From 7b9898e2bf402d0eed9099e126a22d5734d8130f Mon Sep 17 00:00:00 2001 From: "Haoyu (Daniel)" Date: Wed, 4 Dec 2024 11:09:53 +0800 Subject: [PATCH 01/18] migrate to mp_api --- ...-03-Analyze and plot band structures.ipynb | 114 +++--------------- 1 file changed, 15 insertions(+), 99 deletions(-) diff --git a/notebooks/2017-09-03-Analyze and plot band structures.ipynb b/notebooks/2017-09-03-Analyze and plot band structures.ipynb index 907c1e8..c5fe67a 100644 --- a/notebooks/2017-09-03-Analyze and plot band structures.ipynb +++ b/notebooks/2017-09-03-Analyze and plot band structures.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This notebook shows some examples of methods on a BandStructureSymmLine object (gettting band gaps, vbm, etc...) and basic plotting. Written by Geoffroy Hautier (geoffroy.hautier@uclouvain.be)" + "This notebook shows some examples of methods on a BandStructureSymmLine object (gettting band gaps, VBM, etc...) and basic plotting. Written by Geoffroy Hautier (geoffroy.hautier@uclouvain.be)" ] }, { @@ -20,38 +20,22 @@ "metadata": {}, "outputs": [], "source": [ - "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19\n", - "from __future__ import annotations" + "# Uncomment the following line to install dependencies\n", + "!pip install -U mp_api pymatgen" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "0dd84b83815a4ddcb0e4c3802934fd1a", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Retrieving ElectronicStructureDoc documents: 0%| | 0/1 [00:00, {: [15]}), 'kpoint_index': [123], 'kpoint': , 'energy': 6.1023, 'projections': {: array([[0.000e+00, 3.950e-02, 1.500e-03, 1.500e-03],\n", - " [2.000e-04, 0.000e+00, 3.500e-03, 3.500e-03],\n", - " [0.000e+00, 0.000e+00, 1.300e-03, 1.300e-03],\n", - " [0.000e+00, 0.000e+00, 5.740e-02, 5.740e-02],\n", - " [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00],\n", - " [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00],\n", - " [0.000e+00, 3.383e-01, 0.000e+00, 0.000e+00],\n", - " [0.000e+00, 1.137e-01, 0.000e+00, 0.000e+00],\n", - " [0.000e+00, 2.951e-01, 0.000e+00, 0.000e+00]])}}\n" - ] - } - ], + "outputs": [], "source": [ "# is the material a metal (i.e., the fermi level cross a band)\n", "print(bs.is_metal())\n", "# print information on the band gap\n", "print(bs.get_band_gap())\n", - "# print the energy of the 20th band and 10th kpoint\n", + "# print the energy of the 21st band and 11th kpoint\n", "print(bs.bands[Spin.up][20][10])\n", "# print energy of direct band gap\n", "print(bs.get_direct_band_gap())\n", - "# print information on the vbm\n", + "# print information on the VBM\n", "print(bs.get_vbm())" ] }, @@ -104,35 +68,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Here, we plot the bs object. By default for an insulator we have en energy limit of cbm+4eV and vbm-4 eV" + "Here, we plot the bs object. By default for an insulator we have en energy limit of CBM+4eV and VBM-4 eV" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "%matplotlib inline\n", "from pymatgen.electronic_structure.plotter import BSPlotter\n", @@ -152,29 +95,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "plotter.plot_brillouin()" ] @@ -183,7 +104,7 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "venv312", "language": "python", "name": "python3" }, @@ -197,12 +118,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" - }, - "vscode": { - "interpreter": { - "hash": "8022b3e932e045c760cb4633b91dd1cb8bc60d104ca9808334cbd1645adbe837" - } + "version": "3.12.7" } }, "nbformat": 4, From ed615d79e375a01053a0fa2bfc294e5f21f6a1f8 Mon Sep 17 00:00:00 2001 From: "Haoyu (Daniel)" Date: Wed, 4 Dec 2024 11:18:04 +0800 Subject: [PATCH 02/18] recover outputs --- ...-03-Analyze and plot band structures.ipynb | 105 +++++++++++++++++- 1 file changed, 102 insertions(+), 3 deletions(-) diff --git a/notebooks/2017-09-03-Analyze and plot band structures.ipynb b/notebooks/2017-09-03-Analyze and plot band structures.ipynb index c5fe67a..6f966be 100644 --- a/notebooks/2017-09-03-Analyze and plot band structures.ipynb +++ b/notebooks/2017-09-03-Analyze and plot band structures.ipynb @@ -18,9 +18,96 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n", + "Collecting mp_api\n", + " Using cached https://pypi.tuna.tsinghua.edu.cn/packages/9c/5f/3a14476d076019bd22557ffb9a0387546d671cbbf360661fbca55543488f/mp_api-0.43.0-py3-none-any.whl (97 kB)\n", + "Requirement already satisfied: pymatgen in /Users/yang/developer/matgenb/venv312/lib/python3.12/site-packages (2024.11.13)\n", + "Requirement already satisfied: setuptools in /Users/yang/developer/matgenb/venv312/lib/python3.12/site-packages (from mp_api) (75.6.0)\n", + "Requirement already satisfied: msgpack in /Users/yang/developer/matgenb/venv312/lib/python3.12/site-packages (from mp_api) (1.1.0)\n", + "Requirement already satisfied: maggma>=0.57.1 in /Users/yang/developer/matgenb/venv312/lib/python3.12/site-packages (from mp_api) (0.70.0)\n", + "Requirement already satisfied: typing-extensions>=3.7.4.1 in /Users/yang/developer/matgenb/venv312/lib/python3.12/site-packages (from mp_api) (4.12.2)\n", + "Requirement already satisfied: requests>=2.23.0 in /Users/yang/developer/matgenb/venv312/lib/python3.12/site-packages (from mp_api) (2.32.3)\n", + "Requirement already satisfied: monty>=2023.9.25 in /Users/yang/developer/matgenb/venv312/lib/python3.12/site-packages (from mp_api) (2024.10.21)\n", + "Requirement already satisfied: emmet-core>=0.78.0rc3 in /Users/yang/developer/matgenb/venv312/lib/python3.12/site-packages (from mp_api) (0.84.3rc6)\n", + "Requirement already satisfied: smart_open in /Users/yang/developer/matgenb/venv312/lib/python3.12/site-packages (from mp_api) (7.0.5)\n", + 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pydantic-settings>=2.0->emmet-core>=0.78.0rc3->mp_api) (1.0.1)\n", + "Requirement already satisfied: paramiko>=2.7.2 in /Users/yang/developer/matgenb/venv312/lib/python3.12/site-packages (from sshtunnel>=0.1.5->maggma>=0.57.1->mp_api) (3.5.0)\n", + "Requirement already satisfied: bcrypt>=3.2 in /Users/yang/developer/matgenb/venv312/lib/python3.12/site-packages (from paramiko>=2.7.2->sshtunnel>=0.1.5->maggma>=0.57.1->mp_api) (4.2.1)\n", + "Requirement already satisfied: cryptography>=3.3 in /Users/yang/developer/matgenb/venv312/lib/python3.12/site-packages (from paramiko>=2.7.2->sshtunnel>=0.1.5->maggma>=0.57.1->mp_api) (44.0.0)\n", + "Requirement already satisfied: pynacl>=1.5 in /Users/yang/developer/matgenb/venv312/lib/python3.12/site-packages (from paramiko>=2.7.2->sshtunnel>=0.1.5->maggma>=0.57.1->mp_api) (1.5.0)\n", + "Requirement already satisfied: cffi>=1.12 in /Users/yang/developer/matgenb/venv312/lib/python3.12/site-packages (from cryptography>=3.3->paramiko>=2.7.2->sshtunnel>=0.1.5->maggma>=0.57.1->mp_api) (1.17.1)\n", + "Requirement already satisfied: pycparser in /Users/yang/developer/matgenb/venv312/lib/python3.12/site-packages (from cffi>=1.12->cryptography>=3.3->paramiko>=2.7.2->sshtunnel>=0.1.5->maggma>=0.57.1->mp_api) (2.22)\n", + "Installing collected packages: mp_api\n", + "Successfully installed mp_api-0.43.0\n" + ] + } + ], "source": [ - "# Uncomment the following line to install dependencies\n", + "# Run the following line to install dependencies\n", "!pip install -U mp_api pymatgen" ] }, @@ -28,7 +115,19 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'mp_api.client'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmp_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mclient\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m MPRester\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpymatgen\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01melectronic_structure\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Spin\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Put your own MP API key.\u001b[39;00m\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'mp_api.client'" + ] + } + ], "source": [ "from mp_api.client import MPRester\n", "from pymatgen.electronic_structure.core import Spin\n", From 88bc2b9021a10844611ca16b56ef0454c5d3a2b3 Mon Sep 17 00:00:00 2001 From: "Haoyu (Daniel)" Date: Wed, 4 Dec 2024 11:19:28 +0800 Subject: [PATCH 03/18] bump pre-commit hooks --- .pre-commit-config.yaml | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index a57edd7..9db453c 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -5,48 +5,48 @@ ci: repos: - repo: https://github.com/psf/black - rev: 22.10.0 + rev: 24.10.0 hooks: - id: black-jupyter - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.4.0 + rev: v5.0.0 hooks: - id: check-yaml - id: end-of-file-fixer - id: trailing-whitespace - repo: https://github.com/PyCQA/flake8 - rev: 6.0.0 + rev: 7.1.1 hooks: - id: flake8 additional_dependencies: [flake8-bugbear] - repo: https://github.com/asottile/pyupgrade - rev: v3.2.3 + rev: v3.19.0 hooks: - id: pyupgrade args: [--py39-plus] - repo: https://github.com/PyCQA/autoflake - rev: v2.0.0 + rev: v2.3.1 hooks: - id: autoflake - repo: https://github.com/pre-commit/mirrors-mypy - rev: v0.991 + rev: v1.13.0 hooks: - id: mypy - repo: https://github.com/nbQA-dev/nbQA - rev: 1.5.3 + rev: 1.9.1 hooks: - id: nbqa-pyupgrade args: [--py39-plus] - id: nbqa-isort - repo: https://github.com/kynan/nbstripout - rev: 0.6.1 + rev: 0.8.1 hooks: - id: nbstripout args: [--drop-empty-cells, --keep-output] From 8bea0af18574f8da3eb6e2f1e7e1c76c3629b7a5 Mon Sep 17 00:00:00 2001 From: "Haoyu (Daniel)" Date: Wed, 4 Dec 2024 11:21:19 +0800 Subject: [PATCH 04/18] recover outputs --- ...-03-Analyze and plot band structures.ipynb | 172 ++++++++---------- 1 file changed, 73 insertions(+), 99 deletions(-) diff --git a/notebooks/2017-09-03-Analyze and plot band structures.ipynb b/notebooks/2017-09-03-Analyze and plot band structures.ipynb index 6f966be..06ddb1a 100644 --- a/notebooks/2017-09-03-Analyze and plot band structures.ipynb +++ b/notebooks/2017-09-03-Analyze and plot band structures.ipynb @@ -18,94 +18,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n", - "Collecting mp_api\n", - " Using cached https://pypi.tuna.tsinghua.edu.cn/packages/9c/5f/3a14476d076019bd22557ffb9a0387546d671cbbf360661fbca55543488f/mp_api-0.43.0-py3-none-any.whl (97 kB)\n", - "Requirement already satisfied: pymatgen in /Users/yang/developer/matgenb/venv312/lib/python3.12/site-packages (2024.11.13)\n", - "Requirement already satisfied: setuptools in /Users/yang/developer/matgenb/venv312/lib/python3.12/site-packages (from mp_api) (75.6.0)\n", - "Requirement already satisfied: msgpack in /Users/yang/developer/matgenb/venv312/lib/python3.12/site-packages (from mp_api) (1.1.0)\n", - "Requirement already satisfied: maggma>=0.57.1 in /Users/yang/developer/matgenb/venv312/lib/python3.12/site-packages (from mp_api) (0.70.0)\n", - "Requirement already satisfied: typing-extensions>=3.7.4.1 in /Users/yang/developer/matgenb/venv312/lib/python3.12/site-packages (from mp_api) (4.12.2)\n", - 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(4.2.1)\n", - "Requirement already satisfied: cryptography>=3.3 in /Users/yang/developer/matgenb/venv312/lib/python3.12/site-packages (from paramiko>=2.7.2->sshtunnel>=0.1.5->maggma>=0.57.1->mp_api) (44.0.0)\n", - "Requirement already satisfied: pynacl>=1.5 in /Users/yang/developer/matgenb/venv312/lib/python3.12/site-packages (from paramiko>=2.7.2->sshtunnel>=0.1.5->maggma>=0.57.1->mp_api) (1.5.0)\n", - "Requirement already satisfied: cffi>=1.12 in /Users/yang/developer/matgenb/venv312/lib/python3.12/site-packages (from cryptography>=3.3->paramiko>=2.7.2->sshtunnel>=0.1.5->maggma>=0.57.1->mp_api) (1.17.1)\n", - "Requirement already satisfied: pycparser in /Users/yang/developer/matgenb/venv312/lib/python3.12/site-packages (from cffi>=1.12->cryptography>=3.3->paramiko>=2.7.2->sshtunnel>=0.1.5->maggma>=0.57.1->mp_api) (2.22)\n", - "Installing collected packages: mp_api\n", - "Successfully installed mp_api-0.43.0\n" - ] - } - ], + "outputs": [], "source": [ "# Run the following line to install dependencies\n", "!pip install -U mp_api pymatgen" @@ -117,14 +30,12 @@ "metadata": {}, "outputs": [ { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'mp_api.client'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmp_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mclient\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m MPRester\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpymatgen\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01melectronic_structure\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Spin\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Put your own MP API key.