def combination_data(file_ob_list, file_names):
data = pd.read_csv(file_ob_list[0], header=None, sep='\t')
wave_number = data[0]
#data[1] = loren(wave_number,data[1])
data[1] = pre_treatment.pull_baseline_jx(data[1])
#plt.plot(data[1])
data = np.array(data).T
print("0/%d" % (len(file_ob_list)))
for i in range(1, len(file_ob_list)):
data_1 = pd.read_csv(file_ob_list[i], header=None, sep='\t')
#RI = np.array(loren(wave_number,data_1[1])).T
RI = pre_treatment.pull_baseline_jx(data[1])
data = np.vstack([data, RI])
print("%d/%d" % (i, len(file_ob_list)))
data = pd.DataFrame(data.T)
file_names = np.array(file_names)
w = "wave number"
file_names = np.hstack([w, file_names])
#print(file_names)
data.columns = file_names
print(data)
return data
def loren(w, r):
r = pre_treatment.pull_baseline_jx(r)
try:
_, RI = peak_decomposition.my_predict_fit(w, r, 20)
except:
try:
_, RI = peak_decomposition.my_predict_fit(w, r, 10)
except:
_, RI = peak_decomposition.my_predict_fit(w, r, 5)
RI = pre_treatment.pull_baseline_jx(RI + 1000)
RI = pd.DataFrame(RI)
return RI