from __future__ import absolute_import, division, print_function import logging import operator import warnings import numpy as np import pandas as pd from glue.core.subset import (RoiSubsetState, RangeSubsetState, CategoricalROISubsetState, AndState, MultiRangeSubsetState, CategoricalMultiRangeSubsetState, CategoricalROISubsetState2D) from glue.core.roi import (PolygonalROI, CategoricalROI, RangeROI, XRangeROI, YRangeROI, RectangularROI) from glue.core.util import row_lookup from glue.utils import (unique, shape_to_string, coerce_numeric, check_sorted, polygon_line_intersections) __all__ = ['Component', 'DerivedComponent', 'CategoricalComponent', 'CoordinateComponent'] class Component(object): """ Stores the actual, numerical information for a particular quantity Data objects hold one or more components, accessed via ComponentIDs. All Components in a data set must have the same shape and number of dimensions Notes ----- Instead of instantiating Components directly, consider using :meth:`Component.autotyped`, which chooses a subclass most appropriate for the data type. """ def __init__(self, data, units=None): """ :param data: The data to store :type data: :class:`numpy.ndarray` :param units: Optional unit label :type units: str """ # The physical units of the data self.units = units # The actual data # subclasses may pass non-arrays here as placeholders. if isinstance(data, np.ndarray): data = coerce_numeric(data) data.setflags(write=False) # data is read-only self._data = data @property def units(self): return self._units @units.setter def units(self, value): if value is None: self._units = '' else: self._units = str(value) @property def hidden(self): """Whether the Component is hidden by default""" return False @property def data(self): """ The underlying :class:`numpy.ndarray` """ return self._data @property def shape(self): """ Tuple of array dimensions """ return self._data.shape @property def ndim(self): """ The number of dimensions """ return len(self._data.shape) def __getitem__(self, key): logging.debug("Using %s to index data of shape %s", key, self.shape) return self._data[key] @property def numeric(self): """ Whether or not the datatype is numeric """ return np.can_cast(self.data.dtype, np.complex) @property def categorical(self): """ Whether or not the datatype is categorical """ return False def __str__(self): return "Component with shape %s" % shape_to_string(self.shape) def jitter(self, method=None): raise NotImplementedError def subset_from_roi(self, att, roi, other_comp=None, other_att=None, coord='x'): """ Create a SubsetState object from an ROI. This encapsulates the logic for creating subset states with Components. See the documentation for CategoricalComponents for caveats involved with mixed-type plots. :param att: attribute name of this Component :param roi: an ROI object :param other_comp: The other Component for 2D ROIs :param other_att: The attribute name of the other Component :param coord: The orientation of this Component :param is_nested: True if this was passed from another Component. :return: A SubsetState (or subclass) object """ if coord not in ('x', 'y'): raise ValueError('coord should be one of x/y') other_coord = 'y' if coord == 'x' else 'x' if isinstance(roi, RangeROI): # The selection is either an x range or a y range if roi.ori == coord: # The selection applies to the current component lo, hi = roi.range() subset_state = RangeSubsetState(lo, hi, att) else: # The selection applies to the other component, so we delegate return other_comp.subset_from_roi(other_att, roi, other_comp=self, other_att=att, coord=other_coord) else: # The selection is polygon-like. Categorical components require # special care, so if the other component is categorical, we need to # delegate to CategoricalComponent.subset_from_roi. if isinstance(other_comp, CategoricalComponent): return other_comp.subset_from_roi(other_att, roi, other_comp=self, other_att=att, is_nested=True, coord=other_coord) else: subset_state = RoiSubsetState() subset_state.xatt = att subset_state.yatt = other_att x, y = roi.to_polygon() subset_state.roi = PolygonalROI(x, y) return subset_state def to_series(self, **kwargs): """ Convert into a pandas.Series object. :param kwargs: All kwargs are passed to the Series constructor. :return: pandas.Series """ return pd.Series(self.data.ravel(), **kwargs) @classmethod def autotyped(cls, data, units=None): """ Automatically choose between Component and CategoricalComponent, based on the input data type. :param data: The data to pack into a Component (array-like) :param units: Optional units :type units: str :returns: A Component (or subclass) """ data = np.asarray(data) if np.issubdtype(data.dtype, np.object_): return