\u001b[39;00m\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'mp_api.client'" + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/yang/developer/matgenb/venv312/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n", + "Retrieving ElectronicStructureDoc documents: 100%|██████████| 1/1 [00:00<00:00, 18157.16it/s]\n" ] } ], @@ -149,7 +60,27 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "False\n", + "{'direct': False, 'transition': '(0.591,0.409,0.000)-\\\\Gamma', 'energy': 1.7978000000000005}\n", + "18.0201\n", + "2.6903999999999995\n", + "{'band_index': defaultdict(, {: [15]}), 'kpoint_index': [123], 'kpoint': , 'energy': 6.1023, 'projections': {: array([[0.000e+00, 3.950e-02, 1.500e-03, 1.500e-03],\n", + " [2.000e-04, 0.000e+00, 3.500e-03, 3.500e-03],\n", + " [0.000e+00, 0.000e+00, 1.300e-03, 1.300e-03],\n", + " [0.000e+00, 0.000e+00, 5.740e-02, 5.740e-02],\n", + " [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00],\n", + " [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00],\n", + " [0.000e+00, 3.383e-01, 0.000e+00, 0.000e+00],\n", + " [0.000e+00, 1.137e-01, 0.000e+00, 0.000e+00],\n", + " [0.000e+00, 2.951e-01, 0.000e+00, 0.000e+00]])}}\n" + ] + } + ], "source": [ "# is the material a metal (i.e., the fermi level cross a band)\n", "print(bs.is_metal())\n", @@ -174,7 +105,28 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "%matplotlib inline\n", "from pymatgen.electronic_structure.plotter import BSPlotter\n", @@ -194,7 +146,29 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "plotter.plot_brillouin()" ] From c6586475b87876f6d8bd004b0b302410171c64f7 Mon Sep 17 00:00:00 2001 From: "Haoyu (Daniel)" Date: Wed, 4 Dec 2024 11:30:45 +0800 Subject: [PATCH 05/18] tweak test workflow config --- .github/workflows/test.yml | 13 ++++++------- requirements-ci.txt | 2 +- 2 files changed, 7 insertions(+), 8 deletions(-) diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index aec4288..b4baecc 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -1,5 +1,4 @@ -# Runs the complete test suite incl. many external command line dependencies (like Openbabel) -# as well as the pymatgen.ext package. Coverage used to be computed based on this workflow. +# Runs the complete test suite incl. many external dependencies (like Openbabel) name: Tests on: @@ -15,11 +14,10 @@ jobs: test: defaults: run: - shell: bash -l {0} # enables conda/mamba env activation by reading bash profile + shell: bash -l {0} # enables conda/mamba env activation by reading bash profile strategy: fail-fast: false matrix: - # pytest-split automatically distributes work load so parallel jobs finish in similar time os: [ubuntu-latest] python-version: ["3.11"] @@ -27,7 +25,7 @@ jobs: env: PMG_MAPI_KEY: ${{ secrets.PMG_MAPI_KEY }} - PMG_VASP_PSP_DIR: /home/runner/work/matgenb/matgenb/psp + PMG_VASP_PSP_DIR: "${{ github.workspace }}/psp" steps: - name: Check out repo @@ -42,7 +40,9 @@ jobs: micromamba install -n venv -c conda-forge enumlib packmol bader openbabel openff-toolkit --yes - name: Install uv - run: micromamba run -n venv pip install uv + uses: astral-sh/setup-uv@v4 + with: + version: "latest" - name: Install dependencies run: | @@ -54,5 +54,4 @@ jobs: run: | micromamba activate venv cd notebooks - pwd pytest --ignore-glob=*notest.ipynb --nbmake . diff --git a/requirements-ci.txt b/requirements-ci.txt index b6328ba..a4be2f3 100644 --- a/requirements-ci.txt +++ b/requirements-ci.txt @@ -5,5 +5,5 @@ nbmake pymatgen pymatgen-analysis-diffusion mp-api -# BoltzTraP2 # Does not work because numpy not detected. +BoltzTraP2 phonopy From 5f51fe89a94daae5437d4547845c848076591961 Mon Sep 17 00:00:00 2001 From: "Haoyu (Daniel)" Date: Wed, 4 Dec 2024 11:35:12 +0800 Subject: [PATCH 06/18] try to remove seemingly pmg build dep --- .github/workflows/test.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index b4baecc..b99a03c 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -47,7 +47,6 @@ jobs: - name: Install dependencies run: | micromamba activate venv - uv pip install numpy cython uv pip install --upgrade -r requirements-ci.txt - name: pytest From ae0d664d15f74087ffeb294e1c55f9d57a02463b Mon Sep 17 00:00:00 2001 From: "Haoyu (Daniel)" Date: Wed, 4 Dec 2024 11:44:47 +0800 Subject: [PATCH 07/18] migrate mprester to new mp_api.client --- ...tion Energies with the Materials API.ipynb | 4 +- ...ystal structures from online sources.ipynb | 2 +- ...hase Diagram using the Materials API.ipynb | 2 +- ... Phase and Electrochemical Stability.ipynb | 2 +- ...-Getting data from Materials Project.ipynb | 3 +- ...17-12-15-Plotting a Pourbaix Diagram.ipynb | 2 +- ...dination environments in a structure.ipynb | 4 +- ...action Diagram between Two Compounds.ipynb | 4 +- ...8-07-24-Adsorption on solid surfaces.ipynb | 2 +- ...using Pymatgen and the Materials API.ipynb | 2 +- ...-6-Dopant suggestions using Pymatgen.ipynb | 4 +- .../2019-03-11-Interface Reactions.ipynb | 2 +- ...s Input for Initial DFT Calculations.ipynb | 46 +++++++++---------- ...2021-5-12-Explanation of Corrections.ipynb | 4 +- 14 files changed, 41 insertions(+), 42 deletions(-) diff --git a/notebooks/2013-01-01-Calculating Reaction Energies with the Materials API.ipynb b/notebooks/2013-01-01-Calculating Reaction Energies with the Materials API.ipynb index 70fdd28..be67080 100644 --- a/notebooks/2013-01-01-Calculating Reaction Energies with the Materials API.ipynb +++ b/notebooks/2013-01-01-Calculating Reaction Energies with the Materials API.ipynb @@ -47,11 +47,10 @@ } ], "source": [ + "from mp_api.client import MPRester\n", "from pymatgen.analysis.reaction_calculator import ComputedReaction\n", "from pymatgen.core import Composition\n", "from pymatgen.core.units import FloatWithUnit\n", - "from pymatgen.entries.computed_entries import ComputedEntry\n", - "from pymatgen.ext.matproj import MPRester\n", "\n", "# This initializes the REST adaptor. Put your own API key in if necessary.\n", "mpr = MPRester()\n", @@ -59,6 +58,7 @@ "# This gets all entries belonging to the Ca-C-O system.\n", "all_entries = mpr.get_entries_in_chemsys([\"Ca\", \"C\", \"O\"])\n", "\n", + "\n", "# This method simply gets the lowest energy entry for all entry with the same composition.\n", "def get_most_stable_entry(formula):\n", " relevant_entries = [\n", diff --git a/notebooks/2013-01-01-Getting crystal structures from online sources.ipynb b/notebooks/2013-01-01-Getting crystal structures from online sources.ipynb index fb9b9cd..4254917 100644 --- a/notebooks/2013-01-01-Getting crystal structures from online sources.ipynb +++ b/notebooks/2013-01-01-Getting crystal structures from online sources.ipynb @@ -35,7 +35,7 @@ "metadata": {}, "outputs": [], "source": [ - "from pymatgen.ext.matproj import MPRester\n", + "from mp_api.client import MPRester\n", "\n", "# Note that you will need to add your Materials API key in your .pmgrc.yaml file as \"PMG_MAPI_KEY\".\n", "# Alternatively, you will need to supply the API key as an arg to MPRester.\n", diff --git a/notebooks/2013-01-01-Plotting and Analyzing a Phase Diagram using the Materials API.ipynb b/notebooks/2013-01-01-Plotting and Analyzing a Phase Diagram using the Materials API.ipynb index 88ea479..a05e4f3 100644 --- a/notebooks/2013-01-01-Plotting and Analyzing a Phase Diagram using the Materials API.ipynb +++ b/notebooks/2013-01-01-Plotting and Analyzing a Phase Diagram using the Materials API.ipynb @@ -26,8 +26,8 @@ "metadata": {}, "outputs": [], "source": [ + "from mp_api.client import MPRester\n", "from pymatgen.analysis.phase_diagram import PDPlotter, PhaseDiagram\n", - "from pymatgen.ext.matproj import MPRester\n", "\n", "%matplotlib inline" ] diff --git a/notebooks/2016-09-08-Data-driven First Principles Methods for the Study and Design of Alkali Superionic Conductors Part 2 - Phase and Electrochemical Stability.ipynb b/notebooks/2016-09-08-Data-driven First Principles Methods for the Study and Design of Alkali Superionic Conductors Part 2 - Phase and Electrochemical Stability.ipynb index 9b33fb2..806e9e4 100644 --- a/notebooks/2016-09-08-Data-driven First Principles Methods for the Study and Design of Alkali Superionic Conductors Part 2 - Phase and Electrochemical Stability.ipynb +++ b/notebooks/2016-09-08-Data-driven First Principles Methods for the Study and Design of Alkali Superionic Conductors Part 2 - Phase and Electrochemical Stability.ipynb @@ -40,6 +40,7 @@ "\n", "import matplotlib as mpl\n", "import palettable\n", + "from mp_api.client import MPRester\n", "from pymatgen.analysis.phase_diagram import (\n", " CompoundPhaseDiagram,\n", " PDPlotter,\n", @@ -48,7 +49,6 @@ "from pymatgen.core import Composition, Element\n", "from pymatgen.entries.compatibility import MaterialsProjectCompatibility\n", "from pymatgen.entries.computed_entries import ComputedEntry\n", - "from pymatgen.ext.matproj import MPRester\n", "from pymatgen.io.vasp import Vasprun\n", "from pymatgen.util.plotting import pretty_plot" ] diff --git a/notebooks/2017-03-02-Getting data from Materials Project.ipynb b/notebooks/2017-03-02-Getting data from Materials Project.ipynb index c57f87a..3d4cc10 100644 --- a/notebooks/2017-03-02-Getting data from Materials Project.ipynb +++ b/notebooks/2017-03-02-Getting data from Materials Project.ipynb @@ -26,10 +26,9 @@ "outputs": [], "source": [ "import pprint\n", - "import re\n", "\n", + "from mp_api.client import MPRester\n", "from pymatgen.core import Composition\n", - "from pymatgen.ext.matproj import MPRester\n", "\n", "# Make sure that you have the Materials API key. Put the key in the call to\n", "# MPRester if needed, e.g, MPRester(\"MY_API_KEY\")\n", diff --git a/notebooks/2017-12-15-Plotting a Pourbaix Diagram.ipynb b/notebooks/2017-12-15-Plotting a Pourbaix Diagram.ipynb index 7ddd245..852cd60 100644 --- a/notebooks/2017-12-15-Plotting a Pourbaix Diagram.ipynb +++ b/notebooks/2017-12-15-Plotting a Pourbaix Diagram.ipynb @@ -72,8 +72,8 @@ "outputs": [], "source": [ "# Import necessary tools from pymatgen\n", + "from mp_api.client import MPRester\n", "from pymatgen.analysis.pourbaix_diagram import PourbaixDiagram, PourbaixPlotter\n", - "from pymatgen.ext.matproj import MPRester\n", "\n", "%matplotlib inline\n", "\n", diff --git a/notebooks/2018-01-01-ChemEnv - How to automatically identify coordination environments in a structure.ipynb b/notebooks/2018-01-01-ChemEnv - How to automatically identify coordination environments in a structure.ipynb index d96b177..07b9376 100644 --- a/notebooks/2018-01-01-ChemEnv - How to automatically identify coordination environments in a structure.ipynb +++ b/notebooks/2018-01-01-ChemEnv - How to automatically identify coordination environments in a structure.ipynb @@ -86,6 +86,7 @@ "source": [ "import logging\n", "\n", + "from mp_api.client import MPRester\n", "from pymatgen.analysis.chemenv.coordination_environments.chemenv_strategies import (\n", " SimplestChemenvStrategy,\n", ")\n", @@ -94,8 +95,7 @@ ")\n", "from pymatgen.analysis.chemenv.coordination_environments.structure_environments import (\n", " LightStructureEnvironments,\n", - ")\n", - "from pymatgen.ext.matproj import MPRester" + ")" ] }, { diff --git a/notebooks/2018-03-09-Computing the Reaction Diagram between Two Compounds.ipynb b/notebooks/2018-03-09-Computing the Reaction Diagram between Two Compounds.ipynb index a2bd013..abf5723 100644 --- a/notebooks/2018-03-09-Computing the Reaction Diagram between Two Compounds.ipynb +++ b/notebooks/2018-03-09-Computing the Reaction Diagram between Two Compounds.ipynb @@ -27,9 +27,9 @@ "metadata": {}, "outputs": [], "source": [ + "from mp_api.client import MPRester\n", "from pymatgen.analysis.phase_diagram import PhaseDiagram, ReactionDiagram\n", - "from pymatgen.entries.compatibility import MaterialsProjectCompatibility, ComputedEntry\n", - "from pymatgen.ext.matproj import MPRester" + "from pymatgen.entries.compatibility import ComputedEntry, MaterialsProjectCompatibility" ] }, { diff --git a/notebooks/2018-07-24-Adsorption on solid surfaces.ipynb b/notebooks/2018-07-24-Adsorption on solid surfaces.ipynb index 6f269d8..bcbd1e4 