# Determine the preparation name if frame.filter is not None: prep_name = str(frame.filter) else: prep_name = image_name # Set the row entries names_column.append(image_name) paths_column.append(image_path) prep_names_column.append(prep_name) # Create the table data = [names_column, paths_column, prep_names_column] table = tables.new(data, names) # Check whether the preparation directory exists prep_path = fs.join(config.path, "prep") if not fs.is_directory(prep_path): fs.create_directory(prep_path) # Save the table prep_info_table_path = fs.join(prep_path, "prep_info.dat") tables.write(table, prep_info_table_path, format="ascii.ecsv") # ----------------------------------------------------------------- # Create a PreparationInitializer instance initializer = PreparationInitializer(config) # Run the data initializer initializer.run() # -----------------------------------------------------------------
old_galactic_catalog_path = fs.join(galaxy_user_path, "galaxies.dat") old_stellar_catalog_path = fs.join(galaxy_user_path, "stars.dat") if fs.is_file(old_galactic_catalog_path): # Open the 'old' galaxy catalog old_galaxy_catalog = tables.from_file(old_galactic_catalog_path) # Create merged galaxy catalog galaxy_catalog = catalogs.merge_galactic_catalogs( galactic_catalog, old_galaxy_catalog) # Save the merged catalog path = fs.join(galaxy_user_path, "galaxies.dat") tables.write(galaxy_catalog, path) # If a galactic catalog file does not exist yet else: fs.copy_file(galactic_catalog_path, galaxy_user_path, "galaxies.dat") # Check whether there is a stellar catalog file in the galaxy's directory if fs.is_file(old_stellar_catalog_path): # Open the new stellar catalog stellar_catalog = tables.from_file(stellar_catalog_path) # Open the 'old' stellar catalog old_stellar_catalog = tables.from_file(old_stellar_catalog_path) # Create merged stellar catalog
# Give the individual a unique name name = time.unique_name(precision="micro") name_column.append(name) par_a_column.append(ind.genomeList[0]) par_b_column.append(ind.genomeList[1]) # Create the parameters table data = [name_column, par_a_column, par_b_column] names = ["Unique name", "Parameter a", "Parameter b"] new_parameters_table = tables.new(data, names) # ----------------------------------------------------------------- # Save the new parameters table tables.write(new_parameters_table, new_parameters_path, format="ascii.ecsv") # Dump the GA ga.saveto(new_path) # ----------------------------------------------------------------- # Save the state of the random generator new_random_path = fs.join(new_generation_path, "rndstate.pickle") save_state(new_random_path) # ----------------------------------------------------------------- # Initialize evolution: # self.initialize() # done in 'explore' # self.internalPop.evaluate()
if fs.is_directory(galaxy_user_path): old_galactic_catalog_path = fs.join(galaxy_user_path, "galaxies.dat") old_stellar_catalog_path = fs.join(galaxy_user_path, "stars.dat") if fs.is_file(old_galactic_catalog_path): # Open the 'old' galaxy catalog old_galaxy_catalog = tables.from_file(old_galactic_catalog_path) # Create merged galaxy catalog galaxy_catalog = catalogs.merge_galactic_catalogs(galactic_catalog, old_galaxy_catalog) # Save the merged catalog path = fs.join(galaxy_user_path, "galaxies.dat") tables.write(galaxy_catalog, path) # If a galactic catalog file does not exist yet else: fs.copy_file(galactic_catalog_path, galaxy_user_path, "galaxies.dat") # Check whether there is a stellar catalog file in the galaxy's directory if fs.is_file(old_stellar_catalog_path): # Open the new stellar catalog stellar_catalog = tables.from_file(stellar_catalog_path) # Open the 'old' stellar catalog old_stellar_catalog = tables.from_file(old_stellar_catalog_path) # Create merged stellar catalog stellar_catalog = catalogs.merge_stellar_catalogs(stellar_catalog, old_stellar_catalog)
# Add the score to the list name = table["Unique name"][index] names.append(name) scores.append(score) # Create the chi squared table data = [names, scores] names = ["Unique name", "Chi-squared"] chi_squared_table = tables.new(data, names) # Determine the path to the chi squared table chi_squared_path = fs.join(generation_path, "chi_squared.dat") # Write the chi squared table tables.write(chi_squared_table, chi_squared_path, format="ascii.ecsv") # ----------------------------------------------------------------- best_parameter_a = table["Parameter a"][index_lowest] best_parameter_b = table["Parameter b"][index_lowest] best_path = fs.join(generation_path, "best.dat") with open(best_path, 'w') as best_file: best_file.write("Parameter a: " + str(best_parameter_a) + "\n") best_file.write("Parameter b: " + str(best_parameter_b) + "\n") popt, pcov = curve_fit(fit_function, test_data_x, test_data_y) parameter_a_real = popt[0] parameter_b_real = popt[1]