undericepos_estimation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fd022ab9",
   "metadata": {},
   "source": [
    "# Estimating under-ice positions with the terrain-following (Yamazaki + 2020) toolbox"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3234f24",
   "metadata": {},
   "source": [
    "## Accessing the ftp server to download the argo prof netcdf file\n",
    "First access the coriolis ftp server and download the \"prof.nc\" file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "51458f35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'250 Directory successfully changed.'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import library\n",
    "from ftplib import FTP\n",
    "\n",
    "# Set user name and password\n",
    "username = 'anonymous'\n",
    "password = ''\n",
    "\n",
    "# Connect to the ftp server\n",
    "ftp = FTP('ftp.ifremer.fr',username,password)\n",
    "\n",
    "# List the files or directories\n",
    "#ftp.retrlines('LIST')\n",
    "\n",
    "# Change directory to the product we want (Argo DAC)\n",
    "ftp.cwd('ifremer/argo/dac/coriolis/')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03138214",
   "metadata": {},
   "source": [
    "Access the directory of the float (WMO number) and download the prof.nc file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e5729bc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'226 Transfer complete.'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wmo=str(7900549)\n",
    "\n",
    "# Change directory to the float you want\n",
    "ftp.cwd(wmo)\n",
    "\n",
    "# Set the name of the file to download\n",
    "filename=wmo+ '_prof.nc'\n",
    "\n",
    "# Download the file\n",
    "ftp.retrbinary(\"RETR \" + filename ,open(filename, 'wb').write)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10c1bb46",
   "metadata": {},
   "source": [
    "## Excecuting terrain-following code\n",
    "Here we run different scripts in the terrain-following repository (I don't know how to call them from here so I just copy and paste the code)\n",
    "https://github.com/euroargodev/terrain-following\n",
    "I modified: paths, lines that retrieved warnings (SettingWithCopyWarning) and the listing of netcdf files (to read only one specified file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0738daf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>cast</th>\n",
       "      <th>qpos</th>\n",
       "      <th>lons</th>\n",
       "      <th>lats</th>\n",
       "      <th>juld</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>70.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>1</td>\n",
       "      <td>69</td>\n",
       "      <td>3</td>\n",
       "      <td>67</td>\n",
       "      <td>67</td>\n",
       "      <td>70.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>7900549</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>25842.3625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             id  cast qpos lons lats        juld\n",
       "count        70    70   70   70   70     70.0000\n",
       "unique        1    69    3   67   67     70.0000\n",
       "top     7900549     1    8   --   --  25842.3625\n",
       "freq         70     2   35    4    4      1.0000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# https://github.com/euroargodev/terrain-following/blob/main/nc2csv.py\n",
    "# nc2csv.py\n",
    "### This code extracts the trajectry of Argo data\n",
    "### and divides position-interpolated data for Argo netcdf\n",
    "\n",
    "# coding: utf-8\n",
    "import numpy as np\n",
    "import glob\n",
    "import netCDF4\n",
    "import re\n",
    "import pandas as pd\n",
    "import datetime\n",
    "\n",
    "# path \n",
    "names = glob.glob(wmo+ '_prof.nc')\n",
    "names.sort()\n",
    "n_float = len(names)\n",
    "\n",
