Index: trunk/tests/testing_Etienne/SD_ToolBox_Testing.ipynb
===================================================================
diff -u -r122 -r124
--- trunk/tests/testing_Etienne/SD_ToolBox_Testing.ipynb	(.../SD_ToolBox_Testing.ipynb)	(revision 122)
+++ trunk/tests/testing_Etienne/SD_ToolBox_Testing.ipynb	(.../SD_ToolBox_Testing.ipynb)	(revision 124)
@@ -23,7 +23,32 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on package SDToolBox:\n",
+      "\n",
+      "NAME\n",
+      "    SDToolBox\n",
+      "\n",
+      "PACKAGE CONTENTS\n",
+      "    data_processing\n",
+      "    extract_data\n",
+      "    input_data\n",
+      "    output_data\n",
+      "    output_messages\n",
+      "    predictor_definition\n",
+      "    statistical_model\n",
+      "\n",
+      "FILE\n",
+      "    c:\\checkouts\\trunk\\sdtoolbox\\__init__.py\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "# 0, Load all modules\n",
     "\n",
@@ -36,6 +61,7 @@
     "import sys\n",
     "import xarray as xr\n",
     "from netCDF4 import Dataset\n",
+    "from datetime import datetime\n",
     "\n",
     "# non-general imports \n",
     "# install matplotlib seperately (not included in the package)\n",
@@ -60,7 +86,7 @@
     "from SDToolBox import statistical_model as stm\n",
     "\n",
     "# call help functions to get more info on the SDToolBox\n",
-    "#help(SDToolBox)\n",
+    "help(SDToolBox)\n",
     "#help(dap)\n",
     "#help(exd)\n",
     "#help(oud)\n",
@@ -96,7 +122,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 188,
    "metadata": {},
    "outputs": [
     {
@@ -139,7 +165,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 189,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -663,7 +689,7 @@
    "metadata": {},
    "source": [
     "Make use of the following functions:\n",
-    "5. preditor_definition (pde)"
+    "5. predictor_definition (pde)"
    ]
   },
   {
@@ -675,65 +701,134 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 224,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.1279444288311491\n",
-      "1.075649062346249\n",
-      "0.3961807423277801\n",
-      "nan\n",
-      "1.3557984581811098\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# (data_processing function)?\n",
-    "# predictor_definition library -> compute_spatial_gradients\n",
-    "\n",
+    "# dummy netCDF chunk\n",
     "lat = [43.125, 50, 60.125, 13.5]\n",
     "lon = [43.125, 50, 60.125, 13.5]\n",
-    "times = pd.date_range('2000-01-01', periods=5)\n",
+    "times = pd.date_range('2000-01-01', periods=1000)\n",
     "coordinates = ['lon', 'lat']\n",
     "data = np.random.rand(len(times), len(lon), len(lat))\n",
-    "data_array = xr.DataArray(\n",
+    "data_array1 = xr.DataArray(\n",
     "    data,\n",
     "    coords=[times, lon, lat],\n",
     "    dims=['time', 'lon', 'lat'])\n",
     "\n",
-    "result = pde.PredictorDefinition.compute_spatial_gradients(data_array)\n",
-    "\n",
-    "select_grid = 0\n",
-    "temp_csg = []\n",
-    "for i in result:\n",
-    "    print(i[select_grid][select_grid])\n",
-    "#print(help(pde.PredictorDefinition))\n",
-    "#test = pde.PredictorDefinition.compute_spatial_gradients(Extract_Data_ERA5BOXMSL.data_dict['variables']['msl'])\n",
-    "#print(Extract_Data_ERA5BOXMSL)"
+    "# real netCDF chunk\n",
+    "HS_chunk = Dataset(os.path.join(r'C:\\checkouts\\trunk\\tests\\testing_Etienne\\ERA5_data','chunk_era5_Global_Hs_1986.nc'))\n",
+    "data_array2 = xr.DataArray(\n",
+    "    HS_chunk['swh'][0:10,:,:],\n",
+    "    coords=[HS_chunk['time'][0:10], HS_chunk['longitude'][:], HS_chunk['latitude'][:]],\n",
+    "    dims=['time', 'lon', 'lat'])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# verification: for a point in the grid plot temporal series"
+    "# spatial gradient computation -> may take a while\n",
+    "spatgrad_resultWAVE = pde.PredictorDefinition.compute_spatial_gradients(data_array = data_array2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 47,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.04468479199816283, 0.037357616328655106, 0.03178222225966059, 0.027907956547258597, 0.025620354179348492, 0.026068734430703608, 0.026865886319669734, 0.0281849930042335, nan, 0.047256646787964075]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# verification: spatial map of the spatial gradient of mean sea level pressure"
+    "# verification: for a point in the grid plot temporal series\n",
+    "\n",
+    "select_gridx = 0 # the x grid point\n",
+    "select_gridy = 0 # the y grid point\n",
+    "temp_csg = []\n",
+    "for i in spatgrad_resultWAVE:\n",
+    "    temp_csg.append(i[select_gridx][select_gridy])\n",
+    "    \n",
+    "print(temp_csg)\n",
+    "    \n",
+    "plt.figure(figsize=(16,7))\n",
+    "plt.plot(HS_chunk['time'][0:10].tolist(), temp_csg)\n",
+    "plt.grid(alpha=0.6)\n",
+    "plt.xlim(HS_chunk['time'][0:10].tolist()[0], HS_chunk['time'][0:10].tolist()[-1])\n",
+    "plt.title('Squared spatial gradients of mean sea level pressure in gridpoint coordinates %s, %s (lon, lat degree)'%(HS_chunk['longitude'][:][select_gridx], HS_chunk['latitude'][:][select_gridy]));"
    ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 4800x2700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# verification: spatial map of the spatial gradient of mean sea level pressure\n",
+    "fig = plt.figure(figsize=(16, 9), dpi=300, facecolor='white')\n",
+    "\n",
+    "# world map\n",
+    "ax = plt.axes([0, 0.12, 0.83, 0.75],projection=cartopy.crs.PlateCarree())\n",
+    "ax.grid()\n",
+    "\n",
+    "_ = ax.set_xlim(min(HS_chunk['longitude'][:])-1, max(HS_chunk['longitude'][:])+1)                 \n",
+    "_ = ax.set_ylim(min(HS_chunk['latitude'][:])-1, max(HS_chunk['latitude'][:])+1) \n",
+    "_ = ax.set_xticks(np.linspace(min(HS_chunk['longitude'][:])-1, max(HS_chunk['longitude'][:])+1, num=6), crs=cartopy.crs.PlateCarree()) \n",
+    "_ = ax.set_yticks(np.linspace(min(HS_chunk['latitude'])-1, max(HS_chunk['latitude'])+1, num=5), crs=cartopy.crs.PlateCarree()) \n",
+    "\n",
+    "# Set RGB value to ocean colour '#bfd2d9' has 191, G:210, B:217 as 10m ocean cartopy import gives errors\n",
+    "ax.imshow(np.tile(np.array([[[191, 210, 217]]], dtype=np.uint8), [2, 2, 1]), origin='upper', transform=cartopy.crs.PlateCarree(), extent=[-180, 180, -180, 180])\n",
+    "\n",
+    "# load features, add features and add axes\n",
+    "country_10m = cartopy.feature.NaturalEarthFeature('cultural', 'admin_0_countries', '10m')\n",
+    "ax.add_feature(country_10m, edgecolor='k', linestyle='--', facecolor='#EEEFEE')\n",
+    "ax.coastlines(resolution='10m', color='grey', zorder=5);\n",
+    "\n",
+    "lon_formatter = LongitudeFormatter(number_format='.0f',\n",
+    "                                   degree_symbol='',\n",
+    "                                   dateline_direction_label=True)\n",
+    "lat_formatter = LatitudeFormatter(number_format='.0f',\n",
+    "                                  degree_symbol='')\n",
+    "ax.xaxis.set_major_formatter(lon_formatter)\n",
+    "ax.yaxis.set_major_formatter(lat_formatter)\n",
+    "\n",
+    "# plot the data\n",
+    "plotcoordx, plotcoordy = np.meshgrid(HS_chunk['longitude'][:].tolist(), HS_chunk['latitude'][:].tolist())\n",
+    "# insert c, cmap, vmin & vmax \n",
+    "ax.scatter(plotcoordx.flatten(),plotcoordy.flatten(), zorder=13,alpha=0.7,s=50, transform=cartopy.crs.PlateCarree());#,c=metricsdf.loc[ioceaneu,idx[ii,mom]].values,cmap=cm[ii],vmin=dictlims[ii][0], vmax=dictlims[ii][1])"
+   ]
+  },
+  {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -742,20 +837,115 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 77,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'Int64Index'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-77-3396c33e6ff9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     31\u001b[0m resampled_resultWAVE = dap.DataProcessing.mean_time_resampling(dataset = dataset, \n\u001b[0;32m     32\u001b[0m                                                                \u001b[0mscale\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 33\u001b[1;33m                                                                frequency_string = '3H')\n\u001b[0m",
+      "\u001b[1;32mC:\\checkouts\\trunk\\SDToolBox\\data_processing.py\u001b[0m in \u001b[0;36mmean_time_resampling\u001b[1;34m(dataset, scale, frequency_string)\u001b[0m\n\u001b[0;32m     44\u001b[0m             \u001b[0mdataset\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     45\u001b[0m             \u001b[0mscale\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mscale\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 46\u001b[1;33m             \u001b[0mfrequency_string\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfrequency_string\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     47\u001b[0m         ).mean()\n\u001b[0;32m     