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Diffstat (limited to 'src/silx/gui/plot/items/histogram.py')
| -rw-r--r-- | src/silx/gui/plot/items/histogram.py | 389 |
1 files changed, 389 insertions, 0 deletions
diff --git a/src/silx/gui/plot/items/histogram.py b/src/silx/gui/plot/items/histogram.py new file mode 100644 index 0000000..16bbefa --- /dev/null +++ b/src/silx/gui/plot/items/histogram.py @@ -0,0 +1,389 @@ +# 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::t +# +# 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:`Histogram` item of the :class:`Plot`. +""" + +__authors__ = ["H. Payno", "T. Vincent"] +__license__ = "MIT" +__date__ = "28/08/2018" + +import logging +import typing + +import numpy +from collections import OrderedDict, namedtuple +try: + from collections import abc +except ImportError: # Python2 support + import collections as abc + +from ....utils.proxy import docstring +from .core import (DataItem, AlphaMixIn, BaselineMixIn, ColorMixIn, FillMixIn, + LineMixIn, YAxisMixIn, ItemChangedType, Item) +from ._pick import PickingResult + +_logger = logging.getLogger(__name__) + + +def _computeEdges(x, histogramType): + """Compute the edges from a set of xs and a rule to generate the edges + + :param x: the x value of the curve to transform into an histogram + :param histogramType: the type of histogram we wan't to generate. + This define the way to center the histogram values compared to the + curve value. Possible values can be:: + + - 'left' + - 'right' + - 'center' + + :return: the edges for the given x and the histogramType + """ + # for now we consider that the spaces between xs are constant + edges = x.copy() + if histogramType == 'left': + width = 1 + if len(x) > 1: + width = x[1] - x[0] + edges = numpy.append(x[0] - width, edges) + if histogramType == 'center': + edges = _computeEdges(edges, 'right') + widths = (edges[1:] - edges[0:-1]) / 2.0 + widths = numpy.append(widths, widths[-1]) + edges = edges - widths + if histogramType == 'right': + width = 1 + if len(x) > 1: + width = x[-1] - x[-2] + edges = numpy.append(edges, x[-1] + width) + + return edges + + +def _getHistogramCurve(histogram, edges): + """Returns the x and y value of a curve corresponding to the histogram + + :param numpy.ndarray histogram: The values of the histogram + :param numpy.ndarray edges: The bin edges of the histogram + :return: a tuple(x, y) which contains the value of the curve to use + to display the histogram + """ + assert len(histogram) + 1 == len(edges) + x = numpy.empty(len(histogram) * 2, dtype=edges.dtype) + y = numpy.empty(len(histogram) * 2, dtype=histogram.dtype) + # Make a curve with stairs + x[:-1:2] = edges[:-1] + x[1::2] = edges[1:] + y[:-1:2] = histogram + y[1::2] = histogram + + return x, y + + +# TODO: Yerror, test log scale +class Histogram(DataItem, AlphaMixIn, ColorMixIn, FillMixIn, + LineMixIn, YAxisMixIn, BaselineMixIn): + """Description of an histogram""" + + _DEFAULT_Z_LAYER = 1 + """Default overlay layer for histograms""" + + _DEFAULT_SELECTABLE = False + """Default selectable state for histograms""" + + _DEFAULT_LINEWIDTH = 1. + """Default line width of the histogram""" + + _DEFAULT_LINESTYLE = '-' + """Default line style of the histogram""" + + _DEFAULT_BASELINE = None + + def __init__(self): + DataItem.__init__(self) + AlphaMixIn.__init__(self) + BaselineMixIn.__init__(self) + ColorMixIn.__init__(self) + FillMixIn.__init__(self) + LineMixIn.__init__(self) + YAxisMixIn.