w2w cluster aew vort comp

enstools/feature/identification/aew_vortices/configuration.py

@@ -10,16 +10,26 @@ import os
 # lonW = -100
 # lonE = 45
 
-in_data = "/home/ws/he7273/phd_all/data/coll_oper/jjaso2021.nc"
-in_wts = "/home/ws/he7273/phd_all/data/coll_oper/jjaso2021.json"
+in_data = "/project/meteo/w2w/C3/fischer/data/coll_oper/2021/jjaso2021.nc"
+in_wts = "/project/meteo/w2w/C3/fischer/belanger/out/jjaso2021.json"
 
-circles_file = "/home/ws/he7273/phd_all/data/aew/circles.nc"
-fig_dir = '/home/ws/he7273/phd_all/data/vortices/figs'
+# in_data = "/home/ws/he7273/phd_all/data/coll_oper/jjaso2021.nc"
+# in_wts = "/home/ws/he7273/phd_all/data/coll_oper/jjaso2021.json"
+
+circles_file = "/project/meteo/w2w/C3/fischer/data/circles.nc"
+fig_dir = '/project/meteo/w2w/C3/fischer/data/vortices/figs'
+
+# circles_file = "/home/ws/he7273/phd_all/data/aew/circles.nc"
+# fig_dir = '/home/ws/he7273/phd_all/data/vortices/figs'
+
+
+# kw_rain_cm_file = '/home/he7273/phd_all/data/tracked/colorpalette_dyamond_prec_rate.txt'
+kw_rain_cm_file = '/project/meteo/w2w/C3/fischer/belanger/colorpalette_dyamond_prec_rate.txt'
 
 # plot_dir = '/home/ws/he7273/phd_all/data/coll_oper/' # '/project/meteo/w2w/C3/fischer/belanger/plots/' # join('/home/ws/he7273/phd_all/data/aew/plots/') # '/project/meteo/w2w/C3/fischer/belanger/plots/'
 
 field = 'rv'
-bpf = False
+bpf = True
 
 fig_dir = fig_dir + '_' + ('bpf' if bpf else 'no_bpf') + '_' + field + '/'
 try:
@@ -36,8 +46,9 @@ base_threshold = {'cv': 3e-5, 'cva': 1e-5, 'rv': 3e-5}
 # time of interest, if None all
 # june-oct is AEW season
 
-start_date = '2021-08-27T00:00'  # '2022-08-01T00:00' # None # '2008-08-01T00:00' #  # '2008-08-01T00:00'
-end_date = '2021-08-28T10:00'  # '2022-08-15T00:00'  # None # '2008-08-15T00:00' # None # '2008-08-03T00:00'
+## TODO CHANGE DATE AND BPF 2-6D
+start_date = '2021-09-01T00:00'  # '2022-08-01T00:00' # None # '2008-08-01T00:00' #  # '2008-08-01T00:00'
+end_date = '2021-09-15T00:00'  # '2022-08-15T00:00'  # None # '2008-08-15T00:00' # None # '2008-08-03T00:00'
 
 radius = 500000  # 500 km
 prominence_radius = 400000  # 300 km

enstools/feature/identification/aew_vortices/identification.py

@@ -120,7 +120,8 @@ class AEWVortexIdentification(IdentificationStrategy):
         dataset['foci_c'] = xr.zeros_like(dataset[self.field], dtype=int)
 
         l2 = xr.zeros_like(dataset[u_name], dtype=float)
-        # TODO compute CPs in parallel in identify()
+        
+        """
         for time in dataset.time.values:
             for lev in dataset.level.values:
                 print(time)
@@ -150,7 +151,8 @@ class AEWVortexIdentification(IdentificationStrategy):
                 dt['saddle'] = cp
                 dt['foci_c'] = cp_c
                 dataset.loc[dict(level=lev, time=time)] = dt
-
+        
+        """
         dataset = create_wt_troughs_and_areas(dataset, self.json_desc, self.circles, False, d=self.config.radius)
 
