From 7d38aa1843e29a0c3a06f10faac147ba71dc3c95 Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Thu, 6 Jul 2023 17:45:53 -0600 Subject: [PATCH 01/54] adding indexing basics --- fundamentals/02_indexing_Advanced.ipynb | 1816 +++++++++++++++++++++++ 1 file changed, 1816 insertions(+) create mode 100644 fundamentals/02_indexing_Advanced.ipynb diff --git a/fundamentals/02_indexing_Advanced.ipynb b/fundamentals/02_indexing_Advanced.ipynb new file mode 100644 index 00000000..ee2663ab --- /dev/null +++ b/fundamentals/02_indexing_Advanced.ipynb @@ -0,0 +1,1816 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Advanced Indexing\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Learning Objectives\n", + "\n", + "* Vectorized Indexing \n", + "* Dropping/Masking Data Using `where` and `isin`\n", + "* Fancy DateTime Indexing\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Overview\n", + "\n", + "\n", + "In the pervious notebooks, we learned basic forms of indexing with xarray (positional and name based dimensions, integer and label based indexing) and nearest neighbor lookups. In this tutorial, we will discover more advanced options for vectorized indexing and learn about additional useful methods relating to indexing/selecting data such as masking. \n", + "\n", + "\n", + "First, let's import packages: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import xarray as xr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this tutorial, we’ll use air temperature tutorial dataset from the National Center for Environmental Prediction. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ds = xr.tutorial.load_dataset(\"air_temperature\")\n", + "da = ds.air\n", + "ds" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Vectorized Indexing\n", + "\n", + "Like NumPy and pandas, Xarray supports indexing many array elements at once in a\n", + "*vectorized* manner. \n", + "\n", + "If you only provide integers, slices, or unlabeled arrays (array without dimension names, such as `np.ndarray`, `list`, but not `DataArray()`) indexing can be understood as orthogonally." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "da[0,[2,4,10,13],[1,6,7]].plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But for more flexibility, you can supply `DataArray()` objects as indexers. \n", + "\n", + "Vectorized indexing using `DataArrays()` may be used to extract information from the nearest grid cells of interest, for example, the nearest climate model grid cells to a collection specified weather station latitudes and longitudes.\n", + "\n", + "**To trigger vectorized indexing behavior you will need to provide the selection dimensions with a new shared output dimension name.** \n", + "\n", + "In the example below, the selections of the closest latitude and longitude are renamed to an output dimension named `points`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define target latitude and longitude (where weather stations might be)\n", + "lat_points = xr.DataArray([31, 41, 42, 42], dims=\"points\")\n", + "lon_points = xr.DataArray([200, 201, 202, 205], dims=\"points\")\n", + "lat_points" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "lon_points" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, retrieve data at the grid cells nearest to the target latitudes and longitudes (weather stations):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "da.sel(lat=lat_points, lon=lon_points,method=\"nearest\").plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see in the above example, the dimensions on resultant arrays are given by the ordered union of the indexers’ dimensions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "da.sel(lat=lat_points, lon=lon_points,method=\"nearest\").dims" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Please note that slices or sequences/arrays without named-dimensions are treated as if they have the same dimension which is indexed along:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "da.sel(lat=[20, 30, 40], lon=lon_points,method=\"nearest\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "Warning: If an indexer is a DataArray(), its coordinates should not conflict with the selected subpart of the target array (except for the explicitly indexed dimensions with .loc/.sel). Otherwise, IndexError will be raised.\n", + " \n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Masking with `where()`\n", + "\n", + "Indexing methods on Xarray objects generally return a subset of the original data. However, it is sometimes useful to select an object with the same shape as the original data, but with some elements masked. To do this type of selection in Xarray, use `where()`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's replace the missing values (nan) with some placeholder\n", + "\n", + "ds.air.where(ds.air.notnull(), -9999)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also select a condition to create a mask. For example, here we want to mask all the points with latitudes above 60 N. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "da[0,:,:].plot();" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "da_masked = da.where(da.lat<60)\n", + "da_masked[0,:,:].plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By default where maintains the original size of the data. You can use of the option `drop=True` to clips coordinate elements that are fully masked:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "da_masked = da.where(da.lat<60, drop=True)\n", + "da_masked[0,:,:].plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Selecting Values with `isin`\n", + "\n", + "To check whether elements of an xarray object contain a single object, you can compare with the equality operator `==` (e.g., `arr == 3`). To check multiple values, use `isin()`:\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is a simple example: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x_da = xr.DataArray([1, 2, 3, 4, 5], dims=[\"x\"])\n", + "\n", + "#-- select points with values equal to 2 and 4:\n", + "x_da.isin([2, 4])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`isin()` works particularly well with `where()` to support indexing by arrays that are not already labels of an array. \n", + "\n", + "For example, we have another DataArray that displays the status flags of the data-collecting device for our data. Here, flags with value 0 and -1 signifies the device was functioning correctly, while 0 indicates a malfunction, implying that the resulting data collected may not be accurate." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "flags = xr.DataArray(\n", + " np.random.randint(-1, 5, da.shape),\n", + " dims=da.dims,\n", + " coords=da.coords\n", + ")\n", + "flags" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we want to only see the data for points where out measurement device is working correctly: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "da_masked = da.where(flags.isin([1,2,3,4,5]), drop=True)\n", + "da_masked[0,:,:].plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "Warning: Please note that when done repeatedly, this type of indexing is significantly slower than using sel().\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Align and Reindex \n", + "\n", + "Xarray enforces alignment between index Coordinates (that is, coordinates with the same name as a dimension, marked by *) on objects used in binary operations.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "da" + ] + }, + { + "cell_type": "code", + "execution_count": 240, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.DataArray (time: 4, space: 3)>\n",
+       "array([[0.41063639, 0.64438845, 0.34046896],\n",
+       "       [0.87939503, 0.1581048 , 0.34112795],\n",
+       "       [0.85912828, 0.92362903, 0.77612158],\n",
+       "       [0.76501244, 0.20228242, 0.94031457]])\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 2000-01-04\n",
+       "  * space    (space) <U2 'IL' 'IA' 'IN'
" + ], + "text/plain": [ + "\n", + "array([[0.41063639, 0.64438845, 0.34046896],\n", + " [0.87939503, 0.1581048 , 0.34112795],\n", + " [0.85912828, 0.92362903, 0.77612158],\n", + " [0.76501244, 0.20228242, 0.94031457]])\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 2000-01-04\n", + " * space (space) \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.DataArray (time: 4, space: 2)>\n",
+       "array([[0.64438845,        nan],\n",
+       "       [0.1581048 ,        nan],\n",
+       "       [0.92362903,        nan],\n",
+       "       [0.20228242,        nan]])\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 2000-01-04\n",
+       "  * space    (space) <U2 'IA' 'CA'
" + ], + "text/plain": [ + "\n", + "array([[0.64438845, nan],\n", + " [0.1581048 , nan],\n", + " [0.92362903, nan],\n", + " [0.20228242, nan]])\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 2000-01-04\n", + " * space (space) \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.DataArray 'air' (time: 20, lat: 25, lon: 53)>\n",
+       "array([[[238.09999, 238.29999, 238.2    , ..., 238.7    , 240.09999,\n",
+       "         241.7    ],\n",
+       "        [241.2    , 240.5    , 239.2    , ..., 234.89   , 236.     ,\n",
+       "         238.39   ],\n",
+       "        [244.29999, 244.     , 243.09999, ..., 235.59999, 237.79999,\n",
+       "         241.59999],\n",
+       "        ...,\n",
+       "        [296.     , 295.5    , 295.4    , ..., 294.6    , 295.19998,\n",
+       "         295.4    ],\n",
+       "        [297.1    , 297.     , 296.69998, ..., 295.9    , 296.5    ,\n",
+       "         296.1    ],\n",
+       "        [298.1    , 298.     , 297.4    , ..., 296.79   , 297.6    ,\n",
+       "         297.29   ]],\n",
+       "\n",
+       "       [[      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "               nan],\n",
+       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "               nan],\n",
+       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "               nan],\n",
+       "...\n",
+       "        [294.6    , 293.6    , 293.19998, ..., 295.79   , 296.29   ,\n",
+       "         296.4    ],\n",
+       "        [295.9    , 295.4    , 294.69998, ..., 297.1    , 297.5    ,\n",
+       "         296.9    ],\n",
+       "        [297.1    , 296.69998, 296.     , ..., 297.69998, 297.79   ,\n",
+       "         297.     ]],\n",
+       "\n",
+       "       [[      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "               nan],\n",
+       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "               nan],\n",
+       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "               nan],\n",
+       "        ...,\n",
+       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "               nan],\n",
+       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "               nan],\n",
+       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "               nan]]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2013-02-01 ... 2013-02-01T19:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
" + ], + "text/plain": [ + "\n", + "array([[[238.09999, 238.29999, 238.2 , ..., 238.7 , 240.09999,\n", + " 241.7 ],\n", + " [241.2 , 240.5 , 239.2 , ..., 234.89 , 236. ,\n", + " 238.39 ],\n", + " [244.29999, 244. , 243.09999, ..., 235.59999, 237.79999,\n", + " 241.59999],\n", + " ...,\n", + " [296. , 295.5 , 295.4 , ..., 294.6 , 295.19998,\n", + " 295.4 ],\n", + " [297.1 , 297. , 296.69998, ..., 295.9 , 296.5 ,\n", + " 296.1 ],\n", + " [298.1 , 298. , 297.4 , ..., 296.79 , 297.6 ,\n", + " 297.29 ]],\n", + "\n", + " [[ nan, nan, nan, ..., nan, nan,\n", + " nan],\n", + " [ nan, nan, nan, ..., nan, nan,\n", + " nan],\n", + " [ nan, nan, nan, ..., nan, nan,\n", + " nan],\n", + "...\n", + " [294.6 , 293.6 , 293.19998, ..., 295.79 , 296.29 ,\n", + " 296.4 ],\n", + " [295.9 , 295.4 , 294.69998, ..., 297.1 , 297.5 ,\n", + " 296.9 ],\n", + " [297.1 , 296.69998, 296. , ..., 297.69998, 297.79 ,\n", + " 297. ]],\n", + "\n", + " [[ nan, nan, nan, ..., nan, nan,\n", + " nan],\n", + " [ nan, nan, nan, ..., nan, nan,\n", + " nan],\n", + " [ nan, nan, nan, ..., nan, nan,\n", + " nan],\n", + " ...,\n", + " [ nan, nan, nan, ..., nan, nan,\n", + " nan],\n", + " [ nan, nan, nan, ..., nan, nan,\n", + " nan],\n", + " [ nan, nan, nan, ..., nan, nan,\n", + " nan]]], dtype=float32)\n", + "Coordinates:\n", + " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", + " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", + " * time (time) datetime64[ns] 2013-02-01 ... 2013-02-01T19:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 237, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "da.reindex(time=new_time)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fancy DateTime Indexing \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "hide-output" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Additional Resources\n", + "\n", + "- [Xarray Docs - Indexing and Selecting Data](https://docs.xarray.dev/en/stable/indexing.html)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "NPL 2023b", + "language": "python", + "name": "npl-2023b" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": true, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": true + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 4079de5c7c13d2400a1b2962a8d97889844f8b17 Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Thu, 6 Jul 2023 17:46:40 -0600 Subject: [PATCH 02/54] adding indexing basic --- fundamentals/02_indexing_Basic.ipynb | 4831 ++++++++++++++++++++++++++ 1 file changed, 4831 insertions(+) create mode 100644 fundamentals/02_indexing_Basic.ipynb diff --git a/fundamentals/02_indexing_Basic.ipynb b/fundamentals/02_indexing_Basic.ipynb new file mode 100644 index 00000000..d3f45b6a --- /dev/null +++ b/fundamentals/02_indexing_Basic.ipynb @@ -0,0 +1,4831 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "# Indexing and Selecting Data\n", + "\n", + "## Learning Objectives\n", + "\n", + "- Select data by position using `.isel` with values or slices\n", + "- Select data by label using `.sel` with values or slices\n", + "- Select timeseries data by date/time with values or slices\n", + "- Use nearest-neighbor lookups with `.sel`\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Indexing and Selecting Data\n", + "\n", + "Xarray offers extremely flexible indexing routines that combine the best features of NumPy and Pandas for data selection.\n", + "\n", + "The most basic way to access elements of a `DataArray` object is to use Python’s `[]` syntax, such as `array[i, j]`, where `i` and `j` are both integers.\n", + "\n", + "As xarray objects can store coordinates corresponding to each dimension of an array, label-based indexing similar to `pandas.DataFrame.loc` is also possible. In label-based indexing, the element position `i` is automatically looked-up from the coordinate values.\n", + "\n", + "By leveraging the labeled dimensions and coordinates provided by Xarray, users can effortlessly access, subset, and manipulate data along multiple axes, enabling complex operations such as slicing, masking, and aggregating data based on specific criteria. \n", + "\n", + "This indexing and selection capability of Xarray not only enhances data exploration and analysis workflows but also promotes reproducibility and efficiency by providing a convenient interface for working with multi-dimensional data structures.\n", + "\n", + "## Quick Overview \n", + "\n", + "In total, xarray supports four different kinds of indexing, as described below and summarized in this table:\n", + "\n", + "| Dimension lookup | Index lookup | `DataArray` syntax | `Dataset` syntax |\n", + "| ---------------- | ------------ | ---------------------| ---------------------|\n", + "| Positional | By integer | `da[:,0]` | *not available* |\n", + "| Positional | By label | `da.loc[:,'IA']` | *not available* |\n", + "| By name | By integer | `da.isel(space=0)` or `da[dict(space=0)]` | `ds.isel(space=0)` or `ds[dict(space=0)]` |\n", + "| By name | By label | `da.sel(space='IA')` or `da.loc[dict(space='IA')]` | `ds.sel(space='IA')` or `ds.loc[dict(space='IA')]` |\n", + "\n", + "\n", + "----------\n", + "\n", + "In this tutorial, first we cover the positional indexing and label-based indexing, next we will cover more advanced techniques such as nearest neighbor lookups. \n", + "\n", + "First, let's import packages: " + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import xarray as xr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we’ll use air temperature tutorial dataset from the National Center for Environmental Prediction. " + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.Dataset>\n",
+       "Dimensions:  (lat: 25, time: 2920, lon: 53)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Data variables:\n",
+       "    air      (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n",
+       "Attributes:\n",
+       "    Conventions:  COARDS\n",
+       "    title:        4x daily NMC reanalysis (1948)\n",
+       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
+       "    platform:     Model\n",
+       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
" + ], + "text/plain": [ + "\n", + "Dimensions: (lat: 25, time: 2920, lon: 53)\n", + "Coordinates:\n", + " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", + " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Data variables:\n", + " air (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n", + "Attributes:\n", + " Conventions: COARDS\n", + " title: 4x daily NMC reanalysis (1948)\n", + " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", + " platform: Model\n", + " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds = xr.tutorial.load_dataset(\"air_temperature\")\n", + "ds" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "da = ds[\"air\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Position-based Indexing\n", + "\n", + "Indexing a `DataArray` directly works (mostly) just like it does for numpy `ndarrays`, except that the returned object is always another `DataArray`:\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### NumPy Positional Indexing\n", + "\n", + "When working with numpy, indexing is done by position (slices/ranges/scalars).\n", + "\n", + "For example:" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2920, 25, 53)" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np_array = ds[\"air\"].data # numpy array\n", + "np_array.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Indexing is 0-based in NumPy:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "242.09999" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np_array[1,0,0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similarly, we can select a range in NumPy:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([295. , 294.4 , 294.5 , ..., 297.29, 297.79, 297.99], dtype=float32)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# extract a time-series for one spatial location\n", + "np_array[:, 20, 40]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Positional Indexing with Xarray" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Xarray offers extremely flexible indexing routines that combine the best\n", + "features of NumPy and pandas for data selection." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### NumPy style indexing with Xarray\n", + "\n", + "NumPy style indexing works exactly the same with Xarray but it also preserves labels and metadata. " + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.DataArray 'air' (time: 2920)>\n",
+       "array([295.  , 294.4 , 294.5 , ..., 297.29, 297.79, 297.99], dtype=float32)\n",
+       "Coordinates:\n",
+       "    lat      float32 25.0\n",
+       "    lon      float32 300.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
" + ], + "text/plain": [ + "\n", + "array([295. , 294.4 , 294.5 , ..., 297.29, 297.79, 297.99], dtype=float32)\n", + "Coordinates:\n", + " lat float32 25.0\n", + " lon float32 300.0\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "da[:, 20, 40]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Positional Indexing Using Dimension Names" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Remembering the axis order can be challenging even with 2D arrays (is `np_array[0,3]` the first row and third column or first column and third row? or did I store these samples by row or by column when I saved the data?!). The difficulty is compounded with added dimensions. \n", + "\n", + "Xarray objects eliminate much of the mental overhead by adding indexing using dimension names:\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "da.isel(lat=20, lon=40).plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Slicing is also possible similarly:" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "da.isel(time=slice(0,20),lat=20, lon=40).plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Using the isel method, the user can choose/slice the specific elements from a Dataset or DataArray.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But what if I wanted to select data only for 2014, how would I know the indices for it? Xarray reduce this complexity by introducing label-based indexing. \n", + "\n", + "## Label-based Indexing\n", + "\n", + "To select data by coordinate labels instead of integer indices we can use the same syntax, using `sel` instead of `isel`:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For example, let's select the data for one day 2014-01-01 at Lat 25 N and Lon 210 E using `sel` :" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "tags": [ + "hide-output" + ] + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "da.sel(time=\"2014-01-01\",lat=25, lon=210).plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's select data for year 2014 at this coordinate:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "tags": [ + "hide-output" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.DataArray 'air' (time: 1460)>\n",
+       "array([277.6 , 277.5 , 277.4 , ..., 277.59, 277.59, 277.59], dtype=float32)\n",
+       "Coordinates:\n",
+       "    lat      float32 50.0\n",
+       "    lon      float32 200.0\n",
+       "  * time     (time) datetime64[ns] 2014-01-01 ... 2014-12-31T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
" + ], + "text/plain": [ + "\n", + "array([277.6 , 277.5 , 277.4 , ..., 277.59, 277.59, 277.59], dtype=float32)\n", + "Coordinates:\n", + " lat float32 50.0\n", + " lon float32 200.0\n", + " * time (time) datetime64[ns] 2014-01-01 ... 2014-12-31T18:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "da.sel(lat=50.0, lon=200.0, time=\"2014\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similarly we can do slicing or filter a date range using the `.slice` function: " + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.DataArray 'air' (time: 1212, lat: 25, lon: 53)>\n",
+       "array([[[246.     , 245.09999, 244.09999, ..., 240.2    , 242.     ,\n",
+       "         244.2    ],\n",
+       "        [240.59999, 241.7    , 242.09999, ..., 239.09999, 241.2    ,\n",
+       "         244.59999],\n",
+       "        [238.2    , 239.79999, 240.79999, ..., 243.89   , 246.89   ,\n",
+       "         251.2    ],\n",
+       "        ...,\n",
+       "        [295.6    , 295.6    , 296.     , ..., 293.6    , 293.6    ,\n",
+       "         294.1    ],\n",
+       "        [297.     , 297.     , 297.     , ..., 293.69998, 293.9    ,\n",
+       "         294.6    ],\n",
+       "        [297.5    , 297.69998, 297.69998, ..., 294.5    , 294.69998,\n",
+       "         295.19998]],\n",
+       "\n",
+       "       [[245.2    , 244.29999, 243.     , ..., 241.89   , 243.09999,\n",
+       "         244.59999],\n",
+       "        [241.09999, 241.89   , 241.89   , ..., 241.89   , 243.59999,\n",
+       "         246.39   ],\n",
+       "        [239.89   , 241.     , 241.29999, ..., 245.09999, 248.2    ,\n",
+       "         252.2    ],\n",
+       "...\n",
+       "        [296.49   , 295.59   , 295.49   , ..., 297.29   , 297.38998,\n",
+       "         296.79   ],\n",
+       "        [298.09   , 297.79   , 297.19   , ..., 297.69   , 298.09   ,\n",
+       "         297.38998],\n",
+       "        [298.49   , 298.29   , 297.99   , ..., 298.29   , 298.09   ,\n",
+       "         297.99   ]],\n",
+       "\n",
+       "       [[246.29   , 246.39   , 245.98999, ..., 232.29   , 233.39   ,\n",
+       "         234.98999],\n",
+       "        [246.68999, 248.18999, 248.98999, ..., 230.59   , 232.48999,\n",
+       "         235.68999],\n",
+       "        [244.79   , 245.29   , 245.89   , ..., 230.48999, 235.09   ,\n",
+       "         241.39   ],\n",
+       "        ...,\n",
+       "        [296.88998, 296.49   , 296.49   , ..., 297.69   , 297.29   ,\n",
+       "         296.19   ],\n",
+       "        [298.29   , 297.99   , 297.49   , ..., 298.29   , 298.38998,\n",
+       "         297.49   ],\n",
+       "        [298.49   , 298.29   , 297.99   , ..., 298.59   , 298.38998,\n",
+       "         298.29   ]]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
" + ], + "text/plain": [ + "\n", + "array([[[246. , 245.09999, 244.09999, ..., 240.2 , 242. ,\n", + " 244.2 ],\n", + " [240.59999, 241.7 , 242.09999, ..., 239.09999, 241.2 ,\n", + " 244.59999],\n", + " [238.2 , 239.79999, 240.79999, ..., 243.89 , 246.89 ,\n", + " 251.2 ],\n", + " ...,\n", + " [295.6 , 295.6 , 296. , ..., 293.6 , 293.6 ,\n", + " 294.1 ],\n", + " [297. , 297. , 297. , ..., 293.69998, 293.9 ,\n", + " 294.6 ],\n", + " [297.5 , 297.69998, 297.69998, ..., 294.5 , 294.69998,\n", + " 295.19998]],\n", + "\n", + " [[245.2 , 244.29999, 243. , ..., 241.89 , 243.09999,\n", + " 244.59999],\n", + " [241.09999, 241.89 , 241.89 , ..., 241.89 , 243.59999,\n", + " 246.39 ],\n", + " [239.89 , 241. , 241.29999, ..., 245.09999, 248.2 ,\n", + " 252.2 ],\n", + "...\n", + " [296.49 , 295.59 , 295.49 , ..., 297.29 , 297.38998,\n", + " 296.79 ],\n", + " [298.09 , 297.79 , 297.19 , ..., 297.69 , 298.09 ,\n", + " 297.38998],\n", + " [298.49 , 298.29 , 297.99 , ..., 298.29 , 298.09 ,\n", + " 297.99 ]],\n", + "\n", + " [[246.29 , 246.39 , 245.98999, ..., 232.29 , 233.39 ,\n", + " 234.98999],\n", + " [246.68999, 248.18999, 248.98999, ..., 230.59 , 232.48999,\n", + " 235.68999],\n", + " [244.79 , 245.29 , 245.89 , ..., 230.48999, 235.09 ,\n", + " 241.39 ],\n", + " ...,\n", + " [296.88998, 296.49 , 296.49 , ..., 297.69 , 297.29 ,\n", + " 296.19 ],\n", + " [298.29 , 297.99 , 297.49 , ..., 298.29 , 298.38998,\n", + " 297.49 ],\n", + " [298.49 , 298.29 , 297.99 , ..., 298.59 , 298.38998,\n", + " 298.29 ]]], dtype=float32)\n", + "Coordinates:\n", + " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", + " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", + " * time (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# demonstrate slicing\n", + "da.sel(time=slice(\"2014-02-14\", \"2014-12-13\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Xarray also supports label-based indexing, just like pandas using `.loc`. Because we use a `pandas.Index` under the hood, label based indexing is very fast. To do label based indexing, use the `loc` attribute:" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.DataArray 'air' (time: 1212, lat: 25, lon: 53)>\n",
+       "array([[[246.     , 245.09999, 244.09999, ..., 240.2    , 242.     ,\n",
+       "         244.2    ],\n",
+       "        [240.59999, 241.7    , 242.09999, ..., 239.09999, 241.2    ,\n",
+       "         244.59999],\n",
+       "        [238.2    , 239.79999, 240.79999, ..., 243.89   , 246.89   ,\n",
+       "         251.2    ],\n",
+       "        ...,\n",
+       "        [295.6    , 295.6    , 296.     , ..., 293.6    , 293.6    ,\n",
+       "         294.1    ],\n",
+       "        [297.     , 297.     , 297.     , ..., 293.69998, 293.9    ,\n",
+       "         294.6    ],\n",
+       "        [297.5    , 297.69998, 297.69998, ..., 294.5    , 294.69998,\n",
+       "         295.19998]],\n",
+       "\n",
+       "       [[245.2    , 244.29999, 243.     , ..., 241.89   , 243.09999,\n",
+       "         244.59999],\n",
+       "        [241.09999, 241.89   , 241.89   , ..., 241.89   , 243.59999,\n",
+       "         246.39   ],\n",
+       "        [239.89   , 241.     , 241.29999, ..., 245.09999, 248.2    ,\n",
+       "         252.2    ],\n",
+       "...\n",
+       "        [296.49   , 295.59   , 295.49   , ..., 297.29   , 297.38998,\n",
+       "         296.79   ],\n",
+       "        [298.09   , 297.79   , 297.19   , ..., 297.69   , 298.09   ,\n",
+       "         297.38998],\n",
+       "        [298.49   , 298.29   , 297.99   , ..., 298.29   , 298.09   ,\n",
+       "         297.99   ]],\n",
+       "\n",
+       "       [[246.29   , 246.39   , 245.98999, ..., 232.29   , 233.39   ,\n",
+       "         234.98999],\n",
+       "        [246.68999, 248.18999, 248.98999, ..., 230.59   , 232.48999,\n",
+       "         235.68999],\n",
+       "        [244.79   , 245.29   , 245.89   , ..., 230.48999, 235.09   ,\n",
+       "         241.39   ],\n",
+       "        ...,\n",
+       "        [296.88998, 296.49   , 296.49   , ..., 297.69   , 297.29   ,\n",
+       "         296.19   ],\n",
+       "        [298.29   , 297.99   , 297.49   , ..., 298.29   , 298.38998,\n",
+       "         297.49   ],\n",
+       "        [298.49   , 298.29   , 297.99   , ..., 298.59   , 298.38998,\n",
+       "         298.29   ]]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
" + ], + "text/plain": [ + "\n", + "array([[[246. , 245.09999, 244.09999, ..., 240.2 , 242. ,\n", + " 244.2 ],\n", + " [240.59999, 241.7 , 242.09999, ..., 239.09999, 241.2 ,\n", + " 244.59999],\n", + " [238.2 , 239.79999, 240.79999, ..., 243.89 , 246.89 ,\n", + " 251.2 ],\n", + " ...,\n", + " [295.6 , 295.6 , 296. , ..., 293.6 , 293.6 ,\n", + " 294.1 ],\n", + " [297. , 297. , 297. , ..., 293.69998, 293.9 ,\n", + " 294.6 ],\n", + " [297.5 , 297.69998, 297.69998, ..., 294.5 , 294.69998,\n", + " 295.19998]],\n", + "\n", + " [[245.2 , 244.29999, 243. , ..., 241.89 , 243.09999,\n", + " 244.59999],\n", + " [241.09999, 241.89 , 241.89 , ..., 241.89 , 243.59999,\n", + " 246.39 ],\n", + " [239.89 , 241. , 241.29999, ..., 245.09999, 248.2 ,\n", + " 252.2 ],\n", + "...\n", + " [296.49 , 295.59 , 295.49 , ..., 297.29 , 297.38998,\n", + " 296.79 ],\n", + " [298.09 , 297.79 , 297.19 , ..., 297.69 , 298.09 ,\n", + " 297.38998],\n", + " [298.49 , 298.29 , 297.99 , ..., 298.29 , 298.09 ,\n", + " 297.99 ]],\n", + "\n", + " [[246.29 , 246.39 , 245.98999, ..., 232.29 , 233.39 ,\n", + " 234.98999],\n", + " [246.68999, 248.18999, 248.98999, ..., 230.59 , 232.48999,\n", + " 235.68999],\n", + " [244.79 , 245.29 , 245.89 , ..., 230.48999, 235.09 ,\n", + " 241.39 ],\n", + " ...,\n", + " [296.88998, 296.49 , 296.49 , ..., 297.69 , 297.29 ,\n", + " 296.19 ],\n", + " [298.29 , 297.99 , 297.49 , ..., 298.29 , 298.38998,\n", + " 297.49 ],\n", + " [298.49 , 298.29 , 297.99 , ..., 298.59 , 298.38998,\n", + " 298.29 ]]], dtype=float32)\n", + "Coordinates:\n", + " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", + " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", + " * time (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "da.loc[\"2014-02-14\":\"2014-12-13\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dropping using `drop_sel`\n", + "\n", + "If instead of selecting data we want to drop it, we can use `drop_sel` method:" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.DataArray 'air' (time: 2919, lat: 24, lon: 52)>\n",
+       "array([[[242.5    , 243.5    , 244.     , ..., 232.79999, 235.5    ,\n",
+       "         238.59999],\n",
+       "        [244.5    , 244.7    , 244.2    , ..., 232.79999, 235.29999,\n",
+       "         239.29999],\n",
+       "        [249.79999, 248.89   , 247.5    , ..., 233.2    , 236.39   ,\n",
+       "         241.7    ],\n",
+       "        ...,\n",
+       "        [296.19998, 296.4    , 296.5    , ..., 295.4    , 295.1    ,\n",
+       "         294.69998],\n",
+       "        [296.19998, 296.79   , 296.5    , ..., 295.9    , 295.9    ,\n",
+       "         295.19998],\n",
+       "        [296.79   , 297.1    , 297.     , ..., 296.9    , 296.79   ,\n",
+       "         296.6    ]],\n",
+       "\n",
+       "       [[242.7    , 243.09999, 243.39   , ..., 232.     , 233.59999,\n",
+       "         235.79999],\n",
+       "        [244.09999, 244.2    , 244.09999, ..., 231.     , 232.5    ,\n",
+       "         235.7    ],\n",
+       "        [252.89   , 252.09999, 250.79999, ..., 230.79999, 233.39   ,\n",
+       "         238.5    ],\n",
+       "...\n",
+       "        [293.88998, 295.38998, 297.19   , ..., 295.09   , 294.69   ,\n",
+       "         294.29   ],\n",
+       "        [297.19   , 297.59   , 297.88998, ..., 295.29   , 295.09   ,\n",
+       "         294.38998],\n",
+       "        [298.38998, 298.49   , 298.59   , ..., 295.69   , 295.49   ,\n",
+       "         295.19   ]],\n",
+       "\n",
+       "       [[244.29   , 243.29   , 242.18999, ..., 241.68999, 241.48999,\n",
+       "         241.79   ],\n",
+       "        [249.29   , 248.39   , 246.98999, ..., 239.59   , 240.29   ,\n",
+       "         241.68999],\n",
+       "        [262.19   , 261.38998, 259.99   , ..., 239.89   , 242.59   ,\n",
+       "         246.29   ],\n",
+       "        ...,\n",
+       "        [293.69   , 295.09   , 296.69   , ..., 295.29   , 295.09   ,\n",
+       "         294.69   ],\n",
+       "        [296.88998, 297.19   , 297.49   , ..., 295.69   , 295.69   ,\n",
+       "         295.19   ],\n",
+       "        [298.09   , 298.09   , 298.49   , ..., 296.49   , 296.19   ,\n",
+       "         295.69   ]]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 202.5 205.0 207.5 210.0 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
" + ], + "text/plain": [ + "\n", + "array([[[242.5 , 243.5 , 244. , ..., 232.79999, 235.5 ,\n", + " 238.59999],\n", + " [244.5 , 244.7 , 244.2 , ..., 232.79999, 235.29999,\n", + " 239.29999],\n", + " [249.79999, 248.89 , 247.5 , ..., 233.2 , 236.39 ,\n", + " 241.7 ],\n", + " ...,\n", + " [296.19998, 296.4 , 296.5 , ..., 295.4 , 295.1 ,\n", + " 294.69998],\n", + " [296.19998, 296.79 , 296.5 , ..., 295.9 , 295.9 ,\n", + " 295.19998],\n", + " [296.79 , 297.1 , 297. , ..., 296.9 , 296.79 ,\n", + " 296.6 ]],\n", + "\n", + " [[242.7 , 243.09999, 243.39 , ..., 232. , 233.59999,\n", + " 235.79999],\n", + " [244.09999, 244.2 , 244.09999, ..., 231. , 232.5 ,\n", + " 235.7 ],\n", + " [252.89 , 252.09999, 250.79999, ..., 230.79999, 233.39 ,\n", + " 238.5 ],\n", + "...\n", + " [293.88998, 295.38998, 297.19 , ..., 295.09 , 294.69 ,\n", + " 294.29 ],\n", + " [297.19 , 297.59 , 297.88998, ..., 295.29 , 295.09 ,\n", + " 294.38998],\n", + " [298.38998, 298.49 , 298.59 , ..., 295.69 , 295.49 ,\n", + " 295.19 ]],\n", + "\n", + " [[244.29 , 243.29 , 242.18999, ..., 241.68999, 241.48999,\n", + " 241.79 ],\n", + " [249.29 , 248.39 , 246.98999, ..., 239.59 , 240.29 ,\n", + " 241.68999],\n", + " [262.19 , 261.38998, 259.99 , ..., 239.89 , 242.59 ,\n", + " 246.29 ],\n", + " ...,\n", + " [293.69 , 295.09 , 296.69 , ..., 295.29 , 295.09 ,\n", + " 294.69 ],\n", + " [296.88998, 297.19 , 297.49 , ..., 295.69 , 295.69 ,\n", + " 295.19 ],\n", + " [298.09 , 298.09 , 298.49 , ..., 296.49 , 296.19 ,\n", + " 295.69 ]]], dtype=float32)\n", + "Coordinates:\n", + " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", + " * lon (lon) float32 202.5 205.0 207.5 210.0 ... 322.5 325.0 327.5 330.0\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "da.drop_sel(lat=50.0, lon=200.0, time=\"2014\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So far, all the above will require us to specify exact coordinate values, but what if we don't have the exact values? We can use nearest neighbor lookups to address this issue:\n", + "\n", + "## Nearest Neighbor Lookups\n", + "\n", + "The label based selection methods `sel()` support `method` and `tolerance` keyword argument. The `method` parameter allows for enabling nearest neighbor (inexact) lookups by use of the methods `pad`, `backfill` or `nearest`:\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "tags": [ + "hide-output" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.DataArray 'air' (time: 2920)>\n",
+       "array([262.69998, 263.19998, 270.9    , ..., 264.19   , 265.19   ,\n",
+       "       266.99   ], dtype=float32)\n",
+       "Coordinates:\n",
+       "    lat      float32 52.5\n",
+       "    lon      float32 252.5\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
" + ], + "text/plain": [ + "\n", + "array([262.69998, 263.19998, 270.9 , ..., 264.19 , 265.19 ,\n", + " 266.99 ], dtype=float32)\n", + "Coordinates:\n", + " lat float32 52.5\n", + " lon float32 252.5\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "da.sel(lat=52.25, lon=251.8998, method=\"nearest\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`tolerance` argument limits the maximum distance for valid matches with an inexact lookup:" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.DataArray 'air' (time: 2920)>\n",
+       "array([262.69998, 263.19998, 270.9    , ..., 264.19   , 265.19   ,\n",
+       "       266.99   ], dtype=float32)\n",
+       "Coordinates:\n",
+       "    lat      float32 52.5\n",
+       "    lon      float32 252.5\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
" + ], + "text/plain": [ + "\n", + "array([262.69998, 263.19998, 270.9 , ..., 264.19 , 265.19 ,\n", + " 266.99 ], dtype=float32)\n", + "Coordinates:\n", + " lat float32 52.5\n", + " lon float32 252.5\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "da.sel(lat=52.25, lon=251.8998, method=\"nearest\", tolerance=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "- **All of these indexing methods work on the dataset too:**\n", + "\n", + "We can also use these methods to index all variables in a dataset simultaneously, returning a new dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.Dataset>\n",
+       "Dimensions:  (time: 2920)\n",
+       "Coordinates:\n",
+       "    lat      float32 52.5\n",
+       "    lon      float32 252.5\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Data variables:\n",
+       "    air      (time) float32 262.7 263.2 270.9 274.1 ... 261.6 264.2 265.2 267.0\n",
+       "Attributes:\n",
+       "    Conventions:  COARDS\n",
+       "    title:        4x daily NMC reanalysis (1948)\n",
+       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
+       "    platform:     Model\n",
+       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
" + ], + "text/plain": [ + "\n", + "Dimensions: (time: 2920)\n", + "Coordinates:\n", + " lat float32 52.5\n", + " lon float32 252.5\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Data variables:\n", + " air (time) float32 262.7 263.2 270.9 274.1 ... 261.6 264.2 265.2 267.0\n", + "Attributes:\n", + " Conventions: COARDS\n", + " title: 4x daily NMC reanalysis (1948)\n", + " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", + " platform: Model\n", + " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds.sel(lat=52.25, lon=251.8998, method=\"nearest\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "In total, Xarray supports four different kinds of indexing, as described below and summarized in this table:\n", + "\n", + "| Dimension lookup | Index lookup | `DataArray` syntax | `Dataset` syntax |\n", + "| ---------------- | ------------ | ---------------------| ---------------------|\n", + "| Positional | By integer | `da[:,0]` | *not available* |\n", + "| Positional | By label | `da.loc[:,'IA']` | *not available* |\n", + "| By name | By integer | `da.isel(space=0)` or `da[dict(space=0)]` | `ds.isel(space=0)` or `ds[dict(space=0)]` |\n", + "| By name | By label | `da.sel(space='IA')` or `da.loc[dict(space='IA')]` | `ds.sel(space='IA')` or `ds.loc[dict(space='IA')]` |\n", + "\n", + "\n", + "For enhanced indexing capabilities across all methods, you can utilize DataArray objects as an indexer. For more detailed information, please see the Advanced Indexing notebook.\n", + "\n", + "\n", + "## More Resources\n", + "\n", + "- [Xarray Docs - Indexing and Selecting Data](https://docs.xarray.dev/en/stable/indexing.html)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "NPL 2023b", + "language": "python", + "name": "npl-2023b" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": true, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": true + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 5b32a98ea11495f45e7a3e6c259f2887aa98d062 Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Thu, 6 Jul 2023 17:48:15 -0600 Subject: [PATCH 03/54] adding basic and advanced indexing notebooks --- fundamentals/02.1_indexing_Basic.ipynb | 4831 +++++++++++++++++++++ fundamentals/02.2_indexing_Advanced.ipynb | 1816 ++++++++ 2 files changed, 6647 insertions(+) create mode 100644 fundamentals/02.1_indexing_Basic.ipynb create mode 100644 fundamentals/02.2_indexing_Advanced.ipynb diff --git a/fundamentals/02.1_indexing_Basic.ipynb b/fundamentals/02.1_indexing_Basic.ipynb new file mode 100644 index 00000000..d3f45b6a --- /dev/null +++ b/fundamentals/02.1_indexing_Basic.ipynb @@ -0,0 +1,4831 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "# Indexing and Selecting Data\n", + "\n", + "## Learning Objectives\n", + "\n", + "- Select data by position using `.isel` with values or slices\n", + "- Select data by label using `.sel` with values or slices\n", + "- Select timeseries data by date/time with values or slices\n", + "- Use nearest-neighbor lookups with `.sel`\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Indexing and Selecting Data\n", + "\n", + "Xarray offers extremely flexible indexing routines that combine the best features of NumPy and Pandas for data selection.\n", + "\n", + "The most basic way to access elements of a `DataArray` object is to use Python’s `[]` syntax, such as `array[i, j]`, where `i` and `j` are both integers.\n", + "\n", + "As xarray objects can store coordinates corresponding to each dimension of an array, label-based indexing similar to `pandas.DataFrame.loc` is also possible. In label-based indexing, the element position `i` is automatically looked-up from the coordinate values.\n", + "\n", + "By leveraging the labeled dimensions and coordinates provided by Xarray, users can effortlessly access, subset, and manipulate data along multiple axes, enabling complex operations such as slicing, masking, and aggregating data based on specific criteria. \n", + "\n", + "This indexing and selection capability of Xarray not only enhances data exploration and analysis workflows but also promotes reproducibility and efficiency by providing a convenient interface for working with multi-dimensional data structures.\n", + "\n", + "## Quick Overview \n", + "\n", + "In total, xarray supports four different kinds of indexing, as described below and summarized in this table:\n", + "\n", + "| Dimension lookup | Index lookup | `DataArray` syntax | `Dataset` syntax |\n", + "| ---------------- | ------------ | ---------------------| ---------------------|\n", + "| Positional | By integer | `da[:,0]` | *not available* |\n", + "| Positional | By label | `da.loc[:,'IA']` | *not available* |\n", + "| By name | By integer | `da.isel(space=0)` or `da[dict(space=0)]` | `ds.isel(space=0)` or `ds[dict(space=0)]` |\n", + "| By name | By label | `da.sel(space='IA')` or `da.loc[dict(space='IA')]` | `ds.sel(space='IA')` or `ds.loc[dict(space='IA')]` |\n", + "\n", + "\n", + "----------\n", + "\n", + "In this tutorial, first we cover the positional indexing and label-based indexing, next we will cover more advanced techniques such as nearest neighbor lookups. \n", + "\n", + "First, let's import packages: " + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import xarray as xr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we’ll use air temperature tutorial dataset from the National Center for Environmental Prediction. " + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.Dataset>\n",
+       "Dimensions:  (lat: 25, time: 2920, lon: 53)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Data variables:\n",
+       "    air      (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n",
+       "Attributes:\n",
+       "    Conventions:  COARDS\n",
+       "    title:        4x daily NMC reanalysis (1948)\n",
+       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
+       "    platform:     Model\n",
+       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
" + ], + "text/plain": [ + "\n", + "Dimensions: (lat: 25, time: 2920, lon: 53)\n", + "Coordinates:\n", + " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", + " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Data variables:\n", + " air (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n", + "Attributes:\n", + " Conventions: COARDS\n", + " title: 4x daily NMC reanalysis (1948)\n", + " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", + " platform: Model\n", + " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds = xr.tutorial.load_dataset(\"air_temperature\")\n", + "ds" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "da = ds[\"air\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Position-based Indexing\n", + "\n", + "Indexing a `DataArray` directly works (mostly) just like it does for numpy `ndarrays`, except that the returned object is always another `DataArray`:\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### NumPy Positional Indexing\n", + "\n", + "When working with numpy, indexing is done by position (slices/ranges/scalars).\n", + "\n", + "For example:" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2920, 25, 53)" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np_array = ds[\"air\"].data # numpy array\n", + "np_array.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Indexing is 0-based in NumPy:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "242.09999" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np_array[1,0,0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similarly, we can select a range in NumPy:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([295. , 294.4 , 294.5 , ..., 297.29, 297.79, 297.99], dtype=float32)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# extract a time-series for one spatial location\n", + "np_array[:, 20, 40]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Positional Indexing with Xarray" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Xarray offers extremely flexible indexing routines that combine the best\n", + "features of NumPy and pandas for data selection." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### NumPy style indexing with Xarray\n", + "\n", + "NumPy style indexing works exactly the same with Xarray but it also preserves labels and metadata. " + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.DataArray 'air' (time: 2920)>\n",
+       "array([295.  , 294.4 , 294.5 , ..., 297.29, 297.79, 297.99], dtype=float32)\n",
+       "Coordinates:\n",
+       "    lat      float32 25.0\n",
+       "    lon      float32 300.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
" + ], + "text/plain": [ + "\n", + "array([295. , 294.4 , 294.5 , ..., 297.29, 297.79, 297.99], dtype=float32)\n", + "Coordinates:\n", + " lat float32 25.0\n", + " lon float32 300.0\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "da[:, 20, 40]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Positional Indexing Using Dimension Names" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Remembering the axis order can be challenging even with 2D arrays (is `np_array[0,3]` the first row and third column or first column and third row? or did I store these samples by row or by column when I saved the data?!). The difficulty is compounded with added dimensions. \n", + "\n", + "Xarray objects eliminate much of the mental overhead by adding indexing using dimension names:\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "da.isel(lat=20, lon=40).plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Slicing is also possible similarly:" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "da.isel(time=slice(0,20),lat=20, lon=40).plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Using the isel method, the user can choose/slice the specific elements from a Dataset or DataArray.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But what if I wanted to select data only for 2014, how would I know the indices for it? Xarray reduce this complexity by introducing label-based indexing. \n", + "\n", + "## Label-based Indexing\n", + "\n", + "To select data by coordinate labels instead of integer indices we can use the same syntax, using `sel` instead of `isel`:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For example, let's select the data for one day 2014-01-01 at Lat 25 N and Lon 210 E using `sel` :" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "tags": [ + "hide-output" + ] + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "da.sel(time=\"2014-01-01\",lat=25, lon=210).plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's select data for year 2014 at this coordinate:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "tags": [ + "hide-output" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.DataArray 'air' (time: 1460)>\n",
+       "array([277.6 , 277.5 , 277.4 , ..., 277.59, 277.59, 277.59], dtype=float32)\n",
+       "Coordinates:\n",
+       "    lat      float32 50.0\n",
+       "    lon      float32 200.0\n",
+       "  * time     (time) datetime64[ns] 2014-01-01 ... 2014-12-31T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
" + ], + "text/plain": [ + "\n", + "array([277.6 , 277.5 , 277.4 , ..., 277.59, 277.59, 277.59], dtype=float32)\n", + "Coordinates:\n", + " lat float32 50.0\n", + " lon float32 200.0\n", + " * time (time) datetime64[ns] 2014-01-01 ... 2014-12-31T18:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "da.sel(lat=50.0, lon=200.0, time=\"2014\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similarly we can do slicing or filter a date range using the `.slice` function: " + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.DataArray 'air' (time: 1212, lat: 25, lon: 53)>\n",
+       "array([[[246.     , 245.09999, 244.09999, ..., 240.2    , 242.     ,\n",
+       "         244.2    ],\n",
+       "        [240.59999, 241.7    , 242.09999, ..., 239.09999, 241.2    ,\n",
+       "         244.59999],\n",
+       "        [238.2    , 239.79999, 240.79999, ..., 243.89   , 246.89   ,\n",
+       "         251.2    ],\n",
+       "        ...,\n",
+       "        [295.6    , 295.6    , 296.     , ..., 293.6    , 293.6    ,\n",
+       "         294.1    ],\n",
+       "        [297.     , 297.     , 297.     , ..., 293.69998, 293.9    ,\n",
+       "         294.6    ],\n",
+       "        [297.5    , 297.69998, 297.69998, ..., 294.5    , 294.69998,\n",
+       "         295.19998]],\n",
+       "\n",
+       "       [[245.2    , 244.29999, 243.     , ..., 241.89   , 243.09999,\n",
+       "         244.59999],\n",
+       "        [241.09999, 241.89   , 241.89   , ..., 241.89   , 243.59999,\n",
+       "         246.39   ],\n",
+       "        [239.89   , 241.     , 241.29999, ..., 245.09999, 248.2    ,\n",
+       "         252.2    ],\n",
+       "...\n",
+       "        [296.49   , 295.59   , 295.49   , ..., 297.29   , 297.38998,\n",
+       "         296.79   ],\n",
+       "        [298.09   , 297.79   , 297.19   , ..., 297.69   , 298.09   ,\n",
+       "         297.38998],\n",
+       "        [298.49   , 298.29   , 297.99   , ..., 298.29   , 298.09   ,\n",
+       "         297.99   ]],\n",
+       "\n",
+       "       [[246.29   , 246.39   , 245.98999, ..., 232.29   , 233.39   ,\n",
+       "         234.98999],\n",
+       "        [246.68999, 248.18999, 248.98999, ..., 230.59   , 232.48999,\n",
+       "         235.68999],\n",
+       "        [244.79   , 245.29   , 245.89   , ..., 230.48999, 235.09   ,\n",
+       "         241.39   ],\n",
+       "        ...,\n",
+       "        [296.88998, 296.49   , 296.49   , ..., 297.69   , 297.29   ,\n",
+       "         296.19   ],\n",
+       "        [298.29   , 297.99   , 297.49   , ..., 298.29   , 298.38998,\n",
+       "         297.49   ],\n",
+       "        [298.49   , 298.29   , 297.99   , ..., 298.59   , 298.38998,\n",
+       "         298.29   ]]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
" + ], + "text/plain": [ + "\n", + "array([[[246. , 245.09999, 244.09999, ..., 240.2 , 242. ,\n", + " 244.2 ],\n", + " [240.59999, 241.7 , 242.09999, ..., 239.09999, 241.2 ,\n", + " 244.59999],\n", + " [238.2 , 239.79999, 240.79999, ..., 243.89 , 246.89 ,\n", + " 251.2 ],\n", + " ...,\n", + " [295.6 , 295.6 , 296. , ..., 293.6 , 293.6 ,\n", + " 294.1 ],\n", + " [297. , 297. , 297. , ..., 293.69998, 293.9 ,\n", + " 294.6 ],\n", + " [297.5 , 297.69998, 297.69998, ..., 294.5 , 294.69998,\n", + " 295.19998]],\n", + "\n", + " [[245.2 , 244.29999, 243. , ..., 241.89 , 243.09999,\n", + " 244.59999],\n", + " [241.09999, 241.89 , 241.89 , ..., 241.89 , 243.59999,\n", + " 246.39 ],\n", + " [239.89 , 241. , 241.29999, ..., 245.09999, 248.2 ,\n", + " 252.2 ],\n", + "...\n", + " [296.49 , 295.59 , 295.49 , ..., 297.29 , 297.38998,\n", + " 296.79 ],\n", + " [298.09 , 297.79 , 297.19 , ..., 297.69 , 298.09 ,\n", + " 297.38998],\n", + " [298.49 , 298.29 , 297.99 , ..., 298.29 , 298.09 ,\n", + " 297.99 ]],\n", + "\n", + " [[246.29 , 246.39 , 245.98999, ..., 232.29 , 233.39 ,\n", + " 234.98999],\n", + " [246.68999, 248.18999, 248.98999, ..., 230.59 , 232.48999,\n", + " 235.68999],\n", + " [244.79 , 245.29 , 245.89 , ..., 230.48999, 235.09 ,\n", + " 241.39 ],\n", + " ...,\n", + " [296.88998, 296.49 , 296.49 , ..., 297.69 , 297.29 ,\n", + " 296.19 ],\n", + " [298.29 , 297.99 , 297.49 , ..., 298.29 , 298.38998,\n", + " 297.49 ],\n", + " [298.49 , 298.29 , 297.99 , ..., 298.59 , 298.38998,\n", + " 298.29 ]]], dtype=float32)\n", + "Coordinates:\n", + " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", + " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", + " * time (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# demonstrate slicing\n", + "da.sel(time=slice(\"2014-02-14\", \"2014-12-13\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Xarray also supports label-based indexing, just like pandas using `.loc`. Because we use a `pandas.Index` under the hood, label based indexing is very fast. To do label based indexing, use the `loc` attribute:" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.DataArray 'air' (time: 1212, lat: 25, lon: 53)>\n",
+       "array([[[246.     , 245.09999, 244.09999, ..., 240.2    , 242.     ,\n",
+       "         244.2    ],\n",
+       "        [240.59999, 241.7    , 242.09999, ..., 239.09999, 241.2    ,\n",
+       "         244.59999],\n",
+       "        [238.2    , 239.79999, 240.79999, ..., 243.89   , 246.89   ,\n",
+       "         251.2    ],\n",
+       "        ...,\n",
+       "        [295.6    , 295.6    , 296.     , ..., 293.6    , 293.6    ,\n",
+       "         294.1    ],\n",
+       "        [297.     , 297.     , 297.     , ..., 293.69998, 293.9    ,\n",
+       "         294.6    ],\n",
+       "        [297.5    , 297.69998, 297.69998, ..., 294.5    , 294.69998,\n",
+       "         295.19998]],\n",
+       "\n",
+       "       [[245.2    , 244.29999, 243.     , ..., 241.89   , 243.09999,\n",
+       "         244.59999],\n",
+       "        [241.09999, 241.89   , 241.89   , ..., 241.89   , 243.59999,\n",
+       "         246.39   ],\n",
+       "        [239.89   , 241.     , 241.29999, ..., 245.09999, 248.2    ,\n",
+       "         252.2    ],\n",
+       "...\n",
+       "        [296.49   , 295.59   , 295.49   , ..., 297.29   , 297.38998,\n",
+       "         296.79   ],\n",
+       "        [298.09   , 297.79   , 297.19   , ..., 297.69   , 298.09   ,\n",
+       "         297.38998],\n",
+       "        [298.49   , 298.29   , 297.99   , ..., 298.29   , 298.09   ,\n",
+       "         297.99   ]],\n",
+       "\n",
+       "       [[246.29   , 246.39   , 245.98999, ..., 232.29   , 233.39   ,\n",
+       "         234.98999],\n",
+       "        [246.68999, 248.18999, 248.98999, ..., 230.59   , 232.48999,\n",
+       "         235.68999],\n",
+       "        [244.79   , 245.29   , 245.89   , ..., 230.48999, 235.09   ,\n",
+       "         241.39   ],\n",
+       "        ...,\n",
+       "        [296.88998, 296.49   , 296.49   , ..., 297.69   , 297.29   ,\n",
+       "         296.19   ],\n",
+       "        [298.29   , 297.99   , 297.49   , ..., 298.29   , 298.38998,\n",
+       "         297.49   ],\n",
+       "        [298.49   , 298.29   , 297.99   , ..., 298.59   , 298.38998,\n",
+       "         298.29   ]]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
" + ], + "text/plain": [ + "\n", + "array([[[246. , 245.09999, 244.09999, ..., 240.2 , 242. ,\n", + " 244.2 ],\n", + " [240.59999, 241.7 , 242.09999, ..., 239.09999, 241.2 ,\n", + " 244.59999],\n", + " [238.2 , 239.79999, 240.79999, ..., 243.89 , 246.89 ,\n", + " 251.2 ],\n", + " ...,\n", + " [295.6 , 295.6 , 296. , ..., 293.6 , 293.6 ,\n", + " 294.1 ],\n", + " [297. , 297. , 297. , ..., 293.69998, 293.9 ,\n", + " 294.6 ],\n", + " [297.5 , 297.69998, 297.69998, ..., 294.5 , 294.69998,\n", + " 295.19998]],\n", + "\n", + " [[245.2 , 244.29999, 243. , ..., 241.89 , 243.09999,\n", + " 244.59999],\n", + " [241.09999, 241.89 , 241.89 , ..., 241.89 , 243.59999,\n", + " 246.39 ],\n", + " [239.89 , 241. , 241.29999, ..., 245.09999, 248.2 ,\n", + " 252.2 ],\n", + "...\n", + " [296.49 , 295.59 , 295.49 , ..., 297.29 , 297.38998,\n", + " 296.79 ],\n", + " [298.09 , 297.79 , 297.19 , ..., 297.69 , 298.09 ,\n", + " 297.38998],\n", + " [298.49 , 298.29 , 297.99 , ..., 298.29 , 298.09 ,\n", + " 297.99 ]],\n", + "\n", + " [[246.29 , 246.39 , 245.98999, ..., 232.29 , 233.39 ,\n", + " 234.98999],\n", + " [246.68999, 248.18999, 248.98999, ..., 230.59 , 232.48999,\n", + " 235.68999],\n", + " [244.79 , 245.29 , 245.89 , ..., 230.48999, 235.09 ,\n", + " 241.39 ],\n", + " ...,\n", + " [296.88998, 296.49 , 296.49 , ..., 297.69 , 297.29 ,\n", + " 296.19 ],\n", + " [298.29 , 297.99 , 297.49 , ..., 298.29 , 298.38998,\n", + " 297.49 ],\n", + " [298.49 , 298.29 , 297.99 , ..., 298.59 , 298.38998,\n", + " 298.29 ]]], dtype=float32)\n", + "Coordinates:\n", + " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", + " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", + " * time (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "da.loc[\"2014-02-14\":\"2014-12-13\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dropping using `drop_sel`\n", + "\n", + "If instead of selecting data we want to drop it, we can use `drop_sel` method:" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.DataArray 'air' (time: 2919, lat: 24, lon: 52)>\n",
+       "array([[[242.5    , 243.5    , 244.     , ..., 232.79999, 235.5    ,\n",
+       "         238.59999],\n",
+       "        [244.5    , 244.7    , 244.2    , ..., 232.79999, 235.29999,\n",
+       "         239.29999],\n",
+       "        [249.79999, 248.89   , 247.5    , ..., 233.2    , 236.39   ,\n",
+       "         241.7    ],\n",
+       "        ...,\n",
+       "        [296.19998, 296.4    , 296.5    , ..., 295.4    , 295.1    ,\n",
+       "         294.69998],\n",
+       "        [296.19998, 296.79   , 296.5    , ..., 295.9    , 295.9    ,\n",
+       "         295.19998],\n",
+       "        [296.79   , 297.1    , 297.     , ..., 296.9    , 296.79   ,\n",
+       "         296.6    ]],\n",
+       "\n",
+       "       [[242.7    , 243.09999, 243.39   , ..., 232.     , 233.59999,\n",
+       "         235.79999],\n",
+       "        [244.09999, 244.2    , 244.09999, ..., 231.     , 232.5    ,\n",
+       "         235.7    ],\n",
+       "        [252.89   , 252.09999, 250.79999, ..., 230.79999, 233.39   ,\n",
+       "         238.5    ],\n",
+       "...\n",
+       "        [293.88998, 295.38998, 297.19   , ..., 295.09   , 294.69   ,\n",
+       "         294.29   ],\n",
+       "        [297.19   , 297.59   , 297.88998, ..., 295.29   , 295.09   ,\n",
+       "         294.38998],\n",
+       "        [298.38998, 298.49   , 298.59   , ..., 295.69   , 295.49   ,\n",
+       "         295.19   ]],\n",
+       "\n",
+       "       [[244.29   , 243.29   , 242.18999, ..., 241.68999, 241.48999,\n",
+       "         241.79   ],\n",
+       "        [249.29   , 248.39   , 246.98999, ..., 239.59   , 240.29   ,\n",
+       "         241.68999],\n",
+       "        [262.19   , 261.38998, 259.99   , ..., 239.89   , 242.59   ,\n",
+       "         246.29   ],\n",
+       "        ...,\n",
+       "        [293.69   , 295.09   , 296.69   , ..., 295.29   , 295.09   ,\n",
+       "         294.69   ],\n",
+       "        [296.88998, 297.19   , 297.49   , ..., 295.69   , 295.69   ,\n",
+       "         295.19   ],\n",
+       "        [298.09   , 298.09   , 298.49   , ..., 296.49   , 296.19   ,\n",
+       "         295.69   ]]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 202.5 205.0 207.5 210.0 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
" + ], + "text/plain": [ + "\n", + "array([[[242.5 , 243.5 , 244. , ..., 232.79999, 235.5 ,\n", + " 238.59999],\n", + " [244.5 , 244.7 , 244.2 , ..., 232.79999, 235.29999,\n", + " 239.29999],\n", + " [249.79999, 248.89 , 247.5 , ..., 233.2 , 236.39 ,\n", + " 241.7 ],\n", + " ...,\n", + " [296.19998, 296.4 , 296.5 , ..., 295.4 , 295.1 ,\n", + " 294.69998],\n", + " [296.19998, 296.79 , 296.5 , ..., 295.9 , 295.9 ,\n", + " 295.19998],\n", + " [296.79 , 297.1 , 297. , ..., 296.9 , 296.79 ,\n", + " 296.6 ]],\n", + "\n", + " [[242.7 , 243.09999, 243.39 , ..., 232. , 233.59999,\n", + " 235.79999],\n", + " [244.09999, 244.2 , 244.09999, ..., 231. , 232.5 ,\n", + " 235.7 ],\n", + " [252.89 , 252.09999, 250.79999, ..., 230.79999, 233.39 ,\n", + " 238.5 ],\n", + "...\n", + " [293.88998, 295.38998, 297.19 , ..., 295.09 , 294.69 ,\n", + " 294.29 ],\n", + " [297.19 , 297.59 , 297.88998, ..., 295.29 , 295.09 ,\n", + " 294.38998],\n", + " [298.38998, 298.49 , 298.59 , ..., 295.69 , 295.49 ,\n", + " 295.19 ]],\n", + "\n", + " [[244.29 , 243.29 , 242.18999, ..., 241.68999, 241.48999,\n", + " 241.79 ],\n", + " [249.29 , 248.39 , 246.98999, ..., 239.59 , 240.29 ,\n", + " 241.68999],\n", + " [262.19 , 261.38998, 259.99 , ..., 239.89 , 242.59 ,\n", + " 246.29 ],\n", + " ...,\n", + " [293.69 , 295.09 , 296.69 , ..., 295.29 , 295.09 ,\n", + " 294.69 ],\n", + " [296.88998, 297.19 , 297.49 , ..., 295.69 , 295.69 ,\n", + " 295.19 ],\n", + " [298.09 , 298.09 , 298.49 , ..., 296.49 , 296.19 ,\n", + " 295.69 ]]], dtype=float32)\n", + "Coordinates:\n", + " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", + " * lon (lon) float32 202.5 205.0 207.5 210.0 ... 322.5 325.0 327.5 330.0\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "da.drop_sel(lat=50.0, lon=200.0, time=\"2014\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So far, all the above will require us to specify exact coordinate values, but what if we don't have the exact values? We can use nearest neighbor lookups to address this issue:\n", + "\n", + "## Nearest Neighbor Lookups\n", + "\n", + "The label based selection methods `sel()` support `method` and `tolerance` keyword argument. The `method` parameter allows for enabling nearest neighbor (inexact) lookups by use of the methods `pad`, `backfill` or `nearest`:\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "tags": [ + "hide-output" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.DataArray 'air' (time: 2920)>\n",
+       "array([262.69998, 263.19998, 270.9    , ..., 264.19   , 265.19   ,\n",
+       "       266.99   ], dtype=float32)\n",
+       "Coordinates:\n",
+       "    lat      float32 52.5\n",
+       "    lon      float32 252.5\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
" + ], + "text/plain": [ + "\n", + "array([262.69998, 263.19998, 270.9 , ..., 264.19 , 265.19 ,\n", + " 266.99 ], dtype=float32)\n", + "Coordinates:\n", + " lat float32 52.5\n", + " lon float32 252.5\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "da.sel(lat=52.25, lon=251.8998, method=\"nearest\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`tolerance` argument limits the maximum distance for valid matches with an inexact lookup:" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.DataArray 'air' (time: 2920)>\n",
+       "array([262.69998, 263.19998, 270.9    , ..., 264.19   , 265.19   ,\n",
+       "       266.99   ], dtype=float32)\n",
+       "Coordinates:\n",
+       "    lat      float32 52.5\n",
+       "    lon      float32 252.5\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
" + ], + "text/plain": [ + "\n", + "array([262.69998, 263.19998, 270.9 , ..., 264.19 , 265.19 ,\n", + " 266.99 ], dtype=float32)\n", + "Coordinates:\n", + " lat float32 52.5\n", + " lon float32 252.5\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "da.sel(lat=52.25, lon=251.8998, method=\"nearest\", tolerance=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "- **All of these indexing methods work on the dataset too:**\n", + "\n", + "We can also use these methods to index all variables in a dataset simultaneously, returning a new dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.Dataset>\n",
+       "Dimensions:  (time: 2920)\n",
+       "Coordinates:\n",
+       "    lat      float32 52.5\n",
+       "    lon      float32 252.5\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Data variables:\n",
+       "    air      (time) float32 262.7 263.2 270.9 274.1 ... 261.6 264.2 265.2 267.0\n",
+       "Attributes:\n",
+       "    Conventions:  COARDS\n",
+       "    title:        4x daily NMC reanalysis (1948)\n",
+       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
+       "    platform:     Model\n",
+       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
" + ], + "text/plain": [ + "\n", + "Dimensions: (time: 2920)\n", + "Coordinates:\n", + " lat float32 52.5\n", + " lon float32 252.5\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Data variables:\n", + " air (time) float32 262.7 263.2 270.9 274.1 ... 261.6 264.2 265.2 267.0\n", + "Attributes:\n", + " Conventions: COARDS\n", + " title: 4x daily NMC reanalysis (1948)\n", + " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", + " platform: Model\n", + " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds.sel(lat=52.25, lon=251.8998, method=\"nearest\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "In total, Xarray supports four different kinds of indexing, as described below and summarized in this table:\n", + "\n", + "| Dimension lookup | Index lookup | `DataArray` syntax | `Dataset` syntax |\n", + "| ---------------- | ------------ | ---------------------| ---------------------|\n", + "| Positional | By integer | `da[:,0]` | *not available* |\n", + "| Positional | By label | `da.loc[:,'IA']` | *not available* |\n", + "| By name | By integer | `da.isel(space=0)` or `da[dict(space=0)]` | `ds.isel(space=0)` or `ds[dict(space=0)]` |\n", + "| By name | By label | `da.sel(space='IA')` or `da.loc[dict(space='IA')]` | `ds.sel(space='IA')` or `ds.loc[dict(space='IA')]` |\n", + "\n", + "\n", + "For enhanced indexing capabilities across all methods, you can utilize DataArray objects as an indexer. For more detailed information, please see the Advanced Indexing notebook.\n", + "\n", + "\n", + "## More Resources\n", + "\n", + "- [Xarray Docs - Indexing and Selecting Data](https://docs.xarray.dev/en/stable/indexing.html)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "NPL 2023b", + "language": "python", + "name": "npl-2023b" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": true, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": true + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/fundamentals/02.2_indexing_Advanced.ipynb b/fundamentals/02.2_indexing_Advanced.ipynb new file mode 100644 index 00000000..ee2663ab --- /dev/null +++ b/fundamentals/02.2_indexing_Advanced.ipynb @@ -0,0 +1,1816 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Advanced Indexing\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Learning Objectives\n", + "\n", + "* Vectorized Indexing \n", + "* Dropping/Masking Data Using `where` and `isin`\n", + "* Fancy DateTime Indexing\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Overview\n", + "\n", + "\n", + "In the pervious notebooks, we learned basic forms of indexing with xarray (positional and name based dimensions, integer and label based indexing) and nearest neighbor lookups. In this tutorial, we will discover more advanced options for vectorized indexing and learn about additional useful methods relating to indexing/selecting data such as masking. \n", + "\n", + "\n", + "First, let's import packages: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import xarray as xr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this tutorial, we’ll use air temperature tutorial dataset from the National Center for Environmental Prediction. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ds = xr.tutorial.load_dataset(\"air_temperature\")\n", + "da = ds.air\n", + "ds" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Vectorized Indexing\n", + "\n", + "Like NumPy and pandas, Xarray supports indexing many array elements at once in a\n", + "*vectorized* manner. \n", + "\n", + "If you only provide integers, slices, or unlabeled arrays (array without dimension names, such as `np.ndarray`, `list`, but not `DataArray()`) indexing can be understood as orthogonally." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "da[0,[2,4,10,13],[1,6,7]].plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But for more flexibility, you can supply `DataArray()` objects as indexers. \n", + "\n", + "Vectorized indexing using `DataArrays()` may be used to extract information from the nearest grid cells of interest, for example, the nearest climate model grid cells to a collection specified weather station latitudes and longitudes.\n", + "\n", + "**To trigger vectorized indexing behavior you will need to provide the selection dimensions with a new shared output dimension name.** \n", + "\n", + "In the example below, the selections of the closest latitude and longitude are renamed to an output dimension named `points`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define target latitude and longitude (where weather stations might be)\n", + "lat_points = xr.DataArray([31, 41, 42, 42], dims=\"points\")\n", + "lon_points = xr.DataArray([200, 201, 202, 205], dims=\"points\")\n", + "lat_points" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "lon_points" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, retrieve data at the grid cells nearest to the target latitudes and longitudes (weather stations):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "da.sel(lat=lat_points, lon=lon_points,method=\"nearest\").plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see in the above example, the dimensions on resultant arrays are given by the ordered union of the indexers’ dimensions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "da.sel(lat=lat_points, lon=lon_points,method=\"nearest\").dims" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Please note that slices or sequences/arrays without named-dimensions are treated as if they have the same dimension which is indexed along:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "da.sel(lat=[20, 30, 40], lon=lon_points,method=\"nearest\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "Warning: If an indexer is a DataArray(), its coordinates should not conflict with the selected subpart of the target array (except for the explicitly indexed dimensions with .loc/.sel). Otherwise, IndexError will be raised.\n", + " \n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Masking with `where()`\n", + "\n", + "Indexing methods on Xarray objects generally return a subset of the original data. However, it is sometimes useful to select an object with the same shape as the original data, but with some elements masked. To do this type of selection in Xarray, use `where()`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's replace the missing values (nan) with some placeholder\n", + "\n", + "ds.air.where(ds.air.notnull(), -9999)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also select a condition to create a mask. For example, here we want to mask all the points with latitudes above 60 N. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "da[0,:,:].plot();" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "da_masked = da.where(da.lat<60)\n", + "da_masked[0,:,:].plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By default where maintains the original size of the data. You can use of the option `drop=True` to clips coordinate elements that are fully masked:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "da_masked = da.where(da.lat<60, drop=True)\n", + "da_masked[0,:,:].plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Selecting Values with `isin`\n", + "\n", + "To check whether elements of an xarray object contain a single object, you can compare with the equality operator `==` (e.g., `arr == 3`). To check multiple values, use `isin()`:\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is a simple example: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x_da = xr.DataArray([1, 2, 3, 4, 5], dims=[\"x\"])\n", + "\n", + "#-- select points with values equal to 2 and 4:\n", + "x_da.isin([2, 4])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`isin()` works particularly well with `where()` to support indexing by arrays that are not already labels of an array. \n", + "\n", + "For example, we have another DataArray that displays the status flags of the data-collecting device for our data. Here, flags with value 0 and -1 signifies the device was functioning correctly, while 0 indicates a malfunction, implying that the resulting data collected may not be accurate." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "flags = xr.DataArray(\n", + " np.random.randint(-1, 5, da.shape),\n", + " dims=da.dims,\n", + " coords=da.coords\n", + ")\n", + "flags" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we want to only see the data for points where out measurement device is working correctly: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "da_masked = da.where(flags.isin([1,2,3,4,5]), drop=True)\n", + "da_masked[0,:,:].plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "Warning: Please note that when done repeatedly, this type of indexing is significantly slower than using sel().\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Align and Reindex \n", + "\n", + "Xarray enforces alignment between index Coordinates (that is, coordinates with the same name as a dimension, marked by *) on objects used in binary operations.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "da" + ] + }, + { + "cell_type": "code", + "execution_count": 240, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.DataArray (time: 4, space: 3)>\n",
+       "array([[0.41063639, 0.64438845, 0.34046896],\n",
+       "       [0.87939503, 0.1581048 , 0.34112795],\n",
+       "       [0.85912828, 0.92362903, 0.77612158],\n",
+       "       [0.76501244, 0.20228242, 0.94031457]])\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 2000-01-04\n",
+       "  * space    (space) <U2 'IL' 'IA' 'IN'
" + ], + "text/plain": [ + "\n", + "array([[0.41063639, 0.64438845, 0.34046896],\n", + " [0.87939503, 0.1581048 , 0.34112795],\n", + " [0.85912828, 0.92362903, 0.77612158],\n", + " [0.76501244, 0.20228242, 0.94031457]])\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 2000-01-04\n", + " * space (space) \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.DataArray (time: 4, space: 2)>\n",
+       "array([[0.64438845,        nan],\n",
+       "       [0.1581048 ,        nan],\n",
+       "       [0.92362903,        nan],\n",
+       "       [0.20228242,        nan]])\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 2000-01-04\n",
+       "  * space    (space) <U2 'IA' 'CA'
" + ], + "text/plain": [ + "\n", + "array([[0.64438845, nan],\n", + " [0.1581048 , nan],\n", + " [0.92362903, nan],\n", + " [0.20228242, nan]])\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 2000-01-04\n", + " * space (space) \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.DataArray 'air' (time: 20, lat: 25, lon: 53)>\n",
+       "array([[[238.09999, 238.29999, 238.2    , ..., 238.7    , 240.09999,\n",
+       "         241.7    ],\n",
+       "        [241.2    , 240.5    , 239.2    , ..., 234.89   , 236.     ,\n",
+       "         238.39   ],\n",
+       "        [244.29999, 244.     , 243.09999, ..., 235.59999, 237.79999,\n",
+       "         241.59999],\n",
+       "        ...,\n",
+       "        [296.     , 295.5    , 295.4    , ..., 294.6    , 295.19998,\n",
+       "         295.4    ],\n",
+       "        [297.1    , 297.     , 296.69998, ..., 295.9    , 296.5    ,\n",
+       "         296.1    ],\n",
+       "        [298.1    , 298.     , 297.4    , ..., 296.79   , 297.6    ,\n",
+       "         297.29   ]],\n",
+       "\n",
+       "       [[      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "               nan],\n",
+       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "               nan],\n",
+       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "               nan],\n",
+       "...\n",
+       "        [294.6    , 293.6    , 293.19998, ..., 295.79   , 296.29   ,\n",
+       "         296.4    ],\n",
+       "        [295.9    , 295.4    , 294.69998, ..., 297.1    , 297.5    ,\n",
+       "         296.9    ],\n",
+       "        [297.1    , 296.69998, 296.     , ..., 297.69998, 297.79   ,\n",
+       "         297.     ]],\n",
+       "\n",
+       "       [[      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "               nan],\n",
+       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "               nan],\n",
+       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "               nan],\n",
+       "        ...,\n",
+       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "               nan],\n",
+       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "               nan],\n",
+       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
+       "               nan]]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2013-02-01 ... 2013-02-01T19:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
" + ], + "text/plain": [ + "\n", + "array([[[238.09999, 238.29999, 238.2 , ..., 238.7 , 240.09999,\n", + " 241.7 ],\n", + " [241.2 , 240.5 , 239.2 , ..., 234.89 , 236. ,\n", + " 238.39 ],\n", + " [244.29999, 244. , 243.09999, ..., 235.59999, 237.79999,\n", + " 241.59999],\n", + " ...,\n", + " [296. , 295.5 , 295.4 , ..., 294.6 , 295.19998,\n", + " 295.4 ],\n", + " [297.1 , 297. , 296.69998, ..., 295.9 , 296.5 ,\n", + " 296.1 ],\n", + " [298.1 , 298. , 297.4 , ..., 296.79 , 297.6 ,\n", + " 297.29 ]],\n", + "\n", + " [[ nan, nan, nan, ..., nan, nan,\n", + " nan],\n", + " [ nan, nan, nan, ..., nan, nan,\n", + " nan],\n", + " [ nan, nan, nan, ..., nan, nan,\n", + " nan],\n", + "...\n", + " [294.6 , 293.6 , 293.19998, ..., 295.79 , 296.29 ,\n", + " 296.4 ],\n", + " [295.9 , 295.4 , 294.69998, ..., 297.1 , 297.5 ,\n", + " 296.9 ],\n", + " [297.1 , 296.69998, 296. , ..., 297.69998, 297.79 ,\n", + " 297. ]],\n", + "\n", + " [[ nan, nan, nan, ..., nan, nan,\n", + " nan],\n", + " [ nan, nan, nan, ..., nan, nan,\n", + " nan],\n", + " [ nan, nan, nan, ..., nan, nan,\n", + " nan],\n", + " ...,\n", + " [ nan, nan, nan, ..., nan, nan,\n", + " nan],\n", + " [ nan, nan, nan, ..., nan, nan,\n", + " nan],\n", + " [ nan, nan, nan, ..., nan, nan,\n", + " nan]]], dtype=float32)\n", + "Coordinates:\n", + " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", + " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", + " * time (time) datetime64[ns] 2013-02-01 ... 2013-02-01T19:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 237, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "da.reindex(time=new_time)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fancy DateTime Indexing \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "hide-output" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Additional Resources\n", + "\n", + "- [Xarray Docs - Indexing and Selecting Data](https://docs.xarray.dev/en/stable/indexing.html)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "NPL 2023b", + "language": "python", + "name": "npl-2023b" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": true, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": true + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 6df457d26f51831f1d8acdbe4675ada5ae6ce0fe Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Thu, 6 Jul 2023 17:48:39 -0600 Subject: [PATCH 04/54] renaming these --- fundamentals/02_indexing_Advanced.ipynb | 1816 --------- fundamentals/02_indexing_Basic.ipynb | 4831 ----------------------- 2 files changed, 6647 deletions(-) delete mode 100644 fundamentals/02_indexing_Advanced.ipynb delete mode 100644 fundamentals/02_indexing_Basic.ipynb diff --git a/fundamentals/02_indexing_Advanced.ipynb b/fundamentals/02_indexing_Advanced.ipynb deleted file mode 100644 index ee2663ab..00000000 --- a/fundamentals/02_indexing_Advanced.ipynb +++ /dev/null @@ -1,1816 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Advanced Indexing\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Learning Objectives\n", - "\n", - "* Vectorized Indexing \n", - "* Dropping/Masking Data Using `where` and `isin`\n", - "* Fancy DateTime Indexing\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Overview\n", - "\n", - "\n", - "In the pervious notebooks, we learned basic forms of indexing with xarray (positional and name based dimensions, integer and label based indexing) and nearest neighbor lookups. In this tutorial, we will discover more advanced options for vectorized indexing and learn about additional useful methods relating to indexing/selecting data such as masking. \n", - "\n", - "\n", - "First, let's import packages: " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import xarray as xr" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this tutorial, we’ll use air temperature tutorial dataset from the National Center for Environmental Prediction. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ds = xr.tutorial.load_dataset(\"air_temperature\")\n", - "da = ds.air\n", - "ds" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Vectorized Indexing\n", - "\n", - "Like NumPy and pandas, Xarray supports indexing many array elements at once in a\n", - "*vectorized* manner. \n", - "\n", - "If you only provide integers, slices, or unlabeled arrays (array without dimension names, such as `np.ndarray`, `list`, but not `DataArray()`) indexing can be understood as orthogonally." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "da[0,[2,4,10,13],[1,6,7]].plot();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "But for more flexibility, you can supply `DataArray()` objects as indexers. \n", - "\n", - "Vectorized indexing using `DataArrays()` may be used to extract information from the nearest grid cells of interest, for example, the nearest climate model grid cells to a collection specified weather station latitudes and longitudes.\n", - "\n", - "**To trigger vectorized indexing behavior you will need to provide the selection dimensions with a new shared output dimension name.** \n", - "\n", - "In the example below, the selections of the closest latitude and longitude are renamed to an output dimension named `points`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define target latitude and longitude (where weather stations might be)\n", - "lat_points = xr.DataArray([31, 41, 42, 42], dims=\"points\")\n", - "lon_points = xr.DataArray([200, 201, 202, 205], dims=\"points\")\n", - "lat_points" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "lon_points" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, retrieve data at the grid cells nearest to the target latitudes and longitudes (weather stations):" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "da.sel(lat=lat_points, lon=lon_points,method=\"nearest\").plot();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As we can see in the above example, the dimensions on resultant arrays are given by the ordered union of the indexers’ dimensions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "da.sel(lat=lat_points, lon=lon_points,method=\"nearest\").dims" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Please note that slices or sequences/arrays without named-dimensions are treated as if they have the same dimension which is indexed along:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "da.sel(lat=[20, 30, 40], lon=lon_points,method=\"nearest\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - " \n", - "Warning: If an indexer is a DataArray(), its coordinates should not conflict with the selected subpart of the target array (except for the explicitly indexed dimensions with .loc/.sel). Otherwise, IndexError will be raised.\n", - " \n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Masking with `where()`\n", - "\n", - "Indexing methods on Xarray objects generally return a subset of the original data. However, it is sometimes useful to select an object with the same shape as the original data, but with some elements masked. To do this type of selection in Xarray, use `where()`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Let's replace the missing values (nan) with some placeholder\n", - "\n", - "ds.air.where(ds.air.notnull(), -9999)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also select a condition to create a mask. For example, here we want to mask all the points with latitudes above 60 N. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "da[0,:,:].plot();" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "da_masked = da.where(da.lat<60)\n", - "da_masked[0,:,:].plot();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "By default where maintains the original size of the data. You can use of the option `drop=True` to clips coordinate elements that are fully masked:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "da_masked = da.where(da.lat<60, drop=True)\n", - "da_masked[0,:,:].plot();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Selecting Values with `isin`\n", - "\n", - "To check whether elements of an xarray object contain a single object, you can compare with the equality operator `==` (e.g., `arr == 3`). To check multiple values, use `isin()`:\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here is a simple example: " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x_da = xr.DataArray([1, 2, 3, 4, 5], dims=[\"x\"])\n", - "\n", - "#-- select points with values equal to 2 and 4:\n", - "x_da.isin([2, 4])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`isin()` works particularly well with `where()` to support indexing by arrays that are not already labels of an array. \n", - "\n", - "For example, we have another DataArray that displays the status flags of the data-collecting device for our data. Here, flags with value 0 and -1 signifies the device was functioning correctly, while 0 indicates a malfunction, implying that the resulting data collected may not be accurate." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "flags = xr.DataArray(\n", - " np.random.randint(-1, 5, da.shape),\n", - " dims=da.dims,\n", - " coords=da.coords\n", - ")\n", - "flags" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we want to only see the data for points where out measurement device is working correctly: " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "da_masked = da.where(flags.isin([1,2,3,4,5]), drop=True)\n", - "da_masked[0,:,:].plot();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - " \n", - "Warning: Please note that when done repeatedly, this type of indexing is significantly slower than using sel().\n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Align and Reindex \n", - "\n", - "Xarray enforces alignment between index Coordinates (that is, coordinates with the same name as a dimension, marked by *) on objects used in binary operations.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "da" - ] - }, - { - "cell_type": "code", - "execution_count": 240, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.DataArray (time: 4, space: 3)>\n",
-       "array([[0.41063639, 0.64438845, 0.34046896],\n",
-       "       [0.87939503, 0.1581048 , 0.34112795],\n",
-       "       [0.85912828, 0.92362903, 0.77612158],\n",
-       "       [0.76501244, 0.20228242, 0.94031457]])\n",
-       "Coordinates:\n",
-       "  * time     (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 2000-01-04\n",
-       "  * space    (space) <U2 'IL' 'IA' 'IN'
" - ], - "text/plain": [ - "\n", - "array([[0.41063639, 0.64438845, 0.34046896],\n", - " [0.87939503, 0.1581048 , 0.34112795],\n", - " [0.85912828, 0.92362903, 0.77612158],\n", - " [0.76501244, 0.20228242, 0.94031457]])\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 2000-01-04\n", - " * space (space) \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.DataArray (time: 4, space: 2)>\n",
-       "array([[0.64438845,        nan],\n",
-       "       [0.1581048 ,        nan],\n",
-       "       [0.92362903,        nan],\n",
-       "       [0.20228242,        nan]])\n",
-       "Coordinates:\n",
-       "  * time     (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 2000-01-04\n",
-       "  * space    (space) <U2 'IA' 'CA'
" - ], - "text/plain": [ - "\n", - "array([[0.64438845, nan],\n", - " [0.1581048 , nan],\n", - " [0.92362903, nan],\n", - " [0.20228242, nan]])\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 2000-01-04\n", - " * space (space) \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.DataArray 'air' (time: 20, lat: 25, lon: 53)>\n",
-       "array([[[238.09999, 238.29999, 238.2    , ..., 238.7    , 240.09999,\n",
-       "         241.7    ],\n",
-       "        [241.2    , 240.5    , 239.2    , ..., 234.89   , 236.     ,\n",
-       "         238.39   ],\n",
-       "        [244.29999, 244.     , 243.09999, ..., 235.59999, 237.79999,\n",
-       "         241.59999],\n",
-       "        ...,\n",
-       "        [296.     , 295.5    , 295.4    , ..., 294.6    , 295.19998,\n",
-       "         295.4    ],\n",
-       "        [297.1    , 297.     , 296.69998, ..., 295.9    , 296.5    ,\n",
-       "         296.1    ],\n",
-       "        [298.1    , 298.     , 297.4    , ..., 296.79   , 297.6    ,\n",
-       "         297.29   ]],\n",
-       "\n",
-       "       [[      nan,       nan,       nan, ...,       nan,       nan,\n",
-       "               nan],\n",
-       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
-       "               nan],\n",
-       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
-       "               nan],\n",
-       "...\n",
-       "        [294.6    , 293.6    , 293.19998, ..., 295.79   , 296.29   ,\n",
-       "         296.4    ],\n",
-       "        [295.9    , 295.4    , 294.69998, ..., 297.1    , 297.5    ,\n",
-       "         296.9    ],\n",
-       "        [297.1    , 296.69998, 296.     , ..., 297.69998, 297.79   ,\n",
-       "         297.     ]],\n",
-       "\n",
-       "       [[      nan,       nan,       nan, ...,       nan,       nan,\n",
-       "               nan],\n",
-       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
-       "               nan],\n",
-       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
-       "               nan],\n",
-       "        ...,\n",
-       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
-       "               nan],\n",
-       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
-       "               nan],\n",
-       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
-       "               nan]]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-       "  * time     (time) datetime64[ns] 2013-02-01 ... 2013-02-01T19:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
" - ], - "text/plain": [ - "\n", - "array([[[238.09999, 238.29999, 238.2 , ..., 238.7 , 240.09999,\n", - " 241.7 ],\n", - " [241.2 , 240.5 , 239.2 , ..., 234.89 , 236. ,\n", - " 238.39 ],\n", - " [244.29999, 244. , 243.09999, ..., 235.59999, 237.79999,\n", - " 241.59999],\n", - " ...,\n", - " [296. , 295.5 , 295.4 , ..., 294.6 , 295.19998,\n", - " 295.4 ],\n", - " [297.1 , 297. , 296.69998, ..., 295.9 , 296.5 ,\n", - " 296.1 ],\n", - " [298.1 , 298. , 297.4 , ..., 296.79 , 297.6 ,\n", - " 297.29 ]],\n", - "\n", - " [[ nan, nan, nan, ..., nan, nan,\n", - " nan],\n", - " [ nan, nan, nan, ..., nan, nan,\n", - " nan],\n", - " [ nan, nan, nan, ..., nan, nan,\n", - " nan],\n", - "...\n", - " [294.6 , 293.6 , 293.19998, ..., 295.79 , 296.29 ,\n", - " 296.4 ],\n", - " [295.9 , 295.4 , 294.69998, ..., 297.1 , 297.5 ,\n", - " 296.9 ],\n", - " [297.1 , 296.69998, 296. , ..., 297.69998, 297.79 ,\n", - " 297. ]],\n", - "\n", - " [[ nan, nan, nan, ..., nan, nan,\n", - " nan],\n", - " [ nan, nan, nan, ..., nan, nan,\n", - " nan],\n", - " [ nan, nan, nan, ..., nan, nan,\n", - " nan],\n", - " ...,\n", - " [ nan, nan, nan, ..., nan, nan,\n", - " nan],\n", - " [ nan, nan, nan, ..., nan, nan,\n", - " nan],\n", - " [ nan, nan, nan, ..., nan, nan,\n", - " nan]]], dtype=float32)\n", - "Coordinates:\n", - " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", - " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", - " * time (time) datetime64[ns] 2013-02-01 ... 2013-02-01T19:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 237, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "da.reindex(time=new_time)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fancy DateTime Indexing \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "hide-output" - ] - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Additional Resources\n", - "\n", - "- [Xarray Docs - Indexing and Selecting Data](https://docs.xarray.dev/en/stable/indexing.html)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "NPL 2023b", - "language": "python", - "name": "npl-2023b" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": true, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": true - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/fundamentals/02_indexing_Basic.ipynb b/fundamentals/02_indexing_Basic.ipynb deleted file mode 100644 index d3f45b6a..00000000 --- a/fundamentals/02_indexing_Basic.ipynb +++ /dev/null @@ -1,4831 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "# Indexing and Selecting Data\n", - "\n", - "## Learning Objectives\n", - "\n", - "- Select data by position using `.isel` with values or slices\n", - "- Select data by label using `.sel` with values or slices\n", - "- Select timeseries data by date/time with values or slices\n", - "- Use nearest-neighbor lookups with `.sel`\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# Indexing and Selecting Data\n", - "\n", - "Xarray offers extremely flexible indexing routines that combine the best features of NumPy and Pandas for data selection.\n", - "\n", - "The most basic way to access elements of a `DataArray` object is to use Python’s `[]` syntax, such as `array[i, j]`, where `i` and `j` are both integers.\n", - "\n", - "As xarray objects can store coordinates corresponding to each dimension of an array, label-based indexing similar to `pandas.DataFrame.loc` is also possible. In label-based indexing, the element position `i` is automatically looked-up from the coordinate values.\n", - "\n", - "By leveraging the labeled dimensions and coordinates provided by Xarray, users can effortlessly access, subset, and manipulate data along multiple axes, enabling complex operations such as slicing, masking, and aggregating data based on specific criteria. \n", - "\n", - "This indexing and selection capability of Xarray not only enhances data exploration and analysis workflows but also promotes reproducibility and efficiency by providing a convenient interface for working with multi-dimensional data structures.\n", - "\n", - "## Quick Overview \n", - "\n", - "In total, xarray supports four different kinds of indexing, as described below and summarized in this table:\n", - "\n", - "| Dimension lookup | Index lookup | `DataArray` syntax | `Dataset` syntax |\n", - "| ---------------- | ------------ | ---------------------| ---------------------|\n", - "| Positional | By integer | `da[:,0]` | *not available* |\n", - "| Positional | By label | `da.loc[:,'IA']` | *not available* |\n", - "| By name | By integer | `da.isel(space=0)` or `da[dict(space=0)]` | `ds.isel(space=0)` or `ds[dict(space=0)]` |\n", - "| By name | By label | `da.sel(space='IA')` or `da.loc[dict(space='IA')]` | `ds.sel(space='IA')` or `ds.loc[dict(space='IA')]` |\n", - "\n", - "\n", - "----------\n", - "\n", - "In this tutorial, first we cover the positional indexing and label-based indexing, next we will cover more advanced techniques such as nearest neighbor lookups. \n", - "\n", - "First, let's import packages: " - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import xarray as xr" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we’ll use air temperature tutorial dataset from the National Center for Environmental Prediction. " - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.Dataset>\n",
-       "Dimensions:  (lat: 25, time: 2920, lon: 53)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Data variables:\n",
-       "    air      (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n",
-       "Attributes:\n",
-       "    Conventions:  COARDS\n",
-       "    title:        4x daily NMC reanalysis (1948)\n",
-       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
-       "    platform:     Model\n",
-       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
" - ], - "text/plain": [ - "\n", - "Dimensions: (lat: 25, time: 2920, lon: 53)\n", - "Coordinates:\n", - " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", - " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Data variables:\n", - " air (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n", - "Attributes:\n", - " Conventions: COARDS\n", - " title: 4x daily NMC reanalysis (1948)\n", - " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", - " platform: Model\n", - " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ds = xr.tutorial.load_dataset(\"air_temperature\")\n", - "ds" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "da = ds[\"air\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Position-based Indexing\n", - "\n", - "Indexing a `DataArray` directly works (mostly) just like it does for numpy `ndarrays`, except that the returned object is always another `DataArray`:\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### NumPy Positional Indexing\n", - "\n", - "When working with numpy, indexing is done by position (slices/ranges/scalars).\n", - "\n", - "For example:" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2920, 25, 53)" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np_array = ds[\"air\"].data # numpy array\n", - "np_array.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Indexing is 0-based in NumPy:" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "242.09999" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np_array[1,0,0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Similarly, we can select a range in NumPy:" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([295. , 294.4 , 294.5 , ..., 297.29, 297.79, 297.99], dtype=float32)" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# extract a time-series for one spatial location\n", - "np_array[:, 20, 40]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Positional Indexing with Xarray" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Xarray offers extremely flexible indexing routines that combine the best\n", - "features of NumPy and pandas for data selection." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### NumPy style indexing with Xarray\n", - "\n", - "NumPy style indexing works exactly the same with Xarray but it also preserves labels and metadata. " - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.DataArray 'air' (time: 2920)>\n",
-       "array([295.  , 294.4 , 294.5 , ..., 297.29, 297.79, 297.99], dtype=float32)\n",
-       "Coordinates:\n",
-       "    lat      float32 25.0\n",
-       "    lon      float32 300.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
" - ], - "text/plain": [ - "\n", - "array([295. , 294.4 , 294.5 , ..., 297.29, 297.79, 297.99], dtype=float32)\n", - "Coordinates:\n", - " lat float32 25.0\n", - " lon float32 300.0\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "da[:, 20, 40]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Positional Indexing Using Dimension Names" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Remembering the axis order can be challenging even with 2D arrays (is `np_array[0,3]` the first row and third column or first column and third row? or did I store these samples by row or by column when I saved the data?!). The difficulty is compounded with added dimensions. \n", - "\n", - "Xarray objects eliminate much of the mental overhead by adding indexing using dimension names:\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "da.isel(lat=20, lon=40).plot();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Slicing is also possible similarly:" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "da.isel(time=slice(0,20),lat=20, lon=40).plot();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Using the isel method, the user can choose/slice the specific elements from a Dataset or DataArray.\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "But what if I wanted to select data only for 2014, how would I know the indices for it? Xarray reduce this complexity by introducing label-based indexing. \n", - "\n", - "## Label-based Indexing\n", - "\n", - "To select data by coordinate labels instead of integer indices we can use the same syntax, using `sel` instead of `isel`:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For example, let's select the data for one day 2014-01-01 at Lat 25 N and Lon 210 E using `sel` :" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": { - "tags": [ - "hide-output" - ] - }, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "da.sel(time=\"2014-01-01\",lat=25, lon=210).plot();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, let's select data for year 2014 at this coordinate:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": { - "tags": [ - "hide-output" - ] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.DataArray 'air' (time: 1460)>\n",
-       "array([277.6 , 277.5 , 277.4 , ..., 277.59, 277.59, 277.59], dtype=float32)\n",
-       "Coordinates:\n",
-       "    lat      float32 50.0\n",
-       "    lon      float32 200.0\n",
-       "  * time     (time) datetime64[ns] 2014-01-01 ... 2014-12-31T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
" - ], - "text/plain": [ - "\n", - "array([277.6 , 277.5 , 277.4 , ..., 277.59, 277.59, 277.59], dtype=float32)\n", - "Coordinates:\n", - " lat float32 50.0\n", - " lon float32 200.0\n", - " * time (time) datetime64[ns] 2014-01-01 ... 2014-12-31T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "da.sel(lat=50.0, lon=200.0, time=\"2014\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Similarly we can do slicing or filter a date range using the `.slice` function: " - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.DataArray 'air' (time: 1212, lat: 25, lon: 53)>\n",
-       "array([[[246.     , 245.09999, 244.09999, ..., 240.2    , 242.     ,\n",
-       "         244.2    ],\n",
-       "        [240.59999, 241.7    , 242.09999, ..., 239.09999, 241.2    ,\n",
-       "         244.59999],\n",
-       "        [238.2    , 239.79999, 240.79999, ..., 243.89   , 246.89   ,\n",
-       "         251.2    ],\n",
-       "        ...,\n",
-       "        [295.6    , 295.6    , 296.     , ..., 293.6    , 293.6    ,\n",
-       "         294.1    ],\n",
-       "        [297.     , 297.     , 297.     , ..., 293.69998, 293.9    ,\n",
-       "         294.6    ],\n",
-       "        [297.5    , 297.69998, 297.69998, ..., 294.5    , 294.69998,\n",
-       "         295.19998]],\n",
-       "\n",
-       "       [[245.2    , 244.29999, 243.     , ..., 241.89   , 243.09999,\n",
-       "         244.59999],\n",
-       "        [241.09999, 241.89   , 241.89   , ..., 241.89   , 243.59999,\n",
-       "         246.39   ],\n",
-       "        [239.89   , 241.     , 241.29999, ..., 245.09999, 248.2    ,\n",
-       "         252.2    ],\n",
-       "...\n",
-       "        [296.49   , 295.59   , 295.49   , ..., 297.29   , 297.38998,\n",
-       "         296.79   ],\n",
-       "        [298.09   , 297.79   , 297.19   , ..., 297.69   , 298.09   ,\n",
-       "         297.38998],\n",
-       "        [298.49   , 298.29   , 297.99   , ..., 298.29   , 298.09   ,\n",
-       "         297.99   ]],\n",
-       "\n",
-       "       [[246.29   , 246.39   , 245.98999, ..., 232.29   , 233.39   ,\n",
-       "         234.98999],\n",
-       "        [246.68999, 248.18999, 248.98999, ..., 230.59   , 232.48999,\n",
-       "         235.68999],\n",
-       "        [244.79   , 245.29   , 245.89   , ..., 230.48999, 235.09   ,\n",
-       "         241.39   ],\n",
-       "        ...,\n",
-       "        [296.88998, 296.49   , 296.49   , ..., 297.69   , 297.29   ,\n",
-       "         296.19   ],\n",
-       "        [298.29   , 297.99   , 297.49   , ..., 298.29   , 298.38998,\n",
-       "         297.49   ],\n",
-       "        [298.49   , 298.29   , 297.99   , ..., 298.59   , 298.38998,\n",
-       "         298.29   ]]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-       "  * time     (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
" - ], - "text/plain": [ - "\n", - "array([[[246. , 245.09999, 244.09999, ..., 240.2 , 242. ,\n", - " 244.2 ],\n", - " [240.59999, 241.7 , 242.09999, ..., 239.09999, 241.2 ,\n", - " 244.59999],\n", - " [238.2 , 239.79999, 240.79999, ..., 243.89 , 246.89 ,\n", - " 251.2 ],\n", - " ...,\n", - " [295.6 , 295.6 , 296. , ..., 293.6 , 293.6 ,\n", - " 294.1 ],\n", - " [297. , 297. , 297. , ..., 293.69998, 293.9 ,\n", - " 294.6 ],\n", - " [297.5 , 297.69998, 297.69998, ..., 294.5 , 294.69998,\n", - " 295.19998]],\n", - "\n", - " [[245.2 , 244.29999, 243. , ..., 241.89 , 243.09999,\n", - " 244.59999],\n", - " [241.09999, 241.89 , 241.89 , ..., 241.89 , 243.59999,\n", - " 246.39 ],\n", - " [239.89 , 241. , 241.29999, ..., 245.09999, 248.2 ,\n", - " 252.2 ],\n", - "...\n", - " [296.49 , 295.59 , 295.49 , ..., 297.29 , 297.38998,\n", - " 296.79 ],\n", - " [298.09 , 297.79 , 297.19 , ..., 297.69 , 298.09 ,\n", - " 297.38998],\n", - " [298.49 , 298.29 , 297.99 , ..., 298.29 , 298.09 ,\n", - " 297.99 ]],\n", - "\n", - " [[246.29 , 246.39 , 245.98999, ..., 232.29 , 233.39 ,\n", - " 234.98999],\n", - " [246.68999, 248.18999, 248.98999, ..., 230.59 , 232.48999,\n", - " 235.68999],\n", - " [244.79 , 245.29 , 245.89 , ..., 230.48999, 235.09 ,\n", - " 241.39 ],\n", - " ...,\n", - " [296.88998, 296.49 , 296.49 , ..., 297.69 , 297.29 ,\n", - " 296.19 ],\n", - " [298.29 , 297.99 , 297.49 , ..., 298.29 , 298.38998,\n", - " 297.49 ],\n", - " [298.49 , 298.29 , 297.99 , ..., 298.59 , 298.38998,\n", - " 298.29 ]]], dtype=float32)\n", - "Coordinates:\n", - " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", - " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", - " * time (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# demonstrate slicing\n", - "da.sel(time=slice(\"2014-02-14\", \"2014-12-13\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Xarray also supports label-based indexing, just like pandas using `.loc`. Because we use a `pandas.Index` under the hood, label based indexing is very fast. To do label based indexing, use the `loc` attribute:" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.DataArray 'air' (time: 1212, lat: 25, lon: 53)>\n",
-       "array([[[246.     , 245.09999, 244.09999, ..., 240.2    , 242.     ,\n",
-       "         244.2    ],\n",
-       "        [240.59999, 241.7    , 242.09999, ..., 239.09999, 241.2    ,\n",
-       "         244.59999],\n",
-       "        [238.2    , 239.79999, 240.79999, ..., 243.89   , 246.89   ,\n",
-       "         251.2    ],\n",
-       "        ...,\n",
-       "        [295.6    , 295.6    , 296.     , ..., 293.6    , 293.6    ,\n",
-       "         294.1    ],\n",
-       "        [297.     , 297.     , 297.     , ..., 293.69998, 293.9    ,\n",
-       "         294.6    ],\n",
-       "        [297.5    , 297.69998, 297.69998, ..., 294.5    , 294.69998,\n",
-       "         295.19998]],\n",
-       "\n",
-       "       [[245.2    , 244.29999, 243.     , ..., 241.89   , 243.09999,\n",
-       "         244.59999],\n",
-       "        [241.09999, 241.89   , 241.89   , ..., 241.89   , 243.59999,\n",
-       "         246.39   ],\n",
-       "        [239.89   , 241.     , 241.29999, ..., 245.09999, 248.2    ,\n",
-       "         252.2    ],\n",
-       "...\n",
-       "        [296.49   , 295.59   , 295.49   , ..., 297.29   , 297.38998,\n",
-       "         296.79   ],\n",
-       "        [298.09   , 297.79   , 297.19   , ..., 297.69   , 298.09   ,\n",
-       "         297.38998],\n",
-       "        [298.49   , 298.29   , 297.99   , ..., 298.29   , 298.09   ,\n",
-       "         297.99   ]],\n",
-       "\n",
-       "       [[246.29   , 246.39   , 245.98999, ..., 232.29   , 233.39   ,\n",
-       "         234.98999],\n",
-       "        [246.68999, 248.18999, 248.98999, ..., 230.59   , 232.48999,\n",
-       "         235.68999],\n",
-       "        [244.79   , 245.29   , 245.89   , ..., 230.48999, 235.09   ,\n",
-       "         241.39   ],\n",
-       "        ...,\n",
-       "        [296.88998, 296.49   , 296.49   , ..., 297.69   , 297.29   ,\n",
-       "         296.19   ],\n",
-       "        [298.29   , 297.99   , 297.49   , ..., 298.29   , 298.38998,\n",
-       "         297.49   ],\n",
-       "        [298.49   , 298.29   , 297.99   , ..., 298.59   , 298.38998,\n",
-       "         298.29   ]]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-       "  * time     (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
" - ], - "text/plain": [ - "\n", - "array([[[246. , 245.09999, 244.09999, ..., 240.2 , 242. ,\n", - " 244.2 ],\n", - " [240.59999, 241.7 , 242.09999, ..., 239.09999, 241.2 ,\n", - " 244.59999],\n", - " [238.2 , 239.79999, 240.79999, ..., 243.89 , 246.89 ,\n", - " 251.2 ],\n", - " ...,\n", - " [295.6 , 295.6 , 296. , ..., 293.6 , 293.6 ,\n", - " 294.1 ],\n", - " [297. , 297. , 297. , ..., 293.69998, 293.9 ,\n", - " 294.6 ],\n", - " [297.5 , 297.69998, 297.69998, ..., 294.5 , 294.69998,\n", - " 295.19998]],\n", - "\n", - " [[245.2 , 244.29999, 243. , ..., 241.89 , 243.09999,\n", - " 244.59999],\n", - " [241.09999, 241.89 , 241.89 , ..., 241.89 , 243.59999,\n", - " 246.39 ],\n", - " [239.89 , 241. , 241.29999, ..., 245.09999, 248.2 ,\n", - " 252.2 ],\n", - "...\n", - " [296.49 , 295.59 , 295.49 , ..., 297.29 , 297.38998,\n", - " 296.79 ],\n", - " [298.09 , 297.79 , 297.19 , ..., 297.69 , 298.09 ,\n", - " 297.38998],\n", - " [298.49 , 298.29 , 297.99 , ..., 298.29 , 298.09 ,\n", - " 297.99 ]],\n", - "\n", - " [[246.29 , 246.39 , 245.98999, ..., 232.29 , 233.39 ,\n", - " 234.98999],\n", - " [246.68999, 248.18999, 248.98999, ..., 230.59 , 232.48999,\n", - " 235.68999],\n", - " [244.79 , 245.29 , 245.89 , ..., 230.48999, 235.09 ,\n", - " 241.39 ],\n", - " ...,\n", - " [296.88998, 296.49 , 296.49 , ..., 297.69 , 297.29 ,\n", - " 296.19 ],\n", - " [298.29 , 297.99 , 297.49 , ..., 298.29 , 298.38998,\n", - " 297.49 ],\n", - " [298.49 , 298.29 , 297.99 , ..., 298.59 , 298.38998,\n", - " 298.29 ]]], dtype=float32)\n", - "Coordinates:\n", - " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", - " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", - " * time (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "da.loc[\"2014-02-14\":\"2014-12-13\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Dropping using `drop_sel`\n", - "\n", - "If instead of selecting data we want to drop it, we can use `drop_sel` method:" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.DataArray 'air' (time: 2919, lat: 24, lon: 52)>\n",
-       "array([[[242.5    , 243.5    , 244.     , ..., 232.79999, 235.5    ,\n",
-       "         238.59999],\n",
-       "        [244.5    , 244.7    , 244.2    , ..., 232.79999, 235.29999,\n",
-       "         239.29999],\n",
-       "        [249.79999, 248.89   , 247.5    , ..., 233.2    , 236.39   ,\n",
-       "         241.7    ],\n",
-       "        ...,\n",
-       "        [296.19998, 296.4    , 296.5    , ..., 295.4    , 295.1    ,\n",
-       "         294.69998],\n",
-       "        [296.19998, 296.79   , 296.5    , ..., 295.9    , 295.9    ,\n",
-       "         295.19998],\n",
-       "        [296.79   , 297.1    , 297.     , ..., 296.9    , 296.79   ,\n",
-       "         296.6    ]],\n",
-       "\n",
-       "       [[242.7    , 243.09999, 243.39   , ..., 232.     , 233.59999,\n",
-       "         235.79999],\n",
-       "        [244.09999, 244.2    , 244.09999, ..., 231.     , 232.5    ,\n",
-       "         235.7    ],\n",
-       "        [252.89   , 252.09999, 250.79999, ..., 230.79999, 233.39   ,\n",
-       "         238.5    ],\n",
-       "...\n",
-       "        [293.88998, 295.38998, 297.19   , ..., 295.09   , 294.69   ,\n",
-       "         294.29   ],\n",
-       "        [297.19   , 297.59   , 297.88998, ..., 295.29   , 295.09   ,\n",
-       "         294.38998],\n",
-       "        [298.38998, 298.49   , 298.59   , ..., 295.69   , 295.49   ,\n",
-       "         295.19   ]],\n",
-       "\n",
-       "       [[244.29   , 243.29   , 242.18999, ..., 241.68999, 241.48999,\n",
-       "         241.79   ],\n",
-       "        [249.29   , 248.39   , 246.98999, ..., 239.59   , 240.29   ,\n",
-       "         241.68999],\n",
-       "        [262.19   , 261.38998, 259.99   , ..., 239.89   , 242.59   ,\n",
-       "         246.29   ],\n",
-       "        ...,\n",
-       "        [293.69   , 295.09   , 296.69   , ..., 295.29   , 295.09   ,\n",
-       "         294.69   ],\n",
-       "        [296.88998, 297.19   , 297.49   , ..., 295.69   , 295.69   ,\n",
-       "         295.19   ],\n",
-       "        [298.09   , 298.09   , 298.49   , ..., 296.49   , 296.19   ,\n",
-       "         295.69   ]]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 202.5 205.0 207.5 210.0 ... 322.5 325.0 327.5 330.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
" - ], - "text/plain": [ - "\n", - "array([[[242.5 , 243.5 , 244. , ..., 232.79999, 235.5 ,\n", - " 238.59999],\n", - " [244.5 , 244.7 , 244.2 , ..., 232.79999, 235.29999,\n", - " 239.29999],\n", - " [249.79999, 248.89 , 247.5 , ..., 233.2 , 236.39 ,\n", - " 241.7 ],\n", - " ...,\n", - " [296.19998, 296.4 , 296.5 , ..., 295.4 , 295.1 ,\n", - " 294.69998],\n", - " [296.19998, 296.79 , 296.5 , ..., 295.9 , 295.9 ,\n", - " 295.19998],\n", - " [296.79 , 297.1 , 297. , ..., 296.9 , 296.79 ,\n", - " 296.6 ]],\n", - "\n", - " [[242.7 , 243.09999, 243.39 , ..., 232. , 233.59999,\n", - " 235.79999],\n", - " [244.09999, 244.2 , 244.09999, ..., 231. , 232.5 ,\n", - " 235.7 ],\n", - " [252.89 , 252.09999, 250.79999, ..., 230.79999, 233.39 ,\n", - " 238.5 ],\n", - "...\n", - " [293.88998, 295.38998, 297.19 , ..., 295.09 , 294.69 ,\n", - " 294.29 ],\n", - " [297.19 , 297.59 , 297.88998, ..., 295.29 , 295.09 ,\n", - " 294.38998],\n", - " [298.38998, 298.49 , 298.59 , ..., 295.69 , 295.49 ,\n", - " 295.19 ]],\n", - "\n", - " [[244.29 , 243.29 , 242.18999, ..., 241.68999, 241.48999,\n", - " 241.79 ],\n", - " [249.29 , 248.39 , 246.98999, ..., 239.59 , 240.29 ,\n", - " 241.68999],\n", - " [262.19 , 261.38998, 259.99 , ..., 239.89 , 242.59 ,\n", - " 246.29 ],\n", - " ...,\n", - " [293.69 , 295.09 , 296.69 , ..., 295.29 , 295.09 ,\n", - " 294.69 ],\n", - " [296.88998, 297.19 , 297.49 , ..., 295.69 , 295.69 ,\n", - " 295.19 ],\n", - " [298.09 , 298.09 , 298.49 , ..., 296.49 , 296.19 ,\n", - " 295.69 ]]], dtype=float32)\n", - "Coordinates:\n", - " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", - " * lon (lon) float32 202.5 205.0 207.5 210.0 ... 322.5 325.0 327.5 330.0\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "da.drop_sel(lat=50.0, lon=200.0, time=\"2014\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So far, all the above will require us to specify exact coordinate values, but what if we don't have the exact values? We can use nearest neighbor lookups to address this issue:\n", - "\n", - "## Nearest Neighbor Lookups\n", - "\n", - "The label based selection methods `sel()` support `method` and `tolerance` keyword argument. The `method` parameter allows for enabling nearest neighbor (inexact) lookups by use of the methods `pad`, `backfill` or `nearest`:\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": { - "tags": [ - "hide-output" - ] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.DataArray 'air' (time: 2920)>\n",
-       "array([262.69998, 263.19998, 270.9    , ..., 264.19   , 265.19   ,\n",
-       "       266.99   ], dtype=float32)\n",
-       "Coordinates:\n",
-       "    lat      float32 52.5\n",
-       "    lon      float32 252.5\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
" - ], - "text/plain": [ - "\n", - "array([262.69998, 263.19998, 270.9 , ..., 264.19 , 265.19 ,\n", - " 266.99 ], dtype=float32)\n", - "Coordinates:\n", - " lat float32 52.5\n", - " lon float32 252.5\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "da.sel(lat=52.25, lon=251.8998, method=\"nearest\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`tolerance` argument limits the maximum distance for valid matches with an inexact lookup:" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.DataArray 'air' (time: 2920)>\n",
-       "array([262.69998, 263.19998, 270.9    , ..., 264.19   , 265.19   ,\n",
-       "       266.99   ], dtype=float32)\n",
-       "Coordinates:\n",
-       "    lat      float32 52.5\n",
-       "    lon      float32 252.5\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
" - ], - "text/plain": [ - "\n", - "array([262.69998, 263.19998, 270.9 , ..., 264.19 , 265.19 ,\n", - " 266.99 ], dtype=float32)\n", - "Coordinates:\n", - " lat float32 52.5\n", - " lon float32 252.5\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "da.sel(lat=52.25, lon=251.8998, method=\"nearest\", tolerance=2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "- **All of these indexing methods work on the dataset too:**\n", - "\n", - "We can also use these methods to index all variables in a dataset simultaneously, returning a new dataset:" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.Dataset>\n",
-       "Dimensions:  (time: 2920)\n",
-       "Coordinates:\n",
-       "    lat      float32 52.5\n",
-       "    lon      float32 252.5\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Data variables:\n",
-       "    air      (time) float32 262.7 263.2 270.9 274.1 ... 261.6 264.2 265.2 267.0\n",
-       "Attributes:\n",
-       "    Conventions:  COARDS\n",
-       "    title:        4x daily NMC reanalysis (1948)\n",
-       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
-       "    platform:     Model\n",
-       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
" - ], - "text/plain": [ - "\n", - "Dimensions: (time: 2920)\n", - "Coordinates:\n", - " lat float32 52.5\n", - " lon float32 252.5\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Data variables:\n", - " air (time) float32 262.7 263.2 270.9 274.1 ... 261.6 264.2 265.2 267.0\n", - "Attributes:\n", - " Conventions: COARDS\n", - " title: 4x daily NMC reanalysis (1948)\n", - " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", - " platform: Model\n", - " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ds.sel(lat=52.25, lon=251.8998, method=\"nearest\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary\n", - "\n", - "In total, Xarray supports four different kinds of indexing, as described below and summarized in this table:\n", - "\n", - "| Dimension lookup | Index lookup | `DataArray` syntax | `Dataset` syntax |\n", - "| ---------------- | ------------ | ---------------------| ---------------------|\n", - "| Positional | By integer | `da[:,0]` | *not available* |\n", - "| Positional | By label | `da.loc[:,'IA']` | *not available* |\n", - "| By name | By integer | `da.isel(space=0)` or `da[dict(space=0)]` | `ds.isel(space=0)` or `ds[dict(space=0)]` |\n", - "| By name | By label | `da.sel(space='IA')` or `da.loc[dict(space='IA')]` | `ds.sel(space='IA')` or `ds.loc[dict(space='IA')]` |\n", - "\n", - "\n", - "For enhanced indexing capabilities across all methods, you can utilize DataArray objects as an indexer. For more detailed information, please see the Advanced Indexing notebook.\n", - "\n", - "\n", - "## More Resources\n", - "\n", - "- [Xarray Docs - Indexing and Selecting Data](https://docs.xarray.dev/en/stable/indexing.html)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "NPL 2023b", - "language": "python", - "name": "npl-2023b" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": true, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": true - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From bea325ccc2ce4e0e2e1b55a6bdde12ec86fa2d1a Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Thu, 6 Jul 2023 17:50:43 -0600 Subject: [PATCH 05/54] indexing advanced quick update --- fundamentals/02.2_indexing_Advanced.ipynb | 4637 +++++++++++++++++++-- 1 file changed, 4187 insertions(+), 450 deletions(-) diff --git a/fundamentals/02.2_indexing_Advanced.ipynb b/fundamentals/02.2_indexing_Advanced.ipynb index ee2663ab..63b59597 100644 --- a/fundamentals/02.2_indexing_Advanced.ipynb +++ b/fundamentals/02.2_indexing_Advanced.ipynb @@ -4,13 +4,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Advanced Indexing\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ + "\n", + "\n", + "# Advanced Indexing\n", + "\n", "## Learning Objectives\n", "\n", "* Vectorized Indexing \n", @@ -33,7 +30,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -51,235 +48,3515 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ds = xr.tutorial.load_dataset(\"air_temperature\")\n", - "da = ds.air\n", - "ds" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Vectorized Indexing\n", - "\n", - "Like NumPy and pandas, Xarray supports indexing many array elements at once in a\n", - "*vectorized* manner. \n", - "\n", - "If you only provide integers, slices, or unlabeled arrays (array without dimension names, such as `np.ndarray`, `list`, but not `DataArray()`) indexing can be understood as orthogonally." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "da[0,[2,4,10,13],[1,6,7]].plot();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "But for more flexibility, you can supply `DataArray()` objects as indexers. \n", - "\n", - "Vectorized indexing using `DataArrays()` may be used to extract information from the nearest grid cells of interest, for example, the nearest climate model grid cells to a collection specified weather station latitudes and longitudes.\n", - "\n", - "**To trigger vectorized indexing behavior you will need to provide the selection dimensions with a new shared output dimension name.** \n", - "\n", - "In the example below, the selections of the closest latitude and longitude are renamed to an output dimension named `points`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define target latitude and longitude (where weather stations might be)\n", - "lat_points = xr.DataArray([31, 41, 42, 42], dims=\"points\")\n", - "lon_points = xr.DataArray([200, 201, 202, 205], dims=\"points\")\n", - "lat_points" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "lon_points" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, retrieve data at the grid cells nearest to the target latitudes and longitudes (weather stations):" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "da.sel(lat=lat_points, lon=lon_points,method=\"nearest\").plot();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As we can see in the above example, the dimensions on resultant arrays are given by the ordered union of the indexers’ dimensions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "da.sel(lat=lat_points, lon=lon_points,method=\"nearest\").dims" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Please note that slices or sequences/arrays without named-dimensions are treated as if they have the same dimension which is indexed along:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "da.sel(lat=[20, 30, 40], lon=lon_points,method=\"nearest\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - " \n", - "Warning: If an indexer is a DataArray(), its coordinates should not conflict with the selected subpart of the target array (except for the explicitly indexed dimensions with .loc/.sel). Otherwise, IndexError will be raised.\n", - " \n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Masking with `where()`\n", - "\n", - "Indexing methods on Xarray objects generally return a subset of the original data. However, it is sometimes useful to select an object with the same shape as the original data, but with some elements masked. To do this type of selection in Xarray, use `where()`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Let's replace the missing values (nan) with some placeholder\n", - "\n", - "ds.air.where(ds.air.notnull(), -9999)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also select a condition to create a mask. For example, here we want to mask all the points with latitudes above 60 N. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "da[0,:,:].plot();" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "da_masked = da.where(da.lat<60)\n", - "da_masked[0,:,:].plot();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "By default where maintains the original size of the data. You can use of the option `drop=True` to clips coordinate elements that are fully masked:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "da_masked = da.where(da.lat<60, drop=True)\n", - "da_masked[0,:,:].plot();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Selecting Values with `isin`\n", - "\n", - "To check whether elements of an xarray object contain a single object, you can compare with the equality operator `==` (e.g., `arr == 3`). To check multiple values, use `isin()`:\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here is a simple example: " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x_da = xr.DataArray([1, 2, 3, 4, 5], dims=[\"x\"])\n", - "\n", - "#-- select points with values equal to 2 and 4:\n", - "x_da.isin([2, 4])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`isin()` works particularly well with `where()` to support indexing by arrays that are not already labels of an array. \n", - "\n", - "For example, we have another DataArray that displays the status flags of the data-collecting device for our data. Here, flags with value 0 and -1 signifies the device was functioning correctly, while 0 indicates a malfunction, implying that the resulting data collected may not be accurate." - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.Dataset>\n",
+       "Dimensions:  (lat: 25, time: 2920, lon: 53)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Data variables:\n",
+       "    air      (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n",
+       "Attributes:\n",
+       "    Conventions:  COARDS\n",
+       "    title:        4x daily NMC reanalysis (1948)\n",
+       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
+       "    platform:     Model\n",
+       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
" + ], + "text/plain": [ + "\n", + "Dimensions: (lat: 25, time: 2920, lon: 53)\n", + "Coordinates:\n", + " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", + " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Data variables:\n", + " air (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n", + "Attributes:\n", + " Conventions: COARDS\n", + " title: 4x daily NMC reanalysis (1948)\n", + " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", + " platform: Model\n", + " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds = xr.tutorial.load_dataset(\"air_temperature\")\n", + "da = ds.air\n", + "ds" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Vectorized Indexing\n", + "\n", + "Like NumPy and pandas, Xarray supports indexing many array elements at once in a\n", + "*vectorized* manner. \n", + "\n", + "If you only provide integers, slices, or unlabeled arrays (array without dimension names, such as `np.ndarray`, `list`, but not `DataArray()`) indexing can be understood as orthogonally." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "da[0,[2,4,10,13],[1,6,7]].plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But for more flexibility, you can supply `DataArray()` objects as indexers. \n", + "\n", + "Vectorized indexing using `DataArrays()` may be used to extract information from the nearest grid cells of interest, for example, the nearest climate model grid cells to a collection specified weather station latitudes and longitudes.\n", + "\n", + "**To trigger vectorized indexing behavior you will need to provide the selection dimensions with a new shared output dimension name.** \n", + "\n", + "In the example below, the selections of the closest latitude and longitude are renamed to an output dimension named `points`:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.DataArray (points: 4)>\n",
+       "array([31, 41, 42, 42])\n",
+       "Dimensions without coordinates: points
" + ], + "text/plain": [ + "\n", + "array([31, 41, 42, 42])\n", + "Dimensions without coordinates: points" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Define target latitude and longitude (where weather stations might be)\n", + "lat_points = xr.DataArray([31, 41, 42, 42], dims=\"points\")\n", + "lon_points = xr.DataArray([200, 201, 202, 205], dims=\"points\")\n", + "lat_points" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.DataArray (points: 4)>\n",
+       "array([200, 201, 202, 205])\n",
+       "Dimensions without coordinates: points
" + ], + "text/plain": [ + "\n", + "array([200, 201, 202, 205])\n", + "Dimensions without coordinates: points" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lon_points" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, retrieve data at the grid cells nearest to the target latitudes and longitudes (weather stations):" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "da.sel(lat=lat_points, lon=lon_points,method=\"nearest\").plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see in the above example, the dimensions on resultant arrays are given by the ordered union of the indexers’ dimensions." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('time', 'points')" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "da.sel(lat=lat_points, lon=lon_points,method=\"nearest\").dims" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Please note that slices or sequences/arrays without named-dimensions are treated as if they have the same dimension which is indexed along:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.DataArray 'air' (time: 2920, lat: 3, points: 4)>\n",
+       "array([[[296.6    , 296.6    , 296.19998, 296.4    ],\n",
+       "        [293.1    , 293.1    , 293.29   , 293.29   ],\n",
+       "        [284.6    , 284.6    , 284.9    , 284.19998]],\n",
+       "\n",
+       "       [[296.4    , 296.4    , 295.9    , 296.19998],\n",
+       "        [293.19998, 293.19998, 293.9    , 294.19998],\n",
+       "        [283.29   , 283.29   , 285.19998, 285.19998]],\n",
+       "\n",
+       "       [[295.6    , 295.6    , 295.4    , 295.4    ],\n",
+       "        [292.4    , 292.4    , 292.9    , 293.4    ],\n",
+       "        [282.     , 282.     , 283.29   , 284.69998]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[294.79   , 294.79   , 295.29   , 297.49   ],\n",
+       "        [288.88998, 288.88998, 289.19   , 290.88998],\n",
+       "        [282.49   , 282.49   , 281.99   , 281.99   ]],\n",
+       "\n",
+       "       [[293.69   , 293.69   , 293.88998, 295.38998],\n",
+       "        [288.29   , 288.29   , 289.19   , 290.79   ],\n",
+       "        [282.09   , 282.09   , 281.59   , 282.38998]],\n",
+       "\n",
+       "       [[293.79   , 293.79   , 293.69   , 295.09   ],\n",
+       "        [289.49   , 289.49   , 290.38998, 291.59   ],\n",
+       "        [282.09   , 282.09   , 281.99   , 283.09   ]]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 20.0 30.0 40.0\n",
+       "    lon      (points) float32 200.0 200.0 202.5 205.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Dimensions without coordinates: points\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
" + ], + "text/plain": [ + "\n", + "array([[[296.6 , 296.6 , 296.19998, 296.4 ],\n", + " [293.1 , 293.1 , 293.29 , 293.29 ],\n", + " [284.6 , 284.6 , 284.9 , 284.19998]],\n", + "\n", + " [[296.4 , 296.4 , 295.9 , 296.19998],\n", + " [293.19998, 293.19998, 293.9 , 294.19998],\n", + " [283.29 , 283.29 , 285.19998, 285.19998]],\n", + "\n", + " [[295.6 , 295.6 , 295.4 , 295.4 ],\n", + " [292.4 , 292.4 , 292.9 , 293.4 ],\n", + " [282. , 282. , 283.29 , 284.69998]],\n", + "\n", + " ...,\n", + "\n", + " [[294.79 , 294.79 , 295.29 , 297.49 ],\n", + " [288.88998, 288.88998, 289.19 , 290.88998],\n", + " [282.49 , 282.49 , 281.99 , 281.99 ]],\n", + "\n", + " [[293.69 , 293.69 , 293.88998, 295.38998],\n", + " [288.29 , 288.29 , 289.19 , 290.79 ],\n", + " [282.09 , 282.09 , 281.59 , 282.38998]],\n", + "\n", + " [[293.79 , 293.79 , 293.69 , 295.09 ],\n", + " [289.49 , 289.49 , 290.38998, 291.59 ],\n", + " [282.09 , 282.09 , 281.99 , 283.09 ]]], dtype=float32)\n", + "Coordinates:\n", + " * lat (lat) float32 20.0 30.0 40.0\n", + " lon (points) float32 200.0 200.0 202.5 205.0\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Dimensions without coordinates: points\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "da.sel(lat=[20, 30, 40], lon=lon_points,method=\"nearest\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "Warning: If an indexer is a DataArray(), its coordinates should not conflict with the selected subpart of the target array (except for the explicitly indexed dimensions with .loc/.sel). Otherwise, IndexError will be raised.\n", + " \n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Masking with `where()`\n", + "\n", + "Indexing methods on Xarray objects generally return a subset of the original data. However, it is sometimes useful to select an object with the same shape as the original data, but with some elements masked. To do this type of selection in Xarray, use `where()`:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.DataArray 'air' (time: 2920, lat: 25, lon: 53)>\n",
+       "array([[[241.2    , 242.5    , 243.5    , ..., 232.79999, 235.5    ,\n",
+       "         238.59999],\n",
+       "        [243.79999, 244.5    , 244.7    , ..., 232.79999, 235.29999,\n",
+       "         239.29999],\n",
+       "        [250.     , 249.79999, 248.89   , ..., 233.2    , 236.39   ,\n",
+       "         241.7    ],\n",
+       "        ...,\n",
+       "        [296.6    , 296.19998, 296.4    , ..., 295.4    , 295.1    ,\n",
+       "         294.69998],\n",
+       "        [295.9    , 296.19998, 296.79   , ..., 295.9    , 295.9    ,\n",
+       "         295.19998],\n",
+       "        [296.29   , 296.79   , 297.1    , ..., 296.9    , 296.79   ,\n",
+       "         296.6    ]],\n",
+       "\n",
+       "       [[242.09999, 242.7    , 243.09999, ..., 232.     , 233.59999,\n",
+       "         235.79999],\n",
+       "        [243.59999, 244.09999, 244.2    , ..., 231.     , 232.5    ,\n",
+       "         235.7    ],\n",
+       "        [253.2    , 252.89   , 252.09999, ..., 230.79999, 233.39   ,\n",
+       "         238.5    ],\n",
+       "...\n",
+       "        [293.69   , 293.88998, 295.38998, ..., 295.09   , 294.69   ,\n",
+       "         294.29   ],\n",
+       "        [296.29   , 297.19   , 297.59   , ..., 295.29   , 295.09   ,\n",
+       "         294.38998],\n",
+       "        [297.79   , 298.38998, 298.49   , ..., 295.69   , 295.49   ,\n",
+       "         295.19   ]],\n",
+       "\n",
+       "       [[245.09   , 244.29   , 243.29   , ..., 241.68999, 241.48999,\n",
+       "         241.79   ],\n",
+       "        [249.89   , 249.29   , 248.39   , ..., 239.59   , 240.29   ,\n",
+       "         241.68999],\n",
+       "        [262.99   , 262.19   , 261.38998, ..., 239.89   , 242.59   ,\n",
+       "         246.29   ],\n",
+       "        ...,\n",
+       "        [293.79   , 293.69   , 295.09   , ..., 295.29   , 295.09   ,\n",
+       "         294.69   ],\n",
+       "        [296.09   , 296.88998, 297.19   , ..., 295.69   , 295.69   ,\n",
+       "         295.19   ],\n",
+       "        [297.69   , 298.09   , 298.09   , ..., 296.49   , 296.19   ,\n",
+       "         295.69   ]]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
" + ], + "text/plain": [ + "\n", + "array([[[241.2 , 242.5 , 243.5 , ..., 232.79999, 235.5 ,\n", + " 238.59999],\n", + " [243.79999, 244.5 , 244.7 , ..., 232.79999, 235.29999,\n", + " 239.29999],\n", + " [250. , 249.79999, 248.89 , ..., 233.2 , 236.39 ,\n", + " 241.7 ],\n", + " ...,\n", + " [296.6 , 296.19998, 296.4 , ..., 295.4 , 295.1 ,\n", + " 294.69998],\n", + " [295.9 , 296.19998, 296.79 , ..., 295.9 , 295.9 ,\n", + " 295.19998],\n", + " [296.29 , 296.79 , 297.1 , ..., 296.9 , 296.79 ,\n", + " 296.6 ]],\n", + "\n", + " [[242.09999, 242.7 , 243.09999, ..., 232. , 233.59999,\n", + " 235.79999],\n", + " [243.59999, 244.09999, 244.2 , ..., 231. , 232.5 ,\n", + " 235.7 ],\n", + " [253.2 , 252.89 , 252.09999, ..., 230.79999, 233.39 ,\n", + " 238.5 ],\n", + "...\n", + " [293.69 , 293.88998, 295.38998, ..., 295.09 , 294.69 ,\n", + " 294.29 ],\n", + " [296.29 , 297.19 , 297.59 , ..., 295.29 , 295.09 ,\n", + " 294.38998],\n", + " [297.79 , 298.38998, 298.49 , ..., 295.69 , 295.49 ,\n", + " 295.19 ]],\n", + "\n", + " [[245.09 , 244.29 , 243.29 , ..., 241.68999, 241.48999,\n", + " 241.79 ],\n", + " [249.89 , 249.29 , 248.39 , ..., 239.59 , 240.29 ,\n", + " 241.68999],\n", + " [262.99 , 262.19 , 261.38998, ..., 239.89 , 242.59 ,\n", + " 246.29 ],\n", + " ...,\n", + " [293.79 , 293.69 , 295.09 , ..., 295.29 , 295.09 ,\n", + " 294.69 ],\n", + " [296.09 , 296.88998, 297.19 , ..., 295.69 , 295.69 ,\n", + " 295.19 ],\n", + " [297.69 , 298.09 , 298.09 , ..., 296.49 , 296.19 ,\n", + " 295.69 ]]], dtype=float32)\n", + "Coordinates:\n", + " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", + " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Let's replace the missing values (nan) with some placeholder\n", + "\n", + "ds.air.where(ds.air.notnull(), -9999)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also select a condition to create a mask. For example, here we want to mask all the points with latitudes above 60 N. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "da[0,:,:].plot();" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "da_masked = da.where(da.lat<60)\n", + "da_masked[0,:,:].plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By default where maintains the original size of the data. You can use of the option `drop=True` to clips coordinate elements that are fully masked:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "da_masked = da.where(da.lat<60, drop=True)\n", + "da_masked[0,:,:].plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Selecting Values with `isin`\n", + "\n", + "To check whether elements of an xarray object contain a single object, you can compare with the equality operator `==` (e.g., `arr == 3`). To check multiple values, use `isin()`:\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is a simple example: " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.DataArray (x: 5)>\n",
+       "array([False,  True, False,  True, False])\n",
+       "Dimensions without coordinates: x
" + ], + "text/plain": [ + "\n", + "array([False, True, False, True, False])\n", + "Dimensions without coordinates: x" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_da = xr.DataArray([1, 2, 3, 4, 5], dims=[\"x\"])\n", + "\n", + "#-- select points with values equal to 2 and 4:\n", + "x_da.isin([2, 4])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`isin()` works particularly well with `where()` to support indexing by arrays that are not already labels of an array. \n", + "\n", + "For example, we have another DataArray that displays the status flags of the data-collecting device for our data. Here, flags with value 0 and -1 signifies the device was functioning correctly, while 0 indicates a malfunction, implying that the resulting data collected may not be accurate." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.DataArray (time: 2920, lat: 25, lon: 53)>\n",
+       "array([[[ 2,  3,  0, ...,  3,  0,  4],\n",
+       "        [ 3,  1,  0, ...,  4,  2, -1],\n",
+       "        [ 3,  1,  2, ...,  3,  2, -1],\n",
+       "        ...,\n",
+       "        [-1, -1,  3, ...,  0,  3,  3],\n",
+       "        [ 3,  1, -1, ..., -1,  4,  3],\n",
+       "        [ 2, -1,  4, ..., -1,  0,  2]],\n",
+       "\n",
+       "       [[ 0,  4,  2, ...,  3,  4,  3],\n",
+       "        [ 3,  0, -1, ...,  2, -1,  4],\n",
+       "        [ 2,  3,  4, ..., -1,  4, -1],\n",
+       "        ...,\n",
+       "        [ 0,  4,  1, ..., -1,  3,  1],\n",
+       "        [ 4,  1,  3, ...,  1, -1, -1],\n",
+       "        [ 3,  1,  1, ...,  3,  4,  2]],\n",
+       "\n",
+       "       [[ 1,  2, -1, ...,  1,  0,  2],\n",
+       "        [ 4,  3,  2, ...,  3,  4,  3],\n",
+       "        [ 4,  2, -1, ...,  4,  1,  1],\n",
+       "        ...,\n",
+       "...\n",
+       "        ...,\n",
+       "        [ 1,  0,  4, ...,  2, -1,  4],\n",
+       "        [ 3,  4,  3, ...,  1,  0,  1],\n",
+       "        [ 0,  4,  4, ...,  1,  4,  2]],\n",
+       "\n",
+       "       [[ 0,  2,  0, ...,  3,  1,  4],\n",
+       "        [ 3,  2, -1, ...,  0,  4,  2],\n",
+       "        [ 1, -1, -1, ...,  1, -1,  3],\n",
+       "        ...,\n",
+       "        [ 0, -1,  0, ...,  1, -1,  3],\n",
+       "        [ 0,  2,  1, ...,  4,  0,  4],\n",
+       "        [ 2,  2,  0, ...,  0, -1,  2]],\n",
+       "\n",
+       "       [[ 4, -1,  0, ...,  4,  0,  1],\n",
+       "        [ 3, -1,  0, ...,  0,  1, -1],\n",
+       "        [ 4, -1,  2, ..., -1,  3,  3],\n",
+       "        ...,\n",
+       "        [ 2,  0,  0, ...,  4,  3,  0],\n",
+       "        [ 2,  4,  2, ...,  3,  3, -1],\n",
+       "        [ 0,  4,  4, ...,  1,  0,  3]]])\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00
" + ], + "text/plain": [ + "\n", + "array([[[ 2, 3, 0, ..., 3, 0, 4],\n", + " [ 3, 1, 0, ..., 4, 2, -1],\n", + " [ 3, 1, 2, ..., 3, 2, -1],\n", + " ...,\n", + " [-1, -1, 3, ..., 0, 3, 3],\n", + " [ 3, 1, -1, ..., -1, 4, 3],\n", + " [ 2, -1, 4, ..., -1, 0, 2]],\n", + "\n", + " [[ 0, 4, 2, ..., 3, 4, 3],\n", + " [ 3, 0, -1, ..., 2, -1, 4],\n", + " [ 2, 3, 4, ..., -1, 4, -1],\n", + " ...,\n", + " [ 0, 4, 1, ..., -1, 3, 1],\n", + " [ 4, 1, 3, ..., 1, -1, -1],\n", + " [ 3, 1, 1, ..., 3, 4, 2]],\n", + "\n", + " [[ 1, 2, -1, ..., 1, 0, 2],\n", + " [ 4, 3, 2, ..., 3, 4, 3],\n", + " [ 4, 2, -1, ..., 4, 1, 1],\n", + " ...,\n", + "...\n", + " ...,\n", + " [ 1, 0, 4, ..., 2, -1, 4],\n", + " [ 3, 4, 3, ..., 1, 0, 1],\n", + " [ 0, 4, 4, ..., 1, 4, 2]],\n", + "\n", + " [[ 0, 2, 0, ..., 3, 1, 4],\n", + " [ 3, 2, -1, ..., 0, 4, 2],\n", + " [ 1, -1, -1, ..., 1, -1, 3],\n", + " ...,\n", + " [ 0, -1, 0, ..., 1, -1, 3],\n", + " [ 0, 2, 1, ..., 4, 0, 4],\n", + " [ 2, 2, 0, ..., 0, -1, 2]],\n", + "\n", + " [[ 4, -1, 0, ..., 4, 0, 1],\n", + " [ 3, -1, 0, ..., 0, 1, -1],\n", + " [ 4, -1, 2, ..., -1, 3, 3],\n", + " ...,\n", + " [ 2, 0, 0, ..., 4, 3, 0],\n", + " [ 2, 4, 2, ..., 3, 3, -1],\n", + " [ 0, 4, 4, ..., 1, 0, 3]]])\n", + "Coordinates:\n", + " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", + " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "flags = xr.DataArray(\n", " np.random.randint(-1, 5, da.shape),\n", @@ -298,9 +3575,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "da_masked = da.where(flags.isin([1,2,3,4,5]), drop=True)\n", "da_masked[0,:,:].plot();" @@ -328,16 +3616,577 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.DataArray 'air' (time: 2920, lat: 25, lon: 53)>\n",
+       "array([[[241.2    , 242.5    , 243.5    , ..., 232.79999, 235.5    ,\n",
+       "         238.59999],\n",
+       "        [243.79999, 244.5    , 244.7    , ..., 232.79999, 235.29999,\n",
+       "         239.29999],\n",
+       "        [250.     , 249.79999, 248.89   , ..., 233.2    , 236.39   ,\n",
+       "         241.7    ],\n",
+       "        ...,\n",
+       "        [296.6    , 296.19998, 296.4    , ..., 295.4    , 295.1    ,\n",
+       "         294.69998],\n",
+       "        [295.9    , 296.19998, 296.79   , ..., 295.9    , 295.9    ,\n",
+       "         295.19998],\n",
+       "        [296.29   , 296.79   , 297.1    , ..., 296.9    , 296.79   ,\n",
+       "         296.6    ]],\n",
+       "\n",
+       "       [[242.09999, 242.7    , 243.09999, ..., 232.     , 233.59999,\n",
+       "         235.79999],\n",
+       "        [243.59999, 244.09999, 244.2    , ..., 231.     , 232.5    ,\n",
+       "         235.7    ],\n",
+       "        [253.2    , 252.89   , 252.09999, ..., 230.79999, 233.39   ,\n",
+       "         238.5    ],\n",
+       "...\n",
+       "        [293.69   , 293.88998, 295.38998, ..., 295.09   , 294.69   ,\n",
+       "         294.29   ],\n",
+       "        [296.29   , 297.19   , 297.59   , ..., 295.29   , 295.09   ,\n",
+       "         294.38998],\n",
+       "        [297.79   , 298.38998, 298.49   , ..., 295.69   , 295.49   ,\n",
+       "         295.19   ]],\n",
+       "\n",
+       "       [[245.09   , 244.29   , 243.29   , ..., 241.68999, 241.48999,\n",
+       "         241.79   ],\n",
+       "        [249.89   , 249.29   , 248.39   , ..., 239.59   , 240.29   ,\n",
+       "         241.68999],\n",
+       "        [262.99   , 262.19   , 261.38998, ..., 239.89   , 242.59   ,\n",
+       "         246.29   ],\n",
+       "        ...,\n",
+       "        [293.79   , 293.69   , 295.09   , ..., 295.29   , 295.09   ,\n",
+       "         294.69   ],\n",
+       "        [296.09   , 296.88998, 297.19   , ..., 295.69   , 295.69   ,\n",
+       "         295.19   ],\n",
+       "        [297.69   , 298.09   , 298.09   , ..., 296.49   , 296.19   ,\n",
+       "         295.69   ]]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
" + ], + "text/plain": [ + "\n", + "array([[[241.2 , 242.5 , 243.5 , ..., 232.79999, 235.5 ,\n", + " 238.59999],\n", + " [243.79999, 244.5 , 244.7 , ..., 232.79999, 235.29999,\n", + " 239.29999],\n", + " [250. , 249.79999, 248.89 , ..., 233.2 , 236.39 ,\n", + " 241.7 ],\n", + " ...,\n", + " [296.6 , 296.19998, 296.4 , ..., 295.4 , 295.1 ,\n", + " 294.69998],\n", + " [295.9 , 296.19998, 296.79 , ..., 295.9 , 295.9 ,\n", + " 295.19998],\n", + " [296.29 , 296.79 , 297.1 , ..., 296.9 , 296.79 ,\n", + " 296.6 ]],\n", + "\n", + " [[242.09999, 242.7 , 243.09999, ..., 232. , 233.59999,\n", + " 235.79999],\n", + " [243.59999, 244.09999, 244.2 , ..., 231. , 232.5 ,\n", + " 235.7 ],\n", + " [253.2 , 252.89 , 252.09999, ..., 230.79999, 233.39 ,\n", + " 238.5 ],\n", + "...\n", + " [293.69 , 293.88998, 295.38998, ..., 295.09 , 294.69 ,\n", + " 294.29 ],\n", + " [296.29 , 297.19 , 297.59 , ..., 295.29 , 295.09 ,\n", + " 294.38998],\n", + " [297.79 , 298.38998, 298.49 , ..., 295.69 , 295.49 ,\n", + " 295.19 ]],\n", + "\n", + " [[245.09 , 244.29 , 243.29 , ..., 241.68999, 241.48999,\n", + " 241.79 ],\n", + " [249.89 , 249.29 , 248.39 , ..., 239.59 , 240.29 ,\n", + " 241.68999],\n", + " [262.99 , 262.19 , 261.38998, ..., 239.89 , 242.59 ,\n", + " 246.29 ],\n", + " ...,\n", + " [293.79 , 293.69 , 295.09 , ..., 295.29 , 295.09 ,\n", + " 294.69 ],\n", + " [296.09 , 296.88998, 297.19 , ..., 295.69 , 295.69 ,\n", + " 295.19 ],\n", + " [297.69 , 298.09 , 298.09 , ..., 296.49 , 296.19 ,\n", + " 295.69 ]]], dtype=float32)\n", + "Coordinates:\n", + " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", + " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "da" ] }, { "cell_type": "code", - "execution_count": 240, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -707,31 +4556,31 @@ " fill: currentColor;\n", "}\n", "
<xarray.DataArray (time: 4, space: 3)>\n",
-       "array([[0.41063639, 0.64438845, 0.34046896],\n",
-       "       [0.87939503, 0.1581048 , 0.34112795],\n",
-       "       [0.85912828, 0.92362903, 0.77612158],\n",
-       "       [0.76501244, 0.20228242, 0.94031457]])\n",
+       "array([[0.21780543, 0.57711447, 0.86539737],\n",
+       "       [0.14893992, 0.35643873, 0.18224072],\n",
+       "       [0.10407898, 0.95598302, 0.93117286],\n",
+       "       [0.52169098, 0.04393997, 0.97119655]])\n",
        "Coordinates:\n",
        "  * time     (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 2000-01-04\n",
-       "  * space    (space) <U2 'IL' 'IA' 'IN'
    • time
      PandasIndex
      PandasIndex(DatetimeIndex(['2000-01-01', '2000-01-02', '2000-01-03', '2000-01-04'], dtype='datetime64[ns]', name='time', freq='D'))
    • space
      PandasIndex
      PandasIndex(Index(['IL', 'IA', 'IN'], dtype='object', name='space'))
    • " ], "text/plain": [ "\n", - "array([[0.41063639, 0.64438845, 0.34046896],\n", - " [0.87939503, 0.1581048 , 0.34112795],\n", - " [0.85912828, 0.92362903, 0.77612158],\n", - " [0.76501244, 0.20228242, 0.94031457]])\n", + "array([[0.21780543, 0.57711447, 0.86539737],\n", + " [0.14893992, 0.35643873, 0.18224072],\n", + " [0.10407898, 0.95598302, 0.93117286],\n", + " [0.52169098, 0.04393997, 0.97119655]])\n", "Coordinates:\n", " * time (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 2000-01-04\n", " * space (space) 
    <xarray.DataArray (time: 4, space: 2)>\n",
-       "array([[0.64438845,        nan],\n",
-       "       [0.1581048 ,        nan],\n",
-       "       [0.92362903,        nan],\n",
-       "       [0.20228242,        nan]])\n",
+       "array([[0.57711447,        nan],\n",
+       "       [0.35643873,        nan],\n",
+       "       [0.95598302,        nan],\n",
+       "       [0.04393997,        nan]])\n",
        "Coordinates:\n",
        "  * time     (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 2000-01-04\n",
-       "  * space    (space) <U2 'IA' 'CA'
    • time
      PandasIndex
      PandasIndex(DatetimeIndex(['2000-01-01', '2000-01-02', '2000-01-03', '2000-01-04'], dtype='datetime64[ns]', name='time', freq='D'))
    • space
      PandasIndex
      PandasIndex(Index(['IA', 'CA'], dtype='object', name='space'))
    • " ], "text/plain": [ "\n", - "array([[0.64438845, nan],\n", - " [0.1581048 , nan],\n", - " [0.92362903, nan],\n", - " [0.20228242, nan]])\n", + "array([[0.57711447, nan],\n", + " [0.35643873, nan],\n", + " [0.95598302, nan],\n", + " [0.04393997, nan]])\n", "Coordinates:\n", " * time (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 2000-01-04\n", " * space (space) 
    <xarray.DataArray 'air' (time: 20, lat: 25, lon: 53)>\n",
-       "array([[[238.09999, 238.29999, 238.2    , ..., 238.7    , 240.09999,\n",
-       "         241.7    ],\n",
-       "        [241.2    , 240.5    , 239.2    , ..., 234.89   , 236.     ,\n",
-       "         238.39   ],\n",
-       "        [244.29999, 244.     , 243.09999, ..., 235.59999, 237.79999,\n",
-       "         241.59999],\n",
-       "        ...,\n",
-       "        [296.     , 295.5    , 295.4    , ..., 294.6    , 295.19998,\n",
-       "         295.4    ],\n",
-       "        [297.1    , 297.     , 296.69998, ..., 295.9    , 296.5    ,\n",
-       "         296.1    ],\n",
-       "        [298.1    , 298.     , 297.4    , ..., 296.79   , 297.6    ,\n",
-       "         297.29   ]],\n",
-       "\n",
-       "       [[      nan,       nan,       nan, ...,       nan,       nan,\n",
-       "               nan],\n",
-       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
-       "               nan],\n",
-       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
-       "               nan],\n",
-       "...\n",
-       "        [294.6    , 293.6    , 293.19998, ..., 295.79   , 296.29   ,\n",
-       "         296.4    ],\n",
-       "        [295.9    , 295.4    , 294.69998, ..., 297.1    , 297.5    ,\n",
-       "         296.9    ],\n",
-       "        [297.1    , 296.69998, 296.     , ..., 297.69998, 297.79   ,\n",
-       "         297.     ]],\n",
-       "\n",
-       "       [[      nan,       nan,       nan, ...,       nan,       nan,\n",
-       "               nan],\n",
-       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
-       "               nan],\n",
-       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
-       "               nan],\n",
-       "        ...,\n",
-       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
-       "               nan],\n",
-       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
-       "               nan],\n",
-       "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
-       "               nan]]], dtype=float32)\n",
+       "
    <xarray.DataArray (time: 20, space: 3)>\n",
+       "array([[nan, nan, nan],\n",
+       "       [nan, nan, nan],\n",
+       "       [nan, nan, nan],\n",
+       "       [nan, nan, nan],\n",
+       "       [nan, nan, nan],\n",
+       "       [nan, nan, nan],\n",
+       "       [nan, nan, nan],\n",
+       "       [nan, nan, nan],\n",
+       "       [nan, nan, nan],\n",
+       "       [nan, nan, nan],\n",
+       "       [nan, nan, nan],\n",
+       "       [nan, nan, nan],\n",
+       "       [nan, nan, nan],\n",
+       "       [nan, nan, nan],\n",
+       "       [nan, nan, nan],\n",
+       "       [nan, nan, nan],\n",
+       "       [nan, nan, nan],\n",
+       "       [nan, nan, nan],\n",
+       "       [nan, nan, nan],\n",
+       "       [nan, nan, nan]])\n",
        "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
        "  * time     (time) datetime64[ns] 2013-02-01 ... 2013-02-01T19:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
  • space
    PandasIndex
    PandasIndex(Index(['IL', 'IA', 'IN'], dtype='object', name='space'))
    • " ], "text/plain": [ - "\n", - "array([[[238.09999, 238.29999, 238.2 , ..., 238.7 , 240.09999,\n", - " 241.7 ],\n", - " [241.2 , 240.5 , 239.2 , ..., 234.89 , 236. ,\n", - " 238.39 ],\n", - " [244.29999, 244. , 243.09999, ..., 235.59999, 237.79999,\n", - " 241.59999],\n", - " ...,\n", - " [296. , 295.5 , 295.4 , ..., 294.6 , 295.19998,\n", - " 295.4 ],\n", - " [297.1 , 297. , 296.69998, ..., 295.9 , 296.5 ,\n", - " 296.1 ],\n", - " [298.1 , 298. , 297.4 , ..., 296.79 , 297.6 ,\n", - " 297.29 ]],\n", - "\n", - " [[ nan, nan, nan, ..., nan, nan,\n", - " nan],\n", - " [ nan, nan, nan, ..., nan, nan,\n", - " nan],\n", - " [ nan, nan, nan, ..., nan, nan,\n", - " nan],\n", - "...\n", - " [294.6 , 293.6 , 293.19998, ..., 295.79 , 296.29 ,\n", - " 296.4 ],\n", - " [295.9 , 295.4 , 294.69998, ..., 297.1 , 297.5 ,\n", - " 296.9 ],\n", - " [297.1 , 296.69998, 296. , ..., 297.69998, 297.79 ,\n", - " 297. ]],\n", - "\n", - " [[ nan, nan, nan, ..., nan, nan,\n", - " nan],\n", - " [ nan, nan, nan, ..., nan, nan,\n", - " nan],\n", - " [ nan, nan, nan, ..., nan, nan,\n", - " nan],\n", - " ...,\n", - " [ nan, nan, nan, ..., nan, nan,\n", - " nan],\n", - " [ nan, nan, nan, ..., nan, nan,\n", - " nan],\n", - " [ nan, nan, nan, ..., nan, nan,\n", - " nan]]], dtype=float32)\n", + "\n", + "array([[nan, nan, nan],\n", + " [nan, nan, nan],\n", + " [nan, nan, nan],\n", + " [nan, nan, nan],\n", + " [nan, nan, nan],\n", + " [nan, nan, nan],\n", + " [nan, nan, nan],\n", + " [nan, nan, nan],\n", + " [nan, nan, nan],\n", + " [nan, nan, nan],\n", + " [nan, nan, nan],\n", + " [nan, nan, nan],\n", + " [nan, nan, nan],\n", + " [nan, nan, nan],\n", + " [nan, nan, nan],\n", + " [nan, nan, nan],\n", + " [nan, nan, nan],\n", + " [nan, nan, nan],\n", + " [nan, nan, nan],\n", + " [nan, nan, nan]])\n", "Coordinates:\n", - " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", - " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", " * time (time) datetime64[ns] 2013-02-01 ... 2013-02-01T19:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" + " * space (space) Date: Thu, 6 Jul 2023 23:56:30 +0000 Subject: [PATCH 06/54] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- fundamentals/02.1_indexing_Basic.ipynb | 4433 +---------------- fundamentals/02.2_indexing_Advanced.ipynb | 5227 +-------------------- 2 files changed, 88 insertions(+), 9572 deletions(-) diff --git a/fundamentals/02.1_indexing_Basic.ipynb b/fundamentals/02.1_indexing_Basic.ipynb index d3f45b6a..53d97172 100644 --- a/fundamentals/02.1_indexing_Basic.ipynb +++ b/fundamentals/02.1_indexing_Basic.ipynb @@ -54,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -72,483 +72,9 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.Dataset>\n",
-       "Dimensions:  (lat: 25, time: 2920, lon: 53)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Data variables:\n",
-       "    air      (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n",
-       "Attributes:\n",
-       "    Conventions:  COARDS\n",
-       "    title:        4x daily NMC reanalysis (1948)\n",
-       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
-       "    platform:     Model\n",
-       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
    " - ], - "text/plain": [ - "\n", - "Dimensions: (lat: 25, time: 2920, lon: 53)\n", - "Coordinates:\n", - " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", - " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Data variables:\n", - " air (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n", - "Attributes:\n", - " Conventions: COARDS\n", - " title: 4x daily NMC reanalysis (1948)\n", - " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", - " platform: Model\n", - " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "ds = xr.tutorial.load_dataset(\"air_temperature\")\n", "ds" @@ -556,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -586,20 +112,9 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2920, 25, 53)" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "np_array = ds[\"air\"].data # numpy array\n", "np_array.shape" @@ -614,22 +129,11 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "242.09999" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "np_array[1,0,0]" + "np_array[1, 0, 0]" ] }, { @@ -641,20 +145,9 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([295. , 294.4 , 294.5 , ..., 297.29, 297.79, 297.99], dtype=float32)" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# extract a time-series for one spatial location\n", "np_array[:, 20, 40]" @@ -686,434 +179,9 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 2920)>\n",
-       "array([295.  , 294.4 , 294.5 , ..., 297.29, 297.79, 297.99], dtype=float32)\n",
-       "Coordinates:\n",
-       "    lat      float32 25.0\n",
