Fix all lecture notebooks and slides.

.github/workflows/lint.yml

@@ -32,4 +32,4 @@ jobs:
       - name: black
         run: |
           black --version
-          black --check --diff --color ${{ github.event.repository.name }}
+          black --check --diff --color lectures

lectures/slides_tex/example_element_features.py

@@ -1,13 +1,15 @@
+import numpy as np
 import pandas as pd
 from pymatgen.core import Composition
-binaries = pd.read_csv('binary_band_gap.csv')
-# We create a column holding the Composition object. 
+
+binaries = pd.read_csv("binary_band_gap.csv")
+# We create a column holding the Composition object.
 # Note the use of list comprehension in Python.
-binaries['composition'] = [Composition(c) for c in binaries['Formula']]
-electronegs = [[el.X for el in c] for c in binaries['composition']]
+binaries["composition"] = [Composition(c) for c in binaries["Formula"]]
+electronegs = [[el.X for el in c] for c in binaries["composition"]]
 # Create the features mean and difference between electronegativities
-binaries['mean_X'] = [np.mean(e) for e in electronegs]
-binaries['diff_X'] = [max(e) - min(e) for e in electronegs]
+binaries["mean_X"] = [np.mean(e) for e in electronegs]
+binaries["diff_X"] = [max(e) - min(e) for e in electronegs]
 # Label metals (band gap of 0. 1e-5 is used as numerical tolerance) as class 0
 # Insulators are labelled as class 1.
-binaries['class'] = [0 if eg < 1e-5 else 1 for eg in binaries['Eg (eV)']]}
+binaries["class"] = [0 if eg < 1e-5 else 1 for eg in binaries["Eg (eV)"]]

lectures/slides_tex/example_numpy.py

@@ -1,17 +1,16 @@
->>> import numpy as np
->>> x = np.array([[1, 2, 3],
-                  [4, 7, 6],
-                  [9, 4, 2]])
->>> y = np.array([1.5, 0.5, 3])
->>> x * x
-array([[ 1,  4,  9],
-       [16, 49, 36],
-       [81, 16,  4]])
->>> np.dot(x, x)
-array([[36, 28, 21],
-       [86, 81, 66],
-       [43, 54, 55]])
->>> np.linalg.inv(x)
-array([[ 0.16949153, -0.13559322,  0.15254237],
-       [-0.77966102,  0.42372881, -0.10169492],
-       [ 0.79661017, -0.23728814,  0.01694915]])
+import numpy as np
+
+x = np.array([[1, 2, 3], [4, 7, 6], [9, 4, 2]])
+y = np.array([1.5, 0.5, 3])
+x * x
+# array([[ 1,  4,  9],
+#        [16, 49, 36],
+#        [81, 16,  4]])
+np.dot(x, x)
+# array([[36, 28, 21],
+#        [86, 81, 66],
+#        [43, 54, 55]])
+np.linalg.inv(x)
+# array([[ 0.16949153, -0.13559322,  0.15254237],
+#        [-0.77966102,  0.42372881, -0.10169492],
+#        [ 0.79661017, -0.23728814,  0.01694915]])

lectures/slides_tex/example_scipy.py

@@ -1,7 +1,9 @@
->>> from scipy import stats  # Statistics package
->>> dist = stats.norm(0, 1)  # Gaussian distribution
->>> dist.cdf(1.96)
-0.9750021048517795
->>> from scipy import constants  # Physical constants
->>> constants.e
-1.6021766208e-19
+from scipy import stats  # Statistics package
+
+dist = stats.norm(0, 1)  # Gaussian distribution
+print(dist.cdf(1.96))
+# Result: 0.9750021048517795
+from scipy import constants  # Physical constants
+
+print(constants.e)
+# Result: 1.6021766208e-19

lectures/slides_tex/example_sklearn_discriminant_analysis.py

@@ -1,5 +1,8 @@
-from sklearn.discriminant_analysis import LinearDiscriminantAnalysis,
-    QuadraticDiscriminantAnalysis
+from sklearn.discriminant_analysis import (
+    LinearDiscriminantAnalysis,
+    QuadraticDiscriminantAnalysis,
+)
+
 lda = LinearDiscriminantAnalysis(solver="svd", store_covariance=True)
 X = binaries[["mean_X", "diff_X"]]
 y = binaries["class"]
@@ -8,4 +11,3 @@
 
 qda = QuadraticDiscriminantAnalysis(store_covariance=True)
 y_pred = qda.fit(X, y).predict(X)
-\end{minted}