{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Lecture 13\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "-Introduction: [>>](#Introduction) \n", "-Propagating errors: [>>](#Propagating-errors) \n", "-Fitting by minimising chi squared: [>>](#Fitting-by-minimising-chi-squared) \n", "-Electron diffraction experiment: [>>](#Electron-diffraction-experiment) \n", "--Electron diffraction apparatus: [>>](#Electron-diffraction-apparatus) \n", "--Measuring the diffraction rings at variable electron momentum: [>>](#Measuring-the-diffraction-rings-at-variable-electron-momentum) \n", "-Results: [>>](#Results) \n", "-More plots: [>>](#More-plots) \n", "--Lines: [>>](#Lines) \n", "--Points with errorbars: [>>](#Points-with-errorbars) \n", "--Histograms (with mean and standard deviation): [>>](#Histograms-(with-mean-and-standard-deviation)) \n", "--Multiple plots in one figure: [>>](#Multiple-plots-in-one-figure) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction\n", "\n", "This week we will remind ourselves how to propagate errors and look again at fitting and plotting data. As an example of how to do a fit (and some plots!), we will use results from an experiment to measure Planck's constant. We will then look at a few additional plots." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Propagating errors\n", "\n", "Supposing we have measured $x$ and $y$, and their errors, $\\delta x$ and $\\delta y$. We now want to use these to calculate another quantity, $z = z(x, y).$ What is the error on $z$? \n", "\n", "The variation in $z$ for a small change in $x$ is:\n", "\n", "$$\\delta z = \\frac{\\partial z}{\\partial x} \\delta x.$$\n", "\n", "Similarly, the change in $z$ for a small variation in $y$ is:\n", "\n", "$$\\delta z = \\frac{\\partial z}{\\partial y} \\delta y.$$\n", "\n", "Assuming that $x$ and $y$ are independent, the total variation in $z$ resulting from changes in both $x$ and $y$ is then:\n", "\n", "$$\\delta z = \\sqrt{ \\left( \\frac{\\partial z}{\\partial x} \\delta x \\right)^2 + \\left( \\frac{\\partial z}{\\partial y} \\delta y \\right)^2 }.$$\n", "\n", "All the rules you see about calculating errors (e.g. in the case that $z = ax + by$ or $z = kxy$) are derived using this expression.\n", "\n", "## Fitting by minimising chi squared\n", "\n", "Here, we look at fitting a straight line $y = mx + c$ to a set of data points$(x_i, y_i)$, with $i = 1 \\dots N$. \n", "The separation between the line and the data point $(x_i, y_i)$ is given by:\n", "\n", "\\begin{align}\n", "s_i &= y_i - y(x_i) \\\\\n", " &= y_i - (mx_i + c).\n", "\\end{align}\n", "\n", "If we want to find the values of $m$ and $c$ which ensure the line is as close as possible to the points, taking into account the errors involved, we want to minimise something like \n", "\n", "$$\\frac {s_i}{\\delta s_i},$$\n", "\n", "for all the data points. This means that large separations are \"allowed\" if the error involved is large, but not if the error is small, so well constrained points are more significant than poorly constrained ones.\n", "\n", "The total error on $s_i$ is:\n", "\n", "\\begin{align}\n", "\\delta s_i &= \\sqrt{ \\left( \\frac{\\partial s_i}{\\partial y_i} \\delta y_i \\right)^2 + \n", " \\left( \\frac{\\partial s_i}{\\partial x} \\delta x_i \\right)^2} \\\\\n", " &= \\sqrt{ (\\delta y_i)^2 + (m \\delta x_i)^2}.\n", "\\end{align}\n", "\n", "So we want to minimise something like\n", "\n", "$$\\chi_i = \\left| \\frac {y_i - (mx_i + c)}{\\sqrt{ (\\delta y_i)^2 + (m \\delta x_i)^2}} \\right|$$\n", "\n", "for all $N$ data points.\n", "\n", "Many fit programs minimise a closely related quantity defined by:\n", "\n", "\\begin{align}\n", "\\sum_{i = 1}^N \\chi_i^2 &= \\sum_{i = 1}^N \\left( \\frac {y_i - (mx_i + c)}{\\sqrt{ (\\delta y_i)^2 + (m \\delta x_i)^2}} \\right)^2\\\\\n", " &= \\sum_{i = 1}^N \\frac {\\left( y_i - (mx_i + c)\\right)^2}{(\\delta y_i)^2 + (m \\delta x_i)^2}. \n", "\\end{align}\n", "\n", "More complicated fitting functions lead to more complicated expressions for $\\chi_i$, but the principle remains the same!