100644 --- a/notebooks/2018-07-24-Adsorption on solid surfaces.ipynb +++ b/notebooks/2018-07-24-Adsorption on solid surfaces.ipynb @@ -39,10 +39,10 @@ "from __future__ import annotations\n", "\n", "from matplotlib import pyplot as plt\n", + "from mp_api.client import MPRester\n", "from pymatgen.analysis.adsorption import *\n", "from pymatgen.core import Lattice, Molecule, Structure\n", "from pymatgen.core.surface import generate_all_slabs\n", - "from pymatgen.ext.matproj import MPRester\n", "from pymatgen.symmetry.analyzer import SpacegroupAnalyzer\n", "\n", "%matplotlib inline\n", diff --git a/notebooks/2018-09-25-Structure Prediction using Pymatgen and the Materials API.ipynb b/notebooks/2018-09-25-Structure Prediction using Pymatgen and the Materials API.ipynb index c6b1368..b6de3ed 100644 --- a/notebooks/2018-09-25-Structure Prediction using Pymatgen and the Materials API.ipynb +++ b/notebooks/2018-09-25-Structure Prediction using Pymatgen and the Materials API.ipynb @@ -32,13 +32,13 @@ "\n", "from pprint import pprint\n", "\n", + "from mp_api.client import MPRester\n", "from pymatgen.analysis.structure_matcher import ElementComparator, StructureMatcher\n", "from pymatgen.analysis.structure_prediction.substitution_probability import (\n", " SubstitutionPredictor,\n", ")\n", "from pymatgen.analysis.structure_prediction.substitutor import Substitutor\n", "from pymatgen.core.periodic_table import Specie\n", - "from pymatgen.ext.matproj import MPRester\n", "from pymatgen.transformations.standard_transformations import (\n", " AutoOxiStateDecorationTransformation,\n", ")" diff --git a/notebooks/2018-11-6-Dopant suggestions using Pymatgen.ipynb b/notebooks/2018-11-6-Dopant suggestions using Pymatgen.ipynb index 00f71d9..97d3ad4 100644 --- a/notebooks/2018-11-6-Dopant suggestions using Pymatgen.ipynb +++ b/notebooks/2018-11-6-Dopant suggestions using Pymatgen.ipynb @@ -32,12 +32,12 @@ "\n", "from pprint import pprint\n", "\n", + "from mp_api.client import MPRester\n", "from pymatgen.analysis.local_env import CrystalNN\n", "from pymatgen.analysis.structure_prediction.dopant_predictor import (\n", " get_dopants_from_shannon_radii,\n", " get_dopants_from_substitution_probabilities,\n", - ")\n", - "from pymatgen.ext.matproj import MPRester" + ")" ] }, { diff --git a/notebooks/2019-03-11-Interface Reactions.ipynb b/notebooks/2019-03-11-Interface Reactions.ipynb index 1f89e19..1d96e2d 100644 --- a/notebooks/2019-03-11-Interface Reactions.ipynb +++ b/notebooks/2019-03-11-Interface Reactions.ipynb @@ -30,10 +30,10 @@ "metadata": {}, "outputs": [], "source": [ + "from mp_api.client import MPRester\n", "from pymatgen.analysis.interface_reactions import InterfacialReactivity\n", "from pymatgen.analysis.phase_diagram import GrandPotentialPhaseDiagram, PhaseDiagram\n", "from pymatgen.core import Composition, Element\n", - "from pymatgen.ext.matproj import MPRester\n", "\n", "%matplotlib inline\n", "\n", diff --git a/notebooks/2021-08-26-Magnetic Structure Generation as Input for Initial DFT Calculations.ipynb b/notebooks/2021-08-26-Magnetic Structure Generation as Input for Initial DFT Calculations.ipynb index 394b977..5230831 100644 --- a/notebooks/2021-08-26-Magnetic Structure Generation as Input for Initial DFT Calculations.ipynb +++ b/notebooks/2021-08-26-Magnetic Structure Generation as Input for Initial DFT Calculations.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "2feb87ef", + "id": "0", "metadata": {}, "source": [ "# Magnetic Structure Generation as Input for Initial DFT Calculations\n", @@ -14,7 +14,7 @@ }, { "cell_type": "markdown", - "id": "f8548d15", + "id": "1", "metadata": {}, "source": [ "## Introduction\n", @@ -25,7 +25,7 @@ { "cell_type": "code", "execution_count": null, - "id": "e167481e", + "id": "2", "metadata": {}, "outputs": [], "source": [ @@ -37,12 +37,12 @@ { "cell_type": "code", "execution_count": null, - "id": "4622d496", + "id": "3", "metadata": {}, "outputs": [], "source": [ + "from mp_api.client import MPRester\n", "from pymatgen.analysis.magnetism.analyzer import CollinearMagneticStructureAnalyzer\n", - "from pymatgen.ext.matproj import MPRester\n", "from pymatgen.symmetry.analyzer import SpacegroupAnalyzer\n", "from pymatgen.util.testing import PymatgenTest" ] @@ -50,7 +50,7 @@ { "cell_type": "code", "execution_count": null, - "id": "299fd4df", + "id": "4", "metadata": {}, "outputs": [ { @@ -104,7 +104,7 @@ }, { "cell_type": "markdown", - "id": "e5fc1f62", + "id": "5", "metadata": {}, "source": [ "## Add magmoms to initial structure\n", @@ -117,7 +117,7 @@ { "cell_type": "code", "execution_count": null, - "id": "7c7b6ea0", + "id": "6", "metadata": {}, "outputs": [], "source": [ @@ -129,7 +129,7 @@ { "cell_type": "code", "execution_count": null, - "id": "94b18ea1", + "id": "7", "metadata": {}, "outputs": [], "source": [ @@ -140,7 +140,7 @@ { "cell_type": "code", "execution_count": null, - "id": "64729507", + "id": "8", "metadata": {}, "outputs": [ { @@ -193,7 +193,7 @@ { "cell_type": "code", "execution_count": null, - "id": "56698524", + "id": "9", "metadata": {}, "outputs": [ { @@ -211,7 +211,7 @@ }, { "cell_type": "markdown", - "id": "cb607211", + "id": "10", "metadata": {}, "source": [ "## Get space group information" @@ -220,7 +220,7 @@ { "cell_type": "code", "execution_count": null, - "id": "c3e09dbc", + "id": "11", "metadata": {}, "outputs": [ { @@ -242,7 +242,7 @@ { "cell_type": "code", "execution_count": null, - "id": "10867b71", + "id": "12", "metadata": {}, "outputs": [ { @@ -263,7 +263,7 @@ { "cell_type": "code", "execution_count": null, - "id": "4a8ec0de", + "id": "13", "metadata": {}, "outputs": [ { @@ -284,7 +284,7 @@ { "cell_type": "code", "execution_count": null, - "id": "8703b5d7", + "id": "14", "metadata": {}, "outputs": [ { @@ -339,7 +339,7 @@ }, { "cell_type": "markdown", - "id": "9dc72f40", + "id": "15", "metadata": {}, "source": [ "The above structure is saved as a magCIF with .mcif extension. This can be converted back to a CIF with relevant magnetic information associated with each site. OpenBabel does this easily, on command line write-\n", @@ -350,7 +350,7 @@ }, { "cell_type": "markdown", - "id": "130df9f1", + "id": "16", "metadata": {}, "source": [ "## Analyze magnetic moment present in a calculated structure using MAPI\n", @@ -361,7 +361,7 @@ { "cell_type": "code", "execution_count": null, - "id": "e90dcfb8", + "id": "17", "metadata": {}, "outputs": [ { @@ -432,7 +432,7 @@ { "cell_type": "code", "execution_count": null, - "id": "eb53cc57", + "id": "18", "metadata": {}, "outputs": [], "source": [ @@ -444,7 +444,7 @@ { "cell_type": "code", "execution_count": null, - "id": "ccedc00c", + "id": "19", "metadata": {}, "outputs": [ { @@ -465,7 +465,7 @@ { "cell_type": "code", "execution_count": null, - "id": "d400c9af", + "id": "20", "metadata": {}, "outputs": [ { @@ -486,7 +486,7 @@ { "cell_type": "code", "execution_count": null, - "id": "1436c1aa", + "id": "21", "metadata": {}, "outputs": [ { diff --git a/notebooks/2021-5-12-Explanation of Corrections.ipynb b/notebooks/2021-5-12-Explanation of Corrections.ipynb index c0e9ccb..c52444f 100644 --- a/notebooks/2021-5-12-Explanation of Corrections.ipynb +++ b/notebooks/2021-5-12-Explanation of Corrections.ipynb @@ -46,8 +46,8 @@ "metadata": {}, "outputs": [], "source": [ - "from pymatgen.entries.compatibility import MaterialsProjectCompatibility\n", - "from pymatgen.ext.matproj import MPRester" + "from mp_api.client import MPRester\n", + "from pymatgen.entries.compatibility import MaterialsProjectCompatibility" ] }, { From 893ebded9f53bd9c7d54c638c53c7bdabf98663a Mon Sep 17 00:00:00 2001 From: "Haoyu (Daniel)" Date: Wed, 4 Dec 2024 11:50:44 +0800 Subject: [PATCH 08/18] simplify venv preparation --- .github/workflows/test.yml | 25 ++++++++++++++++--------- 1 file changed, 16 insertions(+), 9 deletions(-) diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index b99a03c..be07330 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -14,7 +14,7 @@ jobs: test: defaults: run: - shell: bash -l {0} # enables conda/mamba env activation by reading bash profile + shell: bash -l {0} # enables conda/mamba env activation by reading bash profile strategy: fail-fast: false matrix: @@ -33,11 +33,18 @@ jobs: - name: Set up micromamba uses: mamba-org/setup-micromamba@main - - - name: Create mamba environment - run: | - micromamba create -n venv python=${{ matrix.python-version }} --yes - micromamba install -n venv -c conda-forge enumlib packmol bader openbabel openff-toolkit --yes + with: + environment-name: matgenb + condarc: | + channels: + - conda-forge + create-args: >- + python=${{ matrix.python-version }} + enumlib + packmol + bader + openbabel + openff-toolkit - name: Install uv uses: astral-sh/setup-uv@v4 @@ -46,11 +53,11 @@ jobs: - name: Install dependencies run: | - micromamba activate venv + micromamba activate matgenb uv pip install --upgrade -r requirements-ci.txt - - name: pytest + - name: Run pytest run: | - micromamba activate venv + micromamba activate matgenb cd notebooks pytest --ignore-glob=*notest.ipynb --nbmake . From b98d76ca86db7b9a34e90a844ab9c1b566396107 Mon Sep 17 00:00:00 2001 From: "Haoyu (Daniel)" Date: Wed, 4 Dec 2024 11:50:44 +0800 Subject: [PATCH 09/18] simplify venv preparation --- .github/workflows/test.yml | 30 ++++++++++++++++-------------- 1 file changed, 16 insertions(+), 14 deletions(-) diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index b99a03c..2f91df8 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -14,7 +14,7 @@ jobs: test: defaults: run: - shell: bash -l {0} # enables conda/mamba env activation by reading bash profile + shell: bash -l {0} # enables conda/mamba env activation by reading bash profile strategy: fail-fast: false matrix: @@ -33,24 +33,26 @@ jobs: - name: Set up micromamba uses: mamba-org/setup-micromamba@main - - - name: Create mamba environment - run: | - micromamba create -n venv python=${{ matrix.python-version }} --yes - micromamba install -n venv -c conda-forge enumlib packmol bader openbabel openff-toolkit --yes + with: + environment-name: matgenb + condarc: | + channels: + - conda-forge + create-args: >- + python=${{ matrix.python-version }} + enumlib + packmol + bader + openbabel + openff-toolkit - name: Install uv uses: astral-sh/setup-uv@v4 with: version: "latest" - - name: Install dependencies + - name: Install dependencies and run pytest run: | - micromamba activate venv + micromamba activate matgenb uv pip install --upgrade -r requirements-ci.txt - - - name: pytest - run: | - micromamba activate venv - cd notebooks - pytest --ignore-glob=*notest.ipynb --nbmake . + pytest --ignore-glob=*notest.ipynb --nbmake notebooks/ From 9e9636e7de8e70c844feb0f14160868f2f1d4a1a Mon Sep 17 00:00:00 2001 From: Haoyu Yang Date: Wed, 4 Dec 2024 13:53:26 +0800 Subject: [PATCH 10/18] use non interactive backend --- .github/workflows/test.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 2f91df8..dd78b0e 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -26,6 +26,7 @@ jobs: env: PMG_MAPI_KEY: ${{ secrets.PMG_MAPI_KEY }} PMG_VASP_PSP_DIR: "${{ github.workspace }}/psp" + MPLBACKEND: Agg # non-interactive backend for matplotlib steps: - name: Check out repo From 57e25d5898755d5a7f37af5380d845e73615ea4c Mon Sep 17 00:00:00 2001 From: Haoyu Yang Date: Wed, 4 Dec 2024 13:54:51 +0800 Subject: [PATCH 11/18] remove non-existent ignore pattern --- .github/workflows/test.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index dd78b0e..7496f2e 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -56,4 +56,4 @@ jobs: run: | micromamba activate matgenb uv pip install --upgrade -r requirements-ci.txt - pytest --ignore-glob=*notest.ipynb --nbmake notebooks/ + pytest --nbmake notebooks/ From 793fb58c91d8bfe9e13fb8e04681e35e0d7f411e Mon Sep 17 00:00:00 2001 From: Haoyu Yang Date: Wed, 4 Dec 2024 14:08:56 +0800 Subject: [PATCH 12/18] redirect MP API key --- .github/workflows/test.yml | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 7496f2e..287f0cd 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -24,7 +24,7 @@ jobs: runs-on: ${{ matrix.os }} env: - PMG_MAPI_KEY: ${{ secrets.PMG_MAPI_KEY }} + MP_API_KEY: ${{ secrets.PMG_MAPI_KEY }} PMG_VASP_PSP_DIR: "${{ github.workspace }}/psp" MPLBACKEND: Agg # non-interactive backend for matplotlib @@ -52,8 +52,10 @@ jobs: with: version: "latest" - - name: Install dependencies and run pytest + - name: Install dependencies run: | - micromamba activate matgenb - uv pip install --upgrade -r requirements-ci.txt - pytest --nbmake notebooks/ + micromamba run -n matgenb uv pip install --upgrade -r requirements-ci.txt + + - name: Run pytest + run: | + micromamba run -n matgenb pytest --ignore-glob=*notest.ipynb --nbmake notebooks/ From 43ee13d246fafea94e528ecf3e3a302a8ec34b61 Mon Sep 17 00:00:00 2001 From: Haoyu Yang Date: Wed, 4 Dec 2024 14:24:05 +0800 Subject: [PATCH 13/18] revert to PMG_MAPI_KEY --- .github/workflows/test.