CategoricalComponent(data, units=units) n = coerce_numeric(data) thresh = 0.5 try: use_categorical = np.issubdtype(data.dtype, np.character) and \ np.isfinite(n).mean() <= thresh except TypeError: # isfinite not supported. non-numeric dtype use_categorical = True if use_categorical: return CategoricalComponent(data, units=units) else: return Component(n, units=units) class DerivedComponent(Component): """ A component which derives its data from a function """ def __init__(self, data, link, units=None): """ :param data: The data object to use for calculation :type data: :class:`~glue.core.data.Data` :param link: The link that carries out the function :type link: :class:`~glue.core.component_link.ComponentLink` :param units: Optional unit description """ super(DerivedComponent, self).__init__(data, units=units) self._link = link def set_parent(self, data): """ Reassign the Data object that this DerivedComponent operates on """ self._data = data @property def hidden(self): return self._link.hidden @property def data(self): """ Return the numerical data as a numpy array """ return self._link.compute(self._data) @property def link(self): """ Return the component link """ return self._link def __getitem__(self, key): return self._link.compute(self._data, key) class CoordinateComponent(Component): """ Components associated with pixel or world coordinates The numerical values are computed on the fly. """ def __init__(self, data, axis, world=False): super(CoordinateComponent, self).__init__(None, None) self.world = world self._data = data self.axis = axis @property def data(self): return self._calculate() def _calculate(self, view=None): slices = [slice(0, s, 1) for s in self.shape] grids = np.broadcast_arrays(*np.ogrid[slices]) if view is not None: grids = [g[view] for g in grids] if self.world: world = self._data.coords.pixel2world(*grids[::-1])[::-1] return world[self.axis] else: return grids[self.axis] @property def shape(self): """ Tuple of array dimensions. """ return self._data.shape @property def ndim(self): """ Number of dimensions """ return len(self._data.shape) def __getitem__(self, key): return self._calculate(key) def __lt__(self, other): if self.world == other.world: return self.axis < other.axis return self.world def __gluestate__(self, context): return dict(axis=self.axis, world=self.world) @classmethod def __setgluestate__(cls, rec, context): return cls(None, rec['axis'], rec['world']) class CategoricalComponent(Component): """ Container for categorical data. """ def __init__(self, categorical_data, categories=None, jitter=None, units=None): """ :param categorical_data: The underlying :class:`numpy.ndarray` :param categories: List of unique values in the data :jitter: Strategy for jittering the data """ super(CategoricalComponent, self).__init__(None, units) self._categorical_data = np.asarray(categorical_data) if self._categorical_data.ndim > 1: raise ValueError("Categorical Data must be 1-dimensional") # Disable changing of categories self._categorical_data.setflags(write=False) self._categories = categories self._jitter_method = jitter self._is_jittered = False self._data = None if self._categories is None: self._update_categories() else: self._update_data() @property def codes(self): """ The index of the category for each value in the array. """ return self._data @property def labels(self): """ The original categorical data. """ return self._categorical_data @property def categories(self): """ The categories. """ return self._categories @property def data(self): warnings.warn("The 'data' attribute is deprecated. Use 'codes' " "instead to access the underlying index of the " "categories") return self.codes @property def numeric(self): return False @property def categorical(self): return True def _update_categories(self, categories=None): """ :param categories: A sorted array of categories to find in the dataset. If None the categories are the unique items in the data. :return: None """ if categories is None: categories, inv = unique(self._categorical_data) self._categories = categories self._data = inv.astype(np.float) self._data.setflags(write=False) self.jitter(method=self._jitter_method) else: if check_sorted(categories): self._categories = categories self._update_data() else: raise ValueError("Provided categories must be Sorted") def _update_data(self): """ Converts the categorical data into the numeric representations given self._categories """ self._is_jittered = False self._data = row_lookup(self._categorical_data, self._categories) self.jitter(method=self._jitter_method) self._data.setflags(write=False) def jitter(self, method=None): """ Jitter the data so the density of points can be easily seen in a scatter plot. :param method: None | 'uniform': * None: No jittering is done (or any jittering is undone). * uniform: A unformly distributed random variable (-0.5, 0.5) is applied to each point. :return: None """ if method not in set(['uniform', None]): raise ValueError('%s jitter not supported' % method) self._jitter_method = method seed = 1234567890 rand_state = np.random.RandomState(seed) if (self._jitter_method is None) and self._is_jittered: self._update_data() elif (self._jitter_method is 'uniform') and not self._is_jittered: iswrite = self._data.flags['WRITEABLE'] self._data.setflags(write=True) self._data += rand_state.uniform(-0.5, 0.5, size=self._data.shape) self._is_jittered = True self._data.setflags(write=iswrite) def subset_from_roi(self, att, roi, other_comp=None, other_att=None, coord='x', is_nested=False): """ Create a SubsetState object from an ROI. This encapsulates the logic for creating subset states with CategoricalComponents. There is an important caveat, only RangeROIs and RectangularROIs make sense in mixed type plots. As such, polygons are converted to their outer-most edges in this case. :param att: attribute name of this Component :param roi: an ROI object :param other_comp: The other Component for 2D ROIs :param other_att: The attribute name of the other Component :param coord: The orientation of this Component :param is_nested: True if this was passed from another Component. :return: A SubsetState (or subclass) object """ if coord not in ('x', 'y'): raise ValueError('coord should be one of x/y') if isinstance(roi, RangeROI): # The selection is either an x range or a y range if roi.ori == coord: # The selection applies to the current component return CategoricalROISubsetState.from_range(self, att, roi.min, roi.max) else: # The selection applies to the other component, so we delegate other_coord = 'y' if coord == 'x' else 'x' return other_comp.subset_from_roi(other_att, roi, other_comp=self, other_att=att, coord=other_coord) elif isinstance(roi, RectangularROI): # In this specific case, we can decompose the rectangular # ROI into two RangeROIs that are combined with an 'and' # logical operation. other_coord = 'y' if coord == 'x' else 'x' if coord == 'x': range1 = XRangeROI(roi.xmin, roi.xmax) range2 = YRangeROI(roi.ymin, roi.ymax) else: range2 = XRangeROI(roi.xmin, roi.xmax) range1 = YRangeROI(roi.ymin, roi.ymax) # We get the subset state for the current component subset1 = self.subset_from_roi(att, range1, other_comp=other_comp, other_att=other_att, coord=coord) # We now get the subset state for the other component subset2 = other_comp.subset_from_roi(other_att, range2, other_comp=self, other_att=att, coord=other_coord) return AndState(subset1, subset2) elif isinstance(roi, CategoricalROI): # The selection is categorical itself return CategoricalROISubsetState(roi=roi, att=att) else: # The selection is polygon-like, which requires special care. if isinstance(other_comp, CategoricalComponent): # For each category, we check which categories along the other # axis fall inside the polygon: selection = {} for code, label in enumerate(self.categories): # Determine the coordinates of the points to check n_other = len(other_comp.categories) y = np.arange(n_other) x = np.repeat(code, n_other) if coord == 'y': x, y = y, x # Determine which points are in the polygon, and which # categories these correspond to in_poly = roi.contains(x, y) categories = other_comp.categories[in_poly] if len(categories) > 0: selection[label] = set(categories) return CategoricalROISubsetState2D(selection, att, other_att) else: # If the other component is not categorical, we treat this as if # each categorical component was mapped to a numerical value, # and at each value, we keep track of the polygon intersection # with the component. This will result in zero, one, or # multiple separate numerical ranges for each categorical value. # TODO: if we ever allow the category order to be changed, we # need to figure out how to update this! x, y = roi.to_polygon() if is_nested: x, y = y, x # We loop over each category and for each one we find the # numerical ranges selection = {} for code, label in enumerate(self.categories): # We determine all the numerical segments that represent the # ensemble of points in y that fall in the polygon # TODO: profile the following function segments = polygon_line_intersections(x, y, xval=code) if len(segments) > 0: selection[label] = segments return CategoricalMultiRangeSubsetState(selection, att, other_att) def to_series(self, **kwargs): """ Convert into a pandas.Series object. This will be converted as a dtype=np.object! :param kwargs: All kwargs are passed to the Series constructor. :return: pandas.Series """ return pd.Series(self._categorical_data.ravel(), dtype=np.object, **kwargs)