class Curve(BaseObj):
"""
Simple descriptor of a line.
"""
_defaults = [
Parameter('A', 1.0),
Parameter('p', np.pi),
Parameter('x_shift', 0.0)
]
def __init__(self, interface_factory: InterfaceFactory = None):
"""
Create a line and add an interface if requested
:param interface_factory: interface controller object
:type interface_factory: InterfaceFactory
"""
self.interface = interface_factory
super().__init__(self.__class__.__name__, *self._defaults)
self._set_interface()
def _set_interface(self):
if self.interface:
# If an interface is given, generate bindings
for parameter in self.get_parameters():
name = parameter.name
self.set_binding(name, self.interface.generate_bindings)
def __repr__(self):
return f'Curve: A={self.A}, p={self.p}, x_shift={self.x_shift}'
class Line(BaseObj):
"""
Simple descriptor of a line.
"""
_defaults = [Parameter('m', 1), Parameter('c', 0)]
def __init__(self, interface_factory: InterfaceFactory = None):
"""
Create a line and add an interface if requested
:param interface_factory: interface controller object
:type interface_factory: InterfaceFactory
"""
self.interface = interface_factory
super().__init__(self.__class__.__name__, *self._defaults)
self._set_interface()
def _set_interface(self):
if self.interface:
# If an interface is given, generate bindings
for parameter in self.get_parameters():
name = parameter.name
self.set_binding(name, self.interface.generate_bindings)
def __repr__(self):
return f'Line: m={self.m}, c={self.c}'
def test_parameter_min(value):
d = Parameter('test', -0.1)
if d.raw_value < value:
with pytest.raises(ValueError):
d.min = value
else:
d.min = value
assert d.min == value
def test_parameter_fixed(value):
d = Parameter('test', -np.inf)
if isinstance(value, bool):
d.fixed = value
assert d.fixed == value
else:
with pytest.raises(ValueError):
d.fixed = value
def test_parameter_error(value):
d = Parameter('test', 1)
if value >= 0:
d.error = value
assert d.error == value
else:
with pytest.raises(ValueError):
d.error = value
def test_parameter_max(value):
d = Parameter('test', 2147483649)
if d.raw_value > value:
with pytest.raises(ValueError):
d.max = value
else:
d.max = value
assert d.max == value
def setup_pars():
d = {
'name': 'test',
'par1': Parameter('p1', 0.1, fixed=True),
'des1': Descriptor('d1', 0.1),
'par2': Parameter('p2', 0.1),
'des2': Descriptor('d2', 0.1),
'par3': Parameter('p3', 0.1),
}
return d
class Line(BaseObj):
_defaults = [Parameter('m', 1), Parameter('c', 0)]
def __init__(self, interface=None):
self.interface = interface
super().__init__(self.__class__.__name__, *self._defaults)
if self.interface:
for parameter in self.get_parameters():
name = parameter.name
setattr(self.__class__.__dict__[name], '_callback',
property(self.__gitem(name), self.__sitem(self, name)))
@property
def gradient(self):
if self.interface:
return self.interface.get_value('m')
else:
return self.m.raw_value
@property
def intercept(self):
if self.interface:
return self.interface.get_value('c')
else:
return self.c.raw_value
def fit_func(self, x: np.ndarray) -> np.ndarray:
if self.interface:
return self.interface.fit_func(x)
else:
raise NotImplementedError
def __repr__(self):
return f'Line: m={self.m}, c={self.c}'
@staticmethod
def __gitem(key: str) -> Callable:
def inner(obj):
obj.interface.get_value(key)
return lambda obj: inner(obj)
@staticmethod
def __sitem(obj, key):
def inner(value):
obj.interface.set_value(key, value)
return inner
class Sin(BaseObj):
_defaults = [
Parameter('amplitude', 3.5, min=0.0),
Parameter('period', np.pi, min=0.0),
Parameter('x_shift', 0),
Parameter('y_shift', 0)
]
def __init__(self, interface_factory: InterfaceFactory = None):
super().__init__(self.__class__.__name__, *self._defaults)
self.interface = interface_factory
if self.interface is not None:
self.interface.generate_bindings(self)
def __repr__(self):
return f'{self.__class__.__name__}: amplitude={self.amplitude}, period={self.period}, x_shift={self.x_shift}, ' \
f'y_shift={self.y_shift} '
def test_parameter_repr():
d = Parameter('test', 1)
assert repr(
