flux_gate-Illecillewaet-MP-pw.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "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",
    "from salem import *\n",
    "import math\n",
    "from sklearn.metrics import mean_squared_error as MSE\n",
    "import xarray as xr\n",
    "\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",
    "from hydroeval import *\n",
    "import seaborn as sns\n",
    "import pwlf\n",
    "from scipy.optimize import curve_fit\n",
    "from scipy.interpolate import interp1d\n",
    "import statsmodels.api as stm\n",
    "import pickle\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",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "years = [2016, 2017, 2018]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "# (141+239+150)/(330+603+301)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# len(df_agg),len(df_agg.gpr[np.isnan(df_agg.gpr)].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ipr=D[(D.id==0)&(D.distance>300.)&(D.distance<900.)];dist=ipr.distance[np.isnan(ipr.gpr)].values  \n",
    "# x=ipr.distance[np.logical_not(np.isnan(ipr.gpr))].values;y=ipr.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==0)&(D.distance==dist[f])]=fill[f]\n",
    "\n",
    "# ipr=D[(D.id==1)&(D.distance<600)];dist=ipr.distance[np.isnan(ipr.gpr)].values;\n",
    "# x=ipr.distance[np.logical_not(np.isnan(ipr.gpr))].values;y=ipr.gpr.values; y=y[np.logical_not(np.isnan(y))]\n",
    "# fill=poly(dist);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==1)&(D.distance==dist[f])]=fill[f]\n",
    "\n",
    "# ipr=D[(D.id==3)];dist=ipr.distance[np.isnan(ipr.gpr)].values;\n",
    "# x=ipr.distance[np.logical_not(np.isnan(ipr.gpr))].values;y=ipr.gpr.values; y=y[np.logical_not(np.isnan(y))]\n",
    "# fill=poly(dist)\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==3)&(D.distance==dist[f])]=fill[f]\n",
    "\n",
    "# D.gpr[(D.id==2)&(D.distance==0.0)]=48.3;D.gpr[(D.id==2)&(D.distance==20.0)]=27.9;D.gpr[(D.id==2)&(D.distance==40.0)]=47.5\n",
    "# ct=0;depth=[191.8717,196.46,200.7749,204.8164,208.5844,212.0791,215.3003,218.2481,220.9225,223.3235,225.4511,227.3052,228.886,230.1933,231.2272]\n",
    "# for i in range(460,760,20):\n",
    "#     D.gpr[(D.id==4)&(D.distance==float(i))]=depth[ct]\n",
    "#     ct+=1\n",
    "# ct=0;depth2=[231.6889,230.8089,229.6555,228.2286,226.5284,224.5547,222.3077,219.7872,216.9933,213.9259,210.5852,\n",
    "#             206.971,203.0835,198.9225,194.4881,189.7803,184.799,179.5444,174.0163,168.2149,162.14]\n",
    "# for i in range(860,1280,20):\n",
    "#     D.gpr[(D.id==4)&(D.distance==float(i))]=depth2[ct]\n",
    "#     ct+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "# np.mean(ELA),np.mean(s_ELA)\n",
    "# ELA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "# d=df_agg[(df_agg.id==4)];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",
    "# #     df_agg.gpr[(df_agg.id==4)&(df_agg.distance==dist[f])]=fill[f]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Vel. vz 13.74 10.07\n",
      "Mean Vel. ITS 9.62 7.59\n",
      "3.8659275\n",
      "2.8035376\n",
      "Original vector layer projection:  epsg:32611\n",
      "New vector layer projection (PROJ4):  +init=epsg:2955 +type=crs\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:2955 +type=crs\n",
      "New vector layer projection (PROJ4):  +init=epsg:2955 +type=crs\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:2955 +type=crs\n",
      "New vector layer projection (PROJ4):  +init=epsg:32611 +type=crs\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",
      "OBSFG work start\n",
      "OBSFG work end\n",
      "Mean Vel. vz 12.97 9.58\n",
      "Mean Vel. ITS 10.29 8.1\n",
      "3.747265\n",
      "3.2122195\n",
      "Original vector layer projection:  epsg:32611\n",
      "New vector layer projection (PROJ4):  +init=epsg:2955 +type=crs\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:2955 +type=crs\n",
      "New vector layer projection (PROJ4):  +init=epsg:2955 +type=crs\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:2955 +type=crs\n",
      "New vector layer projection (PROJ4):  +init=epsg:32611 +type=crs\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",
      "OBSFG work start\n",
      "OBSFG work end\n",
      "Mean Vel. vz 10.78 8.82\n",
      "Mean Vel. ITS 7.05 6.45\n",
      "3.2194884\n",
      "2.9682283\n",
      "Original vector layer projection:  epsg:32611\n",
      "New vector layer projection (PROJ4):  +init=epsg:2955 +type=crs\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:2955 +type=crs\n",
      "New vector layer projection (PROJ4):  +init=epsg:2955 +type=crs\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:2955 +type=crs\n",
      "New vector layer projection (PROJ4):  +init=epsg:32611 +type=crs\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",
      "OBSFG work start\n",
      "OBSFG work end\n",
      "OBSFG work start\n",
      "OBSFG work end\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 510.236x141.732 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 510.236x141.732 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 2 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 2 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 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "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",
    "\n",
    "fig1, ax1 = plt.subplots(1,3, sharex=True, sharey=True, figsize=(cm2inch(18, 5.))) #obs. and flux gate SMB boxplot/errorbar plot\n",
    "fig2, ax2 = plt.subplots(1,3, sharex=True, sharey=True, figsize=(cm2inch(18, 5.))) ##mass conservation plot\n",
    "count = 0\n",
    "mfc='none'; mew=1.1; elw=0.9\n",
    "\n",
    "for yr in range(len(years)):\n",
    "    # season\n",
    "    balance = 'Ba'  # Bw or Ba (winter or annual)\n",
    "    # rho_snow = 445. # 470 kg m-3 geodetic or 457 glaciological, or...take given years obs. value\n",
    "    year = years[yr]\n",
    "    I = np.abs(year - 2016) # for selecting raster inputs\n",
    "    vcorr=False ## is velocity corrected by off ice bias and stake bias?\n",
    "\n",
    "    gl = 2 # number to select glacier\n",
    "    Glacier = ['Kokanee', 'Conrad', 'Illecillewaet']\n",
    "    glacier = ['kokanee', 'conrad', 'illecillewaet']\n",
    "#     d = 20. #20.  #distance (m) 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",
    "    sHopt = 0.078 ## ME:10.3 m,  MAE:0.223 (percent ratio)\n",
    "\n",
    "    fl_path = '/home/pelto/Desktop/ice_flux/' + Glacier[gl] + '/'\n",
    "    gpr_path = '/home/pelto/GIS/GPR/ComparisonData/'\n",
    "    path = '/home/pelto/Desktop/lidar_cbt_analysis/' + glacier[gl] + '/'\n",
    "\n",
    "    VDIR = 'individual'  # 'average' or 'individual'\n",
    "    ITS_LIVE = False\n",
    "    firn = False\n",
    "    fit= 'lin'  # lin or 'pw'\n",
    "    \n",
    "    if VDIR == 'individual':\n",
    "    #     vf_list = ['img1_20160912__img1_20170917_vmap_5m_35px_spm2/', 'img1_20160912__img1_20170917_vmap_5m_35px_spm2/',\n",
    "    #            'img1_20170521__img1_20180426_vmap_5m_35px_spm2/']##backups  img1_20170917__img1_20180426_vmap_5m_35px_spm2/\n",
    "\n",
    "#         vf_list = ['img1_20160912__img1_20170521_vmap_5m_35px_spm2/', 'img1_20160912__img1_20170521_vmap_5m_35px_spm2/',\n",
    "#                'img1_20180426__img2_20181016_vmap_5m_35px_spm2/']##backups  img1_20170917__img1_20180426_vmap_5m_35px_spm2/\n",
    "        vf_list = ['illecillewaet_2016_all25m_vx.tif','illecillewaet_2017_all25m_vx.tif','illecillewaet_2018_all25m_vx.tif']\n",
    "    #     vdir = '/home/pelto/Desktop/velocity_mapping/' +glacier[gl] + '/spm2/' + vf_list[I]\n",
    "        vdir = '/home/pelto/Desktop/velocity_mapping/' +glacier[gl] + '/3m/' \n",
    "#         VX = vdir+ vf_list[I][:-1] + '-F_vx.tif' \n",
    "#         VY = vdir+ vf_list[I][:-1] + '-F_vy.tif' \n",
    "        VX = vdir+ vf_list[I]\n",
    "        VY = vdir+ vf_list[I][:-7] + '_vy.tif' \n",
    "        ITS = fl_path + 'ITS_LIVE/' + str(year) + '_illecillewaet_ITS_LIVE.tif'\n",
    "    else:\n",
    "#         vf_list = ['illec_all_spm2_5m', 'illec_all_spm2_5m', 'illec_all_spm2_5m']\n",
    "#         vdir = '/home/pelto/Desktop/velocity_mapping/' +glacier[gl] + '/spm2/'  \n",
    "#         VX = vdir+ 'vx/' +vf_list[I] + '_vx.tif' \n",
    "#         VY = vdir+ 'vy/' +vf_list[I] + '_vy.tif'\n",
    "       \n",
    "        vdir = '/home/pelto/Desktop/velocity_mapping/' +glacier[gl] + '/3m/'  \n",
    "        VX = vdir + vf + 'blur1_25m_vx_neg.tif' #'vx_best_neg_5m.tif'\n",
    "        VY = vdir + vf + 'blur1_25m_vy.tif'\n",
    "#         VM = vdir + vf + 'vm_5m.tif'\n",
    "        ITS = fl_path + 'ITS_LIVE/' + str(year) + '_illecillewaet_ITS_LIVE.tif'\n",
    "    \n",
    "    topo = path + '160912_illecillewaet_dem1_clip_master.tif'\n",
    "\n",
    "    farinotti = gpr_path + glacier[gl] +'/Illec_Geike_combi.tif' #''RGI60-02/RGI60-02.03688_thickness.tif' #'RGI60-02.00147_thickness_srtm_corr.tif' #'RGI60-02/RGI60-02.00147'\n",
    "\n",
    "    dhW_list = ['illecillewaet_2016_winter_dh_dt.tif', 'illecillewaet_2017_winter_dh_dt_dat1_clip.tif', 'illecillewaet_2018_winter_dh_dt_2.tif']\n",
    "    dhA_list = ['illecillewaet_2016_2015_dh_dt_clip_filled_1617.tif', 'illecillewaet_2017_2016_dh_dt.tif','illecillewaet_2018_2017_dh_dt_zmax1800-1m.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/Illec_flux_gates_5_pts_25m.shp'  ##must be WGS84 currently\n",
    "    gates = fl_path+'Illec_flux_gates_5.shp'\n",
    "    shpf = '/home/pelto/GIS/outlines/Illec2016.shp'   #GLIMS_BC/glims_all/all_glaciers_2016.shp' \n",
    "\n",
    "    if year==2016 or year==2017:\n",
    "        bins=fl_path+'illec_flux_bins_16_5.shp'\n",
    "    else:\n",
    "        bins=fl_path+'illec_flux_bins_17_5.shp'\n",
    "\n",
    "    obs = pd.read_csv(fl_path+ 'Illecillewaet_bdot.csv')\n",
    "    rho = pd.read_csv(fl_path + 'RHO.csv')#'illec_rho_5.csv')  ## rho\n",
    "\n",
    "    # open GeoTIFF as array\n",
    "    vx = salem.open_xr_dataset(VX);vy = salem.open_xr_dataset(VY);vx = vx.to_array(name='vx');vy = vy.to_array(name='vy')\n",
    "    msk = salem.open_xr_dataset(fl_path + 'illec_all_glaciers_2016.tif') \n",
    "    extent=salem.open_xr_dataset(path +'illecillewaet_2014_extent_5m.tif') ## 1.0 for glacier nan for off (-9999)\n",
    "    \n",
    "    # msk = msk.to_array(name='msk')\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+'Illecillewaet_251sw_MAE_optimized.tif')#+'Illecillewaet101sw_MAE_optimized.tif') \n",
    " \n",
    "    gates = salem.read_shapefile(gates)\n",
    "    H_opt_reproj = vx.salem.transform(H_opt); H_opt = H_opt_reproj.to_array(name='H_opt')\n",
    "    msk_reproj = vx.salem.transform(msk);msk = msk_reproj.to_array(name='msk')\n",
    "    extent_reproj=vx.salem.transform(extent); extent = extent_reproj.to_array(name='extent')\n",
    "    gpr_reproj = vx.salem.transform(gpr); gpr = gpr_reproj.to_array(name='gpr')\n",
    "    ITS = salem.open_xr_dataset(ITS); ITS_reproj = vx.salem.transform(ITS);ITS = ITS_reproj.to_array(name='ITS')\n",
    "\n",
    "    farin_reproj= vx.salem.transform(farin)\n",
    "    farin = farin_reproj.to_array(name='farin')\n",
    "    srtm_corr = fl_path + 'illecillewaet_SRTM_diff_30m.tif'\n",
    "    srtm_corr = salem.open_xr_dataset(srtm_corr);srtm_corr = vx.salem.transform(srtm_corr)\n",
    "    srtm_corr = srtm_corr.to_array(name='srtm_corr')\n",
    "    srtm_corr.data[srtm_corr.data>10.0] = 0.0  ##remove positive anomalous values\n",
    "    srtm_corr.data[srtm_corr.data<-50.0] = 0.0 ##remove negative anomalous values\n",
    "    farin_corr = farin + srtm_corr; farin_corr= farin_corr.rename('farin_corr')\n",
    "    gpr.data[gpr.data<0.5] = np.nan ## ensure no data read as nan\n",
    "\n",
    "    vx.data[vx.data>0.01]= vx.data[vx.data>0.01]*-1.0 #=np.nan  ## vx for illec\n",
    "    vz = vy.copy(); vz.data = np.sqrt(vx.data**2 + vz.data**2 )\n",
    "    vz.data[extent.data!=1.0] = np.nan;vx.data[extent.data!=1.0] = np.nan;vy.data[extent.data!=1.0] = np.nan \n",
    "    vz=xr.DataArray(vz.data, coords=vz.coords, name='vz')\n",
    "    np.savetxt(fl_path+(str(year)+'_vz.csv'), vz[0].data, delimiter=',')\n",
    "    ITS.data[extent.data!=1.0] = np.nan\n",
    "    VZ_off_ice = vz.copy()\n",
    "    VZ_off_ice.data[msk.data==1.0] = np.nan\n",
    "    # VZ_off_ice[VZ_off_ice==0.0] = np.nan  ## vz[extent.data<1.0] = np.nan\n",
    "    \n",
    "    with open(fl_path+str(year)+'_vz.pkl', 'wb') as f:\n",
    "        pickle.dump(vz[0].data, f)\n",
    "    with open(fl_path+str(year)+'_ITS.pkl', 'wb') as f:\n",
    "        pickle.dump(ITS[0].data, f)\n",
    "    \n",
    "    DIFF = vz - ITS\n",
    "    dem = salem.open_xr_dataset(topo)\n",
    "    dem_reproj = vx.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",
    "    fig, ax = plt.subplots(1,1, sharex=True, sharey=True, figsize=(cm2inch(18, 8.25))) \n",
    "    grid = vx.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",
    "    sm.set_vmax(val=50.)\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('Mean Vel. ITS', round(np.nanmean(ITS),2), round(np.nanstd(ITS),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] +'_thickness_gates.png', dpi=300)\n",
    "    #     plt.show()\n",
    "\n",
    "    ## Functions for calculating zonal statistics over each flux gate bin\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=(15, 15))\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",
    "    df = salem.read_shapefile(pts_file); df_file = loadshp_as_gpd(pts_file); df_file.crs\n",
    "\n",
    "    coords = np.array([p.xy for p in df.geometry]).squeeze()\n",
    "    df['lon'] = coords[:, 0]; 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; df['y'] = yy\n",
    "\n",
    "    vns = ['vx','vy','vz','ITS','gpr','H_opt','dem','farin_corr',]\n",
    "\n",
    "    M = xr.merge([vx,vy,vz,ITS,gpr,H_opt,dem,farin_corr])\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; 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 zip(ii.astype(int), jj.astype(int))]\n",
    "    df_agg = df_agg.groupby('ij').mean()\n",
    "    # Select\n",
    "    for vn in vns:\n",
    "        df_agg[vn] = M[vn][0].isel(x=('z', df_agg.i.astype(int)), y=('z', df_agg.j.astype(int)))  \n",
    "\n",
    "    D = df_agg[['id', 'len', 'distance', 'angle', 'lon', 'lat', 'vx', 'vy', 'vz', 'ITS','gpr', 'H_opt', 'dem', 'farin_corr']].copy()\n",
    "    #     D = D[D.id!='0122_0084'] # removing a magical duplicate point\n",
    "    #     D.reset_index(inplace=True,drop=True);\n",
    "    #         print('starting vel work')\n",
    "#     for i in range(len(D)):\n",
    "#         if D.vx[i]>20:\n",
    "#             D.vx[i]=D.vx[i]/-2\n",
    "#         elif D.vx[i] > 0.0 and D.vx[i] < 20:\n",
    "#             D.vx[i]=D.vx[i]/-1\n",
    "#         elif D.distance[i]==0.0 or D.distance[i]==D.len[i]:\n",
    "#             D.vx[i] = 0.0;D.vy[i] = 0.0\n",
    "#     print('ending vel work')        \n",
    "    #D['vz'] = #np.sqrt(D.vx**2 + D.vy**2 )  # np.sqrt(U**2 + V**2 )\n",
    "    D['vzdir'] = np.arcsin(D.vx/D.vz) *180/math.pi #degrees from north\n",
    "    \n",
    "    ## fill missing IPR data\n",
    "    #last and first points in each line are zero depth (except gates 2 and 4 start)\n",
    "#     D.distance[(D.id==0)&(D.distance==257.5)]=265\n",
    "    D.gpr[(D.distance==0.0)&(D.gpr==np.nan)]=0.0;D.gpr[(D.distance==D.len)]=0.0;\n",
    "    D.gpr[(D.id==2)&(D.distance==0.0)]=np.nan; D.gpr[(D.id==1)&(D.distance==0.0)]=0.0;\n",
    "    D.gpr[(D.id==0)&(D.distance==0.0)]=0.0;\n",
    "    \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",
    "    ipr=D[(D.id==0)&(D.distance>300.)&(D.distance<900.)];dist=ipr.distance[np.isnan(ipr.gpr)].values  \n",
    "    x=ipr.distance[np.logical_not(np.isnan(ipr.gpr))].values;y=ipr.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==0)&(D.distance==dist[f])]=fill[f]\n",
    "\n",
    "    ipr=D[(D.id==1)&(D.distance<600)];dist=ipr.distance[np.isnan(ipr.gpr)].values;\n",
    "    x=ipr.distance[np.logical_not(np.isnan(ipr.gpr))].values;y=ipr.gpr.values; y=y[np.logical_not(np.isnan(y))]\n",
    "    fill=poly(dist);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==1)&(D.distance==dist[f])]=fill[f]\n",
    "\n",
    "    ipr=D[(D.id==3)];dist=ipr.distance[np.isnan(ipr.gpr)].values;\n",
    "    x=ipr.distance[np.logical_not(np.isnan(ipr.gpr))].values;y=ipr.gpr.values; y=y[np.logical_not(np.isnan(y))]\n",
    "    fill=poly(dist)\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==3)&(D.distance==dist[f])]=fill[f]\n",
    "\n",
    "    D.gpr[(D.id==2)&(D.distance==0.0)]=48.3;D.gpr[(D.id==2)&(D.distance==20.0)]=27.9;D.gpr[(D.id==2)&(D.distance==40.0)]=47.5\n",
    "    ct=0;depth=[191.8717,196.46,200.7749,204.8164,208.5844,212.0791,215.3003,218.2481,220.9225,223.3235,225.4511,227.3052,228.886,230.1933,231.2272]\n",
    "    for i in range(460,760,20):\n",
    "        D.gpr[(D.id==4)&(D.distance==float(i))]=depth[ct]\n",
    "        ct+=1\n",
    "    ct=0;depth2=[231.6889,230.8089,229.6555,228.2286,226.5284,224.5547,222.3077,219.7872,216.9933,213.9259,210.5852,\n",
    "                206.971,203.0835,198.9225,194.4881,189.7803,184.799,179.5444,174.0163,168.2149,162.14]\n",
    "    for i in range(860,1280,20):\n",
    "        D.gpr[(D.id==4)&(D.distance==float(i))]=depth2[ct]\n",
    "        ct+=1\n",
    "        \n",
    "        \n",
    "    if year==2018: \n",
    "        with open(fl_path + glacier[2]+'_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",
    "    if ITS_LIVE == True:\n",
    "        D['vfg'] = D.ITS\n",
    "    else:\n",
    "        D['vfg'] = np.abs(D.vz * np.cos((D.vzdir-(D.angle-90.))*(math.pi/180.))) #velocity normal to gate per slice    \n",
    "    \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",
    "    dem_r = topo;vel_r = VX ;shp = loadshp_as_gpd(bins)\n",
    "    rasters = [dh_r, dem_r, vel_r]\n",
    "    names = ['dh','dem','vx']\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",
    "        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\", \"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",
    "        if i == 0:\n",
    "            ZS_df = ZS\n",
    "        else:\n",
    "            ZS_df = pd.concat([ZS_df, ZS], axis=1)\n",
    "            \n",
    "############ correct ice thickness for thinning ##############  \n",
    "    D1=D.copy(); D1.reset_index(inplace=True,drop=True);DH=[]\n",
    "    for i in range(len(D1)):\n",
    "        fl_id=D1.loc[i].id\n",
    "        if year == 2016:\n",
    "            del_h= ZS_df.loc[fl_id].dh_mean * -1##increase thickness to represent 2015 ice srfc\n",
    "        elif year == 2017:\n",
    "            del_h=0.0 ## no change current 2018 ice measurments map 2016 ice surface (2017 srfc+winter SD)\n",
    "        elif year == 2018:\n",
    "            del_h=new17.loc[fl_id].dh_mean\n",
    "        DH.append(del_h)\n",
    "\n",
    "    D['DH']=DH\n",
    "    D.H_opt = D.H_opt + D.DH\n",
    "    D.H_opt[D.H_opt<0.] = 0.0\n",
    "    D.gpr = D.gpr + D.DH \n",
    "    D.gpr[D.gpr<0.] = 0.0\n",
    "    D.farin_corr = D.farin_corr + D.DH\n",
    "    D.farin_corr[D.farin_corr<0.] = 0.0\n",
    "########### end correct ice thickness for thinning ##############\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",
    "#     KP=[];KF=[]\n",
    "#     for loop in D.index:\n",
    "#         id= D[D.index==loop].id.values\n",
    "#         KP.append((D[D.index==loop].vfg * Dint * d * D.H_opt[D.index==loop]).values[0])  ## ice flux per slice *0.9 to est. depth-integrated velocity\n",
    "#         KF.append((D[D.index==loop].vfg * Dint * d * D.farin_corr[D.index==loop]).values[0])\n",
    "#     D['Qopt'] = KP;D['Qfarin'] = KF\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.,] #flux out per gate\n",
    "    cr_area = [0.,] # cross-section area per gate\n",
    "    vzdir = [0.,]; sQout= [0.,];sQout_low= [0.,];sQout_high= [0.,];vfg_all=[];gate_elev=[];v_gate_mean=[];\n",
    "    Len_mod=[0.,];gate_width=[0.,]; GPR=[0.,];HF=[0.,];Hopt=[0.,]\n",
    "    \n",
    "    for n in range(D.id.nunique()):\n",
    "        vfg_all.append(D.vfg[D.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()) ;sQout_high.append(D.sQout_high[D.id==n].sum()) \n",
    "        cr_area.append(D.A_Hopt[D.id==n].sum());Hopt.append(D.H_opt[D.id==n].mean())\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()); \n",
    "        v_gate_mean.append(D.vfg[D.id==n].mean());Len_mod.append(D.Len_mod[D.id==n].sum())\n",
    "        GPR.append(D.gpr[D.id==n].mean())\n",
    "    Q_in = [Q_out[1:]]\n",
    "    sQin = [sQout[1:]+ [0.0]];sQin_low = [sQout_low[1:]+ [0.0]];sQin_high = [sQout_high[1:]+ [0.0]]    \n",
    "    Q_in = np.squeeze(Q_in); sQin = np.squeeze(sQin);sQin_low = np.squeeze(sQin_low);sQin_high = np.squeeze(sQin_high)\n",
    "\n",
    "    Q_outF= [0.,]; cr_areaF = [0.,]; sQoutF = [0.,]; HF = [0.,]\n",
    "    for n in range(D.id.nunique()+1):\n",
    "            Q_outF.append(D.Qfarin[D.id==n].sum()); 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]]; 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",
    "\n",
    "    FG['vfg'] = FG.Q_out / FG.cr_area #net velocity per gate\n",
    "    FG['vfgF'] = FG.Q_outF / FG.cr_area\n",
    "    FG['bin']=np.arange(0,len(range(D.id.nunique()+1)),1)\n",
    "     \n",
    "    FG['spQout'] = FG.sQout / FG.Q_out * 100. #%err on flux\n",
    "    FG['spQin'] = FG.sQin / FG.Q_in * 100. #%err on flux\n",
    "    FG['spQoutF'] = FG.sQoutF / FG.Q_outF * 100. #%err on flux\n",
    "    FG['spQinF'] = FG.sQinF / FG.Q_inF * 100. #%err on flux\n",
    "\n",
    "    ### import data per bin: height change, elevation, surface area, obs.SMB etc. \n",
    "    FG_df = FG.copy(); FG_df =  pd.concat([FG_df, ZS_df], axis=1)\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:]); 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['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",
    "\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",
    "     ## Ilec slope, constant: (0.025293682808396056, 400.09590745271896)\n",
    "#     FG_df['rho_snow'] = (FG_df.dem_mean*(-0.02529))+400.095  #*-0.11255+743.22)\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['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",
    "    ### Uncertainties\n",
    "    if I==0:\n",
    "        sDHdt = 0.45 #0.04 #m Bias dh from Pelto et al. 2019\n",
    "    if I==1:\n",
    "        sDHdt = 0.32\n",
    "    elif I==2:\n",
    "        sDHdt = 0.38\n",
    "        \n",
    "    sVfirn = 0.10; sRHO = 0.10 #0.05 # 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",
    "\n",
    "    D['Agpr']=np.NaN;D = D.dropna(subset=['gpr'])\n",
    "    D.sort_values(by=['distance','id'],ascending=[True,True], inplace=True) \n",
    "    \n",
    "    \n",
    "    for i in range(len((D))):   #D.id.nunique()):\n",
    "        if np.isnan(D.vz[i])==True:\n",
    "            G=D.id[i]\n",
    "            V=np.nanmean(D.vz[D.id==G])\n",
    "            D.vz[i]=V\n",
    "    \n",
    "    for n in range(D.id.nunique()):\n",
    "  \n",
    "        for c in range(len(D.id[D.id==n])):\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 #area of triangle\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]\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]\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 #area of triangle\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]  \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",
    "\n",
    "    D['Qo_gpr'] = D.Agpr * (D.vfg) * Dint#* 0.9\n",
    "    D['sQo_gpr'] =  np.sqrt((sVZ * (D.gpr) * D.Len_gpr)**2 + ((D.vfg*Dint) * D.Len_gpr * (sH_gpr))**2)\n",
    "\n",
    "    Qo_gpr = [0.,] #flux out per gate\n",
    "    cr_area_gpr = [0.,] # cross-section area per gate\n",
    "    vzdir_gpr = [0.,]; sQo_gpr= [0.,];Len_gpr=[0.,]\n",
    "    for n in range(D.id.nunique()):\n",
    "            Qo_gpr.append(D.Qo_gpr[D.id==n].sum()) ;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",
    "    # FG['bin']=np.arange(0,14,1)\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_all = FG_df.merge(FG_gpr, how='left') #on='bin')  \n",
    "    # FG_all\n",
    "    FG_all['Q_net_gpr'] = FG_all.Qin_gpr - FG_all.Qo_gpr \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['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",
    "    ## Plot balance gradients\n",
    "    n = 0\n",
    "    s= 15 #markersize\n",
    "    a = 0.9\n",
    "    color=['b', 'lime', 'green', 'teal']\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",
    "\n",
    "    x_ax_obs=obs[(obs.Year==year)].Elev\n",
    "\n",
    "    # Drop unrealistic FG smb:\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_med;y_ax_fg_gpr=new.b_fg_we_gpr\n",
    "    x_ax_fg_opt=new.dem_med;y_ax_fg_opt=new.b_fg_we;x_ax_fg_F=new.dem_med ;y_ax_fg_F=new.b_fg_weF\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",
    "    sym = ['o', '^', 's', 'd']\n",
    "    ytxt = [0.2, 0.15, 0.1, 0.05]\n",
    "    letter=['D','E','F'] #['A','B','C']\n",
    "    color = ['k', '#51c2d5','#663f3f','#ec4646'] #'teal', '#74c476', '#238b45']\n",
    "    txt= ['Observed', 'FG IPR', 'FG OGGM', 'FG Farinotti']\n",
    "    # Call function to create error bars \n",
    "    shift = [-25,0,25]\n",
    "    for i in range(3):\n",
    "        ax1[count].errorbar((x[i+1]+shift[i]), y[i+1], xerr=None, yerr=yerr[i+1], color=color[i+1],fmt=sym[i+1], \n",
    "                            mec=color[i+1],mfc=mfc, mew=mew, elinewidth=elw, zorder=2, label=txt[i+1], alpha=0.8) #elinewidth=0.7\n",
    "        \n",
    "    ## plot data and regression lines\n",
    "#     if year == 2016 or year == 2017:\n",
    "#     ax1[count].scatter(x[0],y[0],color=color[0], label=label[0], alpha=a, s=s,facecolor='', zorder=3)  ##plot obs data\n",
    "    \n",
    "    ############ obs data box plot ##################################################\n",
    "#     if year != 2018:\n",
    "    obs_data=obs[(obs.Year==year)]\n",
    "    obs_data.reset_index(inplace=True)\n",
    "\n",
    "    bin_size = 100.\n",
    "    z_range = np.arange(2050., 2750., bin_size)\n",
    "    # print(z_range)\n",
    "    # obs_data['Elev_bin'] = np.nan\n",
    "\n",
    "    i = 0\n",
    "    # for n in range(len(obs_data.Ba)):\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(new.dem_mean)):     \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)<1 and bin_size<150:\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",
    "    ######## OBS binned to flux bins ###########\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<150:\n",
    "#                 bin_size=bin_size*(1+(.2*COU))\n",
    "#                 COU=COU+1\n",
    "            if len(OBSFG)<=1:\n",
    "                print('OBSFG work start')\n",
    "                obs_data.sort_values(['Elev'], ascending=[True],inplace=True)\n",
    "                if f > (new.dem_med.min()) and f < (new.dem_med.max()):\n",
    "                    down_one=OB_FG[-1][-1];down_oneH=OB_FG_h[-1][-1]\n",
    "                    up_one=obs_data[obs_data.Elev> (f + bin_size/2.)]\n",
    "                    up_oneH=up_one.Ba_h.iloc[0];up_one=up_one.Ba.iloc[0] #up_one=up_one.Ba[-1:]\n",
    "                    OBSFG.append(down_one);OBSFG.append(up_one);  OBSFGh.append(down_oneH);OBSFGh.append(up_oneH)\n",
    "                if f == (new.dem_med.max()):\n",
    "                        down_one=OB_FG[-1][-1]; d2=OB_FG[-2][-2];down_oneH=OB_FG_h[-1][-1] ;d2h=OB_FG_h[-2][-2]                 \n",
    "                        OBSFG.extend([down_one,d2]);OBSFGh.extend([down_oneH,d2h]);\n",
    "                if f == (new.dem_med.min()):\n",
    "                    up_one=obs_data[obs_data.Elev> (f + bin_size/2.)]; \n",
    "                    up_oneH=up_one.Ba_h.iloc[0];up_one=up_one.Ba.iloc[0]; #up_one=up_one.Ba[-1:]\n",
    "                    OBSFG.append(up_one);  OB_FG_h.append(up_oneH)                \n",
    "                print('OBSFG work end')\n",
    "\n",
    "                OB_FG.append(np.array(OBSFG)); OB_FG_h.append(np.array(OBSFGh));\n",
    "                W=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='--', lw=1., color='0.5');medianprops = dict(linestyle='-', lw=1.,color='k')\n",
    "    boxprops = dict(linewidth=0.5);\n",
    "#     if year == 2016 or year==2018:\n",
    "#         BOX=ax1[count].boxplot(OB_F[:-1],meanprops=meanlineprops,medianprops=medianprops,showmeans=True, meanline=True,sym='',\n",
    "#                        positions=[2050., 2150., 2250., 2350., 2450.,2550.],widths=50, boxprops=boxprops)\n",
    "    BOX=ax1[count].boxplot(OB_FG,meanprops=meanlineprops,medianprops=medianprops,showmeans=True, meanline=True,sym='',\n",
    "                       positions=new.dem_med,widths=50,boxprops=boxprops,  whiskerprops=boxprops)\n",
    "    #[2050., 2150., 2250., 2350., 2450., 2550., 2650.,]\n",
    "#     z_range = z_range[1:] ### drop lowest bin which only has obs data\n",
    "#     OB_F = OB_F[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_med)): #for n in new.bin:  # \n",
    "                if ((z - bin_size/2.) <= new.dem_med[n]) and (new.dem_med[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",
    "################# mass conservation ####################################\n",
    "  \n",
    "#     for i in range(len(OB_F)):\n",
    "#         B = OB_F[i].mean()\n",
    "#         BM.append(B)\n",
    "#     BM = np.squeeze(BM)\n",
    "#     BM = {'SMB': BM}\n",
    "#     BM = pd.DataFrame(data=BM, index=None)\n",
    "#     new['SMB'] = BM\n",
    "#     new['BMC'] = (new.SMB * new['rho_%s'%year]/1000.) + new.Q_netA + new.Vfirn \n",
    "#     new['Bdh'] = new.dh_mean   \n",
    "\n",
    "#     new_gpr['BMC_gpr'] = (new.SMB[:-1] * new_gpr['rho_%s'%year]/1000.) + new_gpr.Q_netA_gpr + new_gpr.Vfirn \n",
    "#     new['BMCF'] = (new.SMB * new['rho_%s'%year]/1000.) + new.Q_netAF + new.Vfirn \n",
    "    \n",
    "# #     new.loc[13, 'BMC_gpr'] = new.BMC[13]\n",
    "#     MC = [new_gpr.BMC_gpr,new.BMC, new.BMCF]\n",
    "    \n",
    "    BM= []; BMh=[]\n",
    "    for i in range(len(OB_FG)):\n",
    "        B = OB_FG[i].mean()\n",
    "        Bh = np.mean(OB_FG_h[i]) # choose median or mean\n",
    "        BM.append(B);BMh.append(Bh)\n",
    "    new['SMB'] = BM; new['BH']= BMh\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",
    "    if I == 0:\n",
    "        overlap=[];overlap_gpr=[];overlapF=[]\n",
    "        MC_LIST=[overlap_gpr,overlap,overlapF]\n",
    "        \n",
    "    for i in range(3):\n",
    "#         ax2[count].scatter(new.dem_med, Yvv[i], edgecolor='r', marker='^', alpha=0.8, label=Yvv_lbl[i], s=s+12, \n",
    "#                            color=color[i+1], zorder=4, linewidth=0.25)\n",
    "        if i == 0:\n",
    "            ax2[count].errorbar(new.dem_med+shift[i]+5,MC[i], xerr=None, yerr=yerr_dh[i], fmt=sym[i+1], \n",
    "                     c=color[i+1], elinewidth=elw, mew=mew, mfc=mfc,mec=color[i+1], label=txt[i+1], alpha=0.8, zorder=3)\n",
    "        else:\n",
    "            ax2[count].errorbar(new.dem_med+shift[i],MC[i], xerr=None, yerr=yerr_dh[i], fmt=sym[i+1], \n",
    "                     c=color[i+1], mec=color[i+1], mew=mew, mfc=mfc, elinewidth=elw, label=txt[i+1], alpha=0.8, zorder=3)\n",
    "    \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",
    "            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*2\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",
    "    ax2[count].errorbar(new.dem_med,new.dh_mean, xerr=50, yerr=sDHdt, fmt='o', c='k', label='LiDAR',alpha=0.7, zorder=2, mfc='none')\n",
    "##################Difference###########################################################\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=[];gpr_bin_all=[];opt_bin_all=[];farin_bin_all=[];NSE=[];PBias=[];\n",
    "        RSR=[];RSQ=[];SE=[];SL=[];ME=[];MAE=[];MSE_ABL=[];MSE_ALL=[];MSE_ACC=[];RLM_SL=[];RLM_SE=[];SL_L=[];\n",
    "        SE_L=[];RSQ_L=[];gpr_bd_fg=[];opt_bd_fg=[];farin_bd_fg=[];obs_fg_all=[];obs_fg_all_med=[];opt_bd_fg_med=[];\n",
    "        farin_bd_fg_med=[];gpr_bd_fg_med=[];ELA_pw=[];ELA_lin=[]\n",
    "\n",
    "    OBF=[];GPRq=[];OPTq=[];FARq=[];OB_FGmean=[];OB_FGmed=[];\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",
    "        OB_FGmed.append(np.round(np.median(OB_FG[i]),3)) \n",
    "    obs_fg_all.extend(OB_FGmean);obs_fg_all_med.extend(OB_FGmed) ## all obs binned averages/medians\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);gpr_all.extend(FG_all.b_fg_we_gpr);opt_all.extend(new.b_fg_we);\n",
