{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Fit 3rd order Polynomial to graph data using numpy" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from matplotlib import pyplot as plt\n", "plt.rcParams[\"font.size\"] = 20\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "x = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], dtype='d')\n", "dx = np.array([0.1,0.1,0.5,0.1,0.5,0.1,0.5,0.1,0.5,0.1], dtype='d')\n", "y = np.array([1.1 ,2.3 ,2.7 ,3.2 ,3.1 ,2.4 ,1.7 ,1.5 ,1.5 ,1.7 ], dtype='d')\n", "dy = np.array([0.15,0.22,0.29,0.39,0.31,0.21,0.13,0.15,0.19,0.13], dtype='d')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "create numpy array with weights as 1/error " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "ones = np.ones(10, dtype='d')\n", "weight = ones/dy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "various fit options" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "n = 3 \n", "#model = np.polyfit(x, y, n, 0 , 0, weight, cov='unscaled' )\n", "#model, fitCov = np.polyfit(x, y, n , 0 , 0, weight, cov='unscaled' )\n", "model, fitCov = np.polyfit(x, y, n , 0 , 0, weight, cov='unscaled' )\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "monitor printout of coefficient polynomial vector (model) and covariance matrix (fitCov)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0.02590585 -0.48405665 2.52045767 -0.98073638]\n", "[[ 1.19801683e-05 -2.07833174e-04 9.95675425e-04 -9.97263377e-04]\n", " [-2.07833174e-04 3.65835615e-03 -1.78482005e-02 1.83096634e-02]\n", " [ 9.95675425e-04 -1.78482005e-02 8.93452262e-02 -9.59887600e-02]\n", " [-9.97263377e-04 1.83096634e-02 -9.59887600e-02 1.19001618e-01]]\n" ] } ], "source": [ "print (model)\n", "print (fitCov)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "prepare errors for plotting, see\n", "https://stackoverflow.com/questions/28505008/numpy-polyfit-how-to-get-1-sigma-uncertainty-around-the-estimated-curve/28528966\n", "write polynomial as np.dot(yy, model) with yy=[x**n, x*n-1, ..., 1] and x can be a single value or \n", "or a vector and model as coefficient vector. Since this a linear equation, with the covariance matrix\n", "fitCov of model, the covariance matrix of the values is np.dot(yy, np.dot(model, yy.T))." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Do the interpolation for plotting" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "x_plot = np.linspace(0.1, 10.5, 500)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Matrix with rows 1, x, x**2, ..." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "YY = np.vstack([x_plot**(n-i) for i in range(n+1)]).T" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "matrix multiplication calculates the polynomial values" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "y_plot = np.dot(YY, model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Covariance_y = YY*Covariance*YY.T" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "fitCov_y_plot = np.dot(YY, np.dot(fitCov, YY.T))\n", "Sigma_y_plot = np.sqrt(np.diag(fitCov_y_plot))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "model contains parameters in order highest power first! Define fit function " ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "def fitpol3(model):\n", " return model[3] + model[2]*x + model[1]*x**2 + model[0]*x**3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "construct polynomial from coefficients in model" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "predict = np.poly1d(model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "r_2 coefficient of the determination of the regression score function\n", "input are the y data values and the prediction from the fit with x data values \n", "this is borrowed from scikit-learn" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.9486643210376879\n" ] } ], "source": [ "from sklearn.metrics import r2_score\n", "p = r2_score(y, predict(x))\n", "print (p)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "plot data" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.errorbar(x, y, dy , dx, fmt=\"o\")\n", "plt.plot(x, fitpol3(model) )\n", "plt.title(\"numpy Fit Test\")\n", "plt.xlim(-0.1, 10.1)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "plot data with covariance" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fg, ax = plt.subplots(1, 1)\n", "ax.set_title(\"numpy Fit Test with $\\pm1\\sigma$ interval\")\n", "ax.fill_between(x_plot, y_plot+Sigma_y_plot, y_plot-Sigma_y_plot, alpha=.25)\n", "ax.plot(x_plot, y_plot,'-')\n", "ax.errorbar(x, y,dy , dx, fmt='o')\n", "ax.axis('tight')\n", "\n", "fg.canvas.draw()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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": "3.8.16" } }, "nbformat": 4, "nbformat_minor": 4 }