diff --git a/environment.yml b/environment.yml
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+++ b/environment.yml
@@ -0,0 +1,634 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3885a60f-ddf0-4c7b-a587-6e7e037ec9b5",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import scipy.stats as stats\n",
+ "import matplotlib.pyplot as plt \n",
+ "\n",
+ "def f(x):\n",
+ " return 2*x + 1\n",
+ "\n",
+ "n = 10\n",
+ "xs = np.linspace(0,4,n)\n",
+ "sigma_y=0.4\n",
+ "ys = stats.multivariate_normal.rvs(f(xs), np.eye(n)*sigma_y**2, 1, random_state=42)\n",
+ " \n",
+ "x_axis = np.linspace(0,4,100)\n",
+ "plt.errorbar(xs,ys,yerr=sigma_y,fmt=\".\")\n",
+ "plt.plot(x_axis, f(x_axis),'--')\n",
+ "plt.xlabel(\"x\")\n",
+ "plt.ylabel(\"y\")\n",
+ "plt.savefig(\"line.png\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9f78c037-4cca-448c-829c-dac9dea11d3c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def chi2(x, y, sy, a, m):\n",
+ " my = m * x + a\n",
+ " r = (y - my)/sy\n",
+ " return np.sum(r**2)\n",
+ "\n",
+ "chi2(xs, ys, sigma_y, 1, 2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "76181537-7b07-4329-9615-0947d4b9dd5a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from matplotlib import cm\n",
+ "a_mesh, m_mesh = np.meshgrid(np.linspace(-3,5,100), np.linspace(-3,5,100))\n",
+ "chi2_vect = np.vectorize(chi2, excluded=[0, 1])\n",
+ "m_axis = np.linspace(-3,5,100)\n",
+ "plt.plot(m_axis, chi2_vect(xs, ys, sigma_y, 1, m_axis))\n",
+ "plt.xlabel(\"m\")\n",
+ "plt.show()\n",
+ "plt.plot(m_axis, chi2_vect(xs, ys, sigma_y, m_axis, 2))\n",
+ "plt.xlabel(\"a\")\n",
+ "plt.show()\n",
+ "\n",
+ "plt.contourf(a_mesh, m_mesh, chi2_vect(xs, ys, sigma_y, a_mesh, m_mesh), vmin=0, vmax=5000, levels=200, cmap=cm.coolwarm)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "34aaaecd-6d6a-452d-8602-d8b7b9d34b72",
+ "metadata": {},
+ "source": [
+ "## Analytisch\n",
+ "$\\hat m = \\frac{\\langle xy \\rangle - \\langle y \\rangle\\langle x \\rangle}{\\langle x^2 \\rangle - \\langle x \\rangle^2}$\n",
+ "\n",
+ "$\\hat a = \\frac{ \\langle y \\rangle \\langle x^2 \\rangle - \\langle x \\rangle \\langle xy \\rangle}{ \\langle x^2 \\rangle - \\langle x \\rangle^2}$\n",
+ "\n",
+ "\n",
+ "$V(\\hat m) = \\frac{1}{\\sum_i 1/\\sigma_i^2} \\frac{1}{\\langle x^2 \\rangle - \\langle x \\rangle^2}$\n",
+ "\n",
+ "\n",
+ "$V(\\hat a) = \\frac{1}{\\sum_i 1/\\sigma_i^2} \\frac{\\langle x^2 \\rangle}{\\langle x^2 \\rangle - \\langle x \\rangle^2}$\n",
+ "\n",
+ "\n",
+ "$\\text{cov}(\\hat m, \\hat a) = - \\frac{1}{\\sum_i 1/\\sigma_i^2} \\frac{<x>}{\\langle x^2 \\rangle - \\langle x \\rangle^2}$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d3b470d1-1b48-46dc-ae1c-d7a44be51e8c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "C = np.eye(n)*sigma_y*sigma_y\n",
+ "W = np.linalg.inv(C)\n",
+ "J = np.ones((len(ys),1))\n",
+ "sum_xy = (np.linalg.inv(J.T@W@J)@(J.T@W@(xs*ys)))[0]\n",
+ "sum_y = (np.linalg.inv(J.T@W@J)@(J.T@W@(ys)))[0]\n",
+ "sum_x = (np.linalg.inv(J.T@W@J)@(J.T@W@(xs)))[0]\n",
+ "sum_x2 = (np.linalg.inv(J.T@W@J)@(J.T@W@(xs*xs)))[0]\n",
+ "print(sum_xy, sum_y, sum_x, sum_x2)\n",
+ "mhat = (sum_xy - sum_x*sum_y)/(sum_x2-sum_x*sum_x)\n",
+ "ahat = (sum_y*sum_x2 - sum_x*sum_xy)/(sum_x2-sum_x*sum_x)\n",
+ "print(mhat, ahat)\n",
+ "sum_siginv = sigma_y*sigma_y/len(ys)\n",
+ "V_am = np.array([[sum_siginv/(sum_x2-sum_x*sum_x), -sum_siginv*sum_x/(sum_x2-sum_x*sum_x)],\n",
+ " [-sum_siginv*sum_x/(sum_x2-sum_x*sum_x), sum_siginv*sum_x2/(sum_x2-sum_x*sum_x)]])\n",
+ "print(V_am)\n",
+ "print(np.sqrt(V_am[0,0]), np.sqrt(V_am[1,1]), V_am[1,0]/(np.sqrt(V_am[0,0])*np.sqrt(V_am[1,1])))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2ba98419-a39d-48e5-8ed1-47fdcab7d237",
+ "metadata": {},
+ "source": [
+ "# Kovarianz direkt aus C\n",
+ "$\\frac{d\\hat m}{y_i} = \\frac{1}{\\sum_i 1/\\sigma_i^2} \\frac{x_i - \\langle x \\rangle}{\\sigma_i^2(\\langle x^2 \\rangle - \\langle x \\rangle^2)}$\n",
+ "\n",
+ "$\\frac{d\\hat a}{y_i} = \\frac{1}{\\sum_i 1/\\sigma_i^2} \\frac{\\langle x^2 \\rangle - \\langle x \\rangle x_i}{\\sigma_i^2(\\langle x^2 \\rangle - \\langle x \\rangle^2)}$\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bf423c2c-0c8f-429e-ab36-4c2d074d3575",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dmdy = sum_siginv / (sum_x2-sum_x*sum_x)*(xs-sum_x)/sigma_y**2\n",
+ "dady = sum_siginv / (sum_x2-sum_x*sum_x)*(sum_x2-xs*sum_x)/sigma_y**2\n",
+ "A=np.array([dmdy,dady]).T\n",
+ "print(A)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "58615ab8-b1a8-433e-9380-666185592201",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "C_am = A.T@C@A\n",
+ "print(C_am)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fe255a0f-c44d-4fc0-ab4b-e4bcb7c16f70",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x_axis = np.linspace(0,4,100)\n",
+ "plt.errorbar(xs,ys,yerr=sigma_y,fmt=\".\")\n",
+ "plt.plot(x_axis, f(x_axis),'--')\n",
+ "plt.plot(x_axis, x_axis*mhat + ahat,'-')\n",
+ "plt.xlabel(\"x\")\n",
+ "plt.ylabel(\"y\")\n",
+ "plt.savefig(\"line.png\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b2b90d30-4e37-4ecc-866d-a45b2243d8bc",
+ "metadata": {},
+ "source": [
+ "# Fehler auf Ausgleichsgerade\n",
+ "\n",
+ "$y = \\hat m x +\\hat a$ \n",
+ "\n",
+ "$\\frac{dy}{dm} = x$ und $\\frac{dy}{da} = 1$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2d61e62e-77c1-41ca-a0e9-2f58bb44de08",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def err(x, V):\n",
+ " A = np.array([x, 1])\n",
+ " return np.sqrt(A.T@V@A)\n",
+ "\n",
+ "err(0, V_am)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c2f71b37-b37c-4802-a356-e57d9c8df0de",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x_axis = np.linspace(0,4,100)\n",
+ "plt.errorbar(xs,ys,yerr=sigma_y,fmt=\".\")\n",
+ "plt.plot(x_axis, f(x_axis),'--')\n",
+ "plt.plot(x_axis, x_axis*mhat + ahat,'-g')\n",
+ "plt.plot(x_axis, x_axis*mhat + ahat - np.vectorize(err,excluded=[1])(x_axis, V_am),'--g')\n",
+ "plt.plot(x_axis, x_axis*mhat + ahat + np.vectorize(err,excluded=[1])(x_axis, V_am),'--g')\n",
+ "\n",
+ "plt.xlabel(\"x\")\n",
+ "plt.ylabel(\"y\")\n",
+ "plt.savefig(\"line.png\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f76530a7-f385-4e58-a504-1f7e75f77129",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.plot(x_axis, np.vectorize(err, excluded=[1])(x_axis, V_am),'-')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "952ccb17-cf14-4561-ad21-acc50b72cf73",
+ "metadata": {},
+ "source": [
+ "$\\chi^2 = (\\sum_i \\frac{1}{\\sigma_i^2}) V(y) ( 1- \\rho^2_{xy})$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "348c5655-6356-4e5b-8f88-f4cbbd4739f7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(len(ys)/(sigma_y*sigma_y)*np.var(ys)*(1-np.corrcoef(xs,ys)[0,1]**2))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e6df3805-e62d-4cba-ba9f-1f0c2f187d55",
+ "metadata": {},
+ "source": [
+ "$\\chi^2 =\\sum \\frac{(y -\\hat m x - \\hat a)^2}{\\sigma_y^2}$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ec6c4161-50dd-475b-8f0f-d02eb9f18cfe",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "np.sum(((ys - (xs * mhat + ahat))/sigma_y)**2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bd398a45-9d44-4a28-8cec-592a9b38ec99",
