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NucRadAnalysis.py
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import copy
from os import execve
import numpy as np
import csv
import math as m
import pandas as pd
from scipy import stats
from scipy.optimize import curve_fit
import matplotlib.pyplot as plt
from lmfit import minimize, Parameters, report_fit, Model, Parameter
from lmfit.models import GaussianModel
from lmfit.models import SplitLorentzianModel
import matplotlib.ticker as tck
import matplotlib.pylab as pl
from matplotlib.offsetbox import AnchoredText
import sys
import glob
#plotpath = str('C:\\Users\\ahosi\\Desktop\\FileTrans4\\NewMethodPlots\\')
#plotpath = str('C:\\Users\\ahosi\\OneDrive\\Desktop\\TestPlots\\')
#style = 'diff'
style = 'diff'
r=4 #number of data points used in analysis
cs = 299792458 #speed of light m/s
hp = 6.62607015*(10)**(-34) #plancks constant
JeV = 1/(1.602176634*10**(-19)) #Joules to eV
crit = 4 #criteria for outlier detection (number of standard deviation)
bins = 10 #number of bins used for histograms
offset1 = 0.001 #offset in photon counts to avoid zeros for weighted fitting
siglev1 = 2 #number of std err used in final calc
binrange = 0.002
JGOsNa = 7.4491
JGOsNae = 0.0013
JGIrNa = 7.2948
JGIrNae = 0.0013
SapIrNa = 7.29662
DiptiIrMg = 7.507248
DiptiOsMg = 7.669836
SteveIrMg = 7.49448
SteveOsMg = 7.65694
theoendiff = 170.028145 - 166.5075315
theoendiffe = np.sqrt((0.0013)**2 + (0.0013)**2)
econv = 8065.543937 #conversion from inverse cm to eV
ftranloc = r'.'
#Desktop Files
wavefile = str(ftranloc + '\\Calibration6bOsIrWavelengthCal6b.csv') ###Adams calibration, but seperated for each individual spectra
wavefile2 = str(ftranloc + '\\Calibration6bOsIrWavelengthCal6b.csv') ###Adam's calibration (cal set basis)
#wavefile2 = str(ftranloc + '\\OsIrWavelengthCalSam.csv') ###Sam's calibration (cal set basis)
sysfile = str(ftranloc + '\\Calibration6bSystematic_Uncertainty.csv')
visualbadfiles = str(ftranloc + '\\badshit.csv')
redDatatab = str(ftranloc + '\\OsIr_NucChargeRadius_ReducedDataTable3.csv')
#specNumtab = str('ftranloc + '\\OsIr_NucChargeRadius_SpectrumNo02.csv')
specNumtab = str(ftranloc + '\\OsIr_NucChargeRadius_SpectrumNo.csv')
dropTab = str(ftranloc + '\\OsIr_NucChargeRadius_SpectrumNo2.csv')
dfw = pd.read_csv(r""+wavefile)
dfvis = pd.read_csv(r""+visualbadfiles)
df = pd.read_csv(r""+redDatatab)
df2 = pd.read_csv(r""+specNumtab)
dfdrop = pd.read_csv(r""+dropTab)
df2copy = copy.deepcopy(df2)
dfwa = pd.read_csv(r""+wavefile2)
dfsys = pd.read_csv(r""+sysfile)
calibrationuncert = []
for i in range(len(dfdrop.columns)):
name = dfdrop.columns[i]
dfwa.drop(dfwa.columns[dfwa.columns.str.contains(name, case=False)], axis=1, inplace=True)
df.drop(df.columns[df.columns.str.contains(name, case=False)], axis=1, inplace=True)
df2.drop(df2.columns[df2.columns.str.contains(name, case=False)], axis=1, inplace=True)
badOs = dfvis['Os']
for i in range(len(badOs)):
name = str(badOs[i])