    "dfs = []\n",
    "for Float in range(n_float):\n",
    "    # if Float%20 == 0:\n",
    "    #     print(Float, 'th data of ', n_float)\n",
    "    Prof = netCDF4.Dataset(names[Float])\n",
    "    idnum = str(Prof.variables['PLATFORM_NUMBER'][0])\n",
    "    idnum = re.sub(r'\\D', '', idnum) # '\\D' means strings except number\n",
    "    # print(idnum)\n",
    "    cast = Prof.variables['CYCLE_NUMBER'][:]\n",
    "    # if len(cast) < 2:\n",
    "    #     return\n",
    "    juld = Prof.variables['JULD'][:]\n",
    "    # juld.units = 'days since 1950-01-01 00:00:00 UTC'\n",
    "    # juld = netCDF4.num2date(juld[:],juld.units)\n",
    "    lons = Prof.variables['LONGITUDE'][:]\n",
    "    lats = Prof.variables['LATITUDE'][:]\n",
    "    qpos = str(Prof.variables['POSITION_QC'][:]) # positioning quality flag\n",
    "    qpos = re.sub(r'\\D', '', qpos) # '\\D' means strings except number\n",
    "    ids = np.array([idnum]*len(cast))\n",
    "    df = pd.DataFrame([ids,cast,qpos,lons,lats,juld]).T\n",
    "    df.columns = ['id','cast','qpos','lons','lats','juld']\n",
    "    dfs.append(df)\n",
    "\n",
    "df_ = pd.concat(dfs, ignore_index=True)\n",
    "df_.to_csv('trajAll.csv')\n",
    "df_.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ad9ddadb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://github.com/euroargodev/terrain-following/blob/main/process.py\n",
    "# process.py\n",
    "### selection for good data(quality flag <= 2)\n",
    "### if there is discontinuity, rename id\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = pd.read_csv('trajAll.csv').values[:,1:]\n",
    "df[np.where(df=='--')] = 999; df = np.float64(df)\n",
    "df[np.isnan(df)] = 999\n",
    "df = pd.DataFrame(df)\n",
    "df.columns = ['id','cast','qpos','lons','lats','juld']\n",
    "### make lons [-180,180] --> [0,360]\n",
    "df.lons[np.where(df.lons.values<0)[0]] += 360\n",
    "\n",
    "ids = np.unique(df.id)\n",
    "dfs = []\n",
    "for idi in ids:\n",
    "    dfi = df[df.id==idi].sort_values(by='cast')\n",
    "    dfi = dfi.reset_index(drop=True)\n",
    "\n",
    "    # selection for good data\n",
    "    discont = []\n",
    "    indx = np.where(dfi.qpos<=2)[0]\n",
    "    for i in range(len(indx)-1):\n",
    "        if indx[i+1] - indx[i] > 1: # search discontinuity for good data\n",
    "            discont.append(i+1) # corresponds to i_bgn\n",
    "\n",
    "    idiscont = 0\n",
    "    for i in range(len(indx)-1):\n",
    "        indxi = indx[i]\n",
    "        if len(discont) == 0:\n",
    "            ### if there is discontinuity, change id\n",
    "            if (np.abs(dfi.lons[indxi+1]-dfi.lons[indxi]) > 10)\\\n",
    "              or (np.abs(dfi.lats[indxi+1]-dfi.lats[indxi]) > 5)\\\n",
    "              or (dfi.cast[indxi+1] - dfi.cast[indxi] > 1):\n",
    "                dfi.id[indxi+1:] += 1e+7\n",
    "        elif indxi == indx[discont[idiscont]-1]: # at discontinuity\n",
    "            if idiscont < len(discont)-1:\n",
    "                idiscont += 1\n",
    "            continue\n",
    "        else:\n",
    "            ### if there is discontinuity, change id\n",
    "            if (np.abs(dfi.lons[indxi+1]-dfi.lons[indxi]) > 10)\\\n",
    "              or (np.abs(dfi.lats[indxi+1]-dfi.lats[indxi]) > 5)\\\n",
    "              or (dfi.cast[indxi+1] - dfi.cast[indxi] > 1):\n",
    "                dfi.id[indxi+1:] += 1e+7\n",
    "    dfs.append(dfi)\n",
    "\n",
    "df = pd.concat(dfs)\n",
    "df = df.sort_values(by='id')\n",
    "df = df.reset_index(drop=True)\n",
    "df.to_csv('traj_processed.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c042fd8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# https://github.com/euroargodev/terrain-following/blob/main/interpolation_v2.py\n",
    "# interpolation_v2\n",
    "# %%\n",
