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petitCODE-hot spectra are downloaded and no longer require the data_f…
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…older parameter of add_model
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Tomas Stolker committed Nov 6, 2020
1 parent 05dea51 commit 66b4580
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Showing 3 changed files with 75 additions and 38 deletions.
6 changes: 3 additions & 3 deletions species/analysis/fit_model.py
View file
Expand Up @@ -1311,10 +1311,10 @@ def lnlike_multinest(cube,

if self.spectrum[item][2] is not None:
# Use the inverted covariance matrix
dot_tmp = np.dot(data_flux-model_flux,
np.dot(data_cov_inv, data_flux-model_flux))
ln_like += -0.5 * np.dot(data_flux-model_flux,
np.dot(data_cov_inv, data_flux-model_flux))

ln_like += -0.5*dot_tmp - 0.5*np.nansum(np.log(2.*np.pi*data_var))
ln_like += -0.5 * np.nansum(np.log(2.*np.pi*data_var))

else:
if item in self.fit_corr:
Expand Down
22 changes: 13 additions & 9 deletions species/data/database.py
View file
Expand Up @@ -27,7 +27,7 @@

class Database:
"""
Class for fitting atmospheric model spectra to photometric data.
Class with reading and writing functionalities for the HDF5 database.
"""

@typechecked
Expand Down Expand Up @@ -343,20 +343,26 @@ def add_model(self,
Effective temperature range (K). Setting the value to None for will add all available
temperatures.
data_folder : str, None
Folder with input data. Only required for the petitCODE hot models which are not
publicly available.
Folder with input data. This parameter has been deprecated since all model spectra
are publicly available.
Returns
-------
NoneType
None
"""

proprietary = ['petitcode-hot-clear', 'petitcode-hot-cloudy']
if data_folder is not None:
warnings.warn('The \'data_folder\' parameter has been deprecated since '
'all supported model spectra are publicly available. The'
'parameter will therefore be ignored and will cause an error '
'in a future release.')

if model in proprietary and data_folder is None:
raise ValueError(f'The {model} model is not publicly available and needs to '
f'be imported by setting the \'data_folder\' parameter.')
# proprietary = ['petitcode-hot-clear', 'petitcode-hot-cloudy']

# if model in proprietary and data_folder is None:
# raise ValueError(f'The {model} model is not publicly available and needs to '
# f'be imported by setting the \'data_folder\' parameter.')

# if model in ['bt-nextgen'] and wavel_range is None:
# raise ValueError(f'The \'wavel_range\' should be set for the \'{model}\' models to '
Expand Down Expand Up @@ -487,7 +493,6 @@ def add_model(self,
elif model == 'petitcode-hot-clear':
petitcode.add_petitcode_hot_clear(self.input_path,
h5_file,
data_folder,
wavel_range,
teff_range,
spec_res)
Expand All @@ -497,7 +502,6 @@ def add_model(self,
elif model == 'petitcode-hot-cloudy':
petitcode.add_petitcode_hot_cloudy(self.input_path,
h5_file,
data_folder,
wavel_range,
teff_range,
spec_res)
Expand Down
85 changes: 59 additions & 26 deletions species/data/petitcode.py
View file
Expand Up @@ -3,15 +3,16 @@
"""

import os
import zipfile
import warnings
import tarfile
import urllib.request
import warnings
import zipfile

from typing import Optional, Tuple

import h5py
import spectres
import numpy as np
import spectres

from typeguard import typechecked

Expand Down Expand Up @@ -57,11 +58,11 @@ def add_petitcode_cool_clear(input_path: str,
data_file = os.path.join(input_path, 'linder_molliere_grid.zip')

if not os.path.isfile(data_file):
print('Downloading petitCODE cool clear model spectra (3.7 GB)...', end='', flush=True)
print('Downloading petitCODE cool model spectra (3.7 GB)...', end='', flush=True)
urllib.request.urlretrieve(url, data_file)
print(' [DONE]')

print('Unpacking petitCODE cool clear model spectra...', end='', flush=True)
print('Unpacking petitCODE cool model spectra (3.7 GB)...', end='', flush=True)

with zipfile.ZipFile(data_file, 'r') as zip_ref:
zip_ref.extractall(input_path)
Expand Down Expand Up @@ -180,11 +181,11 @@ def add_petitcode_cool_cloudy(input_path: str,
data_file = os.path.join(input_path, 'linder_molliere_grid.zip')

if not os.path.isfile(data_file):
print('Downloading petitCODE cool cloudy model spectra (3.7 GB)...', end='', flush=True)
print('Downloading petitCODE cool model spectra (3.7 GB)...', end='', flush=True)
urllib.request.urlretrieve(url, data_file)
print(' [DONE]')

print('Unpacking petitCODE cool cloudy model spectra...', end='', flush=True)
print('Unpacking petitCODE cool model spectra (3.7 GB)...', end='', flush=True)

with zipfile.ZipFile(data_file, 'r') as zip_ref:
zip_ref.extractall(input_path)
Expand Down Expand Up @@ -271,7 +272,6 @@ def add_petitcode_cool_cloudy(input_path: str,
@typechecked
def add_petitcode_hot_clear(input_path: str,
database: h5py._hl.files.File,
data_folder: str,
wavel_range: Optional[Tuple[float, float]] = None,
teff_range: Optional[Tuple[float, float]] = None,
spec_res: Optional[float] = 1000.) -> None:
Expand All @@ -284,8 +284,6 @@ def add_petitcode_hot_clear(input_path: str,
Folder where the data is located.
database : h5py._hl.files.File
Database.
data_folder : str
Path with input data.
wavel_range : tuple(float, float), None
Wavelength range (um). The original wavelength points are used if set to None.
teff_range : tuple(float, float), None
Expand All @@ -302,6 +300,23 @@ def add_petitcode_hot_clear(input_path: str,
if not os.path.exists(input_path):
os.makedirs(input_path)

data_folder = os.path.join(input_path, 'petitcode-hot-clear/')

url = 'https://people.phys.ethz.ch/~ipa/tstolker/petitcode-hot-clear.tgz'

data_file = os.path.join(input_path, 'petitcode-hot-clear.tgz')

if not os.path.isfile(data_file):
print('Downloading petitCODE hot clear model spectra (93 MB)...', end='', flush=True)
urllib.request.urlretrieve(url, data_file)
print(' [DONE]')

print('Unpacking petitCODE hot clear model spectra (93 MB)...', end='', flush=True)
tar = tarfile.open(data_file)
tar.extractall(data_folder)
tar.close()
print(' [DONE]')

teff = []
logg = []
feh = []
Expand All @@ -315,17 +330,19 @@ def add_petitcode_hot_clear(input_path: str,

for _, _, files in os.walk(data_folder):
for filename in files:
teff_val = float(filename[9:13])
logg_val = float(filename[19:23])
feh_val = float(filename[28:32])
co_ratio_val = float(filename[36:40])
file_split = filename.split('_')

teff_val = float(file_split[2])
logg_val = float(file_split[4])
feh_val = float(file_split[6])
co_ratio_val = float(file_split[8])

if teff_range is not None:
if teff_val < teff_range[0] or teff_val > teff_range[1]:
continue

print_message = f'Adding petitCODE hot clear model spectra... {filename}'
print(f'\r{print_message:<100}', end='')
print(f'\r{print_message:<99}', end='')

data = np.loadtxt(os.path.join(data_folder, filename))

Expand Down Expand Up @@ -361,7 +378,7 @@ def add_petitcode_hot_clear(input_path: str,
'original wavelength sampling. Storing zeros instead.')

print_message = 'Adding petitCODE hot clear model spectra... [DONE]'
print(f'\r{print_message:<100}')
print(f'\r{print_message:<99}')

data_sorted = data_util.sort_data(np.asarray(teff),
np.asarray(logg),
Expand All @@ -380,7 +397,6 @@ def add_petitcode_hot_clear(input_path: str,
@typechecked
def add_petitcode_hot_cloudy(input_path: str,
database: h5py._hl.files.File,
data_folder: str,
wavel_range: Optional[Tuple[float, float]] = None,
teff_range: Optional[Tuple[float, float]] = None,
spec_res: Optional[float] = 1000.) -> None:
Expand All @@ -393,8 +409,6 @@ def add_petitcode_hot_cloudy(input_path: str,
Folder where the data is located.
database : h5py._hl.files.File
Database.
data_folder : str
Path with input data.
wavel_range : tuple(float, float), None
Wavelength range (um). The original wavelength points are used if set to None.
teff_range : tuple(float, float), None
Expand All @@ -411,6 +425,23 @@ def add_petitcode_hot_cloudy(input_path: str,
if not os.path.exists(input_path):
os.makedirs(input_path)

data_folder = os.path.join(input_path, 'petitcode-hot-cloudy/')

url = 'https://people.phys.ethz.ch/~ipa/tstolker/petitcode-hot-cloudy.tgz'

data_file = os.path.join(input_path, 'petitcode-hot-cloudy.tgz')

if not os.path.isfile(data_file):
print('Downloading petitCODE hot cloudy model spectra (276 MB)...', end='', flush=True)
urllib.request.urlretrieve(url, data_file)
print(' [DONE]')

print('Unpacking petitCODE hot cloudy model spectra (276 MB)...', end='', flush=True)
tar = tarfile.open(data_file)
tar.extractall(data_folder)
tar.close()
print(' [DONE]')

teff = []
logg = []
feh = []
Expand All @@ -425,18 +456,20 @@ def add_petitcode_hot_cloudy(input_path: str,

for _, _, files in os.walk(data_folder):
for filename in files:
teff_val = float(filename[9:13])
logg_val = float(filename[19:23])
feh_val = float(filename[28:32])
co_ratio_val = float(filename[36:40])
fsed_val = float(filename[46:50])
file_split = filename.split('_')

teff_val = float(file_split[2])
logg_val = float(file_split[4])
feh_val = float(file_split[6])
co_ratio_val = float(file_split[8])
fsed_val = float(file_split[10])

if teff_range is not None:
if teff_val < teff_range[0] or teff_val > teff_range[1]:
continue

print_message = f'Adding petitCODE hot cloudy model spectra... {filename}'
print(f'\r{print_message:<112}', end='')
print(f'\r{print_message:<111}', end='')

data = np.loadtxt(os.path.join(data_folder, filename))

Expand Down Expand Up @@ -473,7 +506,7 @@ def add_petitcode_hot_cloudy(input_path: str,
'original wavelength sampling. Storing zeros instead.')

print_message = 'Adding petitCODE hot cloudy model spectra... [DONE]'
print(f'\r{print_message:<112}')
print(f'\r{print_message:<111}')

data_sorted = data_util.sort_data(np.asarray(teff),
np.asarray(logg),
Expand Down

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