Source code for silx.gui.plot.BackendMatplotlib

# coding: utf-8
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"""Matplotlib Plot backend."""

from __future__ import division

__authors__ = ["V.A. Sole", "T. Vincent"]
__license__ = "MIT"
__date__ = "15/09/2016"


import logging
import sys

import numpy


_logger = logging.getLogger(__name__)

if 'matplotlib' in sys.modules:
    _logger.warning(
        'matplotlib already loaded, setting its backend may not work')


from .. import qt

import matplotlib

if qt.BINDING == 'PySide':
    matplotlib.rcParams['backend'] = 'Qt4Agg'
    matplotlib.rcParams['backend.qt4'] = 'PySide'
    from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg

elif qt.BINDING == 'PyQt4':
    matplotlib.rcParams['backend'] = 'Qt4Agg'
    from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg

elif qt.BINDING == 'PyQt5':
    matplotlib.rcParams['backend'] = 'Qt5Agg'
    from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg

from matplotlib import cm
from matplotlib.container import Container
from matplotlib.figure import Figure
from matplotlib.patches import Rectangle, Polygon
from matplotlib.image import AxesImage
from matplotlib.colors import (LinearSegmentedColormap, ListedColormap,
                               LogNorm, Normalize)
from matplotlib.backend_bases import MouseEvent
from matplotlib.lines import Line2D
from matplotlib.collections import PathCollection, LineCollection

from . import _utils
from .ModestImage import ModestImage
from . import BackendBase
from . import MPLColormap


