philipperemy

#!/usr/bin/env python


import pickle
import sys

if __name__ == '__main__':
    argv = sys.argv
    if len(argv) <= 1:
        print 'Specify pickle file as parameter.'

philipperemy

# On Mac OSX

# Will run AIRODUMP scan of all BSSIDs
# sudo airport -s

# Will run AIRODUMP sniff on channel 11
# sudo airport en0 sniff 11
# Atm, I can't find how to sniff on all channels at the same time.

if [ $# -eq 0 ]

philipperemy

import requests
from bs4 import BeautifulSoup


def get_downloads_count(package_name: str):
    badge_url = f"https://static.pepy.tech/badge/{package_name}"
    response = requests.get(badge_url)
    download_count = int(
        BeautifulSoup(response.text, 'html.parser')
        .find_all('text')[-1].text.replace('k', '000').replace('m', '000000')

philipperemy

import numpy as np

# online computation of the mean and the variance.

class RollingMean:

    def __init__(self):
        self.st = 0.0
        self.n = 0

philipperemy

//  y-cruncher Configuration File
//  Version: 0.7.8 Build 9507
//
//  Load this from y-cruncher or run directly:
//      y-cruncher config filename.cfg
//
//  If you're copying Windows file paths into here, be sure to replace
//  all backslashes "\" with forward slashes "/". Backslash is an
//  escape character.
//

philipperemy

from __future__ import print_function

import numpy as np
from keras.callbacks import Callback
from keras.layers import Dense
from keras.layers import LSTM
from keras.models import Sequential
from numpy.random import choice

from utils import prepare_sequences

philipperemy

from __future__ import print_function

import os

import numpy as np
from keras.layers import RepeatVector
from keras.layers.core import Dropout
from keras.layers.recurrent import LSTM
from keras.models import Sequential
from keras.models import load_model

philipperemy

import numpy as np

# FIND LARGEST K ELEMENTS IN A 2D ARRAY IN NUMPY

def find_k_largest_elements_in_2d_array(arr: np.array, k=1):
    assert len(arr.shape) == 2
    h, w = arr.shape
    top_indices = np.flip(arr.flatten().argsort()[-k:])
    return np.divmod(top_indices, w)

philipperemy

import matplotlib
import matplotlib.pyplot as plt
import numpy as np

matplotlib.use('MACOSX')

img_h, img_w = 250, 500

img = np.zeros((img_h, img_w))

philipperemy

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.applications import Xception
import numpy as np

num_samples = 1000
height = 224
width = 224
num_classes = 1000