    "    farin_all.extend(new.b_fg_weF);elev_all.extend(new.dem_med);\n",
    "        \n",
    "    gpr_bdiff.extend((np.array(OBF) - np.array(GPRq))); opt_bdiff.extend((np.array(OBF) - np.array(OPTq)))\n",
    "    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",
    "    gpr_bd_fg_med.extend((np.array(OB_FGmed) - 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",
    "    opt_bd_fg_med.extend((np.array(OB_FGmed) - np.array(new.b_fg_we))); farin_bd_fg_med.extend((np.array(OB_FGmed) - np.array(new.b_fg_weF)))\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",
    "    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",
    "    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",
    "#         if year==2016:\n",
    "#             NSE.extend(evaluator(nse, EST[e][1:], OBFa[1:]))\n",
    "#             PBias.extend(evaluator(pbias, EST[e][1:], OBFa[1:]))\n",
    "#             RMSE=evaluator(rmse, EST[e][1:], OBFa[1:])\n",
    "#             RSR.extend((RMSE/(np.std(OBFa[1:]))))\n",
    "#             ME.append((OBFa[1:]-EST[e][1:]).mean())\n",
    "#             MAE.append(np.abs(OBFa[1:]-EST[e][1:]).mean())\n",
    "#         else:\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",
    "########### plot data and regression lines  ###############################\n",
    "    \n",
    "    ##plot obs data\n",
    "    ax1[count].scatter(x[0],y[0], marker='o', label=txt[0], alpha=0.9, s=s,facecolors='none', edgecolors='k', zorder=3)  \n",
    "    \n",
    "#     if year == 2018:\n",
    "#         y_bin=[OBF[:-2],GPRq,OPTq,FARq]; x_bin=[z_range[:-2],z_range,z_range,z_range]\n",
    "#     else:\n",
    "#         y_bin=[OBF,GPRq,OPTq,FARq]; x_bin=[z_range,z_range,z_range,z_range]\n",
    "#         y_bin=[OBF,FG_all.b_fg_we_gpr, new.b_fg_we,new.b_fg_weF]; x_bin=[z_range,new.dem_med,new.dem_med,new.dem_med]\n",
    " \n",
    "    bp = [2460,2530,2530] \n",
    "    ELA = [2500,2540,2525];    s_ELA = [75,70,70]\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",
    "    for i in range(4): \n",
    "        x0=bp[I]#[ELA[I]]\n",
    "        Y=np.array(y_bin[i]);X=np.array(x_bin[i])\n",
    "        model = pwlf.PiecewiseLinFit(X, Y)\n",
    "        breakpoints = [2050.,x0,2640.]\n",
    "        model.fit_guess([x0]) #model.fit_with_breaks(breakpoints)\n",
    "        breaks =model.fit_guess([x0]); x0 =breaks[1];\n",
    "        x_hat = np.linspace(2050,2640, 10) \n",
    "        y_hat = model.predict(x_hat)\n",
    "#         ax1[count].plot(x_hat, y_hat, lw=1.1, alpha=0.4, color=color[i], zorder=1)#label=label[i])\n",
    "        sl=model.slopes;  se=model.standard_errors();  rsq=model.r_squared()\n",
    "       # print('sl',sl)\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(2050., 2640., 10.)\n",
    "        ela = (0.0 - model.intercepts[0]) / sl[0]; ELA_pw.append(ela)\n",
    "        if fit == 'PW':\n",
    "            ax1[count].plot(xd, piecewise_linear(xd, *p), color=color[i], lw=1., alpha=0.9)\n",
    "      \n",
    "    # mean square error of the lines  \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",
    "                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",
    "                MSE_ACC.append(MSE(Act,Pred))\n",
    "                \n",
    "########## Linear fit ##################                \n",
    "    for i in range(4): \n",
    "        Y=np.array(y_bin[i]);X=np.array(x_bin[i])\n",
    "        model = pwlf.PiecewiseLinFit(X, Y)\n",
    "        breakpoints = [2050.,2640.]\n",
    "        model.fit_with_breaks(breakpoints)\n",
    "        x_hat = np.linspace(2050,2640, 10) \n",
    "        y_hat = model.predict(x_hat)\n",
    "        if fit == 'lin':\n",
    "            ax1[count].plot(x_hat, y_hat, lw=1.1, alpha=a, color=color[i], zorder=1)#label=label[i])\n",
    "        sll=model.slopes; sel=model.standard_errors(); rsql=model.r_squared()\n",
    "        SL_L.append(sll);SE_L.append(sel);RSQ_L.append(rsql)\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(2050., 2640., 10.)\n",
    "        ela = (0.0 - model.intercepts[0]) / sll[0]; ELA_lin.append(ela)\n",
    "    # mean square error of the lines  \n",
    "        for Line in range(2):\n",
    "            x0=2900\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",
    "                MSE_ALL.append(MSE(Act,Pred))\n",
    "                \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",
    "    ## add ELA to plot                 \n",
    "    ax1[count].errorbar(ELA[I],0., xerr=s_ELA[I], yerr=None, fmt='o', c='limegreen', mec='b',\n",
    "            mfc='limegreen',mew=mew, elinewidth=elw+0.4, label='ELA Satellite', alpha=0.85, zorder=4)   \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",
    "        \n",
    "################## plot specifics ############################\n",
    "#     ax1[count].xaxis.set_major_locator(ticker.MultipleLocator(100));ax2[count].xaxis.set_major_locator(ticker.MultipleLocator(100))\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",
    "    ax1[count].text(0.92, 0.03, letter[count], transform=ax1[count].transAxes)\n",
    "    xticks=[2000, 2100,  2200, 2300, 2400, 2500, 2600];xtick_lbls=[2000, '',  2200, '', 2400, '', 2600]\n",
    "    ax1[count].set_xticks(xticks);ax1[count].set_xticklabels(xtick_lbls)\n",
    "    ax1[count].text(0.03, 0.92, year, transform=ax1[count].transAxes)\n",
    "    ax2[count].text(0.03, 0.92, year, transform=ax2[count].transAxes)\n",
    "    ax1[count].set(xlim=(2000,2700), ylim=(-7.1,2.6))     \n",
    "#     ax1[0].legend(loc='upper left')#, bbox_to_anchor=(0, 0.42, 0.7, 0.5), labelspacing=0.2, handletextpad=0.1)\n",
    "    ax2[count].text(0.92, 0.92, letter[count], transform=ax2[count].transAxes)\n",
    "#     ax2[count].set_xticklabels([2050, 2150,  2250, 2350, 2450, 2550, 2650])\n",
    "    ax2[count].set_xticks(xticks);ax2[count].set_xticklabels(xtick_lbls) \n",
    "#     ax2[2].legend(loc='best',ncol=2,columnspacing=0.2,handletextpad=0.2, labelspacing=0.3, borderaxespad=0.25)\n",
    "    ax2[count].set(xlim=(2075, 2675) , ylim=(-3.5, 2.5)) \n",
    "    ax1[count].tick_params(which='major', length=3); ax2[count].tick_params(which='major', length=3)\n",
    "#     ax2[0].set_ylabel('Height change (m ice $a^{-1}$)'); ax1[0].set_ylabel('Mass balance (m w.e.)')\n",
    "     ################# end mass conservation plot ####################################\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",
    "    count+=1\n",
    "    if year == 2016:\n",
    "        VEL_LIST = [D.vfg.mean()]\n",
    "    else:\n",
    "        VEL_LIST.append(D.vfg.mean())\n",
    "        \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",
    "\n",
    "if ITS_LIVE == True:\n",
    "    vtype='_ITSLIVE'\n",
    "else:\n",
    "    vtype='_indV25m'\n",
    "if firn==True:\n",
    "    FIRN= \"_FIRN\"\n",
    "else:\n",
    "    FIRN =\"_NOFIRN\"\n",
    "\n",
    "fig1.savefig(fl_path+'products/'+glacier[gl]+'_bdot_'+ fit +'_OB_FG_6bins_'+str(Dint)+'_'+balance + FIRN+ vtype + '.pdf', dpi=300)    #_NOfirn\n",
    "fig2.savefig(fl_path+'products/'+glacier[gl]+'_mass_con_' + fit + '_OB_FG_6bins_' + str(Dint)+'_'+balance + FIRN + vtype + '.pdf', dpi=300)\n",
    "\n",
    "SE=np.array(SE);SL=np.array(SL);SE_L=np.array(SE_L);SL_L=np.array(SL_L);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ipr 0.9444444444444444 17 18\n",
      "OGGM 0.8333333333333334 15 18\n",
      "Farin 0.8888888888888888 16 18\n",
      "all 0.8888888888888888 48 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": 21,
   "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>i</th>\n",
       "      <th>j</th>\n",
       "      <th>vx</th>\n",
       "      <th>vy</th>\n",
       "      <th>vz</th>\n",
       "      <th>ITS</th>\n",
       "      <th>gpr</th>\n",
       "      <th>H_opt</th>\n",
       "      <th>dem</th>\n",
       "      <th>farin_corr</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0071_0155</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.450767</td>\n",
       "      <td>51.233473</td>\n",
       "      <td>71</td>\n",
       "      <td>155</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.070843</td>\n",
       "      <td>2436.034912</td>\n",
       "      <td>-10.072021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0072_0153</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>50.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.450305</td>\n",
       "      <td>51.233817</td>\n",
       "      <td>72</td>\n",
       "      <td>153</td>\n",
       "      <td>-0.891504</td>\n",
       "      <td>0.109947</td>\n",
       "      <td>0.898259</td>\n",
       "      <td>0.719045</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.511818</td>\n",
       "      <td>2405.994141</td>\n",
       "      <td>12.412224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0072_0154</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>25.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.450536</td>\n",
       "      <td>51.233645</td>\n",
       "      <td>72</td>\n",
       "      <td>154</td>\n",
       "      <td>-1.176502</td>\n",
       "      <td>0.566844</td>\n",
       "      <td>1.305936</td>\n",
       "      <td>1.843738</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.511818</td>\n",
       "      <td>2416.623535</td>\n",
       "      <td>10.002878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0073_0153</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>75.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.450075</td>\n",
       "      <td>51.233989</td>\n",
       "      <td>73</td>\n",
       "      <td>153</td>\n",
       "      <td>-1.122839</td>\n",
       "      <td>1.305790</td>\n",
       "      <td>1.722166</td>\n",
       "      <td>0.719045</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.511818</td>\n",
       "      <td>2397.521729</td>\n",
       "      <td>17.151449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0074_0151</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>125.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.449613</td>\n",
       "      <td>51.234333</td>\n",
       "      <td>74</td>\n",
       "      <td>151</td>\n",
       "      <td>-2.454499</td>\n",
       "      <td>4.143846</td>\n",
       "      <td>4.816225</td>\n",
       "      <td>0.719045</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.854343</td>\n",
       "      <td>2375.734619</td>\n",
       "      <td>25.525204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0074_0152</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>100.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.449844</td>\n",
       "      <td>51.234161</td>\n",
       "      <td>74</td>\n",
       "      <td>152</td>\n",
       "      <td>-2.060087</td>\n",
       "      <td>3.557255</td>\n",
       "      <td>4.110720</td>\n",
       "      <td>0.719045</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.854343</td>\n",
       "      <td>2380.168945</td>\n",
       "      <td>21.517933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0075_0150</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>150.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.449383</td>\n",
       "      <td>51.234505</td>\n",
       "      <td>75</td>\n",
       "      <td>150</td>\n",
       "      <td>-2.758981</td>\n",
       "      <td>5.623297</td>\n",
       "      <td>6.263660</td>\n",
       "      <td>0.719045</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52.187756</td>\n",
       "      <td>2363.848145</td>\n",
       "      <td>30.777630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0076_0149</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>200.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.448922</td>\n",
       "      <td>51.234849</td>\n",
       "      <td>76</td>\n",
       "      <td>149</td>\n",
       "      <td>-1.194037</td>\n",
       "      <td>4.461414</td>\n",
       "      <td>4.618435</td>\n",
       "      <td>0.719045</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59.944324</td>\n",
       "      <td>2350.191406</td>\n",
       "      <td>33.941872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0076_0150</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>175.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.449152</td>\n",
       "      <td>51.234677</td>\n",
       "      <td>76</td>\n",
       "      <td>150</td>\n",
       "      <td>-1.174731</td>\n",
       "      <td>3.491723</td>\n",
       "      <td>3.684036</td>\n",
       "      <td>0.719045</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59.944324</td>\n",
       "      <td>2358.885498</td>\n",
       "      <td>35.037579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0077_0148</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>225.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.448691</td>\n",
       "      <td>51.235021</td>\n",
       "      <td>77</td>\n",
       "      <td>148</td>\n",
       "      <td>-0.049929</td>\n",
       "      <td>3.367443</td>\n",
       "      <td>3.367813</td>\n",
       "      <td>0.719045</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.990852</td>\n",
       "      <td>2338.738281</td>\n",
       "      <td>38.622501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0078_0147</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>262.5</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.448345</td>\n",
       "      <td>51.235279</td>\n",
       "      <td>78</td>\n",
       "      <td>147</td>\n",
       "      <td>-0.179376</td>\n",
       "      <td>3.375454</td>\n",
       "      <td>3.380217</td>\n",
       "      <td>0.719045</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.158043</td>\n",
       "      <td>2330.911621</td>\n",
       "      <td>42.046001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0079_0146</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>300.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.447999</td>\n",
       "      <td>51.235537</td>\n",
       "      <td>79</td>\n",
       "      <td>146</td>\n",
       "      <td>-0.339423</td>\n",
       "      <td>1.920726</td>\n",
       "      <td>1.950486</td>\n",
       "      <td>5.729864</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.283417</td>\n",
       "      <td>2326.010254</td>\n",
       "      <td>45.780258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0080_0144</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>350.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.447538</td>\n",
       "      <td>51.235881</td>\n",
       "      <td>80</td>\n",
       "      <td>144</td>\n",
       "      <td>-0.254104</td>\n",
       "      <td>1.447165</td>\n",
       "      <td>1.469305</td>\n",
       "      <td>5.729864</td>\n",
       "      <td>31.244942</td>\n",
       "      <td>71.915627</td>\n",
       "      <td>2318.848389</td>\n",
       "      <td>50.989841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0080_0145</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>325.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.447768</td>\n",
       "      <td>51.235709</td>\n",
       "      <td>80</td>\n",
       "      <td>145</td>\n",
       "      <td>-0.372974</td>\n",
       "      <td>1.700551</td>\n",
       "      <td>1.740972</td>\n",
       "      <td>5.729864</td>\n",
       "      <td>33.853920</td>\n",
       "      <td>73.235817</td>\n",
       "      <td>2323.853271</td>\n",
       "      <td>49.947437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0081_0143</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>400.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.447076</td>\n",
       "      <td>51.236225</td>\n",
       "      <td>81</td>\n",
       "      <td>143</td>\n",
       "      <td>-0.488615</td>\n",
       "      <td>1.883482</td>\n",
       "      <td>1.945828</td>\n",
       "      <td>12.222924</td>\n",
       "      <td>31.989626</td>\n",
       "      <td>71.915627</td>\n",
       "      <td>2319.528076</td>\n",
       "      <td>55.103783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0081_0144</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>375.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.447307</td>\n",
       "      <td>51.236053</td>\n",
       "      <td>81</td>\n",
       "      <td>144</td>\n",
       "      <td>-0.236157</td>\n",
       "      <td>1.599982</td>\n",
       "      <td>1.617317</td>\n",
       "      <td>12.222924</td>\n",
       "      <td>33.615875</td>\n",
       "      <td>71.915627</td>\n",
       "      <td>2322.811279</td>\n",
       "      <td>52.365417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0082_0142</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>425.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.446846</td>\n",
       "      <td>51.236397</td>\n",
       "      <td>82</td>\n",
       "      <td>142</td>\n",
       "      <td>-2.005025</td>\n",
       "      <td>3.146753</td>\n",
       "      <td>3.731244</td>\n",
       "      <td>12.222924</td>\n",
       "      <td>35.152126</td>\n",
       "      <td>75.815948</td>\n",
       "      <td>2321.813477</td>\n",
       "      <td>62.632538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0083_0141</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>462.5</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.446500</td>\n",
       "      <td>51.236654</td>\n",
       "      <td>83</td>\n",
       "      <td>141</td>\n",
       "      <td>-4.109261</td>\n",
       "      <td>5.005182</td>\n",
       "      <td>6.475946</td>\n",
       "      <td>12.222924</td>\n",
       "      <td>35.970089</td>\n",
       "      <td>75.815948</td>\n",
       "      <td>2324.301270</td>\n",
       "      <td>67.321701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0084_0140</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>500.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.446154</td>\n",
       "      <td>51.236912</td>\n",
       "      <td>84</td>\n",
       "      <td>140</td>\n",
       "      <td>-6.082923</td>\n",
       "      <td>6.660829</td>\n",
       "      <td>9.020454</td>\n",
       "      <td>12.222924</td>\n",
       "      <td>42.100338</td>\n",
       "      <td>82.390816</td>\n",
       "      <td>2327.738281</td>\n",
       "      <td>71.755089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0085_0138</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>550.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.445692</td>\n",
       "      <td>51.237256</td>\n",
       "      <td>85</td>\n",
       "      <td>138</td>\n",
       "      <td>-10.997713</td>\n",
       "      <td>10.148372</td>\n",
       "      <td>14.964597</td>\n",
       "      <td>18.889126</td>\n",
       "      <td>65.259850</td>\n",
       "      <td>81.267601</td>\n",
       "      <td>2324.895020</td>\n",
       "      <td>74.883125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0085_0139</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>525.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.445923</td>\n",
       "      <td>51.237084</td>\n",
       "      <td>85</td>\n",
       "      <td>139</td>\n",
       "      <td>-8.592056</td>\n",
       "      <td>8.522914</td>\n",
       "      <td>12.102211</td>\n",
       "      <td>18.889126</td>\n",
       "      <td>58.951794</td>\n",
       "      <td>82.390816</td>\n",
       "      <td>2329.271973</td>\n",
       "      <td>74.074547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0086_0137</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>575.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.445462</td>\n",
       "      <td>51.237428</td>\n",
       "      <td>86</td>\n",
       "      <td>137</td>\n",
       "      <td>-13.502338</td>\n",
       "      <td>12.672405</td>\n",
       "      <td>18.517641</td>\n",
       "      <td>18.889126</td>\n",
       "      <td>80.976166</td>\n",
       "      <td>92.043465</td>\n",
       "      <td>2326.557129</td>\n",
       "      <td>76.981903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0087_0136</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>625.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.445000</td>\n",
       "      <td>51.237772</td>\n",
       "      <td>87</td>\n",
       "      <td>136</td>\n",
       "      <td>-16.446974</td>\n",
       "      <td>13.637007</td>\n",
       "      <td>21.365179</td>\n",
       "      <td>18.889126</td>\n",
       "      <td>87.212715</td>\n",
       "      <td>91.974899</td>\n",
       "      <td>2329.011719</td>\n",
       "      <td>83.486778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0087_0137</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>600.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.445231</td>\n",
       "      <td>51.237600</td>\n",
       "      <td>87</td>\n",
       "      <td>137</td>\n",
       "      <td>-14.321509</td>\n",
       "      <td>12.916906</td>\n",
       "      <td>19.286060</td>\n",
       "      <td>18.889126</td>\n",
       "      <td>85.599091</td>\n",
       "      <td>92.043465</td>\n",
       "      <td>2330.806152</td>\n",
       "      <td>81.106499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0088_0135</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>650.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.444770</td>\n",
       "      <td>51.237944</td>\n",
       "      <td>88</td>\n",
       "      <td>135</td>\n",
       "      <td>-18.934896</td>\n",
       "      <td>15.447783</td>\n",
       "      <td>24.436945</td>\n",
       "      <td>18.889126</td>\n",
       "      <td>91.614212</td>\n",
       "      <td>103.363510</td>\n",
       "      <td>2330.921875</td>\n",
       "      <td>91.498276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0089_0134</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>687.5</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.444424</td>\n",
       "      <td>51.238202</td>\n",
       "      <td>89</td>\n",
       "      <td>134</td>\n",
       "      <td>-19.546329</td>\n",
       "      <td>16.354954</td>\n",
       "      <td>25.486143</td>\n",
       "      <td>18.889126</td>\n",
       "      <td>92.311813</td>\n",
       "      <td>103.811371</td>\n",
       "      <td>2331.744385</td>\n",
       "      <td>96.518288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0090_0133</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>725.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.444078</td>\n",
       "      <td>51.238460</td>\n",
       "      <td>90</td>\n",
       "      <td>133</td>\n",
       "      <td>-20.075859</td>\n",
       "      <td>16.318838</td>\n",
       "      <td>25.871695</td>\n",
       "      <td>18.889126</td>\n",
       "      <td>97.084946</td>\n",
       "      <td>113.745529</td>\n",
       "      <td>2332.668213</td>\n",
       "      <td>99.839233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0091_0131</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>775.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.443616</td>\n",
       "      <td>51.238804</td>\n",
       "      <td>91</td>\n",
       "      <td>131</td>\n",
       "      <td>-21.659237</td>\n",
       "      <td>15.738627</td>\n",
       "      <td>26.773624</td>\n",
       "      <td>12.366378</td>\n",
       "      <td>133.130203</td>\n",
       "      <td>113.660614</td>\n",
       "      <td>2332.812500</td>\n",
       "      <td>103.412148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0091_0132</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>750.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.443847</td>\n",
       "      <td>51.238632</td>\n",
       "      <td>91</td>\n",
       "      <td>132</td>\n",
       "      <td>-18.681419</td>\n",
       "      <td>14.698958</td>\n",
       "      <td>23.770882</td>\n",
       "      <td>18.889126</td>\n",
       "      <td>126.766197</td>\n",
       "      <td>113.660614</td>\n",
       "      <td>2333.416992</td>\n",
       "      <td>103.093727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0092_0130</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>825.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.443155</td>\n",
       "      <td>51.239148</td>\n",
       "      <td>92</td>\n",
       "      <td>130</td>\n",
       "      <td>-23.780621</td>\n",
       "      <td>16.767580</td>\n",
       "      <td>29.097589</td>\n",
       "      <td>12.366378</td>\n",
       "      <td>151.204697</td>\n",
       "      <td>117.594673</td>\n",
       "      <td>2333.950439</td>\n",
       "      <td>103.125618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0092_0131</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>800.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.443385</td>\n",
       "      <td>51.238976</td>\n",
       "      <td>92</td>\n",
       "      <td>131</td>\n",
       "      <td>-21.792322</td>\n",
       "      <td>16.385841</td>\n",
       "      <td>27.265383</td>\n",
       "      <td>12.366378</td>\n",
       "      <td>149.555695</td>\n",
       "      <td>119.762009</td>\n",
       "      <td>2334.337891</td>\n",
       "      <td>103.793404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0093_0129</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>850.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.442924</td>\n",
       "      <td>51.239320</td>\n",
       "      <td>93</td>\n",
       "      <td>129</td>\n",
       "      <td>-24.438484</td>\n",
       "      <td>18.444895</td>\n",
       "      <td>30.617865</td>\n",
       "      <td>12.366378</td>\n",
       "      <td>150.884369</td>\n",
       "      <td>117.594673</td>\n",
       "      <td>2334.899902</td>\n",
       "      <td>100.097801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0094_0128</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>887.5</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.442578</td>\n",
       "      <td>51.239578</td>\n",
       "      <td>94</td>\n",
       "      <td>128</td>\n",
       "      <td>-25.312876</td>\n",
       "      <td>17.690983</td>\n",
       "      <td>30.882236</td>\n",
       "      <td>12.366378</td>\n",
       "      <td>150.094376</td>\n",
       "      <td>113.259750</td>\n",
       "      <td>2334.807861</td>\n",
       "      <td>94.205399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0095_0127</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>925.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.442232</td>\n",
       "      <td>51.239836</td>\n",
       "      <td>95</td>\n",
       "      <td>127</td>\n",
       "      <td>-25.834362</td>\n",
       "      <td>17.875206</td>\n",
       "      <td>31.415558</td>\n",
       "      <td>12.366378</td>\n",
       "      <td>142.249146</td>\n",
       "      <td>113.259750</td>\n",
       "      <td>2334.182617</td>\n",
       "      <td>89.275749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0096_0125</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>975.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.441771</td>\n",
       "      <td>51.240179</td>\n",
       "      <td>96</td>\n",
       "      <td>125</td>\n",
       "      <td>-23.408623</td>\n",
       "      <td>17.627037</td>\n",
       "      <td>29.303175</td>\n",
       "      <td>13.465559</td>\n",
       "      <td>123.242241</td>\n",
       "      <td>103.687302</td>\n",
       "      <td>2335.090088</td>\n",
       "      <td>81.956741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0096_0126</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>950.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.442001</td>\n",
       "      <td>51.240007</td>\n",
       "      <td>96</td>\n",
       "      <td>126</td>\n",
       "      <td>-24.363230</td>\n",
       "      <td>17.453924</td>\n",
       "      <td>29.970093</td>\n",
       "      <td>13.465559</td>\n",
       "      <td>129.689896</td>\n",
       "      <td>103.687302</td>\n",
       "      <td>2336.023438</td>\n",
       "      <td>86.110916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0097_0125</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.441540</td>\n",
       "      <td>51.240351</td>\n",
       "      <td>97</td>\n",
       "      <td>125</td>\n",
       "      <td>-21.989729</td>\n",
       "      <td>18.210983</td>\n",
       "      <td>28.551498</td>\n",
       "      <td>13.465559</td>\n",
       "      <td>107.179092</td>\n",
       "      <td>103.687302</td>\n",
       "      <td>2340.223877</td>\n",
       "      <td>83.504616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0098_0123</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.441079</td>\n",
       "      <td>51.240695</td>\n",
       "      <td>98</td>\n",
       "      <td>123</td>\n",
       "      <td>-20.416815</td>\n",
       "      <td>16.741089</td>\n",
       "      <td>26.402847</td>\n",
       "      <td>2.506803</td>\n",
       "      <td>87.851715</td>\n",
       "      <td>87.851044</td>\n",
       "      <td>2346.303467</td>\n",
       "      <td>79.855759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0098_0124</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>1025.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.441309</td>\n",
       "      <td>51.240523</td>\n",
       "      <td>98</td>\n",
       "      <td>124</td>\n",
       "      <td>-21.712559</td>\n",
       "      <td>18.417194</td>\n",
       "      <td>28.471535</td>\n",
       "      <td>2.506803</td>\n",
       "      <td>89.241837</td>\n",
       "      <td>95.532104</td>\n",
       "      <td>2346.734131</td>\n",
       "      <td>82.152206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0099_0122</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>1075.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.440848</td>\n",
       "      <td>51.240867</td>\n",
       "      <td>99</td>\n",
       "      <td>122</td>\n",
       "      <td>-18.867882</td>\n",
       "      <td>14.738803</td>\n",
       "      <td>23.942207</td>\n",
       "      <td>2.506803</td>\n",
       "      <td>86.449753</td>\n",
       "      <td>87.851044</td>\n",
       "      <td>2354.168457</td>\n",
       "      <td>79.991890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0100_0121</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.440386</td>\n",
       "      <td>51.241211</td>\n",
       "      <td>100</td>\n",
       "      <td>121</td>\n",
       "      <td>-16.663286</td>\n",
       "      <td>11.320371</td>\n",
       "      <td>20.144873</td>\n",
       "      <td>2.506803</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.413452</td>\n",
       "      <td>2358.642090</td>\n",
       "      <td>78.945198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0100_0122</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.440617</td>\n",
       "      <td>51.241039</td>\n",
       "      <td>100</td>\n",
       "      <td>122</td>\n",
       "      <td>-18.132820</td>\n",
       "      <td>12.636072</td>\n",
       "      <td>22.101345</td>\n",
       "      <td>2.506803</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93.450508</td>\n",
       "      <td>2360.580322</td>\n",
       "      <td>82.545349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0101_0120</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>1150.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.440156</td>\n",
       "      <td>51.241383</td>\n",
       "      <td>101</td>\n",
       "      <td>120</td>\n",
       "      <td>-14.651629</td>\n",
       "      <td>9.258006</td>\n",
       "      <td>17.331501</td>\n",
       "      <td>2.506803</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.413452</td>\n",
       "      <td>2360.864014</td>\n",
       "      <td>76.649757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0102_0118</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.439694</td>\n",
       "      <td>51.241727</td>\n",
       "      <td>102</td>\n",
       "      <td>118</td>\n",
       "      <td>-9.477534</td>\n",
       "      <td>5.063577</td>\n",
       "      <td>10.745393</td>\n",
       "      <td>2.506803</td>\n",
       "      <td>NaN</td>\n",
       "      <td>92.393913</td>\n",
       "      <td>2365.439453</td>\n",
       "      <td>73.845139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0102_0119</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>1175.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.439925</td>\n",
       "      <td>51.241555</td>\n",
       "      <td>102</td>\n",
       "      <td>119</td>\n",
       "      <td>-11.655224</td>\n",
       "      <td>7.254674</td>\n",
       "      <td>13.728602</td>\n",
       "      <td>2.506803</td>\n",
       "      <td>NaN</td>\n",
       "      <td>92.393913</td>\n",
       "      <td>2370.055420</td>\n",
       "      <td>76.991905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0103_0118</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>1225.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.439464</td>\n",
       "      <td>51.241899</td>\n",
       "      <td>103</td>\n",
       "      <td>118</td>\n",
       "      <td>-8.916608</td>\n",
       "      <td>5.112708</td>\n",
       "      <td>10.278409</td>\n",
       "      <td>2.506803</td>\n",
       "      <td>NaN</td>\n",
       "      <td>92.393913</td>\n",
       "      <td>2374.140137</td>\n",
       "      <td>81.808945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0104_0116</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>1275.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.439002</td>\n",
       "      <td>51.242243</td>\n",
       "      <td>104</td>\n",
       "      <td>116</td>\n",
       "      <td>-2.292946</td>\n",
       "      <td>1.438168</td>\n",
       "      <td>2.706645</td>\n",
       "      <td>0.744128</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102.648254</td>\n",
       "      <td>2378.154541</td>\n",
       "      <td>79.818222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0104_0117</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.439233</td>\n",
       "      <td>51.242071</td>\n",
       "      <td>104</td>\n",
       "      <td>117</td>\n",
       "      <td>-4.719091</td>\n",
       "      <td>2.849271</td>\n",
       "      <td>5.512547</td>\n",
       "      <td>0.744128</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102.648254</td>\n",
       "      <td>2376.739502</td>\n",
       "      <td>84.696411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0105_0115</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>1312.5</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.438656</td>\n",
       "      <td>51.242501</td>\n",
       "      <td>105</td>\n",
       "      <td>115</td>\n",
       "      <td>-2.565575</td>\n",
       "      <td>0.831348</td>\n",
       "      <td>2.696908</td>\n",
       "      <td>0.744128</td>\n",
       "      <td>64.706932</td>\n",
       "      <td>96.731873</td>\n",
       "      <td>2380.593018</td>\n",
       "      <td>79.735291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0106_0114</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.438310</td>\n",
       "      <td>51.242759</td>\n",
       "      <td>106</td>\n",
       "      <td>114</td>\n",
       "      <td>-2.955224</td>\n",
       "      <td>0.990706</td>\n",
       "      <td>3.116865</td>\n",
       "      <td>0.744128</td>\n",
       "      <td>71.924782</td>\n",
       "      <td>109.014015</td>\n",
       "      <td>2380.356445</td>\n",
       "      <td>79.905098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0107_0112</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.437848</td>\n",
       "      <td>51.243102</td>\n",
       "      <td>107</td>\n",
       "      <td>112</td>\n",
       "      <td>-2.071545</td>\n",
       "      <td>0.027409</td>\n",
       "      <td>2.071726</td>\n",
       "      <td>0.744128</td>\n",
       "      <td>67.303650</td>\n",
       "      <td>99.249855</td>\n",
       "      <td>2375.928223</td>\n",
       "      <td>66.260696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0107_0113</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>1375.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.438079</td>\n",
       "      <td>51.242930</td>\n",
       "      <td>107</td>\n",
       "      <td>113</td>\n",
       "      <td>-3.223897</td>\n",
       "      <td>0.814249</td>\n",
       "      <td>3.325133</td>\n",
       "      <td>0.744128</td>\n",
       "      <td>70.023155</td>\n",
       "      <td>99.249855</td>\n",
       "      <td>2378.704834</td>\n",
       "      <td>73.014252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0108_0112</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>1425.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.437618</td>\n",
       "      <td>51.243274</td>\n",
       "      <td>108</td>\n",
       "      <td>112</td>\n",
       "      <td>-2.128311</td>\n",
       "      <td>-0.006086</td>\n",
       "      <td>2.128320</td>\n",
       "      <td>0.744128</td>\n",
       "      <td>67.427155</td>\n",
       "      <td>102.519943</td>\n",
       "      <td>2376.654785</td>\n",
       "      <td>59.838467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0109_0110</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>1475.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.437156</td>\n",
       "      <td>51.243618</td>\n",
       "      <td>109</td>\n",
       "      <td>110</td>\n",
       "      <td>-1.898049</td>\n",
       "      <td>-0.635196</td>\n",
       "      <td>2.001515</td>\n",
       "      <td>0.744128</td>\n",
       "      <td>67.757393</td>\n",
       "      <td>86.946983</td>\n",
       "      <td>2368.579346</td>\n",
       "      <td>40.674210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0109_0111</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>1450.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.437387</td>\n",
       "      <td>51.243446</td>\n",
       "      <td>109</td>\n",
       "      <td>111</td>\n",
       "      <td>-2.480118</td>\n",
       "      <td>-0.308044</td>\n",
       "      <td>2.499175</td>\n",
       "      <td>0.744128</td>\n",
       "      <td>71.485786</td>\n",
       "      <td>86.946983</td>\n",
       "      <td>2374.203369</td>\n",
       "      <td>49.256573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0110_0109</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.436926</td>\n",
       "      <td>51.243790</td>\n",
       "      <td>110</td>\n",
       "      <td>109</td>\n",
       "      <td>-0.778578</td>\n",
       "      <td>-1.068493</td>\n",
       "      <td>1.322067</td>\n",
       "      <td>0.819395</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66.598236</td>\n",
       "      <td>2361.018799</td>\n",
       "      <td>30.958771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0111_0108</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.436498</td>\n",
       "      <td>51.244109</td>\n",
       "      <td>111</td>\n",
       "      <td>108</td>\n",
       "      <td>-0.403374</td>\n",
       "      <td>-0.982031</td>\n",
       "      <td>1.061647</td>\n",
       "      <td>1.533164</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66.598236</td>\n",
       "      <td>2350.401611</td>\n",
       "      <td>-18.039795</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id     len  distance      angle         lon        lat    i    j  \\\n",
       "ij                                                                            \n",
       "0071_0155   1  1546.4       0.0  40.449233 -117.450767  51.233473   71  155   \n",
       "0072_0153   1  1546.4      50.0  40.449233 -117.450305  51.233817   72  153   \n",
       "0072_0154   1  1546.4      25.0  40.449233 -117.450536  51.233645   72  154   \n",
       "0073_0153   1  1546.4      75.0  40.449233 -117.450075  51.233989   73  153   \n",
       "0074_0151   1  1546.4     125.0  40.449233 -117.449613  51.234333   74  151   \n",
       "0074_0152   1  1546.4     100.0  40.449233 -117.449844  51.234161   74  152   \n",
       "0075_0150   1  1546.4     150.0  40.449233 -117.449383  51.234505   75  150   \n",
       "0076_0149   1  1546.4     200.0  40.449233 -117.448922  51.234849   76  149   \n",
       "0076_0150   1  1546.4     175.0  40.449233 -117.449152  51.234677   76  150   \n",
       "0077_0148   1  1546.4     225.0  40.449233 -117.448691  51.235021   77  148   \n",
       "0078_0147   1  1546.4     262.5  40.449233 -117.448345  51.235279   78  147   \n",
       "0079_0146   1  1546.4     300.0  40.449233 -117.447999  51.235537   79  146   \n",
       "0080_0144   1  1546.4     350.0  40.449233 -117.447538  51.235881   80  144   \n",
       "0080_0145   1  1546.4     325.0  40.449233 -117.447768  51.235709   80  145   \n",
       "0081_0143   1  1546.4     400.0  40.449233 -117.447076  51.236225   81  143   \n",
       "0081_0144   1  1546.4     375.0  40.449233 -117.447307  51.236053   81  144   \n",
       "0082_0142   1  1546.4     425.0  40.449233 -117.446846  51.236397   82  142   \n",
       "0083_0141   1  1546.4     462.5  40.449233 -117.446500  51.236654   83  141   \n",
       "0084_0140   1  1546.4     500.0  40.449233 -117.446154  51.236912   84  140   \n",
       "0085_0138   1  1546.4     550.0  40.449233 -117.445692  51.237256   85  138   \n",
       "0085_0139   1  1546.4     525.0  40.449233 -117.445923  51.237084   85  139   \n",
       "0086_0137   1  1546.4     575.0  40.449233 -117.445462  51.237428   86  137   \n",
       "0087_0136   1  1546.4     625.0  40.449233 -117.445000  51.237772   87  136   \n",
       "0087_0137   1  1546.4     600.0  40.449233 -117.445231  51.237600   87  137   \n",
       "0088_0135   1  1546.4     650.0  40.449233 -117.444770  51.237944   88  135   \n",
       "0089_0134   1  1546.4     687.5  40.449233 -117.444424  51.238202   89  134   \n",
       "0090_0133   1  1546.4     725.0  40.449233 -117.444078  51.238460   90  133   \n",
       "0091_0131   1  1546.4     775.0  40.449233 -117.443616  51.238804   91  131   \n",
       "0091_0132   1  1546.4     750.0  40.449233 -117.443847  51.238632   91  132   \n",
       "0092_0130   1  1546.4     825.0  40.449233 -117.443155  51.239148   92  130   \n",
       "0092_0131   1  1546.4     800.0  40.449233 -117.443385  51.238976   92  131   \n",
       "0093_0129   1  1546.4     850.0  40.449233 -117.442924  51.239320   93  129   \n",
       "0094_0128   1  1546.4     887.5  40.449233 -117.442578  51.239578   94  128   \n",
       "0095_0127   1  1546.4     925.0  40.449233 -117.442232  51.239836   95  127   \n",
       "0096_0125   1  1546.4     975.0  40.449233 -117.441771  51.240179   96  125   \n",
       "0096_0126   1  1546.4     950.0  40.449233 -117.442001  51.240007   96  126   \n",
       "0097_0125   1  1546.4    1000.0  40.449233 -117.441540  51.240351   97  125   \n",
       "0098_0123   1  1546.4    1050.0  40.449233 -117.441079  51.240695   98  123   \n",
       "0098_0124   1  1546.4    1025.0  40.449233 -117.441309  51.240523   98  124   \n",
       "0099_0122   1  1546.4    1075.0  40.449233 -117.440848  51.240867   99  122   \n",
       "0100_0121   1  1546.4    1125.0  40.449233 -117.440386  51.241211  100  121   \n",
       "0100_0122   1  1546.4    1100.0  40.449233 -117.440617  51.241039  100  122   \n",
       "0101_0120   1  1546.4    1150.0  40.449233 -117.440156  51.241383  101  120   \n",
       "0102_0118   1  1546.4    1200.0  40.449233 -117.439694  51.241727  102  118   \n",
       "0102_0119   1  1546.4    1175.0  40.449233 -117.439925  51.241555  102  119   \n",
       "0103_0118   1  1546.4    1225.0  40.449233 -117.439464  51.241899  103  118   \n",
       "0104_0116   1  1546.4    1275.0  40.449233 -117.439002  51.242243  104  116   \n",
       "0104_0117   1  1546.4    1250.0  40.449233 -117.439233  51.242071  104  117   \n",
       "0105_0115   1  1546.4    1312.5  40.449233 -117.438656  51.242501  105  115   \n",
       "0106_0114   1  1546.4    1350.0  40.449233 -117.438310  51.242759  106  114   \n",
       "0107_0112   1  1546.4    1400.0  40.449233 -117.437848  51.243102  107  112   \n",
       "0107_0113   1  1546.4    1375.0  40.449233 -117.438079  51.242930  107  113   \n",
       "0108_0112   1  1546.4    1425.0  40.449233 -117.437618  51.243274  108  112   \n",
       "0109_0110   1  1546.4    1475.0  40.449233 -117.437156  51.243618  109  110   \n",
       "0109_0111   1  1546.4    1450.0  40.449233 -117.437387  51.243446  109  111   \n",
       "0110_0109   1  1546.4    1500.0  40.449233 -117.436926  51.243790  110  109   \n",
       "0111_0108   1  1546.4    1546.4  40.449233 -117.436498  51.244109  111  108   \n",
       "\n",
       "                  vx         vy         vz        ITS         gpr       H_opt  \\\n",
       "ij                                                                              \n",
       "0071_0155        NaN        NaN        NaN        NaN         NaN   36.070843   \n",
       "0072_0153  -0.891504   0.109947   0.898259   0.719045         NaN   38.511818   \n",
       "0072_0154  -1.176502   0.566844   1.305936   1.843738         NaN   38.511818   \n",
       "0073_0153  -1.122839   1.305790   1.722166   0.719045         NaN   38.511818   \n",
       "0074_0151  -2.454499   4.143846   4.816225   0.719045         NaN   48.854343   \n",
       "0074_0152  -2.060087   3.557255   4.110720   0.719045         NaN   48.854343   \n",
       "0075_0150  -2.758981   5.623297   6.263660   0.719045         NaN   52.187756   \n",
       "0076_0149  -1.194037   4.461414   4.618435   0.719045         NaN   59.944324   \n",
       "0076_0150  -1.174731   3.491723   3.684036   0.719045         NaN   59.944324   \n",
       "0077_0148  -0.049929   3.367443   3.367813   0.719045         NaN   61.990852   \n",
       "0078_0147  -0.179376   3.375454   3.380217   0.719045         NaN   68.158043   \n",
       "0079_0146  -0.339423   1.920726   1.950486   5.729864         NaN   68.283417   \n",
       "0080_0144  -0.254104   1.447165   1.469305   5.729864   31.244942   71.915627   \n",
       "0080_0145  -0.372974   1.700551   1.740972   5.729864   33.853920   73.235817   \n",
       "0081_0143  -0.488615   1.883482   1.945828  12.222924   31.989626   71.915627   \n",
       "0081_0144  -0.236157   1.599982   1.617317  12.222924   33.615875   71.915627   \n",
       "0082_0142  -2.005025   3.146753   3.731244  12.222924   35.152126   75.815948   \n",
       "0083_0141  -4.109261   5.005182   6.475946  12.222924   35.970089   75.815948   \n",
       "0084_0140  -6.082923   6.660829   9.020454  12.222924   42.100338   82.390816   \n",
       "0085_0138 -10.997713  10.148372  14.964597  18.889126   65.259850   81.267601   \n",
       "0085_0139  -8.592056   8.522914  12.102211  18.889126   58.951794   82.390816   \n",
       "0086_0137 -13.502338  12.672405  18.517641  18.889126   80.976166   92.043465   \n",
       "0087_0136 -16.446974  13.637007  21.365179  18.889126   87.212715   91.974899   \n",
       "0087_0137 -14.321509  12.916906  19.286060  18.889126   85.599091   92.043465   \n",
       "0088_0135 -18.934896  15.447783  24.436945  18.889126   91.614212  103.363510   \n",
       "0089_0134 -19.546329  16.354954  25.486143  18.889126   92.311813  103.811371   \n",
       "0090_0133 -20.075859  16.318838  25.871695  18.889126   97.084946  113.745529   \n",
       "0091_0131 -21.659237  15.738627  26.773624  12.366378  133.130203  113.660614   \n",
       "0091_0132 -18.681419  14.698958  23.770882  18.889126  126.766197  113.660614   \n",
       "0092_0130 -23.780621  16.767580  29.097589  12.366378  151.204697  117.594673   \n",
       "0092_0131 -21.792322  16.385841  27.265383  12.366378  149.555695  119.762009   \n",
       "0093_0129 -24.438484  18.444895  30.617865  12.366378  150.884369  117.594673   \n",
       "0094_0128 -25.312876  17.690983  30.882236  12.366378  150.094376  113.259750   \n",
       "0095_0127 -25.834362  17.875206  31.415558  12.366378  142.249146  113.259750   \n",
       "0096_0125 -23.408623  17.627037  29.303175  13.465559  123.242241  103.687302   \n",
       "0096_0126 -24.363230  17.453924  29.970093  13.465559  129.689896  103.687302   \n",
       "0097_0125 -21.989729  18.210983  28.551498  13.465559  107.179092  103.687302   \n",
       "0098_0123 -20.416815  16.741089  26.402847   2.506803   87.851715   87.851044   \n",
       "0098_0124 -21.712559  18.417194  28.471535   2.506803   89.241837   95.532104   \n",
       "0099_0122 -18.867882  14.738803  23.942207   2.506803   86.449753   87.851044   \n",
       "0100_0121 -16.663286  11.320371  20.144873   2.506803         NaN   86.413452   \n",
       "0100_0122 -18.132820  12.636072  22.101345   2.506803         NaN   93.450508   \n",
       "0101_0120 -14.651629   9.258006  17.331501   2.506803         NaN   86.413452   \n",
       "0102_0118  -9.477534   5.063577  10.745393   2.506803         NaN   92.393913   \n",
       "0102_0119 -11.655224   7.254674  13.728602   2.506803         NaN   92.393913   \n",
       "0103_0118  -8.916608   5.112708  10.278409   2.506803         NaN   92.393913   \n",
       "0104_0116  -2.292946   1.438168   2.706645   0.744128         NaN  102.648254   \n",
       "0104_0117  -4.719091   2.849271   5.512547   0.744128         NaN  102.648254   \n",
       "0105_0115  -2.565575   0.831348   2.696908   0.744128   64.706932   96.731873   \n",
       "0106_0114  -2.955224   0.990706   3.116865   0.744128   71.924782  109.014015   \n",
       "0107_0112  -2.071545   0.027409   2.071726   0.744128   67.303650   99.249855   \n",
       "0107_0113  -3.223897   0.814249   3.325133   0.744128   70.023155   99.249855   \n",
       "0108_0112  -2.128311  -0.006086   2.128320   0.744128   67.427155  102.519943   \n",
       "0109_0110  -1.898049  -0.635196   2.001515   0.744128   67.757393   86.946983   \n",
       "0109_0111  -2.480118  -0.308044   2.499175   0.744128   71.485786   86.946983   \n",
       "0110_0109  -0.778578  -1.068493   1.322067   0.819395         NaN   66.598236   \n",
       "0111_0108  -0.403374  -0.982031   1.061647   1.533164         NaN   66.598236   \n",
       "\n",
       "                   dem  farin_corr  \n",
       "ij                                  \n",
       "0071_0155  2436.034912  -10.072021  \n",
       "0072_0153  2405.994141   12.412224  \n",
       "0072_0154  2416.623535   10.002878  \n",
       "0073_0153  2397.521729   17.151449  \n",
       "0074_0151  2375.734619   25.525204  \n",
       "0074_0152  2380.168945   21.517933  \n",
       "0075_0150  2363.848145   30.777630  \n",
       "0076_0149  2350.191406   33.941872  \n",
       "0076_0150  2358.885498   35.037579  \n",
       "0077_0148  2338.738281   38.622501  \n",
       "0078_0147  2330.911621   42.046001  \n",
       "0079_0146  2326.010254   45.780258  \n",
       "0080_0144  2318.848389   50.989841  \n",
       "0080_0145  2323.853271   49.947437  \n",
       "0081_0143  2319.528076   55.103783  \n",
       "0081_0144  2322.811279   52.365417  \n",
       "0082_0142  2321.813477   62.632538  \n",
       "0083_0141  2324.301270   67.321701  \n",
       "0084_0140  2327.738281   71.755089  \n",
       "0085_0138  2324.895020   74.883125  \n",
       "0085_0139  2329.271973   74.074547  \n",
       "0086_0137  2326.557129   76.981903  \n",
       "0087_0136  2329.011719   83.486778  \n",
       "0087_0137  2330.806152   81.106499  \n",
       "0088_0135  2330.921875   91.498276  \n",
       "0089_0134  2331.744385   96.518288  \n",
       "0090_0133  2332.668213   99.839233  \n",
       "0091_0131  2332.812500  103.412148  \n",
       "0091_0132  2333.416992  103.093727  \n",
       "0092_0130  2333.950439  103.125618  \n",
       "0092_0131  2334.337891  103.793404  \n",
       "0093_0129  2334.899902  100.097801  \n",
       "0094_0128  2334.807861   94.205399  \n",
       "0095_0127  2334.182617   89.275749  \n",
       "0096_0125  2335.090088   81.956741  \n",
       "0096_0126  2336.023438   86.110916  \n",
       "0097_0125  2340.223877   83.504616  \n",
       "0098_0123  2346.303467   79.855759  \n",
       "0098_0124  2346.734131   82.152206  \n",
       "0099_0122  2354.168457   79.991890  \n",
       "0100_0121  2358.642090   78.945198  \n",
       "0100_0122  2360.580322   82.545349  \n",
       "0101_0120  2360.864014   76.649757  \n",
       "0102_0118  2365.439453   73.845139  \n",
       "0102_0119  2370.055420   76.991905  \n",
       "0103_0118  2374.140137   81.808945  \n",
       "0104_0116  2378.154541   79.818222  \n",
       "0104_0117  2376.739502   84.696411  \n",
       "0105_0115  2380.593018   79.735291  \n",
       "0106_0114  2380.356445   79.905098  \n",
       "0107_0112  2375.928223   66.260696  \n",
       "0107_0113  2378.704834   73.014252  \n",
       "0108_0112  2376.654785   59.838467  \n",
       "0109_0110  2368.579346   40.674210  \n",
       "0109_0111  2374.203369   49.256573  \n",
       "0110_0109  2361.018799   30.958771  \n",
       "0111_0108  2350.401611  -18.039795  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg[df_agg.id==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(F_onewayResult(statistic=0.04572278432596661, pvalue=0.8319576736299285),\n",
       " F_onewayResult(statistic=0.10602465048433682, pvalue=0.746711729572094),\n",
       " F_onewayResult(statistic=0.0014617454718427725, pvalue=0.9697256969489758))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.85"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(.75+.97+.83)/3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cr_area % diff gpr to oggm -2.3\n",
      "cr_area % diff gpr to FAR19 32.2\n"
     ]
    }
   ],
   "source": [
    "# compare cross-sectional area\n",
    "GRi=new16.cr_area_gpr/new16.cr_area\n",
    "GR= np.sum(new16.cr_area_gpr)/np.sum(new16.cr_area)\n",
    "FRi=new16.cr_area_gpr/new16.cr_areaF\n",
    "FR= np.sum(new16.cr_area_gpr)/np.sum(new16.cr_areaF)\n",
    "\n",
    "print('cr_area % diff gpr to oggm', ((GR-1)*100).round(1))\n",
    "print('cr_area % diff gpr to FAR19', ((FR-1)*100).round(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Q_out</th>\n",
       "      <th>Q_in</th>\n",
       "      <th>cr_area</th>\n",
       "      <th>sQout</th>\n",
       "      <th>sQout_low</th>\n",
       "      <th>sQout_high</th>\n",
       "      <th>sQin</th>\n",
       "      <th>sQin_low</th>\n",
       "      <th>sQin_high</th>\n",
       "      <th>Q_outF</th>\n",
       "      <th>Q_inF</th>\n",
       "      <th>sQoutF</th>\n",
       "      <th>sQinF</th>\n",
       "      <th>cr_areaF</th>\n",
       "      <th>vzdir</th>\n",
       "      <th>v_gate_mean</th>\n",
       "      <th>gate_width</th>\n",
       "      <th>gpr</th>\n",
       "      <th>Hopt</th>\n",
       "      <th>HF</th>\n",
       "      <th>Len_mod</th>\n",
       "      <th>vfg</th>\n",
       "      <th>vfgF</th>\n",
       "      <th>bin</th>\n",
       "      <th>spQout</th>\n",
       "      <th>spQin</th>\n",
       "      <th>spQoutF</th>\n",
       "      <th>spQinF</th>\n",
       "      <th>dh_count</th>\n",
       "      <th>dh_mean</th>\n",
       "      <th>dh_med</th>\n",
       "      <th>dh_std</th>\n",
       "      <th>dem_count</th>\n",
       "      <th>dem_mean</th>\n",
       "      <th>dem_med</th>\n",
       "      <th>dem_std</th>\n",
       "      <th>vx_count</th>\n",
       "      <th>vx_mean</th>\n",
       "      <th>vx_med</th>\n",
       "      <th>vx_std</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>ba</th>\n",
       "      <th>firn_area</th>\n",
       "      <th>Tavg</th>\n",
       "      <th>Vfirn</th>\n",
       "      <th>column_density</th>\n",
       "      <th>column_depth</th>\n",
       "      <th>time</th>\n",
       "      <th>Q_net</th>\n",
       "      <th>Q_netA</th>\n",
       "      <th>Q_netF</th>\n",
       "      <th>Q_netAF</th>\n",
       "      <th>area_Qin</th>\n",
       "      <th>sQoptA</th>\n",
       "      <th>sQoptA_low</th>\n",
       "      <th>sQoptA_high</th>\n",
       "      <th>sQ_inA</th>\n",
       "      <th>sQ_inA_low</th>\n",
       "      <th>sQ_inA_high</th>\n",
       "      <th>sQnetA_opt</th>\n",
       "      <th>sQnetA_opt_low</th>\n",
       "      <th>sQnetA_opt_high</th>\n",
       "      <th>sQoutFA</th>\n",
       "      <th>sQ_inFA</th>\n",
       "      <th>sQnetFA</th>\n",
       "      <th>b_fg_h</th>\n",
       "      <th>b_fg_hF</th>\n",
       "      <th>RHO</th>\n",
       "      <th>RHO_F</th>\n",
       "      <th>b_fg_we</th>\n",
       "      <th>b_fg_weF</th>\n",
       "      <th>sDH_opt</th>\n",
       "      <th>sBwe_opt</th>\n",
       "      <th>sDH_F</th>\n",
       "      <th>sBwe_F</th>\n",
       "      <th>Qo_gpr</th>\n",
       "      <th>Qin_gpr</th>\n",
       "      <th>cr_area_gpr</th>\n",
       "      <th>sQo_gpr</th>\n",
       "      <th>sQin_gpr</th>\n",
       "      <th>Len_gpr</th>\n",
       "      <th>spQo_gpr</th>\n",
       "      <th>spQin_gpr</th>\n",
       "      <th>Q_net_gpr</th>\n",
       "      <th>Q_netA_gpr</th>\n",
       "      <th>b_fg_h_gpr</th>\n",
       "      <th>sQoutA_gpr</th>\n",
       "      <th>sQ_inA_gpr</th>\n",
       "      <th>sQnetA_gpr</th>\n",
       "      <th>RHO_g</th>\n",
       "      <th>b_fg_we_gpr</th>\n",
       "      <th>sDH_gpr</th>\n",
       "      <th>sBwe_gpr</th>\n",
       "      <th>xerr</th>\n",
       "      <th>SMB</th>\n",
       "      <th>BH</th>\n",
       "      <th>BMC</th>\n",
       "      <th>BMC_gpr</th>\n",
       "      <th>BMCF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>8.579872e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>144608.782613</td>\n",
       "      <td>142387.001277</td>\n",
       "      <td>151761.359317</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9.418587e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>144671.252459</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.632645</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.854422</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.360186</td>\n",
       "      <td>413219</td>\n",
       "      <td>-1.166136</td>\n",
       "      <td>-1.115234</td>\n",
       "      <td>2.387476</td>\n",
       "      <td>413219</td>\n",
       "      <td>2206.260758</td>\n",
       "      <td>2225.477783</td>\n",
       "      <td>109.575024</td>\n",
       "      <td>659</td>\n",
       "      <td>-8.103982</td>\n",
       "      <td>-3.184993</td>\n",
       "      <td>10.318706</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.963464</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.579872e+05</td>\n",
       "      <td>2.076350</td>\n",
       "      <td>9.418587e+05</td>\n",
       "      <td>2.279321</td>\n",
       "      <td>319038</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.453265</td>\n",
       "      <td>0.446301</td>\n",
       "      <td>0.475684</td>\n",
       "      <td>0.453265</td>\n",
       "      <td>0.446301</td>\n",
       "      <td>0.475684</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.453461</td>\n",
       "      <td>0.453461</td>\n",
       "      <td>-3.242486</td>\n",
       "      <td>-3.445457</td>\n",
       "      <td>910.000000</td>\n",
       "      <td>910.000000</td>\n",
       "      <td>-2.950662</td>\n",
       "      <td>-3.135366</td>\n",
       "      <td>0.591481</td>\n",
       "      <td>0.613819</td>\n",
       "      <td>0.591631</td>\n",
       "      <td>0.623027</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.308510e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>179929.234256</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.750698</td>\n",
       "      <td>1.308510e+06</td>\n",
       "      <td>3.166625</td>\n",
       "      <td>-4.332762</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.563974</td>\n",
       "      <td>0.563974</td>\n",
       "      <td>910.000000</td>\n",
       "      <td>-3.942813</td>\n",
       "      <td>0.680049</td>\n",
       "      <td>0.733776</td>\n",
       "      <td>109.575024</td>\n",
       "      <td>-4.156000</td>\n",
       "      <td>-4.568000</td>\n",
       "      <td>-2.491650</td>\n",
       "      <td>-1.401375</td>\n",
       "      <td>-2.288679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.579872e+05</td>\n",
       "      <td>1.615478e+06</td>\n",
       "      <td>45087.173055</td>\n",
       "      <td>144608.782613</td>\n",
       "      <td>142387.001277</td>\n",
       "      <td>151761.359317</td>\n",
       "      <td>385998.907479</td>\n",
       "      <td>382491.131257</td>\n",
       "      <td>397396.805308</td>\n",
       "      <td>9.418587e+05</td>\n",
       "      <td>1.370466e+06</td>\n",
       "      <td>144671.252459</td>\n",
       "      <td>301379.602267</td>\n",
       "      <td>44205.908290</td>\n",
       "      <td>-49.608490</td>\n",
       "      <td>12.654183</td>\n",
       "      <td>918.4</td>\n",
       "      <td>63.334673</td>\n",
       "      <td>48.761864</td>\n",
       "      <td>45.872791</td>\n",
       "      <td>918.4</td>\n",
       "      <td>19.029519</td>\n",
       "      <td>20.889727</td>\n",
       "      <td>1</td>\n",
       "      <td>16.854422</td>\n",
       "      <td>23.893794</td>\n",
       "      <td>15.360186</td>\n",
       "      <td>21.991028</td>\n",
       "      <td>319038</td>\n",
       "      <td>-0.583821</td>\n",
       "      <td>-0.729370</td>\n",
       "      <td>1.720141</td>\n",
       "      <td>319038</td>\n",
       "      <td>2325.640657</td>\n",
       "      <td>2322.429688</td>\n",
       "      <td>30.030002</td>\n",
       "      <td>513</td>\n",
       "      <td>-11.066018</td>\n",
       "      <td>-7.153669</td>\n",
       "      <td>10.215554</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.554041</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.574904e+05</td>\n",
       "      <td>2.374295</td>\n",
       "      <td>4.286075e+05</td>\n",
       "      <td>1.343437</td>\n",
       "      <td>863384</td>\n",
       "      <td>0.453265</td>\n",
       "      <td>0.446301</td>\n",
       "      <td>0.475684</td>\n",
       "      <td>0.447077</td>\n",
       "      <td>0.443014</td>\n",
       "      <td>0.460278</td>\n",
       "      <td>0.636653</td>\n",
       "      <td>0.628845</td>\n",
       "      <td>0.661915</td>\n",
       "      <td>0.453461</td>\n",
       "      <td>0.349068</td>\n",
       "      <td>0.572254</td>\n",
       "      <td>-2.958117</td>\n",
       "      <td>-1.927258</td>\n",
       "      <td>910.000000</td>\n",
       "      <td>910.000000</td>\n",
       "      <td>-2.691886</td>\n",
       "      <td>-1.753805</td>\n",
       "      <td>0.741436</td>\n",
       "      <td>0.726424</td>\n",
       "      <td>0.686932</td>\n",
       "      <td>0.649244</td>\n",
       "      <td>1.308510e+06</td>\n",
       "      <td>1.682739e+06</td>\n",
       "      <td>61701.978461</td>\n",
       "      <td>179929.234256</td>\n",
       "      <td>316223.695314</td>\n",
       "      <td>918.4</td>\n",
       "      <td>13.750698</td>\n",
       "      <td>18.792196</td>\n",
       "      <td>3.742297e+05</td>\n",
       "      <td>1.172994</td>\n",
       "      <td>-1.756815</td>\n",
       "      <td>0.563974</td>\n",
       "      <td>0.366261</td>\n",
       "      <td>0.672469</td>\n",
       "      <td>910.000000</td>\n",
       "      <td>-1.598702</td>\n",
       "      <td>0.772408</td>\n",
       "      <td>0.720843</td>\n",
       "      <td>45.045002</td>\n",
       "      <td>-2.410000</td>\n",
       "      <td>-2.656667</td>\n",
       "      <td>-0.282371</td>\n",
       "      <td>-1.483673</td>\n",
       "      <td>-1.313230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.615478e+06</td>\n",
       "      <td>2.940431e+06</td>\n",
       "      <td>129217.492215</td>\n",
       "      <td>385998.907479</td>\n",
       "      <td>382491.131257</td>\n",
       "      <td>397396.805308</td>\n",
       "      <td>680593.545044</td>\n",
       "      <td>675305.888570</td>\n",
       "      <td>697969.811988</td>\n",
       "      <td>1.370466e+06</td>\n",
       "      <td>2.232358e+06</td>\n",
       "      <td>301379.602267</td>\n",
       "      <td>504823.010721</td>\n",
       "      <td>100000.940695</td>\n",
       "      <td>-46.643524</td>\n",
       "      <td>13.166764</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>71.193643</td>\n",
       "      <td>84.338839</td>\n",
       "      <td>63.809505</td>\n",
       "      <td>1543.2</td>\n",
       "      <td>12.502004</td>\n",
       "      <td>10.605888</td>\n",
       "      <td>2</td>\n",
       "      <td>23.893794</td>\n",
       "      <td>23.146050</td>\n",
       "      <td>21.991028</td>\n",
       "      <td>22.613894</td>\n",
       "      <td>863384</td>\n",
       "      <td>-0.396365</td>\n",
       "      <td>-0.452637</td>\n",
       "      <td>0.813162</td>\n",
       "      <td>863384</td>\n",
       "      <td>2395.147355</td>\n",
       "      <td>2390.628174</td>\n",
       "      <td>41.086899</td>\n",
