def order_of_calling(registry_list, file_list, event_list, process_list):
logging.info("\n---SCANNER CONTROL---")
logging.info("Started calling scanners with given input")
logging.info("Starting registry scan")
rreg.main(registry_list)
logging.info("Starting process scanner")
pss.main(process_list)
logging.info("Starting event log scanner")
els.EventLogScanner(event_list)
logging.info("Starting file scanner")
fs.input_check(file_list[0], file_list[1])
output.main()
print("")
input("Press ENTER to quit")
logging.info("Exiting program")
sys.exit(0)
time1D = np.sqrt(1. / opt_t)
freq_error, time_error = create_acf.err1Dfit(diff, length, rfi_frac,
freq1D, time1D, opt_f, cov_f,
opt_t, cov_t)
params, acffit_2D = create_acf.acf2Dfit(acf_mid, acf_norm, middle_f,
middle_t, intensity, opt_f, opt_t,
mhzperbin, minperbin, f_end, t_end)
C_1, C_2, C_3, C_0 = params
Drift_rate = -(C_2 / (2 * C_3))
slope_visibility = C_2 / np.sqrt(np.abs(4 * C_1 * C_3))
#print('%.f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.6f %.6f' % (mjd_start,freq1D,freq_error,time1D,time_error,diff,length/60,minFreq,maxFreq,Drift_rate,slope_visibility))
secondary, sec_axes = create_sec.main(intensity, args, minFreq, maxFreq,
diff, archive, name, length,
mhzperbin, minperbin)
output.main(args, archive, intensity, minFreq, maxFreq, length,
extrapolx_f, extrapoly_f, fitxplot_f, extrapolx_t, extrapoly_t,
fitxplot_t, freq1D, time1D, freq_error, time_error,
midACF_freq, midACF_time, acf_mid, opt_f, opt_t, secondary,
acffit_2D, f_end, t_end, mhzperbin, minperbin, Drift_rate,
slope_visibility, sec_axes)
#if args.plot:
#output.main(intensity,minFreq, maxFreq,mjd_start,mjd_end,site,ra,dec,name,length,diff,midfreq,args,archive, extrapolx_f,extrapoly_f, fitxplot_f, extrapolx_t, extrapoly_t, fitxplot_t, freq1D, time1D, freq_error,time_error, midACF_freq, midACF_time,middle_f, middle_t,acf_mid,opt_f,opt_t,secondary_log, acffit_2D,f_end,t_end,mhzperbin,minperbin,Drift_rate,slope_visibility,sec_axes)
#plt.imshow(intensity,aspect='auto',extent=[0,length/60,minFreq,maxFreq],vmax=1, vmin=-0.1,cmap='jet', interpolation='None')
#plt.imshow(acf_mid,aspect='auto')
#plt.xlabel("time(mins)")
#plt.ylabel("frequency(Mhz)")
#plt.colorbar(use_gridspec=True)
#plt.savefig('%s_.png' % archive )
#plt.show()
import requests
import json
body = {
"method": "neuralnetwork",
"type": "regression",
"kernel": "linear",
"inputs": 2,
"outputs": 2
}
body = json.dumps(body)
resp = requests.post(
"https://5xpj1pu5y8.execute-api.ap-south-1.amazonaws.com/dev/generator",
data=body)
body = resp.json()
print(body)
# print(body["input"]["body"])
# print(body["code"])
with open('output.py', 'w+') as fl:
fl.write(body["code"])
fl.close()
import output
output.main()
def pagemain(self, urls, page=1):
soup = self.downloading(urls)
datas = self.get_data(soup)
# 生成html文件
output.main(datas, page)
def analyse():
print("Please Wait...")
combine(Area.get())
output.main(Area.get())
print("Thank You for waiting. Your data has been analysed.")