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RatioBuilding.py
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RatioBuilding.py
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import os
import pandas as pd
import numpy as np
from pandas import ExcelWriter
import scipy.interpolate
import ExcelFormatter
import DataFolderUtil
import Util
from scipy.stats import median_abs_deviation, iqr
class RatioBuilder:
def __init__(self):
self.data_root_folder = None
self.ratios = None
self.mean_option = 'median'
self.dev_option = 'std'
def set_path(self, path):
self.data_root_folder = path
def set_constants(self, constants):
# spike impurities
self.R34_33 = constants['R3433']
self.R35_33 = constants['R3533']
self.R30_29 = constants['R3029']
# constants
self.mf48 = constants['mf48']
self.mf36 = constants['mf36']
self.mf56 = constants['mf56']
self.mf68 = constants['mf68']
self.mf92 = constants['mf92']
self.mf38 = constants['mf38']
self.mf35 = constants['mf35']
self.mf43 = constants['mf43']
self.mf45 = constants['mf45']
self.mf09 = constants['mf09']
self.mf29 = constants['mf29']
self.mf34 = constants['mf34']
self.mf58 = constants['mf58']
self.mf02 = constants['mf02']
self.NA = constants['NA']
self.NR85 = constants['NR85']
self.cps = constants['cps']
self.lambda234 = constants['l234']
self.lambda238 = constants['l238']
self.lambda230 = constants['l230']
self.lambda232 = constants['l232']
self.th229SubU238 = constants['th229SubU238']
self.th230SubU238 = constants['th230SubU238']
def set_specific_constants(self, specific_constants):
useBlank = specific_constants['Blank']
if useBlank:
self.blk = 1
else:
self.blk = 0
self.yield_Th = specific_constants['Yield_Th']
self.yield_U = specific_constants['Yield_U']
self.gain = specific_constants['Gain']
self.tailShift = specific_constants['Tail shift']
def set_options(self, mean_option='median', dev_option='std'):
self.mean_option = mean_option
self.dev_option = dev_option
def post_904IC(self, data, i, labNr, tail_mat_cup, ThH_plus, blk, datablkm, UH_plus, cps, yield_U, R34_33, tail_mat,
R35_33,
tail_mat_th, R30_29, yield_Th, mf48, mf36, mf56, mf68, mf92, mf38, mf35,
mf43, mf45, mf09, mf29, mf34, mf58, mf02):
data33 = data[:, 0] - data[:, 4] * tail_mat_cup[3] - data[:, 7] * ThH_plus - blk * datablkm[i, 0] # U233
data34 = data[:, 1] - UH_plus * data33 * cps * yield_U - data33 * R34_33 * cps * yield_U - data[:, 4] * \
tail_mat[4] * cps * yield_U - blk * datablkm[i, 1] # U234
data35 = (data[:, 2] - (data[:, 1] * UH_plus) / (cps * yield_U)) - data33 * R35_33 - data[:, 4] * tail_mat_cup[
5] - blk * datablkm[i, 2] # U235
data36 = data[:, 3] - UH_plus * data[:, 2] - data[:, 4] * tail_mat_cup[6] - blk * datablkm[i, 3] # U236
data38 = data[:, 4] - blk * datablkm[i, 4] # U238
data29 = data[:, 8] - data[:, 4] * tail_mat[0] * cps * yield_U - tail_mat_th[0] * cps * yield_Th * data[:,
7] - blk * \
datablkm[i, 7] - self.th229SubU238 * data[:, 4] # Th229
data30 = data[:, 6] - ThH_plus * data29 - data29 * R30_29 - data[:, 4] * tail_mat[1] * cps * yield_U - \
tail_mat_th[1] * cps * yield_Th * data[:, 7] - blk * datablkm[i, 5] - self.th230SubU238 * data[:,
4] # Th230 U
data32 = data[:, 7] - data[:, 4] * tail_mat_cup[2] - blk * datablkm[i, 6] # Th232
self.intensities_dict[labNr] = pd.DataFrame(
{
'Cycle': list(np.arange(1, len(data29) + 1)),
'229Th': list(data29),
'230Th': list(data30),
'232Th': list(data32),
'233U': list(data33),
'234U': list(data34),
'235U': list(data35),
'236U': list(data36),
'238U': list(data38)
}
)
# calculating atomic ratios, mass fractionation correction, 2 sigma outlier test
R58d = data35 / data38 # U235/U238 for mass fractionation correction
R58u = data35 / data38 # U235/U238 for monitoring machine drift
[R58u, errR58u, R58u_, errRel58u] = self.outliertest(
R58u) # output: outlier corrected R58, 2sigma SE, mean, 2sigma relative SE
R58 = data35 / data38 * (1 / self.NR85 / R58d) ** mf58 # U235/U238
[R58, errR58, R58_, errRel58] = self.outliertest(R58)
R34 = data33 / (data34 / (cps * yield_U)) * (1 / self.NR85 / R58d) ** mf34 # U233/U234
[R34, errR34, R34_, errRel34] = self.outliertest(R34)
R56 = data35 / data36 * (1 / self.NR85 / R58d) ** mf56 # U235/U236
[R56, errR56, R56_, errRel56] = self.outliertest(R56)
R48 = (data34 / (cps * yield_U)) / data38 * (1 / self.NR85 / R58d) ** mf48 # U234/U238
[R48, errR48, R48_, errRel48] = self.outliertest(R48)
