03_panel_attribution_temp.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "947bb114-2b3b-4c46-a540-715b099adf3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "from datetime import datetime\n",
    "import glob\n",
    "import cf_units\n",
    "from scipy import stats\n",
    "import numpy as np\n",
    "import sys\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "if not sys.warnoptions:\n",
    "    import warnings\n",
    "    warnings.simplefilter(\"ignore\")\n",
    "import gc\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4650abf9-dd8e-4358-affe-86b3c9f272a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "## read in observations\n",
    "def linregress_grid(df,df_time,start,end):\n",
    "    x,y=np.meshgrid(np.arange(36),np.arange(72))\n",
    "    slope=np.full([36,72],np.nan)\n",
    "    i_start=np.where(df_time==start)[0][0]\n",
    "    i_end=np.where(df_time==end)[0][0]\n",
    "    \n",
    "    for i_x, i_y in zip(x.flatten(),y.flatten()):\n",
    "        if not np.all(np.isnan(df[i_start:i_end+1,i_x,i_y])):\n",
    "            #print(np.all(df[i_start:i_end+1,i_x,i_y]==np.nan),df[i_start:i_end+1,i_x,i_y])\n",
    "            slope[i_x,i_y]=100*stats.linregress(np.arange(end-start+1)[~np.isnan(df[i_start:i_end+1,i_x,i_y])],\n",
    "                                                df[i_start:i_end+1,i_x,i_y][~np.isnan(df[i_start:i_end+1,i_x,i_y])])[0]\n",
    "    return slope\n",
    "\n",
    "hadcrut_med=xr.open_mfdataset('data/HadCRUT.4.6.0.0.median.nc')\n",
    "hadcrut_med=hadcrut_med.groupby('time.year').mean('time')#.sel(time=slice('1870-01-01', '2101-01-01'))\n",
    "\n",
    "trend_hadcrut_med={}\n",
    "\n",
    "# trends for all years in temperature\n",
    "for year in range(2000,2019):\n",
    " \n",
    "    trend_hadcrut_med[year]=linregress_grid(hadcrut_med.sel(year=slice(1870,2021)).roll(longitude=180, roll_coords=True).temperature_anomaly.values,\n",
    "                         hadcrut_med.sel(year=slice(1870,2021)).year.values,1951,year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d6a87ead-aa37-4f2b-82ab-23fd8870ad1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Got model data for MIROC6\n",
      "AF run number: 50, time length of: [149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149]\n",
      "HN run number: 50, time length of: [149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149]\n",
      "PI run number: 1, time length of: [800]\n",
      "\n",
      "Got model data for IPSL-CM6A-LR\n",
      "AF run number: 33, time length of: [149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149]\n",
      "HN run number: 10, time length of: [149, 149, 149, 149, 149, 149, 149, 149, 149, 149]\n",
      "PI run number: 1, time length of: [2000]\n",
      "\n",
      "Got model data for CanESM5\n",
      "AF run number: 25, time length of: [149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149]\n",
      "HN run number: 25, time length of: [149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149]\n",
      "PI run number: 1, time length of: [1000]\n",
      "\n",
      "Cannot use model data for HadGEM3-GC31-LL\n",
      "AF run number: 0, time length of: []\n",
      "HN run number: 0, time length of: []\n",
      "\n",
      "Cannot use model data for CNRM-CM6-1\n",
      "AF run number: 0, time length of: []\n",
      "HN run number: 0, time length of: []\n",
      "\n",
      "Got model data for GFDL-ESM4\n",
      "AF run number: 3, time length of: [149, 149, 149]\n",
      "HN run number: 3, time length of: [149, 149, 149]\n",
      "PI run number: 1, time length of: [500]\n",
      "\n",
      "Got model data for ACCESS-ESM1-5\n",
      "AF run number: 40, time length of: [149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149]\n",
      "HN run number: 3, time length of: [149, 149, 149]\n",
      "PI run number: 1, time length of: [900]\n",
      "\n",
      "Got model data for BCC-CSM2-MR\n",
      "AF run number: 3, time length of: [149, 149, 149]\n",
      "HN run number: 3, time length of: [149, 149, 149]\n",
      "PI run number: 1, time length of: [600]\n",
      "\n",
      "Got model data for NorESM2-LM\n",
      "AF run number: 3, time length of: [149, 149, 149]\n",
      "HN run number: 3, time length of: [149, 149, 149]\n",
      "PI run number: 1, time length of: [501]\n",
      "\n",
      "Got model data for MRI-ESM2-0\n",
      "AF run number: 10, time length of: [149, 149, 149, 149, 149, 149, 149, 149, 149, 149]\n",
      "HN run number: 5, time length of: [149, 149, 149, 149, 149]\n",
      "PI run number: 1, time length of: [701]\n"
     ]
    }
   ],
   "source": [
    "models=[\n",
    "    'MIROC6', \n",
    "    'IPSL-CM6A-LR', \n",
    "    'CanESM5', \n",
    "    'HadGEM3-GC31-LL', \n",
    "    'CNRM-CM6-1', \n",
    "    'GFDL-ESM4', \n",
    "    'ACCESS-ESM1-5', \n",
    "    'BCC-CSM2-MR', \n",
    "    'NorESM2-LM', \n",
    "    'MRI-ESM2-0'\n",
    "]\n",
    "    \n",
    "dir_var = '/home/sarah/projects/CVM3/data/models/tas/'\n",
    "member = 'r*1p1f1'\n",
    "obs = 'tas' # temperature above soil\n",
    "time_res = 'ann'\n",
    "period = 2018-1870\n",
    "\n",
    "hist_name = 'historical'\n",
    "ssp_name  ='ssp*' \n",
    "hn_name   = 'hist-nat'\n",
    "pi_name   = 'piControl'\n",
    "\n",
    "def get_run_nr(filename):\n",
    "    run = filename.split('_')[-2]\n",
    "    return run.split(\"i1p1f1\")[0][1:]\n",
    "\n",
    "y_af = {}\n",
    "y_hn = {}\n",
    "y_pi = {}\n",
    "\n",
    "time_af={}\n",
    "time_hn={}\n",
    "time_pi={}\n",
    "\n",
    "\n",
    "### get usable models\n",
    "usable_models = []\n",
    "for model in models:\n",
    "    y_af[model] = {}\n",
    "    y_hn[model] = {}\n",
    "    y_pi[model] = {}\n",
    "    \n",
    "    time_af[model]=[]\n",
    "    time_hn[model]=[]\n",
    "    time_pi[model]=[]\n",
    "\n",
    "    \n",
    "    # example file: tas_ann_MIROC6_historical_r3i1p1f1_g5.nc\n",
    "    hist_name_list=sorted(glob.glob(f'{dir_var}{model}/g5/{obs}_{time_res}_{model}_{hist_name}_{member}_g5.nc'))\n",
