Fix imports and indentation
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@ -5,6 +5,7 @@ from pyzebra.comm_export import export_comm
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from pyzebra.fit2 import fitccl
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from pyzebra.fit2 import fitccl
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from pyzebra.h5 import *
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from pyzebra.h5 import *
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from pyzebra.load_1D import load_1D, parse_1D
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from pyzebra.load_1D import load_1D, parse_1D
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from pyzebra.param_study_moduls import add_dict, auto, merge, scan_dict
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from pyzebra.xtal import *
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from pyzebra.xtal import *
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__version__ = "0.1.1"
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__version__ = "0.1.1"
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@ -1,12 +1,14 @@
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from load_1D import load_1D
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import pandas as pd
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from mpl_toolkits.mplot3d import Axes3D # dont delete, otherwise waterfall wont work
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import matplotlib.pyplot as plt
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import matplotlib as mpl
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import numpy as np
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import pickle
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import pickle
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import matplotlib as mpl
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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import scipy.io as sio
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import scipy.io as sio
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import uncertainties as u
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import uncertainties as u
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from mpl_toolkits.mplot3d import Axes3D # dont delete, otherwise waterfall wont work
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from .load_1D import load_1D
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def create_tuples(x, y, y_err):
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def create_tuples(x, y, y_err):
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@ -211,89 +213,89 @@ def save_table(data, filetype, name, path=None):
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if filetype == "json":
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if filetype == "json":
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data.to_json((path + name + ".json"))
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data.to_json((path + name + ".json"))
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def normalize(dict, key, monitor):
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def normalize(dict, key, monitor):
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"""Normalizes the measurement to monitor, checks if sigma exists, otherwise creates it
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"""Normalizes the measurement to monitor, checks if sigma exists, otherwise creates it
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:arg dict : dictionary to from which to tkae the scan
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:arg dict : dictionary to from which to tkae the scan
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:arg key : which scan to normalize from dict1
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:arg key : which scan to normalize from dict1
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:arg monitor : final monitor
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:arg monitor : final monitor
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:return counts - normalized counts
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:return counts - normalized counts
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:return sigma - normalized sigma"""
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:return sigma - normalized sigma"""
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counts = np.array(dict["scan"][key]["Counts"])
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counts = np.array(dict["scan"][key]["Counts"])
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sigma = np.sqrt(counts) if "sigma" not in dict["scan"][key] else dict["scan"][key]["sigma"]
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sigma = np.sqrt(counts) if "sigma" not in dict["scan"][key] else dict["scan"][key]["sigma"]
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monitor_ratio = monitor / dict["scan"][key]["monitor"]
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monitor_ratio = monitor / dict["scan"][key]["monitor"]
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scaled_counts = counts * monitor_ratio
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scaled_counts = counts * monitor_ratio
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scaled_sigma = np.array(sigma) * monitor_ratio
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scaled_sigma = np.array(sigma) * monitor_ratio
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return scaled_counts, scaled_sigma
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return scaled_counts, scaled_sigma
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def merge(dict1, dict2, scand_dict_result, keep=True, monitor=100000):
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def merge(dict1, dict2, scand_dict_result, keep=True, monitor=100000):
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"""merges the two tuples and sorts them, if om value is same, Counts value is average
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"""merges the two tuples and sorts them, if om value is same, Counts value is average
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averaging is propagated into sigma if dict1 == dict2, key[1] is deleted after merging
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averaging is propagated into sigma if dict1 == dict2, key[1] is deleted after merging
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:arg dict1 : dictionary to which measurement will be merged
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:arg dict1 : dictionary to which measurement will be merged
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:arg dict2 : dictionary from which measurement will be merged
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:arg dict2 : dictionary from which measurement will be merged
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:arg scand_dict_result : result of scan_dict after auto function
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:arg scand_dict_result : result of scan_dict after auto function
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:arg keep : if true, when monitors are same, does not change it, if flase, takes monitor
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:arg keep : if true, when monitors are same, does not change it, if flase, takes monitor
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always
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always
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:arg monitor : final monitor after merging
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:arg monitor : final monitor after merging
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note: dict1 and dict2 can be same dict
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note: dict1 and dict2 can be same dict
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:return dict1 with merged scan"""
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:return dict1 with merged scan"""
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for keys in scand_dict_result:
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for keys in scand_dict_result:
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for j in range(len(scand_dict_result[keys])):
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for j in range(len(scand_dict_result[keys])):
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first, second = scand_dict_result[keys][j][0], scand_dict_result[keys][j][1]
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first, second = scand_dict_result[keys][j][0], scand_dict_result[keys][j][1]
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print(first, second)
