def take_data_within(name, start_date, end_date):
#----------------------------------------
# Convert date to JD
times = [
'{0}-{1}-{2}T12:00:00'.format(start_date[:4], start_date[4:6],
start_date[6:]),
'{0}-{1}-{2}T12:00:00'.format(end_date[:4], end_date[4:6],
end_date[6:])
]
t = Time(times, format='isot', scale='utc')
start_jd = t.jd[0]
end_jd = t.jd[1]
#----------------------------------------
# Query data
cnx = TAT_auth()
cursor = cnx.cursor()
print 'target: {0}'.format(name)
print 'start JD : {0}'.format(start_jd)
print 'end JD: {0}'.format(end_jd)
print 'band: {0}, exptime = {1}'.format(band, exptime)
cursor.execute('select * from {0} where `NAME` = "{1}" \
and `JD` between {2} and {3}'\
.format(TAT_env.obs_data_tb_name, name, start_jd, end_jd))
data = cursor.fetchall()
data = np.array(data)
cursor.close()
cnx.close()
return data
def select_data_by_bands_exptime(data, start_date, end_date, band, exptime):
#----------------------------------------
# Convert date to JD
times = [
'{0}-{1}-{2}T12:00:00'.format(start_date[:4], start_date[4:6],
start_date[6:]),
'{0}-{1}-{2}T12:00:00'.format(end_date[:4], end_date[4:6],
end_date[6:])
]
t = Time(times, format='isot', scale='utc')
start_jd = t.jd[0]
end_jd = t.jd[1]
#----------------------------------------
cnx = TAT_auth()
cursor = cnx.cursor()
# Take the ID of selected images.
if band == 'skip' and exptime == 'skip':
print('No band and exptime selection.')
return data
elif band == 'skip':
print('Selected by exptime.')
band_selection = ''
exptime_selection = 'and `EXPTIME` = {0}'.format(exptime)
cursor.execute('select `ID` from {0} where `JD` between {1} and {2}\
{3} {4}'.format(TAT_env.im_tb_name, start_jd, end_jd,
band_selection, exptime_selection))
elif exptime == 'skip':
print('Selected by bands.')
band_selection = 'and `FILTER` = "{0}"'.format(band)
exptime_selection = ''
cursor.execute('select `ID` from {0} where `JD` between {1} and {2}\
{3} {4}'.format(TAT_env.im_tb_name, start_jd, end_jd,
band_selection, exptime_selection))
else:
print('Selected by bands and exptime.')
cursor.execute('select `ID` from {0} where `JD` between {1} and {2}\
and `FILTER` = "{3}"\
and `EXPTIME` = {4}'.format(TAT_env.im_tb_name,
start_jd, end_jd, band,
exptime))
selected_image_ID = cursor.fetchall()
cursor.close()
cnx.close()
# Selected by Bands and Exposure Time.
selected_image_ID = np.array(selected_image_ID)
ID_index = TAT_env.obs_data_titles.index('FILEID')
selected_data = []
for source in data:
dummy_index = np.where(selected_image_ID == source[ID_index])
if len(dummy_index[0]) >= 1:
selected_data.append(source)
selected_data = np.array(selected_data, dtype=object)
print('data points: {0}'.format(len(selected_data)))
return selected_data
def load_data(name):
cnx = TAT_auth()
cursor = cnx.cursor()
cursor.execute(
'select * from observation_data where `NAME` = "{0}"'.format(name))
data = cursor.fetchall()
cursor.close()
cnx.close()
data = np.array(data, dtype=object)
print(data.shape)
return data
def find_filter(fileID):
cnx = TAT_auth()
cursor = cnx.cursor()
cursor.execute(
"select `FILTER` from TAT.data_file where ID='{0}'".format(fileID))
data = cursor.fetchall()
data = np.array(data).flatten()
ans = data[0]
cursor.close()
cnx.close()
return ans
def take_data_within(start_date, end_date, ra_cntr_str, dec_cntr_str):
#----------------------------------------
times = [
'{0}-{1}-{2}T12:00:00'.format(start_date[:4], start_date[4:6],
start_date[6:]),
'{0}-{1}-{2}T12:00:00'.format(end_date[:4], end_date[4:6],
end_date[6:])
]
t = Time(times, format='isot', scale='utc')
start_jd = t.jd[0]
end_jd = t.jd[1]
ra_cntr = float(ra_cntr_str)
dec_cntr = float(dec_cntr_str)
#----------------------------------------
# Query data
cnx = TAT_auth()
cursor = cnx.cursor()
print 'start JD : {0}'.format(start_jd)
print 'end JD : {0}'.format(end_jd)
print "Center at ({0}, {1})".format(ra_cntr, dec_cntr)
print "band: {0}, exptime : {1}".format(band, exptime)
print 'Start ID : {0}, Numbers of aux star : {1}'.format(
begin_of_aux, no_of_aux)
# Selected by Coordinate.
cursor.execute('select * from {0} where `JD` between {1} and {2} \
and `RA` between {3} and {4} \
and `DEC` between {5} and {6}'\
.format(TAT_env.obs_data_tb_name,
start_jd,
end_jd,
ra_cntr-0.5,
ra_cntr+0.5,
dec_cntr-0.5,
dec_cntr+0.5
))
data = cursor.fetchall()
data = np.array(data)
# Take the ID of selected images.
if band == 'skip' and exptime == 'skip':
print('No band and exptime selection.')
