Source code for glue.viewers.profile.state

from collections import OrderedDict
from glue.core.hub import HubListener

import numpy as np

from glue.core import Subset
from echo import delay_callback
from glue.viewers.matplotlib.state import (MatplotlibDataViewerState,
                                           MatplotlibLayerState,
                                           DeferredDrawCallbackProperty as DDCProperty,
                                           DeferredDrawSelectionCallbackProperty as DDSCProperty)
from glue.core.data_combo_helper import ManualDataComboHelper, ComponentIDComboHelper
from glue.utils import defer_draw
from glue.core.link_manager import is_convertible_to_single_pixel_cid
from glue.core.exceptions import IncompatibleDataException
from glue.core.message import SubsetUpdateMessage

__all__ = ['ProfileViewerState', 'ProfileLayerState']


FUNCTIONS = OrderedDict([('maximum', 'Maximum'),
                         ('minimum', 'Minimum'),
                         ('mean', 'Mean'),
                         ('median', 'Median'),
                         ('sum', 'Sum')])


[docs]class ProfileViewerState(MatplotlibDataViewerState): """ A state class that includes all the attributes for a Profile viewer. """ x_att_pixel = DDCProperty(docstring='The component ID giving the pixel component ' 'shown on the x axis') x_att = DDSCProperty(docstring='The component ID giving the pixel or world component ' 'shown on the x axis') reference_data = DDSCProperty(docstring='The dataset that is used to define the ' 'available pixel/world components, and ' 'which defines the coordinate frame in ' 'which the images are shown') function = DDSCProperty(docstring='The function to use for collapsing data') normalize = DDCProperty(False, docstring='Whether to normalize all profiles ' 'to the [0:1] range') # TODO: add function to use def __init__(self, **kwargs): super(ProfileViewerState, self).__init__() self.ref_data_helper = ManualDataComboHelper(self, 'reference_data') self.add_callback('layers', self._layers_changed) self.add_callback('reference_data', self._reference_data_changed, echo_old=True) self.add_callback('x_att', self._update_att) self.add_callback('normalize', self._reset_y_limits) self.add_callback('function', self._reset_y_limits) self.x_att_helper = ComponentIDComboHelper(self, 'x_att', numeric=False, datetime=False, categorical=False, pixel_coord=True) ProfileViewerState.function.set_choices(self, list(FUNCTIONS)) ProfileViewerState.function.set_display_func(self, FUNCTIONS.get) self.update_from_dict(kwargs) def _update_combo_ref_data(self): self.ref_data_helper.set_multiple_data(self.layers_data)
[docs] def reset_limits(self): with delay_callback(self, 'x_min', 'x_max', 'y_min', 'y_max'): self._reset_x_limits() self._reset_y_limits()
@property def _display_world(self): return getattr(self.reference_data, 'coords', None) is not None @defer_draw def _update_att(self, *args): if self.x_att is not None: if self._display_world: if self.x_att in self.reference_data.pixel_component_ids: self.x_att_pixel = self.x_att else: index = self.reference_data.world_component_ids.index(self.x_att) self.x_att_pixel = self.reference_data.pixel_component_ids[index] else: self.x_att_pixel = self.x_att self._reset_x_limits() def _reset_x_limits(self, *event): # NOTE: we don't use AttributeLimitsHelper because we need to avoid # trying to get the minimum of *all* the world coordinates in the # dataset. Instead, we use the same approach as in the layer state below # and in the case of world coordinates we use online the spine of the # data. if self.reference_data is None or self.x_att_pixel is None: return data = self.reference_data if self.x_att in data.pixel_component_ids: x_min, x_max = -0.5, data.shape[self.x_att.axis] - 0.5 else: axis = data.world_component_ids.index(self.x_att) axis_view = [0] * data.ndim axis_view[axis] = slice(None) axis_values = data[self.x_att, tuple(axis_view)] x_min, x_max = np.nanmin(axis_values), np.nanmax(axis_values) with delay_callback(self, 'x_min', 'x_max'): self.x_min = x_min self.x_max = x_max def _reset_y_limits(self, *event): if self.normalize: with delay_callback(self, 'y_min', 'y_max'): self.y_min = -0.1 self.y_max = +1.1 else: y_min, y_max = np.inf, -np.inf for layer in self.layers: try: profile = layer.profile except Exception: # e.g. incompatible subset continue if profile is not None: x, y = profile if len(y) > 0: y_min = min(y_min, np.nanmin(y)) y_max = max(y_max, np.nanmax(y)) with delay_callback(self, 'y_min', 'y_max'): if y_max > y_min: self.y_min = y_min self.y_max = y_max else: self.y_min = 0 self.y_max = 1
[docs] def flip_x(self): """ Flip the x_min/x_max limits. """ with delay_callback(self, 'x_min', 'x_max'): self.x_min, self.x_max = self.x_max, self.x_min