+ "metadata": {},
+ "source": [
+ "# $\\chi^2$ für Ausgleichsgeraden"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5e56c09a-f030-4dad-a095-6b28614d36b2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ys_set = stats.multivariate_normal.rvs(f(xs), np.eye(n)*sigma_y**2, 1000, random_state=42)\n",
+ "chi2s = np.zeros(len(ys_set))\n",
+ "for i in range(len(ys_set)):\n",
+ " sum_xy = (np.linalg.inv(J.T@W@J)@(J.T@W@(xs*ys_set[i])))[0]\n",
+ " sum_y = (np.linalg.inv(J.T@W@J)@(J.T@W@(ys_set[i])))[0]\n",
+ " sum_x = (np.linalg.inv(J.T@W@J)@(J.T@W@(xs)))[0]\n",
+ " sum_x2 = (np.linalg.inv(J.T@W@J)@(J.T@W@(xs*xs)))[0]\n",
+ " mhat = (sum_xy - sum_x*sum_y)/(sum_x2-sum_x*sum_x)\n",
+ " ahat = (sum_y*sum_x2 - sum_x*sum_xy)/(sum_x2-sum_x*sum_x)\n",
+ " sum_siginv = sigma_y*sigma_y/len(ys)\n",
+ " V_am = np.array([[sum_siginv/(sum_x2-sum_x*sum_x), -sum_siginv*sum_x/(sum_x2-sum_x*sum_x)],\n",
+ " [-sum_siginv*sum_x/(sum_x2-sum_x*sum_x), sum_siginv*sum_x2/(sum_x2-sum_x*sum_x)]])\n",
+ " chi2s[i] = np.sum(((ys_set[i] - (xs * mhat + ahat))/sigma_y)**2)\n",
+ " #print(mhat, ahat, ys_set[i], xs * mhat + ahat, (ys_set[i] - (xs * mhat + ahat))/sigma_y)\n",
+ "#print(chi2s)\n",
+ "plt.hist(chi2s, bins=50)\n",
+ "print(np.mean(chi2s))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ccbf4178-bb9e-440e-bed3-9365d2cee9f7",
+ "metadata": {},
+ "source": [
+ "Nochmal für das erste Toy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "683060cb-9968-4bab-b512-b37621536ea2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sum_xy = (np.linalg.inv(J.T@W@J)@(J.T@W@(xs*ys)))[0]\n",
+ "sum_y = (np.linalg.inv(J.T@W@J)@(J.T@W@(ys)))[0]\n",
+ "sum_x = (np.linalg.inv(J.T@W@J)@(J.T@W@(xs)))[0]\n",
+ "sum_x2 = (np.linalg.inv(J.T@W@J)@(J.T@W@(xs*xs)))[0]\n",
+ "print(sum_xy, sum_y, sum_x, sum_x2)\n",
+ "mhat = (sum_xy - sum_x*sum_y)/(sum_x2-sum_x*sum_x)\n",
+ "ahat = (sum_y*sum_x2 - sum_x*sum_xy)/(sum_x2-sum_x*sum_x)\n",
+ "print(mhat, ahat)\n",
+ "sum_siginv = sigma_y*sigma_y/len(ys)\n",
+ "V_am = np.array([[sum_siginv/(sum_x2-sum_x*sum_x), -sum_siginv*sum_x/(sum_x2-sum_x*sum_x)],\n",
+ " [-sum_siginv*sum_x/(sum_x2-sum_x*sum_x), sum_siginv*sum_x2/(sum_x2-sum_x*sum_x)]])\n",
+ "print(\"chi2:\", np.sum(((ys - (xs * mhat + ahat))/sigma_y)**2))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "da5898ca-e7fe-4562-bed1-d9adc5866d1d",
+ "metadata": {},
+ "source": [
+ "# $m$ und $a$ auf Fehlerellipse \n",
+ "\n",
+ "$v = (m, a)$; $(v-\\hat v)V_{am}^{-1}(v - \\hat v)^T=1$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "73d21a5e-fdae-4e46-ab2b-c52056756f2c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import scipy.optimize as opti\n",
+ "\n",
+ "sum_xy = (np.linalg.inv(J.T@W@J)@(J.T@W@(xs*ys)))[0]\n",
+ "sum_y = (np.linalg.inv(J.T@W@J)@(J.T@W@(ys)))[0]\n",
+ "sum_x = (np.linalg.inv(J.T@W@J)@(J.T@W@(xs)))[0]\n",
+ "sum_x2 = (np.linalg.inv(J.T@W@J)@(J.T@W@(xs*xs)))[0]\n",
+ "print(sum_xy, sum_y, sum_x, sum_x2)\n",
+ "mhat = (sum_xy - sum_x*sum_y)/(sum_x2-sum_x*sum_x)\n",
+ "ahat = (sum_y*sum_x2 - sum_x*sum_xy)/(sum_x2-sum_x*sum_x)\n",
+ "print(mhat, ahat)\n",
+ "sum_siginv = sigma_y*sigma_y/len(ys)\n",
+ "V_am = np.array([[sum_siginv/(sum_x2-sum_x*sum_x), -sum_siginv*sum_x/(sum_x2-sum_x*sum_x)],\n",
+ " [-sum_siginv*sum_x/(sum_x2-sum_x*sum_x), sum_siginv*sum_x2/(sum_x2-sum_x*sum_x)]])\n",
+ "\n",
+ "def a_from_m(m, ahat, mhat, V):\n",
+ " Vinv = np.linalg.inv(V)\n",
+ " a = opti.newton(lambda a: (np.array([m - mhat, a - ahat])@Vinv@np.array([[m - mhat], [a - ahat]]))[0]-1,ahat+2)\n",
+ " return a\n",
+ "\n",
+ "def a_from_m2(m, ahat, mhat, V):\n",
+ " Vinv = np.linalg.inv(V)\n",
+ " a = opti.newton(lambda a: (np.array([m - mhat, a - ahat])@Vinv@np.array([[m - mhat], [a - ahat]]))[0]-1,ahat-2)\n",
+ " return a\n",
+ "\n",
+ "a_from_m(mhat, ahat, mhat, V_am)\n",
+ "m_axis = np.linspace(mhat-np.sqrt(V_am[0,0]),mhat+np.sqrt(V_am[0,0]),100)\n",
+ "plt.plot(m_axis, np.vectorize(a_from_m, excluded=[3])(m_axis, ahat, mhat, V_am))\n",
+ "plt.plot(m_axis, np.vectorize(a_from_m2, excluded=[3])(m_axis, ahat, mhat, V_am))\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e47ddf1f-4e91-4479-8fe8-190c1eeb4628",
+ "metadata": {},
+ "source": [
+ "$\\chi^2$ Werte auf Ellipse"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bf1b4f5c-2634-4838-bea8-9b96e5868ffe",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "chi2s_on_cont = np.zeros(2*len(m_axis))\n",
+ "for i in range(len(m_axis)):\n",
+ " m = m_axis[i]\n",
+ " a = a_from_m(m, ahat, mhat, V_am)\n",
+ " chi2s_on_cont[2*i] = np.sum(((ys - (xs * m + a))/sigma_y)**2)\n",
+ " a = a_from_m2(m, ahat, mhat, V_am)\n",
+ " chi2s_on_cont[2*i+1] = np.sum(((ys - (xs * m + a))/sigma_y)**2)\n",
+ " #print(a, m , chi2s_on_cont[2*i], chi2s_on_cont[2*i+1])\n",
+ "\n",
+ " \n",
+ "plt.hist(chi2s_on_cont,range=(0,10), bins=50)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "109c2f87-558b-4038-a929-4e18787a86f7",
+ "metadata": {},
+ "source": [
+ "$\\Delta \\chi^2$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3e43269b-a364-4a1e-9016-4fe7ef983848",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "delta_chi2 = chi2s_on_cont - np.sum(((ys - (xs * mhat + ahat))/sigma_y)**2)\n",
+ "plt.hist(delta_chi2,range=(-2,2), bins=50)\n",
+ "print(np.mean(delta_chi2))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ce34da07-a23e-4773-b11f-4081dd3c80e0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "a_mesh, m_mesh = np.meshgrid(np.linspace(ahat-2*np.sqrt(V_am[1,1]),ahat+2*np.sqrt(V_am[1,1]),100), \n",
+ " np.linspace(mhat-2*np.sqrt(V_am[0,0]),mhat+2*np.sqrt(V_am[0,0]),100))\n",
+ "plt.contourf(m_mesh, a_mesh, chi2_vect(xs, ys, sigma_y, a_mesh, m_mesh)-np.sum(((ys - (xs * mhat + ahat))/sigma_y)**2),[0,1,2,3,4,5,6,7,8], cmap=cm.coolwarm)\n",
+ "plt.xlabel(\"m\")\n",
+ "plt.ylabel(\"a\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e5a79c54-ddb4-45de-9831-f52dcae0676e",
+ "metadata": {},
+ "source": [
+ "# Aus scipy.optimize"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2bff85d9-ffc9-4c5e-819e-33f3ce9c56f8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def fitf(x, m , a):\n",
+ " return m*x + a\n",
+ "pfit, Vfit = opti.curve_fit(fitf , xs, ys, sigma=[sigma_y]*len(ys))\n",
+ "print(pfit, Vfit)\n",
+ "#print(mhat, ahat, V_am)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "27eb4ba5-11c3-4536-8e54-450cf0814871",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(Vfit/V_am)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6c235d04-988c-4a28-bb60-96d88fa3dcf7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(\"chi2 scaled:\", np.sum(((ys - (xs * pfit[0] + pfit[1]))/(sigma_y*np.sqrt(Vfit[0][0]/V_am[0][0])))**2))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c22f8191-5d24-417d-8690-c27edcb6de61",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pfit, Vfit = opti.curve_fit(fitf , xs, ys, sigma=[sigma_y]*len(ys), absolute_sigma=True)\n",
+ "print(pfit, Vfit)\n",
+ "#print(mhat, ahat, V_am)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0e091bf2-fc2c-4bef-93af-506ad38343e5",
