Repository: zincware/MDSuite (language: python)

Issue:
Atom-wise batching for dynamics is broken.
**Describe the bug**
The atom-wise batching for dynamic calculations is broken. 

**To Reproduce**
Run a large analysis on limited memory and it will fail.

Related to #383 


Discussion / hints:

Fix patch:
diff --git a/examples/notebooks/NaCl_walkthrough.ipynb b/examples/notebooks/NaCl_walkthrough.ipynb
index 538bf796..b74c0f27 100644
--- a/examples/notebooks/NaCl_walkthrough.ipynb
+++ b/examples/notebooks/NaCl_walkthrough.ipynb
@@ -65,7 +65,36 @@
    "execution_count": 3,
    "id": "d64da139-7bd9-48e0-b808-0981facea3df",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-12-08 15:46:01,373 - INFO: Loading the class state\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO - 2021-12-08 15:46:01,373 - project - Loading the class state\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2021-12-08 15:46:01,409 - INFO: Available experiments are: [1: NaCl_example_data]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO - 2021-12-08 15:46:01,409 - project - Available experiments are: [1: NaCl_example_data]\n"
+     ]
+    }
+   ],
    "source": [
     "project = mds.Project(\"NaCl_Example\")"
    ]
@@ -88,29 +117,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2021-12-01 13:15:15,348 - INFO: Creating a new experiment!\n"
+      "2021-12-08 15:46:01,420 - INFO: This experiment already exists\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO - 2021-12-01 13:15:15,348 - experiment - Creating a new experiment!\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2021-12-01 13:15:15,349 - INFO: ** An experiment has been added titled NaCl_example_data **\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO - 2021-12-01 13:15:15,349 - experiment - ** An experiment has been added titled NaCl_example_data **\n",
-      "100%|███████████████████████████████████| 1/1 [00:05<00:00,  5.66s/it]\n"
+      "INFO - 2021-12-08 15:46:01,420 - project - This experiment already exists\n"
      ]
     }
    ],
@@ -140,16 +154,6 @@
    "id": "2d58da29",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2021-12-01 13:15:21.257391: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
-      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
-      "Running mini batch loop 1 / 2: 100%|██| 10/10 [00:01<00:00,  7.88it/s]\n",
-      "Running mini batch loop 2 / 2: 100%|██| 10/10 [00:00<00:00, 13.68it/s]\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -471,7 +475,7 @@
        "\n",
        "\n",
        "\n",
-       "  <div class=\"bk-root\" id=\"61d8b353-f711-408d-b743-cdb5f3680675\" data-root-id=\"1198\"></div>\n"
+       "  <div class=\"bk-root\" id=\"d3809714-9847-4b8c-a63f-16699f83d61f\" data-root-id=\"1198\"></div>\n"
       ]
      },
      "metadata": {},
@@ -483,8 +487,8 @@
        "(function(root) {\n",
        "  function embed_document(root) {\n",
        "    \n",
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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[1622]}},\"selected\":{\"id\":\"1049\"},\"selection_policy\":{\"id\":\"1048\"}},\"id\":\"1034\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"axis\":{\"id\":\"1016\"},\"coordinates\":null,\"dimension\":1,\"group\":null,\"ticker\":null},\"id\":\"1019\",\"type\":\"Grid\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1140\",\"type\":\"Line\"},{\"attributes\":{\"label\":{\"value\":\"Na_Na\"},\"renderers\":[{\"id\":\"1038\"}]},\"id\":\"1052\",\"type\":\"LegendItem\"},{\"attributes\":{},\"id\":\"1043\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"1098\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"coordinates\":null,\"group\":null},\"id\":\"1144\",\"type\":\"Title\"},{\"attributes\":{},\"id\":\"1013\",\"type\":\"BasicTicker\"},{\"attributes\":{\"overlay\":{\"id\":\"1130\"}},\"id\":\"1126\",\"type\":\"BoxZoomTool\"},{\"attributes\":{},\"id\":\"1044\",\"type\":\"AllLabels\"},{\"attributes\":{},\"id\":\"1117\",\"type\":\"BasicTicker\"},{\"attributes\":{},\"id\":\"1010\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"1099\",\"type\":\"AllLabels\"},{\"attributes\":{},\"id\":\"1125\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"toolbars\":[{\"id\":\"1027\"},{\"id\":\"1079\"},{\"id\":\"1131\"}],\"tools\":[{\"id\":\"1020\"},{\"id\":\"1021\"},{\"id\":\"1022\"},{\"id\":\"1023\"},{\"id\":\"1024\"},{\"id\":\"1025\"},{\"id\":\"1053\"},{\"id\":\"1072\"},{\"id\":\"1073\"},{\"id\":\"1074\"},{\"id\":\"1075\"},{\"id\":\"1076\"},{\"id\":\"1077\"},{\"id\":\"1105\"},{\"id\":\"1124\"},{\"id\":\"1125\"},{\"id\":\"1126\"},{\"id\":\"1127\"},{\"id\":\"1128\"},{\"id\":\"1129\"},{\"id\":\"1157\"}]},\"id\":\"1196\",\"type\":\"ProxyToolbar\"},{\"attributes\":{},\"id\":\"1006\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"1095\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"1049\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"1124\",\"type\":\"PanTool\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1088\",\"type\":\"Line\"},{\"attributes\":{\"line_color\":\"#1f77b4\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1087\",\"type\":\"Line\"},{\"attributes\":{\"coordinates\":null,\"group\":null,\"items\":[{\"id\":\"1156\"}]},\"id\":\"1155\",\"type\":\"Legend\"},{\"attributes\":{\"line_color\":\"#1f77b4\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1139\",\"type\":\"Line\"},{\"attributes\":{\"label\":{\"value\":\"Na_Cl\"},\"renderers\":[{\"id\":\"1090\"}]},\"id\":\"1104\",\"type\":\"LegendItem\"},{\"attributes\":{\"tools\":[{\"id\":\"1124\"},{\"id\":\"1125\"},{\"id\":\"1126\"},{\"id\":\"1127\"},{\"id\":\"1128\"},{\"id\":\"1129\"},{\"id\":\"1157\"}]},\"id\":\"1131\",\"type\":\"Toolbar\"},{\"attributes\":{},\"id\":\"1025\",\"type\":\"HelpTool\"},{\"attributes\":{},\"id\":\"1129\",\"type\":\"HelpTool\"},{\"attributes\":{\"below\":[{\"id\":\"1064\"}],\"center\":[{\"id\":\"1067\"},{\"id\":\"1071\"},{\"id\":\"1103\"}],\"left\":[{\"id\":\"1068\"}],\"renderers\":[{\"id\":\"1090\"}],\"sizing_mode\":\"scale_both\",\"title\":{\"id\":\"1092\"},\"toolbar\":{\"id\":\"1079\"},\"toolbar_location\":null,\"x_range\":{\"id\":\"1056\"},\"x_scale\":{\"id\":\"1060\"},\"y_range\":{\"id\":\"1058\"},\"y_scale\":{\"id\":\"1062\"}},\"id\":\"1055\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{},\"id\":\"1023\",\"type\":\"SaveTool\"}],\"root_ids\":[\"1198\"]},\"title\":\"Bokeh Application\",\"version\":\"2.4.2\"}};\n",
-       "  const render_items = [{\"docid\":\"dfdf6c7e-33b3-4ac0-8b4f-6f32e45bfaad\",\"root_ids\":[\"1198\"],\"roots\":{\"1198\":\"61d8b353-f711-408d-b743-cdb5f3680675\"}}];\n",
+       "  const docs_json = {\"e1cd6d5b-0355-4613-a813-4b438b7154a0\":{\"defs\":[],\"roots\":{\"references\":[{\"attributes\":{\"children\":[{\"id\":\"1197\"},{\"id\":\"1195\"}],\"sizing_mode\":\"scale_both\"},\"id\":\"1198\",\"type\":\"Column\"},{\"attributes\":{},\"id\":\"1077\",\"type\":\"HelpTool\"},{\"attributes\":{},\"id\":\"1076\",\"type\":\"ResetTool\"},{\"attributes\":{},\"id\":\"1013\",\"type\":\"BasicTicker\"},{\"attributes\":{\"axis_label\":\"$$r / 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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[1622]}},\"selected\":{\"id\":\"1153\"},\"selection_policy\":{\"id\":\"1152\"}},\"id\":\"1138\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"1117\",\"type\":\"BasicTicker\"},{\"attributes\":{\"axis\":{\"id\":\"1116\"},\"coordinates\":null,\"group\":null,\"ticker\":null},\"id\":\"1119\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"1125\",\"type\":\"WheelZoomTool\"},{\"attributes\":{},\"id\":\"1147\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"axis_label\":\"$$g(r)$$\",\"coordinates\":null,\"formatter\":{\"id\":\"1147\"},\"group\":null,\"major_label_policy\":{\"id\":\"1148\"},\"ticker\":{\"id\":\"1121\"}},\"id\":\"1120\",\"type\":\"LinearAxis\"},{\"attributes\":{\"axis\":{\"id\":\"1120\"},\"coordinates\":null,\"dimension\":1,\"group\":null,\"ticker\":null},\"id\":\"1123\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"1006\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"1121\",\"type\":\"BasicTicker\"},{\"attributes\":{},\"id\":\"1148\",\"type\":\"AllLabels\"},{\"attributes\":{},\"id\":\"1004\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"1128\",\"type\":\"ResetTool\"},{\"attributes\":{},\"id\":\"1150\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"1124\",\"type\":\"PanTool\"},{\"attributes\":{},\"id\":\"1129\",\"type\":\"HelpTool\"},{\"attributes\":{\"coordinates\":null,\"group\":null},\"id\":\"1040\",\"type\":\"Title\"},{\"attributes\":{\"callback\":null},\"id\":\"1053\",\"type\":\"HoverTool\"},{\"attributes\":{},\"id\":\"1127\",\"type