Update fit2.py
Added uncertainty propagation to the numerical integration and weighted fit. Case for 'no peak' scenario also added. Parameter export_fit in meas["fit"] added which is decides if fitted area or numerically integrated area will be saved in comm/incomm files.
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JakHolzer
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pyzebra/fit2.py
260
pyzebra/fit2.py
@ -1,21 +1,37 @@
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import numpy as np
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from lmfit import Model, Parameters
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from scipy import integrate
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from scipy.integrate import simps
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import uncertainties as u
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def find_nearest(array, value):
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# find nearest value and return index
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array = np.asarray(array)
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idx = (np.abs(array - value)).argmin()
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return idx
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def create_uncertanities(y, y_err):
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# create array with uncertanities for error propagation
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combined = np.array([])
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for i in range(len(y)):
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part = u.ufloat(y[i], y_err[i])
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combined = np.append(combined, part)
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return combined
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def fitccl(
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data, keys, guess, vary, constraints_min, constraints_max, numfit_min=None, numfit_max=None
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data,
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keys,
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guess,
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vary,
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constraints_min,
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constraints_max,
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numfit_min=None,
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numfit_max=None,
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):
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"""Made for fitting of ccl date where 1 peak is expected. Allows for combination of gaussian, lorentzian and linear model combination
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"""Made for fitting of ccl date where 1 peak is expected. Allows for combination of gaussian and linear model combination
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:param data: dictionary after peak fining
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:param keys: name of the measurement in the data dict (i.e. M123)
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:param guess: initial guess for the fitting, if none, some values are added automatically in order (see below)
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@ -24,133 +40,185 @@ def fitccl(
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:param numfit_max: maximal value on x axis for numerical integration - if none is centre of gaussian plus 3 sigma
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:param constraints_min: min constranits value for fit
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:param constraints_max: max constranits value for fit
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:return data dict with additional values
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order for guess, vary, constraints_min, constraints_max
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[Gaussian centre, Gaussian sigma, Gaussian amplitude, Lorentzian centre, Lorentzian sigma, Lorentzian amplitude, background slope, background intercept]
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order for guess, vary, constraints_min, constraints_max:
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[Gaussian centre, Gaussian sigma, Gaussian amplitude, background slope, background intercept]
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examples:
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guess = [None, None, 100, None, None, None, 0, None]
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vary = [True, True, True, True, False, True, True, True]
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constraints_min = [23, None, 50, None, None, None, 0, 0]
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constraints_min = [80, None, 1000, None, None, None, 0, 100]
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guess = [None, None, 100, 0, None]
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vary = [True, True, True, True, True]
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constraints_min = [23, None, 50, 0, 0]
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constraints_min = [80, None, 1000, 0, 100]
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"""
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meas = data["Measurements"][keys]
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if len(meas["peak_indexes"]) != 1:
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print("NO PEAK or more than 1 peak")
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if len(meas["peak_indexes"]) > 1:
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# return in case of more than 1 peaks
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print("More than 1 peak, measurement skipped")
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return
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x = list(meas["om"])
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y = list(meas["Counts"])
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peak_index = meas["peak_indexes"]
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peak_height = meas["peak_heights"]
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print("before", constraints_min)
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guess[0] = x[int(peak_index)] if guess[0] is None else guess[0]
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guess[1] = 0.1 if guess[1] is None else guess[1]
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guess[2] = float(peak_height / 10) if guess[2] is None else float(guess[2])
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guess[3] = x[int(peak_index)] if guess[3] is None else guess[3]
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guess[4] = 2 * guess[1] if guess[4] is None else guess[4]
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guess[5] = float(peak_height / 10) if guess[5] is None else float(guess[5])
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guess[6] = 0 if guess[6] is None else guess[6]
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guess[7] = np.median(x) if guess[7] is None else guess[7]
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constraints_min[0] = np.min(x) if constraints_min[0] is None else constraints_min[0]
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constraints_min[3] = np.min(x) if constraints_min[3] is None else constraints_min[3]
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constraints_max[0] = np.max(x) if constraints_max[0] is None else constraints_max[0]
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constraints_max[3] = np.max(x) if constraints_max[3] is None else constraints_max[3]
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print("key", keys)
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# if the dictionaries were merged/substracted, takes the errors from them, if not, takes them as sqrt(y)
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y_err = np.sqrt(y) if meas.get("sigma", None) is None else meas[keys].get("sigma")
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print("after", constraints_min)
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if len(meas["peak_indexes"]) == 0:
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# Case for no peak, gaussian in centre, sigma as 20% of range
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print("No peak")
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peak_index = find_nearest(x, np.mean(x))
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guess[0] = x[int(peak_index)]
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guess[1] = (x[-1] - x[0]) / 5
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guess[2] = 10
