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

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

# 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

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

kwcooper

import numpy as np
import matplotlib.pyplot as plt
plt.style.use('seaborn-white')

# Create test data
a, b = np.random.randint(10, 25, 30), np.random.randint(0, 8, 30)
a_mu, b_mu = np.mean(a), np.mean(b)
a_std, b_std = np.std(a), np.std(b)

kwcooper

from itertools import groupby

l1 = [1,1,1,2,2,2,2,2,1,1,1,0,0,0]
l2 = range(len(l1))

[(k, list(group)) for k, group in groupby(l2, key=lambda _, ig=iter(l1): next(ig))]

# Returns [(1, [0, 1, 2]), (2, [3, 4, 5, 6, 7]), (1, [8, 9, 10]), (0, [11, 12, 13])]

kwcooper

import matplotlib.pyplot as plt
# kwc 201208

# Create synthetic data for each plot
np.random.seed(10)
synData_1 = np.random.normal(100, 10, 200)
synData_2 = np.random.normal(80, 30, 200)
synData_3 = np.random.normal(90, 20, 200)
synData_4 = np.random.normal(70, 25, 200)