    "import numpy as np\n",
    "import netCDF4\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "'''\n",
    "This sctipt estimates under-ice Argo tragectries assuming barotropic PV conservation,\n",
    "by interpolating depths which is determined by the position-known end points of a interpolation section.\n",
    "(by Kaihe Yamazaki, September 14, 2017.)\n",
    "'''\n",
    "\n",
    "### set tuning parameters\n",
    "Range = 0.2 # estimate position between +/- 0.2 deg per cycle (length of search range)\n",
    "dGrid = 1/60 # grid size of the ambient PV data (deg)\n",
    "\n",
    "rsl = 2 # resolution of position estimation\n",
    "# rsl=1: 1/60 deg, 25 points(finest)\n",
    "# rsl=2: 1/30 deg, 13 points\n",
    "# rsl=3: 0.05 deg, 9 points\n",
    "\n",
    "### load trajectry data\n",
    "df = pd.read_csv('traj_processed.csv')\n",
    "# df.columns --> Index(['Unnamed: 0', 'id', 'cast', 'qpos', 'lons', 'lats', 'juld'], dtype='object')\n",
    "# 'qpos' is position quality flag originally named as 'POSITION_QC'\n",
    "\n",
    "'''\n",
    "According to the positioning flag attached to each profile, the Argo data were categorized into two groups: \"accurately positioned data\" and \"position-lacking data\".\n",
    "The former is data with a position quality flag of either 1: \"good data\" or 2: \"probably good data\", while the latter is data whose position quality flag is neither 1 nor 2 (without accurate position)\n",
    "Most of the position-lacking data have linearly interpolated positions (with position quality flag being 8: \"interpolated\") and are located within the seasonal ice zone.\n",
    "Here, positions of \"position-lacking data\" are interpolated following PV contours.\n",
    "'''\n",
    "\n",
    "### install topographic(ambient PV) data\n",
    "data = netCDF4.Dataset('GRIDONE_1D.nc')\n",
    "topo = data.variables['z'][:].reshape(10801,21601)\n",
    "dxg = 1/60; xg = np.arange(-180,180+dxg,dxg)# in degree\n",
    "dyg = 1/60; yg = np.arange(-90,90+dyg,dyg)\n",
    "def getDepth(lon,lat,topo,message=False): # this function returns distance-weighted average of depths\n",
    "    if lon >= 180:\n",
    "        ilon = int((lon-180)/dxg)\n",
    "        dlon = (lon-180) - ilon*dxg\n",
    "    else:\n",
    "        ilon = int((lon+180)/dxg)\n",
    "        dlon = (lon+180) - ilon*dxg\n",
    "    ilat = int((90-lat)/dyg)\n",
    "    dlat = (90-ilat*dyg) - lat\n",
    "    if ilon==21600: # e.g. lon = 179.99..\n",
    "        Znw = topo[ilat,ilon];   Zne = topo[ilat,0]\n",
    "        Zsw = topo[ilat+1,ilon]; Zse = topo[ilat+1,0]\n",
    "    else:\n",
    "        Znw = topo[ilat,ilon];   Zne = topo[ilat,ilon+1]\n",
    "        Zsw = topo[ilat+1,ilon]; Zse = topo[ilat+1,ilon+1]\n",
    "    w = np.array([(dxg-dlon)*(dyg-dlat),(dxg-dlon)*dlat,dlon*(dyg-dlat),dlon*dlat])\n",
    "    return np.average([Znw,Zne,Zsw,Zse], weights=w)\n",
    "\n",
    "# %% main code\n",
    "ids = np.unique(df.id)\n",
    "dfs = []\n",
    "spans = []\n",
    "for idi in ids:\n",
    "    dfi = df[df.id==idi].sort_values('cast')\n",
    "    dfi['ilon']=0.0; dfi['ilat']=0.0 # define interpolated coordinates\n",
    "    dfi['span']=0.0\n",
    "    # 'span' is the length of consecutive cycles to be interpolated\n",
    "    dfi = dfi.reset_index(drop=True)\n",
    "\n",
    "    # position that quality flag worse than 2 defined as a bad (position-lacking) data\n",
    "    indxIntp = np.where(dfi.qpos>2)[0] # fetch index of bad data \n",
    "    ### For clarity, this line may be revised so as to fetch positioned data...\n",
    "    \n",
    "    if len(indxIntp)==0:\n",
    "        continue\n",
    "    \n",
    "    discont = [] # search discontinuity in index list 'indxIntp' (e.g., index 1 in indxIntp = [1,2,4,5]; 3 is index of positioned data)\n",
