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Merge pull request #7386 from NelleV/numpy_seed
DOC/ENH: examples are now reproducible
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‎examples/animation/basic_example.py

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fig1 = plt.figure()
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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data = np.random.rand(2, 25)
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l, = plt.plot([], [], 'r-')
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plt.xlim(0, 1)

‎examples/animation/basic_example_writer.py

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line.set_data(data[..., :num])
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return line,
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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# Set up formatting for the movie files
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Writer = animation.writers['ffmpeg']
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writer = Writer(fps=15, metadata=dict(artist='Me'), bitrate=1800)

‎examples/animation/bayes_update.py

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self.line.set_data(self.x, y)
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return self.line,
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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fig, ax = plt.subplots()
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ud = UpdateDist(ax, prob=0.7)
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anim = FuncAnimation(fig, ud, frames=np.arange(100), init_func=ud.init,

‎examples/animation/histogram.py

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fig, ax = plt.subplots()
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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# histogram our data with numpy
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data = np.random.randn(1000)
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n, bins = np.histogram(data, 100)

‎examples/animation/moviewriter.py

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import matplotlib.pyplot as plt
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import matplotlib.animation as manimation
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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FFMpegWriter = manimation.writers['ffmpeg']
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metadata = dict(title='Movie Test', artist='Matplotlib',
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comment='Movie support!')

‎examples/animation/rain.py

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import matplotlib.pyplot as plt
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from matplotlib.animation import FuncAnimation
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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# Create new Figure and an Axes which fills it.
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fig = plt.figure(figsize=(7, 7))

‎examples/animation/random_data.py

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import matplotlib.pyplot as plt
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import matplotlib.animation as animation
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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fig, ax = plt.subplots()
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line, = ax.plot(np.random.rand(10))
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ax.set_ylim(0, 1)

‎examples/animation/simple_3danim.py

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import mpl_toolkits.mplot3d.axes3d as p3
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import matplotlib.animation as animation
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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def Gen_RandLine(length, dims=2):
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"""

‎examples/animation/strip_chart_demo.py

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else:
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yield np.random.rand(1)
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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fig, ax = plt.subplots()
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scope = Scope(ax)
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‎examples/animation/unchained.py

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import matplotlib.pyplot as plt
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import matplotlib.animation as animation
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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# Create new Figure with black background
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fig = plt.figure(figsize=(8, 8), facecolor='black')
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‎examples/api/bbox_intersect.py

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from matplotlib.transforms import Bbox
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from matplotlib.path import Path
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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left, bottom, width, height = (-1, -1, 2, 2)
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rect = plt.Rectangle((left, bottom), width, height, facecolor="#aaaaaa")
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‎examples/api/collections_demo.py

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spiral = list(zip(xx, yy))
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# Make some offsets
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rs = np.random.RandomState([12345678])
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# Fixing random state for reproducibility
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rs = np.random.RandomState(19680801)
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xo = rs.randn(npts)
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yo = rs.randn(npts)
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xyo = list(zip(xo, yo))

‎examples/api/engineering_formatter.py

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from matplotlib.ticker import EngFormatter
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prng = np.random.RandomState(123)
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# Fixing random state for reproducibility
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prng = np.random.RandomState(19680801)
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fig, ax = plt.subplots()
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ax.set_xscale('log')

‎examples/api/filled_step.py

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label_cycle = cycler('label', ['set {n}'.format(n=n) for n in range(4)])
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hatch_cycle = cycler('hatch', ['/', '*', '+', '|'])
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# make some synthetic data
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np.random.seed(0)
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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stack_data = np.random.randn(4, 12250)
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dict_data = OrderedDict(zip((c['label'] for c in label_cycle), stack_data))
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‎examples/api/histogram_path_demo.py

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fig, ax = plt.subplots()
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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# histogram our data with numpy
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data = np.random.randn(1000)
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n, bins = np.histogram(data, 50)
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‎examples/api/image_zcoord.py

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import matplotlib.pyplot as plt
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import matplotlib.cm as cm
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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X = 10*np.random.rand(5, 3)
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fig, ax = plt.subplots()

‎examples/api/line_with_text.py

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lines.Line2D.draw(self, renderer)
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self.text.draw(renderer)
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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fig, ax = plt.subplots()
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x, y = np.random.rand(2, 20)

