Source code for matplotlib.category

Plotting of string "category" data: ``plot(['d', 'f', 'a'], [1, 2, 3])`` will
plot three points with x-axis values of 'd', 'f', 'a'.

See :doc:`/gallery/lines_bars_and_markers/categorical_variables` for an

The module uses Matplotlib's `matplotlib.units` mechanism to convert from
strings to integers and provides a tick locator, a tick formatter, and the
`.UnitData` class that creates and stores the string-to-integer mapping.

from collections import OrderedDict
import dateutil.parser
import itertools
import logging

import numpy as np

from matplotlib import cbook, ticker, units

_log = logging.getLogger(__name__)

[docs]class StrCategoryConverter(units.ConversionInterface):
[docs] @staticmethod def convert(value, unit, axis): """ Convert strings in *value* to floats using mapping information stored in the *unit* object. Parameters ---------- value : str or iterable Value or list of values to be converted. unit : `.UnitData` An object mapping strings to integers. axis : `~matplotlib.axis.Axis` The axis on which the converted value is plotted. .. note:: *axis* is unused. Returns ------- float or ndarray[float] """ if unit is None: raise ValueError( 'Missing category information for StrCategoryConverter; ' 'this might be caused by unintendedly mixing categorical and ' 'numeric data') # dtype = object preserves numerical pass throughs values = np.atleast_1d(np.array(value, dtype=object)) # pass through sequence of non binary numbers if all(units.ConversionInterface.is_numlike(v) and not isinstance(v, (str, bytes)) for v in values): return np.asarray(values, dtype=float) # force an update so it also does type checking unit.update(values) return np.vectorize(unit._mapping.__getitem__, otypes=[float])(values)
[docs] @staticmethod def axisinfo(unit, axis): """ Set the default axis ticks and labels. Parameters ---------- unit : `.UnitData` object string unit information for value axis : `~matplotlib.axis.Axis` axis for which information is being set Returns ------- `~matplotlib.units.AxisInfo` Information to support default tick labeling .. note: axis is not used """ # locator and formatter take mapping dict because # args need to be pass by reference for updates majloc = StrCategoryLocator(unit._mapping) majfmt = StrCategoryFormatter(unit._mapping) return units.AxisInfo(majloc=majloc, majfmt=majfmt)
[docs] @staticmethod def default_units(data, axis): """ Set and update the `~matplotlib.axis.Axis` units. Parameters ---------- data : str or iterable of str axis : `~matplotlib.axis.Axis` axis on which the data is plotted Returns ------- `.UnitData` object storing string to integer mapping """ # the conversion call stack is default_units -> axis_info -> convert if axis.units is None: axis.set_units(UnitData(data)) else: axis.units.update(data) return axis.units
[docs]class StrCategoryLocator(ticker.Locator): """Tick at every integer mapping of the string data.""" def __init__(self, units_mapping): """ Parameters ----------- units_mapping : dict Mapping of category names (str) to indices (int). """ self._units = units_mapping def __call__(self): # docstring inherited return list(self._units.values())
[docs] def tick_values(self, vmin, vmax): # docstring inherited return self()
[docs]class StrCategoryFormatter(ticker.Formatter): """String representation of the data at every tick.""" def __init__(self, units_mapping): """ Parameters ---------- units_mapping : dict Mapping of category names (str) to indices (int). """ self._units = units_mapping def __call__(self, x, pos=None): # docstring inherited return self.format_ticks([x])[0]
[docs] def format_ticks(self, values): # docstring inherited r_mapping = {v: self._text(k) for k, v in self._units.items()} return [r_mapping.get(round(val), '') for val in values]
@staticmethod def _text(value): """Convert text values into utf-8 or ascii strings.""" if isinstance(value, bytes): value = value.decode(encoding='utf-8') elif not isinstance(value, str): value = str(value) return value
[docs]class UnitData: def __init__(self, data=None): """ Create mapping between unique categorical values and integer ids. Parameters ---------- data : iterable sequence of string values """ self._mapping = OrderedDict() self._counter = itertools.count() if data is not None: self.update(data) @staticmethod def _str_is_convertible(val): """ Helper method to check whether a string can be parsed as float or date. """ try: float(val) except ValueError: try: dateutil.parser.parse(val) except (ValueError, TypeError): # TypeError if dateutil >= 2.8.1 else ValueError return False return True
[docs] def update(self, data): """ Map new values to integer identifiers. Parameters ---------- data : iterable of str or bytes Raises ------ TypeError If elements in *data* are neither str nor bytes. """ data = np.atleast_1d(np.array(data, dtype=object)) # check if convertible to number: convertible = True for val in OrderedDict.fromkeys(data): # OrderedDict just iterates over unique values in data. cbook._check_isinstance((str, bytes), value=val) if convertible: # this will only be called so long as convertible is True. convertible = self._str_is_convertible(val) if val not in self._mapping: self._mapping[val] = next(self._counter) if convertible:'Using categorical units to plot a list of strings ' 'that are all parsable as floats or dates. If these ' 'strings should be plotted as numbers, cast to the ' 'appropriate data type before plotting.')
# Register the converter with Matplotlib's unit framework units.registry[str] = StrCategoryConverter() units.registry[np.str_] = StrCategoryConverter() units.registry[bytes] = StrCategoryConverter() units.registry[np.bytes_] = StrCategoryConverter()