def test_as_array(self):
"""
Tests :func:`colour.utilities.array.as_array` definition.
"""
np.testing.assert_equal(as_array([1, 2, 3]), np.array([1, 2, 3]))
self.assertEqual(
as_array([1, 2, 3], DEFAULT_FLOAT_DTYPE).dtype,
DEFAULT_FLOAT_DTYPE)
self.assertEqual(
as_array([1, 2, 3], DEFAULT_INT_DTYPE).dtype, DEFAULT_INT_DTYPE)
def test_as_array(self):
"""
Tests :func:`colour.utilities.array.as_array` definition.
"""
np.testing.assert_equal(as_array([1, 2, 3]), np.array([1, 2, 3]))
self.assertEqual(
as_array([1, 2, 3], DEFAULT_FLOAT_DTYPE).dtype,
DEFAULT_FLOAT_DTYPE)
self.assertEqual(
as_array([1, 2, 3], DEFAULT_INT_DTYPE).dtype, DEFAULT_INT_DTYPE)
def parse_array(a, separator=' ', dtype=DEFAULT_FLOAT_DTYPE):
"""
Converts given string or array of strings to :class:`ndarray` class.
Parameters
----------
a : unicode or array_like
String or array of strings to convert.
separator : unicode
Separator to split the string with.
dtype : object
Type to use for conversion.
Returns
-------
ndarray
Converted string or array of strings.
Examples
--------
>>> parse_array('-0.25 0.5 0.75')
array([-0.25, 0.5 , 0.75])
>>> parse_array(['-0.25', '0.5', '0.75'])
array([-0.25, 0.5 , 0.75])
"""
if is_string(a):
a = a.split(separator)
return as_array([dtype(token) for token in a], dtype)
def test_as_array(self):
"""
Tests :func:`colour.utilities.array.as_array` definition.
"""
np.testing.assert_equal(as_array([1, 2, 3]), np.array([1, 2, 3]))
self.assertEqual(
as_array([1, 2, 3], DEFAULT_FLOAT_DTYPE).dtype,
DEFAULT_FLOAT_DTYPE)
self.assertEqual(
as_array([1, 2, 3], DEFAULT_INT_DTYPE).dtype, DEFAULT_INT_DTYPE)
if six.PY3: # pragma: no cover
np.testing.assert_equal(
as_array(dict(zip('abc', [1, 2, 3])).values()),
np.array([1, 2, 3]))
def signal_unpack_data(data=None, domain=None, dtype=None):
"""
Unpack given data for continuous signal instantiation.
Parameters
----------
data : Series or Signal or array_like or dict_like, optional
Data to unpack for continuous signal instantiation.
domain : array_like, optional
Values to initialise the :attr:`colour.continuous.Signal.domain`
attribute with. If both ``data`` and ``domain`` arguments are
defined, the latter will be used to initialise the
:attr:`colour.continuous.Signal.domain` attribute.
dtype : type, optional
**{np.float16, np.float32, np.float64, np.float128}**,
Floating point data type.
Returns
-------
tuple
Independent domain :math:`x` variable and corresponding range
:math:`y` variable unpacked for continuous signal instantiation.
Examples
--------
Unpacking using implicit *domain*:
>>> range_ = np.linspace(10, 100, 10)
>>> domain, range_ = Signal.signal_unpack_data(range_)
>>> print(domain)
[ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
>>> print(range_)
[ 10. 20. 30. 40. 50. 60. 70. 80. 90. 100.]
Unpacking using explicit *domain*:
>>> domain = np.arange(100, 1100, 100)
>>> domain, range = Signal.signal_unpack_data(range_, domain)
>>> print(domain)
[ 100. 200. 300. 400. 500. 600. 700. 800. 900. 1000.]
>>> print(range_)
[ 10. 20. 30. 40. 50. 60. 70. 80. 90. 100.]
Unpacking using a *dict*:
>>> domain, range_ = Signal.signal_unpack_data(
... dict(zip(domain, range_)))
>>> print(domain)
[ 100. 200. 300. 400. 500. 600. 700. 800. 900. 1000.]
>>> print(range_)
[ 10. 20. 30. 40. 50. 60. 70. 80. 90. 100.]
Unpacking using a *Pandas* `Series`:
>>> if is_pandas_installed():
... from pandas import Series
... domain, range = Signal.signal_unpack_data(
... Series(dict(zip(domain, range_))))
... # doctest: +ELLIPSIS
>>> print(domain) # doctest: +SKIP
[ 100. 200. 300. 400. 500. 600. 700. 800. 900. 1000.]
>>> print(range_) # doctest: +SKIP
[ 10. 20. 30. 40. 50. 60. 70. 80. 90. 100.]
Unpacking using a :class:`colour.continuous.Signal` class:
>>> domain, range_ = Signal.signal_unpack_data(
... Signal(range_, domain))
>>> print(domain)
[ 100. 200. 300. 400. 500. 600. 700. 800. 900. 1000.]