\":\"SaveTool\"},{\"attributes\":{\"bottom_units\":\"screen\",\"coordinates\":null,\"fill_alpha\":0.5,\"fill_color\":\"lightgrey\",\"group\":null,\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":1.0,\"line_color\":\"black\",\"line_dash\":[4,4],\"line_width\":2,\"right_units\":\"screen\",\"syncable\":false,\"top_units\":\"screen\"},\"id\":\"1130\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"tools\":[{\"id\":\"1124\"},{\"id\":\"1125\"},{\"id\":\"1126\"},{\"id\":\"1127\"},{\"id\":\"1128\"},{\"id\":\"1129\"},{\"id\":\"1157\"}]},\"id\":\"1131\",\"type\":\"Toolbar\"},{\"attributes\":{\"toolbar\":{\"id\":\"1196\"},\"toolbar_location\":\"above\"},\"id\":\"1197\",\"type\":\"ToolbarBox\"},{\"attributes\":{},\"id\":\"1062\",\"type\":\"LinearScale\"}],\"root_ids\":[\"1198\"]},\"title\":\"Bokeh Application\",\"version\":\"2.4.2\"}};\n",
+       "  const render_items = [{\"docid\":\"e1cd6d5b-0355-4613-a813-4b438b7154a0\",\"root_ids\":[\"1198\"],\"roots\":{\"1198\":\"d3809714-9847-4b8c-a63f-16699f83d61f\"}}];\n",
        "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
        "\n",
        "  }\n",
@@ -545,13 +549,6 @@
    "id": "72481f26",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "16it [03:25, 12.86s/it]\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -864,6 +861,14 @@
      "metadata": {},
      "output_type": "display_data"
     },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2021-12-08 15:46:01.648316: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
+      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
@@ -873,7 +878,7 @@
        "\n",
        "\n",
        "\n",
-       "  <div class=\"bk-root\" id=\"41f6fff7-82c5-4ac7-875c-da4983e7021e\" data-root-id=\"1657\"></div>\n"
+       "  <div class=\"bk-root\" id=\"509d55c0-b2e0-4dff-b20e-0496f8054135\" data-root-id=\"1657\"></div>\n"
       ]
      },
      "metadata": {},
@@ -885,8 +890,8 @@
        "(function(root) {\n",
        "  function embed_document(root) {\n",
        "    \n",
-       "  const docs_json = 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/ a.u.$$\",\"coordinates\":null,\"formatter\":{\"id\":\"1438\"},\"group\":null,\"major_label_policy\":{\"id\":\"1439\"},\"ticker\":{\"id\":\"1412\"}},\"id\":\"1411\",\"type\":\"LinearAxis\"}],\"root_ids\":[\"1657\"]},\"title\":\"Bokeh Application\",\"version\":\"2.4.2\"}};\n",
+       "  const render_items = [{\"docid\":\"c5dc8cc6-451b-4c17-968c-72bd9283c6a0\",\"root_ids\":[\"1657\"],\"roots\":{\"1657\":\"509d55c0-b2e0-4dff-b20e-0496f8054135\"}}];\n",
        "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
        "\n",
        "  }\n",
@@ -945,14 +950,16 @@
    "cell_type": "code",
    "execution_count": 7,
    "id": "af1016db-732e-4911-8104-2fd956166646",
-   "metadata": {},
+   "metadata": {
+    "scrolled": false
+   },
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Na: 100%|███████████████████████████████| 1/1 [00:01<00:00,  1.72s/it]\n",
-      "Cl: 100%|███████████████████████████████| 1/1 [00:01<00:00,  1.62s/it]\n"
+      "Na: 100%|███████████████████████████████| 1/1 [00:02<00:00,  2.10s/it]\n",
+      "Cl: 100%|███████████████████████████████| 1/1 [00:01<00:00,  1.75s/it]\n"
      ]
     },
     {
@@ -1276,7 +1283,7 @@
        "\n",
        "\n",
        "\n",
-       "  <div class=\"bk-root\" id=\"43f8d3f0-c238-4ab9-bd0c-40eacf0e7562\" data-root-id=\"2131\"></div>\n"
+       "  <div class=\"bk-root\" id=\"3f0b94ec-fa8b-41fa-8d32-d6500d0b2aa2\" data-root-id=\"2131\"></div>\n"
       ]
      },
      "metadata": {},
@@ -1288,8 +1295,8 @@
        "(function(root) {\n",
        "  function embed_document(root) {\n",
        "    \n",
-       "  const docs_json = 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 3.091E-08 +-  4.244E-09\"},\"renderers\":[{\"id\":\"1992\"}]},\"id\":\"2006\",\"type\":\"LegendItem\"},{\"attributes\":{\"coordinates\":null,\"data_source\":{\"id\":\"2008\"},\"glyph\":{\"id\":\"2009\"},\"group\":null,\"hover_glyph\":null,\"muted_glyph\":{\"id\":\"2011\"},\"nonselection_glyph\":{\"id\":\"2010\"},\"view\":{\"id\":\"2013\"},\"y_range_name\":\"Cond_range\"},\"id\":\"2012\",\"type\":\"GlyphRenderer\"}],\"root_ids\":[\"2131\"]},\"title\":\"Bokeh Application\",\"version\":\"2.4.2\"}};\n",