__init__(self) + + self._histogram = () + self._edges = () + self._setBaseline(Histogram._DEFAULT_BASELINE) + + def _addBackendRenderer(self, backend): + """Update backend renderer""" + values, edges, baseline = self.getData(copy=False) + + if values.size == 0: + return None # No data to display, do not add renderer + + if values.size == 0: + return None # No data to display, do not add renderer to backend + + x, y = _getHistogramCurve(values, edges) + + # Filter-out values <= 0 + plot = self.getPlot() + if plot is not None: + xPositive = plot.getXAxis()._isLogarithmic() + yPositive = plot.getYAxis()._isLogarithmic() + else: + xPositive = False + yPositive = False + + if xPositive or yPositive: + clipped = numpy.logical_or( + (x <= 0) if xPositive else False, + (y <= 0) if yPositive else False) + # Make a copy and replace negative points by NaN + x = numpy.array(x, dtype=numpy.float64) + y = numpy.array(y, dtype=numpy.float64) + x[clipped] = numpy.nan + y[clipped] = numpy.nan + + return backend.addCurve(x, y, + color=self.getColor(), + symbol='', + linestyle=self.getLineStyle(), + linewidth=self.getLineWidth(), + yaxis=self.getYAxis(), + xerror=None, + yerror=None, + fill=self.isFill(), + alpha=self.getAlpha(), + baseline=baseline, + symbolsize=1) + + def _getBounds(self): + values, edges, baseline = self.getData(copy=False) + + plot = self.getPlot() + if plot is not None: + xPositive = plot.getXAxis()._isLogarithmic() + yPositive = plot.getYAxis()._isLogarithmic() + else: + xPositive = False + yPositive = False + + if xPositive or yPositive: + values = numpy.array(values, copy=True, dtype=numpy.float64) + + if xPositive: + # Replace edges <= 0 by NaN and corresponding values by NaN + clipped_edges = (edges <= 0) + edges = numpy.array(edges, copy=True, dtype=numpy.float64) + edges[clipped_edges] = numpy.nan + clipped_values = numpy.logical_or(clipped_edges[:-1], + clipped_edges[1:]) + else: + clipped_values = numpy.zeros_like(values, dtype=bool) + + if yPositive: + # Replace values <= 0 by NaN, do not modify edges + clipped_values = numpy.logical_or(clipped_values, values <= 0) + + values[clipped_values] = numpy.nan + + if yPositive: + return (numpy.nanmin(edges), + numpy.nanmax(edges), + numpy.nanmin(values), + numpy.nanmax(values)) + + else: # No log scale on y axis, include 0 in bounds + if numpy.all(numpy.isnan(values)): + return None + return (numpy.nanmin(edges), + numpy.nanmax(edges), + min(0, numpy.nanmin(values)), + max(0, numpy.nanmax(values))) + + def __pickFilledHistogram(self, x: float, y: float) -> typing.Optional[PickingResult]: + """Picking implementation for filled histogram + + :param x: X position in pixels + :param y: Y position in pixels + """ + if not self.isFill(): + return None + + plot = self.getPlot() + if plot is None: + return None + + xData, yData = plot.pixelToData(x, y, axis=self.getYAxis()) + xmin, xmax, ymin, ymax = self.getBounds() + if not xmin < xData < xmax or not ymin < yData < ymax: + return None # Outside bounding box + + # Check x + edges = self.getBinEdgesData(copy=False) + index = numpy.searchsorted(edges, (xData,), side='left')[0] - 1 + # Safe indexing in histogram values + index = numpy.clip(index, 0, len(edges) - 2) + + # Check y + baseline = self.getBaseline(copy=False) + if baseline is None: + baseline = 0 # Default value + + value = self.getValueData(copy=False)[index] + if ((baseline <= value and baseline <= yData <= value) or + (value < baseline and value <= yData <= baseline)): + return PickingResult(self, numpy.array([index])) + else: + return None + + @docstring(DataItem) + def pick(self, x, y): + if self.isFill(): + return self.