Source code for holoviews.element.chart

import numpy as np
import param

from ..core import Dataset, Dimension, Element2D, NdOverlay, Overlay, util
from ..core.dimension import process_dimensions
from .geom import (  # noqa: F401 backward compatible import
from .selection import Selection1DExpr

[docs]class Chart(Dataset, Element2D): """ A Chart is an abstract baseclass for elements representing one or more independent and dependent variables defining a 1D coordinate system with associated values. The independent variables or key dimensions map onto the x-axis while the dependent variables are usually mapped to the location, height or spread along the y-axis. Any number of additional value dimensions may be associated with a Chart. If a chart's independent variable (or key dimension) is numeric the chart will represent a discretely sampled version of the underlying continuously sampled 1D space. Therefore indexing along this variable will automatically snap to the closest coordinate. Since a Chart is a subclass of a Dataset it supports the full set of data interfaces but usually each dimension of a chart represents a column stored in a dictionary, array or DataFrame. """ kdims = param.List(default=[Dimension('x')], bounds=(1,2), doc=""" The key dimension(s) of a Chart represent the independent variable(s).""") 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.""") # Enables adding index if 1D array like data is supplied _auto_indexable_1d = True _max_kdim_count = 1 # Remove once kdims has bounds=(1,1) instead of warning __abstract = True def __init__(self, data, kdims=None, vdims=None, **params): params.update(process_dimensions(kdims, vdims)) if len(params.get('kdims', [])) == self._max_kdim_count + 1: self.param.warning('Chart elements should only be supplied a single kdim') super().__init__(data, **params) def __getitem__(self, index): return super().__getitem__(index)
[docs]class Scatter(Selection1DExpr, Chart): """ Scatter is a Chart element representing a set of points in a 1D coordinate system where the key dimension maps to the points location along the x-axis while the first value dimension represents the location of the point along the y-axis. """ group = param.String(default='Scatter', constant=True)
[docs]class Curve(Selection1DExpr, Chart): """ Curve is a Chart element representing a line in a 1D coordinate system where the key dimension maps on the line x-coordinate and the first value dimension represents the height of the line along the y-axis. """ group = param.String(default='Curve', constant=True)
[docs]class ErrorBars(Selection1DExpr, Chart): """ ErrorBars is a Chart element representing error bars in a 1D coordinate system where the key dimension corresponds to the location along the x-axis and the first value dimension corresponds to the location along the y-axis and one or two extra value dimensions corresponding to the symmetric or asymmetric errors either along x-axis or y-axis. If two value dimensions are given, then the last value dimension will be taken as symmetric errors. If three value dimensions are given then the last two value dimensions will be taken as negative and positive errors. By default the errors are defined along y-axis. A parameter `horizontal`, when set `True`, will define the errors along the x-axis. """ group = param.String(default='ErrorBars', constant=True, doc=""" A string describing the quantity measured by the ErrorBars object.""") vdims = param.List(default=[Dimension('y'), Dimension('yerror')], bounds=(1, None), constant=True) horizontal = param.Boolean(default=False, doc=""" Whether the errors are along y-axis (vertical) or x-axis.""")
[docs] def range(self, dim, data_range=True, dimension_range=True): """Return the lower and upper bounds of values along dimension. Range of the y-dimension includes the symmetric or asymmetric error. Args: dimension: The dimension to compute the range on. data_range (bool): Compute range from data values dimension_range (bool): Include Dimension ranges Whether to include Dimension range and soft_range in range calculation Returns: Tuple containing the lower and upper bound """ dim_with_err = 0 if self.horizontal else 1 didx = self.get_dimension_index(dim) dim = self.get_dimension(dim) if didx == dim_with_err and data_range and len(self): mean = self.dimension_values(didx) 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) if not dimension_range: return (lower, upper) return util.dimension_range(lower, upper, dim.range, dim.soft_range) return super().range(dim, data_range)
[docs]class Spread(ErrorBars): """ Spread is a Chart element representing a spread of values or confidence band in a 1D coordinate system. The key dimension(s) corresponds to the location along the x-axis and the value dimensions define the location along the y-axis as well as the symmetric or asymmetric spread. """ group = param.String(default='Spread', constant=True)
[docs]class Bars(Selection1DExpr, Chart): """ Bars is a Chart element representing categorical observations using the height of rectangular bars. The key dimensions represent the categorical groupings of the data, but may also be used to stack the bars, while the first value dimension represents the height of each bar. """ group = param.String(default='Bars', constant=True) kdims = param.List(default=[Dimension('x')], bounds=(1,3)) _max_kdim_count = 3
[docs]class Histogram(Selection1DExpr, Chart): """ Histogram is a Chart element representing a number of bins in a 1D coordinate system. The key dimension represents the binned values, which may be declared as bin edges or bin centers, while the value dimensions usually defines a count, frequency or density associated with each bin. """ datatype = param.List(default=['grid']) group = param.String(default='Histogram', constant=True) kdims = param.List(default=[Dimension('x')], bounds=(1,1), doc=""" Dimensions on Element2Ds determine the number of indexable dimensions.""") vdims = param.List(default=[Dimension('Frequency')], bounds=(1, None)) _binned = True def __init__(self, data, **params): if data is None: data = [] if (isinstance(data, tuple) and len(data) == 2 and len(data[0])+1 == len(data[1])): data = data[::-1] super().__init__(data, **params) @property def edges(self): "Property to access the Histogram edges provided for backward compatibility" return self.interface.coords(self, self.kdims[0], edges=True)
[docs]class Spikes(Selection1DExpr, Chart): """ Spikes is a Chart element which represents a number of discrete spikes, events or observations in a 1D coordinate system. The key dimension therefore represents the position of each spike along the x-axis while the first value dimension, if defined, controls the height along the y-axis. It may therefore be used to visualize the distribution of discrete events, representing a rug plot, or to draw the strength some signal. """ group = param.String(default='Spikes', constant=True) kdims = param.List(default=[Dimension('x')], bounds=(1, 1)) vdims = param.List(default=[], bounds=(0, None)) _auto_indexable_1d = False
[docs]class Area(Curve): """ Area is a Chart element representing the area under a curve or between two curves in a 1D coordinate system. The key dimension represents the location of each coordinate along the x-axis, while the value dimension(s) represent the height of the area or the lower and upper bounds of the area between curves. Multiple areas may be stacked by overlaying them an passing them to the stack method. """ group = param.String(default='Area', constant=True)
[docs] @classmethod def stack(cls, areas, baseline_name='Baseline'): """ 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 is_overlay = isinstance(areas, Overlay) if is_overlay: areas = NdOverlay({i: el for i, el in enumerate(areas)}) df = areas.dframe(multi_index=True) levels = list(range(areas.ndims)) vdims = [[el.vdims[0], baseline_name] for el in areas] baseline = None stacked = areas.clone(shared_data=False) if len(levels) == 1: # Pandas 2.1 gives the following FutureWarning: # Creating a Groupby object with a length-1 list-like level parameter # will yield indexes as tuples in a future version. levels = levels[0] for (key, sdf), element_vdims in zip(df.groupby(level=levels, sort=False), vdims): vdim = element_vdims[0] sdf = sdf.droplevel(levels).reindex(index=df.index.unique(-1), fill_value=0) if baseline is None: sdf[baseline_name] = 0 else: sdf[] = sdf[] + baseline sdf[baseline_name] = baseline baseline = sdf[] stacked[key] = areas[key].clone(sdf, vdims=element_vdims) return Overlay(stacked.values()) if is_overlay else stacked