+       "  const render_items = [{\"docid\":\"87d8be77-7ab8-43b2-98f7-a9827e780a82\",\"root_ids\":[\"2131\"],\"roots\":{\"2131\":\"3f0b94ec-fa8b-41fa-8d32-d6500d0b2aa2\"}}];\n",
        "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
        "\n",
        "  }\n",
@@ -1324,7 +1331,7 @@
     {
      "data": {
       "text/plain": [
-       "{'NaCl_example_data': Exp1_Green Kubo Self-Diffusion Coefficients_3}"
+       "{'NaCl_example_data': Exp1_Green Kubo Self-Diffusion Coefficients_6}"
       ]
      },
      "execution_count": 7,
@@ -1334,7 +1341,7 @@
    ],
    "source": [
     "project.run.GreenKuboDiffusionCoefficients(\n",
-    "        data_range=100, plot=True, correlation_time=10\n",
+    "        data_range=102, plot=True, correlation_time=10\n",
     "    )"
    ]
   },
@@ -1344,14 +1351,6 @@
    "id": "a6cc2baa",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Ionic Current: 2it [00:00, 32.57it/s]                                 \n",
-      "100%|███████████████████████████████████| 1/1 [00:00<00:00,  1.72it/s]\n"
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1.481E+03 +-  6.097E+01\"},\"renderers\":[{\"id\":\"2462\"}]},\"id\":\"2476\",\"type\":\"LegendItem\"},{\"attributes\":{},\"id\":\"2444\",\"type\":\"PanTool\"},{\"attributes\":{},\"id\":\"2468\",\"type\":\"AllLabels\"},{\"attributes\":{\"toolbars\":[{\"id\":\"2451\"}],\"tools\":[{\"id\":\"2444\"},{\"id\":\"2445\"},{\"id\":\"2446\"},{\"id\":\"2447\"},{\"id\":\"2448\"},{\"id\":\"2449\"},{\"id\":\"2477\"}]},\"id\":\"2492\",\"type\":\"ProxyToolbar\"},{\"attributes\":{},\"id\":\"2432\",\"type\":\"LinearScale\"},{\"attributes\":{\"data\":{\"x\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[300]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[300]}},\"selected\":{\"id\":\"2473\"},\"selection_policy\":{\"id\":\"2472\"}},\"id\":\"2458\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"axis\":{\"id\":\"2436\"},\"coordinates\":null,\"group\":null,\"ticker\":null},\"id\":\"2439\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"2449\",\"type\":\"HelpTool\"},{\"attributes\":{\"bottom_units\":\"screen\",\"coordinates\":null,\"fill_alpha\":0.5,\"fill_color\":\"lightgrey\",\"group\":null,\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":1.0,\"line_color\":\"black\",\"line_dash\":[4,4],\"line_width\":2,\"right_units\":\"screen\",\"syncable\":false,\"top_units\":\"screen\"},\"id\":\"2450\",\"type\":\"BoxAnnotation\"},{\"attributes\":{},\"id\":\"2448\",\"type\":\"ResetTool\"}],\"root_ids\":[\"2494\"]},\"title\":\"Bokeh Application\",\"version\":\"2.4.2\"}};\n",
+       "  const render_items = [{\"docid\":\"f3917fcb-5394-4298-872a-0c81cc4165d6\",\"root_ids\":[\"2494\"],\"roots\":{\"2494\":\"7f78fff5-8eee-478e-85e4-bf7363ab8c4e\"}}];\n",
        "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
        "\n",
        "  }\n",
diff --git a/mdsuite/database/data_manager.py b/mdsuite/database/data_manager.py
index bcfe9d4d..98421003 100644
--- a/mdsuite/database/data_manager.py
+++ b/mdsuite/database/data_manager.py
@@ -72,6 +72,32 @@ def __init__(
         ----------
         database : Database
                 Database object from which tensor_values should be loaded
+        data_path : list
+                Path in the HDF5 database to be loaded.
+        data_range : int
+                Data range used in the calculator.
+        n_batches : int
+                Number of batches required.
+        batch_size : int
+                Size of a batch.
+        ensemble_loop : int
+                Number of ensembles to be looped over.
+        correlation_time : int
+                Correlation time used in the calculator.
+        remainder : int
+                Remainder used in the batching.
+        atom_remainder : int
+                Atom-wise remainder used in the atom-wise batching.
+        minibatch : bool
+                If true, atom-wise batching is required.
+        atom_batch_size : int
+                Size of an atom-wise batch.
+        n_atom_batches : int
+                Number of atom-wise batches.
+        atom_selection : int
+                Selection of atoms in the calculation.
+        offset : int
+                Offset in the data loading if it should not be loaded from the start.
         """
         self.database = database
         self.data_path = data_path
@@ -101,6 +127,13 @@ def batch_generator(
 
