ENH: updated feature detection function and example (#1487)

* Rename functions and variables to remove "convective-stratiform"

We want to make this function more generic so it can be used on a wide variety of fields and not just to detect convective and stratiform elements. I renamed most of the functions and some of the variables to reflect this and hopefully make it easier to use.

* Add new reference

* Incorporate binary closing option to functions

* Add author to functions

* Slight reformat for linting

* More linting formatting

* Add alternate option for getting under/over estimate field

* Create plot_feature_detection.py

* Delete plot_convective_stratiform.py

* Update echo_class.py

* Update plot_feature_detection.py

* Update plot_feature_detection.py

* Update test_echo_class.py

* Keep original function and add warning

* Revert "Delete plot_convective_stratiform.py"

This reverts commit 2bff81a.

* Update __init__.py

* Update echo_class.py

* Update dictionary keys

* Update plot_convective_stratiform.py

* Fix issues

* Update plot_convective_stratiform.py

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
 # 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 +220,19 @@
 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"][0, :, :],
+    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"][0, :, :],
+    vmin=0,
+    vmax=2,
+    cmap=plt.get_cmap("viridis", 3),
 )
 ax3.set_title("Overestimate")
 ax3.set_aspect("equal")
@@ -274,13 +280,13 @@
 
 # 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
 # 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 +399,13 @@
 
 # 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
 # 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