import numpy as np
import param
from ..core import Dataset, Element, Tabular
from ..core.dimension import Dimension, dimension_name
from .selection import SelectionIndexExpr
[docs]class ItemTable(Element):
"""
A tabular element type to allow convenient visualization of either
a standard Python dictionary or a list of tuples
(i.e. input suitable for an dict constructor).
Tables store heterogeneous data with different labels.
Dimension objects are also accepted as keys, allowing dimensional
information (e.g. type and units) to be associated per heading.
"""
kdims = param.List(default=[], bounds=(0, 0), doc="""
ItemTables hold an index Dimension for each value they contain, i.e.
they are equivalent to the keys.""")
vdims = param.List(default=[Dimension('Default')], bounds=(0, None), doc="""
ItemTables should have only index Dimensions.""")
group = param.String(default="ItemTable", constant=True)
@property
def rows(self):
return len(self.vdims)
@property
def cols(self):
return 2
def __init__(self, data, **params):
if data is None:
data = []
if isinstance(data, dict):
pass
elif isinstance(data, list):
data = dict(data)
else:
data = dict(list(data))
if "vdims" not in params:
params['vdims'] = list(data.keys())
str_keys = dict((dimension_name(k), v) for (k,v) in data.items())
super().__init__(str_keys, **params)
def __getitem__(self, heading):
"""
Get the value associated with the given heading (key).
"""
if heading == ():
return self
if heading not in self.vdims:
raise KeyError(f"{heading!r} not in available headings.")
return np.array(self.data.get(heading, np.nan))
[docs] def dimension_values(self, dimension, expanded=True, flat=True):
dimension = self.get_dimension(dimension, strict=True).name
if dimension in self.dimensions('value', label=True):
return np.array([self.data.get(dimension, np.nan)])
else:
return super().dimension_values(dimension)
[docs] def sample(self, samples=None):
if samples is None:
samples = []
if callable(samples):
sampled_data = dict(item for item in self.data.items()
if samples(item))
else:
sampled_data = dict((s, self.data.get(s, np.nan)) for s in samples)
return self.clone(sampled_data)
[docs] def reduce(self, dimensions=None, function=None, **reduce_map):
raise NotImplementedError('ItemTables are for heterogeneous data, which'
'cannot be reduced.')
[docs] def pprint_cell(self, row, col):
"""
Get the formatted cell value for the given row and column indices.
"""
if col > 2:
raise Exception("Only two columns available in a ItemTable.")
elif row >= self.rows:
raise Exception("Maximum row index is %d" % self.rows-1)
elif col == 0:
return self.dimensions('value')[row].pprint_label
else:
dim = self.get_dimension(row)
heading = self.vdims[row]
return dim.pprint_value(self.data.get(heading.name, np.nan))
[docs] def hist(self, *args, **kwargs):
raise NotImplementedError("ItemTables are not homogeneous and "
"don't support histograms.")
[docs] def cell_type(self, row, col):
"""
Returns the cell type given a row and column index. The common
basic cell types are 'data' and 'heading'.
"""
if col == 0: return 'heading'
else: return 'data'
[docs]class Table(SelectionIndexExpr, Dataset, Tabular):
"""
Table is a Dataset type, which gets displayed in a tabular
format and is convertible to most other Element types.
"""
group = param.String(default='Table', constant=True, doc="""
The group is used to describe the Table.""")