Use a ref to the current measurement
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@ -29,6 +29,7 @@ def ccl_findpeaks(
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'peak_heights': [90.], # height of the peaks (if data vere smoothed
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its the heigh of the peaks in smoothed data)
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"""
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meas = data["Measurements"][keys]
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if type(data) is not dict and data["file_type"] != "ccl":
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print("Data is not a dictionary or was not made from ccl file")
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@ -44,7 +45,8 @@ def ccl_findpeaks(
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window_size = 7
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print(
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"Invalid value for window_size, select positive odd integer, new value set to!:",
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window_size)
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window_size,
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)
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if isinstance(poly_order, int) is False or window_size < poly_order:
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poly_order = 3
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@ -57,9 +59,8 @@ def ccl_findpeaks(
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prominence = 50
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print("Invalid value for prominence, select positive number, new value set to:", prominence)
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omega = data["Measurements"][str(keys)]["om"]
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counts = np.array(data["Measurements"][str(keys)]["Counts"])
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omega = meas["om"]
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counts = np.array(meas["Counts"])
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if smooth is True:
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itp = interp1d(omega, counts, kind="linear")
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absintensity = [abs(number) for number in counts]
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@ -73,9 +74,9 @@ def ccl_findpeaks(
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indexes = sc.signal.find_peaks(
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smooth_peaks, height=int_threshold * max(smooth_peaks), prominence=prominence
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)
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data["Measurements"][str(keys)]["num_of_peaks"] = len(indexes[0])
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data["Measurements"][str(keys)]["peak_indexes"] = indexes[0]
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data["Measurements"][str(keys)]["peak_heights"] = indexes[1]["peak_heights"]
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data["Measurements"][str(keys)]["smooth_peaks"] = smooth_peaks # smoothed curve
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meas["num_of_peaks"] = len(indexes[0])
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meas["peak_indexes"] = indexes[0]
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meas["peak_heights"] = indexes[1]["peak_heights"]
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meas["smooth_peaks"] = smooth_peaks # smoothed curve
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return data
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@ -30,36 +30,34 @@ def export_comm(data, path, lorentz=False):
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padding = [4, 6, 10, 8]
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with open(str(path + extension), "w") as out_file:
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for keys in data["Measurements"]:
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print(keys)
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for meas_num, meas in data["Measurements"].items():
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print(meas_num)
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try:
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meas_number_str = f"{keys[1:]:{align}{padding[0]}}"
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h_str = f'{int(data["Measurements"][str(keys)]["h_index"]):{padding[1]}}'
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k_str = f'{int(data["Measurements"][str(keys)]["k_index"]):{padding[1]}}'
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l_str = f'{int(data["Measurements"][str(keys)]["l_index"]):{padding[1]}}'
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if data["Measurements"][str(keys)]["fit"]["export_fit"] is True:
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area = float(data["Measurements"][str(keys)]["fit"]["g_amp"][0]) + float(
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data["Measurements"][str(keys)]["fit"]["l_amp"][0]
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)
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meas_number_str = f"{meas_num[1:]:{align}{padding[0]}}"
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h_str = f'{int(meas["h_index"]):{padding[1]}}'
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k_str = f'{int(meas["k_index"]):{padding[1]}}'
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l_str = f'{int(meas["l_index"]):{padding[1]}}'
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if meas["fit"]["export_fit"] is True:
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area = float(meas["fit"]["g_amp"][0]) + float(meas["fit"]["l_amp"][0])
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else:
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area = float(data["Measurements"][str(keys)]["fit"]["int_area"]) - float(
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data["Measurements"][str(keys)]["fit"]["int_background"][0]
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)
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area = float(meas["fit"]["int_area"]) - float(meas["fit"]["int_background"][0])
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if data["meta"]["zebra_mode"] == "bi":