R09 = data30 / data29 * (1 / self.NR85 / R58d) ** mf09 # Th230/Th229
[R09, errR09, R09_, errRel09] = self.outliertest(R09)
R29 = data32 / (data29 / (cps * yield_Th)) * (1 / self.NR85 / R58d) ** mf29 # Th232/Th229
[R29, errR29, R29_, errRel29] = self.outliertest(R29)
R43 = (data34 / (cps * yield_U)) / data33 * (1 / self.NR85 / R58d) ** mf43 # U233/U234
[R43, errR43, R43_, errRel43] = self.outliertest(R43)
R92 = (data29 / (cps * yield_Th)) / data32 * (1 / self.NR85 / R58d) ** mf92 # Th232/Th229
[R92, errR92, R92_, errRel92] = self.outliertest(R92)
R36 = data33 / data36 * (1 / self.NR85 / R58d) ** mf36 # U233/U234
[R36, errR36, R36_, errRel36] = self.outliertest(R36)
R45 = (data34 / (cps * yield_U)) / data35 * (1 / self.NR85 / R58d) ** mf45 # U233/U234
[R45, errR45, R45_, errRel45] = self.outliertest(R45)
R02 = (data30 / (cps * yield_Th)) / data32 * (1 / self.NR85 / R58d) ** mf02 # Th230/Th229
[R02, errR02, R02_, errRel02] = self.outliertest(R02)
R38 = data33 / data38 * (1 / self.NR85 / R58d) ** mf38 # U233/U238
[R38, errR38, R38_, errRel38] = self.outliertest(R38)
R68 = data36 / data38 * (1 / self.NR85 / R58d) ** mf68 # U236/U238
[R68, errR68, R68_, errRel68] = self.outliertest(R68)
R35 = data33 / data35 * (1 / self.NR85 / R58d) ** mf35 # U233/U235
[R35, errR35, R35_, errRel35] = self.outliertest(R35)
return R36_, errRel36, R58u_, errRel58u, R56_, errRel56, R43_, errRel43, R45_, errRel45, R48_, errRel48, R09_, errRel09, R92_, errRel92, R02_, errRel02, R38_, errRel38, R68_, errRel68, R35_, errRel35
def post_90IC(self, data, i, labNr, tail_mat_cup, ThH_plus, blk, gain13, datablkm, UH_plus, cps, yield_U, R34_33,
tail_mat,
R35_33,
tail_mat_th, R30_29, yield_Th, mf48, mf36, mf56, mf68, mf92, mf38, mf35,
mf43, mf45, mf09, mf29, mf34, mf58, mf02):
data33 = data[:, 0] - data[:, 4] * tail_mat_cup[3] - data[:, 7] * ThH_plus - blk * datablkm[i, 0] # U233
data34 = gain13 * data[:, 1] - UH_plus * data33 - data33 * R34_33 - data[:, 4] * tail_mat_cup[4] - blk * \
datablkm[i, 1] / (cps * yield_U) # U234
data35 = (data[:, 2] - data[:, 1] * UH_plus * gain13) - data33 * R35_33 - data[:, 4] * tail_mat_cup[5] - blk * \
datablkm[i, 2] # U235
data36 = data[:, 3] - UH_plus * data[:, 2] - data[:, 4] * tail_mat_cup[6] - blk * datablkm[i, 3] # U236
data38 = data[:, 4] - blk * datablkm[i, 4] # U238
data29 = data[:, 8] - data[:, 4] * tail_mat[0] * cps * yield_U - tail_mat_th[0] * cps * yield_Th * data[:,
7] - blk * \
datablkm[i, 7] - self.th229SubU238 * data[:, 4] # Th229
data30 = data[:, 6] - ThH_plus * data29 - data29 * R30_29 - data[:, 4] * tail_mat[1] * cps * yield_U - \
tail_mat_th[1] * cps * yield_Th * data[:, 7] - blk * datablkm[i, 5] - self.th230SubU238 * data[:,
4] # Th230
data32 = data[:, 7] - data[:, 4] * tail_mat_cup[2] - blk * datablkm[i, 6] # Th232
self.intensities_dict[labNr] = pd.DataFrame(
{
'Cycle': list(np.arange(1, len(data29) + 1)),
'229Th': list(data29),
'230Th': list(data30),
'232Th': list(data32),
'233U': list(data33),
'234U': list(data34),
'235U': list(data35),
'236U': list(data36),
'238U': list(data38)
}
)
# calculating atomic ratios, mass fractionation correction, 2 sigma outlier test
R58d = data35 / data38 # U235/U238 for mass fractionation correction
R58u = data35 / data38 # U235/U238 for monitoring machine drift
[R58u, errR58u, R58u_, errRel58u] = self.outliertest(
R58u) # output: outlier corrected R58, 2sigma SE, mean, 2sigma relative SE
R58 = data35 / data38 * (1 / self.NR85 / R58d) ** mf58 # U235/U238
[R58, errR58, R58_, errRel58] = self.outliertest(R58)
R34 = data33 / data34 * (1 / self.NR85 / R58d) ** mf34 # U233/U234
[R34, errR34, R34_, errRel34] = self.outliertest(R34)
R56 = data35 / data36 * (1 / self.NR85 / R58d) ** mf56 # U235/U236 and mass fractionation
[R56, errR56, R56_, errRel56] = self.outliertest(R56)
R48 = data34 / data38 * (1 / self.NR85 / R58d) ** mf48 # U234/U238
[R48, errR48, R48_, errRel48] = self.outliertest(R48)
R09 = data30 / data29 * (1 / self.NR85 / R58d) ** mf09 # Th230/Th229
[R09, errR09, R09_, errRel09] = self.outliertest(R09)
R29 = data32 / (data29 / (cps * yield_Th)) * (1 / self.NR85 / R58d) ** mf29 # Th232/Th229
[R29, errR29, R29_, errRel29] = self.outliertest(R29)
R43 = data34 / data33 * (1 / self.NR85 / R58d) ** mf43 # U233/U234
[R43, errR43, R43_, errRel43] = self.outliertest(R43)
R92 = ((data29 / cps) * yield_Th / data32) * (1 / self.NR85 / R58d) ** mf92 # Th232/Th229
[R92, errR92, R92_, errRel92] = self.outliertest(R92);
