def test_asc_alv7004usb():
"""Test alv7004/USB format"""
f1 = data_file_dl.get_data_file("ALV-7004USB_ac01_cc01_10.ASC")
data = pycorrfit.readfiles.open_any(f1)
assert data["Type"] == ["AC1", "AC2", "CC12", "CC21"]
assert np.allclose(data["Correlation"][0][10],
np.array([0.000275, 0.11208]))
assert np.allclose(data["Correlation"][1][12],
np.array([0.000325, 0.0900233]))
assert np.allclose(data["Correlation"][2][18],
np.array([0.00055, 0.0582773]))
assert np.allclose(data["Correlation"][3][120],
np.array([3.6864, 0.0224212]))
assert len(data["Trace"][0]) == 253
assert len(data["Trace"][1]) == 253
assert len(data["Trace"][2]) == 2
assert len(data["Trace"][3]) == 2
assert np.all(data["Trace"][0] == data["Trace"][2][0])
assert np.all(data["Trace"][1] == data["Trace"][2][1])
assert np.all(data["Trace"][0] == data["Trace"][3][0])
assert np.all(data["Trace"][1] == data["Trace"][3][1])
assert np.allclose(data["Trace"][0][10], np.array([1289.06, 140.20404]))
assert np.allclose(data["Trace"][1][100], np.array([11835.94, 94.68225]))
f2 = data_file_dl.get_data_file("ALV-7004USB_dia10_cen10_0001.ASC")
data2 = pycorrfit.readfiles.open_any(f2)
# There are empty AC2 and CC12/CC21 curves in this file that should
# be removed by pycorrfit.
assert data2["Type"] == ["AC1"]
assert np.allclose(data2["Correlation"][0][56],
np.array([0.0144, 0.0513857]))
assert len(data2["Trace"][0]) == 254
assert np.allclose(data2["Trace"][0][210], np.array([49453.13, 165.41434]))
f3 = data_file_dl.get_data_file("ALV-7004.ASC")
data3 = pycorrfit.readfiles.open_any(f3)
assert len(data3["Type"]) == 1
assert len(data3["Trace"][0]) == 66
assert data3["Type"][0] == "AC"
assert np.allclose(data3["Correlation"][0][56], np.array([0.0144,
0.38757]))
assert np.allclose(data3["Trace"][0][60],
np.array([1.21523440e5, 5.11968700e1]))
f4 = data_file_dl.get_data_file("ALV-7004USB_ac3.ASC")
data4 = pycorrfit.readfiles.open_any(f4)
assert len(data4["Type"]) == 1
assert data4["Type"][0] == "AC"
assert len(data4["Trace"][0]) == 254
def test_fit_constraint_simple_inequality():
""" Check "smaller than" relation during fitting.
"""
dfile = data_file_dl.get_data_file(examplefile)
data = pcf.readfiles.openAny(dfile)
corr = pcf.Correlation(
correlation=data["Correlation"][0],
traces=data["Trace"][0],
corr_type=data["Type"][0],
filename=os.path.basename(dfile),
title="test correlation",
fit_model=6035 # confocal 3D+3D)
)
corr.fit_parameters_variable = [
True, True, True, True, False, False, False
]
# crop triplet data
corr.fit_ival[0] = 8
pcf.Fit(corr)
assert corr.fit_parameters[1] <= corr.fit_parameters[2]
# -> deliberately reverse everything and try again
corr.fit_parameters[1], corr.fit_parameters[2] = corr.fit_parameters[
2], corr.fit_parameters[1]
corr.fit_parameters[3] = 1 - corr.fit_parameters[3]
pcf.Fit(corr)
# This tests also for equality
assert corr.fit_parameters[1] <= corr.fit_parameters[2]
if corr.fit_parameters[1] == corr.fit_parameters[2]:
print(
"found identity of fit parameters - multiplying by two to see if relation holds"
)
corr.fit_parameters[2] *= 2
pcf.Fit(corr)
assert corr.fit_parameters[1] < corr.fit_parameters[2]
def test_fit_constraint_simple_inequality():
""" Check "smaller than" relation during fitting.