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 287f0cd..eb1d32f 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -24,7 +24,7 @@ jobs: runs-on: ${{ matrix.os }} env: - MP_API_KEY: ${{ secrets.PMG_MAPI_KEY }} + PMG_MAPI_KEY: ${{ secrets.PMG_MAPI_KEY }} PMG_VASP_PSP_DIR: "${{ github.workspace }}/psp" MPLBACKEND: Agg # non-interactive backend for matplotlib From 82734591a7cb781e37abd60aaf227d660f1393ae Mon Sep 17 00:00:00 2001 From: Haoyu Yang Date: Wed, 4 Dec 2024 16:45:34 +0800 Subject: [PATCH 14/18] update pmg install cmd --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index b58b818..65fac1c 100644 --- a/README.md +++ b/README.md @@ -80,7 +80,7 @@ anyone is welcome to contribute. ```sh # Uncomment the subsequent lines in this cell to install dependencies for Google Colab. - # !pip install pymatgen==2022.2.27 + # !pip install -U pymatgen ``` 4. Ideally, please update notebooks as needed to use more modern versions of the codes, and you may update the date of From d44eb71b5fa8e4f8c6c4986427b40b5402ed3015 Mon Sep 17 00:00:00 2001 From: Haoyu Yang Date: Wed, 4 Dec 2024 17:07:18 +0800 Subject: [PATCH 15/18] revert to legacy key --- ...17-12-15-Plotting a Pourbaix Diagram.ipynb | 36 ++++--------------- 1 file changed, 6 insertions(+), 30 deletions(-) diff --git a/notebooks/2017-12-15-Plotting a Pourbaix Diagram.ipynb b/notebooks/2017-12-15-Plotting a Pourbaix Diagram.ipynb index 852cd60..a2acc2d 100644 --- a/notebooks/2017-12-15-Plotting a Pourbaix Diagram.ipynb +++ b/notebooks/2017-12-15-Plotting a Pourbaix Diagram.ipynb @@ -45,8 +45,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19\n", - "from __future__ import annotations" + "# !pip install pymatgen==2022.7.19" ] }, { @@ -72,13 +71,13 @@ "outputs": [], "source": [ "# Import necessary tools from pymatgen\n", - "from mp_api.client import MPRester\n", "from pymatgen.analysis.pourbaix_diagram import PourbaixDiagram, PourbaixPlotter\n", + "from pymatgen.ext.matproj import MPRester\n", "\n", "%matplotlib inline\n", "\n", "# Initialize the MP Rester\n", - "mpr = MPRester()" + "mpr = MPRester(MP_API_KEY)" ] }, { @@ -92,30 +91,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/shyue/miniconda3/envs/mavrl/lib/python3.11/site-packages/mp_api/client/mprester.py:368: UserWarning: mpcontribs-client not installed. Install the package to query MPContribs data, or construct pourbaix diagrams: 'pip install mpcontribs-client'\n", - " warnings.warn(\n" - ] - }, - { - "ename": "AttributeError", - "evalue": "'NoneType' object has no attribute 'query_contributions'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[4], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Get all pourbaix entries corresponding to the Cu-O-H chemical system.\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m entries \u001b[38;5;241m=\u001b[39m \u001b[43mmpr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_pourbaix_entries\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mCu\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/mavrl/lib/python3.11/site-packages/mp_api/client/mprester.py:851\u001b[0m, in \u001b[0;36mMPRester.get_pourbaix_entries\u001b[0;34m(self, chemsys, solid_compat, use_gibbs)\u001b[0m\n\u001b[1;32m 845\u001b[0m chemsys \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msorted\u001b[39m(e\u001b[38;5;241m.\u001b[39mcapitalize() \u001b[38;5;28;01mfor\u001b[39;00m e \u001b[38;5;129;01min\u001b[39;00m chemsys)\n\u001b[1;32m 847\u001b[0m \u001b[38;5;66;03m# Get ion entries first, because certain ions have reference\u001b[39;00m\n\u001b[1;32m 848\u001b[0m \u001b[38;5;66;03m# solids that aren't necessarily in the chemsys (Na2SO4)\u001b[39;00m\n\u001b[1;32m 849\u001b[0m \n\u001b[1;32m 850\u001b[0m \u001b[38;5;66;03m# download the ion reference data from MPContribs\u001b[39;00m\n\u001b[0;32m--> 851\u001b[0m ion_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_ion_reference_data_for_chemsys\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchemsys\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 853\u001b[0m \u001b[38;5;66;03m# build the PhaseDiagram for get_ion_entries\u001b[39;00m\n\u001b[1;32m 854\u001b[0m ion_ref_comps \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 855\u001b[0m Ion\u001b[38;5;241m.\u001b[39mfrom_formula(d[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRefSolid\u001b[39m\u001b[38;5;124m\"\u001b[39m])\u001b[38;5;241m.\u001b[39mcomposition \u001b[38;5;28;01mfor\u001b[39;00m d \u001b[38;5;129;01min\u001b[39;00m ion_data\n\u001b[1;32m 856\u001b[0m ]\n", - "File \u001b[0;32m~/miniconda3/envs/mavrl/lib/python3.11/site-packages/mp_api/client/mprester.py:989\u001b[0m, in \u001b[0;36mMPRester.get_ion_reference_data_for_chemsys\u001b[0;34m(self, chemsys)\u001b[0m\n\u001b[1;32m 956\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_ion_reference_data_for_chemsys\u001b[39m(\u001b[38;5;28mself\u001b[39m, chemsys: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mlist\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m[\u001b[38;5;28mdict\u001b[39m]:\n\u001b[1;32m 957\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Download aqueous ion reference data used in the construction of Pourbaix diagrams.\u001b[39;00m\n\u001b[1;32m 958\u001b[0m \n\u001b[1;32m 959\u001b[0m \u001b[38;5;124;03m Use this method to examine the ion reference data and to add additional\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 987\u001b[0m \u001b[38;5;124;03m compounds and aqueous species, Wiley, New York (1978)'}}\u001b[39;00m\n\u001b[1;32m 988\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 989\u001b[0m ion_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_ion_reference_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 991\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(chemsys, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m 992\u001b[0m chemsys \u001b[38;5;241m=\u001b[39m chemsys\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m-\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m~/miniconda3/envs/mavrl/lib/python3.11/site-packages/mp_api/client/mprester.py:948\u001b[0m, in \u001b[0;36mMPRester.get_ion_reference_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 919\u001b[0m \u001b[38;5;129m@lru_cache\u001b[39m\n\u001b[1;32m 920\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_ion_reference_data\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m[\u001b[38;5;28mdict\u001b[39m]:\n\u001b[1;32m 921\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Download aqueous ion reference data used in the construction of Pourbaix diagrams.\u001b[39;00m\n\u001b[1;32m 922\u001b[0m \n\u001b[1;32m 923\u001b[0m \u001b[38;5;124;03m Use this method to examine the ion reference data and to add additional\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 946\u001b[0m \u001b[38;5;124;03m compounds and aqueous species, Wiley, New York (1978)'}}\u001b[39;00m\n\u001b[1;32m 947\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 948\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcontribs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mquery_contributions\u001b[49m( \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n\u001b[1;32m 949\u001b[0m query\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mproject\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mion_ref_data\u001b[39m\u001b[38;5;124m\"\u001b[39m},\n\u001b[1;32m 950\u001b[0m fields\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124midentifier\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mformula\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m 951\u001b[0m paginate\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 952\u001b[0m )\u001b[38;5;241m.\u001b[39mget(\n\u001b[1;32m 953\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 954\u001b[0m )\n", - "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'query_contributions'" - ] - } - ], + "outputs": [], "source": [ "# Get all pourbaix entries corresponding to the Cu-O-H chemical system.\n", "entries = mpr.get_pourbaix_entries([\"Cu\"])" @@ -317,7 +293,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "pmg", "language": "python", "name": "python3" }, @@ -331,7 +307,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.12.0" } }, "nbformat": 4, From 1c4c9f95beb41c927c717773cc8fa7d7dc1fc754 Mon Sep 17 00:00:00 2001 From: "Haoyu (Daniel)" Date: Wed, 4 Dec 2024 18:47:20 +0800 Subject: [PATCH 16/18] remove future annotation --- .pre-commit-config.yaml | 5 ----- ...1-01-Getting crystal structures from online sources.ipynb | 3 +-- notebooks/2013-01-01-Molecule.ipynb | 3 +-- ...d Analyzing a Phase Diagram using the Materials API.ipynb | 3 +-- .../2013-01-01-Plotting the electronic structure of Fe.ipynb | 3 +-- notebooks/2013-01-01-Units.ipynb | 3 +-- ...Superionic Conductors Part 1 - Structure Generation.ipynb | 3 +-- ...uctors Part 2 - Phase and Electrochemical Stability.ipynb | 3 +-- ...ductors Part 3 - Diffusivity and Ionic Conductivity.ipynb | 3 +-- .../2016-09-25-Plotting phonon bandstructure and dos.ipynb | 3 +-- notebooks/2017-04-03-Slab generation and Wulff shape.ipynb | 2 +- ...y identify coordination environments in a structure.ipynb | 3 +-- notebooks/2018-07-24-Adsorption on solid surfaces.ipynb | 2 +- ...ure Prediction using Pymatgen and the Materials API.ipynb | 2 +- notebooks/2018-11-6-Dopant suggestions using Pymatgen.ipynb | 2 +- ...2019-01-04-How to use Boltztrap2 interface - notest.ipynb | 3 +-- ...-How to plot and evaluate output files from Lobster.ipynb | 3 +-- ...How to plot a Fermi surface with Boltztrap - notest.ipynb | 3 +-- ...re Generation as Input for Initial DFT Calculations.ipynb | 3 +-- notebooks/2021-5-12-Explanation of Corrections.ipynb | 3 +-- ...eractive Crystal Toolkit Structure Viewer - notest.ipynb | 2 +- notebooks/2023-03-10-Plotting a Pourbaix Diagram-new.ipynb | 3 +-- 22 files changed, 21 insertions(+), 42 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 9db453c..23803ca 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -33,11 +33,6 @@ repos: hooks: - id: autoflake - - repo: https://github.com/pre-commit/mirrors-mypy - rev: v1.13.0 - hooks: - - id: mypy - - repo: https://github.com/nbQA-dev/nbQA rev: 1.9.1 hooks: diff --git a/notebooks/2013-01-01-Getting crystal structures from online sources.ipynb b/notebooks/2013-01-01-Getting crystal structures from online sources.ipynb index 4254917..988f84f 100644 --- a/notebooks/2013-01-01-Getting crystal structures from online sources.ipynb +++ b/notebooks/2013-01-01-Getting crystal structures from online sources.ipynb @@ -16,8 +16,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19\n", - "from __future__ import annotations" + "# !pip install pymatgen==2022.7.19" ] }, { diff --git a/notebooks/2013-01-01-Molecule.ipynb b/notebooks/2013-01-01-Molecule.ipynb index f527ae6..5926635 100644 --- a/notebooks/2013-01-01-Molecule.ipynb +++ b/notebooks/2013-01-01-Molecule.ipynb @@ -21,8 +21,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19\n", - "from __future__ import annotations" + "# !pip install pymatgen==2022.7.19" ] }, { diff --git a/notebooks/2013-01-01-Plotting and Analyzing a Phase Diagram using the Materials API.ipynb b/notebooks/2013-01-01-Plotting and Analyzing a Phase Diagram using the Materials API.ipynb index a05e4f3..cb413c7 100644 --- a/notebooks/2013-01-01-Plotting and Analyzing a Phase Diagram using the Materials API.ipynb +++ b/notebooks/2013-01-01-Plotting and Analyzing a Phase Diagram using the Materials API.ipynb @@ -16,8 +16,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19\n", - "from __future__ import annotations" + "# !pip install pymatgen==2022.7.19" ] }, { diff --git a/notebooks/2013-01-01-Plotting the electronic structure