""" 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 converter themselves to arrays. It also supoprts 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 a ConversionInterface, and the register with the Registry converter dictionary. 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 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 a converter units.registry[datetime.date] = DateConverter() """ from __future__ import (absolute_import, division, print_function, unicode_literals) from matplotlib.externals import six from matplotlib.cbook import iterable, is_numlike import numpy as np class AxisInfo(object): """information to support default axis labeling and tick labeling, and default limits""" def __init__(self, majloc=None, minloc=None, majfmt=None, minfmt=None, label=None, default_limits=None): """ majloc and minloc: TickLocators for the major and minor ticks majfmt and minfmt: TickFormatters for the major and minor ticks label: the default axis label default_limits: the default min, max of the axis if no data is present If any of the above are None, the axis will simply use the default """ self.majloc = majloc self.minloc = minloc self.majfmt = majfmt self.minfmt = minfmt self.label = label self.default_limits = default_limits class ConversionInterface(object): """ The minimal interface for a converter to take custom instances (or sequences) and convert them to values mpl can use """ @staticmethod def axisinfo(unit, axis): 'return an units.AxisInfo instance for axis with the specified units' return None @staticmethod def default_units(x, axis): 'return the default unit for x or None for the given axis' return None @staticmethod def convert(obj, unit, axis): """ convert obj using unit for the specified axis. If obj is a sequence, return the converted sequence. The ouput must be a sequence of scalars that can be used by the numpy array layer """ return obj @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. Derived conversion interfaces may opt to pass plain-ol unitless numbers through the conversion interface and this is a helper function for them. """ if iterable(x): for thisx in x: return is_numlike(thisx) else: return is_numlike(x) class Registry(dict): """ register types with conversion interface """ def __init__(self): dict.__init__(self) self._cached = {} def get_converter(self, x): 'get the converter interface instance for x, or 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 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 converter is None and iterable(x): for thisx in x: # Make sure that recursing might actually lead to a solution, # if we are just going to re-examine another item of the same # kind, then do not look at it. if classx and classx != getattr(thisx, '__class__', None): converter = self.get_converter(thisx) return converter #DISABLED self._cached[idx] = converter return converter registry = Registry()