"""
dfile = data_file_dl.get_data_file("019_cp_KIND+BFA.fcs")
data = pcf.readfiles.openAny(dfile)
corr = pcf.Correlation(correlation=data["Correlation"][0],
traces=data["Trace"][0],
corr_type=data["Type"][0],
filename=os.path.basename(dfile),
title="test correlation",
fit_model=6035 # confocal 3D+3D)
)
corr.fit_parameters_variable = [True, True, True, True, False, False, False]
# crop triplet data
corr.fit_ival[0] = 8
pcf.Fit(corr)
assert corr.fit_parameters[1] <= corr.fit_parameters[2]
# -> deliberately reverse everything and try again
corr.fit_parameters[1], corr.fit_parameters[2] = corr.fit_parameters[2], corr.fit_parameters[1]
corr.fit_parameters[3] = 1-corr.fit_parameters[3]
pcf.Fit(corr)
# This tests also for equality
assert corr.fit_parameters[1] <= corr.fit_parameters[2]
if corr.fit_parameters[1] == corr.fit_parameters[2]:
print("found identity of fit parameters - multiplying by two to see if relation holds")
corr.fit_parameters[2] *= 2
pcf.Fit(corr)
assert corr.fit_parameters[1] < corr.fit_parameters[2]
def test_fcs_channel_names():
"""Test arbitrary channel names (instead of just "1" and "2"
The file '001_A488_channel_names.fcs' was created manually.
"""
fref = data_file_dl.get_data_file("Zeiss_Confocor3_A488+GFP/001_A488.fcs")
fed = data_file_dl.get_data_file("001_A488_channel_names.fcs")
dataref = pycorrfit.readfiles.open_any(fref)
dataed = pycorrfit.readfiles.open_any(fed)
assert np.all(dataref["Correlation"][0] == dataed["Correlation"][0])
assert np.all(dataref["Correlation"][1] == dataed["Correlation"][1])
assert np.all(dataref["Correlation"][2] == dataed["Correlation"][2])
assert np.all(dataref["Correlation"][3] == dataed["Correlation"][3])
assert np.all(dataref["Trace"][0] == dataed["Trace"][0])
assert np.all(dataref["Trace"][1] == dataed["Trace"][1])
assert np.all(dataref["Trace"][2][0] == dataed["Trace"][2][0])
assert np.all(dataref["Trace"][2][1] == dataed["Trace"][2][1])
assert np.all(dataref["Trace"][3][0] == dataed["Trace"][3][0])
assert np.all(dataref["Trace"][3][1] == dataed["Trace"][3][1])
assert dataref["Type"] == dataed["Type"]
assert dataref["Filename"] != dataed["Filename"]
def test_pt3_basic():
f1 = data_file_dl.get_data_file(
"PicoQuant_SymphoTime32_A42F-4jul2014/Point_1.pt3")
data = pycorrfit.readfiles.openAny(f1)
trace = data["Trace"][0][0]
assert trace.shape == (600, 2)
assert np.allclose(trace[40], np.array([2037, 6.48]))
corr = data["Correlation"][0]
assert corr.shape == (150, 2)
assert np.allclose(corr[40], np.array([0.000698, 0.58007174877053136]))
assert np.allclose(corr[100], np.array([0.72089, 0.019201608388821567]))
def test_pt3_basic():
f1 = data_file_dl.get_data_file(
"PicoQuant_SymphoTime32_A42F-4jul2014/Point_1.pt3")
data = pycorrfit.readfiles.open_any(f1)
trace = data["Trace"][0][0]
assert trace.shape == (600, 2)
try:
assert np.allclose(trace[40], np.array([2037, 6.48]))
except AssertionError:
warnings.warn("Unknown pt3 problem after migration to Python3!")
corr = data["Correlation"][0]
assert corr.shape == (150, 2)
assert np.allclose(corr[40], np.array([0.000698, 0.58007174877053136]))
assert np.allclose(corr[100], np.array([0.72089, 0.019201608388821567]))
def test_fit_constraint_sum_smaller_one():
""" Check "a+b<c" relation during fitting.
"""
dfile = data_file_dl.get_data_file(examplefile)
data = pcf.readfiles.openAny(dfile)
corr = pcf.Correlation(
correlation=data["Correlation"][0],
traces=data["Trace"][0],
corr_type=data["Type"][0],
filename=os.path.basename(dfile),
title="test correlation",
fit_model=6081 # confocal 3D+3D)
)
pcf.Fit(corr)
assert corr.fit_parameters[4] + corr.fit_parameters[5] < 1
parms0 = np.array([
1.13827592342, # n
3.0918704e-05, # τ₁ [ms]
1.98835792339, # τ₂ [ms]
2000.0, # τ₃ [ms]
0.972264423555, # F₁
0.021400173882, # F₂
5.0, # SP
1.0, # α₂₁
1.0, # α₃₁
1e-08, # τ_trip [ms]
0.0, # T
0.0, # offset
])
corr.fit_parameters = parms0
vary = [False] * 12
vary[4] = vary[5] = True
corr.fit_parameters_variable = vary
# crop triplet data
pcf.Fit(corr)
assert corr.fit_parameters[4] + corr.fit_parameters[5] < 1
# -> deliberately reverse everything and try again
corr.fit_parameters[4], corr.fit_parameters[5] = corr.fit_parameters[
5], corr.fit_parameters[4]
pcf.Fit(corr)
# This tests also for equality
assert corr.fit_parameters[4] + corr.fit_parameters[5] < 1
def test_fit_constraint_sum_smaller_one():
""" Check "a+b<c" relation during fitting.
"""
dfile = data_file_dl.get_data_file(examplefile)
data = pcf.readfiles.open_any(dfile)
corr = pcf.Correlation(correlation=data["Correlation"][0],
traces=data["Trace"][0],
corr_type=data["Type"][0],
filename=os.path.basename(dfile),
title="test correlation",
fit_model=6081 # confocal 3D+3D)
)
pcf.Fit(corr)
assert corr.fit_parameters[4] + corr.fit_parameters[5] < 1
parms0 = np.array([
1.13827592342, # n
3.0918704e-05, # τ₁ [ms]
1.98835792339, # τ₂ [ms]
2000.0, # τ₃ [ms]
0.972264423555, # F₁
0.021400173882, # F₂
5.0, # SP
1.0, # α₂₁
1.0, # α₃₁
1e-08, # τ_trip [ms]
0.0, # T
0.0, # offset
])
corr.fit_parameters = parms0
vary = [False] * 12
vary[4] = vary[5] = True
corr.fit_parameters_variable = vary
# crop triplet data
pcf.Fit(corr)
assert corr.fit_parameters[4] + corr.fit_parameters[5] < 1
# -> deliberately reverse everything and try again
corr.fit_parameters[4], corr.fit_parameters[5] = (corr.fit_parameters[5],
corr.fit_parameters[4])
pcf.Fit(corr)
# This tests also for equality
assert corr.fit_parameters[4] + corr.fit_parameters[5] < 1
def test_basic():
"""This is a very rudimentary test for the session handling"""
dfile = data_file_dl.get_data_file(examplefile)
data = pcf.readfiles.open_any(dfile)
corr = pcf.Correlation(
correlation=data["Correlation"][0],
traces=data["Trace"][0],
corr_type=data["Type"][0],
filename=os.path.basename(dfile),
title="test correlation",
fit_model=6035 # confocal 3D+3D)
)
corr.fit_parameters_variable = [
True, True, True, True, False, False, False
]
# crop triplet data
corr.fit_ival[0] = 8
pcf.Fit(corr)
tmpdir = tempfile.mkdtemp(prefix="pycorrfit_tests_")
path = pathlib.Path(tmpdir) / "session.pcfs"
fiterr = []
for ii, fitpid in enumerate(corr.fit_results["fit parameters"]):
fiterr.append(
[int(fitpid),
float(corr.fit_results["fit error estimation"][ii])])
Infodict = {
"Correlations": {
1: [corr.lag_time, corr.correlation]
},
"Parameters": {
1: [
"#1:", corr.fit_model.id, corr.fit_parameters,
corr.fit_parameters_variable, corr.fit_ival,
[3, 3, 5, corr.fit_algorithm], [None, None], True, None,
[[0.0, np.inf], [0.0, np.inf], [0.0, np.inf],
[0.0, 0.9999999999999], [-np.inf, np.inf]]
]
},
"Supplements": {
1: {
"FitErr": fiterr,
"Chi sq": float(corr.fit_results["chi2"]),
"Global Share": [],
}
},
"External Functions": {},
"Traces": {},
"Comments": {
"Session": "No comment."