48\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\checkouts\\trunk\\SDToolBox\\data_processing.py\u001b[0m in \u001b[0;36m_time_resampling\u001b[1;34m(dataset, scale, frequency_string)\u001b[0m\n\u001b[0;32m     93\u001b[0m         return dataset.resample(\n\u001b[0;32m     94\u001b[0m             \u001b[0mkeep_attrs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 95\u001b[1;33m             time='{}{}'.format(scale, frequency_string))\n\u001b[0m\u001b[0;32m     96\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     97\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\envs\\SDToolBox_env\\lib\\site-packages\\xarray\\core\\common.py\u001b[0m in \u001b[0;36mresample\u001b[1;34m(self, indexer, skipna, closed, label, base, keep_attrs, loffset, restore_coord_dims, **indexer_kwargs)\u001b[0m\n\u001b[0;32m   1036\u001b[0m             \u001b[0mgrouper\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgrouper\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1037\u001b[0m             \u001b[0mresample_dim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mRESAMPLE_DIM\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1038\u001b[1;33m             \u001b[0mrestore_coord_dims\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrestore_coord_dims\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1039\u001b[0m         )\n\u001b[0;32m   1040\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\envs\\SDToolBox_env\\lib\\site-packages\\xarray\\core\\resample.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, dim, resample_dim, *args, **kwargs)\u001b[0m\n\u001b[0;32m    246\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_resample_dim\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresample_dim\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    247\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 248\u001b[1;33m         \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    249\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    250\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshortcut\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\envs\\SDToolBox_env\\lib\\site-packages\\xarray\\core\\groupby.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, obj, group, squeeze, grouper, bins, restore_coord_dims, cut_kwargs)\u001b[0m\n\u001b[0;32m    334\u001b[0m                 \u001b[1;31m# TODO: sort instead of raising an error\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    335\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"index must be monotonic for resampling\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 336\u001b[1;33m             \u001b[0mfull_index\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfirst_items\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_index_and_items\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgrouper\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    337\u001b[0m             \u001b[0msbins\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfirst_items\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mint64\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    338\u001b[0m             group_indices = [slice(i, j) for i, j in zip(sbins[:-1], sbins[1:])] + [\n",
+      "\u001b[1;32m~\\anaconda3\\envs\\SDToolBox_env\\lib\\site-packages\\xarray\\core\\groupby.py\u001b[0m in \u001b[0;36m_get_index_and_items\u001b[1;34m(self, index, grouper)\u001b[0m\n\u001b[0;32m    432\u001b[0m             \u001b[0mfirst_items\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgrouper\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfirst_items\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    433\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 434\u001b[1;33m             \u001b[0mfirst_items\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgrouper\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfirst\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    435\u001b[0m             \u001b[0m_apply_loffset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgrouper\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfirst_items\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    436\u001b[0m         \u001b[0mfull_index\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfirst_items\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\envs\\SDToolBox_env\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mgroupby\u001b[1;34m(self, by, axis, level, as_index, sort, group_keys, squeeze, observed, **kwargs)\u001b[0m\n\u001b[0;32m   