dfwa.drop(dfwa.columns[dfwa.columns.str.contains(name, case=False)], axis=1, inplace=True)
df.drop(df.columns[df.columns.str.contains(name, case=False)], axis=1, inplace=True)
df2.drop(df2.columns[df2.columns.str.contains(name, case=False)], axis=1, inplace=True)
dfa = copy.deepcopy(df)
df2a = copy.deepcopy(df2)
dfwab = copy.deepcopy(dfwa)
#df = Ir
#dfa = Os
#dfb = Bg
#dfc =Ne
df.drop(df.columns[df.columns.str.contains('Ne', case=False)], axis=1, inplace=True)
df.drop(df.columns[df.columns.str.contains('Os', case=False)], axis=1, inplace=True) #CHANGE
df.drop(df.columns[df.columns.str.contains('BG', case=False)], axis=1, inplace=True)
dfw.drop(dfw.columns[dfw.columns.str.contains('Ne', case=False)], axis=1, inplace=True)
dfw.drop(dfw.columns[dfw.columns.str.contains('Os', case=False)], axis=1, inplace=True) #CHANGE
dfw.drop(dfw.columns[dfw.columns.str.contains('BG', case=False)], axis=1, inplace=True)
df2.drop(df2.columns[df2.columns.str.contains('Ne', case=False)], axis=1, inplace=True)
df2.drop(df2.columns[df2.columns.str.contains('Os', case=False)], axis=1, inplace=True) #CHANGE
df2.drop(df2.columns[df2.columns.str.contains('BG', case=False)], axis=1, inplace=True)
df.head()
df2.head()
dfw.head()
dfa.drop(dfa.columns[dfa.columns.str.contains('Ne', case=False)], axis=1, inplace=True)
dfa.drop(dfa.columns[dfa.columns.str.contains('Ir', case=False)], axis=1, inplace=True) #CHANGE
dfa.drop(dfa.columns[dfa.columns.str.contains('BG', case=False)], axis=1, inplace=True)
dfwab.drop(dfwab.columns[dfwab.columns.str.contains('Ne', case=False)], axis=1, inplace=True)
dfwab.drop(dfwab.columns[dfwab.columns.str.contains('Ir', case=False)], axis=1, inplace=True) #CHANGE
dfwab.drop(dfwab.columns[dfwab.columns.str.contains('BG', case=False)], axis=1, inplace=True)
df2a.drop(df2a.columns[df2a.columns.str.contains('Ne', case=False)], axis=1, inplace=True)
df2a.drop(df2a.columns[df2a.columns.str.contains('Ir', case=False)], axis=1, inplace=True) #CHANGE
df2a.drop(df2a.columns[df2a.columns.str.contains('BG', case=False)], axis=1, inplace=True)
dfa.head()
df2a.head()
dfwab.head()
##########################All data used for fitting
xosna = []
yosna = []
xirna = []
yirna = []
xosmg = []
yosmg = []
xirmg = []
yirmg = []
calibset = []
#Lists for average of wavelength per calibration set
OsNa = []
OsNae = []
OsMg = []
OsMge = []
IrNa = []
IrNae = []
IrMg = []
IrMge = []
sig = np.zeros(np.shape(df)[0],)
sig2 = np.zeros(np.shape(dfa)[0],)
sigIr = np.zeros(np.shape(df)[0],)
sigOs = np.zeros(np.shape(dfa)[0],)
#first order gauss
irna = []
osna = []
irnae = []
osnae = []
irnatime = []
osnatime = []
irmg = []
osmg = []
irmge = []
osmge = []
irmgtime = []
osmgtime = []
#first order centroid
osnacen = []
osmgcen = []
irnacen = []
irmgcen = []
osnacenu = []
osmgcenu = []
irnacenu = []
irmgcenu = []
osnacent = []
osmgcent = []
irnacent = []
irmgcent = []
#2nd order gauss
irna2 = []
osna2 = []
irnae2 = []
osnae2 = []
irnatime2 = []
osnatime2 = []
#centroid 2nd order
