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 +
(0.05)**2 + (0.05)**2 +
(0.05)**2)
self.assertAlmostEqual(R, R_calculated)
self.assertAlmostEqual(sigma_R, sigma_calculated)
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:
N = txt['K']['peaks'][df['element1'][i]]
sigma_N = txt['K']['errors'][df['element1'][i]]
except:
pass
R, sigma_R = responseFactor(N, d, I, tempo, sigma_N)
linha_fatores = [df['element1'][i], R, sigma_R]
fatores.append(linha_fatores)
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