    "    for i in range(len(indxIntp)-1):\n",
    "        if indxIntp[i+1] - indxIntp[i] > 1: # find discontinuity\n",
    "            discont.append(i+1) # to be associated with i_bgn\n",
    "\n",
    "    ### interpolate for every section between discontinuities\n",
    "    ### 'i_bgn' and 'i_end' are the indices of the end of interpolation section (good data separated by consecutive bad data)\n",
    "    for sect in range(len(discont)+1):\n",
    "    \n",
    "        if sect == 0:\n",
    "            i_bgn = indxIntp[0] - 1 # good data at the ends of interpolation\n",
    "        else:\n",
    "            i_bgn = indxIntp[discont[sect-1]] - 1\n",
    "        if i_bgn < 0: # for case the first cast is bad data\n",
    "            continue\n",
    "            \n",
    "        if sect == len(discont):\n",
    "            i_end = indxIntp[-1] + 1 # good data at the ends of interpolation\n",
    "        else:\n",
    "            i_end = indxIntp[discont[sect]-1] + 1\n",
    "        if i_end > len(dfi)-1: # for case the last cast is bad data\n",
    "            continue\n",
    "        \n",
    "        ######\n",
    "        ### first, estimate by forwarding interpolation\n",
    "        dfsec = dfi[i_bgn:i_end+1]\n",
    "        dfsec = dfsec.reset_index(drop=True)\n",
    "\n",
    "        lens = len(dfsec)\n",
    "        if lens < 3: # check if bad data exist (maybe redundunt)\n",
    "            continue\n",
    "        span = lens-2 # length of under-ice cycles\n",
    "        spans.append(span) # the length of malpositioned cycles\n",
    "\n",
    "        x1 = dfsec.lons.values[0]; x2 = dfsec.lons.values[-1]\n",
    "        y1 = dfsec.lats.values[0]; y2 = dfsec.lats.values[-1]\n",
    "        z1 = getDepth(x1, y1, topo)\n",
    "        z2 = getDepth(x2, y2, topo)\n",
    "        dx = (x2 - x1)/(lens-1) # denominator is the number of sections between data points\n",
    "        dy = (y2 - y1)/(lens-1)\n",
    "        dz = (z2 - z1)/(lens-1) # PV will be linearly interpolated\n",
    "\n",
    "        ### define search axis\n",
    "        fact = np.cos(np.deg2rad((y1+y2)/2))\n",
    "        dxf = dx/fact # convert to Cartesian coordinate\n",
    "        def searchAxis(x,y,Lon): # this is normal to the trajectry (tangential) axis\n",
    "            return -dxf/dy*(Lon-x)+y # return latitude\n",
    "\n",
    "        # project to \"x-direction\" (note again, the search axis is normal to the tangential axis)\n",
    "        slope = np.abs(dy / np.sqrt(dxf**2 + dy**2)) \n",
    "        Range_ = Range * slope\n",
    "        dGrid_ = dGrid * slope\n",
    "\n",
    "        ### estimate positions\n",
    "        xref = x1; yref = y1\n",
    "        for i in range(span):\n",
    "            xi = xref + dx\n",
    "            yi = yref + dy\n",
    "            lonSearch = np.arange(xi-Range_, xi+Range_+dGrid_*rsl, dGrid_*rsl)\n",
    "            latSearch = np.array([searchAxis(xi,yi,loni) for loni in lonSearch])\n",
    "            # plt.plot(lonSearch,latSearch,lw=0.1,color='k')\n",
    "            zi = z1 + dz*(i+1) # reference depth\n",
    "            zref = np.array([getDepth(lonSearch[j], latSearch[j], topo)\n",
    "                                for j in range(len(lonSearch))])\n",
    "            indxRef = np.argmin(np.abs(zref - zi))\n",
    "            dfsec.loc[i+1,\"ilon\"] = lonSearch[indxRef] # dfsec.ilon[i+1] = lonSearch[indxRef]\n",
    "            dfsec.loc[i+1,\"ilat\"] = latSearch[indxRef] # dfsec.ilat[i+1] = latSearch[indxRef]\n",
    "            xref = lonSearch[indxRef]; yref = latSearch[indxRef]\n",
    "\n",
    "        ilons1 = dfsec.ilon.values # position obtained with forwarding interpolation\n",
    "        ilats1 = dfsec.ilat.values \n",
    "        \n",
    "        ######\n",
    "        ### second, estimate by backwarding interpolation\n",
