Task 2.3: Finding Maximum Daily Increase in Cases

Our third task is to implement find_max_increase_in_cases which takes in one argument n_cases_increase and finds the maximum daily increase in confirmed cases for each country.

In this case, n_cases_increase is the output obtained from calling compute_increase_in_cases(n_cases_cumulative).

The return value should be a 1D np.ndarray that represents the maximum daily increase in cases for each country. In particular, the -th entry of this array should correspond to the increase in confirmed cases in the -th country as represented in n_cases_increase.

Returning to our previous example in task 2.2, suppose the daily increase in cases is given by np.array([[1, 1, 1, 1], [1, 2, 3, 4]]). Clearly, the maximum daily increase in cases is 1 and 4 for country 0 and 1, respectively. Therefore, we should expect the output of this function to be np.array([1, 4]).

In this task, the goal is to learn how to use np.max, or the equivalent np.amax, to find the max value in a column of a matrix. The following is a sample execution of how these functions work:

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a = np.arange(4).reshape((2,2)) # array([[0, 1], [2, 3]])

np.amax(a) # 3 -> Maximum of the flattened array

np.amax(a, axis=0) # array([2, 3]) -> Maxima along the first axis (first column)

np.amax(a, axis=1) # array([1, 3]) -> Maxima along the second axis (second column)

np.amax(a, where=[False, True], initial=-1, axis=0) # array([-1, 3])

b = np.arange(5, dtype=float) # array([0., 1., 2., 3., 4.])

b[2] = np.NaN # array([ 0., 1., nan, 3., 4.])

np.amax(b) # nan

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def find_max_increase_in_cases(n_cases_increase):

'''

Finds the maximum daily increase in confirmed cases for each country.

Parameters

----------

n_cases_increase: np.ndarray

2D `ndarray` with each row representing the data of a country, and the columns

of each row representing the time series data of the daily increase in the

number of confirmed cases in that country, i.e. the ith row of

`n_cases_increase` contains the data of the ith country, and the (i, j) entry of

`n_cases_increase` is the daily increase in the number of confirmed cases on the

(j + 1)th day in the ith country.

Returns

-------

Maximum daily increase in cases for each country as a 1D `ndarray` such that the

ith entry corresponds to the increase in confirmed cases in the ith country as

represented in `n_cases_increase`.

'''

# TODO: add your solution here and remove `raise NotImplementedError`

raise NotImplementedError

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# Test case for Task 2.3

n_cases_increase = np.ones((100, 20))

actual = find_max_increase_in_cases(n_cases_increase)

expected = np.ones(100)

assert(np.all(actual == expected))

sample_increase = np.array([[1,2,3,4,8,8,10,10,10,10],[1,1,3,5,8,10,15,20,25,30]])

expected2 = np.array([10, 30]) # max of [1,2,3,4,8,8,10,10,10,10] => 10, max of [1,1,3,5,8,10,15,20,25,30] => 30

assert(np.all(find_max_increase_in_cases(sample_increase) == expected2))