         Parameters
         ----------
+        dictionary : bool
+                If true return a dict. This is default now and could be removed.
+        system : bool
+                If true, a system parameter is being called for.
+        remainder : bool
+                If true, a remainder batch must be computed.
+        loop_array : np.ndarray
 
         Returns
         -------
@@ -209,8 +242,8 @@ def atom_generator(
             database = Database(name=database)
             _atom_remainder = [1 if self.atom_remainder else 0][0]
             start = 0
-            for i, atom_batch in tqdm(
-                enumerate(self.n_atom_batches + _atom_remainder),
+            for atom_batch in tqdm(
+                range(self.n_atom_batches + _atom_remainder),
                 total=self.n_atom_batches + _atom_remainder,
                 ncols=70,
                 desc=f"batch loop",
@@ -226,8 +259,7 @@ def atom_generator(
                     if batch == batch_number:
                         stop = int(start + self.remainder)
                         data_size = tf.cast(self.remainder, dtype=tf.int16)
-
-                    select_slice = np.s_[atom_start:atom_stop, start:stop]
+                    select_slice = np.s_[int(atom_start) : int(atom_stop), start:stop]
                     yield database.load_data(
                         data_path,
                         select_slice=select_slice,
@@ -240,9 +272,7 @@ def atom_generator(
         else:
             return generator, args
 
