diff options
Diffstat (limited to 'src/silx/gui/plot/items/scatter.py')
| -rw-r--r-- | src/silx/gui/plot/items/scatter.py | 1002 |
1 files changed, 1002 insertions, 0 deletions
diff --git a/src/silx/gui/plot/items/scatter.py b/src/silx/gui/plot/items/scatter.py new file mode 100644 index 0000000..fdc66f7 --- /dev/null +++ b/src/silx/gui/plot/items/scatter.py @@ -0,0 +1,1002 @@ +# coding: utf-8 +# /*########################################################################## +# +# Copyright (c) 2017-2021 European Synchrotron Radiation Facility +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in +# all copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +# THE SOFTWARE. +# +# ###########################################################################*/ +"""This module provides the :class:`Scatter` item of the :class:`Plot`. +""" + +from __future__ import division + + +__authors__ = ["T. Vincent", "P. Knobel"] +__license__ = "MIT" +__date__ = "29/03/2017" + + +from collections import namedtuple +import logging +import threading +import numpy + +from collections import defaultdict +from concurrent.futures import ThreadPoolExecutor, CancelledError + +from ....utils.proxy import docstring +from ....math.combo import min_max +from ....math.histogram import Histogramnd +from ....utils.weakref import WeakList +from .._utils.delaunay import delaunay +from .core import PointsBase, ColormapMixIn, ScatterVisualizationMixIn +from .axis import Axis +from ._pick import PickingResult + + +_logger = logging.getLogger(__name__) + + +class _GreedyThreadPoolExecutor(ThreadPoolExecutor): + """:class:`ThreadPoolExecutor` with an extra :meth:`submit_greedy` method. + """ + + def __init__(self, *args, **kwargs): + super(_GreedyThreadPoolExecutor, self).__init__(*args, **kwargs) + self.__futures = defaultdict(WeakList) + self.__lock = threading.RLock() + + def submit_greedy(self, queue, fn, *args, **kwargs): + """Same as :meth:`submit` but cancel previous tasks in given queue. + + This means that when a new task is submitted for a given queue, + all other pending tasks of that queue are cancelled. + + :param queue: Identifier of the queue. This must be hashable. + :param callable fn: The callable to call with provided extra arguments + :return: Future corresponding to this task + :rtype: concurrent.futures.Future + """ + with self.__lock: + # Cancel previous tasks in given queue + for future in self.__futures.pop(queue, []): + if not future.done(): + future.cancel() + + future = super(_GreedyThreadPoolExecutor, self).submit( + fn, *args, **kwargs) + self.__futures[queue].append(future) + + return future + + +# Functions to guess grid shape from coordinates + +def _get_z_line_length(array): + """Return length of line if array is a Z-like 2D regular grid. + + :param numpy.ndarray array: The 1D array of coordinates to check + :return: 0 if no line length could be found, + else the number of element per line. + :rtype: int + """ + sign = numpy.sign(numpy.diff(array)) + if len(sign) == 0 or sign[0] == 0: # We don't handle that + return 0 + # Check this way to account for 0 sign (i.e., diff == 0) + beginnings = numpy.where(sign == - sign[0])[0] + 1 + if len(beginnings) == 0: + return 0 + length = beginnings[0] + if numpy.all(numpy.equal(numpy.diff(beginnings), length)): + return length + return 0 + + +def _guess_z_grid_shape(x, y): + """Guess the shape of a grid from (x, y) coordinates. + + The grid might contain more elements than x and y, + as the last line might be partly filled. + + :param numpy.ndarray x: + :paran numpy.ndarray y: + :returns: (order, (height, width)) of the regular grid, + or None if could not guess one. + 'order' is 'row' if X (i.e., column) is the fast dimension, else 'column'. + :rtype: Union[List(str,int),None] + """ + width = _get_z_line_length(x) + if width != 0: + return 'row', (int(numpy.ceil(len(x) / width)), width) + else: + height = _get_z_line_length(y) + if height != 0: + return 'column', (height, int(numpy.ceil(len(y) / height))) + return