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Fix error values calculation

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Fix #40
This commit is contained in:
usov_i
2021-09-29 16:49:43 +02:00
parent 4dae756b3e
commit c2be907113
4 changed files with 19 additions and 3 deletions
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13
pyzebra/ccl_process.py
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@@ -29,6 +29,7 @@ def normalize_dataset(dataset, monitor=100_000):
for scan in dataset:
monitor_ratio = monitor / scan["monitor"]
scan["counts"] *= monitor_ratio
scan["counts_err"] *= monitor_ratio
scan["monitor"] = monitor
@@ -83,6 +84,7 @@ def merge_scans(scan_into, scan_from):
if "init_omega" not in scan_into:
scan_into["init_omega"] = scan_into["omega"]
scan_into["init_counts"] = scan_into["counts"]
scan_into["init_counts_err"] = scan_into["counts_err"]
if "merged_scans" not in scan_into:
scan_into["merged_scans"] = []
@@ -93,15 +95,20 @@ def merge_scans(scan_into, scan_from):
and np.max(np.abs(scan_into["omega"] - scan_from["omega"])) < 0.0005
):
scan_into["counts"] = (scan_into["counts"] + scan_from["counts"]) / 2
scan_into["counts_err"] = np.sqrt(
scan_into["counts_err"] ** 2 + scan_from["counts_err"] ** 2
)
else:
omega = np.concatenate((scan_into["omega"], scan_from["omega"]))
counts = np.concatenate((scan_into["counts"], scan_from["counts"]))
counts_err = np.concatenate((scan_into["counts_err"], scan_from["counts_err"]))
index = np.argsort(omega)
scan_into["omega"] = omega[index]
scan_into["counts"] = counts[index]
scan_into["counts_err"] = counts_err[index]
scan_from["active"] = False
@@ -114,8 +121,10 @@ def restore_scan(scan):
if "init_omega" in scan:
scan["omega"] = scan["init_omega"]
scan["counts"] = scan["init_counts"]
scan["counts_err"] = scan["init_counts_err"]
del scan["init_omega"]
del scan["init_counts"]
del scan["init_counts_err"]
if "merged_scans" in scan:
for merged_scan in scan["merged_scans"]:
@@ -130,11 +139,13 @@ def fit_scan(scan, model_dict, fit_from=None, fit_to=None):
fit_to = np.inf
y_fit = scan["counts"]
y_err = scan["counts_err"]
x_fit = scan[scan["scan_motor"]]
# apply fitting range
fit_ind = (fit_from <= x_fit) & (x_fit <= fit_to)
y_fit = y_fit[fit_ind]
y_err = y_err[fit_ind]
x_fit = x_fit[fit_ind]
model = None
@@ -182,7 +193,7 @@ def fit_scan(scan, model_dict, fit_from=None, fit_to=None):
else:
model += _model
weights = [1 / np.sqrt(val) if val != 0 else 1 for val in y_fit]
weights = [1 / y_err if y_err != 0 else 1 for y_err in y_err]
scan["fit"] = model.fit(y_fit, x=x_fit, weights=weights)