'in_train_file in_test_file in_test_ans in_user_profile_file in_config out_train_file out_test_file'
)
exit()
# arguments
train_file = sys.argv[1]
test_file = sys.argv[2]
test_ans_file = sys.argv[3]
user_profile_file = sys.argv[4]
config_file = sys.argv[5]
out_train_file = sys.argv[6]
out_test_file = sys.argv[7]
# read in data
train_graph = file_io.read_graph(train_file)
config = file_io.read_config(config_file)
user_feature = None
(user_feature,
feature_name) = file_io.read_feature_column_major(user_profile_file,
config)
#normalize features
for column in user_feature:
if column.type == 'numerical':
cf.normalize_column(column)
elif column.type == 'categorical':
cf.convert_to_dummy_variable(column)
test_pair = file_io.read_data(test_file)
train_graph = update_nodes_from_test_data(train_graph, test_pair)
from PyQt5 import QtWidgets
from algorithms.beam_search_solver import BeamSearchSolver
from algorithms.ga_solver import GASolver
from algorithms.csp_solver import CSPSolver
from algorithms.hill_climbing_solver import HillClimbingSolver
from chessboard.chessboard_state_node import ChessboardStateNode
from ui.main_window import MainWindow
import sys
from file_io import read_config, write_config
from chessboard.chessboard_state import ChessboardState
if __name__ == '__main__':
# pass
initial_config = read_config('input2.txt')
state = ChessboardState(initial_config)
# state.print_chessboard()
ga_solver = GASolver(n_population=8)
final_state = ga_solver.solve()
final_state.print_chessboard()
print(final_state.get_attacking_count())
print(ga_solver.get_running_time(), ga_solver.get_expanded_count(),
ga_solver.get_cost())
csp = CSPSolver()
csp.solve(state)
print("Number steps to the final solution =", csp.get_cost())
csp.final_sol.print_chessboard()
print("Expanded node count =", csp.get_expanded_count())
if len(sys.argv) != 8:
print('Usage:', sys.argv[0], 'in_train_file in_test_file in_test_ans in_user_profile_file in_config out_train_file out_test_file')
exit()
# arguments
train_file = sys.argv[1]
test_file = sys.argv[2]
test_ans_file = sys.argv[3]
user_profile_file = sys.argv[4]
config_file = sys.argv[5]
out_train_file = sys.argv[6]
out_test_file = sys.argv[7]
# read in data
train_graph = file_io.read_graph(train_file)
config = file_io.read_config(config_file)
user_feature = None
(user_feature, feature_name) = file_io.read_feature_column_major(user_profile_file, config)
#normalize features
for column in user_feature:
if column.type == 'numerical':
cf.normalize_column(column)
elif column.type == 'categorical':
cf.convert_to_dummy_variable(column)
test_pair = file_io.read_data(test_file)
train_graph = update_nodes_from_test_data(train_graph, test_pair)
test_ans = gen_label_mapping(test_pair, file_io.read_ans(test_ans_file))
gen_training_data(train_graph, user_feature, out_train_file)
def load_file(self):
file, _ = QFileDialog.getOpenFileName(QFileDialog(), 'Open File')
if file:
self.chessboard = read_config(file)
self.refresh_chessboard()
def main():
# Command line args are in sys.argv[1], sys.argv[2] ..
# sys.argv[0] is the script name itself and can be ignored
config = 'V:/DAMAnalysis-Lori/PySolo_Data/pysolo_video.cfg'
key = ''
if len(sys.argv) == 2:
directory = os.path.dirname(os.path.realpath(__file__))
config = os.path.join(directory, 'config.ini')
key = sys.argv[1]
elif len(sys.argv) > 2:
config = sys.argv[1]
key = sys.argv[2]
else:
print """
usage: python process_experiment.py [config_file] key_file
To process trikinetics experimental data, pass a config file (containing
relatively constant parameters) and a key file (containing both parameters
relevant to the specific experiment as well as the genotypes and
monitor/channel positions of flies). If only one argument is passed, the
default configuration is used.
When called, these files are parsed and the raw experimental data is
processed/aggregated into two dictionaries:
activity_dict - a table containing the raw beam crossing events, per minute
sleep_dict - a table where minutes of sleep are marked with a '1'
(sleep is defined as 5+ consecutive minutes of 0 activity)
After the construction of these dictionaries, plots are produced for the
experimental metadata as well as sleep and activity for each line vs all
controls. Other plot types are included in the plot.y module, but not
implemented in this script.
The activity and sleep dictionaries are written as .xls files for later use.
