[Python-modules-commits] [matplotlib] 02/05: remove interpolation_none_vs_nearest.py

[Sandro Tosi]

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morph pushed a commit to branch master
in repository matplotlib.

commit 87576dc0ee642fc0c5a298807705eb13492d6d4f
Author: Sandro Tosi <morph at debian.org>
Date:   Tue Feb 14 19:43:40 2017 -0500

    remove interpolation_none_vs_nearest.py
    
    this example uses necked_tensile_specimen.png, which has been removed due to a
    non distributable color calibration code, and so when it is executed during the
    build process makes it fail to generate the HTML doc.
---
 .../interpolation_none_vs_nearest.py               | 62 ----------------------
 1 file changed, 62 deletions(-)

diff --git a/examples/images_contours_and_fields/interpolation_none_vs_nearest.py b/examples/images_contours_and_fields/interpolation_none_vs_nearest.py
deleted file mode 100644
index 487b401..0000000
--- a/examples/images_contours_and_fields/interpolation_none_vs_nearest.py
+++ /dev/null
@@ -1,62 +0,0 @@
-"""
-Displays the difference between interpolation = 'none' and
-interpolation = 'nearest'.
-
-Interpolation = 'none' and interpolation = 'nearest' are equivalent when
-converting a figure to an image file, such as a PNG.
-Interpolation = 'none' and interpolation = 'nearest' behave quite
-differently, however, when converting a figure to a vector graphics file,
-such as a PDF.  As shown, Interpolation = 'none' works well when a big
-image is scaled down, while interpolation = 'nearest' works well when a
-small image is blown up.
-"""
-
-import numpy as np
-import matplotlib.pyplot as plt
-import matplotlib.cbook as cbook
-
-# Load big image
-big_im_path = cbook.get_sample_data('necked_tensile_specimen.png')
-big_im = plt.imread(big_im_path)
-# Define small image
-small_im = np.array([[0.25, 0.75, 1.0, 0.75], [0.1, 0.65, 0.5, 0.4],
-                     [0.6, 0.3, 0.0, 0.2], [0.7, 0.9, 0.4, 0.6]])
-
-# Create a 2x2 table of plots
-fig, axes = plt.subplots(figsize=[8.0, 7.5], ncols=2, nrows=2)
-
-axes[0, 0].imshow(big_im, interpolation='none')
-axes[0, 1].imshow(big_im, interpolation='nearest')
-axes[1, 0].imshow(small_im, interpolation='none')
-axes[1, 1].imshow(small_im, interpolation='nearest')
-fig.subplots_adjust(left=0.24, wspace=0.2, hspace=0.1,
-                    bottom=0.05, top=0.86)
-
-# Label the rows and columns of the table
-fig.text(0.03, 0.645, 'Big Image\nScaled Down', ha='left')
-fig.text(0.03, 0.225, 'Small Image\nBlown Up', ha='left')
-fig.text(0.383, 0.90, "Interpolation = 'none'", ha='center')
-fig.text(0.75, 0.90, "Interpolation = 'nearest'", ha='center')
-
-# If you were going to run this example on your local machine, you
-# would save the figure as a PNG, save the same figure as a PDF, and
-# then compare them.  The following code would suffice.
-txt = fig.text(0.452, 0.95, 'Saved as a PNG', fontsize=18)
-# plt.savefig('None_vs_nearest-png.png')
-# txt.set_text('Saved as a PDF')
-# plt.savefig('None_vs_nearest-pdf.pdf')
-
-# Here, however, we need to display the PDF on a webpage, which means
-# the PDF must be converted into an image.  For the purposes of this
-# example, the 'Nearest_vs_none-pdf.pdf' has been pre-converted into
-#'Nearest_vs_none-pdf.png' at 80 dpi.  We simply need to load and
-# display it.
-# The conversion is done with:
-#  gs -dNOPAUSE -dBATCH -dDOINTERPOLATE -sDEVICE=pngalpha \
-#     -sOutputFile=None_vs_nearest-pdf.png -r80 None_vs_nearest-pdf.pdf
-pdf_im_path = cbook.get_sample_data('None_vs_nearest-pdf.png')
-pdf_im = plt.imread(pdf_im_path)
-fig2 = plt.figure(figsize=[8.0, 7.5])
-fig2.figimage(pdf_im)
-
-plt.show()

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