from exp_helpers import base_experiment # wang, 30 min base_experiment(expnum = 5, pie_chart = [.3, .45, .2, .05])
from exp_helpers import base_experiment # mostly blockage. some transmission (just a test, should be close to to exp2 and exp6) base_experiment(expnum = 10, pie_chart = [.99, 0, 0, 0.01])
from exp_helpers import base_experiment # mostly blockage. some filtering (just a test, should be close to to exp2 and exp6) base_experiment(expnum = 9, pie_chart = [.99, 0, 0.01, 0])
from exp_helpers import base_experiment from numpy import arange # like 3 and 18, but filter down first base_experiment(expnum = 20, damages_values = arange(0,50,1), histogram_flag = 1, filter_type = "outside", max_trials = 1, sparsity_cutoff = 20.75)
from exp_helpers import base_experiment # like 5, but filter down first base_experiment(expnum = 21, pie_chart = [.3, .45, .2, .05], sparsity_cutoff = 20.75)
from exp_helpers import base_experiment # reflection only, but threshold first base_experiment(expnum = 23, pie_chart = [0, 1, 0, 0], sparsity_cutoff = 20.75)
from exp_helpers import base_experiment # reflection only base_experiment(expnum='6', pie_chart=[0, 1, 0, 0], sparsity_cutoff=34.7)
from exp_helpers import base_experiment base_experiment(expnum=2)
from exp_helpers import base_experiment base_experiment(expnum = 2)
from exp_helpers import base_experiment base_experiment(expnum = 16, sparsity_cutoff = 20.75, max_trials = 1)
from exp_helpers import base_experiment base_experiment(expnum = '2a', sparsity_cutoff = 34.7)
from exp_helpers import base_experiment # combination of damage types, on sparified network # get this distribution of damage types from Wang paper base_experiment(expnum='5', pie_chart=[.3, .45, .2, .05], sparsity_cutoff=34.7)
from exp_helpers import base_experiment # all blockage (just a test, should be equivalent to exp2) base_experiment(expnum = 6, pie_chart = [1, 0, 0, 0])
from exp_helpers import base_experiment
from numpy import tile
base_experiment(expnum='8',
aging_flag=1,
damages_values=tile(.01, 100),
pie_chart=[.3, .45, .2, .05],
sparsity_cutoff=34.7)
from exp_helpers import base_experiment
from numpy import arange
myalpha = 1. / 5050.
base_experiment(expnum='9',
aging_flag=1,
damages_values=(arange(100) + 1) * myalpha,
pie_chart=[.3, .45, .2, .05],
sparsity_cutoff=34.7)
from exp_helpers import base_experiment from numpy import arange base_experiment(expnum = 3, damages_values = arange(0,50,1), histogram_flag = 1, filter_type = "outside", max_trials = 1)
from exp_helpers import base_experiment from numpy import arange base_experiment(expnum = 15, damages_values = arange(0,50,.25), histogram_flag = 1, filter_type = "inside", max_trials = 1)
from exp_helpers import base_experiment base_experiment(detailed_file_flag=1)
from exp_helpers import base_experiment # filtering only base_experiment(expnum = 12, pie_chart = [0, 0, 1, 0])
from exp_helpers import base_experiment # wang, 30 min base_experiment(expnum = 13, pie_chart = [.3, .45, .2, .05], coeff = [-.1411, .8355, -.0127])
from exp_helpers import base_experiment # filtering only base_experiment(expnum = 14, pie_chart = [0, 0, 1, 0], coeff = [-.1411, .8355, -.0127])
from exp_helpers import base_experiment from numpy import tile base_experiment(expnum = 7, aging_flag = 1, damages_values = tile(.01, 100))
from exp_helpers import base_experiment # mostly blockage. some reflection (just a test, should be close to to exp2 and exp6) base_experiment(expnum = 8, pie_chart = [.99, .01, 0, 0])
from exp_helpers import base_experiment
from numpy import arange
base_experiment(expnum=4,
damages_values=arange(0, 50, 1),
histogram_flag=1,
filter_type="inside",
max_trials=1)
from exp_helpers import base_experiment
base_experiment(detailed_file_flag=1, damages_values=[
1,
])
from exp_helpers import base_experiment base_experiment(detailed_file_flag = 1)