Notebook for reading David's Matlab .mat time series of significant wave height and mean period and converting into a time indexed Pandas DataFrame

read_mat_file_timeseries.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import scipy.io as io"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\le12jm\\Downloads\n"
     ]
    }
   ],
   "source": [
    "cd C://Users//le12jm//Downloads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Volume in drive C is Windows\n",
      " Volume Serial Number is FCEA-3AC0\n",
      "\n",
      " Directory of C:\\Users\\le12jm\\Downloads\n",
      "\n",
      "26/08/2015  09:29            71,725 BragarTS.mat\n",
      "26/08/2015  09:29            99,387 SiadarTS.mat\n",
      "               2 File(s)        171,112 bytes\n",
      "               0 Dir(s)  190,927,060,992 bytes free\n"
     ]
    }
   ],
   "source": [
    "ls *.mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bragar_ts_dict = io.loadmat('BragarTS.mat')\n",
    "siadar_ts_dict = io.loadmat('SiadarTS.mat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "bragar_timestamps_strings = bragar_ts_dict.items()[0][1]\n",
    "siadar_timestamps_strings = siadar_ts_dict.items()[0][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def strings_to_timestamps(timestamps_strings):\n",
    "    timestamps = []\n",
    "    for timestamp in timestamps_strings:\n",
    "        timestamps.append(datetime.strptime(timestamp,\"%d-%b-%Y %H:%M:%S\"))\n",
    "    return timestamps\n",
    "bragar_timestamps = strings_to_timestamps(bragar_timestamps_strings)\n",
    "siadar_timestamps = strings_to_timestamps(siadar_timestamps_strings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "bragar_datetimeindex = pd.DatetimeIndex(bragar_timestamps)\n",
    "siadar_datetimeindex = pd.DatetimeIndex(siadar_timestamps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "bragar_df = pd.DataFrame(bragar_ts_dict.items()[1][1], index = bragar_datetimeindex)\n",
    "siadar_df = pd.DataFrame(siadar_ts_dict.items()[1][1], index = siadar_datetimeindex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "bragar_df.columns = ['Hm0','mean_period']\n",
    "siadar_df.columns = ['Hm0','mean_period']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "bragar_df.to_pickle('Bragar_Hm0_mean_period')\n",
    "siadar_df.to_pickle('Siadar_Hm0_mean_period')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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