    "    ssp_name_list=sorted(glob.glob(f'{dir_var}{model}/g5/{obs}_{time_res}_{model}_{ssp_name}_{member}_g5.nc'))\n",
    "    hn_name_list=sorted(glob.glob(f'{dir_var}{model}/g5/{obs}_{time_res}_{model}_{hn_name}_{member}_g5.nc'))\n",
    "    pi_name_list=sorted(glob.glob(f'{dir_var}{model}/g5/{obs}_{time_res}_{model}_{pi_name}_{member}_g5.nc'))\n",
    "    \n",
    "    for i, hist in enumerate(hist_name_list):\n",
    "        run = get_run_nr(hist)\n",
    "        # all forcing: combination of historical and projection of shared socioeconomic pathways\n",
    "        i_ssp = i % (len(ssp_name_list))\n",
    "        try: \n",
    "            data = xr.open_mfdataset([hist, ssp_name_list[i_ssp]],combine='nested', concat_dim='time').sel(time=slice('1870-01-01', '2019-01-01'))\n",
    "            y_af[model][run] = data.tas.values\n",
    "            time_af[model].append(data.time)\n",
    "        except:\n",
    "            pass\n",
    "\n",
    "    for hn in hn_name_list:\n",
    "        run = get_run_nr(hn)\n",
    "        data = xr.open_dataset(hn).sel(time=slice('1870-01-01', '2019-01-01'))\n",
    "        y_hn[model][run] = data.tas.values\n",
    "        time_hn[model].append(data.time)\n",
    "        \n",
    "    for pi in pi_name_list:\n",
    "        run = get_run_nr(pi)\n",
    "        data = xr.open_dataset(pi)\n",
    "        y_pi[model][run] = data.tas.values\n",
    "        time_pi[model].append(data.time)\n",
    "\n",
    "    len_af=len(y_af[model])\n",
    "    len_hn=len(y_hn[model])\n",
    "    len_pi=len(y_pi[model])\n",
    "    \n",
    "    #models only usable if all-forcing run exists, at least 3 histNat runs exist and length of histNat is same as period under investigation\n",
    "    diff_period_hn_time = [len(time) - period for time in time_hn[model] ]\n",
    "    if len_af<1 or len_hn<3 or any(d < 0 for d in diff_period_hn_time):\n",
    "        print('\\nCannot use model data for', model)\n",
    "        print(f'AF run number: {len_af}, time length of: {[len(time) for time in time_af[model] ]}')\n",
    "        print(f'HN run number: {len_hn}, time length of: {[len(time) for time in time_hn[model] ]}')\n",
    "        \n",
    "    else:        \n",
    "        print('\\nGot model data for', model)\n",
    "        usable_models.append(model)\n",
    "        print(f'AF run number: {len(y_af[model])}, time length of: {[len(time) for time in time_af[model] ]}')\n",
    "        print(f'HN run number: {len(y_hn[model])}, time length of: {[len(time) for time in time_hn[model] ]}')\n",
    "        print(f'PI run number: {len(y_pi[model])}, time length of: {[len(time) for time in time_pi[model] ]}')\n",
    "\n",
    "models = usable_models\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9549e74d-0d0f-49d8-89c3-4ad4f68dbd41",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import linregress\n",
    "\n",
    "## Drift correct the models preindustrial control runs\n",
    "y_pi_dc={}\n",
    "\n",
    "for model in models:\n",
    "    first_key = list(y_pi[model].keys())[0]\n",
    "    if len(y_pi[model])>1:\n",
    "        y_pi_temp=np.vstack(([y_pi[model][key] for key in y_pi[model].keys()]))\n",
    "        y_pi_dc[model]=np.zeros_like(y_pi_temp)\n",
    "    else:\n",
    "        y_pi_dc[model]=np.zeros_like(y_pi[model][first_key])\n",
    "        \n",
    "    x,y=np.meshgrid(np.arange(y_pi[model][first_key].shape[1]),np.arange(y_pi[model][first_key].shape[2]))\n",
    "    \n",
    "    i_key=0\n",
    "    for key in y_pi[model].keys():\n",
    "        for i_x, i_y in zip(x.flatten(),y.flatten()):\n",
    "            #print(i_x,i_y)\n",
    "            slope, intercept,_,_,_=linregress(np.arange(y_pi[model][key].shape[0]),\n",
    "                                              y_pi[model][key][:,i_x,i_y])\n",
    "            drift=slope*np.arange(y_pi[model][key].shape[0])+intercept\n",
    "            y_pi_dc[model][i_key:i_key+y_pi[model][key].shape[0],i_x,i_y]=y_pi[model][key][:,i_x,i_y]-drift\n",
    "            \n",
    "        i_key+=y_pi[model][key].shape[0]\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d5b372b7-e69d-418b-9822-16856e40d597",
   "metadata": {},
   "outputs": [],
   "source": [
    "##function to calculate 7 year moving average\n",
    "\n",
    "def butter(df):\n",
    "    \n",
    "    output=np.zeros_like(df)\n",
    "    for i_x,i_y in zip(x.flatten(),y.flatten()):\n",
    "        output[:,i_x,i_y] = np.convolve(df[:,i_x,i_y].flatten(),np.ones(7)/7, mode='full')[:-6]\n",
    "    \n",
    "    output=output[6:,:,:]\n",
    "    output=output[:-6,:,:]\n",
    "    \n",
    "    return output\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b8cf5d33-a796-4a43-911e-c19287ffe8d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5, 13, 36, 72) 10.0\n"
     ]
    }
   ],
   "source": [
    "def create_mask(df):\n",
    "    \n",
    "    ##First check that the year is 'valid' i.e. 6 months are non nana\n",
    "    df=df.reshape(-1,12,df.shape[1],df.shape[2])\n",
    "    valid_idx=np.zeros([df.shape[0],36,72])\n",
    "    \n",
    "    for yr_idx in range(df.shape[0]):\n",
    "        valid_idx[yr_idx,:,:]=np.where(np.isnan(df[yr_idx,:,:,:]).sum(axis=0)>7,1,0)\n",
    "        \n",
    "    cutoff=valid_idx.shape[0]%5   \n",
    "    valid_idx=valid_idx[cutoff:,:,:].reshape(5,-1,36,72)    \n",
    "    mask_idx=np.zeros([5,36,72])\n",
    "    \n",
    "    for idx_block in range(5):\n",
    "        mask_idx[idx_block,:,:]=np.where(valid_idx[idx_block,:,:,:].sum(axis=0)>np.floor(0.8*valid_idx.shape[1]),\n",
    "                                               np.nan,1)\n",
    "    print(valid_idx.shape,np.floor(0.8*valid_idx.shape[1]))\n",
    "    mask_idx=np.prod(mask_idx,axis=0)\n",
    "    \n",
    "    return np.roll(~np.isnan(mask_idx),36,1)\n",
    "\n",
    "## Calculate Internal Variability for smoothed observations\n",
    "y_af_all=np.stack(([y_af[model][key] for model in  y_pi_dc.keys() for key in y_af[model]]))\n",
