Merge pull request #941 from CLIMADA-project/feature/remove-attribute-style-access-tutorials

 Remove attribute-style accesses to (Geo)DataFrames columns and xarray Datasets variables and attributes within tutorials

Author: spjuhel

doc/tutorial/climada_engine_Forecast.ipynb

@@ -255,8 +255,8 @@
     "\n",
     "### generate exposure\n",
     "# find out which hazard coord to consider\n",
-    "CHE_borders = u_plot._get_borders(np.stack([exposure.gdf.latitude.values,\n",
-    "                                            exposure.gdf.longitude.values],\n",
+    "CHE_borders = u_plot._get_borders(np.stack([exposure.gdf['latitude'].values,\n",
+    "                                            exposure.gdf['longitude'].values],\n",
     "                                           axis=1)\n",
     "                                 )\n",
     "centroid_selection = np.logical_and(np.logical_and(hazard.centroids.lat >= CHE_borders[2],\n",
@@ -390,4 +390,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

doc/tutorial/climada_engine_Impact.ipynb

@@ -68,7 +68,7 @@
     "| event_id |list(int)| id (>0) of each hazard event (Hazard.event_id)|\n",
     "| event_name |(list(str))| name of each event (Hazard.event_name)|\n",
     "| date |np.array| date of events (Hazard.date)|\n",
-    "| coord_exp |np.array| exposures coordinates [lat, lon] (in degrees) (Exposure.gdf.latidues, Exposure.gdf.longitude)|\n",
+    "| coord_exp |np.array| exposures coordinates [lat, lon] (in degrees) (Exposure.gdf['latitudes'], Exposure.gdf['longitude'])|\n",
     "| frequency |np.array| frequency of events (Hazard.frequency)|\n",
     "| frequency_unit |str| unit of event frequency, by default '1/year', i.e., annual (Hazard.frequency_unit)|\n",
     "| unit |str| value unit used (Exposure.value_unit)|\n",
@@ -1468,7 +1468,7 @@
     "\n",
     "# Set Hazard in Exposures points\n",
     "# set centroids from exposures coordinates\n",
-    "centr_pnt = Centroids.from_lat_lon(exp_pnt.gdf.latitude.values, exp_pnt.gdf.longitude.values, exp_pnt.crs)\n",
+    "centr_pnt = Centroids.from_lat_lon(exp_pnt.gdf['latitude'].values, exp_pnt.gdf['longitude'].values, exp_pnt.crs)\n",
     "# compute Hazard in that centroids\n",
     "tr_pnt = TCTracks.from_ibtracs_netcdf(storm_id='2007314N10093')\n",
     "tc_pnt = TropCyclone.from_tracks(tr_pnt, centroids=centr_pnt)\n",
@@ -1492,7 +1492,7 @@
     "# Compute Impact\n",
     "imp_pnt = ImpactCalc(exp_pnt, impf_pnt, tc_pnt).impact()\n",
     "# nearest neighbor of exposures to centroids gives identity\n",
-    "print('Nearest neighbor hazard.centroids indexes for each exposure:', exp_pnt.gdf.centr_TC.values)\n",
+    "print('Nearest neighbor hazard.centroids indexes for each exposure:', exp_pnt.gdf['centr_TC'].values)\n",
     "imp_pnt.plot_scatter_eai_exposure(ignore_zero=False, buffer=0.05);"
    ]
   },
@@ -1716,7 +1716,7 @@
     "# Compute impact\n",
     "imp_ras = ImpactCalc(exp_ras, impf_ras, haz_ras).impact(save_mat=False)\n",
     "# nearest neighbor of exposures to centroids is not identity because litpop does not contain data outside the country polygon\n",
-    "print('\\n Nearest neighbor hazard.centroids indexes for each exposure:', exp_ras.gdf.centr_FL.values)\n",
+    "print('\\n Nearest neighbor hazard.centroids indexes for each exposure:', exp_ras.gdf['centr_FL'].values)\n",
     "imp_ras.plot_raster_eai_exposure();"
    ]
   },
@@ -1967,7 +1967,7 @@
     "\n",
     "# compute sequence of hazards using TropCyclone video_intensity method\n",
     "exp_sea = add_sea(exp_video, (100, 5))\n",
-    "centr_video = Centroids.from_lat_lon(exp_sea.gdf.latitude.values, exp_sea.gdf.longitude.values)\n",
+    "centr_video = Centroids.from_lat_lon(exp_sea.gdf['latitude'].values, exp_sea.gdf['longitude'].values)\n",
     "centr_video.check()\n",
     "\n",
     "track_name = '2017242N16333'\n",