d) == f'<{d.__class__.__name__} \'test\' = 1.0+/-0, bounds=[-inf:inf]>'
d = Parameter('test', 1, units='cm')
assert repr(
d
) == f'<{d.__class__.__name__} \'test\' = 1.0+/-0 centimeter, bounds=[-inf:inf]>'
d = Parameter('test', 1, fixed=True)
assert repr(
d
) == f'<{d.__class__.__name__} \'test\' = 1.0+/-0 (fixed), bounds=[-inf:inf]>'
d = Parameter('test', 1, units='cm', fixed=True)
assert repr(
d
) == f'<{d.__class__.__name__} \'test\' = 1.0+/-0 centimeter (fixed), bounds=[-inf:inf]>'
def test_parameter_advanced_creation(element, expected):
if len(element[0]) > 0:
value = element[0][1]
else:
value = element[1]['value']
if 'min' in element[1].keys():
if element[1]['min'] > value:
with pytest.raises(ValueError):
d = Parameter(*element[0], **element[1])
elif 'max' in element[1].keys():
if element[1]['max'] < value:
with pytest.raises(ValueError):
d = Parameter(*element[0], **element[1])
else:
d = Parameter(*element[0], **element[1])
for field in expected.keys():
ref = expected[field]
obtained = getattr(d, field)
if isinstance(obtained, (ureg.Unit, Q_)):
obtained = str(obtained)
assert obtained == ref
class Line(BaseObj):
"""
Simple descriptor of a line.
"""
_defaults = [Parameter('m', 1), Parameter('c', 0)]
def __init__(self):
super().__init__(self.__class__.__name__, *self._defaults)
@property
def gradient(self):
return self.m.raw_value
@property
def intercept(self):
return self.c.raw_value
def fit_func(self, x: np.ndarray) -> np.ndarray:
return self.gradient * x + self.intercept
def __repr__(self):
return f'Line: m={self.m}, c={self.c}'
def from_pars(cls, power: int, amp: float):
"""
Construct a background factor from a power and amplitude as an integer/float respectively.
:param power: Power to which x will be raised.
:type power: int
:param amp: Amplitude for which x will be multiplied by
:type amp: float
:return: Constructed background factor
:rtype: BackgroundFactor
"""
power = Descriptor('power', power)
amp = Parameter('amplitude', amp, fixed=True)
return cls(power, amp)
def from_pars(cls, x: float, y: float):
"""
Construct a background point from x, y floats.
:param x: background x-position.
:type x: float
:param y: background intensity/y-position
:type y: float
:return: Constructed background point
:rtype: BackgroundPoint
"""
x = Descriptor('x', x)
y = Parameter('intensity', y, fixed=True)
return cls(x, y)
def default(cls) -> "Cell":
"""
Default constructor for a crystallographic unit cell.
:return: Default crystallographic unit cell container
:rtype: Cell
"""
length_a = Parameter('length_a', **CELL_DETAILS['length'])
length_b = Parameter('length_b', **CELL_DETAILS['length'])
length_c = Parameter('length_c', **CELL_DETAILS['length'])
angle_alpha = Parameter('angle_alpha', **CELL_DETAILS['angle'])
angle_beta = Parameter('angle_beta', **CELL_DETAILS['angle'])
angle_gamma = Parameter('angle_gamma', **CELL_DETAILS['angle'])
return cls(length_a, length_b, length_c, angle_alpha, angle_beta, angle_gamma)
def test_parameter_as_dict():
d = Parameter('test', 1)
result = d.as_dict()
expected = {
'@module': 'easyCore.Objects.Base',
'@class': 'Parameter',
'@version': '0.0.1',
'name': 'test',
'value': 1.0,
'error': 0.0,
'min': -np.inf,
'max': np.inf,
'fixed': False,
'units': 'dimensionless'
}
for key in expected.keys():
if key == 'callback':
continue
assert result[key] == expected[key]
# Check that additional arguments work
d = Parameter('test', 1, units='km', url='https://www.boo.com')
result = d.as_dict()
expected = {
'@module': 'easyCore.Objects.Base',
'@class': 'Parameter',
'@version': '0.0.1',
'name': 'test',
'units': 'kilometer',
'value': 1.0,
'error': 0.0,
'min': -np.inf,
'max': np.inf,
'fixed': False,
'url': 'https://www.boo.com'
}
for key in expected.keys():
if key == 'callback':
continue
assert result[key] == expected[key]
def from_parameters(cls, length_a: float, length_b: float, length_c: float,
angle_alpha: float, angle_beta: float, angle_gamma: float, ang_unit: str = 'deg') -> "Cell":
"""
Constructor of a crystallographic unit cell when parameters are known.
