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fix whitespace issues
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‎python/2D-Histogram.md

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page_type: u-guide
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permalink: python/2D-Histogram/
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thumbnail: thumbnail/histogram2d.jpg
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### 2D Histogram of a Bivariate Normal Distribution ###

‎python/2d-histogram-contour.md

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page_type: u-guide
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permalink: python/2d-histogram-contour/
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thumbnail: thumbnail/hist2dcontour.png
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#### Basic 2D Histogram Contour

‎python/3d-axes.md

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page_type: example_index
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permalink: python/3d-axes/
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thumbnail: thumbnail/3d-axes.png
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v4upgrade: true
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‎python/3d-bubble-charts.md

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page_type: u-guide
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permalink: python/3d-bubble-charts/
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thumbnail: thumbnail/3dbubble.jpg
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### 3d Bubble chart with Plotly Express

‎python/3d-camera-controls.md

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order: 0.108
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thumbnail: thumbnail/3d-camera-controls.jpg
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### How camera controls work
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The camera position and direction is determined by three vectors: *up*, *center*, *eye*. Their coordinates refer to the 3-d domain, i.e., `(0, 0, 0)` is always the center of the domain, no matter data values.
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The `eye` vector determines the position of the camera. The default is $(x=1.25, y=1.25, z=1.25)$.
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The camera position and direction is determined by three vectors: *up*, *center*, *eye*. Their coordinates refer to the 3-d domain, i.e., `(0, 0, 0)` is always the center of the domain, no matter data values.
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The `eye` vector determines the position of the camera. The default is $(x=1.25, y=1.25, z=1.25)$.
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The `up` vector determines the `up` direction on the page. The default is $(x=0, y=0, z=1)$, that is, the z-axis points up.
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‎python/3d-isosurface-plots.md

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permalink: python/3d-isosurface-plots/
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redirect_from: python/isosurfaces-with-marching-cubes/
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thumbnail: thumbnail/isosurface.jpg
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With ``go.Isosurface``, you can plot [isosurface contours](https://en.wikipedia.org/wiki/Isosurface) of a scalar field ``value``, which is defined on ``x``, ``y`` and ``z`` coordinates.
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X, Y, Z = np.mgrid[-5:5:40j, -5:5:40j, -5:5:40j]
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# ellipsoid
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# ellipsoid
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values = X * X * 0.5 + Y * Y + Z * Z * 2
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fig = go.Figure(data=go.Isosurface(
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X, Y, Z = np.mgrid[-5:5:40j, -5:5:40j, -5:5:40j]
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# ellipsoid
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# ellipsoid
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values = X * X * 0.5 + Y * Y + Z * Z * 2
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fig = go.Figure(data=go.Isosurface(
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X, Y, Z = np.mgrid[-5:5:40j, -5:5:40j, -5:5:40j]
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# ellipsoid
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# ellipsoid
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values = X * X * 0.5 + Y * Y + Z * Z * 2
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fig = go.Figure(data=go.Isosurface(
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#### Isosurface with Addtional Slices
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Here we visualize slices parallel to the axes on top of isosurfaces. For a clearer visualization, the `fill` ratio of isosurfaces is decreased below 1 (completely filled).
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Here we visualize slices parallel to the axes on top of isosurfaces. For a clearer visualization, the `fill` ratio of isosurfaces is decreased below 1 (completely filled).
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```python
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import plotly.graph_objects as go
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import numpy as np
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X, Y, Z = np.mgrid[-5:5:40j, -5:5:40j, -5:5:40j]
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# ellipsoid
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# ellipsoid
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values = X * X * 0.5 + Y * Y + Z * Z * 2
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fig = go.Figure(data=go.Isosurface(
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X, Y, Z = np.mgrid[-5:5:40j, -5:5:40j, -5:5:40j]
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# ellipsoid
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# ellipsoid
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values = X * X * 0.5 + Y * Y + Z * Z * 2
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fig = go.Figure(data=go.Isosurface(

‎python/3d-line-plots.md

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page_type: u-guide
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permalink: python/3d-line-plots/
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### 3D Line plot with Plotly Express

‎python/3d-mesh.md

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v4upgrade: true
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---
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y=[0, 0, 1, 2],
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z=[0, 2, 0, 1],
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colorbar_title='z',
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colorscale=[[0, 'gold'],
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[0.5, 'mediumturquoise'],
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colorscale=[[0, 'gold'],
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[0.5, 'mediumturquoise'],
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# Intensity of each vertex, which will be interpolated and color-coded
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intensity=[0, 0.33, 0.66, 1],
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colorbar_title='z',
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colorscale=[[0, 'gold'],
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[0.5, 'mediumturquoise'],
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[0.5, 'mediumturquoise'],
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[1, 'magenta']],
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# Intensity of each vertex, which will be interpolated and color-coded
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intensity = np.linspace(0, 1, 8, endpoint=True),

‎python/3d-scatter-plots.md

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‎python/3d-subplots.md

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