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Commit 2f4c02d

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timhoffmtimhoffm
authored
Merge pull request #24525 from oscargus/examplespelling
[Doc] Fix spelling and grammar in examples
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‎examples/axes_grid1/demo_axes_divider.py

Copy file name to clipboardExpand all lines: examples/axes_grid1/demo_axes_divider.py
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"""
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============
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Axes Divider
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Axes divider
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============
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Axes divider to calculate location of axes and
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# PLOT 3
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# image and colorbar whose location is adjusted in the drawing time.
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# a easy way
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# an easy way
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ax = fig.add_subplot(2, 2, 3)
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demo_locatable_axes_easy(ax)

‎examples/axes_grid1/inset_locator_demo.py

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"""
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==================
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Inset Locator Demo
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Inset locator demo
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==================
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"""
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###############################################################################
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# The parameters *bbox_to_anchor* and *bbox_transform* can be used for a more
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# fine grained control over the inset position and size or even to position
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# fine-grained control over the inset position and size or even to position
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# the inset at completely arbitrary positions.
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# The *bbox_to_anchor* sets the bounding box in coordinates according to the
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# *bbox_transform*.
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fig = plt.figure(figsize=[5.5, 2.8])
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ax = fig.add_subplot(121)
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# We use the axes transform as bbox_transform. Therefore the bounding box
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# We use the axes transform as bbox_transform. Therefore, the bounding box
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# needs to be specified in axes coordinates ((0, 0) is the lower left corner
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# of the axes, (1, 1) is the upper right corner).
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# The bounding box (.2, .4, .6, .5) starts at (.2, .4) and ranges to (.8, .9)
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# in those coordinates.
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# Inside of this bounding box an inset of half the bounding box' width and
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# Inside this bounding box an inset of half the bounding box' width and
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# three quarters of the bounding box' height is created. The lower left corner
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# of the inset is aligned to the lower left corner of the bounding box (loc=3).
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# The inset is then offset by the default 0.5 in units of the font size.
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###############################################################################
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# In the above the axes transform together with 4-tuple bounding boxes has been
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# used as it mostly is useful to specify an inset relative to the axes it is
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# an inset to. However other use cases are equally possible. The following
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# an inset to. However, other use cases are equally possible. The following
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# example examines some of those.
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#
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‎examples/axes_grid1/inset_locator_demo2.py

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"""
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====================
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Inset Locator Demo 2
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Inset locator demo 2
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====================
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6-
This Demo shows how to create a zoomed inset via `.zoomed_inset_axes`.
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This demo shows how to create a zoomed inset via `.zoomed_inset_axes`.
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In the first subplot an `.AnchoredSizeBar` shows the zoom effect.
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In the second subplot a connection to the region of interest is
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created via `.mark_inset`.
@@ -63,7 +63,7 @@ def add_sizebar(ax, size):
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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
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# subregion of the original image
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x1, x2, y1, y2 = -1.5, -0.9, -2.5, -1.9
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axins2.set_xlim(x1, x2)
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axins2.set_ylim(y1, y2)

‎examples/axes_grid1/simple_axes_divider3.py

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"""
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=====================
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Simple Axes Divider 3
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Simple axes divider 3
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=====================
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See also :doc:`/tutorials/toolkits/axes_grid`.
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fig = plt.figure(figsize=(5.5, 4))
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# the rect parameter will be ignore as we will set axes_locator
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# the rect parameter will be ignored as we will set axes_locator
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rect = (0.1, 0.1, 0.8, 0.8)
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ax = [fig.add_axes(rect, label="%d" % i) for i in range(4)]
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‎examples/event_handling/cursor_demo.py

