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Merge remote-tracking branch 'upstream/v1.3.x'
2 parents 803e538 + eae31b1 commit f1ae145

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‎doc/api/index_backend_api.rst

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@@ -9,5 +9,6 @@ backends
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backend_qt4agg_api.rst
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backend_wxagg_api.rst
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backend_pdf_api.rst
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backend_webagg.rst
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dviread.rst
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type1font.rst

‎examples/pylab_examples/multipage_pdf.py

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import datetime
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import numpy as np
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import matplotlib
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from matplotlib.backends.backend_pdf import PdfPages
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from pylab import *
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import matplotlib.pyplot as plt
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# Create the PdfPages object to which we will save the pages:
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pdf = PdfPages('multipage_pdf.pdf')
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# The with statement makes sure that the PdfPages object is closed properly at
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# the end of the block, even if an Exception occurs.
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with PdfPages('multipage_pdf.pdf') as pdf:
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plt.figure(figsize=(3, 3))
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plt.plot(range(7), [3, 1, 4, 1, 5, 9, 2], 'r-o')
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plt.title('Page One')
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pdf.savefig() # saves the current figure into a pdf page
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plt.close()
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figure(figsize=(3,3))
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plot(range(7), [3,1,4,1,5,9,2], 'r-o')
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title('Page One')
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savefig(pdf, format='pdf') # note the format='pdf' argument!
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close()
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plt.rc('text', usetex=True)
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plt.figure(figsize=(8, 6))
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x = np.arange(0, 5, 0.1)
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plt.plot(x, np.sin(x), 'b-')
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plt.title('Page Two')
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pdf.savefig()
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plt.close()
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rc('text', usetex=True)
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figure(figsize=(8,6))
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x = np.arange(0,5,0.1)
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plot(x, np.sin(x), 'b-')
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title('Page Two')
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pdf.savefig() # here's another way - or you could do pdf.savefig(1)
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close()
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plt.rc('text', usetex=False)
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fig = plt.figure(figsize=(4, 5))
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plt.plot(x, x*x, 'ko')
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plt.title('Page Three')
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pdf.savefig(fig) # or you can pass a Figure object to pdf.savefig
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plt.close()
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rc('text', usetex=False)
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fig=figure(figsize=(4,5))
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plot(x, x*x, 'ko')
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title('Page Three')
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pdf.savefig(fig) # or you can pass a Figure object to pdf.savefig
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close()
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# We can also set the file's metadata via the PdfPages object:
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d = pdf.infodict()
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d['Title'] = 'Multipage PDF Example'
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d['Author'] = u'Jouni K. Sepp\xe4nen'
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d['Subject'] = 'How to create a multipage pdf file and set its metadata'
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d['Keywords'] = 'PdfPages multipage keywords author title subject'
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d['CreationDate'] = datetime.datetime(2009,11,13)
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d['ModDate'] = datetime.datetime.today()
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# Remember to close the object - otherwise the file will not be usable
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pdf.close()
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# We can also set the file's metadata via the PdfPages object:
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d = pdf.infodict()
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d['Title'] = 'Multipage PDF Example'
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d['Author'] = u'Jouni K. Sepp\xe4nen'
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d['Subject'] = 'How to create a multipage pdf file and set its metadata'
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d['Keywords'] = 'PdfPages multipage keywords author title subject'
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d['CreationDate'] = datetime.datetime(2009, 11, 13)
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d['ModDate'] = datetime.datetime.today()

‎lib/matplotlib/backends/backend_pdf.py

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@@ -2252,15 +2252,12 @@ class PdfPages(object):
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Use like this::
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# Initialize:
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pp = PdfPages('foo.pdf')
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with PdfPages('foo.pdf') as pdf:
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# As many times as you like, create a figure fig, then either:
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fig.savefig(pp, format='pdf') # note the format argument!
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# or:
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pp.savefig(fig)
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# Once you are done, remember to close the object:
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pp.close()
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# As many times as you like, create a figure fig and save it:
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# When no figure is specified the current figure is saved
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pdf.savefig(fig)
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pdf.savefig()
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(In reality PdfPages is a thin wrapper around PdfFile, in order to
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avoid confusion when using savefig and forgetting the format
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"""
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self._file = PdfFile(filename)
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def __enter__(self):
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return self
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def __exit__(self, exc_type, exc_val, exc_tb):
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self.close()
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def close(self):
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"""
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Finalize this object, making the underlying file a complete

‎lib/matplotlib/testing/compare.py

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@@ -309,32 +309,6 @@ def compare_images( expected, actual, tol, in_decorator=False ):
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expected_version = version.LooseVersion("1.6")
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found_version = version.LooseVersion(np.__version__)
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# On Numpy 1.6, we can use bincount with minlength, which is much faster than
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# using histogram
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if found_version >= expected_version:
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rms = 0
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for i in xrange(0, 3):
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h1p = expectedImage[:,:,i]
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h2p = actualImage[:,:,i]
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h1h = np.bincount(h1p.ravel(), minlength=256)
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h2h = np.bincount(h2p.ravel(), minlength=256)
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rms += np.sum(np.power((h1h-h2h), 2))
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else:
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rms = 0
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ns = np.arange(257)
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for i in xrange(0, 3):
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h1p = expectedImage[:,:,i]
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h2p = actualImage[:,:,i]
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h1h = np.histogram(h1p, bins=ns)[0]
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h2h = np.histogram(h2p, bins=ns)[0]
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rms += np.sum(np.power((h1h-h2h), 2))
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rms = calculate_rms(expectedImage, actualImage)
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diff_image = make_test_filename(actual, 'failed-diff')

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