-    def ensemble_generator(
-        self, system: bool = False, dictionary: bool = False, glob_data: dict = None
-    ) -> tuple:
+    def ensemble_generator(self, system: bool = False, glob_data: dict = None) -> tuple:
         """
         Build a generator for the ensemble loop
 
@@ -250,8 +280,12 @@ def ensemble_generator(
         ----------
         system : bool
                 If true, the system generator is returned.
-        dictionary : bool
-                If true, data is expected as a dictionary and returned as one.
+        glob_data : dict
+                data to be loaded in ensembles from a tensorflow generator.
+                e.g. {b'Na/Positions': tf.Tensor}.
+                Will usually include a b'data_size' key which is checked in the
+                loop and ignored. All keys are in byte arrays. This appears when you
+                pass a dict to the tensorflow generator.
 
         Returns
         -------
@@ -260,29 +294,6 @@ def ensemble_generator(
 
         args = (self.ensemble_loop, self.correlation_time, self.data_range)
 
-        def generator(ensemble_loop, correlation_time, data_range, data):
-            """
-            Generator for the ensemble loop
-            Parameters
-            ----------
-            ensemble_loop : int
-                    Number of ensembles to loop over
-            correlation_time : int
-                    Distance between ensembles
-            data_range : int
-                    Size of each ensemble
-            data : tf.data.Dataset
-                    Data from which to draw ensembles
-
-            Returns
-            -------
-            None
-            """
-            for ensemble in range(ensemble_loop):
-                start = ensemble * correlation_time
-                stop = start + data_range
-                yield data[:, start:stop]
-
         def dictionary_generator(ensemble_loop, correlation_time, data_range):
             """
             Generator for the ensemble loop
diff --git a/mdsuite/memory_management/memory_manager.py b/mdsuite/memory_management/memory_manager.py
index fe81722d..72c15e14 100644
--- a/mdsuite/memory_management/memory_manager.py
+++ b/mdsuite/memory_management/memory_manager.py
@@ -81,12 +81,23 @@ def __init__(
         Parameters
         ----------
         data_path : list
+                Path to the data to be used in a calculator. This is used when
+                asking the database how big a dataset is.
+                ["Na/Positions", "Ionic_Current/Ionic_Current"]
         database : Database
+                Database class to be used for checking size parameters.
         parallel : bool
+                If true, the memory should be computed for parallel processes.
         memory_fraction : float
+                How much memory is the calculator allowed to use.
         scale_function : dict
+                Scaling function to compute the memory scaling of a calculator.
         gpu : bool
+                If true, gpu should be used.
         offset : int
+                If data is being loaded from a non-zero point in the database the
+                offset is used to take this into account. For example, expanding a
+                transformation.
         """
         if scale_function is None:
             scale_function = {"linear": {"scale_factor": 10}}
@@ -183,10 +194,10 @@ def get_batch_size(self, system: bool = False) -> tuple:
                 number of elements that will be left unloaded after a loop over all
                 batches. This amount can then be loaded to collect unused tensor_values.
         """
-        if self.data_path is []:
+        if self.data_path is None:
             raise ValueError("No tensor_values have been requested.")
 
-        per_configuration_memory: float = 0
+        per_configuration_memory: float = 0.0
         for item in self.data_path:
             n_rows, n_columns, n_bytes = self.database.get_data_size(
                 item, system=system
@@ -212,22 +223,25 @@ def get_batch_size(self, system: bool = False) -> tuple:
 
         return batch_size, number_of_batches, remainder
 
-    def hdf5_load_time(self, N):
+    @staticmethod
+    def hdf5_load_time(n: int):
         """
         Describes the load time of a hdf5 database i.e. O(log N)
 
         Parameters
         ----------
-        N : int
+        n : int
                 Amount of data to be loaded
 
         Returns
         -------
-
+        load_time : float
+                Load time of N data points from a hdf5 database.
         """
-        yield np.log(N)
+        return np.log(n)
 
-    def _get_optimal_batch_size(self, naive_size):
+    @staticmethod
+    def _get_optimal_batch_size(naive_size):
         """
         Use the open/close and read speeds of the hdf5 database_path as well as the
         operation being performed to get an optimal batch size.
@@ -248,9 +262,25 @@ def _get_optimal_batch_size(self, naive_size):
     def _compute_atomwise_minibatch(self, data_range: int):
         """
         Compute the number of atoms which can be loaded in at one time.
+
+        Parameters
+        ----------
+        data_range : int
+                Data range used in the calculator.
+
         Returns
         -------
-
+        Updates the following class attributes:
+            self.batch_size : int
+                    Size of each batch.
+            self.n_batches : int
+                    Number of batches to be looped over.
+            self.remainder :
+                    Remainder of batches after looping.
+            self.n_atom_batches : int
+                    Number of batches over atoms.
+            self.atom_remainder : int
+                    Remainder atoms after even batching.
         """
         per_atom_memory = 0  # memory usage per atom within ONE configuration
         per_configuration_memory = 0  # per configuration memory usage
@@ -261,7 +291,6 @@ def _compute_atomwise_minibatch(self, data_range: int):
             per_atom_memory += per_configuration_memory / n_rows
             total_rows += n_rows
 
