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Merge branch 'det1d'

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This commit is contained in:
usov_i 2020-10-23 16:56:25 +02:00
commit 4ae8890bb8
3 changed files with 351 additions and 3 deletions
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2
pyzebra/comm_export.py
View File

@ -67,8 +67,8 @@ def export_comm(data, path, lorentz=False):
line = (
scan_number_str
+ h_str
+ l_str
+ k_str
+ l_str
+ int_str
+ sigma_str
+ angle_str1

167
pyzebra/fitvol3.py Normal file
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@ -0,0 +1,167 @@
import numpy as np
from lmfit import Model, Parameters
from scipy.integrate import simps
import matplotlib.pyplot as plt
import uncertainties as u
from lmfit.models import GaussianModel
from lmfit.models import VoigtModel
from lmfit.models import PseudoVoigtModel
def bin_data(array, binsize):
if isinstance(binsize, int) and 0 < binsize < len(array):
return [
np.mean(array[binsize * i : binsize * i + binsize])
for i in range(int(np.ceil(len(array) / binsize)))
]
else:
print("Binsize need to be positive integer smaller than lenght of array")
return array
def create_uncertanities(y, y_err):
# create array with uncertanities for error propagation
combined = np.array([])
for i in range(len(y)):
part = u.ufloat(y[i], y_err[i])
combined = np.append(combined, part)
return combined
def find_nearest(array, value):
# find nearest value and return index
array = np.asarray(array)
idx = (np.abs(array - value)).argmin()
return idx
# predefined peak positions
# peaks = [6.2, 8.1, 9.9, 11.5]
peaks = [23.5, 24.5]
# peaks = [24]
def fitccl(scan, variable="om", peak_type="gauss", binning=None):
x = list(scan[variable])
y = list(scan["Counts"])
peak_centre = np.mean(x)
if binning is None or binning == 0 or binning == 1:
x = list(scan["om"])
y = list(scan["Counts"])
y_err = list(np.sqrt(y)) if scan.get("sigma", None) is None else list(scan["sigma"])
print(scan["peak_indexes"])
if not scan["peak_indexes"]:
peak_centre = np.mean(x)
else:
centre = x[int(scan["peak_indexes"])]
else:
x = list(scan["om"])
if not scan["peak_indexes"]:
peak_centre = np.mean(x)
else:
peak_centre = x[int(scan["peak_indexes"])]
x = bin_data(x, binning)
y = list(scan["Counts"])
y_err = list(np.sqrt(y)) if scan.get("sigma", None) is None else list(scan["sigma"])
combined = bin_data(create_uncertanities(y, y_err), binning)
y = [combined[i].n for i in range(len(combined))]
y_err = [combined[i].s for i in range(len(combined))]
def background(x, slope, intercept):
"""background"""
return slope * (x - peak_centre) + intercept
def gaussian(x, center, g_sigma, amplitude):
"""1-d gaussian: gaussian(x, amp, cen, wid)"""
return (amplitude / (np.sqrt(2.0 * np.pi) * g_sigma)) * np.exp(
-((x - center) ** 2) / (2 * g_sigma ** 2)
)
def lorentzian(x, center, l_sigma, amplitude):
"""1d lorentzian"""
return (amplitude / (1 + ((1 * x - center) / l_sigma) ** 2)) / (np.pi * l_sigma)
def pseudoVoigt1(x, center, g_sigma, amplitude, l_sigma, fraction):