R36 = data33 / data36 * (1 / self.NR85 / R58d) ** mf36 # U233/U234
[R36, errR36, R36_, errRel36] = self.outliertest(R36)
R45 = data34 / data35 * (1 / self.NR85 / R58d) ** mf45 # U233/U234
[R45, errR45, R45_, errRel45] = self.outliertest(R45)
R02 = (data30 / (cps * yield_Th)) / data32 * (1 / self.NR85 / R58d) ** mf02 # Th230/Th229
[R02, errR02, R02_, errRel02] = self.outliertest(R02)
R38 = data33 / data38 * (1 / self.NR85 / R58d) ** mf38 # U233/U238
[R38, errR38, R38_, errRel38] = self.outliertest(R38)
R68 = data36 / data38 * (1 / self.NR85 / R58d) ** mf68 # U236/U238
[R68, errR68, R68_, errRel68] = self.outliertest(R68)
R35 = data33 / data35 * (1 / self.NR85 / R58d) ** mf35 # U233/U235
[R35, errR35, R35_, errRel35] = self.outliertest(R35)
return R36_, errRel36, R58u_, errRel58u, R56_, errRel56, R43_, errRel43, R45_, errRel45, R48_, errRel48, R09_, errRel09, R92_, errRel92, R02_, errRel02, R38_, errRel38, R68_, errRel68, R35_, errRel35
def post_0IC(self, data, i, labNr, tail_mat_cup, ThH_plus, blk, gain13, datablkm, UH_plus, cps, yield_U, R34_33,
tail_mat,
R35_33,
tail_mat_th, tail_mat_th_cup, R30_29, yield_Th, mf48, mf36, mf56, mf68, mf92, mf38,
mf35,
mf43, mf45, mf09, mf29, mf34, mf58, mf02):
data33 = data[:, 0] - data[:, 4] * tail_mat_cup[3] - data[:, 7] * ThH_plus - blk * datablkm[i, 0] # U233
data34 = gain13 * data[:, 1] - UH_plus * data33 - data33 * R34_33 - data[:, 4] * tail_mat_cup[4] - blk * \
datablkm[i, 1] # U234
data35 = (data[:, 2] - data[:, 1] * UH_plus * gain13) - data33 * R35_33 - data[:, 4] * tail_mat_cup[5] - blk * \
datablkm[i, 2] # U235
data36 = data[:, 3] - UH_plus * data[:, 2] - data[:, 4] * tail_mat_cup[6] - blk * datablkm[i, 3] # U236
data38 = data[:, 4] - blk * datablkm[i, 4] # U238
data29 = data[:, 5] - data[:, 4] * tail_mat_cup[0] - tail_mat_th_cup[0] * data[:, 7] - blk * datablkm[
i, 7] - data[:, 4] * self.th229SubU238 / cps # Th229
data30 = data[:, 6] - ThH_plus * data29 * cps * yield_Th - data29 * R30_29 * cps * yield_Th - data[:, 4] * \
tail_mat[1] * cps * yield_U - tail_mat_th[1] * cps * yield_Th * data[:, 7] - blk * datablkm[
i, 5] - data[:, 4] * self.th230SubU238 # Th230
data32 = data[:, 7] - data[:, 4] * tail_mat_cup[2] - blk * datablkm[6, i] # Th232
self.intensities_dict[labNr] = pd.DataFrame(
{
'Cycle': list(np.arange(1, len(data29) + 1)),
'229Th': list(data29),
'230Th': list(data30),
'232Th': list(data32),
'233U': list(data33),
'234U': list(data34),
'235U': list(data35),
'236U': list(data36),
'238U': list(data38)
}
)
# calculating atomic ratios, mass fractionation correction, 2 sigma outlier test
R58d = data35 / data38 # U235/U238 for mass fractionation correction
R58u = data35 / data38 # U235/U238 for monitoring machine drift
[R58u, errR58u, R58u_, errRel58u] = self.outliertest(
R58u) # output: outlier corrected R58, 2sigma SE, mean, 2sigma relative SE
R58 = data35 / data38 * (1 / self.NR85 / R58d) ** mf58 # U235/U238
[R58, errR58, R58_, errRel58] = self.outliertest(R58)
R34 = data33 / data34 * (1 / self.NR85 / R58d) ** mf34 # U233/U234
[R34, errR34, R34_, errRel34] = self.outliertest(R34)
R56 = data35 / data36 * (1 / self.NR85 / R58d) ** mf56 # U235/U236 and mass fractionation
[R56, errR56, R56_, errRel56] = self.outliertest(R56)
R48 = data34 / data38 * (1 / self.NR85 / R58d) ** mf48 # U234/U238
[R48, errR48, R48_, errRel48] = self.outliertest(R58u)
R09 = (data30 / (cps * yield_Th)) / data29 * (1 / self.NR85 / R58d) ** mf09 # Th230/Th229
[R09, errR09, R09_, errRel09] = self.outliertest(R09)
R29 = data32 / data29 * (1 / self.NR85 / R58d) ** mf29 # Th232/Th229
[R29, errR29, R29_, errRel29] = self.outliertest(R29)
R43 = data34 / data33 * (1 / self.NR85 / R58d) ** mf43 # U233/U234
[R43, errR43, R43_, errRel43] = self.outliertest(R43)
R92 = data29 / data32 * (1 / self.NR85 / R58d) ** mf92 # Th232/Th229
[R92, errR92, R92_, errRel92] = self.outliertest(R92)
R36 = data33 / data36 * (1 / self.NR85 / R58d) ** mf36 # U233/U234
[R36, errR36, R36_, errRel36] = self.outliertest(R36)
R45 = data34 / data35 * (1 / self.NR85 / R58d) ** mf45 # U233/U234
[R45, errR45, R45_, errRel45] = self.outliertest(R45)
R02 = (data30 / (cps * yield_Th)) / data32 * (1 / self.NR85 / R58d) ** mf02 # Th230/Th229
[R02, errR02, R02_, errRel02] = self.outliertest(R02)
R38 = data33 / data38 * (1 / self.NR85 / R58d) ** mf38 # U233/U238
[R38, errR38, R38_, errRel38] = self.outliertest(R38)