irna2c = []
osna2c = []
irnae2c = []
osnae2c = []
irnatime2c = []
osnatime2c = []
adsysunc = dfsys['after removal']
def gauss(x, A, mu, sig, B):
return A * np.exp(-(x-mu)**2 / (2 * sig**2)) + B
def gauss2(x, A, mu, A2, mu2, sig, B):
return B + (A*np.exp(-(x-mu)**2 / (2 * sig**2)) + A2*np.exp(-(x-mu2)**2 / (2*sig**2)))
#def gauss4(x, A, mu, sig, A2, mu2, A3, mu3, A4, mu4):
# return A*np.exp(-(x-mu)**2 / (2*sig**2)) + A2*np.exp(-(x-mu2)**2 / (2*sig**2)) + A3*np.exp(-(x-mu3)**2 / (2*sig**2)) + A4*np.exp(-(x-mu4)**2 / (2*sig**2))
def gauss4(x, A, mu, sig, A2, mu2, A3, mu3, A4, mu4, K):
return K + A*np.exp(-(x-mu)**2 / (2*sig**2)) + A2*np.exp(-(x-mu2)**2 / (2*sig**2)) + A3*np.exp(-(x-mu3)**2 / (2*sig**2)) + A4*np.exp(-(x-mu4)**2 / (2*sig**2))
def centroid(x,y):
x = np.array(x)
if np.min(y) < 0:
y = np.array(y+np.abs(np.min(y)))
else:
y = np.array(y)
ye = np.sqrt(y)
cnum = 0
cdem = 0
o = 0
res = {}
for i in range(len(y)):
o = y[i] * x[i]
cnum += o
cdem += y[i]
centroid = cnum / cdem
res['centroid'] = centroid
a1 = 0
b1 = 0
for i in range(len(y)):
b1 += y[i]
a1 += ((ye[i])**2)*((x[i] - centroid)**2)
dcentroid = m.sqrt(a1 / (b1**2))
res['cent_unc'] = dcentroid
return res
def parts(x):
arr = np.zeros([4,1])
arr[0,0] = 1
arr[1,0] = x
arr[2,0] = x**2
arr[3,0] = x**3
return arr
def dataResizer2(cenos, cenerros, cenir, cenerrir, specir, specos):
limit = 200
resDat = dict()
counter = 0
try:
cenos = np.reshape(cenos, (1, len(cenos)))
except:
cenos = cenos
try:
cenir = np.reshape(cenir, (1, len(cenir)))
except:
cenir = cenir
osdatalen = np.shape(cenos)[1]
irdatalen = np.shape(cenir)[1]
diff = osdatalen - irdatalen
while counter < diff:
cenir = np.append(cenir, 'nan')
cenerrir = np.append(cenerrir, 'nan')
#specir = np.append(specir, 300+counter)
specir = np.append(specir, 'nan')
if counter == limit:
break
counter+=1
if len(cenir) > 5:
cenir = np.reshape(cenir, (1,len(cenir)))
cenerrir = np.reshape(cenerrir, (1,len(cenerrir)))
specir = np.reshape(specir, (1,len(specir)))
cenir = cenir.astype(np.float64)
cenerrir = cenerrir.astype(np.float64)
specir = specir.astype(np.float64)
resDat['cenir_rs'] = cenir
resDat['cenerrir_rs'] = cenerrir
resDat['specir_rs'] = specir
counter = 0
diff = -osdatalen + irdatalen
while counter < diff:
cenos = np.append(cenos, 'nan')
cenerros = np.append(cenerros, 'nan')
#specos = np.append(specos, 300+counter)
specos = np.append(specos, 'nan')
counter+=1
if counter == limit:
break
if len(cenos) > 5:
cenos = np.reshape(cenos, (1,len(cenos)))
cenerros = np.reshape(cenerros, (1,len(cenerros)))
specos = np.reshape(specos, (1, len(specos)))
cenos = cenos.astype(np.float64)
cenerros = cenerros.astype(np.float64)
specos = specos.astype(np.float64)
resDat['cenos_rs'] = cenos
resDat['cenerros_rs'] = cenerros
resDat['specos_rs'] = specos
return resDat
def nankill0(arr): #checks if values in array == 'nan'
indexlist = np.zeros([1,0])
try:
arrlen = np.shape(arr)[1]
for i in range(arrlen):
if arr[0,i] == 'nan' or arr[0,i] == np.nan:
