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Experiment notebooks (#54)
Co-authored-by: BenSchZA <[email protected]> Co-authored-by: cadlabsgt <[email protected]> Co-authored-by: giutri <[email protected]> Co-authored-by: cadlabsgt <[email protected]>
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‎Makefile

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benchmark-default:
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python -m pytest benchmarks/benchmark_default_experiment.py --benchmark-only --benchmark-json output.json
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profile-memory:
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mprof run --include-children --multiprocess $(target)
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mprof plot

‎README.md

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A notebook exists to perform simulation time profiling of individual State Update Blocks of the model, see [experiments/notebooks/simulation_profiling/simulation_timestep_substep_profiling.ipynb]. Simulation time profiling of PRs is also performed in the GitHub Action pipeline, and included in each PR by a bot. See the Makefile and GitHub Action file for more details.
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Memory profiling can also be performed using `memory-profiler`.
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Memory profiling can also be performed using the `memory-profiler` Python package:
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```bash
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make profile-memory target="tests/test_notebook_memory.py 1_sanity_checks.ipynb"
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```
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## Change Log
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‎experiments/notebooks/0_README.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Experiment Notebook: {Descriptive Experiment Keywords}"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Table of Contents\n",
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"* [Experiment Summary](#Experiment-Summary)\n",
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"* [Experiment Assumptions](#Experiment-Assumptions)\n",
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"* [Experiment Setup](#Experiment-Setup)\n",
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"* [Analysis 1: ...](#Analysis-1:-...)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Experiment Summary \n",
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"\n",
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"The purpose of this notebook is to...\n",
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"\n",
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"# Experiment Assumptions\n",
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"\n",
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"See [assumptions document](../../ASSUMPTIONS.md) for further details."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Experiment Setup\n",
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"\n",
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"We begin with several experiment-notebook-level preparatory setup operations:\n",
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"\n",
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"* Import relevant dependencies\n",
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"* Import relevant experiment templates\n",
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"* Create copies of experiments\n",
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"* Configure and customize experiments \n",
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"\n",
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"Analysis-specific setup operations are handled in their respective notebook sections."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Import the setup module:\n",
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"# * sets up the Python path\n",
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"# * runs shared notebook configuration methods, such as loading IPython modules\n",
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"import setup\n",
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"\n",
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"import copy\n",
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"import logging\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import plotly.express as px\n",
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"\n",
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"import experiments.notebooks.visualizations as visualizations\n",
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"from experiments.run import run\n",
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"from experiments.utils import display_code"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Enable/disable logging\n",
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"logger = logging.getLogger()\n",
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"logger.disabled = False"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Import experiment templates\n",
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"import experiments.default_experiment as default_experiment"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Inspect experiment template\n",
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"display_code(default_experiment)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create a simulation for each analysis\n",
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"simulation_1 = copy.deepcopy(default_experiment.experiment.simulations[0])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Experiment configuration\n",
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"simulation_1.model.initial_state.update({})\n",
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"\n",
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"simulation_1.model.params.update({})"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Analysis 1: ..."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"{Analysis Description}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Analysis-specific setup"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Experiment execution\n",
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"df, exceptions = run(simulation_1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Post-processing and visualizations"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.11"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

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