-        # This does not seem to be used anywhere?
         # per_configuration_memory = self.scale_function(per_configuration_memory,
         # **self.scale_function_parameters)
         per_atom_memory = self.scale_function(


Test patch:
diff --git a/CI/unit_tests/memory_manager/test_memory_manager.py b/CI/unit_tests/memory_manager/test_memory_manager.py
new file mode 100644
index 00000000..6b34da4c
--- /dev/null
+++ b/CI/unit_tests/memory_manager/test_memory_manager.py
@@ -0,0 +1,248 @@
+"""
+MDSuite: A Zincwarecode package.
+License
+-------
+This program and the accompanying materials are made available under the terms
+of the Eclipse Public License v2.0 which accompanies this distribution, and is
+available at https://www.eclipse.org/legal/epl-v20.html
+SPDX-License-Identifier: EPL-2.0
+Copyright Contributors to the Zincwarecode Project.
+Contact Information
+-------------------
+email: zincwarecode@gmail.com
+github: https://github.com/zincware
+web: https://zincwarecode.com/
+Citation
+--------
+If you use this module please cite us with:
+Summary
+-------
+Test for the memory manager module.
+"""
+import unittest
+
+import numpy as np
+
+from mdsuite.memory_management.memory_manager import MemoryManager
+
+
+class TestDatabase:
+    """
+    Test database for the unit testing.
+    """
+
+    def __init__(self, data_size: int = 500, rows: int = 10, columns: int = 10):
+        """
+        Constructor for the test database.
+        """
+        self.data_size = data_size
+        self.rows = rows
+        self.columns = columns
+
+    def get_data_size(self, item, system: bool):
+        """
+        Compute the size of a dataset.
+
+        Returns
+        -------
+
+        """
+        return self.rows, self.columns, self.data_size
+
+
+class TestMemoryManager(unittest.TestCase):
+    """
+    Test the memory manager module.
+    """
+
+    @classmethod
+    def setUpClass(cls) -> None:
+        """
+        Prepare the class.
+
+        Returns
+        -------
+
+        """
+        cls.memory_manager = MemoryManager()
+
+    def test_linear_scale_function(self):
+        """
+        Test the linear scale selection.
+
+        Notes
+        -----
+        Each test will check that the function parameters are return correctly and
+        that the function is called correctly and returns proper values.
+        """
+        # Test linear function
+        scale_function = {"linear": {"scale_factor": 2}}
+        function, parameters = self.memory_manager._select_scale_function(
+            scale_function
+        )
+        self.assertEqual(parameters["scale_factor"], 2)
+        self.assertEqual(function(10, **parameters), 20)
+
+    def test_log_linear_scale_function(self):
+        """
+        Test the log-linear scale selection.
+
+        Notes
+        -----
+        Each test will check that the function parameters are return correctly and
+        that the function is called correctly and returns proper values.
+        """
+        # Test log-linear function
+        scale_function = {"log-linear": {"scale_factor": 2}}
+        function, parameters = self.memory_manager._select_scale_function(
+            scale_function
+        )
+        self.assertEqual(parameters["scale_factor"], 2)
+        self.assertEqual(function(10, **parameters), 20 * np.log(10))
+
+    def test_quadratic_scale_function(self):
+        """
+        Test the quadratic scale selection.
+
+        Notes
+        -----
+        Each test will check that the function parameters are return correctly and
+        that the function is called correctly and returns proper values.
+        """
+        # Test quadratic function
+        scale_function = {
+            "quadratic": {"inner_scale_factor": 2, "outer_scale_factor": 2}
+        }
+        function, parameters = self.memory_manager._select_scale_function(
+            scale_function
+        )
+        self.assertEqual(parameters["inner_scale_factor"], 2)
+        self.assertEqual(parameters["outer_scale_factor"], 2)
+        self.assertEqual(function(10, **parameters), 800)
+
+    def test_polynomial_scale_function(self):
+        """
+        Test the polynomial scale selection.
+
+        Notes
+        -----
+        Each test will check that the function parameters are return correctly and
+        that the function is called correctly and returns proper values.
