forked from 170010011/fr
223 lines
7.1 KiB
Python
223 lines
7.1 KiB
Python
"""
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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 decimal import Decimal
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from numbers import Number
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import numpy as np
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from numpy import ma
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from matplotlib import cbook
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class ConversionError(TypeError):
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pass
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def _is_natively_supported(x):
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"""
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Return whether *x* is of a type that Matplotlib natively supports or an
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array of objects of such types.
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"""
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# Matplotlib natively supports all number types except Decimal.
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if np.iterable(x):
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# Assume lists are homogeneous as other functions in unit system.
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for thisx in x:
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if thisx is ma.masked:
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continue
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return isinstance(thisx, Number) and not isinstance(thisx, Decimal)
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else:
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return isinstance(x, Number) and not isinstance(x, Decimal)
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class AxisInfo:
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"""
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Information to support default axis labeling, tick labeling, and limits.
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An instance of this class must be returned by
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`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:
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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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"""Return an `.AxisInfo` for the axis with the specified units."""
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return None
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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`` for the given axis."""
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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. The output must
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be a sequence of scalars that can be used by the numpy 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 scalars
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which are the units converted to floats given the current unit. The
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converter may be passed these floats, or arrays of them, even when
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units are set.
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"""
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if np.iterable(x):
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for thisx in x:
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if thisx is ma.masked:
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continue
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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 DecimalConverter(ConversionInterface):
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"""Converter for decimal.Decimal data to float."""
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@staticmethod
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def convert(value, unit, axis):
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"""
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Convert Decimals to floats.
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The *unit* and *axis* arguments are not used.
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Parameters
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----------
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value : decimal.Decimal or iterable
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Decimal or list of Decimal need to be converted
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"""
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# If value is a Decimal
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if isinstance(value, Decimal):
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return float(value)
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else:
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# assume x is a list of Decimal
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converter = np.asarray
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if isinstance(value, ma.MaskedArray):
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converter = ma.asarray
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return converter(value, dtype=float)
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@staticmethod
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def axisinfo(unit, axis):
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# Since Decimal is a kind of Number, don't need specific axisinfo.
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return AxisInfo()
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@staticmethod
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def default_units(x, axis):
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# Return None since Decimal is a kind of Number.
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return None
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class Registry(dict):
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"""Register types with conversion interface."""
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def get_converter(self, x):
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"""Get the converter interface instance for *x*, or None."""
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if hasattr(x, "values"):
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x = x.values # Unpack pandas Series and DataFrames.
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if isinstance(x, np.ndarray):
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# In case x in a masked array, access the underlying data (only its
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# type matters). If x is a regular ndarray, getdata() just returns
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# the array itself.
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x = np.ma.getdata(x).ravel()
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# If there are no elements in x, infer the units from its dtype
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if not x.size:
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return self.get_converter(np.array([0], dtype=x.dtype))
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for cls in type(x).__mro__: # Look up in the cache.
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try:
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return self[cls]
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except KeyError:
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pass
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try: # If cache lookup fails, look up based on first element...
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first = cbook.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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# ... and avoid infinite recursion for pathological iterables for
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# which indexing returns instances of the same iterable class.
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if type(first) is not type(x):
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return self.get_converter(first)
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return None
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registry = Registry()
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registry[Decimal] = DecimalConverter()
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