indexlist = np.append(indexlist,i)
else:
continue
indexlist = np.around(indexlist)
indexlist = indexlist.astype(int)
arr = np.delete(arr, indexlist)
newlen = len(arr)
arr = np.reshape(arr, (newlen,))
except:
arrlen = len(arr)
for i in range(arrlen):
if arr[i] == 'nan' or arr[i] == np.nan:
indexlist = np.append(indexlist,i)
else:
continue
indexlist = np.around(indexlist)
indexlist = indexlist.astype(int)
arr = np.delete(arr, indexlist)
newlen = len(arr)
arr = np.reshape(arr, (newlen,))
return arr
def nankill1(arr): #checks if values in array == 'nan'
indexlist = np.zeros([1,0])
try:
arrlen = np.shape(arr)[1]
for i in range(arrlen):
if arr[0,i] == 'nan' or arr[0,i] == np.nan:
indexlist = np.append(indexlist,i)
else:
continue
indexlist = np.around(indexlist)
indexlist = indexlist.astype(int)
arr = np.delete(arr, indexlist)
newlen = len(arr)
arr = np.reshape(arr, (1,newlen))
except:
arrlen = len(arr)
for i in range(arrlen):
if arr[i] == 'nan' or arr[i] == np.nan:
indexlist = np.append(indexlist,i)
else:
continue
indexlist = np.around(indexlist)
indexlist = indexlist.astype(int)
arr = np.delete(arr, indexlist)
newlen = len(arr)
arr = np.reshape(arr, (1,newlen))
return arr
def Histogram(res, bins, bin_range, num):
binneddata = np.histogram(res, bins=bins, range=[-bin_range, bin_range]) #binned data for centroid
Res = dict()
yuncert = np.zeros([1, len(binneddata[0][:])])
uncerterr = np.zeros([1, len(binneddata[0][:])])
freq = np.zeros([1, len(binneddata[0][:])])
totalobs = 0
for i in range(len(binneddata[1][:])-1):
yuncert[0,i] = (1/2) * (binneddata[1][i] + binneddata[1][i+1])
if binneddata[0][i] == 0:
uncerterr[0,i] = np.nan
else:
uncerterr[0,i] = 1 / np.sqrt(binneddata[0][i])
freq[0,i] = (binneddata[0][i])
totalobs += binneddata[0][i]
Res['totalobs'] = totalobs
def gaussfun(x, A, mu, sig):
return A * np.exp((-(x-mu)**2)/ (2. * sig**2))
mod1 = Model(gaussfun)
params = Parameters()
params.add('A', value=1, min=0)
params.add('mu', value = 0)
params.add('sig', value=0.01, min=0)
#print(np.max(np.abs(res)), res, bins, bin_range)
#print(freq, yuncert, uncerterr)
errfit = mod1.fit(freq, params, x=yuncert, nan_policy='omit', weights = uncerterr) #weights = uncerterr
params.update(errfit.params)
xplot = np.linspace(np.min(yuncert), np.max(yuncert), num=500)
yplot = mod1.eval(params=params, x=xplot)
Res['xplot'] = xplot
Res['yplot'] = yplot
Res['xdata'] = yuncert
Res['ydata'] = freq
xgraph = np.linspace(-np.max(yuncert), np.max(yuncert), num=1000)
values1 = params.valuesdict()
cen1 = values1['mu']
###################################Graphing
resideval = errfit.eval(params, x=xgraph)
####################################
calcparams = params.valuesdict()
lineposerroros = calcparams['sig'] / np.sqrt(num)
Res['Line Position Error'] = lineposerroros
Res['sigma']= calcparams['sig']
Res['ABS Line Position Error'] = calcparams['sig'] / np.sqrt(totalobs)
Res['calc params'] = params
Res['mu stderr'] = params['mu'].stderr
Res['mu val'] = params['mu'].value
