linting

conftest_example.py

@@ -15,7 +15,7 @@ def pytest_configure(config):
     seed = config.getvalue("seed")
     # if seed was not set by the user, we set one now
     if seed is None or seed == ('NO', 'DEFAULT'):
-        config.option.seed = int(np.random.randint(2**31-1))
+        config.option.seed = int(np.random.randint(2 ** 31 - 1))
 
 
 def pytest_report_header(config):

plot_logfun.py

@@ -23,9 +23,9 @@ def plot_trajectory(n, r, x0, fname="single_trajectory.png"):
     returns
         fig, ax (matplotlib objects)
     """
-    l = iterate_f(n, x0, r)
+    xs = iterate_f(n, x0, r)
     fig, ax = plt.subplots(figsize=(10, 5))
-    ax.plot(list(range(n)), l)
+    ax.plot(list(range(n)), xs)
     fig.suptitle('Logistic Function')
 
     fig.savefig(fname)
@@ -57,11 +57,11 @@ def plot_bifurcation(start, end, step, fname="bifurcation.png", it=100000,
     y = []
 
     for r in r_range:
-        l = iterate_f(it, 0.1, r)
-        ll = l[len(l) - last::].copy()
-        lll = np.unique(ll)
-        y.extend(lll)
-        x.extend(np.ones(len(lll)) * r)
+        xs = iterate_f(it, 0.1, r)
+        all_xs = xs[len(xs) - last::].copy()
+        unique_xs = np.unique(all_xs)
+        y.extend(unique_xs)
+        x.extend(np.ones(len(unique_xs)) * r)
 
     fig, ax = plt.subplots(figsize=(20, 10))
     ax.scatter(x, y, s=0.1, color='k')

readme.md

@@ -6,8 +6,8 @@ Make a file `logistic.py` and `test_logistic.py` in the same folder as this
 readme and the `plot_logfun.py` file. Implement the code for the logistic map
 in the `logistic.py` file:
 
-a) Implement the logistic map f(𝑥)=𝑟∗𝑥∗(1−𝑥) . Use `@parametrize`
-to test the function for the following cases:
+a) Implement the logistic map f(𝑥)=𝑟∗𝑥∗(1−𝑥) . Import pytest and use
+`@pytest.mark.parametrize` to test the function for the following cases:
 ```
   x=0.1, r=2.2 => f(x, r)=0.198
   x=0.2, r=3.4 => f(x, r)=0.544
@@ -16,7 +16,7 @@ to test the function for the following cases:
 
 b) Implement the function `iterate_f` that runs `f` for `it`
 iterations, each time passing the result back into f.
-Use `@parametrize` to test the function for the following cases:
+Use `@pytest.mark.parametrize` to test the function for the following cases:
 ```
   x=0.1, r=2.2, it=1 => iterate_f(it, x, r)=[0.198]
   x=0.2, r=3.4, it=4 => iterate_f(it, x, r)=[0.544, 0.843418, 0.449019, 0.841163]
@@ -32,9 +32,9 @@ Try with values `r<3`, `r>4` and `3<r<4` to get a feeling for how the function
 behaves differently with different parameters. Note that your input x0 should
 be between 0 and 1.
 
-## Exercise 2 -- Check the convergence of an attractor using randomi testing
+## Exercise 2 -- Check the convergence of an attractor using random testing
 a) Write a randomized test that checks that, for `r=1.5`, all
-starting points converge to the attractor `f(x, r) = 1/3`.
+starting points x0 converge to the same value (attractor) `f(x, r) = 1/3`.
 
 b) Use `pytest.mark` to mark the tests from the previous exercise with one mark
 (they relate to the correct implementation of the logistic map) and the