Version 3.1.2
matplotlib
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Source code for matplotlib.units

"""
The classes here provide support for using custom classes with
Matplotlib, e.g., those that do not expose the array interface but know
how to convert themselves to arrays.  It also supports classes with
units and units conversion.  Use cases include converters for custom
objects, e.g., a list of datetime objects, as well as for objects that
are unit aware.  We don't assume any particular units implementation;
rather a units implementation must provide the register with the Registry
converter dictionary and a `ConversionInterface`.  For example,
here is a complete implementation which supports plotting with native
datetime objects::

    import matplotlib.units as units
    import matplotlib.dates as dates
    import matplotlib.ticker as ticker
    import datetime

    class DateConverter(units.ConversionInterface):

        @staticmethod
        def convert(value, unit, axis):
            'Convert a datetime value to a scalar or array'
            return dates.date2num(value)

        @staticmethod
        def axisinfo(unit, axis):
            'Return major and minor tick locators and formatters'
            if unit!='date': return None
            majloc = dates.AutoDateLocator()
            majfmt = dates.AutoDateFormatter(majloc)
            return AxisInfo(majloc=majloc,
                            majfmt=majfmt,
                            label='date')

        @staticmethod
        def default_units(x, axis):
            'Return the default unit for x or None'
            return 'date'

    # Finally we register our object type with the Matplotlib units registry.
    units.registry[datetime.date] = DateConverter()

"""

from numbers import Number

import numpy as np
from numpy import ma

from matplotlib import cbook


[docs]class ConversionError(TypeError): pass
[docs]class AxisInfo(object): """ Information to support default axis labeling, tick labeling, and limits. An instance of this class must be returned by `ConversionInterface.axisinfo`. """ def __init__(self, majloc=None, minloc=None, majfmt=None, minfmt=None, label=None, default_limits=None): """ Parameters ---------- majloc, minloc : Locator, optional Tick locators for the major and minor ticks. majfmt, minfmt : Formatter, optional Tick formatters for the major and minor ticks. label : str, optional The default axis label. default_limits : optional The default min and max limits of the axis if no data has been plotted. Notes ----- If any of the above are ``None``, the axis will simply use the default value. """ self.majloc = majloc self.minloc = minloc self.majfmt = majfmt self.minfmt = minfmt self.label = label self.default_limits = default_limits
[docs]class ConversionInterface(object): """ The minimal interface for a converter to take custom data types (or sequences) and convert them to values Matplotlib can use. """
[docs] @staticmethod def axisinfo(unit, axis): """ Return an `~units.AxisInfo` for the axis with the specified units. """ return None
[docs] @staticmethod def default_units(x, axis): """ Return the default unit for *x* or ``None`` for the given axis. """ return None
[docs] @staticmethod def convert(obj, unit, axis): """ Convert *obj* using *unit* for the specified *axis*. If *obj* is a sequence, return the converted sequence. The output must be a sequence of scalars that can be used by the numpy array layer. """ return obj
[docs] @staticmethod def is_numlike(x): """ The Matplotlib datalim, autoscaling, locators etc work with scalars which are the units converted to floats given the current unit. The converter may be passed these floats, or arrays of them, even when units are set. """ if np.iterable(x): for thisx in x: if thisx is ma.masked: continue return isinstance(thisx, Number) else: return isinstance(x, Number)
[docs]class Registry(dict): """Register types with conversion interface."""
[docs] def get_converter(self, x): """Get the converter interface instance for *x*, or None.""" if hasattr(x, "values"): x = x.values # Unpack pandas Series and DataFrames. if isinstance(x, np.ndarray): # In case x in a masked array, access the underlying data (only its # type matters). If x is a regular ndarray, getdata() just returns # the array itself. x = np.ma.getdata(x).ravel() # If there are no elements in x, infer the units from its dtype if not x.size: return self.get_converter(np.array([0], dtype=x.dtype)) try: # Look up in the cache. return self[type(x)] except KeyError: try: # If cache lookup fails, look up based on first element... first = cbook.safe_first_element(x) except (TypeError, StopIteration): pass else: # ... and avoid infinite recursion for pathological iterables # where indexing returns instances of the same iterable class. if type(first) is not type(x): return self.get_converter(first) return None
registry = Registry()