Update param_study_moduls.py
Updated parametric study module with merging, adding etc...
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JakHolzer
GitHub
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6099df650b
@ -1,5 +1,4 @@
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from load_1D import load_1D
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from ccl_dict_operation import add_dict
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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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@ -7,6 +6,17 @@ import matplotlib as mpl
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import numpy as np
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import pickle
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import scipy.io as sio
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import uncertainties as u
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def create_tuples(x, y, y_err):
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"""creates tuples for sorting and merginng of the data
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Counts need to be normalized to monitor before"""
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t = list()
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for i in range(len(x)):
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tup = (x[i], y[i], y_err[i])
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t.append(tup)
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return t
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def load_dats(filepath):
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@ -38,10 +48,10 @@ def load_dats(filepath):
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dict1 = add_dict(dict1, load_1D(file_list[i][0]))
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else:
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dict1 = add_dict(dict1, load_1D(file_list[i]))
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dict1["scan"][i + 1]["params"] = {}
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dict1["meas"][i + 1]["params"] = {}
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if data_type == "txt":
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for x in range(len(col_names) - 1):
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dict1["scan"][i + 1]["params"][col_names[x + 1]] = file_list[i][x + 1]
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dict1["meas"][i + 1]["params"][col_names[x + 1]] = file_list[i][x + 1]
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return dict1
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@ -53,7 +63,7 @@ def create_dataframe(dict1):
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# create dictionary to which we pull only wanted items before transforming it to pd.dataframe
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pull_dict = {}
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pull_dict["filenames"] = list()
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for key in dict1["scan"][1]["params"]:
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for key in dict1["meas"][1]["params"]:
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pull_dict[key] = list()
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pull_dict["temperature"] = list()
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pull_dict["mag_field"] = list()
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@ -63,19 +73,19 @@ def create_dataframe(dict1):
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pull_dict["Counts"] = list()
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# populate the dict
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for keys in dict1["scan"]:
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if "file_of_origin" in dict1["scan"][keys]:
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pull_dict["filenames"].append(dict1["scan"][keys]["file_of_origin"].split("/")[-1])
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for keys in dict1["meas"]:
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if "file_of_origin" in dict1["meas"][keys]:
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pull_dict["filenames"].append(dict1["meas"][keys]["file_of_origin"].split("/")[-1])
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else:
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pull_dict["filenames"].append(dict1["meta"]["original_filename"].split("/")[-1])
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for key in dict1["scan"][keys]["params"]:
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pull_dict[str(key)].append(float(dict1["scan"][keys]["params"][key]))
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pull_dict["temperature"].append(dict1["scan"][keys]["temperature"])
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pull_dict["mag_field"].append(dict1["scan"][keys]["mag_field"])
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pull_dict["fit_area"].append(dict1["scan"][keys]["fit"]["fit_area"])
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pull_dict["int_area"].append(dict1["scan"][keys]["fit"]["int_area"])
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pull_dict["om"].append(dict1["scan"][keys]["om"])
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pull_dict["Counts"].append(dict1["scan"][keys]["Counts"])
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for key in dict1["meas"][keys]["params"]:
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pull_dict[str(key)].append(float(dict1["meas"][keys]["params"][key]))
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pull_dict["temperature"].append(dict1["meas"][keys]["temperature"])
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pull_dict["mag_field"].append(dict1["meas"][keys]["mag_field"])
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pull_dict["fit_area"].append(dict1["meas"][keys]["fit"]["fit_area"])
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pull_dict["int_area"].append(dict1["meas"][keys]["fit"]["int_area"])
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pull_dict["om"].append(dict1["meas"][keys]["om"])
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pull_dict["Counts"].append(dict1["meas"][keys]["Counts"])
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return pd.DataFrame(data=pull_dict)
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@ -144,7 +154,7 @@ def make_graph(data, sorting_parameter, style):
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def save_dict(obj, name):
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""" saves dictionary as pickle file in binary format
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"""saves dictionary as pickle file in binary format
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:arg obj - object to save
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:arg name - name of the file
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NOTE: path should be added later"""
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@ -200,3 +210,172 @@ def save_table(data, filetype, name, path=None):
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hdf.close()
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if filetype == "json":
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data.to_json((path + name + ".json"))
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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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: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 monitor : final monitor
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:return counts - normalized counts
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:return sigma - normalized sigma"""
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counts = np.array(dict["meas"][key]["Counts"])
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sigma = np.sqrt(counts) if "sigma" not in dict["meas"][key] else dict["meas"][key]["sigma"]
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monitor_ratio = monitor / dict["meas"][key]["monitor"]
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scaled_counts = counts * 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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def merge(dict1, dict2, scand_dict_result, auto=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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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 dict2 : dictionary from which measurement will be merged
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:arg keys : tuple with key to dict1 and dict2
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:arg auto : if true, when monitors are same, does not change it, if flase, takes monitor always
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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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:return dict1 with merged scan"""
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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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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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if auto:
