docs/how_to_daily_input_daily_output.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "understanding-capture",
   "metadata": {},
   "source": [
    "# A first possibility for OGGM to have a mass balance model with daily input (precipitation and temperature) and daily output via `get_daily_mb` or  `get_specific_daily_mb`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "synthetic-antarctica",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-11 12:22:37: oggm.cfg: Reading default parameters from the OGGM `params.cfg` configuration file.\n",
      "2023-04-11 12:22:37: oggm.cfg: Multiprocessing switched OFF according to the parameter file.\n",
      "2023-04-11 12:22:37: oggm.cfg: Multiprocessing: using all available processors (N=8)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "import seaborn as sns\n",
    "import pickle\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import scipy\n",
    "import scipy.stats as stats\n",
    "import os\n",
    "import oggm\n",
    "from oggm import cfg, utils, workflow, tasks, graphics\n",
    "from oggm.core import massbalance, flowline\n",
    "\n",
    "SMALL_SIZE = 18\n",
    "MEDIUM_SIZE = 20\n",
    "BIGGER_SIZE = 24\n",
    "\n",
    "plt.rc('font', size=SMALL_SIZE)          # controls default text sizes\n",
    "plt.rc('axes', titlesize=SMALL_SIZE)     # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels\n",
    "plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize\n",
    "plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure title\n",
    "\n",
    "# import the MSsandbox modules\n",
    "from MBsandbox.mbmod_daily_oneflowline import process_w5e5_data, TIModel, BASENAMES, process_era5_daily_data\n",
    "from MBsandbox.help_func import compute_stat, minimize_bias_geodetic, optimize_std_quot_brentq_geod\n",
    "\n",
    "cfg.initialize()\n",
    "cfg.PARAMS['use_multiprocessing'] = False\n",
    "\n",
    "# this needs to be changed if working on another computer\n",
    "working_dir = utils.gettempdir(dirname='OGGM_mb_type_intercomparison', reset=True)\n",
    "\n",
    "cfg.PATHS['working_dir'] = working_dir\n",
    "# use elevation band  flowlines\n",
    "base_url = ('https://cluster.klima.uni-bremen.de/~oggm/gdirs/oggm_v1.4/'\n",
    "            'L1-L2_files/elev_bands')\n",
    "\n",
    "from oggm.utils import (floatyear_to_date, date_to_floatyear)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aerial-substitute",
   "metadata": {},
   "source": [
    "we use the Hintereisferner, but it should work for every glacier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "multiple-manual",
   "metadata": {},
   "outputs": [],
   "source": [
    "from MBsandbox.wip.projections_bayescalibration import process_isimip_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "spoken-chest",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-11 12:29:11: oggm.workflow: init_glacier_directories from prepro level 2 on 1 glaciers.\n",
      "2023-04-11 12:29:11: oggm.workflow: Execute entity tasks [gdir_from_prepro] on 1 glaciers\n",
      "2023-04-11 12:29:11: oggm.utils: /home/lilianschuster/OGGM/download_cache/cluster.klima.uni-bremen.de/~oggm/gdirs/oggm_v1.4/L1-L2_files/elev_bands/RGI62/b_010/L2/RGI60-11/RGI60-11.00.tar verified successfully.\n"
     ]
    }
   ],
   "source": [
    "# get HEF glacier>\n",
    "df = ['RGI60-11.00897']\n",
    "#df = ['RGI60-11.03208']\n",
    "gdirs = workflow.init_glacier_directories(df, from_prepro_level=2,\n",
    "                                          prepro_border=10,\n",
    "                                          prepro_base_url=base_url,\n",
    "                                          prepro_rgi_version='62')\n",
    "gdir = gdirs[0]\n",
    "h, w = gdir.get_inversion_flowline_hw()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cooperative-delivery",
   "metadata": {},
   "source": [
    "we don't want hydro years here!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "gross-prefix",
   "metadata": {},
   "outputs": [],
   "source": [
    "cfg.PARAMS['hydro_month_nh'] = 1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "continent-demand",
   "metadata": {},
   "source": [
    "**we use the W5E5 climate with daily temperature and precipitation and a changing lapse rate (data from ERA5)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "south-debate",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-11 12:29:12: MBsandbox.mbmod_daily_oneflowline: (RGI60-11.00897) process_w5e5_data\n"
     ]
    }
   ],
   "source": [
    "climate='W5E5'\n",
    "process_w5e5_data(gdir, climate_type=climate, temporal_resol='daily') \n",
    "# could use here instead as well:\n",
    "# process_era5_daily_data(gdir)\n",
    "mb_type = 'mb_real_daily'\n",
    "grad_type = 'var_an_cycle' # could use here as well 'cte'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "driving-hearing",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "climate_historical_daily_W5E5.nc  glacier_grid.json\n",
      "dem_source.txt                    gridded_data.nc\n",
      "\u001b[0m\u001b[01;35mdem.tif\u001b[0m                           inversion_flowlines.pkl\n",
      "diagnostics.json                  log.txt\n",
      "elevation_band_flowline.csv       \u001b[01;31moutlines.tar.gz\u001b[0m\n",