    "        dfsec = dfi[i_bgn:i_end+1][::-1]\n",
    "        dfsec = dfsec.reset_index(drop=True)\n",
    "\n",
    "        lens = len(dfsec)\n",
    "        if lens < 3: # check if bad data exist (maybe redundunt)\n",
    "            continue\n",
    "        span = lens-2 # length of under-ice cycles\n",
    "        spans.append(span) # the length of malpositioned cycles\n",
    "\n",
    "        x1 = dfsec.lons.values[0]; x2 = dfsec.lons.values[-1]\n",
    "        y1 = dfsec.lats.values[0]; y2 = dfsec.lats.values[-1]\n",
    "        z1 = getDepth(x1, y1, topo)\n",
    "        z2 = getDepth(x2, y2, topo)\n",
    "        dx = (x2 - x1)/(lens-1) # denominator is the number of sections between data points\n",
    "        dy = (y2 - y1)/(lens-1)\n",
    "        dz = (z2 - z1)/(lens-1) # PV will be linearly interpolated\n",
    "\n",
    "        ### define search axis\n",
    "        fact = np.cos(np.deg2rad((y1+y2)/2))\n",
    "        dxf = dx/fact # convert to Cartesian coordinate\n",
    "        def searchAxis(x,y,Lon): # this is normal to the trajectry (tangential) axis\n",
    "            return -dxf/dy*(Lon-x)+y # return latitude\n",
    "\n",
    "        # project to \"x-direction\" (note again, the search axis is normal to the tangential axis)\n",
    "        slope = np.abs(dy / np.sqrt(dxf**2 + dy**2)) \n",
    "        Range_ = Range * slope\n",
    "        dGrid_ = dGrid * slope\n",
    "\n",
    "        ### estimate positions\n",
    "        xref = x1; yref = y1\n",
    "        for i in range(span):\n",
    "            xi = xref + dx\n",
    "            yi = yref + dy\n",
    "            lonSearch = np.arange(xi-Range_, xi+Range_+dGrid_*rsl, dGrid_*rsl)\n",
    "            latSearch = np.array([searchAxis(xi,yi,loni) for loni in lonSearch])\n",
    "            zi = z1 + dz*(i+1) # reference depth\n",
    "            zref = np.array([getDepth(lonSearch[j], latSearch[j], topo)\n",
    "                                for j in range(len(lonSearch))])\n",
    "            indxRef = np.argmin(np.abs(zref - zi))\n",
    "            dfsec.loc[i+1,\"ilon\"] = lonSearch[indxRef] # dfsec.ilon[i+1] = lonSearch[indxRef]\n",
    "            dfsec.loc[i+1,\"ilat\"] = latSearch[indxRef] # dfsec.ilat[i+1] = latSearch[indxRef]\n",
    "            xref = lonSearch[indxRef]; yref = latSearch[indxRef]\n",
    "\n",
    "        ilons2 = np.flipud(dfsec.ilon.values) # position obtained with backwarding interpolation\n",
    "        ilats2 = np.flipud(dfsec.ilat.values) \n",
    "\n",
    "        ###\n",
    "        ### get the average of forwarding/backwarding intepolations weighting by the distance from the end points\n",
    "        lonBgn,latBgn = dfi[i_bgn:i_end+1].lons.values[0], dfi[i_bgn:i_end+1].lats.values[0]\n",
    "        fact = np.cos(np.deg2rad(latBgn))\n",
    "        distBgn1 = np.sqrt(((ilons1-lonBgn)/fact)**2 + (ilats1-latBgn)**2)\n",
    "        distBgn2 = np.sqrt(((ilons2-lonBgn)/fact)**2 + (ilats2-latBgn)**2)\n",
    "\n",
    "        lonEnd,latEnd = dfi[i_bgn:i_end+1].lons.values[-1],dfi[i_bgn:i_end+1].lats.values[-1]\n",
    "        fact = np.cos(np.deg2rad(latEnd))\n",
    "        distEnd1 = np.sqrt(((ilons1-lonEnd)/fact)**2 + (ilats1-latEnd)**2)\n",
    "        distEnd2 = np.sqrt(((ilons2-lonEnd)/fact)**2 + (ilats2-latEnd)**2)\n",
    "\n",
    "        weight1 = 1/distBgn1 + 1/distEnd1 # this shape of weighting function has some arbitrariness...\n",
    "        weight2 = 1/distBgn2 + 1/distEnd2\n",
    "\n",
    "        #dfi.ilon[i_bgn:i_end+1] = (ilons1*weight1 + ilons2*weight2)/(weight1 + weight2)        \n",
    "        dfi.loc[i_bgn:i_end,\"ilon\"]= (ilons1*weight1 + ilons2*weight2)/(weight1 + weight2)\n",
    "        #dfi.ilat[i_bgn:i_end+1] = (ilats1*weight1 + ilats2*weight2)/(weight1 + weight2)\n",
