import numpy as np
from glue.core import BaseData, Subset
from echo import delay_callback
from glue.viewers.matplotlib.state import (MatplotlibDataViewerState,
MatplotlibLayerState,
DeferredDrawCallbackProperty as DDCProperty,
DeferredDrawSelectionCallbackProperty as DDSCProperty)
from glue.core.state_objects import (StateAttributeLimitsHelper,
StateAttributeHistogramHelper)
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 = layer_state.layer.data
try:
if layer.data.get_kind(cid) == 'categorical':
categories.append(layer.data.get_data(cid).categories)
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 = layer_state.layer.data
try:
kinds.add(layer.data.get_kind(cid))
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 = self.layer.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)
