{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Demonstration: Reading Data and fitting\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`````{admonition} Try it yourself with the Jupyter notebook\n", ":class: tip\n", "Note that demonstration sections such as this one correspond to a Jupyter Notebook that you can run yourself (rather than just looking at the static version shown here). You are highly encouraged to download the source files of the lecture notes such that you have local access to these notebooks.\n", "`````" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Our first data set is going to be a classic from nuclear physics, namely all\n", "available data on binding energies. Don't be intimidated if you are not familiar with nuclear physics. It serves simply as an example here of a data set.\n", "\n", "We will show some of the strengths of packages like **Scikit-Learn** in fitting nuclear binding energies to\n", "specific functions using linear regression. First, however, we need to meet the **Pandas**." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# For showing plots inline\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Meet the Pandas\n", "\n", "\n", "\n", "[Pandas](https://pandas.pydata.org/) is a very useful Python package for reading and organizing data. It is an open source library\n", "providing high-performance, easy-to-use data structures and data\n", "analysis tools for Python. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "**pandas** stands for panel data, a term borrowed from econometrics and is an efficient library for data analysis with an emphasis on tabular data.\n", "**pandas** has two major classes, the **DataFrame** class with two-dimensional data objects such as tabular data organized in columns, and the class **Series** with a focus on one-dimensional data objects. Both classes allow you to index data easily as we will see in the examples below. \n", "**pandas** allows you also to perform mathematical operations on the data, spanning from simple reshaping of vectors and matrices to statistical operations. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following simple example shows how we can make tables of our data. Here we define a data set which includes names, place of birth and date of birth, and displays the data in an easy to read way." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " First Name Last Name Place of birth Date of Birth T.A.\n", "0 Frodo Baggins Shire 2968\n", "1 Bilbo Baggins Shire 2890\n", "2 Aragorn II Elessar Eriador 2931\n", "3 Samwise Gamgee Shire 2980" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pandas as pd\n", "from IPython.display import display\n", "data = {'First Name': [\"Frodo\", \"Bilbo\", \"Aragorn II\", \"Samwise\"],\n", " 'Last Name': [\"Baggins\", \"Baggins\",\"Elessar\",\"Gamgee\"],\n", " 'Place of birth': [\"Shire\", \"Shire\", \"Eriador\", \"Shire\"],\n", " 'Date of Birth T.A.': [2968, 2890, 2931, 2980]\n", " }\n", "data_pandas = pd.DataFrame(data)\n", "display(data_pandas)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the above example we have imported **pandas** with the shorthand **pd**, the latter has become the standard way to import **pandas**. We then make a dictionary with various variables\n", "and reorganize the lists of values into a **DataFrame** and then print out a neat table with specific column labels as *Name*, *place of birth* and *date of birth*.\n", "Displaying these results, we see that the indices are given by the default numbers from zero to three.\n", "**pandas** is extremely flexible and we can easily change the above indices by defining a new type of indexing as" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "
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First NameLast NamePlace of birthDate of Birth T.A.
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AragornAragorn IIElessarEriador2931
SamSamwiseGamgeeShire2980
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" ], "text/plain": [ " First Name Last Name Place of birth Date of Birth T.A.\n", "Frodo Frodo Baggins Shire 2968\n", "Bilbo Bilbo Baggins Shire 2890\n", "Aragorn Aragorn II Elessar Eriador 2931\n", "Sam Samwise Gamgee Shire 2980" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data_pandas = pd.DataFrame(data,index=['Frodo','Bilbo','Aragorn','Sam'])\n", "display(data_pandas)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Thereafter we display the content of the row which begins with the index **Aragorn**" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "First Name Aragorn II\n", "Last Name Elessar\n", "Place of birth Eriador\n", "Date of Birth T.A. 2931\n", "Name: Aragorn, dtype: object" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(data_pandas.loc['Aragorn'])" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "We can easily append data to this DataFrame, for example using `concat` (note that the function `append` was deprecated in pandas 2.0)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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First NameLast NamePlace of birthDate of Birth T.A.