[docs]class BackendMatplotlib(BackendBase.BackendBase): """Base class for Matplotlib backend without a FigureCanvas. For interactive on screen plot, see :class:`BackendMatplotlibQt`. See :class:`BackendBase.BackendBase` for public API documentation. """ def __init__(self, plot, parent=None): super(BackendMatplotlib, self).__init__(plot, parent) # matplotlib is handling keep aspect ratio at draw time # When keep aspect ratio is on, and one changes the limits and # ask them *before* next draw has been performed he will get the # limits without applying keep aspect ratio. # This attribute is used to ensure consistent values returned # when getting the limits at the expense of a replot self._dirtyLimits = True self.fig = Figure() self.fig.set_facecolor("w") self.ax = self.fig.add_axes([.15, .15, .75, .75], label="left") self.ax2 = self.ax.twinx() self.ax2.set_label("right") # critical for picking!!!! self.ax2.set_zorder(0) self.ax2.set_autoscaley_on(True) self.ax.set_zorder(1) # this works but the figure color is left self.ax.set_axis_bgcolor('none') self.fig.sca(self.ax) self._overlays = set() self._background = None self._colormaps = {} self._graphCursor = tuple() self.matplotlibVersion = matplotlib.__version__ self.setGraphXLimits(0., 100.) self.setGraphYLimits(0., 100., axis='right') self.setGraphYLimits(0., 100., axis='left') self._enableAxis('right', False) # Add methods def addCurve(self, x, y, legend, color, symbol, linewidth, linestyle, yaxis, xerror, yerror, z, selectable, fill): for parameter in (x, y, legend, color, symbol, linewidth, linestyle, yaxis, z, selectable, fill): assert parameter is not None assert yaxis in ('left', 'right') if (len(color) == 4 and type(color[3]) in [type(1), numpy.uint8, numpy.int8]): color = numpy.array(color, dtype=numpy.float) / 255. if yaxis == "right": axes = self.ax2 self._enableAxis("right", True) else: axes = self.ax picker = 3 if selectable else None artists = [] # All the artists composing the curve # First add errorbars if any so they are behind the curve if xerror is not None or yerror is not None: if hasattr(color, 'dtype') and len(color) == len(x): errorbarColor = 'k' else: errorbarColor = color errorbars = axes.errorbar(x, y, label=legend, xerr=xerror, yerr=yerror, linestyle=' ', color=errorbarColor) artists += list(errorbars.get_children()) if hasattr(color, 'dtype') and len(color) == len(x): # scatter plot if color.dtype not in [numpy.float32, numpy.float]: actualColor = color / 255. else: actualColor = color if linestyle not in ["", " ", None]: # scatter plot with an actual line ... # we need to assign a color ... curveList = axes.plot(x, y, label=legend, linestyle=linestyle, color=actualColor[0], linewidth=linewidth, picker=picker, marker=None) artists += list(curveList) scatter = axes.scatter(x, y, label=legend, color=actualColor, marker=symbol, picker=picker) artists.append(scatter) if fill: artists.append(axes.fill_between( x, 1.0e-8, y, facecolor=actualColor[0], linestyle='')) else: # Curve curveList = axes.plot(x, y, label=legend, linestyle=linestyle, color=color, linewidth=linewidth, marker=symbol, picker=picker) artists += list(curveList) if fill: artists.append( axes.fill_between(x, 1.0e-8, y, facecolor=color, linewidth=0)) for artist in artists: artist.set_zorder(z) return Container(artists) def addImage(self, data, legend, origin, scale, z, selectable, draggable, colormap): # Non-uniform image # http://wiki.scipy.org/Cookbook/Histograms # Non-linear axes # http://stackoverflow.com/questions/11488800/non-linear-axes-for-imshow-in-matplotlib for parameter in (data, legend, origin, scale, z, selectable, draggable): assert parameter is not None h, w = data.shape[0:2] xmin = origin[0] xmax = xmin + scale[0] * w if scale[0] < 0.: xmin, xmax = xmax, xmin ymin = origin[1] ymax = ymin + scale[1] * h if scale[1] < 0.: ymin, ymax = ymax, ymin extent = (xmin, xmax, ymax, ymin) picker = (selectable or draggable) # Debian 7 specific support # No transparent colormap with matplotlib < 1.2.0 # Add support for transparent colormap for uint8 data with # colormap with 256 colors, linear norm, [0, 255] range if matplotlib.