def plot(n):
filename = './data/hist_{}.csv'.format(n)
rows = cc.read_hist_file_rows(filename)
a = cc.array_from_file_rows(rows)
b = a[:, 2:] # get rid of first (identity) row
def chi(x):
xx = [x**i for i in range(2*(n/2)**2+1)]
return np.dot(b, xx)
xx = np.linspace(0, 0.4, 51)
def chi_vals(xx):
return np.array([chi(x) for x in xx])
chivals = chi_vals(xx)
for i in range(1, 2*((n/2)**2+1)):
plt.plot(xx, chivals[:, i])
def plot(n):
filename = './data/hist_{}.csv'.format(n)
rows = cc.read_hist_file_rows(filename)
a = cc.array_from_file_rows(rows)
b = a[:, 2:] # get rid of first (identity) row
def chi(x):
xx = [x**i for i in range(2 * (n / 2)**2 + 1)]
return np.dot(b, xx)
xx = np.linspace(0, 0.4, 51)
def chi_vals(xx):
return np.array([chi(x) for x in xx])
chivals = chi_vals(xx)
for i in range(1, 2 * ((n / 2)**2 + 1)):
plt.plot(xx, chivals[:, i])
def load_memoized(fcn, filename):
path = './data/' + filename + '.csv'
with open(path, 'rb') as f:
reader = csv.reader(f)
for k1, k2, v in reader:
fcn[(float(k1), float(k2))] = float(v)
rows2 = cc.read_hist_file_rows('./data/hist_2.csv')
rows4 = cc.read_hist_file_rows('./data/hist_4.csv')
rows6 = cc.read_hist_file_rows('./data/hist_6.csv')
rows8 = cc.read_hist_file_rows('./data/hist_8.csv')
a2 = cc.array_from_file_rows(rows2)
a4 = cc.array_from_file_rows(rows4)
a6 = cc.array_from_file_rows(rows6)
a8 = cc.array_from_file_rows(rows8)
def p2n(qubit_error_p, stab_error_p):
return cc.noisy_prob(a2, 2, qubit_error_p, stab_error_p)
def p4n(qubit_error_p, stab_error_p):
return cc.noisy_prob(a4, 4, qubit_error_p, stab_error_p)
@memoize
def p6n(qubit_error_p, stab_error_p):
import sys
import os
sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'lib'))
import numpy as np
import conjugacy_classes as cc
import matplotlib.pyplot as plt
from collections import defaultdict
rows2 = cc.read_hist_file_rows('./data/hist_2.csv')
rows4 = cc.read_hist_file_rows('./data/hist_4.csv')
rows6 = cc.read_hist_file_rows('./data/hist_6.csv')
rows8 = cc.read_hist_file_rows('./data/hist_8.csv')
a2 = cc.array_from_file_rows(rows2)
a4 = cc.array_from_file_rows(rows4)
a6 = cc.array_from_file_rows(rows6)
a8 = cc.array_from_file_rows(rows8)
# count the number of unique rows
d2 = defaultdict(int)
for e in a2:
d2[tuple(e[2:])]+=1
d4 = defaultdict(int)
for e in a4:
d4[tuple(e[2:])]+=1
d6 = defaultdict(int)
for e in a6:
d6[tuple(e[2:])]+=1
d8 = defaultdict(int)
for e in a8:
d8[tuple(e[2:])]+=1