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Electron diffraction experiment\n", "\n", "The electron is a fundamental particle which was discovered in 1897 by J.J. Thompson. Its discovery marked the first step in particle physics and was a key stepping stone in the development of quantum theory. One of the prediction of quantum theory is that particles may act like waves. According to de-Broglie’s equation, an electron can be assigned a wavelength, $\\lambda$, that is related to its momentum, $p$, by the equation\n", "\n", "$$\\lambda =h/p,$$\n", "\n", "where $h$ is Planck’s constant. \n", "\n", "When electrons are incident on graphite, due to the their wave-like behaviour, interference can occur as the electrons are reflected by the atomic layers in the graphite. The electrons are diffracted in accordance with the Bragg condition, which relates the angle of diffraction to the wavelength of the incoming waves: \n", "\n", "$$2d\\sin \\theta = n\\lambda.$$\n", "\n", "Here, $d$ is the spacing between the planes of carbon atoms, $\\theta$ is the Bragg angle (the angle between the electron beam and the lattice planes), $\\lambda$ is the wavelength of the incoming waves and $n$ is a positive integer.\n", "\n", "\n", "### Electron diffraction apparatus\n", "\n", "Electrons are generated from a heated cathode at the end of a cathode ray tube and accelerated by an electric field. The electron beam is focussed and then hits a thin piece of graphite. The electrons are diffracted by the lattice planes of the graphite with different intensities. They then hit the fluorescent layer on the front end of the tube causing it to fluoresce. We can observe the fluorescence as a concentric ring pattern. \n", "\n", "![](ElectronsExptOne.png)\n", "\n", "**Figure 1**: *Electron diffraction apparatus showing faint green fluorescence rings generated by electrons hitting the screen.*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Measuring the diffraction rings at variable electron momentum\n", "\n", "Keep the voltage across and the current through the filament in the cathode constant. Vary the voltage betwen the cathode and the anode.\n", "Observe how the fluorescence rings generated by the diffracted electrons change size. Measure the diameters of the inner ring and the outer rings. As the rings have finite width, measure the inner and outer diameter of each ring and take the average. Repeat each reading several times and determine the error on your measurements. \n", "\n", "Calculate the wavelength of the electrons for each ring. The spacings between the atomic layers in graphite are 213 pm and 123 pm. These spacings correspond to the diffraction of the electrons in the inner and outer rings, respectively. \n", "\n", "Measure the radius of the cathode ray tube.\n", "\n", "Use de Broglie’s equation and the relationship between the electron's kinetic and potential energy to derive a relationship between the wavelength of the electron and its potential energy. \n", "\n", "Plot a graph of the wavelength versus the inverse momentum and carry out a fit to the functional form $y = mx + c$ and determine the gradient and intercept of the line. Repeat this for the outer ring. \n", "\n", "If the charge of the electron is $1.6 \\times 10^{-19} {\\rm C}$ and the mass of the electron is $9.11 \\times 10^{-31} {\\rm kg},$ find the values the two datasets give for Planck’s constant. If the values are consistent, combine them to give a better estimate of the value of $h$. Check if this agrees with the accepted value." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " \n", "Number of voltages in dataset 8\n", " \n", "Measurements\n", "Potential (V) \t Radius 1 (cm) \t Radius 2 (cm)\n", "10000.00 \t 1.18 \t\t 1.06\n", "9000.00 \t 1.23 \t\t 1.10\n", "8000.00 \t 1.40 \t\t 1.13\n", "7000.00 \t 1.43 \t\t 1.32\n", "6000.00 \t 1.47 \t\t 1.49\n", "5000.00 \t 1.71 \t\t 1.70\n", "4000.00 \t 1.96 \t\t 1.94\n", "3000.00 \t 2.46 \t\t 2.45\n", " \n", "Calculated wavelengths and momenta\n", "Lamda 1 (m) \t\t Lamda 2 (m) \t\t p (kg*m/s) \t\t 1/p (s/(kg*m))\n", "4.31e-11 +- 3.64e-13 \t 3.87e-11 +- 3.64e-13 \t 5.40e-23 +- 5.40e-25 \t 1.85e+22 +- 1.85e+20\n", "4.50e-11 +- 3.64e-13 \t 4.01e-11 +- 3.64e-13 \t 5.12e-23 +- 5.69e-25 \t 1.95e+22 +- 2.17e+20\n", "5.09e-11 +- 3.64e-13 \t 4.11e-11 +- 3.64e-13 \t 4.83e-23 +- 6.04e-25 \t 2.07e+22 +- 2.59e+20\n", "5.20e-11 +- 3.64e-13 \t 4.82e-11 +- 3.64e-13 \t 4.52e-23 +- 6.45e-25 \t 2.21e+22 +- 3.16e+20\n", "5.36e-11 +- 3.64e-13 \t 5.42e-11 +- 3.64e-13 \t 4.18e-23 +- 6.97e-25 \t 2.39e+22 +- 3.99e+20\n", "6.22e-11 +- 3.64e-13 \t 6.19e-11 +- 3.64e-13 \t 3.82e-23 +- 7.64e-25 \t 2.62e+22 +- 5.24e+20\n", "7.15e-11 +- 3.64e-13 \t 7.05e-11 +- 3.64e-13 \t 3.41e-23 +- 8.54e-25 \t 2.93e+22 +- 7.32e+20\n", "8.97e-11 +- 3.64e-13 \t 8.92e-11 +- 3.64e-13 \t 2.96e-23 +- 9.86e-25 \t 3.38e+22 +- 1.13e+21\n" ] } ], "source": [ "from scipy import stats\n", "from scipy.optimize import least_squares\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "%matplotlib inline\n", "#\n", "# load csv file as an array, assign columns to variables\n", "# Units are:\n", "# V, cm, cm, cm, cm.\n", "Voltage, inner1avg, inner2avg, outer1avg, outer2avg = np.loadtxt('ElectronDiffraction.csv',\n", " delimiter = ',', unpack = True) \n", "#\n", "nLen = len(Voltage)\n", "print(\" \")\n", "print(\"Number of voltages in dataset\",nLen)\n", "#\n", "radius1 = (inner1avg + outer1avg)/4 # cm, radius measurement for first ring\n", "radius2 = (inner2avg + outer2avg)/4 # cm, radius measurement for second ring\n", "#\n", "errorRadius1 = 0.01 # cm\n", "errorRadius2 = 0.01 # cm\n", "#\n", "errorVoltage = 200 # volts\n", "#\n", "#d1 = 213*10**-12 # metres\n", "#d2 = 123*10**-12 # metres\n", "d2 = 213*10**-12 # metres\n", "d1 = 213*10**-12 # metres\n", "#\n", "#SphereRad = 15.85 # cm\n", "SphereRad = 5.85 # cm\n", "#\n", "e = 1.6e-19 # Coulombs\n", "me = 9.11e-31 # kilogrammes\n", "#\n", "Lambda1 = 2*d1*radius1/(2*SphereRad) # m\n", "Lambda2 = 2*d2*radius2/(2*SphereRad) # m\n", "#\n", "Lambda1error = 2*d1/(2*SphereRad)*errorRadius1 # m\n", "Lambda2error = 2*d2/(2*SphereRad)*errorRadius2 # m\n", "#\n", "lambdaarray = np.ones(nLen)\n", "Lambda1error = Lambda1error*lambdaarray\n", "Lambda2error = Lambda2error*lambdaarray\n", "#\n", "# ELectron momentum\n", "p = np.sqrt(2*e*me*Voltage) # kg m/s\n", "#\n", "pError = errorVoltage/np.sqrt(Voltage)*np.sqrt(me*e/2) # kg m/s\n", "#\n", "invP = 1/p \n", "invPerror= pError/p**2\n", "#\n", "print(\" \")\n", "print(\"Measurements\")\n", "print(\"Potential (V) \\t Radius 1 (cm) \\t Radius 2 (cm)\")\n", "for n in range (0, nLen):\n", " print(\"{:3.2f} \\t {:3.2f} \\t\\t {:3.2f}\".format(Voltage[n], radius1[n], radius2[n]))\n", "#\n", "print(\" \")\n", "print(\"Calculated wavelengths and momenta\")\n", "print(\"Lamda 1 (m) \\t\\t Lamda 2 (m) \\t\\t p (kg*m/s) \\t\\t 1/p (s/(kg*m))\")\n", "for n in range (0, nLen):\n", " print(\"{:.2e} +- {:.2e} \\t {:.2e} +- {:.2e} \\t {:.2e} +- {:.2e} \\t {:.2e} +- {:.2e}\"\n", " .format(Lambda1[n], Lambda1error[n], Lambda2[n], Lambda2error[n], p[n], pError[n], invP[n], invPerror[n]))" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " \n", "Choose appropriate scale factors for data before performing fit\n", "Max(1/p) = 3.381467323321354e+22\n", "Max(Lambda 1) = 8.96602564102564e-11\n", " \n", "Scaling factor x data 1e+22\n", "Scaling factor y data 1e-10\n", " \n", "Fit data set 1\n", " \n", "Fit quality:\n", "chisq per point = \n", " [ 0.33 0.421 8.145 0.321 10.523 0.824 0.045 3.182]\n", "chisq = 23.79, chisq/NDF = 3.97.\n", " \n", "Parameters returned by fit:\n", "Intercept = -7.52e-12 +- 2.46e-12 m.