+        """
+        # Test polynomial function
+        scale_function = {
+            "polynomial": {"inner_scale_factor": 2, "outer_scale_factor": 2, "order": 3}
+        }
+        function, parameters = self.memory_manager._select_scale_function(
+            scale_function
+        )
+        self.assertEqual(parameters["inner_scale_factor"], 2)
+        self.assertEqual(parameters["outer_scale_factor"], 2)
+        self.assertEqual(parameters["order"], 3)
+        self.assertEqual(function(10, **parameters), 16000)
+
+    def test_get_batch_size(self):
+        """
+        Test the get_batch_size method.
+
+        Returns
+        -------
+
+        """
+        # Test the exception catch.
+        self.memory_manager.data_path = None
+        self.assertRaises(ValueError, self.memory_manager.get_batch_size)
+
+        # Test correct returns for 1 batch
+        self.memory_manager.database = TestDatabase(data_size=500, rows=10, columns=10)
+        self.memory_manager.data_path = ["Test/Path"]
+        self.memory_manager.memory_fraction = 0.5
+        self.memory_manager.machine_properties["memory"] = 50000
+        batch_size, number_of_batches, remainder = self.memory_manager.get_batch_size(
+            system=False
+        )
+        self.assertEqual(batch_size, 10)
+        self.assertEqual(number_of_batches, 1)
+        self.assertEqual(remainder, 0)
+
+        # Test correct returns for N batches
+        self.memory_manager.database = TestDatabase(data_size=500, rows=11, columns=13)
+        self.memory_manager.data_path = ["Test/Path"]
+        self.memory_manager.memory_fraction = 1.0
+        self.memory_manager.machine_properties["memory"] = 50
+        batch_size, number_of_batches, remainder = self.memory_manager.get_batch_size(
+            system=False
+        )
+        self.assertEqual(batch_size, 1)
+        self.assertEqual(number_of_batches, 13)
+        self.assertEqual(remainder, 0)
+
+    def test_hdf5_load_time(self):
+        """
+        Test the hdf5_load_time method.
+
+        Returns
+        -------
+        Tests that the method returns the correct load time.
+        """
+        data = self.memory_manager.hdf5_load_time(10)
+        self.assertEqual(data, np.log(10))
+
+    def test_get_optimal_batch_size(self):
+        """
+        Test the get_optimal_batch_size method.
+
+        Returns
+        -------
+        Test that this method returns the expected value. Currently this is just
+        the same value that is passed to it.
+        """
+        data = self.memory_manager._get_optimal_batch_size(10)
+        self.assertEqual(data, data)
+
+    def test_compute_atomwise_minibatch(self):
+        """
+        Test the compute atom-wise mini-batch method.
+
+        Returns
+        -------
+        Test the atom wise minibatch method. The test ensures for only a single case
+        that the correct numbers are returned.
+        """
+        self.memory_manager.database = TestDatabase()
+        self.memory_manager.data_path = ["Test/Path"]
+        self.memory_manager._compute_atomwise_minibatch(5)
+        self.assertEqual(self.memory_manager.batch_size, 10)
+        self.assertEqual(self.memory_manager.n_batches, 1)
+        self.assertEqual(self.memory_manager.n_atom_batches, 2)
+        self.assertEqual(self.memory_manager.atom_remainder, 0)
+
+    def test_get_ensemble_loop_standard(self):
+        """
+        Test the get_ensemble_loop method with no mini-batching.
+
+        Returns
+        -------
+
+        """
+        self.memory_manager.database = TestDatabase()
+        self.memory_manager.data_path = ["Test/Path"]
+        self.memory_manager.batch_size = 50
+        data_partitions, minibatch = self.memory_manager.get_ensemble_loop(10, 5)
+        self.assertEqual(minibatch, False)
+        self.assertEqual(data_partitions, 8)
+
+    def test_get_ensemble_loop_minibatching(self):
+        """
+        Test the get_ensemble_loop method with mini-batching.
+
+        Returns
+        -------
+
+        """
+        self.memory_manager.database = TestDatabase()
+        self.memory_manager.data_path = ["Test/Path"]
+        self.memory_manager.batch_size = 5
+        data_partitions, minibatch = self.memory_manager.get_ensemble_loop(10, 5)
+        self.assertEqual(minibatch, True)
+        self.assertEqual(data_partitions, 1)