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SamSamwiseGamgeeShire2980
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" ], "text/plain": [ " First Name Last Name Place of birth Date of Birth T.A.\n", "Frodo Frodo Baggins Shire 2968\n", "Bilbo Bilbo Baggins Shire 2890\n", "Aragorn Aragorn II Elessar Eriador 2931\n", "Sam Samwise Gamgee Shire 2980\n", "Pippin Peregrin Took Shire 2990" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "new_hobbit = {'First Name': [\"Peregrin\"],\n", " 'Last Name': [\"Took\"],\n", " 'Place of birth': [\"Shire\"],\n", " 'Date of Birth T.A.': [2990]\n", " }\n", "data_pandas=pd.concat([data_pandas,pd.DataFrame(new_hobbit, index=['Pippin'])])\n", "display(data_pandas)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Here are other examples where we use the **DataFrame** functionality to handle arrays, now with more interesting features for us, namely numbers. We set up a matrix \n", "of dimensionality $10\\times 5$ and compute the mean value and standard deviation of each column. Similarly, we can perform mathematical operations like squaring the matrix elements and many other operations." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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non-null float64\n", " 4 Fifth 10 non-null float64\n", "dtypes: float64(5)\n", "memory usage: 480.0 bytes\n", "None\n", " First Second Third Fourth Fifth\n", "count 10.000000 10.000000 10.000000 10.000000 10.000000\n", "mean -0.175300 0.083527 -0.044334 -0.399836 0.331939\n", "std 1.069584 0.965548 1.018232 0.793167 0.918992\n", "min -1.749765 -1.443217 -1.690617 -1.613579 -1.232435\n", "25% -0.522836 -0.633949 -0.713163 -0.785061 0.087887\n", "50% -0.280179 0.281930 -0.122282 -0.382187 0.463861\n", "75% 0.441264 0.657041 0.861676 -0.205231 0.923602\n", "max 1.618982 1.541605 1.361556 0.816847 1.470714\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.columns = ['First', 'Second', 'Third', 'Fourth', 'Fifth']\n", "df.index = np.arange(10)\n", "\n", "display(df)\n", "print(df['Second'].mean() )\n", "print(df.info())\n", "print(df.describe())\n", "\n", "from pylab import plt, mpl\n", "mpl.rcParams['font.family'] = 'serif'\n", "\n", "df.cumsum().plot(lw=2.0, figsize=(10,6));\n", "\n", "df.plot.bar(figsize=(10,6), rot=15);" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "We can produce a $4\\times 4$ matrix" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0 1 2 3]\n", " [ 4 5 6 7]\n", " [ 8 9 10 11]\n", " [12 13 14 15]]\n", " 0 1 2 3\n", "0 0 1 2 3\n", "1 4 5 6 7\n", "2 8 9 10 11\n", "3 12 13 14 15\n" ] } ], "source": [ "b = np.arange(16).reshape((4,4))\n", "print(b)\n", "df1 = pd.DataFrame(b)\n", "print(df1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and many other operations. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The **Series** class is another important class included in\n", "**pandas**. You can view it as a specialization of **DataFrame** but where\n", "we have just a single column of data. It shares many of the same features as **DataFrame**. As with **DataFrame**,\n", "most operations are vectorized, achieving thereby a high performance when dealing with computations of arrays, in particular labeled arrays.\n", "As we will see below it leads also to a very concice code close to the mathematical operations we may be interested in.\n", "For multidimensional arrays, we recommend strongly [xarray](http://xarray.pydata.org/en/stable/). **xarray** has much of the same flexibility as **pandas**, but allows for the extension to higher dimensions than two. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## To our real data: nuclear binding energies. Brief reminder on masses and binding energies\n", "\n", "Let us now dive into nuclear physics and remind ourselves briefly about some basic features about binding\n", "energies. A basic quantity which can be measured for the ground\n", "states of nuclei is the atomic mass $M(N, Z)$ of the neutral atom with\n", "atomic mass number $A$ and charge $Z$. The number of neutrons is $N$. There are indeed several sophisticated experiments worldwide which allow us to measure this quantity to high precision (parts per million even)." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Atomic masses are usually tabulated in terms of the mass excess defined by" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\Delta M(N, Z) = M(N, Z) - uA,\n", "$$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "where $u$ is the Atomic Mass Unit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "u = M(^{12}\\mathrm{C})/12 = 931.4940954(57) \\hspace{0.1cm} \\mathrm{MeV}/c^2.\n", "$$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "The nucleon masses are" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "m_p = 1.00727646693(9)u,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "m_n = 939.56536(8)\\hspace{0.1cm} \\mathrm{MeV}/c^2 = 1.0086649156(6)u.\n", "$$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "In the [2016 mass evaluation of by W.J.Huang, G.Audi, M.Wang, F.G.Kondev, S.Naimi and X.Xu](http://nuclearmasses.org/resources_folder/Wang_2017_Chinese_Phys_C_41_030003.pdf) {cite}`Wang2017`\n", "there are data on masses and decays of 3437 nuclei." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "The nuclear binding energy is defined as the energy required to break\n", "up a given nucleus into its constituent parts of $N$ neutrons and $Z$\n", "protons. In terms of the atomic masses $M(N, Z)$ the binding energy is\n", "defined by" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "BE(N, Z) = ZM_H c^2 + Nm_n c^2 - M(N, Z)c^2 ,\n", "$$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "where $M_H$ is the mass of the hydrogen atom and $m_n$ is the mass of the neutron.\n", "In terms of the mass excess the binding energy is given by" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "BE(N, Z) = Z\\Delta_H c^2 + N\\Delta_n c^2 -\\Delta(N, Z)c^2 ,\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where $\\Delta_H c^2 = 7.2890$ MeV and $\\Delta_n c^2 = 8.0713$ MeV." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "A popular and physically intuitive model which can be used to parametrize\n", "the experimental binding energies as function of $A$, is the so-called\n", "**liquid drop model**. The ansatz is based on the following expression" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "BE(N,Z) = a_1A-a_2A^{2/3}-a_3\\frac{Z(Z-1)}{A^{1/3}}-a_4\\frac{(N-Z)^2}{A},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where $A$ stands for the number of nucleons and the $a_i$s are parameters which are determined by a fit\n", "to the experimental data." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "To arrive at the above expression we have assumed that we can make the following assumptions:" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ " * There is a volume term $a_1A$ proportional to the number of nucleons. When an assembly of nucleons of the same size is packed together into the smallest volume, each interior nucleon has a certain number of other nucleons in contact with it. This contribution is proportional to the volume. Note that the nuclear radius is empirically proportional to $A^{1/3}$." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ " * There is a surface energy term $a_2A^{2/3}$. The assumption here is that a nucleon at the surface of a nucleus interacts with fewer other nucleons than one in the interior of the nucleus and hence its binding energy is less. This surface energy term takes that into account and is therefore negative and is proportional to the surface area." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ " * There is a Coulomb energy term $a_3\\frac{Z(Z-1)}{A^{1/3}}$. The electric repulsion between each pair of protons in a nucleus yields less binding." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ " * There is an asymmetry term $a_4\\frac{(N-Z)^2}{A}$. This term is associated with the Pauli exclusion principle and reflects the fact that the proton-neutron interaction is more attractive on the average than the neutron-neutron and proton-proton interactions.\n", "\n", "We could also add a so-called pairing term, which is a correction term that\n", "arises from the tendency of proton pairs and neutron pairs to\n", "occur. An even number of particles is more stable than an odd number." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Import modules\n", "\n", "We import also various modules that we will find useful in order to present various Machine Learning methods. Here we focus mainly on the functionality of **scikit-learn**." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "# Common imports\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import sklearn.linear_model as skl\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n", "import os" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "# Nicer plots\n", "import seaborn as sns\n", "sns.set('talk')" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Organizing our data\n", "\n", "Let us start with reading and organizing our data.\n", "We start with the compilation of masses and binding energies from 2016.\n", "After having downloaded this file to our own computer, we are now ready to read the file and start structuring our data.\n", "\n", "\n", "We start with preparing folders for storing our calculations and the data file over masses and binding energies. " ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "# Where to save the figures and data files\n", "PROJECT_ROOT_DIR = \"Results\"\n", "FIGURE_ID = \"Results/FigureFiles\"\n", "DATA_ID = \"DataFiles/\"\n", "\n", "if not os.path.exists(PROJECT_ROOT_DIR):\n", " os.mkdir(PROJECT_ROOT_DIR)\n", "\n", "if not os.path.exists(FIGURE_ID):\n", " os.makedirs(FIGURE_ID)\n", "\n", "if not os.path.exists(DATA_ID):\n", " os.makedirs(DATA_ID)\n", "\n", "def image_path(fig_id):\n", " return os.path.join(FIGURE_ID, fig_id)\n", "\n", "def data_path(dat_id):\n", " return os.path.join(DATA_ID, dat_id)\n", "\n", "def save_fig(fig_id):\n", " plt.savefig(image_path(fig_id) + \".png\", format='png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our next step is to read the data on experimental binding energies and\n", "reorganize them as functions of the mass number $A$, the number of\n", "protons $Z$ and neutrons $N$ using **pandas**. Before we do this it is\n", "always useful (unless you have a binary file or other types of compressed\n", "data) to actually open the file and simply take a look at it!\n", "\n", "**Note**: In the cell below, `infile` is an iterator and after you show the first 42 lines, the iterator will point to the 43rd line. In order to avoid reading the file from a non-expected start point, we put the iteration and print commands within a `with` block which means that `infile` is only defined inside this block and will then be forgotten. Thanks to (@Daniel Berg} for finding this bug." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 a0boogfu A T O M I C M A S S A D J U S T M E N T\n", "0 DATE 1 Mar 2017 TIME 17:26\n", "0 ********************* A= 0 TO 295\n", " * file : mass16.txt *\n", " *********************\n", "\n", " This is one file out of a series of 3 files published in:\n", " \"The Ame2016 atomic mass evaluation (I)\" by W.J.Huang, G.Audi, M.Wang, F.G.Kondev, S.Naimi and X.Xu\n", " Chinese Physics C41 030002, March 2017.\n", " \"The Ame2016 atomic mass evaluation (II)\" by M.Wang, G.Audi, F.G.Kondev, W.J.Huang, S.Naimi and X.Xu\n", " Chinese Physics C41 030003, March 2017.\n", " for files : mass16.txt : atomic masses\n", " rct1-16.txt : react and sep energies, part 1\n", " rct2-16.txt : react and sep energies, part 2\n", " A fourth file is the \"Rounded\" version of the atomic mass table (the first file)\n", " mass16round.txt : atomic masses \"Rounded\" version\n", "\n", " All files are 3436 lines long with 124 character per line.\n", " Headers are 39 lines long.\n", " Values in files 1, 2 and 3 are unrounded copy of the published ones\n", " Values in file 4 are exact copy of the published ones\n", "\n", " col 1 : Fortran character control: 1 = page feed 0 = line feed\n", " format : a1,i3,i5,i5,i5,1x,a3,a4,1x,f13.5,f11.5,f11.3,f9.3,1x,a2,f11.3,f9.3,1x,i3,1x,f12.5,f11.5\n", " cc NZ N Z A el o mass unc binding unc B beta unc atomic_mass unc\n", " Warnings : this format is identical to the ones used in Ame2003 and Ame2012\n", " in particular \"Mass Excess\" and \"Atomic Mass\" values are given now, when necessary,\n", " with 5 digits after decimal point.\n", " decimal point is replaced by # for (non-experimental) estimated values.\n", " * in place of value : not calculable\n", "\n", "....+....1....+....2....+....3....+....4....+....5....+....6....+....7....+....8....+....9....+...10....+...11....+...12....