\n", "Gradient = 2.71e-33 +- 1.17e-34 Js.\n", " \n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def fitLine(p, x):\n", " '''\n", " Straight line\n", " '''\n", " f = p[0] + p[1]*x\n", " return f\n", "#\n", "def fitLineDiff(p, x):\n", " '''\n", " Differential of straight line\n", " '''\n", " df = p[1]\n", " return df\n", "#\n", "def fitChi(p, x, y, xerr, yerr):\n", " '''\n", " Cost function\n", " '''\n", " e = (y - fitLine(p, x))/(np.sqrt(yerr**2 + fitLineDiff(p, x)**2*xerr**2))\n", " return e\n", "#\n", "# Set initial values of fit parameters, run first fit\n", "print(\" \")\n", "print(\"Choose appropriate scale factors for data before performing fit\")\n", "print(\"Max(1/p) =\",np.amax(invP))\n", "print(\"Max(Lambda 1) =\",np.amax(Lambda1))\n", "scaleX = 1.0e22\n", "scaleY = 1.0e-10\n", "print(\" \")\n", "print(\"Scaling factor x data\",scaleX)\n", "print(\"Scaling factor y data\",scaleY)\n", "print(\" \")\n", "print(\"Fit data set 1\")\n", "#\n", "xData = invP/scaleX\n", "yData = Lambda1/scaleY\n", "xError = invPerror/scaleX\n", "yError = Lambda1error/scaleY\n", "#\n", "nPoints = nLen\n", "#\n", "pInit = [1.0, 1.0]\n", "out = least_squares(fitChi, pInit, args=(xData, yData, xError, yError))\n", "#\n", "fitOK = out.success\n", "#\n", "# Test if fit failed\n", "if not fitOK:\n", " print(\" \")\n", " print(\"Fit failed\")\n", "else:\n", " #\n", " # get output\n", " pFinal = out.x\n", " #\n", " # Apply scale factors to get values of intercept and gradient in same units as input data\n", " cVal1 = pFinal[0]*scaleY\n", " mVal1 = pFinal[1]*scaleY/scaleX\n", " #\n", " # Calculate chis**2 per point, summed chi**2 and chi**2/NDF\n", " chiarr = fitChi(pFinal, xData, yData, xError, yError)**2\n", " chisq = np.sum(chiarr)\n", " NDF = nPoints - 2\n", " redchisq = chisq/NDF\n", "#\n", " np.set_printoptions(precision = 3)\n", " print(\" \")\n", " print(\"Fit quality:\")\n", " print(\"chisq per point = \\n\",chiarr)\n", " print(\"chisq = {:5.2f}, chisq/NDF = {:5.2f}.\".format(chisq, redchisq))\n", " #\n", " # Compute covariance\n", " jMat = out.jac\n", " jMat2 = np.dot(jMat.T, jMat)\n", " detJmat2 = np.linalg.det(jMat2)\n", " #\n", " if detJmat2 < 1E-32:\n", " print(\"Value of determinat detJmat2\",detJmat2)\n", " print(\"Matrix singular, error calculation failed.\")\n", " print(\" \")\n", " print(\"Parameters returned by fit:\")\n", " print(\"Intercept = {:5.2f}\".format(cVal))\n", " print(\"Gradient = {:5.2f}\".format(mVal))\n", " print(\" \")\n", " cErr1 = 0.0\n", " mErr1 = 0.0\n", " else:\n", " covar = np.linalg.inv(jMat2)\n", " #\n", " # Apply scale factors to get errors of intercept and gradient in same units as input data\n", " cErr1 = np.sqrt(covar[0, 0])*scaleY\n", " mErr1 = np.sqrt(covar[1, 1])*scaleY/scaleX\n", " #\n", " print(\" \")\n", " print(\"Parameters returned by fit:\")\n", " print(\"Intercept = {:5.2e} +- {:5.2e} m.\".format(cVal1, cErr1))\n", " print(\"Gradient = {:5.2e} +- {:5.2e} Js.\".format(mVal1, mErr1))\n", " print(\" \")\n", " #\n", " # Calculate fitted function values\n", " nPlot = 2\n", " xPlot = np.linspace(0.0, 1.2*np.max(xData), nPlot)\n", " fitPlot1 = fitLine(pFinal, xPlot)\n", " #\n", " # Make plots\n", " fig = plt.figure(figsize = (8, 6))\n", " plt.title('Data set 1 with fit')\n", " plt.xlabel('1/p (s/(kg $\\\\times$ m)) $\\\\times$ ' + str(scaleX))\n", " plt.ylabel('$\\\\lambda_1$ (m) $\\\\times$ ' + str(scaleY))\n", " plt.errorbar(xData, yData, xerr = xError, yerr = yError, marker = 'o', color = 'r', \n", " linestyle = '', label = \"Data 1\") \n", " plt.errorbar(0.0, cVal1/scaleY, xerr = 0.0, yerr = cErr1/scaleY, marker = 'o', color = 'm', label = \"Intercept 1\") \n", " plt.plot(xPlot, fitPlot1, color = 'b', linestyle = '-', label = \"Fit 1\")\n", " plt.grid(color = 'g')\n", " plt.legend()\n", " plt.show() " ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " \n", "Fit data set 2\n", " \n", "Fit quality:\n", "chisq per point = \n", " [6.164e+00 1.782e-02 1.017e+01 4.892e-02 5.516e-04 8.576e-02 8.764e-02\n", " 9.591e-01]\n", "chisq = 17.54, chisq/NDF = 2.92.