import numpy as np
import matplotlib.pyplot as plt
from astropy.table import Table
from astropy.time import Time
from ccf2rv import get_object_rv
exclude_orders = [0, 11, 12, 13, 15, 16, 20, 21, 22, 47, 48]
# number of median-absolute devs within an epoch to consider a point discrepant
nMAD_cut = 5
tbl = get_object_rv('Gl514',
mask='gl514_neg',
method='all',
force=True,
exclude_orders=exclude_orders,
sanitize=True,
snr_min=120,
weight_type='',
bandpass='******',
velocity_window=10,
do_blacklist=True)
fig, ax = plt.subplots(nrows=2, ncols=1)
ax[0].plot(tbl['RV'], 'g.')
ax[1].plot(tbl['BIS_SLOPE'], 'g.')
tbl['RV'] -= np.nanmedian(tbl['RV'])
plt.plot(tbl['MJDATE'], tbl['RV'], 'g.')
# plt.plot(tbl['MJDATE'],tbl['RV_WAVFP']-tbl['RV_SIMFP'],'r.')
plt.show()
obj = 'TOI-1452'
# number of median-absolute devs within an epoch to consider a point discrepant
nMAD_cut = 5
# you can give some known bad orders
known_bad = [36]
# get the RMS of errors for the case where not additional order is rejected
tbl1 = get_object_rv(
obj,
mask='gl846_neg',
method='template',
force=True,
exclude_orders=known_bad,
snr_min=20.0,
sanitize=False,
weight_type='ccf_depth',
bandpass='******',
doplot=False,
)
ref_rms = np.mean(tbl1['ERROR_RV'])
ERROR_RV = np.zeros(49)
for iord in range(0, 49):
exclude_orders = np.array(np.unique(np.append(iord, known_bad)), dtype=int)
print('\nWe try rejecting order[s] : {0}\n'.format(exclude_orders))
tbl1 = get_object_rv(
obj,
from ccf2rv import get_object_rv
from per_epoch_table import per_epoch_table
object = 'K2-33'
exclude_orders = [47]
# number of median-absolute devs within an epoch to consider a point discrepant
nMAD_cut = 5
method = 'template'
mask = 'gl846_neg'
tbl1 = get_object_rv(object,
bandpass='******',
method=method,
dvmax_per_order=10.0, # large because omewhat fast rot.
exclude_orders=exclude_orders,
force=True,
snr_min=25.0,
weight_type='',
sanitize=True,
doplot=True,
mask=mask,
do_blacklist=True)
tbl_bin1 = per_epoch_table(tbl1)
fig, ax = plt.subplots(nrows=2, ncols=1, sharex=True)
ax[0].errorbar(tbl1['MJDATE'], tbl1['RV'], yerr=tbl1['ERROR_RV'],
linestyle="None", fmt='o', alpha=0.2)
ax[0].errorbar(tbl_bin1['MJDATE_MEAN'], tbl_bin1['RV'],
yerr=tbl_bin1['ERROR_RV'], linestyle="None", fmt='o',
tbl_cube = fits.getdata(cube_file, ext=1)
files_in_cube = np.array(tbl_cube['Filename'])
for i in range(len(files_in_cube)):
files_in_cube[i] = files_in_cube[i].split('o')[0]
obj = 'TOI-1278'
mask = 'gl846_neg'
exclude_orders = [-1]
# number of median-absolute devs within an epoch to consider a point discrepant
tbl = get_object_rv(obj,
mask=mask,
method='template',
force=True,
exclude_orders=exclude_orders,
snr_min=20.0,
sanitize=False,
dvmax_per_order=3.0,
bandpass='******',
doplot=True,
do_blacklist=True)
fig, ax = plt.subplots(nrows=1, ncols=2, sharex=True, sharey=True)
ax[0].imshow(cube, vmin=0.5, vmax=1.5, aspect='auto')
ax[0].set(title='Before alignment', ylabel='Nth frame', xlabel='pixels')
ax[1].set(title='After alignment', ylabel='Nth frame', xlabel='pixels')
xpix = np.arange(cube.shape[1])
for i in range(cube.shape[0]):
if np.max(files_in_cube[i] == tbl['ODOMETER']):
ccf_files = sorted(glob.glob(options.pattern))
#[0,11,12,13,15,16,20,21,22,47,48]
exclude_orders = options.exclude_orders.split(",")
for i in range(len(exclude_orders)) :
exclude_orders[i] = int(exclude_orders[i])
# detect and organize collection of files based on: object, ccfmask, sanitize, and DRS version
ccf_collections = ccf2rv.create_collections(ccf_files, verbose=options.verbose)
save_plots=False
save_csv_table_of_results = False
save_ccf_cube = False
save_weight_table = False
if options.save_all_subproducts :
if options.plot :
save_plots=True
save_csv_table_of_results = True
save_ccf_cube = True
save_weight_table = True
for key in ccf_collections["modes"]:
list_of_files = ccf_collections[key]
if options.verbose:
print("Processing collection {0} containing {1} files".format(key, len(list_of_files)))
tbl = ccf2rv.get_object_rv(list_of_files, collection_key=key, method=options.method, exclude_orders = exclude_orders, snr_min=float(options.min_snr), bandpass = options.bandpass, save_rdb_timeseries = True, save_csv_table_of_results = save_csv_table_of_results, save_ccf_cube = save_ccf_cube, save_weight_table = save_weight_table, doplot=options.plot, showplots=options.plot, saveplots=save_plots, verbose=options.verbose)