Delete todo_r.py

code/todo_r.py

-# coding=utf-8
-import warnings
-def fxn():
-    warnings.warn("deprecated", DeprecationWarning)
-
-with warnings.catch_warnings():
-    warnings.simplefilter("ignore")
-    fxn()
-
-import sys
-input1 = sys.argv[1]
-print(input1)
-# insert at 1, 0 is the script path (or '' in REPL)
-sys.path.insert(1, '../Functions')
-
-from imports import *
-from constants import *
-from functions import *
-
-
-path = input1
-
-AA = []
-#for p in path:
-#print(p)
-col = read_col_names(path)
-df1 = pd.read_csv(path,skiprows=1,header=None,delimiter=r"\s+",names=col)
-# -- quitar errores nulos para evitar problemas con leastsq
-df1 = df1[df1["err_v(km/s)"]!=0]
-print(len(df1))
-
-for labe in ['ra','r','a']:
-    if labe == 'ra':
-        gal = df1.copy()
-    if labe == 'r':
-        dfr = df1.copy()[df1.copy()['Side'] == 'r']
-        gal = dfr
-    if labe == 'a':
-        dfa = df1.copy()[df1.copy()['Side'] == 'a']
-        gal = dfa
-
-    AA.append((path.split("/")[-1][:-4],determine(gal,labe)))
-best_parmeters = return_values(AA)
-
-grafica_data_ybestfit(path, dfr, best_parmeters, ss="r")
-
-y_pred_NFW_bestfit = vnfw(best_parmeters["M200_NFW(Msun)"][1], best_parmeters["c_NFW"][1], dfr["r(kpc)"]*1000/r200(best_parmeters["M200_NFW(Msun)"][1]), G = G, H0 = H0)
-y_pred_ISO_bestfit = viso((best_parmeters["rho0_ISO(Msun/pc3)"][1]*best_parmeters["Rc_ISO(pc)"][1]**2) , dfr["r(kpc)"]*1000/abs(best_parmeters["Rc_ISO(pc)"][1]), G = G)
-
-
-chi2_NFW_bestfit = (((y_pred_NFW_bestfit-dfr["v(km/s)"])/(dfr["err_v(km/s)"]))**2).sum()
-chi2_ISO_bestfit = (((y_pred_ISO_bestfit-dfr["v(km/s)"])/(dfr["err_v(km/s)"]))**2).sum()
-print("Mejor $\chi^2$ para \nNFW: {} \nISO: {}".format(chi2_NFW_bestfit,chi2_ISO_bestfit))
-
-chi2_NFW_bestfit_red = chi2_NFW_bestfit / (len(dfr)-2)
-chi2_ISO_bestfit_red = chi2_ISO_bestfit / (len(dfr)-2)
-print("Mejor $\chi^2$ reducido para \nNFW: {} \nISO: {}".format(chi2_NFW_bestfit_red,chi2_ISO_bestfit_red))
-
-print("La probabilidad cumulativa de encontrar un $\chi^2$ menor para NFW es: {}".format(scipy.stats.chi2.cdf(chi2_NFW_bestfit,len(dfr)-2)))
-print("La probabilidad cumulativa de encontrar un $\chi^2$ menor para ISO es: {}".format(scipy.stats.chi2.cdf(chi2_ISO_bestfit,len(dfr)-2)))
-
-
-combs_NFW_ra = parameters_range(best_parmeters, i = 0, ss = "r")
-combs_ISO_ra = parameters_range(best_parmeters, i = 1, ss = "r")
-
-chis_NFW_ra = chi2_parameters_range(dfr, combs_NFW_ra, i = 0)
-chis_ISO_ra = chi2_parameters_range(dfr, combs_ISO_ra, i = 1)
-
-ch2_inter_NFW_  = chi2_intervals(best_parmeters, dfr, chis_NFW_ra, i = 0, ss = "r")
-ch2_inter_ISO_ = chi2_intervals(best_parmeters, dfr, chis_ISO_ra, i = 1, ss = "r")
-
-para_range_NFW = []
-para_range_ISO = []
-
-for kkk in range(1,5):
-    para_range_NFW.append(parameters_each_interval(combs_NFW_ra, ch2_inter_NFW_, kk = kkk))
-    para_range_ISO.append(parameters_each_interval(combs_ISO_ra, ch2_inter_ISO_, kk = kkk))
-
-
-# -- índice dentro del arreglo de chi^2s que es el nuevo mínimo para NFW e ISO
-# newi_min_ch2_NFW = [i for i in ch2_inter_NFW_[0] if i[1] == min(list(zip(*ch2_inter_NFW_[0]))[1])][0][0]
-# newi_min_ch2_ISO = [i for i in ch2_inter_ISO_[0] if i[1] == min(list(zip(*ch2_inter_ISO_[0]))[1])][0][0]
-
-# new_para_min_ch2_NFW = combs_NFW_ra[newi_min_ch2_NFW]
-# new_para_min_ch2_ISO = combs_ISO_ra[newi_min_ch2_ISO]
-
-# new_para_min_ch2_b = np.concatenate((new_para_min_ch2_NFW,new_para_min_ch2_ISO))
-
-# print("Los nuevos parámetros para NFW son: {} con un $\chi^2 = {}$"
-#       .format(new_para_min_ch2_NFW, chis_NFW_ra[newi_min_ch2_NFW]))
-
-# print("Los nuevos parámetros para ISO son: {} con un $\chi^2 = {}$"
-#       .format(new_para_min_ch2_ISO, chis_ISO_ra[newi_min_ch2_ISO]))
-
-# print("La probabilidad cumulativa de encontrar un $\chi^2$ menor al nuevo para NFW es: {}".format(scipy.stats.chi2.cdf(chis_NFW_ra[newi_min_ch2_NFW]*(len(df1)-2),len(df1)-2)))
-# print("La probabilidad cumulativa de encontrar un $\chi^2$ menor al nuevo para ISO es: {}".format(scipy.stats.chi2.cdf(chis_ISO_ra[newi_min_ch2_ISO]*(len(df1)-2),len(df1)-2)))
-
-
-plot_ellipse_confidence_interval(path,para_range_NFW, best_parmeters, ss = "r", i = 0 )
-
-plot_ellipse_confidence_interval(path,para_range_ISO, best_parmeters, ss = "r", i = 1 )
-
-#grafica_data_ybestfit(path, df1, best_parmeters, ss="ra")
-
-file1 = open("results.txt","a")
-file1.write("\n{}&{}&{:.2f}&{:.2f}&{:.2f}&{:.2f}&{:.2f}&{:.2f}&".format(
-path.split("/")[-1][:-4][5:]
-,len(dfr)
-,best_parmeters["M200_NFW(Msun)"][1]
-,best_parmeters["c_NFW"][1]
-,chi2_NFW_bestfit_red
-,abs(best_parmeters["rho0_ISO(Msun/pc3)"][1])
-,abs(best_parmeters["Rc_ISO(pc)"][1])
-,chi2_ISO_bestfit_red))
-file1.close()