def recreate_star(star_dir):
"""Create a Star from a given directory.
Parameters
----------
star_dir :`pathlib.Path`
The directory in which to find the star's files.
Returns
-------
None.
"""
tqdm.write(f'Creating {star_dir.stem}')
try:
Star(star_dir.stem, star_dir, load_data=False)
except PickleFilesNotFoundError:
newstar_dir = Path('/Volumes/External Storage/data_output') /\
star_dir.stem
tqdm.write('Using external storage files.')
Star(star_dir.stem, new_star_dir, load_data=False, output_dir=star_dir)
def get_star(star_path, verbose=False, recreate=False):
"""Return a varconlib.star.Star object based on its name.
Parameters
----------
star_path : str
A string representing the name of the directory where the HDF5 file
containing a `star.Star`'s data can be found.
Optional
--------
verbose : bool, Default: False
If *True*, write out additional information.
recreate : bool, Default: False
If *True*, first recreate the star from observations before returning
it. This will only work on stars which already have an HDF5 file saved,
and will not create new ones.
Returns
-------
`star.Star`
A Star object from the directory. Note that this will only use already-
existing stars, it will not create ones which do not already exist from
their observations.
"""
assert star_path.exists(), FileNotFoundError('Star directory'
f' {star_path}'
' not found.')
# Flip boolean value, since to recreate (True) the star requires setting its
# load_data argument to False.
recreate = not recreate
try:
return Star(star_path.stem, star_path, load_data=recreate)
except IndexError:
vprint(f'Excluded {star_path.stem}.')
pass
except HDF5FileNotFoundError:
vprint(f'No HDF5 file for {star_path.stem}.')
pass
except AttributeError:
vprint(f'Affected star is {star_path.stem}.')
raise
except PickleFilesNotFoundError:
vprint(f'No pickle files found for {star_path.stem}')
pass
def get_star(star_path, verbose=False):
"""Return a varconlib.star.Star object based on its name.
Parameters
----------
star_path : str
A string representing the name of the directory where the HDF5 file
containing a `star.Star`'s data can be found.
Optional
--------
verbose : bool, Default: False
If *True*, write out additional information.
Returns
-------
`star.Star`
A Star object from the directory. Note that this will only use already-
existing stars, it will not create ones which do not already exist from
their observations.
"""
assert star_path.exists(), FileNotFoundError('Star directory'
f' {star_path}'
' not found.')
try:
return Star(star_path.stem, star_path, load_data=True)
except IndexError:
if verbose:
tqdm.write(f'Excluded {star_path.stem}.')
pass
except HDF5FileNotFoundError:
if verbose:
tqdm.write(f'No HDF5 file for {star_path.stem}.')
pass
except AttributeError:
if verbose:
tqdm.write(f'Affected star is {star_path.stem}.')
raise
def create_transition_model_corrected_arrays(star_dir):
"""
Create the transition model-corrected arrays for a Star from a given
directory.
Parameters
----------
star_dir : `pathlib.Path`
The directory in which to find the star's files.
Returns
-------
None.
"""
tqdm.write(f'Working on {star_dir.stem}')
star = Star(star_dir.stem, star_dir, load_data=True)
star.createTransitionModelCorrectedArrays(model_func='quadratic',
n_sigma=2.5)
star.createPairSeparationArrays()
star.saveDataToDisk()
def main():
"""Run the main routine for this script.
Returns
-------
None.
"""
main_dir = Path(args.main_dir[0])
if not main_dir.exists():
raise FileNotFoundError(f'{main_dir} does not exist!')
tqdm.write(f'Looking in main directory {main_dir}')
rv_diff_list = []
app_mag_list = []
tqdm.write('Collecting stars...')
for star_dir in tqdm(args.star_names):
star_path = main_dir / star_dir
star = Star(star_path.stem, star_path, load_data=True)
vprint(f'Collecting info from {star_dir}.')
rv = star.radialVelocity
berv_list = star.bervArray
rv_diff = [d.to(u.km / u.s) for d in berv_list - rv]
app_mag = [star.apparentMagnitude] * len(rv_diff)
vprint(f'Added {len(rv_diff)} observations.')
rv_diff_list.extend(rv_diff)
app_mag_list.extend(app_mag)
tqdm.write(f'Found {len(rv_diff_list)} total observations.')