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if dict1["meas"][first]["monitor"] == dict2["meas"][second]["monitor"]:
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monitor = dict1["meas"][first]["monitor"]
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# load om and Counts
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x1, x2 = dict1["meas"][first]["om"], dict2["meas"][second]["om"]
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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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# 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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# 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.append((0, 0, 0))
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om, Counts, sigma = [], [], []
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seen = list()
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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] != sorted_t[i + 1][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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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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continue
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if dict1 == dict2:
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del dict1["meas"][second]
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note = (
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f"This measurement was merged with measurement {second} from "
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f'file {dict2["meta"]["original_filename"]} \n'
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)
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if "notes" not in dict1["meas"][first]:
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dict1["meas"][first]["notes"] = note
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else:
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dict1["meas"][first]["notes"] += note
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dict1["meas"][first]["om"] = om
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dict1["meas"][first]["Counts"] = Counts
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dict1["meas"][first]["sigma"] = sigma
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dict1["meas"][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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"""adds two dictionaries, meta of the new is saved as meata+original_filename and
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measurements are shifted to continue with numbering of first dict
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:arg dict1 : dictionarry to add to
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:arg dict2 : dictionarry from which to take the measurements
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:return dict1 : combined dictionary
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Note: dict1 must be made from ccl, otherwise we would have to change the structure of loaded
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dat file"""
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if dict1["meta"]["zebra_mode"] != dict2["meta"]["zebra_mode"]:
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print("You are trying to add scans measured with different zebra modes")
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return
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max_measurement_dict1 = max([keys for keys in dict1["meas"]])
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new_filenames = np.arange(
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max_measurement_dict1 + 1, max_measurement_dict1 + 1 + len(dict2["meas"])
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)
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new_meta_name = "meta" + str(dict2["meta"]["original_filename"])
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if new_meta_name not in dict1:
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for keys, name in zip(dict2["meas"], new_filenames):
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dict2["meas"][keys]["file_of_origin"] = str(dict2["meta"]["original_filename"])
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dict1["meas"][name] = dict2["meas"][keys]
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dict1[new_meta_name] = dict2["meta"]
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else:
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raise KeyError(
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str(
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"The file %s has alredy been added to %s"
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% (dict2["meta"]["original_filename"], dict1["meta"]["original_filename"])
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)
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)
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return dict1
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def auto(dict):
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"""takes just unique tuples from all tuples in dictionary returend by scan_dict
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intendet for automatic merge if you doesent want to specify what scans to merge together
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args: dict - dictionary from scan_dict function
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:return dict - dict without repetitions"""
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for keys in dict:
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tuple_list = dict[keys]
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new = list()
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for i in range(len(tuple_list)):
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if tuple_list[0][0] == tuple_list[i][0]:
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new.append(tuple_list[i])
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dict[keys] = new
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return dict
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def scan_dict(dict, precision=0.5):
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"""scans dictionary for duplicate angles indexes
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:arg dict : dictionary to scan
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:arg precision : in deg, sometimes angles are zero so its easier this way, instead of
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checking zero division
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:return dictionary with matching scans, if there are none, the dict is empty
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note: can be checked by "not d", true if empty
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"""
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if dict["meta"]["zebra_mode"] == "bi":
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angles = ["twotheta_angle", "omega_angle", "chi_angle", "phi_angle"]
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elif dict["meta"]["zebra_mode"] == "nb":
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angles = ["gamma_angle", "omega_angle", "nu_angle"]
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else:
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print("Unknown zebra mode")
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return
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d = {}
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for i in dict["meas"]:
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for j in dict["meas"]:
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if dict["meas"][i] != dict["meas"][j]:
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itup = list()
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for k in angles:
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itup.append(abs(abs(dict["meas"][i][k]) - abs(dict["meas"][j][k])))
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if all(i <= precision for i in itup):
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if str([np.around(dict["meas"][i][k], 1) for k in angles]) not in d:
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d[str([np.around(dict["meas"][i][k], 1) for k in angles])] = list()
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d[str([np.around(dict["meas"][i][k], 1) for k in angles])].append((i, j))
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else:
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d[str([np.around(dict["meas"][i][k], 1) for k in angles])].append((i, j))
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else:
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pass
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else:
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continue
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return d
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