return Res
def poly(x, a, b, c, d):
return (a*x**3 + b*x**2 + c*x + d)
def find_nearest(array, value):
array = np.asarray(array)
idx = (np.abs(array - value)).argmin()
return idx
def OutlierDet(osna, Oserr, osnatime, irna, Irerr, irnatime, poly, crit): #single point removal
osna = osna.astype(np.float64)
Oserr = Oserr.astype(np.float64)
osnatime = osnatime.astype(np.float64)
irna = irna.astype(np.float64)
Irerr = Irerr.astype(np.float64)
irnatime = irnatime.astype(np.float64)
Osnewres = np.zeros([1,])
Irnewres = np.zeros([1,])
sigO = np.zeros([1,])
sigI = np.zeros([1,])
#Ircomp = np.zeros([1,])
#Oscomp = np.zeros([1,])
Oscomp = []
Ircomp = []
for i in range(np.shape(osna)[0]):
arr = copy.deepcopy(osna) #making copy of arrays to avoid altering the original arrays
arrerr = copy.deepcopy(Oserr)
Ostim = copy.deepcopy(osnatime)
arr2 = np.delete(arr, i) #deleting ith row in the data (rotating through all data for single removal)
arrerr2 = np.delete(arrerr, i)
tim2 = np.delete(Ostim, i)
arr2 = np.append(arr2, np.nan)
arrerr2 = np.append(arrerr2, np.nan)
tim2 = np.append(tim2, np.nan)
newfit = poly(tim2, arr2, arrerr2, irnatime, irna, Irerr)
#print('Ostim: ', np.shape(Ostim))
#try:
val = newfit['d_1'] + newfit['c_1']*(Ostim[i]) + newfit['b_1']*(Ostim[i])**2 + newfit['a_1']*(Ostim[i])**3
#except:
#val = newfit['d_1'] + newfit['c_1']*(Ostim[0,i]) + newfit['b_1']*(Ostim[0,i])**2 + newfit['a_1']*(Ostim[0,i])**3
#arr2 = np.reshape(arr2, (1,len(arr2)))
#tim2 = np.reshape(tim2, (1,len(tim2)))
#print(np.shape(arr2))
arr2 = nankill0(arr2)
tim2 = nankill0(tim2)
#print(np.shape(arr2))
resiO = arr - (newfit['d_1'] + newfit['c_1']*Ostim + newfit['b_1']*Ostim**2 + newfit['a_1']*Ostim**3)
OHis = Histogram(resiO, bins=bins, bin_range=(np.average(np.abs(resiO))*1.5), num=newfit['num'])
sigO = np.append(sigO, OHis['sigma'])
Osnewres = np.append(Osnewres, np.abs(arr[i]-val))
#Oscomp.append((np.abs(arr[0,i] - val) / OHis['sigma']))
newval = np.abs(arr[i]-val)/ OHis['sigma']
#Oscomp = np.concatenate((Oscomp, newval))
Oscomp.append(newval)
#Oscomp2.append((np.abs(arr[0,i] - val) / OHis['sigma']))
for i in range(np.shape(irna)[0]):
arr = copy.deepcopy(irna) #making copy of arrays to avoid altering the original arrays
arrerr = copy.deepcopy(Irerr)
Irtim = copy.deepcopy(irnatime)
arr2 = np.delete(arr, i) #deleting ith row in the data (rotating through all data for single removal)
arrerr2 = np.delete(arrerr, i)
tim2 = np.delete(Irtim, i)
arr2 = np.append(arr2, np.nan)
arrerr2 = np.append(arrerr2, np.nan)
tim2 = np.append(tim2, np.nan)
newfit = poly(osnatime, osna, Oserr, tim2, arr2, arrerr2)
#arr2 = np.reshape(arr2, (1,len(arr2)))
#tim2 = np.reshape(tim2, (1,len(tim2)))
arr2 = nankill0(arr2)
tim2 = nankill0(tim2)
val = newfit['d_2'] + newfit['c_2']*(Irtim[i]) + newfit['b_2']*(Irtim[i])**2 + newfit['a_2']*(Irtim[i])**3
resiI = arr - (newfit['d_2'] + newfit['c_1']*Irtim + newfit['b_1']*Irtim**2 + newfit['a_1']*Irtim**3)
