looping.py

import numpy as np
import itertools

def bacteriaGrowth(n0, alpha, K, N): #!f
    """
    Calculate time until bacteria growth exceed N starting from a population of n0 bacteria.
    hints:
        * You need to update the number of bacteria n0 within a loop
    """
    if n0 > N:
        return 0
    for t in itertools.count():
        n0 = (1 + alpha * (1-n0 / K) ) * n0
        if n0 > N:
            break
    t += 1
    return t

def clusterAnalysis(reflectance):
    reflectance = np.asarray(reflectance)
    I1 = np.arange(len(reflectance)) % 2 == 1
    while True:
        m = np.asarray( [np.mean( reflectance[~I1] ),  np.mean( reflectance[I1] ) ] )
        I1_ = np.argmin( np.abs( reflectance[:, np.newaxis] - m[np.newaxis, :] ), axis=1) == 1
        if all(I1_ == I1):
            break
        I1 = I1_
    return I1 + 1

def fermentationRate(measuredRate, lowerBound, upperBound):
    """
    Compute and return the mean value of the rates in 'measuredRate'
    which falls within lowerBound and upperBound.
    """
    mean_value = np.mean( [r for r in measuredRate if lowerBound < r < upperBound] )
    return mean_value

def removeIncomplete(id):
    """
    Hints:
        * Take a look at the example in the exercise.
    """
    id = np.asarray(id)
    id2 = []
    for i, v in enumerate(id):
        if len( [x for x in id if int(x) == int(v) ] ) == 3:
            id2.append(v)
    return np.asarray(id2)

if __name__ == "__main__":
    # I = clusterAnalysis([1.7, 1.6, 1.3, 1.3, 2.8, 1.4, 2.8, 2.6, 1.6, 2.7])
    # print(I)
    print(fermentationRate(np.array([20.1, 19.3, 1.1, 18.2, 19.7, 121.1, 20.3, 20.0]), 15, 25))
    # print(removeIncomplete(np.array([1.3, 2.2, 2.3, 4.2, 5.1, 3.2, 5.3, 3.3, 2.1, 1.1, 5.2, 3.1])))
    # Problem 1: Write a function which add two numbers
    # clusterAnalysis([2, 1, 2, 4, 5])