group_meeting_5-1.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Object Oriented Examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Landscape(object):\n",
    "    density = 5.0 \n",
    "    \n",
    "    def __init__(self, temperature, soil_moist):\n",
    "        self.temperature = temperature\n",
    "        self.soil_moist = soil_moist\n",
    "        \n",
    "    def absorb_rad(self, rad):\n",
    "        self.temperature += 0.25 * rad\n",
    "        \n",
    "    def absorb_precip(self, precip):\n",
    "        self.soil_moist += 0.1 * precip\n",
    "        \n",
    "    def print_summary(self):\n",
    "        print(\"Summary of \" + self.__class__.__name__)\n",
    "        print(\"--------------------------------------\")\n",
    "        print(\"Density: \", self.density)\n",
    "        print(\"Temperature: \", self.temperature)\n",
    "        print(\"Soil Moisture: \", self.soil_moist)\n",
    "        print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary of Landscape\n",
      "--------------------------------------\n",
      "Density:  8.0\n",
      "Temperature:  30.0\n",
      "Soil Moisture:  5\n",
      "\n",
      "Summary of Landscape\n",
      "--------------------------------------\n",
      "Density:  5.0\n",
      "Temperature:  10.0\n",
      "Soil Moisture:  5\n",
      "\n",
      "\n",
      "After changing class level definition:\n",
      "Summary of Landscape\n",
      "--------------------------------------\n",
      "Density:  8.0\n",
      "Temperature:  30.0\n",
      "Soil Moisture:  5\n",
      "\n",
      "Summary of Landscape\n",
      "--------------------------------------\n",
      "Density:  4.0\n",
      "Temperature:  10.0\n",
      "Soil Moisture:  5\n",
      "\n"
     ]
    }
   ],
   "source": [
    "l1 = Landscape(5,5)\n",
    "l1.absorb_rad(100)\n",
    "l1.density = 8.0\n",
    "\n",
    "l2 = Landscape(5,5)\n",
    "l2.absorb_rad(20)\n",
    "\n",
    "l1.print_summary()\n",
    "l2.print_summary()\n",
    "\n",
    "Landscape.density = 4.0\n",
    "print()\n",
    "print(\"After changing class level definition:\")\n",
    "l1.print_summary()\n",
    "l2.print_summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Desert(Landscape):\n",
    "    def absorb_rad(self, rad):\n",
    "        self.temperature += 0.5 * rad\n",
    "            \n",
    "class Forest(Landscape):\n",
    "    def absorb_precip(self, precip):\n",
    "        self.soil_moist += 0.25 * precip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary of Desert\n",
      "--------------------------------------\n",
      "Density:  4.0\n",
      "Temperature:  15.0\n",
      "Soil Moisture:  5\n",
      "\n",
      "Summary of Forest\n",
      "--------------------------------------\n",
      "Density:  4.0\n",
      "Temperature:  5\n",
      "Soil Moisture:  7.5\n",
      "\n"
     ]
    }
   ],
   "source": [
    "d1 = Desert(5,5)\n",
    "d1.absorb_rad(20)\n",
    "\n",
    "f1 = Forest(5,5)\n",
    "f1.absorb_precip(10)\n",
    "\n",
    "d1.print_summary()\n",
    "f1.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Functional Examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 1 2\n"
     ]
    }
   ],
   "source": [
    "def ho_fun(oldfun, arg):\n",
    "    return oldfun(arg)\n",
    "\n",
    "def increment(a):\n",
    "    return a+1\n",
    "\n",
    "start = 0\n",
    "mid = ho_fun(increment, start)\n",
    "end = ho_fun(increment, ho_fun(increment, start))\n",
    "print(start, mid, end)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def add_numbers(a, b):\n",
    "    return a+b\n",
    "\n",
    "def add_five(a):\n",
    "    return add_numbers(a, 5)\n",
    "\n",
    "add_numbers(1, 5) == add_five(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.000400543212890625\n",