    "\n",
    "## create mask based on criteria \n",
    "hadcrut = xr.open_mfdataset('data/HadCRUT.4.6.0.0.median.nc')\n",
    "mask_1951 = create_mask(hadcrut.sel(time=slice('1951-01-01', '2019-01-01')).temperature_anomaly.values)\n",
    "\n",
    "##detrend observations assuming models have accurate trend\n",
    "hadcrut_med_detrend=hadcrut.sel(time=slice('1870','2018')).groupby('time.year').mean('time').roll(longitude=180, roll_coords=True).temperature_anomaly.values-np.mean(y_af_all,axis=0)\n",
    "hadcrut_med_detrend=butter(hadcrut_med_detrend)\n",
    "\n",
    "hadcrut_var=np.nanstd(hadcrut_med_detrend,axis=0)\n",
    "nan_mask=~mask_1951 ##mask out values where observations are considered incomplete \n",
    "hadcrut_var[nan_mask]=np.nan \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3335527e-6e50-40f2-8f73-735cdb2ef28e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x1152 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "\n",
    "#plot difference in internal variability of smoothed model runs and smoothed observations\n",
    "\n",
    "n_col = 4\n",
    "n_row = 4\n",
    "\n",
    "lons=hadcrut.longitude.values\n",
    "lats=hadcrut.latitude.values\n",
    "\n",
    "fig=plt.figure(figsize=(n_col*5, n_row * 4))\n",
    "plt.rcParams.update({'font.size': 12})\n",
    "plt.rcParams.update({'mathtext.default':'regular'}) \n",
    "plt.rcParams.update({'mathtext.default':'it'}) \n",
    "\n",
    "plt.rcParams.update({\n",
    "    \"text.usetex\": True,\n",
    "    \"font.family\": \"sans-serif\",\n",
    "})\n",
    "grid = plt.GridSpec(n_row*3+1, n_col*9+3, wspace=1, hspace=0) # create a grid for the subplots #0.12\n",
    "\n",
    "props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)\n",
    "\n",
    "i=0\n",
    "i_y=0\n",
    "\n",
    "for i in range(len(models)+1):\n",
    "    \n",
    "    if i==0:\n",
    "        mod_pi_var=np.mean(np.stack(([np.nanstd(butter(y_pi[model][key]),axis=0) for model in y_pi.keys() for key in y_pi[model].keys()])),axis=0)\n",
    "        nan_mask=~mask_1951\n",
    "        mod_pi_var[nan_mask]=np.nan \n",
    "\n",
    "        ax = plt.subplot(grid[i+1:i+3,(i_y+1)*10+5:(i_y+1)*10+15], projection=ccrs.EqualEarth())\n",
    "        \n",
    "    elif i<5:\n",
    "        mod_pi_var=np.mean(np.stack(([np.nanstd(butter(y_pi[models[i-1]][key]),axis=0) for key in y_pi[models[i-1]].keys()])),axis=0)\n",
    "        nan_mask=~mask_1951\n",
    "        mod_pi_var[nan_mask]=np.nan \n",
    "        \n",
    "        ax = plt.subplot(grid[4:6,i_y*10:i_y*10+10], projection=ccrs.EqualEarth(central_longitude=0))\n",
    "        i_y+=1\n",
    "    \n",
    "    elif i>4:\n",
    "        mod_pi_var=np.mean(np.stack(([np.nanstd(butter(y_pi[models[i-1]][key]),axis=0) for key in y_pi[models[i-1]].keys()])),axis=0)\n",
    "        nan_mask=~mask_1951\n",
    "        mod_pi_var[nan_mask]=np.nan \n",
    "        if (i-5)%3==0:\n",
    "            i_y=0\n",
    "            ax = plt.subplot(grid[i+2:i+4,i_y*10+5:i_y*10+15], projection=ccrs.EqualEarth(central_longitude=0))\n",
    "\n",
    "        else:\n",
    "            ax = plt.subplot(grid[(i-5)-((i-5)%3)+7:(i-5)-((i-5)%3)+9,i_y*10+5:i_y*10+15], projection=ccrs.EqualEarth(central_longitude=0))\n",
    "        i_y+=1\n",
    "        \n",
    "    ax.coastlines()\n",
    "    mesh_1=ax.pcolormesh(lons, lats, (mod_pi_var-np.roll(hadcrut_var,36,axis=1)), transform=ccrs.PlateCarree(), cmap=plt.cm.get_cmap('RdBu_r',30), vmin=-0.5, vmax=0.5, rasterized=True)\n",
    "    if i==0:\n",
    "        ax.set_title(\"All CMIP6 models\",y=1.02,fontsize=12)\n",
    "    else:\n",
    "        ax.set_title(models[i-1],y=1.02,fontsize=12)\n",
    "    cmap_mesh=mesh_1.get_cmap()\n",
    "    cmap_mesh.set_bad('gray')\n",
    "    mesh_1.set_cmap(cmap_mesh)\n",
    "\n",
    "axtitle = plt.subplot(grid[0,6:-5])\n",
    "plt.title('Difference in temperature internal variability estimates (Model-Obs)',fontsize=20, y=0.4)      \n",
    "plt.axis('off') \n",
    "\n",
    "axcbar = plt.subplot(grid[-1:,5:-5])\n",
    "cbar=plt.colorbar(mesh_1,orientation='horizontal',fraction=0.4,aspect=45)\n",
    "cbar.set_label('[(Model stdev - Obs stdev)]')  \n",
    "plt.axis('off') \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a74ed8fd-c3e1-4e86-8a7c-a5ebbdf4978b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating trend distribution for MIROC6\n",
      "2000\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "37591a0eaaca49b589db830577e8bd1e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cc67481e91d54166b62bf162c76c4a83",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e32d5fb6ba8d44f384bf5387d8c005fb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2001\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4e9e7e4c65bb487a9625fd1cbce153da",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "81361626a841449bbae03a51670eb0f9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ae119cd6118046cd89ec6b902c01a471",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2002\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "49ec1469d08e4824b52789513d774f9e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c12bc54e13ee433897acb99bb459abc3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7dd78bfe26fd4f84a0f4f1c1047fff31",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2003\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "28273291e04e45d290b31e63f6446fd2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bdb9c6907cc444f99e6ee9ca8d842c30",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "af9cfea5d89241d0a343bf23523fe9f5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2004\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "85f7ec73308d47059e73f8906505892a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2854129a3d1d43cc8ba407c3ca387642",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "91d7aaec3dc34ef5b79813d89c9bb646",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2005\