mrtommyb

import exoplanet as xo
import pymc3 as pm
import numpy as np
import matplotlib.pyplot as plt
import theano.tensor as tt

with pm.Model() as model:
    ecs = xo.UnitDisk("ecs", testval=np.array([1e-5, -1.e-5]))
    ecc = pm.Deterministic("ecc", tt.sqrt(tt.sum(ecs ** 2)))
    omega = pm.Deterministic("omega", tt.arctan2(ecs[1], ecs[0]))

Lampsy

remzmike

import os
import time

dirnames = ['classes', 'triggers']
timestamp = '%s' % time.time()

for dirname in dirnames:
    for fname in os.listdir(dirname):

        fpath = os.path.join(dirname, fname)

egramez

#!/usr/local/bin/python3
######################################################################################################################
# Purpose:      Generate rule report of all the security groups                                                      #
# Input Params: None 												     #											 #
# Author:       Ramesh                                                                                               #
# Doc. Ref:     http://boto3.readthedocs.io/en/latest/reference/services/ec2.html#EC2.Client.describe_security_groups#
######################################################################################################################
from __future__ import print_function

nemrosim

async uploadVideo ({ request }) {
        const video = request.file('video');

        await video.move(Helpers.tmpPath('uploads'), {
            name: 'video.mp4',
            overwrite: false,
        });

        if (!video.moved()) {
            return video.error();

maia-18

donrestarone

#show memory speed
sudo lshw -short -C memory

# check core frequencies
watch -n.1 "cat /proc/cpuinfo | grep \"^[c]pu MHz\""

#run stress test
stress --cpu 12 --io 8 --vm 8 --vm-bytes 1024M --timeout 100s

Sarikachima

iamamit-107

function binarySearch(arr, item) {
    let startIndex = 0;
    let endIndex = arr.length - 1;

    while (startIndex < endIndex) {
        let middleIndex = Math.floor((startIndex + endIndex) / 2);

        if (arr[middleIndex] === item) {
            return `Found at index ${middleIndex}`;
        }

siddharththakur26

import pandas as pd
import numpy as np

def most_corr(prices):
    """
    :param prices: (pandas.DataFrame) A dataframe containing each ticker's 
                   daily closing prices.
    :returns: (container of strings) A container, containing the two tickers that 
              are the most highly (linearly) correlated by daily percentage change.
    """