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Commit 3b77af8

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Fix minor typos in colormap manipulation tutorial.
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‎tutorials/colors/colormap-manipulation.py

Copy file name to clipboardExpand all lines: tutorials/colors/colormap-manipulation.py
+12-11Lines changed: 12 additions & 11 deletions
Original file line numberDiff line numberDiff line change
@@ -48,7 +48,7 @@
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# ListedColormap
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# --------------
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#
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# `.ListedColormap` s store their color values in a ``.colors`` attribute.
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# `.ListedColormap`\s store their color values in a ``.colors`` attribute.
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# The list of colors that comprise the colormap can be directly accessed using
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# the ``colors`` property,
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# or it can be accessed indirectly by calling ``viridis`` with an array
@@ -68,7 +68,7 @@
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##############################################################################
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# LinearSegmentedColormap
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# -----------------------
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# `.LinearSegmentedColormap` s do not have a ``.colors`` attribute.
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# `.LinearSegmentedColormap`\s do not have a ``.colors`` attribute.
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# However, one may still call the colormap with an integer array, or with a
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# float array between 0 and 1.
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@@ -114,7 +114,7 @@ def plot_examples(colormaps):
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##############################################################################
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# In fact, that list may contain any valid
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# :doc:`matplotlib color specification </tutorials/colors/colors>`.
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# :doc:`Matplotlib color specification </tutorials/colors/colors>`.
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# Particularly useful for creating custom colormaps are Nx4 numpy arrays.
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# Because with the variety of numpy operations that we can do on a such an
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# array, carpentry of new colormaps from existing colormaps become quite
@@ -168,7 +168,7 @@ def plot_examples(colormaps):
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# Creating linear segmented colormaps
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# ===================================
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#
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# `.LinearSegmentedColormap` class specifies colormaps using anchor points
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# The `.LinearSegmentedColormap` class specifies colormaps using anchor points
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# between which RGB(A) values are interpolated.
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#
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# The format to specify these colormaps allows discontinuities at the anchor
@@ -177,7 +177,7 @@ def plot_examples(colormaps):
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# ``yleft[i]`` and ``yright[i]`` are the values of the color on either
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# side of the anchor point.
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#
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# If there are no discontinuities, then ``yleft[i]=yright[i]``:
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# If there are no discontinuities, then ``yleft[i] == yright[i]``:
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cdict = {'red': [[0.0, 0.0, 0.0],
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[0.5, 1.0, 1.0],
@@ -221,9 +221,10 @@ def plot_linearmap(cdict):
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#
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# In the example below there is a discontinuity in red at 0.5. The
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# interpolation between 0 and 0.5 goes from 0.3 to 1, and between 0.5 and 1
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# it goes from 0.9 to 1. Note that red[0, 1], and red[2, 2] are both
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# superfluous to the interpolation because red[0, 1] is the value to the
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# left of 0, and red[2, 2] is the value to the right of 1.0.
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# it goes from 0.9 to 1. Note that ``red[0, 1]``, and ``red[2, 2]`` are both
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# superfluous to the interpolation because ``red[0, 1]`` (i.e., ``yleft[0]``)
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# is the value to the left of 0, and ``red[2, 2]`` (i.e., ``yright[2]``) is the
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# value to the right of 1, which are outside the color mapping domain.
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cdict['red'] = [[0.0, 0.0, 0.3],
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[0.5, 1.0, 0.9],
@@ -234,7 +235,7 @@ def plot_linearmap(cdict):
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# Directly creating a segmented colormap from a list
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# --------------------------------------------------
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#
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# The above described is a very versatile approach, but admittedly a bit
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# The approach described above is very versatile, but admittedly a bit
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# cumbersome to implement. For some basic cases, the use of
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# `.LinearSegmentedColormap.from_list` may be easier. This creates a segmented
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# colormap with equal spacings from a supplied list of colors.
@@ -243,8 +244,8 @@ def plot_linearmap(cdict):
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cmap1 = LinearSegmentedColormap.from_list("mycmap", colors)
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#############################################################################
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# If desired, the nodes of the colormap can be given as numbers
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# between 0 and 1. E.g. one could have the reddish part take more space in the
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# If desired, the nodes of the colormap can be given as numbers between 0 and
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# 1. For example, one could have the reddish part take more space in the
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# colormap.
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nodes = [0.0, 0.4, 0.8, 1.0]

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