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Merged streamline examples #8336

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Merged
merged 4 commits into from
Apr 12, 2017
Merged

Merged streamline examples #8336

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bf2b43b
Merge all figure in streamplot examples in one plot
patniharshit Mar 19, 2017
92b1196
Add gridspec to have equal aspect ratio and remove references of deleted
patniharshit Mar 23, 2017
913fdb7
Remove data regeneration
patniharshit Mar 28, 2017
d56b5ce
Update example name in docs
patniharshit Apr 10, 2017
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Add gridspec to have equal aspect ratio and remove references of deleted 
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@patniharshit @dstansby
patniharshit authored and dstansby committed Apr 12, 2017
commit 92b11961dd979e811497bb5d69afad4d8beeb570
2 changes: 1 addition & 1 deletion 2 doc/users/prev_whats_new/whats_new_1.2.rst
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Expand Up @@ -153,7 +153,7 @@ In addition to simply plotting the streamlines of the vector field,
line widths of the streamlines to a separate parameter, such as the speed or
local intensity of the vector field.

.. plot:: gallery/images_contours_and_fields/streamplot_features.py
.. plot:: mpl_examples/images_contours_and_fields/streamplot_demo.py
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'streamplot_demo.py' should be changed into 'plot_streamplot.py', shouldn't it?

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Done.



New hist functionality
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2 changes: 1 addition & 1 deletion 2 doc/users/screenshots.rst
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Expand Up @@ -71,7 +71,7 @@ a vector field. In addition to simply plotting the streamlines, it allows you
to map the colors and/or line widths of streamlines to a separate parameter,
such as the speed or local intensity of the vector field.

.. plot:: gallery/images_contours_and_fields/streamplot_features.py
.. plot:: mpl_examples/images_contours_and_fields/streamplot_demo.py
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Here too, 'streamplot_demo.py' should be changed into 'plot_streamplot.py', shouldn't it?

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Done.


This feature complements the :meth:`~matplotlib.pyplot.quiver` function for
plotting vector fields. Thanks to Tom Flannaghan and Tony Yu for adding the
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39 changes: 21 additions & 18 deletions 39 examples/images_contours_and_fields/streamplot_demo.py
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Expand Up @@ -2,39 +2,42 @@
==========
Streamplot
==========
A streamplot, or streamline plot, is used to display 2D vector fields. This

A stream plot, or stream line plot, is used to display 2D vector fields. This
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streamline

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All done.

example shows a few features of the stream plot function:
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stream plot <- streamplot?

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Done.

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Again, when it comes to English, I may be totally wrong, but I think "the streamplot function" may be more correct than the current "the stream plot function", as it is refering to the streamplot function and not to a stream plot. I guess I was not clear enough: it was actually the meaning of my previous comment ^^. However, for the other utterances of "stream plot"/"stream line," I will let a native English speaker (who may be you!) decide if it is better than "streamplot"/"streamline" or not.

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I think generally, "streamline" is a word and "streamplot" is not, unless you're referring to the function itself (as in this case.)


* Varying the color along a streamline.
* Varying the density of streamlines.
* Varying the color along a stream line.
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streamline

* Varying the density of stream lines.
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streamlines

* Varying the line width along a stream line.
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stream line <- streamline?

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Done.

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streamline

* Controlling the start points of streamlines.
* Streamlines skipping masked regions and NaN values.
* Controlling the starting points of stream lines.
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streamlines

* Stream lines skipping masked regions and NaN values.
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Streamlines

"""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec

w = 3
Y, X = np.mgrid[-w:w:100j, -w:w:100j]
U = -1 - X**2 + Y
V = 1 + X - Y**2
speed = np.sqrt(U*U + V*V)

fig = plt.figure()
fig = plt.figure(figsize=(7, 9))
gs = gridspec.GridSpec(nrows=3, ncols=2, height_ratios=[1, 1, 2])

# Varying density along a streamline
ax0 = fig.add_subplot(321)
ax0 = fig.add_subplot(gs[0, 0])
ax0.streamplot(X, Y, U, V, density=[0.5, 1])
ax0.set_title('Varying Density')

# Varying color along a streamline
ax1 = fig.add_subplot(322)
strm = ax1.streamplot(X, Y, U, V, color=U, linewidth=2, cmap=plt.cm.autumn)
ax1 = fig.add_subplot(gs[0, 1])
strm = ax1.streamplot(X, Y, U, V, color=U, linewidth=2, cmap='autumn')
fig.colorbar(strm.lines)
ax1.set_title('Varying color')
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Varying Color?

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Done.


# Varying line width along a streamline
ax2 = fig.add_subplot(323)
ax2 = fig.add_subplot(gs[1, 0])
lw = 5*speed / speed.max()
ax2.streamplot(X, Y, U, V, density=0.6, color='k', linewidth=lw)
ax2.set_title('Varying Line Width')
Expand All @@ -45,16 +48,15 @@
U, V = np.mgrid[-3:3:100j, 0:0:100j]
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Any reason for changing the U, V compared to the former streamplot_demo_start_points.py script? How bad would it be to simply re-use the same X, Y, U and V as for the other subplots? This way, you'd just need to create these (dummy) data at the beginning of the script (NB: it is already the case at ll. 18-22) and after that, the script is focused on the plotting aspect of streamplot. I think that it is also close to what @QuLogic had in mind in one of his remarks.

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@patniharshit patniharshit Mar 25, 2017 •
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I have kept U, V exactly same as they are in previous streamplot_demo_start_points.py.

But if I comment out above lines and use same U,V everywhere then I get this plot -
figure_1-2

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Oh I think that I understand why the current situation was weird for both of us!

The current example in the official docs is indeed the one that you are refering to. However, the starting point example was changed in #6672 (by myself, which is why I was having the feeling that you changed U and V here) to use the same data as the other streamplot examples, which I think is really more demonstrative to show the different plotting options of streamplot. The related version of the example is only in our devdocs gallery (I guess because the PR was milestoned for 2.1 and not backported into 2.0). So I would definitively support to use the same data (built once for all at the top of the script) for all the subplots :).

seed_points = np.array([[-2, -1, 0, 1, 2, -1], [-2, -1, 0, 1, 2, 2]])

ax3 = fig.add_subplot(324)
ax3 = fig.add_subplot(gs[1, 1])
strm = ax3.streamplot(X, Y, U, V, color=U, linewidth=2,
cmap=plt.cm.autumn, start_points=seed_points.T)
cmap='autumn', start_points=seed_points.T)
fig.colorbar(strm.lines)
ax3.set_title('Controlling Starting Points')

# Displaying the starting points with red symbols.
# Displaying the starting points with blue symbols.
ax3.plot(seed_points[0], seed_points[1], 'bo')

ax3.axis((-3, 3, -3, 3))
ax3.axis((-w, w, -w, w))

# Create a mask
w = 3
Expand All @@ -68,12 +70,13 @@
U[:20, :20] = np.nan
U = np.ma.array(U, mask=mask)

ax4 = fig.add_subplot(325)
ax4 = fig.add_subplot(gs[2:, :])
ax4.streamplot(X, Y, U, V, color='r')
ax4.set_title('Streamline with Masking')
ax4.set_title('Streamplot with Masking')

ax4.imshow(~mask, extent=(-w, w, -w, w), alpha=0.5,
interpolation='nearest', cmap=plt.cm.gray, aspect='auto')
interpolation='nearest', cmap='gray', aspect='auto')
ax4.set_aspect('equal')

plt.tight_layout()
plt.show()
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