IHis = Histogram(resiI, bins=bins, bin_range=(np.average(np.abs(resiI[np.abs(resiI)<5]))*1.5), num=newfit['num'])
sigI = np.append(sigI, IHis['sigma'])
Irnewres = np.append(Irnewres, np.abs(arr[i]-val))
newval = [np.abs(arr[i] - val) / IHis['sigma']]
#Ircomp = np.concatenate((Ircomp, newval))
Ircomp.append((np.abs(arr[i] - val) / IHis['sigma']))
Osmaxres = np.max(Oscomp)
Osmaxin = np.argmax(Oscomp)
Irmaxres = np.max(Ircomp)
Irmaxin = np.argmax(Ircomp)
if Osmaxres > Irmaxres:
if Osmaxres >= crit: #rejection criteria based on
#try:
badlines = df2copy.columns[df2copy.eq(str(Ostim[Osmaxin])).any()][0]
#except:
# badlines = df2copy.columns[df2copy.eq(str(Ostim[0,Osmaxin])).any()][0]
newosna = np.delete(osna, Osmaxin)
newOserr = np.delete(Oserr, Osmaxin)
newosnatime = np.delete(osnatime, Osmaxin)
newirna = irna
newIrerr = Irerr
newirnatime = irnatime
else: #(Osmaxres/(newfit['Osconfband'][0,np.around(Ostim[0,Osmaxin]).astype(int)])) < crit:
badlines = None
newosna = osna
newOserr = Oserr
newosnatime = osnatime
newirna = irna
newIrerr = Irerr
newirnatime = irnatime
newfit = poly(osnatime, osna, Oserr, irnatime, irna, Irerr)
if Irmaxres > Osmaxres:
if Irmaxres >= crit: #rejection criteria based on
#try:
badlines = df2copy.columns[df2copy.eq(str(Irtim[Irmaxin])).any()][0] #only the distance from the confidenceband
#except:
# badlines = df2copy.columns[df2copy.eq(str(Irtim[0,Irmaxin])).any()][0]
newirna = np.delete(irna, Irmaxin)
newIrerr = np.delete(Irerr, Irmaxin)
newirnatime = np.delete(irnatime, Irmaxin)
newosna = osna
newOserr = Oserr
newosnatime = osnatime
else: #(Irmaxres/(newfit['Irconfband'][0,np.around(Irtim[0,Irmaxin]).astype(int)])) < crit:
badlines = None
newirna = irna
newIrerr = Irerr
newirnatime = irnatime
newosna = osna
newOserr = Oserr
newosnatime = osnatime
newfit = poly(osnatime, osna, Oserr, irnatime, irna, Irerr)
#print(badlines)
ODres = {}
ODres['osna'] = newosna
ODres['Oserr'] = newOserr
ODres['osnatime'] = newosnatime
ODres['irna'] = newirna
ODres['Irerr'] = newIrerr
ODres['irnatime'] = newirnatime
ODres['badlines'] = badlines
ODres['syspoly'] = poly(newosnatime, newosna, newOserr, newirnatime, newirna, newIrerr)
ODres['a_1'] = newfit['a_1']
ODres['b_1'] = newfit['b_1']
ODres['c_1'] = newfit['c_1']
ODres['d_1'] = newfit['d_1']
ODres['d_2'] = newfit['d_2']
ODres['chi-sq'] = newfit['chi-sq']
ODres['red-chi-sq'] = newfit['red-chi-sq']
return ODres
#pixel = np.linspace(0, 2047, num=2048)
pixel = np.linspace(1, 2048, num=2048)
####uncertainty breakdown
osna1cal = []
osna1sys = []
osna1stat = []
osna2cal = []
osna2sys = []
osna2stat = []
irna1cal = []
irna1stat = []
irna1sys = []
irna2cal = []
irna2stat = []
irna2sys = []
osmg1cal = []
osmg1stat = []
osmg1sys = []
irmg1cal = []
irmg1stat = []
irmg1sys = []
osnastat1ori = []
dispcalunc = []
for v in range(1, 11):
v = 2*v
#v = 2*v-1
tval2 = 2.201
if v==15 or v==16:
continue
cIr = 0
fram = 0
fram2 = 0
framIr = 0
framOs = 0
############################## 4-gauss fit over 1 normalized calibration set.
