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CEREAL_Github.py
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#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Thu Jun 11 19:22:25 2020
@author: Alice Perez-Blanco
"""
#---------------------------------------------
### Packages ###
import numpy as np
import scipy
import matplotlib.pyplot as plt
import warnings
import time
from astropy.io.votable import parse_single_table
import os
#---------------------------------------------
### Path to save the data and plots ###
# Add the directorios where the figures and data would be save
pathfig = '/PATH/'
pathdata = '/PATH/'
path_inputdata= '/PATH/' # This should contain the diretory where the VOTABLE (Gaia data) is save
#---------------------------------------------
### Data input ###
# This file have to contain the general information for each star. This would be the input for the known value of each target.
ClusterInfo = scipy.genfromtxt(path_inputdata + 'FILE.txt', dtype='string',comments='#')
# This file should be a list with the input data of each target for GAIA DR2, or other.
Clusterdata = scipy.genfromtxt(path_inputdata + 'FILE.txt', dtype='string',comments='#')
#---------------------------------------------
### Variables ###
then = time.time() # Time before the operations start
warnings.filterwarnings('ignore') # Do not print the warnings on scream
NumStar = 1 # Print on scream the number of stars on the list and which is runnig at the moment
ClusterNum,Star,A1,A2,A3,A4,A5,A6,A7,A8,A9,B1 = [],[],[],[],[],[],[],[],[],[],[],[] # Empty arrays to created a final file from the clusters clasification
#---------------------------------------------#---------------------------------------------
### Code body ###
for z in range(len(ClusterInfo)):
print '\033[1;31m'
print 'Star '+ '\t' + str(NumStar) + '\t' + 'of' + '\t' + str(len(ClusterInfo))
print '\033[1;30m \n'
# This should be the basic information you should know about the target
# and should be on the file save in 'ClusterInfo'
info = scipy.genfromtxt(ClusterInfo[z], dtype='string',comments='#')
star_name = info[0]
starRA = info[1].astype(float)
starDEC = info[2].astype(float)
spt = info[3]
para_know = info[5].astype(float)
parae_know = info[6].astype(float)
pmra_know = info[7].astype(float)
pmrae_know = info[8].astype(float)
pmdec_know = info[9].astype(float)
pmdece_know = info[10].astype(float)
g_mag_know = info[11].astype(float)
bp_rp_mag_know = info[12].astype(float)
dist_exp = info[13].astype(float)
Mgstars = g_mag_know + 5 * np.log10(para_know) - 10
'''
### The following inputs would depends from the data that would be analysis ###
#From GAIA: this should be VOTABLE files
VOTGaia = parse_single_table(path_inputdata + Clusterdata[z]) #, 'r'
vodat_gaia = VOTGaia.array
IDGaia = np.array(VOTGaia.array['designation'])
ID = VOTGaia.array['source_id']
ra = VOTGaia.array['ra']
ra_error = VOTGaia.array['ra_error']
dec = VOTGaia.array['dec']
dec_error = VOTGaia.array['dec_error']
para = VOTGaia.array['parallax']
para_error = VOTGaia.array['parallax_error']
pmra = VOTGaia.array['pmra']
pmra_error = VOTGaia.array['pmra_error']
pmdec = VOTGaia.array['pmdec']
pmdec_error = VOTGaia.array['pmdec_error']
g_mag = VOTGaia.array['phot_g_mean_mag']
bp_rp_mag = VOTGaia.array['bp_rp']
bp_g_mag = VOTGaia.array['bp_g']
g_rp_mag = VOTGaia.array['g_rp']
'''
# From a txt file: This should be an ASCII file
data = scipy.genfromtxt(path_inputdata + Clusterdata[z], dtype='string',comments='#') #
ID = data[:,0].astype(float)
ra = data[:,1].astype(float)
ra_error = data[:,2].astype(float)
dec = data[:,3].astype(float)
dec_error = data[:,4].astype(float)
para = data[:,5].astype(float)
para_error = data[:,6].astype(float)
pmra = data[:,7].astype(float)
pmra_error = data[:,8].astype(float)
pmdec = data[:,9].astype(float)
pmdec_error = data[:,10].astype(float)
g_mag = data[:,11].astype(float)
bp_rp_mag = data[:,12].astype(float)
bp_g_mag = data[:,13].astype(float)
g_rp_mag = data[:,14].astype(float)
MG = g_mag + 5 * np.log10(para) - 10
print '\033[1;34m'
print("Object name = {0}".format(star_name))
print("Initial number of objects = {0}".format(len(para)))
print("Known value of PARALLAX and error = {:.3f} +/- {:.3f}[mas]".format(para_know,parae_know))
print("Known value of PMRA and error = {:.3f} +/- {:.3f}[mas/yr]".format(pmra_know,pmrae_know))
print("Known value of PMDEC and error = {:.3f} +/- {:.3f}[mas/yr]".format(pmdec_know,pmdece_know))
print '\033[1;30m \n'
# Creating individual folders for each star
filename = str(star_name) # This would be the name for the new folder to save the data for each star
results_dir = os.path.join(pathfig, filename) # This join the original path, to created the new one
if not os.path.isdir(results_dir): # This line and next one would check if the folder have been alreary created or not
os.makedirs(results_dir)
## Building the Density profile
starRA_list = np.linspace(starRA, starRA, len(ra))
starDEC_list = np.linspace(starDEC,starDEC, len(dec))
DeltaRA = np.cos(dec/(180/np.pi)) * (ra - (starRA_list))
DeltaDEC = dec - (starDEC_list)
Distance = np.sqrt(np.power(DeltaRA,2) + np.power(DeltaDEC,2))