      "gcm_datatest_no_correction.nc\n"
     ]
    }
   ],
   "source": [
    "ls /tmp/OGGM/OGGM_mb_type_intercomparison/per_glacier/RGI60-11/RGI60-11.03/RGI60-11.03208/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "danish-breakfast",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0mprocess_isimip_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mgdir\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0moutput_filesuffix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mfpath_temp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mfpath_temp_std\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mfpath_precip\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mfpath_temp_h\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mfpath_temp_std_h\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mfpath_precip_h\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mclimate_historical_filesuffix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mensemble\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'mri-esm2-0_r1i1p1f1'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mssp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ssp126'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mtemporal_resol\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'monthly'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcluster\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0myear_range\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'1979'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'2014'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcorrect\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Read, process and store the isimip3b gcm data for this glacier.\n",
       "\n",
       "It stores the data in a format that can be used by the OGGM mass balance\n",
       "model and in the glacier directory.\n",
       "\n",
       "Currently, this function is built for the ISIMIP3b\n",
       "simulations that are on the OGGM servers.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "filesuffix : str\n",
       "    append a suffix to the filename (useful for ensemble experiments).\n",
       "fpath_temp : str\n",
       "    path to the temp file (default: cfg.PATHS['isimip3b_temp_file'])\n",
       "fpath_precip : str\n",
       "    path to the precip file (default: cfg.PATHS['isimip3b_precip_file'])\n",
       "climate_historical_filesuffix : str\n",
       "    filesuffix of historical climate dataset that should be used to\n",
       "    apply the anomaly method\n",
       "correct : bool\n",
       "    whether the bias correction is applied (default is True) or not. As\n",
       "    we use already internally bias-corrected GCMs, we can also set this\n",
       "    to correct=False!\n",
       "**kwargs: any kwarg to be passed to ref:`process_gcm_data`\n",
       "\n",
       "Notes\n",
       "-----\n",
       "Files written to the glacier directory:\n",
       "    gcm_data.nc\n",
       "        The monthly GCM climate timeseries stored in a netCDF file.\n",
       "\u001b[0;31mFile:\u001b[0m      ~/massbalance-sandbox/MBsandbox/wip/projections_bayescalibration.py\n",
       "\u001b[0;31mType:\u001b[0m      function"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "process_isimip_data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "together-injection",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-11 12:29:14: MBsandbox.wip.projections_bayescalibration: (RGI60-11.00897) process_isimip_datatest\n",
      "2023-04-11 12:29:22: MBsandbox.wip.projections_bayescalibration: (RGI60-11.00897) process_gcm_data_adv_dailytest\n"
     ]
    }
   ],
   "source": [
    "process_isimip_data(gdir, output_filesuffix='test', temporal_resol='daily', correct=False,\n",
    "                   climate_historical_filesuffix='_daily_W5E5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "present-phase",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2252.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xr.open_dataset(gdir.get_filepath('climate_historical', filesuffix='_daily_W5E5')).ref_hgt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "powerful-arrangement",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lilianschuster/massbalance-sandbox/MBsandbox/mbmod_daily_oneflowline.py:1173: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  pd_test.loc[pd_test.index.month >= hydro_month_start,\n"
     ]
    }
   ],
   "source": [
    "mb_mod = TIModel(gdir, None, mb_type=mb_type, grad_type=grad_type, baseline_climate=climate, residual=0)\n",
    "fs = '_daily_{}'.format(climate)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "prescribed-parks",
   "metadata": {},
   "source": [
    "get the geodetic mass balance for the calibration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "separate-reference",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_geodetic = utils.get_geodetic_mb_dataframe()\n",
    "\n",
    "#pd_geodetic = pd.read_csv(path_geodetic, index_col='rgiid')\n",
    "pd_geodetic = pd_geodetic.loc[pd_geodetic.period == '2000-01-01_2020-01-01']\n",
    "# for that glacier\n",
    "mb_geodetic = pd_geodetic.loc[gdir.rgi_id].dmdtda * 1000"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "boxed-remedy",
