Updating the Concentration ROC diagram and adding the ROC and Molchgan plots using the alarm-based approach (#254)

* introduction of Contingency Molchan and ROC diagrams

I added the code to plot the contingency_ROC and the contingency_Molchan in the .\csep\utils\plots.py.
I also inserted the two funcions in the tutorial of "gridded_forecast_evaluation.py"

* Tutorial to draw the contingency_ROC and Molchan diagrams

i inserted the two function in the example "gridded_forecast_evaluation.py"

* Upgrade ROC diagram and new plots Molchan and ROC diagrams.

* fixed plot_gridded_forecast.py

updated function name call

* updated function names in  gridded_forecast_evaluation.py

* removed plot_roc_diagram from plot_gridded_forecast.py

---------

Co-authored-by: Pablo Iturrieta <69042881+pabloitu@users.noreply.github.com>

Author: Biondini

csep/utils/plots.py

@@ -118,9 +118,38 @@
 # When comparing the forecast ROC curve against a catalog, one can evaluate if the forecast is more or less specific (or smooth) at different level or seismic rate.
 #
 # Note: This figure just shows an example of plotting an ROC curve with a catalog forecast.
+#       If "linear=True" the diagram is represented using a linear x-axis.
+#       If "linear=False" the diagram is represented using a logarithmic x-axis.
+
+
+print("Plotting concentration ROC curve")
+_= plots.plot_concentration_ROC_diagram(forecast, catalog, linear=True)
+
+
+
+
+####################################################################################################################################
+# Plot ROC and Molchan curves using the alarm-based approach
+# -----------------------
+#In this script, we generate ROC diagrams and Molchan diagrams using the alarm-based approach to evaluate the predictive
+#performance of models. This method exploits contingency table analysis to evaluate the predictive capabilities of
+#forecasting models. By analysing the contingency table data, we determine the ROC curve and Molchan trajectory and
+#estimate the Area Skill Score to assess the accuracy and reliability of the prediction models. The generated graphs
+#visually represent the prediction performance.
+
+# Note: If "linear=True" the diagram is represented using a linear x-axis.
+#       If "linear=False" the diagram is represented using a logarithmic x-axis.
+
+print("Plotting ROC curve from the contingency table")
+# Set linear True to obtain a linear x-axis, False to obtain a logical x-axis.
+_ = plots.plot_ROC_diagram(forecast, catalog, linear=True)
+
+print("Plotting Molchan curve from the contingency table and the Area Skill Score")
+# Set linear True to obtain a linear x-axis, False to obtain a logical x-axis.
+_ = plots.plot_Molchan_diagram(forecast, catalog, linear=True)
+
+
 
-print("Plotting ROC curve")
-_ = plots.plot_ROC(forecast, catalog)
 
 ####################################################################################################################################
 # Calculate Kagan's I_1 score
@@ -130,4 +159,4 @@
 # (see Kagan, YanY. [2009] Testing long-term earthquake forecasts: likelihood methods and error diagrams, Geophys. J. Int., v.177, pages 532-542).
 
 I_1 = get_Kagan_I1_score(forecast, catalog)
-print("I_1score is: ", I_1)
+print("I_1score is: ", I_1)