       "      <td>1381</td>\n",
       "      <td>-8.389135</td>\n",
       "      <td>-6.209127</td>\n",
       "      <td>7.751619</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.962627</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.324953e+06</td>\n",
       "      <td>1.534605</td>\n",
       "      <td>8.618915e+05</td>\n",
       "      <td>0.998271</td>\n",
       "      <td>1092004</td>\n",
       "      <td>0.447077</td>\n",
       "      <td>0.443014</td>\n",
       "      <td>0.460278</td>\n",
       "      <td>0.623252</td>\n",
       "      <td>0.618410</td>\n",
       "      <td>0.639164</td>\n",
       "      <td>0.767021</td>\n",
       "      <td>0.760718</td>\n",
       "      <td>0.787646</td>\n",
       "      <td>0.349068</td>\n",
       "      <td>0.462290</td>\n",
       "      <td>0.579276</td>\n",
       "      <td>-1.930970</td>\n",
       "      <td>-1.394637</td>\n",
       "      <td>910.000000</td>\n",
       "      <td>910.000000</td>\n",
       "      <td>-1.757183</td>\n",
       "      <td>-1.269119</td>\n",
       "      <td>0.855991</td>\n",
       "      <td>0.798525</td>\n",
       "      <td>0.692792</td>\n",
       "      <td>0.643088</td>\n",
       "      <td>1.682739e+06</td>\n",
       "      <td>2.292828e+06</td>\n",
       "      <td>109551.782716</td>\n",
       "      <td>316223.695314</td>\n",
       "      <td>477886.348142</td>\n",
       "      <td>1543.2</td>\n",
       "      <td>18.792196</td>\n",
       "      <td>20.842656</td>\n",
       "      <td>6.100890e+05</td>\n",
       "      <td>0.706625</td>\n",
       "      <td>-1.102991</td>\n",
       "      <td>0.366261</td>\n",
       "      <td>0.437623</td>\n",
       "      <td>0.570667</td>\n",
       "      <td>910.000000</td>\n",
       "      <td>-1.003721</td>\n",
       "      <td>0.685610</td>\n",
       "      <td>0.631927</td>\n",
       "      <td>61.630349</td>\n",
       "      <td>-0.906667</td>\n",
       "      <td>-1.033333</td>\n",
       "      <td>0.501271</td>\n",
       "      <td>-0.326708</td>\n",
       "      <td>-0.035062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.940431e+06</td>\n",
       "      <td>2.307716e+06</td>\n",
       "      <td>231417.427109</td>\n",
       "      <td>680593.545044</td>\n",
       "      <td>675305.888570</td>\n",
       "      <td>697969.811988</td>\n",
       "      <td>621144.469307</td>\n",
       "      <td>617446.170131</td>\n",
       "      <td>633345.990726</td>\n",
       "      <td>2.232358e+06</td>\n",
       "      <td>1.818863e+06</td>\n",
       "      <td>504823.010721</td>\n",
       "      <td>486505.829812</td>\n",
       "      <td>171326.315578</td>\n",
       "      <td>-27.808907</td>\n",
       "      <td>10.793298</td>\n",
       "      <td>1836.5</td>\n",
       "      <td>91.439333</td>\n",
       "      <td>126.194044</td>\n",
       "      <td>93.050323</td>\n",
       "      <td>1836.5</td>\n",
       "      <td>12.706176</td>\n",
       "      <td>9.646455</td>\n",
       "      <td>3</td>\n",
       "      <td>23.146050</td>\n",
       "      <td>26.915987</td>\n",
       "      <td>22.613894</td>\n",
       "      <td>26.747802</td>\n",
       "      <td>1092004</td>\n",
       "      <td>-0.319800</td>\n",
       "      <td>-0.343018</td>\n",
       "      <td>0.477889</td>\n",
       "      <td>1092004</td>\n",
       "      <td>2479.688734</td>\n",
       "      <td>2460.456055</td>\n",
       "      <td>54.487812</td>\n",
       "      <td>1746</td>\n",
       "      <td>-3.605541</td>\n",
       "      <td>-2.867055</td>\n",
       "      <td>4.527447</td>\n",
       "      <td>3</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.13</td>\n",
       "      <td>-2.381042</td>\n",
       "      <td>0.0</td>\n",
       "      <td>794.113546</td>\n",
       "      <td>4.6085</td>\n",
       "      <td>87.866861</td>\n",
       "      <td>-6.327149e+05</td>\n",
       "      <td>-0.579407</td>\n",
       "      <td>-4.134949e+05</td>\n",
       "      <td>-0.378657</td>\n",
       "      <td>1227849</td>\n",
       "      <td>0.623252</td>\n",
       "      <td>0.618410</td>\n",
       "      <td>0.639164</td>\n",
       "      <td>0.505880</td>\n",
       "      <td>0.502868</td>\n",
       "      <td>0.515817</td>\n",
       "      <td>0.802719</td>\n",
       "      <td>0.797061</td>\n",
       "      <td>0.821339</td>\n",
       "      <td>0.462290</td>\n",
       "      <td>0.396226</td>\n",
       "      <td>0.608858</td>\n",
       "      <td>0.259607</td>\n",
       "      <td>0.058857</td>\n",
       "      <td>905.767924</td>\n",
       "      <td>904.260496</td>\n",
       "      <td>0.235144</td>\n",
       "      <td>0.053222</td>\n",
       "      <td>0.888120</td>\n",
       "      <td>0.804774</td>\n",
       "      <td>0.717710</td>\n",
       "      <td>0.649019</td>\n",
       "      <td>2.292828e+06</td>\n",
       "      <td>2.525386e+06</td>\n",
       "      <td>167611.505015</td>\n",
       "      <td>477886.348142</td>\n",
       "      <td>664939.742177</td>\n",
       "      <td>1836.5</td>\n",
       "      <td>20.842656</td>\n",
       "      <td>26.330220</td>\n",
       "      <td>2.325577e+05</td>\n",
       "      <td>0.212964</td>\n",
       "      <td>-0.532764</td>\n",
       "      <td>0.437623</td>\n",
       "      <td>0.541548</td>\n",
       "      <td>0.696268</td>\n",
       "      <td>904.159376</td>\n",
       "      <td>-0.481704</td>\n",
       "      <td>0.793214</td>\n",
       "      <td>0.718808</td>\n",
       "      <td>81.731718</td>\n",
       "      <td>-0.360000</td>\n",
       "      <td>-0.450000</td>\n",
       "      <td>-1.029407</td>\n",
       "      <td>-0.237036</td>\n",
       "      <td>-0.828657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.307716e+06</td>\n",
       "      <td>1.061525e+06</td>\n",
       "      <td>215099.424250</td>\n",
       "      <td>621144.469307</td>\n",
       "      <td>617446.170131</td>\n",
       "      <td>633345.990726</td>\n",
       "      <td>692147.406276</td>\n",
       "      <td>691356.134701</td>\n",
       "      <td>694793.490027</td>\n",
       "      <td>1.818863e+06</td>\n",
       "      <td>7.153559e+05</td>\n",
       "      <td>486505.829812</td>\n",
       "      <td>427549.089250</td>\n",
       "      <td>168482.183347</td>\n",
       "      <td>-23.349224</td>\n",
       "      <td>5.873972</td>\n",
       "      <td>2014.5</td>\n",
       "      <td>118.563854</td>\n",
       "      <td>107.358864</td>\n",
       "      <td>84.241624</td>\n",
       "      <td>2014.5</td>\n",
       "      <td>10.728600</td>\n",
       "      <td>8.455917</td>\n",
       "      <td>4</td>\n",
       "      <td>26.915987</td>\n",
       "      <td>65.203134</td>\n",
       "      <td>26.747802</td>\n",
       "      <td>59.767325</td>\n",
       "      <td>1227849</td>\n",
       "      <td>-0.100004</td>\n",
       "      <td>-0.134766</td>\n",
       "      <td>0.833556</td>\n",
       "      <td>1227849</td>\n",
       "      <td>2547.144009</td>\n",
       "      <td>2532.567871</td>\n",
       "      <td>50.020876</td>\n",
       "      <td>1963</td>\n",
       "      <td>0.492625</td>\n",
       "      <td>0.334278</td>\n",
       "      <td>3.767042</td>\n",
       "      <td>4</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.66</td>\n",
       "      <td>-2.814219</td>\n",
       "      <td>0.0</td>\n",
       "      <td>794.096532</td>\n",
       "      <td>23.6940</td>\n",
       "      <td>43.806310</td>\n",
       "      <td>-1.246191e+06</td>\n",
       "      <td>-1.014938</td>\n",
       "      <td>-1.103507e+06</td>\n",
       "      <td>-0.898732</td>\n",
       "      <td>2351588</td>\n",
       "      <td>0.505880</td>\n",
       "      <td>0.502868</td>\n",
       "      <td>0.515817</td>\n",
       "      <td>0.294332</td>\n",
       "      <td>0.293995</td>\n",
       "      <td>0.295457</td>\n",
       "      <td>0.585274</td>\n",
       "      <td>0.582503</td>\n",
       "      <td>0.594443</td>\n",
       "      <td>0.396226</td>\n",
       "      <td>0.181813</td>\n",
       "      <td>0.435948</td>\n",
       "      <td>0.914934</td>\n",
       "      <td>0.798728</td>\n",
       "      <td>884.420214</td>\n",
       "      <td>877.400714</td>\n",
       "      <td>0.809186</td>\n",
       "      <td>0.700804</td>\n",
       "      <td>0.697815</td>\n",
       "      <td>0.622444</td>\n",
       "      <td>0.578317</td>\n",
       "      <td>0.512233</td>\n",
       "      <td>2.525386e+06</td>\n",
       "      <td>1.185047e+06</td>\n",
       "      <td>237988.134413</td>\n",
       "      <td>664939.742177</td>\n",
       "      <td>724058.946290</td>\n",
       "      <td>2014.5</td>\n",
       "      <td>26.330220</td>\n",
       "      <td>61.099600</td>\n",
       "      <td>-1.340339e+06</td>\n",
       "      <td>-1.091616</td>\n",
       "      <td>0.991612</td>\n",
       "      <td>0.541548</td>\n",
       "      <td>0.307902</td>\n",
       "      <td>0.622959</td>\n",
       "      <td>886.837656</td>\n",
       "      <td>0.879399</td>\n",
       "      <td>0.729711</td>\n",
       "      <td>0.653083</td>\n",
       "      <td>75.031314</td>\n",
       "      <td>-0.110000</td>\n",
       "      <td>-0.063333</td>\n",
       "      <td>-1.078272</td>\n",
       "      <td>-1.154949</td>\n",
       "      <td>-0.962065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.061525e+06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>250848.331872</td>\n",
       "      <td>692147.406276</td>\n",
       "      <td>691356.134701</td>\n",
       "      <td>694793.490027</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.153559e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>427549.089250</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>154630.972793</td>\n",
       "      <td>-20.000452</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1684.2</td>\n",
       "      <td>163.187862</td>\n",
       "      <td>149.167348</td>\n",
       "      <td>92.321487</td>\n",
       "      <td>1684.2</td>\n",
       "      <td>4.231739</td>\n",
       "      <td>2.851747</td>\n",
       "      <td>5</td>\n",
       "      <td>65.203134</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59.767325</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2351588</td>\n",
       "      <td>-0.045572</td>\n",
       "      <td>-0.053223</td>\n",
       "      <td>0.376738</td>\n",
       "      <td>2351588</td>\n",
       "      <td>2585.203000</td>\n",
       "      <td>2577.216064</td>\n",
       "      <td>38.119369</td>\n",
       "      <td>3761</td>\n",
       "      <td>4.814351</td>\n",
       "      <td>2.771821</td>\n",
       "      <td>6.653227</td>\n",
       "      <td>5</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.96</td>\n",
       "      <td>-3.082116</td>\n",
       "      <td>0.0</td>\n",
       "      <td>794.139112</td>\n",
       "      <td>34.7520</td>\n",
       "      <td>37.781380</td>\n",
       "      <td>-1.061525e+06</td>\n",
       "      <td>-0.451408</td>\n",
       "      <td>-7.153559e+05</td>\n",
       "      <td>-0.304201</td>\n",
       "      <td>0</td>\n",
       "      <td>0.294332</td>\n",
       "      <td>0.293995</td>\n",
       "      <td>0.295457</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.294332</td>\n",
       "      <td>0.293995</td>\n",
       "      <td>0.295457</td>\n",
       "      <td>0.181813</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.181813</td>\n",
       "      <td>0.405836</td>\n",
       "      <td>0.258630</td>\n",
       "      <td>883.007514</td>\n",
       "      <td>866.387211</td>\n",
       "      <td>0.358356</td>\n",
       "      <td>0.224073</td>\n",
       "      <td>0.480657</td>\n",
       "      <td>0.425934</td>\n",
       "      <td>0.421255</td>\n",
       "      <td>0.365657</td>\n",
       "      <td>1.185047e+06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>269575.279011</td>\n",
       "      <td>724058.946290</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1684.2</td>\n",
       "      <td>61.099600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.185047e+06</td>\n",
       "      <td>-0.503935</td>\n",
       "      <td>0.458363</td>\n",
       "      <td>0.307902</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.307902</td>\n",
       "      <td>885.326611</td>\n",
       "      <td>0.405801</td>\n",
       "      <td>0.489085</td>\n",
       "      <td>0.434897</td>\n",
       "      <td>57.179053</td>\n",
       "      <td>-0.110000</td>\n",
       "      <td>-0.063333</td>\n",
       "      <td>-0.514741</td>\n",
       "      <td>-0.567268</td>\n",
       "      <td>-0.367535</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Q_out          Q_in        cr_area          sQout      sQout_low  \\\n",
       "0  0.000000e+00  8.579872e+05       0.000000       0.000000       0.000000   \n",
       "1  8.579872e+05  1.615478e+06   45087.173055  144608.782613  142387.001277   \n",
       "2  1.615478e+06  2.940431e+06  129217.492215  385998.907479  382491.131257   \n",
       "3  2.940431e+06  2.307716e+06  231417.427109  680593.545044  675305.888570   \n",
       "4  2.307716e+06  1.061525e+06  215099.424250  621144.469307  617446.170131   \n",
       "5  1.061525e+06  0.000000e+00  250848.331872  692147.406276  691356.134701   \n",
       "\n",
       "      sQout_high           sQin       sQin_low      sQin_high        Q_outF  \\\n",
       "0       0.000000  144608.782613  142387.001277  151761.359317  0.000000e+00   \n",
       "1  151761.359317  385998.907479  382491.131257  397396.805308  9.418587e+05   \n",
       "2  397396.805308  680593.545044  675305.888570  697969.811988  1.370466e+06   \n",
       "3  697969.811988  621144.469307  617446.170131  633345.990726  2.232358e+06   \n",
       "4  633345.990726  692147.406276  691356.134701  694793.490027  1.818863e+06   \n",
       "5  694793.490027       0.000000       0.000000       0.000000  7.153559e+05   \n",
       "\n",
       "          Q_inF         sQoutF          sQinF       cr_areaF      vzdir  \\\n",
       "0  9.418587e+05       0.000000  144671.252459       0.000000   0.000000   \n",
       "1  1.370466e+06  144671.252459  301379.602267   44205.908290 -49.608490   \n",
       "2  2.232358e+06  301379.602267  504823.010721  100000.940695 -46.643524   \n",
       "3  1.818863e+06  504823.010721  486505.829812  171326.315578 -27.808907   \n",
       "4  7.153559e+05  486505.829812  427549.089250  168482.183347 -23.349224   \n",
       "5  0.000000e+00  427549.089250       0.000000  154630.972793 -20.000452   \n",
       "\n",
       "   v_gate_mean  gate_width         gpr        Hopt         HF  Len_mod  \\\n",
       "0    18.632645         0.0    0.000000    0.000000   0.000000      0.0   \n",
       "1    12.654183       918.4   63.334673   48.761864  45.872791    918.4   \n",
       "2    13.166764      1546.4   71.193643   84.338839  63.809505   1543.2   \n",
       "3    10.793298      1836.5   91.439333  126.194044  93.050323   1836.5   \n",
       "4     5.873972      2014.5  118.563854  107.358864  84.241624   2014.5   \n",
       "5          NaN      1684.2  163.187862  149.167348  92.321487   1684.2   \n",
       "\n",
       "         vfg       vfgF  bin     spQout      spQin    spQoutF     spQinF  \\\n",
       "0        NaN        NaN    0        NaN  16.854422        NaN  15.360186   \n",
       "1  19.029519  20.889727    1  16.854422  23.893794  15.360186  21.991028   \n",
       "2  12.502004  10.605888    2  23.893794  23.146050  21.991028  22.613894   \n",
       "3  12.706176   9.646455    3  23.146050  26.915987  22.613894  26.747802   \n",
       "4  10.728600   8.455917    4  26.915987  65.203134  26.747802  59.767325   \n",
       "5   4.231739   2.851747    5  65.203134        NaN  59.767325        NaN   \n",
       "\n",
       "   dh_count   dh_mean    dh_med    dh_std  dem_count     dem_mean  \\\n",
       "0    413219 -1.166136 -1.115234  2.387476     413219  2206.260758   \n",
       "1    319038 -0.583821 -0.729370  1.720141     319038  2325.640657   \n",
       "2    863384 -0.396365 -0.452637  0.813162     863384  2395.147355   \n",
       "3   1092004 -0.319800 -0.343018  0.477889    1092004  2479.688734   \n",
       "4   1227849 -0.100004 -0.134766  0.833556    1227849  2547.144009   \n",
       "5   2351588 -0.045572 -0.053223  0.376738    2351588  2585.203000   \n",
       "\n",
       "       dem_med     dem_std  vx_count    vx_mean    vx_med     vx_std  \\\n",
       "0  2225.477783  109.575024       659  -8.103982 -3.184993  10.318706   \n",
       "1  2322.429688   30.030002       513 -11.066018 -7.153669  10.215554   \n",
       "2  2390.628174   41.086899      1381  -8.389135 -6.209127   7.751619   \n",
       "3  2460.456055   54.487812      1746  -3.605541 -2.867055   4.527447   \n",
       "4  2532.567871   50.020876      1963   0.492625  0.334278   3.767042   \n",
       "5  2577.216064   38.119369      3761   4.814351  2.771821   6.653227   \n",
       "\n",
       "   Unnamed: 0    ba  firn_area      Tavg  Vfirn  column_density  column_depth  \\\n",
       "0           0  0.00       0.00 -0.963464    0.0        0.000000        0.0000   \n",
       "1           1  0.00       0.00 -1.554041    0.0        0.000000        0.0000   \n",
       "2           2  0.00       0.00 -1.962627    0.0        0.000000        0.0000   \n",
       "3           3  0.32       0.13 -2.381042    0.0      794.113546        4.6085   \n",
       "4           4  0.65       0.66 -2.814219    0.0      794.096532       23.6940   \n",
       "5           5  0.76       0.96 -3.082116    0.0      794.139112       34.7520   \n",
       "\n",
       "        time         Q_net    Q_netA        Q_netF   Q_netAF  area_Qin  \\\n",
       "0   0.000000  8.579872e+05  2.076350  9.418587e+05  2.279321    319038   \n",
       "1   0.000000  7.574904e+05  2.374295  4.286075e+05  1.343437    863384   \n",
       "2   0.000000  1.324953e+06  1.534605  8.618915e+05  0.998271   1092004   \n",
       "3  87.866861 -6.327149e+05 -0.579407 -4.134949e+05 -0.378657   1227849   \n",
       "4  43.806310 -1.246191e+06 -1.014938 -1.103507e+06 -0.898732   2351588   \n",
       "5  37.781380 -1.061525e+06 -0.451408 -7.153559e+05 -0.304201         0   \n",
       "\n",
       "     sQoptA  sQoptA_low  sQoptA_high    sQ_inA  sQ_inA_low  sQ_inA_high  \\\n",
       "0  0.000000    0.000000     0.000000  0.453265    0.446301     0.475684   \n",
       "1  0.453265    0.446301     0.475684  0.447077    0.443014     0.460278   \n",
       "2  0.447077    0.443014     0.460278  0.623252    0.618410     0.639164   \n",
       "3  0.623252    0.618410     0.639164  0.505880    0.502868     0.515817   \n",
       "4  0.505880    0.502868     0.515817  0.294332    0.293995     0.295457   \n",
       "5  0.294332    0.293995     0.295457  0.000000    0.000000     0.000000   \n",
       "\n",
       "   sQnetA_opt  sQnetA_opt_low  sQnetA_opt_high   sQoutFA   sQ_inFA   sQnetFA  \\\n",
       "0    0.453265        0.446301         0.475684  0.000000  0.453461  0.453461   \n",
       "1    0.636653        0.628845         0.661915  0.453461  0.349068  0.572254   \n",
       "2    0.767021        0.760718         0.787646  0.349068  0.462290  0.579276   \n",
       "3    0.802719        0.797061         0.821339  0.462290  0.396226  0.608858   \n",
       "4    0.585274        0.582503         0.594443  0.396226  0.181813  0.435948   \n",
       "5    0.294332        0.293995         0.295457  0.181813  0.000000  0.181813   \n",
       "\n",
       "     b_fg_h   b_fg_hF         RHO       RHO_F   b_fg_we  b_fg_weF   sDH_opt  \\\n",
       "0 -3.242486 -3.445457  910.000000  910.000000 -2.950662 -3.135366  0.591481   \n",
       "1 -2.958117 -1.927258  910.000000  910.000000 -2.691886 -1.753805  0.741436   \n",
       "2 -1.930970 -1.394637  910.000000  910.000000 -1.757183 -1.269119  0.855991   \n",
       "3  0.259607  0.058857  905.767924  904.260496  0.235144  0.053222  0.888120   \n",
       "4  0.914934  0.798728  884.420214  877.400714  0.809186  0.700804  0.697815   \n",
       "5  0.405836  0.258630  883.007514  866.387211  0.358356  0.224073  0.480657   \n",
       "\n",
       "   sBwe_opt     sDH_F    sBwe_F        Qo_gpr       Qin_gpr    cr_area_gpr  \\\n",
       "0  0.613819  0.591631  0.623027  0.000000e+00  1.308510e+06       0.000000   \n",
       "1  0.726424  0.686932  0.649244  1.308510e+06  1.682739e+06   61701.978461   \n",
       "2  0.798525  0.692792  0.643088  1.682739e+06  2.292828e+06  109551.782716   \n",
       "3  0.804774  0.717710  0.649019  2.292828e+06  2.525386e+06  167611.505015   \n",
       "4  0.622444  0.578317  0.512233  2.525386e+06  1.185047e+06  237988.134413   \n",
       "5  0.425934  0.421255  0.365657  1.185047e+06  0.000000e+00  269575.279011   \n",
       "\n",
       "         sQo_gpr       sQin_gpr  Len_gpr   spQo_gpr  spQin_gpr     Q_net_gpr  \\\n",
       "0       0.000000  179929.234256      0.0        NaN  13.750698  1.308510e+06   \n",
       "1  179929.234256  316223.695314    918.4  13.750698  18.792196  3.742297e+05   \n",
       "2  316223.695314  477886.348142   1543.2  18.792196  20.842656  6.100890e+05   \n",
       "3  477886.348142  664939.742177   1836.5  20.842656  26.330220  2.325577e+05   \n",
       "4  664939.742177  724058.946290   2014.5  26.330220  61.099600 -1.340339e+06   \n",
       "5  724058.946290       0.000000   1684.2  61.099600        NaN -1.185047e+06   \n",
       "\n",
       "   Q_netA_gpr  b_fg_h_gpr  sQoutA_gpr  sQ_inA_gpr  sQnetA_gpr       RHO_g  \\\n",
       "0    3.166625   -4.332762    0.000000    0.563974    0.563974  910.000000   \n",
       "1    1.172994   -1.756815    0.563974    0.366261    0.672469  910.000000   \n",
       "2    0.706625   -1.102991    0.366261    0.437623    0.570667  910.000000   \n",
       "3    0.212964   -0.532764    0.437623    0.541548    0.696268  904.159376   \n",
       "4   -1.091616    0.991612    0.541548    0.307902    0.622959  886.837656   \n",
       "5   -0.503935    0.458363    0.307902    0.000000    0.307902  885.326611   \n",
       "\n",
       "   b_fg_we_gpr   sDH_gpr  sBwe_gpr        xerr       SMB        BH       BMC  \\\n",
       "0    -3.942813  0.680049  0.733776  109.575024 -4.156000 -4.568000 -2.491650   \n",
       "1    -1.598702  0.772408  0.720843   45.045002 -2.410000 -2.656667 -0.282371   \n",
       "2    -1.003721  0.685610  0.631927   61.630349 -0.906667 -1.033333  0.501271   \n",
       "3    -0.481704  0.793214  0.718808   81.731718 -0.360000 -0.450000 -1.029407   \n",
       "4     0.879399  0.729711  0.653083   75.031314 -0.110000 -0.063333 -1.078272   \n",
       "5     0.405801  0.489085  0.434897   57.179053 -0.110000 -0.063333 -0.514741   \n",
       "\n",
       "    BMC_gpr      BMCF  \n",
       "0 -1.401375 -2.288679  \n",
       "1 -1.483673 -1.313230  \n",
       "2 -0.326708 -0.035062  \n",
       "3 -0.237036 -0.828657  \n",
       "4 -1.154949 -0.962065  \n",
       "5 -0.567268 -0.367535  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "866.7734105786323"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r=[new16.RHO,new16.RHO_g,new16.RHO_F]\n",
    "np.min(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##mean Vfirn\n",
    "# np.sum(rho.Vfirn[4:]*new.dem_count[4:])/np.sum(new.dem_count[4:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5607660658002256"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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.nanmean(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    16.854422\n",
      "1    29.240126\n",
      "2    33.266395\n",
      "3    35.499436\n",
      "4    70.540195\n",
      "5    65.203134\n",
      "dtype: float64\n",
      "41.7672847217831\n",
      "51.65467672690781\n",
      "0    15.360186\n",
      "1    29.240126\n",
      "2    33.266395\n",
      "3    35.499436\n",
      "4    70.540195\n",
      "5    59.767325\n",
      "dtype: float64\n",
      "40.612277242282865\n",
      "49.51648384260323\n"
     ]
    }
   ],
   "source": [
    "B=np.sqrt(new.spQout**2 + new.spQin**2)\n",
    "B[0]=new.spQin[0];B[5]=new.spQout[5]\n",
    "print(B)\n",
    "print(np.mean(B))\n",
    "print(np.sum(B*new.dem_count)/np.sum(new.dem_count))\n",
    "\n",
    "B=np.sqrt(new.spQout**2 + new.spQin**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": 20,
   "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>i</th>\n",
       "      <th>j</th>\n",
       "      <th>vx</th>\n",
       "      <th>vy</th>\n",
       "      <th>vz</th>\n",
       "      <th>ITS</th>\n",
       "      <th>gpr</th>\n",
       "      <th>H_opt</th>\n",
       "      <th>dem</th>\n",
       "      <th>farin_corr</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0071_0155</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.450767</td>\n",
       "      <td>51.233473</td>\n",
       "      <td>71</td>\n",
       "      <td>155</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.070843</td>\n",
       "      <td>2436.034912</td>\n",
       "      <td>-10.072021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0072_0153</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>50.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.450305</td>\n",
       "      <td>51.233817</td>\n",
       "      <td>72</td>\n",
       "      <td>153</td>\n",
       "      <td>-0.891504</td>\n",
       "      <td>0.109947</td>\n",
       "      <td>0.898259</td>\n",
       "      <td>0.719045</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.511818</td>\n",
       "      <td>2405.994141</td>\n",
       "      <td>12.412224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0072_0154</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>25.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.450536</td>\n",
       "      <td>51.233645</td>\n",
       "      <td>72</td>\n",
       "      <td>154</td>\n",
       "      <td>-1.176502</td>\n",
       "      <td>0.566844</td>\n",
       "      <td>1.305936</td>\n",
       "      <td>1.843738</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.511818</td>\n",
       "      <td>2416.623535</td>\n",
       "      <td>10.002878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0073_0153</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>75.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.450075</td>\n",
       "      <td>51.233989</td>\n",
       "      <td>73</td>\n",
       "      <td>153</td>\n",
       "      <td>-1.122839</td>\n",
       "      <td>1.305790</td>\n",
       "      <td>1.722166</td>\n",
       "      <td>0.719045</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.511818</td>\n",
       "      <td>2397.521729</td>\n",
       "      <td>17.151449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0074_0151</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>125.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.449613</td>\n",
       "      <td>51.234333</td>\n",
       "      <td>74</td>\n",
       "      <td>151</td>\n",
       "      <td>-2.454499</td>\n",
       "      <td>4.143846</td>\n",
       "      <td>4.816225</td>\n",
       "      <td>0.719045</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.854343</td>\n",
       "      <td>2375.734619</td>\n",
       "      <td>25.525204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0074_0152</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>100.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.449844</td>\n",
       "      <td>51.234161</td>\n",
       "      <td>74</td>\n",
       "      <td>152</td>\n",
       "      <td>-2.060087</td>\n",
       "      <td>3.557255</td>\n",
       "      <td>4.110720</td>\n",
       "      <td>0.719045</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.854343</td>\n",
       "      <td>2380.168945</td>\n",
       "      <td>21.517933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0075_0150</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>150.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.449383</td>\n",
       "      <td>51.234505</td>\n",
       "      <td>75</td>\n",
       "      <td>150</td>\n",
       "      <td>-2.758981</td>\n",
       "      <td>5.623297</td>\n",
       "      <td>6.263660</td>\n",
       "      <td>0.719045</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52.187756</td>\n",
       "      <td>2363.848145</td>\n",
       "      <td>30.777630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0076_0149</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>200.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.448922</td>\n",
       "      <td>51.234849</td>\n",
       "      <td>76</td>\n",
       "      <td>149</td>\n",
       "      <td>-1.194037</td>\n",
       "      <td>4.461414</td>\n",
       "      <td>4.618435</td>\n",
       "      <td>0.719045</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59.944324</td>\n",
       "      <td>2350.191406</td>\n",
       "      <td>33.941872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0076_0150</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>175.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.449152</td>\n",
       "      <td>51.234677</td>\n",
       "      <td>76</td>\n",
       "      <td>150</td>\n",
       "      <td>-1.174731</td>\n",
       "      <td>3.491723</td>\n",
       "      <td>3.684036</td>\n",
       "      <td>0.719045</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59.944324</td>\n",
       "      <td>2358.885498</td>\n",
       "      <td>35.037579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0077_0148</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>225.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.448691</td>\n",
       "      <td>51.235021</td>\n",
       "      <td>77</td>\n",
       "      <td>148</td>\n",
       "      <td>-0.049929</td>\n",
       "      <td>3.367443</td>\n",
       "      <td>3.367813</td>\n",
       "      <td>0.719045</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.990852</td>\n",
       "      <td>2338.738281</td>\n",
       "      <td>38.622501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0078_0147</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>262.5</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.448345</td>\n",
       "      <td>51.235279</td>\n",
       "      <td>78</td>\n",
       "      <td>147</td>\n",
       "      <td>-0.179376</td>\n",
       "      <td>3.375454</td>\n",
       "      <td>3.380217</td>\n",
       "      <td>0.719045</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.158043</td>\n",
       "      <td>2330.911621</td>\n",
       "      <td>42.046001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0079_0146</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>300.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.447999</td>\n",
       "      <td>51.235537</td>\n",
       "      <td>79</td>\n",
       "      <td>146</td>\n",
       "      <td>-0.339423</td>\n",
       "      <td>1.920726</td>\n",
       "      <td>1.950486</td>\n",
       "      <td>5.729864</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.283417</td>\n",
       "      <td>2326.010254</td>\n",
       "      <td>45.780258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0080_0144</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>350.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.447538</td>\n",
       "      <td>51.235881</td>\n",
       "      <td>80</td>\n",
       "      <td>144</td>\n",
       "      <td>-0.254104</td>\n",
       "      <td>1.447165</td>\n",
       "      <td>1.469305</td>\n",
       "      <td>5.729864</td>\n",
       "      <td>31.244942</td>\n",
       "      <td>71.915627</td>\n",
       "      <td>2318.848389</td>\n",
       "      <td>50.989841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0080_0145</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>325.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.447768</td>\n",
       "      <td>51.235709</td>\n",
       "      <td>80</td>\n",
       "      <td>145</td>\n",
       "      <td>-0.372974</td>\n",
       "      <td>1.700551</td>\n",
       "      <td>1.740972</td>\n",
       "      <td>5.729864</td>\n",
       "      <td>33.853920</td>\n",
       "      <td>73.235817</td>\n",
       "      <td>2323.853271</td>\n",
       "      <td>49.947437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0081_0143</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>400.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.447076</td>\n",
