flux_gate-Kokanee_MP-pw-Linear-BW.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.7313000567495327"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import pylab\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "import matplotlib.ticker as ticker\n",
    "from osgeo import gdal\n",
    "import salem\n",
    "import seaborn as sns\n",
    "from scipy.optimize import curve_fit\n",
    "from scipy.interpolate import interp1d\n",
    "import statsmodels.api as stm\n",
    "import math\n",
    "from salem import *\n",
    "import pwlf\n",
    "from hydroeval import *\n",
    "import xarray as xr\n",
    "from sklearn.metrics import mean_squared_error as MSE\n",
    "import fiona, rasterio\n",
    "import geopandas as gpd\n",
    "from rasterio.plot import show\n",
    "import rasterio.plot as rplt\n",
    "from rasterio.features import rasterize\n",
    "from rasterstats import zonal_stats\n",
    "import pickle\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "def cm2inch(*tupl):  ### plots in cm ##frontiers 180 mm for full width, 85 mm half width (1 column) figures\n",
    "    inch = 2.54\n",
    "    if isinstance(tupl[0], tuple):\n",
    "        return tuple(i/inch for i in tupl[0])\n",
    "    else:\n",
    "        return tuple(i/inch for i in tupl)\n",
    "    \n",
    "years = [2021, 2021, 2021] #[2016, 2017, 2018]\n",
    "\n",
    "sVSTAKE=1.10; sVOFFice=1.1; sVCoreg=2.0; sVRAND=1.5\n",
    "sVsys=np.sqrt((sVOFFice**2)+(sVCoreg)**2)\n",
    "sVZ=np.sqrt((sVsys**2)+((sVRAND)**2))\n",
    "sVZ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d=D[(D.id==0)]\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Vel. vz 4.79 2.97\n",
      "4.7934384\n",
      "2.971611\n",
      "Original vector layer projection:  {'init': 'epsg:2955'}\n",
      "New vector layer projection (PROJ4):  +init=epsg:32611\n",
      "\n",
      "\n",
      "(0, 'min')\n",
      "(1, 'max')\n",
      "(2, 'mean')\n",
      "(3, 'count')\n",
      "(4, 'sum')\n",
      "(5, 'std')\n",
      "(6, 'median')\n",
      "(7, 'majority')\n",
      "(8, 'minority')\n",
      "(9, 'unique')\n",
      "(10, 'range')\n",
      "Original vector layer projection:  +init=epsg:32611\n",
      "New vector layer projection (PROJ4):  +init=epsg:2955\n",
      "\n",
      "\n",
      "(0, 'min')\n",
      "(1, 'max')\n",
      "(2, 'mean')\n",
      "(3, 'count')\n",
      "(4, 'sum')\n",
      "(5, 'std')\n",
      "(6, 'median')\n",
      "(7, 'majority')\n",
      "(8, 'minority')\n",
      "(9, 'unique')\n",
      "(10, 'range')\n",
      "slope RLM: -0.0009049144712675825\n",
      "slope RLM: 0.001155092677465162\n",
      "slope RLM: 0.0010710555081971437\n",
      "slope RLM: 0.000919715570188079\n",
      "0.7073293333264126\n",
      "0.6830051098239075\n",
      "0.5923218284183569\n",
      "Mean Vel. vz 4.79 2.97\n",
      "4.7934384\n",
      "2.971611\n",
      "Original vector layer projection:  {'init': 'epsg:2955'}\n",
      "New vector layer projection (PROJ4):  +init=epsg:32611\n",
      "\n",
      "\n",
      "(0, 'min')\n",
      "(1, 'max')\n",
      "(2, 'mean')\n",
      "(3, 'count')\n",
      "(4, 'sum')\n",
      "(5, 'std')\n",
      "(6, 'median')\n",
      "(7, 'majority')\n",
      "(8, 'minority')\n",
      "(9, 'unique')\n",
      "(10, 'range')\n",
      "Original vector layer projection:  +init=epsg:32611\n",
      "New vector layer projection (PROJ4):  +init=epsg:2955\n",
      "\n",
      "\n",
      "(0, 'min')\n",
      "(1, 'max')\n",
      "(2, 'mean')\n",
      "(3, 'count')\n",
      "(4, 'sum')\n",
      "(5, 'std')\n",
      "(6, 'median')\n",
      "(7, 'majority')\n",
      "(8, 'minority')\n",
      "(9, 'unique')\n",
      "(10, 'range')\n",
      "slope RLM: -0.0009049144712675825\n",
      "slope RLM: 0.001155092677465162\n",
      "slope RLM: 0.0010710555081971437\n",
      "slope RLM: 0.000919715570188079\n",
      "0.7523106953668623\n",
      "0.734826815037821\n",
      "0.6477713143472478\n",
      "Mean Vel. vz 4.79 2.97\n",
      "4.7934384\n",
      "2.971611\n",
      "Original vector layer projection:  {'init': 'epsg:2955'}\n",
      "New vector layer projection (PROJ4):  +init=epsg:32611\n",
      "\n",
      "\n",
      "(0, 'min')\n",
      "(1, 'max')\n",
      "(2, 'mean')\n",
      "(3, 'count')\n",
      "(4, 'sum')\n",
      "(5, 'std')\n",
      "(6, 'median')\n",
      "(7, 'majority')\n",
      "(8, 'minority')\n",
      "(9, 'unique')\n",
      "(10, 'range')\n",
      "Original vector layer projection:  +init=epsg:32611\n",
      "New vector layer projection (PROJ4):  +init=epsg:2955\n",
      "\n",
      "\n",
      "(0, 'min')\n",
      "(1, 'max')\n",
      "(2, 'mean')\n",
      "(3, 'count')\n",
      "(4, 'sum')\n",
      "(5, 'std')\n",
      "(6, 'median')\n",
      "(7, 'majority')\n",
      "(8, 'minority')\n",
      "(9, 'unique')\n",
      "(10, 'range')\n",
      "slope RLM: -0.0009049144712675825\n",
      "slope RLM: 0.001155092677465162\n",
      "slope RLM: 0.0010710555081971437\n",
      "slope RLM: 0.000919715570188079\n",
      "0.7346688274079111\n",
      "0.7145949045628428\n",
      "0.6262119334442469\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 510.236x233.858 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 510.236x233.858 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "font = {'family' : 'Helvetica', 'weight' : 'normal',  'size'   : 8}\n",
    "mfc='none'; mew=1.1; elw=0.9\n",
    "plt.rc('font', **font)\n",
    "pylab.rcParams['xtick.major.pad']='1.'; pylab.rcParams['ytick.major.pad']='1.'\n",
    "\n",
    "fig1, ax1 = plt.subplots(1,3, sharex=True, sharey=True, figsize=(cm2inch(18, 8.25))) #obs. and flux gate SMB boxplot/errorbar plot\n",
    "fig2, ax2 = plt.subplots(1,3, sharex=True, sharey=True, figsize=(cm2inch(18, 8.25))) ##mass conservation plot\n",
    "\n",
    "count = 0\n",
    "\n",
    "for yr in range(len(years)):\n",
    "    # season\n",
    "    balance = 'Bw'  # Bw or Ba (winter or annual)\n",
    "    rho_snow = 431. # 470 kg m-3 geodetic or 457 glaciological, or...take given years obs. value\n",
    "    year = years[yr] # any of: 2016, 2017, 2018\n",
    "    I = count\n",
    "#     I =  np.abs(year - 2016) # for selecting raster inputs\n",
    "\n",
    "    gl = 0 # number to select glacier\n",
    "    Glacier = ['Kokanee', 'Conrad']\n",
    "    glacier = ['kokanee', 'conrad']\n",
    "#   d = 20. # distance (meters) between flux gate points\n",
    "    Dint = 0.85 #depth integrated velocity ratio, 1.0= all sliding\n",
    "    sVSTAKE=1.10; sVOFFice=1.1; sVCoreg=2.0; sVRAND=1.5\n",
    "    sVsys=np.sqrt((sVOFFice**2)+(sVCoreg)**2)\n",
    "    sVZ=np.sqrt((sVsys**2)+((sVRAND)**2))\n",
    "    \n",
    "#     sVZ = 4.91  #velocity against stake measurements\n",
    "#     spVZ = 0.216 \n",
    "    sHopt = 0.078 ## ME:10.3 m,  MAE:0.223 (percent ratio)\n",
    "\n",
    "    fl_path = '/home/pelto/Desktop/ice_flux/' + Glacier[gl] + '/'\n",
    "    \n",
    "    gpr_path = '/home/pelto/GIS/GPR/ComparisonData/'\n",
    "    path = '/home/pelto/Desktop/lidar_cbt_analysis/kokanee/'\n",
    "        \n",
    "    VDIR = 'average'  # 'average' or 'individual'\n",
    "    firn = False\n",
    "    \n",
    "    if VDIR == 'individual':  \n",
    "        \n",
    "        vf_list = ['kokanee_vy_25m_2016.tif','kokanee_vy_25m_2017.tif','kokanee_vy_25m_2018.tif'] \n",
    "         #['img1_20160417__img1_20170521_vmap_5m_35px_spm2/', 'img1_20160417__img1_20170521_vmap_5m_35px_spm2/',\n",
    "            #   'img1_20160417__img1_20170521_vmap_5m_35px_spm2/' ]#'img1_20150419__img2_20160417_vmap_5m_35px_spm2/'\n",
    "\n",
    "        vdir = '/home/pelto/Desktop/velocity_mapping/kokanee/dem/3m/' \n",
    "        VX = vdir+ vf_list[I][:-15] + 'vx_25m_' +str(year)+ '.tif' \n",
    "        VY = vdir+ vf_list[I][:-15] + 'vy_25m_' +str(year)+ '.tif' \n",
    "        \n",
    "#         VX = vdir+ vf[:-1] + '-F_vx.tif' \n",
    "#         VY = vdir+ vf[:-1] + '-F_vy.tif' \n",
    "       \n",
    "    else:\n",
    "        vdir = '/home/pelto/Desktop/velocity_mapping/kokanee/dem/3m/'  #bedem/3m/old/'\n",
    "        VX = vdir+ 'kokanee_vx_25m_2017.tif' #'kokanee_all_bedem3m_vx_5m.tif'                    #kokanee_best_dem1m_5m_vx.tif' #   #'kokanee_all_bedem3m_vx_5m.tif'\n",
    "        VY =  vdir+ 'kokanee_vy_25m_2017.tif' #'/home/pelto/Desktop/velocity_mapping/kokanee/dem/kokanee_all_dem1m_5m_vy.tif' #kokanee_best_pos_dem1m_5m_vy.tif '     #'kokanee_all_bedem3m_vy_5m.tif'\n",
    "        #VM = vdir+ 'kokanee_all_bedem3m_vm_5m_new.tif'     #'kokanee_all_bedem3m_vm_5m.tif'\n",
    "    \n",
    "    topo = '/home/pelto/Desktop/lidar_cbt_analysis/kokanee/20160913_kokanee_dem1_clip_slave.tif' \n",
    "    farinotti = gpr_path + 'RGI60-02/RGI60-02.00147_thickness.tif' #'RGI60-02.00147_thickness_srtm_corr.tif' #'RGI60-02/RGI60-02.00147'\n",
    "\n",
    "    dhW_list = ['kokanee_2021_winter_dh.tif', 'kokanee_2021_winter_dh.tif', 'kokanee_2021_winter_dh.tif']\n",
    "    dhA_list = ['kokanee_2016_2015_dh_dt.tif', 'kokanee_2017_2016_dh_dt_filled.tif','kokanee_2018_2017_dh_dt_21.tif', 'kokanee_2018_2015_dh_dt_21.tif']\n",
    "    if balance == 'Bw':\n",
    "        dh_r = path+ dhW_list[I]   #winter height change TIFF\n",
    "    else:\n",
    "        dh_r = path+  dhA_list[I] #Annual height change TIFF\n",
    "\n",
    "    pts_file = fl_path + 'gis/kokanee_gates_pts_25m_wgs84.shp'  ##must be WGS84 currently\n",
    "    gates = fl_path+'Kok_flux_gates_6.shp'\n",
    "    shpf = path + 'Kokanee/kok_all_glaciers_2015_dem.shp'   #GLIMS_BC/glims_all/all_glaciers_2016.shp' \n",
    "#     bins=fl_path+'kokanee_flux_bins.shp'\n",
    "    if year==2016:\n",
    "        bins=fl_path+'kokanee_flux_bins_6.shp'\n",
    "    elif year==2017:\n",
    "        bins=fl_path+'kokanee_flux_6bins_16.shp'\n",
    "    else: \n",
    "        bins=fl_path+'kokanee_flux_6bins_17.shp'\n",
    "\n",
    "    obs = pd.read_csv(fl_path+ 'Kokanee_bdot.csv')\n",
    "    rho = pd.read_csv(fl_path + 'RHO.csv') #'kokanee_rho_6.csv')  ## rho\n",
    "    # open GeoTIFF as array\n",
    "    \n",
    "    vy = salem.open_xr_dataset(VY);vy = vy.to_array(name='vy')\n",
    "    vx = salem.open_xr_dataset(VX);vx = vx.to_array(name='vx')\n",
    "    msk = salem.open_xr_dataset(path + 'kokanee_2015_extent_5m.tif')\n",
    "    msk_reproj = vy.salem.transform(msk); msk = msk_reproj.to_array(name='msk')\n",
    "    topo ='/home/pelto/Desktop/lidar_cbt_analysis/kokanee/20181014_Kokanee_dem1_clip_slave.tif' \n",
    "\n",
    "    vz = vy\n",
    "    vz.data = np.sqrt(vx.data**2 + vz.data**2 )\n",
    "    vz.data[vz.data<0.01]=np.nan;# vz.data[vz.data>30.0]=vz.data[vz.data>30.0]*0.5 ##remove high erroneous values\n",
    "    vz.data[msk.data!=1.0] = np.nan; \n",
    "    vz=xr.DataArray(vz.data, coords=vz.coords, name='vz')\n",
    "    with open(fl_path+str(year)+'_vz.pkl', 'wb') as f:\n",
    "        pickle.dump(vz[0].data, f)\n",
    "    \n",
    "    msk_all = salem.open_xr_dataset(fl_path + 'Kokanee_all_glaciers_2015_ice_msk_5m.tif')\n",
    "    gpr = salem.open_xr_dataset(fl_path + 'gpr_50_100m_5m.tif') #'gpr_outlines_all_25_25m_re5m.tif'\n",
    "    farin = salem.open_xr_dataset(farinotti)\n",
    "\n",
    "    H_opt = salem.open_xr_dataset(fl_path+ 'Kokanee_opt_thick_final.tif')#   'opt_thick251sw_MAE.tif'\n",
    "    gates = salem.read_shapefile(gates)\n",
    "\n",
    "    #reproject all images and ensure identical footprint for each \n",
    "    H_opt_reproj = vy.salem.transform(H_opt);H_opt = H_opt_reproj.to_array(name='H_opt')\n",
    "\n",
    "    farin_reproj= vy.salem.transform(farin);farin = farin_reproj.to_array(name='farin')\n",
    "    srtm_corr = fl_path + 'kokanee_SRTM_diff_29m_utm_5m.tif'\n",
    "    srtm_corr = salem.open_xr_dataset(srtm_corr);srtm_corr_reproj = vy.salem.transform(srtm_corr)\n",
    "    srtm_corr = srtm_corr_reproj.to_array(name='srtm_corr')\n",
    "    srtm_corr.data[srtm_corr.data>10.0] = 0.0  ##remove positive anomalous values\n",
    "    farin_corr = farin + srtm_corr\n",
    "    farin_corr= farin_corr.rename('farin_corr')\n",
    "    gpr_reproj = vy.salem.transform(gpr);gpr = gpr_reproj.to_array(name='gpr')\n",
    "\n",
    "    gpr.data[gpr.data<0.5] = np.nan\n",
    "#     vy.data[vy.data<0.0] = np.nan ## remove false velocity vectors (up glacier velocity)\n",
    "#     VZ = np.sqrt(vx.data**2 + vy.data**2 )\n",
    "    \n",
    "    VZ_off_ice = vz.copy()\n",
    "#     VZ[msk.data<1.0] = np.nan;VZ[VZ==0.0]=np.nan; VZ[VZ<0.1]=np.nan;\n",
    "#     VZ_off_ice[msk.data>0.0] = np.nan\n",
    "#     VZ_off_ice[VZ_off_ice==0.0] = np.nan\n",
    "\n",
    "    dem = salem.open_xr_dataset(topo)\n",
    "    dem_reproj = vy.salem.transform(dem)\n",
    "    dem = dem_reproj.to_array(name='dem')\n",
    "    dem.data[dem.data<1] = np.nan\n",
    "\n",
    "    #     # map extent\n",
    "    # grid = mercator_grid(center_ll=(-117.43, 51.238), extent=(4500, 4500)) ##zoomed out view\n",
    "    # 487892.000 5509738.000 491232.000 5512358.000\n",
    "    grid = vy.salem.grid  ##full view\n",
    "    sm = Map(grid, countries=False)\n",
    "    sm.set_lonlat_contours(interval=0)\n",
    "    sm.set_scale_bar()\n",
    "    sm.set_data(vz) #, label='m')\n",
    "    #      Change the lon-lat countour setting\n",
    "    sm.set_lonlat_contours(add_ytick_labels=True, interval=0.05, linewidths=0.75, linestyles='--', colors='0.25')\n",
    "    off_ice_V = np.nanmean(VZ_off_ice)\n",
    "    print('Mean Vel. vz', round(np.nanmean(vz),2), round(np.nanstd(vz),2))\n",
    "    print(off_ice_V)\n",
    "    print(np.nanstd(VZ_off_ice))\n",
    "    gdf = salem.read_shapefile(shpf)\n",
    "    sm.set_shapefile(gdf, linewidth=1)\n",
    "    sm.set_shapefile(gates, linewidth=1.5, color='r')\n",
    "#     sm.visualize()\n",
    "#     plt.savefig(fl_path+ 'products/'+ glacier[gl] +str(year) +'ice_vel_gates.png', dpi=300)\n",
    "#     plt.show()\n",
    "\n",
    "    ## Functions for calculating zonal statistics over each flux gate bin\n",
    "\n",
    "    # https://community.esri.com/groups/python-snippets/blog/2019/05/07/calculating-zonal-statistics-with-python-rasterstats\n",
    "    # For loading shapefiles into geopandas dataframe\n",
    "\n",
    "    def enum_items(source):\n",
    "        print(\"\\n\")\n",
    "        for ele in enumerate(source): \n",
    "            print(ele)\n",
    "\n",
    "    def list_columns(df):\n",
    "        field_list = list(df)\n",
    "#         enum_items(field_list)\n",
    "        return field_list\n",
    "\n",
    "    def loadshp_as_gpd(shp):\n",
    "        data_shp = gpd.read_file(shp)\n",
    "        return data_shp\n",
    "\n",
    "    # For loading feature classes into geopandas dataframe\n",
    "    def loadfc_as_gpd(fgdb):\n",
    "        layers = fiona.listlayers(fgdb)\n",
    "        enum_items(layers)\n",
    "        index = int(input(\"Which index to load? \"))\n",
    "        fcgpd = gpd.read_file(fgdb,layer=layers[index])\n",
    "        return fcgpd\n",
    "\n",
    "    # For re-projecting input vector layer to raster projection\n",
    "    def reproject(fcgpd, raster):\n",
    "        proj = raster.crs.to_proj4()\n",
    "        print(\"Original vector layer projection: \", fcgpd.crs)\n",
    "        reproj = fcgpd.to_crs(proj)\n",
    "        print(\"New vector layer projection (PROJ4): \", reproj.crs)\n",
    "#         fig, ax = plt.subplots(figsize=(10, 10))\n",
    "#         rplt.show(raster, ax=ax)\n",
    "#         reproj.plot(ax=ax, facecolor='none', edgecolor='red')\n",
    "#         fig.show()\n",
    "        return reproj\n",
    "\n",
    "    def dissolve_gpd(df):\n",
    "        field_list = list_columns(df)\n",
    "        index = 1 #int(input(\"Dissolve by which field (index)? \"))\n",
    "        dgpd = df.dissolve(by=field_list[index])\n",
    "        return dgpd\n",
    "\n",
    "    # For selecting which raster statistics to calculate\n",
    "    def stats_select():\n",
    "        stats_list = stats_list = ['min', 'max', 'mean', 'count', \n",
    "                  'sum', 'std', 'median', 'majority', \n",
    "                  'minority', 'unique', 'range']\n",
    "        enum_items(stats_list)\n",
    "    #     indices = input(\"Enter raster statistics selections separated by space: \")\n",
    "        indices='2 3 5 6'\n",
    "        stats  = list(indices.split())\n",
    "        out_stats = list()\n",
    "        for i in stats:\n",
    "            out_stats.append(stats_list[int(i)])\n",
    "        return out_stats\n",
    "\n",
    "    def get_zonal_stats(vector, raster, stats):\n",
    "        # Run zonal statistics, store result in geopandas dataframe\n",
    "        result = zonal_stats(vector, raster, stats=stats, geojson_out=True)\n",
    "        geostats = gpd.GeoDataFrame.from_features(result)\n",
    "        return geostats\n",
    "\n",
    "    ###################### make an ice velocity quiver plot############################\n",
    "\n",
    "    df = salem.read_shapefile(pts_file) \n",
    "    df_file = loadshp_as_gpd(pts_file)\n",
    "\n",
    "    coords = np.array([p.xy for p in df.geometry]).squeeze()\n",
    "    df['lon'] = coords[:, 0]\n",
    "    df['lat'] = coords[:, 1]\n",
    "\n",
    "    # ax.scatter(df.lon, df.lat, s=10, c='r' )#c='depth',cmap='viridis', s=10, ax=ax);\n",
    "    xx, yy = salem.transform_proj(salem.wgs84, grid.proj, df['lon'].values, df['lat'].values)\n",
    "    df['x'] = xx\n",
    "    df['y'] = yy\n",
    "\n",
    "    # shp_plt = reproject(df, VX)\n",
    "\n",
    "    X, Y = np.meshgrid(vx.coords['x'],vx.coords['y'])\n",
    "    U = vx.data[0]\n",
    "    V = vy.data[0]\n",
    "\n",
    "###########################################################################################\n",
    "    vns = ['vx','vy','vz','gpr','H_opt','dem','farin_corr',]\n",
    "\n",
    "    M = xr.merge([vx,vy,vz,gpr,H_opt,dem,farin_corr]) #dem\n",
    "\n",
    "    for vn in vns:\n",
    "        df[vn] = M[vn][0].interp(x=('z', df.x), y=('z', df.y))\n",
    "\n",
    "    df_agg = df[['id', 'len', 'distance', 'angle', 'geometry', 'lon', 'lat']].copy()\n",