"""PseudoVoight peak with different widths of lorenzian and gaussian"""
return (1 - fraction) * gaussian(x, center, g_sigma, amplitude) + fraction * (
lorentzian(x, center, l_sigma, amplitude)
)
mod = Model(background)
params = Parameters()
params.add_many(
("slope", 0, True, None, None, None, None), ("intercept", 0, False, None, None, None, None)
)
for i in range(len(peaks)):
if peak_type == "gauss":
mod = mod + GaussianModel(prefix="p%d_" % (i + 1))
params.add(str("p%d_" % (i + 1) + "amplitude"), 20, True, 0, None, None)
params.add(str("p%d_" % (i + 1) + "center"), peaks[i], True, None, None, None)
params.add(str("p%d_" % (i + 1) + "sigma"), 0.2, True, 0, 5, None)
elif peak_type == "voigt":
mod = mod + VoigtModel(prefix="p%d_" % (i + 1))
params.add(str("p%d_" % (i + 1) + "amplitude"), 20, True, 0, None, None)
params.add(str("p%d_" % (i + 1) + "center"), peaks[i], True, None, None, None)
params.add(str("p%d_" % (i + 1) + "sigma"), 0.2, True, 0, 3, None)
params.add(str("p%d_" % (i + 1) + "gamma"), 0.2, True, 0, 5, None)
elif peak_type == "pseudovoigt":
mod = mod + PseudoVoigtModel(prefix="p%d_" % (i + 1))
params.add(str("p%d_" % (i + 1) + "amplitude"), 20, True, 0, None, None)
params.add(str("p%d_" % (i + 1) + "center"), peaks[i], True, None, None, None)
params.add(str("p%d_" % (i + 1) + "sigma"), 0.2, True, 0, 5, None)
params.add(str("p%d_" % (i + 1) + "fraction"), 0.5, True, -5, 5, None)
elif peak_type == "pseudovoigt1":
mod = mod + Model(pseudoVoigt1, prefix="p%d_" % (i + 1))
params.add(str("p%d_" % (i + 1) + "amplitude"), 20, True, 0, None, None)
params.add(str("p%d_" % (i + 1) + "center"), peaks[i], True, None, None, None)
params.add(str("p%d_" % (i + 1) + "g_sigma"), 0.2, True, 0, 5, None)
params.add(str("p%d_" % (i + 1) + "l_sigma"), 0.2, True, 0, 5, None)
params.add(str("p%d_" % (i + 1) + "fraction"), 0.5, True, 0, 1, None)
# add parameters
result = mod.fit(
y, params, weights=[np.abs(1 / y_err[i]) for i in range(len(y_err))], x=x, calc_covar=True
)
comps = result.eval_components()
reportstring = list()
for keys in result.params:
if result.params[keys].value is not None:
str2 = np.around(result.params[keys].value, 3)
else:
str2 = 0
if result.params[keys].stderr is not None:
str3 = np.around(result.params[keys].stderr, 3)
else:
str3 = 0
reportstring.append("%s = %2.3f +/- %2.3f" % (keys, str2, str3))
reportstring = "\n".join(reportstring)
plt.figure(figsize=(20, 10))
plt.plot(x, result.best_fit, "k-", label="Best fit")
plt.plot(x, y, "b-", label="Original data")
plt.plot(x, comps["background"], "g--", label="Line component")
for i in range(len(peaks)):
plt.plot(
x,
comps[str("p%d_" % (i + 1))],
"r--",
)
plt.fill_between(x, comps[str("p%d_" % (i + 1))], alpha=0.4, label=str("p%d_" % (i + 1)))
plt.legend()
plt.text(
np.min(x),
np.max(y),
reportstring,
fontsize=9,
verticalalignment="top",
)
plt.title(str(peak_type))
plt.xlabel("Omega [deg]")
plt.ylabel("Counts [a.u.]")
plt.show()
print(result.fit_report())

