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
example.

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__)


class StrCategoryConverter(units.ConversionInterface):
    @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)

    @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)

    @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


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())

    def tick_values(self, vmin, vmax):
        # docstring inherited
        return self()


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]

    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


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

    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:
            _log.info('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()