‎examples/api/patch_collection.py

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from matplotlib.collections import PatchCollection
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import matplotlib.pyplot as plt
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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fig, ax = plt.subplots()
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‎examples/api/unicode_minus.py

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import matplotlib
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import matplotlib.pyplot as plt
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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matplotlib.rcParams['axes.unicode_minus'] = False
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fig, ax = plt.subplots()
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ax.plot(10*np.random.randn(100), 10*np.random.randn(100), 'o')

‎examples/api/watermark_image.py

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import matplotlib.image as image
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import matplotlib.pyplot as plt
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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datafile = cbook.get_sample_data('logo2.png', asfileobj=False)
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print('loading %s' % datafile)
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im = image.imread(datafile)

‎examples/api/watermark_text.py

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import matplotlib.pyplot as plt
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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fig, ax = plt.subplots()
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ax.plot(np.random.rand(20), '-o', ms=20, lw=2, alpha=0.7, mfc='orange')
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ax.grid()

‎examples/axes_grid1/scatter_hist.py

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import matplotlib.pyplot as plt
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from mpl_toolkits.axes_grid1 import make_axes_locatable
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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# the random data
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x = np.random.randn(1000)
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y = np.random.randn(1000)

‎examples/axisartist/demo_floating_axes.py

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DictFormatter)
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import matplotlib.pyplot as plt
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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def setup_axes1(fig, rect):
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"""

‎examples/event_handling/data_browser.py

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if __name__ == '__main__':
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import matplotlib.pyplot as plt
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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X = np.random.rand(100, 200)
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xs = np.mean(X, axis=1)

‎examples/event_handling/keypress_demo.py

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xl.set_visible(not visible)
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fig.canvas.draw()
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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fig, ax = plt.subplots()
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fig.canvas.mpl_connect('key_press_event', press)

‎examples/event_handling/looking_glass.py

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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.patches as patches
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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x, y = np.random.rand(2, 200)
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fig, ax = plt.subplots()

‎examples/images_contours_and_fields/interpolation_methods.py

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'spline36', 'hanning', 'hamming', 'hermite', 'kaiser', 'quadric',
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'catrom', 'gaussian', 'bessel', 'mitchell', 'sinc', 'lanczos']
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np.random.seed(0)
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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grid = np.random.rand(4, 4)
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fig, axes = plt.subplots(3, 6, figsize=(12, 6),

‎examples/lines_bars_and_markers/barh_demo.py

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import matplotlib.pyplot as plt
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import numpy as np
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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plt.rcdefaults()
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fig, ax = plt.subplots()

‎examples/misc/multiprocess.py

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import matplotlib.pyplot as plt
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import gobject
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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class ProcessPlotter(object):
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def __init__(self):

‎examples/mplot3d/2dcollections3d_demo.py

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# Plot scatterplot data (20 2D points per colour) on the x and z axes.
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colors = ('r', 'g', 'b', 'k')
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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x = np.random.sample(20*len(colors))
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y = np.random.sample(20*len(colors))
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c_list = []

‎examples/mplot3d/bars3d_demo.py

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import matplotlib.pyplot as plt
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import numpy as np
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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fig = plt.figure()
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ax = fig.add_subplot(111, projection='3d')
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‎examples/mplot3d/hist3d_demo.py

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import matplotlib.pyplot as plt
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import numpy as np
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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fig = plt.figure()
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ax = fig.add_subplot(111, projection='3d')
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x, y = np.random.rand(2, 100) * 4

‎examples/mplot3d/polys3d_demo.py

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from matplotlib import colors as mcolors
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import numpy as np
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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def cc(arg):
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'''

‎examples/mplot3d/scatter3d_demo.py

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import matplotlib.pyplot as plt
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import numpy as np
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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def randrange(n, vmin, vmax):
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'''

‎examples/pie_and_polar_charts/pie_demo_features.py

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"""
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import matplotlib.pyplot as plt
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# The slices will be ordered and plotted counter-clockwise.
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labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'
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sizes = [15, 30, 45, 10]
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ax = fig.gca()
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import numpy as np
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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ax.pie(np.random.random(4), explode=explode, labels=labels,
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autopct='%1.1f%%', shadow=True, startangle=90,
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radius=0.25, center=(0, 0), frame=True)

‎examples/pie_and_polar_charts/polar_bar_demo.py

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import numpy as np
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import matplotlib.pyplot as plt
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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N = 20
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theta = np.linspace(0.0, 2 * np.pi, N, endpoint=False)

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