    "    ii, jj = grid.transform(df['lon'], df['lat'], crs=salem.wgs84, nearest=True)\n",
    "    df_agg['i'] = ii\n",
    "    df_agg['j'] = jj\n",
    "    # # We trick by creating an index of similar i's and j's\n",
    "    # df_agg['ij'] = ['{:04d}_{:04d}'.format(i, j) for i, j in (vx.coords['x'], vx.coords['y'])]\n",
    "    df_agg['ij'] = ['{:04d}_{:04d}'.format(i, j) for i, j in zip(ii, jj)]\n",
    "    df_agg = df_agg.groupby('ij').mean()\n",
    "\n",
    "    # Select\n",
    "    for vn in vns:\n",
    "    #     M_maksed = np.ma.masked_invalid(M[vn][0])\n",
    "    #     df_agg[vn] = .interp(x=('z', df_agg.i), y=('z', df_agg.j))\n",
    "        df_agg[vn] = M[vn][0].isel(x=('z', df_agg.i), y=('z', df_agg.j))  \n",
    "\n",
    "    D = df_agg[['id', 'len', 'distance', 'angle', 'lon', 'lat', 'vx', 'vy','vz' ,'gpr', 'H_opt', 'dem', 'farin_corr']].copy()\n",
    "\n",
    "    D['vzdir'] = np.arcsin(D.vx/D.vz) *180/math.pi #degrees from north\n",
    "    # fill any missing vel. direction\n",
    "    for i in range(len((D))):   \n",
    "        if np.isnan(D.vzdir[i])==True:\n",
    "            G=D.id[i];V=np.nanmedian(D.vzdir[D.id==G]) \n",
    "            D.vzdir[i]=V\n",
    "    \n",
    "    ### fill in velocity nan at edges\n",
    "    for i in range(D.id.nunique()):\n",
    "        d=D[(D.id==i)&(D.distance<300)]\n",
    "        dist=d.distance[np.isnan(d.vz)].values;\n",
    "        if len(dist) > 0:\n",
    "            x=d.distance[np.logical_not(np.isnan(d.vz))].values;y=d.vz.values; y=y[np.logical_not(np.isnan(y))]\n",
    "            coefficients = np.polyfit(x, y, 1); poly = np.poly1d(coefficients); new_x = np.linspace(x[0], x[-1]);\n",
    "            new_y = poly(new_x);fill=poly(dist);#plt.scatter(dist,fill,color='r');plt.plot(x, y, \"o\", new_x, new_y)\n",
    "            for f in range(len(dist)):\n",
    "                D.vz[(D.id==i)&(D.distance==dist[f])]=fill[f] \n",
    "\n",
    "        d=D[(D.id==i)&(D.distance>(D.len - 300))]\n",
    "        dist=d.distance[np.isnan(d.vz)].values;# print(len(dist))\n",
    "        if len(dist) > 0:\n",
    "            x=d.distance[np.logical_not(np.isnan(d.vz))].values;y=d.vz.values; y=y[np.logical_not(np.isnan(y))]\n",
    "            coefficients = np.polyfit(x, y, 1); poly = np.poly1d(coefficients); new_x = np.linspace(x[0], x[-1]);\n",
    "            new_y = poly(new_x);fill=poly(dist);#plt.scatter(dist,fill,color='r');plt.plot(x, y, \"o\", new_x, new_y)\n",
    "            for f in range(len(dist)):\n",
    "                D.vz[(D.id==i)&(D.distance==dist[f])]=fill[f]     \n",
    "    \n",
    "    ### fill ice thickness gaps \n",
    "    #last and first points in each line are zero depth (except gates 2 and 3 start)\n",
    "    D.distance[(D.id==0)&(D.distance==257.5)]=265\n",
    "    D.gpr[D.distance==0.0]=0.0;D.gpr[(D.distance==D.len)]=0.0;\n",
    "    D.gpr[(D.id==2)&(D.distance==0.0)]=12.5;D.gpr[(D.id==3)&(D.distance==0.0)]=42.2;\n",
    "    D.H_opt[(D.gpr==0.0)&(D.distance==0.0)]=0.0;D.H_opt[(D.distance==D.len)]=0.0;\n",
    "    D.farin_corr[(D.gpr==0.0)&(D.distance==0.0)]=0.0;D.farin_corr[(D.distance==D.len)]=0.0;\n",
    "    \n",
    "    \n",
    "    d=D[(D.id==1)&(D.distance<650)];dist=d.distance[np.isnan(d.gpr)].values; \n",
    "    x=d.distance[np.logical_not(np.isnan(d.gpr))].values;y=d.gpr.values; y=y[np.logical_not(np.isnan(y))]\n",
    "    coefficients = np.polyfit(x, y, 5); poly = np.poly1d(coefficients); new_x = np.linspace(x[0], x[-1]);\n",
    "    new_y = poly(new_x);fill=poly(dist);#plt.scatter(dist,fill,color='r');plt.plot(x, y, \"o\", new_x, new_y)\n",
    "    for f in range(len(dist)):\n",
    "        D.gpr[(D.id==1)&(D.distance==dist[f])]=fill[f]\n",
    "\n",
    "    d=D[(D.id==1)&(D.distance>650)];dist=d.distance[np.isnan(d.gpr)].values; \n",
    "    x=d.distance[np.logical_not(np.isnan(d.gpr))].values;y=d.gpr.values; y=y[np.logical_not(np.isnan(y))]\n",
    "    coefficients = np.polyfit(x, y, 5); poly = np.poly1d(coefficients); new_x = np.linspace(x[0], x[-1]);\n",
    "    new_y = poly(new_x);fill=poly(dist);#plt.scatter(dist,fill,color='r');plt.plot(x, y, \"o\", new_x, new_y)\n",
    "    for f in range(len(dist)):\n",
    "        D.gpr[(D.id==1)&(D.distance==dist[f])]=fill[f]\n",
    "\n",
    "    d=D[(D.id==2)&(D.distance<750)]\n",
    "    dist=d.distance[np.isnan(d.gpr)].values; \n",
    "    x=d.distance[np.logical_not(np.isnan(d.gpr))].values;y=d.gpr.values; y=y[np.logical_not(np.isnan(y))]\n",
    "    coefficients = np.polyfit(x, y, 2); poly = np.poly1d(coefficients); new_x = np.linspace(x[0], x[-1]);\n",
    "    new_y = poly(new_x);fill=poly(dist);#plt.scatter(dist,fill,color='r');plt.plot(x, y, \"o\", new_x, new_y)\n",
    "    for f in range(len(dist)):\n",
    "        D.gpr[(D.id==2)&(D.distance==dist[f])]=fill[f]\n",
    "\n",
    "    d=D[(D.id==2)&(D.distance>700)&(D.distance<1000)]\n",
    "    dist=d.distance[np.isnan(d.gpr)].values; \n",
    "    x=d.distance[np.logical_not(np.isnan(d.gpr))].values;y=d.gpr.values; y=y[np.logical_not(np.isnan(y))]\n",
    "    coefficients = np.polyfit(x, y, 3); poly = np.poly1d(coefficients); new_x = np.linspace(x[0], x[-1]);\n",
    "    new_y = poly(new_x);fill=poly(dist);#plt.scatter(dist,fill,color='r');plt.plot(x, y, \"o\", new_x, new_y)\n",
    "    for f in range(len(dist)):\n",
    "        D.gpr[(D.id==2)&(D.distance==dist[f])]=fill[f]\n",
    "\n",
    "    d=D[(D.id==2)&(D.distance>1300)]\n",
    "    dist=d.distance[np.isnan(d.gpr)].values; \n",
    "    x=d.distance[np.logical_not(np.isnan(d.gpr))].values;y=d.gpr.values; y=y[np.logical_not(np.isnan(y))]\n",
    "    coefficients = np.polyfit(x, y, 7); poly = np.poly1d(coefficients); new_x = np.linspace(x[0], x[-1]);\n",
    "    new_y = poly(new_x);fill=poly(dist);#plt.scatter(dist,fill,color='r');plt.plot(x, y, \"o\", new_x, new_y)\n",
    "    for f in range(len(dist)):\n",
    "        D.gpr[(D.id==2)&(D.distance==dist[f])]=fill[f]\n",
    "\n",
    "    d=D[(D.id==3)&(D.distance>500)&(D.distance<1400)]\n",
    "    dist=d.distance[np.isnan(d.gpr)].values; \n",
    "    x=d.distance[np.logical_not(np.isnan(d.gpr))].values;y=d.gpr.values; y=y[np.logical_not(np.isnan(y))]\n",
    "    coefficients = np.polyfit(x, y, 4); poly = np.poly1d(coefficients); new_x = np.linspace(x[0], x[-1]);\n",
    "    new_y = poly(new_x);fill=poly(dist);#plt.scatter(dist,fill,color='r');plt.plot(x, y, \"o\", new_x, new_y)\n",
    "    for f in range(len(dist)):\n",
    "        D.gpr[(D.id==3)&(D.distance==dist[f])]=fill[f]\n",
    "\n",
    "    d=D[(D.id==3)&(D.distance>1600)];dist=d.distance[np.isnan(d.gpr)].values; \n",
    "    x=d.distance[np.logical_not(np.isnan(d.gpr))].values;y=d.gpr.values; y=y[np.logical_not(np.isnan(y))]\n",
    "    coefficients = np.polyfit(x, y, 3); poly = np.poly1d(coefficients); new_x = np.linspace(x[0], x[-1]);\n",
    "    new_y = poly(new_x);fill=poly(dist);#plt.scatter(dist,fill,color='r');plt.plot(x, y, \"o\", new_x, new_y)\n",
    "    for f in range(len(dist)):\n",
    "        D.gpr[(D.id==3)&(D.distance==dist[f])]=fill[f]\n",
    "\n",
    "    d=D[(D.id==4)&(D.distance>830)];dist=d.distance[np.isnan(d.gpr)].values;\n",
    "    x=d.distance[np.logical_not(np.isnan(d.gpr))].values;y=d.gpr.values; y=y[np.logical_not(np.isnan(y))]\n",
    "    coefficients = np.polyfit(x, y,4); poly = np.poly1d(coefficients); new_x = np.linspace(x[0], x[-1]);\n",
    "    new_y = poly(new_x);fill=poly(dist);#plt.scatter(dist,fill,color='r');plt.plot(x, y, \"o\", new_x, new_y)\n",
    "    for f in range(len(dist)):\n",
    "        D.gpr[(D.id==4)&(D.distance==dist[f])]=fill[f]\n",
    "\n",
    "    d=D[(D.id==4)&(D.distance<300)];dist=d.distance[np.isnan(d.gpr)].values;\n",
    "    x=d.distance[np.logical_not(np.isnan(d.gpr))].values;y=d.gpr.values; y=y[np.logical_not(np.isnan(y))]\n",
    "    coefficients = np.polyfit(x, y,3); poly = np.poly1d(coefficients); new_x = np.linspace(x[0], x[-1]);\n",
    "    new_y = poly(new_x);fill=poly(dist);#plt.scatter(dist,fill,color='r');plt.plot(x, y, \"o\", new_x, new_y)\n",
    "    for f in range(len(dist)):\n",
    "        D.gpr[(D.id==4)&(D.distance==dist[f])]=fill[f]\n",
    "\n",
    "    d=D[(D.id==4)&(D.distance>300)&(D.distance<830)];dist=d.distance[np.isnan(d.gpr)].values;\n",
    "    x=d.distance[np.logical_not(np.isnan(d.gpr))].values;y=d.gpr.values; y=y[np.logical_not(np.isnan(y))]\n",
    "    coefficients = np.polyfit(x, y,6); poly = np.poly1d(coefficients); new_x = np.linspace(x[0], x[-1]);\n",
    "    new_y = poly(new_x);fill=poly(dist);#plt.scatter(dist,fill,color='r');plt.plot(x, y, \"o\", new_x, new_y)\n",
    "    for f in range(len(dist)):\n",
    "        D.gpr[(D.id==4)&(D.distance==dist[f])]=fill[f]\n",
    "\n",
    "    \n",
    "#     D.gpr[(D.id==0)&(D.distance==262.5)]=0.0\n",
    "    \n",
    "#     depth=[12.5,22.7,30.95,37.2,41.4,63.2,59.6,51.5,40.4,26.0,8.5,0.0] \n",
    "#     dist=[0.,20.,50.,80.,100.,1000.,1020,1050.,1080,1110.,1140.,1153.]\n",
    "#     for i in range(len(depth)):\n",
    "#         D.gpr[(D.id==1)&(D.distance==dist[i])]=depth[i] \n",
    "        \n",
    "#     dist=[0,20,40,60,80,100,130,160,180,210,240,260,290,320,340,360,380,400,420,440,460,480,560,590,2052]\n",
    "#     depth=[50.2,50.,49.9,49.7,49.5,49.3,49.,48.7,48.5,48.1,47.7,47.5,47.1,46.6,46.3,46.0,45.7,45.4,45.,44.7,44.3,44.,42.4,41.8,0.0]\n",
    "#     for i in range(len(depth)):\n",
    "#         D.gpr[(D.id==2)&(D.distance==dist[i])]=depth[i]\n",
    "        \n",
    "#     dist=[620,640,660,680,710,740,760,790,820,840,860,880,900,920,940,960,980,1000,1020,1040,1060,1090,1120,1140,1170]\n",
    "#     depth=[55.2,54.2,53.4,52.7,51.9,51.4,51.1,51.0,51.1,51.4,51.7,52.2,52.8,53.5,54.3,55.2,56.3,57.5,58.7,60.1,61.6,64.1,66.9,68.9,72.0]\n",
    "    \n",
    "#     for i in range(len(depth)):\n",
    "#         D.gpr[(D.id==3)&(D.distance==dist[i])]=depth[i]\n",
    "        \n",
    "#     dist=[880,990,1020,1040,1060,1080,1100,1120,1150,1180,1200,1230,1260,1290,1320,1340,1370,1400,1420,1440,1460,1480,1500,1530,1560,1590,1620,1700.0,1720,1740,1760,1780,1800,1820]\n",
    "#     depth=[25.76923,39.53941,42.68357,44.63413,46.46823,48.2,49.8,51.27188,53.2807,55.02751,56.0465,57.35665,58.40479,59.19093,59.71506,59.91893,60.0064,59.83186,59.56995,59.19159,58.69678,58.08553,57.35783,56.04795,54.47607,52.64218,50.54629,37.43474,33.35523,29.06548,24.56549,19.85525,14.93477,9.804047]\n",
    "#     for i in range(len(depth)):\n",
    "#         D.gpr[(D.id==4)&(D.distance==dist[i])]=depth[i] \n",
    "    \n",
    "#     d=D[(D.id==4)&(D.distance>800)];dist=d.distance[np.isnan(d.gpr)].values;\n",
    "#     x=d.distance[np.logical_not(np.isnan(d.gpr))].values;y=d.gpr.values; y=y[np.logical_not(np.isnan(y))]\n",
    "#     coefficients = np.polyfit(x, y,5); poly = np.poly1d(coefficients); new_x = np.linspace(x[0], x[-1]);\n",
    "#     new_y = poly(new_x);fill=poly(dist);#plt.scatter(dist,fill,color='r');plt.plot(x, y, \"o\", new_x, new_y)\n",
    "#     for f in range(len(dist)):\n",
    "#         D.gpr[(D.id==4)&(D.distance==dist[f])]=fill[f]\n",
    "\n",
    "#     d=D[(D.id==4)&(D.distance<700)];dist=d.distance[np.isnan(d.gpr)].values;\n",
    "#     x=d.distance[np.logical_not(np.isnan(d.gpr))].values;y=d.gpr.values; y=y[np.logical_not(np.isnan(y))]\n",
    "#     coefficients = np.polyfit(x, y,7); poly = np.poly1d(coefficients); new_x = np.linspace(x[0], x[-1]);\n",
    "#     new_y = poly(new_x);fill=poly(dist)#;plt.scatter(dist,fill,color='r');plt.plot(x, y, \"o\", new_x, new_y)\n",
    "#     for f in range(len(dist)):\n",
    "#         D.gpr[(D.id==4)&(D.distance==dist[f])]=fill[f]\n",
    "    \n",
    "    if year==2016: \n",
    "        with open(fl_path + glacier[0]+'_df_agg.pkl', 'wb') as f:\n",
    "            pickle.dump(D, f)\n",
    "    #multiply velocity vector by cosine of angle between vector and flux gate (line or line segment)\n",
    "    D['vfg'] = np.abs(D.vz * np.cos((D.vzdir-(D.angle-90.))*(math.pi/180.)))  #velocity normal to gate per slice\n",
    "#     for i in range(len((D))):   #D.id.nunique()):\n",
    "#         if np.isnan(D.vfg[i])==True:\n",
    "#             G=D.id[i]\n",
    "#             V=np.nanmean(D.vfg[D.id==G]) \n",
    "#             D.vfg[i]=V     \n",
    "    with open(fl_path + str(year)+'_Dvfg.pkl', 'wb') as f:\n",
    "        pickle.dump(D.vfg.values, f)\n",
    "    \n",
    "    D.sort_values(by=['distance','id'],ascending=[True,True], inplace=True) \n",
    "    \n",
    "    for n in range(D.id.nunique()):\n",
    "\n",
    "        for c in range(len(D.id[D.id==n])): #.count()-1)#range(D.id[D.id==n].count()-1):\n",
    "            idx=D[(D.id==n)&(D.distance==D[D.id==n].distance[c])].index\n",
    "\n",
    "            if c==0:  ## first point in line\n",
    "                L= D.distance[D.id==n][c+1] - D.distance[D.id==n][c]\n",
    "                G= L * D.H_opt[D.id==n][c+1]*0.5;J= L * D.farin_corr[D.id==n][c+1]*0.5 #area of triangle\n",
    "                 \n",
    "            elif c==1: #second point from start\n",
    "                L= (D.distance[D.id==n][c+1] - D.distance[D.id==n][c])/2\n",
    "                G= L * D.H_opt[D.id==n][c]; J= L * D.farin_corr[D.id==n][c]\n",
    "                 \n",
    "            elif c==len(D.id[D.id==n])-2: #second to last point\n",
    "                L= (D.distance[D.id==n][c] - D.distance[D.id==n][c-1])/2 \n",
    "                G= L * D.H_opt[D.id==n]; J= L * D.farin_corr[D.id==n]\n",
    "                \n",
    "            elif c==len(D.id[D.id==n])-1:  #last point in line\n",
    "                L= (D.distance[D.id==n][c] - D.distance[D.id==n][c-1])\n",
    "                G= L * D.H_opt[D.id==n][c-1]*0.5; J= L * D.farin_corr[D.id==n][c-1]*0.5 #area of triangle\n",
    "                \n",
    "            else:\n",
    "                L=(((D.distance[D.id==n][c+1]-D.distance[D.id==n][c])/2) + ((D.distance[D.id==n][c] -\n",
    "                        D.distance[D.id==n][c-1])/2))\n",
    "                G=L* D.H_opt[D.id==n][c] ; J=L* D.farin_corr[D.id==n][c]  \n",
    "                \n",
    "            D.loc[idx,'A_Hopt']= G;D.loc[idx,'A_farin']= J; D.loc[idx,'Len_mod']= L     \n",
    "    D_all=D.copy() \n",
    "        \n",
    "        \n",
    "    D['Qopt'] = D.A_Hopt * (D.vfg) * Dint;D['Qfarin'] = D.A_farin * (D.vfg) * Dint \n",
    "\n",
    "    ### Uncertainties\n",
    "    D['sQout'] = np.sqrt((sVZ * D.A_Hopt)**2 + ((D.vfg*Dint) * D.Len_mod * (sHopt*D.H_opt))**2)\n",
    "    D['sQout_low'] = np.sqrt((sVZ * D.A_Hopt)**2 + ((D.vfg*0.80) * D.Len_mod * (sHopt*D.H_opt))**2)\n",
    "    D['sQout_high'] = np.sqrt((sVZ * D.A_Hopt)**2 + ((D.vfg*1.00) * D.Len_mod * (sHopt*D.H_opt))**2)\n",
    "    D['sQoutF'] = np.sqrt((sVZ * D.A_farin)**2 + ((D.vfg*Dint) * D.Len_mod * (sHopt*D.farin_corr))**2)\n",
    "\n",
    "    Q_out= [0.,]; cr_area = [0.,];gate_width=[0.,];GPR=[0.,];HF=[0.,];Hopt=[0.,];\n",
    "    vzdir = [0.,]; sQout= [0.,];sQout_low= [0.,];sQout_high= [0.,];vfg_all=[];gate_elev=[];v_gate_mean=[];Len_mod=[0.,]\n",
    "    DV= D.copy(); DV = DV.dropna(subset=['vfg'])\n",
    "    \n",
    "    for n in range(D.id.nunique()):\n",
    "        vfg_all.append(DV.vfg[DV.id==n])\n",
    "        gate_elev.append(D.dem[D.id==n].mean())   \n",
    "\n",
    "    for n in range(D.id.nunique()+1):\n",
    "            Q_out.append(D.Qopt[D.id==n].sum()) \n",
    "            cr_area.append(D.A_Hopt[D.id==n].sum())\n",
    "            vzdir.append(D.vzdir[D.id==n].mean());gate_width.append(D.len[D.id==n].max())\n",
    "            sQout.append(D.sQout[D.id==n].sum());sQout_low.append(D.sQout_low[D.id==n].sum()); sQout_high.append(D.sQout_high[D.id==n].sum())  \n",
    "            v_gate_mean.append(np.nanmean(D.vfg[D.id==n]));Len_mod.append(D.Len_mod[D.id==n].sum())\n",
    "            GPR.append(D.gpr[D.id==n].mean());Hopt.append(D.H_opt[D.id==n].mean())\n",
    "    Q_in = [Q_out[1:]]; sQin = [sQout[1:]+ [0.0]]; Q_in = np.squeeze(Q_in);sQin = np.squeeze(sQin)\n",
    "    sQin_low = [sQout_low[1:]+ [0.0]];sQin_high = [sQout_high[1:]+ [0.0]]    \n",
    "    sQin = np.squeeze(sQin);sQin_low = np.squeeze(sQin_low);sQin_high = np.squeeze(sQin_high)\n",
    "    \n",
    "    Q_outF= [0.,] #flux out per gate\n",
    "    cr_areaF = [0.,]; sQoutF = [0.,]\n",
    "    for n in range(D.id.nunique()+1):\n",
    "            Q_outF.append(D.Qfarin[D.id==n].sum()) \n",
    "            cr_areaF.append(D.A_farin[D.id==n].sum()) \n",
    "            sQoutF.append(D.sQoutF[D.id==n].sum());HF.append(D.farin_corr[D.id==n].mean())\n",
    "\n",
    "    Q_inF = [Q_outF[1:]] #flux in per bin\n",
    "    Q_inF = np.squeeze(Q_inF)\n",
    "    sQinF = [sQoutF[1:]+ [0.0]]\n",
    "    sQinF = np.squeeze(sQinF)\n",
    "\n",
    "    FG = pd.DataFrame(list(zip(Q_out, Q_in, cr_area, sQout, sQout_low, sQout_high, sQin, sQin_low, sQin_high, Q_outF, Q_inF, sQoutF, sQinF, cr_areaF, vzdir, \n",
    "           v_gate_mean, gate_width,GPR,Hopt, HF,Len_mod)), columns=['Q_out', 'Q_in', 'cr_area', 'sQout', 'sQout_low', 'sQout_high', 'sQin', 'sQin_low', 'sQin_high','Q_outF', 'Q_inF',\n",
    "            'sQoutF', 'sQinF', 'cr_areaF', 'vzdir', 'v_gate_mean','gate_width','gpr','Hopt','HF','Len_mod']) \n",
    "    FG_df = FG.copy()\n",
    "    dem_r = topo\n",
    "    if VDIR == 'individual':\n",
    "        vel_r = VX  #[:-1] + '-F_vm.tif'\n",
    "    else:\n",
    "        vel_r = VX[:-8] + 'm_5m.tif'\n",
    "    shp = loadshp_as_gpd(bins)\n",
    "\n",
    "    rasters = [dh_r, dem_r]#, vel_r]\n",
    "    names = ['dh','dem']#,'vm']\n",
    "    for i in range(len(rasters)):\n",
    "        raster = rasters[i]\n",
    "        rst = rasterio.open(raster)\n",
    "        shp = reproject(shp, rst) #shp is in correct projection, trying anyway for calculation\n",
    "        # dhdt= vx.salem.transform(dhdt)\n",
    "        name = names[i]\n",
    "    #     shp_vec = dissolve_gpd(shp)  ## only if more columns exist\n",
    "        stats = stats_select()  #'['min', 'max', 'mean', 'count', 'sum', 'std', 'median', 'majority', 'minority', 'unique', 'range']\n",
    "        ZS = (get_zonal_stats(shp, raster, stats))\n",
    "        ZS.drop(['geometry'], axis=1, inplace=True)\n",