R68 = data36 / data38 * (1 / self.NR85 / R58d) ** mf68 # U236/U238
[R68, errR68, R68_, errRel68] = self.outliertest(R68)
R35 = data33 / data35 * (1 / self.NR85 / R58d) ** mf35 # U233/U235
[R35, errR35, R35_, errRel35] = self.outliertest(R35)
return R36_, errRel36, R58u_, errRel58u, R56_, errRel56, R43_, errRel43, R45_, errRel45, R48_, errRel48, R09_, errRel09, R92_, errRel92, R02_, errRel02, R38_, errRel38, R68_, errRel68, R35_, errRel35
def post_allcup(self, data, i, labNr, tail_mat_cup, ThH_plus, blk, gain13, datablkm, UH_plus, cps, yield_U, R34_33,
tail_mat, R35_33,
tail_mat_th, tail_mat_th_cup, R30_29, yield_Th, mf48, mf36, mf56, mf68, mf92,
mf38, mf35,
mf43, mf45, mf09, mf29, mf34, mf58, mf02):
data33 = data[:, 0] - data[:, 4] * tail_mat_cup[3] - data[:, 7] * ThH_plus - blk * datablkm[i, 0] # U233
data34 = gain13 * data[:, 1] - UH_plus * data33 - data33 * R34_33 - data[:, 4] * tail_mat_cup[4] - blk * \
datablkm[i, 1] / cps # U234
data35 = (data[:, 2] - data[:, 1] * UH_plus * gain13) - data33 * R35_33 - data[:, 4] * tail_mat_cup[5] - blk * \
datablkm[i, 2] # U235
data36 = data[:, 3] - UH_plus * data[:, 2] - data[:, 4] * tail_mat_cup[6] - blk * datablkm[i, 3] # U236
data38 = data[:, 4] - blk * datablkm[i, 4] # U238
data29 = data[:, 5] - data[:, 4] * tail_mat_cup[0] - tail_mat_th_cup[0] * data[:, 7] - blk * datablkm[
i, 7] / cps - data[:, 4] * self.th229SubU238 / cps # Th229
data30 = gain13 * data[:, 6] - ThH_plus * data29 - data29 * R30_29 - data[:, 4] * tail_mat_cup[1] - \
tail_mat_th_cup[1] * data[:, 7] - blk * datablkm[i, 5] / cps - data[:,
4] * self.th230SubU238 / cps # Th230
data32 = data[:, 7] - data[:, 4] * tail_mat_cup[2] - blk * datablkm[i, 6] # Th232
self.intensities_dict[labNr] = pd.DataFrame(
{
'Cycle': list(np.arange(1, len(data29) + 1)),
'229Th': list(data29),
'230Th': list(data30),
'232Th': list(data32),
'233U': list(data33),
'234U': list(data34),
'235U': list(data35),
'236U': list(data36),
'238U': list(data38)
}
)
# calculating atomic ratios, mass fractionation correction, 2 sigma outlier test
R58d = data35 / data38 # U235/U238 for mass fractionation correction
R58u = data35 / data38 # U235/U238 for monitoring machine drift
[R58u, errR58u, R58u_, errRel58u] = self.outliertest(
R58u) # output: outlier corrected R58, 2sigma SE, mean, 2sigma relative SE
R58 = data35 / data38 * (1 / self.NR85 / R58d) ** mf58 # U235/U238
[R58, errR58, R58_, errRel58] = self.outliertest(R58)
R34 = data33 / data34 * (1 / self.NR85 / R58d) ** mf34 # U233/U234
[R34, errR34, R34_, errRel34] = self.outliertest(R34)
R56 = data35 / data36 * (1 / self.NR85 / R58d) ** mf56 # U235/U236 and mass fractionation
[R56, errR56, R56_, errRel56] = self.outliertest(R56)
R48 = data34 / data38 * (1 / self.NR85 / R58d) ** mf48 # U234/U238
[R48, errR48, R48_, errRel48] = self.outliertest(R48)
R09 = data30 / data29 * (1 / self.NR85 / R58d) ** mf09 # Th230/Th229
[R09, errR30, R09_, errRel09] = self.outliertest(R09)
R29 = data32 / data29 * (1 / self.NR85 / R58d) ** mf29 # Th232/Th229
[R29, errR32, R29_, errRel29] = self.outliertest(R29)
R43 = data34 / data33 * (1 / self.NR85 / R58d) ** mf43 # U233/U234
[R43, errR43, R43_, errRel43] = self.outliertest(R43)
R92 = data29 / data32 * (1 / self.NR85 / R58d) ** mf92 # Th232/Th229
[R92, errR92, R92_, errRel92] = self.outliertest(R92)
R36 = data33 / data36 * (1 / self.NR85 / R58d) ** mf36 # U233/U234
[R36, errR36, R36_, errRel36] = self.outliertest(R36)
R45 = data34 / data35 * (1 / self.NR85 / R58d) ** mf45 # U233/U234
[R45, errR45, R45_, errRel45] = self.outliertest(R45)
R02 = data30 / data32 * (1 / self.NR85 / R58d) ** mf02 # Th230/Th229
[R02, errR02, R02_, errRel02] = self.outliertest(R02)
R38 = data33 / data38 * (1 / self.NR85 / R58d) ** mf38 # U233/U238
[R38, errR38, R38_, errRel38] = self.outliertest(R38)
R68 = data36 / data38 * (1 / self.NR85 / R58d) ** mf68 # U236/U238
[R68, errR68, R68_, errRel68] = self.outliertest(R68)
R35 = data33 / data35 * (1 / self.NR85 / R58d) ** mf35 # U233/U235
[R35, errR35, R35_, errRel35] = self.outliertest(R35)
return R36_, errRel36, R58u_, errRel58u, R56_, errRel56, R43_, errRel43, R45_, errRel45, R48_, errRel48, R09_, errRel09, R92_, errRel92, R02_, errRel02, R38_, errRel38, R68_, errRel68, R35_, errRel35
def yhas_uranium(self):
# xnew = np.linspace(228.5, 237.5, num=200)
old_path = os.getcwd()
os.chdir(self.data_root_folder)
listyu = os.listdir('yhas_u')
namesyu = np.array(listyu)