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6bf2c3b301c64b6d9626dfb55116f4dd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a203d4cca1914f2ab836c016679bd305",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e3455ce6544e4ee2a2b8ccd00acf5039",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2006\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "55ea5dc745a342449c392900dba82414",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d12b1f51338144ba924462d63618e18c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "46f4362e5bda4491881e39a1ff22e09e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2007\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "489fd93af077483ab04e26b136304851",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fc1f48ad809f44e09ae359d5a20b7514",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b2f4491ce75b461784d55b2465bdf0f7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2008\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0e7611b907f24811a21775f084b9a896",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "09cc5c254ea04625af35495c7fd39d23",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "60e2697550294cef920d8959d905ca1f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2009\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7cf491398cef4d9e9d97c1475414fde8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "79320391912a4a2a8467917e847cb0fd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b82ff8c7b9c64cde85f1f4055417e733",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2010\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cefa7353bdd44fe3a18a6fc86456e04d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "045b182acdee448e9d1dda9c7c6c70a9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d0e034da941b42ca949b876d0338c179",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2011\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "908a3d9bd8f146d2976b5bc2bc1d9b3d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3f7b3652e2d44d8c9b77793c8b26ec19",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6d54f21e719e40658deaaeddce2f08b1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2012\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0617f848c7844765b9cf2d18393c83c0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "19412d11ed184e888ed8c302223a2dc4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "099139fa35494e6a8db9202ebc973778",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2013\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1172f1275d994f648091d331cd855b0a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "39436454ddcf412689451d6ab4622750",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c03a5e9ec8304ba3919dd15718d4f657",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2014\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5e19859d53c644c0a4a0ca82bdc9236d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "43e58df88e3943ceaec927eafaeeec45",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Input \u001b[0;32mIn [11]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     52\u001b[0m \u001b[38;5;28mprint\u001b[39m(year)\n\u001b[1;32m     53\u001b[0m trend_hn[model][year] \u001b[38;5;241m=\u001b[39m mean_trend_from_dict(y_hn[model],time[model],ref_year,year,roll\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m---> 54\u001b[0m trend_af[model][year] \u001b[38;5;241m=\u001b[39m \u001b[43mmean_trend_from_dict\u001b[49m\u001b[43m(\u001b[49m\u001b[43my_af\u001b[49m\u001b[43m[\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43mtime\u001b[49m\u001b[43m[\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43mref_year\u001b[49m\u001b[43m,\u001b[49m\u001b[43myear\u001b[49m\u001b[43m,\u001b[49m\u001b[43mroll\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m     56\u001b[0m \u001b[38;5;66;03m#start resampling procedure for creation of distribution\u001b[39;00m\n\u001b[1;32m     57\u001b[0m resampled_trend_hn[model][year]\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39mzeros([n_samples, trend_hn[model][year]\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m], trend_hn[model][year]\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m]])\n",
      "Input \u001b[0;32mIn [11]\u001b[0m, in \u001b[0;36mmean_trend_from_dict\u001b[0;34m(df, df_time, start, end, roll)\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmean_trend_from_dict\u001b[39m(df,df_time,start,end, roll\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[0;32m----> 5\u001b[0m     all_trends\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39mstack([linregress_grid(df[key],df_time,start,end) \u001b[38;5;28;01mfor\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m tqdm(df\u001b[38;5;241m.\u001b[39mkeys())])\n\u001b[1;32m      7\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m roll:\n\u001b[1;32m      8\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39mroll(np\u001b[38;5;241m.\u001b[39mmean(all_trends,axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m),\u001b[38;5;241m36\u001b[39m,\u001b[38;5;241m1\u001b[39m)\n",
      "Input \u001b[0;32mIn [11]\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmean_trend_from_dict\u001b[39m(df,df_time,start,end, roll\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[0;32m----> 5\u001b[0m     all_trends\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39mstack([\u001b[43mlinregress_grid\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43mdf_time\u001b[49m\u001b[43m,\u001b[49m\u001b[43mstart\u001b[49m\u001b[43m,\u001b[49m\u001b[43mend\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m tqdm(df\u001b[38;5;241m.\u001b[39mkeys())])\n\u001b[1;32m      7\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m roll:\n\u001b[1;32m      8\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39mroll(np\u001b[38;5;241m.\u001b[39mmean(all_trends,axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m),\u001b[38;5;241m36\u001b[39m,\u001b[38;5;241m1\u001b[39m)\n",
