zebra/pyzebra
3
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Update merge_function.py

Browse Source 
Fixed the normalization, changed the structure so we can merge duplicates without merging two dictionaries.
This commit is contained in:
JakHolzer 2021-01-24 15:14:25 +01:00 committed by GitHub
parent 1761003d8a
commit d0b67b8565
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 41 additions and 49 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

90
pyzebra/merge_function.py
View File

@ -12,24 +12,19 @@ def create_tuples(x, y, y_err):
return t
def normalize(scan, 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(scan["Counts"])
sigma = np.sqrt(counts) if "sigma" not in scan else scan["sigma"]
monitor_ratio = scan["monitor"] / monitor
scaled_counts = counts * monitor_ratio
scaled_sigma = np.array(sigma) * monitor_ratio
return scaled_counts, scaled_sigma
def normalize_all(dictionary, monitor=100000):
for keys in dictionary["scan"]:
scan = dictionary["scan"][keys]
counts = np.array(scan["Counts"])
sigma = np.sqrt(counts) if "sigma" not in scan else scan["sigma"]
monitor_ratio = monitor / scan["monitor"]
scan["Counts"] = counts * monitor_ratio
scan["sigma"] = np.array(sigma) * monitor_ratio
scan["monitor"] = monitor
print("Normalized %d scans to monitor %d" % (len(dictionary["scan"]), monitor))
def merge(scan1, scan2, keep=True, monitor=100000):
def merge(scan1, scan2):
"""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
:arg dict1 : dictionary to which measurement will be merged
@ -41,14 +36,13 @@ def merge(scan1, scan2, keep=True, monitor=100000):
note: dict1 and dict2 can be same dict
:return dict1 with merged scan"""
if keep:
if scan1["monitor"] == scan2["monitor"]:
monitor = scan1["monitor"]
# load om and Counts
x1, x2 = scan1["om"], scan2["om"]
cor_y1, y_err1 = normalize(scan1, monitor=monitor)
cor_y2, y_err2 = normalize(scan2, monitor=monitor)
# print(scan1["om"])
# print(scan2["om"])
cor_y1, y_err1 = scan1["Counts"], scan1["sigma"]
cor_y2, y_err2 = scan2["Counts"], scan2["sigma"]
# 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 position
@ -68,7 +62,7 @@ def merge(scan1, scan2, keep=True, monitor=100000):
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) / ((scan1["monitor"] + scan2["monitor"])/monitor)
avg = (count_err1 + count_err2) / 2
Counts = np.append(Counts, avg.n)
sigma = np.append(sigma, avg.s)
seen.append(sorted_t[i][0])
@ -77,7 +71,10 @@ def merge(scan1, scan2, keep=True, monitor=100000):
scan1["om"] = om
scan1["Counts"] = Counts
scan1["sigma"] = sigma
scan1["monitor"] = monitor
if "history" not in scan1:
scan1["history"] = str("Merged with scan %d" % scan2["scan_number"])
else:
scan1["history"] = scan1["history"] + str(", merged with scan %d" % scan2["scan_number"])
print("merging done")
@ -138,19 +135,29 @@ def check_temp_mag(scan1, scan2):
except KeyError:
print("temperature missing")
if all(truth_list):
return True
else:
return False
def merge_dups(dictionary, angles):
def merge_dups(dictionary):
if dictionary["meta"]["data_type"] == "dat":
return
if dictionary["meta"]["zebra_mode"] == "bi":
angles = ["twotheta_angle", "omega_angle", "chi_angle", "phi_angle"]
elif dictionary["meta"]["zebra_mode"] == "nb":
angles = ["gamma_angle", "omega_angle", "nu_angle"]
precision = {
"twotheta_angle": 0.1,
"chi_angle": 0.1,
"nu_angle": 0.1,
"phi_angle": 0.05,
"omega_angle": 0.05,
"omega_angle": 5,
"gamma_angle": 0.05,
}