\n", "\n", "\n", " MASS LIST\n", " for analysis\n", "\n", "1N-Z N Z A EL O MASS EXCESS BINDING ENERGY/A BETA-DECAY ENERGY ATOMIC MASS\n", " (keV) (keV) (keV) (micro-u)\n", "0 1 1 0 1 n 8071.31713 0.00046 0.0 0.0 B- 782.347 0.000 1 008664.91582 0.00049\n", " -1 0 1 1 H 7288.97061 0.00009 0.0 0.0 B- * 1 007825.03224 0.00009\n", "0 0 1 1 2 H 13135.72176 0.00011 1112.283 0.000 B- * 2 014101.77811 0.00012\n", "\n" ] } ], "source": [ "with open(data_path(\"MassEval2016.dat\"),'r') as infile:\n", " head = [next(infile) for x in np.arange(42)]\n", " print(\"\".join(head))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "In particular, the program that outputs the final nuclear masses is written in Fortran with a specific format. It means that we need to figure out the format and which columns contain the data we are interested in. Pandas comes with a function that reads formatted output. After having admired the file, we are now ready to start massaging it with **pandas**. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "- The file begins with some basic format information in the header (that is 39 lines long).\n", "- The data we are interested in are in columns 2, 3, 4 and 11, giving us the number of neutrons, protons, mass numbers and binding energies, respectively. \n", "- We add also for the sake of completeness the element name. \n", "- The data are in fixed-width formatted lines and we will convert them into the **pandas** DataFrame structure.\n", "- The liquid drop model does not work well for the lightest elements so we skip the first few lines of data in the file (we could as well have made this cut after actually reading the data).\n", "- The data is for the binding energy per nucleon (not the total binding energy as in the formula). We will need to be careful to compare the same quantities." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "# Open the file again and read the experimental data with Pandas\n", "with open(data_path(\"MassEval2016.dat\"),'r') as infile:\n", " Masses = pd.read_fwf(infile, usecols=(2,3,4,6,11),\n", " names=('N', 'Z', 'A', 'Element', 'Ebinding'),\n", " widths=(1,3,5,5,5,1,3,4,1,13,11,11,9,1,2,11,9,1,3,1,12,11,1),\n", " header=64,\n", " index_col=False)\n", "\n", "# Extrapolated values are indicated by '#' in place of the decimal place, so\n", "# the Ebinding column won't be numeric. Coerce to float and drop these entries.\n", "Masses['Ebinding'] = pd.to_numeric(Masses['Ebinding'], errors='coerce')\n", "Masses = Masses.dropna()\n", "# Convert from keV to MeV.\n", "Masses['Ebinding'] /= 1000\n", "\n", "# Group the DataFrame by nucleon number, A.\n", "Masses = Masses.groupby('A')\n", "# Find the rows of the grouped DataFrame with the maximum binding energy.\n", "Masses = Masses.apply(lambda t: t[t.Ebinding==t.Ebinding.max()])" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "We have now read in the data, grouped them according to the variables we are interested in.\n", "We see how easy it is to reorganize the data using **pandas**. If we\n", "were to do these operations in C/C++ or Fortran, we would have had to\n", "write various functions/subroutines which perform the above\n", "reorganizations for us. Having reorganized the data, we can now start\n", "to make some simple fits using both the functionalities in **numpy** and\n", "**Scikit-Learn** afterwards.\n", "\n", "Now we define five variables which contain\n", "the number of nucleons $A$, the number of protons $Z$ and the number of neutrons $N$, the element name and finally the energies themselves." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " N Z A Element Ebinding\n", "A \n", "10 2 6 4 10 Be 6.497630\n", "11 8 6 5 11 B 6.927732\n", "12 14 6 6 12 C 7.680144\n", "13 20 7 6 13 C 7.469849\n", "14 25 8 6 14 C 7.520319\n", "... ... ... ... ... ...\n", "264 3272 156 108 264 Hs 7.298375\n", "265 3278 157 108 265 Hs 7.296247\n", "266 3285 158 108 266 Hs 7.298273\n", "269 3306 159 110 269 Ds 7.250154\n", "270 3312 160 110 270 Ds 7.253775\n", "\n", "[258 rows x 5 columns]\n" ] } ], "source": [ "A = Masses['A']\n", "Z = Masses['Z']\n", "N = Masses['N']\n", "Element = Masses['Element']\n", "TotalEnergies = Masses['Ebinding'] * Masses['A']\n", "print(Masses)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "The next step, and we will define this mathematically later, is to set up the so-called **design matrix**. We will throughout call this matrix $\\boldsymbol{X}$.\n", "It has dimensionality $n\\times p$, where $n$ is the number of data points and $p$ are the so-called predictors or features. In our case they are given by the functions of $A$ (and $N$, $Z$) that are included in the liquid drop model and that we wish to include in the fit.