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doc/api/api_changes.rst

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@@ -2078,7 +2078,7 @@ Transformations
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implementations, matplotlib.transforms.SeparableTransformation and
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matplotlib.transforms.Affine. The SeparableTransformation is
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constructed with the bounding box of the input (this determines the
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rectangular coordinate system of the input, ie the x and y view
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rectangular coordinate system of the input, i.e., the x and y view
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limits), the bounding box of the display, and possibly nonlinear
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transformations of x and y. The 2 most frequently used
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transformations, data coordinates -> display and axes coordinates ->

doc/faq/usage_faq.rst

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@@ -295,7 +295,7 @@ others in web application servers to dynamically serve up graphs.
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To support all of these use cases, matplotlib can target different
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outputs, and each of these capabilities is called a backend; the
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"frontend" is the user facing code, ie the plotting code, whereas the
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"frontend" is the user facing code, i.e., the plotting code, whereas the
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"backend" does all the hard work behind-the-scenes to make the figure.
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There are two types of backends: user interface backends (for use in
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pygtk, wxpython, tkinter, qt4, or macosx; also referred to as

examples/pylab_examples/broken_barh.py

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"""
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Make a "broken" horizontal bar plot, ie one with gaps
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Make a "broken" horizontal bar plot, i.e., one with gaps
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"""
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import matplotlib.pyplot as plt
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lib/matplotlib/axes/_axes.py

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@@ -2213,11 +2213,11 @@ def broken_barh(self, xranges, yrange, **kwargs):
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%(BrokenBarHCollection)s
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these can either be a single argument, ie::
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these can either be a single argument, i.e.,::
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facecolors = 'black'
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or a sequence of arguments for the various bars, ie::
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or a sequence of arguments for the various bars, i.e.,::
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facecolors = ('black', 'red', 'green')
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@@ -5420,7 +5420,7 @@ def hist(self, x, bins=10, range=None, normed=False, weights=None,
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normed : boolean, optional, default: False
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If `True`, the first element of the return tuple will
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be the counts normalized to form a probability density, i.e.,
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``n/(len(x)`dbin)``, ie the integral of the histogram will sum to
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``n/(len(x)`dbin)``, i.e., the integral of the histogram will sum to
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1. If *stacked* is also *True*, the sum of the histograms is
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normalized to 1.
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lib/matplotlib/collections.py

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@@ -70,7 +70,7 @@ class Collection(artist.Artist, cm.ScalarMappable):
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The use of :class:`~matplotlib.cm.ScalarMappable` is optional. If
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the :class:`~matplotlib.cm.ScalarMappable` matrix _A is not None
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(ie a call to set_array has been made), at draw time a call to
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(i.e., a call to set_array has been made), at draw time a call to
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scalar mappable will be made to set the face colors.
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"""
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_offsets = np.array([], np.float_)
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The use of :class:`~matplotlib.cm.ScalarMappable` is optional.
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If the :class:`~matplotlib.cm.ScalarMappable` array
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:attr:`~matplotlib.cm.ScalarMappable._A` is not None (ie a call to
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:attr:`~matplotlib.cm.ScalarMappable._A` is not None (i.e., a call to
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:meth:`~matplotlib.cm.ScalarMappable.set_array` has been made), at
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draw time a call to scalar mappable will be made to set the colors.
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"""
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The use of :class:`~matplotlib.cm.ScalarMappable` is optional.
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If the :class:`~matplotlib.cm.ScalarMappable` array
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:attr:`~matplotlib.cm.ScalarMappable._A` is not None (ie a call to
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:attr:`~matplotlib.cm.ScalarMappable._A` is not None (i.e., a call to
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:meth:`~matplotlib.cm.ScalarMappable.set_array` has been made), at
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draw time a call to scalar mappable will be made to set the colors.
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@@ -1517,7 +1517,7 @@ def __init__(self, patches, match_original=False, **kwargs):
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The use of :class:`~matplotlib.cm.ScalarMappable` is optional.
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If the :class:`~matplotlib.cm.ScalarMappable` matrix _A is not
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None (ie a call to set_array has been made), at draw time a
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None (i.e., a call to set_array has been made), at draw time a
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call to scalar mappable will be made to set the face colors.
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"""
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lib/matplotlib/mlab.py

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*Y* : a projected into PCA space
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The factor loadings are in the Wt factor, ie the factor
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The factor loadings are in the Wt factor, i.e., the factor
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loadings for the 1st principal component are given by Wt[0].
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This row is also the 1st eigenvector.
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lib/matplotlib/quiver.py

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@@ -915,7 +915,7 @@ def _find_tails(self, mag, rounding=True, half=5, full=10, flag=50):
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'''
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Find how many of each of the tail pieces is necessary. Flag
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specifies the increment for a flag, barb for a full barb, and half for
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half a barb. Mag should be the magnitude of a vector (ie. >= 0).
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half a barb. Mag should be the magnitude of a vector (i.e., >= 0).
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This returns a tuple of:
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lib/matplotlib/ticker.py

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@@ -993,7 +993,7 @@ class IndexLocator(Locator):
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"""
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Place a tick on every multiple of some base number of points
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plotted, e.g., on every 5th point. It is assumed that you are doing
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index plotting; ie the axis is 0, len(data). This is mainly
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index plotting; i.e., the axis is 0, len(data). This is mainly
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useful for x ticks.
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"""
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def __init__(self, base, offset):

lib/mpl_toolkits/axes_grid1/axes_divider.py

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@@ -390,7 +390,7 @@ def __init__(self, fig, *args, **kwargs):
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# total = rows*cols
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# num -= 1 # convert from matlab to python indexing
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# # ie num in range(0,total)
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# # i.e., num in range(0,total)
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# if num >= total:
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# raise ValueError( 'Subplot number exceeds total subplots')
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# self._rows = rows

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