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import sys
import unittest
import numpy as np
sys.path.insert(0, "../../sidpy/")
from sidpy.proc.fitter import SidFitter
from sidpy import Dataset, Dimension, DataType
import sidpy as sid
from ..sid.test_dataset import validate_dataset_properties
import inspect
class Test_3Ddset_1Dfit(unittest.TestCase):
def setUp(self):
self.data_set_3D, self.xvec = make_3D_dataset(shape=(3, 4, 7))
fitter = SidFitter(self.data_set_3D, xvec=self.xvec,
fit_fn=return_quad, guess_fn=guess_quad, num_workers=3,
threads=2, return_cov=True, return_fit=True, return_std=True,
km_guess=True, n_clus=3)
lb, ub = [-50, -50, -50], [50, 50, 50]
self.fit_results = fitter.do_fit(bounds=(lb, ub), maxfev=20)
self.metadata = self.data_set_3D.metadata.copy()
fit_parms_dict = {'fit_parameters_labels': None,
'fitting_function': inspect.getsource(return_quad),
'guess_function': inspect.getsource(guess_quad),
'ind_dims': (0, 1)
}
self.metadata['fit_parms_dict'] = fit_parms_dict
def test_num_fitter_outputs(self):
self.assertEqual(len(self.fit_results), 4) # add assertion here
def test_fit_parms_dset(self):
# First dataset would be the fitting parameters dataset
self.assertEqual(self.fit_results[0].shape, (3, 4, 3))
## Getting the dimension dict
dim_dict = {0: self.data_set_3D._axes[0].copy(), 1: self.data_set_3D._axes[1].copy(),
2: Dimension(np.arange(3),
name='fit_parms', units='a.u.',
quantity='fit_parameters',
dimension_type='temporal')}
validate_dataset_properties(self, self.fit_results[0], np.array(self.fit_results[0]),
title='Fitting_Map', data_type=DataType.IMAGE_STACK,
dimension_dict=dim_dict,
original_metadata=self.data_set_3D.original_metadata.copy(),
metadata=self.metadata)
def test_cov_dset(self):
# Second dataset is the covariance dataset
self.assertEqual(self.fit_results[1].shape, (3, 4, 3, 3))
## Getting the dim_dict
dim_dict = {0: self.data_set_3D._axes[0].copy(), 1: self.data_set_3D._axes[1].copy(),
2: Dimension(np.arange(3),
name='fit_cov_parms_x', units='a.u.',
quantity='fit_cov_parameters',
dimension_type='spectral'),
3: Dimension(np.arange(3),
name='fit_cov_parms_y', units='a.u.',
quantity='fit_cov_parameters',
dimension_type='spectral')
}
validate_dataset_properties(self, self.fit_results[1], np.array(self.fit_results[1]),
title='Fitting_Map_Covariance', data_type=DataType.IMAGE_4D,
dimension_dict=dim_dict,
original_metadata=self.data_set_3D.original_metadata.copy(),
metadata=self.metadata)
def test_std_dev_dset(self):
# Third dataset is the std_dev dataset
self.assertEqual(self.fit_results[2].shape, (3, 4, 3))
## Getting the dim_dict
dim_dict = {0: self.data_set_3D._axes[0].copy(), 1: self.data_set_3D._axes[1].copy(),
2: Dimension(np.arange(3),
name='std_dev', units='a.u.',
quantity='std_dev_fit_parms',
dimension_type='temporal')}
validate_dataset_properties(self, self.fit_results[2], np.array(self.fit_results[2]),
title='Fitting_Map_std_dev', data_type=DataType.IMAGE_STACK,
dimension_dict=dim_dict,
original_metadata=self.data_set_3D.original_metadata.copy(),
metadata=self.metadata)
def test_fitted_dset(self):
# Fourth dataset is the fitted dataset
self.assertEqual(self.fit_results[3].shape, self.data_set_3D.shape)
validate_dataset_properties(self, self.fit_results[3], np.array(self.fit_results[3]),
title='fitted_' + self.data_set_3D.title, data_type=self.data_set_3D.data_type,
quantity=self.data_set_3D.quantity, modality=self.data_set_3D.modality,
units=self.data_set_3D.units, source=self.data_set_3D.source,
dimension_dict=self.data_set_3D._axes,
original_metadata=self.data_set_3D.original_metadata,
metadata=self.metadata)
class Test_4Ddset_2Dfit(unittest.TestCase):
def setUp(self):
self.data_set_4D, self.xyvec = make_4D_dataset(shape=(3, 5, 9, 13))
# Here we don't provide the xyvec as the input, we let the class figure it
fitter = SidFitter(self.data_set_4D, fit_fn=gauss_2D, num_workers=8, num_fit_parms=5,
threads=2, return_std=True, return_fit=True,
km_guess=True, n_clus=4,
fit_parameter_labels=['amplitude', 'x', 'y', 'sigma', 'offset'])
self.fit_results = fitter.do_fit(maxfev=100)
self.metadata = self.data_set_4D.metadata.copy()
fit_parms_dict = {'fit_parameters_labels': ['amplitude', 'x', 'y', 'sigma', 'offset'],
'fitting_function': inspect.getsource(gauss_2D),
'guess_function': 'Not Provided',
'ind_dims': (0, 1)
}
self.metadata['fit_parms_dict'] = fit_parms_dict
def test_num_fitter_outputs(self):
self.assertEqual(len(self.fit_results), 3) # add assertion here
def test_fit_parms_dset(self):
# First dataset would be the fitting parameters dataset
self.assertEqual(self.fit_results[0].shape, (3, 5, 5))
## Getting the dimension dict
dim_dict = {0: self.data_set_4D._axes[0].copy(), 1: self.data_set_4D._axes[1].copy(),
2: Dimension(np.arange(5),
name='fit_parms', units='a.u.',
quantity='fit_parameters',
dimension_type='temporal')}
validate_dataset_properties(self, self.fit_results[0], np.array(self.fit_results[0]),
title='Fitting_Map', data_type=DataType.IMAGE_STACK,
dimension_dict=dim_dict,
original_metadata=self.data_set_4D.original_metadata.copy(),
metadata=self.metadata)
def test_std_dev_dset(self):
# Third dataset is the std_dev dataset
self.assertEqual(self.fit_results[1].shape, (3, 5, 5))
## Getting the dim_dict
dim_dict = {0: self.data_set_4D._axes[0].copy(), 1: self.data_set_4D._axes[1].copy(),
2: Dimension(np.arange(5),
name='std_dev', units='a.u.',
quantity='std_dev_fit_parms',
dimension_type='temporal')}
validate_dataset_properties(self, self.fit_results[1], np.array(self.fit_results[1]),
title='Fitting_Map_std_dev', data_type=DataType.IMAGE_STACK,
dimension_dict=dim_dict,
original_metadata=self.data_set_4D.original_metadata.copy(),
metadata=self.metadata)
def test_fitted_dset(self):
# Fourth dataset is the fitted dataset
self.assertEqual(self.fit_results[2].shape, self.data_set_4D.shape)
validate_dataset_properties(self, self.fit_results[2], np.array(self.fit_results[2]),
title='fitted_' + self.data_set_4D.title, data_type=self.data_set_4D.data_type,
quantity=self.data_set_4D.quantity, modality=self.data_set_4D.modality,
units=self.data_set_4D.units, source=self.data_set_4D.source,
dimension_dict=self.data_set_4D._axes,
original_metadata=self.data_set_4D.original_metadata,
metadata=self.metadata)
class Test_4Ddset_1Dfit(unittest.TestCase):
def setUp(self):
self.data_set_cycles, self.xvec = make_3D_dataset(shape=(2, 2, 16), cycles=3)
fitter = SidFitter(self.data_set_cycles, xvec=self.xvec,
fit_fn=return_quad, guess_fn=guess_quad, num_workers=8,
threads=2, return_cov=True, return_fit=True, return_std=True,
km_guess=True, n_clus=4, ind_dims=[0, 1, 3])
lb, ub = [-50, -50, -50], [50, 50, 50]
self.fit_results = fitter.do_fit(bounds=(lb, ub), maxfev=10)
self.metadata = self.data_set_cycles.metadata.copy()
fit_parms_dict = {'fit_parameters_labels': None,
'fitting_function': inspect.getsource(return_quad),
'guess_function': inspect.getsource(guess_quad),
'ind_dims': (0, 1, 3)
}
self.metadata['fit_parms_dict'] = fit_parms_dict
def test_num_fitter_outputs(self):
self.assertEqual(len(self.fit_results), 4) # add assertion here
def test_fit_parms_dset(self):
# First dataset would be the fitting parameters dataset
self.assertEqual(self.fit_results[0].shape, (2, 2, 3, 3))
## Getting the dimension dict
dim_dict = {0: self.data_set_cycles._axes[0].copy(), 1: self.data_set_cycles._axes[1].copy(),
2: self.data_set_cycles._axes[3].copy(),
3: Dimension(np.arange(3),
name='fit_parms', units='a.u.',
quantity='fit_parameters',
dimension_type='temporal')}
validate_dataset_properties(self, self.fit_results[0], np.array(self.fit_results[0]),
title='Fitting_Map', data_type=DataType.IMAGE_STACK,
dimension_dict=dim_dict,
original_metadata=self.data_set_cycles.original_metadata.copy(),
metadata=self.metadata)
def test_cov_dset(self):
# Second dataset is the covariance dataset
self.assertEqual(self.fit_results[1].shape, (2, 2, 3, 3, 3))
## Getting the dim_dict
dim_dict = {0: self.data_set_cycles._axes[0].copy(), 1: self.data_set_cycles._axes[1].copy(),
2: self.data_set_cycles._axes[3].copy(),
3: Dimension(np.arange(3),
name='fit_cov_parms_x', units='a.u.',
quantity='fit_cov_parameters',
dimension_type='spectral'),
4: Dimension(np.arange(3),
name='fit_cov_parms_y', units='a.u.',
quantity='fit_cov_parameters',
dimension_type='spectral')
}
validate_dataset_properties(self, self.fit_results[1], np.array(self.fit_results[1]),
title='Fitting_Map_Covariance', data_type=DataType.IMAGE_4D,
dimension_dict=dim_dict,
original_metadata=self.data_set_cycles.original_metadata.copy(),
metadata=self.metadata)
def test_std_dev_dset(self):
# Third dataset is the std_dev dataset
self.assertEqual(self.fit_results[2].shape, (2, 2, 3, 3))
## Getting the dim_dict
dim_dict = {0: self.data_set_cycles._axes[0].copy(), 1: self.data_set_cycles._axes[1].copy(),
2: self.data_set_cycles._axes[3].copy(),
3: Dimension(np.arange(3),
name='std_dev', units='a.u.',
quantity='std_dev_fit_parms',
dimension_type='temporal')}
validate_dataset_properties(self, self.fit_results[2], np.array(self.fit_results[2]),
title='Fitting_Map_std_dev', data_type=DataType.IMAGE_STACK,
dimension_dict=dim_dict,
original_metadata=self.data_set_cycles.original_metadata.copy(),
metadata=self.metadata)
def test_fitted_dset(self):
# Fourth dataset is the fitted dataset
shape = [self.data_set_cycles.shape[i] for i in [0, 1, 3, 2]]
self.assertEqual(self.fit_results[3].shape, self.data_set_cycles.shape)
validate_dataset_properties(self, self.fit_results[3], np.array(self.fit_results[3]),
title='fitted_' + self.data_set_cycles.title,
data_type=self.data_set_cycles.data_type,
quantity=self.data_set_cycles.quantity, modality=self.data_set_cycles.modality,
units=self.data_set_cycles.units, source=self.data_set_cycles.source,
