Add iterating function and the test for it

testing_project/logistic.py

@@ -1,4 +1,13 @@
 # Your code goes here
+import numpy as np
 
 def f(x, r):
     return r * x * (1 - x)
+
+def iterate_f(x, r, n_iterations):
+    trajectory = np.zeros(n_iterations+1)
+    trajectory[0] = x
+    for i in range(n_iterations):
+        trajectory[i+1] = f(x, r)
+        x = trajectory[i+1]
+    return trajectory

testing_project/test_logistic.py

@@ -1,7 +1,7 @@
 import pytest
 from numpy.testing import assert_allclose
 
-from logistic import f
+from logistic import f, iterate_f
 
 
 def test_f_corner_cases():
@@ -14,18 +14,6 @@ def test_f_corner_cases():
         result = f(x, r)
         assert_allclose(result, expected)
 
-@pytest.mark.parametrize(
-    'x, r, expected',
-    [
-        (0.1, 2.2 ,0.198),
-        (0.2, 3.4 ,0.544),
-        (0.5, 2   ,0.5)
-    ]
-)
-def test_f_generic_cases(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
@@ -37,6 +25,30 @@ def test_f_generic_cases(x, r, expected):
 # parametrize the above test using @pytest.mark.parametrize
 
 
+@pytest.mark.parametrize(
+    'x, r, expected',
+    [
+        (0.1, 2.2 ,0.198),
+        (0.2, 3.4 ,0.544),
+        (0.5, 2   ,0.5)
+    ]
+)
+def test_f_generic_cases(x, r, expected):
+    result = f(x, r)
+    assert_allclose(result, expected)
+
+
+@pytest.mark.parametrize(
+    'x, r, n_iterations, 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_generic(x, r, n_iterations, expected):
+    result = iterate_f(x, r, n_iterations)
+    assert_allclose(result, expected, rtol=1e-6)
 # 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]