fix internal links and add troubleshooting guide

nicolaskruchten · nicolaskruchten · commit 53015b94d514 · 2019-11-03T15:01:06.000-05:00
diff --git a/python/3d-volume.md b/python/3d-volume.md
@@ -32,10 +32,10 @@ jupyter:
     page_type: u-guide
     permalink: python/3d-volume-plots/
     thumbnail: thumbnail/3d-volume-plots.jpg
-    
+
 ---
 
-A volume plot with `go.Volume` shows several partially transparent isosurfaces for volume rendering. The API of `go.Volume` is close to the one of `go.Isosurface`. However, whereas [isosurface plots](./3d-isosurface-plots/) show all surfaces with the same opacity, tweaking the `opacityscale` parameter of `go.Volume` results in a depth effect and better volume rendering.
+A volume plot with `go.Volume` shows several partially transparent isosurfaces for volume rendering. The API of `go.Volume` is close to the one of `go.Isosurface`. However, whereas [isosurface plots](/python/3d-isosurface-plots/) show all surfaces with the same opacity, tweaking the `opacityscale` parameter of `go.Volume` results in a depth effect and better volume rendering.
 
 ## Simple volume plot with go.Volume
 
@@ -95,7 +95,7 @@ vol = ndimage.gaussian_filter(vol, 4)
 vol /= vol.max()
 
 fig = go.Figure(data=go.Volume(
-    x=X.flatten(), y=Y.flatten(), z=Z.flatten(), 
+    x=X.flatten(), y=Y.flatten(), z=Z.flatten(),
     value=vol.flatten(),
     isomin=0.2,
     isomax=0.7,
@@ -162,7 +162,7 @@ fig = go.Figure(data=go.Volume(
     isomax=0.5,
     opacity=0.1, # max opacity
     opacityscale=[[-0.5, 1], [-0.2, 0], [0.2, 0], [0.5, 1]],
-    surface_count=21, 
+    surface_count=21,
     colorscale='RdBu'
     ))
 fig.show()
@@ -182,7 +182,7 @@ vol = (X - 1)**2 + (Y - 1)**2 + Z**2
 
 
 fig = go.Figure(data=go.Volume(
-    x=X.flatten(), y=Y.flatten(), z=Z.flatten(), 
+    x=X.flatten(), y=Y.flatten(), z=Z.flatten(),
     value=vol.flatten(),
     isomin=0.2,
     isomax=0.7,
@@ -211,7 +211,7 @@ vol = (X - 1)**2 + (Y - 1)**2 + Z**2
 
 
 fig = go.Figure(data=go.Volume(
-    x=X.flatten(), y=Y.flatten(), z=Z.flatten(), 
+    x=X.flatten(), y=Y.flatten(), z=Z.flatten(),
     value=vol.flatten(),
     isomin=0.2,
     isomax=0.7,
@@ -241,7 +241,7 @@ vol = (X - 1)**2 + (Y - 1)**2 + Z**2
 
 
 fig = go.Figure(data=go.Volume(
-    x=X.flatten(), y=Y.flatten(), z=Z.flatten(), 
+    x=X.flatten(), y=Y.flatten(), z=Z.flatten(),
     value=vol.flatten(),
     isomin=0.2,
     isomax=0.7,
@@ -259,4 +259,4 @@ fig.show()
 See https://plot.ly/python/reference/#volume for more information and chart attribute options!
 
 #### See also
-[3D isosurface documentation](./3d-isosurface-plots/)
+[3D isosurface documentation](/python/3d-isosurface-plots/)
diff --git a/python/annotated_heatmap.md b/python/annotated_heatmap.md
@@ -33,13 +33,13 @@ jupyter:
     page_type: u-guide
     permalink: python/annotated_heatmap/
     thumbnail: thumbnail/ann_heat.jpg
-    
+
     v4upgrade: true
 ---
 
