Merge branch 'main' into master

examples/tutorials/gridded_forecast_evaluation.py

@@ -107,16 +107,15 @@
 plt.show()
 
 ####################################################################################################################################
-# Plot concentration ROC Curves
-# -----------------------
+# Plot ROC Curves
+# ---------------
 #
 # We can also plot the Receiver operating characteristic (ROC) Curves based on forecast and testing-catalog.
-# In the figure below, False Positive Rate is the normalized cumulative forecast rate, after sorting cells in decreasing order of rate.
-# The "True Positive Rate" is the normalized cumulative area. The dashed line is the ROC curve for a uniform forecast,
-# meaning the likelihood for an earthquake to occur at any position is the same. The further the concentration ROC curve of a
-# forecast is to the uniform forecast, the specific the forecast is. 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.
+# In the figure below, True Positive Rate is the normalized cumulative forecast rate, after sorting cells in decreasing order of rate.
+# The “False Positive Rate” is the normalized cumulative area.
+# The dashed line is the ROC curve for a uniform forecast, meaning the likelihood for an earthquake to occur at any position is the same.
+# The further the ROC curve of a forecast is to the uniform forecast, the specific the forecast is.
+# 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.