def __call__(self, data):
"""
Transforms `data` using this scale.
Careful! May return `NaN` if the scale domain doesn't match the data
(ie, applying a log10 scale to negative numbers.)
"""
try:
logicle_min = self._logicle.inverse(0.0)
logicle_max = self._logicle.inverse(1.0 - sys.float_info.epsilon)
if isinstance(data, pd.Series):
data = data.clip(logicle_min, logicle_max)
return data.apply(self._logicle.scale)
elif isinstance(data, np.ndarray):
data = np.clip(data, logicle_min, logicle_max)
scale = np.vectorize(self._logicle.scale)
return scale(data)
elif isinstance(data, float):
data = max(min(data, logicle_max), logicle_min)
return self._logicle.scale(data)
else:
try:
return map(self._logicle.scale, data)
except TypeError:
raise CytoflowError("Unknown data type in LogicleScale.__call__")
except ValueError as e:
raise CytoflowError(e.strerror)
def transform_non_affine(self, values):
try:
logicle_min = self.logicle.inverse(0.0)
logicle_max = self.logicle.inverse(1.0 -
sys.float_info.epsilon)
if isinstance(values, pd.Series):
values = values.clip(logicle_min, logicle_max)
return values.apply(self.logicle.scale)
elif isinstance(values, np.ndarray):
values = np.clip(values, logicle_min, logicle_max)
scale = np.vectorize(self.logicle.scale)
return scale(values)
elif isinstance(values, float):
data = max(min(values, logicle_max), logicle_min)
return self.logicle.scale(data)
elif isinstance(values, int):
data = float(data)
data = max(min(values, logicle_max), logicle_min)
return self.logicle.scale(data)
else:
raise CytoflowError(
"Unknown data type in MatplotlibLogicleScale.transform_non_affine"
)
except ValueError as e:
raise CytoflowError("Bad transform") from e
def transform_non_affine(self, values):
logicle_min = self.logicle.inverse(0.0)
logicle_max = self.logicle.inverse(1.0 - sys.float_info.epsilon)
if isinstance(values, pd.Series):
values = values.clip(logicle_min, logicle_max)
return values.apply(self.logicle.scale)
elif isinstance(values, np.ndarray):
values = np.clip(values, logicle_min, logicle_max)
scale = np.vectorize(self.logicle.scale)
return scale(values)
elif isinstance(values, float):
data = max(min(values, logicle_max), logicle_min)
return self.logicle.scale(data)
else:
raise CytoflowError("Unknown data type in MatplotlibLogicleScale.transform_non_affine")
def __call__(self, data):
"""
Transforms `data` using this scale.
Careful! May return `NaN` if the scale domain doesn't match the data
(ie, applying a log10 scale to negative numbers.)
"""
logicle_min = self.logicle.inverse(0.0)
logicle_max = self.logicle.inverse(1.0 - sys.float_info.epsilon)
if isinstance(data, pd.Series):
data = data.clip(logicle_min, logicle_max)
return data.apply(self.logicle.scale)
elif isinstance(data, np.ndarray):
data = np.clip(data, logicle_min, logicle_max)
scale = np.vectorize(self.logicle.scale)
return scale(data)
elif isinstance(data, float):
data = max(min(data, logicle_max), logicle_min)
return self.logicle.scale(data)
else:
raise CytoflowError("Unknown data type in LogicleScale.__call__")
def transform_non_affine(self, values):
scale = np.vectorize(self.logicle.scale)
return scale(values)
def transform_non_affine(self, values):
scale = np.vectorize(self.logicle.scale)
return scale(values)