diff --git a/modules/icing/pirep_goes.py b/modules/icing/pirep_goes.py
index 8d4439cb23122a24733e096101b37d3c3bb25deb..a7548fe475a4e7aca1b14d55af512edc74a68f0c 100644
--- a/modules/icing/pirep_goes.py
+++ b/modules/icing/pirep_goes.py
@@ -4,7 +4,8 @@ import pickle
import matplotlib.pyplot as plt
import os
from util.util import get_time_tuple_utc, GenericException, add_time_range_to_filename, is_night, is_day, \
- get_grid_values_all, check_oblique, make_times, find_bin_index, get_timestamp, homedir
+ check_oblique, make_times, find_bin_index, get_timestamp, homedir
+from util.plot import make_icing_image
from aeolus.datasource import CLAVRx, CLAVRx_VIIRS, GOESL1B
import h5py
import re
@@ -12,7 +13,7 @@ import datetime
from datetime import timezone
import glob
from skyfield import api, almanac
-import xarray as xr
+from deeplearning.icing_cnn import run_evaluate_static
goes_date_format = '%Y%j%H'
goes16_directory = '/arcdata/goes/grb/goes16' # /year/date/abi/L1b/RadC
@@ -1815,68 +1816,24 @@ def tiles_info(filename):
print('Icing 5: ', np.sum(iint == 5))
print('Icing 6: ', np.sum(iint == 6))
-# ------------ 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 run_make_images(ckpt_dir_s_path='/Users/tomrink/tf_model/', prob_thresh=0.5, domain='CONUS', extent=[-105, -70, 15, 50]):
+ ice_dict, no_ice_dict, neg_ice_dict = setup('/Users/tomrink/data/pirep/pireps_202109140000_202109142359.csv')
-def make_for_full_domain_predict(clvrx_file, name_list=l1b_ds_list, domain='FD'):
- w_x = 16
- w_y = 16
+ clvrx_ds = CLAVRx('/Users/tomrink/data/clavrx/RadC/265/')
+ clvrx_files = clvrx_ds.flist
- 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
-# -------------------------------------------------------------------------------------------
+ for fname in clvrx_files:
+ dto = clvrx_ds.get_datetime(fname)
+ ts = dto.timestamp()
+ clvrx_str_time = dto.strftime('%Y-%m-%d_%H:%M')
+ dto, _ = get_time_tuple_utc(ts)
+ dto_0 = dto - datetime.timedelta(minutes=60)
+ dto_1 = dto + datetime.timedelta(minutes=60)
+ ts_0 = dto_0.timestamp()
+ ts_1 = dto_1.timestamp()
+ obs_times, obs_lons, obs_lats, _ = time_filter_3(ice_dict, ts_0, ts_1)
+ _, ice_lons, ice_lats = run_evaluate_static(filename=fname, ckpt_dir_s_path=ckpt_dir_s_path, prob_thresh=prob_thresh, domain=domain)
+ make_icing_image(fname, ice_lons, ice_lats, clvrx_str_time, ice_lons_vld=obs_lons, ice_lats_vld=obs_lats, extent=extent)
+ print('Done: ', clvrx_str_time)