import functions as f import matplotlib.pyplot as plt import numpy as np import time as t t0 = t.clock() kplr_id = '008191672' kplr_file = 'kplr008191672-2013011073258_llc.fits' jdadj, obsobject, lightdata = f.openfile(kplr_id, kplr_file) time, flux, flux_err = f.fix_data(lightdata) flux, variance = f.rescale(flux, flux_err) time -= np.median(time) depth = 0.00650010001 width = 0.177046694669 period_interval = np.arange(2.00, 8.0, 0.01) offset_intervals = np.arange(0.00, 7.2, 0.01) #Change to numpy arrays to optimize. # z = [[f.sum_chi_squared(flux, f.box(p,o,depth,width,time),variance) for o in offset_intervals] # for p in period_interval] z = [] for p in period_interval: line = [] for o in offset_intervals: if o < p: line.append(f.sum_chi_squared(flux, f.box(p,o,depth,width,time), variance))
import functions as f
import matplotlib.pyplot as plt
import numpy as np
import time as t
t0 = t.clock()
kplr_id = '008191672'
kplr_file = 'kplr008191672-2013011073258_llc.fits'
jdadj, obsobject, lightdata = f.openfile(kplr_id, kplr_file)
time, flux, flux_err = f.fix_data(lightdata)
flux, variance = f.rescale(flux, flux_err)
time -= np.median(time)
depth = 0.00650010001
width = 0.177046694669
period_interval = np.arange(2.00, 8.0, 0.01)
offset_intervals = np.arange(0.00, 7.2, 0.01)
#Change to numpy arrays to optimize.
# z = [[f.sum_chi_squared(flux, f.box(p,o,depth,width,time),variance) for o in offset_intervals]
# for p in period_interval]
z = []
for p in period_interval:
line = []
for o in offset_intervals:
if o < p:
line.append(
def main(kplr_id):
star = client.star(kplr_id)
width = 1.0
lcs = star.get_light_curves(short_cadence=False)
med_flux_list = []
filter_size = 80
time_list, flux_list, var_list, ferr_list = [], [], [], []
start_time = timer.time()
for lc in lcs:
with lc.open() as f:
# The lightcurve data are in the first FITS HDU.
hdu_data = f[1].data
time, flux, ferr = functions.fix_data(hdu_data)
time_list.append(time)
flux_list.append(flux)
median = functions.median_filter(flux, filter_size)
ferr_list.append(ferr / median)
var_list.append((ferr / median)**2)
med_flux_list.append(flux / median)
time = np.concatenate(time_list)
flux = np.concatenate(flux_list)
med_flux = np.concatenate(med_flux_list)
inv_var = 1 / (np.concatenate(var_list))
ferr = np.concatenate(ferr_list)
print "Loading data", timer.time() - start_time
#Run the search
#time_grid makes the time array coarse. This will allow the number of searches to
#be much less than doing the calculations at all times.
time_grid = np.arange(min(time), max(time), width / 8)
print time.shape, time_grid.shape
start_time = timer.time()
# main_array = np.asarray([functions.get_depth_and_ln_like(med_flux, o, width, time, inv_var) for o in time_grid])
main_array = np.array([
functions.get_depth_and_ln_like(med_flux, o, width, time, inv_var)
for o in time_grid
])
transit_boolean_array = np.array(
[functions.get_transit_boolean(o, width, time) for o in time_grid])
print "Search time", timer.time() - start_time
depth_array = main_array[:, 0]
depth_variance_array = main_array[:, 1]
ln_like_array = main_array[:, 2]
#Use the peak_finder function to obtain the boolean arrays used to
#get the required "transit window" values.
