parametrized cases

logistic.py

@@ -1 +1,22 @@
+
 # Your code goes here
+
+def f(x,r):
+    return r*x*(1-x)
+
+
+#print(f(0, 1.1))
+
+def iterate_f(it,x,r):
+    time_series=[]
+    time_series.append(x)
+    for i in range(it):
+        x=r*x*(1-x)
+        time_series.append(x)
+    return time_series
+
+if __name__ == '__main__':
+    import matplotlib.pyplot as plt
+    from plot_logistic import plot_trajectory
+    plot_trajectory(100,4,0)
+    plt.show()

test_logistic.py

@@ -1,10 +1,19 @@
 from numpy.testing import assert_allclose
+from math import isclose
+import numpy as np
+
 
 from logistic import f
+from logistic import iterate_f
+from logistic_fit import fit_r
+import pytest
+
+SEED=np.random.seed(42)
 
 # Add here your test for the logistic map
 
 
+
 def test_f_corner_cases():
     # Test cases are (x, r, expected)
     cases = [
@@ -14,3 +23,79 @@ def test_f_corner_cases():
     for x, r, expected in cases:
         result = f(x, r)
         assert_allclose(result, expected)
+
+def test_f_generic_cases():
+    # Test cases are (x, r, expected)
+    cases = [
+        (0.1, 2.2, 0.198),
+        (0.2, 3.4, 0.544),
+        (0.5, 2, 0.5)
+    ]
+    for x, r, expected in 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_param(x,r,expected):
+    # Test cases are (x, r, expected)
+
+    result = f(x,r)
+    assert_allclose(result, expected)
+
+
+@pytest.mark.parametrize('x,r,expected',[
+        (0, 1.1, 0),
+        (1, 3.7, 0),
+    ])
+def test_f_generic_corner_param(x,r,expected):
+    # Test cases are (x, r, expected)
+    result = f(x,r)
+    assert_allclose(result, expected)
+
+@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_iterative_f(x,r,it,expected):
+    result=iterate_f(it,x,r)
+    assert_allclose(result,expected,rtol=5e-07)
+
+def test_fit_r():
+    expected=2
+    xs=iterate_f(it=23,x=0.3, r=expected)
+    result=fit_r(xs)
+    assert isclose(result, expected)==True
+
+@pytest.mark.parametrize('x,it,r,expected',[
+        (0.3,23,5.3,5.3),
+        (0.5,12,2,2),
+        (0.9,50,3.4,3.4)
+    ])
+def test_fit_r_param(x,it,r,expected):
+
+    xs=iterate_f(it,x,r)
+    result=fit_r(xs)
+    assert isclose(result, expected)==True
+
+def test_fit_r_random():
+    x0=np.random.uniform(0,1,100)
+
+    for x in x0:
+        xs=iterate_f(100,x,1.5)
+        assert  isclose(xs[-1],1/3) 
+
+@pytest.mark.parametrize('x0',[(x)for x in np.random.uniform(0,2,100)] )
+
+
+def test_fit_r_random_param(x0):
+
+
+    xs=iterate_f(100,x0,1.5)
+    assert  isclose(xs[-1],1/3) and isclose(xs[-2],1/3) and isclose(xs[-3],1/3)