>>> print(range_)
[ 10. 20. 30. 40. 50. 60. 70. 80. 90. 100.]
"""
if dtype is None:
dtype = DEFAULT_FLOAT_DTYPE
domain_u, range_u = None, None
if isinstance(data, Signal):
domain_u = data.domain
range_u = data.range
elif (issubclass(type(data), Sequence)
or isinstance(data, (tuple, list, np.ndarray, Iterator))):
data = tsplit(list(data) if isinstance(data, Iterator) else data)
assert data.ndim == 1, 'User "data" must be 1-dimensional!'
domain_u, range_u = np.arange(0, data.size, dtype=dtype), data
elif (issubclass(type(data), Mapping)
or isinstance(data, (dict, OrderedDict))):
domain_u, range_u = tsplit(sorted(data.items()))
elif is_pandas_installed():
from pandas import Series
if isinstance(data, Series):
domain_u = data.index.values
range_u = data.values
if domain is not None and range_u is not None:
assert len(domain) == len(range_u), (
'User "domain" is not compatible with unpacked range!')
domain_u = as_array(domain, dtype)
if range_u is not None:
range_u = as_array(range_u, dtype)
return domain_u, range_u
def signal_unpack_data(data=None, domain=None, dtype=DEFAULT_FLOAT_DTYPE):
"""
Unpack given data for continuous signal instantiation.
Parameters
----------
data : Series or Signal or array_like or dict_like, optional
Data to unpack for continuous signal instantiation.
domain : array_like, optional
Values to initialise the :attr:`colour.continuous.Signal.domain`
attribute with. If both ``data`` and ``domain`` arguments are
defined, the latter will be used to initialise the
:attr:`colour.continuous.Signal.domain` attribute.
dtype : type, optional
**{np.float16, np.float32, np.float64, np.float128}**,
Floating point data type.
Returns
-------
tuple
Independent domain :math:`x` variable and corresponding range
:math:`y` variable unpacked for continuous signal instantiation.
Examples
--------
Unpacking using implicit *domain*:
>>> range_ = np.linspace(10, 100, 10)
>>> domain, range_ = Signal.signal_unpack_data(range_)
>>> print(domain)
[ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
>>> print(range_)
[ 10. 20. 30. 40. 50. 60. 70. 80. 90. 100.]
Unpacking using explicit *domain*:
>>> domain = np.arange(100, 1100, 100)
>>> domain, range = Signal.signal_unpack_data(range_, domain)
>>> print(domain)
[ 100. 200. 300. 400. 500. 600. 700. 800. 900. 1000.]
>>> print(range_)
[ 10. 20. 30. 40. 50. 60. 70. 80. 90. 100.]
Unpacking using a *dict*:
>>> domain, range_ = Signal.signal_unpack_data(
... dict(zip(domain, range_)))
>>> print(domain)
[ 100. 200. 300. 400. 500. 600. 700. 800. 900. 1000.]
>>> print(range_)
[ 10. 20. 30. 40. 50. 60. 70. 80. 90. 100.]
Unpacking using a *Pandas* *Series*:
>>> if is_pandas_installed():
... from pandas import Series
... domain, range = Signal.signal_unpack_data(
... Series(dict(zip(domain, range_))))
... # doctest: +ELLIPSIS
>>> print(domain) # doctest: +SKIP
[ 100. 200. 300. 400. 500. 600. 700. 800. 900. 1000.]
>>> print(range_) # doctest: +SKIP
[ 10. 20. 30. 40. 50. 60. 70. 80. 90. 100.]
Unpacking using a :class:`colour.continuous.Signal` class:
>>> domain, range_ = Signal.signal_unpack_data(
... Signal(range_, domain))
>>> print(domain)
[ 100. 200. 300. 400. 500. 600. 700. 800. 900. 1000.]
>>> print(range_)
[ 10. 20. 30. 40. 50. 60. 70. 80. 90. 100.]
"""
assert dtype in np.sctypes['float'], (
'"dtype" must be one of the following types: {0}'.format(
np.sctypes['float']))
domain_u, range_u = None, None
if isinstance(data, Signal):
domain_u = data.domain
range_u = data.range
elif (issubclass(type(data), Sequence) or
isinstance(data, (tuple, list, np.ndarray, Iterator))):
data = tsplit(list(data) if isinstance(data, Iterator) else data)
assert data.ndim == 1, 'User "data" must be 1-dimensional!'
domain_u, range_u = np.arange(0, data.size, dtype=dtype), data
elif (issubclass(type(data), Mapping) or
isinstance(data, (dict, OrderedDict))):
domain_u, range_u = tsplit(sorted(data.items()))
elif is_pandas_installed():
from pandas import Series
if isinstance(data, Series):
domain_u = data.index.values
range_u = data.values
if domain is not None and range_u is not None:
assert len(domain) == len(range_u), (
'User "domain" is not compatible with unpacked range!')
domain_u = as_array(domain, dtype)
if range_u is not None:
range_u = as_array(range_u, dtype)
return domain_u, range_u