__version__ < '1.2.0': if (len(data.shape) == 2 and colormap['name'] is None and 'colors' in colormap): colors = numpy.array(colormap['colors'], copy=False) if (colors.shape[-1] == 4 and not numpy.all(numpy.equal(colors[3], 255))): # This is a transparent colormap if (colors.shape == (256, 4) and colormap['normalization'] == 'linear' and not colormap['autoscale'] and colormap['vmin'] == 0 and colormap['vmax'] == 255 and data.dtype == numpy.uint8): # Supported case, convert data to RGBA data = colors[data.reshape(-1)].reshape( data.shape + (4,)) else: _logger.warning( 'matplotlib %s does not support transparent ' 'colormap.', matplotlib.__version__) # the normalization can be a source of time waste # Two possibilities, we receive data or a ready to show image if len(data.shape) == 3: if data.shape[-1] == 4: # force alpha? data[:,:,3] = 255 pass # RGBA image # TODO: Possibility to mirror the image # in case of pixmaps just setting # extend = (xmin, xmax, ymax, ymin) # instead of (xmin, xmax, ymin, ymax) extent = (xmin, xmax, ymin, ymax) if tuple(origin) != (0., 0.) or tuple(scale) != (1., 1.): # for the time being not properly handled imageClass = AxesImage elif (data.shape[0] * data.shape[1]) > 5.0e5: imageClass = ModestImage else: imageClass = AxesImage image = imageClass(self.ax, label="__IMAGE__" + legend, interpolation='nearest', picker=picker, zorder=z) if image.origin == 'upper': image.set_extent((xmin, xmax, ymax, ymin)) else: image.set_extent((xmin, xmax, ymin, ymax)) image.set_data(data) else: assert colormap is not None if colormap['name'] is not None: cmap = self.__getColormap(colormap['name']) else: # No name, use custom colors if 'colors' not in colormap: raise ValueError( 'addImage: colormap no name nor list of colors.') colors = numpy.array(colormap['colors'], copy=True) assert len(colors.shape) == 2 assert colors.shape[-1] in (3, 4) if colors.dtype == numpy.uint8: # Convert to float in [0., 1.] colors = colors.astype(numpy.float32) / 255. cmap = ListedColormap(colors) if colormap['normalization'].startswith('log'): vmin, vmax = None, None if not colormap['autoscale']: if colormap['vmin'] > 0.: vmin = colormap['vmin'] if colormap['vmax'] > 0.: vmax = colormap['vmax'] if vmin is None or vmax is None: _logger.warning('Log colormap with negative bounds, ' + 'changing bounds to positive ones.') elif vmin > vmax: _logger.warning('Colormap bounds are inverted.') vmin, vmax = vmax, vmin # Set unset/negative bounds to positive bounds if vmin is None or vmax is None: finiteData = data[numpy.isfinite(data)] posData = finiteData[finiteData > 0] if vmax is None: # 1. as an ultimate fallback vmax = posData.max() if posData.size > 0 else 1. if vmin is None: vmin = posData.min() if posData.size > 0 else vmax if vmin > vmax: vmin = vmax norm = LogNorm(vmin, vmax) else: # Linear normalization if colormap['autoscale']: finiteData = data[numpy.isfinite(data)] vmin = finiteData.min() vmax = finiteData.max() else: vmin = colormap['vmin'] vmax = colormap['vmax'] if vmin > vmax: _logger.warning('Colormap bounds are inverted.') vmin, vmax = vmax, vmin norm = Normalize(vmin, vmax) # try as data if tuple(origin) != (0., 0.) or tuple(scale) != (1., 1.): # for the time being not properly handled imageClass = AxesImage elif (data.shape[0] * data.shape[1]) > 5.0e5: imageClass = ModestImage else: imageClass = AxesImage image = imageClass(self.ax, label="__IMAGE__" + legend, interpolation='nearest', cmap=cmap, extent=extent, picker=picker, zorder=z, norm=norm) if image.origin == 'upper': image.set_extent((xmin, xmax, ymax, ymin)) else: image.set_extent((xmin, xmax, ymin, ymax)) image.set_data(data) self.ax.add_artist(image) return image def addItem(self, x, y, legend, shape, color, fill, overlay, z): xView = numpy.array(x, copy=False) yView = numpy.array(y, copy=False) if shape == "line": item = self.ax.plot(x, y, label=legend, color=color, linestyle='-', marker=None)[0] elif shape == "hline": if hasattr(y, "__len__"): y = y[-1] item = self.ax.axhline(y, label=legend, color=color) elif shape == "vline": if hasattr(x, "__len__"): x = x[-1] item = self.ax.axvline(x, label=legend, color=color) elif shape == 