start_time = timer.time()
complete_boolean_list, grid_boolean_list, peaks, peak_index = functions.peak_finder(
ln_like_array, time, time_grid, width, 5)
print "Peak finding time", timer.time() - start_time
print peaks
print peak_index
pp = PdfPages('../plots/{0}_width_{1}.pdf'.format(kplr_id, width))
fig1 = plt.figure()
sub1 = fig1.add_subplot(211)
sub1.plot(time, med_flux, '.k', markersize=2)
sub1.set_xlabel("Days")
sub1.set_ylabel("Median-filtered Flux")
sub1.grid()
sub2 = fig1.add_subplot(212)
sub2.plot(time_grid, ln_like_array, '.b', markersize=2)
sub2.set_xlabel("Days")
sub2.set_ylabel("ln_Likelihood")
sub2.grid()
pp.savefig()
fig2 = plt.figure()
sub3 = fig2.add_subplot(211)
sub3.plot(time_grid, depth_array, '.b', markersize=2)
sub3.set_xlabel("Days")
sub3.set_ylabel("Depth")
sub3.grid()
sub4 = fig2.add_subplot(212)
sub4.plot(time, flux, '.b', markersize=2)
sub4.set_xlabel("Days")
sub4.set_ylabel("Raw Flux")
sub4.grid()
pp.savefig()
#Plot the peaks and their likelihoods.
functions.plot_peaks(complete_boolean_list, grid_boolean_list, time,
med_flux, ferr, time_grid, ln_like_array, depth_array,
transit_boolean_array, peak_index, pp)
pp.savefig()
pp.close()
plt.close('all')
def main(kplr_id):
star = client.star(kplr_id)
width = 1.0
lcs = star.get_light_curves(short_cadence=False)
med_flux_list = []
filter_size = 80
time_list, flux_list, var_list, ferr_list = [], [], [], []
start_time = timer.time()
for lc in lcs:
with lc.open() as f:
# The lightcurve data are in the first FITS HDU.
hdu_data = f[1].data
time, flux, ferr = functions.fix_data(hdu_data)
time_list.append(time)
flux_list.append(flux)
median = functions.median_filter(flux, filter_size)
ferr_list.append(ferr/median)
var_list.append((ferr / median)**2)
med_flux_list.append(flux / median)
time = np.concatenate(time_list)
flux = np.concatenate(flux_list)
med_flux = np.concatenate(med_flux_list)
inv_var = 1/(np.concatenate(var_list))
ferr = np.concatenate(ferr_list)
print "Loading data", timer.time() - start_time
#Run the search
#time_grid makes the time array coarse. This will allow the number of searches to
#be much less than doing the calculations at all times.
time_grid = np.arange(min(time), max(time), width/8)
print time.shape, time_grid.shape
start_time = timer.time()
# main_array = np.asarray([functions.get_depth_and_ln_like(med_flux, o, width, time, inv_var) for o in time_grid])
main_array = np.array([functions.get_depth_and_ln_like(med_flux, o, width, time, inv_var) for o in time_grid])
transit_boolean_array = np.array([functions.get_transit_boolean(o, width, time) for o in time_grid])
print "Search time", timer.time() - start_time
depth_array = main_array[:,0]
depth_variance_array = main_array[:,1]
ln_like_array = main_array[:,2]
#Use the peak_finder function to obtain the boolean arrays used to
#get the required "transit window" values.
start_time = timer.time()
complete_boolean_list, grid_boolean_list, peaks, peak_index = functions.peak_finder(ln_like_array, time, time_grid, width, 5)
print "Peak finding time", timer.time() - start_time
print peaks
print peak_index
pp = PdfPages('../plots/{0}_width_{1}.pdf'.format(kplr_id, width))
fig1 = plt.figure()
sub1 = fig1.add_subplot(211)
sub1.plot(time, med_flux, '.k', markersize = 2)
sub1.set_xlabel("Days")
sub1.set_ylabel("Median-filtered Flux")
sub1.grid()
sub2 = fig1.add_subplot(212)
sub2.plot(time_grid, ln_like_array, '.b', markersize = 2)
sub2.set_xlabel("Days")
sub2.set_ylabel("ln_Likelihood")
sub2.grid()
pp.savefig()
fig2 = plt.figure()
sub3 = fig2.add_subplot(211)
sub3.plot(time_grid, depth_array, '.b', markersize = 2)
sub3.set_xlabel("Days")
sub3.set_ylabel("Depth")
sub3.grid()
sub4 = fig2.add_subplot(212)
sub4.plot(time, flux, '.b', markersize = 2)
sub4.set_xlabel("Days")
sub4.set_ylabel("Raw Flux")
sub4.grid()
pp.savefig()
#Plot the peaks and their likelihoods.