count, bins, bars = plt.hist(Distance,30, color='white',histtype=u'barstacked', rwidth=0.8, lw=2, alpha=0.8)
plt.close()
newbin = bins - np.min(Distance)
dist_lower = np.linspace(np.min(newbin), np.max(newbin), len(count))
diff = np.diff(dist_lower)
high = np.linspace(np.min(diff), np.max(diff), len(count))
dist_high = dist_lower + high
ringarea = np.pi * np.power(dist_high,2) - np.pi * np.power(dist_lower,2)
density = count / ringarea
count_err = np.sqrt(count)
density_err = count_err / ringarea
# Figures
plt.figure(figsize=(12.5,6.5))
## Parallax
plt.subplot(2,4,1)
count_para, bins_para, bars_para = plt.hist(para, 40, color='greenyellow',histtype=u'barstacked', rwidth=0.8, lw=2, alpha=0.8)
plt.axvline(x=para_know, color='k', linestyle='--', lw=1)
plt.xlabel(r'$\varpi $',fontsize=14)
plt.ylabel('Number objects',fontsize=11)
plt.tick_params(axis='both', which='major', labelsize=11)
plt.subplots_adjust(hspace=0.5,wspace=0.6)
## Proper motion RA
plt.subplot(2,4,2)
count_pmra, bins_pmra, bars_pmra =plt.hist(pmra, 40, color='aqua',histtype=u'barstacked', rwidth=0.8, lw=2, alpha=0.8)
plt.axvline(x=pmra_know, color='k', linestyle='--', lw=1)
plt.xlabel(r'$\mu_{\alpha*} $',fontsize=14)
plt.tick_params(axis='both', which='major', labelsize=11)
plt.subplots_adjust(hspace=0.5,wspace=0.6)
## Proper motion DEC
plt.subplot(2,4,3)
count_pmdec, bins_pmdec, bars_pmdec = plt.hist(pmdec,40, color='royalblue',histtype=u'barstacked', rwidth=0.8, lw=2, alpha=0.8)
plt.axvline(x=pmdec_know, color='k', linestyle='--', lw=1)
plt.xlabel(r'$\mu_{\delta} $',fontsize=14)
plt.tick_params(axis='both', which='major', labelsize=11)
plt.subplots_adjust(hspace=0.5,wspace=0.6)
#Density profile
plt.subplot(2,4,4)
plt.plot(dist_high,density, '--.', color='midnightblue', alpha=0.8)
plt.errorbar(dist_high,density, yerr=density_err, fmt='.', color='midnightblue', alpha=0.8, lw=.8)
plt.xlabel('Distance (deg) ',fontsize=11)
plt.ylabel('Density (deg$^{-2}$)',fontsize=11)
plt.tick_params(axis='both', which='major', labelsize=11)
plt.subplots_adjust(hspace=0.5,wspace=0.6)
## Spatial distribution
plt.subplot(2,4,5)
sc=plt.scatter(ra,dec, c=para, marker='o', s=(para*4.5), cmap= 'YlGnBu', norm=None, vmin=np.min(para), vmax=np.max(para), alpha=None, linewidths=None, verts=None, edgecolors=None, hold=None, data=None)
plt.plot(starRA,starDEC,'*',ms=6,color='darkviolet',alpha=0.65)
plt.xlabel('RA',fontsize=11)
plt.ylabel('DEC',fontsize=11)
plt.gca().invert_xaxis()
plt.colorbar(sc).remove()
plt.tick_params(axis='both', which='major', labelsize=11)
plt.subplots_adjust(wspace=0.5)
## Proper motion distribution
plt.subplot(2,4,6)
sc=plt.scatter(pmra,pmdec, c=para, marker='o',s=(para*4.5), cmap= 'YlGnBu', zorder= 35, norm=None, vmin=np.min(para), vmax=np.max(para), alpha=None, linewidths=None, verts=None, edgecolors=None, hold=None, data=None)
plt.plot(pmra_know,pmdec_know,'*',ms=6, color='darkviolet',alpha=0.65)
plt.colorbar(sc).remove()
plt.xlabel(r'$\mu_{\alpha*} $',fontsize=14)
plt.ylabel(r'$\mu_{\delta} $',fontsize=14)
plt.tick_params(axis='both', which='major', labelsize=11)
plt.subplots_adjust(wspace=0.5)
## CMD
plt.subplot(2,4,7)
sc=plt.scatter(bp_rp_mag,MG, c=para, marker='o',s=(para*4.5), cmap= 'YlGnBu', zorder= 35,norm=None, vmin=np.min(para), vmax=np.max(para), alpha=None, linewidths=None, verts=None, edgecolors=None, hold=None, data=None)
plt.plot(bp_rp_mag_know,Mgstars,'*',ms=6,color='darkviolet',alpha=0.65)
plt.colorbar(sc, aspect=25).set_label(label=r'$\varpi $', size=12)
plt.xlabel('G$_{Bp}$ - G$_{Rp}$',fontsize=11)
plt.ylabel('M$_{G}$',fontsize=11)
plt.gca().invert_yaxis()
plt.tick_params(axis='both', which='major', labelsize=11)
plt.subplots_adjust(wspace=0.5)
# Information of the target at this stage
plt.subplot(2,4,8)
plt.axis([0, 10, 0, 10])
plt.text(0.5, 9, "Object= {0}".format(star_name), wrap=True, fontsize=10)
plt.text(0.5, 8, "Number of objects= {0}".format(len(para)), wrap=True, fontsize=10)
plt.text(0.5, 6, "Known PARALLAX= {:.2f} +/- {:.2f} [mas]".format(para_know,parae_know), wrap=True, fontsize=10)
plt.text(0.5, 5, "Known PMRA= {:.2f} +/- {:.2f} [mas/yr]".format(pmra_know,pmrae_know), wrap=True, fontsize=10)
plt.text(0.5, 4, "Known PMDEC= {:.2f} +/- {:.2f} [mas/yr]".format(pmdec_know,pmdece_know), wrap=True, fontsize=10)
plt.text(0.5, 2, "Mean PARALLAX= {:.2f} [mas]".format(np.mean(para)) , wrap=True, fontsize=10)
plt.text(0.5, 1, "Mean PMRA= {:.2f} [mas/yr]".format(np.mean(pmra)), wrap=True, fontsize=10)
plt.text(0.5, 0, "Mean PMDEC= {:.2f} [mas/yr]".format(np.mean(pmdec)), wrap=True, fontsize=10)
plt.setp(plt.gca(), frame_on=False, xticks=(), yticks=())
plt.subplots_adjust(wspace=0.6)
plt.savefig(results_dir + '/InputData_' + str(star_name) + '.pdf')
plt.tight_layout(h_pad=1.2, w_pad=1.2)
plt.show()
plt.close()
## Started the selection of the data. First parameter: Parallax
again = 'y'
while again == 'y':
IDGaia_l,ID_l,ra_l,rae_l,dec_l,dece_l,para_l,parae_l,pmra_l,pmrae_l,pmdec_l,pmdece_l,gmag_l,BpRp_l,BpG_l, GRp_l=[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[]
print '\033[1;35m'
print ("Make your selection!\n")
print '\033[1;30m'
# Define the range to select the data
para_lower = input("PARALLAX RANGE>=")