"""
# read the configuration file
config_dict = file_io.read_config(config)
# read the key file
(protocol_dict, genotype_dict) = file_io.read_key(key)
# create dataframe for DEnM data
DEnM_df = file_io.read_DEnM_data(protocol_dict['DEnM'],
config_dict['env_monitors'])
# since loading activity monitor data is expensive, find out which
# monitors we need first, then load the data into DAM_dict
dam_monitors = set(item[0] for sublist in genotype_dict.itervalues()
for item in sublist)
DAM_dict = {
'M' + str(monitor): file_io.read_DAM_data(monitor,
config_dict['max_monitor'])
for monitor in dam_monitors
}
# sort/collect data by genotype and create activity dict
activity_dict = analyze.aggregate_by_genotype(genotype_dict, config_dict,
DEnM_df, DAM_dict)
# mark and remove dead fly data so that it isn't plotted
dead_flies = analyze.mark_dead_flies(protocol_dict, DEnM_df, activity_dict,
genotype_dict)
dead_flies_filename = key[:-4] + '_dead_flies' + '.txt'
with open(dead_flies_filename, "a") as myfile:
myfile.write('\n'.join(dead_flies))
# create sleep dict from activity dict
sleep_dict = analyze.calculate_sleep(activity_dict)
# create subfolder for output
f = key[:-4] + '_plots'
try:
os.makedirs(f)
except OSError as exception:
if exception.errno != errno.EEXIST:
raise
shutil.copy(key, f) # copy key file to output folder
shutil.move(dead_flies_filename,
f) # move dead_flies file to output folder
os.chdir(
f
) # move into the output folder, so all subsequent files will be saved there
# plot the DEnM data, including light intensity, temperature, and relative humidity
plot.metadata(protocol_dict, DEnM_df)
# plot the activity and sleep of each genotype individually, with all controls
controls = list()
if set(protocol_dict['control_genotype']) & set(genotype_dict.keys()):
controls = protocol_dict['control_genotype']
for genotype in genotype_dict.keys():
if genotype not in protocol_dict['control_genotype']:
genotype_list = list(controls)
genotype_list.append(genotype)
plot.data(protocol_dict, DEnM_df, activity_dict, genotype_list,
'activity')
plot.data(protocol_dict, DEnM_df, sleep_dict, genotype_list,
'sleep')
# write the data to excel files
file_io.write_data(protocol_dict, DEnM_df, activity_dict,
key[:-4] + '_activity.xls')
file_io.write_data(protocol_dict, DEnM_df, sleep_dict,
key[:-4] + '_sleep.xls')
def main():
# Command line args are in sys.argv[1], sys.argv[2] ..
# sys.argv[0] is the script name itself and can be ignored
config = ''
key = ''
if len(sys.argv) == 2:
directory = os.path.dirname(os.path.realpath(__file__))
config = os.path.join(directory, 'config.ini')
key = sys.argv[1]
elif len(sys.argv) > 2:
config = sys.argv[1]
key = sys.argv[2]
else:
print """
usage: python process_experiment.py [config_file] key_file
To process trikinetics experimental data, pass a config file (containing
relatively constant parameters) and a key file (containing both parameters
relevant to the specific experiment as well as the genotypes and
monitor/channel positions of flies). If only one argument is passed, the
default configuration is used.
When called, these files are parsed and the raw experimental data is
processed/aggregated into two dictionaries:
activity_dict - a table containing the raw beam crossing events, per minute
sleep_dict - a table where minutes of sleep are marked with a '1'
(sleep is defined as 5+ consecutive minutes of 0 activity)
After the construction of these dictionaries, plots are produced for the
experimental metadata as well as sleep and activity for each line vs all
controls. Other plot types are included in the plot.y module, but not
implemented in this script.
The activity and sleep dictionaries are written as .xls files for later use.
"""
# read the configuration file
config_dict = file_io.read_config(config)
# read the key file
(protocol_dict, genotype_dict) = file_io.read_key(key)
# create dataframe for DEnM data
DEnM_df = file_io.read_DEnM_data(protocol_dict['DEnM'], config_dict['env_monitors'])
# since loading activity monitor data is expensive, find out which
# monitors we need first, then load the data into DAM_dict
dam_monitors = set(item[0] for sublist in genotype_dict.itervalues() for item in sublist)
DAM_dict = {'M' + str(monitor): file_io.read_DAM_data(monitor, config_dict['max_monitor']) for monitor in dam_monitors}
# sort/collect data by genotype and create activity dict
activity_dict = analyze.aggregate_by_genotype(genotype_dict, config_dict, DEnM_df, DAM_dict)
# mark and remove dead fly data so that it isn't plotted
dead_flies = analyze.mark_dead_flies(protocol_dict, DEnM_df, activity_dict, genotype_dict)
dead_flies_filename = key[:-4] + '_dead_flies' + '.txt'
with open(dead_flies_filename, "a") as myfile:
myfile.write('\n'.join(dead_flies))
# create sleep dict from activity dict
sleep_dict = analyze.calculate_sleep(activity_dict)
# create subfolder for output
f = key[:-4] + '_plots'
try:
os.makedirs(f)
except OSError as exception:
if exception.errno != errno.EEXIST:
raise
shutil.copy(key, f) # copy key file to output folder
shutil.move(dead_flies_filename, f) # move dead_flies file to output folder
os.chdir(f) # move into the output folder, so all subsequent files will be saved there
# plot the DEnM data, including light intensity, temperature, and relative humidity
plot.metadata(protocol_dict, DEnM_df)
# plot the activity and sleep of each genotype individually, with all controls
controls = list()
if set(protocol_dict['control_genotype']) & set(genotype_dict.keys()):
controls = protocol_dict['control_genotype']
for genotype in genotype_dict.keys():
if genotype not in protocol_dict['control_genotype']:
genotype_list = list(controls)
genotype_list.append(genotype)
plot.data(protocol_dict, DEnM_df, activity_dict, genotype_list, 'activity')
plot.data(protocol_dict, DEnM_df, sleep_dict, genotype_list, 'sleep')
# write the data to excel files
file_io.write_data(protocol_dict, DEnM_df, activity_dict, key[:-4] + '_activity.xls')
file_io.write_data(protocol_dict, DEnM_df, sleep_dict, key[:-4] + '_sleep.xls')
def get_config_if_exist():
if file_io.config_file_exist():
config = file_io.read_config()
return config
def init_config():
logger.info("Read config file --- Start")
config = read_config()
logger.info("Read config file --- Complete")
return config