    "        ZS.rename(columns={\"mean\": name+\"_mean\", \"median\":name+\"_med\",\"min\":name+\"_min\",\"max\":name+\"_max\",\"std\": name+\"_std\", \"count\":name+\"_count\"}, inplace=True)\n",
    "        ZS.sort_values(by=['bin'],ascending=True, inplace=True)\n",
    "        ZS.set_index('bin', inplace=True)\n",
    "        FG_df =  pd.concat([FG_df, ZS], axis=1)\n",
    "    tb = 5 #top full bin\n",
    "    FG_df.loc[tb, 'Q_in'] = 0.0 #FG_df.Q_out[tb+1] + FG_df.Q_out[tb+2] #flux in top full bin\n",
    "    FG_df.loc[tb, 'Q_inF'] = 0.0 # FG_df.Q_outF[tb+1] + FG_df.Q_outF[tb+2] #flux in top full bin\n",
    "#     FG_df.loc[tb+1, 'Q_in'] = 0.0\n",
    "#     FG_df.loc[tb+1, 'Q_inF'] = 0.0\n",
    "#     FG_df.loc[tb+2, 'Q_in'] = 0.0 #flux in bin 6\n",
    "#     FG_df.loc[tb+2, 'Q_inF'] = 0.0 #flux in bin 6\n",
    "    \n",
    "    FG_df['vfg'] = FG_df.Q_out / FG_df.cr_area #net velocity per gate\n",
    "    FG_df['vfg_F'] = FG_df.Q_outF / FG_df.cr_area    \n",
    "\n",
    "    FG_df['bin']=np.arange(0,len(range(D.id.nunique()+1)),1)\n",
    "    # FG_df['sQnet_opt'] = np.sqrt(FG_df.sQopt**2 + FG_df.sQin**2)\n",
    "\n",
    "    FG_df['spQout'] = FG_df.sQout / FG_df.Q_out * 100. #%err on flux\n",
    "    FG_df['spQin'] = FG_df.sQin / FG_df.Q_in * 100. #%err on flux\n",
    "    FG_df['spQoutF'] = FG_df.sQoutF / FG_df.Q_outF * 100. #%err on flux\n",
    "    FG_df['spQinF'] = FG_df.sQinF / FG_df.Q_inF * 100. #%err on flux\n",
    "\n",
    "    ### import data per bin: height change, elevation, surface area, obs.SMB etc. \n",
    "    rho.sort_values(by='bin',ascending=True, inplace=True) # sort by bin\n",
    "    rho.set_index('bin', inplace=True) # set bin as index for sorting\n",
    "    FG_df =  pd.concat([FG_df, rho], axis=1)\n",
    "\n",
    "    WR= np.array(FG_df.dem_count[1:])\n",
    "    WR= np.append(WR,[0])\n",
    "\n",
    "    FG_df['Q_net'] = FG_df.Q_in - FG_df.Q_out  #net flux per gate\n",
    "#     FG_df.loc[4, 'Q_net'] = FG_df.Q_out[5] + FG_df.Q_out[6] - FG_df.Q_out[4] #should be covered by updated Q_in\n",
    "    FG_df['Q_netA']= FG_df.Q_net / FG_df.dem_count\n",
    "\n",
    "    FG_df['Q_netF'] = FG_df.Q_inF - FG_df.Q_outF  #net flux per gate\n",
    "    FG_df['Q_netAF']= FG_df.Q_netF / FG_df.dem_count\n",
    "\n",
    "    FG_df['area_Qin']= WR\n",
    "    FG_df['sQoptA'] = FG_df.sQout / FG_df.dem_count\n",
    "    FG_df['sQoptA_low'] = FG_df.sQout_low / FG_df.dem_count;FG_df['sQoptA_high'] = FG_df.sQout_high / FG_df.dem_count    \n",
    "    FG_df['sQ_inA'] = FG_df.sQin / (FG_df.area_Qin+0.001)\n",
    "    FG_df['sQ_inA_low'] = FG_df.sQin_low / (FG_df.area_Qin+0.001);FG_df['sQ_inA_high'] = FG_df.sQin_high / (FG_df.area_Qin+0.001)    \n",
    "    FG_df['sQnetA_opt'] = np.sqrt(FG_df.sQoptA**2 + FG_df.sQ_inA**2)\n",
    "    FG_df['sQnetA_opt_low'] = np.sqrt(FG_df.sQoptA_low**2 + FG_df.sQ_inA_low**2);FG_df['sQnetA_opt_high'] = np.sqrt(FG_df.sQoptA_high**2 + FG_df.sQ_inA_high**2)\n",
    "    FG_df['sQoutFA'] = FG_df.sQoutF / FG_df.dem_count\n",
    "    FG_df['sQ_inFA'] = FG_df.sQinF / (FG_df.area_Qin+0.001)\n",
    "    FG_df['sQnetFA'] = np.sqrt(FG_df.sQoutFA**2 + FG_df.sQ_inFA**2)\n",
    "\n",
    "    ##calculate height change due to mass balance for highest bins considering firn compaction\n",
    "    if firn == False:\n",
    "        FG_df.Vfirn = 0.0\n",
    "    FG_df['b_fg_h'] = FG_df.dh_mean - FG_df.Q_netA + FG_df.Vfirn\n",
    "    FG_df['b_fg_hF'] = FG_df.dh_mean - FG_df.Q_netAF + FG_df.Vfirn\n",
    "    FG_df.column_depth = FG_df.column_depth * FG_df.firn_area ## adjust firn column height for firn area\n",
    "    FG_df['RHO'] = ((FG_df.column_density * FG_df.column_depth) + (910. * (FG_df.Hopt-FG_df.column_depth)))/ FG_df.Hopt\n",
    "    FG_df['RHO_F'] = ((FG_df.column_density * FG_df.column_depth) + (910. * (FG_df.HF-FG_df.column_depth)))/ FG_df.HF \n",
    "    FG_df.loc[0, 'RHO'] = 910.;FG_df.loc[0, 'RHO_F'] = 910.\n",
    "    \n",
    "    if balance == 'Bw':\n",
    "        FG_df['rho_snow'] = rho_snow\n",
    "        FG_df['b_fg_we'] = FG_df.rho_snow/1000 * FG_df.b_fg_h\n",
    "        FG_df['b_fg_weF'] = FG_df.rho_snow/1000 * FG_df.b_fg_hF\n",
    "    else:\n",
    "        FG_df['b_fg_we'] = FG_df['RHO']/1000. * FG_df.b_fg_h           #FG_df['rho_%s'%year]/1000. * FG_df.b_fg_h\n",
    "        FG_df['b_fg_weF'] = FG_df['RHO_F']/1000. * FG_df.b_fg_hF\n",
    "\n",
    "    ### Uncertainties\n",
    "    if I==0:\n",
    "        sDHdt = 0.15 #NMAD coreg values            #0.04 #m Bias dh from Pelto et al. 2019\n",
    "    if I==1:\n",
    "        sDHdt = 0.34\n",
    "    elif I==2:\n",
    "        sDHdt = 0.28\n",
    "    \n",
    "    #sDHdt = 0.21 #    s_h_dem 0.21m,    0.04 m Bias dh from Pelto et al. 2019 \n",
    "    sVfirn = 0.10\n",
    "    sRHO = 0.10 # percent uncertainty in density\n",
    "\n",
    "    FG_df['sDH_opt'] = np.sqrt(sDHdt**2 + (FG_df.sQnetA_opt)**2 + (FG_df.Vfirn*sVfirn)**2)\n",
    "    FG_df['sBwe_opt'] = np.sqrt((FG_df.sDH_opt * (FG_df['RHO']/1000.))**2+\n",
    "                   (FG_df.b_fg_h * (FG_df['RHO']/1000.*sRHO))**2)\n",
    "\n",
    "    FG_df['sDH_F'] = np.sqrt(sDHdt**2 + (FG_df.sQnetFA)**2 + (FG_df.Vfirn*sVfirn)**2)\n",
    "    FG_df['sBwe_F'] = np.sqrt((FG_df.sDH_F * (FG_df['RHO_F']/1000.))**2+\n",
    "                   (FG_df.b_fg_hF * (FG_df['RHO_F']/1000.*sRHO))**2)\n",
    "\n",
    "    D['Agpr']=np.NaN\n",
    "    pidx=D[(D.id==2)&(D.distance==0.0)].index ; D.loc[D.index == pidx[0],'gpr']=40.0 ## assign a value to first point on gate 2\n",
    "    D = D.dropna(subset=['gpr']);D.sort_values(by=['distance','id'],ascending=[True,True], inplace=True) \n",
    "    \n",
    "    for n in range(D.id.nunique()):\n",
    "\n",
    "        for c in range(len(D.id[D.id==n])): #.count()-1)#range(D.id[D.id==n].count()-1):\n",
    "            idx=D[(D.id==n)&(D.distance==D[D.id==n].distance[c])].index\n",
    "\n",
    "            if c==0:  ## first point in line\n",
    "                L= D.distance[D.id==n][c+1] - D.distance[D.id==n][c]\n",
    "                G= L * D.gpr[D.id==n][c+1]*0.5;J= L * D.farin_corr[D.id==n][c+1]*0.5 #area of triangle\n",
    "                 \n",
    "            elif c==1: #second point from start\n",
    "                L= (D.distance[D.id==n][c+1] - D.distance[D.id==n][c])/2\n",
    "                G= L * D.gpr[D.id==n][c]; J= L * D.farin_corr[D.id==n][c]\n",
    "                 \n",
    "            elif c==len(D.id[D.id==n])-2: #second to last point\n",
    "                L= (D.distance[D.id==n][c] - D.distance[D.id==n][c-1])/2 \n",
    "                G= L * D.gpr[D.id==n]; J= L * D.farin_corr[D.id==n]\n",
    "                \n",
    "            elif c==len(D.id[D.id==n])-1:  #last point in line\n",
    "                L= (D.distance[D.id==n][c] - D.distance[D.id==n][c-1])\n",
    "                G= L * D.gpr[D.id==n][c-1]*0.5; J= L * D.farin_corr[D.id==n][c-1]*0.5 #area of triangle\n",
    "                \n",
    "            else:\n",
    "                L=(((D.distance[D.id==n][c+1]-D.distance[D.id==n][c])/2) + ((D.distance[D.id==n][c] -\n",
    "                        D.distance[D.id==n][c-1])/2))\n",
    "                G=L* D.gpr[D.id==n][c] ; J=L* D.farin_corr[D.id==n][c]  \n",
    "                \n",
    "            D.loc[idx,'Agpr']= G; D.loc[idx,'Len_gpr']= L \n",
    "\n",
    "    #calculate flux out using GPR thicknesses\n",
    "    sGPR = 0.0516 #gpr error %\n",
    "    D['sInterp_gpr'] = np.nan\n",
    "    for n in range(len(D.Len_gpr)):\n",
    "        if D.Len_gpr[n] > 50.:\n",
    "            D.sInterp_gpr[n] = D.gpr[n] * 0.10  \n",
    "        else: \n",
    "            D.sInterp_gpr[n] = 0.0\n",
    "    sH_gpr = np.sqrt(D.sInterp_gpr**2+ (D.gpr*sGPR)**2)\n",
    "    D['Qo_gpr'] = D.Agpr * (D.vfg) * Dint   #* 0.9 #- off_ice_V+sVZ\n",
    "    D['sQo_gpr'] =  np.sqrt(((sVZ) * (D.gpr) * D.Len_gpr)**2 + ((D.vfg*Dint) * D.Len_gpr * (sH_gpr))**2)\n",
    "\n",
    "    # D['sQo_gpr'] = np.sqrt((sVZ**2) * ((D.gpr**2 + (sH_gpr)**2) * (D.vfg*Dint)**2) * d**2)\n",
    "\n",
    "    # D.to_csv(fl_path+'kok_16.csv') #'kokanee_gpr_fluxG_pts.csv')\n",
    "    # for i in range(len(range(D.id.nunique()+1))):\n",
    "    #     print(i, D.Agpr[D.id==i].mean())\n",
    "\n",
    "    Qo_gpr = [0.,];cr_area_gpr = [0.,] ; vzdir_gpr = [0.,]; sQo_gpr= [0.,]; Len_gpr=[0.,]\n",
    "    \n",
    "    for n in range(D.id.nunique()):\n",
    "            Qo_gpr.append(D.Qo_gpr[D.id==n].sum()) \n",
    "            cr_area_gpr.append(D.Agpr[D.id==n].sum())\n",
    "            vzdir_gpr.append(D.vzdir[D.id==n].mean())\n",
    "            sQo_gpr.append(D.sQo_gpr[D.id==n].sum())\n",
    "            Len_gpr.append(D.Len_gpr[D.id==n].sum())\n",
    "    Qin_gpr = [Qo_gpr[1:]+ [0.0]] #flux in per bin, add zero to end for top bin (for now)\n",
    "    Qin_gpr = np.squeeze(Qin_gpr)\n",
    "    sQin_gpr = [sQo_gpr[1:]+ [0.0]]\n",
    "    sQin_gpr = np.squeeze(sQin_gpr)\n",
    "\n",
    "    FG_gpr = pd.DataFrame(list(zip(Qo_gpr, Qin_gpr, cr_area_gpr, sQo_gpr,sQin_gpr,Len_gpr)), \n",
    "                          columns=['Qo_gpr', 'Qin_gpr', 'cr_area_gpr', 'sQo_gpr','sQin_gpr','Len_gpr']) \n",
    "    FG_gpr['bin'] = np.arange(0,len(range(D.id.nunique()+1)),1)#[0,1,2,3]\n",
    "    # FG_gpr['vfg'] = FG_gpr.Qo_gpr / FG_gpr.cr_area_gpr #net velocity per gate\n",
    "\n",
    "    FG_gpr['spQo_gpr'] = FG_gpr.sQo_gpr / FG_gpr.Qo_gpr * 100. #%err on flux\n",
    "    FG_gpr['spQin_gpr'] = FG_gpr.sQin_gpr / FG_gpr.Qin_gpr * 100. #%err on flux\n",
    "\n",
    "    FG_gpr.loc[tb, 'Qin_gpr'] = 0.0 # FG_gpr.Qo_gpr[tb+1] + FG_gpr.Qo_gpr[tb+2] #flux in bin 4\n",
    "#     FG_gpr.loc[tb+1, 'Qin_gpr'] = 0.0\n",
    "#     FG_gpr.loc[tb+2, 'Qin_gpr'] = 0.0\n",
    "\n",
    "    FG_all = FG_df.merge(FG_gpr, left_index=True, right_index=True)#, how='left') #on='bin')  \n",
    "    \n",
    "    FG_all['Q_net_gpr'] = FG_all.Qin_gpr - FG_all.Qo_gpr \n",
    "#     FG_all.loc[4, 'Q_net_gpr'] = FG_all.Qin_gpr[5] + FG_all.Qin_gpr[6] - FG_all.Qo_gpr[4]  ##should be covered by updated Q_in\n",
    "    FG_all['Q_netA_gpr']= FG_all.Q_net_gpr / FG_all.dem_count \n",
    "    FG_all['b_fg_h_gpr'] = FG_all.dh_mean - FG_all.Q_netA_gpr + FG_all.Vfirn #dh1516_mean\n",
    "\n",
    "    FG_all['sQoutA_gpr'] = FG_all.sQo_gpr / FG_all.dem_count\n",
    "    FG_all['sQ_inA_gpr'] = FG_all.sQin_gpr / (FG_all.area_Qin+0.001)\n",
    "    FG_all['sQnetA_gpr'] = np.sqrt(FG_all.sQoutA_gpr**2 + FG_all.sQ_inA_gpr**2)\n",
    "\n",
    "    FG_all['RHO_g'] = ((FG_all.column_density * FG_all.column_depth) + (910. * (FG_all.gpr-FG_all.column_depth)))/ FG_all.gpr; \n",
    "    FG_all.loc[0, 'RHO_g'] = 910.\n",
    "    if balance == 'Bw':\n",
    "        FG_all['b_fg_we_gpr'] = FG_df.rho_snow/1000. * FG_all.b_fg_h_gpr\n",
    "    else:\n",
    "        FG_all['b_fg_we_gpr'] = FG_all['RHO_g']/1000. * FG_all.b_fg_h_gpr\n",
    "    \n",
    "    #FG_all.loc[len(FG_gpr)-1,'b_fg_we_gpr'] = np.nan #no radar data available for flux calculation for top bin\n",
    "    \n",
    "    FG_all['sDH_gpr'] = np.sqrt(sDHdt**2 + (FG_all.sQnetA_gpr)**2 + (FG_all.Vfirn*sVfirn)**2)\n",
    "    FG_all['sBwe_gpr'] = np.sqrt((FG_all.sDH_gpr * (FG_all['RHO_g']/1000.))**2+\n",
    "                   (FG_all.b_fg_h_gpr * (FG_all['RHO_g']/1000.*sRHO))**2)\n",
    "\n",
    "\n",
    "    s= 15 #markersize\n",
    "    a = 0.9; n = 0\n",
    "    sym = ['o', '^', 's', 'd']\n",
    "\n",
    "    ##all_obs\n",
    "    if balance == 'Bw':\n",
    "        obs = obs.dropna(subset=['Bw'])\n",
    "        y_ax_obs=obs[(obs.Year==year)].Bw\n",
    "\n",
    "    else:\n",
    "        obs = obs.dropna(subset=['Ba'])\n",
    "        y_ax_obs=obs[(obs.Year==year)].Ba\n",
    "\n",
    "    x_ax_obs=obs[(obs.Year==year)].Elev\n",
    "    new = FG_all.copy()\n",
    "\n",
    "    yerr = [0.0, new.sBwe_gpr , new.sBwe_opt, new.sBwe_F] #new.sBwe_gpr\n",
    "    new['xerr'] = new.dem_std * 1.5 #2 std dev -- 95% of data\n",
    "    xerr = new.xerr \n",
    "\n",
    "    x_ax_fg_gpr=new.dem_mean; y_ax_fg_gpr=new.b_fg_we_gpr\n",
    "    x_ax_fg_opt=new.dem_mean; y_ax_fg_opt=new.b_fg_we; x_ax_fg_F=new.dem_mean; y_ax_fg_F=new.b_fg_weF\n",
    "\n",
    "    x = [x_ax_obs, x_ax_fg_gpr, x_ax_fg_opt, x_ax_fg_F]\n",
    "    y = [y_ax_obs, y_ax_fg_gpr, y_ax_fg_opt, y_ax_fg_F]\n",
    "    \n",
    "    letter=['G','H','I'] #['A','B','C']\n",
    "    color = ['k', '#51c2d5','#663f3f','#ec4646'] #'teal', '#74c476', '#238b45']\n",
    "    label=['Observed', 'FG IPR', 'FG OGGM', 'FG Farinotti']\n",
    "    txt= ['Observed', 'FG IPR     ', 'FG OGGM   ', 'FG Farinotti']\n",
    "    ##plot obs data\n",
    "    ax1[count].scatter(x[0],y[0], marker='o', label=label[0], alpha=0.9, s=s,facecolors='none', edgecolors='k', zorder=3)  \n",
    "\n",
    "    ############ obs data box plot ################\n",
    "    obs_data=obs[(obs.Year==year)]\n",
    "    obs_data.reset_index(inplace=True)\n",
    "\n",
    "    bin_size = 100.\n",
    "    z_range = np.arange(2350., 2850., bin_size)\n",
    "\n",
    "    i = 0\n",
    "    Z_F = []; Zor_F= []; OB_F=[]\n",
    "\n",
    "    for z in z_range: \n",
    "        W=0;bin_size = 100.; COU=1\n",
    "        while W==0:\n",
    "            OBS = [];Z = [];Zor = [];\n",
    "            for n in range(len(obs_data[balance])):\n",
    "                if ((z - bin_size/2.) <= obs_data.Elev[n]) and (obs_data.Elev[n] <=  (z + bin_size/2.)):\n",
    "                    O = obs_data[balance][n]\n",
    "                    Z.append(z);Zor.append(obs_data.Elev[n]);OBS.append(O)\n",
    "\n",
    "            if len(OBS)<2:\n",
    "                bin_size=bin_size*(1+(.2*COU))\n",
    "                COU=COU+1\n",
    "            else:\n",
    "                OB_F.append(np.array(OBS)); Z_F.append(np.array(Z));  Zor_F.append(np.array(Zor))\n",
    "                W=1\n",
    "        i += 1\n",
    "    \n",
    "    GPR = [];OPT = [];FAR = []  \n",
    "    for z in z_range:\n",
    "        W=0; COU=1;bin_size = 100.\n",
    "        while W==0:\n",
    "            GG=[];PP=[];FF=[]\n",
    "            ####collect balance values for each elevation bin\n",
    "            for n in range(len(new.dem_mean)): #for n in new.bin:  # \n",
    "                if ((z - bin_size/2.) <= new.dem_mean[n]) and (new.dem_mean[n] <=  (z + bin_size/2.)):\n",
    "                    G = new.b_fg_we_gpr[n]\n",
    "                    P = new.b_fg_we[n]\n",
    "                    F = new.b_fg_weF[n]\n",
    "                    GG.append(G);FF.append(F);PP.append(P)\n",
    "            GG=np.array(GG)\n",
    "            GG = GG[np.logical_not(np.isnan(GG))]\n",
    "            \n",
    "            if len(GG)<1:\n",
    "                bin_size=bin_size*(1+(0.1*COU))\n",
    "                COU=COU+1\n",
    "                \n",
    "            else:\n",
    "                GPR.append(GG);OPT.append(np.array(PP));FAR.append(np.array(FF))\n",
    "                W=1\n",
    "\n",
    "    #function to create error bars for flux-derived estimates\n",
    "    shift = [-30,0,30]\n",
    "\n",
    "    for i in range(3):\n",
    "        ax1[count].errorbar((x[i+1]+shift[i]), y[i+1], xerr=None, yerr=yerr[i+1], fmt=sym[i+1], ecolor=color[i+1],\n",
    "                            mew=mew,label=label[i+1], elinewidth=elw,mfc=mfc, alpha=0.8, c=color[i+1], zorder=4) #elinewidth=0.7\n",
    "     \n",
    "    \n",
    "    ###### binned observations for each flux bin ##############\n",
    "    OB_FG=[];OB_FG_h=[]; new.loc[0, 'dem_std'] = new.dem_std[0]*1.5 # lowest bin std too low for method (width-limited)\n",
    "    for i in range(len(new.dem_med)): \n",
    "        f = new.dem_med[i]\n",
    "        W=0;bin_size = new.dem_std[i]*2; COU=1\n",
    "        while W==0:\n",
    "            OBSFG = []; OBSFGh=[]\n",
    "            for n in range(len(obs_data[balance])):\n",
    "                if ((f - bin_size) <= obs_data.Elev[n]) and (obs_data.Elev[n] <=  (f + bin_size)):\n",
    "                    O = obs_data[balance][n]\n",
    "                    Oh= obs_data['Ba_h'][n]\n",
    "                    OBSFG.append(O);OBSFGh.append(Oh)\n",
    "\n",
    "            if len(OBSFG)<2 and bin_size<200:\n",
    "                bin_size=bin_size*(1+(.2*COU))\n",
    "                COU=COU+1\n",
    "            else:\n",
    "                OB_FG.append(np.array(OBSFG));OB_FG_h.append(np.array(OBSFGh));\n",
    "                W=1\n",
    "   \n",
    "    meanlineprops = dict(linestyle='--', linewidth=1., color='0.5')\n",
    "    medianprops = dict(linestyle='-', linewidth=1, color='k'); boxprops = dict(linewidth=0.5);\n",
    "    BOX=ax1[count].boxplot(OB_FG,meanprops=meanlineprops,medianprops=medianprops,showmeans=True, meanline=True,\n",
    "            sym='', positions=new.dem_med,widths=60, zorder=1, boxprops=boxprops)\n",
    "\n",
    "    ################# mass conservation plot ####################################\n",
    "    BM= []; BMh=[]\n",
    "    for i in range(len(OB_FG)):\n",
    "        B = OB_FG[i].mean()\n",
    "        Bh = OB_FG_h[i].mean()\n",
    "        BM.append(B);BMh.append(Bh)\n",
    "    new['SMB'] = BM; new['BH']= BMh\n",
    "#     new['BMC'] = new.b_fg_h + new.Q_netA - new.Vfirn\n",
    "#     new['BMC_gpr'] = new.b_fg_h_gpr + new.Q_netA_gpr - new.Vfirn \n",
    "#     new['BMCF'] = new.b_fg_hF + new.Q_netAF - new.Vfirn \n",
    "    new['BMC'] = new.BH  + new.Q_netA - new.Vfirn\n",
    "    new['BMC_gpr'] = new.BH + new.Q_netA_gpr - new.Vfirn \n",
    "    new['BMCF'] = new.BH + new.Q_netAF - new.Vfirn \n",
    "\n",
    "    MC = [new.BMC_gpr,new.BMC, new.BMCF]\n",
    "    Yvv = [new.Q_netA_gpr, new.Q_netA, new.Q_netAF]\n",
    "    Yvv_lbl=[r'$V^{\\uparrow}_{IPR}$',r'$V^{\\uparrow}_{OGGM}$',r'$V^{\\uparrow}_{Farin}$']\n",
    "    \n",
    "    yerr_dh= [new.sBwe_gpr/(new['RHO_g']/1000) , new.sBwe_opt/(new['RHO']/1000),\n",
    "              new.sBwe_F/(new['RHO_F']/1000)]\n",
    "    \n",
    "    if I == 0:\n",
    "        overlap=[];overlap_gpr=[];overlapF=[]\n",
    "        MC_LIST=[overlap_gpr,overlap,overlapF]\n",
    "    for i in range(3):\n",
    "        if i == 0:\n",