\n", " \n", "Parameters returned by fit:\n", "Intercept = -2.20e-11 +- 2.81e-12 m.\n", "Gradient = 3.18e-33 +- 1.33e-34 Js.\n", " \n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#\n", "# Set initial values of fit parameters, run second fit\n", "print(\" \")\n", "print(\"Fit data set 2\")\n", "#\n", "xData2 = invP/scaleX\n", "yData2 = Lambda2/scaleY\n", "xError2 = invPerror/scaleX\n", "yError2 = Lambda2error/scaleY\n", "#\n", "pInit = [1.0, 1.0]\n", "out = least_squares(fitChi, pInit, args=(xData2, yData2, xError2, yError2))\n", "#\n", "fitOK = out.success\n", "#\n", "# Test if fit failed\n", "if not fitOK:\n", " print(\" \")\n", " print(\"Fit failed\")\n", "else:\n", " #\n", " # get output\n", " pFinal = out.x\n", " #\n", " # Apply scale factors to get values of intercept and gradient in same units as input data\n", " cVal2 = pFinal[0]*scaleY\n", " mVal2 = pFinal[1]*scaleY/scaleX\n", " #\n", " # Calculate chis**2 per point, summed chi**2 and chi**2/NDF\n", " chiarr = fitChi(pFinal, xData2, yData2, xError2, yError2)**2\n", " chisq = np.sum(chiarr)\n", " NDF = nPoints - 2\n", " redchisq = chisq/NDF\n", "#\n", " np.set_printoptions(precision = 3)\n", " print(\" \")\n", " print(\"Fit quality:\")\n", " print(\"chisq per point = \\n\",chiarr)\n", " print(\"chisq = {:5.2f}, chisq/NDF = {:5.2f}.\".format(chisq, redchisq))\n", " #\n", " # Compute covariance\n", " jMat = out.jac\n", " jMat2 = np.dot(jMat.T, jMat)\n", " detJmat2 = np.linalg.det(jMat2)\n", " #\n", " if detJmat2 < 1E-32:\n", " print(\"Value of determinat detJmat2\",detJmat2)\n", " print(\"Matrix singular, error calculation failed.\")\n", " print(\" \")\n", " print(\"Parameters returned by fit:\")\n", " print(\"Intercept = {:5.2e}\".format(cVal))\n", " print(\"Gradient = {:5.2e}\".format(mVal))\n", " print(\" \")\n", " cErr2 = 0.0\n", " mErr2 = 0.0\n", " else:\n", " covar = np.linalg.inv(jMat2)\n", " #\n", " # Apply scale factors to get errors of intercept and gradient in same units as input data\n", " cErr2 = np.sqrt(covar[0, 0])*scaleY\n", " mErr2 = np.sqrt(covar[1, 1])*scaleY/scaleX\n", " #\n", " print(\" \")\n", " print(\"Parameters returned by fit:\")\n", " print(\"Intercept = {:5.2e} +- {:5.2e} m.\".format(cVal2, cErr2))\n", " print(\"Gradient = {:5.2e} +- {:5.2e} Js.\".format(mVal2, mErr2))\n", " print(\" \")\n", " #\n", " # Calculate fitted function values\n", " nPlot = 2\n", " xPlot = np.linspace(0.0, 1.2*np.amax(xData), nPlot)\n", " fitPlot2 = fitLine(pFinal, xPlot)\n", " #\n", " # Make plot\n", " fig = plt.figure(figsize = (8, 6))\n", " plt.title('Data set 2 with fit')\n", " plt.xlabel('1/p (s/(kg $\\\\times$ m)) $\\\\times$ ' + str(scaleX))\n", " plt.ylabel('$\\\\lambda_2$ (m) $\\\\times$ ' + str(scaleY))\n", " plt.errorbar(xData2, yData2, xerr = xError2, yerr = yError2, marker = 'o', color = 'r', \n", " linestyle = '', label = \"Data 2\") \n", " plt.errorbar(0.0, cVal2/scaleY, xerr = 0.0, yerr = cErr2/scaleY, marker = 'o', color = 'c', label = \"Intercept 2\") \n", " plt.plot(xPlot, fitPlot2, color = 'b', linestyle = '-', label = \"Fit 2\")\n", " plt.grid(color = 'g')\n", " plt.legend()\n", " plt.show() " ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " \n", "Combined plot \n", " \n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#\n", "# Make combined plot\n", "print(\" \")\n", "print(\"Combined plot \")\n", "print(\" \")\n", "fig = plt.figure(figsize = (8, 6))\n", "plt.title('Data sets 1 and 2 with fits')\n", "plt.xlabel('1/p (s/(kg $\\\\times$ m)) $\\\\times$ ' + str(scaleX))\n", "plt.ylabel('$\\\\lambda$ (m) $\\\\times$ ' + str(scaleY))\n", "plt.errorbar(xData, yData, xerr = xError, yerr = yError, \n", " marker = 'o', color = 'r', linestyle = '', label = \"Data 1\") \n", "plt.plot(xPlot, fitPlot1, color = 'r', linestyle = '-', label = \"Fit 