-       "    lon      float32 300.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([295. , 294.4 , 294.5 , ..., 297.29, 297.79, 297.99], dtype=float32)\n", - "Coordinates:\n", - " lat float32 25.0\n", - " lon float32 300.0\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da[:, 20, 40]" ] @@ -1144,20 +212,9 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "da.isel(lat=20, lon=40).plot();" ] @@ -1171,22 +228,11 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlkAAAHrCAYAAAD8EoikAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACfE0lEQVR4nOzdd1zU9R/A8dcdeyMIAg7EiXvP3AO1JdrShtqwfqW5smFljixtaktLzVG5Mi2tzLJU3HviRBAnGxmyufv+/sA7RdYdAncH7+fjwaP43ne87+S4N5/P+/v+qBRFURBCCCGEEGVKbeoAhBBCCCEqI0myhBBCCCHKgSRZQgghhBDlQJIsIYQQQohyIEmWEEIIIUQ5kCRLCCGEEKIcSJIlhBBCCFEOJMkSQgghhCgHkmQJIYQQQpQDSbJEiZYtW4ZKpSIyMtLoY/fs2cP06dNJSkoq87hKsnXrVp577jkCAwNxcnKiZs2aDB48mMOHDxfYd9SoUahUqgJfgYGBBl/v33//pUuXLjg6OlK9enVGjRpFbGxsicdt37493zUPHTpU4jGjRo2ibt26Bscmyt7KlSuZN29ege2RkZGoVCo+/fTTEs8xb968fP/28fHxRseh+/nZvn270ceak3///Zf+/fvj5+eHnZ0d3t7e9OnTh02bNhW5vyHvt5ycHGbMmEHdunWxs7MjMDCQr776yuC4bt68yYQJE/Dz88Pe3p7WrVuzevVqg47t1auX/t/2wQcfLHF/3c/OsmXLDI5PmDdJskS52rNnDzNmzDBJkrVgwQIiIyMZP348mzZt4osvviA2NpbOnTuzdevWAvs7ODiwd+/efF9r1qwx6FohISEMGjSIGjVqsGHDBr744gv+/fdf+vbtS1ZWlkHn+Oabb9i7dy9NmjQx6nkK0ygqyTLGsGHD2Lt3L88//3zZBGXBEhISaNasGXPnzuWff/7hu+++w8bGhgceeICffvop377GvN9eeeUVZs+ezZgxY/j7778ZMmQI48eP58MPPzQorqFDh7J8+XKmTZvGX3/9RYcOHRg+fDgrV6406Pg2bdqwd+9ePvvsM8NeCFG5KEKUYOnSpQqgXLx40ehjP/nkk1Ife69iYmIKbEtNTVVq1Kih9O3bN9/2kSNHKk5OTqW+VocOHZSmTZsqOTk5+m27d+9WAGX+/PnFHrtt2zYFULZt22bw9UaOHKn4+/uXMtrSS09PV7RabYVf15ykpaUpiqIoDzzwQKH/BhcvXlQA5ZNPPjH4nNOmTVMAJS4uzuh4SvPzYymys7OVmjVrKt27d8+33dD3W2hoqKJSqZQPP/ww3/GjR49WHBwclISEhGKv/+effyqAsnLlynzb+/fvr/j5+Sm5ubnFHt+zZ0+lZ8+exe5zJ93PztKlSw0+pqykp6dX+DWrAhnJEqWyZcsWBg8eTK1atbC3t6dBgwa89NJL+aY7pk+fzuuvvw5AQECAfti8oqY1vL29C2xzdnamadOmXLlypcyuc+3aNQ4ePMgzzzyDtbW1fnvXrl1p1KgRv/766z2df9myZTRu3Bg7OzuaNGnCDz/8UOh+2dnZzJo1i8DAQOzs7PDy8uLZZ58lLi4u335ZWVm89tpr+Pj44OjoSI8ePTh8+DB169Zl1KhR+a6rUqn4559/eO655/Dy8sLR0VE/UrBmzRq6dOmCk5MTzs7ODBgwgKNHjxaI69ChQzz88MN4eHhgb29PmzZt+Pnnn/Ptk56ezuTJkwkICMDe3h4PDw/at2/PqlWrDH6ddNNmq1at4p133sHPzw9XV1f69evHuXPnCuy/ZMkSWrVqpb/ekCFDOHPmTL59Ro0ahbOzMydPniQoKAgXFxf69u1Lr169+PPPP7l06VK+6b67ff755wQEBODs7EyXLl3Yt2+fwc/nXm3cuFE/nebi4kL//v3Zu3dvvn2mT5+OSqXi1KlTDB8+HDc3N2rUqMFzzz1HcnJyhcVaFBsbG9zd3fO9r4x5v/32228oisKzzz6b77zPPvssGRkZbN68udjr//rrrzg7O/PYY48VOP769evs37+/1M/t+vXrPP7447i4uODm5sYTTzxBdHR0ofsa8h4C2LVrF126dMHe3p6aNWsydepUFi9eXKDco27dujz44IOsX7+eNm3aYG9vz4wZMwCIjo7mpZdeolatWtja2hIQEMCMGTPIzc3Ndy1Df99UddYl7yJEQeHh4XTp0oUXXngBNzc3IiMj+fzzz+nWrRsnT57ExsaGF154gcTERL766ivWr1+Pr68vAE2bNi3yvIqioNFoDIrhzl+whkpOTubIkSP06dOnwGMZGRn4+PgQFxeHr68vwcHBzJw5Ew8Pj2LPGRoaCkDLli0LPNayZUt2795tdJw6y5Yt49lnn2Xw4MF89tlnJCcnM336dLKyslCrb/+NpNVqGTx4MDt37uSNN96ga9euXLp0iWnTptGrVy8OHTqEg4MDkPcBsWbNGt544w369OnD6dOnGTJkCCkpKYXG8Nxzz/HAAw/w448/kpaWho2NDR9++CHvvvsuzz77LO+++y7Z2dl88skndO/enQMHDuj/jbdt28bAgQPp1KkT3377LW5ubqxevZonnniC9PR0fVI3adIkfvzxR2bNmkWbNm1IS0sjNDSUhIQEo1+zt99+m/vuu4/FixeTkpLCm2++yUMPPcSZM2ewsrICYPbs2bz99tsMHz6c2bNnk5CQwPTp0+nSpQsHDx6kYcOG+vNlZ2fz8MMP89JLL/HWW2+Rm5tLrVq1ePHFFwkPDy8yif7mm28IDAzUTylOnTqV+++/n4sXL+Lm5mb08zLGypUreeqppwgKCmLVqlVkZWXx8ccf06tXL/777z+6deuWb/9HHnmEJ554gueff56TJ08yZcoUIC8RLY5Wq0Wr1ZYYj0ql0r/2JdGdMzY2lu+++47z58/z0Ucf6R835v0WGhqKl5cXPj4+Bfa781xFCQ0NpUmTJgV+19x5fNeuXQ16XnfKyMigX79+XL9+ndmzZ9OoUSP+/PNPnnjiiQL7GvoeOnHiBP3796dRo0YsX74cR0dHvv322wJTrTpHjhzhzJkzvPvuuwQEBODk5ER0dDQdO3ZErVbz3nvvUb9+ffbu3cusWbOIjIxk6dKlgHG/b6o8Uw+lCfNX0nShVqtVcnJylEuXLimAsmHDBv1jxk4X6q5lyFdpPPXUU4q1tbVy6NChfNs///xz5fPPP1f++ecf5Z9//lHeeecdxdHRUQkMDFRSU1OLPeeKFSsUQNm7d2+Bx1588UXF1ta22OOLmu7RaDSKn5+f0rZt23xTdJGRkYqNjU2+qapVq1YpgLJu3bp85zh48GC+KZRTp04pgPLmm2/m2093/MiRI/XbdP8WI0aMyLfv5cuXFWtra+XVV1/Ntz01NVXx8fFRHn/8cf22wMBApU2bNvmmdRRFUR588EHF19dX0Wg0iqIoSvPmzZXg4OBiXqWS6V7H+++/P9/2n3/+Od+/z40bNxQHB4cC+12+fFmxs7NTnnzySf22kSNHKoCyZMmSAtcrabqwRYsW+aaTDhw4oADKqlWrChxTltOFup+bFi1a6F9fRcn79/H29la6du1a4Loff/xxvnO+8sorir29fYlTw7rjS/oyZmp7wIAB+uNcXV2V9evX53vcmPdb//79lcaNGxd6HVtbW+XFF18sNpaGDRsqAwYMKLD9+vXrClBgGvJuRU0XLliwoMDvSkXJm8bkrulCQ99Djz32mOLk5JTvZ0ij0ShNmzYt8DvY399fsbKyUs6dO5fvnC+99JLi7OysXLp0Kd/2Tz/9VAGUU6dOKYpi+O8bIdOFopRiY2P53//+R+3atbG2tsbGxgZ/f3+AAlMuxnjooYc4ePCgQV/Gmjp1KitWrGDu3Lm0a9cu32MTJ05k4sSJ9O/fn/79+zNr1ix++OEHzp49y6JFiww6f2HTRcVtL8m5c+e4fv06Tz75ZL5z+Pv7F/jr+Y8//sDd3Z2HHnqI3Nxc/Vfr1q3x8fHRT9GGhIQA8Pjjj+c7/tFHHy1yZPCRRx7J9/3ff/9Nbm4uI0aMyHcte3t7evbsqb/WhQsXOHv2LE899RRAvn3vv/9+oqKi9NN4HTt25K+//uKtt95i+/btZGRklOo1A3j44Yfzfa8bdbh06RIAe/fuJSMjI9/UKEDt2rXp06cP//33X4mvgSEeeOCBfKM3d8dRXnQ/N88880y+0U5nZ2ceeeQR9u3bR3p6er5jCnvNMjMzS7w79sUXXzTovfr7778bHP9XX33FgQMH2LBhAwMGDOCJJ54odNrY0Pdbce8/Q96b93p8YbZt24aLi0uB1/3JJ5/M970x76GQkBD69OlD9erV9cer1eoC73Wdli1b0qhRo3zb/vjjD3r37o2fn1++aw0aNEh/Dd1+hvy+ETJdKEpBq9USFBTE9evXmTp1Ki1atMDJyQmtVkvnzp3v6QPSw8OjXKZSZsyYwaxZs/jggw8YO3asQccMGTIEJyenEutoPD09AQqd2kpMTCxxurEouvPdPdWh23ZnjUVMTAxJSUnY2toWei5drZzunDVq1Mj3uLW1tf553E03zXvntQA6dOhQ6P66D3bdfpMnT2by5MnFxvXll19Sq1Yt1qxZw0cffYS9vT0DBgzgk08+yTd1Z4i7n4ednR2A/udS9xrc/bwA/Pz82LJlS75tjo6OuLq6GhWDIXGUl5Ken1ar5caNGzg6Ouq3lzZWHx+fQmsf72ZMMnLnv/fDDz/MoEGDGDNmDE888QRqtdqo95unpyfHjh0rsF9aWhrZ2dklvjc9PT2LvA5wT+/tu9+DUPC9bsx7qKhzFrYNCv/5iImJ4ffff8fGxqbYaxn6+0ZIkiVKITQ0lOPHj7Ns2TJGjhyp337hwoV7Pvfy5csLFKkWRVEUg/abMWMG06dPZ/r06bz99ttGxaMoSr7RgMI0b94cgJMnT3L//ffne+zkyZP6x42l+zAprBj27m3Vq1fH09OzyEJeFxeXfOeMiYmhZs2a+sdzc3OLrH+6+wNS95fyL7/8oh+9LIxuvylTpjB06NBC92ncuDEATk5OzJgxgxkzZhATE6Mf1XrooYc4e/ZskdcoDd1rEBUVVeCx69ev5xsJgNKPVphKSc9PrVZTrVq1MrnWzJkz9QXTxfH39y9Vnz3IG+XcvHkzcXFx1KhRw6j3W4sWLVi9ejXR0dH5EpiTJ08ClPjebNGiBatWrSI3NzffSK+hxxfF09OTAwcOFNhe2PsaDHsPeXp66pOy4s6pU9jPdfXq1WnZsiUffPBBocf4+fnp9zPk942QJEuUgu7NqftrV+e7774rsK+xf73rpgvLyvvvv8/06dN59913mTZtmlHH/vLLL6Snp9O5c+di96tZsyYdO3bkp59+YvLkyfopon379nHu3DkmTJhQqtgbN26Mr68vq1atYtKkSfrX/dKlS+zZs0f/Cw/gwQcfZPXq1Wg0Gjp16lTkOXv06AHk3RnYtm3bfM/17ruHijJgwACsra0JDw8vdhqtcePGNGzYkOPHjxvckwjy/vIeNWoUx48fZ968eaSnp+cbdblXXbp0wcHBgZ9++infXWNXr15l69atPProowadx87OrtxHpUqjcePG1KxZk5UrVzJ58mT9z01aWhrr1q3T33FYFl588UWDmmze/bvCUIqiEBISgru7uz55NOb9NnjwYN59912WL1/Om2++qd++bNkyHBwcGDhwYLHXHzJkCIsWLWLdunX5itKXL1+On59fse+14vTu3Zuff/6ZjRs35psyvLv3ljHvoZ49e7Jp0ybi4+P1yZlWq2Xt2rUGx/Xggw+yadMm6tevX2wibujvGyFJliiFwMBA6tevz1tvvYWiKHh4ePD7778XmGaBvL8EAb744gtGjhyJjY0NjRs3LvIvHU9PzyKnrYz12Wef8d577zFw4EAeeOCBAtN+uuTp0qVLPPnkkwwbNowGDRqgUqkICQlh3rx5NGvWjBdeeCHfcdbW1vTs2TNf7c5HH31E//79eeyxx3jllVeIjY3lrbfeonnz5gaPzN1NrVbz/vvv88ILLzBkyBBGjx5NUlIS06dPLzCtMGzYMFasWMH999/P+PHj6dixIzY2Nly9epVt27YxePBghgwZQrNmzRg+fDifffYZVlZW9OnTh1OnTvHZZ5/h5uZW4qgd5N3+PXPmTN555x0iIiIYOHAg1apVIyYmhgMHDuhHpSAv8R40aBADBgxg1KhR1KxZk8TERM6cOcORI0f0HwCdOnXiwQcfpGXLllSrVo0zZ87w448/lmlCoOPu7s7UqVN5++23GTFiBMOHDychIYEZM2Zgb29vcDLeokUL1q9fz4IFC2jXrh1qtZr27duXWZzTp09nxowZbNu2jV69ehl8nFqt5uOPP+app57iwQcf5KWXXiIrK4tPPvmEpKQk5syZU2Yx+vn55Uv278XgwYNp1aoVrVu3xtPTk+vXr7Ns2TJCQkL45ptv8o0kGfp+a9asGc8//zzTpk3DysqKDh068M8//7Bw4UJmzZqVb7pv5syZzJw5k//++4+ePXsCMGjQIPr378/LL79MSkoKDRo0YNWqVWzevJmffvrJ4Dsm7zZixAjmzp3LiBEj+OCDD2jYsCGbNm3i77//LrCvoe+hd955h99//52+ffvyzjvv4ODgwLfffktaWhqAQe/tmTNnsmXLFrp27cq4ceNo3LgxmZmZREZGsmnTJr799ltq1apl8O8bgOeff57ly5cTHh6uH/n+4YcfeO6551iyZAkjRowA8n4P169fn5EjR/L999+X6nU1S6atuxeWoLC7C0+fPq30799fcXFxUapVq6Y89thjyuXLlxVAmTZtWr7jp0yZovj5+SlqtbpCmyb27NnToLsTExMTlSFDhih169ZVHBwcFFtbW6Vhw4bKG2+8oSQlJRU4L1DoHUP//POP0rlzZ8Xe3l7x8PBQRowYUWhD1LuV1Exy8eLFSsOGDRVbW1ulUaNGypIlSwptRpqTk6N8+umnSqtWrRR7e3vF2dlZCQwMVF566SUlLCxMv19mZqYyadIkxdvbW7G3t1c6d+6s7N27V3Fzc1MmTpyo30/3737w4MFC4/rtt9+U3r17K66uroqdnZ3i7++vPProo8q///6bb7/jx48rjz/+uOLt7a3Y2NgoPj4+Sp8+fZRvv/1Wv89bb72ltG/fXqlWrZpiZ2en1KtXT5k4caISHx9f4ut39+u4du3afNuLavC4ePFipWXLloqtra3i5uamDB48WH/3lE5xTWoTExOVRx99VHF3d1dUKpX+Z6q4ZqSFvT8UpfC7C1977TVFpVIpZ86cMeh53/3z89tvvymdOnVS7O3tFScnJ6Vv377K7t27S7yuotxbA+LS+uijj5QOHToo1apVU6ysrBRPT09lwIAByh9//FHo/oa+37Kzs5Vp06YpderU0b+HvvzyywL76V6Lu1/H1NRUZdy4cYqPj49ia2urtGzZstA7RAtTXDPSq1evKo888oji7OysuLi4KI888oiyZ8+eQn9WDXkPKYqi7Ny5U+nUqZNiZ2en+Pj4KK+//rry0UcfKUC+32X+/v7KAw88UGhccXFxyrhx45SAgADFxsZG8fDwUNq1a6e88847ys2bN/X7Gfr7RneH7p0/S7qfrzufp+59c+cdzpWBSlEMLGwRQpSL7du307t3b/7991969uxZqv5f92rPnj3cd999rFixosAdTqL8KLf6ws2cOZP333+fuLg4/VRPx44d8ff3N2q6R5iXXr16oSgK//33H2q12qDRpLIWFBREZGQk58+fr/BrC5kuFMJs9OvXD4CDBw+W6bTT3bZs2cLevXtp164dDg4OHD9+nDlz5tCwYcMii2tF+fjiiy+YOHFige0pKSkcP36c5cuXmyAqUZZ27NihX4Pxjz/+KNdrTZo0iTZt2lC7dm0SExNZsWIFW7ZsqVzTbxZGRrKEMLHU1NR8y740bdq0zOuQ7rR//35ee+01Tp8+TWpqKtWrV2fAgAHMnj270Nu6TU032lMcKysri7sLEPL6zV2+fFn/fevWrU0ykinKx7lz50hNTQXyagEbNGhQrtcbP348GzduJDo6GpVKRdOmTZkwYQJPP/10uV5XFE2SLCGEWdNNpxZn6dKlBZqLCiGEqUmSJYQwa3eP9BUmICCgzO5KFUKIsiJJlhBCCCFEOZC1C4UQQgghyoFUWJqIVqvl+vXruLi4WGTBrhBCCFEVKYpCamoqfn5+JbblkCTLRK5fv07t2rVNHYYQQgghSuHKlSvUqlWr2H0kyTIR3bIyV65cwdXV1cTRCCGEEMIQKSkp1K5d26CFsCXJMhHdFKGrq6skWUIIIYSFMaTURwrfhRBCCCHKgSRZQgghhBDlQJIsIYQQQohyIEmWEEIIIUQ5kCRLCCGEEKIcSJIlhBBCCFEOJMkSQgghhCgHkmQJIYQQQpQDSbKEEEIIIcqBJFlCCCGEEOVAkiwhhBBCiHIgSZYQQgghRDmQJEsIIYQQlcqNtGyeW3aQuVvOoyiKyeKQJEsIIYQQlcrJa8lsPRvLxuPXUalUJotDkiwhhBBCVCqh15MBaObnatI4JMkSQgghRKUSei0vyWpR082kcUiSJYQQQohK5aQkWUIIIYQQZSs5PYcriRkANPOTJEsIIYQQokzo6rHqeDji5mhj0lgkyRJCCCFEpaGrx2pe07RF7yBJlhBCCCEqkZP6JMu0U4UgSZYQQgghKhFzubMQJMkSQgghRCWRkplDZEI6AM1NXPQOkmQJIYQQopI4dS0FgJruDlRzsjVxNJJkCSGEEKKSMKepQpAkSwghhBCVhK59gzncWQiSZAkhhBCikjCnOwtBkiwhhBBCVAI3s3K5GJ8GSJIlhBBCCFFmTl9PQVHA182e6s52pg4HkCRLCCGEEJWAuU0VgiRZQgghhKgETumSLDPoj6UjSZYQQgghLJ5uJKtFLfO4sxAkyRJCCCGEhUvPziU87iYg04VCCCGEEGXmTFQKWgW8XezwdrE3dTh6Fp1kzZ49mw4dOuDi4oK3tzfBwcGcO3cu3z4xMTGMGjUKPz8/HB0dGThwIGFhYfrHIyMjUalUhX6tXbu22OvPnz+fgIAA7O3tadeuHTt37iyX5ymEEEKIop28al6d3nUsOskKCQlhzJgx7Nu3jy1btpCbm0tQUBBpaXl9MhRFITg4mIiICDZs2MDRo0fx9/enX79++n1q165NVFRUvq8ZM2bg5OTEoEGDirz2mjVrmDBhAu+88w5Hjx6le/fuDBo0iMuXL1fIcxdCCCFEnpO31ixsZmZJlkpRFMXUQZSVuLg4vL29CQkJoUePHpw/f57GjRsTGhpKs2bNANBoNHh7e/PRRx/xwgsvFHqeNm3a0LZtW77//vsir9WpUyfatm3LggUL9NuaNGlCcHAws2fPLrB/VlYWWVlZ+u9TUlKoXbs2ycnJuLqaT5GeEEIIYWkGztvB2ehUFo1oT/+mNcr1WikpKbi5uRn0+W3RI1l3S07OGy708PAA0Cc19va352etrKywtbVl165dhZ7j8OHDHDt2jOeff77I62RnZ3P48GGCgoLybQ8KCmLPnj2FHjN79mzc3Nz0X7Vr1zb8iQkhhBCiUJk5GsJi84reZbqwnCiKwqRJk+jWrRvNmzcHIDAwEH9/f6ZMmcKNGzfIzs5mzpw5REdHExUVVeh5vv/+e5o0aULXrl2LvFZ8fDwajYYaNfJnyzVq1CA6OrrQY6ZMmUJycrL+68qVK6V8pkIIIYTQOROVgkarUN3Zlhqu5tHpXafSJFljx47lxIkTrFq1Sr/NxsaGdevWcf78eTw8PHB0dGT79u0MGjQIKyurAufIyMhg5cqVxY5i3UmlUuX7XlGUAtt07OzscHV1zfclhBBCiHsTeken96I+g03F2tQBlIVXX32VjRs3smPHDmrVqpXvsXbt2nHs2DGSk5PJzs7Gy8uLTp060b59+wLn+eWXX0hPT2fEiBHFXq969epYWVkVGLWKjY0tMLolhBBCiPKjb0JqZlOFYOEjWYqiMHbsWNavX8/WrVsJCAgocl83Nze8vLwICwvj0KFDDB48uMA+33//PQ8//DBeXl7FXtfW1pZ27dqxZcuWfNu3bNlS7DSjEEIIIcpWqO7OQjNaTkfHokeyxowZw8qVK9mwYQMuLi76kSU3NzccHBwAWLt2LV5eXtSpU4eTJ08yfvx4goODCxStX7hwgR07drBp06ZCr9W3b1+GDBnC2LFjAZg0aRLPPPMM7du3p0uXLixcuJDLly/zv//9rxyfsRBCCCF0MnM0nI9JBaBFLUmyypSufUKvXr3ybV+6dCmjRo0CICoqikmTJhETE4Ovry8jRoxg6tSpBc61ZMkSatasWSD50gkPDyc+Pl7//RNPPEFCQgIzZ84kKiqK5s2bs2nTJvz9/cvmyQkhhBCiWOeiU8nVKlRztMHPzXw6vetUqj5ZlsSYPhtCCCGEKGjF/ku882so3RtW58fnO1XINatsnywhhBBCVB2hZlz0DpJkCSGEEMJC6Yrem0uSJYQQQghRNrJztZyLvlX0LkmWEEIIIUTZOB+TSrZGi5uDDbWqOZg6nEJJkiWEEEIIi3O707ur2XV615EkSwghhBAW5+Qdy+mYK0myhBBCCGFx9CNZZtjpXUeSLCGEEEJYlByNljNmXvQOkmQJIYQQwsKExdwkO1eLi701/p6Opg6nSJJkCSGEEMKihF7Pmyps5me+Re8gSZYQQgghLIy5d3rXkSRLCCGEEBbFEu4sBEmyhBBCCGFBcjVazkSZ93I6OpJkCSGEEMJihMelkZmjxcnWigBPJ1OHUyxJsoQQQghhMXRThc383FCrzbfoHSTJEkIIIYQFCbWQeiyQJEsIIYQQFkR/Z2EtVxNHUjJJsoQQQghhETRahVPXbxW9m/FyOjqSZAkhhBDCIlyMv0lGjgZHWyvqeTmbOpwSSZIlhBBCCIugK3pv6uuKlZkXvYMkWUIIIYSwEKHXLKM/lo4kWUIIIYSwCJbS6V1HkiwhhBBCmD2tVuH0raJ3c1+zUEeSLCGEEEKYvciENG5m5WJvo6a+l3l3eteRJEsIIYQQZk83VdjE1xVrK8tIXywjSiGEEEJUafpO7xbQH0tHkiwhhBBCmD3dnYWWUo8FkmQJIYQQwswpikLodcu6sxAkyRJCCCGEmbucmE5qZi621moa1jD/Tu86kmQJIYQQwqzpi959XLCxkKJ3kCRLCCGEEGbO0pqQ6kiSJYQQQgizdsrCltPRkSRLCCGEEGZLURT9SJYl3VkIkmQJIYQQwoxdvZFBckYONlYqiyp6B0myhBBCCGHGdE1IG/u4YGdtZeJojCNJlhBCCCHMlqVOFYIkWUIIIYQwY6HX84rem1nQcjo6kmQJIYQQwiwpiqKfLpSRLCGEEEKIMnI9OZPEtGys1Soa+7iYOhyjSZIlhBBCCLOkG8VqWMMFexvLKnoHSbKEEEIIYaZuTxW6mjiS0pEkSwghhBBmyVKX09GRJEsIIYQQZufOondJsoQQQgghykhMShbxN7OxUqto6ivThUIIIYQQZUI3itXAy9kii96hFEmWlZUVsbGxBbYnJCRgZWWZL4IQQgghzIul12NBKZIsRVEK3Z6VlYWtre09BySEEEIIYel3FgJYG7rjl19+CYBKpWLx4sU4O99eCVuj0bBjxw4CAwPLPkIhhBBCVDmh1y1/JMvgJGvu3LlA3kjWt99+m29q0NbWlrp16/Ltt9+WfYRCCCGEqFJiUzOJSclCpYKmfpY7kmXwdOHFixe5ePEiPXv25Pjx4/rvL168yLlz5/j777/p1KlTecZawOzZs+nQoQMuLi54e3sTHBzMuXPn8u0TExPDqFGj8PPzw9HRkYEDBxIWFlbgXHv37qVPnz44OTnh7u5Or169yMjIKPLa06dPR6VS5fvy8fEp8+cohBBCVDW6qcL6Xs442ho8HmR2jK7J2rZtG9WqVSuPWIwWEhLCmDFj2LdvH1u2bCE3N5egoCDS0tKAvFG34OBgIiIi2LBhA0ePHsXf359+/frp94G8BGvgwIEEBQVx4MABDh48yNixY1Gri395mjVrRlRUlP7r5MmT5fp8hRBCiKog9FoKYJmLQt+pVOnh1atX2bhxI5cvXyY7OzvfY59//nmZBGaIzZs35/t+6dKleHt7c/jwYXr06EFYWBj79u0jNDSUZs2aATB//ny8vb1ZtWoVL7zwAgATJ05k3LhxvPXWW/pzNWzYsMTrW1tbGzx6lZWVRVZWlv77lJQUg44TQgghqprKcGchlGIk67///qNx48bMnz+fzz77jG3btrF06VKWLFnCsWPHyiFEwyUn5/2jeHh4AOiTGnt7e/0+VlZW2NrasmvXLgBiY2PZv38/3t7edO3alRo1atCzZ0/948UJCwvDz8+PgIAAhg0bRkRERJH7zp49Gzc3N/1X7dq1S/08hRBCiMpM3+ndguuxoBRJ1pQpU3jttdcIDQ3F3t6edevWceXKFXr27Mljjz1WHjEaRFEUJk2aRLdu3WjevDkAgYGB+Pv7M2XKFG7cuEF2djZz5swhOjqaqKgoAH1iNH36dEaPHs3mzZtp27Ytffv2LbR2S6dTp0788MMP/P333yxatIjo6Gi6du1KQkJCoftPmTKF5ORk/deVK1fK+BUQQgghLF/8zSyikjNRqaBZVRvJOnPmDCNHjgTypssyMjJwdnZm5syZfPTRR2UeoKHGjh3LiRMnWLVqlX6bjY0N69at4/z583h4eODo6Mj27dsZNGiQ/u5IrVYLwEsvvcSzzz5LmzZtmDt3Lo0bN2bJkiVFXm/QoEE88sgjtGjRgn79+vHnn38CsHz58kL3t7Ozw9XVNd+XEEIIIfLTjWIFVHfC2c5yi96hFEmWk5OTfhrOz8+P8PBw/WPx8fFlF5kRXn31VTZu3Mi2bduoVatWvsfatWvHsWPHSEpKIioqis2bN5OQkEBAQAAAvr6+ADRt2jTfcU2aNOHy5csGx+Dk5ESLFi2KHf0SQgghRPFOXc+rWW7uZ9mjWFCKJKtz587s3r0bgAceeIDXXnuNDz74gOeee47OnTuXeYDFURSFsWPHsn79erZu3apPnArj5uaGl5cXYWFhHDp0iMGDBwNQt25d/Pz8CrR+OH/+PP7+/gbHkpWVxZkzZ/RJmxBCCCGMd/KqrtO75SdZRo/Dff7559y8eRPIq2O6efMma9asoUGDBvqGpRVlzJgxrFy5kg0bNuDi4kJ0dDSQl1A5ODgAsHbtWry8vKhTpw4nT55k/PjxBAcHExQUBOR1sH/99deZNm0arVq1onXr1ixfvpyzZ8/yyy+/6K/Vt29fhgwZwtixYwGYPHkyDz30EHXq1CE2NpZZs2aRkpKin0oVQgghhPF0dxY2s+DldHSMTrLq1aun/39HR0fmz59fpgEZY8GCBQD06tUr3/alS5cyatQoAKKiopg0aRIxMTH4+voyYsQIpk6dmm//CRMmkJmZycSJE0lMTKRVq1Zs2bKF+vXr6/cJDw/PNx169epVhg8fTnx8PF5eXnTu3Jl9+/YZNfolhBBCiNtupGVzLSmvEbilt28AUClFrfhchnQtFQylUqk4cuRIpU5YUlJScHNzIzk5WYrghRBCCGBnWBzPfH+Aup6ObH+9t6nDKZQxn98VUraflJTEvHnzcHMrOStVFIVXXnkFjUZTAZEJIYQQwlzcniq0/FEsqKAkC2DYsGF4e3sbtO+rr75aztEIIYQQwtycqiTL6ehUSJKl60VlqNTU1HKKRAghhBDmSjeSVVmSLKNbOOhkZ2dz7tw5cnNzDdr/2rVrJe6zYsWK0oYjhBBCCAuWnJ7D5cR0AJpZ+HI6OkYnWenp6Tz//PM4OjrSrFkzfcPOcePGMWfOnCKP69+/Pzdu3Cjy8ZUrV/Lss88aG44QQgghKoFT1/NGsWp7OODuaGviaMpGqdYuPH78ONu3b8+38HK/fv1Ys2ZNkcd5e3szcOBA0tLSCjy2evVqRo0aZdJleYQQQghhOif1i0JXjqlCKEWS9dtvv/H111/TrVs3VCqVfnvTpk3zLbFztz/++AONRsPgwYPJycnRb//5558ZMWIEH374IRMnTjQ2HCGEEEJUAqG65XQqST0WlCLJiouLK/QuwbS0tHxJ192cnZ3566+/uHbtGsOGDUNRFNauXcvTTz/N+++/z+TJk40NRQghhBCVRGglK3qHUiRZHTp04M8//9R/r0usFi1aRJcuXYo91svLi3/++YdDhw7Rr18/nn76aaZNm8abb75pbBhCCCGEqCRSMnO4GJ9XTlSZRrKMbuEwe/ZsBg4cyOnTp8nNzeWLL77g1KlT7N27l5CQkCKPO3HihP7/P/nkE0aMGMGQIUN46KGH8j3WsmVLY0MSQgghhAU7fWuqsKa7Ax5OlaPoHUqRZHXt2pU9e/bwySefUL9+ff755x/atm3L3r17adGiRZHHtW7dGpVKhaIo+v/+/PPPrF27Ft3KPiqVSjq9CyGEEFWMbqqweSVYFPpORiVZOTk5vPjii0ydOpXly5cbdaGLFy8atb8QQgghqobQSnhnIRiZZNnY2PDrr78ydepUoy9UmRd7FkIIIUTp6ds31KrCSRbAkCFD+O2335g0aVKpLnhn/dWdVCoV9vb21KlTBzs7u1KdWwghhBCW5WZWLhG6oveqPJIF0KBBA95//3327NlDu3btcHJyyvf4uHHjij1eV5tVFBsbG5544gm+++67fM1OhRBCCFH5nIlKQVHAx9UeL5fKNchidJK1ePFi3N3dOXz4MIcPH873mEqlKjHJ+vXXX3nzzTd5/fXX6dixI4qicPDgQT777DOmTZtGbm4ub731Fu+++y6ffvqpseEJIYQQwoKcvKoreq9co1hQiiTrXgvYP/jgA7744gsGDBig39ayZUtq1arF1KlTOXDgAE5OTrz22muSZAkhhBBFSErPJuR8HN0aVMfT2XJHgCrrnYVQiiTrXp08ebLQInh/f39OnjwJ5E0pRkVFVXRoQgghhNm7kpjO97su8vOhK6Rna6jt4cDKFzpT28PR1KGVSuj1ytfpXcfoJOu5554r9vElS5YU+3hgYCBz5sxh4cKF2NrmNRzLyclhzpw5BAYGAnDt2jVq1KhhbGhCCCFEpXXiahILd0Sw6WQU2rz2kthaq7mSmMHj3+1lxQudqOflbNogjZSencuF2JuAJFkA3LhxI9/3OTk5hIaGkpSURJ8+fUo8/ptvvuHhhx+mVq1atGzZEpVKxYkTJ9BoNPzxxx8ARERE8MorrxgbmhBCCFGpaLUK28/HsnBHBPsiEvXbuzeszks96tPA25mnFu8jPC6Nx7/bx4oXOtHYx8WEERvnTFQKWgW8XOzwdq18N7sZnWT9+uuvBbZptVpeeeUV6tWrV+LxXbt2JTIykp9++onz58+jKAqPPvooTz75JC4ueT8YzzzzjLFhCSGEEJVGVq6GDceus2hHBGG3Rnqs1SoebuXH6B71aOJ7u35pzUtdeOb7A5yJSmHYwr38+HwniykiD72Wt5xOZRzFAlApujVt7tG5c+fo1auX1FIZKCUlBTc3N5KTk3F1rXzFfkIIIYyXnJ7DigOXWLY7ktjULACc7ax5slMdRnWti5+7Q6HHJaVnM3LJAY5fTcbF3prlz3WkbZ1qFRl6qUxee5xfDl9lXJ8GTApqbOpwDGLM57e6rC4aHh5Obm6uQfv++OOPdOvWDT8/Py5dugTA3Llz2bBhQ1mFI4QQQliMqzfSmfn7abrO+Y+PN58jNjULH1d73r4/kD1T+vD2/U2KTLAA3B1t+emFTnSoW43UzFyeWbyffREJFfgMSuf2nYWVcyTL6OnCuzu9K4pCVFQUf/75JyNHjizx+AULFvDee+8xYcIEZs2apV8Qulq1asybN4/BgwcbG5IQQghhkUKvJbNwRwR/noxCc6uaPdDHhRd71OPBln7YWhs+FuJib8Py5zoy+odD7L6QwMglB1g4oj09G3mVV/j3JDNHo58KbVHJltPRMXq6sHfv3vm+V6vVeHl50adPH5577jmsrYvP25o2bcqHH35IcHAwLi4uHD9+nHr16hEaGkqvXr2Ij483/llYIJkuFEKIqklRFELOx7FwRwR7wm+PNnVrUJ0Xe9Sje8Pqxa6MUpLMHA2vrDjC1rOx2Fqp+frJNgQ18ymL0MvU0cs3GDJ/D55Othx6t989PeeKZMznt9EjWdu2bSt1YJDXzLRNmzYFttvZ2ZGWlnZP5xZCCCHMVXaulo3H84rZz8WkAmClVvFQS19e6F6vzKbM7G2s+PbpdoxffZS/QqN5ZcUR5j7Rmoda+ZXJ+ctK6PW8ovfmNd0sJsEyltE1WX369CEpKanA9pSUFINaOAQEBHDs2LEC2//66y+aNm1qbDhCCCGEWUvOyOHbkHC6f7yVyWuPcy4mFSdbK17oFsCON3ozb1ibMq9JsrVW89XwNgxpU5NcrcL41Uf55fDVMr3GvQq9WnmbkOoYPZK1fft2srOzC2zPzMxk586dJR7/+uuvM2bMGDIzM1EUhQMHDrBq1Spmz57N4sWLjQ1HCCGEMEuKovD5lvMs3R3Jzay8G8NquNrx7H0BDO9YBzcHm3K9vrWVms8ea4W9jZpVB64wee1xMnM0PN254KorFS09O5fDl/P6blbG5XR0DE6yTpw4of//06dPEx0drf9eo9GwefNmatasWeJ5nn32WXJzc3njjTdIT0/nySefpGbNmnzxxRcMGzbMyPCFEEII83Q6KoWvtl4AoHENF0b3qMfDrYwrZr9XarWKD4e0wM7aimV7Inn3t1AyczS80L3kvpblISNbw4r9l/g2JJz4m9nYWKloXdv8W02UlsFJVuvWrVGpVKhUqkKnBR0cHPjqq68MOtfo0aMZPXo08fHxaLVavL29DY9YCCGEsACnb9UcdahbjZ9f6mKyuiOVSsW0h5riYGvFgu3hzPrzDJk5Gsb2aVhhMWTmaFi5/zILQsKJu9X/q46HI2/f3wQft8rX6V3H4CTr4sWLKIpCvXr1OHDgAF5et28JtbW1xdvbGysrK6MuXr16daP2F0IIISzF2ei84nZzKOxWqVS8MaAxDjZWfL7lPJ/+c56MHA2TgxqXa2yZORpWH7jM/O3h+uaqtao5MK5PQ4a0rYmNVcWN6pmCwUmWv3/eHK5WqzX6Im3atDH4H/HIkSNGn18IIYQwN2ej80aymviYR82RSqViXN+GONhY8cGmM3yzLZz0bA3vPdi0zBOtrFwNPx+8wjfbwolOyQSgprsDY/s04JG2tSp0ytSUjC581zl9+jSXL18uUAT/8MMPF9g3ODhY//+ZmZnMnz+fpk2b0qVLFwD27dvHqVOnZFFoIYQQlYKiKJyJyhvJCvQ1rwWbR/eoh72NmqkbTrF0dySZOVo+CG6OWn3viVZ2rpa1h6/wzdYLXE/OS6583ewZ07sBj7WvhZ21cTNels7oJCsiIoIhQ4Zw8uRJVCoVul6muixY18H9TtOmTdP//wsvvMC4ceN4//33C+xz5coVY8MRQgghzE7czSwS07JRq6Cht3klWQDPdKmLnY0Vb607waoDl8nK0fDxoy2xLuX0XY5Gy7rDV/lq6wWuJWUAeXdSjundgCc61K5yyZWO0UnW+PHjCQgI4N9//9XXZyUkJPDaa6/x6aeflnj82rVrOXToUIHtTz/9NO3bt2fJkiXGhiSEEEKYlbO3RrHqVnfCwdY8E4zH29fG3saKiWuOsf7oNTJzNcx7oo1RU3m5Gi3rj17jq61hXEnMS668XOx4pVd9hnesg72NeT73imJ0krV37162bt2Kl5cXarUatVpNt27dmD17NuPGjePo0aPFHu/g4MCuXbto2DD/XQ27du3C3r7y3mEghBCi6jC3eqyiPNzKDztrNWNXHmHTyWiycg7zzVNtS0yOcjVafjt2na+2hnEpIR2A6s62/K9nfZ7u7F/lkysdo5MsjUaDs7MzkHd34PXr12ncuDH+/v6cO3euxOMnTJjAyy+/zOHDh+ncuTOQV5O1ZMkS3nvvPWPDEUIIIcyObiQr0Mf8pgrvNqCZD4tGtOelHw/z39lYXlh+iIUj2uFoWzBF0GgVNh6/xpf/XeBifN5SeJ5OtrzUsx5Pd/Yv9JiqzOhXo3nz5pw4cYJ69erRqVMnPv74Y2xtbVm4cCH16pXc3Oytt96iXr16fPHFF6xcuRKAJk2asGzZMh5//HHjn4EQQghhZs5E64rezXskS6dXY2+WPtuBF5YfYteFeEYtOciSZzvgbJeXJmi0Cn+cuM6X/4URHpeXXFVztOHFHvUZ0cUfJztJrgqjUnSV6wb6+++/SUtLY+jQoURERPDggw9y9uxZPD09WbNmjUHrFwrjVvEWQghhOXI0Wpq+t5kcjcLON3pT28PR1CEZ7PClREYtOUhqVi6taruzbFQHdofHM+/fMC7E3gTAzcGGF3vUY2TXuvokrCox5vPb6CSrMImJiVSrVs3kzdYsiSRZQghROZ2LTmXAvB0421lzcnqQxX02nryazDNL9pOUnoOdtZqs3Lz+mK721ozuXo9R99XFxb581100Z8Z8fht1r2Zubi7W1taEhobm2+7h4VHsD5GHhwfx8fEGX6dOnTpcunTJmNCEEEIIs6Areg/0cbG4BAugRS03Vr/YmerOdmTlanGxs2ZCv4bsfLMPr/ZtWKUTLGMZNc5nbW2Nv79/ob2wipOUlMRff/2Fm5ubQfsnJCQYfQ0hhBDCHJhrE1JjBPq4smHsfewOi2dAMx/cHCWxKg2jJ1PfffddpkyZwk8//YSHh4fBx40cOdLYSwkhhBAW5/ZIlmWXgtR0d+DxDrVNHYZFMzrJ+vLLL7lw4QJ+fn74+/vj5OSU7/HC1h4szXqHQgghhCXStW9oYsEjWaJsGJ1k3bkOoRBCCCFuu5GWrV8QuVENSbKqOqOTrDvXIRRCCCHEbWdv9ceq7eEgBeLCuLsLdZKSkli8eDFTpkwhMTERyJsmvHbtWpkGJ4QQQliSylKPJcqG0SNZJ06coF+/fri5uREZGcno0aPx8PDg119/5dKlS/zwww/lEacQQghh9vT1WBawnI4of0aPZE2aNIlRo0YRFhaWb0HnQYMGsWPHjjINTgghhLAk+pEsC1lOR5Qvo5OsgwcP8tJLLxXYXrNmTaKjo40OIC4ujpycHKOPA5g9ezYdOnTAxcUFb29vgoODCyxSHRMTw6hRo/Dz88PR0ZGBAwcSFhZW4Fx79+6lT58+ODk54e7uTq9evcjIyCj2+vPnzycgIAB7e3vatWvHzp07S/U8hBBCWD6NVuFcjOUsDC3Kn9FJlr29PSkpKQW2nzt3Di8vryKPW7hwIVlZWQAoisKHH35ItWrV8PHxwd3dnUmTJhnd6iEkJIQxY8awb98+tmzZQm5uLkFBQaSlpemvExwcTEREBBs2bODo0aP4+/vTr18//T6Ql2ANHDiQoKAgDhw4wMGDBxk7dixqddEvz5o1a5gwYQLvvPMOR48epXv37gwaNIjLly8b9RyEEEJUDpcS0sjM0WJvo8bf06nkA0Tlpxhp9OjRSnBwsJKdna04OzsrERERyqVLl5Q2bdoo48ePL/I4tVqtxMTEKIqiKN9++63i5OSkfPbZZ8ru3buVr776SnFzc1O++uorY8PJJzY2VgGUkJAQRVEU5dy5cwqghIaG6vfJzc1VPDw8lEWLFum3derUSXn33XeNulbHjh2V//3vf/m2BQYGKm+99Vah+2dmZirJycn6rytXriiAkpycbNR1hRBCmKc/T1xX/N/8Q3n4q52mDkWUo+TkZIM/v40eyfr000+Ji4vD29ubjIwMevbsSYMGDXBxceGDDz4oLpnT///333/P+++/z6RJk+jatStjx47l008/ZdGiRcZniXdITk4G0Hei142c3Vk7ZmVlha2tLbt27QIgNjaW/fv34+3tTdeuXalRowY9e/bUP16Y7OxsDh8+TFBQUL7tQUFB7Nmzp9BjZs+ejZubm/6rdm3poiuEEJXJ2ai8WZ4mUo8lbjE6yXJ1dWXXrl2sW7eOOXPmMHbsWDZt2kRISEiB7u930y2UefHiRfr27ZvvsT59+hAREWFsOHqKojBp0iS6detG8+bNAQgMDMTf358pU6Zw48YNsrOzmTNnDtHR0URFRQHorzl9+nRGjx7N5s2badu2LX379i20dgsgPj4ejUZDjRo18m2vUaNGkXVpU6ZMITk5Wf915cqVUj9XIYQQ5udMtNRjifyMbuGg06dPH/r06WPUMZs3b8bNzQ0HB4cCReUZGRnF1kCVZOzYsZw4cSLfCJSNjQ3r1q3j+eefx8PDAysrK/r168egQYP0++jqwF566SWeffZZANq0acN///3HkiVLmD17dpHXvHt1dUVRilxx3c7ODjs7u1I/PyGEEOZN7iwUdytVVvPff//x4IMPUr9+fRo0aMCDDz7Iv//+W+JxI0eOJDg4mKtXr/Lff//le2zv3r3Ur1+/NOHw6quvsnHjRrZt20atWrXyPdauXTuOHTtGUlISUVFRbN68mYSEBAICAgDw9fUFoGnTpvmOa9KkSZFF7NWrV8fKyqrAqFVsbGyB0S0hhBCVX2pmDlcS8wYPZCRL6BidZH399dcMHDgQFxcXxo8fz7hx43B1deX+++/n66+/LvI4rVab7+vtt9/O97iPj0+xo0aFURSFsWPHsn79erZu3apPnArj5uaGl5cXYWFhHDp0iMGDBwNQt25d/Pz8CrR+OH/+PP7+/oWey9bWlnbt2rFly5Z827ds2ULXrl2Neg5CCCEs3/lbrRt83exxd7Q1cTTCbBhbVe/n51foXYBff/214uvra+zp7snLL7+suLm5Kdu3b1eioqL0X+np6fp9fv75Z2Xbtm1KeHi48ttvvyn+/v7K0KFD851n7ty5iqurq7J27VolLCxMeffddxV7e3vlwoUL+n369OmT73mvXr1asbGxUb7//nvl9OnTyoQJExQnJyclMjLSoNiNuTtBCCGEeftxb6Ti/+Yfyqgl+00diihnxnx+G12TlZKSwsCBAwtsDwoK4s033yzx+IiICHbt2kVUVBRWVlYEBATQv39/XF2Nn8NesGABAL169cq3fenSpYwaNQqAqKgoJk2aRExMDL6+vowYMYKpU6fm23/ChAlkZmYyceJEEhMTadWqFVu2bMk3fRkeHk58fLz++yeeeIKEhARmzpxJVFQUzZs3Z9OmTUWOfgkhhKi8pB5LFEalKHf0VjDAU089RevWrXn99dfzbf/00085fPgwq1atKvS4tLQ0Ro0axbp16/IurFLh7e1NXFwcDg4OzJkzhzFjxpTyaVielJQU3NzcSE5OLlWCKYQQwnw8umAPhy7d4IthrRncuqapwxHlyJjPb6NHspo0acIHH3zA9u3b6dKlCwD79u1j9+7dvPbaa3z55Zf6fceNG6f//0mTJhEVFcXRo0ext7fnnXfeoX79+kybNo3Vq1fz6quvUq1aNZ588kljQxJCCCFMRlEUzt5q3yA9ssSdjB7JKq64PN+JVap8fa+8vLzYvHkz7dq1A+DGjRv4+fmRkJCAo6Mj33zzDYsXL+bo0aPGhGOxZCRLCCEqhyuJ6XT/eBu2VmpOzRyAjVXp2xEJ81euI1kXL14sVVC5ubn5gnF2diY3N5e0tDQcHR0JCgpi8uTJpTq3EEIIYSq6UawG3s6SYIl8KuynoUOHDnzxxRf677/44gu8vLz0i0rfvHkTZ2fnigpHCCGEKBO65XQCfaU/lsjP6JEsRVH45Zdf2LZtG7GxsfqO6Trr168v9Lg5c+bQv39/1q1bh62tLdHR0Sxfvlz/+J49e7j//vuNDUcIIYQwKX09lo+Ufoj8jE6yxo8fz8KFC+nduzc1atQochmZu7Vt25bQ0FD++OMPsrKy6NOnT74u62PGjKlSdxcKIYSoHM5Ey0iWKJzRSdZPP/3E+vXrSzXq5Ovry+jRo40+TgghhDBHGdkaIuPTAAiUkSxxF6OTLDc3N+rVq1fqC27durVAM9KHH36Yhg0blvqcQgghhCmcj0lFq0B1Z1u8XOxMHY4wM0YXvk+fPp0ZM2aQkZFh1HGxsbF06tSJfv36MXPmTBYuXMi+ffv49NNPadKkCW+88YaxoQghhBAmpe/0LqNYohBGj2Q99thjrFq1Cm9vb+rWrYuNjU2+x48cOVLocePGjcPPz4/ExETs7Ox4/fXXSU1N5dChQ2zdupXHH3+cmjVrMn78+NI9EyGEEKKCnYnKK3oP9JF6LFGQ0UnWqFGjOHz4ME8//bRRhe9//fUXe/bswd3dHYCPPvqIatWq8dVXX9GnTx/mzZvHrFmzJMkSQghhMWTNQlEco5OsP//8k7///ptu3boZdZydnV2+hEytVqPRaMjNzQWga9euREZGGhuOEEIIYRJ3LqcjI1miMEbXZNWuXbtUy8B069aN9957j7S0NHJycnj77bepV68eHh4eAMTFxVGtWjWjzyuEEEKYQkxKFknpOVipVTTwlmbaoiCjk6zPPvuMN954w+hRp08//ZRjx47h7u6Ok5MTy5YtY8GCBfrHz5w5w6hRo4wNRwghhDAJXX+setWdsLexMnE0whwZPV349NNPk56eTv369XF0dCxQ+J6YmFjocfXq1ePEiRPs3r2brKwsOnfuTPXq1fWPS4IlhBDCkpzVFb1LPZYogtFJ1rx580p9MUdHR/r371/q44UQQghzcbt9g9RjicIZnWSNHDmyPOIQQgghLIpuJKuJLKcjimB0TRZAeHg47777LsOHDyc2NhaAzZs3c+rUqTINTgghhDBHWbkawuNuAtKIVBTN6CQrJCSEFi1asH//ftavX8/Nm3k/ZCdOnGDatGllHqAQQghhbsJj08jVKrjaW+PrZm/qcISZMjrJeuutt5g1axZbtmzB1tZWv713797s3bu3TIMTQgghzNGdTUgNbcotqh6ja7JOnjzJypUrC2z38vIiISGh0GNSUlIMPn9penAJIYQQFUnXhLSJFL2LYhidZLm7uxMVFUVAQEC+7UePHqVmzZpFHlNSpq8oCiqVCo1GY2xIQgghRIU6EyXL6YiSGZ1kPfnkk7z55pusXbsWlUqFVqtl9+7dTJ48mREjRhR6zLZt2+45UCGEEMJcyHI6whBGJ1kffPABo0aNombNmiiKQtOmTdFoNDz55JO8++67hR7Ts2fPew5UCCGEMAfxN7OIS81CpYJGNSTJEkUzuvDdxsaGFStWEBYWxs8//8xPP/3E2bNn+fHHH7GyMmxZgZ07d/L000/TtWtXrl27BsCPP/7Irl27jA1HCCGEqFDnbo1i+Xs44mRn9FiFqEKMTrJmzpxJeno69erV49FHH+Xxxx+nYcOGZGRkMHPmzBKPX7duHQMGDMDBwYEjR46QlZUFQGpqKh9++KHxz0AIIYSoQPp6LOmPJUpgdJI1Y8YMfW+sO6WnpzNjxowSj581axbffvstixYtyrfuYdeuXTly5Iix4QghhBAVSl+PJZ3eRQmMTrJ0dwHe7fjx43h4eJR4/Llz5+jRo0eB7a6uriQlJRkbjhBCCFGhdD2ymsidhaIEBk8mV6tWDZVKhUqlolGjRvkSLY1Gw82bN/nf//5X4nl8fX25cOECdevWzbd9165d1KtXz/DIhRBCiAqWq9FyPiZvNqeJTBeKEhicZM2bNw9FUXjuueeYMWMGbm5u+sdsbW2pW7cuXbp0KfE8L730EuPHj2fJkiWoVCquX7/O3r17mTx5Mu+9917pnoUQQghRASIT0sjO1eJka0Wtag6mDkeYOYOTrJEjRwIQEBDAfffdh7V16e6oeOONN0hOTqZ3795kZmbSo0cP7OzsmDx5MmPHji3VOYUQQoiKcCYqrx6rsY8LarUspyOKZ3SmVBY9rz744APeeecdTp8+jVarpWnTpjg7O9/zeYUQQojydOeahUKUxOjC93u1fPly0tLScHR0pH379nTs2FESLCGEEBbhbJSsWSgMV+FJ1uTJk/H29mbYsGH88ccf5ObmVnQIQgghRKncbt8gI1miZBWeZEVFRbFmzRqsrKwYNmwYvr6+vPLKK+zZs6eiQxFCCCEMlpyRw7WkDCCvJkuIkhiVZOXm5mJtbU1oaGipL2htbc2DDz7IihUriI2NZd68eVy6dInevXtTv379Up9XCCGEKE+65XRqujvgam9Twt5CGFn4bm1tjb+/PxqNpkwu7ujoyIABA7hx4waXLl3izJkzZXJeIYQQoqzdbkIqo1jCMEZPF7777rtMmTKFxMTEUl80PT2dFStWcP/99+Pn58fcuXMJDg6+pxEyIYQQojzp2jfImoXCUEa3cPjyyy+5cOECfn5++Pv74+TklO/xktYfHD58OL///juOjo489thjbN++na5duxobhhBCCFGhbrdvkJEsYRijk6zg4OB7uqBKpWLNmjUMGDCg1A1NhRBCiIqk1Sr6miwZyRKGMjrLmTZt2j1dcOXKlfr/z8zMxN7e/p7OJ4QQQpS3KzfSSc/WYGetpq6no6nDERaiwls4aLVa3n//fWrWrImzszMREREATJ06le+//76iwxFCCCFKpKvHalTDBWurCv/oFBbKoJ8UDw8P4uPjAahWrRoeHh5FfpVk1qxZLFu2jI8//hhbW1v99hYtWrB48eJSPg0hhBCi/OjrsaQ/ljCCQdOFc+fOxcUl7wdr3rx593TBH374gYULF9K3b1/+97//6be3bNmSs2fP3tO5hRBCiPKgW05HOr0LYxiUZI0cObLQ/79bXFxciee6du0aDRo0KLBdq9WSk5NjSDhCCCFEhdL3yJKRLGGEe55YVhSFTZs2MXToUGrVqlXi/s2aNWPnzp0Ftq9du5Y2bdrcazhCCCFEmUrLyuVSYjogy+kI45S6h0JERARLlixh+fLl3Lx5kwceeIDVq1eXeNy0adN45plnuHbtGlqtlvXr13Pu3Dl++OEH/vjjj9KGI4QQQpSLczGpKAp4u9jh6Wxn6nCEBTEqycrMzOSXX35h8eLF7Nu3j/79+xMVFcWxY8do3ry5Qed46KGHWLNmDR9++CEqlYr33nuPtm3b8vvvv9O/f/9SPQkhhBCivEg9ligtg5OsV155hdWrV9O4cWOefvpp1q1bh6enJzY2NqjVxs06DhgwgAEDBhgdrBBCCFHRpB5LlJbB2dHChQt5+eWX+eeffxgzZgyenp7lGZdBZs+eTYcOHXBxccHb25vg4GDOnTuXb5+YmBhGjRqFn58fjo6ODBw4kLCwsHz79OrVC5VKle9r2LBhxV57+vTpBY7x8fEp8+cohBDCtG6PZEmSJYxjcJL1ww8/cODAAXx9fXniiSf4448/yM3NNejYknprGdNn604hISGMGTOGffv2sWXLFnJzcwkKCiItLQ3IK8oPDg4mIiKCDRs2cPToUfz9/enXr59+H53Ro0cTFRWl//ruu+9KvH6zZs3yHXPy5Emj4hdCCGHeFEXhjL5HlkwXCuMYPF345JNP8uSTTxIZGcnSpUsZM2YM6enpaLVaTp8+TdOmTYs89l57axVl8+bN+b5funQp3t7eHD58mB49ehAWFsa+ffsIDQ2lWbNmAMyfPx9vb29WrVrFCy+8oD/W0dHR6JEoa2trg4/JysoiKytL/31KSopR1xJCFC0zR8OiHRF0qe9J+7rG/bEmRHGuJ2eSmpmLtVpFfS9nU4cjLIzRdxfWrVuXGTNmMH36dP7++2+WLFnC008/zYQJExg6dChffvllgWOK661VlpKTkwH0I2K6pObO9RGtrKywtbVl165d+ZKsFStW8NNPP1GjRg0GDRrEtGnT9A1YixIWFoafnx92dnZ06tSJDz/8kHr16hW67+zZs5kxY8Y9PT8hROE+++cci3ZepO4RR7a/3tvU4YhK5GxU3h/EDbydsbWW5XSEcUr9E6NSqRg4cCA///wz169fZ/LkyYSEhJRlbEZRFIVJkybRrVs3/Z2OgYGB+Pv7M2XKFG7cuEF2djZz5swhOjqaqKgo/bFPPfUUq1atYvv27UydOpV169YxdOjQYq/XqVMnfvjhB/7++28WLVpEdHQ0Xbt2JSEhodD9p0yZQnJysv7rypUrZffkhajCjly+wfe7LgIQmZDOpYS0Eo4QwnBno2/VY0nRuygFlaIoiqmDKAtjxozhzz//ZNeuXfmaoh4+fJjnn3+e48ePY2VlRb9+/fR3Q27atKnQcx0+fJj27dtz+PBh2rZta9D109LSqF+/Pm+88QaTJk0qcf+UlBTc3NxITk7G1VXm+YUojcwcDQ9+tYsLsTf1294f3IxnutQ1XVCiUhm78gh/nIjirUGB/K9nfVOHI8yAMZ/flWLs89VXX2Xjxo1s27atQNf5du3acezYMZKSkoiKimLz5s0kJCQQEBBQ5Pnatm2LjY1NgbsQi+Pk5ESLFi2MOkYIcW++/C+MC7E38XKx48UeeVP1IefjTRyVqExkJEvcC4tOshRFYezYsaxfv56tW7cWmzi5ubnh5eVFWFgYhw4dYvDgwUXue+rUKXJycvD19TU4lqysLM6cOWPUMUKI0jt5NZnvdkQAMCu4OQ+38gNgb3g82blaU4YmKonMHA0RcXmjpE2kEakoBYtOssaMGcNPP/3EypUrcXFxITo6mujoaDIyMvT7rF27lu3bt+vbOPTv35/g4GCCgoIACA8PZ+bMmRw6dIjIyEg2bdrEY489Rps2bbjvvvv05+nbty9ff/21/ntdDdrFixfZv38/jz76KCkpKRVW5C9EVZadq+X1X46j0So81MqPAc18aOrriqeTLWnZGg5fumHqEEUlcCH2JloFqjna4O0iy+kI45V67cJ7cfDgQdauXcvly5fJzs7O99j69esNPs+CBQuAvGaid1q6dCmjRo0CICoqikmTJhETE4Ovry8jRoxg6tSp+n1tbW3577//+OKLL7h58ya1a9fmgQceYNq0aVhZWen3Cw8PJz7+9jTE1atXGT58OPHx8Xh5edG5c2f27duHv7+/wfELIUrn620XOBudiqeTLTMezmvPolar6N6wOr8du86OsDi61Dd9w2Rh2c5E3e6PpVKpTByNsESlauHw3HPPMWrUKOrUqWP0BVevXs2IESMICgpiy5YtBAUFERYWRnR0NEOGDDHqXIbU7I8bN45x48YV+Xjt2rUNuisyMjIy3/eGLIYthCh7p6+nMH/bBQBmDG6Gh5Ot/rGejb347dh1Qs7F8ebAQFOFKCoJfT2WdHoXpWT0dOFrr73Ghg0bqFevHv3792f16tX5mmyW5MMPP2Tu3Ln88ccf2Nra8sUXX3DmzBkef/zxUiVtQoiqI0eTN02Yq1UY2MyHB1rkr4Hs3tALgNNRKcSlGv57SYjC3F6zUOqxROkYnWS9+uqrHD58mMOHD9O0aVPGjRuHr68vY8eO5ciRIyUeHx4ezgMPPACAnZ0daWlpqFQqJk6cyMKFC41/BkKIKuO7kHBOXU/B3dGG94ObF5jCqe5sRzO/vA/EnWFxpghRVBKKonDm1pqFUvQuSqvUhe+tWrXiiy++4Nq1a0ybNo3FixfToUMHWrVqxZIlS4qcyvPw8CA1Ne8Ht2bNmoSGhgKQlJREenp6acMRQlRy52NS+fK/vGnC6Q81w6uIQuSejfJGs0LOS5IlSi/uZhaJadmoVdCwhiynI0qn1ElWTk4OP//8Mw8//DCvvfYa7du3Z/HixTz++OO88847PPXUU4Ue1717d7Zs2QLA448/zvjx4xk9ejTDhw+nb9++pQ1HCINsDo1m0Y4INNpK0YO3ysjVaHl97XGyNVr6BnozuLVfkfv2uJVk7QyLRyv/zqKUzt4axQqo7oS9jVUJewtROKML348cOcLSpUtZtWoVVlZWPPPMM8ydO5fAwNtFpkFBQfTo0aPQ47/++msyMzOBvKVmbGxs2LVrF0OHDs13158QZS07V8uENUfJzNFyJjqFTx5thZVa7hiyBN/vusjxq8m42FvzwZAWxd7p1bZONZxsrUhMy+bU9RRa1HKrwEhFZaGrxwqUqUJxD4xOsjp06ED//v1ZsGABwcHB2NjYFNinadOmDBs2rNDjdYs3A6jVat544w3eeOMNY8MQwmino1LIzMlrUrn+yDXUKhUfP9IStSRaZi087iafbTkPwNQHm+LjZl/s/rbWaro2qM6W0zHsCIuTJEuUim4kq4l0ehf3wOgkKyIiosReUE5OTixdurTYfWJjY4mNjUWrzd+ZuWXLlsaGJIRBjl7Oa1BZ092B6JRMfjl8FSuVitlDW0iiZaY0WoU3fjlBdq6WHo28eKxdrZIPIm/KcMvpGELOxTGmd4NyjlJURmf0y+nISJYoPaOTrHtttnn48GFGjhzJmTNnChTHq1QqNBrNPZ1fiKIcvZwEwPCOtanj6cSE1UdZc+gKarWKD4KbS6JlhpbvieTwpRs421kze2jx04R36nmrlcORyzdIzczBxb7giLsQRcnRaLkQKz2yxL0zKMmqVq2awb/cEhMTi3382WefpVGjRnz//ffUqFFDuuiKCnPk1khWmzrVuK9BdbRahUk/H2PVgctYqeH9wQVbAgjTuZSQxsd/nwVgyv2B1HR3MPjYOp6OBFR34mJ8GnvCExjQzKe8whSVUERcGjkaBRc7a6N+7oS4m0FJ1rx588rsghcvXmT9+vU0aCBD+KLixKZmcvVGBioVtLxVoxPcpiZaReG1tcf5ad9l1CoVMx5uJomWGdDemibMzNHStb4nT3Y0vlFxj4bVuRifRsj5OEmyhFFuF727yO8DcU8MSrLKctHjvn37cvz4cUmyRIU6dmuqsHENl3xTR0Pb1kKrwOu/HOeHvZdQq1RMe6ip/GI1sRX7L7H/YiIONlZ89EjLUv179GjkxfK9l9hxPg5FUeTfVBhM14RU6rHEvTIoyUpJScHV1VX//8XR7VeUxYsXM3LkSEJDQ2nevHmBuxMffvhhQ0ISwihHryQB0KaOe4HHHm1XK2/kZN0Jlu2JRK1SMfXBJvKhbCJXEtOZ/VfeNOGbAxtT28OxVOfpXM8TWys1V29kcDE+jXpe0lBSGObOkSwh7oXBNVlRUVF4e3vj7u5e6IeP7i/FkgrX9+zZw65du/jrr78KPCaF76K8HLl0qx6rdrVCH3+8Q220isJb60+yZPdFrNTw9v2SaFU0RVF4+9eTpGdr6FjXgxFd6pb6XE521rSvW4094QmEnI+TJEsY7KyMZIkyYlCStXXrVn1/q23btt3TBceNG8czzzzD1KlTqVGjxj2dSwhD5Gq0nLiaDBQ+kqUzrGMdNIrCO7+GsmjnRdRqFW8NDJREqwKtOXiFnWHx2Fmr+ejRe+9h1qORF3vCE9hxPo5n7wsooyhFZXYjLZvolLyG2Y2lR5a4RwYlWT179iz0/0sjISGBiRMnSoIlKsy5mFQycjS42FtTv4TRjKc6+aPVKkzdcIrvQiKwUql4fUBjSbQqQFRyBh/8eQaA1wc0JqC60z2fs2cjL+b8dZZ9EYlk5Wqws5blUUTxzt7qj1XHwxFnO6O7HAmRT6l/gtLT07l8+TLZ2dn5tpfUTHTo0KFs27aN+vXrl/bSQhhF1x+rdW13g0ZGnulSF60C0zaeYv72cKzUKib1bySJVjlSFIW3158kNSuXNnXcy2zUKdDHBW8XO2JTszgUeYP7GlQvk/OKyktfjyWjWKIMGJ1kxcXF8eyzzxZaUwWUWFPVqFEjpkyZwq5du2jRokWBwvdx48YZG5IQxbqzP5ahRnati0arMPOP03y19QJqlYqJ/RuVV4hV3voj19h2Lg5bazWfPNqyzNaUVKlUdG/oxbojVwk5HydJliiRvh5L1iwUZcDoJGvChAncuHGDffv20bt3b3799VdiYmKYNWsWn332WYnHL168GGdnZ0JCQggJCcn3mEqlkiRLlDld+4bi6rEK81y3ALSKwqw/z/DFf2FYqVWM69uw7AOs4mJTMpnx+ykAJvRrSAPvsh1B6NGoOuuOXGXH+Tjevr9JmZ5bVD66kSxZs1CUBaOTrK1bt7JhwwY6dOiAWq3G39+f/v374+rqyuzZs3nggQeKPf7ixYulDlYIY91IyyYiPg2ANrXdjT7+he710GgVZv91ls+3nMdKrZK18MqQoii881soKZm5tKjpxovd65X5Nbo39EKlyqu1iUnJpIZr8QtMi6pLo1U4FyMjWaLsqI09IC0tDW9vbwA8PDyIi4sDoEWLFhw5cqRsoxPiHh27mgRAPS8n3B1tS3WOl3rW542BjQH45O9zzN9+oazCq/J+PxHFltMx2Fip+OSxllhbGf0rqUQeTra0rJnX5T/kfFyZn19UHpcS0sjM0eJgY0WdUvZnE+JORo9kNW7cmHPnzlG3bl1at27Nd999R926dfn222/x9fUt8fhJkyYVul2lUmFvb0+DBg0YPHiwvmWEEPfiaAn9sQz1Sq8GKEpekvXx5nNYqVS81FNu3rgX8TezmLYhFICxvRuWa0+iHo28OH41mR3n43i8fe1yu46wbLo7Cxv5uJRZXaCo2kpVkxUVFQXAtGnTGDBgACtWrMDW1pZly5aVePzRo0c5cuQIGo2Gxo0boygKYWFhWFlZERgYyPz583nttdfYtWsXTZs2NfoJCXGn4jq9G2tM7wZotAqfbznP7L/OYqVW8UI5TG9VFdM2nOJGeg5NfF15pXf5Jqw9G3nx1dYL7LoQj0aryAeoKNTZKKnHEmXL6CTrqaee0v9/mzZtiIyM5OzZs9SpU4fq1Uu+c0c3SrV06dJ8S/U8//zzdOvWjdGjR/Pkk08yceJE/v77b2PDE0JPq1X0Re9tjbizsDjj+jZEo1X44r8wZv15BpVKxfPdpMmlsf46GcWfJ6OwVqv45NGW2JTDNOGdWtd2x8XemqT0HE5cTTLqTlNRdZyJ1nV6lyRLlI17/s3m6OhI27ZtDUqwAD755BPef//9fGscurq6Mn36dD7++GMcHR157733OHz48L2GJqq4C3E3Sc3KxdHWikY1ym5JlQn9GvJqn7zi9/f/OM2y3XIzhzES07KZemua8OVe9Wl+q16qPFlbqbmvft7vqB3n48v9esIy3V6zUIreRdkwKslKS0vjvffeo3nz5jg7O+Pi4kLLli2ZOXMm6enpBp0jOTmZ2NjYAtvj4uL0i0+7u7sXaHIqhLGO3uqP1bKWW5kWVKtUec1JX+mVN8U1/ffT/Lg3sszOX9nN/P0U8TezaVTDmbF9Ku5OzZ6NvQDYESbF76Kg1MwcriRmADKSJcqOwdOF2dnZ9OzZk9DQUAYNGsRDDz2EoiicOXOGDz74gL/++osdO3YUaC56t8GDB/Pcc8/x2Wef0aFDB1QqFQcOHGDy5MkEBwcDcODAARo1ksaP4t4c1ffHKvupIdWt5XY0isJ3IRFM3XAKtVrFU538y/xalcm/p2P47dh11Cr45NFWFbrMTY9GeUnW0cs3SE7Pwc2x+N9Vomo5d2uq0NfNvtR3IgtxN4OTrAULFnD16lWOHz9O48aN8z129uxZevXqxbfffsurr75a7Hm+++47Jk6cyLBhw8jNzc0LwtqakSNHMnfuXAACAwNZvHixsc9FiHyOlnE91t1UqrwFpLVahUU7L/LOr6GoVSqGd6xTLtezdMnpObz960kARveoR6tS9C27FzXdHajv5UR4XBq7w+O5v0XJd0OLqkPqsUR5MHgOZf369UydOrVAggV5SdE777zDL7/8UuJ5nJ2dWbRoEQkJCfo7DRMSEli4cCFOTnkLwrZu3ZrWrVsb/iyEuEtKZg7nY/N+abYuxw9zlUrF2/c34blba+1NWX+S9Ueultv1LNmsP08Tm5pFPS8nJvYzzUh1z0Z5Pf52SL8scRfdnYVSjyXKksFJ1unTp+nVq1eRj/fu3ZvTp08bfGFnZ2datmxJq1atcHYuu6JkIQBOXElGUaC2hwNeLnblei2VSsXUB5swqmtdAD7cdAZFUcr1mpbmbHQKaw9fRaWCTx5tib1NxU0T3qlHI13xe5z8G4l8zspIligHBk8XJiUl4enpWeTjnp6eJCcnF/rY0KFDWbZsGa6urgwdOrTY66xfv97QkIQokq7o/V6bkBpKpVIx5f5AVh+8TPzNbC7E3qRhDfllrfPNtnAAHmjhSzt/0zUa7hTgia21muvJmfJvJPS0WkVfk9VERrJEGTJ4JEur1WJlVfRfn2q1Go1GU+hjbm5uqFQq/f8X9yVEWdA1IW1bBk1IDWVnbaWv/9p3MbHCrmvuIuJu8seJ6wAmX/fRwdaKTgF5SZ4ssSN0riVlcDMrF1srNQHVnUwdjqhEDB7JUhSFvn37Ym1d+CG6IvbCLF26tND/F6I8KIpyeySrgptOdgrwZE94AvsjEnims9xpCLBgeziKAv2a1DCLUYKejbzYGRbPjrB46dgvADhzqx6rgbdzuTfGFVWLwUnWtGnTStznkUceKXGfjIwMFEXB0TFv8c1Lly7x66+/0rRpU4KCggwNR4giRSakcyM9B1trdYV/qHeulzdKsi8iEUVR9CO4VdXVG+n8evQaQIX2xCpOj0Ze8OcZ9kckkJmjMVl9mDAf+nosX5k+FmWrTJMsQwwePJihQ4fyv//9j6SkJDp27IitrS3x8fF8/vnnvPzyy2VyHVF16UaxWtR0w9a6Yv8qbVXbHVtrNfE3s4iIT6O+V9W+qeO7kAhytQrdG1Yv17s8jdHQ2xlfN3uikjPZfzGRnrf6Z4mqS9fpvUk5LlIuqiajP4FOnTpV5GObN28u8fgjR47QvXt3AH755Rd8fHy4dOkSP/zwA19++aWx4QhRwO3+WO4Vfm17Gyva3Eom9kdU7bqs2JRM1hy6Api+FutOKpWKHg1vdX+XuiwBnI2SkSxRPoxOstq3b89XX32Vb1tWVhZjx45lyJAhJR6fnp6Oi0veD/I///zD0KFDUavVdO7cmUuXLhkbjhAFHDFRPZZOp3p5d+Huv5hgkuubi0U7I8jO1dKhbjV9sbm50HV/l+J3kZGt4WJCGgCBMpIlypjRSdaKFSuYMWMGgwYNIjo6mmPHjtGmTRu2bt3K7t27Szy+QYMG/Pbbb1y5coW///5bX4cVGxubb9FoIUojPTtXX1/RxgQjWXBnXVZCle3FlJiWzU/7LgN5o1jmVpvWrUF11Cq4EHuT60kZpg5HmND5mFQUBao725V7Tz1R9RidZA0dOpQTJ06Qm5tL8+bN6dKlC7169eLw4cO0bdu2xOPfe+89Jk+eTN26denUqRNdunQB8ka12rRpY/wzEOIOJ68mo9Eq+Lja4+vmYJIY2taphq2VmpiULC4lGLZwemWzdPdFMnI0tKjpZpY1T26ONvoaMZkyrNr09VgyVSjKQamqgjUaDdnZ2Wg0GjQaDT4+PtjZGfYXwKOPPsrly5c5dOhQvhquvn376tcuFKK09P2x/N1NFoO9jRWtauf1fKuKU4YpmTks2xMJmOcolo5MGQqAM1HS6V2UH6OTrNWrV9OyZUvc3Nw4f/48f/75JwsXLqR79+5EREQYdA4fHx/atGmDWn378h07diQwMNDYcITI58iliu30XpTOt+qy9lXB4vcf914iNTOXRjWcCWpaw9ThFEmXZO26EE+uRmviaISp6EaypB5LlAejk6znn3+eDz/8kI0bN+Ll5UX//v05efIkNWvWlEWdhUkpiqIfyTJVPZZOp4Bbxe9VrC4rPTuXxTvz/tga07sBarV5jmIBtKrljpuDDamZuRy/mmTqcIQJhMWkcvjWH2bNakqSJcqe0UnWkSNHCvSyqlatGj///DPffPNNmQUmhLGuJWUQl5qFtVpF85qmXaKprb871moV15MzuXqj6hRWr9x/mRvpOdT1dOSBFr6mDqdYVmoV3RrmLRgdcj7exNGIiqbVKry1/iQ5GoV+TWrQWNaxFOXA6CSrcePG+b6/86/0Z5555t4jEqKUdP2xmvm5mryLt6OtNa1uFVbvjagadVmZORoW7sgbxXq5V32sLWB5kp4NpS6rqlqx/xKHL93A2c6a94ObmW3toLBs9/xb0M7OjjNnzpRFLELcE1P3x7qbrjdUVWlK+svhq8SmZuHnZs+QNrVMHY5BdHVZJ64mcSMt28TRiIoSlZzBR5vPAfDmwMYmuxNZVH4GL6szadKkQrdrNBrmzJmDp2deDcrnn39eNpEJYSTdSJap67F0OtXzZP728Cpxh2GORsuC7eEAvNSzfoUvZ1RaPm72NK7hwrmYVHZdiOehVn6mDkmUM0VRmPrbKW5m5dLOvxpPdZKF3EX5MTjJmjdvHq1atcLd3T3fdkVROHPmDE5OTjLcKkwmK1fD6et5dwmZ+s5CnXb+1bBSq7h6I4OrN9KpVc3R1CGVmw3HrnMtKYPqznY80aG2qcMxSo9G1TkXk0rI+ThJsqqAv0Kj+fdMDDZWKuYMbWHWN2cIy2dwkvXBBx+waNEiPvvsM/r06aPfbmNjw7Jly2jatGm5BCiEIUKvpZCt0VLd2ZbaHuYx9O9sZ02Lmm4cu5LE/ohEarWrnEmWRqswf9sFAEZ3DzB5PZyxejbyZtHOi+wMi0NRFPljsRJLTs/hvQ156+++0qsBDaXYXZQzg8f0p0yZwpo1a3j55ZeZPHkyOTk55RmXEEY5eqseq3Xtamb1Idnp1hI7lXnKcNPJKCLi03B3tOGpzpY39dK+bjXsbfI69J+LSTV1OKIczf7rDPE3s2jg7cwrveubOhxRBRhVONGhQwcOHz5MXFwc7du35+TJk2b1gSaqLnPpj3W3zrp+WRcrZ/G7Vqvwza1RrGe7BuBsZ/DguNmwt7HSN48NOSd3GVZWe8MTWH3wCgCzh7bAztqyRlyFZTK6OtXZ2Znly5czZcoU+vfvj0ajKY+4DDJ79mw6dOiAi4sL3t7eBAcHc+7cuXz7xMTEMGrUKPz8/HB0dGTgwIGEhYXl26dXr16oVKp8X8OGDSvx+vPnzycgIAB7e3vatWvHzp07y/T5CcMdM7Oid532dauhVsGlhHSikitfv6z/zsZyNjoVZztrRnWta+pwSk23vuKOMEmyKqPMHA1v/3oSgKc61aFDXQ8TRySqilLfAjRs2DAOHTrE+vXr8fc3zRRBSEgIY8aMYd++fWzZsoXc3FyCgoJIS0sD8oryg4ODiYiIYMOGDRw9ehR/f3/69eun30dn9OjRREVF6b++++67Yq+9Zs0aJkyYwDvvvMPRo0fp3r07gwYN4vLly+X2fEXhYlIyuZaUgVqV18XbnLjY2+gbo1a2Vg6KovD1rVGsZ7r44+ZoY+KISk/XyuHgxRukZ+eaOBpR1r7aGsbF+DRquNrx5iBZvk1UnHsa269Vqxa1apmuH86dC0wDLF26FG9vbw4fPkyPHj0ICwtj3759hIaG0qxZMyBv9Mnb25tVq1bxwgsv6I91dHTEx8fH4Gt//vnnPP/88/pzzJs3j7///psFCxYwe/bsAvtnZWWRlZWl/z4lJcWo52qoK4np/LA3kowcDbOCW5TLNcyNrh6rsY8rTmY4XdUpwIMTV5PZfzGB4DY1TR1Omdl1IZ7jV5Kwt1HzfLcAU4dzT+pVd6KmuwPXkjLYH5FI70BvU4dUZhRF4af9l8nO1TKsQ22zfI+UpzNRKXwXktckd+bg5rjaW+4fA8LyWEYzGwMlJycD4OGRNxSsS2rs7e31+1hZWWFra8uuXbvyHbtixQqqV69Os2bNmDx5MqmpRRfAZmdnc/jwYYKCgvJtDwoKYs+ePYUeM3v2bNzc3PRftWuXz23uadm5LNp5kZ8PXSU1s2rcnGBu/bHudnsdw8o1kvX11rxRrOEd61Dd2c7E0dwblUpFz8aVs/v7xuPXmfpbKO//cZruH2/j25DwKjNap9EqvLXuBLlahYHNfBjQzPA/pIUoC5UmyVIUhUmTJtGtWzeaN28OQGBgIP7+/kyZMoUbN26QnZ3NnDlziI6OJioqSn/sU089xapVq9i+fTtTp05l3bp1DB06tMhrxcfHo9FoqFGjRr7tNWrUIDo6utBjpkyZQnJysv7rypUrZfCsC2pcw4V61Z3IztWy9WxsuVzD3OiTrFvL2JibDgEeqFQQEZ9GbEqmqcMpEwcjE9l/MRFbKzUv9qhn6nDKRI9bS+zsqERJVlxqFtM25rUscLW3JjEtmzl/naXHx9tYtCOCjGzT1dRWhOV7Ijl+NRkXe2tmDG5m6nBEFVRpkqyxY8dy4sQJVq1apd9mY2PDunXrOH/+PB4eHjg6OrJ9+3YGDRqEldXtO0tGjx5Nv379aN68OcOGDeOXX37h33//5ciRI8Ve8+47K4vrsWNnZ4erq2u+r/KgUqm4/9bCvJtORpWwt+XL0Wg5cS0JgLb+5tGE9G5uDjY09c37995XSe4y1I1iPdKuVqVZkqRrA0+s1Coi4tO4kphu6nDKxLSNoSSl59DU15UD7/Tj08daUcfDkfib2Xyw6QzdP97G97sukplT+ZKtqzfS+fSfvBuhpgxqQg1X+xKOEKLsVYok69VXX2Xjxo1s27atQI1Yu3btOHbsGElJSURFRbF582YSEhIICCi6hqRt27bY2NgUuAtRp3r16lhZWRUYtYqNjS0wumUKg1rkDYlvPxdHWlblnhY4G5VKZo4WNwcbAjydTB1OkW5PGVp+v6wTV5MIOR+HlVrFyz0rT68hV3sb2t6acq4MU4abTkax6WQ01moVnzzWEnsbKx5tV4v/XuvJx4+0pFY1B+JvZvH+H6fp8fE2lu2uPMmWoii8+1so6dkaOtb1YJiFrUIgKg+LTrIURWHs2LGsX7+erVu3Fps4ubm54eXlRVhYGIcOHWLw4MFF7nvq1ClycnLw9fUt9HFbW1vatWvHli1b8m3fsmULXbt2Ld2TKUNNfV3x93QkqwpMGR69omtC6m7Wy2N0vtWUdF8lSLJ0fbEGt/Kjjmfl6mKvb+Vg4UlWYlo2U38LBeCVXvVp5uemf8zGSs3jHWqz9bVezB7agpruDsSmZjH999P0+mQ7P+67RFauZSdbG49fZ/u5OGyt1Mx+RJbOEaZj0UnWmDFj+Omnn1i5ciUuLi5ER0cTHR1NRsbtfkRr165l+/bt+jYO/fv3Jzg4WF+0Hh4ezsyZMzl06BCRkZFs2rSJxx57jDZt2nDffffpz9O3b1++/vpr/feTJk1i8eLFLFmyhDNnzjBx4kQuX77M//73v4p7AYpw55ThX6GVe8rQ3IvedTreqssKj0sjLjWr5APM1LnoVP4+FYNKRaXsmK1r5bAnPIEcjdbE0ZTe9I2nSEjLpnENF8b2aVjoPrbWaoZ3rMO2yb2YFdwcXzd7olMymfpbKL0/2c6K/ZfIzrW81+BGWjYzfz8NwKt9GlDfy9nEEYmqzKKTrAULFpCcnEyvXr3w9fXVf61Zs0a/T1RUFM888wyBgYGMGzeOZ555Jl/dlq2tLf/99x8DBgygcePGjBs3jqCgIP799998dVvh4eHEx8frv3/iiSeYN28eM2fOpHXr1uzYsYNNmzaZrGfY3e5vnpdkbT0bW6nvJDpyq31D2zrmWY+l4+5oS+Nb66QdsOC6rPnb80axBjX3oYF35Vv3rbmfGx5OttzMyuXIpRumDqdU/jkVzcbj11Gr4ONHW2JrXfyveVtrNU939mf7672YObgZNVztuJ6cyTu/htL70+2sPnDZohLOWX+e0SeYL1Wi6WxhmVSKoiimDqIqSklJwc3NjeTk5HIpglcUhR6fbONKYgbzn2qrH9mqTBJuZtFu1r8AHJ8WhJuDefe/mb7xFMv2RPJMZ3/eD25u6nCMFhmfRp/PtqNV4I9Xu+mbrFY241cfZcOx64zpXZ/XB1hW48rk9Bz6zQ0hLjWL//Wsz1ulaLyZmaNh1YHLzN8erh91re3hwKt9GjK0TU2srcz3b/NdYfE8/f1+VCpY93JXs//jS1gmYz6/zffdIu6JSqXSj2ZV1rsMj91ar7CBt7PZJ1hwuy7LUheLXrA9HK0CfQK9K22CBXe2cogvYU/zM/OP08SlZlHfy4kJ/QqfJiyJvY0Vz94XwM43evPuA02o7mzLlcQM3vjlBP0+D2Hd4avkmuHIVkb27aVzRnapKwmWMAuSZFVig1rcnjKsLHcN3cnc+2PdreOtOwzPx9wkMS3bxNEY51pSBuuOXAVgTO8GJo6mfHVvVB2Ak9eSib9pOfVz287Fsu7IVVQq+PjRVtjb3NsCyPY2VrzQvR473ujN2/cH4ulkS2RCOq+tPU7Q3B38dvQaGq35TITM+/c8lxPT8XOzZ/KAxqYORwhAkqxKrVUtN2q6O5CerWH7Ocu+W6ow+nosM+2PdTcPJ1sa1cgrwj1gYaNZC0PCydUqdK3vSTsLeb1Ly9vFXt/XbFeYZYxmpWTmMGVd3ijOc/cFlOm/kaOtNS/2qM+ON3rz5sBAqjnaEBGfxoQ1xwiaG8LG49fRmjjZCr2WzOJdFwF4P7g5zlVs6SBhviTJqsRUKhWDmuf1zKpsU4YarcLxW9OF5n5n4Z0618sbzdpnQUvsxKZmsupg3goFYyv5KJZODwtr5TB70xmiUzKp6+nI5KDyGcVxsrPm5V712flmH14f0Bg3BxvC49IYt+ooA+btYHNoNKYo8c3VaHlz3Qk0WoUHW/rSt4npexUKoSNJViWnmzL870xMpZoyDItNJS1bg5OtFQ0t6C43XVNSS+qX9f3Oi2Tnamlbx50u9T1NHU6F6HFrynBHWLzJR2lKsissnlUH8pLgjx5piYPtvU0TlsTZzpoxvRuw683eTOrfCFd7a8Jib/K/nw7zxHf79H/8VJQluy9y6noKbg42THtIls4R5kWSrEquTW13fN3sScvWsNNCpj4MceRSEgCtartjZUGNBjsG5BW/n4tJJSnd/OuybqRl8+O+SwCM7dOgyGWjKpv2/h442loRfzOL01Eppg6nSDezcnlz3QkARnTxp1O9ikuCXextGNe3ITvf7MPY3g2wt1FzIDKRwd/sZvzqo1y9Uf5LE11OSOfzLecBeOf+Jni5WPZC5aLykSSrklOrVQyshFOGRy2kP9bdvFzsqO/lhKLAfgvol7V0TyTp2Rqa+rrSu7G3qcOpMLbWarreGrXbEWa+U4Yf/XWWa0kZ1KrmwJsDTdNuws3BhskDGrNtci8eaVsLlQo2HLtOn89CmP3XGVIyc8rluoqi8PavJ8nM0dK1viePta9V8kFCVDBJsqqAB25NGf57Osbil8vQOWqB9Vg6urqs/WZel5WamcOy3XnFxFVpFEvH3Ouy9kUk6EcZP3qkJU4mLvb2dXPgs8db8fvYbnSt70l2rpbvQiLo+fE2lu+JLPOGpuuPXGPXhXjsrNV8OKRFlfv5FJZBkqwqoG2dani72JGalcvuC5Y/ZZickcOF2JtA3pqFlkY3pWPu/bJ+3HeJlMxcGng7M7CZj6nDqXC6dQwPRd7gppkttJ6RrdFPEw7vWIf7GlQ3cUS3Na/pxooXOrFkVHsaeDtzIz2HaRtPMWDuDv45VTbF8fE3s3j/z7ylc8b3a0jd6ua7OLyo2iTJqgLU6tt3Gf55ItrE0dw7XRNSf09HPJ0trwaj8626rNNRKSRnlM9Uyr3KyNbw/c68UaxXetWvkgvs+ns64e/pSK5WYW+4eSXEn/x9jksJ6fi62TPlfvPrSq9SqegTWIPN47szK7g5nk62RMSn8eKPh3li4T5OXE26p/O//8dpktJzaOLryuju9comaCHKgSRZVYRuWZ0tp6MtctHXO1lqPZaOt6s99arn1WUdNNO6rFUHLpOQlk1tDwcebuVn6nBM5nb3d/OZMjwUmcjSPXkJ8OyhLXC1N9/VDqytbq+LOKZ3feys1Ry4mMjDX+9mQimL47edi2XDsby1GT96pAU2ZrzMjxDy01lFtK/rQXVnO1Iyc9kdbtlThvpO7xZYj6XTyYyX2MnK1fDdjnAAXu7ZwKzXqitvuinDEDNJsjJzNLzxywkUBR5tV4teFnIzgou9Da8PCGTb5F4MbVsTgN9uFcd/tPmswcXxaVm5vPtrKADP3hdAy1ru5RWyEGWi6v72rGKs7pgy/MuC7zLUahX9dGGb2pY5kgW3+2WZ4x2G6w5fIyYlCx9Xex5pV9PU4ZhUl/qe2FipuJyYTmR8mqnDYe6/54mIT8PbxY6pDzQ1dThG83N34PPHW/PHq93oXM+D7FwtC7aH0+uT7fywt+Ti+M/+Oa+/m/K1oEYVFLUQpSdJVhUyqEVekvXP6Zgyv9OnokTEp5GckYO9jZpAX8tpQno33UhW6LXkcrvFvTRyNVoWhFwA4MUe9bCzLt/GlubOyc5av0SNqVs5HLuSxKIdEQB8MKQFbo7mO01YkuY13Vg1ujOLR7SnnpcTiWnZvLfhFAPm7WDL6ZhCi+OPXUli2a1p0g+GtMDRVpbOEeZPkqwqpGNdDzydbElKzzG7Ql5D6eqxWtZ0t+haDF83B/w9HdEqcDjyhqnD0dt4/DpXEjPwdLJleMc6pg7HLPRslDclZ8q6rKxcDa+vPY5WgcGt/ejf1PKXjlGpVPRrWoO/J/TgfV1xfFwao384xPBF+zh5NVm/b45Gy1vrTqBVILi1n34aVwhzZ7mfUsJo1lZqBuimDEMtc8rQkvtj3a3TrbsM95lJXZZWq/DNtrxRrOe7B5T78iyWQrfEzp7wBJPdNPL11guExd6kurMt0yvZ0jE2VmqeuVUc/0qvvOL4fRGJPPT1LiauOca1pAwW7ojgbHQq1RxtmPqg5U2TiqpLkqwq5v7meXcZ/n0qhlwLnDKsDEXvOrfXMTSPuqzNp6IJj0vD1d6aZzr7mzocs9HEx5XqznakZ2tMcqNC6LVk5m/PuxHh/cHNqeZkW+ExVAQXexveGBjI1sm9GNomrxbw16PX6PPpdr74NwyAqQ82tci2LaLqkiSriulcz4NqjjYkpmWbZdF1cW5m5XIuOm8duTYW2r7hTnfWZZm62aVWq/Dlf3kfZKPuC8DFjNsCVDS1WqUfzXp+2SHe/OUEF2JTK+Ta2blaXv/lBBqtwv0tfPQLvldmNd0d+PyJ1vw+thudAjzIytWSrdHSvWF1hrSp2jdiCMsjSVYVY22lZkAzy1zL8MTVJLRK3i/hGq72pg7nntWq5kitag5otAqHL5m2LmvzqWjORqfiYmfNc/fVNWks5uj1AY1pW8edbI2WNYeu0O/zHbyw/CD7IxLKpIN5URZsD+dMVArVHG2YObh5uV3HHLWo5cbqF/OK45+9ry6fPd5Kls4RFkeSrCpI99fw36ei0WjL7wOirOmmCltXgqlCHX0rhwjT1WVptQrz/j0PwLPdAnB3rJzTUffC182B9a/cx7qXuxDUtAYqFfx7JpYnFu4j+Jvd/HkiqszfS2ejU/h6W97o4vSHm1G9Ck6T6Yrjpz3UDG8Xy//DSlQ9kmRVQV3re+LmYEP8zWwOWNCUob4eywLXKyxK51tThvtMmGT9eTKK8zE3cbG35vluASaLwxK08/dg4Yj2/DepJ092qoOttZrjV5MZs/IIvT7NWwg5Pfvep35zNVpeX3uCHI1C/6Y1qnTXfSEsmSRZVZCNlZqgW7eAW8pdhoqi6Ns3VIZ6LJ3OtxaLPnE1uUw+nI2luWMUa3T3erg5SC2WIep5OfPhkBbseasP4/o2pJqjDVcSM5i28RRd52zls3/OEZeaVerzL9wZwclrybjaW/NBcHOZJhPCQkmSVUXp1jL8K9QypgyvJGaQkJaNrZWa5jVdTR1OmalVzQE/N3tytQpHLiVV+PV/P36d8Lg03BxseFZqsYxW3dmOSf0bseetvrw/uBn+no4kpefw1dYL3PfRVqasP8GF2JtGnfNCbCrztuRNE773UDO8K0H9oRBVlSRZVdR9DarjYm9NXGqWyYuuDXH0Sl6MTf1cK1UXcpVKRad6ulYOFTtlmKvR6u8ofLFHPbmj8B442FrxTJe6bH2tFwueakvr2u5k52pZdeAK/T4P4YXlhzhwMbHEInmNVuH1X06QrdHSq7EXj7SVu+mEsGSSZFVRttZqfddoS7jLsDL1x7pbZxMtFr3h2HUi4tOo5mjDyK51K/TalZWVWsWgFr78+kpX1v6vC/31RfIxPP7dXobM38Omk0UXyS/dfZGjl5NwtrPmwyEtZJpQCAsnSVYVpmtM+ldoFFoznzI8UgnrsXR0dxgev5JMZo6mQq6Zo9Hy5da8UayXetbH2U7WgStLKpWKDnU9WDSiPf9O6snwjnlF8seuJPHKiiP0/jRvQeSM7Nv/3hfj0/jk73MAvPNAE/zcHUwVvhCijEiSVYV1b1QdZztrYlKy9NNx5igzR8Pp63lNSNtWwpEsf09Harjaka3R6pPJ8vbrkWtcSkjH08mWEV2ku3t5qu/lzOyhLdj9Zh/G9WmAu6MNlxPTeW/DKbrO+Y/P/zlHbGomb/5ygqxcLd0aVGdYh9qmDlsIUQYkyarC7Kyt6Nckb/HbP09EmziaooVeSyZXq+DlYkfNSvjXvUql0t9lWBFL7GTn3h7FerlXfRxtZRSrIni52DEpqDF73urDzMHNqOPhyI30HL7ceoEus7dyIDIRR1srZg+VaUIhKgtJsqq423cZmu+UoX6qsLZ7pf3wqcimpOuOXOXqjQy8XOx4qpOMYlU0R1trRnSpy7bJvZj/VFta1XbX12i9NSiQ2h6OJo5QCFFW5E/YKq5HIy+cbK2ISs7k2NUk2pphzdPtonfzi62s6NYxPHolicwcDfY25XMHZVauhq+3XgDg5Z71cbCtPHdqWhortYr7W/gyqLkPhy7dIC41i0HNfUwdlhCiDMlIVhVnb2NFnya3GpOa6V2GuiSrMtZj6dSr7kR1Zzuyc7Ucu5JUbtf5+dBVriVlUMPVjic71Sm36wjD6Yrk72/hW2lHaoWoqiTJEjzQQrdgdHS5LnZbGlHJGUSnZGKlVtGilpupwyk3eXVZt1o5lFNdVmaOhm9ujWKN6d2g3EbLhBBC5JEkS9CzkTcONlZcS8rgxNVkU4eTj64LeqCPS6Uv0NY1JS2vflmrD1wmOiUTXzd7npC714QQotxJkiVwsLWiT2DeXYabzGwtw9vrFbqbNpAK0DkgbyTr8KUbZOWWbb+szBwN87eHAzC2T4NK1TVfCCHMlSRZArjjLkMzmzI8eqs+yRwL8staA29nPJ1sycrVlvmI4or9l4lNzaKmuwOPtZNRLCGEqAiSZAkAegd6YW+j5nJiOqduNf40texcLSev5SUblfnOQp28dQx1dVllN2WYnp3Lgu15tViv9mmArbW87YUQoiLIb1sB5PXu6d341pShmdxleDoqhexcLe6ONtT1rBq9g/T9si6WXfH7T/suEX8zm9oeDjzSrlaZnVcIIUTxJMkSeoNuTRluOhllFlOGR6tAE9K76UayDkXeIEejvefzpWXl8m1IBADj+jTExkre8kIIUVHkN67Q6xPoja21msiEdM5EpZo6nDv6Y1X+qUKdRt4uVHO0ISNHUyZ1WT/svURiWjZ1PR0Z0qZmGUQohBDCUJJkCT1nO2t6NfIC8pbZMTXdotVVoR5LR61W0fHWXYb32sohNTOH73bk3VE4rm9DrGUUSwghKpT81hX56O4y/NPEU4ZxqVlcScxApYKWtStvE9LC6Oqy7nWx6OV7IklKz6GelxMPt/Iri9CEEEIYQZIskU/fJt7YWqmJiEvjfMxNk8Whq8dq6O2Mq72NyeIwhc63mpIejkwkt5R1WSmZOSzckVeLNV5GsYQQwiTkN6/Ix8Xehh6NqgOmvcuwKvXHulugjwtuDjakZWsILWU7jSW7LpKSmUtDb2cebCmjWEIIYQqSZIkCBjW/fZehqVSlTu93U6vzFgwG2FeKflnJ6Tl8v+siABP6NcJKXTXuzBRCCHMjSZYooF/TGthYqQiLvUlYTMXfZZir0XL8StVpQlqYzvfQlPT7XRGkZuYS6OPCoOY+ZR2aEEIIA0mSJQpwc7ChWwPdlGF0hV//XEwqGTkaXOysaeDlXOHXNwe6uqxDkTfQaA2/AeFGWjZLdkcCMKFfQ9QyiiWEECYjSZYolK4xqSlaOej6Y7Wu415lk4Qmvq642FuTmpXLaSPqshbtjOBmVi5NfV0JaiqjWEIIYUqSZIlCBTWtgbVaxdnoVMLjKvYuQ12S1aa2e4Ve15xYlaIuK+FmFsv2RAIwsX+jKpugCiGEuZAkSxTK3dGWrremDP+q4AL420XvVbMeS0dfl2VgU9KFOyNIz9bQoqYb/Zp4l2doQgghDGDRSdbs2bPp0KEDLi4ueHt7ExwczLlz5/LtExMTw6hRo/Dz88PR0ZGBAwcSFhZW6PkURWHQoEGoVCp+++23Yq89ffp0VCpVvi8fn8o1PfNAi7znU5F1WTfSsomITwOgdRUeyYLbTUkPXEwssS4rLjWLH/ZcAmBi/4ZVZq1HIYQwZxadZIWEhDBmzBj27dvHli1byM3NJSgoiLS0vA9pRVEIDg4mIiKCDRs2cPToUfz9/enXr59+nzvNmzfPqA+nZs2aERUVpf86efJkmT03c9C/qQ9WahWno1KIjC/4epWHY1eTAKhX3YlqTrYVck1z1czPFWc7a1IyczkTVXxd1nch4WTkaGhV253ejWUUSwghzIG1qQO4F5s3b873/dKlS/H29ubw4cP06NGDsLAw9u3bR2hoKM2aNQNg/vz5eHt7s2rVKl544QX9scePH+fzzz/n4MGD+Pr6GnR9a2trg0evsrKyyMrK0n+fklK6JpMVycPJli71PNl1IZ5NoVG80qtBuV5PURS2n40F8oreqzprKzXt61Zj+7k49l9MpHnNwpcXik3J5Md9eaNYk/o3klEsIYQwExY9knW35OS83koeHnm1LLqkxt7eXr+PlZUVtra27Nq1S78tPT2d4cOH8/XXXxs15RcWFoafnx8BAQEMGzaMiIiIIvedPXs2bm5u+q/atWsb9dxMRbeW4V/lOGWoKApbTsfw4Fe7WL43L1nocquFQVWnmzIsrl/W/O3hZOVqaedfjR4Nq1dUaEIIIUpQaZIsRVGYNGkS3bp1o3nz5gAEBgbi7+/PlClTuHHjBtnZ2cyZM4fo6Giiom4Xc0+cOJGuXbsyePBgg6/XqVMnfvjhB/7++28WLVpEdHQ0Xbt2JSGh8A/DKVOmkJycrP+6cuXKvT3hChLUrAZqFZy8lszlhPQyPbeiKGw9G8Pgb3Yz+odDnLqegpOtFeP6NOCRtrXK9FqWqtOt4vcDkYloC6nLik7OZOWBywBM7CejWEIIYU4serrwTmPHjuXEiRP5RqhsbGxYt24dzz//PB4eHlhZWdGvXz8GDRqk32fjxo1s3bqVo0ePGnW9O8/RokULunTpQv369Vm+fDmTJk0qsL+dnR12dnaleGamVd3Zjk4BnuyNSOCv0Che6ln/ns+pKAoh5+OY+28Yx2+tUehoa8XIrnUZ3b0eHlW8FutOLWq64WhrRVJ6DudiUmni65rv8fnbL5Cdq6VjXQ/uayCjf0IIYU4qxUjWq6++ysaNG9m2bRu1auUfAWnXrh3Hjh0jKSmJqKgoNm/eTEJCAgEBAQBs3bqV8PBw3N3dsba2xto6L+985JFH6NWrl8ExODk50aJFiyLvXLRk97e8tZZh6L1NGSqKws6wOB5ZsIdRSw9y/EoS9jZqXupRj51v9ObNgYGSYN3FxkpNO/+8VhZ3TxleS8pg9YG8EdGJUoslhBBmx6KTLEVRGDt2LOvXr2fr1q36xKkwbm5ueHl5ERYWxqFDh/RTg2+99RYnTpzg2LFj+i+AuXPnsnTpUoNjycrK4syZMwYXzVuSAc1qoFLB8StJXL1h/JShoijsuRDPY9/u5ZnvD3DkchJ21mpe6BbAzjf6MOX+Jng6W94oX0XRLbGz/2Jivu3fbLtAtkZLl3qedKkvo1hCCGFuLHq6cMyYMaxcuZINGzbg4uJCdHTeSIubmxsODg4ArF27Fi8vL+rUqcPJkycZP348wcHBBAUFAeDj41NosXudOnXyJW19+/ZlyJAhjB07FoDJkyfz0EMPUadOHWJjY5k1axYpKSmMHDmyvJ92hfN2sadjXQ/2X0xkc2g0L3SvZ/Cx+yISmLvlvD5BsLVW81SnOrzcsz7ervYlHC0AOgXompImoigKKpWKK4np/Hzw9iiWEEII82PRSdaCBQsACkzrLV26lFGjRgEQFRXFpEmTiImJwdfXlxEjRjB16lSjrxUeHk58fLz++6tXrzJ8+HDi4+Px8vKic+fO7Nu3D39//1I/H3N2fwtf9l9M5M+TUQYlWQcjE5m75Tx7wvOmuGyt1AzvWJuXezXAx02SK2O0rOWOvY2axLRswmJv0qiGC99su0CuVqFbg+p0vJWECSGEMC8qRVGKbyUtykVKSgpubm4kJyfj6upa8gEmFpOSSefZ/6EosOetPvi5OxS63+FLiczdEsauC3kJqY2Viic61OaVXg2KPEaU7KnF+9h9IYH3BzejRyMv+nwWgkarsO7lLrTzlyRLCCEqijGf3xY9kiUqTg1Xe9r7V+Ng5A02h0bzXLf89W9HL99g7r9h7DgfB4C1WsVj7Wszpnd9alVzNEXIlUqnAE92X0hgX0Qix68mo9Eq9GzkJQmWEEKYMUmyhMEGNfflYOQNNp2M0idZJ64mMXfLebady0uurNQqHm1bi7F9GlDbQ5KrsqKry9pxPo70HA0gtVhCCGHuJMkSBhvUwoeZf5zm0KUbbD0bw8r9l/n3TN4yOFZqFUPa1OTVPg3w93QycaSVT6va7thZq0nNygWgb6B3lV9AWwghzJ0kWcJgvm4OtK3jzpHLSTy37BAAahUEt67Jq30bElBdkqvyYm9jRZs67uyLyLtLc0I/GcUSQghzJ0mWMMrDrfw4cjkJlQoGt/Lj1b4Nqe/lbOqwqoTuDb3YF5FI/6Y1aFGr8MWihRBCmA9JsoRRnulSl2pOtjTzc6WBt4upw6lSnu8WQA1XewY0q2HqUIQQQhhAkixhFCu1isGta5o6jCrJ3saKR9vJwtlCCGEpLHpZHSGEEEIIcyVJlhBCCCFEOZAkSwghhBCiHEiSJYQQQghRDiTJEkIIIYQoB5JkCSGEEEKUA0myhBBCCCHKgSRZQgghhBDlQJIsIYQQQohyIEmWEEIIIUQ5kCRLCCGEEKIcSJIlhBBCCFEOJMkSQgghhCgH1qYOoKpSFAWAlJQUE0cihBBCCEPpPrd1n+PFkSTLRFJTUwGoXbu2iSMRQgghhLFSU1Nxc3Mrdh+VYkgqJsqcVqvl+vXruLi4oFKpyvTcKSkp1K5dmytXruDq6lqm5xbFk9fedOS1Nx157U1HXvuKpygKqamp+Pn5oVYXX3UlI1kmolarqVWrVrlew9XVVd50JiKvvenIa2868tqbjrz2FaukESwdKXwXQgghhCgHkmQJIYQQQpQDSbIqITs7O6ZNm4adnZ2pQ6ly5LU3HXntTUdee9OR1968SeG7EEIIIUQ5kJEsIYQQQohyIEmWEEIIIUQ5kCRLCCGEEKIcSJIlhBBCCFEOJMkSQgghhCgHkmQJIYQQQpQDSbKEEEIUKTY2Fo1GY+owqqQjR46Qmppq6jDEPZAky0LExMTw559/Im3NKl50dDQzZ85k/vz5bNq0ydThVClRUVGMGzeON998ky+//NLU4VQZiqKQnZ3Niy++yIABA9i7d6+pQ6pSrl+/TlBQEL179+bYsWOmDkfcA0myLMDXX3+Nn58fDz30EKdOnTJ1OFXK+++/T4MGDThw4ADLli1jyJAhrFy5EkAS3nI2ffp0GjZsyKX/t3f3QVHVaxzAv3sWMF4VECkjMI18Q8MYZ8oQs9K6XAxfBpskB8KXtLzUSC9aXotrajTkLfWiMpmWKEpWZKaTFiphoxJN0r1lXPElKQhfbnLlbXfZ5/7B5RRqyYu7P876/cw005598Tlf9uw+e87vnN/Jk6iursZTTz2FRYsWAWD2jmYymVBdXY1t27bh9OnTKCgowPnz5wEwe0d79tlnERYWBi8vL3z33XcYOXKk6pKoE9hkdWEigh07diA/Px+vvvoqhg0bhvT0dNjtdtWlubympiZkZGRgx44dyMvLw/bt2/HZZ59h7ty5mD9/PoDmLyK6+mw2GzIyMrB3715s3boVH374IdatW4cFCxZg/fr1AJi9M1itVsTFxWHq1KnIycnBgQMHADB7R7FarfjLX/6CzMxM5OTkID8/H71790Z1dbXq0qgT2GR1YSaTCcHBwZg6dSoee+wx/P3vf8d7772HTz75RHVpLs9sNsNiseCee+7BAw88AADw9fXFqFGj4ObmhvLycsUVui43NzfccccdePHFFzF27Fh9udVqxaxZs1BfX6+wumtHRUUFSktLsXTpUnh7eyM3N1ffm0VXn7u7O0aOHImYmBicOXMGR44cwYQJEzBp0iSMGjUK2dnZsFgsqsukdmKT1YXU1NTgwIED+PHHH/VlUVFRSEpKgo+PD2JiYpCQkIAXXniBgyGvsstl//TTT2Px4sXQNE0/RHLu3Dlcd9116Nevn6pSXc7lsh81ahRGjx4NTdNQU1OD8ePHIyMjA5s3b8Ztt92GrVu3oq6uTmHVruFy2beoqKjAoEGDAADz5s1DYWEhcnNzMXPmTFRWVjq7VJdzuewnTpyIiIgIvPzyy4iOjkZYWBgSEhIwYMAApKamYuXKlfyRYTRCXcKSJUvEz89PIiIixM/PT15//XWpqKgQERGbzSZNTU0iIlJeXi6enp6yfPlyleW6lD/KvqmpSc9eRGTOnDmSmJgoIiIWi0VJva7kStlbLBZZu3atxMbGSlFRkZSWlsrjjz8ugwYNko8//lhx9cb2R9mLiKxevVri4+P12+Hh4eLu7i7Dhg2TyspKsdvtCqp2DZfL/uTJkyIiUlhYKElJSbJt27ZWz0lNTZXbbrtNvvnmGxUlUwexyeoCduzYIQMHDpQPPvhAjh07JosXL5bBgwdLSkqK/pjffqAtWLBAgoOD5dSpUyIiUltbKxcuXHB63a6gLdmL/NpQDR8+XJYtW9bqPn7ZdExbs6+trb3kuf7+/rJp0yZnlepy2pL9vHnzJCsrS3bv3i033nijhISESEBAgGRmZorValVYvbH9XvaPPvqo/pivv/5aGhoaRET0H3lVVVViMpnk4MGDSuqmjmGT1QWkpqbKsGHDWi1bsWKF9O/fX7Kzs0WkeW9WiwsXLkhYWJikpqbKO++8I9HR0ZKXl+fUml1FW7Jv+UL54YcfJCgoSE6cOCEiIjt37pSHH35Yjh8/7tSaXUVb3/cXN7HFxcUSGhoqO3fudFqtruaPsl+1apWINDdZJpNJfH19JT09XX/ekCFDZM+ePc4u2WX8UfarV68WEWm197zl/Z+bmyu9evWSw4cPO69Y6jSOyVLMbrfDarWif//+aGxs1JdPmjQJd999N/7xj3/gwoULMJvN+lmF3t7eSE5OxooVKzB9+nSMHDkSCQkJqlbBsNqavZubGwCgsLAQQ4cOhdlsRmxsLMaNG4fevXujT58+itbAuNrzvjeZTPqYuLKyMqSnp2P48OGIjo5WVb6hXSn71atXo76+Hvfddx8WL16MkpISLFy4EACwYMEC2Gw2aBq/OjriStmvWrUKtbW1er4iApPJhCNHjmDdunWIj4/H0KFDVZVPHcAtRSERgaZpCA0Nxf79+1sNJr3hhhvw5z//Ge7u7sjNzQUAaJqG2tpazJkzB3/729+QkpKCn3/+GUuWLFG1CobV1uw3b96sP37Xrl0oKChA3759oWkaqqqqkJmZqWoVDKu92dfW1iIjIwMzZsxAVFQUfH198dZbb8HHx0fVKhhWW7LXNA15eXm49957MW/ePISHhwNovqxJUFAQvv76a8TExKhaBcNq7+d9bW0tFi1ahEcffRRRUVEICgrCa6+9pqp86iA2WQq17Jl66qmncP78eWzcuLHV/XfffTc0TcPZs2f1ZWfOnIGvry8+//xzvPnmm+jRo4czS3YZbc3+zJkzAJovp+Hh4YGIiAgcOnQI27dvR2BgoNPrdgXtzd7b2xtBQUFoaGjA3r17sWnTJvj5+Tm9blfQluzd3d31BuC318Qym80AAA8PDydV61o68r7v2bMnLly4gH379iEnJwe+vr5Or5s6Sd2RStd39uxZOX36tIi0PsYuIpcMHM3MzBRfX18pLi5utTwyMlIef/xxxxbqghyR/X//+18HVetarlb2s2fP1m/z5IK24WeOOo5431/8OmQ83JPlIC+88AIGDBiA7OxsALhkDIObmxtEBM899xxycnKQlpaGW2+9FfPmzdPnx/vqq68gIhg/fryzyzc0R2XPw1NXdjWznzBhgv48XmX8yviZo46j3vcc+2Z8/AteZb/88gumTZuGTz/9FKGhoThw4AC+/PJLAK3n/Hr77bfRs2dP7Nq1C4MHDwYAbNiwAX5+fpgwYQLuv/9+jBw5EgMHDsRdd92lZF2Mhtmrw+zVYfbqMHu6EpMIZ/vsLPn/GSAA0NjYiIyMDAwdOhT+/v6YO3cu7r//fqSnp8Pd3R0AUFdXh2XLlqFnz56YMWMGzGaz/ho1NTU4ePAgysrKEBkZyQ3uCpi9OsxeHWavDrOn9mCT1Un19fXQNA3dunUD0LwB1tTUoHv37gCap2Y5cOAAnn/+ecTGxurPs9vt3BXcScxeHWavDrNXh9lTe/Gv3gnz589HdHQ04uLisHz5ctTU1MBkMsHPz08/kyQ1NRUAkJ+fr581Iv8/lZc6jtmrw+zVYfbqMHvqCP7lO8BisSAhIQHbtm3Ds88+i969e2PNmjWYMmUKgOZBupqmwW63IzQ0FAkJCfjqq6+wfft2/f6WHYgtGye1DbNXh9mrw+zVYfbUKY45adG1ffvttxIeHi67du3SlxUVFYmnp6e8+uqr+unmLaffNjQ0SGxsrEyePFlKS0slJydHXn75ZSW1Gx2zV4fZq8Ps1WH21BlssjqgpKRETCaTnD17VkR+vYbP0qVLxd/fX8rKyvTHtmx4+fn50rdvXwkMDBQPDw/JzMx0fuEugNmrw+zVYfbqMHvqDB4u7ABN0zBo0CBs2rSp1fK0tDT06NEDa9asAdA8DYWmaSgvL8f777+P48ePY/LkyTh37hzS0tJUlG54zF4dZq8Os1eH2VNnsMnqgLCwMISHh6OoqAiVlZUwmUyw2Wxwd3fHnDlzkJubC7vdrk9DsWbNGhQUFODw4cPIysqCt7e34jUwLmavDrNXh9mrw+ypM9hkXaS6uhqnT5+GxWIB0PzrpIXNZgMA+Pv7Y9y4cThy5Ajy8vIANF/RFwC6d+8Of39/nDp1Sh/k+Morr+DUqVMYMmSIM1fFcJi9OsxeHWavDrMnR2OT9X9WqxWzZs1CTEwMxo0bhwcffBCNjY0wm82wWq0AmjeshoYGbN68GSkpKYiMjMSWLVuwZ88e/XUqKioQFBSEsLAw/bRdnr77x5i9OsxeHWavDrMnp1E9KKwrePfdd6Vfv34yatQoKSgokOzsbOnbt+8lk6S+8cYbEhAQIPHx8SIicvjwYUlMTBQPDw+ZPXu2zJw5U3x9fWXVqlUiwklt24LZq8Ps1WH26jB7ciY2WSLyxBNPyF//+tdWM6UnJSXJ3Llz9dsrVqyQPn36yMaNG1vNjG6322XJkiUyY8YMiY2Nlf379zu1dqNj9uowe3WYvTrMnpzpmp5Wp6mpCWazGVVVVbBarbjpppsAACdPnsTEiRMxZcoU3HnnnRgxYgRsNhsaGxtbDWKU38xhRe3D7NVh9uowe3WYPalwzR083rFjB4DmDablbJDrr79e3+BWrFiBm2++GV5eXvjoo48QFxeHF198ETab7ZKzRLjBtQ+zV4fZq8Ps1WH2pJyaHWjOt337drnxxhvFZDLpu3gvdwx9/fr1UlhYqN+3ceNG8fT0lBMnTji1XlfC7NVh9uowe3WYPXUV18ThwqKiIixevBi33HILysvLcfr0aRQXF7d6jPzOruAjR44gIiICO3fuxJgxY5xVsstg9uowe3WYvTrMnroSN9UFOFLLhhQcHIyxY8di/PjxOHfuHGJiYrB27VpMmzYNdrsdmqb97q7g/Px83HvvvYiOjnZy9cbG7NVh9uowe3WYPXVJanagOVZJSYn88ssvrZbZbDYREbFarZKWliZBQUHS0NBw2eefPHlSjh49KtOnT5fevXvL+vXrRYSn6LYFs1eH2avD7NVh9tSVuVSTtXXrVgkJCZF+/fpJaGioLFy4UCorK0WkeYNp2WiOHTsmN910k6Slpen3tSgrK5O5c+dKSEiIjB49Wr7//nvnr4gBMXt1mL06zF4dZk9G4DJNVnFxsQwYMEBef/11OXz4sGRlZUlQUJDMnj1bnz295deN3W6XrKwscXNzk2PHjomISENDgzQ2Nordbpc9e/bw+iftwOzVYfbqMHt1mD0ZheGbrJZfJatWrZKQkBA5f/68ft/KlSvljjvukEWLFl3yvLNnz8qIESMkPj5eSkpKZMyYMbJhwwbuIm4HZq8Os1eH2avD7MloDH+drJYBjMePH8ett96qT9wJAMnJyYiKisLOnTvxr3/9C8CvE4AGBARgxowZ2LZtG4YPH45u3bph4sSJvBZKOzB7dZi9OsxeHWZPRmO4Jmv37t1ITU3FG2+8gUOHDunL77rrLnzxxReoqqoC0LxxeXt7Iz4+HiaTCbt27QIAmM1mWCwWZGVlYdq0aYiJiUFpaSk++ugjeHl5KVkno2D26jB7dZi9OsyejM4wTVZlZSXGjRuHRx55BOfOncPatWsxduxYfcMbO3Ys+vTpg4yMDAC//uIZM2YMNE3D0aNH9df6z3/+g7KyMqxbtw579+7F4MGDnb9CBsLs1WH26jB7dZg9uQzVxyvbora2VpKSkuShhx7SBy6KiAwfPlySk5NFpHmQ4zvvvCOapl0yiDExMVFGjx7t1JpdBbNXh9mrw+zVYfbkSgyxJ8vLywvdunVDcnIybr75ZthsNgBAXFwcvvvuOwDNu4UnT56M+Ph4TJ8+Hfv27YOIoKqqCv/+97+RmJiochUMi9mrw+zVYfbqMHtyJYaZVsdqtcLd3R3Ar1f2nTp1Kjw9PZGdna0va2howJ/+9Cd8++23iIyMxD//+U+EhoYiLy9PnxSU2ofZq8Ps1WH26jB7chWGabIuJyYmBikpKUhOToaIwG63w2w24+eff0ZpaSmKi4vRp08fTJkyRXWpLofZq8Ps1WH26jB7MiLDNlnHjh3DiBEj8PHHHyMqKgoAYLFY4OHhobgy18fs1WH26jB7dZg9GZUhxmT9VktPWFRUBB8fH32DS09Px5NPPonq6mqV5bk0Zq8Os1eH2avD7Mno3K78kK6l5VTdQ4cOYdKkSdi9ezdmzpyJuro6bNiwAb169VJcoeti9uowe3WYvTrMngzPCWcwXnX19fVyyy23iMlkkm7duskrr7yiuqRrBrNXh9mrw+zVYfZkZIYdkzVmzBiEh4dj2bJluO6661SXc01h9uowe3WYvTrMnozKsE1WU1MTzGaz6jKuScxeHWavDrNXh9mTURm2ySIiIiLqygx3diERERGREbDJIiIiInIANllEREREDsAmi4iIiMgB2GQREREROQCbLCIiIiIHYJNFRNQBL730EiIjI1WXQURdGK+TRUR0kZY5835PUlISVq5cicbGRgQGBjqpKiIyGjZZREQXqaqq0v9/y5YtWLhwIb7//nt9maenJ7p3766iNCIyEB4uJCK6yPXXX6//1717d5hMpkuWXXy4MDk5GePHj8eSJUsQHByMHj16ID09HTabDc888wwCAgIQEhKCt956q9W/9eOPP+Khhx6Cv78/AgMDER8fjxMnTjh3hYnIIdhkERFdJQUFBfjpp59QWFiIZcuW4aWXXkJcXBz8/f1x8OBBzJo1C7NmzcKpU6cAAHV1dRg9ejR8fHxQWFiIoqIi+Pj44IEHHoDFYlG8NkTUWWyyiIiukoCAACxfvhz9+/dHSkoK+vfvj7q6Ojz//PMIDw/H/Pnz4eHhgf379wMANm/eDE3T8Oabb2LIkCEYOHAg1q1bhx9++AF79+5VuzJE1GluqgsgInIVgwcPhqb9+ts1ODgYERER+m2z2YzAwEBUV1cDAEpKSnD06FH4+vq2ep2GhgaUl5c7p2gichg2WUREV4m7u3ur2yaT6bLL7HY7AMButyMqKgobN2685LWCgoIcVygROQWbLCIiRW6//XZs2bIFvXr1gp+fn+pyiOgq45gsIiJFEhMT0bNnT8THx+Pzzz/H8ePHsW/fPjz55JOoqKhQXR4RdRKbLCIiRby8vFBYWIjQ0FBMnDgRAwcOREpKCurr67lni8gF8GKkRERERA7APVlEREREDsAmi4iIiMgB2GQREREROQCbLCIiIiIHYJNFRERE5ABssoiIiIgcgE0WERERkQOwySIiIiJyADZZRERERA7AJouIiIjIAdhkERERETnA/wDVbkINsg7pegAAAABJRU5ErkJggg==", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "da.isel(time=slice(0,20),lat=20, lon=40).plot();" + "da.isel(time=slice(0, 20), lat=20, lon=40).plot();" ] }, { @@ -1218,26 +264,15 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": { "tags": [ "hide-output" ] }, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "da.sel(time=\"2014-01-01\",lat=25, lon=210).plot();" + "da.sel(time=\"2014-01-01\", lat=25, lon=210).plot();" ] }, { @@ -1249,438 +284,13 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": { "tags": [ "hide-output" ] }, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 1460)>\n",
-       "array([277.6 , 277.5 , 277.4 , ..., 277.59, 277.59, 277.59], dtype=float32)\n",
-       "Coordinates:\n",
-       "    lat      float32 50.0\n",
-       "    lon      float32 200.0\n",
-       "  * time     (time) datetime64[ns] 2014-01-01 ... 2014-12-31T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([277.6 , 277.5 , 277.4 , ..., 277.59, 277.59, 277.59], dtype=float32)\n", - "Coordinates:\n", - " lat float32 50.0\n", - " lon float32 200.0\n", - " * time (time) datetime64[ns] 2014-01-01 ... 2014-12-31T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da.sel(lat=50.0, lon=200.0, time=\"2014\")" ] @@ -1694,570 +304,9 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 1212, lat: 25, lon: 53)>\n",
-       "array([[[246.     , 245.09999, 244.09999, ..., 240.2    , 242.     ,\n",
-       "         244.2    ],\n",
-       "        [240.59999, 241.7    , 242.09999, ..., 239.09999, 241.2    ,\n",
-       "         244.59999],\n",
-       "        [238.2    , 239.79999, 240.79999, ..., 243.89   , 246.89   ,\n",
-       "         251.2    ],\n",
-       "        ...,\n",
-       "        [295.6    , 295.6    , 296.     , ..., 293.6    , 293.6    ,\n",
-       "         294.1    ],\n",
-       "        [297.     , 297.     , 297.     , ..., 293.69998, 293.9    ,\n",
-       "         294.6    ],\n",
-       "        [297.5    , 297.69998, 297.69998, ..., 294.5    , 294.69998,\n",
-       "         295.19998]],\n",
-       "\n",
-       "       [[245.2    , 244.29999, 243.     , ..., 241.89   , 243.09999,\n",
-       "         244.59999],\n",
-       "        [241.09999, 241.89   , 241.89   , ..., 241.89   , 243.59999,\n",
-       "         246.39   ],\n",
-       "        [239.89   , 241.     , 241.29999, ..., 245.09999, 248.2    ,\n",
-       "         252.2    ],\n",
-       "...\n",
-       "        [296.49   , 295.59   , 295.49   , ..., 297.29   , 297.38998,\n",
-       "         296.79   ],\n",
-       "        [298.09   , 297.79   , 297.19   , ..., 297.69   , 298.09   ,\n",
-       "         297.38998],\n",
-       "        [298.49   , 298.29   , 297.99   , ..., 298.29   , 298.09   ,\n",
-       "         297.99   ]],\n",
-       "\n",
-       "       [[246.29   , 246.39   , 245.98999, ..., 232.29   , 233.39   ,\n",
-       "         234.98999],\n",
-       "        [246.68999, 248.18999, 248.98999, ..., 230.59   , 232.48999,\n",
-       "         235.68999],\n",
-       "        [244.79   , 245.29   , 245.89   , ..., 230.48999, 235.09   ,\n",
-       "         241.39   ],\n",
-       "        ...,\n",
-       "        [296.88998, 296.49   , 296.49   , ..., 297.69   , 297.29   ,\n",
-       "         296.19   ],\n",
-       "        [298.29   , 297.99   , 297.49   , ..., 298.29   , 298.38998,\n",
-       "         297.49   ],\n",
-       "        [298.49   , 298.29   , 297.99   , ..., 298.59   , 298.38998,\n",
-       "         298.29   ]]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-       "  * time     (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([[[246. , 245.09999, 244.09999, ..., 240.2 , 242. ,\n", - " 244.2 ],\n", - " [240.59999, 241.7 , 242.09999, ..., 239.09999, 241.2 ,\n", - " 244.59999],\n", - " [238.2 , 239.79999, 240.79999, ..., 243.89 , 246.89 ,\n", - " 251.2 ],\n", - " ...,\n", - " [295.6 , 295.6 , 296. , ..., 293.6 , 293.6 ,\n", - " 294.1 ],\n", - " [297. , 297. , 297. , ..., 293.69998, 293.9 ,\n", - " 294.6 ],\n", - " [297.5 , 297.69998, 297.69998, ..., 294.5 , 294.69998,\n", - " 295.19998]],\n", - "\n", - " [[245.2 , 244.29999, 243. , ..., 241.89 , 243.09999,\n", - " 244.59999],\n", - " [241.09999, 241.89 , 241.89 , ..., 241.89 , 243.59999,\n", - " 246.39 ],\n", - " [239.89 , 241. , 241.29999, ..., 245.09999, 248.2 ,\n", - " 252.2 ],\n", - "...\n", - " [296.49 , 295.59 , 295.49 , ..., 297.29 , 297.38998,\n", - " 296.79 ],\n", - " [298.09 , 297.79 , 297.19 , ..., 297.69 , 298.09 ,\n", - " 297.38998],\n", - " [298.49 , 298.29 , 297.99 , ..., 298.29 , 298.09 ,\n", - " 297.99 ]],\n", - "\n", - " [[246.29 , 246.39 , 245.98999, ..., 232.29 , 233.39 ,\n", - " 234.98999],\n", - " [246.68999, 248.18999, 248.98999, ..., 230.59 , 232.48999,\n", - " 235.68999],\n", - " [244.79 , 245.29 , 245.89 , ..., 230.48999, 235.09 ,\n", - " 241.39 ],\n", - " ...,\n", - " [296.88998, 296.49 , 296.49 , ..., 297.69 , 297.29 ,\n", - " 296.19 ],\n", - " [298.29 , 297.99 , 297.49 , ..., 298.29 , 298.38998,\n", - " 297.49 ],\n", - " [298.49 , 298.29 , 297.99 , ..., 298.59 , 298.38998,\n", - " 298.29 ]]], dtype=float32)\n", - "Coordinates:\n", - " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", - " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", - " * time (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# demonstrate slicing\n", "da.sel(time=slice(\"2014-02-14\", \"2014-12-13\"))" @@ -2272,570 +321,9 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 1212, lat: 25, lon: 53)>\n",
-       "array([[[246.     , 245.09999, 244.09999, ..., 240.2    , 242.     ,\n",
-       "         244.2    ],\n",
-       "        [240.59999, 241.7    , 242.09999, ..., 239.09999, 241.2    ,\n",
-       "         244.59999],\n",
-       "        [238.2    , 239.79999, 240.79999, ..., 243.89   , 246.89   ,\n",
-       "         251.2    ],\n",
-       "        ...,\n",
-       "        [295.6    , 295.6    , 296.     , ..., 293.6    , 293.6    ,\n",
-       "         294.1    ],\n",
-       "        [297.     , 297.     , 297.     , ..., 293.69998, 293.9    ,\n",
-       "         294.6    ],\n",
-       "        [297.5    , 297.69998, 297.69998, ..., 294.5    , 294.69998,\n",
-       "         295.19998]],\n",
-       "\n",
-       "       [[245.2    , 244.29999, 243.     , ..., 241.89   , 243.09999,\n",
-       "         244.59999],\n",
-       "        [241.09999, 241.89   , 241.89   , ..., 241.89   , 243.59999,\n",
-       "         246.39   ],\n",
-       "        [239.89   , 241.     , 241.29999, ..., 245.09999, 248.2    ,\n",
-       "         252.2    ],\n",
-       "...\n",
-       "        [296.49   , 295.59   , 295.49   , ..., 297.29   , 297.38998,\n",
-       "         296.79   ],\n",
-       "        [298.09   , 297.79   , 297.19   , ..., 297.69   , 298.09   ,\n",
-       "         297.38998],\n",
-       "        [298.49   , 298.29   , 297.99   , ..., 298.29   , 298.09   ,\n",
-       "         297.99   ]],\n",
-       "\n",
-       "       [[246.29   , 246.39   , 245.98999, ..., 232.29   , 233.39   ,\n",
-       "         234.98999],\n",
-       "        [246.68999, 248.18999, 248.98999, ..., 230.59   , 232.48999,\n",
-       "         235.68999],\n",
-       "        [244.79   , 245.29   , 245.89   , ..., 230.48999, 235.09   ,\n",
-       "         241.39   ],\n",
-       "        ...,\n",
-       "        [296.88998, 296.49   , 296.49   , ..., 297.69   , 297.29   ,\n",
-       "         296.19   ],\n",
-       "        [298.29   , 297.99   , 297.49   , ..., 298.29   , 298.38998,\n",
-       "         297.49   ],\n",
-       "        [298.49   , 298.29   , 297.99   , ..., 298.59   , 298.38998,\n",
-       "         298.29   ]]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-       "  * time     (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([[[246. , 245.09999, 244.09999, ..., 240.2 , 242. ,\n", - " 244.2 ],\n", - " [240.59999, 241.7 , 242.09999, ..., 239.09999, 241.2 ,\n", - " 244.59999],\n", - " [238.2 , 239.79999, 240.79999, ..., 243.89 , 246.89 ,\n", - " 251.2 ],\n", - " ...,\n", - " [295.6 , 295.6 , 296. , ..., 293.6 , 293.6 ,\n", - " 294.1 ],\n", - " [297. , 297. , 297. , ..., 293.69998, 293.9 ,\n", - " 294.6 ],\n", - " [297.5 , 297.69998, 297.69998, ..., 294.5 , 294.69998,\n", - " 295.19998]],\n", - "\n", - " [[245.2 , 244.29999, 243. , ..., 241.89 , 243.09999,\n", - " 244.59999],\n", - " [241.09999, 241.89 , 241.89 , ..., 241.89 , 243.59999,\n", - " 246.39 ],\n", - " [239.89 , 241. , 241.29999, ..., 245.09999, 248.2 ,\n", - " 252.2 ],\n", - "...\n", - " [296.49 , 295.59 , 295.49 , ..., 297.29 , 297.38998,\n", - " 296.79 ],\n", - " [298.09 , 297.79 , 297.19 , ..., 297.69 , 298.09 ,\n", - " 297.38998],\n", - " [298.49 , 298.29 , 297.99 , ..., 298.29 , 298.09 ,\n", - " 297.99 ]],\n", - "\n", - " [[246.29 , 246.39 , 245.98999, ..., 232.29 , 233.39 ,\n", - " 234.98999],\n", - " [246.68999, 248.18999, 248.98999, ..., 230.59 , 232.48999,\n", - " 235.68999],\n", - " [244.79 , 245.29 , 245.89 , ..., 230.48999, 235.09 ,\n", - " 241.39 ],\n", - " ...,\n", - " [296.88998, 296.49 , 296.49 , ..., 297.69 , 297.29 ,\n", - " 296.19 ],\n", - " [298.29 , 297.99 , 297.49 , ..., 298.29 , 298.38998,\n", - " 297.49 ],\n", - " [298.49 , 298.29 , 297.99 , ..., 298.59 , 298.38998,\n", - " 298.29 ]]], dtype=float32)\n", - "Coordinates:\n", - " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", - " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", - " * time (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da.loc[\"2014-02-14\":\"2014-12-13\"]" ] @@ -2851,570 +339,9 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 2919, lat: 24, lon: 52)>\n",
-       "array([[[242.5    , 243.5    , 244.     , ..., 232.79999, 235.5    ,\n",
-       "         238.59999],\n",
-       "        [244.5    , 244.7    , 244.2    , ..., 232.79999, 235.29999,\n",
-       "         239.29999],\n",
-       "        [249.79999, 248.89   , 247.5    , ..., 233.2    , 236.39   ,\n",
-       "         241.7    ],\n",
-       "        ...,\n",
-       "        [296.19998, 296.4    , 296.5    , ..., 295.4    , 295.1    ,\n",
-       "         294.69998],\n",
-       "        [296.19998, 296.79   , 296.5    , ..., 295.9    , 295.9    ,\n",
-       "         295.19998],\n",
-       "        [296.79   , 297.1    , 297.     , ..., 296.9    , 296.79   ,\n",
-       "         296.6    ]],\n",
-       "\n",
-       "       [[242.7    , 243.09999, 243.39   , ..., 232.     , 233.59999,\n",
-       "         235.79999],\n",
-       "        [244.09999, 244.2    , 244.09999, ..., 231.     , 232.5    ,\n",
-       "         235.7    ],\n",
-       "        [252.89   , 252.09999, 250.79999, ..., 230.79999, 233.39   ,\n",
-       "         238.5    ],\n",
-       "...\n",
-       "        [293.88998, 295.38998, 297.19   , ..., 295.09   , 294.69   ,\n",
-       "         294.29   ],\n",
-       "        [297.19   , 297.59   , 297.88998, ..., 295.29   , 295.09   ,\n",
-       "         294.38998],\n",
-       "        [298.38998, 298.49   , 298.59   , ..., 295.69   , 295.49   ,\n",
-       "         295.19   ]],\n",
-       "\n",
-       "       [[244.29   , 243.29   , 242.18999, ..., 241.68999, 241.48999,\n",
-       "         241.79   ],\n",
-       "        [249.29   , 248.39   , 246.98999, ..., 239.59   , 240.29   ,\n",
-       "         241.68999],\n",
-       "        [262.19   , 261.38998, 259.99   , ..., 239.89   , 242.59   ,\n",
-       "         246.29   ],\n",
-       "        ...,\n",
-       "        [293.69   , 295.09   , 296.69   , ..., 295.29   , 295.09   ,\n",
-       "         294.69   ],\n",
-       "        [296.88998, 297.19   , 297.49   , ..., 295.69   , 295.69   ,\n",
-       "         295.19   ],\n",
-       "        [298.09   , 298.09   , 298.49   , ..., 296.49   , 296.19   ,\n",
-       "         295.69   ]]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 202.5 205.0 207.5 210.0 ... 322.5 325.0 327.5 330.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([[[242.5 , 243.5 , 244. , ..., 232.79999, 235.5 ,\n", - " 238.59999],\n", - " [244.5 , 244.7 , 244.2 , ..., 232.79999, 235.29999,\n", - " 239.29999],\n", - " [249.79999, 248.89 , 247.5 , ..., 233.2 , 236.39 ,\n", - " 241.7 ],\n", - " ...,\n", - " [296.19998, 296.4 , 296.5 , ..., 295.4 , 295.1 ,\n", - " 294.69998],\n", - " [296.19998, 296.79 , 296.5 , ..., 295.9 , 295.9 ,\n", - " 295.19998],\n", - " [296.79 , 297.1 , 297. , ..., 296.9 , 296.79 ,\n", - " 296.6 ]],\n", - "\n", - " [[242.7 , 243.09999, 243.39 , ..., 232. , 233.59999,\n", - " 235.79999],\n", - " [244.09999, 244.2 , 244.09999, ..., 231. , 232.5 ,\n", - " 235.7 ],\n", - " [252.89 , 252.09999, 250.79999, ..., 230.79999, 233.39 ,\n", - " 238.5 ],\n", - "...\n", - " [293.88998, 295.38998, 297.19 , ..., 295.09 , 294.69 ,\n", - " 294.29 ],\n", - " [297.19 , 297.59 , 297.88998, ..., 295.29 , 295.09 ,\n", - " 294.38998],\n", - " [298.38998, 298.49 , 298.59 , ..., 295.69 , 295.49 ,\n", - " 295.19 ]],\n", - "\n", - " [[244.29 , 243.29 , 242.18999, ..., 241.68999, 241.48999,\n", - " 241.79 ],\n", - " [249.29 , 248.39 , 246.98999, ..., 239.59 , 240.29 ,\n", - " 241.68999],\n", - " [262.19 , 261.38998, 259.99 , ..., 239.89 , 242.59 ,\n", - " 246.29 ],\n", - " ...,\n", - " [293.69 , 295.09 , 296.69 , ..., 295.29 , 295.09 ,\n", - " 294.69 ],\n", - " [296.88998, 297.19 , 297.49 , ..., 295.69 , 295.69 ,\n", - " 295.19 ],\n", - " [298.09 , 298.09 , 298.49 , ..., 296.49 , 296.19 ,\n", - " 295.69 ]]], dtype=float32)\n", - "Coordinates:\n", - " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", - " * lon (lon) float32 202.5 205.0 207.5 210.0 ... 322.5 325.0 327.5 330.0\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da.drop_sel(lat=50.0, lon=200.0, time=\"2014\")" ] @@ -3433,441 +360,13 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "metadata": { "tags": [ "hide-output" ] }, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 2920)>\n",
-       "array([262.69998, 263.19998, 270.9    , ..., 264.19   , 265.19   ,\n",
-       "       266.99   ], dtype=float32)\n",
-       "Coordinates:\n",
-       "    lat      float32 52.5\n",
-       "    lon      float32 252.5\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([262.69998, 263.19998, 270.9 , ..., 264.19 , 265.19 ,\n", - " 266.99 ], dtype=float32)\n", - "Coordinates:\n", - " lat float32 52.5\n", - " lon float32 252.5\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da.sel(lat=52.25, lon=251.8998, method=\"nearest\")" ] @@ -3881,437 +380,9 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 2920)>\n",
-       "array([262.69998, 263.19998, 270.9    , ..., 264.19   , 265.19   ,\n",
-       "       266.99   ], dtype=float32)\n",
-       "Coordinates:\n",
-       "    lat      float32 52.5\n",
-       "    lon      float32 252.5\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([262.69998, 263.19998, 270.9 , ..., 264.19 , 265.19 ,\n", - " 266.99 ], dtype=float32)\n", - "Coordinates:\n", - " lat float32 52.5\n", - " lon float32 252.5\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da.sel(lat=52.25, lon=251.8998, method=\"nearest\", tolerance=2)" ] @@ -4329,428 +400,9 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.Dataset>\n",
-       "Dimensions:  (time: 2920)\n",
-       "Coordinates:\n",
-       "    lat      float32 52.5\n",
-       "    lon      float32 252.5\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Data variables:\n",
-       "    air      (time) float32 262.7 263.2 270.9 274.1 ... 261.6 264.2 265.2 267.0\n",
-       "Attributes:\n",
-       "    Conventions:  COARDS\n",
-       "    title:        4x daily NMC reanalysis (1948)\n",
-       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
-       "    platform:     Model\n",
-       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
    " - ], - "text/plain": [ - "\n", - "Dimensions: (time: 2920)\n", - "Coordinates:\n", - " lat float32 52.5\n", - " lon float32 252.5\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Data variables:\n", - " air (time) float32 262.7 263.2 270.9 274.1 ... 261.6 264.2 265.2 267.0\n", - "Attributes:\n", - " Conventions: COARDS\n", - " title: 4x daily NMC reanalysis (1948)\n", - " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", - " platform: Model\n", - " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "ds.sel(lat=52.25, lon=251.8998, method=\"nearest\")" ] @@ -4788,11 +440,6 @@ } ], "metadata": { - "kernelspec": { - "display_name": "NPL 2023b", - "language": "python", - "name": "npl-2023b" - }, "language_info": { "codemirror_mode": { "name": "ipython", @@ -4802,8 +449,7 @@ "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" + "pygments_lexer": "ipython3" }, "toc": { "base_numbering": 1, @@ -4817,13 +463,6 @@ "toc_position": {}, "toc_section_display": true, "toc_window_display": true - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } } }, "nbformat": 4, diff --git a/fundamentals/02.2_indexing_Advanced.ipynb b/fundamentals/02.2_indexing_Advanced.ipynb index 63b59597..1ec0ea0c 100644 --- a/fundamentals/02.2_indexing_Advanced.ipynb +++ b/fundamentals/02.2_indexing_Advanced.ipynb @@ -30,7 +30,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -48,483 +48,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.Dataset>\n",
-       "Dimensions:  (lat: 25, time: 2920, lon: 53)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Data variables:\n",
-       "    air      (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n",
-       "Attributes:\n",
-       "    Conventions:  COARDS\n",
-       "    title:        4x daily NMC reanalysis (1948)\n",
-       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
-       "    platform:     Model\n",
-       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
    " - ], - "text/plain": [ - "\n", - "Dimensions: (lat: 25, time: 2920, lon: 53)\n", - "Coordinates:\n", - " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", - " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Data variables:\n", - " air (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n", - "Attributes:\n", - " Conventions: COARDS\n", - " title: 4x daily NMC reanalysis (1948)\n", - " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", - " platform: Model\n", - " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "ds = xr.tutorial.load_dataset(\"air_temperature\")\n", "da = ds.air\n", @@ -545,22 +71,11 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "da[0,[2,4,10,13],[1,6,7]].plot();" + "da[0, [2, 4, 10, 13], [1, 6, 7]].plot();" ] }, { @@ -578,390 +93,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray (points: 4)>\n",
-       "array([31, 41, 42, 42])\n",
-       "Dimensions without coordinates: points
    " - ], - "text/plain": [ - "\n", - "array([31, 41, 42, 42])\n", - "Dimensions without coordinates: points" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Define target latitude and longitude (where weather stations might be)\n", "lat_points = xr.DataArray([31, 41, 42, 42], dims=\"points\")\n", @@ -971,390 +105,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray (points: 4)>\n",
-       "array([200, 201, 202, 205])\n",
-       "Dimensions without coordinates: points
    " - ], - "text/plain": [ - "\n", - "array([200, 201, 202, 205])\n", - "Dimensions without coordinates: points" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "lon_points" ] @@ -1368,22 +121,11 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "da.sel(lat=lat_points, lon=lon_points,method=\"nearest\").plot();" + "da.sel(lat=lat_points, lon=lon_points, method=\"nearest\").plot();" ] }, { @@ -1395,22 +137,11 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "('time', 'points')" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "da.sel(lat=lat_points, lon=lon_points,method=\"nearest\").dims" + "da.sel(lat=lat_points, lon=lon_points, method=\"nearest\").dims" ] }, { @@ -1422,510 +153,11 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 2920, lat: 3, points: 4)>\n",
-       "array([[[296.6    , 296.6    , 296.19998, 296.4    ],\n",
-       "        [293.1    , 293.1    , 293.29   , 293.29   ],\n",
-       "        [284.6    , 284.6    , 284.9    , 284.19998]],\n",
-       "\n",
-       "       [[296.4    , 296.4    , 295.9    , 296.19998],\n",
-       "        [293.19998, 293.19998, 293.9    , 294.19998],\n",
-       "        [283.29   , 283.29   , 285.19998, 285.19998]],\n",
-       "\n",
-       "       [[295.6    , 295.6    , 295.4    , 295.4    ],\n",
-       "        [292.4    , 292.4    , 292.9    , 293.4    ],\n",
-       "        [282.     , 282.     , 283.29   , 284.69998]],\n",
-       "\n",
-       "       ...,\n",
-       "\n",
-       "       [[294.79   , 294.79   , 295.29   , 297.49   ],\n",
-       "        [288.88998, 288.88998, 289.19   , 290.88998],\n",
-       "        [282.49   , 282.49   , 281.99   , 281.99   ]],\n",
-       "\n",
-       "       [[293.69   , 293.69   , 293.88998, 295.38998],\n",
-       "        [288.29   , 288.29   , 289.19   , 290.79   ],\n",
-       "        [282.09   , 282.09   , 281.59   , 282.38998]],\n",
-       "\n",
-       "       [[293.79   , 293.79   , 293.69   , 295.09   ],\n",
-       "        [289.49   , 289.49   , 290.38998, 291.59   ],\n",
-       "        [282.09   , 282.09   , 281.99   , 283.09   ]]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 20.0 30.0 40.0\n",
-       "    lon      (points) float32 200.0 200.0 202.5 205.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Dimensions without coordinates: points\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([[[296.6 , 296.6 , 296.19998, 296.4 ],\n", - " [293.1 , 293.1 , 293.29 , 293.29 ],\n", - " [284.6 , 284.6 , 284.9 , 284.19998]],\n", - "\n", - " [[296.4 , 296.4 , 295.9 , 296.19998],\n", - " [293.19998, 293.19998, 293.9 , 294.19998],\n", - " [283.29 , 283.29 , 285.19998, 285.19998]],\n", - "\n", - " [[295.6 , 295.6 , 295.4 , 295.4 ],\n", - " [292.4 , 292.4 , 292.9 , 293.4 ],\n", - " [282. , 282. , 283.29 , 284.69998]],\n", - "\n", - " ...,\n", - "\n", - " [[294.79 , 294.79 , 295.29 , 297.49 ],\n", - " [288.88998, 288.88998, 289.19 , 290.88998],\n", - " [282.49 , 282.49 , 281.99 , 281.99 ]],\n", - "\n", - " [[293.69 , 293.69 , 293.88998, 295.38998],\n", - " [288.29 , 288.29 , 289.19 , 290.79 ],\n", - " [282.09 , 282.09 , 281.59 , 282.38998]],\n", - "\n", - " [[293.79 , 293.79 , 293.69 , 295.09 ],\n", - " [289.49 , 289.49 , 290.38998, 291.59 ],\n", - " [282.09 , 282.09 , 281.99 , 283.09 ]]], dtype=float32)\n", - "Coordinates:\n", - " * lat (lat) float32 20.0 30.0 40.0\n", - " lon (points) float32 200.0 200.0 202.5 205.0\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Dimensions without coordinates: points\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "da.sel(lat=[20, 30, 40], lon=lon_points,method=\"nearest\")" + "da.sel(lat=[20, 30, 40], lon=lon_points, method=\"nearest\")" ] }, { @@ -1950,570 +182,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 2920, lat: 25, lon: 53)>\n",
-       "array([[[241.2    , 242.5    , 243.5    , ..., 232.79999, 235.5    ,\n",
-       "         238.59999],\n",
-       "        [243.79999, 244.5    , 244.7    , ..., 232.79999, 235.29999,\n",
-       "         239.29999],\n",
-       "        [250.     , 249.79999, 248.89   , ..., 233.2    , 236.39   ,\n",
-       "         241.7    ],\n",
-       "        ...,\n",
-       "        [296.6    , 296.19998, 296.4    , ..., 295.4    , 295.1    ,\n",
-       "         294.69998],\n",
-       "        [295.9    , 296.19998, 296.79   , ..., 295.9    , 295.9    ,\n",
-       "         295.19998],\n",
-       "        [296.29   , 296.79   , 297.1    , ..., 296.9    , 296.79   ,\n",
-       "         296.6    ]],\n",
-       "\n",
-       "       [[242.09999, 242.7    , 243.09999, ..., 232.     , 233.59999,\n",
-       "         235.79999],\n",
-       "        [243.59999, 244.09999, 244.2    , ..., 231.     , 232.5    ,\n",
-       "         235.7    ],\n",
-       "        [253.2    , 252.89   , 252.09999, ..., 230.79999, 233.39   ,\n",
-       "         238.5    ],\n",
-       "...\n",
-       "        [293.69   , 293.88998, 295.38998, ..., 295.09   , 294.69   ,\n",
-       "         294.29   ],\n",
-       "        [296.29   , 297.19   , 297.59   , ..., 295.29   , 295.09   ,\n",
-       "         294.38998],\n",
-       "        [297.79   , 298.38998, 298.49   , ..., 295.69   , 295.49   ,\n",
-       "         295.19   ]],\n",
-       "\n",
-       "       [[245.09   , 244.29   , 243.29   , ..., 241.68999, 241.48999,\n",
-       "         241.79   ],\n",
-       "        [249.89   , 249.29   , 248.39   , ..., 239.59   , 240.29   ,\n",
-       "         241.68999],\n",
-       "        [262.99   , 262.19   , 261.38998, ..., 239.89   , 242.59   ,\n",
-       "         246.29   ],\n",
-       "        ...,\n",
-       "        [293.79   , 293.69   , 295.09   , ..., 295.29   , 295.09   ,\n",
-       "         294.69   ],\n",
-       "        [296.09   , 296.88998, 297.19   , ..., 295.69   , 295.69   ,\n",
-       "         295.19   ],\n",
-       "        [297.69   , 298.09   , 298.09   , ..., 296.49   , 296.19   ,\n",
-       "         295.69   ]]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([[[241.2 , 242.5 , 243.5 , ..., 232.79999, 235.5 ,\n", - " 238.59999],\n", - " [243.79999, 244.5 , 244.7 , ..., 232.79999, 235.29999,\n", - " 239.29999],\n", - " [250. , 249.79999, 248.89 , ..., 233.2 , 236.39 ,\n", - " 241.7 ],\n", - " ...,\n", - " [296.6 , 296.19998, 296.4 , ..., 295.4 , 295.1 ,\n", - " 294.69998],\n", - " [295.9 , 296.19998, 296.79 , ..., 295.9 , 295.9 ,\n", - " 295.19998],\n", - " [296.29 , 296.79 , 297.1 , ..., 296.9 , 296.79 ,\n", - " 296.6 ]],\n", - "\n", - " [[242.09999, 242.7 , 243.09999, ..., 232. , 233.59999,\n", - " 235.79999],\n", - " [243.59999, 244.09999, 244.2 , ..., 231. , 232.5 ,\n", - " 235.7 ],\n", - " [253.2 , 252.89 , 252.09999, ..., 230.79999, 233.39 ,\n", - " 238.5 ],\n", - "...\n", - " [293.69 , 293.88998, 295.38998, ..., 295.09 , 294.69 ,\n", - " 294.29 ],\n", - " [296.29 , 297.19 , 297.59 , ..., 295.29 , 295.09 ,\n", - " 294.38998],\n", - " [297.79 , 298.38998, 298.49 , ..., 295.69 , 295.49 ,\n", - " 295.19 ]],\n", - "\n", - " [[245.09 , 244.29 , 243.29 , ..., 241.68999, 241.48999,\n", - " 241.79 ],\n", - " [249.89 , 249.29 , 248.39 , ..., 239.59 , 240.29 ,\n", - " 241.68999],\n", - " [262.99 , 262.19 , 261.38998, ..., 239.89 , 242.59 ,\n", - " 246.29 ],\n", - " ...,\n", - " [293.79 , 293.69 , 295.09 , ..., 295.29 , 295.09 ,\n", - " 294.69 ],\n", - " [296.09 , 296.88998, 297.19 , ..., 295.69 , 295.69 ,\n", - " 295.19 ],\n", - " [297.69 , 298.09 , 298.09 , ..., 296.49 , 296.19 ,\n", - " 295.69 ]]], dtype=float32)\n", - "Coordinates:\n", - " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", - " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Let's replace the missing values (nan) with some placeholder\n", "\n", @@ -2529,43 +200,21 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "da[0,:,:].plot();" + "da[0, :, :].plot();" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "da_masked = da.where(da.lat<60)\n", - "da_masked[0,:,:].plot();" + "da_masked = da.where(da.lat < 60)\n", + "da_masked[0, :, :].plot();" ] }, { @@ -2577,23 +226,12 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "da_masked = da.where(da.lat<60, drop=True)\n", - "da_masked[0,:,:].plot();" + "da_masked = da.where(da.lat < 60, drop=True)\n", + "da_masked[0, :, :].plot();" ] }, { @@ -2615,394 +253,13 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray (x: 5)>\n",
-       "array([False,  True, False,  True, False])\n",
-       "Dimensions without coordinates: x
    " - ], - "text/plain": [ - "\n", - "array([False, True, False, True, False])\n", - "Dimensions without coordinates: x" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "x_da = xr.DataArray([1, 2, 3, 4, 5], dims=[\"x\"])\n", "\n", - "#-- select points with values equal to 2 and 4:\n", + "# -- select points with values equal to 2 and 4:\n", "x_da.isin([2, 4])" ] }, @@ -3017,552 +274,11 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray (time: 2920, lat: 25, lon: 53)>\n",
-       "array([[[ 2,  3,  0, ...,  3,  0,  4],\n",
-       "        [ 3,  1,  0, ...,  4,  2, -1],\n",
-       "        [ 3,  1,  2, ...,  3,  2, -1],\n",
-       "        ...,\n",
-       "        [-1, -1,  3, ...,  0,  3,  3],\n",
-       "        [ 3,  1, -1, ..., -1,  4,  3],\n",
-       "        [ 2, -1,  4, ..., -1,  0,  2]],\n",
-       "\n",
-       "       [[ 0,  4,  2, ...,  3,  4,  3],\n",
-       "        [ 3,  0, -1, ...,  2, -1,  4],\n",
-       "        [ 2,  3,  4, ..., -1,  4, -1],\n",
-       "        ...,\n",
-       "        [ 0,  4,  1, ..., -1,  3,  1],\n",
-       "        [ 4,  1,  3, ...,  1, -1, -1],\n",
-       "        [ 3,  1,  1, ...,  3,  4,  2]],\n",
-       "\n",
-       "       [[ 1,  2, -1, ...,  1,  0,  2],\n",
-       "        [ 4,  3,  2, ...,  3,  4,  3],\n",
-       "        [ 4,  2, -1, ...,  4,  1,  1],\n",
-       "        ...,\n",
-       "...\n",
-       "        ...,\n",
-       "        [ 1,  0,  4, ...,  2, -1,  4],\n",
-       "        [ 3,  4,  3, ...,  1,  0,  1],\n",
-       "        [ 0,  4,  4, ...,  1,  4,  2]],\n",
-       "\n",
-       "       [[ 0,  2,  0, ...,  3,  1,  4],\n",
-       "        [ 3,  2, -1, ...,  0,  4,  2],\n",
-       "        [ 1, -1, -1, ...,  1, -1,  3],\n",
-       "        ...,\n",
-       "        [ 0, -1,  0, ...,  1, -1,  3],\n",
-       "        [ 0,  2,  1, ...,  4,  0,  4],\n",
-       "        [ 2,  2,  0, ...,  0, -1,  2]],\n",
-       "\n",
-       "       [[ 4, -1,  0, ...,  4,  0,  1],\n",
-       "        [ 3, -1,  0, ...,  0,  1, -1],\n",
-       "        [ 4, -1,  2, ..., -1,  3,  3],\n",
-       "        ...,\n",
-       "        [ 2,  0,  0, ...,  4,  3,  0],\n",
-       "        [ 2,  4,  2, ...,  3,  3, -1],\n",
-       "        [ 0,  4,  4, ...,  1,  0,  3]]])\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00
    " - ], - "text/plain": [ - "\n", - "array([[[ 2, 3, 0, ..., 3, 0, 4],\n", - " [ 3, 1, 0, ..., 4, 2, -1],\n", - " [ 3, 1, 2, ..., 3, 2, -1],\n", - " ...,\n", - " [-1, -1, 3, ..., 0, 3, 3],\n", - " [ 3, 1, -1, ..., -1, 4, 3],\n", - " [ 2, -1, 4, ..., -1, 0, 2]],\n", - "\n", - " [[ 0, 4, 2, ..., 3, 4, 3],\n", - " [ 3, 0, -1, ..., 2, -1, 4],\n", - " [ 2, 3, 4, ..., -1, 4, -1],\n", - " ...,\n", - " [ 0, 4, 1, ..., -1, 3, 1],\n", - " [ 4, 1, 3, ..., 1, -1, -1],\n", - " [ 3, 1, 1, ..., 3, 4, 2]],\n", - "\n", - " [[ 1, 2, -1, ..., 1, 0, 2],\n", - " [ 4, 3, 2, ..., 3, 4, 3],\n", - " [ 4, 2, -1, ..., 4, 1, 1],\n", - " ...,\n", - "...\n", - " ...,\n", - " [ 1, 0, 4, ..., 2, -1, 4],\n", - " [ 3, 4, 3, ..., 1, 0, 1],\n", - " [ 0, 4, 4, ..., 1, 4, 2]],\n", - "\n", - " [[ 0, 2, 0, ..., 3, 1, 4],\n", - " [ 3, 2, -1, ..., 0, 4, 2],\n", - " [ 1, -1, -1, ..., 1, -1, 3],\n", - " ...,\n", - " [ 0, -1, 0, ..., 1, -1, 3],\n", - " [ 0, 2, 1, ..., 4, 0, 4],\n", - " [ 2, 2, 0, ..., 0, -1, 2]],\n", - "\n", - " [[ 4, -1, 0, ..., 4, 0, 1],\n", - " [ 3, -1, 0, ..., 0, 1, -1],\n", - " [ 4, -1, 2, ..., -1, 3, 3],\n", - " ...,\n", - " [ 2, 0, 0, ..., 4, 3, 0],\n", - " [ 2, 4, 2, ..., 3, 3, -1],\n", - " [ 0, 4, 4, ..., 1, 0, 3]]])\n", - "Coordinates:\n", - " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", - " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "flags = xr.DataArray(\n", - " np.random.randint(-1, 5, da.shape),\n", - " dims=da.dims,\n", - " coords=da.coords\n", - ")\n", + "flags = xr.DataArray(np.random.randint(-1, 5, da.shape), dims=da.dims, coords=da.coords)\n", "flags" ] }, @@ -3575,23 +291,12 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "da_masked = da.where(flags.isin([1,2,3,4,5]), drop=True)\n", - "da_masked[0,:,:].plot();" + "da_masked = da.where(flags.isin([1, 2, 3, 4, 5]), drop=True)\n", + "da_masked[0, :, :].plot();" ] }, { @@ -3616,975 +321,18 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 2920, lat: 25, lon: 53)>\n",
-       "array([[[241.2    , 242.5    , 243.5    , ..., 232.79999, 235.5    ,\n",
-       "         238.59999],\n",
-       "        [243.79999, 244.5    , 244.7    , ..., 232.79999, 235.29999,\n",
-       "         239.29999],\n",
-       "        [250.     , 249.79999, 248.89   , ..., 233.2    , 236.39   ,\n",
-       "         241.7    ],\n",
-       "        ...,\n",
-       "        [296.6    , 296.19998, 296.4    , ..., 295.4    , 295.1    ,\n",
-       "         294.69998],\n",
-       "        [295.9    , 296.19998, 296.79   , ..., 295.9    , 295.9    ,\n",
-       "         295.19998],\n",
-       "        [296.29   , 296.79   , 297.1    , ..., 296.9    , 296.79   ,\n",
-       "         296.6    ]],\n",
-       "\n",
-       "       [[242.09999, 242.7    , 243.09999, ..., 232.     , 233.59999,\n",
-       "         235.79999],\n",
-       "        [243.59999, 244.09999, 244.2    , ..., 231.     , 232.5    ,\n",
-       "         235.7    ],\n",
-       "        [253.2    , 252.89   , 252.09999, ..., 230.79999, 233.39   ,\n",
-       "         238.5    ],\n",
-       "...\n",
-       "        [293.69   , 293.88998, 295.38998, ..., 295.09   , 294.69   ,\n",
-       "         294.29   ],\n",
-       "        [296.29   , 297.19   , 297.59   , ..., 295.29   , 295.09   ,\n",
-       "         294.38998],\n",
-       "        [297.79   , 298.38998, 298.49   , ..., 295.69   , 295.49   ,\n",
-       "         295.19   ]],\n",
-       "\n",
-       "       [[245.09   , 244.29   , 243.29   , ..., 241.68999, 241.48999,\n",
-       "         241.79   ],\n",
-       "        [249.89   , 249.29   , 248.39   , ..., 239.59   , 240.29   ,\n",
-       "         241.68999],\n",
-       "        [262.99   , 262.19   , 261.38998, ..., 239.89   , 242.59   ,\n",
-       "         246.29   ],\n",
-       "        ...,\n",
-       "        [293.79   , 293.69   , 295.09   , ..., 295.29   , 295.09   ,\n",
-       "         294.69   ],\n",
-       "        [296.09   , 296.88998, 297.19   , ..., 295.69   , 295.69   ,\n",
-       "         295.19   ],\n",
-       "        [297.69   , 298.09   , 298.09   , ..., 296.49   , 296.19   ,\n",
-       "         295.69   ]]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([[[241.2 , 242.5 , 243.5 , ..., 232.79999, 235.5 ,\n", - " 238.59999],\n", - " [243.79999, 244.5 , 244.7 , ..., 232.79999, 235.29999,\n", - " 239.29999],\n", - " [250. , 249.79999, 248.89 , ..., 233.2 , 236.39 ,\n", - " 241.7 ],\n", - " ...,\n", - " [296.6 , 296.19998, 296.4 , ..., 295.4 , 295.1 ,\n", - " 294.69998],\n", - " [295.9 , 296.19998, 296.79 , ..., 295.9 , 295.9 ,\n", - " 295.19998],\n", - " [296.29 , 296.79 , 297.1 , ..., 296.9 , 296.79 ,\n", - " 296.6 ]],\n", - "\n", - " [[242.09999, 242.7 , 243.09999, ..., 232. , 233.59999,\n", - " 235.79999],\n", - " [243.59999, 244.09999, 244.2 , ..., 231. , 232.5 ,\n", - " 235.7 ],\n", - " [253.2 , 252.89 , 252.09999, ..., 230.79999, 233.39 ,\n", - " 238.5 ],\n", - "...\n", - " [293.69 , 293.88998, 295.38998, ..., 295.09 , 294.69 ,\n", - " 294.29 ],\n", - " [296.29 , 297.19 , 297.59 , ..., 295.29 , 295.09 ,\n", - " 294.38998],\n", - " [297.79 , 298.38998, 298.49 , ..., 295.69 , 295.49 ,\n", - " 295.19 ]],\n", - "\n", - " [[245.09 , 244.29 , 243.29 , ..., 241.68999, 241.48999,\n", - " 241.79 ],\n", - " [249.89 , 249.29 , 248.39 , ..., 239.59 , 240.29 ,\n", - " 241.68999],\n", - " [262.99 , 262.19 , 261.38998, ..., 239.89 , 242.59 ,\n", - " 246.29 ],\n", - " ...,\n", - " [293.79 , 293.69 , 295.09 , ..., 295.29 , 295.09 ,\n", - " 294.69 ],\n", - " [296.09 , 296.88998, 297.19 , ..., 295.69 , 295.69 ,\n", - " 295.19 ],\n", - " [297.69 , 298.09 , 298.09 , ..., 296.49 , 296.19 ,\n", - " 295.69 ]]], dtype=float32)\n", - "Coordinates:\n", - " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", - " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray (time: 4, space: 3)>\n",
-       "array([[0.21780543, 0.57711447, 0.86539737],\n",
-       "       [0.14893992, 0.35643873, 0.18224072],\n",
-       "       [0.10407898, 0.95598302, 0.93117286],\n",
-       "       [0.52169098, 0.04393997, 0.97119655]])\n",
-       "Coordinates:\n",
-       "  * time     (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 2000-01-04\n",
-       "  * space    (space) <U2 'IL' 'IA' 'IN'
    " - ], - "text/plain": [ - "\n", - "array([[0.21780543, 0.57711447, 0.86539737],\n", - " [0.14893992, 0.35643873, 0.18224072],\n", - " [0.10407898, 0.95598302, 0.93117286],\n", - " [0.52169098, 0.04393997, 0.97119655]])\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 2000-01-04\n", - " * space (space) \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray (time: 4, space: 2)>\n",
-       "array([[0.57711447,        nan],\n",
-       "       [0.35643873,        nan],\n",
-       "       [0.95598302,        nan],\n",
-       "       [0.04393997,        nan]])\n",
-       "Coordinates:\n",
-       "  * time     (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 2000-01-04\n",
-       "  * space    (space) <U2 'IA' 'CA'
    " - ], - "text/plain": [ - "\n", - "array([[0.57711447, nan],\n", - " [0.35643873, nan],\n", - " [0.95598302, nan],\n", - " [0.04393997, nan]])\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 2000-01-04\n", - " * space (space) \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray (time: 20, space: 3)>\n",
-       "array([[nan, nan, nan],\n",
-       "       [nan, nan, nan],\n",
-       "       [nan, nan, nan],\n",
-       "       [nan, nan, nan],\n",
-       "       [nan, nan, nan],\n",
-       "       [nan, nan, nan],\n",
-       "       [nan, nan, nan],\n",
-       "       [nan, nan, nan],\n",
-       "       [nan, nan, nan],\n",
-       "       [nan, nan, nan],\n",
-       "       [nan, nan, nan],\n",
-       "       [nan, nan, nan],\n",
-       "       [nan, nan, nan],\n",
-       "       [nan, nan, nan],\n",
-       "       [nan, nan, nan],\n",
-       "       [nan, nan, nan],\n",
-       "       [nan, nan, nan],\n",
-       "       [nan, nan, nan],\n",
-       "       [nan, nan, nan],\n",
-       "       [nan, nan, nan]])\n",
-       "Coordinates:\n",
-       "  * time     (time) datetime64[ns] 2013-02-01 ... 2013-02-01T19:00:00\n",
-       "  * space    (space) <U2 'IL' 'IA' 'IN'
    " - ], - "text/plain": [ - "\n", - "array([[nan, nan, nan],\n", - " [nan, nan, nan],\n", - " [nan, nan, nan],\n", - " [nan, nan, nan],\n", - " [nan, nan, nan],\n", - " [nan, nan, nan],\n", - " [nan, nan, nan],\n", - " [nan, nan, nan],\n", - " [nan, nan, nan],\n", - " [nan, nan, nan],\n", - " [nan, nan, nan],\n", - " [nan, nan, nan],\n", - " [nan, nan, nan],\n", - " [nan, nan, nan],\n", - " [nan, nan, nan],\n", - " [nan, nan, nan],\n", - " [nan, nan, nan],\n", - " [nan, nan, nan],\n", - " [nan, nan, nan],\n", - " [nan, nan, nan]])\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 2013-02-01 ... 2013-02-01T19:00:00\n", - " * space (space) Date: Thu, 6 Jul 2023 18:08:54 -0600 Subject: [PATCH 07/54] fix typo --- fundamentals/02.2_manipulating_dimensions.ipynb | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/fundamentals/02.2_manipulating_dimensions.ipynb b/fundamentals/02.2_manipulating_dimensions.ipynb index b379a126..5affde9f 100644 --- a/fundamentals/02.2_manipulating_dimensions.ipynb +++ b/fundamentals/02.2_manipulating_dimensions.ipynb @@ -102,6 +102,11 @@ } ], "metadata": { + "kernelspec": { + "display_name": "16318-311", + "language": "python", + "name": "16318-311" + }, "language_info": { "codemirror_mode": { "name": "ipython", @@ -112,6 +117,13 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } } }, "nbformat": 4, From 36ff650baa374e13e8d00c1e9368febc2dc332fe Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 7 Jul 2023 00:09:09 +0000 Subject: [PATCH 08/54] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- fundamentals/02.2_manipulating_dimensions.ipynb | 12 ------------ 1 file changed, 12 deletions(-) diff --git a/fundamentals/02.2_manipulating_dimensions.ipynb b/fundamentals/02.2_manipulating_dimensions.ipynb index 5affde9f..b379a126 100644 --- a/fundamentals/02.2_manipulating_dimensions.ipynb +++ b/fundamentals/02.2_manipulating_dimensions.ipynb @@ -102,11 +102,6 @@ } ], "metadata": { - "kernelspec": { - "display_name": "16318-311", - "language": "python", - "name": "16318-311" - }, "language_info": { "codemirror_mode": { "name": "ipython", @@ -117,13 +112,6 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } } }, "nbformat": 4, From 5efafb6937d530ead8d44ce17f4c071845713cab Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Fri, 7 Jul 2023 10:27:20 -0600 Subject: [PATCH 09/54] moving advanced indexing into intermediate curriculum --- .../02.2_indexing_Advanced.ipynb | 17 +++++++++++++++-- 1 file changed, 15 insertions(+), 2 deletions(-) rename {fundamentals => intermediate}/02.2_indexing_Advanced.ipynb (96%) diff --git a/fundamentals/02.2_indexing_Advanced.ipynb b/intermediate/02.2_indexing_Advanced.ipynb similarity index 96% rename from fundamentals/02.2_indexing_Advanced.ipynb rename to intermediate/02.2_indexing_Advanced.ipynb index 1ec0ea0c..ea7b9936 100644 --- a/fundamentals/02.2_indexing_Advanced.ipynb +++ b/intermediate/02.2_indexing_Advanced.ipynb @@ -22,7 +22,7 @@ "## Overview\n", "\n", "\n", - "In the pervious notebooks, we learned basic forms of indexing with xarray (positional and name based dimensions, integer and label based indexing) and nearest neighbor lookups. In this tutorial, we will discover more advanced options for vectorized indexing and learn about additional useful methods relating to indexing/selecting data such as masking. \n", + "In the previous notebooks, we learned basic forms of indexing with xarray (positional and name based dimensions, integer and label based indexing) and nearest neighbor lookups. In this tutorial, we will discover more advanced options for vectorized indexing and learn about additional useful methods relating to indexing/selecting data such as masking. \n", "\n", "\n", "First, let's import packages: " @@ -400,6 +400,11 @@ } ], "metadata": { + "kernelspec": { + "display_name": "NPL 2023b", + "language": "python", + "name": "npl-2023b" + }, "language_info": { "codemirror_mode": { "name": "ipython", @@ -409,7 +414,8 @@ "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3" + "pygments_lexer": "ipython3", + "version": "3.10.12" }, "toc": { "base_numbering": 1, @@ -423,6 +429,13 @@ "toc_position": {}, "toc_section_display": true, "toc_window_display": true + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } } }, "nbformat": 4, From 9f4732cb4a029b3189b84cbe13ac646b47f58533 Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Fri, 7 Jul 2023 10:28:58 -0600 Subject: [PATCH 10/54] updating _toc.yml --- _toc.yml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/_toc.yml b/_toc.yml index 33d72793..abc423e0 100644 --- a/_toc.yml +++ b/_toc.yml @@ -17,7 +17,8 @@ parts: - file: fundamentals/01.1_io - file: fundamentals/02_labeled_data.md sections: - - file: fundamentals/02.1_working_with_labeled_data + - file: fundamentals/02.1_indexing_Basic.ipynb + - file: intermediate/02.2_indexing_Advanced.ipynb - file: fundamentals/02.2_manipulating_dimensions - file: fundamentals/03_computation.md sections: From f8182c94bf2823cecf57b8cd3f40f717fadc6fd0 Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Fri, 7 Jul 2023 10:37:48 -0600 Subject: [PATCH 11/54] updating readme --- workshops/scipy2023/README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/workshops/scipy2023/README.md b/workshops/scipy2023/README.md index e3d68c3f..ae3c7e89 100644 --- a/workshops/scipy2023/README.md +++ b/workshops/scipy2023/README.md @@ -46,6 +46,8 @@ Once your codespace is launched, the following happens: ``` ```{dropdown} Indexing +{doc}`../../fundamentals/02.1_indexing_Basic.ipynb` +{doc}`../../intermediate/02.2_indexing_Advanced.ipynb` ``` From 2dad3cc8357cde1ac1ef528cb95ba6319e83db7d Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Fri, 7 Jul 2023 10:57:08 -0600 Subject: [PATCH 12/54] updating indexing --- fundamentals/02.1_indexing_Basic.ipynb | 85 +++++++++++++++++++++----- 1 file changed, 71 insertions(+), 14 deletions(-) diff --git a/fundamentals/02.1_indexing_Basic.ipynb b/fundamentals/02.1_indexing_Basic.ipynb index 53d97172..802e719c 100644 --- a/fundamentals/02.1_indexing_Basic.ipynb +++ b/fundamentals/02.1_indexing_Basic.ipynb @@ -4,12 +4,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n", - "\n", "# Indexing and Selecting Data\n", "\n", "## Learning Objectives\n", "\n", + "- Understanding the difference between position and label-based indexing\n", "- Select data by position using `.isel` with values or slices\n", "- Select data by label using `.sel` with values or slices\n", "- Select timeseries data by date/time with values or slices\n", @@ -21,7 +20,7 @@ "metadata": {}, "source": [ "\n", - "# Indexing and Selecting Data\n", + "## Introduction\n", "\n", "Xarray offers extremely flexible indexing routines that combine the best features of NumPy and Pandas for data selection.\n", "\n", @@ -391,9 +390,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n", - "\n", - "- **All of these indexing methods work on the dataset too:**\n", + "
    \n", + " All of these indexing methods work on the dataset too! \n", + "
    \n", "\n", "We can also use these methods to index all variables in a dataset simultaneously, returning a new dataset:" ] @@ -407,6 +406,58 @@ "ds.sel(lat=52.25, lon=251.8998, method=\"nearest\")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise\n", + "\n", + "Practice the syntax you’ve learned so far:\n", + "\n", + "1. Select the first 30 entries of latitude and 30th to 40th entries of longitude:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#write your answer here!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. Select all data at 75 degree north and between Jan 1, 2013 and Oct 15, 2013 : " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#write your answer here!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3. Remove all entries at 260 and 270 degrees: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#write your answer here!" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -430,16 +481,14 @@ "\n", "- [Xarray Docs - Indexing and Selecting Data](https://docs.xarray.dev/en/stable/indexing.html)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { + "kernelspec": { + "display_name": "NPL 2023b", + "language": "python", + "name": "npl-2023b" + }, "language_info": { "codemirror_mode": { "name": "ipython", @@ -449,7 +498,8 @@ "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3" + "pygments_lexer": "ipython3", + "version": "3.10.12" }, "toc": { "base_numbering": 1, @@ -463,6 +513,13 @@ "toc_position": {}, "toc_section_display": true, "toc_window_display": true + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } } }, "nbformat": 4, From 6170e1fa32b29e38128d08f271a90ee8aa3cefa6 Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Fri, 7 Jul 2023 12:06:35 -0600 Subject: [PATCH 13/54] using myst syntax --- fundamentals/02.1_indexing_Basic.ipynb | 66 ++++++++++++++++++-------- 1 file changed, 45 insertions(+), 21 deletions(-) diff --git a/fundamentals/02.1_indexing_Basic.ipynb b/fundamentals/02.1_indexing_Basic.ipynb index 802e719c..2a2ced35 100644 --- a/fundamentals/02.1_indexing_Basic.ipynb +++ b/fundamentals/02.1_indexing_Basic.ipynb @@ -238,9 +238,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "
    \n", - "Using the isel method, the user can choose/slice the specific elements from a Dataset or DataArray.\n", - "
    " + "```{note}\n", + "Using the `isel` method, the user can choose/slice the specific elements from a Dataset or DataArray.\n", + "```" ] }, { @@ -390,9 +390,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "
    \n", - " All of these indexing methods work on the dataset too! \n", - "
    \n", + "```{tip}\n", + "All of these indexing methods work on the dataset too!\n", + "```\n", "\n", "We can also use these methods to index all variables in a dataset simultaneously, returning a new dataset:" ] @@ -414,48 +414,72 @@ "\n", "Practice the syntax you’ve learned so far:\n", "\n", - "1. Select the first 30 entries of latitude and 30th to 40th entries of longitude:" + "\n", + "```{exercise}\n", + ":label: indexing-1\n", + "\n", + "Select the first 30 entries of `latitude` and 30th to 40th entries of `longitude`:\n", + "```" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "#write your answer here!" + "```{solution} indexing-1\n", + ":class: dropdown\n", + "\n", + "`ds.isel(lat=slice(None, 30), lon=slice(30, 40))`\n", + "\n", + "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "2. Select all data at 75 degree north and between Jan 1, 2013 and Oct 15, 2013 : " + "```{exercise}\n", + ":label: indexing-1\n", + "\n", + "Select all data at 75 degree north and between Jan 1, 2013 and Oct 15, 2013 :\n", + "```" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "#write your answer here!" + "```{solution} indexing-1\n", + ":class: dropdown\n", + "\n", + "`ds.sel(lat=75, time=slice(\"2013-01-01\", \"2013-10-15\"))`\n", + "\n", + "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "3. Remove all entries at 260 and 270 degrees: " + "```{exercise}\n", + ":label: indexing-1\n", + "\n", + "Remove all entries at 260 and 270 degrees :\n", + "\n", + "```" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "#write your answer here!" + "\n", + "```{solution} indexing-1\n", + ":class: dropdown\n", + "\n", + "`ds.drop_sel(lon=[260, 270])`\n", + "\n", + "```" ] }, { From a22174dd823ff51b03540606550ef2fd48e2799b Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 7 Jul 2023 18:07:03 +0000 Subject: [PATCH 14/54] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- fundamentals/02.1_indexing_Basic.ipynb | 15 +-------------- intermediate/02.2_indexing_Advanced.ipynb | 15 +-------------- 2 files changed, 2 insertions(+), 28 deletions(-) diff --git a/fundamentals/02.1_indexing_Basic.ipynb b/fundamentals/02.1_indexing_Basic.ipynb index 2a2ced35..01c7154b 100644 --- a/fundamentals/02.1_indexing_Basic.ipynb +++ b/fundamentals/02.1_indexing_Basic.ipynb @@ -508,11 +508,6 @@ } ], "metadata": { - "kernelspec": { - "display_name": "NPL 2023b", - "language": "python", - "name": "npl-2023b" - }, "language_info": { "codemirror_mode": { "name": "ipython", @@ -522,8 +517,7 @@ "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" + "pygments_lexer": "ipython3" }, "toc": { "base_numbering": 1, @@ -537,13 +531,6 @@ "toc_position": {}, "toc_section_display": true, "toc_window_display": true - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } } }, "nbformat": 4, diff --git a/intermediate/02.2_indexing_Advanced.ipynb b/intermediate/02.2_indexing_Advanced.ipynb index ea7b9936..8a23c805 100644 --- a/intermediate/02.2_indexing_Advanced.ipynb +++ b/intermediate/02.2_indexing_Advanced.ipynb @@ -400,11 +400,6 @@ } ], "metadata": { - "kernelspec": { - "display_name": "NPL 2023b", - "language": "python", - "name": "npl-2023b" - }, "language_info": { "codemirror_mode": { "name": "ipython", @@ -414,8 +409,7 @@ "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" + "pygments_lexer": "ipython3" }, "toc": { "base_numbering": 1, @@ -429,13 +423,6 @@ "toc_position": {}, "toc_section_display": true, "toc_window_display": true - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } } }, "nbformat": 4, From a60a5110ce6dfbcae22890ea3c798b46b2b6d2ce Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Fri, 7 Jul 2023 12:21:16 -0600 Subject: [PATCH 15/54] update this --- workshops/scipy2023/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/workshops/scipy2023/README.md b/workshops/scipy2023/README.md index ae3c7e89..11195679 100644 --- a/workshops/scipy2023/README.md +++ b/workshops/scipy2023/README.md @@ -46,8 +46,8 @@ Once your codespace is launched, the following happens: ``` ```{dropdown} Indexing -{doc}`../../fundamentals/02.1_indexing_Basic.ipynb` -{doc}`../../intermediate/02.2_indexing_Advanced.ipynb` +{doc}`../../fundamentals/02.1_indexing_Basic` +{doc}`../../intermediate/02.2_indexing_Advanced` ``` From 2d77bfae533ec8b1db4ea65a9fb83ba40b37db9a Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Fri, 7 Jul 2023 12:42:42 -0600 Subject: [PATCH 16/54] updating the syntax --- fundamentals/02.1_indexing_Basic.ipynb | 80 +++++++++++++------------- 1 file changed, 40 insertions(+), 40 deletions(-) diff --git a/fundamentals/02.1_indexing_Basic.ipynb b/fundamentals/02.1_indexing_Basic.ipynb index 01c7154b..0615d5f2 100644 --- a/fundamentals/02.1_indexing_Basic.ipynb +++ b/fundamentals/02.1_indexing_Basic.ipynb @@ -412,26 +412,26 @@ "source": [ "## Exercise\n", "\n", - "Practice the syntax you’ve learned so far:\n", - "\n", - "\n", - "```{exercise}\n", - ":label: indexing-1\n", - "\n", - "Select the first 30 entries of `latitude` and 30th to 40th entries of `longitude`:\n", - "```" + "Practice the syntax you’ve learned so far:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "```{solution} indexing-1\n", - ":class: dropdown\n", + "```{exercise}\n", + ":label: indexing-1\n", "\n", - "`ds.isel(lat=slice(None, 30), lon=slice(30, 40))`\n", + "Select the first 30 entries of `latitude` and 30th to 40th entries of `longitude`:\n", + "```\n", "\n", - "```" + "````{solution} indexing-1\n", + ":class: dropdown\n", + "```python\n", + "ds.isel(lat=slice(None, 30), lon=slice(30, 40))\n", + "```\n", + "\n", + "````" ] }, { @@ -439,22 +439,16 @@ "metadata": {}, "source": [ "```{exercise}\n", - ":label: indexing-1\n", + ":label: indexing-2\n", "\n", "Select all data at 75 degree north and between Jan 1, 2013 and Oct 15, 2013 :\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```{solution} indexing-1\n", + "```\n", + "````{solution} indexing-1\n", ":class: dropdown\n", - "\n", - "`ds.sel(lat=75, time=slice(\"2013-01-01\", \"2013-10-15\"))`\n", - "\n", - "```" + "```python\n", + "ds.sel(lat=75, time=slice(\"2013-01-01\", \"2013-10-15\"))\n", + "```\n", + "````" ] }, { @@ -462,24 +456,17 @@ "metadata": {}, "source": [ "```{exercise}\n", - ":label: indexing-1\n", + ":label: indexing-3\n", "\n", "Remove all entries at 260 and 270 degrees :\n", "\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "```{solution} indexing-1\n", + "```\n", + "````{solution} indexing-1\n", ":class: dropdown\n", - "\n", - "`ds.drop_sel(lon=[260, 270])`\n", - "\n", - "```" + "```python\n", + "ds.drop_sel(lon=[260, 270])\n", + "```\n", + "````" ] }, { @@ -508,6 +495,11 @@ } ], "metadata": { + "kernelspec": { + "display_name": "NPL 2023b", + "language": "python", + "name": "npl-2023b" + }, "language_info": { "codemirror_mode": { "name": "ipython", @@ -517,7 +509,8 @@ "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3" + "pygments_lexer": "ipython3", + "version": "3.10.12" }, "toc": { "base_numbering": 1, @@ -531,6 +524,13 @@ "toc_position": {}, "toc_section_display": true, "toc_window_display": true + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } } }, "nbformat": 4, From 92c532dad12ec60510ee22316668a2a0203212ea Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 7 Jul 2023 18:43:51 +0000 Subject: [PATCH 17/54] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- fundamentals/02.1_indexing_Basic.ipynb | 15 +-------------- 1 file changed, 1 insertion(+), 14 deletions(-) diff --git a/fundamentals/02.1_indexing_Basic.ipynb b/fundamentals/02.1_indexing_Basic.ipynb index 0615d5f2..5b3a4c1f 100644 --- a/fundamentals/02.1_indexing_Basic.ipynb +++ b/fundamentals/02.1_indexing_Basic.ipynb @@ -495,11 +495,6 @@ } ], "metadata": { - "kernelspec": { - "display_name": "NPL 2023b", - "language": "python", - "name": "npl-2023b" - }, "language_info": { "codemirror_mode": { "name": "ipython", @@ -509,8 +504,7 @@ "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" + "pygments_lexer": "ipython3" }, "toc": { "base_numbering": 1, @@ -524,13 +518,6 @@ "toc_position": {}, "toc_section_display": true, "toc_window_display": true - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } } }, "nbformat": 4, From 5ef4981063da6631c22c9f0212c0b724e55fd1a6 Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Fri, 7 Jul 2023 12:50:35 -0600 Subject: [PATCH 18/54] update --- workshops/scipy2023/README.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/workshops/scipy2023/README.md b/workshops/scipy2023/README.md index 11195679..e3d68c3f 100644 --- a/workshops/scipy2023/README.md +++ b/workshops/scipy2023/README.md @@ -46,8 +46,6 @@ Once your codespace is launched, the following happens: ``` ```{dropdown} Indexing -{doc}`../../fundamentals/02.1_indexing_Basic` -{doc}`../../intermediate/02.2_indexing_Advanced` ``` From 61220c0e0fe476397b6815cec2497893a79aef98 Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Fri, 7 Jul 2023 13:03:36 -0600 Subject: [PATCH 19/54] adding notebooks to scipy 2023 --- workshops/scipy2023/README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/workshops/scipy2023/README.md b/workshops/scipy2023/README.md index e3d68c3f..2b802816 100644 --- a/workshops/scipy2023/README.md +++ b/workshops/scipy2023/README.md @@ -46,6 +46,8 @@ Once your codespace is launched, the following happens: ``` ```{dropdown} Indexing +-{doc}`../../fundamentals/02.1_indexing_Basic` +-{doc}`../../intermediate/02.2_indexing_Advanced` ``` From 25e435f93c5c8ae6595e1588f8b578bd62dc8ece Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Fri, 7 Jul 2023 14:38:56 -0600 Subject: [PATCH 20/54] updating stuff --- intermediate/02.2_indexing_Advanced.ipynb | 5020 ++++++++++++++++++++- 1 file changed, 4964 insertions(+), 56 deletions(-) diff --git a/intermediate/02.2_indexing_Advanced.ipynb b/intermediate/02.2_indexing_Advanced.ipynb index 8a23c805..baa17237 100644 --- a/intermediate/02.2_indexing_Advanced.ipynb +++ b/intermediate/02.2_indexing_Advanced.ipynb @@ -4,15 +4,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n", - "\n", "# Advanced Indexing\n", "\n", "## Learning Objectives\n", "\n", - "* Vectorized Indexing \n", - "* Dropping/Masking Data Using `where` and `isin`\n", - "* Fancy DateTime Indexing\n" + "* Vectorized and Pointwise Indexing \n", + "* Boolean Indexing & Masking\n", + " * Dropping/Masking Data Using `where` and `isin`" ] }, { @@ -21,16 +19,15 @@ "source": [ "## Overview\n", "\n", - "\n", - "In the previous notebooks, we learned basic forms of indexing with xarray (positional and name based dimensions, integer and label based indexing) and nearest neighbor lookups. In this tutorial, we will discover more advanced options for vectorized indexing and learn about additional useful methods relating to indexing/selecting data such as masking. \n", + "In the previous notebooks, we learned basic forms of indexing with xarray (positional and name based dimensions, integer and label based indexing), Datetime Indexing, and nearest neighbor lookups. In this tutorial, we will discover more advanced options for vectorized indexing and learn about additional useful methods relating to indexing/selecting data such as masking. \n", "\n", "\n", - "First, let's import packages: " + "First, let's import packages needed for this repository: " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -48,9 +45,483 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.Dataset>\n",
+       "Dimensions:  (lat: 25, time: 2920, lon: 53)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Data variables:\n",
+       "    air      (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n",
+       "Attributes:\n",
+       "    Conventions:  COARDS\n",
+       "    title:        4x daily NMC reanalysis (1948)\n",
+       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
+       "    platform:     Model\n",
+       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
    " + ], + "text/plain": [ + "\n", + "Dimensions: (lat: 25, time: 2920, lon: 53)\n", + "Coordinates:\n", + " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", + " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Data variables:\n", + " air (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n", + "Attributes:\n", + " Conventions: COARDS\n", + " title: 4x daily NMC reanalysis (1948)\n", + " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", + " platform: Model\n", + " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ds = xr.tutorial.load_dataset(\"air_temperature\")\n", "da = ds.air\n", @@ -66,25 +537,586 @@ "Like NumPy and pandas, Xarray supports indexing many array elements at once in a\n", "*vectorized* manner. \n", "\n", - "If you only provide integers, slices, or unlabeled arrays (array without dimension names, such as `np.ndarray`, `list`, but not `DataArray()`) indexing can be understood as orthogonally." + "If you only provide integers, slices, or unlabeled arrays (array without dimension names, such as `np.ndarray`, `list`, but not `DataArray()`) indexing can be understood as orthogonally (i.e. along independent axes, instead of using numpy’s broadcasting rules to vectorize indexers). \n", + "\n", + "*Orthogonal* or *outer* indexing considers one-dimensional arrays in the same way as slices when deciding the output shapes. The principle of outer or orthogonal indexing is that the result mirrors the effect of independently indexing along each dimension with integer or boolean arrays, treating both the indexed and indexing arrays as one-dimensional. This method of indexing is analogous to vector indexing in programming languages like MATLAB, Fortran, and R, where each indexer component independently selects along its corresponding dimension. \n", + "\n", + "For example : " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "da[0, [2, 4, 10, 13], [1, 6, 7]].plot();" + "da[0, [2, 4, 10, 13], [1, 6, 7]].plot(); # -- orthogonal indexing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "But for more flexibility, you can supply `DataArray()` objects as indexers. \n", + "For more flexibility, you can supply `DataArray()` objects as indexers. Dimensions on resultant arrays are given by the ordered union of the indexers’ dimensions:\n", "\n", - "Vectorized indexing using `DataArrays()` may be used to extract information from the nearest grid cells of interest, for example, the nearest climate model grid cells to a collection specified weather station latitudes and longitudes.\n", + "For example, in the example below we do orthogonal indexing using `DataArray()` objects. " + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'air' (time: 2920, degrees_north: 4, degrees_east: 4)>\n",
+       "array([[[293.1    , 293.1    , 293.29   , 293.29   ],\n",
+       "        [284.6    , 284.6    , 284.9    , 284.19998],\n",
+       "        [282.79   , 282.79   , 283.19998, 282.6    ],\n",
+       "        [282.79   , 282.79   , 283.19998, 282.6    ]],\n",
+       "\n",
+       "       [[293.19998, 293.19998, 293.9    , 294.19998],\n",
+       "        [283.29   , 283.29   , 285.19998, 285.19998],\n",
+       "        [281.4    , 281.4    , 282.79   , 283.5    ],\n",
+       "        [281.4    , 281.4    , 282.79   , 283.5    ]],\n",
+       "\n",
+       "       [[292.4    , 292.4    , 292.9    , 293.4    ],\n",
+       "        [282.     , 282.     , 283.29   , 284.69998],\n",
+       "        [280.     , 280.     , 280.79   , 282.4    ],\n",
+       "        [280.     , 280.     , 280.79   , 282.4    ]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[288.88998, 288.88998, 289.19   , 290.88998],\n",
+       "        [282.49   , 282.49   , 281.99   , 281.99   ],\n",
+       "        [281.29   , 281.29   , 281.29   , 280.99   ],\n",
+       "        [281.29   , 281.29   , 281.29   , 280.99   ]],\n",
+       "\n",
+       "       [[288.29   , 288.29   , 289.19   , 290.79   ],\n",
+       "        [282.09   , 282.09   , 281.59   , 282.38998],\n",
+       "        [280.99   , 280.99   , 280.38998, 280.59   ],\n",
+       "        [280.99   , 280.99   , 280.38998, 280.59   ]],\n",
+       "\n",
+       "       [[289.49   , 289.49   , 290.38998, 291.59   ],\n",
+       "        [282.09   , 282.09   , 281.99   , 283.09   ],\n",
+       "        [281.38998, 281.38998, 280.59   , 280.99   ],\n",
+       "        [281.38998, 281.38998, 280.59   , 280.99   ]]], dtype=float32)\n",
+       "Coordinates:\n",
+       "    lat      (degrees_north) float32 30.0 40.0 42.5 42.5\n",
+       "    lon      (degrees_east) float32 200.0 200.0 202.5 205.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Dimensions without coordinates: degrees_north, degrees_east\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
    " + ], + "text/plain": [ + "\n", + "array([[[293.1 , 293.1 , 293.29 , 293.29 ],\n", + " [284.6 , 284.6 , 284.9 , 284.19998],\n", + " [282.79 , 282.79 , 283.19998, 282.6 ],\n", + " [282.79 , 282.79 , 283.19998, 282.6 ]],\n", + "\n", + " [[293.19998, 293.19998, 293.9 , 294.19998],\n", + " [283.29 , 283.29 , 285.19998, 285.19998],\n", + " [281.4 , 281.4 , 282.79 , 283.5 ],\n", + " [281.4 , 281.4 , 282.79 , 283.5 ]],\n", + "\n", + " [[292.4 , 292.4 , 292.9 , 293.4 ],\n", + " [282. , 282. , 283.29 , 284.69998],\n", + " [280. , 280. , 280.79 , 282.4 ],\n", + " [280. , 280. , 280.79 , 282.4 ]],\n", + "\n", + " ...,\n", + "\n", + " [[288.88998, 288.88998, 289.19 , 290.88998],\n", + " [282.49 , 282.49 , 281.99 , 281.99 ],\n", + " [281.29 , 281.29 , 281.29 , 280.99 ],\n", + " [281.29 , 281.29 , 281.29 , 280.99 ]],\n", + "\n", + " [[288.29 , 288.29 , 289.19 , 290.79 ],\n", + " [282.09 , 282.09 , 281.59 , 282.38998],\n", + " [280.99 , 280.99 , 280.38998, 280.59 ],\n", + " [280.99 , 280.99 , 280.38998, 280.59 ]],\n", + "\n", + " [[289.49 , 289.49 , 290.38998, 291.59 ],\n", + " [282.09 , 282.09 , 281.99 , 283.09 ],\n", + " [281.38998, 281.38998, 280.59 , 280.99 ],\n", + " [281.38998, 281.38998, 280.59 , 280.99 ]]], dtype=float32)\n", + "Coordinates:\n", + " lat (degrees_north) float32 30.0 40.0 42.5 42.5\n", + " lon (degrees_east) float32 200.0 200.0 202.5 205.0\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Dimensions without coordinates: degrees_north, degrees_east\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "target_lat = xr.DataArray([31, 41, 42, 42], dims=\"degrees_north\")\n", + "target_lon = xr.DataArray([200, 201, 202, 205], dims=\"degrees_east\")\n", + "\n", + "da.sel(lat=target_lat, lon=target_lon, method=\"nearest\") # -- orthogonal indexing " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the above example, you can see how the output shape is time x lats x lons. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But what if we would like to find the information from the nearest grid cell to collection of specified points (for example weather stations or tower data)?\n", + "\n", + "\n", + "**Vectorized indexing** using `DataArrays()` may be used to extract information from the nearest grid cells of interest, for example, the nearest climate model grid cells to a collection specified weather station latitudes and longitudes.\n", "\n", "**To trigger vectorized indexing behavior you will need to provide the selection dimensions with a new shared output dimension name.** \n", "\n", @@ -93,9 +1125,390 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 57, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray (points: 4)>\n",
+       "array([31, 41, 42, 42])\n",
+       "Dimensions without coordinates: points
    " + ], + "text/plain": [ + "\n", + "array([31, 41, 42, 42])\n", + "Dimensions without coordinates: points" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Define target latitude and longitude (where weather stations might be)\n", "lat_points = xr.DataArray([31, 41, 42, 42], dims=\"points\")\n", @@ -105,9 +1518,390 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 58, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray (points: 4)>\n",
+       "array([200, 201, 202, 205])\n",
+       "Dimensions without coordinates: points
    " + ], + "text/plain": [ + "\n", + "array([200, 201, 202, 205])\n", + "Dimensions without coordinates: points" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "lon_points" ] @@ -121,25 +1915,1008 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 59, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'air' (time: 2920, points: 4)>\n",
+       "array([[293.1    , 284.6    , 283.19998, 282.6    ],\n",
+       "       [293.19998, 283.29   , 282.79   , 283.5    ],\n",
+       "       [292.4    , 282.     , 280.79   , 282.4    ],\n",
+       "       ...,\n",
+       "       [288.88998, 282.49   , 281.29   , 280.99   ],\n",
+       "       [288.29   , 282.09   , 280.38998, 280.59   ],\n",
+       "       [289.49   , 282.09   , 280.59   , 280.99   ]], dtype=float32)\n",
+       "Coordinates:\n",
+       "    lat      (points) float32 30.0 40.0 42.5 42.5\n",
+       "    lon      (points) float32 200.0 200.0 202.5 205.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Dimensions without coordinates: points\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
    " + ], + "text/plain": [ + "\n", + "array([[293.1 , 284.6 , 283.19998, 282.6 ],\n", + " [293.19998, 283.29 , 282.79 , 283.5 ],\n", + " [292.4 , 282. , 280.79 , 282.4 ],\n", + " ...,\n", + " [288.88998, 282.49 , 281.29 , 280.99 ],\n", + " [288.29 , 282.09 , 280.38998, 280.59 ],\n", + " [289.49 , 282.09 , 280.59 , 280.99 ]], dtype=float32)\n", + "Coordinates:\n", + " lat (points) float32 30.0 40.0 42.5 42.5\n", + " lon (points) float32 200.0 200.0 202.5 205.0\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Dimensions without coordinates: points\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "da.sel(lat=lat_points, lon=lon_points, method=\"nearest\").plot();" + "da.sel(lat=lat_points, lon=lon_points, method=\"nearest\")" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'air' (time: 2920, lat: 4, lon: 4)>\n",
+       "array([[[293.1    , 293.1    , 293.1    , 293.1    ],\n",
+       "        [284.6    , 284.6    , 284.6    , 284.6    ],\n",
+       "        [282.79   , 282.79   , 282.79   , 282.79   ],\n",
+       "        [282.79   , 282.79   , 282.79   , 282.79   ]],\n",
+       "\n",
+       "       [[293.19998, 293.19998, 293.19998, 293.19998],\n",
+       "        [283.29   , 283.29   , 283.29   , 283.29   ],\n",
+       "        [281.4    , 281.4    , 281.4    , 281.4    ],\n",
+       "        [281.4    , 281.4    , 281.4    , 281.4    ]],\n",
+       "\n",
+       "       [[292.4    , 292.4    , 292.4    , 292.4    ],\n",
+       "        [282.     , 282.     , 282.     , 282.     ],\n",
+       "        [280.     , 280.     , 280.     , 280.     ],\n",
+       "        [280.     , 280.     , 280.     , 280.     ]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[288.88998, 288.88998, 288.88998, 288.88998],\n",
+       "        [282.49   , 282.49   , 282.49   , 282.49   ],\n",
+       "        [281.29   , 281.29   , 281.29   , 281.29   ],\n",
+       "        [281.29   , 281.29   , 281.29   , 281.29   ]],\n",
+       "\n",
+       "       [[288.29   , 288.29   , 288.29   , 288.29   ],\n",
+       "        [282.09   , 282.09   , 282.09   , 282.09   ],\n",
+       "        [280.99   , 280.99   , 280.99   , 280.99   ],\n",
+       "        [280.99   , 280.99   , 280.99   , 280.99   ]],\n",
+       "\n",
+       "       [[289.49   , 289.49   , 289.49   , 289.49   ],\n",
+       "        [282.09   , 282.09   , 282.09   , 282.09   ],\n",
+       "        [281.38998, 281.38998, 281.38998, 281.38998],\n",
+       "        [281.38998, 281.38998, 281.38998, 281.38998]]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 30.0 40.0 42.5 42.5\n",
+       "  * lon      (lon) float32 200.0 200.0 200.0 200.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
    " + ], + "text/plain": [ + "\n", + "array([[[293.1 , 293.1 , 293.1 , 293.1 ],\n", + " [284.6 , 284.6 , 284.6 , 284.6 ],\n", + " [282.79 , 282.79 , 282.79 , 282.79 ],\n", + " [282.79 , 282.79 , 282.79 , 282.79 ]],\n", + "\n", + " [[293.19998, 293.19998, 293.19998, 293.19998],\n", + " [283.29 , 283.29 , 283.29 , 283.29 ],\n", + " [281.4 , 281.4 , 281.4 , 281.4 ],\n", + " [281.4 , 281.4 , 281.4 , 281.4 ]],\n", + "\n", + " [[292.4 , 292.4 , 292.4 , 292.4 ],\n", + " [282. , 282. , 282. , 282. ],\n", + " [280. , 280. , 280. , 280. ],\n", + " [280. , 280. , 280. , 280. ]],\n", + "\n", + " ...,\n", + "\n", + " [[288.88998, 288.88998, 288.88998, 288.88998],\n", + " [282.49 , 282.49 , 282.49 , 282.49 ],\n", + " [281.29 , 281.29 , 281.29 , 281.29 ],\n", + " [281.29 , 281.29 , 281.29 , 281.29 ]],\n", + "\n", + " [[288.29 , 288.29 , 288.29 , 288.29 ],\n", + " [282.09 , 282.09 , 282.09 , 282.09 ],\n", + " [280.99 , 280.99 , 280.99 , 280.99 ],\n", + " [280.99 , 280.99 , 280.99 , 280.99 ]],\n", + "\n", + " [[289.49 , 289.49 , 289.49 , 289.49 ],\n", + " [282.09 , 282.09 , 282.09 , 282.09 ],\n", + " [281.38998, 281.38998, 281.38998, 281.38998],\n", + " [281.38998, 281.38998, 281.38998, 281.38998]]], dtype=float32)\n", + "Coordinates:\n", + " * lat (lat) float32 30.0 40.0 42.5 42.5\n", + " * lon (lon) float32 200.0 200.0 200.0 200.0\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lat_points = [31, 41, 42, 42]\n", + "lon_points = [31, 41, 42, 42]\n", + "da.sel(lat=lat_points, lon=lon_points, method=\"nearest\") # --orthogonal indexing\n", + "#da.sel_points(lat=lat_points, lon=lon_points, method=\"nearest\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "As we can see in the above example, the dimensions on resultant arrays are given by the ordered union of the indexers’ dimensions." + "👆 Please notice how the shape of our `DataArray` is time x points, extracting time series for each weather stations. \n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "('time', 'points')" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "da.sel(lat=lat_points, lon=lon_points, method=\"nearest\").dims" ] @@ -148,14 +2925,517 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Please note that slices or sequences/arrays without named-dimensions are treated as if they have the same dimension which is indexed along:" + ":::{attention}\n", + "Please note that slices or sequences/arrays without named-dimensions are treated as if they have the same dimension which is indexed along.\n", + ":::\n", + "\n", + "For example:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'air' (time: 2920, lat: 3, points: 4)>\n",
+       "array([[[296.6    , 296.6    , 296.19998, 296.4    ],\n",
+       "        [293.1    , 293.1    , 293.29   , 293.29   ],\n",
+       "        [284.6    , 284.6    , 284.9    , 284.19998]],\n",
+       "\n",
+       "       [[296.4    , 296.4    , 295.9    , 296.19998],\n",
+       "        [293.19998, 293.19998, 293.9    , 294.19998],\n",
+       "        [283.29   , 283.29   , 285.19998, 285.19998]],\n",
+       "\n",
+       "       [[295.6    , 295.6    , 295.4    , 295.4    ],\n",
+       "        [292.4    , 292.4    , 292.9    , 293.4    ],\n",
+       "        [282.     , 282.     , 283.29   , 284.69998]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[294.79   , 294.79   , 295.29   , 297.49   ],\n",
+       "        [288.88998, 288.88998, 289.19   , 290.88998],\n",
+       "        [282.49   , 282.49   , 281.99   , 281.99   ]],\n",
+       "\n",
+       "       [[293.69   , 293.69   , 293.88998, 295.38998],\n",
+       "        [288.29   , 288.29   , 289.19   , 290.79   ],\n",
+       "        [282.09   , 282.09   , 281.59   , 282.38998]],\n",
+       "\n",
+       "       [[293.79   , 293.79   , 293.69   , 295.09   ],\n",
+       "        [289.49   , 289.49   , 290.38998, 291.59   ],\n",
+       "        [282.09   , 282.09   , 281.99   , 283.09   ]]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 20.0 30.0 40.0\n",
+       "    lon      (points) float32 200.0 200.0 202.5 205.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Dimensions without coordinates: points\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
    " + ], + "text/plain": [ + "\n", + "array([[[296.6 , 296.6 , 296.19998, 296.4 ],\n", + " [293.1 , 293.1 , 293.29 , 293.29 ],\n", + " [284.6 , 284.6 , 284.9 , 284.19998]],\n", + "\n", + " [[296.4 , 296.4 , 295.9 , 296.19998],\n", + " [293.19998, 293.19998, 293.9 , 294.19998],\n", + " [283.29 , 283.29 , 285.19998, 285.19998]],\n", + "\n", + " [[295.6 , 295.6 , 295.4 , 295.4 ],\n", + " [292.4 , 292.4 , 292.9 , 293.4 ],\n", + " [282. , 282. , 283.29 , 284.69998]],\n", + "\n", + " ...,\n", + "\n", + " [[294.79 , 294.79 , 295.29 , 297.49 ],\n", + " [288.88998, 288.88998, 289.19 , 290.88998],\n", + " [282.49 , 282.49 , 281.99 , 281.99 ]],\n", + "\n", + " [[293.69 , 293.69 , 293.88998, 295.38998],\n", + " [288.29 , 288.29 , 289.19 , 290.79 ],\n", + " [282.09 , 282.09 , 281.59 , 282.38998]],\n", + "\n", + " [[293.79 , 293.79 , 293.69 , 295.09 ],\n", + " [289.49 , 289.49 , 290.38998, 291.59 ],\n", + " [282.09 , 282.09 , 281.99 , 283.09 ]]], dtype=float32)\n", + "Coordinates:\n", + " * lat (lat) float32 20.0 30.0 40.0\n", + " lon (points) float32 200.0 200.0 202.5 205.0\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Dimensions without coordinates: points\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "da.sel(lat=[20, 30, 40], lon=lon_points, method=\"nearest\")" ] @@ -164,18 +3444,1651 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "
    \n", - " \n", - "Warning: If an indexer is a DataArray(), its coordinates should not conflict with the selected subpart of the target array (except for the explicitly indexed dimensions with .loc/.sel). Otherwise, IndexError will be raised.\n", - " \n", - "
    \n" + ":::{warning}\n", + "If an indexer is a `DataArray()`, its coordinates should not conflict with the selected subpart of the target array (except for the explicitly indexed dimensions with `.loc`/`.sel`). Otherwise, `IndexError` will be raised!\n", + ":::" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pointwise Indexing\n", + "\n", + "Positional indexing deviates from the NumPy when indexing with multiple arrays. \n", + "\n", + "**Xarray pointwise indexing supports the indexing along multiple labeled dimensions using list-like objects similar to NumPy indexing behavior.** \n" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray (x: 7, y: 8)>\n",
+       "array([[ 0,  1,  2,  3,  4,  5,  6,  7],\n",
+       "       [ 8,  9, 10, 11, 12, 13, 14, 15],\n",
+       "       [16, 17, 18, 19, 20, 21, 22, 23],\n",
+       "       [24, 25, 26, 27, 28, 29, 30, 31],\n",
+       "       [32, 33, 34, 35, 36, 37, 38, 39],\n",
+       "       [40, 41, 42, 43, 44, 45, 46, 47],\n",
+       "       [48, 49, 50, 51, 52, 53, 54, 55]])\n",
+       "Dimensions without coordinates: x, y
    " + ], + "text/plain": [ + "\n", + "array([[ 0, 1, 2, 3, 4, 5, 6, 7],\n", + " [ 8, 9, 10, 11, 12, 13, 14, 15],\n", + " [16, 17, 18, 19, 20, 21, 22, 23],\n", + " [24, 25, 26, 27, 28, 29, 30, 31],\n", + " [32, 33, 34, 35, 36, 37, 38, 39],\n", + " [40, 41, 42, 43, 44, 45, 46, 47],\n", + " [48, 49, 50, 51, 52, 53, 54, 55]])\n", + "Dimensions without coordinates: x, y" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "da = xr.DataArray(np.arange(56).reshape((7, 8)), dims=[\"x\", \"y\"])\n", + "da" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray (z: 3)>\n",
+       "array([ 0,  9, 48])\n",
+       "Dimensions without coordinates: z
    " + ], + "text/plain": [ + "\n", + "array([ 0, 9, 48])\n", + "Dimensions without coordinates: z" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "da.isel(x=xr.DataArray([0, 1, 6], dims=\"z\"), y=xr.DataArray([0, 1, 0], dims=\"z\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Masking with `where()`\n", + "In the above three elements at `(ix, iy) = ((0, 0), (1, 1), (6, 0))` are selected and mapped along a new dimension `z`. \n", + "If you want to add a coordinate to the new dimension `z`, you can supply a `DataArray` with a coordinate:\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray (z: 3)>\n",
+       "array([ 0,  9, 48])\n",
+       "Coordinates:\n",
+       "  * z        (z) <U1 'a' 'b' 'c'
    " + ], + "text/plain": [ + "\n", + "array([ 0, 9, 48])\n", + "Coordinates:\n", + " * z (z) \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray (new_time: 3)>\n",
+       "array([0.17400012, 0.37301067, 0.59544555])\n",
+       "Coordinates:\n",
+       "    time      (new_time) datetime64[ns] 2000-01-03 2000-01-02 2000-01-01\n",
+       "    space     (new_time) <U2 'IA' 'IL' 'IN'\n",
+       "  * new_time  (new_time) datetime64[ns] 2000-01-03 2000-01-02 2000-01-01
    " + ], + "text/plain": [ + "\n", + "array([0.17400012, 0.37301067, 0.59544555])\n", + "Coordinates:\n", + " time (new_time) datetime64[ns] 2000-01-03 2000-01-02 2000-01-01\n", + " space (new_time) Date: Fri, 7 Jul 2023 20:40:22 +0000 Subject: [PATCH 21/54] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- intermediate/02.2_indexing_Advanced.ipynb | 4867 +-------------------- 1 file changed, 33 insertions(+), 4834 deletions(-) diff --git a/intermediate/02.2_indexing_Advanced.ipynb b/intermediate/02.2_indexing_Advanced.ipynb index baa17237..393a1e34 100644 --- a/intermediate/02.2_indexing_Advanced.ipynb +++ b/intermediate/02.2_indexing_Advanced.ipynb @@ -27,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -45,483 +45,9 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.Dataset>\n",
-       "Dimensions:  (lat: 25, time: 2920, lon: 53)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Data variables:\n",
-       "    air      (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n",
-       "Attributes:\n",
-       "    Conventions:  COARDS\n",
-       "    title:        4x daily NMC reanalysis (1948)\n",
-       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
-       "    platform:     Model\n",
-       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
    " - ], - "text/plain": [ - "\n", - "Dimensions: (lat: 25, time: 2920, lon: 53)\n", - "Coordinates:\n", - " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", - " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Data variables:\n", - " air (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n", - "Attributes:\n", - " Conventions: COARDS\n", - " title: 4x daily NMC reanalysis (1948)\n", - " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", - " platform: Model\n", - " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "ds = xr.tutorial.load_dataset(\"air_temperature\")\n", "da = ds.air\n", @@ -546,22 +72,11 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "da[0, [2, 4, 10, 13], [1, 6, 7]].plot(); # -- orthogonal indexing" + "da[0, [2, 4, 10, 13], [1, 6, 7]].plot(); # -- orthogonal indexing" ] }, { @@ -575,531 +90,14 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 2920, degrees_north: 4, degrees_east: 4)>\n",
-       "array([[[293.1    , 293.1    , 293.29   , 293.29   ],\n",
-       "        [284.6    , 284.6    , 284.9    , 284.19998],\n",
-       "        [282.79   , 282.79   , 283.19998, 282.6    ],\n",
-       "        [282.79   , 282.79   , 283.19998, 282.6    ]],\n",
-       "\n",
-       "       [[293.19998, 293.19998, 293.9    , 294.19998],\n",
-       "        [283.29   , 283.29   , 285.19998, 285.19998],\n",
-       "        [281.4    , 281.4    , 282.79   , 283.5    ],\n",
-       "        [281.4    , 281.4    , 282.79   , 283.5    ]],\n",
-       "\n",
-       "       [[292.4    , 292.4    , 292.9    , 293.4    ],\n",
-       "        [282.     , 282.     , 283.29   , 284.69998],\n",
-       "        [280.     , 280.     , 280.79   , 282.4    ],\n",
-       "        [280.     , 280.     , 280.79   , 282.4    ]],\n",
-       "\n",
-       "       ...,\n",
-       "\n",
-       "       [[288.88998, 288.88998, 289.19   , 290.88998],\n",
-       "        [282.49   , 282.49   , 281.99   , 281.99   ],\n",
-       "        [281.29   , 281.29   , 281.29   , 280.99   ],\n",
-       "        [281.29   , 281.29   , 281.29   , 280.99   ]],\n",
-       "\n",
-       "       [[288.29   , 288.29   , 289.19   , 290.79   ],\n",
-       "        [282.09   , 282.09   , 281.59   , 282.38998],\n",
-       "        [280.99   , 280.99   , 280.38998, 280.59   ],\n",
-       "        [280.99   , 280.99   , 280.38998, 280.59   ]],\n",
-       "\n",
-       "       [[289.49   , 289.49   , 290.38998, 291.59   ],\n",
-       "        [282.09   , 282.09   , 281.99   , 283.09   ],\n",
-       "        [281.38998, 281.38998, 280.59   , 280.99   ],\n",
-       "        [281.38998, 281.38998, 280.59   , 280.99   ]]], dtype=float32)\n",
-       "Coordinates:\n",
-       "    lat      (degrees_north) float32 30.0 40.0 42.5 42.5\n",
-       "    lon      (degrees_east) float32 200.0 200.0 202.5 205.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Dimensions without coordinates: degrees_north, degrees_east\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([[[293.1 , 293.1 , 293.29 , 293.29 ],\n", - " [284.6 , 284.6 , 284.9 , 284.19998],\n", - " [282.79 , 282.79 , 283.19998, 282.6 ],\n", - " [282.79 , 282.79 , 283.19998, 282.6 ]],\n", - "\n", - " [[293.19998, 293.19998, 293.9 , 294.19998],\n", - " [283.29 , 283.29 , 285.19998, 285.19998],\n", - " [281.4 , 281.4 , 282.79 , 283.5 ],\n", - " [281.4 , 281.4 , 282.79 , 283.5 ]],\n", - "\n", - " [[292.4 , 292.4 , 292.9 , 293.4 ],\n", - " [282. , 282. , 283.29 , 284.69998],\n", - " [280. , 280. , 280.79 , 282.4 ],\n", - " [280. , 280. , 280.79 , 282.4 ]],\n", - "\n", - " ...,\n", - "\n", - " [[288.88998, 288.88998, 289.19 , 290.88998],\n", - " [282.49 , 282.49 , 281.99 , 281.99 ],\n", - " [281.29 , 281.29 , 281.29 , 280.99 ],\n", - " [281.29 , 281.29 , 281.29 , 280.99 ]],\n", - "\n", - " [[288.29 , 288.29 , 289.19 , 290.79 ],\n", - " [282.09 , 282.09 , 281.59 , 282.38998],\n", - " [280.99 , 280.99 , 280.38998, 280.59 ],\n", - " [280.99 , 280.99 , 280.38998, 280.59 ]],\n", - "\n", - " [[289.49 , 289.49 , 290.38998, 291.59 ],\n", - " [282.09 , 282.09 , 281.99 , 283.09 ],\n", - " [281.38998, 281.38998, 280.59 , 280.99 ],\n", - " [281.38998, 281.38998, 280.59 , 280.99 ]]], dtype=float32)\n", - "Coordinates:\n", - " lat (degrees_north) float32 30.0 40.0 42.5 42.5\n", - " lon (degrees_east) float32 200.0 200.0 202.5 205.0\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Dimensions without coordinates: degrees_north, degrees_east\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "target_lat = xr.DataArray([31, 41, 42, 42], dims=\"degrees_north\")\n", "target_lon = xr.DataArray([200, 201, 202, 205], dims=\"degrees_east\")\n", "\n", - "da.sel(lat=target_lat, lon=target_lon, method=\"nearest\") # -- orthogonal indexing " + "da.sel(lat=target_lat, lon=target_lon, method=\"nearest\") # -- orthogonal indexing" ] }, { @@ -1125,390 +123,9 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray (points: 4)>\n",
-       "array([31, 41, 42, 42])\n",
-       "Dimensions without coordinates: points
    " - ], - "text/plain": [ - "\n", - "array([31, 41, 42, 42])\n", - "Dimensions without coordinates: points" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Define target latitude and longitude (where weather stations might be)\n", "lat_points = xr.DataArray([31, 41, 42, 42], dims=\"points\")\n", @@ -1518,390 +135,9 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray (points: 4)>\n",
-       "array([200, 201, 202, 205])\n",
-       "Dimensions without coordinates: points
    " - ], - "text/plain": [ - "\n", - "array([200, 201, 202, 205])\n", - "Dimensions without coordinates: points" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "lon_points" ] @@ -1915,983 +151,23 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 2920, points: 4)>\n",
-       "array([[293.1    , 284.6    , 283.19998, 282.6    ],\n",
-       "       [293.19998, 283.29   , 282.79   , 283.5    ],\n",
-       "       [292.4    , 282.     , 280.79   , 282.4    ],\n",
-       "       ...,\n",
-       "       [288.88998, 282.49   , 281.29   , 280.99   ],\n",
-       "       [288.29   , 282.09   , 280.38998, 280.59   ],\n",
-       "       [289.49   , 282.09   , 280.59   , 280.99   ]], dtype=float32)\n",
-       "Coordinates:\n",
-       "    lat      (points) float32 30.0 40.0 42.5 42.5\n",
-       "    lon      (points) float32 200.0 200.0 202.5 205.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Dimensions without coordinates: points\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([[293.1 , 284.6 , 283.19998, 282.6 ],\n", - " [293.19998, 283.29 , 282.79 , 283.5 ],\n", - " [292.4 , 282. , 280.79 , 282.4 ],\n", - " ...,\n", - " [288.88998, 282.49 , 281.29 , 280.99 ],\n", - " [288.29 , 282.09 , 280.38998, 280.59 ],\n", - " [289.49 , 282.09 , 280.59 , 280.99 ]], dtype=float32)\n", - "Coordinates:\n", - " lat (points) float32 30.0 40.0 42.5 42.5\n", - " lon (points) float32 200.0 200.0 202.5 205.0\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Dimensions without coordinates: points\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da.sel(lat=lat_points, lon=lon_points, method=\"nearest\")" ] }, { "cell_type": "code", - "execution_count": 61, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 2920, lat: 4, lon: 4)>\n",
-       "array([[[293.1    , 293.1    , 293.1    , 293.1    ],\n",
-       "        [284.6    , 284.6    , 284.6    , 284.6    ],\n",
-       "        [282.79   , 282.79   , 282.79   , 282.79   ],\n",
-       "        [282.79   , 282.79   , 282.79   , 282.79   ]],\n",
-       "\n",
-       "       [[293.19998, 293.19998, 293.19998, 293.19998],\n",
-       "        [283.29   , 283.29   , 283.29   , 283.29   ],\n",
-       "        [281.4    , 281.4    , 281.4    , 281.4    ],\n",
-       "        [281.4    , 281.4    , 281.4    , 281.4    ]],\n",
-       "\n",
-       "       [[292.4    , 292.4    , 292.4    , 292.4    ],\n",
-       "        [282.     , 282.     , 282.     , 282.     ],\n",
-       "        [280.     , 280.     , 280.     , 280.     ],\n",
-       "        [280.     , 280.     , 280.     , 280.     ]],\n",
-       "\n",
-       "       ...,\n",
-       "\n",
-       "       [[288.88998, 288.88998, 288.88998, 288.88998],\n",
-       "        [282.49   , 282.49   , 282.49   , 282.49   ],\n",
-       "        [281.29   , 281.29   , 281.29   , 281.29   ],\n",
-       "        [281.29   , 281.29   , 281.29   , 281.29   ]],\n",
-       "\n",
-       "       [[288.29   , 288.29   , 288.29   , 288.29   ],\n",
-       "        [282.09   , 282.09   , 282.09   , 282.09   ],\n",
-       "        [280.99   , 280.99   , 280.99   , 280.99   ],\n",
-       "        [280.99   , 280.99   , 280.99   , 280.99   ]],\n",
-       "\n",
-       "       [[289.49   , 289.49   , 289.49   , 289.49   ],\n",
-       "        [282.09   , 282.09   , 282.09   , 282.09   ],\n",
-       "        [281.38998, 281.38998, 281.38998, 281.38998],\n",
-       "        [281.38998, 281.38998, 281.38998, 281.38998]]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 30.0 40.0 42.5 42.5\n",
-       "  * lon      (lon) float32 200.0 200.0 200.0 200.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([[[293.1 , 293.1 , 293.1 , 293.1 ],\n", - " [284.6 , 284.6 , 284.6 , 284.6 ],\n", - " [282.79 , 282.79 , 282.79 , 282.79 ],\n", - " [282.79 , 282.79 , 282.79 , 282.79 ]],\n", - "\n", - " [[293.19998, 293.19998, 293.19998, 293.19998],\n", - " [283.29 , 283.29 , 283.29 , 283.29 ],\n", - " [281.4 , 281.4 , 281.4 , 281.4 ],\n", - " [281.4 , 281.4 , 281.4 , 281.4 ]],\n", - "\n", - " [[292.4 , 292.4 , 292.4 , 292.4 ],\n", - " [282. , 282. , 282. , 282. ],\n", - " [280. , 280. , 280. , 280. ],\n", - " [280. , 280. , 280. , 280. ]],\n", - "\n", - " ...,\n", - "\n", - " [[288.88998, 288.88998, 288.88998, 288.88998],\n", - " [282.49 , 282.49 , 282.49 , 282.49 ],\n", - " [281.29 , 281.29 , 281.29 , 281.29 ],\n", - " [281.29 , 281.29 , 281.29 , 281.29 ]],\n", - "\n", - " [[288.29 , 288.29 , 288.29 , 288.29 ],\n", - " [282.09 , 282.09 , 282.09 , 282.09 ],\n", - " [280.99 , 280.99 , 280.99 , 280.99 ],\n", - " [280.99 , 280.99 , 280.99 , 280.99 ]],\n", - "\n", - " [[289.49 , 289.49 , 289.49 , 289.49 ],\n", - " [282.09 , 282.09 , 282.09 , 282.09 ],\n", - " [281.38998, 281.38998, 281.38998, 281.38998],\n", - " [281.38998, 281.38998, 281.38998, 281.38998]]], dtype=float32)\n", - "Coordinates:\n", - " * lat (lat) float32 30.0 40.0 42.5 42.5\n", - " * lon (lon) float32 200.0 200.0 200.0 200.0\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "lat_points = [31, 41, 42, 42]\n", "lon_points = [31, 41, 42, 42]\n", - "da.sel(lat=lat_points, lon=lon_points, method=\"nearest\") # --orthogonal indexing\n", - "#da.sel_points(lat=lat_points, lon=lon_points, method=\"nearest\")" + "da.sel(lat=lat_points, lon=lon_points, method=\"nearest\") # --orthogonal indexing\n", + "# da.sel_points(lat=lat_points, lon=lon_points, method=\"nearest\")" ] }, { @@ -2903,20 +179,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "('time', 'points')" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da.sel(lat=lat_points, lon=lon_points, method=\"nearest\").dims" ] @@ -2934,508 +199,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 2920, lat: 3, points: 4)>\n",
-       "array([[[296.6    , 296.6    , 296.19998, 296.4    ],\n",
-       "        [293.1    , 293.1    , 293.29   , 293.29   ],\n",
-       "        [284.6    , 284.6    , 284.9    , 284.19998]],\n",
-       "\n",
-       "       [[296.4    , 296.4    , 295.9    , 296.19998],\n",
-       "        [293.19998, 293.19998, 293.9    , 294.19998],\n",
-       "        [283.29   , 283.29   , 285.19998, 285.19998]],\n",
-       "\n",
-       "       [[295.6    , 295.6    , 295.4    , 295.4    ],\n",
-       "        [292.4    , 292.4    , 292.9    , 293.4    ],\n",
-       "        [282.     , 282.     , 283.29   , 284.69998]],\n",
-       "\n",
-       "       ...,\n",
-       "\n",
-       "       [[294.79   , 294.79   , 295.29   , 297.49   ],\n",
-       "        [288.88998, 288.88998, 289.19   , 290.88998],\n",
-       "        [282.49   , 282.49   , 281.99   , 281.99   ]],\n",
-       "\n",
-       "       [[293.69   , 293.69   , 293.88998, 295.38998],\n",
-       "        [288.29   , 288.29   , 289.19   , 290.79   ],\n",
-       "        [282.09   , 282.09   , 281.59   , 282.38998]],\n",
-       "\n",
-       "       [[293.79   , 293.79   , 293.69   , 295.09   ],\n",
-       "        [289.49   , 289.49   , 290.38998, 291.59   ],\n",
-       "        [282.09   , 282.09   , 281.99   , 283.09   ]]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 20.0 30.0 40.0\n",
-       "    lon      (points) float32 200.0 200.0 202.5 205.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Dimensions without coordinates: points\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([[[296.6 , 296.6 , 296.19998, 296.4 ],\n", - " [293.1 , 293.1 , 293.29 , 293.29 ],\n", - " [284.6 , 284.6 , 284.9 , 284.19998]],\n", - "\n", - " [[296.4 , 296.4 , 295.9 , 296.19998],\n", - " [293.19998, 293.19998, 293.9 , 294.19998],\n", - " [283.29 , 283.29 , 285.19998, 285.19998]],\n", - "\n", - " [[295.6 , 295.6 , 295.4 , 295.4 ],\n", - " [292.4 , 292.4 , 292.9 , 293.4 ],\n", - " [282. , 282. , 283.29 , 284.69998]],\n", - "\n", - " ...,\n", - "\n", - " [[294.79 , 294.79 , 295.29 , 297.49 ],\n", - " [288.88998, 288.88998, 289.19 , 290.88998],\n", - " [282.49 , 282.49 , 281.99 , 281.99 ]],\n", - "\n", - " [[293.69 , 293.69 , 293.88998, 295.38998],\n", - " [288.29 , 288.29 , 289.19 , 290.79 ],\n", - " [282.09 , 282.09 , 281.59 , 282.38998]],\n", - "\n", - " [[293.79 , 293.79 , 293.69 , 295.09 ],\n", - " [289.49 , 289.49 , 290.38998, 291.59 ],\n", - " [282.09 , 282.09 , 281.99 , 283.09 ]]], dtype=float32)\n", - "Coordinates:\n", - " * lat (lat) float32 20.0 30.0 40.0\n", - " lon (points) float32 200.0 200.0 202.5 205.0\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Dimensions without coordinates: points\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da.sel(lat=[20, 30, 40], lon=lon_points, method=\"nearest\")" ] @@ -3462,408 +228,9 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray (x: 7, y: 8)>\n",
-       "array([[ 0,  1,  2,  3,  4,  5,  6,  7],\n",
-       "       [ 8,  9, 10, 11, 12, 13, 14, 15],\n",
-       "       [16, 17, 18, 19, 20, 21, 22, 23],\n",
-       "       [24, 25, 26, 27, 28, 29, 30, 31],\n",
-       "       [32, 33, 34, 35, 36, 37, 38, 39],\n",
-       "       [40, 41, 42, 43, 44, 45, 46, 47],\n",
-       "       [48, 49, 50, 51, 52, 53, 54, 55]])\n",
-       "Dimensions without coordinates: x, y
    " - ], - "text/plain": [ - "\n", - "array([[ 0, 1, 2, 3, 4, 5, 6, 7],\n", - " [ 8, 9, 10, 11, 12, 13, 14, 15],\n", - " [16, 17, 18, 19, 20, 21, 22, 23],\n", - " [24, 25, 26, 27, 28, 29, 30, 31],\n", - " [32, 33, 34, 35, 36, 37, 38, 39],\n", - " [40, 41, 42, 43, 44, 45, 46, 47],\n", - " [48, 49, 50, 51, 52, 53, 54, 55]])\n", - "Dimensions without coordinates: x, y" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da = xr.DataArray(np.arange(56).reshape((7, 8)), dims=[\"x\", \"y\"])\n", "da" @@ -3871,390 +238,9 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray (z: 3)>\n",
-       "array([ 0,  9, 48])\n",
-       "Dimensions without coordinates: z
    " - ], - "text/plain": [ - "\n", - "array([ 0, 9, 48])\n", - "Dimensions without coordinates: z" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da.isel(x=xr.DataArray([0, 1, 6], dims=\"z\"), y=xr.DataArray([0, 1, 0], dims=\"z\"))" ] @@ -4270,392 +256,9 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray (z: 3)>\n",
-       "array([ 0,  9, 48])\n",
-       "Coordinates:\n",
-       "  * z        (z) <U1 'a' 'b' 'c'
    " - ], - "text/plain": [ - "\n", - "array([ 0, 9, 48])\n", - "Coordinates:\n", - " * z (z) \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray (new_time: 3)>\n",
-       "array([0.17400012, 0.37301067, 0.59544555])\n",
-       "Coordinates:\n",
-       "    time      (new_time) datetime64[ns] 2000-01-03 2000-01-02 2000-01-01\n",
-       "    space     (new_time) <U2 'IA' 'IL' 'IN'\n",
-       "  * new_time  (new_time) datetime64[ns] 2000-01-03 2000-01-02 2000-01-01
    " - ], - "text/plain": [ - "\n", - "array([0.17400012, 0.37301067, 0.59544555])\n", - "Coordinates:\n", - " time (new_time) datetime64[ns] 2000-01-03 2000-01-02 2000-01-01\n", - " space (new_time) Date: Fri, 7 Jul 2023 16:11:43 -0600 Subject: [PATCH 22/54] updating config --- _config.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/_config.yml b/_config.yml index 78978501..f7a087d5 100644 --- a/_config.yml +++ b/_config.yml @@ -79,5 +79,6 @@ sphinx: rediraffe_redirects: scipy-tutorial/00_overview.ipynb: overview/get-started.md workshops/scipy2022/README.md: overview/fundamental-path/README.md + fundamentals/02.1_working_with_labeled_data.ipynb : fundamentals/02.1_indexing_Basic.ipynb bibtex_reference_style: author_year # or label, super, \supercite From e18d02991ea66b87cc379c060d9efff01b7bc6e9 Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Fri, 7 Jul 2023 16:13:36 -0600 Subject: [PATCH 23/54] updating README --- overview/fundamental-path/README.md | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/overview/fundamental-path/README.md b/overview/fundamental-path/README.md index 7c768049..83291d31 100644 --- a/overview/fundamental-path/README.md +++ b/overview/fundamental-path/README.md @@ -31,9 +31,7 @@ _Below are links to sections of this website that are part of this journey_: ``` ```{dropdown} Working with Labeled Data -{doc}`../../fundamentals/02.1_working_with_labeled_data` - - +{doc}`../../fundamentals/02.1_indexing_Basic.ipynb` ``` ```{dropdown} Computation From 12dfbe2937aa0f3ac024cf7a9e2e1775b571b8c9 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 7 Jul 2023 22:13:59 +0000 Subject: [PATCH 24/54] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- _config.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_config.yml b/_config.yml index f7a087d5..6ec14e9d 100644 --- a/_config.yml +++ b/_config.yml @@ -79,6 +79,6 @@ sphinx: rediraffe_redirects: scipy-tutorial/00_overview.ipynb: overview/get-started.md workshops/scipy2022/README.md: overview/fundamental-path/README.md - fundamentals/02.1_working_with_labeled_data.ipynb : fundamentals/02.1_indexing_Basic.ipynb + fundamentals/02.1_working_with_labeled_data.ipynb: fundamentals/02.1_indexing_Basic.ipynb bibtex_reference_style: author_year # or label, super, \supercite From be89087503fdb8acbee8a35041c57dcaa71eb3bd Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Fri, 7 Jul 2023 16:34:39 -0600 Subject: [PATCH 25/54] update README --- overview/fundamental-path/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/overview/fundamental-path/README.md b/overview/fundamental-path/README.md index 83291d31..2dd373f0 100644 --- a/overview/fundamental-path/README.md +++ b/overview/fundamental-path/README.md @@ -31,7 +31,7 @@ _Below are links to sections of this website that are part of this journey_: ``` ```{dropdown} Working with Labeled Data -{doc}`../../fundamentals/02.1_indexing_Basic.ipynb` +{doc}`../../fundamentals/02.1_indexing_Basic` ``` ```{dropdown} Computation From 39f019c5a889fa7f40352f1552e05e1925d0a908 Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Fri, 7 Jul 2023 16:41:51 -0600 Subject: [PATCH 26/54] removing the old notebook --- .../02.1_working_with_labeled_data.ipynb | 319 ------------------ 1 file changed, 319 deletions(-) delete mode 100644 fundamentals/02.1_working_with_labeled_data.ipynb diff --git a/fundamentals/02.1_working_with_labeled_data.ipynb b/fundamentals/02.1_working_with_labeled_data.ipynb deleted file mode 100644 index 716d8265..00000000 --- a/fundamentals/02.1_working_with_labeled_data.ipynb +++ /dev/null @@ -1,319 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "# Working with labeled data\n", - "\n", - "Learning goals:\n", - "\n", - "- Use different forms of indexing to select data based on position and\n", - " coordinates\n", - "- Select datetime ranges\n", - "\n", - "Scientific data is inherently *labeled*. For example, time series data includes timestamps that label individual periods or points in time, spatial data has coordinates (e.g. longitude, latitude, elevation), and model or laboratory experiments are often identified by unique identifiers. In this notebook we'll see that labeled dimensions make code much easier to understand!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import xarray as xr" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We'll start by comparing common indexing operations with a `numpy` array and equivalent `xarray` DataArray:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# axis0: x, axis1: y\n", - "np_array = np.arange(10).reshape(2, 5)\n", - "np_array" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "da = xr.DataArray(np_array, dims=(\"x\", \"y\"))\n", - "da" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Position-based indexing\n", - "\n", - "### Indexing\n", - "\n", - "Recall that *indexing* is selecting a value from an array based on its position" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np_array[0, 3]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "da.isel(x=0, y=3) # or da[{\"x\": 0, \"y\": 3}]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Slicing\n", - "\n", - "And *slicing* retrieves a range of values" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np_array[:2, 1:]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "da.isel(x=slice(None, 2), y=slice(1, None))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Label-based indexing\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Remembering the axis order can be challenging even with 2D arrays (is np_array[0,3] the first row and third column *or first column and third row*? or did I store these samples by row or by column when I saved the data?!). The difficulty is compounded with added dimensions. Xarray objects eliminate much of the mental overhead by adding coordinate labels:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "arr = xr.DataArray(\n", - " data=np.arange(48).reshape(4, 2, 6),\n", - " dims=(\"u\", \"v\", \"time\"),\n", - " coords={\n", - " \"u\": [-3.2, 2.1, 5.3, 6.5],\n", - " \"v\": [-1, 2.6],\n", - " \"time\": pd.date_range(\"2009-01-05\", periods=6, freq=\"M\"),\n", - " },\n", - ")\n", - "arr" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To select data by coordinate **labels** instead of *integer indices* we can use the\n", - "same syntax, using `sel` instead of `isel`:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "arr.sel(u=5.3, time=\"2009-04-30\") # or arr.loc[{\"u\": 5.3, \"time\": \"2009-04-30\"}]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "this will require us to specify exact coordinate values. If we don't have those, we can use the `method` parameter (see `Dataset.sel` for documentation):" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "arr.sel(u=5, time=\"2009-04-28\", method=\"nearest\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also select multiple values:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "arr.sel(u=[-3.2, 6.5], time=slice(\"2009-02-28\", \"2009-05-31\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If instead of selecting data we want to drop it, we can use `drop_sel`:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "arr.drop_sel(u=[-3.2, 6.5])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Exercises\n", - "\n", - "Practice the syntax you've learned with the xarray tutorial dataset! " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ds = xr.tutorial.open_dataset(\"air_temperature\")\n", - "ds" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "1. Select the first 30 entries of latitude and 20th to 40th entries of longitude\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "jupyter": { - "source_hidden": true - }, - "tags": [ - "hide-output" - ] - }, - "outputs": [], - "source": [ - "ds.isel(lat=slice(None, 30), lon=slice(20, 40))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "2. Select all data at 75 degree north and between Jan 1, 2013 and Oct 15, 2013\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "jupyter": { - "source_hidden": true - }, - "tags": [ - "hide-output" - ] - }, - "outputs": [], - "source": [ - "ds.sel(lat=75, time=slice(\"2013-01-01\", \"2013-10-15\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "3. Remove all entries at 260 and 270 degrees" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "jupyter": { - "source_hidden": true - }, - "tags": [ - "hide-output" - ] - }, - "outputs": [], - "source": [ - "ds.drop_sel(lon=[260, 270])" - ] - } - ], - "metadata": { - "interpreter": { - "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From 89deda12d6f7cf51037c2eba9b87ef77052a5e3d Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Fri, 7 Jul 2023 16:49:39 -0600 Subject: [PATCH 27/54] removing the redirect --- _config.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/_config.yml b/_config.yml index 6ec14e9d..78978501 100644 --- a/_config.yml +++ b/_config.yml @@ -79,6 +79,5 @@ sphinx: rediraffe_redirects: scipy-tutorial/00_overview.ipynb: overview/get-started.md workshops/scipy2022/README.md: overview/fundamental-path/README.md - fundamentals/02.1_working_with_labeled_data.ipynb: fundamentals/02.1_indexing_Basic.ipynb bibtex_reference_style: author_year # or label, super, \supercite From 8ec458153b66db6b2081460e7e591bb0738c9963 Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Fri, 7 Jul 2023 19:43:58 -0600 Subject: [PATCH 28/54] boolean indexing --- .../02.3_indexing_BooleanMasking.ipynb | 2311 +++++++++++++++++ 1 file changed, 2311 insertions(+) create mode 100644 intermediate/02.3_indexing_BooleanMasking.ipynb diff --git a/intermediate/02.3_indexing_BooleanMasking.ipynb b/intermediate/02.3_indexing_BooleanMasking.ipynb new file mode 100644 index 00000000..a69ae0db --- /dev/null +++ b/intermediate/02.3_indexing_BooleanMasking.ipynb @@ -0,0 +1,2311 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Boolean Indexing & Masking\n", + "\n", + "## Learning Objectives\n", + "\n", + "* The concept of boolean masks\n", + "* Dropping/Masking data using `where`\n", + "* Using `isin` for creating a boolean mask" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Overview\n", + "\n", + "*Boolean masking*, known as *boolean indexing*, is a functionality in Python that enables the filtering of values based on a specific condition.\n", + "\n", + "A boolean mask refers to a binary array or a boolean-valued array that is used as a *filter* to select specific elements from another array. The boolean mask acts as a criterion or condition, where each element in the mask corresponds to an element in the target array. The mask determines whether the corresponding element in the target array should be selected or not. \n", + "\n", + "Xarray provides different capabilities to allow filtering and boolean indexing. In this notebook, we will learn more about it.\n", + "\n", + "First, let's import the packages needed for this notebook: " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import xarray as xr\n", + "\n", + "import cartopy.crs as ccrs\n", + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this tutorial, we’ll use the Regional Arctic System Mode (RASM) dataset from the `xarray-data` repository." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.Dataset>\n",
+       "Dimensions:  (time: 36, y: 205, x: 275)\n",
+       "Coordinates:\n",
+       "  * time     (time) object 1980-09-16 12:00:00 ... 1983-08-17 00:00:00\n",
+       "    xc       (y, x) float64 189.2 189.4 189.6 189.7 ... 17.65 17.4 17.15 16.91\n",
+       "    yc       (y, x) float64 16.53 16.78 17.02 17.27 ... 28.26 28.01 27.76 27.51\n",
+       "Dimensions without coordinates: y, x\n",
+       "Data variables:\n",
+       "    Tair     (time, y, x) float64 nan nan nan nan nan ... 29.8 28.66 28.19 28.21\n",
+       "Attributes:\n",
+       "    title:                     /workspace/jhamman/processed/R1002RBRxaaa01a/l...\n",
+       "    institution:               U.W.\n",
+       "    source:                    RACM R1002RBRxaaa01a\n",
+       "    output_frequency:          daily\n",
+       "    output_mode:               averaged\n",
+       "    convention:                CF-1.4\n",
+       "    references:                Based on the initial model of Liang et al., 19...\n",
+       "    comment:                   Output from the Variable Infiltration Capacity...\n",
+       "    nco_openmp_thread_number:  1\n",
+       "    NCO:                       netCDF Operators version 4.7.9 (Homepage = htt...\n",
+       "    history:                   Fri Aug  7 17:57:38 2020: ncatted -a bounds,,d...