7892\u001b[0m             \u001b[0msqueeze\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   7893\u001b[0m             \u001b[0mobserved\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mobserved\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 7894\u001b[1;33m             \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   7895\u001b[0m         )\n\u001b[0;32m   7896\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\envs\\SDToolBox_env\\lib\\site-packages\\pandas\\core\\groupby\\groupby.py\u001b[0m in \u001b[0;36mgroupby\u001b[1;34m(obj, by, **kwds)\u001b[0m\n\u001b[0;32m   2520\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"invalid type: {}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2521\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2522\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mklass\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mby\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m~\\anaconda3\\envs\\SDToolBox_env\\lib\\site-packages\\pandas\\core\\groupby\\groupby.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, squeeze, observed, **kwargs)\u001b[0m\n\u001b[0;32m    389\u001b[0m                 \u001b[0msort\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msort\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    390\u001b[0m                 \u001b[0mobserved\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mobserved\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 391\u001b[1;33m                 \u001b[0mmutated\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmutated\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    392\u001b[0m             )\n\u001b[0;32m    393\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\envs\\SDToolBox_env\\lib\\site-packages\\pandas\\core\\groupby\\grouper.py\u001b[0m in \u001b[0;36m_get_grouper\u001b[1;34m(obj, key, axis, level, sort, observed, mutated, validate)\u001b[0m\n\u001b[0;32m    511\u001b[0m     \u001b[1;31m# a passed-in Grouper, directly convert\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    512\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mGrouper\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 513\u001b[1;33m         \u001b[0mbinner\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgrouper\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_grouper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalidate\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    514\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkey\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    515\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mgrouper\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\envs\\SDToolBox_env\\lib\\site-packages\\pandas\\core\\resample.py\u001b[0m in \u001b[0;36m_get_grouper\u001b[1;34m(self, obj, validate)\u001b[0m\n\u001b[0;32m   1446\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_grouper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalidate\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1447\u001b[0m         \u001b[1;31m# create the resampler and return our binner\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1448\u001b[1;33m         \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_resampler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1449\u001b[0m         \u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_set_binner\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1450\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbinner\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgrouper\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\envs\\SDToolBox_env\\lib\\site-packages\\pandas\\core\\resample.py\u001b[0m in \u001b[0;36m_get_resampler\u001b[1;34m(self, obj, kind)\u001b[0m\n\u001b[0;32m   1441\u001b[0m             \u001b[1;34m\"Only valid with DatetimeIndex, \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1442\u001b[0m             \u001b[1;34m\"TimedeltaIndex or PeriodIndex, \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1443\u001b[1;33m             \u001b[1;34m\"but got an instance of %r\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1444\u001b[0m         )\n\u001b[0;32m   1445\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mTypeError\u001b[0m: Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'Int64Index'"
+     ]
+    }
+   ],
    "source": [