      "Input \u001b[0;32mIn [11]\u001b[0m, in \u001b[0;36mlinregress_grid\u001b[0;34m(df, df_time, start, end)\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i_x, i_y \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(x\u001b[38;5;241m.\u001b[39mflatten(),y\u001b[38;5;241m.\u001b[39mflatten()):\n\u001b[1;32m     19\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39mall(np\u001b[38;5;241m.\u001b[39misnan(df[i_start:i_end\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m,i_x,i_y])):\n\u001b[1;32m     20\u001b[0m         \u001b[38;5;66;03m#print(np.all(df[i_start:i_end+1,i_x,i_y]==np.nan),df[i_start:i_end+1,i_x,i_y])\u001b[39;00m\n\u001b[0;32m---> 21\u001b[0m         slope[i_x,i_y]\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m100\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[43mstats\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinregress\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marange\u001b[49m\u001b[43m(\u001b[49m\u001b[43mend\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43mstart\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m~\u001b[39;49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43misnan\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi_start\u001b[49m\u001b[43m:\u001b[49m\u001b[43mi_end\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mi_x\u001b[49m\u001b[43m,\u001b[49m\u001b[43mi_y\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     22\u001b[0m \u001b[43m                                            \u001b[49m\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi_start\u001b[49m\u001b[43m:\u001b[49m\u001b[43mi_end\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mi_x\u001b[49m\u001b[43m,\u001b[49m\u001b[43mi_y\u001b[49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m~\u001b[39;49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43misnan\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi_start\u001b[49m\u001b[43m:\u001b[49m\u001b[43mi_end\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mi_x\u001b[49m\u001b[43m,\u001b[49m\u001b[43mi_y\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m     23\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m slope\n",
      "File \u001b[0;32m~/projects/climate_change_impact/climate/lib/python3.8/site-packages/scipy/stats/_stats_mstats_common.py:160\u001b[0m, in \u001b[0;36mlinregress\u001b[0;34m(x, y, alternative)\u001b[0m\n\u001b[1;32m    155\u001b[0m ymean \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mmean(y, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m    157\u001b[0m \u001b[38;5;66;03m# Average sums of square differences from the mean\u001b[39;00m\n\u001b[1;32m    158\u001b[0m \u001b[38;5;66;03m#   ssxm = mean( (x-mean(x))^2 )\u001b[39;00m\n\u001b[1;32m    159\u001b[0m \u001b[38;5;66;03m#   ssxym = mean( (x-mean(x)) * (y-mean(y)) )\u001b[39;00m\n\u001b[0;32m--> 160\u001b[0m ssxm, ssxym, _, ssym \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcov\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbias\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mflat\n\u001b[1;32m    162\u001b[0m \u001b[38;5;66;03m# R-value\u001b[39;00m\n\u001b[1;32m    163\u001b[0m \u001b[38;5;66;03m#   r = ssxym / sqrt( ssxm * ssym )\u001b[39;00m\n\u001b[1;32m    164\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ssxm \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0.0\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m ssym \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0.0\u001b[39m:\n\u001b[1;32m    165\u001b[0m     \u001b[38;5;66;03m# If the denominator was going to be 0\u001b[39;00m\n",
      "File \u001b[0;32m<__array_function__ internals>:180\u001b[0m, in \u001b[0;36mcov\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "File \u001b[0;32m~/projects/climate_change_impact/climate/lib/python3.8/site-packages/numpy/lib/function_base.py:2656\u001b[0m, in \u001b[0;36mcov\u001b[0;34m(m, y, rowvar, bias, ddof, fweights, aweights, dtype)\u001b[0m\n\u001b[1;32m   2653\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   2654\u001b[0m         w \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m=\u001b[39m aweights\n\u001b[0;32m-> 2656\u001b[0m avg, w_sum \u001b[38;5;241m=\u001b[39m \u001b[43maverage\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweights\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mw\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreturned\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m   2657\u001b[0m w_sum \u001b[38;5;241m=\u001b[39m w_sum[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m   2659\u001b[0m \u001b[38;5;66;03m# Determine the normalization\u001b[39;00m\n",
      "File \u001b[0;32m<__array_function__ internals>:180\u001b[0m, in \u001b[0;36maverage\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "File \u001b[0;32m~/projects/climate_change_impact/climate/lib/python3.8/site-packages/numpy/lib/function_base.py:531\u001b[0m, in \u001b[0;36maverage\u001b[0;34m(a, axis, weights, returned)\u001b[0m\n\u001b[1;32m    529\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m returned:\n\u001b[1;32m    530\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m scl\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m!=\u001b[39m avg\u001b[38;5;241m.\u001b[39mshape:\n\u001b[0;32m--> 531\u001b[0m         scl \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbroadcast_to\u001b[49m\u001b[43m(\u001b[49m\u001b[43mscl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mavg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[1;32m    532\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m avg, scl\n\u001b[1;32m    533\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "File \u001b[0;32m<__array_function__ internals>:180\u001b[0m, in \u001b[0;36mbroadcast_to\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "File \u001b[0;32m~/projects/climate_change_impact/climate/lib/python3.8/site-packages/numpy/lib/stride_tricks.py:412\u001b[0m, in \u001b[0;36mbroadcast_to\u001b[0;34m(array, shape, subok)\u001b[0m\n\u001b[1;32m    366\u001b[0m \u001b[38;5;129m@array_function_dispatch\u001b[39m(_broadcast_to_dispatcher, module\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnumpy\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m    367\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mbroadcast_to\u001b[39m(array, shape, subok\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[1;32m    368\u001b[0m     \u001b[38;5;124;03m\"\"\"Broadcast an array to a new shape.