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"""

==========

Evans test

==========

A mockup "Foo" units class which supports conversion and different tick

formatting depending on the "unit". Here the "unit" is just a scalar

conversion factor, but this example shows that Matplotlib is entirely agnostic

to what kind of units client packages use.

"""

import numpy as np

import matplotlib.units as units

import matplotlib.ticker as ticker

import matplotlib.pyplot as plt

class Foo:

def __init__(self, val, unit=1.0):

self.unit = unit

self._val = val * unit

def value(self, unit):

if unit is None:

unit = self.unit

return self._val / unit

class FooConverter(units.ConversionInterface):

@staticmethod

def axisinfo(unit, axis):

"""Return the Foo AxisInfo."""

if unit == 1.0 or unit == 2.0:

return units.AxisInfo(

majloc=ticker.IndexLocator(8, 0),

majfmt=ticker.FormatStrFormatter("VAL: %s"),

label='foo',

)

else:

return None

@staticmethod

def convert(obj, unit, axis):

"""

Convert *obj* using *unit*.

If *obj* is a sequence, return the converted sequence.

"""

if units.ConversionInterface.is_numlike(obj):

return obj

if np.iterable(obj):

return [o.value(unit) for o in obj]

else:

return obj.value(unit)

@staticmethod

def default_units(x, axis):

"""Return the default unit for *x* or None."""

if np.iterable(x):

for thisx in x:

return thisx.unit

else:

return x.unit

units.registry[Foo] = FooConverter()

# create some Foos

x = [Foo(val, 1.0) for val in range(0, 50, 2)]

# and some arbitrary y data

y = [i for i in range(len(x))]

fig, (ax1, ax2) = plt.subplots(1, 2)

fig.suptitle("Custom units")

fig.subplots_adjust(bottom=0.2)

# plot specifying units

ax2.plot(x, y, 'o', xunits=2.0)

ax2.set_title("xunits = 2.0")

plt.setp(ax2.get_xticklabels(), rotation=30, ha='right')

# plot without specifying units; will use the None branch for axisinfo

ax1.plot(x, y) # uses default units

ax1.set_title('default units')

plt.setp(ax1.get_xticklabels(), rotation=30, ha='right')

plt.show()

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