    "            ax2[count].errorbar(new.dem_mean+shift[i]+5,MC[i], xerr=None, yerr=yerr_dh[i], fmt=sym[i+1],\n",
    "                            c=color[i+1], mfc=mfc,mew=mew, elinewidth=elw, label=label[i+1], alpha=0.8, zorder=2)\n",
    "        else:\n",
    "            ax2[count].errorbar(new.dem_mean+shift[i],MC[i], xerr=None, yerr=yerr_dh[i], fmt=sym[i+1],\n",
    "                            c=color[i+1], mfc=mfc,mew=mew, elinewidth=elw, label=label[i+1], alpha=0.8, zorder=2)\n",
    "    ####count the number of SMB obs. +/- 1-error which fall within the uncertainty bounds of the LiDAR height change            \n",
    "        OL = MC_LIST[i] ## select which list to append to\n",
    "        for r in range(len(MC[i])):\n",
    "            #W=1\n",
    "            M_min = MC[i][r] - yerr_dh[i][r]; M_max = MC[i][r]+ yerr_dh[i][r]\n",
    "            L_min = new.dh_mean[r] - sDHdt; L_max = new.dh_mean[r] + sDHdt\n",
    "            #while W==1:\n",
    "            if M_min > L_min and M_min < L_max:\n",
    "                OL.append(1);# W=0\n",
    "            elif M_max < L_max and M_max > L_min:\n",
    "                OL.append(1); #W=0\n",
    "            elif M_max > L_max and M_min < L_min:\n",
    "                OL.append(1); #W=0\n",
    "            elif M_max < L_max and M_min > L_min:\n",
    "                OL.append(1); #W=0\n",
    "            else:\n",
    "                OL.append(0)\n",
    "    \n",
    "    Ybd = [BM, new.b_fg_we_gpr, new.b_fg_we, new.b_fg_weF]\n",
    "    ax1[count].tick_params(which='major', length=3);ax2[count].tick_params(which='major', length=3) \n",
    "           \n",
    "#################Difference###########################################################\n",
    "####initialize lists for first run\n",
    "    if count == 0:\n",
    "        gpr_bdiff=[];opt_bdiff=[];farin_bdiff=[];z_range_all=[];obs_all=obs_data;obs_bin_all=[];gpr_all=[];\n",
    "        opt_all=[];farin_all=[];elev_all=[];elev_gpr_all=[];gpr_bin_all=[];opt_bin_all=[];farin_bin_all=[];\n",
    "        NSE=[];PBias=[];RSR=[];RSQ=[];SE=[];SL=[];ME=[];MAE=[];RLM_SL=[];RLM_SE=[];MSE_ABL=[];MSE_ACC=[];\n",
    "        gpr_bd_fg=[];opt_bd_fg=[];farin_bd_fg=[];obs_fg_all=[]\n",
    "       \n",
    "    OBF=[];GPRq=[];OPTq=[];FARq=[];OB_FGmean=[]\n",
    "    ###calculate mean of balance values within each elevation bin\n",
    "    for i in range(len(OB_F)):\n",
    "        OBF.append(np.round(OB_F[i].mean(),3));GPRq.append(np.round(GPR[i].mean(),3))\n",
    "        OPTq.append(np.round(OPT[i].mean(),3));FARq.append(np.round(FAR[i].mean(),3))\n",
    "    for i in range(len(OB_FG)): ##OBS means for each FG bin\n",
    "        OB_FGmean.append(np.round(OB_FG[i].mean(),3))   \n",
    "    obs_fg_all.extend(OB_FGmean)\n",
    "    ## do I need -1 for Kokanee??\n",
    "    FG_all.dropna(subset=['b_fg_we_gpr'], inplace=True)  ## remove data for bins where GPR is nan \n",
    "    obs_bin_all.extend(OBF);gpr_bin_all.extend(GPRq);opt_bin_all.extend(OPTq);farin_bin_all.extend(FARq);\n",
    "    z_range_all.extend(z_range);elev_gpr_all.extend(FG_all.dem_mean);gpr_all.extend(FG_all.b_fg_we_gpr);\n",
    "    opt_all.extend(new.b_fg_we);farin_all.extend(new.b_fg_weF);elev_all.extend(new.dem_mean) \n",
    "    \n",
    "    gpr_bdiff.extend((np.array(OBF) - np.array(GPRq)))\n",
    "    opt_bdiff.extend((np.array(OBF) - np.array(OPTq))); farin_bdiff.extend((np.array(OBF) - np.array(FARq)))\n",
    "    \n",
    "    gpr_bd_fg.extend((np.array(OB_FGmean) - np.array(new.b_fg_we_gpr)))\n",
    "    opt_bd_fg.extend((np.array(OB_FGmean) - np.array(new.b_fg_we))); farin_bd_fg.extend((np.array(OB_FGmean) - np.array(new.b_fg_weF)))\n",
    "        \n",
    "######################  https://pypi.org/project/hydroeval/  ##############################\n",
    "    EST=[np.array(GPRq),np.array(OPTq),np.array(FARq)] ## must be arrays not lists\n",
    "    OBFa=np.array(OBF);OBFGma=np.array(OB_FGmean)\n",
    "    \n",
    "    y_bin=[OB_FGmean,new.b_fg_we_gpr,new.b_fg_we,new.b_fg_weF]; x_bin=[new.dem_med,new.dem_med,new.dem_med,new.dem_med]\n",
    "    \n",
    "    ME.append(0.0);MAE.append(0.0),RSR.append(0.0);PBias.append(0.0);NSE.append(0.0)\n",
    "    for e in range(3):\n",
    "        FLB=np.array(y_bin[e+1])\n",
    "#         NSE.extend(evaluator(nse, EST[e], OBFGma))\n",
    "        PBias.extend(evaluator(pbias, FLB, OBFGma))\n",
    "#         RMSE=evaluator(rmse, FLB[e], OBFGma)\n",
    "#         RSR.extend((RMSE/(np.std(OBFGma))))\n",
    "        ME.append((OBFGma-FLB).mean())\n",
    "        MAE.append(np.abs(OBFGma-FLB).mean())\n",
    "    \n",
    "################### End difference ##########################    \n",
    "    \n",
    "###################End difference##########################\n",
    "\n",
    "    ytxt = [0.2, 0.15, 0.1, 0.05]\n",
    "#     y_bin=[OBF,GPRq,OPTq,FARq]; x_bin=[z_range[:-1],z_range[:-1],z_range[:-1],z_range[:-1]]\n",
    "    #         y_bin=[OBF,FG_all.b_fg_we_gpr, new.b_fg_we,new.b_fg_weF]; x_bin=[z_range,new.dem_mean,new.dem_mean,new.dem_mean]\n",
    "#     if year==2016:\n",
    "#         x0=2540\n",
    "#     elif year==2017:\n",
    "#         x0=2560\n",
    "#     else:\n",
    "#         x0=2580\n",
    "    x0=2900\n",
    "    def piecewise_linear(x, x0, y0, k1, k2):\n",
    "        return np.piecewise(x, [x < x0], [lambda x:k1*x + y0-k1*x0, lambda x:k2*x + y0-k2*x0])   \n",
    "    \n",
    "    for i in range(4):   \n",
    "        ##pwlf\n",
    "#         if i==0:\n",
    "#             Y=y_bin[i];X=x_bin[i]\n",
    "#         else:\n",
    "        Y=np.array(y_bin[i]);X=np.array(x_bin[i])\n",
    "        model = pwlf.PiecewiseLinFit(X, Y)\n",
    "        breakpoints = [2200.,2900.]\n",
    "        model.fit_with_breaks(breakpoints)\n",
    "#         if i > 0 and year==2016:\n",
    "#             x_hat = np.arange(2200., 2690., 10.)#np.linspace(X.min(), X.max(), 100)\n",
    "#         else:\n",
    "        x_hat = np.arange(2260., 2800., 10.)\n",
    "        y_hat = model.predict(x_hat)\n",
    "        ax1[count].plot(x_hat, y_hat, lw=1.1, alpha=a, color=color[i])#label=label[i])\n",
    "        sl=model.slopes\n",
    "        se=model.standard_errors()\n",
    "        rsq=model.r_squared()\n",
    "        SL.append(sl);SE.append(se);RSQ.append(rsq)\n",
    "\n",
    "        x1=np.array(x_bin[i]); y1=np.array(y_bin[i])\n",
    "        p , e = curve_fit(piecewise_linear, x1, y1 )\n",
    "        xd = np.arange(2260., 2800., 10.)\n",
    "#         xd = np.linspace(1950, 3150, 50)\n",
    "#         ax1[count].plot(xd, piecewise_linear(xd, *p), color=color[i], lw=1., alpha=0.9)\n",
    "        # mean square error of the lines\n",
    "        \n",
    "        for Line in range(1):\n",
    "            Pred=[];Act=[]\n",
    "            if Line ==0:\n",
    "                INDEX= np.where(x1<x0)[0]\n",
    "                for ix in INDEX:\n",
    "                    Pred.append(sl[0]*x1[ix]+model.intercepts[0])\n",
    "                    Act.append(y1[ix])\n",
    "\n",
    "                MSE_ABL.append(MSE(Act,Pred))\n",
    "#             if Line==1:\n",
    "#                 INDEX= np.where(x1>=x0)[0]\n",
    "#                 for ix in INDEX:\n",
    "#                     Pred.append( p[3]*x1[ix]+(p[1]-p[3]*x0))\n",
    "#                     Act.append(y1[ix])\n",
    "\n",
    "#                 MSE_ACC.append(MSE(Act,Pred))\n",
    "#         ax1[count].text(0.2, ytxt[i], txt[i]+ ' L1: '+ str(np.round(p[2]*1000,2)) +' L2: ' + \n",
    "#                         str(np.round(p[3]*1000,2)) + ' AABR: ' + str(np.round(p[2]/p[3],2)),\n",
    "#                         fontsize=6, transform=ax1[count].transAxes)  \n",
    "#         ax1[count].text(0.18, ytxt[i]-0.05, txt[i] + ' L1: '+ str(round(sl[0]*1000,2))+u\" \\u00B1 \"+ str(round(se[1]*1000*1.96,2)) +' L2: ' + \n",
    "#              str(round(sl[1]*1000,2))+u\" \\u00B1 \"+ str(round(se[2]*1000*1.96,2)),fontsize=6,transform=ax1[count].transAxes)\n",
    "########################### end piecewise ################################\n",
    "\n",
    "        #################### RLM  https://www.statsmodels.org/stable/rlm.html\n",
    "        ###https://www.statsmodels.org/devel/generated/statsmodels.robust.robust_linear_model.RLMResults.html\n",
    "    \n",
    "    for i in range(4):\n",
    "        exog = stm.add_constant(x[i]) #obs_data.Elev)\n",
    "        rlm_model = stm.RLM(y[i],exog, M=stm.robust.norms.HuberT()) #data.endog, data.exog,\n",
    "        rlm_results = rlm_model.fit()\n",
    "        stdE=rlm_results.bse[1]; RLM_SE.append(stdE)\n",
    "        # print(rlm_results.params)\n",
    "#         ax1[count].plot(x[i],rlm_results.params[1]*x[i]+rlm_results.params[0], color=color[i],label='', alpha=a, \n",
    "#                 linewidth=0.5, zorder=4, linestyle='--')\n",
    "        slp=rlm_results.params[1];RLM_SL.append(slp)\n",
    "        print('slope RLM:', slp)\n",
    "#         ax1[count].text(0.2, ytxt[i], txt[i]+str(np.round(1000*rlm_results.params[1],2))+ ' +/- ' + \n",
    "#                 str(np.round(1000*stdE,2)), transform=ax1[count].transAxes)\n",
    "\n",
    "################## plot specifics ############################\n",
    "    ax1[count].text(0.92, 0.03, letter[count], transform=ax1[count].transAxes)\n",
    "   \n",
    "    ax1[count].text(0.03, 0.95, year, transform=ax1[count].transAxes)\n",
    "    ax2[count].text(0.03, 0.95, year, transform=ax2[count].transAxes)\n",
    "    ax1[count].set(xlim=(2225,2820)),#ylim=(-3.35,2.25))\n",
    "     \n",
    "#     ax1[0].legend(loc='upper left')#, bbox_to_anchor=(0, 0.42, 0.7, 0.5), labelspacing=0.2, handletextpad=0.1)\n",
    "   \n",
    "    if balance == 'Ba':\n",
    "        ax1[count].axhline(linewidth=1, color='k', ls='--', alpha=a, zorder=0)\n",
    "        ax2[count].axhline(linewidth=1, color='k', ls='--', alpha=a, zorder=0)\n",
    "    if year==2016:\n",
    "        new16=new.copy();new16.to_pickle(fl_path+glacier[gl]+'_new16.pkl')\n",
    "    elif year==2017:\n",
    "        new17=new.copy();new17.to_pickle(fl_path+glacier[gl]+'_new17.pkl')\n",
    "    else:\n",
    "        new.to_pickle(fl_path+glacier[gl]+'_new18.pkl')\n",
    "        \n",
    "    ax2[count].errorbar(new.dem_mean,new.dh_mean, xerr=50, yerr=sDHdt, fmt='o', c='k', label='LiDAR',alpha=0.7, zorder=1, mfc='none', elinewidth=elw)\n",
    "    ax2[count].text(0.92, 0.95, letter[count], transform=ax2[count].transAxes)\n",
    "    xticks=[2300, 2400,  2500, 2600, 2700];xtick_lbls=[2300, '',  2500, '', 2700]\n",
    "    ax1[count].set_xticks(xticks);ax1[count].set_xticklabels(xtick_lbls);ax2[count].set_xticks(xticks);ax2[count].set_xticklabels(xtick_lbls)\n",
    "#     ax2[0].legend(loc='best')\n",
    "#     ax1[0].set_ylabel('Mass balance (m w.e.)'); ax2[0].set_ylabel('Height change (m ice $a^{-1}$)')\n",
    "    ax1[count].tick_params(which='major', length=3)\n",
    "    ax2[count].tick_params(which='major', length=3)\n",
    "   \n",
    "     ################# end mass conservation plot ####################################\n",
    "    print(FG_all.sBwe_gpr.median());print(FG_all.sBwe_opt.median());print(FG_all.sBwe_F.median())\n",
    "        \n",
    "    count+=1\n",
    "# fig1.text(0.008, 0.7, 'Mass balance (m w.e.)',rotation=90);fig2.text(0.008, 0.7,'Height change (m ice $a^{-1}$)',rotation=90);\n",
    "fig1.text(0.45, 0.01, 'Elevation (m a.s.l.)');fig2.text(0.45, 0.01,'Elevation (m a.s.l.)');\n",
    "fig1.subplots_adjust(bottom=0.09, top=0.98, hspace=0.1, left=0.06, right=0.99, wspace=0.05) \n",
    "fig2.subplots_adjust(bottom=0.09, top=0.98, hspace=0.1, left=0.06, right=0.99, wspace=0.05)\n",
    "if firn==True:\n",
    "    fig1.savefig(fl_path + 'products/' + glacier[gl]+'_indV25m_bdot_LIN_6bins_OB_FGmean_'+balance+'_'+str(Dint)+'dint.pdf', dpi=300) #_NOfirn\n",
    "    fig2.savefig(fl_path + 'products/' + glacier[gl]+'_indV25m_mass_con_LIN_6bins_'+balance+'_'+str(Dint)+'dint.pdf', dpi=300)\n",
    "else:\n",
    "    fig1.savefig(fl_path + 'products/' + glacier[gl]+'_indV25m_bdot_LIN_6bins_OB_FGmean_'+balance+'_'+str(Dint)+'dint_NOfirn.pdf', dpi=300) #_NOfirn\n",
    "    fig2.savefig(fl_path + 'products/' + glacier[gl]+'_indV25m_mass_con_LIN_6bins_'+balance+'_'+str(Dint)+'dint_NOfirn.pdf', dpi=300)\n",
    "\n",
    "SE=np.array(SE);SL=np.array(SL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.1722992587331036"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum((new.dh_mean*new.dem_count))/np.sum(new.dem_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ipr 0.9444444444444444 17 18\n",
      "OGGM 0.8888888888888888 16 18\n",
      "Farin 0.8888888888888888 16 18\n",
      "all 0.9074074074074074 49 54\n"
     ]
    }
   ],
   "source": [
    "print('ipr',np.sum(MC_LIST[0])/ len(MC_LIST[0]), np.sum(MC_LIST[0]), len(MC_LIST[0]))\n",
    "print('OGGM',np.sum(MC_LIST[1])/ len(MC_LIST[1]), np.sum(MC_LIST[1]), len(MC_LIST[1]))\n",
    "print('Farin',np.sum(MC_LIST[2])/ len(MC_LIST[2]), np.sum(MC_LIST[2]), len(MC_LIST[2]))\n",
    "print('all',np.sum(MC_LIST)/ (len(MC_LIST[0])+len(MC_LIST[1])+len(MC_LIST[2])), np.sum(MC_LIST), (len(MC_LIST[0])+len(MC_LIST[1])+len(MC_LIST[2])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "new16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "FG_df.dh_mean - FG_df.Q_netA + FG_df.Vfirn\n",
    "FG_df['b_fg_hF'] = FG_df.dh_mean - FG_df.Q_netAF + FG_df.Vfirn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.mean([4.79,4.79,5.36]),np.std([4.79,4.79,5.36])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "k=[new16.sBwe_F,new16.sBwe_gpr,new16.sBwe_opt,new17.sBwe_F,new17.sBwe_gpr,new17.sBwe_opt,new.sBwe_F,new.sBwe_gpr,new.sBwe_opt]\n",
    "np.mean(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "B=np.sqrt(new.spQoutF**2 + new.spQinF**2)\n",
    "B[0]=new.spQinF[0];B[5]=new.spQoutF[5]\n",
    "print(B)\n",
    "print(np.mean(B))\n",
    "print(np.sum(B*new.dem_count)/np.sum(new.dem_count))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(np.mean(sVZ/new.v_gate_mean*100))\n",
    "print(sHopt*(new.cr_area[1:]/new.gate_width)/(new.cr_area[1:]/new.gate_width)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(df_agg),len(df_agg.gpr[np.isnan(df_agg.gpr)].values)\n",
    "                        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig,ax=plt.subplots(1,1, figsize=cm2inch(24,12))\n",
    "plt.plot(new.sQnetA_opt_high/new.sQnetA_opt-1, label='full slip (1.0)')\n",
    "plt.plot(new.sQnetA_opt_low/new.sQnetA_opt-1, label='plug flow (0.80)')\n",
    "plt.plot(new.sQnetA_opt/new.sQnetA_opt-1, label= 'best estimate (0.85)')\n",
    "# plt.plot(new.sQnetA_opt_high/np.abs(new.Q_netA))\n",
    "# plt.plot(new.sQnetA_opt_low/np.abs(new.Q_netA))\n",
    "# plt.plot(new.sQnetA_opt/np.abs(new.Q_netA))\n",
    "ax.set(ylabel='Percent Deviation',xlabel='Flux bin') #ylim=(0.2,0.6))\n",
    "ax.legend()\n",
    "plt.savefig(fl_path+'products/'+glacier[gl]+'Plug_flow_vs_full_slip effect.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=1-(new.sQnetA_opt_low/new.sQnetA_opt)\n",
    "b=(new.sQnetA_opt_high/new.sQnetA_opt)-1\n",
    "print(np.mean([a,b]),np.std([a,b]))\n",
    "print(np.max(new.sQnetA_opt_high/new.sQnetA_opt-1),np.min(new.sQnetA_opt_low/new.sQnetA_opt)-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "new16.Q_out.sum()/new16.cr_area.sum()/0.85"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "####### Net flux uncertainty and MB unc #################\n",
    "print(np.sum(new.sQout)/np.sum(new.Q_out))\n",
    "print(np.mean([new.sBwe_opt,FG_all.sBwe_gpr,new.sBwe_F])) #new.sBwe_opt*new.dem_count)/np.sum(new.dem_count))\n",
    "# print(np.mean(FG_all.sBwe_gpr))\n",
    "# print(np.mean(new.sBwe_F))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "v=sVZ/new.v_gate_mean[:-1]\n",
    "v"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate glacier-wide SMB and compare with field obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "SMB = [new16.b_fg_we_gpr,new17.b_fg_we_gpr,new.b_fg_we_gpr, new16.b_fg_we,new17.b_fg_we,new.b_fg_we,\n",
    "      new16.b_fg_weF,new17.b_fg_weF,new.b_fg_weF]\n",
    "Bn=[]\n",
    "for i in range(len(SMB)):\n",
    "    MB=SMB[i]*new.dem_count\n",
    "    A=np.sum(MB)/np.sum(new.dem_count)\n",
    "    Bn.append(round(A,2))\n",
    "field=[0.13,-0.03,-0.29,0.13,-0.03,-0.29,0.13,-0.03,-0.29]\n",
    "\n",
    "MB_df = pd.DataFrame(list(zip(field, Bn)), \n",
    "            columns=['field', 'flux_bn'])\n",
    "\n",
    "MB_df['MBAL_diff']=MB_df.field-MB_df.flux_bn\n",
    "\n",
    "print(np.round(MB_df.MBAL_diff.mean(),2))\n",
    "MB_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "residuals= [z_range_all,gpr_bdiff,farin_bdiff,opt_bdiff,obs_bin_all]\n",
    "\n",
    "r = pd.DataFrame(list(zip(z_range_all,elev_all,gpr_bdiff,farin_bdiff,opt_bdiff,gpr_bd_fg,opt_bd_fg,farin_bd_fg,obs_bin_all,obs_fg_all)), \n",
    "                  columns=['z_range_all','elev_all','gpr_bdiff','farin_bdiff','opt_bdiff','gpr_bd_fg','opt_bd_fg','farin_bd_fg',\n",
    "                           'obs_bin_all','obs_fg_all']) \n",
    "r['zrel'] = r.elev_all-r.z_range_all.min()\n",
    "r.zrel= r.zrel / r.zrel.max()\n",
    "##where bin_all is 100 m elev bins and fg_all for flux bins\n",
    "r['gpr_fr']=r.gpr_bdiff/np.abs(r.obs_bin_all);r['far_fr']=r.farin_bdiff/np.abs(r.obs_bin_all);\n",
    "r['opt_fr']=r.opt_bdiff/np.abs(r.obs_bin_all); \n",
    "## for FG bins\n",
    "r['opt_bd_fr']=r.opt_bd_fg/np.abs(r.obs_fg_all)\n",
    "r['farin_bd_fr']=r.farin_bd_fg/np.abs(r.obs_fg_all);r['gpr_bd_fr']=r.gpr_bd_fg/np.abs(r.obs_fg_all);\n",
    "\n",
    "fig_path= '/home/pelto/Desktop/ice_flux/figures/'\n",
    "with open(fig_path + glacier[gl]+'_residuals.pkl', 'wb') as f: #_NOfirn\n",
    "    pickle.dump(r, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.stats as stats\n",
    "stats.f_oneway(obs_fg_all,gpr_all),stats.f_oneway(obs_fg_all,opt_all) ,stats.f_oneway(obs_fg_all,farin_all) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y1=np.array([2016]*5);y2=np.array([2017]*5);y3=np.array([2018]*5);Years=np.concatenate([y1,y2,y3])\n",
    "r['year']=Years\n",
    "\n",
    "for n in range(len(r)):\n",
    "    if (r.z_range_all[n]==2750.0) and (r.year[n]==2017):\n",