of Fe.ipynb b/notebooks/2013-01-01-Plotting the electronic structure of Fe.ipynb index 74c7f66..90627a7 100644 --- a/notebooks/2013-01-01-Plotting the electronic structure of Fe.ipynb +++ b/notebooks/2013-01-01-Plotting the electronic structure of Fe.ipynb @@ -14,8 +14,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19\n", - "from __future__ import annotations" + "# !pip install pymatgen==2022.7.19" ] }, { diff --git a/notebooks/2013-01-01-Units.ipynb b/notebooks/2013-01-01-Units.ipynb index e89a06a..c53dc2a 100644 --- a/notebooks/2013-01-01-Units.ipynb +++ b/notebooks/2013-01-01-Units.ipynb @@ -28,8 +28,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19\n", - "from __future__ import annotations" + "# !pip install pymatgen==2022.7.19" ] }, { diff --git a/notebooks/2016-09-08-Data-driven First Principles Methods for the Study and Design of Alkali Superionic Conductors Part 1 - Structure Generation.ipynb b/notebooks/2016-09-08-Data-driven First Principles Methods for the Study and Design of Alkali Superionic Conductors Part 1 - Structure Generation.ipynb index b997654..41a4746 100644 --- a/notebooks/2016-09-08-Data-driven First Principles Methods for the Study and Design of Alkali Superionic Conductors Part 1 - Structure Generation.ipynb +++ b/notebooks/2016-09-08-Data-driven First Principles Methods for the Study and Design of Alkali Superionic Conductors Part 1 - Structure Generation.ipynb @@ -23,8 +23,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19\n", - "from __future__ import annotations" + "# !pip install pymatgen==2022.7.19" ] }, { diff --git a/notebooks/2016-09-08-Data-driven First Principles Methods for the Study and Design of Alkali Superionic Conductors Part 2 - Phase and Electrochemical Stability.ipynb b/notebooks/2016-09-08-Data-driven First Principles Methods for the Study and Design of Alkali Superionic Conductors Part 2 - Phase and Electrochemical Stability.ipynb index 806e9e4..b67292e 100644 --- a/notebooks/2016-09-08-Data-driven First Principles Methods for the Study and Design of Alkali Superionic Conductors Part 2 - Phase and Electrochemical Stability.ipynb +++ b/notebooks/2016-09-08-Data-driven First Principles Methods for the Study and Design of Alkali Superionic Conductors Part 2 - Phase and Electrochemical Stability.ipynb @@ -23,8 +23,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19 pymatgen-analysis-diffusion\n", - "from __future__ import annotations" + "# !pip install pymatgen==2022.7.19 pymatgen-analysis-diffusion" ] }, { diff --git a/notebooks/2016-09-08-Data-driven First Principles Methods for the Study and Design of Alkali Superionic Conductors Part 3 - Diffusivity and Ionic Conductivity.ipynb b/notebooks/2016-09-08-Data-driven First Principles Methods for the Study and Design of Alkali Superionic Conductors Part 3 - Diffusivity and Ionic Conductivity.ipynb index 6f158ce..ed0b10a 100644 --- a/notebooks/2016-09-08-Data-driven First Principles Methods for the Study and Design of Alkali Superionic Conductors Part 3 - Diffusivity and Ionic Conductivity.ipynb +++ b/notebooks/2016-09-08-Data-driven First Principles Methods for the Study and Design of Alkali Superionic Conductors Part 3 - Diffusivity and Ionic Conductivity.ipynb @@ -23,8 +23,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19 pymatgen-analysis-diffusion\n", - "from __future__ import annotations" + "# !pip install pymatgen==2022.7.19 pymatgen-analysis-diffusion" ] }, { diff --git a/notebooks/2016-09-25-Plotting phonon bandstructure and dos.ipynb b/notebooks/2016-09-25-Plotting phonon bandstructure and dos.ipynb index 3974832..592a6b7 100644 --- a/notebooks/2016-09-25-Plotting phonon bandstructure and dos.ipynb +++ b/notebooks/2016-09-25-Plotting phonon bandstructure and dos.ipynb @@ -16,8 +16,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19 phonopy\n", - "from __future__ import annotations" + "# !pip install pymatgen==2022.7.19 phonopy" ] }, { diff --git a/notebooks/2017-04-03-Slab generation and Wulff shape.ipynb b/notebooks/2017-04-03-Slab generation and Wulff shape.ipynb index dd8fa23..c27f23b 100644 --- a/notebooks/2017-04-03-Slab generation and Wulff shape.ipynb +++ b/notebooks/2017-04-03-Slab generation and Wulff shape.ipynb @@ -37,7 +37,7 @@ "outputs": [], "source": [ "# Import the necessary tools for making a Wulff shape\n", - "from __future__ import annotations\n", + "\n", "\n", "from pymatgen.analysis.wulff import WulffShape\n", "\n", diff --git a/notebooks/2018-01-01-ChemEnv - How to automatically identify coordination environments in a structure.ipynb b/notebooks/2018-01-01-ChemEnv - How to automatically identify coordination environments in a structure.ipynb index 07b9376..c9e64fa 100644 --- a/notebooks/2018-01-01-ChemEnv - How to automatically identify coordination environments in a structure.ipynb +++ b/notebooks/2018-01-01-ChemEnv - How to automatically identify coordination environments in a structure.ipynb @@ -74,8 +74,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19\n", - "from __future__ import annotations" + "# !pip install pymatgen==2022.7.19" ] }, { diff --git a/notebooks/2018-07-24-Adsorption on solid surfaces.ipynb b/notebooks/2018-07-24-Adsorption on solid surfaces.ipynb index bcbd1e4..fcb9669 100644 --- a/notebooks/2018-07-24-Adsorption on solid surfaces.ipynb +++ b/notebooks/2018-07-24-Adsorption on solid surfaces.ipynb @@ -36,7 +36,7 @@ "outputs": [], "source": [ "# Import statements\n", - "from __future__ import annotations\n", + "\n", "\n", "from matplotlib import pyplot as plt\n", "from mp_api.client import MPRester\n", diff --git a/notebooks/2018-09-25-Structure Prediction using Pymatgen and the Materials API.ipynb b/notebooks/2018-09-25-Structure Prediction using Pymatgen and the Materials API.ipynb index b6de3ed..ce7988c 100644 --- a/notebooks/2018-09-25-Structure Prediction using Pymatgen and the Materials API.ipynb +++ b/notebooks/2018-09-25-Structure Prediction using Pymatgen and the Materials API.ipynb @@ -28,7 +28,7 @@ "outputs": [], "source": [ "# Imports we need for running structure prediction\n", - "from __future__ import annotations\n", + "\n", "\n", "from pprint import pprint\n", "\n", diff --git a/notebooks/2018-11-6-Dopant suggestions using Pymatgen.ipynb b/notebooks/2018-11-6-Dopant suggestions using Pymatgen.ipynb index 97d3ad4..4dcb18e 100644 --- a/notebooks/2018-11-6-Dopant suggestions using Pymatgen.ipynb +++ b/notebooks/2018-11-6-Dopant suggestions using Pymatgen.ipynb @@ -28,7 +28,7 @@ "outputs": [], "source": [ "# Imports we need for generating dopant suggestions\n", - "from __future__ import annotations\n", + "\n", "\n", "from pprint import pprint\n", "\n", diff --git a/notebooks/2019-01-04-How to use Boltztrap2 interface - notest.ipynb b/notebooks/2019-01-04-How to use Boltztrap2 interface - notest.ipynb index 5507bb9..fc35a52 100644 --- a/notebooks/2019-01-04-How to use Boltztrap2 interface - notest.ipynb +++ b/notebooks/2019-01-04-How to use Boltztrap2 interface - notest.ipynb @@ -26,8 +26,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19\n", - "from __future__ import annotations" + "# !pip install pymatgen==2022.7.19" ] }, { diff --git a/notebooks/2019-01-11-How to plot and evaluate output files from Lobster.ipynb b/notebooks/2019-01-11-How to plot and evaluate output files from Lobster.ipynb index 3c36ed2..94a6347 100644 --- a/notebooks/2019-01-11-How to plot and evaluate output files from Lobster.ipynb +++ b/notebooks/2019-01-11-How to plot and evaluate output files from Lobster.ipynb @@ -18,8 +18,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19\n", - "from __future__ import annotations" + "# !pip install pymatgen==2022.7.19" ] }, { diff --git a/notebooks/2020-07-15-How to plot a Fermi surface with Boltztrap - notest.ipynb b/notebooks/2020-07-15-How to plot a Fermi surface with Boltztrap - notest.ipynb index 3f3c692..011aed5 100644 --- a/notebooks/2020-07-15-How to plot a Fermi surface with Boltztrap - notest.ipynb +++ b/notebooks/2020-07-15-How to plot a Fermi surface with Boltztrap - notest.ipynb @@ -28,8 +28,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19\n", - "from __future__ import annotations" + "# !pip install pymatgen==2022.7.19" ] }, { diff --git a/notebooks/2021-08-26-Magnetic Structure Generation as Input for Initial DFT Calculations.ipynb b/notebooks/2021-08-26-Magnetic Structure Generation as Input for Initial DFT Calculations.ipynb index 5230831..8f754d9 100644 --- a/notebooks/2021-08-26-Magnetic Structure Generation as Input for Initial DFT Calculations.ipynb +++ b/notebooks/2021-08-26-Magnetic Structure Generation as Input for Initial DFT Calculations.ipynb @@ -30,8 +30,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19\n", - "from __future__ import annotations" + "# !pip install pymatgen==2022.7.19" ] }, { diff --git a/notebooks/2021-5-12-Explanation of Corrections.ipynb b/notebooks/2021-5-12-Explanation of Corrections.ipynb index c52444f..25c9401 100644 --- a/notebooks/2021-5-12-Explanation of Corrections.ipynb +++ b/notebooks/2021-5-12-Explanation of Corrections.ipynb @@ -36,8 +36,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19\n", - "from __future__ import annotations" + "# !pip install pymatgen==2022.7.19" ] }, { diff --git a/notebooks/2022-07-23 Interactive Crystal Toolkit Structure Viewer - notest.ipynb b/notebooks/2022-07-23 Interactive Crystal Toolkit Structure Viewer - notest.ipynb index 38ef0c3..4bfe723 100644 --- a/notebooks/2022-07-23 Interactive Crystal Toolkit Structure Viewer - notest.ipynb +++ b/notebooks/2022-07-23 Interactive Crystal Toolkit Structure Viewer - notest.ipynb @@ -29,7 +29,7 @@ "metadata": {}, "outputs": [], "source": [ - "from __future__ import annotations\n", + "\n", "\n", "!pip install dash crystal-toolkit" ] diff --git a/notebooks/2023-03-10-Plotting a Pourbaix Diagram-new.ipynb b/notebooks/2023-03-10-Plotting a Pourbaix Diagram-new.ipynb index 24f9817..48c1d60 100644 --- a/notebooks/2023-03-10-Plotting a Pourbaix Diagram-new.ipynb +++ b/notebooks/2023-03-10-Plotting a Pourbaix Diagram-new.ipynb @@ -50,8 +50,7 @@ "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", "# !pip install pymatgen==2022.7.19\n", - "# !pip install mp-api\n", - "from __future__ import annotations" + "# !pip install mp-api" ] }, { From 2a986bd21d62a8b6296e604dbe4ccb12eda2f691 Mon Sep 17 00:00:00 2001 From: "Haoyu (Daniel)" Date: Wed, 4 Dec 2024 18:56:23 +0800 Subject: [PATCH 17/18] update pymatgen install command --- docs/index.md | 10 +- ...tion Energies with the Materials API.ipynb | 2 +- .../2013-01-01-Calculating XRD patterns.ipynb | 2 +- ...ystal structures from online sources.ipynb | 2 +- notebooks/2013-01-01-Molecule.ipynb | 2 +- ...01-01-Ordering Disordered Structures.ipynb | 2 +- ...hase Diagram using the Materials API.ipynb | 2 +- ...tting the electronic structure of Fe.ipynb | 2 +- notebooks/2013-01-01-Units.ipynb | 2 +- ...uctors Part 1 - Structure Generation.ipynb | 2 +- ... Phase and Electrochemical Stability.ipynb | 2 +- ...- Diffusivity and Ionic Conductivity.ipynb | 2 +- ...lotting phonon bandstructure and dos.ipynb | 2 +- ...-Getting data from Materials Project.ipynb | 2 +- ...4-03-Slab generation and Wulff shape.ipynb | 2 +- ...-14-Inputs and Analysis of VASP runs.ipynb | 2 +- ...17-12-15-Plotting a Pourbaix Diagram.ipynb | 2 +- ...018-03-14-Plotting COHP from LOBSTER.ipynb | 2 +- ...8-07-24-Adsorption on solid surfaces.ipynb | 2 +- ...