None + + +def is_monotonic(array): + """Returns whether array is monotonic (increasing or decreasing). + + :param numpy.ndarray array: 1D array-like container. + :returns: 1 if array is monotonically increasing, + -1 if array is monotonically decreasing, + 0 if array is not monotonic + :rtype: int + """ + diff = numpy.diff(numpy.ravel(array)) + with numpy.errstate(invalid='ignore'): + if numpy.all(diff >= 0): + return 1 + elif numpy.all(diff <= 0): + return -1 + else: + return 0 + + +def _guess_grid(x, y): + """Guess a regular grid from the points. + + Result convention is (x, y) + + :param numpy.ndarray x: X coordinates of the points + :param numpy.ndarray y: Y coordinates of the points + :returns: (order, (height, width) + order is 'row' or 'column' + :rtype: Union[List[str,List[int]],None] + """ + x, y = numpy.ravel(x), numpy.ravel(y) + + guess = _guess_z_grid_shape(x, y) + if guess is not None: + return guess + + else: + # Cannot guess a regular grid + # Let's assume it's a single line + order = 'row' # or 'column' doesn't matter for a single line + y_monotonic = is_monotonic(y) + if is_monotonic(x) or y_monotonic: # we can guess a line + x_min, x_max = min_max(x) + y_min, y_max = min_max(y) + + if not y_monotonic or x_max - x_min >= y_max - y_min: + # x only is monotonic or both are and X varies more + # line along X + shape = 1, len(x) + else: + # y only is monotonic or both are and Y varies more + # line along Y + shape = len(y), 1 + + else: # Cannot guess a line from the points + return None + + return order, shape + + +def _quadrilateral_grid_coords(points): + """Compute an irregular grid of quadrilaterals from a set of points + + The input points are expected to lie on a grid. + + :param numpy.ndarray points: + 3D data set of 2D input coordinates (height, width, 2) + height and width must be at least 2. + :return: 3D dataset of 2D coordinates of the grid (height+1, width+1, 2) + """ + assert points.ndim == 3 + assert points.shape[0] >= 2 + assert points.shape[1] >= 2 + assert points.shape[2] == 2 + + dim0, dim1 = points.shape[:2] + grid_points = numpy.zeros((dim0 + 1, dim1 + 1, 2), dtype=numpy.float64) + + # Compute inner points as mean of 4 neighbours + neighbour_view = numpy.lib.stride_tricks.as_strided( + points, + shape=(dim0 - 1, dim1 - 1, 2, 2, points.shape[2]), + strides=points.strides[:2] + points.strides[:2] + points.strides[-1:], writeable=False) + inner_points = numpy.mean(neighbour_view, axis=(2, 3)) + grid_points[1:-1, 1:-1] = inner_points + + # Compute 'vertical' sides + # Alternative: grid_points[1:-1, [0, -1]] = points[:-1, [0, -1]] + points[1:, [0, -1]] - inner_points[:, [0, -1]] + grid_points[1:-1, [0, -1], 0] = points[:-1, [0, -1], 0] + points[1:, [0, -1], 0] - inner_points[:, [0, -1], 0] + grid_points[1:-1, [0, -1], 1] = inner_points[:, [0, -1], 1] + + # Compute 'horizontal' sides + grid_points[[0, -1], 1:-1, 0] = inner_points[[0, -1], :, 0] + grid_points[[0, -1], 1:-1, 1] = points[[0, -1], :-1, 1] + points[[0, -1], 1:, 1] - inner_points[[0, -1], :, 1] + + # Compute corners + d0, d1 = [0, 0, -1, -1], [0, -1, -1, 0] + grid_points[d0, d1] = 2 * points[d0, d1] - inner_points[d0, d1] + return grid_points + + +def _quadrilateral_grid_as_triangles(points): + """Returns the points and indices to make a grid of quadirlaterals + + :param numpy.ndarray points: + 3D array of points (height, width, 2) + :return: triangle corners (4 * N, 2), triangle indices (2 * N, 3) + With N = height * width, the number of input points + """ + nbpoints = numpy.prod(points.shape[:2]) + + grid = _quadrilateral_grid_coords(points) + coords = numpy.empty((4 * nbpoints, 2), dtype=grid.dtype) + coords[::4] = grid[:-1, :-1].reshape(-1, 2) + coords[1::4] = grid[1:, :-1].reshape(-1, 2) + coords[2::4] = grid[:-1, 1:].reshape(-1, 2) + coords[3::4] = grid[1:, 1:].reshape(-1, 2) + + indices = numpy.empty((2 * nbpoints, 3), dtype=numpy.uint32) + indices[::2, 0] = numpy.arange(0, 4 * nbpoints, 4) + indices[::2, 1] = numpy.arange(1, 4 * nbpoints, 4) + indices[::2, 2] = numpy.arange(2, 4 * nbpoints, 4) + indices[1::2, 0] = indices[::2, 1] + indices[1::2, 1] = indices[::2, 2] + indices[1::2, 2] = numpy.arange(3, 4 * nbpoints, 4) + + return coords, indices + + +_RegularGridInfo = namedtuple( + '_RegularGridInfo', ['bounds', 'origin', 'scale', 'shape', 'order']) + + +_HistogramInfo = namedtuple( + '_HistogramInfo', ['mean', 'count', 'sum', 'origin', 'scale', 'shape']) + + +class Scatter(PointsBase, ColormapMixIn, ScatterVisualizationMixIn): + """Description of a scatter""" + + _DEFAULT_SELECTABLE = True + """Default selectable state for scatter plots""" + + _SUPPORTED_SCATTER_VISUALIZATION = ( + ScatterVisualizationMixIn.Visualization.POINTS, + ScatterVisualizationMixIn.Visualization.SOLID, + ScatterVisualizationMixIn.Visualization.REGULAR_GRID, + ScatterVisualizationMixIn.Visualization.IRREGULAR_GRID, + ScatterVisualizationMixIn.Visualization.BINNED_STATISTIC, + ) + """Overrides supported Visualizations""" + + def __init__(self): + PointsBase.__init__(self) + ColormapMixIn.__init__(self) + ScatterVisualizationMixIn.__init__(self) + self._value = () + self.__alpha = None + # Cache Delaunay triangulation future object + self.__delaunayFuture = None + # Cache interpolator future object + self.__interpolatorFuture = None + self.__executor = None + + # Cache triangles: x, y, indices + self.__cacheTriangles = None, None, None + + # Cache regular grid and histogram info + self.__cacheRegularGridInfo = None + self.__cacheHistogramInfo = None + + def _updateColormappedData(self): + """Update the colormapped data, to be called when changed""" + if self.getVisualization() is self.Visualization.BINNED_STATISTIC: + histoInfo = self.__getHistogramInfo() + if histoInfo is None: + data = None + else: + data = getattr( + histoInfo, + self.getVisualizationParameter( + self.VisualizationParameter.BINNED_STATISTIC_FUNCTION)) + else: + data = self.getValueData(copy=False) + self._setColormappedData(data, copy=False) + + @docstring(ScatterVisualizationMixIn) + def setVisualization(self, mode): + previous = self.getVisualization() + if super().setVisualization(mode): + if (bool(mode is self.Visualization.BINNED_STATISTIC) ^ + bool(previous is self.Visualization.BINNED_STATISTIC)): + self._updateColormappedData() + return True + else: + return False + + @docstring(ScatterVisualizationMixIn) + def setVisualizationParameter(self, parameter, value): + parameter = self.VisualizationParameter.from_value(parameter) + + if super(Scatter, self).setVisualizationParameter(parameter, value): + if parameter in (self.VisualizationParameter.GRID_BOUNDS, + self.VisualizationParameter.GRID_MAJOR_ORDER, + self.VisualizationParameter.GRID_SHAPE): + self.__cacheRegularGridInfo = None + + if parameter in (self.VisualizationParameter.BINNED_STATISTIC_SHAPE, + self.VisualizationParameter.BINNED_STATISTIC_FUNCTION, + self.VisualizationParameter.DATA_BOUNDS_HINT): + if parameter in (self.VisualizationParameter.BINNED_STATISTIC_SHAPE, + self.VisualizationParameter.DATA_BOUNDS_HINT): + self.__cacheHistogramInfo = None # Clean-up cache + if self.getVisualization() is self.Visualization.BINNED_STATISTIC: + self._updateColormappedData() + return True + else: + return False + + @docstring(ScatterVisualizationMixIn) + def getCurrentVisualizationParameter(self, parameter): + value = self.getVisualizationParameter(parameter) + if (parameter is self.VisualizationParameter.DATA_BOUNDS_HINT or + value is not None): + return value # Value has been set, return it + + elif parameter is self.VisualizationParameter.GRID_BOUNDS: + grid = self.__getRegularGridInfo() + return None if grid is None else grid.bounds + + elif parameter is self.VisualizationParameter.GRID_MAJOR_ORDER: + grid = self.__getRegularGridInfo() + return None if grid is None else grid.order + + elif parameter is self.VisualizationParameter.GRID_SHAPE: + grid = self.__getRegularGridInfo() + return None if grid is None else grid.shape + + elif parameter is self.VisualizationParameter.BINNED_STATISTIC_SHAPE: + info = self.