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jdh2358jdh2358
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new transformation / artist constructors
svn path=/trunk/matplotlib/; revision=290
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‎.matplotlibrc‎

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@@ -110,11 +110,18 @@ axes.labelsize : 12 # fontsize of the x any y labels
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axes.labelcolor : k # black
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### TICKS
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tick.major.size : 5 # major tick size in points
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tick.minor.size : 3 # minor tick size in points
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tick.major.size : 4 # major tick size in points
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tick.minor.size : 2 # minor tick size in points
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tick.major.pad : 3 # distance to major tick label in points
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tick.minor.pad : 3 # distance to the minor tick label in points
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tick.color : k # color of the tick labels
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tick.labelsize : 10 # fontsize of the tick labels
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### Grids
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grid.color : k # grid color
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grid.linestyle : : # dotted
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grid.linewidth : 0.5 # in points
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### FIGURE
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figure.figsize : 8, 6 # figure size in inches
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figure.dpi : 80 # figure dots per inch

‎API_CHANGES‎

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API CHANGES in matplotlib-0.54
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Autoscaling:
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The x and y axis instances no longer have autoscale view. These are
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handled by axes.autoscale_view
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Axes creation:
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You should not instantiate your own Axes any more using the OO API.
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Rather, create a Figure as before and in place of
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f = Figure(figsize=(5,4), dpi=100)
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a = Subplot(f, 111)
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f.add_axis(a)
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use
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f = Figure(figsize=(5,4), dpi=100)
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a = f.add_subplot(111)
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That is, add_axis no longer exists and is replaced by
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add_axes(rect, axisbg=defaultcolor, frameon=True)
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add_subplot(num, axisbg=defaultcolor, frameon=True)
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Artist methods:
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If you define your own Artists, you need to rename the _draw method
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to draw
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Bounding boxes.
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matplotlib.transforms.Bound2D is replaced by
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matplotlib.transforms.Bbox. If you want to construct a bbox from
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left, bottom, width, height (the signature for Bound2D), use
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matplotlib.transforms.lbwh_to_bbox, as in
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bbox = clickBBox = lbwh_to_bbox(left, bottom, width, height)
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The Bbox has a different API than the Bound2D. Eg, if you want to
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get the width and height of the bbox
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OLD
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width = self.figure.bbox.x.interval()
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height = self.figure.bbox.y.interval()
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New
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width = self.figure.bbox.width()
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height = self.figure.bbox.height()
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Object constructors:
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You no longer pass the bbox, dpi, or transforms to the various
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Artist constructors. The old way or creating lines and rectangles
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was cumbersome because you had to pass so many attributes to the
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Line2D and Rectangle classes not related directly to the gemoetry
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and properties of the object. Now default values are added to the
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object when you call axes.add_line or axes.add_patch, so they are
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hidden from the user.
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If you want to define a custom transformation on these objects, call
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o.set_transform(trans) where trans is a Transformation instance.
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In prior versions of you wanted to add a custom line in data coords,
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you would have to do
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l = Line2D(dpi, bbox, x, y,
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color = color,
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transx = transx,
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transy = transy,
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)
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now all you need is
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l = Line2D(x, y, color=color)
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and the axes will set the transformation for you (unless you have
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set your own already, in which case it will eave it unchanged)
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Transformations:
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The entire transformation architecture has been rewritten.
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Previously the x and y transformations where stored in the xaxis and
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yaxis insstances. The problem with this approach is it only allows
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for separable transforms (where the x and y transformations don't
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depend on one another). But for cases like polar, they do. Now
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transformations operate on x,y together. There is a new base class
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matplotlib.transforms.Transformation and two concrete
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implemetations, 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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limits), the bounding box of the display, and possibily nonlinear
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transformations of x and y. The 2 most frequently used
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transformations, data cordinates -> display and axes coordinates ->
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display are available as ax.transData and ax.transAxes. See
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alignment_demo.py which uses axes coords.
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Also, the transformations should be much faster now, for two reasons
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* they are written entirely in extension code
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* because they operate on x and y together, they can do the entire
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transformation in one loop. Earlier I did something along the
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lines of
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xt = sx*func(x) + tx
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yt = sy*func(y) + ty
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Although this was done in numerix, it still involves 6 length(x)
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for-loops (the multiply, add, and function evaluation each for x
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and y). Now all of that is done in a single pass.
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See unit/transforms_unit.py for many examples using the new
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transformations.
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API changes at 0.50
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* refactored Figure class so it is no longer backend dependent.

