CristinaSolana

1. Clone your fork:

git clone [email protected]:YOUR-USERNAME/YOUR-FORKED-REPO.git

2. Add remote from original repository in your forked repository:

cd into/cloned/fork-repo
git remote add upstream git://github.com/ORIGINAL-DEV-USERNAME/REPO-YOU-FORKED-FROM.git
git fetch upstream

yanofsky

This is free and unencumbered software released into the public domain.

Anyone is free to copy, modify, publish, use, compile, sell, or
distribute this software, either in source code form or as a compiled
binary, for any purpose, commercial or non-commercial, and by any
means.

In jurisdictions that recognize copyright laws, the author or authors
of this software dedicate any and all copyright interest in the
software to the public domain. We make this dedication for the benefit

sloria

The Best of the Best Practices (BOBP) Guide for Python

A "Best of the Best Practices" (BOBP) guide to developing in Python.

In General

Values

• "Build tools for others that you want to be built for you." - Kenneth Reitz
• "Simplicity is alway better than functionality." - Pieter Hintjens

hzbd

#!/usr/bin/env python
import os
import time

username = 'root'
defaultdb = 'postgres'
port = '5433'
backupdir='/www/backup/'
date = time.strftime('%Y-%m-%d')

brenopolanski

A simple Ghostscript command to merge two PDFs in a single file is shown below:

gs -dNOPAUSE -sDEVICE=pdfwrite -sOUTPUTFILE=combine.pdf -dBATCH 1.pdf 2.pdf

Install Ghostscript:

Type the command sudo apt-get install ghostscript to download and install the ghostscript package and all of the packages it depends on.

fortruce

import matplotlib.pyplot as plt
import matplotlib.animation as anim
from collections import deque
import random

MAX_X = 100   #width of graph
MAX_Y = 1000  #height of graph

# intialize line to horizontal line on 0
line = deque([0.0]*MAX_X, maxlen=MAX_X)

hnykda

""" From:  http://danielhnyk.cz/predicting-sequences-vectors-keras-using-rnn-lstm/ """
from keras.models import Sequential  
from keras.layers.core import TimeDistributedDense, Activation, Dropout  
from keras.layers.recurrent import GRU
import numpy as np

def _load_data(data, steps = 40):  
    docX, docY = [], []
    for i in range(0, data.shape[0]/steps-1):
        docX.append(data[i*steps:(i+1)*steps,:])

Nemitek

import pandas as pd  
from random import random

flow = (list(range(1,10,1)) + list(range(10,1,-1)))*1000  
pdata = pd.DataFrame({"a":flow, "b":flow})  
pdata.b = pdata.b.shift(9)  
data = pdata.iloc[10:] * random()  # some noise  

import numpy as np

jkleint

#!/usr/bin/env python
"""
Example of using Keras to implement a 1D convolutional neural network (CNN) for timeseries prediction.
"""

from __future__ import print_function, division

import numpy as np
from keras.layers import Convolution1D, Dense, MaxPooling1D, Flatten
from keras.models import Sequential

wolever

"""
A simple watchdog for long running processes which may stall for some reason or
another.

If the main thread hasn't logged progress (by updating
``self.last_progress_time``) in WATCHDOG_HARD_KILL_TIMEOUT, the watchdog
thread will log an error containing the stack trace of all currently running
threads then use ``kill -9`` to kill the main process.

Assumes that a process monitor like supervisor or systemd will then restart