Fixed examples

examples/advanced/ex02_shuffle_data.py

@@ -53,6 +53,14 @@
     "Be_snapshot1.out.npy",
     data_path,
 )
+# On-the-fly snapshots can be added as well.
+# data_shuffler.add_snapshot(
+#     "Be_snapshot2.info.json",
+#     data_path,
+#     "Be_snapshot2.out.npy",
+#     data_path,
+# )
+
 
 # Shuffle the snapshots using the "shuffle_to_temporary" option.
 data_shuffler.shuffle_snapshots(

examples/basic/ex01_train_network.py

@@ -65,15 +65,25 @@
 data_handler.add_snapshot(
     "Be_snapshot0.in.npy", data_path, "Be_snapshot0.out.npy", data_path, "tr"
 )
-# Add snapshots with "raw" (=MALA formatted) JSON, computation of descriptors
-# will be performed "on-the-fly".
 data_handler.add_snapshot(
-    "Be_snapshot1.info.json",
-    data_path,
-    "Be_snapshot1.out.npy",
-    data_path,
-    "va",
+    "Be_snapshot1.in.npy", data_path, "Be_snapshot1.out.npy", data_path, "va"
 )
+# Add snapshots with "raw" (=MALA formatted) JSON, computation of descriptors
+# will be performed "on-the-fly".
+# data_handler.add_snapshot(
+#     "Be_snapshot0.info.json",
+#     data_path,
+#     "Be_snapshot0.out.npy",
+#     data_path,
+#     "tr",
+# )
+# data_handler.add_snapshot(
+#     "Be_snapshot1.info.json",
+#     data_path,
+#     "Be_snapshot1.out.npy",
+#     data_path,
+#     "va",
+# )
 
 data_handler.prepare_data()
 

examples/basic/ex02_test_network.py

@@ -50,17 +50,33 @@
     "te",
     calculation_output_file=os.path.join(data_path, "Be_snapshot2.info.json"),
 )
-
-# Add snapshots with "raw" (=MALA formatted) JSON, computation of descriptors
-# will be performed "on-the-fly".
 data_handler.add_snapshot(
-    "Be_snapshot3.info.json",
+    "Be_snapshot3.in.npy",
     data_path,
     "Be_snapshot3.out.npy",
     data_path,
     "te",
     calculation_output_file=os.path.join(data_path, "Be_snapshot3.info.json"),
 )
+
+# Add snapshots with "raw" (=MALA formatted) JSON, computation of descriptors
+# will be performed "on-the-fly".
+# data_handler.add_snapshot(
+#     "Be_snapshot2.info.json",
+#     data_path,
+#     "Be_snapshot2.out.npy",
+#     data_path,
+#     "te",
+#     calculation_output_file=os.path.join(data_path, "Be_snapshot2.info.json"),
+# )
+# data_handler.add_snapshot(
+#     "Be_snapshot3.info.json",
+#     data_path,
+#     "Be_snapshot3.out.npy",
+#     data_path,
+#     "te",
+#     calculation_output_file=os.path.join(data_path, "Be_snapshot3.info.json"),
+# )
 data_handler.prepare_data(reparametrize_scaler=False)
 
 

examples/basic/ex03_preprocess_data.py

@@ -51,7 +51,9 @@
 # more convenient *.json files that can be used in their stead. This saves
 # on disk space and makes the process more reproducible.
 # To only process parts of the data, omit/add descriptor_input*, target_input_*
-# and simulation_output_* at your leisure.
+# and simulation_output_* at your leisure. This is especially useful if you,
+# e.g., do not need to convert the descriptor data, since it will be
+# calculated on-the-fly during training.
 # Make sure to set the correct units - for QE, this should always be
 # 1/(Ry*Bohr^3).
 ####################
@@ -60,6 +62,7 @@
 outfile = os.path.join(data_path, "Be_snapshot0.out")
 ldosfile = os.path.join(data_path, "cubes/tmp.pp*Be_ldos.cube")
 
+# Converting a snapshot for training on precomputed descriptor data.
 data_converter.add_snapshot(
     descriptor_input_type="espresso-out",
     descriptor_input_path=outfile,
@@ -70,6 +73,16 @@
     target_units="1/(Ry*Bohr^3)",
 )
 
+# Converting a snapshot for training with on-the-fly descriptor calculation.
+# data_converter.add_snapshot(
+#     target_input_type=".cube",
+#     target_input_path=ldosfile,
+#     simulation_output_type="espresso-out",
+#     simulation_output_path=outfile,
+#     target_units="1/(Ry*Bohr^3)",
+# )
+
+
 ####################
 # 3. Converting the data
 # To convert the data we now simply have to call the convert_snapshot function.
@@ -82,9 +95,11 @@
 ####################
 
 data_converter.convert_snapshots(
-    descriptor_save_path="./",
     target_save_path="./",
     simulation_output_save_path="./",
+    # The next line should be omitted, if the descriptor data is to be
+    # calculated on-the-fly during training.
+    descriptor_save_path="./",
     naming_scheme="Be_snapshot*.npy",
     descriptor_calculation_kwargs={"working_directory": data_path},
 )