para_upper = input("PARALLAX RANGE<=")
for i in range(len(ID)):
if para[i]>=para_lower and para[i]<=para_upper:
IDGaia_l.append(IDGaia[i])
ID_l.append(ID[i])
ra_l.append(ra[i])
rae_l.append(ra_error[i])
dec_l.append(dec[i])
dece_l.append(dec_error[i])
para_l.append(para[i])
parae_l.append(para_error[i])
pmra_l.append(pmra[i])
pmrae_l.append(pmra_error[i])
pmdec_l.append(pmdec[i])
pmdece_l.append(pmdec_error[i])
gmag_l.append(g_mag[i])
BpRp_l.append(bp_rp_mag[i])
BpG_l.append(bp_g_mag[i])
GRp_l.append(g_rp_mag[i])
Mg_l = gmag_l + 5*np.log10(para_l) - 10
mu_para,mu_pmra,mu_pmdec = np.mean(para_l),np.mean(pmra_l),np.mean(pmdec_l)
# General information before the selection
print '\033[1;34m'
print("Number of objects in this selection = {0}".format(len(para_l)))
print '\033[1;30m'
print '\033[1;34m'
print("Known value of PARALLAX = {0:.3f}[mas]".format(para_know))
print("Known value of PMRA = {0:.3f}[mas/yr]".format(pmra_know))
print("Known value of PMDEC = {0:.3f}[mas/yr]".format(pmdec_know))
print '\033[1;30m'
print '\033[1;34m'
print("Mean value of PARALLAX = {0:.3f} [mas]".format(mu_para))
print("Mean value of PMRA = {0:.3f} [mas/yr]".format(mu_pmra))
print("Mean value of PMDEC = {0:.3f} [mas/yr]".format(mu_pmdec))
print '\033[1;30m'
starRA_list0 = np.linspace(starRA, starRA, len(ra_l))
starDEC_list0 = np.linspace(starDEC,starDEC, len(dec_l))
DECresult0 = np.asarray(dec_l)
DeltaRA0 = np.cos(DECresult0/(180/np.pi)) * (ra_l - (starRA_list0))
DeltaDEC0 = dec_l - (starDEC_list0)
Distance0 = np.sqrt(np.power(DeltaRA0,2) + np.power(DeltaDEC0,2))
count0, bins0, bars0 = plt.hist(Distance0, 30, color='white',histtype=u'barstacked', rwidth=0.8, lw=2, alpha=0.8)
plt.close()
newbin0 = bins0 - np.min(Distance0)
dist_lower0 = np.linspace(np.min(newbin0), np.max(newbin0), len(count0))
diff0 = np.diff(dist_lower0)
high0 = np.linspace(np.min(diff0), np.max(diff0), len(count0))
dist_high0 = dist_lower0 + high0
ringarea0 = np.pi * np.power(dist_high0,2) - np.pi * np.power(dist_lower0,2)
density0 = count0 / ringarea0
count_err0 = np.sqrt(count0)
density_err0 = count_err0 / ringarea0
plt.figure(figsize=(12.5,6.5))
## Parallax
plt.subplot(2,4,1)
count_para0, bins_para0, bars_para0 = plt.hist(para_l, 35, color='greenyellow',histtype=u'barstacked', rwidth=0.8, lw=2, alpha=0.8)
plt.axvline(x=para_know, color='k', linestyle='--', lw=1)
plt.xlabel(r'$\varpi $',fontsize=14)
plt.ylabel('Number objects',fontsize=11)
plt.tick_params(axis='both', which='major', labelsize=11)
plt.subplots_adjust(hspace=0.5,wspace=0.6)
## Proper motion RA
plt.subplot(2,4,2)
count_pmra0, bins_pmra0, bars_pmra0 = plt.hist(pmra_l, 35, color='aqua',histtype=u'barstacked', rwidth=0.8, lw=2, alpha=0.8)
plt.axvline(x=pmra_know, color='k', linestyle='--', lw=1)
plt.xlabel(r'$\mu_{\alpha*} $',fontsize=14)
plt.tick_params(axis='both', which='major', labelsize=11)
plt.subplots_adjust(hspace=0.5,wspace=0.6)
## Proper motion DEC
plt.subplot(2,4,3)
count_pmdec0, bins_pmdec0, bars_pmdec0 = plt.hist(pmdec_l, 35, color='royalblue',histtype=u'barstacked', rwidth=0.8, lw=2, alpha=0.8)
plt.axvline(x=pmdec_know, color='k', linestyle='--', lw=1)
plt.xlabel(r'$\mu_{\delta} $',fontsize=14)
plt.tick_params(axis='both', which='major', labelsize=11)
plt.subplots_adjust(hspace=0.5,wspace=0.6)
## Density profile
plt.subplot(2,4,4)
plt.plot(dist_high0,density0, '--.', color='midnightblue', alpha=0.8)
plt.errorbar(dist_high0,density0, yerr=density_err0, fmt='.', color='midnightblue', alpha=0.8, lw=.8)
plt.xlabel('Distance (deg) ',fontsize=11)
plt.ylabel('Density (deg$^{-2}$)',fontsize=11)
plt.tick_params(axis='both', which='major', labelsize=11)
plt.subplots_adjust(hspace=0.5,wspace=0.6)
## Spatial distribution
plt.subplot(2,4,5)
sc=plt.scatter(ra_l,dec_l, c=para_l, marker='o', s=(np.array(para_l)*4.5) ,cmap= 'YlGnBu', norm=None, vmin=np.min(para_l), vmax=np.max(para_l), alpha=None, linewidths=None, verts=None, edgecolors=None, hold=None, data=None)
plt.plot(starRA,starDEC,'*',ms=6,color='darkviolet',alpha=0.65)
plt.xlabel('RA',fontsize=11)
plt.ylabel('DEC',fontsize=11)
plt.gca().invert_xaxis()
plt.colorbar(sc).remove()
plt.tick_params(axis='both', which='major', labelsize=11)
plt.subplots_adjust(wspace=0.5)
## Proper motion distribution
plt.subplot(2,4,6)
sc=plt.scatter(pmra_l,pmdec_l, c=para_l, marker='o',s=(np.array(para_l)*4.5), cmap= 'YlGnBu', zorder= 35, norm=None, vmin=np.min(para_l), vmax=np.max(para_l), alpha=None, linewidths=None, verts=None, edgecolors=None, hold=None, data=None)
plt.plot(pmra_know,pmdec_know,'*',ms=6, color='darkviolet',alpha=0.65)
plt.colorbar(sc).remove()
plt.xlabel(r'$\mu_{\alpha} $',fontsize=14)
plt.ylabel(r'$\mu_{\delta} $',fontsize=14)
plt.tick_params(axis='both', which='major', labelsize=11)
plt.subplots_adjust(wspace=0.5)
## CMD
plt.subplot(2,4,7)
sc=plt.scatter(BpRp_l,Mg_l, c=para_l, marker='o',s=(np.array(para_l)*4.5), cmap= 'YlGnBu', zorder= 35,norm=None, vmin=np.min(para_l), vmax=np.max(para_l), alpha=None, linewidths=None, verts=None, edgecolors=None, hold=None, data=None)
plt.plot(bp_rp_mag_know,Mgstars,'*',ms=6,color='darkviolet',alpha=0.65)
