def main(project=None, unit=None, folders_file=None, parallel=False, remote=False, remove_after=False, verbose=False, **kwargs):
pan = Panoptes(simulator=['all'])
folders = get_file_contents(folders_file).strip().split('\n')
# See if the remote path exists in the HDF5 data store
store = pd.HDFStore(kwargs.get('hdf5_file'))
with console.ProgressBarOrSpinner(len(folders), "Folders") as bar:
for idx, folder in enumerate(folders):
folder = folder.rstrip('/')
hdf_path = '/observing/{}'.format(folder)
if hdf_path not in store.keys():
local_dir = '/var/panoptes/images/fields/{}/'.format(folder)
if not os.path.exists(local_dir) and remote:
# Get the data
remote_path = 'gs://{}/{}/{}'.format(project, unit, folder)
get_remote_dir(remote_path, local_dir=local_dir, extension='cr2')
# Make data
make_pec_data(folder, observer=pan.observatory.scheduler, parallel=parallel, verbose=verbose)
if remove_after:
# Remove the data
try:
shutil.rmtree(local_dir)
except Exception as e:
if verbose:
print("Error removing dir: {}".format(e))
else:
if verbose:
print("{} already in HDF5 table".format(folder))
bar.update(idx)
from panoptes.utils import images
parser = argparse.ArgumentParser(
description='Convert Canon .cr2 file(s) to a FITS')
parser.add_argument('--directory', help="Convert all .cr2 files in directory.")
parser.add_argument('-v',
'--verbose',
action='store_true',
default=False,
help='Verbose mode')
def out(msg):
if args.verbose:
console.color_print(msg)
args = parser.parse_args()
if args.directory:
out("Converting all files in {}".format(args.directory))
cr2_files = glob.glob("{}/*.cr2".format(args.directory))
with console.ProgressBarOrSpinner(len(cr2_files), "CR2 to FITS") as bar:
for num, cr2 in enumerate(cr2_files):
images.get_solve_field(cr2)
bar.update(num)
def output_seds(image_stack):
"""
Makes the SEDs.
Parameters
----------
image_stack: HDU list
A structure containing headers and image data for all FITS input
images.
"""
# make new directory for output, if needed
new_directory = image_directory + "/seds/"
if not os.path.exists(new_directory):
os.makedirs(new_directory)
all_image_data = []
wavelengths = []
num_wavelengths = len(image_stack)
# print("In seds")
# print("num_wavelenghts : ", num_wavelengths)
for i in range(1, num_wavelengths):
original_filename = os.path.basename(image_stack[i].header['FILENAME'])
original_directory = os.path.dirname(image_stack[i].header['FILENAME'])
input_directory = original_directory + "/resampled/"
input_filename = (input_directory + original_filename +
"_resampled.fits")
wavelength = image_stack[i].header['WAVELNTH']
wavelengths.append(wavelength)
# Load the data for each image and append it to a master list of
# all image data.
##NOTETOSELF: change to use nddata structure?
hdulist = image_stack[i]
image_data = hdulist.data
all_image_data.append(image_data)
print(len(all_image_data))
sed_data = []
for i in range(0, num_wavelengths - 1):
print(i, "on")
for j in range(len(all_image_data[i])):
for k in range(len(all_image_data[i][j])):
sed_data.append(
(int(j), int(k), wavelengths[i], all_image_data[i][j][k]))
print(i, "done")