:param length_a: Unit cell length a
:type length_a: float
:param length_b: Unit cell length b
:type length_b: float
:param length_c: Unit cell length c
:type length_c: float
:param angle_alpha: Unit cell angle alpha
:type angle_alpha: float
:param angle_beta: Unit cell angle beta
:type angle_beta: float
:param angle_gamma: Unit cell angle gamma
:type angle_gamma: float
:param ang_unit: unit for supplied angles. Default is degree ('deg'). Radian is also valid ('rad'/'radian')
:type ang_unit: str
:return: Crystallographic unit cell container
:rtype: Cell
"""
if ang_unit.startswith('rad'):
angle_alpha = np.rad2deg(angle_alpha)
angle_beta = np.rad2deg(angle_beta)
angle_gamma = np.rad2deg(angle_gamma)
default_options = deepcopy(CELL_DETAILS)
del default_options['length']['value']
del default_options['angle']['value']
length_a = Parameter('length_a', length_a, **default_options['length'])
length_b = Parameter('length_b', length_b, **default_options['length'])
length_c = Parameter('length_c', length_c, **default_options['length'])
angle_alpha = Parameter('angle_alpha', angle_alpha, **default_options['angle'])
angle_beta = Parameter('angle_beta', angle_beta, **default_options['angle'])
angle_gamma = Parameter('angle_gamma', angle_gamma, **default_options['angle'])
return cls(length_a=length_a, length_b=length_b, length_c=length_c,
angle_alpha=angle_alpha, angle_beta=angle_beta, angle_gamma=angle_gamma)
def test_parameter_advanced_convert_unit(conv_unit: str, data_in: dict,
result: dict):
d = Parameter('test', 273, units='kelvin', **data_in)
d.convert_unit(conv_unit)
for key in result.keys():
assert pytest.approx(getattr(d, key), result[key])
def test_Parameter_value_get(element, expected):
d = Parameter('test', 1, units=element)
assert str(d.value) == expected
__author__ = 'github.com/wardsimon'
__version__ = '0.0.1'
import numpy as np
from easyCore.Objects.Base import Parameter, BaseObj
from easyCore.Fitting.Fitting import Fitter
# This is a simple example of creating an object which has fitable parameters
b = BaseObj('line', m=Parameter('m', 1), c=Parameter('c', 1))
def fit_fun(x):
# In the real case we would gust call the evaluation fn without reference to the BaseObj
return b.c.raw_value + b.m.raw_value * x
f = Fitter()
f.initialize(b, fit_fun)
x = np.array([1, 2, 3])
y = np.array([2, 4, 6]) - 1
f_res = f.fit(x, y)
print(f_res.fit_report())
__author__ = 'github.com/wardsimon'
__version__ = '0.1.0'
from easyCore import np
from easyCore.Datasets.xarray import xr
import matplotlib.pyplot as plt
from easyCore.Objects.Base import Parameter, BaseObj
from easyCore.Fitting.Fitting import Fitter
d = xr.Dataset()
m_starting_point = 1
c_starting_point = 1
b = BaseObj('line',
m=Parameter('m', m_starting_point),
c=Parameter('c', c_starting_point))
def fit_fun(x, *args, **kwargs):
# In the real case we would gust call the evaluation fn without reference to the BaseObj
return b.c.raw_value + b.m.raw_value * x
nx = 1E3
x_min = 0
x_max = 100
x = np.linspace(x_min, x_max, num=int(nx))
y = 2 * x - 1 + 5 * (np.random.random(size=x.shape) - 0.5)
