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199 lines
6.5 KiB
199 lines
6.5 KiB
"""
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The classes here provide support for using custom classes with
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Matplotlib, e.g., those that do not expose the array interface but know
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how to convert themselves to arrays. It also supports classes with
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units and units conversion. Use cases include converters for custom
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objects, e.g., a list of datetime objects, as well as for objects that
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are unit aware. We don't assume any particular units implementation;
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rather a units implementation must provide the register with the Registry
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converter dictionary and a `ConversionInterface`. For example,
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here is a complete implementation which supports plotting with native
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datetime objects::
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import matplotlib.units as units
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import matplotlib.dates as dates
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import matplotlib.ticker as ticker
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import datetime
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class DateConverter(units.ConversionInterface):
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@staticmethod
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def convert(value, unit, axis):
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'Convert a datetime value to a scalar or array'
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return dates.date2num(value)
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@staticmethod
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def axisinfo(unit, axis):
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'Return major and minor tick locators and formatters'
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if unit!='date': return None
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majloc = dates.AutoDateLocator()
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majfmt = dates.AutoDateFormatter(majloc)
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return AxisInfo(majloc=majloc,
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majfmt=majfmt,
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label='date')
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@staticmethod
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def default_units(x, axis):
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'Return the default unit for x or None'
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return 'date'
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# Finally we register our object type with the Matplotlib units registry.
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units.registry[datetime.date] = DateConverter()
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"""
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from numbers import Number
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import numpy as np
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from matplotlib.cbook import iterable, safe_first_element
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class AxisInfo(object):
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"""
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Information to support default axis labeling, tick labeling, and
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default limits. An instance of this class must be returned by
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:meth:`ConversionInterface.axisinfo`.
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"""
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def __init__(self, majloc=None, minloc=None,
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majfmt=None, minfmt=None, label=None,
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default_limits=None):
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"""
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Parameters
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----------
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majloc, minloc : Locator, optional
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Tick locators for the major and minor ticks.
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majfmt, minfmt : Formatter, optional
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Tick formatters for the major and minor ticks.
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label : str, optional
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The default axis label.
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default_limits : optional
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The default min and max limits of the axis if no data has
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been plotted.
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Notes
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-----
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If any of the above are ``None``, the axis will simply use the
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default value.
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"""
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self.majloc = majloc
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self.minloc = minloc
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self.majfmt = majfmt
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self.minfmt = minfmt
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self.label = label
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self.default_limits = default_limits
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class ConversionInterface(object):
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"""
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The minimal interface for a converter to take custom data types (or
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sequences) and convert them to values Matplotlib can use.
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"""
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@staticmethod
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def axisinfo(unit, axis):
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"""
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Return an `~units.AxisInfo` instance for the axis with the
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specified units.
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"""
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return None
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@staticmethod
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def default_units(x, axis):
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"""
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Return the default unit for *x* or ``None`` for the given axis.
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"""
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return None
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@staticmethod
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def convert(obj, unit, axis):
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"""
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Convert *obj* using *unit* for the specified *axis*.
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If *obj* is a sequence, return the converted sequence.
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The output must be a sequence of scalars that can be used by the numpy
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array layer.
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"""
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return obj
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@staticmethod
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def is_numlike(x):
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"""
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The Matplotlib datalim, autoscaling, locators etc work with
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scalars which are the units converted to floats given the
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current unit. The converter may be passed these floats, or
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arrays of them, even when units are set.
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"""
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if iterable(x):
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for thisx in x:
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return isinstance(thisx, Number)
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else:
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return isinstance(x, Number)
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class Registry(dict):
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"""
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A register that maps types to conversion interfaces.
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"""
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def __init__(self):
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dict.__init__(self)
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self._cached = {}
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def get_converter(self, x):
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"""
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Get the converter for data that has the same type as *x*. If no
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converters are registered for *x*, returns ``None``.
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"""
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if not len(self):
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return None # nothing registered
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# DISABLED idx = id(x)
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# DISABLED cached = self._cached.get(idx)
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# DISABLED if cached is not None: return cached
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converter = None
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classx = getattr(x, '__class__', None)
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if classx is not None:
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converter = self.get(classx)
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if converter is None and hasattr(x, "values"):
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# this unpacks pandas series or dataframes...
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x = x.values
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# If x is an array, look inside the array for data with units
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if isinstance(x, np.ndarray) and x.size:
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xravel = x.ravel()
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try:
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# pass the first value of x that is not masked back to
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# get_converter
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if not np.all(xravel.mask):
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# some elements are not masked
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converter = self.get_converter(
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xravel[np.argmin(xravel.mask)])
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return converter
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except AttributeError:
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# not a masked_array
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# Make sure we don't recurse forever -- it's possible for
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# ndarray subclasses to continue to return subclasses and
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# not ever return a non-subclass for a single element.
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next_item = xravel[0]
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if (not isinstance(next_item, np.ndarray) or
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next_item.shape != x.shape):
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converter = self.get_converter(next_item)
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return converter
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# If we haven't found a converter yet, try to get the first element
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if converter is None:
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try:
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thisx = safe_first_element(x)
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except (TypeError, StopIteration):
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pass
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else:
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if classx and classx != getattr(thisx, '__class__', None):
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converter = self.get_converter(thisx)
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return converter
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# DISABLED self._cached[idx] = converter
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return converter
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registry = Registry()
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