with open(datafile, newline='') as f:
reader = csv.reader(f)
data = list(reader)
f.close()
# Read in the correct config file from the database
config_file = config.config_file(db_init_filename='database_archi.ini',
section='radex_fit_results')
bestfit_config_file = config.config_file(db_init_filename='database_archi.ini',
section='radex_bestfit_conditions')
db_pool = db.dbpool(config_file)
db_bestfit_pool = db.dbpool(bestfit_config_file)
# Parse the data to a dict list
observed_data = workerfunctions.parse_data(data, db_pool, db_bestfit_pool)
# Filter the observed data to contain only those species that we can use
# (normally limited by those with Radex data)
filtered_data = workerfunctions.filter_data(observed_data,
["SIO", "SO", "OCS", "H2CS"])
# Read the image data from Farhad
fits_table_filename = 'data/images/plw.fits'
observation = fits.open(fits_table_filename)[1]
image_data = observation.data
w = wcs.WCS(observation.header)
# Determine size of image from pixel coords and use to determine pixel sizes
origin_x_pos, origin_y_pos = w.wcs_pix2world(0, 0, 1)
max_x_pos, max_y_pos = w.wcs_pix2world(len(image_data), len(image_data[0]), 1)
# Declare the database connections
config_file = config.config_file(db_init_filename='database_archi.ini', section='shock_fit_results')
bestfit_config_file = config.config_file(db_init_filename='database_archi.ini', section='shock_bestfit_conditions')
# Set up the connection pools
db_pool = db.dbpool(config_file)
db_bestfit_pool = db.dbpool(bestfit_config_file)
# Read in the whole data file containing sources and source flux
with open(datafile, newline='') as f:
reader = csv.reader(f)
data = list(reader)
f.close()
# Parse the data to a dict list
observed_data = workerfunctions.parse_data(data)
# Filter the observed data to contain only those species that we can use
# (normally limited by those with Radex data)
filtered_data = workerfunctions.filter_data(observed_data, relevant_species)
nWalkers = 500 # Number of random walkers to sample parameter space
nDim = 5 # Number of dimensions within the parameters
nSteps = int(1e3) # Number of steps per walker
for obs in filtered_data:
if (len(obs["species"]) >= 2 and "SIO" in obs["species"]) or \
(len(obs["species"]) >= 2 and "SO" in obs["species"]) or \
(len(obs["species"]) >= 2 and "CH3OH" in obs["species"]) or \
(len(obs["species"]) >= 2 and "OCS" in obs["species"]) or \
(len(obs["species"]) >= 2 and "H2CS" in obs["species"]):