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Diffstat (limited to 'silx/gui/plot/items/scatter.py')
| -rw-r--r-- | silx/gui/plot/items/scatter.py | 973 |
1 files changed, 0 insertions, 973 deletions
diff --git a/silx/gui/plot/items/scatter.py b/silx/gui/plot/items/scatter.py deleted file mode 100644 index 2d54223..0000000 --- a/silx/gui/plot/items/scatter.py +++ /dev/null @@ -1,973 +0,0 @@ -# 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 _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()) - - # Compute colors - cmap = self.getColormap() - rgbacolors = cmap.applyToData(self) - - if self.__alpha is not None: - rgbacolors[:, -1] = (rgbacolors[:, -1] * self.__alpha).astype(numpy.uint8) - - visualization = self.getVisualization() - - if visualization is self.Visualization.POINTS: - 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: - 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 - - if len(rgbacolors) == dim0 * dim1: - image = rgbacolors.reshape(dim0, dim1, -1) - else: - # The points do not fill the whole image - image = numpy.empty((dim0 * dim1, 4), dtype=rgbacolors.dtype) - image[:len(rgbacolors)] = rgbacolors - image[len(rgbacolors):] = 0, 0, 0, 0 # Transparent pixels - image.shape = dim0, dim1, -1 - - if gridInfo.order == 'column': - image = numpy.transpose(image, axes=(1, 0, 2)) - - return backend.addImage( - data=image, - origin=gridInfo.origin, - scale=gridInfo.scale, - colormap=None, - 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 - - 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 - self._updateColormappedData() - - 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) |