from numpy.testing import assert_allclose
import pytest
from logistic import f, iterate_f


@pytest.mark.parametrize('x,r,expected', [(0, 1.1, 0),(1, 3.7, 0)])
def test_f_corner_cases(x, r, expected):
    # Test cases are (x, r, expected)
    result = f(x, r)
    assert_allclose(result, expected)

# Hands on 1
#Add a new test for these generic cases using the for-loop pattern:
# x=0.1, r=2.2 => f(x, r)=0.198
# x=0.2, r=3.4 => f(x, r)=0.544
# x=0.5, r=2   => f(x, r)=0.5

def test_f_normal_cases():
    cases = [
        (.1, 2.2, .198),
        (.2, 3.4, .544),
        (.5, 2, .5),
    ]
    for x, r, expected in cases:
        result = f(x, r)
        assert_allclose(result, expected)

# Hands on 2:
# parametrize the above test using @pytest.mark.parametrize


# Hands on 3
# Implement a function iterate_f that runs f for it iterations. Write tests for the following cases:
# x=0.1, r=2.2, it=1 => iterate_f(it, x, r)=[0.1, 0.198]
# x=0.2, r=3.4, it=4 => iterate_f(it, x, r)=[0.2, 0.544, 0.843418, 0.449019, 0.841163]
# x=0.5, r=2, it=3 => iterate_f(it, x, r)=[0.5, 0.5, 0.5]
@pytest.mark.parametrize('x,r,it,expected', [
    (0.1, 2.2, 1, [0.1, 0.198]),
    (0.2, 3.4, 4, [0.2, 0.544, 0.843418, 0.449019, 0.841163]),
    (0.5, 2, 3, [0.5, 0.5, 0.5, 0.5]),
])
def test_iterate_f(x, r, it, expected):
    result = iterate_f(it, x, r)
    assert_allclose(result, expected, rtol=1e-06)