StephenFordham

# Function for baseline correction using a simple rolling minimum 
import numpy as np
from scipy.signal import savgol_filter


def baseline_correction(intensities, window_size=50, poly_order=3):
    baseline = savgol_filter(intensities, window_length=window_size, polyorder=poly_order)
    corrected_intensities = intensities - baseline
    corrected_intensities[corrected_intensities < 0] = 0  # Ensuring no negative intensities
    return corrected_intensities

StephenFordham

from Bio import SeqIO

def read_fasta_sequence(file_path):
  
    sequence = ""
    with open(file_path, 'r') as file:
        for line in file:
            if not line.startswith('>'):
                sequence += line.strip()
    return sequence

StephenFordham

def depth_calc(file, read_depth=0):
    df = pd.read_csv(file, sep='\t', header=None)
    df.columns = ['consensus', 'base_position', 'depth']
    plasmid_length = df['base_position'].max()
    base_coverage = df[df['depth'] > read_depth].__len__()
    cov_perc = (base_coverage/plasmid_length) * 100
    return cov_perc 

StephenFordham

for i in tqdm(range(1, 21)):

    player = WebDriverWait(browser, 20).until(expected_conditions.visibility_of_element_located((By.XPATH, '//*[@id="rankingDetailAjaxContainer"]/table/tbody/tr['+str(i)+']/td[4]/a')))
    Tennis_data_collection.write(player.text + ',')
    WebDriverWait(browser, 20).until(expected_conditions.visibility_of_element_located((By.XPATH, '//*[@id="rankingDetailAjaxContainer"]/table/tbody/tr['+str(i)+']/td[4]/a'))).click()
    turned_pro = WebDriverWait(browser, 20).until(expected_conditions.visibility_of_element_located((By.XPATH, '//*[@id="playerProfileHero"]/div[2]/div[2]/div/table/tbody/tr[1]/td[2]/div/div[2]'))).text
    career_length = 2019 - int(turned_pro)
    Tennis_data_collection.write(str(career_length) + ',')

    WebDriverWait(browser, 20).until(expected_conditions.visibility_of_element_located((By.XPATH, '//*[@id="profileTabs"]/ul/li[6]/a'))).click()

StephenFordham

import matplotlib.pyplot as plt 
from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import precision_recall_curve, auc
import seaborn as sns

X, y = make_classification(n_samples=1000, n_classes=2, random_state=1)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=2)
model = LogisticRegression(solver='lbfgs')

StephenFordham

import matplotlib.pyplot as plt 
# ROC and AUC modules
from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_curve, roc_auc_score
import seaborn as sns 

# generate 2 class dataset
X, y = make_classification(n_samples=1000, n_classes=2, weights=[0.5], random_state=1)

StephenFordham

def meta_decorator(arg):
    def decorator_list(fnc):
        def inner(list_of_tuples):
            return [(fnc(val[0], val[1])) ** power for val in list_of_tuples]
        return inner
    if callable(arg):
        power = 2
        return decorator_list(arg)
    else:
        power = arg

StephenFordham

def integer_check(method):
	def inner(ref, expo=None):
		if expo == None:
			if not isinstance(ref._val1, int) or not isinstance(ref._val2, int):
				raise TypeError('Please enter numerical numbers for val1 and val2')
			else:
				return method(ref)
		if expo:
			if not isinstance(ref._val1, int) or not isinstance(ref._val2, int)\
					or not isinstance(expo, int):

StephenFordham

# Class Decorators: Using Decorators with methods defined in a Class

def integer_check(method):
	def inner(ref):
		if not isinstance(ref._val1, int) or not isinstance(ref._val2, int):
			raise TypeError('val1 and val2 must be integers')
		else:
			return method(ref)
	return inner

StephenFordham

class CustomIterTeams(object):
	def __init__(self, division, teams=[]):
		self._mng = division
		self._teams = teams
		self._index = -1

	def __iter__(self):
		return self

# 	def __iter__(self):