@defer_draw def _layers_changed(self, *args): self._update_combo_ref_data() @defer_draw def _reference_data_changed(self, before=None, after=None): # A callback event for reference_data is triggered if the choices change # but the actual selection doesn't - so we avoid resetting the WCS in # this case. if before is after: return for layer in self.layers: layer.reset_cache() # This signal can get emitted if just the choices but not the actual # reference data change, so we check here that the reference data has # actually changed if self.reference_data is not getattr(self, '_last_reference_data', None): self._last_reference_data = self.reference_data with delay_callback(self, 'x_att'): if self.reference_data is None: self.x_att_helper.set_multiple_data([]) else: self.x_att_helper.set_multiple_data([self.reference_data]) if self._display_world: self.x_att_helper.world_coord = True self.x_att = self.reference_data.world_component_ids[0] else: self.x_att_helper.world_coord = False self.x_att = self.reference_data.pixel_component_ids[0] self._update_att() self.reset_limits() def _update_priority(self, name): if name == 'layers': return 2 elif name == 'reference_data': return 1.5 elif name.endswith(('_min', '_max')): return 0 else: return 1
[docs]class ProfileLayerState(MatplotlibLayerState, HubListener): """ A state class that includes all the attributes for layers in a Profile plot. """ linewidth = DDCProperty(1, docstring='The width of the line') attribute = DDSCProperty(docstring='The attribute shown in the layer') v_min = DDCProperty(docstring='The lower level shown') v_max = DDCProperty(docstring='The upper level shown') percentile = DDSCProperty(docstring='The percentile value used to ' 'automatically calculate levels') as_steps = DDCProperty(True, docstring='Whether to display the profile as steps') _viewer_callbacks_set = False _layer_subset_updates_subscribed = False _profile_cache = None def __init__(self, layer=None, viewer_state=None, **kwargs): super(ProfileLayerState, self).__init__(layer=layer, viewer_state=viewer_state) self.attribute_att_helper = ComponentIDComboHelper(self, 'attribute', numeric=True, categorical=False) percentile_display = {100: 'Min/Max', 99.5: '99.5%', 99: '99%', 95: '95%', 90: '90%', 'Custom': 'Custom'} ProfileLayerState.percentile.set_choices(self, [100, 99.5, 99, 95, 90, 'Custom']) ProfileLayerState.percentile.set_display_func(self, percentile_display.get) self.add_callback('layer', self._on_layer_change, priority=1000) self.add_callback('visible', self.reset_cache, priority=1000) if layer is not None: self._on_layer_change() self.update_from_dict(kwargs) def _on_layer_change(self, *args): if self.layer is not None: # Set the available attributes self.attribute_att_helper.set_multiple_data([self.layer]) # We only subscribe to SubsetUpdateMessage the first time that 'layer' # is not None, and then do any filtering in the callback function. if not self._layer_subset_updates_subscribed and self.layer.hub is not None: self.layer.hub.subscribe(self, SubsetUpdateMessage, handler=self._on_subset_update) self._layer_subset_updates_subscribed = True self.reset_cache() def _on_subset_update(self, msg): if msg.subset is self.layer: self.reset_cache()
[docs] @property def independent_x_att(self): return is_convertible_to_single_pixel_cid(self.layer, self.viewer_state.x_att) is not None
[docs] def normalize_values(self, values): return (np.asarray(values) - self.v_min) / (self.v_max - self.v_min)
[docs] def reset_cache(self, *args): self._profile_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 profile(self): self.update_profile() return self._profile_cache
[docs] def update_profile(self, update_limits=True): if self._profile_cache is not None: return self._profile_cache if not self.visible: return if not self._viewer_callbacks_set: self.viewer_state.add_callback('x_att', self.reset_cache, priority=100000) self.viewer_state.add_callback('function', self.reset_cache, priority=100000) if self.is_callback_property('attribute'): self.add_callback('attribute', self.reset_cache, priority=100000) self._viewer_callbacks_set = True if self.viewer_state is None or self.viewer_state.x_att is None or self.attribute is None: raise IncompatibleDataException() # Check what pixel axis in the current dataset x_att corresponds to pix_cid = is_convertible_to_single_pixel_cid(self.layer, self.viewer_state.x_att_pixel) if pix_cid is None: raise IncompatibleDataException() # If we get here, then x_att does correspond to a single pixel axis in # the cube, so we now prepare a list of axes to collapse over. axes = tuple(i for i in range(self.layer.ndim) if i != pix_cid.axis) # We now get the y values for the data # TODO: in future we should optimize the case where the mask is much # smaller than the data to just average the relevant 'spaxels' in the # data rather than collapsing the whole cube. if isinstance(self.layer, Subset): data = self.layer.data subset_state = self.layer.subset_state else: data = self.layer subset_state = None profile_values = data.compute_statistic(self.viewer_state.function, self.attribute, axis=axes, subset_state=subset_state) if np.all(np.isnan(profile_values)): self._profile_cache = [], [] else: axis_view = [0] * data.ndim axis_view[pix_cid.axis] = slice(None) axis_values = data[self.viewer_state.x_att, tuple(axis_view)] self._profile_cache = axis_values, profile_values if update_limits: self.update_limits(update_profile=False)
[docs] def update_limits(self, update_profile=True): with delay_callback(self, 'v_min', 'v_max'): if update_profile: self.update_profile(update_limits=False) if self._profile_cache is not None and len(self._profile_cache[1]) > 0: self.v_min = np.nanmin(self._profile_cache[1]) self.v_max = np.nanmax(self._profile_cache[1])