       "      <td>51.236225</td>\n",
       "      <td>81</td>\n",
       "      <td>143</td>\n",
       "      <td>-0.488615</td>\n",
       "      <td>1.883482</td>\n",
       "      <td>1.945828</td>\n",
       "      <td>12.222924</td>\n",
       "      <td>31.989626</td>\n",
       "      <td>71.915627</td>\n",
       "      <td>2319.528076</td>\n",
       "      <td>55.103783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0081_0144</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>375.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.447307</td>\n",
       "      <td>51.236053</td>\n",
       "      <td>81</td>\n",
       "      <td>144</td>\n",
       "      <td>-0.236157</td>\n",
       "      <td>1.599982</td>\n",
       "      <td>1.617317</td>\n",
       "      <td>12.222924</td>\n",
       "      <td>33.615875</td>\n",
       "      <td>71.915627</td>\n",
       "      <td>2322.811279</td>\n",
       "      <td>52.365417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0082_0142</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>425.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.446846</td>\n",
       "      <td>51.236397</td>\n",
       "      <td>82</td>\n",
       "      <td>142</td>\n",
       "      <td>-2.005025</td>\n",
       "      <td>3.146753</td>\n",
       "      <td>3.731244</td>\n",
       "      <td>12.222924</td>\n",
       "      <td>35.152126</td>\n",
       "      <td>75.815948</td>\n",
       "      <td>2321.813477</td>\n",
       "      <td>62.632538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0083_0141</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>462.5</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.446500</td>\n",
       "      <td>51.236654</td>\n",
       "      <td>83</td>\n",
       "      <td>141</td>\n",
       "      <td>-4.109261</td>\n",
       "      <td>5.005182</td>\n",
       "      <td>6.475946</td>\n",
       "      <td>12.222924</td>\n",
       "      <td>35.970089</td>\n",
       "      <td>75.815948</td>\n",
       "      <td>2324.301270</td>\n",
       "      <td>67.321701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0084_0140</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>500.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.446154</td>\n",
       "      <td>51.236912</td>\n",
       "      <td>84</td>\n",
       "      <td>140</td>\n",
       "      <td>-6.082923</td>\n",
       "      <td>6.660829</td>\n",
       "      <td>9.020454</td>\n",
       "      <td>12.222924</td>\n",
       "      <td>42.100338</td>\n",
       "      <td>82.390816</td>\n",
       "      <td>2327.738281</td>\n",
       "      <td>71.755089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0085_0138</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>550.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.445692</td>\n",
       "      <td>51.237256</td>\n",
       "      <td>85</td>\n",
       "      <td>138</td>\n",
       "      <td>-10.997713</td>\n",
       "      <td>10.148372</td>\n",
       "      <td>14.964597</td>\n",
       "      <td>18.889126</td>\n",
       "      <td>65.259850</td>\n",
       "      <td>81.267601</td>\n",
       "      <td>2324.895020</td>\n",
       "      <td>74.883125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0085_0139</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>525.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.445923</td>\n",
       "      <td>51.237084</td>\n",
       "      <td>85</td>\n",
       "      <td>139</td>\n",
       "      <td>-8.592056</td>\n",
       "      <td>8.522914</td>\n",
       "      <td>12.102211</td>\n",
       "      <td>18.889126</td>\n",
       "      <td>58.951794</td>\n",
       "      <td>82.390816</td>\n",
       "      <td>2329.271973</td>\n",
       "      <td>74.074547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0086_0137</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>575.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.445462</td>\n",
       "      <td>51.237428</td>\n",
       "      <td>86</td>\n",
       "      <td>137</td>\n",
       "      <td>-13.502338</td>\n",
       "      <td>12.672405</td>\n",
       "      <td>18.517641</td>\n",
       "      <td>18.889126</td>\n",
       "      <td>80.976166</td>\n",
       "      <td>92.043465</td>\n",
       "      <td>2326.557129</td>\n",
       "      <td>76.981903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0087_0136</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>625.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.445000</td>\n",
       "      <td>51.237772</td>\n",
       "      <td>87</td>\n",
       "      <td>136</td>\n",
       "      <td>-16.446974</td>\n",
       "      <td>13.637007</td>\n",
       "      <td>21.365179</td>\n",
       "      <td>18.889126</td>\n",
       "      <td>87.212715</td>\n",
       "      <td>91.974899</td>\n",
       "      <td>2329.011719</td>\n",
       "      <td>83.486778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0087_0137</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>600.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.445231</td>\n",
       "      <td>51.237600</td>\n",
       "      <td>87</td>\n",
       "      <td>137</td>\n",
       "      <td>-14.321509</td>\n",
       "      <td>12.916906</td>\n",
       "      <td>19.286060</td>\n",
       "      <td>18.889126</td>\n",
       "      <td>85.599091</td>\n",
       "      <td>92.043465</td>\n",
       "      <td>2330.806152</td>\n",
       "      <td>81.106499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0088_0135</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>650.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.444770</td>\n",
       "      <td>51.237944</td>\n",
       "      <td>88</td>\n",
       "      <td>135</td>\n",
       "      <td>-18.934896</td>\n",
       "      <td>15.447783</td>\n",
       "      <td>24.436945</td>\n",
       "      <td>18.889126</td>\n",
       "      <td>91.614212</td>\n",
       "      <td>103.363510</td>\n",
       "      <td>2330.921875</td>\n",
       "      <td>91.498276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0089_0134</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>687.5</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.444424</td>\n",
       "      <td>51.238202</td>\n",
       "      <td>89</td>\n",
       "      <td>134</td>\n",
       "      <td>-19.546329</td>\n",
       "      <td>16.354954</td>\n",
       "      <td>25.486143</td>\n",
       "      <td>18.889126</td>\n",
       "      <td>92.311813</td>\n",
       "      <td>103.811371</td>\n",
       "      <td>2331.744385</td>\n",
       "      <td>96.518288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0090_0133</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>725.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.444078</td>\n",
       "      <td>51.238460</td>\n",
       "      <td>90</td>\n",
       "      <td>133</td>\n",
       "      <td>-20.075859</td>\n",
       "      <td>16.318838</td>\n",
       "      <td>25.871695</td>\n",
       "      <td>18.889126</td>\n",
       "      <td>97.084946</td>\n",
       "      <td>113.745529</td>\n",
       "      <td>2332.668213</td>\n",
       "      <td>99.839233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0091_0131</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>775.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.443616</td>\n",
       "      <td>51.238804</td>\n",
       "      <td>91</td>\n",
       "      <td>131</td>\n",
       "      <td>-21.659237</td>\n",
       "      <td>15.738627</td>\n",
       "      <td>26.773624</td>\n",
       "      <td>12.366378</td>\n",
       "      <td>133.130203</td>\n",
       "      <td>113.660614</td>\n",
       "      <td>2332.812500</td>\n",
       "      <td>103.412148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0091_0132</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>750.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.443847</td>\n",
       "      <td>51.238632</td>\n",
       "      <td>91</td>\n",
       "      <td>132</td>\n",
       "      <td>-18.681419</td>\n",
       "      <td>14.698958</td>\n",
       "      <td>23.770882</td>\n",
       "      <td>18.889126</td>\n",
       "      <td>126.766197</td>\n",
       "      <td>113.660614</td>\n",
       "      <td>2333.416992</td>\n",
       "      <td>103.093727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0092_0130</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>825.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.443155</td>\n",
       "      <td>51.239148</td>\n",
       "      <td>92</td>\n",
       "      <td>130</td>\n",
       "      <td>-23.780621</td>\n",
       "      <td>16.767580</td>\n",
       "      <td>29.097589</td>\n",
       "      <td>12.366378</td>\n",
       "      <td>151.204697</td>\n",
       "      <td>117.594673</td>\n",
       "      <td>2333.950439</td>\n",
       "      <td>103.125618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0092_0131</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>800.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.443385</td>\n",
       "      <td>51.238976</td>\n",
       "      <td>92</td>\n",
       "      <td>131</td>\n",
       "      <td>-21.792322</td>\n",
       "      <td>16.385841</td>\n",
       "      <td>27.265383</td>\n",
       "      <td>12.366378</td>\n",
       "      <td>149.555695</td>\n",
       "      <td>119.762009</td>\n",
       "      <td>2334.337891</td>\n",
       "      <td>103.793404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0093_0129</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>850.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.442924</td>\n",
       "      <td>51.239320</td>\n",
       "      <td>93</td>\n",
       "      <td>129</td>\n",
       "      <td>-24.438484</td>\n",
       "      <td>18.444895</td>\n",
       "      <td>30.617865</td>\n",
       "      <td>12.366378</td>\n",
       "      <td>150.884369</td>\n",
       "      <td>117.594673</td>\n",
       "      <td>2334.899902</td>\n",
       "      <td>100.097801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0094_0128</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>887.5</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.442578</td>\n",
       "      <td>51.239578</td>\n",
       "      <td>94</td>\n",
       "      <td>128</td>\n",
       "      <td>-25.312876</td>\n",
       "      <td>17.690983</td>\n",
       "      <td>30.882236</td>\n",
       "      <td>12.366378</td>\n",
       "      <td>150.094376</td>\n",
       "      <td>113.259750</td>\n",
       "      <td>2334.807861</td>\n",
       "      <td>94.205399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0095_0127</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>925.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.442232</td>\n",
       "      <td>51.239836</td>\n",
       "      <td>95</td>\n",
       "      <td>127</td>\n",
       "      <td>-25.834362</td>\n",
       "      <td>17.875206</td>\n",
       "      <td>31.415558</td>\n",
       "      <td>12.366378</td>\n",
       "      <td>142.249146</td>\n",
       "      <td>113.259750</td>\n",
       "      <td>2334.182617</td>\n",
       "      <td>89.275749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0096_0125</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>975.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.441771</td>\n",
       "      <td>51.240179</td>\n",
       "      <td>96</td>\n",
       "      <td>125</td>\n",
       "      <td>-23.408623</td>\n",
       "      <td>17.627037</td>\n",
       "      <td>29.303175</td>\n",
       "      <td>13.465559</td>\n",
       "      <td>123.242241</td>\n",
       "      <td>103.687302</td>\n",
       "      <td>2335.090088</td>\n",
       "      <td>81.956741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0096_0126</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>950.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.442001</td>\n",
       "      <td>51.240007</td>\n",
       "      <td>96</td>\n",
       "      <td>126</td>\n",
       "      <td>-24.363230</td>\n",
       "      <td>17.453924</td>\n",
       "      <td>29.970093</td>\n",
       "      <td>13.465559</td>\n",
       "      <td>129.689896</td>\n",
       "      <td>103.687302</td>\n",
       "      <td>2336.023438</td>\n",
       "      <td>86.110916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0097_0125</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.441540</td>\n",
       "      <td>51.240351</td>\n",
       "      <td>97</td>\n",
       "      <td>125</td>\n",
       "      <td>-21.989729</td>\n",
       "      <td>18.210983</td>\n",
       "      <td>28.551498</td>\n",
       "      <td>13.465559</td>\n",
       "      <td>107.179092</td>\n",
       "      <td>103.687302</td>\n",
       "      <td>2340.223877</td>\n",
       "      <td>83.504616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0098_0123</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.441079</td>\n",
       "      <td>51.240695</td>\n",
       "      <td>98</td>\n",
       "      <td>123</td>\n",
       "      <td>-20.416815</td>\n",
       "      <td>16.741089</td>\n",
       "      <td>26.402847</td>\n",
       "      <td>2.506803</td>\n",
       "      <td>87.851715</td>\n",
       "      <td>87.851044</td>\n",
       "      <td>2346.303467</td>\n",
       "      <td>79.855759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0098_0124</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>1025.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.441309</td>\n",
       "      <td>51.240523</td>\n",
       "      <td>98</td>\n",
       "      <td>124</td>\n",
       "      <td>-21.712559</td>\n",
       "      <td>18.417194</td>\n",
       "      <td>28.471535</td>\n",
       "      <td>2.506803</td>\n",
       "      <td>89.241837</td>\n",
       "      <td>95.532104</td>\n",
       "      <td>2346.734131</td>\n",
       "      <td>82.152206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0099_0122</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>1075.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.440848</td>\n",
       "      <td>51.240867</td>\n",
       "      <td>99</td>\n",
       "      <td>122</td>\n",
       "      <td>-18.867882</td>\n",
       "      <td>14.738803</td>\n",
       "      <td>23.942207</td>\n",
       "      <td>2.506803</td>\n",
       "      <td>86.449753</td>\n",
       "      <td>87.851044</td>\n",
       "      <td>2354.168457</td>\n",
       "      <td>79.991890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0100_0121</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.440386</td>\n",
       "      <td>51.241211</td>\n",
       "      <td>100</td>\n",
       "      <td>121</td>\n",
       "      <td>-16.663286</td>\n",
       "      <td>11.320371</td>\n",
       "      <td>20.144873</td>\n",
       "      <td>2.506803</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.413452</td>\n",
       "      <td>2358.642090</td>\n",
       "      <td>78.945198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0100_0122</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.440617</td>\n",
       "      <td>51.241039</td>\n",
       "      <td>100</td>\n",
       "      <td>122</td>\n",
       "      <td>-18.132820</td>\n",
       "      <td>12.636072</td>\n",
       "      <td>22.101345</td>\n",
       "      <td>2.506803</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93.450508</td>\n",
       "      <td>2360.580322</td>\n",
       "      <td>82.545349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0101_0120</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>1150.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.440156</td>\n",
       "      <td>51.241383</td>\n",
       "      <td>101</td>\n",
       "      <td>120</td>\n",
       "      <td>-14.651629</td>\n",
       "      <td>9.258006</td>\n",
       "      <td>17.331501</td>\n",
       "      <td>2.506803</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.413452</td>\n",
       "      <td>2360.864014</td>\n",
       "      <td>76.649757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0102_0118</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.439694</td>\n",
       "      <td>51.241727</td>\n",
       "      <td>102</td>\n",
       "      <td>118</td>\n",
       "      <td>-9.477534</td>\n",
       "      <td>5.063577</td>\n",
       "      <td>10.745393</td>\n",
       "      <td>2.506803</td>\n",
       "      <td>NaN</td>\n",
       "      <td>92.393913</td>\n",
       "      <td>2365.439453</td>\n",
       "      <td>73.845139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0102_0119</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>1175.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.439925</td>\n",
       "      <td>51.241555</td>\n",
       "      <td>102</td>\n",
       "      <td>119</td>\n",
       "      <td>-11.655224</td>\n",
       "      <td>7.254674</td>\n",
       "      <td>13.728602</td>\n",
       "      <td>2.506803</td>\n",
       "      <td>NaN</td>\n",
       "      <td>92.393913</td>\n",
       "      <td>2370.055420</td>\n",
       "      <td>76.991905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0103_0118</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>1225.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.439464</td>\n",
       "      <td>51.241899</td>\n",
       "      <td>103</td>\n",
       "      <td>118</td>\n",
       "      <td>-8.916608</td>\n",
       "      <td>5.112708</td>\n",
       "      <td>10.278409</td>\n",
       "      <td>2.506803</td>\n",
       "      <td>NaN</td>\n",
       "      <td>92.393913</td>\n",
       "      <td>2374.140137</td>\n",
       "      <td>81.808945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0104_0116</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>1275.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.439002</td>\n",
       "      <td>51.242243</td>\n",
       "      <td>104</td>\n",
       "      <td>116</td>\n",
       "      <td>-2.292946</td>\n",
       "      <td>1.438168</td>\n",
       "      <td>2.706645</td>\n",
       "      <td>0.744128</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102.648254</td>\n",
       "      <td>2378.154541</td>\n",
       "      <td>79.818222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0104_0117</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.439233</td>\n",
       "      <td>51.242071</td>\n",
       "      <td>104</td>\n",
       "      <td>117</td>\n",
       "      <td>-4.719091</td>\n",
       "      <td>2.849271</td>\n",
       "      <td>5.512547</td>\n",
       "      <td>0.744128</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102.648254</td>\n",
       "      <td>2376.739502</td>\n",
       "      <td>84.696411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0105_0115</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>1312.5</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.438656</td>\n",
       "      <td>51.242501</td>\n",
       "      <td>105</td>\n",
       "      <td>115</td>\n",
       "      <td>-2.565575</td>\n",
       "      <td>0.831348</td>\n",
       "      <td>2.696908</td>\n",
       "      <td>0.744128</td>\n",
       "      <td>64.706932</td>\n",
       "      <td>96.731873</td>\n",
       "      <td>2380.593018</td>\n",
       "      <td>79.735291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0106_0114</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.438310</td>\n",
       "      <td>51.242759</td>\n",
       "      <td>106</td>\n",
       "      <td>114</td>\n",
       "      <td>-2.955224</td>\n",
       "      <td>0.990706</td>\n",
       "      <td>3.116865</td>\n",
       "      <td>0.744128</td>\n",
       "      <td>71.924782</td>\n",
       "      <td>109.014015</td>\n",
       "      <td>2380.356445</td>\n",
       "      <td>79.905098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0107_0112</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.437848</td>\n",
       "      <td>51.243102</td>\n",
       "      <td>107</td>\n",
       "      <td>112</td>\n",
       "      <td>-2.071545</td>\n",
       "      <td>0.027409</td>\n",
       "      <td>2.071726</td>\n",
       "      <td>0.744128</td>\n",
       "      <td>67.303650</td>\n",
       "      <td>99.249855</td>\n",
       "      <td>2375.928223</td>\n",
       "      <td>66.260696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0107_0113</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>1375.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.438079</td>\n",
       "      <td>51.242930</td>\n",
       "      <td>107</td>\n",
       "      <td>113</td>\n",
       "      <td>-3.223897</td>\n",
       "      <td>0.814249</td>\n",
       "      <td>3.325133</td>\n",
       "      <td>0.744128</td>\n",
       "      <td>70.023155</td>\n",
       "      <td>99.249855</td>\n",
       "      <td>2378.704834</td>\n",
       "      <td>73.014252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0108_0112</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>1425.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.437618</td>\n",
       "      <td>51.243274</td>\n",
       "      <td>108</td>\n",
       "      <td>112</td>\n",
       "      <td>-2.128311</td>\n",
       "      <td>-0.006086</td>\n",
       "      <td>2.128320</td>\n",
       "      <td>0.744128</td>\n",
       "      <td>67.427155</td>\n",
       "      <td>102.519943</td>\n",
       "      <td>2376.654785</td>\n",
       "      <td>59.838467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0109_0110</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>1475.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.437156</td>\n",
       "      <td>51.243618</td>\n",
       "      <td>109</td>\n",
       "      <td>110</td>\n",
       "      <td>-1.898049</td>\n",
       "      <td>-0.635196</td>\n",
       "      <td>2.001515</td>\n",
       "      <td>0.744128</td>\n",
       "      <td>67.757393</td>\n",
       "      <td>86.946983</td>\n",
       "      <td>2368.579346</td>\n",
       "      <td>40.674210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0109_0111</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>1450.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.437387</td>\n",
       "      <td>51.243446</td>\n",
       "      <td>109</td>\n",
       "      <td>111</td>\n",
       "      <td>-2.480118</td>\n",
       "      <td>-0.308044</td>\n",
       "      <td>2.499175</td>\n",
       "      <td>0.744128</td>\n",
       "      <td>71.485786</td>\n",
       "      <td>86.946983</td>\n",
       "      <td>2374.203369</td>\n",
       "      <td>49.256573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0110_0109</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.436926</td>\n",
       "      <td>51.243790</td>\n",
       "      <td>110</td>\n",
       "      <td>109</td>\n",
       "      <td>-0.778578</td>\n",
       "      <td>-1.068493</td>\n",
       "      <td>1.322067</td>\n",
       "      <td>0.819395</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66.598236</td>\n",
       "      <td>2361.018799</td>\n",
       "      <td>30.958771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0111_0108</th>\n",
       "      <td>1</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>1546.4</td>\n",
       "      <td>40.449233</td>\n",
       "      <td>-117.436498</td>\n",
       "      <td>51.244109</td>\n",
       "      <td>111</td>\n",
       "      <td>108</td>\n",
       "      <td>-0.403374</td>\n",
       "      <td>-0.982031</td>\n",
       "      <td>1.061647</td>\n",
       "      <td>1.533164</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66.598236</td>\n",
       "      <td>2350.401611</td>\n",
       "      <td>-18.039795</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id     len  distance      angle         lon        lat    i    j  \\\n",
       "ij                                                                            \n",
       "0071_0155   1  1546.4       0.0  40.449233 -117.450767  51.233473   71  155   \n",
       "0072_0153   1  1546.4      50.0  40.449233 -117.450305  51.233817   72  153   \n",
       "0072_0154   1  1546.4      25.0  40.449233 -117.450536  51.233645   72  154   \n",
       "0073_0153   1  1546.4      75.0  40.449233 -117.450075  51.233989   73  153   \n",
       "0074_0151   1  1546.4     125.0  40.449233 -117.449613  51.234333   74  151   \n",
       "0074_0152   1  1546.4     100.0  40.449233 -117.449844  51.234161   74  152   \n",
       "0075_0150   1  1546.4     150.0  40.449233 -117.449383  51.234505   75  150   \n",
       "0076_0149   1  1546.4     200.0  40.449233 -117.448922  51.234849   76  149   \n",
       "0076_0150   1  1546.4     175.0  40.449233 -117.449152  51.234677   76  150   \n",
       "0077_0148   1  1546.4     225.0  40.449233 -117.448691  51.235021   77  148   \n",
       "0078_0147   1  1546.4     262.5  40.449233 -117.448345  51.235279   78  147   \n",
       "0079_0146   1  1546.4     300.0  40.449233 -117.447999  51.235537   79  146   \n",
       "0080_0144   1  1546.4     350.0  40.449233 -117.447538  51.235881   80  144   \n",
       "0080_0145   1  1546.4     325.0  40.449233 -117.447768  51.235709   80  145   \n",
       "0081_0143   1  1546.4     400.0  40.449233 -117.447076  51.236225   81  143   \n",
       "0081_0144   1  1546.4     375.0  40.449233 -117.447307  51.236053   81  144   \n",
       "0082_0142   1  1546.4     425.0  40.449233 -117.446846  51.236397   82  142   \n",
       "0083_0141   1  1546.4     462.5  40.449233 -117.446500  51.236654   83  141   \n",
       "0084_0140   1  1546.4     500.0  40.449233 -117.446154  51.236912   84  140   \n",
       "0085_0138   1  1546.4     550.0  40.449233 -117.445692  51.237256   85  138   \n",
       "0085_0139   1  1546.4     525.0  40.449233 -117.445923  51.237084   85  139   \n",
       "0086_0137   1  1546.4     575.0  40.449233 -117.445462  51.237428   86  137   \n",
       "0087_0136   1  1546.4     625.0  40.449233 -117.445000  51.237772   87  136   \n",
       "0087_0137   1  1546.4     600.0  40.449233 -117.445231  51.237600   87  137   \n",
       "0088_0135   1  1546.4     650.0  40.449233 -117.444770  51.237944   88  135   \n",
       "0089_0134   1  1546.4     687.5  40.449233 -117.444424  51.238202   89  134   \n",
       "0090_0133   1  1546.4     725.0  40.449233 -117.444078  51.238460   90  133   \n",
       "0091_0131   1  1546.4     775.0  40.449233 -117.443616  51.238804   91  131   \n",
       "0091_0132   1  1546.4     750.0  40.449233 -117.443847  51.238632   91  132   \n",
       "0092_0130   1  1546.4     825.0  40.449233 -117.443155  51.239148   92  130   \n",
       "0092_0131   1  1546.4     800.0  40.449233 -117.443385  51.238976   92  131   \n",
       "0093_0129   1  1546.4     850.0  40.449233 -117.442924  51.239320   93  129   \n",
       "0094_0128   1  1546.4     887.5  40.449233 -117.442578  51.239578   94  128   \n",
       "0095_0127   1  1546.4     925.0  40.449233 -117.442232  51.239836   95  127   \n",
       "0096_0125   1  1546.4     975.0  40.449233 -117.441771  51.240179   96  125   \n",
       "0096_0126   1  1546.4     950.0  40.449233 -117.442001  51.240007   96  126   \n",
       "0097_0125   1  1546.4    1000.0  40.449233 -117.441540  51.240351   97  125   \n",
       "0098_0123   1  1546.4    1050.0  40.449233 -117.441079  51.240695   98  123   \n",
       "0098_0124   1  1546.4    1025.0  40.449233 -117.441309  51.240523   98  124   \n",
       "0099_0122   1  1546.4    1075.0  40.449233 -117.440848  51.240867   99  122   \n",
       "0100_0121   1  1546.4    1125.0  40.449233 -117.440386  51.241211  100  121   \n",
       "0100_0122   1  1546.4    1100.0  40.449233 -117.440617  51.241039  100  122   \n",
       "0101_0120   1  1546.4    1150.0  40.449233 -117.440156  51.241383  101  120   \n",
       "0102_0118   1  1546.4    1200.0  40.449233 -117.439694  51.241727  102  118   \n",
       "0102_0119   1  1546.4    1175.0  40.449233 -117.439925  51.241555  102  119   \n",
       "0103_0118   1  1546.4    1225.0  40.449233 -117.439464  51.241899  103  118   \n",
       "0104_0116   1  1546.4    1275.0  40.449233 -117.439002  51.242243  104  116   \n",
       "0104_0117   1  1546.4    1250.0  40.449233 -117.439233  51.242071  104  117   \n",
       "0105_0115   1  1546.4    1312.5  40.449233 -117.438656  51.242501  105  115   \n",
       "0106_0114   1  1546.4    1350.0  40.449233 -117.438310  51.242759  106  114   \n",
       "0107_0112   1  1546.4    1400.0  40.449233 -117.437848  51.243102  107  112   \n",
       "0107_0113   1  1546.4    1375.0  40.449233 -117.438079  51.242930  107  113   \n",
       "0108_0112   1  1546.4    1425.0  40.449233 -117.437618  51.243274  108  112   \n",
       "0109_0110   1  1546.4    1475.0  40.449233 -117.437156  51.243618  109  110   \n",
       "0109_0111   1  1546.4    1450.0  40.449233 -117.437387  51.243446  109  111   \n",
       "0110_0109   1  1546.4    1500.0  40.449233 -117.436926  51.243790  110  109   \n",
       "0111_0108   1  1546.4    1546.4  40.449233 -117.436498  51.244109  111  108   \n",
       "\n",
       "                  vx         vy         vz        ITS         gpr       H_opt  \\\n",
       "ij                                                                              \n",
       "0071_0155        NaN        NaN        NaN        NaN         NaN   36.070843   \n",
       "0072_0153  -0.891504   0.109947   0.898259   0.719045         NaN   38.511818   \n",
       "0072_0154  -1.176502   0.566844   1.305936   1.843738         NaN   38.511818   \n",
       "0073_0153  -1.122839   1.305790   1.722166   0.719045         NaN   38.511818   \n",
       "0074_0151  -2.454499   4.143846   4.816225   0.719045         NaN   48.854343   \n",
       "0074_0152  -2.060087   3.557255   4.110720   0.719045         NaN   48.854343   \n",
       "0075_0150  -2.758981   5.623297   6.263660   0.719045         NaN   52.187756   \n",
       "0076_0149  -1.194037   4.461414   4.618435   0.719045         NaN   59.944324   \n",
       "0076_0150  -1.174731   3.491723   3.684036   0.719045         NaN   59.944324   \n",
       "0077_0148  -0.049929   3.367443   3.367813   0.719045         NaN   61.990852   \n",
       "0078_0147  -0.179376   3.375454   3.380217   0.719045         NaN   68.158043   \n",
       "0079_0146  -0.339423   1.920726   1.950486   5.729864         NaN   68.283417   \n",
       "0080_0144  -0.254104   1.447165   1.469305   5.729864   31.244942   71.915627   \n",
       "0080_0145  -0.372974   1.700551   1.740972   5.729864   33.853920   73.235817   \n",
       "0081_0143  -0.488615   1.883482   1.945828  12.222924   31.989626   71.915627   \n",
       "0081_0144  -0.236157   1.599982   1.617317  12.222924   33.615875   71.915627   \n",
       "0082_0142  -2.005025   3.146753   3.731244  12.222924   35.152126   75.815948   \n",
       "0083_0141  -4.109261   5.005182   6.475946  12.222924   35.970089   75.815948   \n",
       "0084_0140  -6.082923   6.660829   9.020454  12.222924   42.100338   82.390816   \n",
       "0085_0138 -10.997713  10.148372  14.964597  18.889126   65.259850   81.267601   \n",
       "0085_0139  -8.592056   8.522914  12.102211  18.889126   58.951794   82.390816   \n",
       "0086_0137 -13.502338  12.672405  18.517641  18.889126   80.976166   92.043465   \n",
       "0087_0136 -16.446974  13.637007  21.365179  18.889126   87.212715   91.974899   \n",
       "0087_0137 -14.321509  12.916906  19.286060  18.889126   85.599091   92.043465   \n",
       "0088_0135 -18.934896  15.447783  24.436945  18.889126   91.614212  103.363510   \n",
       "0089_0134 -19.546329  16.354954  25.486143  18.889126   92.311813  103.811371   \n",
       "0090_0133 -20.075859  16.318838  25.871695  18.889126   97.084946  113.745529   \n",
       "0091_0131 -21.659237  15.738627  26.773624  12.366378  133.130203  113.660614   \n",
       "0091_0132 -18.681419  14.698958  23.770882  18.889126  126.766197  113.660614   \n",
       "0092_0130 -23.780621  16.767580  29.097589  12.366378  151.204697  117.594673   \n",
       "0092_0131 -21.792322  16.385841  27.265383  12.366378  149.555695  119.762009   \n",
       "0093_0129 -24.438484  18.444895  30.617865  12.366378  150.884369  117.594673   \n",
       "0094_0128 -25.312876  17.690983  30.882236  12.366378  150.094376  113.259750   \n",
       "0095_0127 -25.834362  17.875206  31.415558  12.366378  142.249146  113.259750   \n",
