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Misc. docstring cleanups. #14441

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Jun 4, 2019
Merged

Misc. docstring cleanups. #14441

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12 changes: 8 additions & 4 deletions examples/animation/random_walk.py
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Expand Up @@ -15,10 +15,14 @@

def gen_rand_line(length, dims=2):
"""
Create a line using a random walk algorithm

length is the number of points for the line.
dims is the number of dimensions the line has.
Create a line using a random walk algorithm.

Parameters
----------
length : int
The number of points of the line.
dims : int
The number of dimensions of the line.
"""
line_data = np.empty((dims, length))
line_data[:, 0] = np.random.rand(dims)
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2 changes: 1 addition & 1 deletion examples/animation/strip_chart.py
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Expand Up @@ -40,7 +40,7 @@ def update(self, y):


def emitter(p=0.03):
'return a random value with probability p, else 0'
"""Return a random value in [0, 1) with probability p, else 0."""
while True:
v = np.random.rand(1)
if v > p:
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4 changes: 1 addition & 3 deletions examples/axisartist/demo_axis_direction.py
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Expand Up @@ -19,9 +19,7 @@


def setup_axes(fig, rect):
"""
polar projection, but in a rectangular box.
"""
"""Polar projection, but in a rectangular box."""

# see demo_curvelinear_grid.py for details
tr = Affine2D().scale(np.pi/180., 1.) + PolarAxes.PolarTransform()
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4 changes: 1 addition & 3 deletions examples/axisartist/demo_curvelinear_grid2.py
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Expand Up @@ -22,9 +22,7 @@


def curvelinear_test1(fig):
"""
grid for custom transform.
"""
"""Grid for custom transform."""

def tr(x, y):
sgn = np.sign(x)
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4 changes: 1 addition & 3 deletions examples/axisartist/simple_axis_pad.py
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Expand Up @@ -19,9 +19,7 @@


def setup_axes(fig, rect):
"""
polar projection, but in a rectangular box.
"""
"""Polar projection, but in a rectangular box."""

# see demo_curvelinear_grid.py for details
tr = Affine2D().scale(np.pi/180., 1.) + PolarAxes.PolarTransform()
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6 changes: 3 additions & 3 deletions examples/event_handling/pick_event_demo2.py
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Expand Up @@ -3,9 +3,9 @@
Pick Event Demo2
================

compute the mean and standard deviation (stddev) of 100 data sets and plot
mean vs stddev. When you click on one of the mu, sigma points, plot the raw
data from the dataset that generated the mean and stddev.
Compute the mean (mu) and standard deviation (sigma) of 100 data sets and plot
mu vs. sigma. When you click on one of the (mu, sigma) points, plot the raw
data from the dataset that generated this point.
"""
import numpy as np
import matplotlib.pyplot as plt
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