1\")\n", "plt.errorbar(0.0, cVal1/scaleY, xerr = 0.0, yerr = cErr1/scaleY, marker = 'o', color = 'm', label = \"Intercept 1\") \n", "plt.errorbar(xData2, yData2, xerr = xError2, yerr = yError2, \n", " marker = 'o', color = 'b', linestyle = '', label = \"Data 2\") \n", "plt.errorbar(0.0, cVal2/scaleY, xerr = 0.0, yerr = cErr2/scaleY, marker = 'o', color = 'c', label = 'Intercept 2') \n", "plt.plot(xPlot, fitPlot2, color = 'c', linestyle = '-', label = \"Fit 2\")\n", "plt.grid(color = 'g')\n", "plt.legend()\n", "plt.savefig('ElectronDiffractionPlot.png')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " \n", "Intercept values are compatible.\n", "Combined intercept -1.38e-11 +- 1.85e-12 m.\n", "The intercept is not compatible with zero.\n", " \n", "Gradient values are compatible.\n", "Combined gradient 2.92e-33 +- 8.78e-35 Js.\n", "The accepted value of Planck's constant is 6.625e-34 Js.\n", "The two values differ by 25.7 times the measured error.\n", "The measurements here cannot be considered compatible with the standard result.\n" ] } ], "source": [ "#\n", "# Compare values from two datasets and combine if appropriate\n", "sigC = abs(cVal1 - cVal2)/(np.sqrt(cErr1**2 + cErr2**2))\n", "sigM = abs(mVal1 - mVal2)/(np.sqrt(mErr1**2 + mErr2**2))\n", "#\n", "sigTest = 5\n", "if sigC < sigTest:\n", " cVal = (cVal1/cErr1**2 + cVal2/cErr2**2)/(1/cErr1**2 + 1/cErr2**2)\n", " cErr = 1/np.sqrt(1/cErr1**2 + 1/cErr2**2)\n", " print(\" \")\n", " print(\"Intercept values are compatible.\")\n", " print(f\"Combined intercept {cVal:.2e} +- {cErr:.2e} m.\")\n", " sigZero = abs(cVal/cErr)\n", " if sigZero < sigTest:\n", " print(\"The intercept is compatible with zero.\")\n", " else:\n", " print(\"The intercept is not compatible with zero.\")\n", "else:\n", " print(\"Intercept values not compatible.\")\n", "#\n", "if sigM < sigTest:\n", " mVal = (mVal1/mErr1**2 + mVal2/mErr2**2)/(1/mErr1**2 + 1/mErr2**2)\n", " mErr = 1/np.sqrt(1/mErr1**2 + 1/mErr2**2)\n", " print(\" \")\n", " print(\"Gradient values are compatible.\")\n", " print(f\"Combined gradient {mVal:.2e} +- {mErr:.2e} Js.\")\n", " #\n", " # Compare with expected value\n", " hBook = 6.625e-34\n", " sigResult = abs(hBook - mVal)/mErr\n", " print(f\"The accepted value of Planck's constant is {hBook:.3e} Js.\")\n", " print(f\"The two values differ by {sigResult:.1f} times the measured error.\")\n", " if sigResult < sigTest:\n", " print(\"The measurements here can be considered compatible with the standard result.\")\n", " else:\n", " print(\"The measurements here cannot be considered compatible with the standard result.\")\n", "else:\n", " print(\"Gradient values not compatible.\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Results\n", "\n", "The value of Planck's constant obtained here is $(7.8 \\pm 0.5) \\times 10^{-34}$ Js. This is compatible with the accepted value \n", "$h = 6.626 \\times 10^{-34}$ Js." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## More plots\n", "\n", "### Lines" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "xArr = np.linspace(1, 6, 51)\n", "yArr05 = np.sqrt(xArr)\n", "yArr2 = xArr**2\n", "yArr3 = xArr**3\n", "plt.figure(figsize = (6, 4))\n", "plt.title(\"Some functions\")\n", "plt.xlabel(\"x\")\n", "plt.ylabel(\"y\")\n", "plt.plot(xArr, yArr05, linestyle = '-', color = 'c', label = \"sqrt\")\n", "plt.plot(xArr, yArr2, linestyle = ':', color = 'b', label = \"quadratic\")\n", "plt.plot(xArr, yArr3, linestyle = '--', color = 'r', label = \"cubic\")\n", "plt.legend()\n", "plt.grid(color = 'g')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize = (6, 4))\n", "plt.title(\"Some functions, log y axis\")\n", "plt.xlabel(\"x\")\n", "plt.ylabel(\"y\")\n", "plt.plot(xArr, yArr05, linestyle = '-', color = 'c', label = \"sqrt\")\n", "plt.plot(xArr, yArr2, linestyle = ':', color = 'b', label = \"quadratic\")\n", "plt.plot(xArr, yArr3, linestyle = '--', color = 'r', label = \"cubic\")\n", "plt.yscale('log')\n", "plt.legend()\n", "plt.grid(color = 'g')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Points with errorbars" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#\n", "# nPoints is the number of data points\n", "nPoints = 10\n", "#\n", "# Define numpy arrays, initially filled with zeros, to store x and y values\n", "xData = np.zeros(nPoints)\n", "yData = np.zeros(nPoints)\n", "#\n", "# Define arrays to store the errors in x and y\n", "xError = np.zeros(nPoints)\n", "yError = np.zeros(nPoints)\n", "#\n", "# Enter the data\n", "xData[0] = 1.50\n", "xData[1] = 2.31\n", "xData[2] = 2.78\n", "xData[3] = 3.58\n", "xData[4] = 4.08\n", "xData[5] = 4.76\n", "xData[6] = 5.62\n", "xData[7] = 7.02\n", "xData[8] = 8.45\n", "xData[9] = 9.65\n", "#\n", "yData[0] = 14.3\n", "yData[1] = 20.2\n", "yData[2] = 30.1\n", "yData[3] = 36.5\n", "yData[4] = 42.7\n", "yData[5] = 47.1\n", "yData[6] = 52.9\n", "yData[7] = 68.8\n", "yData[8] = 85.2\n", "yData[9] = 99.4\n", "#\n", "# Enter the errors\n", "xError[0] = 0.21\n", "xError[1] = 0.11\n", "xError[2] = 0.43\n", "xError[3] = 0.13\n", "xError[4] = 0.17\n", "xError[5] = 0.18\n", "xError[6] = 0.15\n", "xError[7] = 0.19\n", "xError[8] = 0.17\n", "xError[9] = 0.11\n", "#\n", "yError[0] = 2.1\n", "yError[1] = 1.7\n", "yError[2] = 3.3\n", "yError[3] = 1.1\n", "yError[4] = 0.9\n", "yError[5] = 1.1\n", "yError[6] = 1.5\n", "yError[7] = 0.9\n", "yError[8] = 1.2\n", "yError[9] = 2.9\n", "#\n", "# Plot data\n", "fig = plt.figure(figsize = (8, 6))\n", "plt.title('Data with errors')\n", "plt.xlabel('x')\n", "plt.ylabel('y')\n", "plt.errorbar(xData, yData, xerr = xError, yerr = yError, color = 'r', \n", " marker = '+', linestyle = '', label = \"Data\") \n", "plt.xlim(1.0, 11.0)\n", "plt.ylim(10.0, 110.0)\n", "plt.grid(color = 'g')\n", "plt.legend()\n", "plt.savefig(\"ErrorBarPlot.png\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Histograms (with mean and standard deviation)\n", "\n", "A histogram shows the frequency with which values occur in a dataset. The data are split into \"bins\", the lower and upper limits of which are indicated by the edges of the bars in the plot. The area of each of the bars is proportional to the number of data points that fall within the bin. (Most histograms use bins of equal width, in which case the height of the bin indicates the number of entries it contains.) Here, a histogram is plotted using defined, rather than accepting those calculated by matplotlib. (A complete description of `plt.hist` is provided [here](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.hist.html).)\n", "\n", "The mean and standard deviation of the distribution are also calculated and displayed on the plot." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " \n", "gaussArr\n", " [ 3.501 5.479 6.768 5.229 3.83 10.654 6.862 6.865 4.04 4.736\n", " 7.155 5.75 7.958 9.19 3.596 3.247 8.109 5.922 7.361 8.658\n", " 8.567 2.483 7.229 9.033 5.608 4.366 4.108 6.441 4.799 5.695\n", " 3.625 6.598 4.104 2.313 7.621 4.495 5.127 6.095 5.498 6.334\n", " 7.331 9.394 8.107 6.585 4.351 6.202 6.497 9.431 5.245 1.803\n", " 4.794 4.962 1.575 5.188 5.407 6.543 5.082 6.928 3.945 6.345\n", " 7.187 5.384 4.153 4.594 8.027 9.057 7.052 9.378 6.025 7.681\n", " 5.818 8.064 6.968 5.22 5.796 9.695 7.349 3.214 4.842 5.503\n", " 4.954 6.94 4.159 5.94 6.163 8.071 3.523 5.809 2.616 7.319\n", " 6.732 6.121 7.118 2.56 6.821 4.766 9.242 6.503 9.556 9.467\n", " 5.061 4.749 4.759 6.978 9.18 4.651 5.62 2.469 8.745 6.662\n", " 12.211 4.865 5.729 4.675 6.623 5.843 8.017 5.903 5.347 4.104\n", " 5.465 4.441 5.358 4.93 7.49 8.439 4.319 7.216 6.859 3.971\n", " 3.705 9.736 8.673 2.48 4.591 6.99 5.839 3.619 6.939 5.181\n", " 9.829 9.407 3.157 5.746 4.773 1.779 4.491 6.465 3.288 6.942\n", " 6.025 5.832 9.285 3.981 4.45 7.08 5.502 7.005 4.307 6.729\n", " 9.311 3.968 4.407 7.583 6.509 4.378 4.62 4.975 6.674 6.11\n", " 2.828 4.471 