def test_missing_dependencies(fixed_fid_sum):
data = MRSData(fixed_fid_sum, 5e-4, 123)
model = {
"phase0": 0.0,
"phase1": 0.0,
"pcr2": {
"amplitude": 1.0,
"frequency": "pcr3_frequency+200",
"fwhm": {
"value": 45.0,
"min": 42.0,
"max": 55.0,
},
"phase": "0",
},
"pcr": {
"amplitude": 1.0,
"fwhm": {
"value": 45.0,
"min": 42.0,
"max": 55.0,
},
"phase": "0",
"frequency": 0
}
}
with pytest.raises(NameError):
fitting_result = singlet.fit(data, model)
def test_missing_dependencies(fixed_fid_sum):
data = MRSData(fixed_fid_sum, 5e-4, 123)
model = {
"phase0": 0.0,
"phase1": 0.0,
"pcr2": {
"amplitude": 1.0,
"frequency": "pcr3_frequency+200",
"fwhm": {
"value": 45.0,
"min": 42.0,
"max": 55.0,
},
"phase": "0",
},
"pcr": {
"amplitude": 1.0,
"fwhm": {
"value": 45.0,
"min": 42.0,
"max": 55.0,
},
"phase": "0",
"frequency": 0
}
}
with pytest.raises(NameError):
fitting_result = singlet.fit(data, model)
def test_gaussian(fixed_fid):
data = MRSData(fixed_fid, 5e-4, 123)
# Original test with all parameters passed in; correct data types; integer values
model = {
"phase0": 0,
"phase1": 0,
"pcr": {
"amplitude": 1,
"fwhm": {
"value": 45,
"min": 42.0,
"max": 55
},
"phase": "0",
"frequency": 0.0
}
}
fitting_result = singlet.fit(data, model)
numpy.testing.assert_allclose(fitting_result["model"]["pcr"]["fwhm"], 50.0, rtol=1e-2)
numpy.testing.assert_allclose(fitting_result["model"]["pcr"]["amplitude"], 1.0, rtol=2e-2)
numpy.testing.assert_allclose(fitting_result["model"]["pcr"]["frequency"], 0.0, atol=5e-2)
numpy.testing.assert_allclose(fitting_result["fit"], fixed_fid, atol=0.001)
def test_circular_dependencies(fixed_fid):
data = MRSData(fixed_fid, 5e-4, 123)
model = {
"phase0": 0.0,
"phase1": 0.0,
"pcr": {
"amplitude": 1.0,
"fwhm": {
"value": 45.0,
"min": 42.0,
"max": 55.0,
},
"phase": "0",
"frequency": "pcr2_frequency+200"
},
"pcr2": {
"amplitude": 1.0,
"fwhm": {
"value": 45.0,
"min": 42.0,
"max": 55.0,
},
"phase": "0",
"frequency": "pcr_frequency-200"
}
}
with pytest.raises(ReferenceError):
fitting_result = singlet.fit(data, model)
def test_missing_peak_phase(fixed_fid):
data = MRSData(fixed_fid, 5e-4, 123)
# No phase value passed in, to test whether phase is fixed to 0 by default
model = {
"phase0": 0,
"phase1": 0,
"pcr": {
"amplitude": 1,
"fwhm": {
"value": 45,
"min": 42,
"max": 55,
},
# "phase": "0",
"frequency": 0
}
}
fitting_result = singlet.fit(data, model)
numpy.testing.assert_allclose(fitting_result["model"]["pcr"]["fwhm"], 50.0, rtol=5e-2)
numpy.testing.assert_allclose(fitting_result["model"]["pcr"]["amplitude"], 1.0, rtol=5e-2)
numpy.testing.assert_allclose(fitting_result["model"]["pcr"]["frequency"], 0.0, atol=5e-2)
numpy.testing.assert_allclose(fitting_result["fit"], fixed_fid, atol=0.001)
def test_gaussian(fixed_fid):
data = MRSData(fixed_fid, 5e-4, 123)
# Original test with all parameters passed in; correct data types; integer values
model = {
"phase0": 0,
"phase1": 0,
"pcr": {
"amplitude": 1,
"fwhm": {
"value": 45,
"min": 42.0,
"max": 55
},
"phase": "0",
"frequency": 0.0
}
}
fitting_result = singlet.fit(data, model)
numpy.testing.assert_allclose(fitting_result["model"]["pcr"]["fwhm"],
50.0,
rtol=1e-2)
numpy.testing.assert_allclose(fitting_result["model"]["pcr"]["amplitude"],
1.0,
rtol=2e-2)
numpy.testing.assert_allclose(fitting_result["model"]["pcr"]["frequency"],
0.0,
atol=1e-1)
numpy.testing.assert_allclose(fitting_result["fit"], fixed_fid, atol=0.001)
def test_circular_dependencies(fixed_fid):
data = MRSData(fixed_fid, 5e-4, 123)
model = {
"phase0": 0.0,
"phase1": 0.0,
"pcr": {
"amplitude": 1.0,
"fwhm": {
"value": 45.0,
"min": 42.0,
"max": 55.0,
},
"phase": "0",
"frequency": "pcr2_frequency+200"
},
"pcr2": {
"amplitude": 1.0,
"fwhm": {
"value": 45.0,
"min": 42.0,
"max": 55.0,
},
"phase": "0",
"frequency": "pcr_frequency-200"
}
}
with pytest.raises(ReferenceError):
fitting_result = singlet.fit(data, model)
def test_missing_peak_phase(fixed_fid):
data = MRSData(fixed_fid, 5e-4, 123)
# No phase value passed in, to test whether phase is fixed to 0 by default
model = {
"phase0": 0,
"phase1": 0,
"pcr": {
"amplitude": 1,
"fwhm": {
"value": 45,
"min": 42,
"max": 55,
},
# "phase": "0",
"frequency": 0
}
}
fitting_result = singlet.fit(data, model)
numpy.testing.assert_allclose(fitting_result["model"]["pcr"]["fwhm"],
50.0,
rtol=5e-2)
numpy.testing.assert_allclose(fitting_result["model"]["pcr"]["amplitude"],
1.0,
rtol=5e-2)
numpy.testing.assert_allclose(fitting_result["model"]["pcr"]["frequency"],
0.0,
atol=1e-1)
numpy.testing.assert_allclose(fitting_result["fit"], fixed_fid, atol=0.001)
def test_reordered_dependencies(fixed_fid_sum):
data = MRSData(fixed_fid_sum, 5e-4, 123)
model = {
"phase0": 0.0,
"phase1": 0.0,
"pcr": {
"amplitude": 1.0,
"fwhm": {
"value": 45.0,
"min": 42.0,
"max": 55.0,
},
"phase": "0",
"frequency": "pcr2_frequency+200"
},
"pcr2": {
"amplitude": 1.0,
"fwhm": {
"value": 45.0,
"min": 42.0,
"max": 55.0,
},
"phase": "0",
"frequency": 0
}
}
fitting_result = singlet.fit(data, model)
numpy.testing.assert_allclose(fitting_result["model"]["pcr"]["fwhm"],
50.0,
rtol=1e-2)
numpy.testing.assert_allclose(fitting_result["model"]["pcr"]["amplitude"],
1.0,
rtol=2e-2)
numpy.testing.assert_allclose(fitting_result["model"]["pcr"]["frequency"],
200.0,
atol=1e-1)
numpy.testing.assert_allclose(fitting_result["fit"],
fixed_fid_sum,
atol=0.001)
def test_missing_param(fixed_fid):
data = MRSData(fixed_fid, 5e-4, 123)
# No width value passed in, to test whether KeyError is raised
model = {
"phase0": 0,
"phase1": 0,
"pcr": {
"amplitude": 1,
"fwhm": {
# "value": 45,
"min": 42,
"max": 55,
},
"phase": "0",