+ "metadata": {},
+ "source": [
+ "Least-squares"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "aba1dc09-d2ce-4615-8944-9b46b69907db",
+ "metadata": {},
+ "source": [
+ "Minimize $Chi^2$:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "28332217-8875-49a0-be90-c7a6d00af311",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "opti.minimize?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6fba2b83-6cd9-4525-a15e-cb2994b1512a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "res = opti.minimize( lambda p: chi2(xs, ys, sigma_y, p[1], p[0]),x0=np.zeros(2))\n",
+ "print(res)\n",
+ "print(res.x, res.hess_inv)\n",
+ "#print(mhat, ahat, V_am)\n",
+ "#print(res.hess_inv/V_am)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9cadcf6e-a776-4ded-bb83-23e94e03add4",
+ "metadata": {},
+ "source": [
+ "## Maximum-Likelihood\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d02a8b77-5f51-4a5e-bf85-2522297e36e7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import scipy.stats as stats\n",
+ "\n",
+ "def like(m, a, xs, ys, sigma_y):\n",
+ " return np.prod(stats.norm.pdf(ys,xs*m+a,sigma_y))\n",
+ "\n",
+ "like_vect = np.vectorize(like, excluded=[2,3])\n",
+ "m_axis = np.linspace(-0,3,200)\n",
+ "\n",
+ "plt.plot(m_axis, like_vect(m_axis,1,xs, ys, sigma_y))\n",
+ "plt.xlabel(\"m\")\n",
+ "#plt.yscale('log')\n",
+ "plt.show()\n",
+ "plt.plot(m_axis, like_vect(2, m_axis, xs, ys, sigma_y))\n",
+ "plt.xlabel(\"a\")\n",
+ "plt.ylabel(\"$L(a; m=2)$\")\n",
+ "plt.savefig(\"like_a.png\")\n",
+ "#plt.yscale('log')\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "09a251ef-627e-4811-85fa-dcd26ba7b589",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def logL(m, a, xs, ys, sigma_y):\n",
+ " return np.sum(-stats.norm.logpdf(ys,xs*m+a,sigma_y))\n",
+ "\n",
+ "logL_vect = np.vectorize(logL, excluded=[2,3])\n",
+ "\n",
+ "m_axis = np.linspace(-5,5,200)\n",
+ "plt.plot(m_axis, logL_vect(m_axis,1,xs, ys, sigma_y))\n",
+ "plt.xlabel(\"m\")\n",
+ "plt.show()\n",
+ "plt.plot(m_axis, logL_vect(2, m_axis, xs, ys, sigma_y))\n",
+ "plt.xlabel(\"a\")\n",
+ "plt.ylabel(\"$-\\ln L(a; m=2)$\")\n",
+ "plt.savefig(\"loglike_a.png\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9dc61515-4f0c-4951-a318-cb86508622e7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "res = opti.minimize( lambda p: logL(p[0], p[1],xs, ys, sigma_y), x0=np.zeros(2))\n",
+ "print(res)\n",
+ "print(res.x, res.hess_inv)\n",
+ "#print(mhat, ahat, V_am)\n",
+ "#print(res.hess_inv/V_am)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fa7a2440-b9f1-417e-8f5c-eb122cf9d786",
+ "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": "3.11.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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new file mode 100644
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+<?xml version="1.0" encoding="utf-8" standalone="yes"?>
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" version="1.1"
+width="390px" height="390px" viewBox="-18 -120 135 135">
+<title>Cumulative distribution function of log-normal distribution</title>
+<defs>
+ <pattern id="grid" width="20" height="20" patternUnits="userSpaceOnUse">
+ <path d="M0,20v-20h20v20h-20" fill="none" stroke="#555" stroke-width=".25"/>
+ </pattern>
+</defs>
+<rect y="-120" width="130" height="120" fill="url(#grid)"/>
+<g font-size="6" stroke="none" fill="black" text-anchor="middle" font-family="New Century Schoolbook">
+ <text y="6"><tspan x="0">0</tspan><tspan x="20">0.5</tspan><tspan x="40">1.0</tspan><tspan x="60">1.5</tspan><tspan x="80">2.0</tspan><tspan x="100">2.5</tspan></text>
+ <text x="-5" y="0">0</text><text x="-5" y="-18">0.2</text><text x="-5" y="-38">0.4</text><text x="-5" y="-58">0.6</text><text x="-5" y="-78">0.8</text><text x="-5" y="-98">1.0</text>
+ <g font-size="8"><text font-style="italic" x="60" y="12">x</text><text x="-12" y="-60" transform="rotate(-90,-12,-60)">CDF</text></g>
+ <text fill="#f00" x="105" y="-47">σ=10</text>
+ <text fill="#0f0" x="105" y="-67">σ=1.5</text>
+ <text fill="#00f" x="105" y="-77">σ=1</text>
+ <text fill="#aaf" x="105" y="-90">σ=0.5</text>
+ <text fill="#005" x="105" y="-102">σ=0.125</text>
+ <text fill="#070" x="60" y="-102">σ=0.25</text>
+</g>
+<g fill="none">
+ <path d="M0,0h130 M0,-120v120" stroke="black" stroke-width=".75"/>
+ <path d="M0,0 L0.4,-32 Q 0.47,-33 0.53,-33 0.62,-34 0.71,-34 0.82,-35 0.94,-35 1.1,-36 1.3,-36 1.5,-37 1.7,-38 1.9,-38 2.2,-39 2.6,-39 2.9,-40 3.4,-40 3.9,-41 4.6,-41 5.2,-42 6.1,-43 6.9,-43 8.1,-44 9.2,-44 11,-45 12,-45 14,-46 16,-46 19,-47 22,-48 25,-48 29,-49 34,-49 38,-50 45,-50 51,-51 59,-52 68,-52 79,-53 90,-53 110,-54 120,-54" stroke="#f00"/>
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+ <path d="M0,0 H6.9 Q8.1,-0.042 9.2,-0.17 11,-0.32 12,-0.9 14,-1.7 16,-3.6 19,-6.3 22,-11 25,-17 29,-26 34,-37 38,-47 45,-60 51,-69 59,-80 68,-85 79,-93 90,-95 110,-98 120,-99" stroke="#aaf"/>
+ <path d="M0,0 H16 Q 19,0.077 22,-0.71 25,-1.5 29,-9.5 34,-22 38,-43 45,-71 51,-83 59,-98 68,-98 79,-100 90,-100 H120" stroke="#070"/>
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+</svg>
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diff --git a/figures/08/Lognormal_distribution_PDF.svg b/figures/08/Lognormal_distribution_PDF.svg
new file mode 100644
index 0000000000000000000000000000000000000000..734bd7eacd286a579bd038579189113c34a618ff
--- /dev/null
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+<?xml version="1.0" encoding="utf-8" standalone="no"?>
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN"
+ "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
+<svg width="1024" height="768" viewBox="0 0 1024 768"
+ xmlns="http://www.w3.org/2000/svg"
+ xmlns:xlink="http://www.w3.org/1999/xlink">
+<desc>Produced by GNUPLOT 4.2 patchlevel 5 </desc>
+
+<defs>
+
+ <circle id='gpDot' r='0.5' stroke-width='0.5'/>
+ <path id='gpPt0' stroke-width='0.222' stroke='currentColor' d='M-1,0 h2 M0,-1 v2'/>
+ <path id='gpPt1' stroke-width='0.222' stroke='currentColor' d='M-1,-1 L1,1 M1,-1 L-1,1'/>
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+ <use xlink:href='#gpPt5' id='gpPt6' fill='currentColor' stroke='none'/>
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+ <use xlink:href='#gpPt7' id='gpPt8' fill='currentColor' stroke='none'/>
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+ <use xlink:href='#gpPt11' id='gpPt12' fill='currentColor' stroke='none'/>
+</defs>
+<g style="fill:none; color:white; stroke:currentColor; stroke-width:1.00; stroke-linecap:butt; stroke-linejoin:miter">
+</g>
+<g style="fill:none; color:black; stroke:currentColor; stroke-width:1.00; stroke-linecap:butt; stroke-linejoin:miter">
+ <path d='M70.5,732.0 L79.5,732.0 '></path>
+ <g transform="translate(62.2,736.5)" style="stroke:none; fill:black; font-family:Arial; font-size:12.00pt; text-anchor:end">
+ <text> 0</text>
+ </g>
+ <path d='M70.5,714.1 L75.0,714.1 M70.5,696.2 L75.0,696.2 M70.5,678.3 L75.0,678.3 M70.5,660.5 L75.0,660.5
+ M70.5,642.6 L75.0,642.6 M70.5,624.7 L75.0,624.7 M70.5,606.8 L75.0,606.8 M70.5,588.9 L75.0,588.9
+ M70.5,571.0 L75.0,571.0 M70.5,553.1 L75.0,553.1 M70.5,553.1 L79.5,553.1 '></path>
