{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Example of a class" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from numpy.polynomial import Polynomial\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [], "source": [ "class MyFamousClass:\n", " \"\"\" This class represents famous data and provides utilities to plot them \"\"\"\n", " \n", " def __init__(self, data, xcol=None):\n", " \"\"\"\n", " Set up the class with the data\n", " \n", " Args:\n", " data (DataFrame): a pandas data frame\n", " xcol (string or int): column for absissa, set None or empty string for pandas index\n", " \"\"\"\n", " if not isinstance(data, pd.DataFrame):\n", " raise TypeError(\"data must be a pandas DataFrame\")\n", " self.data = data\n", " \n", " if xcol is None or xcol == \"\":\n", " self._xcol = None\n", " \n", " elif isinstance(xcol, int):\n", " if xcol < len(self.data.columns):\n", " self._xcol = xcol\n", " else:\n", " raise IndexError(f\"The value of {xcol} for xcol is out of range.\"\n", " f\" Data contains {len(self.data.columns)} columns.\")\n", " elif isinstance(xcol, str):\n", " if xcol in self.data.columns:\n", " self._xcol = self.data.columns.get_loc(xcol)\n", " else:\n", " raise KeyError(f\"Columns {xcol} does not exist in the data.\")\n", " \n", " else:\n", " raise TypeError(f\"xcol is {xcol} of type {type(xcol)} and must be int or str.\")\n", " \n", " @property\n", " def xcol(self):\n", " \"\"\" Index of the column use as x value \"\"\"\n", " if self._xcol is None:\n", " return \"index\"\n", " else:\n", " return self._xcol\n", " \n", " @property\n", " def xcol_name(self):\n", " \"\"\" Name of the column use as x value \"\"\"\n", " if self._xcol is None:\n", " if self.data.index.name:\n", " return self.data.index.name\n", " else:\n", " return \"index\"\n", " else:\n", " return self.data.columns[self._xcol]\n", " \n", " @staticmethod\n", " def from_file(filename, *args, **kwargs):\n", " \"\"\" \n", " Return the object from a data file. Additional arguments are\n", " passed to the read_csv function of pandas.\n", " \n", " If `index_col` is given as an argument, the index of the\n", " pandas dataframe is used as x values. Else, the first columns is \n", " used as x values.\n", " \"\"\"\n", "\n", " data = pd.read_csv(filename, *args, **kwargs)\n", " if \"index_col\" in kwargs:\n", " index = None\n", " else:\n", " # use first column as x values\n", " index = 0\n", " \n", " return MyFamousClass(data, index)\n", " \n", " # this is supposed to be private, because of the _ at the begining\n", " def _get_y_data(self, column):\n", " \"\"\" Check column and return y values \"\"\"\n", " if column is None:\n", " # select the second column\n", " ydata = self.data.iloc[:, 1].values\n", " else:\n", " # check and select the column\n", " if isinstance(column, int):\n", " try:\n", " ydata = self.data.iloc[:, column].values\n", " except IndexError:\n", " raise IndexError(f\"The value of {column} for column is out of range.\"\n", " f\" Data contains {len(self.data.columns)} columns.\")\n", " elif isinstance(column, str):\n", " try:\n", " ydata = self.data.loc[:, column].values\n", " except KeyError:\n", " raise KeyError(f\"Column {column} is not known.\")\n", " else:\n", " raise TypeError(f\"column is {column} of type {type(column)} and must be int or str.\")\n", " \n", " return ydata\n", " \n", " def make_polyfit(self, column=None, deg=1):\n", " \"\"\"\n", " make a polyfit using the data and return the coefficient in\n", " ascending degree order.\n", " \"\"\"\n", " \n", " # x values:\n", " if self._xcol is None:\n", " xdata = self.data.index.values\n", " else:\n", " xdata = self.data.iloc[:, self._xcol].values\n", " \n", " # y values:\n", " ydata = self._get_y_data(column)\n", " \n", " # make the fit and return coefs\n", " p = Polynomial.fit(xdata, ydata, deg)\n", " return p.convert().coef\n", " \n", " def get_plot(self, column=None, deg=None, ax=None):\n", " \"\"\"\n", " Plot the column with or without a polynomial fit with a polynom degree deg:\n", " \n", " Args:\n", " column (str or int): column for y value\n", " deg (int): degree of the polynom\n", " ax (matplotlib Axes): axes on which to plot\n", " \"\"\"\n", " \n", " # set up axes\n", " if not ax:\n", " ax = plt.subplot(111)\n", "\n", " # x values:\n", " if self._xcol is None:\n", " xdata = self.data.index.values\n", " else:\n", " xdata = self.data.iloc[:, self._xcol].values\n", " \n", " # yvalues\n", " ydata = self._get_y_data(column)\n", " \n", " # plot\n", " ax.plot(xdata, ydata, \"o\", label=\"data\")\n", " \n", " # polynomial fit\n", " if deg > 0:\n", " polynom = Polynomial(self.make_polyfit(column, deg))\n", " xfit = np.linspace(xdata.min(), xdata.max(), 