if self.tailShift:
x_axis_tail_u = np.array([227.5, 233.5, 236.5, 236.7, 237.05, 237.5]) # half-masses tailing SEM/RPQ
else:
x_axis_tail_u = np.array([228.5, 233.5, 236.5, 236.7, 237.05, 237.5]) # half-masses tailing SEM/RPQ
os.chdir('yhas_u')
for i in range(len(namesyu)): # read yhas data
aa = pd.read_table(namesyu[i], sep='\t') # read in files from neptune software
raw = pd.DataFrame(aa)
sub_raw = raw['Unnamed: 1']
peakstct = 'C\(C\)'
searchIC2 = sub_raw.str.contains(peakstct)
peakstrt = 'Mean'
searchMean = sub_raw.str.contains(peakstrt)
for k in range(len(searchMean)):
if searchMean[k] == True:
rowtu = k
dummyu = raw.loc[
rowtu, ['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4', 'Unnamed: 5', 'Unnamed: 6', 'Unnamed: 7']]
dummyu = [float(x) for x in dummyu]
dummyu = np.array(dummyu)
u238tail = raw.loc[rowtu, ['Unnamed: 9']]
u238tail = float(u238tail)
u238tail = np.array(u238tail)
self.u238tail = np.mean(u238tail[u238tail != 0])
uh1 = raw.loc[rowtu, ['Unnamed: 8']]
uh1 = float(uh1)
uh1 = np.array(uh1)
uh2 = raw.loc[rowtu, ['Unnamed: 9']]
uh2 = float(uh2)
uh2 = np.array(uh2)
uh = uh1 / (uh2 * self.cps)
self.UH_plus = np.mean(uh[uh != 0])
check = 0
for m in range(len(dummyu)):
if dummyu[m] == 0:
check = check + 1
else:
check = check
if check == len(dummyu):
dummyu = []
self.x_axis_tail_u = x_axis_tail_u
self.aatsu = dummyu
self.f_u238 = scipy.interpolate.PchipInterpolator(self.x_axis_tail_u, self.aatsu)
os.chdir(old_path)
self.uranium_tailing()
def yhas_thorium(self):
old_path = os.getcwd()
os.chdir(self.data_root_folder)
listyth = os.listdir('yhas_th')
namesyth = np.array(listyth)
x_axis_tail_th = np.array([227.5, 228.5, 229.5, 230.5, 231.5]) # half-masses tailing SEM/RPQ
os.chdir('yhas_th')
for i in range(len(namesyth)): # read yhas data
aa = pd.read_table(namesyth[i], sep='\t') # read in files from neptune software
raw = pd.DataFrame(aa)
sub_raw = raw['Unnamed: 1']
peakstrt = 'Mean'
searchMean = sub_raw.str.contains(peakstrt)
for k in range(len(searchMean)):
if searchMean[k] == True:
rowt = k
dummyt = raw.loc[rowt, ['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4', 'Unnamed: 5', 'Unnamed: 6']]
dummyt = [float(x) for x in dummyt]
dummyt = np.array(dummyt)
th232tail = raw.loc[rowt, ['Unnamed: 7']]
th232tail = float(th232tail)
th232tail = np.array(th232tail)
self.th232tail = np.mean(th232tail[th232tail != 0])
thh1 = raw.loc[rowt, ['Unnamed: 8']]
thh1 = float(thh1)
thh1 = np.array(thh1)
thh2 = raw.loc[rowt, ['Unnamed: 7']]
thh2 = float(thh2)
thh2 = np.array(thh2)
thh = thh1 / (thh2 * self.cps)
ThH_plus = np.mean(thh[thh != 0])
check = 0
for m in range(len(dummyt)):
if dummyt[m] == 0:
check = check + 1
else:
check = check
if check == len(dummyt):
dummyt = []
self.x_axis_tail_th = x_axis_tail_th
self.aats = dummyt
self.g_th232 = scipy.interpolate.PchipInterpolator(self.x_axis_tail_th, self.aats)
self.ThH_plus = ThH_plus
os.chdir(old_path)
self.thorium_tailing()
def uranium_tailing(self):
rpq_factor_u = [164, 94, 46, 37, 18, 7, 5, 4] # RPQon/RPQoff
f_u238 = self.f_u238
tail_mat = [f_u238(229), f_u238(230), f_u238(232), f_u238(233), f_u238(234), f_u238(235), f_u238(236),
f_u238(237)] / (self.yield_U * self.cps * self.u238tail)
if tail_mat[0] < 1e-10: # empirically determined threshold for too low signals
tail_mat[0] = 1e-10
if tail_mat[1] < 4e-10: # empirically determined threshold for too low signals
tail_mat[1] = 4e-10
for j in range(len(tail_mat)): # Sets all negative elements of {tail_mat_cup} to zero
if tail_mat[j] < 0:
tail_mat[j] = 0
tail_mat_cup = tail_mat * rpq_factor_u # tailing U-238 on cup converted from tailing on SEM/RPQ using factors
self.tail_mat = tail_mat
self.tail_mat_cup = tail_mat_cup
self.uTailData = pd.DataFrame(
{
'229': [self.tail_mat[0], self.tail_mat_cup[0]],
'230': [self.tail_mat[1], self.tail_mat_cup[1]],
'232': [self.tail_mat[2], self.tail_mat_cup[2]],
'233': [self.tail_mat[3], self.tail_mat_cup[3]],
'234': [self.tail_mat[4], self.tail_mat_cup[4]],
'235': [self.tail_mat[5], self.tail_mat_cup[5]],
'236': [self.tail_mat[6], self.tail_mat_cup[6]],
'237': [self.tail_mat[7], self.tail_mat_cup[7]],
}, index=['Tailing U SEM', 'Tailing U Cup'])
def thorium_tailing(self):
rpq_factor_th = [52, 10] # RPQon/RPQoff
g_th232 = self.g_th232
tail_mat_th = [g_th232(229), g_th232(230)] / (
self.yield_Th * self.cps * self.th232tail) # tailing Th-232 on SEM/RPQ
if tail_mat_th[0] < 2e-9: # empirically determined threshold for too low signals
