Source code for glue.viewers.histogram.state

import numpy as np

from glue.core import BaseData, Subset

from echo import delay_callback
from glue.viewers.matplotlib.state import (MatplotlibDataViewerState,
                                           DeferredDrawCallbackProperty as DDCProperty,
                                           DeferredDrawSelectionCallbackProperty as DDSCProperty)
from glue.core.state_objects import (StateAttributeLimitsHelper,
from glue.core.exceptions import IncompatibleAttribute, IncompatibleDataException
from glue.core.data_combo_helper import ComponentIDComboHelper
from glue.utils import defer_draw, datetime64_to_mpl
from glue.utils.decorators import avoid_circular

__all__ = ['HistogramViewerState', 'HistogramLayerState']

[docs]class HistogramViewerState(MatplotlibDataViewerState): """ A state class that includes all the attributes for a histogram viewer. """ x_att = DDSCProperty(docstring='The attribute to compute the histograms for') cumulative = DDCProperty(False, docstring='Whether to show the histogram as ' 'a cumulative histogram') normalize = DDCProperty(False, docstring='Whether to normalize the histogram ' '(based on the total sum)') hist_x_min = DDCProperty(docstring='The minimum value used to compute the ' 'histogram') hist_x_max = DDCProperty(docstring='The maximum value used to compute the ' 'histogram') hist_n_bin = DDCProperty(docstring='The number of bins in the histogram') common_n_bin = DDCProperty(True, docstring='The number of bins to use for ' 'all numerical components') x_limits_percentile = DDCProperty(100, docstring="Percentile to use when automatically determining x limits") update_bins_on_reset_limits = DDCProperty(True, docstring="Whether to update the bins to match the view when resetting limits") random_subset = DDCProperty(None, docstring='The maximum number of elements to use ' 'when computing the histogram. If the data ' 'is larger than this, a random subset of ' 'the data will be used.') def __init__(self, **kwargs): super(HistogramViewerState, self).__init__() self.hist_helper = StateAttributeHistogramHelper(self, 'x_att', lower='hist_x_min', upper='hist_x_max', n_bin='hist_n_bin', common_n_bin='common_n_bin') self.x_lim_helper = StateAttributeLimitsHelper(self, 'x_att', lower='x_min', upper='x_max', log='x_log') self.add_callback('layers', self._layers_changed) self.x_att_helper = ComponentIDComboHelper(self, 'x_att', pixel_coord=True, world_coord=True) self.update_from_dict(kwargs) # This should be added after update_from_dict since we don't want to # influence the restoring of sessions. self.add_callback('hist_x_min', self.update_view_to_bins) self.add_callback('hist_x_max', self.update_view_to_bins) self.add_callback('x_log', self._reset_x_limits, priority=1000) def _reset_x_limits(self, *args): if self.x_att is None: return with delay_callback(self, 'hist_x_min', 'hist_x_max', 'x_min', 'x_max', 'x_log'): self.x_lim_helper.percentile = self.x_limits_percentile self.x_lim_helper.update_values(force=True) if self.update_bins_on_reset_limits: self.update_bins_to_view()
[docs] def reset_limits(self): self._reset_x_limits() y_min = min(getattr(layer, '_y_min', np.inf) for layer in self.layers) if np.isfinite(y_min): self.y_min = y_min y_max = max(getattr(layer, '_y_max', 0) for layer in self.layers) if np.isfinite(y_max): self.y_max = y_max
def _update_priority(self, name): if name == 'layers': return 2 elif name.endswith('_log'): return 0.5 elif name.endswith(('_min', '_max', '_bin')): return 0 else: return 1
[docs] def flip_x(self): """ Flip the x_min/x_max limits. """ self.x_lim_helper.flip_limits()
[docs] @avoid_circular def update_bins_to_view(self, *args): """ Update the bins to match the current view. """ with delay_callback(self, 'hist_x_min', 'hist_x_max'): if self.x_max > self.x_min: self.hist_x_min = self.x_min self.hist_x_max = self.x_max else: self.hist_x_min = self.x_max self.hist_x_max = self.x_min