\n", "\n", "We will make the fit with respect to the total binding energy. One could argue that it would be better to fit to the binding energy per nucleon. Can you argue why that is?\n", "\n", "Note that we also include a constant term (an intercept) since this is expected by the LinearRegression method that we will be using." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "# Now we set up the design matrix X\n", "X = np.zeros((len(A),5))\n", "X[:,0] = np.ones_like(A)\n", "X[:,1] = A\n", "X[:,2] = A**(2.0/3.0)\n", "X[:,3] = Z*(Z-1) * A**(-1.0/3.0)\n", "X[:,4] = (N-Z)**2 * A**(-1.0)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "With **scikitlearn** we are now ready to use linear regression and fit our data. Note that we have included an intercept column into our design matrix, which corresponds to a constant predictor term in our model. It is very common to have such a term in a linear regression fit and we include it here although our model actually does not have such a predictor. In fact, the built-in linear regression function that we will use does usually add such an offset automatically and we need to explicitly turn it off using the argument `fit_intercept=False` since we already have it in our design matrix." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "clf = skl.LinearRegression(fit_intercept=False).fit(X, TotalEnergies)\n", "fity = clf.predict(X)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Pretty simple!\n", "Now we can print measures of how our fit is doing, the coefficients from the fits and plot the final fit together with our data." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mean squared error (for energy per nucleon): 0.002\n", "Variance score (for energy per nucleon): 0.990\n", "Mean absolute error (for energy per nucleon): 0.025\n" ] } ], "source": [ "# The mean squared error \n", "print(f\"Mean squared error (for energy per nucleon): {mean_squared_error(TotalEnergies/A, fity/A):.3f}\")\n", "# Explained variance score: 1 is perfect prediction \n", "print(f'Variance score (for energy per nucleon): {r2_score(TotalEnergies/A, fity/A):.3f}')\n", "# Mean absolute error \n", "print(f'Mean absolute error (for energy per nucleon): {mean_absolute_error(TotalEnergies/A, fity/A):.3f}')" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 19.06802716 17.20433313 -24.05418781 -0.78693299 -26.3490481 ]\n", "***\n", "intercept volume surface coulomb asymmetry\n", "19.068 17.204 -24.054 -0.787 -26.349\n" ] } ], "source": [ "print(clf.coef_)\n", "# Or nicer\n", "print('***')\n", "print('intercept volume surface coulomb asymmetry')\n", "print(\" \".join([\"%.3f\"%coef for coef in clf.coef_]))" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Generate a plot comparing the experimental with the fitted values values.\n", "Masses['Eapprox'] = fity/A\n", "fig, ax = plt.subplots(figsize=(8,5))\n", "ax.set_xlabel(r'$A = N + Z$')\n", "ax.set_ylabel(r'$E_\\mathrm{bind}\\,/A$ MeV')\n", "ax.scatter(Masses['A'], Masses['Ebinding'], alpha=0.2, marker='o',\n", " label='Ame2016')\n", "ax.plot(Masses['A'], Masses['Eapprox'], alpha=0.9, lw=2, c='k',\n", " label='Fit')\n", "ax.legend()\n", "save_fig(\"Masses2016\")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## A first summary\n", "\n", "The aim behind these introductory words was to present to you various\n", "Python libraries and their functionalities, in particular libraries like\n", "**numpy**, **pandas**, and **matplotlib** and other that make our life much easier\n", "in handling various data sets and visualizing data.\n", "\n", "Furthermore, **Scikit-Learn** allows us with few lines of code to implement popular\n", "Machine Learning algorithms for supervised learning.\n", "\n", "A physics remark that concerns the liquid drop model for nuclear binding energies is that we fail to reproduce some slow variations in the target data. In fact, we know that these variations are due to shell effects, with local maxima appearing in binding energies as different shells and subshells are filled." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "celltoolbar": "Slideshow", "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": "3.9.17" } }, "nbformat": 4, "nbformat_minor": 4 }