tail_mat_th[0] = 2e-9
if tail_mat_th[1] < 6e-8: # empirically determined threshold for too low signals
tail_mat_th[1] = 6e-8
tail_mat_th_cup = tail_mat_th * rpq_factor_th # tailing Th-232 on cup
self.tail_mat_th = tail_mat_th
self.tail_mat_th_cup = tail_mat_th_cup
self.thTailData = pd.DataFrame(
{
'229': [self.tail_mat_th[0], self.tail_mat_th_cup[0]],
'230': [self.tail_mat_th[1], self.tail_mat_th_cup[1]]
}, index=['Tailing Th SEM', 'Tailing Th Cup'])
# RPQ_Norm_Th = tail_mat_th * self.cps
# IC5_Norm_Th = tail_mat_th_cup * self.cps
def calc_blank_correction(self):
old_path = os.getcwd()
folder_blank = self.data_root_folder + '\\blank'
list_blank = os.listdir(folder_blank)
names_blank = np.array(list_blank)
names_blank = np.sort(names_blank)
peakstra = '1:233U'
peakstrb = 'Cup'
peakstrc = '2:230Th'
peakstrhelp = 'Cycle'
datablkm = np.empty([len(names_blank), 8])
if self.blk == 1.0:
for k in range(len(names_blank)):
os.chdir(folder_blank)
bb = pd.read_table(names_blank[k], sep='\t') # read in files from neptune software
raw = pd.DataFrame(bb)
sub_bb = raw['Unnamed: 1']
searchCup = sub_bb.str.contains(peakstrb)
colpb = 1
for i in range(len(searchCup)):
if searchCup[i] == True:
rowpb = i
sub_help = raw['Neptune Analysis Data Report']
searchCycle = sub_help.str.contains(peakstrhelp)
for i in range(len(searchCycle)):
if searchCycle[i] == True:
rowphelp = i
rawnew = raw.iloc[rowphelp]
search233U = rawnew.str.contains(peakstra)
for i in range(len(search233U)):
if search233U[i] == True:
cola = i
search230Th = rawnew.str.contains(peakstrc)
for i in range(len(search230Th)):
if search230Th[i] == True:
colc = i
os.chdir(self.data_root_folder)
datablk = raw.iloc[(rowphelp + 1):(rowpb), (colpb + 1):(colpb + 6)]
datablk = datablk.values
datablk = datablk.astype(np.float)
datablk2 = raw.iloc[(rowphelp + 1):(rowpb), (colc):(colc + 3)]
datablk2 = datablk2.values
datablk2 = datablk2.astype(np.float)
datablk = np.append(datablk, datablk2, axis=1)
smaller = np.mean(datablk, axis=0) + 0.5 * np.std(datablk, axis=0, ddof=1)
larger = np.mean(datablk, axis=0) - 2 * np.std(datablk, axis=0, ddof=1)
index1 = (rowpb) - (rowphelp + 1)
index2 = ((colc + 3) - (colc)) + ((colpb + 6) - (colpb + 1))
for m in range(index1):
for n in range(index2):
if ((datablk[m, n] > smaller[n]) or (datablk[m, n] < larger[n])):
datablk[m, n] = float('nan')
else:
datablk[m, n] = datablk[m, n]
[nn, mm] = np.shape(datablk)
for n in range(nn):
for m in range(mm):
if datablk[n, m] < 0:
datablk[n, m] = 0
datablk_mean = np.nanmean(datablk, axis=0)
datablkm[k, :] = datablk_mean
blank = pd.DataFrame(
{'Lab. #': self.labNrs, 'U-233': datablkm[:, 0], 'U-234': datablkm[:, 1], 'U-235': datablkm[:, 2],
'U-236': datablkm[:, 3],
'U-238': datablkm[:, 4], 'Th-230': datablkm[:, 5], 'Th-232': datablkm[:, 6], 'Th-229': datablkm[:, 7]},
index=names_blank)
writer = ExcelWriter(self.data_root_folder + '\\PrBlank.xlsx', engine='xlsxwriter')
ExcelFormatter.format(writer, {'Sheet1': blank})
writer.save()
else:
datablkm[:, :] = 0
blank = pd.DataFrame(
{'Lab. #': self.labNrs, 'U-233': datablkm[:, 0], 'U-234': datablkm[:, 1], 'U-235': datablkm[:, 2],
'U-236': datablkm[:, 3],
'U-238': datablkm[:, 4], 'Th-230': datablkm[:, 5], 'Th-232': datablkm[:, 6], 'Th-229': datablkm[:, 7]},
index=names_blank)
writer = ExcelWriter(self.data_root_folder + '\\PrBlank.xlsx', engine='xlsxwriter')
ExcelFormatter.format(writer, {'Sheet1': blank})
writer.save()
self.datablkm = datablkm
os.chdir(old_path)
def data_correction(self):
old_path = os.getcwd()
folder_data = os.path.join(self.data_root_folder, 'data')
list_data = os.listdir(folder_data)
names_data = np.sort(np.array(list_data))
# get the laboratory numbers from the data filenames
self.labNrs = DataFolderUtil.getLabNrsFromList(names_data)
standard = DataFolderUtil.findStandardNumber(self.data_root_folder)
standardCount = 1
# Modify lab nr strings to prevent overwriting in intensities sheet
for i, labNr in enumerate(self.labNrs):
if labNr == standard:
self.labNrs[i] = self.labNrs[i] + '_{}'.format(standardCount)
standardCount += 1
self.yhas_uranium()
self.yhas_thorium()
self.calc_blank_correction()
peakstrhelp = 'Cycle'
self.intensities_dict = {}
matrix_ratios = np.empty([len(names_data), 18])
matrix_ratios_add = np.empty([len(names_data), 24])
for i in range(len(names_data)):
os.chdir(folder_data)