"frequency": 0
}
}
with pytest.raises(KeyError):
fitting_result = singlet.fit(data, model)
def test_missing_global_phase(fixed_fid):
data = MRSData(fixed_fid, 5e-4, 123)
# None value supplied for phase0 and phase1, to test whether TypeError is raised
model = {
"phase0": None,
"phase1": None,
"pcr": {
"amplitude": 1.0,
"fwhm": {
"value": 45.0,
"min": 42.0,
"max": 55.0,
},
"phase": "0",
"frequency": 0.0
}
}
with pytest.raises(TypeError):
fitting_result = singlet.fit(data, model)
def test_missing_param(fixed_fid):
data = MRSData(fixed_fid, 5e-4, 123)
# No width value passed in, to test whether KeyError is raised
model = {
"phase0": 0,
"phase1": 0,
"pcr": {
"amplitude": 1,
"fwhm": {
# "value": 45,
"min": 42,
"max": 55,
},
"phase": "0",
"frequency": 0
}
}
with pytest.raises(KeyError):
fitting_result = singlet.fit(data, model)
def test_missing_global_phase(fixed_fid):
data = MRSData(fixed_fid, 5e-4, 123)
# None value supplied for phase0 and phase1, to test whether TypeError is raised
model = {
"phase0": None,
"phase1": None,
"pcr": {
"amplitude": 1.0,
"fwhm": {
"value": 45.0,
"min": 42.0,
"max": 55.0,
},
"phase": "0",
"frequency": 0.0
}
}
with pytest.raises(TypeError):
fitting_result = singlet.fit(data, model)
def test_bad_param(fixed_fid):
data = MRSData(fixed_fid, 5e-4, 123)
# invalid key added to width dict, to test whether KeyError is raised
model = {
"phase0": 0.0,
"phase1": 0.0,
"pcr": {
"amplitude": 1.0,
"fwhm": {
"value": 45.0,
"min": 42.0,
"max": 55.0,
"avg": 47 # this is the bad key
},
"phase": "0",
"frequency": 0.0
}
}
with pytest.raises(KeyError):
fitting_result = singlet.fit(data, model)
def test_bad_param(fixed_fid):
data = MRSData(fixed_fid, 5e-4, 123)
# invalid key added to width dict, to test whether KeyError is raised
model = {
"phase0": 0.0,
"phase1": 0.0,
"pcr": {
"amplitude": 1.0,
"fwhm": {
"value": 45.0,
"min": 42.0,
"max": 55.0,
"avg": 47 # this is the bad key
},
"phase": "0",
"frequency": 0.0
}
}
with pytest.raises(KeyError):
fitting_result = singlet.fit(data, model)
def test_reordered_dependencies(fixed_fid_sum):
data = MRSData(fixed_fid_sum, 5e-4, 123)
model = {
"phase0": 0.0,
"phase1": 0.0,
"pcr": {
"amplitude": 1.0,
"fwhm": {
"value": 45.0,
"min": 42.0,
"max": 55.0,
},
"phase": "0",
"frequency": "pcr2_frequency+200"
},
"pcr2": {
"amplitude": 1.0,
"fwhm": {
"value": 45.0,
"min": 42.0,
"max": 55.0,
},
"phase": "0",
"frequency": 0
}
}
fitting_result = singlet.fit(data, model)
numpy.testing.assert_allclose(fitting_result["model"]["pcr"]["fwhm"], 50.0, rtol=1e-2)
numpy.testing.assert_allclose(fitting_result["model"]["pcr"]["amplitude"], 1.0, rtol=2e-2)
numpy.testing.assert_allclose(fitting_result["model"]["pcr"]["frequency"], 200.0, atol=1e-1)
numpy.testing.assert_allclose(fitting_result["fit"], fixed_fid_sum, atol=0.001)