# plot_histogram(rv_diff_list)
plot_apparent_magnitude(rv_diff_list, app_mag_list)
axis.set_ylim(bottom=-0.5, top=len(stars_to_use) - 0.5)
axis.xaxis.set_minor_locator(ticker.MultipleLocator(base=5))
axis.axvline(x=0, linestyle='--', color='Black', linewidth=3, zorder=100)
axis.xaxis.set_tick_params(which='major', width=2, length=6)
axis.xaxis.set_tick_params(which='minor', width=1.5, length=4)
ax_dict[pairs_to_use[0]].set_yticks(range(0, len(stars_to_use)))
labels = []
stars = []
pair_means = {}
pair_stds = {}
pair_mean_errors = {}
for star_label in stars_to_use:
tqdm.write(f'Analysing {star_label}')
star = Star(star_label, f'/Users/dberke/data_output/{star_label}')
stars.append(star)
pre_slice = slice(None, star.fiberSplitIndex)
num_obs = star.getNumObs(pre_slice)
if star.name != "Vesta":
labels.append(star.name) # + f'\n #obs: {num_obs}')
else:
labels.append('Sun\n(Vesta)')
for label in pairs_to_use:
col_index = star.p_index(label)
means = star.pairSeparationsArray[pre_slice, col_index]
errors = star.pairSepErrorsArray[pre_slice, col_index]
tqdm.write(f'Found {len(transitions_list)} individual transitions.')
# Read the list of chosen pairs.
with open(vcl.final_pair_selection_file, 'r+b') as f:
pairs_list = pickle.load(f)
tqdm.write(f'Found {len(pairs_list)} transition pairs (total) in list.')
# Initialize Star objects from the given directories.
if args.recreate_stars:
tqdm.write('Creating stars from directories...')
tqdm.write('Reading in first star...')
star1 = Star(name=data_dir1.name,
star_dir=data_dir1,
suffix=args.suffix,
transitions_list=transitions_list,
pairs_list=pairs_list,
load_data=args.recreate_stars)
tqdm.write('Reading in second star...')
star2 = Star(name=data_dir2.name,
star_dir=data_dir2,
suffix=args.suffix,
transitions_list=transitions_list,
pairs_list=pairs_list,
load_data=args.recreate_stars)
if args.compare_pairs or args.sort_blended:
# Create a plot comparing the two stars.
tqdm.write('Creating plot...')
if (star1.fiberSplitIndex not in (0, None))\
return [
star.name,
str(star.temperature.value),
str(star.metallicity),
str(star.logg),
str(star.absoluteMagnitude),
str(star.apparentMagnitude),
str(star.numObsPre),
str(star.numObsPost)
]
# Get star directories starting with H or V.
star_dirs = [Path(s) for s in glob(str(vcl.output_dir) + '/[HV]*')]
stars = [Star(s.stem, s) for s in tqdm(star_dirs)]
categories = {
'SP1': {
'temperature': 100 * u.K,
'metallicity': 0.1,
'logg': 0.2
},
'SP2': {
'temperature': 200 * u.K,
'metallicity': 0.2,
'logg': 0.3
},
'SP3': {
'temperature': 300 * u.K,
'metallicity': 0.3,
from varconlib.miscellaneous import remove_nans
from varconlib.star import Star
parser = argparse.ArgumentParser(description='Compare the values between pre-'
' and post-fiber change observations in'
' various ways.')
parser.add_argument('star_name', action='store', type=str,
help='The name of the star (directory) where the star to be'
' used is.')
args = parser.parse_args()
plots_dir = vcl.output_dir / 'pair_result_plots'
star = Star(args.star_name, vcl.output_dir / args.star_name)
# KS stands for the 2-sided Kolmogorov-Smirnov test, TT for Welch's t-test.
ks_p_values = []
ks_stats = []
tt_p_values = []
tt_stats = []
# !!! Need to check if star hasObsPost before making post_slice.
pre_slice = slice(None, star.fiberSplitIndex)
post_slice = slice(star.fiberSplitIndex, None)
for pair_label, num in tqdm(star._pair_bidict.items()):
tqdm.write('Matching stars...')
for name in tqdm(names):
for row in data:
if row[0] == name:
if 'HD' not in name:
try:
name = hd_dict[name]
except KeyError:
continue
elif name == 'HD68978A':
name = hd_dict[name]
path = Path(f'/Users/dberke/data_output/{name}')
# tqdm.write(str(path))
if path.exists():
try:
star = Star(name, path, load_data=True)
except HDF5FileNotFoundError:
continue
matched_star_names.add(name)
# tqdm.write(f'Matched {name} ({row[0]})')
num_matched_stars += 1
teff_list.append(float(row[1]))
logg_list.append(float(row[2]))
feh_list.append(float(row[3]))
teff_err_list.append(float(row[4]))
logg_err_list.append(float(row[5]))
feh_err_list.append(float(row[6]))
casagrande_teff.append(star.temperature.value)
casagrande_logg.append(star.logg)
casagrande_feh.append(star.metallicity)