plt.colorbar(sc, aspect=25).set_label(label=r'$\varpi $', size=10)
plt.xlabel('G$_{Bp}$ - G$_{Rp}$',fontsize=11)
plt.ylabel('M$_{G}$',fontsize=11)
plt.gca().invert_yaxis()
plt.tick_params(axis='both', which='major', labelsize=12)
plt.subplots_adjust(wspace=0.5)
# Information of the target at this stage
plt.subplot(2,4,8)
plt.axis([0, 10, 0, 10])
plt.text(0.5, 9, "Object= {0}".format(star_name), wrap=True, fontsize=10)
plt.text(0.5, 8, "Number of objects= {0}".format(len(para_l)), wrap=True, fontsize=10)
plt.text(0.5, 6, "Known PARALLAX= {:.2f} +/- {:.2f} [mas]".format(para_know,parae_know), wrap=True, fontsize=10)
plt.text(0.5, 5, "Known PMRA= {:.2f} +/- {:.2f} [mas/yr]".format(pmra_know,pmrae_know), wrap=True, fontsize=10)
plt.text(0.5, 4, "Known PMDEC= {:.2f} +/- {:.2f} [mas/yr]".format(pmdec_know,pmdece_know), wrap=True, fontsize=10)
plt.text(0.5, 2, "Mean PARALLAX = {:.2f} [mas]".format(mu_para) , wrap=True, fontsize=10)
plt.text(0.5, 1, "Mean PMRA= {:.2f} [mas/yr]".format(mu_pmra), wrap=True, fontsize=10)
plt.text(0.5, 0, "Mean PMDEC= {:.2f} [mas/yr]".format(mu_pmdec), wrap=True, fontsize=10)
plt.setp(plt.gca(), frame_on=False, xticks=(), yticks=())
plt.subplots_adjust(wspace=0.6)
plt.savefig(results_dir + '/PARALLAX_' + str(star_name) + '[' +str(para_lower)+ '_' + str(para_upper)+ ';' +str(len(para_l))+ ']' + '.pdf')
plt.tight_layout(h_pad=1.2, w_pad=1.2)
plt.show()
plt.close()
again=str(input('Choose a new Parallax range? (y/n):'))
## Started the selection of another parameter: Proper motion in RA
again='y'
while again=='y':
IDGaia1_l,ID1_l,ra1_l,rae1_l,dec1_l,dece1_l,para1_l,parae1_l,pmra1_l,pmrae1_l,pmdec1_l,pmdece1_l,gmag1_l,BpRp1_l,BpG1_l, GRp1_l=[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[]
print '\033[1;35m'
print ("Make your selection!\n")
print '\033[1;30m'
# Define the range to select the data
pmra_lower = input("PMRA RANGE >=")
pmra_upper = input("PMRA RANGE<=")
for i in range(len(ID_l)):
if pmra_l[i]>=pmra_lower and pmra_l[i]<=pmra_upper:
IDGaia1_l.append(IDGaia_l[i])
ID1_l.append(ID_l[i])
ra1_l.append(ra_l[i])
rae1_l.append(rae_l[i])
dec1_l.append(dec_l[i])
dece1_l.append(dece_l[i])
para1_l.append(para_l[i])
parae1_l.append(parae_l[i])
pmra1_l.append(pmra_l[i])
pmrae1_l.append(pmrae_l[i])
pmdec1_l.append(pmdec_l[i])
pmdece1_l.append(pmdece_l[i])
gmag1_l.append(gmag_l[i])
BpRp1_l.append(BpRp_l[i])
BpG1_l.append(BpG_l[i])
GRp1_l.append(GRp_l[i])
Mg1_l = gmag1_l + 5*np.log10(para1_l) - 10
mu_para1,mu_pmra1,mu_pmdec1 = np.mean(para1_l),np.mean(pmra1_l),np.mean(pmdec1_l)
print '\033[1;34m'
print("Number of objects in this selection = {0}".format(len(para1_l)))
print '\033[1;30m'
print '\033[1;34m'
print("Known value of PARALLAX = {0:.3f}[mas]".format(para_know))
print("Known value of PMRA = {0:.3f}[mas/yr]".format(pmra_know))
print("Known value of PMDEC = {0:.3f}[mas/yr]".format(pmdec_know))
print '\033[1;30m'
print '\033[1;34m'
print("Mean value of PARALLAX = {0:.3f} [mas]".format(mu_para1))
print("Mean value of PMRA = {0:.3f} [mas/yr]".format(mu_pmra1))
print("Mean value of PMDEC = {0:.3f} [mas/yr]".format(mu_pmdec1))
print '\033[1;30m'
starRA_list1 = np.linspace(starRA, starRA, len(ra1_l))
starDEC_list1 = np.linspace(starDEC,starDEC, len(dec1_l))
DECresult1 = np.asarray(dec1_l)
DeltaRA1 = np.cos(DECresult1/(180/np.pi)) * (ra1_l - (starRA_list1))
DeltaDEC1 = dec1_l - (starDEC_list1)
Distance1 = np.sqrt(np.power(DeltaRA1,2) + np.power(DeltaDEC1,2))
count1, bins1, bars1 = plt.hist(Distance1, 30,color='white',histtype=u'barstacked', rwidth=0.8, lw=2, alpha=0.8)
plt.close()
newbin1 = bins1 - np.min(Distance1)
dist_lower1 = np.linspace(np.min(newbin1), np.max(newbin1), len(count1))
diff1 = np.diff(dist_lower1)
high1 = np.linspace(np.min(diff1), np.max(diff1), len(count1))
dist_high1 = dist_lower1 + high1
ringarea1 = np.pi * np.power(dist_high1,2) - np.pi * np.power(dist_lower1,2)
density1 = count1 / ringarea1
count_err1 = np.sqrt(count1)
density_err1 = count_err1 / ringarea1
plt.figure(figsize=(12.5,6.5))
## Parallax
plt.subplot(2,4,1)
count_para1, bins_para1, bars_para1 = plt.hist(para1_l, 35, color='greenyellow',histtype=u'barstacked', rwidth=0.8, lw=2, alpha=0.8)
plt.axvline(x=para_know, color='k', linestyle='--', lw=1)
plt.xlabel(r'$\varpi $',fontsize=14)
plt.ylabel('Number objects',fontsize=11)
plt.tick_params(axis='both', which='major', labelsize=11)
plt.subplots_adjust(hspace=0.5,wspace=0.6)
## Proper motion RA
plt.subplot(2,4,2)
count_pmra1, bins_pmra1, bars_pmra1 = plt.hist(pmra1_l, 35, color='aqua',histtype=u'barstacked', rwidth=0.8, lw=2, alpha=0.8)
plt.axvline(x=pmra_know, color='k', linestyle='--', lw=1)
plt.xlabel(r'$\mu_{\alpha} $',fontsize=14)
plt.tick_params(axis='both', which='major', labelsize=11)
plt.subplots_adjust(hspace=0.5,wspace=0.6)
## Proper motion DEC
plt.subplot(2,4,3)
count_pmdec1, bins_pmdec1, bars_pmdec1 = plt.hist(pmdec1_l, 35, color='royalblue',histtype=u'barstacked', rwidth=0.8, lw=2, alpha=0.8)
plt.axvline(x=pmdec_know, color='k', linestyle='--', lw=1)
plt.xlabel(r'$\mu_{\delta} $',fontsize=14)
plt.tick_params(axis='both', which='major', labelsize=11)
plt.subplots_adjust(hspace=0.5,wspace=0.6)
# Density profile
plt.subplot(2,4,4)