    "        r.z_range_all[n]=np.nan\n",
    "\n",
    "r.dropna(subset=['z_range_all'],inplace=True)\n",
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(fig_path + glacier[gl] +'_residuals.pkl', 'rb') as f:\n",
    "    tester = pickle.load(f)\n",
    "tester"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "AV=[]\n",
    "for i in range(len(new)):\n",
    "    AV.append(np.nanmean([new.b_fg_h[i]-new.Vfirn[i], new16.b_fg_h_gpr[i]-new16.Vfirn[i], new17.b_fg_hF[i]-new17.Vfirn[i]]))\n",
    "AV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('mean percent smb error:', round(np.nanmean([np.abs(new16.sBwe_F/new16.b_fg_weF),np.abs(new16.sBwe_gpr/new16.b_fg_we_gpr),\n",
    "                                             np.abs(new16.sBwe_opt/new16.b_fg_we),np.abs(new17.sBwe_F/new17.b_fg_weF),np.abs(new17.sBwe_gpr/new17.b_fg_we_gpr),\n",
    "                                             np.abs(new17.sBwe_opt/new17.b_fg_we),np.abs(new.sBwe_F/new.b_fg_weF),np.abs(new.sBwe_gpr/new.b_fg_we_gpr),\n",
    "                                             np.abs(new.sBwe_opt/new.b_fg_we)]),2))\n",
    "\n",
    "print('mean flux error:',round(np.nanmean([new16.spQo_gpr,new16.spQout,new16.spQoutF]),1))\n",
    "\n",
    "mean_error= np.mean([new16.sBwe_opt.mean(),new16.sBwe_F.mean(),new16.sBwe_gpr.mean(),new17.sBwe_opt.mean(),\n",
    "            new17.sBwe_F.mean(),new17.sBwe_gpr.mean(),new.sBwe_opt.mean(),new.sBwe_F.mean(),new.sBwe_gpr.mean()])\n",
    "print('mean SMB error:', round(mean_error,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "new16.spQout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "R= np.sum(new.cr_area_gpr)/np.sum(new.cr_area)\n",
    "Ri=new16.cr_area/new16.cr_areaF\n",
    "R=R-1\n",
    "print(R)\n",
    "# R[R>0.4]=np.nan\n",
    "# R.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=[];b=[];c=[]\n",
    "for i in range(len(new.cr_area)-1):\n",
    "    a.append(new.cr_area_gpr[i]/new.cr_area_gpr[i+1])\n",
    "    b.append(new.cr_area[i]/new.cr_area[i+1])\n",
    "    c.append(new.cr_areaF[i]/new.cr_areaF[i+1])\n",
    "dcr=pd.DataFrame(list(zip(a,b,c)),columns=['a','b','c'])\n",
    "dcr['ab']=1-dcr.a/dcr.b;dcr['bc']=1-dcr.b/dcr.c;dcr['ac']=1-dcr.a/dcr.c\n",
    "dcr.ab.median(),dcr.bc.median(),dcr.ac.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.mean([-0.10266655970446315, -0.04846731259747927, -0.0538312801448646,-0.1000502727441146, 0.12702149053722184,\n",
    "0.034982457814228096,0.012644901657839103, -0.15337537258449963, -0.12656652428335724])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "GR=new.cr_area_gpr/new.cr_area\n",
    "\n",
    "GR=GR-1\n",
    "print(GR)\n",
    "GR.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "GRi=new16.cr_area_gpr/new16.cr_area\n",
    "GR= np.sum(new.cr_area_gpr)/np.sum(new.cr_area)\n",
    "GR-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "GRi-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig_path= '/home/pelto/Desktop/ice_flux/figures/'\n",
    "with open(fig_path + glacier[0]+'_vfg_all.pkl', 'wb') as f:\n",
    "    pickle.dump(vfg_all, f)\n",
    "with open(fig_path + glacier[0]+ '_gate_elev.pkl', 'wb') as f:\n",
    "    pickle.dump(gate_elev, f)\n",
    "new.to_csv(fig_path + glacier[0]+'_new_2018.csv')\n",
    "\n",
    "# with open(fig_path + glacier[1] +'_vfg_all.pkl', 'rb') as f:\n",
    "#     vfg_all = pickle.load(f)\n",
    "# with open(fig_path + glacier[1] + 'gate_elev.pkl', 'rb') as f:\n",
    "#     gates_elev = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "D.vfg.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "v_gate_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "new"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kokanee Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "M= ['Observed','FG IPR','FG OGGM','FG Farinotti']\n",
    "# method=[]\n",
    "# for i in range(4):\n",
    "#     method.extend(label)\n",
    "# len(method)\n",
    "# STATS=pd.DataFrame(list(zip(method,SL[:,0], SE[:,1],SL[:,1], SE[:,2], RSQ, RSR, NSE, PBias,ME)), \n",
    "#                      columns=['method','ABL', 'ABLse', 'ACC', 'ACCse', 'RSQ', 'RSR', 'NSE', 'PBias','ME'])\n",
    "# STATS.ABL=STATS.ABL*1000;STATS.ABLse=STATS.ABLse*1000;STATS.ACC=STATS.ACC*1000;STATS.ACCse=STATS.ACCse*1000\n",
    "# STATS\n",
    "method=[]\n",
    "for i in range(4):\n",
    "    method.extend(M)\n",
    "len(method)\n",
    "STATS=pd.DataFrame(list(zip(method,SL[:,0], SE[:,1], PBias,ME,MAE)), \n",
    "                     columns=['method','ALL', 'ALLse', 'PBias','ME','MAE'])\n",
    "STATS.ALL=STATS.ALL*1000;STATS.ALLse=STATS.ALLse*1000;\n",
    "STATS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rng_ab=[];rng_ac=[]\n",
    "# for m in M:\n",
    "#     mn= STATS.ALL[STATS.method==m].min(); mx=STATS.ALL[STATS.method==m].max()\n",
    "# #     mnc= STATS.ACC[STATS.method==m].min(); mxc=STATS.ACC[STATS.method==m].max()\n",
    "#     rng_ab.append(mn/mx);#rng_ac.append(mnc/mxc);\n",
    "# rng_ab#, rng_ac"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for m in M:\n",
    "    L= STATS[STATS.method==m].values\n",
    "    MEAN= np.nanmean(L[:,1:],axis=0)\n",
    "    MEAN= np.insert(MEAN,0,m)\n",
    "    STATS= STATS.append(dict(zip(STATS.columns, MEAN)),ignore_index=True)\n",
    "STATS=STATS.round(2);STATS.ALLse=STATS.ALLse.round(1);STATS.PBias=STATS.PBias.round(1)\n",
    "STATS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "S1=STATS.copy()\n",
    "AB=[]\n",
    "# AC=[]\n",
    "for i in range(len(S1)):\n",
    "    AB.append(str(S1.ALL[i])+' $\\pm$ '+str(S1.ALLse[i]))\n",
    "S1.ALL=AB;\n",
    "S1.drop(columns=['ALLse'], inplace=True)\n",
    "S1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(S1.to_latex(index=False)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "firn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Could add PBias, MSE(ACC and ABL), RMSE (redunbdant with RSR), MAE, ME\n",
    "AB=[];AC=[];AL=[]\n",
    "for i in LNFS.index:\n",
    "    AL.append(str(LNFS.ALL[i])+' $\\pm$ '+str(LNFS.ALLse[i]))\n",
    "LNFS.ABL=AB;LNFS.ACC=AC;LNFS.ALL=AL\n",
    "LNFS.drop(columns=['ALLse'], inplace=True)\n",
    "LNFS\n",
    "print(LNFS.to_latex(index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "STATS.to_pickle(fl_path+'kokanee_LIN_firn_stats.pkl') #_NOfirn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LIN_firn_STATS=STATS\n",
    "# LIN_firn_STATSirn_STATS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "new.Vfirn[2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.sum(new.Vfirn[2:]*new.dem_count[2:])/np.sum(new.dem_count[2:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "LIN_NOfirn_STATS=STATS\n",
    "LIN_NOfirn_STATS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "LFS=LIN_firn_STATS.tail(4)\n",
    "LNFS=LIN_NOfirn_STATS.tail(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "LNFS['ALL_firn']=LFS.ALL\n",
    "LNFS['pdiff_obs']=(LNFS.ALL-LNFS.ALL_firn)/LNFS.ALL\n",
    "LNFS.pdiff_obs[1:].mean(),LNFS.pdiff_obs[1:].std(),"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "LNFS['ALL_firn']=LFS.ALL\n",
    "LNFS['pdiff_obs']=(LNFS.ALL-LNFS.ALL[12])/LNFS.ALL[12]\n",
    "LNFS.pdiff_obs[1:].mean(),LNFS.pdiff_obs[1:].std(),"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "MEAN=[np.mean([-0.040984,0.094340,-0.092992]),np.mean([-0.061475,0.052502,-0.092992]),np.mean([-0.024590, -0.054143,-0.173854]),]\n",
    "np.mean(MEAN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "STD=[np.std([-0.040984,0.094340,-0.092992]),np.std([-0.061475,0.052502,-0.092992]),np.std([-0.024590, -0.054143,-0.173854])]\n",
    "np.mean(STD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "LNFS['ALL_firn']=LFS.ALL\n",
    "LNFS['pdiff_obs']=(LNFS.ALL_firn-LNFS.ALL[12])/LNFS.ALL[12]\n",
    "LNFS.pdiff_obs[1:].mean(),LNFS.pdiff_obs[1:].std(),"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Calc PBIAS for slopes\n",
    "for i in range(3):\n",
    "    D=STATS.ALL[STATS.method==(method[i+1])][:-1].values\n",
    "    O=STATS.ALL[STATS.method=='Observed'][:-1].values\n",
    "    e=evaluator(pbias, D,O)\n",
    "    print(method[i+1],e )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Could add PBias, MSE(ACC and ABL), RMSE (redunbdant with RSR), MAE, ME\n",
    "AB=[];AC=[];AL=[]\n",
    "for i in LNFS.index:\n",
    "    AL.append(str(LNFS.ALL[i])+' $\\pm$ '+str(LNFS.ALLse[i]))\n",
    "LNFS.ABL=AB;LNFS.ACC=AC;LNFS.ALL=AL\n",
    "LNFS.drop(columns=['ALLse'], inplace=True)\n",
    "LNFS\n",
    "print(LNFS.to_latex(index=False))\n",
    "# S1.to_pickle(fl_path+'kokanee_stats.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_bin=[obs.Ba[(obs.Year>2015) & (obs.Year < 2019)],gpr_all,opt_all,farin_all,obs.Elev[(obs.Year>2015) & (obs.Year < 2019)],elev_all,elev_all,elev_all]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.nanmean(vz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(fl_path+ 'ALL_list.pkl', 'wb') as f:\n",
    "    pickle.dump(all_bin, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "font = {'family' : 'Helvetica', 'weight' : 'normal',  'size'   : 8}\n",
    "plt.rc('font', **font)\n",
    "\n",
    "obs = pd.read_csv(fl_path+ 'Kokanee_bdot.csv') \n",
    "obs=obs[obs.Year>2015]\n",
    "obs.dropna(subset=['Ba'],inplace=True)\n",
    "# obs=obs[obs.Year==2016 and obs.Year==2017 and obs.Year==2018]\n",
    "label=['Observed', 'FG IPR', 'FG OGGM', 'FG Farinotti']\n",
    "############## piecewise function ########################################         \n",
    "fig3, ax3 = plt.subplots(1, sharex=True, sharey=True, figsize=(4,4))#(3.5,3.5)) \n",
    "y_bin=[obs.Ba,gpr_all,opt_all,farin_all]; x_bin=[obs.Elev,elev_all,elev_all,elev_all]\n",
    "# y_bin=[obs_all,gpr_ALL,opt_all,farin_all]; x_bin=[z_range_all,zr_gpr,z_range_all,z_range_all]\n",
    " \n",
    "    \n",
    "for i in range(4):   \n",
    "    x0=2570\n",
    "    def piecewise_linear(x, x0, y0, k1, k2):\n",
    "        return np.piecewise(x, [x < x0], [lambda x:k1*x + y0-k1*x0, lambda x:k2*x + y0-k2*x0])  \n",
    "    Y=np.array(y_bin[i]);X=np.array(x_bin[i])\n",
    "    model = pwlf.PiecewiseLinFit(X, Y)\n",
    "    breakpoints = [2200.,x0,2900.]\n",
    "    model.fit_with_breaks(breakpoints)\n",
    "#         if i > 0 and year==2016:\n",
    "#             x_hat = np.arange(2200., 2690., 10.)#np.linspace(X.min(), X.max(), 100)\n",
    "#         else:\n",
    "    x_hat = np.arange(2280., 2800., 10.)\n",
    "    y_hat = model.predict(x_hat)\n",
    "    ax3.plot(x_hat, y_hat, lw=1.5, alpha=a, color=color[i],label=label[i])\n",
    "    \n",
    "    SL=model.slopes\n",
    "    SE=model.standard_errors()\n",
    "    rsq=model.r_squared()\n",
    "#     SL.append(sl);SE.append(se);RSQ.append(rsq)\n",
    "\n",
    "    x1=np.array(x_bin[i]); y1=np.array(y_bin[i])\n",
    "    p , e = curve_fit(piecewise_linear, x1, y1 )\n",
    "    xd = np.arange(2280., 2800., 10.)\n",
    "#         xd = np.linspace(1950, 3150, 50)\n",
    "#         ax1[count].plot(xd, piecewise_linear(xd, *p), color=color[i], lw=1., alpha=0.9)\n",
    "    # mean square error of the lines\n",
    "\n",
    "    MSE_results=[]\n",
    "    for Line in range(2):\n",
    "        Pred=[];Act=[]\n",
    "        if Line ==0:\n",
    "            INDEX= np.where(x1<x0)[0]\n",
    "            for ix in INDEX:\n",
    "                Pred.append( p[2]*x1[ix]+(p[1]-p[2]*x0))\n",
    "                Act.append(y1[ix])\n",
    "\n",
    "            MSE_results.append(MSE(Act,Pred))\n",
    "        if Line==1:\n",
    "            INDEX= np.where(x1>=x0)[0]\n",
    "            for ix in INDEX:\n",
    "                Pred.append( p[3]*x1[ix]+(p[1]-p[3]*x0))\n",
    "                Act.append(y1[ix])\n",
    "\n",
    "            MSE_results.append(MSE(Act,Pred))\n",
    "    ax3.scatter(x_bin[i], y_bin[i], color=color[i], s=10, marker=sym[i], facecolors='none')\n",
    "#     meanlineprops = dict(linestyle='--', linewidth=1., color=color[i])\n",
    "#     medianprops = dict(linestyle='-', linewidth=1, color=color[i])\n",
    "#     if i == 1:\n",
    "#         BOX2=ax3.boxplot(y_bin[i],meanprops=meanlineprops,medianprops=medianprops,showmeans=True, meanline=True,sym='',\n",
    "#                     positions=[1950, 2050, 2150, 2250, 2350, 2450, 2550, 2650, 2750, 2850, 2950, 3050],widths=75)\n",
    "#     else:\n",
    "#         BOX2=ax3.boxplot(y_bin[i],meanprops=meanlineprops,medianprops=medianprops,showmeans=True, meanline=True,sym='',\n",
    "#                     positions=[1950, 2050, 2150, 2250, 2350, 2450, 2550, 2650, 2750, 2850, 2950, 3050,3150,3250],widths=75)\n",
    "\n",
    "#     ax3.text(0.2, ytxt[i], txt[i]+ ' L1: '+ str(np.round(p[2]*1000,2)) +' L2: ' + \n",
    "#                     str(np.round(p[3]*1000,2)) + ' AABR: ' + str(np.round(p[2]/p[3],2)),\n",
    "#                      transform=ax3.transAxes)  \n",
    "#     ax3.text(0.55, ytxt[i], txt[i] + ' L1: '+ str(round(SL[0]*1000,2))+u\" \\u00B1 \"+ str(round(SE[1]*1000*1.96,2)) +' L2: ' + \n",
    "#                    str(round(SL[1]*1000,2))+u\" \\u00B1 \"+ str(round(SE[2]*1000*1.96,2)) ,transform=ax3.transAxes) #+ ' AABR: ' + str(np.round(p[2]/p[3],2)),\n",
    "    ax3.text(0.20, ytxt[i], txt[i] + ' L1: '+ str(round(SL[0]*1000,2))+u\" \\u00B1 \"+ str(round(SE[1]*1000*1.96,2)) +' L2: ' + \n",
    "          str(round(SL[1]*1000,2))+u\" \\u00B1 \"+ str(round(SE[2]*1000*1.96,2)) ,transform=ax3.transAxes) #+ ' AABR: ' + str(np.round(p[2]/p[3],2)),\n",
    "ax3.legend(loc=2)\n",
    "ax3.axhline(linewidth=1., color='k', ls='--', alpha=a, zorder=0)\n",
    "ax3.set(ylim=(-3.5,2.),xlim=(2270,2800),ylabel='Mass balance (m w.e.)',xlabel='Elevation (m a.s.l.)')\n",
    "#         print(MSE_results)\n",
    "fig3.savefig(fl_path + 'products/' + glacier[gl]+'_' + balance +'_all_years_combined.pdf', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(np.mean(NSE), np.std(NSE))\n",
    "print(np.mean(PBias), np.std(PBias))\n",
    "print(np.mean(RSR), np.mean(RSR))\n",
    "print(np.mean(OBF), np.mean(OPTq))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "new.b_fg_we_gpr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(FG_all.sBwe_gpr.mean())\n",
    "print(FG_all.sBwe_opt.mean())\n",
    "print(FG_all.sBwe_F.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gpr_bdiff_abs = np.abs(gpr_bdiff)\n",
    "opt_bdiff_abs = np.abs(opt_bdiff)\n",
    "farin_bdiff_abs = np.abs(farin_bdiff)\n",
    "m_gpr_bdiff = round(np.mean(gpr_bdiff),2)\n",
    "ma_gpr_bdiff = round(np.mean(gpr_bdiff_abs),2)\n",
    "s_gpr_bdiff = round(np.std(gpr_bdiff),2)\n",
    "sa_gpr_bdiff = round(np.std(gpr_bdiff_abs),2)\n",
    "m_opt_bdiff = round(np.mean(opt_bdiff),2)\n",
    "ma_opt_bdiff = round(np.mean(opt_bdiff_abs),2)\n",
    "s_opt_bdiff = round(np.std(opt_bdiff),2)\n",
    "sa_opt_bdiff = round(np.std(opt_bdiff_abs),2)\n",
    "m_far_bdiff = round(np.mean(farin_bdiff),2)\n",
    "ma_far_bdiff = round(np.mean(farin_bdiff_abs),2)\n",
    "s_far_bdiff = round(np.std(farin_bdiff),2)\n",
    "sa_far_bdiff = round(np.std(farin_bdiff_abs),2)\n",
    "\n",
    "\n",
    "print('mean gpr diff:', m_gpr_bdiff, '+/-',s_gpr_bdiff,', abs. diff mean:', ma_gpr_bdiff,'+/-',sa_gpr_bdiff)\n",
    "print('mean opt diff:', m_opt_bdiff, '+/-',s_opt_bdiff,', abs. diff mean:', ma_opt_bdiff,'+/-',sa_opt_bdiff)\n",
    "print('mean farin diff:', m_far_bdiff, '+/-',s_far_bdiff,', abs. diff mean:', ma_far_bdiff,'+/-',sa_far_bdiff)\n",
    "\n",
    "mean_diff_std = np.round(np.mean([sa_gpr_bdiff, sa_opt_bdiff, sa_far_bdiff]),2) #np.round(np.std([m_gpr_bdiff, m_opt_bdiff, m_far_bdiff]),2)\n",
    "mean_diff =  np.round(np.mean([m_gpr_bdiff, m_opt_bdiff, m_far_bdiff]),2)\n",
    "meanabs_diff_std = np.round(np.mean([s_gpr_bdiff, s_opt_bdiff, s_far_bdiff]),2)#np.round(np.std([ma_gpr_bdiff, ma_opt_bdiff, ma_far_bdiff]),2)\n",
    "meanabs_diff =  np.round(np.mean([ma_gpr_bdiff, ma_opt_bdiff, ma_far_bdiff]),2)\n",
    "\n",
    "print(mean_diff, '+/-', mean_diff_std)\n",
    "print(meanabs_diff, '+/-', meanabs_diff_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt_bdiff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.mean([new.dh_mean[0], new16.dh_mean[0], new17.dh_mean[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.mean([new.Q_netAF[0], new.Q_netA_gpr[0], new.Q_netA[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "OB_F[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for n in range(len(new.dem_mean)):\n",
    "    print(new.dem_mean[n])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fig1.savefig(fl_path + 'products/' + glacier[gl]+'_bdot_3_panel_' + balance +  '_all_years.pdf', dpi=300) #+ Glacier[gl]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gpr_bdiff_abs = np.abs(gpr_bdiff)\n",
    "opt_bdiff_abs = np.abs(opt_bdiff)\n",
    "farin_bdiff_abs = np.abs(farin_bdiff)\n",
    "print('mean gpr diff:', round(np.mean(gpr_bdiff),2), '+/-',round(np.std(gpr_bdiff),2),'abs. diff mean:',\n",
    "      round(np.mean(gpr_bdiff_abs),2), '+/-',round(np.std(gpr_bdiff_abs),2))\n",
    "print('mean opt diff:', round(np.mean(opt_bdiff),2), '+/-',round(np.std(opt_bdiff),2),'abs. diff mean:',\n",
    "      round(np.mean(opt_bdiff_abs),2), '+/-',round(np.std(opt_bdiff_abs),2))\n",
    "print('mean farin diff:', round(np.mean(farin_bdiff),2), '+/-',round(np.std(farin_bdiff),2),'abs. diff mean:',\n",
    "      round(np.mean(farin_bdiff_abs),2), '+/-',round(np.std(farin_bdiff_abs),2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# ax.errorbar(FG_all.dem_mean+shift[i],MC_y[i], xerr=None, yerr=FG_all.sBwe_opt, fmt=sym[i+1], c='r', label='', \n",
    "#         alpha=a, zorder=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "new['FG_opt_m3_we'] = new.b_fg_we * new.dem_count\n",
    "new['FG_gpr_m3_we'] = new.b_fg_we_gpr * new.dem_count\n",
    "\n",
    "B_opt = (new.FG_opt_m3_we.sum()) / (new.dem_count.sum())\n",
    "B_gpr = new.FG_gpr_m3_we.sum() / new.dem_count.sum()\n",
    "print(B_opt)\n",
    "print(B_gpr)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot flux gates as cross-sections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pylab.rcParams['xtick.major.pad']='1.'\n",
    "pylab.rcParams['ytick.major.pad']='1.'\n",
    "# pylab.rcParams['xtick.major.size']='0.2'\n",
