142 lines
4.0 KiB
Python
142 lines
4.0 KiB
Python
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#!/usr/bin/env python
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try:
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from collections.abc import Iterable, Sequence, Mapping
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except ImportError:
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from collections import Iterable, Sequence, Mapping
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from functools import partial
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import six
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from leather.data_types import DataType
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from leather.utils import DIMENSION_NAMES, X, Y, Datum
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class Series(object):
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"""
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A series of data and its associated metadata.
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Series object does not modify the data it is passed.
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:param data:
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A sequence (rows) of sequences (columns), a.k.a. :func:`csv.reader`
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format. If the :code:`x` and :code:`y` are not specified then the first
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column is used as the X values and the second column is used for Y.
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Or, a sequence of (rows) of dicts (columns), a.k.a.
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:class:`csv.DictReader` format. If this format is used then :code:`x`
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and :code:`y` arguments must specify the columns to be charted.
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Or, a custom data format, in which case :code:`x` and :code:`y` must
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specify :func:`.key_function`.
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:param x:
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If using sequence row data, then this may be either an integer index
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identifying the X column, or a :func:`.key_function`.
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If using dict row data, then this may be either a key name identifying
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the X column, or a :func:`.key_function`.
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If using a custom data format, then this must be a
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:func:`.key_function`.`
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:param y:
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See :code:`x`.
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:param name:
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An optional name to be used in labeling this series. This will be
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used as the chart title if rendered in a :class:`.Lattice`.
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"""
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def __init__(self, data, x=None, y=None, name=None):
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self._data = data
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self._name = name
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self._keys = [
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self._make_key(x if x is not None else X),
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self._make_key(y if y is not None else Y)
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]
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self._types = [
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self._infer_type(X),
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self._infer_type(Y)
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]
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def _make_key(self, key):
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"""
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Process a user-specified data key and convert to a function if needed.
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"""
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if callable(key):
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return key
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else:
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return lambda row, index: row[key]
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def _infer_type(self, dimension):
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"""
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Infer the datatype of this column by sampling the data.
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"""
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key = self._keys[dimension]
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for i, row in enumerate(self._data):
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v = key(row, i)
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if v is not None:
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break
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if v is None:
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raise ValueError('All values in %s dimension are null.' % DIMENSION_NAMES[dimension])
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return DataType.infer(v)
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@property
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def name(self):
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return self._name
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def data_type(self, dimension):
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"""
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Return the data type for a dimension of this series.
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"""
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return self._types[dimension]
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def data(self):
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"""
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Return data for this series.
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"""
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x = self._keys[X]
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y = self._keys[Y]
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for i, row in enumerate(self._data):
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yield Datum(i, x(row, i), y(row, i), None, row)
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def values(self, dimension):
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"""
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Get a flattened list of values for a given dimension of the data.
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"""
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key = self._keys[dimension]
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return [key(row, i) for i, row in enumerate(self._data)]
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def min(self, dimension):
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"""
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Compute the minimum value of a given dimension.
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"""
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return min(v for v in self.values(dimension) if v is not None)
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def max(self, dimension):
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"""
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Compute the minimum value of a given dimension.
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"""
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return max(v for v in self.values(dimension) if v is not None)
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def key_function(row, index):
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"""
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This example shows how to define a function to extract X and Y values
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from custom data.
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:param row:
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The function will be called with the row data, in whatever format it
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was provided to the :class:`.Series`.
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:param index:
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The row index in the series data will also be provided.
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:returns:
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The function must return a chartable value.
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"""
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pass
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