Exemple #1
0
# 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)

pixel_width = sgrA_dist * ((origin_x_pos - max_x_pos) / len(image_data))
pixel_height = sgrA_dist * ((max_y_pos - origin_y_pos) / len(image_data[0]))

############################## Plot the data ############################
Exemple #2
0
    # 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"]):

            if obs["source"] == "K13a" or obs["source"] == "G2" or obs["source"] == "G5":
                print("Source = {}".format(obs["source"]))
            else: