Merge pull request #194 from esowc/branch-alba

Fix bug and add warning

Author: albavilanova

README.md

@@ -104,7 +104,8 @@ Lately, there have been problems retrieving the data from NACIS Natural Earth to
 In case users want to see the distribution of the components by countries, they will need a Google API Key, Client User ID and Secret. The library Geocoder is used to get the data from Google, but it also allows the users of the ADC-Toolbox to use other APIs to do reverse geocoding (retrieving location information by coordinates). In the function scatter_plot, they will find the following line. You can rewrite as they wish. The list of providers can be found in <a href = "https://github.com/DenisCarriere/geocoder" target = "_blank"> <em>Geocoder</em>'s Github repository</a>.
 ```python
 >>> merge_df['Country'] = merge_df.apply(lambda row: geocoder.google([row['latitude'], row['longitude']], 
-                                         method='reverse', key = google_api_key).country_long, axis = 1)
+                                                     method = 'reverse', key = google_api_key).country_long, 
+                                                     axis = 1)
 ```
 If they do not want to edit anything and prefer to run the code using Google API, then they should edit the file <em>keys.txt</em> under the folder <em>data</em>, and write three lines, under their ADS API key. They should contain, in this order, the Google API Key, Client User ID and Secret. They can get their credentials in <a href = "https://console.cloud.google.com/projectselector/google/maps-apis/credentials" target = "_blank">Google Cloud Platform</a>.
 

functions/functions_general.ipynb

@@ -259,11 +259,16 @@
     "\n",
     "    \"\"\"\n",
     "\n",
-    "    bbox = ((lon_min, lat_min), (lon_max, lat_max))\n",
+    "    if lon_min > lon_max or lat_min > lat_max:\n",
+    "        print('ERROR: The minimum longitude/latitude is larger than the maximum longitude/latitude.')\n",
+    "        raise KeyboardInterrupt()\n",
+    "    \n",
+    "    else:\n",
+    "        bbox = ((lon_min, lat_min), (lon_max, lat_max))\n",
     "\n",
-    "    print('SEARCH BOUNDING BOX')\n",
-    "    print(f'Latitudes: from {lat_min} to {lat_max}')\n",
-    "    print(f'Longitudes: from {lon_min} to {lon_max}')\n",
+    "        print('SEARCH BOUNDING BOX')\n",
+    "        print(f'Latitudes: from {lat_min} to {lat_max}')\n",
+    "        print(f'Longitudes: from {lon_min} to {lon_max}')\n",
     "\n",
     "    return bbox"
    ]
@@ -832,7 +837,7 @@
     "                print('The sum will be matched to the sensor data by nearest neighbours.')\n",
     "\n",
     "                model_ds_time = model_ds_time.component.sum(dim = 'hybrid', skipna = False)\n",
-    "               \n",
+    "                \n",
     "                match_df_time['step_index'] = match_df_time.apply(lambda row: nearest_neighbour(model_times, row['delta_time']), axis = 1)\n",
     "                match_df_time['model_time'] = match_df_time.apply(lambda row: model_ds_time.valid_time[row['step_index']].values, axis = 1)\n",
     "                match_df_time['model_column'] = match_df_time.apply(lambda row: model_ds_time.sel(\n",
@@ -2230,7 +2235,8 @@
     "\n",
     "            # Reverse geocoding\n",
     "            merge_df['country'] = merge_df.apply(lambda row: geocoder.google([row['latitude'], row['longitude']], \n",
-    "                                                 method='reverse', key = google_api_key).country_long, axis = 1)\n",
+    "                                                             method = 'reverse', key = google_api_key).country_long, \n",
+    "                                                             axis = 1)\n",
     "\n",
     "            # Find data for the countries in search list\n",
     "            merge_df = merge_df[merge_df['country'].isin(plot_countries)]\n",
@@ -2463,6 +2469,10 @@
     "    # Drop NaN values\n",
     "    merge_df = merge_df.dropna()\n",
     "\n",
+    "    # Transform string to tuple (if there is only one element)\n",
+    "    if isinstance(regions_names, str):\n",
+    "        regions_names = tuple([regions_names])\n",
+    "        \n",
     "    # Drop the dates that have NaN values\n",
     "    plot_dates = np.intersect1d(plot_dates, np.unique(merge_df.index.get_level_values(2)))\n",
     "\n",
@@ -2665,13 +2675,16 @@
     "        Returns:\n",
     "            trends_table (dataframe): Trends table\n",
     "    \"\"\"\n",
-    "\n",
+    "        \n",
     "    if len(np.unique(merge_df.reset_index()['time'])) >= 12:\n",
     "    \n",
     "        # Sinusoidal model\n",
     "        def objective_function_sin(X, C, D, E, N):\n",
     "            return C * np.sin(D * X + E) + N\n",
     "\n",
+    "        # Drop NaN values\n",
+    "        merge_df = merge_df.dropna()\n",
+    "\n",
     "        # Transform string to tuple (if there is only one element)\n",
     "        if isinstance(regions_names, str):\n",
     "            regions_names = tuple([regions_names])\n",
@@ -2681,9 +2694,6 @@
     "\n",
     "        trends_table = []\n",
     "\n",
-    "        # Drop NaN values\n",
-    "        merge_df = merge_df.dropna()\n",
-    "\n",
     "        for region_lats, region_lons, region_name in zip(regions_lats, regions_lons, regions_names):\n",
     "\n",
     "            summary_region = []\n",
@@ -3099,7 +3109,7 @@
     "            coords_list (list): List of search coordinates (e.g. (lat, lon, lat, lon, ...)\n",
     "            regions_names (list): Region names\n",
     "    \"\"\"\n",
-    "    \n",
+    "\n",
     "    if ((sensor_break_date != None and model_break_date != None and sensor_break_date != model_break_date) or\n",
     "        (sensor_break_date != None and model_break_date == None) or\n",
     "        (sensor_break_date == None and model_break_date != None)):\n",