snapshot..

modules/aeolus/aeolus_amv.py

@@ -6,6 +6,7 @@ import xarray as xr
 from netCDF4 import Dataset, Dimension, Variable
 from aeolus.geos_nav import GEOSNavigation
 from util.util import haversine_np
+import math
 
 
 amv_file_duration = 60  # minutes
@@ -263,8 +264,8 @@ def get_aeolus_time_dict_s(files_path, lon360=False, do_sort=True, chan='mie'):
     dct_s = []
     for fname in sorted_filenames:
         a_dct = get_aeolus_time_dict(fname, lon360=lon360, do_sort=do_sort)
-        t_dct = time_dict_to_nd(a_dct)
-        dct_s.append(t_dct)
+        # t_dct = time_dict_to_nd(a_dct)
+        dct_s.append(a_dct)
 
     t_dct = dct_s[0]
 
@@ -274,6 +275,61 @@ def get_aeolus_time_dict_s(files_path, lon360=False, do_sort=True, chan='mie'):
     return t_dct
 
 
+def time_dict_to_cld_layers(time_dict):
+    time_dict_layers = {}
+
+    keys = list(time_dict.keys())
+    for key in keys:
+        prof_s = time_dict[key]
+        layers = []
+        prof = prof_s[0]
+
+        if len(prof) == 1:
+            tup = prof[0]
+            layers.append((tup[0], tup[1], tup[3], tup[4]))
+            time_dict_layers[key] = layers
+            continue
+
+        top = -9999.9
+        last_bot = -9999.9
+        tup = None
+        for i in range(len(prof)):
+            tup = prof[i]
+
+            if i == 0:
+                top = tup[3]
+                bot = tup[4]
+                last_bot = bot
+            else:
+                if math.fabs(last_bot - tup[3]) > 10.0:
+                    layers.append((tup[0], tup[1], top, last_bot))
+                    top = tup[3]
+                last_bot = tup[4]
+
+        layers.append((tup[0], tup[1], top, tup[4]))
+
+        time_dict_layers[key] = layers
+
+    return time_dict_layers
+
+
+def time_dict_to_nd_2(time_dict):
+    keys = list(time_dict.keys())
+    for key in keys:
+        vals = time_dict[key]
+        if vals is not None:
+            time_dict[key] = np.stack(vals)
+
+    return time_dict
+
+
+def get_cloud_layers_dict(filename, lon360=False):
+    a_d = get_aeolus_time_dict(filename, lon360=lon360)
+    c_d = time_dict_to_cld_layers(a_d)
+    cld_lyr_dct = time_dict_to_nd_2(c_d)
+    return cld_lyr_dct
+
+
 def run_amv_aeolus_best_fit(match_dict, aeolus_dict):
     keys = list(match_dict.keys())