# write the SED data to a test file
# NOTETOSELF: make this optional?
data = np.copy(sorted(sed_data))
np.savetxt('test.out',
data,
fmt='%f,%f,%f,%f',
header='x, y, wavelength (um), flux units (Jy/pixel)')
num_seds = int(len(data) / num_wavelengths)
with console.ProgressBarOrSpinner(num_seds, "Creating SEDs") as bar:
for i in range(0, num_seds):
# change to the desired fonts
rc('font', family='Times New Roman')
rc('text', usetex=True)
# grab the data from the cube
wavelength_values = data[:, 2][i * num_wavelengths:(i + 1) *
num_wavelengths]
flux_values = data[:, 3][i * num_wavelengths:(i + 1) *
num_wavelengths]
# NOTETOSELF: change from 0-index to 1-index
x_values = data[:, 0][i * num_wavelengths:(i + 1) *
num_wavelengths] # pixel pos
y_values = data[:, 1][i * num_wavelengths:(i + 1) *
num_wavelengths] # pixel pos
fig, ax = plt.subplots()
ax.scatter(wavelength_values, flux_values)
# axes specific
ax.set_xlabel(r'Wavelength ($\mu$m)')
ax.set_ylabel(r'Flux density (Jy/pixel)')
rc('axes', labelsize=14, linewidth=2, labelcolor='black')
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_xlim(min(wavelength_values), max(
wavelength_values)) #NOTETOSELF: doesn't quite seem to work
ax.set_ylim(min(flux_values), max(flux_values))
fig.savefig(new_directory + '/' + str(int(x_values[0])) + '_' +
str(int(y_values[0])) + '_sed.eps')
bar.update(i)
return
def output_seds(images_with_headers):
"""
Makes the SEDs.
Parameters
----------
images_with_headers: zipped list structure
A structure containing headers and image data for all FITS input
images.
"""
all_image_data = []
wavelengths = []
num_wavelengths = len(images_with_headers)
for i in range(0, num_wavelengths):
original_filename = os.path.basename(images_with_headers[i][2])
original_directory = os.path.dirname(images_with_headers[i][2])
new_directory = original_directory + "/seds/"
input_directory = original_directory + "/resampled/"
input_filename = (input_directory + original_filename +
"_resampled.fits")
wavelength = images_with_headers[i][1]['WAVELNTH']
wavelengths.append(wavelength)
if not os.path.exists(new_directory):
os.makedirs(new_directory)
# Load the data for each image and append it to a master list of
# all image data.
##NOTETOSELF: change to use nddata structure?
hdulist = fits.open(input_filename)
image_data = hdulist[0].data
all_image_data.append(image_data)
hdulist.close()
sed_data = []
for i in range(0, num_wavelengths):
for j in range(len(all_image_data[i])):
for k in range(len(all_image_data[i][j])):
sed_data.append(
(int(j), int(k), wavelengths[i], all_image_data[i][j][k]))
data = np.copy(sorted(sed_data))
np.savetxt('test.out',
data,
fmt='%f,%f,%f,%f',
header='x, y, wavelength (um), flux units (Jy/pixel)')
num_seds = int(len(data) / num_wavelengths)
with console.ProgressBarOrSpinner(num_seds, "Creating SEDs") as bar:
for i in range(0, num_seds):
# change to the desired fonts
rc('font', family='Times New Roman')
rc('text', usetex=True)
wavelength_values = data[:, 2][i * num_wavelengths:(i + 1) *
num_wavelengths]
flux_values = data[:, 3][i * num_wavelengths:(i + 1) *
num_wavelengths]
x_values = data[:,
0][i * num_wavelengths:(i + 1) * num_wavelengths]
y_values = data[:,
1][i * num_wavelengths:(i + 1) * num_wavelengths]
fig, ax = plt.subplots()
ax.scatter(wavelength_values, flux_values)
# axes specific
ax.set_xlabel(r'Wavelength ($\mu$m)')
ax.set_ylabel(r'Flux density (Jy/pixel)')
rc('axes', labelsize=14, linewidth=2, labelcolor='black')
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_xlim(min(wavelength_values), max(
wavelength_values)) #NOTETOSELF: doesn't quite seem to work
ax.set_ylim(min(flux_values), max(flux_values))
fig.savefig(new_directory + '/' + ` int(x_values[0]) ` + '_' +
` int(y_values[0]) ` + '_sed.eps')
bar.update(i)
return