'rectangle': xMin = numpy.nanmin(xView) xMax = numpy.nanmax(xView) yMin = numpy.nanmin(yView) yMax = numpy.nanmax(yView) w = xMax - xMin h = yMax - yMin item = Rectangle(xy=(xMin, yMin), width=w, height=h, fill=False, color=color) if fill: item.set_hatch('.') self.ax.add_patch(item) elif shape in ('polygon', 'polylines'): xView = xView.reshape(1, -1) yView = yView.reshape(1, -1) item = Polygon(numpy.vstack((xView, yView)).T, closed=(shape == 'polygon'), fill=False, label=legend, color=color) if fill and shape == 'polygon': item.set_hatch('/') self.ax.add_patch(item) else: raise NotImplementedError("Unsupported item shape %s" % shape) item.set_zorder(z) if overlay: item.set_animated(True) self._overlays.add(item) return item def addMarker(self, x, y, legend, text, color, selectable, draggable, symbol, constraint, overlay): legend = "__MARKER__" + legend if x is not None and y is not None: line = self.ax.plot(x, y, label=legend, linestyle=" ", color=color, marker=symbol, markersize=10.)[-1] if text is not None: xtmp, ytmp = self.ax.transData.transform_point((x, y)) inv = self.ax.transData.inverted() xtmp, ytmp = inv.transform_point((xtmp, ytmp)) if symbol is None: valign = 'baseline' else: valign = 'top' text = " " + text line._infoText = self.ax.text(x, ytmp, text, color=color, horizontalalignment='left', verticalalignment=valign) elif x is not None: line = self.ax.axvline(x, label=legend, color=color) if text is not None: text = " " + text ymin, ymax = self.getGraphYLimits(axis='left') delta = abs(ymax - ymin) if ymin > ymax: ymax = ymin ymax -= 0.005 * delta line._infoText = self.ax.text(x, ymax, text, color=color, horizontalalignment='left', verticalalignment='top') elif y is not None: line = self.ax.axhline(y, label=legend, color=color) if text is not None: text = " " + text xmin, xmax = self.getGraphXLimits() delta = abs(xmax - xmin) if xmin > xmax: xmax = xmin xmax -= 0.005 * delta line._infoText = self.ax.text(xmax, y, text, color=color, horizontalalignment='right', verticalalignment='top') else: raise RuntimeError('A marker must at least have one coordinate') if selectable or draggable: line.set_picker(5) if overlay: line.set_animated(True) self._overlays.add(line) return line # Remove methods def remove(self, item): # Warning: It also needs to remove extra stuff if added as for markers if hasattr(item, "_infoText"): # For markers text item._infoText.remove() item._infoText = None self._overlays.discard(item) item.remove() # Interaction methods def setGraphCursor(self, flag, color, linewidth, linestyle): if flag: lineh = self.ax.axhline( self.ax.get_ybound()[0], visible=False, color=color, linewidth=linewidth, linestyle=linestyle) lineh.set_animated(True) linev = self.ax.axvline( self.ax.get_xbound()[0], visible=False, color=color, linewidth=linewidth, linestyle=linestyle) linev.set_animated(True) self._graphCursor = lineh, linev else: if self._graphCursor is not None: lineh, linev = self._graphCursor lineh.remove() linev.remove() self._graphCursor = tuple() # Active curve def setActiveCurve(self, curve, active, color=None): # Store Line2D and PathCollection for artist in curve.get_children(): if active: if isinstance(artist, (Line2D, LineCollection)): artist._initialColor = artist.get_color() artist.set_color(color) elif isinstance(artist, PathCollection): artist._initialColor = artist.get_facecolors() artist.set_facecolors(color) artist.set_edgecolors(color) else: _logger.warning( 'setActiveCurve ignoring artist %s', str(artist)) else: if hasattr(artist, '_initialColor'): if isinstance(artist, (Line2D, LineCollection)): artist.set_color(artist._initialColor) elif isinstance(artist, PathCollection): artist.set_facecolors(artist._initialColor) artist.set_edgecolors(artist._initialColor) else: _logger.info( 'setActiveCurve ignoring artist %s', str(artist)) del artist._initialColor # Misc. def getWidgetHandle(self): return self.fig.canvas def _enableAxis(self, axis, flag=True): """Show/hide Y axis :param str axis: Axis name: 'left' or 'right' :param bool flag: Default, True """ assert axis in ('right', 'left') axes = self.ax2 if axis == 'right' else self.ax axes.get_yaxis().set_visible(flag)