-    "# wave climate data: hourly to 3 hourly"
+    "# dummy dataset chunk\n",
+    "dataset = xr.Dataset(\n",
+    "    {\n",
+    "        'var_name': (\n",
+    "            ('time', 'lat', 'lon'),\n",
+    "            20 * np.random.rand(1464).reshape(366, 2, 2)),\n",
+    "    },\n",
+    "    coords={\n",
+    "        'time': pd.date_range(\n",
+    "                '01/01/1979',\n",
+    "                periods=366,\n",
+    "                freq=pd.DateOffset(days=1)),\n",
+    "        'lat': [10, 20],\n",
+    "        'lon': [150, 160]})\n",
+    "dataset.attrs['cdm_data_type'] = True\n",
+    "\n",
+    "# real dataset chunk (TODO: to be filled in)\n",
+    "dataset = xr.Dataset(\n",
+    "    {\n",
+    "        'var_name': (\n",
+    "            ('time', 'lat', 'lon'),\n",
+    "            HS_chunk['swh'][0:10,:,:]),\n",
+    "    },\n",
+    "    coords={\n",
+    "        'time': HS_chunk['time'][0:10],\n",
+    "        'lat': HS_chunk['latitude'][:],\n",
+    "        'lon': HS_chunk['longitude'][:]})\n",
+    "dataset.attrs['cdm_data_type'] = True\n",
+    "\n",
+    "# wave climate data: hourly to 3 hourly\n",
+    "resampled_resultWAVE = dap.DataProcessing.mean_time_resampling(dataset = dataset, \n",
+    "                                                               scale = 0,\n",
+    "                                                               frequency_string = '3H')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 116,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DatetimeIndex(['1979-01-01', '1979-01-02', '1979-01-03', '1979-01-04',\n",
+      "               '1979-01-05', '1979-01-06', '1979-01-07', '1979-01-08',\n",
+      "               '1979-01-09', '1979-01-10',\n",
+      "               ...\n",
+      "               '1979-12-23', '1979-12-24', '1979-12-25', '1979-12-26',\n",
+      "               '1979-12-27', '1979-12-28', '1979-12-29', '1979-12-30',\n",
+      "               '1979-12-31', '1980-01-01'],\n",
+      "              dtype='datetime64[ns]', length=366, freq='<DateOffset: days=1>')\n",
+      "[753864 753865 753866 753867 753868 753869 753870 753871 753872 753873]\n",
+      "<class 'numpy.ma.core.MaskedArray'>\n",
+      "1986-01-01 00:00:00\n",
+      "1986-02-11 15:00:00\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(pd.date_range('01/01/1979',periods=366,freq=pd.DateOffset(days=1)))\n",
+    "print(HS_chunk['time'][0:10])\n",
+    "print(type(HS_chunk['time'][0]))\n",
+    "\n",
+    "print(datetime(1900, 1, 1) + timedelta(hours=int(HS_chunk['time'][0])))\n",
+    "print(datetime(1900, 1, 1) + timedelta(hours=int(HS_chunk['time'][-1])))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "# mean sea level pressure and gradient: hourly to 6hourly"
+    "# mean sea level pressure and gradient: hourly to 6hourly\n",
+    "resampled_resultMSLP = dap.DataProcessing.mean_time_resampling(dataset = dataset, \n",
+    "                                                               scale = 0,\n",
+    "                                                               frequency_string = '6H')\n",
+    "resampled_resultGMSLP = dap.DataProcessing.mean_time_resampling(dataset = dataset, \n",
+    "                                                                scale = 0,\n",
+    "                                                                frequency_string = '6H')"
    ]
   },
   {
@@ -764,7 +954,24 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# verification: for a point in the grid plot temporal series"
+    "# verification: for a point in the grid plot temporal series\n",
+    "select_gridx = 0 # the x grid point\n",
+    "select_gridy = 0 # the y grid point\n",
+    "temp_resamWAVE = []\n",
+    "temp_resamMSLP = []\n",
+    "temp_resamGMSLP = []\n",
+    "for i in resampled_resultWAVE:\n",
+    "    temp_resamWAVE.append(i[select_gridx][select_gridy])\n",
+    "for i in resampled_resultWAVE:\n",
+    "    temp_resamWAVE.append(i[select_gridx][select_gridy])\n",
+    "for i in resampled_resultWAVE:\n",
+    "    temp_resamWAVE.append(i[select_gridx][select_gridy])\n",
+    "    \n",
+    "plt.figure(figsize=(16,7))\n",
+    "plt.plot(times.tolist(), temp_csg)\n",
+    "plt.grid(alpha=0.6)\n",
+    "plt.xlim(times.tolist()[0], times.tolist()[-1])\n",
+    "plt.title('Squared spatial gradients of mean sea level pressure in gridpoint coordinates %s, %s (lon, lat degree)'%(lon[select_gridx], lat[select_gridy]));"
    ]
   },
   {