# 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()
# 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()
# 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()
# -----------------------------------------------------------------
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]