[docs] def replot(self): """Do not perform rendering. Override in subclass to actually draw something. """ # TODO images, markers? scatter plot? move in remove? # Right Y axis only support curve for now # Hide right Y axis if no line is present self._dirtyLimits = False if not self.ax2.lines: self._enableAxis('right', False)
def saveGraph(self, fileName, fileFormat, dpi): # fileName can be also a StringIO or file instance if dpi is not None: self.fig.savefig(fileName, format=fileFormat, dpi=dpi) else: self.fig.savefig(fileName, format=fileFormat) self._plot._setDirtyPlot() # Graph labels def setGraphTitle(self, title): self.ax.set_title(title) def setGraphXLabel(self, label): self.ax.set_xlabel(label) def setGraphYLabel(self, label, axis): axes = self.ax if axis == 'left' else self.ax2 axes.set_ylabel(label) # Graph limits def resetZoom(self, dataMargins): xAuto = self._plot.isXAxisAutoScale() yAuto = self._plot.isYAxisAutoScale() if not xAuto and not yAuto: _logger.debug("Nothing to autoscale") else: # Some axes to autoscale xLimits = self.getGraphXLimits() yLimits = self.getGraphYLimits(axis='left') y2Limits = self.getGraphYLimits(axis='right') # Get data range ranges = self._plot.getDataRange() xmin, xmax = (1., 100.) if ranges.x is None else ranges.x ymin, ymax = (1., 100.) if ranges.y is None else ranges.y if ranges.yright is None: ymin2, ymax2 = None, None else: ymin2, ymax2 = ranges.yright # Add margins around data inside the plot area newLimits = list(_utils.addMarginsToLimits( dataMargins, self.ax.get_xscale() == 'log', self.ax.get_yscale() == 'log', xmin, xmax, ymin, ymax, ymin2, ymax2)) if self.isKeepDataAspectRatio(): # Compute bbox wth figure aspect ratio figW, figH = self.fig.get_size_inches() figureRatio = figH / figW dataRatio = (ymax - ymin) / (xmax - xmin) if dataRatio < figureRatio: # Increase y range ycenter = 0.5 * (newLimits[3] + newLimits[2]) yrange = (xmax - xmin) * figureRatio newLimits[2] = ycenter - 0.5 * yrange newLimits[3] = ycenter + 0.5 * yrange elif dataRatio > figureRatio: # Increase x range xcenter = 0.5 * (newLimits[1] + newLimits[0]) xrange_ = (ymax - ymin) / figureRatio newLimits[0] = xcenter - 0.5 * xrange_ newLimits[1] = xcenter + 0.5 * xrange_ self.setLimits(*newLimits) if not xAuto and yAuto: self.setGraphXLimits(*xLimits) elif xAuto and not yAuto: if y2Limits is not None: self.setGraphYLimits( y2Limits[0], y2Limits[1], axis='right') if yLimits is not None: self.setGraphYLimits(yLimits[0], yLimits[1], axis='left') def setLimits(self, xmin, xmax, ymin, ymax, y2min=None, y2max=None): # Let matplotlib taking care of keep aspect ratio if any self._dirtyLimits = True self.ax.set_xlim(min(xmin, xmax), max(xmin, xmax)) if y2min is not None and y2max is not None: if not self.isYAxisInverted(): self.ax2.set_ylim(min(y2min, y2max), max(y2min, y2max)) else: self.ax2.set_ylim(max(y2min, y2max), min(y2min, y2max)) if not self.isYAxisInverted(): self.ax.set_ylim(min(ymin, ymax), max(ymin, ymax)) else: self.ax.set_ylim(max(ymin, ymax), min(ymin, ymax)) def getGraphXLimits(self): if self._dirtyLimits and self.isKeepDataAspectRatio(): self.replot() # makes sure we get the right limits return self.ax.get_xbound() def setGraphXLimits(self, xmin, xmax): self._dirtyLimits = True self.ax.set_xlim(min(xmin, xmax), max(xmin, xmax)) def getGraphYLimits(self, axis): assert axis in ('left', 'right') ax = self.ax2 if axis == 'right' else self.ax if not ax.get_visible(): return None if self._dirtyLimits and self.isKeepDataAspectRatio(): self.replot() # makes sure we get the right limits return ax.get_ybound() def setGraphYLimits(self, ymin, ymax, axis): ax = self.ax2 if axis == 'right' else self.ax if ymax < ymin: ymin, ymax = ymax, ymin self._dirtyLimits = True if self.isKeepDataAspectRatio(): # matplotlib keeps limits of shared axis when keeping aspect ratio # So x limits are kept when changing y limits.... # Change x limits first by taking into account aspect ratio # and then change y limits.. so matplotlib does not need # to make change (to y) to keep aspect ratio xmin, xmax = ax.get_xbound() curYMin, curYMax = ax.get_ybound() newXRange = (xmax - xmin) * (ymax - ymin) / (curYMax - curYMin) xcenter = 0.5 * (xmin + xmax) ax.set_xlim(xcenter - 0.5 * newXRange, xcenter + 0.5 * newXRange) if not self.isYAxisInverted(): ax.set_ylim(ymin, ymax) else: ax.set_ylim(ymax, ymin) # Graph axes def setXAxisLogarithmic(self, flag): self.ax2.set_xscale('log' if flag else 'linear') self.ax.set_xscale('log' if flag else 'linear') def setYAxisLogarithmic(self, flag): self.ax2.set_yscale('log' if flag else 'linear') self.ax.set_yscale('log' if flag else 'linear') def setYAxisInverted(self, flag): if self.ax.yaxis_inverted() != bool(flag): self.ax.invert_yaxis() def isYAxisInverted(self): return self.ax.yaxis_inverted() def isKeepDataAspectRatio(self): return self.ax.get_aspect() in (1.0, 'equal') def setKeepDataAspectRatio(self, flag): self.ax.set_aspect(1.0 if flag else 'auto') self.ax2.set_aspect(1.0 if flag else 'auto') def setGraphGrid(self, which): self.ax.grid(False, which='both') # Disable all grid first if which is not None: self.ax.grid(True, which=which) # colormap def getSupportedColormaps(self): default = super(BackendMatplotlib, self).getSupportedColormaps() maps = [m for m in cm.datad] maps.sort() return default + maps def __getColormap(self, name): if not self._colormaps: # Lazy initialization of own colormaps cdict = {'red': ((0.0, 0.0, 0.0), (1.0, 1.0, 1.0)), 'green': ((0.0, 0.0, 0.0), (1.0, 0.0, 0.0)), 'blue': ((0.0, 0.0, 0.0), (1.0, 0.0, 0.0))} self._colormaps['red'] = LinearSegmentedColormap( 'red', cdict, 256) cdict = {'red': ((0.0, 0.0, 0.0), (1.0, 0.0, 0.0)), 'green': ((0.0, 0.0, 0.0), (1.0, 1.0, 1.0)), 'blue': ((0.0, 0.0, 0.0), (1.0, 0.0, 0.0))} self._colormaps['green'] = LinearSegmentedColormap( 'green', cdict, 256) cdict = {'red': ((0.0, 0.0, 0.0), (1.0, 0.0, 0.0)), 'green': ((0.0, 0.0, 0.0), (1.0, 0.0, 0.0)), 'blue': ((0.0, 0.0, 0.0), (1.0, 1.0, 1.0))} self._colormaps['blue'] = LinearSegmentedColormap( 'blue', cdict, 256) # Temperature as defined in spslut cdict = {'red': ((0.0, 0.0, 0.0), (0.5, 0.0, 0.0), (0.75, 1.0, 1.0), (1.0, 1.0, 1.0)), 'green': ((0.0, 0.0, 0.0), (0.25, 1.0, 1.0), (0.75, 1.0, 1.0), (1.0, 0.0, 0.0)), 'blue': ((0.0, 1.0, 1.0), (0.25, 1.0, 1.0), (0.5, 0.0, 0.0), (1.0, 0.0, 0.0))} # but limited to 256 colors for a faster display (of the colorbar) self._colormaps['temperature'] = LinearSegmentedColormap( 'temperature', cdict, 256) # reversed gray cdict = {'red': ((0.0, 1.0, 1.0), (1.0, 0.0, 0.0)), 'green': ((0.0, 1.0, 1.0), (1.0, 0.0, 0.0)), 'blue': ((0.0, 1.0, 1.0), (1.0, 0.0, 0.0))} self._colormaps['reversed gray'] = LinearSegmentedColormap( 'yerg', cdict, 256) if name in self._colormaps: return self._colormaps[name] elif hasattr(MPLColormap, name): # viridis and sister colormaps return getattr(MPLColormap, name) else: # matplotlib built-in return cm.get_cmap(name) # Data <-> Pixel coordinates conversion def dataToPixel(self, x, y, axis): ax = self.ax2 if axis == "right" else self.ax pixels = ax.transData.transform_point((x, y)) xPixel, yPixel = pixels.T return xPixel, yPixel def pixelToData(self, x, y, axis, check): ax = self.ax2 if axis == "right" else self.ax inv = ax.transData.inverted() x, y = inv.transform_point((x, y)) if check: xmin, xmax = self.getGraphXLimits() ymin, ymax = self.getGraphYLimits(axis=axis) if x > xmax or x < xmin or y > ymax or y < ymin: return None # (x, y) is out of plot area return x, y def getPlotBoundsInPixels(self): bbox = self.ax.get_window_extent().transformed( self.fig.dpi_scale_trans.inverted()) dpi = self.fig.dpi # Warning this is not returning int... return (bbox.bounds[0] * dpi, bbox.bounds[1] * dpi, bbox.bounds[2] * dpi, bbox.bounds[3] * dpi)