functions.plot_peaks(complete_boolean_list, grid_boolean_list, time, med_flux, ferr, time_grid, ln_like_array, depth_array, transit_boolean_array, peak_index, pp)
pp.savefig()
pp.close()
plt.close('all')
import functions
client = kplr.API()
star = client.star(8800954)
lcs = star.get_light_curves(short_cadence=False)
med_flux_list = []
filter_size = 80
time_list, flux_list, ferr_list = [], [], []
for lc in lcs:
with lc.open() as f:
# The lightcurve data are in the first FITS HDU.
hdu_data = f[1].data
time, flux, ferr = functions.fix_data(hdu_data)
time_list.append(time)
flux_list.append(flux)
median = functions.median_filter(flux, filter_size)
ferr_list.append((ferr / median)**2)
med_flux_list.append(flux / median)
time = np.concatenate(time_list)
flux = np.concatenate(flux_list)
med_flux = np.concatenate(med_flux_list)
depth = 0.003
width = 0.1
#Run the search
ln_like_perfect = np.asarray([functions.pre_ln_like(med_flux, functions.push_box_model(o, depth, width, time)) for o in time])
def main(kplr_id, width):
star = client.star(kplr_id)
lcs = star.get_light_curves(short_cadence=False)
med_flux_list = []
filter_size = 80
time_list, flux_list, var_list, ferr_list = [], [], [], []
start_time = timer.time()
for lc in lcs:
with lc.open() as f:
# The lightcurve data are in the first FITS HDU.
hdu_data = f[1].data
time, flux, ferr = functions.fix_data(hdu_data)
time_list.append(time)
flux_list.append(flux)
median = functions.median_filter(flux, filter_size)
ferr_list.append(ferr/median)
var_list.append((ferr / median)**2)
med_flux_list.append(flux / median)
time = np.concatenate(time_list)
med_flux = np.concatenate(med_flux_list)
inv_var = 1/(np.concatenate(var_list))
ferr = np.concatenate(ferr_list)
print "Loading data", timer.time() - start_time
#Run the search
#time_grid makes the time array coarse. This will allow the number of searches to
#be much less than doing the calculations at all times.
time_grid = np.arange(min(time), max(time), width/8)
print time.shape, time_grid.shape
start_time = timer.time()
# main_array = np.asarray([functions.get_depth_and_ln_like(med_flux, o, width, time, inv_var) for o in time_grid])
main_array = np.array([functions.get_depth_and_ln_like(med_flux, o, width, time, inv_var) for o in time_grid])
# transit_boolean_array = np.array([functions.get_transit_boolean(o, width, time) for o in time_grid])
print "Search time", timer.time() - start_time
depth_array = main_array[:,0]
depth_variance_array = main_array[:,1]
ln_like_array = main_array[:,2]
###OPEN, WRITE, CLOSE DATA TO FILE###
# file_name = '../picklefiles/{0}_width_{1}.hdf5'.format(kplr_id, width)
file_name = '../automatically_created_files/{0}_width_{1}.hdf5'.format(kplr_id, width)
f = h5py.File(file_name, 'w')
f.create_dataset("time", data=time)
f.create_dataset("med_flux", data=med_flux)
f.create_dataset("inv_var", data=inv_var)
f.create_dataset("ferr", data=ferr)
f.create_dataset("time_grid", data=time_grid)
# f.create_dataset("transit_boolean_array", data=transit_boolean_array)
f.create_dataset("depth_array", data=depth_array)
f.create_dataset("depth_variance_array", data=depth_variance_array)
f.create_dataset("ln_like_array", data=ln_like_array)
print time.shape
# print transit_boolean_array.shape
# assert 0
f.close()
