Source code for holoviews.element.chart

import numpy as np

import param

from ..core import util
from ..core import Dimension, Dataset, Element2D
from .util import compute_edges

[docs]class Chart(Dataset, Element2D): """ The data held within Chart is a numpy array of shape (N, D), where N is the number of samples and D the number of dimensions. Chart Elements are sliceable along up to two key dimensions. The data may be supplied in one of three formats: 1) As a numpy array of shape (N, D). 2) As a list of length N containing tuples of length D. 3) As a tuple of length D containing iterables of length N. """ kdims = param.List(default=[Dimension('x')], bounds=(1,2), doc=""" The key dimensions of the Chart, determining the number of indexable dimensions.""") group = param.String(default='Chart', constant=True) vdims = param.List(default=[Dimension('y')], bounds=(1,None), doc=""" The value dimensions of the Chart, usually corresponding to a number of dependent variables.""") def __getitem__(self, index): sliced = super(Chart, self).__getitem__(index) if not isinstance(sliced, Chart): return sliced if not isinstance(index, tuple): index = (index,) ndims = len(self.extents)//2 lower_bounds, upper_bounds = [None]*ndims, [None]*ndims for i, slc in enumerate(index[:ndims]): if isinstance(slc, slice): lbound = self.extents[i] ubound = self.extents[ndims:][i] lower_bounds[i] = lbound if slc.start is None else slc.start upper_bounds[i] = ubound if slc.stop is None else slc.stop sliced.extents = tuple(lower_bounds+upper_bounds) return sliced
[docs]class Scatter(Chart): """ Scatter is a Element2D type which gets displayed as a number of disconnected points. """ group = param.String(default='Scatter', constant=True)
[docs]class Curve(Chart): """ Curve is a simple Chart Element providing 1D indexing along the x-axis. """ group = param.String(default='Curve', constant=True)
[docs]class ErrorBars(Chart): """ ErrorBars is a Chart Element type representing any number of errorbars situated in a 2D space. The errors must be supplied as an Nx3 or Nx4 array representing the x/y-positions and either the symmetric error or asymmetric errors respectively. """ group = param.String(default='ErrorBars', constant=True, doc=""" A string describing the quantity measured by the ErrorBars object.""") kdims = param.List(default=[Dimension('x')], bounds=(1, 2), constant=True, doc=""" The Dimensions corresponding to the x- and y-positions of the error bars.""") vdims = param.List(default=[Dimension('y'), Dimension('yerror')], bounds=(1, 3), constant=True) def range(self, dim, data_range=True): didx = self.get_dimension_index(dim) dim = self.get_dimension(dim) if didx == 1 and data_range and len(self): mean = self.dimension_values(1) neg_error = self.dimension_values(2) if len(self.dimensions()) > 3: pos_error = self.dimension_values(3) else: pos_error = neg_error lower = np.nanmin(mean-neg_error) upper = np.nanmax(mean+pos_error) return util.dimension_range(lower, upper, dim) return super(ErrorBars, self).range(dim, data_range)
[docs]class Spread(ErrorBars): """ Spread is a Chart Element type representing a spread of values as given by a mean and standard error or confidence intervals. Just like the ErrorBars Element type, mean and deviations from the mean should be supplied as either an Nx3 or Nx4 array representing the x-values, mean values and symmetric or asymmetric errors respective. Internally the data is always expanded to an Nx4 array. """ group = param.String(default='Spread', constant=True)
[docs]class Bars(Chart): """ Bars is an Element type, representing a number of stacked and grouped bars, depending the dimensionality of the key and value dimensions. Bars is useful for categorical data, which may be laid via groups, categories and stacks. """ group = param.String(default='Bars', constant=True) kdims = param.List(default=[Dimension('x')], bounds=(1,3)) vdims = param.List(default=[Dimension('y')], bounds=(1, None))