__getHistogramInfo() + return None if info is None else info.shape + + else: + raise NotImplementedError() + + def __getRegularGridInfo(self): + """Get grid info""" + if self.__cacheRegularGridInfo is None: + shape = self.getVisualizationParameter( + self.VisualizationParameter.GRID_SHAPE) + order = self.getVisualizationParameter( + self.VisualizationParameter.GRID_MAJOR_ORDER) + if shape is None or order is None: + guess = _guess_grid(self.getXData(copy=False), + self.getYData(copy=False)) + if guess is None: + _logger.warning( + 'Cannot guess a grid: Cannot display as regular grid image') + return None + if shape is None: + shape = guess[1] + if order is None: + order = guess[0] + + nbpoints = len(self.getXData(copy=False)) + if nbpoints > shape[0] * shape[1]: + # More data points that provided grid shape: enlarge grid + _logger.warning( + "More data points than provided grid shape size: extends grid") + dim0, dim1 = shape + if order == 'row': # keep dim1, enlarge dim0 + dim0 = nbpoints // dim1 + (1 if nbpoints % dim1 else 0) + else: # keep dim0, enlarge dim1 + dim1 = nbpoints // dim0 + (1 if nbpoints % dim0 else 0) + shape = dim0, dim1 + + bounds = self.getVisualizationParameter( + self.VisualizationParameter.GRID_BOUNDS) + if bounds is None: + x, y = self.getXData(copy=False), self.getYData(copy=False) + min_, max_ = min_max(x) + xRange = (min_, max_) if (x[0] - min_) < (max_ - x[0]) else (max_, min_) + min_, max_ = min_max(y) + yRange = (min_, max_) if (y[0] - min_) < (max_ - y[0]) else (max_, min_) + bounds = (xRange[0], yRange[0]), (xRange[1], yRange[1]) + + begin, end = bounds + scale = ((end[0] - begin[0]) / max(1, shape[1] - 1), + (end[1] - begin[1]) / max(1, shape[0] - 1)) + if scale[0] == 0 and scale[1] == 0: + scale = 1., 1. + elif scale[0] == 0: + scale = scale[1], scale[1] + elif scale[1] == 0: + scale = scale[0], scale[0] + + origin = begin[0] - 0.5 * scale[0], begin[1] - 0.5 * scale[1] + + self.__cacheRegularGridInfo = _RegularGridInfo( + bounds=bounds, origin=origin, scale=scale, shape=shape, order=order) + + return self.__cacheRegularGridInfo + + def __getHistogramInfo(self): + """Get histogram info""" + if self.__cacheHistogramInfo is None: + shape = self.getVisualizationParameter( + self.VisualizationParameter.BINNED_STATISTIC_SHAPE) + if shape is None: + shape = 100, 100 # TODO compute auto shape + + x, y, values = self.getData(copy=False)[:3] + if len(x) == 0: # No histogram + return None + + if not numpy.issubdtype(x.dtype, numpy.floating): + x = x.astype(numpy.float64) + if not numpy.issubdtype(y.dtype, numpy.floating): + y = y.astype(numpy.float64) + if not numpy.issubdtype(values.dtype, numpy.floating): + values = values.astype(numpy.float64) + + ranges = (tuple(min_max(y, finite=True)), + tuple(min_max(x, finite=True))) + rangesHint = self.getVisualizationParameter( + self.VisualizationParameter.DATA_BOUNDS_HINT) + if rangesHint is not None: + ranges = tuple((min(dataMin, hintMin), max(dataMax, hintMax)) + for (dataMin, dataMax), (hintMin, hintMax) in zip(ranges, rangesHint)) + + points = numpy.transpose(numpy.array((y, x))) + counts, sums, bin_edges = Histogramnd( + points, + histo_range=ranges, + n_bins=shape, + weights=values) + yEdges, xEdges = bin_edges + origin = xEdges[0], yEdges[0] + scale = ((xEdges[-1] - xEdges[0]) / (len(xEdges) - 1), + (yEdges[-1] - yEdges[0]) / (len(yEdges) - 1)) + + with numpy.errstate(divide='ignore', invalid='ignore'): + histo = sums / counts + + self.__cacheHistogramInfo = _HistogramInfo( + mean=histo, count=counts, sum=sums, + origin=origin, scale=scale, shape=shape) + + return self.__cacheHistogramInfo + + def __applyColormapToData(self): + """Compute colors by applying colormap to values. + + :returns: Array of RGBA colors + """ + cmap = self.getColormap() + rgbacolors = cmap.applyToData(self) + + if self.__alpha is not None: + rgbacolors[:, -1] = (rgbacolors[:, -1] * self.