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Update fit2.py

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added option for data binning
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JakHolzer 2020-10-09 11:26:27 +02:00 committed by GitHub
parent 94551acc64
commit d0d993a813
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1 changed files with 27 additions and 8 deletions
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pyzebra/fit2.py
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@ -1,9 +1,15 @@
import numpy as np
from lmfit import Model, Parameters
from scipy.integrate import simps
import uncertainties as u
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 find_nearest(array, value):
# find nearest value and return index
@ -30,6 +36,7 @@ def fitccl(
constraints_max,
numfit_min=None,
numfit_max=None,
binning=None
):
"""Made for fitting of ccl date where 1 peak is expected. Allows for combination of gaussian and linear model combination
:param data: dictionary after peak fining
@ -40,6 +47,7 @@ def fitccl(
:param numfit_max: maximal value on x axis for numerical integration - if none is centre of gaussian plus 3 sigma
:param constraints_min: min constranits value for fit
:param constraints_max: max constranits value for fit
:param binning : binning of the data
:return data dict with additional values
order for guess, vary, constraints_min, constraints_max:
[Gaussian centre, Gaussian sigma, Gaussian amplitude, background slope, background intercept]
@ -56,10 +64,21 @@ def fitccl(
print("More than 1 peak, measurement skipped")
return
x = list(meas["om"])
y = list(meas["Counts"])
# if the dictionaries were merged/substracted, takes the errors from them, if not, takes them as sqrt(y)
y_err = np.sqrt(y) if meas.get("sigma", None) is None else meas.get("sigma")
if binning is None or binning == 0 or binning == 1:
x = list(meas["om"])
y = list(meas["Counts"])
y_err = list(np.sqrt(y)) if meas.get("sigma", None) is None else list(meas["sigma"])
centre = x[int(meas["peak_indexes"])]
else:
x = list(meas["om"])
centre = x[int(meas["peak_indexes"])]
x = bin_data(x, binning)
y = list(meas["Counts"])
y_err = list(np.sqrt(y)) if meas.get("sigma", None) is None else list(meas["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))]
if len(meas["peak_indexes"]) == 0:
# Case for no peak, gaussian in centre, sigma as 20% of range
@ -75,8 +94,8 @@ def fitccl(
elif len(meas["peak_indexes"]) == 1:
# case for one peak, takse into account users guesses
print("one peak")
peak_index, peak_height = meas["peak_indexes"], meas["peak_heights"]
guess[0] = x[int(peak_index)] if guess[0] is None else guess[0]
peak_height = meas["peak_heights"]
guess[0] = centre if guess[0] is None else guess[0]
guess[1] = 0.1 if guess[1] is None else guess[1]
guess[2] = float(peak_height / 10) if guess[2] is None else float(guess[2])
guess[3] = 0 if guess[3] is None else guess[3]
@ -84,7 +103,7 @@ def fitccl(
constraints_min[0] = np.min(x) if constraints_min[0] is None else constraints_min[0]
constraints_max[0] = np.max(x) if constraints_max[0] is None else constraints_max[0]
centre = x[int(peak_index)]
def gaussian(x, g_cen, g_width, g_amp):
"""1-d gaussian: gaussian(x, amp, cen, wid)"""