[docs]class BoxWhisker(Chart): """ BoxWhisker allows representing the distribution of data grouped into one or more groups by summarizing the data using quartiles. The boxes of a BoxWhisker element represent the first, second and third quartiles. The whiskers follow the Tukey boxplot definition representing the lowest datum still within 1.5 IQR of the lower quartile, and the highest datum still within 1.5 IQR of the upper quartile. Any points falling outside this range are shown as distinct outlier points. """ group = param.String(default='BoxWhisker', constant=True) kdims = param.List(default=[], bounds=(0,None)) vdims = param.List(default=[Dimension('y')], bounds=(1,1)) _auto_indexable_1d = False
[docs]class Histogram(Element2D): """ Histogram contains a number of bins, which are defined by the upper and lower bounds of their edges and the computed bin values. """ kdims = param.List(default=[Dimension('x')], bounds=(1,1), doc=""" Dimensions on Element2Ds determine the number of indexable dimensions.""") group = param.String(default='Histogram', constant=True) vdims = param.List(default=[Dimension('Frequency')], bounds=(1,1)) def __init__(self, values, edges=None, **params): if edges is not None: self.warning("Histogram edges should be supplied as a tuple " "along with the values, passing the edges will " "be deprecated in holoviews 2.0.") self.values, self.edges, settings = self._process_data(values, edges) settings.update(params) super(Histogram, self).__init__((self.values, self.edges), **settings) def __getitem__(self, key): """ Implements slicing or indexing of the Histogram """ if key in self.dimensions(): return self.dimension_values(key) if key is () or key is Ellipsis: return self # May no longer be necessary key = util.process_ellipses(self, key) if not isinstance(key, tuple): pass elif len(key) == self.ndims + 1: if key[-1] != slice(None) and (key[-1] not in self.vdims): raise KeyError("%r is the only selectable value dimension" % self.vdims[0].name) key = key[0] elif len(key) == self.ndims + 1: key = key[0] else: raise KeyError("Histogram cannot slice more than %d dimension." % len(self.kdims)+1) centers = [(float(l)+r)/2 for (l,r) in zip(self.edges, self.edges[1:])] if isinstance(key, slice): start, stop = key.start, key.stop if [start, stop] == [None,None]: return self start_idx, stop_idx = None,None if start is not None: start_idx = np.digitize([start], centers, right=True)[0] if stop is not None: stop_idx = np.digitize([stop], centers, right=True)[0] slice_end = stop_idx+1 if stop_idx is not None else None slice_values = self.values[start_idx:stop_idx] slice_edges = self.edges[start_idx: slice_end] extents = (min(slice_edges), self.extents[1], max(slice_edges), self.extents[3]) return self.clone((slice_values, slice_edges), extents=extents) else: if not (self.edges.min() <= key < self.edges.max()): raise KeyError("Key value %s is out of the histogram bounds" % key) idx = np.digitize([key], self.edges)[0] return self.values[idx-1 if idx>0 else idx] def _process_data(self, values, edges): """ Ensure that edges are specified as left and right edges of the histogram bins rather than bin centers. """ settings = {} (values, edges) = values if isinstance(values, tuple) else (values, edges) if isinstance(values, Chart): settings = dict(values.get_param_values(onlychanged=True)) edges = values.dimension_values(0) values = values.dimension_values(1) elif isinstance(values, np.ndarray) and len(values.shape) == 2: edges = values[:, 0] values = values[:, 1] elif all(isinstance(el, tuple) for el in values): edges, values = zip(*values) else: values = np.array(values) if edges is None: edges = np.arange(len(values), dtype=np.float) else: edges = np.array(edges, dtype=np.float) if len(edges) == len(values): edges = compute_edges(edges) return values, edges, settings def range(self, dimension, data_range=True): if self.get_dimension_index(dimension) == 0 and data_range: dim = self.get_dimension(dimension) lower, upper = np.min(self.edges), np.max(self.edges) return util.dimension_range(lower, upper, dim) else: return super(Histogram, self).range(dimension, data_range) def dimension_values(self, dim): dim = self.get_dimension(dim, strict=True).name if dim in self.vdims: return self.values elif dim in self.kdims: return np.convolve(self.edges, np.ones((2,))/2, mode='valid') else: return super(Histogram, self).dimension_values(dim) def sample(self, samples=[], **sample_values): raise NotImplementedError('Cannot sample a Histogram.') def reduce(self, dimensions=None, function=None, **reduce_map): raise NotImplementedError('Reduction of Histogram not implemented.')
[docs]class Points(Chart): """ Allows sets of points to be positioned over a sheet coordinate system. Each points may optionally be associated with a chosen numeric value. The input data can be a Nx2 or Nx3 Numpy array where the first two columns corresponds to the X,Y coordinates in sheet coordinates, within the declared bounding region. For Nx3 arrays, the third column corresponds to the magnitude values of the points. Any additional columns will be ignored (use VectorFields instead). The input data may be also be passed as a tuple of elements that may be numpy arrays or values that can be cast to arrays. When such a tuple is supplied, the elements are joined column-wise into a single array, allowing the magnitudes to be easily supplied separately. Note that if magnitudes are to be rendered correctly by default, they should lie in the range [0,1]. """ kdims = param.List(default=[Dimension('x'), Dimension('y')], bounds=(2, 2), constant=True, doc=""" The label of the x- and y-dimension of the Points in form of a string or dimension object.""") group = param.String(default='Points', constant=True) vdims = param.List(default=[]) _min_dims = 2 # Minimum number of columns
[docs]class VectorField(Points): """ A VectorField contains is a collection of vectors where each vector has an associated position in sheet coordinates. The constructor of VectorField is similar to the constructor of Points: the input data can be an NxM Numpy array where the first two columns corresponds to the X,Y coordinates in sheet coordinates, within the declared bounding region. As with Points, the input can be a tuple of array objects or of objects that can be cast to arrays (the tuple elements are joined column-wise). The third column maps to the vector angle which must be specified in radians. Note that it is possible to supply a collection which isn't a numpy array, whereby each element of the collection is assumed to be an iterable corresponding to a single column of the NxM array. The visualization of any additional columns is decided by the plotting code. For instance, the fourth and fifth columns could correspond to arrow length and colour map value. All that is assumed is that these additional dimension are normalized between 0.0 and 1.0 for the default visualization to work well. The only restriction is that the final data array is NxM where M>3. In other words, the vector must have a dimensionality of 2 or higher. """ group = param.String(default='VectorField', constant=True) vdims = param.List(default=[Dimension('Angle', cyclic=True, range=(0,2*np.pi)), Dimension('Magnitude')], bounds=(1, None)) _null_value = np.array([[], [], [], []]).T # For when data is None _min_dims = 3 # Minimum number of columns def __init__(self, data, kdims=None, vdims=None, **params): if isinstance(data, list) and data and all(isinstance(d, np.ndarray) for d in data): data = np.column_stack([d.flat if d.ndim > 1 else d for d in data]) super(VectorField, self).__init__(data, kdims=kdims, vdims=vdims, **params)
[docs]class Spikes(Chart): """ Spikes is a 1D or 2D Element, which represents a series of vertical or horizontal lines distributed along some dimension. If an additional dimension is supplied it will be used to specify the height of the lines. The Element may therefore be used to represent 1D distributions, spectrograms or spike trains in electrophysiology. """ group = param.String(default='Spikes', constant=True) kdims = param.List(default=[Dimension('x')], bounds=(1, 1)) vdims = param.List(default=[]) _auto_indexable_1d = False
[docs]class Area(Curve): """ An Area Element represents the area under a Curve and is specified in the same format as a regular Curve, with the key dimension corresponding to a column of x-values and the value dimension corresponding to a column of y-values. Optionally a second value dimension may be supplied to shade the region between the curves. """ group = param.String(default='Area', constant=True)
[docs] @classmethod def stack(cls, areas): """ Stacks an (Nd)Overlay of Area or Curve Elements by offsetting their baselines. To stack a HoloMap or DynamicMap use the map method. """ if not len(areas): return areas baseline = np.zeros(len(areas.values()[0])) stacked = areas.clone(shared_data=False) vdims = [areas.values()[0].vdims[0], 'Baseline'] for k, area in areas.items(): x, y = (area.dimension_values(i) for i in range(2)) stacked[k] = area.clone((x, y+baseline, baseline), vdims=vdims, new_type=Area) baseline += y return stacked