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int_str = f'{"{:8.2f}".format(correction(area, lorentz, data["meta"]["zebra_mode"], data["Measurements"][str(keys)]["twotheta_angle"])):{align}{padding[2]}}'
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angle_str1 = f'{data["Measurements"][str(keys)]["twotheta_angle"]:{padding[3]}}'
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angle_str2 = f'{data["Measurements"][str(keys)]["omega_angle"]:{padding[3]}}'
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angle_str3 = f'{data["Measurements"][str(keys)]["chi_angle"]:{padding[3]}}'
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angle_str4 = f'{data["Measurements"][str(keys)]["phi_angle"]:{padding[3]}}'
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int_str = f'{"{:8.2f}".format(correction(area, lorentz, data["meta"]["zebra_mode"], meas["twotheta_angle"])):{align}{padding[2]}}'
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angle_str1 = f'{meas["twotheta_angle"]:{padding[3]}}'
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angle_str2 = f'{meas["omega_angle"]:{padding[3]}}'
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angle_str3 = f'{meas["chi_angle"]:{padding[3]}}'
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angle_str4 = f'{meas["phi_angle"]:{padding[3]}}'
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elif data["meta"]["zebra_mode"] == "nb":
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int_str = f'{"{:8.2f}".format(correction(area, lorentz, data["meta"]["zebra_mode"], data["Measurements"][str(keys)]["gamma_angle"],data["Measurements"][str(keys)]["nu_angle"])):{align}{padding[2]}}'
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angle_str1 = f'{data["Measurements"][str(keys)]["gamma_angle"]:{padding[3]}}'
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angle_str2 = f'{data["Measurements"][str(keys)]["omega_angle"]:{padding[3]}}'
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angle_str3 = f'{data["Measurements"][str(keys)]["nu_angle"]:{padding[3]}}'
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angle_str4 = f'{data["Measurements"][str(keys)]["unkwn_angle"]:{padding[3]}}'
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int_str = f'{"{:8.2f}".format(correction(area, lorentz, data["meta"]["zebra_mode"], meas["gamma_angle"],meas["nu_angle"])):{align}{padding[2]}}'
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angle_str1 = f'{meas["gamma_angle"]:{padding[3]}}'
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angle_str2 = f'{meas["omega_angle"]:{padding[3]}}'
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angle_str3 = f'{meas["nu_angle"]:{padding[3]}}'
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angle_str4 = f'{meas["unkwn_angle"]:{padding[3]}}'
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sigma_str = f'{"{:8.2f}".format(float(data["Measurements"][str(keys)]["fit"]["g_width"][0])):{align}{padding[2]}}'
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sigma_str = (
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f'{"{:8.2f}".format(float(meas["fit"]["g_width"][0])):{align}{padding[2]}}'
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)
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line = (
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meas_number_str
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+ h_str
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@ -76,5 +74,4 @@ def export_comm(data, path, lorentz=False):
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out_file.write(line)
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except KeyError:
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print("Measurement skipped - no fit value for:", keys)
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print("Measurement skipped - no fit value for:", meas_num)
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@ -35,15 +35,16 @@ def fitccl(
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constraints_min = [23, None, 50, None, None, None, 0, 0]
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constraints_min = [80, None, 1000, None, None, None, 0, 100]
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"""
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meas = data["Measurements"][keys]
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if len(data["Measurements"][str(keys)]["peak_indexes"]) != 1:
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if len(meas["peak_indexes"]) != 1:
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print("NO PEAK or more than 1 peak")
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return
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x = list(data["Measurements"][str(keys)]["om"])
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y = list(data["Measurements"][str(keys)]["Counts"])
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peak_index = data["Measurements"][str(keys)]["peak_indexes"]
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peak_height = data["Measurements"][str(keys)]["peak_heights"]
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x = list(meas["om"])
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y = list(meas["Counts"])
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peak_index = meas["peak_indexes"]
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peak_height = meas["peak_heights"]
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print("before", constraints_min)
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guess[0] = x[int(peak_index)] if guess[0] is None else guess[0]
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guess[1] = 0.1 if guess[1] is None else guess[1]
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@ -151,6 +152,6 @@ def fitccl(
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d["int_area"] = num_int_area
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d["int_background"] = num_int_bacground
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d["full_report"] = result.fit_report()
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data["Measurements"][str(keys)]["fit"] = d
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meas["fit"] = d
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return data
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