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Merge pull request #15641 from anntzer/get_sample_data-npy
API: Make get_sample_data autoload npy/npz files.
2 parents 56d509f + d7d11a7 commit a1c51a0

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‎doc/api/api_changes_3.3/behaviour.rst

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@@ -294,3 +294,10 @@ FT_LOAD_DEFAULT, etc. The old synonyms (respectively "either", "native",
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"auto", and "none") are still supported, but their use is discouraged. To get
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normalized values, use `.backend_agg.get_hinting_flag`, which returns integer
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flag values.
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`.cbook.get_sample_data` auto-loads numpy arrays
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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When `.cbook.get_sample_data` is used to load a npy or npz file and the
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keyword-only parameter ``np_load`` is True, the file is automatically loaded
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using `numpy.load`. ``np_load`` defaults to False for backwards compatibility,
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but will become True in a later release.

‎examples/axes_grid1/demo_axes_divider.py

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@@ -6,14 +6,13 @@
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Axes divider to calculate location of axes and
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create a divider for them using existing axes instances.
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"""
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from matplotlib import cbook
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import matplotlib.pyplot as plt
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def get_demo_image():
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import numpy as np
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from matplotlib.cbook import get_sample_data
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f = get_sample_data("axes_grid/bivariate_normal.npy", asfileobj=False)
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z = np.load(f)
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z = cbook.get_sample_data("axes_grid/bivariate_normal.npy", np_load=True)
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# z is a numpy array of 15x15
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return z, (-3, 4, -4, 3)
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‎examples/axes_grid1/demo_axes_grid.py

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@@ -6,6 +6,7 @@
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Grid of 2x2 images with single or own colorbar.
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"""
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from matplotlib import cbook
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import matplotlib.pyplot as plt
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from mpl_toolkits.axes_grid1 import ImageGrid
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def get_demo_image():
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import numpy as np
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from matplotlib.cbook import get_sample_data
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f = get_sample_data("axes_grid/bivariate_normal.npy", asfileobj=False)
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z = np.load(f)
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z = cbook.get_sample_data("axes_grid/bivariate_normal.npy", np_load=True)
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# z is a numpy array of 15x15
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return z, (-3, 4, -4, 3)
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‎examples/axes_grid1/demo_axes_grid2.py

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@@ -7,23 +7,15 @@
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"""
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import numpy as np
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from matplotlib import cbook
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import matplotlib.colors
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import matplotlib.pyplot as plt
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from mpl_toolkits.axes_grid1 import ImageGrid
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import matplotlib.colors
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plt.rcParams["mpl_toolkits.legacy_colorbar"] = False
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def get_demo_image():
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from matplotlib.cbook import get_sample_data
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f = get_sample_data("axes_grid/bivariate_normal.npy", asfileobj=False)
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z = np.load(f)
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# z is a numpy array of 15x15
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return z, (-3, 4, -4, 3)
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def add_inner_title(ax, title, loc, **kwargs):
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from matplotlib.offsetbox import AnchoredText
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from matplotlib.patheffects import withStroke
@@ -39,7 +31,8 @@ def add_inner_title(ax, title, loc, **kwargs):
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fig = plt.figure(figsize=(6, 6))
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# Prepare images
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Z, extent = get_demo_image()
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Z = cbook.get_sample_data("axes_grid/bivariate_normal.npy", np_load=True)
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extent = (-3, 4, -4, 3)
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ZS = [Z[i::3, :] for i in range(3)]
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extent = extent[0], extent[1]/3., extent[2], extent[3]
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‎examples/axes_grid1/demo_axes_rgb.py

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def get_rgb():
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f = cbook.get_sample_data("axes_grid/bivariate_normal.npy")
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Z = np.load(f) # 15x15 numpy array.
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Z = cbook.get_sample_data("axes_grid/bivariate_normal.npy", np_load=True)
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Z[Z < 0] = 0.
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Z = Z / Z.max()
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‎examples/axes_grid1/demo_colorbar_of_inset_axes.py

