Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=np.linspace?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "ncell=51\n",
    "nodes=np.linspace(0,1,ncell)\n",
    "dx=np.diff(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dx\n",
    "len (dx)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "x\n",
    "nodes=x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "boxmin=0.4\n",
    "boxmax=0.5\n",
    "amplitude=1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "cellcenters= x[:-1] + dx/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cell=cellcenters\n",
    "ncell=len (cell)\n",
    "ncell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 0.2)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d6aad30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(nodes, np.zeros_like(nodes), '|')\n",
    "plt.plot(cell, np.zeros_like(cell), '.')\n",
    "plt.xlim([0., 0.2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "m=np.zeros(ncell)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "inds_boxcar = (cell >= 0.4) & (cell <= 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False,  True,  True,  True,  True,  True, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inds_boxcar\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "m[inds_boxcar]=amplitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 1., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10cbb7438>]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d704a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(cell,m)\n"
   ]
  },
  {
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}