100)\n", " \n", " ax.plot(xfit, polynom(xfit), label=\"fit\")\n", " \n", " line = f\"{polynom.coef[0]:.5f} \" \n", " line += f\"{polynom.coef[1]:+.5f} x \"\n", " if deg > 1:\n", " line += \" \".join([f\"{polynom.coef[i]:+.5f} x^{i}\" for i in range(2, deg + 1)])\n", " \n", " _ = ax.set_title(line)\n", " \n", " ax.legend()\n", " \n", " return ax\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example of use\n", "\n", "### Example 1" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [], "source": [ "my_data = MyFamousClass.from_file(\"../../data_FeSCN.csv\", sep=\",\")" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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V[Fe3+]o[SCN-]oC_cplxAbs[Fe3+]eq[SCN-]eqKxyEps*l*Kresteb
010.0009900.0001980.00017617.6348700.0008140.00002210000.0106867.3095788.994665e+071.000000e+091.586197e+131.586297e+13
120.0019610.0001960.00018618.5621820.0017750.00001010000.0104133.8417104.828024e+071.000000e+098.961865e+128.962865e+12
230.0029130.0001940.00018718.7302090.0027250.00000710000.0102891.2821333.311813e+071.000000e+096.203095e+126.204095e+12
340.0038460.0001920.00018718.7191710.0036590.00000510000.0102206.6757712.530832e+071.000000e+094.737508e+124.738508e+12
450.0047620.0001900.00018618.6402270.0045760.00000410000.0101775.6401542.055085e+071.000000e+093.830725e+123.831725e+12
\n", "
" ], "text/plain": [ " V [Fe3+]o [SCN-]o C_cplx Abs [Fe3+]eq [SCN-]eq K \\\n", "0 1 0.000990 0.000198 0.000176 17.634870 0.000814 0.000022 10000.0 \n", "1 2 0.001961 0.000196 0.000186 18.562182 0.001775 0.000010 10000.0 \n", "2 3 0.002913 0.000194 0.000187 18.730209 0.002725 0.000007 10000.0 \n", "3 4 0.003846 0.000192 0.000187 18.719171 0.003659 0.000005 10000.0 \n", "4 5 0.004762 0.000190 0.000186 18.640227 0.004576 0.000004 10000.0 \n", "\n", " x y Eps*l*K reste b \n", "0 106867.309578 8.994665e+07 1.000000e+09 1.586197e+13 1.586297e+13 \n", "1 104133.841710 4.828024e+07 1.000000e+09 8.961865e+12 8.962865e+12 \n", "2 102891.282133 3.311813e+07 1.000000e+09 6.203095e+12 6.204095e+12 \n", "3 102206.675771 2.530832e+07 1.000000e+09 4.737508e+12 4.738508e+12 \n", "4 101775.640154 2.055085e+07 1.000000e+09 3.830725e+12 3.831725e+12 " ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_data.data.head()" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "my_data.get_plot(deg=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example 2" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv(\"../../data_FeSCN.csv\", sep=\",\")\n", "my_data = MyFamousClass(data=df, xcol=\"V\")" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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120.0019610.0001960.00018618.5621820.0017750.00001010000.0104133.8417104.828024e+071.000000e+098.961865e+128.962865e+12
230.0029130.0001940.00018718.7302090.0027250.00000710000.0102891.2821333.311813e+071.000000e+096.203095e+126.204095e+12
340.0038460.0001920.00018718.7191710.0036590.00000510000.0102206.6757712.530832e+071.000000e+094.737508e+124.738508e+12
450.0047620.0001900.00018618.6402270.0045760.00000410000.0101775.6401542.055085e+071.000000e+093.830725e+123.831725e+12
\n", "
" ], "text/plain": [ " V [Fe3+]o [SCN-]o C_cplx Abs [Fe3+]eq [SCN-]eq K \\\n", "0 1 0.000990 0.000198 0.000176 17.634870 0.000814 0.000022 10000.0 \n", "1 2 0.001961 0.000196 0.000186 18.562182 0.001775 0.000010 10000.0 \n", "2 3 0.002913 0.000194 0.000187 18.730209 0.002725 0.000007 10000.0 \n", "3 4 0.003846 0.000192 0.000187 18.719171 0.003659 0.000005 10000.0 \n", "4 5 0.004762 0.000190 0.000186 18.640227 0.004576 0.000004 10000.0 \n", "\n", " x y Eps*l*K reste b \n", "0 106867.309578 8.994665e+07 1.000000e+09 1.586197e+13 1.586297e+13 \n", "1 104133.841710 4.828024e+07 1.000000e+09 8.961865e+12 8.962865e+12 \n", "2 102891.282133 3.311813e+07 1.000000e+09 6.203095e+12 6.204095e+12 \n", "3 102206.675771 2.530832e+07 1.000000e+09 4.737508e+12 4.738508e+12 \n", "4 101775.640154 2.055085e+07 1.000000e+09 3.830725e+12 3.831725e+12 " ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_data.data.head()" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "poly([ 1.68381790e+01 1.10562332e+00 -1.91775327e-01 9.33245415e-03])\n", "16.83818 +1.10562 x -0.19178 x^2 +0.00933 x^3\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax = my_data.get_plot(column=\"Abs\", deg=3)\n", "ax.set_xlabel(\"V\")\n", "ax.set_ylabel(\"Abs\");" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "poly([0.00019492 0.00089964])\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax = my_data.get_plot(column=\"[Fe3+]o\", deg=1)\n", "ax.set_xlabel(\"V\")\n", "ax.set_ylabel(\"[Fe3+]o\");" ] } ], "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": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 4 }