‎CHANGELOG‎

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New entries should be added at the top
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------------------------------------------------------------
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2004-05-15 Rewrote transformation class in extension code, simplified
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all the artist constructors - JDH
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2004-05-14 Modified the type definitions in the numarray side of
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numerix so that they are Numeric typecodes and can be
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used with Numeric compilex extensions. The original

‎TODO‎

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-- expose line set dashes
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-- fix vertical layout in PS
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-- fix vertical layout in PS
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-- DONE y clipping broken
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-- fix data lim problem with image; see image_demo2
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-- handle space in roman/font text
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-- scatter demos whacked
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-- fix small artist bboxing problems
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-- fix backend image area problem for vert text in GTK - related to
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rect I think

‎examples/alignment_test.py‎

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from matplotlib.matlab import *
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from matplotlib.lines import Line2D
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from matplotlib.patches import Rectangle
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from matplotlib.transforms import Transform
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# build a rectangle in axes coords
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left, width = .25, .5
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bottom, height = .25, .5
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right = left + width
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top = bottom + height
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ax = gca()
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p = Rectangle(ax.dpi, ax.bbox,
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(left, bottom), width, height,
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p = Rectangle((left, bottom), width, height,
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fill=False,
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transx=ax.xaxis.transAxis,
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transy=ax.yaxis.transAxis)
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)
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p.set_transform(ax.transAxes)
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p.set_clip_on(False)
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ax.add_patch(p)
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ax.text(left, bottom, 'left top',
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horizontalalignment='left',
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verticalalignment='top',
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transx=ax.xaxis.transAxis,
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transy=ax.yaxis.transAxis)
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transform=ax.transAxes)
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ax.text(left, bottom, 'left bottom',
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horizontalalignment='left',
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verticalalignment='bottom',
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transx=ax.xaxis.transAxis,
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transy=ax.yaxis.transAxis)
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transform=ax.transAxes)
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ax.text(right, top, 'right bottom',
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horizontalalignment='right',
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verticalalignment='bottom',
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transx=ax.xaxis.transAxis,
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transy=ax.yaxis.transAxis)
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transform=ax.transAxes)
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ax.text(right, top, 'right top',
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horizontalalignment='right',
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verticalalignment='top',
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transx=ax.xaxis.transAxis,
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transy=ax.yaxis.transAxis)
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transform=ax.transAxes)
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ax.text(right, bottom, 'center top',
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horizontalalignment='center',
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verticalalignment='top',
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transx=ax.xaxis.transAxis,
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transy=ax.yaxis.transAxis)
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transform=ax.transAxes)
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ax.text(left, 0.5*(bottom+top), 'right center',
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horizontalalignment='right',
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verticalalignment='center',
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rotation='vertical',
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transx=ax.xaxis.transAxis,
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transy=ax.yaxis.transAxis)
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transform=ax.transAxes)
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ax.text(left, 0.5*(bottom+top), 'left center',
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horizontalalignment='left',
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verticalalignment='center',
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rotation='vertical',
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transx=ax.xaxis.transAxis,
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transy=ax.yaxis.transAxis)
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transform=ax.transAxes)
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ax.text(0.5*(left+right), 0.5*(bottom+top), 'middle',
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horizontalalignment='center',
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verticalalignment='center',
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transx=ax.xaxis.transAxis,
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transy=ax.yaxis.transAxis)
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transform=ax.transAxes)
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ax.text(right, 0.5*(bottom+top), 'centered',
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horizontalalignment='center',
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verticalalignment='center',
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rotation='vertical',
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transx=ax.xaxis.transAxis,
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transy=ax.yaxis.transAxis)
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transform=ax.transAxes)
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ax.text(left, top, 'rotated',
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horizontalalignment='center',
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verticalalignment='center',
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rotation=45,
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transx=ax.xaxis.transAxis,
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transy=ax.yaxis.transAxis)
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transform=ax.transAxes)
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axis('off')
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savefig('alignment_test', dpi=100)
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#savefig('alignment_test', dpi=100)
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show()

‎examples/anim.py‎

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def updatefig(*args):
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updatefig.count += 1
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if updatefig.count>59: updatefig.count=0
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lines[0].set_data(ind,X[:,updatefig.count])
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lines[0].set_ydata(X[:,updatefig.count])
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manager.canvas.draw()
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return gtk.TRUE
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‎examples/embedding_in_gtk.py‎

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vbox.show()
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f = Figure(figsize=(5,4), dpi=100)
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a = Subplot(f, 111)
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a = f.add_subplot(111)
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t = arange(0.0,3.0,0.01)
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s = sin(2*pi*t)
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a.plot(t,s)
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f.add_axis(a)
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canvas = FigureCanvasGTK(f) # a gtk.DrawingArea
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canvas.show()

‎examples/embedding_in_gtk2.py‎

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vbox.show()
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fig = Figure(figsize=(5,4), dpi=100)
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ax = Subplot(fig, 111)
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ax = fig.add_subplot(111)
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t = arange(0.0,3.0,0.01)
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s = sin(2*pi*t)
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ax.plot(t,s)
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fig.add_axis(ax)
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canvas = FigureCanvasGTK(fig) # a gtk.DrawingArea
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canvas.show()

‎examples/embedding_in_tk.py‎

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f = Figure(figsize=(5,4), dpi=100)
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a = Subplot(f, 111)
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a = f.add_subplot(111)
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t = arange(0.0,3.0,0.01)
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s = sin(2*pi*t)
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a.plot(t,s)
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f.add_axis(a)
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# a tk.DrawingArea
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canvas = FigureCanvasTkAgg(f, master=root)

‎examples/histogram_demo.py‎

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ylabel('Probability')
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title(r'$\rm{Histogram of IQ: }\mu=100, \sigma=15$')
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axis([40, 160, 0, 0.03])
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grid(True)
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#set(gca(), 'xlim', [40, 160])
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#savefig('histogram_demo',dpi=72)
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show()

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