[docs] @avoid_circular def update_view_to_bins(self, *args): """ Update the view to match the histogram interval """ with delay_callback(self, 'x_min', 'x_max'): self.x_min = self.hist_x_min self.x_max = self.hist_x_max
[docs] @property def x_categories(self): return self._categories(self.x_att)
def _categories(self, cid): categories = [] for layer_state in self.layers: if isinstance(layer_state.layer, BaseData): layer = layer_state.layer else: layer = try: if == 'categorical': categories.append( except IncompatibleAttribute: pass if len(categories) == 0: return None else: return np.unique(np.hstack(categories))
[docs] @property def x_kinds(self): return self._component_kinds(self.x_att)
def _component_kinds(self, cid): # Construct list of component kinds over all layers kinds = set() for layer_state in self.layers: if isinstance(layer_state.layer, BaseData): layer = layer_state.layer else: layer = try: kinds.add( except IncompatibleAttribute: pass return kinds
[docs] @property def bins(self): """ The position of the bins for the histogram based on the current state. """ if self.hist_x_min is None or self.hist_x_max is None or self.hist_n_bin is None: return None if self.x_log: return np.logspace(np.log10(self.hist_x_min), np.log10(self.hist_x_max), self.hist_n_bin + 1) elif isinstance(self.hist_x_min, np.datetime64): x_min = self.hist_x_min.astype(int) x_max = self.hist_x_max.astype(self.hist_x_min.dtype).astype(int) return np.linspace(x_min, x_max, self.hist_n_bin + 1).astype(self.hist_x_min.dtype) else: return np.linspace(self.hist_x_min, self.hist_x_max, self.hist_n_bin + 1)
@defer_draw def _layers_changed(self, *args): self.x_att_helper.set_multiple_data(self.layers_data)
[docs]class HistogramLayerState(MatplotlibLayerState): """ A state class that includes all the attributes for layers in a histogram plot. """ _histogram_cache = None
[docs] def reset_cache(self, *args): self._histogram_cache = None
@property def viewer_state(self): return self._viewer_state
[docs] @viewer_state.setter def viewer_state(self, viewer_state): self._viewer_state = viewer_state
[docs] @property def histogram(self): self.update_histogram() edges, unscaled = self._histogram_cache[1] scaled = unscaled.astype(float) dx = edges[1] - edges[0] if self.viewer_state.cumulative: scaled = scaled.cumsum() if self.viewer_state.normalize: scaled /= scaled.max() elif self.viewer_state.normalize: scaled /= (scaled.sum() * dx) return edges, scaled
[docs] def update_histogram(self): current_settings = (id(self.viewer_state.x_att), self.viewer_state.x_log, self.viewer_state.hist_x_min, self.viewer_state.hist_x_max, self.viewer_state.hist_n_bin) if self._histogram_cache is not None and self._histogram_cache[0] == current_settings: return self._histogram_cache[1] if (self.viewer_state is None or self.viewer_state.x_att is None or self.viewer_state.hist_x_min is None or self.viewer_state.hist_x_max is None or self.viewer_state.hist_n_bin is None or self.viewer_state.x_log is None): raise IncompatibleDataException() if isinstance(self.layer, Subset): data = subset_state = self.layer.subset_state else: data = self.layer subset_state = None range = sorted((self.viewer_state.hist_x_min, self.viewer_state.hist_x_max)) hist_values = data.compute_histogram([self._viewer_state.x_att], range=[range], bins=[self._viewer_state.hist_n_bin], log=[self._viewer_state.x_log], subset_state=subset_state, random_subset=self._viewer_state.random_subset) # TODO: determine whether this belongs here or in the layer artist if isinstance(range[0], np.datetime64): range = [datetime64_to_mpl(range[0]), datetime64_to_mpl(range[1])] if self._viewer_state.x_log: hist_edges = np.logspace(np.log10(range[0]), np.log10(range[1]), self._viewer_state.hist_n_bin + 1) else: hist_edges = np.linspace(range[0], range[1], self._viewer_state.hist_n_bin + 1) self._histogram_cache = current_settings, (hist_edges, hist_values)