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guess[3] = 0
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guess[4] = np.mean(y)
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constraints_min[2] = 0
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def find_nearest(array, value):
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array = np.asarray(array)
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idx = (np.abs(array - value)).argmin()
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return idx
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elif len(meas["peak_indexes"]) == 1:
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# case for one peak, takse into account users guesses
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print("one peak")
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peak_index, peak_height = meas["peak_indexes"], meas["peak_heights"]
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guess[0] = x[int(peak_index)] if guess[0] is None else guess[0]
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guess[1] = 0.1 if guess[1] is None else guess[1]
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guess[2] = float(peak_height / 10) if guess[2] is None else float(guess[2])
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guess[3] = 0 if guess[3] is None else guess[3]
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guess[4] = np.median(x) if guess[4] is None else guess[4]
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constraints_min[0] = np.min(x) if constraints_min[0] is None else constraints_min[0]
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constraints_max[0] = np.max(x) if constraints_max[0] is None else constraints_max[0]
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centre = x[int(peak_index)]
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def gaussian(x, g_cen, g_width, g_amp):
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"""1-d gaussian: gaussian(x, amp, cen, wid)"""
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return (g_amp / (np.sqrt(2.0 * np.pi) * g_width)) * np.exp(
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return (g_amp / (np.sqrt(2 * np.pi) * g_width)) * np.exp(
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-((x - g_cen) ** 2) / (2 * g_width ** 2)
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)
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def lorentzian(x, l_cen, l_width, l_amp):
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"""1d lorentzian"""
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return (l_amp / (1 + ((1 * x - l_cen) / l_width) ** 2)) / (np.pi * l_width)
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def background(x, slope, intercept):
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"""background"""
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return slope * x + intercept
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return slope * (x - centre) + intercept
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mod = Model(gaussian) + Model(lorentzian) + Model(background)
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mod = Model(gaussian) + Model(background)
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params = Parameters()
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params.add_many(
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("g_cen", x[int(peak_index)], bool(vary[0]), np.min(x), np.max(x), None, None),
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("g_width", guess[1], bool(vary[1]), constraints_min[1], constraints_max[1], None, None),
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("g_amp", guess[2], bool(vary[2]), constraints_min[2], constraints_max[2], None, None),
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("l_cen", guess[3], bool(vary[3]), np.min(x), np.max(x), None, None),
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("l_width", guess[4], bool(vary[4]), constraints_min[4], constraints_max[4], None, None),
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("l_amp", guess[5], bool(vary[5]), constraints_min[5], constraints_max[5], None, None),
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("slope", guess[6], bool(vary[6]), constraints_min[6], constraints_max[6], None, None),
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("intercept", guess[7], bool(vary[7]), constraints_min[7], constraints_max[7], None, None),
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(
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"g_width",
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guess[1],
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bool(vary[1]),
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constraints_min[1],
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constraints_max[1],
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None,
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None,
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),
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(
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"g_amp",
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guess[2],
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bool(vary[2]),
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constraints_min[2],
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constraints_max[2],
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None,
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None,
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),
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(
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"slope",
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guess[3],
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bool(vary[3]),
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constraints_min[3],
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constraints_max[3],
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None,
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None,
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),
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(
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"intercept",
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guess[4],
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bool(vary[4]),
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constraints_min[4],
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constraints_max[4],
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None,
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None,
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),
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)
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result = mod.fit(y, params, x=x)
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print("Chi-sqr: ", result.chisqr)
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# the weighted fit
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result = mod.fit(y, params, weights=y_err, x=x, calc_covar=True)
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# u.ufloat to work with uncertanities
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fit_area = u.ufloat(result.params["g_amp"].value, result.params["g_amp"].stderr)
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comps = result.eval_components()
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gauss_3sigmamin = find_nearest(
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x, result.params["g_cen"].value - 3 * result.params["g_width"].value
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)
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gauss_3sigmamax = find_nearest(
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x, result.params["g_cen"].value + 3 * result.params["g_width"].value
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)
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numfit_min = gauss_3sigmamin if numfit_min is None else find_nearest(x, numfit_min)
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numfit_max = gauss_3sigmamax if numfit_max is None else find_nearest(x, numfit_max)
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it = -1
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if len(meas["peak_indexes"]) == 0:
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# for case of no peak, there is no reason to integrate, therefore fit and int are equal
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int_area = fit_area
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while numfit_max == numfit_min:
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it = it + 1
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numfit_min = find_nearest(
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x, result.params["g_cen"].value - 3 * (1 + it / 10) * result.params["g_width"].value
