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generate_quad.py
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generate_quad.py
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from isochrones.dartmouth import Dartmouth_Isochrone
from isochrones.utils import addmags
import numpy as np
import pandas as pd
from isochrones.observation import ObservationTree
#generate 4 stars all in different systems
#file = open('/tigress/np5/true_params.txt','a')
# def get_index(n):
# if n < 10:
# return '000' + str(n)
# elif n < 100:
# return '00' + str(n)
# elif n < 1000:
# return '0' + str(n)
# else:
# return str(n)
#
# def arrange_value(array):
# if array[0] > array[1]:
# if array[0] > array[2]:
# return array[0], array[1], array[2]
# else:
# return array[2], array[0], array[1]
# else:
# if array[1] > array[2]:
# return array[1], array[2], array[0]
# else:
# return array[2], array[0], array[1]
#for n in range(2000,2250,1):
#index = get_index(n)
#file.write('test: ' + index + '\n')
dar = Dartmouth_Isochrone()
M1, M2, M3, M4 = [1.0, 1.0, 1.0, 1.0]
distance1, distance2, distance3, distance4 = [400, 800, 1200, 1600]
AV = 0.0
feh = 0.0
age = np.log10(1e9)
# array = np.random.rand(3) + 0.5
# M1, M2, M3 = arrange_value(array)
#
# age1 = np.log10(5e8)
# age2 = np.log10(9e8)
# feh1 = 0.0
#
# array = 1400*np.random.rand(2) + 100
# if array[0] > array[1]:
# distance1 = array[0]
# distance2 = array[1]
# else:
# distance1 = array[1]
# distance2 = array[0]
#
# AV1 = 0.0
# feh2 = 0.2
# AV2 = 0.1
#
# params = (M1,M2,M3,age1,age2,feh1,feh2,distance1,distance2,AV1,AV2)
# params = str(params)
# file.write('(M1,M2,M3,age1,age2,feh1,feh2,distance1,distance2,AV1,AV2) = ' + params + '\n')
# file.write('\n')
#Simulate true magnitudes
unresolved_bands = ['J','H','K']
resolved_bands = ['i','K']
args1 = (age, feh, distance1, AV)
args2 = (age, feh, distance2, AV)
args3 = (age, feh, distance3, AV)
args4 = (age, feh, distance4, AV)
unresolved = {b:addmags(dar.mag[b](M1, *args1), dar.mag[b](M2, *args2), dar.mag[b](M3, *args3), dar.mag[b](M4, *args4)) for b in unresolved_bands}
resolved_1 = {b:dar.mag[b](M1, *args1) for b in resolved_bands}
resolved_2 = {b:dar.mag[b](M2, *args2) for b in resolved_bands}
resolved_3 = {b:dar.mag[b](M3, *args3) for b in resolved_bands}
resolved_4 = {b:dar.mag[b](M3, *args4) for b in resolved_bands}
#print dar.mag['K'](M2, *args2)
#print unresolved, resolved_1, resolved_2
instruments = ['twomass','RAO']
bands = {'twomass':['J','H','K'],
'RAO':['i','K']}
mag_unc = {'twomass': 0.02, 'RAO':0.1}
resolution = {'twomass':4.0, 'RAO':0.1}
relative = {'twomass':False, 'RAO':True}
separation2 = 0.5
separation3 = 1.1
separation4 = 0.8
PA2 = 100.
PA3 = 45.
PA4 = 235.
columns = ['name', 'band', 'resolution', 'relative', 'separation', 'pa', 'mag', 'e_mag']
df = pd.DataFrame(columns=columns)
i=0
for inst in ['twomass']: #Unresolved observations
for b in bands[inst]:
row = {}
row['name'] = inst
row['band'] = b
row['resolution'] = resolution[inst]
row['relative'] = relative[inst]
row['separation'] = 0.
row['pa'] = 0.
row['mag'] = unresolved[b]
row['e_mag'] = mag_unc[inst]
df = df.append(pd.DataFrame(row, index=[i]))
i += 1
for inst in ['RAO']: #Resolved observations
for b in bands[inst]:
mags = [resolved_1[b], resolved_2[b], resolved_3[b], resolved_4[b]]
pas = [0, PA2, PA3, PA4]
seps = [0., separation2, separation3, separation4]
for mag,sep,pa in zip(mags,seps,pas):
row = {}
row['name'] = inst
row['band'] = b
row['resolution'] = resolution[inst]
row['relative'] = relative[inst]
row['separation'] = sep
row['pa'] = pa
row['mag'] = mag
row['e_mag'] = mag_unc[inst]
df = df.append(pd.DataFrame(row, index=[i]))
i += 1
#print df
df.to_csv(path_or_buf='/tigress/np5/dataFrame/df_quad_test3000.csv')
t = ObservationTree.from_df(df, name='test-quad')
t.define_models(dar)
t.print_ascii()
#file.close()