def analyze_individual_status_by_generation(self, csv_output, status, status_analysis, aggregate_functions = None, generations = 'all'):
print('\n[INDIVIDUAL ' + status.upper() + ' STATUS ANALYSIS]')
print('Opening outputfile: ' + csv_output + '...')
outputfile = open(csv_output, 'w')
print('Getting population size...')
pop_size = database_calls.db_reconstruct_simulation_parameters(self.cur, self.starting_time)['population_size']
print('Writing outputfile header...')
header = ['Generation'] + [str(i) for i in range(1, pop_size + 1)]
if aggregate_functions != None:
header = header + [key for key in list(aggregate_functions.keys())]
outputfile.write(','.join(header) + '\n')
print('Starting main analysis...')
if generations == 'all':
generation_list = database_calls.db_list_generations(self.cur, self.starting_time)
else:
generation_list = [str(gen) for gen in generations]
for generation in generation_list:
print('Analyzing generation ' + str(generation) + '...')
print()
status_list = [status_analysis(stat) for stat in self.get_individual_status_list_by_generation(status, generation)]
status_row = [str(generation)] + [str(stat_result) for stat_result in status_list]
if aggregate_functions != None:
for key in list(aggregate_functions.keys()):
status_row.append(str(aggregate_functions[key](status_list)))
outputfile.write(','.join(status_row) + '\n')
print('\nIndividual [' + status + '] analysis complete!')
def analyze_status_group_genome_by_generation(self, csv_output, genome_analysis, status, stats, aggregate_functions = None, generations = 'all'):
print('\n[' + status.upper() + ' STATUS GROUP GENOME ANALYSIS]')
print('Opening outputfile: ' + csv_output + '...')
outputfile = open(csv_output, 'w')
print('Constructing generations list...')
if generations == 'all':
generation_list = database_calls.db_list_generations(self.cur, self.starting_time)
else:
generation_list = generations
print('Writing outputfile header...')
header = [str(stat).replace(", ","-") for stat in stats]
if aggregate_functions == None:
header = ['Generation'] + header
else:
header = ['Generation'] + header + [key for key in list(aggregate_functions.keys())]
outputfile.write(','.join(header) + '\n')
print('Starting main analysis...')
for generation in generation_list:
print('Analyzing generation ' + str(generation) + '...')
print()
analyzed_genome_list = [genome_analysis(self.get_status_group_genome_by_generation(status, target_status, generation)) for target_status in stats]
status_row = [str(generation)] + [str(status_result) for status_result in analyzed_genome_list]
if aggregate_functions != None:
for key in list(aggregate_functions.keys()):
status_row.append(str(aggregate_functions[key](analyzed_genome_list)))
outputfile.write(','.join(status_row) + '\n')
print('\nGrouped [' + status + '] genome analysis complete!')
def analyze_status_group_count_by_generation(self,
csv_output,
status,
stats,
aggregate_functions=None,
generations='all'):
print('\n[' + status.upper() + ' STATUS GROUP COUNT ANALYSIS]')
print('Constructing generations list...')
if generations == 'all':
generation_list = database_calls.db_list_generations(
self.cur, self.starting_time)
else:
generation_list = generations
print('Preparing outputfile: ' + csv_output + '...')
outputfile = open(csv_output, 'w')
print('Writing outputfile header...')
header = [str(stat).replace(", ", "-") for stat in stats]
if aggregate_functions == None:
header = ['Generation'] + header
else:
header = ['Generation'] + header + \
[key for key in list(aggregate_functions.keys())]
outputfile.write(','.join(header) + '\n')
outputfile.close()
print('Starting main analysis...')
for generation in generation_list:
print('Analyzing generation ' + str(generation) + '...')
status_list = \
self.get_individual_status_list_by_generation(status,
generation)
status_row = [str(generation)] + \
[str(status_list.count(target_stat))
for target_stat in stats]
if aggregate_functions != None:
for key in list(aggregate_functions.keys()):
status_row.append(
str(aggregate_functions[key]([
status_list.count(target_stat)
for target_stat in stats
])))
outputfile = open(csv_output, 'a')
outputfile.write(','.join(status_row) + '\n')
outputfile.close()
print('\nGrouped [' + status + '] count analysis complete!')
def analyze_individual_status_by_generation(self,
csv_output,
status,
status_analysis,
aggregate_functions=None,
generations='all'):
print('\n[INDIVIDUAL ' + status.upper() + ' STATUS ANALYSIS]')
print('Getting population size...')
pop_size = \
database_calls.db_reconstruct_simulation_parameters(self.cur,
self.starting_time)['population_size']
print('Constructing generations list...')
if generations == 'all':
generation_list = database_calls.db_list_generations(
self.cur, self.starting_time)
else:
generation_list = [str(gen) for gen in generations]
print('Preparing outputfile: ' + csv_output + '...')
outputfile = open(csv_output, 'w')
print('Writing outputfile header...')
header = ['Generation'] + [str(i) for i in range(1, pop_size + 1)]
if aggregate_functions != None:
header = header + \
[key for key in list(aggregate_functions.keys())]
outputfile.write(','.join(header) + '\n')
outputfile.close()
print('Starting main analysis...')
for generation in generation_list:
print('Analyzing generation ' + str(generation) + '...')
status_list = \
[status_analysis(stat)
for stat in self.get_individual_status_list_by_generation(status, generation)]
status_row = [str(generation)] + \
[str(stat_result) for stat_result in status_list]
if aggregate_functions != None:
for key in list(aggregate_functions.keys()):
status_row.append(
str(aggregate_functions[key](status_list)))
outputfile = open(csv_output, 'a')
outputfile.write(','.join(status_row) + '\n')
outputfile.close()
print('\nIndividual [' + status + '] analysis complete!')
def analyze_status_group_count_by_generation(self, csv_output, status,
stats,
aggregate_functions = None,
generations = 'all'):
print('\n[' + status.upper() + ' STATUS GROUP COUNT ANALYSIS]')
print('Constructing generations list...')
if generations == 'all':
generation_list = database_calls.db_list_generations(self.cur,
self.starting_time)
else:
generation_list = generations
print('Preparing outputfile: ' + csv_output + '...')
outputfile = open(csv_output, 'w')
print('Writing outputfile header...')
header = [str(stat).replace(", ","-") for stat in stats]
if aggregate_functions == None:
header = ['Generation'] + header
else:
header = ['Generation'] + header + \
[key for key in list(aggregate_functions.keys())]
outputfile.write(','.join(header) + '\n')
outputfile.close()
print('Starting main analysis...')
for generation in generation_list:
print('Analyzing generation ' + str(generation) + '...')
status_list = \
self.get_individual_status_list_by_generation(status,
generation)
status_row = [str(generation)] + \
[str(status_list.count(target_stat))
for target_stat in stats]
if aggregate_functions != None:
for key in list(aggregate_functions.keys()):
status_row.append(str(aggregate_functions[key]([status_list.count(target_stat) for target_stat in stats])))
outputfile = open(csv_output, 'a')
outputfile.write(','.join(status_row) + '\n')
outputfile.close()
print('\nGrouped [' + status + '] count analysis complete!')
