fixed exercise 4 plotting of their own calibration thanks to Luuk, also some typos etc

Author: MarkMelotto

book/2_modelling_advanced_ewatercycle/exercise4_calibrate_HBV_eWaterCycle.ipynb

@@ -229,24 +229,23 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
     "def calibrationObjective(modelOutput, observation, start_calibration, end_calibration):\n",
-    "    # A function that takes in two dataFrames, interpolates the model output to the\n",
-    "    # observations and calculates the average absolute difference between the two.\n",
+    "    '''A function that takes in two dataFrames, interpolates the model output to the\n",
+    "    observations and calculates the average absolute difference between the two. '''\n",
     "\n",
-    "    #combine the two in one dataFrame\n",
+    "    # Combine the two in one dataFrame\n",
     "    hydro_data = pd.concat([modelOutput.reindex(observation.index, method = 'ffill'), observation], axis=1)\n",
     "\n",
-    "    #only select the calibration period\n",
+    "    # Only select the calibration period\n",
     "    hydro_data = hydro_data[hydro_data.index > pd.to_datetime(pd.Timestamp(start_calibration).date())]\n",
     "    hydro_data = hydro_data[hydro_data.index < pd.to_datetime(pd.Timestamp(end_calibration).date())]\n",
     "\n",
-    "    #calculate mean absolute difference\n",
-    "\n",
+    "    # Calculate mean absolute difference\n",
     "    diff = hydro_data['Observations from GRDC'] - hydro_data['model output']\n",
     "    absDiff = np.abs(diff)\n",
     "    meanAbsDiff = np.mean(absDiff)\n",
@@ -308,12 +307,12 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
-    "# Finaly, just like before, we remove the models themselves to save up space and memory.\n",
+    "# Finally, just like before, we remove the models themselves to save up space and memory.\n",
     "for ensembleMember in ensemble:\n",
     "    ensembleMember.finalize()"
    ]
@@ -434,10 +433,10 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
     "# An object to show a progress bar, since this can take a while:\n",
     "f = IntProgress(min=0, max=N) # instantiate the bar\n",
@@ -464,19 +463,19 @@
     "    # It is good practice to remove any variable you don't need anymore to save memory.\n",
     "    del Q_m, time, discharge_dataframe, objective_this_model\n",
     "\n",
-    "    #update progress bar\n",
+    "    # Update progress bar\n",
     "    f.value += 1\n",
     "\n",
-    "# Finaly, just like before, we remove the models themselves to save up space and memory.\n",
+    "# Finally, just like before, we remove the models themselves to save up space and memory.\n",
     "for ensembleMember in ensemble:\n",
     "    ensembleMember.finalize()"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
     "xFigNr = 2\n",
     "yFigNr = 4\n",
@@ -488,17 +487,19 @@
     "    for yFig in range(yFigNr):\n",
     "        paramCounter = xFig*yFigNr + yFig\n",
     "        # Extract NSE and log NSE for all runs\n",
-    "        nse_values = objectives[:, 0]\n",
-    "        log_nse_values = objectives[:, 1]\n",
-    "        \n",
-    "        axs[xFig_nse,yFig_nse].plot(parameters[paramCounter,:],nse_values,'.', label='NSE')\n",
-    "        axs[xFig_nse,yFig_nse].set_title(p_names[paramCounter])\n",
-    "        \n",
-    "        axs[xFig_nselog,yFig_nselog].plot(parameters[paramCounter,:],log_nse_values,'.', label='log NSE')\n",
-    "        axs[xFig_nselog,yFig_nselog].set_title(p_names[paramCounter])\n",
-    "\n",
-    "        axs[xFig_nse,yFig_nse].legend()\n",
-    "        axs[xFig_nselog,yFig_nselog].legend()\n"
+    "        # nse_values = objectives[:, 0] #objectives is a list of tuples, so this goes wrong\n",
+    "        # log_nse_values = objectives[:, 1]\n",
+    "        nse_values = [i[0] for i in objectives]\n",
+    "        log_nse_values = [i[1] for i in objectives]\n",
+    "\n",
+    "        axs_nse[xFig,yFig].plot(parameters[paramCounter,:],nse_values,'.', label='NSE')\n",
+    "        axs_nse[xFig,yFig].set_title(p_names[paramCounter])\n",
+    "\n",
+    "        axs_nselog[xFig,yFig].plot(parameters[paramCounter,:],log_nse_values,'.', label='log NSE')\n",
+    "        axs_nselog[xFig,yFig].set_title(p_names[paramCounter])\n",
+    "\n",
+    "        axs_nse[xFig,yFig].legend()\n",
+    "        axs_nselog[xFig,yFig].legend()\n"
    ]
   },
   {