From b85ee9d64a536937912544c7bbd5b98b635b7e8d Mon Sep 17 00:00:00 2001 From: Christian C Date: Mon, 11 Nov 2024 12:29:32 -0800 Subject: Initial commit --- notebooks/Outline.ipynb | 280 ++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 280 insertions(+) create mode 100644 notebooks/Outline.ipynb (limited to 'notebooks/Outline.ipynb') diff --git a/notebooks/Outline.ipynb b/notebooks/Outline.ipynb new file mode 100644 index 0000000..207cf77 --- /dev/null +++ b/notebooks/Outline.ipynb @@ -0,0 +1,280 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ebfce4c6", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "# Imports and Functions" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "ff590465", + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [ + "from sunlab.environment.base.cpu import *\n", + "from sunlab.environment.base.extras import *\n", + "from sunlab.globals import FILES\n", + "from sunlab.sunflow import *" + ] + }, + { + "cell_type": "markdown", + "id": "cb02bc7e", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "# Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a2bab12d", + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [ + "XYM = np.load(DIR_ROOT + \"extra_data/OutlineXYM.npy\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a860112e", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(4, 800, 800)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "XYM.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b2e37fd3", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([False, True])" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "base_nt = np.zeros((XYM.shape[1],XYM.shape[2]))\n", + "base_nt = XYM[2,:,:]\n", + "base_nt = base_nt + 1\n", + "base_nt[base_nt != 1] = 0\n", + "base_nt = base_nt.astype(np.bool_)\n", + "np.unique(base_nt)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "58229165", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([False, True])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "base_wt = np.zeros((XYM.shape[1],XYM.shape[2]))\n", + "base_wt = XYM[3,:,:]\n", + "base_wt = base_wt + 1\n", + "base_wt[base_wt != 1] = 0\n", + "base_wt = base_wt.astype(np.bool_)\n", + "np.unique(base_wt)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b6e2ceaf", + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [ + "exclude = (base_nt ^ base_wt) & base_nt\n", + "overlap = base_nt & base_wt" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "3230e8e7", + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [ + "outline = np.ones((XYM.shape[1],XYM.shape[2],4))\n", + "outline[...,0] -= exclude.astype(np.float32)\n", + "outline[...,1] -= base_nt.astype(np.float32)\n", + "outline[...,2] -= base_nt.astype(np.float32)\n", + "outline[...,3] = base_nt.astype(np.float32)\n", + "SemanticOutline = outline.copy()\n", + "SemanticExtent = [np.min(XYM[0,...]),np.max(XYM[0,...]),np.min(XYM[1,...]),np.max(XYM[1,...])]" + ] + }, + { + "cell_type": "markdown", + "id": "001c1fbb", + "metadata": {}, + "source": [ + "# Runtime Parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "5ce1ed49", + "metadata": {}, + "outputs": [], + "source": [ + "SAVE_EXTRAS = False" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "a85a22d9", + "metadata": {}, + "outputs": [], + "source": [ + "g_Base = lambda *args: DIR_ROOT + \"Figures/\"" + ] + }, + { + "cell_type": "markdown", + "id": "27e84e37", + "metadata": {}, + "source": [ + "# Plots" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "34b9e2bf", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.apply_boundary()\n", + "if SAVE_EXTRAS:\n", + " plt.save(g_Base() + \"Model/Theory/PhenotypeBoundaries\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "71519985", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(SemanticOutline, extent=SemanticExtent, origin='lower')\n", + "if SAVE_EXTRAS:\n", + " plt.save(g_Base() + \"Model/Theory/PhenotypeBoundariesNonPhysicalBoundaryRed\")" + ] + }, + { + "cell_type": "markdown", + "id": "ae236fe1", + "metadata": {}, + "source": [ + "# END" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f3b5ed0b", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tfnb", + "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": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} -- cgit v1.2.1