         """
@@ -190,6 +192,33 @@ class AEWVortexIdentification(IdentificationStrategy):
     def identify(self, data_chunk: xr.Dataset, **kwargs):
 
         objs = []
+        
+        cp = data_chunk.saddle
+        cp_c = data_chunk.foci_c
+        u_name = self.config.u_dim
+        v_name = self.config.v_dim
+
+        sx = ScalarField()
+        sx.import_from_arrays(data_chunk.latitude.values.tolist(), data_chunk.longitude.values.tolist(), data_chunk[u_name].data)
+        sy = ScalarField()
+        sy.import_from_arrays(data_chunk.latitude.values.tolist(), data_chunk.longitude.values.tolist(), data_chunk[v_name].data)
+
+        v = VectorField()
+        v.import_from_sfs(sx, sy)
+
+        cpp = IMTreatment.vortex_detection.vortex_detection.get_critical_points(v)
+        foci = cpp.sadd[0].xy
+        foci_c = cpp.foc_c[0].xy
+
+        for i in range(foci.shape[0]):
+            cp.loc[dict(longitude=round_off_rating(foci[i][1]), latitude=round_off_rating(foci[i][0]))] = 1
+
+        for i in range(foci_c.shape[0]):
+            cp_c.loc[dict(longitude=round_off_rating(foci_c[i][1]), latitude=round_off_rating(foci_c[i][0]))] = 1
+
+        data_chunk['saddle'] = cp
+        data_chunk['foci_c'] = cp_c
+                
         # v = data_chunk.vortices
         cp = data_chunk.saddle
 

enstools/feature/identification/aew_vortices/plotting.py

@@ -10,12 +10,10 @@ from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
 from PIL import Image
 from datetime import datetime
 
-def get_kitweather_rain_cm():
+def get_kitweather_rain_cm(rain_cm_file):
     rgb_colors = []
-    pathtotxtfile = '/home/he7273/phd_all/data/tracked/'
-    filename_colorpalette = 'colorpalette_dyamond_prec_rate.txt'
 
-    with open(pathtotxtfile + filename_colorpalette, 'r') as f:
+    with open(rain_cm_file, 'r') as f:
         lines = f.readlines()
     for i, line in enumerate(lines):
         rgb_colors.append([float(line[0:3])/255, float(line[4:7])/255, float(line[8:11])/255, 1])
@@ -79,8 +77,8 @@ def plot_kw_style(dataset, dataset_desc, config, lev=700):
                            subplot_kw=dict(projection=ccrs.PlateCarree()))
 
         extent = [-75, 45, -10, 40]
-
-        levels_rain, rain_cm, norm = get_kitweather_rain_cm()
+        
+        levels_rain, rain_cm, norm = get_kitweather_rain_cm(config.kw_rain_cm_file)
         distance_plot_to_cbar = 0.010
         axins = ax.inset_axes([1 + distance_plot_to_cbar, 0.05, 0.015, 0.93],
                           transform=ax.transAxes)
@@ -131,9 +129,9 @@ def plot_kw_style(dataset, dataset_desc, config, lev=700):
 
         # plot vortices
         # ds_t.sel(level=700).vortices.plot.contourf(levels=[-0.5,0.5,99], colors=('#00000000', 'blue'), subplot_kws={'transform_first': True}, add_colorbar=False)
-        ds_t.sel(level=lv).saddle.plot.contourf(levels=[-0.5,0.5,99], colors=('#00000000', 'green'), subplot_kws={'transform_first': True}, add_colorbar=False)
-        ds_t.sel(level=lv).foci_c.plot.contourf(levels=[-0.5, 0.5, 99], colors=('#00000000', 'grey'),
-                                            subplot_kws={'transform_first': True}, add_colorbar=False)
+        ds_t.sel(level=lv).saddle.plot.contourf(levels=[-0.5,0.5,99], colors=('#00000000', 'orange'), subplot_kws={'transform_first': True}, add_colorbar=False)
+        ds_t.sel(level=lv).foci_c.plot.contourf(levels=[-0.5, 0.5, 99], colors=('#00000000', 'orange'), 
+                                            subplot_kws={'transform_first': True}, add_colorbar=False) #### TODO all points seem to end up here!!
 