 #### Simple Annotated Heatmap
 
-For more examples with Heatmaps, see [this page](../heatmaps/).
+For more examples with Heatmaps, see [this page](/python/heatmaps/).
 
 ```python
 import plotly.figure_factory as ff
diff --git a/python/choropleth-maps.md b/python/choropleth-maps.md
@@ -23,11 +23,11 @@ jupyter:
     page_type: u-guide
     permalink: python/choropleth-maps/
     thumbnail: thumbnail/choropleth.jpg
-    
+
     v4upgrade: true
 ---
 
-A [Choropleth Map](https://en.wikipedia.org/wiki/Choropleth_map) is a heatmap using geographical boundaries. It is used to represent spatial variations of a quantity. See also the [index of other geographical charts](../maps/).
+A [Choropleth Map](https://en.wikipedia.org/wiki/Choropleth_map) is a heatmap using geographical boundaries. It is used to represent spatial variations of a quantity. See also the [index of other geographical charts](/python/maps/).
 
 Below we show how to create Choropleth Maps using either Plotly Express' `px.choropleth` or the lower-level `go.Choropleth`.
 
diff --git a/python/cone-plot.md b/python/cone-plot.md
@@ -34,10 +34,10 @@ jupyter:
     permalink: python/cone-plot/
     redirect_from: python/3d-cone/
     thumbnail: thumbnail/3dcone.png
-    
+
 ---
 
-A cone plot is the 3D equivalent of a 2D [quiver plot](./quiver-plots/), i.e., it represents a 3D vector field using cones to represent the direction and norm of the vectors. 3-D coordinates are given by `x`, `y` and `z`, and the coordinates of the vector field by `u`, `v` and `w`.
+A cone plot is the 3D equivalent of a 2D [quiver plot](/python/quiver-plots/), i.e., it represents a 3D vector field using cones to represent the direction and norm of the vectors. 3-D coordinates are given by `x`, `y` and `z`, and the coordinates of the vector field by `u`, `v` and `w`.
 
 ### Basic 3D Cone
 
@@ -48,7 +48,7 @@ import plotly.graph_objects as go
 fig = go.Figure(data=go.Cone(x=[1], y=[1], z=[1], u=[1], v=[1], w=[0]))
 
 fig.update_layout(scene_camera_eye=dict(x=-0.76, y=1.8, z=0.92))
-   
+
 fig.show()
 ```
 
@@ -91,7 +91,7 @@ fig.add_trace(go.Cone(x=[7,] * 3, lighting_fresnel=2, name="lighting.fresnel:2")
 fig.add_trace(go.Cone(x=[8,] * 3, lightposition=dict(x=0, y=0, z=1e5),
                                   name="lighting.position x:0,y:0,z:1e5"))
 
-fig.update_traces(y=[1, 2, 3], z=[1, 1, 1], 
+fig.update_traces(y=[1, 2, 3], z=[1, 1, 1],
                   u=[1, 2, 3], v=[1, 1, 2], w=[4, 4, 1],
                   hoverinfo="u+v+w+name",
                   showscale=False)
diff --git a/python/dot-plots.md b/python/dot-plots.md
@@ -33,13 +33,13 @@ jupyter:
     page_type: u-guide
     permalink: python/dot-plots/
     thumbnail: thumbnail/dot-plot.jpg
-    
+
     v4upgrade: true
 ---
 
 #### Basic Dot Plot
 
-Dot plots (also known as [Cleveland dot plots](https://en.wikipedia.org/wiki/Dot_plot_(statistics))) show changes between two (or more) points in time or between two (or more) conditions. Compared to a [bar chart](../bar-charts/), dot plots can be less cluttered and allow for an easier comparison between conditions.
+Dot plots (also known as [Cleveland dot plots](https://en.wikipedia.org/wiki/Dot_plot_(statistics))) show changes between two (or more) points in time or between two (or more) conditions. Compared to a [bar chart](/python/bar-charts/), dot plots can be less cluttered and allow for an easier comparison between conditions.
 
 For the same data, we show below how to create a dot plot using either `px.scatter` (for a tidy pandas DataFrame) or `go.Scatter`.
 
diff --git a/python/getting-started.md b/python/getting-started.md
@@ -31,7 +31,7 @@ jupyter:
     page_type: u-guide
     permalink: python/getting-started/
     redirect_from: python/getting_started/
-    
+
     v4upgrade: true
 ---
 
