matplot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "x = np.linspace(0,5,11)\n",
    "y = x ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. ])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.  ,  0.25,  1.  ,  2.25,  4.  ,  6.25,  9.  , 12.25, 16.  ,\n",
       "       20.25, 25.  ])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Functions:\n",
    "plt.plot(x,y,'g-')\n",
    "plt.xlabel('X - Label')\n",
    "plt.ylabel('Y - Label')\n",
    "plt.title('Fucntional')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(3,1,1)\n",
    "plt.plot(x,y,'b-') # where x = np.linspace(0,5,11)\n",
    "\n",
    "plt.subplot(3,2,1)\n",
    "plt.plot(x,y,'r-')# where y = x ** 2\n",
    "\n",
    "plt.subplot(3,1,3)\n",
    "plt.plot(x,y,'g-')# where y = x ** 2\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# object oriented"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'B-Plot')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "axes = fig.add_axes([0.1,0.1,0.8,0.8]) #([left,bottom,width,height])\n",
    "axes1 = fig.add_axes([0.23,0.55,0.4,0.2])\n",
    "\n",
    "axes.plot(x,y,'r')\n",
    "axes.set_title('A -Plot')\n",
    "axes1.plot(y,x)\n",
    "axes1.set_title('B-Plot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'numpy.ndarray' object has no attribute 'plot'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-44-e4c316e36284>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maxes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnrows\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mncols\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'plot'"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes = plt.subplots(nrows = 3,ncols = 2)\n",
    "\n",
    "axes[0].plot(x,y)\n",
    "axes[1].plot(y,x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Figure Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes = plt.subplots(nrows = 2,ncols = 1,figsize = (8,3),dpi = 200)\n",
    "axes[0].plot(x,y)\n",
    "\n",
    "axes[1].plot(y,x)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x131ea9267b8>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "ax.plot(x,x ** 2,label = 'X sq')\n",
    "ax.plot(y,x*3,label = '3 times Y')\n",
    "\n",
    "ax.legend(loc = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "fig.savefig('my_picture.png',dpi = 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot Appearance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "x = np.linspace(0,5,11)\n",
    "y = x ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x22612770ac8>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "\n",
    "ax.plot(x,y,color='purple',lw= 0.75,ls='-',marker='*',markersize = 12,markerfacecolor = 'yellow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax"
   ]
  },
  {
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}