Py_lectures_02_basicElements_Dict_and_Func_1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic Elements II - a\n",
    "\n",
    "Here we introduce the first dictionary and a function to be called"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's start we a revised and improved version of the little script we did so far \n",
    "# to plot a distribution of duration T\n",
    "import os, sys\n",
    "import numpy as np\n",
    "import matplotlib.pylab as plt\n",
    "\n",
    "# Put your main directory here, where there are the files to analyse\n",
    "mainDir = \"/home/gf/src/Python/Python-in-the-lab/Bk\"\n",
    "\n",
    "# Check if the directory exists\n",
    "if not os.path.isdir(mainDir):\n",
    "    print(\"Houston, we have a problem!\")\n",
    "    # Otherwise exit\n",
    "    sys.exit()\n",
    "    \n",
    "# We then explicitly create a figure\n",
    "fig = plt.figure()\n",
    "# and add a plot\n",
    "ax = fig.add_subplot(111)\n",
    "# Because we are going to use errobar\n",
    "# it is better to set the scale to log now\n",
    "ax.set_xscale(\"log\")\n",
    "ax.set_yscale(\"log\")\n",
    "\n",
    "# Now we need to plot the three files \n",
    "# F64ac_0.0i_T.dat where i = [1,2,3]\n",
    "# We could make a loop for \"i\" but it is not very pythonics\n",
    "# Let's use glob\n",
    "\n",
    "import glob # Check the documentation for its methods\n",
    "\n",
    "# In particular glob1 finds files given a pattern in a directory\n",
    "# * stands for any character: it does not work here, try yourself\n",
    "# ? stands for a single character, which is what we need\n",
    "filenames = sorted(glob.glob1(mainDir, \"F64ac_0.0?_T.dat\"))\n",
    "\n",
    "for filename in filenames:\n",
    "    # We need the full pathname to load the data\n",
    "    fname = os.path.join(mainDir, filename)\n",
    "    x, y = np.loadtxt(fname,unpack=True)\n",
    "    # We need to get rid of the zeros\n",
    "    # Let's make a boolean np.array\n",
    "    # telling if the y is not zero\n",
    "    is_not_zero = y!=0\n",
    "    # Redefine x and y\n",
    "    x, y = x[is_not_zero], y[is_not_zero]\n",
    "    # Add a fake \n",
    "    yerr = y * 0.6\n",
    "    # Extract the frequency to make a label\n",
    "    # See what we use filename and not fname?\n",
    "    material, freq, meas = filename.split(\"_\")\n",
    "    # Create the label\n",
    "    lb = \"{0} Hz\".format(freq)\n",
    "    ax.errorbar(x,y,yerr,fmt=\"o\", label=lb)\n",
    "ax.legend()\n",
    "ax.set_xlabel(\"$Duration T (s)$\", size=16)\n",
    "ax.set_ylabel(\"$P(T)$\", size=16); # Suppress the output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have a little script that analyses some data (i.e. duration distributions), but there are  two other sets of data we are interested in. \n",
    "\n",
    "These are the size (S) and the energy (E) distributions. Can we plot them together?\n",
    "\n",
    "Clearly writing three times the same code just to plot similar data is not very efficient and for sure not pythonic. Thus it seems very natural to create a FUNCTION that makes this job.\n",
    "\n",
    "At the same time we need to pass to the function the information regarding the differences of the 3 distributions, so the fuction can plot them.\n",
    "\n",
    "This is done with a dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's define a dictionary\n",
    "distributions = dict()\n",
    "# This works as well\n",
    "distributions = {}\n",
    "\n",
    "# A dictionary is an object charaterised by one or more keys\n",
    "# It can contain any\n",
    "distributions['Duration'] = ['T', 'Duration T (s)', r'$P(T)$']\n",
    "distributions['Size'] = ['S', 'Size S (Wb)', r'$P(S)$']\n",
    "distributions['Energy'] = ['E', 'Energy E (xx)', r'$P(E)$']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let us now define the function\n",
    "# We add a comment to explain how to use it\n",
    "\n",
    "def plot_dis(distribution_type):\n",
    "    \"\"\"\n",
    "    This is my first comment\n",
    "    This function plot a distribution of my choice\n",
    "    Parameters:\n",
    "    distribution_type: str\n",
    "        The distribution you want to plot\n",
    "    \"\"\"\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    ax.set_xscale(\"log\")\n",
    "    ax.set_yscale(\"log\")\n",
    "\n",
    "    # We extract the info from the dictionary \n",
    "    symb, xlabel, ylabel = distributions[distribution_type]\n",
    "\n",
    "    filenames = sorted(glob.glob1(mainDir, \"F64ac_0.0?_%s.dat\" % symb))\n",
    "\n",
    "    for filename in filenames:\n",
    "        fname = os.path.join(mainDir, filename)\n",
    "        x, y = np.loadtxt(fname, unpack=True)\n",
    "        yerr = y * 0.6\n",
    "        material, freq, meas = filename.split(\"_\")\n",
    "        freq = \"%s Hz\" % freq\n",