-6-Dopant suggestions using Pymatgen.ipynb | 2 +- ...to use Boltztrap2 interface - notest.ipynb | 2 +- ...d evaluate output files from Lobster.ipynb | 2 +- .../2019-03-11-Interface Reactions.ipynb | 3 +- ...ermi surface with Boltztrap - notest.ipynb | 2 +- ...s Input for Initial DFT Calculations.ipynb | 2 +- ...2021-5-12-Explanation of Corrections.ipynb | 2 +- ...3-10-Plotting a Pourbaix Diagram-new.ipynb | 3 +- ...2024-06-28-Charge_Density_Difference.ipynb | 118 ++++++++++-------- 28 files changed, 95 insertions(+), 87 deletions(-) diff --git a/docs/index.md b/docs/index.md index 2babda8..9e43439 100644 --- a/docs/index.md +++ b/docs/index.md @@ -2,12 +2,12 @@ Visit the [Github Pages](http://matgenb.materialsvirtuallab.org) for a nicely fo # Introduction -This repo is started by the [Materials Virtual Lab](http://www.materialsvirtuallab.org) as a useful collection of +This repo is started by the [Materials Virtual Lab](http://www.materialsvirtuallab.org) as a useful collection of Jupyter notebooks that demonstrate the utilization of open-source codes for the study of materials science. We frequently get requests (from students, postdocs, collaborators, or just general users) for example codes that demonstrate various capabilities in the open-source software we maintain and contribute to, such as the Materials -Project software stack comprising [Python Materials Genomics (pymatgen)](http://www.pymatgen.org), +Project software stack comprising [Python Materials Genomics (pymatgen)](http://www.pymatgen.org), [Custodian](https://materialsprojecthub.io/custodian/), and [Fireworks](https://pythonhosted.org/FireWorks/). This repo is a start at building a more sustainable path towards sharing of code examples. @@ -18,7 +18,7 @@ anyone is welcome to contribute. ## Option 1: Google Colab -You can easily run all the notebooks via [Google Colab](https://colab.research.google.com/). +You can easily run all the notebooks via [Google Colab](https://colab.research.google.com/). ## Option 2: BinderHub @@ -46,13 +46,13 @@ output. 3. Notebooks should be well-documented and simple. The idea here is to be pedagogical. A newcomer to the software (with the right materials science background) should be able to follow the logic without too much difficulty. Feel - free to add authorship and contact information, as well as works to cite and acknowledge your contributions. In view + free to add authorship and contact information, as well as works to cite and acknowledge your contributions. In view that scientific codes tend to be continuously being updated, please put in a list of the key pinned dependencies so that other users can install the exact version of software to run the notebook if needed. The best practice is to put a section that provides a commented out pip install instructure that can be used in Google Colab. For example, ```sh # Uncomment the subsequent lines in this cell to install dependencies for Google Colab. - # !pip install pymatgen==2022.2.27 + # !pip install -U pymatgen ``` 4. Ideally, please update notebooks as needed to use more modern versions of the codes, and you may update the date of diff --git a/notebooks/2013-01-01-Calculating Reaction Energies with the Materials API.ipynb b/notebooks/2013-01-01-Calculating Reaction Energies with the Materials API.ipynb index be67080..c8fc570 100644 --- a/notebooks/2013-01-01-Calculating Reaction Energies with the Materials API.ipynb +++ b/notebooks/2013-01-01-Calculating Reaction Energies with the Materials API.ipynb @@ -14,7 +14,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19" + "# !pip install -U pymatgen" ] }, { diff --git a/notebooks/2013-01-01-Calculating XRD patterns.ipynb b/notebooks/2013-01-01-Calculating XRD patterns.ipynb index 97c74d2..2170c62 100644 --- a/notebooks/2013-01-01-Calculating XRD patterns.ipynb +++ b/notebooks/2013-01-01-Calculating XRD patterns.ipynb @@ -16,7 +16,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.2.27" + "# !pip install -U pymatgen" ] }, { diff --git a/notebooks/2013-01-01-Getting crystal structures from online sources.ipynb b/notebooks/2013-01-01-Getting crystal structures from online sources.ipynb index 988f84f..c3ff116 100644 --- a/notebooks/2013-01-01-Getting crystal structures from online sources.ipynb +++ b/notebooks/2013-01-01-Getting crystal structures from online sources.ipynb @@ -16,7 +16,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19" + "# !pip install -U pymatgen" ] }, { diff --git a/notebooks/2013-01-01-Molecule.ipynb b/notebooks/2013-01-01-Molecule.ipynb index 5926635..1264eb9 100644 --- a/notebooks/2013-01-01-Molecule.ipynb +++ b/notebooks/2013-01-01-Molecule.ipynb @@ -21,7 +21,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19" + "# !pip install -U pymatgen" ] }, { diff --git a/notebooks/2013-01-01-Ordering Disordered Structures.ipynb b/notebooks/2013-01-01-Ordering Disordered Structures.ipynb index 0d399e0..31e183f 100644 --- a/notebooks/2013-01-01-Ordering Disordered Structures.ipynb +++ b/notebooks/2013-01-01-Ordering Disordered Structures.ipynb @@ -16,7 +16,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19" + "# !pip install -U pymatgen" ] }, { diff --git a/notebooks/2013-01-01-Plotting and Analyzing a Phase Diagram using the Materials API.ipynb b/notebooks/2013-01-01-Plotting and Analyzing a Phase Diagram using the Materials API.ipynb index cb413c7..bbfe045 100644 --- a/notebooks/2013-01-01-Plotting and Analyzing a Phase Diagram using the Materials API.ipynb +++ b/notebooks/2013-01-01-Plotting and Analyzing a Phase Diagram using the Materials API.ipynb @@ -16,7 +16,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19" + "# !pip install -U pymatgen" ] }, { diff --git a/notebooks/2013-01-01-Plotting the electronic structure of Fe.ipynb b/notebooks/2013-01-01-Plotting the electronic structure of Fe.ipynb index 90627a7..e6502f4 100644 --- a/notebooks/2013-01-01-Plotting the electronic structure of Fe.ipynb +++ b/notebooks/2013-01-01-Plotting the electronic structure of Fe.ipynb @@ -14,7 +14,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19" + "# !pip install -U pymatgen" ] }, { diff --git a/notebooks/2013-01-01-Units.ipynb b/notebooks/2013-01-01-Units.ipynb index c53dc2a..90bf5b3 100644 --- a/notebooks/2013-01-01-Units.ipynb +++ b/notebooks/2013-01-01-Units.ipynb @@ -28,7 +28,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19" + "# !pip install -U pymatgen" ] }, { diff --git a/notebooks/2016-09-08-Data-driven First Principles Methods for the Study and Design of Alkali Superionic Conductors Part 1 - Structure Generation.ipynb b/notebooks/2016-09-08-Data-driven First Principles Methods for the Study and Design of Alkali Superionic Conductors Part 1 - Structure Generation.ipynb index 41a4746..6e7f000 100644 --- a/notebooks/2016-09-08-Data-driven First Principles Methods for the Study and Design of Alkali Superionic Conductors Part 1 - Structure Generation.ipynb +++ b/notebooks/2016-09-08-Data-driven First Principles Methods for the Study and Design of Alkali Superionic Conductors Part 1 - Structure Generation.ipynb @@ -23,7 +23,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19" + "# !pip install -U pymatgen" ] }, { diff --git a/notebooks/2016-09-08-Data-driven First Principles Methods for the Study and Design of Alkali Superionic Conductors Part 2 - Phase and Electrochemical Stability.ipynb b/notebooks/2016-09-08-Data-driven First Principles Methods for the Study and Design of Alkali Superionic Conductors Part 2 - Phase and Electrochemical Stability.ipynb index b67292e..3fbf80a 100644 --- a/notebooks/2016-09-08-Data-driven First Principles Methods for the Study and Design of Alkali Superionic Conductors Part 2 - Phase and Electrochemical Stability.ipynb +++ b/notebooks/2016-09-08-Data-driven First Principles Methods for the Study and Design of Alkali Superionic Conductors Part 2 - Phase and Electrochemical Stability.ipynb @@ -23,7 +23,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19 pymatgen-analysis-diffusion" + "# !pip install -U pymatgen pymatgen-analysis-diffusion" ] }, { diff --git a/notebooks/2016-09-08-Data-driven First Principles Methods for the Study and Design of Alkali Superionic Conductors Part 3 - Diffusivity and Ionic Conductivity.ipynb b/notebooks/2016-09-08-Data-driven First Principles Methods for the Study and Design of Alkali Superionic Conductors Part 3 - Diffusivity and Ionic Conductivity.ipynb index ed0b10a..0c27c2d 100644 --- a/notebooks/2016-09-08-Data-driven First Principles Methods for the Study and Design of Alkali Superionic Conductors Part 3 - Diffusivity and Ionic Conductivity.ipynb +++ b/notebooks/2016-09-08-Data-driven First Principles Methods for the Study and Design of Alkali Superionic Conductors Part 3 - Diffusivity and Ionic Conductivity.ipynb @@ -23,7 +23,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19 pymatgen-analysis-diffusion" + "# !pip install -U pymatgen pymatgen-analysis-diffusion" ] }, { diff --git a/notebooks/2016-09-25-Plotting phonon bandstructure and dos.ipynb b/notebooks/2016-09-25-Plotting phonon bandstructure and dos.ipynb index 592a6b7..1cb9949 100644 --- a/notebooks/2016-09-25-Plotting phonon bandstructure and dos.ipynb +++ b/notebooks/2016-09-25-Plotting phonon bandstructure and dos.ipynb @@ -16,7 +16,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19 phonopy" + "# !pip install -U pymatgen phonopy" ] }, { diff --git a/notebooks/2017-03-02-Getting data from Materials Project.ipynb b/notebooks/2017-03-02-Getting data from Materials Project.ipynb index 3d4cc10..bbdb47e 100644 --- a/notebooks/2017-03-02-Getting data from Materials Project.ipynb +++ b/notebooks/2017-03-02-Getting data from Materials Project.ipynb @@ -16,7 +16,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19" + "# !pip install -U pymatgen" ] }, { diff --git a/notebooks/2017-04-03-Slab generation and Wulff shape.ipynb b/notebooks/2017-04-03-Slab generation and Wulff shape.ipynb index c27f23b..95746a2 100644 --- a/notebooks/2017-04-03-Slab generation and Wulff shape.ipynb +++ b/notebooks/2017-04-03-Slab generation and Wulff shape.ipynb @@ -27,7 +27,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19" + "# !pip install -U pymatgen" ] }, { diff --git a/notebooks/2017-04-14-Inputs and Analysis of VASP runs.ipynb b/notebooks/2017-04-14-Inputs and Analysis of VASP runs.ipynb index 2a228d1..8c282d8 100644 --- a/notebooks/2017-04-14-Inputs and Analysis of VASP runs.ipynb +++ b/notebooks/2017-04-14-Inputs and Analysis of VASP runs.ipynb @@ -16,7 +16,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19" + "# !pip install -U pymatgen" ] }, { diff --git a/notebooks/2017-12-15-Plotting a Pourbaix Diagram.ipynb b/notebooks/2017-12-15-Plotting a Pourbaix Diagram.ipynb index a2acc2d..c5e436e 100644 --- a/notebooks/2017-12-15-Plotting a Pourbaix Diagram.ipynb +++ b/notebooks/2017-12-15-Plotting a Pourbaix Diagram.ipynb @@ -45,7 +45,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19" + "# !pip install -U pymatgen" ] }, { diff --git a/notebooks/2018-03-14-Plotting COHP from LOBSTER.ipynb b/notebooks/2018-03-14-Plotting COHP from LOBSTER.ipynb index a148ab8..1ff0a58 100644 --- a/notebooks/2018-03-14-Plotting COHP from LOBSTER.ipynb +++ b/notebooks/2018-03-14-Plotting COHP from LOBSTER.ipynb @@ -16,7 +16,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19" + "# !pip install -U pymatgen" ] }, { diff --git a/notebooks/2018-07-24-Adsorption on solid surfaces.ipynb b/notebooks/2018-07-24-Adsorption on solid surfaces.ipynb index fcb9669..4598674 100644 --- a/notebooks/2018-07-24-Adsorption on solid surfaces.ipynb +++ b/notebooks/2018-07-24-Adsorption on solid surfaces.ipynb @@ -26,7 +26,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19 atomate" + "# !pip install -U pymatgen atomate" ] }, { diff --git a/notebooks/2018-11-6-Dopant suggestions using Pymatgen.ipynb b/notebooks/2018-11-6-Dopant suggestions using Pymatgen.ipynb index 4dcb18e..161e85f 100644 --- a/notebooks/2018-11-6-Dopant suggestions using Pymatgen.ipynb +++ b/notebooks/2018-11-6-Dopant suggestions using Pymatgen.ipynb @@ -18,7 +18,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19" + "# !pip install -U pymatgen" ] }, { diff --git a/notebooks/2019-01-04-How to use Boltztrap2 interface - notest.ipynb b/notebooks/2019-01-04-How to use Boltztrap2 interface - notest.ipynb index fc35a52..298aaa0 100644 --- a/notebooks/2019-01-04-How to use Boltztrap2 interface - notest.ipynb +++ b/notebooks/2019-01-04-How to use Boltztrap2 interface - notest.ipynb @@ -26,7 +26,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19" + "# !pip install -U pymatgen" ] }, { diff --git a/notebooks/2019-01-11-How to plot and evaluate output files from Lobster.ipynb b/notebooks/2019-01-11-How to plot and evaluate output files from Lobster.ipynb index 94a6347..0148956 100644 --- a/notebooks/2019-01-11-How to plot and evaluate output files from Lobster.ipynb +++ b/notebooks/2019-01-11-How to plot and evaluate output files from Lobster.ipynb @@ -18,7 +18,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19" + "# !pip install -U pymatgen" ] }, { diff --git a/notebooks/2019-03-11-Interface Reactions.ipynb b/notebooks/2019-03-11-Interface Reactions.ipynb index 1d96e2d..eddad04 