+ <g transform="translate(62.2,557.6)" style="stroke:none; fill:black; font-family:Arial; font-size:12.00pt; text-anchor:end">
+ <text> 0.5</text>
+ </g>
+ <path d='M70.5,535.3 L75.0,535.3 M70.5,517.4 L75.0,517.4 M70.5,499.5 L75.0,499.5 M70.5,481.6 L75.0,481.6
+ M70.5,463.7 L75.0,463.7 M70.5,445.8 L75.0,445.8 M70.5,428.0 L75.0,428.0 M70.5,410.1 L75.0,410.1
+ M70.5,392.2 L75.0,392.2 M70.5,374.3 L75.0,374.3 M70.5,374.3 L79.5,374.3 '></path>
+ <g transform="translate(62.2,378.8)" style="stroke:none; fill:black; font-family:Arial; font-size:12.00pt; text-anchor:end">
+ <text> 1</text>
+ </g>
+ <path d='M70.5,356.4 L75.0,356.4 M70.5,338.5 L75.0,338.5 M70.5,320.6 L75.0,320.6 M70.5,302.8 L75.0,302.8
+ M70.5,284.9 L75.0,284.9 M70.5,267.0 L75.0,267.0 M70.5,249.1 L75.0,249.1 M70.5,231.2 L75.0,231.2
+ M70.5,213.3 L75.0,213.3 M70.5,195.4 L75.0,195.4 M70.5,195.4 L79.5,195.4 '></path>
+ <g transform="translate(62.2,199.9)" style="stroke:none; fill:black; font-family:Arial; font-size:12.00pt; text-anchor:end">
+ <text> 1.5</text>
+ </g>
+ <path d='M70.5,177.6 L75.0,177.6 M70.5,159.7 L75.0,159.7 M70.5,141.8 L75.0,141.8 M70.5,123.9 L75.0,123.9
+ M70.5,106.0 L75.0,106.0 M70.5,88.1 L75.0,88.1 M70.5,70.3 L75.0,70.3 M70.5,52.4 L75.0,52.4
+ M70.5,34.5 L75.0,34.5 M70.5,16.6 L75.0,16.6 M70.5,16.6 L79.5,16.6 '></path>
+ <g transform="translate(62.2,21.1)" style="stroke:none; fill:black; font-family:Arial; font-size:12.00pt; text-anchor:end">
+ <text> 2</text>
+ </g>
+ <path d='M70.5,732.0 L70.5,723.0 '></path>
+ <g transform="translate(70.5,754.5)" style="stroke:none; fill:black; font-family:Arial; font-size:12.00pt; text-anchor:middle">
+ <text> 0</text>
+ </g>
+ <path d='M86.0,732.0 L86.0,727.5 M101.6,732.0 L101.6,727.5 M117.1,732.0 L117.1,727.5 M132.7,732.0 L132.7,727.5
+ M148.2,732.0 L148.2,727.5 M163.8,732.0 L163.8,727.5 M179.3,732.0 L179.3,727.5 M194.9,732.0 L194.9,727.5
+ M210.4,732.0 L210.4,727.5 M226.0,732.0 L226.0,727.5 M226.0,732.0 L226.0,723.0 '></path>
+ <g transform="translate(226.0,754.5)" style="stroke:none; fill:black; font-family:Arial; font-size:12.00pt; text-anchor:middle">
+ <text> 0.5</text>
+ </g>
+ <path d='M241.5,732.0 L241.5,727.5 M257.0,732.0 L257.0,727.5 M272.6,732.0 L272.6,727.5 M288.1,732.0 L288.1,727.5
+ M303.7,732.0 L303.7,727.5 M319.2,732.0 L319.2,727.5 M334.8,732.0 L334.8,727.5 M350.3,732.0 L350.3,727.5
+ M365.9,732.0 L365.9,727.5 M381.4,732.0 L381.4,727.5 M381.4,732.0 L381.4,723.0 '></path>
+ <g transform="translate(381.4,754.5)" style="stroke:none; fill:black; font-family:Arial; font-size:12.00pt; text-anchor:middle">
+ <text> 1</text>
+ </g>
+ <path d='M396.9,732.0 L396.9,727.5 M412.5,732.0 L412.5,727.5 M428.0,732.0 L428.0,727.5 M443.6,732.0 L443.6,727.5
+ M459.1,732.0 L459.1,727.5 M474.7,732.0 L474.7,727.5 M490.2,732.0 L490.2,727.5 M505.8,732.0 L505.8,727.5
+ M521.3,732.0 L521.3,727.5 M536.9,732.0 L536.9,727.5 M536.9,732.0 L536.9,723.0 '></path>
+ <g transform="translate(536.9,754.5)" style="stroke:none; fill:black; font-family:Arial; font-size:12.00pt; text-anchor:middle">
+ <text> 1.5</text>
+ </g>
+ <path d='M552.4,732.0 L552.4,727.5 M567.9,732.0 L567.9,727.5 M583.5,732.0 L583.5,727.5 M599.0,732.0 L599.0,727.5
+ M614.6,732.0 L614.6,727.5 M630.1,732.0 L630.1,727.5 M645.7,732.0 L645.7,727.5 M661.2,732.0 L661.2,727.5
+ M676.8,732.0 L676.8,727.5 M692.3,732.0 L692.3,727.5 M692.3,732.0 L692.3,723.0 '></path>
+ <g transform="translate(692.3,754.5)" style="stroke:none; fill:black; font-family:Arial; font-size:12.00pt; text-anchor:middle">
+ <text> 2</text>
+ </g>
+ <path d='M707.8,732.0 L707.8,727.5 M723.4,732.0 L723.4,727.5 M738.9,732.0 L738.9,727.5 M754.5,732.0 L754.5,727.5
+ M770.0,732.0 L770.0,727.5 M785.6,732.0 L785.6,727.5 M801.1,732.0 L801.1,727.5 M816.7,732.0 L816.7,727.5
+ M832.2,732.0 L832.2,727.5 M847.8,732.0 L847.8,727.5 M847.8,732.0 L847.8,723.0 '></path>
+ <g transform="translate(847.8,754.5)" style="stroke:none; fill:black; font-family:Arial; font-size:12.00pt; text-anchor:middle">
+ <text> 2.5</text>
+ </g>
+ <path d='M863.3,732.0 L863.3,727.5 M878.8,732.0 L878.8,727.5 M894.4,732.0 L894.4,727.5 M909.9,732.0 L909.9,727.5
+ M925.5,732.0 L925.5,727.5 M941.0,732.0 L941.0,727.5 M956.6,732.0 L956.6,727.5 M972.1,732.0 L972.1,727.5
+ M987.7,732.0 L987.7,727.5 M1003.2,732.0 L1003.2,727.5 M1003.2,732.0 L1003.2,723.0 '></path>
+ <g transform="translate(1003.2,754.5)" style="stroke:none; fill:black; font-family:Arial; font-size:12.00pt; text-anchor:middle">
+ <text> 3</text>
+ </g>
+ <path d='M70.5,16.6 L70.5,732.0 L1003.2,732.0 M1003.2,16.6 M70.5,16.6 '></path>
+</g>
+<g style="fill:none; color:red; stroke:currentColor; stroke-width:1.00; stroke-linecap:butt; stroke-linejoin:miter">
+</g>
+<g style="fill:none; color:black; stroke:currentColor; stroke-width:1.00; stroke-linecap:butt; stroke-linejoin:miter">
+ <g transform="translate(137.6,39.1)" style="stroke:none; fill:black; font-family:Arial; font-size:12.00pt; text-anchor:start">
+ <text>σ=10</text>
+ </g>
+</g>
+<g style="fill:none; color:red; stroke:currentColor; stroke-width:1.00; stroke-linecap:butt; stroke-linejoin:miter">
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+</g>
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+
diff --git a/lecture_1.css b/lecture_1.css
index 52f2af64ec3e4897c4e80e8e2671870f8fbee110..d5d484f41c6072c927ccc92bb1e69842745f5075 100644
--- a/lecture_1.css
+++ b/lecture_1.css
@@ -1,16 +1,11 @@
-div.myheader {
- position: absolute;
- margin: 30px;
- left: 8%;
- background: blue;
- font-size: xx-large;
-}
-
div.myfooter {
+ height: 7%;
position: absolute;
+ bottom: 0;
+ width: 100%;
background: red;
- font-size: 120%;
- right: 10%;
+ font-size: 150%;
+ left: 0;
}
.rise-enabled .cm-editor {
diff --git a/lecture_1.ipynb b/lecture_1.ipynb
index 0cae9aee134f224484fae52550552f74f6c7ed8a..8a7b5cbe9f40b27c6a08008ff8a444e194d76690 100644
--- a/lecture_1.ipynb
+++ b/lecture_1.ipynb
@@ -4,7 +4,6 @@
"cell_type": "markdown",
"id": "87c9d786-6d5c-4366-9701-5fa127727caa",
"metadata": {
- "editable": true,
"slideshow": {
"slide_type": "slide"
},
@@ -17,6 +16,8 @@
"\n",
"## Basic statistics \n",
"\n",
+ "# \n",
+ "\n",
"<br>\n",
"<br>\n",
"\n",
@@ -31,7 +32,8 @@
"metadata": {
"slideshow": {
"slide_type": "slide"
- }
+ },
+ "tags": []
},
"source": [
"## Samples\n",
@@ -40,15 +42,33 @@
"\n",
"Sample: $X = x_1, x_2,\\dots, x_N$ \n",
"\n",
- "Expectation value: $<f(x)> = \\frac{1}{N}\\sum_i^N f(x_i)$\n",
+ "Expectation value:\n",
+ "$$<f(x)> = E[f(x)] = \\frac{1}{N}\\sum_i^N f(x_i)$$\n",
+ "\n",
+ "linear operator:\n",
+ "$$E[a f(x) + b g(x)] = aE[f(x)] + bE[g(x)]$$ \n",
"\n",
"Describing samples: minimum, maximum, frequency/histogram, means, variance, standard deviation,....\n",
"\n"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "37cae6fd",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "### Describing samples\n",
+ "\n",
+ "minimum, maximum, frequency/histogram, means, variance, standard deviation,....\n"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 29,
"id": "b44e356e-b829-4879-b5fc-9706fffe873d",
"metadata": {},
"outputs": [
@@ -66,7 +86,7 @@
" [1., 3.]])"