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elif len(meas["peak_indexes"]) == 1:
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gauss_3sigmamin = find_nearest(
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x, result.params["g_cen"].value - 3 * result.params["g_width"].value
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)
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numfit_max = find_nearest(
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x, result.params["g_cen"].value + 3 * (1 + it / 10) * result.params["g_width"].value
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gauss_3sigmamax = find_nearest(
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x, result.params["g_cen"].value + 3 * result.params["g_width"].value
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)
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numfit_min = gauss_3sigmamin if numfit_min is None else find_nearest(x, numfit_min)
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numfit_max = gauss_3sigmamax if numfit_max is None else find_nearest(x, numfit_max)
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if x[numfit_min] < np.min(x):
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numfit_min = gauss_3sigmamin
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print("Minimal integration value outside of x range")
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elif x[numfit_min] >= x[numfit_max]:
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numfit_min = gauss_3sigmamin
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print("Minimal integration value higher than maximal")
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else:
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pass
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if x[numfit_max] > np.max(x):
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numfit_max = gauss_3sigmamax
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print("Maximal integration value outside of x range")
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elif x[numfit_max] <= x[numfit_min]:
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numfit_max = gauss_3sigmamax
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print("Maximal integration value lower than minimal")
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else:
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pass
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print(result.params["g_width"].value)
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print(result.params["g_cen"].value)
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num_int_area = simps(y[numfit_min:numfit_max], x[numfit_min:numfit_max])
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num_int_bacground = integrate.quad(
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background,
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x[numfit_min],
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x[numfit_max],
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args=(result.params["slope"].value, result.params["intercept"].value),
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)
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it = -1
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while numfit_max == numfit_min:
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# in the case the peak is very thin and numerical integration would be on zero omega difference, finds closes values
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it = it + 1
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numfit_min = find_nearest(
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x,
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result.params["g_cen"].value - 3 * (1 + it / 10) * result.params["g_width"].value,
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)
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numfit_max = find_nearest(
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x,
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result.params["g_cen"].value + 3 * (1 + it / 10) * result.params["g_width"].value,
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)
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if x[numfit_min] < np.min(x):
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# makes sure that the values supplied by user lay in the omega range
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# can be ommited for users who know what they're doing
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numfit_min = gauss_3sigmamin
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print("Minimal integration value outside of x range")
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elif x[numfit_min] >= x[numfit_max]:
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numfit_min = gauss_3sigmamin
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print("Minimal integration value higher than maximal")
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else:
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pass
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if x[numfit_max] > np.max(x):
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numfit_max = gauss_3sigmamax
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print("Maximal integration value outside of x range")
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elif x[numfit_max] <= x[numfit_min]:
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numfit_max = gauss_3sigmamax
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print("Maximal integration value lower than minimal")
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else:
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pass
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count_errors = create_uncertanities(y, y_err)
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# create error vector for numerical integration propagation
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num_int_area = simps(count_errors[numfit_min:numfit_max], x[numfit_min:numfit_max])
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slope_err = u.ufloat(result.params["slope"].value, result.params["slope"].stderr)
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# pulls the nominal and error values from fit (slope)
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intercept_err = u.ufloat(
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result.params["intercept"].value, result.params["intercept"].stderr
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)
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# pulls the nominal and error values from fit (intercept)
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background_errors = np.array([])
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for j in range(len(x[numfit_min:numfit_max])):
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# creates nominal and error vector for numerical integration of background
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bg = slope_err * (x[j] - centre) + intercept_err
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background_errors = np.append(background_errors, bg)
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num_int_background = simps(background_errors, x[numfit_min:numfit_max])
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int_area = num_int_area - num_int_background
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d = {}
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for pars in result.params:
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d[str(pars)] = (result.params[str(pars)].value, result.params[str(pars)].vary)
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print(result.fit_report())
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print((result.params["g_amp"].value - int_area.n) / result.params["g_amp"].value)
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d["export_fit"] = False
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d["int_area"] = num_int_area
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d["int_background"] = num_int_bacground
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# ["export_fit"] = False if user wants num. int. value in comm/incomm, otherwise true
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d["ratio"] = (result.params["g_amp"].value - int_area.n) / result.params["g_amp"].value
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d["int_area"] = int_area
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d["fit_area"] = fit_area
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d["full_report"] = result.fit_report()
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meas["fit"] = d
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