{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "51b9920d", "metadata": {}, "outputs": [], "source": [ "import sys\n", "sys.path.append(\"../bandfit\")\n", "\n", "import bandfit as bf\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "id": "8c04c562", "metadata": {}, "source": [ "## Import the Data" ] }, { "cell_type": "code", "execution_count": 2, "id": "96979275", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Code.txt\r\n", "\u001b[0m\u001b[01;35mSB_0.5_0.5_2.0_0.0_pc_1.0_pd_1.0.png\u001b[0m\r\n", "\u001b[01;35mSB_1.0_1.0_0.75_0.0_pc_1.0_pd_1.0.png\u001b[0m\r\n", "\u001b[01;35mSB_1.0_1.0_0.75_0.2_pc_1.0_pd_1.0.png\u001b[0m\r\n", "\u001b[01;35mSB_1.0_1.0_0.75_0.4_pc_1.0_pd_1.0.png\u001b[0m\r\n", "\u001b[01;35mSB_1.0_1.0_0.75_0.8_pc_1.0_pd_1.0.png\u001b[0m\r\n", "\u001b[01;35mSB_1.3_1.3_0.4_0.2_pc_0.0_pd_1.0.png\u001b[0m\r\n", "\u001b[01;35mSB_points_0.5_0.5_2.0_0.0_pc_1.0_pd_1.0.png\u001b[0m\r\n", "\u001b[01;35mSB_points_1.0_1.0_0.75_0.0_pc_1.0_pd_1.0.png\u001b[0m\r\n", "\u001b[01;35mSB_points_1.0_1.0_0.75_0.2_pc_1.0_pd_1.0.png\u001b[0m\r\n", "\u001b[01;35mSB_points_1.0_1.0_0.75_0.4_pc_1.0_pd_1.0.png\u001b[0m\r\n", "\u001b[01;35mSB_points_1.0_1.0_0.75_0.8_pc_1.0_pd_1.0.png\u001b[0m\r\n", "\u001b[01;35mSB_points_1.3_1.3_0.4_0.2_pc_0.0_pd_1.0.png\u001b[0m\r\n", "t_t_0.5_c_2.0_d_0.0_pc_1.0_pd_1.0.txt\r\n", "t_t_1.0_c_0.75_d_0.0_pc_1.0_pd_1.0.txt\r\n", "t_t_1.0_c_0.75_d_0.2_pc_1.0_pd_1.0.txt\r\n", "t_t_1.0_c_0.75_d_0.4_pc_1.0_pd_1.0.txt\r\n", "t_t_1.0_c_0.75_d_0.8_pc_1.0_pd_1.0.txt\r\n", "t_t_1.3_c_0.4_d_0.2_pc_0.0_pd_1.0.txt\r\n" ] } ], "source": [ "%ls ../data" ] }, { "cell_type": "code", "execution_count": 3, "id": "b9ce6ee1", "metadata": {}, "outputs": [], "source": [ "data = bf.load_data(\"../data/t_t_0.5_c_2.0_d_0.0_pc_1.0_pd_1.0.txt\")" ] }, { "cell_type": "code", "execution_count": 4, "id": "c6f4546d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/svg+xml": [ "\n", "\n", "\n" ], "text/plain": [ "

" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.matshow(data)" ] }, { "cell_type": "markdown", "id": "0d8ae14a", "metadata": {}, "source": [ "## Detect the Bands" ] }, { "cell_type": "code", "execution_count": 5, "id": "993475a2", "metadata": {}, "outputs": [], "source": [ "bands = bf.detect_bands(data)" ] }, { "cell_type": "code", "execution_count": 6, "id": "e9eeb951", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(

, )" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/svg+xml": [ "\n", "\n", "\n" ], "text/plain": [ "

" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bf.plot_data_with_bands(data, bands)" ] }, { "cell_type": "markdown", "id": "d050a311", "metadata": {}, "source": [ "## Fit the Theory Bands" ] }, { "cell_type": "code", "execution_count": 13, "id": "3030d4d1", "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n" ], "text/plain": [ "

" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "p, sigma = bf.fit_to_bands(bands,\n", " c_d_order=1, \n", " ic_scan_steps=20,\n", " debug_plots=True)" ] }, { "cell_type": "code", "execution_count": 16, "id": "9e8e731a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a = 1.0000 +- 0.0060\n", "b = 0.5136 +- 0.0113\n", "c = -0.0659 +- 0.0089\n", "d = 0.4029 +- 0.0039\n", "k_scale = 1.0329 +- 0.0048\n", "k_shift = -0.0087 +- 0.0027\n" ] } ], "source": [ "for name, val, unc in zip([\"a\", \"b\", \"c\", \"d\", \"k_scale\", \"k_shift\"], p, sigma):\n", " print(f\"{name} = {val:.4f} +- {unc:.4f}\")" ] }, { "cell_type": "code", "execution_count": 17, "id": "89d6802b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(

, )" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/svg+xml": [ "\n", "\n", "\n" ], "text/plain": [ "

" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bf.plot_band_with_error_funnel(p[:4], sigma[:4])" ] }, { "cell_type": "markdown", "id": "9bbe5981", "metadata": {}, "source": [ "## Constraining a, b to be closer to each other..." ] }, { "cell_type": "code", "execution_count": 11, "id": "31590615", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((1.0,\n", " 0.9,\n", " -0.3184041488763748,\n", " 0.3068125893496051,\n", " 1.4453651848389524,\n", " 0.0005592129046892522),\n", " array([0.01868871, 0.02834105, 0.01595615, 0.01343736, 0.00428216,\n", " 0.00884451]))" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/svg+xml": [ "\n", "\n", "\n" ], "text/plain": [ "

" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bf.fit_to_bands(bands,\n", " c_d_order=1, \n", " debug_plots=True, \n", " ic_scan_steps=20,\n", " bounds=[(-10, -10, 0.5, -0.1, 0.2, -0.5), (10, 10, 10, 0.1, 5, 0.5)])" ] }, { "cell_type": "markdown", "id": "2a4b5a5e", "metadata": {}, "source": [ "This sucks!" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.10.10" } }, "nbformat": 4, "nbformat_minor": 5 }