@@ -103,7 +103,7 @@ fig
 ```
 
 <!-- #region -->
-See [*Displaying Figures in Python*](../renderers/) for more information on the renderers framework, and see [*Plotly FigureWidget Overview*](../figurewidget/) for more information on using `FigureWidget`.
+See [*Displaying Figures in Python*](/python/renderers/) for more information on the renderers framework, and see [*Plotly FigureWidget Overview*](/python/figurewidget/) for more information on using `FigureWidget`.
 
 #### JupyterLab Support (Python 3.5+)
 For use in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/), install the `jupyterlab` and `ipywidgets`
@@ -169,7 +169,7 @@ fig
 ```
 
 <!-- #region -->
-See [*Displaying Figures in Python*](../renderers/) for more information on the renderers framework, and see [*Plotly FigureWidget Overview*](../figurewidget/) for more information on using `FigureWidget`.
+See [*Displaying Figures in Python*](/python/renderers/) for more information on the renderers framework, and see [*Plotly FigureWidget Overview*](/python/figurewidget/) for more information on using `FigureWidget`.
 
 #### Static Image Export Support
 plotly.py supports static image export using the `to_image` and `write_image`
@@ -202,7 +202,7 @@ fig = go.FigureWidget(data=go.Bar(y=[2, 3, 1]))
 fig.write_image('figure.png')
 ```
 <!-- #region -->
-See [*Static Image Export in Python*](../static-image-export/) for more information on static image export.
+See [*Static Image Export in Python*](/python/static-image-export/) for more information on static image export.
 
 #### Extended Geo Support
 Some plotly.py features rely on fairly large geographic shape files. The county
@@ -238,15 +238,15 @@ $ conda install -c plotly chart-studio=1.0.0
 > **Note:** This package is optional, and if it is not installed it is not possible for figures to be uploaded to the Chart Studio cloud service.
 
 ### Where to next?
-Now that you have everything installed, you are ready to start reading and running examples of [basic charts](../basic-charts/), [statistical charts](../statistical-charts/), [scientific charts](../scientific-charts/), [financial charts](../#financial-charts), [geographic charts](../maps/), and [3-dimensional charts](../3d-charts/).
+Now that you have everything installed, you are ready to start reading and running examples of [basic charts](/python/basic-charts/), [statistical charts](/python/statistical-charts/), [scientific charts](/python/scientific-charts/), [financial charts](/python/#financial-charts), [geographic charts](/python/maps/), and [3-dimensional charts](/python/3d-charts/).
 
-For a complete overview of all of the ways that figures can be created and updated, see the [*Plotly User Guide for Python*](../user-guide/).
+For a complete overview of all of the ways that figures can be created and updated, see the [*Plotly User Guide for Python*](/python/user-guide/).
 
-For information on configuring figure layout options (e.g. axes, titles, legends, etc) and styling figures (e.g. colors, fonts, annotations, images, shapes, etc.), see [*Plotly Fundamentals*](../plotly-fundamentals).
+For information on configuring figure layout options (e.g. axes, titles, legends, etc) and styling figures (e.g. colors, fonts, annotations, images, shapes, etc.), see [*Plotly Fundamentals*](/python/plotly-fundamentals).
 
-For information on theming plotly figures, see [*Theming and templates with plotly for Python*](../templates/).
+For information on theming plotly figures, see [*Theming and templates with plotly for Python*](/python/templates/).
 
-For information on all of the ways that plotly figures can be displayed, see [*Displaying plotly figures with plotly for Python*](../renderers/).
+For information on all of the ways that plotly figures can be displayed, see [*Displaying plotly figures with plotly for Python*](/python/renderers/).
 
 For the full searchable reference of every figure property, see the [*Python figure reference*](https://plot.ly/python/reference/).
 
diff --git a/python/lines-on-maps.md b/python/lines-on-maps.md
@@ -33,11 +33,11 @@ jupyter:
     page_type: example_index
     permalink: python/lines-on-maps/
     thumbnail: thumbnail/flight-paths.jpg
-    
+
     v4upgrade: true
 ---
 
-For other geographical and map charts see [the maps index page](../maps/).
+For other geographical and map charts see [the maps index page](/python/maps/).
 
 Below we show how to create geographical line plots using either Plotly Express with `px.line_geo` or the lower-level `go.Scattergeo`.
 
diff --git a/python/multiple-axes.md b/python/multiple-axes.md
@@ -32,7 +32,7 @@ jupyter:
     order: 14
     permalink: python/multiple-axes/
     thumbnail: thumbnail/multiple-axes.jpg
-    
+
     v4upgrade: true
 ---
 
@@ -229,4 +229,4 @@ fig.show()
 ```
 