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print(first, second)
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if keep:
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if keep:
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if dict1["scan"][first]["monitor"] == dict2["scan"][second]["monitor"]:
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if dict1["scan"][first]["monitor"] == dict2["scan"][second]["monitor"]:
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monitor = dict1["scan"][first]["monitor"]
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monitor = dict1["scan"][first]["monitor"]
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# load om and Counts
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# load om and Counts
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x1, x2 = dict1["scan"][first]["om"], dict2["scan"][second]["om"]
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x1, x2 = dict1["scan"][first]["om"], dict2["scan"][second]["om"]
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cor_y1, y_err1 = normalize(dict1, first, monitor=monitor)
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cor_y1, y_err1 = normalize(dict1, first, monitor=monitor)
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cor_y2, y_err2 = normalize(dict2, second, monitor=monitor)
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cor_y2, y_err2 = normalize(dict2, second, monitor=monitor)
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# creates touples (om, Counts, sigma) for sorting and further processing
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# creates touples (om, Counts, sigma) for sorting and further processing
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tuple_list = create_tuples(x1, cor_y1, y_err1) + create_tuples(x2, cor_y2, y_err2)
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tuple_list = create_tuples(x1, cor_y1, y_err1) + create_tuples(x2, cor_y2, y_err2)
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# Sort the list on om and add 0 0 0 tuple to the last position
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# Sort the list on om and add 0 0 0 tuple to the last position
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sorted_t = sorted(tuple_list, key=lambda tup: tup[0])
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sorted_t = sorted(tuple_list, key=lambda tup: tup[0])
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sorted_t.append((0, 0, 0))
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sorted_t.append((0, 0, 0))
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om, Counts, sigma = [], [], []
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om, Counts, sigma = [], [], []
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seen = list()
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seen = list()
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for i in range(len(sorted_t) - 1):
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for i in range(len(sorted_t) - 1):
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if sorted_t[i][0] not in seen:
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if sorted_t[i][0] not in seen:
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if sorted_t[i][0] != sorted_t[i + 1][0]:
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if sorted_t[i][0] != sorted_t[i + 1][0]:
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om = np.append(om, sorted_t[i][0])
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om = np.append(om, sorted_t[i][0])
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Counts = np.append(Counts, sorted_t[i][1])
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Counts = np.append(Counts, sorted_t[i][1])
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sigma = np.append(sigma, sorted_t[i][2])
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sigma = np.append(sigma, sorted_t[i][2])
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else:
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om = np.append(om, sorted_t[i][0])
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counts1, counts2 = sorted_t[i][1], sorted_t[i + 1][1]
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sigma1, sigma2 = sorted_t[i][2], sorted_t[i + 1][2]
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count_err1 = u.ufloat(counts1, sigma1)
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count_err2 = u.ufloat(counts2, sigma2)
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avg = (count_err1 + count_err2) / 2
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Counts = np.append(Counts, avg.n)
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sigma = np.append(sigma, avg.s)
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seen.append(sorted_t[i][0])
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else:
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else:
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continue
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om = np.append(om, sorted_t[i][0])
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counts1, counts2 = sorted_t[i][1], sorted_t[i + 1][1]
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if dict1 == dict2:
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sigma1, sigma2 = sorted_t[i][2], sorted_t[i + 1][2]
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del dict1["scan"][second]
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count_err1 = u.ufloat(counts1, sigma1)
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count_err2 = u.ufloat(counts2, sigma2)
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note = (
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avg = (count_err1 + count_err2) / 2
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f"This measurement was merged with measurement {second} from "
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Counts = np.append(Counts, avg.n)
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f'file {dict2["meta"]["original_filename"]} \n'
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sigma = np.append(sigma, avg.s)
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)
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seen.append(sorted_t[i][0])
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if "notes" not in dict1["scan"][first]:
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dict1["scan"][first]["notes"] = note
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else:
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else:
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dict1["scan"][first]["notes"] += note
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continue
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dict1["scan"][first]["om"] = om
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if dict1 == dict2:
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dict1["scan"][first]["Counts"] = Counts
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del dict1["scan"][second]
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dict1["scan"][first]["sigma"] = sigma
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dict1["scan"][first]["monitor"] = monitor
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note = (
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print("merging done")
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f"This measurement was merged with measurement {second} from "
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return dict1
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f'file {dict2["meta"]["original_filename"]} \n'
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)
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if "notes" not in dict1["scan"][first]:
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dict1["scan"][first]["notes"] = note
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else:
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dict1["scan"][first]["notes"] += note
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dict1["scan"][first]["om"] = om
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dict1["scan"][first]["Counts"] = Counts
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dict1["scan"][first]["sigma"] = sigma
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dict1["scan"][first]["monitor"] = monitor
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print("merging done")
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return dict1
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def add_dict(dict1, dict2):
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def add_dict(dict1, dict2):
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