]
},
- "execution_count": 1,
+ "execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
@@ -80,7 +100,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 30,
"id": "2aeacb94-518d-464f-a7ab-5282b97bc225",
"metadata": {},
"outputs": [
@@ -90,7 +110,7 @@
"array([5., 2., 0., 0., 0., 2., 4., 2., 1.])"
]
},
- "execution_count": 3,
+ "execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
@@ -101,7 +121,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 31,
"id": "1829fa78-7d7b-4bac-9f24-5129dca63629",
"metadata": {},
"outputs": [
@@ -111,7 +131,7 @@
"(np.float64(0.0), np.float64(6.0))"
]
},
- "execution_count": 2,
+ "execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
@@ -134,9 +154,16 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 20,
"id": "871f915d-09f8-4d14-a79a-8fbd2f16ab75",
- "metadata": {},
+ "metadata": {
+ "cell_style": "center",
+ "scrolled": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
"outputs": [
{
"data": {
@@ -146,13 +173,13 @@
" <BarContainer object of 10 artists>)"
]
},
- "execution_count": 4,
+ "execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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",
+ "image/png": "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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -164,14 +191,30 @@
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
- "plt.hist(data[:, 0])"
+ "plt.hist(data[:, 0])\n",
+ "#plt.hist(data[:, 0], bins=np.arange(-0.25,6.25,0.5))\n",
+ "#plt.xlabel(\"k\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0ba7c3f0",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "subslide"
+ }
+ },
+ "source": [
+ "### Histograms"
]
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 19,
"id": "f8263279-48b5-4421-be05-604ddbfd8d6f",
- "metadata": {},
+ "metadata": {
+ "cell_style": "center"
+ },
"outputs": [
{
"data": {
@@ -179,13 +222,13 @@
"Text(0.5, 0, 'k')"
]
},
- "execution_count": 5,
+ "execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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",
+ "image/png": "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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -199,41 +242,60 @@
"plt.xlabel(\"k\")"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "dcc75692",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "### Histograms"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 32,
"id": "5056a717-6561-41ec-be39-1757984863a9",
"metadata": {
- "editable": true,
+ "cell_style": "split",
"slideshow": {
- "slide_type": ""
+ "slide_type": "-"
},
"tags": []
},
"outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": "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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
- },
- {
- "data": {
- "text/plain": [
- "Text(0.5, 0, 'l')"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- },
+ }
+ ],
+ "source": [
+ "\n",
+ "plt.hist(data[:, 0], bins=np.arange(-0.25,6.26,0.5))\n",
+ "plt.xlabel(\"k\")\n",
+ "#plt.savefig(\"hist.pdf\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "e30c1224",
+ "metadata": {
+ "cell_style": "split"
+ },
+ "outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAigAAAGwCAYAAACD0J42AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAcYElEQVR4nO3df2xd9X3/8ZebgEmYk0IYdiwCuJs3aAOUJiwisCUb4CkLrFW00g7aMbWbiBIobtbRZNmGi4ZNszXL1oxsYRMNQxn8sdIyMUq8tQ1lWdUQmpWFCloRIKNY1rrMDhA5IznfPyquvq7TH9Cbno/Tx0M6Uu7nnnv99hHIT318r29LVVVVAAAK8qa6BwAA+G4CBQAojkABAIojUACA4ggUAKA4AgUAKI5AAQCKM7XuAd6II0eO5Fvf+lba2trS0tJS9zgAwA+hqqocOHAgnZ2dedObvv8eyaQMlG9961uZM2dO3WMAAG/Avn37csYZZ3zfcyZloLS1tSX5zjc4Y8aMmqcBAH4Yo6OjmTNnTuPn+PczKQPltV/rzJgxQ6AAwCTzw7w8w4tkAYDiCBQAoDgCBQAojkABAIojUACA4ggUAKA4AgUAKI5AAQCKI1AAgOIIFACgOAIFACiOQAEAiiNQAIDiCBQAoDgCBQAoztS6B2ByOHv1g3WP0PDs7UvrHgGAY8wOCgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxXndgfLII4/kqquuSmdnZ1paWvKZz3xm3P1VVaWvry+dnZ2ZNm1aFi9enD179ow7Z2xsLDfeeGNOO+20nHzyyfn1X//1/Nd//deP9I0AAMeP1x0oL7/8ci644IJs3LjxqPevW7cu69evz8aNG7Nz5850dHTkiiuuyIEDBxrn9Pb25v7778+9996bRx99NC+99FKuvPLKHD58+I1/JwDAcWPq633AkiVLsmTJkqPeV1VVNmzYkLVr12bZsmVJki1btqS9vT1bt27N9ddfn5GRkfzd3/1d/v7v/z6XX355kuSee+7JnDlz8i//8i/51V/91R/h2wEAjgdNfQ3K3r17MzQ0lJ6ensZaa2trFi1alB07diRJdu3alf/7v/8bd05nZ2fmzp3bOOe7jY2NZXR0dNwBABy/mhooQ0NDSZL29vZx6+3t7Y37hoaGcuKJJ+aUU075nud8t4GBgcycObNxzJkzp5ljAwCFOSbv4mlpaRl3u6qqCWvf7fuds2bNmoyMjDSOffv2NW1WAKA8TQ2Ujo6OJJmwEzI8PNzYVeno6MihQ4eyf//+73nOd2ttbc2MGTPGHQDA8aupgdLV1ZWOjo4MDg421g4dOpTt27dn4cKFSZJ58+blhBNOGHfOiy++mP/8z/9snAMA/GR73e/ieemll/LNb36zcXvv3r3ZvXt3Tj311Jx55pnp7e1Nf39/uru7093dnf7+/kyfPj3XXHNNkmTmzJn54Ac/mN/7vd/LrFmzcuqpp+YjH/lIzjvvvMa7egCAn2yvO1Aee+yx/PIv/3Lj9qpVq5Ik1113XT71qU/l5ptvzsGDB7NixYrs378/CxYsyLZt29LW1tZ4zJ//+Z9n6tSpufrqq3Pw4MFcdtll+dSnPpUpU6Y04VsCACa7lqqqqrqHeL1GR0czc+bMjIyMeD3Kj8nZqx+se4SGZ29fWvcIALwBr+fnt8/iAQCKI1AAgOIIFACgOAIFACiOQAEAiiNQAIDiCBQAoDgCBQAojkABAIojUACA4ggUAKA4AgUAKI5AAQCKI1AAgOIIFACgOAIFACiOQAEAiiNQAIDiCBQAoDgCBQAojkABAIojUACA4ggUAKA4AgUAKI5AAQCKI1AAgOIIFACgOAIFACjO1LoHKNHZqx+se4SGZ29fWvcIAPBjZwcFACiOQAEAiiNQAIDiCBQAoDgCBQAojkABAIojUACA4ggUAKA4AgUAKI5AAQCKI1AAgOIIFACgOAIFACiOQAEAiiNQAIDiCBQAoDgCBQAojkABAIojUACA4ggUAKA4AgUAKI5AAQCKI1AAgOIIFACgOE0PlFdffTV/+Id/mK6urkybNi1vectbcuutt+bIkSONc6qqSl9fXzo7OzNt2rQsXrw4e/bsafYoAMAk1fRA+fjHP56//uu/zsaNG/P1r38969aty5/+6Z/mk5/8ZOOcdevWZf369dm4cWN27tyZjo6OXHHFFTlw4ECzxwEAJqGmB8q///u/553vfGeWLl2as88+O7/xG7+Rnp6ePPbYY0m+s3uyYcOGrF27NsuWLcvcuXOzZcuWvPLKK9m6dWuzxwEAJqGmB8qll16af/3Xf83TTz+dJPmP//iPPProo/m1X/u1JMnevXszNDSUnp6exmNaW1uzaNGi7Nix46jPOTY2ltHR0XEHAHD8mtrsJ/zoRz+akZGRnHPOOZkyZUoOHz6c2267Lb/5m7+ZJBkaGkqStLe3j3tce3t7nnvuuaM+58DAQD72sY81e1QAoFBN30G57777cs8992Tr1q15/PHHs2XLlvzZn/1ZtmzZMu68lpaWcberqpqw9po1a9ZkZGSkcezbt6/ZYwMABWn6Dsrv//7vZ/Xq1Xnve9+bJDnvvPPy3HPPZWBgINddd106OjqSfGcnZfbs2Y3HDQ8PT9hVeU1ra2taW1ubPSoAUKim76C88soredObxj/tlClTGm8z7urqSkdHRwYHBxv3Hzp0KNu3b8/ChQubPQ4AMAk1fQflqquuym233ZYzzzwzb3vb2/LVr34169evzwc+8IEk3/nVTm9vb/r7+9Pd3Z3u7u709/dn+vTpueaaa5o9DgAwCTU9UD75yU/mj/7oj7JixYoMDw+ns7Mz119/ff74j/+4cc7NN9+cgwcPZsWKFdm/f38WLFiQbdu2pa2trdnjAACTUEtVVVXdQ7xeo6OjmTlzZkZGRjJjxoymP//Zqx9s+nO+Uc/evrTuEZK4JgD86F7Pz2+fxQMAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABTnmATKCy+8kPe9732ZNWtWpk+fnre//e3ZtWtX4/6qqtLX15fOzs5MmzYtixcvzp49e47FKADAJNT0QNm/f38uueSSnHDCCXnooYfy5JNP5hOf+ETe/OY3N85Zt25d1q9fn40bN2bnzp3p6OjIFVdckQMHDjR7HABgEpra7Cf8+Mc/njlz5uSuu+5qrJ199tmNf1dVlQ0bNmTt2rVZtmxZkmTLli1pb2/P1q1bc/311zd7JABgkmn6DsoDDzyQ+fPn593vfndOP/30XHjhhbnzzjsb9+/duzdDQ0Pp6elprLW2tmbRokXZsWPHUZ9zbGwso6Oj4w4A4PjV9EB55plnsmnTpnR3d+fhhx/O8uXL86EPfSh33313kmRoaChJ0t7ePu5x7e3tjfu+28DAQGbOnNk45syZ0+yxAYCCND1Qjhw5kne84x3p7+/PhRdemOuvvz6/+7u/m02bNo07r6WlZdztqqomrL1mzZo1GRkZaRz79u1r9tgAQEGaHiizZ8/OW9/61nFr5557bp5//vkkSUdHR5JM2C0ZHh6esKvymtbW1syYMWPcAQAcv5oeKJdcckmeeuqpcWtPP/10zjrrrCRJV1dXOjo6Mjg42Lj/0KFD2b59exYuXNjscQCASajp7+L58Ic/nIULF6a/vz9XX311vvKVr2Tz5s3ZvHlzku/8aqe3tzf9/f3p7u5Od3d3+vv7M3369FxzzTXNHgcAmISaHigXXXRR7r///qxZsya33nprurq6smHDhlx77bWNc26++eYcPHgwK1asyP79+7NgwYJs27YtbW1tzR4HAJiEmh4oSXLllVfmyiuv/J73t7S0pK+vL319fcfiywMAk5zP4gEAiiNQAIDiCBQAoDgCBQAojkABAIojUACA4hyTtxnDT4qzVz9Y9whJkmdvX1r3CABNZQcFACiOQAEAiiNQAIDiCBQAoDgCBQAojkABAIojUACA4ggUAKA4AgUAKI5AAQCKI1AAgOIIFACgOAIFACiOQAEAiiNQAIDiCBQAoDgCBQAojkABAIojUACA4ggUAKA4AgUAKI5AAQCKI1AAgOIIFACgOAIFACiOQAEAiiNQAIDiCBQAoDgCBQAojkABAIojUACA4ggUAKA4AgUAKI5AAQCKI1AAgOIIFACgOAIFACiOQAEAiiNQAIDiCBQAoDgCBQAojkABAIojUACA4ggUAKA4AgUAKI5AAQCKc8wDZWBgIC0tLent7W2sVVWVvr6+dHZ2Ztq0aVm8eHH27NlzrEcBACaJYxooO3fuzObNm3P++eePW1+3bl3Wr1+fjRs3ZufOneno6MgVV1yRAwcOHMtxAIBJ4pgFyksvvZRrr702d955Z0455ZTGelVV2bBhQ9auXZtly5Zl7ty52bJlS1555ZVs3br1WI0DAEwixyxQVq5cmaVLl+byyy8ft753794MDQ2lp6ensdba2ppFixZlx44dR32usbGxjI6OjjsAgOPX1GPxpPfee28ef/zx7Ny5c8J9Q0NDSZL29vZx6+3t7XnuueeO+nwDAwP52Mc+1vxBgaY7e/WDdY/Q8OztS+seAXiDmr6Dsm/fvtx000255557ctJJJ33P81paWsbdrqpqwtpr1qxZk5GRkcaxb9++ps4MAJSl6Tsou3btyvDwcObNm9dYO3z4cB555JFs3LgxTz31VJLv7KTMnj27cc7w8PCEXZXXtLa2prW1tdmjAgCFavoOymWXXZYnnngiu3fvbhzz58/Ptddem927d+ctb3lLOjo6Mjg42HjMoUOHsn379ixcuLDZ4wAAk1DTd1Da2toyd+7ccWsnn3xyZs2a1Vjv7e1Nf39/uru7093dnf7+/kyfPj3XXHNNs8cBACahY/Ii2R/k5ptvzsGDB7NixYrs378/CxYsyLZt29LW1lbHOABAYX4sgfLFL35x3O2Wlpb09fWlr6/vx/HlAYBJxmfxAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxWl6oAwMDOSiiy5KW1tbTj/99LzrXe/KU089Ne6cqqrS19eXzs7OTJs2LYsXL86ePXuaPQoAMEk1PVC2b9+elStX5stf/nIGBwfz6quvpqenJy+//HLjnHXr1mX9+vXZuHFjdu7cmY6OjlxxxRU5cOBAs8cBACahqc1+ws997nPjbt911105/fTTs2vXrvzSL/1SqqrKhg0bsnbt2ixbtixJsmXLlrS3t2fr1q25/vrrmz0SADDJHPPXoIyMjCRJTj311CTJ3r17MzQ0lJ6ensY5ra2tWbRoUXbs2HHU5xgbG8vo6Oi4AwA4fh3TQKmqKqtWrcqll16auXPnJkmGhoaSJO3t7ePObW9vb9z33QYGBjJz5szGMWfOnGM5NgBQs2MaKDfccEO+9rWv5R/+4R8m3NfS0jLudlVVE9Zes2bNmoyMjDSOffv2HZN5AYAyNP01KK+58cYb88ADD+SRRx7JGWec0Vjv6OhI8p2dlNmzZzfWh4eHJ+yqvKa1tTWtra3HalQAoDBN30Gpqio33HBDPv3pT+fzn/98urq6xt3f1dWVjo6ODA4ONtYOHTqU7du3Z+HChc0eBwCYhJq+g7Jy5cps3bo1n/3sZ9PW1tZ4XcnMmTMzbdq0tLS0pLe3N/39/enu7k53d3f6+/szffr0XHPNNc0eBwCYhJoeKJs2bUqSLF68eNz6XXfdld/+7d9Oktx88805ePBgVqxYkf3792fBggXZtm1b2tramj0OADAJNT1Qqqr6gee0tLSkr68vfX19zf7yAMBxwGfxAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFmVr3AADHu7NXP1j3CA3P3r607hHgh2IHBQAojkABAIojUACA4ggUAKA4AgUAKI5AAQCKI1AAgOIIFACgOAIFACiOQAEAiiNQAIDiCBQAoDgCBQAojkABAIojUACA4ggUAKA4AgUAKI5AAQCKI1AAgOIIFACgOAIFACiOQAEAiiNQAIDiCBQAoDgCBQAojkABAIojUACA4tQaKHfccUe6urpy0kknZd68efnSl75U5zgAQCGm1vWF77vvvvT29uaOO+7IJZdckr/5m7/JkiVL8uSTT+bMM8+saywAfkzOXv1g3SMkSZ69fWndI3AUte2grF+/Ph/84AfzO7/zOzn33HOzYcOGzJkzJ5s2baprJACgELXsoBw6dCi7du3K6tWrx6339PRkx44dE84fGxvL2NhY4/bIyEiSZHR09JjMd2TslWPyvG/EsfoeXy/X5OhKuS6uydGVcl1ck6Mr5bqUdE2Od69d66qqfvDJVQ1eeOGFKkn1b//2b+PWb7vtturnfu7nJpx/yy23VEkcDofD4XAcB8e+fft+YCvU9hqUJGlpaRl3u6qqCWtJsmbNmqxatapx+8iRI/mf//mfzJo166jn1210dDRz5szJvn37MmPGjLrHKYbrMpFrMpFrcnSuy0SuyUSlX5OqqnLgwIF0dnb+wHNrCZTTTjstU6ZMydDQ0Lj14eHhtLe3Tzi/tbU1ra2t49be/OY3H8sRm2LGjBlF/gdSN9dlItdkItfk6FyXiVyTiUq+JjNnzvyhzqvlRbInnnhi5s2bl8HBwXHrg4ODWbhwYR0jAQAFqe1XPKtWrcr73//+zJ8/PxdffHE2b96c559/PsuXL69rJACgELUFynve8558+9vfzq233poXX3wxc+fOzT//8z/nrLPOqmukpmltbc0tt9wy4ddSP+lcl4lck4lck6NzXSZyTSY6nq5JS1X9MO/1AQD48fFZPABAcQQKAFAcgQIAFEegAADFESjHwB133JGurq6cdNJJmTdvXr70pS/VPVKtHnnkkVx11VXp7OxMS0tLPvOZz9Q9Uu0GBgZy0UUXpa2tLaeffnre9a535amnnqp7rFpt2rQp559/fuMPTF188cV56KGH6h6rKAMDA2lpaUlvb2/do9Sqr68vLS0t446Ojo66x6rdCy+8kPe9732ZNWtWpk+fnre//e3ZtWtX3WO9YQKlye6777709vZm7dq1+epXv5pf/MVfzJIlS/L888/XPVptXn755VxwwQXZuHFj3aMUY/v27Vm5cmW+/OUvZ3BwMK+++mp6enry8ssv1z1abc4444zcfvvteeyxx/LYY4/lV37lV/LOd74ze/bsqXu0IuzcuTObN2/O+eefX/coRXjb296WF198sXE88cQTdY9Uq/379+eSSy7JCSeckIceeihPPvlkPvGJT0yKv7r+PTXl0/9o+IVf+IVq+fLl49bOOeecavXq1TVNVJYk1f3331/3GMUZHh6uklTbt2+ve5SinHLKKdXf/u3f1j1G7Q4cOFB1d3dXg4OD1aJFi6qbbrqp7pFqdcstt1QXXHBB3WMU5aMf/Wh16aWX1j1GU9lBaaJDhw5l165d6enpGbfe09OTHTt21DQVk8HIyEiS5NRTT615kjIcPnw49957b15++eVcfPHFdY9Tu5UrV2bp0qW5/PLL6x6lGN/4xjfS2dmZrq6uvPe9780zzzxT90i1euCBBzJ//vy8+93vzumnn54LL7wwd955Z91j/UgEShP993//dw4fPjzhAw/b29snfDAivKaqqqxatSqXXnpp5s6dW/c4tXriiSfyUz/1U2ltbc3y5ctz//33561vfWvdY9Xq3nvvzeOPP56BgYG6RynGggULcvfdd+fhhx/OnXfemaGhoSxcuDDf/va36x6tNs8880w2bdqU7u7uPPzww1m+fHk+9KEP5e677657tDestj91fzxraWkZd7uqqglr8JobbrghX/va1/Loo4/WPUrtfv7nfz67d+/O//7v/+Yf//Efc91112X79u0/sZGyb9++3HTTTdm2bVtOOumkuscpxpIlSxr/Pu+883LxxRfnZ37mZ7Jly5asWrWqxsnqc+TIkcyfPz/9/f1JkgsvvDB79uzJpk2b8lu/9Vs1T/fG2EFpotNOOy1TpkyZsFsyPDw8YVcFkuTGG2/MAw88kC984Qs544wz6h6ndieeeGJ+9md/NvPnz8/AwEAuuOCC/MVf/EXdY9Vm165dGR4ezrx58zJ16tRMnTo127dvz1/+5V9m6tSpOXz4cN0jFuHkk0/Oeeedl2984xt1j1Kb2bNnTwj5c889d1K/QUOgNNGJJ56YefPmZXBwcNz64OBgFi5cWNNUlKiqqtxwww359Kc/nc9//vPp6uqqe6QiVVWVsbGxuseozWWXXZYnnngiu3fvbhzz58/Ptddem927d2fKlCl1j1iEsbGxfP3rX8/s2bPrHqU2l1xyyYQ/VfD0009P6g/g9SueJlu1alXe//73Z/78+bn44ouzefPmPP/881m+fHndo9XmpZdeyje/+c3G7b1792b37t059dRTc+aZZ9Y4WX1WrlyZrVu35rOf/Wza2toau24zZ87MtGnTap6uHn/wB3+QJUuWZM6cOTlw4EDuvffefPGLX8znPve5ukerTVtb24TXJZ188smZNWvWT/TrlT7ykY/kqquuyplnnpnh4eH8yZ/8SUZHR3PdddfVPVptPvzhD2fhwoXp7+/P1Vdfna985SvZvHlzNm/eXPdob1y9byI6Pv3VX/1VddZZZ1Unnnhi9Y53vOMn/q2jX/jCF6okE47rrruu7tFqc7TrkaS666676h6tNh/4wAca/9/89E//dHXZZZdV27Ztq3us4nibcVW95z3vqWbPnl2dcMIJVWdnZ7Vs2bJqz549dY9Vu3/6p3+q5s6dW7W2tlbnnHNOtXnz5rpH+pG0VFVV1dRGAABH5TUoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFEShAcRYvXpze3t66xwBqJFAAgOIIFACgOAIFACiOQAEAiiNQAIDiCBQAoDgCBQAojkABAIojUACA4ggUAKA4LVVVVXUPAQDw/7ODAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBx/h894AS6MKHZXQAAAABJRU5ErkJggg==",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -243,19 +305,15 @@
}
],
"source": [
- "plt.hist(data[:, 0], bins=np.arange(-0.25,6.26,0.5))\n",
- "plt.xlabel(\"k\")\n",
- "#plt.savefig(\"hist.pdf\")\n",
- "plt.show()\n",