    "# pylab.rcParams['ytick.major.width']='250'\n",
    "\n",
    "font = {'family' : 'Helfricht', 'weight' : 'normal', 'size'   : 8}\n",
    "\n",
    "n=0\n",
    "# df_agg['area_opt'] = df_agg.thick_opt * 10 \n",
    "a=0.8\n",
    "letter=['W','X','Y','Z$_a$', 'Z$_b$', 'Z$_c$']\n",
    "gate=['K0', 'K1', 'K2', 'K3', 'K4']\n",
    "\n",
    "div=1000\n",
    "\n",
    "obs_H = D #, inplace=True)   \n",
    "\n",
    "fig, ax = plt.subplots(1,5, figsize=(7.08,1.3))\n",
    "for i in range(5):\n",
    "    \n",
    "    # plot glacier surface\n",
    "    ax[i].plot(df_agg[df_agg.id==i].distance, (df_agg[df_agg.id==i].dem/div), color='#43a2ca', alpha=a, label='Ice surf.')\n",
    "    \n",
    "    #plot gpr thickness gates\n",
    "    gpr=(obs_H[obs_H.id==i].dem/div)-(obs_H[obs_H.id==i].gpr/div)\n",
    "    gpr_mask=np.isfinite(gpr)\n",
    "    ax[i].plot(obs_H[obs_H.id==i].distance, gpr, marker='o'\n",
    "                 , ms=1.5, ls='--', color='k', alpha=a, label='Obs. bed')\n",
    "    \n",
    "#         ax[i].set_ylim(((df_agg.elev[df_agg.id_opt==i])-(df_agg[df_agg.id_opt==i].thick_opt)+5).max(), -5)\n",
    "    \n",
    "    ##plot optimized thickness gates\n",
    "    ax[i].plot(df_agg[df_agg.id==i].distance, (df_agg[df_agg.id==i].dem/div)-(df_agg[df_agg.id==i].H_opt/div), \n",
    "                 ls='--', c='r', alpha=a, label='Model bed')  #marker='o')\n",
    "    \n",
    "    ##plot Farinotti thickness gates\n",
    "    ax[i].plot(df_agg[df_agg.id==i].distance, (df_agg[df_agg.id==i].dem/div)-(df_agg[df_agg.id==i].farin_corr/div), \n",
    "                 ls='--', c='salmon', alpha=a, label='Farinotti bed')  #marker='o')\n",
    "    \n",
    "    ax[i].yaxis.set_major_locator(ticker.MultipleLocator(100/div)) ##\n",
    "    ax[i].tick_params(which='major', length=2)\n",
    "#     if i > 3:\n",
    "#     if i > 2:\n",
    "#         ax[i].set_ylim(2600, 2810)\n",
    "    ax[0].set_ylim(2280/div, 2405/div) \n",
    "    ax[3].set_ylim(2510/div, 2710/div)\n",
    "    ax[4].set_ylim(2590/div, 2750/div)\n",
    "#     ax[5].set_ylim(2590/div, 2740/div)\n",
    "    ax[i].text(0.1, 0.08, letter[i], transform=ax[i].transAxes, verticalalignment='center', horizontalalignment='center',)\n",
    "    ax[i].text(0.9, 0.9, gate[i], transform=ax[i].transAxes, verticalalignment='center', horizontalalignment='center',)\n",
    "      \n",
    "# fig.text(0.01, 0.6, 'Altitude (m a.s.l.)', rotation=90)\n",
    "fig.text(0.48, 0.02, 'Distance (m)')     \n",
    "fig.subplots_adjust(bottom=0.21, top=0.98, left=0.055, right=0.99, wspace=0.22)#, hspace=0.18) \n",
    "# fig.subplots_adjust(bottom=0.13, top=0.95, left=0.05, right=0.99, wspace=0.4, hspace=0.15) \n",
    "plt.savefig(fl_path + 'products/' + glacier[gl]+'_flux_gates_onerow.pdf', dpi=300)\n",
    "plt.show()\n",
    "\n",
    "# for i in range(4):\n",
    "#     area=df_agg[df_agg.id_opt==i].area_opt\n",
    "#     area_total = area.sum()\n",
    "#     print(area_total)        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gates.sort_values(by='id',ascending=True, inplace=True) # sort by bin\n",
    "\n",
    "WID =[]\n",
    "for i in range(len(gates.id)):\n",
    "    W1=gates[gates.id == i].len.to_numpy()\n",
    "    W2=gates[gates.id == i+1].len.to_numpy()\n",
    "    WID.append(1-(W1/W2))   #,ignore_index=True)\n",
    "gates['p_len_diff']= WID\n",
    "\n",
    "gates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "font = {'family' : 'Helvetica', 'weight' : 'normal', 'size'   : 8};plt.rc('font', **font)\n",
    "pylab.rcParams['xtick.major.pad']='1';pylab.rcParams['ytick.major.pad']='1'\n",
    "topo_big = '/home/pelto/Desktop/lidar_cbt_analysis/kokanee/20181014_Kokanee_dem1_clip_slave.tif'\n",
    "\n",
    "fig, ax = plt.subplots(1, figsize=cm2inch(8.5,5.7))\n",
    "\n",
    "grid = mercator_grid(center_ll=(-117.137, 49.75), extent=(2250, 2000)) ##zoomed out view\n",
    "# 487892.000 5509738.000 491232.000 5512358.000\n",
    "# grid = vx.salem.grid  ##full view\n",
    "sm = Map(grid, countries=False)\n",
    "sm.set_lonlat_contours(interval=0)\n",
    "# b = sm.set_geometry(linewidth=0.1, alpha=0.8, facecolor='p') ############???????????????????\n",
    "sm.set_scale_bar(location=(0.16, 0.93), add_bbox=True, bbox_dy=0.95, linewidth=1.2)\n",
    "\n",
    "VZA=xr.DataArray(data=VZ, coords=vx.coords, dims=vx.dims, name='VZA', attrs=vx.attrs) #,indexes=vx.indexes fastpath=False)\n",
    "\n",
    "VZA.data=VZA.data*(msk)\n",
    "sm.set_data(VZA) \n",
    "sm.set_vmax(val=30.)\n",
    "#      Change the lon-lat countour setting\n",
    "sm.set_lonlat_contours(add_ytick_labels=True, interval=0.05, lw=0.75, linestyles='--', colors='0.25')\n",
    "# off_ice_V = np.nanmean(VZ_off_ice)\n",
    "sm.set_cmap(\"Spectral_r\")\n",
    "print(off_ice_V)\n",
    "print(np.nanstd(VZ_off_ice))\n",
    "sm.set_topography(topo_big,  relief_factor=0.9)\n",
    "gdf = salem.read_shapefile(shpf)\n",
    "sm.set_shapefile(gdf, lw=1.5, color='w')\n",
    "\n",
    "sm.set_shapefile(gates, color='w', lw=1.0)\n",
    "sm.append_colorbar(ax=ax, pad=0.1, label='Ice Velocity (m yr$^{-1}$)')#, cbar_title='Ice Velocity (m yr$^-1$)')\n",
    "sm.plot(ax=ax)\n",
    "\n",
    "# sm.visualize(addcbar=True, cbar_title='Ice Velocity (m yr$^-1$)')\n",
    "# plt.append_colorbar(ax, label='Velocity (m yr-1)')\n",
    "fig.subplots_adjust(bottom=0.05, top=0.99, left=0.14, right=0.88) #, wspace=0.22, hspace=0.15\n",
    "plt.savefig(fl_path+ 'products/'+ glacier[gl]+ '_velocity_gates_all.pdf', dpi=300)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "VX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "v[v>25]=v[v>25]*0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 240.945x162.992 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vf_list = ['kokanee_vy_25m_2016.tif','kokanee_vy_25m_2017.tif','kokanee_vy_25m_2018.tif']\n",
    "vdir = '/home/pelto/Desktop/velocity_mapping/' +glacier[gl] + '/dem/3m/'\n",
    "ITS = fl_path + 'ITS_Live/' + str(year) + '_conrad_ITS_LIVE.tif'\n",
    "\n",
    "year=2018\n",
    "I = np.abs(year - 2016) \n",
    "\n",
    "VX = vdir+ vf_list[I][:-15] + 'vx_25m_' +str(year)+ '.tif' \n",
    "VY = vdir+ vf_list[I][:-15] + 'vy_25m_' +str(year)+ '.tif' \n",
    "\n",
    "vy = salem.open_xr_dataset(VY);vy = vy.to_array(name='vy')\n",
    "vx = salem.open_xr_dataset(VX);vx = vx.to_array(name='vx')\n",
    "msk = salem.open_xr_dataset(path + 'kokanee_2015_extent_5m.tif')\n",
    "msk_reproj = vy.salem.transform(msk); msk = msk_reproj.to_array(name='msk')\n",
    "topo ='/home/pelto/Desktop/lidar_cbt_analysis/kokanee/20181014_Kokanee_dem1_clip_slave.tif' \n",
    "with open('/home/pelto/Desktop/ice_flux/Kokanee/' + glacier[0] +'_df_agg.pkl', 'rb') as f:\n",
    "    df_agg = pickle.load(f)\n",
    "obs_shp = '/home/pelto/GIS/GlacierPoints/ALL_gps_point_SB_topos.shp'\n",
    "obs_pts = salem.read_shapefile(obs_shp)\n",
    "\n",
    "vz = vy\n",
    "vz.data = np.sqrt(vx.data**2 + vz.data**2 )\n",
    "vz.data[vz.data<0.01]=np.nan;# vz.data[vz.data>30.0]=vz.data[vz.data>30.0]*0.5 ##remove high erroneous values\n",
    "vz.data[msk.data!=1.0] = np.nan;\n",
    "\n",
    "font = {'family' : 'Helvetica', 'weight' : 'normal', 'size'   : 8}\n",
    "plt.rc('font', **font)\n",
    "pylab.rcParams['xtick.major.pad']='1'\n",
    "pylab.rcParams['ytick.major.pad']='1'\n",
    "\n",
    "fig, ax = plt.subplots(1, figsize=cm2inch(8.5,5.75))\n",
    "grid = mercator_grid(center_ll=(-117.137, 49.75), extent=(2250, 2000)) ##zoomed out view\n",
    "sm1 = Map(grid, countries=False)\n",
    "sm1.set_lonlat_contours(interval=0)\n",
    "sm1.set_scale_bar(location=(0.15, 0.09),add_bbox=True,bbox_dy=1.1,linewidth=1.0)\n",
    "\n",
    "VZA=xr.DataArray(data=vz, coords=vz.coords, dims=vz.dims, name='VZA', attrs=vz.attrs) #,indexes=vx.indexes fastpath=False)\n",
    "\n",
    "# VZA.data=VZA.data*(msk+1.0)\n",
    "sm1.set_data(VZA) \n",
    "\n",
    "sm1.set_cmap(\"Spectral_r\")\n",
    "sm1.set_vmax(val=30.)\n",
    "#      Change the lon-lat countour setting\n",
    "sm1.set_lonlat_contours(add_ytick_labels=True, interval=0.05, linewidths=0.75, linestyles='--', colors='0.25')\n",
    "sm1.set_topography(topo,  relief_factor=0.8)\n",
    "sm1.set_shapefile(shpf, lw=1.5, color='w')\n",
    "sm1.set_shapefile(gates, color='w', lw=1.1)\n",
    "\n",
    "x, y = sm1.grid.transform(obs_pts.lon.values, obs_pts.lat.values)\n",
    "ax.scatter(x, y, color='w', s=8, facecolor='None')\n",
    "\n",
    "sm1.append_colorbar(ax=ax, pad=0.1, label='')#'Ice Velocity (m yr$^{-1}$)')\n",
    "sm1.plot(ax=ax)\n",
    "\n",
    "df_agg.vx[df_agg.lon<-117.146]=df_agg.vx[df_agg.lon<-117.146]*-1.0\n",
    "u = df_agg.vx.values\n",
    "v = df_agg.vy.values;#v[v>25]=v[v>25]*0.2\n",
    "\n",
    "X, Y = np.meshgrid(df_agg.lon,df_agg.lat)\n",
    "\n",
    "# transform their coordinates to the map reference system and plot the arrows\n",
    "xx, yy = sm1.grid.transform(df_agg.lon, df_agg.lat, crs=salem.wgs84)#sm1.grid.proj\n",
    "\n",
    "# xx, yy = np.meshgrid(xx,yy)\n",
    "\n",
    "# qu = ax.quiver(xx, yy, u, v)\n",
    "\n",
    "###### start:stop:step\n",
    "Q = ax.quiver(xx[::1], yy[::1], u[::1], v[::1])  #, pivot='mid')\n",
    "qk = ax.quiverkey(Q, 0.07, 0.10, 20, r'$40 \\frac{m}{a}$', labelpos='N', coordinates='figure', labelsep=0.025)\n",
    "\n",
    "# Q = ax.quiver(X[::1, ::1], Y[::1, ::1], u[::1, ::1], v[::1, ::1], pivot='mid')\n",
    "#Q= ax2.quiver(X[::3, ::3], Y[::3, ::3], U[::3, ::3], V[::3, ::3],pivot='mid', units='inches')\n",
    "\n",
    "# X, Y = np.meshgrid(df_agg.lon,df_agg.lat)\n",
    "# U = df_agg.vx.values\n",
    "# V = df_agg.vy.values\n",
    "# U,V = np.meshgrid(U, V)\n",
    "\n",
    "# # Q = ax.quiver(X, Y, U, V)#units='width', angles='uv') \n",
    "# Q = ax.quiver(X[::30, ::30], Y[::30, ::30], U[::30, ::30], V[::30, ::30], units='xy')\n",
    "\n",
    "\n",
    "fig.subplots_adjust(bottom=0.05, top=0.99, left=0.14, right=0.88) #, wspace=0.22, hspace=0.15\n",
    "plt.savefig(fl_path+ 'products/'+ glacier[gl]+ '_' +str(year) +'_vel_gates_quiver.pdf', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>len</th>\n",
       "      <th>distance</th>\n",
       "      <th>angle</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>vx</th>\n",
       "      <th>vy</th>\n",
       "      <th>vz</th>\n",
       "      <th>gpr</th>\n",
       "      <th>H_opt</th>\n",
       "      <th>dem</th>\n",
       "      <th>farin_corr</th>\n",
       "      <th>vzdir</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ij</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0087_0118</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>0.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.150273</td>\n",
       "      <td>49.751861</td>\n",
       "      <td>1.463031</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.812812</td>\n",
       "      <td>42.200001</td>\n",
       "      <td>34.952103</td>\n",
       "      <td>2692.644531</td>\n",
       "      <td>6.540831</td>\n",
       "      <td>-20.009487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0088_0119</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>25.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.149955</td>\n",
       "      <td>49.751772</td>\n",
       "      <td>1.699979</td>\n",
       "      <td>14.954845</td>\n",
       "      <td>14.954845</td>\n",
       "      <td>39.716499</td>\n",
       "      <td>33.477638</td>\n",
       "      <td>2687.949951</td>\n",
       "      <td>39.903928</td>\n",
       "      <td>-13.141000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0089_0119</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>50.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.149636</td>\n",
       "      <td>49.751683</td>\n",
       "      <td>1.626611</td>\n",
       "      <td>13.221420</td>\n",
       "      <td>13.221420</td>\n",
       "      <td>38.549000</td>\n",
       "      <td>33.473923</td>\n",
       "      <td>2679.099365</td>\n",
       "      <td>41.528717</td>\n",
       "      <td>-14.244299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0090_0119</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>75.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.149318</td>\n",
       "      <td>49.751594</td>\n",
       "      <td>1.470872</td>\n",
       "      <td>11.294090</td>\n",
       "      <td>11.294090</td>\n",
       "      <td>33.038998</td>\n",
       "      <td>35.657833</td>\n",
       "      <td>2670.854004</td>\n",
       "      <td>40.660488</td>\n",
       "      <td>-15.097804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0091_0120</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>100.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.148999</td>\n",
       "      <td>49.751504</td>\n",
       "      <td>1.584921</td>\n",
       "      <td>7.504434</td>\n",
       "      <td>7.504434</td>\n",
       "      <td>27.422318</td>\n",
       "      <td>39.618732</td>\n",
       "      <td>2667.141113</td>\n",
       "      <td>39.038415</td>\n",
       "      <td>-24.985943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0092_0120</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>125.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.148681</td>\n",
       "      <td>49.751415</td>\n",
       "      <td>1.603650</td>\n",
       "      <td>5.546185</td>\n",
       "      <td>5.546185</td>\n",
       "      <td>20.936953</td>\n",
       "      <td>43.506130</td>\n",
       "      <td>2662.265381</td>\n",
       "      <td>41.985172</td>\n",
       "      <td>-35.330307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0093_0121</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>150.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.148362</td>\n",
       "      <td>49.751326</td>\n",
       "      <td>1.242517</td>\n",
       "      <td>5.144938</td>\n",
       "      <td>5.144938</td>\n",
       "      <td>15.642700</td>\n",
       "      <td>47.145554</td>\n",
       "      <td>2662.538818</td>\n",
       "      <td>46.325237</td>\n",
       "      <td>-28.881874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0094_0121</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>175.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.148043</td>\n",
       "      <td>49.751237</td>\n",
       "      <td>1.386238</td>\n",
       "      <td>4.702446</td>\n",
       "      <td>4.702446</td>\n",
       "      <td>17.807199</td>\n",
       "      <td>50.201744</td>\n",
       "      <td>2659.213379</td>\n",
       "      <td>49.881763</td>\n",
       "      <td>-36.127335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0095_0121</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>200.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.147725</td>\n",
       "      <td>49.751147</td>\n",
       "      <td>-2.934101</td>\n",
       "      <td>4.111371</td>\n",
       "      <td>4.111371</td>\n",
       "      <td>19.998913</td>\n",
       "      <td>52.240566</td>\n",
       "      <td>2656.314941</td>\n",
       "      <td>53.381650</td>\n",
       "      <td>-45.533092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0096_0122</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>225.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.147406</td>\n",
       "      <td>49.751058</td>\n",
       "      <td>-1.968246</td>\n",
       "      <td>3.027712</td>\n",
       "      <td>3.027712</td>\n",
       "      <td>19.544662</td>\n",
       "      <td>53.179119</td>\n",
       "      <td>2655.419434</td>\n",
       "      <td>54.561545</td>\n",
       "      <td>-40.547405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0097_0122</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>250.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.147088</td>\n",
       "      <td>49.750969</td>\n",
       "      <td>-1.402215</td>\n",
       "      <td>4.098421</td>\n",
       "      <td>4.098421</td>\n",
       "      <td>21.801212</td>\n",
       "      <td>54.701904</td>\n",
       "      <td>2652.634766</td>\n",
       "      <td>55.647996</td>\n",
       "      <td>-20.007034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0097_0123</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>275.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.146769</td>\n",
       "      <td>49.750880</td>\n",
       "      <td>-1.061054</td>\n",
       "      <td>3.941034</td>\n",
       "      <td>3.941034</td>\n",
       "      <td>18.515837</td>\n",
       "      <td>55.859760</td>\n",
       "      <td>2655.162598</td>\n",
       "      <td>56.734866</td>\n",
       "      <td>-15.618594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0098_0123</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>300.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.146451</td>\n",
       "      <td>49.750790</td>\n",
       "      <td>-1.044961</td>\n",
       "      <td>3.053514</td>\n",
       "      <td>3.053514</td>\n",
       "      <td>32.689213</td>\n",
       "      <td>56.645973</td>\n",
       "      <td>2652.900391</td>\n",
       "      <td>57.103909</td>\n",
       "      <td>-20.011940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0099_0123</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>325.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.146132</td>\n",
       "      <td>49.750701</td>\n",
       "      <td>-0.074828</td>\n",
       "      <td>4.128119</td>\n",
       "      <td>4.128119</td>\n",
       "      <td>45.433224</td>\n",
       "      <td>56.511395</td>\n",
       "      <td>2650.289062</td>\n",
       "      <td>55.985535</td>\n",
       "      <td>-1.038627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0100_0124</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>350.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.145814</td>\n",
       "      <td>49.750612</td>\n",
       "      <td>1.530835</td>\n",
       "      <td>5.357755</td>\n",
       "      <td>5.357755</td>\n",
       "      <td>49.421112</td>\n",
       "      <td>57.093010</td>\n",
       "      <td>2650.734619</td>\n",
       "      <td>56.431485</td>\n",
