parent
4dae756b3e
commit
c2be907113
@ -207,10 +207,11 @@ def create():
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scan_motor = scan["scan_motor"]
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y = scan["counts"]
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y_err = scan["counts_err"]
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x = scan[scan_motor]
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plot.axis[0].axis_label = scan_motor
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plot_scatter_source.data.update(x=x, y=y, y_upper=y + np.sqrt(y), y_lower=y - np.sqrt(y))
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plot_scatter_source.data.update(x=x, y=y, y_upper=y + y_err, y_lower=y - y_err)
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fit = scan.get("fit")
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if fit is not None:
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@ -235,10 +235,11 @@ def create():
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scan_motor = scan["scan_motor"]
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y = scan["counts"]
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y_err = scan["counts_err"]
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x = scan[scan_motor]
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plot.axis[0].axis_label = scan_motor
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plot_scatter_source.data.update(x=x, y=y, y_upper=y + np.sqrt(y), y_lower=y - np.sqrt(y))
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plot_scatter_source.data.update(x=x, y=y, y_upper=y + y_err, y_lower=y - y_err)
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fit = scan.get("fit")
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if fit is not None:
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@ -169,6 +169,7 @@ def parse_1D(fileobj, data_type):
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while len(counts) < s["n_points"]:
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counts.extend(map(float, next(fileobj).split()))
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s["counts"] = np.array(counts)
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s["counts_err"] = np.sqrt(s["counts"])
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if s["h"].is_integer() and s["k"].is_integer() and s["l"].is_integer():
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s["h"], s["k"], s["l"] = map(int, (s["h"], s["k"], s["l"]))
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@ -204,6 +205,8 @@ def parse_1D(fileobj, data_type):
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for name in col_names:
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s[name] = np.array(s[name])
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s["counts_err"] = np.sqrt(s["counts"])
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s["scan_motors"] = []
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for motor, step in zip(motors, steps):
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if step == 0:
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@ -29,6 +29,7 @@ def normalize_dataset(dataset, monitor=100_000):
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for scan in dataset:
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monitor_ratio = monitor / scan["monitor"]
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scan["counts"] *= monitor_ratio
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scan["counts_err"] *= monitor_ratio
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scan["monitor"] = monitor
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@ -83,6 +84,7 @@ def merge_scans(scan_into, scan_from):
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if "init_omega" not in scan_into:
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scan_into["init_omega"] = scan_into["omega"]
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scan_into["init_counts"] = scan_into["counts"]
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scan_into["init_counts_err"] = scan_into["counts_err"]
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if "merged_scans" not in scan_into:
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scan_into["merged_scans"] = []
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@ -93,15 +95,20 @@ def merge_scans(scan_into, scan_from):
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and np.max(np.abs(scan_into["omega"] - scan_from["omega"])) < 0.0005
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):
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scan_into["counts"] = (scan_into["counts"] + scan_from["counts"]) / 2
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scan_into["counts_err"] = np.sqrt(
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scan_into["counts_err"] ** 2 + scan_from["counts_err"] ** 2
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)
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else:
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omega = np.concatenate((scan_into["omega"], scan_from["omega"]))
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counts = np.concatenate((scan_into["counts"], scan_from["counts"]))
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counts_err = np.concatenate((scan_into["counts_err"], scan_from["counts_err"]))
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index = np.argsort(omega)
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scan_into["omega"] = omega[index]
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scan_into["counts"] = counts[index]
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scan_into["counts_err"] = counts_err[index]
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scan_from["active"] = False
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@ -114,8 +121,10 @@ def restore_scan(scan):
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if "init_omega" in scan:
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scan["omega"] = scan["init_omega"]
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scan["counts"] = scan["init_counts"]
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scan["counts_err"] = scan["init_counts_err"]
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del scan["init_omega"]
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del scan["init_counts"]
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del scan["init_counts_err"]
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if "merged_scans" in scan:
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for merged_scan in scan["merged_scans"]:
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@ -130,11 +139,13 @@ def fit_scan(scan, model_dict, fit_from=None, fit_to=None):
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fit_to = np.inf
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y_fit = scan["counts"]
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y_err = scan["counts_err"]
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x_fit = scan[scan["scan_motor"]]
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# apply fitting range
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fit_ind = (fit_from <= x_fit) & (x_fit <= fit_to)
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y_fit = y_fit[fit_ind]
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y_err = y_err[fit_ind]
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x_fit = x_fit[fit_ind]
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model = None
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@ -182,7 +193,7 @@ def fit_scan(scan, model_dict, fit_from=None, fit_to=None):
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else:
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model += _model
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weights = [1 / np.sqrt(val) if val != 0 else 1 for val in y_fit]
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weights = [1 / y_err if y_err != 0 else 1 for y_err in y_err]
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scan["fit"] = model.fit(y_fit, x=x_fit, weights=weights)
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