def test_auto_bin_count(ds):
b = Bins(source=ds, column='mpg')
assert len(b.bins) == 12
# this should test it still matches
# http://stats.stackexchange.com/questions/114490/optimal-bin-width-for-two-dimensional-histogram
# with iterables with the same value
b = Bins(values=[5, 5, 5, 5, 5], bins=None)
assert len(b.bins) == 3
class ColorAttr(AttrSpec):
"""An attribute specification for mapping unique data values to colors.
.. note::
Should be expanded to support more complex coloring options.
"""
attrname = Override(default='color')
iterable = Override(default=DEFAULT_PALETTE)
bin = Bool(default=False)
def __init__(self, **kwargs):
iterable = kwargs.pop('palette', None)
if iterable is not None:
kwargs['iterable'] = iterable
super(ColorAttr, self).__init__(**kwargs)
def _generate_items(self, df, columns):
"""Produce list of unique tuples that identify each item."""
if not self.bin:
super(ColorAttr, self)._generate_items(df, columns)
else:
if len(columns) == 1 and ChartDataSource.is_number(df[columns[0]]):
self.bins = Bins(source=ColumnDataSource(df),
column=columns[0],
bins=len(self.iterable),
aggregate=False)
if self.sort:
self.bins.sort(ascending=self.ascending)
self.items = [bin.label[0] for bin in self.bins]
else:
raise ValueError(
'Binned colors can only be created for one column of \
numerical data.')
def add_bin_labels(self, data):
col = self.columns[0]
# save original values into new column
data._data[col + '_values'] = data._data[col]
for bin in self.bins:
# set all rows associated to each bin to the bin label being mapped to colors
data._data.ix[data._data[col + '_values'].isin(bin.values),
col] = bin.label[0]
data._data[col] = pd.Categorical(data._data[col],
categories=list(self.items),
ordered=self.sort)
class ColorAttr(AttrSpec):
"""An attribute specification for mapping unique data values to colors.
.. note::
Should be expanded to support more complex coloring options.
"""
attrname = Override(default='color')
iterable = Override(default=DEFAULT_PALETTE)
bin = Bool(default=False)
def __init__(self, **kwargs):
iterable = kwargs.pop('palette', None)
if iterable is not None:
kwargs['iterable'] = iterable
super(ColorAttr, self).__init__(**kwargs)
def _generate_items(self, df, columns):
"""Produce list of unique tuples that identify each item."""
if not self.bin:
super(ColorAttr, self)._generate_items(df, columns)
else:
if len(columns) == 1 and ChartDataSource.is_number(df[columns[0]]):
self.bins = Bins(source=ColumnDataSource(df), column=columns[0],
bin_count=len(self.iterable), aggregate=False)
if self.sort:
self.bins.sort(ascending=self.ascending)
self.items = [bin.label[0] for bin in self.bins]
else:
raise ValueError('Binned colors can only be created for one column of \
numerical data.')
def add_bin_labels(self, data):
col = self.columns[0]
# save original values into new column
data._data[col + '_values'] = data._data[col]
for bin in self.bins:
# set all rows associated to each bin to the bin label being mapped to colors
data._data.ix[data._data[col + '_values'].isin(bin.values),
col] = bin.label[0]
data._data[col] = pd.Categorical(data._data[col], categories=list(self.items),
ordered=self.sort)
def _generate_items(self, df, columns):
"""Produce list of unique tuples that identify each item."""
if not self.bin:
super(ColorAttr, self)._generate_items(df, columns)
else:
if len(columns) == 1 and ChartDataSource.is_number(df[columns[0]]):
self.bins = Bins(source=ColumnDataSource(df), column=columns[0],
bin_count=len(self.iterable), aggregate=False)
if self.sort:
self.bins.sort(ascending=self.ascending)
self.items = [bin.label[0] for bin in self.bins]
else:
raise ValueError('Binned colors can only be created for one column of \
numerical data.')
def test_bin_labeling(ds):
Bins(source=ds, column='cyl', bins=2)
assert len(pd.Series(ds.data['cyl_bin']).drop_duplicates()) == 2
def test_explicit_bin_count(ds):
b = Bins(source=ds, column='mpg', bins=2)
assert len(b.bins) == 2
def test_auto_bin_count(ds):
b = Bins(source=ds, column='mpg')
assert len(b.bins) == 12