    "        ax.errorbar(x,y,yerr,fmt='o', label=freq)\n",
    "    ax.set_xlabel(xlabel, size=16)\n",
    "    ax.set_ylabel(ylabel, size=16)\n",
    "    ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now we only need to call the function\n",
    "plot_dis(\"Size\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The \"distribution_type\" value is needed in input to call the function. This is because it does not have any default value.\n",
    "\n",
    "We can set the scale (lin/log), and give a default value.\n",
    "\n",
    "Even better, we can tell the function to plot where we want. The reason will be clear soon... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_dis(distribution_type, scale='log', ax=None):\n",
    "    \"\"\"\n",
    "    This is my first comment\n",
    "    This function plot a distribution of my choice\n",
    "    Parameters:\n",
    "    distribution_type: str\n",
    "        The distribution you want to plot\n",
    "    scale : str\n",
    "        Set the scale of the plot (lin/log)\n",
    "    ax : obj\n",
    "        A matplotlib axis to plot the data\n",
    "    \"\"\"\n",
    "    if ax is None:\n",
    "        fig = plt.figure()\n",
    "        ax = fig.add_subplot(111)\n",
    "\n",
    "    ax.set_xscale(scale)\n",
    "    ax.set_yscale(scale)\n",
    "\n",
    "    symb, xlabel, ylabel = distributions[distribution_type]\n",
    "\n",
    "    filenames = sorted(glob.glob1(mainDir, \"F64ac_0.0?_%s.dat\" % symb))\n",
    "\n",
    "    for filename in filenames:\n",
    "        #filename = \"F64ac_0.0%i_T.dat\" % i # no????\n",
    "        fname = os.path.join(mainDir, filename)\n",
    "        x, y = np.loadtxt(fname, unpack=True)\n",
    "        yerr = y * 0.6\n",
    "        material, freq, meas = filename.split(\"_\")\n",
    "        freq = \"%s Hz\" % freq\n",
    "        #ax.loglog(x,y,'o', label=freq)\n",
    "        ax.errorbar(x,y,yerr,fmt='o', label=freq)\n",
    "    ax.set_xlabel(xlabel, size=16)\n",
    "    ax.set_ylabel(ylabel, size=16)\n",
    "    ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A function is better defined in a separated script. The main reason is that we can reuse it as many time we want.\n",
    "\n",
    "Let us call the script *plot_distribution.py\n",
    "\n",
    "Here is the final version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import glob\n",
    "import matplotlib.pylab as plt\n",
    "import numpy as np\n",
    "\n",
    "mainDir = \"/home/gf/src/Python/Python-in-the-lab/Bk\"\n",
    "\n",
    "\n",
    "if not os.path.isdir(mainDir):\n",
    "    print(\"Houston, we have a problem!\")\n",
    "    sys.exit()\n",
    "\n",
    "# Let's define a dictionary\n",
    "distributions = dict()\n",
    "# This works as well\n",
    "distributions = {}\n",
    "\n",
    "distributions['Duration'] = ['T', 'Duration T (s)', r'$P(T)$']\n",
    "distributions['Size'] = ['S', 'Size S (Wb)', r'$P(S)$']\n",
    "distributions['Energy'] = ['E', 'Energy E (xx)', r'$P(E)$']\n",
    "\n",
    "    \n",
    "def plot_dis(distribution_type, scale='log', ax=None):\n",
    "    \"\"\"\n",
    "    This is my first comment\n",
    "    This function plot a distribution of my choice\n",
    "    Parameters:\n",
    "    distribution_type: str\n",
    "        The distribution you want to plot\n",
    "    \"\"\"\n",
    "    if ax is None:\n",
    "        fig = plt.figure()\n",
    "        ax = fig.add_subplot(111)\n",
    "\n",
    "    ax.set_xscale(scale)\n",
    "    ax.set_yscale(scale)\n",
    "\n",
    "    symb, xlabel, ylabel = distributions[distribution_type]\n",
    "\n",
    "    filenames = sorted(glob.glob1(mainDir, \"F64ac_0.0?_%s.dat\" % symb))\n",
    "\n",
    "    for filename in filenames:\n",
    "        #filename = \"F64ac_0.0%i_T.dat\" % i # no????\n",
    "        fname = os.path.join(mainDir, filename)\n",
    "        x, y = np.loadtxt(fname, unpack=True)\n",
    "        yerr = y * 0.6\n",
    "        material, freq, meas = filename.split(\"_\")\n",
    "        freq = \"%s Hz\" % freq\n",
    "        #ax.loglog(x,y,'o', label=freq)\n",
    "        ax.errorbar(x,y,yerr,fmt='o', label=freq)\n",
    "    ax.set_xlabel(xlabel, size=16)\n",
    "    ax.set_ylabel(ylabel, size=16)\n",
    "    ax.legend()\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    my_choice = \"Size\"\n",
    "    plot_dis(distribution_type=my_choice)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"__name__\" is an internal variable. It is set to \"__main__\" when the script is run, when not when is called (using import) from another script.\n",
    "\n",
    "It is a smart way to test your code\n",
    "\n",
    "Now we want to use \"plot_distribution.py\" in another notebook. It is time to move to...\n",
    "\n",
    "Py_lectures_02_basicElements_Dict_and_Func2.ipynb"
   ]
  }
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