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===========================
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"""
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import matplotlib.pyplot as plt
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from matplotlib import cbook
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import matplotlib.pyplot as plt
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from mpl_toolkits.axes_grid1.inset_locator import inset_axes, zoomed_inset_axes
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def get_demo_image():
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from matplotlib.cbook import get_sample_data
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import numpy as np
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f = get_sample_data("axes_grid/bivariate_normal.npy", asfileobj=False)
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z = np.load(f)
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# z is a numpy array of 15x15
18-
return z, (-3, 4, -4, 3)
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fig, ax = plt.subplots(figsize=[5, 4])
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Z, extent = get_demo_image()
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ax.set(aspect=1,
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xlim=(-15, 15),
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ylim=(-20, 5))
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Z = cbook.get_sample_data("axes_grid/bivariate_normal.npy", np_load=True)
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extent = (-3, 4, -4, 3)
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ax.set(aspect=1, xlim=(-15, 15), ylim=(-20, 5))
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axins = zoomed_inset_axes(ax, zoom=2, loc='upper left')
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im = axins.imshow(Z, extent=extent, origin="lower")
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plt.xticks(visible=False)
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plt.yticks(visible=False)
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# colorbar
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cax = inset_axes(axins,
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width="5%", # width = 10% of parent_bbox width
@@ -43,7 +32,6 @@ def get_demo_image():
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bbox_transform=axins.transAxes,
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borderpad=0,
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)
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fig.colorbar(im, cax=cax)
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plt.show()

‎examples/axes_grid1/demo_edge_colorbar.py

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of an image grid.
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"""
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from matplotlib import cbook
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import matplotlib.pyplot as plt
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from mpl_toolkits.axes_grid1 import AxesGrid
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def get_demo_image():
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import numpy as np
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from matplotlib.cbook import get_sample_data
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f = get_sample_data("axes_grid/bivariate_normal.npy", asfileobj=False)
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z = np.load(f)
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z = cbook.get_sample_data("axes_grid/bivariate_normal.npy", np_load=True)
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# z is a numpy array of 15x15
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return z, (-3, 4, -4, 3)
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‎examples/axes_grid1/inset_locator_demo2.py

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created via `~.mark_inset`.
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"""
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from matplotlib import cbook
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import matplotlib.pyplot as plt
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from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes, mark_inset
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from mpl_toolkits.axes_grid1.anchored_artists import AnchoredSizeBar
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import numpy as np
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def get_demo_image():
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from matplotlib.cbook import get_sample_data
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import numpy as np
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f = get_sample_data("axes_grid/bivariate_normal.npy", asfileobj=False)
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z = np.load(f)
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z = cbook.get_sample_data("axes_grid/bivariate_normal.npy", np_load=True)
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# z is a numpy array of 15x15
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return z, (-3, 4, -4, 3)
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@@ -60,11 +57,9 @@ def add_sizebar(ax, size):
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ny, nx = Z.shape
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Z2[30:30+ny, 30:30+nx] = Z
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# extent = [-3, 4, -4, 3]
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ax2.imshow(Z2, extent=extent, origin="lower")
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axins2 = zoomed_inset_axes(ax2, 6, loc=1) # zoom = 6
62+
axins2 = zoomed_inset_axes(ax2, zoom=6, loc=1)
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axins2.imshow(Z2, extent=extent, origin="lower")
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# sub region of the original image

‎examples/axes_grid1/simple_axesgrid2.py

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Align multiple images of different sizes using
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`~mpl_toolkits.axes_grid1.axes_grid.ImageGrid`.
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"""
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from matplotlib import cbook
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import matplotlib.pyplot as plt
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from mpl_toolkits.axes_grid1 import ImageGrid
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def get_demo_image():
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import numpy as np
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from matplotlib.cbook import get_sample_data
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f = get_sample_data("axes_grid/bivariate_normal.npy", asfileobj=False)
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z = np.load(f)
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# z is a numpy array of 15x15
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return z, (-3, 4, -4, 3)
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fig = plt.figure(figsize=(5.5, 3.5))
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grid = ImageGrid(fig, 111, # similar to subplot(111)
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nrows_ncols=(1, 3),
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axes_pad=0.1,
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label_mode="L",
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)
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29-
Z, extent = get_demo_image() # demo image
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# demo image
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Z = cbook.get_sample_data("axes_grid/bivariate_normal.npy", np_load=True)
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im1 = Z
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im2 = Z[:, :10]
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im3 = Z[:, 10:]

