L1-python/source/Plotting1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f8579363",
   "metadata": {},
   "source": [
    "# **Libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e4d24b04",
   "metadata": {},
   "outputs": [],
   "source": [
    "# libraries\n",
    "import scipy \n",
    "# \"SciPy\" provides algorithms for optimization, integration, interpolation, eigenvalue problems, \n",
    "# algebraic equations, differential equations, statistics and many other classes of problems.\n",
    "import numpy as np\n",
    "# Fast and versatile, the \"NumPy\" vectorization, indexing, and broadcasting concepts are the \n",
    "# de-facto standards of array computing today.\n",
    "import matplotlib.pyplot as plt\n",
    "# \"Matplotlib\" is a comprehensive library for creating static, animated, and interactive \n",
    "# visualizations in Python."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c41a20d",
   "metadata": {},
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9e9a38b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "x= np.linspace(0,2*np.pi)\n",
    "y= np.sin(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a8dcbb5",
   "metadata": {},
   "source": [
    "## Anatomy of a figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b5768c9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from PIL import Image\n",
    "img = Image.open('anatomy.webp')\n",
    "img.save(\"anatomy.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8f75cee3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"anatomy.png\" width=\"500\" height=\"500\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import image module\n",
    "from IPython.display import Image\n",
    "  \n",
    "# get the image\n",
    "Image(url=\"anatomy.png\", width=500, height=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82c13c31",
   "metadata": {},
   "source": [
    "## Implicit or explicit?\n",
    "**Using figures**\n",
    "- Explicitly create Figures and Axes, and call methods on them (the \"object-oriented (OO) style\").\n",
    "- Rely on pyplot to implicitly create and manage the Figures and Axes, and use pyplot functions for plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0f84f0db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# implicit \n",
    "plt.plot(x,y,label=\"sin\")\n",
    "plt.xlabel('x label')\n",
    "plt.ylabel('y label')\n",
    "plt.title(\"Simple Plot\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3f2d03de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fcca3b14dc0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# explicity\n",
    "fig = plt.figure(figsize=(6,6)) # size\n",
    "ax = plt.subplot(aspect=1)   # aspect ratio\n",
    "ax.plot(x,y,label=\"sin\") # label\n",
    "ax.set_xlabel('x')  # Add an x-label to the axes.\n",
    "ax.set_ylabel('y')  # Add a y-label to the axes.\n",
    "ax.set_title(\"Simple Plot\")  # Add a title to the axes.\n",
    "ax.legend() # Add a legend."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d5ac225",
   "metadata": {},
   "source": [
    "### Figure : lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "568d7656",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fdc69fe4b90>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6,6))\n",
    "ax = plt.subplot(aspect=1)\n",
    "ax.plot(x,y,label=\"sin\",color='blue', linewidth=3, linestyle='--') \n",
    "ax.plot(x,y*y,label=\"$\\sin^2$\",color='red', linewidth=1, linestyle='dotted') \n",
    "ax.set_xlabel('x')  # Add an x-label to the axes.\n",
    "ax.set_ylabel('y')  # Add a y-label to the axes.\n",
    "ax.set_title(\"Simple Plot\")  # Add a title to the axes.\n",
    "ax.legend() # Add a legend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b316fd3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "ax.plot?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "287d0813",
   "metadata": {},
   "source": [
    "## Figure and Axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7cbcc514",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'y')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "fig = plt.figure(figsize=(6,6))\n",
    "ax = plt.subplot(aspect=1)\n",
    "ax.plot(x,y,\".r\",label=\"$\\sin(x)$\") \n",
    "ax.plot(x,y*y,\".g\",label=\"$\\sin(x)^2$\") \n",
    "ax.legend()\n",
    "ax.set_xlabel(\"x\")\n",
    "ax.set_ylabel(\"y\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf3594a3",
   "metadata": {},
   "source": [
    "### Figures : axes and text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8cd16580",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'y')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# adding text\n",
    "fig = plt.figure(figsize=(6,6))\n",
    "ax = plt.subplot(aspect=1)\n",
    "ax.plot(x,y,\".\",label=\"sin\") \n",
    "ax.legend()\n",
    "\n",
    "\n",
    "ax.text(0.3,  0.1, \"-> Mot\",family=\"cursive\",size=14)\n",
    "ax.text(0.3, -0.5, \"-> Mot\",family=\"serif\",size = 14)\n",
    "\n",
    "ax.annotate('point (3,0)', xy=(3, 0), xytext=(4, 0.5),\n",
    "            arrowprops=dict(facecolor='black', shrink=0.05))\n",
    "\n",
    "ax.set_title('Title')\n",
    "ax.set_xlabel(\"x\")\n",
    "ax.set_ylabel(\"y\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "977fb62b",
   "metadata": {},
   "source": [
    "### figure : scales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5f4d1736",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fdc5860e790>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6,6)) # size\n",
    "ax = plt.subplot(aspect=1)   # aspect ratio\n",
    "\n",
    "ax.plot(x,y,label=\"sin\") # label\n",
    "ax.set_xscale('log')\n",
    "ax.set_yscale('log')\n",
    "ax.set_xlabel('x')  # Add an x-label to the axes.\n",
    "ax.set_ylabel('y')  # Add a y-label to the axes.\n",
    "ax.set_title(\"Title\")  # Add a title to the axes.\n",
    "ax.legend() # Add a legend."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2266d48",
   "metadata": {},
   "source": [
    "### figures multiples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "45b0275c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fdc69e73190>]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax0, ax1, ax2) = plt.subplots(nrows=1, ncols=3,\n",
    "                                    figsize=(6, 6))\n",
    "fig.tight_layout()\n",
    "ax0.set_title('a')\n",
    "ax0.plot(x,y,label=\"sin\") # label\n",
    "\n",
    "ax1.plot(x,y,label=\"sin\") # label\n",
    "\n",
    "ax2.set_title('title')\n",
    "ax2.plot(x,y,label=\"sin\") # label\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fecf66d0",
   "metadata": {},
   "source": [
    "### figure : save"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "2f6abdd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#save\n",
    "fig.savefig(\"output.png\")\n",
    "fig.savefig(\"output.pdf\")"
   ]
  }
 ],
 "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}