@ -160,12 +167,13 @@ def merge_dups(dictionary, angles):
continue
else:
# print(i, j)
if check_angles(dictionary["scan"][i], dictionary["scan"][j], angles, precision):
if check_angles(dictionary["scan"][i], dictionary["scan"][j], angles, precision) \
and check_temp_mag(dictionary["scan"][i], dictionary["scan"][j]):
merge(dictionary["scan"][i], dictionary["scan"][j])
print("merged %d with %d" % (i, j))
print("merged %d with %d within the dictionary" % (i, j))
del dictionary["scan"][j]
merge_dups(dictionary, angles)
merge_dups(dictionary)
break
else:
continue
@ -175,9 +183,6 @@ def merge_dups(dictionary, angles):
def add_scan(dict1, dict2, scan_to_add):
max_scan = np.max(list(dict1["scan"]))
dict1["scan"][max_scan + 1] = dict2["scan"][scan_to_add]
if dict1.get("extra_meta") is None:
dict1["extra_meta"] = {}
dict1["extra_meta"][max_scan + 1] = dict2["meta"]
del dict2["scan"][scan_to_add]
@ -185,7 +190,6 @@ def process(dict1, dict2, angles, precision):
# stop when the second dict is empty
# print(dict2["scan"])
if dict2["scan"]:
print("doing something")
# check UB matrixes
if check_UB(dict1, dict2):
# iterate over second dict and check for matches
@ -195,18 +199,18 @@ def process(dict1, dict2, angles, precision):
# angles good, see the mag and temp
if check_temp_mag(dict1["scan"][j], dict2["scan"][i]):
merge(dict1["scan"][j], dict2["scan"][i])
print("merged")
print("merged %d with %d from different dictionaries" % (i, j))
del dict2["scan"][i]
process(dict1, dict2, angles, precision)
break
else:
add_scan(dict1, dict2, i)
print("scan added r")
print("Diffrent T or M, scan added")
process(dict1, dict2, angles, precision)
break
else:
add_scan(dict1, dict2, i)
print("scan added l")
print("Mismatch in angles, scan added")
process(dict1, dict2, angles, precision)
break
else:
@ -223,7 +227,7 @@ def process(dict1, dict2, angles, precision):
"""
1. check for bisecting or normal beam geometry in data files; select stt, om, chi, phi for bisecting; select stt, om, nu for normal beam
2. in the ccl files, check for identical stt, chi and nu within 0.1 degree, and, at the same time, for identical om and phi within 0.05 degree;
3. in the dat files, check for identical stt, chi and nu within 0.1 degree, and, at the same time,
3. in the dat files, check for identical stt, chi and nu within 0.1 degree, and, at the same time,
for identical phi within 0.05 degree, and, at the same time, for identical om within 5 degree."""
@ -245,18 +249,11 @@ def unified_merge(dict1, dict2):
"nu_angle": 0.1,
"phi_angle": 0.05,
"omega_angle": 5,
"gamma_angle": 0.05,
"gamma_angle": 0.1,
}
if (dict1["meta"]["data_type"] == "ccl") and (dict2["meta"]["data_type"] == "ccl"):
precision["omega_angle"] = 0.05
# check for duplicates in original files
for d in dict1, dict2:
# no duplicates in dats
if d["meta"]["data_type"] == "dat":
continue
else:
merge_dups(d, angles)
process(dict1, dict2, angles, precision)
@ -280,16 +277,11 @@ def add_dict(dict1, dict2):
new_filenames = np.arange(
max_measurement_dict1 + 1, max_measurement_dict1 + 1 + len(dict2["scan"])
)
if dict1.get("extra_meta") is None:
dict1["extra_meta"] = {}
new_meta_name = "meta" + str(dict2["meta"]["original_filename"])
if new_meta_name not in dict1:
for keys, name in zip(dict2["scan"], new_filenames):
dict2["scan"][keys]["file_of_origin"] = str(dict2["meta"]["original_filename"])
dict1["scan"][name] = dict2["scan"][keys]
dict1["extra_meta"][name] = dict2["meta"]
dict1[new_meta_name] = dict2["meta"]
else: