/usr/lib/python3/dist-packages/ginga/mplw/transform.py is in python3-ginga 2.6.1-2.
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# transform.py -- a custom projection for supporting matplotlib plotting
# on ginga
#
# Eric Jeschke (eric@naoj.org)
#
# Copyright (c) Eric R. Jeschke. All rights reserved.
# This is open-source software licensed under a BSD license.
# Please see the file LICENSE.txt for details.
#
# NOTE: this code is based on "custom_projection_example.py", an example
# script developed by matplotlib developers
# See http://matplotlib.org/examples/api/custom_projection_example.html
#
from __future__ import print_function
import matplotlib
from matplotlib.axes import Axes
from matplotlib.path import Path
from matplotlib.transforms import Affine2D, BboxTransformTo, Transform, \
blended_transform_factory
from matplotlib.projections import register_projection
import numpy as np
from ginga.util.six.moves import map, zip
class GingaAxes(Axes):
"""
This is a custom matplotlib projection to support matplotlib plotting
on a ginga-rendered image in a matplotlib Figure.
This code is based on 'custom_projection_example.py', an example
script developed by matplotlib developers.
"""
# The projection must specify a name. This will be used be the
# user to select the projection, i.e. ``subplot(111,
# projection='ginga')``.
name = 'ginga'
def __init__(self, *args, **kwargs):
# this is the Ginga object
self.viewer = kwargs.pop('viewer', None)
Axes.__init__(self, *args, **kwargs)
## self.set_aspect(0.5, adjustable='box', anchor='C')
self.cla()
def set_viewer(self, viewer):
self.viewer = viewer
self.transData.viewer = viewer
def _set_lim_and_transforms(self):
"""
This is called once when the plot is created to set up all the
transforms for the data, text and grids.
"""
# There are three important coordinate spaces going on here:
#
# 1. Data space: The space of the data itself
#
# 2. Axes space: The unit rectangle (0, 0) to (1, 1)
# covering the entire plot area.
#
# 3. Display space: The coordinates of the resulting image,
# often in pixels or dpi/inch.
# This function makes heavy use of the Transform classes in
# ``lib/matplotlib/transforms.py.`` For more information, see
# the inline documentation there.
# The goal of the first two transformations is to get from the
# data space to axes space. It is separated into a non-affine
# and affine part so that the non-affine part does not have to be
# recomputed when a simple affine change to the figure has been
# made (such as resizing the window or changing the dpi).
# 3) This is the transformation from axes space to display
# space.
self.transAxes = BboxTransformTo(self.bbox)
# Now put these 3 transforms together -- from data all the way
# to display coordinates. Using the '+' operator, these
# transforms will be applied "in order". The transforms are
# automatically simplified, if possible, by the underlying
# transformation framework.
#self.transData = \
# self.transProjection + self.transAffine + self.transAxes
self.transData = self.GingaTransform()
self.transData.viewer = self.viewer
# self._xaxis_transform = blended_transform_factory(
# self.transData, self.transAxes)
# self._yaxis_transform = blended_transform_factory(
# self.transAxes, self.transData)
self._xaxis_transform = self.transData
self._yaxis_transform = self.transData
# Prevent the user from applying scales to one or both of the
# axes. In this particular case, scaling the axes wouldn't make
# sense, so we don't allow it.
def set_xscale(self, *args, **kwargs):
if args[0] != 'linear':
raise NotImplementedError
Axes.set_xscale(self, *args, **kwargs)
def set_yscale(self, *args, **kwargs):
if args[0] != 'linear':
raise NotImplementedError
Axes.set_yscale(self, *args, **kwargs)
# Prevent the user from changing the axes limits. This also
# applies to interactive panning and zooming in the GUI interfaces.
## def set_xlim(self, *args, **kwargs):
## print "Setting xlim!", args
## def set_ylim(self, *args, **kwargs):
## print "Setting ylim!", args
def format_coord(self, x, y):
"""
Override this method to change how the values are displayed in
the status bar.
"""
return 'x=%f, y=%f' % (x, y)
def get_data_ratio(self):
"""
Return the aspect ratio of the data itself.
This method should be overridden by any Axes that have a
fixed data ratio.
"""
return 1.0
def can_zoom(self):
"""
Return True if this axes support the zoom box
"""
# TODO: get zoom box working
return False
def can_pan(self):
"""
Return True if this axes support the zoom box
"""
return True
def start_pan(self, x, y, button):
"""
Called when a pan operation has started.
*x*, *y* are the mouse coordinates in display coords.
button is the mouse button number:
* 1: LEFT
* 2: MIDDLE
* 3: RIGHT
.. note::
Intended to be overridden by new projection types.
"""
bd = self.viewer.get_bindings()
data_x, data_y = self.viewer.get_data_xy(x, y)
bd.ms_pan(self.viewer, 'down', data_x, data_y)
def end_pan(self):
"""
Called when a pan operation completes (when the mouse button
is up.)
.. note::
Intended to be overridden by new projection types.
"""
bd = self.viewer.get_bindings()
data_x, data_y = self.viewer.get_last_data_xy()
bd.ms_pan(self.viewer, 'up', data_x, data_y)
def drag_pan(self, button, key, x, y):
"""
Called when the mouse moves during a pan operation.
*button* is the mouse button number:
* 1: LEFT
* 2: MIDDLE
* 3: RIGHT
*key* is a "shift" key
*x*, *y* are the mouse coordinates in display coords.
.. note::
Intended to be overridden by new projection types.
"""
bd = self.viewer.get_bindings()
data_x, data_y = self.viewer.get_data_xy(x, y)
bd.ms_pan(self.viewer, 'move', data_x, data_y)
# Now, the transforms themselves.
class GingaTransform(Transform):
"""
The base Ginga transform.
"""
input_dims = 2
output_dims = 2
is_separable = False
viewer = None
#pass_through = True
def invalidate(self):
#print("I don't feel validated! (%s)" % (self.pass_through))
return Transform.invalidate(self)
def transform_non_affine(self, xy):
"""
Override the transform_non_affine method to implement the custom
transform.
The input and output are Nx2 numpy arrays.
"""
#print(("transform in:", xy))
if self.viewer is None:
return xy
res = np.dstack(self.viewer.get_canvas_xy(xy.T[0], xy.T[1]))[0]
#print(("transform out:", res))
return res
# This is where things get interesting. With this projection,
# straight lines in data space become curves in display space.
# This is done by interpolating new values between the input
# values of the data. Since ``transform`` must not return a
# differently-sized array, any transform that requires
# changing the length of the data array must happen within
# ``transform_path``.
def transform_path_non_affine(self, path):
ipath = path.interpolated(path._interpolation_steps)
return Path(self.transform(ipath.vertices), ipath.codes)
transform_path_non_affine.__doc__ = \
Transform.transform_path_non_affine.__doc__
if matplotlib.__version__ < '1.2':
# Note: For compatibility with matplotlib v1.1 and older, you'll
# need to explicitly implement a ``transform`` method as well.
# Otherwise a ``NotImplementedError`` will be raised. This isn't
# necessary for v1.2 and newer, however.
transform = transform_non_affine
# Similarly, we need to explicitly override ``transform_path`` if
# compatibility with older matplotlib versions is needed. With v1.2
# and newer, only overriding the ``transform_path_non_affine``
# method is sufficient.
transform_path = transform_path_non_affine
transform_path.__doc__ = Transform.transform_path.__doc__
def inverted(self):
tform = GingaAxes.InvertedGingaTransform()
tform.viewer = self.viewer
return tform
inverted.__doc__ = Transform.inverted.__doc__
class InvertedGingaTransform(Transform):
input_dims = 2
output_dims = 2
is_separable = False
viewer = None
def transform_non_affine(self, xy):
#print "transform in:", xy
if self.viewer is None:
return xy
res = np.dstack(self.viewer.get_data_xy(xy.T[0], xy.T[1]))[0]
#print "transform out:", res
return res
transform_non_affine.__doc__ = Transform.transform_non_affine.__doc__
# As before, we need to implement the "transform" method for
# compatibility with matplotlib v1.1 and older.
if matplotlib.__version__ < '1.2':
transform = transform_non_affine
def inverted(self):
# The inverse of the inverse is the original transform... ;)
tform = GingaAxes.GingaTransform()
tform.viewer = self.viewer
return tform
inverted.__doc__ = Transform.inverted.__doc__
# Now register the projection with matplotlib so the user can select
# it.
register_projection(GingaAxes)
#END
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