 #### Reference
-All of the y-axis properties are found here: https://plot.ly/python/reference/#YAxis.  For more information on creating subplots see the [Subplots in Python](../subplots/) section.
+All of the y-axis properties are found here: https://plot.ly/python/reference/#YAxis.  For more information on creating subplots see the [Subplots in Python](/python/subplots/) section.
diff --git a/python/orca-management.md b/python/orca-management.md
@@ -33,12 +33,12 @@ jupyter:
     order: 1.5
     permalink: python/orca-management/
     thumbnail: thumbnail/orca-management.png
-    
+
     v4upgrade: true
 ---
 
 ### Overview
-This section covers the lower-level details of how plotly.py uses orca to perform static image generation. Please refer to the [Static Image Export](../static-image-export/) section for general information on creating static images from plotly.py figures.
+This section covers the lower-level details of how plotly.py uses orca to perform static image generation. Please refer to the [Static Image Export](/python/static-image-export/) section for general information on creating static images from plotly.py figures.
 
 ### What is Orca?
 Orca is an [Electron](https://electronjs.org/) application that inputs plotly figure specifications and converts them into static images.  Orca can run as a command-line utility or as a long-running server process. In order to provide the fastest possible image export experience, plotly.py launches orca in server mode, and communicates with it over a local port. See https://github.com/plotly/orca for more information.
diff --git a/python/parallel-coordinates-plot.md b/python/parallel-coordinates-plot.md
@@ -35,13 +35,13 @@ jupyter:
     page_type: u-guide
     permalink: python/parallel-coordinates-plot/
     thumbnail: thumbnail/parcoords.jpg
-    
+
     v4upgrade: true
 ---
 
