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

def mutate_amino(protein_str, mutations, offset=1):
    ''' mutations: {int>mutation_idx:str>AminoID}'''
    mut_protein_str = list(protein_str)
    
    for mut_idx in mutations.keys():
        mut_protein_str[mut_idx-offset] = mutations[mut_idx] # Arg 71 His | R > H 

    mut_protein_str = ''.join(mut_protein_str)
    
    return mut_protein_str

kwcooper

import cv2


class PointGrabber:
    def __init__(self, imgPath, printCords=True):
        self.points = []
        self.img = cv2.imread(imgPath, 1)
        self.printCords = printCords

    def select_point(self, event, x, y, flags, param):

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

# Computes an ICS file of birthdays from a spreadsheet
# spreadsheet in the format of 
# Name - persons name
# Date - Birthdate in %m/%d/%Y format
# Description - some links or other info you want to add

# this is built ontop of ICSps
# https://icspy.readthedocs.io/en/stable/
# install with pip install ics

kwcooper

import re
import pandas as pd

# kwc 210310

data_fName = 'linkGrabberData.txt'
saveName = 'linkedinLinks.csv'
re_exp = '(((//)|(\\\\))+([\w\d:#@%/;$()~_?\+-=\\\.&](#!)?)*)'

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

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)

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 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)