{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "#%load_ext autoreload\n", "#%autoreload 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Tutorial: Lorenz-96 \n", "\n", "This notebook runs the Lorenz96 example using ICESEE's data assimilation driver." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import os, sys\n", "import platform\n", "import numpy as np\n", "from pathlib import Path\n", "\n", "from IPython.display import HTML" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Setup and preprocessing:\n", "\n", "# Current path\n", "Path = os.path.realpath(\".\")\n", "#print ('Path: %s\\n' %Path)\n", "\n", "# Parent path\n", "Parentpath = os.path.dirname(Path)\n", "#print ('Parentpath: %s\\n' %Parentpath)\n", "\n", "# Add to PYTHONPATH\n", "sys.path.append(Parentpath)\n", "\n", "# Tool path\n", "Toolpath = os.path.dirname(Parentpath)\n", "#print ('Toolpath: %s\\n' %Toolpath)\n", "\n", "import utils\n", "from utils.configuration import VERBOSE\n", "\n", "if VERBOSE:\n", " \n", " print (\"Operating System Platform: \" + platform.system(), platform.release())\n", " print (\"\\n\")\n", "\n", " print (\"os.environ['PATH']: \", os.environ['PATH'])\n", " print (\"sys.path: \", sys.path)\n", " print (\"\\n\")\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#https://api.jquery.com/ready/\n", "HTML('''\n", "\n", "''')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "application/javascript": "IPython.OutputArea.prototype._should_scroll = function(lines) {\n return false;\n}\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%javascript\n", "IPython.OutputArea.prototype._should_scroll = function(lines) {\n", " return false;\n", "}" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Directory tree of\n", "/Users/bkyanjo3/da_project/ICESEE-GHUB:\n", "\n" ] } ], "source": [ "print ('\\nDirectory tree of\\n%s:\\n' %Toolpath)\n", "from utils.Tree import Tree\n", "results_tree = Tree(Toolpath)\n", "# results_tree.print_tree()\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ICESEE bootstrap] external path enabled: /Users/bkyanjo3/da_project/ICESEE-GHUB/external\n", "[ICESEE bootstrap] ICESEE loaded from: /Users/bkyanjo3/da_project/ICESEE-GHUB/external/ICESEE/__init__.py\n" ] }, { "data": { "text/plain": [ "PosixPath('/Users/bkyanjo3/da_project/ICESEE-GHUB/external')" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from _bootstrap._bootstrap import enable_icesee\n", "enable_icesee()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ICESEE] Running default parallel mode\n", "[ICESEE] Generating true state ...\n", "[ICESEE] Generating nurged state ...\n", "[ICESEE] Generating synthetic observations ...\n", "[ICESEE] observation times requested: [0.1 0.3 0.5 0.7 0.9 1.1 1.3 1.5 1.7 1.9]\n", "[ICESEE] observation model indices ind_m: [ 10 30 50 70 90 110 130 150 170 190], total m_obs=10\n", "[ICESEE] bed_snaps: []\n", "[ICESEE] bed_snap_cols: []\n", "[ICESEE] Initializing the ensemble ...\n", "[ICESEE] Launching lorenz with data assimilation using the EnKF filter across 1 MPI ranks.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[ICESEE] Assimilation progress (1 ranks): 98%|█████████▊| 976/1000 [00:11<00:00, 177.88it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[ICESEE] Existing file results/true-wrong-lorenz.h5 removed.\n", "[ICESEE] Writing data to results/true-wrong-lorenz.h5\n", "[ICESEE] Dataset 't' written to file\n", "[ICESEE] Dataset 'b_io' written to file\n", "[ICESEE] Dataset 'Lxy' written to file\n", "[ICESEE] Dataset 'nxy' written to file\n", "[ICESEE] Dataset 'obs_max_time' written to file\n", "[ICESEE] Dataset 'obs_index' written to file\n", "[ICESEE] Dataset 'run_mode' written to file\n", "[ICESEE] Data successfully written to results/true-wrong-lorenz.h5\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[ICESEE] Assimilation progress (1 ranks): 100%|██████████| 1000/1000 [00:11<00:00, 85.96it/s]\n" ] }, { "data": { "text/plain": [ "0" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# --- call the lorenz96 data assimilation example ---\n", "# go to external/ICESEE\n", "# os.system('cd ../../external/ICESEE/applications/lorenz_model/examples/lorenz96')\n", "import os\n", "os.system('python3 -m ICESEE.applications.lorenz_model.examples.lorenz96.run_da_lorenz96')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# make read results and postprocess notebook\n", "# from utils.notebook_runner import find_repo_root, run_postprocess_to_wrapper\n", "\n", "# root = find_repo_root()\n", "\n", "# postprocess_nb = (\n", "# root / \"external\" / \"ICESEE\"\n", "# / \"applications\" / \"lorenz_model\" / \"examples\" / \"lorenz96\"\n", "# / \"read_results.ipynb\"\n", "# )\n", "\n", "# out_nb = run_postprocess_to_wrapper(\n", "# postprocess_nb,\n", "# wrapper_out_dir=\"icesee_jupyter_book/_static/generated\",\n", "# out_name=\"read_results_report.ipynb\",\n", "# )\n", "\n", "# print(\"Executed report notebook:\", out_nb)\n", "# print(\"Mirrored new/changed artifacts into: icesee_jupyter_book/_static/generated//\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# # Display generated figures\n", "# from pathlib import Path\n", "# from IPython.display import display, Image\n", "# from utils.notebook_runner import find_repo_root\n", "\n", "# root = find_repo_root()\n", "# # same convention as runner: _static/generated//figures\n", "# tag = Path.cwd().name\n", "# fig_dir = root / \"icesee_jupyter_book\" / \"_static\" / \"generated\" / tag / \"figures\"\n", "\n", "# pngs = sorted(fig_dir.glob(\"*.png\"))\n", "# print(f\"Found {len(pngs)} saved figures in:\", fig_dir)\n", "\n", "# for p in pngs[-12:]:\n", "# display(Image(filename=str(p)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# from IPython.display import Image, display\n", "# from pathlib import Path\n", "\n", "# for f in sorted((Path(\"results\")/\"figures\").glob(\"*.png\")):\n", "# display(Image(filename=str(f)))" ] } ], "metadata": { "kernelspec": { "display_name": "Python3 (icesee1)", "language": "python", "name": "icesee1" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.6" } }, "nbformat": 4, "nbformat_minor": 4 }