\u001b[39;00m\n\u001b[1;32m    369\u001b[0m \n\u001b[1;32m    370\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    410\u001b[0m \u001b[38;5;124;03m           [1, 2, 3]])\u001b[39;00m\n\u001b[1;32m    411\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 412\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_broadcast_to\u001b[49m\u001b[43m(\u001b[49m\u001b[43marray\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mshape\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msubok\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msubok\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreadonly\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/projects/climate_change_impact/climate/lib/python3.8/site-packages/numpy/lib/stride_tricks.py:344\u001b[0m, in \u001b[0;36m_broadcast_to\u001b[0;34m(array, shape, subok, readonly)\u001b[0m\n\u001b[1;32m    342\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m shape \u001b[38;5;129;01mand\u001b[39;00m array\u001b[38;5;241m.\u001b[39mshape:\n\u001b[1;32m    343\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcannot broadcast a non-scalar to a scalar array\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 344\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28;43many\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msize\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m<\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43msize\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mshape\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m    345\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mall elements of broadcast shape must be non-\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    346\u001b[0m                      \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnegative\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m    347\u001b[0m extras \u001b[38;5;241m=\u001b[39m []\n",
      "File \u001b[0;32m~/projects/climate_change_impact/climate/lib/python3.8/site-packages/numpy/lib/stride_tricks.py:344\u001b[0m, in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    342\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m shape \u001b[38;5;129;01mand\u001b[39;00m array\u001b[38;5;241m.\u001b[39mshape:\n\u001b[1;32m    343\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcannot broadcast a non-scalar to a scalar array\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 344\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[43msize\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m<\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m \u001b[38;5;28;01mfor\u001b[39;00m size \u001b[38;5;129;01min\u001b[39;00m shape):\n\u001b[1;32m    345\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mall elements of broadcast shape must be non-\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    346\u001b[0m                      \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnegative\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m    347\u001b[0m extras \u001b[38;5;241m=\u001b[39m []\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#create trend distributions for all-forcing and natural-forcing runs by resampling variability \n",
    "#from piControl runs\n",
    "\n",
    "def mean_trend_from_dict(df,df_time,start,end, roll=False):\n",
    "    all_trends=np.stack([linregress_grid(df[key],df_time,start,end) for key in tqdm(df.keys())])\n",
    "    \n",
    "    if roll:\n",
    "        return np.roll(np.mean(all_trends,axis=0),36,1)\n",
    "    else:\n",
    "        return np.mean(all_trends,axis=0)\n",
    "\n",
    "def linregress_grid(df,df_time,start,end):\n",
    "    x,y=np.meshgrid(np.arange(36),np.arange(72))\n",
    "    slope=np.full([36,72],np.nan)\n",
    "    i_start=np.where(df_time==start)[0][0]\n",
    "    i_end=np.where(df_time==end)[0][0]\n",
    "    \n",
    "    for i_x, i_y in zip(x.flatten(),y.flatten()):\n",
    "        if not np.all(np.isnan(df[i_start:i_end+1,i_x,i_y])):\n",
    "            #print(np.all(df[i_start:i_end+1,i_x,i_y]==np.nan),df[i_start:i_end+1,i_x,i_y])\n",
    "            slope[i_x,i_y]=100*stats.linregress(np.arange(end-start+1)[~np.isnan(df[i_start:i_end+1,i_x,i_y])],\n",
    "                                                df[i_start:i_end+1,i_x,i_y][~np.isnan(df[i_start:i_end+1,i_x,i_y])])[0]\n",
    "    return slope\n",
    "\n",
    "n_samples=50\n",
    "\n",
    "y_af_all=np.stack(([y_af[model][key] for model in  y_pi_dc.keys() for key in y_af[model]]))\n",
    "hadcrut=xr.open_mfdataset('data/HadCRUT.4.6.0.0.median.nc')\n",
    "hadcrut_med_detrend=hadcrut.sel(time=slice('1870','2018')).groupby('time.year').mean('time').roll(longitude=180, roll_coords=True).temperature_anomaly.values-np.mean(y_af_all,axis=0)\n",
    "obs_std=np.nanstd(butter(hadcrut_med_detrend),axis=0)\n",
    "\n",
    "trend_hn = {}\n",
    "trend_af = {}\n",
    "resampled_trend_hn = {}\n",
    "resampled_trend_af = {}\n",
    "time = {}\n",
    "ref_year = 1951\n",
    "\n",
    "for model in models:\n",
    "    time[model]=np.arange(1870,2019)\n",
    "    trend_hn[model]={}\n",
    "    trend_af[model]={}\n",
    "    resampled_trend_hn[model] = {}\n",
    "    resampled_trend_af[model] = {}\n",
    "\n",
    "    mod_std=np.mean(np.stack(([np.nanstd(butter(y_pi[model][key]),axis=0) for key in y_pi[model].keys()])),axis=0)\n",
    "    std_corr=obs_std/mod_std\n",
    "    \n",
    "    print('Creating trend distribution for', model)\n",
    "    \n",
    "    for year in range(2000,2019):\n",
    "        print(year)\n",
