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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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3
pyzebra/app/panel_ccl_integrate.py
View File

@ -207,10 +207,11 @@ def create():
scan_motor = scan["scan_motor"] scan_motor = scan["scan_motor"]
y = scan["counts"] y = scan["counts"]
y_err = scan["counts_err"]
x = scan[scan_motor] x = scan[scan_motor]
plot.axis[0].axis_label = scan_motor plot.axis[0].axis_label = scan_motor
plot_scatter_source.data.update(x=x, y=y, y_upper=y + np.sqrt(y), y_lower=y - np.sqrt(y)) plot_scatter_source.data.update(x=x, y=y, y_upper=y + y_err, y_lower=y - y_err)
fit = scan.get("fit") fit = scan.get("fit")
if fit is not None: if fit is not None:

3
pyzebra/app/panel_param_study.py
View File

@ -235,10 +235,11 @@ def create():
scan_motor = scan["scan_motor"] scan_motor = scan["scan_motor"]
y = scan["counts"] y = scan["counts"]
y_err = scan["counts_err"]
x = scan[scan_motor] x = scan[scan_motor]
plot.axis[0].axis_label = scan_motor plot.axis[0].axis_label = scan_motor
plot_scatter_source.data.update(x=x, y=y, y_upper=y + np.sqrt(y), y_lower=y - np.sqrt(y)) plot_scatter_source.data.update(x=x, y=y, y_upper=y + y_err, y_lower=y - y_err)
fit = scan.get("fit") fit = scan.get("fit")
if fit is not None: if fit is not None:

3
pyzebra/ccl_io.py
View File

@ -169,6 +169,7 @@ def parse_1D(fileobj, data_type):
while len(counts) < s["n_points"]: while len(counts) < s["n_points"]:
counts.extend(map(float, next(fileobj).split())) counts.extend(map(float, next(fileobj).split()))
s["counts"] = np.array(counts) s["counts"] = np.array(counts)
s["counts_err"] = np.sqrt(s["counts"])
if s["h"].is_integer() and s["k"].is_integer() and s["l"].is_integer(): if s["h"].is_integer() and s["k"].is_integer() and s["l"].is_integer():
s["h"], s["k"], s["l"] = map(int, (s["h"], s["k"], s["l"])) s["h"], s["k"], s["l"] = map(int, (s["h"], s["k"], s["l"]))
@ -204,6 +205,8 @@ def parse_1D(fileobj, data_type):
for name in col_names: for name in col_names:
s[name] = np.array(s[name]) s[name] = np.array(s[name])
s["counts_err"] = np.sqrt(s["counts"])
s["scan_motors"] = [] s["scan_motors"] = []
for motor, step in zip(motors, steps): for motor, step in zip(motors, steps):
if step == 0: if step == 0:

13
pyzebra/ccl_process.py
View File

@ -29,6 +29,7 @@ def normalize_dataset(dataset, monitor=100_000):
for scan in dataset: for scan in dataset:
monitor_ratio = monitor / scan["monitor"] monitor_ratio = monitor / scan["monitor"]
scan["counts"] *= monitor_ratio scan["counts"] *= monitor_ratio
scan["counts_err"] *= monitor_ratio
scan["monitor"] = monitor scan["monitor"] = monitor
@ -83,6 +84,7 @@ def merge_scans(scan_into, scan_from):
if "init_omega" not in scan_into: if "init_omega" not in scan_into:
scan_into["init_omega"] = scan_into["omega"] scan_into["init_omega"] = scan_into["omega"]
scan_into["init_counts"] = scan_into["counts"] scan_into["init_counts"] = scan_into["counts"]
scan_into["init_counts_err"] = scan_into["counts_err"]
if "merged_scans" not in scan_into: if "merged_scans" not in scan_into:
scan_into["merged_scans"] = [] 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 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"] = (scan_into["counts"] + scan_from["counts"]) / 2
scan_into["counts_err"] = np.sqrt(
scan_into["counts_err"] ** 2 + scan_from["counts_err"] ** 2
)
else: else:
omega = np.concatenate((scan_into["omega"], scan_from["omega"])) omega = np.concatenate((scan_into["omega"], scan_from["omega"]))
counts = np.concatenate((scan_into["counts"], scan_from["counts"])) 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) index = np.argsort(omega)
scan_into["omega"] = omega[index] scan_into["omega"] = omega[index]
scan_into["counts"] = counts[index] scan_into["counts"] = counts[index]
scan_into["counts_err"] = counts_err[index]
scan_from["active"] = False scan_from["active"] = False
@ -114,8 +121,10 @@ def restore_scan(scan):
if "init_omega" in scan: if "init_omega" in scan:
scan["omega"] = scan["init_omega"] scan["omega"] = scan["init_omega"]
scan["counts"] = scan["init_counts"] scan["counts"] = scan["init_counts"]
scan["counts_err"] = scan["init_counts_err"]
del scan["init_omega"] del scan["init_omega"]
del scan["init_counts"] del scan["init_counts"]
del scan["init_counts_err"]
if "merged_scans" in scan: if "merged_scans" in scan:
for merged_scan in scan["merged_scans"]: 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 fit_to = np.inf
y_fit = scan["counts"] y_fit = scan["counts"]
y_err = scan["counts_err"]
x_fit = scan[scan["scan_motor"]] x_fit = scan[scan["scan_motor"]]
# apply fitting range # apply fitting range
fit_ind = (fit_from <= x_fit) & (x_fit <= fit_to) fit_ind = (fit_from <= x_fit) & (x_fit <= fit_to)
y_fit = y_fit[fit_ind] y_fit = y_fit[fit_ind]
y_err = y_err[fit_ind]
x_fit = x_fit[fit_ind] x_fit = x_fit[fit_ind]
model = None model = None
@ -182,7 +193,7 @@ def fit_scan(scan, model_dict, fit_from=None, fit_to=None):
else: else:
model += _model 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) scan["fit"] = model.fit(y_fit, x=x_fit, weights=weights)