100644 --- a/notebooks/2019-03-11-Interface Reactions.ipynb +++ b/notebooks/2019-03-11-Interface Reactions.ipynb @@ -20,8 +20,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19\n", - "from __future__ import annotations" + "# !pip install -U pymatgen" ] }, { diff --git a/notebooks/2020-07-15-How to plot a Fermi surface with Boltztrap - notest.ipynb b/notebooks/2020-07-15-How to plot a Fermi surface with Boltztrap - notest.ipynb index 011aed5..a4cc213 100644 --- a/notebooks/2020-07-15-How to plot a Fermi surface with Boltztrap - notest.ipynb +++ b/notebooks/2020-07-15-How to plot a Fermi surface with Boltztrap - notest.ipynb @@ -28,7 +28,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19" + "# !pip install -U pymatgen" ] }, { diff --git a/notebooks/2021-08-26-Magnetic Structure Generation as Input for Initial DFT Calculations.ipynb b/notebooks/2021-08-26-Magnetic Structure Generation as Input for Initial DFT Calculations.ipynb index 8f754d9..9575b0e 100644 --- a/notebooks/2021-08-26-Magnetic Structure Generation as Input for Initial DFT Calculations.ipynb +++ b/notebooks/2021-08-26-Magnetic Structure Generation as Input for Initial DFT Calculations.ipynb @@ -30,7 +30,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19" + "# !pip install -U pymatgen" ] }, { diff --git a/notebooks/2021-5-12-Explanation of Corrections.ipynb b/notebooks/2021-5-12-Explanation of Corrections.ipynb index 25c9401..3cf85d5 100644 --- a/notebooks/2021-5-12-Explanation of Corrections.ipynb +++ b/notebooks/2021-5-12-Explanation of Corrections.ipynb @@ -36,7 +36,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19" + "# !pip install -U pymatgen" ] }, { diff --git a/notebooks/2023-03-10-Plotting a Pourbaix Diagram-new.ipynb b/notebooks/2023-03-10-Plotting a Pourbaix Diagram-new.ipynb index 48c1d60..db12b13 100644 --- a/notebooks/2023-03-10-Plotting a Pourbaix Diagram-new.ipynb +++ b/notebooks/2023-03-10-Plotting a Pourbaix Diagram-new.ipynb @@ -49,8 +49,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19\n", - "# !pip install mp-api" + "# !pip install -U pymatgen mp-api" ] }, { diff --git a/notebooks/2024-06-28-Charge_Density_Difference.ipynb b/notebooks/2024-06-28-Charge_Density_Difference.ipynb index e0d93be..1a7f4f4 100644 --- a/notebooks/2024-06-28-Charge_Density_Difference.ipynb +++ b/notebooks/2024-06-28-Charge_Density_Difference.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "42d1bcc9", + "id": "0", "metadata": {}, "source": [ "# Charge density difference \n", @@ -20,30 +20,31 @@ { "cell_type": "code", "execution_count": null, - "id": "bba1b004", + "id": "1", "metadata": {}, "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2024.6.10" + "# !pip install -U pymatgen" ] }, { "cell_type": "code", - "execution_count": 1, - "id": "f000232c", + "execution_count": null, + "id": "2", "metadata": {}, "outputs": [], "source": [ "import os\n", - "from pymatgen.io.vasp.outputs import Chgcar, VolumetricData\n", + "\n", + "import numpy as np\n", "from pymatgen.io.common import VolumetricData as CommonVolumetricData\n", - "import numpy as np" + "from pymatgen.io.vasp.outputs import Chgcar, VolumetricData" ] }, { "cell_type": "markdown", - "id": "e669ef33", + "id": "3", "metadata": {}, "source": [ "The working directory contains 3 CHGCAR files ... the total system {LiMoS2_F}(AB) and its constituent systems {LiMoS2}(A) and {G}(F)....\n", @@ -53,8 +54,8 @@ }, { "cell_type": "code", - "execution_count": 5, - "id": "068ee94b", + "execution_count": null, + "id": "4", "metadata": {}, "outputs": [ { @@ -66,7 +67,7 @@ " '2024-06-25-Charge_Density_Difference.ipynb']" ] }, - "execution_count": 5, + "execution_count": null, "metadata": {}, "output_type": "execute_result" } @@ -77,7 +78,7 @@ }, { "cell_type": "markdown", - "id": "e974cf22", + "id": "5", "metadata": {}, "source": [ "### Read the CHGCAR files \n", @@ -86,8 +87,8 @@ }, { "cell_type": "code", - "execution_count": 2, - "id": "1171d9bc", + "execution_count": null, + "id": "6", "metadata": {}, "outputs": [ { @@ -105,13 +106,13 @@ "chgcar_B = Chgcar.from_file(\"chgcar_data/F_CHGCAR\")\n", "\n", "# Get the grid shape (same for all CHGCAR files)\n", - "print(np.shape(chgcar_B.data['total']))\n", - "grid=np.shape(chgcar_B.data['total'])" + "print(np.shape(chgcar_B.data[\"total\"]))\n", + "grid = np.shape(chgcar_B.data[\"total\"])" ] }, { "cell_type": "markdown", - "id": "5a21f84c", + "id": "7", "metadata": {}, "source": [ "### new CHGDIFF.vasp is created \n" @@ -119,23 +120,25 @@ }, { "cell_type": "code", - "execution_count": 3, - "id": "51cce32e", + "execution_count": null, + "id": "8", "metadata": {}, "outputs": [], "source": [ - "poscar = chgcar_AB.poscar # strucutre of total system \n", - "data_diff = chgcar_AB.data['total'] - chgcar_A.data['total'] - chgcar_B.data['total'] # difference of grid data\n", - "del chgcar_AB, chgcar_A, chgcar_B \n", + "poscar = chgcar_AB.poscar # strucutre of total system\n", + "data_diff = (\n", + " chgcar_AB.data[\"total\"] - chgcar_A.data[\"total\"] - chgcar_B.data[\"total\"]\n", + ") # difference of grid data\n", + "del chgcar_AB, chgcar_A, chgcar_B\n", "\n", - "#writing the new grid file\n", - "volumetric_diff = VolumetricData(structure=poscar.structure, data={'total': data_diff})\n", - "volumetric_diff.write_file('CHGDIFF.vasp')" + "# writing the new grid file\n", + "volumetric_diff = VolumetricData(structure=poscar.structure, data={\"total\": data_diff})\n", + "volumetric_diff.write_file(\"CHGDIFF.vasp\")" ] }, { "cell_type": "markdown", - "id": "ec7c1ec4", + "id": "9", "metadata": {}, "source": [ "### VESTA visualization of CHGDIFF file\n", @@ -144,7 +147,7 @@ }, { "cell_type": "markdown", - "id": "2eefe3c9", + "id": "10", "metadata": {}, "source": [ "### Planar averaged density " @@ -152,23 +155,25 @@ }, { "cell_type": "code", - "execution_count": 4, - "id": "2eaee023", + "execution_count": null, + "id": "11", "metadata": {}, "outputs": [], "source": [ - "#planar averaged density\n", - "volumetric_diff = CommonVolumetricData(structure=poscar.structure, data={'total': data_diff})\n", - "planar_avg = volumetric_diff.get_average_along_axis(ind=2) # ind 2 is for 001 direction \n", + "# planar averaged density\n", + "volumetric_diff = CommonVolumetricData(\n", + " structure=poscar.structure, data={\"total\": data_diff}\n", + ")\n", + "planar_avg = volumetric_diff.get_average_along_axis(ind=2) # ind 2 is for 001 direction\n", "\n", - "c_latt=poscar.structure.lattice.c\n", - "planar_avg=planar_avg/c_latt # linear charge density profile\n", - "c_arr=np.linspace(0,c_latt,grid[2])" + "c_latt = poscar.structure.lattice.c\n", + "planar_avg = planar_avg / c_latt # linear charge density profile\n", + "c_arr = np.linspace(0, c_latt, grid[2])" ] }, { "cell_type": "markdown", - "id": "a0074a76", + "id": "12", "metadata": {}, "source": [ "### Plotting the linear charge density \n", @@ -177,8 +182,8 @@ }, { "cell_type": "code", - "execution_count": 5, - "id": "f269d5e3", + "execution_count": null, + "id": "13", "metadata": {}, "outputs": [ { @@ -187,7 +192,7 @@ "" ] }, - "execution_count": 5, + "execution_count": null, "metadata": {}, "output_type": "execute_result" }, @@ -203,7 +208,7 @@ } ], "source": [ - "from matplotlib import pyplot as plt \n", + "from matplotlib import pyplot as plt\n", "from scipy.integrate import cumulative_trapezoid\n", "\n", "desired_aspect_ratio = 1\n", @@ -211,29 +216,34 @@ "fig_height = fig_width / desired_aspect_ratio\n", "fig, ax = plt.subplots(figsize=(fig_width, fig_height))\n", "\n", - "plt.xticks(fontweight='bold')\n", - "plt.yticks(fontsize=20, fontweight='bold')\n", - "plt.xticks(fontsize=20, fontweight='bold')\n", - "plt.tick_params(axis='x', which='both', length=4, width=2, pad=15)\n", - "plt.tick_params(axis='y', which='both', length=4, width=2)\n", + "plt.xticks(fontweight=\"bold\")\n", + "plt.yticks(fontsize=20, fontweight=\"bold\")\n", + "plt.xticks(fontsize=20, fontweight=\"bold\")\n", + "plt.tick_params(axis=\"x\", which=\"both\", length=4, width=2, pad=15)\n", + "plt.tick_params(axis=\"y\", which=\"both\", length=4, width=2)\n", "\n", - "ax.set_xlabel(\"Distance (Å)\", color=\"black\", fontsize=22, fontweight='bold')\n", - "ax.set_ylabel(r\"$\\mathregular {\\int \\Delta \\rho (z) dz (e)}$\", color=\"black\", fontsize=26, fontweight='bold')\n", - "ax.set_xlim([0,35])\n", + "ax.set_xlabel(\"Distance (Å)\", color=\"black\", fontsize=22, fontweight=\"bold\")\n", + "ax.set_ylabel(\n", + " r\"$\\mathregular {\\int \\Delta \\rho (z) dz (e)}$\",\n", + " color=\"black\",\n", + " fontsize=26,\n", + " fontweight=\"bold\",\n", + ")\n", + "ax.set_xlim([0, 35])\n", "\n", - "ax.set_xlim([0,35])\n", + "ax.set_xlim([0, 35])\n", "\n", - "#cumulative integral \n", + "# cumulative integral\n", "integ_elec = cumulative_trapezoid(planar_avg, c_arr, initial=0)\n", "\n", - "ax.plot(c_arr,planar_avg,lw=2,color='purple',label='density')\n", - "ax.plot(c_arr, integ_elec, lw=2, color='black', ls='--',label='cumulative integral')\n", - "plt.legend(fontsize='12')" + "ax.plot(c_arr, planar_avg, lw=2, color=\"purple\", label=\"density\")\n", + "ax.plot(c_arr, integ_elec, lw=2, color=\"black\", ls=\"--\", label=\"cumulative integral\")\n", + "plt.legend(fontsize=\"12\")" ] }, { "cell_type": "markdown", - "id": "4d17bb9b", + "id": "14", "metadata": {}, "source": [ "Roughly 0.55 electrons have been transferred from $Li_1Mo_{16}S_{32}$ to the Flourine sheet.\n", From 8b5b4c37e51535a102c834c058415b31015e8a81 Mon Sep 17 00:00:00 2001 From: Haoyu Yang Date: Sat, 7 Dec 2024 15:46:48 +0800 Subject: [PATCH 18/18] update pmg install command --- ...ing Reaction Energies with the Materials API.ipynb | 2 +- .../2013-01-01-Ordering Disordered Structures.ipynb | 2 +- ...17-03-02-Getting data from Materials Project.ipynb | 2 +- .../2017-04-03-Slab generation and Wulff shape.ipynb | 2 +- .../2017-04-14-Inputs and Analysis of VASP runs.ipynb | 2 +- .../2017-12-15-Plotting a Pourbaix Diagram.ipynb | 2 +- ...g the Reaction Diagram between Two Compounds.ipynb | 4 ++-- notebooks/2018-03-14-Plotting COHP from LOBSTER.ipynb | 6 +++--- ...diction using Pymatgen and the Materials API.ipynb | 2 +- .../2018-11-6-Dopant suggestions using Pymatgen.ipynb | 2 +- ... plot and evaluate output files from Lobster.ipynb | 11 +++++------ notebooks/2019-03-11-Interface Reactions.ipynb | 3 +-- 12 files changed, 19 insertions(+), 21 deletions(-) diff --git a/notebooks/2013-01-01-Calculating Reaction Energies with the Materials API.ipynb b/notebooks/2013-01-01-Calculating Reaction Energies with the Materials API.ipynb index be67080..c8fc570 100644 --- a/notebooks/2013-01-01-Calculating Reaction Energies with the Materials API.ipynb +++ b/notebooks/2013-01-01-Calculating Reaction Energies with the Materials API.ipynb @@ -14,7 +14,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19" + "# !pip install -U pymatgen" ] }, { diff --git a/notebooks/2013-01-01-Ordering Disordered Structures.ipynb b/notebooks/2013-01-01-Ordering Disordered Structures.ipynb index 0d399e0..31e183f 100644 --- a/notebooks/2013-01-01-Ordering Disordered Structures.ipynb +++ b/notebooks/2013-01-01-Ordering Disordered Structures.ipynb @@ -16,7 +16,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19" + "# !pip install -U pymatgen" ] }, { diff --git a/notebooks/2017-03-02-Getting data from Materials Project.ipynb b/notebooks/2017-03-02-Getting data from Materials Project.ipynb index 3d4cc10..bbdb47e 100644 --- a/notebooks/2017-03-02-Getting data from Materials Project.ipynb +++ b/notebooks/2017-03-02-Getting data from Materials Project.ipynb @@ -16,7 +16,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19" + "# !pip install -U pymatgen" ] }, { diff --git a/notebooks/2017-04-03-Slab generation and Wulff shape.ipynb b/notebooks/2017-04-03-Slab generation and Wulff shape.ipynb index dd8fa23..aff082e 100644 --- a/notebooks/2017-04-03-Slab generation and Wulff shape.ipynb +++ b/notebooks/2017-04-03-Slab generation and Wulff shape.ipynb @@ -27,7 +27,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19" + "# !pip install -U pymatgen" ] }, { diff --git a/notebooks/2017-04-14-Inputs and Analysis of VASP runs.ipynb b/notebooks/2017-04-14-Inputs and Analysis of VASP runs.ipynb index 2a228d1..8c282d8 100644 --- a/notebooks/2017-04-14-Inputs and Analysis of VASP runs.ipynb +++ b/notebooks/2017-04-14-Inputs