diff --git a/notebooks/damask.ipynb b/notebooks/damask.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..b00354ff754c94b72b05c2519bf96502f7db94d6 --- /dev/null +++ b/notebooks/damask.ipynb @@ -0,0 +1,403 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d0c61026-3a4c-4e5e-9d61-fdfafca74244", + "metadata": {}, + "source": [ + "# DAMASK tutorial\n", + "- creating necessary inputs for damask\n", + "- runing the damask jobs\n", + "\n", + "here more option is given to the user to select from damask python package itself." + ] + }, + { + "cell_type": "markdown", + "id": "ef3b0f8f-f536-445e-b5a5-80127c731dc5", + "metadata": {}, + "source": [ + "## Importing libraries and creating Project" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "705dade7-e2db-493d-a361-ad81b731528c", + "metadata": {}, + "outputs": [], + "source": [ + "from pyiron_continuum import damask\n", + "from damask import Result\n", + "from pathlib import Path\n", + "import numpy as np\n", + "import matplotlib.pylab as plt\n", + "%config InlineBackend.figure_format = \"retina\"\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "1dfa818a-b6e3-4abd-8a00-39a3ffd2424c", + "metadata": {}, + "source": [ + "## Creating the necessary inputs" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "75f70557-9750-4272-873c-2c6b709655a0", + "metadata": {}, + "outputs": [], + "source": [ + "path = Path(\"TEST\")\n", + "path.mkdir(exist_ok=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "6512f434-10fe-49d8-82da-555001502ea4", + "metadata": {}, + "outputs": [], + "source": [ + "grains=8; grids=16 # defines the number of grains and grids" + ] + }, + { + "cell_type": "markdown", + "id": "af0167a1-2c9d-49c0-8d68-ffa248d00de9", + "metadata": {}, + "source": [ + "### Homogenization" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2a96e114-7cea-49dd-959c-50e63498e47c", + "metadata": {}, + "outputs": [], + "source": [ + "homogenization = damask.factory.get_homogenization()" + ] + }, + { + "cell_type": "markdown", + "id": "b1af7a42-951f-4d0f-bb56-5d6ea18b92e5", + "metadata": {}, + "source": [ + "### Elasticity in combination with DFT" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "c0d643a2-7843-4019-b680-57f9b9fb3338", + "metadata": {}, + "outputs": [], + "source": [ + "# Retrieve data from database\n", + "def get_elasticity(key=\"Hooke_Al\"):\n", + " return damask.reference.yaml.list_elasticity()[key]\n" + ] + }, + { + "cell_type": "markdown", + "id": "71b5659e-ade4-4bfb-a80c-79d77f3366bf", + "metadata": {}, + "source": [ + "### Plasticity" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "00e86ebe-8cc6-49f0-ac5f-05b5689fb6f7", + "metadata": {}, + "outputs": [], + "source": [ + "def get_plasticity(key=\"phenopowerlaw_Al\"):\n", + " return damask.reference.yaml.list_plasticity()[key]" + ] + }, + { + "cell_type": "markdown", + "id": "c4599715-e2e7-43aa-ab86-c0a26fe862a7", + "metadata": {}, + "source": [ + "### Phase" + ] + }, + { + "cell_type": "markdown", + "id": "b84fe488-fabb-4729-a1ff-2ce6d7cec22d", + "metadata": {}, + "source": [ + "#### Expert user variant: Define all parameters\n", + "\n", + "pyiron allows the user to insert all parameters required by Damask" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "7d018837-f07e-4f01-8436-683e12257489", + "metadata": {}, + "outputs": [], + "source": [ + "elasticity = get_elasticity()\n", + "plasticity = get_plasticity()\n", + "phase = damask.factory.get_phase(\n", + " composition='Aluminum', elasticity=elasticity, plasticity=plasticity\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "ce4d182d-85ee-40f1-946e-eaa1005a7e03", + "metadata": {}, + "source": [ + "### Rotation" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "d93eda97-b7ce-4572-8ce3-b8d34672e274", + "metadata": {}, + "outputs": [], + "source": [ + "rotation = damask.factory.get_rotation(shape=grains)" + ] + }, + { + "cell_type": "markdown", + "id": "e39e9e82-c289-4322-ba9e-f03c3580e03e", + "metadata": {}, + "source": [ + "### Material" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "dfb6237e-1dbf-47ba-9f50-e580456e9760", + "metadata": {}, + "outputs": [], + "source": [ + "def save_material(rotation, composition, phase, homogenization, file_name=\"material.yaml\", path=path):\n", + " material = damask.factory.generate_material([rotation],[composition], phase, homogenization)\n", + " material.save(path / file_name)\n", + " return file_name\n", + "\n", + "material = save_material(rotation=rotation, composition=\"Aluminum\", phase=phase, homogenization=homogenization)" + ] + }, + { + "cell_type": "markdown", + "id": "583ae5d3-d057-41b7-ad15-249564316cec", + "metadata": {}, + "source": [ + "### Grid" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "038cb767-89ba-4fce-971e-7b71625ac748", + "metadata": {}, + "outputs": [], + "source": [ + "def save_grid(box_size, spatial_discretization, num_grains, file_name=\"damask\", path=path):\n", + " grid = damask.factory.generate_grid_from_voronoi_tessellation(box_size=box_size, spatial_discretization=spatial_discretization, num_grains=num_grains)\n", + " grid.save(path / file_name)\n", + " return file_name\n", + "\n", + "grid = save_grid(box_size=1.0e-5, spatial_discretization=grids, num_grains=grains)" + ] + }, + { + "cell_type": "markdown", + "id": "0f2fda22-7d5c-42bb-9aba-f1aebe58279d", + "metadata": {}, + "source": [ + "### Loading" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "88fa77c8-b2c2-4357-9701-20405b08d6c4", + "metadata": {}, + "outputs": [], + "source": [ + "def save_loading(strain=1.0e-3, file_name=\"loading.yaml\", path=path):\n", + " keys, values = damask.factory.generate_loading_tensor(\"dot_F\")\n", + " values[0, 0] = strain\n", + " keys[1, 1] = keys[2, 2] = \"P\"\n", + " data = damask.factory.loading_tensor_to_dict(keys, values)\n", + " load_step = [\n", + " damask.factory.generate_load_step(N=40, t=10, f_out=4, **data),\n", + " damask.factory.generate_load_step(N=60, t=60, f_out=4, **data)\n", + " ]\n", + " loading = damask.factory.get_loading(solver={\"mechanical\": \"spectral_basic\"}, load_steps=load_step)\n", + " loading.save(path / file_name)\n", + " return file_name\n", + "\n", + "loading = save_loading()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "23ec01ce-413c-46fd-8167-d2e9d847d4ed", + "metadata": {}, + "outputs": [], + "source": [ + "def run_damask(material, loading, grid):\n", + " command = f\"DAMASK_grid -m {material} -l {loading} -g {grid}.vti\".split()\n", + " import subprocess\n", + " process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, cwd=path)\n", + " stdout, stderr = process.communicate()\n", + " return process, stdout, stderr\n", + "\n", + "process, stdout, stderr = run_damask(material, loading, grid)" + ] + }, + { + "cell_type": "markdown", + "id": "841da73c-cc70-4131-b27c-ed6918c0c8d9", + "metadata": {}, + "source": [ + "## Post-processing" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "ed8c7f2a-9669-429a-a879-27c0e03a893c", + "metadata": {}, + "outputs": [], + "source": [ + "def average(d):\n", + " return np.average(list(d.values()), axis=1)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "eedbfac7-4e0f-4ca2-af08-2ad816a77471", + "metadata": {}, + "outputs": [], + "source": [ + "def get_hdf_file_name(material, loading, grid):\n", + " return \"{}_{}_{}.hdf5\".format(grid, loading.split(\".\")[0], material.split(\".