       "      <td>16.602079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0101_0124</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>375.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.145495</td>\n",
       "      <td>49.750523</td>\n",
       "      <td>1.643123</td>\n",
       "      <td>5.988144</td>\n",
       "      <td>5.988144</td>\n",
       "      <td>48.634338</td>\n",
       "      <td>56.067986</td>\n",
       "      <td>2647.916504</td>\n",
       "      <td>54.726943</td>\n",
       "      <td>15.926026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0102_0125</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>400.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.145177</td>\n",
       "      <td>49.750433</td>\n",
       "      <td>2.389611</td>\n",
       "      <td>6.382704</td>\n",
       "      <td>6.382704</td>\n",
       "      <td>53.692337</td>\n",
       "      <td>54.860321</td>\n",
       "      <td>2648.668945</td>\n",
       "      <td>54.643809</td>\n",
       "      <td>21.986528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0103_0125</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>425.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.144858</td>\n",
       "      <td>49.750344</td>\n",
       "      <td>2.098716</td>\n",
       "      <td>6.150829</td>\n",
       "      <td>6.150829</td>\n",
       "      <td>55.093575</td>\n",
       "      <td>54.860321</td>\n",
       "      <td>2645.690918</td>\n",
       "      <td>52.646081</td>\n",
       "      <td>19.950523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0104_0125</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>450.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.144540</td>\n",
       "      <td>49.750255</td>\n",
       "      <td>2.552134</td>\n",
       "      <td>6.813757</td>\n",
       "      <td>6.813757</td>\n",
       "      <td>61.954323</td>\n",
       "      <td>53.584194</td>\n",
       "      <td>2642.197021</td>\n",
       "      <td>50.890643</td>\n",
       "      <td>21.996880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0105_0126</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>475.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.144221</td>\n",
       "      <td>49.750166</td>\n",
       "      <td>2.980043</td>\n",
       "      <td>7.597724</td>\n",
       "      <td>7.597724</td>\n",
       "      <td>69.704239</td>\n",
       "      <td>52.603374</td>\n",
       "      <td>2642.767578</td>\n",
       "      <td>49.198640</td>\n",
       "      <td>23.093225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0106_0126</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>500.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.143903</td>\n",
       "      <td>49.750076</td>\n",
       "      <td>3.250309</td>\n",
       "      <td>7.828930</td>\n",
       "      <td>7.828930</td>\n",
       "      <td>68.556610</td>\n",
       "      <td>51.111721</td>\n",
       "      <td>2639.316650</td>\n",
       "      <td>48.871607</td>\n",
       "      <td>24.529797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0107_0127</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>525.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.143584</td>\n",
       "      <td>49.749987</td>\n",
       "      <td>2.743552</td>\n",
       "      <td>6.791433</td>\n",
       "      <td>6.791433</td>\n",
       "      <td>65.738884</td>\n",
       "      <td>48.838585</td>\n",
       "      <td>2641.336914</td>\n",
       "      <td>46.567578</td>\n",
       "      <td>23.826752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0108_0127</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>562.5</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.143106</td>\n",
       "      <td>49.749853</td>\n",
       "      <td>2.733854</td>\n",
       "      <td>6.886071</td>\n",
       "      <td>6.886071</td>\n",
       "      <td>58.015263</td>\n",
       "      <td>47.162056</td>\n",
       "      <td>2637.970947</td>\n",
       "      <td>46.013769</td>\n",
       "      <td>23.391521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0109_0128</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>600.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.142628</td>\n",
       "      <td>49.749719</td>\n",
       "      <td>1.752285</td>\n",
       "      <td>6.450512</td>\n",
       "      <td>6.450512</td>\n",
       "      <td>51.046249</td>\n",
       "      <td>43.947521</td>\n",
       "      <td>2641.412109</td>\n",
       "      <td>44.848214</td>\n",
       "      <td>15.762508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0110_0128</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>625.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.142310</td>\n",
       "      <td>49.749630</td>\n",
       "      <td>1.864836</td>\n",
       "      <td>6.224868</td>\n",
       "      <td>6.224868</td>\n",
       "      <td>48.622280</td>\n",
       "      <td>43.947521</td>\n",
       "      <td>2637.004883</td>\n",
       "      <td>42.015336</td>\n",
       "      <td>17.432283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0111_0129</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>650.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.141991</td>\n",
       "      <td>49.749541</td>\n",
       "      <td>1.974978</td>\n",
       "      <td>5.100935</td>\n",
       "      <td>5.100935</td>\n",
       "      <td>46.427277</td>\n",
       "      <td>40.445938</td>\n",
       "      <td>2640.526611</td>\n",
       "      <td>39.819757</td>\n",
       "      <td>22.779123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0112_0129</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>675.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.141673</td>\n",
       "      <td>49.749451</td>\n",
       "      <td>1.141310</td>\n",
       "      <td>4.773098</td>\n",
       "      <td>4.773098</td>\n",
       "      <td>44.921764</td>\n",
       "      <td>39.142967</td>\n",
       "      <td>2634.243164</td>\n",
       "      <td>36.969448</td>\n",
       "      <td>13.834205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0113_0129</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>700.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.141354</td>\n",
       "      <td>49.749362</td>\n",
       "      <td>0.821080</td>\n",
       "      <td>4.662340</td>\n",
       "      <td>4.662340</td>\n",
       "      <td>44.038036</td>\n",
       "      <td>38.209541</td>\n",
       "      <td>2626.884766</td>\n",
       "      <td>35.670620</td>\n",
       "      <td>10.143200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0114_0130</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>725.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.141036</td>\n",
       "      <td>49.749273</td>\n",
       "      <td>0.670674</td>\n",
       "      <td>4.803851</td>\n",
       "      <td>4.803851</td>\n",
       "      <td>43.710896</td>\n",
       "      <td>35.838097</td>\n",
       "      <td>2632.643799</td>\n",
       "      <td>37.130676</td>\n",
       "      <td>8.025385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0115_0130</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>750.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.140717</td>\n",
       "      <td>49.749184</td>\n",
       "      <td>0.148405</td>\n",
       "      <td>4.585234</td>\n",
       "      <td>4.585234</td>\n",
       "      <td>43.877647</td>\n",
       "      <td>35.867321</td>\n",
       "      <td>2627.365723</td>\n",
       "      <td>37.470716</td>\n",
       "      <td>1.854755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0131_0137</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>1187.5</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.135144</td>\n",
       "      <td>49.747621</td>\n",
       "      <td>-1.865977</td>\n",
       "      <td>4.417768</td>\n",
       "      <td>4.417768</td>\n",
       "      <td>73.042252</td>\n",
       "      <td>59.395332</td>\n",
       "      <td>2590.085205</td>\n",
       "      <td>37.947042</td>\n",
       "      <td>-24.984930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0132_0138</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>1225.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.134666</td>\n",
       "      <td>49.747487</td>\n",
       "      <td>-2.063198</td>\n",
       "      <td>4.567921</td>\n",
       "      <td>4.567921</td>\n",
       "      <td>73.632248</td>\n",
       "      <td>55.554073</td>\n",
       "      <td>2595.284424</td>\n",
       "      <td>38.715327</td>\n",
       "      <td>-26.850956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0133_0138</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.134347</td>\n",
       "      <td>49.747398</td>\n",
       "      <td>-2.399386</td>\n",
       "      <td>4.867803</td>\n",
       "      <td>4.867803</td>\n",
       "      <td>77.198090</td>\n",
       "      <td>58.351925</td>\n",
       "      <td>2593.273926</td>\n",
       "      <td>46.290257</td>\n",
       "      <td>-29.531992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0134_0139</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>1275.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.134029</td>\n",
       "      <td>49.747309</td>\n",
       "      <td>-3.331510</td>\n",
       "      <td>5.605476</td>\n",
       "      <td>5.605476</td>\n",
       "      <td>76.556091</td>\n",
       "      <td>60.877014</td>\n",
       "      <td>2597.843994</td>\n",
       "      <td>47.181625</td>\n",
       "      <td>-36.464973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0135_0139</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.133710</td>\n",
       "      <td>49.747219</td>\n",
       "      <td>-3.307630</td>\n",
       "      <td>6.063656</td>\n",
       "      <td>6.063656</td>\n",
       "      <td>78.217712</td>\n",
       "      <td>62.942539</td>\n",
       "      <td>2596.146484</td>\n",
       "      <td>47.539489</td>\n",
       "      <td>-33.057777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0136_0139</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>1325.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.133392</td>\n",
       "      <td>49.747130</td>\n",
       "      <td>-3.242635</td>\n",
       "      <td>6.729495</td>\n",
       "      <td>6.729495</td>\n",
       "      <td>79.261497</td>\n",
       "      <td>64.349045</td>\n",
       "      <td>2595.286621</td>\n",
       "      <td>47.801742</td>\n",
       "      <td>-28.806561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0137_0140</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.133074</td>\n",
       "      <td>49.747041</td>\n",
       "      <td>-3.279752</td>\n",
       "      <td>6.459460</td>\n",
       "      <td>6.459460</td>\n",
       "      <td>79.818497</td>\n",
       "      <td>62.152580</td>\n",
       "      <td>2597.959961</td>\n",
       "      <td>45.491020</td>\n",
       "      <td>-30.513670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0138_0140</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>1375.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.132755</td>\n",
       "      <td>49.746951</td>\n",
       "      <td>-2.974355</td>\n",
       "      <td>6.777437</td>\n",
       "      <td>6.777437</td>\n",
       "      <td>73.403496</td>\n",
       "      <td>62.414249</td>\n",
       "      <td>2597.479248</td>\n",
       "      <td>46.995062</td>\n",
       "      <td>-26.031250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0139_0141</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.132437</td>\n",
       "      <td>49.746862</td>\n",
       "      <td>-2.421928</td>\n",
       "      <td>5.726070</td>\n",
       "      <td>5.726070</td>\n",
       "      <td>66.884995</td>\n",
       "      <td>59.308441</td>\n",
       "      <td>2600.906982</td>\n",
       "      <td>43.992452</td>\n",
       "      <td>-25.021933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0140_0141</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>1425.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.132118</td>\n",
       "      <td>49.746773</td>\n",
       "      <td>-2.942743</td>\n",
       "      <td>6.134447</td>\n",
       "      <td>6.134447</td>\n",
       "      <td>59.434998</td>\n",
       "      <td>59.272831</td>\n",
       "      <td>2601.374512</td>\n",
       "      <td>44.692851</td>\n",
       "      <td>-28.666325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0141_0141</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>1450.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.131800</td>\n",
       "      <td>49.746684</td>\n",
       "      <td>-3.348574</td>\n",
       "      <td>6.544652</td>\n",
       "      <td>6.544652</td>\n",
       "      <td>51.223434</td>\n",
       "      <td>58.491379</td>\n",
       "      <td>2601.748291</td>\n",
       "      <td>45.026524</td>\n",
       "      <td>-30.773830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0142_0142</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>1487.5</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.131322</td>\n",
       "      <td>49.746550</td>\n",
       "      <td>-2.906687</td>\n",
       "      <td>5.845273</td>\n",
       "      <td>5.845273</td>\n",
       "      <td>36.451801</td>\n",
       "      <td>55.234852</td>\n",
       "      <td>2606.592773</td>\n",
       "      <td>42.527060</td>\n",
       "      <td>-29.819632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0143_0143</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>1525.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.130844</td>\n",
       "      <td>49.746416</td>\n",
       "      <td>-2.387891</td>\n",
       "      <td>5.447091</td>\n",
       "      <td>5.447091</td>\n",
       "      <td>29.903540</td>\n",
       "      <td>52.596802</td>\n",
       "      <td>2611.898193</td>\n",
       "      <td>39.200474</td>\n",
       "      <td>-26.000509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0144_0143</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.130526</td>\n",
       "      <td>49.746326</td>\n",
       "      <td>-1.943454</td>\n",
       "      <td>5.473351</td>\n",
       "      <td>5.473351</td>\n",
       "      <td>31.307703</td>\n",
       "      <td>51.861816</td>\n",
       "      <td>2611.087646</td>\n",
       "      <td>38.975769</td>\n",
       "      <td>-20.798082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0145_0143</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.130207</td>\n",
       "      <td>49.746237</td>\n",
       "      <td>-1.468108</td>\n",
       "      <td>4.833486</td>\n",
       "      <td>4.833486</td>\n",
       "      <td>33.467999</td>\n",
       "      <td>50.814560</td>\n",
       "      <td>2609.727783</td>\n",
       "      <td>39.206867</td>\n",
       "      <td>-17.682186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0146_0144</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.129889</td>\n",
       "      <td>49.746148</td>\n",
       "      <td>-0.484076</td>\n",
       "      <td>4.563340</td>\n",
       "      <td>4.563340</td>\n",
       "      <td>38.947998</td>\n",
       "      <td>49.505199</td>\n",
       "      <td>2614.870850</td>\n",
       "      <td>37.482712</td>\n",
       "      <td>-6.089352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0147_0144</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>1625.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.129570</td>\n",
       "      <td>49.746058</td>\n",
       "      <td>-0.338416</td>\n",
       "      <td>4.267499</td>\n",
       "      <td>4.267499</td>\n",
       "      <td>43.972740</td>\n",
       "      <td>49.505199</td>\n",
       "      <td>2612.071289</td>\n",
       "      <td>37.681465</td>\n",
       "      <td>-4.548381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0148_0145</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>1650.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.129252</td>\n",
       "      <td>49.745969</td>\n",
       "      <td>-0.066485</td>\n",
       "      <td>5.805495</td>\n",
       "      <td>5.805495</td>\n",
       "      <td>43.514851</td>\n",
       "      <td>48.556561</td>\n",
       "      <td>2617.416992</td>\n",
       "      <td>37.008652</td>\n",
       "      <td>-0.656171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0149_0145</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>1675.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.128933</td>\n",
       "      <td>49.745880</td>\n",
       "      <td>-0.165122</td>\n",
       "      <td>5.718610</td>\n",
       "      <td>5.718610</td>\n",
       "      <td>43.238354</td>\n",
       "      <td>47.443596</td>\n",
       "      <td>2612.760498</td>\n",
       "      <td>35.396542</td>\n",
       "      <td>-1.654612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0150_0145</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.128615</td>\n",
       "      <td>49.745790</td>\n",
       "      <td>-1.142349</td>\n",
       "      <td>6.185821</td>\n",
       "      <td>6.185821</td>\n",
       "      <td>41.048634</td>\n",
       "      <td>46.296764</td>\n",
       "      <td>2607.182861</td>\n",
       "      <td>32.661827</td>\n",
       "      <td>-10.642021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0151_0146</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>1725.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.128296</td>\n",
       "      <td>49.745701</td>\n",
       "      <td>-4.409619</td>\n",
       "      <td>8.908995</td>\n",
       "      <td>8.908995</td>\n",
       "      <td>39.099998</td>\n",
       "      <td>45.249908</td>\n",
       "      <td>2609.977051</td>\n",
       "      <td>30.805347</td>\n",
       "      <td>-29.667284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0152_0146</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.127978</td>\n",
       "      <td>49.745612</td>\n",
       "      <td>-4.243895</td>\n",
       "      <td>9.232656</td>\n",
       "      <td>9.232656</td>\n",
       "      <td>40.961060</td>\n",
