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import numpy as np

import matplotlib.pyplot as plt

from matplotlib.pyplot import figure

from sympy import sieve, prime

import itertools

import pandas as pd

matrix_size = 200

matrix = np.zeros((matrix_size, matrix_size))

with open('C:/Users/esultano/git/elliptic_curves/data/elliptic_curves.csv') as f:

with open('elliptic_curves.csv') as f:

for line in f.readlines():

line = line.strip()

p, q, cases = eval(line)

idx_p = sieve.search(p)[0]-2

idx_q = sieve.search(q)[0]-2

if idx_p < matrix_size and idx_q < matrix_size:

cases_set = set()

for case in cases:

cases_set.add(case[0])

cases_list = list(cases_set)

val = sum(i*i for i in cases_list)

matrix[idx_p,idx_q] = val

matrix[idx_q,idx_p] = val

max_prime = prime(matrix_size+2)

axis_labels = list(sieve.primerange(3, max_prime))

axis_labels = list(enumerate(sieve.primerange(3, max_prime)))

axis_labels = axis_labels[::len(axis_labels) // 9][:-1] + [axis_labels[-1]]

ticks = [e[0] for e in axis_labels]

ticklabels = [e[1] for e in axis_labels]

fig = plt.figure(figsize=(8, 8))

ax = fig.add_subplot(111)

matrix_plot = ax.matshow(matrix, interpolation='nearest')

ax.set_xticklabels(axis_labels)

ax.set_xticks(ticks); ax.set_xticklabels(ticklabels)

ax.set_yticklabels(axis_labels)

ax.set_yticks(ticks); ax.set_yticklabels(ticklabels)

plt.show()

Différences enregistrées

Texte d'origine

Ouvrir un fichier

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
from sympy import sieve, prime
import itertools
import pandas as pd

matrix_size = 200
matrix = np.zeros((matrix_size, matrix_size))

with open('C:/Users/esultano/git/elliptic_curves/data/elliptic_curves.csv') as f:
    for line in f.readlines():
        line = line.strip()
        p, q, cases = eval(line)
        idx_p = sieve.search(p)[0]-2
        idx_q = sieve.search(q)[0]-2
        if idx_p < matrix_size and idx_q < matrix_size:
            cases_set = set()
            for case in cases:
                cases_set.add(case[0])
            cases_list = list(cases_set)
            val = sum(i*i for i in cases_list)
            matrix[idx_p,idx_q] = val
            matrix[idx_q,idx_p] = val

max_prime = prime(matrix_size+2)
axis_labels = list(sieve.primerange(3, max_prime))

fig = plt.figure(figsize=(8, 8))
ax = fig.add_subplot(111)
matrix_plot = ax.matshow(matrix, interpolation='nearest')
ax.set_xticklabels(axis_labels)
ax.set_yticklabels(axis_labels)
plt.show()

Texte modifié

Ouvrir un fichier

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
from sympy import sieve, prime
import itertools
import pandas as pd

matrix_size = 200
matrix = np.zeros((matrix_size, matrix_size))

with open('elliptic_curves.csv') as f:
    for line in f.readlines():
        line = line.strip()
        p, q, cases = eval(line)
        idx_p = sieve.search(p)[0]-2
        idx_q = sieve.search(q)[0]-2
        if idx_p < matrix_size and idx_q < matrix_size:
            cases_set = set()
            for case in cases:
                cases_set.add(case[0])
            cases_list = list(cases_set)
            val = sum(i*i for i in cases_list)
            matrix[idx_p,idx_q] = val
            matrix[idx_q,idx_p] = val

max_prime = prime(matrix_size+2)
axis_labels = list(enumerate(sieve.primerange(3, max_prime)))
axis_labels = axis_labels[::len(axis_labels) // 9][:-1] + [axis_labels[-1]]
ticks = [e[0] for e in axis_labels]
ticklabels = [e[1] for e in axis_labels]

fig = plt.figure(figsize=(8, 8))
ax = fig.add_subplot(111)
matrix_plot = ax.matshow(matrix, interpolation='nearest')
ax.set_xticks(ticks); ax.set_xticklabels(ticklabels)
ax.set_yticks(ticks); ax.set_yticklabels(ticklabels)
plt.show()