for k in range(np.shape(df)[1]): #Ir
if int(df2.iloc[2,k][3:]) == int(v):
wave = dfwa[df2.iloc[2,k]]
sig += df.iloc[:,k] #/ int(df2.iloc[4,k])
fram += int(df2.iloc[4,k])
calstring = df2.iloc[2,k]
cIr +=1
sigtestIr = sig
energy = JeV*hp*cs / (wave * 10**(-9))
photc = ((3 * sig))/ (energy / 3.6)
photc = photc /(cIr * fram)
sig = sig/fram #normalized average Ir signal for a given calibration set
cOs = 0
for b in range(np.shape(dfa)[1]): #Os
if int(df2a.iloc[2,b][3:]) == int(v):
wave2 = dfwab[df2a.iloc[2,b]]
sig2 += dfa.iloc[:,b] #/
fram2 += int(df2a.iloc[4,b])
calstring2 = df2a.iloc[2,b]
cOs +=1
sigtestOs = sig2
energy2 = JeV*hp*cs / (wave2 * 10**(-9))
photc2 = ((3 * sig2))/ (energy2 / 3.6)
photc2 = photc2 / (cOs * fram2)
sig2 = sig2 / fram2 #normalized average Os spectra for a given calibration set
difference = photc - photc2 ### Ir minus Os in photon counts
difference2 = sig - sig2 ### Ir minus Os in ADU signal
# plt.figure()
# plt.plot(pixel, sigtestOs, label='Os',c='r')
# plt.plot(pixel, sigtestIr, label='Ir',c='b')
# plt.xlabel('Pixel')
# plt.xlim(np.min(pixel), np.max(pixel))
# plt.ylabel('ADU')
# plt.legend()
# plt.show()
# plt.close()
####################### calibration uncertainty determination from dispersion relation
centerIr = 544
centerOs = 566
testIr = []
testOs = []
pOs = []
pIr = []
for i in range(1, 2*r):
testIr.append(sigtestIr[centerIr-r+i])
testOs.append(sigtestOs[centerOs-r+i])
pOs.append(pixel[centerOs-r+i])
pIr.append(pixel[centerIr-r+i])
modtestIr = Model(gauss)
paramstestIr = Parameters()
paramstestIr.add('A', value = np.max(testIr)-np.min(testIr), min=0)
paramstestIr.add('mu', value=np.mean(pIr))
paramstestIr.add('sig', value = 0.001, min=0)
paramstestIr.add('B', value = np.min(testIr))
resIrtest = modtestIr.fit(np.array(testIr), params=paramstestIr, x=np.array(pIr), max_nfev=None, nan_policy='omit')
paramstestIr.update(resIrtest.params)
modtestOs = Model(gauss)
paramstestOs = Parameters()
paramstestOs.add('A', value = np.max(testOs)-np.min(testOs), min=0)
paramstestOs.add('mu', value=np.mean(pOs))
paramstestOs.add('sig', value = 0.001, min=0)
paramstestOs.add('B', value = np.min(testOs))
resOstest = modtestOs.fit(np.array(testOs), params=paramstestOs, x=np.array(pOs), max_nfev=None, nan_policy='omit')
paramstestOs.update(resOstest.params)
ostestx = np.rint(paramstestOs['mu'].value)
irtestx = np.rint(paramstestIr['mu'].value)
testrang = np.linspace(irtestx, ostestx, num=(int(ostestx)-int(irtestx)+int(1)))
calteststr = 'Cal'+str(v)
for name in glob.glob(ftranloc+'\\Calibration6b\\'+calstring+'\\*PolyParameters.csv'):
nam = name
testdf = pd.read_csv(r""+nam)
bet1 = testdf['Uncertainty'][1]
gam1 = testdf['Uncertainty'][2]
omeg1 = testdf['Uncertainty'][3]
def Funcert(x, beta, gamma, omega):
val = np.sqrt(beta**2 + (2*gamma*x)**2 + (3*omega*x**2)**2)
sumfun = np.sum(val)
return sumfun
estcalunc = Funcert(testrang, bet1, gam1, omeg1)
dispcalunc.append(estcalunc)
#print(estcalunc)
estcalunc = 0
######################### Individual spectra analysis (single gauss fit)
for k in range(np.shape(df)[1]): #Ir individual spectra(Na-like)
if int(df2.iloc[2,k][3:]) == int(v):
wave = dfwa[df2.iloc[2,k]]
sigIr = df.iloc[:,k]
framIr = int(df2.iloc[4,k])
calstring = df2.iloc[2,k]
sigIr = sigIr / framIr
diffIr = sigIr - sig2
irfit = []
irwave = []
irpix = []
c=544
if style == 'diff':
for i in range(1, 2*r):
irfit.append(diffIr.iloc[c-r+i])