       "      <td>44.386234</td>\n",
       "      <td>2602.615723</td>\n",
       "      <td>29.496879</td>\n",
       "      <td>-27.365252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0153_0147</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>1787.5</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.127500</td>\n",
       "      <td>49.745478</td>\n",
       "      <td>-1.746952</td>\n",
       "      <td>7.440915</td>\n",
       "      <td>7.440915</td>\n",
       "      <td>40.735504</td>\n",
       "      <td>42.830536</td>\n",
       "      <td>2604.433594</td>\n",
       "      <td>29.091456</td>\n",
       "      <td>-13.578451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0154_0147</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>1825.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.127023</td>\n",
       "      <td>49.745344</td>\n",
       "      <td>-0.931317</td>\n",
       "      <td>6.767874</td>\n",
       "      <td>6.767874</td>\n",
       "      <td>39.728001</td>\n",
       "      <td>42.830536</td>\n",
       "      <td>2595.579346</td>\n",
       "      <td>27.535065</td>\n",
       "      <td>-7.909482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0155_0148</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>1850.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.126704</td>\n",
       "      <td>49.745255</td>\n",
       "      <td>-1.512908</td>\n",
       "      <td>6.450754</td>\n",
       "      <td>6.450754</td>\n",
       "      <td>37.626999</td>\n",
       "      <td>40.764256</td>\n",
       "      <td>2596.177002</td>\n",
       "      <td>26.860533</td>\n",
       "      <td>-13.564040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0156_0148</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>1875.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.126386</td>\n",
       "      <td>49.745165</td>\n",
       "      <td>-3.751306</td>\n",
       "      <td>6.882661</td>\n",
       "      <td>6.882661</td>\n",
       "      <td>34.362583</td>\n",
       "      <td>39.520168</td>\n",
       "      <td>2586.000244</td>\n",
       "      <td>25.417480</td>\n",
       "      <td>-33.027203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0157_0149</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.126067</td>\n",
       "      <td>49.745076</td>\n",
       "      <td>-3.086375</td>\n",
       "      <td>5.913600</td>\n",
       "      <td>5.913600</td>\n",
       "      <td>29.867140</td>\n",
       "      <td>36.242722</td>\n",
       "      <td>2585.234375</td>\n",
       "      <td>22.600906</td>\n",
       "      <td>-31.460550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0158_0149</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>1925.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.125749</td>\n",
       "      <td>49.744987</td>\n",
       "      <td>-1.454132</td>\n",
       "      <td>3.742982</td>\n",
       "      <td>3.742982</td>\n",
       "      <td>23.705832</td>\n",
       "      <td>34.346447</td>\n",
       "      <td>2575.501953</td>\n",
       "      <td>20.423409</td>\n",
       "      <td>-22.860920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0159_0149</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>113.499646</td>\n",
       "      <td>-117.125430</td>\n",
       "      <td>49.744897</td>\n",
       "      <td>0.005207</td>\n",
       "      <td>2.162810</td>\n",
       "      <td>2.162810</td>\n",
       "      <td>15.618391</td>\n",
       "      <td>32.297230</td>\n",
       "      <td>2565.339111</td>\n",
       "      <td>18.820283</td>\n",
       "      <td>0.137951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0160_0150</th>\n",
       "      <td>3</td>\n",
       "      <td>1986</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>113.499000</td>\n",
       "      <td>-117.124971</td>\n",
       "      <td>49.744769</td>\n",
       "      <td>0.178689</td>\n",
       "      <td>0.178689</td>\n",
       "      <td>0.178689</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2565.199951</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>90.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>75 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id   len  distance       angle         lon        lat        vx  \\\n",
       "ij                                                                           \n",
       "0087_0118   3  1986       0.0  113.499646 -117.150273  49.751861  1.463031   \n",
       "0088_0119   3  1986      25.0  113.499646 -117.149955  49.751772  1.699979   \n",
       "0089_0119   3  1986      50.0  113.499646 -117.149636  49.751683  1.626611   \n",
       "0090_0119   3  1986      75.0  113.499646 -117.149318  49.751594  1.470872   \n",
       "0091_0120   3  1986     100.0  113.499646 -117.148999  49.751504  1.584921   \n",
       "0092_0120   3  1986     125.0  113.499646 -117.148681  49.751415  1.603650   \n",
       "0093_0121   3  1986     150.0  113.499646 -117.148362  49.751326  1.242517   \n",
       "0094_0121   3  1986     175.0  113.499646 -117.148043  49.751237  1.386238   \n",
       "0095_0121   3  1986     200.0  113.499646 -117.147725  49.751147 -2.934101   \n",
       "0096_0122   3  1986     225.0  113.499646 -117.147406  49.751058 -1.968246   \n",
       "0097_0122   3  1986     250.0  113.499646 -117.147088  49.750969 -1.402215   \n",
       "0097_0123   3  1986     275.0  113.499646 -117.146769  49.750880 -1.061054   \n",
       "0098_0123   3  1986     300.0  113.499646 -117.146451  49.750790 -1.044961   \n",
       "0099_0123   3  1986     325.0  113.499646 -117.146132  49.750701 -0.074828   \n",
       "0100_0124   3  1986     350.0  113.499646 -117.145814  49.750612  1.530835   \n",
       "0101_0124   3  1986     375.0  113.499646 -117.145495  49.750523  1.643123   \n",
       "0102_0125   3  1986     400.0  113.499646 -117.145177  49.750433  2.389611   \n",
       "0103_0125   3  1986     425.0  113.499646 -117.144858  49.750344  2.098716   \n",
       "0104_0125   3  1986     450.0  113.499646 -117.144540  49.750255  2.552134   \n",
       "0105_0126   3  1986     475.0  113.499646 -117.144221  49.750166  2.980043   \n",
       "0106_0126   3  1986     500.0  113.499646 -117.143903  49.750076  3.250309   \n",
       "0107_0127   3  1986     525.0  113.499646 -117.143584  49.749987  2.743552   \n",
       "0108_0127   3  1986     562.5  113.499646 -117.143106  49.749853  2.733854   \n",
       "0109_0128   3  1986     600.0  113.499646 -117.142628  49.749719  1.752285   \n",
       "0110_0128   3  1986     625.0  113.499646 -117.142310  49.749630  1.864836   \n",
       "0111_0129   3  1986     650.0  113.499646 -117.141991  49.749541  1.974978   \n",
       "0112_0129   3  1986     675.0  113.499646 -117.141673  49.749451  1.141310   \n",
       "0113_0129   3  1986     700.0  113.499646 -117.141354  49.749362  0.821080   \n",
       "0114_0130   3  1986     725.0  113.499646 -117.141036  49.749273  0.670674   \n",
       "0115_0130   3  1986     750.0  113.499646 -117.140717  49.749184  0.148405   \n",
       "...        ..   ...       ...         ...         ...        ...       ...   \n",
       "0131_0137   3  1986    1187.5  113.499646 -117.135144  49.747621 -1.865977   \n",
       "0132_0138   3  1986    1225.0  113.499646 -117.134666  49.747487 -2.063198   \n",
       "0133_0138   3  1986    1250.0  113.499646 -117.134347  49.747398 -2.399386   \n",
       "0134_0139   3  1986    1275.0  113.499646 -117.134029  49.747309 -3.331510   \n",
       "0135_0139   3  1986    1300.0  113.499646 -117.133710  49.747219 -3.307630   \n",
       "0136_0139   3  1986    1325.0  113.499646 -117.133392  49.747130 -3.242635   \n",
       "0137_0140   3  1986    1350.0  113.499646 -117.133074  49.747041 -3.279752   \n",
       "0138_0140   3  1986    1375.0  113.499646 -117.132755  49.746951 -2.974355   \n",
       "0139_0141   3  1986    1400.0  113.499646 -117.132437  49.746862 -2.421928   \n",
       "0140_0141   3  1986    1425.0  113.499646 -117.132118  49.746773 -2.942743   \n",
       "0141_0141   3  1986    1450.0  113.499646 -117.131800  49.746684 -3.348574   \n",
       "0142_0142   3  1986    1487.5  113.499646 -117.131322  49.746550 -2.906687   \n",
       "0143_0143   3  1986    1525.0  113.499646 -117.130844  49.746416 -2.387891   \n",
       "0144_0143   3  1986    1550.0  113.499646 -117.130526  49.746326 -1.943454   \n",
       "0145_0143   3  1986    1575.0  113.499646 -117.130207  49.746237 -1.468108   \n",
       "0146_0144   3  1986    1600.0  113.499646 -117.129889  49.746148 -0.484076   \n",
       "0147_0144   3  1986    1625.0  113.499646 -117.129570  49.746058 -0.338416   \n",
       "0148_0145   3  1986    1650.0  113.499646 -117.129252  49.745969 -0.066485   \n",
       "0149_0145   3  1986    1675.0  113.499646 -117.128933  49.745880 -0.165122   \n",
       "0150_0145   3  1986    1700.0  113.499646 -117.128615  49.745790 -1.142349   \n",
       "0151_0146   3  1986    1725.0  113.499646 -117.128296  49.745701 -4.409619   \n",
       "0152_0146   3  1986    1750.0  113.499646 -117.127978  49.745612 -4.243895   \n",
       "0153_0147   3  1986    1787.5  113.499646 -117.127500  49.745478 -1.746952   \n",
       "0154_0147   3  1986    1825.0  113.499646 -117.127023  49.745344 -0.931317   \n",
       "0155_0148   3  1986    1850.0  113.499646 -117.126704  49.745255 -1.512908   \n",
       "0156_0148   3  1986    1875.0  113.499646 -117.126386  49.745165 -3.751306   \n",
       "0157_0149   3  1986    1900.0  113.499646 -117.126067  49.745076 -3.086375   \n",
       "0158_0149   3  1986    1925.0  113.499646 -117.125749  49.744987 -1.454132   \n",
       "0159_0149   3  1986    1950.0  113.499646 -117.125430  49.744897  0.005207   \n",
       "0160_0150   3  1986    1986.0  113.499000 -117.124971  49.744769  0.178689   \n",
       "\n",
       "                  vy         vz        gpr      H_opt          dem  \\\n",
       "ij                                                                   \n",
       "0087_0118        NaN  13.812812  42.200001  34.952103  2692.644531   \n",
       "0088_0119  14.954845  14.954845  39.716499  33.477638  2687.949951   \n",
       "0089_0119  13.221420  13.221420  38.549000  33.473923  2679.099365   \n",
       "0090_0119  11.294090  11.294090  33.038998  35.657833  2670.854004   \n",
       "0091_0120   7.504434   7.504434  27.422318  39.618732  2667.141113   \n",
       "0092_0120   5.546185   5.546185  20.936953  43.506130  2662.265381   \n",
       "0093_0121   5.144938   5.144938  15.642700  47.145554  2662.538818   \n",
       "0094_0121   4.702446   4.702446  17.807199  50.201744  2659.213379   \n",
       "0095_0121   4.111371   4.111371  19.998913  52.240566  2656.314941   \n",
       "0096_0122   3.027712   3.027712  19.544662  53.179119  2655.419434   \n",
       "0097_0122   4.098421   4.098421  21.801212  54.701904  2652.634766   \n",
       "0097_0123   3.941034   3.941034  18.515837  55.859760  2655.162598   \n",
       "0098_0123   3.053514   3.053514  32.689213  56.645973  2652.900391   \n",
       "0099_0123   4.128119   4.128119  45.433224  56.511395  2650.289062   \n",
       "0100_0124   5.357755   5.357755  49.421112  57.093010  2650.734619   \n",
       "0101_0124   5.988144   5.988144  48.634338  56.067986  2647.916504   \n",
       "0102_0125   6.382704   6.382704  53.692337  54.860321  2648.668945   \n",
       "0103_0125   6.150829   6.150829  55.093575  54.860321  2645.690918   \n",
       "0104_0125   6.813757   6.813757  61.954323  53.584194  2642.197021   \n",
       "0105_0126   7.597724   7.597724  69.704239  52.603374  2642.767578   \n",
       "0106_0126   7.828930   7.828930  68.556610  51.111721  2639.316650   \n",
       "0107_0127   6.791433   6.791433  65.738884  48.838585  2641.336914   \n",
       "0108_0127   6.886071   6.886071  58.015263  47.162056  2637.970947   \n",
       "0109_0128   6.450512   6.450512  51.046249  43.947521  2641.412109   \n",
       "0110_0128   6.224868   6.224868  48.622280  43.947521  2637.004883   \n",
       "0111_0129   5.100935   5.100935  46.427277  40.445938  2640.526611   \n",
       "0112_0129   4.773098   4.773098  44.921764  39.142967  2634.243164   \n",
       "0113_0129   4.662340   4.662340  44.038036  38.209541  2626.884766   \n",
       "0114_0130   4.803851   4.803851  43.710896  35.838097  2632.643799   \n",
       "0115_0130   4.585234   4.585234  43.877647  35.867321  2627.365723   \n",
       "...              ...        ...        ...        ...          ...   \n",
       "0131_0137   4.417768   4.417768  73.042252  59.395332  2590.085205   \n",
       "0132_0138   4.567921   4.567921  73.632248  55.554073  2595.284424   \n",
       "0133_0138   4.867803   4.867803  77.198090  58.351925  2593.273926   \n",
       "0134_0139   5.605476   5.605476  76.556091  60.877014  2597.843994   \n",
       "0135_0139   6.063656   6.063656  78.217712  62.942539  2596.146484   \n",
       "0136_0139   6.729495   6.729495  79.261497  64.349045  2595.286621   \n",
       "0137_0140   6.459460   6.459460  79.818497  62.152580  2597.959961   \n",
       "0138_0140   6.777437   6.777437  73.403496  62.414249  2597.479248   \n",
       "0139_0141   5.726070   5.726070  66.884995  59.308441  2600.906982   \n",
       "0140_0141   6.134447   6.134447  59.434998  59.272831  2601.374512   \n",
       "0141_0141   6.544652   6.544652  51.223434  58.491379  2601.748291   \n",
       "0142_0142   5.845273   5.845273  36.451801  55.234852  2606.592773   \n",
       "0143_0143   5.447091   5.447091  29.903540  52.596802  2611.898193   \n",
       "0144_0143   5.473351   5.473351  31.307703  51.861816  2611.087646   \n",
       "0145_0143   4.833486   4.833486  33.467999  50.814560  2609.727783   \n",
       "0146_0144   4.563340   4.563340  38.947998  49.505199  2614.870850   \n",
       "0147_0144   4.267499   4.267499  43.972740  49.505199  2612.071289   \n",
       "0148_0145   5.805495   5.805495  43.514851  48.556561  2617.416992   \n",
       "0149_0145   5.718610   5.718610  43.238354  47.443596  2612.760498   \n",
       "0150_0145   6.185821   6.185821  41.048634  46.296764  2607.182861   \n",
       "0151_0146   8.908995   8.908995  39.099998  45.249908  2609.977051   \n",
       "0152_0146   9.232656   9.232656  40.961060  44.386234  2602.615723   \n",
       "0153_0147   7.440915   7.440915  40.735504  42.830536  2604.433594   \n",
       "0154_0147   6.767874   6.767874  39.728001  42.830536  2595.579346   \n",
       "0155_0148   6.450754   6.450754  37.626999  40.764256  2596.177002   \n",
       "0156_0148   6.882661   6.882661  34.362583  39.520168  2586.000244   \n",
       "0157_0149   5.913600   5.913600  29.867140  36.242722  2585.234375   \n",
       "0158_0149   3.742982   3.742982  23.705832  34.346447  2575.501953   \n",
       "0159_0149   2.162810   2.162810  15.618391  32.297230  2565.339111   \n",
       "0160_0150   0.178689   0.178689   0.000000   0.000000  2565.199951   \n",
       "\n",
       "           farin_corr      vzdir  \n",
       "ij                                \n",
       "0087_0118    6.540831 -20.009487  \n",
       "0088_0119   39.903928 -13.141000  \n",
       "0089_0119   41.528717 -14.244299  \n",
       "0090_0119   40.660488 -15.097804  \n",
       "0091_0120   39.038415 -24.985943  \n",
       "0092_0120   41.985172 -35.330307  \n",
       "0093_0121   46.325237 -28.881874  \n",
       "0094_0121   49.881763 -36.127335  \n",
       "0095_0121   53.381650 -45.533092  \n",
       "0096_0122   54.561545 -40.547405  \n",
       "0097_0122   55.647996 -20.007034  \n",
       "0097_0123   56.734866 -15.618594  \n",
       "0098_0123   57.103909 -20.011940  \n",
       "0099_0123   55.985535  -1.038627  \n",
       "0100_0124   56.431485  16.602079  \n",
       "0101_0124   54.726943  15.926026  \n",
       "0102_0125   54.643809  21.986528  \n",
       "0103_0125   52.646081  19.950523  \n",
       "0104_0125   50.890643  21.996880  \n",
       "0105_0126   49.198640  23.093225  \n",
       "0106_0126   48.871607  24.529797  \n",
       "0107_0127   46.567578  23.826752  \n",
       "0108_0127   46.013769  23.391521  \n",
       "0109_0128   44.848214  15.762508  \n",
       "0110_0128   42.015336  17.432283  \n",
       "0111_0129   39.819757  22.779123  \n",
       "0112_0129   36.969448  13.834205  \n",
       "0113_0129   35.670620  10.143200  \n",
       "0114_0130   37.130676   8.025385  \n",
       "0115_0130   37.470716   1.854755  \n",
       "...               ...        ...  \n",
       "0131_0137   37.947042 -24.984930  \n",
       "0132_0138   38.715327 -26.850956  \n",
       "0133_0138   46.290257 -29.531992  \n",
       "0134_0139   47.181625 -36.464973  \n",
       "0135_0139   47.539489 -33.057777  \n",
       "0136_0139   47.801742 -28.806561  \n",
       "0137_0140   45.491020 -30.513670  \n",
       "0138_0140   46.995062 -26.031250  \n",
       "0139_0141   43.992452 -25.021933  \n",
       "0140_0141   44.692851 -28.666325  \n",
       "0141_0141   45.026524 -30.773830  \n",
       "0142_0142   42.527060 -29.819632  \n",
       "0143_0143   39.200474 -26.000509  \n",
       "0144_0143   38.975769 -20.798082  \n",
       "0145_0143   39.206867 -17.682186  \n",
       "0146_0144   37.482712  -6.089352  \n",
       "0147_0144   37.681465  -4.548381  \n",
       "0148_0145   37.008652  -0.656171  \n",
       "0149_0145   35.396542  -1.654612  \n",
       "0150_0145   32.661827 -10.642021  \n",
       "0151_0146   30.805347 -29.667284  \n",
       "0152_0146   29.496879 -27.365252  \n",
       "0153_0147   29.091456 -13.578451  \n",
       "0154_0147   27.535065  -7.909482  \n",
       "0155_0148   26.860533 -13.564040  \n",
       "0156_0148   25.417480 -33.027203  \n",
       "0157_0149   22.600906 -31.460550  \n",
       "0158_0149   20.423409 -22.860920  \n",
       "0159_0149   18.820283   0.137951  \n",
       "0160_0150    0.000000  90.000000  \n",
       "\n",
       "[75 rows x 14 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "type(obs_pts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "obs_pts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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