    " + ], + "text/plain": [ + "\n", + "Dimensions: (time: 36, y: 205, x: 275)\n", + "Coordinates:\n", + " * time (time) object 1980-09-16 12:00:00 ... 1983-08-17 00:00:00\n", + " xc (y, x) float64 189.2 189.4 189.6 189.7 ... 17.65 17.4 17.15 16.91\n", + " yc (y, x) float64 16.53 16.78 17.02 17.27 ... 28.26 28.01 27.76 27.51\n", + "Dimensions without coordinates: y, x\n", + "Data variables:\n", + " Tair (time, y, x) float64 nan nan nan nan nan ... 29.8 28.66 28.19 28.21\n", + "Attributes:\n", + " title: /workspace/jhamman/processed/R1002RBRxaaa01a/l...\n", + " institution: U.W.\n", + " source: RACM R1002RBRxaaa01a\n", + " output_frequency: daily\n", + " output_mode: averaged\n", + " convention: CF-1.4\n", + " references: Based on the initial model of Liang et al., 19...\n", + " comment: Output from the Variable Infiltration Capacity...\n", + " nco_openmp_thread_number: 1\n", + " NCO: netCDF Operators version 4.7.9 (Homepage = htt...\n", + " history: Fri Aug 7 17:57:38 2020: ncatted -a bounds,,d..." + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds = xr.tutorial.load_dataset(\"rasm\")\n", + "ds" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this dataset, the logical coordinates are `x` and `y`, while the physical coordinates are `xc` and `yc`, which represent the latitudes and longitude of the data." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'long_name': 'longitude of grid cell center', 'units': 'degrees_east'}\n", + "{'long_name': 'latitude of grid cell center', 'units': 'degrees_north'}\n" + ] + } + ], + "source": [ + "print(ds.xc.attrs)\n", + "print(ds.yc.attrs)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'Tair' (time: 36, y: 205, x: 275)>\n",
+       "array([[[        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        ...,\n",
+       "        [        nan,         nan,         nan, ..., 27.03290153,\n",
+       "         27.03125761, 27.33531541],\n",
+       "        [        nan,         nan,         nan, ..., 27.2784053 ,\n",
+       "         26.80261869, 27.08603517],\n",
+       "        [        nan,         nan,         nan, ..., 27.02344402,\n",
+       "         26.56473862, 26.73064933]],\n",
+       "\n",
+       "       [[        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "...\n",
+       "        [        nan,         nan,         nan, ..., 27.8597472 ,\n",
+       "         27.82928439, 28.09249224],\n",
+       "        [        nan,         nan,         nan, ..., 27.89704094,\n",
+       "         27.31104941, 27.67387171],\n",
+       "        [        nan,         nan,         nan, ..., 27.46837113,\n",
+       "         27.0088944 , 27.23017976]],\n",
+       "\n",
+       "       [[        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        ...,\n",
+       "        [        nan,         nan,         nan, ..., 28.95929072,\n",
+       "         28.87672039, 29.04890862],\n",
+       "        [        nan,         nan,         nan, ..., 29.036132  ,\n",
+       "         28.42273578, 28.68721201],\n",
+       "        [        nan,         nan,         nan, ..., 28.66381585,\n",
+       "         28.18595533, 28.20753022]]])\n",
+       "Coordinates:\n",
+       "  * time     (time) object 1980-09-16 12:00:00 ... 1983-08-17 00:00:00\n",
+       "    xc       (y, x) float64 189.2 189.4 189.6 189.7 ... 17.65 17.4 17.15 16.91\n",
+       "    yc       (y, x) float64 16.53 16.78 17.02 17.27 ... 28.26 28.01 27.76 27.51\n",
+       "Dimensions without coordinates: y, x\n",
+       "Attributes:\n",
+       "    units:           C\n",
+       "    long_name:       Surface air temperature\n",
+       "    type_preferred:  double\n",
+       "    time_rep:        instantaneous
    " + ], + "text/plain": [ + "\n", + "array([[[ nan, nan, nan, ..., nan,\n", + " nan, nan],\n", + " [ nan, nan, nan, ..., nan,\n", + " nan, nan],\n", + " [ nan, nan, nan, ..., nan,\n", + " nan, nan],\n", + " ...,\n", + " [ nan, nan, nan, ..., 27.03290153,\n", + " 27.03125761, 27.33531541],\n", + " [ nan, nan, nan, ..., 27.2784053 ,\n", + " 26.80261869, 27.08603517],\n", + " [ nan, nan, nan, ..., 27.02344402,\n", + " 26.56473862, 26.73064933]],\n", + "\n", + " [[ nan, nan, nan, ..., nan,\n", + " nan, nan],\n", + " [ nan, nan, nan, ..., nan,\n", + " nan, nan],\n", + " [ nan, nan, nan, ..., nan,\n", + " nan, nan],\n", + "...\n", + " [ nan, nan, nan, ..., 27.8597472 ,\n", + " 27.82928439, 28.09249224],\n", + " [ nan, nan, nan, ..., 27.89704094,\n", + " 27.31104941, 27.67387171],\n", + " [ nan, nan, nan, ..., 27.46837113,\n", + " 27.0088944 , 27.23017976]],\n", + "\n", + " [[ nan, nan, nan, ..., nan,\n", + " nan, nan],\n", + " [ nan, nan, nan, ..., nan,\n", + " nan, nan],\n", + " [ nan, nan, nan, ..., nan,\n", + " nan, nan],\n", + " ...,\n", + " [ nan, nan, nan, ..., 28.95929072,\n", + " 28.87672039, 29.04890862],\n", + " [ nan, nan, nan, ..., 29.036132 ,\n", + " 28.42273578, 28.68721201],\n", + " [ nan, nan, nan, ..., 28.66381585,\n", + " 28.18595533, 28.20753022]]])\n", + "Coordinates:\n", + " * time (time) object 1980-09-16 12:00:00 ... 1983-08-17 00:00:00\n", + " xc (y, x) float64 189.2 189.4 189.6 189.7 ... 17.65 17.4 17.15 16.91\n", + " yc (y, x) float64 16.53 16.78 17.02 17.27 ... 28.26 28.01 27.76 27.51\n", + "Dimensions without coordinates: y, x\n", + "Attributes:\n", + " units: C\n", + " long_name: Surface air temperature\n", + " type_preferred: double\n", + " time_rep: instantaneous" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "da = ds.Tair\n", + "da" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Masking with `where()`\n", + "\n", + "Indexing methods on Xarray objects generally return a subset of the original data. However, it is sometimes useful to select an object with the same shape as the original data, but with some elements masked. \n", + "\n", + "By applying `.where()`, the original data's shape is maintained, with values masked based on a Boolean condition. Values that satisfy the condition (`True`) are returned unchanged, while values that do not meet the condition (`False`) are replaced with a predefined value.\n", + "\n", + "In the example below, we replace all `nan` values with `-9999`:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'Tair' (time: 36, y: 205, x: 275)>\n",
+       "array([[[-9999.        , -9999.        , -9999.        , ...,\n",
+       "         -9999.        , -9999.        , -9999.        ],\n",
+       "        [-9999.        , -9999.        , -9999.        , ...,\n",
+       "         -9999.        , -9999.        , -9999.        ],\n",
+       "        [-9999.        , -9999.        , -9999.        , ...,\n",
+       "         -9999.        , -9999.        , -9999.        ],\n",
+       "        ...,\n",
+       "        [-9999.        , -9999.        , -9999.        , ...,\n",
+       "            27.03290153,    27.03125761,    27.33531541],\n",
+       "        [-9999.        , -9999.        , -9999.        , ...,\n",
+       "            27.2784053 ,    26.80261869,    27.08603517],\n",
+       "        [-9999.        , -9999.        , -9999.        , ...,\n",
+       "            27.02344402,    26.56473862,    26.73064933]],\n",
+       "\n",
+       "       [[-9999.        , -9999.        , -9999.        , ...,\n",
+       "         -9999.        , -9999.        , -9999.        ],\n",
+       "        [-9999.        , -9999.        , -9999.        , ...,\n",
+       "         -9999.        , -9999.        , -9999.        ],\n",
+       "        [-9999.        , -9999.        , -9999.        , ...,\n",
+       "         -9999.        , -9999.        , -9999.        ],\n",
+       "...\n",
+       "        [-9999.        , -9999.        , -9999.        , ...,\n",
+       "            27.8597472 ,    27.82928439,    28.09249224],\n",
+       "        [-9999.        , -9999.        , -9999.        , ...,\n",
+       "            27.89704094,    27.31104941,    27.67387171],\n",
+       "        [-9999.        , -9999.        , -9999.        , ...,\n",
+       "            27.46837113,    27.0088944 ,    27.23017976]],\n",
+       "\n",
+       "       [[-9999.        , -9999.        , -9999.        , ...,\n",
+       "         -9999.        , -9999.        , -9999.        ],\n",
+       "        [-9999.        , -9999.        , -9999.        , ...,\n",
+       "         -9999.        , -9999.        , -9999.        ],\n",
+       "        [-9999.        , -9999.        , -9999.        , ...,\n",
+       "         -9999.        , -9999.        , -9999.        ],\n",
+       "        ...,\n",
+       "        [-9999.        , -9999.        , -9999.        , ...,\n",
+       "            28.95929072,    28.87672039,    29.04890862],\n",
+       "        [-9999.        , -9999.        , -9999.        , ...,\n",
+       "            29.036132  ,    28.42273578,    28.68721201],\n",
+       "        [-9999.        , -9999.        , -9999.        , ...,\n",
+       "            28.66381585,    28.18595533,    28.20753022]]])\n",
+       "Coordinates:\n",
+       "  * time     (time) object 1980-09-16 12:00:00 ... 1983-08-17 00:00:00\n",
+       "    xc       (y, x) float64 189.2 189.4 189.6 189.7 ... 17.65 17.4 17.15 16.91\n",
+       "    yc       (y, x) float64 16.53 16.78 17.02 17.27 ... 28.26 28.01 27.76 27.51\n",
+       "Dimensions without coordinates: y, x\n",
+       "Attributes:\n",
+       "    units:           C\n",
+       "    long_name:       Surface air temperature\n",
+       "    type_preferred:  double\n",
+       "    time_rep:        instantaneous
    " + ], + "text/plain": [ + "\n", + "array([[[-9999. , -9999. , -9999. , ...,\n", + " -9999. , -9999. , -9999. ],\n", + " [-9999. , -9999. , -9999. , ...,\n", + " -9999. , -9999. , -9999. ],\n", + " [-9999. , -9999. , -9999. , ...,\n", + " -9999. , -9999. , -9999. ],\n", + " ...,\n", + " [-9999. , -9999. , -9999. , ...,\n", + " 27.03290153, 27.03125761, 27.33531541],\n", + " [-9999. , -9999. , -9999. , ...,\n", + " 27.2784053 , 26.80261869, 27.08603517],\n", + " [-9999. , -9999. , -9999. , ...,\n", + " 27.02344402, 26.56473862, 26.73064933]],\n", + "\n", + " [[-9999. , -9999. , -9999. , ...,\n", + " -9999. , -9999. , -9999. ],\n", + " [-9999. , -9999. , -9999. , ...,\n", + " -9999. , -9999. , -9999. ],\n", + " [-9999. , -9999. , -9999. , ...,\n", + " -9999. , -9999. , -9999. ],\n", + "...\n", + " [-9999. , -9999. , -9999. , ...,\n", + " 27.8597472 , 27.82928439, 28.09249224],\n", + " [-9999. , -9999. , -9999. , ...,\n", + " 27.89704094, 27.31104941, 27.67387171],\n", + " [-9999. , -9999. , -9999. , ...,\n", + " 27.46837113, 27.0088944 , 27.23017976]],\n", + "\n", + " [[-9999. , -9999. , -9999. , ...,\n", + " -9999. , -9999. , -9999. ],\n", + " [-9999. , -9999. , -9999. , ...,\n", + " -9999. , -9999. , -9999. ],\n", + " [-9999. , -9999. , -9999. , ...,\n", + " -9999. , -9999. , -9999. ],\n", + " ...,\n", + " [-9999. , -9999. , -9999. , ...,\n", + " 28.95929072, 28.87672039, 29.04890862],\n", + " [-9999. , -9999. , -9999. , ...,\n", + " 29.036132 , 28.42273578, 28.68721201],\n", + " [-9999. , -9999. , -9999. , ...,\n", + " 28.66381585, 28.18595533, 28.20753022]]])\n", + "Coordinates:\n", + " * time (time) object 1980-09-16 12:00:00 ... 1983-08-17 00:00:00\n", + " xc (y, x) float64 189.2 189.4 189.6 189.7 ... 17.65 17.4 17.15 16.91\n", + " yc (y, x) float64 16.53 16.78 17.02 17.27 ... 28.26 28.01 27.76 27.51\n", + "Dimensions without coordinates: y, x\n", + "Attributes:\n", + " units: C\n", + " long_name: Surface air temperature\n", + " type_preferred: double\n", + " time_rep: instantaneous" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Let's replace the missing values (nan) with some placeholder\n", + "ds.Tair.where(ds.Tair.notnull(), -9999)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see, in the example above `.where()` preserved the **shape** of the original data by masking the values with a boolean condition. \n", + "\n", + "Most uses of `.where()` check whether or not specific data values are less than or greater than a constant value. \n", + "\n", + "The data values specified in the boolean condition of `.where()` can be any of the following:\n", + "\n", + "* a `DataArray`\n", + "* a `Dataset`\n", + "* a function\n", + "\n", + "In the following example, we make use of `.where()` to mask all temperature below 0°C.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "da_masked = da.where(da >= 0)\n", + "\n", + "# -- making both plots for comparison:\n", + "fig, axes = plt.subplots(ncols=2, figsize=(15,5))\n", + "\n", + "# -- for reference (without masking):\n", + "da[0, :, :].plot(ax=axes[0]);\n", + "\n", + "# -- masked DataArray\n", + "da_masked[0, :, :].plot(ax=axes[1]);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```tip\n", + "By default Xarray set the masked values to `nan`. But as we saw in the first example, we can set it to other values too. \n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```{exercise}\n", + ":label: boolean-2\n", + "\n", + "Using the syntax you’ve learned so far, mask all the points with latitudes above 60° N.\n", + "```\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# write your answer here!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "````{solution} boolean-2\n", + ":class: dropdown\n", + "```python\n", + "da_masked = da.where(da.yc >= 60)\n", + "da_masked[0, :, :].plot();\n", + "```\n", + "````" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As mentioned above, by default `where` maintains the original size of the data. You can use the option `drop=True` to clip coordinate elements that are fully masked:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "da_masked = da.where(da.yc > 60, drop=True)\n", + "da_masked[0, :, :].plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Please note that in this dataset, the variables `xc` (longitude) and `yc` (latitude) are two-dimensional scalar fields.\n", + "\n", + "When we plotted the data variable `Tair`, by default we get the logical coordinates (i.e. `x` and `y`) as we show in the example above. \n", + "\n", + "In order to visualize the data on a conventional latitude-longitude grid, we can take advantage of Xarray’s ability to apply `cartopy` map projections." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(14, 6))\n", + "ax = plt.axes(projection=ccrs.PlateCarree())\n", + "ax.set_global()\n", + "ds.Tair[0].plot.pcolormesh(\n", + " ax=ax, transform=ccrs.PlateCarree(), x=\"xc\", y=\"yc\", add_colorbar=False\n", + ")\n", + "ax.coastlines()\n", + "ax.set_ylim([20, 90]);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Using `where` with Multiple Conditions\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In Xarray's `.where()` function, boolean conditions can be combined using logical operators. The bitwise `and` operator (`&`) and the bitwise `or` operator (`|`) are relevant in this case. This allows for specifying multiple masking conditions within a single `.where()` statement.\n", + "\n", + "We can select data for one specific region using bound boxes. For example, here we want to access data over a region over Alaska :" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# -- define a region\n", + "min_lon = 190\n", + "min_lat = 55\n", + "max_lon = 230\n", + "max_lat = 85" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First we have to create our boolean masks:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "mask_lon = (ds.xc >= min_lon) & (ds.xc <= max_lon)\n", + "mask_lat = (ds.yc >= min_lat) & (ds.yc <= max_lat)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we can use the boolean masks for filtering data for that region: " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "da_masked = da.where(mask_lon & mask_lat, drop=True)\n", + "\n", + "da_masked[0, :, :].plot();" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(5, 5))\n", + "ax = plt.axes(projection=ccrs.PlateCarree())\n", + "ax.set_global()\n", + "da_masked[0, :, :].plot.pcolormesh(\n", + " ax=ax, transform=ccrs.PlateCarree(), x=\"xc\", y=\"yc\", add_colorbar=False\n", + ")\n", + "ax.coastlines();\n", + "ax.set_ylim([50, 80]);\n", + "ax.set_xlim([-180, -120]);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Excercise\n", + "\n", + "If we load air temperature dataset from NCEP, we could use `sel` method for selecting a region:\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "````{exercise}\n", + ":label: boolean-1\n", + "\n", + "If we load air temperature dataset from NCEP, we could use `sel` method for selecting a region:\n", + "\n", + "```python\n", + "ds = xr.tutorial.open_dataset(\"air_temperature\")\n", + "ds_region = ds.sel(lat=slice(75,50), lon=slice(250,300))\n", + "\n", + "ds_region.air[0].plot();\n", + "```\n", + "Can you use a similar method as above using `sel` to crop a region using the RASM dataset? Why?\n", + "\n", + "````\n", + "\n", + "````{solution} indexing-1\n", + ":class: dropdown\n", + "This method will not work here as the dimensions are different from coordinates here. Specifically, the variables xc (longitude) and yc (latitude) are two-dimensional scalar fields, which differ from the logical coordinates represented by x and y.\n", + "\n", + "So the code below will not give the correct answer!\n", + "```python\n", + "cropped_ds = ds.sel(x=slice(min_lat,max_lat), y=slice(min_lon,max_lon))\n", + "cropped_ds.Tair[0].plot()\n", + "```\n", + "````\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Using `xr.where` with a Function\n", + "\n", + "We can use `xr.where` with a function as a condition too. For example, here we want to convert temperature to Kelvin and find if temperature is greater than 300 K. " + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Define a function to use as a condition\n", + "def is_greater_than_threshold(x, threshhold=300):\n", + " # function to convert temp to K\n", + " # and compare with threshhold\n", + " x= x+273.15 \n", + " return x > threshhold\n", + "\n", + "# Apply the condition using xarray.where()\n", + "masked_data = xr.where(is_greater_than_threshold(da,50), da, 0)\n", + "\n", + "masked_data[0].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Selecting Values with `isin`\n", + "\n", + "To check whether elements of an xarray object contain a single object, you can compare with the equality operator `==` (e.g., `arr == 3`). \n", + "\n", + "To check multiple values, we use `isin()`:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is a simple example: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x_da = xr.DataArray([1, 2, 3, 4, 5], dims=[\"x\"])\n", + "\n", + "# -- select points with values equal to 2 and 4:\n", + "x_da.isin([2, 4])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```tip\n", + "`isin()` works particularly well with `where()` to support indexing by arrays that are not already labels of an array. \n", + "```\n", + "\n", + "For example, we have another `DataArray` that displays the status flags of the data-collecting device for our data. \n", + "\n", + "Here, flags with value 0 and -1 signifies the device was functioning correctly, while 0 indicates a malfunction, implying that the resulting data collected may not be accurate." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "flags = xr.DataArray(np.random.randint(-1, 5, da.shape), dims=da.dims, coords=da.coords)\n", + "flags" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we want to only see the data for points where out measurement device is working correctly: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "da_masked = da.where(flags.isin([1, 2, 3, 4, 5]), drop=True)\n", + "da_masked[0, :, :].plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```warning\n", + "Please note that when done repeatedly, this type of indexing is significantly slower than using `sel()`.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Additional Resources\n", + "\n", + "- [Xarray Docs - Indexing and Selecting Data](https://docs.xarray.dev/en/stable/indexing.html)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "NPL 2023b", + "language": "python", + "name": "npl-2023b" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": true, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": true + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 5c044dd1c1fb80cc52059054e324d38db3b1c374 Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Fri, 7 Jul 2023 19:44:17 -0600 Subject: [PATCH 29/54] boolean indexing update --- .../02.3_indexing_BooleanMasking.ipynb | 997 +++++++++++++++++- 1 file changed, 985 insertions(+), 12 deletions(-) diff --git a/intermediate/02.3_indexing_BooleanMasking.ipynb b/intermediate/02.3_indexing_BooleanMasking.ipynb index a69ae0db..15d707e4 100644 --- a/intermediate/02.3_indexing_BooleanMasking.ipynb +++ b/intermediate/02.3_indexing_BooleanMasking.ipynb @@ -2135,27 +2135,27 @@ "source": [ "### Using `xr.where` with a Function\n", "\n", - "We can use `xr.where` with a function as a condition too. For example, here we want to convert temperature to Kelvin and find if temperature is greater than 300 K. " + "We can use `xr.where` with a function as a condition too. For example, here we want to convert temperature to Kelvin and find if temperature is greater than 280 K:\n" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 27, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAj4AAAHFCAYAAADyj/PrAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOy9ebgkRZk1fiIiMyur6q693e5mbRQQaUE2UURpZG13YdzHQUYdvgdkhh+iI4OfggsojA4zgDP6jQKDIuMC6IyozY6IKLuCytpsTS9wuX2XulWVS8Tvj1gyIqtur7f3OM/TT9fNjMiMzKrKijjvec9LhBACHh4eHh4eHh47AOiWHoCHh4eHh4eHx+aCn/h4eHh4eHh47DDwEx8PDw8PDw+PHQZ+4uPh4eHh4eGxw8BPfDw8PDw8PDx2GPiJj4eHh4eHh8cOAz/x8fDw8PDw8Nhh4Cc+Hh4eHh4eHjsM/MTHw8PDw8PDY4eBn/h4bDTuuusunHvuuVi9enXHvkWLFmHRokWbfUybCxdffDFOOOEELFiwAISQNV7rr371K7zxjW9EtVpFf38/3vGOd+CRRx7paNdut3HRRRdh4cKFqNfrGBoawuLFi3HXXXd1tE3TFOeddx523313VCoVvOpVr8Ill1yyXtewatUqfPSjH8WsWbNQq9Xwhje8ATfffHNHuyRJ8PnPfx4LFixAFEXYbbfdcPbZZ6PZbK7zudbnfgHAT3/6UxxxxBHo6+tDvV7Hvvvui29/+9trPc8jjzyCU089FW94wxtQr9dBCMFtt93W0W758uX43Oc+hze84Q2YNWsW+vr6cNBBB+Hb3/428jxf5+t66qmncMIJJ2BgYAA9PT045phjcP/993dte8011+C1r30t4jjG/PnzccYZZ2BiYmKrPJeHx3YJ4eGxkbjooosEALF06dKOfY888oh45JFHNv+gNhP23ntvceCBB4q//du/FbNnzxZHHHFE13bXX3+9IISId7/73eLnP/+5uPrqq8Xee+8tBgcHxRNPPOG0/chHPiIopeKcc84RN998s/jRj34kDjroIBEEgfjd737ntP34xz8uKpWKuPDCC8Wtt94qPvvZzwpCiPjKV76yTuNvtVpi4cKFYueddxbf+973xJIlS8S73vUuEQSBuO2225y2J5xwgojjWJx//vnixhtvFF/84hdFFEXiHe94x7TfLyGEuOCCCwSlVJx66qniF7/4hbjpppvEpZdeKi655JK1nueKK64Q8+bNE29961vFO97xDgFA3HrrrR3t/ud//kfssssu4pxzzhE///nPxZIlS8T/9//9f4JSKk4++eR1uqZVq1aJ+fPni3333Vf85Cc/ET//+c/F4YcfLnp7e8Vf/vIXp+33vvc9AUB8/OMfF7fccov4j//4D9Hf3y+OOeaYre5cHh7bK/zEx2OjsaaJz/aOPM/N63333XfKH/K9995b7LfffoJzbrY9/fTTIooi8aEPfchsa7VagjEm/vqv/9rp/8ILLwgA4u///u/NtocfflgQQsT555/vtP3EJz4hqtWqGB4eXuv4L7vsMgFA3HXXXWZbmqbi1a9+tXjd615ntv32t78VAMTXv/51p//5558vAIglS5as9VxCrPv9uvfeewWlVHzta19bp+Ou6Tw/+tGPppz4vPzyyyJJko7tp512mgAgnn322bWe69Of/rQIw1A8/fTTZtvo6KiYNWuWeN/73me2ZVkm5s2bJ4499lin//e//30BQNxwww1b1bk8PLZX+FCXx0bh3HPPxac//WkAMOELO6xQDnU9/fTTIITgoosuwte+9jXsvvvuqFarWLRoER577DGkaYrPfvazmD9/Pvr7+/Ge97wHq1at6jjvf//3f5swRk9PD4477jg88MADm+OSHVC69q/Q8PAwHn30USxevBiEELN9t912w8KFC3H99debsAqlFJRS9Pf3O8fo6+sDpRRxHJtt119/PYQQOPnkk522J598MprNJn75y1+udWzXXXcd9t57b7zhDW8w24IgwF//9V/j97//PZYtWwYA+M1vfgMAeOtb3+r0f/vb3w4A+MlPfrLWc+nrWxdceumlqFQqOP3009ep/YaeZ3BwEGEYdmx/3eteBwB4/vnn13qM6667Dm95y1uw2267mW19fX044YQT8D//8z/IsgwAcPfdd2P58uUd79d73/te9PT04LrrrtuqzuXhsb3CT3w8Ngof//jHzY/Ttddei9/+9rf47W9/iwMPPHCN/S677DL85je/wWWXXYb//M//xF/+8he84x3vwMc+9jG8+OKL+O53v4sLL7wQN910Ez7+8Y87fc8//3x88IMfxKtf/Wr88Ic/xFVXXYXx8XG86U1vwp/+9Ke1jjnLsnX6J4TY8BtjIUkSAEClUunYV6lUMDk5iSeffBIAEIYhTj31VFx55ZW4/vrrMTY2hqeffhqf+MQn0N/fj0984hOm78MPP4zZs2dj7ty5zjH3228/s39tePjhh037bsfQGqSprkH//Yc//GGt51of3HHHHdhnn33wk5/8BHvvvTcYY9h5553x2c9+1oxlU+KWW25BEATYa6+9nO1lXVKz2cSTTz455T1sNpt46qmnABTvR7ltGIZ41ate1fF+bc5zeXjsSAi29AA8tm3svPPO2HXXXQEABxxwAHbfffd16jcwMIDrr7/erMxfeuklnHHGGXjVq16Fn/70p6bdX/7yF1x88cUYGxtDX18fnnvuOXzhC1/AJz/5Sfzbv/2baXfMMcdgzz33xHnnnYf//u//nvK8Tz/9NBYsWLBOY7z11lunRZg9NDSEGTNmGNZEY/Xq1eYHaHh42Gz/l3/5F/T39+PEE08E5xwAsOuuu+KWW27BK1/5StNueHgYM2bM6DhfvV5HFEXOMafCVMfQ2/QxXv3qVwOQzI99/+68886O8U8Hli1bhhdffBF///d/jy996Ut49atfjZtvvhlf/epX8dxzz+H73//+tJ7PxpIlS3DVVVfhH/7hHzBz5kxnH2MMjDHz98jICIQQ63QP9f9TtX366ae32Lk8PHYk+ImPxxbBW9/6Viccsc8++wAA3va2tznt9PZnn30WCxcuxK9+9StkWYa/+Zu/MbQ+AMRxjCOOOAK33nrrGs87f/583HPPPes0xr333nud2q0NlFKcdtpp+NKXvoQvfelLOOWUUzA2NoYzzjgDk5OTpo3GV77yFfzzP/8zzj33XLzpTW/C2NgYLr30UhxzzDFYsmQJDjjgANPWDp2VofcJIToylIIg6Gi3pmMsXrwYr3zlK/GP//iPGBoawiGHHIK7774b//RP/wTGmDN++30B5A/4ms7RDZxzjI+P4wc/+AE+8IEPAACOPPJINBoNXHzxxTjvvPOcSeB04f7778f73vc+vP71r8cFF1zQsb98bRrrcg/X1ra8fXOey8NjR4Kf+HhsEZRXolEUrXF7q9UCAKxcuRIAcMghh3Q97tq0HVEU4bWvfe06jdFebW8sPv/5z2NiYgJf/vKX8fnPfx6AnOSdfPLJ+M///E/stNNOAIA///nP+PznP48LL7wQZ511lum/ePFivPrVr8aZZ55pJnczZ87Egw8+2HGuRqOBJEnMvbzyyis7tB46jDdz5syubM3LL78MoHg/oijCL37xC3zkIx/BscceC0AyS+effz6+9KUvmfED6NDMXH755fjoRz+6bjdKYebMmVixYgWOO+44Z/vixYtx8cUX4/7775/2ic8DDzxgmMMbbriha2iyjMHBQRBC1ukeavZoeHgYQ0NDHW27sTNb6lweHtsz/MTHY5vCrFmzAAA//vGPHYHnumJLhLoAybB84xvfwBe/+EUsXboUs2bNwrx583DcccdhwYIF2HnnnQEADz30EIQQHRO7MAyx//774/bbbzfbXvOa1+Caa67BihUrHJ3PH//4RwDAwoULAQDveMc7pmS5XvOa15j2NsrHAIBXvvKV+O1vf4tly5bh5Zdfxite8QqMjo7iH/7hH/DmN7/ZtCufa13vt4399tsPK1as6NiuJ2zrKl5eVzzwwAM4+uijsdtuu2HJkiUd4vKpUK1W8cpXvnLKe1itVrHHHnsAkPdab9ehQ0AyO3/5y1/wwQ9+cKs5l4fH9gw/8fHYaOiV8foY2W0ojjvuOARBgCeffBInnnjievffEqEuGz09PeZH6f7778fNN9+Mr3/96874AJmVc8QRR5jt7XYb999/v5kgAcC73vUufO5zn8OVV16Jf/zHfzTbr7jiClSrVRx//PEA5Oq/rFXReM973oNTTz0Vv/vd73DooYcCkD+O3/ve93DooYea8djYaaedDMPzuc99DvV6HR/72MfM/oMPPnj9bkoXnHjiiViyZAl+8Ytf4EMf+pDZfsMNN4BSOiXjtyF48MEHcfTRR2PnnXfGjTfeiMHBwfXq/573vAcXX3wxnnvuOeyyyy4AgPHxcVx77bV45zvfacKKhx56KObNm4crrrgC73//+03/H//4x5iYmMAJJ5ywVZ3Lw2N7hZ/4eGw09A/5v/7rv+Kkk05CGIbYe++90dvbO+3n2n333fHFL34R55xzDp566ikcf/zxGBwcxMqVK/H73/8e9Xod55133pT9oyialh9mjXvvvdcIRcfGxiCEwI9//GMAMhynWanbbrsN99xzD/bbbz8IIfD73/8eX/va13D88cfjk5/8pDne4YcfjkMOOQTnnnsuJicn8eY3vxmjo6O45JJLsHTpUlx11VWm7b777ouPfexj+MIXvgDGGA455BAsWbIE3/72t/HlL395ncIZf/u3f4vLLrsM733ve/HVr34Vc+bMwTe/+U08+uijuOmmm5y2F154IebOnYtdd90VK1euxA9/+ENcf/31uOqqq5xQ13Tcr5NPPhnf+ta3cOqpp+Kll17Cq1/9atx000247LLLcOqpp66V7ZucnMQNN9wAQE4iAeD222/HSy+9hHq9jsWLFwMAHn30URx99NEApLbq8ccfx+OPP26O84pXvAKzZ882fwdBgCOOOMJxtj7rrLNw1VVX4W1vexu++MUvolKp4Ktf/SparRbOPfdc044xhgsvvBAf+chHcMopp+CDH/wgHn/8cXzmM5/BMcccYyaqW+JcHh47FLaQf5DHdoazzz5bzJ8/X1BKHbO4I444wjGpW7p0qQAgLrroIqf/rbfeKgCIH/3oR872yy+/XAAQ99xzj7P9+uuvF0ceeaTo6+sTlUpF7LbbbuKv/uqvxE033bRJrm8qnHTSSQJA13+XX365afeb3/xGHHrooWa8CxcuFP/8z//c1Txv9erV4pxzzhH77LOPqNVqYs6cOWLRokVdTeeSJBFf+MIXxK677iqiKBJ77bWX+Ld/+7f1uoYVK1aIv/mbvxEzZswQcRyL17/+9eLGG2/saHfeeeeJV7ziFaJSqYiBgQFx/PHHizvuuGO9zrWu90sIIYaHh8Upp5wihoaGRBiGYq+99hIXXXSRY044FfTnrNu/3XbbzbTTn691HROArqaLTzzxhHj3u98t+vr6RK1WE0cddZS47777uo7t6quvFvvtt5+IokjMnTtX/P3f/70YHx/vaLc5z+XhsSOBCDFNZiUeHh4eHh4eHls5vIGhh4eHh4eHxw4DP/Hx8PDw8PDw2GHgJz4eHh4eHh4eOwz8xMfDw8PDw8Njh4Gf+Hh4eHh4eHjsMPATHw8PDw8PD48dBt7AELIg4gsvvIDe3l5fvM/Dw8PDY40QQmB8fBzz58+f9vIpNlqtFpIk2ejjRFGEOI6nYUTbB/zEB8ALL7xg7N89PDw8PDzWBc8995xTRmY60Wq1MLPag0nkG32suXPnYunSpX7yo+AnPoAprfDE449vkjIL04l/2e0NGAjlCiNmBEMV+RaGFVlJvNHMAACtnDv9+kK5P+ECufKszAXACFBl8nhRTwSm2uVpDsGVt2XOwXMBGsl9LHRXOGFdVuOuz6qhf4Esk1CbMwNBHGL8+RfleVc3wLkck+ACLJDHqszoRXX2gLyeGX2gYYDGCy/Ja3h5DPGMPgBAdfYAWCwrtYucg7BiDCLnyBqyTlh7tFEcb6edEAzJ0gakWgdqsvCkIBS03QDUfRDtBpJnH5PtKAWIus7BolRB+8mHIbhAc6Wsgj354mqk45NynKMt06535xkI1DizVoJ8soW00Za3McvRHpWrt9bqFoKqfO/a4wlaw3L8JGTonVvHzm/aUx5v17mo7FSUZ2gvewYAMPLYs2BhgDyV73f75TFkrVSdN0emPgeEUeTtzPQfOmBXzDvxr+R1z1kA8oIszzD6u9ux+tHn1HiaCGL5nrI4AuzPknXfUfqMsZp8qIZ1+T8hsq0QHFlLXjdPMvAsM/31+AmlEJxDWMcUuXx/OOfms8it3wBROr/dR157J3srcgGeC9OXZxx5knf07QZ9vDW146UxdfOHZQEzx8qTHFF/BXu+W9ZJq+//evC2/FyN3/87LP3FQwCAhdf8eI1js/Gnv34fAKBnXg/CnhoAgEaBe+8BZA35uc3THHlajFPfm6yZgTBqPqeV3sjsa48nyBP5mjJi7uH4CxN45oUxNDJ5vJOf+M2U47z9wLeY/jwXOOL+W9b5Gm98zRGI65F5FiVjCYYTeW0TGUcrL55xCS+ujRGCLh8L01b+LyDg9qvpZySV7dqC46utpzfp70WSJJhEjg9jJ0QboUpJwPH9FcuQJImf+Cj4iQ9gwlu9vb3o6+vbwqNZMyqEIlRfgp0qIerqocRzAcoIKBOmXUTldSVcIFLXWAsImurhJSdBsi0A9EQBglgeT0QB8jQ3x+ZJLicFABij5jUARJGa+MQRetXrKOfIVzcQZ/IYAWMQ6jwsZohnygdG1FdHZaBHnqedYOwvzyBZLR/8lXqEYLwhX/fVEPdUZf+eKrh6yAnOIQhBqiZSYRggVg+pWkCAUVnhmz8/AqEmXjSugfUXRTvz4RVoPv4sAPljoH+M81YbrRF5/qxV/GAAQBAHGJglr6G2965gYedXKRmfRBIwvLRsHADQWtlAe0xOAJqNxDxYq4yib0AWet359Tth12Nfh2gPWRWdRDFoXZ4nW/kcnr/tTtn/LyshcuH8aNvvl/5BGNxjAHMPfZUZk8g56s3V8tirHsPI734tj/fwMwjV56I20ANqTXBIFIJa16fvow0WVxCqiQ+NAqd/1kyQq/c+DzLwVE7QeJIB6vMix82dY/MkN9s18tQ6d9HVXJveRhgFtX7huPpVEzlHnnAIKo+Tg4MTexLdOVEpT6BELky7bpOrclt9DfY9oZF8zQlDQBiwQk6o43lLwQbnAAB6XnsAoqa8V4+f+Fc46MYlazyXxut/9ksAwB/e8zZU9HdTLSD0BDRvtZGozzQHBasz019/1hNBwSKK6sy6HDOj5r1IQc1nOWkkYOo6e6sRdq/GeElNtm9aeCROePa+ruN8xxP3AgDuPPjNAIB7Dj4GAHDUY79b6zWe+MwDuHWf1yMM5fWJQYZ4tVxgTDCOiUyOcyLjSEjxnkaUgKlnYV6alOq3Xn8vG0IegxEghOwTgoAChoPZHNKIKigisuETH+ZrM3TAT3w8PDw8PDy2UkiWasMnWAxEVp7zMNiiE58LLrgA1157Lf7yl7+gWq3isMMOw9e+9jXsvffepo0QAueddx6+/e1vY2RkBIceeiguu+wy7LvvvqZNu93GWWedhR/84AdoNps46qij8M1vfnOTxV43Bl8cWIi+oJi9j2XFqkJ/uD/78h+n7E9RtEu4QF1tZxFDnuRm1RJRglCxMrngZrt9Hv1t0H8HcWCFkDhIrlalzU5xnV5N04iZ1XTWytBYNa5e69BGEZvQbFJYjxHEkuFgcYRkTDI8Lz3yLCZfmkRtlqTn45n9pm9zeMywDpE1DpFzJ/QV1mPk6tyNZ583zFBjxcsmHEajALW5M1FVx8+T1ISn8lYCPpmaY2uGgUUM1Zk95jw2wxHEkRkrDQMTAgOAkceWoT2qQl0JNwxB78waoh61Gp9Tx+ArZVhtxj67gw3OQapCb+nYGJIxzTolmFw1Ks9DCUjohkx0SIIwgr6d5Qp/9mv3RH0vyfgQSjH+p4fxwi9vUdeXI1FjpRFDpa4/TUW4gzAKGoaG0SKMGkaMUgoaBea6o76aOQ8AcN3OYnI454C8vaavBrXOmycZqH6jk4L1YSG1wl7yf/1ZJCXWTTMsPOdgiv3MAbAIEHnnDwnPuRPO0vdT/23AivGIXJg+NqNjH1NfgwZhxLSlVQrCCMafXaX2UVRnrwQAVIdmY3ChDHnmrQT3vEVWkT/klps6ztMN+133czx60gkAgKi3iaivjqhXvUcDPWDxREef9upxwyJSRsAiZsZKGDXhTcqoYYaaLzVNGJwnOVhE0ZOvO0ORpzmieujc73XBkX++G7fu83rztw7396BgcxJevM+nrnrI6f+fc1/rPBftt7iZcxMSsyceuZDHLrNFmxKUYMrw3Dr1B/zEp4Qtms5+++2347TTTsPdd9+NG2+8EVmW4dhjj0Wj0TBtLrzwQnzjG9/ApZdeinvuuQdz587FMcccg/HxcdPmjDPOwHXXXYdrrrkGd955JyYmJvD2t78deb7xojAPDw8PDw+P7QdblPH55S9/6fx9+eWXY86cObjvvvvw5je/GUIIXHzxxTjnnHNwwgly5XLllVdiaGgIV199NU455RSMjo7iO9/5Dq666iocfbRcEX3ve9/DLrvsgptuugnHHXfcZr+uNYERoEcxPrvXI4woRmJpI0W6DqsIGaMujlUIjhlEzo2uhwWWxqHJTdzaFjfrbZQUq1yl61XMhqW3yAVYIQMwq1wWUrMiTCaKZW17rI0wDsyqPKgGiAcloxDUq45QeWKZFEA3VjYQ1UP07jRo9mmGReTcMEM0DEDVYLJW2zALgGQKDFqJYX8IK9gJkXOkY5Nmnz4mAKSNZiF8zTkqfZKZIpSCKZZKXnfBjgVxxfQnUQwaKh1FmqE2pw+De+1kjqEZlqi3hnimEm4PzQZPCoF0c+lTePkvT8vXL407+iLNmvXM70XWTJE2JH1CqbskrAxIXRBlDO3nlsp71WxjcvnLmFg2rK5PIDQsXHFthEqWR54vUqxPwfjohwa1tF40CgyLZ+6fel80K2cf37y2PlSEUYdpShvFPbHZG6KulQLmtd1G9i+2M8YcxkYph8x5WJc+QHFPCaNA6IqpjRYu5+Dqe6QTA8z1cwHG9DbRVT8UxAGCamCYobGlK8z3YXCvFnoXyGzT3l2H0DO0rKP/2tAaaamxjSKIK6jNm2H22d8te1s30bjex9R3KFdCegBq/EpMnOZG9AwAb3/63rWO8YiHfoPbFh4GpnRPt+7zehz557vX6fp0u5/udpB59klWRu5nhCCaYnkvWZyC9bbffkaoI3bWWIv+fZNgWkJdHg62Ko3P6Kik8WfMkF/OpUuXYsWKFTj22GNNm0qlgiOOOAJ33XUXTjnlFNx3331I09RpM3/+fCxcuBB33XVX14lPu91Gu902f4+NjW2qS+rAOSMPm9e37/9G7L2HDHHsvLKBB54YAQB8ZXCh085GRAmq6hvaVwvNDxcgH0B6IkQZMT8wvRFDe1z+GE9kHBOZLfYDGkqAHLYyI1IUnJuHWdp2mTORc/Pgp4wWkyDGTDYRIMMvTI0n6gmLMFEYmElIOjaJ1mqV0cQIarNqiPrq5jwm64dR0z+bbCFQYSbCKNLVLRNaY6WQi+4TWT+kIudg6gcdAFrDY8hb7eJ4pl1xn8K6HQZ0J2V5mprwFmEtZE15LJ5k6NlpNvpf+1p5jF33Ah+X77HIUohG8bkjk5LBbK+QP3w6RFfpq6I6S4eTwiIkl2bIGk1Q1lR/507oR2dW0SjA+LMr1XWOgoYB6nMH5PisCSNxJjHFeVgcOaJW+0eRA2C0+PHM9D1UGVrciMQT8z66P7LM3EOq3isjOm61u/4AO+Ek1rG7a+aVPaER3P3looyAK+Kbwp1o69CL/gzzKX717HAujZgVruPWRI44gugOwbRemLQSkJYcw8hjz6E9IsNRLI5QVSHg9cH+P7sBgBQ6h/VRs+CgUWDGJnJuPi/MCj/mrTayZmLee4pislS+hkKILsA3IAy06OG7TNhKP8PWB+96pruAek2wJzT229EfUuRCmMnGaMpNQohsSzbrZIJtZKhr/e/m9o+txrlZCIEzzzwThx9+OBYulBktK1bIjJyhoSGn7dDQkNm3YsUKRFGEwcHBKduUccEFF6C/v9/88x4+Hh4eHh4eOwa2Gsbnk5/8JP7whz/gzjvv7NhXThkUQqw1jXBNbc4++2yceeaZ5u+xsbEtMvl5/qVJDL1GMj6zFr0CWetRAMBvnh7F12fuh08N/6FrP0PBJtykMBOz8laUcy7AlDg0qocm7DX64qRZPWhKuKn6VMcTE/YCXC8gRgBoIXYUFudJcwSs82PEcw4kQFSXgwjiwAkVpSpslU420R6TTAFlFCwucpRtVkbkBYNAGHXCKIRS8KSg3jUIowjUTaBhYFa7PMkUo6UFs4ERPmdW+Ku8KudJWqRbx5XC02RkAgmbVPcmKN4LzjG5ajXw4IMAgJ7h5SaNntT6kLwgPXnaq8fBVGiptXochFHUd5rd9Xh6zESNU4egbFaFhQHqc+V5KvN2MuOsDPQgrFcRqNV91mgh1f5Hqyecay/DhJpQsDYsDBDWq8X9UZo6wTnyVoKsiyjeZu6oxchpxilNC1bOFtp3Qzl0ZL9fkpGwGbpcjZmBUF6kIzMAmq2w1oFBNTBMj7y24ly0vPyO3BCXBouoEVHnKXdU+XrsgnJkzcwcU56n8NFpviQ/Vz3zCpuNh098Oxb+5H+xPtjvup/jD+95m/ne2IkDeauN5ovquutVw36a74kRO7tp/5Hy72qsbKA5YoUlrRDS+kCHrZbscch6990QfHzFg/jW0GsByOdg1Xpfq4x2DXEZmcFmGaE+pw91TTe2Csbn9NNPx89+9jPceuutTibW3LlzAaCDuVm1apVhgebOnYskSTAyMjJlmzIqlQr6+vqcfx4eHh4eHlsbdKhrY/55uNiijI8QAqeffjquu+463HbbbViwYIGzf8GCBZg7dy5uvPFGHHDAAQCkm+Xtt9+Or33tawCAgw46CGEY4sYbb8T73icdS5cvX46HH34YF1544ea9oPXEilaOlX+US614sIo5r5HGZfuPtPDERCeDUUYjy8GUpiYejCFyYoSFeZKbFVmlr4JEiWC1W7MN/XfCBZgy+wopddI5gSJd1E49ZSFzVsZa40OV42ulTy5ze3aabRiX9uoJk6LdHC4y+Cp9Uk+iNTZhPS5EtbE7Rzf6BMUu2Nvt/kZE3cV0z7AY1j4yRd0dnnPwRstoIKRbdLHKtZmLuFdqlNJGC43lL+PFh6UoldI/IR6UbEttTr8R9oo8N7omGgbo2Wk24p0lA8nHV6P98qgaQ+7ocgCAU53GXzVGkD277YRg51eo62HQXq2xug+iLVmeKAgdkbm+12WWSY9L/l9cKwuL/gAAbWFQunc281ZmRQz7EwZrFEF3A2GdLt52X/u9so9LrfWe4AJCp6Nb/YM4cMwSO1geC2YfYyYVvBtYF+0K5wLgOXKd4k+JEUvLsctjjz0/2qE5Wl/sd93Pcf/xUvNYealpxMS2GBkYKewBlC5JM2lpq2CmkonUsM1BHKCqPtdJI0XWzIzQeENw7FP3bHDf9UXB6hCT7BFR4rg2A+4zU2p8PLZlbNGJz2mnnYarr74aP/3pT9Hb22uYnf7+flSrVRBCcMYZZ+D888/HnnvuiT333BPnn38+arUaPvShD5m2H/vYx/CpT30KM2fOxIwZM3DWWWfhNa95jcny8vDw8PDw2BbhQ13Tjy068fn3f/93AMCiRYuc7Zdffjk++tGPAgA+85nPoNls4tRTTzUGhkuWLHFqpPzLv/wLgiDA+973PmNgeMUVV5hU0q0Vnxr+Ay6etR8AgP7mOSw4Sq7S93n7nkh+9ljXPqeuegj/Ofe1AKQ2p54UBnuBlT4uLDO2pJEiVQySXIkVX4SEC1Ory85kCKoBYp2yy6WORq/2gmpgUnd5XtQ6IowYK36tYdCr02S8AajyE1kzQTLeUq8z0zaqR6gM9BoGhlup6RR2+nhRM6isI+Fpiqh3hhm3ZoOCWmxS4LWeJlHjoZQW+p9GqyjNYWW+lNN8RZ4jUAyLsAz6WBiY7CaeZgiqIWqqOoZj+d9oIajKc1YGeo0mh8WR1FWMr5bHyFKTAZeNFan7lNGO0oVatxHtsS+EKmqVPvsY8obMDGquGkEyPmmuL+qrGY0OjQLzMGBxBKZKHYicG+2OPC8z92qqLDehSlJ0Y2J026lgM2drVviosXYpFaKvx7BWjJtr4zmHYNwYCnJwJ2vL6JciZhgfkXNH/2PXpiqzL+VsMpsBsi0HOO+ugeFcmHYu82Ol0leDDdL5AMCBv/zVWtv8ftFR8jyx1Dnp8id56tY1M9mc1r1CI11vI0INre3ZnIyPNjW8fN4BZptkvt3aXW6qvNisae0EG6dJ8dOeTmzxUNfaQAjBueeei3PPPXfKNnEc45JLLsEll1wyjaPz8PDw8PDw2N6w1WR17ag44yWZuXX5vAMw6/nVAIDqrBr6eyuGDdJtND6+4kEAwH/NP8B48MQTKSp9FdSHpNdH86XCNyRPMrM662cBcpWdlQthqrYDMotLMz7xYGz0OZRRZM2s8HLhAmlLMw8Ea6pW3VgpWZXWSMuwUSyiZqVsG8jRiDl6G845uGZsADBWVD3PGm72jzbPy1ptk92kxy7HzGE7MPI8N2aHZWaCWWUYdNFLwbnD+lCruKaN9uoJh7Xof8VOxiMl6q0bZigZaxiGhlVrxsCQJ5n0L1I+UzQMLH1N4GadUWruFwXQfFEK/Mkj96C5Sr5uDY+ZNnmrDZ5kXXVP1DJkzFuJ2RfEFTC7uKbVjqeZw8ppSJaoVKWcc2d/GRxZh5GeBisZG9qw/Yc0Mym328aIrNieZiYTsPhb+etQbvQ+TpFTxpysLvu17VVFuuhaNGvjGCpGzHzmbYa2jLIppe2RtaGsyrrgdbfdDAC4+01vQZgLJBOKXW1lRg/EImq8vIJqYPzE0ojiTfd3ZuYCwG0LDzOv8yQ39/3oJ3+PW/d5PZgydv3f3Q9eJ+PD6UTChaNLsrWQbphJIOGb18jQh7qmH37is5Xg5OUP4Ke7HQQAGBiI0WwkaxUI2g6lWStDMpGYOle6dhcAtMcSpI3iB5NZP9ossiqtWympLKQmFV1WtCamJLH94CeUFD9yueuwq48DSGGkfnBH9dAcm0U5NJHLwgA8SY04Waa3q7BKLTY/sjwtfiQ552CWKzO1wlsi50bwa1fu5nmObLJVGOylmXkdVCNHVKt/sPUESzsiA3KSA8jQmz05CSzBL4sjVIdUavrsncwPcqUxBjZTZi2SIAKfWC3PkyYg1TpoXNTN0ghfXAb29JMA5AQvySeL+4Ai7Dfy52dMuC2wq6ZTijzMTHhIT8j0PdX3gFvGkTzJHOdmAKbWVjkM5kykGO0aohKcOw7atokeYRRUT2LCEIIW6fFmMllyfbYnQnQNYmjOigkRT1MQO2zJinpa3KSSF8kFhBJT60uOR8CuhqM/14QR6XAeWeE6y/2ZdbEQZoyoGm6FbUO31zb0YuOhd74VQGFSON14/a9vwe8XHeV83w+/9w4AsqI6i1TNNKsGXRAH+PWBh3ed/BBGzASJx4Gxsfj1gYejf7f+Ioz2zOYzlNU4ZWWR2u7WM+ys4xVRgikilZsE3sBw+uEnPh4eHh4eHlspyhOx9e4/jWPZXuAnPlsRtO36N+fsbwTHa8LJyx/Aj3Y5EIAW2AqnrpOGTaOH9cjQ5DzJHXv4oBWY1Wt7LHGqYPMk76Dru6EwVJSrUi16zJPcrOjCoTqivsL4TofNAiW01cxBbrEDiVXx3Ba0ahM8zQTYbEtQj53Vc65EtzzJpMBapdQLzh1zxbJoF5Dp9JWBHlPRvbF8GFyLtQHUVJmMoBYbwXDQ1wdwDhIWDBvtk8Jr2jMAWpfskQiroEPKyoFQCBaCV3rUDbWYunl7onf+7vKcq5aBT6xGtvrljnHnrcSwY9FAD2ivdDVPVi7H5KoRw/TEM/uNkWRzeLSrkJsnmSN8tkGtcJLN9khhcgCqGbY0c0JfhuWh3LAt5VAWQ2BYGvvza7/3thmivHW0I3ynYVhAqs9tMVV2iC0vSl7wEmdlhPqNtMTKFH0oI1Z6uCtItk0KnfR6J1eadX+NIlymS2Hovx8+8e3me8ZC6oi19/3hz7AxeN1tNxuxMwkD85pF1E3dt9w3pgp1UUbM84bkHFGP/IDEAzFqs6rGBLEbM7Y54FZjh8O46/R2RghyAXDhw0fbMvzEx8PDw8PDYyuFD3VNP/zEZyvEqasewsWz9usQNXeDXomk7dzEzMvIE25WoWHdWmFGDGEcGJYGKFaVNnOUp7kseprrNG9aCJVL2oeiDwdl1KnYrleioVW+QvIz0lBP5DkqA72mvEBreBwp5Cow6i1WgZxRs/I32hqt8SkV1Owmis3TDMn4pGEH7GKmdqo8DQOTch7UYkR9dbMvqEaIZ0j2pzZnEBX1mlRisJnzZP++GSChZfBHGURTsUQvLgN/SgqQo933AYZ2l02SpmR8iNJKVXogAsVGVQfB6zLtnQ7tCdYe17IlkLQNsXqVOg8tzktZURi12UCcZAgVO0WD0NHoaNjp+YBkfVLrPmodF7PulQ2tCTLmhwhAqNXOEoxzi1kSnDssDe9S2JSW0tedEhiMGsG6rY+xSy3krQSEW7ogWBofADm0XgfQsnv9mTDFOlnWtWCp/u7YfAWJOh+xdmFUdaUd11SGXRj4Vd/7MQDgkfe9s2NfpS9CPFgYaD584tsBYINS38141He80l8p9EyUmO+7THyQr7UGqBvedP+dhjHioAiVzq9nqA4WMaTKZLVcFHlzQ7M9RQp7YfOh9T75ZmR8vLh5+rFVlKzw8PDw8PDw8Ngc8IzPVop1YXsAQMsJRtMcbCI1xoLE0hqkjQRceSZRRswKLooDiFw4abLU6BW4YX3WFHOXzI7KXKHEsflPLdZI5Bw9Q3IlWp0zYLZHvXVEqsQD55Jp0GnePM2slHNmWB47y0hmy3T/GAsrO6na128ynXTKOWOSSWFhYAp32tlfQRyhMih1OEEtRjo+aYp6BrUY1dlSOxMNzQNVxUdZ/0ygX5YeETRwSjXSdgPZC0sBSMbH3MORVaDKcDBrTUK0m0aXE86cC16Tr3l9Jngo9UO8NgjEfYBeCRIK0q+yxJImwJXdwOQIhEqVB88R9tVAY8n4pKtXG+1LhxmhlSKu/8lORZkMnnOjqeoo0BmGDo/RVTfFqGGCaBeWw6TN20aWUeC0tdk6Gzznpp3dRn9utNaHl4wpjU7KOqfpa85JihWjY5GgU9fVWK3vms1EUkbB1GcsT/MOLY99PDulvgxbv6OZnTzlaI00zOvpBLOsL2wNVFgP0R6busCtxu37v9FkdUm7DPn9S1sZmiMtJMpkdXMaGGp8a+i1pkhplRGj8ylDh502Zzo73chQl2c3OuEnPh4eHh4eHlspfKhr+uEnPts4Tl7+AABpZqiNCQG5StVmd3nCEdWKzCIdm2cRQ3us7TAzTpaMtcywS1ZMBXuFWfizKKYpYghV0dSwbnnycI7qzD75OudoDY+hphih3l2H0Fg+3DE220TPzuiSY8gMi2EzBUE9njI7KOqrOcU2dbuwXkXYI9kowXOkjaalG2EIZ86SY4hiU6ZBtFugTeVDkrQAykCCUJ/UePfQeh94Q7YTrQay5U/LYwUhEETIlktmKH3+CdC6vD/hPodCzNhNtuMZSDIJobRAWX0WeKxYoiAGSWS2lqABmGJ8RLMBmqUgUeHfkzXbzr0FOrOwtP4GcH10RM6NDqdcUJTnudPHzgDjVkYVSj6Qtm+SyQorGShq6PfePnau35+8u/ePyDkoAsu7J0NuNF2Fd5CtUWJxBTxJkbTkPaWMOopR41mldC+a5bEzH2WmmzY6LLLPyoVARS4KZsi9NeZ7++hJJzjbuZVdxZPc+XuqsiHrit8vOsroh2wQRhAopjWMAzO2Ow9+Mw6/9w7cefCbTVut+7GNToNqgLpigBsrG3j58REkk2svzLypENGC4dHaHnuyoUlvo/Xxk4ltGn7is51A0q+ioNutdNewJzSTlrTlCjPtWlmAJdBkxPxg2NWa5f9u3aI1uc/akydNhzdXjZjUdRYGSFRKddpoOu67UV/d/Ai0hsfcyY4lRgYKIWzeSsyEj1kTJABGyFtOgSe0cH4O6jGomhiQMASoqqDeGFNiZ9ku7OszEwiRtCDUj6JoNYBhWWyX1nulEaGauBDKQatqIsWYE4KykQ8vdyYnoqXS7levBK32q40CJE8AFfpieRtUV0APKoCaEIFSoFeG4cIFNSBPwZXAGjxHNZM/NsnouAkFkpyCUfs9pig7MZdhi6Hl9RXCchaGRnxOKEVgvY+2gaHgvJisWhOi3HqvKHMnE877yChoWpgj2uHQTPUTnDthLGqFSTkvmwcyZ2wm1FpKm7dT4ElErOr1QWkh0Zmeb4djZZtCVD2VmaFQ9fH0ZEMw4kyy9PdxbQuVqaAruAMyJGWHuKg1qSueLxH6lEXG5EtN3Hnwm825acTw6wMPByDNDU3yRJfw+pYIcdmwJzxV637mpdJKCRdg3sBwm4af+Hh4eHh4eGyl8BOf6Yef+Gwn0OUrdEp71BMW9vC5MCUr0naOsCK/Clq8bFeaLoTKrsV+uR6XLWjWKe2iVI3a7iPyHI1VDfN3387y/8pOsx2BbJ5mZgUsco5YlYiwTfloGJjVt16N63INIueOCNo2NMwmW2a7LXgN61UT6qJRDNo7UNzXEZkiztMM8cx+sEEpXCZBCK5ZnnazuDE8B63JMbPBOSCVmklHF5QCimERzYZJ2yfVOohihUSzAZq0INpqrJUYdECek/YMQDSUYWG7AdEzAyKSQmWStQ1zJMIqSFuW02ATL4EooTMIBYIKqDKOE0kL+agVStSsil3ZXId79OV1qc2lYcJUirmx2RT72FSH/qglls2STtZIWwew7uaS+j00zA5lzmtSidENWauocg8AedhZCkVY6fecc1CLgqLW2NxSKNwpA2LfExt2uE4w7oSlASsMG8J8F+zPMaeFKai+J1pQLUN5BdbXwPCBtx1vRMcsoir0VtTY0wG4PE0KQ9I4QO+u8jNaWfYiCCMmNZ1QAq4Yo0pfpTA4tX7JuRXq21I4efkDuHZXWTIoopJB07UK8VITTXWtOrWdboTmxmPLw098PDw8PDw8tlJ4cfP0w098thP8zQsP4Ps7HWAo0SAPQKiqzp7mJrU94QK5YnqiUDI866IFsFdk5fZ2aQutNXDs7FV/HdcfXz5hjlHfaXah16nHIPbKN44Q1KSGJRlvoNkqihfaRTRtXZC9mg6qkUmNT8cmDSukxdF6dc7iqNBPZAm4YkHSiQbaI+OyTxQgntlvBMm0WjeiZUIpEERmO+0ZkNvjHuT1mRA6d5kFIG3JejHBjcmgaLcMW8MBkFbBjIk8L7RAhELEkk2ieQKkbQiqrpcQkFQyTySZBG3LcROeQSQWI5Ulhmnio8MFA2XpTgKrfEeeqP3ajHAK6wD7GN20Xg5oJ/lOggjIkiLd3EqpB7qLdA1zp+49CcPiER9EJm1fZClYWlynrS3K09Sc0xZ128wWSTPkyIz9gc3yyH7y2EEYOgaYnHPnXkxlTChoDjbFPWPh1Pdbp+sLbmn7GC2YREaMyWE35ucP73lbxzbb0JRaej9AVZVX7E1Ujwy7nLYycw9n7rsAwFK8/Lg0zWyPtU2JnJ75Pc6zQ3//ksYI0oktJ2zWaFrvQb9VPZ5FDCzVjI/SUorNJ/Jh2MhQ12bUI20r8BMfDw8PDw+PrRR0IxkfH5brhJ/4bEf48LKiaGk4kYJHamWfC7S6rCjzjEPkxLA5ZY+0otiijO8bQznLWM0uU6H3AcXKs1vsPmtmmFgpWY3wz8+iOkuyGFFvXeodGpLhyFuF7iOoxahYjAI3xoYUQTUyK84grjjZXzq13U5F52mGsF4UE7UhODdaILsPoRQ8yUCoSg0PI7DZUtfA+mcWGV4sMhlVgjGIqAoR6H0hqNqXAyCKvSFJE2JkhToPA+0dNLohkaVAJq8hW/YE2C6vkv37ZFq8zZ6QVI6NtscBrbehlp6k3YRoTRa6pdFh5G25aqdRAIqiDIhtMsiTrGTUpxkBd5thaEK5j+eFBsVOU+eKwSKUgtg6HyurqlyaohsIZdIuQBeBDaIiw6vWV2xvt4zeJ+A5BM+LNPyWVZA1DIoyGWFQyrayjA7DUoaW1ocpJtEcO+eFUSLnoFb2mTkWpcgBozETJZbIhimlwigEp9ClLkhOHHNFu32ZndUMEABHA6jb5UluMrfSVmZea2iTwfpQHT3zpC6tPdZE6+VRANIaojpnEBX1/SaMIFI2FrWZdef9TdX3nCdrYQg3E/QwcsGRN1P0KJZnYyYdHlsn/MTHw8PDw8NjK8VGZ3X5eVsH/MRnO4MuWjqW5oislaP2oojomr8FxgMErsZiTTCGbVbhQp4LBNWgQ+sDSM2AzvoYfWYMk8OS3eid14OenWajMtAjr2W8YTxXKgM9iHqlZqO9esIcK+qroTLQ62TzaBYkm5hArvQXzuq9VObAMS+0snBsRoiGgVzRay1PzwCC2TvJY/fMhqhof56oyKISHIJFRZFRwJSSIIIXzFAQFplkXBr/6WKm+egwuCo+mjz7GJjSH0UL3wheGwCdeEkeI6pCVOR94zwHbcoVOEmbyFWRUj6+GqLVAG9I/U86UWiJ9PWZYaK7qSUAsC5aFXtbUeKh0/fG8aMpZTw5mh6HCcqLv0v6IBKGheeRZRZJq3XpYSRvSNEvCEEQggUlRgtSy6NZrKzVBtMMWMnHaCpfoyCOwNPMYYMMS2Pp0GxWSOQcDIExabT1Q8IqIGuzYTzJpPbH6IwSo0fhFsvDUWR7/eWv3w2RCydT02Z0bW8vXio2rFnb1C6CvBLo21nq56qzek3ZmayVQOTcmBNWWpkpRkoYNf5dUW8NyfikGfPRT/6+435ubmgz2MvnHYCJTCDhBePTE1DzGti8opmNFjd7xqoDfuKzneHDy+SX95qdDzTbTnj2Pvx0t4PM3/qLQIkMc9kGhXY6u0ZZ5GiDUGLEizzhJu9Z12DqZtpmh7+SRoKkoR2mc/TsNBvVOdKBOGu0MLlK/mjnrcRMROzaXJWBXgQDM0z6t8gSZOoHPWu2LRO9wq3XpGrrEIeVxgwUEyEnFEMZQKkxICSVuDAJBIDcmtAYwTFVDsvaMDCTdbQAKXJW7wPJ0yIVnXPkI6vAx1fLv1sNJMPDZrzhkE5fb4G+/JypvE7n7wneM1u2Cypm8pWtfM4IsvORVc4Pc1ArUq/tCSN4blyT7bCOfc/0WG3YP+z2/2V0S2fvbERNuI6oey+Pydx+lBlhOQlC43JNohgi0xYHtpOx6qv+pwAIVaL3KHBEzTpUJzif0gnaDunpaya6QjyzJxbuvdKTQkG5TGnvlkZvm28mhfjXCZVBTlr1RJWCmhpazA57WUajGsrv0rGd4OoIxd/Ffrtd0kgw9rycXNeH6uaeBPUYhFEkamHDkxzxQM2MW4f7kvFJY7Ex3TXFNhYnL38A/zX/ACf0pbG2haPHtgE/8fHw8PDw8NhK4UNd0w8/8dlO8YHn759ynw57USIZnrIpnEaZ4bGt8O2q06ZadhExQhiFshyGos3z3GV6ujFBANAaHkXeGnLaADJUFam/dYo6ALBqDSJNjGCWJ5lJQbdLW5RXyfY+Z/VsmfeRKDYp56AMJIpB6yqdPK5DNOV5WJ4WtZeiqklfF1FNvlZsDslakhECDNsjG1osSmMMot0yKfUiSxDNlCUnaM8Agp1fKa9zfLUsbaEYDsoz0MkRcx6++kXVrqjOToIILLDeJAtGCAxAOucVLAYLeMGeMPf96pau3S1ESsosTemcUghfamOX8rCYN+f4QVTYClTr5v0BLUqCiKTVcSxis076/c7clGrKrdR2i/mipVIU3LJW4ChS/u1QFyvdE30fNRNULt0BKHG0DpXFFROulbLmgplijBoW1C6B4ZbGEIaFLcZQmJBq1iVgxISmRM7RHmsbpidBYVPBE452LkNflb4IYV3ZA7QSsDAwwumcEVSUCSmLI6QqcWD82Rcx8tRqADDMz9YERmCqtU9khYRAV2bvDOBvyrH4UNd0Y+Mq2Hl4eHh4eHh4bEPwjM8Ognc9c595vWSPQwDAEToCclVo62DKxQ7NCpIxw/IQRorq1JbBGQDHlGwqvQdhRdV3FjEE9aoxzWNRaEzObIYm7KmDKK0NoMs/yDG0V4+b7TQMjFiV57mTos3CsGsBSWLpeJz06CiWjEKtz/TJlQ7HZhNorQ+0b4b6gwKCmzRzUCrT3QGAZIW4OYwN60OiOhhgsTSF8JlEsRE9AwDtnwk6S9b+EISCjMqUeD46bBgaEtcLDYzWEWlmg+duOrzWPmWpy9BY10dUOni5D1mjXsdmcqjDohk2rNy/xBAZhsbap40jdao6ja3PRLtl7oFznaX/SWRphhiz0sILQbZd7FTv0+MV3P1cFUnmnejKMKr/udXGLpuhv49aBA0AWZ477crfrUB9xuxirAglgzVVqrw2HpUauwEAQDI2idbwKBorVpt2qZX2rpmg9liC6iy5P2sl4EkKFsv3qzJYR9hXM9eQjsnP78hTq9FQKe88F1iyxyHGbyblHIuX3mvOefNeh3aMU+QcjWaGtz8t2/3v7gejp0dedzKZbnTB01wUep4qK1hybWBYLly6KUE3skSG9/HphJ/4eHh4eHh4bKUglrRgg/r7iU8H/MRnB0aedKaR3rrP6wEoVkQxQhwUIs2LYoUJR3VQrZgZMeyPNioDgKSRllKA3aKE3bZTShDEkWFp2qvHzWo76qt31Y5oxkCbHlIrzZcyVqyGLcYniCtgceRoMcxqPoiK9GiL8ZFakqjQuliFRAEULAYAkUrtAwFA0ARyVS4hrJqSCoJQY2xo631EWAXjGZhiZXizUTA0anzyfCFo3wwInb6dNE32lsgS086F1BsZs8Wk1aUNQKyngtGbmBRrCsK7s0EFm0JdvY4Na1yE0jUmBrs6nE5mSpsX2mwQV4yYe88KnVXXchma6aIM0HohzgqGjOZu1psNnjtlPATjUgwDVX7E+syae1liGkXOjQZIcO4wRiaF3dL0BErvw2Fdo30ew8C6j3eRc1Pg1AZlFGGfZMtqc2eY7191Zj+ivpp5LyeWDRudXjsXsEfaHtGGhZOgYWCY2qivbkxEs1aCkaWyyG5zpIUj/3y36b9kj0OK5027+FTctvAwzN53lhq/QDKhDUnltd++/xsBADPn95g+3Sw0NgZVRsAUO5twgYlMIN+8Ge0e0ww/8fHw8PDw8NhKQRnpcNBer/6e8emAn/jsgFhT/Nteha0Jv190FAAgqAaG2dHeHQCcjK4yWFjSFunMr1RqELQviq1REDk37E/eboNpjUWWmBWlOX6kVu2qiGUZhFEne4sGYbHSD0uMD9OagtwwKgAc3QgJIxBVEBM8h9DMC2WSfVHHo/Ji5T4r+4vkKUgqzdx01pdhMXhumCUaW6Z89UEIykCa8lzZ8ArTjvUOOOyLKV+RJiDVOoQydaS9A84+UcpqAqT2RlhSEQIYDxyR58V4YHnkdGNGbF2OtW1dtEGEUpfxsdg1qf/pZLdIEBbjDiJzneZ968pUFYVjwXOIXLGAnDnX6ZyHsg4mpzBwLGB7/dht9GtXr6OyxFCwN/Z9y5UXE8m7Zc91/8zL42WlMRXnrM0eACAZmkTpcPJWgqivjr7dZXmUqLeG0SdfkPss88NKXwRqaW+CaoRIMUgsjgwj3Fw1gsmXpI/VEQ/9xhkbCygqfdLoM3+pKKpbn1ND7zzJ5ow9P46x56WGL0lzxHGAgd36zRiaI/Lzbz+HNgZa4xNSaoqSJlwonc+0nGLdMMVzbJ1BPD1Vhp/4eGwQNJ1s08q2mLls2EYsV+egGpiHYdbMjOlhVA+RTjbNRMaeuHQIkdUPV95yJz5BtXBJLlf31hOiwqDQcvNVP3gkigsxMLNSnSGFrE7IxAhkY+fH1xbVOj/sLIQIVYXvIAaY+vpZ6ezgHIIGoP0yhZ1U64UAOIoB5QItKANpTyAfkWnr+fAKx8FYp3XzZqMIaVEmxdHG8C8yQmWCLuEhDWuiILLCbHGNgmQ+RbihLFq2QjzlsBDpYnRImBWOCkIZmlTiZuecPC+ebvb7xpVouctY5QRLC7xRVHovmSaKNCkmUkDnj5L1u+uYG07x4yU4n6I2WeaEvfQEi5XqiNkgdnjX7ptzUFhp9InrzG3MQSsVCC4nF63hMSTjk6iqSVHPzrOLSd3Ty01/FhaLn6yZytCd+o7V5gya6179xLKORY+GtNWQd7zSH+G3hy0CAAzuMYCWmtCsfmYURz32O6ffnQe/Wb3qMSn0lb6KSeDYUJGz7eRBGEGiJnpy4rN5xc2EFvdmg/rDMz5l+HR2Dw8PDw8Pjx0GnvHx2CBoqjtP8sLAELKiMyCt6nUpC8C1pReWMNItX6FEwGplG/XWDGXusD+UgWvxb2nl6xjqUWpWnjzNCtFzGDirfju8ZVgTQIp09d+cu2Ej23DPYgpIWDAQIk3c0BmhRbV0FhZhLxYAuoJ7ANB0EoKoOmFhFVCrSwEYITRpTyB9/knwySJ939yDLDHXRnsGzHY+9jKEJUwWScuwJ861l9PceQ5LMW6xJ1OLSMvrYZu9scXJZn+5BpfNqDnGhkUquSllYaeo5yXWR5/TDo+lbmjUhMTs9PoA5l51Y2qEdQx7PW0L5YGihpkdwrWr2hNGkSeps60wMKROursd2qI5hSgdB4ApCaG32VXgYTFFhFJAMaVhrYpI1cdDEJqq6elkE2K8YOJoFJg6evW5M9FYIU0200bbfO+1IWo3Y8vanEHUZnV+XgEZYtcsT21mDbVZ8vNf6avg5celMWc5PAYAh997BwApctZhr6gerrOu5Ue7HGhe2+GriBKEurxGmqOprmci40i4QLo509mtUkIb1N8zPh3wEx8PDw8PD4+tFGvSba1T/804SdtW4Cc+HhsN28CQW5qfPMkNM6RXZgBw95veYlLfbcaHhRRZMzHHqwz2gimtikjtQp650fWUV9iEUaOXoGFQsAOsMG8zehKzui+KW9pwUtsBkCwszPtKqeCalbC1MSQIgaACoVJhiV2aggUgKu1dhBWISK5wSdoGOAfJrErY5oAUUNvz0WFpNGiJoM2xs9QIsWm9r7i2LJUMkd3HNiPUDEIYGs2S1CuV9D5aw2Rv1/vU+QlgtbMvocTQlGGzNDajZp+j9CPgaIOc7YVJoU77N2MzxysMFaV2R2uOmNPONjQE506xUyOCzuTnrJvoWDbt1KuJnBvtmTlX3tkOTvmJQusjh8M7mB79f9k00TZB1GxoZbCneL+TlqOZ01XXAan5aaqiwWmjZb7r7bG2saio9EXOdzJrthGpcQb12GGBy9CMb21WDfFgPGW7bjjiod+YhAueCyNGXhOu3fUgVK17rMXMSSkpo5nLFHa9L+ECiZ9LbNPwEx8PDw8PD4+tFD7UNf3YohOfO+64AxdddBHuu+8+LF++HNdddx3e/e53m/1TOU5eeOGF+PSnPw0AWLRoEW6//XZn//vf/35cc801m2zc04VLZu+P0198aEsPY6OhbeRZxAyD84a7butop2P4UT0yfQCY1V1UDxH11c1KlkaxxQ4U7ISt6+mWwcIqKrMrCAE7U0nrIIJQMgoVS9djm+JZ2VqGLaEBRNIEtEFe0rJSpLlJESdRLPU7gNT0sLDQ6FAGKI0PyTPD8oAGhhUyGiJd2kJYGUg8dx5htN5rzluG1v6IpGVdAyvS7gEQGptMLlsXJNsWho5yGIodCwHo4qJWZhOg0tsh9TGgrMS+FEaQa0JRzqKUwt7ttSkbYetJrG22oaJJbYdzXKf8hq0Dyl02zGHGKIWwjRiVY6FAKYU9yUpjK0pe2CnwNkMicm70PnZfpNZnvfSWB/EUhWftgsNhIHVGKlWeRiF4Yh3I0s8VWjqKsK+GqFd+Zlovj2Ji+ai6tuJ+2IakaSNFpY8XjGw9Nq95kjn9bNy28DBjYJg1MydbNE/XzZBw/IUJeSmMrFPWlT2XiGhRFiJWGWZ5VmRyaY2PTmXfnOnshPmsrunGFs3qajQa2H///XHppZd23b98+XLn33e/+10QQnDiiSc67T7xiU847b71rW9tjuF7eHh4eHh4bGPYoozP4sWLsXjx4in3z5071/n7pz/9KY488kjssccezvZardbRdlvARkzitzi0Zuf+449z2JupipECQBBrJseKq9dDY1xWnzsTLI6MpwgAK4OIF6wDo6Dqo6tXsN2NCgvWwd5LKtKrhyr2g1SqBWNTYUWGV7UOUZEaBx7WQAkFsXQrmuGAZVIIWGwNIbALkwoaFCwFC4p2lEEXLAWlEFEN4IqiIJZ2I20V2Vr1PqDeV2QXZanDXhjmKEvBVTFVoXyFOnRIGt0ElCqLiqDQyJjrLGuEdH+dDaX0WQ4zZGXG2TBZcl2Yt6nMA2ExE+Y4mokpGQYWZogFeyb/p4B9bbanD7eMGlFkb4kgMuUswHNzTkJzh/WxmR3bN6djzDnvmgXFECLL22r4FPkURUkJ7dTy6Hbl7Vr/JnJussJ4zo3XUzbZKsacZphcMezoekzBYkZMkVI5vuJh1h5rojLQ2zEewblhLu5+01vw+l/fYvaxiKI2U34faURNFqjIOZKJdTMkLHv8lPGLBQebTC0A6KkUP38sYsXzSY1RZ6qxl5uG4dEZXdnm9PFRmXIb3H/Ksrk7LrYZjc/KlSvx85//HFdeeWXHvu9///v43ve+h6GhISxevBhf+MIX0NvbO+Wx2u022u1CPDo2NjZl202JU1c9hEtm7w8A21zI665DjwAAR4RYNi0sQ6e9a6MxAAjrRQV2FkeI+mpF7SNKnVAOsaqM6zpFtPQRZpWKU7kddn9tdKcmPaSiQ01FFFwKndVDO6yChzU9eIioCtJumL8JW8sDhdKiP6S4mdPCwJDoyQ3PIJQxIVgEEWRFOjsLTNo7oUExgVOTJhpIl1vebJhwTtmBWdg/0gBM2Sc7NTwI3bBPyWiweJ2biQuxw1nl1HZHkEy777PTx/X2cqp6eSz2eJTFQFFPrAKi40BhVIjhLYdpwpj7N7fS8yl3r8eZWIXFhM0WQVtGh4LnzmSb8EIIPtUPV1mcDxSTpKncnrX4eapK6/bx9LH0JMhMgKIAnBeTC32P00YT7dXj6nVL7dfO6kW9PnkOJZjusoJrDsuQWFCPjSVFdfYA4kFpuEmfd9Pa33T/ncaMsD5UM5OP9oQbLtwYLF56L5bscQjCirzWsB4ZsXUYBwh7ikWAyIVxgq6OtgEow9QtkCHlNT7Tj21m4nPllVeit7cXJ5xwgrP9wx/+MBYsWIC5c+fi4Ycfxtlnn42HHnoIN95445THuuCCC3Deeedt6iF7eHh4eHh4bGXYZiY+3/3ud/HhD38YceymOX7iE58wrxcuXIg999wTBx98MO6//34ceOCB5cMAAM4++2yceeaZ5u+xsTHssssum2bg2yF+feDhCOsqfJMLtMdUinWSdxU1a9gCPc0UVfqqCGuS3QjrMcJ6FUFNhZoYAypW2MtKo9ZrWkFzyQTp1XmlakJYImk5RnNmHJRKka0T8omc/wFZT8thOwg1oSEShFNWFjep08qUkLbU6lZwEG1gyDMZuoIyMORqe54oEbNiK8rHVmUuCM9BeGbYHcKKmlG0HhumTLQaRedy6IgXDAcJIoe56Ohjw06d1n0AizlxhcllQ0T3WOUSFpYgeaqaX9b45djV/WbFNTiidov9QRBJ8XVWCJIdYa9VmkN0sFjMXLMd/jJGh+o1oeoYaWI+TxSFWLnM1Dip8kDBeCaukL+jnw5j8e7hLTu8xlM3JEzDAMwJQ2l2jDv9RC4cJlczDzwXXVksymRpmrwl739reNSYHgbVihFiky5FN7WIOapHiAfkd7g92nbqgm0sKHHDRpp9jgdjRIrxYSEDz4uwXP2lSVRV2KuRq5T2zRnqIqTr/Vrn/twzPmVsEyUrfv3rX+PRRx/Fxz/+8bW2PfDAAxGGIR5//PEp21QqFfT19Tn/PDw8PDw8tjZQRjf63/rgggsuwCGHHILe3l7MmTMH7373u/Hoo486bYQQOPfcczF//nxUq1UsWrQIjzzyyHRe9ibFNsH4fOc738FBBx2E/ffff61tH3nkEaRpinnz5m2GkW08tLbnW0OvxSkrH9zs5795r0PXKgrshmLlx03qqc32/H7RUSCU4JBbbjLbtAi60l9BWJealqBeRWVQrgjDehVhX1+xog+iouRCloBPdjIRBAAJw0L0G9ccjY/9lTcrY9scT16EVeDTWn0LblLRjR5HMTa0WgfXqe1ZWhQC7R0ozt0clf1MqnoG1pA2/2gMm+rsIowhqNTq0PaEPKe2crBek6wNoU0PG2MQsHRL+j6UQAJLm6Kuz9ln3QOzfQ3GgrIauf26i96nfIzS8bppida6fu4mdKZUMl1aPB4UZUDKTFK5OrwtSDbXbpsRAkbHY1+zOZ5mHxkzJTAEJBOn+7lmjyjYJGqlgqPQ4ACKcZmiAGkZdoHXqbCmfTbzkU3Kz28QV4yxYZ5moCEFUeOxU8yZVXTYZiP0j6yu1s7TDO3VE2Z/rtgkOyGi6EvUPop4piw/wXOOiVWTU17D+oILYZgcykhRXLWVmfMTSlDpq5rrq/RVEI0WmtDNXqR0Y9PZxfr1vf3223HaaafhkEMOQZZlOOecc3DsscfiT3/6E+p1+Wy98MIL8Y1vfANXXHEF9tprL3z5y1/GMcccg0cffXSN+tqtBVt04jMxMYEnnnjC/L106VI8+OCDmDFjBnbddVcAMgz1ox/9CF//+tc7+j/55JP4/ve/j7e+9a2YNWsW/vSnP+FTn/oUDjjgALzxjW/cbNfh4eHh4eGxPeCXv/yl8/fll1+OOXPm4L777sOb3/xmCCFw8cUX45xzzjGa2yuvvBJDQ0O4+uqrccopp2yJYa8XtujE595778WRRx5p/ta6m5NOOglXXHEFAOCaa66BEAIf/OAHO/pHUYSbb74Z//qv/4qJiQnssssueNvb3oYvfOELYGwKTcFWilwIfHOOZLROXbX5Mrw2JFvAzsCgjOCw393e0YaF8v7ff/xxAIADf/krs2rhuTArQ56mJnuEhoFZbQIA4xzQep0sdQuI6tVzOwdvNkA1y2Ot7kGZY5hnUpD1/nJqNmDStgFIk8FAGwqGknEJVfZVfbAoINkYtzKVAtBEZVqNvgRhlUQAANG0NDdaL1SpFmxAmoAwBhIXrJVmkwTPIdotcz8cUKbKS8AtsxFEZrvZZt8fWy9j359u28v9ga56nw7YLA3nTv8OHY3VzuiPygVM7e92WaulC7/q60DJ6sBiCk07+9jWdmIVdCVWX6c8iG0qqbLUjO7KygwDZUZvRqwMMabYJEenwwv2x04/nyozrKwRWhNj1I0BsnU9hFHHTkJwDm5KYGRF2ZdcAPbHwGJwWBgUOiUAWUOzSVGRwdmF8dHp5JW+gnUKqpEpb3PbwsOw6OG7pry2dcGxT92Dm/c6VI6ZEQS8GKdmtHSmV9Qbd1xbwsW2Z2CoGJ9y9nKlUkFFm72uAaOjMkNvxowZACRBsWLFChx77LHOsY444gjcddddfuKzNixatAhiLZTh3/3d3+Hv/u7vuu7bZZddOlybPTw8PDw8thdsiE7H6S9k33ICzxe+8AWce+65a+wrhMCZZ56Jww8/HAsXLgQArFixAgAwNDTktB0aGsIzzzyzwePcnNgmND47CtgUJTqmA7fu83oAkm2xNT1H/vlu3LbwMABYr9WUXXS0GwgjCKqBY3K233U/BwA88fG/MrH1IAzNijxrJRhbuhxBVa72qrN5wWIEEWj/TPU6dDxaeKMNQpXGpsyKaLbD1nkoRsSswCuxk81lYGcnlUpKgFDneEZfIzhEW2oSaL0XgnOIdrP7TdLlIpQfjT6+AEwRVIfloswtMKq36bHqsWWpo3Hp0NtMaQho6566a0OmZGj0GNaGcoHRNe2zmClzf0vZYoRScGo9xqxCsN0KuJb/JpS6rI/xNbLa8Rx2QVaRWxlnWWIxUxQiVX0BxxxRZpMpTRfnRVZZbpXTwJq9eajWDjHqZFqJnBcZYzZzVLqfZbbHZpPShvyMhvUq4pky2SOoxxA5N4xNOtk0zFBul42h7g8ziyMng81uqz19ygzGnQe/Gb3zpdavOmfQ3If2SMM8QzaW7dHQz7+b9zq08CWixJyfUII8zUzpnN55Peh5ZtT039wan+nCc8895yTyrAvb88lPfhJ/+MMfcOedd3bsK5eUEkJMWWZqa4Of+OzA2JAJz/qgOhg7dXw0BC9CXSIvaH79oEnG5MSB59xsqwz2ItJCXisMBFi1uQDw1iRIpB66ScuqwF78EBKmwg5dQjuEMRAVzuKEFhMewZ0fVcCalJTFwOaHVU3qdO2lPHcnKqa99WNViR1jP9FqFOG6LLEqlncJ/5gf1nyN4mJzvjU4IhNnzMWkisA2ROwyycCaJ0edQuEpJjv2uNc0UWOhIx63YUJO5fT68r2bSsxtxMPl9pYg2kq7N+FHq30R3rIE1Ty3QqvK0VgPLSxEx4QWwmLKGHJrckEtt+ccWYeRZzfYBoZOhXjOkVkV2XWYqj53JgQvJkXNVavNazuURSgt0uWZG3br5kgNFIsgc05GUOlTqf+UovmidIseXz5hHJTXB78+8HAAMiw/FY567He46RWvAwC0GEHUI8/PIgae5GbiWB+qY84c+czpm1TGqQLYbIbIGxnqggp1rW8G8+mnn46f/exnuOOOO7Dzzjub7bpKwooVK5wkolWrVnWwQFsrtol0dg8PDw8Pjx0RlBBQuhH/1pOFEULgk5/8JK699lrccsstWLBggbNfGwbbJsFJkuD222/HYYcdNi3XvKnhGZ+tCJsynf3IP99tXuuwVzwYQ+RinVZH64r7jpGCt6AagIUMQSy/dH/+4LvMqiWqR4b+plHg0Od5KzGrxHRsEugrSj7YYlUTXmDMKU+ALOluwGcLaqcKPUEyHIVYNSiOK3hRWwsyvV0ItYInFMhT087U4wqrIIKDqLR1ovdDibXt0g02bLYhgxUWsa4rcFPXRZ4Xqe12xfZu7Eu3kJYtUrbEw4b5sboXDEfi1AQjQWc6fRlTsUFdy1JYLFy395FQKuuf6ZIeeWEd0K12m9VxreO0l4Qd9b0oLUwLaW4YvY4wos18pIkrnNZsoTZ31MdOUyN0t6+BIwOzwm2c5iYVPmBR1xBZObQ1VUkZQgsmKGslCHWtsDgCrfeBxavl0Botq127MMyMQsvWgBZlQdQxtIEhYRSN5dLOYVZpDGE9NOLmyRdHjCkqWY9yDb9fdBQAoD6njv7dZDr83W96C5ovSQbZfgaWkTUzc854MAahHNS6f1VluDrjBfletwSAtOMw2wVOO+00XH311fjpT3+K3t5eo+np7+9HtVoFIQRnnHEGzj//fOy5557Yc889cf7556NWq+FDH/rQFh79usFPfDw8PDw8PLZS2G7cG9Sfr1/ff//3fwcgk49sXH755fjoRz8KAPjMZz6DZrOJU089FSMjIzj00EOxZMmSbcLDB/ATn60GmyuF/ea9DjWppEEcOKLEOw9+81pFy4BcRWnYFZYB4KAblwCQDA+NmElfBVwDNA2Rc1Mssb16ApRSZK22Gl+h3SGUFqtkq7yCAVWC5VKZCmKVvDAajHKac5YWqe22CZ7gxrhQECpTpYlDBZj/RUWtavPOZaBQ4lsRViQrAchipNZxjEGi4JJt0MwFFOsDVwgs8txJk3cEu5XYEn/T4tqUSNgwR5ao2kkRtxkIlJgmxwDRKgRq6VY6UBJvO8ewmByRFSUeYNsAlFgeZ5w2UyRE8d4x631cF4anjG599HdF2Cn5hdAeQQSShUW5kCwpLAusgrukJJaGpUWz096R5I6WRlhFcUlOQa33LLeL8VrGhkX6OUdRVrQTdjvD6ky2EEYxqLqGeKAXudYCtQpDP56koKHWz8lz24WH7WeAxkPvfCv2/9kN5m8WMWMYyK3nRBgHSMOprUm0rUbfLr2YtfdMdS9yTKyU46RrqWx+9JO/B+AKndOJBMwqvpw2iufJjIghogRNkQNTE8fTio0uUrqeJSvWlmkNSGHzueeeu9assK0VXuPj4eHh4eHhscPAMz7bGa7ZWRZm/cDz93fdL/LCsl1wgbA/NExM1BM6xoQa2qBQMz06+6IbHnjb8QBkCqjOyALkCpAwi4mxVu7U1jLwIjU3TzMEavXJ08wY+ZEoNuyNyFJp9qd1EVHs6lpM0Uk386gj00dvDyNntS8088IzN6uLc+hSEoIGEKEqrJqnILFkf3hYAXIrI4UG5niyY6FNMecRHLDTs4OK0QhB8CJTKehyDY6xXqFBcfRQzrUW7I/I82J/2bDQ6uewP3abLvdSWJoYgzLbY7M5drFZS9ezputcYwp9N3auc5BT91/Tsex+mviDytzSBUwpK56wdmYY73KvTEYYL+53EBWmm4CTkWUzM4JzwwyJUpq7JpY0i9rtLhDre+oYISYZ8saE01YbC4ZWmjoLA5MCD0gtUFWVnCCMIk/ktemyGN1AGDGFj1kYmEyuPMlN8dJu0GaDYRyYds2RFhorJ9V1Z+uUtXrUY7/D7ftLt/+oHqLSXzHPKG4VYu7pidAfUTTyHBhZ62GnBRttYOiLlHbAT3w8PDw8PDy2Umxujc+OAD/x2QbwX/MPwN+88EDH9m8NvRZ6IfDxFQ8CgDHW+tEuB+K9z3WyPkc/+XuzsiGUIIwDhCqbgkUMlb5CV5M1Xf8MzfTUZtUMI3T/8cfhwF/+CoBke2LVP6xL23ltWMaTwu6el7w9uOXjk0+6gXPjVZKkRbmHuGYZzYVFZgwgM6C6lBawQSiVq2mTjUOdwpuGickSl32xMohspkEEsdHxgGfFeav9TibYlKAUuhiqZhaEpR8ilj+N9hhCSW8kaABiO+GpVTzhWWHYqLQk3bK25Di637cOlqebuWE39sw+RrciqB2ZXBRT+RR1PReh8j5Zmqyu6Mb2rE3/Yx9Ln8c+ZNdxqe06E87y5xGA0S8JnpdYKyazwwAgCN0SI7r4bsQ6S4/YXjya/aEFY0oc358cgvFORggys9J+rZmOPE0Bq8KKHI5m5CiY+vkI6jHq8+fI7ZVYfl7UGHhSsDyNZS9hcpVkkPb/2Q2mnE2e5Ih6isy0LE+6ZqnZuO+YY8Et1mt8+YTJCmuNtNZJq1jGEQ/9BgBw16FHIKqHRckKi20hjKBnXg9IlgGPr/cpNgiSGN0Yjc80DmY7gZ/4bMX41tBrAQD2Z/6/5h+ASFVDrjKCRAkC9eTow8vkBOlHuxzY9Zg373WooYdZxEAYKSY7Iy0T9uJcmHYPvO14hHGAniE5iWEhM5OX3vk9+MN73gYA6Bmqozqzx5yLRoUrM2HU0OS02w8nYMzR7DCYk5Jr/3jqsJUOg+iQVhAZ12MHtsNuFBcmhtBiYG7aFfWVqGuKZxsaohAt87gXRNXnEiwCQaIHL9uYSQxxr8GuK6Xb5KSYBBU3ofN69Fj0JE2fyzquHo+p7k4DgGdFWCVLnElNt0frWic93Zyjy/vKx+5S68uEtmzLgjVUFleNABYBeeJuK1+DtY10MaKc6tjlyU63ECgEL10bjOEkASDUrXZqfWWWPcCazB4rhcBWpInjLk7Kk1M9cQqL74wdDmNRaEJOel+313KYcpw558jTFFFvXV0qN99he/ES1mNkasES9Q6AKYd1ABDNBuioTGGny140z5SHT3y7SbIw1dLVZD2oRia0lDRSvO62mwHIUHv/zjJraNarZ2Ny1QSGH5fxpjfcdZtZ0OkJzIbisN/djnvecjQq/cUiMFT1wnguwCIGSrc952aPAn7i4+Hh4eHhsZWCUAJCN0LjsxF9t1f4ic9WDG1o+K2h1+LyeQcAAHoCYmp6zYgYRpWgLxeS5alq8zHLrfN/dz/YsEQsoA5tmiccSUOuBINqYCoRpxMJiJVGqldqAMDi0BRmzlspepSdO4tDszrknCOsxabuVoYirEKj7kZ3hLEOAa6GY4OftEz1cqKqsuv6XODcDe1MUY5AtJvdK1WnaVEZnXMQrmt9UXA7bBXEEIFcEQoWAWqXyFpAZtX3YqGzqtd9SJ4WDJTNJDDF2tjV17sIosv7hFU7THA4TEzBTgiAhZL5Ufu6sWPrFNoqo0tqfFchdNkosVRKwjZ1dKqem0608x7o6yFpYRBZYufKoSsbHcxOuR3pwsKZ75dlpqn/16VDbPbQGW9RDkEkMuxlPos8LxijICwE/ZZJZ+f7UxKj24ypJYi2WZ+pQkki58hRtCGMIqhJSwjBc8P4RL31otRMHBkTUj6+GqAMweyd5Hn7ZxpLicFXtYwIevVjz2H0GcnWZK0MPOfIW/K8tTmDYNEYANcCg1oiX55maI21nZDWxjI9NrJWhvaostWoBsbAkDKKPMnBszXUrJtmULqRRUpzr/Epw98RDw8PDw8Pjx0GnvHZBmCXsvjRLgeiTzExtVlV1JTp1vjwJHIBw+zkQuDaXQ8CILfpei1HPfY73HXoEeZ4lBEjYqaMmFg2egp2I4wlE6RXkpXB3kJMWc+RqurNYT1GoMzKCJOrFL1CdOz3rWrNtoGhFmNytSqlUegaq5nU9NTRONC47hizFdWyWaFnscegtCTEqtxu9BO28JTnlvYmB2GZESsLFOyMrf0RYbVgF/JElq3AFGUjVPFAh40QXBklqn12eY5SEU6g0BmBkO5p45yDCCXczhOZeq9LaPBsaiG4zfJMkTK+tjR3Y0DIrbIOQNfyEx3XZZf0mCItXV9LN9NI2WBqAbPodkyb2elg1qx9a6t9ZBsaKr1XueSHnb5uf0YdpssW7dvIrc8noMwjrYQBzbxR6qS5c2RgUVEh3v5umb5WIVPN+NjQx2Zx5IjUqXqrk5HVCK3PUjBvdwRDu8hmPQNgg88CkFXgWfwMAODlR1cgswqREkbB1DPukFtuMttfd9vNpizF6267vvO+TCOSRormiHyuVREjVM9DwijaY21TMX5zYKPT2TemwOl2Cj/x8fDw8PDw2Eqx0ensG9F3e4Wf+GxjGE05IpXBUYNkfQCARRRZK0OeyNVac7yNE569r+sxtDlYJRdIWxkixfIEcYBKnzyeneJKGEVYq5rVo72SZGFgGB8aBca0sGOlyCgqg8rYL8lMH8E4oPUBnIMnaVedT9ZKDFNE2i0IXQqgWgeCELRnQB6Djht9jF2MVJTIEhKGgJU1Y7LEpiq0SYjM8Ap0poeVxUQZBHQmzyREaBVWZVYRzaztsga2bqRDt1IwTaZJWbeij1OCYIEpjUHy1MlMc4wYLV0QgK7Gj2tMK18DnGKvU6CjkGg5C6yctg44ZUNEEMo0/kyVTxC8O5NjvRZd95Eur219UBempwszJAhV6ey6TIVVniNpFWaRpfIexpJBj8+yZnAKv2o/lhLDU4b5nljfQcIoKAJT2JSFVsHTxGVgnX6W1QNXRYQBmcJeFCbNja6OhgFaw6OINKtLGaLdXyVf9g6AtmR+fNgYQ++u8vuZt9pY/dSwYSbSRmtKlkJneG1qHPHQb0yWGGXUyoSlYBED81ld2zT8xMfDw8PDw2MrBaG0c5Gwnv09XPiJzzaGj6940GR4VV+cNCuRSl8FLGKYfEmuotgadAg6++HXBx6OOI9NPD0eDBFbVvPGPFCt3vTfyVjDsDKVgR70LZgHQK729IqQp5mzesyT1DFW04ULk/FJ04Z1oWTt1WemWCLCqGRsoLKwGCsKmHIOPikzQkAZSNRFT6I0PboPCafIMuOWpocFEIFVDoNQs5oXNCiM6mgAEUnGhzZHQZImeG1Q/b3asBOCRU4Gksm0Elyu+hVLQ8QU2V3mWrrcM5uFsH2EKHMZIEKtDCR095Tp0P7khhVzdD1WVpddBgPonr3V9bV9jRQmW6ucdWX+ZpE0aMxdYz/nOECJFaNdttm7rX7685qnim1TY+3I8Cplj9l/20O3dD32+UgYFfcKcNgfu+BoocfiQJo6ppvGHNEyObSZHDM8YyJqMU4lI0S7FAZPM6PlYdXIsEm2LojapWDUsZIxyezQcDkylXlJ+2YYjx8a18FmyudG3krQGmkY757KQG48fR553zuN/scuaro5oIultsfahoGq9FVQ6YuQpptvMkHZRmZ1+VBXB/zEZxvEyculSeF/zT8AeFb+yFd65YOvqSoJR5TgFwsOBgAsXnpv1+O86f47AcAIBikj5kFLGUXWsmprMYqsqWrutDLLfKyIPwezdwJtyRo5+eiw8+BNG00ndKYnUzQs6HeRc6dul+M8y6h5WPMkA430j0gO3myA6hTgMHQmQdYBHKdmEsUmFZ4EYZESbRn8kYAWLrxB7BgllsFVeIPGvWgROZa4JwRtDJsfWR73gU28KDtoEbOCCeWYi7eEvZbQ2YS+dF9u7bPh/G2JkEVQnNc29qPF/QXnbijGOpLgOYxBNGXrJHzeoHBZNwNByIkcj6WJnYh7QVrjTjhwKidmI0SHmhiuJUVfhq2siangENZ9nFI4XTZI7JaCTmlHvTFiidGnrHJfguOArQXNQWRS4G0BM2EUPMncCU+XtHfdz7RhFOmEnMQEtRhBTX63ssmWeS03yO+MnhhpETVPM2QvLJX9g9CkuaM+iLAu38feBcsw8thzaI/J5834shETfu/fbRBBXYbfHz3pBIw9J593tuh5U+HIP99tXuuEEBYxsGgd3Ng9tmr4iY+Hh4eHh8fWio0UN8MzPh3wE59tGH/zwgP4/k4y7IV2BkYIegL5Ie+dWTXU8dqwMYLB5f/378xr2jtYmOg1GwBahvWxQ2c0DMB0GA0ZSMlgy2F6ws6PKE8zQ+mLVgMijCC0uLJaN9EFbq2eaVzrWOUX6e1F1XRCA1M3SZBCwNyx4rePwzOTIt5EaBiSnIYQPbPBVYiEkVSmuwPAFCnY9jgAQATW6ttOc+9iweUIgI3wuiQMZgyC6HpfohBeA8ZoTrSbhk0QJZFzR4DIOk8HbEZiKhbDPn438TYgtykGiUc94NX+4hRlhmUqJsZi0ITgIOgMAznNhSVMFkKGH+0Q15rqf9nQ9y2MXJPCMkrsJACQoNRWf+bbTfCkBaqZzdK91985m2XV/4eKPbG/Y3YpC3kZWrQsv7O6lIzIi5IVNAzMZ4RUYohUHkOmuVPD/OStpAh3r3zWXA/rnynLy0CKnnt2mm1CS0kjMaaqlYEezNx3gXkd1Z8DAPzhPW9DOpHioBuXdN7LTQAznqY0W2xla/78TCcI3cisLq/x6YCf+Hh4eHh4eGyl8OLm6Yef+GzDuGbnAzG3LldNecbxcpKjbsTOmzYO/ehJJwAA+hfMMeJGYRuuUQoaFEVKQ87RTsYBAEG1YlaElFOIjvTZghnSKx17u8h50T9pyfITWtcQRoXlfxS7DIVlgGiKm6KUJi4sESmhRhti9CZdDP8EDZCrYrGEEFQ0uZC3kdIIQjEF1BI+k9Y41ikh1jbsA4zuRDI31n3Ls2Kfrd2wxM2CZ0qUrfpkie326KROm1V/eTxOejV3X0+Rnk6oJQSfqpCpeg+mMhbkyiJAsBC0NaauOQXJWu57V1yAfRJj/ii6CKZJN6bGvj6lx+r6fk1V7kLv08emzBHRa7aET46rauaW9kYbP1qFdKX2RwmTW5NK56baM1YIyrPEMDQ9n/iyM5yxyz7rWFKY8yn9DwBkebtD+2MLmjVzFA30GI2cNFp0j0d48bdOSkjHJhE25DOA9Q4g3OkV8vXgHMx+02EY2EuyOePPrjSC3PpOs006fNC3DK1h+d7nSY403nw/X4sevgsAcPv+bwQLGRrdyrF4bDPwEx8PDw8PD4+tFDKBZMP8tGR/P0krw098tmHkQiDqKcwHsaIBrqzUWcQ2Keuz95XXAgBWffFUcG0Y2GyYFTybOQ98bFgWLYRcYWp9AE8yo93hadZhtGZnokwFo11IU4gsLRifKDZaFWIZFMoVtTKWq/dJEzhddDQIzepesNAY0JkUdkCVmOiexiwsM0JKYNgFkqegrFI056mbvWVnO5XMDE27EhslrOyzrgU7NWz2Ret48qxjn8N2qGOTAIC2C2jnTh+bXZjqfGt7SK+JetfjEVY2m4hq5h7Q9jhIamnXrOy4KYuNCg5dNYSgC9uzDkVYy32cUiGmEdz3pJQ1J4zeBxBK10biGviLy4qyFZQZM07XBiB1DDkJs1i0PDef/3R8EvWPfXHK67A1d+sLnmZItU4vCsB0kdJ6b5HVlWRdy2doNFfJwqR0ZBxxQ7I30Sv3Q7jHaxDu8RoAQLzwZQiVHUrrvcjVMyRvjBfWFxFzylxsLhzx0G9w0yteh/Y6Zt5NB7xz8/TD3xEPDw8PDw+PHQae8dmG8eFlD+CmV7wOgDTWCivMzO5FLozXzm0LDzMx6jXht4ctwhvuum29xpC12sa0MAoLU0CRJVJjYxc1NGPj4EqHQCgtZt96ZWLpevK0c1VHmKX3yRKIpGWySuzVO4ksw0GERUYV1Kpdr8jtFb8QRUFNFhn2x9ZqAADJMrPqz7hAK5OMTUgBrstpEAoKURQX5Xlhtic4jCEeoYDW55jyCOpesahgFXheMDSWAaIc19RsBzGMj2LmePe2tjEgAnU/01QVbi20RQ4sJsjd3t3HZ21lLPQYRFgpSn9QBpKqz0vadhkVQtdPe8O5u4vzTiYPgFuUtJNZM5ohFnZkf9lmlAaUSSYN0izS3IcgAonrgCrlgCM/CnH7VWqfa6yp77HOtsobE/J4OUf0gbMBYIq8Q4m+076K0Us+UxxPe3ZF7s+A/T0tswX6e9ceGUdlsFcdp2H28zRDnqSgaqw0CgzLpAsYA/K50Vj2our/CPIXl4EOzpF/W9edjw4jH5HtskbLML1ZK0PWzPDA244HABzw81+u4cqnF0c/+XuMjY0Bc+dulvNRSh1/sw3p7+HCT3y2cWhHURYy8FyAQlPR1Ex8WLRuH/z1nfQA8kHXGh4FAIQrnkUwd1e5fXxETkisSQWryLAPz1KwVBn+MWbEmB0P2Zyb2l+yDlhotjtteV6k+irKHwBQrYOEKtRkh4wYk+PSkyIdogCcsAgYNym3spJ7kTptQh26qfolY5Qg0eFGQhHmifkBJTwrKsKvTRRrw3KLFqV2Jsyjxw43fCPnVNaYbeNE+4e5JCo29y1LinCX3q1NHa3XAKY2KnRE72sOgwl1XhHWXCG5njwyBvPzrictawpx6XOWH/68NNEs9yGsmHAK4U6UAYhEhZ0qpePa75H1/kh0qX9Gc7D+meBK0Mxvv8qEvUTS6jqBJEEEwjmCEz7d/brXgP7TL+zY9vLXzwRTkx87oaAbTOV3SxBNehjydttsp4whT7XZadscj6ZBR1gbABovrAJhL6EysBKACn+r8+iJFAC0V0+gvVqKo7Nmps7VOdb7jz/OqZ6+uVLeNxV8qGv64e+Ih4eHh4eHxw4Dz/hsw7hm5wOx2659ACTDQxlxQl0aPF+nxOkNws4XXYllnz0ZAJA1EwwWJ0XebhsRc95KDE3NKhUEKhO2NTzaQbVr0DBwGCO7nT5uEVqzVsmmUeF/IaK6FTLiTtjIEcXaImbBDUMjgggkbbntVNgqIhQVdaw87EE7k68DIkoVz+3K6KWgj2GccmmIaDNKU6R42waEHWnv+nBW6QV5TjpFhXLqjslO3w9Cq9zHFBXUy6/L6JZy7lyPSmXXtdHCirn3dsq6sMJONpvVgZL41DFxzHP3vllMmfksCGExPhyiNVGUQiFUGXTKOlUCkdu/9F6Y85pLLt4DEkYQAGhNhY2i2HyGebMBoerOkeP+D/jPLlbDyadke5JrLnBsH+KP/N/u9wcwYa8Zn/qG2Tb+H/9kXtMwMKns8ryFgWEQV8yzhmdpweqonxTN+Iicm+/q5KqRrgwNiyOwMCzS3htNJONS3Jy1EgRWUkRohcvaY21QRbXqkBcARPXQ1DAEgIfe+VZ5/pcmN4jV3tLwjM/0w098PDw8PDw8tlIQspEGhmsKq++g8BOfbRgfeP5+3LzXoQCk2C9t56j0Kv1FLpBOqDh7MzPtWMTWSei8Ptjpq5cDAF74p49h4pll8jx6ZViVmg279ASp1o0Ik4yMm+1aEKlXi5QxI6a1hZE0DEw1ag3D+LSbJt2aRXFhxGeVlTB9LBGqY3CnV/qlqtuCFgU+RRCZVHeSFUyQEECkV7/JRMe96gq7ojch8jzdxmNDCaLtazAakDwtWJ2pjPf0YXSKu611sTU1dnHXMoLudgkdD2ldjqJ7Y3n6buUq1iAuLo49hXandBwnld96v8vvsXl/aWDoGsIzJAsORfik/N6QuG7OyxtjJv1ca3q6anzsgrTl0imMFQwQLHFvEEKo8+Q3XAb6zjO63oLs2ovMaxZHJtkAANpXf0UNRSYEVD50TnFe696NXHyWPGXsvqeOvYTVnlntnAKnqsRF2mh1tI16a+bag7hi+jWHRyFyjqAq2yVjkyapgScphHp21ObNMMfkSYb26gmjL6RRw5S8qM6qmTIX7dUTGP7z83IcIcXvFx211hI9vz7wcIT1yBzvsN/djiV7HAIAOPape9bY12PbgJ/4eHh4eHh4bKXwoa7pxxad+Nxxxx246KKLcN9992H58uW47rrr8O53v9vs/+hHP4orr7zS6XPooYfi7rvvNn+3222cddZZ+MEPfoBms4mjjjoK3/zmN7HzzjtvrsvYonhxUhUHbKZghKAWqlU/I6ZI6VGP/W6zjGX++d/B8IVnAJCr2qDmsjRaL5Ctftl8GcN61VmhEkbBtNEgK0wPCaNFUUagc6VvsrqoYYNEloBkxepZG/SBMVcfY6FDX6O0IoTnMsVa5STzSi9aXDIxcVyYFHIhEApLF2ExNrbxHdEGhLqdbWwo8qk1PuVMLEMGlUtYpKaNMWEkRekGZ3yATLO3CrLaZoaCWyUVSjoeh92xy09MlTGWp4VBJO3y+NF6myzpGCug7luXAqxm3CU7gA5wyfYIZYJIgrAoZcJ5wba0ixRtzcakrzgMAAzzA6AoLwGLtbKywYqGtCgVAov1KRV/FYAxAySUFVmFYQT+v5fILm8/3b0nlIK9+1Pmb/0OZddeJM0FobRDnCO55gIAUnPH4k7Gzs6YFJx3TWkHZGq50fWEgTlWnidgYQg2IO+jZm/t4wMwmZwaeSsxuh4AJpuT1avm2CwMEfZJiwOeZMjTDBXLULH5kmSP63NnoneffQAA8aoXMKHS5gGgBuC+Y46Vx8g58lSVAUlyHPa72wEAYT1Cz5wauCpDc/eb3oLqgPyO37rP63Hkn4vfn80BP/GZfmzRO9JoNLD//vvj0ksvnbLN8ccfj+XLl5t/N9xwg7P/jDPOwHXXXYdrrrkGd955JyYmJvD2t78dua+l4uHh4eGxjYOqtP2N+efhYosyPosXL8bixYvX2KZSqWDuFEZRo6Oj+M53voOrrroKRx99NADge9/7HnbZZRfcdNNNOO6446Z9zFsD/mv+AQCA+dUQcwfkan5iQjIedjZDnqzdin+6odmboBoh6quDVZUJHc9BmFrtjU06xRJNIVJGEfbUTQkMvd+gi028KfyoC45yDpiCpWGxYrbIIli6nw4I7pgWGsNBdY68PlNeAyhytaJfnXDEKrskZkRmDUEyAIKF4MqokNIAVDMh1rkFoUX5AVCQ8nWWDPu6wfGYoYHLNuh7E0QQghd6F8tQUbAIoIV+yWRUCcmCCG4VHLVhszw2phprWe/jsFQJoG+3zYjZ7W3DwW5mhN30UKLIrpNZXSnS3Q4GAETP3W+a6W0AEC17qChpotoluxwo2ynmx/Rfg3jUZfIslsfW+ZQy5YxezWLabGaK33AZ6FtPA7/hMrnPYkLz679u/g5mzrW8f0IgSxHOHgIAhFmCbEzqY8b/45/Mj2OeZkZfw8JgyoxLnufmc04YNUVXdXv9HAjrhWlo1mw7eiANFgUgrAamfIF4mjlla3QmV56mIFbmVzo2iWRcMnNZKzElLFrDoyYzjlVrpv/EC5IRqs2SY4oHY6M9bA43TPZXz5waarNqZnwiFwhVQVSec/z2sEW+SOk2jq1e43Pbbbdhzpw5GBgYwBFHHIGvfOUrmDNHOnzed999SNMUxx57rGk/f/58LFy4EHfdddeUE592u412u6j3MzY2tmkvYpqhzfLiwRj1IfkFjV5qOumdSSM1oa7NCW5VVmeVShGS4rnj4qwR9PRYoYYcpBKD1ftMH12dmoSRE24x26NYGdR1Vk13B2a5JqtQjA53gOdFiIMyU5EdojAsBACSufcz0vc6F1CMOSJGQNXESbfPTfiFgbDCENGcp1TlXLDQFd92E/by3AihDaxxCx0uFNy9HmfSUfQn9j5biEsDIKgUobPyZLHb5AToEoKyr1EdI4idiYot6kbKjXOxnJSpa6CZCQUBdM11tixbgnTuPmZz9MIfEb3wR/kHC2X4Te9b9pDsUqm7wnFCET5zrxzabgcjXKrCx5Vaca1rmQCZK7W+C2a/tc/+LJsadFnqhsVuvQJMOR2D5xC3XiEvZ3CO81m2Q3HO+bLEhJ5pGCBQ4dqs1UZmh54pddqZCvFWjT0pZpahqyCuGMNBAAjC2EmJt12hCwGzrO+lJyEUAVJlHZFbIun63Jmmf3v1BJLxBpIxed6kkSBryuO1x5pIXlwFAIhmz0FYkxOdPOXg1sSrMtCDnp1my2OPT6KxfNjso4ya+xAPxqCzVLiaEqSNFEGaAX/semunHYSSjcvqomvzS9/xsFVzYIsXL8b3v/993HLLLfj617+Oe+65B295y1vMpGXFihWIogiDg4NOv6GhIaxYsWLK415wwQXo7+83/3bZZZdNeh0eHh4eHh4bAq3x2Zh/Hi62asbn/e9/v3m9cOFCHHzwwdhtt93w85//HCeccMKU/YQQIN0ob4Wzzz4bZ555pvl7bGxsm5r8fHjZAwCAm/c61NC2dogLAFojLSTp5qdjeaKt6hOkE4VAlIaBWdGxOCpWgTw3oS1jh29WrLGh7WW4hZs+zqo2CE1FdgBOFWuhRc9ZWjAFOvTT1qUymAnlIKobEz3HcLCENBdgaiVVDYh5TQhAeBGaIiWWxTAadlVwaoWFNOmCLkyM7qeuQbCgYELU+YrXKh2+PH7bpI9Q019YYyNZ24QIzTF1yIUGBWNjhbM6KqOXTfzKDBAgb5Yjji5qsKVz9kK46rHiWvQ4aSALoqljZjN2N93Dl54wr9NZryy2r3wU4Yo/y+1z94ExS1Rj0SEse9yCWveWZxBBCBJLoXD4zL3FZ4gGFutVNqUkRUr8VOn4gFM+ReS5+czyxjhwxEdkf7v9jf8PpNZXbKDMtXewWc+y+DyT38l0ZMRhaXRoqVsoyhY7l7fpPrpfW7EklYHe4rT6e5/z4v1ttJykBpFzUwLDHoewQkpZq41MlaxIGy3kSYZUhbdELsyYslaGieeloHnG7DnGCqPSFyFppM7Y9evKQI/Zlow1HKF1pa/iGCdW+jIgcUXbHtsWtuqJTxnz5s3DbrvthscffxwAMHfuXCRJgpGREYf1WbVqFQ477LCpDoNKpYJKpTLlfg8PDw8Pj60BPqtr+rFNTXyGh4fx3HPPYd68eQCAgw46CGEY4sYbb8T73vc+AMDy5cvx8MMP48ILO4vxbW846rHf4fb93wgACOMAhBET595SoFFRSHRy1YjZHg/0mlRUXV0aALLJpln5sTiSBRhrSsvgVFcHqGJ1RJqAjxXxeEGZYVlIEBpjPWGl0oog6tT/aC0FY1YhUOIKUk3bADysmpV7yAKzP6DE6HlIuwVb9yIoQ6DYIJq1Tdq9k1ourLRsKnUrxkiPlb6iNktEg0KHUtLudGWZdDutpRXclOEQhLosj6V7sUtG8LAiK6Sr4zpMz1QaF6esBS1W8ZR2prSTglVI5+xlXgcjzxbn0KwMc/UrNsvjHpMApBhDMm9fRMsf6d62a391XdrIsVI31eIdBg3u++Wk1/PMSZu3IdrN4n3lOURLsQ1v+lD38azJVNIap8hz8x3QSQBaOJ23EvR84ssA3DIVtv6unO5us0GOsaH1WkOzSclYwxE4a1bHSZsv9eWcm8+I4AUT1BoeK1idRhM8zYymkQPmdZ5ww9jwxhgi9dwJ4gBZM3PYca3jiXoLEXSqqsDrsj+EyXI8xXhypJuR8fHOzdOPLTrxmZiYwBNPFPT00qVL8eCDD2LGjBmYMWMGzj33XJx44omYN28enn76afzTP/0TZs2ahfe85z0AgP7+fnzsYx/Dpz71KcycORMzZszAWWedhde85jUmy8vDw8PDw8PDQ2OLTnzuvfdeHHnkkeZvrbs56aST8O///u/44x//iP/6r//C6tWrMW/ePBx55JH47//+b/T2FvHjf/mXf0EQBHjf+95nDAyvuOIKsDWtiLYjrF6t0jYJQcyo0fz079aH6CW56lqyxyGbxWr95a+fid5dZKaJMStUK6rm8KhZBdopsiwMnb950gJRmSwkCE1Wishz8IbMvuONMXeFzHMQneVVqTr6H3PcidVG70CCSBUwtdKIVR/OgiL7y85uYpFcwTdlCnAljMFDea8J55Y+JjHsj2aIWKK0TnnmljCwsqgMu5QX1wWo9GabDWIFwzOlvsQyy4MQHcxIV2SW3iKMzXEJzwAWucySSocXQAeb0w1lVkizc06ZCp29pu5P8PLTjn4nG9zVvGajL5hrZuMrkfcOrf36Skjm7QsAHcxPMv81AKQuSETW2GxdUMc1yXtvNFf6Pc4tjVieGrbFZh5FmprPMCAZGVuj1g0kCOXnd4qSIVojhDQpWKYsgeAc2eqXAQDVj34Bje98Xl5rvWpYF57nDkOTp6kpPyFyblgRnmRTMjZA8f3nOQdpFdmQdsZYR58uGXoi5yZlXabNU3NcAMgTnVJfFGgmOUdbaYFWP/aceb5QRhFUA5OabrNmyfikKZKat9oQuUCuNJJpKzNMetKQ429km49ZJ4yZcj4b2t/DxRad+CxatAhCTF1J6Fe/+tVajxHHMS655BJccskl0zk0Dw8PDw+PLQ6v8Zl+bFMaH4+pUY0YgmqASp8UbVcHY8Tqtc582NSI+mqI+uoAihIVeuWVjE2auHtYL4zDEBZZHyLnyFsJCFXaoCwpsld4XqyYs9SskAllDnMjC2yqlV8UF6sdSi2Tw5KPShBBhMUq14Hxj1G6I6294ZnxtiF5UjAmdlmKPFWZYXbpAqsAqp3pY5sUipIRH3WZBwBdtDHW8ZysHlZs12U6DIPEIIgS+dusECEQkXwfCc9AkoYxPSRps9CxMItxsq+hNNbyOEVgvS5lpa2LHiHvny+H3xgG4RnY+EpzfXlfp9mprRUqQzM/HcPMEwgoTZm5X9ZYaWfxUXMPbe8n83nhxnuKT46bDENRMsITh06drUp+c4089eAcx7TQAS+ywsoQrYZhYlpXfQlM6fHsAsAi56ZN2miCJxkGz/hns//lr0tWPoijIvOK8w4mxoZmjDb0Bzi3sr2YYm94miFP8q7HJIyiNSJZMxYOI1TPJMIIKn0V1OdKE1IaBUZfmDZaSMYa5thpKzNFStNWZvQ+rZEWRC7Q9AaG2zT8xGcbR09FvoW983sQ1kP0DMkveX3ugHngVJ4fx20LZZabyPkmqzXDwlCaFgIAZeCTrSJFnVGQXLnDJpmzPbNMygAgpypsVTIm5E2rfpIRM5fofp4DQd2MoWv4hecAL/aRqFqEMbIUQhvnRXUZ9gHcEIYcXBHeSibNJEjY7QhVRoNdwlH25MTergWx5fAJppjslENcQEmVDWciRvLEhL4EKeqXcRCkqjaREEKqRQEQEqESB6BJce+NsNc+px67td1OGe+WQm9XlDeHEOoz0mUCUwavzwRtjhRhuSwxYTA9OdpgCG7S9om+li4O2uU0fsd+gFJAvc2i3ZQTHgD8tW91+tAHizI8xHotG2vn5ghk9k7yNVPfCysF3oSEm41ikh5GZrIlstSEuQAg7C2ciZ3LttLKtfjZxoxPfQOArOau62nxnJsQbTnsVa7cPhVoFADauXkNppQ6VJYnOQQXRtBsm/RlVmgqs2qSRX01hLUqKoM9Ztx64iPyHHlLfoeTRqLS49V3MC8WLkEcoLFqEslURqmbAJ7xmX74iY+Hh4eHh8dWCkI3MqtrI/pur/ATn20cLJKru9rMKnrm9yKe2Q9ApmfqME8YB2go2lavkDYF7ErMpBKDhpOgahUXxBHsgJtmbLJWUb+HhgEoYx3pvoAMW5mvbxA55oOycxcBX5bAVpCR0HodhKC6NIZdk4tQCKpCHFGtCIEJLkXLdpp4IkN3xEr97iwLUWJlrDIKxTaU+lilKOyU9VKdK6HS5QGACAFhnYeo8xCeFcxUqdwEJ8yU08g5N2U3hBCohsV5Wpygqu9DljjhqEL0TNxrs65biFJYz4yzkznhqhbaOoNFDpu0JqPA9UGy0/5G+MwjyFIjOmxFA3Mem80iFksEqGvSbFulakT7NoK/3A46c65TH27Ksit2KQtesGWEWu2D0NR6EwCgvifZi8sQvvcfi+tTVdoBybZolidvJah+9AtrvDcAMHjGP5uwl7CMCWd99t+w6ounmnaE0SKsDZd90KLjDiYoyTThCMKoCW/lSeam1FNiqquLJMervnd9xzgfPekERH2yDYsrCOqxmQiIJDNjC+IKwrp8BrTH2uC5ANNskmUTQhkFiyhYPrU2dbrhGZ/ph78jHh4eHh4eHjsMPOOzDeOnux2E/l6pqQmqAeKZ/QhrqpJxKwFXDEylL8JRj8miirfu8/pNNp600UKu6qgF9T6weo9TeV2vrrLJlom72zSs4BwcMFognqXGa49EMUistDs8B2KpURCZlSbcDVojEUZmhUxrvbJMhRFLJyBVaZEgwgpEUCm2Wyt4kmdOarnDNNhlHEz7RDEiRdq62WelbrsCX8X2MEvX4xgiqu2EIOMC4MXKU98rCuEyLAqCUFlyQh2P8RSgkmnIIYrityEDFdrQkSJkBEQzbFahVlnWwWLaNJEjXANFuZ0V98DSInUYNK4n7DEIypy0/I2GZsrStssclttZrBWZoqgsiapg/bIdfebe4nPfO+DaDVj9O9grU64EIEHFPU9upcprA8+kZYqclsMd0QfOnuKi1x1a71PGnM9/0/n7xa98EoA0RJyqJAa1mJ0kn+xoA0hhsymYyijyNMfeV167xjHyJDdsFosrUhRtTBALBo5GgWHL22NNtEZaMK4WuTBp83nKEdYjhPlmTGenZOMYH1+ktAN+4uPh4eHh4bGVwmt8ph9+4rMV41tDrwUAnLLyQWf7NTsfCACoMoKoXghXcksvA8Ap+qexqTK69Pn0KopVGyCV2BhJEkoBbSOfFuOijJl4Pk9ltpdtjKazUmBnb1EGUpHMFqUMIkvAR4sSFrA0DrrMBYmsgqeMSeO4tlrxBaFZxfOwVmJtXM2GYXayvNBlWCnjNpNjtEMWoyNsTUsXEzx1UV33CUKNjifnrsbAXtOVtTx2AVSdNaXHx7hiBYPQSHRY3naYHVimjAAczVG51IZzvi5wim2WDQw3wFpfBBVkatyhSKZN4wNInQ8ARMsekmxbZFkedHsfjbZJ/c2zonwEAKLlUJblQUdWGwvMZ05wdB5bn5sxx8TTuW6tEaIMtFfWMCRxHeLXVxtGjDcbhg1i7/7U+t6aDYJdlHT2OZcaJiiox+A5B1XfexoGCGLJumaW+aHIeWF9wTj2+NYP13rOfX7wUzx9+gcBABU1gUjH5HMonWyaZyRhFFGvZJT7dp0DwVdiUhnAJhMJMqsYKo28IeC2Dj/x8fDw8PDw2ErR4VW2Af09XPiJz1aMMtOj8YHn7wcgNT5JQ67YGysnkTRSsFB+yPt3G0SgrOcJGzU+PoQR0+bwe++Y1vHO/MzFGLn4LAAynh/Ve42nDqUMTDE9uVXgjzAK5Ll5LTgvsrxq1sfTynQhldh8mUkUgwQhhGJvhJU5Qyi1ylSEhiUSaeL4+5AwMroeInih56AUAsUqW7AIpD0h/+SW7sPS3tiFO4m9WrfaOv+XUd7umBYyh+khAJiK38ssLuXDY3kJkY5zc+t4VrYNz0G1piVPi/5aM6PYrbJvDWyNDitdmzlecU8cTY+V9SQzoNbvAS0IQZIX2iSStZH3zF6vY6wLkp32R7jiz0XGlpXh5ZYUYU4Gm2PGSAgEK9hZYZUe6fr56LJPhPIzKgLFXOrx2AxfkBpWjVolX8BzaZaoWCI+OYb8xWXrcRfWHTrbS0Pr+YI4Mt5igGR9AGDVF08FjQJQZaQYVF1vLpv10azQLv/6/XUejzYi5JwjjAKkky21PUOunkmixY1RYn3eTNTm9KE50lLnz5AnllkjI463zyaH7We2of09HPjgn4eHh4eHh8cOA8/4bOPQ/hJjz4+BRsxofnrm9aI6W8b3a7OqiAflaqo91u7qkzNd0Pb2E//vcwhqq0H7lS8LZWB16ZgaTuHuygGH8eFphqBaeJcYnQ9lpmQFkhZIXAPtHQAA5CMvFq7QlUKTQep9RfFS3Vfrj+IecOXQ7GRY2dk2SmMh9BjytFNLo9p1lF0or+IBqdvpwgh1MCprgWaAGCXGu0cQAmJlkpWLhBb6FMv3RwqA5B9psSoXLHDvQ2m83ZybBVMZdMbfqNWV6eKV3pJ/0vo9jtoZRy6ASqCuo9v7MU1I5+6DcNVjADB10VfNqNksodHylNrZmWD2uAmFCAvGzGbsNNMjoqpkbnQiIQChHNwJZYWvVFgxfXQBWH0uNhoiH3lxfW7BOsMpSVOC1u8NX3gGZn7mYgAyC+zFr3zSlLugJa8ffaSs1XYKGq8r7JI9YT02up72yHjX9slYAzzJQBWbmifc+J/lXP6dr8GFetphMdcb3N/DgZ/4bOMIqsVbSC0KtjncMHWzwnqM3nly0iFyjjfcddsmH1faaCGdaCCqqjo5cd1MPMIwktb6AATPEdTkBCUZlQ8iOwXenuyQUPYnQVhQ9s0GKGCEy3oCBAA0iguTtyA06eva4p/E8p7ktcGudLCwDfF4BoIi9EN4NvUPbam0gVuuwUrrttpNNTkBIc4+qiYrXAgpmFXbcy7MPmKPQR8PQJtLc0L9u8IAU6YipMSkw6NbmYy1mTB2E2rrbSwC17W/srZzrHWqHF9CWxc8F0CFEVNCg1cH1/tY64pw5aPgsbQ86DClVNBCZ1O+hNBi0lhKU3fCXtZnT1gifpIlxeRR8OKzmFrCc6h7qD8X6j4DcN5HALK0yqSsgyfaramNEjcSMj2dmddmnDk39cHsBAdACpwzqwq8rh9Go8AsZPI0w/zzv7Pe49Ep7y/808ekQFpNnlgUmBIY9kQtbyVOFXnb9DWIAxP22lwgjG1UhXVfnb0Tfiro4eHh4eHhAQC444478I53vAPz588HIQTXX3+9s18IgXPPPRfz589HtVrFokWL8Mgjj2yZwW4gPOOzDeNdz9yHJXscAgCo9MqVok61zBOO1vAoACkq7JknV6tRT4j7jz8OAHDgL3+1ycY2eMY/Y/SSzxhhY9gzAFBd8LMQ6xGegzfG5OYwcFaChFI3NVdVaieV2NpOQSgDrfeafToFnoRREd6iAQC5eqbVOhBUkKvyCLza7zA70JXIs5a7Ym435Da4KeMdqeldill27LMKkerrsC7c7aPDIkIyPYBkOwAYYS8lxCVl1P3lghlWp51Jg0hdi5uTIlQmhEBVGwEGscOATRV+c1L37XGXyi7YLIQxh9xAtDnQzOQ5Q0oQiqwoK7Kp0S1kCev+MCkqF9S6Rh1WtkTdvAvLZUJ+drgvSwqBOM9MRXcCAEEIHikWN6oWxWbDqmGcSNaCceHjHHRyBHzFUnnoF5eZhIDptLcb/49/QhBXDKti21MARVg76q1j9JLPAAD6T7/Qsd6Y8/lv4qWv/j0AoFLvAVf97XT4DUHaaCFrJQh00dLeOphKmxd5jkTZbWTqGRT2qGdXPTTp7CxiIIwi2JzF2TezuLnRaGD//ffHySefjBNPPLFj/4UXXohvfOMbuOKKK7DXXnvhy1/+Mo455hg8+uij6O3t3fBxbkb4iY+Hh4eHh8fWCrr+GY8d/dcDixcvxuLFi7vuE0Lg4osvxjnnnIMTTjgBAHDllVdiaGgIV199NU455ZQNH+dmhJ/4bOM49ql7AAA373Woo/cBYFYpAMwqpzqzB0Es2z30zreiOdIyMWxCCQ655aZpG1tuGRoGSasoChqEhokRWQqijQSROKvDvJWABGofz0H7Zsg+1qqYVXsh0jag09GDCkjQNsfW6e2i2ZBMDwBenwERViEqcsUMyoCWKjhqMxhZBh4XqcMknTT7Hf2PDaf4KDd9Aa1nsZkhRdsQTJ3ejkKEzLlwSidQUmh+nPai0O7kXCAxrI4kmTRbpMXQgBRHC+OwR13diVBlMNRYChG1m25NLDZrY5mdMrSuZ7ydm/FX4/UXRG8o0qG9EQw/Jf+ggSlkC54Xafw0kNodm/nS9yqoILXePKbeNwohPx+KmSHtiaLMStYCbSstXNo25oMiz2XRU8UcCVExmiEiOGhLMqgkaZj3h7YbyF96AZlKYRftJujbT5+2+9P4zucBAJWBXqeoZtk1WDD1nWAUPC9ok5mfuRjDF55h/jYlbRgFVT9TG1tsM2+lUrTcK8vdxDP7jJan+eKIYaNELsDiEGEs37DarBpaKrWd57z4Am0mTJdz89jYmLO9UqmgUlm/7+nSpUuxYsUKHHvssc5xjjjiCNx1113bzMTHa3w8PDw8PDy2c+yyyy7o7+83/y644IL1PsaKFSsAAENDQ872oaEhs29bgGd8thPoIqQa97zlaAByZZMnuTE6jAeqCFXGRN/OQDwYI52Qq560leGBtx0PADjg57/c6DHN+NQ3TBw/qK9AoFaytHcQtGcAAMBbDbPCZVmCdHyysKXnHHlTMjEMatULgEQUYlLql0R9EKKnbpgQkmdGs0AA5COrZbssAe2VjBGv9AJhXBgNtsZBE7WypoFTcJRYJnwkz7rTxlMyH9zR/zjmiLofShqaMoMhBIi+P5TJwqSQbI/N9XAhtH8hBCQlDQAZF7AzbxkppcBb98qUwxAUomSUCIt1MkMDTNo8JQTUzl6zSyxsJMZTgclUXkSSC/RE8riVzbxso4nMHrMLxwo7i4sFRRkTQDJfxviRIFSFX4ngIOpYOruPtlRGY9I0eh/RHEeusx/tQrw8B83S4nNRbYDXVEYbZSbLjaRto0nLXlyGfHgFyFF/K/dNzy0BALSu+hKqs9V3K0slQ2GXmJkieyysu9osnd4OWBYXlsngxoLnHOlkyzBHPM2cEj/6ucPqxfMHkJmzYSrfx3QiRZ5u3qwukI3U+BDZ97nnnkNfX5/ZvL5sj3PIEtMshOjYtjXDT3w8PDw8PDy2VkyTuLmvr8+Z+GwI5s6dC0AyP/PmzTPbV61a1cECbc3wE5/tFLZW575jjgVROh6Rc8SDcqUV9dUQWd+DtNEy9u5/eM/bjCdQEAeIekIQZej1yv/88TqPo//0CwHIbI+aNhaM64WOgTL5NwA2OAckWI1UxaIJpUXcPWkVhUgpLQosBhEQVU1mj+C5WTFTm4Wx/VHyFCKI3IKZWiOUp05pAqLM/Ghr1C2bMVXFCYsVMmxPt2wgwQ2L4jBBuoClXShV64rAnDEzSmDJdMwxci6giRlGCfTRcyU50e+jZU8CARg2iYuCFaJEru5yi0Fyrtc6gi6fQQlFaB07E0CwnovBRiaQaE+qjBvWqiei6K/oH4HNq7VI5u0LAIhe+COIMg61S22IdBIirDleTaaUiZXpRngGorQ7ht1RbKZoNw27k736LV3HQe/9KfjkODCpWKJoNWiv+s7EPYWRZHMcmfrO8NFhw/ZMN1gcmXIwrBI7BYVFu2kNnIJwlXU6MWF0PNm1F6E1PIqeT3zZNJ3xqW8AgKP70SUuNhSv/M8fY9lnT4bODRM5B09VpmcYIlTf27TRguDcPAtFzk2ZH/QAhBGk2bbDbkwnFixYgLlz5+LGG2/EAQccAABIkgS33347vva1r23h0a07/MRnB8BBNy7BfcdIMVrSSI24OYgjRL11Q/3SMER7tXyYspCBxvLL3TOvz9T92lCkjRaaL0rztBpl5uFOq3UjOob6XwcLsomJgpZujBlBNJ8cNz8uvDEONjgCOmtnAICo1IvU3mo/aK8MlYHnViq5CsOYCU7g/oTa4SZtIGeHGgCZptwlBV2woPtER//JrR9MW9ycF2MhttuyZWAo1vF3Xro4d26nkHS0nvDYcxhm/W1PbnIhRdBaz5lYwk5GC5EgIQRUDbBMeWdcIGBr/6FoZMISZRfbqwFFpPpXA2qE1lsKyfzXOH9Hyx4CAKRz9ke0/BHHnLB4jy2X7PYEuAphpXu9ab3Pzw9+l/M3+d215niEsWLi3WxAvPED6338dUX6I/lDF/T1m8ULKINIWsagFJSaSREoKwTalns8oRQstO6ZBTv8NR0QOQdXtQJtl/g8b5taXXkrBc+5CffahoVhHIBSgiTdfBOf6RI3rysmJibwxBNPmL+XLl2KBx98EDNmzMCuu+6KM844A+effz723HNP7Lnnnjj//PNRq9XwoQ99aIPHuLnhJz4eHh4eHh5bKzazj8+9996LI4880vx95plnAgBOOukkXHHFFfjMZz6DZrOJU089FSMjIzj00EOxZMmSbcbDBwCIEOu6jtx+MTY2hv7+fqxcsWKjY6DbAv78Qbli7N15AFFv3YgHk7EG0oak3O1U+J55/SCMojLQa9q1hiUztMe3frje55/4f59DbWgWAIDNnAvaK4WZJIrBJ8fAx1cDkPQ8N2UmOJgtxrO+zCQMjViazZxnylaIsAo0ZQiAj4+AzZwPAMh7ZkFUekASxQblqftwMNXFcyMOpe1GZwVxvbJmkVu+YqoU6xL7062UhSCyTEdR44mZsg4cxBgYdoO9R5M2a/p656IIVcmwmWyb8qIfMYJnFerKi5CWfR5GJRsDdIqOJzOB2jrEulYn3DBKISWoKJZnXfpuLYiWPYRkp/2Lv5+7X76wS1SkbSdsmi44dLONb7qQX/91Y09Boriwp0hayEeHjdlgODhYhL54bpigZHQceeKyqJoFskNe042nT/8gmBIx0ygw1eJFzp1n3p7f/ckajzM2NoahuXMxOjq6yX4z9O/Sqp/9B/o2gnEfazQx553/Z5OOdVuDZ3w8PDw8PDy2VmxmA8MdAX7iswNinx/8FADw6EknoDXSMCsdXekdkGZh+/7wZwCAp055HyqDdZPuGdarxt59Q5C3ElOQtFKtg1StwoqUGcaGBCGgK0hTWhTbKz8EKANXBoRi5bOgk0roGcVFGnCWuqUk8qQQm+YJwBV7QwMQropAWtWxO8pSUKuw6FTFSrsooKesvq7NDgEIXqTkT9XepO932WeLkwUsVoZIsTLvQgKVmSGtaqBCgMPV3Gi5Sm4xQyGloFMQM+vK2AxEFKuVnmIg2jYf1jbbAwDJLgduoZFsWpAoNoWBSRCaZAPeGEc22ULlQ+fIv392sWF58lbiaHvqH/uic8yJ//e5TT7u3S/5AZ4+/YPmb63xWfDN/97k595Q+CKl049t8+ni4eHh4eHh4bEB8IzPDoy9r7x2ndrt8a0f4unTPwgaTphtOlPg6dM/iN0v+cF6nbf/9AsxdtlnAQAsftkwOLTWCxJGgNILkEoVtF8WEiVBWBQ2ZQzCsrvXmSIaeoUp0hR8YrUzXrmDS3O31GKtnLTjwsDQpCMDAOcya0vtcxgkZwDudlvLI6JqcT6rgKSGPD8HSGfpC21EplFmbjT7UnbU19sT4a50GCWGOWKUGJYoF4XpYQ6XDSKElNgflZIPOCnsG4ptlenZXiF+9R8gujgwpTKjEpAFgKO4aMe14WDLMMMAkE02zT7NAk2FTantsaGfVzbzs1WD0o0LV/lQVwf8xMfDw8PDw2NrxWbO6toR4Cc+HmvFss+ejJ6dZhvWhKeZKXqatRI8/+mTAAA7X3Qlln32ZADATl+9fI3H7DvtqwCksaFeN7JmA7TeW6wwwxBscLbcyTn4uPQBQqUPVK02Bc8BxiBS5Q/SahS6HitzRgQRRFOuVlGfKctRWAaHYqqsLGJpf5B13eegVL7C1gbx2iBSJjPTJlOOmvI+CcALLZHgIFm7OJ4o9hHKTHHLjAsIy1+nTLZ00/EAUrujR51z12ZeZ1RluYBF6ji+PjYhw2jhCVTW8axOuMPeDLfkEWfGfvW5NYD85ppCb/OmNfuvaJ8t2jNgMjB5Y6xk6FkUJbXLQEQfONv4/WTXXgSuvHIE52tlgDY11pep3lIglIFsxORlY/pur/ATH48poangnp1moz53pjETzFpt8wCrRoF50L389TMR1mQoZ/n//TvM+9K313qO3v9zvgl7hfUWguYkggFlbljrNY7LIkvMhEaMrAIdnCMPoB++Oh08aUHoau+UgdZlCr7gHLkSStPB+eDVfvPjTrJW17CVU7dL/W1ccZnlBD3FREcfTxvapayC4aYcbyPliFXNn2pAQUwfimoYIRYqDGaHmYQAh5tK7pgQdgk1CeFOYoQAuNVOmw7mvBA026EyHfbShbHtiZPtUziWcPRZEx07HAb4Cc/WAvI7Fd6mhQu4AIBbr5CveemNsx3PwxBEGw1SWthOjA1DKAdkQhlYhSG//uvFIfr6zTmp+m7qGnweHlsCfuLj4eHh4eGxtYJspMZnmooFb0/wEx+PtSJttDCx7EVUBnoAALV5M82+9si4YX94WlRSnvelb2P0ks8YoSNPM1O3qwwd9gJk6CtSxwgH3PRXw+q0GqZul0mpDeXKlM2ch2zV87L5W08Dbr9K7kdh9R8se0gyPlW5EqVtBpJoi/2gMA8EnLCVXXGcWHWYBFA8XGwaRFXn5nV5v0ZbHBMqXTvlHE0VkWsEHKHKBecCYDTHYEUeuydipkSDIEU1dX1bbEGzWIu4WGiGSDE61YCYFHQZ8tK1ugTaeVG3SwhAVzYjEKCq00BEMdLuXqnaMzxbJ8ShJ3TfceRHAQDk1ivMd47GNZBqvWBd09QwrCKzXluJBkSbGuoNpcrsmjEKwn5k114kX5/w6Y26pu0dPtQ1/fBPJw8PDw8PD48dBluU8bnjjjtw0UUX4b777sPy5ctx3XXX4d3vfjcAIE1TfO5zn8MNN9yAp556Cv39/Tj66KPx1a9+FfPnzzfHWLRoEW6//XbnuO9///txzTXXbM5L2S6hxX/PnfkR5K22qabMkwxcsTKt4TFT2HT++d9x+veffqExJavNnGlEjslYw/SnYeBY1ff+n/OdYyTXXAAACHp6TGo77RmYOlMhCEEqlr37ER/paJKtfBYsrCLvnwdACp/NypQQiFDJre3q7jyTf9siaNGd7Sg6EYioZkz5Rtu5IYT6KwyZ6s4hDOOT5AJjrdwIjVu5MNqZiiUgpkSmmmvYpoV2SQnShQXSep2QuXohfc6JlKORFEfvjQJT4T3hbmmMwXJ9iu0UbOJF5D2zt/QwNj2O/Gih/fn11ZIt0KVZklZhVNhsQLQaHd0JlSVXCmYIxXeL5873lsY1ALLYafjef9w017M9wDs3Tzu26B1pNBrYf//9cemll3bsm5ycxP3334//+3//L+6//35ce+21eOyxx/DOd76zo+0nPvEJLF++3Pz71re+tTmG7+Hh4eHhsWmhfXw25p+Hgy3K+CxevBiLFy/uuq+/vx833nijs+2SSy7B6173Ojz77LPYddddzfZarYa5c+du0rHuyNjlG1dh2WdPNtlbjRXDyFRxv7TR7GB6AGDk4rMgcm50QSLnpnCh4BzxTKmvYdWa6aPTXW3Ds+gDZ5v9/H8vkX1m72SVr1CrUZVVgjRxzQq7gB/4DtA/3QKqmB1e7YeoqLIZlo5HsAgilOMjPCuKmup2Jf1Cx3niPkyQGMMNq+CrYm+qFnuTWsRRRCWf8rISACWZQM7ltdZDgUixNIHWBKl+dso5g5XtJQqGhxHZTx/DNhxsW+aGFMSkzcchRcDkNnk+4WRsrZyU92Cotv4r0uWNDFSdZ039X2oW93lWdcvoFXYItqeMLJFaOm0cSqlhfETSMtoe+tbTzHdTZGnXMioa2nbCLsPAqjUk11zgfNc9PDYltqmp4OjoKAghGBgYcLZ///vfx6xZs7DvvvvirLPOwvj4+BqP0263MTY25vzz8PDw8PDY2qAniRvzz8PFNpPV1Wq18NnPfhYf+tCH0NfXZ7Z/+MMfxoIFCzB37lw8/PDDOPvss/HQQw91sEU2LrjgApx33nmbY9jbDWzGBgCScRnfn8qoMIgjp0+5IKFG+qOvgdUlKxQMznZcSsXN30X64krTNhyU5mmi1TBlLUSWApQZvQE55hPrdD281QBrK41CfSZEII0FkaeFL4/t6RNUwMNCO0R4BtJS9v1Z29L7UIhQHqvFqhiZzIw+JmbUMDWEFBlVjBb6HICgBxRcyOvLuMBLk5IxmgwZ+pWmRjNH2uaHC2HYE5mJVTBAsaJ8tPanW2mJCgWSQB1buHqiiBGzQkotjc/zE5lhmp6fEMVYlGZpfn3Nj5d5a9mvkXBhWIRVzdywUYDPHtukOPKjMivS6HUKLy1AZU3q128/HYBkZTWrAxTZloDK/tJeXFlqvsOyALF/H6eEd26edhBRLsu8hUAIccTNNtI0xXvf+148++yzuO2225yJTxn33XcfDj74YNx333048MDulZHb7Tba7cIdd2xsDLvssgtWrlixxmNPFy6fdwAA4OTlD2zyc00Xnv/0SWCx/EFnYYCsJe9ftzDX+sIImPv6QKt149yMLDFV14kl8CNBWJgZthoQee48hNcV4TP3AgDyWQsgYml0CCEKRbAQ5pwJCRwBMCNANVQTiuZqkKQpu7DQHGuMh5hIciNijhgxhw6tkFPg1MmC48iccoF2poTFuUCgnmE9IUNIpfgZkBMkPR2QjsryL0qAmhrnuuqQVyfchLOEEFOGl55cnWJcWRn0RgFa6kLbeY6BODTXRwnWOgkq47nxDLn1aNLXE1AYIbi+RxsSZnMwVc01D4ku5obkqL9dp67iV/9RLGTSxHxPs2svMiFpwfk2l9I+NjaGoblzMTo6usl+M8bGxtDf//+39+/hklXluTd8jzEPVbVqHfrcqxuatlWIQhNRMIiECCa0ohCVd6OE7+KCreGLGzRBJG6JnxswEYwHQi48JGb7Amrc+LoDbqO8kTYclKAEGg8IHlAbuoVumu5ex1pVNeccY3x/jOOsqtWntbrX6l7P77oWzJo1D6NmVVc98x738zxD2P3I/4vB/vqBH2eygSWvPuegjvVwY94rPnme4+1vfzs2b96Me++9d69v3Kte9SokSYKnnnpq2sCnUqmgUqkcjOESBEEQBDGPmdeBjw16nnrqKdx3331YunTpXvd54oknkOc5Vq1adQhGeGAcTkqPRQmJoqFVDZnGezUQ7w88NR3QI1+gENDKDguntKzMHki/rFoHWg2ojf/kntvnu9G2fj1RYxekMApSWoOyU1o8guD6/FIqNAvpFB+lvKG4rzKExOyjohS2pl8hJThjSI0gEbGgLYRUyIMpJ5tZzhmQSwbbNz3hDNWKT3XPpFd4VGBITksp7OW5rEz4abR9UX321CH9+SnRU4mRUM5EzSVDIxMQiTVy9z7ebycLHN3f+ytIQvVMxdfPmf/3EKttTzCgPA02VfhU//Aa5AqwE3mJyFwrEla0wYSespGVgd4DWSjY4oYHsKtOOrDTW34K7HBTeOYSxvmMvm9n87v6SGFOA5/JyUn86le/co83b96MH/3oR1iyZAlWr16N//Jf/gsee+wxfPOb34QQAtu3bwcALFmyBGma4te//jX++Z//GW9605uwbNkyPPnkk3j/+9+PV77ylTj99NPn6mURBEEQxOzAZujxYeTx6WROA59HH30UZ511lnt81VVXAQAuueQSXHfddfjGN74BADjppJNK+913330488wzkaYp/v3f/x1///d/j8nJSaxZswZvfvObce211yIiJ/ussuamL7nl5/7qXeBpMqPj2WKGSkiXxiq/eQvYG97ttgnv58O7TXn3Z4J0dv2lwJxRct/f9+K4MwAAyeaHXbNOJQunKKi07lQH58Fxiguc8tEsFJj5comYVxwKqZBLBS/GsFIjUavEJJx1dVN3XdiV76DelzCkZkPGvEcIANqFLDUXdWdk3uis/3cg9+2eTj/N1glr1vbragnHZFtitGlaj9R7f1amU3sAYO1AgmcmTLNaeG/UqnqMHSa9vZc5OrQshh3hO7vHW3KhXCFHGVd8T9i4Ch4fWdPh8ZP3er9NkUGsP/ugn9OanucLW6+6GNIY2Nb+/T/P8WiIuWJOA58zzzwTe/JW7813vWbNmq6qzQRBEARxxMDYzIz3080ZL2DmtceHmF9s+/D/FwBmpS5ErxL1+3p3eCAZXHsiX3cqks0PAzCvTRiPjxRgphFpxBlEoZwKlUuFhqk8mAdSSyXmTq1hgW8HANLI+28SzhC79f6LqWW8KC7VnXlliTPm0uHTiCGRmWuUmlYiTJnxhOnsOsNLH6sazf4X4JqB3l8hT6vceXAY034eYM8qTydrB7RS9MxEjrX9XjVasYcihmEGWlj40C7nUiGNuLvmImjIGjGf8l+JWKDUHRkUx79eqz7AIVF7iFmC8RkGPuTx6YSuCEEQBEEQCwZSfIh9Jq76YmS24Niuj1+JpR+4eY5GNHvk604FAKTbngBLTfZY0fJ1fESETPisrkxIPD+ps1SSiKEv0du1C+k8IwnX7R7sjG2rUE6JqSUM/SbrKVSMCqmMytM9xkz4lhWZUOBxBZE0Y1Xc1evRPiS9nULvgoUHmxcNJs7/M50qtK9Y5Wd/CdWfXHo/ViElCumvo1XmajH3DVxVAcWOvK/H4vjXz/UQ5pTQq3i4oBiHmoFqM5N9j1SOvH/ZxEFj+Yd8M9mxWz4AQKdKjtx8NQBg8ZWfnJNxzSpFG6zQ/YjAI6hY9/NKeISEM5dObov1AcBQJUF/pVxJGdBTPBwMTWOmHGnmqJjU7lxyNHM/FWR/cGsxB4uYKyDImU91B/M9uDjrnrpn5uTT+HgPOTMNeGYTW/QQsQ4qW+YCtwtVmvZKzRtxJAY9xGEKTXXNOnRFCIIgCIJYMNBtDXFADL334wCA8c98EFFyBH2MeOSK2MnqIPJIpzQLoVPTm8ZAnHCOowe1GlRLmEsS95NMOuV9vC2wo6Hbe+RSoVKYe41q7P715UI5BWlpX4JazFzfLcaA2KhEEfMp/gwAk8KZm4k9Y6e9bIq7LT6plHQqWsLZnEwLEsQeYT3k3f3dnyhB35oEQRAEMV/hXP/NZH+iBAU+xIwYvOJjmPiHv5rrYcwaKq66Tu0irmIq02nQzUKnPVtlJmIMfYlNLedoC59Kbj05QinkUmKo6v+ZWRP0UCVyTU7bhcJkZpUkb7oF9M1azeweBc1Mj7RU60NFZzf30YyhMNe6fw+tOghiriBz8+xDV4QgCIIgiAUDKT4EASB99scAANm32K2LihZiblP4lUuJBnQxQd8wVJaesxlauVSoxhz9pktpwn0Bwoj7DLBKzBCZuzIJuz5sM6ER0hdQ1IUQe1c2L5RPh9+XpqQLmVrMEZls+fmSDUcQJSira9ahwIcgCIIg5isU+Mw6dEWIGcOTGDyJ0bzt+rkeyoFjvlwUj8GUBFMSULoYYRLptg8J14UK+5IIScTQLhTahUIjF6a4oXRKjlJAZtpPpJFuKFqLGQqpUEi9X2Rq9ESMoRJzVGIODu0NsokctZhBKV34UCitIuVSQUiFYpp/vjHTSo9VeyZyhYlcYTyTPbdfyFS4vl5zpfZIMCjKuiGIQwopPsSMqb/rIwCA5m3Xu+Cndum1czmk/cfeFYVGQh659PVMKhOg6KdSztxEky1ECABCwu0z0sqxst9Xux5pC0y29XPL67EzN0vl06vTiEEEBuk46K9VSOVSr9tKIZPKVX/mwY8nD6bARjNZKoZIzC84FAo5u4FXvPtpAECx5EWzd1Bi7iDFZ9ahwIcgCIIg5imKsRlmddEdTycU+BCzhu3fdTjivlgY09NcAGSUotn2Xb2rEXctDew6AIi4QsNMI+VSYirX+1iFxaaqN3OJoapPZ7dfRyJQciTgDNCWXhNUUgEIlCLGfEd2GZibJzPfXyzhDIwBiyhte14xm2oPb41BDK4CAESTL4BP7gQA5MMvn72TEMRhDgU+BEEQBDFfoamuWYcCH2LWqF78YbS/8tG5HsYBYVUeSAFV6wcANAoFaVSUyMjFYdq6VVuERGm75XXt6+FgyKWEMnf0S2oRlphqhGEHdh60olAKAO+ddSCZXy+VbVSqD94upFuOOhQE3wCVUTbDEQhv7AJi02alMoDcvMtR/wrEcbqnXYnDAWpZMevQ9yBBEARBEAsGUnyIWaVy0YcAAPnX/hbJBf99jkez72SrTgAAxKO/RVHRik+rKVy2FGda1bF+mSi4i4o4MFCJ3Hph1J9MKKQRx6Dx1NhtgHKGFme+sWnEGfxWQCAwgSsFAb9PJWLOR5RGzCk+Fa5T2PV4/FgjztxYiCMIzl1jXcUj96HZ3SwgoT/LK4u2a8VCHGbQVNesQ4EPQRAEQcxTqFfX7EOBD3FQSC747yju/ATi8/9yroeyV9Ktj8H2LRADK1ybiEIqp94IqafK+xOvx9jnOPOZWBFjrmGpbmTKUU/KtXj08byUw5hXbjobUWilCcG2fjmJGOyhkw5jj1eCuFteTP0rjlyMR42LHIzpr/Vc6hpBAKBqFaTbnnDKJnEYwWbYnZ0Cny4o8CEOGoxziK9/CgAQvfX9czya6VGVfvfFouIUTfNrIZRyxf8kFKq8HMRMZjoiiTlzXdVjzlCLTW8uE4zYwEUp5Y3H01QTFEpvZ1PYow5jov0KS4Ogp+dxXKo9pa8f6cjaYvDWGABARYkzzdcS7j6XUBKib7HrSZcd9Yq5GCpBzAso8CEIgiCI+Qp5fGYduiLEQSN66/vBeATGI8hv3DzXw5kWFcUQ9aUQ9aVQlQHXD0sqrZhEHKhG3KWg2zvqiDP9x1jQdwuuv1co7oSp5wBK24ezVPYGnds/5v8i09095qxUSLGT8UyiJRRawitWxBGO6S2nGHefqarpEZdGDEwWejqXx/qPOHywgc9M/g5T8jzHf/2v/xW/+c1vZvW4h+8VIQiCIAjiiCVJEtx1112zflwK/YmDCv/jK7vWyW/eAn7uew/9YKaBicIvF23UkzoAoF0oZyZOenhyrKE59OQIBSihSttZRSfivhBZaFgWUiE8etRxLrt/GnFwZRPay+cImU7k2dYoUEhgzQD9s7c8sbONXOorlnCOE5bNLOV7W6Nw71/EGJZWD829JSsyAADPm4iTGgBABYZYljXAp0bI3Hw4ssCnut72trfh61//Oq666qpZOyZ9AxIEQRDEPGWhNyl96Utfir/+67/GQw89hJNPPhn1er30/J//+Z/v9zEp8CEOPZxD3v0Z//BNV8zhYABIAd4cM4OJkQzpO+aY+2KEjGnVJ2wgyplRXRhz/5CE8m0tlDLV5u0+UjmfT5im3ikmhWpQEjGkUt/NK6T7dPe2KOUYaWsVI0xhX1Wnf+6d5FJipJm7xzun9LXuSyL0mdIFES+XBdD7+UKWaWDSahcKFXOZhZKutEEl4kjMW3Ewikgq69sRmc/84zFY0QIA8HYDrGjP+nkJ4mDzP//n/8SiRYuwadMmbNq0qfQcY4wCH4IgCII4oljgU12bN2+e9WNS4EMccvibrnCKT7jMogjsDe8+9ANqN8Dzpl6OEkSmtH9/33JMtLWnphAKSvn6OIwxKBXU8bF3/UK5Nhe51C0ubGZVNWaIjDdHwhctFKqc8cWhgoajDJDG1xP2stgLodLzXEN7mFaT4tOFVMqpNzunMkiz3JdEGKwmbpujBqqoG6WmVUhM5fo9GajEaBZ6nxcaGUaauXv/k4ijz8g8Q5UEy831F23pVL2EAy2hMGEUOgmFlX16u5gz9MX7Nk2h0j63zLIpAKaYYa6XWZEf9j+ACxZqUjrr0DchMSeE01ssMr/oPIL69/8b7A/fufcDfO8rfn/OAe6PEWKPLU95y/THkgLKBBf5mlch3fYEAKAWxeDVxQCA8bbUKe6u75UqFRf0BQPLVZiLwOjMgNJUV9ifqzxonwrPmO8cP72dec9QwFPmwd9OomLKAeycyjDe1oHhRLtAZopXjjRzvGCmvYRU2DbRxrI+HQj1p7ELdDkT7rgjzRw/3zGJXZN6v6X9KVYNVQEAi6sFIq6nUBPOIeGnyjhDaUq1ZT4zWSahqvrzW99LAMSEmQ6NUp26btZZ436+8nf29zIRxJxx1VVX4a//+q9Rr9f3amq+6aab9vv49I1IEARBEPOVOZrq+uxnP4tPfOIT2LZtG0444QTcfPPNOOOMMw58HPvBD3/4Q+R57pangx2gmrXfgc+ll16Kd77znfiDP/iDAzohQXRip7fUxn/SKwI1x03zSAGW6rtnVqmBLV6ul3kE8Ags0XfjSgig8GZVq+Twx/4V8lXn9Tx/vu7U0mOb8pts/xmquTaEJoPDmMiV67WlFCCcBlMuTOhaVACIA+OrUCg5mqMe/2YZAM6YU6eZOlCdhwh5+LkGAGCsXWCyXSA2is94K8fOhld2rOJj/w8Ao80cQiqsGNBToLU0cgqfkMotT2UCL0y0nOJz9JI+9Ff1V2xfEmGsVbhla6IeaeZ40eIajhnUx44jhqdH9WduRyPD8ct1BkvfQLLHz4KsDgEAovHtTvGBLKbdnjh8mIsmpV/96ldx5ZVX4rOf/SxOP/10/OM//iPOOeccPPnkkzjmmGMOeCz7yn333ddzebbY7ysyMTGBDRs24Nhjj8UNN9yAZ599dtYHRRAEQRAE5qRy80033YR3vetd+NM//VO8/OUvx80334w1a9bgc5/73EF4gYee/VZ8/uVf/gW7du3Cl7/8Zdx222249tpr8Ud/9Ed417vehbe85S1IzJ33vvDd734Xn/jEJ7Bp0yZs27YNd911F9761re655VSuP766/H5z38eIyMjOPXUU/GZz3wGJ5zgi3C1221cffXV+F//63+h2WziD//wD/HZz34WRx999P6+NGKOYWdfBtx3G3h9QD9Oq4D0d94s1p8tPrQUqqLvhBWPgfCOSEmwXKfwsvYkZLPhjsWfvFdvUuQQv/uGvY4nH365W453P436knUYaWkFKRPSeXMSDqhAvbH35VYJCL927HPK5rrDqzwAupUeRT0nZoOJTL9v2ybaGJnKkMb6XRmbytG0z401ndKTxhxDtRQAMNnKMTqVY8d4yzwu0DYp8K2pHNLsk1RiVGqxM0g/I5VrLTI2mOP5Sa3k1JMIz0+03XniiJfUoN3m2JtHptxnqBYP7lMxRDE4fGAXiDjiGR8fLz2uVCqoVLoLdmZZhk2bNuGDH/xgaf2GDRvw0EMPHdQxTscjjzyCr33ta9iyZQuyLCs9d+edd+738Q5IP1u6dCn+4i/+Aj/84Q/xn//5n3jpS1+Kiy++GKtXr8b73vc+PPXUU/t0nEajgVe84hX49Kc/3fP5j3/847jpppvw6U9/Go888giGh4dx9tlnY2Jiwm1z5ZVX4q677sIdd9yBBx98EJOTkzj33HMhhOh5TIIgCII4XNAFDGf2BwBr1qzB0NCQ+7vxxht7nm/nzp0QQmDlypWl9StXrsT27dsP+uvt5I477sDpp5+OJ598EnfddRfyPMeTTz6Je++9F0NDQwd0zBmZm7dt24Z77rkH99xzD6Iowpve9CY88cQTOP744/Hxj38c73vf+/a4/znnnINzzjmn53NKKdx888340Ic+hPPPPx8AcPvtt2PlypX4yle+gj/7sz/D2NgYvvCFL+BLX/oS/uiP/ggA8OUvfxlr1qzBd77zHbzhDXu/qyfmGWddCvUfd+hlHoH3afWH1+q6ySKgFZ5I340rU57fotI+KLMdnxpBFO/UTxRtKOv9yTNEP/n2Pqk+Ft5uIBJtpObYQjFYUaZVSKfYxByuoB1jDAkrFyQMbT024SvhHZ6eQOVhIievxixgixSOTGUYm8pLz00ZxWd0KsekeW7VkhqaJmV912SGZqtAbkobtKYyTI5q9afVaEGadhFJtQ8DS2qITeHD1lSOHxv/0NLFNSzt13fXUdBk9oSjBtHMBX47psspLO1LUTP7j03l+N7uXQCAY4ZqWJaYFinms08sDJQCZmL1s/tu3boVg4ODbn0vtSek0zislDpgM/FMuOGGG/B3f/d3uOKKKzAwMIC///u/x7p16/Bnf/ZnWLVq1QEdc78VnzzP8S//8i8499xzsXbtWnzta1/D+973Pmzbtg2333477rnnHnzpS1/CRz7ykQMakGXz5s3Yvn07NmzY4NZVKhW87nWvc3Lbpk2bkOd5aZvVq1dj/fr1cybJEQRBEMR8Y3BwsPQ3XeCzbNkyRFHUpe7s2LGjSwU6FPz617/Gm9/8ZgA6Bmg0GmCM4X3vex8+//nPH9Ax91vxWbVqFaSU+JM/+RP853/+J0466aSubd7whjdg0aJFBzQgi73oveS2Z555xm2TpikWL17ctc2eJLl2u41225dv75z7JOYWdfqF+v/33eayulgUQQnj10lSINVKDyvaYO2Gq2MiqwOQ/TrjS/YtdqX8o8YuoBhxx0KcIPrJtwFg35QfWYA3x9A/sMKtahtvh/R2HQAo1fEJb9TCLC6pANu5oHQTpaQuphioPDZjhzhwbMHBsakcw4uqpUai28e0erO70S5la/12ty7+NzneRtYukJt6P61GjuaE/iyKdtOdIxMCzSRyik+RC0waL1HWLDA2qD+jtWqMo5fogoOTJsvMeo4SzjDe0qrTM7umnK/oFzsn8ZLFSwAA1dm9NMQ8RyoFOQPJZ3/3TdMUJ598MjZu3Ii3ve1tbv3GjRvxlrfsoR7aQWLJkiXO3nLUUUfhpz/9KU488USMjo5iamrqgI6534HP3/3d3+GCCy5AtTr9P7/FixfPWpnpA5Hb9rbNjTfeiOuvv35WxkccRM661HUalwCYmQKLlq4CM2nmYeo6ALCx5xGbvltiYCWUCZBkbQgs0h93VuTg1RZkQwe88ZP3ojj+9V2nT555FLAm6qQGljcRNUcBALXKEKxgGknlprNqsa/iLBXQDNKibbE6QFfldTXppChnXihJwc4sY/tuCamwpJZirK0/N089P4ntZpppNJgC2zHawtS4/oy1pnIUmUBmAp+s2UTe0J8xFRS/FEWGojWJKPXTrzzR01J5u8CUSXOv1hM0jZnZBjZHLzaVl4f7sd1Moz2zY9JVB98+2caYqe5c7duPEt7EYY/CgRcvtfvvL1dddRUuvvhinHLKKTjttNPw+c9/Hlu2bMG7333oKuu/853vxN///d/jjDPOwMaNG3HiiSfi7W9/O/7iL/4C9957LzZu3Ig//MM/PKBj7/dU18UXX7zHoGe2GB7W2Ql7ktuGh4eRZRlGRkam3aYX11xzDcbGxtzf1q1bZ3n0BEEQBHF48o53vAM333wzPvKRj+Ckk07Cd7/7Xdx9991Yu3btIRvD7bffjmaziU9/+tO48EI9C3DNNdfg6quvxvPPP4/zzz8fX/jCFw7o2PO2cvO6deswPDyMjRs34pWvfCUAnWb3wAMP4G//9m8BACeffDKSJMHGjRvx9re/HYA2XP/0pz/Fxz/+8WmPPV0aHzG/sVNg8rF/BctMynq1D2zJKog+PQXF0wkokwLP8inXqwhR6qbAIAvwdgOoaVWFyQLpc4/rpyZHURynq5OyOIEw26hKXafKWyNr1ARP9F16q5DOqKwAZMJPlyjlp7iSyHd3Z1K4dgKKxyjc/hwpn7f/LA9brNw/lQm0C+HS1nc3MldwsNXMXSp6ayrH1JhWfPQ0V+amtfKpMYjCp9TKXC8XrQZyPoa41g8AiNMaEq4/P62xEWRTWv1p1fuRNfV7P2JuPbf3TQIAfvbsWGkMff16nzTmrqcYb42RIriAkKqcIHEg+x8Il19+OS6//PIDP/EMsWrnkiVL3DrOOT7wgQ/gAx/4wIyOPaffsJOTk/jVr37lHm/evBk/+tGPsGTJEhxzzDG48sorccMNN+DYY491BRP7+vpw0UUXAQCGhobwrne9C+9///uxdOlSLFmyBFdffTVOPPFEl+VFEARBEIcrSikXBBzo/ocrByuLbE4Dn0cffRRnnXWWe2ybkV1yySW47bbb8IEPfADNZhOXX365K2B4zz33YGBgwO3zd3/3d4jjGG9/+9tdAcPbbrsNUUTz4EcqcmIUeN3F+sH3vqI/xMP6LlsMrnLGYJY1talZ7wSIoPBVlEIaNQc8dvvwSl17ewBka09xSpBg3PmF9LGnwE0X9yTiULaxpFBO8QG02hMb42rY1FTxCMo0VGXSN7/IhUJKXbRnHZsivrQ/RS4Vltf1e3fsyn5khfboPNcu0DLqT3MiQ2EM0UUukDfGkE1pX0/RnPStUGKfWm7XMfO+8jh1qe5KCrcMAFJ475coCkSx/ioercRIKnq5UosxsEJ7zAbS2PnDqMs6sVA47rjj9hr87N69e7+PO6eBz5lnnrnHaJQxhuuuuw7XXXfdtNtUq1XccsstuOWWWw7CCAmCIAhi7pirqa75wPXXX3/ARQr3BJkJiMMPq/YAwBkXQTx8J5L+RQAAlU36NhdS+LRwHoONv+DX8whxVatEslKHSk32VloH9GqkWx9DtuZVblnWl2h1CAAY18UFASCqoNc9ib1RsY1NhVKIzUohlSt6qFjkPCcRA5gooEAetNnkj4/VJS/GWzlaQrqsrogz9Fd1Ucoo4lC2CW3wa1E0J5FNjblMLpG1SqqOhccJorTmnpNF5pYBrwQVzUkUzUm3TQiPU9QWa79atT6AVYu0yliJOUaaWlEaWtSP+swuB3GYcRjHLjPiwgsvxIoVK/a+4X5CgQ9BEARBzFMWquJzMKtEU+BDHBGInc8BAFSRecWHc0RDSwEArDYA2LtvKSDGdoFNjOrnqn2IrGIUV6BMvR+V1JBsflgvA+CN3a5thuxbDGbUpDipOv9OxBVgqg8JqSAUwMwXTyZ8vR9da8qvt9RiDuTUomK2eWCL7++XFb7BLIDSMrNZd5y5uj3tyd3IJkaQBbV7KgM60yStD0HaOj4m68uqOBIoKT42+0tJAWGyEjtVIR4nTkUqlvZh26g+5mNbRl2T08lsEC9dUsOyWKtWKiZ1kDjyOJimbAp8iMMeder56NWSlj/2r1C5/nGQze0Q688GAEQ/3QjGI1exOf75A5CjehqsOP71SLc9ofep1IHE/6ioIM1cMe56ajEzdQYAFSYhTP76VMetViaUS0nWae56WUJX7AXM9Bjj4E1dm0rWylXJiZnRyAXSmLtAdftUC7snfdq6neJinDmzMqCDFRugxJUa6svXAACS+hCyCW2ulEUGJUQpwAmDmjDYCYMoxiO3XVLrR8tMyY6+4L0Nu0aaWLpYT3s9u7uJE1YP4jVr9PPr0gnIik/4II4sFmpWl5Ry7xsdIBT4EARBEMQ8RQKYSQhw8MKHwxcKfIgjFvmq88B69OOyyk+J4M487JOlzJ20Ylz3BbOd0xkHy00RRR4DdrqBcZjG26jE5bTjQioUZlorjpib4oqDwoZc5GDthuswTxwY33l63PXnihjQn+qvunoSoZELNE0Ke7uQrueaktrgDABRzFyKeVLtR7Q8dSpNVKmhMqSLYXLOII1JHjBtK4xxGfBTXWFrCxkoQZ3/B/yUWXvsBUyYz5AUCk2Tar9zVxO7GhmGTbf35av70Tejq0UQCwsKfAiCIAhinqKU/pvJ/kQZCnyII5p96bxevOx1pcfKtsNIalCRNpoyq8BYX4/IYI1FXEmoVN9zq6QGzvQ/q1rMwBlzvh7At6+QgGtzkQKowhRQnDJ95xLdD49nDciUkpf3hQe2TDiVZ0cjw8iUVkgW96VY1qffx8FqAs4ZprjerhJzDFT1+9UcSNFOjcKiFLhVfyo1JPUhp/IwHqFSS8wyA4+016ao9ZfUHiUFImNUloFfiBcJYlMMk3Fe8vjwJC2pP3lLfxanGEOl0OdMKjGEVGibIohTuUS/NP4h+qwccSzUrK6DCZUAJQiCIAhiwUCKD0F0wGKTsh4Up4PIAR7pbC5ANyy1rTFEDrQbbtMkvOtWQGLv5hkglZZ8moW3HKYR00UVAchKP8Aj5FyfO5HlAnfE9LSFxEhLZ/E9+dw4hLnVXVZPneq2sq6vq/X8hG1Eamns0sfbzQKRbTWS1pBWYsSJLVrIESf6OR6kwgsh0Wok4Ik+h/XqAGX1RxSZW+4shMh4BBZ5349dzzlDpWZ8SoMVvHh5Hf2pyTKLWCnjkDiyWKhZXQcT+tdCEARBEPMUyuqafSjwIY4o2MN3ujtmecpbDugYrk3Fc4+7RqYqrgCMQ5m7eSgJBO0G3B23kuBZw2/DYyCxfo7Ita/gMEoPdIVSxZPgWBG4vUmju7W98vBz+npP5QI/36YLFY41cxy70jSuVcC2CV2rZ0U9RbuQTi2pxBxL+vV7GnGGZqZVvNGEo1rX70mcREgqEVLTPJRx5tQg/bxeFoVCnEROGcrbfZDm/S5yUWpiavfvrE7LOQOPux0IaSVG1ahVS/tT1NIIuTGJtQsFFpnaQVTMkCD2CgU+xBGFDXpmg2z1ib47e30pVJpCmSBGRak3Oodp7nnLL+ujgJvHYZG5WsKd6ZAzYEqYHl4KSCPlChqW0uyJnlhDMwD0G6PyaDN3/c9GpjK0TKDzokU1LOtL0GeDE6nc/lkhMVTTwcXAQMVNY0mpAxo39cWZm0YDgJo5djMTYBwu8Cly4Tu8Z/4zoQOncsBjAyDeUUXaFlTkMQeP/HOTrQI7GjrYWTVQwfBQbd8vGHFYoTDDrK5ZG8mRAwU+BEEQBDFPkUpBziDymcm+RyoU+BBHFEoIqFPPPzgHZ9ylt08Vvrt6NamBmemtUO2xPb/slBhnDaQuLV45NUfyxE1bCKXQLhS4Nc8yDt7W0zfUlqA3VrHZOtpEM9PLRy+uITUKzdhU7lSZaszx6lWD2NbQU1q7A2WomQmX2v7i5f1oLvJKUtjmAgCagcpke33tmmxjVCo3JSalQt42ik8uXB8wWyQRsD3bejdj7DSlWjWoXUhMtApMmmm5HY0MS2u6nMJyNUJtTo4wFGam2lDY0w2lsxMEQRAEsWAgxYcgpiHZ/DDUwFK/Qknk5vapkAo1o8owEZickyoQpLxDFk4FUuEyj6FY4o4Vkkv7HyBKIyBQepz6k9b1sezxohQLkfueGXfLS+spJltaBRnqS1CNtcoTdmNvGXXHeqiSiGPQKDTP7JxybUaW9qdOMaqlEeqp/6qUSkEYNSZizKk/v9mht+9LvS9rwoynmQl3vPD5diFLfqGskCjMGIWQzuMDwBVUbGYCL0y0nDrVn8ZY1qc/S7XBRajnOo2eT424z2ax5EV7vpDEvIUKGM4+FPgQBEEQxHxlhi0raK6rGwp8iCOK2fT35OtORfrsjwHoOWERV5DAKzGR0CnSTOROeQHjrsghExlYkflUdx675qOKx5DMeHykcu0rMqGgFJCau3uuBHhLN8eUtcXO58NkgZzFSOAbqi5EOGMuQ6sviTBk1JtcKow0dTHDiDMMVRK3TUhfwtEwis2qRVWnDDUzgSGjoiypJhgeqPhMMKF8I9oockUTU9P+wmaWAcCO8bZbtgpNGnPnOcoKibGp3DVKFVJhsq3f04lW7jxLWeHVn6KQpeNmhXQ+p+cX1XDsUp3hNTx0FOLm7v24mgSxMKDAhyAIgiDmKRIKcgayzUz2PVKhwIcg9kB21CvccrLjl2CFvtPmcQWIvJLjfD2BjwdSAkq51hbIpgDbBiOuukJ1nMF5RoTUNXxcccOiBSZ6qDoiRxLBK00LkIeencRYu0BQ3gZHDVbdss2660siV7CwbpuQmm1yobDE1O5Zscy3ttg61nQKz7FL61g7lLpjtIryT8ny3HptIrxkcZ/zCU3lErsWaY9NJeZBmwyvPAmlsKOROZWnJaTLMhubyjE6pdWksWbuiisC2hdmnxOygV2mps/TOxv4zYjO8HrlqkG8bOki/brHt0MMDu/LZSXmGdSdffahwIcg9pF8xXFu6kslNdeRXSY1100dPAbLpvSy7cjtOnMLuN9oxn1xQha7VOlqzBEx38WdiQyif3nXWGwhxYVMxBikUhhp6oBgcS1xUz5TuXTTXhGDC2h+O95GzVxjAFjZn2LNkH7vqjHDjoYOJqRSOMasP2YwRV/MYM0SfTHz1bkB9Fd0teTBtIqWqLhU2clcopGbY0ccfcYMr5RygZeQCqsHKmiYAocjrRy56bqeS4WdpsP8jom2M25PZQK7Gxl2jOuO7KNTuTNR7xhv4Zldemy7Gxmk0ub831m6Ev15E3xqRJ93aPX+Xm6COGKgwIcgCIIg5imU1TX7UOBDEPtBaepr+88AAFwWkGbaS6V1wExt8ZZOPXcd3cPu7rJw01QRZ6gntmWFAgOQKNP5vaDu7NNRiTmGKrFTRXZNZW4KKeHe9DxQifDj7fq9aBcSRw9W8KJBbVxeVovQNvLLC1MF6mY66yVpH1bU9XvaF3cXGJRpvWtdX9ZAjXFIowQyxjBgjjcYCdgJthfaDE0znbWsFqMSMUyZ8gVD1cgt50I51WqoEjuz9kgzR8wZhNTbjU75IowAnCH6qecnnYl6d7PAqv4KjjLTXUvaE+CNXQAo1X2+Q1Ndsw8VMCQIgiAIYsFAig9BHCD58MsBaOWHGQWgq5Cgks7sLENDtCh0GjwAzmO3TWQ7eBcts11+MF/CYckj27SHKuLAUDXGMaZB586pDNsn9HV78ZI+JJH3TVnlBECp9QQAVJi+9ivrsWsdUkiF/tQWoty3W2arAjGz/SBazsfFsiZkdchsKdBv1Kg0YkjzBhLTVT3mkSuumAmF/ooew+Ja4pqSVuI20pi7tPldkxnGjBrUzApMGcVndCpz3ep3T2YY6kuwdrFWo161agBrFh2tzzmyBcXiY/bpNRKHHsrqmn0o8CEIgiCIeQpNdc0+FPgQxAzJh1+OZOevup9QEixvAdykreccSsTuOavmsKCwIZdtnRVmnqPsm25evarPLT+xs411i/U1XVxNsG1SlxtIOHcZXlIpHL+iH4BuStp1B2x8VwmAxPl52Ix/McLMOxWlLhNsoFLHaEuPrVUoJGndlUmoxokrolhI5bIA20Ihtm02ONMZbAP6GLsGMmwf00rXRKtwqfGTLe/9GTWNWguTMbZtIsNQRY9vqDaEePfT+pzk95l3UHf22Yc8PgRBEARBLBhI8SGI2aAwXpzAk8PyFvIVx5U2s8qQYhwq1jVeWNECrO9ESUAKX/SQAAA8/JxWS0Rw9/rao/qRS4lI6vu3RbXYZXKlQSZWVihXzHCokqCZS+xsarVkWa3cwuJgYj1AtfYEqqb2DxMtSPgMMQ4FFRSztCpPxODaZCyvp1hcU67NxZJa4jxM2yfb2D2pvUD9ldgVUxzqS3D8ygGcNKyVr6FKhIq97VXxgm1yezggpP6byf5EGQp8iMMa9h936DTx0y6Y66EAAHg+5ZY7gx4AyJe9FIAOgNxPuCjAmOm9ZAOeBVyR2WKDnRCp4LqZP/TsJBLOkQsdxCyqxaiZIoEvHkqwdcIGj+UO6K1ComHTyXHoAh+L7bcGeDM8a0/65+GDENvDbXdLuHX9KUfCmavPsrQvdoFPHHSbB3TwA+hK0esW17DCBHrx6G/9Z43HgKCyCfMVmuqafWiqiyAIgiCIBQMpPgQxC9jU9n3eftlLnaFUxRV3983yNhjdfeORbVNd01qAVnl4ZKd/mJv+AYBWLt1U169Hc9RsUchcYbdJ9x6qJFjVn6JmpsIKBfSoT3jIkXXdWiKafAH9pv2JiitoKG16jzlD1bzu/pQjDV64VNyl6FdijjHTviKJmFN/Ig4MppEzUbOi5dRHYn4jlSr9WziQ/YkyFPgQBEEQxDxFt6yYSeAzi4M5Qpj3U10vetGLwBjr+rviiisAAJdeemnXc695zWvmeNTEoUJJOW/8PftDsuOXUFEKFaVgSmqlJ29rUzOb9/8sDxqPPT+Fx57XPinboygsOJhwjr4kQl8SoRJzJJyjEkWoRL7buVAKES+bQpfUEiypJRjuT9CXcGRCIRMKk/n88lKJ/uWQlQHIygBUlKJfTqFfTmFNH7A8FVieCtRkC3E2Ca4EuBKIIbG0FmFpLcK6RSlOXFHDiStqOGF5DesWpVi3KMXagQRLeBu8OQreHAXk/HrdBHEomfeKzyOPPAIhvLHvpz/9Kc4++2xccIH/sXvjG9+IW2+91T1OU8pQIAiCIA5/KKtr9pn3gc/y5ctLjz/2sY/hJS95CV73ute5dZVKBcPDw4d6aMR84IyL5noEBwbnrp2BimIw06pCxRWoKAZvd2c0Hek8/kLLpZ3nymdi2RYOnZy0Qhfg+/lu7Vtp5xIi1/u0C4bBqr6+S2ox6olX0bZPFvj5Tp1FddRgFcct0Z6aXs1I55pezVARAXzsOUSmCS4QqGJSlDa1rVB0mYTCPe6VcUjMTyira/Y5rDT1LMvw5S9/Ge985zvBAvn7/vvvx4oVK3Dcccfhsssuw44dO+ZwlARBEARBzFfmveIT8vWvfx2jo6O49NJL3bpzzjkHF1xwAdauXYvNmzfjwx/+MF7/+tdj06ZNqJgiYZ2022202233eHx8/GAPnSC6MV4eJgpXsFAxBiaKBVfH5/EXWmgV/jWfurru6vicutqrHknEnLrxxM42pPLFCRPu7+M4Y5hs6+OlnCEzBXFahcREVqASazWoGnNMIyjNa6iVycJBzDCrayb7HqkcVoHPF77wBZxzzjlYvdr/o3/HO97hltevX49TTjkFa9euxbe+9S2cf/75PY9z44034vrrrz/o4yUIgiCImSAxs8yshXULtW8cNoHPM888g+985zu4884797jdqlWrsHbtWjz11FPTbnPNNdfgqquuco/Hx8exZs2aWRsrQewLtqYKRKZVHgBIdQNOlrf2uG+y45dQaR+UVY2URLHoaAAAb44ApiKw4rGuEzQP+eVIhjxQYqRSSEytmseen3JKz2PPTzlV56QVNTz+gr42uZRoF9I9N1CJnOoTcTiVZyKTLntLSIVqzHHiCn3s/pS7+jgtodAwHiGhFPqNL2g+en+IhYOQqlR5/ED2J8ocNoHPrbfeihUrVuDNb37zHrfbtWsXtm7dilWrVk27TaVSmXYajCAOBfmyl7q+XWEhuWTHLwGl9l7EUCnd76uif8Bl0P1bxVVvnOb+n7hiDEXwJWp/8OcKxrzxUk9hceRBmrVNaxcSANfbPf5CywVHuQlmQvOm7bwupTf85lLC9gfhjKE/5RgyjaqqEcN4po8zmUuMt23XdImlNV08cCpmqMQcqTmvkMq1kginyZh5bLurJxQvEcS85LAwN0spceutt+KSSy5BHPsv8snJSVx99dX4/ve/j6effhr3338/zjvvPCxbtgxve9vb5nDEBEEQBDFzlMnqOtA/RR6fLg4Lxec73/kOtmzZgne+852l9VEU4fHHH8cXv/hFjI6OYtWqVTjrrLPw1a9+FQMDA9McjTii+N5X9P8Pk7T29LnH3XK2+sSu5/MVxyHd+hiyNa/a43FY2GASgEpqkEbhYEF3d6aka4bKwMEA1/F7rjl2UYpfjnhlq11IZ3BuF9J1FteNSG3HcomKufkZqMRQCpjKfQq3/Y6XSqFqpqqskRnQCk8cMbTM8ZqFwohpACqUQn/qU+CnjKL024kc9TRyKfFSwT0XMYYgwRScAUtMGr2Mue+AvgckGCKhpz2pSzrRiVC+We2B7k+UOSwCnw0bNvSMWmu1Gr797W/PwYgIgiAIgjgcOSwCH4LYE4xzHDY3NUGaerr1MQDoUneyNa/yypCSUIVusMk471aCgjR4a+aNGEdslAMmMlhBQgGIeORUkUKpOW/QedxiPc5fj+al9UnEXSZLLmWpbYUlYgwvXpTgN2N+XytmSVXe3q5fVovw28kChTl4JWJuuV1I1JzKxErnbGSipCbZpqcRY+hP9deoVZ7sbss5Qxv+GE2jZmVCOfWomUu0hMIy4yeq9r5MxAKGChjOPhT4EARBEMQ8hbK6Zh8KfIjDmzMuAv7jjrkexT6THfUKAEDyzKPTbhP/8ntAfRAAdNZWbDwsInP7qUodTBZO6eIiR8SM3wfaNwIAnMclvw9T0mV6cRZsN8ea2UsWJXhqVIEzrXzkUjpfT8ThmpAmEXMqkeXFQ4lbfnpcKzGcAS3TpEh1NDrNhC8IV4mY8zzJiKFd2HR24Xoc9SVR6a47F8qlzedSOqUnMn4ee4yxtoCtyZgJhYlMvw8jzRyLrcITa/+SzRirpYJ8PgRxkDkssroIgiAIYiEyk4yumU6T7Y2PfvSjeO1rX4u+vj4sWrSo5zZbtmzBeeedh3q9jmXLluHP//zPkWV7KddxkCHFhzi8+d5XAM7BjOqjTr9wToeTPvtjp+rsESm6GkqWYP6eRCWm5pSSANrl7WzhQyURMxnsq7/smJI+1Umf2GeEAeD2POzQ3QNtndDnF0qVMqIixnDCMq12/Hx3G7avaMSBly3Ze92tp0Yzk7em97EvOxcK8Ild4Kyc6VKL7T7c+aSEZE6FSSMGpXzp/zSCyxgLpxEirvPmbB7dVO5fXy4lRowvaCoX7vxDlRgDlcgdJ6y7RBDA/M7qyrIMF1xwAU477TR84Qtf6D63EHjzm9+M5cuX48EHH8SuXbtwySWXQCmFW2655eANbC/QvzKCmAPydadO+xyLIv8DKKXTZVVaAzeFDRUAqCCIyZuuWjN4sH+4jZ3mYuabkDHfvRuHJpV6Z9MHe0KiFPgIpZzJeU+Bjt2GMT3N9dSoviZZoZCYACeCTzOPOFwQA+h0/lz64KSW2F5fzAVOEn46rBpxRAxgJjjMhAQC07KdzmJMvwZljNXh9FrEOdYuqrnzWCQUUs5Qs6nyLJrzaUeC2Fds66fbbrut5/P33HMPnnzySWzdutW1mvrUpz6FSy+9FB/96EcxODh4qIZagqa6CIIgCGKeMp+nuvbG97//faxfv77UX/MNb3gD2u02Nm3aNGfjIsWHOLw546J5dX+8T9Nce0E2G+6OhPUNQcVeiVFJzW+oJJhpU8GKNmRVF+2U1SHk5ggCCaqp3p+3J8CKlit6CMZLvb7s1FvpHLMMYwxuGo4FBQfhjcx74tejuVNiImfM9gn71hBdif1UleIMlUDxiRhKyo6ZgUJfwtz5WZAOL5VCzDnqRhnqT2M3NSWCKTCldHHDsBu2fznMvHYg4bqvGKCLTYJxV+aAFa1521uNmBukVJAzyMyy+46Pj5fWH4rWTdu3b8fKlStL6xYvXow0TbF9+/aDeu49QYoPQRAEQcxTpPI+nwP5szHTmjVrMDQ05P5uvPHGnue77rrrwBjb49+jj06fldoJ61GDSynVc/2hghQf4rCH/ccdvinnaRfM8Whmjlh/NvATXZE8qtbBcmNoVtKbkKOovJMs/HY1jsykVGdCoWmWF1UHEbXGgcJkVHAOFjQF5UZ1ULKArBy8li/2+44zOKtMzLS/xj7320lvwFYKzpMjA32PMZ2+HqpFNgW9XShXjFB0aIIRZ6hYC5RiTqHpNIFaK07Cmakcb707QBLb9hXKGcStgpUHBRHtMcPveAlgwjRD7Us4YgDKNZWNwAv9PjKRQ6b1XpeQIPabrVu3ljw106k973nPe3DhhXtOEnnRi160T+ccHh7Gww8/XFo3MjKCPM+7lKBDCQU+BEEQBDFPma3KzYODg/tkJl62bBmWLVt2wOcLOe200/DRj34U27Ztw6pVqwBow3OlUsHJJ588K+c4ECjwIQ57whR29vCdfv2p58/FcPYZ/qO7AQCMR4ApiKeKHPKkN0H87hv0Rj/dCJbqRga8f5Hz8fRKP2cm44vlTaSx9ukweCVjoi2Qxv2opsZjU7TdPiquAnb/bArcqEKyvnRWXutUYX0wyikpjPnMp9S0jhCye185jYuLM+BFgwmemdAmnQIMnHn/kIUxn3m1oymwohZhV0uvEMqfU8GrNRFjSM1AKzEHZ16yV/AFHxlnzu/DwcChXGNSnnA0c/+C7FImlPM2NfLCHdNSM01YK1GCet7Uzx9E3xUxvxFKlXxjB7L/wWLLli3YvXs3tmzZAiEEfvSjHwEAXvrSl6K/vx8bNmzA8ccfj4svvhif+MQnsHv3blx99dW47LLL5iyjC6DAhyAIgiCIA+B//I//gdtvv909fuUrXwkAuO+++3DmmWciiiJ861vfwuWXX47TTz8dtVoNF110ET75yU/O1ZABUOBDHIEw43+ZT9leFv7YvwKAVnFi32qBGY8Hq6bgT97rGpMCADftK2SlDmU9HyIHK1pgtoAh9woQb44h7TPNNqPEqUNTBTCZSTSNktGX1FAwrSYJqcC4zv4a7KuAtSYAANHkCxD9y2f+up3K47O6Es6cD0cqoAjeMQmvioQqUMSY28qaNtcO6Ov428nC1dABfO2ellCufYVQDKhFWFrV5x1py1JdndQcNJcKmfUVFRLViLncMR4xGAELHF5RE8qrPXas/vWU60g6X5Esq06MAcL4rliFo97p5SIWHLOV1XUwuO2226at4WM55phj8M1vfvOgjeFAoMCHOPIwQQB/9P9AnvKWOR1K9JNvQ5k0cXnSm3RBQsD/3+B6btXqAI90mjOgixHGpmc3j8Gs8bVoAUL44oTCV3hmIgOfGtHrGYNK9P59lQEocDed0yyk+zHOhII05uY2j7CoT09xpa0R8OYIZG3xjK5D1QQhQgGF9CZh2ycrEwoRY5C2uGKQjSKhXPp5wr0ZWSpdBXrNgP4aO7o/xraGN0Wv7NNBw9YJ5YKJrFB4ZiJH3QRcy2q9A4udTeFM4bmUUIqjPzVjYEDuAh9VCpw6w203PSaVez0RY4js9eAm6DHD4Mz3Dks4833V2hMH1XBOzF8EZli5edZGcuRA6ewEQRAEQSwYSPEhjijUqecf1Cmu+OcPAACKl71u38YT9OTij/6f0no7vQX4qS7wCCxOAK7NrIpx3129NQEVmekxWzTPFiOMpmlTEWzDlEQtThD1cBDXYub6VEkAbXuLWV2MpGiCN3bp52Zodq7FvNQ+wk0fMaBVhMUAGcCt5FOu9+HS4VE2MYesqvuvtjRiEMp2Uy+nrluz854oBNCCRMWNgyEJzjtdiwnG/HSbUApKWuO1L5QIqdWfsC+YvTwxA5gtPaAkeFOreEwUszL9SBwezFZWF+GhwIcgCIIg5inzOavrcIUCH+KIxpqJAUC+6rwZHSv++QP7rPQ4ApMyAJearjo7s1vFRwqoTLjHjHOo0A9k3bM2nd0WHYzrbrm0PqkBkVF88iaiSKCUGG09KDxGJTbnVArStnRQCiqugLcnAQDRxPNeWeLcj4PxUmdxZhuiAqUWDBw+nT0kg051t4ZgxhVU0Iqis7WFRSjlih1GTHtkOokYnIm6olQpbZ0z4Pkp/170UpAkFArBMJmZa6qAxZW9uwSY6v1alVIQRj1SShnfky166LeLI4aauXY1XrhyAwRBzAwKfAiCIAhiniKlcrWiDnR/ogwFPsQRjVV5QuXnQNlvtQcoZZXxR/8PlGsX4YsWQkq/XorScwo+PZ+lvsiginTqObMqTzYFFfl/zjbtXTEO1jaNTGVRaoipeAwYz5Cs9IM59YYhco1AYTLLzPmypjunkuiZHsFEBhWl3pvUzpwyNF0LhqptL9Ej5buZS1eAMMRlgLntUVJYdjS7lZxazJEJ5RQWIeG9RNCtMwCTeWXWc6EzyewYMqFcQca+uFvSCdUy65viQMnb5GGl1xxz7/GpxhxRPqWPldYB+GsXjW8vyVNi4NCU/09+/RAAIH/Jaw/J+QhdamImgc9M9j1SocCHIAiCIOYpFPjMPhT4EAsGq/rM1OtzoCghwIySw3jkavkoKQI3i37OqjyIUzBTn6erTQWPoJRf54oZAoAw3iKlvCqkpG5mah9L7xvhUkClJpMsrrpWFpAFVFp3NWS4KHzGGOPuNTBVBH6jqj9HcF4A4G3ZU/2JZI6Ix8gK76NxdW+4L3pYup7mqtlrF3HWsz6HUuVMsFB56fxRcO0nuC9AGEcKMZj3H7HeSo8lM1lzuYRpbqqVHHveTh+SbnRqlKai5a6vRH3aVhVicBjR5At6u77F7tozWbjikzxr+PcRAKREvuK4acdtSbc+5pZV1io/abxnB+R3I4h5AgU+xIJAvuq8WZnumgnq1POBoJeY/RHhaRWsZoIAKcrBTtiRXRYuNZ5FCVQwbRXOOTGRgZnAR0WJm6ZScVWnupvpEyYKX05YSX+s4JxMFFBKQTIz1jgF2vrYTEk9BsD9H9A/viWjM+OlYMceK/yRVzwGD6bqpPJBAwfAzRxWOOMVMd01PVxnCwbuakkXqKhSR3edil6I7o7ugJ9eKyRgZt/0tBezAdh0U1bdiMCsXUs4mMuuKQdbnAE80+Zxmdah0LtrdvcJbHAr0VKROWeCWl03mIzjFDBTqEzkYCJDsuOXeh8puoNTQJvUK/1+Gx6VDOxuOx4h/uX39K5Zy1Ualye9ad/GTuwzQs5MtenV/26hQ4EPQRAEQcxTaKpr9qHAh1gwhFNc/Ed3H/K7U/bwnWCJNhPzah18YBEAb0QGfId1levWFAjS3lmcBFMPLa0YhWnxvLtHmU2f18/HUIw7dYaLdjkF3himFeCnrRgHK1rgxgTN8pbfRwK+Ep+falNSgkVeNVI8dmOTLELLTGelUQWJnfaRAqkqEFf0eQrpjcF58MUdliRJIwapmFOGOm9sXY1AlBWa6X4IhPJtJXTRN9udXU9T2eMxxvZobrYtJ+o8mB7bSy2V6UzftmihrC0uLQOAGFoNQPdTq5qCho1CoWmubyUdQJyaKbCirUsSBOqhHRGTBSD050jFvrcbKzLIZMBPoUrhn0vgpm0l/JQY+/7XoE67oPza7v6MXijyUhkHW6aBcY7ore/f4/UhiNmEAh+CIAiCmKeQ4jP7UOBDLEjkSW8C/9HdALSZWPzuGw7auVgPXw+rBZ3WowgsawIIlB7jzVDtFljFqDZRHzDg/ReIk3KbCnNnzvKmU4JkYxwsMXfjnEOl/U7ZAZql/V07DB57b4gsoDgHb2vDLETu09kZLy07g60yZQYj3ypDMd+J3pIJBW4K9HFuvEtm3KmSbv+Iee8OZ/6LPDcSj1VYOtWXhlFlmOquXmvVIYYgK1x5RYkxXyxRqbJpmrNeVmtPeRiBh6rTnA6t8IkoKPCohGtECx5BJX16sTlSNq+Hxyja7v3pqw46payQypVFiOMKlCxKXh7nr2IcjPn1fjBcq4S25Unujc56H6seTQT79Gj/YYt48shpb7LIoQLzSf61v3XLyQX/vefrXKhQHZ/Zh5qUEgRBEASxYCDFh1iwhB6f6Cffdsuzrf6oU88HALD/uMMpBarZ8I1JiwxSBF6eKIIKH9d0KnkxtNqpIFASiFK0E60aCalQg8m2yhpOHYj4c5CNcb1eSoAxdwcv0xqYuRtXsc/+gpIl1QGMu7v9sPkpC30i6PYalbLEbKNWHiOJbIG/stoSHp+JHCkzSgHjcEnryhdvBJROGTePGoX36HRme3Xe4dnHoYYSMQYWqdLjEHs8Ibz/qJED1ch6gTg4g2tgqoL9O2otuteZ8xRTmXDqlW5SWnXnr0TB9tMoPnpwplxA3kLFKHcqipy3SL/33L/HUrrsMVa0AdajHUaRa/XPerWiuKw6mc+IbDWgcvPZq1ShHviS+8zpY9gGvNx73HjkinbKvACLuFOAsjtuRHrhNdO/1gWGUDOc6qJeXV1Q4EMQBEEQ8xTy+Mw+FPgQRAjniH66EQDA4nRWi7Sp0y8EvvcVAICcmvBqS5GVavooACwxLSLqg5BVrfjI+lIY2woqk8+Dj21HrWJaU0SpL0CY1lEY34hK+xDLX+id8iZQHYQy+4AxMGX8GU5F0dk8zrujeEmxYSLzhRej2NeCCbLFoBh0yUGfQeS0D8YQB01Lw2wnFtQpgpJ+DDwGmPeOWDUqiiqlu1nZ4eUJb3SlLWDYcfcbajoRB6JgTeTaV2iPUWqeagvfLTvcXkilFSSjACUovzarVBUKEOar19YTsq02lFIlpcguqygBUuP3aezSdZpcwcjgeoqs5N3xT5hl12AWXpETuX9eybKql7ehehTQZCJztXtUYxyypWtDRdFSsGod3LyPSvrPTghLUFJ8OOKS54cgDiYU+BAE/PQW/9Hd4DYFPAiCIOWsToGpdhOq3TTniVyg49pfJb7ooOvLVbQRm2kMPjWK4pmfuamD6Kjj/NSOKBCZl5AldWDJWr3N5E690vYFU6pUgNBNYwSd1XUl4MxNpaDIg8rPAnDTUcylN+sxJz69nfEgt1yAZ1P+QthpEFvk0KTHy+DHXAWGWaaUS/lPGCA5c323AH8aEVR+7tUhPTRL28myMCTS+wYTeUq5fmKFVK7bO2O6OKHeSu+R9DgfgoBGKeVS8Bl0YcTQEmzN2hEL6kuGgSngp6yA7uKT1mTeGfiE29lt7f+DIoXK7m96xrlASMpSwUvV1D3gVNYC+8N36k3uuw18aCnQN6i3y1q+BEOYyi56BEPmvSdzcxlSfGYfCnwIgiAIYp5SSIVoBsFLQYFPF/M68Lnuuutw/fXXl9atXLkS27dvB6DvnK6//np8/vOfx8jICE499VR85jOfwQknnDAXwyWOAORJb+oqhAdo83P85L2ldbYYm1h/9r6f4IyL9L4PfMmntsPfATPOIbMWeJy451ih75jjXU8DU6P6nLu2A0UGVjfTYH2LXKsB1hwDn9BqEutbrHs5QStGLJ8CK/zdfdi3S5qpMvDYKzwiAst9R3bEiS88V7R9+j3g22xICaR9Lj2ewU9rMGTlFHjXTqNSni5j3CkkQvpeVgoM3CpgskDCE6eeAL67O2NuxqknVgUKU+V1f7Dy1JM9FodXckRU7qge9vByB7L7Br2yuDGIJ1Hi0swhlTu/PS9zy6ycau9KD1jD8h7MzkBZyemh9jiFr3TdmT+uUl0qj3vvgh5eSvoSkbIxoYtmWpVO+v50yjwGABQ5iil9DCUkkotI5ZkOUnxmn3mfzn7CCSdg27Zt7u/xxx93z3384x/HTTfdhE9/+tN45JFHMDw8jLPPPhsTExN7OCJBEARBEAuVea34AEAcxxgeHu5ar5TCzTffjA996EM4/3ydLnz77bdj5cqV+MpXvoI/+7M/O9RDJY5gQg8QoP0Pth1E/MvvuSJtqsj9to/96x47wTOr6iQpWLXPrVdFDmXMoiza5bwUxa7tJb8E6xsEN81NRdrnTK5cFuCmQzfLpsBqQ/q4aU23LbB34GEj0awJZpQUWav69hWxBEuqXgHisUtt51MjQFOfR+W5u7NHXCkbms1+7lxBd3fvotHjcb6UoDlm1FkUzwoQjGtlxzYwFV496TQxd2KVlDhiJRN06K+1x4q4993ox8x9cQoFuFehyins5TFL12md5U0kZjmKyvee4e7MKi6ddLYq6XGunpSa2nac1KpIgWkaQKlNBRgHAgXLmptLQzv3vRDfuNm1Y0GcemWzyCCNr62YapKZeR+hAoazz7xXfJ566imsXr0a69atw4UXXojf/OY3AIDNmzdj+/bt2LBhg9u2Uqngda97HR566KG5Gi5BEARBzBpCqRn/EWXmteJz6qmn4otf/CKOO+44PP/88/ibv/kbvPa1r8UTTzzhfD4rV64s7bNy5Uo888wzezxuu91Gu+29CePj47M/eOKIxBY95I/9q/P4sFCR4Nx7gap1sJ9826s0nPtU8GrdtaKIBhb5ZqI8ghjZATHyAgBA7NrmG0BW64iWavWTxSlYnID1GTUnSoN0cumKyyFK/N18kellm1HD/HigJHhzzKxnUGmgQPHYp7vzyClFKqmCBynWbpxxDnCv5nSmSFsYvPqjZGHUAfOVJODOyYIss5Kiwnw2FaCVGKv0CMXQ60bXeXvMY+l7kUIphcgWV4S/K0wjhjTisFJTAu+dicMtldSFCoNsKpuNxUWj3BQ0b5oxm+vp3rtgsIHywjo9Ooy595uF24YFBqPg671HuwyHFEHmVvl9Cs+rGHdKk2q3fMPRDvWJ//GVkN+8Re9fq5cb5VplNFCySPkhDjXzOvA555xz3PKJJ56I0047DS95yUtw++234zWveQ0AlIyBgP7y6lzXyY033thlmiYIgiCI+QaZm2efeR34dFKv13HiiSfiqaeewlvf+lYAwPbt27Fq1Sq3zY4dO7pUoE6uueYaXHXVVe7x+Pg41qxZc1DGTByZdHp3wkakrix/tQ4WJ2VlyPpgirx8p2zbDNgsKZv99e1/cB6JaOkwosUr9PooBcubELueAwAkSkIMGi8c4+54sm8xWvXlAIC0aGpVxxUjLHzGTthkNG9qrwd04TzGOJRRD1TsM3ZkXHGKhoor4Lu00qqmxnSbDasSMQZpM86Klq/vEyoSUoAVGVQoUrgCfVXvFwq8QlASDNxlfAG+Vg5TcNle4fd+Z00fFqyTKigcGGwTcwYerulVGBCBehXU3HEvzzakBSArA+DNEb9/lLr3q5OSiheeczofT4goyqpPqBoFhQVZ2LyUx345inQxTdtGJG9CGr+ZbDUgJ0b1YdstdN5q5kZFTwDn/ZLNBvJJvX/log/tffwEAAp8DgaHVeDTbrfxs5/9DGeccQbWrVuH4eFhbNy4Ea985SsBAFmW4YEHHsDf/u3f7vE4lUoFlUplj9sQxP4Q9uOSZll9/2v6STs9ALigQQWBj8pzsGSXXm63ACmg/v3/dse2JmjZmHCGUhYn2kj9/BYAQL7ll6j8jv53oJYe46o9qyh2faVyVsVA32JwW8gQcG7asA9TqQeX+6E0P+ZRWu60bvaX1UGw/iV61eh2qPYUULNfLzzoweWDLdfFPTgns8UVgxRrxjIoFXR653Fpf26348x9yUeltuu9ihNqEh4UOuzouW5T1iMeVBLcA70CnumQNV1igDd26cDSPsG4H7dSvmu6eay3Mc+FBQhtQMh413SVIwxug8BJRalPu+8MjsJrKKWbzpQTo5ATOnjj5763+/VlegzF5CRiE/gU4+MU8BDzgnkd+Fx99dU477zzcMwxx2DHjh34m7/5G4yPj+OSSy4BYwxXXnklbrjhBhx77LE49thjccMNN6Cvrw8XXXTRXA+dIAiCIGYMKT6zz7wOfH7729/iT/7kT7Bz504sX74cr3nNa/CDH/wAa9fqEvwf+MAH0Gw2cfnll7sChvfccw8GBgbmeOTEQqVk2jztgj1vbPp2AQg6WEelNGH2hnf748ErF/Luz5S7uBc5ip3a8B/XF0OaFHbebsD+a2hWFkNWB8Ebu/WxRe77MIVICYRKQzj9IQvAdvUOe2vBTINB9xuTzQZ41RpuM68oMO7OqXhcMjeHJl0VJW76RBUZWGTWI/X9vRhzbSL04wix28kbkEu9rxTAAmWn8ych9AdGsMrU7P5w8MYuyPpSc2wJFRYGZLxUMDA0TrtpNSmmnepSPPbTTp2G6E7saw2PF4xFP1ZOQVLNBmTDlC+YGndG5V7ULr0WANC87XoAetorbzSx75oYYRFKQvQqa7Af+xNl5nXgc8cdd+zxecYYrrvuOlx33XWHZkAEQRAEQRzWzOvAhyAOO87Yx2nWB77kFlmSuMaoEuV2ANPB33SF3jdYJ+7+jF5XqYKvXQ8AUEnN+WZUqjAmEyzuW6S3yxrl4nQuJVqUO6OHiEDl4RFUbPw2IveqVVwBq0jvNZECOj8dprih8Zmg8MvcpLQHKdrOy8NkqdChU3J4DPDgGnTe2Do/C4LXycCDqyaV8oZnoyABul2EtS2zol3qgD5TyoqK1NfUjjW4Bt078vJ+4Xq7/3QnFUX3ea25eU+KgAp8PVPjkJOjep83vBvizk8AAIo7bkR64TU9d7fKDwBSew4QKmA4+1DgQxAEQRDzFCEVOHl8ZhUKfAjiUPG9r3hF6HUXO4+PbEy4thRKSuCMi6a/c98DVgUSG/8JvKrTp9mKtRCDutzDSEtgIpPoX6zT3pOxZ4N2Ar6gJxjzhQ0jmLRxg8iBwhZkjF0bBplUwdom5Z3HOjPNdqaIk6BAn1caoFTgLVFgEXoWPSx5gQBXEkAX14vLhfys6tShmoQ+oLDMFwfrbhMBAEr4lP5ZVHsAQPQv98sDKxGNPRecX7rXo+KKL38Qvp6gfYjeUHY/Z9a7LDl7rcP0+9C3FWZ72fdeCqDt09blxChUQ/t11DdvcSqlElP79fqJ/aOQAJtRd/ZZHMwRwrxvWUEQBEEQBDFbkOJDEIeKTv9P8Hg2xWh29mXIv/0PAIC4yMBN8bwk0nV2xtpacVkWJT6LKq4EPpMMKjF380kNLG+65pQMAKxniBVgRnVoK46qbYbaGtMeHaNioVb3DUx5VMrw8oPmOrvJKjaB8lGu3aMAZWohuTo/3d6XTm9SqR5OeG7GnMpTKuQHXWzRXZuDiBha3XN9vPtplykHHvliho5A3ZFeseneLqCHOqSz8wKVx8BkAdlsQBqVx/7fbmcLcyYX/Peu04zd8gEAwNB7P46Jf/grfWohUbQyt7z0AzdPP07CQVNdsw8FPgRxBGLT4PNv3oLYpB2vXHcSBoZWu4KGKk51h3WY4nu2H1bsf+xVnOopE9HDWBtUBY4YILmpWJ30lfuXIUjzD1OgeVQOiAKTrpLcBzRROWhxxQx5BMV7/8izjikfFzjpHXubgUPDrywgq0M9jz3bxKO/RbHo6K71xZIXIdnxSz20tM9PbdkimKG/WfnAhVkjeQ/Tcqno4XTP22nFVkNXaDaGZpW13PsYn/+Xe3xNMui/ZXtxVRYPgDd0j7JsnKbH9hUKfGYfmuoiCIIgCGLBQIoPQRzB8HPfi8J0yuYjOzDwslejWKSnVliR+fTxcJqHcT8VJHR6tTQmZiZyX2CPx04ZSkQGlhs1h0dgcQJpjbVtDhYZlUIEUylRpBUlWGOyHzcLCxACYDLocO9Ui6BNA8zUVy8zMOM+PV9xIOiu3tlZ3CkiRWbrNB589pBO7gzWwr9XULYfWfB+Sa/yOLWrw8wcnovJwqlvpemxIH1djO2CGHkB7OzLAGife3bHjQCA/Gt/C2GmraoXf7j7JQWKz+AVHwOgixlahY86su87pPjMPhT4EARBEMQ8her4zD4U+BDEEY5tIim/cTOKbb9BbFLXZd8iyD7dLBNSgrd1OwJnPgZMZ/YEMMqMTpFum8UCPNMGZtYcc/vZZp3O5yOl84awJDhWkUG1tecDSQpw6T0/AYz7on6Kx2DMNHeNZdn4zHjZ+ByqP4HSodPtvdrhCHwvpfT+g0yx+BgkO3+lH0jpFLV85e8gW3UCAOjnnf8JHQ1Hc2/0Dt47v7EhbD0ihN9WiuC9ErpRLgDVbkE1G84HJb9xM9JV2oukshbkCzumfU1L3n8TAGDXx6907z1PYqSDfft2UQjiIEKBD0EQBEHMU4RUM6rjQ1Nd3VDgQxALBP7HV0J9/2tQtUEAgEr6gkKFsVdqsiaY8X+opAJEiSuEx/Km9wUVGZC3zXLLZR0xWUAVuUt3DjOnVFsA7aAlR6g05BkQlbPBHNYLFEuf3VSgnPHFuG9twQEI29gUJa8LE1lvX01QvG+PbRwOAvmylwKAzuLqdW4ZqFGAGavPXGPKj9v5dcI2JHafnq0xhG+T0uF5ghQQX/8UACBefhRYRXu9RNaCzPT5xz/zQefjsez+1FV6n1qKoqk/O3E1dYoeiyivZl9RSkHNIHhRs9xk90iAPn0EQRAEQSwYSPEhiAUEq1RdywSWtxCPbwcAyOqA34hHzmfClGkQarKLWN7y3hnAF8FTslyEsejwmhiUFF5l4j4bSRUZWJyC1XSxRdls+O3gVSPdb9QoSbF5DO8tKrW2ML0omCi8CmLH3kvZCZb3WATwYCJFaUzpsz/W46nUg2w2rdzsVZXq3EZkgBDd2/EIkIGvx2Z7JSkQJ0Dmm9JK21ql3YIwWXxJvYbxz3xQnyLX1z+uaoUub7TAU/0zU1k8QNlcB4CUakYGZTI3d0OBD0EsIFSeIx7TwQ5aE+6HDGkNMFNdKihM6FLbmfnylL6HFaQsBRI2AIEQUEKAmV5OLIp8GnsmSlNgbplHUEXm+kIheC6EAYA5rk3dVrbZFgu6u4fp8Dz2lZuFcvu619erkJ8skDz/CwDaZHyoyIdf3vsJKXU3++BxTzgHEzadPfjBYwxQyhuag2KNKjiWkn7aS06MAEXujM+q1Sidp7JIB8syL1BJ9XJ7ZAIqMLOng31I6qYUAuduesyan4m9o5Sa0XQVTXV1Q4EPQRAEQcxTlJyhx4cUny4o8CGIBYQ69Xzk993mHvOhpQCAKG/7lOgid9NEtmdXCTu9VbSBSLepUGnNt6NojJTVIMAVMEScegVBikB1MMvcT4O57Yrcbye4n6bislSkjynlxg3Og/5ePQr5uQvSe6oLSjqjdbL9ZyVlaFpVZpZJnnlUK3GAfv3hnXupX0Xw2oQoG5jDbcLHUnilRwqo3Kt4tsSAKnLIIkdU99OgbhqsUkOcaIVQTIw6Jae6tNzmI1STZF6gaB26MgEEMR0U+BAEQRDEPIU8PrMPBT4EscCwvh5eq0OMvODWR6t1SrVKa2W1QPjGoqo6AGS6wSTLm74LuhBAu+GOz2yRQmhfkUuTjhOnBDEkTtWRLW1mdl0o4NUCJYVrRM4AXewQgCq0b4Qx+yQPWi8ob4gOC/e5QU2jmATPu2MluncFz5rd280C1sAMANlRr3DL+dpTkPz6IT3Eat3vwOOy+To0MYfNRztUHQSNY1loLG+3oKwZnXOXso6pCbPOKnTSLTPOwap9fn+hPxNKSPA0dgpQCIs4mZsPgGn6ye7X/kQZSmcnCIIgCGLBQIoPQSwwQr+NmhgBAIgXnkW8/CgAgKwvAWwriqwJxIkudghA8ShoX6F0mjUAiMwVOYSUOj3dKgw88t4QXi5QaNcD0FlgtollsL4TlWfudSge+QKJPNJtKyzSprCL6QsjAr4VRIfy05kuroyfKX32x17BMuv0Drx0DCYLZKtP7HlKl6YepVBJzR0veeZR5GtPcdvlL3ktACD++QNgFZMlFydg4XlDRF5WeXoUJNT/82UFVNYCi83xeAQYW5dbZzPv4sS9L2HGHatUwUwmmBISSkhkEw1zeRKXzq6E7Cp0SOwdyuqafUjxIQiCIIh5ivX4zOTvYPD000/jXe96F9atW4darYaXvOQluPbaa5Fl5RpeW7ZswXnnnYd6vY5ly5bhz//8z7u2OdSQ4kMQCw3r00irukgdjE/D3LUzkQe1YKRejkxhwCiBiq0kwCGt4iNriOomY6fZgMpaWh2CUQ5cvR9fx0c3LxVuLLw+6NQFMbarVKtG8UC5sKqDObYyygRL0pIHpaTGdBbuYwyddFl9wkKLgM+q4rFrm6F4XD5WkMmmZKRbULgVvoaQSmp+e7mHrDO7a5G594fXBwCel15rmD1li0eqztecB16tIvOqWvCe8LRaUtt4tc8pfKrIndoGKf2yGQPQ3YqCp3HPxrPE4c/Pf/5zSCnxj//4j3jpS1+Kn/70p7jsssvQaDTwyU9+EgAghMCb3/xmLF++HA8++CB27dqFSy65BEop3HLLLXM2dgp8CGKBwc6+TC88fKdflyQ+pVlkkFXdz8sWCXRI4VPYWcVPl1QHIG2a+/hOyIlRH1SFJ+eyNE3iKzeXTbYlQ3SAynM/Vcd1B3Ymjfk2+CFG4tPmS+OHmdJi5hjhNIAQ5SDGPmc7pu/NJdrrB75U5doWTYy6txE2AKwh/uX39Pqg3xmkhPjdN+hhPfav4NW6Noqb1+rPIXzA01HkUBVZOZi0Xdgz3ztNxUkpMOZA0E8tMESHx5fCmdk5gGKqhf7L/gYA0P7KR92xGeeY/Kf/HwC454m9M1/r+LzxjW/EG9/4Rvf4xS9+MX7xi1/gc5/7nAt87rnnHjz55JPYunUrVq9eDQD41Kc+hUsvvRQf/ehHMTg4eFDGtjcoFCcIgiCI+YoJfA70D4cwnX1sbAxLlixxj7///e9j/fr1LugBgDe84Q1ot9vYtGnTIRtXJ6T4EMQCRbUaJaOwGNsFAEjqA5ADQTp6XIGy3dHDvleM6yKGACCqrtghT1KwJHXKAwO8OhESKDqq3YIIu7Z3qEJ+Kid3LStYnJTM0qzTwNxDMQIAFnZx7+zP1es3QpnteqXAKwl3/zhdG4mu4wUn6bEPr+u7YFXkYNaMPDEK/uj/0euFgIrzUv+yvZ47LBRpHtvrVVbkIkSmqKUqMohd28vX0X4OkJX3C9pfFM02ii/8D715reJf9r5eH+KgMD4+XnpcqVRQqVSm2Xr/+fWvf41bbrkFn/rUp9y67du3Y+XKlaXtFi9ejDRNsX379lk79/5Cig9BEARBzFOkUjP+A4A1a9ZgaGjI/d144409z3fdddeBMbbHv0cffbS0z3PPPYc3vvGNuOCCC/Cnf/qnpedYDz+dUqrn+kMFKT4EsUApeTvyHGpK3xHKxgR4rO8EZVrTRt6wU3pQLM/5Z7IprwpV+8H7F/mieEAptd2Zb7OwVUJ36rUtpKeEAGyDzFIRvkg/nsY86026OTqL9/XqbM5sQ1bz2qYleI4hUEx06/jwgGUlJjA3I9wHwTWVhb8OwTVhadVfT2F9Uf7adZYJ8EMN/D5BGxDwqGQsd+ep+JICpfcsPJ9dZz1CwfmVkIhSr+7JrNCp83Y8VMBwv1Fqhh4f87nbunVryVMzndrznve8BxdeeOEej/miF73ILT/33HM466yzcNppp+Hzn/98abvh4WE8/PDDpXUjIyPI87xLCTqUUOBDEARBEPOU2TI3Dw4O7pOZeNmyZVi2bNk+HfvZZ5/FWWedhZNPPhm33noreMdNyGmnnYaPfvSj2LZtG1atWgVAG54rlQpOPvnk/XwlswcFPgSxQGFnXwZ192f0cuKL08lWA3zQGBQZBytaYPmUe+zS2XnkG4QWbadkyLRWUg5K5wx8OKHiZIvzWQWIJYlWIgCdvh6qR0ngFwpVoumKFIZqEo+m9cSU0q5DBaWj3UOJsF2EaWTqixhO7yToUpxCr1HYPFQEnhznbUrLalqcdvhtwmN1Z9DpoZa9UW7/Ioe0rSqKvFxgMihgGB5b5gVYZBqjRhws4qhc9KHpXjpxBzlMpQAAJcpJREFUhPDcc8/hzDPPxDHHHINPfvKTeOEF3/5meHgYALBhwwYcf/zxuPjii/GJT3wCu3fvxtVXX43LLrtszjK6AAp8CIIgCGLeIiXAZtSkdBYHE3DPPffgV7/6FX71q1/h6KOPLj1np9eiKMK3vvUtXH755Tj99NNRq9Vw0UUXuXT3uYICH4JYwPiaOJHPzGmMu8akTBZgzXHAeH5UUgGE8ejwmvakAGB5G0i1J4cpaVSWoHVCr6wu+xwAcKNmmM1YnPr9Q9WhUguaZurmpcx8i6kc3f6UjtfW9X8Ehf6iKFB28g7VR3YU4vMKkvP1yEIrZO7AsqsNBjCN2hM0FnXvQ9BWoovw/SoyV0dH9WpT0YOun1HruyoAmEw/OTWh1wcFDJlrKRK0JEFR8u6Q2jO7zNeWFZdeeikuvfTSvW53zDHH4Jvf/OZBGcOBQoEPQRAA/DSTbE25ru1cSsh2E9wUGkZSKf9Im2UmMihpohZhpmikN+G61Gspg+Aj+FF3/aC6f8CV6JhqsoEQN2nptj4h94X0Sj/+wb5KiHIVZ2P67fmcwaV9d5h9AZOqX4qH8u7ngekrM5sgsTS9FVRHDtPQQzNzaRxSlKa+Sq/N0jnt2Bn4hccyRnLVbGhDNEzJg7Tq+7QVOUS7bfbvMU1HEPMYCnwIgiAIYp7SWULqQPYnyszrOj433ngjXv3qV2NgYAArVqzAW9/6VvziF78obXPppZd21Rh4zWteM0cjJojDC9GYgGhMQDbGIbMWZNaCajchmw3IZgMoMpdWDgAocj39JQuwfApMFLqoYZSCFTlYkXuTc6sB2dJ9u+yynBqHnJqAnJrQyoZNSZfSdXVXRaaNtXkemJ1NUcQ80wX8hEmFN20dlJlas/urPA+28/tASmOWNn92Sq7Xn9nGHstNQ4VqiVVlwnXWFBxuL/Kef27bIgOKTF+TYGz2talgzCrPu83WvcYPlF93J8F27n0A3HsvM92/zb4Pnens1sgs88L9UV+u2We+Nik9nJnXn9IHHngAV1xxBX7wgx9g48aNKIoCGzZsQKPRKG33xje+Edu2bXN/d9999xyNmCAIgiCI+cy8nur6t3/7t9LjW2+9FStWrMCmTZvwB3/wB259pVJx6XMEQewb2R2+cmt64TWuoSTjvMv07Fo8iMwZmsHjUsE/W8xQ8RisUgNr6RT4sDmmyrNyijS3HhatODgvUJ51pKrbbuZlpUPlmd+Hh14V2e1rgTY9W3VIn3f64n/OKA2UfThclK6PuzYoe5i67rN7NWftVGOm6V7fZViWHT4lgxJBK4rQZ4Vyur4KOrWHqMI3gZWZfj/j/sA3ZbxEjPPy8YzPJ734mp7HJQ6c+dqk9HBmXis+nYyNjQFAqQkaANx///1YsWIFjjvuOFx22WXYsWPHXAyPIAiCIGaVmTQonWnQdKQyrxWfEKUUrrrqKvz+7/8+1q9f79afc845uOCCC7B27Vps3rwZH/7wh/H6178emzZtmrYkd7vdRttkJADdzdsIYiHAU//P36o9gFFFhG+Oyap94LU6AFN00CgkLA4ytIL0atuUlNcH9DGaDciGLornvC9mH5/NJLX6EwV+ogDVI9sKxvtiFSQWJ17xiYJWFqXUdq/0uGOHGVG9MqHiVC8HmWd23CzI0ldF1l34sGO8Xa/HeJTC85dUnkDBck1fo6jLs+OPl2n1rMf5VSjyhFliHcimthLIvPCvC6bIpX3dnLuCijzJkGfNnsciiPnIYRP4vOc978FPfvITPPjgg6X173jHO9zy+vXrccopp2Dt2rX41re+hfPPP7/nsW688UZcf/31B3W8BEEQBDFTpFJgM6jFIw9SHZ/DmcMi8Hnve9+Lb3zjG/jud7/bVSGyk1WrVmHt2rV46qmnpt3mmmuuwVVXXeUej4+PY82aNbM2XoI4HFBCgif6KyAsOie/eYur5SIBxENLfdHAvAGW2IJ2gGrrO31WahfRAqsNgNW04sNCP0mRedUgNioN4LKKbA2bkjKEoB5OnJaLEJaUm6CYIeBq8pRVmbzbB+NeeEeTVPs6sxYQJ94LFJ4zBwBfVwgdx2Y92mh0+3pkWfUJlZge2Vhd6lensmMVMM79te48SFgnKWxD0S4rNyLLwVu+jo/zZAXjZJE/DzH7kMdn9pnXgY9SCu9973tx11134f7778e6dev2us+uXbuwdetW1xCtF5VKZdppMIJYKNipDAAIf56LyUn3xRDXB3VwYntyVWq+ym/up4h0urMvvMcBsL4hvU+tDmaL4mUt9yPMOPfTZrU6IAVko+W2C/tzoVp3x/YvQJRMx4wHJuzwm01GQVFAvb3brsMAHU7llPpkwZinUZ7SYkF3eNst3pugw2N0BAZBYcLwMYDSlFZpequTYN+wAGLJXB1UuWZxRx8wG/CY9H/72kTLX/fK4gFncgZQKq5Y6tVlrsHEP/wVBt59Q/dYiQNmtrqzE555bW6+4oor8OUvfxlf+cpXMDAwgO3bt2P79u1oNvVdyeTkJK6++mp8//vfx9NPP437778f5513HpYtW4a3ve1tczx6giAIgiDmG/Na8fnc5z4HADjzzDNL62+99VZceumliKIIjz/+OL74xS9idHQUq1atwllnnYWvfvWrGBgYmIMRE8ThgxISindPUaQXXoPizk/obfIMqtXw01ZxAhlMh7hpnfAAnEO1W24f1b8EcaIVVplWndJQNhULqGbDdWxXzUbpuRJhW4qwg3kUpMMH+7kCgfDp5naKTQZKDIuTslrSoY6UpuXs9FYU+T5k8TRTSnZMvXpohQUEzfahutNT6ek4rhs7zPRjOFUWGL9V1tEao91ym4mmKT0QtJ+I61VEA4vA216Fs4bmzvNb9TAbn8L4Zz4IFvl7alKAZoaaYRFCmurqZl4HPnuT6Gq1Gr797W8fotEQBEEQxKFlvjYpPZyZ14EPQRAHj+rFH0brS38NAGh84X+g/q6PuOesr0NOjkLWBxE5j02gSFjlBdCNLK1qYdWHtlYRWFyBrBrFaGkKNrkLACAmRr3CkuddhuaQUJ1AuBx4bJQQTiliSWDYzX0BRf1cUk757vTbWFwzVKPwBGn4rks50kBtyV26vN6u04QcNvMMjNsdXeBLr7tXGr8dU69SApx7BSoohth5bUvXMzyflE71iftq+rUmRh3LWoBV5ILtADiFh6cxilbmFKCkXu09foKYQyjwIQiCIIh5CmV1zT4U+BDEAqZ68Yd7rs8bNk19N/jgUvCwzYT1ecQV176is3gfq9TAjK9HSaHbWwCQ1RSRMOnRzQZUq3cRvRAVZi2F643i4NUGry7IxnhpH6vQsDiFyvOe6fHTqSAwLTx8JpiAslld8OoNq1TNa+1dQNAPTvjmq1HkssX0axJlZWk6jw8CX1XnOQOVJ8y8Y1HUpcoB0I1nrScneDuUFGClbDOJwmTdRdXUXX8lpLu+POLIW5lTDDMxNe34iX1DSgXMIHihJqXdzOusLoIgCIIgiNmEFB+CILqwfp/Wl/5aFzO0bQv6hqBMewImi7KqYX0vWUsrGRXjC4oTsMKoKYxDxVoJ4vVBCOMZQbtZVi6AwHsjS+qCxSk6oUoRPt+hCAEAiswrVuZxWK9H2oyzDv8KjxPAFmJMkrLCEnidtErTo5gf5901iACnEDllJ8x0A3TWGEwxxMR6d+S0RQxV1vJZc+1W2QfUkS3m2pIUuVNoimYb3DYpzQutyrmGqhJFq+2WbcuTotV2+4usQN5oQZprddTHbu2+FsR+ocLGtQe4P1GGAh+CIKbFBg0+ZTwBasbM2xjxaeedXdPbLfCamQYTHFAmCGEcyk6BLVmD2AQh+ZZfQkk/ZaKC3lhKSN83qkdgExIW23NTYB1fc0zKcof4ih6DkhLSBHUyL0rTPyqRwfHMSsBMLZlxFJnv69UJj7quEWBTz4N+XaVAMioFf6UgJtw2mApUWatUjNC9RgCqXZ4Ss+csGi2ILHev2x6L53GpmCGLOCJrdBYCME8pIZGbKbD26ASyiSm86Jb/1X0NiAOCAp/Zh6a6CIIgCIJYMJDiQxDEtCghtYpgpqR43oRKdAd1FifOYCtbjSD9mwNpFSrX0yIMAIRRIaQErOKT1iBruq1FtHiFPo9pbdHVtbxjTBYphJuaKZptpwzxJEZkFBrRypxawyJeUoUAICoVGdTHFlnujssiDonCizxZ4aZ5uBSuJxhM1/Se7TDi1PW5Cs/jOsz3KAqJOO0oRpi59XZfAOX9ixyi7aejwvOF42ERd2qOyHK/nBeITP+2TDTAkxhxreJeTzLYBwDIx6fctJfMC+RTVvGZhMhIYZhNVGcvtwPYnyhDgQ9BEARBzFOUENPXc9rH/YkyFPgQBNFF87br3bKYGAWrvgAAUK0pRIuX6+WBZWC1QQBA3ByHGDHb2PYQYafv0G9ifDRcSSDS6gVbPAw+OYqiMQ6gbLgN71iVkNpfAm96dv6WDoXD+lb0i9D7RGkCxrlThljESypR3Kf9Poxzl9JvPTPW31IySwM+5TtOSy0rVJjejyD9PPDuyKZXuNx+djnPy34h0y0eoXkZ+nqHDWedEbzDFF4ad+63K6ZaEJm/HoVRf+Jqiqju095DlUgK4c5ZNDPk4/p1iExA5KQwzCZKzdDjoyjw6YQ8PgRBEARBLBhI8SEIogurDsT1qlZZRnYAANRU1RXFS2oDzqODSj/4gFEibAq0USZk1nKZT6zap5UeWOXHZFGlNfD+RWC7tpl9ilIml8h9mnmoaAAopbO7rLBA4eiV/WVRQkKYgoqdKez2+J2eICY4uPHBhMX7VJF1pOQHik24PmxrISVk1nLnYBEPlJlcp9EDAI9KxQhR5C71Psy86rwGoeoVp7E7T95oQpjrG74+JSTiWpCeH1wXicIv54VTibKJKXcskUv8zu13gpg9KKtr9qHAhyAIgiDmKRT4zD4U+BAE0YX1t0TVFPHgkDNI5iMjiG27hVod8XKjXvDYtUGQUkBlvogdAJetFS0d9gUQ48RlizHGgUrVZSxpRcFngrkCeXkxrYLDIl6au3eKjZDgRkWReQEeRaUsL3u8TuXEwtNYqyfm+TA7Kq5VSh4hoCh5aXjYwLSjTQQAFFOBD8qMLzK+p1CJ4cE3tSoyiKARaOh7Cl9b6H9iHbWFOpuMWpJ61WWsRUnSVTdJBkZZr/7kyBv62pDaQxwOUOBDEEQXA+++AQAw8Q9/hXqtD3xgkX5ifBztnbsB+MJ/ABANLfU7d6bPhv2jshbkmO7Ozqp94DVd3Vm1G2Bx4o/z3LOlqSZruO1ZudmOISpP0YSIoLIwEEzlVSvBMEXJJGzhSVwyROv0Yj82eywJc2wRpM6beTgeBj3wAY+SEkWzjShNSucCTD8sa9yWArLt08e7Kkvb15nnPStZM67T+G3gIkU58LHTW+lg3U/jdUz1iSCQzcanvNE5KxBX9T4//b/Oxfp/+WbXNSQOHFJ8Zh8KfAiCIAhinkJ1fGYfCnwIgpiWgXffgNaX/hoVo+7EfTW0RycAAK1nn4PTfMIigHne1Zmc9Q365+30Vpy41G05MQqWVsH7BgBotSOzKdJ50VOJYVwbkK1CwYMppk5FIyRvtBCl3pzsxhVMDfEk9lNGpvhfVNWqiGhlTu0ITdQ23T2uB73A7HiK3E1viVZWOq9VewC4aSa3X5DS76bkzBQgD3pvWSWnc7rOXhvRyiDgfwTD1xpuVzZXm+KEDa9OycDcnBvVSu7BPE4Q8xEKfAiCIAhiniKDqeID3p8oQYEPQRB7pLlrzKkM6WAd6aD25bR2jSH7+VMAgMqi7UiXLgEA8Fpdd0A3RmU1Ne4VHyl9ijePIE3BQjG2C7w+ABZ0OheBedelaAcKT9KhrOi0d7/PntLYi6YxKmdlM7Iz7ArpWjd0psVL4c3WLCsQGyWIcQ4eRSV1yvp9wuOERmOZFT0VIqDcMLTkz8m7Czva8UyndFlvU5iObpfjerX3NTAGc3ueoukN1Xmj2XEsV56RmGXI4zP7UAFDgiAIgiAWDKT4EASxRxZf+Uns/tRV7nFSr7nl9sgkAO2bGTQKSTqwGKw+6FsqZK1ys01DyQskBcTICy6rK6r1OUVHZl75EIFC06lsdKa6hypGV5G/qPuer9T+oiUhrXcojcEjXlJqfLaVzeUKjhmcqtQcNVCCrGplfUMhrhVEo9Wl8oTjDNUkm/VWUps4R95o+X2Ca2NT9LvO3VGsUeZF2ddj/D5hAUMlFNrjOuMsb3V7sYiZQYrP7EOBD0EQBEHMV4SA4jMIXqhJaRcU+BAEsVeWvP8mAMDIzVc7VUEK6RQLmRclDwuLE8iJUQC66KHNVpJZ0ZW5BOgMLxSZa9rJKlWnLOVoOiUiSmOnQOSNVum8QO/2FCXlx6hCoX+Hyd4z/iWVpUMhchlWeeG+RItWhjjYj0Xc1dSxGWiAKQwY9S4sGGZ8hXWFQuWFB9lmnWPNG63SawvHqYQACzLBwtcietRJsvuHjU7LY9UvLm8VkMbjIzOJ/zzzD/F79/9713mIA0OpmZmbqUlpNxT4EASxz8isAEywk9SraDZGAegffTedxCPIiVG0t24GALRHJpwhOpxOktlOFwTxtAqWJK7CM0urSAYH3Tl9V/Dyj3TnFI4oTQnpL3wWRW7ZBxImJTwpdx/nvNuM3D3uIjBRC+TTmKjD9Pow8AlhEYdq+ZT4sLeWzH3hxrB3Wef0mGhlQYFHAfszFyFG0Wq7bQAgNsGkCAPGVlaq8OzS8ztS/sOyAkUrQ2aqNRdNf6y4Frv1BDFfocCHIAiCIOYpSsqZKT5UwLALCnwIgthnZF64KSiexMjGpwDo1HY3JVLkELu2YWLL8/pxkGYeqiA8iV3LiLguEaEPMF4GlWeuJUYy2IdswhQzLKkbspTSXrQyp+R0KkHhNBGLuC8MGBQgRA6nlvAkcVNgburJKT45Ots56O0E4o70eEtpui0rnJpiTc4h9nxFMO0V/nhZ9cmlsMuwE71w6lYhhFN6RF6AcQ5hFCAWcURRxb+u4FiWdKBPrwvUrV7TXiKXQO5T/YnZRc2wjg+Zm7uhdHaCIAiCIBYMpPgQBLHPiLxAY5tpMhpxtHaNAdDp0U51yFpo7RpzrS2KZuaaYIaemnSgz/tJRKpT2ptT7thRYvap9jkTcNHR7qFoZc7M22nEDYsZhsqKCLueB68tVH+EaLv1LOLgSeKUFNHKnLIRKjHWAxMFjU9VkFFjjcVREkPmXvHRr9U3BrXjk3nhCyVGvORfCpFZ7o4dmpeVEG4fmQmwyHep50kcXCuBXjqNErK7hYbbR7mihVHC0RzRafNFq4DISPWZTfRU14FfU5rq6oYCH4IgCIKYp9BU1+xDgQ9BEPuF9XyIKZ/lkw7UnapS7H4B7dFJ1xaiNdpE1NAKSpRGrsVDFKoOUqKYaroCeUm9hkj6rCyrPERJDN7nCwsWpkCfpVfLCQGvlFgFpFSMMPSrhJ6dINMpCtQkrTopMzYWHEchFy13DOsnsrAw/dwsx9BtLXp5Z0LVKky5tyqXLPl+giaxYYuJzKhUuQATDDwxKfXwmW2dGWf22nSWCuBpgtgWkmzl7rWziCPt1+9p3sghcvqhJeY3FPgQBEEQxDyFFJ/ZhwIfgiAOCCWk87PUj1rm6stMbn0ejWdfQHOXzsQqmgUKVxvGqyBSSpehFVcrXX4S2/IC8CpEOtiHuE9ncsW1CtojEyUvT+iCCTOMbLE9S1TtzuuQYcsKISGMWsI72k3Y9QAQRVEpo0pkwqlBMmKloo42k0siB4f2OhWtDJzznplgneqPXxaQQXFCoDvrCygXPWSclTLqOosfsh7PFa3MeZAAAHl3gUN9fXyDUp5GQKvAfS9/DQDgrJ/9oOt1EfuHlAKMAp9ZhbK6CIIgCIJYMJDiQxDEPlM0mq55J+AblvIoQjaqG5Zm41No7JjA1E7t1ymCxpV9S2uQdeGOZevKKCFRWzoEbrKSGtt3uX1qS4dcPZrQj+I8N8bnk081nYKUNfKe7SsAIK7GkMZzpH04ZZ8OYNUfvZznBXiwjRSq3PAz8n6fOE2cryYcY+eyzRpjEUd4P975+nrVItIXqBW0wIh7ZpnZ12JfJxC0tpgm00efU++XDtYgq6lX6LLcNT3NGhmyhq2ZpFA0gwy6iLkWH/e8+NXIzHU89+lHe56T2DNKSIDNQPGh2kpdUOBDEMQBIfPCpazzJHY/qu3RCSih0DIpznmrQN9SHSD1rx5w+7dGGi4o4kmCpO77c6UDfYhrpsBe0N9LBK0xRCtDe2QS41t2ANABlv0xBoC07lPnLSziLqABACG16RcAlFSlaSyLEgoScP2oeMRKXcjjqjFbgwOQJdNv6XrZIoqlY8uubXpNW3WNJwyIeozZEk5vSSFdhUYelY8h8u5zJvVK6bFoZe76FM3CLctMOkOzDZpKQZ7y15vYf6hX1+xzxEx1ffazn8W6detQrVZx8skn43vf+95cD4kgCIIgZoSSYsZ/RJkjQvH56le/iiuvvBKf/exncfrpp+Mf//Efcc455+DJJ5/EMcccM9fDI4gjCqtIZONNpIOm6WUrc8rL1K4m8smy8rLoRUN6eaDP7d8ebyOtp259WGSQR1HpsW1ZMb55m2+IWa+Z6Rd9rqyRO/WlPd52ikRtcTUwIEtIcDd1JYWCbHr1xqo6YUq2VVhC9SRKuDlP5o8tlU4ZD8zOofrj2lwE15JHHFLIcif4aVLlO7HKlUQ47dZbZQq3B7TSVTqWVXWy8tSayAs3HZk32u5ai1yWpgVdd3ah3DIAcMYQMf86CGI+cEQoPjfddBPe9a534U//9E/x8pe/HDfffDPWrFmDz33uc3M9NIIgCII4YEjxmX0Oe8UnyzJs2rQJH/zgB0vrN2zYgIceeqjnPu12G+22L0k/Pj5+UMdIEIc7z/zF/weAVhSswVXkArHx5OiWClY1EEj6E1QX67TzwaOHXKp7HLRzGDiqfN8lWplTPngSOy+PEtK1yQCA6lKtHlUW9YMnMXiqvTw7frwV/au0h6gymLrtJ7ZNOmVJKz0SMCbqfLJs0uUpd8udRmfGtXIRpVGp6Wl7XH+XpPVEPxcqMO7eMmgkGqaSdzwOlZ1eKk+IHU/IdApPL2Tgx7HmZCkVEqOaybxAPt5wx8wDD1XRDAs9erO3krI0bkn+nhmjpJiZuZkCny4O+8Bn586dEEJg5cqVpfUrV67E9u3be+5z44034vrrr+9aPzExcVDGSBCHO5OZ/sHjEUdhlou8ANo6OImkxKQJWppFAc4VIhgzcJaDm+mOOJj2KNp+OixrZUiCuje8EOBBNeMJs63MchTmnFmrDV4ITJjHk0WByIwtz/2xG0WBLDfHlQxMMkTMTLflBfLCZiQpcD5N4KMUmDSBD1dgSi9zxQGznOb6uGFAwoWp4xO8thCbxeUrN+9boCD3of/S3gMfs52Q+r2EDlQSc+mKLEfMmQt8WnmBtrlWRRHUNRICwmbYCQER9JbKpXBZXUfSDab9rVCHIrATOWZ0FpHvfZsFxmEf+FhYxzyyUqprneWaa67BVVdd5R4/++yzOP744/HSY489qGMkCIJYsAwPz/UIZp2JiQkMDQ0dlGOnaYrh4WFsf/L/mfGxhoeHkabp3jdcIBz2gc+yZcsQRVGXurNjx44uFchSqVRQqXjJvb+/H08++SSOP/54bN26FYODgwd1zIcj4+PjWLNmDV2faaDrs2fo+uwZuj57Zr5dH6UUJiYmsHr16oN2jmq1is2bNyPLshkfK01TVKvVWRjVkcFhH/ikaYqTTz4ZGzduxNve9ja3fuPGjXjLW96yT8fgnOOoo44CAAwODs6Lf1jzFbo+e4auz56h67Nn6Prsmfl0fQ6W0hNSrVYpYDkIHPaBDwBcddVVuPjii3HKKafgtNNOw+c//3ls2bIF7373u+d6aARBEARBzCOOiMDnHe94B3bt2oWPfOQj2LZtG9avX4+7774ba9euneuhEQRBEAQxjzgiAh8AuPzyy3H55Zcf8P6VSgXXXnttyftDeOj67Bm6PnuGrs+eoeuzZ+j6ELMJU4ckH48gCIIgCGLuOSIqNxMEQRAEQewLFPgQBEEQBLFgoMCHIAiCIIgFAwU+BEEQBEEsGCjwAfDZz34W69atQ7Vaxcknn4zvfe97cz2kOeG6664DY6z0NxyUmVdK4brrrsPq1atRq9Vw5pln4oknnpjDER9cvvvd7+K8887D6tWrwRjD17/+9dLz+3I92u023vve92LZsmWo1+v44z/+Y/z2t789hK/i4LG363PppZd2fZ5e85rXlLY5kq/PjTfeiFe/+tUYGBjAihUr8Na3vhW/+MUvStss5M/Qvlyfhf4ZIg4OCz7w+epXv4orr7wSH/rQh/DDH/4QZ5xxBs455xxs2bJlroc2J5xwwgnYtm2b+3v88cfdcx//+Mdx00034dOf/jQeeeQRDA8P4+yzzz5im7s2Gg284hWvwKc//emez+/L9bjyyitx11134Y477sCDDz6IyclJnHvuuRDi8O+YvLfrAwBvfOMbS5+nu+++u/T8kXx9HnjgAVxxxRX4wQ9+gI0bN6IoCmzYsAGNRsNts5A/Q/tyfYCF/RkiDhJqgfN7v/d76t3vfndp3cte9jL1wQ9+cI5GNHdce+216hWveEXP56SUanh4WH3sYx9z61qtlhoaGlL/8A//cIhGOHcAUHfddZd7vC/XY3R0VCVJou644w63zbPPPqs45+rf/u3fDtnYDwWd10cppS655BL1lre8Zdp9FtL1UUqpHTt2KADqgQceUErRZ6iTzuujFH2GiIPDglZ8sizDpk2bsGHDhtL6DRs24KGHHpqjUc0tTz31FFavXo1169bhwgsvxG9+8xsAwObNm7F9+/bStapUKnjd6163IK/VvlyPTZs2Ic/z0jarV6/G+vXrF8w1u//++7FixQocd9xxuOyyy7Bjxw733EK7PmNjYwCAJUuWAKDPUCed18dCnyFitlnQgc/OnTshhOjq4r5y5cqubu8LgVNPPRVf/OIX8e1vfxv/9E//hO3bt+O1r30tdu3a5a4HXSvNvlyP7du3I01TLF68eNptjmTOOecc/PM//zPuvfdefOpTn8IjjzyC17/+9Wi32wAW1vVRSuGqq67C7//+72P9+vUA6DMU0uv6APQZIg4OR0zLipnAGCs9Vkp1rVsInHPOOW75xBNPxGmnnYaXvOQluP32252hkK5VmQO5Hgvlmr3jHe9wy+vXr8cpp5yCtWvX4lvf+hbOP//8afc7Eq/Pe97zHvzkJz/Bgw8+2PUcfYamvz70GSIOBgta8Vm2bBmiKOq6M9ixY0fXXdhCpF6v48QTT8RTTz3lsrvoWmn25XoMDw8jyzKMjIxMu81CYtWqVVi7di2eeuopAAvn+rz3ve/FN77xDdx33304+uij3Xr6DGmmuz69WKifIWJ2WdCBT5qmOPnkk7Fx48bS+o0bN+K1r33tHI1q/tBut/Gzn/0Mq1atwrp16zA8PFy6VlmW4YEHHliQ12pfrsfJJ5+MJElK22zbtg0//elPF+Q127VrF7Zu3YpVq1YBOPKvj1IK73nPe3DnnXfi3nvvxbp160rPL/TP0N6uTy8W2meIOEjMjad6/nDHHXeoJEnUF77wBfXkk0+qK6+8UtXrdfX000/P9dAOOe9///vV/fffr37zm9+oH/zgB+rcc89VAwMD7lp87GMfU0NDQ+rOO+9Ujz/+uPqTP/kTtWrVKjU+Pj7HIz84TExMqB/+8Ifqhz/8oQKgbrrpJvXDH/5QPfPMM0qpfbse7373u9XRRx+tvvOd76jHHntMvf71r1eveMUrVFEUc/WyZo09XZ+JiQn1/ve/Xz300ENq8+bN6r777lOnnXaaOuqooxbM9flv/+2/qaGhIXX//ferbdu2ub+pqSm3zUL+DO3t+tBniDhYLPjARymlPvOZz6i1a9eqNE3Vq171qlI65ULiHe94h1q1apVKkkStXr1anX/++eqJJ55wz0sp1bXXXquGh4dVpVJRf/AHf6Aef/zxORzxweW+++5TALr+LrnkEqXUvl2PZrOp3vOe96glS5aoWq2mzj33XLVly5Y5eDWzz56uz9TUlNqwYYNavny5SpJEHXPMMeqSSy7peu1H8vXpdW0AqFtvvdVts5A/Q3u7PvQZIg4WTCmlDp2+RBAEQRAEMXcsaI8PQRAEQRALCwp8CIIgCIJYMFDgQxAEQRDEgoECH4IgCIIgFgwU+BAEQRAEsWCgwIcgCIIgiAUDBT4EQRAEQSwYKPAhCIIgCGLBQIEPQRAEQRALBgp8CIIgCIJYMFDgQxBEiRdeeAHDw8O44YYb3LqHH34YaZrinnvumcOREQRBzBzq1UUQRBd333033vrWt+Khhx7Cy172Mrzyla/Em9/8Ztx8881zPTSCIIgZQYEPQRA9ueKKK/Cd73wHr371q/HjH/8YjzzyCKrV6lwPiyAIYkZQ4EMQRE+azSbWr1+PrVu34tFHH8Xv/u7vzvWQCIIgZgx5fAiC6MlvfvMbPPfcc5BS4plnnpnr4RAEQcwKpPgQBNFFlmX4vd/7PZx00kl42ctehptuugmPP/44Vq5cOddDIwiCmBEU+BAE0cVf/uVf4n//7/+NH//4x+jv78dZZ52FgYEBfPOb35zroREEQcwImuoiCKLE/fffj5tvvhlf+tKXMDg4CM45vvSlL+HBBx/E5z73ubkeHkEQxIwgxYcgCIIgiAUDKT4EQRAEQSwYKPAhCIIgCGLBQIEPQRAEQRALBgp8CIIgCIJYMFDgQxAEQRDEgoECH4IgCIIgFgwU+BAEQRAEsWCgwIcgCIIgiAUDBT4EQRAEQSwYKPAhCIIgCGLBQIEPQRAEQRALBgp8CIIgCIJYMPz/AcpvE6RK8P+CAAAAAElFTkSuQmCC", + "image/png": "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", "text/plain": [ "
    " ] @@ -2173,7 +2173,7 @@ " return x > threshhold\n", "\n", "# Apply the condition using xarray.where()\n", - "masked_data = xr.where(is_greater_than_threshold(da,50), da, 0)\n", + "masked_data = xr.where(is_greater_than_threshold(da,280), da, 0)\n", "\n", "masked_data[0].plot()" ] @@ -2198,9 +2198,390 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray (x: 5)>\n",
+       "array([False,  True, False,  True, False])\n",
+       "Dimensions without coordinates: x
    " + ], + "text/plain": [ + "\n", + "array([False, True, False, True, False])\n", + "Dimensions without coordinates: x" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "x_da = xr.DataArray([1, 2, 3, 4, 5], dims=[\"x\"])\n", "\n", @@ -2223,9 +2604,590 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray (time: 36, y: 205, x: 275)>\n",
+       "array([[[ 0,  2,  0, ...,  1, -1,  1],\n",
+       "        [ 4, -1,  4, ..., -1,  1,  2],\n",
+       "        [ 0,  0,  2, ...,  0, -1, -1],\n",
+       "        ...,\n",
+       "        [ 4,  1,  4, ...,  1, -1,  2],\n",
+       "        [ 2,  1,  2, ...,  4,  3,  0],\n",
+       "        [ 4,  3, -1, ...,  2,  3,  3]],\n",
+       "\n",
+       "       [[ 1,  0,  3, ...,  1,  3,  0],\n",
+       "        [ 3,  2, -1, ...,  0, -1, -1],\n",
+       "        [ 3,  4,  2, ..., -1,  0,  0],\n",
+       "        ...,\n",
+       "        [ 4,  3,  4, ...,  0,  4,  3],\n",
+       "        [-1,  1,  1, ...,  2,  0, -1],\n",
+       "        [ 0,  4,  1, ...,  4,  4,  3]],\n",
+       "\n",
+       "       [[-1,  0,  1, ...,  2,  3,  0],\n",
+       "        [-1,  1,  2, ..., -1,  0,  4],\n",
+       "        [-1,  0,  0, ...,  0,  1,  4],\n",
+       "        ...,\n",
+       "...\n",
+       "        ...,\n",
+       "        [ 4,  2,  1, ...,  4, -1,  2],\n",
+       "        [ 0,  1,  0, ...,  2,  1,  3],\n",
+       "        [ 2,  1,  1, ...,  4, -1,  0]],\n",
+       "\n",
+       "       [[ 4, -1,  1, ...,  4,  3,  0],\n",
+       "        [ 4,  0,  1, ..., -1,  4,  4],\n",
+       "        [ 1, -1,  4, ...,  0,  2,  2],\n",
+       "        ...,\n",
+       "        [ 3,  4, -1, ...,  4,  0,  2],\n",
+       "        [ 0,  1,  3, ...,  3,  2, -1],\n",
+       "        [ 4,  0, -1, ...,  3,  0, -1]],\n",
+       "\n",
+       "       [[ 3,  4,  2, ...,  0,  2, -1],\n",
+       "        [ 0,  3,  2, ..., -1,  1,  2],\n",
+       "        [ 4,  3,  0, ...,  0,  0,  3],\n",
+       "        ...,\n",
+       "        [ 3, -1,  0, ...,  0,  1, -1],\n",
+       "        [-1,  1,  2, ...,  1,  0, -1],\n",
+       "        [ 2,  2,  0, ...,  0,  4,  0]]])\n",
+       "Coordinates:\n",
+       "  * time     (time) object 1980-09-16 12:00:00 ... 1983-08-17 00:00:00\n",
+       "    xc       (y, x) float64 189.2 189.4 189.6 189.7 ... 17.65 17.4 17.15 16.91\n",
+       "    yc       (y, x) float64 16.53 16.78 17.02 17.27 ... 28.26 28.01 27.76 27.51\n",
+       "Dimensions without coordinates: y, x
    " + ], + "text/plain": [ + "\n", + "array([[[ 0, 2, 0, ..., 1, -1, 1],\n", + " [ 4, -1, 4, ..., -1, 1, 2],\n", + " [ 0, 0, 2, ..., 0, -1, -1],\n", + " ...,\n", + " [ 4, 1, 4, ..., 1, -1, 2],\n", + " [ 2, 1, 2, ..., 4, 3, 0],\n", + " [ 4, 3, -1, ..., 2, 3, 3]],\n", + "\n", + " [[ 1, 0, 3, ..., 1, 3, 0],\n", + " [ 3, 2, -1, ..., 0, -1, -1],\n", + " [ 3, 4, 2, ..., -1, 0, 0],\n", + " ...,\n", + " [ 4, 3, 4, ..., 0, 4, 3],\n", + " [-1, 1, 1, ..., 2, 0, -1],\n", + " [ 0, 4, 1, ..., 4, 4, 3]],\n", + "\n", + " [[-1, 0, 1, ..., 2, 3, 0],\n", + " [-1, 1, 2, ..., -1, 0, 4],\n", + " [-1, 0, 0, ..., 0, 1, 4],\n", + " ...,\n", + "...\n", + " ...,\n", + " [ 4, 2, 1, ..., 4, -1, 2],\n", + " [ 0, 1, 0, ..., 2, 1, 3],\n", + " [ 2, 1, 1, ..., 4, -1, 0]],\n", + "\n", + " [[ 4, -1, 1, ..., 4, 3, 0],\n", + " [ 4, 0, 1, ..., -1, 4, 4],\n", + " [ 1, -1, 4, ..., 0, 2, 2],\n", + " ...,\n", + " [ 3, 4, -1, ..., 4, 0, 2],\n", + " [ 0, 1, 3, ..., 3, 2, -1],\n", + " [ 4, 0, -1, ..., 3, 0, -1]],\n", + "\n", + " [[ 3, 4, 2, ..., 0, 2, -1],\n", + " [ 0, 3, 2, ..., -1, 1, 2],\n", + " [ 4, 3, 0, ..., 0, 0, 3],\n", + " ...,\n", + " [ 3, -1, 0, ..., 0, 1, -1],\n", + " [-1, 1, 2, ..., 1, 0, -1],\n", + " [ 2, 2, 0, ..., 0, 4, 0]]])\n", + "Coordinates:\n", + " * time (time) object 1980-09-16 12:00:00 ... 1983-08-17 00:00:00\n", + " xc (y, x) float64 189.2 189.4 189.6 189.7 ... 17.65 17.4 17.15 16.91\n", + " yc (y, x) float64 16.53 16.78 17.02 17.27 ... 28.26 28.01 27.76 27.51\n", + "Dimensions without coordinates: y, x" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "flags = xr.DataArray(np.random.randint(-1, 5, da.shape), dims=da.dims, coords=da.coords)\n", "flags" @@ -2240,9 +3202,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "da_masked = da.where(flags.isin([1, 2, 3, 4, 5]), drop=True)\n", "da_masked[0, :, :].plot();" From 9cffe767b318688dd0b12234fbb89ef72f77c9bc Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Fri, 7 Jul 2023 19:48:54 -0600 Subject: [PATCH 30/54] updating to remove the masking stuff --- intermediate/02.2_indexing_Advanced.ipynb | 3420 +++++++++++++++++++-- 1 file changed, 3172 insertions(+), 248 deletions(-) diff --git a/intermediate/02.2_indexing_Advanced.ipynb b/intermediate/02.2_indexing_Advanced.ipynb index 393a1e34..b21f239d 100644 --- a/intermediate/02.2_indexing_Advanced.ipynb +++ b/intermediate/02.2_indexing_Advanced.ipynb @@ -8,9 +8,9 @@ "\n", "## Learning Objectives\n", "\n", - "* Vectorized and Pointwise Indexing \n", - "* Boolean Indexing & Masking\n", - " * Dropping/Masking Data Using `where` and `isin`" + "* Orthogonal vs. Vectorized and Pointwise Indexing\n", + "\n", + "* Fancy DateTime Indexing" ] }, { @@ -21,13 +21,12 @@ "\n", "In the previous notebooks, we learned basic forms of indexing with xarray (positional and name based dimensions, integer and label based indexing), Datetime Indexing, and nearest neighbor lookups. In this tutorial, we will discover more advanced options for vectorized indexing and learn about additional useful methods relating to indexing/selecting data such as masking. \n", "\n", - "\n", "First, let's import packages needed for this repository: " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 90, "metadata": {}, "outputs": [], "source": [ @@ -40,14 +39,488 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In this tutorial, we’ll use air temperature tutorial dataset from the National Center for Environmental Prediction. " + "In this notebook, we’ll use air temperature tutorial dataset from the National Center for Environmental Prediction. " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 92, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.Dataset>\n",
+       "Dimensions:  (lat: 25, time: 2920, lon: 53)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Data variables:\n",
+       "    air      (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n",
+       "Attributes:\n",
+       "    Conventions:  COARDS\n",
+       "    title:        4x daily NMC reanalysis (1948)\n",
+       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
+       "    platform:     Model\n",
+       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
    " + ], + "text/plain": [ + "\n", + "Dimensions: (lat: 25, time: 2920, lon: 53)\n", + "Coordinates:\n", + " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", + " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Data variables:\n", + " air (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n", + "Attributes:\n", + " Conventions: COARDS\n", + " title: 4x daily NMC reanalysis (1948)\n", + " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", + " platform: Model\n", + " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ds = xr.tutorial.load_dataset(\"air_temperature\")\n", "da = ds.air\n", @@ -58,12 +531,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Vectorized Indexing\n", + "## Orthogonal Indexing \n", "\n", - "Like NumPy and pandas, Xarray supports indexing many array elements at once in a\n", - "*vectorized* manner. \n", + "As we learned in the previous tutorial, positional indexing deviates from the behavior exhibited by NumPy when indexing with multiple arrays. However, Xarray pointwise indexing supports the indexing along multiple labeled dimensions using list-like objects similar to NumPy indexing behavior.\n", "\n", - "If you only provide integers, slices, or unlabeled arrays (array without dimension names, such as `np.ndarray`, `list`, but not `DataArray()`) indexing can be understood as orthogonally (i.e. along independent axes, instead of using numpy’s broadcasting rules to vectorize indexers). \n", + "If you only provide integers, slices, or unlabeled arrays (array without dimension names, such as `np.ndarray`, `list`, but not `DataArray()`) indexing can be understood as orthogonally (i.e. along independent axes, instead of using NumPy’s broadcasting rules to vectorize indexers). \n", "\n", "*Orthogonal* or *outer* indexing considers one-dimensional arrays in the same way as slices when deciding the output shapes. The principle of outer or orthogonal indexing is that the result mirrors the effect of independently indexing along each dimension with integer or boolean arrays, treating both the indexed and indexing arrays as one-dimensional. This method of indexing is analogous to vector indexing in programming languages like MATLAB, Fortran, and R, where each indexer component independently selects along its corresponding dimension. \n", "\n", @@ -72,11 +544,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 93, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "da[0, [2, 4, 10, 13], [1, 6, 7]].plot(); # -- orthogonal indexing" + "da[0, [2, 4, 10, 13], [1, 6, 7]].plot(); # -- orthogonal indexing" ] }, { @@ -90,42 +573,947 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 94, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'air' (time: 2920, degrees_north: 4, degrees_east: 4)>\n",
+       "array([[[293.1    , 293.1    , 293.29   , 293.29   ],\n",
+       "        [284.6    , 284.6    , 284.9    , 284.19998],\n",
+       "        [282.79   , 282.79   , 283.19998, 282.6    ],\n",
+       "        [282.79   , 282.79   , 283.19998, 282.6    ]],\n",
+       "\n",
+       "       [[293.19998, 293.19998, 293.9    , 294.19998],\n",
+       "        [283.29   , 283.29   , 285.19998, 285.19998],\n",
+       "        [281.4    , 281.4    , 282.79   , 283.5    ],\n",
+       "        [281.4    , 281.4    , 282.79   , 283.5    ]],\n",
+       "\n",
+       "       [[292.4    , 292.4    , 292.9    , 293.4    ],\n",
+       "        [282.     , 282.     , 283.29   , 284.69998],\n",
+       "        [280.     , 280.     , 280.79   , 282.4    ],\n",
+       "        [280.     , 280.     , 280.79   , 282.4    ]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[288.88998, 288.88998, 289.19   , 290.88998],\n",
+       "        [282.49   , 282.49   , 281.99   , 281.99   ],\n",
+       "        [281.29   , 281.29   , 281.29   , 280.99   ],\n",
+       "        [281.29   , 281.29   , 281.29   , 280.99   ]],\n",
+       "\n",
+       "       [[288.29   , 288.29   , 289.19   , 290.79   ],\n",
+       "        [282.09   , 282.09   , 281.59   , 282.38998],\n",
+       "        [280.99   , 280.99   , 280.38998, 280.59   ],\n",
+       "        [280.99   , 280.99   , 280.38998, 280.59   ]],\n",
+       "\n",
+       "       [[289.49   , 289.49   , 290.38998, 291.59   ],\n",
+       "        [282.09   , 282.09   , 281.99   , 283.09   ],\n",
+       "        [281.38998, 281.38998, 280.59   , 280.99   ],\n",
+       "        [281.38998, 281.38998, 280.59   , 280.99   ]]], dtype=float32)\n",
+       "Coordinates:\n",
+       "    lat      (degrees_north) float32 30.0 40.0 42.5 42.5\n",
+       "    lon      (degrees_east) float32 200.0 200.0 202.5 205.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Dimensions without coordinates: degrees_north, degrees_east\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
    " + ], + "text/plain": [ + "\n", + "array([[[293.1 , 293.1 , 293.29 , 293.29 ],\n", + " [284.6 , 284.6 , 284.9 , 284.19998],\n", + " [282.79 , 282.79 , 283.19998, 282.6 ],\n", + " [282.79 , 282.79 , 283.19998, 282.6 ]],\n", + "\n", + " [[293.19998, 293.19998, 293.9 , 294.19998],\n", + " [283.29 , 283.29 , 285.19998, 285.19998],\n", + " [281.4 , 281.4 , 282.79 , 283.5 ],\n", + " [281.4 , 281.4 , 282.79 , 283.5 ]],\n", + "\n", + " [[292.4 , 292.4 , 292.9 , 293.4 ],\n", + " [282. , 282. , 283.29 , 284.69998],\n", + " [280. , 280. , 280.79 , 282.4 ],\n", + " [280. , 280. , 280.79 , 282.4 ]],\n", + "\n", + " ...,\n", + "\n", + " [[288.88998, 288.88998, 289.19 , 290.88998],\n", + " [282.49 , 282.49 , 281.99 , 281.99 ],\n", + " [281.29 , 281.29 , 281.29 , 280.99 ],\n", + " [281.29 , 281.29 , 281.29 , 280.99 ]],\n", + "\n", + " [[288.29 , 288.29 , 289.19 , 290.79 ],\n", + " [282.09 , 282.09 , 281.59 , 282.38998],\n", + " [280.99 , 280.99 , 280.38998, 280.59 ],\n", + " [280.99 , 280.99 , 280.38998, 280.59 ]],\n", + "\n", + " [[289.49 , 289.49 , 290.38998, 291.59 ],\n", + " [282.09 , 282.09 , 281.99 , 283.09 ],\n", + " [281.38998, 281.38998, 280.59 , 280.99 ],\n", + " [281.38998, 281.38998, 280.59 , 280.99 ]]], dtype=float32)\n", + "Coordinates:\n", + " lat (degrees_north) float32 30.0 40.0 42.5 42.5\n", + " lon (degrees_east) float32 200.0 200.0 202.5 205.0\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Dimensions without coordinates: degrees_north, degrees_east\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "target_lat = xr.DataArray([31, 41, 42, 42], dims=\"degrees_north\")\n", "target_lon = xr.DataArray([200, 201, 202, 205], dims=\"degrees_east\")\n", "\n", - "da.sel(lat=target_lat, lon=target_lon, method=\"nearest\") # -- orthogonal indexing" + "da.sel(lat=target_lat, lon=target_lon, method=\"nearest\") # -- orthogonal indexing " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "In the above example, you can see how the output shape is time x lats x lons. " + "In the above example, you can see how the output shape is `time` x `lats` x `lons`. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "But what if we would like to find the information from the nearest grid cell to collection of specified points (for example weather stations or tower data)?\n", "\n", + "But what if we would like to find the information from the nearest grid cell to a collection of specified points (for example, weather stations or tower data)?\n", + "\n", + "## Vectorized or Pointwise Indexing\n", "\n", - "**Vectorized indexing** using `DataArrays()` may be used to extract information from the nearest grid cells of interest, for example, the nearest climate model grid cells to a collection specified weather station latitudes and longitudes.\n", + "Like NumPy and pandas, Xarray supports indexing many array elements at once in a\n", + "*vectorized* manner. \n", + "\n", + "**Vectorized indexing** or **Pointwise Indexing** using `DataArrays()` can be used to extract information from the nearest grid cells of interest, for example, the nearest climate model grid cells to a collection of specified weather station latitudes and longitudes.\n", "\n", - "**To trigger vectorized indexing behavior you will need to provide the selection dimensions with a new shared output dimension name.** \n", + "```{hint}\n", + "To trigger vectorized indexing behavior, you will need to provide the selection dimensions with a new shared output dimension name. \n", + "```\n", "\n", "In the example below, the selections of the closest latitude and longitude are renamed to an output dimension named `points`:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 95, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray (points: 4)>\n",
+       "array([31, 41, 42, 42])\n",
+       "Dimensions without coordinates: points
    " + ], + "text/plain": [ + "\n", + "array([31, 41, 42, 42])\n", + "Dimensions without coordinates: points" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Define target latitude and longitude (where weather stations might be)\n", "lat_points = xr.DataArray([31, 41, 42, 42], dims=\"points\")\n", @@ -135,9 +1523,390 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 96, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray (points: 4)>\n",
+       "array([200, 201, 202, 205])\n",
+       "Dimensions without coordinates: points
    " + ], + "text/plain": [ + "\n", + "array([200, 201, 202, 205])\n", + "Dimensions without coordinates: points" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "lon_points" ] @@ -151,37 +1920,481 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 97, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'air' (time: 2920, points: 4)>\n",
+       "array([[293.1    , 284.6    , 283.19998, 282.6    ],\n",
+       "       [293.19998, 283.29   , 282.79   , 283.5    ],\n",
+       "       [292.4    , 282.     , 280.79   , 282.4    ],\n",
+       "       ...,\n",
+       "       [288.88998, 282.49   , 281.29   , 280.99   ],\n",
+       "       [288.29   , 282.09   , 280.38998, 280.59   ],\n",
+       "       [289.49   , 282.09   , 280.59   , 280.99   ]], dtype=float32)\n",
+       "Coordinates:\n",
+       "    lat      (points) float32 30.0 40.0 42.5 42.5\n",
+       "    lon      (points) float32 200.0 200.0 202.5 205.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Dimensions without coordinates: points\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
    " + ], + "text/plain": [ + "\n", + "array([[293.1 , 284.6 , 283.19998, 282.6 ],\n", + " [293.19998, 283.29 , 282.79 , 283.5 ],\n", + " [292.4 , 282. , 280.79 , 282.4 ],\n", + " ...,\n", + " [288.88998, 282.49 , 281.29 , 280.99 ],\n", + " [288.29 , 282.09 , 280.38998, 280.59 ],\n", + " [289.49 , 282.09 , 280.59 , 280.99 ]], dtype=float32)\n", + "Coordinates:\n", + " lat (points) float32 30.0 40.0 42.5 42.5\n", + " lon (points) float32 200.0 200.0 202.5 205.0\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Dimensions without coordinates: points\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "da.sel(lat=lat_points, lon=lon_points, method=\"nearest\")" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "lat_points = [31, 41, 42, 42]\n", - "lon_points = [31, 41, 42, 42]\n", - "da.sel(lat=lat_points, lon=lon_points, method=\"nearest\") # --orthogonal indexing\n", - "# da.sel_points(lat=lat_points, lon=lon_points, method=\"nearest\")" - ] - }, { "cell_type": "markdown", "metadata": {}, "source": [ - "👆 Please notice how the shape of our `DataArray` is time x points, extracting time series for each weather stations. \n" + "👆 Please notice how the shape of our `DataArray` is `time` x `points`, extracting time series for each weather stations. \n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 98, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "('time', 'points')" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "da.sel(lat=lat_points, lon=lon_points, method=\"nearest\").dims" ] @@ -190,18 +2403,517 @@ "cell_type": "markdown", "metadata": {}, "source": [ - ":::{attention}\n", + "```attention}\n", "Please note that slices or sequences/arrays without named-dimensions are treated as if they have the same dimension which is indexed along.\n", - ":::\n", + "```\n", "\n", "For example:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 99, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'air' (time: 2920, lat: 3, points: 4)>\n",
+       "array([[[296.6    , 296.6    , 296.19998, 296.4    ],\n",
+       "        [293.1    , 293.1    , 293.29   , 293.29   ],\n",
+       "        [284.6    , 284.6    , 284.9    , 284.19998]],\n",
+       "\n",
+       "       [[296.4    , 296.4    , 295.9    , 296.19998],\n",
+       "        [293.19998, 293.19998, 293.9    , 294.19998],\n",
+       "        [283.29   , 283.29   , 285.19998, 285.19998]],\n",
+       "\n",
+       "       [[295.6    , 295.6    , 295.4    , 295.4    ],\n",
+       "        [292.4    , 292.4    , 292.9    , 293.4    ],\n",
+       "        [282.     , 282.     , 283.29   , 284.69998]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[294.79   , 294.79   , 295.29   , 297.49   ],\n",
+       "        [288.88998, 288.88998, 289.19   , 290.88998],\n",
+       "        [282.49   , 282.49   , 281.99   , 281.99   ]],\n",
+       "\n",
+       "       [[293.69   , 293.69   , 293.88998, 295.38998],\n",
+       "        [288.29   , 288.29   , 289.19   , 290.79   ],\n",
+       "        [282.09   , 282.09   , 281.59   , 282.38998]],\n",
+       "\n",
+       "       [[293.79   , 293.79   , 293.69   , 295.09   ],\n",
+       "        [289.49   , 289.49   , 290.38998, 291.59   ],\n",
+       "        [282.09   , 282.09   , 281.99   , 283.09   ]]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 20.0 30.0 40.0\n",
+       "    lon      (points) float32 200.0 200.0 202.5 205.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Dimensions without coordinates: points\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
    " + ], + "text/plain": [ + "\n", + "array([[[296.6 , 296.6 , 296.19998, 296.4 ],\n", + " [293.1 , 293.1 , 293.29 , 293.29 ],\n", + " [284.6 , 284.6 , 284.9 , 284.19998]],\n", + "\n", + " [[296.4 , 296.4 , 295.9 , 296.19998],\n", + " [293.19998, 293.19998, 293.9 , 294.19998],\n", + " [283.29 , 283.29 , 285.19998, 285.19998]],\n", + "\n", + " [[295.6 , 295.6 , 295.4 , 295.4 ],\n", + " [292.4 , 292.4 , 292.9 , 293.4 ],\n", + " [282. , 282. , 283.29 , 284.69998]],\n", + "\n", + " ...,\n", + "\n", + " [[294.79 , 294.79 , 295.29 , 297.49 ],\n", + " [288.88998, 288.88998, 289.19 , 290.88998],\n", + " [282.49 , 282.49 , 281.99 , 281.99 ]],\n", + "\n", + " [[293.69 , 293.69 , 293.88998, 295.38998],\n", + " [288.29 , 288.29 , 289.19 , 290.79 ],\n", + " [282.09 , 282.09 , 281.59 , 282.38998]],\n", + "\n", + " [[293.79 , 293.79 , 293.69 , 295.09 ],\n", + " [289.49 , 289.49 , 290.38998, 291.59 ],\n", + " [282.09 , 282.09 , 281.99 , 283.09 ]]], dtype=float32)\n", + "Coordinates:\n", + " * lat (lat) float32 20.0 30.0 40.0\n", + " lon (points) float32 200.0 200.0 202.5 205.0\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Dimensions without coordinates: points\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "da.sel(lat=[20, 30, 40], lon=lon_points, method=\"nearest\")" ] @@ -210,75 +2922,412 @@ "cell_type": "markdown", "metadata": {}, "source": [ - ":::{warning}\n", + "```{warning}\n", "If an indexer is a `DataArray()`, its coordinates should not conflict with the selected subpart of the target array (except for the explicitly indexed dimensions with `.loc`/`.sel`). Otherwise, `IndexError` will be raised!\n", - ":::" + "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Pointwise Indexing\n", - "\n", - "Positional indexing deviates from the NumPy when indexing with multiple arrays. \n", - "\n", - "**Xarray pointwise indexing supports the indexing along multiple labeled dimensions using list-like objects similar to NumPy indexing behavior.** \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "da = xr.DataArray(np.arange(56).reshape((7, 8)), dims=[\"x\", \"y\"])\n", - "da" + "Analogously, label-based pointwise-indexing is also possible by the `.sel()` method:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 100, "metadata": {}, - "outputs": [], - "source": [ - "da.isel(x=xr.DataArray([0, 1, 6], dims=\"z\"), y=xr.DataArray([0, 1, 0], dims=\"z\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the above three elements at `(ix, iy) = ((0, 0), (1, 1), (6, 0))` are selected and mapped along a new dimension `z`. \n", - "If you want to add a coordinate to the new dimension `z`, you can supply a `DataArray` with a coordinate:\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "da.isel(\n", - " x=xr.DataArray([0, 1, 6], dims=\"z\", coords={\"z\": [\"a\", \"b\", \"c\"]}),\n", - " y=xr.DataArray([0, 1, 0], dims=\"z\"),\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Analogously, label-based pointwise-indexing is also possible by the `.sel` method:\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray (new_time: 3)>\n",
+       "array([0.87902737, 0.06690113, 0.01799672])\n",
+       "Coordinates:\n",
+       "    time      (new_time) datetime64[ns] 2000-01-03 2000-01-02 2000-01-01\n",
+       "    space     (new_time) <U2 'IA' 'IL' 'IN'\n",
+       "  * new_time  (new_time) datetime64[ns] 2000-01-03 2000-01-02 2000-01-01
    " + ], + "text/plain": [ + "\n", + "array([0.87902737, 0.06690113, 0.01799672])\n", + "Coordinates:\n", + " time (new_time) datetime64[ns] 2000-01-03 2000-01-02 2000-01-01\n", + " space (new_time) \n", - " \n", - "Warning: Please note that when done repeatedly, this type of indexing is significantly slower than using sel().\n", - " \n", - "" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -507,6 +3418,11 @@ } ], "metadata": { + "kernelspec": { + "display_name": "NPL 2023b", + "language": "python", + "name": "npl-2023b" + }, "language_info": { "codemirror_mode": { "name": "ipython", @@ -516,7 +3432,8 @@ "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3" + "pygments_lexer": "ipython3", + "version": "3.10.12" }, "toc": { "base_numbering": 1, @@ -530,6 +3447,13 @@ "toc_position": {}, "toc_section_display": true, "toc_window_display": true + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } } }, "nbformat": 4, From 006714150a02a4d8b2936f4661ee04099c7534c5 Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Fri, 7 Jul 2023 19:49:24 -0600 Subject: [PATCH 31/54] updating basic --- fundamentals/02.1_indexing_Basic.ipynb | 4057 +++++++++++++++++++++++- 1 file changed, 4026 insertions(+), 31 deletions(-) diff --git a/fundamentals/02.1_indexing_Basic.ipynb b/fundamentals/02.1_indexing_Basic.ipynb index 5b3a4c1f..2fa03fa1 100644 --- a/fundamentals/02.1_indexing_Basic.ipynb +++ b/fundamentals/02.1_indexing_Basic.ipynb @@ -53,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -71,9 +71,483 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.Dataset>\n",
+       "Dimensions:  (lat: 25, time: 2920, lon: 53)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Data variables:\n",
+       "    air      (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n",
+       "Attributes:\n",
+       "    Conventions:  COARDS\n",
+       "    title:        4x daily NMC reanalysis (1948)\n",
+       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
+       "    platform:     Model\n",
+       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
    " + ], + "text/plain": [ + "\n", + "Dimensions: (lat: 25, time: 2920, lon: 53)\n", + "Coordinates:\n", + " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", + " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Data variables:\n", + " air (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n", + "Attributes:\n", + " Conventions: COARDS\n", + " title: 4x daily NMC reanalysis (1948)\n", + " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", + " platform: Model\n", + " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ds = xr.tutorial.load_dataset(\"air_temperature\")\n", "ds" @@ -81,7 +555,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -111,9 +585,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(2920, 25, 53)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "np_array = ds[\"air\"].data # numpy array\n", "np_array.shape" @@ -128,9 +613,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "242.09999" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "np_array[1, 0, 0]" ] @@ -144,9 +640,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([295. , 294.4 , 294.5 , ..., 297.29, 297.79, 297.99], dtype=float32)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# extract a time-series for one spatial location\n", "np_array[:, 20, 40]" @@ -178,9 +685,434 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'air' (time: 2920)>\n",
+       "array([295.  , 294.4 , 294.5 , ..., 297.29, 297.79, 297.99], dtype=float32)\n",
+       "Coordinates:\n",
+       "    lat      float32 25.0\n",
+       "    lon      float32 300.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
    " + ], + "text/plain": [ + "\n", + "array([295. , 294.4 , 294.5 , ..., 297.29, 297.79, 297.99], dtype=float32)\n", + "Coordinates:\n", + " lat float32 25.0\n", + " lon float32 300.0\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "da[:, 20, 40]" ] @@ -189,7 +1121,62 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n" + "```caution\n", + "Positional indexing deviates from the NumPy behavior when indexing with multiple arrays. \n", + "\n", + "```\n", + "We can show this with an example: " + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2920, 2)" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np_array[:,[0, 1], [0, 1]].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2920, 2, 2)" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "da[:,[0, 1], [0, 1]].shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Please note how the dimension of the `DataArray()` object is different from the `numpy.ndarray`.\n", + "\n", + "``` tip\n", + " However, users can still achieve NumPy-like pointwise indexing across multiple labeled dimensions by using Xarray vectorized indexing techniques. We will delve further into this topic in the advanced indexing notebook.\n", + " ```" ] }, { @@ -211,9 +1198,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkwAAAHgCAYAAABXUjLLAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAC5AUlEQVR4nOyddXgUVxeHfxt3JSEkQPCEAMG1OASnSIsWa2nLVyhOKRQoLhWsBqUUaXGKFivuHiwQHIImBAgR4snO90fYzcrsjuys5rw8ediduXPvndm5d86cc+45MoZhGBAEQRAEQRA6sTN3BwiCIAiCICwdEpgIgiAIgiA4IIGJIAiCIAiCAxKYCIIgCIIgOCCBiSAIgiAIggMSmAiCIAiCIDgggYkgCIIgCIIDEpgIgiAIgiA4IIGJIAiCIAiCAxKYihCrVq2CTCZDXFyc4GNPnz6NadOmITk5WfJ+cXH48GF88sknCA8Ph7u7O0JCQtClSxdER0drlR00aBBkMpnWX3h4OO/2Dh48iIYNG8LNzQ3FihXDoEGDkJiYyHnc0aNH1dq8ePEi5zGDBg1CmTJlePeNkJ5169Zh0aJFWtvj4uIgk8nw448/ctaxaNEitd/+1atXgvuhuH+OHj0q+FhL4uDBg4iKikJwcDCcnZ0RGBiIli1bYs+ePTrL8xlvubm5mD59OsqUKQNnZ2eEh4fj559/5t2vt2/fYtSoUQgODoaLiwtq1KiBDRs28Dq2efPmyt+2U6dOnOUV986qVat494+wfEhgInhx+vRpTJ8+3SwC05IlSxAXF4eRI0diz549WLx4MRITE9GgQQMcPnxYq7yrqyvOnDmj9rdx40ZebR07dgzt27dH8eLFsWPHDixevBgHDx5Eq1atkJ2dzauOX3/9FWfOnEHlypUFnSdhHnQJTELo3bs3zpw5g8GDB0vTKSvm9evXqFKlChYuXIj9+/fj999/h6OjIzp27Ig1a9aolRUy3oYOHYq5c+di2LBh+O+//9CtWzeMHDkSc+bM4dWv7t27Y/Xq1Zg6dSr27t2LunXrok+fPli3bh2v42vWrIkzZ85g/vz5/C4EYXswRJFh5cqVDADm4cOHgo/94YcfRB9rKC9evNDalpaWxhQvXpxp1aqV2vaBAwcy7u7uotuqW7cuExERweTm5iq3nTp1igHA/Pbbb3qPPXLkCAOAOXLkCO/2Bg4cyISGhorsrXgyMjIYuVxu8nYtifT0dIZhGKZjx46sv8HDhw8ZAMwPP/zAu86pU6cyAJiXL18K7o+Y+8dayMnJYUJCQpgmTZqobec73q5fv87IZDJmzpw5asd/9tlnjKurK/P69Wu97e/evZsBwKxbt05te1RUFBMcHMzk5eXpPb5Zs2ZMs2bN9JZRRXHvrFy5kvcxUpGRkWHyNosKpGEq4hw4cABdunRByZIl4eLiggoVKmDIkCFqJoVp06bhq6++AgCULVtWqZo2lekgMDBQa5uHhwciIiLw5MkTydp59uwZLly4gP79+8PBwUG5vVGjRqhUqRK2bdtmUP2rVq1CWFgYnJ2dUblyZfz111+s5XJycjBr1iyEh4fD2dkZAQEB+Pjjj/Hy5Uu1ctnZ2Rg7diyCgoLg5uaGpk2bIjo6GmXKlMGgQYPU2pXJZNi/fz8++eQTBAQEwM3NTfkGv3HjRjRs2BDu7u7w8PBA27ZtcfnyZa1+Xbx4Ee+//z78/Pzg4uKCmjVrYtOmTWplMjIyMG7cOJQtWxYuLi7w8/NDnTp1sH79et7XSWGaWr9+PSZNmoTg4GB4eXmhdevWuH37tlb5FStWoHr16sr2unXrhps3b6qVGTRoEDw8PBATE4M2bdrA09MTrVq1QvPmzbF79248evRIzaSmyYIFC1C2bFl4eHigYcOGOHv2LO/zMZSdO3cqTVaenp6IiorCmTNn1MpMmzYNMpkMN27cQJ8+feDt7Y3ixYvjk08+QUpKisn6qgtHR0f4+PiojSsh42379u1gGAYff/yxWr0ff/wxMjMzsW/fPr3tb9u2DR4eHujRo4fW8c+fP8e5c+dEn9vz58/Rs2dPeHp6wtvbG7169UJCQgJrWT5jCABOnjyJhg0bwsXFBSEhIZgyZQqWL1+u5VJRpkwZdOrUCVu3bkXNmjXh4uKC6dOnAwASEhIwZMgQlCxZEk5OTihbtiymT5+OvLw8tbb4zjcE4MBdhLBl7t+/j4YNG+LTTz+Ft7c34uLisGDBAjRu3BgxMTFwdHTEp59+iqSkJPz888/YunUrSpQoAQCIiIjQWS/DMMjPz+fVB9XJki8pKSm4dOkSWrZsqbUvMzMTQUFBePnyJUqUKIGuXbtixowZ8PPz01vn9evXAQCRkZFa+yIjI3Hq1CnB/VSwatUqfPzxx+jSpQvmz5+PlJQUTJs2DdnZ2bCzK3xvkcvl6NKlC06cOIHx48ejUaNGePToEaZOnYrmzZvj4sWLcHV1BVAw2W/cuBHjx49Hy5YtERsbi27duiE1NZW1D5988gk6duyIv//+G+np6XB0dMScOXMwefJkfPzxx5g8eTJycnLwww8/oEmTJjh//rzyNz5y5AjatWuH+vXrY+nSpfD29saGDRvQq1cvZGRkKAW0MWPG4O+//8asWbNQs2ZNpKen4/r163j9+rXga/bNN9/gvffew/Lly5Gamoqvv/4anTt3xs2bN2Fvbw8AmDt3Lr755hv06dMHc+fOxevXrzFt2jQ0bNgQFy5cQMWKFZX15eTk4P3338eQIUMwYcIE5OXloWTJkvj8889x//59nQLxr7/+ivDwcKXZbsqUKejQoQMePnwIb29vweclhHXr1uGjjz5CmzZtsH79emRnZ+P7779H8+bNcejQITRu3Fit/AcffIBevXph8ODBiImJwcSJEwEUCJX6kMvlkMvlnP2RyWTKa8+Fos7ExET8/vvvuHPnDr777jvlfiHj7fr16wgICEBQUJBWOdW6dHH9+nVUrlxZa65RPb5Ro0a8zkuVzMxMtG7dGs+fP8fcuXNRqVIl7N69G7169dIqy3cMXbt2DVFRUahUqRJWr14NNzc3LF26VMucqeDSpUu4efMmJk+ejLJly8Ld3R0JCQmoV68e7Ozs8O2336J8+fI4c+YMZs2ahbi4OKxcuRKAsPmGAJnkihJcJjm5XM7k5uYyjx49YgAwO3bsUO4TapJTtMXnTwwfffQR4+DgwFy8eFFt+4IFC5gFCxYw+/fvZ/bv389MmjSJcXNzY8LDw5m0tDS9da5du5YBwJw5c0Zr3+eff844OTnpPV6XSSU/P58JDg5matWqpWYGi4uLYxwdHdXMQevXr2cAMFu2bFGr48KFC2pmihs3bjAAmK+//lqtnOL4gQMHKrcpfosBAwaolX38+DHj4ODADB8+XG17WloaExQUxPTs2VO5LTw8nKlZs6aa6YRhGKZTp05MiRIlmPz8fIZhGKZq1apM165d9VwlbhTXsUOHDmrbN23apPb7vHnzhnF1ddUq9/jxY8bZ2Znp27evctvAgQMZAMyKFSu02uMyyVWrVk3NZHP+/HkGALN+/XqtY6Q0ySnum2rVqimvL8MU/D6BgYFMo0aNtNr9/vvv1eocOnQo4+Liwml+VRzP9SfEfNy2bVvlcV5eXszWrVvV9gsZb1FRUUxYWBhrO05OTsznn3+uty8VK1Zk2rZtq7X9+fPnDAAtU58mukxyS5Ys0ZorGabAVAgNkxzfMdSjRw/G3d1d7R7Kz89nIiIitObg0NBQxt7enrl9+7ZanUOGDGE8PDyYR48eqW3/8ccfGQDMjRs3GIbhP98QBZBJroiTmJiI//3vfyhVqhQcHBzg6OiI0NBQANAyawihc+fOuHDhAq8/oUyZMgVr167FwoULUbt2bbV9o0ePxujRoxEVFYWoqCjMmjULf/31F27duoU//viDV/1sJhl927m4ffs2nj9/jr59+6rVERoaqvVWu2vXLvj4+KBz587Iy8tT/tWoUQNBQUFKM+ixY8cAAD179lQ7/sMPP9Spsfvggw/Uvv/333/Iy8vDgAED1NpycXFBs2bNlG3du3cPt27dwkcffQQAamU7dOiA+Ph4pamsXr162Lt3LyZMmICjR48iMzNT1DUDgPfff1/tu0Ib8OjRIwDAmTNnkJmZqWZ+BIBSpUqhZcuWOHToEOc14EPHjh3VtCqa/TAWivumf//+alpIDw8PfPDBBzh79iwyMjLUjmG7ZllZWZyrPD///HNeY/Xff//l3f+ff/4Z58+fx44dO9C2bVv06tWL1TTLd7zpG398xqahx7Nx5MgReHp6al33vn37qn0XMoaOHTuGli1bolixYsrj7ezstMa6gsjISFSqVElt265du9CiRQsEBwertdW+fXtlG4pyfOYbogAyyRVh5HI52rRpg+fPn2PKlCmoVq0a3N3dIZfL0aBBA4Medn5+fkYxV0yfPh2zZs3C7Nmz8eWXX/I6plu3bnB3d+f0O/H39wcAVvNRUlISp0lPF4r6NM0Jim2qPgkvXrxAcnIynJycWOtS+JYp6ixevLjafgcHB+V5aKIwpaq2BQB169ZlLa94SCvKjRs3DuPGjdPbr59++gklS5bExo0b8d1338HFxQVt27bFDz/8oGYe44PmeTg7OwOA8r5UXAPN8wKA4OBgHDhwQG2bm5sbvLy8BPWBTz+MBdf5yeVyvHnzBm5ubsrtYvsaFBTE6iuoiRDBQvX3fv/999G+fXsMGzYMvXr1gp2dnaDx5u/vjytXrmiVS09PR05ODufY9Pf319kOAIPGtuYYBLTHupAxpKtOtm0A+/3x4sUL/Pvvv3B0dNTbFt/5hiiABKYizPXr13H16lWsWrUKAwcOVG6/d++ewXWvXr1ay0FTFwzD8Co3ffp0TJs2DdOmTcM333wjqD8Mw6i9pbNRtWpVAEBMTAw6dOigti8mJka5XyiKBwObI6jmtmLFisHf31+nE6unp6danS9evEBISIhyf15enk5/Ic2HneIN9p9//lFqFdlQlJs4cSK6d+/OWiYsLAwA4O7ujunTp2P69Ol48eKFUtvUuXNn3Lp1S2cbYlBcg/j4eK19z58/V3tDB8RrEcwF1/nZ2dnB19dXkrZmzJihdBbWR2hoqKg4bkCB9nHfvn14+fIlihcvLmi8VatWDRs2bEBCQoKaMBITEwMAnGOzWrVqWL9+PfLy8tQ0sHyP14W/vz/Onz+vtZ1tXAP8xpC/v79SwNJXpwK2+7pYsWKIjIzE7NmzWY8JDg5WluMz3xAFkMBUhFEMNMVbqILff/9dq6zQt2qFSU4qZs6ciWnTpmHy5MmYOnWqoGP/+ecfZGRkoEGDBnrLhYSEoF69elizZg3GjRunNMOcPXsWt2/fxqhRo0T1PSwsDCVKlMD69esxZswY5XV/9OgRTp8+rZy8AKBTp07YsGED8vPzUb9+fZ11Nm3aFEDBCrdatWqpnavmKhhdtG3bFg4ODrh//75eU1VYWBgqVqyIq1ev8o55AxS8EQ8aNAhXr17FokWLkJGRoaYNMZSGDRvC1dUVa9asUVv99PTpUxw+fBgffvghr3qcnZ2Nri0SQ1hYGEJCQrBu3TqMGzdOed+kp6djy5YtypVzUvD555/zCsioOVfwhWEYHDt2DD4+PkpBUMh469KlCyZPnozVq1fj66+/Vm5ftWoVXF1d0a5dO73td+vWDX/88Qe2bNmi5pC9evVqBAcH6x1r+mjRogU2bdqEnTt3qpnlNGM7CRlDzZo1w549e/Dq1SuloCWXy7F582be/erUqRP27NmD8uXL6xWq+c43RAEkMBVhwsPDUb58eUyYMAEMw8DPzw///vuvlikDKHhDA4DFixdj4MCBcHR0RFhYmM43EH9/f52mIaHMnz8f3377Ldq1a4eOHTtqmdYUgtCjR4/Qt29f9O7dGxUqVIBMJsOxY8ewaNEiVKlSBZ9++qnacQ4ODmjWrJmar8t3332HqKgo9OjRA0OHDkViYiImTJiAqlWr8taYaWJnZ4eZM2fi008/Rbdu3fDZZ58hOTkZ06ZN01Ld9+7dG2vXrkWHDh0wcuRI1KtXD46Ojnj69CmOHDmCLl26oFu3bqhSpQr69OmD+fPnw97eHi1btsSNGzcwf/58eHt7c2rTgIIlyTNmzMCkSZPw4MEDtGvXDr6+vnjx4gXOnz+v1BYBBUJ0+/bt0bZtWwwaNAghISFISkrCzZs3cenSJeVkXr9+fXTq1AmRkZHw9fXFzZs38ffff0v6cFfg4+ODKVOm4JtvvsGAAQPQp08fvH79GtOnT4eLiwtvwbpatWrYunUrlixZgtq1a8POzg516tSRrJ/Tpk3D9OnTceTIETRv3pz3cXZ2dvj+++/x0UcfoVOnThgyZAiys7Pxww8/IDk5GfPmzZOsj8HBwWqCuyF06dIF1atXR40aNeDv74/nz59j1apVOHbsGH799Vc1DQ/f8ValShUMHjwYU6dOhb29PerWrYv9+/dj2bJlmDVrlppJbcaMGZgxYwYOHTqEZs2aAQDat2+PqKgofPHFF0hNTUWFChWwfv167Nu3D2vWrOG98k+TAQMGYOHChRgwYABmz56NihUrYs+ePfjvv/+0yvIdQ5MmTcK///6LVq1aYdKkSXB1dcXSpUuRnp4OALzG9owZM3DgwAE0atQII0aMQFhYGLKyshAXF4c9e/Zg6dKlKFmyJO/5BgAGDx6M1atX4/79+0qN9F9//YVPPvkEK1aswIABAwAUzMPly5fHwIED8eeff4q6rhaLeX3OCVPCtkouNjaWiYqKYjw9PRlfX1+mR48ezOPHjxkAzNSpU9WOnzhxIhMcHMzY2dmZNMBes2bNeK2yS0pKYrp168aUKVOGcXV1ZZycnJiKFSsy48ePZ5KTk7XqBcC68mX//v1MgwYNGBcXF8bPz48ZMGAAa/BMTbgCDy5fvpypWLEi4+TkxFSqVIlZsWIFa+DK3Nxc5scff2SqV6/OuLi4MB4eHkx4eDgzZMgQ5u7du8pyWVlZzJgxY5jAwEDGxcWFadCgAXPmzBnG29ubGT16tLKc4ne/cOECa7+2b9/OtGjRgvHy8mKcnZ2Z0NBQ5sMPP2QOHjyoVu7q1atMz549mcDAQMbR0ZEJCgpiWrZsySxdulRZZsKECUydOnUYX19fxtnZmSlXrhwzevRo5tWrV5zXT/M6bt68WW27rmCAy5cvZyIjIxknJyfG29ub6dKli3IVkAJ9AU2TkpKYDz/8kPHx8WFkMpnyntIXuJJtfDAM+yq5sWPHMjKZjLl58yav89a8f7Zv387Ur1+fcXFxYdzd3ZlWrVoxp06d4myXYQwLViuW7777jqlbty7j6+vL2NvbM/7+/kzbtm2ZXbt2sZbnO95ycnKYqVOnMqVLl1aOoZ9++kmrnOJaaF7HtLQ0ZsSIEUxQUBDj5OTEREZGsq50ZENf4MqnT58yH3zwAePh4cF4enoyH3zwAXP69GnWe5XPGGIYhjlx4gRTv359xtnZmQkKCmK++uor5rvvvmMAqM1loaGhTMeOHVn79fLlS2bEiBFM2bJlGUdHR8bPz4+pXbs2M2nSJObt27fKcnznG8VKU9V7SXF/qZ6nYtyortS1FWQMw9OBhCAIvRw9ehQtWrTAwYMH0axZM1HxpQzl9OnTeO+997B27VqtlTqE8WDexR2bMWMGZs6ciZcvXyrNKfXq1UNoaKggkwphWTRv3hwMw+DQoUOws7PjpeWRmjZt2iAuLg537twxedtEAWSSIwiJad26NQDgwoULkpp2NDlw4ADOnDmD2rVrw9XVFVevXsW8efNQsWJFnY6lhHFYvHgxRo8erbU9NTUVV69exerVq83QK0JKjh8/rsyJt2vXLqO2NWbMGNSsWROlSpVCUlIS1q5diwMHDtieicvKIA0TQUhEWlqaWuqOiIgIyf12VDl37hzGjh2L2NhYpKWloVixYmjbti3mzp3LutTY3Ci0MPqwt7e3utVsQEE8s8ePHyu/16hRwywaRsI43L59G2lpaQAKfOcqVKhg1PZGjhyJnTt3IiEhATKZDBERERg1ahT69etn1HYJ/ZDARBCESVCYLPWxcuVKrUCUBEEQlgAJTARBmARNDRwbZcuWlWx1JUEQhJSQwEQQBEEQBMEB5ZIjCIIgCILggLwSJUAul+P58+fw9PS0SodVgiAIgiiKMAyDtLQ0BAcHc4aLIIFJAp4/f45SpUqZuxsEQRAEQYjgyZMnKFmypN4yJDBJgCI9yJMnT0RlQycIgiAIwvSkpqaiVKlSvBINk8AkAQoznJeXFwlMBEEQBGFl8HGnIadvgiAIgiAIDkhgIgiCIAiC4IAEJoIgCIIgCA5IYCIIgiAIguCABCaCIAiCIAgOSGAiCIIgCILggAQmgiAIgiAIDkhgIgiCIAiC4IAEJoIgCIIgCA5IYCIIgiAIguCABCaCIAiCIAgOSGAiCIIgCILggAQmgiAIgpCYpPQc3H/51qA6cvPluPY0GXI5I1GvCEMggYkgCIIgJKbWzANoNf8Y4l6li65j3OareP+XU1h86K6EPSPEQgITQRAEQRiJ6EdvRB+748pzAMCSY/el6g5hACQwEQRBEARBcEACE0EQBEEYQL6cwbSdN7AnJt44DZALk0VAAhNBEARBGMDOq8+w6nQchq69pLWPZB3bgQQmgiAIgjCAl2nZOvcxjOEiE0Nil0VAAhNBEARBCGTlqYf4ZluMlkDEMAymbL8uaVt8Za6cPDlGb7yCrZeeSto+UYCDuTtAEARBENbG9H9jAQCdI4Mhg0y5/dLjN/j77COz9GnjxSfYdvkZtl1+hu61SpqlD7YMaZgIgiAIQiRvs/PUvqdn56t9l8KYxreON+k5ErRG6IIEJoIgcOjmC5y8+8rc3SAIq0Qm07NTRdrJzZdj7blHeGhAMEtd/HcjAWfuv9baHv3oDXZefS55e0URMskRRBHn9dtsDF59EQDwYE4H2Nnpm/0JgtCHPuFp9ek4zNp9EwAQN68j7zq5HMdfpGZhyN/RrPs+WHIaAFCumDuqhnjzbpPQhjRMBFHESc7MVX6mtTgEIQxNYUbVn0mTC3FJ4trg2K9vlZ6CR68zRLVNFEICE0EQBEGIRFOY0dQwqYYESMtS93d6kpSBzBx1nyfWNhjg3IPXSFF5uSFMDwlMBEEokSJmDEEUNWQqUpI+g/ZpFR+j2OepaPL9EbSaf5RXG72WnUX16ftF9pCQAhKYCIJQQuISQQhHpvOLbv67kQAAeJ6SJXl/2KDgl4ZDAhNBFHFIqUQQ4tEcP5o+TLrGl53epXWEJUICE0EQSkh4IgihMHpXxk3YGoMBK85rO4eLlJeiFhzT8nv660ycuMoIQZDARBBFHNWJm9T2BGEYbILQ8TsvtRy2xUbvuJv4Frtj4tW2bbpIqVBMAQlMBEEoIQ0TQQhHpuOzKvlyTQ2TeJOcXMRApbFtOCQwEUQRh2siTcnIxcHYF8jNl5umQwRhRWiOn9c60pM8T1Z37n6SxB4X6U16Dg7dfIE8nuNNLtc9gPmELCD4Q5G+CYLQS69lZ3ArIQ2jWlfEqNaVzN0dgrA4VLVFQ9deYi3T+ZeTat83XHjCWq7bb6cQ9zoD33QI59X21svPdO77ess1XnUQ/CANE0EQSti0TbcS0gAAO69QPiqCMDZx7yJy745J0F1IZZweuZWosxjlkJMWEpgIwgq5l5iGPRqOnwCQmJqFLdFPkZ1XqIp/npyJbZefKlX8F+KScPo+e6JdfU7fD4yQMJQgrB0G4le8KWAdj3ydjnS0rWmOIxcmwyGTHEFYIa0XHAcA/PVJPTStFKDc3uXXU4hPycL9l28xvl2BSr/V/GPIzM3Hq7QcfPxeGfRYegYAcHVqG3i7OgpqNzMnH65O9hKdBUFYP1I4U/f94xyufBsFHzcnwcfqktVm7Y41rFOEFqRhIggrJuZZitr3+HdRgw/efKHclplb8KZ59E4i8lQcRFNZ8lJxTf4ZOXn6CxBEEUSKEJS6nMU529ah3tp7XY9JjxAFCUwEYcWoBsPLytW/IiY3n/tVmO/LslzO4EWqaVI6EISloZVzUYKo3Xk8xqeyfZWRqqtlygspPVYtMC1ZsgSRkZHw8vKCl5cXGjZsiL179yr3MwyDadOmITg4GK6urmjevDlu3Lih3J+UlIThw4cjLCwMbm5uKF26NEaMGIGUlBS25gjC4lCdE5t+f0Rv2dx8OacG6dvt1/XuV7zNDl17CfXnHMLR27odTgnCVvn58D3lZ6mCvebJxYXt4CurkQBlOFYtMJUsWRLz5s3DxYsXcfHiRbRs2RJdunRRCkXff/89FixYgF9++QUXLlxAUFAQoqKikJZWsOrn+fPneP78OX788UfExMRg1apV2LdvHwYPHmzO0yII3qhOgYlp2XrL8nmD1bdEWZV97xKHLjv+gFd5grAlFhy4o/zMMNKY5IRomFShjHSmw6qdvjt37qz2ffbs2ViyZAnOnj2LiIgILFq0CJMmTUL37t0BAKtXr0bx4sWxbt06DBkyBFWrVsWWLVuUx5cvXx6zZ89Gv379kJeXBwcHq748RBFA10sj2/bcfLnkqU80oxcTBCGOPJFjyZCI4YQwrFrDpEp+fj42bNiA9PR0NGzYEA8fPkRCQgLatGmjLOPs7IxmzZrh9OnTOutJSUmBl5eXXmEpOzsbqampan8EYQyGrbuEz/66qFOdvvDgHdbtdxPfov3iE0jJKHTs1jTJbeepTdKHaoqGFScfou3C43jJoekiCEIbzcjeV5/qdg1RHce6xKU3GdqLOgjDsHqBKSYmBh4eHnB2dsb//vc/bNu2DREREUhIKDAZFC9eXK188eLFlfs0ef36NWbOnIkhQ4bobXPu3Lnw9vZW/pUqVUqakyEIFd5m52H3tXgciH2hXP0mhJvxqfjjRKHJTM6om/DmH2AXtoSg+lI8Y1csbr9Iw+JDhtdLENaCVDrWfLE+RqRgMhlWLzCFhYXhypUrOHv2LL744gsMHDgQsbGF8Sc01ZUMw7CqMFNTU9GxY0dERERg6tSpetucOHEiUlJSlH9PnrCHuCcIQ1DVKomdlFVXzskgLmmnPthMctm5lHOOIAQjWl4iiclUWL3A5OTkhAoVKqBOnTqYO3cuqlevjsWLFyMoKAgAtLRJiYmJWlqntLQ0tGvXDh4eHti2bRscHfUH83N2dlauzFP8EYQxURWelh2/z/u4fA2hi4+8NGX7dSTo0GjN3BWLnLxCgejKk2TM3XtTbwJQgiC4ETOEHr5Kx5ZLT3mVZVMU5ObLcfreK86QJEQBVi8wacIwDLKzs1G2bFkEBQXhwIEDyn05OTk4duwYGjVqpNyWmpqKNm3awMnJCTt37oSLi4s5uk0QWrBNcM+SMzFnzy3edWgJMjwm5b/PPsKX69gTiG67/Axrzj5S2/b7sQfYzZKmhSCKAlIt1xezIEMRtZ8PdiyKqDl7bqLv8nMYs+mK4LaLIla9DOybb75B+/btUapUKaSlpWHDhg04evQo9u3bB5lMhlGjRmHOnDmoWLEiKlasiDlz5sDNzQ19+/YFUKBZatOmDTIyMrBmzRo1B+6AgADY21MKCMIyUMzJb7OERdrW9IvgOylff67b4fTJmwytbapBLEnXRBQ1pLjnxWiYXr3lv8CCzXS38lQcAGCPvkS/hBKrFphevHiB/v37Iz4+Ht7e3oiMjMS+ffsQFRUFABg/fjwyMzMxdOhQvHnzBvXr18f+/fvh6ekJAIiOjsa5c+cAABUqVFCr++HDhyhTpoxJz4cgACAnTw4nB2HKX1UzmSqqkzDDMMjWUU4TfS/NbPtoaTNRFMiXM0YLAJmTJ+ddt5geSB1SpChi1QLTn3/+qXe/TCbDtGnTMG3aNNb9zZs3p+inhEUxe3cs/jjxELtHNEaov7vWfja5ZNrOG1h1Oo61PlWTXNzrDNSfc4hXP/SNChozRFGEYRhELTymtahhyvbr+OpdomtD+Oyvi2hYzp9X2YlbY9CnXmlB9dOwNRyb82EiCGvmjxMPAQAL9t9RXyXHqP+vii5hCeAbWJKljD4NE48aCcLWSM/Jx4OX6XiWnKm2PVWgmVwfZx68lqwuTWjcGg4JTARhgTBQn+DEqtNz86Vf4s+WxFdV8ZWXL8fbbMMeIqlZuRRFnJAUhmGQkllwX6VlCQ/qyOY0rYBWiRYNrNokRxC2CsMwkqjQt195zqMtlm16BLT15x9ztrn9ynNcnhIFX3cnzvY1eZKUgSbfH0GdUF/880Uj7gMIggcTtsRg48XCmHknv26Bkr5uvI/XF+9o6s4bOvdZCmRKNxzSMBGEpaLmsG2SZkS3x+ZbdVakeWHn1QIh7+KjN6KOJwg2VIUlANh1TVgoDHKaJkhgIggLxVQTNL14EkURofe9tY8Ta++/JUACE0FYIJpRufuvOIf5+29L3s4P/93C7mvaZjsp5lYhkQZevc1G119PYeMF/eY+gpAKoS8kRUXeOP8wCWUm7MbErTHm7orFQQITQVgBT5Iy8fPhe5JrnX49ch8/Hb6ntd3U/g7z99/BlSfJ+HoLTdKEaRCuYbJukYnv3NHz94Lo4evPP8bN+FRjdsnqIIGJICwQhjHvG63Qtg0NW5lu4Ko6gjA21i0uiTPJJaSy55QsqpDARBAWiiW80eYZISyBGA7EvsDSY/yTDhMEF4rxFZ+Sibl7b+Lpu5Q/Z+6/xoIDd7TCWljAcDQIrv6nZOZaxJxjyVBYAYKwQDTjMJm8/XeNr7/wRH/Bd7CnRuGvd+Lyd/rsr4sAgFqlfVGvrB/veglCF4p7fPCqi4iNT8W+6wk49lUL9PnjLAAg2NsFvVWjadu4LNH8hyP47aPa6htt/JyFQhomgrBQLOFlL+5Vukna4StakYmAkArF8Ip956fz6LV6UumHr9M1ylvAgDQArt6/ychFYhqNL32QwEQQFgjDMKwT9LUnKSbtR0IKvwmUS0MklzM4de8VkjNydBxPyXsJ01L0wgqIPwGKZF4ACUwEYamwzFHjt1wzaRd2xwgL7qeLzdFP8NHyc+j400lJ6iMIQ+HUGDF6v5qd5xo57bgQ2//kjBw0mHsIU7ZfF1mD7UACE0EQRkFVaaSIqqyZuFRZlmed5JRKmArNO83S7j3B5mke3WfT9K499xiJadn4++wjYe3ZICQwEYSFYlnTs2GQyY2wNLjkH00BydLGozFGlGadDBiLExTNCQlMBGGBnLj7CmM3XTVrH6pO/Y932Vdp2VrbZDo+LzvOEh6A5+w/csMVvCDHb4KFg7EvMHRttE4/OU24xABVOWH16TjUmXVQfOeMgNCXED5O62w+iyQvFUICE0FYKCfvvTJr+28FBJNkixauizl7bmlt05cJXpNvKGUDwcKnf13EnpgE/Mg3hRCHJKC6d+rOG+I7ZiHwEXxm77mpfZwR+mKtkMBEEITR4XoZFvKyrMsPiiAAICFFW9vJhhANkyUi1CQn9nQs/TqYEgpcSRCE0cjJk2Po2mgcvf3S3F0hiiiXHr/B9/u0tZrxKVno+y5IJRuv3mbjo+VnUau0rzG7J5oNJkpUbe3xp6SEBCaCIIyCTCbDjivPcPBmorm7QhQp1B/w3X87zVrqn+inemvZefU5AODUvdfSdEti1p/nF4VfgVhNEWmYCiGTHEEQRoNvUl1aQ0dIBT3g2RGrKaLLWQgJTARhZhiGwcpTD3HmvmW+yZoCijpASAU94NmJeWraLAG2CAlMBGFmTtx9hen/xiqTfhZFhKySIw0CoQ+KG8TOBp6JtFVhGNCAU4EEJoIwM4+SMrgLWSGkNCLMAaU9kxa6nIWQwEQQZsZWBYtHSRm4/jyVdd+thILtKZm5iHmaQiY5QjIuPXqDnDw55HIGlx6/MXd3LAoxSXTZFEwv07Jx50WaBD2yLmiVHEEQRmHmrlid+9otOoGDY5qh3/JzSEjNQklfV971knBF6CMtOw9fb7mG6iW9Me1f3fdgUWTt+cfo3yBU0DFszuJ1ZxdEPT86rjnKFHOXpG/WAGmYCIIwCxfjkpQJRJ++YQ9GyeaPQi4VBBfbLj/D2nOmiVNkTWwUEbtJ33i78iRZfGesEBKYCMLMFFWNiS7rgKqQRMIRQZgXfUNQXsQGKAlMBEGYBV2TrermojUdE2KRyp/m+B2KSK+J6njUTEskZwr8or7dcR3bLusPBGoLkMBEEIRZ0LX8m+FRhiBUmb1bO2msGM3tgBXnJeiN5SJmOKn6MI1Yf1ltn1zOYH9sAv468wijN141tHsWDwlMBGFmhMQgsiV0zd0kIxFCYROOiuq40ofQsaVZ/v7Lt2rf5QyDpPRcA3tlPdAqOYIwMgzDYE9MAsKCPODsYI+jd17C2d4ObasGwdvV0dzdMxvnHiSxbj9864Xycz7DaE1SlAyU0IREI+Pw5E2G2ptNcoa6cKTph/g2Ow8ezrYrVtjumRGEhXD87isMW3dJa/u/157j78H1i6zT9+6YeNbtV1VSOPwT/RQf1Re2DJooeshYBlFRHVdSMv3fWHzetJzatldvs5Wf5QwDO5UL/c3WGPzUp6bJ+mdqyCRHEEYm5mky6/YTd1+ZtiNWyLkHSeTHRFgtni6WpZMQM5I0x9/rtzk69+28+lxMt6wGEpgIQgQn777C7mvsGhJN2N5+1fZL0SEbhQGw/cozc3eDsHAsdQy1rRIkWV1hxT0RN6+jZPXx5fgd9Re7NxmFAlNOPqOmybN1rR4JTAQhgn5/nsOwdZe0ltkS0nIrPrVIrL4hDMNSH9RSdqtheX8JawNSs/g5a9/WCNnw5brClXIzd8WqnaOF/gySQQITQRjAq7Rs7kKEaB6zJCYmCx2hjWU+qtkEuTFRlQTXU7eML8a3C5OgR4XcThAXu0rVh0kTLm26tUMCE0GYkRepWTh9/7W5u2GxkGxE8MFSn9NsoQ3cnOwF1zOwURm4OUnrD2Un0UVTM8lJUqPlYlkeaQRhg+ibl+rPOWS6jlgjJDERPLD1B7UYIYsNVSdtO4kuWlGKd0UaJoIwAD4vaUVpQpEairlE8MESNUwbP2/A2a8O1YIQ4uPKWVfzSoES9aoQqTRMqlji7yAlggUme3t7JCYmam1//fo17O2lkYIJwpaw9UnEmOhK0EsQqrC9lJjTnyY8yBP1y/mzRyBX2ejt6ojf+9fmrM9OKnUQCrRMDMPAXsI6Fdi6D5Ngk5yumCjZ2dlwcnIyuEMEYemojgFyQDYubPMNXXJCE7bn9M34VNN35B2F2hsuAcK0AsathDSUnbgH9nYyTOpQWZI6x2+5pvxs2+KSAIHpp59+AlAgQS5fvhweHh7Kffn5+Th+/DjCw8Ol7yFBWBhChSRbn0SMCWmYCD5YmmJjbvdqALiTR8tkxjGNcZEvZzBjV6zk9Vra7yA1vAWmhQsXAii4AZYuXapmfnNyckKZMmWwdOlS6XtIEBYGPcMJgtDF/TkdlOauPA6J304mjZDxYE4HlPtmj+EVGYit+2vyFpgePnwIAGjRogW2bt0KX19fo3WKIKwFPpPdjiu2nS6AIMzNnpgEc3dBiapvUF6+XGu/6pRhJ5NJIjBJ6eNkCLauYRLs9H3kyBESlogijdDcZrFm9KWwRSi3HGGp9KlXSu17rg4Nk797gb9vm4ggo2tlPJ1NFz3IxuUlcXGYnj59ip07d+Lx48fIyclR27dgwQJJOkYQlgo9rgnCcpBbkKPbjC5V1b7n52v3TSYDDo9rjkev0xFZ0kd0xG1N5navholbY7S2/9SnJj5edUGSNoo6ggWmQ4cO4f3330fZsmVx+/ZtVK1aFXFxcWAYBrVq1TJGHwnCouCr4EjJyIWXK8WGlZqsXG0zB1F0yZVbzv3gaK9utMlj6ZsMBeEEIkv6SNq2pwv7XOPiaLpwP5YjuhoHwSa5iRMnYuzYsbh+/TpcXFywZcsWPHnyBM2aNUOPHj2M0UeCsCj4BFO8+iQZ1Wfsx9C1l0zQo6LFs+RMi9IqEOYll0WLYym48khnIlVwVgc79se5MeIt6UJu4+ZywQLTzZs3MXDgQACAg4MDMjMz4eHhgRkzZuC7776TvIMEYWnwmRP+OPEAALD3uuU4o9oSOSzOtETRJDfPcu+Fie3DUTXECz98GGn0tlpVDkSj8v74X7PyattN6Q9u4/KScIHJ3d0d2dkF2YqDg4Nx//595b5Xr15J1zOCsAJ0OWzaesRbgrAEnr7JQFZevtnab1jOX+/+YB9X7BreBD3qlNJZRiqnb0d7O6z7rAEmtFePh2jKFXQ2Li8J92Fq0KABTp06hYiICHTs2BFjx45FTEwMtm7digYNGhijjwRhUfB5iyJxybjY+psswc3GC4/x9RZtJ2dTIsacpvkyZex8ifamfHmz8XEpWMO0YMEC1K9fHwAwbdo0REVFYePGjQgNDcWff/4peQcJwtLgM8FZSFgUQSzqVcPcXeANJeUlFh64a9b2R7WuaNb2FczoUkXvflP6MNn6uBSsYSpXrpzys5ubG3777TdJO0QQlg4vDZMVmuSqhnjj9/61MeTvaHN3hRPSMBHmfimpX9YfZ+6/Vn7/oFZJk7TbIiwAR26/VH6vV9ZPb3lTpl6x9XEpWMMkBj8/P0F//v7+ePToEWe9S5YsQWRkJLy8vODl5YWGDRti7969yv0Mw2DatGkIDg6Gq6srmjdvjhs3bqjVkZ2djeHDh6NYsWJwd3fH+++/j6dPn0p+DQjbgc+cYH3ikvkfQEKw8XmZ4IG5X0oYDX0K3+5I3W0uHyhTaphsHZMEiUlOTsaiRYvg7e3NWZZhGAwdOhT5+dyOfCVLlsS8efNQoUIFAMDq1avRpUsXXL58GVWqVMH333+PBQsWYNWqVahUqRJmzZqFqKgo3L59G56engCAUaNG4d9//8WGDRvg7++PsWPHolOnToiOjlbLl0cQCnhFmrbCOUoms55MUBTtm9Cxit5sDGlajrsQ9E8NDnYyzvxzQrE34XWy9VFpsqh6vXv3RmBgIK+yw4cP51Wuc+fOat9nz56NJUuW4OzZs4iIiMCiRYswadIkdO/eHUCBQFW8eHGsW7cOQ4YMQUpKCv7880/8/fffaN26NQBgzZo1KFWqFA4ePIi2bdsKOEOiqMBnUjBHBnJDkcH8b+18sfWJmeDGIsaYyo1YsbinwdWJOSWuY0xrkrPtkWkS2VMul/MWlgAgLS1NzVeKD/n5+diwYQPS09PRsGFDPHz4EAkJCWjTpo2yjLOzM5o1a4bTp08DAKKjo5Gbm6tWJjg4GFWrVlWWYSM7Oxupqalqf0TRgc+cYI1acIt4APHExudlQgePXqdj4tZrePgq3fz3qxHuQT4vLJpluI4wbeBKkzVlFkQLTDk5Obh9+zby8vJ4lX/27BlnmbVr1wruR0xMDDw8PODs7Iz//e9/2LZtGyIiIpCQUBAwsHjx4mrlixcvrtyXkJAAJycnrWTCqmXYmDt3Lry9vZV/pUrpjrFB2CC8LHKmn8zrldHv/MkFn+dPt5ohys/ODma0idj4xEyw0//P81h//gn6/nFWcl8goTAQuSpMT8fZZJvO1YPFVveuTut5EbJ0BM94GRkZGDx4MNzc3FClShU8fvwYADBixAjMmzdP53FRUVF48+aNzv3r1q3Dxx9/LLQ7CAsLw5UrV3D27Fl88cUXGDhwIGJjY5X7tWJeMAynFM9VZuLEiUhJSVH+PXnyRHC/CeuFzyRpjjlqxcd1lZ9bhAUgKqK4ntLs6Ov22k/rY36P6tj4eQOc/6YVTk1oKaKX0mDry5cJdh4nZQAA4lOybFIQ0HzRKubhxCNKODl9mwpRueSuXr2Ko0ePwsXFRbm9devW2Lhxo87jAgMD0a5dO6Snp2vt27BhAwYNGiQqtYqTkxMqVKiAOnXqYO7cuahevToWL16MoKAgANDSFCUmJiq1TkFBQcjJydES5FTLsOHs7Kxcmaf4I4oOXOagY3deYve1eNN05h3VQrzh4Vzoklgl2Buhfm6C6pDJ9At671UoBjs7GeqX80eglwuKeTjDyZQepSqQSa7osCX6KYb8fREn76pnkrAEOUDq+1Bz/DWpGGBw8lxLFJiycs0Xnd0QBM9227dvxy+//ILGjRuraWEiIiLU0qRosmvXLuTn56NLly7Izc1Vbt+0aRMGDBiAOXPmYPTo0UK7owXDMMjOzkbZsmURFBSEAwcOKPfl5OTg2LFjaNSoEQCgdu3acHR0VCsTHx+P69evK8sQhD40J7gbz1MwcMV5pGXzM1VLRZVgdaG9tEBhCRCnujeXpofkpaLBibsvMXbzVfx34wX6/XlObZ+5NUxihSXNXnu6OOrcx0Z4kLpzOddlsDRx6WDsC4RP2Yelx3TLC5aKYIHp5cuXrA7c6enpes1YHh4e2Lt3L549e4bevXuDYRhs3rwZ/fr1w8yZMzFu3DihXcE333yDEydOIC4uDjExMZg0aRKOHj2Kjz76CDKZDKNGjcKcOXOwbds2XL9+HYMGDYKbmxv69u0LAPD29sbgwYMxduxYHDp0CJcvX0a/fv1QrVo15ao5gtBE3zx5OyHNZP0AgD8H1sH/mpXHNx0rAwDWfVYfI1pVxAe1hQfR49IwsWGuTPG2vhqHKODy42Sd+8y9olMzDhNfNLsd4uOKGV2q8I60P7yleoRxzqvA4zKZUgs17p+rAIB5e2+ZrE2pEBxWoG7duti9e7dy6b/ipv3jjz/QsGFDvccGBARg//79aNy4MVq3bo2TJ09i6tSp+Prrr0V0HXjx4gX69++P+Ph4eHt7IzIyEvv27UNUVBQAYPz48cjMzMTQoUPx5s0b1K9fH/v371fGYAKAhQsXwsHBAT179kRmZiZatWqFVatWUQwmQieW9LBuVbk4WlUuNB83Kl8MjcoXAyBc+JG9+2cIdcv44kKcbl9FqbCcX4AwFxZoaRLNgIZlAACTt1/nLOvqpP5sMlRwLBfgjsNjm6PMhN0G1VMUECwwzZ07F+3atUNsbCzy8vKwePFi3LhxA2fOnMGxY8d0Hnft2jXl5x9++AEDBgxAt27d0LlzZ7V9kZFcDm6FcOWuk8lkmDZtGqZNm6azjIuLC37++Wf8/PPPvNslihYpmbkYtvYSutYMwYe1S6o9rC1IdjIYa3oAaV73jJw8/G/NJbSJKI5+DULN0ylCcvTdkuY2yQGW8fLEdRW4XoLMfxWtB8ECU6NGjXD69Gn88MMPKF++PPbv349atWrhzJkzqFatms7jatSoAZlMplyBxjAMNm3ahM2bNytvOplMxivCN0GYkt+O3MPJe69w8t6rAoHJxHNkw3L+OPPgNXdBDQT3UwZUL+Uj6JBRrSti0UHTJ0HVNIasOh2H43de4vidlyQw2RD6ZCJzy0vifZh0d1yqUwr1d8Oj1wUrCjl9nMx9Ia0IQQJTbm4uPv/8c0yZMgWrV68W1NDDhw8FlScISyElM1ftu+rD2hROz38ProcKk/ZyF5QAP3cnQeVHtqqID2qVRJPvjxipRzrQuOxvs0zrZE8QDCzDNMwm7yz5qDY6/HSiYD/H8dakWTY3ggQmR0dHbNu2DVOmTBHcUGgovfUR1onWhKQyS6q+Zd5/+RZjNl2VvH0HkUv3hb44ZuYI1+7KZDKUErEiz1D4PKhepmXjk1UX0KtuKdI62SAWYA2THjGpUVgOUnXi5tIgWU8GSfMjeCbu1q0btm/fLrrBa9eusf7FxMTg7t27yM7OFl03QZgCRsfncZulEZZ83Rz17p/ZtSoAoIy/eEHFTcNx1MneDkHeLjpKc9O7bkG0+5GtKuFznklIDYHPw3LhwTuIeZbCy5GWIPgS4uMKAKhZ2gdjoioBAPrU4872ULlEQeiP1pX5pwkb1KgMZxlDwwqwHV81hGILsiHYh6lChQqYOXMmTp8+jdq1a8Pd3V1t/4gRI/Qer/Bl0oWjoyN69eqF33//XS0wJkFYCoyahqnwS7pEsZf83J3wJiNX5/7+DULRrWaI6NQkN6a3RdPvjyBDRaN0ZWoUnB3Erwyd270aJravDG83R9Qp44tlxx+IrosPmqZQtilFjMaMsCws0b/m6FfNkZ0nh4ezA5pUDMDVb9vAy5X7Ufrvl+8hMzdfLe6SPq5ObQNvV35l9SGTAfXL+uHcwyTex6z+uB4azj2MnHy5we3bEoIFpuXLl8PHxwfR0dGIjo5W2yeTyTgFpm3btuHrr7/GV199hXr16oFhGFy4cAHz58/H1KlTkZeXhwkTJmDy5Mn48ccfhXaPIIyO6sP6jxMP0LZKELrUCJFs1Q6fh4RqVG+h9bg7OyBXYyJ0cxI8FWi15f1OM2aK1UuaGiYyKxQ9zBU01dHeDo4qZnJvDo2wAgd7O3hymNdV72IphKWCOmV64yyxzRP+Hs5wcrAjgUkDwbOkoc7bs2fPxuLFi9G2bVvltsjISJQsWRJTpkzB+fPn4e7ujrFjx5LARFgImvkICz/viUnAnpgEdKkRItnbsDGWKpfwdkF8Spbye/daJbHqdJzk7QCmcSK1RfcVghAD57TDEZD2ZnyqZH1xtOce/KolrjxJRg2BK3PNickTQcXExLA6gIeGhiImJgZAgdkuPt60ubgIQheak42uh7WlrTZRFbxGtVaPDjyhfbjR2jWFGUVTqLRAyw0hAcb4XU+Mb4EDo5tKX7EEiBk7nE7dIq+hmBe3luHc/lmqtZ68+1JwG+ZEsIbpk08+0bt/xYoVeveHh4dj3rx5WLZsGZycCpYw5+bmYt68eQgPL5jEnz17pjf5LUGYE7aJ5EVqFm48l+5NTWocNUwBhib01IdJNExaJjlt4l5rJ/omrIvnyZmS1ufqaG+WVZ3GhIeCyWToMo1n5uTjzINXyiwECuRWpioWLDC9eaOe9iA3NxfXr19HcnIyWrZsyXn8r7/+ivfffx8lS5ZEZGQkZDIZrl27hvz8fOzatQsA8ODBAwwdOlRo1wjCKGhFFWAZ5PXnHJKsvbAgT9x/afjDvqRv4YOhmIezwfXxxVIcdfXlISOsgzVnH0tan6Wv/qoa4oVT94QHqdVHQaBo4ceJkWV0+ZWN++cqdl+LR/eaIWrz6YIDd1CztA+aVAwQ0ZrpESwwbdu2TWubXC7H0KFDUa4c93LiRo0aIS4uDmvWrMGdO3fAMAw+/PBD9O3bV5njrX///kK7RRBWz5KPauH0/dcY1boi9sQkGFxf3/ql8eh1BppWKoYmFYthWIvyyqXNALDli4b4YMkZLOhZ3eC2TI1NxuAhjM78HjXM3QW9LOxZAz8dvisobpihYQUULOtfGxO3xmBx75q829ZE17jcfa3AxWbr5Wfw0XCS/9/f0bgxo53oNk2JYUtj3mFnZ4fRo0ejefPmGD9+PGd5Dw8P/O9//5OiaYIwOcZ6WFcN8Ub7aiUkq8/R3g7fdo5Qfv+qrbrfUu1QP8TN6yhZe6oYOwmv1pushWi1CNORkyd8BVeAp+k0rWII9HLBrK66U4yxwWYGUx0OfIdGmypBiIoortQQi5nnrM3EJhTJnL7v37+PvDx+cWj+/vtvNG7cGMHBwXj06BEAYOHChdixY4dU3SEIo2Gu5czWhLGX+fPxYSJsmzsv3go+xhblam4NE/+TVjWni5vnuI/R7I2lmPD5IFjDNGbMGLXvDMMgPj4eu3fvxsCBAzmPX7JkCb799luMGjUKs2bNUibb9fX1xaJFi9ClSxehXSIIo6K1Ss5I8pIVzRtmh0RWwhws7l3D3F0AAHSvFYKtl57p3C/FVCLK78nGB6ZgDdPly5fV/q5duwYAmD9/PhYtWsR5/M8//4w//vgDkyZNgoNDobxWp04dZVgBgrBk5EaaFeQUI443xohVRRAKOkaWQPTk1srvFye3Rty8juhSI8SMvSrko/qllZ/5pD4xdLhUC/HmVU5MM9b0nihYw3TkiGFZyR8+fIiaNbWdypydnZGeTsuACfNy+fEbTN15A1M6RSDmaQpaVQ7EhYfqvjgJKgEgpSQrj1J58EVzYhaqndtx5RkCPV3QsLy/ZH0iLB/e94k1yeNGkjhUL0GQtwtinqVwHnP/5VusPPUQH9UPhZPI1E2WjOAzatmyJZKTk7W2p6am8gorULZsWVy5ckVr+969exEREaF9AEGYkG6/nca1pynosfQMZuyKRYsfj+L2izS1MlN33jBK254ukqzBKBIYkhrlXmIaRm64gj5/nJW4V4SlI+Q+cVVJUO1qxLhl4pCpfDJO4EpV+MZWe/Q6A9P/jcWy4/f5V25FKibBM/TRo0eRk5OjtT0rKwsnTpzgPP6rr77CsGHDkJWVBYZhcP78eaxfvx5z587F8uXLhXaHIIwK26qPu4nCnU35UMLb1Sj1WiJ+7k4Y1KgMFhy4w/uYoc3L47ejiolYvArgebJxNISE5SNEeHBzcsCfA+uAYQryL1oSQlbBiV6AoTLEhNYR/ch4K2TNCe+7QOGrBACxsbFISCiME5Ofn499+/YhJITbvvvxxx8jLy8P48ePR0ZGBvr27YuQkBAsXrwYvXv3Fth9gjAtYzddNXcXTEYxD2e8epttlLoblvPHiFYVcTM+FXuv84s55eRgBx83RyRn5Brkk0HO9ZbPzfhUfLr6ouT1Cv3pW1W2/owTkmiYBNqiTt57hVozD+D3/rVRt4yf3rLWNBx5C0w1atSATCaDTCZjNb25urri559/5lXXZ599hs8++wyvXr2CXC5HYCB3/hmCsAS2XHpq7i6YDINSnPA8tlF5f94Ck2q1hvgwGTvkAWE47RdzWyt0UTHQQ6cW2JqWsOtDpuMzV1khqIYVsBN43XLzGSSl56DH0jNGi/VmDngLTA8fPgTDMChXrhzOnz+PgIDCUOZOTk4IDAyEvb0wO2+xYsW4CxGEDVC2mDsevtK9qOHIuOZ6j+9TrxTWn38ica/0Y2+IxMSiAfqsSVn8ceIhAMDhXVbzvvVD4eniiCrBXohaeJyzWl1B9YT01EaemYQOlg2ogxY/HmXdx9/n23q8vtmEQHWTnczg85FS0NSsy5qEWN4CU2hoQah2uYi1zzVr1uR9US5duiS4foKwdKqFeOsVmMoWc9d7fJCX6f2bVN8qpZjTKgZ6Kj87vNPx29vJ0LVmCDJz+K0QLNQwiX8AWM/0XDR5ZmDCXV+N1BuqWNGzmTdG0zCpDDEpE2pbc0gQ0Z5ssbGxePz4sZYD+Pvvv69VtmvXrsrPWVlZ+O233xAREYGGDRsCAM6ePYsbN25Qwl3CZjF0wjFHSgehfgtcFPN0Un52tNd8y+RXh6KcQXOuDT40bYn35h026HgHe903Lt8X92ArX4DhorGqz1AztDGHjDUJsYIFpgcPHqBbt26IiYl5lwW5YOZS3IiKyN2qTJ06Vfn5008/xYgRIzBz5kytMk+emNbkQBCmQt9E3bVGMOfxPeqURMyzFDSpaDoztlC/BX0MalQGLcIKfRUd7PXX/d0H1fD1FvVAtgWTvg6TnEgfJoZhrMokQOhnfLsweKisaAsP8kRECS9svaw7KrYmYcU9MbJ1RWN0zyiw3b4lfd0wslVFeLo4wM5Ov0luVteqrNvzGfE+TPqw5vEm+B1y5MiRKFu2LF68eAE3NzfcuHEDx48fR506dXD06FHO4zdv3owBAwZobe/Xrx+2bNkitDsEYRWozhGRJdWj5vKJHuxob4e53auhg4TJebmQcpKc9n4VtYnSgUN91atuaa1tDJhCDZPGA0DIJKxa1IqtA4QGfwyog6HNK6ht+6ptGAY2KiOonu8/jISni26znqWhS3s0OqoSPm1SjvP42qG+rNtVx4a+8bX6k3oGpYyxJvFJsMB05swZzJgxAwEBAbCzs4OdnR0aN26MuXPnYsSIEZzHu7q64uTJk1rbT548CRcXF6HdIQirQK9K3EJnDIPkJY5jnR3Vpx7eJrl3/xsUVkDl8z9FaNWjNbAlWvzvwXYL2dnJBC9ekPJFwVio3f48uuuox0zJ5/o4OeguYy+TFZkXD8Emufz8fHh4eAAoWOX2/PlzhIWFITQ0FLdv3+Y8ftSoUfjiiy8QHR2NBg0aACjwYVqxYgW+/fZbod0hCKtA35xkqdOzVA+O7rUKNWjDW1bA9ivP8DmPN19NZJDxFqz0OZaqvi2P/+caetYpJbgvhHEYu1naOGd2Mhkql/BCrdI+KO6l/4W8Q7UgxKdkISLYS9I+GBs+Y2La+1XQav4x1n185MmRrSrh+J1XOh3yDcmvaU0mOsECU9WqVXHt2jWUK1cO9evXx/fffw8nJycsW7YM5cpxT4ITJkxAuXLlsHjxYqxbtw4AULlyZaxatQo9e/YUfgYEYQVY0ZygxF6iTi/oWUP5eWybMIxtEya6LpkOHyZN9O23xt+CEIe9rEDDtHXoe5xlf/uotgl6JA1Cb+HyAR469/F5MQrydsGpCS1Re+YBvE5XX+glRXJfa0GwwDR58mRlktxZs2ahU6dOaNKkCfz9/bFx40ZedfTs2ZOEI6JIobZEX2Ofpb5h2Um5lpgDvhOubh8mjfr01LEnJl7te76cMSzmlJVx90UaZu6+iVGtK6JWaXb/FWuE7TeXeqWnpaB6robeuYZqkmUQpmFK0hS4DGrdtAi+ndq2bYvu3bsDAMqVK4fY2Fi8evUKiYmJvJLvEkRRpFvNQrMUn6ll0DtH1ZGtTL9ap/W7dBCDG5fFR/ULnK/HRlUyaptOenwsVOHrw6TPJLfyVJza90M3X/Bq21YYvPoijt95ie6/nTZ3VySF7TevHGRd5jUxGPrCZbDpXWZIZkfrQpCGKS8vDy4uLrhy5QqqVi1ciujnpz9XjJ+fH+7cucM7snfp0qVx4sQJZbBMgrB26pfz17mPbbr6tlME+jUorVeVbiyW9quFR0kZKB/gge41Q/Dxe2WE90PgDMpHmyWTqUT61tyncRWFNJ+ekyegtPXz3MDAkNbA1altkJmTD193J+7CVo7BGiYDtXAyAyUmC1WwsyJIYHJwcEBoaChrrCV9JCcnY+/evfD29uYuDOD169eC2yAIa4HP/GBnJ0MFlcjYpsTB3k4pIJmzH/rQ1CZk5eZr7Ddlb6yLAo2C6S/Qk6QMhPi4Gs3Uq3pG3q6O8Ha1ntAAhmCowCFEw8RWVCazrlQyhiDKh2nixIlYs2YNp2ZJlYEDBwptiiCKBNb0hsUXfw/p3+zdnR1UfJjUWXzortp3IRN4UROu7O1kgInfR9eee4RJ266jV51S+O7DSKO0UdR+R6nQ5b/nYCdDnpyBs4N+FZQMhl5765kABQtMP/30E+7du4fg4GCEhobC3V09BxZbLjgx+ecIoqhgaNoCS2Ta+1WQkpmLAQ3LSFJfKT9XfFS/NFadLkjea8gquaKOORzcFx64AwDYePGJ0QQmoVqzPwbUwWd/XTRSX0yHwWlPdBz+zxeNMHfPTUzuGMFxvAzyIjLeBAtMqnnhCMKaePw6A+7O9vD3MH1eNn3YooapuJcL1n3WQLL6Vn1cDy6O9ioPB+lmaKmFqzsv0hDs46qWosOSMLa8xDAMYuNTUT7AQ5nTTN81zs7Lx90Xb43bKRaiIorD391Ja5m8tWHo/KErfEiNUj7YOKSh2jZdv6NBybCtaP4TPKJV88IRhLXwMi0bTX84AgCIm9fRvJ2xphnCQlD4WfBNvmsuDdOFuCT0WHoGxb2cce6b1ubpBAfG1jBtvvgU47dcQ4NyftjweUPO8v/7OxpHbr80uF3SKopDiA9TKT831jhMRUXDJMo/Pjk5GcuXL8fEiRORlJQEoMAU9+wZ/wSHBGFKbiWkmrzNAE9ntKsShLWf1tfat+6zwm0kPnGjeMbz0S8xDCPMh0l0r7T573oCAOBFaraEtUqLvZGDE/199hEA4OyDJF7lpRCWgKKztF1qhAhMP/epiTYRxbFeRXssA5Cdq9spTl+ID8Xx1oJgDdO1a9fQunVreHt7Iy4uDp999hn8/Pywbds2PHr0CH/99Zcx+kkQVseQpuV0Jr+sWUolYKA1zRhmQmGKU4YV0DMHyxlh2gaGYcAwjCQBRO3tjf9jGtpXniGvRMPWNVMIM0VVw2RwGCUB90MpPzcsG1AHufmFfskyGZCRo09g4l+/VOPQWAgeOmPGjMGgQYNw9+5dtWS57du3x/HjxyXtHEHYKhY8J1gkMk0Nk55ZuEDDxJ+v/rmGXsvOcr4J80GqdDK6yMzJR/Mfj2LsJvE514zdR83a5XJGK7ozIR2GOn2Lue1lGt80w3qo1c9V17vKxm2+imY/HEWGBcdFEywwXbhwAUOGDNHaHhISgoSEBMEdePnyJXJzcwUfRxC2gi2ukpMaOw2bnF6THLjNAJqcf5iE5AzD5yEHI/sH7bsRj0evM7Dl0lPRdRg95Y2GQPYoKcO47b1DjOOxtb64qN7efM9hdOtK8HVzRLNKAWrbXRyFqxxVtUAyGTDovTI6y/Idi/9EP8XjpAzsuy5cjjAVgq+Ui4sLUlO1/UFu376NgIAAliMKWLZsGbKzC+z6DMNgzpw58PX1RVBQEHx8fDBmzBgKP0AYDRJKrBstHya9Jjlxa3aksOgYWxiRwuxkbKHOXCOtyJrkeJYb2boioidHIdTfTW27s4O9QW3KAAR6uugqyq1hsqK5WbDA1KVLF8yYMUOpFZLJZHj8+DEmTJiADz74QOdxX3zxBVJSUgAUCE9z5szBlClTcOLECXz33XdYsWIFfvvtN5GnQRDi+fPkQ0zbeUMSk4wYrPUt15Ro+TCBwR/HH2DGv7FavxvD4sOk6nOhC2swyUmBsYU61eq/WBONuy/SjNqegqIkL4m9zTR/+wqB4lIvSX2b30ssDCthyUNIsNP3jz/+iA4dOiAwMBCZmZlo1qwZEhIS0LBhQ8yePVvncaqT0Z9//omZM2di9OjRAIBGjRrBxcUFP//8M7788ksRp0EQ4pm5KxaAeoJcwrLQ1DCBAWbvuQkA+KA2y++m8fTceukpetUtrbcNKZZGG93cJQFG92FSqX/v9QTstWATiy0g1Elail9f3SSnv0au9xCZDBi44nzhdwvWOAkWmLy8vHDy5EkcPnwYly5dglwuR61atdC6NXfMEcWFffjwIVq1aqW2r2XLlkoBiiCkho+RxlRJWGVQf4uy3OnBclDMHWypUTJztPPIaf7eSenc/klSaJiMbu6SoHpjx2Eyl8xoLg2xOVDzYTJfN3i1z2fufaaSENqmNEwKWrZsiZYtWwo6Zt++ffD29oarqysyM9UzZmdmZsLOyPFBCMuFYRhsu/wMYUGeqBLML0kzX87cf41DNxM5y6VnS5tgy5KXx1obhRomPmEFGK39ch4PUyk0TFIII6/eZmPv9QR0rREMTxf1BLJSyASafbyXmIboR2/Qo3YpNQ1ZYloW/rvxAt1qhrBGLd9x5RnKFnNHZEkfte0X4t7w7su1p8mC+k5oY+nTDNc9m5tvPYKuKIHp0KFDWLhwIW7evAmZTIbw8HCMGjWKU8ukmoD30KFDqF+/MHjfmTNnUL58eTHdIWyAY3deYsy7pdJSR+Lu88dZXuUmb4+RtF1985iq2pkEqwKqhnjh+rNU1An11dqnrWHSE1YA4vxZ+AhVXAgJAqiL/n+ex834VJy9/xq/flTL4Po00RSYWi8oCAdjJ5OhR51Syu19lp3F/ZfpiI5LwqLeNdWOuRiXhJEbrgBQH698fMVUef+XU4LKEwVY0pThwBF7LDVTv3b31VvLDfKqiWCVzi+//IJ27drB09MTI0eOxIgRI+Dl5YUOHTrgl19+0XmcXC5X+/vmm2/U9gcFBWHu3LnCz4CwCW7Gm8YxFNCtujdVdGZLmuwsiRUD6+KrtmFY2r+21j5NxY0+2UYRiFIVOQ/1kRQCkxS/7c34glXI+24Yx/dHl1B35Umy2vf7L9MBAAdiX2iVVXXSVSXfjDkyipBFTiOsgEAfJokmoEGNyqBdlSBElPDSW+7JG9OElTAFgjVMc+fOxcKFC9Wcs0eMGIH33nsPs2fPFu203alTJ1HHEbaBKYUIhjG/0KLmw0QCFAAg0MsFw1pUYN1XqGEq+D9f5Ymhef3YNEx8nqVSRDUx9k8pxb0i1M+K7QErhSZNasQEkyhKQpbUTHu/Cq9y2XnCBpYla9wFC0ypqalo166d1vY2bdrg66+/5jz+wYMHOHnyJOLj42Fvb4+yZcsiKioKXl76pVTCNhiz8Qqy8+X4pU9NkwwMNm0SzZHWh+YqOX0aI0au/SDk82DMl/jpeS8xDRUCPQUdoy9islTwWcn365F7ys9spS1xNSAJP5ZJjlCByUj9kALBJrn3338f27Zt09q+Y8cOdO7cWedx6enp6NGjBypUqIBBgwbhm2++wfz589GrVy+EhITg119/FdoVwspIy8rF1svPsPtaPBLTTGP+YptEVU0vxlxZoykP9q1fsKx9TFQlrcBvhH40fZjUTT/qV5CBdvJdPuY2KcxJqi8BY0SkL/n36nOD+8CFLllH9ex/+O+2qDrMSdNKugMn62J6lwItydDmRcd/9qP6heE1TPEzWpNTNxeCNUyVK1fG7NmzcfToUTRs2BAAcPbsWZw6dQpjx47FTz/9pCw7YsQI5ecxY8YgPj4ely9fhouLCyZNmoTy5ctj6tSp2LBhA4YPHw5fX1/07dtXgtMiLBHVYaP5/DLWwGUbqqptm/KtdHbXqvi6XTi8XR2Rp5G8ktCP4gGtMAWpC0DagSs1f3heJjmJb4a32YaFqTCWMC84zg1LcV0mOamvoSZeLg5IzVK/ri3DA/FTn5qsK/m46BQZjCYVA+Dt6shd2EaoWFyY1tNQBGuYLHg+FHyH/fnnn/D19UVsbCxiY2OV2318fPDnn38qv8tkMjWBaevWrdi3bx+qV68OAPjjjz8QHByMqVOn4pNPPkFmZiZ++OEHEpisgNSsXHT79RTaVgnC+HbhouoQl7xCRDusJrnCbcae4FWRyWTKidmS7fSWiJ2Whkl3WbbUKHyED4WG6dDNF5iy/Trm96yBhuX9lfuP33mJCVuu4bsPI9GkIrs2QzO+1uFbLzB5m3ZdunB1KkxTIaX/9IHYF5i64zoW9a4JodFb0rLyIJczamY4XbevsYdTMQ9nLYHJx9VRlLCkoCgJS+YgJ1+YmdmSA1cKNsk9fPiQ19+DBw/UjsvLy1PzU/Lw8EBeXh7S0wtWYrRp0wa3bt0y8HQIU7Du3GPcf5mO347eF3ScOYYB20NHTcNkxLb5n6/lThCWhuJKqfsbaZrkxPkwKYTnwasv4nlKFvr9eU5t/4AV5/E8JQv9/zzPdjgrn6wqqItvaAsHI8Wi++yvwnMS80C6laC+ilVV4FcVRo39+sG1hJ3gR/kAdwBAx8gSRm/LljRMJosUWbduXSxevFj5ffHixQgICFAm7H379i08PMTltSFMi1hfD3NYsrk0WabUMKmi5sNkwROEpaA0ASlMcvqcvlkiffP5nTWLiLnP1X9X4T+ssaNV5+TJRd1vmtdT1YdJ9TIZu/9sAqXteMiYjn/+1wi/96+tc1WqlAgVmCwZwQITwzDYvHkzhg4dig8//BDdu3dX+9PFvHnzsH79epQoUQKhoaGYNGkSFixYoNx/+vRpdOjQQVBf5s6di7p168LT0xOBgYHo2rUrbt9Wd1Z88eIFBg0ahODgYLi5uaFdu3a4e/euWpmEhAT0798fQUFBcHd3R61atfDPP/8I6ktRwhhLio0lNOhz+pbLGWUeOXNC8hI3Snnp3XdVYeaDJafVyn71z1VEP1KPNi32oTp5ewzWn3+M8f/wc+DefkW403ZmTj4mbo3BkduJWhrRM/dfAwCuPknGV5uv4qWOxRLxKZkYt/kqrj9LEdy+GA6rRM5/+KowJpOxhRdH0jBJgq+7E9pWCYKjvfF1JkLDCuTJGRy+9QIpHAEvzYFgw+/IkSOxbNkytGjRAsWLF+f9FlWrVi1cv34du3btQnZ2Nlq2bImIiAjl/mHDhmHYsGGC+nLs2DEMGzYMdevWRV5eHiZNmoQ2bdogNjYW7u7uYBgGXbt2haOjI3bs2AEvLy8sWLAArVu3VpYBgP79+yMlJQU7d+5EsWLFsG7dOvTq1QsXL15EzZo1OXpR9BAr3JhDmcPWpmLbkduJWHP2sdHa1jc2SKskDHsNHyZ9GqOjt1/i6O2Xatv4pUbRLiP0/lAV1PhqW5afeID15x9j/fnH+LmP+nzT54+ziJvXEV1+1R8Re8T6y7gQ9wb/RD/ljJQvxQvP1svPlJ8HrbyAk18XpMky1hivXMILN+NT8WHtkrj6VF0oLEo55BSE+ruZuwu8yREY/f23I/dwKyENVUO8sGt4EyP1ShyCBaY1a9Zg69atgrVBAFCiRAl89tlngo/Txb59+9S+r1y5EoGBgYiOjkbTpk1x9+5dnD17FtevX0eVKgXLR3/77TcEBgZi/fr1+PTTTwEUpGVZsmQJ6tWrBwCYPHkyFi5ciEuXLpHAxILoJcXmEJhYGlVsSUrPMW1ndEAO4NwoHI6VcZgEPiT5FJf6ucu3uucpWcrPYk3Esc9TeZeV+nZ7+qYwL6ixhJdNQxrgxvNU1Cvjhyk7bqjtK3riUoHz+96RTQxydjcVQk1yCn+568/439OmQvDV9vb2Rrly5UQ3ePjwYa3Ale+//z4qVqwouk4FKSkFbx5+fn4AgOzsAvW1i4uLsoy9vT2cnJxw8uRJpcDUuHFjbNy4ER07doSPjw82bdqE7OxsNG/e3OA+WSN3XqTh8esMtI4ozrpfqNNoenYe/ruRgFqltXOEia2TL+waJtNMsfoeTCQk8aekr6vyszLSt0C3CD6/udQ5rR68Sy3CheqtIFZgyhXgb6V652Xnaa9gYgueybdb/ARTRvD97+niiAbluFcZFiUqc6QksRQ0U+6IITkjB8kZufBydYSfu5PhnRKJYAPmtGnTMH36dGRmZnIXViExMRH169dH69atMWPGDCxbtgxnz57Fjz/+iMqVK2P8+PFCu6IGwzAYM2YMGjdujKpVqwIAwsPDERoaiokTJ+LNmzfIycnBvHnzkJCQgPj4eOWxGzduRF5eHvz9/eHs7IwhQ4Zg27ZtOpMBZ2dnIzU1Ve3Plmiz8Dg+/euizhtd6LP+m20xGLPpKj7/+6Jym6m06KxxmN79bwxfLGeHwiEV4uOqp2QhJDrpRzVZLPsqOW74yBODV1/kLmQEVH9/IelZVIXAPD0S5KXH6v5cqsLK0qMPNItj1m7xfn18fpWT916Jrp+wPjTN42JYc/YRmv94FN/vM+9KesECU48ePfDmzRsEBgaiWrVqqFWrltqfLkaMGIHg4GAkJSUhLS0NX3zxBapUqYL4+Hjs378fK1asUFtFJ5Qvv/wS165dw/r165XbHB0dsWXLFty5cwd+fn5wc3PD0aNH0b59e9jbF8Y7mTx5Mt68eYODBw/i4sWLGDNmDHr06IGYGPbs9XPnzoW3t7fyr1SpUqzlrJ3bCeyCoNC3wx3vHGHvvGBP2FlQp6AqecP2xs7Ijdfm4t41Ub2UD/rWL42W4YHSN1AEsVf5oZQ+TAJXsFmym4tYDZNqUX2XQ9Ncp3rbH7mdCE12XYvX2sa/T9z9VyQXlgpL/m2LEgfHNFVmM5AaxfuAvZlDzAs2yQ0aNAjR0dHo16+fIKfvvXv34vTp0/Dx8QEAfPfdd/D19cXPP/+Mli1bYtGiRZg1axZGjhwptEsYPnw4du7ciePHj6NkyZJq+2rXro0rV64gJSUFOTk5CAgIQP369VGnTh0AwP379/HLL7+o+TlVr14dJ06cwK+//oqlS5dqtTdx4kSMGTNG+T01NdVkQlNSeg48nB3g5KBf1n2Zlg1/dyeDcj7pMpNJccuaao5jNcm9a90YAlMxDyfsGPaeoGPIOqcftYCJYIv0zY3U4SMevHyLIG8XuDkZ7kOiOs7YupmYmqW9EfzHkKYmNU0l8KObSqDMwv4IJyUzF072drz65Oqo3SZh/VQI9MS4NmFYd076hTT571SvVicw7d69G//99x8aN24s6DhnZ2c14crOzg75+fnIyysYvI0aNUJcXJygOhmGwfDhw7Ft2zYcPXoUZcuW1VnW29sbAHD37l1cvHgRM2fOBABkZGQo+6OKvb095Dr0487OznB2dhbUVyl4/DoDTX84ggqBHjg4ppnOcucevEavZWfRKjwQfw6qK3k/pLhnhWoIRKNnlZylRJS1lH5YKqoaJsWlEhojSWq/tZbzjwEAHs7tYLA/mh2HhqnenEOsxy07/gBf8MiBptm983FJys+sApOI86k+fT+c7O1wakJLzrIuEgtM7lbg+FxUMNZMpjDBm1tgEmySK1WqlFrEbr40btwY3377LdLT05Gbm4tvvvkG5cqVUzpov3z5Er6+up2C2Rg2bBjWrFmDdevWwdPTEwkJCUhISFDzr9q8eTOOHj2KBw8eYMeOHYiKikLXrl3Rpk0bAAV+ThUqVMCQIUNw/vx53L9/H/Pnz8eBAwfQtWtXwedpTP67kQAAuJeo27QFACtOPQQAHLqlrW4XhI5705oclvWtkjNOe4TUqGqYFB+Fa5ik7FEheRIn7BVS3Xc8/Tn0PWNUA0EWvkiIIydfzivlkdTRuse2qSRpfYR4jPVoUIwzezM/ewQLTPPnz8f48eMFa4N+/PFHXLlyBT4+PnB3d8eqVauwZMkS5f6bN29i0KBBgupcsmQJUlJS0Lx5c5QoUUL5t3HjRmWZ+Ph49O/fH+Hh4RgxYgT69++v5ee0Z88eBAQEoHPnzoiMjMRff/2F1atXiwqdUBSQRMNkIscDtgeQom1LkfsspR+WimpsPYU2TugqOXNFdBeKUGd2PgjVYBpyP5ojfEMxD9Nr+wl2hNxr3WqG8C6rsEjYmzlwqWBdZr9+/ZCRkYHy5cvDzc0Njo7qiQuTkpJYjytXrhyuXbuGU6dOITs7Gw0aNECxYsWU+4UKSwA/NfuIESPUkgCzUbFiRWzZskVw+5aKVBOSzltTgie8Zh/ZtFZHbyfiXuJbfNpEfBiLx0kZOtu2Jk1ZUcZezZRf8P9Ph+7qKM0OX4FJocU1Jaq3YarE0Y1TMnKx6OAdnfvZNULix4WVyKWEBeDM4YeriqVomAQLTIsWLRLdmJubG6KiokQfT/DD2HOWqTRMg1ZeAABUC/FGfZExWLqyREhWOn2LqlF6Aj3pDZkNf3cnvE7PQcvwwnhgCgfmTJZYQfrgu1x/yN/RguoVIiDcSkhFeJC2O4OqU/YP/93W2m8IU3ZcVwuMqYn0wTqFrPIj6crmMNZq53cCk4O1OX0PHDjQGP0gLBBdGhgpnJSFTJXxeiZ8QxoX8rLSt35pyVd/bB3aCOnZeQj0cuEuXATZPaIJTt9/hU6RwcptYrWCQh7kxqr3VVoOEKS93ZiPAM2cepqw6pcM6BAfHyyFnCSFvKSZSoYwL8b2YTJk1bcUiMq8d//+fUyePBl9+vRBYmKBY/G+fftw48YNjiMJU3Ag9oXoY58ncwckPWyoMzkKJ8t8OYO5e25i4wV1YWTi1msGt6GzbRHHVAjw4Fe3gMprlfZFk4oBInpTNAjydkH3WiXVQmiInS6F+jzxhWGAzRefYNnx+5xlbyWkYtrOG1oJdI35EOB6gLHdr4b0RsjqVylE2GCewWEJ02CsO1mRLsXcGibBAtOxY8dQrVo1nDt3Dlu3bsXbtwUrtq5du4apU6dK3kGiEFOYb1Wjcevi4M1CgUysWl1x3NkHr/H78QdqQS2vPU3G+vNPRNXLB4U5UEikbwaAO8sSbABoUrHQF6+0n/UkxbRGxM6XxjT/fPXPNczZcwtxr/SnQpm1+yZWnY7D11vUXwakHtaqLz2mdvngY2pXlJDCEd/Mz09CA2P4hSal5yg1pebWMAk2yU2YMAGzZs3CmDFj4OnpqdzeokULgyJ1E5aBasJDPrcmw4iblBVTpWoQPQXGToordvn0ka+aI+ZpCuJeZyDY2wUlfd0gkwFli7lj9Zk41CjpgyBvMq8ZE7HpbIy1Sk61WrZ7mY3rz1LUN0j8DMjIKewH9/XSvi6GPPOExMfi+5McGddc5z5zx+UhjM8TlYU75tYwCRaYYmJisG7dOq3tAQEBeP36NesxQnKtiYnxRIjnTXoOLsQloWV4IBzshVto5QwDOxEzfqGWR/ChvLjyJBmOOpagHrr5AtVL+eCGgAzvDMMg0NMFrSqzC0RDm1cQ1U9CGGLfYBXP8Vs60v2IRQrfKKkDl2blyvHDf7fQPCyQs2a2McDVH83cdKrwEUwVD0C+165sMXed+4yRD5IQj9S/xtHbicjMKVzgYe7fW7DA5OPjg/j4eK2o2pcvX0ZICHtcBR8fH86JTpHBOj9f2OoXwjB6/H4G9xLf4qu2YRjWQv2hz+feFPu4UMyrxnhDTMnIZV0dp2DKDvK1s1bEzpeK+EbtFp2QsDfqWhK+fUvU8GGS+hkwdtNV3H6Rhl+P3EcpP/0+PmwLKtj6ozjPzJx8dP/ttM76+PiKLT50F6OjKkni9O3l4shdiDAZUt/LipXSloJggalv3774+uuvsXnzZshkMsjlcpw6dQrjxo3DgAEDWI85cuSIwR0ljIMiavi/V59rCUx8EGvqEOJHJHQQJqZJvKqOsBjEytdyOWMUPyYpTH1SvzLcfpGm/JyTx9/bXWHu0NeftCz9caLEJg/WBdcquNL+5DNoSRg7zZO5Y+cJFphmz56NQYMGISQkBAzDICIiAvn5+ejbty8mT57MekyzZrrznhHGJTUrF57ODpw3GpvvAS8Nk8jnhfI4trdZcVUqMVWaOsL0iFXJZ+fJjRJU0dB7LSMnzyjRvRUIWR2o0PYa8lAS5MPEY6RXDfHWuY+S+Foetm4hFey04ujoiLVr1+Lu3bvYtGkT1qxZg1u3buHvv/+GvT2/G/jEiRPo168fGjVqhGfPngEA/v77b5w8eVJod4osO64841Uuctp+jNl0lXXfvuvxys9iJ22xb9iKw6btlN48JjQxK2E9iBWYDt9KNIrjtyFaq5TMXER8+x9+P/ZAdB1pWbl4picUSD7fiJ2ATp8/VbjOVsjl2HiBeyWsvh55ulDS3aKGueUxwQLTjBkzkJGRgXLlyuHDDz9Ez549UbFiRWRmZmLGjBmcx2/ZsgVt27aFq6srLl26hOzsAnt+Wloa5syZI/wMiijfbI3hXXbbZXbh6lsVXx4h8VNUESubKN4uH73WTl1iKNaSN4wQgSEruIxwX6gK50JlubMP2BfJCCH60Rts1zG+ASAvn/8527/LO8MmBPJ10BZyjaf/G8tZhu2arhhUBxUDPfDnwLq82yIIKRAsME2fPl0Ze0mVjIwMTJ8+nfP4WbNmYenSpfjjjz/U8tA1atQIly5dEtqdIosUU7/q2zrbRMfHHi3eh0nPTgNPzjiaBMmrJERgyCoZAcoW/nVawH2h75LkCjhphQ8T21zA9/6XWrvLNge1DC+OA2OaoVpJ3eY6wjwIGZ7WaL4TLDApVrNpcvXqVfj5+XEef/v2bTRt2lRru5eXF5KTk4V2p8iSZ+DExDAMElILnaPZ5lVFdFW99Yh8COkzZcTGG7b0m0xytoshiyrzjCAxiQ0rkCUwF57u9vW/2AgZC4qggGyHfLDkNF69zeZ8hZL6ZcUaH6pFGeM7fRu1ek54C0y+vr7w8/ODTCZDpUqV4Ofnp/zz9vZGVFQUevbsyVlPiRIlcO/ePa3tJ0+eRLly4rPSFzWErH5hQ9PvgW1i5RMkTOwDQ99RhiYglWrSblqJ0pZYGpamYRJ7q22+KF0ke32XRMy7A9vLTJ6cwfz9dzhHu1jTPmEbmFugMTa8veYWLVoEhmHwySefYPr06fD2LlSHOjk5oUyZMmjYsCFnPUOGDMHIkSOxYsUKyGQyPH/+HGfOnMG4cePw7bffijuLIoKUSyo1Hx6sJjkezYn2YTKijUuqOXvVoLoo980eaSojJMGQEWAMDZOqcC7k7TotOw8BUtynjP5rIuR6KcakrqH5NjuPU0CU2k/M1h/ARZUVg+qIynlq7tuBt8A0cOBAAEDZsmXx3nvvwcFB3AqF8ePHIyUlBS1atEBWVhaaNm0KZ2dnjBs3Dl9++aWoOosK2XnSBfXM0QgQyvZmyDb3aQo6YrU52bnSPLxy8uS4nZCGKsFekMkKIhdn5EhznVTzFhkr2z0hDIOWvBtBSBer6RXijM2FVELF63cpifRdJ67xbqjmWxNzx90hhMH31yrla53xswRLPVLEVJo9ezYmTZqE2NhYyOVyREREwMODXzb4osz3+7hNVXw1N5+sUk+yy+roySIkHL39Uu27WIFp6s4bODDG8Hvpy3WXsD/2BSZ1qAxvN0eM/+ca90GE1WKID5MxfNt0hezgIi9fDimWbjBg9Gq2hMgb6849xpxu1XSa1RiG4Rzv4zbT+CvKGFvA9TRzZHeTB7JYvXo1PvzwQ7i7u6NOnTqmbt7m4Su/PE5SX87P9jBhq2u7RvwnsS/tdxO1V1qKYf87te7ykw8Q5K0/DQRh/Rjiw2QMgUmRRV0oUmZd13dJCoQpYeet76HHNd5fvc3WX4CwaYytD2xfLcjILehHeLZVAxk3bhwCAwPRu3dv7Nq1C3l5/DJ8F3XyeIbszRJptmM1yfE5zkzr7WOfp+LR63TldyERjcVAYQUsA0NeYN9mW85c42hvJ8k9xTDAyzQ9QoqI66Urv6NMJpNsvBvTh5EwH3zHp5hxXKm4B9yczBus1OQCU3x8PDZu3Ah7e3v07t0bJUqUwNChQ3H6tO6EjkRBwko+jNkozkTAN/aK5n1uikUxmm+8r95mo8NPJ9Dsh6PKbTQBFw0MUfkPW2vcOG9CuiZl0unfj4uPFM6Grq4xDCPZiwOF/iCESvN3XkhjlTAEQQJTXl4eHBwccP36ddENOjg4oFOnTli7di0SExOxaNEiPHr0CC1atED58uVF12vrrDv3mFe5fTcSRNXvaKd9K/ARQkyxjFizH2zRwSm6d9HAkPxh91+mcxcyACG3IJ+QHVK0KaYVfUKpVOOM5CXbxNad9AXptxwcHBAaGor8fGlWIbm5uaFt27Z48+YNHj16hJs3b0pSry0i5Y3IJgg5OrAITEbuh1jYupAvZ4xqP6f53TJwd7bchKu5AuzCUglMxkCfTCSVoEMvOIQ1ItgkN3nyZEycOBFJSUmiG83IyMDatWvRoUMHBAcHY+HChejatatBmitbR0o5pfeys1rb2CbwZSyqfm2TnPEnPk0hje1S0PxbNHB3ttyEq11+PcW7rL2dTBIhnKsOofNGbr5cr+O2VKZvvpkKLFesJAxDxivZs6UhePb56aefcO/ePQQHByM0NBTu7u5q+7nywfXp0wf//vsv3Nzc0KNHDxw9ehSNGjUS2o0ih+atVTvUV3Rd5x5qC7tiVx9ZimpdzjAU5K4I4GhvcrdLq0ZoqoqHr/SbLaUa73wXsRC2Q1REcbVglcNbVsRfZx6ZsUfCESwwde3a1aAGZTIZNm7ciLZt24oOflkU0RRoAj2dJa2fd1A/jfnXUlTr+RI6pLJhIadZ5LEVmVjOSHNPcWl8hL5EmCpXXK6EgTsJy6d7rRBM6lBZTWAKkPgZZgoESyxTp041qMF169YpP2dlZcHFxcWg+ooKmhOfocl3NZHLGey69lxr+9Qd1zG9S1Wdx91LfIvyAcYNOvpP9FO8Xz0YALDgwB3WflqKposwLraiRVx77pFFrPrRRN/13XUtHo3KF5OkHSH+XoQNwFi2OZ0vJtdvy+VyzJw5EyEhIfDw8MCDBwV+MlOmTMGff/5p6u5YDZoaJqlXp8kZBl+uu6y1ffWZR0h6lzKBjaFGXqoNAMfvFEYX/+nQXTxgWe3EGMkkF+JTEAwzKiJQ+soJwUj9E5f2M0+KBqmEJU4fJklaKeSbbTGS1CNlahjCMlGdjxkYFnTWUuAlMPn5+eHVq1cAAF9fX/j5+en842LWrFlYtWoVvv/+ezg5OSm3V6tWDcuXLxd5GkUPqTVM+uLCqMZM0fSJsJR4Ksbqx6GxzXB6QktUCPQ0Sv2EMPSt0hzSrJzg+naNaIwj45ob0CPLRviqVtM81HKNkAiZsCxUBSRjvdCaGl46soULF8LTs+CBsWjRIoMa/Ouvv7Bs2TK0atUK//vf/5TbIyMjcevWLYPqtmU0bzahvgQFN6y+nFO6971Oz1bamzVTo1gKco6s7WJxcbRHsA+lXLEU9E26bo7CVf5eLo7wMnN+KkOQ2rfOVA+1y4+TTdMQYTZUbyVG47vedD4yy/UZ5TXDDBw4kPWzJi9fvtS5T8GzZ89QoUIFre1yuRy5ubl8ulMk0VRnClVpH739Ei3CdZuV9IWFGbb2Eg6NbY74lEzBmhyuVTdSIuUYax4WIGFthCkQ+rD3cbNeQYkvlrIoQ5Nxm/llJPBwsX6/l6KKmkmOUX+GsQVKVmAvkyHPQu9bg32YGIbBnj170L17d5QsWZKzfJUqVXDixAmt7Zs3b0bNmjUN7Y7NoinQ8F7V9o4HHIKLPvuyIkJycoZwgTYxNUvwMeZmdreqWNyL7kVLRN9tL1Q5suULWwhnon8eEGq6txSrSafIEljar5ZVa/+KOqruG3KGgZ2dDP9rVh4f1S+N0v66fQct2ddJtPj+4MEDrFixAqtXr8bbt2/RsWNHbNiwgfO4qVOnon///nj27Bnkcjm2bt2K27dv46+//sKuXbvEdsfm0TSZmSIliSZS5r8yBlL17qP6oRLVRFgyxb10r9AtH+Bu9FQqpkDIPGFvJ7OISP4A8EvfWubuAmEoGk7fADChfbhZuiIVgjRMWVlZWLNmDZo3b46IiAhcvXoV8fHxOHHiBNasWYNu3bpx1tG5c2ds3LgRe/bsgUwmw7fffoubN2/i33//RVRUlOgTsXU0pzGhb45SROjVJfmfuPsSDecewtHbiVr7LMQnnLARpEyyrE80sJbblutyCJknCpLrWsuZE5aO2vgScFsxFjz6eGuYhg4dig0bNiAsLAz9+vXDli1b4O/vD0dHR9jpsUey0bZtW7Rt21ZwZ4symrKK1KvC+MyTuhRM/f88DwAYtPIC4uZ1VNsnZbwV7iB9lvF2TJgHoSNC7+1iuXO2GlJ2kwG94BCGU6u0D+JeZyA8yBOn778GII0Q1LSS+f1KeQtMy5Ytw9dff40JEyYoV8wRpkNTGJA6rACfG1qMSU4qp1NLCV9AWC5C7zV9aUOs5W6TUiFUUJe1nDlhqWz5ohHy5AyG/B2t3Cb0Pm0eFoCjt9UXkQ1pKjxsiNTwFpj++usvrFy5EiVKlEDHjh3Rv39/tGvXjtexvr6+vN/+DUnqa8toXr18ieOY8NMwCReYfj1yT0RvtCn/zR7M6FJFbxlLXRFESIeUv7AtKCSlvucpADdhKDKZdmJdobcpWzJ4SxivvAWmvn37om/fvoiLi8PKlSsxbNgwZGRkQC6XIzY2FhERETqPNTR2E8ESVkByDZNxuBD3RrK6vt1xQ+/+Z28yJWuLsEz0TbzSalusQ/jmEpjCgzxxKyGNd33m0uQ2qxSAY3e4w9IQ1oPqE0uw9pdFOhKaSNoYCF4lV6ZMGUyfPh3Tpk3Df//9hxUrVqBfv34YNWoUunfvjp9++knrGH2xmwh+WIIPk6U/Qyx5OSphfKT25xHLJ++VxYpTDwEALcMDcfiW9mIIQ3ivgj8YBjh9/zXnmHSwFzYmzKWlXf1JPZSZsNssbRPGQTM1Cl8YHUGILWGRtuiwAjKZDO3atUO7du2QlJSkNNkRpkFo4ErueVB/gT+OP7D4N0BLGFCEcdF7l0r4sJeqKmPckjLIYP9OEOJ6cRJ6HmTWJoyB0NuKrbidBUzwkiTf9fPzw6hRo3D1Kr/orYThmFrDNHvPTZy890rSNqVGilVy9cpw50MkbANHe93T3+DGZUXXy4BBZElvAECPOtzBfMWgeHZwzQNCpwljmuRqh/qyblf4u4xoVREA8GFt41wzwtRoJkfhR72yfjjCopXVlwTeVEgiMBHGR/PNT2ofJlOja/I0BEPkpbMTW2HFoDpY82l96TpESI4+3yIhIyLA01nvqs8BDbWDl16Y1JpX3QwDbBrSEPtGNUHbKkECesUfhfmZax4Q6oslpYZp94jGat91BS28OLkg/t6oVhWxe0RjzOteTbI+EJYBn9tq4+cNMLxlBfzUpybrfS1liBqxUKIeK0Hz/nn1NhsPX6WjbDF3XsczYLDxwmNUCfbWsd+0GMOp9qkBTt9B3i4I8tYd+ZmwfIQ87APfJZPWBZu2MsDTGY72MuTyMIe7ONojPMiLd3+EIJOpapj0P0SEDrMrT1JE9kobzblGl4Dq7VqQ/sTOTqZzfiKsD6E+TKH+7qhfzh8A4GRvhxwNASk71/wCE2mYrAS2h0GLH4+qfY9+pHtF2oHYF/h6Sww6/XySdb+pVwVZt36MsERMcQu3EaExqhjoIXk/ZHw1TALjq83cFWtYx/Rgfg8UwpQIXSWnKk+7OGqLJpaQmsssGqYLFy5g8+bNePz4MXJy1O2SW7duNUeXLB4+D4Nnybo1LLfi9S8tNr2GycQNEjaB0Pume60QbL30TNAxn+sJkDe3ezVUL+mNOXtu8a5vbvdq+HDpGUF90IdMJuPtw8THdC/2MfReBX+cuvead3mKxF904TNuVe8PNuGofTXjmLeFIFjDVKZMGcyYMQOPHz8W1eCGDRvw3nvvITY2Ftu2bUNubi5iY2Nx+PBheHuTOlYXfJwxXR3tde7LzpNWdW8oJC8RUqAaII/tntI1JvQ9u8OK685k4OXiiM+bltfbJ01tbSk/3ZnZxaLwYXqTod8Rlk/yXbHhOIQeR+JS0UKoSU6fAqmUnyucHXQ/30yFYIFp7Nix2LFjB8qVK4eoqChs2LAB2dnZvI+fM2cOFi5ciF27dsHJyQmLFy/GzZs30bNnT5QuXVpod4oMfFSa+gQmTXuwJiYP1EcqJkICKqkIN2y3lK6HumYQvBIq/muuTtJOzFILCgEezrj8OBkA8OuR+3rL8tIwmUiSoThpRRc+zxfV+8NStZGCBabhw4cjOjoa0dHRiIiIwIgRI1CiRAl8+eWXuHTpEufx9+/fR8eOBQlanZ2dkZ6eDplMhtGjR2PZsmXCz6CIwEe+cHUq/Dl93RyF1S+0QwZC4hIhBlWfnPerB2PjkIZ6y/Odd1d/Ug8AUNrPDW0iigMAlg+og9qhvqhRyge/9q0loI+aneB9KC8mdghHQmoWr7IWpWGSWUZ6C8I0CI3MrSYwaeyzlPdr0U7f1atXx+LFi/Hs2TNMnToVy5cvR926dVG9enWsWLFCp0Tp5+eHtLQCf5qQkBBcv34dAJCcnIyMjAyx3bF5+GiYVKXy4S0rCmuAFEyElfFTn5rwcNbvhsn3oV6puCfi5nXE8fEt4PAuPlPriOLY8kUjbB/2HjpGluDdL817W2rNSjEP/Sv8VOGjYRLrTCvmtAa/Jz6+FWFdqJnk+PgwWcESNNFO37m5udi2bRtWrlyJAwcOoEGDBhg8eDCeP3+OSZMm4eDBg1i3bp3WcU2aNMGBAwdQrVo19OzZEyNHjsThw4dx4MABtGrVyqCTsWXiU7jfKE/csezAkqrwWb1DEJrozSVnofeUOZUqiWnc7hJi5Tmhh5GGqejCb5Wcfn9ES0CwwHTp0iWsXLkS69evh729Pfr374+FCxciPLwwKFmbNm3QtGlT1uN/+eUXZGUVPPwnTpwIR0dHnDx5Et27d8eUKVNEngYBAAsP3lF+rlHaR9CxtEqOsAaE3ja6HtCdBGiMhKIpuFmqP4YCsRowoedlJ5NZ/LUgpKNV5eLYez0BANCuKvcKN0OS9ZoKwQJT3bp1ERUVhSVLlqBr165wdNT2lYmIiEDv3r1Zj/fzK0w9YWdnh/Hjx2P8+PFCu1GkUDVvFvNwwqu33CHiqwoMAGfyOEwmHg8jW1XE7YQ07LuRYNqGCUkRmrqDTRjoXjPEoNQngP4l9domOYOaUmPJR/x9qfiieolcHO2QxTNAoL7zGtVa2yVAJqOVckWJ7jVDYG9XkPe0ey1h6W74+N6ZA8EC04MHDxAaqp02QBV3d3fORLyJiYlITEyEXCNSbWRkpNAu2TyqE1hYkCde8Yh9InSStnWn72oh3rCTyUhgsnKETqRsw6Bl5UCln5JYhPgRCXV+1Yefu5NkdSlQ7Z0QbZM+bVEASyR1GUhiKkrY2cnQraa4vICaLx2WonASLDBxCUtcREdHY+DAgbh586aWVkMmkyE/P9+g+m2RzNzCa+LEc6IXqvo2fVQB0zZI/hO2gdAcimwZzqW49fT7Uqlj6c6sar4jAq5N5+rBOBD7grPOwm3awqODBURvJiwDZ4fCgWIh8pEWvAQmX19f3g/gpKQkvfs//vhjVKpUCX/++SeKFy9ONm0eqNpznRx4CkwC27AEh9lG5f1x+j7/yMFCIHOAbaDXt4FlF9tvbuw7XbOLln7fqQqVfH1Hvmobhs6RJTBi/WX2OnWctOZ0/8fAOrzaI2wfVa2vyeMC8oSXwLRo0SLJGnz48CG2bt2KChUqSFanraN67zjq0DBl5apr5oTKoXk8EopKCdt4KOHtarT2ZJCRhskGEOrDxCatmF67Kd2NJ0VdgZ7OaqvnclSyAPC9MrVD9b9E69qnuVVfsF2i6FBaIxq+hbow8ROYBg4cKFmDrVq1wtWrVyURmObOnYutW7fi1q1bcHV1RaNGjfDdd98hLCxMWebFixf4+uuvsX//fiQnJ6Np06b4+eefUbGiulPimTNnMGnSJJw7dw6Ojo6oUaMG9u7dC1dX4z3E+aKq/akW4o1d1+K1yszff1vtu9CJVaipw1DYNFqVS+hOSWEwei5HSV/z/8YEP/IFCjtS+g+pEl7CEzuv6tqr4WogYbsKYa+MvxviXouLW6c5NaRk5qo0wK8OLl8nnRHWNTbTOwwBaGskLcHiwQYv+05qaqraZ31/XCxfvhwrVqzA9OnTsWXLFuzcuVPtTwjHjh3DsGHDcPbsWRw4cAB5eXlo06YN0tPTARRMLl27dsWDBw+wY8cOXL58GaGhoWjdurWyDFAgLLVr1w5t2rTB+fPnceHCBXz55Zews7MM5wPVZ8QnOlb36PIlsFTYnnsDG5UxaptsQuTIVhU5o0UTloNQp29jucgMaFhG5z4pFFjrP2ugd//8ntVF161PiFSY5DpWK4EP9Kxs4rquOk1yGm2TSwYBaAvYmsPcUkx0vH2Y4uPjERgYCB8fH9abnGEYXk7bp0+fxsmTJ7F3716tfUKdvvft26f2feXKlQgMDER0dDSaNm2Ku3fv4uzZs7h+/TqqVKkCAPjtt98QGBiI9evX49NPPwUAjB49GiNGjMCECROUdWlqoMyJ4l6xt5PpNMlZG2y3v1HPjWE3U46OqmS8NgnJ0ZcSke2eMtbz2IWnLyEgzmeqYnEPvfv93Pmv0tNEn7Cj6OvQFuXx95lHOstxCTq6dmtpmEheIqB9H1iKgKQJL4Hp8OHDyvhJR44cMajBESNGoH///pgyZQqKFy9uUF2apKSkACiM9aRICuziUphY097eHk5OTjh58iQ+/fRTJCYm4ty5c/joo4/QqFEj3L9/H+Hh4Zg9ezYaN27M2k52drZawmE+mjVDUKgn9c0t1vamZqkDgrBsPJyF+bwYyySnL52IFHGYdJm0FFUbclb65grFuOS6blzTje6kx/q/E0UTzfvFUh8PvASmZs2asX4Ww+vXrzF69GjJhSWGYTBmzBg0btwYVatWBQCEh4cjNDQUEydOxO+//w53d3csWLAACQkJiI8v8AN68OABAGDatGn48ccfUaNGDfz1119o1aoVrl+/zqppmjt3LqZPny5p//WfW8H/+iYpKSee9lWDlBFajYXmePiqbRhrOenaY4z28CRMx6dNyyH68Rt0jgzmVZ5NWJHi5UJIHW5O/KK3hAd54lZCQZ5Ne5EaHF183S4c3+27pbc+hik0hXDVz9W8zuujsd3K3vMII6FtkrNMiUm0DSQjIwO3bt3CtWvX1P646N69u8FaKja+/PJLXLt2DevXr1duc3R0xJYtW3Dnzh34+fnBzc0NR48eRfv27WFvX/CmqgicOWTIEHz88ceoWbMmFi5ciLCwMKxYsYK1rYkTJyIlJUX59+TJE8nPR5XCt0rTSEz9GhgWa4sX707Ky6XgYdK2CnfofEOhydn68XJxxNpPG6B3vdL8DmD50Y19G7A5rIYHcS9o+Lp9YXopqWM3fdG8fGHdPC6AoWNFtw8T9xai6KFlkjNPNzgRHLjy5cuX+Pjjj1l9kABw+iBVqlQJEydOxMmTJ1GtWjWt1CojRowQ2iUMHz4cO3fuxPHjx1GypLqjYu3atXHlyhWkpKQgJycHAQEBqF+/PurUKYj/UaJEQU6piIgIteMqV66Mx48fs7bn7OwMZ2fxPgRCUZqvTDS3iM1eruDnQ3exOyZerzM1o/G/KeLX0dRc9DBHXESxL8dCIm4L0Zby8RuSQdqHFFv/GR1tEwSXSc5SBCjB7zGjRo3CmzdvcPbsWbi6umLfvn1YvXo1KlasyGuV2/Lly+Hh4YFjx47hl19+wcKFC5V/QuM9MQyDL7/8Elu3bsXhw4dRtqzu/FDe3t4ICAjA3bt3cfHiRXTp0gUAUKZMGQQHB+P2bfVl+Xfu3DE4qrlU8JGXpJyH+ETf/VRPLq75B+7gVkIaVp2K01lGIQQWmhuNO5MyOpy+CdtGBhkCWdJ0mJpvOxW8kPWso3vlmeoYYBuCjvYyRJb0fleWf9vafkMseh5NU5mBMwr/VXIGNUNYOcHeBf7Fbaqou+h0rxViju5wIljDdPjwYezYsQN169aFnZ0dQkNDERUVBS8vL8ydOxcdO3bUe/zDhw9Fd1aTYcOGYd26ddixYwc8PT2RkFDgd+Pt7a2Mn7R582YEBASgdOnSiImJwciRI9G1a1e0adMGQMFE8dVXX2Hq1KmoXr06atSogdWrV+PWrVv4559/JOurFIjNKi4ULg1TiI8rKhXnNjHkyXUvaVJqmJROpsaHfJhsG7aFBDIZsLBXDXy0/Jzp+sGyrVGFYrg5ox0ycvKw6eJT1uP0aZhuzWwHAHDREeixdeVAHLyZyF6vCL8hw6cafhXQiCza7PiyMc48eI12Gi4Zs7pWxdZLz8zUK90IFpjS09MRGBgIoGA12suXL1GpUiVUq1YNly5dkryD+liyZAkAoHnz5mrbV65ciUGDBgEA4uPjMWbMGLx48QIlSpTAgAEDMGXKFLXyo0aNQlZWFkaPHo2kpCRUr14dBw4cQPny5WEJ8HL6llCYcuCIP+XsaGfwhKo4J6V/lilMcjQ7FznsZCypSox8H+gyybk62avlhdREtV+afdQlKClwdtC9n8/KNKlXr/FNjWJtq3sJaQnwdMb71bUXcPBdKGFqBPcqLCwMt2/fRpkyZVCjRg38/vvvKFOmDJYuXar0B9LHmDFjWLfLZDK4uLigQoUK6NKlizI0gD74LE0fMWIEL7+oCRMmqMVhsiT4hBWQEi4N04OX6Tonugcv3/JqQ3FOheZG45vkiKKHpT2Q9Q0t1THA6cOksVtfyhjNshUCPbWihLM1F+IjPgI+hRUgbBHBAtOoUaOUS/KnTp2Ktm3bYu3atXBycsKqVas4j798+TIuXbqE/Px8hIWFgWEY3L17F/b29ggPD8dvv/2GsWPH4uTJk1qO2EUVPn4+YiaePvVKoVmlQPxvTbTadgd77tp0Tfw3nvOLSVWoYXonDL6rb273api4NYZXHUIgealosGlIQ/T8/YzaNlOnWdDXHt8XA6Hmd/0hRwp27hj2Htade4xxbcNwcLb+zAAyGfBZ03LYcumpqBQsupTUFLiSEIOlvPAKFpg++ugj5eeaNWsiLi4Ot27dQunSpVGsWDHO4xXao5UrV8LLywtAQeDHwYMHo3Hjxvjss8/Qt29fjB49Gv/995/Q7tkkciP5+cztHomk9Byt7Xza4TPR6XvrZZiCNBdZuXK1+mqH+vJoXRym8gEjzEe9sn7wcHbA2+w8AIXxhVQxui+bnsmdb7gAOxlQq7QPLj1OZq9HgF+SYl/1Uj6oXsqHvYzWOjkZXBztMatrNfT7U7j/l87kuxI7lxOEKTHYUOjm5oZatWrxLv/DDz/gwIEDSmEJALy8vDBt2jS0adMGI0eOxLfffqt0yiZUpjEek6JQ2A5z4JGihI/wsf48e1gGBUfvFDqpGn+VHENvszYO21uonUxmUdpFvregjKPffFa+FdbFp0H2Y8Rq59jmBw9n7ccNjUmCD5aSjFdQWIH09HR8++23qFq1Kjw8PODp6YnIyEjMmDEDGRn81LYpKSlITNRezfHy5UtlihEfHx/k5GhrPooqmmEFZnUtiGTu4+bIfoAA2FTkrhwOpgXluGe6Nxm5OvcxDINYFfOdTON/Y6Ba95Bm5bB92HtGbI2wBMwRh0kfQrScUpkhjKnFWT6gjo42tQn2cSUBiRCFPmuFKeGtYcrJyUGzZs1w/fp1tG/fHp07dwbDMLh58yZmz56NvXv34vjx41qBKDXp0qULPvnkE8yfPx9169aFTCbD+fPnMW7cOHTt2hUAcP78eVSqRElRC1H4+RTMNlWCC7Rzni6FP59Uk2KfeqX5LTs2sB0GgJ3K00zRpqkm1IntK5umIcLkqC4GkUFm8ryFejVDeu5vIW/RWvUYqH0W64wdpiOCuaZg6O3q+K5eisNECCc338oEpiVLluDp06e4evUqwsLU837dunULzZs3x9KlSzF8+HC99fz+++8YPXo0evfujby8Aj8DBwcHDBw4EAsXLgRQkANu+fLlQs/FZtEMK6AQnPSEOeKN5gRmJ+M3WXIlB+WCYdTzZcmMrGNiULCsm7Bd2Jbeuzjaad2Txg8roHsUSOVHpyV46C3Loz6By/25Qo/wDitAPkwEDyxFw8TbJLd161ZMmTJFS1gCCgScSZMm8Qr06OHhgT/++AOvX79Wrph7/fo1li1bBnd3dwBAjRo1UKNGDf5nYeMUpg+Rvftfu4zoeVjjOHsZP4nJUFMHA0bt4cHW/4W9qhvUxshW6omTu9QIQb2yfhjVWjuhMmG9fNspAjVK+eCzpuUAqAvtPeqUsqglkno1TAb0UyaT4csWFXTuE1zfu/8ddfgzVg3xelc3RwUcm0nDRPAhN18C7YAE8BaYYmNjtQJEqtKiRQvExsbybtjDwwORkZGoXr06PDw8eB9XFNH0YVK8lUmR0ZntzZLPW5+hE52c0TDJsZRpXbk4y1b+dNYIiObiaI9NQxpiVGsy99oSnzQui+3D3lOafVSHhYujvRnCCuhGiEZFqGmvbln22HX8fL7ZS7mxaGVL+bkqhTChwphmcVq5SvDBUgQm3ia55ORk+Pv769zv7++PlJQU1n3du3fHqlWr4OXlhe7du+ttZ+vWrXy7VCTIy5ej/7tlvZp+PlK4ZmhOV3YyGc/UCeyFRqy/zKvdApOcyndlvdxt8KfwAllKHA/C9tF3r+nTzAq5Rdl8jnTWLWIYKYYem8DES8jRcTKa18bSnPIJy8RCLHL8NUxyuRz29rp9QOzs7JCfzx7239vbW/nw8/b21vtHqHP87kskpmW/+6Z4qyv4pqphKlvMXevYJhW542JpCiWG+jDxh1GLKM5mo5ah0FlUDCV93UQfS9gOmg/pBuV0v/gJYVpn9sC6+jVDusdNjXcxkop7iUsWrEtLJMqH6d1Rpfy0x5CaKV1HfbquwaXHbzTaJYmJ4EbTWmAueGuYGIZBq1at4ODAfojCgZuNlStXsn4muMnOLVRFKjVM76Yp1UmJLY3B6o/r4dcj9zD/wB2d9WtpmOxkvCYxg8UlBmqztEJgUq1XJgPOT2qF0/de4+NVFwTV//2HkRpOwBbyikKYHFWB6dq0NvByMTwcBwAMeq8s3q8RglozD/A+Rp9GxdvVETHT2ijzwuld3ccSDkS3o7Xw8aw4xNnBHrEz2iLi2/9YywqVdzRjvJG8RPBhQU/D/FmlgrfANHXqVM4yH3zwAWeZzMxMMAwDN7eCN5dHjx5h27ZtiIiIoGCVLOy5nqD8rJhbFAtUVOdTNpWlnZ0MPu5Ogtqzk/HzsuBYJMMJA/UJXhnNXGUGtZPJ4OxgL2p1m2aQPDLJFR00fZZUv0klLCnwYxlf+gQdLuHFU6V/+nwU2VbJ6Y6urbdJTrQSoaqazXm+OilNfBorGcmHieCiZXigzsUHpkZSgYkPXbp0Qffu3fG///0PycnJqFevHpycnPDq1SssWLAAX3zxhSTt2Ar/Xn2u/KylYVKZUHU5tnL5CGg7YfKM22LgRMcwDBxVpC4fV2GCHRc0DRMKTB2HSSozk9Bu69QwcRzn4mgnus98D1Oci7Oj+oOPfJgILoK8XczdBSWCxbYbN27o3Ldv3z7O4y9duoQmTZoAAP755x8EBQXh0aNH+Ouvv/DTTz8J7U6RQiEoKSYZ1flU1+TK9QaoHYeJ5yo5zhL6UbXIeTo7wJslarkhzu2aEzkpmIoO5tYmujhI8zasz9GVbXWrnQ7pg0sYOjquBedy/xGthIXi4DsfkYaJ0MXqT+rh/erBGN9WO5SRuRA8suvUqYOff/5ZbVt2dja+/PJLdOvWjfP4jIwMeHoWRIfdv38/unfvDjs7OzRo0ACPHj0S2p0iheYqOYXKPl/O6FTfi9Ew8YvDZKiGqVCIqVPGt7A/bGVFiTs0ERMFmFp+kipAql7THss2sRomtjd4TSGrlG+hj6SaD5OOOjXHLMlFhFCaVQrAT31qwsdNWuuDIQgWmNauXYvp06ejffv2SEhIwJUrV1CzZk0cPnwYp06d4jy+QoUK2L59O548eYL//vtP6beUmJiolpCX0KZQSFGY5ICElCzUmnkAf51hFzaFTlQynmEFDBeYCiUm1clZtdrC+FPC26IJuuhib2Y7j3QCE/+yhvowpWXrXrQD6BE6eV5qXfOFLq0YQVgiggWm7t2749q1a8jLy0PVqlXRsGFDNG/eHNHR0ahVqxbn8d9++y3GjRuHMmXKoH79+mjYsCGAAm1TzZo1hZ9BEURpkmMYLD12HymZupPccqnjNXfb2/ETTwwVSBioOHqrbC/l64ZapX3QpGIxOL8zbahqoBR4smQ+V6V5WIB6e2STKzKsHFQXvm6O+LlPwXzSrFIAyge4o2sN4y9N9nFzxOdNyklSlz7Nqta4lul7iRHxwqHdGda2uWaLRb1qwM/dCcv612bdT/ISYU3wdvpWJT8/Hzk5OcjPz0d+fj6CgoLg7MwvdsiHH36Ixo0bIz4+HtWrFy4VbNWqFS+TXlFGM5ccHyGAaz5iyyUnpC+iUTHJqdZlZyfDli8avdtesMPR3g4P53YAAJSduAdAQeJhXW/FX7cLVy7NJooe9cv549KUKOX94+Joj4Njmhk95o+zgx0uq7RrKHp9mLS+6xZdxHRH2wdQ3BtH15oh6FIjWOc1IR8mwpoQrGHasGEDIiMj4e3tjTt37mD37t1YtmwZmjRpggcPHvCqIygoCDVr1oSdyiqpevXqITw8XGh3ihSKuUXV6ZtrvhE6IRWY5LiPMdgkB9WkwhpLpFn6oLnNw0W3rE9zMMF2/xi/TWnb0RtWgKUZXWNSX4/4viDpdOLWcTyjQyOl5dvEr3mCsAgEC0yDBw/GnDlzsHPnTgQEBCAqKgoxMTEICQmhhLlGRvEOqZpLjnMVnGCnb54mOR5l9MEwjHLyFFLX4t41UMbfDT/1qYnVn9RDqD+/aN6mzidGEAYj8JbVNdbZtivGzobPG7Ifwze+Et/O6Tqe3m4IK0KwSe7SpUsIC1Nf5ufr64tNmzbh77//lqxjhDZicslxaYI09/KNw2Sos6a6hon/cV1qhKBLjRAAQHgQcOyrFigzYbdaGdaVdiQvEVaGoMCVenyY2ISfZpUCcOyrFrrr5xmWQ5fAw3e4kQ8TYU0I1jBpCkuqS1/79+9veI8ITthyyXGV1b1fvUB2ntw0cZgYYNHBO8rPBEGoo3dYaPt864y+L8qHSbMvDPs+Q+cB8mEirAmDI6w5Ozvj5s2bUvSF4InS6RvCBSKt/RrfV5+OM02kbzB49TYHAHA+LsmgujRR7ZrXO1+n+mX9JG2DIDRx4JkvyNWR34KEUa0LgkV+UKskZ1n9GibhaAprQk3akSH8EqmTvERYE7xNcmPGjGHdnp+fj3nz5sHfvyAD+IIFC6TpGaGFYkJUqrEZ7slQaODK1+k5PPvCqxgAoJiHM169zVbbpvrGmpsnh7E4P6k13mbnoZiHuAzwBMEXvvGXoqe0RmZOPuztZFhxKg4/HbrLWq5bzZKoV9YfJbzYAktql1cdkw52MuQpElqLkEr4aK919QMAfHnmsCQfJsKa4C0wLVq0CNWrV4ePj4/adoZhcPPmTbi7u9PNLzHnH6prXrJy8wEU+iTk5Mux/ORDvXVwO4Vr75daw+Tl4qAtMKl8NqZa3sXRHi483+gJwhDceQpMbk4OyoS2Hs76jwnxcWXdzhZWQHVrwZgSb+vWDGkgNvUSF/TIIKwJ3gLT7Nmz8ccff2D+/Plo2bKlcrujoyNWrVqFiIgIo3SwKHMv8a3a9/iULADc2p2apX2Un4U6VTYq789rEhRSL+vbqtqyY/518aFKMD9zAEFIQbC3C56nZKFd1RJm60OdMr5qY1I9tpnw+uQaEpPqN7XxauDYJXmJsCZ4D6WJEydi48aN+OKLLzBu3Djk5uqOLk1Ig9jAvSsG1uWuQwfDW1aUPDUKmzZK1SdCas3kexWKSVofQehj69D38P2HkRgdJSxBrSFojpkPapVUG5Oqux3thUtMWu84KhsM1SoRhLUiaCTVrVsX0dHRePnyJerUqYOYmBgywxkR3fKS7mvetkpxNf8Bob+Pq5O9SabD3HxVgckEDRKEkQjydkHPOqVERZcXu0JUKxyInUxdYFIp4SRCYDLUh4kgbBHBcZg8PDywevVqbNiwAVFRUcjPzzdGvwg96DOHlfBW93kQOp/Z84z0LeVESUuLiaKKv4SLEdTMcAZqmJwc1I9RFZ8qFvcobFNwzeqYO1EyQQhBVC45AOjduzcaN26M6OhohIaGStkn4h26I/fqnmRGR1VS+y48NQq/STBHwpVtNGcSRZWuNYIR/SgJDcr5CzqOdZWcnapJrvCzo73wARasw9kcAMa3LUxhJVSDraq4Gt8uTOn8ThDWgEF3a8mSJVGyJHeMEEJa9AkY3q6Oat+FKm/sZDJex2TmSqlZJImJKJo42NthbvdIwcexmeV1OX2L0TBpoirolOaZjoiLoc0rSFIPQZgKw0cSYTR0+SoJcboUqmHiu6ImS0KBiTRMBGE4qvOC6pDSNK+JgdHh00RDlyhKkMBkyeiYjfgGyNNXhy74+jCFBXlJ1gXyYSIIYagOGYVWWfXFQ9U8J4WGqT4Pk2HDd2UGNiQXDcI2IQOyFTGpQ2UABW+MFQM9cFcjThMbuoSRPvVKsW7nIywt7VdLZ0A9MZC8RBDiWftpfQDqY1d1SInxYdKkcgkv7B7RGMU1oo6r6p3mfVANiWnZqFHKx+D2CMISIQ2TBaMVzVdlQ7kAd1F1KPB3Z1+dw8c85vrOUdNBIlsayUsEIR5354LxqO7DVPhFM2q3WKoEe2ulGMpXqdzZwR51y/hJotEiCEuE7mwLRlPb06xSgPIzXzOWrnK6ZB0+9ZZ/J6wJ0Qwp1PRfNC+vtU/KWF4NylGSXcL2UR0yio+qS/RVBZkDsS+M1g83FfcAX3dHPSUJwvohk5wVUbG4p/IzX4FJaGgCfXFROlYrga/ahqGkb8EqmQInU36vr1M7V0Hf+qEoH+COJUfvq+1zkMBkoOCHD6tLVhdBWCoMS2ohVc1OvlRqJQ4c7e1wYVJrMGBEBe4kCGuCBCYLRp8YwVcpo1tgEl5vgKczyhRTMQUKkHPs7GQIC/JkXW0jZfA6MgcQRQ3F6jjVez83X7o4aVwEeEoXfJMgLBl6ulgwqsKLplDB14zloCNOgG5Tne56NXeJEXPY+m0voUmOQhQQRQHV+cDLteC9V9W521QaJoIoSpCGyUrQFJj4CgZlirEHmRPjw6QZ/0kqOUdKDZMdSUxEEcDR3g6rPq6LnDw5fNwKckeqvozkkcBEEJJDApMFo6ZhkmkKTPwEA13aG10aKn2BKzVlEaniJ0kZh0lKbRVBWDLNwwLN3QWCKFKQSc5K0BRW+MoFuoQRHzf2FS3GNsmxIamGiQQmgpAs5AdBEIWQhsmCUU1wq2lqErtKrlwxd1QJ8UaP2uyBKxX1ft0uHM+SM7Al+pkyb5ymVop3OACOYlKa0fimdiEIW+TbThG4GZ+KF2nZOH7npbm7QxA2BQlMFkxmTmG+Nk0Bia+MoSnU9KxbCv9rph0LSYHCpKWIl7T10jPddfPrAidSvgtLqa0iCGvjk8ZlAQCDVp43c08Iwvag93ELJltVwyTSf0jLlMdRXibkjpBINpFSxiGTHEFQ9HyCMAYkMFkwPeoUms1++6i22j6xPky96rKb4nSV14dkGiZJwwrQo4IgVJnRpYqg8ot71zBORwjCyiGTnAXj5+6E+3M6AGAzNYnzYVIsQdaFEI2UVIKOlBomMskRhPrYjCzpw1l++vtVMHXnDQBAg3L+xuoWQVg1JDBZOLoEAENXyfEtrzrxakbpFiubyGQaqR0kNCCQvEQQ6vAZEmy56QiCUIdMclYK30lNqBJIS2BS+cyS1UQUmm1UDfGWpmJIa94jCGtFdRTwGRIynV+kpaxqaiWCsDJIw2SlsE2CC3tpJ54VrmES1qYYNKsZ1kL3qj2CIAyDlwZXZXAb0w9wQMMyeJORg6YVA4zWBkEYCxKYrBS2SbCUr3YaFKFTn/7Aler7+CqcNGssaKPgaE8XB/h7UPJOgpASNRObQA2TMXW0Tg52+KptuBFbIAjjQSY5K+VRUgavcoI1THpUTGIn0rpl/HRWJMXbbEQJLwBAnVBfg+siiKIOmbUJgh3SMFkpz95oC0xs85y55776Zf0wuVOE2jah/hVcrPy4LjZffIJedUsbXhlB2ASFA0voGCNxiSDYIYHJSuGfGkXC6U9EVcNaVICHs/ptJvWKnOJeLviyZUUJaiII20PoKlRzv2QRhKVi1Sa5uXPnom7duvD09ERgYCC6du2K27dvq5V58eIFBg0ahODgYLi5uaFdu3a4e/cua30Mw6B9+/aQyWTYvn27Cc5APKaKN6TqpyRm+T+bYCdlGAGCILQR6sOkdiyNT4JgxaoFpmPHjmHYsGE4e/YsDhw4gLy8PLRp0wbp6ekACgSgrl274sGDB9ixYwcuX76M0NBQtG7dWllGlUWLFlmN/Z69n6btO58wA1xmQmu53gRhTRhk9qYhSRCsWLVJbt++fWrfV65cicDAQERHR6Np06a4e/cuzp49i+vXr6NKlYL0AL/99hsCAwOxfv16fPrpp8pjr169igULFuDChQsoUaKESc9DDOwKJokCJanWqCIViVFqkTxEEOaFTHIEIQ1WrWHSJCUlBQDg51ewKis7OxsA4OLioixjb28PJycnnDx5UrktIyMDffr0wS+//IKgoCDOdrKzs5Gamqr2Z2pMlTNNzSSn0aRm5G822CZrUy1hJoiiyuv0HOVncvomCGmwGYGJYRiMGTMGjRs3RtWqVQEA4eHhCA0NxcSJE/HmzRvk5ORg3rx5SEhIQHx8vPLY0aNHo1GjRujSpQuvtubOnQtvb2/lX6lS+hPaGgN9y/+lxNAUJmzdJDMcQRiX6EdvlJ8FW+RofBIEKzYjMH355Ze4du0a1q9fr9zm6OiILVu24M6dO/Dz84ObmxuOHj2K9u3bw97eHgCwc+dOHD58GIsWLeLd1sSJE5GSkqL8e/LkidSnwwm7vCRuovu0cVmd+xg9Zj4+BkA2wY6mY4IwHaRhIghpsGofJgXDhw/Hzp07cfz4cZQsWVJtX+3atXHlyhWkpKQgJycHAQEBqF+/PurUqQMAOHz4MO7fvw8fHx+14z744AM0adIER48e1WrP2dkZzs7mjU4tpUlOn+AjV9UwifFh4tioajogCMIYkA8TQUiBVQtMDMNg+PDh2LZtG44ePYqyZXVrSry9CxK83r17FxcvXsTMmTMBABMmTFBz/gaAatWqYeHChejcubPxOm8gbBqm0n7aqVEMRs0kp7GL1yo50jARhDkxNAE3QRAFWLXANGzYMKxbtw47duyAp6cnEhISABQIR66urgCAzZs3IyAgAKVLl0ZMTAxGjhyJrl27ok2bNgCAoKAgVkfv0qVL6xXAzI2qINKnXmn0qlsKAZ7sWq/uNUOw9fIznXXpE3zUTHIaE2lmbj6PfrJtowmZIEwFjTaCkAarFpiWLFkCAGjevLna9pUrV2LQoEEAgPj4eIwZMwYvXrxAiRIlMGDAAEyZMsXEPZUeVQ1Tn3qlEFnSR2fZkr6uotvRJ0zlq9jrXB3tWQUotrdVE/mrEwQB4S8o9D5DEOxYtcDEZ1n7iBEjMGLECMnrtSQMjcyrz7Hb1ckeaVl579rRjZ+7E54lZ7L0TRvSMBGE6RAet5LGJ0GwYTOr5IoaqjJdlWAvvWWz8+Wi21n9ST3lZ31yzu/9a7NuZ0+NQhCEqRC8So4GKEGwQgKTDcAVkyk9O0/vfn0KtVqlfZWfdb15ero4oGqINxb3rqG1j2vyHdAwVH8BgiAMQnCkbyP1gyCsHRKYrBQhRkNHe9P8zKo+TQRBWAbCNUwkMhEEG1btw0TwY1iLCrj6JBk96xgWkZxrHmWTl9iENbV0Kwb1iCAIqaExSRDskMBkrQhQ5hTzcMbWoe/proqnkzvXRCpnkZgc7Wn6JQhrghRMBMEOmeSsFH0r24wF10SazyJ4sWqYrGwVIkFYM0JN5WSSIwh2SGCyUqSUOQytSjG9sk3MpvKfIgiCnTweAhO9whAEN/Q0I3jD9ebJpjmyZ1nBR5MzQZgOXhom0voSBCckMFkpUk5vhs6VCkGKbWJ2oLDeBGFW8uTccdhIXCIIbkhgIvBehWIGHa/QLEWW8tHa5+Zsz1LeoOYIghBAcS8XzjI0JgmCG1olZ6VI6TjdtkpxrBxUF+ElPPWW4/IFVQ1yqcDZQVtgUq+TNFAEYUyKebAn5VZFThITQXBCApOVIuX0JpPJ0CI8kLMcW5oTxfFCUBX2aMUcQZgfGoYEwQ2Z5KwUc0xwUumCaG4mCMuCNEwEwQ0JTFaKJWlmdCmYtnzRiH2HStfJJEcQBEFYAyQwWSnmSNsmRLbxd3dC7VBtnyZNLEnwI4iiCg1DguCGBCYrxRwqdKFZz3VBczNBWBZkkiMIbkhgslIsXcPEtyyZ5AjC/JC4RBDckMBkpVizKcua+04QtggNSYLghgQmK4VU6ARBSIU5knkThLVBApOVYh6TnI44TALrUe26rthOBEGYDnr/IghuSGCyUuRmkJiEiDZ8fZPcnPRHAicIwviQmZwguCGByUqxJJMcm3CkT1xS7borCUwEYXYsaDohCIuFBCYrxZJWybG9nfK1tLHlnyMIwrSwJc4mCEIdyiVnpZgnDpOQsrpLqzqYNizvb0CPCIKQgqYVi2HJR7VQKUh/Am6CKMqQwGSlmCWXnIDku+TLTRDmR0g8tPbVShi3MwRh5ZBJzkopF+Bu8jb9PZx4l9W3+i2seMFbLAlVBGFcaIgRhHSQhslK+bFHdfzw3230bxBq9LYW966BS4/eoH1Vad5Af/2oFhYeuIvBjctKUh9BEOxQ2A6CkA4SmKyU4l4u+LFHdZO01aVGCLrUCNG5n21K1jdPl/R1w/yepuk7QRRlSF4iCOkgkxxhFGiiJgjz4+xAYTsIQipIYCIMxsGeLQ4TSUwEYS6W9quFEt4uWP1JXXN3hSBsBjLJEQbj4qj9FmtH8hJBmI12VUugnUQ+hwRBFEAaJsJgXFkEJr6pUQiCIAjCGiCBiTAYtuCTJC4RBEEQtgSZ5AjRHBzTDPtjE/BxI5bwACQxEQRBEDYECUyEaCoEeqBCYAXWfSQvEQRBELYEmeQIo0AB8wiCIAhbggQmwiiQvEQQBEHYEiQwEUaB4jARBEEQtgQJTIRRqBDoYe4uEARBEIRkkMBESMryAXXQKjwQX7UNM3dXCIIgCEIyaJUcISmtI4qjdURxc3eDIAiCICSFNEwEQRAEQRAckMBEEARBEATBAQlMBEEQBEEQHJDARBAEQRAEwQEJTARBEARBEByQwEQQBEEQBMEBCUwEQRAEQRAckMBEEARBEATBAQlMBEEQBEEQHJDARBAEQRAEwQEJTARBEARBEByQwEQQBEEQBMEBCUwEQRAEQRAckMBEEARBEATBgYO5O2ALMAwDAEhNTTVzTwiCIAiC4Iviua14juuDBCYJSEtLAwCUKlXKzD0hCIIgCEIoaWlp8Pb21ltGxvARqwi9yOVyPH/+HJ6enpDJZJLWnZqailKlSuHJkyfw8vKStG5zY8vnBtj++QF0jrYAnZ/1Q+coHoZhkJaWhuDgYNjZ6fdSIg2TBNjZ2aFkyZJGbcPLy8tmB4Itnxtg++cH0DnaAnR+1g+dozi4NEsKyOmbIAiCIAiCAxKYCIIgCIIgOCCBycJxdnbG1KlT4ezsbO6uSI4tnxtg++cH0DnaAnR+1g+do2kgp2+CIAiCIAgOSMNEEARBEATBAQlMBEEQBEEQHJDARBAEQRAEwQEJTARBEARBEByQwEQYHVteV3Dx4kVkZWWZuxsEwYmtjkMag4SpIIHJTCQlJeHVq1cAClKr2BLx8fHo0aMHNm7cCMD2zg8AHjx4gC5duqBevXrYtGmTubsjOU+ePME///yDS5cuITc3F4DtPXBteQwCtj8ObX0MAjQOLQ0SmMzApEmTEB4ejmXLlgEAZ/4aa+PPP//Eli1bsGjRImRkZMDe3t7iBwJfGIbB0KFDUbFiRchkMnh7e8PDw8Pc3ZKUiRMnolKlSpg/fz4aNWqEL774Ag8ePIBMJrOZydrWxyBgu+OwKIxBgMahJWLZvbMxkpOTMXjwYBw8eBClS5fG2bNnceHCBQC29dZw+vRp9OrVC87Ozvj+++/N3R3J2L59O9zd3REdHY3Tp09j+/btqFy5Mvbu3QvANn7Dc+fOYceOHfjnn39w5MgRLF++HHfv3kX//v0BQPLk0qamqIxBwDbHYVEYgwCNQ0uFBCYjo/rju7q6IjQ0FBMnTsT8+fPx7NkzbNu2Dbm5uVb51qDZ37y8PABAiRIl0KtXLzRq1AibNm3CzZs3YWdnZ3XnB6if48uXL7FmzRqcO3cO9evXR2ZmJsqXL4+kpCRkZGRY/SQGFDyQ8vPz0bFjR7i4uKBfv36YN28erl27hoULFwKw7AmNDVseg4Dtj8OiNgYBGocWe24MYTQyMjKYrKws5Xe5XM4kJycrv48dO5Z57733mN27dyv3Wwts56agWrVqzI0bN5jz588zLVq0YEaMGMFkZ2cz169fN0dXRaN5jvn5+crPeXl5DMMwzKhRo5jIyEit/daA4jdT7feCBQuY6tWrM+np6Wrlpk2bxvj6+qpdD2vAlscgw9j+OLT1McgwNA4ZxnrGIWmYjMTEiRPRuHFjdOrUCT/99BNSU1Mhk8ng5eWl9CMYMWIEGIbB9u3b8erVK8uWrFXQdW5yuRzPnj2Du7s7ypQpg7p166Jz585Yt24dXFxccPjwYeTk5Ji7+7zQPMe0tDTY2dkpfzvFm2zr1q0RFxeHx48fW7z9XZUFCxZgzpw5ANT9Bry8vODg4IBDhw4pt8lkMgwcOBBubm5W9XZry2MQsP1xaOtjEPh/e/ceU3X9x3H8fc5BBIfC4QQoESGGN2RjXqZhodRkZCikggoZhMogNJumrVZMplPnlJnX4TRvWVqurFgrDU1EmyKW5BW5iCgi5qVNgcOB8/r94e/71RPi4ejhfM/34/uxNeRc6PPc93y++5zv+Z5zeB6qbR6q69GlAs3NzZSQkEA//vgjLViwgPz9/SkvL4+SkpKI6P6DXpr0gYGBlJiYSCdPnqT8/Hz5emd8oBBZb9NqtdSjRw/q0qULaTQa+v7772nx4sVkMpkoLCyMZs+eTa6urk7bR9R+49SpU4nowU5N+tna2koGg4FqamoUG7MtiouLKSoqij788EP67rvv6I8//iAikt+Bk5CQQM3NzfTLL79QfX29fL9evXrRmDFjqKysjFpbW536pQ+R5yCR+PNQ9DlIxPOQSKXzUJHjWgI7e/YsQkJCsG/fPvmyoqIiuLu7Y/ny5W0OvzY1NWHs2LFITExEaWkpvvzySyxevFiRsVtjrQ0ACgoK0KtXLwwaNAheXl5YsWIF8vLyEB4ejnXr1gFw7sPmtm6/mzdvwtXVFfn5+RaXO6tFixZh0qRJ2LJlC6KjozFjxgz5uubmZgDAunXr0LdvX2zcuNHiviNHjsT06dMdOt4nIfIcBMSfh6LPQYDnoVrnIS+Y7KykpAQajQY3b94E8OC12KVLl0Kv16OsrEy+rfRA2bt3L4KDg2EwGODq6ooVK1Y4fuAd8Lg2Ly8vVFZWwmQyYeDAgUhPT0dVVRUAoLa2FomJiYiMjHT6195t2X4AcOfOHURGRmLevHkOH6stpI7q6mocPXoUwP2m4cOH45tvvgEAmEwm+fZJSUkIDw9HXl4ebt++jZKSEgwePBi7du1y/OBtJPIcBMSfh6LOQYDnIaDuecgLJjv7888/ERoaijVr1gB48CBpbm5G79695UktnbBYXl6Od955BxqNBpmZmbh7964yA++Ax7UFBQXhgw8+AABcv369zUl7Z86cceqdtKSj20/aqbW0tCAkJAQZGRnyM0O1qKioQHx8POLj43Hr1i0AgNFolK/Lzs6GTqfDkCFD4O7ujunTp6uiUeQ5CIg/D5+lOQjwPFTTPOQFk53dunUL8fHxmDx5MmprawE8mNgrV66Ev7+/xSHj+fPnIyAgAKWlpYqM1xbW2nr16tXmcLizvtuhPbZsP2mib9++HRcuXFBmwE9I2i6bN2/G8OHDkZub+8jbnT59Gvn5+Th37pwjh/dURJ6DgPjz8FmZgwDPQ7XNQz7p2wb19fV048YN+R0mra2t8nXSZ5/o9XoaN24cnT9/Xv64fhcXFyIi8vT0JL1eTzU1NfK7A5YtW0Y1NTUUFhbmyJQ27NHm7e3d5sRLZzop0Z7bj4hIp9MREdG0adOob9++DutoT0f6JNJ1kyZNooEDB1J+fj5dvHiRiIhOnjxJRPe/piA0NJTefPNN6t+/vyMSrCovL6f9+/c/8jq1z0Ei+/Q58zy05/Yjcr45SNSxRola5+GZM2dowYIFVFZW1uY6EeZhe3jB1AEmk4kyMjIoMjKSxo0bR+PHjyej0Ug6nU5+V4OLiws1NTXRrl27KC0tjcLDw2n37t108OBB+e9cuXKFfHx86MUXX2zzTg+ldEabsxG9saN9JpOJtm3bJv9uNpupR48elJCQQGazmXJycuj111+noUOH0u3btxV/bP5XaWkp9e3bl5KSkqi6ulq+XNrhqnUOSuzd52xE7yPqWKOa52FzczO9++67FBYWRk1NTRQUFCRfh/+/o03t8/CxlD7E5ey+/fZb9OnTB6NGjcKBAwewceNGBAcH47333rO43eeffw5vb2/ExcUBAE6dOoXk5GS4uroiMzMT6enp6N69OzZs2ADAOQ6Ri9wmEb3R1r6JEyfK50lIqqur0adPH2g0GkyZMgV1dXWOTOiw4uJixMTEoGfPnm36APVuQwn3qbsP6HijGufh5s2b0b17d0RERLR52ezhbSHCdmwPL5isyMrKwmeffWbxzoWUlBTMnTtX/n3NmjUICgrCzp07LV6TNZvNWLJkCWbOnImxY8fiyJEjDh27NSK3SURvtLXvvzungoICeHh4IDw8HCdOnHDYuJ9EXl4epk6dioKCAri4uODYsWPydWvXrlXtNpRwn7r7gI43qnEeRkREYMCAAbh9+zaA+++C+/nnn3HhwgU0NjYCUPe+tCN4wdQO6WTCa9eu4fLly/Llly5dwuDBg7FixQp5o5tMpjZn9DvzqlnkNonojU/bJ/nnn3/w1Vdfdf6A7WDr1q346KOPAAAvv/wyxo4dC+DB59Y0NDRY3N7Zt+F/cZ+6+wDbGyXOPA+lJ2NHjx5FcHAwcnJyMH78eAQHByM0NBR+fn5ISEiQb6u2fakteMH0EGvfY7N69WpoNBq88sorGDVqFPR6PbKzs+XVtTMTuU0ieqO9+5xxR/a4xvfffx+zZs0CAFRVVUGr1SImJgbDhw/H2bNnHTrOJ8V96u4D7N+ohnko/UxLS4ObmxtSU1Px119/obS0FD/99BPc3NywcOFCxcbrKLxgApCfn4/nn38eGo1Gflb+qAfx1q1bUVhYKF+3c+dOuLu749KlSw4dry1EbpOI3ih6H/D4RunnlClT8NtvvwEANm3aBHd3d3Tp0gV79uxRZtA24D519wHPdqN0RPvGjRv49NNPcfXqVYv7rVy5EgaDQRWfD/U0nvkF0+HDhxETE4NZs2bhjTfewNChQ9vcpr1nAOfOnYNOp7P46HdnInKbRPRG0fsA643SuRApKSmYNm0ahg0bBh8fHyxatAheXl5YuXKlEsPuMO5Tdx/AjcCD/cy9e/fa3Pfrr7+GXq/H33//7ZCxKuWZXTBJG7+srAy5ubmorKzEiRMn0K1bN2zatAmA9e8kWrp0KaKjo9t9XVopIrdJRG8UvQ+wrbGhoQFvvfUWDAYDsrKycOXKFQDAsmXLoNFo5K//cCbcp+4+gBs7uq/JzMzEhAkTOn2sSnvmFkwlJSW4c+eOxWXS4UaTyYR58+bBx8en3a8PqK6uRnl5OWbMmAF/f39s3boVgHO8Di1ym0T0RtH7ANsbpeuOHz+OM2fOWNyvqakJy5cvd6ovXOU+dfcB3NiRfU1VVRXKy8sxffp0BAYGYu/evQCca19jb8/MgmnPnj0ICAhAnz59EBgYiOzsbFy7dg3A/Q0sbeTKykq88MIL8vfcPLzxy8rKMHfuXAQEBCAqKsppPopf5DaJ6I2i9wFP3ijtxJ0d96m7D+DGju5rzp8/j6ysLPj6+mL06NFOt6/pLM/Egqm4uBj9+/fHqlWrcOrUKaxfvx4+Pj7IzMyUv0lZesCbzWasX78eLi4uqKysBHD/GYLRaITZbMbBgwed6jMkRG6TiN4oeh/w9I1Go1E+d8IZn8Fyn7r7AG60ZV/T0tKCX3/9FYWFhYq1KEHoBZP0oN2wYQMCAgLw77//ytetXbsWI0aMwKJFi9rc7+bNm4iIiEBcXBxKSkowZswY7Nixw6kmgchtEtEbRe8D7NcYHR3tlI3cp+4+gBtF2dc4gtALJsmCBQvw2muvWZzdf/fuXWRlZSEiIgKnT58GYHlYdcuWLdBoNNBqtYiNjX3kOwOcgchtEtEbRe8D7NPorCevA9yn9j6AG0XZ13QmoRZM+/btw+zZs7Fq1SqLj6T/4Ycf4ObmhoqKCgAPHgz79u3DyJEjkZubK9/WaDRi3bp10Gq1GDVqlPwAUprIbRLRG0XvA8Rv5D519wHcKEqjEoRYMNXW1iI2Nha+vr5ITk5GWFgYPD095QdKY2Mj+vfvj/T0dACWb5F89dVXLb4ksa6uDnPmzMG2bdscG9EOkdskojeK3geI38h96u4DuBEQo1FJql8w3bt3DykpKZg8ebJ8YhoADBs2DKmpqQDur6K3b98OrVbb5oTY5ORkREVFOXTMHSVym0T0RtH7APEbuU/dfQA3itKoNC2pXLdu3ahr166UmppKvXv3ppaWFiIiio2NpXPnzhERkU6no8TERIqLi6MZM2bQoUOHCADV1dXRxYsXKTk5WcmEdoncJhG9UfQ+IvEbuU/dfUTcKEqj4hRbqtnRw99fI529//bbb2PmzJkWlzU2NmL06NHw9fVFdHQ0/P39MWLECItve3c2IrdJRG8UvQ8Qv5H71N0HcOPDl6m5UUkaAFB60dYZIiMjKS0tjVJTUwkAmc1m0ul0dP36dSotLaXi4mIKCgqipKQkpYdqM5HbJKI3it5HJH4j96m7j4gbRWl0GIUWap2qoqICfn5+OHHihHyZ0WhUcET2I3KbRPRG0fsA8Ru5T/24kdlK9ecwPQz/P1hWVFREHh4eNGTIECIiysnJoTlz5lB9fb2Sw3sqIrdJRG8UvY9I/EbuU3cfETeK0qgEF6UHYE8ajYaIiI4fP04TJ06k/fv3U3p6OjU0NNCOHTvI19dX4RE+OZHbJKI3it5HJH4j96m7j4gbRWlUhGLHtjpJY2MjXnrpJWg0GnTt2hXLli1Tekh2I3KbRPRG0fsA8Ru5T/24kT0JIU/6HjNmDIWEhFBubi65ubkpPRy7ErlNInqj6H1E4jdyn/pxI7OVkAum1tZW0ul0Sg+jU4jcJhG9UfQ+IvEbuU/9uJHZSsgFE2OMMcaYPQn1LjnGGGOMsc7ACybGGGOMMSt4wcQYY4wxZgUvmBhjjDHGrOAFE2OMMcaYFbxgYowxxhizghdMjLFn3sKFCyk8PFzpYTDGnBh/DhNjTGjS92q1JyUlhdauXUtGo5EMBoODRsUYUxteMDHGhFZXVyf/e/fu3ZSdnU0XLlyQL3N3dydPT08lhsYYUxF+SY4xJrSePXvK/3l6epJGo2lz2X9fkktNTaX4+HhasmQJ+fn5kZeXF+Xk5FBLSwvNnz+fvL29KSAggL744guL/9fVq1dp8uTJpNfryWAwUFxcHF26dMmxwYyxTsELJsYYe4QDBw5QbW0tFRYWUm5uLi1cuJBiY2NJr9fTsWPHKCMjgzIyMqimpoaIiBoaGigqKoo8PDyosLCQioqKyMPDg2JiYqi5uVnhGsbY0+IFE2OMPYK3tzetXr2a+vXrR2lpadSvXz9qaGigTz75hEJCQujjjz8mV1dXOnLkCBER7dq1i7RaLW3atInCwsJowIABtGXLFrp8+TL9/vvvysYwxp6ai9IDYIwxZxQaGkpa7YPnlH5+fjRo0CD5d51ORwaDgerr64mIqKSkhMrLy6l79+4Wf6epqYkqKiocM2jGWKfhBRNjjD1Cly5dLH7XaDSPvMxsNhMRkdlspiFDhtDOnTvb/C0fH5/OGyhjzCF4wcQYY3YwePBg2r17N/n6+lKPHj2UHg5jzM74HCbGGLOD5ORkeu655yguLo4OHz5MVVVVdOjQIZozZw5duXJF6eExxp4SL5gYY8wOunXrRoWFhRQYGEgTJkygAQMGUFpaGjU2NvIRJ8YEwB9cyRhjjDFmBR9hYowxxhizghdMjDHGGGNW8IKJMcYYY8wKXjAxxhhjjFnBCybGGGOMMSt4wcQYY4wxZgUvmBhjjDHGrOAFE2OMMcaYFbxgYowxxhizghdMjDHGGGNW8IKJMcYYY8wKXjAxxhhjjFnxPxErRkAxQKWqAAAAAElFTkSuQmCC", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "da.isel(lat=20, lon=40).plot();" ] @@ -227,9 +1225,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "da.isel(time=slice(0, 20), lat=20, lon=40).plot();" ] @@ -263,13 +1272,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": { "tags": [ "hide-output" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "da.sel(time=\"2014-01-01\", lat=25, lon=210).plot();" ] @@ -283,13 +1303,438 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": { "tags": [ "hide-output" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'air' (time: 1460)>\n",