\")[0])\n", + "\n", + "def get_results(file_name, path=path):\n", + " results = Result(path / file_name)\n", + " results.add_stress_Cauchy()\n", + " results.add_strain()\n", + " results.add_equivalent_Mises(\"sigma\")\n", + " results.add_equivalent_Mises(\"epsilon_V^0.0(F)\")\n", + " stress = average(results.get(\"sigma\"))\n", + " strain = average(results.get(\"epsilon_V^0.0(F)\"))\n", + " stress_von_Mises = average(results.get(\"sigma_vM\"))\n", + " strain_von_Mises = average(results.get(\"epsilon_V^0.0(F)_vM\"))\n", + " return stress, strain, stress_von_Mises, strain_von_Mises" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "39177b74", + "metadata": {}, + "outputs": [], + "source": [ + "def get_hdf_file_name(material, loading, grid):\n", + " return \"{}_{}_{}.hdf5\".format(grid, loading.split(\".\")[0], material.split(\".\")[0])\n", + "\n", + "def get_results(file_name, path=path):\n", + " results = Result(path / file_name)\n", + " results.add_stress_Cauchy()\n", + " results.add_strain()\n", + " results.add_equivalent_Mises(\"sigma\")\n", + " results.add_equivalent_Mises(\"epsilon_V^0.0(F)\")\n", + " stress = average(results.get(\"sigma\"))\n", + " strain = average(results.get(\"epsilon_V^0.0(F)\"))\n", + " stress_von_Mises = average(results.get(\"sigma_vM\"))\n", + " strain_von_Mises = average(results.get(\"epsilon_V^0.0(F)_vM\"))\n", + " return stress, strain, stress_von_Mises, strain_von_Mises" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "04fbd2e0-38b7-4652-9be2-c84932459b5a", + "metadata": {}, + "outputs": [], + "source": [ + "file_name = get_hdf_file_name(material, loading, grid)\n", + "stress, strain, stress_von_Mises, strain_von_Mises = get_results(file_name)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "eaacdbe4-d6d8-47d5-8ba5-75a7feedd79c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": { + "image/png": { + "height": 448, + "width": 554 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.xlabel(\"Strain\")\n", + "plt.ylabel(\"Stress\")\n", + "plt.plot(strain_von_Mises, stress_von_Mises);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "74a8b88b-8639-45e2-a0a4-098def9e6af6", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pyiron", + "language": "python", + "name": "pyiron" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/tutorial.ipynb b/notebooks/tutorial.ipynb deleted file mode 100644 index d577c78e8cf4819de6b3e21e84051de6d2ca772e..0000000000000000000000000000000000000000 --- a/notebooks/tutorial.ipynb +++ /dev/null @@ -1,379 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "d0c61026-3a4c-4e5e-9d61-fdfafca74244", - "metadata": {}, - "source": [ - "# DAMASK tutorial\n", - "- creating necessary inputs for damask\n", - "- runing the damask jobs\n", - "\n", - "here more option is given to the user to select from damask python package itself." - ] - }, - { - "cell_type": "markdown", - "id": "ef3b0f8f-f536-445e-b5a5-80127c731dc5", - "metadata": {}, - "source": [ - "## Importing libraries and creating Project" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "705dade7-e2db-493d-a361-ad81b731528c", - "metadata": {}, - "outputs": [], - "source": [ - "from pyiron_continuum import damask\n", - "from damask import Result\n", - "from pathlib import Path\n", - "import numpy as np\n", - "import matplotlib.pylab as plt\n", - "%config InlineBackend.figure_format = \"retina\"\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "1dfa818a-b6e3-4abd-8a00-39a3ffd2424c", - "metadata": {}, - "source": [ - "## Creating the necessary inputs" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "75f70557-9750-4272-873c-2c6b709655a0", - "metadata": {}, - "outputs": [], - "source": [ - "path = \"TEST\"" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "6512f434-10fe-49d8-82da-555001502ea4", - "metadata": {}, - "outputs": [], - "source": [ - "grains=8; grids=16 # defines the number of grains and grids" - ] - }, - { - "cell_type": "markdown", - "id": "af0167a1-2c9d-49c0-8d68-ffa248d00de9", - "metadata": {}, - "source": [ - "### Homogenization" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "2a96e114-7cea-49dd-959c-50e63498e47c", - "metadata": {}, - "outputs": [], - "source": [ - "homogenization = damask.factory.get_homogenization()" - ] - }, - { - "cell_type": "markdown", - "id": "b1af7a42-951f-4d0f-bb56-5d6ea18b92e5", - "metadata": {}, - "source": [ - "### Elasticity in combination with DFT" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "c0d643a2-7843-4019-b680-57f9b9fb3338", - "metadata": {}, - "outputs": [], - "source": [ - "# Retrieve data from database\n", - "def get_elasticity(key=\"Hooke_Al\"):\n", - " return damask.reference.yaml.list_elasticity()[key]\n" - ] - }, - { - "cell_type": "markdown", - "id": "71b5659e-ade4-4bfb-a80c-79d77f3366bf", - "metadata": {}, - "source": [ - "### Plasticity" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "00e86ebe-8cc6-49f0-ac5f-05b5689fb6f7", - "metadata": {}, - "outputs": [], - "source": [ - "def get_plasticity(key=\"phenopowerlaw_Al\"):\n", - " return damask.reference.yaml.list_plasticity()[key]" - ] - }, - { - "cell_type": "markdown", - "id": "c4599715-e2e7-43aa-ab86-c0a26fe862a7", - "metadata": {}, - "source": [ - "### Phase" - ] - }, - { - "cell_type": "markdown", - "id": "b84fe488-fabb-4729-a1ff-2ce6d7cec22d", - "metadata": {}, - "source": [ - "#### Expert user variant: Define all parameters\n", - "\n", - "pyiron allows the user to insert all parameters required by Damask" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "7d018837-f07e-4f01-8436-683e12257489", - "metadata": {}, - "outputs": [], - "source": [ - "elasticity = get_elasticity()\n", - "plasticity = get_plasticity()\n", - "phase = damask.factory.get_phase(\n", - " composition='Aluminum', elasticity=elasticity, plasticity=plasticity\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "ce4d182d-85ee-40f1-946e-eaa1005a7e03", - "metadata": {}, - "source": [ - "### Rotation" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "d93eda97-b7ce-4572-8ce3-b8d34672e274", - "metadata": {}, - "outputs": [], - "source": [ - "rotation = damask.factory.get_rotation(shape=grains)" - ] - }, - { - "cell_type": "markdown", - "id": "e39e9e82-c289-4322-ba9e-f03c3580e03e", - "metadata": {}, - "source": [ - "### Material" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "dfb6237e-1dbf-47ba-9f50-e580456e9760", - "metadata": {}, - "outputs": [], - "source": [ - "def save_material(rotation, composition, phase, homogenization, file_name=\"material.yaml\", path=path):\n", - " material = damask.factory.generate_material([rotation],[composition], phase, homogenization)\n", - " material.save(Path(path) / file_name)\n", - " return file_name\n", - "\n", - "material = save_material(rotation=rotation, composition=\"Aluminum\", phase=phase, homogenization=homogenization)" - ] - }, - { - "cell_type": "markdown", - "id": "583ae5d3-d057-41b7-ad15-249564316cec", - "metadata": {}, - "source": [ - "### Grid" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "038cb767-89ba-4fce-971e-7b71625ac748", - "metadata": {}, - "outputs": [], - "source": [ - "def save_grid(box_size, spatial_discretization, num_grains, file_name=\"damask\", path=path):\n", - " grid = damask.factory.generate_grid_from_voronoi_tessellation(box_size=box_size, spatial_discretization=spatial_discretization, num_grains=num_grains)\n", - " grid.save(Path(path) / file_name)\n", - " return file_name\n", - "\n", - "grid = save_grid(box_size=1.0e-5, spatial_discretization=grids, num_grains=grains)" - ] - }, - { - "cell_type": "markdown", - "id": "0f2fda22-7d5c-42bb-9aba-f1aebe58279d", - "metadata": {}, - "source": [ - "### Loading" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "88fa77c8-b2c2-4357-9701-20405b08d6c4", - "metadata": {}, - "outputs": [], - "source": [ - "def save_loading(strain=1.0e-3, file_name=\"loading.yaml\", path=path):\n", - " keys, values = damask.factory.generate_loading_tensor(\"dot_F\")\n", - " values[0, 0] = strain\n", - " keys[1, 1] = keys[2, 2] = \"P\"\n", - " data = damask.factory.loading_tensor_to_dict(keys, values)\n", - " load_step = [\n", - " damask.factory.generate_load_step(N=40, t=10, f_out=4, **data),\n", - " damask.factory.generate_load_step(N=60, t=60, f_out=4, **data)\n", - " ]\n", - " loading = damask.factory.get_loading(solver={\"mechanical\": \"spectral_basic\"}, load_steps=load_step)\n", - " loading.save(Path(path) / file_name)\n", - " return file_name\n", - "\n", - "loading = save_loading()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "23ec01ce-413c-46fd-8167-d2e9d847d4ed", - "metadata": {}, - "outputs": [], - "source": [ - "def run_damask(material, loading, grid):\n", - " command = f\"DAMASK_grid -m {material} -l {loading} -g {grid}.vti\".split()\n", - " import subprocess\n", - " process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, cwd=path)\n", - " stdout, stderr = process.communicate()\n", - " return process, stdout, stderr\n", - "\n", - "process, stdout, stderr = run_damask(material, loading, grid)" - ] - }, - { - "cell_type": "markdown", - "id": "841da73c-cc70-4131-b27c-ed6918c0c8d9", - "metadata": {}, - "source": [ - "## Post-processing" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "ed8c7f2a-9669-429a-a879-27c0e03a893c", - "metadata": {}, - "outputs": [], - "source": [ - "def average(d):\n", - " return np.average(list(d.values()), axis=1)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "eedbfac7-4e0f-4ca2-af08-2ad816a77471", - "metadata": {}, - "outputs": [], - "source": [ - "def get_hdf_file_name(material, loading, grid):\n", - " return \"{}_{}_{}.hdf5\".format(grid, loading.split(\".\")[0], material.split(\".\")[0])\n", - "\n", - "def get_results(file_name, path=path):\n", - " results = Result(Path(path) / file_name)\n", - " results.add_stress_Cauchy()\n", - " results.add_strain()\n", - " results.add_equivalent_Mises(\"sigma\")\n", - " results.add_equivalent_Mises(\"epsilon_V^0.0(F)\")\n", - " stress = average(results.get(\"sigma\"))\n", - " strain = average(results.get(\"epsilon_V^0.0(F)\"))\n", - " stress_von_Mises = average(results.get(\"sigma_vM\"))\n", - " strain_von_Mises = average(results.get(\"epsilon_V^0.0(F)_vM\"))\n", - " return stress, strain, stress_von_Mises, strain_von_Mises" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "04fbd2e0-38b7-4652-9be2-c84932459b5a", - "metadata": {}, - "outputs": [], - "source": [ - "file_name = get_hdf_file_name(material, loading, grid)\n", - "stress, strain, stress_von_Mises, strain_von_Mises = get_results(file_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "eaacdbe4-d6d8-47d5-8ba5-75a7feedd79c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "<Figure size 640x480 with 1 Axes>" - ] - }, - "metadata": { - "image/png": { - "height": 448, - "width": 554 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.xlabel(\"Strain\")\n", - "plt.ylabel(\"Stress\")\n", - "plt.plot(strain_von_Mises, stress_von_Mises);" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "74a8b88b-8639-45e2-a0a4-098def9e6af6", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "pyiron", - "language": "python", - "name": "pyiron" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.9" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -}