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[WIP] Categorical Color Mapping#6934

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Categorical mapping via units on norm
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@story645
story645 committedAug 18, 2016
commit9707a0f8b6c7b17da8c087b122bd0b5f22e7d1d7
36 changes: 0 additions & 36 deletionsbuild_alllocal.cmd
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100 changes: 58 additions & 42 deletionslib/matplotlib/category.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -38,12 +38,11 @@ class StrCategoryConverter(munits.ConversionInterface):

Conversion typically happens in the following order:
1. default_units:
creates unit_data category-integer mapping and binds to axis
create unit_data category-integer mapping and binds to axis
2. axis_info:
sets ticks/locator andlabel/formatter
set ticks/locator andlabels/formatter
3. convert:
maps input category data to integers using unit_data

map input category data to integers using unit_data
"""
@staticmethod
def convert(value, unit, axis):
Expand All@@ -53,13 +52,13 @@ def convert(value, unit, axis):
vmap = dict(zip(axis.unit_data.seq, axis.unit_data.locs))

if isinstance(value, six.string_types):
return vmap[value]
return vmap.get(value, None)

vals = to_array(value)
for lab, loc in vmap.items():
vals[vals == lab] = loc

return vals.astype('float')
return vals.astype('float64')

@staticmethod
def axisinfo(unit, axis):
Expand All@@ -74,16 +73,19 @@ def axisinfo(unit, axis):
return munits.AxisInfo(majloc=majloc, majfmt=majfmt)

@staticmethod
def default_units(data, axis):
def default_units(data, axis, sort=True, normed=False):
"""
Create mapping between string categories in *data*
and integers,then store in *axis.unit_data*
and integers,and store in *axis.unit_data*
"""
if axis.unit_data is None:
axis.unit_data = UnitData(data)
else:
axis.unit_data.update(data)
return None
if axis and axis.unit_data:
axis.unit_data.update(data, sort)
return axis.unit_data

unit_data = UnitData(data, sort)
if axis:
axis.unit_data = unit_data
return unit_data


class StrCategoryLocator(mticker.FixedLocator):
Expand All@@ -110,35 +112,37 @@ class CategoryNorm(mcolors.Normalize):
"""
Preserves ordering of discrete values
"""
def __init__(self,categories):
def __init__(self,data):
"""
*categories*
distinct values for mapping

Out-of-range values are mapped to a value not in categories;
these are then converted to valid indices by :meth:`Colormap.__call__`.
Out-of-range values are mapped to np.nan
"""
self.categories = categories
self.N = len(self.categories)
self.vmin = 0
self.vmax = self.N
self._interp = False

def __call__(self, value, clip=None):
if not cbook.iterable(value):
value = [value]

value = np.asarray(value)
ret = np.ones(value.shape) * np.nan

for i, c in enumerate(self.categories):
ret[value == c] = i / (self.N * 1.0)
self.units = StrCategoryConverter()
self.unit_data = None
self.units.default_units(data,
self, sort=False)
self.loc2seq = dict(zip(self.unit_data.locs, self.unit_data.seq))
self.vmin = min(self.unit_data.locs)
self.vmax = max(self.unit_data.locs)

return np.ma.array(ret, mask=np.isnan(ret))
def __call__(self, value, clip=None):
# gonna have to go into imshow and undo casting
value = np.asarray(value, dtype=np.int)
ret = self.units.convert(value, None, self)
# knock out values not in the norm
mask = np.in1d(ret, self.unit_data.locs).reshape(ret.shape)
# normalize ret & locs
ret /= self.vmax
return np.ma.array(ret, mask=~mask)

def inverse(self, value):
# not quite sure what invertible means in this context
return ValueError("CategoryNorm is not invertible")
if not cbook.iterable(value):
value = np.asarray(value)
vscaled = np.asarray(value) * self.vmax
return [self.loc2seq[int(vs)] for vs in vscaled]


def colors_from_categories(codings):
Expand All@@ -156,8 +160,7 @@ def colors_from_categories(codings):
:class:`Normalize` instance
"""
if isinstance(codings, dict):
codings = codings.items()

codings = cbook.sanitize_sequence(codings.items())
values, colors = zip(*codings)
cmap = mcolors.ListedColormap(list(colors))
norm = CategoryNorm(list(values))
Expand All@@ -184,30 +187,43 @@ def convert_to_string(value):


class UnitData(object):
# debatable makes sense to special code missing values
# debatableif itmakes sense to special code missing values
spdict = {'nan': -1.0, 'inf': -2.0, '-inf': -3.0}