and Analysis of VASP runs.ipynb @@ -16,7 +16,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19" + "# !pip install -U pymatgen" ] }, { diff --git a/notebooks/2017-12-15-Plotting a Pourbaix Diagram.ipynb b/notebooks/2017-12-15-Plotting a Pourbaix Diagram.ipynb index a2acc2d..c5e436e 100644 --- a/notebooks/2017-12-15-Plotting a Pourbaix Diagram.ipynb +++ b/notebooks/2017-12-15-Plotting a Pourbaix Diagram.ipynb @@ -45,7 +45,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19" + "# !pip install -U pymatgen" ] }, { diff --git a/notebooks/2018-03-09-Computing the Reaction Diagram between Two Compounds.ipynb b/notebooks/2018-03-09-Computing the Reaction Diagram between Two Compounds.ipynb index abf5723..9122785 100644 --- a/notebooks/2018-03-09-Computing the Reaction Diagram between Two Compounds.ipynb +++ b/notebooks/2018-03-09-Computing the Reaction Diagram between Two Compounds.ipynb @@ -346,7 +346,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "pmg", "language": "python", "name": "python3" }, @@ -360,7 +360,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.8" + "version": "3.12.0" } }, "nbformat": 4, diff --git a/notebooks/2018-03-14-Plotting COHP from LOBSTER.ipynb b/notebooks/2018-03-14-Plotting COHP from LOBSTER.ipynb index a148ab8..eac9b0a 100644 --- a/notebooks/2018-03-14-Plotting COHP from LOBSTER.ipynb +++ b/notebooks/2018-03-14-Plotting COHP from LOBSTER.ipynb @@ -16,7 +16,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19" + "# !pip install -U pymatgen" ] }, { @@ -61,7 +61,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "pmg", "language": "python", "name": "python3" }, @@ -75,7 +75,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.8" + "version": "3.12.0" } }, "nbformat": 4, diff --git a/notebooks/2018-09-25-Structure Prediction using Pymatgen and the Materials API.ipynb b/notebooks/2018-09-25-Structure Prediction using Pymatgen and the Materials API.ipynb index b6de3ed..80f941a 100644 --- a/notebooks/2018-09-25-Structure Prediction using Pymatgen and the Materials API.ipynb +++ b/notebooks/2018-09-25-Structure Prediction using Pymatgen and the Materials API.ipynb @@ -18,7 +18,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19" + "# !pip install -U pymatgen" ] }, { diff --git a/notebooks/2018-11-6-Dopant suggestions using Pymatgen.ipynb b/notebooks/2018-11-6-Dopant suggestions using Pymatgen.ipynb index 97d3ad4..6fa476e 100644 --- a/notebooks/2018-11-6-Dopant suggestions using Pymatgen.ipynb +++ b/notebooks/2018-11-6-Dopant suggestions using Pymatgen.ipynb @@ -18,7 +18,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19" + "# !pip install -U pymatgen" ] }, { diff --git a/notebooks/2019-01-11-How to plot and evaluate output files from Lobster.ipynb b/notebooks/2019-01-11-How to plot and evaluate output files from Lobster.ipynb index 3c36ed2..87a60a0 100644 --- a/notebooks/2019-01-11-How to plot and evaluate output files from Lobster.ipynb +++ b/notebooks/2019-01-11-How to plot and evaluate output files from Lobster.ipynb @@ -18,8 +18,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19\n", - "from __future__ import annotations" + "# !pip install -U pymatgen" ] }, { @@ -68,8 +67,8 @@ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[3], line 4\u001b[0m\n\u001b[1;32m 1\u001b[0m COHPCAR_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlobster_data/GaAs/COHPCAR.lobster_new\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 2\u001b[0m POSCAR_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlobster_data/GaAs/POSCAR_new\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m----> 4\u001b[0m completecohp \u001b[38;5;241m=\u001b[39m \u001b[43mCompleteCohp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_file\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 5\u001b[0m \u001b[43m \u001b[49m\u001b[43mfmt\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mLOBSTER\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mCOHPCAR_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstructure_file\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mPOSCAR_path\u001b[49m\n\u001b[1;32m 6\u001b[0m \u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/mavrl/lib/python3.11/site-packages/pymatgen/electronic_structure/cohp.py:717\u001b[0m, in \u001b[0;36mCompleteCohp.from_file\u001b[0;34m(cls, fmt, filename, structure_file, are_coops, are_cobis, are_multi_center_cobis)\u001b[0m\n\u001b[1;32m 715\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 716\u001b[0m filename \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCOHPCAR.lobster\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 717\u001b[0m cohp_file \u001b[38;5;241m=\u001b[39m \u001b[43mCohpcar\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 718\u001b[0m \u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 719\u001b[0m \u001b[43m \u001b[49m\u001b[43mare_coops\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mare_coops\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 720\u001b[0m \u001b[43m \u001b[49m\u001b[43mare_cobis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mare_cobis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 721\u001b[0m \u001b[43m \u001b[49m\u001b[43mare_multi_center_cobis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mare_multi_center_cobis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 722\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 723\u001b[0m orb_res_cohp \u001b[38;5;241m=\u001b[39m cohp_file\u001b[38;5;241m.\u001b[39morb_res_cohp\n\u001b[1;32m 724\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", - "File \u001b[0;32m~/miniconda3/envs/mavrl/lib/python3.11/site-packages/pymatgen/io/lobster/outputs.py:126\u001b[0m, in \u001b[0;36mCohpcar.__init__\u001b[0;34m(self, are_coops, are_cobis, are_multi_center_cobis, filename)\u001b[0m\n\u001b[1;32m 123\u001b[0m cohp_data: \u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Any]] \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m 124\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mare_multi_center_cobis:\n\u001b[1;32m 125\u001b[0m \u001b[38;5;66;03m# The COHP data start in row num_bonds + 3\u001b[39;00m\n\u001b[0;32m--> 126\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrow\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msplit\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mfloat\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mcontents\u001b[49m\u001b[43m[\u001b[49m\u001b[43mnum_bonds\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mtranspose()\n\u001b[1;32m 127\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39menergies \u001b[38;5;241m=\u001b[39m data[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 128\u001b[0m cohp_data \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 129\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maverage\u001b[39m\u001b[38;5;124m\"\u001b[39m: {\n\u001b[1;32m 130\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCOHP\u001b[39m\u001b[38;5;124m\"\u001b[39m: {spin: data[\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;241m*\u001b[39m s \u001b[38;5;241m*\u001b[39m (num_bonds \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m)] \u001b[38;5;28;01mfor\u001b[39;00m s, spin \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(spins)},\n\u001b[1;32m 131\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mICOHP\u001b[39m\u001b[38;5;124m\"\u001b[39m: {spin: data[\u001b[38;5;241m2\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;241m*\u001b[39m s \u001b[38;5;241m*\u001b[39m (num_bonds \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m)] \u001b[38;5;28;01mfor\u001b[39;00m s, spin \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(spins)},\n\u001b[1;32m 132\u001b[0m }\n\u001b[1;32m 133\u001b[0m }\n", + "File \u001b[0;32m~/developer/pymatgen/src/pymatgen/electronic_structure/cohp.py:788\u001b[0m, in \u001b[0;36mCompleteCohp.from_file\u001b[0;34m(cls, fmt, filename, structure_file, are_coops, are_cobis, are_multi_center_cobis)\u001b[0m\n\u001b[1;32m 786\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 787\u001b[0m filename \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCOHPCAR.lobster\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 788\u001b[0m cohp_file \u001b[38;5;241m=\u001b[39m \u001b[43mCohpcar\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 789\u001b[0m \u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 790\u001b[0m \u001b[43m \u001b[49m\u001b[43mare_coops\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mare_coops\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 791\u001b[0m \u001b[43m \u001b[49m\u001b[43mare_cobis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mare_cobis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 792\u001b[0m \u001b[43m \u001b[49m\u001b[43mare_multi_center_cobis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mare_multi_center_cobis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 793\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 794\u001b[0m orb_res_cohp \u001b[38;5;241m=\u001b[39m cohp_file\u001b[38;5;241m.\u001b[39morb_res_cohp\n\u001b[1;32m 796\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", + "File \u001b[0;32m~/developer/pymatgen/src/pymatgen/io/lobster/outputs.py:138\u001b[0m, in \u001b[0;36mCohpcar.__init__\u001b[0;34m(self, are_coops, are_cobis, are_multi_center_cobis, is_lcfo, filename)\u001b[0m\n\u001b[1;32m 135\u001b[0m cohp_data: \u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Any]] \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m 136\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mare_multi_center_cobis:\n\u001b[1;32m 137\u001b[0m \u001b[38;5;66;03m# The COHP data start in line num_bonds + 3\u001b[39;00m\n\u001b[0;32m--> 138\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m(\u001b[49m\u001b[43mline\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msplit\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mfloat\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mline\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mlines\u001b[49m\u001b[43m[\u001b[49m\u001b[43mnum_bonds\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mtranspose()\n\u001b[1;32m 139\u001b[0m cohp_data \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 140\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maverage\u001b[39m\u001b[38;5;124m\"\u001b[39m: {\n\u001b[1;32m 141\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCOHP\u001b[39m\u001b[38;5;124m\"\u001b[39m: {spin: data[\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;241m*\u001b[39m s \u001b[38;5;241m*\u001b[39m (num_bonds \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m)] \u001b[38;5;28;01mfor\u001b[39;00m s, spin \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(spins)},\n\u001b[1;32m 142\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mICOHP\u001b[39m\u001b[38;5;124m\"\u001b[39m: {spin: data[\u001b[38;5;241m2\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;241m*\u001b[39m s \u001b[38;5;241m*\u001b[39m (num_bonds \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m)] \u001b[38;5;28;01mfor\u001b[39;00m s, spin \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(spins)},\n\u001b[1;32m 143\u001b[0m }\n\u001b[1;32m 144\u001b[0m }\n\u001b[1;32m 145\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 146\u001b[0m \u001b[38;5;66;03m# The COBI data start in line num_bonds + 3 if multi-center cobis exist\u001b[39;00m\n", "\u001b[0;31mValueError\u001b[0m: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (402,) + inhomogeneous part." ] } @@ -722,7 +721,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "pmg", "language": "python", "name": "python3" }, @@ -736,7 +735,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.12.0" } }, "nbformat": 4, diff --git a/notebooks/2019-03-11-Interface Reactions.ipynb b/notebooks/2019-03-11-Interface Reactions.ipynb index 1d96e2d..eddad04 100644 --- a/notebooks/2019-03-11-Interface Reactions.ipynb +++ b/notebooks/2019-03-11-Interface Reactions.ipynb @@ -20,8 +20,7 @@ "outputs": [], "source": [ "# Uncomment the subsequent lines in this cell to install dependencies for Google Colab.\n", - "# !pip install pymatgen==2022.7.19\n", - "from __future__ import annotations" + "# !pip install -U pymatgen" ] }, {