+       "array([277.6 , 277.5 , 277.4 , ..., 277.59, 277.59, 277.59], dtype=float32)\n",
+       "Coordinates:\n",
+       "    lat      float32 50.0\n",
+       "    lon      float32 200.0\n",
+       "  * time     (time) datetime64[ns] 2014-01-01 ... 2014-12-31T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
    " + ], + "text/plain": [ + "\n", + "array([277.6 , 277.5 , 277.4 , ..., 277.59, 277.59, 277.59], dtype=float32)\n", + "Coordinates:\n", + " lat float32 50.0\n", + " lon float32 200.0\n", + " * time (time) datetime64[ns] 2014-01-01 ... 2014-12-31T18:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "da.sel(lat=50.0, lon=200.0, time=\"2014\")" ] @@ -303,9 +1748,570 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'air' (time: 1212, lat: 25, lon: 53)>\n",
+       "array([[[246.     , 245.09999, 244.09999, ..., 240.2    , 242.     ,\n",
+       "         244.2    ],\n",
+       "        [240.59999, 241.7    , 242.09999, ..., 239.09999, 241.2    ,\n",
+       "         244.59999],\n",
+       "        [238.2    , 239.79999, 240.79999, ..., 243.89   , 246.89   ,\n",
+       "         251.2    ],\n",
+       "        ...,\n",
+       "        [295.6    , 295.6    , 296.     , ..., 293.6    , 293.6    ,\n",
+       "         294.1    ],\n",
+       "        [297.     , 297.     , 297.     , ..., 293.69998, 293.9    ,\n",
+       "         294.6    ],\n",
+       "        [297.5    , 297.69998, 297.69998, ..., 294.5    , 294.69998,\n",
+       "         295.19998]],\n",
+       "\n",
+       "       [[245.2    , 244.29999, 243.     , ..., 241.89   , 243.09999,\n",
+       "         244.59999],\n",
+       "        [241.09999, 241.89   , 241.89   , ..., 241.89   , 243.59999,\n",
+       "         246.39   ],\n",
+       "        [239.89   , 241.     , 241.29999, ..., 245.09999, 248.2    ,\n",
+       "         252.2    ],\n",
+       "...\n",
+       "        [296.49   , 295.59   , 295.49   , ..., 297.29   , 297.38998,\n",
+       "         296.79   ],\n",
+       "        [298.09   , 297.79   , 297.19   , ..., 297.69   , 298.09   ,\n",
+       "         297.38998],\n",
+       "        [298.49   , 298.29   , 297.99   , ..., 298.29   , 298.09   ,\n",
+       "         297.99   ]],\n",
+       "\n",
+       "       [[246.29   , 246.39   , 245.98999, ..., 232.29   , 233.39   ,\n",
+       "         234.98999],\n",
+       "        [246.68999, 248.18999, 248.98999, ..., 230.59   , 232.48999,\n",
+       "         235.68999],\n",
+       "        [244.79   , 245.29   , 245.89   , ..., 230.48999, 235.09   ,\n",
+       "         241.39   ],\n",
+       "        ...,\n",
+       "        [296.88998, 296.49   , 296.49   , ..., 297.69   , 297.29   ,\n",
+       "         296.19   ],\n",
+       "        [298.29   , 297.99   , 297.49   , ..., 298.29   , 298.38998,\n",
+       "         297.49   ],\n",
+       "        [298.49   , 298.29   , 297.99   , ..., 298.59   , 298.38998,\n",
+       "         298.29   ]]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
    " + ], + "text/plain": [ + "\n", + "array([[[246. , 245.09999, 244.09999, ..., 240.2 , 242. ,\n", + " 244.2 ],\n", + " [240.59999, 241.7 , 242.09999, ..., 239.09999, 241.2 ,\n", + " 244.59999],\n", + " [238.2 , 239.79999, 240.79999, ..., 243.89 , 246.89 ,\n", + " 251.2 ],\n", + " ...,\n", + " [295.6 , 295.6 , 296. , ..., 293.6 , 293.6 ,\n", + " 294.1 ],\n", + " [297. , 297. , 297. , ..., 293.69998, 293.9 ,\n", + " 294.6 ],\n", + " [297.5 , 297.69998, 297.69998, ..., 294.5 , 294.69998,\n", + " 295.19998]],\n", + "\n", + " [[245.2 , 244.29999, 243. , ..., 241.89 , 243.09999,\n", + " 244.59999],\n", + " [241.09999, 241.89 , 241.89 , ..., 241.89 , 243.59999,\n", + " 246.39 ],\n", + " [239.89 , 241. , 241.29999, ..., 245.09999, 248.2 ,\n", + " 252.2 ],\n", + "...\n", + " [296.49 , 295.59 , 295.49 , ..., 297.29 , 297.38998,\n", + " 296.79 ],\n", + " [298.09 , 297.79 , 297.19 , ..., 297.69 , 298.09 ,\n", + " 297.38998],\n", + " [298.49 , 298.29 , 297.99 , ..., 298.29 , 298.09 ,\n", + " 297.99 ]],\n", + "\n", + " [[246.29 , 246.39 , 245.98999, ..., 232.29 , 233.39 ,\n", + " 234.98999],\n", + " [246.68999, 248.18999, 248.98999, ..., 230.59 , 232.48999,\n", + " 235.68999],\n", + " [244.79 , 245.29 , 245.89 , ..., 230.48999, 235.09 ,\n", + " 241.39 ],\n", + " ...,\n", + " [296.88998, 296.49 , 296.49 , ..., 297.69 , 297.29 ,\n", + " 296.19 ],\n", + " [298.29 , 297.99 , 297.49 , ..., 298.29 , 298.38998,\n", + " 297.49 ],\n", + " [298.49 , 298.29 , 297.99 , ..., 298.59 , 298.38998,\n", + " 298.29 ]]], dtype=float32)\n", + "Coordinates:\n", + " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", + " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", + " * time (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# demonstrate slicing\n", "da.sel(time=slice(\"2014-02-14\", \"2014-12-13\"))" @@ -320,9 +2326,570 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'air' (time: 1212, lat: 25, lon: 53)>\n",
+       "array([[[246.     , 245.09999, 244.09999, ..., 240.2    , 242.     ,\n",
+       "         244.2    ],\n",
+       "        [240.59999, 241.7    , 242.09999, ..., 239.09999, 241.2    ,\n",
+       "         244.59999],\n",
+       "        [238.2    , 239.79999, 240.79999, ..., 243.89   , 246.89   ,\n",
+       "         251.2    ],\n",
+       "        ...,\n",
+       "        [295.6    , 295.6    , 296.     , ..., 293.6    , 293.6    ,\n",
+       "         294.1    ],\n",
+       "        [297.     , 297.     , 297.     , ..., 293.69998, 293.9    ,\n",
+       "         294.6    ],\n",
+       "        [297.5    , 297.69998, 297.69998, ..., 294.5    , 294.69998,\n",
+       "         295.19998]],\n",
+       "\n",
+       "       [[245.2    , 244.29999, 243.     , ..., 241.89   , 243.09999,\n",
+       "         244.59999],\n",
+       "        [241.09999, 241.89   , 241.89   , ..., 241.89   , 243.59999,\n",
+       "         246.39   ],\n",
+       "        [239.89   , 241.     , 241.29999, ..., 245.09999, 248.2    ,\n",
+       "         252.2    ],\n",
+       "...\n",
+       "        [296.49   , 295.59   , 295.49   , ..., 297.29   , 297.38998,\n",
+       "         296.79   ],\n",
+       "        [298.09   , 297.79   , 297.19   , ..., 297.69   , 298.09   ,\n",
+       "         297.38998],\n",
+       "        [298.49   , 298.29   , 297.99   , ..., 298.29   , 298.09   ,\n",
+       "         297.99   ]],\n",
+       "\n",
+       "       [[246.29   , 246.39   , 245.98999, ..., 232.29   , 233.39   ,\n",
+       "         234.98999],\n",
+       "        [246.68999, 248.18999, 248.98999, ..., 230.59   , 232.48999,\n",
+       "         235.68999],\n",
+       "        [244.79   , 245.29   , 245.89   , ..., 230.48999, 235.09   ,\n",
+       "         241.39   ],\n",
+       "        ...,\n",
+       "        [296.88998, 296.49   , 296.49   , ..., 297.69   , 297.29   ,\n",
+       "         296.19   ],\n",
+       "        [298.29   , 297.99   , 297.49   , ..., 298.29   , 298.38998,\n",
+       "         297.49   ],\n",
+       "        [298.49   , 298.29   , 297.99   , ..., 298.59   , 298.38998,\n",
+       "         298.29   ]]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
    " + ], + "text/plain": [ + "\n", + "array([[[246. , 245.09999, 244.09999, ..., 240.2 , 242. ,\n", + " 244.2 ],\n", + " [240.59999, 241.7 , 242.09999, ..., 239.09999, 241.2 ,\n", + " 244.59999],\n", + " [238.2 , 239.79999, 240.79999, ..., 243.89 , 246.89 ,\n", + " 251.2 ],\n", + " ...,\n", + " [295.6 , 295.6 , 296. , ..., 293.6 , 293.6 ,\n", + " 294.1 ],\n", + " [297. , 297. , 297. , ..., 293.69998, 293.9 ,\n", + " 294.6 ],\n", + " [297.5 , 297.69998, 297.69998, ..., 294.5 , 294.69998,\n", + " 295.19998]],\n", + "\n", + " [[245.2 , 244.29999, 243. , ..., 241.89 , 243.09999,\n", + " 244.59999],\n", + " [241.09999, 241.89 , 241.89 , ..., 241.89 , 243.59999,\n", + " 246.39 ],\n", + " [239.89 , 241. , 241.29999, ..., 245.09999, 248.2 ,\n", + " 252.2 ],\n", + "...\n", + " [296.49 , 295.59 , 295.49 , ..., 297.29 , 297.38998,\n", + " 296.79 ],\n", + " [298.09 , 297.79 , 297.19 , ..., 297.69 , 298.09 ,\n", + " 297.38998],\n", + " [298.49 , 298.29 , 297.99 , ..., 298.29 , 298.09 ,\n", + " 297.99 ]],\n", + "\n", + " [[246.29 , 246.39 , 245.98999, ..., 232.29 , 233.39 ,\n", + " 234.98999],\n", + " [246.68999, 248.18999, 248.98999, ..., 230.59 , 232.48999,\n", + " 235.68999],\n", + " [244.79 , 245.29 , 245.89 , ..., 230.48999, 235.09 ,\n", + " 241.39 ],\n", + " ...,\n", + " [296.88998, 296.49 , 296.49 , ..., 297.69 , 297.29 ,\n", + " 296.19 ],\n", + " [298.29 , 297.99 , 297.49 , ..., 298.29 , 298.38998,\n", + " 297.49 ],\n", + " [298.49 , 298.29 , 297.99 , ..., 298.59 , 298.38998,\n", + " 298.29 ]]], dtype=float32)\n", + "Coordinates:\n", + " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", + " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", + " * time (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "da.loc[\"2014-02-14\":\"2014-12-13\"]" ] @@ -338,9 +2905,570 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'air' (time: 2919, lat: 24, lon: 52)>\n",
+       "array([[[242.5    , 243.5    , 244.     , ..., 232.79999, 235.5    ,\n",
+       "         238.59999],\n",
+       "        [244.5    , 244.7    , 244.2    , ..., 232.79999, 235.29999,\n",
+       "         239.29999],\n",
+       "        [249.79999, 248.89   , 247.5    , ..., 233.2    , 236.39   ,\n",
+       "         241.7    ],\n",
+       "        ...,\n",
+       "        [296.19998, 296.4    , 296.5    , ..., 295.4    , 295.1    ,\n",
+       "         294.69998],\n",
+       "        [296.19998, 296.79   , 296.5    , ..., 295.9    , 295.9    ,\n",
+       "         295.19998],\n",
+       "        [296.79   , 297.1    , 297.     , ..., 296.9    , 296.79   ,\n",
+       "         296.6    ]],\n",
+       "\n",
+       "       [[242.7    , 243.09999, 243.39   , ..., 232.     , 233.59999,\n",
+       "         235.79999],\n",
+       "        [244.09999, 244.2    , 244.09999, ..., 231.     , 232.5    ,\n",
+       "         235.7    ],\n",
+       "        [252.89   , 252.09999, 250.79999, ..., 230.79999, 233.39   ,\n",
+       "         238.5    ],\n",
+       "...\n",
+       "        [293.88998, 295.38998, 297.19   , ..., 295.09   , 294.69   ,\n",
+       "         294.29   ],\n",
+       "        [297.19   , 297.59   , 297.88998, ..., 295.29   , 295.09   ,\n",
+       "         294.38998],\n",
+       "        [298.38998, 298.49   , 298.59   , ..., 295.69   , 295.49   ,\n",
+       "         295.19   ]],\n",
+       "\n",
+       "       [[244.29   , 243.29   , 242.18999, ..., 241.68999, 241.48999,\n",
+       "         241.79   ],\n",
+       "        [249.29   , 248.39   , 246.98999, ..., 239.59   , 240.29   ,\n",
+       "         241.68999],\n",
+       "        [262.19   , 261.38998, 259.99   , ..., 239.89   , 242.59   ,\n",
+       "         246.29   ],\n",
+       "        ...,\n",
+       "        [293.69   , 295.09   , 296.69   , ..., 295.29   , 295.09   ,\n",
+       "         294.69   ],\n",
+       "        [296.88998, 297.19   , 297.49   , ..., 295.69   , 295.69   ,\n",
+       "         295.19   ],\n",
+       "        [298.09   , 298.09   , 298.49   , ..., 296.49   , 296.19   ,\n",
+       "         295.69   ]]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 202.5 205.0 207.5 210.0 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
    " + ], + "text/plain": [ + "\n", + "array([[[242.5 , 243.5 , 244. , ..., 232.79999, 235.5 ,\n", + " 238.59999],\n", + " [244.5 , 244.7 , 244.2 , ..., 232.79999, 235.29999,\n", + " 239.29999],\n", + " [249.79999, 248.89 , 247.5 , ..., 233.2 , 236.39 ,\n", + " 241.7 ],\n", + " ...,\n", + " [296.19998, 296.4 , 296.5 , ..., 295.4 , 295.1 ,\n", + " 294.69998],\n", + " [296.19998, 296.79 , 296.5 , ..., 295.9 , 295.9 ,\n", + " 295.19998],\n", + " [296.79 , 297.1 , 297. , ..., 296.9 , 296.79 ,\n", + " 296.6 ]],\n", + "\n", + " [[242.7 , 243.09999, 243.39 , ..., 232. , 233.59999,\n", + " 235.79999],\n", + " [244.09999, 244.2 , 244.09999, ..., 231. , 232.5 ,\n", + " 235.7 ],\n", + " [252.89 , 252.09999, 250.79999, ..., 230.79999, 233.39 ,\n", + " 238.5 ],\n", + "...\n", + " [293.88998, 295.38998, 297.19 , ..., 295.09 , 294.69 ,\n", + " 294.29 ],\n", + " [297.19 , 297.59 , 297.88998, ..., 295.29 , 295.09 ,\n", + " 294.38998],\n", + " [298.38998, 298.49 , 298.59 , ..., 295.69 , 295.49 ,\n", + " 295.19 ]],\n", + "\n", + " [[244.29 , 243.29 , 242.18999, ..., 241.68999, 241.48999,\n", + " 241.79 ],\n", + " [249.29 , 248.39 , 246.98999, ..., 239.59 , 240.29 ,\n", + " 241.68999],\n", + " [262.19 , 261.38998, 259.99 , ..., 239.89 , 242.59 ,\n", + " 246.29 ],\n", + " ...,\n", + " [293.69 , 295.09 , 296.69 , ..., 295.29 , 295.09 ,\n", + " 294.69 ],\n", + " [296.88998, 297.19 , 297.49 , ..., 295.69 , 295.69 ,\n", + " 295.19 ],\n", + " [298.09 , 298.09 , 298.49 , ..., 296.49 , 296.19 ,\n", + " 295.69 ]]], dtype=float32)\n", + "Coordinates:\n", + " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", + " * lon (lon) float32 202.5 205.0 207.5 210.0 ... 322.5 325.0 327.5 330.0\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "da.drop_sel(lat=50.0, lon=200.0, time=\"2014\")" ] @@ -359,13 +3487,441 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": { "tags": [ "hide-output" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'air' (time: 2920)>\n",
+       "array([262.69998, 263.19998, 270.9    , ..., 264.19   , 265.19   ,\n",
+       "       266.99   ], dtype=float32)\n",
+       "Coordinates:\n",
+       "    lat      float32 52.5\n",
+       "    lon      float32 252.5\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
    " + ], + "text/plain": [ + "\n", + "array([262.69998, 263.19998, 270.9 , ..., 264.19 , 265.19 ,\n", + " 266.99 ], dtype=float32)\n", + "Coordinates:\n", + " lat float32 52.5\n", + " lon float32 252.5\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "da.sel(lat=52.25, lon=251.8998, method=\"nearest\")" ] @@ -379,9 +3935,437 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'air' (time: 2920)>\n",
+       "array([262.69998, 263.19998, 270.9    , ..., 264.19   , 265.19   ,\n",
+       "       266.99   ], dtype=float32)\n",
+       "Coordinates:\n",
+       "    lat      float32 52.5\n",
+       "    lon      float32 252.5\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
    " + ], + "text/plain": [ + "\n", + "array([262.69998, 263.19998, 270.9 , ..., 264.19 , 265.19 ,\n", + " 266.99 ], dtype=float32)\n", + "Coordinates:\n", + " lat float32 52.5\n", + " lon float32 252.5\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "da.sel(lat=52.25, lon=251.8998, method=\"nearest\", tolerance=2)" ] @@ -495,6 +4479,10 @@ } ], "metadata": { + "kernelspec": { + "display_name": "", + "name": "" + }, "language_info": { "codemirror_mode": { "name": "ipython", @@ -518,6 +4506,13 @@ "toc_position": {}, "toc_section_display": true, "toc_window_display": true + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } } }, "nbformat": 4, From 6b5d50a8ea1b75f11fdb24d7aecfdeffd11ec715 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Sat, 8 Jul 2023 01:49:44 +0000 Subject: [PATCH 32/54] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- fundamentals/02.1_indexing_Basic.ipynb | 4034 +---------------- intermediate/02.2_indexing_Advanced.ipynb | 3172 +------------ .../02.3_indexing_BooleanMasking.ipynb | 2864 +----------- 3 files changed, 99 insertions(+), 9971 deletions(-) diff --git a/fundamentals/02.1_indexing_Basic.ipynb b/fundamentals/02.1_indexing_Basic.ipynb index 2fa03fa1..89011625 100644 --- a/fundamentals/02.1_indexing_Basic.ipynb +++ b/fundamentals/02.1_indexing_Basic.ipynb @@ -53,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -71,483 +71,9 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.Dataset>\n",
-       "Dimensions:  (lat: 25, time: 2920, lon: 53)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Data variables:\n",
-       "    air      (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n",
-       "Attributes:\n",
-       "    Conventions:  COARDS\n",
-       "    title:        4x daily NMC reanalysis (1948)\n",
-       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
-       "    platform:     Model\n",
-       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
    " - ], - "text/plain": [ - "\n", - "Dimensions: (lat: 25, time: 2920, lon: 53)\n", - "Coordinates:\n", - " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", - " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Data variables:\n", - " air (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n", - "Attributes:\n", - " Conventions: COARDS\n", - " title: 4x daily NMC reanalysis (1948)\n", - " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", - " platform: Model\n", - " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "ds = xr.tutorial.load_dataset(\"air_temperature\")\n", "ds" @@ -555,7 +81,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -585,20 +111,9 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2920, 25, 53)" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "np_array = ds[\"air\"].data # numpy array\n", "np_array.shape" @@ -613,20 +128,9 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "242.09999" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "np_array[1, 0, 0]" ] @@ -640,20 +144,9 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([295. , 294.4 , 294.5 , ..., 297.29, 297.79, 297.99], dtype=float32)" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# extract a time-series for one spatial location\n", "np_array[:, 20, 40]" @@ -685,434 +178,9 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 2920)>\n",
-       "array([295.  , 294.4 , 294.5 , ..., 297.29, 297.79, 297.99], dtype=float32)\n",
-       "Coordinates:\n",
-       "    lat      float32 25.0\n",
-       "    lon      float32 300.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([295. , 294.4 , 294.5 , ..., 297.29, 297.79, 297.99], dtype=float32)\n", - "Coordinates:\n", - " lat float32 25.0\n", - " lon float32 300.0\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da[:, 20, 40]" ] @@ -1130,42 +198,20 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2920, 2)" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "np_array[:,[0, 1], [0, 1]].shape" + "np_array[:, [0, 1], [0, 1]].shape" ] }, { "cell_type": "code", - "execution_count": 49, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2920, 2, 2)" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "da[:,[0, 1], [0, 1]].shape" + "da[:, [0, 1], [0, 1]].shape" ] }, { @@ -1198,20 +244,9 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "da.isel(lat=20, lon=40).plot();" ] @@ -1225,20 +260,9 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "da.isel(time=slice(0, 20), lat=20, lon=40).plot();" ] @@ -1272,24 +296,13 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": { "tags": [ "hide-output" ] }, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "da.sel(time=\"2014-01-01\", lat=25, lon=210).plot();" ] @@ -1303,438 +316,13 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": { "tags": [ "hide-output" ] }, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 1460)>\n",
-       "array([277.6 , 277.5 , 277.4 , ..., 277.59, 277.59, 277.59], dtype=float32)\n",
-       "Coordinates:\n",
-       "    lat      float32 50.0\n",
-       "    lon      float32 200.0\n",
-       "  * time     (time) datetime64[ns] 2014-01-01 ... 2014-12-31T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([277.6 , 277.5 , 277.4 , ..., 277.59, 277.59, 277.59], dtype=float32)\n", - "Coordinates:\n", - " lat float32 50.0\n", - " lon float32 200.0\n", - " * time (time) datetime64[ns] 2014-01-01 ... 2014-12-31T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da.sel(lat=50.0, lon=200.0, time=\"2014\")" ] @@ -1748,570 +336,9 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 1212, lat: 25, lon: 53)>\n",
-       "array([[[246.     , 245.09999, 244.09999, ..., 240.2    , 242.     ,\n",
-       "         244.2    ],\n",
-       "        [240.59999, 241.7    , 242.09999, ..., 239.09999, 241.2    ,\n",
-       "         244.59999],\n",
-       "        [238.2    , 239.79999, 240.79999, ..., 243.89   , 246.89   ,\n",
-       "         251.2    ],\n",
-       "        ...,\n",
-       "        [295.6    , 295.6    , 296.     , ..., 293.6    , 293.6    ,\n",
-       "         294.1    ],\n",
-       "        [297.     , 297.     , 297.     , ..., 293.69998, 293.9    ,\n",
-       "         294.6    ],\n",
-       "        [297.5    , 297.69998, 297.69998, ..., 294.5    , 294.69998,\n",
-       "         295.19998]],\n",
-       "\n",
-       "       [[245.2    , 244.29999, 243.     , ..., 241.89   , 243.09999,\n",
-       "         244.59999],\n",
-       "        [241.09999, 241.89   , 241.89   , ..., 241.89   , 243.59999,\n",
-       "         246.39   ],\n",
-       "        [239.89   , 241.     , 241.29999, ..., 245.09999, 248.2    ,\n",
-       "         252.2    ],\n",
-       "...\n",
-       "        [296.49   , 295.59   , 295.49   , ..., 297.29   , 297.38998,\n",
-       "         296.79   ],\n",
-       "        [298.09   , 297.79   , 297.19   , ..., 297.69   , 298.09   ,\n",
-       "         297.38998],\n",
-       "        [298.49   , 298.29   , 297.99   , ..., 298.29   , 298.09   ,\n",
-       "         297.99   ]],\n",
-       "\n",
-       "       [[246.29   , 246.39   , 245.98999, ..., 232.29   , 233.39   ,\n",
-       "         234.98999],\n",
-       "        [246.68999, 248.18999, 248.98999, ..., 230.59   , 232.48999,\n",
-       "         235.68999],\n",
-       "        [244.79   , 245.29   , 245.89   , ..., 230.48999, 235.09   ,\n",
-       "         241.39   ],\n",
-       "        ...,\n",
-       "        [296.88998, 296.49   , 296.49   , ..., 297.69   , 297.29   ,\n",
-       "         296.19   ],\n",
-       "        [298.29   , 297.99   , 297.49   , ..., 298.29   , 298.38998,\n",
-       "         297.49   ],\n",
-       "        [298.49   , 298.29   , 297.99   , ..., 298.59   , 298.38998,\n",
-       "         298.29   ]]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-       "  * time     (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([[[246. , 245.09999, 244.09999, ..., 240.2 , 242. ,\n", - " 244.2 ],\n", - " [240.59999, 241.7 , 242.09999, ..., 239.09999, 241.2 ,\n", - " 244.59999],\n", - " [238.2 , 239.79999, 240.79999, ..., 243.89 , 246.89 ,\n", - " 251.2 ],\n", - " ...,\n", - " [295.6 , 295.6 , 296. , ..., 293.6 , 293.6 ,\n", - " 294.1 ],\n", - " [297. , 297. , 297. , ..., 293.69998, 293.9 ,\n", - " 294.6 ],\n", - " [297.5 , 297.69998, 297.69998, ..., 294.5 , 294.69998,\n", - " 295.19998]],\n", - "\n", - " [[245.2 , 244.29999, 243. , ..., 241.89 , 243.09999,\n", - " 244.59999],\n", - " [241.09999, 241.89 , 241.89 , ..., 241.89 , 243.59999,\n", - " 246.39 ],\n", - " [239.89 , 241. , 241.29999, ..., 245.09999, 248.2 ,\n", - " 252.2 ],\n", - "...\n", - " [296.49 , 295.59 , 295.49 , ..., 297.29 , 297.38998,\n", - " 296.79 ],\n", - " [298.09 , 297.79 , 297.19 , ..., 297.69 , 298.09 ,\n", - " 297.38998],\n", - " [298.49 , 298.29 , 297.99 , ..., 298.29 , 298.09 ,\n", - " 297.99 ]],\n", - "\n", - " [[246.29 , 246.39 , 245.98999, ..., 232.29 , 233.39 ,\n", - " 234.98999],\n", - " [246.68999, 248.18999, 248.98999, ..., 230.59 , 232.48999,\n", - " 235.68999],\n", - " [244.79 , 245.29 , 245.89 , ..., 230.48999, 235.09 ,\n", - " 241.39 ],\n", - " ...,\n", - " [296.88998, 296.49 , 296.49 , ..., 297.69 , 297.29 ,\n", - " 296.19 ],\n", - " [298.29 , 297.99 , 297.49 , ..., 298.29 , 298.38998,\n", - " 297.49 ],\n", - " [298.49 , 298.29 , 297.99 , ..., 298.59 , 298.38998,\n", - " 298.29 ]]], dtype=float32)\n", - "Coordinates:\n", - " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", - " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", - " * time (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# demonstrate slicing\n", "da.sel(time=slice(\"2014-02-14\", \"2014-12-13\"))" @@ -2326,570 +353,9 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 1212, lat: 25, lon: 53)>\n",
-       "array([[[246.     , 245.09999, 244.09999, ..., 240.2    , 242.     ,\n",
-       "         244.2    ],\n",
-       "        [240.59999, 241.7    , 242.09999, ..., 239.09999, 241.2    ,\n",
-       "         244.59999],\n",
-       "        [238.2    , 239.79999, 240.79999, ..., 243.89   , 246.89   ,\n",
-       "         251.2    ],\n",
-       "        ...,\n",
-       "        [295.6    , 295.6    , 296.     , ..., 293.6    , 293.6    ,\n",
-       "         294.1    ],\n",
-       "        [297.     , 297.     , 297.     , ..., 293.69998, 293.9    ,\n",
-       "         294.6    ],\n",
-       "        [297.5    , 297.69998, 297.69998, ..., 294.5    , 294.69998,\n",
-       "         295.19998]],\n",
-       "\n",
-       "       [[245.2    , 244.29999, 243.     , ..., 241.89   , 243.09999,\n",
-       "         244.59999],\n",
-       "        [241.09999, 241.89   , 241.89   , ..., 241.89   , 243.59999,\n",
-       "         246.39   ],\n",
-       "        [239.89   , 241.     , 241.29999, ..., 245.09999, 248.2    ,\n",
-       "         252.2    ],\n",
-       "...\n",
-       "        [296.49   , 295.59   , 295.49   , ..., 297.29   , 297.38998,\n",
-       "         296.79   ],\n",
-       "        [298.09   , 297.79   , 297.19   , ..., 297.69   , 298.09   ,\n",
-       "         297.38998],\n",
-       "        [298.49   , 298.29   , 297.99   , ..., 298.29   , 298.09   ,\n",
-       "         297.99   ]],\n",
-       "\n",
-       "       [[246.29   , 246.39   , 245.98999, ..., 232.29   , 233.39   ,\n",
-       "         234.98999],\n",
-       "        [246.68999, 248.18999, 248.98999, ..., 230.59   , 232.48999,\n",
-       "         235.68999],\n",
-       "        [244.79   , 245.29   , 245.89   , ..., 230.48999, 235.09   ,\n",
-       "         241.39   ],\n",
-       "        ...,\n",
-       "        [296.88998, 296.49   , 296.49   , ..., 297.69   , 297.29   ,\n",
-       "         296.19   ],\n",
-       "        [298.29   , 297.99   , 297.49   , ..., 298.29   , 298.38998,\n",
-       "         297.49   ],\n",
-       "        [298.49   , 298.29   , 297.99   , ..., 298.59   , 298.38998,\n",
-       "         298.29   ]]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-       "  * time     (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([[[246. , 245.09999, 244.09999, ..., 240.2 , 242. ,\n", - " 244.2 ],\n", - " [240.59999, 241.7 , 242.09999, ..., 239.09999, 241.2 ,\n", - " 244.59999],\n", - " [238.2 , 239.79999, 240.79999, ..., 243.89 , 246.89 ,\n", - " 251.2 ],\n", - " ...,\n", - " [295.6 , 295.6 , 296. , ..., 293.6 , 293.6 ,\n", - " 294.1 ],\n", - " [297. , 297. , 297. , ..., 293.69998, 293.9 ,\n", - " 294.6 ],\n", - " [297.5 , 297.69998, 297.69998, ..., 294.5 , 294.69998,\n", - " 295.19998]],\n", - "\n", - " [[245.2 , 244.29999, 243. , ..., 241.89 , 243.09999,\n", - " 244.59999],\n", - " [241.09999, 241.89 , 241.89 , ..., 241.89 , 243.59999,\n", - " 246.39 ],\n", - " [239.89 , 241. , 241.29999, ..., 245.09999, 248.2 ,\n", - " 252.2 ],\n", - "...\n", - " [296.49 , 295.59 , 295.49 , ..., 297.29 , 297.38998,\n", - " 296.79 ],\n", - " [298.09 , 297.79 , 297.19 , ..., 297.69 , 298.09 ,\n", - " 297.38998],\n", - " [298.49 , 298.29 , 297.99 , ..., 298.29 , 298.09 ,\n", - " 297.99 ]],\n", - "\n", - " [[246.29 , 246.39 , 245.98999, ..., 232.29 , 233.39 ,\n", - " 234.98999],\n", - " [246.68999, 248.18999, 248.98999, ..., 230.59 , 232.48999,\n", - " 235.68999],\n", - " [244.79 , 245.29 , 245.89 , ..., 230.48999, 235.09 ,\n", - " 241.39 ],\n", - " ...,\n", - " [296.88998, 296.49 , 296.49 , ..., 297.69 , 297.29 ,\n", - " 296.19 ],\n", - " [298.29 , 297.99 , 297.49 , ..., 298.29 , 298.38998,\n", - " 297.49 ],\n", - " [298.49 , 298.29 , 297.99 , ..., 298.59 , 298.38998,\n", - " 298.29 ]]], dtype=float32)\n", - "Coordinates:\n", - " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", - " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", - " * time (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da.loc[\"2014-02-14\":\"2014-12-13\"]" ] @@ -2905,570 +371,9 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 2919, lat: 24, lon: 52)>\n",
-       "array([[[242.5    , 243.5    , 244.     , ..., 232.79999, 235.5    ,\n",
-       "         238.59999],\n",
-       "        [244.5    , 244.7    , 244.2    , ..., 232.79999, 235.29999,\n",
-       "         239.29999],\n",
-       "        [249.79999, 248.89   , 247.5    , ..., 233.2    , 236.39   ,\n",
-       "         241.7    ],\n",
-       "        ...,\n",
-       "        [296.19998, 296.4    , 296.5    , ..., 295.4    , 295.1    ,\n",
-       "         294.69998],\n",
-       "        [296.19998, 296.79   , 296.5    , ..., 295.9    , 295.9    ,\n",
-       "         295.19998],\n",
-       "        [296.79   , 297.1    , 297.     , ..., 296.9    , 296.79   ,\n",
-       "         296.6    ]],\n",
-       "\n",
-       "       [[242.7    , 243.09999, 243.39   , ..., 232.     , 233.59999,\n",
-       "         235.79999],\n",
-       "        [244.09999, 244.2    , 244.09999, ..., 231.     , 232.5    ,\n",
-       "         235.7    ],\n",
-       "        [252.89   , 252.09999, 250.79999, ..., 230.79999, 233.39   ,\n",
-       "         238.5    ],\n",
-       "...\n",
-       "        [293.88998, 295.38998, 297.19   , ..., 295.09   , 294.69   ,\n",
-       "         294.29   ],\n",
-       "        [297.19   , 297.59   , 297.88998, ..., 295.29   , 295.09   ,\n",
-       "         294.38998],\n",
-       "        [298.38998, 298.49   , 298.59   , ..., 295.69   , 295.49   ,\n",
-       "         295.19   ]],\n",
-       "\n",
-       "       [[244.29   , 243.29   , 242.18999, ..., 241.68999, 241.48999,\n",
-       "         241.79   ],\n",
-       "        [249.29   , 248.39   , 246.98999, ..., 239.59   , 240.29   ,\n",
-       "         241.68999],\n",
-       "        [262.19   , 261.38998, 259.99   , ..., 239.89   , 242.59   ,\n",
-       "         246.29   ],\n",
-       "        ...,\n",
-       "        [293.69   , 295.09   , 296.69   , ..., 295.29   , 295.09   ,\n",
-       "         294.69   ],\n",
-       "        [296.88998, 297.19   , 297.49   , ..., 295.69   , 295.69   ,\n",
-       "         295.19   ],\n",
-       "        [298.09   , 298.09   , 298.49   , ..., 296.49   , 296.19   ,\n",
-       "         295.69   ]]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 202.5 205.0 207.5 210.0 ... 322.5 325.0 327.5 330.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([[[242.5 , 243.5 , 244. , ..., 232.79999, 235.5 ,\n", - " 238.59999],\n", - " [244.5 , 244.7 , 244.2 , ..., 232.79999, 235.29999,\n", - " 239.29999],\n", - " [249.79999, 248.89 , 247.5 , ..., 233.2 , 236.39 ,\n", - " 241.7 ],\n", - " ...,\n", - " [296.19998, 296.4 , 296.5 , ..., 295.4 , 295.1 ,\n", - " 294.69998],\n", - " [296.19998, 296.79 , 296.5 , ..., 295.9 , 295.9 ,\n", - " 295.19998],\n", - " [296.79 , 297.1 , 297. , ..., 296.9 , 296.79 ,\n", - " 296.6 ]],\n", - "\n", - " [[242.7 , 243.09999, 243.39 , ..., 232. , 233.59999,\n", - " 235.79999],\n", - " [244.09999, 244.2 , 244.09999, ..., 231. , 232.5 ,\n", - " 235.7 ],\n", - " [252.89 , 252.09999, 250.79999, ..., 230.79999, 233.39 ,\n", - " 238.5 ],\n", - "...\n", - " [293.88998, 295.38998, 297.19 , ..., 295.09 , 294.69 ,\n", - " 294.29 ],\n", - " [297.19 , 297.59 , 297.88998, ..., 295.29 , 295.09 ,\n", - " 294.38998],\n", - " [298.38998, 298.49 , 298.59 , ..., 295.69 , 295.49 ,\n", - " 295.19 ]],\n", - "\n", - " [[244.29 , 243.29 , 242.18999, ..., 241.68999, 241.48999,\n", - " 241.79 ],\n", - " [249.29 , 248.39 , 246.98999, ..., 239.59 , 240.29 ,\n", - " 241.68999],\n", - " [262.19 , 261.38998, 259.99 , ..., 239.89 , 242.59 ,\n", - " 246.29 ],\n", - " ...,\n", - " [293.69 , 295.09 , 296.69 , ..., 295.29 , 295.09 ,\n", - " 294.69 ],\n", - " [296.88998, 297.19 , 297.49 , ..., 295.69 , 295.69 ,\n", - " 295.19 ],\n", - " [298.09 , 298.09 , 298.49 , ..., 296.49 , 296.19 ,\n", - " 295.69 ]]], dtype=float32)\n", - "Coordinates:\n", - " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", - " * lon (lon) float32 202.5 205.0 207.5 210.0 ... 322.5 325.0 327.5 330.0\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da.drop_sel(lat=50.0, lon=200.0, time=\"2014\")" ] @@ -3487,441 +392,13 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": { "tags": [ "hide-output" ] }, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 2920)>\n",
-       "array([262.69998, 263.19998, 270.9    , ..., 264.19   , 265.19   ,\n",
-       "       266.99   ], dtype=float32)\n",
-       "Coordinates:\n",
-       "    lat      float32 52.5\n",
-       "    lon      float32 252.5\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([262.69998, 263.19998, 270.9 , ..., 264.19 , 265.19 ,\n", - " 266.99 ], dtype=float32)\n", - "Coordinates:\n", - " lat float32 52.5\n", - " lon float32 252.5\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da.sel(lat=52.25, lon=251.8998, method=\"nearest\")" ] @@ -3935,437 +412,9 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 2920)>\n",
-       "array([262.69998, 263.19998, 270.9    , ..., 264.19   , 265.19   ,\n",
-       "       266.99   ], dtype=float32)\n",
-       "Coordinates:\n",
-       "    lat      float32 52.5\n",
-       "    lon      float32 252.5\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([262.69998, 263.19998, 270.9 , ..., 264.19 , 265.19 ,\n", - " 266.99 ], dtype=float32)\n", - "Coordinates:\n", - " lat float32 52.5\n", - " lon float32 252.5\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da.sel(lat=52.25, lon=251.8998, method=\"nearest\", tolerance=2)" ] @@ -4479,10 +528,6 @@ } ], "metadata": { - "kernelspec": { - "display_name": "", - "name": "" - }, "language_info": { "codemirror_mode": { "name": "ipython", @@ -4506,13 +551,6 @@ "toc_position": {}, "toc_section_display": true, "toc_window_display": true - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } } }, "nbformat": 4, diff --git a/intermediate/02.2_indexing_Advanced.ipynb b/intermediate/02.2_indexing_Advanced.ipynb index b21f239d..88284af7 100644 --- a/intermediate/02.2_indexing_Advanced.ipynb +++ b/intermediate/02.2_indexing_Advanced.ipynb @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -44,483 +44,9 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.Dataset>\n",
-       "Dimensions:  (lat: 25, time: 2920, lon: 53)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Data variables:\n",
-       "    air      (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n",
-       "Attributes:\n",
-       "    Conventions:  COARDS\n",
-       "    title:        4x daily NMC reanalysis (1948)\n",
-       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
-       "    platform:     Model\n",
-       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
    " - ], - "text/plain": [ - "\n", - "Dimensions: (lat: 25, time: 2920, lon: 53)\n", - "Coordinates:\n", - " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", - " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Data variables:\n", - " air (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n", - "Attributes:\n", - " Conventions: COARDS\n", - " title: 4x daily NMC reanalysis (1948)\n", - " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", - " platform: Model\n", - " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." - ] - }, - "execution_count": 92, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "ds = xr.tutorial.load_dataset(\"air_temperature\")\n", "da = ds.air\n", @@ -544,22 +70,11 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "da[0, [2, 4, 10, 13], [1, 6, 7]].plot(); # -- orthogonal indexing" + "da[0, [2, 4, 10, 13], [1, 6, 7]].plot(); # -- orthogonal indexing" ] }, { @@ -573,531 +88,14 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 2920, degrees_north: 4, degrees_east: 4)>\n",
-       "array([[[293.1    , 293.1    , 293.29   , 293.29   ],\n",
-       "        [284.6    , 284.6    , 284.9    , 284.19998],\n",
-       "        [282.79   , 282.79   , 283.19998, 282.6    ],\n",
-       "        [282.79   , 282.79   , 283.19998, 282.6    ]],\n",
-       "\n",
-       "       [[293.19998, 293.19998, 293.9    , 294.19998],\n",
-       "        [283.29   , 283.29   , 285.19998, 285.19998],\n",
-       "        [281.4    , 281.4    , 282.79   , 283.5    ],\n",
-       "        [281.4    , 281.4    , 282.79   , 283.5    ]],\n",
-       "\n",
-       "       [[292.4    , 292.4    , 292.9    , 293.4    ],\n",
-       "        [282.     , 282.     , 283.29   , 284.69998],\n",
-       "        [280.     , 280.     , 280.79   , 282.4    ],\n",
-       "        [280.     , 280.     , 280.79   , 282.4    ]],\n",
-       "\n",
-       "       ...,\n",
-       "\n",
-       "       [[288.88998, 288.88998, 289.19   , 290.88998],\n",
-       "        [282.49   , 282.49   , 281.99   , 281.99   ],\n",
-       "        [281.29   , 281.29   , 281.29   , 280.99   ],\n",
-       "        [281.29   , 281.29   , 281.29   , 280.99   ]],\n",
-       "\n",
-       "       [[288.29   , 288.29   , 289.19   , 290.79   ],\n",
-       "        [282.09   , 282.09   , 281.59   , 282.38998],\n",
-       "        [280.99   , 280.99   , 280.38998, 280.59   ],\n",
-       "        [280.99   , 280.99   , 280.38998, 280.59   ]],\n",
-       "\n",
-       "       [[289.49   , 289.49   , 290.38998, 291.59   ],\n",
-       "        [282.09   , 282.09   , 281.99   , 283.09   ],\n",
-       "        [281.38998, 281.38998, 280.59   , 280.99   ],\n",
-       "        [281.38998, 281.38998, 280.59   , 280.99   ]]], dtype=float32)\n",
-       "Coordinates:\n",
-       "    lat      (degrees_north) float32 30.0 40.0 42.5 42.5\n",
-       "    lon      (degrees_east) float32 200.0 200.0 202.5 205.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Dimensions without coordinates: degrees_north, degrees_east\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([[[293.1 , 293.1 , 293.29 , 293.29 ],\n", - " [284.6 , 284.6 , 284.9 , 284.19998],\n", - " [282.79 , 282.79 , 283.19998, 282.6 ],\n", - " [282.79 , 282.79 , 283.19998, 282.6 ]],\n", - "\n", - " [[293.19998, 293.19998, 293.9 , 294.19998],\n", - " [283.29 , 283.29 , 285.19998, 285.19998],\n", - " [281.4 , 281.4 , 282.79 , 283.5 ],\n", - " [281.4 , 281.4 , 282.79 , 283.5 ]],\n", - "\n", - " [[292.4 , 292.4 , 292.9 , 293.4 ],\n", - " [282. , 282. , 283.29 , 284.69998],\n", - " [280. , 280. , 280.79 , 282.4 ],\n", - " [280. , 280. , 280.79 , 282.4 ]],\n", - "\n", - " ...,\n", - "\n", - " [[288.88998, 288.88998, 289.19 , 290.88998],\n", - " [282.49 , 282.49 , 281.99 , 281.99 ],\n", - " [281.29 , 281.29 , 281.29 , 280.99 ],\n", - " [281.29 , 281.29 , 281.29 , 280.99 ]],\n", - "\n", - " [[288.29 , 288.29 , 289.19 , 290.79 ],\n", - " [282.09 , 282.09 , 281.59 , 282.38998],\n", - " [280.99 , 280.99 , 280.38998, 280.59 ],\n", - " [280.99 , 280.99 , 280.38998, 280.59 ]],\n", - "\n", - " [[289.49 , 289.49 , 290.38998, 291.59 ],\n", - " [282.09 , 282.09 , 281.99 , 283.09 ],\n", - " [281.38998, 281.38998, 280.59 , 280.99 ],\n", - " [281.38998, 281.38998, 280.59 , 280.99 ]]], dtype=float32)\n", - "Coordinates:\n", - " lat (degrees_north) float32 30.0 40.0 42.5 42.5\n", - " lon (degrees_east) float32 200.0 200.0 202.5 205.0\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Dimensions without coordinates: degrees_north, degrees_east\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 94, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "target_lat = xr.DataArray([31, 41, 42, 42], dims=\"degrees_north\")\n", "target_lon = xr.DataArray([200, 201, 202, 205], dims=\"degrees_east\")\n", "\n", - "da.sel(lat=target_lat, lon=target_lon, method=\"nearest\") # -- orthogonal indexing " + "da.sel(lat=target_lat, lon=target_lon, method=\"nearest\") # -- orthogonal indexing" ] }, { @@ -1130,390 +128,9 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray (points: 4)>\n",
-       "array([31, 41, 42, 42])\n",
-       "Dimensions without coordinates: points
    " - ], - "text/plain": [ - "\n", - "array([31, 41, 42, 42])\n", - "Dimensions without coordinates: points" - ] - }, - "execution_count": 95, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Define target latitude and longitude (where weather stations might be)\n", "lat_points = xr.DataArray([31, 41, 42, 42], dims=\"points\")\n", @@ -1523,390 +140,9 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray (points: 4)>\n",
-       "array([200, 201, 202, 205])\n",
-       "Dimensions without coordinates: points
    " - ], - "text/plain": [ - "\n", - "array([200, 201, 202, 205])\n", - "Dimensions without coordinates: points" - ] - }, - "execution_count": 96, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "lon_points" ] @@ -1920,454 +156,9 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 2920, points: 4)>\n",
-       "array([[293.1    , 284.6    , 283.19998, 282.6    ],\n",
-       "       [293.19998, 283.29   , 282.79   , 283.5    ],\n",
-       "       [292.4    , 282.     , 280.79   , 282.4    ],\n",
-       "       ...,\n",
-       "       [288.88998, 282.49   , 281.29   , 280.99   ],\n",
-       "       [288.29   , 282.09   , 280.38998, 280.59   ],\n",
-       "       [289.49   , 282.09   , 280.59   , 280.99   ]], dtype=float32)\n",
-       "Coordinates:\n",
-       "    lat      (points) float32 30.0 40.0 42.5 42.5\n",
-       "    lon      (points) float32 200.0 200.0 202.5 205.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Dimensions without coordinates: points\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([[293.1 , 284.6 , 283.19998, 282.6 ],\n", - " [293.19998, 283.29 , 282.79 , 283.5 ],\n", - " [292.4 , 282. , 280.79 , 282.4 ],\n", - " ...,\n", - " [288.88998, 282.49 , 281.29 , 280.99 ],\n", - " [288.29 , 282.09 , 280.38998, 280.59 ],\n", - " [289.49 , 282.09 , 280.59 , 280.99 ]], dtype=float32)\n", - "Coordinates:\n", - " lat (points) float32 30.0 40.0 42.5 42.5\n", - " lon (points) float32 200.0 200.0 202.5 205.0\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Dimensions without coordinates: points\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 97, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da.sel(lat=lat_points, lon=lon_points, method=\"nearest\")" ] @@ -2381,20 +172,9 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "('time', 'points')" - ] - }, - "execution_count": 98, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da.sel(lat=lat_points, lon=lon_points, method=\"nearest\").dims" ] @@ -2412,508 +192,9 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 2920, lat: 3, points: 4)>\n",
-       "array([[[296.6    , 296.6    , 296.19998, 296.4    ],\n",
-       "        [293.1    , 293.1    , 293.29   , 293.29   ],\n",
-       "        [284.6    , 284.6    , 284.9    , 284.19998]],\n",
-       "\n",
-       "       [[296.4    , 296.4    , 295.9    , 296.19998],\n",
-       "        [293.19998, 293.19998, 293.9    , 294.19998],\n",
-       "        [283.29   , 283.29   , 285.19998, 285.19998]],\n",
-       "\n",
-       "       [[295.6    , 295.6    , 295.4    , 295.4    ],\n",
-       "        [292.4    , 292.4    , 292.9    , 293.4    ],\n",
-       "        [282.     , 282.     , 283.29   , 284.69998]],\n",
-       "\n",
-       "       ...,\n",
-       "\n",
-       "       [[294.79   , 294.79   , 295.29   , 297.49   ],\n",
-       "        [288.88998, 288.88998, 289.19   , 290.88998],\n",
-       "        [282.49   , 282.49   , 281.99   , 281.99   ]],\n",
-       "\n",
-       "       [[293.69   , 293.69   , 293.88998, 295.38998],\n",
-       "        [288.29   , 288.29   , 289.19   , 290.79   ],\n",
-       "        [282.09   , 282.09   , 281.59   , 282.38998]],\n",
-       "\n",
-       "       [[293.79   , 293.79   , 293.69   , 295.09   ],\n",
-       "        [289.49   , 289.49   , 290.38998, 291.59   ],\n",
-       "        [282.09   , 282.09   , 281.99   , 283.09   ]]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 20.0 30.0 40.0\n",
-       "    lon      (points) float32 200.0 200.0 202.5 205.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Dimensions without coordinates: points\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([[[296.6 , 296.6 , 296.19998, 296.4 ],\n", - " [293.1 , 293.1 , 293.29 , 293.29 ],\n", - " [284.6 , 284.6 , 284.9 , 284.19998]],\n", - "\n", - " [[296.4 , 296.4 , 295.9 , 296.19998],\n", - " [293.19998, 293.19998, 293.9 , 294.19998],\n", - " [283.29 , 283.29 , 285.19998, 285.19998]],\n", - "\n", - " [[295.6 , 295.6 , 295.4 , 295.4 ],\n", - " [292.4 , 292.4 , 292.9 , 293.4 ],\n", - " [282. , 282. , 283.29 , 284.69998]],\n", - "\n", - " ...,\n", - "\n", - " [[294.79 , 294.79 , 295.29 , 297.49 ],\n", - " [288.88998, 288.88998, 289.19 , 290.88998],\n", - " [282.49 , 282.49 , 281.99 , 281.99 ]],\n", - "\n", - " [[293.69 , 293.69 , 293.88998, 295.38998],\n", - " [288.29 , 288.29 , 289.19 , 290.79 ],\n", - " [282.09 , 282.09 , 281.59 , 282.38998]],\n", - "\n", - " [[293.79 , 293.79 , 293.69 , 295.09 ],\n", - " [289.49 , 289.49 , 290.38998, 291.59 ],\n", - " [282.09 , 282.09 , 281.99 , 283.09 ]]], dtype=float32)\n", - "Coordinates:\n", - " * lat (lat) float32 20.0 30.0 40.0\n", - " lon (points) float32 200.0 200.0 202.5 205.0\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Dimensions without coordinates: points\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 99, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da.sel(lat=[20, 30, 40], lon=lon_points, method=\"nearest\")" ] @@ -2936,398 +217,9 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray (new_time: 3)>\n",
-       "array([0.87902737, 0.06690113, 0.01799672])\n",
-       "Coordinates:\n",
-       "    time      (new_time) datetime64[ns] 2000-01-03 2000-01-02 2000-01-01\n",
-       "    space     (new_time) <U2 'IA' 'IL' 'IN'\n",
-       "  * new_time  (new_time) datetime64[ns] 2000-01-03 2000-01-02 2000-01-01
    " - ], - "text/plain": [ - "\n", - "array([0.87902737, 0.06690113, 0.01799672])\n", - "Coordinates:\n", - " time (new_time) datetime64[ns] 2000-01-03 2000-01-02 2000-01-01\n", - " space (new_time) \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.Dataset>\n",
-       "Dimensions:  (time: 36, y: 205, x: 275)\n",
-       "Coordinates:\n",
-       "  * time     (time) object 1980-09-16 12:00:00 ... 1983-08-17 00:00:00\n",
-       "    xc       (y, x) float64 189.2 189.4 189.6 189.7 ... 17.65 17.4 17.15 16.91\n",
-       "    yc       (y, x) float64 16.53 16.78 17.02 17.27 ... 28.26 28.01 27.76 27.51\n",
-       "Dimensions without coordinates: y, x\n",
-       "Data variables:\n",
-       "    Tair     (time, y, x) float64 nan nan nan nan nan ... 29.8 28.66 28.19 28.21\n",
-       "Attributes:\n",
-       "    title:                     /workspace/jhamman/processed/R1002RBRxaaa01a/l...\n",
-       "    institution:               U.W.\n",
-       "    source:                    RACM R1002RBRxaaa01a\n",
-       "    output_frequency:          daily\n",
-       "    output_mode:               averaged\n",
-       "    convention:                CF-1.4\n",
-       "    references:                Based on the initial model of Liang et al., 19...\n",
-       "    comment:                   Output from the Variable Infiltration Capacity...\n",
-       "    nco_openmp_thread_number:  1\n",
-       "    NCO:                       netCDF Operators version 4.7.9 (Homepage = htt...\n",
-       "    history:                   Fri Aug  7 17:57:38 2020: ncatted -a bounds,,d...
    " - ], - "text/plain": [ - "\n", - "Dimensions: (time: 36, y: 205, x: 275)\n", - "Coordinates:\n", - " * time (time) object 1980-09-16 12:00:00 ... 1983-08-17 00:00:00\n", - " xc (y, x) float64 189.2 189.4 189.6 189.7 ... 17.65 17.4 17.15 16.91\n", - " yc (y, x) float64 16.53 16.78 17.02 17.27 ... 28.26 28.01 27.76 27.51\n", - "Dimensions without coordinates: y, x\n", - "Data variables:\n", - " Tair (time, y, x) float64 nan nan nan nan nan ... 29.8 28.66 28.19 28.21\n", - "Attributes:\n", - " title: /workspace/jhamman/processed/R1002RBRxaaa01a/l...\n", - " institution: U.W.\n", - " source: RACM R1002RBRxaaa01a\n", - " output_frequency: daily\n", - " output_mode: averaged\n", - " convention: CF-1.4\n", - " references: Based on the initial model of Liang et al., 19...\n", - " comment: Output from the Variable Infiltration Capacity...\n", - " nco_openmp_thread_number: 1\n", - " NCO: netCDF Operators version 4.7.9 (Homepage = htt...\n", - " history: Fri Aug 7 17:57:38 2020: ncatted -a bounds,,d..." - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "ds = xr.tutorial.load_dataset(\"rasm\")\n", "ds" @@ -599,18 +67,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'long_name': 'longitude of grid cell center', 'units': 'degrees_east'}\n", - "{'long_name': 'latitude of grid cell center', 'units': 'degrees_north'}\n" - ] - } - ], + "outputs": [], "source": [ "print(ds.xc.attrs)\n", "print(ds.yc.attrs)" @@ -618,600 +77,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'Tair' (time: 36, y: 205, x: 275)>\n",
-       "array([[[        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        ...,\n",
-       "        [        nan,         nan,         nan, ..., 27.03290153,\n",
-       "         27.03125761, 27.33531541],\n",
-       "        [        nan,         nan,         nan, ..., 27.2784053 ,\n",
-       "         26.80261869, 27.08603517],\n",
-       "        [        nan,         nan,         nan, ..., 27.02344402,\n",
-       "         26.56473862, 26.73064933]],\n",
-       "\n",
-       "       [[        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "...\n",
-       "        [        nan,         nan,         nan, ..., 27.8597472 ,\n",
-       "         27.82928439, 28.09249224],\n",
-       "        [        nan,         nan,         nan, ..., 27.89704094,\n",
-       "         27.31104941, 27.67387171],\n",
-       "        [        nan,         nan,         nan, ..., 27.46837113,\n",
-       "         27.0088944 , 27.23017976]],\n",
-       "\n",
-       "       [[        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        ...,\n",
-       "        [        nan,         nan,         nan, ..., 28.95929072,\n",
-       "         28.87672039, 29.04890862],\n",
-       "        [        nan,         nan,         nan, ..., 29.036132  ,\n",
-       "         28.42273578, 28.68721201],\n",
-       "        [        nan,         nan,         nan, ..., 28.66381585,\n",
-       "         28.18595533, 28.20753022]]])\n",
-       "Coordinates:\n",
-       "  * time     (time) object 1980-09-16 12:00:00 ... 1983-08-17 00:00:00\n",
-       "    xc       (y, x) float64 189.2 189.4 189.6 189.7 ... 17.65 17.4 17.15 16.91\n",
-       "    yc       (y, x) float64 16.53 16.78 17.02 17.27 ... 28.26 28.01 27.76 27.51\n",
-       "Dimensions without coordinates: y, x\n",
-       "Attributes:\n",
-       "    units:           C\n",
-       "    long_name:       Surface air temperature\n",
-       "    type_preferred:  double\n",
-       "    time_rep:        instantaneous
    " - ], - "text/plain": [ - "\n", - "array([[[ nan, nan, nan, ..., nan,\n", - " nan, nan],\n", - " [ nan, nan, nan, ..., nan,\n", - " nan, nan],\n", - " [ nan, nan, nan, ..., nan,\n", - " nan, nan],\n", - " ...,\n", - " [ nan, nan, nan, ..., 27.03290153,\n", - " 27.03125761, 27.33531541],\n", - " [ nan, nan, nan, ..., 27.2784053 ,\n", - " 26.80261869, 27.08603517],\n", - " [ nan, nan, nan, ..., 27.02344402,\n", - " 26.56473862, 26.73064933]],\n", - "\n", - " [[ nan, nan, nan, ..., nan,\n", - " nan, nan],\n", - " [ nan, nan, nan, ..., nan,\n", - " nan, nan],\n", - " [ nan, nan, nan, ..., nan,\n", - " nan, nan],\n", - "...\n", - " [ nan, nan, nan, ..., 27.8597472 ,\n", - " 27.82928439, 28.09249224],\n", - " [ nan, nan, nan, ..., 27.89704094,\n", - " 27.31104941, 27.67387171],\n", - " [ nan, nan, nan, ..., 27.46837113,\n", - " 27.0088944 , 27.23017976]],\n", - "\n", - " [[ nan, nan, nan, ..., nan,\n", - " nan, nan],\n", - " [ nan, nan, nan, ..., nan,\n", - " nan, nan],\n", - " [ nan, nan, nan, ..., nan,\n", - " nan, nan],\n", - " ...,\n", - " [ nan, nan, nan, ..., 28.95929072,\n", - " 28.87672039, 29.04890862],\n", - " [ nan, nan, nan, ..., 29.036132 ,\n", - " 28.42273578, 28.68721201],\n", - " [ nan, nan, nan, ..., 28.66381585,\n", - " 28.18595533, 28.20753022]]])\n", - "Coordinates:\n", - " * time (time) object 1980-09-16 12:00:00 ... 1983-08-17 00:00:00\n", - " xc (y, x) float64 189.2 189.4 189.6 189.7 ... 17.65 17.4 17.15 16.91\n", - " yc (y, x) float64 16.53 16.78 17.02 17.27 ... 28.26 28.01 27.76 27.51\n", - "Dimensions without coordinates: y, x\n", - "Attributes:\n", - " units: C\n", - " long_name: Surface air temperature\n", - " type_preferred: double\n", - " time_rep: instantaneous" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da = ds.Tair\n", "da" @@ -1232,600 +100,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'Tair' (time: 36, y: 205, x: 275)>\n",
-       "array([[[-9999.        , -9999.        , -9999.        , ...,\n",
-       "         -9999.        , -9999.        , -9999.        ],\n",
-       "        [-9999.        , -9999.        , -9999.        , ...,\n",
-       "         -9999.        , -9999.        , -9999.        ],\n",
-       "        [-9999.        , -9999.        , -9999.        , ...,\n",
-       "         -9999.        , -9999.        , -9999.        ],\n",
-       "        ...,\n",
-       "        [-9999.        , -9999.        , -9999.        , ...,\n",
-       "            27.03290153,    27.03125761,    27.33531541],\n",
-       "        [-9999.        , -9999.        , -9999.        , ...,\n",
-       "            27.2784053 ,    26.80261869,    27.08603517],\n",
-       "        [-9999.        , -9999.        , -9999.        , ...,\n",
-       "            27.02344402,    26.56473862,    26.73064933]],\n",
-       "\n",
-       "       [[-9999.        , -9999.        , -9999.        , ...,\n",
-       "         -9999.        , -9999.        , -9999.        ],\n",
-       "        [-9999.        , -9999.        , -9999.        , ...,\n",
-       "         -9999.        , -9999.        , -9999.        ],\n",
-       "        [-9999.        , -9999.        , -9999.        , ...,\n",
-       "         -9999.        , -9999.        , -9999.        ],\n",
-       "...\n",
-       "        [-9999.        , -9999.        , -9999.        , ...,\n",
-       "            27.8597472 ,    27.82928439,    28.09249224],\n",
-       "        [-9999.        , -9999.        , -9999.        , ...,\n",
-       "            27.89704094,    27.31104941,    27.67387171],\n",
-       "        [-9999.        , -9999.        , -9999.        , ...,\n",
-       "            27.46837113,    27.0088944 ,    27.23017976]],\n",
-       "\n",
-       "       [[-9999.        , -9999.        , -9999.        , ...,\n",
-       "         -9999.        , -9999.        , -9999.        ],\n",
-       "        [-9999.        , -9999.        , -9999.        , ...,\n",
-       "         -9999.        , -9999.        , -9999.        ],\n",
-       "        [-9999.        , -9999.        , -9999.        , ...,\n",
-       "         -9999.        , -9999.        , -9999.        ],\n",
-       "        ...,\n",
-       "        [-9999.        , -9999.        , -9999.        , ...,\n",
-       "            28.95929072,    28.87672039,    29.04890862],\n",
-       "        [-9999.        , -9999.        , -9999.        , ...,\n",
-       "            29.036132  ,    28.42273578,    28.68721201],\n",
-       "        [-9999.        , -9999.        , -9999.        , ...,\n",
-       "            28.66381585,    28.18595533,    28.20753022]]])\n",
-       "Coordinates:\n",
-       "  * time     (time) object 1980-09-16 12:00:00 ... 1983-08-17 00:00:00\n",
-       "    xc       (y, x) float64 189.2 189.4 189.6 189.7 ... 17.65 17.4 17.15 16.91\n",
-       "    yc       (y, x) float64 16.53 16.78 17.02 17.27 ... 28.26 28.01 27.76 27.51\n",
-       "Dimensions without coordinates: y, x\n",
-       "Attributes:\n",
-       "    units:           C\n",
-       "    long_name:       Surface air temperature\n",
-       "    type_preferred:  double\n",
-       "    time_rep:        instantaneous
    " - ], - "text/plain": [ - "\n", - "array([[[-9999. , -9999. , -9999. , ...,\n", - " -9999. , -9999. , -9999. ],\n", - " [-9999. , -9999. , -9999. , ...,\n", - " -9999. , -9999. , -9999. ],\n", - " [-9999. , -9999. , -9999. , ...,\n", - " -9999. , -9999. , -9999. ],\n", - " ...,\n", - " [-9999. , -9999. , -9999. , ...,\n", - " 27.03290153, 27.03125761, 27.33531541],\n", - " [-9999. , -9999. , -9999. , ...,\n", - " 27.2784053 , 26.80261869, 27.08603517],\n", - " [-9999. , -9999. , -9999. , ...,\n", - " 27.02344402, 26.56473862, 26.73064933]],\n", - "\n", - " [[-9999. , -9999. , -9999. , ...,\n", - " -9999. , -9999. , -9999. ],\n", - " [-9999. , -9999. , -9999. , ...,\n", - " -9999. , -9999. , -9999. ],\n", - " [-9999. , -9999. , -9999. , ...,\n", - " -9999. , -9999. , -9999. ],\n", - "...\n", - " [-9999. , -9999. , -9999. , ...,\n", - " 27.8597472 , 27.82928439, 28.09249224],\n", - " [-9999. , -9999. , -9999. , ...,\n", - " 27.89704094, 27.31104941, 27.67387171],\n", - " [-9999. , -9999. , -9999. , ...,\n", - " 27.46837113, 27.0088944 , 27.23017976]],\n", - "\n", - " [[-9999. , -9999. , -9999. , ...,\n", - " -9999. , -9999. , -9999. ],\n", - " [-9999. , -9999. , -9999. , ...,\n", - " -9999. , -9999. , -9999. ],\n", - " [-9999. , -9999. , -9999. , ...,\n", - " -9999. , -9999. , -9999. ],\n", - " ...,\n", - " [-9999. , -9999. , -9999. , ...,\n", - " 28.95929072, 28.87672039, 29.04890862],\n", - " [-9999. , -9999. , -9999. , ...,\n", - " 29.036132 , 28.42273578, 28.68721201],\n", - " [-9999. , -9999. , -9999. , ...,\n", - " 28.66381585, 28.18595533, 28.20753022]]])\n", - "Coordinates:\n", - " * time (time) object 1980-09-16 12:00:00 ... 1983-08-17 00:00:00\n", - " xc (y, x) float64 189.2 189.4 189.6 189.7 ... 17.65 17.4 17.15 16.91\n", - " yc (y, x) float64 16.53 16.78 17.02 17.27 ... 28.26 28.01 27.76 27.51\n", - "Dimensions without coordinates: y, x\n", - "Attributes:\n", - " units: C\n", - " long_name: Surface air temperature\n", - " type_preferred: double\n", - " time_rep: instantaneous" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Let's replace the missing values (nan) with some placeholder\n", "ds.Tair.where(ds.Tair.notnull(), -9999)" @@ -1851,28 +128,17 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "da_masked = da.where(da >= 0)\n", "\n", "# -- making both plots for comparison:\n", - "fig, axes = plt.subplots(ncols=2, figsize=(15,5))\n", + "fig, axes = plt.subplots(ncols=2, figsize=(15, 5))\n", "\n", "# -- for reference (without masking):\n", - "da[0, :, :].plot(ax=axes[0]);\n", + "da[0, :, :].plot(ax=axes[0])\n", "\n", "# -- masked DataArray\n", "da_masked[0, :, :].plot(ax=axes[1]);" @@ -1929,20 +195,9 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "da_masked = da.where(da.yc > 60, drop=True)\n", "da_masked[0, :, :].plot();" @@ -1961,27 +216,14 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure(figsize=(14, 6))\n", "ax = plt.axes(projection=ccrs.PlateCarree())\n", "ax.set_global()\n", - "ds.Tair[0].plot.pcolormesh(\n", - " ax=ax, transform=ccrs.PlateCarree(), x=\"xc\", y=\"yc\", add_colorbar=False\n", - ")\n", + "ds.Tair[0].plot.pcolormesh(ax=ax, transform=ccrs.PlateCarree(), x=\"xc\", y=\"yc\", add_colorbar=False)\n", "ax.coastlines()\n", "ax.set_ylim([20, 90]);" ] @@ -2004,7 +246,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2024,12 +266,12 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mask_lon = (ds.xc >= min_lon) & (ds.xc <= max_lon)\n", - "mask_lat = (ds.yc >= min_lat) & (ds.yc <= max_lat)\n" + "mask_lat = (ds.yc >= min_lat) & (ds.yc <= max_lat)" ] }, { @@ -2041,20 +283,9 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjUAAAHFCAYAAAAKbwgcAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABjFUlEQVR4nO3deXgURd4H8G/PJDO5Q7hySAgBwk0AATlUAqugiCcuHnggiuICSmBdVhZXwpVwrIguii8egAeL6wGv+3qQoBwiopwCQZEjkICEcIQkhCSTzNT7B5uRYapCMjNJJzPfz/P0o6nuqq7uDMkv1b+u0oQQAkREREQNnEHvDhARERF5AoMaIiIi8goMaoiIiMgrMKghIiIir8CghoiIiLwCgxoiIiLyCgxqiIiIyCswqCEiIiKvwKCGiIiIvAKDGqrSli1bkJKSgvPnzzvtGzhwIAYOHFjnfaorixYtwvDhwxEfHw9N06q81rVr1+L6669HYGAgwsPDcccddyAzM9PpuLKyMixYsABdunRBcHAwIiMjMXToUGzZssXp2PLycsyYMQOtWrWC2WxGhw4d8M9//rNG15CXl4fHHnsMTZs2RVBQEPr164evv/7a6TiLxYIXX3wR8fHxMJlMiIuLw9SpU1FSUlLtc9XkfgHA//7v/yIpKQlhYWEIDg5G586dsXTp0queJzMzE+PGjUO/fv0QHBwMTdOwYcMGp+NOnjyJF154Af369UPTpk0RFhaGnj17YunSpbBardW+riNHjmD48OFo1KgRQkJCMHjwYOzcuVN67KpVq9C9e3cEBAQgJiYGycnJuHDhQr08F5FXEkRVWLBggQAgsrKynPZlZmaKzMzMuu9UHWnfvr249tprxeOPPy6aNWsmkpKSpMetWbNGaJom7r77bvH555+LlStXivbt24uIiAhx6NAhh2MfeeQRYTAYxLRp08TXX38tPvroI9GzZ0/h5+cnfvjhB4djx4wZI8xms5g/f75Yv369eP7554WmaWLOnDnV6n9paano0qWLaNGihXj//fdFenq6uOuuu4Sfn5/YsGGDw7HDhw8XAQEBIjU1VWRkZIiZM2cKk8kk7rjjDo/fLyGESEtLEwaDQYwbN058+eWXYt26dWLx4sXin//851XPs3z5chEdHS1uu+02cccddwgAYv369U7H/ec//xGxsbFi2rRp4vPPPxfp6eli0qRJwmAwiNGjR1frmvLy8kRMTIzo3Lmz+OSTT8Tnn38ubrjhBhEaGip++eUXh2Pff/99AUCMGTNGfPPNN+KNN94Q4eHhYvDgwfXuXETeikENVamqoMbbWa1W+/937txZ+Uu6ffv2IjExUdhsNnvZ0aNHhclkEiNHjrSXlZaWCqPRKB5++GGH+r/99psAIJ599ll72b59+4SmaSI1NdXh2CeffFIEBgaKs2fPXrX/r732mgAgtmzZYi8rLy8XnTp1Etddd5297PvvvxcAxEsvveRQPzU1VQAQ6enpVz2XENW/X9u3bxcGg0HMmzevWu1WdZ6PPvpIGdScO3dOWCwWp/Lx48cLACI7O/uq5/rLX/4i/P39xdGjR+1lBQUFomnTpuK+++6zl1VUVIjo6GgxZMgQh/offPCBACC++OKLenUuIm/Fx0+klJKSgr/85S8AYH+kcPlQ/5WPn44ePQpN07BgwQLMmzcPrVq1QmBgIAYOHIhff/0V5eXleP755xETE4Pw8HDcc889yMvLczrvhx9+aH+0EBISgltuuQW7du2qi0t2YDBc/Z/H2bNnceDAAQwdOhSaptnL4+Li0KVLF6xZs8b+qMNgMMBgMCA8PNyhjbCwMBgMBgQEBNjL1qxZAyEERo8e7XDs6NGjUVJSgq+++uqqfVu9ejXat2+Pfv362cv8/Pzw8MMP48cff8SJEycAAN999x0A4LbbbnOof/vttwMAPvnkk6ueq/L6qmPx4sUwm8145plnqnW8q+eJiIiAv7+/U/l1110HADh+/PhV21i9ejX+8Ic/IC4uzl4WFhaG4cOH4z//+Q8qKioAAFu3bsXJkyedvl8jRoxASEgIVq9eXa/OReStGNSQ0pgxY+y/eD799FN8//33+P7773HttddWWe+1117Dd999h9deew1vvfUWfvnlF9xxxx144okncPr0abzzzjuYP38+1q1bhzFjxjjUTU1NxYMPPohOnTrh3//+N9577z0UFRXhxhtvxP79+6/a54qKimptwkOL01ssFgCA2Wx22mc2m3Hx4kUcPnwYAODv749x48ZhxYoVWLNmDQoLC3H06FE8+eSTCA8Px5NPPmmvu2/fPjRr1gxRUVEObSYmJtr3X82+ffvsx8vaqMz5UV1D5dd79uy56rlqYtOmTejYsSM++eQTtG/fHkajES1atMDzzz9v70tt+uabb+Dn54d27do5lF+ZB1RSUoLDhw8r72FJSQmOHDkC4Pfvx5XH+vv7o0OHDk7fr7o8F5Ev8dO7A1R/tWjRAi1btgQA9OjRA61atapWvUaNGmHNmjX2v6jPnDmD5ORkdOjQAf/7v/9rP+6XX37BokWLUFhYiLCwMOTk5GD69OmYMGECXn31VftxgwcPRkJCAmbMmIEPP/xQed6jR48iPj6+Wn1cv369R5KcIyMj0bhxY/toR6Xz58/bf7mcPXvWXv7yyy8jPDwc9957L2w2GwCgZcuW+Oabb9C2bVv7cWfPnkXjxo2dzhccHAyTyeTQpoqqjcqyyjY6deoE4NKIzeX3b/PmzU7994QTJ07g9OnTePbZZzFr1ix06tQJX3/9NebOnYucnBx88MEHHj3f5dLT0/Hee+9h4sSJaNKkicM+o9EIo9Fo/zo/Px9CiGrdw8r/qo49evSobuci8iUMasjjbrvtNodHBB07dgQADBs2zOG4yvLs7Gx06dIFa9euRUVFBR599FH7UDsABAQEICkpCevXr6/yvDExMdi2bVu1+ti+fftqHXc1BoMB48ePx6xZszBr1iyMHTsWhYWFSE5OxsWLF+3HVJozZw7+8Y9/ICUlBTfeeCMKCwuxePFiDB48GOnp6ejRo4f92MsfZ12pcp8QwulNHj8/P6fjqmpj6NChaNu2Lf76178iMjISvXv3xtatW/G3v/0NRqPRof+Xf1+AS7+cqzqHjM1mQ1FREf71r3/hgQceAAAMGjQIxcXFWLRoEWbMmOEQ4HnKzp07cd9996Fv375IS0tz2n/ltVWqzj282rFXltfluYh8CYMa8rgr/4I0mUxVlpeWlgIATp06BQDo3bu3tN2r5VKYTCZ07969Wn28/K9kd7344ou4cOECZs+ejRdffBHApQBu9OjReOutt3DNNdcAAH7++We8+OKLmD9/Pp577jl7/aFDh6JTp06YPHmyPXBr0qQJdu/e7XSu4uJiWCwW+71csWKFU25F5aO1Jk2aSEdZzp07B+D374fJZMKXX36JRx55BEOGDAFwaUQoNTUVs2bNsvcfgFOOyrJly/DYY49V70b9V5MmTZCbm4tbbrnFoXzo0KFYtGgRdu7c6fGgZteuXfYRvy+++EL6uPBKERER0DStWvewctTn7NmziIyMdDpWNqqi17mIvBmDGqo3mjZtCgD4+OOPHZIlq0uPx0/ApZGRhQsXYubMmcjKykLTpk0RHR2NW265BfHx8WjRogUA4KeffoIQwilo8/f3R7du3bBx40Z7WdeuXbFq1Srk5uY65NXs3bsXANClSxcAwB133KEcneratav9+Mtd2QYAtG3bFt9//z1OnDiBc+fOoU2bNigoKMDEiRMxYMAA+3FXnqu69/tyiYmJyM3NdSqvDMaqmwhcXbt27cLNN9+MuLg4pKenOyVqqwQGBqJt27bKexgYGIjWrVsDuHSvK8srH+cBl0ZkfvnlFzz44IP15lxE3oxBDVWp8i/amkzC5qpbbrkFfn5+OHz4MO69994a19fj8dPlQkJC7L9wdu7cia+//hovvfSSQ/+AS2+vJCUl2cvLysqwc+dOe/ADAHfddRdeeOEFrFixAn/961/t5cuXL0dgYCBuvfVWAJf+ar8yN6TSPffcg3HjxuGHH35Anz59AFz6xff++++jT58+9v5c7pprrrGPzLzwwgsIDg7GE088Yd/fq1evmt0UiXvvvRfp6en48ssvMXLkSHv5F198AYPBoBypc8Xu3btx8803o0WLFsjIyEBERESN6t9zzz1YtGgRcnJyEBsbCwAoKirCp59+ijvvvNP+qK9Pnz6Ijo7G8uXLcf/999vrf/zxx7hw4QKGDx9er85F5K0Y1FCVKn9Jv/LKKxg1ahT8/f3Rvn17hIaGevxcrVq1wsyZMzFt2jQcOXIEt956KyIiInDq1Cn8+OOPCA4OxowZM5T1TSaTR37pVtq+fbs96bKwsBBCCHz88ccALj0iqxxN2rBhA7Zt24bExEQIIfDjjz9i3rx5uPXWWzFhwgR7ezfccAN69+6NlJQUXLx4EQMGDEBBQQH++c9/IisrC++995792M6dO+OJJ57A9OnTYTQa0bt3b6Snp2Pp0qWYPXt2tR4xPP7443jttdcwYsQIzJ07F82bN8frr7+OAwcOYN26dQ7Hzp8/H1FRUWjZsiVOnTqFf//731izZg3ee+89h8dPnrhfo0ePxv/8z/9g3LhxOHPmDDp16oR169bhtddew7hx4646Snfx4kV88cUXAC4FiACwceNGnDlzBsHBwRg6dCgA4MCBA7j55psBXMplOnjwIA4ePGhvp02bNmjWrJn9az8/PyQlJTnMuPzcc8/hvffew7BhwzBz5kyYzWbMnTsXpaWlSElJsR9nNBoxf/58PPLIIxg7diwefPBBHDx4EFOmTMHgwYPtQage5yLyKTrNj0MNyNSpU0VMTIwwGAwOE50lJSU5TLCWlZUlAIgFCxY41F+/fr0AID766COH8mXLlgkAYtu2bQ7la9asEYMGDRJhYWHCbDaLuLg48cc//lGsW7euVq5PZdSoUQKAdFu2bJn9uO+++0706dPH3t8uXbqIf/zjH9KJ386fPy+mTZsmOnbsKIKCgkTz5s3FwIEDpROmWSwWMX36dNGyZUthMplEu3btxKuvvlqja8jNzRWPPvqoaNy4sQgICBB9+/YVGRkZTsfNmDFDtGnTRpjNZtGoUSNx6623ik2bNtXoXNW9X0IIcfbsWTF27FgRGRkp/P39Rbt27cSCBQscJtZTqfycyba4uDj7cZWfr+r2CYB0wsBDhw6Ju+++W4SFhYmgoCBx0003iR07dkj7tnLlSpGYmChMJpOIiooSzz77rCgqKnI6ri7PReRLNCE8NGEHERERkY44+R4RERF5BQY1RERE5BUY1BAREZFXYFBDREREXoFBDREREXkFXYOaVq1aQdM0p238+PEALs0wmpKSgpiYGAQGBmLgwIH2lYWJiIiILqfr5Hvbtm1zWIxv3759GDx4MEaMGAHg0oRgCxcuxPLly9GuXTvMnj0bgwcPxoEDB6o9+ZvNZsNvv/2G0NBQLvRGRERVEkKgqKgIMTExHl+y43KlpaWwWCxut2MymRAQEOCBHnkJfafJcTRx4kTRpk0bYbPZhM1mE1FRUWLu3Ln2/aWlpSI8PFy88cYb1W4zJyenygm4uHHjxo0btyu3nJyc2vg1J4QQoqSkRATB6JF+RkVFiZKSklrra0NTb5ZJsFgseP/99zF58mRomoYjR44gNzfXvmowcGkdoqSkJGzZsgVjx46tVruVIzo5OTkICwurlb4TEZF3KCwsRGxsbK0sBVPJYrHgIqx4CNfA5EYWiAU2fJB7AhaLhaM1/1Vvgpo1a9bg/PnzeOyxxwDAvopvZGSkw3GRkZE4duyYsp2ysjKUlZXZvy4qKgIAhIWFMaghIqJqqYt0hUAYYNJcD2qMwoOd8RL15u2nt99+G0OHDnVaOfjKD5YQosoPW1paGsLDw+1b5Wq3RERE9YlR09zeyFG9CGqOHTuGdevWYcyYMfayqKgoAL+P2FTKy8tzGr253NSpU1FQUGDfcnJyaqfTREREbjBogNGNzcCYxkm9CGqWLVuG5s2bY9iwYfay+Ph4REVFISMjw15msViwceNG9O/fX9mW2Wy2P2riIyciIiLfoXtOjc1mw7JlyzBq1Cj4+f3eHU3TkJycjNTUVCQkJCAhIQGpqakICgrCyJEjdewxERGR+9x9hGQEh2qupHtQs27dOmRnZ+Pxxx932jdlyhSUlJRg3LhxyM/PR58+fZCenl6rWelERER1ofIxksv1PdcVr6EJIbw6f7qwsBDh4eEoKCjgoygiIqpSXfzOqDzHRL84mN14+6lM2PBKxTH+fruM7iM1REREvoiPnzyPQQ0REZEO+PjJ8+rF209ERERE7uJIDRERkQ74+MnzGNQQERHpQIN7j0sY0jjj4yciIiLyChypISIi0gEfP3kegxoiIiId8O0nz+PjJyIiIh1cCmrcWaW7ZufbtGkT7rjjDsTExEDTNKxZs8ZhvxACKSkpiImJQWBgIAYOHIjMzEzPXXAdYFBDRETkA4qLi9GtWzcsXrxYun/+/PlYuHAhFi9ejG3btiEqKgqDBw9GUVFRHffUdXz8REREpIO6fvw0dOhQDB06VLpPCIFFixZh2rRpGD58OABgxYoViIyMxMqVKzF27FjXO1qHOFJDRESkA/cePbmXZHylrKws5ObmYsiQIfYys9mMpKQkbNmyxWPnqW0cqSEiImrACgsLHb42m80wm801aiM3NxcAEBkZ6VAeGRmJY8eOudfBOsSRGiIiIh0YtN8fQbmyGf47UBMbG4vw8HD7lpaW5nKftCtGf4QQTmX1GUdqiIiIdOCpeWpycnIQFhZmL6/pKA0AREVFAbg0YhMdHW0vz8vLcxq9qc84UkNERNSAhYWFOWyuBDXx8fGIiopCRkaGvcxisWDjxo3o37+/J7tbqzhSQ0REpIO6fvvpwoULOHTokP3rrKws7N69G40bN0bLli2RnJyM1NRUJCQkICEhAampqQgKCsLIkSNd72QdY1BDRESkg7oOarZv345BgwbZv548eTIAYNSoUVi+fDmmTJmCkpISjBs3Dvn5+ejTpw/S09MRGhrqeifrmCaEEHp3ojYVFhYiPDwcBQUFDs8ciYiIrlQXvzMqz/HPRm0RqLm+2EGJsOKZ84f4++0yHKkhIiLSARe09DwGNURERDowws3HT179nMU1DGqIiIh0YHBzpMbQgOaPqSt8pZuIiIi8AkdqiIiIdOD2208cqHHCoIaIiEgHbicK8/GTEz5+IiIiIq/AkRoiIiId8PGT5zGoISIi0gEfP3keHz8RERGRV+BIDRERkQ4MmubWXDOcp8YZgxoiIiIdaEYNmsH1wERjUOOEj5+IiIjIK3CkhoiISAcGowaDGyM1fPzkjEENERGRHowGaAY3HphoXNHySgxqiIiIdKAZNGhuTDajgSM1V2JODREREXkFjtQQERHpwGDUYHBjpMbAkRonDGqIiIh0oBncy6nRBHNqrsTHT0REROQVOFJDRESkAz5+8jwGNURERDrQjHz7ydN0f/x04sQJPPzww2jSpAmCgoLQvXt37Nixw75fCIGUlBTExMQgMDAQAwcORGZmpo49JiIiovpI16AmPz8f119/Pfz9/fHll19i//79eOmll9CoUSP7MfPnz8fChQuxePFibNu2DVFRURg8eDCKior06zgREZGbLo3UGNzYOFJzJV0fP82bNw+xsbFYtmyZvaxVq1b2/xdCYNGiRZg2bRqGDx8OAFixYgUiIyOxcuVKjB07tq67TERE5BHMqfE8XUdqPvvsM/Tq1QsjRoxA8+bN0aNHD7z55pv2/VlZWcjNzcWQIUPsZWazGUlJSdiyZYseXSYiIqJ6Steg5siRI1iyZAkSEhKwdu1aPP3003j22Wfx7rvvAgByc3MBAJGRkQ71IiMj7fuuVFZWhsLCQoeNiIiovtE07dJSCa5uXNDSia6Pn2w2G3r16oXU1FQAQI8ePZCZmYklS5bg0UcftR935TdOCKH8ZqalpWHGjBm112kiIiIPMBgNMBhdH1swCN3f9al3dL0j0dHR6NSpk0NZx44dkZ2dDQCIiooCAKdRmby8PKfRm0pTp05FQUGBfcvJyamFnhMREbmn8pVudzZypGtQc/311+PAgQMOZb/++ivi4uIAAPHx8YiKikJGRoZ9v8ViwcaNG9G/f39pm2azGWFhYQ4bEREReT9dHz9NmjQJ/fv3R2pqKu677z78+OOPWLp0KZYuXQrg0mOn5ORkpKamIiEhAQkJCUhNTUVQUBBGjhypZ9eJiIjc4vbke4IjNVfSNajp3bs3Vq9ejalTp2LmzJmIj4/HokWL8NBDD9mPmTJlCkpKSjBu3Djk5+ejT58+SE9PR2hoqI49JyIicg9zajxPE8K7l/ksLCxEeHg4CgoK+CiKiIiqVBe/MyrP8fXAGxDs5/rYQnFFBW7asJm/3y7DtZ+IiIj04G6yLx8/OWFQQ0REpAODpsFgcGNGYc5T44QP5IiIiMgrcKSGiIhIB5ULU7pc38ZxiSsxqCEiItKB2wta2vj46UoM84iIiMgrcKSGiIhIB25PvseRGicMaoiIiHTAnBrPY1BDRESkA4MRbubUeLAzXoJhHhEREXkFjtQQERHpQDNo0NyYfM+dut6KQQ0REZEODAY3F7S08mHLlXhHiIiIyCtwpIaIiEgHbr/S7c5imF6KQQ0REZEO3H6l24263op3hIiIiLwCR2qIiIh0oBkM0AxujNS4UddbMaghIiLSgcHo5ttPfPzkhHeEiIiIvAJHaoiIiPTgZqIwOFLjhEENERGRDjSDm28/MafGCYMaIiIiHTBR2PN4R4iIiMgrcKSGiIhIB5cm3zO6Ud/qwd54BwY1REREOuCMwp7HO0JERERegSM1REREOjAYDDC4kezrTl1vxaCGiIhIB3z85Hm8I0REROQVOFJDRESkA18cqXn11VdrXGf06NEIDQ2t1rEMaoiIiHSgaW5Ovqc1vKAmOTkZLVq0gLGar7Ln5OTg9ttvZ1BDRERE9c/27dvRvHnzah1b3WCmEoMaIiIiHfji46fp06cjJCSk2sf/7W9/Q+PGjat9PIMaIiIiHfhqUFMTU6dOrdHxDe+OEBEReQGD0eD21hCVlJTgs88+Q1FRkdO+wsJCfPbZZygrK3Op7YZ5R4iIiKhBWrp0KV555RVpvkxYWBheffVVvPXWWy61zaCGiIhIB5pBg2YwuLFpel+CSz744AMkJycr9ycnJ2PFihUutc2cGiIiIh34Yk4NABw8eBDdunVT7k9MTMTBgwddarth3hEiIiKqkZSUFGia5rBFRUXVeT8qKipw+vRp5f7Tp0+joqLCpbY5UkNERKQDPUZqOnfujHXr1tm/ru4keJ5U2YeePXtK92dkZKBz584utc2ghoiISAd6zCjs5+eny+jM5R5//HFMnjwZnTt3xu233+6w7z//+Q9mz56NhQsXutQ2gxoiIqIGrLCw0OFrs9kMs9ksPfbgwYOIiYmB2WxGnz59kJqaitatW9dFN+2eeuopbNq0CXfeeSc6dOiA9u3bQ9M0/Pzzz/j1119x33334amnnnKpbV1zaq72fE8IgZSUFMTExCAwMBADBw5EZmamjj0mIiLyDM1ohMGNTfvvo6PY2FiEh4fbt7S0NOn5+vTpg3fffRdr167Fm2++idzcXPTv3x9nz56ty8sGALz//vtYtWoV2rVrh19//RW//PIL2rdvj3/961/417/+5XK7uo/UVPV8b/78+Vi4cCGWL1+Odu3aYfbs2Rg8eDAOHDhQ4/UgiIiI6hNP5dTk5OQgLCzMXq4apRk6dKj9/7t27Yp+/fqhTZs2WLFiBSZPnuxyP1x133334b777vNom7oHNarne0IILFq0CNOmTcPw4cMBACtWrEBkZCRWrlyJsWPH1nVXiYiI6p2wsDCHoKa6goOD0bVrV5dfn3ZFYWFhjfpaVFRUo0EM3V/prny+Fx8fjwceeABHjhwBAGRlZSE3NxdDhgyxH2s2m5GUlIQtW7Yo2ysrK0NhYaHDRkREVN9UjtS4s7mjrKwMP//8M6Kjoz10RVcXERGBvLy8ah9/zTXX2OOC6tB1pKby+V67du1w6tQpzJ49G/3790dmZiZyc3MBAJGRkQ51IiMjcezYMWWbaWlpmDFjRq32m4iIyF2VMwO7U78mnnvuOdxxxx1o2bIl8vLyMHv2bBQWFmLUqFEu96GmhBB46623qr1Sd3l5eY3a1zWoqer5Xt++fQEAmuY4DbQQwqnsclOnTnV4NlhYWIjY2FgP95yIiMg9dT1PzfHjx/Hggw/izJkzaNasGfr27YutW7ciLi7O5T7UVMuWLfHmm29W+/ioqCj4+/tX+3jdc2oud/nzvbvvvhsAkJub6zA0lpeX5zR6c7mqXmUjIiLyVatWrdK7Czh69Gittq97Ts3lLn++Fx8fj6ioKGRkZNj3WywWbNy4Ef3799exl0RERO7TDJp7OTUNdEHL2qTrSE1Vz/c0TUNycjJSU1ORkJCAhIQEpKamIigoCCNHjtSz20RERG6r65waX6BrUHO153tTpkxBSUkJxo0bh/z8fPTp0wfp6emco4aIiIic6BrUXO35nqZpSElJQUpKSt10iIiIqI5oBiM0g+sLSrpT11vVq0RhIiIin2EwXtrcqU8O+ECOiIiIdPHtt9/i4YcfRr9+/XDixAkAwHvvvYfNmze71B6DGiIiIj0YDO5vDdgnn3yCW265BYGBgdi1axfKysoAXFoaITU11aU2G/YdISIiaqC0/6607c7WkM2ePRtvvPEG3nzzTYcJ9vr374+dO3e61CaDGiIiIqpzBw4cwIABA5zKw8LCcP78eZfaZFBDRESkh8pEYXe2Biw6OhqHDh1yKt+8eTNat27tUpt8+4lIJ4XLXlTuM8fGS8s1c6Cyjii3yNv6w6PSctvhH5Vt2QLD5eWmYGUdU9MWyn1EJGEwuPn2U8Melxg7diwmTpyId955B5qm4bfffsP333+P5557Di++qP75WBUGNURERDrw9RmFp0yZgoKCAgwaNAilpaUYMGAAzGYznnvuOUyYMMGlNhnUEBERUZ2yWq3YvHkz/vznP2PatGnYv38/bDYbOnXqhJCQEJfbZVBDRESkB83NvBit4ebUGI1G3HLLLfj555/RuHFj9OrVyyPtNuyxKyIioobKxxOFu3btiiNHjni0TY7UkNfLX/K8tLzkbIGyTswLS2qrO3bWUnliLwBogfKEXOM17ZR1hFlep+LEz/IKQRHKtmyKtoR/kLJOaUmJvC1o0vLicpuyrbIK+b4WjV0fliai+mXOnDl47rnnMGvWLPTs2RPBwY4/d8LCwmrcJoMaIiIiHfh6ovCtt94KALjzzjuhab//8SOEgKZpsFqtNW6TQQ0REZEefHxBy/Xr13u8TQY1REREVOeSkpI83iaDGiIiIj34+OR7mzZtqnK/bAmFq2FQQ16v4OhJaXmreSvquCeOyovlibUAAJs8UVaY1DMKVzSSz+grNHmirqGiTNmWVq7om1An92pWeeKzwWiSlvtX8fPYWNVOIi/h7qKUDX1By4EDBzqVXZ5b40pODX9yEBERUZ3Lz8932PLy8vDVV1+hd+/eSE9Pd6lNjtQQERHpwWBw7xFSA3/8FB7uvMbc4MGDYTabMWnSJOzYsaPGbTKoISIi0oOPv/2k0qxZMxw4cMClugxqiIiIdKAZjNDcCEzcqVsf7Nmzx+FrIQROnjyJuXPnolu3bi61yaCGvJ4qIfjEjLHKOkKRqGspLFbWMZj8a3R+V2hl6vOrEoLPl8qT7UxGeX8BIMQkH9bWyorUnVMkBEMIabHZP0DZlNUmr3P+wkVlnUYh6tmOiaj+6d69OzRNg7jiZ0Tfvn3xzjvvuNQmgxoiIiI9aG7m1GgNO6cmKyvL4WuDwYBmzZohIED9B8/VNOw7QkRE1EBVPn5yZ2vINm7ciKioKMTFxSEuLg6xsbEICAiAxWLBu+++61KbDGqIiIiozo0ePRoFBc4LCxcVFWH06NEutcnHT0RERHrw8RmFKxeuvNLx48elr3tXB4MaIiIiPfjoPDU9evSApmnQNA033XQT/Px+D0WsViuysrLsK3jXFIMa0s2hCfcp97Vd/G+Pneen+4ZKyxu3i1HWiegYJy2vKC5V1rmYly8tPzJppLQ8LD5a2ZatSN6WXxXLFBgs8jeDzEZ50p1F8YYRAFiM8h8NJn/1G0Zaufz8yuUTqlpywc8sLfczqn+IF12UL+0QGqReWoKI6t7dd98NANi9ezduueUWhISE2PeZTCa0atUK9957r0ttM6ghIiLSga+u/TR9+nQAQKtWrXD//fe79bbTlRjUEBER6cHHZxQeNWqUx9tkUENERKQHHw9qrFYrXn75Zfz73/9GdnY2LBbHR9Xnzp2rcZsNM8uIiIiIGrQZM2Zg4cKFuO+++1BQUIDJkydj+PDhMBgMSElJcalNjtRQrftlzN16d0HKGKCY1h+AwU++hIA5qpGyjtVSLi0vOS1P+hVWdaKs6i8wYZEnwwKAZquQlgcb5Ym6Qf7qZRIgX3FBeQ4AgGJpCWUdD8+GalK2x0Rhqp80gwGaG28wuVO3Pvjggw/w5ptvYtiwYZgxYwYefPBBtGnTBomJidi6dSueffbZGrfZsO8IERFRQ6UZf38E5cqmNezHT7m5uejatSsAICQkxD4R3+23347PP//cpTYZ1BAREVGda9GiBU6ePAkAaNu2LdLT0wEA27Ztg9ksn9bhahjUEBER6UHTLj2GdXlTPCduIO655x58/fXXAICJEyfi73//OxISEvDoo4/i8ccfd6lN5tQQERHpoTI4cad+AzZ37lz7///xj39EbGwsvvvuO7Rt2xZ33nmnS20yqKFa1+GtNbqe3z9AMTtumHp23PILxfI6wWHKOkGtWknLS06fV9ZREaXy84viQmUd4/nfpOW2gFB5W+Zg9fn9Fcm1itmBAcBgkfcZqkRhm1XZFvyqSEhWUcyCbMnPlZabIqJqfg4i8ojy8nI89dRT+Pvf/47WrVsDAPr06YM+ffq41W7DDvOIiIgaKKEZ3N4aKn9/f6xevdrj7TbcO0JERNSQuZVP4+ajq3rgnnvuwZo1azzaJh8/ERERUZ1r27YtZs2ahS1btqBnz54IDnZ8JN6g56lJS0uDpmlITk62lwkhkJKSgpiYGAQGBmLgwIHIzMzUr5NERESeomnubw3YW2+9hUaNGmHHjh1YunQpXn75Zfu2aNEil9qsFyM127Ztw9KlS5GYmOhQPn/+fCxcuBDLly9Hu3btMHv2bAwePBgHDhxAaKg8+ZHoSkUnL0jLDXsOK+tEtIuVlpuaNFGfSDELcHiba6TlVc0oLMrlsxNbjv6sPv3pE9JyU9tEaXl5sPpaLgj5bMMXhDpROrJRuLTc/+wRabnhonymZQAQ/vI5KoRRPQu0ap+mSG4uL1MkNgPwj2qj3EfkMQbDpc2d+g1YVlaWx9vU/Y5cuHABDz30EN58801ERETYy4UQWLRoEaZNm4bhw4ejS5cuWLFiBS5evIiVK1fq2GMiIiL3+XKi8OUsFgsOHDiAigoX3nq8gu53ZPz48Rg2bBhuvvlmh/KsrCzk5uZiyJAh9jKz2YykpCRs2bJF2V5ZWRkKCwsdNiIiIqpfLl68iCeeeAJBQUHo3LkzsrOzAVzKpbl8Dpua0DWoWbVqFXbu3Im0tDSnfbm5l+aWiIyMdCiPjIy075NJS0tDeHi4fYuNlT9GICIi0pWPv/00depU/PTTT9iwYQMCAgLs5TfffDM+/PBDl9rU7Y7k5ORg4sSJeP/99x0u5kraFYlQQginsstNnToVBQUF9i0nJ8djfSYiIvIYHw9q1qxZg8WLF+OGG25w+L3eqVMnHD6sznmsim6Jwjt27EBeXh569uxpL7Nardi0aRMWL16MAwcOALg0YhMdHW0/Ji8vz2n05nJms9nlhbDIO/XJWC8t/+6GG5V1gppFSMtLT55S1rEqknuN/vKkW1OjEGVbVc62q6pSdF5aXqFIIBbXdFO2daZIfi35Jepn3op8bHSLTJCWm04dULallcgfG2tG+b0EAFF2UblP2lYV+6xl8osxxqnvGRHVzOnTp9G8eXOn8uLi4ioHL6qiW5h30003Ye/evdi9e7d969WrFx566CHs3r0brVu3RlRUFDIyMux1LBYLNm7ciP79++vVbSIiIs/w8ZGa3r174/PPP7d/XRnIvPnmm+jXr59Lbeo2UhMaGoouXbo4lAUHB6NJkyb28uTkZKSmpiIhIQEJCQlITU1FUFAQRo4cqUeXiYiIPEZomltvMIkGPk9NWloabr31Vuzfvx8VFRV45ZVXkJmZie+//x4bN250qc16HeZNmTIFycnJGDduHHr16oUTJ04gPT2dc9QQERE1cP3798d3332Hixcvok2bNkhPT0dkZCS+//57h9SUmqgXk+9V2rBhg8PXmqYhJSUFKSkpuvSHiIio1rj7CKmBP34CgK5du2LFihUea69eBTVEREQ+w92lDhr44yfg0gtCq1evxs8//wxN09CxY0fcdddd8PNzLTxhUEO62dBLnQhm9Jf/BWIOV7/ZVppfKi0f8IN8ssbrN3+rbGtL0gBpeVh2nrJORLsW0vLG3TpIy7WAYGk5APXbT4qlGKpiy5f32Vionu8pIsD5jQQA8Deof4iWlAtp+cFzZdLyRqHqpQgiG8vfsqpqaQWDsUBabgtuLD++VH48AIgz8jfGrIq3sgDA2EH9Nh0ROdu3bx/uuusu5Obmon379gCAX3/9Fc2aNcNnn32Grl271rjNhj92RURE1BD5+NtPY8aMQefOnXH8+HHs3LkTO3fuRE5ODhITE/HUU0+51CZHaoiIiHTg7vpNDX3tp59++gnbt293WPcxIiICc+bMQe/evV1qs2HfESIiooZKM/y+UrcrWwMPatq3b49Tp5wnNM3Ly0Pbtm1darNh3xEiIiJqkFJTU/Hss8/i448/xvHjx3H8+HF8/PHHSE5Oxrx581xamFoTQsiz+7xEYWEhwsPDUVBQgLCwML2749W+SbxOWh7YNLDGbRlNNU+I9Q+QP021WeUf8Ytn1NPqG4zyeD/2RvVfD017dZaWm9omyvtVeE7ZVkVutrz8gmItAgDGAJO0XFMkF/u1UCfqau36SsttAep/Q8JPnsT9W7E86feIIrEbAMLN8u9lTKj8GgGgpMImLb9YLi/3qyLpua1Z/tkwnvxFWUeV3G3scpO6DtU7dfE7o/Icp4/+irAw1+ddKywsQrNW7Rrs7zeD4fefs5WzCVeGJJd/rWkarNbqLR3DnBoiIiI9+Pg8NevXy9flcweDGiIiIqpzSUlJHm+TQQ0REZEefHykBgBKS0uxZ88e5OXlwWZzfFR855131rg9BjVEREQ68PUFLb/66is8+uijOHPmjNO+muTRXI5BjQ/7tt/1yn0VpfLkzkG7flDWsZbLP4AVJYrZYRXJuABgsyiSOwPVH9nApkHSclOovDyshUXZlsEkP09YfLS6TrA8Uc96Vj5zb8UpeTIwAFjOyxOCLUXFyjoqwdc0k5bbis4r6xiP7JSXmwKUdbTG8ntzTZNW0vLyKmaHvlAm//7/VqT+npXb5HVOXZDXKbPKjweAY4pE5T6x8mR4AGh0ao+03HZoq7Tc0FaejE3kKyZMmIARI0bgxRdfRGRkpEfaZFBDRESkBx9//JSXl4fJkyd7LKABOE8NERGRPioXtHRna8D++Mc/YsOGDR5tkyM1REREVOcWL16MESNG4Ntvv0XXrl3h7+/vsP/ZZ5+tcZsMaoiIiPSg0+On119/HQsWLMDJkyfRuXNnLFq0CDfeWPerzK9cuRJr165FYGAgNmzYYJ9wD7iUKFwnQc1jjz2Gxx9/HAMGDKjxyah+sSpmWgUAoZiFd227a5V1DEb5UKiwyduyQX1+FaO/+h+xf7A8idUUFixvq3mEtBxQJxdbCtWJupZ9mdLyQMV5rKXqpFcVcyP17KP+ij6rEphFRbmyLWvBWWm55ucvLQcA/2B537SKMml5lOJ4ACjyq/lbD6pP0zWh8oTkgjL1OcoVn/+zJeo6hij5zNHBxc5r2wAAfv1O3VY7dRI/eQ89FrT88MMPkZycjNdffx3XX389/ud//gdDhw7F/v370bJlS5f74ooXXngBM2fOxPPPP+8wu7A7atxKUVERhgwZgoSEBKSmpuLEiRMe6QgREZFPqRypcWeroYULF+KJJ57AmDFj0LFjRyxatAixsbFYsmRJLVxg1SwWC+6//36PBTSAC0HNJ598ghMnTmDChAn46KOP0KpVKwwdOhQff/wxysvVf/kRERGR512+8GNhYSHKyuSjoxaLBTt27MCQIUMcyocMGYItW7bURVcdjBo1Ch9++KFH23Qpp6ZJkyaYOHEiJk6ciF27duGdd97BI488gpCQEDz88MMYN24cEhISPNpRIiIib3Jp8j3X32CqrBsbG+tQPn36dKSkpDgdf+bMGVitVqdXqCMjI5GbK59PqzZZrVbMnz8fa9euRWJiolOi8MKFC2vcpluJwidPnkR6ejrS09NhNBpx2223ITMzE506dcL8+fMxadIkd5onIiLyWkJc2typDwA5OTkOq3SbzeqJLQE4JOReakc4ldWFvXv3okePHgCAffv2OexztT81DmrKy8vx2WefYdmyZUhPT0diYiImTZqEhx56CKGhlxL/Vq1ahT/96U8MaoiIiGpZWFiYQ1Cj0rRpUxiNRqdRmby8PI9OgFdd9WKV7ujoaNhsNjz44IP48ccf0b17d6djbrnlFjRq1MgD3aPaVF6sfvvGqlimwGhSp2EFNJK/faR6y8poUEfiqiUU/ENMyjoVJfLrqSiRv8ljVCyFAABlwYHScmup/Fk1AAjFtPuqJReM/uo3iVSsFnXeWmCza+T9Ur3lZKviDSPFPmOTWGk5AAhziLTccOG0tDzIdFHZVqBBfs80m3zJDQAQ/vLPnzDL335rHqz+a1ZT/PlcXsVf1RbFG1O2IPnbb1ruQWVbth2fy+so3mQD1G+mCaPi30wVb7IJP/m99ItmWoEn2YSAzY2hmprWNZlM6NmzJzIyMnDPPffYyzMyMnDXXXe53A93HTp0CIcPH8aAAQMQGBjo1shRjYOal19+GSNGjEBAgHoNmIiICGRlZbnUISIiIl8g/ru5U7+mJk+ejEceeQS9evVCv379sHTpUmRnZ+Ppp592oyeuOXv2LO677z6sX78emqbh4MGDaN26NcaMGYNGjRrhpZdeqnGbNX776ZFHHqkyoCEiIqL66f7778eiRYswc+ZMdO/eHZs2bcIXX3yBuLi4Ou/LpEmT4O/vj+zsbAQF/T7P1v3334+vvvrKpTY5ozAREZEObOLS5k59V4wbNw7jxo1z/cQekp6ejrVr16JFixYO5QkJCTh27JhLbTKoISIi0oEQAsKNnBp36tYHxcXFDiM0lc6cOXPVN7hUGNT4sME/76hxnaqWSSgvliekGkxGablfoPrjV1EiTwgtzS+tcR3V8g3+wVUkSp69oKijTlRWLWFQerZQWh7YrJGyLZWKi+rrL8uR57H5hcgTeKtiat1ZWq75q6/fmv2ztNxWIl9aQqtiFlFVcrNqyQcAMIQ1lrcV2UZ+vEH+uQTUSccBFerketVDeZsigVoLa6psy5aXLd9RVqKso7qfmqZIrq5imRSbqeafGaKaGjBgAN59913MmjULwKXXuG02GxYsWIBBgwa51CaDGiIiIh3o9fipvliwYAEGDhyI7du3w2KxYMqUKcjMzMS5c+fw3XfqtdGq4rkFF4iIiKhGhBtbQ9epUyfs2bMH1113HQYPHozi4mIMHz4cu3btQps28hHWq+FIDRERkQ58faQmOzsbsbGxmDFjhnSfK6uGc6SGiIiI6lx8fDxOn3aenPPs2bOIj493qU2O1FCNqJJxgZon5FbVVkWpep+KTTGjb1BT5+x6APALVCcK+wXIE2KDr2mmrGP0r9k/J2FTJ2qaQuWz4BpN6j5rilmYKy7Ik55VxwNAxVn54naG0EbKOkKRxCpK5YnCtnJ10q2tVD7bsKFAPjs0AGj5edJyP0VbxmYtpOUAYA2RJ/EayorU57corj9Cfp6Kpq2VbfmXy9uyFavPr/o8aZAnXQuLOuncqJq5OdK1XzQk5+tvP6lmDr5w4YLL8+ExqCEiItKB7b+bO/UbosmTJwO49LbT3//+d4fXuq1WK3744QfpEkzVwaCGiIiI6syuXbsAXBqp2bt3L0ym30fGTSYTunXrhueee86lthnUEBER6UCIS5s79RuiytW5R48ejVdeeaVaK4xXF4MaIiIiHfj620/Lli3zeJsMaqhGhuXsUe77v5iu0nLVTKdGkzpRNTRaPqNpeFyEso61XJ7c6B+smB22cbiyraqSeFUsRfKE1PJieUKmKVSewAyo75kr/RKKBOrS8+qkU0OuPCE3sJn6/vs3kSfXGptdI+9XmTpRFafkM+pay8qUVSrOFUjLzYqEZNVMxwDg30L+WRJ+6uRFUSb//muKWYjLAtT30k8xC7HId35T5KrM8j5rJvW1GNr2rfl5iOoBBjVEREQ68PW3n2oDgxoiIiId+OrbT7WJk+8RERFRnSovL8fo0aNx5MgRj7ara1CzZMkSJCYmIiwsDGFhYejXrx++/PJL+34hBFJSUhATE4PAwEAMHDgQmZmZOvaYiIjIMwR+fwPKpU3vC3CDv78/Vq9e7fF2dX381KJFC8ydOxdt27YFAKxYsQJ33XUXdu3ahc6dO2P+/PlYuHAhli9fjnbt2mH27NkYPHgwDhw4gNDQUD277rNUycAAENQ0UFoe1kL+vWrSKUbZVkS72Jp1DMDFvHxpuSpRtqyKRFlV0m9Vswb7Bcuvv/SsPIFVVQ4AFxWJuhWl6ll4hVX+I84/2CwtV83AXJWyfPnsxAAQ3lbenjlAPjuyIbiKf8MGo7T4womaJ8qqZk4OjGheRSV5Hc2qvv/wk8/2bLh4XlpuCm6ibEqcOykvVyQdXzqRos+KRGFXks7Js2xCwOZGXow7deuDe+65B2vWrLFPxucJugY1d9xxh8PXc+bMwZIlS7B161Z06tQJixYtwrRp0zB8+HAAl4KeyMhIrFy5EmPHjtWjy0RERB7h7mrbDTukAdq2bYtZs2Zhy5Yt6NmzJ4KDHf8AevbZZ2vcZr1JFLZarfjoo49QXFyMfv36ISsrC7m5uRgyZIj9GLPZjKSkJGzZskUZ1JSVlaHsstc+CwsLa73vREREVDNvvfUWGjVqhB07dmDHjh0O+zRNa5hBzd69e9GvXz+UlpYiJCQEq1evRqdOnbBlyxYAQGRkpMPxkZGROHbsmLK9tLQ06TLmRERE9YmvT76XlZXl8TZ1f/upffv22L17N7Zu3Yo//elPGDVqFPbv32/ff+UKnqpVPStNnToVBQUF9i0nJ6fW+k5EROQyd5KE3X125aV0H6kxmUz2ROFevXph27ZteOWVV/DXv/4VAJCbm4vo6Gj78Xl5eU6jN5czm80wm+WJkURERKSfyZMnY9asWQgODr5qgvDChQtr3L7uQc2VhBAoKytDfHw8oqKikJGRgR49egAALBYLNm7ciHnz5uncS+/3eWyitFwzqkfJDCb5GyuhLeTLETRqI58+HwD8FEsbFBw6oayjepvJplg+wVJYomzrQp58Cv2Q5vI3eQAg+Jpm0vKmiW2k5VW9faVajsAQrF74TSiWA6hQ5JUVHpW/YQOol3YoOHpKWadE8TZXbIji7afQRsq2DOHyN4MMRvXIq0HxZlpFqXxpharOL/zkfxhpZeq3v6wF8jfWjE3kbfmdPqxu68J5eb8U32MA0PxM0nJVHc1ffjzVHRsEbG4Mt7hTVy+7du1CeXm5/f9VqnoiUxVdg5q//e1vGDp0KGJjY1FUVIRVq1Zhw4YN+Oqrr6BpGpKTk5GamoqEhAQkJCQgNTUVQUFBGDlypJ7dJiIicpsvrtJduUL3lf/vKboGNadOncIjjzyCkydPIjw8HImJifjqq68wePBgAMCUKVNQUlKCcePGIT8/H3369EF6ejrnqCEiIiInugY1b7/9dpX7NU1DSkoKUlJS6qZDREREdcTX334CgG3btuGjjz5CdnY2LBbHR6WffvppjdvT/e0nIiIiX+TWEgluPrqqD1atWoXrr78e+/fvx+rVq1FeXo79+/fjm2++QXi4PBfzaupdojDVD8Ny9kjLP47srK50Sp5cW3RcnkBqNKkXMlMlqlaUypN+q+IXIP+YW8utyjpGf3nSc6N2LZR1Iv+oyPUKkD8uDa6QXyMA2ALl/6DPhbRU1impkP+EizbKl3wIOZetPn/BGWl57ppPlHXOH/pNWm4tkyfqmkzyZHAAgGIJhdA2ccoqJSflScyB0fK3JY2No5RtCZv8s1FxqopE5SB5n4VB/vmzna7i/hcrJg1V9AsArBXl8n5VUUdF/ukn8qzU1FS8/PLLGD9+PEJDQ/HKK68gPj4eY8eOdXjruSY4UkNERKSDyref3NkassOHD2PYsGEALk3HUlxcDE3TMGnSJCxdutSlNhnUEBER6cDXHz81btwYRUWXpra45pprsG/fPgDA+fPncfGifIT5avj4iYiISAe+vkr3jTfeiIyMDHTt2hX33XcfJk6ciG+++QYZGRm46aabXGqTQQ0RERHVucWLF6O09FJu4dSpU+Hv74/Nmzdj+PDh+Pvf/+5SmwxqqEb+eCpTuW9ls07ScuOBc9Lyknx1omxZoXwWVFOIv7JOUJNAabnNKv9rJqCR/HgAaNRGnqQW0b2Lsk55y2ul5Ret8pkxw35VTzx1/ouPpOWhrVsr60Q0k8/QbGwmT26uaBSjbKssSp4QHvWUOlG34tW50vLzv8qTa80JXZVtGVu0l5YbgqqYUdki/zwZI5orKtjUbRXKE6WrmtHXpji/n1VeR5Sqh9dtitmJVUnXAGAMDJLvqJCfP+C2PynborphtV3a3KnfkDVu3Nj+/waDAVOmTMGUKVPcapNBDRERkQ58/fFTbWCiMBEREXkFjtQQERHpwCYErByp8SgGNURERDq4tEyCO0GNBzvjJRjUkMeo/uI4VyhPbrxwQZ10GWCUPxmtahZgZVuKf/lVJQqHtlTMQhveRH2ii/nS4mBziLS84PsNyqa2LVwnLTeHmZV1rp1ws7Q8qKv8nvlVqJNO0ViegVgYpk4Ujh01Wl5n41fq86gUy++lrVQ+azUAaIoZijV/k7wt1ay9AKCanTe0kbKKrei8tNxyeK/8eEUyMAAU/5Ynr1OunlHb4C+fuds/WN6vKuZzJqpThw4dwuHDhzFgwAAEBgZCCAFNk79gcTXMqSEiItJB5dtP7mwN2dmzZ3HzzTejXbt2uO2223Dy5EkAwJgxY/DnP//ZpTYZ1BAREemg8u0nd7aGbNKkSfDz80N2djaCgn6fkuD+++/HV1+5MMILPn4iIiIiHaSnp2Pt2rVo0cJxLq2EhAQcO3bMpTYZ1BAREenA6ubbT+7UrQ+Ki4sdRmgqnTlzBmazOn+wKgxq6sAPgwdJy/tkqGeUbYgeOfOztHxpRAdpuclQ80QwrUSdKGnxlyd3qmYUDoyoeapkRd5x5b6yfYul5Xm7fpWWZ3+r/kskv1h+LfGtGynrhPQeIN8R1UZaXHFkl7ItcfywtDysYz9lnYqWPaTlwYnypNcKxTkAwHoqW1quSgYG1DMK24rkScd+MerZmYVJnkRusJQo6xiC5bMdl2fLv/+qGYgBILB5hKKO+vNflH1KWl5w+IS0vJGyJaorNrj3BlMDT6nBgAED8O6772LWrFkAAE3TYLPZsGDBAgwaJP+9eTUMaoiIiHRgtQlY3Yhq3KlbHyxYsAADBw7E9u3bYbFYMGXKFGRmZuLcuXP47rvvXGqTicJERERU5zp16oQ9e/bguuuuw+DBg1FcXIzhw4dj165daNNGPsJ8NRypISIi0oFw8w0m0cBzagAgKioKM2bM8Fh7HKkhIiLSgVW4vzVky5Ytw0cffeRU/tFHH2HFihUutcmghoiIiOrc3Llz0bRpU6fy5s2bIzU11aU2+fipDpjD5NO0733wNmWdsgL5FPYl+eo3Jm78Xp5Yta5zb2n5zZnblG15klHxklNjk1FZJ7CR/HW+kGj5kgMAYFS0d/GM/I2Vot8uKNv6bbN8avsLeepp+nf/8Ju0PLdU/sZKeRVDx22C5Z+ZRnHhyjpa02uk5dZAeZ2qpukv3P+LtNycJX+TBwCCEq+TlhujWknLS3/dp2zLYJL/aDIa1J8ZlRLFfBeGiObq87dKlJbbzKHqEwXLl6PwD5UvrWGq4k0q1dtXKJC/SXbJRmnpNdP/p4o6pCd3J9Br6JPvHTt2DPHx8U7lcXFxyM6WvwF5NRypISIi0kHl20/ubA1Z8+bNsWfPHqfyn376CU2aVLHOXhUY1BAREVGde+CBB/Dss89i/fr1sFqtsFqt+OabbzBx4kQ88MADLrXJx09EREQ68PXHT7Nnz8axY8dw0003wc/vUjhis9nw6KOPMqeGiIioIXH3DaaG/vaTyWTChx9+iFmzZuGnn35CYGAgunbtiri4OJfbZFBTB7p/slZa/m2/62vclilEnkAKANtvu0la3ihOPn37dzfcqGzr4tmLNetYFaIUSa/BkcHKOs07OWfEA0DINfJyQJ1cmvOtPLn1wE/qpMuCXfIp5/PL5cmgABBplp+/f2v5lPdVLdMQoNjXpLNzUl0la6D8PMJfnnSqWlYAAC6ePi/vVxN1orKoUCxTcf60tNxaalG3ZZNPAG8IUNexlcsTsq3l8n5d3LtD2VZIgPyzaWgcrayj7JdZkdweUMW99Jd//zV/53VyKgXfrE58JqrP2rVrh3bt2nmkLQY1REREOvD1x08AcPz4cXz22WfIzs6GxeL4R8vChQtr3B6DGiIiIh3YbAI2N95gcqduffD111/jzjvvRHx8PA4cOIAuXbrg6NGjEELg2muvdalNvv1ERESkA5ubswk38JgGU6dOxZ///Gfs27cPAQEB+OSTT5CTk4OkpCSMGDHCpTYZ1BAREVGd+/nnnzFq1CgAgJ+fH0pKShASEoKZM2di3rx5LrXJx086Us0ADABftupW4/b8lbPQyhOF/UP8lW2Zy+Uz+hr91TO6+gXKP05lhfLZkS0X5AmcAKAppiE2BqgTpUvPFUjLDUZ57P7Y2Z+Vbf3V1FpaHuanvv5u3eSJmi0HtpeWBzSRf18AoChbnsR8+IvdyjqtytOk5c1uu0taLkzqRGVTqDwhNbjPH5R1Tl8jn7m6San8Wox7flC2ZSmSz9xs8Ff/yKoolic++wXIP8tWi/rzV54jTy435KuTy42KGYoN/vJ/Z1qgenZia7B84jFhVifXVyjq1HwOZqorvp5TExwcjLKyS78fYmJicPjwYXTu3BkAcObMGZfaZFBDRESkA6sQsLoRmLhTtz7o27cvvvvuO3Tq1AnDhg3Dn//8Z+zduxeffvop+vbt61KbDGqIiIiozi1cuBAXLlxagy8lJQUXLlzAhx9+iLZt2+Lll192qU0GNURERDrwxbefXn31VTz11FMICAiAn58funbtCgAICgrC66+/7nb7TBQmIiLSgRXuvf2kngq0/po8eTIKCwsBAPHx8Th9Wj45p6s4UlNPDT36k7T848jOyjp/VNRZHaWuo+LK9NuK3F6UKBq7UCGfNRYAytbJk4vNYSeVdUrz5YmihYpZgL8LVc9gaTLILybcX/13QP6R89LyitJMabl/sDpRu6xQPnPu1/vUiaqWzTnS8lE/Z0vLWz98t7KtiC7ye6MFKGbHBVBQJr/PJUb5LNAtq0g6Lv/mc2m5atZgQJ1E7hcmT8i2Fl9QtiVK5InK6k9s1TM0yxjC1asQG1rKk4iFQT0LMTTFP0CieiQmJgaffPIJbrvtNgghcPz4cZSWyv/ttGzZssbtM6ghIiLSgS++/fTCCy/gmWeewYQJE6BpGnr3dn5rUggBTdNgtdZ8LIpBDRERkQ588e2np556Cg8++CCOHTuGxMRErFu3Dk2aqEcta4pBDREREdWZ0NBQdOzYEe+88w46duyI6OiaLxSromuicFpaGnr37o3Q0FA0b94cd999Nw4cOOBwjBACKSkpiImJQWBgIAYOHIjMTHmOAhERUUNhswlY3dga4ttPlYxGI55++mllPo2rdB2p2bhxI8aPH4/evXujoqIC06ZNw5AhQ7B//34EB1+aOXP+/PlYuHAhli9fjnbt2mH27NkYPHgwDhw4gNBQ9Yyc3uqPp2oe0N2TWz+DwLcbd1DuM6hmFDap43DVPqtFnlxqqeIHwqzSw8p9nqKatRhQJyqrkq4BdeJ11vpj0vLmvfYr2wpNlC8mZzkkT0YHgIQ28vOfaiJPVLc1baVsy2CUz4NbelY+azQABF/TTL7DVlV6r1zFxRJpuZ+fOrkbNvnzfy1IMXO04ngA0MrlifIQ6msxh1SRREz1UmVw4k79hqxr1644cuQI4uPjPdamrkHNV1995fD1smXL0Lx5c+zYsQMDBgyAEAKLFi3CtGnTMHz4cADAihUrEBkZiZUrV2Ls2LF6dJuIiMhtvh7UzJkzB8899xxmzZqFnj172gczKoUp3lysSr2ap6ag4NJfYY0bNwYAZGVlITc3F0OGDLEfYzabkZSUhC1btkjbKCsrQ2FhocNGRERE1deqVStomuawPf/88x49x6233oqffvoJd955J1q0aIGIiAhERESgUaNGiIiIcKnNepMoLITA5MmTccMNN6BLly4AgNzcXABAZGSkw7GRkZE4dkw+pJ6WloYZM2bUbmeJiIjcZLW5N9pirfmT1RqZOXMmnnzySfvXISHqeapcsX79eo+2B9SjoGbChAnYs2cPNm/e7LRPu2JSqcp32GWmTp2KyZMn278uLCxEbGysZztLRETkpvr++Ck0NBRRUVG11n5SUpLH26wXQc0zzzyDzz77DJs2bUKLFi3s5ZU3Mzc31+GVr7y8PKfRm0pmsxlms7l2O0xEROTl5s2bh1mzZiE2NhYjRozAX/7yF5hM8pm7XbFp06Yq9w8YMKDGbeoa1Agh8Mwzz2D16tXYsGGDUwZ0fHw8oqKikJGRgR49egAALBYLNm7ciHnz5unRZXLB0gj5W06qZRUAICAiQFoeGq0e/jSa5G/MnLkgX3JAb/MsR+rkPM/5y9+yClrmPCpaqV9ae2m5X6R62nLruVx5eUQnafmv1sbKttpfd6O03HL0Z2UdzRwoL1e8sSQq1J+L8tKL8h0V5erzBwYr90lV8VaWZpEv06BZFP2iBslTIzVX5o564o/7iRMn4tprr0VERAR+/PFHTJ06FVlZWXjrrbfcavdyAwcOdCq7/CmMKzMK65ooPH78eLz//vtYuXIlQkNDkZubi9zcXJSUXHqdUtM0JCcnIzU1FatXr8a+ffvw2GOPISgoCCNHjtSz60RERG7x1Dw1sbGxCA8Pt29paWnS86WkpDgl/165bd++HQAwadIkJCUlITExEWPGjMEbb7yBt99+G2fPnvXY9efn5ztseXl5+Oqrr9C7d2+kp6e71KauIzVLliwB4BytLVu2DI899hgAYMqUKSgpKcG4ceOQn5+PPn36ID093SfnqCEiIrpSTk6Ow+vPqlGaCRMm4IEHHqiyrVatWknL+/btCwA4dOiQx5Y1CA93nltp8ODBMJvNmDRpEnbs2FHjNnV//HQ1mqYhJSUFKSkptd8hIiKiOmIVbj5++u/v0LCwsGrN6dK0aVM0bdrUpXPt2rULADy6pIFKs2bNnFYXqK56kShMRETka+rr20/ff/89tm7dikGDBiE8PBzbtm3DpEmTcOedd6JlS3VeXU3t2bPH4WshBE6ePIm5c+eiW7duLrXJoIZqpKqlDZ4494u0/Kl8efnyJh2VbR3PkU+aaDpRpKyjWkHgkTPq5FJf8I9yeULynKAEZZ1Gb3wsLe/w+O3KOn5975SWb86RL23wVeYpZVv/vPsP0nLzRfX33xAkfyRdcfqEtFxUkahr8Jf/aLSWKZYvAGAIlyeqq5ZDEOXqROWKs/Kka6O/PBn6klZV7COqPrPZjA8//BAzZsxAWVkZ4uLi8OSTT2LKlCkePU/37t2haZrTU5u+ffvinXfecalNBjVEREQ6qK8jNddeey22bt1aK21fLisry+Frg8GAZs2aISBA/vZrdTCoISIi0kGFTcDoRmBS0cDXfoqLi/N4m/Vq7SciIiJf4c7r3O6O8ujphx9+wJdffulQ9u677yI+Ph7NmzfHU089hbIqHvVWhUENERER1ZmUlBSHJOG9e/fiiSeewM0334znn38e//nPf5Rz7VwNHz9RjaiSgV3x2FnfTuDV27SLB5X7/h7QRlp+e/FqZZ1erTtLy5sGJUrLTX7qv6l+OVMqLb82opmyjiiXz/armgVZWOTnAADr6TxpuSm6hbQcAPyiW0nLK07lyM+hSAYGAGMT+Xo7xvhrlXWo4bG5Odpia6AjNbt378asWbPsX69atQp9+vTBm2++CeDSZILTp093aSoXBjVEREQ6sAphn2vG1foNUX5+vsP6jRs3bsStt95q/7p3797IyZH/MXA1fPxEREREdSYyMtL+5pPFYsHOnTvRr18/+/6ioiL4+8vXbLsajtQQERHpoL6+0l3bbr31Vjz//POYN28e1qxZg6CgINx44++L2O7Zswdt2sgfgV8NgxoiIiId+GpQM3v2bAwfPhxJSUkICQnBihUrYDKZ7PvfeecdDBkyxKW2GdQQkZNZpYel5ZP84pV1Sh57QVp+4z8mSMs7D7lb2da5EvksvAiOUNYp37tZWq5KulXNQAwApiby8/s1u0ZZxxDaWF6nQp7AbDWon/4LRR0ib9CsWTN8++23KCgoQEhICIxGx9m4P/roI4SEhLjUNoMaIiIiHfjqSE0l2SrdANC4sfwPhOpgUENERKQDq7DBWsUaZNWpT4749hMRERF5BY7UEBER6cBXJ9+rTQxqiKjaXq7IUu4bb2glLd9310xp+Yhp6lWAO977iLTcVnhOWUcLCJbXKTgrLa8qGVfVlmZWrx4sSi/I6wTK2/IPbadsy6roM3kXq03A4MM5NbWBQQ0REZEOKmyA5tYq3R7sjJdgTg0RERF5BY7UEBER6YCPnzyPQQ0REZEOGNR4Hh8/ERERkVfgSA0RecRrtqPS8qe1VtLyTu//oGwrPu+8tDyyXzdlnYBuN8h32ORLHpT9skPZluqNqXJFWwCgmeRvRhnCm8jLm7aocVvkXThS43kMaoiIiHTAeWo8j4+fiIiIyCtwpIaIiEgHVptwa54aPn5yxqCGiIhIB0IICDcCEyEY1FyJQQ0R1ao3xFFp+eqozso6zXokSMv9W6qXFhCWUvmO4AhpsbFJlLKtol3b5DtOq5dpCIxsJj9PRHNpuSg8o2zLr9sQ5T4iUmNQQ0REpAObTbiV7MtEYWcMaoiIiHQghHDrERIfPzljUENERKQDYXMzp4YjNU74SjcRERF5BY7UEJEuwpsHK/cF3nCXtFycOa6sc/aL1dLysE4d5BUMRmVbpjB532zlFco6NlWisur4QnXSMfkG5tR4HoMaIiIiHQjbpc2d+uSIj5+IiIjIK3CkhoiISAd8+8nzGNQQERHpgDk1nseghoh0YSkuV+67sO4jafnpXb+q65wskJaHxF0jLTdGyGcABgBjYJC83GxV1tGCw+Q7/PxrVk5ELmNQQ0REpAPOU+N5DGqIiIj04GZQAwY1Tvj2ExEREXkFXYOaTZs24Y477kBMTAw0TcOaNWsc9gshkJKSgpiYGAQGBmLgwIHIzMzUp7NEREQeZBPC7Y0c6fr4qbi4GN26dcPo0aNx7733Ou2fP38+Fi5ciOXLl6Ndu3aYPXs2Bg8ejAMHDiA0NFSHHhORp9x6eJdy3yth7aXlRk3dXqe4cGl58fGT0vIQc4CyLUv+eWm5ZlT/HWhSJQrb5DOkmfo5/8wj3yKEmzk1DGqc6BrUDB06FEOHDpXuE0Jg0aJFmDZtGoYPHw4AWLFiBSIjI7Fy5UqMHTu2LrtKRETkUUwU9rx6m1OTlZWF3NxcDBkyxF5mNpuRlJSELVu2KOuVlZWhsLDQYSMiIiLvV2+DmtzcXABAZGSkQ3lkZKR9n0xaWhrCw8PtW2xsbK32k4iIyBU22+8T8Lm26X0F9U+9DWoqaZrjQ3QhhFPZ5aZOnYqCggL7lpOTU9tdJCIiqrHKZRLc2chRvZ2nJioqCsClEZvo6Gh7eV5entPozeXMZjPMZnOt94+IiIjql3ob1MTHxyMqKgoZGRno0aMHAMBisWDjxo2YN2+ezr0jotqUUyJfQiEmQP0ja8eR89Jy21ubpeXdx6oHqoNbtpC3VVKsrKMZ5O3ZLsj7RSRslzZ36pMjXYOaCxcu4NChQ/avs7KysHv3bjRu3BgtW7ZEcnIyUlNTkZCQgISEBKSmpiIoKAgjR47UsddERETus9kENC5o6VG6BjXbt2/HoEGD7F9PnjwZADBq1CgsX74cU6ZMQUlJCcaNG4f8/Hz06dMH6enpnKOGiIiInOga1AwcOLDKRCdN05CSkoKUlJS66xQREVEd4Dw1nldvc2qIiIi8GYMaz2NQQ0T1TqBiPYRmZqOyzk8FZdLybw6dk5a3PXFG2VZ4vyRpefnRX5R1So/Lp48wNZEnF5uULRGRqxjUEBER6cAmBDQ35prhgpbOGNQQERHpgI+fPI9BDRERkQ64Srfn1ftlEoiIiIiqgyM1RFTvBChm540wqX9kRfhXSMtPlcnLS88WKNuyFZ2XlltLLirrWC3yWZCtxReUdci3if8uTOlOfXLEoIaIiEgH7i5KycdPzvj4iYiIiLwCR2qIiIh0wLefPI9BDRERkQ5sNgFwQUuPYlBDRPXOtIsHpeWfxyYq65Qr8gtC/ORP2csK5TMQA4D1bK60PKB9d2UdU4l85mDzwIeUdYjIsxjUEBER6UDYrBA2q1v1yRGDGiIiIh0wqPE8vv1EREREXoEjNURERDoQNpubIzU2D/bGOzCoIaIGI7vIotxXYpX/gDcZNGn5bz/+pmwrPD5TWh4d0UzdOYNRvY9IQlitEFY3gho36norBjVEREQ6EMLNnBrBoOZKzKkhIiIir8CRGiIiIh3w7SfPY1BDRESkAwY1nsfHT0REROQVOFJDRA3GOYv6L1OLYh2cCxXyt6J+PluibCvqwElpeVCz3co6IXHXKPcRydTnkZo5c+bg888/x+7du2EymXD+/HmnY7KzszF+/Hh88803CAwMxMiRI/GPf/wDJpOp1vp1NQxqiIiIdFCf56mxWCwYMWIE+vXrh7fffttpv9VqxbBhw9CsWTNs3rwZZ8+exahRoyCEwD//+c9a69fVMKghIiIiBzNmzAAALF++XLo/PT0d+/fvR05ODmJiYgAAL730Eh577DHMmTMHYWFhddVVBwxqiIiIdGCzWQE3Rmps/61bWFjoUG42m2E2m93q29V8//336NKliz2gAYBbbrkFZWVl2LFjBwYNGlSr51dhojAREZEOKnNq3NkAIDY2FuHh4fYtLS2t1vuem5uLyMhIh7KIiAiYTCbk5ubW+vlVOFJDRA3GtIsHlfue828tLW9skv/tpkogBoCC40XS8ubFpco6QSOmKPcR1aacnByHxz2qUZqUlBT7YyWVbdu2oVevXtU6r6Y5L0EihJCW1xUGNURERDrw1NtPYWFh1cphmTBhAh544IEqj2nVqlW1zh0VFYUffvjBoSw/Px/l5eVOIzh1iUENERGRHqxWCIMbr2XXcEHLpk2bomnTpq6f7zL9+vXDnDlzcPLkSURHRwO4lDxsNpvRs2dPj5zDFQxqiIiIdCCEe4nCtbmgZXZ2Ns6dO4fs7GxYrVbs3r0bANC2bVuEhIRgyJAh6NSpEx555BEsWLAA586dw3PPPYcnn3xStzefAAY1REREdIUXX3wRK1assH/do0cPAMD69esxcOBAGI1GfP755xg3bhyuv/56h8n39MSghoi8gskgT070VyQtlipmIAaAU9kF0vLWpZaad4xIQdhs7o3U1OLke8uXL1fOUVOpZcuW+L//+79a64MrGNQQERHpQLg5Tw0XtHTGeWqIiIjIK3CkhoiISAeXHj+5/gipNh8/NVQMaoiIiHTAx0+ex6CGiLxCatlhafnMwLbS8oJy9S+ErOJyaXnsFvk5AKBlFX0jorrBoIaIiEgHHKnxPAY1REREOrDZrNAY1HgU334iIiIir9AggprXX38d8fHxCAgIQM+ePfHtt9/q3SUiIiK3CKsNwmp1Y+PbT1eq90HNhx9+iOTkZEybNg27du3CjTfeiKFDhyI7O1vvrhEREblMCKt9pW6Xtlpc+6mhqvc5NQsXLsQTTzyBMWPGAAAWLVqEtWvXYsmSJUhLS9O5d0RU371YcshjbX0em+ixtoiEzQpozKnxpHo9UmOxWLBjxw4MGTLEoXzIkCHYsmWLtE5ZWRkKCwsdNiIiIvJ+9TqoOXPmDKxWKyIjIx3KIyMjkZubK62TlpaG8PBw+xYbG1sXXSUiIqoRtx49/XcjR/U6qKmkXbHKrhDCqazS1KlTUVBQYN9ycnLqootEREQ1wqDG8+p1Tk3Tpk1hNBqdRmXy8vKcRm8qmc1mmM1m+9dCCADgYygictvFKn6J8GeMd6j8Plb+7qhV1nK4dRarfOZrX1avgxqTyYSePXsiIyMD99xzj708IyMDd911V7XaKCoqAgA+hiKi2hUerncPyIOKiooQXkvfU5PJhKioKOTu/7fbbUVFRcFkMnmgV96hXgc1ADB58mQ88sgj6NWrF/r164elS5ciOzsbTz/9dLXqx8TEICcnB6GhoSgqKkJsbCxycnIQFhZWyz2vfwoLC3n9vH5eP6+f11/F9QshUFRUhJiYmFrrS0BAALKysmCxWNxuy2QyISAgwAO98g71Pqi5//77cfbsWcycORMnT55Ely5d8MUXXyAuLq5a9Q0GA1q0aAHg99ycsLAwn/xHXYnXz+vn9fP6fVV1rr+2RmguFxAQwGCkFtT7oAYAxo0bh3HjxundDSIiIqrHGsTbT0RERERX41NBjdlsxvTp0x3ejvIlvH5eP6+f18/r983r9xWaqJP31oiIiIhql0+N1BAREZH3YlBDREREXoFBDREREXkFBjVERETkFXwqqHn99dcRHx+PgIAA9OzZE99++63eXaoVmzZtwh133IGYmBhomoY1a9Y47BdCICUlBTExMQgMDMTAgQORmZmpT2c9LC0tDb1790ZoaCiaN2+Ou+++GwcOHHA4xpuvf8mSJUhMTLRPMNavXz98+eWX9v3efO0yaWlp0DQNycnJ9jJvvgcpKSnQNM1hi4qKsu/35muvdOLECTz88MNo0qQJgoKC0L17d+zYscO+3xfugS/zmaDmww8/RHJyMqZNm4Zdu3bhxhtvxNChQ5Gdna131zyuuLgY3bp1w+LFi6X758+fj4ULF2Lx4sXYtm0boqKiMHjwYPs6WQ3Zxo0bMX78eGzduhUZGRmoqKjAkCFDUFxcbD/Gm6+/RYsWmDt3LrZv347t27fjD3/4A+666y77D21vvvYrbdu2DUuXLkViYqJDubffg86dO+PkyZP2be/evfZ93n7t+fn5uP766+Hv748vv/wS+/fvx0svvYRGjRrZj/H2e+DzhI+47rrrxNNPP+1Q1qFDB/H888/r1KO6AUCsXr3a/rXNZhNRUVFi7ty59rLS0lIRHh4u3njjDR16WLvy8vIEALFx40YhhO9dvxBCREREiLfeesunrr2oqEgkJCSIjIwMkZSUJCZOnCiE8P7v//Tp00W3bt2k+7z92oUQ4q9//au44YYblPt94R74Op8YqbFYLNixYweGDBniUD5kyBBs2bJFp17pIysrC7m5uQ73wmw2IykpySvvRUFBAQCgcePGAHzr+q1WK1atWoXi4mL069fPp659/PjxGDZsGG6++WaHcl+4BwcPHkRMTAzi4+PxwAMP4MiRIwB849o/++wz9OrVCyNGjEDz5s3Ro0cPvPnmm/b9vnAPfJ1PBDVnzpyB1WpFZGSkQ3lkZCRyc3N16pU+Kq/XF+6FEAKTJ0/GDTfcgC5dugDwjevfu3cvQkJCYDab8fTTT2P16tXo1KmTT1w7AKxatQo7d+5EWlqa0z5vvwd9+vTBu+++i7Vr1+LNN99Ebm4u+vfvj7Nnz3r9tQPAkSNHsGTJEiQkJGDt2rV4+umn8eyzz+Ldd98F4P3ff2ogC1p6SuUq3ZWEEE5lvsIX7sWECROwZ88ebN682WmfN19/+/btsXv3bpw/fx6ffPIJRo0ahY0bN9r3e/O15+TkYOLEiUhPT69yBWRvvQdDhw61/3/Xrl3Rr18/tGnTBitWrEDfvn0BeO+1A4DNZkOvXr2QmpoKAOjRowcyMzOxZMkSPProo/bjvPke+DqfGKlp2rQpjEajUySel5fnFLF7u8o3Ibz9XjzzzDP47LPPsH79erRo0cJe7gvXbzKZ0LZtW/Tq1QtpaWno1q0bXnnlFZ+49h07diAvLw89e/aEn58f/Pz8sHHjRrz66qvw8/OzX6c334PLBQcHo2vXrjh48KBPfP+jo6PRqVMnh7KOHTvaXwjxhXvg63wiqDGZTOjZsycyMjIcyjMyMtC/f3+deqWP+Ph4REVFOdwLi8WCjRs3esW9EEJgwoQJ+PTTT/HNN98gPj7eYb+3X7+MEAJlZWU+ce033XQT9u7di927d9u3Xr164aGHHsLu3bvRunVrr78HlysrK8PPP/+M6Ohon/j+X3/99U5TOPz666+Ii4sD4Jv//n2OXhnKdW3VqlXC399fvP3222L//v0iOTlZBAcHi6NHj+rdNY8rKioSu3btErt27RIAxMKFC8WuXbvEsWPHhBBCzJ07V4SHh4tPP/1U7N27Vzz44IMiOjpaFBYW6txz9/3pT38S4eHhYsOGDeLkyZP27eLFi/ZjvPn6p06dKjZt2iSysrLEnj17xN/+9jdhMBhEenq6EMK7r13l8refhPDue/DnP/9ZbNiwQRw5ckRs3bpV3H777SI0NNT+c86br10IIX788Ufh5+cn5syZIw4ePCg++OADERQUJN5//337Md5+D3ydzwQ1Qgjx2muvibi4OGEymcS1115rf83X26xfv14AcNpGjRolhLj0WuP06dNFVFSUMJvNYsCAAWLv3r36dtpDZNcNQCxbtsx+jDdf/+OPP27/jDdr1kzcdNNN9oBGCO++dpUrgxpvvgf333+/iI6OFv7+/iImJkYMHz5cZGZm2vd787VX+s9//iO6dOkizGaz6NChg1i6dKnDfl+4B75ME0IIfcaIiIiIiDzHJ3JqiIiIyPsxqCEiIiKvwKCGiIiIvAKDGiIiIvIKDGqIiIjIKzCoISIiIq/AoIaIiIi8AoMaIiIi8goMaoiIiMgrMKghIiIir8CghsiHnD59GlFRUUhNTbWX/fDDDzCZTEhPT9exZ0RE7uPaT0Q+5osvvsDdd9+NLVu2oEOHDujRoweGDRuGRYsW6d01IiK3MKgh8kHjx4/HunXr0Lt3b/z000/Ytm0bAgIC9O4WEZFbGNQQ+aCSkhJ06dIFOTk52L59OxITE/XuEhGR25hTQ+SDjhw5gt9++w02mw3Hjh3TuztERB7BkRoiH2OxWHDdddehe/fu6NChAxYuXIi9e/ciMjJS764REbmFQQ2Rj/nLX/6Cjz/+GD/99BNCQkIwaNAghIaG4v/+7//07hoRkVv4+InIh2zYsAGLFi3Ce++9h7CwMBgMBrz33nvYvHkzlixZonf3iIjcwpEaIiIi8gocqSEiIiKvwKCGiIiIvAKDGiIiIvIKDGqIiIjIKzCoISIiIq/AoIaIiIi8AoMaIiIi8goMaoiIiMgrMKghIiIir8CghoiIiLwCgxoiIiLyCgxqiIiIyCv8P066yrDdfqfKAAAAAElFTkSuQmCC", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "da_masked = da.where(mask_lon & mask_lat, drop=True)\n", "\n", @@ -2063,20 +294,9 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure(figsize=(5, 5))\n", "ax = plt.axes(projection=ccrs.PlateCarree())\n", @@ -2084,8 +304,8 @@ "da_masked[0, :, :].plot.pcolormesh(\n", " ax=ax, transform=ccrs.PlateCarree(), x=\"xc\", y=\"yc\", add_colorbar=False\n", ")\n", - "ax.coastlines();\n", - "ax.set_ylim([50, 80]);\n", + "ax.coastlines()\n", + "ax.set_ylim([50, 80])\n", "ax.set_xlim([-180, -120]);" ] }, @@ -2140,40 +360,20 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Define a function to use as a condition\n", "def is_greater_than_threshold(x, threshhold=300):\n", " # function to convert temp to K\n", " # and compare with threshhold\n", - " x= x+273.15 \n", + " x = x + 273.15\n", " return x > threshhold\n", "\n", + "\n", "# Apply the condition using xarray.where()\n", - "masked_data = xr.where(is_greater_than_threshold(da,280), da, 0)\n", + "masked_data = xr.where(is_greater_than_threshold(da, 280), da, 0)\n", "\n", "masked_data[0].plot()" ] @@ -2198,390 +398,9 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray (x: 5)>\n",
