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| -rw-r--r-- | Docs/source/visualization/ascent.rst | 26 | ||||
| -rw-r--r-- | Tools/Ascent/ascent_replay_warpx.ipynb | 310 |
2 files changed, 333 insertions, 3 deletions
diff --git a/Docs/source/visualization/ascent.rst b/Docs/source/visualization/ascent.rst index a48b936aa..6033e1333 100644 --- a/Docs/source/visualization/ascent.rst +++ b/Docs/source/visualization/ascent.rst @@ -286,10 +286,30 @@ Workflow .. note:: This section is in-progress. - It will describe how to quickly create and iterate on ``ascent_actions.yaml`` files before running at scale. + TODOs: finalize acceptance testing; update 3D LWFA example - TODOs: finalize acceptance testing; update 3D LWFA example; share Jupyter notebook file +In the preparation of simulations, it is generally useful to run small, under-resolved versions of the planned simulation layout first. +Ascent replay is helpful in the setup of an in situ visualization pipeline during this process. +In the following, a Jupyter-based workflow is shown that can be used to quickly iterate on the design of a ``ascent_actions.yaml`` file, repeatedly rendering the same (small) data. + +First, run a small simulation, e.g. on a local computer, and create conduit blueprint files (see above). +Second, copy the Jupyter Notebook file :download:`ascent_replay_warpx.ipynb <../../../Tools/Ascent/ascent_replay_warpx.ipynb>` into the simulation output directory. +Third, download and start a Docker container with a prepared Jupyter installation and Ascent Python bindings from the simulation output directory: .. code-block:: bash - docker run -v$PWD:/home/user/ascent/install-debug/examples/ascentutorial/ascent_intro/notebooks/replay -p 8000:8000 -p 8888:8888 -p 9000:9000 -p 10000:10000 -t -i alpinedav/ascent-jupyter:latest + docker pull alpinedav/ascent-jupyter:latest + docker run -v$PWD:/home/user/ascent/install-debug/examples/ascent/tutorial/ascent_intro/notebooks/replay -p 8000:8000 -p 8888:8888 -p 9000:9000 -p 10000:10000 -t -i alpinedav/ascent-jupyter:latest + +Now, access Jupyter Lab via: http://localhost:8888/lab (password: ``learn``). + +Inside the Jupyter Lab is a ``replay/`` directory, which mounts the outer working directory. +You can now open ``ascent_replay_warpx.ipynb`` and execute all cells. +The last two cells are the replay action that can be quickly iterated: change ``replay_actions.yaml`` cell and execute both. + +.. note:: + + * Keep an eye on the terminal, if a replay action is erroneous it will show up on the terminal that started the docker container. + (TODO: We might want to catch that inside python and print it in Jupyter instead.) + * If you remove a "key" from the replay action, you might see an error in the ``AscentViewer``. + Restart and execute all cells in that case. diff --git a/Tools/Ascent/ascent_replay_warpx.ipynb b/Tools/Ascent/ascent_replay_warpx.ipynb new file mode 100644 index 000000000..636e9721c --- /dev/null +++ b/Tools/Ascent/ascent_replay_warpx.ipynb @@ -0,0 +1,310 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Replay Data\n", + "This notebook allows quick playing with Ascent actions on created \"conduit blueprint data\". See Ascent's [Extracts](https://ascent.readthedocs.io/en/latest/Actions/Extracts.html) docs for deeper details on Extracts." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import conduit\n", + "import conduit.blueprint\n", + "import conduit.relay\n", + "import ascent\n", + "\n", + "from IPython.display import Image\n", + "\n", + "import glob" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#print(conduit.about())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# show existing conduit blueprint files\n", + "sim = \"./\"\n", + "glob.glob(sim + \"*root\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# VisIt 2.13 or newer, when built with Conduit support, can visualize meshes from these files.\n", + "# Look at the Blueprint HDF5 extract with VisIt\n", + "#!visit -o conduit_blueprint.cycle_000400.root" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Prepare Data\n", + "Initialize Ascent and Load a Blueprint File" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# this step can take a while if the simulation used high resolution\n", + "data = conduit.Node()\n", + "conduit.relay.io.blueprint.read_mesh(data, sim + \"conduit_blueprint.cycle_000400.root\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# we publish this data now once to visualize it in multiple ways below\n", + "a = ascent.Ascent()\n", + "opts = conduit.Node()\n", + "opts[\"actions_file\"] = \"\" # work-around: ascent_actions.yaml file must not overwrite our replay action\n", + "#opts[\"exceptions\"] = \"forward\"\n", + "a.open(opts)\n", + "a.publish(data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def