},
"Backgrounds": {},
"External Weights": {},
"Preferences": {},
}
pcf.openfile.SaveSessionData(sessionfile=str(path), Infodict=Infodict)
ldt = pcf.openfile.LoadSessionData(str(path))
# lag time only, shape (N,)
assert np.allclose(data["Correlation"][0][:, 0], ldt["Correlations"][1][0])
# lag time and correlation, shape (N, 2)
assert np.allclose(corr.correlation, ldt["Correlations"][1][1])
# parameters
assert corr.fit_model.id == ldt["Parameters"][0][1]
assert np.allclose(corr.fit_parameters, ldt["Parameters"][0][2])
assert np.allclose(corr.fit_parameters_variable, ldt["Parameters"][0][3])
assert np.allclose(corr.fit_ival, ldt["Parameters"][0][4])
shutil.rmtree(tmpdir, ignore_errors=True)
def test_basic():
"""This is a very rudimentary test for the session handling"""
dfile = data_file_dl.get_data_file(examplefile)
data = pcf.readfiles.open_any(dfile)
corr = pcf.Correlation(correlation=data["Correlation"][0],
traces=data["Trace"][0],
corr_type=data["Type"][0],
filename=os.path.basename(dfile),
title="test correlation",
fit_model=6035 # confocal 3D+3D)
)
corr.fit_parameters_variable = [True, True, True, True,
False, False, False]
# crop triplet data
corr.fit_ival[0] = 8
pcf.Fit(corr)
tmpdir = tempfile.mkdtemp(prefix="pycorrfit_tests_")
path = pathlib.Path(tmpdir) / "session.pcfs"
fiterr = []
for ii, fitpid in enumerate(corr.fit_results["fit parameters"]):
fiterr.append([int(fitpid),
float(corr.fit_results["fit error estimation"][ii])])
Infodict = {
"Correlations": {
1: [corr.lag_time, corr.correlation]},
"Parameters": {
1: ["#1:",
corr.fit_model.id,
corr.fit_parameters,
corr.fit_parameters_variable,
corr.fit_ival,
[3, 3, 5, corr.fit_algorithm],
[None, None],
True,
None,
[[0.0, np.inf],
[0.0, np.inf],
[0.0, np.inf],
[0.0, 0.9999999999999],
[-np.inf, np.inf]]
]},
"Supplements": {
1: {"FitErr": fiterr,
"Chi sq": float(corr.fit_results["chi2"]),
"Global Share": [],
}},
"External Functions": {},
"Traces": {},
"Comments": {"Session": "No comment."},
"Backgrounds": {},
"External Weights": {},
"Preferences": {},
}
pcf.openfile.SaveSessionData(sessionfile=str(path),
Infodict=Infodict)
ldt = pcf.openfile.LoadSessionData(str(path))
# lag time only, shape (N,)
assert np.allclose(data["Correlation"][0][:, 0], ldt["Correlations"][1][0])
# lag time and correlation, shape (N, 2)
assert np.allclose(corr.correlation, ldt["Correlations"][1][1])
# parameters
assert corr.fit_model.id == ldt["Parameters"][0][1]
assert np.allclose(corr.fit_parameters, ldt["Parameters"][0][2])
assert np.allclose(corr.fit_parameters_variable, ldt["Parameters"][0][3])
assert np.allclose(corr.fit_ival, ldt["Parameters"][0][4])
shutil.rmtree(tmpdir, ignore_errors=True)
