def test_fit_single_dataset(IR_dataset_2D, script):
dataset = IR_dataset_2D[54, 3700.0:3400.0]
f1 = Fit(dataset, script, silent=True)
f1.run(maxiter=10, every=1)
# assert_approx_equal(dataset.model_A, -116.40475, significant=4)
# assert_approx_equal(f1.fp['width_line_2'], 195.7273, significant=4)
dataset.plot(plot_model=True)
dataset2 = dataset.copy() * 2.34
f2 = Fit(dataset2, script, silent=True)
f2.run(maxiter=10, every=1)
dataset2.plot(plot_model=True)
assert_approx_equal(dataset2.model_A, 1389.6235438214812, significant=4)
assert_approx_equal(f2.fp["width_line_2"], 184.6313, significant=4)
f2 = Fit(dataset2, script, silent=False)
f2.run(maxiter=100, every=1)
dataset2.plot(plot_model=True)
show()
def test_fit_multiple_dataset(IR_dataset_2D, script):
dataset = IR_dataset_2D[54, 3700.0:3400.0]
datasets = [dataset.copy(), dataset.copy() * 2.23456]
f = Fit(datasets, script, silent=True)
f.run(maxiter=10, every=1)
assert_approx_equal(datasets[0].model_A, 593.8562153083558, significant=4)
assert_approx_equal(datasets[1].model_A,
593.8562153083558 * 2.23456,
significant=4)
assert_approx_equal(f.fp["width_line_2"], 184.6313, significant=4)
def test_fit_multiple_dataset(IR_dataset_2D, script):
dataset = IR_dataset_2D[54, 3700.:3400.]
datasets = [dataset.copy(), dataset.copy() * 2.23456]
f = Fit(datasets, script, silent=True)
f.run(maxiter=10, every=1)
assert_approx_equal(datasets[0].model_A, 116.3807504474709, significant=4)
assert_approx_equal(datasets[1].model_A,
116.3807504474709 * 2.23456,
significant=4)
assert_approx_equal(f.fp['width_line_2'], 195.7273, significant=4)
def test_ndcomplex_slicing_byindex_quaternion(ndarrayquaternion):
ndc = ndarrayquaternion.copy()
ndc1 = ndc[1, 1].real
assert_approx_equal(ndc1.values.magnitude, 4.646475973719301, 3)
def test_linearcoord():
coord1 = Coord([1, 2.5, 4, 5])
coord2 = Coord(np.array([1, 2.5, 4, 5]))
assert coord2 == coord1
coord3 = Coord(range(10))
coord4 = Coord(np.arange(10))
assert coord4 == coord3
coord5 = coord4.copy()
coord5 += 1
assert np.all(coord5.data == coord4.data + 1)
assert coord5 is not None
coord5.linear = True
coord6 = Coord(linear=True, offset=2.0, increment=2.0, size=10)
assert np.all(coord6.data == (coord4.data + 1.0) * 2.0)
LinearCoord(offset=2.0, increment=2.0, size=10)
coord0 = LinearCoord.linspace(
200.0,
300.0,
3,
labels=["cold", "normal", "hot"],
units="K",
title="temperature",
)
coord1 = LinearCoord.linspace(0.0,
60.0,
100,
labels=None,
units="minutes",
title="time-on-stream")
coord2 = LinearCoord.linspace(4000.0,
1000.0,
100,
labels=None,
units="cm^-1",
title="wavenumber")
assert coord0.size == 3
assert coord1.size == 100
assert coord2.size == 100
coordc = coord0.copy()
assert coord0 == coordc
coordc = coord1.copy()
assert coord1 == coordc
assert_approx_equal(coord1.spacing.m, 0.606060606)
assert coord1.author is None
assert not coord1.history
assert not coord1.descendant
assert coord2.descendant
assert coord1.is_1d
assert coord0.transpose() == coord0
def test_models():
model = scp.asymmetricvoigtmodel()
assert model.args == ["ampl", "pos", "width", "ratio", "asym"]
x = np.arange(1000)
ampl = 1000.0
width = 100
ratio = 0
asym = 1.5
pos = 500
max = 6.366197723675813
array = model.f(x, ampl, pos, width, ratio, asym)
assert array.shape == (1000, )
assert_approx_equal(array[pos], max, significant=4)
array = model.f(x, 2.0 * ampl, pos, width, ratio, asym) # ampl=2.
assert_approx_equal(array[pos], max * 2.0, significant=4)
# x array with units
x1 = x * ur("cm")
array = model.f(x1, ampl, pos, width, ratio, asym)
assert_approx_equal(array[pos], max, significant=4)
assert not hasattr(array, "units")
# amplitude with units
ampl = 1000.0 * ur("g")
array = model.f(x1, ampl, pos, width, ratio, asym)
assert hasattr(array, "units")
assert array.units == ur("g")
assert_approx_equal(array[pos].m, max, significant=4)
# use keyword instead of positional parameters
array = model.f(x1, ampl, pos, asym=asym, width=width, ratio=ratio)
assert_approx_equal(array[pos].m, max, significant=4)
# rescale some parameters
array = model.f(x1,
width=1000.0 * ur("mm"),
ratio=ratio,
asym=asym,
ampl=ampl,
pos=pos)
assert_approx_equal(array[pos].m, max, significant=4)
# x is a Coord object
x2 = scp.LinearCoord.arange(1000)
width = 100.0
array = model.f(x2, ampl, pos, width, ratio, asym)
assert isinstance(array, scp.NDDataset)
assert_approx_equal(array[pos].value.m, max, significant=4)
assert array.units == ampl.units
# x is a Coord object with units
x3 = scp.LinearCoord.linspace(0.0,
0.999,
1000,
units="m",
title="distance")
width = 100.0 * ur("mm")
pos = 0.5
array = model.f(x3, ampl, pos, width, ratio, asym)
assert hasattr(array, "units")
assert_approx_equal(array[500].m, max, significant=4)
# do the same for various models
kwargs = dict(
ampl=1.0 * ur["g"],
width=100.0 * ur("mm"),
ratio=0.5,
asym=2,
pos=0.5,
c_2=1.0,
)
for modelname, expected in [
("gaussianmodel", 0.9394292818892936),
("lorentzianmodel", 0.6366197723675814),
("voigtmodel", 0.8982186579508358),
("asymmetricvoigtmodel", 0.8982186579508358),
("polynomialbaseline", 0.0),
("sigmoidmodel", 50),
]:
model = getattr(scp, modelname)()
if modelname == "sigmoid":
kwargs["width"] = 0.01
array = model.f(x3, **kwargs)
actual = array[pos].value
if modelname != "sigmoid":
actual = actual * 100
assert_approx_equal(actual.m, expected, 4)