   "metadata": {},
   "source": [
    "get the precipitation factor by optimising the standard deviation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "molecular-fantasy",
   "metadata": {},
   "outputs": [],
   "source": [
    "mb_glaciological = gdir.get_ref_mb_data(input_filesuffix=fs)['ANNUAL_BALANCE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "eastern-deputy",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.122746865797822\n"
     ]
    }
   ],
   "source": [
    "pf_opt = scipy.optimize.brentq(optimize_std_quot_brentq_geod, 0.05, 10, disp=True, xtol=0.1,\n",
    "                                            args=(mb_mod, mb_geodetic, mb_glaciological, h, w,\n",
    "                                                  np.arange(2000, 2019, 1)# glaciological data only goes till 2018\n",
    "                                                 ))\n",
    "print(pf_opt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "suitable-gathering",
   "metadata": {},
   "source": [
    "get again the calibrated melt factor by minimising the bias of the direct glaciological observations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "proved-bottle",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "199.89641770954523\n"
     ]
    }
   ],
   "source": [
    "melt_f_opt = scipy.optimize.brentq(minimize_bias_geodetic, 1, 1000,\n",
    "                                       disp=True, xtol=0.1,\n",
    "                                       args=(mb_mod, mb_geodetic, h, w,\n",
    "                                             pf_opt, False,\n",
    "                                             np.arange(2000, 2020, 1)  # time period that we want to calibrate\n",
    "                                             ))\n",
    "print(melt_f_opt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "crucial-motorcycle",
   "metadata": {},
   "outputs": [],
   "source": [
    "mb_mod.melt_f = melt_f_opt\n",
    "mb_mod.prcp_fac = pf_opt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "flush-construction",
   "metadata": {},
   "source": [
    "## get the daily MB (depending on height)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "honest-dodge",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lilianschuster/massbalance-sandbox/MBsandbox/mbmod_daily_oneflowline.py:2207: UserWarning: be cautiuous when using get_daily_mb and test yourself if it does what you expect\n",
      "  warnings.warn('be cautiuous when using get_daily_mb and test yourself if it does '\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(56, 366)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(mb_mod.get_daily_mb(h, year=1980))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "civic-briefs",
   "metadata": {},
   "outputs": [],
   "source": [
    "mb_daily, t, temp2dformelt, prcp, prcpsol = mb_mod.get_daily_mb(h, year=1980, add_climate=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "strong-agreement",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(56, 366)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(prcpsol)\n",
    "# for each day prcp, prcpsol available  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "gentle-aquatic",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7f354bfa4a60>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(mb_daily[-1])\n",
    "plt.axvline(30, color='grey', ls='--')\n",
    "plt.axvline(60, color='grey', ls='--')\n",
    "plt.axvline(90, color='grey', ls='--')\n",
    "plt.axvline(120, color='grey', ls='--')\n",
    "plt.axvline(150, color='grey', ls='--')\n",
    "plt.axvline(180, color='grey', ls='--')\n",
    "plt.axvline(210, color='grey', ls='--')\n",
    "plt.axvline(240, color='grey', ls='--')\n",
    "plt.axvline(270, color='grey', ls='--')\n",
    "plt.axvline(120, color='grey', ls='--')\n",
    "plt.axvline(150, color='grey', ls='--')\n",
    "plt.axvline(180, color='grey', ls='--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "comparable-victoria",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "reduced-upgrade",
   "metadata": {},
   "source": [
    "## get the specific MB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "parental-print",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lilianschuster/massbalance-sandbox/MBsandbox/mbmod_daily_oneflowline.py:2234: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  return np.asarray(out)\n"
     ]
    }
   ],
   "source": [
    "spec_mb_daily = mb_mod.get_specific_daily_mb(heights=h, widths=w, year=np.arange(1980, 2020))\n",
    "# just a quick check:\n",
    "spec_mb_yearly = mb_mod.get_specific_mb(heights=h, widths=w,\n",
    "                                         year=np.arange(1980,\n",
    "                                                        2020))\n",
    "spec_mb_daily_yearly_sum = []\n",
    "for mb in spec_mb_daily:\n",
    "    spec_mb_daily_yearly_sum.append(mb.sum())\n",
    "np.testing.assert_allclose(spec_mb_daily_yearly_sum,\n",
    "                           spec_mb_yearly, rtol=0.05)\n",
    "# daily specific mass balance summed up over the year is equal to the annual specific mass balance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "surgical-flash",
   "metadata": {},
   "source": [