__alpha).astype(numpy.uint8) + return rgbacolors + + def _addBackendRenderer(self, backend): + """Update backend renderer""" + # Filter-out values <= 0 + xFiltered, yFiltered, valueFiltered, xerror, yerror = self.getData( + copy=False, displayed=True) + + # Remove not finite numbers (this includes filtered out x, y <= 0) + mask = numpy.logical_and(numpy.isfinite(xFiltered), numpy.isfinite(yFiltered)) + xFiltered = xFiltered[mask] + yFiltered = yFiltered[mask] + + if len(xFiltered) == 0: + return None # No data to display, do not add renderer to backend + + visualization = self.getVisualization() + + if visualization is self.Visualization.BINNED_STATISTIC: + plot = self.getPlot() + if (plot is None or + plot.getXAxis().getScale() != Axis.LINEAR or + plot.getYAxis().getScale() != Axis.LINEAR): + # Those visualizations are not available with log scaled axes + return None + + histoInfo = self.__getHistogramInfo() + if histoInfo is None: + return None + data = getattr(histoInfo, self.getVisualizationParameter( + self.VisualizationParameter.BINNED_STATISTIC_FUNCTION)) + + return backend.addImage( + data=data, + origin=histoInfo.origin, + scale=histoInfo.scale, + colormap=self.getColormap(), + alpha=self.getAlpha()) + + elif visualization is self.Visualization.POINTS: + rgbacolors = self.__applyColormapToData() + return backend.addCurve(xFiltered, yFiltered, + color=rgbacolors[mask], + symbol=self.getSymbol(), + linewidth=0, + linestyle="", + yaxis='left', + xerror=xerror, + yerror=yerror, + fill=False, + alpha=self.getAlpha(), + symbolsize=self.getSymbolSize(), + baseline=None) + + else: + plot = self.getPlot() + if (plot is None or + plot.getXAxis().getScale() != Axis.LINEAR or + plot.getYAxis().getScale() != Axis.LINEAR): + # Those visualizations are not available with log scaled axes + return None + + if visualization is self.Visualization.SOLID: + triangulation = self._getDelaunay().result() + if triangulation is None: + _logger.warning( + 'Cannot get a triangulation: Cannot display as solid surface') + return None + else: + rgbacolors = self.__applyColormapToData() + triangles = triangulation.simplices.astype(numpy.int32) + return backend.addTriangles(xFiltered, + yFiltered, + triangles, + color=rgbacolors[mask], + alpha=self.getAlpha()) + + elif visualization is self.Visualization.REGULAR_GRID: + gridInfo = self.__getRegularGridInfo() + if gridInfo is None: + return None + + dim0, dim1 = gridInfo.shape + if gridInfo.order == 'column': # transposition needed + dim0, dim1 = dim1, dim0 + + values = self.getValueData(copy=False) + if self.__alpha is None and len(values) == dim0 * dim1: + image = values.reshape(dim0, dim1) + else: + # The points do not fill the whole image + if (self.__alpha is None and + numpy.issubdtype(values.dtype, numpy.floating)): + image = numpy.empty(dim0 * dim1, dtype=values.dtype) + image[:len(values)] = values + image[len(values):] = float('nan') # Transparent pixels + image.shape = dim0, dim1 + else: # Per value alpha or no NaN, so convert to RGBA + rgbacolors = self.__applyColormapToData() + image = numpy.empty((dim0 * dim1, 4), dtype=numpy.uint8) + image[:len(rgbacolors)] = rgbacolors + image[len(rgbacolors):] = (0, 0, 0, 0) # Transparent pixels + image.shape = dim0, dim1, 4 + + if gridInfo.order == 'column': + if image.ndim == 2: + image = numpy.transpose(image) + else: + image = numpy.transpose(image, axes=(1, 0, 2)) + + if image.ndim == 2: + colormap = self.getColormap() + if colormap.isAutoscale(): + # Avoid backend to compute autoscale: use item cache + colormap = colormap.copy() + colormap.setVRange(*colormap.getColormapRange(self)) + else: + colormap = None + + return backend.addImage( + data=image, + origin=gridInfo.origin, + scale=gridInfo.scale, + colormap=colormap, + alpha=self.getAlpha()) + + elif visualization is self.Visualization.IRREGULAR_GRID: + gridInfo = self.__getRegularGridInfo() + if gridInfo is None: + return None + + shape = gridInfo.shape + if shape is None: # No shape, no display + return None + + rgbacolors = self.