From cd6ceaf846dc01bc37b391a25870e333da002317 Mon Sep 17 00:00:00 2001 From: Hatem Helal Date: Wed, 15 May 2024 13:35:46 +0100 Subject: [PATCH] fix typo --- docs/batching.ipynb | 168 ++++++++++++++++++++++++++------------------ 1 file changed, 101 insertions(+), 67 deletions(-) diff --git a/docs/batching.ipynb b/docs/batching.ipynb index 2949a4e..65bca95 100644 --- a/docs/batching.ipynb +++ b/docs/batching.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "tags": [ "remove-input" @@ -37,8 +37,8 @@ "a batch of hydrogen molecules where the bond length (H-H distance) is varied. To setup\n", "the batch we build a `Hamiltonian` for each bond length and stack the built modules to\n", "create a batched-Hamiltonian. This example uses the `sto-3g` basis set and the simple\n", - "local density approximation of density functional theory but the this formulation isn't\n", - "unique to these choices for how the Hamiltonian is represented.\n", + "local density approximation of density functional theory but this batching formulation isn't\n", + "limited to these choices for how the Hamiltonian is represented.\n", "\n", ":::{note}\n", "The following code cell will install MESS into the Google Colab runtime.\n", @@ -48,13 +48,13 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": { "tags": [ "hide-cell" ], "id": "T36emVKN5u4f", - "outputId": "fbb0f9fb-ed2e-4fa7-e7e4-e700a80ce71d", + "outputId": "59faa045-366f-40e5-cb87-835710a24506", "colab": { "base_uri": "https://localhost:8080/" } @@ -65,49 +65,70 @@ "name": "stdout", "text": [ "Collecting git+https://github.com/graphcore-research/mess.git\n", - " Cloning https://github.com/graphcore-research/mess.git to /tmp/pip-req-build-yle2xylj\n", - " Running command git clone --filter=blob:none --quiet https://github.com/graphcore-research/mess.git /tmp/pip-req-build-yle2xylj\n", - " Resolved https://github.com/graphcore-research/mess.git to commit 099a73ba3135854519986351090884c9b9dc0cb8\n", + " Cloning https://github.com/graphcore-research/mess.git to /tmp/pip-req-build-3s3kdd6e\n", + " Running command git clone --filter=blob:none --quiet https://github.com/graphcore-research/mess.git /tmp/pip-req-build-3s3kdd6e\n", + " Resolved https://github.com/graphcore-research/mess.git to commit d0eb339067294e9124095d3ea99846944abe4b02\n", " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", " Installing backend dependencies ... \u001b[?25l\u001b[?25hdone\n", " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", "Collecting pyquante2@ git+https://github.com/rpmuller/pyquante2@pure (from mess==0.0.0)\n", - " Cloning https://github.com/rpmuller/pyquante2 (to revision pure) to /tmp/pip-install-25dcsqwy/pyquante2_a1d8959b4fb34b83a3c262912498bcac\n", - " Running command git clone --filter=blob:none --quiet https://github.com/rpmuller/pyquante2 /tmp/pip-install-25dcsqwy/pyquante2_a1d8959b4fb34b83a3c262912498bcac\n", + " Cloning https://github.com/rpmuller/pyquante2 (to revision pure) to /tmp/pip-install-wqyc34e7/pyquante2_3994315c991141ecb7b209b8ae3217bb\n", + " Running command git clone --filter=blob:none --quiet https://github.com/rpmuller/pyquante2 /tmp/pip-install-wqyc34e7/pyquante2_3994315c991141ecb7b209b8ae3217bb\n", " Running command git checkout -b pure --track origin/pure\n", " Switched to a new branch 'pure'\n", " Branch 'pure' set up to track remote branch 'pure' from 'origin'.\n", " Resolved https://github.com/rpmuller/pyquante2 to commit 822a1755c83f1730b1b063bc4ab2580a23342c02\n", " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - "Requirement already satisfied: equinox in /usr/local/lib/python3.10/dist-packages (from mess==0.0.0) (0.11.4)\n", - "Requirement already satisfied: jax[cpu] in /usr/local/lib/python3.10/dist-packages (from mess==0.0.0) (0.4.26)\n", - 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"outputId": "f07c3bce-ef1f-4289-fef9-64b1886116bd", + "outputId": "4632c7b9-f141-455d-ba70-eeefd9d2795b", "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ - "50cca93f45b44bdc909eece88dcd329b", - "bb637d9775264341a1718d84e3a50932", - "22473c7f6c31442d950d2bfd29866f1d", - "c4c8327bcfed4ea4928f6391fe9fc381", - "f84b898485e54152b1f2ca9d0ae94340", - "24427445af634738b2e93e0b9ec897b6", - "3142b7bc060d4c97b87b0350a9e8e1bf", - "5655128e51db4c79bc2fd11053ae5990", - "525f173820c049b2887a3d71f946c473", - "48bec612857b40549e4eba73b2241d0c", - "a5f0575bcd974403aefdd3e8b1f39606" + "5ababf5717a549dc863348050f74c1f6", + "b5338eaae29d4e1aadbc9a9134ad68d4", + "54407ee1c19b42f6b657a9efa3cb4865", + "7b1d2cd1741849d3a893adb1daa2dda3", + "5751062bfa9e412488c6737d0bb8f0af", + "e850c38a03494ae0adb59cf9b46ce032", + "dd786da37bf644eda8fed98a45eb542d", + "ae081d5f46e24d2399b3605d49395a4e", + "f4b476b1a61d47a3b522dcbfeef40d71", + "872620e1c557417da9ae41c71c4ac591", + "60b9c5965cba421e931e9e6398600c3c" ] } }, @@ -284,7 +318,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "50cca93f45b44bdc909eece88dcd329b" + "model_id": "5ababf5717a549dc863348050f74c1f6" } }, "metadata": {} @@ -320,7 +354,7 @@ "execution_count": 8, "metadata": { "id": "59E1Oq1S5u4i", - "outputId": "f0308f22-38cb-4bba-d5c6-9ffeae033d63", + "outputId": "491f4824-dced-4b1b-8b99-2fa7973eb2b9", "colab": { "base_uri": "https://localhost:8080/", "height": 458 @@ -364,7 +398,7 @@ "execution_count": 9, "metadata": { "id": "z5iXpTTI5u4i", - "outputId": "99e57303-d8b5-4ac4-983c-83d949108c03", + "outputId": "16d150cd-69dd-40f1-bde4-2c1490ab33ef", "colab": { "base_uri": "https://localhost:8080/", "height": 462 @@ -417,7 +451,7 @@ "accelerator": "GPU", "widgets": { "application/vnd.jupyter.widget-state+json": { - "50cca93f45b44bdc909eece88dcd329b": { + "5ababf5717a549dc863348050f74c1f6": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", @@ -432,14 +466,14 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_bb637d9775264341a1718d84e3a50932", - "IPY_MODEL_22473c7f6c31442d950d2bfd29866f1d", - "IPY_MODEL_c4c8327bcfed4ea4928f6391fe9fc381" + "IPY_MODEL_b5338eaae29d4e1aadbc9a9134ad68d4", + "IPY_MODEL_54407ee1c19b42f6b657a9efa3cb4865", + "IPY_MODEL_7b1d2cd1741849d3a893adb1daa2dda3" ], - "layout": "IPY_MODEL_f84b898485e54152b1f2ca9d0ae94340" + "layout": "IPY_MODEL_5751062bfa9e412488c6737d0bb8f0af" } }, - "bb637d9775264341a1718d84e3a50932": { + "b5338eaae29d4e1aadbc9a9134ad68d4": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", @@ -454,13 +488,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_24427445af634738b2e93e0b9ec897b6", + "layout": "IPY_MODEL_e850c38a03494ae0adb59cf9b46ce032", "placeholder": "​", - "style": "IPY_MODEL_3142b7bc060d4c97b87b0350a9e8e1bf", + "style": "IPY_MODEL_dd786da37bf644eda8fed98a45eb542d", "value": "Loss -77.213536: 100%" } }, - "22473c7f6c31442d950d2bfd29866f1d": { + "54407ee1c19b42f6b657a9efa3cb4865": { "model_module": "@jupyter-widgets/controls", "model_name": "FloatProgressModel", "model_module_version": "1.5.0", @@ -476,15 +510,15 @@ "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_5655128e51db4c79bc2fd11053ae5990", + "layout": "IPY_MODEL_ae081d5f46e24d2399b3605d49395a4e", "max": 128, "min": 0, "orientation": "horizontal", - "style": "IPY_MODEL_525f173820c049b2887a3d71f946c473", + "style": "IPY_MODEL_f4b476b1a61d47a3b522dcbfeef40d71", "value": 128 } }, - "c4c8327bcfed4ea4928f6391fe9fc381": { + "7b1d2cd1741849d3a893adb1daa2dda3": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", @@ -499,13 +533,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_48bec612857b40549e4eba73b2241d0c", + "layout": "IPY_MODEL_872620e1c557417da9ae41c71c4ac591", "placeholder": "​", - "style": "IPY_MODEL_a5f0575bcd974403aefdd3e8b1f39606", - "value": " 128/128 [00:03<00:00, 76.29it/s]" + "style": "IPY_MODEL_60b9c5965cba421e931e9e6398600c3c", + "value": " 128/128 [00:03<00:00, 76.15it/s]" } }, - "f84b898485e54152b1f2ca9d0ae94340": { + "5751062bfa9e412488c6737d0bb8f0af": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -557,7 +591,7 @@ "width": null } }, - "24427445af634738b2e93e0b9ec897b6": { + "e850c38a03494ae0adb59cf9b46ce032": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -609,7 +643,7 @@ "width": null } }, - "3142b7bc060d4c97b87b0350a9e8e1bf": { + "dd786da37bf644eda8fed98a45eb542d": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", @@ -624,7 +658,7 @@ "description_width": "" } }, - "5655128e51db4c79bc2fd11053ae5990": { + "ae081d5f46e24d2399b3605d49395a4e": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -676,7 +710,7 @@ "width": null } }, - "525f173820c049b2887a3d71f946c473": { + "f4b476b1a61d47a3b522dcbfeef40d71": { "model_module": "@jupyter-widgets/controls", "model_name": "ProgressStyleModel", "model_module_version": "1.5.0", @@ -692,7 +726,7 @@ "description_width": "" } }, - "48bec612857b40549e4eba73b2241d0c": { + "872620e1c557417da9ae41c71c4ac591": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -744,7 +778,7 @@ "width": null } }, - "a5f0575bcd974403aefdd3e8b1f39606": { + "60b9c5965cba421e931e9e6398600c3c": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0",