def _calculate(self, data):
x = pop(data, 'x', None)
y = pop(data, 'y', None)
# intercept and slope may be one of:
# - aesthetics to geom_abline or
# - parameter settings to stat_abline
slope = pop(data, 'slope', self.params['slope'])
intercept = pop(data, 'intercept', self.params['intercept'])
if hasattr(slope, '__call__'):
if x is None or y is None:
raise GgplotError(
'To compute the slope, x & y aesthetics are needed')
try:
slope = slope(x, y)
except TypeError as err:
raise GgplotError(*err.args)
if hasattr(intercept, '__call__'):
if x is None or y is None:
raise GgplotError(
'To compute the intercept, x & y aesthetics are needed')
try:
intercept = intercept(x, y)
except TypeError as err:
raise GgplotError(*err.args)
try:
n = len(slope)
except TypeError:
n = 1
try:
_n = len(intercept)
except TypeError:
_n = 1
if n != _n:
raise GgplotError('Specified {} slopes but {} intercepts'.format(
n, _n))
slope = make_iterable(slope)
intercept = make_iterable(intercept)
new_data = pd.DataFrame({'slope': slope, 'intercept': intercept})
# Copy the other aesthetics into the new dataframe
n = len(slope)
for ae in data:
new_data[ae] = make_iterable_ntimes(data[ae].iloc[0], n)
return new_data
def _calculate(self, data):
x = pop(data, 'x', None)
y = pop(data, 'y', None)
# intercept and slope may be one of:
# - aesthetics to geom_abline or
# - parameter settings to stat_abline
slope = pop(data, 'slope', self.params['slope'])
intercept = pop(data, 'intercept', self.params['intercept'])
if hasattr(slope, '__call__'):
if x is None or y is None:
raise GgplotError(
'To compute the slope, x & y aesthetics are needed')
try:
slope = slope(x, y)
except TypeError as err:
raise GgplotError(*err.args)
if hasattr(intercept, '__call__'):
if x is None or y is None:
raise GgplotError(
'To compute the intercept, x & y aesthetics are needed')
try:
intercept = intercept(x, y)
except TypeError as err:
raise GgplotError(*err.args)
try:
n = len(slope)
except TypeError:
n = 1
try:
_n = len(intercept)
except TypeError:
_n = 1
if n != _n:
raise GgplotError(
'Specified {} slopes but {} intercepts'.format(n, _n))
slope = make_iterable(slope)
intercept = make_iterable(intercept)
new_data = pd.DataFrame({'slope': slope, 'intercept': intercept})
# Copy the other aesthetics into the new dataframe
n = len(slope)
for ae in data:
new_data[ae] = make_iterable_ntimes(data[ae].iloc[0], n)
return new_data
def _calculate(self, data):
x = pop(data, 'x', None)
# xintercept may be one of:
# - aesthetic to geom_vline or
# - parameter setting to stat_vline
xintercept = pop(data, 'xintercept', self.params['xintercept'])
if hasattr(xintercept, '__call__'):
if x is None:
raise GgplotError(
'To compute the intercept, x aesthetic is needed')
try:
xintercept = xintercept(x)
except TypeError as err:
raise GgplotError(*err.args)
xintercept = make_iterable(xintercept)
new_data = pd.DataFrame({'xintercept': xintercept})
# Copy the other aesthetics into the new dataframe
n = len(xintercept)
for ae in data:
new_data[ae] = make_iterable_ntimes(data[ae].iloc[0], n)
return new_data