reduce bottom margin

2-produce-graph/pie-data-type.py

@@ -15,14 +15,13 @@ df_type = df["resourceTypeGeneral"].value_counts()
 
 
 
-#define Seaborn color palette to use
-# a set of color via plt
+#define a color palette to use
 ### see color palett https://matplotlib.org/stable/users/explain/colors/colormaps.html
 colors = [plt.cm.Pastel1(i) for i in range(len(df_type))]
 random.shuffle(colors) ## so that blue is not more the first item
 
 plt.subplots(figsize=(10, 7))
-# plt.subplots_adjust(0.5, 1 , 0, 1) # pour modifier la taille du pie 
+# plt.subplots_adjust(1, 0.01 , 0.9, 0.2) # pour modifier la taille du pie 
 plt.pie(df_type, colors = colors, autopct=lambda p: '{:.0f}%'.format(round(p)) if p > 1 else '', startangle = 160)
 ## auto pct only if value > 1
 
@@ -30,6 +29,7 @@ plt.legend(df_type.index, loc = (0.85, 0.2) )
 
 plt.title(f"Type of datasets", fontsize = 20, x = 0.5, y = 1.03, alpha = 0.6)
 plt.suptitle(f"n = {len(df)}", fontsize = 11, x = 0.5, y = 0.9, alpha = 0.6)
+plt.tight_layout(h_pad = 0)
 plt.savefig("pie--datacite-type.png")
 print(f"\ngraph produced pie--datacite-type.png")
 

2-produce-graph/pie-datacite-client.py

@@ -34,6 +34,7 @@ plt.subplots(figsize=(10, 7))
 plt.pie(df_client, labels = df_client.index, colors = colors, autopct='%.0f%%')
 plt.title(f"Distribution of datasets by DataCite client", fontsize = 20, x = 0.5, y = 1.03, alpha = 0.6)
 plt.suptitle(f"n = {len(df)}", fontsize = 11, x = 0.5, y = 0.90, alpha = 0.6)
+plt.tight_layout(h_pad = 0)
 plt.savefig("pie--datacite-client.png")
 print(f"\ngraph produced pie--datacite-client.png")