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"""
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=================
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Cross hair cursor
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Cross-hair cursor
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=================
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This example adds a cross hair as a data cursor. The cross hair is
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This example adds a cross-hair as a data cursor. The cross-hair is
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implemented as regular line objects that are updated on mouse move.
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We show three implementations:
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1) A simple cursor implementation that redraws the figure on every mouse move.
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This is a bit slow and you may notice some lag of the cross hair movement.
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This is a bit slow, and you may notice some lag of the cross-hair movement.
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2) A cursor that uses blitting for speedup of the rendering.
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3) A cursor that snaps to data points.
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# Faster redrawing using blitting
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# """""""""""""""""""""""""""""""
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# This technique stores the rendered plot as a background image. Only the
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# changed artists (cross hair lines and text) are rendered anew. They are
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# changed artists (cross-hair lines and text) are rendered anew. They are
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# combined with the background using blitting.
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#
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# This technique is significantly faster. It requires a bit more setup because
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# the background has to be stored without the cross hair lines (see
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# the background has to be stored without the cross-hair lines (see
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# ``create_new_background()``). Additionally, a new background has to be
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# created whenever the figure changes. This is achieved by connecting to the
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# ``'draw_event'``.
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class BlittedCursor:
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"""
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A cross hair cursor using blitting for faster redraw.
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A cross-hair cursor using blitting for faster redraw.
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"""
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def __init__(self, ax):
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self.ax = ax
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class SnappingCursor:
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"""
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A cross hair cursor that snaps to the data point of a line, which is
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A cross-hair cursor that snaps to the data point of a line, which is
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closest to the *x* position of the cursor.
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For simplicity, this assumes that *x* values of the data are sorted.

‎examples/event_handling/data_browser.py

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"""
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============
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Data Browser
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Data browser
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============
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Connecting data between multiple canvases.
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This example covers how to interact data with multiple canvases. This
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let's you select and highlight a point on one axis, and generating the
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lets you select and highlight a point on one axis, and generating the
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data of that point on the other axis.
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.. note::

‎examples/event_handling/legend_picking.py

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"""
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==============
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Legend Picking
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Legend picking
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==============
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Enable picking on the legend to toggle the original line on and off
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origline = lined[legline]
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visible = not origline.get_visible()
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origline.set_visible(visible)
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# Change the alpha on the line in the legend so we can see what lines
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# Change the alpha on the line in the legend, so we can see what lines
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# have been toggled.
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legline.set_alpha(1.0 if visible else 0.2)
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fig.canvas.draw()

‎examples/event_handling/path_editor.py

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"""
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===========
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Path Editor
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Path editor
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===========
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Sharing events across GUIs.
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class PathInteractor:
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"""
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An path editor.
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A path editor.
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Press 't' to toggle vertex markers on and off. When vertex markers are on,
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they can be dragged with the mouse.

‎examples/event_handling/pick_event_demo.py

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"""
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===============
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Pick Event Demo
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Pick event demo
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===============
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You can enable picking by setting the "picker" property of an artist
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the matplotlib.artist that generated the pick event.
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Additionally, certain artists like Line2D and PatchCollection may
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attach additional meta data like the indices into the data that meet
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attach additional metadata like the indices into the data that meet
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the picker criteria (for example, all the points in the line that are within
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the specified epsilon tolerance)
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‎examples/event_handling/pick_event_demo2.py

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"""
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================
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Pick Event Demo2
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================
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=================
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Pick event demo 2
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=================
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Compute the mean (mu) and standard deviation (sigma) of 100 data sets and plot
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mu vs. sigma. When you click on one of the (mu, sigma) points, plot the raw

‎examples/images_contours_and_fields/colormap_normalizations.py

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"""
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=======================
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Colormap Normalizations
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Colormap normalizations
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=======================
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Demonstration of using norm to map colormaps onto data in non-linear ways.
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X, Y = np.mgrid[-3:3:complex(0, N), -2:2:complex(0, N)]
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# A low hump with a spike coming out of the top. Needs to have
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# z/colour axis on a log scale so we see both hump and spike. linear
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# scale only shows the spike.
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# z/colour axis on a log scale, so we see both hump and spike.
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# A linear scale only shows the spike.
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Z1 = np.exp(-X**2 - Y**2)
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Z2 = np.exp(-(X * 10)**2 - (Y * 10)**2)
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# Note that colorbar labels do not come out looking very good.
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X, Y = np.mgrid[-3:3:complex(0, N), -2:2:complex(0, N)]
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Z1 = 5 * np.exp(-X**2 - Y**2)
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Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
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Z = (Z1 - Z2) * 2
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Z = 5 * np.exp(-X**2 - Y**2)
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fig, ax = plt.subplots(2, 1)
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pcm = ax[0].pcolormesh(X, Y, Z1,
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pcm = ax[0].pcolormesh(X, Y, Z,
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norm=colors.SymLogNorm(linthresh=0.03, linscale=0.03,
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vmin=-1.0, vmax=1.0, base=10),
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cmap='RdBu_r', shading='nearest')
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fig.colorbar(pcm, ax=ax[0], extend='both')
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pcm = ax[1].pcolormesh(X, Y, Z1, cmap='RdBu_r', vmin=-np.max(Z1),
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pcm = ax[1].pcolormesh(X, Y, Z, cmap='RdBu_r', vmin=-np.max(Z),
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shading='nearest')
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fig.colorbar(pcm, ax=ax[1], extend='both')
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‎examples/images_contours_and_fields/colormap_normalizations_symlognorm.py

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"""
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==================================
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Colormap Normalizations SymLogNorm
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Colormap normalizations SymLogNorm
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==================================
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Demonstration of using norm to map colormaps onto data in non-linear ways.