dimension_dict=self.data_set_cycles._axes,
original_metadata=self.data_set_cycles.original_metadata,
metadata=self.metadata)
# The following functions are used to generate datasets
def return_quad(x, *parms):
a, b, c, = parms
return a * x ** 2 + b * x + c
def guess_quad(xvec, yvec):
popt = np.polyfit(xvec, yvec, 2)
return popt
def make_3D_dataset(shape=(60, 60, 32), cycles=1):
data_mat = np.zeros(shape=(shape[0], shape[1], shape[2], cycles))
xvec = np.linspace(0, 5, shape[-1])
for row in range(data_mat.shape[0]):
for col in range(data_mat.shape[1]):
for cycle in range(cycles):
if row ** 2 + col ** 2 < data_mat.shape[0] * data_mat.shape[1] / 3:
a = np.random.normal(loc=3.0, scale=0.4)
b = np.random.normal(loc=1.0, scale=2.4)
c = np.random.normal(loc=0.5, scale=1.0)
else:
a = np.random.normal(loc=1.0, scale=0.8)
b = np.random.normal(loc=0.0, scale=1.4)
c = np.random.normal(loc=-0.5, scale=1.3)
data_mat[row, col, :, cycle] = return_quad(xvec, *[a, b, c]) + 2.5 * np.random.normal(size=len(xvec))
data_mat = np.squeeze(data_mat)
parms_dict = {'info_1': np.linspace(0, 1, 100), 'instrument': 'perseverence rover AFM'}
# Let's convert it to a sidpy dataset
# Specify dimensions
x_dim = np.linspace(0, 1E-6,
data_mat.shape[0])
y_dim = np.linspace(0, 1E-6,
data_mat.shape[1])
z_dim = xvec
# Make a sidpy dataset
data_set = sid.Dataset.from_array(data_mat, title='Current_spectral_map')
# Set the data type
data_set.data_type = sid.DataType.SPECTRAL_IMAGE
# Add quantity and units
data_set.units = 'nA'
data_set.quantity = 'Current'
# Add dimension info
data_set.set_dimension(0, sid.Dimension(x_dim,
name='x',
units='m', quantity='x',
dimension_type='spatial'))
data_set.set_dimension(1, sid.Dimension(y_dim,
name='y',
units='m', quantity='y',
dimension_type='spatial'))
data_set.set_dimension(2, sid.Dimension(z_dim,
name='Voltage',
units='V', quantity='Voltage',
dimension_type='spectral'))
if cycles > 1:
cycles_dim = np.arange(cycles)
data_set.set_dimension(3, sid.Dimension(cycles_dim,
name='Cycle',
units='#', quantity='Cycle',
dimension_type='spectral'))
# append metadata
data_set.metadata = parms_dict
return data_set, xvec
def gauss_2D(fitting_space, *parms):
x = fitting_space[0]
y = fitting_space[1]
amplitude, xo, yo, sigma, offset = parms
xo = float(xo)
yo = float(yo)
r = ((x - xo) ** 2 + (y - yo) ** 2) ** .5
g = amplitude * np.exp(-(r / sigma) ** 2) + offset
return g.ravel()
def make_4D_dataset(shape=(32, 16, 64, 48)):
dataset = np.zeros(shape=shape, dtype=np.float64)
xlen, ylen, kxlen, kylen = shape
kx, ky = np.meshgrid(np.linspace(0, 11, kxlen), np.linspace(0, 7, kylen), indexing='ij')
true_parms = np.zeros((dataset.shape[0], dataset.shape[1], 5))
for row in range(xlen):
for col in range(ylen):
amp = np.sqrt(row * col // xlen * ylen) + 3.5
sigma = np.random.normal(loc=2.5, scale=0.34)
offset = 0.1 # col
xo = np.random.uniform(low=0, high=6)
yo = np.random.uniform(low=0, high=5)
cur_parms = [amp, xo, yo, sigma, offset]
true_parms[row, col, :] = cur_parms
# amplitude, xo, yo, sigma, offset
gauss_mat = gauss_2D([kx.ravel(), ky.ravel()], *cur_parms)
gauss_mat += np.mean(gauss_mat) / 2 * np.random.normal(size=len(gauss_mat))
gauss_mat = gauss_mat.reshape([kxlen, kylen])