return data
elif band == 'skip':
print('Selected by exptime.')
band_selection = ''
exptime_selection = 'and `EXPTIME` = {0}'.format(exptime)
cursor.execute('select `ID` from {0} where `JD` between {1} and {2}\
{3} {4}'.format(TAT_env.im_tb_name, start_jd, end_jd,
band_selection, exptime_selection))
elif exptime == 'skip':
print('Selected by bands.')
band_selection = 'and `FILTER` = "{0}"'.format(band)
exptime_selection = ''
cursor.execute('select `ID` from {0} where `JD` between {1} and {2}\
{3} {4}'.format(TAT_env.im_tb_name, start_jd, end_jd,
band_selection, exptime_selection))
else:
print('Selected by bands and exptime.')
cursor.execute('select `ID` from {0} where `JD` between {1} and {2}\
and `FILTER` = "{3}"\
and `EXPTIME` = {4}'.format(TAT_env.im_tb_name,
start_jd, end_jd, band,
exptime))
selected_image_ID = cursor.fetchall()
cursor.close()
cnx.close()
# Selected by Bands and Exposure Time.
selected_image_ID = np.array(selected_image_ID)
ID_index = TAT_env.obs_data_titles.index('FILEID')
selected_data = []
for source in data:
dummy_index = np.where(selected_image_ID == source[ID_index])
if len(dummy_index[0]) >= 1:
selected_data.append(source)
selected_data = np.array(selected_data)
return selected_data
def EP_process(data):
#----------------------------------------
# Load the index of some parameters
bjd_index = TAT_env.obs_data_titles.index('BJD')
inst_mag_index = TAT_env.obs_data_titles.index('INST_MAG')
e_inst_mag_index = TAT_env.obs_data_titles.index('E_INST_MAG')
target_name_index = TAT_env.obs_data_titles.index('NAME')
fileID_index = TAT_env.obs_data_titles.index("FILEID")
# Pick 10 brightest stars from each frame (They have to be the same star in diff. frames.)
# Take all the data in the first frame
first_bjd = np.amin(data[:, bjd_index])
first_frame_data = data[data[:, bjd_index] == first_bjd]
# Sort the first frame data by the brightness
first_frame_data = first_frame_data[np.argsort(
first_frame_data[:, inst_mag_index])]
# Take the data from all frames.
all_fileIDs = data[:, fileID_index]
fileIDs = [
item for item, count in collections.Counter(all_fileIDs).items()
if count > 1
]
source_list = []
selected_source_name = []
# Find sources found in all frames.
for source in first_frame_data[int(begin_of_aux):]:
if source[target_name_index] == var_star:
continue
source_data = data[data[:, target_name_index] ==
source[target_name_index]]
source_fileIDs = source_data[:, fileID_index]
if len(source_fileIDs) == len(fileIDs):
source_error = source_data[:, e_inst_mag_index]
source_error[source_error == 0.0] = 1e-4
source_data_lite = np.transpose(
np.array([
source_data[:, bjd_index], source_data[:, inst_mag_index],
source_error
]))
source_list.append(source_data_lite)
selected_source_name.append(source[target_name_index])
if len(source_list) > int(no_of_aux):
break
#################################
# Print Auxiliary Star Names
print
#################################
exit()
#----------------------------------------
# Do photometry on Bright Stars only, save the result.
source_data_array = np.array(source_list)
stu = photometry_lib.EP(source_data_array[0], source_data_array)
ems, var_ems, m0s, var_m0s = stu.make_airmass_model()
#----------------------------------------
# Pick a image, find the center position.
cnx = TAT_auth()
cursor = cnx.cursor()
print fileIDs
cursor.execute('select * from {0} where `ID` = {1}'.format(
TAT_env.im_tb_name, fileIDs[0]))
img_data = cursor.fetchall()
cursor.close()
cnx.close()
img_ra_cntr = float(img_data[0][4])
img_dec_cntr = float(img_data[0][5])
# Get all possible target within the region.
observed_targets = find_source_match_coords(img_ra_cntr,
img_dec_cntr,
margin=TAT_env.pix1 * 1024. /
3600.)
# Pick a target star, we make a photometry on it.
for source in observed_targets:
# Take the name of the source
source_name = source[target_name_index]
# Get all the data of the source
data2 = data[np.isin(data[:, target_name_index], source_name)]
# Take time, magnitude, and error of magnitude.
observation_data_ID = data2[:, 0]
time_array = data2[:, bjd_index]
mag_array = data2[:, inst_mag_index]
err_mag_array = data2[:, e_inst_mag_index]
# Combine and do EP phot.
source_data = np.transpose(
np.array([time_array, mag_array, err_mag_array]))
failure, correlated_target, matched = stu.phot(source_data)
if failure:
print 'One event {0} cannot be measure.'.format(source_name)
continue
observation_data_ID = observation_data_ID[matched]
save2sql_EP(correlated_target, observation_data_ID)
return False