def setup_parameter_files(parameters):
# 1 is subtracted because pcp only runes N-1 processes in parallel.
max_proc = min(
parameters['num_threads'] - 1,
len(parameters['q1_list']) * len(parameters['q2_list']) *
parameters['num_reservoir_samplings'])
parallel_input_sequence = generate_parallel_input_sequence(
parameters['q1_list'], parameters['q2_list'], parameters)
parallel_input_chunks = utilities.split_list(parallel_input_sequence,
max_proc)
parameter_file_names = write_command_file(parameters['worker_file'],
parameters['command_prefix'],
len(parallel_input_chunks),
parameters['full_path'])
write_parameter_files(parameter_file_names, parallel_input_chunks)
write_output_filenamelist(parameters['command_prefix'],
len(parameter_file_names),
parameters['full_path'])
utilities.save_object(
{
'parameters': parameters,
'q1_list': parameters['q1_list'],
'q2_list': parameters['q2_list']
}, parameters['full_path'] + parameters['command_prefix'] +
"_paramfile.pyobj")
def write_output_filenamelist(command_prefix, num_chunks, full_path):
output_paths = [ full_path + command_prefix + "_output" + str(i) + ".pyobj" \
for i in xrange(num_chunks) ]
utilities.save_object(output_paths,
full_path + command_prefix + "_outputfilelist.pyobj")
def main(argv):
experiment_parameter_file = str(argv[0])
chunkID = str(argv[1])
experimental_parameters = utilities.load_object(experiment_parameter_file)
results = worker(experimental_parameters)
utilities.save_object(results, experimental_parameters[0]['full_path'] +\
experimental_parameters[0]['command_prefix'] + "_output" + chunkID + ".pyobj")
def spectral_analysis(network_parameters, savefile=True):
largest_eigvals_adj_by_mu = []
second_largest_eigvals_adj_by_mu = []
# second_smallest_eigvals_lap_by_mu = []
spectrums = {'adj_eigvals': [], 'mus': network_parameters['mus']}
for mu in network_parameters['mus']:
adj_eigvals_by_trial = []
# lap_eigvals_by_trial = []
largest_eigvals_adj_by_trial = []
second_largest_eigvals_adj_by_trial = []
# second_smallest_eigvals_lap_by_trial = []
for j in range(network_parameters['num_reservoir_samplings']):
network = generate_network(
N=network_parameters['N'],
mu=mu,
k=network_parameters['k'],
maxk=network_parameters['maxk'],
minc=network_parameters['minc'],
maxc=network_parameters['maxc'],
deg_exp=network_parameters['deg_exp'],
temp_dir_ID=network_parameters['temp_dir_ID'],
full_path=network_parameters['full_path'],
weight_scale=network_parameters['reservoir_weight_scale'],
lower_weight_bound=network_parameters['lower_reservoir_bound'],
upper_weight_bound=network_parameters['upper_reservoir_bound'])
adj_eigvals = adj_spectrum(network)
# lap_eigvals = norm_laplacian_spectrum(network)
largest_eigvals_adj_by_trial.append(adj_eigvals[-1])
second_largest_eigvals_adj_by_trial.append(adj_eigvals[-2])
# second_smallest_eigvals_lap_by_trial.append(lap_eigvals[1])
adj_eigvals_by_trial.append(adj_eigvals)
# lap_eigvals_by_trial.append(lap_eigvals)
largest_eigvals_adj_by_mu.append(largest_eigvals_adj_by_trial)
second_largest_eigvals_adj_by_mu.append(
second_largest_eigvals_adj_by_trial)
spectrums['adj_eigvals'].append(adj_eigvals_by_trial)
# spectrums['lap_eigvals'].append(lap_eigvals_by_trial)
utilities.save_object(
spectrums, network_parameters['command_prefix'] + "_spectrum.pyobj")
return np.array(largest_eigvals_adj_by_mu), \
np.array(second_largest_eigvals_adj_by_mu)
def main(argv):
if len(argv) == 0:
print """
Call as: bigred2_result_consolidation command_prefix path
Where path is the full working directory where the files are locations
"""
command_prefix = str(argv[0])
full_path = str(argv[1])
parameters = utilities.load_object(full_path + command_prefix + "_paramfile.pyobj")
output_file_list = utilities.load_object(full_path + command_prefix + "_outputfilelist.pyobj")
results = consolidate_data(parameters, output_file_list)
utilities.save_object({'parameters': parameters, 'results': results}, full_path + command_prefix + "_final_results.pyobj")
cleanup(command_prefix, full_path, output_file_list)
def XmlParser(path, index_path):
global index_folder_path
index_folder_path = index_path
parser = xml.sax.make_parser()
parser.setFeature(xml.sax.handler.feature_namespaces, 0)
Handler = WikiContentHandler()
parser.setContentHandler(Handler)
parser.parse(path)
global i_count
# return doc_list, docid_title_map
if len(doc_list) >= 1:
i_count += 1
# path_to_save = "../index/i_index" + str(i_count) + ".txt"
fname = create_inverted_index(doc_list, i_count, index_folder_path)
filenames.append(fname)
# write_index_to_file(path_to_save, i_index)
save_object(docid_title_map, index_folder_path + "doc_title_map")
return filenames
def write_parameter_files(parameter_file_names, parallel_input_chunks):
for i in xrange(len(parallel_input_chunks)):
utilities.save_object(parallel_input_chunks[i], parameter_file_names[i])
for k in xrange(num_trials)] \
for i in xrange(num_mus)] \
for j in xrange(num_ratios) ]
listRatios_listMus_listMeanResults = [ [ np.mean([ model_results[(num_trials*num_ratios*i + num_trials*j + k)] \
for k in xrange(num_trials)], axis=0) \
for i in xrange(num_mus)] \
for j in xrange(num_ratios) ]
# Sort
sorted_listRatios_listMus_listMeanResults = sorted([
sorted(inner_list, key=lambda x: x[0])
for inner_list in listRatios_listMus_listMeanResults
],
key=lambda x: x[0][1])
utilities.save_object((sorted(list_mus), sorted(list_signal_ratio),
sorted_listRatios_listMus_listMeanResults,
listRatios_listMus_listTrials_listResults),
prefix + "_simresults_vs_mu_vs_signal.pyobj")
# Plot results
# list_mus, list_signal_ratio, sorted_listRatios_listMus_listMeanResults, \
# listRatios_listMus_listTrials_listResults = utilities.load_object("test0.4_simresults_vs_mu_vs_signal.pyobj")
# activity_contour_plot(prefix, list_mus, list_signal_ratio, sorted_listRatios_listMus_listMeanResults)
# activity_vs_mu_plot(prefix, 9, list_mus, list_signal_ratio, listRatios_listMus_listTrials_listResults)
fixed_point_contour_plot(prefix, list_mus, list_signal_ratio,
sorted_listRatios_listMus_listMeanResults)
# fixed_point_vs_mu_plot(prefix, 5, list_mus, list_signal_ratio, listRatios_listMus_listTrials_listResults)
# halting_time_contour_plot(prefix, list_mus, list_signal_ratio, sorted_listRatios_listMus_listMeanResults)
# time_to_activation_contour_plot(prefix, list_mus, list_signal_ratio, sorted_listRatios_listMus_listMeanResults)
# halting_time_vs_mu_plot(prefix, 9, list_mus, list_signal_ratio, listRatios_listMus_listTrials_listResults)
# time_to_activation_vs_mu_plot(prefix, 0, list_mus, list_signal_ratio, listRatios_listMus_listTrials_listResults)
