Use plt.show to avoid extra output (#78)

Author: frostedoyster

examples/dos-align/dos-align.py

@@ -848,6 +848,7 @@ def train_model(model_to_train, fixed_DOS, structure_splines, spline_positions,
 plt.xlabel("Epochs")
 plt.ylabel("RMSE")
 plt.title("Train Loss vs Epoch")
+plt.show()
 # %%
 # Lets plot the val loss histories to compare their learning behaviour
 #
@@ -861,6 +862,7 @@ def train_model(model_to_train, fixed_DOS, structure_splines, spline_positions,
 plt.xlabel("Epochs")
 plt.ylabel("RMSE")
 plt.title("Validation Loss vs Epoch")
+plt.show()
 
 
 # %%
@@ -918,6 +920,7 @@ def train_model(model_to_train, fixed_DOS, structure_splines, spline_positions,
 plt.title("Energy Reference - Average Hartree Potential")
 plt.xlabel("Energy [eV]")
 plt.ylabel("DOS")
+plt.show()
 print("The DOSes, despite looking similar, are offset along the energy axis")
 
 # %%
@@ -927,6 +930,7 @@ def train_model(model_to_train, fixed_DOS, structure_splines, spline_positions,
 plt.title("Energy Reference - Fermi Level")
 plt.xlabel("Energy [eV]")
 plt.ylabel("DOS")
+plt.show()
 
 print("It is better aligned but still quite some offset")
 # %%
@@ -943,6 +947,7 @@ def train_model(model_to_train, fixed_DOS, structure_splines, spline_positions,
 plt.title("Energy Reference - Optimized")
 plt.xlabel("Energy [eV]")
 plt.ylabel("DOS")
+plt.show()
 print("The DOS alignment is better under the optimized energy reference")
 print("The difference will increase with more training epochs")
 # %%

examples/gaas-map/gaas-map.py

@@ -101,6 +101,7 @@
 ax.plot(energy / natoms, atom_energy / natoms, "b.")
 ax.set_xlabel("Energy / (eV/atom)")
 ax.set_ylabel("Atomic e. / (eV/atom)")
+plt.show()
 print(f"RMSE / (eV/atom): {np.sqrt(np.mean((cohesive_peratom)**2))}")
 
 
@@ -172,6 +173,7 @@
 ax.set_ylabel("PCA[2]")
 cbar = fig.colorbar(scatter, ax=ax)
 cbar.set_label("energy / eV/at.")
+plt.show()
 
 # computes PCovR map
 pcovr = PCovR(n_components=4)
@@ -183,6 +185,7 @@
 ax.set_ylabel("PCovR[2]")
 cbar = fig.colorbar(scatter, ax=ax)
 cbar.set_label("energy / (eV/at.)")
+plt.show()
 
 
 ######################################################################

examples/lpr/lpr.py

@@ -206,6 +206,7 @@
 ax.set_ylabel("PC2")
 fig.colorbar(im, ax=ax, label="LPR")
 ax.tick_params(left=False, bottom=False, labelleft=False, labelbottom=False)
+plt.show()
 
 # %%
 # In the PCA map, where each point corresponds to an
@@ -253,6 +254,7 @@
 ax.set_ylabel("PC2")
 fig.colorbar(im, ax=ax, label=r"LPR$_{\mathrm{new}}$ / LPR$_{\mathrm{old}}$")
 ax.tick_params(left=False, bottom=False, labelleft=False, labelbottom=False)
+plt.show()
 
 # %%
 # It is apparent that while the LPR stays more or less consistent for the

examples/path-integrals/path-integrals.py

@@ -150,6 +150,7 @@
 ax.set_xlabel(r"$t$ / ps")
 ax.set_ylabel(r"energy / eV")
 ax.legend()
+plt.show()
 
 # %%
 # While the potential energy is simply the mean over the beads of the
@@ -179,6 +180,7 @@
 ax.set_xlabel(r"$t$ / ps")
 ax.set_ylabel(r"energy / eV")
 ax.legend()
+plt.show()
 
 # %%
 # You can also visualize the (very short) trajectory in a way that highlights the
@@ -252,6 +254,7 @@
 ax.set_xlabel(r"$t$ / ps")
 ax.set_ylabel(r"energy / eV")
 ax.legend()
+plt.show()
 
 # %%
 # However, you should be somewhat careful: PIGLET converges *some* but not all the
@@ -270,6 +273,7 @@
 ax.set_xlabel(r"$t$ / ps")
 ax.set_ylabel(r"energy / eV")
 ax.legend()
+plt.show()
 
 # %%
 # Kinetic energy tensors

examples/pi-metad/pi-metad.py

@@ -317,6 +317,7 @@
 ax.set_xlabel(r"$t$ / ps")
 ax.set_ylabel(r"energy / eV")
 ax.legend(loc="upper left", ncols=1)
+plt.show()
 
 # %%
 #
@@ -360,6 +361,7 @@ def moving_average(arr, window_size):
 ax.set_xlabel(r"$t$ / ps")
 ax.set_ylabel(r"temperature / K")
 ax.legend(loc="upper left", ncols=2)
+plt.show()
 
 # %%
 # It is clear that the very high rate of biasing used in this demonstrative
@@ -458,6 +460,7 @@ def moving_average(arr, window_size):
 ax[0].set_title(r"$t=0.8$ ps")
 ax[1].set_title(r"$t=2.5$ ps")
 ax[2].set_title(r"$t=5.0$ ps")
+plt.show()
 
 # %%
 # Biasing a path integral calculation
@@ -667,6 +670,7 @@ def moving_average(arr, window_size):
 ax[0].set_title(r"$t=0.8$ ps")
 ax[1].set_title(r"$t=2.5$ ps")
 ax[2].set_title(r"$t=5.0$ ps")
+plt.show()
 
 # %%
 # Assessing quantum nuclear effects
@@ -694,6 +698,7 @@ def moving_average(arr, window_size):
         plt.Line2D([0], [0], color="r", label="PIMD"),
     ]
 )
+plt.show()
 
 # %%
 #
@@ -730,6 +735,7 @@ def moving_average(arr, window_size):
         plt.Line2D([0], [0], color="r", label="PIMD"),
     ]
 )
+plt.show()
 
 # %%
 #
@@ -765,6 +771,7 @@ def moving_average(arr, window_size):
 ax.legend(ncols=2, loc="upper right", fontsize=9)
 ax.set_ylabel(r"$F$ / eV")
 ax.set_xlabel(r"$\Delta C_\mathrm{H}$")
+plt.show()
 
 # %%
 # This model system is representative of the behavior of protons

examples/roy-gch/roy-gch.py

@@ -90,6 +90,7 @@
 ax.plot(density[ssel], energy[ssel], "k--")
 ax.set_xlabel("density / g/cm$^3$")
 ax.set_ylabel("energy / kJ/mol")
+plt.show()
 
 print(
     f"Mean hull energy for 'known' stable structures {dch_dist[iknown].mean()} kJ/mol"
@@ -194,6 +195,7 @@
 ax.set_ylabel("PCA[2]")
 cbar = fig.colorbar(scatter, ax=ax)
 cbar.set_label("energy / kJ/mol")
+plt.show()
 
 
 # %%
@@ -221,6 +223,7 @@
 ax.set_ylabel("PCA[2]")
 ax.set_zlabel("energy / kJ/mol\n \n", labelpad=11)
 ax.view_init(25, 110)
+plt.show()
 
 
 # %%