"plt.hist(data[:, 1], bins=np.arange(-0.25,6.26,0.5))\n",
- "plt.xlabel(\"l\")"
+ "plt.xlabel(\"l\")\n",
+ "plt.show()"
]
},
{
"cell_type": "markdown",
"id": "4183331e-8a9f-4af5-829f-3fcb9b2abb31",
"metadata": {
- "editable": true,
"slideshow": {
"slide_type": "slide"
},
@@ -267,10 +325,9 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 7,
"id": "4ecfb198-c621-4ee4-9d24-9f9a8cb8bec7",
"metadata": {
- "editable": true,
"slideshow": {
"slide_type": ""
},
@@ -286,7 +343,7 @@
},
{
"data": {
- "image/png": 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",
+ "image/png": "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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -307,9 +364,8 @@
"execution_count": null,
"id": "d00cadf3-0cfa-4f59-9962-4b9e6b707e0b",
"metadata": {
- "editable": true,
"slideshow": {
- "slide_type": ""
+ "slide_type": "skip"
},
"tags": []
},
@@ -320,7 +376,6 @@
"cell_type": "markdown",
"id": "ceb5ca27-a96a-4ee3-967b-5f02efe66540",
"metadata": {
- "editable": true,
"slideshow": {
"slide_type": "slide"
},
@@ -332,9 +387,32 @@
"---\n",
"\n",
"different means:\n",
- " - arithmetric mean: $ \\overline{x} = \\frac{1}{n}\\sum\\limits_{i=1}^n x_i (= \\mu)$\n",
- " - geometric mean: $ \\overline{{x}}_\\mathrm {geom} = \\sqrt[n]{\\prod\\limits_{i=1}^{n}{x_i}}$\n",
- " - quadratic mean: $ \\overline{{x}}_\\mathrm{quadr} = \\sqrt {\\frac {1}{n} \\sum\\limits_{i=1}^{n}x_i^2} = \\sqrt{\\overline{x^2}}$\n",
+ " - arithmetric mean: $$ \\overline{x} = E[x] = <x> = \\frac{1}{n}\\sum\\limits_{i=1}^n x_i (= \\mu)$$\n",
+ " - geometric mean: $$ \\overline{{x}}_\\mathrm {geom} = \\sqrt[n]{\\prod\\limits_{i=1}^{n}{x_i}}$$\n",
+ " - quadratic mean: $$ \\overline{{x}}_\\mathrm{quadr} = \\sqrt{E[x^2]} = \\sqrt {\\frac {1}{n} \\sum\\limits_{i=1}^{n}x_i^2} = \\sqrt{\\overline{x^2}} $$\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3f0e1262",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "### Variance\n",
+ "\n",
+ " - variance \n",
+ " $$V = E[(x - \\mu)^2]$$\n",
+ " with $\\mu = E[x] = \\overline x$:\n",
+ " $$V = E[(x - {\\overline x}^2]$ = E[x^2 - 2x{\\overline x} + {\\overline x}^2] = E[x^2] - 2{\\overline x}E[x] + {\\overline x}^2 = E[x^2] - 2 {\\overline x}^2 + {\\overline x}^2 = E[x^2] - (E[x])^2$$\n",
+ " for sample $X = x_1, x_2,\\dots, x_N$; \n",
+ " $$V= \\frac{1}{n}\\sum\\limits_{i=1}^n (x_i - \\overline x)^2$$\n",
+ "\n",
+ " - standard deviation: \n",
+ " $$\\sigma = \\sqrt{V}$$ \n",
"\n"
]
},
@@ -342,7 +420,6 @@
"cell_type": "markdown",
"id": "68cea392-8d4f-48d7-aacc-0f10d46af3af",
"metadata": {
- "editable": true,
"slideshow": {
"slide_type": "slide"
},
@@ -354,10 +431,17 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": null,
+ "id": "2ea96198",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
"id": "76d92d18-1d77-40d2-a910-592183635d3b",
"metadata": {
- "editable": true,
"slideshow": {
"slide_type": "fragment"
},
@@ -378,10 +462,9 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 9,
"id": "5f0f34b4-5bbe-439b-8b4e-fbb05794b790",
"metadata": {
- "editable": true,
"slideshow": {
"slide_type": ""
},
@@ -402,10 +485,9 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 10,
"id": "70a1920f-beda-4154-ad77-22a9ffe2e39f",
"metadata": {
- "editable": true,
"slideshow": {
"slide_type": ""
},
@@ -424,11 +506,95 @@
"print(\"standard deviation:\", np.std(data, axis=0))"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "539b2083",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "### Covariance\n",
+ "\n",
+ "- covariance: $$\\text{cov}(X,Y) = E[(X-E[X])(Y-E[Y])] = E[XY] - E[X]E[Y]$$\n",
+ "\n",
+ "for samples $X = x_1, x_2,\\dots, x_N$ and $Y = y_1, y_2,\\dots, y_N$:\n",
+ "$$\\text{cov}(X,Y) = \\frac{1}{n}\\sum\\limits_{i=1}^n (x_i - \\overline x)(y_i - \\overline y)$$\n",
+ "\n",
+ "- correlation $$\\rho_{xy} = \\frac{\\text{cov}(X,Y)}{\\sqrt{\\text{cov}(X,X)\\text{cov}(Y,Y)}}$$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "27a53f23",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "### Exercise: compute covariance and correlation column 1 and 2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ef09b3b5",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "-"
+ }
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "de7df23f",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "fragment"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ 1.85964856 -0.1927676 ]\n",
+ " [-0.1927676 1.27724204]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(np.cov(data, rowvar=False))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "1edc9156",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ 1. -0.12507831]\n",
+ " [-0.12507831 1. ]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(np.corrcoef(data, rowvar=False))"
+ ]
+ },
{
"cell_type": "markdown",
"id": "b8ab42a5-b7af-4942-9a7a-91b73e0ce625",
"metadata": {
- "editable": true,
"slideshow": {
"slide_type": "slide"
},
@@ -458,7 +624,6 @@
"cell_type": "markdown",
"id": "13563524-0626-4a69-a6ab-f87d7f19a016",
"metadata": {
- "editable": true,
"slideshow": {
"slide_type": "slide"
},
@@ -468,17 +633,33 @@
"### Example\n",
"\n",
"$$P(a \\le x \\le b) = \\int_a^b f(x)\\,dx = F(b) - F(a)$$\n",
- "\n",
- "<img src=\"./figures/08/lognormal_pdf.png\"\n",
- "style=\"width:55%\" /> <img src=\"./figures/08/lognormal_cdf.png\"\n",
- "style=\"width:41.4%\" /> CC0, via Wikimedia Commons\n"
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f087bd39",
+ "metadata": {
+ "cell_style": "split"
+ },
+ "source": [
+ "<img src=\"./figures/08/Lognormal_distribution_PDF.svg\" style=\"width:100%\" /> \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3f86b608",
+ "metadata": {
+ "cell_style": "split"
+ },
+ "source": [
+ "<img src=\"./figures/08/CDF-log_normal_distributions.svg\" style=\"width:200%\" />"
]
},
{
"cell_type": "markdown",
"id": "14e4b782-9573-4e87-89d0-901dade25538",
"metadata": {
- "editable": true,
"slideshow": {
"slide_type": "slide"
},
@@ -488,20 +669,22 @@
"### Quantiles \n",
"\n",
"\n",
- "Quantile $x_\\alpha$ Wert der Zufallsvariable $x$ mit\n",
- "$$F(x_\\alpha) = \\alpha$$ Also: $$x_\\alpha = F^{-1}(\\alpha)$$\n",
+ "Quantile $x_\\alpha$ is the value of the random variable $x$ with\n",
+ "$$F(x_\\alpha) = \\int_{-\\infty}^{x_\\alpha} f(x)\\,dx = \\alpha$$ \n",
