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Diffstat (limited to 'silx/gui/plot/items/histogram.py')
| -rw-r--r-- | silx/gui/plot/items/histogram.py | 332 |
1 files changed, 0 insertions, 332 deletions
diff --git a/silx/gui/plot/items/histogram.py b/silx/gui/plot/items/histogram.py deleted file mode 100644 index 389e8a6..0000000 --- a/silx/gui/plot/items/histogram.py +++ /dev/null @@ -1,332 +0,0 @@ -# coding: utf-8 -# /*########################################################################## -# -# Copyright (c) 2017 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:`Histogram` item of the :class:`Plot`. -""" - -__authors__ = ["H. Payno", "T. Vincent"] -__license__ = "MIT" -__date__ = "28/08/2018" - -import logging - -import numpy - -from .core import (Item, AlphaMixIn, ColorMixIn, FillMixIn, - LineMixIn, YAxisMixIn, ItemChangedType) - -_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 is 'left': - width = 1 - if len(x) > 1: - width = x[1] - x[0] - edges = numpy.append(x[0] - width, edges) - if histogramType is 'center': - edges = _computeEdges(edges, 'right') - widths = (edges[1:] - edges[0:-1]) / 2.0 - widths = numpy.append(widths, widths[-1]) - edges = edges - widths - if histogramType is '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(Item, AlphaMixIn, ColorMixIn, FillMixIn, - LineMixIn, YAxisMixIn): - """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""" - - def __init__(self): - Item.__init__(self) - AlphaMixIn.__init__(self) - ColorMixIn.__init__(self) - FillMixIn.__init__(self) - LineMixIn.__init__(self) - YAxisMixIn.__init__(self) - - self._histogram = () - self._edges = () - - def _addBackendRenderer(self, backend): - """Update backend renderer""" - values, edges = 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.float) - y = numpy.array(y, dtype=numpy.float) - x[clipped] = numpy.nan - y[clipped] = numpy.nan - - return backend.addCurve(x, y, self.getLegend(), - color=self.getColor(), - symbol='', - linestyle=self.getLineStyle(), - linewidth=self.getLineWidth(), - yaxis=self.getYAxis(), - xerror=None, - yerror=None, - z=self.getZValue(), - selectable=self.isSelectable(), - fill=self.isFill(), - alpha=self.getAlpha(), - symbolsize=1) - - def _getBounds(self): - values, edges = 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.float) - - if xPositive: - # Replace edges <= 0 by NaN and corresponding values by NaN - clipped_edges = (edges <= 0) - edges = numpy.array(edges, copy=True, dtype=numpy.float) - edges[clipped_edges] = numpy.nan - clipped_values = numpy.logical_or(clipped_edges[:-1], - clipped_edges[1:]) - else: - clipped_values = numpy.zeros_like(values, dtype=numpy.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 xPositive or yPositive: - return (numpy.nanmin(edges), - numpy.nanmax(edges), - numpy.nanmin(values), - numpy.nanmax(values)) - - else: # No log scale, include 0 in bounds - return (numpy.nanmin(edges), - numpy.nanmax(edges), - min(0, numpy.nanmin(values)), - max(0, numpy.nanmax(values))) - - def setVisible(self, visible): - """Set visibility of item. - - :param bool visible: True to display it, False otherwise - """ - visible = bool(visible) - # TODO hackish data range implementation - if self.isVisible() != visible: - plot = self.getPlot() - if plot is not None: - plot._invalidateDataRange() - super(Histogram, self).setVisible(visible) - - 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 bin edges 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 and the bin edges - - :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) - - def setData(self, histogram, edges, align='center', 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 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) - assert numpy.all(edgesDiff >= 0) or numpy.all(edgesDiff <= 0) - - self._histogram = histogram - self._edges = edges - self._alignement = align - - if self.isVisible(): - plot = self.getPlot() - if plot is not None: - plot._invalidateDataRange() - - 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 is 'left': - return edges[1:] - if histogramType is 'center': - edges = (edges[1:] + edges[:-1]) / 2.0 - if histogramType is 'right': - width = 1 - if len(x) > 1: - width = x[-1] + x[-2] - edges = edges[:-1] - return edges |