def __init__(self, data):
def __init__(self, data, sort=True):
"""Create mapping between unique categorical values
and numerical identifier
Paramters
---------
data: iterable
sequence of values
sort: bool
sort input data, default is True
False preserves input order
"""
self.seq, self.locs = [], []
self._set_seq_locs(data, 0)
self._set_seq_locs(data, 0, sort)
self.sort = sort

def update(self, new_data):
def update(self, new_data, sort=True):
if sort:
self.sort = sort
# so as not to conflict with spdict
value = max(max(self.locs) + 1, 0)
self._set_seq_locs(new_data, value)
self._set_seq_locs(new_data, value, self.sort)

def _set_seq_locs(self, data, value):
def _set_seq_locs(self, data, value, sort):
# magic to make it work under np1.6
strdata = to_array(data)

# np.unique makes dateframes work
new_s = [d for d in np.unique(strdata) if d not in self.seq]
if sort:
unq = np.unique(strdata)
else:
_, idx = np.unique(strdata, return_index=~sort)
unq = strdata[np.sort(idx)]

new_s = [d for d in unq if d not in self.seq]
for ns in new_s:
self.seq.append(convert_to_string(ns))
if ns in UnitData.spdict.keys():
Expand Down
5 changes: 5 additions & 0 deletionslib/matplotlib/colorbar.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -30,6 +30,7 @@

import matplotlib as mpl
import matplotlib.artist as martist
import matplotlib.category as category
import matplotlib.cbook as cbook
import matplotlib.collections as collections
import matplotlib.colors as colors
Expand DownExpand Up@@ -312,6 +313,8 @@ def __init__(self, ax, cmap=None,
if format is None:
if isinstance(self.norm, colors.LogNorm):
self.formatter = ticker.LogFormatterMathtext()
elif isinstance(self.norm, category.CategoryNorm):
self.formatter = ticker.FixedFormatter(self.norm.unit_data.seq)
else:
self.formatter = ticker.ScalarFormatter()
elif cbook.is_string_like(format):
Expand DownExpand Up@@ -580,6 +583,8 @@ def _ticker(self):
locator = ticker.FixedLocator(b, nbins=10)
elif isinstance(self.norm, colors.LogNorm):
locator = ticker.LogLocator()
elif isinstance(self.norm, category.CategoryNorm):
locator = ticker.FixedLocator(self.norm.unit_data.locs)
else:
if mpl.rcParams['_internal.classic_mode']:
locator = ticker.MaxNLocator()
Expand Down
30 changes: 24 additions & 6 deletionslib/matplotlib/tests/test_category.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -106,7 +106,7 @@ def test_axisinfo(self):

def test_default_units(self):
axis = FakeAxis(None)
assert self.cc.default_units(["a"], axis) is None
assertisinstance(self.cc.default_units(["a"], axis), cat.UnitData)


class TestStrCategoryLocator(object):
Expand All@@ -129,17 +129,35 @@ def test_StrCategoryFormatterUnicode(self):


class TestCategoryNorm(object):
testdata = [[[205, 302, 205, 101], [0,2. / 3., 0,1. / 3.]],
[[205, np.nan, 101, 305], [0,9999, 1. / 3., 2. / 3.]],
[[205, 101, 504, 101], [0,9999, 1. / 3., 1. / 3.]]]
testdata = [[[205, 302, 205, 101], [0,1, 0,.5]],
[[205, np.nan, 101, 305], [0,np.nan, .5, 1]],
[[205, 101, 504, 101], [0,.5, np.nan, .5]]]

ids = ["regular", "nan", "exclude"]

@pytest.mark.parametrize("data, nmap", testdata, ids=ids)
def test_norm(self, data, nmap):
norm = cat.CategoryNorm([205, 101, 302])
test = np.ma.masked_equal(nmap, 9999)
np.testing.assert_allclose(norm(data), test)
masked_nmap = np.ma.masked_equal(nmap, np.nan)
assert np.ma.allequal(norm(data), masked_nmap)

def test_invert(self):
data = [205, 302, 101]
strdata = ['205', '302', '101']
value = [0, .5, 1]
norm = cat.CategoryNorm(data)
assert norm.inverse(value) == strdata


class TestColorsFromCategories(object):
testdata = [[{'101': "blue", '205': "red", '302': "green"}, dict],
[[('205', "red"), ('101', "blue"), ('302', "green")], list]]
ids = ["dict", "tuple"]

@pytest.mark.parametrize("codings, mtype", testdata, ids=ids)
def test_colors_from_categories(self, codings, mtype):
cmap, norm = cat.colors_from_categories(codings)
assert mtype(zip(norm.unit_data.seq, cmap.colors)) == codings


def lt(tl):
Expand Down
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