doc/tutorial/climada_engine_unsequa.ipynb

@@ -177,7 +177,7 @@
     "# Here x_exp is the input uncertainty parameter and exp_func the inputvar.func.\n",
     "def exp_func(x_exp, exp_base=exp_base):\n",
     "    exp = exp_base.copy()\n",
-    "    exp.gdf.value *= x_exp\n",
+    "    exp.gdf['value'] *= x_exp\n",
     "    return exp"
    ]
   },
@@ -989,7 +989,7 @@
     "exp_base.assign_centroids(haz)\n",
     "def exp_base_func(x_exp, exp_base):\n",
     "    exp = exp_base.copy()\n",
-    "    exp.gdf.value *= x_exp\n",
+    "    exp.gdf['value'] *= x_exp\n",
     "    return exp\n",
     "from functools import partial\n",
     "exp_func = partial(exp_base_func, exp_base=exp_base)"
@@ -2462,7 +2462,7 @@
     "exp_base.assign_centroids(haz)\n",
     "def exp_base_func(x_exp, exp_base):\n",
     "    exp = exp_base.copy()\n",
-    "    exp.gdf.value *= x_exp\n",
+    "    exp.gdf['value'] *= x_exp\n",
     "    return exp\n",
     "from functools import partial\n",
     "exp_func = partial(exp_base_func, exp_base=exp_base)"
@@ -2797,7 +2797,7 @@
     "    from climada.util.constants import ENT_DEMO_TODAY\n",
     "    entity = Entity.from_excel(ENT_DEMO_TODAY)\n",
     "    entity.exposures.ref_year = 2018\n",
-    "    entity.exposures.gdf.value *= x_ent\n",
+    "    entity.exposures.gdf['value'] *= x_ent\n",
     "    return entity\n",
     "\n",
     "# Entity in the future has a +- 10% uncertainty in the cost of all the adapatation measures\n",
@@ -5267,7 +5267,7 @@
     "\n",
     "def exp_func(cnt, x_exp, exp_list=exp_list):\n",
     "    exp = exp_list[int(cnt)].copy()\n",
-    "    exp.gdf.value *= x_exp\n",
+    "    exp.gdf['value'] *= x_exp\n",
     "    return exp\n",
     "\n",
     "exp_distr = {\"x_exp\": sp.stats.uniform(0.9, 0.2),\n",
@@ -5519,15 +5519,15 @@
     "    global exp_base\n",
     "    if 'exp_base' in globals():\n",
     "        if isinstance(exp_base, Exposures):\n",
-    "            if exp_base.gdf.filename != str(filename):\n",
+    "            if exp_base.gdf['filename'] != str(filename):\n",
     "                exp_base = Exposures.from_hdf5(filename)\n",
-    "                exp_base.gdf.filename = str(filename)\n",
+    "                exp_base.gdf['filename'] = str(filename)\n",
     "    else:\n",
     "        exp_base = Exposures.from_hdf5(filename)\n",
-    "        exp_base.gdf.filename = str(filename)\n",
+    "        exp_base.gdf['filename'] = str(filename)\n",
     "\n",
     "    exp = exp_base.copy()\n",
-    "    exp.gdf.value *= x_exp\n",
+    "    exp.gdf['value'] *= x_exp\n",
     "    return exp\n",
     "\n",
     "exp_distr = {\"x_exp\": sp.stats.uniform(0.9, 0.2),\n",
@@ -5727,4 +5727,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