‎examples/images_contours_and_fields/contourf_demo.py

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"""
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=============
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Contourf Demo
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Contourf demo
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=============
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How to use the `.axes.Axes.contourf` method to create filled contour plots.
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cmap = plt.colormaps["winter"].with_extremes(under="magenta", over="yellow")
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# Note: contouring simply excludes masked or nan regions, so
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# instead of using the "bad" colormap value for them, it draws
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# nothing at all in them. Therefore the following would have
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# nothing at all in them. Therefore, the following would have
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# no effect:
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# cmap.set_bad("red")
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‎examples/images_contours_and_fields/contourf_log.py

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X, Y = np.meshgrid(x, y)
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# A low hump with a spike coming out.
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# Needs to have z/colour axis on a log scale so we see both hump and spike.
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# linear scale only shows the spike.
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# Needs to have z/colour axis on a log scale, so we see both hump and spike.
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# A linear scale only shows the spike.
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Z1 = np.exp(-X**2 - Y**2)
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Z2 = np.exp(-(X * 10)**2 - (Y * 10)**2)
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z = Z1 + 50 * Z2

‎examples/images_contours_and_fields/image_annotated_heatmap.py

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# tick labels (`~matplotlib.axes.Axes.set_xticklabels`),
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# otherwise they would become out of sync. The locations are just
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# the ascending integer numbers, while the ticklabels are the labels to show.
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# Finally we can label the data itself by creating a `~matplotlib.text.Text`
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# Finally, we can label the data itself by creating a `~matplotlib.text.Text`
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# within each cell showing the value of that cell.
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‎examples/images_contours_and_fields/image_antialiasing.py

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When subsampling data, aliasing is reduced by smoothing first and then
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subsampling the smoothed data. In Matplotlib, we can do that
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smoothing before mapping the data to colors, or we can do the smoothing
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on the RGB(A) data in the final image. The difference between these is
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on the RGB(A) data in the final image. The differences between these are
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shown below, and controlled with the *interpolation_stage* keyword argument.
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The default image interpolation in Matplotlib is 'antialiased', and
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###############################################################################
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# The following images are subsampled from 450 data pixels to either
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# 125 pixels or 250 pixels (depending on your display).
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# The Moire patterns in the 'nearest' interpolation are caused by the
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# The Moiré patterns in the 'nearest' interpolation are caused by the
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# high-frequency data being subsampled. The 'antialiased' imaged
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# still has some Moire patterns as well, but they are greatly reduced.
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# still has some Moiré patterns as well, but they are greatly reduced.
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#
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# There are substantial differences between the 'data' interpolation and
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# the 'rgba' interpolation. The alternating bands of red and blue on the
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plt.show()
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###############################################################################
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# Even up-sampling an image with 'nearest' interpolation will lead to Moire
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# Even up-sampling an image with 'nearest' interpolation will lead to Moiré
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# patterns when the upsampling factor is not integer. The following image
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# upsamples 500 data pixels to 530 rendered pixels. You may note a grid of
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# 30 line-like artifacts which stem from the 524 - 500 = 24 extra pixels that

‎examples/images_contours_and_fields/image_demo.py

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"""
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==========
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Image Demo
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Image demo
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==========
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Many ways to plot images in Matplotlib.

‎examples/images_contours_and_fields/image_nonuniform.py

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"""
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================
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Image Nonuniform
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Image nonuniform
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================
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This illustrates the NonUniformImage class. It is not
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available via an Axes method but it is easily added to an
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available via an Axes method, but it is easily added to an
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Axes instance as shown here.
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"""
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‎examples/images_contours_and_fields/matshow.py

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"""
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=======
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Matshow
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=======
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===============================
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Visualize matrices with matshow
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===============================
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`~.axes.Axes.matshow` visualizes a 2D matrix or array as color-coded image.
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"""

‎examples/images_contours_and_fields/multi_image.py

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"""
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===========
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Multi Image
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===========
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===============
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Multiple images
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===============
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Make a set of images with a single colormap, norm, and colorbar.
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"""

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