Update dictionary keys

examples/retrieve/plot_convective_stratiform.py

@@ -142,13 +142,13 @@
 
 # add to grid object
 # mask zero values (no surface echo)
-convsf_masked = np.ma.masked_equal(convsf_dict["convsf"]["data"], 0)
+convsf_masked = np.ma.masked_equal(convsf_dict["feature_detection"]["data"], 0)
 # mask 3 values (weak echo)
 convsf_masked = np.ma.masked_equal(convsf_masked, 3)
 # add dimension to array to add to grid object
-convsf_dict["convsf"]["data"] = convsf_masked[None, :, :]
+convsf_dict["feature_detection"]["data"] = convsf_masked[None, :, :]
 # add field
-grid.add_field("convsf", convsf_dict["convsf"], replace_existing=True)
+grid.add_field("convsf", convsf_dict["feature_detection"], replace_existing=True)
 
 # create plot using GridMapDisplay
 # plot variables
@@ -195,23 +195,23 @@
 # ``estimate_flag=False``), but we recommend keeping it turned on.
 
 # mask weak echo and no surface echo
-convsf_masked = np.ma.masked_equal(convsf_dict["convsf"]["data"], 0)
+convsf_masked = np.ma.masked_equal(convsf_dict["feature_detection"]["data"], 0)
 convsf_masked = np.ma.masked_equal(convsf_masked, 3)
-convsf_dict["convsf"]["data"] = convsf_masked
+convsf_dict["feature_detection"]["data"] = convsf_masked
 # underest.
-convsf_masked = np.ma.masked_equal(convsf_dict["convsf_under"]["data"], 0)
+convsf_masked = np.ma.masked_equal(convsf_dict["feature_under"]["data"], 0)
 convsf_masked = np.ma.masked_equal(convsf_masked, 3)
-convsf_dict["convsf_under"]["data"] = convsf_masked
+convsf_dict["feature_under"]["data"] = convsf_masked
 # overest.
-convsf_masked = np.ma.masked_equal(convsf_dict["convsf_over"]["data"], 0)
+convsf_masked = np.ma.masked_equal(convsf_dict["feature_over"]["data"], 0)
 convsf_masked = np.ma.masked_equal(convsf_masked, 3)
-convsf_dict["convsf_over"]["data"] = convsf_masked
+convsf_dict["feature_over"]["data"] = convsf_masked
 
 # Plot each estimation
 plt.figure(figsize=(10, 4))
 ax1 = plt.subplot(131)
 ax1.pcolormesh(
-    convsf_dict["convsf"]["data"][0, :, :],
+    convsf_dict["feature_detection"]["data"][0, :, :],
     vmin=0,
     vmax=2,
     cmap=plt.get_cmap("viridis", 3),
@@ -220,13 +220,13 @@
 ax1.set_aspect("equal")
 ax2 = plt.subplot(132)
 ax2.pcolormesh(
-    convsf_dict["convsf_under"]["data"], vmin=0, vmax=2, cmap=plt.get_cmap("viridis", 3)
+    convsf_dict["feature_under"]["data"], vmin=0, vmax=2, cmap=plt.get_cmap("viridis", 3)
 )
 ax2.set_title("Underestimate")
 ax2.set_aspect("equal")
 ax3 = plt.subplot(133)
 ax3.pcolormesh(
-    convsf_dict["convsf_over"]["data"], vmin=0, vmax=2, cmap=plt.get_cmap("viridis", 3)
+    convsf_dict["feature_over"]["data"], vmin=0, vmax=2, cmap=plt.get_cmap("viridis", 3)
 )
 ax3.set_title("Overestimate")
 ax3.set_aspect("equal")
@@ -274,13 +274,13 @@
 
 # add to grid object
 # mask zero values (no surface echo)
-convsf_masked = np.ma.masked_equal(convsf_dict["convsf"]["data"], 0)
+convsf_masked = np.ma.masked_equal(convsf_dict["feature_detection"]["data"], 0)
 # mask 3 values (weak echo)
 convsf_masked = np.ma.masked_equal(convsf_masked, 3)
 # add dimension to array to add to grid object
-convsf_dict["convsf"]["data"] = convsf_masked[None, :, :]
+convsf_dict["feature_detection"]["data"] = convsf_masked[None, :, :]
 # add field
-grid.add_field("convsf", convsf_dict["convsf"], replace_existing=True)
+grid.add_field("convsf", convsf_dict["feature_detection"], replace_existing=True)
 
 # create plot using GridMapDisplay
 # plot variables
@@ -393,13 +393,13 @@
 
 # add to grid object
 # mask zero values (no surface echo)
-convsf_masked = np.ma.masked_equal(convsf_dict["convsf"]["data"], 0)
+convsf_masked = np.ma.masked_equal(convsf_dict["feature_detection"]["data"], 0)
 # mask 3 values (weak echo)
 convsf_masked = np.ma.masked_equal(convsf_masked, 3)
 # add dimension to array to add to grid object
-convsf_dict["convsf"]["data"] = convsf_masked[None, :, :]
+convsf_dict["feature_detection"]["data"] = convsf_masked[None, :, :]
 # add field
-grid.add_field("convsf", convsf_dict["convsf"], replace_existing=True)
+grid.add_field("convsf", convsf_dict["feature_detection"], replace_existing=True)
 
 # create plot using GridMapDisplay
 # plot variables