irwave.append(wave[c-r+i])
irpix.append(c-r+i)
elif style == 'reg':
for i in range(1, 2*r):
irfit.append(framIr*sigIr.iloc[c-r+i])
irwave.append(wave[c-r+i])
irpix.append(c-r+i)
xirna += irwave
irfit = np.array(irfit)
irwave = np.array(irwave)
irenergy = JeV*hp*cs / (irwave * 10**(-9))
irphot = (3*irfit)/(irenergy / 3.6)
irphot = framIr*(irphot+np.abs(np.min(irphot)))+offset1
###calibration uncertainty determination
modcalIr = Model(gauss)
params=Parameters()
params.add('A', value = np.max(irfit)-np.min(irfit), min=0)
params.add('B', value = np.min(irfit))
params.add('mu', value = c)
params.add('sig', value = 0.5)
resultcalir = modcalIr.fit(np.array(irfit), params=params, x=np.array(irpix), weights=None, nan_policy='omit', max_nfev=None)
params.update(resultcalir.params)
#print(params['A'].value)
for name in glob.glob(ftranloc+'\\Calibration6b\\'+calstring+'\\*CovarianceMatrix.csv'):
#for name in glob.glob('C:\\Users\\ahosi\\OneDrive\\Desktop\\FileTrans4\\Calibration\\'+df2.iloc[2,k]+'\\*CovarianceMatrix.csv'):
nam = name
tempdf = pd.read_csv(r""+nam)
coma = np.array(tempdf)
colvec = parts(resultcalir.params['mu'].value)
temp1 = np.matmul(coma, colvec)
temp = tval2*np.sqrt(np.matmul(colvec.T, temp1))
calunc = temp[0,0]
calibrationuncert.append(calunc)
irnacen.append(centroid(irwave,irphot)['centroid'])
irnacenu.append(np.sqrt(centroid(irwave,irphot)['cent_unc']**2 + calunc**2))
irnacent.append(np.float64(df2.iloc[1,k]))
####single gauss fit
modIr = Model(gauss)
params = Parameters()
params.add('A', value = np.max(irphot)-np.min(irphot), min=0)
params.add('B', value= np.min(irphot))
params.add('mu', value = np.mean(irwave))
params.add('sig', value = 0.01, min=0)
resultir = modIr.fit(irphot, params=params, x=irwave, max_nfev = None, nan_policy='omit', weights=np.sqrt(irphot))
params.update(resultir.params)
xplot = np.linspace(np.min(irwave), np.max(irwave), num=1000)
yplot = modIr.eval(params=params, x=xplot)
if str(resultir.params['mu'].stderr) == 'None':
pass
elif resultir.params['mu'].stderr < 0.05:
irna.append(resultir.params['mu'].value-estcalunc)
irnae.append(np.sqrt(siglev1*resultir.params['mu'].stderr**2 + calunc**2))
irnatime.append(np.float64(df2.iloc[1,k]))
irna1cal.append(calunc)
irna1stat.append(siglev1*resultir.params['mu'].stderr)
irna1sys.append(adsysunc[v])
else:
pass
###Double gauss fit
modIr2 = Model(gauss2)
params2 = Parameters()
params2.add('A', value = np.max(irphot)-np.min(irphot), min=0)
params2.add('A2', value = np.max(irphot)/2, min=0)
params2.add('B', value= np.min(irphot))
params2.add('mu', value = np.mean(irwave))
params2.add('mu2', value = np.mean(irwave))
params2.add('sig', value = 0.01, min=0)
resultir2 = modIr2.fit(irphot, params=params2, x=irwave, max_nfev = None, nan_policy='omit', weights=None)
params2.update(resultir2.params)
xplot2 = np.linspace(np.min(irwave), np.max(irwave), num=1000)
yplot2 = modIr2.eval(params=params2, x=xplot2)
# plt.figure()
# plt.plot(irwave, irphot, c='b')
# plt.plot(xplot, yplot, c='r')
# #plt.xlim(7.28, 7.51)
# #plt.axhline(y=0)
# #plt.show()
# plt.close()
for k in range(np.shape(df)[1]): #Ir individual spectra(Mg-like)
if int(df2.iloc[2,k][3:]) == int(v):
wave = dfwa[df2.iloc[2,k]]
sigIr = df.iloc[:,k]
framIr = int(df2.iloc[4,k])
calstring = df2.iloc[2,k]
sigIr = sigIr / framIr
diffIr = sigIr - sig2
irfit = []
irwave = []
irpix = []
c=574
if style == 'diff':
for i in range(1, 2*r):