       "0096_0125 -23.408623  17.627037  29.303175  13.465559  123.242241  103.687302   \n",
       "0096_0126 -24.363230  17.453924  29.970093  13.465559  129.689896  103.687302   \n",
       "0097_0125 -21.989729  18.210983  28.551498  13.465559  107.179092  103.687302   \n",
       "0098_0123 -20.416815  16.741089  26.402847   2.506803   87.851715   87.851044   \n",
       "0098_0124 -21.712559  18.417194  28.471535   2.506803   89.241837   95.532104   \n",
       "0099_0122 -18.867882  14.738803  23.942207   2.506803   86.449753   87.851044   \n",
       "0100_0121 -16.663286  11.320371  20.144873   2.506803         NaN   86.413452   \n",
       "0100_0122 -18.132820  12.636072  22.101345   2.506803         NaN   93.450508   \n",
       "0101_0120 -14.651629   9.258006  17.331501   2.506803         NaN   86.413452   \n",
       "0102_0118  -9.477534   5.063577  10.745393   2.506803         NaN   92.393913   \n",
       "0102_0119 -11.655224   7.254674  13.728602   2.506803         NaN   92.393913   \n",
       "0103_0118  -8.916608   5.112708  10.278409   2.506803         NaN   92.393913   \n",
       "0104_0116  -2.292946   1.438168   2.706645   0.744128         NaN  102.648254   \n",
       "0104_0117  -4.719091   2.849271   5.512547   0.744128         NaN  102.648254   \n",
       "0105_0115  -2.565575   0.831348   2.696908   0.744128   64.706932   96.731873   \n",
       "0106_0114  -2.955224   0.990706   3.116865   0.744128   71.924782  109.014015   \n",
       "0107_0112  -2.071545   0.027409   2.071726   0.744128   67.303650   99.249855   \n",
       "0107_0113  -3.223897   0.814249   3.325133   0.744128   70.023155   99.249855   \n",
       "0108_0112  -2.128311  -0.006086   2.128320   0.744128   67.427155  102.519943   \n",
       "0109_0110  -1.898049  -0.635196   2.001515   0.744128   67.757393   86.946983   \n",
       "0109_0111  -2.480118  -0.308044   2.499175   0.744128   71.485786   86.946983   \n",
       "0110_0109  -0.778578  -1.068493   1.322067   0.819395         NaN   66.598236   \n",
       "0111_0108  -0.403374  -0.982031   1.061647   1.533164         NaN   66.598236   \n",
       "\n",
       "                   dem  farin_corr  \n",
       "ij                                  \n",
       "0071_0155  2436.034912  -10.072021  \n",
       "0072_0153  2405.994141   12.412224  \n",
       "0072_0154  2416.623535   10.002878  \n",
       "0073_0153  2397.521729   17.151449  \n",
       "0074_0151  2375.734619   25.525204  \n",
       "0074_0152  2380.168945   21.517933  \n",
       "0075_0150  2363.848145   30.777630  \n",
       "0076_0149  2350.191406   33.941872  \n",
       "0076_0150  2358.885498   35.037579  \n",
       "0077_0148  2338.738281   38.622501  \n",
       "0078_0147  2330.911621   42.046001  \n",
       "0079_0146  2326.010254   45.780258  \n",
       "0080_0144  2318.848389   50.989841  \n",
       "0080_0145  2323.853271   49.947437  \n",
       "0081_0143  2319.528076   55.103783  \n",
       "0081_0144  2322.811279   52.365417  \n",
       "0082_0142  2321.813477   62.632538  \n",
       "0083_0141  2324.301270   67.321701  \n",
       "0084_0140  2327.738281   71.755089  \n",
       "0085_0138  2324.895020   74.883125  \n",
       "0085_0139  2329.271973   74.074547  \n",
       "0086_0137  2326.557129   76.981903  \n",
       "0087_0136  2329.011719   83.486778  \n",
       "0087_0137  2330.806152   81.106499  \n",
       "0088_0135  2330.921875   91.498276  \n",
       "0089_0134  2331.744385   96.518288  \n",
       "0090_0133  2332.668213   99.839233  \n",
       "0091_0131  2332.812500  103.412148  \n",
       "0091_0132  2333.416992  103.093727  \n",
       "0092_0130  2333.950439  103.125618  \n",
       "0092_0131  2334.337891  103.793404  \n",
       "0093_0129  2334.899902  100.097801  \n",
       "0094_0128  2334.807861   94.205399  \n",
       "0095_0127  2334.182617   89.275749  \n",
       "0096_0125  2335.090088   81.956741  \n",
       "0096_0126  2336.023438   86.110916  \n",
       "0097_0125  2340.223877   83.504616  \n",
       "0098_0123  2346.303467   79.855759  \n",
       "0098_0124  2346.734131   82.152206  \n",
       "0099_0122  2354.168457   79.991890  \n",
       "0100_0121  2358.642090   78.945198  \n",
       "0100_0122  2360.580322   82.545349  \n",
       "0101_0120  2360.864014   76.649757  \n",
       "0102_0118  2365.439453   73.845139  \n",
       "0102_0119  2370.055420   76.991905  \n",
       "0103_0118  2374.140137   81.808945  \n",
       "0104_0116  2378.154541   79.818222  \n",
       "0104_0117  2376.739502   84.696411  \n",
       "0105_0115  2380.593018   79.735291  \n",
       "0106_0114  2380.356445   79.905098  \n",
       "0107_0112  2375.928223   66.260696  \n",
       "0107_0113  2378.704834   73.014252  \n",
       "0108_0112  2376.654785   59.838467  \n",
       "0109_0110  2368.579346   40.674210  \n",
       "0109_0111  2374.203369   49.256573  \n",
       "0110_0109  2361.018799   30.958771  \n",
       "0111_0108  2350.401611  -18.039795  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_agg[df_agg.id==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28.532984364669606\n"
     ]
    }
   ],
   "source": [
    "print(np.nanmean(sVZ/v_gate_mean[1:]*100))\n",
    "# print(sHopt*new.HF[1:]/new.HF[1:]*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "FG_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 297.638x170.079 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# interpolation figure supplemental\n",
    "\n",
    "fig, ax = plt.subplots(1,1, figsize=(cm2inch(10.5, 6.)))\n",
    "df_agg.gpr[(df_agg.id==0)&(df_agg['distance']==620.)]=107.1;df_agg.gpr[(df_agg.id==0)&(df_agg['distance']==560.)]=106.2;\n",
    "d=df_agg[(df_agg.id==0)&(df_agg['distance']>300)&(df_agg['distance']<900)];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',label='Interpolted points');\n",
    "plt.plot(x, y, \"o\", color='k',label='IPR Obs.')\n",
    "plt.plot(new_x, new_y,color='#0570b0',alpha=0.95,label='Polynomial best fit')\n",
    "ax.legend(loc='best')\n",
    "ax.set(ylim=(30,115),ylabel='Ice thickness (m)',xlabel='Distance along flux gate (m)')\n",
    "ax.text(0.95,0.9, 'A', transform=ax.transAxes)\n",
    "fig.subplots_adjust(bottom=0.15, top=0.98, left=0.13, right=0.98)\n",
    "plt.savefig(fl_path + 'products/' + glacier[gl] + '_poly_interp_gate0.pdf', dpi=300)\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": 357,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 240.945x184.252 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "year =2018\n",
    "I = np.abs(year - 2016) \n",
    "vf_list = ['illecillewaet_2016_all25m_vx.tif','illecillewaet_2017_all25m_vx.tif','illecillewaet_2018_all25m_vx.tif']\n",
    "vdir = '/home/pelto/Desktop/velocity_mapping/' +glacier[gl] + '/3m/' \n",
    "VX = vdir+ vf_list[I]\n",
    "VY = vdir+ vf_list[I][:-7] + '_vy.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+'illecillewaet_2014_extent_5m.tif') \n",
    "msk_reproj = vy.salem.transform(msk); msk = msk_reproj.to_array(name='msk')\n",
    "msk.data[msk.data<0.0]=np.nan\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>50.0]=np.nan\n",
    "vz.data[msk.data!=1.0] = np.nan;\n",
    "# np.count_nonzero(~np.isnan(vz.data))/np.count_nonzero(~np.isnan(msk))\n",
    "\n",
    "\n",
    "font = {'family' : 'Helvetica', 'weight' : 'normal', 'size'   : 8}\n",
    "plt.rc('font', **font)\n",
    "pylab.rcParams['xtick.major.pad']='1';pylab.rcParams['ytick.major.pad']='1'\n",
    "\n",
    "fig, ax = plt.subplots(1, figsize=cm2inch(8.5,6.5))\n",
    "grid1 = mercator_grid(center_ll=(-117.43, 51.237), extent=(4400, 4400)) ##zoomed out view\n",
    "# 487892.000 5509738.000 491232.000 5512358.000\n",
    "# grid = vx.salem.grid  ##full view\n",
    "sm1 = Map(grid1, countries=False)\n",
    "sm1.set_lonlat_contours(interval=0)\n",
    "sm1.set_scale_bar(location=(0.15, 0.08),add_bbox=True,bbox_dy=1.1,linewidth=1.0)\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=50.)\n",
    "# sm1.set_vmin(val=0)\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(gdf, lw=1.5, color='w')\n",
    "sm1.set_shapefile(gates, color='w', lw=1.1)\n",
    "sm1.append_colorbar(ax=ax, pad=0.1, label='Surface ice velocity (m a$^{-1}$)')#, cbar_title='Ice Velocity (m yr$^-1$)')\n",
    "sm1.plot(ax=ax)\n",
    "\n",
    "u = df_agg.vx.values\n",
    "v = df_agg.vy.values\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[::2], yy[::2], u[::2], v[::2])  #, pivot='mid')\n",
    "qk = ax.quiverkey(Q, 0.07, 0.10, 40, r'$40 \\frac{m}{a}$', labelpos='N', coordinates='figure', labelsep=0.025)\n",
    "\n",
    "\n",
    "\n",
    "# Q = ax.quiver(X[::2, ::2], Y[::2, ::2], u[::2, ::2], v[::2, ::2], 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]+ '_vel_gates_quiver'+str(year)+'.pdf', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ITS_LIVE_mean = np.mean(VEL_LIST), np.std(VEL_LIST)\n",
    "ITS_LIVE_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[17.082045375654065, 17.082045375654065, 17.082045375654065]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VEL_LIST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13.82949919157234, 1.3761437644833465)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IND_V_mean = np.mean(VEL_LIST), np.std(VEL_LIST)\n",
    "IND_V_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[15.349118230425972, 14.122650247549355, 12.016729096741694]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VEL_LIST_indV=VEL_LIST\n",
    "VEL_LIST_indV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.2479907901620169, 0.12889290383947863)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = [i - j for i, j in zip(VEL_LIST_indV, VEL_LIST)] \n",
    "res2 =[i / j for i, j in zip(res, VEL_LIST_indV)]\n",
    "np.mean(res2), np.std(res2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.06296913937843866,\n",
       " [0.08684403857480226, 0.27731249550993436, -0.17524911594942064])"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vi=[((VEL_LIST_indV[0]-VEL_LIST_indV[1])/VEL_LIST_indV[1]),\n",
    "((VEL_LIST_indV[0]-VEL_LIST_indV[2])/VEL_LIST_indV[2]),\n",
    "((VEL_LIST_indV[2]-VEL_LIST_indV[1])/VEL_LIST_indV[2]),]\n",
    "np.mean(vi),vi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "year=2018\n",
    "VDIR = 'individual'  # 'average' or 'individual'\n",
    "ITS_LIVE = True\n",
    "I = np.abs(year - 2016)\n",
    "\n",
    "if VDIR == 'individual':\n",
    "#     vf_list = ['img1_20160912__img1_20170917_vmap_5m_35px_spm2/', 'img1_20160912__img1_20170917_vmap_5m_35px_spm2/',\n",
    "#            'img1_20170521__img1_20180426_vmap_5m_35px_spm2/']##backups  img1_20170917__img1_20180426_vmap_5m_35px_spm2/\n",
    "\n",
    "#         vf_list = ['img1_20160912__img1_20170521_vmap_5m_35px_spm2/', 'img1_20160912__img1_20170521_vmap_5m_35px_spm2/',\n",
    "#                'img1_20180426__img2_20181016_vmap_5m_35px_spm2/']##backups  img1_20170917__img1_20180426_vmap_5m_35px_spm2/\n",
    "    vf_list = ['illecillewaet_2016_all25m_vx.tif','illecillewaet_2017_all25m_vx.tif','illecillewaet_2018_all25m_vx.tif']\n",
    "#     vdir = '/home/pelto/Desktop/velocity_mapping/' +glacier[gl] + '/spm2/' + vf_list[I]\n",
    "    vdir = '/home/pelto/Desktop/velocity_mapping/' +glacier[gl] + '/3m/' \n",
    "#         VX = vdir+ vf_list[I][:-1] + '-F_vx.tif' \n",
    "#         VY = vdir+ vf_list[I][:-1] + '-F_vy.tif' \n",
    "    VX = vdir+ vf_list[I]\n",
    "    VY = vdir+ vf_list[I][:-7] + '_vy.tif' \n",
    "    ITS = fl_path + 'ITS_LIVE/' + str(year) + '_illecillewaet_ITS_LIVE.tif'\n",
    "else:\n",
    "#         vf_list = ['illec_all_spm2_5m', 'illec_all_spm2_5m', 'illec_all_spm2_5m']\n",
    "#         vdir = '/home/pelto/Desktop/velocity_mapping/' +glacier[gl] + '/spm2/'  \n",
    "#         VX = vdir+ 'vx/' +vf_list[I] + '_vx.tif' \n",
    "#         VY = vdir+ 'vy/' +vf_list[I] + '_vy.tif'\n",
    "    vf = 'illecillewaet_all_dem3m_'\n",
    "    vdir = '/home/pelto/Desktop/velocity_mapping/' +glacier[gl] + '/3m/'  \n",
    "    VX = vdir + vf + 'blur1_25m_vx_neg.tif' #'vx_best_neg_5m.tif'\n",
    "    VY = vdir + vf + 'blur1_25m_vy.tif'\n",
    "#         VM = vdir + vf + 'vm_5m.tif'\n",
    "    ITS = fl_path + 'ITS_LIVE/' + str(year) + '_illecillewaet_ITS_LIVE.tif'\n",
    "\n",
    "topo = path + '160912_illecillewaet_dem1_clip_master.tif'\n",
    "\n",
    "vx = salem.open_xr_dataset(VX);vy = salem.open_xr_dataset(VY);vx = vx.to_array(name='vx');vy = vy.to_array(name='vy')\n",
    "msk = salem.open_xr_dataset(fl_path + 'illec_all_glaciers_2016.tif') \n",
    "extent=salem.open_xr_dataset(path +'illecillewaet_2014_extent_5m.tif')\n",
    "\n",
    "# msk = msk.to_array(name='msk')\n",
    "gpr = salem.open_xr_dataset(fl_path + 'gpr_outline_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+'Illecillewaet_251sw_MAE_optimized.tif')#+'Illecillewaet101sw_MAE_optimized.tif') \n",
    "\n",
    "msk_reproj = vx.salem.transform(msk);msk = msk_reproj.to_array(name='msk')\n",
    "extent_reproj=vx.salem.transform(extent); extent = extent_reproj.to_array(name='extent')\n",
    "gpr_reproj = vx.salem.transform(gpr); gpr = gpr_reproj.to_array(name='gpr')\n",
    "ITS = salem.open_xr_dataset(ITS); ITS_reproj = vx.salem.transform(ITS);ITS = ITS_reproj.to_array(name='ITS')\n",
    "\n",
    "gpr.data[gpr.data<0] = np.nan\n",
    "gpr.data[gpr.data<1.5] = 0.0   #not having any effect\n",
    "VZ = np.sqrt(vx.data**2 + vy.data**2 )\n",
    "vx.data[msk.data==0] = np.nan\n",
    "vy.data[msk.data==0] = np.nan\n",
    "\n",
    "VZ_off_ice = VZ.copy()\n",
    "VZ_off_ice[msk.data>0.0] = np.nan\n",
    "# VZ_off_ice[VZ_off_ice==0.0] = np.nan\n",
    "VZ[extent.data<1.0] = np.nan\n",
    "DIFF = VZ - ITS\n",
    "dem = salem.open_xr_dataset(topo)\n",
    "dem_reproj = vx.salem.transform(dem)\n",
    "dem = dem_reproj.to_array(name='dem')\n",
    "dem.data[dem.data<1] = np.nan\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig3, ax3 = plt.subplots(1,1, sharex=True, sharey=True, figsize=(cm2inch(12, 12.))) \n",
    "grid = vx.salem.grid  ##full view\n",
    "sm = Map(grid, countries=False)\n",
    "sm.set_lonlat_contours(interval=0)\n",
    "sm.set_scale_bar()\n",
    "VZ[VZ<0.50] = np.nan\n",
    "\n",
    "sm.set_data(VZ-ITS) #, label='m')\n",
    "sm.set_vmax(val=15.);sm.set_vmin(val=-15.);\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",
    "diff = np.nanmean(VZ-ITS)\n",
    "print(diff)\n",
    "print(diff/np.nanmean(VZ))\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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.std([0.30,0.21,0.35])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "####### Net flux uncertainty #################\n",
    "np.sum(new.sQout)/np.sum(new.Q_out)\n",
    "print(np.mean([new.sBwe_opt,FG_all.sBwe_gpr,new.sBwe_F]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', None)\n",
    "new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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,\n",
    "                          obs_bin_all,obs_fg_all,obs_fg_all_med,gpr_bd_fg_med,opt_bd_fg_med,farin_bd_fg_med)), \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','obs_fg_all_med','gpr_bd_fg_med','opt_bd_fg_med','farin_bd_fg_med']) \n",
    "r['zrel'] = r.elev_all-r.z_range_all.min()\n",
    "r.zrel= r.zrel / r.zrel.max()\n",
    "\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 by mean\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",
    "## for FG bins by mean\n",
    "r['opt_bd_med_fr']=r.opt_bd_fg_med/np.abs(r.obs_fg_all_med)\n",
    "r['farin_bd_med_fr']=r.farin_bd_fg_med/np.abs(r.obs_fg_all_med);r['gpr_bd_med_fr']=r.gpr_bd_fg_med/np.abs(r.obs_fg_all_med);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>z_range_all</th>\n",
       "      <th>elev_all</th>\n",
       "      <th>gpr_bdiff</th>\n",
       "      <th>farin_bdiff</th>\n",
       "      <th>opt_bdiff</th>\n",
       "      <th>gpr_bd_fg</th>\n",
       "      <th>opt_bd_fg</th>\n",
       "      <th>farin_bd_fg</th>\n",
       "      <th>obs_bin_all</th>\n",
       "      <th>obs_fg_all</th>\n",
       "      <th>obs_fg_all_med</th>\n",
       "      <th>gpr_bd_fg_med</th>\n",
       "      <th>opt_bd_fg_med</th>\n",
       "      <th>farin_bd_fg_med</th>\n",
       "      <th>zrel</th>\n",
       "      <th>gpr_fr</th>\n",
       "      <th>far_fr</th>\n",
       "      <th>opt_fr</th>\n",
       "      <th>opt_bd_fr</th>\n",
       "      <th>farin_bd_fr</th>\n",
       "      <th>gpr_bd_fr</th>\n",
       "      <th>opt_bd_med_fr</th>\n",
       "      <th>farin_bd_med_fr</th>\n",
       "      <th>gpr_bd_med_fr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2050.0</td>\n",
       "      <td>2224.108643</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.150198</td>\n",
       "      <td>0.009107</td>\n",
       "      <td>0.212939</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.763</td>\n",
       "      <td>-4.495</td>\n",
       "      <td>0.418198</td>\n",
       "      <td>-0.722893</td>\n",
       "      <td>-0.519061</td>\n",
       "      <td>0.330241</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.002420</td>\n",
       "      <td>0.056587</td>\n",
       "      <td>0.305660</td>\n",
       "      <td>-0.160822</td>\n",
       "      <td>-0.115475</td>\n",
       "      <td>0.093036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2150.0</td>\n",
       "      <td>2322.538086</td>\n",
       "      <td>0.213</td>\n",
       "      <td>-0.724</td>\n",
       "      <td>-0.928</td>\n",
       "      <td>0.206202</td>\n",
       "      <td>1.658052</td>\n",
       "      <td>0.437223</td>\n",
       "      <td>-4.700</td>\n",
       "      <td>-1.845</td>\n",
       "      <td>-1.845</td>\n",
       "      <td>0.206202</td>\n",
       "      <td>1.658052</td>\n",
       "      <td>0.437223</td>\n",
       "      <td>0.516937</td>\n",
       "      <td>0.045319</td>\n",
       "      <td>-0.154043</td>\n",
       "      <td>-0.197447</td>\n",
       "      <td>0.898673</td>\n",
       "      <td>0.236977</td>\n",
       "      <td>0.111763</td>\n",
       "      <td>0.898673</td>\n",
       "      <td>0.236977</td>\n",
       "      <td>0.111763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2250.0</td>\n",
       "      <td>2390.635742</td>\n",
       "      <td>1.703</td>\n",
       "      <td>0.766</td>\n",
       "      <td>0.562</td>\n",
       "      <td>-0.017102</td>\n",
       "      <td>0.773993</td>\n",
       "      <td>0.241317</td>\n",
       "      <td>-3.210</td>\n",
       "      <td>-0.997</td>\n",
       "      <td>-0.480</td>\n",
       "      <td>0.499898</td>\n",
       "      <td>1.290993</td>\n",
       "      <td>0.758317</td>\n",
       "      <td>0.646101</td>\n",
       "      <td>0.530530</td>\n",
       "      <td>0.238629</td>\n",
       "      <td>0.175078</td>\n",
       "      <td>0.776322</td>\n",
       "      <td>0.242043</td>\n",
       "      <td>-0.017153</td>\n",
       "      <td>2.689569</td>\n",
       "      <td>1.579827</td>\n",
       "      <td>1.041455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2350.0</td>\n",
       "      <td>2460.371582</td>\n",
       "      <td>-0.329</td>\n",
       "      <td>-0.085</td>\n",
       "      <td>0.792</td>\n",
       "      <td>1.001344</td>\n",
       "      <td>0.169480</td>\n",
       "      <td>0.208489</td>\n",
       "      <td>-1.845</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.928344</td>\n",
       "      <td>0.096480</td>\n",
       "      <td>0.135489</td>\n",
       "      <td>0.778373</td>\n",
       "      <td>-0.178320</td>\n",
       "      <td>-0.046070</td>\n",
       "      <td>0.429268</td>\n",
       "      <td>2.321647</td>\n",
       "      <td>2.856020</td>\n",
       "      <td>13.717040</td>\n",
       "      <td>inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2450.0</td>\n",
       "      <td>2532.567871</td>\n",
       "      <td>1.628</td>\n",
       "      <td>0.835</td>\n",
       "      <td>0.796</td>\n",
       "      <td>-0.756861</td>\n",
       "      <td>-0.633525</td>\n",
       "      <td>-0.446498</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.640</td>\n",
       "      <td>0.700</td>\n",
       "      <td>-0.696861</td>\n",
       "      <td>-0.573525</td>\n",
       "      <td>-0.386498</td>\n",
       "      <td>0.915311</td>\n",
       "      <td>2.325714</td>\n",
       "      <td>1.192857</td>\n",
       "      <td>1.137143</td>\n",
       "      <td>-0.989883</td>\n",
       "      <td>-0.697653</td>\n",
       "      <td>-1.182596</td>\n",
       "      <td>-0.819322</td>\n",
       "      <td>-0.552140</td>\n",
       "      <td>-0.995516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2550.0</td>\n",
       "      <td>2577.217285</td>\n",
       "      <td>-0.368</td>\n",
       "      <td>-0.086</td>\n",
       "      <td>-0.285</td>\n",
       "      <td>0.051608</td>\n",
       "      <td>0.094132</td>\n",
       "      <td>0.304802</td>\n",
       "      <td>0.610</td>\n",
       "      <td>0.610</td>\n",
       "      <td>0.610</td>\n",
       "      <td>0.051608</td>\n",
       "      <td>0.094132</td>\n",
       "      <td>0.304802</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.603279</td>\n",
       "      <td>-0.140984</td>\n",
       "      <td>-0.467213</td>\n",
       "      <td>0.154314</td>\n",
       "      <td>0.499676</td>\n",
       "      <td>0.084604</td>\n",
       "      <td>0.154314</td>\n",
       "      <td>0.499676</td>\n",
       "      <td>0.084604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2650.0</td>\n",
       "      <td>2224.108643</td>\n",
       "      <td>0.662</td>\n",
       "      <td>0.915</td>\n",
       "      <td>0.704</td>\n",
       "      <td>0.840652</td>\n",
       "      <td>-0.172908</td>\n",
       "      <td>0.015543</td>\n",
       "      <td>1.220</td>\n",
       "      <td>-3.950</td>\n",
       "      <td>-4.640</td>\n",
       "      <td>0.150652</td>\n",
       "      <td>-0.862908</td>\n",
       "      <td>-0.674457</td>\n",
       "      <td>0.330241</td>\n",
       "      <td>0.542623</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.577049</td>\n",
       "      <td>-0.043774</td>\n",
       "      <td>0.003935</td>\n",
       "      <td>0.212823</td>\n",
       "      <td>-0.185972</td>\n",
       "      <td>-0.145357</td>\n",
       "      <td>0.032468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2050.0</td>\n",
       "      <td>2322.538086</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.450647</td>\n",
       "      <td>1.688477</td>\n",
       "      <td>0.600850</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.230</td>\n",
       "      <td>-2.230</td>\n",
       "      <td>0.450647</td>\n",
       "      <td>1.688477</td>\n",
       "      <td>0.600850</td>\n",
       "      <td>0.516937</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.757165</td>\n",
       "      <td>0.269439</td>\n",
       "      <td>0.202084</td>\n",
       "      <td>0.757165</td>\n",
       "      <td>0.269439</td>\n",
       "      <td>0.202084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2150.0</td>\n",
       "      <td>2390.635742</td>\n",
       "      <td>0.151</td>\n",
       "      <td>-0.674</td>\n",
       "      <td>-0.863</td>\n",
       "      <td>0.367204</td>\n",
       "      <td>1.222301</td>\n",
       "      <td>0.638489</td>\n",
       "      <td>-4.640</td>\n",
       "      <td>-1.677</td>\n",
       "      <td>-1.500</td>\n",
       "      <td>0.544204</td>\n",
       "      <td>1.399301</td>\n",
       "      <td>0.815489</td>\n",
       "      <td>0.646101</td>\n",
       "      <td>0.032543</td>\n",
       "      <td>-0.145259</td>\n",
       "      <td>-0.185991</td>\n",
       "      <td>0.728862</td>\n",
       "      <td>0.380733</td>\n",
       "      <td>0.218965</td>\n",
       "      <td>0.932867</td>\n",
       "      <td>0.543659</td>\n",
       "      <td>0.362803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2250.0</td>\n",
       "      <td>2460.371582</td>\n",
       "      <td>1.831</td>\n",
       "      <td>1.006</td>\n",
       "      <td>0.817</td>\n",
       "      <td>0.455173</td>\n",
       "      <td>-0.431440</td>\n",
       "      <td>-0.227179</td>\n",
       "      <td>-2.960</td>\n",
       "      <td>-1.035</td>\n",
       "      <td>-1.035</td>\n",
       "      <td>0.455173</td>\n",
       "      <td>-0.431440</td>\n",
       "      <td>-0.227179</td>\n",
       "      <td>0.778373</td>\n",
       "      <td>0.618581</td>\n",
       "      <td>0.339865</td>\n",
       "      <td>0.276014</td>\n",
       "      <td>-0.416851</td>\n",
       "      <td>-0.219496</td>\n",
       "      <td>0.439781</td>\n",
       "      <td>-0.416851</td>\n",
       "      <td>-0.219496</td>\n",
       "      <td>0.439781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2350.0</td>\n",
       "      <td>2532.567871</td>\n",
       "      <td>0.132</td>\n",
       "      <td>0.343</td>\n",
       "      <td>1.179</td>\n",
       "      <td>-0.041011</td>\n",
       "      <td>0.121796</td>\n",
       "      <td>0.159105</td>\n",
       "      <td>-2.230</td>\n",
       "      <td>0.173</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.214011</td>\n",
       "      <td>-0.051204</td>\n",
       "      <td>-0.013895</td>\n",
       "      <td>0.915311</td>\n",
       "      <td>0.059193</td>\n",
       "      <td>0.153812</td>\n",
       "      <td>0.528700</td>\n",
       "      <td>0.704020</td>\n",
       "      <td>0.919680</td>\n",
       "      <td>-0.237061</td>\n",
       "      <td>-inf</td>\n",
       "      <td>-inf</td>\n",
       "      <td>-inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2450.0</td>\n",
       "      <td>2577.217285</td>\n",
       "      <td>0.920</td>\n",
       "      <td>0.238</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.926959</td>\n",
       "      <td>0.953271</td>\n",
       "      <td>1.148466</td>\n",
       "      <td>-0.570</td>\n",
       "      <td>0.545</td>\n",
       "      <td>0.545</td>\n",
       "      <td>0.926959</td>\n",
       "      <td>0.953271</td>\n",
       "      <td>1.148466</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.614035</td>\n",
       "      <td>0.417544</td>\n",
       "      <td>0.059649</td>\n",
       "      <td>1.749121</td>\n",
       "      <td>2.107278</td>\n",
       "      <td>1.700842</td>\n",
       "      <td>1.749121</td>\n",
       "      <td>2.107278</td>\n",
       "      <td>1.700842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2550.0</td>\n",
       "      <td>2225.477783</td>\n",
       "      <td>1.174</td>\n",
       "      <td>1.385</td>\n",
       "      <td>1.269</td>\n",
       "      <td>-0.213187</td>\n",
       "      <td>-1.205338</td>\n",
       "      <td>-1.020634</td>\n",
       "      <td>1.090</td>\n",
       "      <td>-4.156</td>\n",
       "      <td>-4.560</td>\n",
       "      <td>-0.617187</td>\n",
       "      <td>-1.609338</td>\n",
       "      <td>-1.424634</td>\n",
       "      <td>0.332838</td>\n",
       "      <td>1.077064</td>\n",
       "      <td>1.270642</td>\n",
       "      <td>1.164220</td>\n",
       "      <td>-0.290023</td>\n",
       "      <td>-0.245581</td>\n",
       "      <td>-0.051296</td>\n",
       "      <td>-0.352925</td>\n",
       "      <td>-0.312420</td>\n",
       "      <td>-0.135348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2650.0</td>\n",
       "      <td>2322.429688</td>\n",
       "      <td>0.382</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.408</td>\n",
       "      <td>-0.811298</td>\n",
       "      <td>0.281886</td>\n",
       "      <td>-0.656195</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-2.410</td>\n",
       "      <td>-2.000</td>\n",
       "      <td>-0.401298</td>\n",
       "      <td>0.691886</td>\n",
       "      <td>-0.246195</td>\n",
       "      <td>0.516731</td>\n",
       "      <td>inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.116965</td>\n",
       "      <td>-0.272280</td>\n",
       "      <td>-0.336638</td>\n",
       "      <td>0.345943</td>\n",
       "      <td>-0.123097</td>\n",
       "      <td>-0.200649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2050.0</td>\n",
       "      <td>2390.628174</td>\n",
       "      <td>-0.897</td>\n",
       "      <td>-1.705</td>\n",
       "      <td>-1.889</td>\n",
       "      <td>0.096721</td>\n",
       "      <td>0.850183</td>\n",
       "      <td>0.362119</td>\n",
       "      <td>-4.840</td>\n",
       "      <td>-0.907</td>\n",
       "      <td>-0.610</td>\n",
       "      <td>0.393721</td>\n",
       "      <td>1.147183</td>\n",
       "      <td>0.659119</td>\n",
       "      <td>0.646087</td>\n",
       "      <td>-0.185331</td>\n",
       "      <td>-0.352273</td>\n",
       "      <td>-0.390289</td>\n",
       "      <td>0.937357</td>\n",
       "      <td>0.399249</td>\n",
       "      <td>0.106639</td>\n",
       "      <td>1.880627</td>\n",
       "      <td>1.080523</td>\n",
       "      <td>0.645445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2150.0</td>\n",
       "      <td>2460.456055</td>\n",
       "      <td>-0.607</td>\n",
       "      <td>-1.415</td>\n",
       "      <td>-1.599</td>\n",
       "      <td>0.121704</td>\n",
       "      <td>-0.595144</td>\n",
       "      <td>-0.413222</td>\n",
       "      <td>-4.550</td>\n",
       "      <td>-0.360</td>\n",
       "      <td>-0.360</td>\n",
       "      <td>0.121704</td>\n",
       "      <td>-0.595144</td>\n",
       "      <td>-0.413222</td>\n",
       "      <td>0.778533</td>\n",
       "      <td>-0.133407</td>\n",
       "      <td>-0.310989</td>\n",
       "      <td>-0.351429</td>\n",
       "      <td>-1.653177</td>\n",
       "      <td>-1.147839</td>\n",
       "      <td>0.338066</td>\n",
       "      <td>-1.653177</td>\n",
       "      <td>-1.147839</td>\n",
       "      <td>0.338066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2250.0</td>\n",
       "      <td>2532.567871</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.989399</td>\n",
       "      <td>-0.919186</td>\n",
       "      <td>-0.810804</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.110</td>\n",
       "      <td>-0.110</td>\n",
       "      <td>-0.989399</td>\n",
       "      <td>-0.919186</td>\n",
       "      <td>-0.810804</td>\n",
       "      <td>0.915311</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-8.356240</td>\n",
       "      <td>-7.370948</td>\n",
       "      <td>-8.994533</td>\n",
       "      <td>-8.356240</td>\n",
       "      <td>-7.370948</td>\n",
       "      <td>-8.994533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2350.0</td>\n",
       "      <td>2577.216064</td>\n",
       "      <td>-0.699</td>\n",
       "      <td>-0.489</td>\n",
       "      <td>0.225</td>\n",
       "      <td>-0.515801</td>\n",
       "      <td>-0.468356</td>\n",
       "      <td>-0.334073</td>\n",
       "      <td>-2.000</td>\n",
       "      <td>-0.110</td>\n",
       "      <td>-0.110</td>\n",
       "      <td>-0.515801</td>\n",
       "      <td>-0.468356</td>\n",
       "      <td>-0.334073</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>-0.349500</td>\n",
       "      <td>-0.244500</td>\n",
       "      <td>0.112500</td>\n",
       "      <td>-4.257785</td>\n",
       "      <td>-3.037031</td>\n",
       "      <td>-4.689102</td>\n",
       "      <td>-4.257785</td>\n",
       "      <td>-3.037031</td>\n",
       "      <td>-4.689102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    z_range_all     elev_all  gpr_bdiff  farin_bdiff  opt_bdiff  gpr_bd_fg  \\\n",
       "0        2050.0  2224.108643        NaN          NaN        NaN   1.150198   \n",
       "1        2150.0  2322.538086      0.213       -0.724     -0.928   0.206202   \n",
       "2        2250.0  2390.635742      1.703        0.766      0.562  -0.017102   \n",
       "3        2350.0  2460.371582     -0.329       -0.085      0.792   1.001344   \n",
       "4        2450.0  2532.567871      1.628        0.835      0.796  -0.756861   \n",
       "5        2550.0  2577.217285     -0.368       -0.086     -0.285   0.051608   \n",
       "6        2650.0  2224.108643      0.662        0.915      0.704   0.840652   \n",
       "7        2050.0  2322.538086        NaN          NaN        NaN   0.450647   \n",
       "8        2150.0  2390.635742      0.151       -0.674     -0.863   0.367204   \n",
       "9        2250.0  2460.371582      1.831        1.006      0.817   0.455173   \n",