doc/tutorial/climada_entity_Exposures.ipynb

@@ -1639,7 +1639,7 @@
     "# Example 3: plot_raster method\n",
     "from climada.util.plot import add_cntry_names # use climada's plotting utilities\n",
     "ax = exp.plot_raster(); # plot with same resolution as data\n",
-    "add_cntry_names(ax, [exp.gdf.longitude.min(), exp.gdf.longitude.max(), exp.gdf.latitude.min(), exp.gdf.latitude.max()])\n",
+    "add_cntry_names(ax, [exp.gdf['longitude'].min(), exp.gdf['longitude'].max(), exp.gdf['latitude'].min(), exp.gdf['latitude'].max()])\n",
     "\n",
     "# use keyword argument save_tiff='filepath.tiff' to save the corresponding raster in tiff format\n",
     "# use keyword argument raster_res='desired number' to change resolution of the raster."

doc/tutorial/climada_entity_LitPop.ipynb

@@ -161,9 +161,9 @@
     "    raise err\n",
     "exp.plot_scatter();\n",
     "\n",
-    "# Note that `exp.gdf.region_id` is a number identifying each country:\n",
+    "# Note that `exp.gdf['region_id']` is a number identifying each country:\n",
     "print('\\n Region IDs (`region_id`) in this exposure:')\n",
-    "print(exp.gdf.region_id.unique())"
+    "print(exp.gdf['region_id'].unique())"
    ]
   },
   {

doc/tutorial/climada_hazard_TropCyclone.ipynb

@@ -1743,14 +1743,14 @@
     "# 1. Which is the time frequency of the data?\n",
     "# The values of a DataArray are numpy.arrays.\n",
     "# The nummpy.ediff1d computes the different between elements in an array\n",
-    "diff_time_ns = np.ediff1d(tc_syn.time)\n",
+    "diff_time_ns = np.ediff1d(tc_syn[\"time\"])\n",
     "diff_time_h = diff_time_ns.astype(int)/1000/1000/1000/60/60\n",
     "print('Mean time frequency in hours:', diff_time_h.mean())\n",
     "print('Std time frequency in hours:', diff_time_h.std())\n",
     "print()\n",
     "\n",
     "# 2. Compute the maximum sustained wind for each day.\n",
-    "print('Daily max sustained wind:', tc_syn.max_sustained_wind.groupby('time.day').max())"
+    "print('Daily max sustained wind:', tc_syn[\"max_sustained_wind\"].groupby('time.day').max())"
    ]
   },
   {

doc/tutorial/climada_util_calibrate.ipynb

@@ -1039,7 +1039,7 @@
     "from climada.util.constants import SYSTEM_DIR\n",
     "\n",
     "emdat = pd.read_csv(SYSTEM_DIR / \"tc_impf_cal_v01_EDR.csv\")\n",
-    "emdat_subset = emdat[(emdat.cal_region2 == \"NA1\") & (emdat.year >= 2010)]\n",
+    "emdat_subset = emdat[(emdat[\"cal_region2\"] == \"NA1\") & (emdat[\"year\"] >= 2010)]\n",
     "emdat_subset"
    ]
   },
@@ -1689,7 +1689,7 @@
     "# tracks.plot()\n",
     "\n",
     "# # Calculate windfield for the tracks\n",
-    "# centroids = Centroids.from_lat_lon(exposure.gdf.latitude, exposure.gdf.longitude)\n",
+    "# centroids = Centroids.from_lat_lon(exposure.gdf['latitude'], exposure.gdf['longitude'])\n",
     "# hazard = TropCyclone.from_tracks(tracks, centroids)"
    ]
   },