+ "Hence: $$x_\\alpha = F^{-1}(\\alpha)$$\n",
+ "\n",
+ "Median: $x_{\\frac{1}{2}}$ \n",
+ "$$F(x_{\\frac{1}{2}}) = 0.5$$ $$x_{\\frac{1}{2}} = F^{-1}(0.5)$$\n",
"\n",
- "Median: $x_{\\sfrac{1}{2}}$ \n",
- "$$F(x_{\\sfrac{1}{2}}) = 0{,}5$$ $$x_{\\sfrac{1}{2}} = F^{-1}(0{,}5)$$\n",
"\n",
- "<img src=\"./figures/09/Normalverteilung.png\" alt=\"image\" />"
+ "<img src=\"./figures/09/Normalverteilung.png\" width=38% alt=\"image\" />\n",
+ "\n"
]
},
{
"cell_type": "markdown",
"id": "8e74c511-b730-4781-a751-e1895983cc4c",
"metadata": {
- "editable": true,
"slideshow": {
"slide_type": "slide"
},
@@ -527,7 +710,6 @@
"cell_type": "markdown",
"id": "7624d391-9fd5-4567-bf84-632eead1bb20",
"metadata": {
- "editable": true,
"slideshow": {
"slide_type": "slide"
},
@@ -553,7 +735,6 @@
"cell_type": "markdown",
"id": "ba8fae9b-636b-4b7e-a36d-3be582c000f5",
"metadata": {
- "editable": true,
"slideshow": {
"slide_type": "slide"
},
@@ -579,7 +760,6 @@
"cell_type": "markdown",
"id": "fdd11743-1e7b-48b6-9e9b-41bee7d4f23b",
"metadata": {
- "editable": true,
"slideshow": {
"slide_type": "slide"
},
@@ -605,7 +785,6 @@
"cell_type": "markdown",
"id": "525847ad-2ddf-468c-883f-35bc853bac8c",
"metadata": {
- "editable": true,
"slideshow": {
"slide_type": "slide"
},
@@ -629,7 +808,6 @@
"cell_type": "markdown",
"id": "0c669bab-6c81-45fb-a506-8ef709cd4687",
"metadata": {
- "editable": true,
"slideshow": {
"slide_type": "slide"
},
@@ -655,7 +833,6 @@
"cell_type": "markdown",
"id": "4b7f6667-c019-4b23-83fd-a1758d748762",
"metadata": {
- "editable": true,
"slideshow": {
"slide_type": "slide"
},
@@ -685,7 +862,6 @@
"cell_type": "markdown",
"id": "2907584f-bb17-463e-b084-749f2011bd4c",
"metadata": {
- "editable": true,
"slideshow": {
"slide_type": "slide"
},
@@ -711,7 +887,6 @@
"cell_type": "markdown",
"id": "5cd273e7-3129-454b-a31c-4e91c4316bf4",
"metadata": {
- "editable": true,
"slideshow": {
"slide_type": "slide"
},
@@ -738,7 +913,6 @@
"cell_type": "markdown",
"id": "30d01c8b-50a5-4d90-8e0b-89a68f52f9bd",
"metadata": {
- "editable": true,
"slideshow": {
"slide_type": "slide"
},
@@ -761,7 +935,6 @@
"cell_type": "markdown",
"id": "ae67472e-f9b2-46fa-9679-15553d31aaaf",
"metadata": {
- "editable": true,
"slideshow": {
"slide_type": "slide"
},
@@ -784,7 +957,6 @@
"cell_type": "markdown",
"id": "576b4570-8a06-4224-b867-31459483e6bb",
"metadata": {
- "editable": true,
"slideshow": {
"slide_type": "slide"
},
@@ -828,7 +1000,6 @@
"cell_type": "markdown",
"id": "ba08bfd1-4c17-4a1d-bdf6-9c517edffdd8",
"metadata": {
- "editable": true,
"slideshow": {
"slide_type": "slide"
},
@@ -855,7 +1026,6 @@
"cell_type": "markdown",
"id": "c1dbdd5b-c1f4-40b3-bad6-d6639a9a635c",
"metadata": {
- "editable": true,
"slideshow": {
"slide_type": "slide"
},
@@ -883,8 +1053,7 @@
"Die Zahlen $k$ folgen einer Binomialverteilung mit der\n",
"Wahrscheinlichkeit $p_k = E[h_k] = E[\\frac{k}{n}]$: $$\\begin{aligned}\n",
" \\sigma(h_k) &= &\\sqrt{V[\\frac{k}{n}]} = \\sqrt{\\frac{1}{n^2} V[k]} = \\sqrt{\\frac{1}{n^2}\\cdot np_k(1-p_k)}\\\\\n",
- " &=& \\sqrt{\\frac{p_k(1-p_k)}{n}}\\\\\n",
- " \n",
+ " &=& \\sqrt{\\frac{p_k(1-p_k)}{n}}\\\\ \n",
"\\end{aligned}$$"
]
},
@@ -892,7 +1061,6 @@
"cell_type": "markdown",
"id": "60849c4a-dad2-4acd-9780-f9560da2ed9b",
"metadata": {
- "editable": true,
"slideshow": {
"slide_type": "slide"
},
@@ -925,7 +1093,6 @@
"cell_type": "markdown",
"id": "7e80723a-ac12-4af1-a43a-70593ef791b8",
"metadata": {
- "editable": true,
"slideshow": {
"slide_type": "slide"
},
@@ -944,7 +1111,7 @@
"|:------------------------------------------|----:|----:|----:|----:|----:|----:|----:|\n",
"| Zahl der Korps-Jahre mit $k$ Todesf\"allen | 144 | 91 | 32 | 11 | 2 | 0 | 0 |\n",
"\n",
- "<img src=\"./figures/08/poisson70.pdf\" style=\"width:55.0%\" />\n",
+ "<img src=\"./figures/08/poisson70.png\" style=\"width:55.0%\" />\n",
"\n",
"Poisson-Verteilung f\"ur $\\mu = \\frac{196}{280} = 0.70$"
]
@@ -953,7 +1120,6 @@
"cell_type": "markdown",
"id": "b9772cfd-74fb-4a8b-9c0c-fd2c3554a986",
"metadata": {
- "editable": true,
"slideshow": {
"slide_type": "slide"
},
@@ -967,14 +1133,13 @@
"cell_type": "markdown",
"id": "42e65c7a-4636-4319-b21a-acc95140c2de",
"metadata": {
- "editable": true,
"slideshow": {
"slide_type": "fragment"
},
"tags": []
},
"source": [
- "If we quote $a = 1 \\pm 0.5$, we usually mean that the probability for the *true* value of $a$ is Gaussian $G(a, \\mu, \\sigma)$ distributed with $\\mu = 1$ and $\\sigma = 0.5$ "
+ "If we quote $a = 1 \\pm 0.5$, we usually mean that the probability for the *true* value of $a$ is Gaussian $G(a, \\mu, \\sigma)$ distributed with $\\mu = 1$ and $\\sigma = 0.5$. "
]
},
{
@@ -1023,7 +1188,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 11,
"id": "5e55929e-7028-4ae2-9e05-29812e933733",
"metadata": {
"slideshow": {
@@ -1036,10 +1201,9 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "[ 2.04228058 2.0372654 0.16987033 ... 0.95302626 1.32747258\n",
- " -0.02868072]\n",
- "[ True True True ... False False True]\n",
- "fraction outside one sigma: 0.3203\n"
+ "[0.62483765 1.14886134 0.26443731 ... 1.71203625 1.6365167 1.52227868]\n",
+ "[False False True ... True True True]\n",
+ "fraction outside one sigma: 0.3178\n"
]
}
],
@@ -1092,6 +1256,7 @@
}
],
"metadata": {
+ "celltoolbar": "Slideshow",
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
@@ -1107,11 +1272,11 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.11.10"
+ "version": "3.9.20"
},
"rise": {
"autolaunch": true,
- "overlay": "<div class='myheader'><h2>my company</h2></div><div class='myfooter'><h2>the date</h2></div>"
+ "overlay": "<div class='myfooter'><h2 style='text-align:center;'>Hartmut Stadie - University of Hamburg</h2></div>"
},
"toc": {
"base_numbering": 1
diff --git a/lecture_4.ipynb b/lecture_4.ipynb
index c2f9b5c19893176b53d487e803036c536d8d1175..d17b914a5b4dfde0929888b5c3bf3d92ec3d0acf 100644
--- a/lecture_4.ipynb
+++ b/lecture_4.ipynb
@@ -1,19 +1,5 @@
{
"cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "60c7547b-61ea-4709-bb5d-30ad3c94c340",
- "metadata": {
- "editable": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": []
- },
{
"cell_type": "markdown",
"id": "d2193805-8359-4380-82da-4b1cb5358290",