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):

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

        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,

        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[] = DateConverter()


from decimal import Decimal
from numbers import Number

import numpy as np
from numpy import ma

from matplotlib import cbook

[docs]class ConversionError(TypeError): pass
def _is_natively_supported(x): """ Return whether *x* is of a type that Matplotlib natively supports or an array of objects of such types. """ # Matplotlib natively supports all number types except Decimal. if np.iterable(x): # Assume lists are homogeneous as other functions in unit system. for thisx in x: if thisx is ma.masked: continue return isinstance(thisx, Number) and not isinstance(thisx, Decimal) else: return isinstance(x, Number) and not isinstance(x, Decimal)
[docs]class AxisInfo: """ 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: """ 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 `.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 DecimalConverter(ConversionInterface): """Converter for decimal.Decimal data to float."""
[docs] @staticmethod def convert(value, unit, axis): """ Convert Decimals to floats. The *unit* and *axis* arguments are not used. Parameters ---------- value : decimal.Decimal or iterable Decimal or list of Decimal need to be converted """ # If value is a Decimal if isinstance(value, Decimal): return float(value) else: # assume x is a list of Decimal converter = np.asarray if isinstance(value, ma.MaskedArray): converter = ma.asarray return converter(value, dtype=float)
[docs] @staticmethod def axisinfo(unit, axis): # Since Decimal is a kind of Number, don't need specific axisinfo. return AxisInfo()
[docs] @staticmethod def default_units(x, axis): # Return None since Decimal is a kind of Number. return None
[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 = # 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)) for cls in type(x).__mro__: # Look up in the cache. try: return self[cls] except KeyError: pass 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 for # which indexing returns instances of the same iterable class. if type(first) is not type(x): return self.get_converter(first) return None
registry = Registry() registry[Decimal] = DecimalConverter()