-       "array([False,  True, False,  True, False])\n",
-       "Dimensions without coordinates: x
    " - ], - "text/plain": [ - "\n", - "array([False, True, False, True, False])\n", - "Dimensions without coordinates: x" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "x_da = xr.DataArray([1, 2, 3, 4, 5], dims=[\"x\"])\n", "\n", @@ -2604,590 +423,9 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray (time: 36, y: 205, x: 275)>\n",
-       "array([[[ 0,  2,  0, ...,  1, -1,  1],\n",
-       "        [ 4, -1,  4, ..., -1,  1,  2],\n",
-       "        [ 0,  0,  2, ...,  0, -1, -1],\n",
-       "        ...,\n",
-       "        [ 4,  1,  4, ...,  1, -1,  2],\n",
-       "        [ 2,  1,  2, ...,  4,  3,  0],\n",
-       "        [ 4,  3, -1, ...,  2,  3,  3]],\n",
-       "\n",
-       "       [[ 1,  0,  3, ...,  1,  3,  0],\n",
-       "        [ 3,  2, -1, ...,  0, -1, -1],\n",
-       "        [ 3,  4,  2, ..., -1,  0,  0],\n",
-       "        ...,\n",
-       "        [ 4,  3,  4, ...,  0,  4,  3],\n",
-       "        [-1,  1,  1, ...,  2,  0, -1],\n",
-       "        [ 0,  4,  1, ...,  4,  4,  3]],\n",
-       "\n",
-       "       [[-1,  0,  1, ...,  2,  3,  0],\n",
-       "        [-1,  1,  2, ..., -1,  0,  4],\n",
-       "        [-1,  0,  0, ...,  0,  1,  4],\n",
-       "        ...,\n",
-       "...\n",
-       "        ...,\n",
-       "        [ 4,  2,  1, ...,  4, -1,  2],\n",
-       "        [ 0,  1,  0, ...,  2,  1,  3],\n",
-       "        [ 2,  1,  1, ...,  4, -1,  0]],\n",
-       "\n",
-       "       [[ 4, -1,  1, ...,  4,  3,  0],\n",
-       "        [ 4,  0,  1, ..., -1,  4,  4],\n",
-       "        [ 1, -1,  4, ...,  0,  2,  2],\n",
-       "        ...,\n",
-       "        [ 3,  4, -1, ...,  4,  0,  2],\n",
-       "        [ 0,  1,  3, ...,  3,  2, -1],\n",
-       "        [ 4,  0, -1, ...,  3,  0, -1]],\n",
-       "\n",
-       "       [[ 3,  4,  2, ...,  0,  2, -1],\n",
-       "        [ 0,  3,  2, ..., -1,  1,  2],\n",
-       "        [ 4,  3,  0, ...,  0,  0,  3],\n",
-       "        ...,\n",
-       "        [ 3, -1,  0, ...,  0,  1, -1],\n",
-       "        [-1,  1,  2, ...,  1,  0, -1],\n",
-       "        [ 2,  2,  0, ...,  0,  4,  0]]])\n",
-       "Coordinates:\n",
-       "  * time     (time) object 1980-09-16 12:00:00 ... 1983-08-17 00:00:00\n",
-       "    xc       (y, x) float64 189.2 189.4 189.6 189.7 ... 17.65 17.4 17.15 16.91\n",
-       "    yc       (y, x) float64 16.53 16.78 17.02 17.27 ... 28.26 28.01 27.76 27.51\n",
-       "Dimensions without coordinates: y, x
    " - ], - "text/plain": [ - "\n", - "array([[[ 0, 2, 0, ..., 1, -1, 1],\n", - " [ 4, -1, 4, ..., -1, 1, 2],\n", - " [ 0, 0, 2, ..., 0, -1, -1],\n", - " ...,\n", - " [ 4, 1, 4, ..., 1, -1, 2],\n", - " [ 2, 1, 2, ..., 4, 3, 0],\n", - " [ 4, 3, -1, ..., 2, 3, 3]],\n", - "\n", - " [[ 1, 0, 3, ..., 1, 3, 0],\n", - " [ 3, 2, -1, ..., 0, -1, -1],\n", - " [ 3, 4, 2, ..., -1, 0, 0],\n", - " ...,\n", - " [ 4, 3, 4, ..., 0, 4, 3],\n", - " [-1, 1, 1, ..., 2, 0, -1],\n", - " [ 0, 4, 1, ..., 4, 4, 3]],\n", - "\n", - " [[-1, 0, 1, ..., 2, 3, 0],\n", - " [-1, 1, 2, ..., -1, 0, 4],\n", - " [-1, 0, 0, ..., 0, 1, 4],\n", - " ...,\n", - "...\n", - " ...,\n", - " [ 4, 2, 1, ..., 4, -1, 2],\n", - " [ 0, 1, 0, ..., 2, 1, 3],\n", - " [ 2, 1, 1, ..., 4, -1, 0]],\n", - "\n", - " [[ 4, -1, 1, ..., 4, 3, 0],\n", - " [ 4, 0, 1, ..., -1, 4, 4],\n", - " [ 1, -1, 4, ..., 0, 2, 2],\n", - " ...,\n", - " [ 3, 4, -1, ..., 4, 0, 2],\n", - " [ 0, 1, 3, ..., 3, 2, -1],\n", - " [ 4, 0, -1, ..., 3, 0, -1]],\n", - "\n", - " [[ 3, 4, 2, ..., 0, 2, -1],\n", - " [ 0, 3, 2, ..., -1, 1, 2],\n", - " [ 4, 3, 0, ..., 0, 0, 3],\n", - " ...,\n", - " [ 3, -1, 0, ..., 0, 1, -1],\n", - " [-1, 1, 2, ..., 1, 0, -1],\n", - " [ 2, 2, 0, ..., 0, 4, 0]]])\n", - "Coordinates:\n", - " * time (time) object 1980-09-16 12:00:00 ... 1983-08-17 00:00:00\n", - " xc (y, x) float64 189.2 189.4 189.6 189.7 ... 17.65 17.4 17.15 16.91\n", - " yc (y, x) float64 16.53 16.78 17.02 17.27 ... 28.26 28.01 27.76 27.51\n", - "Dimensions without coordinates: y, x" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "flags = xr.DataArray(np.random.randint(-1, 5, da.shape), dims=da.dims, coords=da.coords)\n", "flags" @@ -3202,20 +440,9 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "da_masked = da.where(flags.isin([1, 2, 3, 4, 5]), drop=True)\n", "da_masked[0, :, :].plot();" @@ -3241,11 +468,6 @@ } ], "metadata": { - "kernelspec": { - "display_name": "NPL 2023b", - "language": "python", - "name": "npl-2023b" - }, "language_info": { "codemirror_mode": { "name": "ipython", @@ -3255,8 +477,7 @@ "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" + "pygments_lexer": "ipython3" }, "toc": { "base_numbering": 1, @@ -3270,13 +491,6 @@ "toc_position": {}, "toc_section_display": true, "toc_window_display": true - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } } }, "nbformat": 4, From dad0c54f6a3aafd312199e24040b0d0a265d4381 Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Fri, 7 Jul 2023 19:52:12 -0600 Subject: [PATCH 33/54] updates --- fundamentals/02.1_indexing_Basic.ipynb | 4461 +++++++++++++++++++++++- 1 file changed, 4421 insertions(+), 40 deletions(-) diff --git a/fundamentals/02.1_indexing_Basic.ipynb b/fundamentals/02.1_indexing_Basic.ipynb index 89011625..0e0053a4 100644 --- a/fundamentals/02.1_indexing_Basic.ipynb +++ b/fundamentals/02.1_indexing_Basic.ipynb @@ -53,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -66,14 +66,488 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Here we’ll use air temperature tutorial dataset from the National Center for Environmental Prediction. " + "Here we’ll use air temperature tutorial dataset from the [National Center for Environmental Prediction](https://www.weather.gov/ncep/). " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.Dataset>\n",
+       "Dimensions:  (lat: 25, time: 2920, lon: 53)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Data variables:\n",
+       "    air      (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n",
+       "Attributes:\n",
+       "    Conventions:  COARDS\n",
+       "    title:        4x daily NMC reanalysis (1948)\n",
+       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
+       "    platform:     Model\n",
+       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
    " + ], + "text/plain": [ + "\n", + "Dimensions: (lat: 25, time: 2920, lon: 53)\n", + "Coordinates:\n", + " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", + " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Data variables:\n", + " air (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n", + "Attributes:\n", + " Conventions: COARDS\n", + " title: 4x daily NMC reanalysis (1948)\n", + " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", + " platform: Model\n", + " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ds = xr.tutorial.load_dataset(\"air_temperature\")\n", "ds" @@ -81,7 +555,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -111,9 +585,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 52, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(2920, 25, 53)" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "np_array = ds[\"air\"].data # numpy array\n", "np_array.shape" @@ -128,9 +613,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "242.09999" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "np_array[1, 0, 0]" ] @@ -144,9 +640,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 54, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([295. , 294.4 , 294.5 , ..., 297.29, 297.79, 297.99], dtype=float32)" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# extract a time-series for one spatial location\n", "np_array[:, 20, 40]" @@ -178,9 +685,434 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'air' (time: 2920)>\n",
+       "array([295.  , 294.4 , 294.5 , ..., 297.29, 297.79, 297.99], dtype=float32)\n",
+       "Coordinates:\n",
+       "    lat      float32 25.0\n",
+       "    lon      float32 300.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
    " + ], + "text/plain": [ + "\n", + "array([295. , 294.4 , 294.5 , ..., 297.29, 297.79, 297.99], dtype=float32)\n", + "Coordinates:\n", + " lat float32 25.0\n", + " lon float32 300.0\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "da[:, 20, 40]" ] @@ -198,20 +1130,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 56, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(2920, 2)" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "np_array[:, [0, 1], [0, 1]].shape" + "np_array[:,[0, 1], [0, 1]].shape" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 57, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(2920, 2, 2)" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "da[:, [0, 1], [0, 1]].shape" + "da[:,[0, 1], [0, 1]].shape" ] }, { @@ -221,7 +1175,7 @@ "Please note how the dimension of the `DataArray()` object is different from the `numpy.ndarray`.\n", "\n", "``` tip\n", - " However, users can still achieve NumPy-like pointwise indexing across multiple labeled dimensions by using Xarray vectorized indexing techniques. We will delve further into this topic in the advanced indexing notebook.\n", + "However, users can still achieve NumPy-like pointwise indexing across multiple labeled dimensions by using Xarray vectorized indexing techniques. We will delve further into this topic in the advanced indexing notebook.\n", " ```" ] }, @@ -244,9 +1198,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 58, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "da.isel(lat=20, lon=40).plot();" ] @@ -260,9 +1225,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 59, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "da.isel(time=slice(0, 20), lat=20, lon=40).plot();" ] @@ -296,13 +1272,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 60, "metadata": { "tags": [ "hide-output" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "da.sel(time=\"2014-01-01\", lat=25, lon=210).plot();" ] @@ -316,13 +1303,438 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 61, "metadata": { "tags": [ "hide-output" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'air' (time: 1460)>\n",
+       "array([277.6 , 277.5 , 277.4 , ..., 277.59, 277.59, 277.59], dtype=float32)\n",
+       "Coordinates:\n",
+       "    lat      float32 50.0\n",
+       "    lon      float32 200.0\n",
+       "  * time     (time) datetime64[ns] 2014-01-01 ... 2014-12-31T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
    " + ], + "text/plain": [ + "\n", + "array([277.6 , 277.5 , 277.4 , ..., 277.59, 277.59, 277.59], dtype=float32)\n", + "Coordinates:\n", + " lat float32 50.0\n", + " lon float32 200.0\n", + " * time (time) datetime64[ns] 2014-01-01 ... 2014-12-31T18:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "da.sel(lat=50.0, lon=200.0, time=\"2014\")" ] @@ -336,9 +1748,570 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 62, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'air' (time: 1212, lat: 25, lon: 53)>\n",
+       "array([[[246.     , 245.09999, 244.09999, ..., 240.2    , 242.     ,\n",
+       "         244.2    ],\n",
+       "        [240.59999, 241.7    , 242.09999, ..., 239.09999, 241.2    ,\n",
+       "         244.59999],\n",
+       "        [238.2    , 239.79999, 240.79999, ..., 243.89   , 246.89   ,\n",
+       "         251.2    ],\n",
+       "        ...,\n",
+       "        [295.6    , 295.6    , 296.     , ..., 293.6    , 293.6    ,\n",
+       "         294.1    ],\n",
+       "        [297.     , 297.     , 297.     , ..., 293.69998, 293.9    ,\n",
+       "         294.6    ],\n",
+       "        [297.5    , 297.69998, 297.69998, ..., 294.5    , 294.69998,\n",
+       "         295.19998]],\n",
+       "\n",
+       "       [[245.2    , 244.29999, 243.     , ..., 241.89   , 243.09999,\n",
+       "         244.59999],\n",
+       "        [241.09999, 241.89   , 241.89   , ..., 241.89   , 243.59999,\n",
+       "         246.39   ],\n",
+       "        [239.89   , 241.     , 241.29999, ..., 245.09999, 248.2    ,\n",
+       "         252.2    ],\n",
+       "...\n",
+       "        [296.49   , 295.59   , 295.49   , ..., 297.29   , 297.38998,\n",
+       "         296.79   ],\n",
+       "        [298.09   , 297.79   , 297.19   , ..., 297.69   , 298.09   ,\n",
+       "         297.38998],\n",
+       "        [298.49   , 298.29   , 297.99   , ..., 298.29   , 298.09   ,\n",
+       "         297.99   ]],\n",
+       "\n",
+       "       [[246.29   , 246.39   , 245.98999, ..., 232.29   , 233.39   ,\n",
+       "         234.98999],\n",
+       "        [246.68999, 248.18999, 248.98999, ..., 230.59   , 232.48999,\n",
+       "         235.68999],\n",
+       "        [244.79   , 245.29   , 245.89   , ..., 230.48999, 235.09   ,\n",
+       "         241.39   ],\n",
+       "        ...,\n",
+       "        [296.88998, 296.49   , 296.49   , ..., 297.69   , 297.29   ,\n",
+       "         296.19   ],\n",
+       "        [298.29   , 297.99   , 297.49   , ..., 298.29   , 298.38998,\n",
+       "         297.49   ],\n",
+       "        [298.49   , 298.29   , 297.99   , ..., 298.59   , 298.38998,\n",
+       "         298.29   ]]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
    " + ], + "text/plain": [ + "\n", + "array([[[246. , 245.09999, 244.09999, ..., 240.2 , 242. ,\n", + " 244.2 ],\n", + " [240.59999, 241.7 , 242.09999, ..., 239.09999, 241.2 ,\n", + " 244.59999],\n", + " [238.2 , 239.79999, 240.79999, ..., 243.89 , 246.89 ,\n", + " 251.2 ],\n", + " ...,\n", + " [295.6 , 295.6 , 296. , ..., 293.6 , 293.6 ,\n", + " 294.1 ],\n", + " [297. , 297. , 297. , ..., 293.69998, 293.9 ,\n", + " 294.6 ],\n", + " [297.5 , 297.69998, 297.69998, ..., 294.5 , 294.69998,\n", + " 295.19998]],\n", + "\n", + " [[245.2 , 244.29999, 243. , ..., 241.89 , 243.09999,\n", + " 244.59999],\n", + " [241.09999, 241.89 , 241.89 , ..., 241.89 , 243.59999,\n", + " 246.39 ],\n", + " [239.89 , 241. , 241.29999, ..., 245.09999, 248.2 ,\n", + " 252.2 ],\n", + "...\n", + " [296.49 , 295.59 , 295.49 , ..., 297.29 , 297.38998,\n", + " 296.79 ],\n", + " [298.09 , 297.79 , 297.19 , ..., 297.69 , 298.09 ,\n", + " 297.38998],\n", + " [298.49 , 298.29 , 297.99 , ..., 298.29 , 298.09 ,\n", + " 297.99 ]],\n", + "\n", + " [[246.29 , 246.39 , 245.98999, ..., 232.29 , 233.39 ,\n", + " 234.98999],\n", + " [246.68999, 248.18999, 248.98999, ..., 230.59 , 232.48999,\n", + " 235.68999],\n", + " [244.79 , 245.29 , 245.89 , ..., 230.48999, 235.09 ,\n", + " 241.39 ],\n", + " ...,\n", + " [296.88998, 296.49 , 296.49 , ..., 297.69 , 297.29 ,\n", + " 296.19 ],\n", + " [298.29 , 297.99 , 297.49 , ..., 298.29 , 298.38998,\n", + " 297.49 ],\n", + " [298.49 , 298.29 , 297.99 , ..., 298.59 , 298.38998,\n", + " 298.29 ]]], dtype=float32)\n", + "Coordinates:\n", + " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", + " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", + " * time (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# demonstrate slicing\n", "da.sel(time=slice(\"2014-02-14\", \"2014-12-13\"))" @@ -353,9 +2326,570 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 63, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'air' (time: 1212, lat: 25, lon: 53)>\n",
+       "array([[[246.     , 245.09999, 244.09999, ..., 240.2    , 242.     ,\n",
+       "         244.2    ],\n",
+       "        [240.59999, 241.7    , 242.09999, ..., 239.09999, 241.2    ,\n",
+       "         244.59999],\n",
+       "        [238.2    , 239.79999, 240.79999, ..., 243.89   , 246.89   ,\n",
+       "         251.2    ],\n",
+       "        ...,\n",
+       "        [295.6    , 295.6    , 296.     , ..., 293.6    , 293.6    ,\n",
+       "         294.1    ],\n",
+       "        [297.     , 297.     , 297.     , ..., 293.69998, 293.9    ,\n",
+       "         294.6    ],\n",
+       "        [297.5    , 297.69998, 297.69998, ..., 294.5    , 294.69998,\n",
+       "         295.19998]],\n",
+       "\n",
+       "       [[245.2    , 244.29999, 243.     , ..., 241.89   , 243.09999,\n",
+       "         244.59999],\n",
+       "        [241.09999, 241.89   , 241.89   , ..., 241.89   , 243.59999,\n",
+       "         246.39   ],\n",
+       "        [239.89   , 241.     , 241.29999, ..., 245.09999, 248.2    ,\n",
+       "         252.2    ],\n",
+       "...\n",
+       "        [296.49   , 295.59   , 295.49   , ..., 297.29   , 297.38998,\n",
+       "         296.79   ],\n",
+       "        [298.09   , 297.79   , 297.19   , ..., 297.69   , 298.09   ,\n",
+       "         297.38998],\n",
+       "        [298.49   , 298.29   , 297.99   , ..., 298.29   , 298.09   ,\n",
+       "         297.99   ]],\n",
+       "\n",
+       "       [[246.29   , 246.39   , 245.98999, ..., 232.29   , 233.39   ,\n",
+       "         234.98999],\n",
+       "        [246.68999, 248.18999, 248.98999, ..., 230.59   , 232.48999,\n",
+       "         235.68999],\n",
+       "        [244.79   , 245.29   , 245.89   , ..., 230.48999, 235.09   ,\n",
+       "         241.39   ],\n",
+       "        ...,\n",
+       "        [296.88998, 296.49   , 296.49   , ..., 297.69   , 297.29   ,\n",
+       "         296.19   ],\n",
+       "        [298.29   , 297.99   , 297.49   , ..., 298.29   , 298.38998,\n",
+       "         297.49   ],\n",
+       "        [298.49   , 298.29   , 297.99   , ..., 298.59   , 298.38998,\n",
+       "         298.29   ]]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
    " + ], + "text/plain": [ + "\n", + "array([[[246. , 245.09999, 244.09999, ..., 240.2 , 242. ,\n", + " 244.2 ],\n", + " [240.59999, 241.7 , 242.09999, ..., 239.09999, 241.2 ,\n", + " 244.59999],\n", + " [238.2 , 239.79999, 240.79999, ..., 243.89 , 246.89 ,\n", + " 251.2 ],\n", + " ...,\n", + " [295.6 , 295.6 , 296. , ..., 293.6 , 293.6 ,\n", + " 294.1 ],\n", + " [297. , 297. , 297. , ..., 293.69998, 293.9 ,\n", + " 294.6 ],\n", + " [297.5 , 297.69998, 297.69998, ..., 294.5 , 294.69998,\n", + " 295.19998]],\n", + "\n", + " [[245.2 , 244.29999, 243. , ..., 241.89 , 243.09999,\n", + " 244.59999],\n", + " [241.09999, 241.89 , 241.89 , ..., 241.89 , 243.59999,\n", + " 246.39 ],\n", + " [239.89 , 241. , 241.29999, ..., 245.09999, 248.2 ,\n", + " 252.2 ],\n", + "...\n", + " [296.49 , 295.59 , 295.49 , ..., 297.29 , 297.38998,\n", + " 296.79 ],\n", + " [298.09 , 297.79 , 297.19 , ..., 297.69 , 298.09 ,\n", + " 297.38998],\n", + " [298.49 , 298.29 , 297.99 , ..., 298.29 , 298.09 ,\n", + " 297.99 ]],\n", + "\n", + " [[246.29 , 246.39 , 245.98999, ..., 232.29 , 233.39 ,\n", + " 234.98999],\n", + " [246.68999, 248.18999, 248.98999, ..., 230.59 , 232.48999,\n", + " 235.68999],\n", + " [244.79 , 245.29 , 245.89 , ..., 230.48999, 235.09 ,\n", + " 241.39 ],\n", + " ...,\n", + " [296.88998, 296.49 , 296.49 , ..., 297.69 , 297.29 ,\n", + " 296.19 ],\n", + " [298.29 , 297.99 , 297.49 , ..., 298.29 , 298.38998,\n", + " 297.49 ],\n", + " [298.49 , 298.29 , 297.99 , ..., 298.59 , 298.38998,\n", + " 298.29 ]]], dtype=float32)\n", + "Coordinates:\n", + " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", + " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", + " * time (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "da.loc[\"2014-02-14\":\"2014-12-13\"]" ] @@ -371,9 +2905,570 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 64, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'air' (time: 2919, lat: 24, lon: 52)>\n",
+       "array([[[242.5    , 243.5    , 244.     , ..., 232.79999, 235.5    ,\n",
+       "         238.59999],\n",
+       "        [244.5    , 244.7    , 244.2    , ..., 232.79999, 235.29999,\n",
+       "         239.29999],\n",
+       "        [249.79999, 248.89   , 247.5    , ..., 233.2    , 236.39   ,\n",
+       "         241.7    ],\n",
+       "        ...,\n",
+       "        [296.19998, 296.4    , 296.5    , ..., 295.4    , 295.1    ,\n",
+       "         294.69998],\n",
+       "        [296.19998, 296.79   , 296.5    , ..., 295.9    , 295.9    ,\n",
+       "         295.19998],\n",
+       "        [296.79   , 297.1    , 297.     , ..., 296.9    , 296.79   ,\n",
+       "         296.6    ]],\n",
+       "\n",
+       "       [[242.7    , 243.09999, 243.39   , ..., 232.     , 233.59999,\n",
+       "         235.79999],\n",
+       "        [244.09999, 244.2    , 244.09999, ..., 231.     , 232.5    ,\n",
+       "         235.7    ],\n",
+       "        [252.89   , 252.09999, 250.79999, ..., 230.79999, 233.39   ,\n",
+       "         238.5    ],\n",
+       "...\n",
+       "        [293.88998, 295.38998, 297.19   , ..., 295.09   , 294.69   ,\n",
+       "         294.29   ],\n",
+       "        [297.19   , 297.59   , 297.88998, ..., 295.29   , 295.09   ,\n",
+       "         294.38998],\n",
+       "        [298.38998, 298.49   , 298.59   , ..., 295.69   , 295.49   ,\n",
+       "         295.19   ]],\n",
+       "\n",
+       "       [[244.29   , 243.29   , 242.18999, ..., 241.68999, 241.48999,\n",
+       "         241.79   ],\n",
+       "        [249.29   , 248.39   , 246.98999, ..., 239.59   , 240.29   ,\n",
+       "         241.68999],\n",
+       "        [262.19   , 261.38998, 259.99   , ..., 239.89   , 242.59   ,\n",
+       "         246.29   ],\n",
+       "        ...,\n",
+       "        [293.69   , 295.09   , 296.69   , ..., 295.29   , 295.09   ,\n",
+       "         294.69   ],\n",
+       "        [296.88998, 297.19   , 297.49   , ..., 295.69   , 295.69   ,\n",
+       "         295.19   ],\n",
+       "        [298.09   , 298.09   , 298.49   , ..., 296.49   , 296.19   ,\n",
+       "         295.69   ]]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 202.5 205.0 207.5 210.0 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
    " + ], + "text/plain": [ + "\n", + "array([[[242.5 , 243.5 , 244. , ..., 232.79999, 235.5 ,\n", + " 238.59999],\n", + " [244.5 , 244.7 , 244.2 , ..., 232.79999, 235.29999,\n", + " 239.29999],\n", + " [249.79999, 248.89 , 247.5 , ..., 233.2 , 236.39 ,\n", + " 241.7 ],\n", + " ...,\n", + " [296.19998, 296.4 , 296.5 , ..., 295.4 , 295.1 ,\n", + " 294.69998],\n", + " [296.19998, 296.79 , 296.5 , ..., 295.9 , 295.9 ,\n", + " 295.19998],\n", + " [296.79 , 297.1 , 297. , ..., 296.9 , 296.79 ,\n", + " 296.6 ]],\n", + "\n", + " [[242.7 , 243.09999, 243.39 , ..., 232. , 233.59999,\n", + " 235.79999],\n", + " [244.09999, 244.2 , 244.09999, ..., 231. , 232.5 ,\n", + " 235.7 ],\n", + " [252.89 , 252.09999, 250.79999, ..., 230.79999, 233.39 ,\n", + " 238.5 ],\n", + "...\n", + " [293.88998, 295.38998, 297.19 , ..., 295.09 , 294.69 ,\n", + " 294.29 ],\n", + " [297.19 , 297.59 , 297.88998, ..., 295.29 , 295.09 ,\n", + " 294.38998],\n", + " [298.38998, 298.49 , 298.59 , ..., 295.69 , 295.49 ,\n", + " 295.19 ]],\n", + "\n", + " [[244.29 , 243.29 , 242.18999, ..., 241.68999, 241.48999,\n", + " 241.79 ],\n", + " [249.29 , 248.39 , 246.98999, ..., 239.59 , 240.29 ,\n", + " 241.68999],\n", + " [262.19 , 261.38998, 259.99 , ..., 239.89 , 242.59 ,\n", + " 246.29 ],\n", + " ...,\n", + " [293.69 , 295.09 , 296.69 , ..., 295.29 , 295.09 ,\n", + " 294.69 ],\n", + " [296.88998, 297.19 , 297.49 , ..., 295.69 , 295.69 ,\n", + " 295.19 ],\n", + " [298.09 , 298.09 , 298.49 , ..., 296.49 , 296.19 ,\n", + " 295.69 ]]], dtype=float32)\n", + "Coordinates:\n", + " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", + " * lon (lon) float32 202.5 205.0 207.5 210.0 ... 322.5 325.0 327.5 330.0\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "da.drop_sel(lat=50.0, lon=200.0, time=\"2014\")" ] @@ -392,13 +3487,441 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 65, "metadata": { "tags": [ "hide-output" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'air' (time: 2920)>\n",
+       "array([262.69998, 263.19998, 270.9    , ..., 264.19   , 265.19   ,\n",
+       "       266.99   ], dtype=float32)\n",
+       "Coordinates:\n",
+       "    lat      float32 52.5\n",
+       "    lon      float32 252.5\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
    " + ], + "text/plain": [ + "\n", + "array([262.69998, 263.19998, 270.9 , ..., 264.19 , 265.19 ,\n", + " 266.99 ], dtype=float32)\n", + "Coordinates:\n", + " lat float32 52.5\n", + " lon float32 252.5\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "da.sel(lat=52.25, lon=251.8998, method=\"nearest\")" ] @@ -412,9 +3935,437 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 66, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'air' (time: 2920)>\n",
+       "array([262.69998, 263.19998, 270.9    , ..., 264.19   , 265.19   ,\n",
+       "       266.99   ], dtype=float32)\n",
+       "Coordinates:\n",
+       "    lat      float32 52.5\n",
+       "    lon      float32 252.5\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
    " + ], + "text/plain": [ + "\n", + "array([262.69998, 263.19998, 270.9 , ..., 264.19 , 265.19 ,\n", + " 266.99 ], dtype=float32)\n", + "Coordinates:\n", + " lat float32 52.5\n", + " lon float32 252.5\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "da.sel(lat=52.25, lon=251.8998, method=\"nearest\", tolerance=2)" ] @@ -432,9 +4383,428 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 67, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.Dataset>\n",
+       "Dimensions:  (time: 2920)\n",
+       "Coordinates:\n",
+       "    lat      float32 52.5\n",
+       "    lon      float32 252.5\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Data variables:\n",
+       "    air      (time) float32 262.7 263.2 270.9 274.1 ... 261.6 264.2 265.2 267.0\n",
+       "Attributes:\n",
+       "    Conventions:  COARDS\n",
+       "    title:        4x daily NMC reanalysis (1948)\n",
+       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
+       "    platform:     Model\n",
+       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
    " + ], + "text/plain": [ + "\n", + "Dimensions: (time: 2920)\n", + "Coordinates:\n", + " lat float32 52.5\n", + " lon float32 252.5\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Data variables:\n", + " air (time) float32 262.7 263.2 270.9 274.1 ... 261.6 264.2 265.2 267.0\n", + "Attributes:\n", + " Conventions: COARDS\n", + " title: 4x daily NMC reanalysis (1948)\n", + " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", + " platform: Model\n", + " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ds.sel(lat=52.25, lon=251.8998, method=\"nearest\")" ] @@ -528,6 +4898,10 @@ } ], "metadata": { + "kernelspec": { + "display_name": "", + "name": "" + }, "language_info": { "codemirror_mode": { "name": "ipython", @@ -551,6 +4925,13 @@ "toc_position": {}, "toc_section_display": true, "toc_window_display": true + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } } }, "nbformat": 4, From c8650d4b8493621042e72b01b115182991bf23d4 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Sat, 8 Jul 2023 01:52:32 +0000 Subject: [PATCH 34/54] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- fundamentals/02.1_indexing_Basic.ipynb | 4457 +----------------------- 1 file changed, 38 insertions(+), 4419 deletions(-) diff --git a/fundamentals/02.1_indexing_Basic.ipynb b/fundamentals/02.1_indexing_Basic.ipynb index 0e0053a4..8c89f467 100644 --- a/fundamentals/02.1_indexing_Basic.ipynb +++ b/fundamentals/02.1_indexing_Basic.ipynb @@ -53,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -71,483 +71,9 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.Dataset>\n",
-       "Dimensions:  (lat: 25, time: 2920, lon: 53)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Data variables:\n",
-       "    air      (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n",
-       "Attributes:\n",
-       "    Conventions:  COARDS\n",
-       "    title:        4x daily NMC reanalysis (1948)\n",
-       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
-       "    platform:     Model\n",
-       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
    " - ], - "text/plain": [ - "\n", - "Dimensions: (lat: 25, time: 2920, lon: 53)\n", - "Coordinates:\n", - " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", - " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Data variables:\n", - " air (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n", - "Attributes:\n", - " Conventions: COARDS\n", - " title: 4x daily NMC reanalysis (1948)\n", - " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", - " platform: Model\n", - " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "ds = xr.tutorial.load_dataset(\"air_temperature\")\n", "ds" @@ -555,7 +81,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -585,20 +111,9 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2920, 25, 53)" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "np_array = ds[\"air\"].data # numpy array\n", "np_array.shape" @@ -613,20 +128,9 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "242.09999" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "np_array[1, 0, 0]" ] @@ -640,20 +144,9 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([295. , 294.4 , 294.5 , ..., 297.29, 297.79, 297.99], dtype=float32)" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# extract a time-series for one spatial location\n", "np_array[:, 20, 40]" @@ -685,434 +178,9 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 2920)>\n",
-       "array([295.  , 294.4 , 294.5 , ..., 297.29, 297.79, 297.99], dtype=float32)\n",
-       "Coordinates:\n",
-       "    lat      float32 25.0\n",
-       "    lon      float32 300.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([295. , 294.4 , 294.5 , ..., 297.29, 297.79, 297.99], dtype=float32)\n", - "Coordinates:\n", - " lat float32 25.0\n", - " lon float32 300.0\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da[:, 20, 40]" ] @@ -1130,42 +198,20 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2920, 2)" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "np_array[:,[0, 1], [0, 1]].shape" + "np_array[:, [0, 1], [0, 1]].shape" ] }, { "cell_type": "code", - "execution_count": 57, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2920, 2, 2)" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "da[:,[0, 1], [0, 1]].shape" + "da[:, [0, 1], [0, 1]].shape" ] }, { @@ -1198,20 +244,9 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkwAAAHgCAYAAABXUjLLAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAC5AUlEQVR4nOyddXgUVxeHfxt3JSEkQPCEAMG1OASnSIsWa2nLVyhOKRQoLhWsBqUUaXGKFivuHiwQHIImBAgR4snO90fYzcrsjuys5rw8ediduXPvndm5d86cc+45MoZhGBAEQRAEQRA6sTN3BwiCIAiCICwdEpgIgiAIgiA4IIGJIAiCIAiCAxKYCIIgCIIgOCCBiSAIgiAIggMSmAiCIAiCIDgggYkgCIIgCIIDEpgIgiAIgiA4IIGJIAiCIAiCAxKYihCrVq2CTCZDXFyc4GNPnz6NadOmITk5WfJ+cXH48GF88sknCA8Ph7u7O0JCQtClSxdER0drlR00aBBkMpnWX3h4OO/2Dh48iIYNG8LNzQ3FihXDoEGDkJiYyHnc0aNH1dq8ePEi5zGDBg1CmTJlePeNkJ5169Zh0aJFWtvj4uIgk8nw448/ctaxaNEitd/+1atXgvuhuH+OHj0q+FhL4uDBg4iKikJwcDCcnZ0RGBiIli1bYs+ePTrL8xlvubm5mD59OsqUKQNnZ2eEh4fj559/5t2vt2/fYtSoUQgODoaLiwtq1KiBDRs28Dq2efPmyt+2U6dOnOUV986qVat494+wfEhgInhx+vRpTJ8+3SwC05IlSxAXF4eRI0diz549WLx4MRITE9GgQQMcPnxYq7yrqyvOnDmj9rdx40ZebR07dgzt27dH8eLFsWPHDixevBgHDx5Eq1atkJ2dzauOX3/9FWfOnEHlypUFnSdhHnQJTELo3bs3zpw5g8GDB0vTKSvm9evXqFKlChYuXIj9+/fj999/h6OjIzp27Ig1a9aolRUy3oYOHYq5c+di2LBh+O+//9CtWzeMHDkSc+bM4dWv7t27Y/Xq1Zg6dSr27t2LunXrok+fPli3bh2v42vWrIkzZ85g/vz5/C4EYXswRJFh5cqVDADm4cOHgo/94YcfRB9rKC9evNDalpaWxhQvXpxp1aqV2vaBAwcy7u7uotuqW7cuExERweTm5iq3nTp1igHA/Pbbb3qPPXLkCAOAOXLkCO/2Bg4cyISGhorsrXgyMjIYuVxu8nYtifT0dIZhGKZjx46sv8HDhw8ZAMwPP/zAu86pU6cyAJiXL18K7o+Y+8dayMnJYUJCQpgmTZqobec73q5fv87IZDJmzpw5asd/9tlnjKurK/P69Wu97e/evZsBwKxbt05te1RUFBMcHMzk5eXpPb5Zs2ZMs2bN9JZRRXHvrFy5kvcxUpGRkWHyNosKpGEq4hw4cABdunRByZIl4eLiggoVKmDIkCFqJoVp06bhq6++AgCULVtWqZo2lekgMDBQa5uHhwciIiLw5MkTydp59uwZLly4gP79+8PBwUG5vVGjRqhUqRK2bdtmUP2rVq1CWFgYnJ2dUblyZfz111+s5XJycjBr1iyEh4fD2dkZAQEB+Pjjj/Hy5Uu1ctnZ2Rg7diyCgoLg5uaGpk2bIjo6GmXKlMGgQYPU2pXJZNi/fz8++eQTBAQEwM3NTfkGv3HjRjRs2BDu7u7w8PBA27ZtcfnyZa1+Xbx4Ee+//z78/Pzg4uKCmjVrYtOmTWplMjIyMG7cOJQtWxYuLi7w8/NDnTp1sH79et7XSWGaWr9+PSZNmoTg4GB4eXmhdevWuH37tlb5FStWoHr16sr2unXrhps3b6qVGTRoEDw8PBATE4M2bdrA09MTrVq1QvPmzbF79248evRIzaSmyYIFC1C2bFl4eHigYcOGOHv2LO/zMZSdO3cqTVaenp6IiorCmTNn1MpMmzYNMpkMN27cQJ8+feDt7Y3ixYvjk08+QUpKisn6qgtHR0f4+PiojSsh42379u1gGAYff/yxWr0ff/wxMjMzsW/fPr3tb9u2DR4eHujRo4fW8c+fP8e5c+dEn9vz58/Rs2dPeHp6wtvbG7169UJCQgJrWT5jCABOnjyJhg0bwsXFBSEhIZgyZQqWL1+u5VJRpkwZdOrUCVu3bkXNmjXh4uKC6dOnAwASEhIwZMgQlCxZEk5OTihbtiymT5+OvLw8tbb4zjcE4MBdhLBl7t+/j4YNG+LTTz+Ft7c34uLisGDBAjRu3BgxMTFwdHTEp59+iqSkJPz888/YunUrSpQoAQCIiIjQWS/DMMjPz+fVB9XJki8pKSm4dOkSWrZsqbUvMzMTQUFBePnyJUqUKIGuXbtixowZ8PPz01vn9evXAQCRkZFa+yIjI3Hq1CnB/VSwatUqfPzxx+jSpQvmz5+PlJQUTJs2DdnZ2bCzK3xvkcvl6NKlC06cOIHx48ejUaNGePToEaZOnYrmzZvj4sWLcHV1BVAw2W/cuBHjx49Hy5YtERsbi27duiE1NZW1D5988gk6duyIv//+G+np6XB0dMScOXMwefJkfPzxx5g8eTJycnLwww8/oEmTJjh//rzyNz5y5AjatWuH+vXrY+nSpfD29saGDRvQq1cvZGRkKAW0MWPG4O+//8asWbNQs2ZNpKen4/r163j9+rXga/bNN9/gvffew/Lly5Gamoqvv/4anTt3xs2bN2Fvbw8AmDt3Lr755hv06dMHc+fOxevXrzFt2jQ0bNgQFy5cQMWKFZX15eTk4P3338eQIUMwYcIE5OXloWTJkvj8889x//59nQLxr7/+ivDwcKXZbsqUKejQoQMePnwIb29vweclhHXr1uGjjz5CmzZtsH79emRnZ+P7779H8+bNcejQITRu3Fit/AcffIBevXph8ODBiImJwcSJEwEUCJX6kMvlkMvlnP2RyWTKa8+Fos7ExET8/vvvuHPnDr777jvlfiHj7fr16wgICEBQUJBWOdW6dHH9+nVUrlxZa65RPb5Ro0a8zkuVzMxMtG7dGs+fP8fcuXNRqVIl7N69G7169dIqy3cMXbt2DVFRUahUqRJWr14NNzc3LF26VMucqeDSpUu4efMmJk+ejLJly8Ld3R0JCQmoV68e7Ozs8O2336J8+fI4c+YMZs2ahbi4OKxcuRKAsPmGAJnkihJcJjm5XM7k5uYyjx49YgAwO3bsUO4TapJTtMXnTwwfffQR4+DgwFy8eFFt+4IFC5gFCxYw+/fvZ/bv389MmjSJcXNzY8LDw5m0tDS9da5du5YBwJw5c0Zr3+eff844OTnpPV6XSSU/P58JDg5matWqpWYGi4uLYxwdHdXMQevXr2cAMFu2bFGr48KFC2pmihs3bjAAmK+//lqtnOL4gQMHKrcpfosBAwaolX38+DHj4ODADB8+XG17WloaExQUxPTs2VO5LTw8nKlZs6aa6YRhGKZTp05MiRIlmPz8fIZhGKZq1apM165d9VwlbhTXsUOHDmrbN23apPb7vHnzhnF1ddUq9/jxY8bZ2Znp27evctvAgQMZAMyKFSu02uMyyVWrVk3NZHP+/HkGALN+/XqtY6Q0ySnum2rVqimvL8MU/D6BgYFMo0aNtNr9/vvv1eocOnQo4+Liwml+VRzP9SfEfNy2bVvlcV5eXszWrVvV9gsZb1FRUUxYWBhrO05OTsznn3+uty8VK1Zk2rZtq7X9+fPnDAAtU58mukxyS5Ys0ZorGabAVAgNkxzfMdSjRw/G3d1d7R7Kz89nIiIitObg0NBQxt7enrl9+7ZanUOGDGE8PDyYR48eqW3/8ccfGQDMjRs3GIbhP98QBZBJroiTmJiI//3vfyhVqhQcHBzg6OiI0NBQANAyawihc+fOuHDhAq8/oUyZMgVr167FwoULUbt2bbV9o0ePxujRoxEVFYWoqCjMmjULf/31F27duoU//viDV/1sJhl927m4ffs2nj9/jr59+6rVERoaqvVWu2vXLvj4+KBz587Iy8tT/tWoUQNBQUFKM+ixY8cAAD179lQ7/sMPP9Spsfvggw/Uvv/333/Iy8vDgAED1NpycXFBs2bNlG3du3cPt27dwkcffQQAamU7dOiA+Ph4pamsXr162Lt3LyZMmICjR48iMzNT1DUDgPfff1/tu0Ib8OjRIwDAmTNnkJmZqWZ+BIBSpUqhZcuWOHToEOc14EPHjh3VtCqa/TAWivumf//+alpIDw8PfPDBBzh79iwyMjLUjmG7ZllZWZyrPD///HNeY/Xff//l3f+ff/4Z58+fx44dO9C2bVv06tWL1TTLd7zpG398xqahx7Nx5MgReHp6al33vn37qn0XMoaOHTuGli1bolixYsrj7ezstMa6gsjISFSqVElt265du9CiRQsEBwertdW+fXtlG4pyfOYbogAyyRVh5HI52rRpg+fPn2PKlCmoVq0a3N3dIZfL0aBBA4Medn5+fkYxV0yfPh2zZs3C7Nmz8eWXX/I6plu3bnB3d+f0O/H39wcAVvNRUlISp0lPF4r6NM0Jim2qPgkvXrxAcnIynJycWOtS+JYp6ixevLjafgcHB+V5aKIwpaq2BQB169ZlLa94SCvKjRs3DuPGjdPbr59++gklS5bExo0b8d1338HFxQVt27bFDz/8oGYe44PmeTg7OwOA8r5UXAPN8wKA4OBgHDhwQG2bm5sbvLy8BPWBTz+MBdf5yeVyvHnzBm5ubsrtYvsaFBTE6iuoiRDBQvX3fv/999G+fXsMGzYMvXr1gp2dnaDx5u/vjytXrmiVS09PR05ODufY9Pf319kOAIPGtuYYBLTHupAxpKtOtm0A+/3x4sUL/Pvvv3B0dNTbFt/5hiiABKYizPXr13H16lWsWrUKAwcOVG6/d++ewXWvXr1ay0FTFwzD8Co3ffp0TJs2DdOmTcM333wjqD8Mw6i9pbNRtWpVAEBMTAw6dOigti8mJka5XyiKBwObI6jmtmLFisHf31+nE6unp6danS9evEBISIhyf15enk5/Ic2HneIN9p9//lFqFdlQlJs4cSK6d+/OWiYsLAwA4O7ujunTp2P69Ol48eKFUtvUuXNn3Lp1S2cbYlBcg/j4eK19z58/V3tDB8RrEcwF1/nZ2dnB19dXkrZmzJihdBbWR2hoqKg4bkCB9nHfvn14+fIlihcvLmi8VatWDRs2bEBCQoKaMBITEwMAnGOzWrVqWL9+PfLy8tQ0sHyP14W/vz/Onz+vtZ1tXAP8xpC/v79SwNJXpwK2+7pYsWKIjIzE7NmzWY8JDg5WluMz3xAFkMBUhFEMNMVbqILff/9dq6zQt2qFSU4qZs6ciWnTpmHy5MmYOnWqoGP/+ecfZGRkoEGDBnrLhYSEoF69elizZg3GjRunNMOcPXsWt2/fxqhRo0T1PSwsDCVKlMD69esxZswY5XV/9OgRTp8+rZy8AKBTp07YsGED8vPzUb9+fZ11Nm3aFEDBCrdatWqpnavmKhhdtG3bFg4ODrh//75eU1VYWBgqVqyIq1ev8o55AxS8EQ8aNAhXr17FokWLkJGRoaYNMZSGDRvC1dUVa9asUVv99PTpUxw+fBgffvghr3qcnZ2Nri0SQ1hYGEJCQrBu3TqMGzdOed+kp6djy5YtypVzUvD555/zCsioOVfwhWEYHDt2DD4+PkpBUMh469KlCyZPnozVq1fj66+/Vm5ftWoVXF1d0a5dO73td+vWDX/88Qe2bNmi5pC9evVqBAcH6x1r+mjRogU2bdqEnTt3qpnlNGM7CRlDzZo1w549e/Dq1SuloCWXy7F582be/erUqRP27NmD8uXL6xWq+c43RAEkMBVhwsPDUb58eUyYMAEMw8DPzw///vuvlikDKHhDA4DFixdj4MCBcHR0RFhYmM43EH9/f52mIaHMnz8f3377Ldq1a4eOHTtqmdYUgtCjR4/Qt29f9O7dGxUqVIBMJsOxY8ewaNEiVKlSBZ9++qnacQ4ODmjWrJmar8t3332HqKgo9OjRA0OHDkViYiImTJiAqlWr8taYaWJnZ4eZM2fi008/Rbdu3fDZZ58hOTkZ06ZN01Ld9+7dG2vXrkWHDh0wcuRI1KtXD46Ojnj69CmOHDmCLl26oFu3bqhSpQr69OmD+fPnw97eHi1btsSNGzcwf/58eHt7c2rTgIIlyTNmzMCkSZPw4MEDtGvXDr6+vnjx4gXOnz+v1BYBBUJ0+/bt0bZtWwwaNAghISFISkrCzZs3cenSJeVkXr9+fXTq1AmRkZHw9fXFzZs38ffff0v6cFfg4+ODKVOm4JtvvsGAAQPQp08fvH79GtOnT4eLiwtvwbpatWrYunUrlixZgtq1a8POzg516tSRrJ/Tpk3D9OnTceTIETRv3pz3cXZ2dvj+++/x0UcfoVOnThgyZAiys7Pxww8/IDk5GfPmzZOsj8HBwWqCuyF06dIF1atXR40aNeDv74/nz59j1apVOHbsGH799Vc1DQ/f8ValShUMHjwYU6dOhb29PerWrYv9+/dj2bJlmDVrlppJbcaMGZgxYwYOHTqEZs2aAQDat2+PqKgofPHFF0hNTUWFChWwfv167Nu3D2vWrOG98k+TAQMGYOHChRgwYABmz56NihUrYs+ePfjvv/+0yvIdQ5MmTcK///6LVq1aYdKkSXB1dcXSpUuRnp4OALzG9owZM3DgwAE0atQII0aMQFhYGLKyshAXF4c9e/Zg6dKlKFmyJO/5BgAGDx6M1atX4/79+0qN9F9//YVPPvkEK1aswIABAwAUzMPly5fHwIED8eeff4q6rhaLeX3OCVPCtkouNjaWiYqKYjw9PRlfX1+mR48ezOPHjxkAzNSpU9WOnzhxIhMcHMzY2dmZNMBes2bNeK2yS0pKYrp168aUKVOGcXV1ZZycnJiKFSsy48ePZ5KTk7XqBcC68mX//v1MgwYNGBcXF8bPz48ZMGAAa/BMTbgCDy5fvpypWLEi4+TkxFSqVIlZsWIFa+DK3Nxc5scff2SqV6/OuLi4MB4eHkx4eDgzZMgQ5u7du8pyWVlZzJgxY5jAwEDGxcWFadCgAXPmzBnG29ubGT16tLKc4ne/cOECa7+2b9/OtGjRgvHy8mKcnZ2Z0NBQ5sMPP2QOHjyoVu7q1atMz549mcDAQMbR0ZEJCgpiWrZsySxdulRZZsKECUydOnUYX19fxtnZmSlXrhwzevRo5tWrV5zXT/M6bt68WW27rmCAy5cvZyIjIxknJyfG29ub6dKli3IVkAJ9AU2TkpKYDz/8kPHx8WFkMpnyntIXuJJtfDAM+yq5sWPHMjKZjLl58yav89a8f7Zv387Ur1+fcXFxYdzd3ZlWrVoxp06d4myXYQwLViuW7777jqlbty7j6+vL2NvbM/7+/kzbtm2ZXbt2sZbnO95ycnKYqVOnMqVLl1aOoZ9++kmrnOJaaF7HtLQ0ZsSIEUxQUBDj5OTEREZGsq50ZENf4MqnT58yH3zwAePh4cF4enoyH3zwAXP69GnWe5XPGGIYhjlx4gRTv359xtnZmQkKCmK++uor5rvvvmMAqM1loaGhTMeOHVn79fLlS2bEiBFM2bJlGUdHR8bPz4+pXbs2M2nSJObt27fKcnznG8VKU9V7SXF/qZ6nYtyortS1FWQMw9OBhCAIvRw9ehQtWrTAwYMH0axZM1HxpQzl9OnTeO+997B27VqtlTqE8WDexR2bMWMGZs6ciZcvXyrNKfXq1UNoaKggkwphWTRv3hwMw+DQoUOws7PjpeWRmjZt2iAuLg537twxedtEAWSSIwiJad26NQDgwoULkpp2NDlw4ADOnDmD2rVrw9XVFVevXsW8efNQsWJFnY6lhHFYvHgxRo8erbU9NTUVV69exerVq83QK0JKjh8/rsyJt2vXLqO2NWbMGNSsWROlSpVCUlIS1q5diwMHDtieicvKIA0TQUhEWlqaWuqOiIgIyf12VDl37hzGjh2L2NhYpKWloVixYmjbti3mzp3LutTY3Ci0MPqwt7e3utVsQEE8s8ePHyu/16hRwywaRsI43L59G2lpaQAKfOcqVKhg1PZGjhyJnTt3IiEhATKZDBERERg1ahT69etn1HYJ/ZDARBCESVCYLPWxcuVKrUCUBEEQlgAJTARBmARNDRwbZcuWlWx1JUEQhJSQwEQQBEEQBMEB5ZIjCIIgCILggLwSJUAul+P58+fw9PS0SodVgiAIgiiKMAyDtLQ0BAcHc4aLIIFJAp4/f45SpUqZuxsEQRAEQYjgyZMnKFmypN4yJDBJgCI9yJMnT0RlQycIgiAIwvSkpqaiVKlSvBINk8AkAQoznJeXFwlMBEEQBGFl8HGnIadvgiAIgiAIDkhgIgiCIAiC4IAEJoIgCIIgCA5IYCIIgiAIguCABCaCIAiCIAgOSGAiCIIgCILggAQmgiAIgiAIDkhgIgiCIAiC4IAEJoIgCIIgCA5IYCIIgiAIguCABCaCIAiCIAgOSGAiCIIgCILggAQmgiAIgpCYpPQc3H/51qA6cvPluPY0GXI5I1GvCEMggYkgCIIgJKbWzANoNf8Y4l6li65j3OareP+XU1h86K6EPSPEQgITQRAEQRiJ6EdvRB+748pzAMCSY/el6g5hACQwEQRBEARBcEACE0EQBEEYQL6cwbSdN7AnJt44DZALk0VAAhNBEARBGMDOq8+w6nQchq69pLWPZB3bgQQmgiAIgjCAl2nZOvcxjOEiE0Nil0VAAhNBEARBCGTlqYf4ZluMlkDEMAymbL8uaVt8Za6cPDlGb7yCrZeeSto+UYCDuTtAEARBENbG9H9jAQCdI4Mhg0y5/dLjN/j77COz9GnjxSfYdvkZtl1+hu61SpqlD7YMaZgIgiAIQiRvs/PUvqdn56t9l8KYxreON+k5ErRG6IIEJoIgcOjmC5y8+8rc3SAIq0Qm07NTRdrJzZdj7blHeGhAMEtd/HcjAWfuv9baHv3oDXZefS55e0URMskRRBHn9dtsDF59EQDwYE4H2Nnpm/0JgtCHPuFp9ek4zNp9EwAQN68j7zq5HMdfpGZhyN/RrPs+WHIaAFCumDuqhnjzbpPQhjRMBFHESc7MVX6mtTgEIQxNYUbVn0mTC3FJ4trg2K9vlZ6CR68zRLVNFEICE0EQBEGIRFOY0dQwqYYESMtS93d6kpSBzBx1nyfWNhjg3IPXSFF5uSFMDwlMBEEokSJmDEEUNWQqUpI+g/ZpFR+j2OepaPL9EbSaf5RXG72WnUX16ftF9pCQAhKYCIJQQuISQQhHpvOLbv67kQAAeJ6SJXl/2KDgl4ZDAhNBFHFIqUQQ4tEcP5o+TLrGl53epXWEJUICE0EQSkh4IgihMHpXxk3YGoMBK85rO4eLlJeiFhzT8nv660ycuMoIQZDARBBFHNWJm9T2BGEYbILQ8TsvtRy2xUbvuJv4Frtj4tW2bbpIqVBMAQlMBEEoIQ0TQQhHpuOzKvlyTQ2TeJOcXMRApbFtOCQwEUQRh2siTcnIxcHYF8jNl5umQwRhRWiOn9c60pM8T1Z37n6SxB4X6U16Dg7dfIE8nuNNLtc9gPmELCD4Q5G+CYLQS69lZ3ArIQ2jWlfEqNaVzN0dgrA4VLVFQ9deYi3T+ZeTat83XHjCWq7bb6cQ9zoD33QI59X21svPdO77ess1XnUQ/CANE0EQSti0TbcS0gAAO69QPiqCMDZx7yJy745J0F1IZZweuZWosxjlkJMWEpgIwgq5l5iGPRqOnwCQmJqFLdFPkZ1XqIp/npyJbZefKlX8F+KScPo+e6JdfU7fD4yQMJQgrB0G4le8KWAdj3ydjnS0rWmOIxcmwyGTHEFYIa0XHAcA/PVJPTStFKDc3uXXU4hPycL9l28xvl2BSr/V/GPIzM3Hq7QcfPxeGfRYegYAcHVqG3i7OgpqNzMnH65O9hKdBUFYP1I4U/f94xyufBsFHzcnwcfqktVm7Y41rFOEFqRhIggrJuZZitr3+HdRgw/efKHclplb8KZ59E4i8lQcRFNZ8lJxTf4ZOXn6CxBEEUSKEJS6nMU529ah3tp7XY9JjxAFCUwEYcWoBsPLytW/IiY3n/tVmO/LslzO4EWqaVI6EISloZVzUYKo3Xk8xqeyfZWRqqtlygspPVYtMC1ZsgSRkZHw8vKCl5cXGjZsiL179yr3MwyDadOmITg4GK6urmjevDlu3Lih3J+UlIThw4cjLCwMbm5uKF26NEaMGIGUlBS25gjC4lCdE5t+f0Rv2dx8OacG6dvt1/XuV7zNDl17CfXnHMLR27odTgnCVvn58D3lZ6mCvebJxYXt4CurkQBlOFYtMJUsWRLz5s3DxYsXcfHiRbRs2RJdunRRCkXff/89FixYgF9++QUXLlxAUFAQoqKikJZWsOrn+fPneP78OX788UfExMRg1apV2LdvHwYPHmzO0yII3qhOgYlp2XrL8nmD1bdEWZV97xKHLjv+gFd5grAlFhy4o/zMMNKY5IRomFShjHSmw6qdvjt37qz2ffbs2ViyZAnOnj2LiIgILFq0CJMmTUL37t0BAKtXr0bx4sWxbt06DBkyBFWrVsWWLVuUx5cvXx6zZ89Gv379kJeXBwcHq748RBFA10sj2/bcfLnkqU80oxcTBCGOPJFjyZCI4YQwrFrDpEp+fj42bNiA9PR0NGzYEA8fPkRCQgLatGmjLOPs7IxmzZrh9OnTOutJSUmBl5eXXmEpOzsbqampan8EYQyGrbuEz/66qFOdvvDgHdbtdxPfov3iE0jJKHTs1jTJbeepTdKHaoqGFScfou3C43jJoekiCEIbzcjeV5/qdg1RHce6xKU3GdqLOgjDsHqBKSYmBh4eHnB2dsb//vc/bNu2DREREUhIKDAZFC9eXK188eLFlfs0ef36NWbOnIkhQ4bobXPu3Lnw9vZW/pUqVUqakyEIFd5m52H3tXgciH2hXP0mhJvxqfjjRKHJTM6om/DmH2AXtoSg+lI8Y1csbr9Iw+JDhtdLENaCVDrWfLE+RqRgMhlWLzCFhYXhypUrOHv2LL744gsMHDgQsbGF8Sc01ZUMw7CqMFNTU9GxY0dERERg6tSpetucOHEiUlJSlH9PnrCHuCcIQ1DVKomdlFVXzskgLmmnPthMctm5lHOOIAQjWl4iiclUWL3A5OTkhAoVKqBOnTqYO3cuqlevjsWLFyMoKAgAtLRJiYmJWlqntLQ0tGvXDh4eHti2bRscHfUH83N2dlauzFP8EYQxURWelh2/z/u4fA2hi4+8NGX7dSTo0GjN3BWLnLxCgejKk2TM3XtTbwJQgiC4ETOEHr5Kx5ZLT3mVZVMU5ObLcfreK86QJEQBVi8wacIwDLKzs1G2bFkEBQXhwIEDyn05OTk4duwYGjVqpNyWmpqKNm3awMnJCTt37oSLi4s5uk0QWrBNcM+SMzFnzy3edWgJMjwm5b/PPsKX69gTiG67/Axrzj5S2/b7sQfYzZKmhSCKAlIt1xezIEMRtZ8PdiyKqDl7bqLv8nMYs+mK4LaLIla9DOybb75B+/btUapUKaSlpWHDhg04evQo9u3bB5lMhlGjRmHOnDmoWLEiKlasiDlz5sDNzQ19+/YFUKBZatOmDTIyMrBmzRo1B+6AgADY21MKCMIyUMzJb7OERdrW9IvgOylff67b4fTJmwytbapBLEnXRBQ1pLjnxWiYXr3lv8CCzXS38lQcAGCPvkS/hBKrFphevHiB/v37Iz4+Ht7e3oiMjMS+ffsQFRUFABg/fjwyMzMxdOhQvHnzBvXr18f+/fvh6ekJAIiOjsa5c+cAABUqVFCr++HDhyhTpoxJz4cgACAnTw4nB2HKX1UzmSqqkzDDMMjWUU4TfS/NbPtoaTNRFMiXM0YLAJmTJ+ddt5geSB1SpChi1QLTn3/+qXe/TCbDtGnTMG3aNNb9zZs3p+inhEUxe3cs/jjxELtHNEaov7vWfja5ZNrOG1h1Oo61PlWTXNzrDNSfc4hXP/SNChozRFGEYRhELTymtahhyvbr+OpdomtD+Oyvi2hYzp9X2YlbY9CnXmlB9dOwNRyb82EiCGvmjxMPAQAL9t9RXyXHqP+vii5hCeAbWJKljD4NE48aCcLWSM/Jx4OX6XiWnKm2PVWgmVwfZx68lqwuTWjcGg4JTARhgTBQn+DEqtNz86Vf4s+WxFdV8ZWXL8fbbMMeIqlZuRRFnJAUhmGQkllwX6VlCQ/qyOY0rYBWiRYNrNokRxC2CsMwkqjQt195zqMtlm16BLT15x9ztrn9ynNcnhIFX3cnzvY1eZKUgSbfH0GdUF/880Uj7gMIggcTtsRg48XCmHknv26Bkr5uvI/XF+9o6s4bOvdZCmRKNxzSMBGEpaLmsG2SZkS3x+ZbdVakeWHn1QIh7+KjN6KOJwg2VIUlANh1TVgoDHKaJkhgIggLxVQTNL14EkURofe9tY8Ta++/JUACE0FYIJpRufuvOIf5+29L3s4P/93C7mvaZjsp5lYhkQZevc1G119PYeMF/eY+gpAKoS8kRUXeOP8wCWUm7MbErTHm7orFQQITQVgBT5Iy8fPhe5JrnX49ch8/Hb6ntd3U/g7z99/BlSfJ+HoLTdKEaRCuYbJukYnv3NHz94Lo4evPP8bN+FRjdsnqIIGJICwQhjHvG63Qtg0NW5lu4Ko6gjA21i0uiTPJJaSy55QsqpDARBAWiiW80eYZISyBGA7EvsDSY/yTDhMEF4rxFZ+Sibl7b+Lpu5Q/Z+6/xoIDd7TCWljAcDQIrv6nZOZaxJxjyVBYAYKwQDTjMJm8/XeNr7/wRH/Bd7CnRuGvd+Lyd/rsr4sAgFqlfVGvrB/veglCF4p7fPCqi4iNT8W+6wk49lUL9PnjLAAg2NsFvVWjadu4LNH8hyP47aPa6htt/JyFQhomgrBQLOFlL+5Vukna4StakYmAkArF8Ip956fz6LV6UumHr9M1ylvAgDQArt6/ychFYhqNL32QwEQQFgjDMKwT9LUnKSbtR0IKvwmUS0MklzM4de8VkjNydBxPyXsJ01L0wgqIPwGKZF4ACUwEYamwzFHjt1wzaRd2xwgL7qeLzdFP8NHyc+j400lJ6iMIQ+HUGDF6v5qd5xo57bgQ2//kjBw0mHsIU7ZfF1mD7UACE0EQRkFVaaSIqqyZuFRZlmed5JRKmArNO83S7j3B5mke3WfT9K499xiJadn4++wjYe3ZICQwEYSFYlnTs2GQyY2wNLjkH00BydLGozFGlGadDBiLExTNCQlMBGGBnLj7CmM3XTVrH6pO/Y932Vdp2VrbZDo+LzvOEh6A5+w/csMVvCDHb4KFg7EvMHRttE4/OU24xABVOWH16TjUmXVQfOeMgNCXED5O62w+iyQvFUICE0FYKCfvvTJr+28FBJNkixauizl7bmlt05cJXpNvKGUDwcKnf13EnpgE/Mg3hRCHJKC6d+rOG+I7ZiHwEXxm77mpfZwR+mKtkMBEEITR4XoZFvKyrMsPiiAAICFFW9vJhhANkyUi1CQn9nQs/TqYEgpcSRCE0cjJk2Po2mgcvf3S3F0hiiiXHr/B9/u0tZrxKVno+y5IJRuv3mbjo+VnUau0rzG7J5oNJkpUbe3xp6SEBCaCIIyCTCbDjivPcPBmorm7QhQp1B/w3X87zVrqn+inemvZefU5AODUvdfSdEti1p/nF4VfgVhNEWmYCiGTHEEQRoNvUl1aQ0dIBT3g2RGrKaLLWQgJTARhZhiGwcpTD3HmvmW+yZoCijpASAU94NmJeWraLAG2CAlMBGFmTtx9hen/xiqTfhZFhKySIw0CoQ+KG8TOBp6JtFVhGNCAU4EEJoIwM4+SMrgLWSGkNCLMAaU9kxa6nIWQwEQQZsZWBYtHSRm4/jyVdd+thILtKZm5iHmaQiY5QjIuPXqDnDw55HIGlx6/MXd3LAoxSXTZFEwv07Jx50WaBD2yLmiVHEEQRmHmrlid+9otOoGDY5qh3/JzSEjNQklfV971knBF6CMtOw9fb7mG6iW9Me1f3fdgUWTt+cfo3yBU0DFszuJ1ZxdEPT86rjnKFHOXpG/WAGmYCIIwCxfjkpQJRJ++YQ9GyeaPQi4VBBfbLj/D2nOmiVNkTWwUEbtJ33i78iRZfGesEBKYCMLMFFWNiS7rgKqQRMIRQZgXfUNQXsQGKAlMBEGYBV2TrermojUdE2KRyp/m+B2KSK+J6njUTEskZwr8or7dcR3bLusPBGoLkMBEEIRZ0LX8m+FRhiBUmb1bO2msGM3tgBXnJeiN5SJmOKn6MI1Yf1ltn1zOYH9sAv468wijN141tHsWDwlMBGFmhMQgsiV0zd0kIxFCYROOiuq40ofQsaVZ/v7Lt2rf5QyDpPRcA3tlPdAqOYIwMgzDYE9MAsKCPODsYI+jd17C2d4ObasGwdvV0dzdMxvnHiSxbj9864Xycz7DaE1SlAyU0IREI+Pw5E2G2ptNcoa6cKTph/g2Ow8ezrYrVtjumRGEhXD87isMW3dJa/u/157j78H1i6zT9+6YeNbtV1VSOPwT/RQf1Re2DJooeshYBlFRHVdSMv3fWHzetJzatldvs5Wf5QwDO5UL/c3WGPzUp6bJ+mdqyCRHEEYm5mky6/YTd1+ZtiNWyLkHSeTHRFgtni6WpZMQM5I0x9/rtzk69+28+lxMt6wGEpgIQgQn777C7mvsGhJN2N5+1fZL0SEbhQGw/cozc3eDsHAsdQy1rRIkWV1hxT0RN6+jZPXx5fgd9Re7NxmFAlNOPqOmybN1rR4JTAQhgn5/nsOwdZe0ltkS0nIrPrVIrL4hDMNSH9RSdqtheX8JawNSs/g5a9/WCNnw5brClXIzd8WqnaOF/gySQQITQRjAq7Rs7kKEaB6zJCYmCx2hjWU+qtkEuTFRlQTXU7eML8a3C5OgR4XcThAXu0rVh0kTLm26tUMCE0GYkRepWTh9/7W5u2GxkGxE8MFSn9NsoQ3cnOwF1zOwURm4OUnrD2Un0UVTM8lJUqPlYlkeaQRhg+ibl+rPOWS6jlgjJDERPLD1B7UYIYsNVSdtO4kuWlGKd0UaJoIwAD4vaUVpQpEairlE8MESNUwbP2/A2a8O1YIQ4uPKWVfzSoES9aoQqTRMqlji7yAlggUme3t7JCYmam1//fo17O2lkYIJwpaw9UnEmOhK0EsQqrC9lJjTnyY8yBP1y/mzRyBX2ejt6ojf+9fmrM9OKnUQCrRMDMPAXsI6Fdi6D5Ngk5yumCjZ2dlwcnIyuEMEYemojgFyQDYubPMNXXJCE7bn9M34VNN35B2F2hsuAcK0AsathDSUnbgH9nYyTOpQWZI6x2+5pvxs2+KSAIHpp59+AlAgQS5fvhweHh7Kffn5+Th+/DjCw8Ol7yFBWBhChSRbn0SMCWmYCD5YmmJjbvdqALiTR8tkxjGNcZEvZzBjV6zk9Vra7yA1vAWmhQsXAii4AZYuXapmfnNyckKZMmWwdOlS6XtIEBYGPcMJgtDF/TkdlOauPA6J304mjZDxYE4HlPtmj+EVGYit+2vyFpgePnwIAGjRogW2bt0KX19fo3WKIKwFPpPdjiu2nS6AIMzNnpgEc3dBiapvUF6+XGu/6pRhJ5NJIjBJ6eNkCLauYRLs9H3kyBESlogijdDcZrFm9KWwRSi3HGGp9KlXSu17rg4Nk797gb9vm4ggo2tlPJ1NFz3IxuUlcXGYnj59ip07d+Lx48fIyclR27dgwQJJOkYQlgo9rgnCcpBbkKPbjC5V1b7n52v3TSYDDo9rjkev0xFZ0kd0xG1N5navholbY7S2/9SnJj5edUGSNoo6ggWmQ4cO4f3330fZsmVx+/ZtVK1aFXFxcWAYBrVq1TJGHwnCouCr4EjJyIWXK8WGlZqsXG0zB1F0yZVbzv3gaK9utMlj6ZsMBeEEIkv6SNq2pwv7XOPiaLpwP5YjuhoHwSa5iRMnYuzYsbh+/TpcXFywZcsWPHnyBM2aNUOPHj2M0UeCsCj4BFO8+iQZ1Wfsx9C1l0zQo6LFs+RMi9IqEOYll0WLYym48khnIlVwVgc79se5MeIt6UJu4+ZywQLTzZs3MXDgQACAg4MDMjMz4eHhgRkzZuC7776TvIMEYWnwmRP+OPEAALD3uuU4o9oSOSzOtETRJDfPcu+Fie3DUTXECz98GGn0tlpVDkSj8v74X7PyattN6Q9u4/KScIHJ3d0d2dkF2YqDg4Nx//595b5Xr15J1zOCsAJ0OWzaesRbgrAEnr7JQFZevtnab1jOX+/+YB9X7BreBD3qlNJZRiqnb0d7O6z7rAEmtFePh2jKFXQ2Li8J92Fq0KABTp06hYiICHTs2BFjx45FTEwMtm7digYNGhijjwRhUfB5iyJxybjY+psswc3GC4/x9RZtJ2dTIsacpvkyZex8ifamfHmz8XEpWMO0YMEC1K9fHwAwbdo0REVFYePGjQgNDcWff/4peQcJwtLgM8FZSFgUQSzqVcPcXeANJeUlFh64a9b2R7WuaNb2FczoUkXvflP6MNn6uBSsYSpXrpzys5ubG3777TdJO0QQlg4vDZMVmuSqhnjj9/61MeTvaHN3hRPSMBHmfimpX9YfZ+6/Vn7/oFZJk7TbIiwAR26/VH6vV9ZPb3lTpl6x9XEpWMMkBj8/P0F//v7+ePToEWe9S5YsQWRkJLy8vODl5YWGDRti7969yv0Mw2DatGkIDg6Gq6srmjdvjhs3bqjVkZ2djeHDh6NYsWJwd3fH+++/j6dPn0p+DQjbgc+cYH3ikvkfQEKw8XmZ4IG5X0oYDX0K3+5I3W0uHyhTaphsHZMEiUlOTsaiRYvg7e3NWZZhGAwdOhT5+dyOfCVLlsS8efNQoUIFAMDq1avRpUsXXL58GVWqVMH333+PBQsWYNWqVahUqRJmzZqFqKgo3L59G56engCAUaNG4d9//8WGDRvg7++PsWPHolOnToiOjlbLl0cQCnhFmrbCOUoms55MUBTtm9Cxit5sDGlajrsQ9E8NDnYyzvxzQrE34XWy9VFpsqh6vXv3RmBgIK+yw4cP51Wuc+fOat9nz56NJUuW4OzZs4iIiMCiRYswadIkdO/eHUCBQFW8eHGsW7cOQ4YMQUpKCv7880/8/fffaN26NQBgzZo1KFWqFA4ePIi2bdsKOEOiqMBnUjBHBnJDkcH8b+18sfWJmeDGIsaYyo1YsbinwdWJOSWuY0xrkrPtkWkS2VMul/MWlgAgLS1NzVeKD/n5+diwYQPS09PRsGFDPHz4EAkJCWjTpo2yjLOzM5o1a4bTp08DAKKjo5Gbm6tWJjg4GFWrVlWWYSM7Oxupqalqf0TRgc+cYI1acIt4APHExudlQgePXqdj4tZrePgq3fz3qxHuQT4vLJpluI4wbeBKkzVlFkQLTDk5Obh9+zby8vJ4lX/27BlnmbVr1wruR0xMDDw8PODs7Iz//e9/2LZtGyIiIpCQUBAwsHjx4mrlixcvrtyXkJAAJycnrWTCqmXYmDt3Lry9vZV/pUrpjrFB2CC8LHKmn8zrldHv/MkFn+dPt5ohys/ODma0idj4xEyw0//P81h//gn6/nFWcl8goTAQuSpMT8fZZJvO1YPFVveuTut5EbJ0BM94GRkZGDx4MNzc3FClShU8fvwYADBixAjMmzdP53FRUVF48+aNzv3r1q3Dxx9/LLQ7CAsLw5UrV3D27Fl88cUXGDhwIGJjY5X7tWJeMAynFM9VZuLEiUhJSVH+PXnyRHC/CeuFzyRpjjlqxcd1lZ9bhAUgKqK4ntLs6Ov22k/rY36P6tj4eQOc/6YVTk1oKaKX0mDry5cJdh4nZQAA4lOybFIQ0HzRKubhxCNKODl9mwpRueSuXr2Ko0ePwsXFRbm9devW2Lhxo87jAgMD0a5dO6Snp2vt27BhAwYNGiQqtYqTkxMqVKiAOnXqYO7cuahevToWL16MoKAgANDSFCUmJiq1TkFBQcjJydES5FTLsOHs7Kxcmaf4I4oOXOagY3deYve1eNN05h3VQrzh4Vzoklgl2Buhfm6C6pDJ9At671UoBjs7GeqX80eglwuKeTjDyZQepSqQSa7osCX6KYb8fREn76pnkrAEOUDq+1Bz/DWpGGBw8lxLFJiycs0Xnd0QBM9227dvxy+//ILGjRuraWEiIiLU0qRosmvXLuTn56NLly7Izc1Vbt+0aRMGDBiAOXPmYPTo0UK7owXDMMjOzkbZsmURFBSEAwcOKPfl5OTg2LFjaNSoEQCgdu3acHR0VCsTHx+P69evK8sQhD40J7gbz1MwcMV5pGXzM1VLRZVgdaG9tEBhCRCnujeXpofkpaLBibsvMXbzVfx34wX6/XlObZ+5NUxihSXNXnu6OOrcx0Z4kLpzOddlsDRx6WDsC4RP2Yelx3TLC5aKYIHp5cuXrA7c6enpes1YHh4e2Lt3L549e4bevXuDYRhs3rwZ/fr1w8yZMzFu3DihXcE333yDEydOIC4uDjExMZg0aRKOHj2Kjz76CDKZDKNGjcKcOXOwbds2XL9+HYMGDYKbmxv69u0LAPD29sbgwYMxduxYHDp0CJcvX0a/fv1QrVo15ao5gtBE3zx5OyHNZP0AgD8H1sH/mpXHNx0rAwDWfVYfI1pVxAe1hQfR49IwsWGuTPG2vhqHKODy42Sd+8y9olMzDhNfNLsd4uOKGV2q8I60P7yleoRxzqvA4zKZUgs17p+rAIB5e2+ZrE2pEBxWoG7duti9e7dy6b/ipv3jjz/QsGFDvccGBARg//79aNy4MVq3bo2TJ09i6tSp+Prrr0V0HXjx4gX69++P+Ph4eHt7IzIyEvv27UNUVBQAYPz48cjMzMTQoUPx5s0b1K9fH/v371fGYAKAhQsXwsHBAT179kRmZiZatWqFVatWUQwmQieW9LBuVbk4WlUuNB83Kl8MjcoXAyBc+JG9+2cIdcv44kKcbl9FqbCcX4AwFxZoaRLNgIZlAACTt1/nLOvqpP5sMlRwLBfgjsNjm6PMhN0G1VMUECwwzZ07F+3atUNsbCzy8vKwePFi3LhxA2fOnMGxY8d0Hnft2jXl5x9++AEDBgxAt27d0LlzZ7V9kZFcDm6FcOWuk8lkmDZtGqZNm6azjIuLC37++Wf8/PPPvNslihYpmbkYtvYSutYMwYe1S6o9rC1IdjIYa3oAaV73jJw8/G/NJbSJKI5+DULN0ylCcvTdkuY2yQGW8fLEdRW4XoLMfxWtB8ECU6NGjXD69Gn88MMPKF++PPbv349atWrhzJkzqFatms7jatSoAZlMplyBxjAMNm3ahM2bNytvOplMxivCN0GYkt+O3MPJe69w8t6rAoHJxHNkw3L+OPPgNXdBDQT3UwZUL+Uj6JBRrSti0UHTJ0HVNIasOh2H43de4vidlyQw2RD6ZCJzy0vifZh0d1yqUwr1d8Oj1wUrCjl9nMx9Ia0IQQJTbm4uPv/8c0yZMgWrV68W1NDDhw8FlScISyElM1ftu+rD2hROz38ProcKk/ZyF5QAP3cnQeVHtqqID2qVRJPvjxipRzrQuOxvs0zrZE8QDCzDNMwm7yz5qDY6/HSiYD/H8dakWTY3ggQmR0dHbNu2DVOmTBHcUGgovfUR1onWhKQyS6q+Zd5/+RZjNl2VvH0HkUv3hb44ZuYI1+7KZDKUErEiz1D4PKhepmXjk1UX0KtuKdI62SAWYA2THjGpUVgOUnXi5tIgWU8GSfMjeCbu1q0btm/fLrrBa9eusf7FxMTg7t27yM7OFl03QZgCRsfncZulEZZ83Rz17p/ZtSoAoIy/eEHFTcNx1MneDkHeLjpKc9O7bkG0+5GtKuFznklIDYHPw3LhwTuIeZbCy5GWIPgS4uMKAKhZ2gdjoioBAPrU4872ULlEQeiP1pX5pwkb1KgMZxlDwwqwHV81hGILsiHYh6lChQqYOXMmTp8+jdq1a8Pd3V1t/4gRI/Qer/Bl0oWjoyN69eqF33//XS0wJkFYCoyahqnwS7pEsZf83J3wJiNX5/7+DULRrWaI6NQkN6a3RdPvjyBDRaN0ZWoUnB3Erwyd270aJravDG83R9Qp44tlxx+IrosPmqZQtilFjMaMsCws0b/m6FfNkZ0nh4ezA5pUDMDVb9vAy5X7Ufrvl+8hMzdfLe6SPq5ObQNvV35l9SGTAfXL+uHcwyTex6z+uB4azj2MnHy5we3bEoIFpuXLl8PHxwfR0dGIjo5W2yeTyTgFpm3btuHrr7/GV199hXr16oFhGFy4cAHz58/H1KlTkZeXhwkTJmDy5Mn48ccfhXaPIIyO6sP6jxMP0LZKELrUCJFs1Q6fh4RqVG+h9bg7OyBXYyJ0cxI8FWi15f1OM2aK1UuaGiYyKxQ9zBU01dHeDo4qZnJvDo2wAgd7O3hymNdV72IphKWCOmV64yyxzRP+Hs5wcrAjgUkDwbOkoc7bs2fPxuLFi9G2bVvltsjISJQsWRJTpkzB+fPn4e7ujrFjx5LARFgImvkICz/viUnAnpgEdKkRItnbsDGWKpfwdkF8Spbye/daJbHqdJzk7QCmcSK1RfcVghAD57TDEZD2ZnyqZH1xtOce/KolrjxJRg2BK3PNickTQcXExLA6gIeGhiImJgZAgdkuPt60ubgIQheak42uh7WlrTZRFbxGtVaPDjyhfbjR2jWFGUVTqLRAyw0hAcb4XU+Mb4EDo5tKX7EEiBk7nE7dIq+hmBe3luHc/lmqtZ68+1JwG+ZEsIbpk08+0bt/xYoVeveHh4dj3rx5WLZsGZycCpYw5+bmYt68eQgPL5jEnz17pjf5LUGYE7aJ5EVqFm48l+5NTWocNUwBhib01IdJNExaJjlt4l5rJ/omrIvnyZmS1ufqaG+WVZ3GhIeCyWToMo1n5uTjzINXyiwECuRWpioWLDC9eaOe9iA3NxfXr19HcnIyWrZsyXn8r7/+ivfffx8lS5ZEZGQkZDIZrl27hvz8fOzatQsA8ODBAwwdOlRo1wjCKGhFFWAZ5PXnHJKsvbAgT9x/afjDvqRv4YOhmIezwfXxxVIcdfXlISOsgzVnH0tan6Wv/qoa4oVT94QHqdVHQaBo4ceJkWV0+ZWN++cqdl+LR/eaIWrz6YIDd1CztA+aVAwQ0ZrpESwwbdu2TWubXC7H0KFDUa4c93LiRo0aIS4uDmvWrMGdO3fAMAw+/PBD9O3bV5njrX///kK7RRBWz5KPauH0/dcY1boi9sQkGFxf3/ql8eh1BppWKoYmFYthWIvyyqXNALDli4b4YMkZLOhZ3eC2TI1NxuAhjM78HjXM3QW9LOxZAz8dvisobpihYQUULOtfGxO3xmBx75q829ZE17jcfa3AxWbr5Wfw0XCS/9/f0bgxo53oNk2JYUtj3mFnZ4fRo0ejefPmGD9+PGd5Dw8P/O9//5OiaYIwOcZ6WFcN8Ub7aiUkq8/R3g7fdo5Qfv+qrbrfUu1QP8TN6yhZe6oYOwmv1pushWi1CNORkyd8BVeAp+k0rWII9HLBrK66U4yxwWYGUx0OfIdGmypBiIoortQQi5nnrM3EJhTJnL7v37+PvDx+cWj+/vtvNG7cGMHBwXj06BEAYOHChdixY4dU3SEIo2Gu5czWhLGX+fPxYSJsmzsv3go+xhblam4NE/+TVjWni5vnuI/R7I2lmPD5IFjDNGbMGLXvDMMgPj4eu3fvxsCBAzmPX7JkCb799luMGjUKs2bNUibb9fX1xaJFi9ClSxehXSIIo6K1Ss5I8pIVzRtmh0RWwhws7l3D3F0AAHSvFYKtl57p3C/FVCLK78nGB6ZgDdPly5fV/q5duwYAmD9/PhYtWsR5/M8//4w//vgDkyZNgoNDobxWp04dZVgBgrBk5EaaFeQUI443xohVRRAKOkaWQPTk1srvFye3Rty8juhSI8SMvSrko/qllZ/5pD4xdLhUC/HmVU5MM9b0nihYw3TkiGFZyR8+fIiaNbWdypydnZGeTsuACfNy+fEbTN15A1M6RSDmaQpaVQ7EhYfqvjgJKgEgpSQrj1J58EVzYhaqndtx5RkCPV3QsLy/ZH0iLB/e94k1yeNGkjhUL0GQtwtinqVwHnP/5VusPPUQH9UPhZPI1E2WjOAzatmyJZKTk7W2p6am8gorULZsWVy5ckVr+969exEREaF9AEGYkG6/nca1pynosfQMZuyKRYsfj+L2izS1MlN33jBK254ukqzBKBIYkhrlXmIaRm64gj5/nJW4V4SlI+Q+cVVJUO1qxLhl4pCpfDJO4EpV+MZWe/Q6A9P/jcWy4/f5V25FKibBM/TRo0eRk5OjtT0rKwsnTpzgPP6rr77CsGHDkJWVBYZhcP78eaxfvx5z587F8uXLhXaHIIwK26qPu4nCnU35UMLb1Sj1WiJ+7k4Y1KgMFhy4w/uYoc3L47ejiolYvArgebJxNISE5SNEeHBzcsCfA+uAYQryL1oSQlbBiV6AoTLEhNYR/ch4K2TNCe+7QOGrBACxsbFISCiME5Ofn499+/YhJITbvvvxxx8jLy8P48ePR0ZGBvr27YuQkBAsXrwYvXv3Fth9gjAtYzddNXcXTEYxD2e8epttlLoblvPHiFYVcTM+FXuv84s55eRgBx83RyRn5Brkk0HO9ZbPzfhUfLr6ouT1Cv3pW1W2/owTkmiYBNqiTt57hVozD+D3/rVRt4yf3rLWNBx5C0w1atSATCaDTCZjNb25urri559/5lXXZ599hs8++wyvXr2CXC5HYCB3/hmCsAS2XHpq7i6YDINSnPA8tlF5f94Ck2q1hvgwGTvkAWE47RdzWyt0UTHQQ6cW2JqWsOtDpuMzV1khqIYVsBN43XLzGSSl56DH0jNGi/VmDngLTA8fPgTDMChXrhzOnz+PgIDCUOZOTk4IDAyEvb0wO2+xYsW4CxGEDVC2mDsevtK9qOHIuOZ6j+9TrxTWn38ica/0Y2+IxMSiAfqsSVn8ceIhAMDhXVbzvvVD4eniiCrBXohaeJyzWl1B9YT01EaemYQOlg2ogxY/HmXdx9/n23q8vtmEQHWTnczg85FS0NSsy5qEWN4CU2hoQah2uYi1zzVr1uR9US5duiS4foKwdKqFeOsVmMoWc9d7fJCX6f2bVN8qpZjTKgZ6Kj87vNPx29vJ0LVmCDJz+K0QLNQwiX8AWM/0XDR5ZmDCXV+N1BuqWNGzmTdG0zCpDDEpE2pbc0gQ0Z5ssbGxePz4sZYD+Pvvv69VtmvXrsrPWVlZ+O233xAREYGGDRsCAM6ePYsbN25Qwl3CZjF0wjFHSgehfgtcFPN0Un52tNd8y+RXh6KcQXOuDT40bYn35h026HgHe903Lt8X92ArX4DhorGqz1AztDGHjDUJsYIFpgcPHqBbt26IiYl5lwW5YOZS3IiKyN2qTJ06Vfn5008/xYgRIzBz5kytMk+emNbkQBCmQt9E3bVGMOfxPeqURMyzFDSpaDoztlC/BX0MalQGLcIKfRUd7PXX/d0H1fD1FvVAtgWTvg6TnEgfJoZhrMokQOhnfLsweKisaAsP8kRECS9svaw7KrYmYcU9MbJ1RWN0zyiw3b4lfd0wslVFeLo4wM5Ov0luVteqrNvzGfE+TPqw5vEm+B1y5MiRKFu2LF68eAE3NzfcuHEDx48fR506dXD06FHO4zdv3owBAwZobe/Xrx+2bNkitDsEYRWozhGRJdWj5vKJHuxob4e53auhg4TJebmQcpKc9n4VtYnSgUN91atuaa1tDJhCDZPGA0DIJKxa1IqtA4QGfwyog6HNK6ht+6ptGAY2KiOonu8/jISni26znqWhS3s0OqoSPm1SjvP42qG+rNtVx4a+8bX6k3oGpYyxJvFJsMB05swZzJgxAwEBAbCzs4OdnR0aN26MuXPnYsSIEZzHu7q64uTJk1rbT548CRcXF6HdIQirQK9K3EJnDIPkJY5jnR3Vpx7eJrl3/xsUVkDl8z9FaNWjNbAlWvzvwXYL2dnJBC9ekPJFwVio3f48uuuox0zJ5/o4OeguYy+TFZkXD8Emufz8fHh4eAAoWOX2/PlzhIWFITQ0FLdv3+Y8ftSoUfjiiy8QHR2NBg0aACjwYVqxYgW+/fZbod0hCKtA35xkqdOzVA+O7rUKNWjDW1bA9ivP8DmPN19NZJDxFqz0OZaqvi2P/+caetYpJbgvhHEYu1naOGd2Mhkql/BCrdI+KO6l/4W8Q7UgxKdkISLYS9I+GBs+Y2La+1XQav4x1n185MmRrSrh+J1XOh3yDcmvaU0mOsECU9WqVXHt2jWUK1cO9evXx/fffw8nJycsW7YM5cpxT4ITJkxAuXLlsHjxYqxbtw4AULlyZaxatQo9e/YUfgYEYQVY0ZygxF6iTi/oWUP5eWybMIxtEya6LpkOHyZN9O23xt+CEIe9rEDDtHXoe5xlf/uotgl6JA1Cb+HyAR469/F5MQrydsGpCS1Re+YBvE5XX+glRXJfa0GwwDR58mRlktxZs2ahU6dOaNKkCfz9/bFx40ZedfTs2ZOEI6JIobZEX2Ofpb5h2Um5lpgDvhOubh8mjfr01LEnJl7te76cMSzmlJVx90UaZu6+iVGtK6JWaXb/FWuE7TeXeqWnpaB6robeuYZqkmUQpmFK0hS4DGrdtAi+ndq2bYvu3bsDAMqVK4fY2Fi8evUKiYmJvJLvEkRRpFvNQrMUn6ll0DtH1ZGtTL9ap/W7dBCDG5fFR/ULnK/HRlUyaptOenwsVOHrw6TPJLfyVJza90M3X/Bq21YYvPoijt95ie6/nTZ3VySF7TevHGRd5jUxGPrCZbDpXWZIZkfrQpCGKS8vDy4uLrhy5QqqVi1ciujnpz9XjJ+fH+7cucM7snfp0qVx4sQJZbBMgrB26pfz17mPbbr6tlME+jUorVeVbiyW9quFR0kZKB/gge41Q/Dxe2WE90PgDMpHmyWTqUT61tyncRWFNJ+ekyegtPXz3MDAkNbA1altkJmTD193J+7CVo7BGiYDtXAyAyUmC1WwsyJIYHJwcEBoaChrrCV9JCcnY+/evfD29uYuDOD169eC2yAIa4HP/GBnJ0MFlcjYpsTB3k4pIJmzH/rQ1CZk5eZr7Ddlb6yLAo2C6S/Qk6QMhPi4Gs3Uq3pG3q6O8Ha1ntAAhmCowCFEw8RWVCazrlQyhiDKh2nixIlYs2YNp2ZJlYEDBwptiiCKBNb0hsUXfw/p3+zdnR1UfJjUWXzortp3IRN4UROu7O1kgInfR9eee4RJ266jV51S+O7DSKO0UdR+R6nQ5b/nYCdDnpyBs4N+FZQMhl5765kABQtMP/30E+7du4fg4GCEhobC3V09BxZbLjgx+ecIoqhgaNoCS2Ta+1WQkpmLAQ3LSFJfKT9XfFS/NFadLkjea8gquaKOORzcFx64AwDYePGJ0QQmoVqzPwbUwWd/XTRSX0yHwWlPdBz+zxeNMHfPTUzuGMFxvAzyIjLeBAtMqnnhCMKaePw6A+7O9vD3MH1eNn3YooapuJcL1n3WQLL6Vn1cDy6O9ioPB+lmaKmFqzsv0hDs46qWosOSMLa8xDAMYuNTUT7AQ5nTTN81zs7Lx90Xb43bKRaiIorD391Ja5m8tWHo/KErfEiNUj7YOKSh2jZdv6NBybCtaP4TPKJV88IRhLXwMi0bTX84AgCIm9fRvJ2xphnCQlD4WfBNvmsuDdOFuCT0WHoGxb2cce6b1ubpBAfG1jBtvvgU47dcQ4NyftjweUPO8v/7OxpHbr80uF3SKopDiA9TKT831jhMRUXDJMo/Pjk5GcuXL8fEiRORlJQEoMAU9+wZ/wSHBGFKbiWkmrzNAE9ntKsShLWf1tfat+6zwm0kPnGjeMbz0S8xDCPMh0l0r7T573oCAOBFaraEtUqLvZGDE/199hEA4OyDJF7lpRCWgKKztF1qhAhMP/epiTYRxbFeRXssA5Cdq9spTl+ID8Xx1oJgDdO1a9fQunVreHt7Iy4uDp999hn8/Pywbds2PHr0CH/99Zcx+kkQVseQpuV0Jr+sWUolYKA1zRhmQmGKU4YV0DMHyxlh2gaGYcAwjCQBRO3tjf9jGtpXniGvRMPWNVMIM0VVw2RwGCUB90MpPzcsG1AHufmFfskyGZCRo09g4l+/VOPQWAgeOmPGjMGgQYNw9+5dtWS57du3x/HjxyXtHEHYKhY8J1gkMk0Nk55ZuEDDxJ+v/rmGXsvOcr4J80GqdDK6yMzJR/Mfj2LsJvE514zdR83a5XJGK7ozIR2GOn2Lue1lGt80w3qo1c9V17vKxm2+imY/HEWGBcdFEywwXbhwAUOGDNHaHhISgoSEBMEdePnyJXJzcwUfRxC2gi2ukpMaOw2bnF6THLjNAJqcf5iE5AzD5yEHI/sH7bsRj0evM7Dl0lPRdRg95Y2GQPYoKcO47b1DjOOxtb64qN7efM9hdOtK8HVzRLNKAWrbXRyFqxxVtUAyGTDovTI6y/Idi/9EP8XjpAzsuy5cjjAVgq+Ui4sLUlO1/UFu376NgIAAliMKWLZsGbKzC+z6DMNgzpw58PX1RVBQEHx8fDBmzBgKP0AYDRJKrBstHya9Jjlxa3aksOgYWxiRwuxkbKHOXCOtyJrkeJYb2boioidHIdTfTW27s4O9QW3KAAR6uugqyq1hsqK5WbDA1KVLF8yYMUOpFZLJZHj8+DEmTJiADz74QOdxX3zxBVJSUgAUCE9z5szBlClTcOLECXz33XdYsWIFfvvtN5GnQRDi+fPkQ0zbeUMSk4wYrPUt15Ro+TCBwR/HH2DGv7FavxvD4sOk6nOhC2swyUmBsYU61eq/WBONuy/SjNqegqIkL4m9zTR/+wqB4lIvSX2b30ssDCthyUNIsNP3jz/+iA4dOiAwMBCZmZlo1qwZEhIS0LBhQ8yePVvncaqT0Z9//omZM2di9OjRAIBGjRrBxcUFP//8M7788ksRp0EQ4pm5KxaAeoJcwrLQ1DCBAWbvuQkA+KA2y++m8fTceukpetUtrbcNKZZGG93cJQFG92FSqX/v9QTstWATiy0g1Elail9f3SSnv0au9xCZDBi44nzhdwvWOAkWmLy8vHDy5EkcPnwYly5dglwuR61atdC6NXfMEcWFffjwIVq1aqW2r2XLlkoBiiCkho+RxlRJWGVQf4uy3OnBclDMHWypUTJztPPIaf7eSenc/klSaJiMbu6SoHpjx2Eyl8xoLg2xOVDzYTJfN3i1z2fufaaSENqmNEwKWrZsiZYtWwo6Zt++ffD29oarqysyM9UzZmdmZsLOyPFBCMuFYRhsu/wMYUGeqBLML0kzX87cf41DNxM5y6VnS5tgy5KXx1obhRomPmEFGK39ch4PUyk0TFIII6/eZmPv9QR0rREMTxf1BLJSyASafbyXmIboR2/Qo3YpNQ1ZYloW/rvxAt1qhrBGLd9x5RnKFnNHZEkfte0X4t7w7su1p8mC+k5oY+nTDNc9m5tvPYKuKIHp0KFDWLhwIW7evAmZTIbw8HCMGjWKU8ukmoD30KFDqF+/MHjfmTNnUL58eTHdIWyAY3deYsy7pdJSR+Lu88dZXuUmb4+RtF1985iq2pkEqwKqhnjh+rNU1An11dqnrWHSE1YA4vxZ+AhVXAgJAqiL/n+ex834VJy9/xq/flTL4Po00RSYWi8oCAdjJ5OhR51Syu19lp3F/ZfpiI5LwqLeNdWOuRiXhJEbrgBQH698fMVUef+XU4LKEwVY0pThwBF7LDVTv3b31VvLDfKqiWCVzi+//IJ27drB09MTI0eOxIgRI+Dl5YUOHTrgl19+0XmcXC5X+/vmm2/U9gcFBWHu3LnCz4CwCW7Gm8YxFNCtujdVdGZLmuwsiRUD6+KrtmFY2r+21j5NxY0+2UYRiFIVOQ/1kRQCkxS/7c34glXI+24Yx/dHl1B35Umy2vf7L9MBAAdiX2iVVXXSVSXfjDkyipBFTiOsgEAfJokmoEGNyqBdlSBElPDSW+7JG9OElTAFgjVMc+fOxcKFC9Wcs0eMGIH33nsPs2fPFu203alTJ1HHEbaBKYUIhjG/0KLmw0QCFAAg0MsFw1pUYN1XqGEq+D9f5Ymhef3YNEx8nqVSRDUx9k8pxb0i1M+K7QErhSZNasQEkyhKQpbUTHu/Cq9y2XnCBpYla9wFC0ypqalo166d1vY2bdrg66+/5jz+wYMHOHnyJOLj42Fvb4+yZcsiKioKXl76pVTCNhiz8Qqy8+X4pU9NkwwMNm0SzZHWh+YqOX0aI0au/SDk82DMl/jpeS8xDRUCPQUdoy9islTwWcn365F7ys9spS1xNSAJP5ZJjlCByUj9kALBJrn3338f27Zt09q+Y8cOdO7cWedx6enp6NGjBypUqIBBgwbhm2++wfz589GrVy+EhITg119/FdoVwspIy8rF1svPsPtaPBLTTGP+YptEVU0vxlxZoykP9q1fsKx9TFQlrcBvhH40fZjUTT/qV5CBdvJdPuY2KcxJqi8BY0SkL/n36nOD+8CFLllH9ex/+O+2qDrMSdNKugMn62J6lwItydDmRcd/9qP6heE1TPEzWpNTNxeCNUyVK1fG7NmzcfToUTRs2BAAcPbsWZw6dQpjx47FTz/9pCw7YsQI5ecxY8YgPj4ely9fhouLCyZNmoTy5ctj6tSp2LBhA4YPHw5fX1/07dtXgtMiLBHVYaP5/DLWwGUbqqptm/KtdHbXqvi6XTi8XR2Rp5G8ktCP4gGtMAWpC0DagSs1f3heJjmJb4a32YaFqTCWMC84zg1LcV0mOamvoSZeLg5IzVK/ri3DA/FTn5qsK/m46BQZjCYVA+Dt6shd2EaoWFyY1tNQBGuYLHg+FHyH/fnnn/D19UVsbCxiY2OV2318fPDnn38qv8tkMjWBaevWrdi3bx+qV68OAPjjjz8QHByMqVOn4pNPPkFmZiZ++OEHEpisgNSsXHT79RTaVgnC+HbhouoQl7xCRDusJrnCbcae4FWRyWTKidmS7fSWiJ2Whkl3WbbUKHyED4WG6dDNF5iy/Trm96yBhuX9lfuP33mJCVuu4bsPI9GkIrs2QzO+1uFbLzB5m3ZdunB1KkxTIaX/9IHYF5i64zoW9a4JodFb0rLyIJczamY4XbevsYdTMQ9nLYHJx9VRlLCkoCgJS+YgJ1+YmdmSA1cKNsk9fPiQ19+DBw/UjsvLy1PzU/Lw8EBeXh7S0wtWYrRp0wa3bt0y8HQIU7Du3GPcf5mO347eF3ScOYYB20NHTcNkxLb5n6/lThCWhuJKqfsbaZrkxPkwKYTnwasv4nlKFvr9eU5t/4AV5/E8JQv9/zzPdjgrn6wqqItvaAsHI8Wi++yvwnMS80C6laC+ilVV4FcVRo39+sG1hJ3gR/kAdwBAx8gSRm/LljRMJosUWbduXSxevFj5ffHixQgICFAm7H379i08PMTltSFMi1hfD3NYsrk0WabUMKmi5sNkwROEpaA0ASlMcvqcvlkiffP5nTWLiLnP1X9X4T+ssaNV5+TJRd1vmtdT1YdJ9TIZu/9sAqXteMiYjn/+1wi/96+tc1WqlAgVmCwZwQITwzDYvHkzhg4dig8//BDdu3dX+9PFvHnzsH79epQoUQKhoaGYNGkSFixYoNx/+vRpdOjQQVBf5s6di7p168LT0xOBgYHo2rUrbt9Wd1Z88eIFBg0ahODgYLi5uaFdu3a4e/euWpmEhAT0798fQUFBcHd3R61atfDPP/8I6ktRwhhLio0lNOhz+pbLGWUeOXNC8hI3Snnp3XdVYeaDJafVyn71z1VEP1KPNi32oTp5ewzWn3+M8f/wc+DefkW403ZmTj4mbo3BkduJWhrRM/dfAwCuPknGV5uv4qWOxRLxKZkYt/kqrj9LEdy+GA6rRM5/+KowJpOxhRdH0jBJgq+7E9pWCYKjvfF1JkLDCuTJGRy+9QIpHAEvzYFgw+/IkSOxbNkytGjRAsWLF+f9FlWrVi1cv34du3btQnZ2Nlq2bImIiAjl/mHDhmHYsGGC+nLs2DEMGzYMdevWRV5eHiZNmoQ2bdogNjYW7u7uYBgGXbt2haOjI3bs2AEvLy8sWLAArVu3VpYBgP79+yMlJQU7d+5EsWLFsG7dOvTq1QsXL15EzZo1OXpR9BAr3JhDmcPWpmLbkduJWHP2sdHa1jc2SKskDHsNHyZ9GqOjt1/i6O2Xatv4pUbRLiP0/lAV1PhqW5afeID15x9j/fnH+LmP+nzT54+ziJvXEV1+1R8Re8T6y7gQ9wb/RD/ljJQvxQvP1svPlJ8HrbyAk18XpMky1hivXMILN+NT8WHtkrj6VF0oLEo55BSE+ruZuwu8yREY/f23I/dwKyENVUO8sGt4EyP1ShyCBaY1a9Zg69atgrVBAFCiRAl89tlngo/Txb59+9S+r1y5EoGBgYiOjkbTpk1x9+5dnD17FtevX0eVKgXLR3/77TcEBgZi/fr1+PTTTwEUpGVZsmQJ6tWrBwCYPHkyFi5ciEuXLpHAxILoJcXmEJhYGlVsSUrPMW1ndEAO4NwoHI6VcZgEPiT5FJf6ucu3uucpWcrPYk3Esc9TeZeV+nZ7+qYwL6ixhJdNQxrgxvNU1Cvjhyk7bqjtK3riUoHz+96RTQxydjcVQk1yCn+568/439OmQvDV9vb2Rrly5UQ3ePjwYa3Ale+//z4qVqwouk4FKSkFbx5+fn4AgOzsAvW1i4uLsoy9vT2cnJxw8uRJpcDUuHFjbNy4ER07doSPjw82bdqE7OxsNG/e3OA+WSN3XqTh8esMtI4ozrpfqNNoenYe/ruRgFqltXOEia2TL+waJtNMsfoeTCQk8aekr6vyszLSt0C3CD6/udQ5rR68Sy3CheqtIFZgyhXgb6V652Xnaa9gYgueybdb/ARTRvD97+niiAbluFcZFiUqc6QksRQ0U+6IITkjB8kZufBydYSfu5PhnRKJYAPmtGnTMH36dGRmZnIXViExMRH169dH69atMWPGDCxbtgxnz57Fjz/+iMqVK2P8+PFCu6IGwzAYM2YMGjdujKpVqwIAwsPDERoaiokTJ+LNmzfIycnBvHnzkJCQgPj4eOWxGzduRF5eHvz9/eHs7IwhQ4Zg27ZtOpMBZ2dnIzU1Ve3Plmiz8Dg+/euizhtd6LP+m20xGLPpKj7/+6Jym6m06KxxmN79bwxfLGeHwiEV4uOqp2QhJDrpRzVZLPsqOW74yBODV1/kLmQEVH9/IelZVIXAPD0S5KXH6v5cqsLK0qMPNItj1m7xfn18fpWT916Jrp+wPjTN42JYc/YRmv94FN/vM+9KesECU48ePfDmzRsEBgaiWrVqqFWrltqfLkaMGIHg4GAkJSUhLS0NX3zxBapUqYL4+Hjs378fK1asUFtFJ5Qvv/wS165dw/r165XbHB0dsWXLFty5cwd+fn5wc3PD0aNH0b59e9jbF8Y7mTx5Mt68eYODBw/i4sWLGDNmDHr06IGYGPbs9XPnzoW3t7fyr1SpUqzlrJ3bCeyCoNC3wx3vHGHvvGBP2FlQp6AqecP2xs7Ijdfm4t41Ub2UD/rWL42W4YHSN1AEsVf5oZQ+TAJXsFmym4tYDZNqUX2XQ9Ncp3rbH7mdCE12XYvX2sa/T9z9VyQXlgpL/m2LEgfHNFVmM5AaxfuAvZlDzAs2yQ0aNAjR0dHo16+fIKfvvXv34vTp0/Dx8QEAfPfdd/D19cXPP/+Mli1bYtGiRZg1axZGjhwptEsYPnw4du7ciePHj6NkyZJq+2rXro0rV64gJSUFOTk5CAgIQP369VGnTh0AwP379/HLL7+o+TlVr14dJ06cwK+//oqlS5dqtTdx4kSMGTNG+T01NdVkQlNSeg48nB3g5KBf1n2Zlg1/dyeDcj7pMpNJccuaao5jNcm9a90YAlMxDyfsGPaeoGPIOqcftYCJYIv0zY3U4SMevHyLIG8XuDkZ7kOiOs7YupmYmqW9EfzHkKYmNU0l8KObSqDMwv4IJyUzF072drz65Oqo3SZh/VQI9MS4NmFYd076hTT571SvVicw7d69G//99x8aN24s6DhnZ2c14crOzg75+fnIyysYvI0aNUJcXJygOhmGwfDhw7Ft2zYcPXoUZcuW1VnW29sbAHD37l1cvHgRM2fOBABkZGQo+6OKvb095Dr0487OznB2dhbUVyl4/DoDTX84ggqBHjg4ppnOcucevEavZWfRKjwQfw6qK3k/pLhnhWoIRKNnlZylRJS1lH5YKqoaJsWlEhojSWq/tZbzjwEAHs7tYLA/mh2HhqnenEOsxy07/gBf8MiBptm983FJys+sApOI86k+fT+c7O1wakJLzrIuEgtM7lbg+FxUMNZMpjDBm1tgEmySK1WqlFrEbr40btwY3377LdLT05Gbm4tvvvkG5cqVUzpov3z5Er6+up2C2Rg2bBjWrFmDdevWwdPTEwkJCUhISFDzr9q8eTOOHj2KBw8eYMeOHYiKikLXrl3Rpk0bAAV+ThUqVMCQIUNw/vx53L9/H/Pnz8eBAwfQtWtXwedpTP67kQAAuJeo27QFACtOPQQAHLqlrW4XhI5705oclvWtkjNOe4TUqGqYFB+Fa5ik7FEheRIn7BVS3Xc8/Tn0PWNUA0EWvkiIIydfzivlkdTRuse2qSRpfYR4jPVoUIwzezM/ewQLTPPnz8f48eMFa4N+/PFHXLlyBT4+PnB3d8eqVauwZMkS5f6bN29i0KBBgupcsmQJUlJS0Lx5c5QoUUL5t3HjRmWZ+Ph49O/fH+Hh4RgxYgT69++v5ee0Z88eBAQEoHPnzoiMjMRff/2F1atXiwqdUBSQRMNkIscDtgeQom1LkfsspR+WimpsPYU2TugqOXNFdBeKUGd2PgjVYBpyP5ojfEMxD9Nr+wl2hNxr3WqG8C6rsEjYmzlwqWBdZr9+/ZCRkYHy5cvDzc0Njo7qiQuTkpJYjytXrhyuXbuGU6dOITs7Gw0aNECxYsWU+4UKSwA/NfuIESPUkgCzUbFiRWzZskVw+5aKVBOSzltTgie8Zh/ZtFZHbyfiXuJbfNpEfBiLx0kZOtu2Jk1ZUcZezZRf8P9Ph+7qKM0OX4FJocU1Jaq3YarE0Y1TMnKx6OAdnfvZNULix4WVyKWEBeDM4YeriqVomAQLTIsWLRLdmJubG6KiokQfT/DD2HOWqTRMg1ZeAABUC/FGfZExWLqyREhWOn2LqlF6Aj3pDZkNf3cnvE7PQcvwwnhgCgfmTJZYQfrgu1x/yN/RguoVIiDcSkhFeJC2O4OqU/YP/93W2m8IU3ZcVwuMqYn0wTqFrPIj6crmMNZq53cCk4O1OX0PHDjQGP0gLBBdGhgpnJSFTJXxeiZ8QxoX8rLSt35pyVd/bB3aCOnZeQj0cuEuXATZPaIJTt9/hU6RwcptYrWCQh7kxqr3VVoOEKS93ZiPAM2cepqw6pcM6BAfHyyFnCSFvKSZSoYwL8b2YTJk1bcUiMq8d//+fUyePBl9+vRBYmKBY/G+fftw48YNjiMJU3Ag9oXoY58ncwckPWyoMzkKJ8t8OYO5e25i4wV1YWTi1msGt6GzbRHHVAjw4Fe3gMprlfZFk4oBInpTNAjydkH3WiXVQmiInS6F+jzxhWGAzRefYNnx+5xlbyWkYtrOG1oJdI35EOB6gLHdr4b0RsjqVylE2GCewWEJ02CsO1mRLsXcGibBAtOxY8dQrVo1nDt3Dlu3bsXbtwUrtq5du4apU6dK3kGiEFOYb1Wjcevi4M1CgUysWl1x3NkHr/H78QdqQS2vPU3G+vNPRNXLB4U5UEikbwaAO8sSbABoUrHQF6+0n/UkxbRGxM6XxjT/fPXPNczZcwtxr/SnQpm1+yZWnY7D11vUXwakHtaqLz2mdvngY2pXlJDCEd/Mz09CA2P4hSal5yg1pebWMAk2yU2YMAGzZs3CmDFj4OnpqdzeokULgyJ1E5aBasJDPrcmw4iblBVTpWoQPQXGToordvn0ka+aI+ZpCuJeZyDY2wUlfd0gkwFli7lj9Zk41CjpgyBvMq8ZE7HpbIy1Sk61WrZ7mY3rz1LUN0j8DMjIKewH9/XSvi6GPPOExMfi+5McGddc5z5zx+UhjM8TlYU75tYwCRaYYmJisG7dOq3tAQEBeP36NesxQnKtiYnxRIjnTXoOLsQloWV4IBzshVto5QwDOxEzfqGWR/ChvLjyJBmOOpagHrr5AtVL+eCGgAzvDMMg0NMFrSqzC0RDm1cQ1U9CGGLfYBXP8Vs60v2IRQrfKKkDl2blyvHDf7fQPCyQs2a2McDVH83cdKrwEUwVD0C+165sMXed+4yRD5IQj9S/xtHbicjMKVzgYe7fW7DA5OPjg/j4eK2o2pcvX0ZICHtcBR8fH86JTpHBOj9f2OoXwjB6/H4G9xLf4qu2YRjWQv2hz+feFPu4UMyrxnhDTMnIZV0dp2DKDvK1s1bEzpeK+EbtFp2QsDfqWhK+fUvU8GGS+hkwdtNV3H6Rhl+P3EcpP/0+PmwLKtj6ozjPzJx8dP/ttM76+PiKLT50F6OjKkni9O3l4shdiDAZUt/LipXSloJggalv3774+uuvsXnzZshkMsjlcpw6dQrjxo3DgAEDWI85cuSIwR0ljIMiavi/V59rCUx8EGvqEOJHJHQQJqZJvKqOsBjEytdyOWMUPyYpTH1SvzLcfpGm/JyTx9/bXWHu0NeftCz9caLEJg/WBdcquNL+5DNoSRg7zZO5Y+cJFphmz56NQYMGISQkBAzDICIiAvn5+ejbty8mT57MekyzZrrznhHGJTUrF57ODpw3GpvvAS8Nk8jnhfI4trdZcVUqMVWaOsL0iFXJZ+fJjRJU0dB7LSMnzyjRvRUIWR2o0PYa8lAS5MPEY6RXDfHWuY+S+Foetm4hFey04ujoiLVr1+Lu3bvYtGkT1qxZg1u3buHvv/+GvT2/G/jEiRPo168fGjVqhGfPngEA/v77b5w8eVJod4osO64841Uuctp+jNl0lXXfvuvxys9iJ22xb9iKw6btlN48JjQxK2E9iBWYDt9KNIrjtyFaq5TMXER8+x9+P/ZAdB1pWbl4picUSD7fiJ2ATp8/VbjOVsjl2HiBeyWsvh55ulDS3aKGueUxwQLTjBkzkJGRgXLlyuHDDz9Ez549UbFiRWRmZmLGjBmcx2/ZsgVt27aFq6srLl26hOzsAnt+Wloa5syZI/wMiijfbI3hXXbbZXbh6lsVXx4h8VNUESubKN4uH73WTl1iKNaSN4wQgSEruIxwX6gK50JlubMP2BfJCCH60Rts1zG+ASAvn/8527/LO8MmBPJ10BZyjaf/G8tZhu2arhhUBxUDPfDnwLq82yIIKRAsME2fPl0Ze0mVjIwMTJ8+nfP4WbNmYenSpfjjjz/U8tA1atQIly5dEtqdIosUU7/q2zrbRMfHHi3eh0nPTgNPzjiaBMmrJERgyCoZAcoW/nVawH2h75LkCjhphQ8T21zA9/6XWrvLNge1DC+OA2OaoVpJ3eY6wjwIGZ7WaL4TLDApVrNpcvXqVfj5+XEef/v2bTRt2lRru5eXF5KTk4V2p8iSZ+DExDAMElILnaPZ5lVFdFW99Yh8COkzZcTGG7b0m0xytoshiyrzjCAxiQ0rkCUwF57u9vW/2AgZC4qggGyHfLDkNF69zeZ8hZL6ZcUaH6pFGeM7fRu1ek54C0y+vr7w8/ODTCZDpUqV4Ofnp/zz9vZGVFQUevbsyVlPiRIlcO/ePa3tJ0+eRLly4rPSFzWErH5hQ9PvgW1i5RMkTOwDQ99RhiYglWrSblqJ0pZYGpamYRJ7q22+KF0ke32XRMy7A9vLTJ6cwfz9dzhHu1jTPmEbmFugMTa8veYWLVoEhmHwySefYPr06fD2LlSHOjk5oUyZMmjYsCFnPUOGDMHIkSOxYsUKyGQyPH/+HGfOnMG4cePw7bffijuLIoKUSyo1Hx6sJjkezYn2YTKijUuqOXvVoLoo980eaSojJMGQEWAMDZOqcC7k7TotOw8BUtynjP5rIuR6KcakrqH5NjuPU0CU2k/M1h/ARZUVg+qIynlq7tuBt8A0cOBAAEDZsmXx3nvvwcFB3AqF8ePHIyUlBS1atEBWVhaaNm0KZ2dnjBs3Dl9++aWoOosK2XnSBfXM0QgQyvZmyDb3aQo6YrU52bnSPLxy8uS4nZCGKsFekMkKIhdn5EhznVTzFhkr2z0hDIOWvBtBSBer6RXijM2FVELF63cpifRdJ67xbqjmWxNzx90hhMH31yrla53xswRLPVLEVJo9ezYmTZqE2NhYyOVyREREwMODXzb4osz3+7hNVXw1N5+sUk+yy+roySIkHL39Uu27WIFp6s4bODDG8Hvpy3WXsD/2BSZ1qAxvN0eM/+ca90GE1WKID5MxfNt0hezgIi9fDimWbjBg9Gq2hMgb6849xpxu1XSa1RiG4Rzv4zbT+CvKGFvA9TRzZHeTB7JYvXo1PvzwQ7i7u6NOnTqmbt7m4Su/PE5SX87P9jBhq2u7RvwnsS/tdxO1V1qKYf87te7ykw8Q5K0/DQRh/Rjiw2QMgUmRRV0oUmZd13dJCoQpYeet76HHNd5fvc3WX4CwaYytD2xfLcjILehHeLZVAxk3bhwCAwPRu3dv7Nq1C3l5/DJ8F3XyeIbszRJptmM1yfE5zkzr7WOfp+LR63TldyERjcVAYQUsA0NeYN9mW85c42hvJ8k9xTDAyzQ9QoqI66Urv6NMJpNsvBvTh5EwH3zHp5hxXKm4B9yczBus1OQCU3x8PDZu3Ah7e3v07t0bJUqUwNChQ3H6tO6EjkRBwko+jNkozkTAN/aK5n1uikUxmm+8r95mo8NPJ9Dsh6PKbTQBFw0MUfkPW2vcOG9CuiZl0unfj4uPFM6Grq4xDCPZiwOF/iCESvN3XkhjlTAEQQJTXl4eHBwccP36ddENOjg4oFOnTli7di0SExOxaNEiPHr0CC1atED58uVF12vrrDv3mFe5fTcSRNXvaKd9K/ARQkyxjFizH2zRwSm6d9HAkPxh91+mcxcyACG3IJ+QHVK0KaYVfUKpVOOM5CXbxNad9AXptxwcHBAaGor8fGlWIbm5uaFt27Z48+YNHj16hJs3b0pSry0i5Y3IJgg5OrAITEbuh1jYupAvZ4xqP6f53TJwd7bchKu5AuzCUglMxkCfTCSVoEMvOIQ1ItgkN3nyZEycOBFJSUmiG83IyMDatWvRoUMHBAcHY+HChejatatBmitbR0o5pfeys1rb2CbwZSyqfm2TnPEnPk0hje1S0PxbNHB3ttyEq11+PcW7rL2dTBIhnKsOofNGbr5cr+O2VKZvvpkKLFesJAxDxivZs6UhePb56aefcO/ePQQHByM0NBTu7u5q+7nywfXp0wf//vsv3Nzc0KNHDxw9ehSNGjUS2o0ih+atVTvUV3Rd5x5qC7tiVx9ZimpdzjAU5K4I4GhvcrdLq0ZoqoqHr/SbLaUa73wXsRC2Q1REcbVglcNbVsRfZx6ZsUfCESwwde3a1aAGZTIZNm7ciLZt24oOflkU0RRoAj2dJa2fd1A/jfnXUlTr+RI6pLJhIadZ5LEVmVjOSHNPcWl8hL5EmCpXXK6EgTsJy6d7rRBM6lBZTWAKkPgZZgoESyxTp041qMF169YpP2dlZcHFxcWg+ooKmhOfocl3NZHLGey69lxr+9Qd1zG9S1Wdx91LfIvyAcYNOvpP9FO8Xz0YALDgwB3WflqKposwLraiRVx77pFFrPrRRN/13XUtHo3KF5OkHSH+XoQNwFi2OZ0vJtdvy+VyzJw5EyEhIfDw8MCDBwV+MlOmTMGff/5p6u5YDZoaJqlXp8kZBl+uu6y1ffWZR0h6lzKBjaFGXqoNAMfvFEYX/+nQXTxgWe3EGMkkF+JTEAwzKiJQ+soJwUj9E5f2M0+KBqmEJU4fJklaKeSbbTGS1CNlahjCMlGdjxkYFnTWUuAlMPn5+eHVq1cAAF9fX/j5+en842LWrFlYtWoVvv/+ezg5OSm3V6tWDcuXLxd5GkUPqTVM+uLCqMZM0fSJsJR4Ksbqx6GxzXB6QktUCPQ0Sv2EMPSt0hzSrJzg+naNaIwj45ob0CPLRviqVtM81HKNkAiZsCxUBSRjvdCaGl46soULF8LTs+CBsWjRIoMa/Ouvv7Bs2TK0atUK//vf/5TbIyMjcevWLYPqtmU0bzahvgQFN6y+nFO6971Oz1bamzVTo1gKco6s7WJxcbRHsA+lXLEU9E26bo7CVf5eLo7wMnN+KkOQ2rfOVA+1y4+TTdMQYTZUbyVG47vedD4yy/UZ5TXDDBw4kPWzJi9fvtS5T8GzZ89QoUIFre1yuRy5ubl8ulMk0VRnClVpH739Ei3CdZuV9IWFGbb2Eg6NbY74lEzBmhyuVTdSIuUYax4WIGFthCkQ+rD3cbNeQYkvlrIoQ5Nxm/llJPBwsX6/l6KKmkmOUX+GsQVKVmAvkyHPQu9bg32YGIbBnj170L17d5QsWZKzfJUqVXDixAmt7Zs3b0bNmjUN7Y7NoinQ8F7V9o4HHIKLPvuyIkJycoZwgTYxNUvwMeZmdreqWNyL7kVLRN9tL1Q5suULWwhnon8eEGq6txSrSafIEljar5ZVa/+KOqruG3KGgZ2dDP9rVh4f1S+N0v66fQct2ddJtPj+4MEDrFixAqtXr8bbt2/RsWNHbNiwgfO4qVOnon///nj27Bnkcjm2bt2K27dv46+//sKuXbvEdsfm0TSZmSIliSZS5r8yBlL17qP6oRLVRFgyxb10r9AtH+Bu9FQqpkDIPGFvJ7OISP4A8EvfWubuAmEoGk7fADChfbhZuiIVgjRMWVlZWLNmDZo3b46IiAhcvXoV8fHxOHHiBNasWYNu3bpx1tG5c2ds3LgRe/bsgUwmw7fffoubN2/i33//RVRUlOgTsXU0pzGhb45SROjVJfmfuPsSDecewtHbiVr7LMQnnLARpEyyrE80sJbblutyCJknCpLrWsuZE5aO2vgScFsxFjz6eGuYhg4dig0bNiAsLAz9+vXDli1b4O/vD0dHR9jpsUey0bZtW7Rt21ZwZ4symrKK1KvC+MyTuhRM/f88DwAYtPIC4uZ1VNsnZbwV7iB9lvF2TJgHoSNC7+1iuXO2GlJ2kwG94BCGU6u0D+JeZyA8yBOn778GII0Q1LSS+f1KeQtMy5Ytw9dff40JEyYoV8wRpkNTGJA6rACfG1qMSU4qp1NLCV9AWC5C7zV9aUOs5W6TUiFUUJe1nDlhqWz5ohHy5AyG/B2t3Cb0Pm0eFoCjt9UXkQ1pKjxsiNTwFpj++usvrFy5EiVKlEDHjh3Rv39/tGvXjtexvr6+vN/+DUnqa8toXr18ieOY8NMwCReYfj1yT0RvtCn/zR7M6FJFbxlLXRFESIeUv7AtKCSlvucpADdhKDKZdmJdobcpWzJ4SxivvAWmvn37om/fvoiLi8PKlSsxbNgwZGRkQC6XIzY2FhERETqPNTR2E8ESVkByDZNxuBD3RrK6vt1xQ+/+Z28yJWuLsEz0TbzSalusQ/jmEpjCgzxxKyGNd33m0uQ2qxSAY3e4w9IQ1oPqE0uw9pdFOhKaSNoYCF4lV6ZMGUyfPh3Tpk3Df//9hxUrVqBfv34YNWoUunfvjp9++knrGH2xmwh+WIIPk6U/Qyx5OSphfKT25xHLJ++VxYpTDwEALcMDcfiW9mIIQ3ivgj8YBjh9/zXnmHSwFzYmzKWlXf1JPZSZsNssbRPGQTM1Cl8YHUGILWGRtuiwAjKZDO3atUO7du2QlJSkNNkRpkFo4ErueVB/gT+OP7D4N0BLGFCEcdF7l0r4sJeqKmPckjLIYP9OEOJ6cRJ6HmTWJoyB0NuKrbidBUzwkiTf9fPzw6hRo3D1Kr/orYThmFrDNHvPTZy890rSNqVGilVy9cpw50MkbANHe93T3+DGZUXXy4BBZElvAECPOtzBfMWgeHZwzQNCpwljmuRqh/qyblf4u4xoVREA8GFt41wzwtRoJkfhR72yfjjCopXVlwTeVEgiMBHGR/PNT2ofJlOja/I0BEPkpbMTW2HFoDpY82l96TpESI4+3yIhIyLA01nvqs8BDbWDl16Y1JpX3QwDbBrSEPtGNUHbKkECesUfhfmZax4Q6oslpYZp94jGat91BS28OLkg/t6oVhWxe0RjzOteTbI+EJYBn9tq4+cNMLxlBfzUpybrfS1liBqxUKIeK0Hz/nn1NhsPX6WjbDF3XsczYLDxwmNUCfbWsd+0GMOp9qkBTt9B3i4I8tYd+ZmwfIQ87APfJZPWBZu2MsDTGY72MuTyMIe7ONojPMiLd3+EIJOpapj0P0SEDrMrT1JE9kobzblGl4Dq7VqQ/sTOTqZzfiKsD6E+TKH+7qhfzh8A4GRvhxwNASk71/wCE2mYrAS2h0GLH4+qfY9+pHtF2oHYF/h6Sww6/XySdb+pVwVZt36MsERMcQu3EaExqhjoIXk/ZHw1TALjq83cFWtYx/Rgfg8UwpQIXSWnKk+7OGqLJpaQmsssGqYLFy5g8+bNePz4MXJy1O2SW7duNUeXLB4+D4Nnybo1LLfi9S8tNr2GycQNEjaB0Pume60QbL30TNAxn+sJkDe3ezVUL+mNOXtu8a5vbvdq+HDpGUF90IdMJuPtw8THdC/2MfReBX+cuvead3mKxF904TNuVe8PNuGofTXjmLeFIFjDVKZMGcyYMQOPHz8W1eCGDRvw3nvvITY2Ftu2bUNubi5iY2Nx+PBheHuTOlYXfJwxXR3tde7LzpNWdW8oJC8RUqAaII/tntI1JvQ9u8OK685k4OXiiM+bltfbJ01tbSk/3ZnZxaLwYXqTod8Rlk/yXbHhOIQeR+JS0UKoSU6fAqmUnyucHXQ/30yFYIFp7Nix2LFjB8qVK4eoqChs2LAB2dnZvI+fM2cOFi5ciF27dsHJyQmLFy/GzZs30bNnT5QuXVpod4oMfFSa+gQmTXuwJiYP1EcqJkICKqkIN2y3lK6HumYQvBIq/muuTtJOzFILCgEezrj8OBkA8OuR+3rL8tIwmUiSoThpRRc+zxfV+8NStZGCBabhw4cjOjoa0dHRiIiIwIgRI1CiRAl8+eWXuHTpEufx9+/fR8eOBQlanZ2dkZ6eDplMhtGjR2PZsmXCz6CIwEe+cHUq/Dl93RyF1S+0QwZC4hIhBlWfnPerB2PjkIZ6y/Odd1d/Ug8AUNrPDW0iigMAlg+og9qhvqhRyge/9q0loI+aneB9KC8mdghHQmoWr7IWpWGSWUZ6C8I0CI3MrSYwaeyzlPdr0U7f1atXx+LFi/Hs2TNMnToVy5cvR926dVG9enWsWLFCp0Tp5+eHtLQCf5qQkBBcv34dAJCcnIyMjAyx3bF5+GiYVKXy4S0rCmuAFEyElfFTn5rwcNbvhsn3oV6puCfi5nXE8fEt4PAuPlPriOLY8kUjbB/2HjpGluDdL817W2rNSjEP/Sv8VOGjYRLrTCvmtAa/Jz6+FWFdqJnk+PgwWcESNNFO37m5udi2bRtWrlyJAwcOoEGDBhg8eDCeP3+OSZMm4eDBg1i3bp3WcU2aNMGBAwdQrVo19OzZEyNHjsThw4dx4MABtGrVyqCTsWXiU7jfKE/csezAkqrwWb1DEJrozSVnofeUOZUqiWnc7hJi5Tmhh5GGqejCb5Wcfn9ES0CwwHTp0iWsXLkS69evh729Pfr374+FCxciPLwwKFmbNm3QtGlT1uN/+eUXZGUVPPwnTpwIR0dHnDx5Et27d8eUKVNEngYBAAsP3lF+rlHaR9CxtEqOsAaE3ja6HtCdBGiMhKIpuFmqP4YCsRowoedlJ5NZ/LUgpKNV5eLYez0BANCuKvcKN0OS9ZoKwQJT3bp1ERUVhSVLlqBr165wdNT2lYmIiEDv3r1Zj/fzK0w9YWdnh/Hjx2P8+PFCu1GkUDVvFvNwwqu33CHiqwoMAGfyOEwmHg8jW1XE7YQ07LuRYNqGCUkRmrqDTRjoXjPEoNQngP4l9domOYOaUmPJR/x9qfiieolcHO2QxTNAoL7zGtVa2yVAJqOVckWJ7jVDYG9XkPe0ey1h6W74+N6ZA8EC04MHDxAaqp02QBV3d3fORLyJiYlITEyEXCNSbWRkpNAu2TyqE1hYkCde8Yh9InSStnWn72oh3rCTyUhgsnKETqRsw6Bl5UCln5JYhPgRCXV+1Yefu5NkdSlQ7Z0QbZM+bVEASyR1GUhiKkrY2cnQraa4vICaLx2WonASLDBxCUtcREdHY+DAgbh586aWVkMmkyE/P9+g+m2RzNzCa+LEc6IXqvo2fVQB0zZI/hO2gdAcimwZzqW49fT7Uqlj6c6sar4jAq5N5+rBOBD7grPOwm3awqODBURvJiwDZ4fCgWIh8pEWvAQmX19f3g/gpKQkvfs//vhjVKpUCX/++SeKFy9ONm0eqNpznRx4CkwC27AEh9lG5f1x+j7/yMFCIHOAbaDXt4FlF9tvbuw7XbOLln7fqQqVfH1Hvmobhs6RJTBi/WX2OnWctOZ0/8fAOrzaI2wfVa2vyeMC8oSXwLRo0SLJGnz48CG2bt2KChUqSFanraN67zjq0DBl5apr5oTKoXk8EopKCdt4KOHtarT2ZJCRhskGEOrDxCatmF67Kd2NJ0VdgZ7OaqvnclSyAPC9MrVD9b9E69qnuVVfsF2i6FBaIxq+hbow8ROYBg4cKFmDrVq1wtWrVyURmObOnYutW7fi1q1bcHV1RaNGjfDdd98hLCxMWebFixf4+uuvsX//fiQnJ6Np06b4+eefUbGiulPimTNnMGnSJJw7dw6Ojo6oUaMG9u7dC1dX4z3E+aKq/akW4o1d1+K1yszff1vtu9CJVaipw1DYNFqVS+hOSWEwei5HSV/z/8YEP/IFCjtS+g+pEl7CEzuv6tqr4WogYbsKYa+MvxviXouLW6c5NaRk5qo0wK8OLl8nnRHWNTbTOwwBaGskLcHiwQYv+05qaqraZ31/XCxfvhwrVqzA9OnTsWXLFuzcuVPtTwjHjh3DsGHDcPbsWRw4cAB5eXlo06YN0tPTARRMLl27dsWDBw+wY8cOXL58GaGhoWjdurWyDFAgLLVr1w5t2rTB+fPnceHCBXz55Zews7MM5wPVZ8QnOlb36PIlsFTYnnsDG5UxaptsQuTIVhU5o0UTloNQp29jucgMaFhG5z4pFFjrP2ugd//8ntVF161PiFSY5DpWK4EP9Kxs4rquOk1yGm2TSwYBaAvYmsPcUkx0vH2Y4uPjERgYCB8fH9abnGEYXk7bp0+fxsmTJ7F3716tfUKdvvft26f2feXKlQgMDER0dDSaNm2Ku3fv4uzZs7h+/TqqVKkCAPjtt98QGBiI9evX49NPPwUAjB49GiNGjMCECROUdWlqoMyJ4l6xt5PpNMlZG2y3v1HPjWE3U46OqmS8NgnJ0ZcSke2eMtbz2IWnLyEgzmeqYnEPvfv93Pmv0tNEn7Cj6OvQFuXx95lHOstxCTq6dmtpmEheIqB9H1iKgKQJL4Hp8OHDyvhJR44cMajBESNGoH///pgyZQqKFy9uUF2apKSkACiM9aRICuziUphY097eHk5OTjh58iQ+/fRTJCYm4ty5c/joo4/QqFEj3L9/H+Hh4Zg9ezYaN27M2k52drZawmE+mjVDUKgn9c0t1vamZqkDgrBsPJyF+bwYyySnL52IFHGYdJm0FFUbclb65grFuOS6blzTje6kx/q/E0UTzfvFUh8PvASmZs2asX4Ww+vXrzF69GjJhSWGYTBmzBg0btwYVatWBQCEh4cjNDQUEydOxO+//w53d3csWLAACQkJiI8v8AN68OABAGDatGn48ccfUaNGDfz1119o1aoVrl+/zqppmjt3LqZPny5p//WfW8H/+iYpKSee9lWDlBFajYXmePiqbRhrOenaY4z28CRMx6dNyyH68Rt0jgzmVZ5NWJHi5UJIHW5O/KK3hAd54lZCQZ5Ne5EaHF183S4c3+27pbc+hik0hXDVz9W8zuujsd3K3vMII6FtkrNMiUm0DSQjIwO3bt3CtWvX1P646N69u8FaKja+/PJLXLt2DevXr1duc3R0xJYtW3Dnzh34+fnBzc0NR48eRfv27WFvX/CmqgicOWTIEHz88ceoWbMmFi5ciLCwMKxYsYK1rYkTJyIlJUX59+TJE8nPR5XCt0rTSEz9GhgWa4sX707Ky6XgYdK2CnfofEOhydn68XJxxNpPG6B3vdL8DmD50Y19G7A5rIYHcS9o+Lp9YXopqWM3fdG8fGHdPC6AoWNFtw8T9xai6KFlkjNPNzgRHLjy5cuX+Pjjj1l9kABw+iBVqlQJEydOxMmTJ1GtWjWt1CojRowQ2iUMHz4cO3fuxPHjx1GypLqjYu3atXHlyhWkpKQgJycHAQEBqF+/PurUKYj/UaJEQU6piIgIteMqV66Mx48fs7bn7OwMZ2fxPgRCUZqvTDS3iM1eruDnQ3exOyZerzM1o/G/KeLX0dRc9DBHXESxL8dCIm4L0Zby8RuSQdqHFFv/GR1tEwSXSc5SBCjB7zGjRo3CmzdvcPbsWbi6umLfvn1YvXo1KlasyGuV2/Lly+Hh4YFjx47hl19+wcKFC5V/QuM9MQyDL7/8Elu3bsXhw4dRtqzu/FDe3t4ICAjA3bt3cfHiRXTp0gUAUKZMGQQHB+P2bfVl+Xfu3DE4qrlU8JGXpJyH+ETf/VRPLq75B+7gVkIaVp2K01lGIQQWmhuNO5MyOpy+CdtGBhkCWdJ0mJpvOxW8kPWso3vlmeoYYBuCjvYyRJb0fleWf9vafkMseh5NU5mBMwr/VXIGNUNYOcHeBf7Fbaqou+h0rxViju5wIljDdPjwYezYsQN169aFnZ0dQkNDERUVBS8vL8ydOxcdO3bUe/zDhw9Fd1aTYcOGYd26ddixYwc8PT2RkFDgd+Pt7a2Mn7R582YEBASgdOnSiImJwciRI9G1a1e0adMGQMFE8dVXX2Hq1KmoXr06atSogdWrV+PWrVv4559/JOurFIjNKi4ULg1TiI8rKhXnNjHkyXUvaVJqmJROpsaHfJhsG7aFBDIZsLBXDXy0/Jzp+sGyrVGFYrg5ox0ycvKw6eJT1uP0aZhuzWwHAHDREeixdeVAHLyZyF6vCL8hw6cafhXQiCza7PiyMc48eI12Gi4Zs7pWxdZLz8zUK90IFpjS09MRGBgIoGA12suXL1GpUiVUq1YNly5dkryD+liyZAkAoHnz5mrbV65ciUGDBgEA4uPjMWbMGLx48QIlSpTAgAEDMGXKFLXyo0aNQlZWFkaPHo2kpCRUr14dBw4cQPny5WEJ8HL6llCYcuCIP+XsaGfwhKo4J6V/lilMcjQ7FznsZCypSox8H+gyybk62avlhdREtV+afdQlKClwdtC9n8/KNKlXr/FNjWJtq3sJaQnwdMb71bUXcPBdKGFqBPcqLCwMt2/fRpkyZVCjRg38/vvvKFOmDJYuXar0B9LHmDFjWLfLZDK4uLigQoUK6NKlizI0gD74LE0fMWIEL7+oCRMmqMVhsiT4hBWQEi4N04OX6Tonugcv3/JqQ3FOheZG45vkiKKHpT2Q9Q0t1THA6cOksVtfyhjNshUCPbWihLM1F+IjPgI+hRUgbBHBAtOoUaOUS/KnTp2Ktm3bYu3atXBycsKqVas4j798+TIuXbqE/Px8hIWFgWEY3L17F/b29ggPD8dvv/2GsWPH4uTJk1qO2EUVPn4+YiaePvVKoVmlQPxvTbTadgd77tp0Tfw3nvOLSVWoYXonDL6rb273api4NYZXHUIgealosGlIQ/T8/YzaNlOnWdDXHt8XA6Hmd/0hRwp27hj2Htade4xxbcNwcLb+zAAyGfBZ03LYcumpqBQsupTUFLiSEIOlvPAKFpg++ugj5eeaNWsiLi4Ot27dQunSpVGsWDHO4xXao5UrV8LLywtAQeDHwYMHo3Hjxvjss8/Qt29fjB49Gv/995/Q7tkkciP5+cztHomk9Byt7Xza4TPR6XvrZZiCNBdZuXK1+mqH+vJoXRym8gEjzEe9sn7wcHbA2+w8AIXxhVQxui+bnsmdb7gAOxlQq7QPLj1OZq9HgF+SYl/1Uj6oXsqHvYzWOjkZXBztMatrNfT7U7j/l87kuxI7lxOEKTHYUOjm5oZatWrxLv/DDz/gwIEDSmEJALy8vDBt2jS0adMGI0eOxLfffqt0yiZUpjEek6JQ2A5z4JGihI/wsf48e1gGBUfvFDqpGn+VHENvszYO21uonUxmUdpFvregjKPffFa+FdbFp0H2Y8Rq59jmBw9n7ccNjUmCD5aSjFdQWIH09HR8++23qFq1Kjw8PODp6YnIyEjMmDEDGRn81LYpKSlITNRezfHy5UtlihEfHx/k5GhrPooqmmEFZnUtiGTu4+bIfoAA2FTkrhwOpgXluGe6Nxm5OvcxDINYFfOdTON/Y6Ba95Bm5bB92HtGbI2wBMwRh0kfQrScUpkhjKnFWT6gjo42tQn2cSUBiRCFPmuFKeGtYcrJyUGzZs1w/fp1tG/fHp07dwbDMLh58yZmz56NvXv34vjx41qBKDXp0qULPvnkE8yfPx9169aFTCbD+fPnMW7cOHTt2hUAcP78eVSqRElRC1H4+RTMNlWCC7Rzni6FP59Uk2KfeqX5LTs2sB0GgJ3K00zRpqkm1IntK5umIcLkqC4GkUFm8ryFejVDeu5vIW/RWvUYqH0W64wdpiOCuaZg6O3q+K5eisNECCc338oEpiVLluDp06e4evUqwsLU837dunULzZs3x9KlSzF8+HC99fz+++8YPXo0evfujby8Aj8DBwcHDBw4EAsXLgRQkANu+fLlQs/FZtEMK6AQnPSEOeKN5gRmJ+M3WXIlB+WCYdTzZcmMrGNiULCsm7Bd2Jbeuzjaad2Txg8roHsUSOVHpyV46C3Loz6By/25Qo/wDitAPkwEDyxFw8TbJLd161ZMmTJFS1gCCgScSZMm8Qr06OHhgT/++AOvX79Wrph7/fo1li1bBnd3dwBAjRo1UKNGDf5nYeMUpg+Rvftfu4zoeVjjOHsZP4nJUFMHA0bt4cHW/4W9qhvUxshW6omTu9QIQb2yfhjVWjuhMmG9fNspAjVK+eCzpuUAqAvtPeqUsqglkno1TAb0UyaT4csWFXTuE1zfu/8ddfgzVg3xelc3RwUcm0nDRPAhN18C7YAE8BaYYmNjtQJEqtKiRQvExsbybtjDwwORkZGoXr06PDw8eB9XFNH0YVK8lUmR0ZntzZLPW5+hE52c0TDJsZRpXbk4y1b+dNYIiObiaI9NQxpiVGsy99oSnzQui+3D3lOafVSHhYujvRnCCuhGiEZFqGmvbln22HX8fL7ZS7mxaGVL+bkqhTChwphmcVq5SvDBUgQm3ia55ORk+Pv769zv7++PlJQU1n3du3fHqlWr4OXlhe7du+ttZ+vWrXy7VCTIy5ej/7tlvZp+PlK4ZmhOV3YyGc/UCeyFRqy/zKvdApOcyndlvdxt8KfwAllKHA/C9tF3r+nTzAq5Rdl8jnTWLWIYKYYem8DES8jRcTKa18bSnPIJy8RCLHL8NUxyuRz29rp9QOzs7JCfzx7239vbW/nw8/b21vtHqHP87kskpmW/+6Z4qyv4pqphKlvMXevYJhW542JpCiWG+jDxh1GLKM5mo5ah0FlUDCV93UQfS9gOmg/pBuV0v/gJYVpn9sC6+jVDusdNjXcxkop7iUsWrEtLJMqH6d1Rpfy0x5CaKV1HfbquwaXHbzTaJYmJ4EbTWmAueGuYGIZBq1at4ODAfojCgZuNlStXsn4muMnOLVRFKjVM76Yp1UmJLY3B6o/r4dcj9zD/wB2d9WtpmOxkvCYxg8UlBmqztEJgUq1XJgPOT2qF0/de4+NVFwTV//2HkRpOwBbyikKYHFWB6dq0NvByMTwcBwAMeq8s3q8RglozD/A+Rp9GxdvVETHT2ijzwuld3ccSDkS3o7Xw8aw4xNnBHrEz2iLi2/9YywqVdzRjvJG8RPBhQU/D/FmlgrfANHXqVM4yH3zwAWeZzMxMMAwDN7eCN5dHjx5h27ZtiIiIoGCVLOy5nqD8rJhbFAtUVOdTNpWlnZ0MPu5Ogtqzk/HzsuBYJMMJA/UJXhnNXGUGtZPJ4OxgL2p1m2aQPDLJFR00fZZUv0klLCnwYxlf+gQdLuHFU6V/+nwU2VbJ6Y6urbdJTrQSoaqazXm+OilNfBorGcmHieCiZXigzsUHpkZSgYkPXbp0Qffu3fG///0PycnJqFevHpycnPDq1SssWLAAX3zxhSTt2Ar/Xn2u/KylYVKZUHU5tnL5CGg7YfKM22LgRMcwDBxVpC4fV2GCHRc0DRMKTB2HSSozk9Bu69QwcRzn4mgnus98D1Oci7Oj+oOPfJgILoK8XczdBSWCxbYbN27o3Ldv3z7O4y9duoQmTZoAAP755x8EBQXh0aNH+Ouvv/DTTz8J7U6RQiEoKSYZ1flU1+TK9QaoHYeJ5yo5zhL6UbXIeTo7wJslarkhzu2aEzkpmIoO5tYmujhI8zasz9GVbXWrnQ7pg0sYOjquBedy/xGthIXi4DsfkYaJ0MXqT+rh/erBGN9WO5SRuRA8suvUqYOff/5ZbVt2dja+/PJLdOvWjfP4jIwMeHoWRIfdv38/unfvDjs7OzRo0ACPHj0S2p0iheYqOYXKPl/O6FTfi9Ew8YvDZKiGqVCIqVPGt7A/bGVFiTs0ERMFmFp+kipAql7THss2sRomtjd4TSGrlG+hj6SaD5OOOjXHLMlFhFCaVQrAT31qwsdNWuuDIQgWmNauXYvp06ejffv2SEhIwJUrV1CzZk0cPnwYp06d4jy+QoUK2L59O548eYL//vtP6beUmJiolpCX0KZQSFGY5ICElCzUmnkAf51hFzaFTlQynmEFDBeYCiUm1clZtdrC+FPC26IJuuhib2Y7j3QCE/+yhvowpWXrXrQD6BE6eV5qXfOFLq0YQVgiggWm7t2749q1a8jLy0PVqlXRsGFDNG/eHNHR0ahVqxbn8d9++y3GjRuHMmXKoH79+mjYsCGAAm1TzZo1hZ9BEURpkmMYLD12HymZupPccqnjNXfb2/ETTwwVSBioOHqrbC/l64ZapX3QpGIxOL8zbahqoBR4smQ+V6V5WIB6e2STKzKsHFQXvm6O+LlPwXzSrFIAyge4o2sN4y9N9nFzxOdNyklSlz7Nqta4lul7iRHxwqHdGda2uWaLRb1qwM/dCcv612bdT/ISYU3wdvpWJT8/Hzk5OcjPz0d+fj6CgoLg7MwvdsiHH36Ixo0bIz4+HtWrFy4VbNWqFS+TXlFGM5ccHyGAaz5iyyUnpC+iUTHJqdZlZyfDli8avdtesMPR3g4P53YAAJSduAdAQeJhXW/FX7cLVy7NJooe9cv549KUKOX94+Joj4Njmhk95o+zgx0uq7RrKHp9mLS+6xZdxHRH2wdQ3BtH15oh6FIjWOc1IR8mwpoQrGHasGEDIiMj4e3tjTt37mD37t1YtmwZmjRpggcPHvCqIygoCDVr1oSdyiqpevXqITw8XGh3ihSKuUXV6ZtrvhE6IRWY5LiPMdgkB9WkwhpLpFn6oLnNw0W3rE9zMMF2/xi/TWnb0RtWgKUZXWNSX4/4viDpdOLWcTyjQyOl5dvEr3mCsAgEC0yDBw/GnDlzsHPnTgQEBCAqKgoxMTEICQmhhLlGRvEOqZpLjnMVnGCnb54mOR5l9MEwjHLyFFLX4t41UMbfDT/1qYnVn9RDqD+/aN6mzidGEAYj8JbVNdbZtivGzobPG7Ifwze+Et/O6Tqe3m4IK0KwSe7SpUsIC1Nf5ufr64tNmzbh77//lqxjhDZicslxaYI09/KNw2Sos6a6hon/cV1qhKBLjRAAQHgQcOyrFigzYbdaGdaVdiQvEVaGoMCVenyY2ISfZpUCcOyrFrrr5xmWQ5fAw3e4kQ8TYU0I1jBpCkuqS1/79+9veI8ITthyyXGV1b1fvUB2ntw0cZgYYNHBO8rPBEGoo3dYaPt864y+L8qHSbMvDPs+Q+cB8mEirAmDI6w5Ozvj5s2bUvSF4InS6RvCBSKt/RrfV5+OM02kbzB49TYHAHA+LsmgujRR7ZrXO1+n+mX9JG2DIDRx4JkvyNWR34KEUa0LgkV+UKskZ1n9GibhaAprQk3akSH8EqmTvERYE7xNcmPGjGHdnp+fj3nz5sHfvyAD+IIFC6TpGaGFYkJUqrEZ7slQaODK1+k5PPvCqxgAoJiHM169zVbbpvrGmpsnh7E4P6k13mbnoZiHuAzwBMEXvvGXoqe0RmZOPuztZFhxKg4/HbrLWq5bzZKoV9YfJbzYAktql1cdkw52MuQpElqLkEr4aK919QMAfHnmsCQfJsKa4C0wLVq0CNWrV4ePj4/adoZhcPPmTbi7u9PNLzHnH6prXrJy8wEU+iTk5Mux/ORDvXVwO4Vr75daw+Tl4qAtMKl8NqZa3sXRHi483+gJwhDceQpMbk4OyoS2Hs76jwnxcWXdzhZWQHVrwZgSb+vWDGkgNvUSF/TIIKwJ3gLT7Nmz8ccff2D+/Plo2bKlcrujoyNWrVqFiIgIo3SwKHMv8a3a9/iULADc2p2apX2Un4U6VTYq789rEhRSL+vbqtqyY/518aFKMD9zAEFIQbC3C56nZKFd1RJm60OdMr5qY1I9tpnw+uQaEpPqN7XxauDYJXmJsCZ4D6WJEydi48aN+OKLLzBu3Djk5uqOLk1Ig9jAvSsG1uWuQwfDW1aUPDUKmzZK1SdCas3kexWKSVofQehj69D38P2HkRgdJSxBrSFojpkPapVUG5Oqux3thUtMWu84KhsM1SoRhLUiaCTVrVsX0dHRePnyJerUqYOYmBgywxkR3fKS7mvetkpxNf8Bob+Pq5O9SabD3HxVgckEDRKEkQjydkHPOqVERZcXu0JUKxyInUxdYFIp4SRCYDLUh4kgbBHBcZg8PDywevVqbNiwAVFRUcjPzzdGvwg96DOHlfBW93kQOp/Z84z0LeVESUuLiaKKv4SLEdTMcAZqmJwc1I9RFZ8qFvcobFNwzeqYO1EyQQhBVC45AOjduzcaN26M6OhohIaGStkn4h26I/fqnmRGR1VS+y48NQq/STBHwpVtNGcSRZWuNYIR/SgJDcr5CzqOdZWcnapJrvCzo73wARasw9kcAMa3LUxhJVSDraq4Gt8uTOn8ThDWgEF3a8mSJVGyJHeMEEJa9AkY3q6Oat+FKm/sZDJex2TmSqlZJImJKJo42NthbvdIwcexmeV1OX2L0TBpoirolOaZjoiLoc0rSFIPQZgKw0cSYTR0+SoJcboUqmHiu6ImS0KBiTRMBGE4qvOC6pDSNK+JgdHh00RDlyhKkMBkyeiYjfgGyNNXhy74+jCFBXlJ1gXyYSIIYagOGYVWWfXFQ9U8J4WGqT4Pk2HDd2UGNiQXDcI2IQOyFTGpQ2UABW+MFQM9cFcjThMbuoSRPvVKsW7nIywt7VdLZ0A9MZC8RBDiWftpfQDqY1d1SInxYdKkcgkv7B7RGMU1oo6r6p3mfVANiWnZqFHKx+D2CMISIQ2TBaMVzVdlQ7kAd1F1KPB3Z1+dw8c85vrOUdNBIlsayUsEIR5354LxqO7DVPhFM2q3WKoEe2ulGMpXqdzZwR51y/hJotEiCEuE7mwLRlPb06xSgPIzXzOWrnK6ZB0+9ZZ/J6wJ0Qwp1PRfNC+vtU/KWF4NylGSXcL2UR0yio+qS/RVBZkDsS+M1g83FfcAX3dHPSUJwvohk5wVUbG4p/IzX4FJaGgCfXFROlYrga/ahqGkb8EqmQInU36vr1M7V0Hf+qEoH+COJUfvq+1zkMBkoOCHD6tLVhdBWCoMS2ohVc1OvlRqJQ4c7e1wYVJrMGBEBe4kCGuCBCYLRp8YwVcpo1tgEl5vgKczyhRTMQUKkHPs7GQIC/JkXW0jZfA6MgcQRQ3F6jjVez83X7o4aVwEeEoXfJMgLBl6ulgwqsKLplDB14zloCNOgG5Tne56NXeJEXPY+m0voUmOQhQQRQHV+cDLteC9V9W521QaJoIoSpCGyUrQFJj4CgZlirEHmRPjw6QZ/0kqOUdKDZMdSUxEEcDR3g6rPq6LnDw5fNwKckeqvozkkcBEEJJDApMFo6ZhkmkKTPwEA13aG10aKn2BKzVlEaniJ0kZh0lKbRVBWDLNwwLN3QWCKFKQSc5K0BRW+MoFuoQRHzf2FS3GNsmxIamGiQQmgpAs5AdBEIWQhsmCUU1wq2lqErtKrlwxd1QJ8UaP2uyBKxX1ft0uHM+SM7Al+pkyb5ymVop3OACOYlKa0fimdiEIW+TbThG4GZ+KF2nZOH7npbm7QxA2BQlMFkxmTmG+Nk0Bia+MoSnU9KxbCv9rph0LSYHCpKWIl7T10jPddfPrAidSvgtLqa0iCGvjk8ZlAQCDVp43c08Iwvag93ELJltVwyTSf0jLlMdRXibkjpBINpFSxiGTHEFQ9HyCMAYkMFkwPeoUms1++6i22j6xPky96rKb4nSV14dkGiZJwwrQo4IgVJnRpYqg8ot71zBORwjCyiGTnAXj5+6E+3M6AGAzNYnzYVIsQdaFEI2UVIKOlBomMskRhPrYjCzpw1l++vtVMHXnDQBAg3L+xuoWQVg1JDBZOLoEAENXyfEtrzrxakbpFiubyGQaqR0kNCCQvEQQ6vAZEmy56QiCUIdMclYK30lNqBJIS2BS+cyS1UQUmm1UDfGWpmJIa94jCGtFdRTwGRIynV+kpaxqaiWCsDJIw2SlsE2CC3tpJ54VrmES1qYYNKsZ1kL3qj2CIAyDlwZXZXAb0w9wQMMyeJORg6YVA4zWBkEYCxKYrBS2SbCUr3YaFKFTn/7Aler7+CqcNGssaKPgaE8XB/h7UPJOgpASNRObQA2TMXW0Tg52+KptuBFbIAjjQSY5K+VRUgavcoI1THpUTGIn0rpl/HRWJMXbbEQJLwBAnVBfg+siiKIOmbUJgh3SMFkpz95oC0xs85y55776Zf0wuVOE2jah/hVcrPy4LjZffIJedUsbXhlB2ASFA0voGCNxiSDYIYHJSuGfGkXC6U9EVcNaVICHs/ptJvWKnOJeLviyZUUJaiII20PoKlRzv2QRhKVi1Sa5uXPnom7duvD09ERgYCC6du2K27dvq5V58eIFBg0ahODgYLi5uaFdu3a4e/cua30Mw6B9+/aQyWTYvn27Cc5APKaKN6TqpyRm+T+bYCdlGAGCILQR6sOkdiyNT4JgxaoFpmPHjmHYsGE4e/YsDhw4gLy8PLRp0wbp6ekACgSgrl274sGDB9ixYwcuX76M0NBQtG7dWllGlUWLFlmN/Z69n6btO58wA1xmQmu53gRhTRhk9qYhSRCsWLVJbt++fWrfV65cicDAQERHR6Np06a4e/cuzp49i+vXr6NKlYL0AL/99hsCAwOxfv16fPrpp8pjr169igULFuDChQsoUaKESc9DDOwKJokCJanWqCIViVFqkTxEEOaFTHIEIQ1WrWHSJCUlBQDg51ewKis7OxsA4OLioixjb28PJycnnDx5UrktIyMDffr0wS+//IKgoCDOdrKzs5Gamqr2Z2pMlTNNzSSn0aRm5G822CZrUy1hJoiiyuv0HOVncvomCGmwGYGJYRiMGTMGjRs3RtWqVQEA4eHhCA0NxcSJE/HmzRvk5ORg3rx5SEhIQHx8vPLY0aNHo1GjRujSpQuvtubOnQtvb2/lX6lS+hPaGgN9y/+lxNAUJmzdJDMcQRiX6EdvlJ8FW+RofBIEKzYjMH355Ze4du0a1q9fr9zm6OiILVu24M6dO/Dz84ObmxuOHj2K9u3bw97eHgCwc+dOHD58GIsWLeLd1sSJE5GSkqL8e/LkidSnwwm7vCRuovu0cVmd+xg9Zj4+BkA2wY6mY4IwHaRhIghpsGofJgXDhw/Hzp07cfz4cZQsWVJtX+3atXHlyhWkpKQgJycHAQEBqF+/PurUqQMAOHz4MO7fvw8fHx+14z744AM0adIER48e1WrP2dkZzs7mjU4tpUlOn+AjV9UwifFh4tioajogCMIYkA8TQUiBVQtMDMNg+PDh2LZtG44ePYqyZXVrSry9CxK83r17FxcvXsTMmTMBABMmTFBz/gaAatWqYeHChejcubPxOm8gbBqm0n7aqVEMRs0kp7GL1yo50jARhDkxNAE3QRAFWLXANGzYMKxbtw47duyAp6cnEhISABQIR66urgCAzZs3IyAgAKVLl0ZMTAxGjhyJrl27ok2bNgCAoKAgVkfv0qVL6xXAzI2qINKnXmn0qlsKAZ7sWq/uNUOw9fIznXXpE3zUTHIaE2lmbj6PfrJtowmZIEwFjTaCkAarFpiWLFkCAGjevLna9pUrV2LQoEEAgPj4eIwZMwYvXrxAiRIlMGDAAEyZMsXEPZUeVQ1Tn3qlEFnSR2fZkr6uotvRJ0zlq9jrXB3tWQUotrdVE/mrEwQB4S8o9D5DEOxYtcDEZ1n7iBEjMGLECMnrtSQMjcyrz7Hb1ckeaVl579rRjZ+7E54lZ7L0TRvSMBGE6RAet5LGJ0GwYTOr5IoaqjJdlWAvvWWz8+Wi21n9ST3lZ31yzu/9a7NuZ0+NQhCEqRC8So4GKEGwQgKTDcAVkyk9O0/vfn0KtVqlfZWfdb15ero4oGqINxb3rqG1j2vyHdAwVH8BgiAMQnCkbyP1gyCsHRKYrBQhRkNHe9P8zKo+TQRBWAbCNUwkMhEEG1btw0TwY1iLCrj6JBk96xgWkZxrHmWTl9iENbV0Kwb1iCAIqaExSRDskMBkrQhQ5hTzcMbWoe/proqnkzvXRCpnkZgc7Wn6JQhrghRMBMEOmeSsFH0r24wF10SazyJ4sWqYrGwVIkFYM0JN5WSSIwh2SGCyUqSUOQytSjG9sk3MpvKfIgiCnTweAhO9whAEN/Q0I3jD9ebJpjmyZ1nBR5MzQZgOXhom0voSBCckMFkpUk5vhs6VCkGKbWJ2oLDeBGFW8uTccdhIXCIIbkhgIvBehWIGHa/QLEWW8tHa5+Zsz1LeoOYIghBAcS8XzjI0JgmCG1olZ6VI6TjdtkpxrBxUF+ElPPWW4/IFVQ1yqcDZQVtgUq+TNFAEYUyKebAn5VZFThITQXBCApOVIuX0JpPJ0CI8kLMcW5oTxfFCUBX2aMUcQZgfGoYEwQ2Z5KwUc0xwUumCaG4mCMuCNEwEwQ0JTFaKJWlmdCmYtnzRiH2HStfJJEcQBEFYAyQwWSnmSNsmRLbxd3dC7VBtnyZNLEnwI4iiCg1DguCGBCYrxRwqdKFZz3VBczNBWBZkkiMIbkhgslIsXcPEtyyZ5AjC/JC4RBDckMBkpVizKcua+04QtggNSYLghgQmK4VU6ARBSIU5knkThLVBApOVYh6TnI44TALrUe26rthOBEGYDnr/IghuSGCyUuRmkJiEiDZ8fZPcnPRHAicIwviQmZwguCGByUqxJJMcm3CkT1xS7borCUwEYXYsaDohCIuFBCYrxZJWybG9nfK1tLHlnyMIwrSwJc4mCEIdyiVnpZgnDpOQsrpLqzqYNizvb0CPCIKQgqYVi2HJR7VQKUh/Am6CKMqQwGSlmCWXnIDku+TLTRDmR0g8tPbVShi3MwRh5ZBJzkopF+Bu8jb9PZx4l9W3+i2seMFbLAlVBGFcaIgRhHSQhslK+bFHdfzw3230bxBq9LYW966BS4/eoH1Vad5Af/2oFhYeuIvBjctKUh9BEOxQ2A6CkA4SmKyU4l4u+LFHdZO01aVGCLrUCNG5n21K1jdPl/R1w/yepuk7QRRlSF4iCOkgkxxhFGiiJgjz4+xAYTsIQipIYCIMxsGeLQ4TSUwEYS6W9quFEt4uWP1JXXN3hSBsBjLJEQbj4qj9FmtH8hJBmI12VUugnUQ+hwRBFEAaJsJgXFkEJr6pUQiCIAjCGiCBiTAYtuCTJC4RBEEQtgSZ5AjRHBzTDPtjE/BxI5bwACQxEQRBEDYECUyEaCoEeqBCYAXWfSQvEQRBELYEmeQIo0AB8wiCIAhbggQmwiiQvEQQBEHYEiQwEUaB4jARBEEQtgQJTIRRqBDoYe4uEARBEIRkkMBESMryAXXQKjwQX7UNM3dXCIIgCEIyaJUcISmtI4qjdURxc3eDIAiCICSFNEwEQRAEQRAckMBEEARBEATBAQlMBEEQBEEQHJDARBAEQRAEwQEJTARBEARBEByQwEQQBEEQBMEBCUwEQRAEQRAckMBEEARBEATBAQlMBEEQBEEQHJDARBAEQRAEwQEJTARBEARBEByQwEQQBEEQBMEBCUwEQRAEQRAckMBEEARBEATBgYO5O2ALMAwDAEhNTTVzTwiCIAiC4Iviua14juuDBCYJSEtLAwCUKlXKzD0hCIIgCEIoaWlp8Pb21ltGxvARqwi9yOVyPH/+HJ6enpDJZJLWnZqailKlSuHJkyfw8vKStG5zY8vnBtj++QF0jrYAnZ/1Q+coHoZhkJaWhuDgYNjZ6fdSIg2TBNjZ2aFkyZJGbcPLy8tmB4Itnxtg++cH0DnaAnR+1g+dozi4NEsKyOmbIAiCIAiCAxKYCIIgCIIgOCCBycJxdnbG1KlT4ezsbO6uSI4tnxtg++cH0DnaAnR+1g+do2kgp2+CIAiCIAgOSMNEEARBEATBAQlMBEEQBEEQHJDARBAEQRAEwQEJTARBEARBEByQwEQYHVteV3Dx4kVkZWWZuxsEwYmtjkMag4SpIIHJTCQlJeHVq1cAClKr2BLx8fHo0aMHNm7cCMD2zg8AHjx4gC5duqBevXrYtGmTubsjOU+ePME///yDS5cuITc3F4DtPXBteQwCtj8ObX0MAjQOLQ0SmMzApEmTEB4ejmXLlgEAZ/4aa+PPP//Eli1bsGjRImRkZMDe3t7iBwJfGIbB0KFDUbFiRchkMnh7e8PDw8Pc3ZKUiRMnolKlSpg/fz4aNWqEL774Ag8ePIBMJrOZydrWxyBgu+OwKIxBgMahJWLZvbMxkpOTMXjwYBw8eBClS5fG2bNnceHCBQC29dZw+vRp9OrVC87Ozvj+++/N3R3J2L59O9zd3REdHY3Tp09j+/btqFy5Mvbu3QvANn7Dc+fOYceOHfjnn39w5MgRLF++HHfv3kX//v0BQPLk0qamqIxBwDbHYVEYgwCNQ0uFBCYjo/rju7q6IjQ0FBMnTsT8+fPx7NkzbNu2Dbm5uVb51qDZ37y8PABAiRIl0KtXLzRq1AibNm3CzZs3YWdnZ3XnB6if48uXL7FmzRqcO3cO9evXR2ZmJsqXL4+kpCRkZGRY/SQGFDyQ8vPz0bFjR7i4uKBfv36YN28erl27hoULFwKw7AmNDVseg4Dtj8OiNgYBGocWe24MYTQyMjKYrKws5Xe5XM4kJycrv48dO5Z57733mN27dyv3Wwts56agWrVqzI0bN5jz588zLVq0YEaMGMFkZ2cz169fN0dXRaN5jvn5+crPeXl5DMMwzKhRo5jIyEit/daA4jdT7feCBQuY6tWrM+np6Wrlpk2bxvj6+qpdD2vAlscgw9j+OLT1McgwNA4ZxnrGIWmYjMTEiRPRuHFjdOrUCT/99BNSU1Mhk8ng5eWl9CMYMWIEGIbB9u3b8erVK8uWrFXQdW5yuRzPnj2Du7s7ypQpg7p166Jz585Yt24dXFxccPjwYeTk5Ji7+7zQPMe0tDTY2dkpfzvFm2zr1q0RFxeHx48fW7z9XZUFCxZgzpw5ANT9Bry8vODg4IBDhw4pt8lkMgwcOBBubm5W9XZry2MQsP1xaOtjEPh/e/ceU3X9x3H8fc5BBIfC4QQoESGGN2RjXqZhodRkZCikggoZhMogNJumrVZMplPnlJnX4TRvWVqurFgrDU1EmyKW5BW5iCgi5qVNgcOB8/r94e/71RPi4ejhfM/34/uxNeRc6PPc93y++5zv+Z5zeB6qbR6q69GlAs3NzZSQkEA//vgjLViwgPz9/SkvL4+SkpKI6P6DXpr0gYGBlJiYSCdPnqT8/Hz5emd8oBBZb9NqtdSjRw/q0qULaTQa+v7772nx4sVkMpkoLCyMZs+eTa6urk7bR9R+49SpU4nowU5N+tna2koGg4FqamoUG7MtiouLKSoqij788EP67rvv6I8//iAikt+Bk5CQQM3NzfTLL79QfX29fL9evXrRmDFjqKysjFpbW536pQ+R5yCR+PNQ9DlIxPOQSKXzUJHjWgI7e/YsQkJCsG/fPvmyoqIiuLu7Y/ny5W0OvzY1NWHs2LFITExEaWkpvvzySyxevFiRsVtjrQ0ACgoK0KtXLwwaNAheXl5YsWIF8vLyEB4ejnXr1gFw7sPmtm6/mzdvwtXVFfn5+RaXO6tFixZh0qRJ2LJlC6KjozFjxgz5uubmZgDAunXr0LdvX2zcuNHiviNHjsT06dMdOt4nIfIcBMSfh6LPQYDnoVrnIS+Y7KykpAQajQY3b94E8OC12KVLl0Kv16OsrEy+rfRA2bt3L4KDg2EwGODq6ooVK1Y4fuAd8Lg2Ly8vVFZWwmQyYeDAgUhPT0dVVRUAoLa2FomJiYiMjHT6195t2X4AcOfOHURGRmLevHkOH6stpI7q6mocPXoUwP2m4cOH45tvvgEAmEwm+fZJSUkIDw9HXl4ebt++jZKSEgwePBi7du1y/OBtJPIcBMSfh6LOQYDnIaDuecgLJjv7888/ERoaijVr1gB48CBpbm5G79695UktnbBYXl6Od955BxqNBpmZmbh7964yA++Ax7UFBQXhgw8+AABcv369zUl7Z86cceqdtKSj20/aqbW0tCAkJAQZGRnyM0O1qKioQHx8POLj43Hr1i0AgNFolK/Lzs6GTqfDkCFD4O7ujunTp6uiUeQ5CIg/D5+lOQjwPFTTPOQFk53dunUL8fHxmDx5MmprawE8mNgrV66Ev7+/xSHj+fPnIyAgAKWlpYqM1xbW2nr16tXmcLizvtuhPbZsP2mib9++HRcuXFBmwE9I2i6bN2/G8OHDkZub+8jbnT59Gvn5+Th37pwjh/dURJ6DgPjz8FmZgwDPQ7XNQz7p2wb19fV048YN+R0mra2t8nXSZ5/o9XoaN24cnT9/Xv64fhcXFyIi8vT0JL1eTzU1NfK7A5YtW0Y1NTUUFhbmyJQ27NHm7e3d5sRLZzop0Z7bj4hIp9MREdG0adOob9++DutoT0f6JNJ1kyZNooEDB1J+fj5dvHiRiIhOnjxJRPe/piA0NJTefPNN6t+/vyMSrCovL6f9+/c/8jq1z0Ei+/Q58zy05/Yjcr45SNSxRola5+GZM2dowYIFVFZW1uY6EeZhe3jB1AEmk4kyMjIoMjKSxo0bR+PHjyej0Ug6nU5+V4OLiws1NTXRrl27KC0tjcLDw2n37t108OBB+e9cuXKFfHx86MUXX2zzTg+ldEabsxG9saN9JpOJtm3bJv9uNpupR48elJCQQGazmXJycuj111+noUOH0u3btxV/bP5XaWkp9e3bl5KSkqi6ulq+XNrhqnUOSuzd52xE7yPqWKOa52FzczO9++67FBYWRk1NTRQUFCRfh/+/o03t8/CxlD7E5ey+/fZb9OnTB6NGjcKBAwewceNGBAcH47333rO43eeffw5vb2/ExcUBAE6dOoXk5GS4uroiMzMT6enp6N69OzZs2ADAOQ6Ri9wmEb3R1r6JEyfK50lIqqur0adPH2g0GkyZMgV1dXWOTOiw4uJixMTEoGfPnm36APVuQwn3qbsP6HijGufh5s2b0b17d0RERLR52ezhbSHCdmwPL5isyMrKwmeffWbxzoWUlBTMnTtX/n3NmjUICgrCzp07LV6TNZvNWLJkCWbOnImxY8fiyJEjDh27NSK3SURvtLXvvzungoICeHh4IDw8HCdOnHDYuJ9EXl4epk6dioKCAri4uODYsWPydWvXrlXtNpRwn7r7gI43qnEeRkREYMCAAbh9+zaA+++C+/nnn3HhwgU0NjYCUPe+tCN4wdQO6WTCa9eu4fLly/Llly5dwuDBg7FixQp5o5tMpjZn9DvzqlnkNonojU/bJ/nnn3/w1Vdfdf6A7WDr1q346KOPAAAvv/wyxo4dC+DB59Y0NDRY3N7Zt+F/cZ+6+wDbGyXOPA+lJ2NHjx5FcHAwcnJyMH78eAQHByM0NBR+fn5ISEiQb6u2fakteMH0EGvfY7N69WpoNBq88sorGDVqFPR6PbKzs+XVtTMTuU0ieqO9+5xxR/a4xvfffx+zZs0CAFRVVUGr1SImJgbDhw/H2bNnHTrOJ8V96u4D7N+ohnko/UxLS4ObmxtSU1Px119/obS0FD/99BPc3NywcOFCxcbrKLxgApCfn4/nn38eGo1Gflb+qAfx1q1bUVhYKF+3c+dOuLu749KlSw4dry1EbpOI3ih6H/D4RunnlClT8NtvvwEANm3aBHd3d3Tp0gV79uxRZtA24D519wHPdqN0RPvGjRv49NNPcfXqVYv7rVy5EgaDQRWfD/U0nvkF0+HDhxETE4NZs2bhjTfewNChQ9vcpr1nAOfOnYNOp7P46HdnInKbRPRG0fsA643SuRApKSmYNm0ahg0bBh8fHyxatAheXl5YuXKlEsPuMO5Tdx/AjcCD/cy9e/fa3Pfrr7+GXq/H33//7ZCxKuWZXTBJG7+srAy5ubmorKzEiRMn0K1bN2zatAmA9e8kWrp0KaKjo9t9XVopIrdJRG8UvQ+wrbGhoQFvvfUWDAYDsrKycOXKFQDAsmXLoNFo5K//cCbcp+4+gBs7uq/JzMzEhAkTOn2sSnvmFkwlJSW4c+eOxWXS4UaTyYR58+bBx8en3a8PqK6uRnl5OWbMmAF/f39s3boVgHO8Di1ym0T0RtH7ANsbpeuOHz+OM2fOWNyvqakJy5cvd6ovXOU+dfcB3NiRfU1VVRXKy8sxffp0BAYGYu/evQCca19jb8/MgmnPnj0ICAhAnz59EBgYiOzsbFy7dg3A/Q0sbeTKykq88MIL8vfcPLzxy8rKMHfuXAQEBCAqKsppPopf5DaJ6I2i9wFP3ijtxJ0d96m7D+DGju5rzp8/j6ysLPj6+mL06NFOt6/pLM/Egqm4uBj9+/fHqlWrcOrUKaxfvx4+Pj7IzMyUv0lZesCbzWasX78eLi4uqKysBHD/GYLRaITZbMbBgwed6jMkRG6TiN4oeh/w9I1Go1E+d8IZn8Fyn7r7AG60ZV/T0tKCX3/9FYWFhYq1KEHoBZP0oN2wYQMCAgLw77//ytetXbsWI0aMwKJFi9rc7+bNm4iIiEBcXBxKSkowZswY7Nixw6kmgchtEtEbRe8D7NcYHR3tlI3cp+4+gBtF2dc4gtALJsmCBQvw2muvWZzdf/fuXWRlZSEiIgKnT58GYHlYdcuWLdBoNNBqtYiNjX3kOwOcgchtEtEbRe8D7NPorCevA9yn9j6AG0XZ13QmoRZM+/btw+zZs7Fq1SqLj6T/4Ycf4ObmhoqKCgAPHgz79u3DyJEjkZubK9/WaDRi3bp10Gq1GDVqlPwAUprIbRLRG0XvA8Rv5D519wHcKEqjEoRYMNXW1iI2Nha+vr5ITk5GWFgYPD095QdKY2Mj+vfvj/T0dACWb5F89dVXLb4ksa6uDnPmzMG2bdscG9EOkdskojeK3geI38h96u4DuBEQo1FJql8w3bt3DykpKZg8ebJ8YhoADBs2DKmpqQDur6K3b98OrVbb5oTY5ORkREVFOXTMHSVym0T0RtH7APEbuU/dfQA3itKoNC2pXLdu3ahr166UmppKvXv3ppaWFiIiio2NpXPnzhERkU6no8TERIqLi6MZM2bQoUOHCADV1dXRxYsXKTk5WcmEdoncJhG9UfQ+IvEbuU/dfUTcKEqj4hRbqtnRw99fI529//bbb2PmzJkWlzU2NmL06NHw9fVFdHQ0/P39MWLECItve3c2IrdJRG8UvQ8Qv5H71N0HcOPDl6m5UUkaAFB60dYZIiMjKS0tjVJTUwkAmc1m0ul0dP36dSotLaXi4mIKCgqipKQkpYdqM5HbJKI3it5HJH4j96m7j4gbRWl0GIUWap2qoqICfn5+OHHihHyZ0WhUcET2I3KbRPRG0fsA8Ru5T/24kdlK9ecwPQz/P1hWVFREHh4eNGTIECIiysnJoTlz5lB9fb2Sw3sqIrdJRG8UvY9I/EbuU3cfETeK0qgEF6UHYE8ajYaIiI4fP04TJ06k/fv3U3p6OjU0NNCOHTvI19dX4RE+OZHbJKI3it5HJH4j96m7j4gbRWlUhGLHtjpJY2MjXnrpJWg0GnTt2hXLli1Tekh2I3KbRPRG0fsA8Ru5T/24kT0JIU/6HjNmDIWEhFBubi65ubkpPRy7ErlNInqj6H1E4jdyn/pxI7OVkAum1tZW0ul0Sg+jU4jcJhG9UfQ+IvEbuU/9uJHZSsgFE2OMMcaYPQn1LjnGGGOMsc7ACybGGGOMMSt4wcQYY4wxZgUvmBhjjDHGrOAFE2OMMcaYFbxgYowxxhizghdMjLFn3sKFCyk8PFzpYTDGnBh/DhNjTGjS92q1JyUlhdauXUtGo5EMBoODRsUYUxteMDHGhFZXVyf/e/fu3ZSdnU0XLlyQL3N3dydPT08lhsYYUxF+SY4xJrSePXvK/3l6epJGo2lz2X9fkktNTaX4+HhasmQJ+fn5kZeXF+Xk5FBLSwvNnz+fvL29KSAggL744guL/9fVq1dp8uTJpNfryWAwUFxcHF26dMmxwYyxTsELJsYYe4QDBw5QbW0tFRYWUm5uLi1cuJBiY2NJr9fTsWPHKCMjgzIyMqimpoaIiBoaGigqKoo8PDyosLCQioqKyMPDg2JiYqi5uVnhGsbY0+IFE2OMPYK3tzetXr2a+vXrR2lpadSvXz9qaGigTz75hEJCQujjjz8mV1dXOnLkCBER7dq1i7RaLW3atInCwsJowIABtGXLFrp8+TL9/vvvysYwxp6ai9IDYIwxZxQaGkpa7YPnlH5+fjRo0CD5d51ORwaDgerr64mIqKSkhMrLy6l79+4Wf6epqYkqKiocM2jGWKfhBRNjjD1Cly5dLH7XaDSPvMxsNhMRkdlspiFDhtDOnTvb/C0fH5/OGyhjzCF4wcQYY3YwePBg2r17N/n6+lKPHj2UHg5jzM74HCbGGLOD5ORkeu655yguLo4OHz5MVVVVdOjQIZozZw5duXJF6eExxp4SL5gYY8wOunXrRoWFhRQYGEgTJkygAQMGUFpaGjU2NvIRJ8YEwB9cyRhjjDFmBR9hYowxxhizghdMjDHGGGNW8IKJMcYYY8wKXjAxxhhjjFnBCybGGGOMMSt4wcQYY4wxZgUvmBhjjDHGrOAFE2OMMcaYFbxgYowxxhizghdMjDHGGGNW8IKJMcYYY8wKXjAxxhhjjFnxPxErRkAxQKWqAAAAAElFTkSuQmCC", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "da.isel(lat=20, lon=40).plot();" ] @@ -1225,20 +260,9 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "da.isel(time=slice(0, 20), lat=20, lon=40).plot();" ] @@ -1272,24 +296,13 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": null, "metadata": { "tags": [ "hide-output" ] }, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "da.sel(time=\"2014-01-01\", lat=25, lon=210).plot();" ] @@ -1303,438 +316,13 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": null, "metadata": { "tags": [ "hide-output" ] }, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 1460)>\n",
-       "array([277.6 , 277.5 , 277.4 , ..., 277.59, 277.59, 277.59], dtype=float32)\n",
-       "Coordinates:\n",
-       "    lat      float32 50.0\n",
-       "    lon      float32 200.0\n",
-       "  * time     (time) datetime64[ns] 2014-01-01 ... 2014-12-31T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([277.6 , 277.5 , 277.4 , ..., 277.59, 277.59, 277.59], dtype=float32)\n", - "Coordinates:\n", - " lat float32 50.0\n", - " lon float32 200.0\n", - " * time (time) datetime64[ns] 2014-01-01 ... 2014-12-31T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da.sel(lat=50.0, lon=200.0, time=\"2014\")" ] @@ -1748,570 +336,9 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 1212, lat: 25, lon: 53)>\n",
-       "array([[[246.     , 245.09999, 244.09999, ..., 240.2    , 242.     ,\n",
-       "         244.2    ],\n",
-       "        [240.59999, 241.7    , 242.09999, ..., 239.09999, 241.2    ,\n",
-       "         244.59999],\n",
-       "        [238.2    , 239.79999, 240.79999, ..., 243.89   , 246.89   ,\n",
-       "         251.2    ],\n",
-       "        ...,\n",
-       "        [295.6    , 295.6    , 296.     , ..., 293.6    , 293.6    ,\n",
-       "         294.1    ],\n",
-       "        [297.     , 297.     , 297.     , ..., 293.69998, 293.9    ,\n",
-       "         294.6    ],\n",
-       "        [297.5    , 297.69998, 297.69998, ..., 294.5    , 294.69998,\n",
-       "         295.19998]],\n",
-       "\n",
-       "       [[245.2    , 244.29999, 243.     , ..., 241.89   , 243.09999,\n",
-       "         244.59999],\n",
-       "        [241.09999, 241.89   , 241.89   , ..., 241.89   , 243.59999,\n",
-       "         246.39   ],\n",
-       "        [239.89   , 241.     , 241.29999, ..., 245.09999, 248.2    ,\n",
-       "         252.2    ],\n",
-       "...\n",
-       "        [296.49   , 295.59   , 295.49   , ..., 297.29   , 297.38998,\n",
-       "         296.79   ],\n",
-       "        [298.09   , 297.79   , 297.19   , ..., 297.69   , 298.09   ,\n",
-       "         297.38998],\n",
-       "        [298.49   , 298.29   , 297.99   , ..., 298.29   , 298.09   ,\n",
-       "         297.99   ]],\n",
-       "\n",
-       "       [[246.29   , 246.39   , 245.98999, ..., 232.29   , 233.39   ,\n",
-       "         234.98999],\n",
-       "        [246.68999, 248.18999, 248.98999, ..., 230.59   , 232.48999,\n",
-       "         235.68999],\n",
-       "        [244.79   , 245.29   , 245.89   , ..., 230.48999, 235.09   ,\n",
-       "         241.39   ],\n",
-       "        ...,\n",
-       "        [296.88998, 296.49   , 296.49   , ..., 297.69   , 297.29   ,\n",
-       "         296.19   ],\n",
-       "        [298.29   , 297.99   , 297.49   , ..., 298.29   , 298.38998,\n",
-       "         297.49   ],\n",
-       "        [298.49   , 298.29   , 297.99   , ..., 298.59   , 298.38998,\n",
-       "         298.29   ]]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-       "  * time     (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([[[246. , 245.09999, 244.09999, ..., 240.2 , 242. ,\n", - " 244.2 ],\n", - " [240.59999, 241.7 , 242.09999, ..., 239.09999, 241.2 ,\n", - " 244.59999],\n", - " [238.2 , 239.79999, 240.79999, ..., 243.89 , 246.89 ,\n", - " 251.2 ],\n", - " ...,\n", - " [295.6 , 295.6 , 296. , ..., 293.6 , 293.6 ,\n", - " 294.1 ],\n", - " [297. , 297. , 297. , ..., 293.69998, 293.9 ,\n", - " 294.6 ],\n", - " [297.5 , 297.69998, 297.69998, ..., 294.5 , 294.69998,\n", - " 295.19998]],\n", - "\n", - " [[245.2 , 244.29999, 243. , ..., 241.89 , 243.09999,\n", - " 244.59999],\n", - " [241.09999, 241.89 , 241.89 , ..., 241.89 , 243.59999,\n", - " 246.39 ],\n", - " [239.89 , 241. , 241.29999, ..., 245.09999, 248.2 ,\n", - " 252.2 ],\n", - "...\n", - " [296.49 , 295.59 , 295.49 , ..., 297.29 , 297.38998,\n", - " 296.79 ],\n", - " [298.09 , 297.79 , 297.19 , ..., 297.69 , 298.09 ,\n", - " 297.38998],\n", - " [298.49 , 298.29 , 297.99 , ..., 298.29 , 298.09 ,\n", - " 297.99 ]],\n", - "\n", - " [[246.29 , 246.39 , 245.98999, ..., 232.29 , 233.39 ,\n", - " 234.98999],\n", - " [246.68999, 248.18999, 248.98999, ..., 230.59 , 232.48999,\n", - " 235.68999],\n", - " [244.79 , 245.29 , 245.89 , ..., 230.48999, 235.09 ,\n", - " 241.39 ],\n", - " ...,\n", - " [296.88998, 296.49 , 296.49 , ..., 297.69 , 297.29 ,\n", - " 296.19 ],\n", - " [298.29 , 297.99 , 297.49 , ..., 298.29 , 298.38998,\n", - " 297.49 ],\n", - " [298.49 , 298.29 , 297.99 , ..., 298.59 , 298.38998,\n", - " 298.29 ]]], dtype=float32)\n", - "Coordinates:\n", - " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", - " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", - " * time (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# demonstrate slicing\n", "da.sel(time=slice(\"2014-02-14\", \"2014-12-13\"))" @@ -2326,570 +353,9 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 1212, lat: 25, lon: 53)>\n",
-       "array([[[246.     , 245.09999, 244.09999, ..., 240.2    , 242.     ,\n",
-       "         244.2    ],\n",
-       "        [240.59999, 241.7    , 242.09999, ..., 239.09999, 241.2    ,\n",
-       "         244.59999],\n",
-       "        [238.2    , 239.79999, 240.79999, ..., 243.89   , 246.89   ,\n",
-       "         251.2    ],\n",
-       "        ...,\n",
-       "        [295.6    , 295.6    , 296.     , ..., 293.6    , 293.6    ,\n",
-       "         294.1    ],\n",
-       "        [297.     , 297.     , 297.     , ..., 293.69998, 293.9    ,\n",
-       "         294.6    ],\n",
-       "        [297.5    , 297.69998, 297.69998, ..., 294.5    , 294.69998,\n",
-       "         295.19998]],\n",
-       "\n",
-       "       [[245.2    , 244.29999, 243.     , ..., 241.89   , 243.09999,\n",
-       "         244.59999],\n",
-       "        [241.09999, 241.89   , 241.89   , ..., 241.89   , 243.59999,\n",
-       "         246.39   ],\n",
-       "        [239.89   , 241.     , 241.29999, ..., 245.09999, 248.2    ,\n",
-       "         252.2    ],\n",
-       "...\n",
-       "        [296.49   , 295.59   , 295.49   , ..., 297.29   , 297.38998,\n",
-       "         296.79   ],\n",
-       "        [298.09   , 297.79   , 297.19   , ..., 297.69   , 298.09   ,\n",
-       "         297.38998],\n",
-       "        [298.49   , 298.29   , 297.99   , ..., 298.29   , 298.09   ,\n",
-       "         297.99   ]],\n",
-       "\n",
-       "       [[246.29   , 246.39   , 245.98999, ..., 232.29   , 233.39   ,\n",
-       "         234.98999],\n",
-       "        [246.68999, 248.18999, 248.98999, ..., 230.59   , 232.48999,\n",
-       "         235.68999],\n",
-       "        [244.79   , 245.29   , 245.89   , ..., 230.48999, 235.09   ,\n",
-       "         241.39   ],\n",
-       "        ...,\n",
-       "        [296.88998, 296.49   , 296.49   , ..., 297.69   , 297.29   ,\n",
-       "         296.19   ],\n",
-       "        [298.29   , 297.99   , 297.49   , ..., 298.29   , 298.38998,\n",
-       "         297.49   ],\n",
-       "        [298.49   , 298.29   , 297.99   , ..., 298.59   , 298.38998,\n",
-       "         298.29   ]]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-       "  * time     (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([[[246. , 245.09999, 244.09999, ..., 240.2 , 242. ,\n", - " 244.2 ],\n", - " [240.59999, 241.7 , 242.09999, ..., 239.09999, 241.2 ,\n", - " 244.59999],\n", - " [238.2 , 239.79999, 240.79999, ..., 243.89 , 246.89 ,\n", - " 251.2 ],\n", - " ...,\n", - " [295.6 , 295.6 , 296. , ..., 293.6 , 293.6 ,\n", - " 294.1 ],\n", - " [297. , 297. , 297. , ..., 293.69998, 293.9 ,\n", - " 294.6 ],\n", - " [297.5 , 297.69998, 297.69998, ..., 294.5 , 294.69998,\n", - " 295.19998]],\n", - "\n", - " [[245.2 , 244.29999, 243. , ..., 241.89 , 243.09999,\n", - " 244.59999],\n", - " [241.09999, 241.89 , 241.89 , ..., 241.89 , 243.59999,\n", - " 246.39 ],\n", - " [239.89 , 241. , 241.29999, ..., 245.09999, 248.2 ,\n", - " 252.2 ],\n", - "...\n", - " [296.49 , 295.59 , 295.49 , ..., 297.29 , 297.38998,\n", - " 296.79 ],\n", - " [298.09 , 297.79 , 297.19 , ..., 297.69 , 298.09 ,\n", - " 297.38998],\n", - " [298.49 , 298.29 , 297.99 , ..., 298.29 , 298.09 ,\n", - " 297.99 ]],\n", - "\n", - " [[246.29 , 246.39 , 245.98999, ..., 232.29 , 233.39 ,\n", - " 234.98999],\n", - " [246.68999, 248.18999, 248.98999, ..., 230.59 , 232.48999,\n", - " 235.68999],\n", - " [244.79 , 245.29 , 245.89 , ..., 230.48999, 235.09 ,\n", - " 241.39 ],\n", - " ...,\n", - " [296.88998, 296.49 , 296.49 , ..., 297.69 , 297.29 ,\n", - " 296.19 ],\n", - " [298.29 , 297.99 , 297.49 , ..., 298.29 , 298.38998,\n", - " 297.49 ],\n", - " [298.49 , 298.29 , 297.99 , ..., 298.59 , 298.38998,\n", - " 298.29 ]]], dtype=float32)\n", - "Coordinates:\n", - " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", - " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", - " * time (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da.loc[\"2014-02-14\":\"2014-12-13\"]" ] @@ -2905,570 +371,9 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 2919, lat: 24, lon: 52)>\n",