    "this is a very stupid way to do this,  but it works:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "color-orientation",
   "metadata": {},
   "outputs": [],
   "source": [
    "j = 0\n",
    "a = 1980\n",
    "# we don't use hydro years here \n",
    "first_date = np.array('1980-01-01', dtype=np.datetime64)\n",
    "pd_mb = pd.DataFrame(columns=['daily_specific_mb', 'year'])\n",
    "for mb_d in spec_mb_daily:\n",
    "    for m in mb_d:\n",
    "        pd_mb.loc[first_date + j] = m\n",
    "        pd_mb.loc[first_date + j, 'year'] = a\n",
    "        j += 1\n",
    "    a+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "juvenile-facing",
   "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>daily_specific_mb</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.099869</td>\n",
       "      <td>1999.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.644163</td>\n",
       "      <td>1999.486726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.033226</td>\n",
       "      <td>1999.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.648258</td>\n",
       "      <td>1999.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.393937</td>\n",
       "      <td>1999.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-7.505738</td>\n",
       "      <td>1999.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-18.053747</td>\n",
       "      <td>1999.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-18.272268</td>\n",
       "      <td>1999.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-4.790850</td>\n",
       "      <td>1999.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.637296</td>\n",
       "      <td>1999.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3.967056</td>\n",
       "      <td>1999.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3.034400</td>\n",
       "      <td>1999.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    daily_specific_mb         year\n",
       "1            3.099869  1999.500000\n",
       "2            2.644163  1999.486726\n",
       "3            3.033226  1999.500000\n",
       "4            3.648258  1999.500000\n",
       "5            2.393937  1999.500000\n",
       "6           -7.505738  1999.500000\n",
       "7          -18.053747  1999.500000\n",
       "8          -18.272268  1999.500000\n",
       "9           -4.790850  1999.500000\n",
       "10           1.637296  1999.500000\n",
       "11           3.967056  1999.500000\n",
       "12           3.034400  1999.500000"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd_mb.groupby(pd_mb.index.month).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "final-characteristic",
   "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>daily_specific_mb</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.623102</td>\n",
       "      <td>1999.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.524010</td>\n",
       "      <td>1999.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.731563</td>\n",
       "      <td>1999.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.962731</td>\n",
       "      <td>1999.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.061281</td>\n",
       "      <td>1999.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>2.653338</td>\n",
       "      <td>1999.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>2.990428</td>\n",
       "      <td>1999.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>2.308232</td>\n",
       "      <td>1999.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>2.230183</td>\n",
       "      <td>1999.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>0.347130</td>\n",
       "      <td>1998.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>366 rows &times; 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     daily_specific_mb    year\n",
       "1             2.623102  1999.5\n",
       "2             3.524010  1999.5\n",
       "3             2.731563  1999.5\n",
       "4             4.962731  1999.5\n",
       "5             4.061281  1999.5\n",
       "..                 ...     ...\n",
       "362           2.653338  1999.5\n",
       "363           2.990428  1999.5\n",
       "364           2.308232  1999.5\n",
       "365           2.230183  1999.5\n",
       "366           0.347130  1998.0\n",
       "\n",
       "[366 rows x 2 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd_mb.groupby(pd_mb.index.day_of_year).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "secondary-webster",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.plot(pd_mb.groupby(pd_mb.index.day_of_year).mean()['daily_specific_mb'])\n",
    "plt.xlabel('day of year')\n",
    "plt.ylabel('daily specific mass balance \\n(kg / m&sup2; / day)');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "preceding-spotlight",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f354c618730>]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "\n",
    "plt.plot(pd_mb.groupby(pd_mb.index.month).mean()['daily_specific_mb'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "closed-centre",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "oggmv153_mb_sandbox",
   "language": "python",
   "name": "oggmv153_mb_sandbox"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}