In [16]:

    

# Import numpy
import numpy as np
from glob import glob
from astropy.io import fits, ascii

# Importing plotting stuff
import matplotlib.pyplot as plt
%matplotlib inline
from scipy import stats

# Use seaborn plotting style defaults
#import seaborn as sns; sns.set()

# Import Astropy things we need
from astropy.io import ascii

import astropy.coordinates as coord
import astropy.units as u
from astropy.table import Table

from scipy.ndimage import median_filter
from sklearn.neighbors import NearestNeighbors


    

In [15]:

    

# Loop through fits files to get times, DEC, RA. for HAT-P-53b.

#   0 NUMBER                 Running object number                                     
#   1 FLUX_ISO               Isophotal flux                                             [count]
#   2 FLUXERR_ISO            RMS error for isophotal flux                               [count]
#   3 MAG_ISO                Isophotal magnitude                                        [mag]
#   4 MAGERR_ISO             RMS error for isophotal magnitude                          [mag]
#   5 XWIN_IMAGE             Windowed position estimate along x                         [pixel]
#   6 YWIN_IMAGE             Windowed position estimate along y                         [pixel]
#   7 ALPHAWIN_J2000         Windowed right ascension (J2000)                           [deg]
#   8 DELTAWIN_J2000         windowed declination (J2000)                               [deg]

# Coordinates of Star, beginning
#RA = 3.90615
#DEC = -11.93665

# For Reference Star 1:
#RA_ref1 = 3.93338
#DEC_ref1= -11.9410

# For Reference Star 2:
#RA_ref2 = 21.923235
#DEC_ref2= 38.991684

times = []
newTime = [] # Getting the UTC time.
newMag = []
newMag_err = []
magRef1 = []
magRef1_err = []
magRef2 = []
magRef2_err = []
mag = []
mag_err = []

# Number of catalogue.
listNum = np.arange(478,597,1)

for i in listNum:
    data = fits.open('wasp44b_end_i_24.0' + str(i) + ".fits")
    times.append(data[0].header['DATE-OBS'])
      

for i in np.arange(0, len(listNum)-1, 1):    
    tempTime = times[i][11:]
    newTime.append(tempTime)

# Now we want to read the fluxes from the catalogue.

# Choosing stars by Magnitude.

for i in listNum:
    data = np.loadtxt(str(i) + ".cat")
    stars = np.sort(np.array(data[1]).flatten())
    magRef2.append(stars[2])
    magRef1.append(stars[1])
    mag.append(stars[0])

print(stars[0])


    

    




-12.0080958

In [41]:

    

#ascii.read('479.cat')

target_RA = 3.903
target_DEC = -11.938


ref_lc = []

def findTarget(arr, RA, DEC):
    index1 = np.where(np.min(np.abs(arr['ALPHAWIN_J2000'] - RA)))
    print(index1)
    index2 = np.where(np.min(np.abs(arr['DELTAWIN_J2000'] - DEC)))
    print(index2)
    for i in index1:
        if i in index2:
            return i
    return -1

data = ascii.read(str(479) + ".cat")
index = findTarget(data, target_RA, target_DEC)
    

for i in listNum:
    data = ascii.read(str(i) + ".cat")
    data.sort('MAG_ISO')
    #ref_lc.append(np.mean(data('FLUX_ISO')[0:10]))
      
#data


    

    




(array([0]),)
(array([0]),)

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