    "        trend_hn[model][year] = mean_trend_from_dict(y_hn[model],time[model],ref_year,year,roll=True)\n",
    "        trend_af[model][year] = mean_trend_from_dict(y_af[model],time[model],ref_year,year,roll=True)\n",
    "  \n",
    "        #start resampling procedure for creation of distribution\n",
    "        resampled_trend_hn[model][year]=np.zeros([n_samples, trend_hn[model][year].shape[0], trend_hn[model][year].shape[1]])\n",
    "        resampled_trend_af[model][year]=np.zeros([n_samples, trend_af[model][year].shape[0], trend_af[model][year].shape[1]])\n",
    "  \n",
    "\n",
    "        for sample in tqdm(range(n_samples)):\n",
    "\n",
    "            start_pi=np.random.randint(0,y_pi_dc[model].shape[0]-(year-ref_year))\n",
    "            end_pi=start_pi+(year-ref_year)\n",
    "\n",
    "            resampled_trend_hn[model][year][sample,:,:] = (\n",
    "                (linregress_grid(y_pi_dc[model],np.arange(y_pi_dc[model].shape[0]),start_pi,end_pi)*std_corr)\n",
    "                + trend_hn[model][year])\n",
    "            resampled_trend_af[model][year][sample,:,:] = (\n",
    "                (linregress_grid(y_pi_dc[model],np.arange(y_pi_dc[model].shape[0]),start_pi,end_pi)*std_corr)\n",
    "                + trend_af[model][year])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0cf7b55-11d5-4cc4-a1f4-3b58f01ba717",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create array of trends containing all model values for starting time of interest\n",
    "for year in range(2000,2019):\n",
    "    resampled_trend_hn[year] = np.vstack(([resampled_trend_hn[mod][year] for mod in models]))\n",
    "    resampled_trend_af[year] = np.vstack(([resampled_trend_af[mod][year] for mod in models]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e0e6d35-6310-49fd-9321-33f60a931ff6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import percentileofscore\n",
    "import scipy\n",
    "\n",
    "##categorize observations according to location between model all-forcing and natural-forcing\n",
    "##trend distirbutions\n",
    "## need to find a more elegant method, this is quick and dirty\n",
    "\n",
    "def attr_categorize(af_quantiles, hn_quantiles, obs_trend):\n",
    "    \n",
    "    try:\n",
    "        sign=np.sign(obs_trend)\n",
    "\n",
    "        #percentile_af=percentileofscore(af_quantiles,obs_trend)\n",
    "        #percentile_hn=percentileofscore(hn_quantiles,obs_trend)\n",
    "\n",
    "        loc_af,scale_af=scipy.stats.norm.fit(af_quantiles)\n",
    "        loc_hn,scale_hn=scipy.stats.norm.fit(hn_quantiles)\n",
    "\n",
    "        percentile_af=scipy.stats.norm.cdf(obs_trend,loc_af,scale_af)*100\n",
    "        percentile_hn=scipy.stats.norm.cdf(obs_trend,loc_hn,scale_hn)*100\n",
    "\n",
    "        if sign==1:\n",
    "            if percentile_af>5 and percentile_af<95:\n",
    "                if percentile_hn<=96:\n",
    "                    return 0\n",
    "                else:\n",
    "                    return 2\n",
    "            elif (percentile_hn<95 and percentile_hn>5):\n",
    "                return 0\n",
    "            elif (percentile_af<=5 and percentile_hn>=95):\n",
    "                return 1\n",
    "            elif percentile_af>=95:\n",
    "                return 3\n",
    "            elif percentile_hn<=5:\n",
    "                return 0\n",
    "        elif sign==-1:\n",
    "\n",
    "            if (percentile_af<95 and percentile_af>5):\n",
    "                if percentile_hn>=4:\n",
    "                    return 0\n",
    "                else:\n",
    "                    return -2\n",
    "            elif (percentile_hn>5 and percentile_hn<95):\n",
    "                return 0\n",
    "            elif (percentile_hn<=5 and percentile_af>=95):\n",
    "                return -1\n",
    "            elif percentile_af<=5:\n",
    "                return -3\n",
    "            if percentile_hn>=95:\n",
    "                return 0\n",
    "    except:\n",
    "        return(np.nan)\n",
    "        \n",
    "        \n",
    "        \n",
    "\n",
    "\n",
    "    \n",
    "\n",
    "## Assign attribution categories based on median values for select ref year\n",
    "attr = {}\n",
    "for year in range(2000,2019):\n",
    "    attr[year] = np.full([36,72],np.nan)\n",
    "\n",
    "    x,y = np.meshgrid(np.arange(36),np.arange(72))\n",
    "\n",
    "    for i_x, i_y in zip(x.flatten(),y.flatten()):\n",
    "        attr[year][i_x,i_y] = attr_categorize(resampled_trend_af[year][:,i_x,i_y], \n",
    "                                              resampled_trend_hn[year][:,i_x,i_y],\n",
    "                                              trend_hadcrut_med[year].reshape(1,36,72)[:,i_x,i_y])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2372434e-034c-4536-9fc3-eec29a853882",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "import matplotlib as mpl\n",
    "\n",
    "n_col = 2\n",
    "n_row = 3\n",
    "\n",
    "fs_title=16\n",
    "\n",
    "fig=plt.figure(figsize=(n_col*13, n_row * 11))\n",
    "plt.rcParams.update({'font.size': 12})\n",
    "plt.rcParams.update({'mathtext.default':'regular'}) \n",
    "plt.rcParams.update({'mathtext.default':'it'}) \n",
    "\n",
    "\n",
    "grid = plt.GridSpec(n_row*3, n_col*9+3, wspace=1, hspace=0) # create a grid for the subplots #0.12\n",
    "\n",
    "props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)\n",
    "\n",
    "cmap = mpl.colors.ListedColormap(['blue','royalblue', 'turquoise','white',\n",
    "                                  'orange',\"orangered\",'firebrick'])\n",
    "\n",
    "bounds = [ -3.5, -2.5, -1.5, -0.5, 0.5, 1.5, 2.5, 3.5]\n",
    "norm = mpl.colors.BoundaryNorm(bounds, cmap.N)\n",
    "i=0\n",
    "for ref in [1951]:\n",
    "    attr_2010 = attr[2010]\n",
    "    attr_2018 = attr[2018]\n",
    "    \n",
    "    attr_2010[~mask_1951]=np.nan\n",
    "    attr_2018[~mask_1951]=np.nan\n",
    "    \n",
    "    res_2010_plt=attr_2010\n",
    "    \n",
    "    res_2018_plt=attr_2018\n",
    "\n",