    "        dfi.loc[i_bgn:i_end,\"ilat\"] = (ilats1*weight1 + ilats2*weight2)/(weight1 + weight2)\n",
    "\n",
    "    dfs.append(dfi)\n",
    "\n",
    "df = pd.concat(dfs)\n",
    "df = df.sort_values(by='id').reset_index(drop=True)\n",
    "# %% plot interpolated positions\n",
    "xx,yy = np.meshgrid(xg[::10],yg[::10])\n",
    "for idx in np.unique(df.id).astype('int'):\n",
    "    df1 = df[df.id==idx]\n",
    "    plt.contour(xx,yy,np.flipud(topo[::10,::10]),levels=np.arange(-5000,0,500),colors='gray',linestyles='-')\n",
    "    plt.title('id = '+str(idx))\n",
    "    plt.scatter(df1.lons,df1.lats,c=df1.qpos,s=10,zorder=2); plt.xlabel('lons'); plt.ylabel('lats')\n",
    "    plt.scatter(df1.ilon,df1.ilat,c='lime',s=10,zorder=2,label='interpolated')\n",
    "    plt.legend()\n",
    "    plt.xlim(0,130); plt.ylim(75,85)\n",
    "    plt.savefig(str(idx)+'_interpolated.png',dpi=200)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ad2d8db",
   "metadata": {},
   "source": [
    "From here forward is my own code.\n",
    "\n",
    "## Saving the data in csv\n",
    "Before exporting the data in csv format I order the cycles for them to be as in the original file. I order the lines, then create three new columns in the dataframe: flon and flat (final lon and lat positions) and nqpos (new corresponding quality flags for the positions). Finally I export the data in a csv file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "53fca5e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# here I order by WMO number (one can run the script for several floats but that is to complicated for me atm)\n",
    "df = df.sort_values(by=['id','cast','juld'])\n",
    "\n",
    "# create a vector with the new position QC\n",
    "indx = np.where(df.ilat>0)[0]\n",
    "nqpos=df.qpos.to_numpy()\n",
    "nqpos[indx]=8\n",
    "df['fqpos']=nqpos\n",
    "#print(nqpos)\n",
    "\n",
    "# create a vector with the best lon, lat\n",
    "flon=df.ilon.to_numpy() #copy the interpolated\n",
    "olon=df.lons.to_numpy() #copy the interpolated\n",
    "indx = np.where(df.ilat==0)[0] #if there is no interpolated\n",
    "flon[indx]=olon[indx]\n",
    "df['flon']=flon\n",
    "#print(flon)\n",
    "\n",
    "flat=df.ilat.to_numpy() #copy the interpolated\n",
    "olat=df.lats.to_numpy() #copy the interpolated\n",
    "indx = np.where(df.ilat==0)[0] #if there is no interpolated\n",
    "flat[indx]=olat[indx]\n",
    "df['flat']=flat\n",
    "#print(flat)\n",
    "\n",
    "#print(df)\n",
    "df.to_csv('TrajectoryCSVinterppos_'+str(idx)+'.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13e85bf8",
   "metadata": {},
   "source": [
    "## Modifying the netcdf file \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "271bc30e",
   "metadata": {},
   "outputs": [],
   "source": [
    "dset = netCDF4.Dataset(names[0], 'r+')\n",
    "dset.variables['LONGITUDE'][:]= flon\n",
    "dset.variables['LATITUDE'][:]=flat;\n",
    "#dset['LONGITUDE'][:]=flon;\n",
    "\n",
    "qpos =dset['POSITION_QC'][:]\n",
    "\n",
    "for i in range(0,len(nqpos)):\n",
    "  qpos[i]=nqpos[i].astype('|S1')\n",
    "\n",
    "dset.variables['POSITION_QC'][:]=qpos;\n",
    "dset.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54d990f6",
   "metadata": {},
   "source": [
    "Here I proof that the new data are stored in the netcdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "043e9dc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "names = glob.glob('*_prof.nc')\n",
    "names.sort()\n",
    "dset = netCDF4.Dataset(names[0])\n",
    "\n",
    "plt.scatter(dset.variables['LONGITUDE'][:],dset.variables['LATITUDE'][:],s=10,zorder=2); plt.xlabel('lons'); plt.ylabel('lats')\n",
    "plt.xlim(0,130); plt.ylim(75,85)\n",
    "#plt.savefig(str(idx)+'_interpolated.png',dpi=200)\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}