      "0.003988504409790039\n",
      "0.039566755294799805\n"
     ]
    }
   ],
   "source": [
    " def timer(newfun):\n",
    "    import time\n",
    "    def wrapper(args):\n",
    "        t1 = time.time()\n",
    "        newfun(args)\n",
    "        t2 = time.time()\n",
    "        print(t2-t1)\n",
    "    return wrapper\n",
    "\n",
    "@timer\n",
    "def random_srt(n):\n",
    "    import numpy.random\n",
    "    a = numpy.random.rand(n)\n",
    "    return sorted(a)\n",
    "\n",
    "random_srt(500)\n",
    "random_srt(5000)\n",
    "random_srt(50000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "def multi(dispatch_fn):\n",
    "    \"\"\"\n",
    "    Keeps a function `inner` that holds a dictionary\n",
    "    with function pointers.\n",
    "    \"\"\"\n",
    "    def inner(arg):\n",
    "        # Get a function out of the dictionary\n",
    "        # If the key doesn't exist, just return a \n",
    "        # function that always returns None\n",
    "        return inner.__multi__.get(\n",
    "            dispatch_fn(arg),\n",
    "            lambda arg: None\n",
    "        )(arg)\n",
    "    # Initialize the dictionary\n",
    "    inner.__multi__ = {}\n",
    "    return inner\n",
    "\n",
    "def method(dispatch_fn, key=None):\n",
    "    \"\"\"\n",
    "    Put new key, value pairs into the `multi` function's\n",
    "    inner function for retrieval later.\n",
    "    \"\"\"\n",
    "    def apply_decorator(fn):\n",
    "        # This is assigning to the __multi__ dictionary\n",
    "        dispatch_fn.__multi__[key] = fn\n",
    "        return dispatch_fn\n",
    "    return apply_decorator\n",
    "\n",
    "import xarray as xr\n",
    "import pandas as pd\n",
    "\n",
    "@multi\n",
    "def read(filename):\n",
    "    return filename.split('.')[-1]\n",
    "\n",
    "@method(read, 'nc')\n",
    "def read(filename):\n",
    "    return xr.open_dataset(filename)\n",
    "\n",
    "@method(read, 'txt')\n",
    "def read(filename):\n",
    "    return pd.read_table(filename, header=None, delim_whitespace=True)\n",
    "\n",
    "read_dict = {}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Frozen(SortedKeysDict({'lat': 4, 'lon': 5}))"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ncfile = read(\"/home/bzq/workspace/test/stehekin.nc\")\n",
    "ncfile.dims"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.675</td>\n",
       "      <td>-4.20</td>\n",
       "      <td>-13.080000</td>\n",
       "      <td>4.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.675</td>\n",
       "      <td>-4.20</td>\n",
       "      <td>-13.080000</td>\n",
       "      <td>4.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000</td>\n",
       "      <td>-4.72</td>\n",
       "      <td>-23.030001</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000</td>\n",
       "      <td>-4.72</td>\n",
       "      <td>-23.030001</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000</td>\n",
       "      <td>-17.33</td>\n",
       "      <td>-20.990000</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       0      1          2     3\n",
       "0  3.675  -4.20 -13.080000  4.51\n",
       "1  3.675  -4.20 -13.080000  4.51\n",
       "2  0.000  -4.72 -23.030001  2.63\n",
       "3  0.000  -4.72 -23.030001  2.63\n",
       "4  0.000 -17.33 -20.990000  0.64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "txtfile = read(\"/home/bzq/workspace/test/data.txt\")\n",
    "txtfile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "badfile = read(\"I don't have an entry for this.\")\n",
    "print(badfile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def read_nc(fn):\n",
    "    pass\n",
    "read_dict = {\"netcdf\": read_nc}\n",
    "\n",
    "read_dict['netcdf'](fn)"
   ]
  }
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