cc = pd.read_table(names_data[i], sep='\t') # read in files from Neptune software
raw = pd.DataFrame(cc)
sub_cc = raw['Unnamed: 1'] # gets second column
sub_cc = sub_cc.values
rowCup = np.where(sub_cc == 'Cup')[0][0]
rawnew = raw.iloc[rowCup]
rawnew = rawnew.values
c3 = []
for j in range(len(rawnew)):
if rawnew[j] == 'C(C)':
c3.append(j)
sub_help = raw['Neptune Analysis Data Report']
searchCycle = sub_help.str.contains(peakstrhelp)
for k in range(len(searchCycle)):
if searchCycle[k] == True:
rowphelp = k
rawnew2 = raw.iloc[rowphelp]
rawnew2 = rawnew2.values
for j in range(len(rawnew2)):
if rawnew2[j] == '1:233U':
col233 = j
os.chdir(self.data_root_folder)
data = raw.iloc[(rowphelp + 1):(rowCup), (col233):(col233 + 9)]
data = data.values
data = data.astype(np.float)
if len(c3) == 3:
[R36_, errRel36, R58u_, errRel58u, R56_, errRel56, R43_, errRel43, R45_, errRel45, R48_, errRel48, R09_,
errRel09, R92_, errRel92, R02_, errRel02, R38_, errRel38, R68_, errRel68, R35_,
errRel35] = self.post_904IC(
data, i, self.labNrs[i], self.tail_mat_cup, self.ThH_plus, self.blk, self.datablkm, self.UH_plus,
self.cps,
self.yield_U, self.R34_33, self.tail_mat, self.R35_33,
self.tail_mat_th, self.R30_29, self.yield_Th, self.mf48, self.mf36,
self.mf56, self.mf68, self.mf92, self.mf38, self.mf35,
self.mf43, self.mf45, self.mf09, self.mf29, self.mf34, self.mf58,
self.mf02) # measurement method: 234, 230, and 229 on SEM/RPQ
elif len(c3) == 2:
[R36_, errRel36, R58u_, errRel58u, R56_, errRel56, R43_, errRel43, R45_, errRel45, R48_, errRel48, R09_,
errRel09, R92_, errRel92, R02_, errRel02, R38_, errRel38, R68_, errRel68, R35_,
errRel35] = self.post_90IC(
data, i, self.labNrs[i], self.tail_mat_cup, self.ThH_plus, self.blk, self.gain, self.datablkm,
self.UH_plus,
self.cps, self.yield_U, self.R34_33, self.tail_mat, self.R35_33,
self.tail_mat_th, self.R30_29, self.yield_Th, self.mf48, self.mf36,
self.mf56, self.mf68, self.mf92, self.mf38, self.mf35,
self.mf43, self.mf45, self.mf09, self.mf29, self.mf34, self.mf58,
self.mf02) # measurement method: 230 and 229 on SEM/RPQ
elif len(c3) == 1:
[R36_, errRel36, R58u_, errRel58u, R56_, errRel56, R43_, errRel43, R45_, errRel45, R48_, errRel48, R09_,
errRel09, R92_, errRel92, R02_, errRel02, R38_, errRel38, R68_, errRel68, R35_,
errRel35] = self.post_0IC(
data, i, self.labNrs[i], self.tail_mat_cup, self.ThH_plus, self.blk, self.gain, self.datablkm,
self.UH_plus,
self.cps, self.yield_U, self.R34_33, self.tail_mat, self.R35_33,
self.tail_mat_th, self.tail_mat_th_cup, self.R30_29, self.yield_Th,
self.mf48, self.mf36, self.mf56, self.mf68, self.mf92, self.mf38, self.mf35,
self.mf43, self.mf45, self.mf09, self.mf29, self.mf34, self.mf58,
self.mf02) # measurement method: 230 on SEM/RPQ
else:
[R36_, errRel36, R58u_, errRel58u, R56_, errRel56, R43_, errRel43, R45_, errRel45, R48_, errRel48, R09_,
errRel09, R92_, errRel92, R02_, errRel02, R38_, errRel38, R68_, errRel68, R35_,
errRel35] = self.post_allcup(data, i, self.labNrs[i], self.tail_mat_cup, self.ThH_plus, self.blk,
self.gain,
self.datablkm, self.UH_plus, self.cps, self.yield_U, self.R34_33,
self.tail_mat, self.R35_33, self.tail_mat_th, self.tail_mat_th_cup,
self.R30_29, self.yield_Th, self.mf48,
self.mf36, self.mf56, self.mf68, self.mf92, self.mf38, self.mf35,
self.mf43, self.mf45, self.mf09, self.mf29, self.mf34, self.mf58,
self.mf02) # measurement method: all isotopes on cup
matrix_ratios[i, :] = [R36_, errRel36 * 100, R58u_, errRel58u * 100, R56_, errRel56 * 100, R43_,
errRel43 * 100, R45_, errRel45 * 100, R48_, errRel48 * 100, R09_, errRel09 * 100,
R92_, errRel92 * 100, R02_, errRel02 * 100]
matrix_ratios_add[i, :] = [R36_, errRel36 * 100, R58u_, errRel58u * 100, R56_, errRel56 * 100, R43_,
errRel43 * 100, R45_, errRel45 * 100, R48_, errRel48 * 100, R09_, errRel09 * 100,
R92_, errRel92 * 100, R02_, errRel02 * 100, R38_, errRel38 * 100, R68_,
errRel68 * 100, R35_, errRel35 * 100]
# reset labNrs
self.labNrs = DataFolderUtil.getLabNrsFromList(names_data)
# Save ratios to file
self.ratios = pd.DataFrame({'Lab. #': self.labNrs, 'dU234': (matrix_ratios[:, 10] * self.lambda234 / self.lambda238 - 1) * 1000,
'Error dU234 (abs.)': (matrix_ratios[:,
10] * self.lambda234 / self.lambda238) * matrix_ratios[:,
11] / 100,
'Ratio 233/236': matrix_ratios[:, 0], 'Error (%) 233/236': matrix_ratios[:, 1],
'Ratio 235/238': matrix_ratios[:, 2], 'Error (%) 235/238': matrix_ratios[:, 3],