    "    \n",
    "    plt.title('Trend assessment using observation median values')\n",
    "    i_y=0\n",
    "\n",
    "    ax = plt.subplot(grid[i+1:i+2,i_y*15:i_y*15+15], projection=ccrs.PlateCarree(central_longitude=0))\n",
    "    \n",
    "    ## make fake mesh so colorbar can be plotted without the extra grey bar\n",
    "    ax.coastlines()\n",
    "    mesh_1=ax.pcolormesh(lons, lats, np.roll(res_2010_plt,-36,axis=1), transform=ccrs.PlateCarree(central_longitude=0),cmap=cmap,norm=norm,rasterized=True)\n",
    "    ax.set_title(\"%i-2010\"%(ref),y=1.02,fontsize=12)\n",
    "    cmap_mesh=mesh_1.get_cmap()\n",
    "    cmap_mesh.set_bad('gray')\n",
    "    mesh_1.set_cmap(cmap_mesh)\n",
    "    \n",
    "    i_y+=1\n",
    "    i+=1\n",
    "\n",
    "    ax = plt.subplot(grid[i-(i%2)+1:i-(i%2)+2,i_y*15:i_y*15+15], projection=ccrs.PlateCarree(central_longitude=0))\n",
    "\n",
    "    i_y+=1\n",
    "    i+=1\n",
    "    ax.coastlines()\n",
    "\n",
    "    \n",
    "    ax.coastlines()\n",
    "    mesh_1=ax.pcolormesh(lons, lats, np.roll(res_2018_plt,-36,axis=1), transform=ccrs.PlateCarree(central_longitude=0),cmap=cmap,norm=norm,rasterized=True)\n",
    "    ax.set_title(\"%i-2018\"%(ref),y=1.02,fontsize=12)\n",
    "    cmap_mesh=mesh_1.get_cmap()\n",
    "    cmap_mesh.set_bad('gray')\n",
    "    mesh_1.set_cmap(cmap_mesh)\n",
    "    \n",
    "axcbar = plt.subplot(grid[i-(i%2):i-(i%2)+1,5:25])\n",
    "cbar=plt.colorbar(mesh_1,orientation='horizontal',ticks=[-3,-2,-1,0,1,2,3],fraction=0.4,aspect=45,extend='neither')\n",
    "cbar.set_label('Attribution category %s'%model)\n",
    "cbar.ax.set_xlim(-3.5,3.5)\n",
    "cbar.ax.set_xticklabels(np.arange(-3,4))\n",
    "plt.axis('off')   \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "998d86ee-089e-4d0d-a97d-84368ee54e32",
   "metadata": {},
   "outputs": [],
   "source": [
    "year_attr_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf8996ba-3a29-497d-ab12-dc73d819ff19",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save data\n",
    "\n",
    "degrees = 5\n",
    "lons = np.linspace(degrees*.5,360-degrees*.5,int(360/degrees))\n",
    "lats = np.linspace(-90+degrees*.5,90-degrees*.5,int(180/degrees))\n",
    "xv, yv = np.meshgrid(lons,lats)\n",
    "\n",
    "attribut = list()\n",
    "years = list()\n",
    "lon = list()\n",
    "lat = list()\n",
    "\n",
    "\n",
    "for year in range(2000,2019):\n",
    "    attrs = attr[year].flatten()\n",
    "    attribut.extend(attrs)\n",
    "    years.extend([year for i in range(len(attrs))])\n",
    "    lon.extend(list(xv.flatten()))\n",
    "    lat.extend(list(yv.flatten()))\n",
    "    \n",
    "    \n",
    "year_attr_grid = pd.DataFrame({\"year\": years, \"Attribution_Category\": attribut, \"LON_5\":lon,\"LAT_5\":lat})\n",
    "year_attr_grid.to_csv(\"data/grid5_tas_adj_attribution_categories_years.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b93e752b-f6cc-4e6a-81da-81127b1722de",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "\n",
    "n_col = 2\n",
    "n_row = 3\n",
    "\n",
    "fs_title=16\n",
    "\n",
    "fig=plt.figure(figsize=(n_col*13, n_row * 11))\n",
    "plt.rcParams.update({'font.size': 12})\n",
    "plt.rcParams.update({'mathtext.default':'regular'}) \n",
    "plt.rcParams.update({'mathtext.default':'it'}) \n",
    "\n",
    "\n",
    "grid = plt.GridSpec(n_row*3, n_col*9+3, wspace=1, hspace=0) # create a grid for the subplots #0.12\n",
    "\n",
    "props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)\n",
    "\n",
    "cmap = mpl.colors.ListedColormap(['blue','royalblue', 'turquoise','white',\n",
    "                                  'orange',\"orangered\",'firebrick'])\n",
    "\n",
    "bounds = [ -3.5, -2.5, -1.5, -0.5, 0.5, 1.5, 2.5, 3.5]\n",
    "norm = mpl.colors.BoundaryNorm(bounds, cmap.N)\n",
    "i=0\n",
    "for ref in [1951]:\n",
    "    attr_2010 = attr[2010]\n",
    "    attr_2018 = attr[2018]\n",
    "    \n",
    "    attr_2010[~mask_1951]=np.nan\n",
    "    attr_2018[~mask_1951]=np.nan\n",
    "    \n",
    "    res_2010_plt=attr_2010\n",
    "    \n",
    "    res_2018_plt=attr_2018\n",
    "\n",
    "    \n",
    "    plt.title('Trend assessment using observation median values')\n",
    "    i_y=0\n",
    "\n",
    "    ax = plt.subplot(grid[i+1:i+2,i_y*15:i_y*15+15], projection=ccrs.PlateCarree(central_longitude=0))\n",
    "    \n",
    "    ## make fake mesh so colorbar can be plotted without the extra grey bar\n",
    "    ax.coastlines()\n",
    "    mesh_1=ax.pcolormesh(lons, lats, np.roll(res_2010_plt,-36,axis=1), transform=ccrs.PlateCarree(central_longitude=0),cmap=cmap,norm=norm,rasterized=True)\n",
    "    ax.set_title(\"%i-2010\"%(ref),y=1.02,fontsize=12)\n",
    "    cmap_mesh=mesh_1.get_cmap()\n",
    "    cmap_mesh.set_bad('gray')\n",
    "    mesh_1.set_cmap(cmap_mesh)\n",
    "    \n",
    "    i_y+=1\n",
    "    i+=1\n",
    "\n",
    "    ax = plt.subplot(grid[i-(i%2)+1:i-(i%2)+2,i_y*15:i_y*15+15], projection=ccrs.PlateCarree(central_longitude=0))\n",
    "\n",
    "    i_y+=1\n",
    "    i+=1\n",
    "    ax.coastlines()\n",
    "\n",
    "    \n",
    "    ax.coastlines()\n",
    "    mesh_1=ax.pcolormesh(lons, lats, np.roll(res_2018_plt,-36,axis=1), transform=ccrs.PlateCarree(central_longitude=0),cmap=cmap,norm=norm,rasterized=True)\n",
    "    ax.set_title(\"%i-2018\"%(ref),y=1.02,fontsize=12)\n",
    "    cmap_mesh=mesh_1.get_cmap()\n",
    "    cmap_mesh.set_bad('gray')\n",
    "    mesh_1.set_cmap(cmap_mesh)\n",
    "    \n",
    "axcbar = plt.subplot(grid[i-(i%2):i-(i%2)+1,5:25])\n",
    "cbar=plt.colorbar(mesh_1,orientation='horizontal',ticks=[-3,-2,-1,0,1,2,3],fraction=0.4,aspect=45,extend='neither')\n",
    "cbar.set_label('Attribution category %s'%model)\n",
    "cbar.ax.set_xlim(-3.5,3.5)\n",
    "cbar.ax.set_xticklabels(np.arange(-3,4))\n",
    "plt.axis('off')   \n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "climate",
   "language": "python",
   "name": "climate"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}