__pickFilledHistogram(x, y) + else: + result = super().pick(x, y) + if result is None: + return None + else: # Convert from curve indices to histogram indices + return PickingResult(self, numpy.unique(result.getIndices() // 2)) + + def getValueData(self, copy=True): + """The values of the histogram + + :param copy: True (Default) to get a copy, + False to use internal representation (do not modify!) + :returns: The values of the histogram + :rtype: numpy.ndarray + """ + return numpy.array(self._histogram, copy=copy) + + def getBinEdgesData(self, copy=True): + """The bin edges of the histogram (number of histogram values + 1) + + :param copy: True (Default) to get a copy, + False to use internal representation (do not modify!) + :returns: The bin edges of the histogram + :rtype: numpy.ndarray + """ + return numpy.array(self._edges, copy=copy) + + def getData(self, copy=True): + """Return the histogram values, bin edges and baseline + + :param copy: True (Default) to get a copy, + False to use internal representation (do not modify!) + :returns: (N histogram value, N+1 bin edges) + :rtype: 2-tuple of numpy.nadarray + """ + return (self.getValueData(copy), + self.getBinEdgesData(copy), + self.getBaseline(copy)) + + def setData(self, histogram, edges, align='center', baseline=None, + copy=True): + """Set the histogram values and bin edges. + + :param numpy.ndarray histogram: The values of the histogram. + :param numpy.ndarray edges: + The bin edges of the histogram. + If histogram and edges have the same length, the bin edges + are computed according to the align parameter. + :param str align: + In case histogram values and edges have the same length N, + the N+1 bin edges are computed according to the alignment in: + 'center' (default), 'left', 'right'. + :param baseline: histogram baseline + :type baseline: Union[None,float,numpy.ndarray] + :param bool copy: True make a copy of the data (default), + False to use provided arrays. + """ + histogram = numpy.array(histogram, copy=copy) + edges = numpy.array(edges, copy=copy) + + assert histogram.ndim == 1 + assert edges.ndim == 1 + assert edges.size in (histogram.size, histogram.size + 1) + assert align in ('center', 'left', 'right') + + if histogram.size == 0: # No data + self._histogram = () + self._edges = () + else: + if edges.size == histogram.size: # Compute true bin edges + edges = _computeEdges(edges, align) + + # Check that bin edges are monotonic + edgesDiff = numpy.diff(edges) + edgesDiff = edgesDiff[numpy.logical_not(numpy.isnan(edgesDiff))] + assert numpy.all(edgesDiff >= 0) or numpy.all(edgesDiff <= 0) + # manage baseline + if (isinstance(baseline, abc.Iterable)): + baseline = numpy.array(baseline) + if baseline.size == histogram.size: + new_baseline = numpy.empty(baseline.shape[0] * 2) + for i_value, value in enumerate(baseline): + new_baseline[i_value*2:i_value*2+2] = value + baseline = new_baseline + self._histogram = histogram + self._edges = edges + self._alignement = align + self._setBaseline(baseline) + + self._boundsChanged() + self._updated(ItemChangedType.DATA) + + def getAlignment(self): + """ + + :return: histogram alignement. Value in ('center', 'left', 'right'). + """ + return self._alignement + + def _revertComputeEdges(self, x, histogramType): + """Compute the edges from a set of xs and a rule to generate the edges + + :param x: the x value of the curve to transform into an histogram + :param histogramType: the type of histogram we wan't to generate. + This define the way to center the histogram values compared to the + curve value. Possible values can be:: + + - 'left' + - 'right' + - 'center' + + :return: the edges for the given x and the histogramType + """ + # for now we consider that the spaces between xs are constant + edges = x.copy() + if histogramType == 'left': + return edges[1:] + if histogramType == 'center': + edges = (edges[1:] + edges[:-1]) / 2.0 + if histogramType == 'right': + width = 1 + if len(x) > 1: + width = x[-1] + x[-2] + edges = edges[:-1] + return edges |