 ## Parallel Coordinates plot with Plotly Express
 
-[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/). In a parallel coordinates plot with `px.parallel_coordinates`, each row of the DataFrame is represented by a polyline mark which traverses a set of parallel axes, one for each of the dimensions. For other representations of multivariate data, also see [radar charts](../radar-chart/) and [scatterplot matrix (SPLOM)](../splom/).
+[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/). In a parallel coordinates plot with `px.parallel_coordinates`, each row of the DataFrame is represented by a polyline mark which traverses a set of parallel axes, one for each of the dimensions. For other representations of multivariate data, also see [radar charts](/python/radar-chart/) and [scatterplot matrix (SPLOM)](/python/splom/).
 
 ```python
 import plotly.express as px
diff --git a/python/plotly-express.md b/python/plotly-express.md
@@ -34,7 +34,7 @@ jupyter:
     page_type: example_index
     permalink: python/plotly-express/
     thumbnail: thumbnail/plotly-express.png
-    
+
     v4upgrade: true
 ---
 
@@ -44,7 +44,7 @@ Plotly Express is a terse, consistent, high-level wrapper around `plotly.graph_o
 
 **Note**: Plotly Express was previously its own separately-installed `plotly_express` package but is now part of `plotly`!
 
-This notebook demonstrates various `plotly.express` features. [Reference documentation](https://plotly.github.io/plotly_express/plotly_express/) is also available, as well as a [tutorial on input argument types](./px-arguments).
+This notebook demonstrates various `plotly.express` features. [Reference documentation](https://plotly.github.io/plotly_express/plotly_express/) is also available, as well as a [tutorial on input argument types](/python/px-arguments).
 
 You can also read our original [Medium announcement article](https://medium.com/@plotlygraphs/introducing-plotly-express-808df010143d) for more information on this library.
 
diff --git a/python/quiver-plots.md b/python/quiver-plots.md
@@ -33,7 +33,7 @@ jupyter:
     order: 12
     permalink: python/quiver-plots/
     thumbnail: thumbnail/quiver-plot.jpg
-    
+
 ---
 
 #### Basic Quiver Plot
@@ -82,7 +82,7 @@ fig.show()
 
 #### See also
 
-[Cone plots](./cone-plot) for the 3D equivalent of quiver plots.
+[Cone plot](/python/cone-plot) for the 3D equivalent of quiver plots.
 
 #### Reference
 
diff --git a/python/radar-chart.md b/python/radar-chart.md
@@ -33,13 +33,13 @@ jupyter:
     page_type: u-guide
     permalink: python/radar-chart/
     thumbnail: thumbnail/radar.gif
-    
+
     v4upgrade: true
 ---
 
-A [Radar Chart](https://en.wikipedia.org/wiki/Radar_chart) (also known as a spider plot or star plot) displays multivariate data in the form of a two-dimensional chart of quantitative variables represented on axes originating from the center. The relative position and angle of the axes is typically uninformative.  It is equivalent to a [parallel coordinates plot](../parallel-coordinates-plot/) with the axes arranged radially.
+A [Radar Chart](https://en.wikipedia.org/wiki/Radar_chart) (also known as a spider plot or star plot) displays multivariate data in the form of a two-dimensional chart of quantitative variables represented on axes originating from the center. The relative position and angle of the axes is typically uninformative.  It is equivalent to a [parallel coordinates plot](/python/parallel-coordinates-plot/) with the axes arranged radially.
 
-For a Radar Chart, use a [polar chart](../polar-chart/) with categorical angular variables, with `px.line_polar` for data available as a tidy pandas DataFrame, or with `go.Scatterpolar` in the general case. See more examples of [polar charts here](../polar-chart/).
+For a Radar Chart, use a [polar chart](/python/polar-chart/) with categorical angular variables, with `px.line_polar` for data available as a tidy pandas DataFrame, or with `go.Scatterpolar` in the general case. See more examples of [polar charts here](/python/polar-chart/).
 
 #### Radar Chart with Plotly Express
 
diff --git a/python/static-image-export.md b/python/static-image-export.md
@@ -35,7 +35,7 @@ jupyter:
     page_type: u-guide
     permalink: python/static-image-export/
     thumbnail: thumbnail/static-image-export.png
-    
+
     v4upgrade: true
 ---
 
@@ -194,4 +194,4 @@ Image(img_bytes)
 ### Summary
 In summary, to export high-quality static images from plotly.py, all you need to do is install orca, psutil, and requests and then use the `plotly.io.write_image` and `plotly.io.to_image` functions (or the `.write_image` and `.to_image` graph object figure methods).
 
-If you want to know more about how the orca integration works, or if you need to troubleshoot an issue, please check out the [Orca Management](../orca-management/) section.
+If you want to know more about how the orca integration works, or if you need to troubleshoot an issue, please check out the [Orca Management](/python/orca-management/) section.
diff --git a/python/streamline-plots.md b/python/streamline-plots.md
@@ -33,7 +33,7 @@ jupyter:
     order: 13
     permalink: python/streamline-plots/
     thumbnail: thumbnail/streamline.jpg
-    
+
 ---
 
 A Streamline plot is a representation based on a 2-D vector field interpreted as a velocity field, consisting of closed curves tangent to the velocity field. In the case of a stationary velocity field, streamlines coincide with trajectories (see also the [Wikipedia page on streamlines, streaklines and pathlines](https://en.wikipedia.org/wiki/Streamlines,_streaklines,_and_pathlines)).
@@ -42,7 +42,7 @@ For the streamline figure factory, one needs to provide
 - uniformly spaced ranges of `x` and `y` values (1D)
 - 2-D velocity values `u` and `v` defined on the cross-product (`np.meshgrid(x, y)`) of `x` and `y`.
 
-Velocity values are interpolated when determining the streamlines. Streamlines are initialized on the boundary of the `x-y` domain. 
+Velocity values are interpolated when determining the streamlines. Streamlines are initialized on the boundary of the `x-y` domain.
 
 #### Basic Streamline Plot
 
@@ -100,9 +100,9 @@ fig.show()
 
 #### See also
 
-For a 3D version of streamlines, use the trace `go.Streamtube` documented [here](./streamtube-plot/).
+For a 3D version of streamlines, use the trace `go.Streamtube` documented [here](/python/streamtube-plot/).
 
-For representing the 2-D vector field as arrows, see the [quiver plot tutorial](./quiver-plots/).
+For representing the 2-D vector field as arrows, see the [quiver plot tutorial](/python/quiver-plots/).
 
 
 #### Reference
diff --git a/python/troubleshooting.md b/python/troubleshooting.md
diff --git a/python/v4-migration.md b/python/v4-migration.md
