def csv_to_constant_volume(filepath,weight_column,V_max,has_header=False,delim=',',quotechar='"',lower_bound=None,upper_bound=None):
data, weight_column, header = load_csv(filepath,weight_column,has_header=has_header,delim=',',quotechar='"')
bins = to_constant_volume(data,V_max,weight_pos=weight_column,lower_bound=lower_bound,upper_bound=upper_bound)
print_binsizes(bins,weight_column)
save_csvs(bins,filepath,header,delim=delim,quotechar=quotechar)
def csv_to_constant_volume(filepath,weight_column,V_max,has_header=False,delim=',',quotechar='"',lower_bound=None,upper_bound=None):
data, weight_column, header = load_csv(filepath,weight_column,has_header=has_header,delim=',',quotechar='"')
bins = to_constant_volume(data,V_max,weight_pos=weight_column,lower_bound=lower_bound,upper_bound=upper_bound)
print_binsizes(bins,weight_column)
save_csvs(bins,filepath,header,delim=delim,quotechar=quotechar)
new_bins[b].append(new_dict[key])
return new_bins
if __name__=="__main__":
a = np.random.power(0.01,size=10000)
V_max = 1.
bins = to_constant_volume(a,V_max)
a = np.sort(a)[::-1]
print(a[:10])
print([ np.sum(b) for b in bins ])
print(a.sum(), sum([ np.sum(b) for b in bins ]))
w = [ np.sum(b) for b in bins ]
import pylab as pl
pl.plot(np.arange(len(w)),w)
pl.show()
b = { 'a': 10, 'b': 10, 'c':11, 'd':1, 'e': 2,'f':7 }
V_max = max(b.values())
bins = to_constant_volume(b,V_max)
print_binsizes(bins)