plt.plot(dist_high1,density1, '--.', color='midnightblue', alpha=0.8)
plt.errorbar(dist_high1,density1, yerr=density_err1, fmt='.', color='midnightblue', alpha=0.8, lw=.8)
plt.xlabel('Distance (deg) ',fontsize=11)
plt.ylabel('Density (deg$^{-2}$)',fontsize=11)
plt.tick_params(axis='both', which='major', labelsize=11)
plt.subplots_adjust(hspace=0.5,wspace=0.6)
## Spatial distribution
plt.subplot(2,4,5)
sc=plt.scatter(ra1_l,dec1_l, c=para1_l, marker='o',s=(np.array(para1_l)*4.5), cmap= 'YlGnBu', norm=None, vmin=np.min(para1_l), vmax=np.max(para1_l), alpha=None, linewidths=None, verts=None, edgecolors=None, hold=None, data=None)
plt.plot(starRA,starDEC,'*',ms=6,color='darkviolet',alpha=0.65)
plt.xlabel('RA',fontsize=11)
plt.ylabel('DEC',fontsize=11)
plt.gca().invert_xaxis()
plt.colorbar(sc).remove()
plt.tick_params(axis='both', which='major', labelsize=11)
plt.subplots_adjust(wspace=0.5)
## Proper motion distribution
plt.subplot(2,4,6)
sc=plt.scatter(pmra1_l,pmdec1_l, c=para1_l,s=(np.array(para1_l)*4.5), marker='o',cmap= 'YlGnBu', zorder= 35, norm=None, vmin=np.min(para1_l), vmax=np.max(para1_l), alpha=None, linewidths=None, verts=None, edgecolors=None, hold=None, data=None)
plt.plot(pmra_know,pmdec_know,'*',ms=6, color='darkviolet',alpha=0.65)
plt.colorbar(sc).remove()
plt.xlabel(r'$\mu_{\alpha*} $',fontsize=14)
plt.ylabel(r'$\mu_{\delta} $',fontsize=14)
plt.tick_params(axis='both', which='major', labelsize=11)
plt.subplots_adjust(wspace=0.5)
## CMD
plt.subplot(2,4,7)
sc=plt.scatter(BpRp1_l,Mg1_l, c=para1_l, marker='o',s=(np.array(para1_l)*4.5),cmap= 'YlGnBu', zorder= 35,norm=None, vmin=np.min(para1_l), vmax=np.max(para1_l), alpha=None, linewidths=None, verts=None, edgecolors=None, hold=None, data=None)
plt.plot(bp_rp_mag_know,Mgstars,'*',ms=6,color='darkviolet',alpha=0.65)
plt.colorbar(sc, aspect=25).set_label(label=r'$\varpi $', size=10)
plt.xlabel('G$_{Bp}$ - G$_{Rp}$',fontsize=11)
plt.ylabel('M$_{G}$',fontsize=11)
plt.gca().invert_yaxis()
plt.tick_params(axis='both', which='major', labelsize=11)
plt.subplots_adjust(wspace=0.5)
# Information of the target at this stage
plt.subplot(2,4,8)
plt.axis([0, 10, 0, 10])
plt.text(0.5, 9, "Object= {0}".format(star_name), wrap=True, fontsize=10)
plt.text(0.5, 8, "Number of objects= {0}".format(len(para1_l)), wrap=True, fontsize=10)
plt.text(0.5, 6, "Known PARALLAX= {:.2f} +/- {:.2f} [mas]".format(para_know,parae_know), wrap=True, fontsize=10)
plt.text(0.5, 5, "Known PMRA= {:.2f} +/- {:.2f} [mas/yr]".format(pmra_know,pmrae_know), wrap=True, fontsize=10)
plt.text(0.5, 4, "Known PMDEC= {:.2f} +/- {:.2f} [mas/yr]".format(pmdec_know,pmdece_know), wrap=True, fontsize=10)
plt.text(0.5, 2, "Mean PARALLAX = {:.2f} [mas]".format(mu_para1) , wrap=True, fontsize=10)
plt.text(0.5, 1, "Mean PMRA= {:.2f} [mas/yr]".format(mu_pmra1), wrap=True, fontsize=10)
plt.text(0.5, 0, "Mean PMDEC= {:.2f} [mas/yr]".format(mu_pmdec1), wrap=True, fontsize=10)
plt.setp(plt.gca(), frame_on=False, xticks=(), yticks=())
plt.subplots_adjust(wspace=0.6)
plt.savefig(results_dir + '/PMRA_' + str(star_name) + '[' +str(pmra_lower)+ '_' + str(pmra_upper)+ ';' +str(len(pmra1_l))+ ']' + '.pdf')
plt.tight_layout(h_pad=1.2, w_pad=1.2)
plt.show()
plt.close()
again=str(input('Choose a new Proper Motion in RA range? (y/n):'))
## Started the selection of another parameter: Proper motion in DEC
again='y'
while again=='y':
IDGaia2_l,ID2_l,ra2_l,rae2_l,dec2_l,dece2_l,para2_l,parae2_l,pmra2_l,pmrae2_l,pmdec2_l,pmdece2_l,gmag2_l,BpRp2_l,BpG2_l, GRp2_l=[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[]
print '\033[1;35m'
print ("Make your selection!\n")
print '\033[1;30m'
# Define the range to select the data
pmdec_lower = input("PMDEC RANGE>=")
pmdec_upper = input("PMDEC RANGE<=")
for i in range(len(ID1_l)):
if pmdec1_l[i]>=pmdec_lower and pmdec1_l[i]<=pmdec_upper:
IDGaia2_l.append(IDGaia1_l[i])
ID2_l.append(ID1_l[i])
ra2_l.append(ra1_l[i])
rae2_l.append(rae1_l[i])
dec2_l.append(dec1_l[i])
dece2_l.append(dece1_l[i])
para2_l.append(para1_l[i])
parae2_l.append(parae1_l[i])
pmra2_l.append(pmra1_l[i])
pmrae2_l.append(pmrae1_l[i])
pmdec2_l.append(pmdec1_l[i])
pmdece2_l.append(pmdece1_l[i])
gmag2_l.append(gmag1_l[i])
BpRp2_l.append(BpRp1_l[i])
BpG2_l.append(BpG1_l[i])
GRp2_l.append(GRp1_l[i])
Mg2_l = gmag2_l + 5*np.log10(para2_l) - 10
mu_para2,mu_pmra2,mu_pmdec2 = np.mean(para2_l),np.mean(pmra2_l),np.mean(pmdec2_l)
print '\033[1;34m'
print("Number of objects in this selection = {0}".format(len(para2_l)))
print '\033[1;30m'
print '\033[1;34m'
print("Known value of PARALLAX = {0:.3f}[mas]".format(para_know))
print("Known value of PMRA = {0:.3f}[mas/yr]".format(pmra_know))
print("Known value of PMDEC = {0:.3f}[mas/yr]".format(pmdec_know))
print '\033[1;30m'
print '\033[1;34m'
print("Mean value of PARALLAX = {0:.3f} [mas]".format(mu_para2))
print("Mean value of PMRA = {0:.3f} [mas/yr]".format(mu_pmra2))
print("Mean value of PMDEC = {0:.3f} [mas/yr]".format(mu_pmdec2))
print '\033[1;30m'
starRA_list2 = np.linspace(starRA, starRA, len(ra2_l))
starDEC_list2 = np.linspace(starDEC,starDEC, len(dec2_l))
DECresult2 = np.asarray(dec2_l)
DeltaRA2 = np.cos(DECresult2/(180/np.pi)) * (ra2_l - (starRA_list2))