183
pyzebra/param_study_moduls.py
View File

@ -1,5 +1,4 @@
from load_1D import load_1D
from ccl_dict_operation import add_dict
import pandas as pd
from mpl_toolkits.mplot3d import Axes3D # dont delete, otherwise waterfall wont work
import matplotlib.pyplot as plt
@ -7,6 +6,17 @@ import matplotlib as mpl
import numpy as np
import pickle
import scipy.io as sio
import uncertainties as u
def create_tuples(x, y, y_err):
"""creates tuples for sorting and merginng of the data
Counts need to be normalized to monitor before"""
t = list()
for i in range(len(x)):
tup = (x[i], y[i], y_err[i])
t.append(tup)
return t
def load_dats(filepath):
@ -200,3 +210,174 @@ def save_table(data, filetype, name, path=None):
hdf.close()
if filetype == "json":
data.to_json((path + name + ".json"))
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["scan"][key]["Counts"])
sigma = np.sqrt(counts) if "sigma" not in dict["scan"][key] else dict["scan"][key]["sigma"]
monitor_ratio = monitor / dict["scan"][key]["monitor"]
scaled_counts = counts * monitor_ratio
scaled_sigma = np.array(sigma) * monitor_ratio
return scaled_counts, scaled_sigma
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
averaging is propagated into sigma if dict1 == dict2, key[1] is deleted after merging
:arg dict1 : dictionary to 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 keep : if true, when monitors are same, does not change it, if flase, takes monitor
always
:arg monitor : final monitor after merging
note: dict1 and dict2 can be same dict
:return dict1 with merged scan"""
for keys in scand_dict_result:
for j in range(len(scand_dict_result[keys])):
first, second = scand_dict_result[keys][j][0], scand_dict_result[keys][j][1]
print(first, second)
if keep:
if dict1["scan"][first]["monitor"] == dict2["scan"][second]["monitor"]:
monitor = dict1["scan"][first]["monitor"]
# load om and Counts
x1, x2 = dict1["scan"][first]["om"], dict2["scan"][second]["om"]
cor_y1, y_err1 = normalize(dict1, first, monitor=monitor)
cor_y2, y_err2 = normalize(dict2, second, 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 position
sorted_t = sorted(tuple_list, key=lambda tup: tup[0])
sorted_t.append((0, 0, 0))
om, Counts, sigma = [], [], []
seen = list()
for i in range(len(sorted_t) - 1):
if sorted_t[i][0] not in seen:
if sorted_t[i][0] != sorted_t[i + 1][0]:
om = np.append(om, sorted_t[i][0])
Counts = np.append(Counts, sorted_t[i][1])
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:
continue
if dict1 == dict2:
del dict1["scan"][second]
note = (
f"This measurement was merged with measurement {second} from "
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):
"""adds two dictionaries, meta of the new is saved as meata+original_filename and
measurements are shifted to continue with numbering of first dict
:arg dict1 : dictionarry to add to
:arg dict2 : dictionarry from which to take the measurements
:return dict1 : combined dictionary
Note: dict1 must be made from ccl, otherwise we would have to change the structure of loaded
dat file"""
if dict1["meta"]["zebra_mode"] != dict2["meta"]["zebra_mode"]:
print("You are trying to add scans measured with different zebra modes")
return
max_measurement_dict1 = max([keys for keys in dict1["scan"]])
new_filenames = np.arange(
max_measurement_dict1 + 1, max_measurement_dict1 + 1 + len(dict2["scan"])
)
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[new_meta_name] = dict2["meta"]
else:
raise KeyError(
str(
"The file %s has alredy been added to %s"
% (dict2["meta"]["original_filename"], dict1["meta"]["original_filename"])
)
)
return dict1
def auto(dict):
"""takes just unique tuples from all tuples in dictionary returend by scan_dict
intendet for automatic merge if you doesent want to specify what scans to merge together
args: dict - dictionary from scan_dict function
:return dict - dict without repetitions"""
for keys in dict:
tuple_list = dict[keys]
new = list()
for i in range(len(tuple_list)):
if tuple_list[0][0] == tuple_list[i][0]:
new.append(tuple_list[i])
dict[keys] = new
return dict
def scan_dict(dict, precision=0.5):
"""scans dictionary for duplicate angles indexes
:arg dict : dictionary to scan
:arg precision : in deg, sometimes angles are zero so its easier this way, instead of
checking zero division
:return dictionary with matching scans, if there are none, the dict is empty
note: can be checked by "not d", true if empty
"""
if dict["meta"]["zebra_mode"] == "bi":
angles = ["twotheta_angle", "omega_angle", "chi_angle", "phi_angle"]
elif dict["meta"]["zebra_mode"] == "nb":
angles = ["gamma_angle", "omega_angle", "nu_angle"]
else:
print("Unknown zebra mode")
return
d = {}
for i in dict["scan"]:
for j in dict["scan"]:
if dict["scan"][i] != dict["scan"][j]:
itup = list()
for k in angles:
itup.append(abs(abs(dict["scan"][i][k]) - abs(dict["scan"][j][k])))
if all(i <= precision for i in itup):
if str([np.around(dict["scan"][i][k], 1) for k in angles]) not in d:
d[str([np.around(dict["scan"][i][k], 1) for k in angles])] = list()
d[str([np.around(dict["scan"][i][k], 1) for k in angles])].append((i, j))
else:
d[str([np.around(dict["scan"][i][k], 1) for k in angles])].append((i, j))
else:
pass
else:
continue
return d