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Fix imports and indentation

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usov_i 2020-10-26 16:33:30 +01:00
parent dba2dc6149
commit 42c6e6b921
2 changed files with 85 additions and 82 deletions
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1
pyzebra/__init__.py
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@ -5,6 +5,7 @@ from pyzebra.comm_export import export_comm
from pyzebra.fit2 import fitccl from pyzebra.fit2 import fitccl
from pyzebra.h5 import * from pyzebra.h5 import *
from pyzebra.load_1D import load_1D, parse_1D from pyzebra.load_1D import load_1D, parse_1D
from pyzebra.param_study_moduls import add_dict, auto, merge, scan_dict
from pyzebra.xtal import * from pyzebra.xtal import *
__version__ = "0.1.1" __version__ = "0.1.1"

166
pyzebra/param_study_moduls.py
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@ -1,12 +1,14 @@
from load_1D import load_1D
import pandas as pd
from mpl_toolkits.mplot3d import Axes3D # dont delete, otherwise waterfall wont work
import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np
import pickle import pickle
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scipy.io as sio import scipy.io as sio
import uncertainties as u import uncertainties as u
from mpl_toolkits.mplot3d import Axes3D # dont delete, otherwise waterfall wont work
from .load_1D import load_1D
def create_tuples(x, y, y_err): def create_tuples(x, y, y_err):
@ -211,89 +213,89 @@ def save_table(data, filetype, name, path=None):
if filetype == "json": if filetype == "json":
data.to_json((path + name + ".json")) data.to_json((path + name + ".json"))
def normalize(dict, key, monitor): def normalize(dict, key, monitor):
"""Normalizes the measurement to monitor, checks if sigma exists, otherwise creates it """Normalizes the measurement to monitor, checks if sigma exists, otherwise creates it
:arg dict : dictionary to from which to tkae the scan :arg dict : dictionary to from which to tkae the scan
:arg key : which scan to normalize from dict1 :arg key : which scan to normalize from dict1
:arg monitor : final monitor :arg monitor : final monitor
:return counts - normalized counts :return counts - normalized counts
:return sigma - normalized sigma""" :return sigma - normalized sigma"""
counts = np.array(dict["scan"][key]["Counts"]) counts = np.array(dict["scan"][key]["Counts"])
sigma = np.sqrt(counts) if "sigma" not in dict["scan"][key] else dict["scan"][key]["sigma"] sigma = np.sqrt(counts) if "sigma" not in dict["scan"][key] else dict["scan"][key]["sigma"]
monitor_ratio = monitor / dict["scan"][key]["monitor"] monitor_ratio = monitor / dict["scan"][key]["monitor"]
scaled_counts = counts * monitor_ratio scaled_counts = counts * monitor_ratio
scaled_sigma = np.array(sigma) * monitor_ratio scaled_sigma = np.array(sigma) * monitor_ratio
return scaled_counts, scaled_sigma return scaled_counts, scaled_sigma
def merge(dict1, dict2, scand_dict_result, keep=True, monitor=100000): def merge(dict1, dict2, scand_dict_result, keep=True, monitor=100000):
"""merges the two tuples and sorts them, if om value is same, Counts value is average """merges the two tuples and sorts them, if om value is same, Counts value is average
averaging is propagated into sigma if dict1 == dict2, key[1] is deleted after merging averaging is propagated into sigma if dict1 == dict2, key[1] is deleted after merging
:arg dict1 : dictionary to which measurement will be merged :arg dict1 : dictionary to which measurement will be merged
:arg dict2 : dictionary from which measurement will be merged :arg dict2 : dictionary from which measurement will be merged
:arg scand_dict_result : result of scan_dict after auto function :arg scand_dict_result : result of scan_dict after auto function
:arg keep : if true, when monitors are same, does not change it, if flase, takes monitor :arg keep : if true, when monitors are same, does not change it, if flase, takes monitor
always always
:arg monitor : final monitor after merging :arg monitor : final monitor after merging