'Ratio 235/236': matrix_ratios[:, 4], 'Error (%) 235/236': matrix_ratios[:, 5],
'Ratio 234/233': matrix_ratios[:, 6], 'Error (%) 234/233': matrix_ratios[:, 7],
'Ratio 234/235': matrix_ratios[:, 8], 'Error (%) 234/235': matrix_ratios[:, 9],
'Ratio 234/238': matrix_ratios[:, 10], 'Error (%) 234/238': matrix_ratios[:, 11],
'Ratio 230/229': matrix_ratios[:, 12], 'Error (%) 230/229': matrix_ratios[:, 13],
'Ratio 229/232': matrix_ratios[:, 14], 'Error (%) 229/232': matrix_ratios[:, 15],
'Ratio 230/232': matrix_ratios[:, 16], 'Error (%) 230/232': matrix_ratios[:, 17]},
index=names_data)
datacorr = self.ratios.copy()
writer = ExcelWriter(self.data_root_folder + '\\Ratios.xlsx', engine='xlsxwriter')
ExcelFormatter.format(writer, {'Ratios': datacorr})
writer.save()
# os.remove(data_root_folder + '\\Ratios_python_add.xlsx')
datacorr_add = pd.DataFrame(
{
'Lab. #': self.labNrs,
'Ratio 233/236': matrix_ratios_add[:, 0], 'Error (%) 233/236': matrix_ratios_add[:, 1],
'Ratio 235/238': matrix_ratios_add[:, 2], 'Error (%) 235/238': matrix_ratios_add[:, 3],
'Ratio 235/236': matrix_ratios_add[:, 4], 'Error (%) 235/236': matrix_ratios_add[:, 5],
'Ratio 234/233': matrix_ratios_add[:, 6], 'Error (%) 234/233': matrix_ratios_add[:, 7],
'Ratio 234/235': matrix_ratios_add[:, 8], 'Error (%) 234/235': matrix_ratios_add[:, 9],
'Ratio 234/238': matrix_ratios_add[:, 10], 'Error (%) 234/238': matrix_ratios_add[:, 11],
'Ratio 230/229': matrix_ratios_add[:, 12], 'Error (%) 230/229': matrix_ratios_add[:, 13],
'Ratio 229/232': matrix_ratios_add[:, 14], 'Error (%) 229/232': matrix_ratios_add[:, 15],
'Ratio 230/232': matrix_ratios_add[:, 16], 'Error (%) 230/232': matrix_ratios_add[:, 17],
'Ratio 233/238': matrix_ratios_add[:, 18], 'Error (%) 233/238': matrix_ratios_add[:, 19],
'Ratio 236/238': matrix_ratios_add[:, 20], 'Error (%) 236/238': matrix_ratios_add[:, 21],
'Ratio 233/235': matrix_ratios_add[:, 22], 'Error (%) 233/235': matrix_ratios_add[:, 23],
}, index=names_data
)
writer = ExcelWriter(self.data_root_folder + '\\Ratios_add.xlsx', engine='xlsxwriter')
ExcelFormatter.format(writer, {'Ratios': datacorr_add})
writer.save()
# Save tailing to file
writer = ExcelWriter(self.data_root_folder + '\\Tailing.xlsx', engine='xlsxwriter')
# Get Tailing in CPS / V_Uran or CPS / V_Thorium
dfUTail = self.uTailData.loc[['Tailing U SEM'],:] * self.cps * self.yield_U
dfThTail = self.thTailData.loc[['Tailing Th SEM'],:] * self.cps * self.yield_Th
dfUTail = pd.concat([pd.DataFrame([['[CPS/V_U]'] * len(dfUTail.columns)], columns=dfUTail.columns), dfUTail])
dfThTail = pd.concat([pd.DataFrame([['[CPS/V_Th]'] * len(dfThTail.columns)], columns=dfThTail.columns), dfThTail])
ExcelFormatter.format(writer, {'U-Tailing': dfUTail,
'Th-Tailing': dfThTail})
writer.save()
# Save intensitites to file
units = {
'Cycle': '',
'229Th': '[V]' if len(c3) not in [2, 3] else '[CPS]',
'230Th': '[V]' if len(c3) not in [1, 2, 3] else '[CPS]',
'232Th': '[V]',
'233U': '[V]',
'234U': '[V]' if len(c3) not in [3] else '[CPS]',
'235U': '[V]',
'236U': '[V]',
'238U': '[V]'
}
for labNr in self.intensities_dict:
df = self.intensities_dict[labNr]
medians = ['Median']
errs = ['Error (%)']
for col in df.columns[1:]:
_, _, mean, err = self.outliertest(df.loc[:, col])
medians.append(mean)
errs.append(err * 100)
# add units
df = pd.concat([df, pd.DataFrame([[''] * len(df.columns)], columns=df.columns)])
# add empty row
df = pd.concat([pd.DataFrame(units, index=['']), df])
# add median and errors
df = pd.concat([df, pd.DataFrame([medians, errs], columns=df.columns)])
self.intensities_dict[labNr] = df
writer = ExcelWriter(self.data_root_folder + '\\Intensities.xlsx', engine='xlsxwriter')
ExcelFormatter.format(writer, self.intensities_dict)
writer.save()
os.chdir(old_path)
def outliertest(self, X):
mean = None
std = None
if self.mean_option == 'mean':
mean = np.mean(X)
elif self.mean_option == 'median':
mean = np.median(X)
if self.dev_option == 'std':
std = np.std(X, ddof=1)
elif self.dev_option == 'mad':
std = median_abs_deviation(X)
elif self.dev_option == 'iqr':
std = iqr(X)
inRangeIndices = np.where(np.logical_and(X >= mean - 2 * std, X <= mean + 2 * std))[0]
X = X[inRangeIndices]
errX = 2 * np.std(X, ddof=1) / np.sqrt(len(X)) # 2 sigma SE
meanX = None
if self.mean_option == 'mean':
meanX = np.mean(X)
elif self.mean_option == 'median':
meanX = np.median(X)
errRelX = errX / meanX # 2 sigma relative error
return X, errX, meanX, errRelX