-       "array([[[242.5    , 243.5    , 244.     , ..., 232.79999, 235.5    ,\n",
-       "         238.59999],\n",
-       "        [244.5    , 244.7    , 244.2    , ..., 232.79999, 235.29999,\n",
-       "         239.29999],\n",
-       "        [249.79999, 248.89   , 247.5    , ..., 233.2    , 236.39   ,\n",
-       "         241.7    ],\n",
-       "        ...,\n",
-       "        [296.19998, 296.4    , 296.5    , ..., 295.4    , 295.1    ,\n",
-       "         294.69998],\n",
-       "        [296.19998, 296.79   , 296.5    , ..., 295.9    , 295.9    ,\n",
-       "         295.19998],\n",
-       "        [296.79   , 297.1    , 297.     , ..., 296.9    , 296.79   ,\n",
-       "         296.6    ]],\n",
-       "\n",
-       "       [[242.7    , 243.09999, 243.39   , ..., 232.     , 233.59999,\n",
-       "         235.79999],\n",
-       "        [244.09999, 244.2    , 244.09999, ..., 231.     , 232.5    ,\n",
-       "         235.7    ],\n",
-       "        [252.89   , 252.09999, 250.79999, ..., 230.79999, 233.39   ,\n",
-       "         238.5    ],\n",
-       "...\n",
-       "        [293.88998, 295.38998, 297.19   , ..., 295.09   , 294.69   ,\n",
-       "         294.29   ],\n",
-       "        [297.19   , 297.59   , 297.88998, ..., 295.29   , 295.09   ,\n",
-       "         294.38998],\n",
-       "        [298.38998, 298.49   , 298.59   , ..., 295.69   , 295.49   ,\n",
-       "         295.19   ]],\n",
-       "\n",
-       "       [[244.29   , 243.29   , 242.18999, ..., 241.68999, 241.48999,\n",
-       "         241.79   ],\n",
-       "        [249.29   , 248.39   , 246.98999, ..., 239.59   , 240.29   ,\n",
-       "         241.68999],\n",
-       "        [262.19   , 261.38998, 259.99   , ..., 239.89   , 242.59   ,\n",
-       "         246.29   ],\n",
-       "        ...,\n",
-       "        [293.69   , 295.09   , 296.69   , ..., 295.29   , 295.09   ,\n",
-       "         294.69   ],\n",
-       "        [296.88998, 297.19   , 297.49   , ..., 295.69   , 295.69   ,\n",
-       "         295.19   ],\n",
-       "        [298.09   , 298.09   , 298.49   , ..., 296.49   , 296.19   ,\n",
-       "         295.69   ]]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 202.5 205.0 207.5 210.0 ... 322.5 325.0 327.5 330.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([[[242.5 , 243.5 , 244. , ..., 232.79999, 235.5 ,\n", - " 238.59999],\n", - " [244.5 , 244.7 , 244.2 , ..., 232.79999, 235.29999,\n", - " 239.29999],\n", - " [249.79999, 248.89 , 247.5 , ..., 233.2 , 236.39 ,\n", - " 241.7 ],\n", - " ...,\n", - " [296.19998, 296.4 , 296.5 , ..., 295.4 , 295.1 ,\n", - " 294.69998],\n", - " [296.19998, 296.79 , 296.5 , ..., 295.9 , 295.9 ,\n", - " 295.19998],\n", - " [296.79 , 297.1 , 297. , ..., 296.9 , 296.79 ,\n", - " 296.6 ]],\n", - "\n", - " [[242.7 , 243.09999, 243.39 , ..., 232. , 233.59999,\n", - " 235.79999],\n", - " [244.09999, 244.2 , 244.09999, ..., 231. , 232.5 ,\n", - " 235.7 ],\n", - " [252.89 , 252.09999, 250.79999, ..., 230.79999, 233.39 ,\n", - " 238.5 ],\n", - "...\n", - " [293.88998, 295.38998, 297.19 , ..., 295.09 , 294.69 ,\n", - " 294.29 ],\n", - " [297.19 , 297.59 , 297.88998, ..., 295.29 , 295.09 ,\n", - " 294.38998],\n", - " [298.38998, 298.49 , 298.59 , ..., 295.69 , 295.49 ,\n", - " 295.19 ]],\n", - "\n", - " [[244.29 , 243.29 , 242.18999, ..., 241.68999, 241.48999,\n", - " 241.79 ],\n", - " [249.29 , 248.39 , 246.98999, ..., 239.59 , 240.29 ,\n", - " 241.68999],\n", - " [262.19 , 261.38998, 259.99 , ..., 239.89 , 242.59 ,\n", - " 246.29 ],\n", - " ...,\n", - " [293.69 , 295.09 , 296.69 , ..., 295.29 , 295.09 ,\n", - " 294.69 ],\n", - " [296.88998, 297.19 , 297.49 , ..., 295.69 , 295.69 ,\n", - " 295.19 ],\n", - " [298.09 , 298.09 , 298.49 , ..., 296.49 , 296.19 ,\n", - " 295.69 ]]], dtype=float32)\n", - "Coordinates:\n", - " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", - " * lon (lon) float32 202.5 205.0 207.5 210.0 ... 322.5 325.0 327.5 330.0\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da.drop_sel(lat=50.0, lon=200.0, time=\"2014\")" ] @@ -3487,441 +392,13 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": null, "metadata": { "tags": [ "hide-output" ] }, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 2920)>\n",
-       "array([262.69998, 263.19998, 270.9    , ..., 264.19   , 265.19   ,\n",
-       "       266.99   ], dtype=float32)\n",
-       "Coordinates:\n",
-       "    lat      float32 52.5\n",
-       "    lon      float32 252.5\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([262.69998, 263.19998, 270.9 , ..., 264.19 , 265.19 ,\n", - " 266.99 ], dtype=float32)\n", - "Coordinates:\n", - " lat float32 52.5\n", - " lon float32 252.5\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da.sel(lat=52.25, lon=251.8998, method=\"nearest\")" ] @@ -3935,437 +412,9 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 2920)>\n",
-       "array([262.69998, 263.19998, 270.9    , ..., 264.19   , 265.19   ,\n",
-       "       266.99   ], dtype=float32)\n",
-       "Coordinates:\n",
-       "    lat      float32 52.5\n",
-       "    lon      float32 252.5\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([262.69998, 263.19998, 270.9 , ..., 264.19 , 265.19 ,\n", - " 266.99 ], dtype=float32)\n", - "Coordinates:\n", - " lat float32 52.5\n", - " lon float32 252.5\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da.sel(lat=52.25, lon=251.8998, method=\"nearest\", tolerance=2)" ] @@ -4383,428 +432,9 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.Dataset>\n",
-       "Dimensions:  (time: 2920)\n",
-       "Coordinates:\n",
-       "    lat      float32 52.5\n",
-       "    lon      float32 252.5\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Data variables:\n",
-       "    air      (time) float32 262.7 263.2 270.9 274.1 ... 261.6 264.2 265.2 267.0\n",
-       "Attributes:\n",
-       "    Conventions:  COARDS\n",
-       "    title:        4x daily NMC reanalysis (1948)\n",
-       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
-       "    platform:     Model\n",
-       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
    " - ], - "text/plain": [ - "\n", - "Dimensions: (time: 2920)\n", - "Coordinates:\n", - " lat float32 52.5\n", - " lon float32 252.5\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Data variables:\n", - " air (time) float32 262.7 263.2 270.9 274.1 ... 261.6 264.2 265.2 267.0\n", - "Attributes:\n", - " Conventions: COARDS\n", - " title: 4x daily NMC reanalysis (1948)\n", - " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", - " platform: Model\n", - " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "ds.sel(lat=52.25, lon=251.8998, method=\"nearest\")" ] @@ -4898,10 +528,6 @@ } ], "metadata": { - "kernelspec": { - "display_name": "", - "name": "" - }, "language_info": { "codemirror_mode": { "name": "ipython", @@ -4925,13 +551,6 @@ "toc_position": {}, "toc_section_display": true, "toc_window_display": true - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } } }, "nbformat": 4, From 662bbd316ee459a8266de565b0e592644f53420a Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Fri, 7 Jul 2023 19:54:03 -0600 Subject: [PATCH 35/54] update _config.yml --- _config.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/_config.yml b/_config.yml index 78978501..6ec14e9d 100644 --- a/_config.yml +++ b/_config.yml @@ -79,5 +79,6 @@ sphinx: rediraffe_redirects: scipy-tutorial/00_overview.ipynb: overview/get-started.md workshops/scipy2022/README.md: overview/fundamental-path/README.md + fundamentals/02.1_working_with_labeled_data.ipynb: fundamentals/02.1_indexing_Basic.ipynb bibtex_reference_style: author_year # or label, super, \supercite From d4803a1e6df9449254142b1de82ead1ce727c319 Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Fri, 7 Jul 2023 19:57:15 -0600 Subject: [PATCH 36/54] more updates on toc --- _toc.yml | 1 + workshops/scipy2023/README.md | 1 + 2 files changed, 2 insertions(+) diff --git a/_toc.yml b/_toc.yml index 4f5313e4..e38ef891 100644 --- a/_toc.yml +++ b/_toc.yml @@ -21,6 +21,7 @@ parts: sections: - file: fundamentals/02.1_indexing_Basic.ipynb - file: intermediate/02.2_indexing_Advanced.ipynb + - file: intermediate/02.3_indexing_BooleanMasking.ipynb - file: fundamentals/02.2_manipulating_dimensions - file: fundamentals/03_computation.md sections: diff --git a/workshops/scipy2023/README.md b/workshops/scipy2023/README.md index 138b381c..b3a03f0b 100644 --- a/workshops/scipy2023/README.md +++ b/workshops/scipy2023/README.md @@ -61,6 +61,7 @@ Once your codespace is launched, the following happens: ```{dropdown} Indexing -{doc}`../../fundamentals/02.1_indexing_Basic` -{doc}`../../intermediate/02.2_indexing_Advanced` +-{doc}`../../intermediate/02.3_indexing_BooleanMasking` ``` From 7ec8242287ea98f461c7a2d1f42076bdc004f719 Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Fri, 7 Jul 2023 20:08:58 -0600 Subject: [PATCH 37/54] fix typo --- .../02.3_indexing_BooleanMasking.ipynb | 21 +++++++++++++++---- 1 file changed, 17 insertions(+), 4 deletions(-) diff --git a/intermediate/02.3_indexing_BooleanMasking.ipynb b/intermediate/02.3_indexing_BooleanMasking.ipynb index fa977f64..aa6b7d14 100644 --- a/intermediate/02.3_indexing_BooleanMasking.ipynb +++ b/intermediate/02.3_indexing_BooleanMasking.ipynb @@ -313,7 +313,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Excercise\n", + "### Exercise\n", "\n", "If we load air temperature dataset from NCEP, we could use `sel` method for selecting a region:\n" ] @@ -365,7 +365,7 @@ "outputs": [], "source": [ "# Define a function to use as a condition\n", - "def is_greater_than_threshold(x, threshhold=300):\n", + "def is_greater_than_threshhold(x, threshhold=300):\n", " # function to convert temp to K\n", " # and compare with threshhold\n", " x = x + 273.15\n", @@ -373,7 +373,7 @@ "\n", "\n", "# Apply the condition using xarray.where()\n", - "masked_data = xr.where(is_greater_than_threshold(da, 280), da, 0)\n", + "masked_data = xr.where(is_greater_than_threshhold(da, 280), da, 0)\n", "\n", "masked_data[0].plot()" ] @@ -468,6 +468,11 @@ } ], "metadata": { + "kernelspec": { + "display_name": "NPL 2023b", + "language": "python", + "name": "npl-2023b" + }, "language_info": { "codemirror_mode": { "name": "ipython", @@ -477,7 +482,8 @@ "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3" + "pygments_lexer": "ipython3", + "version": "3.10.12" }, "toc": { "base_numbering": 1, @@ -491,6 +497,13 @@ "toc_position": {}, "toc_section_display": true, "toc_window_display": true + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } } }, "nbformat": 4, From 4d9f2096a3d308a7df3263a262a69108bbc8a918 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Sat, 8 Jul 2023 02:09:18 +0000 Subject: [PATCH 38/54] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- intermediate/02.3_indexing_BooleanMasking.ipynb | 15 +-------------- 1 file changed, 1 insertion(+), 14 deletions(-) diff --git a/intermediate/02.3_indexing_BooleanMasking.ipynb b/intermediate/02.3_indexing_BooleanMasking.ipynb index aa6b7d14..586814e1 100644 --- a/intermediate/02.3_indexing_BooleanMasking.ipynb +++ b/intermediate/02.3_indexing_BooleanMasking.ipynb @@ -468,11 +468,6 @@ } ], "metadata": { - "kernelspec": { - "display_name": "NPL 2023b", - "language": "python", - "name": "npl-2023b" - }, "language_info": { "codemirror_mode": { "name": "ipython", @@ -482,8 +477,7 @@ "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" + "pygments_lexer": "ipython3" }, "toc": { "base_numbering": 1, @@ -497,13 +491,6 @@ "toc_position": {}, "toc_section_display": true, "toc_window_display": true - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } } }, "nbformat": 4, From 312a44ae722188573c22d2249321b54281f48079 Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Fri, 7 Jul 2023 20:18:43 -0600 Subject: [PATCH 39/54] updates for typo --- fundamentals/02.1_indexing_Basic.ipynb | 4464 ++++++++++++++++++++- intermediate/02.2_indexing_Advanced.ipynb | 3172 ++++++++++++++- 2 files changed, 7570 insertions(+), 66 deletions(-) diff --git a/fundamentals/02.1_indexing_Basic.ipynb b/fundamentals/02.1_indexing_Basic.ipynb index 8c89f467..b568b66a 100644 --- a/fundamentals/02.1_indexing_Basic.ipynb +++ b/fundamentals/02.1_indexing_Basic.ipynb @@ -53,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -71,9 +71,483 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.Dataset>\n",
+       "Dimensions:  (lat: 25, time: 2920, lon: 53)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Data variables:\n",
+       "    air      (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n",
+       "Attributes:\n",
+       "    Conventions:  COARDS\n",
+       "    title:        4x daily NMC reanalysis (1948)\n",
+       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
+       "    platform:     Model\n",
+       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
    " + ], + "text/plain": [ + "\n", + "Dimensions: (lat: 25, time: 2920, lon: 53)\n", + "Coordinates:\n", + " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", + " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Data variables:\n", + " air (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n", + "Attributes:\n", + " Conventions: COARDS\n", + " title: 4x daily NMC reanalysis (1948)\n", + " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", + " platform: Model\n", + " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ds = xr.tutorial.load_dataset(\"air_temperature\")\n", "ds" @@ -81,7 +555,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -111,9 +585,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 52, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(2920, 25, 53)" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "np_array = ds[\"air\"].data # numpy array\n", "np_array.shape" @@ -128,9 +613,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "242.09999" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "np_array[1, 0, 0]" ] @@ -144,9 +640,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 54, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([295. , 294.4 , 294.5 , ..., 297.29, 297.79, 297.99], dtype=float32)" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# extract a time-series for one spatial location\n", "np_array[:, 20, 40]" @@ -178,9 +685,434 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'air' (time: 2920)>\n",
+       "array([295.  , 294.4 , 294.5 , ..., 297.29, 297.79, 297.99], dtype=float32)\n",
+       "Coordinates:\n",
+       "    lat      float32 25.0\n",
+       "    lon      float32 300.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
    " + ], + "text/plain": [ + "\n", + "array([295. , 294.4 , 294.5 , ..., 297.29, 297.79, 297.99], dtype=float32)\n", + "Coordinates:\n", + " lat float32 25.0\n", + " lon float32 300.0\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "da[:, 20, 40]" ] @@ -189,29 +1121,50 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "```caution\n", + "```{caution}\n", "Positional indexing deviates from the NumPy behavior when indexing with multiple arrays. \n", - "\n", "```\n", "We can show this with an example: " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 56, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(2920, 2)" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "np_array[:, [0, 1], [0, 1]].shape" + "np_array[:,[0, 1], [0, 1]].shape" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 57, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(2920, 2, 2)" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "da[:, [0, 1], [0, 1]].shape" + "da[:,[0, 1], [0, 1]].shape" ] }, { @@ -220,9 +1173,9 @@ "source": [ "Please note how the dimension of the `DataArray()` object is different from the `numpy.ndarray`.\n", "\n", - "``` tip\n", + "```{tip}\n", "However, users can still achieve NumPy-like pointwise indexing across multiple labeled dimensions by using Xarray vectorized indexing techniques. We will delve further into this topic in the advanced indexing notebook.\n", - " ```" + "```" ] }, { @@ -244,9 +1197,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 58, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "da.isel(lat=20, lon=40).plot();" ] @@ -260,9 +1224,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 59, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlkAAAHrCAYAAAD8EoikAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACfE0lEQVR4nOzdd1zU9R/A8dcdeyMIAg7EiXvP3AO1JdrShtqwfqW5smFljixtaktLzVG5Mi2tzLJU3HviRBAnGxmyufv+/sA7RdYdAncH7+fjwaP43ne87+S4N5/P+/v+qBRFURBCCCGEEGVKbeoAhBBCCCEqI0myhBBCCCHKgSRZQgghhBDlQJIsIYQQQohyIEmWEEIIIUQ5kCRLCCGEEKIcSJIlhBBCCFEOJMkSQgghhCgHkmQJIYQQQpQDSbJEiZYtW4ZKpSIyMtLoY/fs2cP06dNJSkoq87hKsnXrVp577jkCAwNxcnKiZs2aDB48mMOHDxfYd9SoUahUqgJfgYGBBl/v33//pUuXLjg6OlK9enVGjRpFbGxsicdt37493zUPHTpU4jGjRo2ibt26Bscmyt7KlSuZN29ege2RkZGoVCo+/fTTEs8xb968fP/28fHxRseh+/nZvn270ceak3///Zf+/fvj5+eHnZ0d3t7e9OnTh02bNhW5vyHvt5ycHGbMmEHdunWxs7MjMDCQr776yuC4bt68yYQJE/Dz88Pe3p7WrVuzevVqg47t1auX/t/2wQcfLHF/3c/OsmXLDI5PmDdJskS52rNnDzNmzDBJkrVgwQIiIyMZP348mzZt4osvviA2NpbOnTuzdevWAvs7ODiwd+/efF9r1qwx6FohISEMGjSIGjVqsGHDBr744gv+/fdf+vbtS1ZWlkHn+Oabb9i7dy9NmjQx6nkK0ygqyTLGsGHD2Lt3L88//3zZBGXBEhISaNasGXPnzuWff/7hu+++w8bGhgceeICffvop377GvN9eeeUVZs+ezZgxY/j7778ZMmQI48eP58MPPzQorqFDh7J8+XKmTZvGX3/9RYcOHRg+fDgrV6406Pg2bdqwd+9ePvvsM8NeCFG5KEKUYOnSpQqgXLx40ehjP/nkk1Ife69iYmIKbEtNTVVq1Kih9O3bN9/2kSNHKk5OTqW+VocOHZSmTZsqOTk5+m27d+9WAGX+/PnFHrtt2zYFULZt22bw9UaOHKn4+/uXMtrSS09PV7RabYVf15ykpaUpiqIoDzzwQKH/BhcvXlQA5ZNPPjH4nNOmTVMAJS4uzuh4SvPzYymys7OVmjVrKt27d8+33dD3W2hoqKJSqZQPP/ww3/GjR49WHBwclISEhGKv/+effyqAsnLlynzb+/fvr/j5+Sm5ubnFHt+zZ0+lZ8+exe5zJ93PztKlSw0+pqykp6dX+DWrAhnJEqWyZcsWBg8eTK1atbC3t6dBgwa89NJL+aY7pk+fzuuvvw5AQECAfti8oqY1vL29C2xzdnamadOmXLlypcyuc+3aNQ4ePMgzzzyDtbW1fnvXrl1p1KgRv/766z2df9myZTRu3Bg7OzuaNGnCDz/8UOh+2dnZzJo1i8DAQOzs7PDy8uLZZ58lLi4u335ZWVm89tpr+Pj44OjoSI8ePTh8+DB169Zl1KhR+a6rUqn4559/eO655/Dy8sLR0VE/UrBmzRq6dOmCk5MTzs7ODBgwgKNHjxaI69ChQzz88MN4eHhgb29PmzZt+Pnnn/Ptk56ezuTJkwkICMDe3h4PDw/at2/PqlWrDH6ddNNmq1at4p133sHPzw9XV1f69evHuXPnCuy/ZMkSWrVqpb/ekCFDOHPmTL59Ro0ahbOzMydPniQoKAgXFxf69u1Lr169+PPPP7l06VK+6b67ff755wQEBODs7EyXLl3Yt2+fwc/nXm3cuFE/nebi4kL//v3Zu3dvvn2mT5+OSqXi1KlTDB8+HDc3N2rUqMFzzz1HcnJyhcVaFBsbG9zd3fO9r4x5v/32228oisKzzz6b77zPPvssGRkZbN68udjr//rrrzg7O/PYY48VOP769evs37+/1M/t+vXrPP7447i4uODm5sYTTzxBdHR0ofsa8h4C2LVrF126dMHe3p6aNWsydepUFi9eXKDco27dujz44IOsX7+eNm3aYG9vz4wZMwCIjo7mpZdeolatWtja2hIQEMCMGTPIzc3Ndy1Df99UddYl7yJEQeHh4XTp0oUXXngBNzc3IiMj+fzzz+nWrRsnT57ExsaGF154gcTERL766ivWr1+Pr68vAE2bNi3yvIqioNFoDIrhzl+whkpOTubIkSP06dOnwGMZGRn4+PgQFxeHr68vwcHBzJw5Ew8Pj2LPGRoaCkDLli0LPNayZUt2795tdJw6y5Yt49lnn2Xw4MF89tlnJCcnM336dLKyslCrb/+NpNVqGTx4MDt37uSNN96ga9euXLp0iWnTptGrVy8OHTqEg4MDkPcBsWbNGt544w369OnD6dOnGTJkCCkpKYXG8Nxzz/HAAw/w448/kpaWho2NDR9++CHvvvsuzz77LO+++y7Z2dl88skndO/enQMHDuj/jbdt28bAgQPp1KkT3377LW5ubqxevZonnniC9PR0fVI3adIkfvzxR2bNmkWbNm1IS0sjNDSUhIQEo1+zt99+m/vuu4/FixeTkpLCm2++yUMPPcSZM2ewsrICYPbs2bz99tsMHz6c2bNnk5CQwPTp0+nSpQsHDx6kYcOG+vNlZ2fz8MMP89JLL/HWW2+Rm5tLrVq1ePHFFwkPDy8yif7mm28IDAzUTylOnTqV+++/n4sXL+Lm5mb08zLGypUreeqppwgKCmLVqlVkZWXx8ccf06tXL/777z+6deuWb/9HHnmEJ554gueff56TJ08yZcoUIC8RLY5Wq0Wr1ZYYj0ql0r/2JdGdMzY2lu+++47z58/z0Ucf6R835v0WGhqKl5cXPj4+Bfa781xFCQ0NpUmTJgV+19x5fNeuXQ16XnfKyMigX79+XL9+ndmzZ9OoUSP+/PNPnnjiiQL7GvoeOnHiBP3796dRo0YsX74cR0dHvv322wJTrTpHjhzhzJkzvPvuuwQEBODk5ER0dDQdO3ZErVbz3nvvUb9+ffbu3cusWbOIjIxk6dKlgHG/b6o8Uw+lCfNX0nShVqtVcnJylEuXLimAsmHDBv1jxk4X6q5lyFdpPPXUU4q1tbVy6NChfNs///xz5fPPP1f++ecf5Z9//lHeeecdxdHRUQkMDFRSU1OLPeeKFSsUQNm7d2+Bx1588UXF1ta22OOLmu7RaDSKn5+f0rZt23xTdJGRkYqNjU2+qapVq1YpgLJu3bp85zh48GC+KZRTp04pgPLmm2/m2093/MiRI/XbdP8WI0aMyLfv5cuXFWtra+XVV1/Ntz01NVXx8fFRHn/8cf22wMBApU2bNvmmdRRFUR588EHF19dX0Wg0iqIoSvPmzZXg4OBiXqWS6V7H+++/P9/2n3/+Od+/z40bNxQHB4cC+12+fFmxs7NTnnzySf22kSNHKoCyZMmSAtcrabqwRYsW+aaTDhw4oADKqlWrChxTltOFup+bFi1a6F9fRcn79/H29la6du1a4Loff/xxvnO+8sorir29fYlTw7rjS/oyZmp7wIAB+uNcXV2V9evX53vcmPdb//79lcaNGxd6HVtbW+XFF18sNpaGDRsqAwYMKLD9+vXrClBgGvJuRU0XLliwoMDvSkXJm8bkrulCQ99Djz32mOLk5JTvZ0ij0ShNmzYt8DvY399fsbKyUs6dO5fvnC+99JLi7OysXLp0Kd/2Tz/9VAGUU6dOKYpi+O8bIdOFopRiY2P53//+R+3atbG2tsbGxgZ/f3+AAlMuxnjooYc4ePCgQV/Gmjp1KitWrGDu3Lm0a9cu32MTJ05k4sSJ9O/fn/79+zNr1ix++OEHzp49y6JFiww6f2HTRcVtL8m5c+e4fv06Tz75ZL5z+Pv7F/jr+Y8//sDd3Z2HHnqI3Nxc/Vfr1q3x8fHRT9GGhIQA8Pjjj+c7/tFHHy1yZPCRRx7J9/3ff/9Nbm4uI0aMyHcte3t7evbsqb/WhQsXOHv2LE899RRAvn3vv/9+oqKi9NN4HTt25K+//uKtt95i+/btZGRklOo1A3j44Yfzfa8bdbh06RIAe/fuJSMjI9/UKEDt2rXp06cP//33X4mvgSEeeOCBfKM3d8dRXnQ/N88880y+0U5nZ2ceeeQR9u3bR3p6er5jCnvNMjMzS7w79sUXXzTovfr7778bHP9XX33FgQMH2LBhAwMGDOCJJ54odNrY0Pdbce8/Q96b93p8YbZt24aLi0uB1/3JJ5/M970x76GQkBD69OlD9erV9cer1eoC73Wdli1b0qhRo3zb/vjjD3r37o2fn1++aw0aNEh/Dd1+hvy+ETJdKEpBq9USFBTE9evXmTp1Ki1atMDJyQmtVkvnzp3v6QPSw8OjXKZSZsyYwaxZs/jggw8YO3asQccMGTIEJyenEutoPD09AQqd2kpMTCxxurEouvPdPdWh23ZnjUVMTAxJSUnY2toWei5drZzunDVq1Mj3uLW1tf553E03zXvntQA6dOhQ6P66D3bdfpMnT2by5MnFxvXll19Sq1Yt1qxZw0cffYS9vT0DBgzgk08+yTd1Z4i7n4ednR2A/udS9xrc/bwA/Pz82LJlS75tjo6OuLq6GhWDIXGUl5Ken1ar5caNGzg6Ouq3lzZWHx+fQmsf72ZMMnLnv/fDDz/MoEGDGDNmDE888QRqtdqo95unpyfHjh0rsF9aWhrZ2dklvjc9PT2LvA5wT+/tu9+DUPC9bsx7qKhzFrYNCv/5iImJ4ffff8fGxqbYaxn6+0ZIkiVKITQ0lOPHj7Ns2TJGjhyp337hwoV7Pvfy5csLFKkWRVEUg/abMWMG06dPZ/r06bz99ttGxaMoSr7RgMI0b94cgJMnT3L//ffne+zkyZP6x42l+zAprBj27m3Vq1fH09OzyEJeFxeXfOeMiYmhZs2a+sdzc3OLrH+6+wNS95fyL7/8oh+9LIxuvylTpjB06NBC92ncuDEATk5OzJgxgxkzZhATE6Mf1XrooYc4e/ZskdcoDd1rEBUVVeCx69ev5xsJgNKPVphKSc9PrVZTrVq1MrnWzJkz9QXTxfH39y9Vnz3IG+XcvHkzcXFx1KhRw6j3W4sWLVi9ejXR0dH5EpiTJ08ClPjebNGiBatWrSI3NzffSK+hxxfF09OTAwcOFNhe2PsaDHsPeXp66pOy4s6pU9jPdfXq1WnZsiUffPBBocf4+fnp9zPk942QJEuUgu7NqftrV+e7774rsK+xf73rpgvLyvvvv8/06dN59913mTZtmlHH/vLLL6Snp9O5c+di96tZsyYdO3bkp59+YvLkyfopon379nHu3DkmTJhQqtgbN26Mr68vq1atYtKkSfrX/dKlS+zZs0f/Cw/gwQcfZPXq1Wg0Gjp16lTkOXv06AHk3RnYtm3bfM/17ruHijJgwACsra0JDw8vdhqtcePGNGzYkOPHjxvckwjy/vIeNWoUx48fZ968eaSnp+cbdblXXbp0wcHBgZ9++infXWNXr15l69atPProowadx87OrtxHpUqjcePG1KxZk5UrVzJ58mT9z01aWhrr1q3T33FYFl588UWDmmze/bvCUIqiEBISgru7uz55NOb9NnjwYN59912WL1/Om2++qd++bNkyHBwcGDhwYLHXHzJkCIsWLWLdunX5itKXL1+On59fse+14vTu3Zuff/6ZjRs35psyvLv3ljHvoZ49e7Jp0ybi4+P1yZlWq2Xt2rUGx/Xggw+yadMm6tevX2wibujvGyFJliiFwMBA6tevz1tvvYWiKHh4ePD7778XmGaBvL8EAb744gtGjhyJjY0NjRs3LvIvHU9PzyKnrYz12Wef8d577zFw4EAeeOCBAtN+uuTp0qVLPPnkkwwbNowGDRqgUqkICQlh3rx5NGvWjBdeeCHfcdbW1vTs2TNf7c5HH31E//79eeyxx3jllVeIjY3lrbfeonnz5gaPzN1NrVbz/vvv88ILLzBkyBBGjx5NUlIS06dPLzCtMGzYMFasWMH999/P+PHj6dixIzY2Nly9epVt27YxePBghgwZQrNmzRg+fDifffYZVlZW9OnTh1OnTvHZZ5/h5uZW4qgd5N3+PXPmTN555x0iIiIYOHAg1apVIyYmhgMHDuhHpSAv8R40aBADBgxg1KhR1KxZk8TERM6cOcORI0f0HwCdOnXiwQcfpGXLllSrVo0zZ87w448/lmlCoOPu7s7UqVN5++23GTFiBMOHDychIYEZM2Zgb29vcDLeokUL1q9fz4IFC2jXrh1qtZr27duXWZzTp09nxowZbNu2jV69ehl8nFqt5uOPP+app57iwQcf5KWXXiIrK4tPPvmEpKQk5syZU2Yx+vn55Uv278XgwYNp1aoVrVu3xtPTk+vXr7Ns2TJCQkL45ptv8o0kGfp+a9asGc8//zzTpk3DysqKDh068M8//7Bw4UJmzZqVb7pv5syZzJw5k//++4+ePXsCMGjQIPr378/LL79MSkoKDRo0YNWqVWzevJmffvrJ4Dsm7zZixAjmzp3LiBEj+OCDD2jYsCGbNm3i77//LrCvoe+hd955h99//52+ffvyzjvv4ODgwLfffktaWhqAQe/tmTNnsmXLFrp27cq4ceNo3LgxmZmZREZGsmnTJr799ltq1apl8O8bgOeff57ly5cTHh6uH/n+4YcfeO6551iyZAkjRowA8n4P169fn5EjR/L999+X6nU1S6atuxeWoLC7C0+fPq30799fcXFxUapVq6Y89thjyuXLlxVAmTZtWr7jp0yZovj5+SlqtbpCmyb27NnToLsTExMTlSFDhih169ZVHBwcFFtbW6Vhw4bKG2+8oSQlJRU4L1DoHUP//POP0rlzZ8Xe3l7x8PBQRowYUWhD1LuV1Exy8eLFSsOGDRVbW1ulUaNGypIlSwptRpqTk6N8+umnSqtWrRR7e3vF2dlZCQwMVF566SUlLCxMv19mZqYyadIkxdvbW7G3t1c6d+6s7N27V3Fzc1MmTpyo30/3737w4MFC4/rtt9+U3r17K66uroqdnZ3i7++vPProo8q///6bb7/jx48rjz/+uOLt7a3Y2NgoPj4+Sp8+fZRvv/1Wv89bb72ltG/fXqlWrZpiZ2en1KtXT5k4caISHx9f4ut39+u4du3afNuLavC4ePFipWXLloqtra3i5uamDB48WH/3lE5xTWoTExOVRx99VHF3d1dUKpX+Z6q4ZqSFvT8UpfC7C1977TVFpVIpZ86cMeh53/3z89tvvymdOnVS7O3tFScnJ6Vv377K7t27S7yuotxbA+LS+uijj5QOHToo1apVU6ysrBRPT09lwIAByh9//FHo/oa+37Kzs5Vp06YpderU0b+HvvzyywL76V6Lu1/H1NRUZdy4cYqPj49ia2urtGzZstA7RAtTXDPSq1evKo888oji7OysuLi4KI888oiyZ8+eQn9WDXkPKYqi7Ny5U+nUqZNiZ2en+Pj4KK+//rry0UcfKUC+32X+/v7KAw88UGhccXFxyrhx45SAgADFxsZG8fDwUNq1a6e88847ys2bN/X7Gfr7RneH7p0/S7qfrzufp+59c+cdzpWBSlEMLGwRQpSL7du307t3b/7991969uxZqv5f92rPnj3cd999rFixosAdTqL8KLf6ws2cOZP333+fuLg4/VRPx44d8ff3N2q6R5iXXr16oSgK//33H2q12qDRpLIWFBREZGQk58+fr/BrC5kuFMJs9OvXD4CDBw+W6bTT3bZs2cLevXtp164dDg4OHD9+nDlz5tCwYcMii2tF+fjiiy+YOHFige0pKSkcP36c5cuXmyAqUZZ27NihX4Pxjz/+KNdrTZo0iTZt2lC7dm0SExNZsWIFW7ZsqVzTbxZGRrKEMLHU1NR8y740bdq0zOuQ7rR//35ee+01Tp8+TWpqKtWrV2fAgAHMnj270Nu6TU032lMcKysri7sLEPL6zV2+fFn/fevWrU0ykinKx7lz50hNTQXyagEbNGhQrtcbP348GzduJDo6GpVKRdOmTZkwYQJPP/10uV5XFE2SLCGEWdNNpxZn6dKlBZqLCiGEqUmSJYQwa3eP9BUmICCgzO5KFUKIsiJJlhBCCCFEOZC1C4UQQgghyoFUWJqIVqvl+vXruLi4WGTBrhBCCFEVKYpCamoqfn5+JbblkCTLRK5fv07t2rVNHYYQQgghSuHKlSvUqlWr2H0kyTIR3bIyV65cwdXV1cTRCCGEEMIQKSkp1K5d26CFsCXJMhHdFKGrq6skWUIIIYSFMaTURwrfhRBCCCHKgSRZQgghhBDlQJIsIYQQQohyIEmWEEIIIUQ5kCRLCCGEEKIcSJIlhBBCCFEOJMkSQgghhCgHkmQJIYQQQpQDSbKEEEIIIcqBJFlCCCGEEOVAkiwhhBBCiHIgSZYQQgghRDmQJEsIIYQQlcqNtGyeW3aQuVvOoyiKyeKQJEsIIYQQlcrJa8lsPRvLxuPXUalUJotDkiwhhBBCVCqh15MBaObnatI4JMkSQgghRKUSei0vyWpR082kcUiSJYQQQohK5aQkWUIIIYQQZSs5PYcriRkANPOTJEsIIYQQokzo6rHqeDji5mhj0lgkyRJCCCFEpaGrx2pe07RF7yBJlhBCCCEqkZP6JMu0U4UgSZYQQgghKhFzubMQJMkSQgghRCWRkplDZEI6AM1NXPQOkmQJIYQQopI4dS0FgJruDlRzsjVxNJJkCSGEEKKSMKepQpAkSwghhBCVhK59gzncWQiSZAkhhBCikjCnOwtBkiwhhBBCVAI3s3K5GJ8GSJIlhBBCCFFmTl9PQVHA182e6s52pg4HkCRLCCGEEJWAuU0VgiRZQgghhKgETumSLDPoj6UjSZYQQgghLJ5uJKtFLfO4sxAkyRJCCCGEhUvPziU87iYg04VCCCGEEGXmTFQKWgW8XezwdrE3dTh6Fp1kzZ49mw4dOuDi4oK3tzfBwcGcO3cu3z4xMTGMGjUKPz8/HB0dGThwIGFhYfrHIyMjUalUhX6tXbu22OvPnz+fgIAA7O3tadeuHTt37iyX5ymEEEKIop28al6d3nUsOskKCQlhzJgx7Nu3jy1btpCbm0tQUBBpaXl9MhRFITg4mIiICDZs2MDRo0fx9/enX79++n1q165NVFRUvq8ZM2bg5OTEoEGDirz2mjVrmDBhAu+88w5Hjx6le/fuDBo0iMuXL1fIcxdCCCFEnpO31ixsZmZJlkpRFMXUQZSVuLg4vL29CQkJoUePHpw/f57GjRsTGhpKs2bNANBoNHh7e/PRRx/xwgsvFHqeNm3a0LZtW77//vsir9WpUyfatm3LggUL9NuaNGlCcHAws2fPLrB/VlYWWVlZ+u9TUlKoXbs2ycnJuLqaT5GeEEIIYWkGztvB2ehUFo1oT/+mNcr1WikpKbi5uRn0+W3RI1l3S07OGy708PAA0Cc19va352etrKywtbVl165dhZ7j8OHDHDt2jOeff77I62RnZ3P48GGCgoLybQ8KCmLPnj2FHjN79mzc3Nz0X7Vr1zb8iQkhhBCiUJk5GsJi84reZbqwnCiKwqRJk+jWrRvNmzcHIDAwEH9/f6ZMmcKNGzfIzs5mzpw5REdHExUVVeh5vv/+e5o0aULXrl2LvFZ8fDwajYYaNfJnyzVq1CA6OrrQY6ZMmUJycrL+68qVK6V8pkIIIYTQOROVgkarUN3Zlhqu5tHpXafSJFljx47lxIkTrFq1Sr/NxsaGdevWcf78eTw8PHB0dGT79u0MGjQIKyurAufIyMhg5cqVxY5i3UmlUuX7XlGUAtt07OzscHV1zfclhBBCiHsTeken96I+g03F2tQBlIVXX32VjRs3smPHDmrVqpXvsXbt2nHs2DGSk5PJzs7Gy8uLTp060b59+wLn+eWXX0hPT2fEiBHFXq969epYWVkVGLWKjY0tMLolhBBCiPKjb0JqZlOFYOEjWYqiMHbsWNavX8/WrVsJCAgocl83Nze8vLwICwvj0KFDDB48uMA+33//PQ8//DBeXl7FXtfW1pZ27dqxZcuWfNu3bNlS7DSjEEIIIcpWqO7OQjNaTkfHokeyxowZw8qVK9mwYQMuLi76kSU3NzccHBwAWLt2LV5eXtSpU4eTJ08yfvx4goODCxStX7hwgR07drBp06ZCr9W3b1+GDBnC2LFjAZg0aRLPPPMM7du3p0uXLixcuJDLly/zv//9rxyfsRBCCCF0MnM0nI9JBaBFLUmyypSufUKvXr3ybV+6dCmjRo0CICoqikmTJhETE4Ovry8jRoxg6tSpBc61ZMkSatasWSD50gkPDyc+Pl7//RNPPEFCQgIzZ84kKiqK5s2bs2nTJvz9/cvmyQkhhBCiWOeiU8nVKlRztMHPzXw6vetUqj5ZlsSYPhtCCCGEKGjF/ku882so3RtW58fnO1XINatsnywhhBBCVB2hZlz0DpJkCSGEEMJC6Yrem0uSJYQQQghRNrJztZyLvlX0LkmWEEIIIUTZOB+TSrZGi5uDDbWqOZg6nEJJkiWEEEIIi3O707ur2XV615EkSwghhBAW5+Qdy+mYK0myhBBCCGFx9CNZZtjpXUeSLCGEEEJYlByNljNmXvQOkmQJIYQQwsKExdwkO1eLi701/p6Opg6nSJJkCSGEEMKihF7Pmyps5me+Re8gSZYQQgghLIy5d3rXkSRLCCGEEBbFEu4sBEmyhBBCCGFBcjVazkSZ93I6OpJkCSGEEMJihMelkZmjxcnWigBPJ1OHUyxJsoQQQghhMXRThc383FCrzbfoHSTJEkIIIYQFCbWQeiyQJEsIIYQQFkR/Z2EtVxNHUjJJsoQQQghhETRahVPXbxW9m/FyOjqSZAkhhBDCIlyMv0lGjgZHWyvqeTmbOpwSSZIlhBBCCIugK3pv6uuKlZkXvYMkWUIIIYSwEKHXLKM/lo4kWUIIIYSwCJbS6V1HkiwhhBBCmD2tVuH0raJ3c1+zUEeSLCGEEEKYvciENG5m5WJvo6a+l3l3eteRJEsIIYQQZk83VdjE1xVrK8tIXywjSiGEEEJUafpO7xbQH0tHkiwhhBBCmD3dnYWWUo8FkmQJIYQQwswpikLodcu6sxAkyRJCCCGEmbucmE5qZi621moa1jD/Tu86kmQJIYQQwqzpi959XLCxkKJ3kCRLCCGEEGbO0pqQ6kiSJYQQQgizdsrCltPRkSRLCCGEEGZLURT9SJYl3VkIkmQJIYQQwoxdvZFBckYONlYqiyp6B0myhBBCCGHGdE1IG/u4YGdtZeJojCNJlhBCCCHMlqVOFYIkWUIIIYQwY6HX84rem1nQcjo6kmQJIYQQwiwpiqKfLpSRLCGEEEKIMnI9OZPEtGys1Soa+7iYOhyjSZIlhBBCCLOkG8VqWMMFexvLKnoHSbKEEEIIYaZuTxW6mjiS0pEkSwghhBBmyVKX09GRJEsIIYQQZufOondJsoQQQgghykhMShbxN7OxUqto6ivThUIIIYQQZUI3itXAy9kii96hFEmWlZUVsbGxBbYnJCRgZWWZL4IQQgghzIul12NBKZIsRVEK3Z6VlYWtre09BySEEEIIYel3FgJYG7rjl19+CYBKpWLx4sU4O99eCVuj0bBjxw4CAwPLPkIhhBBCVDmh1y1/JMvgJGvu3LlA3kjWt99+m29q0NbWlrp16/Ltt9+WfYRCCCGEqFJiUzOJSclCpYKmfpY7kmXwdOHFixe5ePEiPXv25Pjx4/rvL168yLlz5/j777/p1KlTecZawOzZs+nQoQMuLi54e3sTHBzMuXPn8u0TExPDqFGj8PPzw9HRkYEDBxIWFlbgXHv37qVPnz44OTnh7u5Or169yMjIKPLa06dPR6VS5fvy8fEp8+cohBBCVDW6qcL6Xs442ho8HmR2jK7J2rZtG9WqVSuPWIwWEhLCmDFj2LdvH1u2bCE3N5egoCDS0tKAvFG34OBgIiIi2LBhA0ePHsXf359+/frp94G8BGvgwIEEBQVx4MABDh48yNixY1Gri395mjVrRlRUlP7r5MmT5fp8hRBCiKog9FoKYJmLQt+pVOnh1atX2bhxI5cvXyY7OzvfY59//nmZBGaIzZs35/t+6dKleHt7c/jwYXr06EFYWBj79u0jNDSUZs2aATB//ny8vb1ZtWoVL7zwAgATJ05k3LhxvPXWW/pzNWzYsMTrW1tbGzx6lZWVRVZWlv77lJQUg44TQgghqprKcGchlGIk67///qNx48bMnz+fzz77jG3btrF06VKWLFnCsWPHyiFEwyUn5/2jeHh4AOiTGnt7e/0+VlZW2NrasmvXLgBiY2PZv38/3t7edO3alRo1atCzZ0/948UJCwvDz8+PgIAAhg0bRkRERJH7zp49Gzc3N/1X7dq1S/08hRBCiMpM3+ndguuxoBRJ1pQpU3jttdcIDQ3F3t6edevWceXKFXr27Mljjz1WHjEaRFEUJk2aRLdu3WjevDkAgYGB+Pv7M2XKFG7cuEF2djZz5swhOjqaqKgoAH1iNH36dEaPHs3mzZtp27Ytffv2LbR2S6dTp0788MMP/P333yxatIjo6Gi6du1KQkJCoftPmTKF5ORk/deVK1fK+BUQQgghLF/8zSyikjNRqaBZVRvJOnPmDCNHjgTypssyMjJwdnZm5syZfPTRR2UeoKHGjh3LiRMnWLVqlX6bjY0N69at4/z583h4eODo6Mj27dsZNGiQ/u5IrVYLwEsvvcSzzz5LmzZtmDt3Lo0bN2bJkiVFXm/QoEE88sgjtGjRgn79+vHnn38CsHz58kL3t7Ozw9XVNd+XEEIIIfLTjWIFVHfC2c5yi96hFEmWk5OTfhrOz8+P8PBw/WPx8fFlF5kRXn31VTZu3Mi2bduoVatWvsfatWvHsWPHSEpKIioqis2bN5OQkEBAQAAAvr6+ADRt2jTfcU2aNOHy5csGx+Dk5ESLFi2KHf0SQgghRPFOXc+rWW7uZ9mjWFCKJKtz587s3r0bgAceeIDXXnuNDz74gOeee47OnTuXeYDFURSFsWPHsn79erZu3apPnArj5uaGl5cXYWFhHDp0iMGDBwNQt25d/Pz8CrR+OH/+PP7+/gbHkpWVxZkzZ/RJmxBCCCGMd/KqrtO75SdZRo/Dff7559y8eRPIq2O6efMma9asoUGDBvqGpRVlzJgxrFy5kg0bNuDi4kJ0dDSQl1A5ODgAsHbtWry8vKhTpw4nT55k/PjxBAcHExQUBOR1sH/99deZNm0arVq1onXr1ixfvpyzZ8/yyy+/6K/Vt29fhgwZwtixYwGYPHkyDz30EHXq1CE2NpZZs2aRkpKin0oVQgghhPF0dxY2s+DldHSMTrLq1aun/39HR0fmz59fpgEZY8GCBQD06tUr3/alS5cyatQoAKKiopg0aRIxMTH4+voyYsQIpk6dmm//CRMmkJmZycSJE0lMTKRVq1Zs2bKF+vXr6/cJDw/PNx169epVhg8fTnx8PF5eXnTu3Jl9+/YZNfolhBBCiNtupGVzLSmvEbilt28AUClFrfhchnQtFQylUqk4cuRIpU5YUlJScHNzIzk5WYrghRBCCGBnWBzPfH+Aup6ObH+9t6nDKZQxn98VUraflJTEvHnzcHMrOStVFIVXXnkFjUZTAZEJIYQQwlzcniq0/FEsqKAkC2DYsGF4e3sbtO+rr75aztEIIYQQwtycqiTL6ehUSJKl60VlqNTU1HKKRAghhBDmSjeSVVmSLKNbOOhkZ2dz7tw5cnNzDdr/2rVrJe6zYsWK0oYjhBBCCAuWnJ7D5cR0AJpZ+HI6OkYnWenp6Tz//PM4OjrSrFkzfcPOcePGMWfOnCKP69+/Pzdu3Cjy8ZUrV/Lss88aG44QQgghKoFT1/NGsWp7OODuaGviaMpGqdYuPH78ONu3b8+38HK/fv1Ys2ZNkcd5e3szcOBA0tLSCjy2evVqRo0aZdJleYQQQghhOif1i0JXjqlCKEWS9dtvv/H111/TrVs3VCqVfnvTpk3zLbFztz/++AONRsPgwYPJycnRb//5558ZMWIEH374IRMnTjQ2HCGEEEJUAqG65XQqST0WlCLJiouLK/QuwbS0tHxJ192cnZ3566+/uHbtGsOGDUNRFNauXcvTTz/N+++/z+TJk40NRQghhBCVRGglK3qHUiRZHTp04M8//9R/r0usFi1aRJcuXYo91svLi3/++YdDhw7Rr18/nn76aaZNm8abb75pbBhCCCGEqCRSMnO4GJ9XTlSZRrKMbuEwe/ZsBg4cyOnTp8nNzeWLL77g1KlT7N27l5CQkCKPO3HihP7/P/nkE0aMGMGQIUN46KGH8j3WsmVLY0MSQgghhAU7fWuqsKa7Ax5OlaPoHUqRZHXt2pU9e/bwySefUL9+ff755x/atm3L3r17adGiRZHHtW7dGpVKhaIo+v/+/PPPrF27Ft3KPiqVSjq9CyGEEFWMbqqweSVYFPpORiVZOTk5vPjii0ydOpXly5cbdaGLFy8atb8QQgghqobQSnhnIRiZZNnY2PDrr78ydepUoy9UmRd7FkIIIUTp6ds31KrCSRbAkCFD+O2335g0aVKpLnhn/dWdVCoV9vb21KlTBzs7u1KdWwghhBCW5WZWLhG6oveqPJIF0KBBA95//3327NlDu3btcHJyyvf4uHHjij1eV5tVFBsbG5544gm+++67fM1OhRBCCFH5nIlKQVHAx9UeL5fKNchidJK1ePFi3N3dOXz4MIcPH873mEqlKjHJ+vXXX3nzzTd5/fXX6dixI4qicPDgQT777DOmTZtGbm4ub731Fu+++y6ffvqpseEJIYQQwoKcvKoreq9co1hQiiTrXgvYP/jgA7744gsGDBig39ayZUtq1arF1KlTOXDgAE5OTrz22muSZAkhhBBFSErPJuR8HN0aVMfT2XJHgCrrnYVQiiTrXp08ebLQInh/f39OnjwJ5E0pRkVFVXRoQgghhNm7kpjO97su8vOhK6Rna6jt4cDKFzpT28PR1KGVSuj1ytfpXcfoJOu5554r9vElS5YU+3hgYCBz5sxh4cKF2NrmNRzLyclhzpw5BAYGAnDt2jVq1KhhbGhCCCFEpXXiahILd0Sw6WQU2rz2kthaq7mSmMHj3+1lxQudqOflbNogjZSencuF2JuAJFkA3LhxI9/3OTk5hIaGkpSURJ8+fUo8/ptvvuHhhx+mVq1atGzZEpVKxYkTJ9BoNPzxxx8ARERE8MorrxgbmhBCCFGpaLUK28/HsnBHBPsiEvXbuzeszks96tPA25mnFu8jPC6Nx7/bx4oXOtHYx8WEERvnTFQKWgW8XOzwdq18N7sZnWT9+uuvBbZptVpeeeUV6tWrV+LxXbt2JTIykp9++onz58+jKAqPPvooTz75JC4ueT8YzzzzjLFhCSGEEJVGVq6GDceus2hHBGG3Rnqs1SoebuXH6B71aOJ7u35pzUtdeOb7A5yJSmHYwr38+HwniykiD72Wt5xOZRzFAlApujVt7tG5c+fo1auX1FIZKCUlBTc3N5KTk3F1rXzFfkIIIYyXnJ7DigOXWLY7ktjULACc7ax5slMdRnWti5+7Q6HHJaVnM3LJAY5fTcbF3prlz3WkbZ1qFRl6qUxee5xfDl9lXJ8GTApqbOpwDGLM57e6rC4aHh5Obm6uQfv++OOPdOvWDT8/Py5dugTA3Llz2bBhQ1mFI4QQQliMqzfSmfn7abrO+Y+PN58jNjULH1d73r4/kD1T+vD2/U2KTLAA3B1t+emFTnSoW43UzFyeWbyffREJFfgMSuf2nYWVcyTL6OnCuzu9K4pCVFQUf/75JyNHjizx+AULFvDee+8xYcIEZs2apV8Qulq1asybN4/BgwcbG5IQQghhkUKvJbNwRwR/noxCc6uaPdDHhRd71OPBln7YWhs+FuJib8Py5zoy+odD7L6QwMglB1g4oj09G3mVV/j3JDNHo58KbVHJltPRMXq6sHfv3vm+V6vVeHl50adPH5577jmsrYvP25o2bcqHH35IcHAwLi4uHD9+nHr16hEaGkqvXr2Ij483/llYIJkuFEKIqklRFELOx7FwRwR7wm+PNnVrUJ0Xe9Sje8Pqxa6MUpLMHA2vrDjC1rOx2Fqp+frJNgQ18ymL0MvU0cs3GDJ/D55Othx6t989PeeKZMznt9EjWdu2bSt1YJDXzLRNmzYFttvZ2ZGWlnZP5xZCCCHMVXaulo3H84rZz8WkAmClVvFQS19e6F6vzKbM7G2s+PbpdoxffZS/QqN5ZcUR5j7Rmoda+ZXJ+ctK6PW8ovfmNd0sJsEyltE1WX369CEpKanA9pSUFINaOAQEBHDs2LEC2//66y+aNm1qbDhCCCGEWUvOyOHbkHC6f7yVyWuPcy4mFSdbK17oFsCON3ozb1ibMq9JsrVW89XwNgxpU5NcrcL41Uf55fDVMr3GvQq9WnmbkOoYPZK1fft2srOzC2zPzMxk586dJR7/+uuvM2bMGDIzM1EUhQMHDrBq1Spmz57N4sWLjQ1HCCGEMEuKovD5lvMs3R3Jzay8G8NquNrx7H0BDO9YBzcHm3K9vrWVms8ea4W9jZpVB64wee1xMnM0PN254KorFS09O5fDl/P6blbG5XR0DE6yTpw4of//06dPEx0drf9eo9GwefNmatasWeJ5nn32WXJzc3njjTdIT0/nySefpGbNmnzxxRcMGzbMyPCFEEII83Q6KoWvtl4AoHENF0b3qMfDrYwrZr9XarWKD4e0wM7aimV7Inn3t1AyczS80L3kvpblISNbw4r9l/g2JJz4m9nYWKloXdv8W02UlsFJVuvWrVGpVKhUqkKnBR0cHPjqq68MOtfo0aMZPXo08fHxaLVavL29DY9YCCGEsACnb9UcdahbjZ9f6mKyuiOVSsW0h5riYGvFgu3hzPrzDJk5Gsb2aVhhMWTmaFi5/zILQsKJu9X/q46HI2/f3wQft8rX6V3H4CTr4sWLKIpCvXr1OHDgAF5et28JtbW1xdvbGysrK6MuXr16daP2F0IIISzF2ei84nZzKOxWqVS8MaAxDjZWfL7lPJ/+c56MHA2TgxqXa2yZORpWH7jM/O3h+uaqtao5MK5PQ4a0rYmNVcWN6pmCwUmWv3/eHK5WqzX6Im3atDH4H/HIkSNGn18IIYQwN2ej80aymviYR82RSqViXN+GONhY8cGmM3yzLZz0bA3vPdi0zBOtrFwNPx+8wjfbwolOyQSgprsDY/s04JG2tSp0ytSUjC581zl9+jSXL18uUAT/8MMPF9g3ODhY//+ZmZnMnz+fpk2b0qVLFwD27dvHqVOnZFFoIYQQlYKiKJyJyhvJCvQ1rwWbR/eoh72NmqkbTrF0dySZOVo+CG6OWn3viVZ2rpa1h6/wzdYLXE/OS6583ewZ07sBj7WvhZ21cTNels7oJCsiIoIhQ4Zw8uRJVCoVul6muixY18H9TtOmTdP//wsvvMC4ceN4//33C+xz5coVY8MRQgghzE7czSwS07JRq6Cht3klWQDPdKmLnY0Vb607waoDl8nK0fDxoy2xLuX0XY5Gy7rDV/lq6wWuJWUAeXdSjundgCc61K5yyZWO0UnW+PHjCQgI4N9//9XXZyUkJPDaa6/x6aeflnj82rVrOXToUIHtTz/9NO3bt2fJkiXGhiSEEEKYlbO3RrHqVnfCwdY8E4zH29fG3saKiWuOsf7oNTJzNcx7oo1RU3m5Gi3rj17jq61hXEnMS668XOx4pVd9hnesg72NeT73imJ0krV37162bt2Kl5cXarUatVpNt27dmD17NuPGjePo0aPFHu/g4MCuXbto2DD/XQ27du3C3r7y3mEghBCi6jC3eqyiPNzKDztrNWNXHmHTyWiycg7zzVNtS0yOcjVafjt2na+2hnEpIR2A6s62/K9nfZ7u7F/lkysdo5MsjUaDs7MzkHd34PXr12ncuDH+/v6cO3euxOMnTJjAyy+/zOHDh+ncuTOQV5O1ZMkS3nvvPWPDEUIIIcyObiQr0Mf8pgrvNqCZD4tGtOelHw/z39lYXlh+iIUj2uFoWzBF0GgVNh6/xpf/XeBifN5SeJ5OtrzUsx5Pd/Yv9JiqzOhXo3nz5pw4cYJ69erRqVMnPv74Y2xtbVm4cCH16pXc3Oytt96iXr16fPHFF6xcuRKAJk2asGzZMh5//HHjn4EQQghhZs5E64rezXskS6dXY2+WPtuBF5YfYteFeEYtOciSZzvgbJeXJmi0Cn+cuM6X/4URHpeXXFVztOHFHvUZ0cUfJztJrgqjUnSV6wb6+++/SUtLY+jQoURERPDggw9y9uxZPD09WbNmjUHrFwrjVvEWQghhOXI0Wpq+t5kcjcLON3pT28PR1CEZ7PClREYtOUhqVi6taruzbFQHdofHM+/fMC7E3gTAzcGGF3vUY2TXuvokrCox5vPb6CSrMImJiVSrVs3kzdYsiSRZQghROZ2LTmXAvB0421lzcnqQxX02nryazDNL9pOUnoOdtZqs3Lz+mK721ozuXo9R99XFxb581100Z8Z8fht1r2Zubi7W1taEhobm2+7h4VHsD5GHhwfx8fEGX6dOnTpcunTJmNCEEEIIs6Areg/0cbG4BAugRS03Vr/YmerOdmTlanGxs2ZCv4bsfLMPr/ZtWKUTLGMZNc5nbW2Nv79/ob2wipOUlMRff/2Fm5ubQfsnJCQYfQ0hhBDCHJhrE1JjBPq4smHsfewOi2dAMx/cHCWxKg2jJ1PfffddpkyZwk8//YSHh4fBx40cOdLYSwkhhBAW5/ZIlmWXgtR0d+DxDrVNHYZFMzrJ+vLLL7lw4QJ+fn74+/vj5OSU7/HC1h4szXqHQgghhCXStW9oYsEjWaJsGJ1k3bkOoRBCCCFuu5GWrV8QuVENSbKqOqOTrDvXIRRCCCHEbWdv9ceq7eEgBeLCuLsLdZKSkli8eDFTpkwhMTERyJsmvHbtWpkGJ4QQQliSylKPJcqG0SNZJ06coF+/fri5uREZGcno0aPx8PDg119/5dKlS/zwww/lEacQQghh9vT1WBawnI4of0aPZE2aNIlRo0YRFhaWb0HnQYMGsWPHjjINTgghhLAk+pEsC1lOR5Qvo5OsgwcP8tJLLxXYXrNmTaKjo40OIC4ujpycHKOPA5g9ezYdOnTAxcUFb29vgoODCyxSHRMTw6hRo/Dz88PR0ZGBAwcSFhZW4Fx79+6lT58+ODk54e7uTq9evcjIyCj2+vPnzycgIAB7e3vatWvHzp07S/U8hBBCWD6NVuFcjOUsDC3Kn9FJlr29PSkpKQW2nzt3Di8vryKPW7hwIVlZWQAoisKHH35ItWrV8PHxwd3dnUmTJhnd6iEkJIQxY8awb98+tmzZQm5uLkFBQaSlpemvExwcTEREBBs2bODo0aP4+/vTr18//T6Ql2ANHDiQoKAgDhw4wMGDBxk7dixqddEvz5o1a5gwYQLvvPMOR48epXv37gwaNIjLly8b9RyEEEJUDpcS0sjM0WJvo8bf06nkA0Tlpxhp9OjRSnBwsJKdna04OzsrERERyqVLl5Q2bdoo48ePL/I4tVqtxMTEKIqiKN9++63i5OSkfPbZZ8ru3buVr776SnFzc1O++uorY8PJJzY2VgGUkJAQRVEU5dy5cwqghIaG6vfJzc1VPDw8lEWLFum3derUSXn33XeNulbHjh2V//3vf/m2BQYGKm+99Vah+2dmZirJycn6rytXriiAkpycbNR1hRBCmKc/T1xX/N/8Q3n4q52mDkWUo+TkZIM/v40eyfr000+Ji4vD29ubjIwMevbsSYMGDXBxceGDDz4oLpnT///333/P+++/z6RJk+jatStjx47l008/ZdGiRcZniXdITk4G0Hei142c3Vk7ZmVlha2tLbt27QIgNjaW/fv34+3tTdeuXalRowY9e/bUP16Y7OxsDh8+TFBQUL7tQUFB7Nmzp9BjZs+ejZubm/6rdm3poiuEEJXJ2ai8WZ4mUo8lbjE6yXJ1dWXXrl2sW7eOOXPmMHbsWDZt2kRISEiB7u930y2UefHiRfr27ZvvsT59+hAREWFsOHqKojBp0iS6detG8+bNAQgMDMTf358pU6Zw48YNsrOzmTNnDtHR0URFRQHorzl9+nRGjx7N5s2badu2LX379i20dgsgPj4ejUZDjRo18m2vUaNGkXVpU6ZMITk5Wf915cqVUj9XIYQQ5udMtNRjifyMbuGg06dPH/r06WPUMZs3b8bNzQ0HB4cCReUZGRnF1kCVZOzYsZw4cSLfCJSNjQ3r1q3j+eefx8PDAysrK/r168egQYP0++jqwF566SWeffZZANq0acN///3HkiVLmD17dpHXvHt1dUVRilxx3c7ODjs7u1I/PyGEEOZN7iwUdytVVvPff//x4IMPUr9+fRo0aMCDDz7Iv//+W+JxI0eOJDg4mKtXr/Lff//le2zv3r3Ur1+/NOHw6quvsnHjRrZt20atWrXyPdauXTuOHTtGUlISUVFRbN68mYSEBAICAgDw9fUFoGnTpvmOa9KkSZFF7NWrV8fKyqrAqFVsbGyB0S0hhBCVX2pmDlcS8wYPZCRL6BidZH399dcMHDgQFxcXxo8fz7hx43B1deX+++/n66+/LvI4rVab7+vtt9/O97iPj0+xo0aFURSFsWPHsn79erZu3apPnArj5uaGl5cXYWFhHDp0iMGDBwNQt25d/Pz8CrR+OH/+PP7+/oWey9bWlnbt2rFly5Z827ds2ULXrl2Neg5CCCEs3/lbrRt83exxd7Q1cTTCbBhbVe/n51foXYBff/214uvra+zp7snLL7+suLm5Kdu3b1eioqL0X+np6fp9fv75Z2Xbtm1KeHi48ttvvyn+/v7K0KFD851n7ty5iqurq7J27VolLCxMeffddxV7e3vlwoUL+n369OmT73mvXr1asbGxUb7//nvl9OnTyoQJExQnJyclMjLSoNiNuTtBCCGEeftxb6Ti/+Yfyqgl+00diihnxnx+G12TlZKSwsCBAwtsDwoK4s033yzx+IiICHbt2kVUVBRWVlYEBATQv39/XF2Nn8NesGABAL169cq3fenSpYwaNQqAqKgoJk2aRExMDL6+vowYMYKpU6fm23/ChAlkZmYyceJEEhMTadWqFVu2bMk3fRkeHk58fLz++yeeeIKEhARmzpxJVFQUzZs3Z9OmTUWOfgkhhKi8pB5LFEalKHf0VjDAU089RevWrXn99dfzbf/00085fPgwq1atKvS4tLQ0Ro0axbp16/IurFLh7e1NXFwcDg4OzJkzhzFjxpTyaVielJQU3NzcSE5OLlWCKYQQwnw8umAPhy7d4IthrRncuqapwxHlyJjPb6NHspo0acIHH3zA9u3b6dKlCwD79u1j9+7dvPbaa3z55Zf6fceNG6f//0mTJhEVFcXRo0ext7fnnXfeoX79+kybNo3Vq1fz6quvUq1aNZ588kljQxJCCCFMRlEUzt5q3yA9ssSdjB7JKq64PN+JVap8fa+8vLzYvHkz7dq1A+DGjRv4+fmRkJCAo6Mj33zzDYsXL+bo0aPGhGOxZCRLCCEqhyuJ6XT/eBu2VmpOzRyAjVXp2xEJ81euI1kXL14sVVC5ubn5gnF2diY3N5e0tDQcHR0JCgpi8uTJpTq3EEIIYSq6UawG3s6SYIl8KuynoUOHDnzxxRf677/44gu8vLz0i0rfvHkTZ2fnigpHCCGEKBO65XQCfaU/lsjP6JEsRVH45Zdf2LZtG7GxsfqO6Trr168v9Lg5c+bQv39/1q1bh62tLdHR0Sxfvlz/+J49e7j//vuNDUcIIYQwKX09lo+Ufoj8jE6yxo8fz8KFC+nduzc1atQochmZu7Vt25bQ0FD++OMPsrKy6NOnT74u62PGjKlSdxcKIYSoHM5Ey0iWKJzRSdZPP/3E+vXrSzXq5Ovry+jRo40+TgghhDBHGdkaIuPTAAiUkSxxF6OTLDc3N+rVq1fqC27durVAM9KHH36Yhg0blvqcQgghhCmcj0lFq0B1Z1u8XOxMHY4wM0YXvk+fPp0ZM2aQkZFh1HGxsbF06tSJfv36MXPmTBYuXMi+ffv49NNPadKkCW+88YaxoQghhBAmpe/0LqNYohBGj2Q99thjrFq1Cm9vb+rWrYuNjU2+x48cOVLocePGjcPPz4/ExETs7Ox4/fXXSU1N5dChQ2zdupXHH3+cmjVrMn78+NI9EyGEEKKCnYnKK3oP9JF6LFGQ0UnWqFGjOHz4ME8//bRRhe9//fUXe/bswd3dHYCPPvqIatWq8dVXX9GnTx/mzZvHrFmzJMkSQghhMWTNQlEco5OsP//8k7///ptu3boZdZydnV2+hEytVqPRaMjNzQWga9euREZGGhuOEEIIYRJ3LqcjI1miMEbXZNWuXbtUy8B069aN9957j7S0NHJycnj77bepV68eHh4eAMTFxVGtWjWjzyuEEEKYQkxKFknpOVipVTTwlmbaoiCjk6zPPvuMN954w+hRp08//ZRjx47h7u6Ok5MTy5YtY8GCBfrHz5w5w6hRo4wNRwghhDAJXX+setWdsLexMnE0whwZPV349NNPk56eTv369XF0dCxQ+J6YmFjocfXq1ePEiRPs3r2brKwsOnfuTPXq1fWPS4IlhBDCkpzVFb1LPZYogtFJ1rx580p9MUdHR/r371/q44UQQghzcbt9g9RjicIZnWSNHDmyPOIQQgghLIpuJKuJLKcjimB0TRZAeHg47777LsOHDyc2NhaAzZs3c+rUqTINTgghhDBHWbkawuNuAtKIVBTN6CQrJCSEFi1asH//ftavX8/Nm3k/ZCdOnGDatGllHqAQQghhbsJj08jVKrjaW+PrZm/qcISZMjrJeuutt5g1axZbtmzB1tZWv713797s3bu3TIMTQgghzNGdTUgNbcotqh6ja7JOnjzJypUrC2z38vIiISGh0GNSUlIMPn9penAJIYQQFUnXhLSJFL2LYhidZLm7uxMVFUVAQEC+7UePHqVmzZpFHlNSpq8oCiqVCo1GY2xIQgghRIU6EyXL6YiSGZ1kPfnkk7z55pusXbsWlUqFVqtl9+7dTJ48mREjRhR6zLZt2+45UCGEEMJcyHI6whBGJ1kffPABo0aNombNmiiKQtOmTdFoNDz55JO8++67hR7Ts2fPew5UCCGEMAfxN7OIS81CpYJGNSTJEkUzuvDdxsaGFStWEBYWxs8//8xPP/3E2bNn+fHHH7GyMmxZgZ07d/L000/TtWtXrl27BsCPP/7Irl27jA1HCCGEqFDnbo1i+Xs44mRn9FiFqEKMTrJmzpxJeno69erV49FHH+Xxxx+nYcOGZGRkMHPmzBKPX7duHQMGDMDBwYEjR46QlZUFQGpqKh9++KHxz0AIIYSoQPp6LOmPJUpgdJI1Y8YMfW+sO6WnpzNjxowSj581axbffvstixYtyrfuYdeuXTly5Iix4QghhBAVSl+PJZ3eRQmMTrJ0dwHe7fjx43h4eJR4/Llz5+jRo0eB7a6uriQlJRkbjhBCCFGhdD2ymsidhaIEBk8mV6tWDZVKhUqlolGjRvkSLY1Gw82bN/nf//5X4nl8fX25cOECdevWzbd9165d1KtXz/DIhRBCiAqWq9FyPiZvNqeJTBeKEhicZM2bNw9FUXjuueeYMWMGbm5u+sdsbW2pW7cuXbp0KfE8L730EuPHj2fJkiWoVCquX7/O3r17mTx5Mu+9917pnoUQQghRASIT0sjO1eJka0Wtag6mDkeYOYOTrJEjRwIQEBDAfffdh7V16e6oeOONN0hOTqZ3795kZmbSo0cP7OzsmDx5MmPHji3VOYUQQoiKcCYqrx6rsY8LarUspyOKZ3SmVBY9rz744APeeecdTp8+jVarpWnTpjg7O9/zeYUQQojydOeahUKUxOjC93u1fPly0tLScHR0pH379nTs2FESLCGEEBbhbJSsWSgMV+FJ1uTJk/H29mbYsGH88ccf5ObmVnQIQgghRKncbt8gI1miZBWeZEVFRbFmzRqsrKwYNmwYvr6+vPLKK+zZs6eiQxFCCCEMlpyRw7WkDCCvJkuIkhiVZOXm5mJtbU1oaGipL2htbc2DDz7IihUriI2NZd68eVy6dInevXtTv379Up9XCCGEKE+65XRqujvgam9Twt5CGFn4bm1tjb+/PxqNpkwu7ujoyIABA7hx4waXLl3izJkzZXJeIYQQoqzdbkIqo1jCMEZPF7777rtMmTKFxMTEUl80PT2dFStWcP/99+Pn58fcuXMJDg6+pxEyIYQQojzp2jfImoXCUEa3cPjyyy+5cOECfn5++Pv74+TklO/xktYfHD58OL///juOjo489thjbN++na5duxobhhBCCFGhbrdvkJEsYRijk6zg4OB7uqBKpWLNmjUMGDCg1A1NhRBCiIqk1Sr6miwZyRKGMjrLmTZt2j1dcOXKlfr/z8zMxN7e/p7OJ4QQQpS3KzfSSc/WYGetpq6no6nDERaiwls4aLVa3n//fWrWrImzszMREREATJ06le+//76iwxFCCCFKpKvHalTDBWurCv/oFBbKoJ8UDw8P4uPjAahWrRoeHh5FfpVk1qxZLFu2jI8//hhbW1v99hYtWrB48eJSPg0hhBCi/OjrsaQ/ljCCQdOFc+fOxcUl7wdr3rx593TBH374gYULF9K3b1/+97//6be3bNmSs2fP3tO5hRBCiPKgW05HOr0LYxiUZI0cObLQ/79bXFxciee6du0aDRo0KLBdq9WSk5NjSDhCCCFEhdL3yJKRLGGEe55YVhSFTZs2MXToUGrVqlXi/s2aNWPnzp0Ftq9du5Y2bdrcazhCCCFEmUrLyuVSYjogy+kI45S6h0JERARLlixh+fLl3Lx5kwceeIDVq1eXeNy0adN45plnuHbtGlqtlvXr13Pu3Dl++OEH/vjjj9KGI4QQQpSLczGpKAp4u9jh6Wxn6nCEBTEqycrMzOSXX35h8eLF7Nu3j/79+xMVFcWxY8do3ry5Qed46KGHWLNmDR9++CEqlYr33nuPtm3b8vvvv9O/f/9SPQkhhBCivEg9ligtg5OsV155hdWrV9O4cWOefvpp1q1bh6enJzY2NqjVxs06DhgwgAEDBhgdrBBCCFHRpB5LlJbB2dHChQt5+eWX+eeffxgzZgyenp7lGZdBZs+eTYcOHXBxccHb25vg4GDOnTuXb5+YmBhGjRqFn58fjo6ODBw4kLCwsHz79OrVC5VKle9r2LBhxV57+vTpBY7x8fEp8+cohBDCtG6PZEmSJYxjcJL1ww8/cODAAXx9fXniiSf4448/yM3NNejYknprGdNn604hISGMGTOGffv2sWXLFnJzcwkKCiItLQ3IK8oPDg4mIiKCDRs2cPToUfz9/enXr59+H53Ro0cTFRWl//ruu+9KvH6zZs3yHXPy5Emj4hdCCGHeFEXhjL5HlkwXCuMYPF345JNP8uSTTxIZGcnSpUsZM2YM6enpaLVaTp8+TdOmTYs89l57axVl8+bN+b5funQp3t7eHD58mB49ehAWFsa+ffsIDQ2lWbNmAMyfPx9vb29WrVrFCy+8oD/W0dHR6JEoa2trg4/JysoiKytL/31KSopR1xJCFC0zR8OiHRF0qe9J+7rG/bEmRHGuJ2eSmpmLtVpFfS9nU4cjLIzRdxfWrVuXGTNmMH36dP7++2+WLFnC008/zYQJExg6dChffvllgWOK661VlpKTkwH0I2K6pObO9RGtrKywtbVl165d+ZKsFStW8NNPP1GjRg0GDRrEtGnT9A1YixIWFoafnx92dnZ06tSJDz/8kHr16hW67+zZs5kxY8Y9PT8hROE+++cci3ZepO4RR7a/3tvU4YhK5GxU3h/EDbydsbWW5XSEcUr9E6NSqRg4cCA///wz169fZ/LkyYSEhJRlbEZRFIVJkybRrVs3/Z2OgYGB+Pv7M2XKFG7cuEF2djZz5swhOjqaqKgo/bFPPfUUq1atYvv27UydOpV169YxdOjQYq/XqVMnfvjhB/7++28WLVpEdHQ0Xbt2JSEhodD9p0yZQnJysv7rypUrZffkhajCjly+wfe7LgIQmZDOpYS0Eo4QwnBno2/VY0nRuygFlaIoiqmDKAtjxozhzz//ZNeuXfmaoh4+fJjnn3+e48ePY2VlRb9+/fR3Q27atKnQcx0+fJj27dtz+PBh2rZta9D109LSqF+/Pm+88QaTJk0qcf+UlBTc3NxITk7G1VXm+YUojcwcDQ9+tYsLsTf1294f3IxnutQ1XVCiUhm78gh/nIjirUGB/K9nfVOHI8yAMZ/flWLs89VXX2Xjxo1s27atQNf5du3acezYMZKSkoiKimLz5s0kJCQQEBBQ5Pnatm2LjY1NgbsQi+Pk5ESLFi2MOkYIcW++/C+MC7E38XKx48UeeVP1IefjTRyVqExkJEvcC4tOshRFYezYsaxfv56tW7cWmzi5ubnh5eVFWFgYhw4dYvDgwUXue+rUKXJycvD19TU4lqysLM6cOWPUMUKI0jt5NZnvdkQAMCu4OQ+38gNgb3g82blaU4YmKonMHA0RcXmjpE2kEakoBYtOssaMGcNPP/3EypUrcXFxITo6mujoaDIyMvT7rF27lu3bt+vbOPTv35/g4GCCgoIACA8PZ+bMmRw6dIjIyEg2bdrEY489Rps2bbjvvvv05+nbty9ff/21/ntdDdrFixfZv38/jz76KCkpKRVW5C9EVZadq+X1X46j0So81MqPAc18aOrriqeTLWnZGg5fumHqEEUlcCH2JloFqjna4O0iy+kI45V67cJ7cfDgQdauXcvly5fJzs7O99j69esNPs+CBQuAvGaid1q6dCmjRo0CICoqikmTJhETE4Ovry8jRoxg6tSp+n1tbW3577//+OKLL7h58ya1a9fmgQceYNq0aVhZWen3Cw8PJz7+9jTE1atXGT58OPHx8Xh5edG5c2f27duHv7+/wfELIUrn620XOBudiqeTLTMezmvPolar6N6wOr8du86OsDi61Dd9w2Rh2c5E3e6PpVKpTByNsESlauHw3HPPMWrUKOrUqWP0BVevXs2IESMICgpiy5YtBAUFERYWRnR0NEOGDDHqXIbU7I8bN45x48YV+Xjt2rUNuisyMjIy3/eGLIYthCh7p6+nMH/bBQBmDG6Gh5Ot/rGejb347dh1Qs7F8ebAQFOFKCoJfT2WdHoXpWT0dOFrr73Ghg0bqFevHv3792f16tX5mmyW5MMPP2Tu3Ln88ccf2Nra8sUXX3DmzBkef/zxUiVtQoiqI0eTN02Yq1UY2MyHB1rkr4Hs3tALgNNRKcSlGv57SYjC3F6zUOqxROkYnWS9+uqrHD58mMOHD9O0aVPGjRuHr68vY8eO5ciRIyUeHx4ezgMPPACAnZ0daWlpqFQqJk6cyMKFC41/BkKIKuO7kHBOXU/B3dGG94ObF5jCqe5sRzO/vA/EnWFxpghRVBKKonDm1pqFUvQuSqvUhe+tWrXiiy++4Nq1a0ybNo3FixfToUMHWrVqxZIlS4qcyvPw8CA1Ne8Ht2bNmoSGhgKQlJREenp6acMRQlRy52NS+fK/vGnC6Q81w6uIQuSejfJGs0LOS5IlSi/uZhaJadmoVdCwhiynI0qn1ElWTk4OP//8Mw8//DCvvfYa7du3Z/HixTz++OO88847PPXUU4Ue1717d7Zs2QLA448/zvjx4xk9ejTDhw+nb9++pQ1HCINsDo1m0Y4INNpK0YO3ysjVaHl97XGyNVr6BnozuLVfkfv2uJVk7QyLRyv/zqKUzt4axQqo7oS9jVUJewtROKML348cOcLSpUtZtWoVVlZWPPPMM8ydO5fAwNtFpkFBQfTo0aPQ47/++msyMzOBvKVmbGxs2LVrF0OHDs13158QZS07V8uENUfJzNFyJjqFTx5thZVa7hiyBN/vusjxq8m42FvzwZAWxd7p1bZONZxsrUhMy+bU9RRa1HKrwEhFZaGrxwqUqUJxD4xOsjp06ED//v1ZsGABwcHB2NjYFNinadOmDBs2rNDjdYs3A6jVat544w3eeOMNY8MQwmino1LIzMlrUrn+yDXUKhUfP9IStSRaZi087iafbTkPwNQHm+LjZl/s/rbWaro2qM6W0zHsCIuTJEuUim4kq4l0ehf3wOgkKyIiosReUE5OTixdurTYfWJjY4mNjUWrzd+ZuWXLlsaGJIRBjl7Oa1BZ092B6JRMfjl8FSuVitlDW0iiZaY0WoU3fjlBdq6WHo28eKxdrZIPIm/KcMvpGELOxTGmd4NyjlJURmf0y+nISJYoPaOTrHtttnn48GFGjhzJmTNnChTHq1QqNBrNPZ1fiKIcvZwEwPCOtanj6cSE1UdZc+gKarWKD4KbS6JlhpbvieTwpRs421kze2jx04R36nmrlcORyzdIzczBxb7giLsQRcnRaLkQKz2yxL0zKMmqVq2awb/cEhMTi3382WefpVGjRnz//ffUqFFDuuiKCnPk1khWmzrVuK9BdbRahUk/H2PVgctYqeH9wQVbAgjTuZSQxsd/nwVgyv2B1HR3MPjYOp6OBFR34mJ8GnvCExjQzKe8whSVUERcGjkaBRc7a6N+7oS4m0FJ1rx588rsghcvXmT9+vU0aCBD+KLixKZmcvVGBioVtLxVoxPcpiZaReG1tcf5ad9l1CoVMx5uJomWGdDemibMzNHStb4nT3Y0vlFxj4bVuRifRsj5OEmyhFFuF727yO8DcU8MSrLKctHjvn37cvz4cUmyRIU6dmuqsHENl3xTR0Pb1kKrwOu/HOeHvZdQq1RMe6ip/GI1sRX7L7H/YiIONlZ89EjLUv179GjkxfK9l9hxPg5FUeTfVBhM14RU6rHEvTIoyUpJScHV1VX//8XR7VeUxYsXM3LkSEJDQ2nevHmBuxMffvhhQ0ISwihHryQB0KaOe4HHHm1XK2/kZN0Jlu2JRK1SMfXBJvKhbCJXEtOZ/VfeNOGbAxtT28OxVOfpXM8TWys1V29kcDE+jXpe0lBSGObOkSwh7oXBNVlRUVF4e3vj7u5e6IeP7i/FkgrX9+zZw65du/jrr78KPCaF76K8HLl0qx6rdrVCH3+8Q220isJb60+yZPdFrNTw9v2SaFU0RVF4+9eTpGdr6FjXgxFd6pb6XE521rSvW4094QmEnI+TJEsY7KyMZIkyYlCStXXrVn1/q23btt3TBceNG8czzzzD1KlTqVGjxj2dSwhD5Gq0nLiaDBQ+kqUzrGMdNIrCO7+GsmjnRdRqFW8NDJREqwKtOXiFnWHx2Fmr+ejRe+9h1qORF3vCE9hxPo5n7wsooyhFZXYjLZvolLyG2Y2lR5a4RwYlWT179iz0/0sjISGBiRMnSoIlKsy5mFQycjS42FtTv4TRjKc6+aPVKkzdcIrvQiKwUql4fUBjSbQqQFRyBh/8eQaA1wc0JqC60z2fs2cjL+b8dZZ9EYlk5Wqws5blUUTxzt7qj1XHwxFnO6O7HAmRT6l/gtLT07l8+TLZ2dn5tpfUTHTo0KFs27aN+vXrl/bSQhhF1x+rdW13g0ZGnulSF60C0zaeYv72cKzUKib1bySJVjlSFIW3158kNSuXNnXcy2zUKdDHBW8XO2JTszgUeYP7GlQvk/OKyktfjyWjWKIMGJ1kxcXF8eyzzxZaUwWUWFPVqFEjpkyZwq5du2jRokWBwvdx48YZG5IQxbqzP5ahRnati0arMPOP03y19QJqlYqJ/RuVV4hV3voj19h2Lg5bazWfPNqyzNaUVKlUdG/oxbojVwk5HydJliiRvh5L1iwUZcDoJGvChAncuHGDffv20bt3b3799VdiYmKYNWsWn332WYnHL168GGdnZ0JCQggJCcn3mEqlkiRLlDld+4bi6rEK81y3ALSKwqw/z/DFf2FYqVWM69uw7AOs4mJTMpnx+ykAJvRrSAPvsh1B6NGoOuuOXGXH+Tjevr9JmZ5bVD66kSxZs1CUBaOTrK1bt7JhwwY6dOiAWq3G39+f/v374+rqyuzZs3nggQeKPf7ixYulDlYIY91IyyYiPg2ANrXdjT7+he710GgVZv91ls+3nMdKrZK18MqQoii881soKZm5tKjpxovd65X5Nbo39EKlyqu1iUnJpIZr8QtMi6pLo1U4FyMjWaLsqI09IC0tDW9vbwA8PDyIi4sDoEWLFhw5cqRsoxPiHh27mgRAPS8n3B1tS3WOl3rW542BjQH45O9zzN9+oazCq/J+PxHFltMx2Fip+OSxllhbGf0rqUQeTra0rJnX5T/kfFyZn19UHpcS0sjM0eJgY0WdUvZnE+JORo9kNW7cmHPnzlG3bl1at27Nd999R926dfn222/x9fUt8fhJkyYVul2lUmFvb0+DBg0YPHiwvmWEEPfiaAn9sQz1Sq8GKEpekvXx5nNYqVS81FNu3rgX8TezmLYhFICxvRuWa0+iHo28OH41mR3n43i8fe1yu46wbLo7Cxv5uJRZXaCo2kpVkxUVFQXAtGnTGDBgACtWrMDW1pZly5aVePzRo0c5cuQIGo2Gxo0boygKYWFhWFlZERgYyPz583nttdfYtWsXTZs2NfoJCXGn4jq9G2tM7wZotAqfbznP7L/OYqVW8UI5TG9VFdM2nOJGeg5NfF15pXf5Jqw9G3nx1dYL7LoQj0aryAeoKNTZKKnHEmXL6CTrqaee0v9/mzZtiIyM5OzZs9SpU4fq1Uu+c0c3SrV06dJ8S/U8//zzdOvWjdGjR/Pkk08yceJE/v77b2PDE0JPq1X0Re9tjbizsDjj+jZEo1X44r8wZv15BpVKxfPdpMmlsf46GcWfJ6OwVqv45NGW2JTDNOGdWtd2x8XemqT0HE5cTTLqTlNRdZyJ1nV6lyRLlI17/s3m6OhI27ZtDUqwAD755BPef//9fGscurq6Mn36dD7++GMcHR157733OHz48L2GJqq4C3E3Sc3KxdHWikY1ym5JlQn9GvJqn7zi9/f/OM2y3XIzhzES07KZemua8OVe9Wl+q16qPFlbqbmvft7vqB3n48v9esIy3V6zUIreRdkwKslKS0vjvffeo3nz5jg7O+Pi4kLLli2ZOXMm6enpBp0jOTmZ2NjYAtvj4uL0i0+7u7sXaHIqhLGO3uqP1bKWW5kWVKtUec1JX+mVN8U1/ffT/Lg3sszOX9nN/P0U8TezaVTDmbF9Ku5OzZ6NvQDYESbF76Kg1MwcriRmADKSJcqOwdOF2dnZ9OzZk9DQUAYNGsRDDz2EoiicOXOGDz74gL/++osdO3YUaC56t8GDB/Pcc8/x2Wef0aFDB1QqFQcOHGDy5MkEBwcDcODAARo1ksaP4t4c1ffHKvupIdWt5XY0isJ3IRFM3XAKtVrFU538y/xalcm/p2P47dh11Cr45NFWFbrMTY9GeUnW0cs3SE7Pwc2x+N9Vomo5d2uq0NfNvtR3IgtxN4OTrAULFnD16lWOHz9O48aN8z129uxZevXqxbfffsurr75a7Hm+++47Jk6cyLBhw8jNzc0LwtqakSNHMnfuXAACAwNZvHixsc9FiHyOlnE91t1UqrwFpLVahUU7L/LOr6GoVSqGd6xTLtezdMnpObz960kARveoR6tS9C27FzXdHajv5UR4XBq7w+O5v0XJd0OLqkPqsUR5MHgOZf369UydOrVAggV5SdE777zDL7/8UuJ5nJ2dWbRoEQkJCfo7DRMSEli4cCFOTnkLwrZu3ZrWrVsb/iyEuEtKZg7nY/N+abYuxw9zlUrF2/c34blba+1NWX+S9Ueultv1LNmsP08Tm5pFPS8nJvYzzUh1z0Z5Pf52SL8scRfdnYVSjyXKksFJ1unTp+nVq1eRj/fu3ZvTp08bfGFnZ2datmxJq1atcHYuu6JkIQBOXElGUaC2hwNeLnblei2VSsXUB5swqmtdAD7cdAZFUcr1mpbmbHQKaw9fRaWCTx5tib1NxU0T3qlHI13xe5z8G4l8zspIligHBk8XJiUl4enpWeTjnp6eJCcnF/rY0KFDWbZsGa6urgwdOrTY66xfv97QkIQokq7o/V6bkBpKpVIx5f5AVh+8TPzNbC7E3qRhDfllrfPNtnAAHmjhSzt/0zUa7hTgia21muvJmfJvJPS0WkVfk9VERrJEGTJ4JEur1WJlVfRfn2q1Go1GU+hjbm5uqFQq/f8X9yVEWdA1IW1bBk1IDWVnbaWv/9p3MbHCrmvuIuJu8seJ6wAmX/fRwdaKTgF5SZ4ssSN0riVlcDMrF1srNQHVnUwdjqhEDB7JUhSFvn37Ym1d+CG6IvbCLF26tND/F6I8KIpyeySrgptOdgrwZE94AvsjEnims9xpCLBgeziKAv2a1DCLUYKejbzYGRbPjrB46dgvADhzqx6rgbdzuTfGFVWLwUnWtGnTStznkUceKXGfjIwMFEXB0TFv8c1Lly7x66+/0rRpU4KCggwNR4giRSakcyM9B1trdYV/qHeulzdKsi8iEUVR9CO4VdXVG+n8evQaQIX2xCpOj0Ze8OcZ9kckkJmjMVl9mDAf+nosX5k+FmWrTJMsQwwePJihQ4fyv//9j6SkJDp27IitrS3x8fF8/vnnvPzyy2VyHVF16UaxWtR0w9a6Yv8qbVXbHVtrNfE3s4iIT6O+V9W+qeO7kAhytQrdG1Yv17s8jdHQ2xlfN3uikjPZfzGRnrf6Z4mqS9fpvUk5LlIuqiajP4FOnTpV5GObN28u8fgjR47QvXt3AH755Rd8fHy4dOkSP/zwA19++aWx4QhRwO3+WO4Vfm17Gyva3Eom9kdU7bqs2JRM1hy6Api+FutOKpWKHg1vdX+XuiwBnI2SkSxRPoxOstq3b89XX32Vb1tWVhZjx45lyJAhJR6fnp6Oi0veD/I///zD0KFDUavVdO7cmUuXLhkbjhAFHDFRPZZOp3p5d+Huv5hgkuubi0U7I8jO1dKhbjV9sbm50HV/l+J3kZGt4WJCGgCBMpIlypjRSdaKFSuYMWMGgwYNIjo6mmPHjtGmTRu2bt3K7t27Szy+QYMG/Pbbb1y5coW///5bX4cVGxubb9FoIUojPTtXX1/RxgQjWXBnXVZCle3FlJiWzU/7LgN5o1jmVpvWrUF11Cq4EHuT60kZpg5HmND5mFQUBao725V7Tz1R9RidZA0dOpQTJ06Qm5tL8+bN6dKlC7169eLw4cO0bdu2xOPfe+89Jk+eTN26denUqRNdunQB8ka12rRpY/wzEOIOJ68mo9Eq+Lja4+vmYJIY2taphq2VmpiULC4lGLZwemWzdPdFMnI0tKjpZpY1T26ONvoaMZkyrNr09VgyVSjKQamqgjUaDdnZ2Wg0GjQaDT4+PtjZGfYXwKOPPsrly5c5dOhQvhquvn376tcuFKK09P2x/N1NFoO9jRWtauf1fKuKU4YpmTks2xMJmOcolo5MGQqAM1HS6V2UH6OTrNWrV9OyZUvc3Nw4f/48f/75JwsXLqR79+5EREQYdA4fHx/atGmDWn378h07diQwMNDYcITI58iliu30XpTOt+qy9lXB4vcf914iNTOXRjWcCWpaw9ThFEmXZO26EE+uRmviaISp6EaypB5LlAejk6znn3+eDz/8kI0bN+Ll5UX//v05efIkNWvWlEWdhUkpiqIfyTJVPZZOp4Bbxe9VrC4rPTuXxTvz/tga07sBarV5jmIBtKrljpuDDamZuRy/mmTqcIQJhMWkcvjWH2bNakqSJcqe0UnWkSNHCvSyqlatGj///DPffPNNmQUmhLGuJWUQl5qFtVpF85qmXaKprb871moV15MzuXqj6hRWr9x/mRvpOdT1dOSBFr6mDqdYVmoV3RrmLRgdcj7exNGIiqbVKry1/iQ5GoV+TWrQWNaxFOXA6CSrcePG+b6/86/0Z5555t4jEqKUdP2xmvm5mryLt6OtNa1uFVbvjagadVmZORoW7sgbxXq5V32sLWB5kp4NpS6rqlqx/xKHL93A2c6a94ObmW3toLBs9/xb0M7OjjNnzpRFLELcE1P3x7qbrjdUVWlK+svhq8SmZuHnZs+QNrVMHY5BdHVZJ64mcSMt28TRiIoSlZzBR5vPAfDmwMYmuxNZVH4GL6szadKkQrdrNBrmzJmDp2deDcrnn39eNpEJYSTdSJap67F0OtXzZP728Cpxh2GORsuC7eEAvNSzfoUvZ1RaPm72NK7hwrmYVHZdiOehVn6mDkmUM0VRmPrbKW5m5dLOvxpPdZKF3EX5MTjJmjdvHq1atcLd3T3fdkVROHPmDE5OTjLcKkwmK1fD6et5dwmZ+s5CnXb+1bBSq7h6I4OrN9KpVc3R1CGVmw3HrnMtKYPqznY80aG2qcMxSo9G1TkXk0rI+ThJsqqAv0Kj+fdMDDZWKuYMbWHWN2cIy2dwkvXBBx+waNEiPvvsM/r06aPfbmNjw7Jly2jatGm5BCiEIUKvpZCt0VLd2ZbaHuYx9O9sZ02Lmm4cu5LE/ohEarWrnEmWRqswf9sFAEZ3DzB5PZyxejbyZtHOi+wMi0NRFPljsRJLTs/hvQ156+++0qsBDaXYXZQzg8f0p0yZwpo1a3j55ZeZPHkyOTk55RmXEEY5eqseq3Xtamb1Idnp1hI7lXnKcNPJKCLi03B3tOGpzpY39dK+bjXsbfI69J+LSTV1OKIczf7rDPE3s2jg7cwrveubOhxRBRhVONGhQwcOHz5MXFwc7du35+TJk2b1gSaqLnPpj3W3zrp+WRcrZ/G7Vqvwza1RrGe7BuBsZ/DguNmwt7HSN48NOSd3GVZWe8MTWH3wCgCzh7bAztqyRlyFZTK6OtXZ2Znly5czZcoU+vfvj0ajKY+4DDJ79mw6dOiAi4sL3t7eBAcHc+7cuXz7xMTEMGrUKPz8/HB0dGTgwIGEhYXl26dXr16oVKp8X8OGDSvx+vPnzycgIAB7e3vatWvHzp07y/T5CcMdM7Oid532dauhVsGlhHSikitfv6z/zsZyNjoVZztrRnWta+pwSk23vuKOMEmyKqPMHA1v/3oSgKc61aFDXQ8TRySqilLfAjRs2DAOHTrE+vXr8fc3zRRBSEgIY8aMYd++fWzZsoXc3FyCgoJIS0sD8oryg4ODiYiIYMOGDRw9ehR/f3/69eun30dn9OjRREVF6b++++67Yq+9Zs0aJkyYwDvvvMPRo0fp3r07gwYN4vLly+X2fEXhYlIyuZaUgVqV18XbnLjY2+gbo1a2Vg6KovD1rVGsZ7r44+ZoY+KISk/XyuHgxRukZ+eaOBpR1r7aGsbF+DRquNrx5iBZvk1UnHsa269Vqxa1apmuH86dC0wDLF26FG9vbw4fPkyPHj0ICwtj3759hIaG0qxZMyBv9Mnb25tVq1bxwgsv6I91dHTEx8fH4Gt//vnnPP/88/pzzJs3j7///psFCxYwe/bsAvtnZWWRlZWl/z4lJcWo52qoK4np/LA3kowcDbOCW5TLNcyNrh6rsY8rTmY4XdUpwIMTV5PZfzGB4DY1TR1Omdl1IZ7jV5Kwt1HzfLcAU4dzT+pVd6KmuwPXkjLYH5FI70BvU4dUZhRF4af9l8nO1TKsQ22zfI+UpzNRKXwXktckd+bg5rjaW+4fA8LyWEYzGwMlJycD4OGRNxSsS2rs7e31+1hZWWFra8uuXbvyHbtixQqqV69Os2bNmDx5MqmpRRfAZmdnc/jwYYKCgvJtDwoKYs+ePYUeM3v2bNzc3PRftWuXz23uadm5LNp5kZ8PXSU1s2rcnGBu/bHudnsdw8o1kvX11rxRrOEd61Dd2c7E0dwblUpFz8aVs/v7xuPXmfpbKO//cZruH2/j25DwKjNap9EqvLXuBLlahYHNfBjQzPA/pIUoC5UmyVIUhUmTJtGtWzeaN28OQGBgIP7+/kyZMoUbN26QnZ3NnDlziI6OJioqSn/sU089xapVq9i+fTtTp05l3bp1DB06tMhrxcfHo9FoqFGjRr7tNWrUIDo6utBjpkyZQnJysv7rypUrZfCsC2pcw4V61Z3IztWy9WxsuVzD3OiTrFvL2JibDgEeqFQQEZ9GbEqmqcMpEwcjE9l/MRFbKzUv9qhn6nDKRI9bS+zsqERJVlxqFtM25rUscLW3JjEtmzl/naXHx9tYtCOCjGzT1dRWhOV7Ijl+NRkXe2tmDG5m6nBEFVRpkqyxY8dy4sQJVq1apd9mY2PDunXrOH/+PB4eHjg6OrJ9+3YGDRqEldXtO0tGjx5Nv379aN68OcOGDeOXX37h33//5ciRI8Ve8+47K4vrsWNnZ4erq2u+r/KgUqm4/9bCvJtORpWwt+XL0Wg5cS0JgLb+5tGE9G5uDjY09c37995XSe4y1I1iPdKuVqVZkqRrA0+s1Coi4tO4kphu6nDKxLSNoSSl59DU15UD7/Tj08daUcfDkfib2Xyw6QzdP97G97sukplT+ZKtqzfS+fSfvBuhpgxqQg1X+xKOEKLsVYok69VXX2Xjxo1s27atQI1Yu3btOHbsGElJSURFRbF582YSEhIICCi6hqRt27bY2NgUuAtRp3r16lhZWRUYtYqNjS0wumUKg1rkDYlvPxdHWlblnhY4G5VKZo4WNwcbAjydTB1OkW5PGVp+v6wTV5MIOR+HlVrFyz0rT68hV3sb2t6acq4MU4abTkax6WQ01moVnzzWEnsbKx5tV4v/XuvJx4+0pFY1B+JvZvH+H6fp8fE2lu2uPMmWoii8+1so6dkaOtb1YJiFrUIgKg+LTrIURWHs2LGsX7+erVu3Fps4ubm54eXlRVhYGIcOHWLw4MFF7nvq1ClycnLw9fUt9HFbW1vatWvHli1b8m3fsmULXbt2Ld2TKUNNfV3x93QkqwpMGR69omtC6m7Wy2N0vtWUdF8lSLJ0fbEGt/Kjjmfl6mKvb+Vg4UlWYlo2U38LBeCVXvVp5uemf8zGSs3jHWqz9bVezB7agpruDsSmZjH999P0+mQ7P+67RFauZSdbG49fZ/u5OGyt1Mx+RJbOEaZj0UnWmDFj+Omnn1i5ciUuLi5ER0cTHR1NRsbtfkRr165l+/bt+jYO/fv3Jzg4WF+0Hh4ezsyZMzl06BCRkZFs2rSJxx57jDZt2nDffffpz9O3b1++/vpr/feTJk1i8eLFLFmyhDNnzjBx4kQuX77M//73v4p7AYpw55ThX6GVe8rQ3IvedTreqssKj0sjLjWr5APM1LnoVP4+FYNKRaXsmK1r5bAnPIEcjdbE0ZTe9I2nSEjLpnENF8b2aVjoPrbWaoZ3rMO2yb2YFdwcXzd7olMymfpbKL0/2c6K/ZfIzrW81+BGWjYzfz8NwKt9GlDfy9nEEYmqzKKTrAULFpCcnEyvXr3w9fXVf61Zs0a/T1RUFM888wyBgYGMGzeOZ555Jl/dlq2tLf/99x8DBgygcePGjBs3jqCgIP799998dVvh4eHEx8frv3/iiSeYN28eM2fOpHXr1uzYsYNNmzaZrGfY3e5vnpdkbT0bW6nvJDpyq31D2zrmWY+l4+5oS+Nb66QdsOC6rPnb80axBjX3oYF35Vv3rbmfGx5OttzMyuXIpRumDqdU/jkVzcbj11Gr4ONHW2JrXfyveVtrNU939mf7672YObgZNVztuJ6cyTu/htL70+2sPnDZohLOWX+e0SeYL1Wi6WxhmVSKoiimDqIqSklJwc3NjeTk5HIpglcUhR6fbONKYgbzn2qrH9mqTBJuZtFu1r8AHJ8WhJuDefe/mb7xFMv2RPJMZ3/eD25u6nCMFhmfRp/PtqNV4I9Xu+mbrFY241cfZcOx64zpXZ/XB1hW48rk9Bz6zQ0hLjWL//Wsz1ulaLyZmaNh1YHLzN8erh91re3hwKt9GjK0TU2srcz3b/NdYfE8/f1+VCpY93JXs//jS1gmYz6/zffdIu6JSqXSj2ZV1rsMj91ar7CBt7PZJ1hwuy7LUheLXrA9HK0CfQK9K22CBXe2cogvYU/zM/OP08SlZlHfy4kJ/QqfJiyJvY0Vz94XwM43evPuA02o7mzLlcQM3vjlBP0+D2Hd4avkmuHIVkb27aVzRnapKwmWMAuSZFVig1rcnjKsLHcN3cnc+2PdreOtOwzPx9wkMS3bxNEY51pSBuuOXAVgTO8GJo6mfHVvVB2Ak9eSib9pOfVz287Fsu7IVVQq+PjRVtjb3NsCyPY2VrzQvR473ujN2/cH4ulkS2RCOq+tPU7Q3B38dvQaGq35TITM+/c8lxPT8XOzZ/KAxqYORwhAkqxKrVUtN2q6O5CerWH7Ocu+W6ow+nosM+2PdTcPJ1sa1cgrwj1gYaNZC0PCydUqdK3vSTsLeb1Ly9vFXt/XbFeYZYxmpWTmMGVd3ijOc/cFlOm/kaOtNS/2qM+ON3rz5sBAqjnaEBGfxoQ1xwiaG8LG49fRmjjZCr2WzOJdFwF4P7g5zlVs6SBhviTJqsRUKhWDmuf1zKpsU4YarcLxW9OF5n5n4Z0618sbzdpnQUvsxKZmsupg3goFYyv5KJZODwtr5TB70xmiUzKp6+nI5KDyGcVxsrPm5V712flmH14f0Bg3BxvC49IYt+ooA+btYHNoNKYo8c3VaHlz3Qk0WoUHW/rSt4npexUKoSNJViWnmzL870xMpZoyDItNJS1bg5OtFQ0t6C43XVNSS+qX9f3Oi2Tnamlbx50u9T1NHU6F6HFrynBHWLzJR2lKsissnlUH8pLgjx5piYPtvU0TlsTZzpoxvRuw683eTOrfCFd7a8Jib/K/nw7zxHf79H/8VJQluy9y6noKbg42THtIls4R5kWSrEquTW13fN3sScvWsNNCpj4MceRSEgCtartjZUGNBjsG5BW/n4tJJSnd/OuybqRl8+O+SwCM7dOgyGWjKpv2/h442loRfzOL01Eppg6nSDezcnlz3QkARnTxp1O9ikuCXextGNe3ITvf7MPY3g2wt1FzIDKRwd/sZvzqo1y9Uf5LE11OSOfzLecBeOf+Jni5WPZC5aLykSSrklOrVQyshFOGRy2kP9bdvFzsqO/lhKLAfgvol7V0TyTp2Rqa+rrSu7G3qcOpMLbWarreGrXbEWa+U4Yf/XWWa0kZ1KrmwJsDTdNuws3BhskDGrNtci8eaVsLlQo2HLtOn89CmP3XGVIyc8rluoqi8PavJ8nM0dK1viePta9V8kFCVDBJsqqAB25NGf57Osbil8vQOWqB9Vg6urqs/WZel5WamcOy3XnFxFVpFEvH3Ouy9kUk6EcZP3qkJU4mLvb2dXPgs8db8fvYbnSt70l2rpbvQiLo+fE2lu+JLPOGpuuPXGPXhXjsrNV8OKRFlfv5FJZBkqwqoG2dani72JGalcvuC5Y/ZZickcOF2JtA3pqFlkY3pWPu/bJ+3HeJlMxcGng7M7CZj6nDqXC6dQwPRd7gppkttJ6RrdFPEw7vWIf7GlQ3cUS3Na/pxooXOrFkVHsaeDtzIz2HaRtPMWDuDv45VTbF8fE3s3j/z7ylc8b3a0jd6ua7OLyo2iTJqgLU6tt3Gf55ItrE0dw7XRNSf09HPJ0trwaj8626rNNRKSRnlM9Uyr3KyNbw/c68UaxXetWvkgvs+ns64e/pSK5WYW+4eSXEn/x9jksJ6fi62TPlfvPrSq9SqegTWIPN47szK7g5nk62RMSn8eKPh3li4T5OXE26p/O//8dpktJzaOLryuju9comaCHKgSRZVYRuWZ0tp6MtctHXO1lqPZaOt6s99arn1WUdNNO6rFUHLpOQlk1tDwcebuVn6nBM5nb3d/OZMjwUmcjSPXkJ8OyhLXC1N9/VDqytbq+LOKZ3feys1Ry4mMjDX+9mQimL47edi2XDsby1GT96pAU2ZrzMjxDy01lFtK/rQXVnO1Iyc9kdbtlThvpO7xZYj6XTyYyX2MnK1fDdjnAAXu7ZwKzXqitvuinDEDNJsjJzNLzxywkUBR5tV4teFnIzgou9Da8PCGTb5F4MbVsTgN9uFcd/tPmswcXxaVm5vPtrKADP3hdAy1ru5RWyEGWi6v72rGKs7pgy/MuC7zLUahX9dGGb2pY5kgW3+2WZ4x2G6w5fIyYlCx9Xex5pV9PU4ZhUl/qe2FipuJyYTmR8mqnDYe6/54mIT8PbxY6pDzQ1dThG83N34PPHW/PHq93oXM+D7FwtC7aH0+uT7fywt+Ti+M/+Oa+/m/K1oEYVFLUQpSdJVhUyqEVekvXP6Zgyv9OnokTEp5GckYO9jZpAX8tpQno33UhW6LXkcrvFvTRyNVoWhFwA4MUe9bCzLt/GlubOyc5av0SNqVs5HLuSxKIdEQB8MKQFbo7mO01YkuY13Vg1ujOLR7SnnpcTiWnZvLfhFAPm7WDL6ZhCi+OPXUli2a1p0g+GtMDRVpbOEeZPkqwqpGNdDzydbElKzzG7Ql5D6eqxWtZ0t+haDF83B/w9HdEqcDjyhqnD0dt4/DpXEjPwdLJleMc6pg7HLPRslDclZ8q6rKxcDa+vPY5WgcGt/ejf1PKXjlGpVPRrWoO/J/TgfV1xfFwao384xPBF+zh5NVm/b45Gy1vrTqBVILi1n34aVwhzZ7mfUsJo1lZqBuimDEMtc8rQkvtj3a3TrbsM95lJXZZWq/DNtrxRrOe7B5T78iyWQrfEzp7wBJPdNPL11guExd6kurMt0yvZ0jE2VmqeuVUc/0qvvOL4fRGJPPT1LiauOca1pAwW7ojgbHQq1RxtmPqg5U2TiqpLkqwq5v7meXcZ/n0qhlwLnDKsDEXvOrfXMTSPuqzNp6IJj0vD1d6aZzr7mzocs9HEx5XqznakZ2tMcqNC6LVk5m/PuxHh/cHNqeZkW+ExVAQXexveGBjI1sm9GNomrxbw16PX6PPpdr74NwyAqQ82tci2LaLqkiSriulcz4NqjjYkpmWbZdF1cW5m5XIuOm8duTYW2r7hTnfWZZm62aVWq/Dlf3kfZKPuC8DFjNsCVDS1WqUfzXp+2SHe/OUEF2JTK+Ta2blaXv/lBBqtwv0tfPQLvldmNd0d+PyJ1vw+thudAjzIytWSrdHSvWF1hrSp2jdiCMsjSVYVY22lZkAzy1zL8MTVJLRK3i/hGq72pg7nntWq5kitag5otAqHL5m2LmvzqWjORqfiYmfNc/fVNWks5uj1AY1pW8edbI2WNYeu0O/zHbyw/CD7IxLKpIN5URZsD+dMVArVHG2YObh5uV3HHLWo5cbqF/OK45+9ry6fPd5Kls4RFkeSrCpI99fw36ei0WjL7wOirOmmCltXgqlCHX0rhwjT1WVptQrz/j0PwLPdAnB3rJzTUffC182B9a/cx7qXuxDUtAYqFfx7JpYnFu4j+Jvd/HkiqszfS2ejU/h6W97o4vSHm1G9Ck6T6Yrjpz3UDG8Xy//DSlQ9kmRVQV3re+LmYEP8zWwOWNCUob4eywLXKyxK51tThvtMmGT9eTKK8zE3cbG35vluASaLwxK08/dg4Yj2/DepJ092qoOttZrjV5MZs/IIvT7NWwg5Pfvep35zNVpeX3uCHI1C/6Y1qnTXfSEsmSRZVZCNlZqgW7eAW8pdhoqi6Ns3VIZ6LJ3OtxaLPnE1uUw+nI2luWMUa3T3erg5SC2WIep5OfPhkBbseasP4/o2pJqjDVcSM5i28RRd52zls3/OEZeaVerzL9wZwclrybjaW/NBcHOZJhPCQkmSVUXp1jL8K9QypgyvJGaQkJaNrZWa5jVdTR1OmalVzQE/N3tytQpHLiVV+PV/P36d8Lg03BxseFZqsYxW3dmOSf0bseetvrw/uBn+no4kpefw1dYL3PfRVqasP8GF2JtGnfNCbCrztuRNE773UDO8K0H9oRBVlSRZVdR9DarjYm9NXGqWyYuuDXH0Sl6MTf1cK1UXcpVKRad6ulYOFTtlmKvR6u8ofLFHPbmj8B442FrxTJe6bH2tFwueakvr2u5k52pZdeAK/T4P4YXlhzhwMbHEInmNVuH1X06QrdHSq7EXj7SVu+mEsGSSZFVRttZqfddoS7jLsDL1x7pbZxMtFr3h2HUi4tOo5mjDyK51K/TalZWVWsWgFr78+kpX1v6vC/31RfIxPP7dXobM38Omk0UXyS/dfZGjl5NwtrPmwyEtZJpQCAsnSVYVpmtM+ldoFFoznzI8UgnrsXR0dxgev5JMZo6mQq6Zo9Hy5da8UayXetbH2U7WgStLKpWKDnU9WDSiPf9O6snwjnlF8seuJPHKiiP0/jRvQeSM7Nv/3hfj0/jk73MAvPNAE/zcHUwVvhCijEiSVYV1b1QdZztrYlKy9NNx5igzR8Pp63lNSNtWwpEsf09Harjaka3R6pPJ8vbrkWtcSkjH08mWEV2ku3t5qu/lzOyhLdj9Zh/G9WmAu6MNlxPTeW/DKbrO+Y/P/zlHbGomb/5ygqxcLd0aVGdYh9qmDlsIUQYkyarC7Kyt6Nckb/HbP09EmziaooVeSyZXq+DlYkfNSvjXvUql0t9lWBFL7GTn3h7FerlXfRxtZRSrIni52DEpqDF73urDzMHNqOPhyI30HL7ceoEus7dyIDIRR1srZg+VaUIhKgtJsqq423cZmu+UoX6qsLZ7pf3wqcimpOuOXOXqjQy8XOx4qpOMYlU0R1trRnSpy7bJvZj/VFta1XbX12i9NSiQ2h6OJo5QCFFW5E/YKq5HIy+cbK2ISs7k2NUk2pphzdPtonfzi62s6NYxPHolicwcDfY25XMHZVauhq+3XgDg5Z71cbCtPHdqWhortYr7W/gyqLkPhy7dIC41i0HNfUwdlhCiDMlIVhVnb2NFnya3GpOa6V2GuiSrMtZj6dSr7kR1Zzuyc7Ucu5JUbtf5+dBVriVlUMPVjic71Sm36wjD6Yrk72/hW2lHaoWoqiTJEjzQQrdgdHS5LnZbGlHJGUSnZGKlVtGilpupwyk3eXVZt1o5lFNdVmaOhm9ujWKN6d2g3EbLhBBC5JEkS9CzkTcONlZcS8rgxNVkU4eTj64LeqCPS6Uv0NY1JS2vflmrD1wmOiUTXzd7npC714QQotxJkiVwsLWiT2DeXYabzGwtw9vrFbqbNpAK0DkgbyTr8KUbZOWWbb+szBwN87eHAzC2T4NK1TVfCCHMlSRZArjjLkMzmzI8eqs+yRwL8staA29nPJ1sycrVlvmI4or9l4lNzaKmuwOPtZNRLCGEqAiSZAkAegd6YW+j5nJiOqduNf40texcLSev5SUblfnOQp28dQx1dVllN2WYnp3Lgu15tViv9mmArbW87YUQoiLIb1sB5PXu6d341pShmdxleDoqhexcLe6ONtT1rBq9g/T9si6WXfH7T/suEX8zm9oeDjzSrlaZnVcIIUTxJMkSeoNuTRluOhllFlOGR6tAE9K76UayDkXeIEejvefzpWXl8m1IBADj+jTExkre8kIIUVHkN67Q6xPoja21msiEdM5EpZo6nDv6Y1X+qUKdRt4uVHO0ISNHUyZ1WT/svURiWjZ1PR0Z0qZmGUQohBDCUJJkCT1nO2t6NfIC8pbZMTXdotVVoR5LR61W0fHWXYb32sohNTOH73bk3VE4rm9DrGUUSwghKpT81hX56O4y/NPEU4ZxqVlcScxApYKWtStvE9LC6Oqy7nWx6OV7IklKz6GelxMPt/Iri9CEEEIYQZIskU/fJt7YWqmJiEvjfMxNk8Whq8dq6O2Mq72NyeIwhc63mpIejkwkt5R1WSmZOSzckVeLNV5GsYQQwiTkN6/Ix8Xehh6NqgOmvcuwKvXHulugjwtuDjakZWsILWU7jSW7LpKSmUtDb2cebCmjWEIIYQqSZIkCBjW/fZehqVSlTu93U6vzFgwG2FeKflnJ6Tl8v+siABP6NcJKXTXuzBRCCHMjSZYooF/TGthYqQiLvUlYTMXfZZir0XL8StVpQlqYzvfQlPT7XRGkZuYS6OPCoOY+ZR2aEEIIA0mSJQpwc7ChWwPdlGF0hV//XEwqGTkaXOysaeDlXOHXNwe6uqxDkTfQaA2/AeFGWjZLdkcCMKFfQ9QyiiWEECYjSZYolK4xqSlaOej6Y7Wu415lk4Qmvq642FuTmpXLaSPqshbtjOBmVi5NfV0JaiqjWEIIYUqSZIlCBTWtgbVaxdnoVMLjKvYuQ12S1aa2e4Ve15xYlaIuK+FmFsv2RAIwsX+jKpugCiGEuZAkSxTK3dGWrremDP+q4AL420XvVbMeS0dfl2VgU9KFOyNIz9bQoqYb/Zp4l2doQgghDGDRSdbs2bPp0KEDLi4ueHt7ExwczLlz5/LtExMTw6hRo/Dz88PR0ZGBAwcSFhZW6PkURWHQoEGoVCp+++23Yq89ffp0VCpVvi8fn8o1PfNAi7znU5F1WTfSsomITwOgdRUeyYLbTUkPXEwssS4rLjWLH/ZcAmBi/4ZVZq1HIYQwZxadZIWEhDBmzBj27dvHli1byM3NJSgoiLS0vA9pRVEIDg4mIiKCDRs2cPToUfz9/enXr59+nzvNmzfPqA+nZs2aERUVpf86efJkmT03c9C/qQ9WahWno1KIjC/4epWHY1eTAKhX3YlqTrYVck1z1czPFWc7a1IyczkTVXxd1nch4WTkaGhV253ejWUUSwghzIG1qQO4F5s3b873/dKlS/H29ubw4cP06NGDsLAw9u3bR2hoKM2aNQNg/vz5eHt7s2rVKl544QX9scePH+fzzz/n4MGD+Pr6GnR9a2trg0evsrKyyMrK0n+fklK6JpMVycPJli71PNl1IZ5NoVG80qtBuV5PURS2n40F8oreqzprKzXt61Zj+7k49l9MpHnNwpcXik3J5Md9eaNYk/o3klEsIYQwExY9knW35OS83koeHnm1LLqkxt7eXr+PlZUVtra27Nq1S78tPT2d4cOH8/XXXxs15RcWFoafnx8BAQEMGzaMiIiIIvedPXs2bm5u+q/atWsb9dxMRbeW4V/lOGWoKApbTsfw4Fe7WL43L1nocquFQVWnmzIsrl/W/O3hZOVqaedfjR4Nq1dUaEIIIUpQaZIsRVGYNGkS3bp1o3nz5gAEBgbi7+/PlClTuHHjBtnZ2cyZM4fo6Giiom4Xc0+cOJGuXbsyePBgg6/XqVMnfvjhB/7++28WLVpEdHQ0Xbt2JSGh8A/DKVOmkJycrP+6cuXKvT3hChLUrAZqFZy8lszlhPQyPbeiKGw9G8Pgb3Yz+odDnLqegpOtFeP6NOCRtrXK9FqWqtOt4vcDkYloC6nLik7OZOWBywBM7CejWEIIYU4serrwTmPHjuXEiRP5RqhsbGxYt24dzz//PB4eHlhZWdGvXz8GDRqk32fjxo1s3bqVo0ePGnW9O8/RokULunTpQv369Vm+fDmTJk0qsL+dnR12dnaleGamVd3Zjk4BnuyNSOCv0Che6ln/ns+pKAoh5+OY+28Yx2+tUehoa8XIrnUZ3b0eHlW8FutOLWq64WhrRVJ6DudiUmni65rv8fnbL5Cdq6VjXQ/uayCjf0IIYU4qxUjWq6++ysaNG9m2bRu1auUfAWnXrh3Hjh0jKSmJqKgoNm/eTEJCAgEBAQBs3bqV8PBw3N3dsba2xto6L+985JFH6NWrl8ExODk50aJFiyLvXLRk97e8tZZh6L1NGSqKws6wOB5ZsIdRSw9y/EoS9jZqXupRj51v9ObNgYGSYN3FxkpNO/+8VhZ3TxleS8pg9YG8EdGJUoslhBBmx6KTLEVRGDt2LOvXr2fr1q36xKkwbm5ueHl5ERYWxqFDh/RTg2+99RYnTpzg2LFj+i+AuXPnsnTpUoNjycrK4syZMwYXzVuSAc1qoFLB8StJXL1h/JShoijsuRDPY9/u5ZnvD3DkchJ21mpe6BbAzjf6MOX+Jng6W94oX0XRLbGz/2Jivu3fbLtAtkZLl3qedKkvo1hCCGFuLHq6cMyYMaxcuZINGzbg4uJCdHTeSIubmxsODg4ArF27Fi8vL+rUqcPJkycZP348wcHBBAUFAeDj41NosXudOnXyJW19+/ZlyJAhjB07FoDJkyfz0EMPUadOHWJjY5k1axYpKSmMHDmyvJ92hfN2sadjXQ/2X0xkc2g0L3SvZ/Cx+yISmLvlvD5BsLVW81SnOrzcsz7ervYlHC0AOgXompImoigKKpWKK4np/Hzw9iiWEEII82PRSdaCBQsACkzrLV26lFGjRgEQFRXFpEmTiImJwdfXlxEjRjB16lSjrxUeHk58fLz++6tXrzJ8+HDi4+Px8vKic+fO7Nu3D39//1I/H3N2fwtf9l9M5M+TUQYlWQcjE5m75Tx7wvOmuGyt1AzvWJuXezXAx02SK2O0rOWOvY2axLRswmJv0qiGC99su0CuVqFbg+p0vJWECSGEMC8qRVGKbyUtykVKSgpubm4kJyfj6upa8gEmFpOSSefZ/6EosOetPvi5OxS63+FLiczdEsauC3kJqY2Viic61OaVXg2KPEaU7KnF+9h9IYH3BzejRyMv+nwWgkarsO7lLrTzlyRLCCEqijGf3xY9kiUqTg1Xe9r7V+Ng5A02h0bzXLf89W9HL99g7r9h7DgfB4C1WsVj7Wszpnd9alVzNEXIlUqnAE92X0hgX0Qix68mo9Eq9GzkJQmWEEKYMUmyhMEGNfflYOQNNp2M0idZJ64mMXfLebady0uurNQqHm1bi7F9GlDbQ5KrsqKry9pxPo70HA0gtVhCCGHuJMkSBhvUwoeZf5zm0KUbbD0bw8r9l/n3TN4yOFZqFUPa1OTVPg3w93QycaSVT6va7thZq0nNygWgb6B3lV9AWwghzJ0kWcJgvm4OtK3jzpHLSTy37BAAahUEt67Jq30bElBdkqvyYm9jRZs67uyLyLtLc0I/GcUSQghzJ0mWMMrDrfw4cjkJlQoGt/Lj1b4Nqe/lbOqwqoTuDb3YF5FI/6Y1aFGr8MWihRBCmA9JsoRRnulSl2pOtjTzc6WBt4upw6lSnu8WQA1XewY0q2HqUIQQQhhAkixhFCu1isGta5o6jCrJ3saKR9vJwtlCCGEpLHpZHSGEEEIIcyVJlhBCCCFEOZAkSwghhBCiHEiSJYQQQghRDiTJEkIIIYQoB5JkCSGEEEKUA0myhBBCCCHKgSRZQgghhBDlQJIsIYQQQohyIEmWEEIIIUQ5kCRLCCGEEKIcSJIlhBBCCFEOJMkSQgghhCgH1qYOoKpSFAWAlJQUE0cihBBCCEPpPrd1n+PFkSTLRFJTUwGoXbu2iSMRQgghhLFSU1Nxc3Mrdh+VYkgqJsqcVqvl+vXruLi4oFKpyvTcKSkp1K5dmytXruDq6lqm5xbFk9fedOS1Nx157U1HXvuKpygKqamp+Pn5oVYXX3UlI1kmolarqVWrVrlew9XVVd50JiKvvenIa2868tqbjrz2FaukESwdKXwXQgghhCgHkmQJIYQQQpQDSbIqITs7O6ZNm4adnZ2pQ6ly5LU3HXntTUdee9OR1968SeG7EEIIIUQ5kJEsIYQQQohyIEmWEEIIIUQ5kCRLCCGEEKIcSJIlhBBCCFEOJMkSQgghhCgHkmQJIYQQQpQDSbKEEEIUKTY2Fo1GY+owqqQjR46Qmppq6jDEPZAky0LExMTw559/Im3NKl50dDQzZ85k/vz5bNq0ydThVClRUVGMGzeON998ky+//NLU4VQZiqKQnZ3Niy++yIABA9i7d6+pQ6pSrl+/TlBQEL179+bYsWOmDkfcA0myLMDXX3+Nn58fDz30EKdOnTJ1OFXK+++/T4MGDThw4ADLli1jyJAhrFy5EkAS3nI2ffp0GjZsyKX/t3f3QVHVaxzAv3sWMF4VECkjMI18Q8MYZ8oQs9K6XAxfBpskB8KXtLzUSC9aXotrajTkLfWiMpmWKEpWZKaTFiphoxJN0r1lXPElKQhfbnLlbXfZ5/7B5RRqyYu7P876/cw005598Tlf9uw+e87vnN/Jk6iursZTTz2FRYsWAWD2jmYymVBdXY1t27bh9OnTKCgowPnz5wEwe0d79tlnERYWBi8vL3z33XcYOXKk6pKoE9hkdWEigh07diA/Px+vvvoqhg0bhvT0dNjtdtWlubympiZkZGRgx44dyMvLw/bt2/HZZ59h7ty5mD9/PoDmLyK6+mw2GzIyMrB3715s3boVH374IdatW4cFCxZg/fr1AJi9M1itVsTFxWHq1KnIycnBgQMHADB7R7FarfjLX/6CzMxM5OTkID8/H71790Z1dbXq0qgT2GR1YSaTCcHBwZg6dSoee+wx/P3vf8d7772HTz75RHVpLs9sNsNiseCee+7BAw88AADw9fXFqFGj4ObmhvLycsUVui43NzfccccdePHFFzF27Fh9udVqxaxZs1BfX6+wumtHRUUFSktLsXTpUnh7eyM3N1ffm0VXn7u7O0aOHImYmBicOXMGR44cwYQJEzBp0iSMGjUK2dnZsFgsqsukdmKT1YXU1NTgwIED+PHHH/VlUVFRSEpKgo+PD2JiYpCQkIAXXniBgyGvsstl//TTT2Px4sXQNE0/RHLu3Dlcd9116Nevn6pSXc7lsh81ahRGjx4NTdNQU1OD8ePHIyMjA5s3b8Ztt92GrVu3oq6uTmHVruFy2beoqKjAoEGDAADz5s1DYWEhcnNzMXPmTFRWVjq7VJdzuewnTpyIiIgIvPzyy4iOjkZYWBgSEhIwYMAApKamYuXKlfyRYTRCXcKSJUvEz89PIiIixM/PT15//XWpqKgQERGbzSZNTU0iIlJeXi6enp6yfPlyleW6lD/KvqmpSc9eRGTOnDmSmJgoIiIWi0VJva7kStlbLBZZu3atxMbGSlFRkZSWlsrjjz8ugwYNko8//lhx9cb2R9mLiKxevVri4+P12+Hh4eLu7i7Dhg2TyspKsdvtCqp2DZfL/uTJkyIiUlhYKElJSbJt27ZWz0lNTZXbbrtNvvnmGxUlUwexyeoCduzYIQMHDpQPPvhAjh07JosXL5bBgwdLSkqK/pjffqAtWLBAgoOD5dSpUyIiUltbKxcuXHB63a6gLdmL/NpQDR8+XJYtW9bqPn7ZdExbs6+trb3kuf7+/rJp0yZnlepy2pL9vHnzJCsrS3bv3i033nijhISESEBAgGRmZorValVYvbH9XvaPPvqo/pivv/5aGhoaRET0H3lVVVViMpnk4MGDSuqmjmGT1QWkpqbKsGHDWi1bsWKF9O/fX7Kzs0WkeW9WiwsXLkhYWJikpqbKO++8I9HR0ZKXl+fUml1FW7Jv+UL54YcfJCgoSE6cOCEiIjt37pSHH35Yjh8/7tSaXUVb3/cXN7HFxcUSGhoqO3fudFqtruaPsl+1apWINDdZJpNJfH19JT09XX/ekCFDZM+ePc4u2WX8UfarV68WEWm197zl/Z+bmyu9evWSw4cPO69Y6jSOyVLMbrfDarWif//+aGxs1JdPmjQJd999N/7xj3/gwoULMJvN+lmF3t7eSE5OxooVKzB9+nSMHDkSCQkJqlbBsNqavZubGwCgsLAQQ4cOhdlsRmxsLMaNG4fevXujT58+itbAuNrzvjeZTPqYuLKyMqSnp2P48OGIjo5WVb6hXSn71atXo76+Hvfddx8WL16MkpISLFy4EACwYMEC2Gw2aBq/OjriStmvWrUKtbW1er4iApPJhCNHjmDdunWIj4/H0KFDVZVPHcAtRSERgaZpCA0Nxf79+1sNJr3hhhvw5z//Ge7u7sjNzQUAaJqG2tpazJkzB3/729+QkpKCn3/+GUuWLFG1CobV1uw3b96sP37Xrl0oKChA3759oWkaqqqqkJmZqWoVDKu92dfW1iIjIwMzZsxAVFQUfH198dZbb8HHx0fVKhhWW7LXNA15eXm49957MW/ePISHhwNovqxJUFAQvv76a8TExKhaBcNq7+d9bW0tFi1ahEcffRRRUVEICgrCa6+9pqp86iA2WQq17Jl66qmncP78eWzcuLHV/XfffTc0TcPZs2f1ZWfOnIGvry8+//xzvPnmm+jRo4czS3YZbc3+zJkzAJovp+Hh4YGIiAgcOnQI27dvR2BgoNPrdgXtzd7b2xtBQUFoaGjA3r17sWnTJvj5+Tm9blfQluzd3d31BuC318Qym80AAA8PDydV61o68r7v2bMnLly4gH379iEnJwe+vr5Or5s6Sd2RStd39uxZOX36tIi0PsYuIpcMHM3MzBRfX18pLi5utTwyMlIef/xxxxbqghyR/X//+18HVetarlb2s2fP1m/z5IK24WeOOo5431/8OmQ83JPlIC+88AIGDBiA7OxsALhkDIObmxtEBM899xxycnKQlpaGW2+9FfPmzdPnx/vqq68gIhg/fryzyzc0R2XPw1NXdjWznzBhgv48XmX8yviZo46j3vcc+2Z8/AteZb/88gumTZuGTz/9FKGhoThw4AC+/PJLAK3n/Hr77bfRs2dP7Nq1C4MHDwYAbNiwAX5+fpgwYQLuv/9+jBw5EgMHDsRdd92lZF2Mhtmrw+zVYfbqMHu6EpMIZ/vsLPn/GSAA0NjYiIyMDAwdOhT+/v6YO3cu7r//fqSnp8Pd3R0AUFdXh2XLlqFnz56YMWMGzGaz/ho1NTU4ePAgysrKEBkZyQ3uCpi9OsxeHWavDrOn9mCT1Un19fXQNA3dunUD0LwB1tTUoHv37gCap2Y5cOAAnn/+ecTGxurPs9vt3BXcScxeHWavDrNXh9lTe/Gv3gnz589HdHQ04uLisHz5ctTU1MBkMsHPz08/kyQ1NRUAkJ+fr581Iv8/lZc6jtmrw+zVYfbqMHvqCP7lO8BisSAhIQHbtm3Ds88+i969e2PNmjWYMmUKgOZBupqmwW63IzQ0FAkJCfjqq6+wfft2/f6WHYgtGye1DbNXh9mrw+zVYfbUKY45adG1ffvttxIeHi67du3SlxUVFYmnp6e8+uqr+unmLaffNjQ0SGxsrEyePFlKS0slJydHXn75ZSW1Gx2zV4fZq8Ps1WH21BlssjqgpKRETCaTnD17VkR+vYbP0qVLxd/fX8rKyvTHtmx4+fn50rdvXwkMDBQPDw/JzMx0fuEugNmrw+zVYfbqMHvqDB4u7ABN0zBo0CBs2rSp1fK0tDT06NEDa9asAdA8DYWmaSgvL8f777+P48ePY/LkyTh37hzS0tJUlG54zF4dZq8Os1eH2VNnsMnqgLCwMISHh6OoqAiVlZUwmUyw2Wxwd3fHnDlzkJubC7vdrk9DsWbNGhQUFODw4cPIysqCt7e34jUwLmavDrNXh9mrw+ypM9hkXaS6uhqnT5+GxWIB0PzrpIXNZgMA+Pv7Y9y4cThy5Ajy8vIANF/RFwC6d+8Of39/nDp1Sh/k+Morr+DUqVMYMmSIM1fFcJi9OsxeHWavDrMnR2OT9X9WqxWzZs1CTEwMxo0bhwcffBCNjY0wm82wWq0AmjeshoYGbN68GSkpKYiMjMSWLVuwZ88e/XUqKioQFBSEsLAw/bRdnr77x5i9OsxeHWavDrMnp1E9KKwrePfdd6Vfv34yatQoKSgokOzsbOnbt+8lk6S+8cYbEhAQIPHx8SIicvjwYUlMTBQPDw+ZPXu2zJw5U3x9fWXVqlUiwklt24LZq8Ps1WH26jB7ciY2WSLyxBNPyF//+tdWM6UnJSXJ3Llz9dsrVqyQPn36yMaNG1vNjG6322XJkiUyY8YMiY2Nlf379zu1dqNj9uowe3WYvTrMnpzpmp5Wp6mpCWazGVVVVbBarbjpppsAACdPnsTEiRMxZcoU3HnnnRgxYgRsNhsaGxtbDWKU38xhRe3D7NVh9uowe3WYPalwzR083rFjB4DmDablbJDrr79e3+BWrFiBm2++GV5eXvjoo48QFxeHF198ETab7ZKzRLjBtQ+zV4fZq8Ps1WH2pJyaHWjOt337drnxxhvFZDLpu3gvdwx9/fr1UlhYqN+3ceNG8fT0lBMnTji1XlfC7NVh9uowe3WYPXUV18ThwqKiIixevBi33HILysvLcfr0aRQXF7d6jPzOruAjR44gIiICO3fuxJgxY5xVsstg9uowe3WYvTrMnroSN9UFOFLLhhQcHIyxY8di/PjxOHfuHGJiYrB27VpMmzYNdrsdmqb97q7g/Px83HvvvYiOjnZy9cbG7NVh9uowe3WYPXVJanagOVZJSYn88ssvrZbZbDYREbFarZKWliZBQUHS0NBw2eefPHlSjh49KtOnT5fevXvL+vXrRYSn6LYFs1eH2avD7NVh9tSVuVSTtXXrVgkJCZF+/fpJaGioLFy4UCorK0WkeYNp2WiOHTsmN910k6Slpen3tSgrK5O5c+dKSEiIjB49Wr7//nvnr4gBMXt1mL06zF4dZk9G4DJNVnFxsQwYMEBef/11OXz4sGRlZUlQUJDMnj1bnz295deN3W6XrKwscXNzk2PHjomISENDgzQ2Nordbpc9e/bw+iftwOzVYfbqMHt1mD0ZheGbrJZfJatWrZKQkBA5f/68ft/KlSvljjvukEWLFl3yvLNnz8qIESMkPj5eSkpKZMyYMbJhwwbuIm4HZq8Os1eH2avD7MloDH+drJYBjMePH8ett96qT9wJAMnJyYiKisLOnTvxr3/9C8CvE4AGBARgxowZ2LZtG4YPH45u3bph4sSJvBZKOzB7dZi9OsxeHWZPRmO4Jmv37t1ITU3FG2+8gUOHDunL77rrLnzxxReoqqoC0LxxeXt7Iz4+HiaTCbt27QIAmM1mWCwWZGVlYdq0aYiJiUFpaSk++ugjeHl5KVkno2D26jB7dZi9OsyejM4wTVZlZSXGjRuHRx55BOfOncPatWsxduxYfcMbO3Ys+vTpg4yMDAC//uIZM2YMNE3D0aNH9df6z3/+g7KyMqxbtw579+7F4MGDnb9CBsLs1WH26jB7dZg9uQzVxyvbora2VpKSkuShhx7SBy6KiAwfPlySk5NFpHmQ4zvvvCOapl0yiDExMVFGjx7t1JpdBbNXh9mrw+zVYfbkSgyxJ8vLywvdunVDcnIybr75ZthsNgBAXFwcvvvuOwDNu4UnT56M+Ph4TJ8+Hfv27YOIoKqqCv/+97+RmJiochUMi9mrw+zVYfbqMHtyJYaZVsdqtcLd3R3Ar1f2nTp1Kjw9PZGdna0va2howJ/+9Cd8++23iIyMxD//+U+EhoYiLy9PnxSU2ofZq8Ps1WH26jB7chWGabIuJyYmBikpKUhOToaIwG63w2w24+eff0ZpaSmKi4vRp08fTJkyRXWpLofZq8Ps1WH26jB7MiLDNlnHjh3DiBEj8PHHHyMqKgoAYLFY4OHhobgy18fs1WH26jB7dZg9GZUhxmT9VktPWFRUBB8fH32DS09Px5NPPonq6mqV5bk0Zq8Os1eH2avD7Mno3K78kK6l5VTdQ4cOYdKkSdi9ezdmzpyJuro6bNiwAb169VJcoeti9uowe3WYvTrMngzPCWcwXnX19fVyyy23iMlkkm7duskrr7yiuqRrBrNXh9mrw+zVYfZkZIYdkzVmzBiEh4dj2bJluO6661SXc01h9uowe3WYvTrMnozKsE1WU1MTzGaz6jKuScxeHWavDrNXh9mTURm2ySIiIiLqygx3diERERGREbDJIiIiInIANllEREREDsAmi4iIiMgB2GQREREROQCbLCIiIiIHYJNFRNQBL730EiIjI1WXQURdGK+TRUR0kZY5835PUlISVq5cicbGRgQGBjqpKiIyGjZZREQXqaqq0v9/y5YtWLhwIb7//nt9maenJ7p3766iNCIyEB4uJCK6yPXXX6//1717d5hMpkuWXXy4MDk5GePHj8eSJUsQHByMHj16ID09HTabDc888wwCAgIQEhKCt956q9W/9eOPP+Khhx6Cv78/AgMDER8fjxMnTjh3hYnIIdhkERFdJQUFBfjpp59QWFiIZcuW4aWXXkJcXBz8/f1x8OBBzJo1C7NmzcKpU6cAAHV1dRg9ejR8fHxQWFiIoqIi+Pj44IEHHoDFYlG8NkTUWWyyiIiukoCAACxfvhz9+/dHSkoK+vfvj7q6Ojz//PMIDw/H/Pnz4eHhgf379wMANm/eDE3T8Oabb2LIkCEYOHAg1q1bhx9++AF79+5VuzJE1GluqgsgInIVgwcPhqb9+ts1ODgYERER+m2z2YzAwEBUV1cDAEpKSnD06FH4+vq2ep2GhgaUl5c7p2gichg2WUREV4m7u3ur2yaT6bLL7HY7AMButyMqKgobN2685LWCgoIcVygROQWbLCIiRW6//XZs2bIFvXr1gp+fn+pyiOgq45gsIiJFEhMT0bNnT8THx+Pzzz/H8ePHsW/fPjz55JOoqKhQXR4RdRKbLCIiRby8vFBYWIjQ0FBMnDgRAwcOREpKCurr67lni8gF8GKkRERERA7APVlEREREDsAmi4iIiMgB2GQREREROQCbLCIiIiIHYJNFRERE5ABssoiIiIgcgE0WERERkQOwySIiIiJyADZZRERERA7AJouIiIjIAdhkERERETnA/wDVbkINsg7pegAAAABJRU5ErkJggg==", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "da.isel(time=slice(0, 20), lat=20, lon=40).plot();" ] @@ -296,13 +1271,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 60, "metadata": { "tags": [ "hide-output" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "da.sel(time=\"2014-01-01\", lat=25, lon=210).plot();" ] @@ -316,13 +1302,438 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 61, "metadata": { "tags": [ "hide-output" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'air' (time: 1460)>\n",
+       "array([277.6 , 277.5 , 277.4 , ..., 277.59, 277.59, 277.59], dtype=float32)\n",
+       "Coordinates:\n",
+       "    lat      float32 50.0\n",
+       "    lon      float32 200.0\n",
+       "  * time     (time) datetime64[ns] 2014-01-01 ... 2014-12-31T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
    " + ], + "text/plain": [ + "\n", + "array([277.6 , 277.5 , 277.4 , ..., 277.59, 277.59, 277.59], dtype=float32)\n", + "Coordinates:\n", + " lat float32 50.0\n", + " lon float32 200.0\n", + " * time (time) datetime64[ns] 2014-01-01 ... 2014-12-31T18:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "da.sel(lat=50.0, lon=200.0, time=\"2014\")" ] @@ -336,9 +1747,570 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 62, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'air' (time: 1212, lat: 25, lon: 53)>\n",
+       "array([[[246.     , 245.09999, 244.09999, ..., 240.2    , 242.     ,\n",
+       "         244.2    ],\n",
+       "        [240.59999, 241.7    , 242.09999, ..., 239.09999, 241.2    ,\n",
+       "         244.59999],\n",
+       "        [238.2    , 239.79999, 240.79999, ..., 243.89   , 246.89   ,\n",
+       "         251.2    ],\n",
+       "        ...,\n",
+       "        [295.6    , 295.6    , 296.     , ..., 293.6    , 293.6    ,\n",
+       "         294.1    ],\n",
+       "        [297.     , 297.     , 297.     , ..., 293.69998, 293.9    ,\n",
+       "         294.6    ],\n",
+       "        [297.5    , 297.69998, 297.69998, ..., 294.5    , 294.69998,\n",
+       "         295.19998]],\n",
+       "\n",
+       "       [[245.2    , 244.29999, 243.     , ..., 241.89   , 243.09999,\n",
+       "         244.59999],\n",
+       "        [241.09999, 241.89   , 241.89   , ..., 241.89   , 243.59999,\n",
+       "         246.39   ],\n",
+       "        [239.89   , 241.     , 241.29999, ..., 245.09999, 248.2    ,\n",
+       "         252.2    ],\n",
+       "...\n",
+       "        [296.49   , 295.59   , 295.49   , ..., 297.29   , 297.38998,\n",
+       "         296.79   ],\n",
+       "        [298.09   , 297.79   , 297.19   , ..., 297.69   , 298.09   ,\n",
+       "         297.38998],\n",
+       "        [298.49   , 298.29   , 297.99   , ..., 298.29   , 298.09   ,\n",
+       "         297.99   ]],\n",
+       "\n",
+       "       [[246.29   , 246.39   , 245.98999, ..., 232.29   , 233.39   ,\n",
+       "         234.98999],\n",
+       "        [246.68999, 248.18999, 248.98999, ..., 230.59   , 232.48999,\n",
+       "         235.68999],\n",
+       "        [244.79   , 245.29   , 245.89   , ..., 230.48999, 235.09   ,\n",
+       "         241.39   ],\n",
+       "        ...,\n",
+       "        [296.88998, 296.49   , 296.49   , ..., 297.69   , 297.29   ,\n",
+       "         296.19   ],\n",
+       "        [298.29   , 297.99   , 297.49   , ..., 298.29   , 298.38998,\n",
+       "         297.49   ],\n",
+       "        [298.49   , 298.29   , 297.99   , ..., 298.59   , 298.38998,\n",
+       "         298.29   ]]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
    " + ], + "text/plain": [ + "\n", + "array([[[246. , 245.09999, 244.09999, ..., 240.2 , 242. ,\n", + " 244.2 ],\n", + " [240.59999, 241.7 , 242.09999, ..., 239.09999, 241.2 ,\n", + " 244.59999],\n", + " [238.2 , 239.79999, 240.79999, ..., 243.89 , 246.89 ,\n", + " 251.2 ],\n", + " ...,\n", + " [295.6 , 295.6 , 296. , ..., 293.6 , 293.6 ,\n", + " 294.1 ],\n", + " [297. , 297. , 297. , ..., 293.69998, 293.9 ,\n", + " 294.6 ],\n", + " [297.5 , 297.69998, 297.69998, ..., 294.5 , 294.69998,\n", + " 295.19998]],\n", + "\n", + " [[245.2 , 244.29999, 243. , ..., 241.89 , 243.09999,\n", + " 244.59999],\n", + " [241.09999, 241.89 , 241.89 , ..., 241.89 , 243.59999,\n", + " 246.39 ],\n", + " [239.89 , 241. , 241.29999, ..., 245.09999, 248.2 ,\n", + " 252.2 ],\n", + "...\n", + " [296.49 , 295.59 , 295.49 , ..., 297.29 , 297.38998,\n", + " 296.79 ],\n", + " [298.09 , 297.79 , 297.19 , ..., 297.69 , 298.09 ,\n", + " 297.38998],\n", + " [298.49 , 298.29 , 297.99 , ..., 298.29 , 298.09 ,\n", + " 297.99 ]],\n", + "\n", + " [[246.29 , 246.39 , 245.98999, ..., 232.29 , 233.39 ,\n", + " 234.98999],\n", + " [246.68999, 248.18999, 248.98999, ..., 230.59 , 232.48999,\n", + " 235.68999],\n", + " [244.79 , 245.29 , 245.89 , ..., 230.48999, 235.09 ,\n", + " 241.39 ],\n", + " ...,\n", + " [296.88998, 296.49 , 296.49 , ..., 297.69 , 297.29 ,\n", + " 296.19 ],\n", + " [298.29 , 297.99 , 297.49 , ..., 298.29 , 298.38998,\n", + " 297.49 ],\n", + " [298.49 , 298.29 , 297.99 , ..., 298.59 , 298.38998,\n", + " 298.29 ]]], dtype=float32)\n", + "Coordinates:\n", + " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", + " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", + " * time (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# demonstrate slicing\n", "da.sel(time=slice(\"2014-02-14\", \"2014-12-13\"))" @@ -353,9 +2325,570 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 63, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'air' (time: 1212, lat: 25, lon: 53)>\n",
+       "array([[[246.     , 245.09999, 244.09999, ..., 240.2    , 242.     ,\n",
+       "         244.2    ],\n",
+       "        [240.59999, 241.7    , 242.09999, ..., 239.09999, 241.2    ,\n",
+       "         244.59999],\n",
+       "        [238.2    , 239.79999, 240.79999, ..., 243.89   , 246.89   ,\n",
+       "         251.2    ],\n",
+       "        ...,\n",
+       "        [295.6    , 295.6    , 296.     , ..., 293.6    , 293.6    ,\n",
+       "         294.1    ],\n",
+       "        [297.     , 297.     , 297.     , ..., 293.69998, 293.9    ,\n",
+       "         294.6    ],\n",
+       "        [297.5    , 297.69998, 297.69998, ..., 294.5    , 294.69998,\n",
+       "         295.19998]],\n",
+       "\n",
+       "       [[245.2    , 244.29999, 243.     , ..., 241.89   , 243.09999,\n",
+       "         244.59999],\n",
+       "        [241.09999, 241.89   , 241.89   , ..., 241.89   , 243.59999,\n",
+       "         246.39   ],\n",
+       "        [239.89   , 241.     , 241.29999, ..., 245.09999, 248.2    ,\n",
+       "         252.2    ],\n",
+       "...\n",
+       "        [296.49   , 295.59   , 295.49   , ..., 297.29   , 297.38998,\n",
+       "         296.79   ],\n",
+       "        [298.09   , 297.79   , 297.19   , ..., 297.69   , 298.09   ,\n",
+       "         297.38998],\n",
+       "        [298.49   , 298.29   , 297.99   , ..., 298.29   , 298.09   ,\n",
+       "         297.99   ]],\n",
+       "\n",
+       "       [[246.29   , 246.39   , 245.98999, ..., 232.29   , 233.39   ,\n",
+       "         234.98999],\n",
+       "        [246.68999, 248.18999, 248.98999, ..., 230.59   , 232.48999,\n",
+       "         235.68999],\n",
+       "        [244.79   , 245.29   , 245.89   , ..., 230.48999, 235.09   ,\n",
+       "         241.39   ],\n",
+       "        ...,\n",
+       "        [296.88998, 296.49   , 296.49   , ..., 297.69   , 297.29   ,\n",
+       "         296.19   ],\n",
+       "        [298.29   , 297.99   , 297.49   , ..., 298.29   , 298.38998,\n",
+       "         297.49   ],\n",
+       "        [298.49   , 298.29   , 297.99   , ..., 298.59   , 298.38998,\n",
+       "         298.29   ]]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
    " + ], + "text/plain": [ + "\n", + "array([[[246. , 245.09999, 244.09999, ..., 240.2 , 242. ,\n", + " 244.2 ],\n", + " [240.59999, 241.7 , 242.09999, ..., 239.09999, 241.2 ,\n", + " 244.59999],\n", + " [238.2 , 239.79999, 240.79999, ..., 243.89 , 246.89 ,\n", + " 251.2 ],\n", + " ...,\n", + " [295.6 , 295.6 , 296. , ..., 293.6 , 293.6 ,\n", + " 294.1 ],\n", + " [297. , 297. , 297. , ..., 293.69998, 293.9 ,\n", + " 294.6 ],\n", + " [297.5 , 297.69998, 297.69998, ..., 294.5 , 294.69998,\n", + " 295.19998]],\n", + "\n", + " [[245.2 , 244.29999, 243. , ..., 241.89 , 243.09999,\n", + " 244.59999],\n", + " [241.09999, 241.89 , 241.89 , ..., 241.89 , 243.59999,\n", + " 246.39 ],\n", + " [239.89 , 241. , 241.29999, ..., 245.09999, 248.2 ,\n", + " 252.2 ],\n", + "...\n", + " [296.49 , 295.59 , 295.49 , ..., 297.29 , 297.38998,\n", + " 296.79 ],\n", + " [298.09 , 297.79 , 297.19 , ..., 297.69 , 298.09 ,\n", + " 297.38998],\n", + " [298.49 , 298.29 , 297.99 , ..., 298.29 , 298.09 ,\n", + " 297.99 ]],\n", + "\n", + " [[246.29 , 246.39 , 245.98999, ..., 232.29 , 233.39 ,\n", + " 234.98999],\n", + " [246.68999, 248.18999, 248.98999, ..., 230.59 , 232.48999,\n", + " 235.68999],\n", + " [244.79 , 245.29 , 245.89 , ..., 230.48999, 235.09 ,\n", + " 241.39 ],\n", + " ...,\n", + " [296.88998, 296.49 , 296.49 , ..., 297.69 , 297.29 ,\n", + " 296.19 ],\n", + " [298.29 , 297.99 , 297.49 , ..., 298.29 , 298.38998,\n", + " 297.49 ],\n", + " [298.49 , 298.29 , 297.99 , ..., 298.59 , 298.38998,\n", + " 298.29 ]]], dtype=float32)\n", + "Coordinates:\n", + " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", + " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", + " * time (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "da.loc[\"2014-02-14\":\"2014-12-13\"]" ] @@ -371,9 +2904,570 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 64, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'air' (time: 2919, lat: 24, lon: 52)>\n",
+       "array([[[242.5    , 243.5    , 244.     , ..., 232.79999, 235.5    ,\n",
+       "         238.59999],\n",
+       "        [244.5    , 244.7    , 244.2    , ..., 232.79999, 235.29999,\n",
+       "         239.29999],\n",
+       "        [249.79999, 248.89   , 247.5    , ..., 233.2    , 236.39   ,\n",
+       "         241.7    ],\n",
+       "        ...,\n",
+       "        [296.19998, 296.4    , 296.5    , ..., 295.4    , 295.1    ,\n",
+       "         294.69998],\n",
+       "        [296.19998, 296.79   , 296.5    , ..., 295.9    , 295.9    ,\n",
+       "         295.19998],\n",
+       "        [296.79   , 297.1    , 297.     , ..., 296.9    , 296.79   ,\n",
+       "         296.6    ]],\n",
+       "\n",
+       "       [[242.7    , 243.09999, 243.39   , ..., 232.     , 233.59999,\n",
+       "         235.79999],\n",
+       "        [244.09999, 244.2    , 244.09999, ..., 231.     , 232.5    ,\n",
+       "         235.7    ],\n",
+       "        [252.89   , 252.09999, 250.79999, ..., 230.79999, 233.39   ,\n",
+       "         238.5    ],\n",
+       "...\n",
+       "        [293.88998, 295.38998, 297.19   , ..., 295.09   , 294.69   ,\n",
+       "         294.29   ],\n",
+       "        [297.19   , 297.59   , 297.88998, ..., 295.29   , 295.09   ,\n",
+       "         294.38998],\n",
+       "        [298.38998, 298.49   , 298.59   , ..., 295.69   , 295.49   ,\n",
+       "         295.19   ]],\n",
+       "\n",
+       "       [[244.29   , 243.29   , 242.18999, ..., 241.68999, 241.48999,\n",
+       "         241.79   ],\n",
+       "        [249.29   , 248.39   , 246.98999, ..., 239.59   , 240.29   ,\n",
+       "         241.68999],\n",
+       "        [262.19   , 261.38998, 259.99   , ..., 239.89   , 242.59   ,\n",
+       "         246.29   ],\n",
+       "        ...,\n",
+       "        [293.69   , 295.09   , 296.69   , ..., 295.29   , 295.09   ,\n",
+       "         294.69   ],\n",
+       "        [296.88998, 297.19   , 297.49   , ..., 295.69   , 295.69   ,\n",
+       "         295.19   ],\n",
+       "        [298.09   , 298.09   , 298.49   , ..., 296.49   , 296.19   ,\n",
+       "         295.69   ]]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 202.5 205.0 207.5 210.0 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
    " + ], + "text/plain": [ + "\n", + "array([[[242.5 , 243.5 , 244. , ..., 232.79999, 235.5 ,\n", + " 238.59999],\n", + " [244.5 , 244.7 , 244.2 , ..., 232.79999, 235.29999,\n", + " 239.29999],\n", + " [249.79999, 248.89 , 247.5 , ..., 233.2 , 236.39 ,\n", + " 241.7 ],\n", + " ...,\n", + " [296.19998, 296.4 , 296.5 , ..., 295.4 , 295.1 ,\n", + " 294.69998],\n", + " [296.19998, 296.79 , 296.5 , ..., 295.9 , 295.9 ,\n", + " 295.19998],\n", + " [296.79 , 297.1 , 297. , ..., 296.9 , 296.79 ,\n", + " 296.6 ]],\n", + "\n", + " [[242.7 , 243.09999, 243.39 , ..., 232. , 233.59999,\n", + " 235.79999],\n", + " [244.09999, 244.2 , 244.09999, ..., 231. , 232.5 ,\n", + " 235.7 ],\n", + " [252.89 , 252.09999, 250.79999, ..., 230.79999, 233.39 ,\n", + " 238.5 ],\n", + "...\n", + " [293.88998, 295.38998, 297.19 , ..., 295.09 , 294.69 ,\n", + " 294.29 ],\n", + " [297.19 , 297.59 , 297.88998, ..., 295.29 , 295.09 ,\n", + " 294.38998],\n", + " [298.38998, 298.49 , 298.59 , ..., 295.69 , 295.49 ,\n", + " 295.19 ]],\n", + "\n", + " [[244.29 , 243.29 , 242.18999, ..., 241.68999, 241.48999,\n", + " 241.79 ],\n", + " [249.29 , 248.39 , 246.98999, ..., 239.59 , 240.29 ,\n", + " 241.68999],\n", + " [262.19 , 261.38998, 259.99 , ..., 239.89 , 242.59 ,\n", + " 246.29 ],\n", + " ...,\n", + " [293.69 , 295.09 , 296.69 , ..., 295.29 , 295.09 ,\n", + " 294.69 ],\n", + " [296.88998, 297.19 , 297.49 , ..., 295.69 , 295.69 ,\n", + " 295.19 ],\n", + " [298.09 , 298.09 , 298.49 , ..., 296.49 , 296.19 ,\n", + " 295.69 ]]], dtype=float32)\n", + "Coordinates:\n", + " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", + " * lon (lon) float32 202.5 205.0 207.5 210.0 ... 322.5 325.0 327.5 330.0\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "da.drop_sel(lat=50.0, lon=200.0, time=\"2014\")" ] @@ -392,13 +3486,441 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 65, "metadata": { "tags": [ "hide-output" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'air' (time: 2920)>\n",
+       "array([262.69998, 263.19998, 270.9    , ..., 264.19   , 265.19   ,\n",
+       "       266.99   ], dtype=float32)\n",
+       "Coordinates:\n",
+       "    lat      float32 52.5\n",
+       "    lon      float32 252.5\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
    " + ], + "text/plain": [ + "\n", + "array([262.69998, 263.19998, 270.9 , ..., 264.19 , 265.19 ,\n", + " 266.99 ], dtype=float32)\n", + "Coordinates:\n", + " lat float32 52.5\n", + " lon float32 252.5\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "da.sel(lat=52.25, lon=251.8998, method=\"nearest\")" ] @@ -412,9 +3934,437 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 66, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'air' (time: 2920)>\n",
+       "array([262.69998, 263.19998, 270.9    , ..., 264.19   , 265.19   ,\n",
+       "       266.99   ], dtype=float32)\n",
+       "Coordinates:\n",
+       "    lat      float32 52.5\n",
+       "    lon      float32 252.5\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
    " + ], + "text/plain": [ + "\n", + "array([262.69998, 263.19998, 270.9 , ..., 264.19 , 265.19 ,\n", + " 266.99 ], dtype=float32)\n", + "Coordinates:\n", + " lat float32 52.5\n", + " lon float32 252.5\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "da.sel(lat=52.25, lon=251.8998, method=\"nearest\", tolerance=2)" ] @@ -432,9 +4382,428 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 67, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.Dataset>\n",
+       "Dimensions:  (time: 2920)\n",
+       "Coordinates:\n",
+       "    lat      float32 52.5\n",
+       "    lon      float32 252.5\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Data variables:\n",
+       "    air      (time) float32 262.7 263.2 270.9 274.1 ... 261.6 264.2 265.2 267.0\n",
+       "Attributes:\n",
+       "    Conventions:  COARDS\n",
+       "    title:        4x daily NMC reanalysis (1948)\n",
+       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
+       "    platform:     Model\n",
+       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
    " + ], + "text/plain": [ + "\n", + "Dimensions: (time: 2920)\n", + "Coordinates:\n", + " lat float32 52.5\n", + " lon float32 252.5\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Data variables:\n", + " air (time) float32 262.7 263.2 270.9 274.1 ... 261.6 264.2 265.2 267.0\n", + "Attributes:\n", + " Conventions: COARDS\n", + " title: 4x daily NMC reanalysis (1948)\n", + " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", + " platform: Model\n", + " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ds.sel(lat=52.25, lon=251.8998, method=\"nearest\")" ] @@ -528,6 +4897,10 @@ } ], "metadata": { + "kernelspec": { + "display_name": "", + "name": "" + }, "language_info": { "codemirror_mode": { "name": "ipython", @@ -551,6 +4924,13 @@ "toc_position": {}, "toc_section_display": true, "toc_window_display": true + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } } }, "nbformat": 4, diff --git a/intermediate/02.2_indexing_Advanced.ipynb b/intermediate/02.2_indexing_Advanced.ipynb index 88284af7..b21f239d 100644 --- a/intermediate/02.2_indexing_Advanced.ipynb +++ b/intermediate/02.2_indexing_Advanced.ipynb @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 90, "metadata": {}, "outputs": [], "source": [ @@ -44,9 +44,483 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 92, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.Dataset>\n",
+       "Dimensions:  (lat: 25, time: 2920, lon: 53)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Data variables:\n",
+       "    air      (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n",
+       "Attributes:\n",
+       "    Conventions:  COARDS\n",
+       "    title:        4x daily NMC reanalysis (1948)\n",
+       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
+       "    platform:     Model\n",
+       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
    " + ], + "text/plain": [ + "\n", + "Dimensions: (lat: 25, time: 2920, lon: 53)\n", + "Coordinates:\n", + " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", + " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Data variables:\n", + " air (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n", + "Attributes:\n", + " Conventions: COARDS\n", + " title: 4x daily NMC reanalysis (1948)\n", + " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", + " platform: Model\n", + " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ds = xr.tutorial.load_dataset(\"air_temperature\")\n", "da = ds.air\n", @@ -70,11 +544,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 93, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "da[0, [2, 4, 10, 13], [1, 6, 7]].plot(); # -- orthogonal indexing" + "da[0, [2, 4, 10, 13], [1, 6, 7]].plot(); # -- orthogonal indexing" ] }, { @@ -88,14 +573,531 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 94, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'air' (time: 2920, degrees_north: 4, degrees_east: 4)>\n",
+       "array([[[293.1    , 293.1    , 293.29   , 293.29   ],\n",
+       "        [284.6    , 284.6    , 284.9    , 284.19998],\n",
+       "        [282.79   , 282.79   , 283.19998, 282.6    ],\n",
+       "        [282.79   , 282.79   , 283.19998, 282.6    ]],\n",
+       "\n",
+       "       [[293.19998, 293.19998, 293.9    , 294.19998],\n",
+       "        [283.29   , 283.29   , 285.19998, 285.19998],\n",
+       "        [281.4    , 281.4    , 282.79   , 283.5    ],\n",
+       "        [281.4    , 281.4    , 282.79   , 283.5    ]],\n",
+       "\n",
+       "       [[292.4    , 292.4    , 292.9    , 293.4    ],\n",
+       "        [282.     , 282.     , 283.29   , 284.69998],\n",
+       "        [280.     , 280.     , 280.79   , 282.4    ],\n",
+       "        [280.     , 280.     , 280.79   , 282.4    ]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[288.88998, 288.88998, 289.19   , 290.88998],\n",
+       "        [282.49   , 282.49   , 281.99   , 281.99   ],\n",
+       "        [281.29   , 281.29   , 281.29   , 280.99   ],\n",
+       "        [281.29   , 281.29   , 281.29   , 280.99   ]],\n",
+       "\n",
+       "       [[288.29   , 288.29   , 289.19   , 290.79   ],\n",
+       "        [282.09   , 282.09   , 281.59   , 282.38998],\n",
+       "        [280.99   , 280.99   , 280.38998, 280.59   ],\n",
+       "        [280.99   , 280.99   , 280.38998, 280.59   ]],\n",
+       "\n",
+       "       [[289.49   , 289.49   , 290.38998, 291.59   ],\n",
+       "        [282.09   , 282.09   , 281.99   , 283.09   ],\n",
+       "        [281.38998, 281.38998, 280.59   , 280.99   ],\n",
+       "        [281.38998, 281.38998, 280.59   , 280.99   ]]], dtype=float32)\n",
+       "Coordinates:\n",
+       "    lat      (degrees_north) float32 30.0 40.0 42.5 42.5\n",
+       "    lon      (degrees_east) float32 200.0 200.0 202.5 205.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Dimensions without coordinates: degrees_north, degrees_east\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
    " + ], + "text/plain": [ + "\n", + "array([[[293.1 , 293.1 , 293.29 , 293.29 ],\n", + " [284.6 , 284.6 , 284.9 , 284.19998],\n", + " [282.79 , 282.79 , 283.19998, 282.6 ],\n", + " [282.79 , 282.79 , 283.19998, 282.6 ]],\n", + "\n", + " [[293.19998, 293.19998, 293.9 , 294.19998],\n", + " [283.29 , 283.29 , 285.19998, 285.19998],\n", + " [281.4 , 281.4 , 282.79 , 283.5 ],\n", + " [281.4 , 281.4 , 282.79 , 283.5 ]],\n", + "\n", + " [[292.4 , 292.4 , 292.9 , 293.4 ],\n", + " [282. , 282. , 283.29 , 284.69998],\n", + " [280. , 280. , 280.79 , 282.4 ],\n", + " [280. , 280. , 280.79 , 282.4 ]],\n", + "\n", + " ...,\n", + "\n", + " [[288.88998, 288.88998, 289.19 , 290.88998],\n", + " [282.49 , 282.49 , 281.99 , 281.99 ],\n", + " [281.29 , 281.29 , 281.29 , 280.99 ],\n", + " [281.29 , 281.29 , 281.29 , 280.99 ]],\n", + "\n", + " [[288.29 , 288.29 , 289.19 , 290.79 ],\n", + " [282.09 , 282.09 , 281.59 , 282.38998],\n", + " [280.99 , 280.99 , 280.38998, 280.59 ],\n", + " [280.99 , 280.99 , 280.38998, 280.59 ]],\n", + "\n", + " [[289.49 , 289.49 , 290.38998, 291.59 ],\n", + " [282.09 , 282.09 , 281.99 , 283.09 ],\n", + " [281.38998, 281.38998, 280.59 , 280.99 ],\n", + " [281.38998, 281.38998, 280.59 , 280.99 ]]], dtype=float32)\n", + "Coordinates:\n", + " lat (degrees_north) float32 30.0 40.0 42.5 42.5\n", + " lon (degrees_east) float32 200.0 200.0 202.5 205.0\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Dimensions without coordinates: degrees_north, degrees_east\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "target_lat = xr.DataArray([31, 41, 42, 42], dims=\"degrees_north\")\n", "target_lon = xr.DataArray([200, 201, 202, 205], dims=\"degrees_east\")\n", "\n", - "da.sel(lat=target_lat, lon=target_lon, method=\"nearest\") # -- orthogonal indexing" + "da.sel(lat=target_lat, lon=target_lon, method=\"nearest\") # -- orthogonal indexing " ] }, { @@ -128,9 +1130,390 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 95, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray (points: 4)>\n",
+       "array([31, 41, 42, 42])\n",
+       "Dimensions without coordinates: points
    " + ], + "text/plain": [ + "\n", + "array([31, 41, 42, 42])\n", + "Dimensions without coordinates: points" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Define target latitude and longitude (where weather stations might be)\n", "lat_points = xr.DataArray([31, 41, 42, 42], dims=\"points\")\n", @@ -140,9 +1523,390 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 96, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray (points: 4)>\n",
+       "array([200, 201, 202, 205])\n",
+       "Dimensions without coordinates: points
    " + ], + "text/plain": [ + "\n", + "array([200, 201, 202, 205])\n", + "Dimensions without coordinates: points" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "lon_points" ] @@ -156,9 +1920,454 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 97, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'air' (time: 2920, points: 4)>\n",
+       "array([[293.1    , 284.6    , 283.19998, 282.6    ],\n",
+       "       [293.19998, 283.29   , 282.79   , 283.5    ],\n",
+       "       [292.4    , 282.     , 280.79   , 282.4    ],\n",
+       "       ...,\n",
+       "       [288.88998, 282.49   , 281.29   , 280.99   ],\n",
+       "       [288.29   , 282.09   , 280.38998, 280.59   ],\n",
+       "       [289.49   , 282.09   , 280.59   , 280.99   ]], dtype=float32)\n",
+       "Coordinates:\n",
+       "    lat      (points) float32 30.0 40.0 42.5 42.5\n",
+       "    lon      (points) float32 200.0 200.0 202.5 205.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Dimensions without coordinates: points\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
    " + ], + "text/plain": [ + "\n", + "array([[293.1 , 284.6 , 283.19998, 282.6 ],\n", + " [293.19998, 283.29 , 282.79 , 283.5 ],\n", + " [292.4 , 282. , 280.79 , 282.4 ],\n", + " ...,\n", + " [288.88998, 282.49 , 281.29 , 280.99 ],\n", + " [288.29 , 282.09 , 280.38998, 280.59 ],\n", + " [289.49 , 282.09 , 280.59 , 280.99 ]], dtype=float32)\n", + "Coordinates:\n", + " lat (points) float32 30.0 40.0 42.5 42.5\n", + " lon (points) float32 200.0 200.0 202.5 205.0\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Dimensions without coordinates: points\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "da.sel(lat=lat_points, lon=lon_points, method=\"nearest\")" ] @@ -172,9 +2381,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 98, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "('time', 'points')" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "da.sel(lat=lat_points, lon=lon_points, method=\"nearest\").dims" ] @@ -192,9 +2412,508 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 99, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'air' (time: 2920, lat: 3, points: 4)>\n",
+       "array([[[296.6    , 296.6    , 296.19998, 296.4    ],\n",
+       "        [293.1    , 293.1    , 293.29   , 293.29   ],\n",
+       "        [284.6    , 284.6    , 284.9    , 284.19998]],\n",
+       "\n",
+       "       [[296.4    , 296.4    , 295.9    , 296.19998],\n",
+       "        [293.19998, 293.19998, 293.9    , 294.19998],\n",
+       "        [283.29   , 283.29   , 285.19998, 285.19998]],\n",
+       "\n",
+       "       [[295.6    , 295.6    , 295.4    , 295.4    ],\n",
+       "        [292.4    , 292.4    , 292.9    , 293.4    ],\n",
+       "        [282.     , 282.     , 283.29   , 284.69998]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[294.79   , 294.79   , 295.29   , 297.49   ],\n",
+       "        [288.88998, 288.88998, 289.19   , 290.88998],\n",
+       "        [282.49   , 282.49   , 281.99   , 281.99   ]],\n",
+       "\n",
+       "       [[293.69   , 293.69   , 293.88998, 295.38998],\n",
+       "        [288.29   , 288.29   , 289.19   , 290.79   ],\n",
+       "        [282.09   , 282.09   , 281.59   , 282.38998]],\n",
+       "\n",
+       "       [[293.79   , 293.79   , 293.69   , 295.09   ],\n",
+       "        [289.49   , 289.49   , 290.38998, 291.59   ],\n",
+       "        [282.09   , 282.09   , 281.99   , 283.09   ]]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 20.0 30.0 40.0\n",
+       "    lon      (points) float32 200.0 200.0 202.5 205.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Dimensions without coordinates: points\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
    " + ], + "text/plain": [ + "\n", + "array([[[296.6 , 296.6 , 296.19998, 296.4 ],\n", + " [293.1 , 293.1 , 293.29 , 293.29 ],\n", + " [284.6 , 284.6 , 284.9 , 284.19998]],\n", + "\n", + " [[296.4 , 296.4 , 295.9 , 296.19998],\n", + " [293.19998, 293.19998, 293.9 , 294.19998],\n", + " [283.29 , 283.29 , 285.19998, 285.19998]],\n", + "\n", + " [[295.6 , 295.6 , 295.4 , 295.4 ],\n", + " [292.4 , 292.4 , 292.9 , 293.4 ],\n", + " [282. , 282. , 283.29 , 284.69998]],\n", + "\n", + " ...,\n", + "\n", + " [[294.79 , 294.79 , 295.29 , 297.49 ],\n", + " [288.88998, 288.88998, 289.19 , 290.88998],\n", + " [282.49 , 282.49 , 281.99 , 281.99 ]],\n", + "\n", + " [[293.69 , 293.69 , 293.88998, 295.38998],\n", + " [288.29 , 288.29 , 289.19 , 290.79 ],\n", + " [282.09 , 282.09 , 281.59 , 282.38998]],\n", + "\n", + " [[293.79 , 293.79 , 293.69 , 295.09 ],\n", + " [289.49 , 289.49 , 290.38998, 291.59 ],\n", + " [282.09 , 282.09 , 281.99 , 283.09 ]]], dtype=float32)\n", + "Coordinates:\n", + " * lat (lat) float32 20.0 30.0 40.0\n", + " lon (points) float32 200.0 200.0 202.5 205.0\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Dimensions without coordinates: points\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "da.sel(lat=[20, 30, 40], lon=lon_points, method=\"nearest\")" ] @@ -217,9 +2936,398 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 100, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray (new_time: 3)>\n",
+       "array([0.87902737, 0.06690113, 0.01799672])\n",
+       "Coordinates:\n",
+       "    time      (new_time) datetime64[ns] 2000-01-03 2000-01-02 2000-01-01\n",
+       "    space     (new_time) <U2 'IA' 'IL' 'IN'\n",
+       "  * new_time  (new_time) datetime64[ns] 2000-01-03 2000-01-02 2000-01-01
    " + ], + "text/plain": [ + "\n", + "array([0.87902737, 0.06690113, 0.01799672])\n", + "Coordinates:\n", + " time (new_time) datetime64[ns] 2000-01-03 2000-01-02 2000-01-01\n", + " space (new_time) Date: Fri, 7 Jul 2023 20:19:09 -0600 Subject: [PATCH 40/54] adding syntax myst updates --- .../02.3_indexing_BooleanMasking.ipynb | 1788 ++++++++++++++++- 1 file changed, 1773 insertions(+), 15 deletions(-) diff --git a/intermediate/02.3_indexing_BooleanMasking.ipynb b/intermediate/02.3_indexing_BooleanMasking.ipynb index 586814e1..9fe747d2 100644 --- a/intermediate/02.3_indexing_BooleanMasking.ipynb +++ b/intermediate/02.3_indexing_BooleanMasking.ipynb @@ -30,7 +30,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -50,9 +50,541 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.Dataset>\n",
+       "Dimensions:  (time: 36, y: 205, x: 275)\n",
+       "Coordinates:\n",
+       "  * time     (time) object 1980-09-16 12:00:00 ... 1983-08-17 00:00:00\n",
+       "    xc       (y, x) float64 189.2 189.4 189.6 189.7 ... 17.65 17.4 17.15 16.91\n",
+       "    yc       (y, x) float64 16.53 16.78 17.02 17.27 ... 28.26 28.01 27.76 27.51\n",
+       "Dimensions without coordinates: y, x\n",
+       "Data variables:\n",
+       "    Tair     (time, y, x) float64 nan nan nan nan nan ... 29.8 28.66 28.19 28.21\n",
+       "Attributes:\n",
+       "    title:                     /workspace/jhamman/processed/R1002RBRxaaa01a/l...\n",
+       "    institution:               U.W.\n",
+       "    source:                    RACM R1002RBRxaaa01a\n",
+       "    output_frequency:          daily\n",
+       "    output_mode:               averaged\n",
+       "    convention:                CF-1.4\n",
+       "    references:                Based on the initial model of Liang et al., 19...\n",
+       "    comment:                   Output from the Variable Infiltration Capacity...\n",
+       "    nco_openmp_thread_number:  1\n",
+       "    NCO:                       netCDF Operators version 4.7.9 (Homepage = htt...\n",
+       "    history:                   Fri Aug  7 17:57:38 2020: ncatted -a bounds,,d...
