#Define meta list and properties that will store optimal networks
results = []
results_header = []
#Start looping the columns; e.g., year 2000, then 2001, then 2002
for column in arange(len(data.columns.values)):
current = data.columns.values[column]
print("")
print("Starting {0}".format(current))
#Find none blank values and assign them in list distribution purely to get the zeta values in default 'D' mode
if zeta_type != 'S' and zeta_type != 'M':
from NFA_zeta import zeta_default
zeta_list = zeta_default(pd.to_numeric(data.ix[:,column]).dropna())
#Define list to store all the temporarily results for the current column
temp_results = []
#Compute, store, and save all networks for list of zeta coefficients
for i in arange(len(zeta_list)):
temp_results.append(network_calc(pd.to_numeric(data.ix[:,column]).dropna(), zeta_list[i], column))
if record_all == 'Y':
temp_zeta = [temp_results[i][1]]
temp_zeta_current = int(current)
save_individual_results(temp_results[i], temp_results[i][1], file_name, current)
plot_final_network(temp_results[i], temp_results[i][1], file_name, current, x_label, coef_show, column_name, mode_value, x_scale, y_scale, x_limits1, x_limits2, y_limits1, y_limits2, grid_lines)
#Determine "optimal" network
giant_size = []
grid_lines = ""
#Define meta list and properties that will store optimal networks
results = []
results_header = []
#Start looping the columns; e.g., year 2000, then 2001, then 2002
for column in arange(len(data.columns.values)):
current = data.columns.values[column]
print("")
print("Starting {0}".format(current))
#Find none blank values and assign them in list distribution purely to get the zeta values in default 'D' mode
if zeta_type != 'S' and zeta_type != 'M':
from NFA_zeta import zeta_default
zeta_list = zeta_default(pd.to_numeric(data.ix[:, column]).dropna())
#Define list to store all the temporarily results for the current column
temp_results = []
#Compute, store, and save all networks for list of zeta coefficients
for i in arange(len(zeta_list)):
temp_results.append(
network_calc(
pd.to_numeric(data.ix[:, column]).dropna(), zeta_list[i],
column))
if record_all == 'Y':
temp_zeta = [temp_results[i][1]]
temp_zeta_current = int(current)
save_individual_results(temp_results[i], temp_results[i][1],
file_name, current)
grid_lines = ""
#Define meta list and properties that will store optimal networks
results = []
results_header = []
#Start looping the columns; e.g., year 2000, then 2001, then 2002
for column in arange(len(data.columns.values)):
current = data.columns.values[column]
print("")
print("Starting {0}".format(current))
#Find none blank values and assign them in list distribution purely to get the zeta values in default 'D' mode
if zeta_type != 'S' and zeta_type != 'M':
from NFA_zeta import zeta_default
zeta_list = zeta_default(
data.ix[:, column].convert_objects(convert_numeric=True).dropna())
#Define list to store all the temporarily results for the current column
temp_results = []
#Compute, store, and save all networks for list of zeta coefficients
for i in arange(len(zeta_list)):
temp_results.append(
network_calc(
data.ix[:,
column].convert_objects(convert_numeric=True).dropna(),
zeta_list[i], column))
if record_all == 'Y':
temp_zeta = [temp_results[i][1]]
temp_zeta_current = int(current)
save_individual_results(temp_results[i], temp_results[i][1],
