def produce_data(parameters_file, data_file):
"""
Produce data for 'pooled' condition using multiprocessing
:param parameters_file: Path to parameters file (string)
:param data_file: Path to the future data files (dictionary with two entries)
:return: a 'pool backup' (arbitrary Python object)
"""
json_parameters = parameters.load(parameters_file)
pool_parameters = parameters.extract_parameters(json_parameters)
pool = mlt.Pool()
backups = []
for bkp in tqdm.tqdm(pool.imap_unordered(run, pool_parameters),
total=len(pool_parameters)):
backups.append(bkp)
pool_backup = backup.PoolBackup(parameters=json_parameters,
backups=backups)
pool_backup.save(parameters_file, data_file)
return pool_backup
def individual_data(args):
"""
Produce figures for 'individual' data
:param args: Parsed args from command line ('Namespace' object)
:return: None
"""
for move in (str(i).replace("Move.", "")
for i in (model.Move.max_profit, model.Move.strategic,
model.Move.max_diff, model.Move.equal_sharing)):
run_backups = []
for r in ("25", "50"): # , "75"):
parameters_file = "data/json/{}_{}.json".format(r, move)
data_file = "data/pickle/{}_{}.p".format(r, move)
if not data_already_produced(parameters_file,
data_file) or args.force:
json_parameters = parameters.load(parameters_file)
param = parameters.extract_parameters(json_parameters)
run_backup = run(param)
run_backup.save(parameters_file, data_file)
else:
run_backup = backup.RunBackup.load(data_file)
run_backups.append(run_backup)
analysis.separate.separate(backups=run_backups,
fig_name='fig/separate_{}.pdf'.format(move))
def produce_data(parameters_file, data_file):
json_parameters = parameters.load(parameters_file)
pool_parameters = parameters.extract_parameters(json_parameters)
pool = mlt.Pool()
backups = []
for bkp in tqdm.tqdm(pool.imap_unordered(run, pool_parameters),
total=len(pool_parameters)):
backups.append(bkp)
pool_backup = backup.PoolBackup(parameters=json_parameters,
backups=backups)
pool_backup.save(data_file)
return pool_backup
def individual_data(args):
for condition in ("75", "50", "25"):
parameters_file, data_file, fig_files = get_files_names(condition)
if not data_already_produced(data_file) or args.force:
json_parameters = parameters.load(parameters_file)
param = parameters.extract_parameters(json_parameters)
run_backup = run(param)
run_backup.save(data_file)
else:
run_backup = backup.RunBackup.load(data_file["pickle"])
analysis.separate.eeg_like(backup=run_backup,
fig_name=fig_files["eeg_like"])
analysis.separate.pos_firmA_over_pos_firmB(
backup=run_backup, fig_name=fig_files["positions"])
terminal_msg(condition, parameters_file, data_file, fig_files)
def clustered_data(args):
for move in (str(i).replace("Move.", "")
for i in (model.Move.max_profit, model.Move.strategic,
model.Move.max_diff, model.Move.equal_sharing)):
parameters_file = "data/json/pool_{}.json".format(move)
data_file = "data/pickle/pool_{}.p".format(move)
if not data_already_produced(data_file) or args.force:
pool_backup = produce_data(parameters_file, data_file)
else:
pool_backup = backup.PoolBackup.load(data_file)
run_backups = []
for r in ("25", "50"): # , "75"):
parameters_file = "data/json/{}_{}.json".format(r, move)
data_file = "data/pickle/{}_{}.p".format(r, move)
if not data_already_produced(parameters_file,
data_file) or args.force:
json_parameters = parameters.load(parameters_file)
param = parameters.extract_parameters(json_parameters)
run_backup = run(param)
run_backup.save(parameters_file, data_file)
else:
run_backup = backup.RunBackup.load(data_file)
run_backups.append(run_backup)
parameters_file = "data/json/batch_{}.json".format(move)
data_file = "data/pickle/batch_{}.p".format(move)
if not data_already_produced(data_file) or args.force:
batch_backup = produce_data(parameters_file, data_file)
else:
batch_backup = backup.PoolBackup.load(data_file)
fig = plt.figure(figsize=(13.5, 7))
gs = matplotlib.gridspec.GridSpec(nrows=2,
ncols=2,
width_ratios=[1, 0.7])
analysis.pool.distance_price_and_profit(pool_backup=pool_backup,
subplot_spec=gs[0, 0])
analysis.separate.separate(backups=run_backups, subplot_spec=gs[:, 1])
analysis.batch.plot(batch_backup=batch_backup, subplot_spec=gs[1, 0])
plt.tight_layout()
ax = fig.add_subplot(gs[:, :], zorder=-10)
plt.axis("off")
ax.text(s="B",
x=-0.05,
y=0,
horizontalalignment='center',
verticalalignment='center',
transform=ax.transAxes,
fontsize=20)
ax.text(s="A",
x=-0.05,
y=0.55,
horizontalalignment='center',
verticalalignment='center',
transform=ax.transAxes,
fontsize=20)
ax.text(s="C",
x=0.58,
y=0,
horizontalalignment='center',
verticalalignment='center',
transform=ax.transAxes,
fontsize=20)
fig_name = "fig/clustered_{}.pdf".format(move)
os.makedirs(os.path.dirname(fig_name), exist_ok=True)
plt.savefig(fig_name)
plt.show()