kwcooper

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

image = img.imread('school.png') # Grab the image

def toGreyscale(image):
    # convert the RGB to Greyscale
    return np.dot(image[...,:3], [0.114, 0.299, 0.587])

kwcooper

import math as m
import kMath as km
import matplotlib.pyplot as plt

A = .96 #initial height
ma = 5.0 #mass
k = 2.1 #spring constant
b = 0.09  #dampening
theta = 0 #phase angle
w = m.sqrt(k / ma)

kwcooper

# make a movie from images

import glob
import imageio
import os

path_name = "img/*.png"

# grab files, sorted by modification time
#filenames = glob.glob("img/*.png")

kwcooper

import matplotlib.pyplot as plt
from matplotlib import dates
import datetime

from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()

# TODO: Turn this bad boy into a function

# Raw data example (Format will vary)

kwcooper

# Wald-Wolfowitz Runs Test (Actual)
# *** For educational purposes only, 
# use more robust code for actual analysis

import math
import scipy.stats as st # for pvalue 

# Example data (Current script only works for binary ints)
L = [1,1,1,0,1,1,1,0,0,1,1,0,0,1,1,1,0,1,1,0,0,0,1,0,1,0,0,1,0,0,1,1,1,0,0,0,1]

kwcooper

# To clean and convert a whatsapp txt file export to a CSV file

import pandas as pd

# read file by lines
file_path = "whatsapp.txt"
f = open(file_path, 'r')
data = f.readlines()
f.close()

kwcooper

# Modified 190812 k
# Be sure to add to the list of packages in deployment.py
# /usr/lib/python3/dist-packages/landscape/sysinfo/deployment.py

from twisted.internet.defer import succeed
from requests import get

current_instance = None

kwcooper

def strpMailTime(timestamp):
	if len(timestamp) < 27:
		# Ex. '5 Nov 2018 12:05:07 -0500' len 25
		#    '28 Oct 2019 03:33:57 -0400' len 26
		dt = datetime.datetime.strptime(timestamp, "%d %b %Y %H:%M:%S %z")
	elif len(timestamp) > 29 and len(timestamp) < 32:
		# 'Fri, 6 Dec 2019 15:22:49 +0000' len 30
		# 'Mon, 09 Dec 2019 14:19:26 +0000' len 31
		dt = datetime.datetime.strptime(timestamp, "%a, %d %b %Y %H:%M:%S %z")
	elif len(timestamp) > 35 and len(timestamp) < 39:

kwcooper

import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import datetime
import random

import matplotlib.ticker as mticker

from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()

kwcooper

  def scaleInRange(lon, a, b):
    r_max, r_min = max(lon), min(lon)
    slon = []
    for i in lon:  
      slon.append(((i - r_min) / (r_max - r_min)) * (b - a) + a)
    return slon

# Example
lon = range(1,100)
a, b, = 4, 8