3.714 4.883 3.972 8.306 7.667 10.861 7.494 8.885\n", " 2.344 5.938 3.178 11.319 8.202 8.116 3.119 2.521 5.646 6.015\n", " -0.354 5.07 4.113 2.907 7.56 5.218 3.773 6.298 5.901 5.581\n", " 5.314 7.076 2.205 8.397 5.726 4.866 7.333 4.92 7.663 5.871\n", " 8.209 2.922 3.285 5.325 4.949 6.844 6.463 7.637 5.401 3.609\n", " 6.564 9.051 4.395 8.109 6.643 5.543 9.829 7.736 5.825 9.994\n", " 1.882 4.939 6.328 4.961 2.762 6.371 4.212 8.454 3.092 5.579\n", " 4.276 8.263 6.508 7.398 3.576 6.471 3.742 5.07 8.728 0.788\n", " 8.671 8.745 2.525 6.692 5.23 3.511 6.853 8.167 6.488 4.903\n", " 7.451 8.079 5.308 5.76 6.334 7.615 2.862 3.362 3.064 6.268\n", " 3.217 7.349 7.339 4.834 6.353 7.384 5.814 6.784 1.493 7.34\n", " -0.276 5.032 7.896 2.971 5.631 2.391 7.096 6.012 8.051 6.855\n", " 6.131 6.341 1.957 9.503 4.893 7.172 3.262 5.604 4.512 7.244]\n", " \n", "Histogram bins start at -1.0 finish at 13.0\n", "Number of bins is 14 and width of bins is 1.0\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#\n", "# Read in an array\n", "gaussArr = np.loadtxt(\"normDistArr.csv\")\n", "print(\" \")\n", "print(\"gaussArr\\n\",gaussArr)\n", "#\n", "binBot = -1.0\n", "binTop = 13.0\n", "binNumber = 14\n", "binEdges = np.linspace(binBot, binTop, binNumber + 1)\n", "binWidth = (binTop - binBot)/binNumber\n", "print(\" \")\n", "print(\"Histogram bins start at\",binBot,\"finish at\",binTop)\n", "print(\"Number of bins is\",binNumber,\"and width of bins is\",binWidth)\n", "#\n", "nEvents = len(gaussArr) # determine length of gaussArr\n", "mu = np.mean(gaussArr) # calculate arithmetic mean of numbers in array\n", "sigma = np.std(gaussArr) # calculate standard deviation (error on single value)\n", "muError = sigma/np.sqrt(nEvents) # calculate error of mean\n", "yMu = nEvents/20\n", "ySigma = 1.2*nEvents/20\n", "#\n", "plt.figure(figsize = (8, 6))\n", "plt.title('Normal distribution', fontsize = 14)\n", "plt.xlabel('Data')\n", "plt.ylabel('Relative frequency')\n", "plt.hist(gaussArr, bins = binEdges)\n", "plt.errorbar(mu, yMu, xerr = muError, marker = 'o', color = 'r', \n", " label = 'Mean with its error')\n", "plt.errorbar(mu, ySigma, xerr = sigma/2, marker = '', color = 'b', label = 'RMS')\n", "plt.grid(color = 'g')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Multiple plots in one figure" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "xArr = np.linspace(0, 15, 15)\n", "yArr = xArr**2\n", "#\n", "fig = plt.figure(figsize = (12, 16)) # opens a figure \n", "fig.suptitle('Overall title', fontsize=20) # overall title\n", "#\n", "plt.subplot(3, 2, 1) # creates 3 row, 2 column grid, starts in top left square \n", "plt.title(\"Plot 1\")\n", "plt.xlabel(\"x\")\n", "plt.ylabel(\"y\")\n", "plt.plot(xArr, yArr, linestyle = '--', color = 'b', label = 'curve 1')\n", "plt.legend()\n", "plt.grid(color = 'g')\n", "#\n", "plt.subplot(3, 2, 2) # second square, reading from left to right, top to bottom\n", "plt.title(\"Plot 2\")\n", "plt.xlabel(\"x\")\n", "plt.ylabel(\"y\")\n", "plt.plot(xArr, yArr, linestyle = ':', color = 'r', label = 'curve 2')\n", "plt.grid(color = 'g')\n", "plt.legend()\n", "#\n", "plt.subplot(3, 2, 3) # plot in third square\n", "plt.title(\"Plot 3\")\n", "plt.xlabel(\"x\")\n", "plt.ylabel(\"y\")\n", "plt.plot(xArr, yArr, linestyle = '-.', color = 'c', label = 'curve 3')\n", "plt.grid(color = 'g')\n", "plt.legend()\n", "#\n", "plt.subplot(3, 2, 4) # plot in fourth square\n", "plt.title(\"Plot 4\")\n", "plt.xlabel(\"x\")\n", "plt.ylabel(\"y\")\n", "plt.plot(xArr, yArr, linestyle = '-', color = 'm', label = 'curve 4')\n", "plt.grid(color = 'g')\n", "plt.legend()\n", "#\n", "plt.subplot(3, 2, 5) # plot in fifth square\n", "plt.title(\"Plot 5\")\n", "plt.xlabel(\"x\")\n", "plt.ylabel(\"y\")\n", "plt.plot(xArr, yArr, linestyle = '-', color = 'k', label = 'curve 5')\n", "plt.grid(color = 'g')\n", "plt.legend()\n", "#\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 4 }