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Update ccl_dict_operation.py

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Corrected some lines, no major changes
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JakHolzer 2020-09-29 13:43:48 +02:00 committed by GitHub
parent 1302e4f93f
commit 7afd41ea8b
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1 changed files with 28 additions and 42 deletions
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pyzebra/ccl_dict_operation.py
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@ -106,6 +106,21 @@ def create_tuples(x, y, y_err):
t.append(tup)
return t
def normalize(dict, key, monitor):
"""Normalizes the measurement to monitor, checks if sigma exists, otherwise creates it
:arg dict : dictionary to from which to tkae the scan
:arg key : which scan to normalize from dict1
:arg monitor : final monitor
:return counts - normalized counts
:return sigma - normalized sigma"""
counts = np.array(dict["Measurements"][key]["Counts"])
sigma = np.sqrt(counts) if "sigma" not in dict["Measurements"][key] else dict["Measurements"][key]['sigma']
monitor_ratio = monitor/dict["Measurements"][key]["monitor"]
scaled_counts = counts*monitor_ratio
scaled_sigma = np.array(sigma)*monitor_ratio
return scaled_counts, scaled_sigma
def merge(dict1, dict2, keys, auto=True, monitor=100000):
"""merges the two tuples and sorts them, if om value is same, Counts value is average
@ -123,25 +138,8 @@ def merge(dict1, dict2, keys, auto=True, monitor=100000):
# load om and Counts
x1, x2 = dict1["Measurements"][keys[0]]["om"], dict2["Measurements"][keys[1]]["om"]
y1, y2 = np.array(dict1["Measurements"][keys[0]]["Counts"]), np.array(
dict2["Measurements"][keys[1]]["Counts"]
)
# normalize y to monitors
cor_y1 = (y1 / dict1["Measurements"][keys[0]]["monitor"]) * monitor
cor_y2 = (y2 / dict2["Measurements"][keys[0]]["monitor"]) * monitor
# check if sigma errors for y exist, otherwise create them as sqrt(y)
y_err1 = (
np.sqrt(cor_y1)
if "sigma" not in dict1["Measurements"][keys[0]]
else np.array(dict1["Measurements"][keys[0]]["sigma"])
* np.sqrt(monitor / dict1["Measurements"][keys[0]]["monitor"])
)
y_err2 = (
np.sqrt(cor_y2)
if "sigma" not in dict2["Measurements"][keys[1]]
else np.array(dict2["Measurements"][keys[1]]["sigma"])
* np.sqrt(monitor / dict2["Measurements"][keys[1]]["monitor"])
)
cor_y1, y_err1 = normalize(dict1, keys[0], monitor=monitor)
cor_y2, y_err2 = normalize(dict2, keys[1], monitor=monitor)
# 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)
# Sort the list on om and add 0 0 0 tuple to the last postion
@ -174,6 +172,7 @@ def merge(dict1, dict2, keys, auto=True, monitor=100000):
dict1["Measurements"][keys[0]]["om"] = om
dict1["Measurements"][keys[0]]["Counts"] = Counts
dict1["Measurements"][keys[0]]["sigma"] = sigma
dict1["Measurements"][keys[0]]["monitor"] = monitor
return dict1
@ -193,28 +192,14 @@ def substract_measurement(dict1, dict2, keys, auto=True, monitor=100000):
if dict1["Measurements"][keys[0]]["monitor"] == dict2["Measurements"][keys[1]]["monitor"]:
monitor = dict1["Measurements"][keys[0]]["monitor"]
monitor_ratio_prim = monitor / dict1["Measurements"][str(keys[0])]["monitor"]
monitor_ratio_sec = monitor / dict2["Measurements"][str(keys[1])]["monitor"]
y1 = np.array(dict1["Measurements"][str(keys[0])]["Counts"]) * monitor_ratio_prim
y2 = np.array(dict2["Measurements"][str(keys[1])]["Counts"]) * monitor_ratio_sec
y_err1 = (
np.sqrt(y1)
if "sigma" not in dict1["Measurements"][keys[0]]
else dict1["Measurements"][keys[0]]["sigma"]
* np.sqrt(monitor / dict1["Measurements"][keys[0]]["monitor"])
)
y_err2 = (
np.sqrt(y2)
if "sigma" not in dict2["Measurements"][keys[1]]
else dict2["Measurements"][keys[1]]["sigma"]
* np.sqrt(monitor / dict2["Measurements"][keys[1]]["monitor"])
)
dict1_count_err = create_uncertanities(y1, y_err1)
dict2_count_err = create_uncertanities(y2, y_err2)
if np.average(y1) > np.average(y2):
res = np.subtract(dict1_count_err, dict2_count_err)
else:
res = np.subtract(dict2_count_err, dict1_count_err)
cor_y1, y_err1 = normalize(dict1, keys[0], monitor=monitor)
cor_y2, y_err2 = normalize(dict2, keys[1], monitor=monitor)
dict1_count_err = create_uncertanities(cor_y1, y_err1)
dict2_count_err = create_uncertanities(cor_y2, y_err2)
res = np.subtract(dict1_count_err, dict2_count_err)
res_nom = []
res_err = []
for k in range(len(res)):
@ -222,7 +207,8 @@ def substract_measurement(dict1, dict2, keys, auto=True, monitor=100000):
res_err = np.append(res_err, res[k].s)
dict1["Measurements"][str(keys[0])]["Counts"] = res_nom
dict1["Measurements"][str(keys[0])]["sigma"] = res_err
dict1["Measurements"][str(keys[0])]["monitor"] = monitor
return dict1
def compare_dict(dict1, dict2):
"""takes two ccl dictionaries and compare different values for each key