DeltaDEC2 = dec2_l - (starDEC_list2)
Distance2 = np.sqrt(np.power(DeltaRA2,2) + np.power(DeltaDEC2,2))
count2, bins2, bars2 = plt.hist(Distance2, 30, color='white',histtype=u'barstacked', rwidth=0.8, lw=2, alpha=0.8)
plt.close()
newbin2 = bins2 - np.min(Distance2)
dist_lower2 = np.linspace(np.min(newbin2), np.max(newbin2), len(count2))
diff2 = np.diff(dist_lower2)
high2 = np.linspace(np.min(diff2), np.max(diff2), len(count2))
dist_high2 = dist_lower2 + high2
ringarea2 = np.pi * np.power(dist_high2,2) - np.pi * np.power(dist_lower2,2)
density2 = count2 / ringarea2
count_err2 = np.sqrt(count2)
density_err2 = count_err2 / ringarea2
plt.figure(figsize=(12.5,6.5))
## Parallax
plt.subplot(2,4,1)
count_para2, bins_para2, bars_para2 = plt.hist(para2_l, 35, color='greenyellow',histtype=u'barstacked', rwidth=0.8, lw=2, alpha=0.8)
plt.axvline(x=para_know, color='k', linestyle='--', lw=1)
plt.xlabel(r'$\varpi $',fontsize=14)
plt.ylabel('Number objects',fontsize=11)
plt.tick_params(axis='both', which='major', labelsize=11)
plt.subplots_adjust(hspace=0.5,wspace=0.6)
## Proper motion RA
plt.subplot(2,4,2)
count_pmra2, bins_pmra2, bars_pmra2 = plt.hist(pmra2_l, 35, color='aqua',histtype=u'barstacked', rwidth=0.8, lw=2, alpha=0.8)
plt.axvline(x=pmra_know, color='k', linestyle='--', lw=1)
plt.xlabel(r'$\mu_{\alpha*} $',fontsize=14)
plt.tick_params(axis='both', which='major', labelsize=11)
plt.subplots_adjust(hspace=0.5,wspace=0.6)
## Proper motion DEC
plt.subplot(2,4,3)
count_pmdec2, bins_pmdec2, bars_pmdec2 = plt.hist(pmdec2_l, 35, color='royalblue',histtype=u'barstacked', rwidth=0.8, lw=2, alpha=0.8)
plt.axvline(x=pmdec_know, color='k', linestyle='--', lw=1)
plt.xlabel(r'$\mu_{\delta} $',fontsize=14)
plt.tick_params(axis='both', which='major', labelsize=11)
plt.subplots_adjust(hspace=0.5,wspace=0.6)
# Density profile
plt.subplot(2,4,4)
plt.plot(dist_high2,density2, '--.', color='midnightblue', alpha=0.8)
plt.errorbar(dist_high2,density2, yerr=density_err2, fmt='.', color='midnightblue', alpha=0.8, lw=.8)
plt.xlabel('Distance (deg) ',fontsize=11)
plt.ylabel('Density (deg$^{-2}$)',fontsize=11)
plt.tick_params(axis='both', which='major', labelsize=11)
plt.subplots_adjust(hspace=0.5, wspace=0.6)
## Spatial distribution
plt.subplot(2,4,5)
sc=plt.scatter(ra2_l,dec2_l, c=para2_l, marker='o',s=(np.array(para2_l)*4.5), cmap= 'YlGnBu', norm=None, vmin=np.min(para2_l), vmax=np.max(para2_l), alpha=None, linewidths=None, verts=None, edgecolors=None, hold=None, data=None)
plt.plot(starRA,starDEC,'*',ms=6,color='darkviolet',alpha=0.65)
plt.xlabel('RA',fontsize=11)
plt.ylabel('DEC',fontsize=11)
plt.gca().invert_xaxis()
plt.colorbar(sc).remove()
plt.tick_params(axis='both', which='major', labelsize=11)
plt.subplots_adjust(wspace=0.5)
## Proper motion distribution
plt.subplot(2,4,6)
sc=plt.scatter(pmra2_l,pmdec2_l, c=para2_l, marker='o',s=(np.array(para2_l)*4.5),cmap= 'YlGnBu', zorder= 35, norm=None, vmin=np.min(para2_l), vmax=np.max(para2_l), alpha=None, linewidths=None, verts=None, edgecolors=None, hold=None, data=None)
plt.plot(pmra_know,pmdec_know,'*',ms=6, color='darkviolet',alpha=0.65)
plt.colorbar(sc).remove()
plt.xlabel(r'$\mu_{\alpha*} $',fontsize=14)
plt.ylabel(r'$\mu_{\delta} $',fontsize=14)
plt.tick_params(axis='both', which='major', labelsize=11)
plt.subplots_adjust(wspace=0.5)
## CMD
plt.subplot(2,4,7)
sc=plt.scatter(BpRp2_l,Mg2_l, c=para2_l, marker='o',s=(np.array(para2_l)*4.5),cmap= 'YlGnBu', zorder= 35,norm=None, vmin=np.min(para2_l), vmax=np.max(para2_l), alpha=None, linewidths=None, verts=None, edgecolors=None, hold=None, data=None)
plt.plot(bp_rp_mag_know,Mgstars,'*',ms=6,color='darkviolet',alpha=0.65)
plt.colorbar(sc, aspect=25).set_label(label=r'$\varpi $', size=10)
plt.xlabel('G$_{Bp}$ - G$_{Rp}$',fontsize=11)
plt.ylabel('M$_{G}$',fontsize=11)
plt.gca().invert_yaxis()
plt.tick_params(axis='both', which='major', labelsize=11)
plt.subplots_adjust(wspace=0.5)
# Information of the target at this stage
plt.subplot(2,4,8)
plt.axis([0, 10, 0, 10])
plt.text(0.5, 9, "Object= {0}".format(star_name), wrap=True, fontsize=10)
plt.text(0.5, 8, "Number of objects= {0}".format(len(para2_l)), wrap=True, fontsize=10)
plt.text(0.5, 6, "Known PARALLAX= {:.2f} +/- {:.2f} [mas]".format(para_know,parae_know), wrap=True, fontsize=10)
plt.text(0.5, 5, "Known PMRA= {:.2f} +/- {:.2f} [mas/yr]".format(pmra_know,pmrae_know), wrap=True, fontsize=10)
plt.text(0.5, 4, "Known PMDEC= {:.2f} +/- {:.2f} [mas/yr]".format(pmdec_know,pmdece_know), wrap=True, fontsize=10)
plt.text(0.5, 2, "Mean PARALLAX = {:.2f} [mas]".format(mu_para2) , wrap=True, fontsize=10)
plt.text(0.5, 1, "Mean PMRA= {:.2f} [mas/yr]".format(mu_pmra2), wrap=True, fontsize=10)
plt.text(0.5, 0, "Mean PMDEC= {:.2f} [mas/yr]".format(mu_pmdec2), wrap=True, fontsize=10)
plt.setp(plt.gca(), frame_on=False, xticks=(), yticks=())
plt.subplots_adjust(wspace=0.6)
plt.savefig(results_dir + '/PMDEC_' + str(star_name) + '[' +str(pmdec_lower)+ '_' + str(pmdec_upper)+ ';' + str(len(pmdec2_l))+ ']' + '.pdf')
plt.tight_layout(h_pad=1.2, w_pad=1.2)
plt.show()
plt.close()
again=str(input('Choose a new Proper Motion in DEC range? (y/n):'))
# Up to this point the code would finish, if you select 'y' or continue if
# you decide to make more selection over the paramate (Parallax, PMRA or PMDEC)
# in that order.
loop=str(input('Would you like to start again the selection? (y/n): '))
while loop=='n':
print('Well done! This are your results =D \n')
print '\033[1;34m'
print("Number of objects = {0}".format(len(pmra2_l)))
print '\033[1;30m'
print '\033[1;34m'
print("Known value of PARALLAX = {0:.3f}[mas]".format(para_know))
print("Known value of PMRA = {0:.3f}[mas/yr]".format(pmra_know))
print("Known value of PMDEC = {0:.3f}[mas/yr]".format(pmdec_know))
print '\033[1;30m'
print '\033[1;34m'
print("Mean value of PARALLAX = {0:.3f} [mas]".format(mu_para2))
print("Mean value of PMRA = {0:.3f} [mas/yr]".format(mu_pmra2))
print("Mean value of PMDEC = {0:.3f} [mas/yr]".format(mu_pmdec2))
print '\033[1;30m'
# This would be the output file from CEREAL for the star is running
# at the moment.
file= open(pathdata + '/OutputData_' + str(star_name) + '.txt','w')
file.write('#ID\t' 'RA\t' 'RA_Error\t' 'DEC\t' 'DEC_Error\t' 'PARALLAX\t' 'PARALLAX_Error\t' 'PMRA\t' 'PMRA_Error\t' ' PMDEC\t' ' PMDEC_Error\t' ' G_mag\t' 'Bp-Rp_mag\t' 'Bp-G_mag\t' 'G-Rp_mag\n' )
for i in range(len(ID2_l)):
file.write("%f %f %f %f %f %f %f %f %f %f %f %f %f %f %f\n" % (ID2_l[i], ra2_l[i], rae2_l[i], dec2_l[i], dece2_l[i], para2_l[i], parae2_l[i], pmra2_l[i], pmrae2_l[i], pmdec2_l[i], pmdece2_l[i], gmag2_l[i], BpRp2_l[i], BpG2_l[i], GRp2_l[i]))
file.close()
## FINAL PLOT WITH THE RESULTS OF THE LAST SELECTION
## Estimate de error trought the weighted mean ecuation
#Parallax#
wpara = 1/np.power(parae2_l,2)
Sigma_wparasqueare = np.sqrt(1/wpara)
Xpara = (wpara*para2_l)/(wpara)
Sigma_wparaT = np.sqrt(1/np.sum(wpara))
XparaT = np.sum(wpara*para2_l)/np.sum(wpara)
#PmRA
wpmra = 1/np.power(pmrae2_l,2)
Sigma_wpmrasqueare = np.sqrt(1/wpmra)
Xpmra = (wpmra*pmra2_l)/(wpmra)
Sigma_wpmraT = np.sqrt(1/np.sum(wpmra))
XpmraT = np.sum(wpmra*pmra2_l)/np.sum(wpmra)
#PmDEC
wpmdec = 1/np.power(pmdece2_l,2)
Sigma_wpmdecsqueare = np.sqrt(1/wpmdec)
Xpmdec = (wpmdec*pmdec2_l)/(wpmdec)
Sigma_wpmdecT = np.sqrt(1/np.sum(wpmdec))
XpmdecT = np.sum(wpmdec*pmdec2_l)/np.sum(wpmdec)
plt.figure(figsize=(12.5,6.5))
plt.subplot(2,4,1)
count_para2, bins_para2, bars_para2 = plt.hist(para2_l, 35, color='greenyellow',histtype=u'barstacked', rwidth=0.8, lw=2, alpha=0.8)
plt.axvline(x=para_know, color='k', linestyle='--', lw=1)
plt.xlabel(r'$\varpi $',fontsize=14)
plt.ylabel('Number objects',fontsize=11)
plt.tick_params(axis='both', which='major', labelsize=11)
plt.subplots_adjust(hspace=0.5,wspace=0.6)
## Proper motion RA
plt.subplot(2,4,2)
count_pmra2, bins_pmra2, bars_pmra2 = plt.hist(pmra2_l, 35, color='aqua',histtype=u'barstacked', rwidth=0.8, lw=2, alpha=0.8)
plt.axvline(x=pmra_know, color='k', linestyle='--', lw=1)
plt.xlabel(r'$\mu_{\alpha*} $',fontsize=14)
plt.tick_params(axis='both', which='major', labelsize=11)
plt.subplots_adjust(hspace=0.5,wspace=0.6)
## Proper motion DEC
plt.subplot(2,4,3)
count_pmdec2, bins_pmdec2, bars_pmdec2 = plt.hist(pmdec2_l, 35, color='royalblue',histtype=u'barstacked', rwidth=0.8, lw=2, alpha=0.8)
plt.axvline(x=pmdec_know, color='k', linestyle='--', lw=1)
plt.xlabel(r'$\mu_{\delta} $',fontsize=14)
plt.tick_params(axis='both', which='major', labelsize=11)
plt.subplots_adjust(hspace=0.5,wspace=0.6)
#Density profile
plt.subplot(2,4,4)
plt.plot(dist_high2,density2, '--.',color='midnightblue', alpha=0.8)
plt.errorbar(dist_high2,density2, yerr=density_err2, fmt='.', color='midnightblue', alpha=0.8, lw=.8)
plt.xlabel('Distance (deg) ',fontsize=11)
plt.ylabel('Density (deg$^{-2}$)',fontsize=11)
plt.tick_params(axis='both', which='major', labelsize=10)
plt.subplots_adjust(hspace=0.5,wspace=0.6)
## Spatial distribution
plt.subplot(2,4,5)
sc=plt.scatter(ra2_l,dec2_l, c=para2_l, marker='o', s=(np.array(para2_l)*4.5),cmap= 'YlGnBu', norm=None, vmin=np.min(para2_l), vmax=np.max(para2_l), alpha=None, linewidths=None, verts=None, edgecolors=None, hold=None, data=None)
plt.plot(starRA,starDEC,'*',ms=6,color='darkviolet',alpha=0.65)
plt.xlabel('RA',fontsize=11)
plt.ylabel('DEC',fontsize=11)
plt.gca().invert_xaxis()
plt.colorbar(sc).remove()
plt.tick_params(axis='both', which='major', labelsize=11)
plt.subplots_adjust(hspace=0.5, wspace=0.6)
## Proper motion distribution
plt.subplot(2,4,6)
sc=plt.scatter(pmra2_l,pmdec2_l, c=para2_l, marker='o', s=(np.array(para2_l)*4.5),cmap= 'YlGnBu', zorder= 35, norm=None, vmin=np.min(para2_l), vmax=np.max(para2_l), linewidths=None, verts=None, edgecolors=None, hold=None, data=None)
plt.plot(pmra_know,pmdec_know,'s',ms=5, color='darkviolet',label='Known Value')
plt.plot(XpmraT,XpmdecT,'o',ms=5,color='darkorange',label='This programme')
plt.xlabel(r'$\mu_{\alpha*} $',fontsize=14)
plt.ylabel(r'$\mu_{\delta} $',fontsize=14)
plt.colorbar(sc).remove()
plt.tick_params(axis='both', which='major', labelsize=11)
plt.subplots_adjust(hspace=0.5, wspace=0.6)
## CMD
plt.subplot(2,4,7)