__applyColormapToData() + + nbpoints = len(xFiltered) + if nbpoints == 1: + # single point, render as a square points + return backend.addCurve(xFiltered, yFiltered, + color=rgbacolors[mask], + symbol='s', + linewidth=0, + linestyle="", + yaxis='left', + xerror=None, + yerror=None, + fill=False, + alpha=self.getAlpha(), + symbolsize=7, + baseline=None) + + # Make shape include all points + gridOrder = gridInfo.order + if nbpoints != numpy.prod(shape): + if gridOrder == 'row': + shape = int(numpy.ceil(nbpoints / shape[1])), shape[1] + else: # column-major order + shape = shape[0], int(numpy.ceil(nbpoints / shape[0])) + + if shape[0] < 2 or shape[1] < 2: # Single line, at least 2 points + points = numpy.ones((2, nbpoints, 2), dtype=numpy.float64) + # Use row/column major depending on shape, not on info value + gridOrder = 'row' if shape[0] == 1 else 'column' + + if gridOrder == 'row': + points[0, :, 0] = xFiltered + points[0, :, 1] = yFiltered + else: # column-major order + points[0, :, 0] = yFiltered + points[0, :, 1] = xFiltered + + # Add a second line that will be clipped in the end + points[1, :-1] = points[0, :-1] + numpy.cross( + points[0, 1:] - points[0, :-1], (0., 0., 1.))[:, :2] + points[1, -1] = points[0, -1] + numpy.cross( + points[0, -1] - points[0, -2], (0., 0., 1.))[:2] + + points.shape = 2, nbpoints, 2 # Use same shape for both orders + coords, indices = _quadrilateral_grid_as_triangles(points) + + elif gridOrder == 'row': # row-major order + if nbpoints != numpy.prod(shape): + points = numpy.empty((numpy.prod(shape), 2), dtype=numpy.float64) + points[:nbpoints, 0] = xFiltered + points[:nbpoints, 1] = yFiltered + # Index of last element of last fully filled row + index = (nbpoints // shape[1]) * shape[1] + points[nbpoints:, 0] = xFiltered[index - (numpy.prod(shape) - nbpoints):index] + points[nbpoints:, 1] = yFiltered[-1] + else: + points = numpy.transpose((xFiltered, yFiltered)) + points.shape = shape[0], shape[1], 2 + + else: # column-major order + if nbpoints != numpy.prod(shape): + points = numpy.empty((numpy.prod(shape), 2), dtype=numpy.float64) + points[:nbpoints, 0] = yFiltered + points[:nbpoints, 1] = xFiltered + # Index of last element of last fully filled column + index = (nbpoints // shape[0]) * shape[0] + points[nbpoints:, 0] = yFiltered[index - (numpy.prod(shape) - nbpoints):index] + points[nbpoints:, 1] = xFiltered[-1] + else: + points = numpy.transpose((yFiltered, xFiltered)) + points.shape = shape[1], shape[0], 2 + + coords, indices = _quadrilateral_grid_as_triangles(points) + + # Remove unused extra triangles + coords = coords[:4*nbpoints] + indices = indices[:2*nbpoints] + + if gridOrder == 'row': + x, y = coords[:, 0], coords[:, 1] + else: # column-major order + y, x = coords[:, 0], coords[:, 1] + + rgbacolors = rgbacolors[mask] # Filter-out not finite points + gridcolors = numpy.empty( + (4 * nbpoints, rgbacolors.shape[-1]), dtype=rgbacolors.dtype) + for first in range(4): + gridcolors[first::4] = rgbacolors[:nbpoints] + + return backend.addTriangles(x, + y, + indices, + color=gridcolors, + alpha=self.getAlpha()) + + else: + _logger.error("Unhandled visualization %s", visualization) + return None + + @docstring(PointsBase) + def pick(self, x, y): + result = super(Scatter, self).pick(x, y) + + if result is not None: + visualization = self.getVisualization() + + if visualization is self.Visualization.IRREGULAR_GRID: + # Specific handling of picking for the irregular grid mode + index = result.getIndices(copy=False)[0] // 4 + result = PickingResult(self, (index,)) + + elif visualization is self.Visualization.REGULAR_GRID: + # Specific handling of picking for the regular grid mode + picked = result.getIndices(copy=False) + if picked is None: + return None + row, column = picked[0][0], picked[1][0] + + gridInfo = self.