‎examples/frontpage/3D.py

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import matplotlib.pyplot as plt
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import numpy as np
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with cbook.get_sample_data('jacksboro_fault_dem.npz') as file, \
16-
np.load(file) as dem:
17-
z = dem['elevation']
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nrows, ncols = z.shape
19-
x = np.linspace(dem['xmin'], dem['xmax'], ncols)
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y = np.linspace(dem['ymin'], dem['ymax'], nrows)
21-
x, y = np.meshgrid(x, y)
15+
dem = cbook.get_sample_data('jacksboro_fault_dem.npz', np_load=True)
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z = dem['elevation']
17+
nrows, ncols = z.shape
18+
x = np.linspace(dem['xmin'], dem['xmax'], ncols)
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y = np.linspace(dem['ymin'], dem['ymax'], nrows)
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x, y = np.meshgrid(x, y)
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region = np.s_[5:50, 5:50]
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x, y, z = x[region], y[region], z[region]

‎examples/images_contours_and_fields/shading_example.py

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x, y = np.mgrid[-5:5:0.05, -5:5:0.05]
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z = 5 * (np.sqrt(x**2 + y**2) + np.sin(x**2 + y**2))
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24-
with cbook.get_sample_data('jacksboro_fault_dem.npz') as file, \
25-
np.load(file) as dem:
26-
elev = dem['elevation']
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dem = cbook.get_sample_data('jacksboro_fault_dem.npz', np_load=True)
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elev = dem['elevation']
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2827
fig = compare(z, plt.cm.copper)
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fig.suptitle('HSV Blending Looks Best with Smooth Surfaces', y=0.95)

‎examples/lines_bars_and_markers/scatter_demo2.py

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# Load a numpy record array from yahoo csv data with fields date, open, close,
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# volume, adj_close from the mpl-data/example directory. The record array
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# stores the date as an np.datetime64 with a day unit ('D') in the date column.
15-
with cbook.get_sample_data('goog.npz') as datafile:
16-
price_data = np.load(datafile)['price_data'].view(np.recarray)
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price_data = (cbook.get_sample_data('goog.npz', np_load=True)['price_data']
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.view(np.recarray))
1717
price_data = price_data[-250:] # get the most recent 250 trading days
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delta1 = np.diff(price_data.adj_close) / price_data.adj_close[:-1]

‎examples/mplot3d/custom_shaded_3d_surface.py

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import numpy as np
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# Load and format data
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with cbook.get_sample_data('jacksboro_fault_dem.npz') as file, \
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np.load(file) as dem:
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z = dem['elevation']
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nrows, ncols = z.shape
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x = np.linspace(dem['xmin'], dem['xmax'], ncols)
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y = np.linspace(dem['ymin'], dem['ymax'], nrows)
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x, y = np.meshgrid(x, y)
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dem = cbook.get_sample_data('jacksboro_fault_dem.npz', np_load=True)
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z = dem['elevation']
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nrows, ncols = z.shape
19+
x = np.linspace(dem['xmin'], dem['xmax'], ncols)
20+
y = np.linspace(dem['ymin'], dem['ymax'], nrows)
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x, y = np.meshgrid(x, y)
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region = np.s_[5:50, 5:50]
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x, y, z = x[region], y[region], z[region]

‎examples/recipes/common_date_problems.py

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import numpy as np
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import matplotlib.pyplot as plt
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with cbook.get_sample_data('goog.npz') as datafile:
42-
r = np.load(datafile)['price_data'].view(np.recarray)
41+
r = (cbook.get_sample_data('goog.npz', np_load=True)['price_data']
42+
.view(np.recarray))
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fig, ax = plt.subplots()
4545
ax.plot(r.date, r.close)

‎examples/recipes/fill_between_alpha.py

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np.random.seed(19680801)
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# load up some sample financial data
24-
with cbook.get_sample_data('goog.npz') as datafile:
25-
r = np.load(datafile)['price_data'].view(np.recarray)
24+
r = (cbook.get_sample_data('goog.npz', np_load=True)['price_data']
25+
.view(np.recarray))
2626
# create two subplots with the shared x and y axes
2727
fig, (ax1, ax2) = plt.subplots(1, 2, sharex=True, sharey=True)
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‎examples/specialty_plots/topographic_hillshading.py

