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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):

        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 numbers import Number

import numpy as np

from matplotlib.cbook import iterable, safe_first_element

[docs]class AxisInfo(object): """ Information to support default axis labeling, tick labeling, and default limits. An instance of this class must be returned by :meth:`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` instance 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 iterable(x): for thisx in x: return isinstance(thisx, Number) else: return isinstance(x, Number)
[docs]class Registry(dict): """ A register that maps types to conversion interfaces. """ def __init__(self): dict.__init__(self) self._cached = {}
[docs] def get_converter(self, x): """ Get the converter for data that has the same type as *x*. If no converters are registered for *x*, returns ``None``. """ if not len(self): return None # nothing registered # DISABLED idx = id(x) # DISABLED cached = self._cached.get(idx) # DISABLED if cached is not None: return cached converter = None classx = getattr(x, '__class__', None) if classx is not None: converter = self.get(classx) if converter is None and hasattr(x, "values"): # this unpacks pandas series or dataframes... x = x.values # If x is an array, look inside the array for data with units if isinstance(x, np.ndarray) and x.size: xravel = x.ravel() try: # pass the first value of x that is not masked back to # get_converter if not np.all(xravel.mask): # some elements are not masked converter = self.get_converter( xravel[np.argmin(xravel.mask)]) return converter except AttributeError: # not a masked_array # Make sure we don't recurse forever -- it's possible for # ndarray subclasses to continue to return subclasses and # not ever return a non-subclass for a single element. next_item = xravel[0] if (not isinstance(next_item, np.ndarray) or next_item.shape != x.shape): converter = self.get_converter(next_item) return converter # If we haven't found a converter yet, try to get the first element if converter is None: try: thisx = safe_first_element(x) except (TypeError, StopIteration): pass else: if classx and classx != getattr(thisx, '__class__', None): converter = self.get_converter(thisx) return converter # DISABLED self._cached[idx] = converter return converter
registry = Registry()