[docs]class BackendMatplotlibQt(FigureCanvasQTAgg, BackendMatplotlib): """QWidget matplotlib backend using a QtAgg canvas. It adds fast overlay drawing and mouse event management. """ _sigPostRedisplay = qt.Signal() """Signal handling automatic asynchronous replot""" def __init__(self, plot, parent=None): self._insideResizeEventMethod = False BackendMatplotlib.__init__(self, plot, parent) FigureCanvasQTAgg.__init__(self, self.fig) self.setParent(parent) FigureCanvasQTAgg.setSizePolicy( self, qt.QSizePolicy.Expanding, qt.QSizePolicy.Expanding) FigureCanvasQTAgg.updateGeometry(self) # Make postRedisplay asynchronous using Qt signal self._sigPostRedisplay.connect( super(BackendMatplotlibQt, self).postRedisplay, qt.Qt.QueuedConnection) self.mpl_connect('button_press_event', self._onMousePress) self.mpl_connect('button_release_event', self._onMouseRelease) self.mpl_connect('motion_notify_event', self._onMouseMove) self.mpl_connect('scroll_event', self._onMouseWheel) def postRedisplay(self): self._sigPostRedisplay.emit() # Mouse event forwarding _MPL_TO_PLOT_BUTTONS = {1: 'left', 2: 'middle', 3: 'right'} def _onMousePress(self, event): self._plot.onMousePress( event.x, event.y, self._MPL_TO_PLOT_BUTTONS[event.button]) def _onMouseMove(self, event): if self._graphCursor: lineh, linev = self._graphCursor if event.inaxes != self.ax and lineh.get_visible(): lineh.set_visible(False) linev.set_visible(False) self._plot._setDirtyPlot(overlayOnly=True) else: linev.set_visible(True) linev.set_xdata((event.xdata, event.xdata)) lineh.set_visible(True) lineh.set_ydata((event.ydata, event.ydata)) self._plot._setDirtyPlot(overlayOnly=True) # onMouseMove must trigger replot if dirty flag is raised self._plot.onMouseMove(event.x, event.y) def _onMouseRelease(self, event): self._plot.onMouseRelease( event.x, event.y, self._MPL_TO_PLOT_BUTTONS[event.button]) def _onMouseWheel(self, event): self._plot.onMouseWheel(event.x, event.y, event.step) # picking def _onPick(self, event): # TODO not very nice and fragile, find a better way? # Make a selection according to kind label = event.artist.get_label() if label.startswith('__MARKER__'): self._picked.append({'kind': 'marker', 'legend': label[10:]}) elif label.startswith('__IMAGE__'): self._picked.append({'kind': 'image', 'legend': label[9:]}) else: # it's a curve, item have no picker for now if isinstance(event.artist, PathCollection): data = event.artist.get_offsets()[event.ind, :] xdata, ydata = data[:, 0], data[:, 1] elif isinstance(event.artist, Line2D): xdata = event.artist.get_xdata()[event.ind] ydata = event.artist.get_ydata()[event.ind] else: _logger.info('Unsupported artist, ignored') return self._picked.append({'kind': 'curve', 'legend': label, 'xdata': xdata, 'ydata': ydata}) def pickItems(self, x, y): self._picked = [] # Weird way to do an explicit picking: Simulate a button press event mouseEvent = MouseEvent('button_press_event', self, x, y) cid = self.mpl_connect('pick_event', self._onPick) self.fig.pick(mouseEvent) self.mpl_disconnect(cid) picked = self._picked del self._picked return picked # replot control def resizeEvent(self, event): self._insideResizeEventMethod = True # Need to dirty the whole plot on resize. self._plot._setDirtyPlot() FigureCanvasQTAgg.resizeEvent(self, event) self._insideResizeEventMethod = False
[docs] def draw(self): """Override canvas draw method to support faster draw of overlays.""" if self._plot._getDirtyPlot(): # Need a full redraw FigureCanvasQTAgg.draw(self) self._background = None # Any saved background is dirty if (self._overlays or self._graphCursor or self._plot._getDirtyPlot() == 'overlay'): # There are overlays or crosshair, or they is just no more overlays # Specific case: called from resizeEvent: # avoid store/restore background, just draw the overlay if not self._insideResizeEventMethod: if self._background is None: # First store the background self._background = self.copy_from_bbox(self.fig.bbox) self.restore_region(self._background) # This assume that items are only on left/bottom Axes for item in self._overlays: self.ax.draw_artist(item) for item in self._graphCursor: self.ax.draw_artist(item) self.blit(self.fig.bbox)
def replot(self): BackendMatplotlib.replot(self) self.draw() # cursor _QT_CURSORS = { None: qt.Qt.ArrowCursor, BackendBase.CURSOR_DEFAULT: qt.Qt.ArrowCursor, BackendBase.CURSOR_POINTING: qt.Qt.PointingHandCursor, BackendBase.CURSOR_SIZE_HOR: qt.Qt.SizeHorCursor, BackendBase.CURSOR_SIZE_VER: qt.Qt.SizeVerCursor, BackendBase.CURSOR_SIZE_ALL: qt.Qt.SizeAllCursor, } def setGraphCursorShape(self, cursor): cursor = self._QT_CURSORS[cursor] FigureCanvasQTAgg.setCursor(self, qt.QCursor(cursor))