sc=plt.scatter(BpRp2_l,Mg2_l, c=para2_l, marker='o',s=(np.array(para2_l)*4.5),cmap= 'YlGnBu', zorder= 35,norm=None, vmin=np.min(para2_l), vmax=np.max(para2_l), alpha=None, linewidths=None, verts=None, edgecolors=None, hold=None, data=None)
plt.plot(bp_rp_mag_know,Mgstars,'*',ms=6,color='darkviolet',alpha=0.65)
plt.colorbar(sc, aspect=25).set_label(label=r'$\varpi $', size=10)
plt.xlabel('G$_{Bp}$ - G$_{Rp}$',fontsize=11)
plt.ylabel('M$_{G}$',fontsize=11)
plt.gca().invert_yaxis()
plt.tick_params(axis='both', which='major', labelsize=11)
plt.subplots_adjust(hspace=0.5, wspace=0.6)
## Text of the last result
plt.subplot(2,4,8)
plt.axis([0, 10, 0, 10])
plt.text(0.5, 9, "Object= {0}".format(star_name), wrap=True, fontsize=10)
plt.text(0.5, 8, "Number of objects= {0}".format(len(para2_l)), wrap=True, fontsize=10)
plt.text(0.5, 7, "Known PARALLAX= {:.2f} +/- {:.2f} [mas]".format(para_know,parae_know), wrap=True, fontsize=10)
plt.text(0.5, 6, "Known PMRA= {:.2f} +/- {:.2f} [mas/yr]".format(pmra_know,pmrae_know), wrap=True, fontsize=10)
plt.text(0.5, 5, "Known PMDEC= {:.2f} +/- {:.2f} [mas/yr]".format(pmdec_know,pmdece_know), wrap=True, fontsize=10)
plt.text(0.5, 4, "Mean PARALLAX = {:.2f} +/- {:.2f} [mas]".format(XparaT,Sigma_wparaT) , wrap=True, fontsize=10)
plt.text(0.5, 3, "Mean PMRA= {:.2f} +/- {:.2f} [mas/yr]".format(XpmraT,Sigma_wpmraT), wrap=True, fontsize=10)
plt.text(0.5, 2, "Mean PMDEC= {:.2f} +/- {:.2f} [mas/yr]".format(XpmdecT,Sigma_wpmdecT), wrap=True, fontsize=10)
plt.text(0.5, 0, "Square-> Known Value; Circle-> CEREAL", wrap=True, fontsize=8)
plt.setp(plt.gca(), frame_on=False, xticks=(), yticks=())
plt.subplots_adjust(wspace=0.6)
plt.savefig(results_dir + '/OutputData_' + str(star_name) + '.pdf')
plt.tight_layout(h_pad=1.2, w_pad=1.2)
plt.show()
plt.close()
break
#New loop after the started again
while loop=='y':
again=str(input('Introduce a new Parallax range (y): '))
while again=='y':
IDGaia3_l,ID3_l,ra3_l,rae3_l,dec3_l,dece3_l,para3_l,parae3_l,pmra3_l,pmrae3_l,pmdec3_l,pmdece3_l,gmag3_l,BpRp3_l,BpG3_l,GRp3_l = [],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[]
print '\033[1;35m'
print ("Make your selection!\n")
print '\033[1;30m'
# Define the range to select the data
para_lower = input("PARALLAX RANGE>=")
para_upper = input("PARALLAX RANGE<=")
for i in range(len(ID2_l)):
if para2_l[i]>=para_lower and para2_l[i]<=para_upper:
IDGaia3_l.append(IDGaia2_l[i])
ID3_l.append(ID2_l[i])
ra3_l.append(ra2_l[i])
rae3_l.append(rae2_l[i])
dec3_l.append(dec2_l[i])
dece3_l.append(dec2_l[i])
para3_l.append(para2_l[i])
parae3_l.append(parae2_l[i])
pmra3_l.append(pmra2_l[i])
pmrae3_l.append(pmrae2_l[i])
pmdec3_l.append(pmdec2_l[i])
pmdece3_l.append(pmdece2_l[i])
gmag3_l.append(gmag2_l[i])
BpRp3_l.append(BpRp2_l[i])
BpG3_l.append(BpG2_l[i])
GRp3_l.append(GRp2_l[i])
Mg3_l = gmag3_l + 5*np.log10(para3_l) - 10
mu_para3,mu_pmra3,mu_pmdec3 = np.mean(para3_l),np.mean(pmra3_l),np.mean(pmdec3_l)
print '\033[1;34m'
print("Number of objects in this selection = {0}".format(len(para3_l)))
print '\033[1;30m'
print '\033[1;34m'
print("Known value of PARALLAX = {0:.3f}[mas]".format(para_know))
print("Known value of PMRA = {0:.3f}[mas/yr]".format(pmra_know))
print("Known value of PMDEC = {0:.3f}[mas/yr]".format(pmdec_know))
print '\033[1;30m'
print '\033[1;34m'
print("Mean value of PARALLAX = {0:.3f} [mas]".format(mu_para3))
print("Mean value of PMRA = {0:.3f} [mas/yr]".format(mu_pmra3))
print("Mean value of PMDEC = {0:.3f} [mas/yr]".format(mu_pmdec3))
print '\033[1;30m'
starRA_list3 = np.linspace(starRA, starRA, len(ra3_l))
starDEC_list3 = np.linspace(starDEC,starDEC, len(dec3_l))
DECresult3 = np.asarray(dec3_l)
DeltaRA3 = np.cos(DECresult3/(180/np.pi)) * (ra3_l - (starRA_list3))
DeltaDEC3 = dec3_l - (starDEC_list3)
Distance3 = np.sqrt(np.power(DeltaRA3,2) + np.power(DeltaDEC3,2))
count3, bins3, bars3 = plt.hist(Distance3, 30, color='white',histtype=u'barstacked', rwidth=0.8, lw=2, alpha=0.8)
plt.close()
newbin3 = bins3 - np.min(Distance3)
dist_lower3 = np.linspace(np.min(newbin3), np.max(newbin3), len(count3))
diff3 = np.diff(dist_lower3)
high3 = np.linspace(np.min(diff3), np.max(diff3), len(count3))
dist_high3 = dist_lower3 + high3
ringarea3 = np.pi * np.power(dist_high3,2) - np.pi * np.power(dist_lower3,2)
density3 = count3 / ringarea3
count_err3 = np.sqrt(count3)
density_err3 = count_err3 / ringarea3
plt.figure(figsize=(12.5,6.5))
## Parallax
plt.subplot(2,4,1)
count_para3, bins_para3, bars_para3 = plt.hist(para3_l, 35, color='greenyellow',histtype=u'barstacked', rwidth=0.8, lw=2, alpha=0.8)
plt.axvline(x=para_know, color='k', linestyle='--', lw=1)
plt.xlabel(r'$\varpi $',fontsize=14)
plt.ylabel('Number objects',fontsize=11)
plt.tick_params(axis='both', which='major', labelsize=11)
plt.subplots_adjust(hspace=0.5,wspace=0.6)
## Proper motion RA
plt.subplot(2,4,2)
count_pmra3, bins_pmra3, bars_pmra3 = plt.hist(pmra3_l, 35, color='aqua',histtype=u'barstacked', rwidth=0.8, lw=2, alpha=0.8)
plt.axvline(x=pmra_know, color='k', linestyle='--', lw=1)
plt.xlabel(r'$\mu_{\alpha*} $',fontsize=14)
plt.tick_params(axis='both', which='major', labelsize=11)
plt.subplots_adjust(hspace=0.5,wspace=0.6)