       "10       2350.0  2532.567871      0.132        0.343      1.179  -0.041011   \n",
       "11       2450.0  2577.217285      0.920        0.238      0.034   0.926959   \n",
       "12       2550.0  2225.477783      1.174        1.385      1.269  -0.213187   \n",
       "13       2650.0  2322.429688      0.382        0.603      0.408  -0.811298   \n",
       "14       2050.0  2390.628174     -0.897       -1.705     -1.889   0.096721   \n",
       "15       2150.0  2460.456055     -0.607       -1.415     -1.599   0.121704   \n",
       "16       2250.0  2532.567871        NaN          NaN        NaN  -0.989399   \n",
       "17       2350.0  2577.216064     -0.699       -0.489      0.225  -0.515801   \n",
       "\n",
       "    opt_bd_fg  farin_bd_fg  obs_bin_all  obs_fg_all  obs_fg_all_med  \\\n",
       "0    0.009107     0.212939          NaN      -3.763          -4.495   \n",
       "1    1.658052     0.437223       -4.700      -1.845          -1.845   \n",
       "2    0.773993     0.241317       -3.210      -0.997          -0.480   \n",
       "3    0.169480     0.208489       -1.845       0.073           0.000   \n",
       "4   -0.633525    -0.446498        0.700       0.640           0.700   \n",
       "5    0.094132     0.304802        0.610       0.610           0.610   \n",
       "6   -0.172908     0.015543        1.220      -3.950          -4.640   \n",
       "7    1.688477     0.600850          NaN      -2.230          -2.230   \n",
       "8    1.222301     0.638489       -4.640      -1.677          -1.500   \n",
       "9   -0.431440    -0.227179       -2.960      -1.035          -1.035   \n",
       "10   0.121796     0.159105       -2.230       0.173           0.000   \n",
       "11   0.953271     1.148466       -0.570       0.545           0.545   \n",
       "12  -1.205338    -1.020634        1.090      -4.156          -4.560   \n",
       "13   0.281886    -0.656195        0.000      -2.410          -2.000   \n",
       "14   0.850183     0.362119       -4.840      -0.907          -0.610   \n",
       "15  -0.595144    -0.413222       -4.550      -0.360          -0.360   \n",
       "16  -0.919186    -0.810804          NaN      -0.110          -0.110   \n",
       "17  -0.468356    -0.334073       -2.000      -0.110          -0.110   \n",
       "\n",
       "    gpr_bd_fg_med  opt_bd_fg_med  farin_bd_fg_med      zrel    gpr_fr  \\\n",
       "0        0.418198      -0.722893        -0.519061  0.330241       NaN   \n",
       "1        0.206202       1.658052         0.437223  0.516937  0.045319   \n",
       "2        0.499898       1.290993         0.758317  0.646101  0.530530   \n",
       "3        0.928344       0.096480         0.135489  0.778373 -0.178320   \n",
       "4       -0.696861      -0.573525        -0.386498  0.915311  2.325714   \n",
       "5        0.051608       0.094132         0.304802  1.000000 -0.603279   \n",
       "6        0.150652      -0.862908        -0.674457  0.330241  0.542623   \n",
       "7        0.450647       1.688477         0.600850  0.516937       NaN   \n",
       "8        0.544204       1.399301         0.815489  0.646101  0.032543   \n",
       "9        0.455173      -0.431440        -0.227179  0.778373  0.618581   \n",
       "10      -0.214011      -0.051204        -0.013895  0.915311  0.059193   \n",
       "11       0.926959       0.953271         1.148466  1.000000  1.614035   \n",
       "12      -0.617187      -1.609338        -1.424634  0.332838  1.077064   \n",
       "13      -0.401298       0.691886        -0.246195  0.516731       inf   \n",
       "14       0.393721       1.147183         0.659119  0.646087 -0.185331   \n",
       "15       0.121704      -0.595144        -0.413222  0.778533 -0.133407   \n",
       "16      -0.989399      -0.919186        -0.810804  0.915311       NaN   \n",
       "17      -0.515801      -0.468356        -0.334073  0.999998 -0.349500   \n",
       "\n",
       "      far_fr    opt_fr  opt_bd_fr  farin_bd_fr  gpr_bd_fr  opt_bd_med_fr  \\\n",
       "0        NaN       NaN   0.002420     0.056587   0.305660      -0.160822   \n",
       "1  -0.154043 -0.197447   0.898673     0.236977   0.111763       0.898673   \n",
       "2   0.238629  0.175078   0.776322     0.242043  -0.017153       2.689569   \n",
       "3  -0.046070  0.429268   2.321647     2.856020  13.717040            inf   \n",
       "4   1.192857  1.137143  -0.989883    -0.697653  -1.182596      -0.819322   \n",
       "5  -0.140984 -0.467213   0.154314     0.499676   0.084604       0.154314   \n",
       "6   0.750000  0.577049  -0.043774     0.003935   0.212823      -0.185972   \n",
       "7        NaN       NaN   0.757165     0.269439   0.202084       0.757165   \n",
       "8  -0.145259 -0.185991   0.728862     0.380733   0.218965       0.932867   \n",
       "9   0.339865  0.276014  -0.416851    -0.219496   0.439781      -0.416851   \n",
       "10  0.153812  0.528700   0.704020     0.919680  -0.237061           -inf   \n",
       "11  0.417544  0.059649   1.749121     2.107278   1.700842       1.749121   \n",
       "12  1.270642  1.164220  -0.290023    -0.245581  -0.051296      -0.352925   \n",
       "13       inf       inf   0.116965    -0.272280  -0.336638       0.345943   \n",
       "14 -0.352273 -0.390289   0.937357     0.399249   0.106639       1.880627   \n",
       "15 -0.310989 -0.351429  -1.653177    -1.147839   0.338066      -1.653177   \n",
       "16       NaN       NaN  -8.356240    -7.370948  -8.994533      -8.356240   \n",
       "17 -0.244500  0.112500  -4.257785    -3.037031  -4.689102      -4.257785   \n",
       "\n",
       "    farin_bd_med_fr  gpr_bd_med_fr  \n",
       "0         -0.115475       0.093036  \n",
       "1          0.236977       0.111763  \n",
       "2          1.579827       1.041455  \n",
       "3               inf            inf  \n",
       "4         -0.552140      -0.995516  \n",
       "5          0.499676       0.084604  \n",
       "6         -0.145357       0.032468  \n",
       "7          0.269439       0.202084  \n",
       "8          0.543659       0.362803  \n",
       "9         -0.219496       0.439781  \n",
       "10             -inf           -inf  \n",
       "11         2.107278       1.700842  \n",
       "12        -0.312420      -0.135348  \n",
       "13        -0.123097      -0.200649  \n",
       "14         1.080523       0.645445  \n",
       "15        -1.147839       0.338066  \n",
       "16        -7.370948      -8.994533  \n",
       "17        -3.037031      -4.689102  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1848.8326"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nanmin(dem.data[msk.data==1.0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DATA=[];D2=[]\n",
    "# z=[]\n",
    "# # s= [r.opt_bdiff, r.farin_bdiff,r.gpr_bdiff]\n",
    "# s= [r.opt_bd_fg, r.farin_bd_fg,r.gpr_bd_fg]\n",
    "# s1=[r.opt_bd_med_fr, r.farin_bd_med_fr,r.gpr_bd_med_fr]\n",
    "# for i in range(3):\n",
    "#     DATA.extend(s[i])\n",
    "#     D2.extend(s1[i])\n",
    "#     z.extend(r.zrel)\n",
    "    \n",
    "# DF = pd.DataFrame(list(zip(DATA,D2,z)),   columns=['r_data','r_frac','zrel'])\n",
    "# DF.dropna(subset=['r_data'],inplace=True)\n",
    "# # DF.r_frac[np.abs(DF.r_frac)>4]=np.nan;DF.dropna(subset=['r_frac'],inplace=True)\n",
    "# print(len(DF))\n",
    "# DF.r_data[np.abs(DF.r_frac)>0.5]=np.nan;DF.dropna(subset=['r_data'],inplace=True)\n",
    "# print(len(DF))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1-(26/54)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "y1=np.array([2016]*6);y2=np.array([2017]*6);y3=np.array([2018]*6);Years=np.concatenate([y1,y2,y3])\n",
    "r['year']=Years\n",
    "# r.z_range_all[r.z_range_all==2750.0]=np.nan;#\n",
    "# for n in range(len(r)):\n",
    "#     if (r.z_range_all[n]>2400) and (r.year[n]==2018):\n",
    "#         r.z_range_all[n]=np.nan\n",
    "#     if (r.z_range_all[n]>2750.0) and (r.year[n]==2017):\n",
    "#         r.z_range_all[n]=np.nan\n",
    "#     if (r.z_range_all[n]>2450.0 or r.z_range_all[n]==2550.0) and (r.year[n]==2017):\n",
    "#         r.z_range_all[n]=np.nan\n",
    "\n",
    "# # r.z_range_all[ & r.year<2018]=np.nan\n",
    "r.dropna(subset=['z_range_all'],inplace=True)\n",
    "# r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(F_onewayResult(statistic=0.08820838656765408, pvalue=0.7682742536555284),\n",
       " F_onewayResult(statistic=0.18269146824258015, pvalue=0.6717653024361637),\n",
       " F_onewayResult(statistic=0.010139420990552272, pvalue=0.9203842759991278))"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": [
    "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": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean percent smb error: 1.98\n",
      "mean flux error: 24.2\n",
      "mean SMB error: 0.56\n"
     ]
    }
   ],
   "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": 291,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3648710095792036\n",
      "0         NaN\n",
      "1    1.395786\n",
      "2    1.095508\n",
      "3    0.978317\n",
      "4    1.412542\n",
      "5    1.743346\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1.39578578629272"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### cross section area comparison ###\n",
    "R= np.sum(new.cr_area)/np.sum(new.cr_areaF)\n",
    "Ri=new16.cr_area/new16.cr_area_gpr\n",
    "R=R-1\n",
    "print(R)\n",
    "# R[R>0.4]=np.nan\n",
    "R.mean()\n",
    "GR=new.cr_area_gpr/new.cr_areaF\n",
    "\n",
    "# GR=GR-1\n",
    "print(GR)\n",
    "GR.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.08152526326052212, 0.12782929574488694, 0.05925392713172983)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=[];b=[];c=[]\n",
    "for i in range(len(new16.cr_area)-1):\n",
    "    a.append(new16.cr_area_gpr[i]/new16.cr_area_gpr[i+1])\n",
    "    b.append(new16.cr_area[i]/new16.cr_area[i+1])\n",
    "    c.append(new16.cr_areaF[i]/new16.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": 293,
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>ab</th>\n",
       "      <th>bc</th>\n",
       "      <th>ac</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.563222</td>\n",
       "      <td>0.348925</td>\n",
       "      <td>0.442055</td>\n",
       "      <td>-0.614165</td>\n",
       "      <td>0.210676</td>\n",
       "      <td>-0.274100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.653605</td>\n",
       "      <td>0.558374</td>\n",
       "      <td>0.583687</td>\n",
       "      <td>-0.170551</td>\n",
       "      <td>0.043367</td>\n",
       "      <td>-0.119787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.704285</td>\n",
       "      <td>1.075863</td>\n",
       "      <td>1.016881</td>\n",
       "      <td>0.345376</td>\n",
       "      <td>-0.058003</td>\n",
       "      <td>0.307406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.882826</td>\n",
       "      <td>0.857488</td>\n",
       "      <td>1.089576</td>\n",
       "      <td>-0.029549</td>\n",
       "      <td>0.213008</td>\n",
       "      <td>0.189752</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a         b         c        ab        bc        ac\n",
       "0  0.000000  0.000000  0.000000       NaN       NaN       NaN\n",
       "1  0.563222  0.348925  0.442055 -0.614165  0.210676 -0.274100\n",
       "2  0.653605  0.558374  0.583687 -0.170551  0.043367 -0.119787\n",
       "3  0.704285  1.075863  1.016881  0.345376 -0.058003  0.307406\n",
       "4  0.882826  0.857488  1.089576 -0.029549  0.213008  0.189752"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dcr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.028957258220220217"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.70550570568146"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nanmedian(D_all.vfg)"
   ]
  },
  {
   "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": [
    "########## Calculate glacier-wide SMB and compare with field obs #######################\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=[-0.19,-0.84,-1.05,-0.19,-0.84,-1.05,-0.19,-0.84,-1.05]\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(MB_df.MBAL_diff.mean())\n",
    "MB_df"
   ]
  },
  {
   "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": [
    "glacier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig_path= '/home/pelto/Desktop/ice_flux/figures/'\n",
    "\n",
    "with open(fig_path + glacier[2]+'_vfg_all.pkl', 'wb') as f:\n",
    "    pickle.dump(vfg_all, f)\n",
    "with open(fig_path + glacier[2]+ '_gate_elev.pkl', 'wb') as f:\n",
    "    pickle.dump(gate_elev, f)\n",
    "new.to_csv(fig_path + glacier[2]+'_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": [
    "pd.set_option('display.max_columns', None)\n",
    "new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig3, ax3 = plt.subplots(1,1, sharex=True, sharey=True, figsize=(2.85,2.75)) ##mass conservation plot\n",
    "\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",
    "AX=ax3.twinx()\n",
    "for i in range(3):\n",
    "    ax3.scatter(new.dem_mean, Yvv[i], edgecolor='k', marker=sym[i+1], alpha=0.6, label=Yvv_lbl[i], s=35, \n",
    "                       color=color[i+1], zorder=2, linewidth=0.25)\n",
    "meanlineprops = dict(linestyle='--', lw=1., color='#cb181d');medianprops = dict(linestyle='-', lw=1.,color='k')\n",
    "BOXVEL=AX.boxplot(vfg_all,meanprops=meanlineprops,medianprops=medianprops,showmeans=True, meanline=True,\n",
    "                  sym='', positions=[2188., 2288., 2351., 2440., 2517.,2536.,2565., 2591.],widths=25, zorder=1)\n",
    "\n",
    "ax3.set_xticks([2100, 2200,  2300, 2400, 2500, 2600])  #, 2650])\n",
    "ax3.set_xticklabels([2100, 2200,  2300, 2400, 2500, 2600]) \n",
    "ax3.set(xlim=(2090, 2650))\n",
    "ax3.legend(loc='upper right', labelspacing=0.35)\n",
    "\n",
    "fig3.text(0.005, 0.65, r'$V^{\\uparrow}$' + '(m ice $a^{-1}$)',rotation=90) #transform=ax3.transAxes)\n",
    "fig3.text(0.94, 0.7, 'Ice velocity (m $a^{-1}$)',rotation=90)\n",
    "\n",
    "ax3.set_zorder(1)\n",
    "ax3.patch.set_visible(False)\n",
    "\n",
    "fig3.subplots_adjust(bottom=0.11, top=0.98, hspace=0.1, left=0.12, right=0.85, wspace=0.05)\n",
    "fig3.savefig(fl_path + 'products/' + glacier[gl] + '_Velocities.pdf', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Illec Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>method</th>\n",
       "      <th>ALL</th>\n",
       "      <th>ALLse</th>\n",
       "      <th>ABL</th>\n",
       "      <th>ABLse</th>\n",
       "      <th>ACC</th>\n",
       "      <th>ACCse</th>\n",
       "      <th>ELA</th>\n",
       "      <th>ELA_sat</th>\n",
       "      <th>sELA_sat</th>\n",
       "      <th>ME</th>\n",
       "      <th>MAE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Observed</td>\n",
       "      <td>12.62</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2488</td>\n",
       "      <td>2500</td>\n",
       "      <td>75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FG IPR</td>\n",
       "      <td>15.83</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2491</td>\n",
       "      <td>2500</td>\n",
       "      <td>75</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FG OGGM</td>\n",
       "      <td>15.30</td>\n",
       "      <td>2.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2498</td>\n",
       "      <td>2500</td>\n",
       "      <td>75</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FG Farinotti</td>\n",
       "      <td>13.59</td>\n",
       "      <td>1.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2494</td>\n",
       "      <td>2500</td>\n",
       "      <td>75</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Observed</td>\n",
       "      <td>12.40</td>\n",
       "      <td>0.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2528</td>\n",
       "      <td>2540</td>\n",
       "      <td>70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FG IPR</td>\n",
       "      <td>12.92</td>\n",
       "      <td>1.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2561</td>\n",
       "      <td>2540</td>\n",
       "      <td>70</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>FG OGGM</td>\n",
       "      <td>12.55</td>\n",
       "      <td>2.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2571</td>\n",
       "      <td>2540</td>\n",
       "      <td>70</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>FG Farinotti</td>\n",
       "      <td>11.10</td>\n",
       "      <td>1.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2576</td>\n",
       "      <td>2540</td>\n",
       "      <td>70</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Observed</td>\n",
       "      <td>11.60</td>\n",
       "      <td>2.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2534</td>\n",
       "      <td>2525</td>\n",
       "      <td>70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FG IPR</td>\n",
       "      <td>12.44</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2495</td>\n",
       "      <td>2525</td>\n",
       "      <td>70</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>FG OGGM</td>\n",
       "      <td>11.90</td>\n",
       "      <td>2.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2502</td>\n",
       "      <td>2525</td>\n",
       "      <td>70</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>FG Farinotti</td>\n",
       "      <td>10.63</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2499</td>\n",
       "      <td>2525</td>\n",
       "      <td>70</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Observed</td>\n",
       "      <td>12.20</td>\n",
       "      <td>1.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2517</td>\n",
       "      <td>2522</td>\n",
       "      <td>72</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>FG IPR</td>\n",
       "      <td>13.73</td>\n",
       "      <td>2.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2516</td>\n",
       "      <td>2522</td>\n",
       "      <td>72</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>FG OGGM</td>\n",
       "      <td>13.25</td>\n",
       "      <td>2.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2524</td>\n",
       "      <td>2522</td>\n",
       "      <td>72</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>FG Farinotti</td>\n",
       "      <td>11.77</td>\n",
       "      <td>1.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2523</td>\n",
       "      <td>2522</td>\n",
       "      <td>72</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          method    ALL  ALLse  ABL  ABLse  ACC  ACCse   ELA  ELA_sat  \\\n",
       "0       Observed  12.62    1.5  NaN    NaN  NaN    NaN  2488     2500   \n",
       "1         FG IPR  15.83    3.0  NaN    NaN  NaN    NaN  2491     2500   \n",
       "2        FG OGGM  15.30    2.4  NaN    NaN  NaN    NaN  2498     2500   \n",
       "3   FG Farinotti  13.59    1.9  NaN    NaN  NaN    NaN  2494     2500   \n",
       "4       Observed  12.40    0.7  NaN    NaN  NaN    NaN  2528     2540   \n",
       "5         FG IPR  12.92    1.9  NaN    NaN  NaN    NaN  2561     2540   \n",
       "6        FG OGGM  12.55    2.6  NaN    NaN  NaN    NaN  2571     2540   \n",
       "7   FG Farinotti  11.10    1.6  NaN    NaN  NaN    NaN  2576     2540   \n",
       "8       Observed  11.60    2.1  NaN    NaN  NaN    NaN  2534     2525   \n",
       "9         FG IPR  12.44    2.0  NaN    NaN  NaN    NaN  2495     2525   \n",
       "10       FG OGGM  11.90    2.1  NaN    NaN  NaN    NaN  2502     2525   \n",
       "11  FG Farinotti  10.63    1.5  NaN    NaN  NaN    NaN  2499     2525   \n",
       "12      Observed  12.20    1.4  NaN    NaN  NaN    NaN  2517     2522   \n",
       "13        FG IPR  13.73    2.3  NaN    NaN  NaN    NaN  2516     2522   \n",
       "14       FG OGGM  13.25    2.4  NaN    NaN  NaN    NaN  2524     2522   \n",
       "15  FG Farinotti  11.77    1.7  NaN    NaN  NaN    NaN  2523     2522   \n",
       "\n",
       "    sELA_sat    ME   MAE  \n",
       "0         75  0.00  0.00  \n",
       "1         75  0.27  0.57  \n",
       "2         75  0.34  0.57  \n",
       "3         75  0.16  0.32  \n",
       "4         70  0.00  0.00  \n",
       "5         70  0.49  0.52  \n",
       "6         70  0.56  0.74  \n",
       "7         70  0.38  0.46  \n",
       "8         70  0.00  0.00  \n",
       "9         70 -0.39  0.49  \n",
       "10        70 -0.35  0.70  \n",
       "11        70 -0.48  0.61  \n",
       "12        72  0.00  0.00  \n",
       "13        72  0.12  0.53  \n",
       "14        72  0.18  0.67  \n",
       "15        72  0.02  0.46  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MSE_ABL,MSE_ACC, 'MSE_ABL','MSE_ACC',RSR, NSE,\n",
    "label=['Observed', 'FG IPR', 'FG OGGM', 'FG Farinotti']\n",
    "ELA_sat=[];sELA_sat=[]\n",
    "for i in range(len(ELA)):\n",
    "    ELA_sat= ELA_sat+ ([ELA[i]] * 4)\n",
    "    sELA_sat= sELA_sat+ ([s_ELA[i]] * 4)\n",
    "ELA_sat,sELA_sat\n",
    "\n",
    "method=[]\n",
    "for i in range(4):\n",
    "    method.extend(label)\n",
    "len(method)\n",
    "STATS=pd.DataFrame(list(zip(method,SL_L[:,0], SE_L[:,1],SL[:,0], SE[:,1],SL[:,1], SE[:,2], ELA_lin ,ELA_sat,sELA_sat,ME,MAE)),#PBias \n",
    "                     columns=['method','ALL','ALLse','ABL', 'ABLse', 'ACC', 'ACCse', 'ELA', 'ELA_sat' ,'sELA_sat','ME','MAE']) #'PBias'\n",
    "STATS.ALL=STATS.ALL*1000;STATS.ALLse=STATS.ALLse*1000;STATS.ABL=STATS.ABL*1000;STATS.ABLse=STATS.ABLse*1000;\n",
    "STATS.ACC=STATS.ACC*1000;STATS.ACCse=STATS.ACCse*1000\n",
    "STATS\n",
    "\n",
    "# STATS.ACC[STATS.method=='Observed'].min()/STATS.ACC[STATS.method=='Observed'].max()\n",
    "# STATS.ACC.mean()/STATS.ABLse.mean()\n",
    "\n",
    "# STATS=pd.read_pickle(FLP+Glacier[1]+ '/' + glacier[1]+ '_NOfirn_PW_stats.pkl')\n",
    "# STATS\n",
    "\n",
    "M= ['Observed','FG IPR','FG OGGM','FG Farinotti']\n",
    "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.ABLse=STATS.ABLse.round(1);STATS.ACCse=STATS.ACCse.round(1);\n",
    "STATS.ACCse=np.nan;STATS.ACC=np.nan;STATS.ABL=np.nan;STATS.ABLse=np.nan\n",
    "STATS.ELA=STATS.ELA.round(0);STATS.ELA=pd.to_numeric(STATS.ELA, downcast='integer')  #.round(0)#STATS.PBias=STATS.PBias.round(1)\n",
    "STATS.ELA_sat=STATS.ELA_sat.round(0);STATS.ELA_sat=pd.to_numeric(STATS.ELA_sat, downcast='integer') \n",
    "STATS.sELA_sat=STATS.sELA_sat.round(0);STATS.sELA_sat=pd.to_numeric(STATS.sELA_sat, downcast='integer') \n",
    "if firn == True:\n",
    "    STATS.to_pickle(fl_path+glacier[gl]+'_stats_firn_LIN_stats.pkl')\n",
    "else:\n",
    "    STATS.to_pickle(fl_path+glacier[gl]+'_stats_NOfirn_LIN_stats.pkl')\n",
    "#     STATS.to_pickle(fl_path+'conrad_stats_ITS_LIVE_NOfirn.pkl')\n",
    "#     STATS.to_pickle(fl_path+glacier[gl]+'_stats_mosaic_NOfirn.pkl')\n",
    "STATS\n",
    "\n",
    "e=(STATS[:-4].ELA_sat - STATS[:-4].ELA)\n",
    "e.mean(),e\n",
    "STATS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.9166666666666667,\n",
       " 0     12\n",
       " 1      9\n",
       " 2      2\n",
       " 3      6\n",
       " 4     12\n",
       " 5    -21\n",
       " 6    -31\n",
       " 7    -36\n",
       " 8     -9\n",
       " 9     30\n",
       " 10    23\n",
       " 11    26\n",
       " dtype: int16)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(e[e>100])\n",
    "e=(STATS[:-4].ELA_sat - STATS[:-4].ELA)\n",
    "e.mean(),e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    910.000000\n",
       "1    910.000000\n",
       "2    910.000000\n",
       "3    904.439747\n",
       "4    887.809097\n",
       "5    882.845909\n",
       "Name: RHO_g, dtype: float64"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new16.RHO_g.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rng_ab=[];rng_ac=[]\n",
    "# for m in M:\n",
    "#     mn= STATS.ABL[STATS.method==m].min(); mx=STATS.ABL[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": 306,
   "metadata": {},
   "outputs": [],
   "source": [
    "# np.mean(STATS.ABLse), np.mean(STATS.ACCse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "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>method</th>\n",
       "      <th>ALL</th>\n",
       "      <th>ALLse</th>\n",
       "      <th>ABL</th>\n",
       "      <th>ABLse</th>\n",
       "      <th>ACC</th>\n",
       "      <th>ACCse</th>\n",
       "      <th>PBias</th>\n",
       "      <th>ME</th>\n",
       "      <th>MAE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Observed</td>\n",
       "      <td>12.61</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FG IPR</td>\n",
       "      <td>14.99</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-40.4</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FG OGGM</td>\n",
       "      <td>14.51</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-48.6</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FG Farinotti</td>\n",
       "      <td>13.02</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-25.1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Observed</td>\n",
       "      <td>12.39</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FG IPR</td>\n",
       "      <td>13.18</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-35.5</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>FG OGGM</td>\n",
       "      <td>12.77</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-39.8</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>FG Farinotti</td>\n",
       "      <td>11.43</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-26.3</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Observed</td>\n",
       "      <td>11.59</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FG IPR</td>\n",
       "      <td>11.85</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.2</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>FG OGGM</td>\n",
       "      <td>11.21</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.3</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>FG Farinotti</td>\n",
       "      <td>10.13</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.5</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Observed</td>\n",
       "      <td>12.19</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>FG IPR</td>\n",
       "      <td>13.34</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-16.9</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>FG OGGM</td>\n",
       "      <td>12.83</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-23.0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>FG Farinotti</td>\n",
       "      <td>11.53</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-6.6</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          method    ALL  ALLse   ABL  ABLse   ACC  ACCse  PBias    ME   MAE\n",
       "0       Observed  12.61    1.5  0.02    0.0  0.00    0.0    0.0  0.00  0.00\n",
       "1         FG IPR  14.99    2.6  0.02    0.0  0.01    0.0  -40.4  0.36  0.49\n",
       "2        FG OGGM  14.51    2.3  0.02    0.0  0.01    0.0  -48.6  0.43  0.53\n",
       "3   FG Farinotti  13.02    1.8  0.02    0.0  0.00    0.0  -25.1  0.22  0.28\n",
       "4       Observed  12.39    0.7  0.01    0.0  0.01    0.0    0.0  0.00  0.00\n",
       "5         FG IPR  13.18    1.6  0.01    0.0 -0.01    0.0  -35.5  0.48  0.49\n",
       "6        FG OGGM  12.77    2.6  0.01    0.0 -0.00    0.0  -39.8  0.54  0.75\n",
       "7   FG Farinotti  11.43    1.4  0.01    0.0 -0.01    0.0  -26.3  0.36  0.43\n",
       "8       Observed  11.59    2.1  0.01    0.0 -0.01    0.0    0.0  0.00  0.00\n",
       "9         FG IPR  11.85    1.9  0.01    0.0 -0.01    0.0   25.2 -0.34  0.37\n",
       "10       FG OGGM  11.21    2.0  0.01    0.0 -0.01    0.0   19.3 -0.26  0.64\n",
       "11  FG Farinotti  10.13    1.4  0.01    0.0 -0.01    0.0   31.5 -0.42  0.54\n",
       "12      Observed  12.19    1.4  0.01    0.0  0.00    0.0    0.0  0.00  0.00\n",
       "13        FG IPR  13.34    2.0  0.02    0.0 -0.00    0.0  -16.9  0.17  0.45\n",
       "14       FG OGGM  12.83    2.3  0.01    0.0  0.00    0.0  -23.0  0.24  0.64\n",
       "15  FG Farinotti  11.53    1.5  0.01    0.0 -0.00    0.0   -6.6  0.05  0.42"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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.ABLse=STATS.ABLse.round(1);STATS.ACCse=STATS.ACCse.round(1);STATS.ALLse=STATS.ALLse.round(1)\n",
    "# STATS.PBias=STATS.PBias.round(1)\n",
    "# STATS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>method</th>\n",
       "      <th>ALL</th>\n",
       "      <th>ELA</th>\n",
       "      <th>ELA_sat</th>\n",
       "      <th>ME</th>\n",
       "      <th>MAE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Observed</td>\n",
       "      <td>12.62 $\\pm$ 1.5</td>\n",
       "      <td>2488</td>\n",
       "      <td>2500 $\\pm$ 75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FG IPR</td>\n",
       "      <td>15.83 $\\pm$ 3.0</td>\n",
       "      <td>2491</td>\n",
       "      <td>2500 $\\pm$ 75</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FG OGGM</td>\n",
       "      <td>15.3 $\\pm$ 2.4</td>\n",
       "      <td>2498</td>\n",
       "      <td>2500 $\\pm$ 75</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FG Farinotti</td>\n",
       "      <td>13.59 $\\pm$ 1.9</td>\n",
       "      <td>2494</td>\n",
       "      <td>2500 $\\pm$ 75</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Observed</td>\n",
       "      <td>12.4 $\\pm$ 0.7</td>\n",
       "      <td>2528</td>\n",
       "      <td>2540 $\\pm$ 70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FG IPR</td>\n",
       "      <td>12.92 $\\pm$ 1.9</td>\n",
       "      <td>2561</td>\n",
       "      <td>2540 $\\pm$ 70</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>FG OGGM</td>\n",
       "      <td>12.55 $\\pm$ 2.6</td>\n",
       "      <td>2571</td>\n",
       "      <td>2540 $\\pm$ 70</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>FG Farinotti</td>\n",
       "      <td>11.1 $\\pm$ 1.6</td>\n",
       "      <td>2576</td>\n",
       "      <td>2540 $\\pm$ 70</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Observed</td>\n",
       "      <td>11.6 $\\pm$ 2.1</td>\n",
       "      <td>2534</td>\n",
       "      <td>2525 $\\pm$ 70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FG IPR</td>\n",
       "      <td>12.44 $\\pm$ 2.0</td>\n",
       "      <td>2495</td>\n",
       "      <td>2525 $\\pm$ 70</td>\n",
       "      <td>-0.39</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>FG OGGM</td>\n",
       "      <td>11.9 $\\pm$ 2.1</td>\n",
       "      <td>2502</td>\n",
       "      <td>2525 $\\pm$ 70</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>FG Farinotti</td>\n",
       "      <td>10.63 $\\pm$ 1.5</td>\n",
       "      <td>2499</td>\n",
       "      <td>2525 $\\pm$ 70</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Observed</td>\n",
       "      <td>12.2 $\\pm$ 1.4</td>\n",
       "      <td>2517</td>\n",
       "      <td>2522 $\\pm$ 72</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>FG IPR</td>\n",
       "      <td>13.73 $\\pm$ 2.3</td>\n",
       "      <td>2516</td>\n",
       "      <td>2522 $\\pm$ 72</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>FG OGGM</td>\n",
       "      <td>13.25 $\\pm$ 2.4</td>\n",
       "      <td>2524</td>\n",
       "      <td>2522 $\\pm$ 72</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>FG Farinotti</td>\n",
       "      <td>11.77 $\\pm$ 1.7</td>\n",
       "      <td>2523</td>\n",
       "      <td>2522 $\\pm$ 72</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          method              ALL   ELA        ELA_sat    ME   MAE\n",
       "0       Observed  12.62 $\\pm$ 1.5  2488  2500 $\\pm$ 75  0.00  0.00\n",
       "1         FG IPR  15.83 $\\pm$ 3.0  2491  2500 $\\pm$ 75  0.27  0.57\n",
       "2        FG OGGM   15.3 $\\pm$ 2.4  2498  2500 $\\pm$ 75  0.34  0.57\n",
       "3   FG Farinotti  13.59 $\\pm$ 1.9  2494  2500 $\\pm$ 75  0.16  0.32\n",
       "4       Observed   12.4 $\\pm$ 0.7  2528  2540 $\\pm$ 70  0.00  0.00\n",
       "5         FG IPR  12.92 $\\pm$ 1.9  2561  2540 $\\pm$ 70  0.49  0.52\n",
       "6        FG OGGM  12.55 $\\pm$ 2.6  2571  2540 $\\pm$ 70  0.56  0.74\n",
       "7   FG Farinotti   11.1 $\\pm$ 1.6  2576  2540 $\\pm$ 70  0.38  0.46\n",
       "8       Observed   11.6 $\\pm$ 2.1  2534  2525 $\\pm$ 70  0.00  0.00\n",
       "9         FG IPR  12.44 $\\pm$ 2.0  2495  2525 $\\pm$ 70 -0.39  0.49\n",
       "10       FG OGGM   11.9 $\\pm$ 2.1  2502  2525 $\\pm$ 70 -0.35  0.70\n",
       "11  FG Farinotti  10.63 $\\pm$ 1.5  2499  2525 $\\pm$ 70 -0.48  0.61\n",
       "12      Observed   12.2 $\\pm$ 1.4  2517  2522 $\\pm$ 72  0.00  0.00\n",
       "13        FG IPR  13.73 $\\pm$ 2.3  2516  2522 $\\pm$ 72  0.12  0.53\n",
       "14       FG OGGM  13.25 $\\pm$ 2.4  2524  2522 $\\pm$ 72  0.18  0.67\n",
       "15  FG Farinotti  11.77 $\\pm$ 1.7  2523  2522 $\\pm$ 72  0.02  0.46"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S1=STATS.copy()\n",
    "AB=[];AC=[];AL=[];EL=[];\n",
    "for i in range(len(S1)):\n",
    "    AL.append(str(S1.ALL[i])+' $\\pm$ '+str(S1.ALLse[i]))\n",
    "    AB.append(str(S1.ABL[i])+' $\\pm$ '+str(S1.ABLse[i]))\n",
    "    AC.append((str(S1.ACC[i])+' $\\pm$ '+str(S1.ACCse[i])))\n",
    "    EL.append((str(S1.ELA_sat[i])+' $\\pm$ '+str(S1.sELA_sat[i])))\n",
    "S1.ABL=AB;S1.ACC=AC;S1.ALL=AL;S1.ELA_sat=EL\n",
    "S1.drop(columns=['ABLse','ABL','ACCse','ACC','ALLse','sELA_sat'], inplace=True)\n",
    "S1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{llrlrr}\n",
      "\\toprule\n",
      "       method &              ALL &   ELA &        ELA\\_sat &    ME &   MAE \\\\\n",
      "\\midrule\n",
      "     Observed &  12.62 \\$\\textbackslash pm\\$ 1.5 &  2488 &  2500 \\$\\textbackslash pm\\$ 75 &  0.00 &  0.00 \\\\\n",
      "       FG IPR &  15.83 \\$\\textbackslash pm\\$ 3.0 &  2491 &  2500 \\$\\textbackslash pm\\$ 75 &  0.27 &  0.57 \\\\\n",
      "      FG OGGM &   15.3 \\$\\textbackslash pm\\$ 2.4 &  2498 &  2500 \\$\\textbackslash pm\\$ 75 &  0.34 &  0.57 \\\\\n",
      " FG Farinotti &  13.59 \\$\\textbackslash pm\\$ 1.9 &  2494 &  2500 \\$\\textbackslash pm\\$ 75 &  0.16 &  0.32 \\\\\n",
      "     Observed &   12.4 \\$\\textbackslash pm\\$ 0.7 &  2528 &  2540 \\$\\textbackslash pm\\$ 70 &  0.00 &  0.00 \\\\\n",
      "       FG IPR &  12.92 \\$\\textbackslash pm\\$ 1.9 &  2561 &  2540 \\$\\textbackslash pm\\$ 70 &  0.49 &  0.52 \\\\\n",
      "      FG OGGM &  12.55 \\$\\textbackslash pm\\$ 2.6 &  2571 &  2540 \\$\\textbackslash pm\\$ 70 &  0.56 &  0.74 \\\\\n",
      " FG Farinotti &   11.1 \\$\\textbackslash pm\\$ 1.6 &  2576 &  2540 \\$\\textbackslash pm\\$ 70 &  0.38 &  0.46 \\\\\n",
      "     Observed &   11.6 \\$\\textbackslash pm\\$ 2.1 &  2534 &  2525 \\$\\textbackslash pm\\$ 70 &  0.00 &  0.00 \\\\\n",
      "       FG IPR &  12.44 \\$\\textbackslash pm\\$ 2.0 &  2495 &  2525 \\$\\textbackslash pm\\$ 70 & -0.39 &  0.49 \\\\\n",
      "      FG OGGM &   11.9 \\$\\textbackslash pm\\$ 2.1 &  2502 &  2525 \\$\\textbackslash pm\\$ 70 & -0.35 &  0.70 \\\\\n",
      " FG Farinotti &  10.63 \\$\\textbackslash pm\\$ 1.5 &  2499 &  2525 \\$\\textbackslash pm\\$ 70 & -0.48 &  0.61 \\\\\n",
      "     Observed &   12.2 \\$\\textbackslash pm\\$ 1.4 &  2517 &  2522 \\$\\textbackslash pm\\$ 72 &  0.00 &  0.00 \\\\\n",
      "       FG IPR &  13.73 \\$\\textbackslash pm\\$ 2.3 &  2516 &  2522 \\$\\textbackslash pm\\$ 72 &  0.12 &  0.53 \\\\\n",
      "      FG OGGM &  13.25 \\$\\textbackslash pm\\$ 2.4 &  2524 &  2522 \\$\\textbackslash pm\\$ 72 &  0.18 &  0.67 \\\\\n",
      " FG Farinotti &  11.77 \\$\\textbackslash pm\\$ 1.7 &  2523 &  2522 \\$\\textbackslash pm\\$ 72 &  0.02 &  0.46 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(S1.to_latex(index=False)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# STATS=STATS.append(Sall,ignore_index=True,sort=False)\n",
    "# STATS=STATS.round(2)\n",
    "# STATS\n",
    "firn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [],
   "source": [
    "STATS.to_pickle(fl_path+'illecillewaet_stats_ITS_LIVE_NOfirn.pkl')#'illecillewaet_LIN_NOfirn_stats.pkl') #_NOfirn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "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>method</th>\n",
       "      <th>ALL</th>\n",
       "      <th>ALLse</th>\n",
       "      <th>ABL</th>\n",
       "      <th>ABLse</th>\n",
       "      <th>ACC</th>\n",
       "      <th>ACCse</th>\n",
       "      <th>PBias</th>\n",
       "      <th>ME</th>\n",
       "      <th>MAE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Observed</td>\n",
       "      <td>12.61</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FG IPR</td>\n",
       "      <td>14.99</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-40.4</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FG OGGM</td>\n",
       "      <td>14.51</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-48.6</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FG Farinotti</td>\n",
       "      <td>13.02</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-25.1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Observed</td>\n",
       "      <td>12.39</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FG IPR</td>\n",
       "      <td>13.18</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-35.5</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>FG OGGM</td>\n",
       "      <td>12.77</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-39.8</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>FG Farinotti</td>\n",
       "      <td>11.43</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-26.3</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Observed</td>\n",
       "      <td>11.59</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FG IPR</td>\n",
       "      <td>11.85</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.2</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>FG OGGM</td>\n",
       "      <td>11.21</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.3</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>FG Farinotti</td>\n",
       "      <td>10.13</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.5</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Observed</td>\n",
       "      <td>12.19</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>FG IPR</td>\n",
       "      <td>13.34</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-16.9</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>FG OGGM</td>\n",
       "      <td>12.83</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-23.0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>FG Farinotti</td>\n",
       "      <td>11.53</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-6.6</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          method    ALL  ALLse   ABL  ABLse   ACC  ACCse  PBias    ME   MAE\n",
       "0       Observed  12.61    1.5  0.02    0.0  0.00    0.0    0.0  0.00  0.00\n",
       "1         FG IPR  14.99    2.6  0.02    0.0  0.01    0.0  -40.4  0.36  0.49\n",
       "2        FG OGGM  14.51    2.3  0.02    0.0  0.01    0.0  -48.6  0.43  0.53\n",
       "3   FG Farinotti  13.02    1.8  0.02    0.0  0.00    0.0  -25.1  0.22  0.28\n",
       "4       Observed  12.39    0.7  0.01    0.0  0.01    0.0    0.0  0.00  0.00\n",
       "5         FG IPR  13.18    1.6  0.01    0.0 -0.01    0.0  -35.5  0.48  0.49\n",
       "6        FG OGGM  12.77    2.6  0.01    0.0 -0.00    0.0  -39.8  0.54  0.75\n",
       "7   FG Farinotti  11.43    1.4  0.01    0.0 -0.01    0.0  -26.3  0.36  0.43\n",
       "8       Observed  11.59    2.1  0.01    0.0 -0.01    0.0    0.0  0.00  0.00\n",
       "9         FG IPR  11.85    1.9  0.01    0.0 -0.01    0.0   25.2 -0.34  0.37\n",
       "10       FG OGGM  11.21    2.0  0.01    0.0 -0.01    0.0   19.3 -0.26  0.64\n",
       "11  FG Farinotti  10.13    1.4  0.01    0.0 -0.01    0.0   31.5 -0.42  0.54\n",
       "12      Observed  12.19    1.4  0.01    0.0  0.00    0.0    0.0  0.00  0.00\n",
       "13        FG IPR  13.34    2.0  0.02    0.0 -0.00    0.0  -16.9  0.17  0.45\n",
       "14       FG OGGM  12.83    2.3  0.01    0.0  0.00    0.0  -23.0  0.24  0.64\n",
       "15  FG Farinotti  11.53    1.5  0.01    0.0 -0.00    0.0   -6.6  0.05  0.42"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# LIN_firn_STATS=STATS\n",
    "LIN_NOfirn_STATS=STATS\n",
    "LIN_NOfirn_STATS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "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>method</th>\n",
       "      <th>ALL</th>\n",
       "      <th>ALLse</th>\n",
       "      <th>ABL</th>\n",
       "      <th>ABLse</th>\n",
       "      <th>ACC</th>\n",
       "      <th>ACCse</th>\n",
       "      <th>PBias</th>\n",
       "      <th>ME</th>\n",
       "      <th>MAE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Observed</td>\n",
       "      <td>12.19</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>FG IPR</td>\n",
       "      <td>14.33</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-7.9</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>FG OGGM</td>\n",
       "      <td>13.82</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-14.1</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>FG Farinotti</td>\n",
       "      <td>12.52</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          method    ALL  ALLse   ABL  ABLse  ACC  ACCse  PBias    ME   MAE\n",
       "12      Observed  12.19    1.4  0.01    0.0  0.0    0.0    0.0  0.00  0.00\n",
       "13        FG IPR  14.33    1.9  0.02    0.0  0.0    0.0   -7.9  0.06  0.49\n",
       "14       FG OGGM  13.82    2.3  0.02    0.0  0.0    0.0  -14.1  0.13  0.66\n",
       "15  FG Farinotti  12.52    1.3  0.01    0.0 -0.0    0.0    2.3 -0.05  0.42"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LFS=LIN_firn_STATS.tail(4) \n",
    "LFS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "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>method</th>\n",
       "      <th>ALL</th>\n",
       "      <th>ALLse</th>\n",
       "      <th>ABL</th>\n",
       "      <th>ABLse</th>\n",
       "      <th>ACC</th>\n",
       "      <th>ACCse</th>\n",
       "      <th>PBias</th>\n",
       "      <th>ME</th>\n",
       "      <th>MAE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Observed</td>\n",
       "      <td>12.19</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>FG IPR</td>\n",
       "      <td>13.34</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-16.9</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>FG OGGM</td>\n",
       "      <td>12.83</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-23.0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>FG Farinotti</td>\n",
       "      <td>11.53</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-6.6</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          method    ALL  ALLse   ABL  ABLse  ACC  ACCse  PBias    ME   MAE\n",
       "12      Observed  12.19    1.4  0.01    0.0  0.0    0.0    0.0  0.00  0.00\n",
       "13        FG IPR  13.34    2.0  0.02    0.0 -0.0    0.0  -16.9  0.17  0.45\n",
       "14       FG OGGM  12.83    2.3  0.01    0.0  0.0    0.0  -23.0  0.24  0.64\n",
       "15  FG Farinotti  11.53    1.5  0.01    0.0 -0.0    0.0   -6.6  0.05  0.42"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LNFS=LIN_NOfirn_STATS.tail(4) \n",
    "LNFS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.03089964451736399, 0.076562072660916)"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.22444626743232152, 0.12771649418901967)"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "STATS['ALL_firn']=LFS.ALL\n",
    "STATS['pdiff_obs']=(STATS.ALL-STATS.ALL[12])/STATS.ALL[12]\n",
    "STATS.pdiff_obs[1:].mean(),STATS.pdiff_obs[1:].std(),"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.11211375444353298, 0.076562072660916)"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 258,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FG IPR [28.47772615]\n",
      "FG OGGM [23.91363761]\n",
      "FG Farinotti [31.07406395]\n"
     ]
    }
   ],
   "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": 205,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{llrrr}\n",
      "\\toprule\n",
      "       method &              ALL &  PBias &    ME &   MAE \\\\\n",
      "\\midrule\n",
      "     Observed &  12.19 \\$\\textbackslash pm\\$ 1.4 &    0.0 &  0.00 &  0.00 \\\\\n",
      "       FG IPR &  13.34 \\$\\textbackslash pm\\$ 2.0 &  -16.9 &  0.17 &  0.45 \\\\\n",
      "      FG OGGM &  12.83 \\$\\textbackslash pm\\$ 2.3 &  -23.0 &  0.24 &  0.64 \\\\\n",
      " FG Farinotti &  11.53 \\$\\textbackslash pm\\$ 1.5 &   -6.6 &  0.05 &  0.42 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "STATS_LIN=STATS[['method','ALL','ALLse','PBias','ME','MAE']]\n",
    "SL1=STATS_LIN.tail(4)\n",
    "AB=[];AC=[];AL=[]\n",
    "for i in SL1.index:\n",
    "    AL.append(str(SL1.ALL[i])+' $\\pm$ '+str(SL1.ALLse[i]))\n",
    "SL1.ABL=AB;SL1.ACC=AC;SL1.ALL=AL\n",
    "SL1.drop(columns=['ALLse'], inplace=True)\n",
    "SL1\n",
    "print(SL1.to_latex(index=False)) \n",
    "# SL1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.29418053050043436"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1-STATS.ALL.tail(3).mean()/LNFS.ALL.mean()  #.MAE.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# method=[]\n",
    "# for i in range(4):\n",
    "#     method.extend(M)\n",
    "# len(method)\n",
    "Sall=pd.DataFrame(list(zip(method,np.array(SLa)[:,0], np.array(SEa)[:,1],np.array(SLa)[:,1], np.array(SEa)[:,2])), \n",
    "                     columns=['method','ABL', 'ABLse', 'ACC', 'ACCse'])\n",
    "Sall.ABL=Sall.ABL*1000;Sall.ABLse=Sall.ABLse*1000;Sall.ACC=Sall.ACC*1000;Sall.ACCse=Sall.ACCse*1000\n",
    "Sall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_bin=x_bin\n",
    "all_bin.append(y_bin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "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": 325,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(fl_path+ 'ALL_list.pkl', 'wb') as f:\n",
    "    pickle.dump(all_bin, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46.55332"
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": [
    "font = {'family' : 'Helvetica', 'weight' : 'normal',  'size'   : 8}\n",
    "plt.rc('font', **font)\n",
    "label=['Observed', 'FG IPR', 'FG OGGM', 'FG Farinotti']\n",
    "obs = pd.read_csv(fl_path+ 'Illecillewaet_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",
    "SLa=[];SEa=[]\n",
    "############## piecewise function ########################################         \n",
    "fig3, ax3 = plt.subplots(1, sharex=True, sharey=True, figsize=(cm2inch(8.5, 8.5)))\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",
    "x0=2500\n",
    "def piecewise_linear(x, x0, y0, k1, k2):\n",
    "   \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",
    "#     if i > 0 and year==2016:\n",
    "#         x0=2470\n",
    "#     else:\n",
    "#         x0=2430\n",
    "    ##pwlf\n",
    "    Y=np.array(y_bin[i])\n",
    "    X=np.array(x_bin[i])\n",
    "    model = pwlf.PiecewiseLinFit(X, Y)\n",
    "    breakpoints = [2040.,x0,2680.]\n",
    "    model.fit_with_breaks(breakpoints)\n",
    "    x_hat = np.arange(2040., 2660., 10.)\n",
    "    y_hat = model.predict(x_hat)\n",
    "    plt.plot(x_hat, y_hat, lw=1., alpha=1, color=color[i],zorder=1)\n",
    "    SLa.append(model.slopes)\n",
    "    SEa.append(model.standard_errors())\n",
    "    \n",
    "    x1=np.array(x_bin[i]); y1=np.array(y_bin[i])\n",
    "    p , e = curve_fit(piecewise_linear, x1, y1 )\n",
    "#     perr = np.sqrt(np.diag(e))\n",
    "#     print(perr)\n",
    "    xd = np.linspace(2040, 2660, 10)\n",
    "#     ax3.plot(xd, piecewise_linear(xd, *p), color=color[i], label=label[i], lw=1.0)\n",
    "    # mean square error of the lines\n",
    "   \n",
    "\n",
    "    MSE_res=[]\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_res.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_res.append(MSE(Act,Pred))\n",
    "    ax3.scatter(x_bin[i], y_bin[i], color=color[i], s=10, marker=sym[i], facecolors='none', lw=0.5, label=label[i],\n",
    "               zorder=2,alpha=0.7)\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",
    "                      \n",
    "    ax3.legend(loc='best')\n",
    "    ax3.axhline(linewidth=1., color='k', ls='--', alpha=a, zorder=0)\n",
    "    ax3.set(ylim=(-8.5,3.2),xlim=(2030,2660),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_combinedBIG.pdf', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "SLa, SEa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import optimize\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "y = np.array(obs_data.Ba)   #new.b_fg_we.values\n",
    "x = np.array(obs_data.Elev) #new.dem_mean.values\n",
    "\n",
    "def piecewise_linear(x, x0, y0, k1, k2):\n",
    "    return np.piecewise(x, [x < x0, x >= x0], [lambda x:k1*x + y0-k1*x0, lambda x:k2*x + y0-k2*x0])\n",
    "#     return np.piecewise(x, [x < 0, x >= 0], [lambda x:k1*x + y0-k1*0, lambda x:k2*x + y0-k2*0])\n",
    "p , e = optimize.curve_fit(piecewise_linear, x, y)\n",
    "xd = np.linspace(2000, 2800, 15)\n",
    "plt.plot(x, y, \"o\")\n",
    "plt.plot(xd, piecewise_linear(xd, *p))\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "FIT = np.polyfit(x,y,3)\n",
    "poly = np.poly1d(FIT)\n",
    "new_x = np.linspace(x[0], x[-1])\n",
    "new_y = poly(new_x)\n",
    "plt.plot(x, y, \"o\", new_x, new_y)\n",
    "plt.show()\n",
    "# plt.scatter(x,y)\n",
    "FIT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def piecewise_linear(x, x0, y0, k1, k2):\n",
    "    x0=2400\n",
    "    return np.piecewise(x, [x < x0], [lambda x:k1*x + y0-k1*x0, lambda x:k2*x + y0-k2*x0])\n",
    "\n",
    "p , e = optimize.curve_fit(piecewise_linear, x, y)\n",
    "xd = np.linspace(2000, 2800, 15)\n",
    "plt.plot(x, y, \"o\")\n",
    "plt.plot(xd, piecewise_linear(xd, *p), color='k')\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "# y = new.b_fg_we.values\n",
    "y=np.array([9,-3,-5,-4,7,-2,-1,3,5,6])\n",
    "x = new.dem_mean.values\n",
    "\n",
    "Neg= np.where(y > 0 )[0]\n",
    "breakpoint=np.where(Neg==min(Neg))[0][0]\n",
    "\n",
    "C=0\n",
    "while C==0:\n",
    "    BP_check=Neg[breakpoint+1]-Neg[breakpoint]\n",
    "    if BP_check >1:\n",
    "        breakpoint= breakpoint+1\n",
    "    else:\n",
    "        C=1\n",
    "    \n",
    "BP=Neg[breakpoint]\n",
    "first_y=y[:BP]\n",
    "first_x=x[:BP]\n",
    "\n",
    "second_y=y[BP:]\n",
    "second_x=x[BP:]\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g=0,3\n",
    "o=1,4\n",
    "f=2,5\n",
    "GPR_dat=[]; OPT_dat=[];FAR_dat=[]\n",
    "for n in range(2):\n",
    "    GPR_dat.append([NSE[g[n]],PBias[g[n]],RSR[g[n]]])\n",
    "    OPT_dat.append([NSE[o[n]],PBias[o[n]],RSR[o[n]]])\n",
    "    FAR_dat.append([NSE[f[n]],PBias[f[n]],RSR[f[n]]])\n",
    "\n",
    "\n",
    "GPR_dat_FIN=np.mean(GPR_dat,axis=0)\n",
    "OPT_dat_FIN=np.mean(OPT_dat,axis=0)\n",
    "FAR_dat_FIN=np.mean(FAR_dat,axis=0)\n",
    "    \n",
    "    #     NSE.mean\n",
    "# print(GPR_dat)\n",
    "# print(OPT_dat)\n",
    "# print(FAR_dat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gpr_bdiff = pd.Series(gpr_bdiff)\n",
    "gpr_bdiff.dropna(inplace=True)\n",
    "opt_bdiff = pd.Series(opt_bdiff)\n",
    "opt_bdiff.dropna(inplace=True)\n",
    "farin_bdiff = pd.Series(farin_bdiff)\n",
    "farin_bdiff.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gpr_bdiff = pd.Series(gpr_bdiff)\n",
    "\n",
    "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": [
    "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot flux gates as cross-sections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(D.id.nunique()):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_agg[df_agg.id==0.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df_agg = df_agg.astype({\"id\": int})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_agg[df_agg.id==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(fl_path + glacier[2]+'_df_agg.pkl', 'wb') as f:\n",
    "    pickle.dump(df_agg, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(D.id.nunique()):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "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",
    "j,k = 0,0\n",
    "n=0\n",
    "# df_agg['area_opt'] = df_agg.thick_opt * 10 \n",
    "a=0.8\n",
    "div=1000\n",
    "letter=['M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U']\n",
    "\n",
    "obs_H = df_agg.copy()\n",
    "obs_H = obs_H.dropna(subset=['gpr']) #, inplace=True)   \n",
    "\n",
    "fig, ax = plt.subplots(2,6, figsize=(7.08,2.5))\n",
    "for i in range(D.id.nunique()):\n",
    "    K=df_agg[df_agg.id==i].sort_values(by=['distance'])\n",
    "    O=obs_H[obs_H.id==i].sort_values(by=['distance'])\n",
    "    \n",
    "    # plot glacier surface\n",
    "    ax[j,k].plot(K.distance, (K.dem/div), color='#43a2ca', alpha=a, \n",
    "                 label='Ice surface')\n",
    "    \n",
    "    #plot gpr thickness gates\n",
    "    gpr=(O.dem/div)-(O.gpr/div)\n",
    "    gpr_mask=np.isfinite(gpr)\n",
    "    ax[j,k].plot(O.distance, gpr, marker='o'\n",
    "                 , ms=1.5, ls='--', color='k', alpha=a, label='Observed bed')\n",
    "    \n",
    "#         ax[j,k].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",
    "    \n",
    "    ax[j,k].plot(K.distance, (K.dem/div)-(K.H_opt/div), \n",
    "                 ls='--', c='r', alpha=a, label='OGGM bed')  #marker='o')\n",
    "    \n",
    "    ##plot Farinotti thickness gates\n",
    "    ax[j,k].plot(K.distance, (K.dem/div)-(K.farin_corr/div), \n",
    "                 ls='--', c='salmon', alpha=a, label='Farinotti et al. (2019) bed')  #marker='o')\n",
    "    ax[1,4].axis('off')\n",
    "\n",
    "#     if i <= 2:\n",
    "#         ax[j,k].yaxis.set_major_locator(ticker.MultipleLocator(50/div))\n",
    "#     else:\n",
    "    ax[j,k].yaxis.set_major_locator(ticker.MultipleLocator(100/div))\n",
    "    ax[j,k].tick_params(which='major', length=2)\n",
    "    if i == 7:\n",
    "        ax[j,k].text(0.9, 0.08, letter[i], transform=ax[j,k].transAxes, fontweight='bold', verticalalignment='center', horizontalalignment='center')\n",
    "    else:\n",
    "        ax[j,k].text(0.1, 0.08, letter[i], transform=ax[j,k].transAxes, fontweight='bold', verticalalignment='center', horizontalalignment='center')\n",
    "#     ax[0,0].set_ylim(2.0, 2.12)\n",
    "    ax[0,1].set_ylim(2.18, 2.35)\n",
    "    ax[0,5].set_xlim(-100, 2200)\n",
    "#     if i > 8:\n",
    "    ax[1,2].axis('off');ax[1,3].axis('off'); ax[1,4].axis('off'); ax[1,5].axis('off')\n",
    "    n += 1\n",
    "\n",
    "    k += 1\n",
    "    if n == 6: \n",
    "        j += 1\n",
    "        k = 0  \n",
    "         \n",
    "    ax[0,5].legend(loc=4,  bbox_to_anchor=(0.35, -1.1),)\n",
    "    ax[0,0].set_ylabel('Altitude (m a.s.l.)')\n",
    "# fig.text(0.005, 0.65, 'Altitude (m a.s.l.)', rotation=90)\n",
    "# fig.text(0.48, 0.01, 'Distance (m)')     \n",
    "fig.subplots_adjust(bottom=0.115, top=0.98, left=0.055, right=0.99, wspace=0.25, hspace=0.18) \n",
    "\n",
    "plt.savefig(fl_path + 'products/' + glacier[gl]+'_flux_gates_trial.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": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.2194884\n",
      "2.9682283\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 240.945x187.087 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "year=2017\n",
    "I = np.abs(year - 2016) \n",
    "\n",
    "font = {'family' : 'Helvetica', 'weight' : 'normal', 'size'   : 8}\n",
    "thick=H_opt.copy();thick.data[thick.data<0.1] = np.nan\n",
    "plt.rc('font', **font)\n",
    "pylab.rcParams['xtick.major.pad']='1';pylab.rcParams['ytick.major.pad']='1'\n",
    "\n",
    "if VDIR == 'individual':\n",
    "        vf_list = ['illecillewaet_2016_all25m_vx.tif','illecillewaet_2017_all25m_vx.tif','illecillewaet_2018_all25m_vx.tif']\n",
    "        vdir = '/home/pelto/Desktop/velocity_mapping/' +glacier[gl] + '/3m/' \n",
    "        VX = vdir+ vf_list[I]\n",
    "        VY = vdir+ vf_list[I][:-7] + '_vy.tif' \n",
    "        ITS = fl_path + 'ITS_LIVE/' + str(year) + '_illecillewaet_ITS_LIVE.tif'\n",
    "else:\n",
    "        vf = 'illecillewaet_all_dem3m_'\n",
    "        vdir = '/home/pelto/Desktop/velocity_mapping/' +glacier[gl] + '/3m/'  \n",
    "        VX = vdir + vf + 'blur1_25m_vx_neg.tif' #'vx_best_neg_5m.tif'\n",
    "        VY = vdir + vf + 'blur1_25m_vy.tif'\n",
    "#         VM = vdir + vf + 'vm_5m.tif'\n",
    "        ITS = fl_path + 'ITS_LIVE/' + str(year) + '_illecillewaet_ITS_LIVE.tif'\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",
    "\n",
    "vx.data[vx.data>0.01]= vx.data[vx.data>0.01]*-1.0 #=np.nan  ## vx for illec\n",
    "vz = vy.copy(); vz.data = np.sqrt(vx.data**2 + vz.data**2 )\n",
    "vz.data[extent.data!=1.0] = np.nan;vx.data[extent.data!=1.0] = np.nan;vy.data[extent.data!=1.0] = np.nan \n",
    "vz=xr.DataArray(vz.data, coords=vz.coords, name='vz')\n",
    "\n",
    "VZA=xr.DataArray(data=vz, coords=vx.coords, dims=vx.dims, name='VZA', attrs=vx.attrs) #,indexes=vx.indexes fastpath=False)\n",
    "# VZA.data=VZA.data*(msk)\n",
    "# VZA.data[VZA.data>50.]=np.nan\n",
    "fig, ax = plt.subplots(1,figsize=cm2inch(8.5,6.6))\n",
    "\n",
    "grid = mercator_grid(center_ll=(-117.428, 51.238), extent=(4100, 4200)) ##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(add_ytick_labels=True, xinterval=0.05, yinterval=0.2, linewidths=0.75, linestyles='--', colors='0.25')\n",
    "sm.set_scale_bar(location=(0.16, 0.92), add_bbox=True, bbox_dy=.98, bbox_kwargs=b, linewidth=1.0)\n",
    "\n",
    "sm.set_data(VZA) \n",
    "sm.set_vmax(val=50.)\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",
    "print(off_ice_V)\n",
    "print(np.nanstd(VZ_off_ice))\n",
    "sm.set_topography(topo,  relief_factor=0.9)\n",
    "shpf14 = '/home/pelto/GIS/outlines/Illec2014.shp'  \n",
    "gdf = salem.read_shapefile(shpf14)\n",
    "sm.set_shapefile(gdf, lw=1.2, color='k',)\n",
    "sm.set_cmap(\"Spectral_r\")\n",
    "sm.set_shapefile(gates, color='w', lw=1.1)\n",
    "sm.append_colorbar(ax=ax, pad=0.1, label='Ice velocity (m yr$^{-1}$)') #'Ice Velocity (m yr$^-1$)')#, cbar_title='Ice Velocity (m yr$^-1$)')\n",
    "sm.plot(ax=ax)\n",
    "\n",
    "obs_shp = '/home/pelto/Desktop/ice_flux/Flux_pts.csv'\n",
    "obs_pts = pd.read_csv(obs_shp)  #salem.read_shapefile(obs_shp)\n",
    "x, y = sm.grid.transform(obs_pts.lon.values, obs_pts.lat.values)\n",
    "ax.scatter(x, y, color='k', s=8, facecolor='w',zorder=3)\n",
    "\n",
    "u = D.vx.values\n",
    "v = D.vy.values\n",
    "\n",
    "X, Y = np.meshgrid(D.lon,D.lat)\n",
    "\n",
    "# transform their coordinates to the map reference system and plot the arrows\n",
    "xx, yy = sm.grid.transform(D.lon, D.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",
    "n=1\n",
    "Q = ax.quiver(xx[::n], yy[::n], u[::n], v[::n], scale=350)  #, pivot='mid')\n",
    "qk = ax.quiverkey(Q, 0.07, 0.10, 40, r'$40 \\frac{m}{a}$', labelpos='N', coordinates='figure', labelsep=0.025)\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=0.04, 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)\n",
    "# plt.savefig(fl_path+ 'products/'+ glacier[gl]+ '_thickness.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.2194884\n",
      "2.9682283\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 240.945x187.087 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "year=2017\n",
    "I = np.abs(year - 2016) \n",
    "\n",
    "font = {'family' : 'Helvetica', 'weight' : 'normal', 'size'   : 8}\n",
    "thick=H_opt.copy();thick.data[thick.data<0.1] = np.nan\n",
    "plt.rc('font', **font)\n",
    "pylab.rcParams['xtick.major.pad']='1';pylab.rcParams['ytick.major.pad']='1'\n",
    "\n",
    "if VDIR == 'individual':\n",
    "        vf_list = ['illecillewaet_2016_all25m_vx.tif','illecillewaet_2017_all25m_vx.tif','illecillewaet_2018_all25m_vx.tif']\n",
    "        vdir = '/home/pelto/Desktop/velocity_mapping/' +glacier[gl] + '/3m/' \n",
    "        VX = vdir+ vf_list[I]\n",
    "        VY = vdir+ vf_list[I][:-7] + '_vy.tif' \n",
    "        ITS = fl_path + 'ITS_LIVE/' + str(year) + '_illecillewaet_ITS_LIVE.tif'\n",
    "else:\n",
    "        vf = 'illecillewaet_all_dem3m_'\n",
    "        vdir = '/home/pelto/Desktop/velocity_mapping/' +glacier[gl] + '/3m/'  \n",
    "        VX = vdir + vf + 'blur1_25m_vx_neg.tif' #'vx_best_neg_5m.tif'\n",
    "        VY = vdir + vf + 'blur1_25m_vy.tif'\n",
    "#         VM = vdir + vf + 'vm_5m.tif'\n",
    "        ITS = fl_path + 'ITS_LIVE/' + str(year) + '_illecillewaet_ITS_LIVE.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",
    "    \n",
    "firn = '/home/pelto/Desktop/ice_flux/Illecillewaet/AAR_Illec_2012_2018.tif'\n",
    "firn =salem.open_xr_dataset(firn); firn_reproj = vx.salem.transform(firn);firn = firn_reproj.to_array(name='firn')\n",
    "firn.data[extent.data!=1.0] = np.nan;\n",
    "\n",
    "fig, ax = plt.subplots(1,figsize=cm2inch(8.5,6.6))\n",
    "\n",
    "grid = mercator_grid(center_ll=(-117.428, 51.238), extent=(4100, 4200)) ##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(add_ytick_labels=True, xinterval=0.05, yinterval=0.2, linewidths=0.75, linestyles='--', colors='0.25')\n",
    "sm.set_scale_bar(location=(0.16, 0.92), add_bbox=True, bbox_dy=.98, linewidth=1.0) #bbox_kwargs=b\n",
    "\n",
    "sm.set_data(firn) \n",
    "# sm.set_vmax(val=50.)\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",
    "print(off_ice_V)\n",
    "print(np.nanstd(VZ_off_ice))\n",
    "sm.set_topography(topo,  relief_factor=0.9)\n",
    "shpf14 = '/home/pelto/GIS/outlines/Illec2014.shp'  \n",
    "gdf = salem.read_shapefile(shpf14)\n",
    "sm.set_shapefile(gdf, lw=1.2, color='k',)\n",
    "sm.set_cmap(\"Blues\")\n",
    "sm.set_shapefile(gates, color='w', lw=1.1)\n",
    "sm.append_colorbar(ax=ax, pad=0.1, label='Firn cover (years)') #'Ice Velocity (m yr$^-1$)')#, cbar_title='Ice Velocity (m yr$^-1$)')\n",
    "sm.plot(ax=ax)\n",
    "\n",
    "obs_shp = '/home/pelto/Desktop/ice_flux/Flux_pts.csv'\n",
    "obs_pts = pd.read_csv(obs_shp)  #salem.read_shapefile(obs_shp)\n",
    "x, y = sm.grid.transform(obs_pts.lon.values, obs_pts.lat.values)\n",
    "ax.scatter(x, y, color='k', s=8, facecolor='w',zorder=3)\n",
    "\n",
    "fig.subplots_adjust(bottom=0.04, top=0.99, left=0.14, right=0.88) #, wspace=0.22, hspace=0.15\n",
    "plt.savefig(fl_path+ 'products/'+ glacier[gl] +'_firn_map.pdf', dpi=300)\n",
    "# plt.savefig(fl_path+ 'products/'+ glacier[gl]+ '_thickness.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## trying to fill nan velocity points\n",
    "#     import more_itertools as mit\n",
    "#     #IDS= unique(D.id)\n",
    "\n",
    "# import more_itertools as mit\n",
    "# IDS= unique(D.id)\n",
    "\n",
    "# Sorted_D= D.sort_values(['id', 'distance'], ascending=[True,True])\n",
    "# Missing_V= np.where(Sorted_D['vz'].isnull()==True)[0]\n",
    "# Grouped_MV= [list(group) for group in mit.consecutive_groups(Missing_V)]\n",
    "# for i in Grouped_MV:\n",
    "#     if len(i)==1:\n",
    "#         index=i[0]\n",
    "#         replace= np.mean([Sorted_D['vz'][index-1],Sorted_D['vz'][index+1]])\n",
    "#         Sorted_D['vz'][index]= replace\n",
    "#     else:\n",
    "#         start_index=i[0]\n",
    "#         end_index=i[-1]\n",
    "#         replace= np.mean([Sorted_D['vz'][start_index-4:start_index-1],Sorted_D['vz'][end_index+1:end_index+4]])\n",
    "#         Sorted_D['vx'][i]=replace\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.322"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs = pd.read_csv(fl_path+ 'Illecillewaet_bdot.csv')\n",
    "b=obs[(obs.Elev>2400)&(obs.Elev<2475)&(obs.Ba>-0.1)]\n",
    "b.Ba.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2544915018837891\n",
      "0.23775426112957673\n",
      "0.23280580382612634\n",
      "0.24\n",
      "0.589\n"
     ]
    }
   ],
   "source": [
    "####### Net flux uncertainty #################\n",
    "\n",
    "### average ice velcoity uncertainty\n",
    "vel=[sVZ/18.7,sVZ/13.3,sVZ/5.0,sVZ/8.0,sVZ/8.1]\n",
    "np.mean(vel),np.min(vel),np.max(vel),\n",
    "\n",
    "# print(np.mean(sVZ/new.v_gate_mean[1:]*100))\n",
    "# print((sHopt*new.HF[1:])/new.HF[1:]*100)\n",
    "n=[new16,new17,new]; sQ=[];sQF=[];sB=[] ;sQI=[];sBF=[];sBI=[]\n",
    "for i in range(len(n)):\n",
    "    sQ.append(np.sum(n[i].sQout)/np.sum(n[i].Q_out))\n",
    "    sQF.append(np.sum(n[i].sQoutF)/np.sum(n[i].Q_outF))\n",
    "    sQI.append(np.sum(n[i].sQo_gpr)/np.sum(n[i].Qo_gpr))\n",
    "    \n",
    "    sBF.append(np.sum(n[i].sBwe_F*n[i].dem_count)/np.sum(n[i].dem_count))\n",
    "    sB.append(np.sum(n[i].sBwe_opt*n[i].dem_count)/np.sum(n[i].dem_count))\n",
    "    sBI.append(np.sum(n[i].sBwe_gpr*n[i].dem_count)/np.sum(n[i].dem_count))\n",
    "print(np.mean(sQ))\n",
    "print(np.nanmean(sQF))\n",
    "print(np.nanmean(sQI))\n",
    "print(np.nanmean([sQI,sQ,sQI]).round(3))\n",
    "print(np.nanmean([sBI,sB,sBF]).round(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}