note: dict1 and dict2 can be same dict note: dict1 and dict2 can be same dict
:return dict1 with merged scan""" :return dict1 with merged scan"""
for keys in scand_dict_result: for keys in scand_dict_result:
for j in range(len(scand_dict_result[keys])): for j in range(len(scand_dict_result[keys])):
first, second = scand_dict_result[keys][j][0], scand_dict_result[keys][j][1] first, second = scand_dict_result[keys][j][0], scand_dict_result[keys][j][1]
print(first, second) print(first, second)
if keep: if keep:
if dict1["scan"][first]["monitor"] == dict2["scan"][second]["monitor"]: if dict1["scan"][first]["monitor"] == dict2["scan"][second]["monitor"]:
monitor = dict1["scan"][first]["monitor"] monitor = dict1["scan"][first]["monitor"]
# load om and Counts # load om and Counts
x1, x2 = dict1["scan"][first]["om"], dict2["scan"][second]["om"] x1, x2 = dict1["scan"][first]["om"], dict2["scan"][second]["om"]
cor_y1, y_err1 = normalize(dict1, first, monitor=monitor) cor_y1, y_err1 = normalize(dict1, first, monitor=monitor)
cor_y2, y_err2 = normalize(dict2, second, monitor=monitor) cor_y2, y_err2 = normalize(dict2, second, monitor=monitor)
# creates touples (om, Counts, sigma) for sorting and further processing # creates touples (om, Counts, sigma) for sorting and further processing
tuple_list = create_tuples(x1, cor_y1, y_err1) + create_tuples(x2, cor_y2, y_err2) tuple_list = create_tuples(x1, cor_y1, y_err1) + create_tuples(x2, cor_y2, y_err2)
# Sort the list on om and add 0 0 0 tuple to the last position # Sort the list on om and add 0 0 0 tuple to the last position
sorted_t = sorted(tuple_list, key=lambda tup: tup[0]) sorted_t = sorted(tuple_list, key=lambda tup: tup[0])
sorted_t.append((0, 0, 0)) sorted_t.append((0, 0, 0))
om, Counts, sigma = [], [], [] om, Counts, sigma = [], [], []
seen = list() seen = list()
for i in range(len(sorted_t) - 1): for i in range(len(sorted_t) - 1):
if sorted_t[i][0] not in seen: if sorted_t[i][0] not in seen:
if sorted_t[i][0] != sorted_t[i + 1][0]: if sorted_t[i][0] != sorted_t[i + 1][0]:
om = np.append(om, sorted_t[i][0]) om = np.append(om, sorted_t[i][0])
Counts = np.append(Counts, sorted_t[i][1]) Counts = np.append(Counts, sorted_t[i][1])
sigma = np.append(sigma, sorted_t[i][2]) sigma = np.append(sigma, sorted_t[i][2])
else:
om = np.append(om, sorted_t[i][0])
counts1, counts2 = sorted_t[i][1], sorted_t[i + 1][1]
sigma1, sigma2 = sorted_t[i][2], sorted_t[i + 1][2]
count_err1 = u.ufloat(counts1, sigma1)
count_err2 = u.ufloat(counts2, sigma2)
avg = (count_err1 + count_err2) / 2
Counts = np.append(Counts, avg.n)
sigma = np.append(sigma, avg.s)
seen.append(sorted_t[i][0])
else: else:
continue om = np.append(om, sorted_t[i][0])
counts1, counts2 = sorted_t[i][1], sorted_t[i + 1][1]
if dict1 == dict2: sigma1, sigma2 = sorted_t[i][2], sorted_t[i + 1][2]
del dict1["scan"][second] count_err1 = u.ufloat(counts1, sigma1)
count_err2 = u.ufloat(counts2, sigma2)
note = ( avg = (count_err1 + count_err2) / 2
f"This measurement was merged with measurement {second} from " Counts = np.append(Counts, avg.n)
f'file {dict2["meta"]["original_filename"]} \n' sigma = np.append(sigma, avg.s)
) seen.append(sorted_t[i][0])
if "notes" not in dict1["scan"][first]:
dict1["scan"][first]["notes"] = note
else: else:
dict1["scan"][first]["notes"] += note continue
dict1["scan"][first]["om"] = om if dict1 == dict2:
dict1["scan"][first]["Counts"] = Counts del dict1["scan"][second]
dict1["scan"][first]["sigma"] = sigma
dict1["scan"][first]["monitor"] = monitor note = (
print("merging done") f"This measurement was merged with measurement {second} from "
return dict1 f'file {dict2["meta"]["original_filename"]} \n'
)
if "notes" not in dict1["scan"][first]:
dict1["scan"][first]["notes"] = note
else:
dict1["scan"][first]["notes"] += note
dict1["scan"][first]["om"] = om
dict1["scan"][first]["Counts"] = Counts
dict1["scan"][first]["sigma"] = sigma
dict1["scan"][first]["monitor"] = monitor
print("merging done")
return dict1
def add_dict(dict1, dict2): def add_dict(dict1, dict2):