try:
N = txt['K']['peaks'][df['element1'][i]]
sigma_N = txt['K']['errors'][df['element1'][i]]
except:
pass
#print(txt['K']['errors'][11])
# ler corrente e tempo dos arquivos csv
import shimadzu as sd
doc_csv = 'data/calibration/2010-10/csv/'
for j in range(len(serial)):
doc_csv += serial[j]
for j in range(len(pto_csv)):
doc_csv += pto_csv[j]
file_content = pathlib.Path(doc_csv).read_text()
It = sd.parseCsv(file_content)
I = It['current']
tempo = It['livetime']
# subtrair branco ??
# caculcar fator de resposta ponto a ponto
from responseFactor import responseFactor
R, sigma_R = responseFactor(N, d, I, tempo, sigma_N)
linha_fatores = [df['element1'][i], R, sigma_R]
fatores.append(linha_fatores)
#calcular fator de resposta para element2
d = df['density2'][i]
try:
}
# current * live time for each file
it1 = 1000 * 959
it2 = 1000 * 960
it3 = 1000 * 959
# all lists above indexed by file names
peaks = {}
errors = {}
irradiation_parameters = {}
# parser files
keys = csvs.keys() # or txts.keys()
for key in keys:
irradiation_parameters[key] = parseCsv(csvs[key])
txt_content = parseTxt(txts[key])
peaks[key] = txt_content['K']['peaks']
errors[key] = txt_content['K']['errors']
class blankCorrectionTest(unittest.TestCase):
def test_13(self):
# teste peak
testcase = blankCorrection(irradiation_parameters, peaks,
errors)['peaks_correction'][13]
calculed = (0 / it1 + 226 / it2 + 212 / it3) / 3
self.assertAlmostEqual(testcase, calculed)
# test error
testcase = blankCorrection(irradiation_parameters, peaks,
import sys
import math
sys.path.append('./lib/')
from responseFactor import responseFactor
from shimadzu import parseCsv
from winqxas import parseTxt
import numpy as np
# test data
micromatter = pathlib.Path(
'data/calibration/micromatter-table-iag.csv').read_text()
csv_34671 = pathlib.Path('data/calibration/2010-10/csv/34671.csv').read_text()
txt_34671 = pathlib.Path('data/calibration/2010-10/txt/34671.txt').read_text()
irradiation_parameters = parseCsv(csv_34671)
txt_content = parseTxt(txt_34671)
peaks = txt_content['K']['peaks']
errors = txt_content['K']['errors']
i = irradiation_parameters['current']
t = irradiation_parameters['livetime']
N = peaks[22]
sigma_N = errors[22]
class calculateResponseFactorTest(unittest.TestCase):
def test_Ti(self):
R, sigma_R = responseFactor(N, 49.4, i, t, sigma_N)
R_calculated = 454712 / (268 * 179 * 49.4)
sigma_calculated = R_calculated * np.sqrt((676 / 454712)**2 +
def test_livetime(self):
self.assertEqual(parseCsv(file_content)['livetime'],959)
def test_current(self):
self.assertEqual(parseCsv(file_content)['current'],1000)
def test_sample(self):
self.assertEqual(parseCsv(file_content)['sample'],"AFR390")
import pandas as pd
densidades = pd.read_csv('data/calibration/micromatter-table-iag.csv')
df = pd.DataFrame(densidades)
d = df['density1'][0]
# ler contagens do arquivos txt
import sys
import pathlib
sys.path.append('lib')
from winqxas import parseTxt
file_content = pathlib.Path('data/calibration/2010-10/txt/34662.txt').read_text()
txt = parseTxt(file_content)
N = txt['K']['peaks'][11]
#print(txt['K']['errors'][11])
# ler corrente e tempo dos arquivos csv
import shimadzu
file_content = pathlib.Path('data/calibration/2010-10/csv/34662.csv').read_text()
It = shimadzu.parseCsv(file_content)
I = It['current']
tempo = It['livetime']
# subtrair branco ??
# caculcar fator de resposta ponto a ponto
from responseFactor import responseFactor
R = responseFactor(N,d,I,tempo)
print(R)
# Ajustar polinômio