plot fixes

exercises/02450Toolbox_Python/Scripts/ex2_2_2.py

@@ -14,6 +14,7 @@ from matplotlib.pyplot import (
     xlabel,
     ylabel,
     yticks,
+    tight_layout
 )
 from scipy.io import loadmat
 
@@ -99,6 +100,7 @@ for d in range(D):
     I = np.reshape(W[digit_ix, :] + X.mean(0), (16, 16))
     imshow(I, cmap=cm.gray_r)
     title("Reconstr.")
+tight_layout()
 
 
 # Visualize the pricipal components
@@ -110,6 +112,7 @@ for k in range(K):
     I = np.reshape(V[:, k], (16, 16))
     imshow(I, cmap=cm.hot)
     title("PC{0}".format(k + 1))
+tight_layout()
 
 # output to screen
 show()

exercises/02450Toolbox_Python/Scripts/ex3_3_1.py

@@ -37,6 +37,9 @@ sim_to_index = sorted(zip(sim,noti))
 plt.figure(figsize=(12,8))
 plt.subplot(3,1,1)
 
+# set font size
+plt.rcParams.update({'font.size': 16})
+
 img_hw = int(np.sqrt(len(X[0])))
 img = np.reshape(X[i], (img_hw,img_hw))
 if transpose: img = img.T

exercises/02450Toolbox_Python/Scripts/ex4_1_1.py

 # exercise 4.1.1
 
 import numpy as np
-from matplotlib.pyplot import figure, hist, plot, show, subplot, title
+from matplotlib.pyplot import figure, hist, plot, show, subplots, title
 
 # Number of samples
 N = 200
@@ -21,12 +21,10 @@ X = np.random.normal(mu, s, N).T
 X = np.random.randn(N).T * s + mu
 
 # Plot the samples and histogram
-figure(figsize=(12, 4))
-title("Normal distribution")
-subplot(1, 2, 1)
-plot(X, ".")
-subplot(1, 3, 3)
-hist(X, bins=nbins)
+fig, ax = subplots(1,2, figsize = (12,4))
+fig.suptitle("Normal distribution")
+ax[0].plot(X,".")
+ax[1].hist(X,bins=nbins)
 show()
 
 print("Ran Exercise 4.1.1")

exercises/02450Toolbox_Python/Scripts/ex4_3_1.py

@@ -13,6 +13,7 @@ from matplotlib.pyplot import (
     xticks,
     ylim,
     yticks,
+    tight_layout
 )
 from scipy.io import loadmat
 from scipy.stats import zscore
@@ -34,6 +35,7 @@ figure()
 title("Wine: Boxplot")
 boxplot(X)
 xticks(range(1, M + 1), attributeNames, rotation=45)
+tight_layout()
 
 # From this it is clear that there are some outliers in the Alcohol
 # attribute (10x10^14 is clearly not a proper value for alcohol content)
@@ -44,6 +46,7 @@ figure(figsize=(12, 6))
 title("Wine: Boxplot (standarized)")
 boxplot(zscore(X, ddof=1), attributeNames)
 xticks(range(1, M + 1), attributeNames, rotation=45)
+tight_layout()
 
 # This plot reveals that there are clearly some outliers in the Volatile
 # acidity, Density, and Alcohol attributes, i.e. attribute number 2, 8,

exercises/02450Toolbox_Python/Scripts/ex5_2_4.py

@@ -3,7 +3,7 @@ import sklearn.linear_model as lm
 
 # requires wine data from exercise 5.1.5
 from ex5_1_5 import *
-from matplotlib.pylab import figure, hist, plot, show, subplot, xlabel, ylabel
+from matplotlib.pylab import figure, hist, plot, show, subplots, xlabel, ylabel, tight_layout
 
 # Split dataset into features and target vector
 alcohol_idx = attributeNames.index("Alcohol")
@@ -21,13 +21,15 @@ y_est = model.predict(X)
 residual = y_est - y
 
 # Display scatter plot
-figure()
-subplot(2, 1, 1)
-plot(y, y_est, ".")
-xlabel("Alcohol content (true)")
-ylabel("Alcohol content (estimated)")
-subplot(2, 1, 2)
-hist(residual, 40)
+fig, ax = subplots(nrows=2,ncols=1)
+ax[0].plot(y, y_est, ".")
+ax[0].set_xlabel("Alcohol content (true)")
+ax[0].set_ylabel("Alcohol content (estimated)")
+
+ax[1].hist(residual, 40)
+ax[1].set_xlabel("Residual")
+ax[1].set_ylabel("Count")
+tight_layout()
 
 show()
 

exercises/02450Toolbox_Python/Scripts/ex5_2_5.py

@@ -3,7 +3,7 @@ import sklearn.linear_model as lm
 
 # requires data from exercise 5.1.4
 from ex5_1_5 import *
-from matplotlib.pylab import figure, hist, plot, show, subplot, xlabel, ylabel
+from matplotlib.pylab import figure, hist, plot, show, subplot, xlabel, ylabel, tight_layout
 
 # Split dataset into features and target vector
 alcohol_idx = attributeNames.index("Alcohol")
@@ -54,6 +54,7 @@ plot(Xfava, residual, ".r")
 xlabel("Fixed*Volatile Acidity")
 ylabel("Residual")
 
+tight_layout()
 show()
 
 print("Ran Exercise 5.2.5")

exercises/02450Toolbox_Python/Scripts/ex6_2_1.py

@@ -2,7 +2,7 @@
 import importlib_resources
 import numpy as np
 import sklearn.linear_model as lm
-from matplotlib.pyplot import clim, figure, plot, show, subplot, title, xlabel, ylabel
+from matplotlib.pyplot import clim, figure, plot, show, subplot, title, xlabel, ylabel, subplots, suptitle
 from scipy.io import loadmat
 from sklearn import model_selection
 
@@ -117,13 +117,18 @@ else:
     y_est= m.predict(X[:,ff])
     residual=y-y_est
     
-    figure(k+1, figsize=(12,6))
-    title('Residual error vs. Attributes for features selected in cross-validation fold {0}'.format(f))
+    # Determine the number of rows and columns for subplots
+    nrows = int(len(ff) ** 0.5)
+    ncols = int(len(ff) / nrows) + (len(ff) % nrows > 0)
+
+    fig, ax = subplots(nrows=nrows, ncols=ncols, num=k+1, figsize=(12,6))
+    ax = ax.flatten()
+
+    suptitle('Residual error vs. Attributes for features selected in cross-validation fold {0}'.format(f))
     for i in range(0,len(ff)):
-       subplot(2, int( np.ceil(len(ff)/2)), i+1)
-       plot(X[:,ff[i]],residual,'.')
-       xlabel(attributeNames[ff[i]])
-       ylabel('residual error')
+       ax[i].plot(X[:,ff[i]],residual,'.')
+       ax[i].set_xlabel(attributeNames[ff[i]])
+       ax[i].set_ylabel('residual error')
     
     
 show()

exercises/02450Toolbox_Python/Scripts/ex8_2_2.py

@@ -149,6 +149,7 @@ summaries_axes[1].set_xlabel("Fold")
 summaries_axes[1].set_xticks(np.arange(1, K + 1))
 summaries_axes[1].set_ylabel("Error rate")
 summaries_axes[1].set_title("Test misclassification rates")
+plt.tight_layout()
 
 # Show the plots
 # plt.show(decision_boundaries.number) # try these lines if the following code fails (depends on package versions)