Check nan in plot dl1 param

hiperta_stream/stream_plot_dl1.py

@@ -20,13 +20,15 @@ def plotCalibEvent(pdf, tabImage, varName, nbbin):
 		strValue = varName + ", size = " + str(tabValue.size) + ", Min : " + str(tabValue.min()) + ", max : " + str(tabValue.max()) + ", mean : " + str(tabValue.mean()) + ", std : " + str(tabValue.std())
 		print(strValue)
 		
-		plt.hist(tabValue, bins=nbbin, alpha=1.0)
-		
-		plt.xlabel(varName)
-		plt.title("Signal "+varName)
-		plt.ylabel("nb")
-		plt.yscale("log")
-		plt.grid()
+		try:
+			plt.hist(tabValue, bins=nbbin, alpha=1.0)
+			plt.xlabel(varName)
+			plt.title("Signal "+varName)
+			plt.ylabel("nb")
+			plt.yscale("log")
+			plt.grid()
+		except ValueError:
+			print("Only NaN in",varName," => skip plot")
 		plt.text(0.05, 0.95, strValue, transform=fig.transFigure, size=14)
 		pdf.savefig()  # saves the current figure into a pdf page
 		plt.close()
@@ -50,14 +52,16 @@ def plotParam(pdf, tabParam, tabIsGoodEvent, varName, nbbin):
 		
 		strValue = varName + ", size = " + str(tabValue.size) + ", Min : " + str(tabValue.min()) + ", max : " + str(tabValue.max()) + ", mean : " + str(tabValue.mean()) + ", std : " + str(tabValue.std())
 		print(strValue)
-		
-		plt.hist(tabValue, bins=nbbin, alpha=1.0)
-		
-		plt.xlabel(varName)
-		plt.title("Parameter "+varName)
-		plt.ylabel("nb")
-		plt.yscale("log")
-		plt.grid()
+		try:
+			plt.hist(tabValue, bins=nbbin, alpha=1.0)
+			plt.xlabel(varName)
+			plt.title("Parameter "+varName)
+			plt.ylabel("nb")
+			plt.yscale("log")
+			plt.grid()
+		except ValueError:
+			print("Only NaN in",varName," => skip plot")
+		plt.text(0.05, 0.95, strValue, transform=fig.transFigure, size=14)
 		pdf.savefig()  # saves the current figure into a pdf page
 		plt.close()
 	except KeyError: