8000 Doc yinleon rebase by tacaswell · Pull Request #8860 · matplotlib/matplotlib · GitHub
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Merged
merged 7 commits into from
Jul 15, 2017
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Doc yinleon rebase #8860

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82c52f3
changes for MEP12/sphinx-gallery compliance
yinleon Mar 7, 2017
f9c9db1
DOC FIX reviewer's comments
yinleon Mar 7, 2017
e679205
FIX blank space issue for pep8
NelleV Mar 10, 2017
860a331
DOC: fix typos / markup
tacaswell Jul 10, 2017
e3429cd
DOC: tweak re sampling demo to use different resampling method
tacaswell Jul 11, 2017
c3a6534
DOC: fix typos and links
tacaswell Jul 11, 2017
20b6309
DOC: Minor typos, https, and pep8
tacaswell Jul 11, 2017
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DOC: tweak re sampling demo to use different resampling method 
Fixed target number of points instead of fixed data reduction.
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@tacaswell
tacaswell committed Jul 11, 2017
commit e3429cde480dd2aee9f7350e45cda74e69dd2aa2
29 changes: 19 additions & 10 deletions examples/event_handling/resample.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,11 +3,9 @@
Resampling Data
===============


Downsampling lowers the sample rate or sample size of a signal. In
this tutorial, the signal is downsampled when the plot is adjusted
through dragging and zooming.
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... except that the example is completely broken (try it). It should either be fixed or removed.

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It does what it claims, but in a weird way. It always down-samples to every 5th point but also clips the data.

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re worked this a bit.

It works on zoom, but not pan which seems like a bug Mpl, not in the example.


"""

import numpy as np
Expand All @@ -19,18 +17,28 @@ class DataDisplayDownsampler(object):
def __init__(self, xdata, ydata):
self.origYData = ydata
self.origXData = xdata
self.ratio = 5
self.max_points = 50
self.delta = xdata[-1] - xdata[0]

def downsample(self, xstart, xend):
# Very simple downsampling that takes the points within the range
# and picks every Nth point
# get the points in the view range
mask = (self.origXData > xstart) & (self.origXData < xend)
xdata = self.origXData[mask]
xdata = xdata[::self.ratio]
# dilate the mask by one to catch the points just outside
# of the view range to not truncate the line
mask = np.convolve([1, 1], mask, mode='same').astype(bool)
# sort out how many points to drop
ratio = max(np.sum(mask) // self.max_points, 1)

# mask data
xdata = self.origXData[mask]
ydata = self.origYData[mask]
ydata = ydata[::self.ratio]

# downsample data
xdata = xdata[::ratio]
ydata = ydata[::ratio]

print("using {} of {} visible points".format(
len(ydata), np.sum(mask)))

return xdata, ydata

Expand All @@ -43,8 +51,9 @@ def update(self, ax):
self.line.set_data(*self.downsample(xstart, xend))
ax.figure.canvas.draw_idle()


# Create a signal
xdata = np.linspace(16, 365, 365-16)
xdata = np.linspace(16, 365, (365-16)*4)
ydata = np.sin(2*np.pi*xdata/153) + np.cos(2*np.pi*xdata/127)

d = DataDisplayDownsampler(xdata, ydata)
Expand All @@ -57,5 +66,5 @@ def update(self, ax):

# Connect for changing the view limits
ax.callbacks.connect('xlim_changed', d.update)

ax.set_xlim(16, 365)
plt.show()
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