dataset[row, col, :, :] = gauss_mat
# Now make it a sidpy dataset
parms_dict = {'info_1': np.linspace(0, 5.6, 30), 'instrument': 'opportunity rover AFM'}
# Let's convert it to a sidpy dataset
# Specify dimensions
x_dim = np.linspace(0, 1E-6,
dataset.shape[0])
y_dim = np.linspace(0, 2E-6,
dataset.shape[1])
kx_dim = np.linspace(0, 11, kxlen)
ky_dim = np.linspace(0, 5, kylen)
# Make a sidpy dataset
data_set = sid.Dataset.from_array(dataset, name='4D_STEM')
# Set the data type
data_set.data_type = sid.DataType.IMAGE_4D
# Add quantity and units
data_set.units = 'nA'
data_set.quantity = 'Current'
# Add dimension info
data_set.set_dimension(0, sid.Dimension(x_dim,
name='x',
units='m', quantity='x',
dimension_type='spatial'))
data_set.set_dimension(1, sid.Dimension(y_dim,
name='y',
units='m', quantity='y',
dimension_type='spatial'))
data_set.set_dimension(2, sid.Dimension(kx_dim,
name='Intensity KX',
units='counts', quantity='Intensity',
dimension_type='spectral'))
data_set.set_dimension(3, sid.Dimension(ky_dim,
name='Intensity KY',
units='counts', quantity='Intensity',
dimension_type='spectral'))
# append metadata
data_set.metadata = parms_dict
return data_set, [kx.ravel(), ky.ravel()]
def make_4D_dataset_spectral_image(shape=(32, 16, 64, 48)):
dataset = np.zeros(shape=shape, dtype=np.float64)
xlen, ylen, kxlen, kylen = shape
kx, ky = np.meshgrid(np.linspace(0, 11, kxlen), np.linspace(0, 7, kylen), indexing='ij')
true_parms = np.zeros((dataset.shape[0], dataset.shape[1], 5))
for row in range(xlen):
for col in range(ylen):
amp = np.sqrt(row * col // xlen * ylen) + 3.5
sigma = np.random.normal(loc=2.5, scale=0.34)
offset = 0.1 # col
xo = np.random.uniform(low=0, high=6)
yo = np.random.uniform(low=0, high=5)
cur_parms = [amp, xo, yo, sigma, offset]
true_parms[row, col, :] = cur_parms
# amplitude, xo, yo, sigma, offset
gauss_mat = gauss_2D([kx.ravel(), ky.ravel()], *cur_parms)
gauss_mat += np.mean(gauss_mat) / 2 * np.random.normal(size=len(gauss_mat))
gauss_mat = gauss_mat.reshape([kxlen, kylen])
dataset[row, col, :, :] = gauss_mat
# Now make it a sidpy dataset
parms_dict = {'info_1': np.linspace(0, 5.6, 30), 'instrument': 'opportunity rover AFM'}
# Let's convert it to a sidpy dataset
# Specify dimensions
x_dim = np.linspace(0, 1E-6,
dataset.shape[0])
y_dim = np.linspace(0, 2E-6,
dataset.shape[1])
kx_dim = np.linspace(0, 11, kxlen)
ky_dim = np.linspace(0, 5, kylen)
# Make a sidpy dataset
data_set = sid.Dataset.from_array(dataset)
# Set the data type
data_set.data_type = sid.DataType.SPECTRAL_IMAGE
# Add quantity and units
data_set.units = 'nA'
data_set.quantity = 'Current'
# Add dimension info
data_set.set_dimension(0, sid.Dimension(x_dim,
name='x',
units='m', quantity='x',
dimension_type='spatial'))
data_set.set_dimension(1, sid.Dimension(y_dim,
name='y',
units='m', quantity='y',
dimension_type='spatial'))
data_set.set_dimension(2, sid.Dimension(kx_dim,
name='Intensity KX',
units='counts', quantity='Intensity',
dimension_type='spectral'))
data_set.set_dimension(3, sid.Dimension(ky_dim,
name='Intensity KY',
units='counts', quantity='Intensity',
dimension_type='channel'))
# append metadata
data_set.metadata = parms_dict
return data_set, [kx.ravel(), ky.ravel()]
if __name__ == '__main__':
unittest.main()
|