rerender():\n", + " \"\"\"This is reads an updated Ascent Action and rerenders the image\"\"\"\n", + " actions = conduit.Node()\n", + " actions.load(\"replay_actions.yaml\")\n", + " a.execute(actions)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Relay Example\n", + "The next two cells are as follows:\n", + "1. modify the Ascent action (examples: https://ascent.readthedocs.io/en/latest/Actions/Examples.html)\n", + "2. rerender\n", + "\n", + "By only modifying and executing only those two cells one can quickly iterate over new visualizations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%writefile replay_actions.yaml\n", + "\n", + "# this block are data pipelines\n", + "# each entry in pipelines: executes a series of functions from top to bottom,\n", + "# results of prior functions can be used in later calls of the same pipeline\n", + "-\n", + " action: \"add_pipelines\"\n", + " pipelines:\n", + " slice_field:\n", + " f1:\n", + " type: \"slice\"\n", + " params:\n", + " topology: topo\n", + " point: {x: 0.0, y: 0.0, z: 0.0}\n", + " normal: {x: 1.0, y: 1.0, z: 0.0}\n", + " \n", + " sampled_particles:\n", + " f1:\n", + " type: histsampling\n", + " params:\n", + " field: particle_electrons_uz\n", + " bins: 64\n", + " sample_rate: 0.90\n", + " f2:\n", + " type: \"clip\"\n", + " params:\n", + " topology: particle_electrons # particle data\n", + " multi_plane:\n", + " point1: {x: 0.0, y: 0.0, z: 0.0}\n", + " normal1: {x: 1.0, y: 0.0, z: 0.0}\n", + " point2: {x: 0.0, y: 0.0, z: 0.0}\n", + " normal2: {x: 0.7, y: -0.7, z: 0.0}\n", + "\n", + " clipped_volume:\n", + " f0:\n", + " type: \"contour\"\n", + " params:\n", + " field: \"Ez\"\n", + " levels: 10\n", + " f1:\n", + " type: \"clip\"\n", + " params:\n", + " topology: topo # name of the amr mesh\n", + " multi_plane:\n", + " point1: {x: 0.0, y: 0.0, z: 0.0}\n", + " normal1: {x: 1.0, y: 0.0, z: 0.0}\n", + " point2: {x: 0.0, y: 0.0, z: 0.0}\n", + " normal2: {x: 0.3, y: -0.3, z: 0.0}\n", + "\n", + " mag_clipped_volume_E:\n", + " f0: \n", + " type: \"composite_vector\"\n", + " params: \n", + " field1: \"Ex\"\n", + " field2: \"Ey\"\n", + " field3: \"Ez\"\n", + " output_name: \"E_vec\"\n", + " f1: \n", + " type: \"vector_magnitude\"\n", + " params: \n", + " field: \"E_vec\"\n", + " output_name: \"E_mag\"\n", + " f2:\n", + " type: \"contour\"\n", + " params:\n", + " field: \"E_mag\"\n", + " levels: 4\n", + " f3:\n", + " type: \"clip\"\n", + " params:\n", + " topology: topo # name of the amr mesh\n", + " multi_plane:\n", + " point1: {x: 0.0, y: 0.0, z: 0.0}\n", + " normal1: {x: 1.0, y: 0.0, z: 0.0}\n", + " point2: {x: 0.0, y: 0.0, z: 0.0}\n", + " normal2: {x: 0.7, y: -0.7, z: 0.0}\n", + "\n", + "# A scene is describes the things to be rendered and how\n", + "# here one selects either data directly or data that goes into a data pipeline;\n", + "# then, the result gets represented with a visualization \"type\" and according\n", + "# parameters such as colorbars and color ranges (aka transfer functions)\n", + "# reference: https://ascent.readthedocs.io/en/latest/Actions/Scenes.html\n", + "# color tables: https://ascent.readthedocs.io/en/latest/Actions/VTKmColorTables.html\n", + "-\n", + " action: \"add_scenes\"\n", + " scenes:\n", + " scene1:\n", + " plots:\n", + "# p0:\n", + "# type: \"pseudocolor\"\n", + "# field: \"particle_electrons_uz\"\n", + "# pipeline: \"sampled_particles\"\n", + " p1:\n", + " type: \"pseudocolor\"\n", + " field: \"Ez\"\n", + " pipeline: \"clipped_volume\"\n", + "# color_table: \n", + "# name: \"plasma\"\n", + "# reverse: true\n", + "# control_points: \n", + "# - \n", + "# type: \"alpha\"\n", + "# position: 0.0\n", + "# alpha: 0.5\n", + "# - \n", + "# type: \"alpha\"\n", + "# position: 1.0\n", + "# alpha: 0.5\n", + "# p2:\n", + "# type: \"pseudocolor\"\n", + "# field: \"E_mag\"\n", + "# pipeline: \"mag_clipped_volume_E\"\n", + "# color_table: \n", + "# name: \"Black-Body Radiation\"\n", + "# reverse: true\n", + " #p3:\n", + " # type: \"pseudocolor\"\n", + " # field: \"Ez\"\n", + " # pipeline: \"slice_field\"\n", + " # min_value: -3.0e11\n", + " # max_value: 3.0e11\n", + " renders:\n", + " image1:\n", + " image_width: 512\n", + " image_height: 512\n", + " bg_color: [1.0, 1.0, 1.0]\n", + " fg_color: [0.0, 0.0, 0.0]\n", + " image_prefix: \"./replay_%06d\"\n", + " camera:\n", + " azimuth: -70\n", + " elevation: 30\n", + " zoom: 1.5" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rerender()\n", + "ascent.jupyter.AscentViewer(a).show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#!ls *png" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Image(filename=\"lwfa_Ex_e-uz_000400.png\")\n", + "# print(conduit.about())" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} |