def get_edgecolor(self):
# We need this check here to make sure we do not double-apply the depth
# based alpha shading when the edge color is "face" which means the
# edge colour should be identical to the face colour.
if cbook._str_equal(self._edgecolors, 'face'):
return self.get_facecolor()
return self._maybe_depth_shade_and_sort_colors(super().get_edgecolor())
def set_color(self, color):
# docstring inherited
# Unlike the base Line2D.set_color, this also supports "auto".
if not cbook._str_equal(color, "auto"):
mcolors._check_color_like(color=color)
self._color = color
self.stale = True
def _validate_color_or_linecolor(s):
if cbook._str_equal(s, 'linecolor'):
return s
elif cbook._str_equal(s, 'mfc') or cbook._str_equal(s, 'markerfacecolor'):
return 'markerfacecolor'
elif cbook._str_equal(s, 'mec') or cbook._str_equal(s, 'markeredgecolor'):
return 'markeredgecolor'
elif s is None:
return None
elif isinstance(s, str) and len(s) == 6 or len(s) == 8:
stmp = '#' + s
if is_color_like(stmp):
return stmp
if s.lower() == 'none':
return None
elif is_color_like(s):
return s
raise ValueError(f'{s!r} does not look like a color arg')
def __init__(self, dataset, bw_method=None):
self.dataset = np.atleast_2d(dataset)
if not np.array(self.dataset).size > 1:
raise ValueError("`dataset` input should have multiple elements.")
self.dim, self.num_dp = np.array(self.dataset).shape
if bw_method is None:
pass
elif cbook._str_equal(bw_method, 'scott'):
self.covariance_factor = self.scotts_factor
elif cbook._str_equal(bw_method, 'silverman'):
self.covariance_factor = self.silverman_factor
elif isinstance(bw_method, Number):
self._bw_method = 'use constant'
self.covariance_factor = lambda: bw_method
elif callable(bw_method):
self._bw_method = bw_method
self.covariance_factor = lambda: self._bw_method(self)
else:
raise ValueError("`bw_method` should be 'scott', 'silverman', a "
"scalar or a callable")
# Computes the covariance matrix for each Gaussian kernel using
# covariance_factor().
self.factor = self.covariance_factor()
# Cache covariance and inverse covariance of the data
if not hasattr(self, '_data_inv_cov'):
self.data_covariance = np.atleast_2d(
np.cov(
self.dataset,
rowvar=1,
bias=False))
self.data_inv_cov = np.linalg.inv(self.data_covariance)
self.covariance = self.data_covariance * self.factor ** 2
self.inv_cov = self.data_inv_cov / self.factor ** 2
self.norm_factor = np.sqrt(
np.linalg.det(
2 * np.pi * self.covariance)) * self.num_dp
def __init__(self, dataset, bw_method=None):
self.dataset = np.atleast_2d(dataset)
if not np.array(self.dataset).size > 1:
raise ValueError("`dataset` input should have multiple elements.")
self.dim, self.num_dp = np.array(self.dataset).shape
if bw_method is None:
pass
elif cbook._str_equal(bw_method, 'scott'):
self.covariance_factor = self.scotts_factor
elif cbook._str_equal(bw_method, 'silverman'):
self.covariance_factor = self.silverman_factor
elif isinstance(bw_method, Number):
self._bw_method = 'use constant'
self.covariance_factor = lambda: bw_method
elif callable(bw_method):
self._bw_method = bw_method
self.covariance_factor = lambda: self._bw_method(self)
else:
raise ValueError("`bw_method` should be 'scott', 'silverman', a "
"scalar or a callable")
# Computes the covariance matrix for each Gaussian kernel using
# covariance_factor().
self.factor = self.covariance_factor()
# Cache covariance and inverse covariance of the data
if not hasattr(self, '_data_inv_cov'):
self.data_covariance = np.atleast_2d(
np.cov(
self.dataset,
rowvar=1,
bias=False))
self.data_inv_cov = np.linalg.inv(self.data_covariance)
self.covariance = self.data_covariance * self.factor ** 2
self.inv_cov = self.data_inv_cov / self.factor ** 2
self.norm_factor = (np.sqrt(np.linalg.det(2 * np.pi * self.covariance))
* self.num_dp)
def score_weight(self, weight1, weight2):
"""
Return a match score between *weight1* and *weight2*.
The result is 0.0 if both weight1 and weight 2 are given as strings
and have the same value.
Otherwise, the result is the absolute value of the difference between
the CSS numeric values of *weight1* and *weight2*, normalized between
0.05 and 1.0.
"""
# exact match of the weight names, e.g. weight1 == weight2 == "regular"
if cbook._str_equal(weight1, weight2):
return 0.0
w1 = weight1 if isinstance(weight1, Number) else weight_dict[weight1]
w2 = weight2 if isinstance(weight2, Number) else weight_dict[weight2]
return 0.95 * (abs(w1 - w2) / 1000) + 0.05
def score_weight(self, weight1, weight2):
"""
Returns a match score between *weight1* and *weight2*.
The result is 0.0 if both weight1 and weight 2 are given as strings
and have the same value.
Otherwise, the result is the absolute value of the difference between
the CSS numeric values of *weight1* and *weight2*, normalized between
0.05 and 1.0.
"""
# exact match of the weight names, e.g. weight1 == weight2 == "regular"
if cbook._str_equal(weight1, weight2):
return 0.0
w1 = weight1 if isinstance(weight1, Number) else weight_dict[weight1]
w2 = weight2 if isinstance(weight2, Number) else weight_dict[weight2]
return 0.95 * (abs(w1 - w2) / 1000) + 0.05
def validate_color_or_auto(s):
if cbook._str_equal(s, 'auto'):
return s
return validate_color(s)
def validate_color_or_inherit(s):
"""Return a valid color arg."""
if cbook._str_equal(s, 'inherit'):
return s
return validate_color(s)