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from matplotlib.colors import LightSource
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25-
with np.load(get_sample_data('jacksboro_fault_dem.npz')) as dem:
26-
z = dem['elevation']
27-
28-
#-- Optional dx and dy for accurate vertical exaggeration ----------------
29-
# If you need topographically accurate vertical exaggeration, or you don't
30-
# want to guess at what *vert_exag* should be, you'll need to specify the
31-
# cellsize of the grid (i.e. the *dx* and *dy* parameters). Otherwise, any
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# *vert_exag* value you specify will be relative to the grid spacing of
33-
# your input data (in other words, *dx* and *dy* default to 1.0, and
34-
# *vert_exag* is calculated relative to those parameters). Similarly, *dx*
35-
# and *dy* are assumed to be in the same units as your input z-values.
36-
# Therefore, we'll need to convert the given dx and dy from decimal degrees
37-
# to meters.
38-
dx, dy = dem['dx'], dem['dy']
39-
dy = 111200 * dy
40-
dx = 111200 * dx * np.cos(np.radians(dem['ymin']))
41-
#-------------------------------------------------------------------------
25+
dem = get_sample_data('jacksboro_fault_dem.npz', np_load=True)
26+
z = dem['elevation']
27+
#-- Optional dx and dy for accurate vertical exaggeration ---------------------
28+
# If you need topographically accurate vertical exaggeration, or you don't want
29+
# to guess at what *vert_exag* should be, you'll need to specify the cellsize
30+
# of the grid (i.e. the *dx* and *dy* parameters). Otherwise, any *vert_exag*
31+
# value you specify will be relative to the grid spacing of your input data
32+
# (in other words, *dx* and *dy* default to 1.0, and *vert_exag* is calculated
33+
# relative to those parameters). Similarly, *dx* and *dy* are assumed to be in
34+
# the same units as your input z-values. Therefore, we'll need to convert the
35+
# given dx and dy from decimal degrees to meters.
36+
dx, dy = dem['dx'], dem['dy']
37+
dy = 111200 * dy
38+
dx = 111200 * dx * np.cos(np.radians(dem['ymin']))
39+
#------------------------------------------------------------------------------
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4341
# Shade from the northwest, with the sun 45 degrees from horizontal
4442
ls = LightSource(azdeg=315, altdeg=45)

‎examples/subplots_axes_and_figures/zoom_inset_axes.py

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======================
55
66
Example of an inset axes and a rectangle showing where the zoom is located.
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87
"""
98

9+
from matplotlib import cbook
1010
import matplotlib.pyplot as plt
1111
import numpy as np
1212

1313

1414
def get_demo_image():
15-
from matplotlib.cbook import get_sample_data
16-
import numpy as np
17-
f = get_sample_data("axes_grid/bivariate_normal.npy", asfileobj=False)
18-
z = np.load(f)
15+
z = cbook.get_sample_data("axes_grid/bivariate_normal.npy", np_load=True)
1916
# z is a numpy array of 15x15
2017
return z, (-3, 4, -4, 3)
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‎examples/text_labels_and_annotations/date.py

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2929
# close, volume, adj_close from the mpl-data/example directory. This array
3030
# stores the date as an np.datetime64 with a day unit ('D') in the 'date'
3131
# column.
32-
with cbook.get_sample_data('goog.npz') as datafile:
33-
data = np.load(datafile)['price_data']
32+
data = cbook.get_sample_data('goog.npz', np_load=True)['price_data']
3433

3534
fig, ax = plt.subplots()
3635
ax.plot('date', 'adj_close', data=data)

‎examples/text_labels_and_annotations/date_index_formatter.py

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1414
# Load a numpy record array from yahoo csv data with fields date, open, close,
1515
# volume, adj_close from the mpl-data/example directory. The record array
1616
# stores the date as an np.datetime64 with a day unit ('D') in the date column.
17-
with cbook.get_sample_data('goog.npz') as datafile:
18-
r = np.load(datafile)['price_data'].view(np.recarray)
17+
r = (cbook.get_sample_data('goog.npz', np_load=True)['price_data']
18+
.view(np.recarray))
1919
r = r[-30:] # get the last 30 days
2020

2121
# first we'll do it the default way, with gaps on weekends

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