refactor

modules/util/util.py

 import numpy as np
+import xarray as xr
 import datetime
 from datetime import timezone
 from metpy.units import units
 from metpy.calc import thickness_hydrostatic
 from collections import namedtuple
 import os
+import h5py
 
 LatLonTuple = namedtuple('LatLonTuple', ['lat', 'lon'])
 
@@ -350,4 +352,69 @@ def normalize(data, param, mean_std_dict, add_noise=False, noise_scale=1.0, seed
 
     data = np.reshape(data, shape)
 
-    return data
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+    return data
+
+# ------------ This code will not be needed when we implement a Fully Connected CNN -----------------------------------
+# Example GOES file to retrieve GEOS parameters in MetPy form (CONUS)
+exmp_file_conus = '/Users/tomrink/data/OR_ABI-L1b-RadC-M6C14_G16_s20193140811215_e20193140813588_c20193140814070.nc'
+# Full Disk
+exmp_file_fd = '/Users/tomrink/data/OR_ABI-L1b-RadF-M6C16_G16_s20212521800223_e20212521809542_c20212521809596.nc'
+
+
+def make_for_full_domain_predict(clvrx_file, name_list=None, domain='FD'):
+    w_x = 16
+    w_y = 16
+
+    if domain == 'CONUS':
+        exmpl_ds = xr.open_dataset(exmp_file_conus)
+    elif domain == 'FD':
+        exmpl_ds = xr.open_dataset(exmp_file_fd)
+    mdat = exmpl_ds.metpy.parse_cf('Rad')
+    geos = mdat.metpy.cartopy_crs
+    xlen = mdat.x.values.size
+    ylen = mdat.y.values.size
+    exmpl_ds.close()
+
+    h5f = h5py.File(clvrx_file, 'r')
+
+    grd_dct = {name: None for name in name_list}
+
+    cnt_a = 0
+    for didx, ds_name in enumerate(name_list):
+        gvals = get_grid_values_all(h5f, ds_name)
+        if gvals is not None:
+            grd_dct[ds_name] = gvals
+            cnt_a += 1
+
+    if cnt_a > 0 and cnt_a != len(name_list):
+        raise GenericException('weirdness')
+
+    grd_dct_n = {name: [] for name in name_list}
+
+    n_x = int(xlen/w_x)
+    n_y = int(ylen/w_y)
+
+    i_0 = 0
+    j_0 = 0
+
+    cc = []
+    ll = []
+
+    for didx, ds_name in enumerate(name_list):
+        for j in range(4, n_y-4, 1):
+            j_ul = j_0 + j * w_y
+            for i in range(4, n_x-4, 1):
+                i_ul = i_0 + i * w_x
+                if didx == 0:
+                    ll.append(j_ul)
+                    cc.append(i_ul)
+                grd_dct_n[ds_name].append(grd_dct[ds_name][j_ul:j_ul+w_y, i_ul:i_ul+w_x])
+
+    grd_dct = {name: None for name in name_list}
+    for didx, ds_name in enumerate(name_list):
+        grd_dct[ds_name] = np.stack(grd_dct_n[ds_name])
+
+    h5f.close()
+
+    return grd_dct, ll, cc
+# -------------------------------------------------------------------------------------------
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