__getRegularGridInfo() + if gridInfo is None: + return None + + if gridInfo.order == 'row': + index = row * gridInfo.shape[1] + column + else: + index = row + column * gridInfo.shape[0] + if index >= len(self.getXData(copy=False)): # OK as long as not log scale + return None # Image can be larger than scatter + + result = PickingResult(self, (index,)) + + elif visualization is self.Visualization.BINNED_STATISTIC: + picked = result.getIndices(copy=False) + if picked is None or len(picked) == 0 or len(picked[0]) == 0: + return None + row, col = picked[0][0], picked[1][0] + histoInfo = self.__getHistogramInfo() + if histoInfo is None: + return None + sx, sy = histoInfo.scale + ox, oy = histoInfo.origin + xdata = self.getXData(copy=False) + ydata = self.getYData(copy=False) + indices = numpy.nonzero(numpy.logical_and( + numpy.logical_and(xdata >= ox + sx * col, xdata < ox + sx * (col + 1)), + numpy.logical_and(ydata >= oy + sy * row, ydata < oy + sy * (row + 1))))[0] + result = None if len(indices) == 0 else PickingResult(self, indices) + + return result + + def __getExecutor(self): + """Returns async greedy executor + + :rtype: _GreedyThreadPoolExecutor + """ + if self.__executor is None: + self.__executor = _GreedyThreadPoolExecutor(max_workers=2) + return self.__executor + + def _getDelaunay(self): + """Returns a :class:`Future` which result is the Delaunay object. + + :rtype: concurrent.futures.Future + """ + if self.__delaunayFuture is None or self.__delaunayFuture.cancelled(): + # Need to init a new delaunay + x, y = self.getData(copy=False)[:2] + # Remove not finite points + mask = numpy.logical_and(numpy.isfinite(x), numpy.isfinite(y)) + + self.__delaunayFuture = self.__getExecutor().submit_greedy( + 'delaunay', delaunay, x[mask], y[mask]) + + return self.__delaunayFuture + + @staticmethod + def __initInterpolator(delaunayFuture, values): + """Returns an interpolator for the given data points + + :param concurrent.futures.Future delaunayFuture: + Future object which result is a Delaunay object + :param numpy.ndarray values: The data value of valid points. + :rtype: Union[callable,None] + """ + # Wait for Delaunay to complete + try: + triangulation = delaunayFuture.result() + except CancelledError: + triangulation = None + + if triangulation is None: + interpolator = None # Error case + else: + # Lazy-loading of interpolator + try: + from scipy.interpolate import LinearNDInterpolator + except ImportError: + LinearNDInterpolator = None + + if LinearNDInterpolator is not None: + interpolator = LinearNDInterpolator(triangulation, values) + + # First call takes a while, do it here + interpolator([(0., 0.)]) + + else: + # Fallback using matplotlib interpolator + import matplotlib.tri + + x, y = triangulation.points.T + tri = matplotlib.tri.Triangulation( + x, y, triangles=triangulation.simplices) + mplInterpolator = matplotlib.tri.LinearTriInterpolator( + tri, values) + + # Wrap interpolator to have same API as scipy's one + def interpolator(points): + return mplInterpolator(*points.T) + + return interpolator + + def _getInterpolator(self): + """Returns a :class:`Future` which result is the interpolator. + + The interpolator is a callable taking an array Nx2 of points + as a single argument. + The :class:`Future` result is None in case the interpolator cannot + be initialized. + + :rtype: concurrent.futures.Future + """ + if (self.__interpolatorFuture is None or + self.__interpolatorFuture.cancelled()): + # Need to init a new interpolator + x, y, values = self.getData(copy=False)[:3] + # Remove not finite points + mask = numpy.logical_and(numpy.isfinite(x), numpy.isfinite(y)) + x, y, values = x[mask], y[mask], values[mask] + + self.__interpolatorFuture = self.__getExecutor().submit_greedy( + 'interpolator', + self.__initInterpolator, self._getDelaunay(), values) + return self.__interpolatorFuture + + def _logFilterData(self, xPositive, yPositive): + """Filter out values with x or y <= 0 on log axes + + :param bool xPositive: True to filter arrays according to X coords. + :param bool yPositive: True to filter arrays according to Y coords. + :return: The filtered arrays or unchanged object if not filtering needed + :rtype: (x, y, value, xerror, yerror) + """ + # overloaded from PointsBase to filter also value. + value = self.getValueData(copy=False) + + if xPositive or yPositive: + clipped = self._getClippingBoolArray(xPositive, yPositive) + + if numpy.any(clipped): + # copy to keep original array and convert to float + value = numpy.array(value, copy=True, dtype=numpy.float64) + value[clipped] = numpy.nan + + x, y, xerror, yerror = PointsBase._logFilterData(self, xPositive, yPositive) + + return x, y, value, xerror, yerror + + def getValueData(self, copy=True): + """Returns the value assigned to the scatter data points. + + :param copy: True (Default) to get a copy, + False to use internal representation (do not modify!) + :rtype: numpy.ndarray + """ + return numpy.array(self._value, copy=copy) + + def getAlphaData(self, copy=True): + """Returns the alpha (transparency) assigned to the scatter data points. + + :param copy: True (Default) to get a copy, + False to use internal representation (do not modify!) + :rtype: numpy.ndarray + """ + return numpy.array(self.__alpha, copy=copy) + + def getData(self, copy=True, displayed=False): + """Returns the x, y coordinates and the value of the data points + + :param copy: True (Default) to get a copy, + False to use internal representation (do not modify!) + :param bool displayed: True to only get curve points that are displayed + in the plot. Default: False. + Note: If plot has log scale, negative points + are not displayed. + :returns: (x, y, value, xerror, yerror) + :rtype: 5-tuple of numpy.ndarray + """ + if displayed: + data = self._getCachedData() + if data is not None: + assert len(data) == 5 + return data + + return (self.getXData(copy), + self.getYData(copy), + self.getValueData(copy), + self.getXErrorData(copy), + self.getYErrorData(copy)) + + # reimplemented from PointsBase to handle `value` + def setData(self, x, y, value, xerror=None, yerror=None, alpha=None, copy=True): + """Set the data of the scatter. + + :param numpy.ndarray x: The data corresponding to the x coordinates. + :param numpy.ndarray y: The data corresponding to the y coordinates. + :param numpy.ndarray value: The data corresponding to the value of + the data points. + :param xerror: Values with the uncertainties on the x values + :type xerror: A float, or a numpy.ndarray of float32. + If it is an array, it can either be a 1D array of + same length as the data or a 2D array with 2 rows + of same length as the data: row 0 for positive errors, + row 1 for negative errors. + :param yerror: Values with the uncertainties on the y values + :type yerror: A float, or a numpy.ndarray of float32. See xerror. + :param alpha: Values with the transparency (between 0 and 1) + :type alpha: A float, or a numpy.ndarray of float32 + :param bool copy: True make a copy of the data (default), + False to use provided arrays. + """ + value = numpy.array(value, copy=copy) + assert value.ndim == 1 + assert len(x) == len(value) + + # Convert complex data + if numpy.iscomplexobj(value): + _logger.warning( + 'Converting value data to absolute value to plot it.') + value = numpy.absolute(value) + + # Reset triangulation and interpolator + if self.__delaunayFuture is not None: + self.__delaunayFuture.cancel() + self.__delaunayFuture = None + if self.__interpolatorFuture is not None: + self.__interpolatorFuture.cancel() + self.__interpolatorFuture = None + + # Data changed, this needs update + self.__cacheRegularGridInfo = None + self.__cacheHistogramInfo = None + + self._value = value + + if alpha is not None: + # Make sure alpha is an array of float in [0, 1] + alpha = numpy.array(alpha, copy=copy) + assert alpha.ndim == 1 + assert len(x) == len(alpha) + if alpha.dtype.kind != 'f': + alpha = alpha.astype(numpy.float32) + if numpy.any(numpy.logical_or(alpha < 0., alpha > 1.)): + alpha = numpy.clip(alpha, 0., 1.) + self.__alpha = alpha + + # set x, y, xerror, yerror + + # call self._updated + plot._invalidateDataRange() + PointsBase.setData(self, x, y, xerror, yerror, copy) + + self._updateColormappedData() |