    " + ], + "text/plain": [ + "\n", + "Dimensions: (time: 36, y: 205, x: 275)\n", + "Coordinates:\n", + " * time (time) object 1980-09-16 12:00:00 ... 1983-08-17 00:00:00\n", + " xc (y, x) float64 189.2 189.4 189.6 189.7 ... 17.65 17.4 17.15 16.91\n", + " yc (y, x) float64 16.53 16.78 17.02 17.27 ... 28.26 28.01 27.76 27.51\n", + "Dimensions without coordinates: y, x\n", + "Data variables:\n", + " Tair (time, y, x) float64 nan nan nan nan nan ... 29.8 28.66 28.19 28.21\n", + "Attributes:\n", + " title: /workspace/jhamman/processed/R1002RBRxaaa01a/l...\n", + " institution: U.W.\n", + " source: RACM R1002RBRxaaa01a\n", + " output_frequency: daily\n", + " output_mode: averaged\n", + " convention: CF-1.4\n", + " references: Based on the initial model of Liang et al., 19...\n", + " comment: Output from the Variable Infiltration Capacity...\n", + " nco_openmp_thread_number: 1\n", + " NCO: netCDF Operators version 4.7.9 (Homepage = htt...\n", + " history: Fri Aug 7 17:57:38 2020: ncatted -a bounds,,d..." + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ds = xr.tutorial.load_dataset(\"rasm\")\n", "ds" @@ -67,9 +599,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'long_name': 'longitude of grid cell center', 'units': 'degrees_east'}\n", + "{'long_name': 'latitude of grid cell center', 'units': 'degrees_north'}\n" + ] + } + ], "source": [ "print(ds.xc.attrs)\n", "print(ds.yc.attrs)" @@ -77,9 +618,600 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'Tair' (time: 36, y: 205, x: 275)>\n",
+       "array([[[        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        ...,\n",
+       "        [        nan,         nan,         nan, ..., 27.03290153,\n",
+       "         27.03125761, 27.33531541],\n",
+       "        [        nan,         nan,         nan, ..., 27.2784053 ,\n",
+       "         26.80261869, 27.08603517],\n",
+       "        [        nan,         nan,         nan, ..., 27.02344402,\n",
+       "         26.56473862, 26.73064933]],\n",
+       "\n",
+       "       [[        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "...\n",
+       "        [        nan,         nan,         nan, ..., 27.8597472 ,\n",
+       "         27.82928439, 28.09249224],\n",
+       "        [        nan,         nan,         nan, ..., 27.89704094,\n",
+       "         27.31104941, 27.67387171],\n",
+       "        [        nan,         nan,         nan, ..., 27.46837113,\n",
+       "         27.0088944 , 27.23017976]],\n",
+       "\n",
+       "       [[        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        [        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan],\n",
+       "        ...,\n",
+       "        [        nan,         nan,         nan, ..., 28.95929072,\n",
+       "         28.87672039, 29.04890862],\n",
+       "        [        nan,         nan,         nan, ..., 29.036132  ,\n",
+       "         28.42273578, 28.68721201],\n",
+       "        [        nan,         nan,         nan, ..., 28.66381585,\n",
+       "         28.18595533, 28.20753022]]])\n",
+       "Coordinates:\n",
+       "  * time     (time) object 1980-09-16 12:00:00 ... 1983-08-17 00:00:00\n",
+       "    xc       (y, x) float64 189.2 189.4 189.6 189.7 ... 17.65 17.4 17.15 16.91\n",
+       "    yc       (y, x) float64 16.53 16.78 17.02 17.27 ... 28.26 28.01 27.76 27.51\n",
+       "Dimensions without coordinates: y, x\n",
+       "Attributes:\n",
+       "    units:           C\n",
+       "    long_name:       Surface air temperature\n",
+       "    type_preferred:  double\n",
+       "    time_rep:        instantaneous
    " + ], + "text/plain": [ + "\n", + "array([[[ nan, nan, nan, ..., nan,\n", + " nan, nan],\n", + " [ nan, nan, nan, ..., nan,\n", + " nan, nan],\n", + " [ nan, nan, nan, ..., nan,\n", + " nan, nan],\n", + " ...,\n", + " [ nan, nan, nan, ..., 27.03290153,\n", + " 27.03125761, 27.33531541],\n", + " [ nan, nan, nan, ..., 27.2784053 ,\n", + " 26.80261869, 27.08603517],\n", + " [ nan, nan, nan, ..., 27.02344402,\n", + " 26.56473862, 26.73064933]],\n", + "\n", + " [[ nan, nan, nan, ..., nan,\n", + " nan, nan],\n", + " [ nan, nan, nan, ..., nan,\n", + " nan, nan],\n", + " [ nan, nan, nan, ..., nan,\n", + " nan, nan],\n", + "...\n", + " [ nan, nan, nan, ..., 27.8597472 ,\n", + " 27.82928439, 28.09249224],\n", + " [ nan, nan, nan, ..., 27.89704094,\n", + " 27.31104941, 27.67387171],\n", + " [ nan, nan, nan, ..., 27.46837113,\n", + " 27.0088944 , 27.23017976]],\n", + "\n", + " [[ nan, nan, nan, ..., nan,\n", + " nan, nan],\n", + " [ nan, nan, nan, ..., nan,\n", + " nan, nan],\n", + " [ nan, nan, nan, ..., nan,\n", + " nan, nan],\n", + " ...,\n", + " [ nan, nan, nan, ..., 28.95929072,\n", + " 28.87672039, 29.04890862],\n", + " [ nan, nan, nan, ..., 29.036132 ,\n", + " 28.42273578, 28.68721201],\n", + " [ nan, nan, nan, ..., 28.66381585,\n", + " 28.18595533, 28.20753022]]])\n", + "Coordinates:\n", + " * time (time) object 1980-09-16 12:00:00 ... 1983-08-17 00:00:00\n", + " xc (y, x) float64 189.2 189.4 189.6 189.7 ... 17.65 17.4 17.15 16.91\n", + " yc (y, x) float64 16.53 16.78 17.02 17.27 ... 28.26 28.01 27.76 27.51\n", + "Dimensions without coordinates: y, x\n", + "Attributes:\n", + " units: C\n", + " long_name: Surface air temperature\n", + " type_preferred: double\n", + " time_rep: instantaneous" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "da = ds.Tair\n", "da" @@ -100,9 +1232,600 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'Tair' (time: 36, y: 205, x: 275)>\n",
+       "array([[[-9999.        , -9999.        , -9999.        , ...,\n",
+       "         -9999.        , -9999.        , -9999.        ],\n",
+       "        [-9999.        , -9999.        , -9999.        , ...,\n",
+       "         -9999.        , -9999.        , -9999.        ],\n",
+       "        [-9999.        , -9999.        , -9999.        , ...,\n",
+       "         -9999.        , -9999.        , -9999.        ],\n",
+       "        ...,\n",
+       "        [-9999.        , -9999.        , -9999.        , ...,\n",
+       "            27.03290153,    27.03125761,    27.33531541],\n",
+       "        [-9999.        , -9999.        , -9999.        , ...,\n",
+       "            27.2784053 ,    26.80261869,    27.08603517],\n",
+       "        [-9999.        , -9999.        , -9999.        , ...,\n",
+       "            27.02344402,    26.56473862,    26.73064933]],\n",
+       "\n",
+       "       [[-9999.        , -9999.        , -9999.        , ...,\n",
+       "         -9999.        , -9999.        , -9999.        ],\n",
+       "        [-9999.        , -9999.        , -9999.        , ...,\n",
+       "         -9999.        , -9999.        , -9999.        ],\n",
+       "        [-9999.        , -9999.        , -9999.        , ...,\n",
+       "         -9999.        , -9999.        , -9999.        ],\n",
+       "...\n",
+       "        [-9999.        , -9999.        , -9999.        , ...,\n",
+       "            27.8597472 ,    27.82928439,    28.09249224],\n",
+       "        [-9999.        , -9999.        , -9999.        , ...,\n",
+       "            27.89704094,    27.31104941,    27.67387171],\n",
+       "        [-9999.        , -9999.        , -9999.        , ...,\n",
+       "            27.46837113,    27.0088944 ,    27.23017976]],\n",
+       "\n",
+       "       [[-9999.        , -9999.        , -9999.        , ...,\n",
+       "         -9999.        , -9999.        , -9999.        ],\n",
+       "        [-9999.        , -9999.        , -9999.        , ...,\n",
+       "         -9999.        , -9999.        , -9999.        ],\n",
+       "        [-9999.        , -9999.        , -9999.        , ...,\n",
+       "         -9999.        , -9999.        , -9999.        ],\n",
+       "        ...,\n",
+       "        [-9999.        , -9999.        , -9999.        , ...,\n",
+       "            28.95929072,    28.87672039,    29.04890862],\n",
+       "        [-9999.        , -9999.        , -9999.        , ...,\n",
+       "            29.036132  ,    28.42273578,    28.68721201],\n",
+       "        [-9999.        , -9999.        , -9999.        , ...,\n",
+       "            28.66381585,    28.18595533,    28.20753022]]])\n",
+       "Coordinates:\n",
+       "  * time     (time) object 1980-09-16 12:00:00 ... 1983-08-17 00:00:00\n",
+       "    xc       (y, x) float64 189.2 189.4 189.6 189.7 ... 17.65 17.4 17.15 16.91\n",
+       "    yc       (y, x) float64 16.53 16.78 17.02 17.27 ... 28.26 28.01 27.76 27.51\n",
+       "Dimensions without coordinates: y, x\n",
+       "Attributes:\n",
+       "    units:           C\n",
+       "    long_name:       Surface air temperature\n",
+       "    type_preferred:  double\n",
+       "    time_rep:        instantaneous
    " + ], + "text/plain": [ + "\n", + "array([[[-9999. , -9999. , -9999. , ...,\n", + " -9999. , -9999. , -9999. ],\n", + " [-9999. , -9999. , -9999. , ...,\n", + " -9999. , -9999. , -9999. ],\n", + " [-9999. , -9999. , -9999. , ...,\n", + " -9999. , -9999. , -9999. ],\n", + " ...,\n", + " [-9999. , -9999. , -9999. , ...,\n", + " 27.03290153, 27.03125761, 27.33531541],\n", + " [-9999. , -9999. , -9999. , ...,\n", + " 27.2784053 , 26.80261869, 27.08603517],\n", + " [-9999. , -9999. , -9999. , ...,\n", + " 27.02344402, 26.56473862, 26.73064933]],\n", + "\n", + " [[-9999. , -9999. , -9999. , ...,\n", + " -9999. , -9999. , -9999. ],\n", + " [-9999. , -9999. , -9999. , ...,\n", + " -9999. , -9999. , -9999. ],\n", + " [-9999. , -9999. , -9999. , ...,\n", + " -9999. , -9999. , -9999. ],\n", + "...\n", + " [-9999. , -9999. , -9999. , ...,\n", + " 27.8597472 , 27.82928439, 28.09249224],\n", + " [-9999. , -9999. , -9999. , ...,\n", + " 27.89704094, 27.31104941, 27.67387171],\n", + " [-9999. , -9999. , -9999. , ...,\n", + " 27.46837113, 27.0088944 , 27.23017976]],\n", + "\n", + " [[-9999. , -9999. , -9999. , ...,\n", + " -9999. , -9999. , -9999. ],\n", + " [-9999. , -9999. , -9999. , ...,\n", + " -9999. , -9999. , -9999. ],\n", + " [-9999. , -9999. , -9999. , ...,\n", + " -9999. , -9999. , -9999. ],\n", + " ...,\n", + " [-9999. , -9999. , -9999. , ...,\n", + " 28.95929072, 28.87672039, 29.04890862],\n", + " [-9999. , -9999. , -9999. , ...,\n", + " 29.036132 , 28.42273578, 28.68721201],\n", + " [-9999. , -9999. , -9999. , ...,\n", + " 28.66381585, 28.18595533, 28.20753022]]])\n", + "Coordinates:\n", + " * time (time) object 1980-09-16 12:00:00 ... 1983-08-17 00:00:00\n", + " xc (y, x) float64 189.2 189.4 189.6 189.7 ... 17.65 17.4 17.15 16.91\n", + " yc (y, x) float64 16.53 16.78 17.02 17.27 ... 28.26 28.01 27.76 27.51\n", + "Dimensions without coordinates: y, x\n", + "Attributes:\n", + " units: C\n", + " long_name: Surface air temperature\n", + " type_preferred: double\n", + " time_rep: instantaneous" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Let's replace the missing values (nan) with some placeholder\n", "ds.Tair.where(ds.Tair.notnull(), -9999)" @@ -148,7 +1871,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "```tip\n", + "```{tip}\n", "By default Xarray set the masked values to `nan`. But as we saw in the first example, we can set it to other values too. \n", "```" ] @@ -294,9 +2017,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "type", + "evalue": "name 'da_masked' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[6], line 4\u001b[0m\n\u001b[1;32m 2\u001b[0m ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39maxes(projection\u001b[38;5;241m=\u001b[39mccrs\u001b[38;5;241m.\u001b[39mPlateCarree())\n\u001b[1;32m 3\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_global()\n\u001b[0;32m----> 4\u001b[0m \u001b[43mda_masked\u001b[49m[\u001b[38;5;241m0\u001b[39m, :, :]\u001b[38;5;241m.\u001b[39mplot\u001b[38;5;241m.\u001b[39mpcolormesh(\n\u001b[1;32m 5\u001b[0m ax\u001b[38;5;241m=\u001b[39max, transform\u001b[38;5;241m=\u001b[39mccrs\u001b[38;5;241m.\u001b[39mPlateCarree(), x\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxc\u001b[39m\u001b[38;5;124m\"\u001b[39m, y\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myc\u001b[39m\u001b[38;5;124m\"\u001b[39m, add_colorbar\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m 6\u001b[0m )\n\u001b[1;32m 7\u001b[0m ax\u001b[38;5;241m.\u001b[39mcoastlines()\n\u001b[1;32m 8\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_ylim([\u001b[38;5;241m50\u001b[39m, \u001b[38;5;241m80\u001b[39m])\n", + "\u001b[0;31mNameError\u001b[0m: name 'da_masked' is not defined" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.figure(figsize=(5, 5))\n", "ax = plt.axes(projection=ccrs.PlateCarree())\n", @@ -412,7 +2157,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "```tip\n", + "```{tip}\n", "`isin()` works particularly well with `where()` to support indexing by arrays that are not already labels of an array. \n", "```\n", "\n", @@ -452,7 +2197,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "```warning\n", + "```{warning}\n", "Please note that when done repeatedly, this type of indexing is significantly slower than using `sel()`.\n", "```" ] @@ -468,6 +2213,11 @@ } ], "metadata": { + "kernelspec": { + "display_name": "NPL 2023b", + "language": "python", + "name": "npl-2023b" + }, "language_info": { "codemirror_mode": { "name": "ipython", @@ -477,7 +2227,8 @@ "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3" + "pygments_lexer": "ipython3", + "version": "3.10.12" }, "toc": { "base_numbering": 1, @@ -491,6 +2242,13 @@ "toc_position": {}, "toc_section_display": true, "toc_window_display": true + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } } }, "nbformat": 4, From cd44e5f888f61e3a776852b7bf8bb8fddb7defcc Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Sat, 8 Jul 2023 02:19:35 +0000 Subject: [PATCH 41/54] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- fundamentals/02.1_indexing_Basic.ipynb | 4457 +---------------- intermediate/02.2_indexing_Advanced.ipynb | 3172 +----------- .../02.3_indexing_BooleanMasking.ipynb | 1782 +------ 3 files changed, 74 insertions(+), 9337 deletions(-) diff --git a/fundamentals/02.1_indexing_Basic.ipynb b/fundamentals/02.1_indexing_Basic.ipynb index b568b66a..7f2eec30 100644 --- a/fundamentals/02.1_indexing_Basic.ipynb +++ b/fundamentals/02.1_indexing_Basic.ipynb @@ -53,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -71,483 +71,9 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.Dataset>\n",
-       "Dimensions:  (lat: 25, time: 2920, lon: 53)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Data variables:\n",
-       "    air      (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n",
-       "Attributes:\n",
-       "    Conventions:  COARDS\n",
-       "    title:        4x daily NMC reanalysis (1948)\n",
-       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
-       "    platform:     Model\n",
-       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
    " - ], - "text/plain": [ - "\n", - "Dimensions: (lat: 25, time: 2920, lon: 53)\n", - "Coordinates:\n", - " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", - " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Data variables:\n", - " air (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n", - "Attributes:\n", - " Conventions: COARDS\n", - " title: 4x daily NMC reanalysis (1948)\n", - " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", - " platform: Model\n", - " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "ds = xr.tutorial.load_dataset(\"air_temperature\")\n", "ds" @@ -555,7 +81,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -585,20 +111,9 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2920, 25, 53)" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "np_array = ds[\"air\"].data # numpy array\n", "np_array.shape" @@ -613,20 +128,9 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "242.09999" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "np_array[1, 0, 0]" ] @@ -640,20 +144,9 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([295. , 294.4 , 294.5 , ..., 297.29, 297.79, 297.99], dtype=float32)" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# extract a time-series for one spatial location\n", "np_array[:, 20, 40]" @@ -685,434 +178,9 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 2920)>\n",
-       "array([295.  , 294.4 , 294.5 , ..., 297.29, 297.79, 297.99], dtype=float32)\n",
-       "Coordinates:\n",
-       "    lat      float32 25.0\n",
-       "    lon      float32 300.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([295. , 294.4 , 294.5 , ..., 297.29, 297.79, 297.99], dtype=float32)\n", - "Coordinates:\n", - " lat float32 25.0\n", - " lon float32 300.0\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da[:, 20, 40]" ] @@ -1129,42 +197,20 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2920, 2)" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "np_array[:,[0, 1], [0, 1]].shape" + "np_array[:, [0, 1], [0, 1]].shape" ] }, { "cell_type": "code", - "execution_count": 57, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2920, 2, 2)" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "da[:,[0, 1], [0, 1]].shape" + "da[:, [0, 1], [0, 1]].shape" ] }, { @@ -1197,20 +243,9 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "da.isel(lat=20, lon=40).plot();" ] @@ -1224,20 +259,9 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "da.isel(time=slice(0, 20), lat=20, lon=40).plot();" ] @@ -1271,24 +295,13 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": null, "metadata": { "tags": [ "hide-output" ] }, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "da.sel(time=\"2014-01-01\", lat=25, lon=210).plot();" ] @@ -1302,438 +315,13 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": null, "metadata": { "tags": [ "hide-output" ] }, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 1460)>\n",
-       "array([277.6 , 277.5 , 277.4 , ..., 277.59, 277.59, 277.59], dtype=float32)\n",
-       "Coordinates:\n",
-       "    lat      float32 50.0\n",
-       "    lon      float32 200.0\n",
-       "  * time     (time) datetime64[ns] 2014-01-01 ... 2014-12-31T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([277.6 , 277.5 , 277.4 , ..., 277.59, 277.59, 277.59], dtype=float32)\n", - "Coordinates:\n", - " lat float32 50.0\n", - " lon float32 200.0\n", - " * time (time) datetime64[ns] 2014-01-01 ... 2014-12-31T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da.sel(lat=50.0, lon=200.0, time=\"2014\")" ] @@ -1747,570 +335,9 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 1212, lat: 25, lon: 53)>\n",
-       "array([[[246.     , 245.09999, 244.09999, ..., 240.2    , 242.     ,\n",
-       "         244.2    ],\n",
-       "        [240.59999, 241.7    , 242.09999, ..., 239.09999, 241.2    ,\n",
-       "         244.59999],\n",
-       "        [238.2    , 239.79999, 240.79999, ..., 243.89   , 246.89   ,\n",
-       "         251.2    ],\n",
-       "        ...,\n",
-       "        [295.6    , 295.6    , 296.     , ..., 293.6    , 293.6    ,\n",
-       "         294.1    ],\n",
-       "        [297.     , 297.     , 297.     , ..., 293.69998, 293.9    ,\n",
-       "         294.6    ],\n",
-       "        [297.5    , 297.69998, 297.69998, ..., 294.5    , 294.69998,\n",
-       "         295.19998]],\n",
-       "\n",
-       "       [[245.2    , 244.29999, 243.     , ..., 241.89   , 243.09999,\n",
-       "         244.59999],\n",
-       "        [241.09999, 241.89   , 241.89   , ..., 241.89   , 243.59999,\n",
-       "         246.39   ],\n",
-       "        [239.89   , 241.     , 241.29999, ..., 245.09999, 248.2    ,\n",
-       "         252.2    ],\n",
-       "...\n",
-       "        [296.49   , 295.59   , 295.49   , ..., 297.29   , 297.38998,\n",
-       "         296.79   ],\n",
-       "        [298.09   , 297.79   , 297.19   , ..., 297.69   , 298.09   ,\n",
-       "         297.38998],\n",
-       "        [298.49   , 298.29   , 297.99   , ..., 298.29   , 298.09   ,\n",
-       "         297.99   ]],\n",
-       "\n",
-       "       [[246.29   , 246.39   , 245.98999, ..., 232.29   , 233.39   ,\n",
-       "         234.98999],\n",
-       "        [246.68999, 248.18999, 248.98999, ..., 230.59   , 232.48999,\n",
-       "         235.68999],\n",
-       "        [244.79   , 245.29   , 245.89   , ..., 230.48999, 235.09   ,\n",
-       "         241.39   ],\n",
-       "        ...,\n",
-       "        [296.88998, 296.49   , 296.49   , ..., 297.69   , 297.29   ,\n",
-       "         296.19   ],\n",
-       "        [298.29   , 297.99   , 297.49   , ..., 298.29   , 298.38998,\n",
-       "         297.49   ],\n",
-       "        [298.49   , 298.29   , 297.99   , ..., 298.59   , 298.38998,\n",
-       "         298.29   ]]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-       "  * time     (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([[[246. , 245.09999, 244.09999, ..., 240.2 , 242. ,\n", - " 244.2 ],\n", - " [240.59999, 241.7 , 242.09999, ..., 239.09999, 241.2 ,\n", - " 244.59999],\n", - " [238.2 , 239.79999, 240.79999, ..., 243.89 , 246.89 ,\n", - " 251.2 ],\n", - " ...,\n", - " [295.6 , 295.6 , 296. , ..., 293.6 , 293.6 ,\n", - " 294.1 ],\n", - " [297. , 297. , 297. , ..., 293.69998, 293.9 ,\n", - " 294.6 ],\n", - " [297.5 , 297.69998, 297.69998, ..., 294.5 , 294.69998,\n", - " 295.19998]],\n", - "\n", - " [[245.2 , 244.29999, 243. , ..., 241.89 , 243.09999,\n", - " 244.59999],\n", - " [241.09999, 241.89 , 241.89 , ..., 241.89 , 243.59999,\n", - " 246.39 ],\n", - " [239.89 , 241. , 241.29999, ..., 245.09999, 248.2 ,\n", - " 252.2 ],\n", - "...\n", - " [296.49 , 295.59 , 295.49 , ..., 297.29 , 297.38998,\n", - " 296.79 ],\n", - " [298.09 , 297.79 , 297.19 , ..., 297.69 , 298.09 ,\n", - " 297.38998],\n", - " [298.49 , 298.29 , 297.99 , ..., 298.29 , 298.09 ,\n", - " 297.99 ]],\n", - "\n", - " [[246.29 , 246.39 , 245.98999, ..., 232.29 , 233.39 ,\n", - " 234.98999],\n", - " [246.68999, 248.18999, 248.98999, ..., 230.59 , 232.48999,\n", - " 235.68999],\n", - " [244.79 , 245.29 , 245.89 , ..., 230.48999, 235.09 ,\n", - " 241.39 ],\n", - " ...,\n", - " [296.88998, 296.49 , 296.49 , ..., 297.69 , 297.29 ,\n", - " 296.19 ],\n", - " [298.29 , 297.99 , 297.49 , ..., 298.29 , 298.38998,\n", - " 297.49 ],\n", - " [298.49 , 298.29 , 297.99 , ..., 298.59 , 298.38998,\n", - " 298.29 ]]], dtype=float32)\n", - "Coordinates:\n", - " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", - " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", - " * time (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# demonstrate slicing\n", "da.sel(time=slice(\"2014-02-14\", \"2014-12-13\"))" @@ -2325,570 +352,9 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 1212, lat: 25, lon: 53)>\n",
-       "array([[[246.     , 245.09999, 244.09999, ..., 240.2    , 242.     ,\n",
-       "         244.2    ],\n",
-       "        [240.59999, 241.7    , 242.09999, ..., 239.09999, 241.2    ,\n",
-       "         244.59999],\n",
-       "        [238.2    , 239.79999, 240.79999, ..., 243.89   , 246.89   ,\n",
-       "         251.2    ],\n",
-       "        ...,\n",
-       "        [295.6    , 295.6    , 296.     , ..., 293.6    , 293.6    ,\n",
-       "         294.1    ],\n",
-       "        [297.     , 297.     , 297.     , ..., 293.69998, 293.9    ,\n",
-       "         294.6    ],\n",
-       "        [297.5    , 297.69998, 297.69998, ..., 294.5    , 294.69998,\n",
-       "         295.19998]],\n",
-       "\n",
-       "       [[245.2    , 244.29999, 243.     , ..., 241.89   , 243.09999,\n",
-       "         244.59999],\n",
-       "        [241.09999, 241.89   , 241.89   , ..., 241.89   , 243.59999,\n",
-       "         246.39   ],\n",
-       "        [239.89   , 241.     , 241.29999, ..., 245.09999, 248.2    ,\n",
-       "         252.2    ],\n",
-       "...\n",
-       "        [296.49   , 295.59   , 295.49   , ..., 297.29   , 297.38998,\n",
-       "         296.79   ],\n",
-       "        [298.09   , 297.79   , 297.19   , ..., 297.69   , 298.09   ,\n",
-       "         297.38998],\n",
-       "        [298.49   , 298.29   , 297.99   , ..., 298.29   , 298.09   ,\n",
-       "         297.99   ]],\n",
-       "\n",
-       "       [[246.29   , 246.39   , 245.98999, ..., 232.29   , 233.39   ,\n",
-       "         234.98999],\n",
-       "        [246.68999, 248.18999, 248.98999, ..., 230.59   , 232.48999,\n",
-       "         235.68999],\n",
-       "        [244.79   , 245.29   , 245.89   , ..., 230.48999, 235.09   ,\n",
-       "         241.39   ],\n",
-       "        ...,\n",
-       "        [296.88998, 296.49   , 296.49   , ..., 297.69   , 297.29   ,\n",
-       "         296.19   ],\n",
-       "        [298.29   , 297.99   , 297.49   , ..., 298.29   , 298.38998,\n",
-       "         297.49   ],\n",
-       "        [298.49   , 298.29   , 297.99   , ..., 298.59   , 298.38998,\n",
-       "         298.29   ]]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-       "  * time     (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([[[246. , 245.09999, 244.09999, ..., 240.2 , 242. ,\n", - " 244.2 ],\n", - " [240.59999, 241.7 , 242.09999, ..., 239.09999, 241.2 ,\n", - " 244.59999],\n", - " [238.2 , 239.79999, 240.79999, ..., 243.89 , 246.89 ,\n", - " 251.2 ],\n", - " ...,\n", - " [295.6 , 295.6 , 296. , ..., 293.6 , 293.6 ,\n", - " 294.1 ],\n", - " [297. , 297. , 297. , ..., 293.69998, 293.9 ,\n", - " 294.6 ],\n", - " [297.5 , 297.69998, 297.69998, ..., 294.5 , 294.69998,\n", - " 295.19998]],\n", - "\n", - " [[245.2 , 244.29999, 243. , ..., 241.89 , 243.09999,\n", - " 244.59999],\n", - " [241.09999, 241.89 , 241.89 , ..., 241.89 , 243.59999,\n", - " 246.39 ],\n", - " [239.89 , 241. , 241.29999, ..., 245.09999, 248.2 ,\n", - " 252.2 ],\n", - "...\n", - " [296.49 , 295.59 , 295.49 , ..., 297.29 , 297.38998,\n", - " 296.79 ],\n", - " [298.09 , 297.79 , 297.19 , ..., 297.69 , 298.09 ,\n", - " 297.38998],\n", - " [298.49 , 298.29 , 297.99 , ..., 298.29 , 298.09 ,\n", - " 297.99 ]],\n", - "\n", - " [[246.29 , 246.39 , 245.98999, ..., 232.29 , 233.39 ,\n", - " 234.98999],\n", - " [246.68999, 248.18999, 248.98999, ..., 230.59 , 232.48999,\n", - " 235.68999],\n", - " [244.79 , 245.29 , 245.89 , ..., 230.48999, 235.09 ,\n", - " 241.39 ],\n", - " ...,\n", - " [296.88998, 296.49 , 296.49 , ..., 297.69 , 297.29 ,\n", - " 296.19 ],\n", - " [298.29 , 297.99 , 297.49 , ..., 298.29 , 298.38998,\n", - " 297.49 ],\n", - " [298.49 , 298.29 , 297.99 , ..., 298.59 , 298.38998,\n", - " 298.29 ]]], dtype=float32)\n", - "Coordinates:\n", - " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", - " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", - " * time (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da.loc[\"2014-02-14\":\"2014-12-13\"]" ] @@ -2904,570 +370,9 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 2919, lat: 24, lon: 52)>\n",
-       "array([[[242.5    , 243.5    , 244.     , ..., 232.79999, 235.5    ,\n",
-       "         238.59999],\n",
-       "        [244.5    , 244.7    , 244.2    , ..., 232.79999, 235.29999,\n",
-       "         239.29999],\n",
-       "        [249.79999, 248.89   , 247.5    , ..., 233.2    , 236.39   ,\n",
-       "         241.7    ],\n",
-       "        ...,\n",
-       "        [296.19998, 296.4    , 296.5    , ..., 295.4    , 295.1    ,\n",
-       "         294.69998],\n",
-       "        [296.19998, 296.79   , 296.5    , ..., 295.9    , 295.9    ,\n",
-       "         295.19998],\n",
-       "        [296.79   , 297.1    , 297.     , ..., 296.9    , 296.79   ,\n",
-       "         296.6    ]],\n",
-       "\n",
-       "       [[242.7    , 243.09999, 243.39   , ..., 232.     , 233.59999,\n",
-       "         235.79999],\n",
-       "        [244.09999, 244.2    , 244.09999, ..., 231.     , 232.5    ,\n",
-       "         235.7    ],\n",
-       "        [252.89   , 252.09999, 250.79999, ..., 230.79999, 233.39   ,\n",
-       "         238.5    ],\n",
-       "...\n",
-       "        [293.88998, 295.38998, 297.19   , ..., 295.09   , 294.69   ,\n",
-       "         294.29   ],\n",
-       "        [297.19   , 297.59   , 297.88998, ..., 295.29   , 295.09   ,\n",
-       "         294.38998],\n",
-       "        [298.38998, 298.49   , 298.59   , ..., 295.69   , 295.49   ,\n",
-       "         295.19   ]],\n",
-       "\n",
-       "       [[244.29   , 243.29   , 242.18999, ..., 241.68999, 241.48999,\n",
-       "         241.79   ],\n",
-       "        [249.29   , 248.39   , 246.98999, ..., 239.59   , 240.29   ,\n",
-       "         241.68999],\n",
-       "        [262.19   , 261.38998, 259.99   , ..., 239.89   , 242.59   ,\n",
-       "         246.29   ],\n",
-       "        ...,\n",
-       "        [293.69   , 295.09   , 296.69   , ..., 295.29   , 295.09   ,\n",
-       "         294.69   ],\n",
-       "        [296.88998, 297.19   , 297.49   , ..., 295.69   , 295.69   ,\n",
-       "         295.19   ],\n",
-       "        [298.09   , 298.09   , 298.49   , ..., 296.49   , 296.19   ,\n",
-       "         295.69   ]]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 202.5 205.0 207.5 210.0 ... 322.5 325.0 327.5 330.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([[[242.5 , 243.5 , 244. , ..., 232.79999, 235.5 ,\n", - " 238.59999],\n", - " [244.5 , 244.7 , 244.2 , ..., 232.79999, 235.29999,\n", - " 239.29999],\n", - " [249.79999, 248.89 , 247.5 , ..., 233.2 , 236.39 ,\n", - " 241.7 ],\n", - " ...,\n", - " [296.19998, 296.4 , 296.5 , ..., 295.4 , 295.1 ,\n", - " 294.69998],\n", - " [296.19998, 296.79 , 296.5 , ..., 295.9 , 295.9 ,\n", - " 295.19998],\n", - " [296.79 , 297.1 , 297. , ..., 296.9 , 296.79 ,\n", - " 296.6 ]],\n", - "\n", - " [[242.7 , 243.09999, 243.39 , ..., 232. , 233.59999,\n", - " 235.79999],\n", - " [244.09999, 244.2 , 244.09999, ..., 231. , 232.5 ,\n", - " 235.7 ],\n", - " [252.89 , 252.09999, 250.79999, ..., 230.79999, 233.39 ,\n", - " 238.5 ],\n", - "...\n", - " [293.88998, 295.38998, 297.19 , ..., 295.09 , 294.69 ,\n", - " 294.29 ],\n", - " [297.19 , 297.59 , 297.88998, ..., 295.29 , 295.09 ,\n", - " 294.38998],\n", - " [298.38998, 298.49 , 298.59 , ..., 295.69 , 295.49 ,\n", - " 295.19 ]],\n", - "\n", - " [[244.29 , 243.29 , 242.18999, ..., 241.68999, 241.48999,\n", - " 241.79 ],\n", - " [249.29 , 248.39 , 246.98999, ..., 239.59 , 240.29 ,\n", - " 241.68999],\n", - " [262.19 , 261.38998, 259.99 , ..., 239.89 , 242.59 ,\n", - " 246.29 ],\n", - " ...,\n", - " [293.69 , 295.09 , 296.69 , ..., 295.29 , 295.09 ,\n", - " 294.69 ],\n", - " [296.88998, 297.19 , 297.49 , ..., 295.69 , 295.69 ,\n", - " 295.19 ],\n", - " [298.09 , 298.09 , 298.49 , ..., 296.49 , 296.19 ,\n", - " 295.69 ]]], dtype=float32)\n", - "Coordinates:\n", - " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", - " * lon (lon) float32 202.5 205.0 207.5 210.0 ... 322.5 325.0 327.5 330.0\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da.drop_sel(lat=50.0, lon=200.0, time=\"2014\")" ] @@ -3486,441 +391,13 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": null, "metadata": { "tags": [ "hide-output" ] }, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 2920)>\n",
-       "array([262.69998, 263.19998, 270.9    , ..., 264.19   , 265.19   ,\n",
-       "       266.99   ], dtype=float32)\n",
-       "Coordinates:\n",
-       "    lat      float32 52.5\n",
-       "    lon      float32 252.5\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([262.69998, 263.19998, 270.9 , ..., 264.19 , 265.19 ,\n", - " 266.99 ], dtype=float32)\n", - "Coordinates:\n", - " lat float32 52.5\n", - " lon float32 252.5\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da.sel(lat=52.25, lon=251.8998, method=\"nearest\")" ] @@ -3934,437 +411,9 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 2920)>\n",
-       "array([262.69998, 263.19998, 270.9    , ..., 264.19   , 265.19   ,\n",
-       "       266.99   ], dtype=float32)\n",
-       "Coordinates:\n",
-       "    lat      float32 52.5\n",
-       "    lon      float32 252.5\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([262.69998, 263.19998, 270.9 , ..., 264.19 , 265.19 ,\n", - " 266.99 ], dtype=float32)\n", - "Coordinates:\n", - " lat float32 52.5\n", - " lon float32 252.5\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da.sel(lat=52.25, lon=251.8998, method=\"nearest\", tolerance=2)" ] @@ -4382,428 +431,9 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.Dataset>\n",
-       "Dimensions:  (time: 2920)\n",
-       "Coordinates:\n",
-       "    lat      float32 52.5\n",
-       "    lon      float32 252.5\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Data variables:\n",
-       "    air      (time) float32 262.7 263.2 270.9 274.1 ... 261.6 264.2 265.2 267.0\n",
-       "Attributes:\n",
-       "    Conventions:  COARDS\n",
-       "    title:        4x daily NMC reanalysis (1948)\n",
-       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
-       "    platform:     Model\n",
-       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
    " - ], - "text/plain": [ - "\n", - "Dimensions: (time: 2920)\n", - "Coordinates:\n", - " lat float32 52.5\n", - " lon float32 252.5\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Data variables:\n", - " air (time) float32 262.7 263.2 270.9 274.1 ... 261.6 264.2 265.2 267.0\n", - "Attributes:\n", - " Conventions: COARDS\n", - " title: 4x daily NMC reanalysis (1948)\n", - " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", - " platform: Model\n", - " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "ds.sel(lat=52.25, lon=251.8998, method=\"nearest\")" ] @@ -4897,10 +527,6 @@ } ], "metadata": { - "kernelspec": { - "display_name": "", - "name": "" - }, "language_info": { "codemirror_mode": { "name": "ipython", @@ -4924,13 +550,6 @@ "toc_position": {}, "toc_section_display": true, "toc_window_display": true - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } } }, "nbformat": 4, diff --git a/intermediate/02.2_indexing_Advanced.ipynb b/intermediate/02.2_indexing_Advanced.ipynb index b21f239d..88284af7 100644 --- a/intermediate/02.2_indexing_Advanced.ipynb +++ b/intermediate/02.2_indexing_Advanced.ipynb @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -44,483 +44,9 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.Dataset>\n",
-       "Dimensions:  (lat: 25, time: 2920, lon: 53)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Data variables:\n",
-       "    air      (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n",
-       "Attributes:\n",
-       "    Conventions:  COARDS\n",
-       "    title:        4x daily NMC reanalysis (1948)\n",
-       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
-       "    platform:     Model\n",
-       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
    " - ], - "text/plain": [ - "\n", - "Dimensions: (lat: 25, time: 2920, lon: 53)\n", - "Coordinates:\n", - " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", - " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Data variables:\n", - " air (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n", - "Attributes:\n", - " Conventions: COARDS\n", - " title: 4x daily NMC reanalysis (1948)\n", - " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", - " platform: Model\n", - " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." - ] - }, - "execution_count": 92, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "ds = xr.tutorial.load_dataset(\"air_temperature\")\n", "da = ds.air\n", @@ -544,22 +70,11 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "da[0, [2, 4, 10, 13], [1, 6, 7]].plot(); # -- orthogonal indexing" + "da[0, [2, 4, 10, 13], [1, 6, 7]].plot(); # -- orthogonal indexing" ] }, { @@ -573,531 +88,14 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 2920, degrees_north: 4, degrees_east: 4)>\n",
-       "array([[[293.1    , 293.1    , 293.29   , 293.29   ],\n",
-       "        [284.6    , 284.6    , 284.9    , 284.19998],\n",
-       "        [282.79   , 282.79   , 283.19998, 282.6    ],\n",
-       "        [282.79   , 282.79   , 283.19998, 282.6    ]],\n",
-       "\n",
-       "       [[293.19998, 293.19998, 293.9    , 294.19998],\n",
-       "        [283.29   , 283.29   , 285.19998, 285.19998],\n",
-       "        [281.4    , 281.4    , 282.79   , 283.5    ],\n",
-       "        [281.4    , 281.4    , 282.79   , 283.5    ]],\n",
-       "\n",
-       "       [[292.4    , 292.4    , 292.9    , 293.4    ],\n",
-       "        [282.     , 282.     , 283.29   , 284.69998],\n",
-       "        [280.     , 280.     , 280.79   , 282.4    ],\n",
-       "        [280.     , 280.     , 280.79   , 282.4    ]],\n",
-       "\n",
-       "       ...,\n",
-       "\n",
-       "       [[288.88998, 288.88998, 289.19   , 290.88998],\n",
-       "        [282.49   , 282.49   , 281.99   , 281.99   ],\n",
-       "        [281.29   , 281.29   , 281.29   , 280.99   ],\n",
-       "        [281.29   , 281.29   , 281.29   , 280.99   ]],\n",
-       "\n",
-       "       [[288.29   , 288.29   , 289.19   , 290.79   ],\n",
-       "        [282.09   , 282.09   , 281.59   , 282.38998],\n",
-       "        [280.99   , 280.99   , 280.38998, 280.59   ],\n",
-       "        [280.99   , 280.99   , 280.38998, 280.59   ]],\n",
-       "\n",
-       "       [[289.49   , 289.49   , 290.38998, 291.59   ],\n",
-       "        [282.09   , 282.09   , 281.99   , 283.09   ],\n",
-       "        [281.38998, 281.38998, 280.59   , 280.99   ],\n",
-       "        [281.38998, 281.38998, 280.59   , 280.99   ]]], dtype=float32)\n",
-       "Coordinates:\n",
-       "    lat      (degrees_north) float32 30.0 40.0 42.5 42.5\n",
-       "    lon      (degrees_east) float32 200.0 200.0 202.5 205.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Dimensions without coordinates: degrees_north, degrees_east\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([[[293.1 , 293.1 , 293.29 , 293.29 ],\n", - " [284.6 , 284.6 , 284.9 , 284.19998],\n", - " [282.79 , 282.79 , 283.19998, 282.6 ],\n", - " [282.79 , 282.79 , 283.19998, 282.6 ]],\n", - "\n", - " [[293.19998, 293.19998, 293.9 , 294.19998],\n", - " [283.29 , 283.29 , 285.19998, 285.19998],\n", - " [281.4 , 281.4 , 282.79 , 283.5 ],\n", - " [281.4 , 281.4 , 282.79 , 283.5 ]],\n", - "\n", - " [[292.4 , 292.4 , 292.9 , 293.4 ],\n", - " [282. , 282. , 283.29 , 284.69998],\n", - " [280. , 280. , 280.79 , 282.4 ],\n", - " [280. , 280. , 280.79 , 282.4 ]],\n", - "\n", - " ...,\n", - "\n", - " [[288.88998, 288.88998, 289.19 , 290.88998],\n", - " [282.49 , 282.49 , 281.99 , 281.99 ],\n", - " [281.29 , 281.29 , 281.29 , 280.99 ],\n", - " [281.29 , 281.29 , 281.29 , 280.99 ]],\n", - "\n", - " [[288.29 , 288.29 , 289.19 , 290.79 ],\n", - " [282.09 , 282.09 , 281.59 , 282.38998],\n", - " [280.99 , 280.99 , 280.38998, 280.59 ],\n", - " [280.99 , 280.99 , 280.38998, 280.59 ]],\n", - "\n", - " [[289.49 , 289.49 , 290.38998, 291.59 ],\n", - " [282.09 , 282.09 , 281.99 , 283.09 ],\n", - " [281.38998, 281.38998, 280.59 , 280.99 ],\n", - " [281.38998, 281.38998, 280.59 , 280.99 ]]], dtype=float32)\n", - "Coordinates:\n", - " lat (degrees_north) float32 30.0 40.0 42.5 42.5\n", - " lon (degrees_east) float32 200.0 200.0 202.5 205.0\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Dimensions without coordinates: degrees_north, degrees_east\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 94, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "target_lat = xr.DataArray([31, 41, 42, 42], dims=\"degrees_north\")\n", "target_lon = xr.DataArray([200, 201, 202, 205], dims=\"degrees_east\")\n", "\n", - "da.sel(lat=target_lat, lon=target_lon, method=\"nearest\") # -- orthogonal indexing " + "da.sel(lat=target_lat, lon=target_lon, method=\"nearest\") # -- orthogonal indexing" ] }, { @@ -1130,390 +128,9 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray (points: 4)>\n",
-       "array([31, 41, 42, 42])\n",
-       "Dimensions without coordinates: points
    " - ], - "text/plain": [ - "\n", - "array([31, 41, 42, 42])\n", - "Dimensions without coordinates: points" - ] - }, - "execution_count": 95, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Define target latitude and longitude (where weather stations might be)\n", "lat_points = xr.DataArray([31, 41, 42, 42], dims=\"points\")\n", @@ -1523,390 +140,9 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray (points: 4)>\n",
-       "array([200, 201, 202, 205])\n",
-       "Dimensions without coordinates: points
    " - ], - "text/plain": [ - "\n", - "array([200, 201, 202, 205])\n", - "Dimensions without coordinates: points" - ] - }, - "execution_count": 96, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "lon_points" ] @@ -1920,454 +156,9 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 2920, points: 4)>\n",
-       "array([[293.1    , 284.6    , 283.19998, 282.6    ],\n",
-       "       [293.19998, 283.29   , 282.79   , 283.5    ],\n",
-       "       [292.4    , 282.     , 280.79   , 282.4    ],\n",
-       "       ...,\n",
-       "       [288.88998, 282.49   , 281.29   , 280.99   ],\n",
-       "       [288.29   , 282.09   , 280.38998, 280.59   ],\n",
-       "       [289.49   , 282.09   , 280.59   , 280.99   ]], dtype=float32)\n",
-       "Coordinates:\n",
-       "    lat      (points) float32 30.0 40.0 42.5 42.5\n",
-       "    lon      (points) float32 200.0 200.0 202.5 205.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Dimensions without coordinates: points\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([[293.1 , 284.6 , 283.19998, 282.6 ],\n", - " [293.19998, 283.29 , 282.79 , 283.5 ],\n", - " [292.4 , 282. , 280.79 , 282.4 ],\n", - " ...,\n", - " [288.88998, 282.49 , 281.29 , 280.99 ],\n", - " [288.29 , 282.09 , 280.38998, 280.59 ],\n", - " [289.49 , 282.09 , 280.59 , 280.99 ]], dtype=float32)\n", - "Coordinates:\n", - " lat (points) float32 30.0 40.0 42.5 42.5\n", - " lon (points) float32 200.0 200.0 202.5 205.0\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Dimensions without coordinates: points\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 97, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da.sel(lat=lat_points, lon=lon_points, method=\"nearest\")" ] @@ -2381,20 +172,9 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "('time', 'points')" - ] - }, - "execution_count": 98, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da.sel(lat=lat_points, lon=lon_points, method=\"nearest\").dims" ] @@ -2412,508 +192,9 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 2920, lat: 3, points: 4)>\n",
-       "array([[[296.6    , 296.6    , 296.19998, 296.4    ],\n",
-       "        [293.1    , 293.1    , 293.29   , 293.29   ],\n",
-       "        [284.6    , 284.6    , 284.9    , 284.19998]],\n",
-       "\n",
-       "       [[296.4    , 296.4    , 295.9    , 296.19998],\n",
-       "        [293.19998, 293.19998, 293.9    , 294.19998],\n",
-       "        [283.29   , 283.29   , 285.19998, 285.19998]],\n",
-       "\n",
-       "       [[295.6    , 295.6    , 295.4    , 295.4    ],\n",
-       "        [292.4    , 292.4    , 292.9    , 293.4    ],\n",
-       "        [282.     , 282.     , 283.29   , 284.69998]],\n",
-       "\n",
-       "       ...,\n",
-       "\n",
-       "       [[294.79   , 294.79   , 295.29   , 297.49   ],\n",
-       "        [288.88998, 288.88998, 289.19   , 290.88998],\n",
-       "        [282.49   , 282.49   , 281.99   , 281.99   ]],\n",
-       "\n",
-       "       [[293.69   , 293.69   , 293.88998, 295.38998],\n",
-       "        [288.29   , 288.29   , 289.19   , 290.79   ],\n",
-       "        [282.09   , 282.09   , 281.59   , 282.38998]],\n",
-       "\n",
-       "       [[293.79   , 293.79   , 293.69   , 295.09   ],\n",
-       "        [289.49   , 289.49   , 290.38998, 291.59   ],\n",
-       "        [282.09   , 282.09   , 281.99   , 283.09   ]]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 20.0 30.0 40.0\n",
-       "    lon      (points) float32 200.0 200.0 202.5 205.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Dimensions without coordinates: points\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([[[296.6 , 296.6 , 296.19998, 296.4 ],\n", - " [293.1 , 293.1 , 293.29 , 293.29 ],\n", - " [284.6 , 284.6 , 284.9 , 284.19998]],\n", - "\n", - " [[296.4 , 296.4 , 295.9 , 296.19998],\n", - " [293.19998, 293.19998, 293.9 , 294.19998],\n", - " [283.29 , 283.29 , 285.19998, 285.19998]],\n", - "\n", - " [[295.6 , 295.6 , 295.4 , 295.4 ],\n", - " [292.4 , 292.4 , 292.9 , 293.4 ],\n", - " [282. , 282. , 283.29 , 284.69998]],\n", - "\n", - " ...,\n", - "\n", - " [[294.79 , 294.79 , 295.29 , 297.49 ],\n", - " [288.88998, 288.88998, 289.19 , 290.88998],\n", - " [282.49 , 282.49 , 281.99 , 281.99 ]],\n", - "\n", - " [[293.69 , 293.69 , 293.88998, 295.38998],\n", - " [288.29 , 288.29 , 289.19 , 290.79 ],\n", - " [282.09 , 282.09 , 281.59 , 282.38998]],\n", - "\n", - " [[293.79 , 293.79 , 293.69 , 295.09 ],\n", - " [289.49 , 289.49 , 290.38998, 291.59 ],\n", - " [282.09 , 282.09 , 281.99 , 283.09 ]]], dtype=float32)\n", - "Coordinates:\n", - " * lat (lat) float32 20.0 30.0 40.0\n", - " lon (points) float32 200.0 200.0 202.5 205.0\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Dimensions without coordinates: points\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 99, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da.sel(lat=[20, 30, 40], lon=lon_points, method=\"nearest\")" ] @@ -2936,398 +217,9 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray (new_time: 3)>\n",
-       "array([0.87902737, 0.06690113, 0.01799672])\n",
-       "Coordinates:\n",
-       "    time      (new_time) datetime64[ns] 2000-01-03 2000-01-02 2000-01-01\n",
-       "    space     (new_time) <U2 'IA' 'IL' 'IN'\n",
-       "  * new_time  (new_time) datetime64[ns] 2000-01-03 2000-01-02 2000-01-01
    " - ], - "text/plain": [ - "\n", - "array([0.87902737, 0.06690113, 0.01799672])\n", - "Coordinates:\n", - " time (new_time) datetime64[ns] 2000-01-03 2000-01-02 2000-01-01\n", - " space (new_time) \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.Dataset>\n",
-       "Dimensions:  (time: 36, y: 205, x: 275)\n",
-       "Coordinates:\n",
-       "  * time     (time) object 1980-09-16 12:00:00 ... 1983-08-17 00:00:00\n",
-       "    xc       (y, x) float64 189.2 189.4 189.6 189.7 ... 17.65 17.4 17.15 16.91\n",
-       "    yc       (y, x) float64 16.53 16.78 17.02 17.27 ... 28.26 28.01 27.76 27.51\n",
-       "Dimensions without coordinates: y, x\n",
-       "Data variables:\n",
-       "    Tair     (time, y, x) float64 nan nan nan nan nan ... 29.8 28.66 28.19 28.21\n",
-       "Attributes:\n",
-       "    title:                     /workspace/jhamman/processed/R1002RBRxaaa01a/l...\n",
-       "    institution:               U.W.\n",
-       "    source:                    RACM R1002RBRxaaa01a\n",
-       "    output_frequency:          daily\n",
-       "    output_mode:               averaged\n",
-       "    convention:                CF-1.4\n",
-       "    references:                Based on the initial model of Liang et al., 19...\n",
-       "    comment:                   Output from the Variable Infiltration Capacity...\n",
-       "    nco_openmp_thread_number:  1\n",
-       "    NCO:                       netCDF Operators version 4.7.9 (Homepage = htt...\n",
-       "    history:                   Fri Aug  7 17:57:38 2020: ncatted -a bounds,,d...
    " - ], - "text/plain": [ - "\n", - "Dimensions: (time: 36, y: 205, x: 275)\n", - "Coordinates:\n", - " * time (time) object 1980-09-16 12:00:00 ... 1983-08-17 00:00:00\n", - " xc (y, x) float64 189.2 189.4 189.6 189.7 ... 17.65 17.4 17.15 16.91\n", - " yc (y, x) float64 16.53 16.78 17.02 17.27 ... 28.26 28.01 27.76 27.51\n", - "Dimensions without coordinates: y, x\n", - "Data variables:\n", - " Tair (time, y, x) float64 nan nan nan nan nan ... 29.8 28.66 28.19 28.21\n", - "Attributes:\n", - " title: /workspace/jhamman/processed/R1002RBRxaaa01a/l...\n", - " institution: U.W.\n", - " source: RACM R1002RBRxaaa01a\n", - " output_frequency: daily\n", - " output_mode: averaged\n", - " convention: CF-1.4\n", - " references: Based on the initial model of Liang et al., 19...\n", - " comment: Output from the Variable Infiltration Capacity...\n", - " nco_openmp_thread_number: 1\n", - " NCO: netCDF Operators version 4.7.9 (Homepage = htt...\n", - " history: Fri Aug 7 17:57:38 2020: ncatted -a bounds,,d..." - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "ds = xr.tutorial.load_dataset(\"rasm\")\n", "ds" @@ -599,18 +67,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'long_name': 'longitude of grid cell center', 'units': 'degrees_east'}\n", - "{'long_name': 'latitude of grid cell center', 'units': 'degrees_north'}\n" - ] - } - ], + "outputs": [], "source": [ "print(ds.xc.attrs)\n", "print(ds.yc.attrs)" @@ -618,600 +77,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'Tair' (time: 36, y: 205, x: 275)>\n",
-       "array([[[        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        ...,\n",
-       "        [        nan,         nan,         nan, ..., 27.03290153,\n",
-       "         27.03125761, 27.33531541],\n",
-       "        [        nan,         nan,         nan, ..., 27.2784053 ,\n",
-       "         26.80261869, 27.08603517],\n",
-       "        [        nan,         nan,         nan, ..., 27.02344402,\n",
-       "         26.56473862, 26.73064933]],\n",
-       "\n",
-       "       [[        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "...\n",
-       "        [        nan,         nan,         nan, ..., 27.8597472 ,\n",
-       "         27.82928439, 28.09249224],\n",
-       "        [        nan,         nan,         nan, ..., 27.89704094,\n",
-       "         27.31104941, 27.67387171],\n",
-       "        [        nan,         nan,         nan, ..., 27.46837113,\n",
-       "         27.0088944 , 27.23017976]],\n",
-       "\n",
-       "       [[        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan],\n",
-       "        ...,\n",
-       "        [        nan,         nan,         nan, ..., 28.95929072,\n",
-       "         28.87672039, 29.04890862],\n",
-       "        [        nan,         nan,         nan, ..., 29.036132  ,\n",
-       "         28.42273578, 28.68721201],\n",
-       "        [        nan,         nan,         nan, ..., 28.66381585,\n",
-       "         28.18595533, 28.20753022]]])\n",
-       "Coordinates:\n",
-       "  * time     (time) object 1980-09-16 12:00:00 ... 1983-08-17 00:00:00\n",
-       "    xc       (y, x) float64 189.2 189.4 189.6 189.7 ... 17.65 17.4 17.15 16.91\n",
-       "    yc       (y, x) float64 16.53 16.78 17.02 17.27 ... 28.26 28.01 27.76 27.51\n",
-       "Dimensions without coordinates: y, x\n",
-       "Attributes:\n",
-       "    units:           C\n",
-       "    long_name:       Surface air temperature\n",
-       "    type_preferred:  double\n",
-       "    time_rep:        instantaneous
    " - ], - "text/plain": [ - "\n", - "array([[[ nan, nan, nan, ..., nan,\n", - " nan, nan],\n", - " [ nan, nan, nan, ..., nan,\n", - " nan, nan],\n", - " [ nan, nan, nan, ..., nan,\n", - " nan, nan],\n", - " ...,\n", - " [ nan, nan, nan, ..., 27.03290153,\n", - " 27.03125761, 27.33531541],\n", - " [ nan, nan, nan, ..., 27.2784053 ,\n", - " 26.80261869, 27.08603517],\n", - " [ nan, nan, nan, ..., 27.02344402,\n", - " 26.56473862, 26.73064933]],\n", - "\n", - " [[ nan, nan, nan, ..., nan,\n", - " nan, nan],\n", - " [ nan, nan, nan, ..., nan,\n", - " nan, nan],\n", - " [ nan, nan, nan, ..., nan,\n", - " nan, nan],\n", - "...\n", - " [ nan, nan, nan, ..., 27.8597472 ,\n", - " 27.82928439, 28.09249224],\n", - " [ nan, nan, nan, ..., 27.89704094,\n", - " 27.31104941, 27.67387171],\n", - " [ nan, nan, nan, ..., 27.46837113,\n", - " 27.0088944 , 27.23017976]],\n", - "\n", - " [[ nan, nan, nan, ..., nan,\n", - " nan, nan],\n", - " [ nan, nan, nan, ..., nan,\n", - " nan, nan],\n", - " [ nan, nan, nan, ..., nan,\n", - " nan, nan],\n", - " ...,\n", - " [ nan, nan, nan, ..., 28.95929072,\n", - " 28.87672039, 29.04890862],\n", - " [ nan, nan, nan, ..., 29.036132 ,\n", - " 28.42273578, 28.68721201],\n", - " [ nan, nan, nan, ..., 28.66381585,\n", - " 28.18595533, 28.20753022]]])\n", - "Coordinates:\n", - " * time (time) object 1980-09-16 12:00:00 ... 1983-08-17 00:00:00\n", - " xc (y, x) float64 189.2 189.4 189.6 189.7 ... 17.65 17.4 17.15 16.91\n", - " yc (y, x) float64 16.53 16.78 17.02 17.27 ... 28.26 28.01 27.76 27.51\n", - "Dimensions without coordinates: y, x\n", - "Attributes:\n", - " units: C\n", - " long_name: Surface air temperature\n", - " type_preferred: double\n", - " time_rep: instantaneous" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da = ds.Tair\n", "da" @@ -1232,600 +100,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'Tair' (time: 36, y: 205, x: 275)>\n",
-       "array([[[-9999.        , -9999.        , -9999.        , ...,\n",
-       "         -9999.        , -9999.        , -9999.        ],\n",
-       "        [-9999.        , -9999.        , -9999.        , ...,\n",
-       "         -9999.        , -9999.        , -9999.        ],\n",
-       "        [-9999.        , -9999.        , -9999.        , ...,\n",
-       "         -9999.        , -9999.        , -9999.        ],\n",
-       "        ...,\n",
-       "        [-9999.        , -9999.        , -9999.        , ...,\n",
-       "            27.03290153,    27.03125761,    27.33531541],\n",
-       "        [-9999.        , -9999.        , -9999.        , ...,\n",
-       "            27.2784053 ,    26.80261869,    27.08603517],\n",
-       "        [-9999.        , -9999.        , -9999.        , ...,\n",
-       "            27.02344402,    26.56473862,    26.73064933]],\n",
-       "\n",
-       "       [[-9999.        , -9999.        , -9999.        , ...,\n",
-       "         -9999.        , -9999.        , -9999.        ],\n",
-       "        [-9999.        , -9999.        , -9999.        , ...,\n",
-       "         -9999.        , -9999.        , -9999.        ],\n",
-       "        [-9999.        , -9999.        , -9999.        , ...,\n",
-       "         -9999.        , -9999.        , -9999.        ],\n",
-       "...\n",
-       "        [-9999.        , -9999.        , -9999.        , ...,\n",
-       "            27.8597472 ,    27.82928439,    28.09249224],\n",
-       "        [-9999.        , -9999.        , -9999.        , ...,\n",
-       "            27.89704094,    27.31104941,    27.67387171],\n",
-       "        [-9999.        , -9999.        , -9999.        , ...,\n",
-       "            27.46837113,    27.0088944 ,    27.23017976]],\n",
-       "\n",
-       "       [[-9999.        , -9999.        , -9999.        , ...,\n",
-       "         -9999.        , -9999.        , -9999.        ],\n",
-       "        [-9999.        , -9999.        , -9999.        , ...,\n",
-       "         -9999.        , -9999.        , -9999.        ],\n",
-       "        [-9999.        , -9999.        , -9999.        , ...,\n",
-       "         -9999.        , -9999.        , -9999.        ],\n",
-       "        ...,\n",
-       "        [-9999.        , -9999.        , -9999.        , ...,\n",
-       "            28.95929072,    28.87672039,    29.04890862],\n",
-       "        [-9999.        , -9999.        , -9999.        , ...,\n",
-       "            29.036132  ,    28.42273578,    28.68721201],\n",
-       "        [-9999.        , -9999.        , -9999.        , ...,\n",
-       "            28.66381585,    28.18595533,    28.20753022]]])\n",
-       "Coordinates:\n",
-       "  * time     (time) object 1980-09-16 12:00:00 ... 1983-08-17 00:00:00\n",
-       "    xc       (y, x) float64 189.2 189.4 189.6 189.7 ... 17.65 17.4 17.15 16.91\n",
-       "    yc       (y, x) float64 16.53 16.78 17.02 17.27 ... 28.26 28.01 27.76 27.51\n",
-       "Dimensions without coordinates: y, x\n",
-       "Attributes:\n",
-       "    units:           C\n",
-       "    long_name:       Surface air temperature\n",
-       "    type_preferred:  double\n",
-       "    time_rep:        instantaneous
    " - ], - "text/plain": [ - "\n", - "array([[[-9999. , -9999. , -9999. , ...,\n", - " -9999. , -9999. , -9999. ],\n", - " [-9999. , -9999. , -9999. , ...,\n", - " -9999. , -9999. , -9999. ],\n", - " [-9999. , -9999. , -9999. , ...,\n", - " -9999. , -9999. , -9999. ],\n", - " ...,\n", - " [-9999. , -9999. , -9999. , ...,\n", - " 27.03290153, 27.03125761, 27.33531541],\n", - " [-9999. , -9999. , -9999. , ...,\n", - " 27.2784053 , 26.80261869, 27.08603517],\n", - " [-9999. , -9999. , -9999. , ...,\n", - " 27.02344402, 26.56473862, 26.73064933]],\n", - "\n", - " [[-9999. , -9999. , -9999. , ...,\n", - " -9999. , -9999. , -9999. ],\n", - " [-9999. , -9999. , -9999. , ...,\n", - " -9999. , -9999. , -9999. ],\n", - " [-9999. , -9999. , -9999. , ...,\n", - " -9999. , -9999. , -9999. ],\n", - "...\n", - " [-9999. , -9999. , -9999. , ...,\n", - " 27.8597472 , 27.82928439, 28.09249224],\n", - " [-9999. , -9999. , -9999. , ...,\n", - " 27.89704094, 27.31104941, 27.67387171],\n", - " [-9999. , -9999. , -9999. , ...,\n", - " 27.46837113, 27.0088944 , 27.23017976]],\n", - "\n", - " [[-9999. , -9999. , -9999. , ...,\n", - " -9999. , -9999. , -9999. ],\n", - " [-9999. , -9999. , -9999. , ...,\n", - " -9999. , -9999. , -9999. ],\n", - " [-9999. , -9999. , -9999. , ...,\n", - " -9999. , -9999. , -9999. ],\n", - " ...,\n", - " [-9999. , -9999. , -9999. , ...,\n", - " 28.95929072, 28.87672039, 29.04890862],\n", - " [-9999. , -9999. , -9999. , ...,\n", - " 29.036132 , 28.42273578, 28.68721201],\n", - " [-9999. , -9999. , -9999. , ...,\n", - " 28.66381585, 28.18595533, 28.20753022]]])\n", - "Coordinates:\n", - " * time (time) object 1980-09-16 12:00:00 ... 1983-08-17 00:00:00\n", - " xc (y, x) float64 189.2 189.4 189.6 189.7 ... 17.65 17.4 17.15 16.91\n", - " yc (y, x) float64 16.53 16.78 17.02 17.27 ... 28.26 28.01 27.76 27.51\n", - "Dimensions without coordinates: y, x\n", - "Attributes:\n", - " units: C\n", - " long_name: Surface air temperature\n", - " type_preferred: double\n", - " time_rep: instantaneous" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Let's replace the missing values (nan) with some placeholder\n", "ds.Tair.where(ds.Tair.notnull(), -9999)" @@ -2017,31 +294,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "type", - "evalue": "name 'da_masked' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[6], line 4\u001b[0m\n\u001b[1;32m 2\u001b[0m ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39maxes(projection\u001b[38;5;241m=\u001b[39mccrs\u001b[38;5;241m.\u001b[39mPlateCarree())\n\u001b[1;32m 3\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_global()\n\u001b[0;32m----> 4\u001b[0m \u001b[43mda_masked\u001b[49m[\u001b[38;5;241m0\u001b[39m, :, :]\u001b[38;5;241m.\u001b[39mplot\u001b[38;5;241m.\u001b[39mpcolormesh(\n\u001b[1;32m 5\u001b[0m ax\u001b[38;5;241m=\u001b[39max, transform\u001b[38;5;241m=\u001b[39mccrs\u001b[38;5;241m.\u001b[39mPlateCarree(), x\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxc\u001b[39m\u001b[38;5;124m\"\u001b[39m, y\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myc\u001b[39m\u001b[38;5;124m\"\u001b[39m, add_colorbar\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m 6\u001b[0m )\n\u001b[1;32m 7\u001b[0m ax\u001b[38;5;241m.\u001b[39mcoastlines()\n\u001b[1;32m 8\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_ylim([\u001b[38;5;241m50\u001b[39m, \u001b[38;5;241m80\u001b[39m])\n", - "\u001b[0;31mNameError\u001b[0m: name 'da_masked' is not defined" - ] - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAADVCAYAAACbgb3eAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAADt0lEQVR4nO3ZoY7bQBhG0dlq5QQFhxjk/R8qICTYKDFxUWnqtndVZXUO/jX62AXzsW3bNgAg9ON/DwDg+xEXAHLiAkBOXADIiQsAOXEBICcuAOQ+9x4+Ho+xrutXbgHgDUzTNI7H48ubXXF5PB7jcrmM+/2eDAPgfZ3P53G9Xl8GZldc1nUd9/t93G63cTqdsoEAvJdlWcY8z2Nd13+Pyy+n00lcAPgtH/oA5MQFgJy4AJATFwBy4gJATlwAyIkLADlxASAnLgDkxAWAnLgAkBMXAHLiAkBOXADIiQsAOXEBICcuAOTEBYCcuACQExcAcuICQE5cAMiJCwA5cQEgJy4A5MQFgJy4AJATFwBy4gJATlwAyIkLADlxASAnLgDkxAWAnLgAkBMXAHLiAkBOXADIiQsAOXEBICcuAOTEBYCcuACQExcAcuICQE5cAMiJCwA5cQEgJy4A5MQFgJy4AJATFwBy4gJATlwAyIkLADlxASAnLgDkxAWAnLgAkBMXAHLiAkBOXADIiQsAOXEBICcuAOTEBYCcuACQExcAcuICQE5cAMiJCwA5cQEgJy4A5MQFgJy4AJATFwBy4gJATlwAyIkLADlxASAnLgDkxAWAnLgAkBMXAHLiAkBOXADIiQsAOXEBICcuAOTEBYCcuACQExcAcuICQE5cAMiJCwA5cQEgJy4A5MQFgJy4AJATFwBy4gJATlwAyIkLADlxASAnLgDkxAWAnLgAkBMXAHLiAkBOXADIiQsAOXEBICcuAOTEBYCcuACQExcAcuICQE5cAMiJCwA5cQEgJy4A5MQFgJy4AJATFwBy4gJATlwAyIkLADlxASAnLgDkxAWAnLgAkBMXAHLiAkBOXADIiQsAOXEBICcuAOTEBYCcuACQExcAcuICQE5cAMiJCwA5cQEgJy4A5MQFgJy4AJATFwBy4gJATlwAyIkLADlxASAnLgDkxAWAnLgAkBMXAHLiAkBOXADIiQsAOXEBICcuAOTEBYCcuACQExcAcuICQE5cAMiJCwA5cQEgJy4A5MQFgJy4AJATFwBy4gJATlwAyIkLADlxASAnLgDkxAWAnLgAkBMXAHLiAkBOXADIiQsAOXEBICcuAOTEBYCcuACQExcAcuICQE5cAMh9/snxsixftQOAN7C3A7vicjgcxhhjzPP894sA+BbO5/OYpunlzce2bduex57P53g+n8kwAN7XNE3jeDy+vNkdFwDYy4c+ADlxASAnLgDkxAWAnLgAkBMXAHLiAkDuJ6MsOBqz/3v5AAAAAElFTkSuQmCC", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure(figsize=(5, 5))\n", "ax = plt.axes(projection=ccrs.PlateCarree())\n", @@ -2213,11 +468,6 @@ } ], "metadata": { - "kernelspec": { - "display_name": "NPL 2023b", - "language": "python", - "name": "npl-2023b" - }, "language_info": { "codemirror_mode": { "name": "ipython", @@ -2227,8 +477,7 @@ "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" + "pygments_lexer": "ipython3" }, "toc": { "base_numbering": 1, @@ -2242,13 +491,6 @@ "toc_position": {}, "toc_section_display": true, "toc_window_display": true - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } } }, "nbformat": 4, From a9327ea335810c38aa84a78273edb04ea8913b90 Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Fri, 7 Jul 2023 20:22:50 -0600 Subject: [PATCH 42/54] update --- intermediate/02.2_indexing_Advanced.ipynb | 17 +++++++++++++++-- 1 file changed, 15 insertions(+), 2 deletions(-) diff --git a/intermediate/02.2_indexing_Advanced.ipynb b/intermediate/02.2_indexing_Advanced.ipynb index 88284af7..161dcea6 100644 --- a/intermediate/02.2_indexing_Advanced.ipynb +++ b/intermediate/02.2_indexing_Advanced.ipynb @@ -183,7 +183,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "```attention}\n", + "```{attention}\n", "Please note that slices or sequences/arrays without named-dimensions are treated as if they have the same dimension which is indexed along.\n", "```\n", "\n", @@ -307,6 +307,11 @@ } ], "metadata": { + "kernelspec": { + "display_name": "NPL 2023b", + "language": "python", + "name": "npl-2023b" + }, "language_info": { "codemirror_mode": { "name": "ipython", @@ -316,7 +321,8 @@ "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3" + "pygments_lexer": "ipython3", + "version": "3.10.12" }, "toc": { "base_numbering": 1, @@ -330,6 +336,13 @@ "toc_position": {}, "toc_section_display": true, "toc_window_display": true + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } } }, "nbformat": 4, From 2dfd6566389a5286ef939cfa58c4a5ba3f3dd34f Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Fri, 7 Jul 2023 20:24:48 -0600 Subject: [PATCH 43/54] fix typo --- .../02.3_indexing_BooleanMasking.ipynb | 23 +++++++++++++++---- 1 file changed, 18 insertions(+), 5 deletions(-) diff --git a/intermediate/02.3_indexing_BooleanMasking.ipynb b/intermediate/02.3_indexing_BooleanMasking.ipynb index 358dc9d2..686a53e0 100644 --- a/intermediate/02.3_indexing_BooleanMasking.ipynb +++ b/intermediate/02.3_indexing_BooleanMasking.ipynb @@ -365,15 +365,15 @@ "outputs": [], "source": [ "# Define a function to use as a condition\n", - "def is_greater_than_threshhold(x, threshhold=300):\n", + "def is_greater_than_threshold(x, threshold=300):\n", " # function to convert temp to K\n", - " # and compare with threshhold\n", + " # and compare with threshold\n", " x = x + 273.15\n", - " return x > threshhold\n", + " return x > threshold\n", "\n", "\n", "# Apply the condition using xarray.where()\n", - "masked_data = xr.where(is_greater_than_threshhold(da, 280), da, 0)\n", + "masked_data = xr.where(is_greater_than_threshold(da, 280), da, 0)\n", "\n", "masked_data[0].plot()" ] @@ -468,6 +468,11 @@ } ], "metadata": { + "kernelspec": { + "display_name": "NPL 2023b", + "language": "python", + "name": "npl-2023b" + }, "language_info": { "codemirror_mode": { "name": "ipython", @@ -477,7 +482,8 @@ "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3" + "pygments_lexer": "ipython3", + "version": "3.10.12" }, "toc": { "base_numbering": 1, @@ -491,6 +497,13 @@ "toc_position": {}, "toc_section_display": true, "toc_window_display": true + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } } }, "nbformat": 4, From 386f1a56939592a002710c75718c4265ce995a38 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Sat, 8 Jul 2023 02:25:06 +0000 Subject: [PATCH 44/54] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- intermediate/02.2_indexing_Advanced.ipynb | 15 +-------------- intermediate/02.3_indexing_BooleanMasking.ipynb | 15 +-------------- 2 files changed, 2 insertions(+), 28 deletions(-) diff --git a/intermediate/02.2_indexing_Advanced.ipynb b/intermediate/02.2_indexing_Advanced.ipynb index 161dcea6..0ce0b0e0 100644 --- a/intermediate/02.2_indexing_Advanced.ipynb +++ b/intermediate/02.2_indexing_Advanced.ipynb @@ -307,11 +307,6 @@ } ], "metadata": { - "kernelspec": { - "display_name": "NPL 2023b", - "language": "python", - "name": "npl-2023b" - }, "language_info": { "codemirror_mode": { "name": "ipython", @@ -321,8 +316,7 @@ "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" + "pygments_lexer": "ipython3" }, "toc": { "base_numbering": 1, @@ -336,13 +330,6 @@ "toc_position": {}, "toc_section_display": true, "toc_window_display": true - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } } }, "nbformat": 4, diff --git a/intermediate/02.3_indexing_BooleanMasking.ipynb b/intermediate/02.3_indexing_BooleanMasking.ipynb index 686a53e0..cf872472 100644 --- a/intermediate/02.3_indexing_BooleanMasking.ipynb +++ b/intermediate/02.3_indexing_BooleanMasking.ipynb @@ -468,11 +468,6 @@ } ], "metadata": { - "kernelspec": { - "display_name": "NPL 2023b", - "language": "python", - "name": "npl-2023b" - }, "language_info": { "codemirror_mode": { "name": "ipython", @@ -482,8 +477,7 @@ "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" + "pygments_lexer": "ipython3" }, "toc": { "base_numbering": 1, @@ -497,13 +491,6 @@ "toc_position": {}, "toc_section_display": true, "toc_window_display": true - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } } }, "nbformat": 4, From 54165ce618a8fce7f5a0f0ed5ced7ddc86193475 Mon Sep 17 00:00:00 2001 From: Deepak Cherian Date: Fri, 7 Jul 2023 22:12:08 -0600 Subject: [PATCH 45/54] new cache --- .github/workflows/main.yaml | 2 +- .github/workflows/preview.yaml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/main.yaml b/.github/workflows/main.yaml index d39ecb1a..9736c134 100644 --- a/.github/workflows/main.yaml +++ b/.github/workflows/main.yaml @@ -30,7 +30,7 @@ jobs: with: path: _build # NOTE: change key to "jupyterbook-DATE" to force rebuilding cache - key: jupyterbook-20230626 + key: jupyterbook-20230707 - name: Install Conda environment with Micromamba uses: mamba-org/setup-micromamba@v1 diff --git a/.github/workflows/preview.yaml b/.github/workflows/preview.yaml index 36e52d50..42e95e97 100644 --- a/.github/workflows/preview.yaml +++ b/.github/workflows/preview.yaml @@ -21,7 +21,7 @@ jobs: with: path: _build # NOTE: change key to "jupyterbook-DATE" to force rebuilding cache - key: jupyterbook-20230626 + key: jupyterbook-20230707 - name: Install Conda environment with Micromamba uses: mamba-org/setup-micromamba@v1 From 71d07c9c2e6585fed676b4b80303bf331d3e89ea Mon Sep 17 00:00:00 2001 From: Anderson Banihirwe Date: Sun, 9 Jul 2023 10:14:51 -0700 Subject: [PATCH 46/54] remove unused imports --- fundamentals/02.1_indexing_Basic.ipynb | 2 -- 1 file changed, 2 deletions(-) diff --git a/fundamentals/02.1_indexing_Basic.ipynb b/fundamentals/02.1_indexing_Basic.ipynb index 7f2eec30..ebb84fa7 100644 --- a/fundamentals/02.1_indexing_Basic.ipynb +++ b/fundamentals/02.1_indexing_Basic.ipynb @@ -57,8 +57,6 @@ "metadata": {}, "outputs": [], "source": [ - "import numpy as np\n", - "import pandas as pd\n", "import xarray as xr" ] }, From 267fa29d889c38ab53d7409f8217ecb9f329b6fa Mon Sep 17 00:00:00 2001 From: Anderson Banihirwe Date: Sun, 9 Jul 2023 10:49:25 -0700 Subject: [PATCH 47/54] update link --- advanced/backends/backends.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/advanced/backends/backends.md b/advanced/backends/backends.md index a7312b44..a53df382 100644 --- a/advanced/backends/backends.md +++ b/advanced/backends/backends.md @@ -22,7 +22,7 @@ Xarray bundles several backends internally for the following formats: External Backends that use the new backend API (xarray >= v0.18.0) that allows to add support for backend without any change to Xarray - [cfgrib](https://github.com/ecmwf/cfgrib) - GRIB -- [tiledb](https://pythonrepo.com/repo/TileDB-Inc-TileDB-xarray) - TileDB +- [tiledb](https://github.com/TileDB-Inc/TileDB-CF-Py) - TileDB - [rioxarray](https://corteva.github.io/rioxarray/stable/) - GeoTIFF, JPEG-2000, ESRI-hdr, etc (via GDAL) - [xarray-sentinel](https://github.com/bopen/xarray-sentinel) - Sentinel-1 SAFE - ... From a7a0bd947b4b71fedf9a81f63e6286ab33910351 Mon Sep 17 00:00:00 2001 From: Anderson Banihirwe Date: Sun, 9 Jul 2023 15:50:15 -0700 Subject: [PATCH 48/54] update notebook with datetime indexing section --- fundamentals/02.1_indexing_Basic.ipynb | 114 ++++++++++++++++++ .../01-high-level-computation-patterns.ipynb | 2 +- 2 files changed, 115 insertions(+), 1 deletion(-) diff --git a/fundamentals/02.1_indexing_Basic.ipynb b/fundamentals/02.1_indexing_Basic.ipynb index ebb84fa7..1447eaa3 100644 --- a/fundamentals/02.1_indexing_Basic.ipynb +++ b/fundamentals/02.1_indexing_Basic.ipynb @@ -499,6 +499,114 @@ "````" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Datetime Indexing\n", + "\n", + "\n", + "Datetime indexing is a critical feature when working with time series data, which is a common occurrence in many fields, including finance, economics, and environmental sciences. Essentially, datetime indexing allows you to select data points or a series of data points that correspond to certain date or time criteria. This becomes essential for time-series analysis where the date or time information associated with each data point can be as critical as the data point itself.\n", + "\n", + "Let's see some of the techniques to perform datetime indexing in Xarray:\n", + "\n", + "### Selecting data based on single datetime\n", + "\n", + "Let's say we have a Dataset ds and we want to select data at a particular date and time, for instance, '2013-01-01'. We can do this by using the sel (select) method, like so:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ds.sel(time='2013-01-01')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Selecting data for a range of dates\n", + "\n", + "Now, let's say we want to select data between a certain range of dates. We can still use the `sel` method, but this time we will combine it with slice:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# This will return a subset of the dataset corresponding to the entire year of 2013.\n", + "ds.sel(time=slice('2013-01-01', '2013-12-31'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```{note}\n", + "\n", + "The slice function takes two arguments, start and stop, to make a slice that includes these endpoints. When we use `slice` with the `sel` method, it provides an efficient way to select a range of dates. The above example shows the usage of slice for datetime indexing.\n", + "\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Indexing with a DatetimeIndex or date string list\n", + "\n", + "Another technique is to use a list of datetime objects or date strings for indexing. For example, you could select data for specific, non-contiguous dates like this:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dates = ['2013-07-09', '2013-10-11', '2013-12-24']\n", + "ds.sel(time=dates)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fancy indexing based on year, month, day, or other datetime components\n", + "\n", + "In addition to the basic datetime indexing techniques, Xarray also supports \"fancy\" indexing options, which can provide more flexibility and efficiency in your data analysis tasks. You can directly access datetime components such as year, month, day, hour, etc. using the `.dt` accessor. Here is an example of selecting all data points from July across all years:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ds.sel(time=ds.time.dt.month == 7)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Or, if you wanted to select data from a specific day of each month, you could use:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ds.sel(time=ds.time.dt.day == 15)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -507,6 +615,7 @@ "\n", "In total, Xarray supports four different kinds of indexing, as described below and summarized in this table:\n", "\n", + "\n", "| Dimension lookup | Index lookup | `DataArray` syntax | `Dataset` syntax |\n", "| ---------------- | ------------ | ---------------------| ---------------------|\n", "| Positional | By integer | `da[:,0]` | *not available* |\n", @@ -522,6 +631,11 @@ "\n", "- [Xarray Docs - Indexing and Selecting Data](https://docs.xarray.dev/en/stable/indexing.html)" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] } ], "metadata": { diff --git a/intermediate/01-high-level-computation-patterns.ipynb b/intermediate/01-high-level-computation-patterns.ipynb index ba429693..72aaa688 100644 --- a/intermediate/01-high-level-computation-patterns.ipynb +++ b/intermediate/01-high-level-computation-patterns.ipynb @@ -83,7 +83,7 @@ "\n", "\n", "\n", - "```{Note}\n", + "```{note}\n", "the documentation links in this tutorial point to the DataArray implementations of each function, but they are also available for DataSet objects.\n", "```\n" ] From 3b6f5435c0ac609ccdf36527b07878e290333628 Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Mon, 10 Jul 2023 00:22:49 -0600 Subject: [PATCH 49/54] a few updates --- fundamentals/02.1_indexing_Basic.ipynb | 7347 +++++++++++++++++++++++- 1 file changed, 7298 insertions(+), 49 deletions(-) diff --git a/fundamentals/02.1_indexing_Basic.ipynb b/fundamentals/02.1_indexing_Basic.ipynb index 1447eaa3..9f7453e6 100644 --- a/fundamentals/02.1_indexing_Basic.ipynb +++ b/fundamentals/02.1_indexing_Basic.ipynb @@ -11,8 +11,8 @@ "- Understanding the difference between position and label-based indexing\n", "- Select data by position using `.isel` with values or slices\n", "- Select data by label using `.sel` with values or slices\n", - "- Select timeseries data by date/time with values or slices\n", - "- Use nearest-neighbor lookups with `.sel`\n" + "- Use nearest-neighbor lookups with `.sel`\n", + "- Select timeseries data by date/time with values or slices\n" ] }, { @@ -53,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -69,9 +69,483 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.Dataset>\n",
+       "Dimensions:  (lat: 25, time: 2920, lon: 53)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Data variables:\n",
+       "    air      (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n",
+       "Attributes:\n",
+       "    Conventions:  COARDS\n",
+       "    title:        4x daily NMC reanalysis (1948)\n",
+       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
+       "    platform:     Model\n",
+       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
    " + ], + "text/plain": [ + "\n", + "Dimensions: (lat: 25, time: 2920, lon: 53)\n", + "Coordinates:\n", + " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", + " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Data variables:\n", + " air (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n", + "Attributes:\n", + " Conventions: COARDS\n", + " title: 4x daily NMC reanalysis (1948)\n", + " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", + " platform: Model\n", + " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ds = xr.tutorial.load_dataset(\"air_temperature\")\n", "ds" @@ -79,7 +553,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -109,9 +583,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(2920, 25, 53)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "np_array = ds[\"air\"].data # numpy array\n", "np_array.shape" @@ -126,9 +611,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "242.09999" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "np_array[1, 0, 0]" ] @@ -142,9 +638,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([295. , 294.4 , 294.5 , ..., 297.29, 297.79, 297.99], dtype=float32)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# extract a time-series for one spatial location\n", "np_array[:, 20, 40]" @@ -176,9 +683,434 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'air' (time: 2920)>\n",
+       "array([295.  , 294.4 , 294.5 , ..., 297.29, 297.79, 297.99], dtype=float32)\n",
+       "Coordinates:\n",
+       "    lat      float32 25.0\n",
+       "    lon      float32 300.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
    " + ], + "text/plain": [ + "\n", + "array([295. , 294.4 , 294.5 , ..., 297.29, 297.79, 297.99], dtype=float32)\n", + "Coordinates:\n", + " lat float32 25.0\n", + " lon float32 300.0\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "da[:, 20, 40]" ] @@ -195,18 +1127,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(2920, 2)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "np_array[:, [0, 1], [0, 1]].shape" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(2920, 2, 2)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "da[:, [0, 1], [0, 1]].shape" ] @@ -241,9 +1195,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "da.isel(lat=20, lon=40).plot();" ] @@ -257,9 +1222,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "da.isel(time=slice(0, 20), lat=20, lon=40).plot();" ] @@ -293,13 +1269,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": { "tags": [ "hide-output" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "da.sel(time=\"2014-01-01\", lat=25, lon=210).plot();" ] @@ -313,13 +1300,438 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { "tags": [ "hide-output" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'air' (time: 1460)>\n",
+       "array([277.6 , 277.5 , 277.4 , ..., 277.59, 277.59, 277.59], dtype=float32)\n",
+       "Coordinates:\n",
+       "    lat      float32 50.0\n",
+       "    lon      float32 200.0\n",
+       "  * time     (time) datetime64[ns] 2014-01-01 ... 2014-12-31T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
    " + ], + "text/plain": [ + "\n", + "array([277.6 , 277.5 , 277.4 , ..., 277.59, 277.59, 277.59], dtype=float32)\n", + "Coordinates:\n", + " lat float32 50.0\n", + " lon float32 200.0\n", + " * time (time) datetime64[ns] 2014-01-01 ... 2014-12-31T18:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "da.sel(lat=50.0, lon=200.0, time=\"2014\")" ] @@ -333,9 +1745,570 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'air' (time: 1212, lat: 25, lon: 53)>\n",
+       "array([[[246.     , 245.09999, 244.09999, ..., 240.2    , 242.     ,\n",
+       "         244.2    ],\n",
+       "        [240.59999, 241.7    , 242.09999, ..., 239.09999, 241.2    ,\n",
+       "         244.59999],\n",
+       "        [238.2    , 239.79999, 240.79999, ..., 243.89   , 246.89   ,\n",
+       "         251.2    ],\n",
+       "        ...,\n",
+       "        [295.6    , 295.6    , 296.     , ..., 293.6    , 293.6    ,\n",
+       "         294.1    ],\n",
+       "        [297.     , 297.     , 297.     , ..., 293.69998, 293.9    ,\n",
+       "         294.6    ],\n",
+       "        [297.5    , 297.69998, 297.69998, ..., 294.5    , 294.69998,\n",
+       "         295.19998]],\n",
+       "\n",
+       "       [[245.2    , 244.29999, 243.     , ..., 241.89   , 243.09999,\n",
+       "         244.59999],\n",
+       "        [241.09999, 241.89   , 241.89   , ..., 241.89   , 243.59999,\n",
+       "         246.39   ],\n",
+       "        [239.89   , 241.     , 241.29999, ..., 245.09999, 248.2    ,\n",
+       "         252.2    ],\n",
+       "...\n",
+       "        [296.49   , 295.59   , 295.49   , ..., 297.29   , 297.38998,\n",
+       "         296.79   ],\n",
+       "        [298.09   , 297.79   , 297.19   , ..., 297.69   , 298.09   ,\n",
+       "         297.38998],\n",
+       "        [298.49   , 298.29   , 297.99   , ..., 298.29   , 298.09   ,\n",
+       "         297.99   ]],\n",
+       "\n",
+       "       [[246.29   , 246.39   , 245.98999, ..., 232.29   , 233.39   ,\n",
+       "         234.98999],\n",
+       "        [246.68999, 248.18999, 248.98999, ..., 230.59   , 232.48999,\n",
+       "         235.68999],\n",
+       "        [244.79   , 245.29   , 245.89   , ..., 230.48999, 235.09   ,\n",
+       "         241.39   ],\n",
+       "        ...,\n",
+       "        [296.88998, 296.49   , 296.49   , ..., 297.69   , 297.29   ,\n",
+       "         296.19   ],\n",
+       "        [298.29   , 297.99   , 297.49   , ..., 298.29   , 298.38998,\n",
+       "         297.49   ],\n",
+       "        [298.49   , 298.29   , 297.99   , ..., 298.59   , 298.38998,\n",
+       "         298.29   ]]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
    " + ], + "text/plain": [ + "\n", + "array([[[246. , 245.09999, 244.09999, ..., 240.2 , 242. ,\n", + " 244.2 ],\n", + " [240.59999, 241.7 , 242.09999, ..., 239.09999, 241.2 ,\n", + " 244.59999],\n", + " [238.2 , 239.79999, 240.79999, ..., 243.89 , 246.89 ,\n", + " 251.2 ],\n", + " ...,\n", + " [295.6 , 295.6 , 296. , ..., 293.6 , 293.6 ,\n", + " 294.1 ],\n", + " [297. , 297. , 297. , ..., 293.69998, 293.9 ,\n", + " 294.6 ],\n", + " [297.5 , 297.69998, 297.69998, ..., 294.5 , 294.69998,\n", + " 295.19998]],\n", + "\n", + " [[245.2 , 244.29999, 243. , ..., 241.89 , 243.09999,\n", + " 244.59999],\n", + " [241.09999, 241.89 , 241.89 , ..., 241.89 , 243.59999,\n", + " 246.39 ],\n", + " [239.89 , 241. , 241.29999, ..., 245.09999, 248.2 ,\n", + " 252.2 ],\n", + "...\n", + " [296.49 , 295.59 , 295.49 , ..., 297.29 , 297.38998,\n", + " 296.79 ],\n", + " [298.09 , 297.79 , 297.19 , ..., 297.69 , 298.09 ,\n", + " 297.38998],\n", + " [298.49 , 298.29 , 297.99 , ..., 298.29 , 298.09 ,\n", + " 297.99 ]],\n", + "\n", + " [[246.29 , 246.39 , 245.98999, ..., 232.29 , 233.39 ,\n", + " 234.98999],\n", + " [246.68999, 248.18999, 248.98999, ..., 230.59 , 232.48999,\n", + " 235.68999],\n", + " [244.79 , 245.29 , 245.89 , ..., 230.48999, 235.09 ,\n", + " 241.39 ],\n", + " ...,\n", + " [296.88998, 296.49 , 296.49 , ..., 297.69 , 297.29 ,\n", + " 296.19 ],\n", + " [298.29 , 297.99 , 297.49 , ..., 298.29 , 298.38998,\n", + " 297.49 ],\n", + " [298.49 , 298.29 , 297.99 , ..., 298.59 , 298.38998,\n", + " 298.29 ]]], dtype=float32)\n", + "Coordinates:\n", + " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", + " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", + " * time (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# demonstrate slicing\n", "da.sel(time=slice(\"2014-02-14\", \"2014-12-13\"))" @@ -350,13 +2323,995 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'air' (time: 1212, lat: 25, lon: 53)>\n",
+       "array([[[246.     , 245.09999, 244.09999, ..., 240.2    , 242.     ,\n",
+       "         244.2    ],\n",
+       "        [240.59999, 241.7    , 242.09999, ..., 239.09999, 241.2    ,\n",
+       "         244.59999],\n",
+       "        [238.2    , 239.79999, 240.79999, ..., 243.89   , 246.89   ,\n",
+       "         251.2    ],\n",
+       "        ...,\n",
+       "        [295.6    , 295.6    , 296.     , ..., 293.6    , 293.6    ,\n",
+       "         294.1    ],\n",
+       "        [297.     , 297.     , 297.     , ..., 293.69998, 293.9    ,\n",
+       "         294.6    ],\n",
+       "        [297.5    , 297.69998, 297.69998, ..., 294.5    , 294.69998,\n",
+       "         295.19998]],\n",
+       "\n",
+       "       [[245.2    , 244.29999, 243.     , ..., 241.89   , 243.09999,\n",
+       "         244.59999],\n",
+       "        [241.09999, 241.89   , 241.89   , ..., 241.89   , 243.59999,\n",
+       "         246.39   ],\n",
+       "        [239.89   , 241.     , 241.29999, ..., 245.09999, 248.2    ,\n",
+       "         252.2    ],\n",
+       "...\n",
+       "        [296.49   , 295.59   , 295.49   , ..., 297.29   , 297.38998,\n",
+       "         296.79   ],\n",
+       "        [298.09   , 297.79   , 297.19   , ..., 297.69   , 298.09   ,\n",
+       "         297.38998],\n",
+       "        [298.49   , 298.29   , 297.99   , ..., 298.29   , 298.09   ,\n",
+       "         297.99   ]],\n",
+       "\n",
+       "       [[246.29   , 246.39   , 245.98999, ..., 232.29   , 233.39   ,\n",
+       "         234.98999],\n",
+       "        [246.68999, 248.18999, 248.98999, ..., 230.59   , 232.48999,\n",
+       "         235.68999],\n",
+       "        [244.79   , 245.29   , 245.89   , ..., 230.48999, 235.09   ,\n",
+       "         241.39   ],\n",
+       "        ...,\n",
+       "        [296.88998, 296.49   , 296.49   , ..., 297.69   , 297.29   ,\n",
+       "         296.19   ],\n",
+       "        [298.29   , 297.99   , 297.49   , ..., 298.29   , 298.38998,\n",
+       "         297.49   ],\n",
+       "        [298.49   , 298.29   , 297.99   , ..., 298.59   , 298.38998,\n",
+       "         298.29   ]]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
    " + ], + "text/plain": [ + "\n", + "array([[[246. , 245.09999, 244.09999, ..., 240.2 , 242. ,\n", + " 244.2 ],\n", + " [240.59999, 241.7 , 242.09999, ..., 239.09999, 241.2 ,\n", + " 244.59999],\n", + " [238.2 , 239.79999, 240.79999, ..., 243.89 , 246.89 ,\n", + " 251.2 ],\n", + " ...,\n", + " [295.6 , 295.6 , 296. , ..., 293.6 , 293.6 ,\n", + " 294.1 ],\n", + " [297. , 297. , 297. , ..., 293.69998, 293.9 ,\n", + " 294.6 ],\n", + " [297.5 , 297.69998, 297.69998, ..., 294.5 , 294.69998,\n", + " 295.19998]],\n", + "\n", + " [[245.2 , 244.29999, 243. , ..., 241.89 , 243.09999,\n", + " 244.59999],\n", + " [241.09999, 241.89 , 241.89 , ..., 241.89 , 243.59999,\n", + " 246.39 ],\n", + " [239.89 , 241. , 241.29999, ..., 245.09999, 248.2 ,\n", + " 252.2 ],\n", + "...\n", + " [296.49 , 295.59 , 295.49 , ..., 297.29 , 297.38998,\n", + " 296.79 ],\n", + " [298.09 , 297.79 , 297.19 , ..., 297.69 , 298.09 ,\n", + " 297.38998],\n", + " [298.49 , 298.29 , 297.99 , ..., 298.29 , 298.09 ,\n", + " 297.99 ]],\n", + "\n", + " [[246.29 , 246.39 , 245.98999, ..., 232.29 , 233.39 ,\n", + " 234.98999],\n", + " [246.68999, 248.18999, 248.98999, ..., 230.59 , 232.48999,\n", + " 235.68999],\n", + " [244.79 , 245.29 , 245.89 , ..., 230.48999, 235.09 ,\n", + " 241.39 ],\n", + " ...,\n", + " [296.88998, 296.49 , 296.49 , ..., 297.69 , 297.29 ,\n", + " 296.19 ],\n", + " [298.29 , 297.99 , 297.49 , ..., 298.29 , 298.38998,\n", + " 297.49 ],\n", + " [298.49 , 298.29 , 297.99 , ..., 298.59 , 298.38998,\n", + " 298.29 ]]], dtype=float32)\n", + "Coordinates:\n", + " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", + " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", + " * time (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "da.loc[\"2014-02-14\":\"2014-12-13\"]" ] }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'time' (time: 2920)>\n",
+       "array(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n",
+       "       '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\n",
+       "       '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\n",
+       "      dtype='datetime64[ns]')\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Attributes:\n",
+       "    standard_name:  time\n",
+       "    long_name:      Time
    " + ], + "text/plain": [ + "\n", + "array(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n", + " '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\n", + " '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\n", + " dtype='datetime64[ns]')\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Attributes:\n", + " standard_name: time\n", + " long_name: Time" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "da.time" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -368,9 +3323,570 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'air' (time: 2919, lat: 24, lon: 52)>\n",
+       "array([[[242.5    , 243.5    , 244.     , ..., 232.79999, 235.5    ,\n",
+       "         238.59999],\n",
+       "        [244.5    , 244.7    , 244.2    , ..., 232.79999, 235.29999,\n",
+       "         239.29999],\n",
+       "        [249.79999, 248.89   , 247.5    , ..., 233.2    , 236.39   ,\n",
+       "         241.7    ],\n",
+       "        ...,\n",
+       "        [296.19998, 296.4    , 296.5    , ..., 295.4    , 295.1    ,\n",
+       "         294.69998],\n",
+       "        [296.19998, 296.79   , 296.5    , ..., 295.9    , 295.9    ,\n",
+       "         295.19998],\n",
+       "        [296.79   , 297.1    , 297.     , ..., 296.9    , 296.79   ,\n",
+       "         296.6    ]],\n",
+       "\n",
+       "       [[242.7    , 243.09999, 243.39   , ..., 232.     , 233.59999,\n",
+       "         235.79999],\n",
+       "        [244.09999, 244.2    , 244.09999, ..., 231.     , 232.5    ,\n",
+       "         235.7    ],\n",
+       "        [252.89   , 252.09999, 250.79999, ..., 230.79999, 233.39   ,\n",
+       "         238.5    ],\n",
+       "...\n",
+       "        [293.88998, 295.38998, 297.19   , ..., 295.09   , 294.69   ,\n",
+       "         294.29   ],\n",
+       "        [297.19   , 297.59   , 297.88998, ..., 295.29   , 295.09   ,\n",
+       "         294.38998],\n",
+       "        [298.38998, 298.49   , 298.59   , ..., 295.69   , 295.49   ,\n",
+       "         295.19   ]],\n",
+       "\n",
+       "       [[244.29   , 243.29   , 242.18999, ..., 241.68999, 241.48999,\n",
+       "         241.79   ],\n",
+       "        [249.29   , 248.39   , 246.98999, ..., 239.59   , 240.29   ,\n",
+       "         241.68999],\n",
+       "        [262.19   , 261.38998, 259.99   , ..., 239.89   , 242.59   ,\n",
+       "         246.29   ],\n",
+       "        ...,\n",
+       "        [293.69   , 295.09   , 296.69   , ..., 295.29   , 295.09   ,\n",
+       "         294.69   ],\n",
+       "        [296.88998, 297.19   , 297.49   , ..., 295.69   , 295.69   ,\n",
+       "         295.19   ],\n",
+       "        [298.09   , 298.09   , 298.49   , ..., 296.49   , 296.19   ,\n",
+       "         295.69   ]]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 202.5 205.0 207.5 210.0 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
    " + ], + "text/plain": [ + "\n", + "array([[[242.5 , 243.5 , 244. , ..., 232.79999, 235.5 ,\n", + " 238.59999],\n", + " [244.5 , 244.7 , 244.2 , ..., 232.79999, 235.29999,\n", + " 239.29999],\n", + " [249.79999, 248.89 , 247.5 , ..., 233.2 , 236.39 ,\n", + " 241.7 ],\n", + " ...,\n", + " [296.19998, 296.4 , 296.5 , ..., 295.4 , 295.1 ,\n", + " 294.69998],\n", + " [296.19998, 296.79 , 296.5 , ..., 295.9 , 295.9 ,\n", + " 295.19998],\n", + " [296.79 , 297.1 , 297. , ..., 296.9 , 296.79 ,\n", + " 296.6 ]],\n", + "\n", + " [[242.7 , 243.09999, 243.39 , ..., 232. , 233.59999,\n", + " 235.79999],\n", + " [244.09999, 244.2 , 244.09999, ..., 231. , 232.5 ,\n", + " 235.7 ],\n", + " [252.89 , 252.09999, 250.79999, ..., 230.79999, 233.39 ,\n", + " 238.5 ],\n", + "...\n", + " [293.88998, 295.38998, 297.19 , ..., 295.09 , 294.69 ,\n", + " 294.29 ],\n", + " [297.19 , 297.59 , 297.88998, ..., 295.29 , 295.09 ,\n", + " 294.38998],\n", + " [298.38998, 298.49 , 298.59 , ..., 295.69 , 295.49 ,\n", + " 295.19 ]],\n", + "\n", + " [[244.29 , 243.29 , 242.18999, ..., 241.68999, 241.48999,\n", + " 241.79 ],\n", + " [249.29 , 248.39 , 246.98999, ..., 239.59 , 240.29 ,\n", + " 241.68999],\n", + " [262.19 , 261.38998, 259.99 , ..., 239.89 , 242.59 ,\n", + " 246.29 ],\n", + " ...,\n", + " [293.69 , 295.09 , 296.69 , ..., 295.29 , 295.09 ,\n", + " 294.69 ],\n", + " [296.88998, 297.19 , 297.49 , ..., 295.69 , 295.69 ,\n", + " 295.19 ],\n", + " [298.09 , 298.09 , 298.49 , ..., 296.49 , 296.19 ,\n", + " 295.69 ]]], dtype=float32)\n", + "Coordinates:\n", + " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", + " * lon (lon) float32 202.5 205.0 207.5 210.0 ... 322.5 325.0 327.5 330.0\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "da.drop_sel(lat=50.0, lon=200.0, time=\"2014\")" ] @@ -389,13 +3905,441 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": { "tags": [ "hide-output" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'air' (time: 2920)>\n",
+       "array([262.69998, 263.19998, 270.9    , ..., 264.19   , 265.19   ,\n",
+       "       266.99   ], dtype=float32)\n",
+       "Coordinates:\n",
+       "    lat      float32 52.5\n",
+       "    lon      float32 252.5\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
    " + ], + "text/plain": [ + "\n", + "array([262.69998, 263.19998, 270.9 , ..., 264.19 , 265.19 ,\n", + " 266.99 ], dtype=float32)\n", + "Coordinates:\n", + " lat float32 52.5\n", + " lon float32 252.5\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "da.sel(lat=52.25, lon=251.8998, method=\"nearest\")" ] @@ -409,9 +4353,437 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'air' (time: 2920)>\n",
+       "array([262.69998, 263.19998, 270.9    , ..., 264.19   , 265.19   ,\n",
+       "       266.99   ], dtype=float32)\n",
+       "Coordinates:\n",
+       "    lat      float32 52.5\n",
+       "    lon      float32 252.5\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]
    " + ], + "text/plain": [ + "\n", + "array([262.69998, 263.19998, 270.9 , ..., 264.19 , 265.19 ,\n", + " 266.99 ], dtype=float32)\n", + "Coordinates:\n", + " lat float32 52.5\n", + " lon float32 252.5\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Attributes:\n", + " long_name: 4xDaily Air temperature at sigma level 995\n", + " units: degK\n", + " precision: 2\n", + " GRIB_id: 11\n", + " GRIB_name: TMP\n", + " var_desc: Air temperature\n", + " dataset: NMC Reanalysis\n", + " level_desc: Surface\n", + " statistic: Individual Obs\n", + " parent_stat: Other\n", + " actual_range: [185.16 322.1 ]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "da.sel(lat=52.25, lon=251.8998, method=\"nearest\", tolerance=2)" ] @@ -429,9 +4801,428 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.Dataset>\n",
+       "Dimensions:  (time: 2920)\n",
+       "Coordinates:\n",
+       "    lat      float32 52.5\n",
+       "    lon      float32 252.5\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Data variables:\n",
+       "    air      (time) float32 262.7 263.2 270.9 274.1 ... 261.6 264.2 265.2 267.0\n",
+       "Attributes:\n",
+       "    Conventions:  COARDS\n",
+       "    title:        4x daily NMC reanalysis (1948)\n",
+       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
+       "    platform:     Model\n",
+       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
    " + ], + "text/plain": [ + "\n", + "Dimensions: (time: 2920)\n", + "Coordinates:\n", + " lat float32 52.5\n", + " lon float32 252.5\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Data variables:\n", + " air (time) float32 262.7 263.2 270.9 274.1 ... 261.6 264.2 265.2 267.0\n", + "Attributes:\n", + " Conventions: COARDS\n", + " title: 4x daily NMC reanalysis (1948)\n", + " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", + " platform: Model\n", + " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ds.sel(lat=52.25, lon=251.8998, method=\"nearest\")" ] @@ -517,9 +5308,473 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.Dataset>\n",
+       "Dimensions:  (lat: 25, time: 4, lon: 53)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2013-01-01T18:00:00\n",
+       "Data variables:\n",
+       "    air      (time, lat, lon) float32 241.2 242.5 243.5 ... 297.8 298.0 297.9\n",
+       "Attributes:\n",
+       "    Conventions:  COARDS\n",
+       "    title:        4x daily NMC reanalysis (1948)\n",
+       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
+       "    platform:     Model\n",
+       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
    " + ], + "text/plain": [ + "\n", + "Dimensions: (lat: 25, time: 4, lon: 53)\n", + "Coordinates:\n", + " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", + " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2013-01-01T18:00:00\n", + "Data variables:\n", + " air (time, lat, lon) float32 241.2 242.5 243.5 ... 297.8 298.0 297.9\n", + "Attributes:\n", + " Conventions: COARDS\n", + " title: 4x daily NMC reanalysis (1948)\n", + " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", + " platform: Model\n", + " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ds.sel(time='2013-01-01')" ] @@ -535,9 +5790,483 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.Dataset>\n",
+       "Dimensions:  (lat: 25, time: 1460, lon: 53)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2013-12-31T18:00:00\n",
+       "Data variables:\n",
+       "    air      (time, lat, lon) float32 241.2 242.5 243.5 ... 296.1 295.1 294.7\n",
+       "Attributes:\n",
+       "    Conventions:  COARDS\n",
+       "    title:        4x daily NMC reanalysis (1948)\n",
+       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
+       "    platform:     Model\n",
+       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
    " + ], + "text/plain": [ + "\n", + "Dimensions: (lat: 25, time: 1460, lon: 53)\n", + "Coordinates:\n", + " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", + " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2013-12-31T18:00:00\n", + "Data variables:\n", + " air (time, lat, lon) float32 241.2 242.5 243.5 ... 296.1 295.1 294.7\n", + "Attributes:\n", + " Conventions: COARDS\n", + " title: 4x daily NMC reanalysis (1948)\n", + " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", + " platform: Model\n", + " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# This will return a subset of the dataset corresponding to the entire year of 2013.\n", "ds.sel(time=slice('2013-01-01', '2013-12-31'))" @@ -565,9 +6294,470 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.Dataset>\n",
+       "Dimensions:  (lat: 25, time: 3, lon: 53)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2013-07-09 2013-10-11 2013-12-24\n",
+       "Data variables:\n",
+       "    air      (time, lat, lon) float32 279.0 278.6 278.1 ... 296.8 296.6 296.5\n",
+       "Attributes:\n",
+       "    Conventions:  COARDS\n",
+       "    title:        4x daily NMC reanalysis (1948)\n",
+       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
+       "    platform:     Model\n",
+       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
    " + ], + "text/plain": [ + "\n", + "Dimensions: (lat: 25, time: 3, lon: 53)\n", + "Coordinates:\n", + " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", + " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", + " * time (time) datetime64[ns] 2013-07-09 2013-10-11 2013-12-24\n", + "Data variables:\n", + " air (time, lat, lon) float32 279.0 278.6 278.1 ... 296.8 296.6 296.5\n", + "Attributes:\n", + " Conventions: COARDS\n", + " title: 4x daily NMC reanalysis (1948)\n", + " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", + " platform: Model\n", + " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "dates = ['2013-07-09', '2013-10-11', '2013-12-24']\n", "ds.sel(time=dates)" @@ -584,9 +6774,491 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/glade/u/apps/opt/conda/envs/npl-2023b/lib/python3.10/site-packages/xarray/core/accessor_dt.py:72: FutureWarning: Index.ravel returning ndarray is deprecated; in a future version this will return a view on self.\n", + " values_as_series = pd.Series(values.ravel(), copy=False)\n" + ] + }, + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.Dataset>\n",
+       "Dimensions:  (lat: 25, time: 248, lon: 53)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2013-07-01 ... 2014-07-31T18:00:00\n",
+       "Data variables:\n",
+       "    air      (time, lat, lon) float32 273.7 273.0 272.5 ... 297.5 297.6 297.8\n",
+       "Attributes:\n",
+       "    Conventions:  COARDS\n",
+       "    title:        4x daily NMC reanalysis (1948)\n",
+       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
+       "    platform:     Model\n",
+       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
    " + ], + "text/plain": [ + "\n", + "Dimensions: (lat: 25, time: 248, lon: 53)\n", + "Coordinates:\n", + " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", + " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", + " * time (time) datetime64[ns] 2013-07-01 ... 2014-07-31T18:00:00\n", + "Data variables:\n", + " air (time, lat, lon) float32 273.7 273.0 272.5 ... 297.5 297.6 297.8\n", + "Attributes:\n", + " Conventions: COARDS\n", + " title: 4x daily NMC reanalysis (1948)\n", + " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", + " platform: Model\n", + " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ds.sel(time=ds.time.dt.month == 7)" ] @@ -600,9 +7272,573 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/glade/u/apps/opt/conda/envs/npl-2023b/lib/python3.10/site-packages/xarray/core/accessor_dt.py:72: FutureWarning: Index.ravel returning ndarray is deprecated; in a future version this will return a view on self.\n", + " values_as_series = pd.Series(values.ravel(), copy=False)\n" + ] + }, + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.Dataset>\n",
+       "Dimensions:  (lat: 25, time: 96, lon: 53)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-15 ... 2014-12-15T18:00:00\n",
+       "Data variables:\n",
+       "    air      (time, lat, lon) float32 243.8 243.4 242.8 ... 297.1 296.9 296.9\n",
+       "Attributes:\n",
+       "    Conventions:  COARDS\n",
+       "    title:        4x daily NMC reanalysis (1948)\n",
+       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
+       "    platform:     Model\n",
+       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
    " + ], + "text/plain": [ + "\n", + "Dimensions: (lat: 25, time: 96, lon: 53)\n", + "Coordinates:\n", + " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", + " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", + " * time (time) datetime64[ns] 2013-01-15 ... 2014-12-15T18:00:00\n", + "Data variables:\n", + " air (time, lat, lon) float32 243.8 243.4 242.8 ... 297.1 296.9 296.9\n", + "Attributes:\n", + " Conventions: COARDS\n", + " title: 4x daily NMC reanalysis (1948)\n", + " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", + " platform: Model\n", + " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ds.sel(time=ds.time.dt.day == 15)" ] @@ -639,6 +7875,11 @@ } ], "metadata": { + "kernelspec": { + "display_name": "NPL 2023b", + "language": "python", + "name": "npl-2023b" + }, "language_info": { "codemirror_mode": { "name": "ipython", @@ -648,7 +7889,8 @@ "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3" + "pygments_lexer": "ipython3", + "version": "3.10.12" }, "toc": { "base_numbering": 1, @@ -662,6 +7904,13 @@ "toc_position": {}, "toc_section_display": true, "toc_window_display": true + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } } }, "nbformat": 4, From a822b64427ee80aa6cd5b19f201d6c681e1aebc1 Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Mon, 10 Jul 2023 00:24:06 -0600 Subject: [PATCH 50/54] removing align/reindex and quick intro update --- intermediate/02.2_indexing_Advanced.ipynb | 563 +++++++++++++++++++--- 1 file changed, 493 insertions(+), 70 deletions(-) diff --git a/intermediate/02.2_indexing_Advanced.ipynb b/intermediate/02.2_indexing_Advanced.ipynb index 0ce0b0e0..eefc001b 100644 --- a/intermediate/02.2_indexing_Advanced.ipynb +++ b/intermediate/02.2_indexing_Advanced.ipynb @@ -8,9 +8,7 @@ "\n", "## Learning Objectives\n", "\n", - "* Orthogonal vs. Vectorized and Pointwise Indexing\n", - "\n", - "* Fancy DateTime Indexing" + "* Orthogonal vs. Vectorized and Pointwise Indexing" ] }, { @@ -19,14 +17,13 @@ "source": [ "## Overview\n", "\n", - "In the previous notebooks, we learned basic forms of indexing with xarray (positional and name based dimensions, integer and label based indexing), Datetime Indexing, and nearest neighbor lookups. In this tutorial, we will discover more advanced options for vectorized indexing and learn about additional useful methods relating to indexing/selecting data such as masking. \n", - "\n", + "In the previous notebooks, we learned basic forms of indexing with xarray (positional and name based dimensions, integer and label based indexing), Datetime Indexing, and nearest neighbor lookups. In this tutorial, we will lean how Xarray indexing is different from Numpy and how to do vectorized/pointwise indexing using Xarray. \n", "First, let's import packages needed for this repository: " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -44,9 +41,483 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.Dataset>\n",
+       "Dimensions:  (lat: 25, time: 2920, lon: 53)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Data variables:\n",
+       "    air      (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n",
+       "Attributes:\n",
+       "    Conventions:  COARDS\n",
+       "    title:        4x daily NMC reanalysis (1948)\n",
+       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
+       "    platform:     Model\n",
+       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
    " + ], + "text/plain": [ + "\n", + "Dimensions: (lat: 25, time: 2920, lon: 53)\n", + "Coordinates:\n", + " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", + " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", + " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", + "Data variables:\n", + " air (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n", + "Attributes:\n", + " Conventions: COARDS\n", + " title: 4x daily NMC reanalysis (1948)\n", + " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", + " platform: Model\n", + " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ds = xr.tutorial.load_dataset(\"air_temperature\")\n", "da = ds.air\n", @@ -235,67 +706,6 @@ "da.sel(space=xr.DataArray([\"IA\", \"IL\", \"IN\"], dims=[\"new_time\"]), time=times)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Align and Reindex \n", - "\n", - "Xarray enforces alignment between index Coordinates (that is, coordinates with the same name as a dimension, marked by *) on objects used in binary operations.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "da" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "da = xr.DataArray(\n", - " np.random.rand(4, 3),\n", - " [\n", - " (\"time\", pd.date_range(\"2000-01-01\", periods=4)),\n", - " (\"space\", [\"IL\", \"IA\", \"IN\"]),\n", - " ],\n", - ")\n", - "da" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "da.reindex(space=[\"IA\", \"CA\"])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "new_time = pd.date_range('2013-02-01', periods=20, freq='H')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "da.reindex(time=new_time)" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -307,6 +717,11 @@ } ], "metadata": { + "kernelspec": { + "display_name": "NPL 2023b", + "language": "python", + "name": "npl-2023b" + }, "language_info": { "codemirror_mode": { "name": "ipython", @@ -316,7 +731,8 @@ "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3" + "pygments_lexer": "ipython3", + "version": "3.10.12" }, "toc": { "base_numbering": 1, @@ -330,6 +746,13 @@ "toc_position": {}, "toc_section_display": true, "toc_window_display": true + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } } }, "nbformat": 4, From 52babd4d8ef837255a37133cc33cd49db79edfa4 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 10 Jul 2023 06:24:47 +0000 Subject: [PATCH 51/54] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- fundamentals/02.1_indexing_Basic.ipynb | 7338 +-------------------- intermediate/02.2_indexing_Advanced.ipynb | 495 +- 2 files changed, 53 insertions(+), 7780 deletions(-) diff --git a/fundamentals/02.1_indexing_Basic.ipynb b/fundamentals/02.1_indexing_Basic.ipynb index 9f7453e6..d3a88afb 100644 --- a/fundamentals/02.1_indexing_Basic.ipynb +++ b/fundamentals/02.1_indexing_Basic.ipynb @@ -53,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -69,483 +69,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.Dataset>\n",
-       "Dimensions:  (lat: 25, time: 2920, lon: 53)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Data variables:\n",
-       "    air      (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n",
-       "Attributes:\n",
-       "    Conventions:  COARDS\n",
-       "    title:        4x daily NMC reanalysis (1948)\n",
-       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
-       "    platform:     Model\n",
-       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
    " - ], - "text/plain": [ - "\n", - "Dimensions: (lat: 25, time: 2920, lon: 53)\n", - "Coordinates:\n", - " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", - " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Data variables:\n", - " air (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n", - "Attributes:\n", - " Conventions: COARDS\n", - " title: 4x daily NMC reanalysis (1948)\n", - " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", - " platform: Model\n", - " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "ds = xr.tutorial.load_dataset(\"air_temperature\")\n", "ds" @@ -553,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -583,20 +109,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2920, 25, 53)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "np_array = ds[\"air\"].data # numpy array\n", "np_array.shape" @@ -611,20 +126,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "242.09999" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "np_array[1, 0, 0]" ] @@ -638,20 +142,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([295. , 294.4 , 294.5 , ..., 297.29, 297.79, 297.99], dtype=float32)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# extract a time-series for one spatial location\n", "np_array[:, 20, 40]" @@ -683,434 +176,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 2920)>\n",
-       "array([295.  , 294.4 , 294.5 , ..., 297.29, 297.79, 297.99], dtype=float32)\n",
-       "Coordinates:\n",
-       "    lat      float32 25.0\n",
-       "    lon      float32 300.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([295. , 294.4 , 294.5 , ..., 297.29, 297.79, 297.99], dtype=float32)\n", - "Coordinates:\n", - " lat float32 25.0\n", - " lon float32 300.0\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da[:, 20, 40]" ] @@ -1127,40 +195,18 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2920, 2)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "np_array[:, [0, 1], [0, 1]].shape" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2920, 2, 2)" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da[:, [0, 1], [0, 1]].shape" ] @@ -1195,20 +241,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "da.isel(lat=20, lon=40).plot();" ] @@ -1222,20 +257,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "da.isel(time=slice(0, 20), lat=20, lon=40).plot();" ] @@ -1269,24 +293,13 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { "tags": [ "hide-output" ] }, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "da.sel(time=\"2014-01-01\", lat=25, lon=210).plot();" ] @@ -1300,438 +313,13 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "tags": [ "hide-output" ] }, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 1460)>\n",
-       "array([277.6 , 277.5 , 277.4 , ..., 277.59, 277.59, 277.59], dtype=float32)\n",
-       "Coordinates:\n",
-       "    lat      float32 50.0\n",
-       "    lon      float32 200.0\n",
-       "  * time     (time) datetime64[ns] 2014-01-01 ... 2014-12-31T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([277.6 , 277.5 , 277.4 , ..., 277.59, 277.59, 277.59], dtype=float32)\n", - "Coordinates:\n", - " lat float32 50.0\n", - " lon float32 200.0\n", - " * time (time) datetime64[ns] 2014-01-01 ... 2014-12-31T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da.sel(lat=50.0, lon=200.0, time=\"2014\")" ] @@ -1745,570 +333,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 1212, lat: 25, lon: 53)>\n",
-       "array([[[246.     , 245.09999, 244.09999, ..., 240.2    , 242.     ,\n",
-       "         244.2    ],\n",
-       "        [240.59999, 241.7    , 242.09999, ..., 239.09999, 241.2    ,\n",
-       "         244.59999],\n",
-       "        [238.2    , 239.79999, 240.79999, ..., 243.89   , 246.89   ,\n",
-       "         251.2    ],\n",
-       "        ...,\n",
-       "        [295.6    , 295.6    , 296.     , ..., 293.6    , 293.6    ,\n",
-       "         294.1    ],\n",
-       "        [297.     , 297.     , 297.     , ..., 293.69998, 293.9    ,\n",
-       "         294.6    ],\n",
-       "        [297.5    , 297.69998, 297.69998, ..., 294.5    , 294.69998,\n",
-       "         295.19998]],\n",
-       "\n",
-       "       [[245.2    , 244.29999, 243.     , ..., 241.89   , 243.09999,\n",
-       "         244.59999],\n",
-       "        [241.09999, 241.89   , 241.89   , ..., 241.89   , 243.59999,\n",
-       "         246.39   ],\n",
-       "        [239.89   , 241.     , 241.29999, ..., 245.09999, 248.2    ,\n",
-       "         252.2    ],\n",
-       "...\n",
-       "        [296.49   , 295.59   , 295.49   , ..., 297.29   , 297.38998,\n",
-       "         296.79   ],\n",
-       "        [298.09   , 297.79   , 297.19   , ..., 297.69   , 298.09   ,\n",
-       "         297.38998],\n",
-       "        [298.49   , 298.29   , 297.99   , ..., 298.29   , 298.09   ,\n",
-       "         297.99   ]],\n",
-       "\n",
-       "       [[246.29   , 246.39   , 245.98999, ..., 232.29   , 233.39   ,\n",
-       "         234.98999],\n",
-       "        [246.68999, 248.18999, 248.98999, ..., 230.59   , 232.48999,\n",
-       "         235.68999],\n",
-       "        [244.79   , 245.29   , 245.89   , ..., 230.48999, 235.09   ,\n",
-       "         241.39   ],\n",
-       "        ...,\n",
-       "        [296.88998, 296.49   , 296.49   , ..., 297.69   , 297.29   ,\n",
-       "         296.19   ],\n",
-       "        [298.29   , 297.99   , 297.49   , ..., 298.29   , 298.38998,\n",
-       "         297.49   ],\n",
-       "        [298.49   , 298.29   , 297.99   , ..., 298.59   , 298.38998,\n",
-       "         298.29   ]]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-       "  * time     (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([[[246. , 245.09999, 244.09999, ..., 240.2 , 242. ,\n", - " 244.2 ],\n", - " [240.59999, 241.7 , 242.09999, ..., 239.09999, 241.2 ,\n", - " 244.59999],\n", - " [238.2 , 239.79999, 240.79999, ..., 243.89 , 246.89 ,\n", - " 251.2 ],\n", - " ...,\n", - " [295.6 , 295.6 , 296. , ..., 293.6 , 293.6 ,\n", - " 294.1 ],\n", - " [297. , 297. , 297. , ..., 293.69998, 293.9 ,\n", - " 294.6 ],\n", - " [297.5 , 297.69998, 297.69998, ..., 294.5 , 294.69998,\n", - " 295.19998]],\n", - "\n", - " [[245.2 , 244.29999, 243. , ..., 241.89 , 243.09999,\n", - " 244.59999],\n", - " [241.09999, 241.89 , 241.89 , ..., 241.89 , 243.59999,\n", - " 246.39 ],\n", - " [239.89 , 241. , 241.29999, ..., 245.09999, 248.2 ,\n", - " 252.2 ],\n", - "...\n", - " [296.49 , 295.59 , 295.49 , ..., 297.29 , 297.38998,\n", - " 296.79 ],\n", - " [298.09 , 297.79 , 297.19 , ..., 297.69 , 298.09 ,\n", - " 297.38998],\n", - " [298.49 , 298.29 , 297.99 , ..., 298.29 , 298.09 ,\n", - " 297.99 ]],\n", - "\n", - " [[246.29 , 246.39 , 245.98999, ..., 232.29 , 233.39 ,\n", - " 234.98999],\n", - " [246.68999, 248.18999, 248.98999, ..., 230.59 , 232.48999,\n", - " 235.68999],\n", - " [244.79 , 245.29 , 245.89 , ..., 230.48999, 235.09 ,\n", - " 241.39 ],\n", - " ...,\n", - " [296.88998, 296.49 , 296.49 , ..., 297.69 , 297.29 ,\n", - " 296.19 ],\n", - " [298.29 , 297.99 , 297.49 , ..., 298.29 , 298.38998,\n", - " 297.49 ],\n", - " [298.49 , 298.29 , 297.99 , ..., 298.59 , 298.38998,\n", - " 298.29 ]]], dtype=float32)\n", - "Coordinates:\n", - " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", - " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", - " * time (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# demonstrate slicing\n", "da.sel(time=slice(\"2014-02-14\", \"2014-12-13\"))" @@ -2323,991 +350,18 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 1212, lat: 25, lon: 53)>\n",
-       "array([[[246.     , 245.09999, 244.09999, ..., 240.2    , 242.     ,\n",
-       "         244.2    ],\n",
-       "        [240.59999, 241.7    , 242.09999, ..., 239.09999, 241.2    ,\n",
-       "         244.59999],\n",
-       "        [238.2    , 239.79999, 240.79999, ..., 243.89   , 246.89   ,\n",
-       "         251.2    ],\n",
-       "        ...,\n",
-       "        [295.6    , 295.6    , 296.     , ..., 293.6    , 293.6    ,\n",
-       "         294.1    ],\n",
-       "        [297.     , 297.     , 297.     , ..., 293.69998, 293.9    ,\n",
-       "         294.6    ],\n",
-       "        [297.5    , 297.69998, 297.69998, ..., 294.5    , 294.69998,\n",
-       "         295.19998]],\n",
-       "\n",
-       "       [[245.2    , 244.29999, 243.     , ..., 241.89   , 243.09999,\n",
-       "         244.59999],\n",
-       "        [241.09999, 241.89   , 241.89   , ..., 241.89   , 243.59999,\n",
-       "         246.39   ],\n",
-       "        [239.89   , 241.     , 241.29999, ..., 245.09999, 248.2    ,\n",
-       "         252.2    ],\n",
-       "...\n",
-       "        [296.49   , 295.59   , 295.49   , ..., 297.29   , 297.38998,\n",
-       "         296.79   ],\n",
-       "        [298.09   , 297.79   , 297.19   , ..., 297.69   , 298.09   ,\n",
-       "         297.38998],\n",
-       "        [298.49   , 298.29   , 297.99   , ..., 298.29   , 298.09   ,\n",
-       "         297.99   ]],\n",
-       "\n",
-       "       [[246.29   , 246.39   , 245.98999, ..., 232.29   , 233.39   ,\n",
-       "         234.98999],\n",
-       "        [246.68999, 248.18999, 248.98999, ..., 230.59   , 232.48999,\n",
-       "         235.68999],\n",
-       "        [244.79   , 245.29   , 245.89   , ..., 230.48999, 235.09   ,\n",
-       "         241.39   ],\n",
-       "        ...,\n",
-       "        [296.88998, 296.49   , 296.49   , ..., 297.69   , 297.29   ,\n",
-       "         296.19   ],\n",
-       "        [298.29   , 297.99   , 297.49   , ..., 298.29   , 298.38998,\n",
-       "         297.49   ],\n",
-       "        [298.49   , 298.29   , 297.99   , ..., 298.59   , 298.38998,\n",
-       "         298.29   ]]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-       "  * time     (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([[[246. , 245.09999, 244.09999, ..., 240.2 , 242. ,\n", - " 244.2 ],\n", - " [240.59999, 241.7 , 242.09999, ..., 239.09999, 241.2 ,\n", - " 244.59999],\n", - " [238.2 , 239.79999, 240.79999, ..., 243.89 , 246.89 ,\n", - " 251.2 ],\n", - " ...,\n", - " [295.6 , 295.6 , 296. , ..., 293.6 , 293.6 ,\n", - " 294.1 ],\n", - " [297. , 297. , 297. , ..., 293.69998, 293.9 ,\n", - " 294.6 ],\n", - " [297.5 , 297.69998, 297.69998, ..., 294.5 , 294.69998,\n", - " 295.19998]],\n", - "\n", - " [[245.2 , 244.29999, 243. , ..., 241.89 , 243.09999,\n", - " 244.59999],\n", - " [241.09999, 241.89 , 241.89 , ..., 241.89 , 243.59999,\n", - " 246.39 ],\n", - " [239.89 , 241. , 241.29999, ..., 245.09999, 248.2 ,\n", - " 252.2 ],\n", - "...\n", - " [296.49 , 295.59 , 295.49 , ..., 297.29 , 297.38998,\n", - " 296.79 ],\n", - " [298.09 , 297.79 , 297.19 , ..., 297.69 , 298.09 ,\n", - " 297.38998],\n", - " [298.49 , 298.29 , 297.99 , ..., 298.29 , 298.09 ,\n", - " 297.99 ]],\n", - "\n", - " [[246.29 , 246.39 , 245.98999, ..., 232.29 , 233.39 ,\n", - " 234.98999],\n", - " [246.68999, 248.18999, 248.98999, ..., 230.59 , 232.48999,\n", - " 235.68999],\n", - " [244.79 , 245.29 , 245.89 , ..., 230.48999, 235.09 ,\n", - " 241.39 ],\n", - " ...,\n", - " [296.88998, 296.49 , 296.49 , ..., 297.69 , 297.29 ,\n", - " 296.19 ],\n", - " [298.29 , 297.99 , 297.49 , ..., 298.29 , 298.38998,\n", - " 297.49 ],\n", - " [298.49 , 298.29 , 297.99 , ..., 298.59 , 298.38998,\n", - " 298.29 ]]], dtype=float32)\n", - "Coordinates:\n", - " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", - " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", - " * time (time) datetime64[ns] 2014-02-14 ... 2014-12-13T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da.loc[\"2014-02-14\":\"2014-12-13\"]" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'time' (time: 2920)>\n",
-       "array(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n",
-       "       '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\n",
-       "       '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\n",
-       "      dtype='datetime64[ns]')\n",
-       "Coordinates:\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Attributes:\n",
-       "    standard_name:  time\n",
-       "    long_name:      Time
    " - ], - "text/plain": [ - "\n", - "array(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\n", - " '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\n", - " '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\n", - " dtype='datetime64[ns]')\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Attributes:\n", - " standard_name: time\n", - " long_name: Time" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da.time" ] @@ -3323,570 +377,9 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 2919, lat: 24, lon: 52)>\n",
-       "array([[[242.5    , 243.5    , 244.     , ..., 232.79999, 235.5    ,\n",
-       "         238.59999],\n",
-       "        [244.5    , 244.7    , 244.2    , ..., 232.79999, 235.29999,\n",
-       "         239.29999],\n",
-       "        [249.79999, 248.89   , 247.5    , ..., 233.2    , 236.39   ,\n",
-       "         241.7    ],\n",
-       "        ...,\n",
-       "        [296.19998, 296.4    , 296.5    , ..., 295.4    , 295.1    ,\n",
-       "         294.69998],\n",
-       "        [296.19998, 296.79   , 296.5    , ..., 295.9    , 295.9    ,\n",
-       "         295.19998],\n",
-       "        [296.79   , 297.1    , 297.     , ..., 296.9    , 296.79   ,\n",
-       "         296.6    ]],\n",
-       "\n",
-       "       [[242.7    , 243.09999, 243.39   , ..., 232.     , 233.59999,\n",
-       "         235.79999],\n",
-       "        [244.09999, 244.2    , 244.09999, ..., 231.     , 232.5    ,\n",
-       "         235.7    ],\n",
-       "        [252.89   , 252.09999, 250.79999, ..., 230.79999, 233.39   ,\n",
-       "         238.5    ],\n",
-       "...\n",
-       "        [293.88998, 295.38998, 297.19   , ..., 295.09   , 294.69   ,\n",
-       "         294.29   ],\n",
-       "        [297.19   , 297.59   , 297.88998, ..., 295.29   , 295.09   ,\n",
-       "         294.38998],\n",
-       "        [298.38998, 298.49   , 298.59   , ..., 295.69   , 295.49   ,\n",
-       "         295.19   ]],\n",
-       "\n",
-       "       [[244.29   , 243.29   , 242.18999, ..., 241.68999, 241.48999,\n",
-       "         241.79   ],\n",
-       "        [249.29   , 248.39   , 246.98999, ..., 239.59   , 240.29   ,\n",
-       "         241.68999],\n",
-       "        [262.19   , 261.38998, 259.99   , ..., 239.89   , 242.59   ,\n",
-       "         246.29   ],\n",
-       "        ...,\n",
-       "        [293.69   , 295.09   , 296.69   , ..., 295.29   , 295.09   ,\n",
-       "         294.69   ],\n",
-       "        [296.88998, 297.19   , 297.49   , ..., 295.69   , 295.69   ,\n",
-       "         295.19   ],\n",
-       "        [298.09   , 298.09   , 298.49   , ..., 296.49   , 296.19   ,\n",
-       "         295.69   ]]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 202.5 205.0 207.5 210.0 ... 322.5 325.0 327.5 330.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([[[242.5 , 243.5 , 244. , ..., 232.79999, 235.5 ,\n", - " 238.59999],\n", - " [244.5 , 244.7 , 244.2 , ..., 232.79999, 235.29999,\n", - " 239.29999],\n", - " [249.79999, 248.89 , 247.5 , ..., 233.2 , 236.39 ,\n", - " 241.7 ],\n", - " ...,\n", - " [296.19998, 296.4 , 296.5 , ..., 295.4 , 295.1 ,\n", - " 294.69998],\n", - " [296.19998, 296.79 , 296.5 , ..., 295.9 , 295.9 ,\n", - " 295.19998],\n", - " [296.79 , 297.1 , 297. , ..., 296.9 , 296.79 ,\n", - " 296.6 ]],\n", - "\n", - " [[242.7 , 243.09999, 243.39 , ..., 232. , 233.59999,\n", - " 235.79999],\n", - " [244.09999, 244.2 , 244.09999, ..., 231. , 232.5 ,\n", - " 235.7 ],\n", - " [252.89 , 252.09999, 250.79999, ..., 230.79999, 233.39 ,\n", - " 238.5 ],\n", - "...\n", - " [293.88998, 295.38998, 297.19 , ..., 295.09 , 294.69 ,\n", - " 294.29 ],\n", - " [297.19 , 297.59 , 297.88998, ..., 295.29 , 295.09 ,\n", - " 294.38998],\n", - " [298.38998, 298.49 , 298.59 , ..., 295.69 , 295.49 ,\n", - " 295.19 ]],\n", - "\n", - " [[244.29 , 243.29 , 242.18999, ..., 241.68999, 241.48999,\n", - " 241.79 ],\n", - " [249.29 , 248.39 , 246.98999, ..., 239.59 , 240.29 ,\n", - " 241.68999],\n", - " [262.19 , 261.38998, 259.99 , ..., 239.89 , 242.59 ,\n", - " 246.29 ],\n", - " ...,\n", - " [293.69 , 295.09 , 296.69 , ..., 295.29 , 295.09 ,\n", - " 294.69 ],\n", - " [296.88998, 297.19 , 297.49 , ..., 295.69 , 295.69 ,\n", - " 295.19 ],\n", - " [298.09 , 298.09 , 298.49 , ..., 296.49 , 296.19 ,\n", - " 295.69 ]]], dtype=float32)\n", - "Coordinates:\n", - " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", - " * lon (lon) float32 202.5 205.0 207.5 210.0 ... 322.5 325.0 327.5 330.0\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da.drop_sel(lat=50.0, lon=200.0, time=\"2014\")" ] @@ -3905,441 +398,13 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": { "tags": [ "hide-output" ] }, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 2920)>\n",
-       "array([262.69998, 263.19998, 270.9    , ..., 264.19   , 265.19   ,\n",
-       "       266.99   ], dtype=float32)\n",
-       "Coordinates:\n",
-       "    lat      float32 52.5\n",
-       "    lon      float32 252.5\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([262.69998, 263.19998, 270.9 , ..., 264.19 , 265.19 ,\n", - " 266.99 ], dtype=float32)\n", - "Coordinates:\n", - " lat float32 52.5\n", - " lon float32 252.5\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da.sel(lat=52.25, lon=251.8998, method=\"nearest\")" ] @@ -4353,437 +418,9 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.DataArray 'air' (time: 2920)>\n",
-       "array([262.69998, 263.19998, 270.9    , ..., 264.19   , 265.19   ,\n",
-       "       266.99   ], dtype=float32)\n",
-       "Coordinates:\n",
-       "    lat      float32 52.5\n",
-       "    lon      float32 252.5\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]
    " - ], - "text/plain": [ - "\n", - "array([262.69998, 263.19998, 270.9 , ..., 264.19 , 265.19 ,\n", - " 266.99 ], dtype=float32)\n", - "Coordinates:\n", - " lat float32 52.5\n", - " lon float32 252.5\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Attributes:\n", - " long_name: 4xDaily Air temperature at sigma level 995\n", - " units: degK\n", - " precision: 2\n", - " GRIB_id: 11\n", - " GRIB_name: TMP\n", - " var_desc: Air temperature\n", - " dataset: NMC Reanalysis\n", - " level_desc: Surface\n", - " statistic: Individual Obs\n", - " parent_stat: Other\n", - " actual_range: [185.16 322.1 ]" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "da.sel(lat=52.25, lon=251.8998, method=\"nearest\", tolerance=2)" ] @@ -4801,428 +438,9 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.Dataset>\n",
-       "Dimensions:  (time: 2920)\n",
-       "Coordinates:\n",
-       "    lat      float32 52.5\n",
-       "    lon      float32 252.5\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Data variables:\n",
-       "    air      (time) float32 262.7 263.2 270.9 274.1 ... 261.6 264.2 265.2 267.0\n",
-       "Attributes:\n",
-       "    Conventions:  COARDS\n",
-       "    title:        4x daily NMC reanalysis (1948)\n",
-       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
-       "    platform:     Model\n",
-       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
    " - ], - "text/plain": [ - "\n", - "Dimensions: (time: 2920)\n", - "Coordinates:\n", - " lat float32 52.5\n", - " lon float32 252.5\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Data variables:\n", - " air (time) float32 262.7 263.2 270.9 274.1 ... 261.6 264.2 265.2 267.0\n", - "Attributes:\n", - " Conventions: COARDS\n", - " title: 4x daily NMC reanalysis (1948)\n", - " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", - " platform: Model\n", - " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "ds.sel(lat=52.25, lon=251.8998, method=\"nearest\")" ] @@ -5308,473 +526,9 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.Dataset>\n",
-       "Dimensions:  (lat: 25, time: 4, lon: 53)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2013-01-01T18:00:00\n",
-       "Data variables:\n",
-       "    air      (time, lat, lon) float32 241.2 242.5 243.5 ... 297.8 298.0 297.9\n",
-       "Attributes:\n",
-       "    Conventions:  COARDS\n",
-       "    title:        4x daily NMC reanalysis (1948)\n",
-       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
-       "    platform:     Model\n",
-       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
    " - ], - "text/plain": [ - "\n", - "Dimensions: (lat: 25, time: 4, lon: 53)\n", - "Coordinates:\n", - " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", - " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2013-01-01T18:00:00\n", - "Data variables:\n", - " air (time, lat, lon) float32 241.2 242.5 243.5 ... 297.8 298.0 297.9\n", - "Attributes:\n", - " Conventions: COARDS\n", - " title: 4x daily NMC reanalysis (1948)\n", - " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", - " platform: Model\n", - " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "ds.sel(time='2013-01-01')" ] @@ -5790,483 +544,9 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.Dataset>\n",
-       "Dimensions:  (lat: 25, time: 1460, lon: 53)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2013-12-31T18:00:00\n",
-       "Data variables:\n",
-       "    air      (time, lat, lon) float32 241.2 242.5 243.5 ... 296.1 295.1 294.7\n",
-       "Attributes:\n",
-       "    Conventions:  COARDS\n",
-       "    title:        4x daily NMC reanalysis (1948)\n",
-       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
-       "    platform:     Model\n",
-       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
    " - ], - "text/plain": [ - "\n", - "Dimensions: (lat: 25, time: 1460, lon: 53)\n", - "Coordinates:\n", - " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", - " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2013-12-31T18:00:00\n", - "Data variables:\n", - " air (time, lat, lon) float32 241.2 242.5 243.5 ... 296.1 295.1 294.7\n", - "Attributes:\n", - " Conventions: COARDS\n", - " title: 4x daily NMC reanalysis (1948)\n", - " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", - " platform: Model\n", - " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# This will return a subset of the dataset corresponding to the entire year of 2013.\n", "ds.sel(time=slice('2013-01-01', '2013-12-31'))" @@ -6294,470 +574,9 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.Dataset>\n",
-       "Dimensions:  (lat: 25, time: 3, lon: 53)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-       "  * time     (time) datetime64[ns] 2013-07-09 2013-10-11 2013-12-24\n",
-       "Data variables:\n",
-       "    air      (time, lat, lon) float32 279.0 278.6 278.1 ... 296.8 296.6 296.5\n",
-       "Attributes:\n",
-       "    Conventions:  COARDS\n",
-       "    title:        4x daily NMC reanalysis (1948)\n",
-       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
-       "    platform:     Model\n",
-       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
    " - ], - "text/plain": [ - "\n", - "Dimensions: (lat: 25, time: 3, lon: 53)\n", - "Coordinates:\n", - " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", - " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", - " * time (time) datetime64[ns] 2013-07-09 2013-10-11 2013-12-24\n", - "Data variables:\n", - " air (time, lat, lon) float32 279.0 278.6 278.1 ... 296.8 296.6 296.5\n", - "Attributes:\n", - " Conventions: COARDS\n", - " title: 4x daily NMC reanalysis (1948)\n", - " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", - " platform: Model\n", - " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "dates = ['2013-07-09', '2013-10-11', '2013-12-24']\n", "ds.sel(time=dates)" @@ -6774,491 +593,9 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/glade/u/apps/opt/conda/envs/npl-2023b/lib/python3.10/site-packages/xarray/core/accessor_dt.py:72: FutureWarning: Index.ravel returning ndarray is deprecated; in a future version this will return a view on self.\n", - " values_as_series = pd.Series(values.ravel(), copy=False)\n" - ] - }, - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.Dataset>\n",
-       "Dimensions:  (lat: 25, time: 248, lon: 53)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-       "  * time     (time) datetime64[ns] 2013-07-01 ... 2014-07-31T18:00:00\n",
-       "Data variables:\n",
-       "    air      (time, lat, lon) float32 273.7 273.0 272.5 ... 297.5 297.6 297.8\n",
-       "Attributes:\n",
-       "    Conventions:  COARDS\n",
-       "    title:        4x daily NMC reanalysis (1948)\n",
-       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
-       "    platform:     Model\n",
-       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
    " - ], - "text/plain": [ - "\n", - "Dimensions: (lat: 25, time: 248, lon: 53)\n", - "Coordinates:\n", - " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", - " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", - " * time (time) datetime64[ns] 2013-07-01 ... 2014-07-31T18:00:00\n", - "Data variables:\n", - " air (time, lat, lon) float32 273.7 273.0 272.5 ... 297.5 297.6 297.8\n", - "Attributes:\n", - " Conventions: COARDS\n", - " title: 4x daily NMC reanalysis (1948)\n", - " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", - " platform: Model\n", - " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "ds.sel(time=ds.time.dt.month == 7)" ] @@ -7272,573 +609,9 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/glade/u/apps/opt/conda/envs/npl-2023b/lib/python3.10/site-packages/xarray/core/accessor_dt.py:72: FutureWarning: Index.ravel returning ndarray is deprecated; in a future version this will return a view on self.\n", - " values_as_series = pd.Series(values.ravel(), copy=False)\n" - ] - }, - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.Dataset>\n",
-       "Dimensions:  (lat: 25, time: 96, lon: 53)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-15 ... 2014-12-15T18:00:00\n",
-       "Data variables:\n",
-       "    air      (time, lat, lon) float32 243.8 243.4 242.8 ... 297.1 296.9 296.9\n",
-       "Attributes:\n",
-       "    Conventions:  COARDS\n",
-       "    title:        4x daily NMC reanalysis (1948)\n",
-       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
-       "    platform:     Model\n",
-       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
    " - ], - "text/plain": [ - "\n", - "Dimensions: (lat: 25, time: 96, lon: 53)\n", - "Coordinates:\n", - " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", - " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", - " * time (time) datetime64[ns] 2013-01-15 ... 2014-12-15T18:00:00\n", - "Data variables:\n", - " air (time, lat, lon) float32 243.8 243.4 242.8 ... 297.1 296.9 296.9\n", - "Attributes:\n", - " Conventions: COARDS\n", - " title: 4x daily NMC reanalysis (1948)\n", - " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", - " platform: Model\n", - " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "ds.sel(time=ds.time.dt.day == 15)" ] @@ -7875,11 +648,6 @@ } ], "metadata": { - "kernelspec": { - "display_name": "NPL 2023b", - "language": "python", - "name": "npl-2023b" - }, "language_info": { "codemirror_mode": { "name": "ipython", @@ -7889,8 +657,7 @@ "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" + "pygments_lexer": "ipython3" }, "toc": { "base_numbering": 1, @@ -7904,13 +671,6 @@ "toc_position": {}, "toc_section_display": true, "toc_window_display": true - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } } }, "nbformat": 4, diff --git a/intermediate/02.2_indexing_Advanced.ipynb b/intermediate/02.2_indexing_Advanced.ipynb index eefc001b..9566c5c4 100644 --- a/intermediate/02.2_indexing_Advanced.ipynb +++ b/intermediate/02.2_indexing_Advanced.ipynb @@ -23,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -41,483 +41,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.Dataset>\n",
-       "Dimensions:  (lat: 25, time: 2920, lon: 53)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Data variables:\n",
-       "    air      (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n",
-       "Attributes:\n",
-       "    Conventions:  COARDS\n",
-       "    title:        4x daily NMC reanalysis (1948)\n",
-       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
-       "    platform:     Model\n",
-       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
    " - ], - "text/plain": [ - "\n", - "Dimensions: (lat: 25, time: 2920, lon: 53)\n", - "Coordinates:\n", - " * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n", - " * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n", - " * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n", - "Data variables:\n", - " air (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n", - "Attributes:\n", - " Conventions: COARDS\n", - " title: 4x daily NMC reanalysis (1948)\n", - " description: Data is from NMC initialized reanalysis\\n(4x/day). These a...\n", - " platform: Model\n", - " references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..." - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "ds = xr.tutorial.load_dataset(\"air_temperature\")\n", "da = ds.air\n", @@ -717,11 +243,6 @@ } ], "metadata": { - "kernelspec": { - "display_name": "NPL 2023b", - "language": "python", - "name": "npl-2023b" - }, "language_info": { "codemirror_mode": { "name": "ipython", @@ -731,8 +252,7 @@ "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" + "pygments_lexer": "ipython3" }, "toc": { "base_numbering": 1, @@ -746,13 +266,6 @@ "toc_position": {}, "toc_section_display": true, "toc_window_display": true - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } } }, "nbformat": 4, From 469e385755e9c13b733951d0fd0d8c5f6ea4c35c Mon Sep 17 00:00:00 2001 From: Negin Sobhani Date: Mon, 10 Jul 2023 00:30:05 -0600 Subject: [PATCH 52/54] quick update --- intermediate/02.3_indexing_BooleanMasking.ipynb | 12 ++++++++++++ workshops/scipy2023/README.md | 2 ++ 2 files changed, 14 insertions(+) diff --git a/intermediate/02.3_indexing_BooleanMasking.ipynb b/intermediate/02.3_indexing_BooleanMasking.ipynb index cf872472..122da3cf 100644 --- a/intermediate/02.3_indexing_BooleanMasking.ipynb +++ b/intermediate/02.3_indexing_BooleanMasking.ipynb @@ -468,6 +468,11 @@ } ], "metadata": { + "kernelspec": { + "display_name": "16318-311", + "language": "python", + "name": "16318-311" + }, "language_info": { "codemirror_mode": { "name": "ipython", @@ -491,6 +496,13 @@ "toc_position": {}, "toc_section_display": true, "toc_window_display": true + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } } }, "nbformat": 4, diff --git a/workshops/scipy2023/README.md b/workshops/scipy2023/README.md index c1009b84..56249b96 100644 --- a/workshops/scipy2023/README.md +++ b/workshops/scipy2023/README.md @@ -60,7 +60,9 @@ Once your codespace is launched, the following happens: ```{dropdown} Indexing -{doc}`../../fundamentals/02.1_indexing_Basic` + -{doc}`../../intermediate/02.2_indexing_Advanced` + -{doc}`../../intermediate/02.3_indexing_BooleanMasking` ``` From 3b6cf0d03db468513f410f2f07b2be4dac6c8852 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 10 Jul 2023 06:30:40 +0000 Subject: [PATCH 53/54] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- intermediate/02.3_indexing_BooleanMasking.ipynb | 12 ------------ 1 file changed, 12 deletions(-) diff --git a/intermediate/02.3_indexing_BooleanMasking.ipynb b/intermediate/02.3_indexing_BooleanMasking.ipynb index 122da3cf..cf872472 100644 --- a/intermediate/02.3_indexing_BooleanMasking.ipynb +++ b/intermediate/02.3_indexing_BooleanMasking.ipynb @@ -468,11 +468,6 @@ } ], "metadata": { - "kernelspec": { - "display_name": "16318-311", - "language": "python", - "name": "16318-311" - }, "language_info": { "codemirror_mode": { "name": "ipython", @@ -496,13 +491,6 @@ "toc_position": {}, "toc_section_display": true, "toc_window_display": true - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } } }, "nbformat": 4, From f0485decc6b9946cdf7062dd4bb23d3fb14473bc Mon Sep 17 00:00:00 2001 From: dcherian Date: Mon, 10 Jul 2023 08:20:39 -0600 Subject: [PATCH 54/54] Review/updates --- _toc.yml | 6 +- fundamentals/02.1_indexing_Basic.ipynb | 93 ++++++++++++++++--- .../advanced-indexing.ipynb} | 10 +- .../boolean-masking-indexing.ipynb} | 41 ++++---- intermediate/indexing/indexing.md | 5 + workshops/scipy2023/README.md | 5 +- 6 files changed, 118 insertions(+), 42 deletions(-) rename intermediate/{02.2_indexing_Advanced.ipynb => indexing/advanced-indexing.ipynb} (95%) rename intermediate/{02.3_indexing_BooleanMasking.ipynb => indexing/boolean-masking-indexing.ipynb} (91%) create mode 100644 intermediate/indexing/indexing.md diff --git a/_toc.yml b/_toc.yml index e38ef891..976b745a 100644 --- a/_toc.yml +++ b/_toc.yml @@ -20,8 +20,6 @@ parts: - file: fundamentals/02_labeled_data.md sections: - file: fundamentals/02.1_indexing_Basic.ipynb - - file: intermediate/02.2_indexing_Advanced.ipynb - - file: intermediate/02.3_indexing_BooleanMasking.ipynb - file: fundamentals/02.2_manipulating_dimensions - file: fundamentals/03_computation.md sections: @@ -39,6 +37,10 @@ parts: - caption: Intermediate chapters: - file: intermediate/01-high-level-computation-patterns + - file: intermediate/indexing/indexing + sections: + - file: intermediate/indexing/advanced-indexing.ipynb + - file: intermediate/indexing/boolean-masking-indexing.ipynb - file: intermediate/xarray_and_dask - file: intermediate/xarray_ecosystem - file: intermediate/hvplot diff --git a/fundamentals/02.1_indexing_Basic.ipynb b/fundamentals/02.1_indexing_Basic.ipynb index d3a88afb..8fc5d018 100644 --- a/fundamentals/02.1_indexing_Basic.ipynb +++ b/fundamentals/02.1_indexing_Basic.ipynb @@ -19,14 +19,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n", "## Introduction\n", "\n", "Xarray offers extremely flexible indexing routines that combine the best features of NumPy and Pandas for data selection.\n", "\n", "The most basic way to access elements of a `DataArray` object is to use Python’s `[]` syntax, such as `array[i, j]`, where `i` and `j` are both integers.\n", "\n", - "As xarray objects can store coordinates corresponding to each dimension of an array, label-based indexing similar to `pandas.DataFrame.loc` is also possible. In label-based indexing, the element position `i` is automatically looked-up from the coordinate values.\n", + "As xarray objects can store coordinates corresponding to each dimension of an array, label-based indexing is also possible (e.g. `.sel(latitude=0)`, similar to `pandas.DataFrame.loc`). In label-based indexing, the element position `i` is automatically looked-up from the coordinate values.\n", "\n", "By leveraging the labeled dimensions and coordinates provided by Xarray, users can effortlessly access, subset, and manipulate data along multiple axes, enabling complex operations such as slicing, masking, and aggregating data based on specific criteria. \n", "\n", @@ -57,7 +56,9 @@ "metadata": {}, "outputs": [], "source": [ - "import xarray as xr" + "import xarray as xr\n", + "\n", + "xr.set_options(display_expand_attrs=False, display_expand_data=False);" ] }, { @@ -152,7 +153,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, "source": [ "### Positional Indexing with Xarray" ] @@ -167,11 +171,15 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ "#### NumPy style indexing with Xarray\n", "\n", - "NumPy style indexing works exactly the same with Xarray but it also preserves labels and metadata. " + "NumPy style indexing works exactly the same with Xarray but it also preserves labels and metadata. \n", + "\n", + "This approach however does not take advantage of the dimension names and coordinate location information that is present in a Xarray object." ] }, { @@ -233,10 +241,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Remembering the axis order can be challenging even with 2D arrays (is `np_array[0,3]` the first row and third column or first column and third row? or did I store these samples by row or by column when I saved the data?!). The difficulty is compounded with added dimensions. \n", + "Remembering the axis order can be challenging even with 2D arrays:\n", + "- is `np_array[0,3]` the first row and third column or first column and third row? \n", + "- or did I store these samples by row or by column when I saved the data?!. \n", "\n", - "Xarray objects eliminate much of the mental overhead by adding indexing using dimension names:\n", - "\n" + "The difficulty is compounded with added dimensions. \n", + "\n", + "Xarray objects eliminate much of the mental overhead by allowing indexing using dimension names instead of axes numbers:" ] }, { @@ -345,7 +356,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Xarray also supports label-based indexing, just like pandas using `.loc`. Because we use a `pandas.Index` under the hood, label based indexing is very fast. To do label based indexing, use the `loc` attribute:" + "Xarray also supports label-based indexing, just like pandas using `.loc`. To do label based indexing, use the `loc` attribute:" ] }, { @@ -372,7 +383,7 @@ "source": [ "### Dropping using `drop_sel`\n", "\n", - "If instead of selecting data we want to drop it, we can use `drop_sel` method:" + "If instead of selecting data we want to drop it, we can use `drop_sel` method with syntax similar to `sel`:" ] }, { @@ -449,7 +460,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Exercise\n", + "## Exercises\n", "\n", "Practice the syntax you’ve learned so far:" ] @@ -482,7 +493,7 @@ "\n", "Select all data at 75 degree north and between Jan 1, 2013 and Oct 15, 2013 :\n", "```\n", - "````{solution} indexing-1\n", + "````{solution} indexing-2\n", ":class: dropdown\n", "```python\n", "ds.sel(lat=75, time=slice(\"2013-01-01\", \"2013-10-15\"))\n", @@ -500,7 +511,7 @@ "Remove all entries at 260 and 270 degrees :\n", "\n", "```\n", - "````{solution} indexing-1\n", + "````{solution} indexing-3\n", ":class: dropdown\n", "```python\n", "ds.drop_sel(lon=[260, 270])\n", @@ -521,7 +532,23 @@ "\n", "### Selecting data based on single datetime\n", "\n", - "Let's say we have a Dataset ds and we want to select data at a particular date and time, for instance, '2013-01-01'. We can do this by using the sel (select) method, like so:\n" + "Let's say we have a Dataset ds and we want to select data at a particular date and time, for instance, '2013-01-01' at 6AM. We can do this by using the `sel` (select) method, like so:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ds.sel(time='2013-01-01 06:00')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By default, datetime selection will return a range of values that match the provided string. For e.g. `time=\"2013-01-01\"` will return all timestamps for that day (4 of them here):" ] }, { @@ -533,6 +560,42 @@ "ds.sel(time='2013-01-01')" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can use this feature to select all points in a month" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "ds.sel(time=\"2014-May\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "or a year" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "ds.sel(time=\"2014\")" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/intermediate/02.2_indexing_Advanced.ipynb b/intermediate/indexing/advanced-indexing.ipynb similarity index 95% rename from intermediate/02.2_indexing_Advanced.ipynb rename to intermediate/indexing/advanced-indexing.ipynb index 9566c5c4..0f57cae1 100644 --- a/intermediate/02.2_indexing_Advanced.ipynb +++ b/intermediate/indexing/advanced-indexing.ipynb @@ -29,7 +29,11 @@ "source": [ "import numpy as np\n", "import pandas as pd\n", - "import xarray as xr" + "import xarray as xr\n", + "\n", + "\n", + "xr.set_options(display_expand_attrs=False)\n", + "np.set_printoptions(threshold=10, edgeitems=2)" ] }, { @@ -60,7 +64,7 @@ "\n", "If you only provide integers, slices, or unlabeled arrays (array without dimension names, such as `np.ndarray`, `list`, but not `DataArray()`) indexing can be understood as orthogonally (i.e. along independent axes, instead of using NumPy’s broadcasting rules to vectorize indexers). \n", "\n", - "*Orthogonal* or *outer* indexing considers one-dimensional arrays in the same way as slices when deciding the output shapes. The principle of outer or orthogonal indexing is that the result mirrors the effect of independently indexing along each dimension with integer or boolean arrays, treating both the indexed and indexing arrays as one-dimensional. This method of indexing is analogous to vector indexing in programming languages like MATLAB, Fortran, and R, where each indexer component independently selects along its corresponding dimension. \n", + "*Orthogonal* or *outer* indexing considers one-dimensional arrays in the same way as slices when deciding the output shapes. The principle of outer or orthogonal indexing is that the result mirrors the effect of independently indexing along each dimension with integer or boolean arrays, treating both the indexed and indexing arrays as one-dimensional. This method of indexing is analogous to vector indexing in programming languages like MATLAB, Fortran, and R, where each indexer component *independently* selects along its corresponding dimension. \n", "\n", "For example : " ] @@ -71,7 +75,7 @@ "metadata": {}, "outputs": [], "source": [ - "da[0, [2, 4, 10, 13], [1, 6, 7]].plot(); # -- orthogonal indexing" + "da.isel(time=0, lat=[2, 4, 10, 13], lon=[1, 6, 7]).plot(); # -- orthogonal indexing" ] }, { diff --git a/intermediate/02.3_indexing_BooleanMasking.ipynb b/intermediate/indexing/boolean-masking-indexing.ipynb similarity index 91% rename from intermediate/02.3_indexing_BooleanMasking.ipynb rename to intermediate/indexing/boolean-masking-indexing.ipynb index cf872472..821dba20 100644 --- a/intermediate/02.3_indexing_BooleanMasking.ipynb +++ b/intermediate/indexing/boolean-masking-indexing.ipynb @@ -21,7 +21,7 @@ "\n", "*Boolean masking*, known as *boolean indexing*, is a functionality in Python that enables the filtering of values based on a specific condition.\n", "\n", - "A boolean mask refers to a binary array or a boolean-valued array that is used as a *filter* to select specific elements from another array. The boolean mask acts as a criterion or condition, where each element in the mask corresponds to an element in the target array. The mask determines whether the corresponding element in the target array should be selected or not. \n", + "A boolean mask refers to a binary array or a boolean-valued (`True`/`False`) array that is used as a *filter* to select specific elements from another array. The boolean mask acts as a criterion or condition, where each element in the mask corresponds to an element in the target array. An element in the target array is selected when the corresponding `mask` value is `True`. \n", "\n", "Xarray provides different capabilities to allow filtering and boolean indexing. In this notebook, we will learn more about it.\n", "\n", @@ -34,18 +34,21 @@ "metadata": {}, "outputs": [], "source": [ + "import cartopy.crs as ccrs\n", "import numpy as np\n", "import xarray as xr\n", + "from matplotlib import pyplot as plt\n", + "import matplotlib as mpl\n", "\n", - "import cartopy.crs as ccrs\n", - "from matplotlib import pyplot as plt" + "xr.set_options(display_expand_attrs=False)\n", + "np.set_printoptions(threshold=10, edgeitems=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "In this tutorial, we’ll use the Regional Arctic System Mode (RASM) dataset from the `xarray-data` repository." + "In this tutorial, we’ll use the Regional Arctic System Mode (RASM) example dataset" ] }, { @@ -54,7 +57,7 @@ "metadata": {}, "outputs": [], "source": [ - "ds = xr.tutorial.load_dataset(\"rasm\")\n", + "ds = xr.tutorial.load_dataset(\"rasm\").isel(time=0)\n", "ds" ] }, @@ -138,10 +141,10 @@ "fig, axes = plt.subplots(ncols=2, figsize=(15, 5))\n", "\n", "# -- for reference (without masking):\n", - "da[0, :, :].plot(ax=axes[0])\n", + "da.plot(ax=axes[0], vmin=-30, vmax=30, cmap=mpl.cm.RdBu_r)\n", "\n", "# -- masked DataArray\n", - "da_masked[0, :, :].plot(ax=axes[1]);" + "da_masked.plot(ax=axes[1], vmin=-30, vmax=30, cmap=mpl.cm.RdBu_r);" ] }, { @@ -200,7 +203,7 @@ "outputs": [], "source": [ "da_masked = da.where(da.yc > 60, drop=True)\n", - "da_masked[0, :, :].plot();" + "da_masked.plot();" ] }, { @@ -223,7 +226,7 @@ "plt.figure(figsize=(14, 6))\n", "ax = plt.axes(projection=ccrs.PlateCarree())\n", "ax.set_global()\n", - "ds.Tair[0].plot.pcolormesh(ax=ax, transform=ccrs.PlateCarree(), x=\"xc\", y=\"yc\", add_colorbar=False)\n", + "ds.Tair.plot.pcolormesh(ax=ax, transform=ccrs.PlateCarree(), x=\"xc\", y=\"yc\", add_colorbar=False)\n", "ax.coastlines()\n", "ax.set_ylim([20, 90]);" ] @@ -289,7 +292,7 @@ "source": [ "da_masked = da.where(mask_lon & mask_lat, drop=True)\n", "\n", - "da_masked[0, :, :].plot();" + "da_masked.plot();" ] }, { @@ -301,9 +304,7 @@ "plt.figure(figsize=(5, 5))\n", "ax = plt.axes(projection=ccrs.PlateCarree())\n", "ax.set_global()\n", - "da_masked[0, :, :].plot.pcolormesh(\n", - " ax=ax, transform=ccrs.PlateCarree(), x=\"xc\", y=\"yc\", add_colorbar=False\n", - ")\n", + "da_masked.plot.pcolormesh(ax=ax, transform=ccrs.PlateCarree(), x=\"xc\", y=\"yc\", add_colorbar=False)\n", "ax.coastlines()\n", "ax.set_ylim([50, 80])\n", "ax.set_xlim([-180, -120]);" @@ -331,20 +332,20 @@ "ds = xr.tutorial.open_dataset(\"air_temperature\")\n", "ds_region = ds.sel(lat=slice(75,50), lon=slice(250,300))\n", "\n", - "ds_region.air[0].plot();\n", + "ds_region.air.plot();\n", "```\n", "Can you use a similar method as above using `sel` to crop a region using the RASM dataset? Why?\n", "\n", "````\n", "\n", - "````{solution} indexing-1\n", + "````{solution} boolean-1\n", ":class: dropdown\n", "This method will not work here as the dimensions are different from coordinates here. Specifically, the variables xc (longitude) and yc (latitude) are two-dimensional scalar fields, which differ from the logical coordinates represented by x and y.\n", "\n", "So the code below will not give the correct answer!\n", "```python\n", "cropped_ds = ds.sel(x=slice(min_lat,max_lat), y=slice(min_lon,max_lon))\n", - "cropped_ds.Tair[0].plot()\n", + "cropped_ds.Tair.plot()\n", "```\n", "````\n" ] @@ -375,7 +376,7 @@ "# Apply the condition using xarray.where()\n", "masked_data = xr.where(is_greater_than_threshold(da, 280), da, 0)\n", "\n", - "masked_data[0].plot()" + "masked_data.plot()" ] }, { @@ -445,7 +446,7 @@ "outputs": [], "source": [ "da_masked = da.where(flags.isin([1, 2, 3, 4, 5]), drop=True)\n", - "da_masked[0, :, :].plot();" + "da_masked.plot();" ] }, { @@ -453,7 +454,9 @@ "metadata": {}, "source": [ "```{warning}\n", - "Please note that when done repeatedly, this type of indexing is significantly slower than using `sel()`.\n", + "Please note that when done repeatedly, this type of indexing is significantly slower than using `sel()`. \n", + "\n", + "Use `sel` instead of `where` as much as possible.\n", "```" ] }, diff --git a/intermediate/indexing/indexing.md b/intermediate/indexing/indexing.md new file mode 100644 index 00000000..72dae2a4 --- /dev/null +++ b/intermediate/indexing/indexing.md @@ -0,0 +1,5 @@ +# Indexing + +```{tableofcontents} + +``` diff --git a/workshops/scipy2023/README.md b/workshops/scipy2023/README.md index 56249b96..aa967eef 100644 --- a/workshops/scipy2023/README.md +++ b/workshops/scipy2023/README.md @@ -61,10 +61,9 @@ Once your codespace is launched, the following happens: ```{dropdown} Indexing -{doc}`../../fundamentals/02.1_indexing_Basic` --{doc}`../../intermediate/02.2_indexing_Advanced` - --{doc}`../../intermediate/02.3_indexing_BooleanMasking` +-{doc}`../../intermediate/indexing/boolean-masking-indexing` +-{doc}`../../intermediate/indexing/advanced-indexing` ``` ```{dropdown} Computational Patterns