import inspect
import warnings
from traitlets import TraitType, TraitError, Undefined, Sentinel
class _DelayedImportError(object):
def __init__(self, package_name):
self.package_name = package_name
def __getattribute__(self, name):
package_name = super(_DelayedImportError, self).__getattribute__('package_name')
raise RuntimeError('Missing dependency: %s' % package_name)
try:
import numpy as np
except ImportError:
np = _DelayedImportError('numpy')
Empty = Sentinel('Empty', 'traittypes',
"""
Used in traittypes to specify that the default value should
be an empty dataset
""")
class SciType(TraitType):
"""A base trait type for numpy arrays, pandas dataframes, pandas series, xarray datasets and xarray dataarrays."""
def __init__(self, **kwargs):
super(SciType, self).__init__(**kwargs)
self.validators = []
def valid(self, *validators):
"""
Register new trait validators
Validators are functions that take two arguments.
- The trait instance
- The proposed value
Validators return the (potentially modified) value, which is either
assigned to the HasTraits attribute or input into the next validator.
They are evaluated in the order in which they are provided to the `valid`
function.
Example
-------
.. code:: python
# Test with a shape constraint
def shape(*dimensions):
def validator(trait, value):
if value.shape != dimensions:
raise TraitError('Expected an of shape %s and got and array with shape %s' % (dimensions, value.shape))
else:
return value
return validator
class Foo(HasTraits):
bar = Array(np.identity(2)).valid(shape(2, 2))
foo = Foo()
foo.bar = [1, 2] # Should raise a TraitError
"""
self.validators.extend(validators)
return self
def validate(self, obj, value):
"""Validate the value against registered validators."""
try:
for validator in self.validators:
value = validator(self, value)
return value
except (ValueError, TypeError) as e:
raise TraitError(e)
class Array(SciType):
"""A numpy array trait type."""
info_text = 'a numpy array'
dtype = None
def validate(self, obj, value):
if value is None and not self.allow_none:
self.error(obj, value)
if value is None or value is Undefined:
return super(Array, self).validate(obj, value)
try:
r = np.asarray(value, dtype=self.dtype)
if isinstance(value, np.ndarray) and r is not value:
warnings.warn(
'Given trait value dtype "%s" does not match required type "%s". '
'A coerced copy has been created.' % (
np.dtype(value.dtype).name,
np.dtype(self.dtype).name))
value = r
except (ValueError, TypeError) as e:
raise TraitError(e)
return super(Array, self).validate(obj, value)
def set(self, obj, value):
new_value = self._validate(obj, value)
old_value = obj._trait_values.get(self.name, self.default_value)
obj._trait_values[self.name] = new_value
if not np.array_equal(old_value, new_value):
obj._notify_trait(self.name, old_value, new_value)
def __init__(self, default_value=Empty, allow_none=False, dtype=None, **kwargs):
self.dtype = dtype
if default_value is Empty:
default_value = np.array(0, dtype=self.dtype)
elif default_value is not None and default_value is not Undefined:
default_value = np.asarray(default_value, dtype=self.dtype)
super(Array, self).__init__(default_value=default_value, allow_none=allow_none, **kwargs)
def make_dynamic_default(self):
if self.default_value is None or self.default_value is Undefined:
return self.default_value
else:
return np.copy(self.default_value)
class PandasType(SciType):
"""A pandas dataframe or series trait type."""
info_text = 'a pandas dataframe or series'
klass = None
def validate(self, obj, value):
if value is None and not self.allow_none:
self.error(obj, value)
if value is None or value is Undefined:
return super(PandasType, self).validate(obj, value)
try:
value = self.klass(value)
except (ValueError, TypeError) as e:
raise TraitError(e)
return super(PandasType, self).validate(obj, value)
def set(self, obj, value):
new_value = self._validate(obj, value)
old_value = obj._trait_values.get(self.name, self.default_value)
obj._trait_values[self.name] = new_value
if ((old_value is None and new_value is not None) or
(old_value is Undefined and new_value is not Undefined) or
not old_value.equals(new_value)):
obj._notify_trait(self.name, old_value, new_value)
def __init__(self, default_value=Empty, allow_none=False, klass=None, **kwargs):
if klass is None:
klass = self.klass
if (klass is not None) and inspect.isclass(klass):
self.klass = klass
else:
raise TraitError('The klass attribute must be a class'
' not: %r' % klass)
if default_value is Empty:
default_value = klass()
elif default_value is not None and default_value is not Undefined:
default_value = klass(default_value)
super(PandasType, self).__init__(default_value=default_value, allow_none=allow_none, **kwargs)
def make_dynamic_default(self):
if self.default_value is None or self.default_value is Undefined:
return self.default_value
else:
return self.default_value.copy()
class DataFrame(PandasType):
"""A pandas dataframe trait type."""
info_text = 'a pandas dataframe'
def __init__(self, default_value=Empty, allow_none=False, dtype=None, **kwargs):
if 'klass' not in kwargs and self.klass is None:
import pandas as pd
kwargs['klass'] = pd.DataFrame
super(DataFrame, self).__init__(
default_value=default_value, allow_none=allow_none, dtype=dtype, **kwargs)
class Series(PandasType):
"""A pandas series trait type."""
info_text = 'a pandas series'
dtype = None
def __init__(self, default_value=Empty, allow_none=False, dtype=None, **kwargs):
if 'klass' not in kwargs and self.klass is None:
import pandas as pd
kwargs['klass'] = pd.Series
super(Series, self).__init__(
default_value=default_value, allow_none=allow_none, dtype=dtype, **kwargs)
self.dtype = dtype
class XarrayType(SciType):
"""An xarray dataset or dataarray trait type."""
info_text = 'an xarray dataset or dataarray'
klass = None
def validate(self, obj, value):
if value is None and not self.allow_none:
self.error(obj, value)
if value is None or value is Undefined:
return super(XarrayType, self).validate(obj, value)
try:
value = self.klass(value)
except (ValueError, TypeError) as e:
raise TraitError(e)
return super(XarrayType, self).validate(obj, value)
def set(self, obj, value):
new_value = self._validate(obj, value)
old_value = obj._trait_values.get(self.name, self.default_value)
obj._trait_values[self.name] = new_value
if ((old_value is None and new_value is not None) or
(old_value is Undefined and new_value is not Undefined) or
not old_value.equals(new_value)):
obj._notify_trait(self.name, old_value, new_value)
def __init__(self, default_value=Empty, allow_none=False, klass=None, **kwargs):
if klass is None:
klass = self.klass
if (klass is not None) and inspect.isclass(klass):
self.klass = klass
else:
raise TraitError('The klass attribute must be a class'
' not: %r' % klass)
if default_value is Empty:
default_value = klass()
elif default_value is not None and default_value is not Undefined:
default_value = klass(default_value)
super(XarrayType, self).__init__(default_value=default_value, allow_none=allow_none, **kwargs)
def make_dynamic_default(self):
if self.default_value is None or self.default_value is Undefined:
return self.default_value
else:
return self.default_value.copy()
class Dataset(XarrayType):
"""An xarray dataset trait type."""
info_text = 'an xarray dataset'
def __init__(self, default_value=Empty, allow_none=False, dtype=None, **kwargs):
if 'klass' not in kwargs and self.klass is None:
import xarray as xr
kwargs['klass'] = xr.Dataset
super(Dataset, self).__init__(
default_value=default_value, allow_none=allow_none, dtype=dtype, **kwargs)
class DataArray(XarrayType):
"""An xarray dataarray trait type."""
info_text = 'an xarray dataarray'
dtype = None
def __init__(self, default_value=Empty, allow_none=False, dtype=None, **kwargs):
if 'klass' not in kwargs and self.klass is None:
import xarray as xr
kwargs['klass'] = xr.DataArray
super(DataArray, self).__init__(
default_value=default_value, allow_none=allow_none, dtype=dtype, **kwargs)
self.dtype = dtype