         # ds_t.prec_rate_rea.plot.contourf(levels=levels_rain, extend='max', subplot_kws={'transform_first': True},
         #                  cmap=rain_cm, norm=norm, add_colorbar=False)

enstools/feature/identification/aew_vortices/util.py

@@ -255,8 +255,8 @@ def bpf(dataset, u_str, v_str, min_days=2, max_days=6): # 2-6day bpf as default
     time_res_days = time_res_hrs / 24.0  # 6/24 = 1/4
     print("Execute bpf for " + str(min_days) + " to " + str(max_days) + " days.")
 
-    u_filtered = butter_bandpass_filter(dataset[u_str].data, time_res_days / 6, time_res_days / 2, 1, axis=0)  # time is dim0
-    v_filtered = butter_bandpass_filter(dataset[v_str].data, time_res_days / 6, time_res_days / 2, 1, axis=0)  # time is dim0
+    u_filtered = butter_bandpass_filter(dataset[u_str].data, time_res_days / max_days, time_res_days / min_days, 1, axis=0)  # time is dim0
+    v_filtered = butter_bandpass_filter(dataset[v_str].data, time_res_days / max_days, time_res_days / min_days, 1, axis=0)  # time is dim0
 
     u_bpf = xr.zeros_like(dataset[u_str])
     v_bpf = xr.zeros_like(dataset[v_str])

enstools/feature/identification/african_easterly_waves/configuration.py

@@ -13,11 +13,13 @@ data_lat = (3, 35)
 data_lon = (-100, 45)
 
 aew_clim_dir = '/project/meteo/w2w/C3/fischer/belanger/aew_clim/cv_clim_era5.nc' # '/home/ws/he7273/phd_all/data/aew/clim/cv_clim_era5.nc' # # '/lsdf/MOD/Gruppe_Transregio/Gruppe_Knippertz/kitweather/data/era5/cv_clim_era5.nc' 'C:\\Users\\Christoph\\phd\\data\\enstools-feature\\cv_clim_era5.nc' # '/home/christoph/phd/data/aew/clim/cv_clim_era5.nc' # '/home/christoph/phd/data/framework_example_ds/aew/' # '/project/meteo/w2w/C3/fischer/belanger/aew_clim/' #
-in_files = '/project/meteo/w2w/C3/fischer/data/coll_oper/2021/jjaso2021.nc' # '/home/ws/he7273/phd_all/data/coll_oper/jja2021/jja2021.nc'  # 'C:\\Users\\Christoph\\phd\\data\\enstools-feature\\2008_sum_uv.nc' # '/home/christoph/phd/data/framework_example_ds/aew/cv_aug_08.nc'
-out_dir = '/project/meteo/w2w/C3/fischer/belanger/out/' # join('/home/ws/he7273/phd_all/data/aew/out/') # '/project/meteo/w2w/C3/fischer/belanger/out/'
+in_files = '/project/meteo/w2w/C3/fischer/data/aew_new/2009/jjaso2009_uv_05.nc' # 
+'/home/ws/he7273/phd_all/data/coll_oper/jja2021/jja2021.nc'  # 'C:\\Users\\Christoph\\phd\\data\\enstools-feature\\2008_sum_uv.nc' # '/home/christoph/phd/data/framework_example_ds/aew/cv_aug_08.nc'
+out_dir = '/project/meteo/w2w/C3/fischer/data/aew_new/2009/' # 
+join('/home/ws/he7273/phd_all/data/aew/out/') # '/project/meteo/w2w/C3/fischer/belanger/out/'
 
-out_json_path = out_dir + 'jjaso2021.json'
-out_data_path = out_dir + 'jjaso2021_wts.nc'
+out_json_path = out_dir + 'jjaso2009.json'
+out_data_path = out_dir + 'jjaso2009_wts.nc'
 generate_output = True
 
 plot_dir = '/home/ws/he7273/phd_all/data/coll_oper/' # '/project/meteo/w2w/C3/fischer/belanger/plots/' # join('/home/ws/he7273/phd_all/data/aew/plots/') # '/project/meteo/w2w/C3/fischer/belanger/plots/'

enstools/feature/identification/african_easterly_waves/run_identify.py

@@ -176,6 +176,12 @@ if enable_out:
     ### add WTs to orig DS. field tracks and field WTs
     ds = add_wts_to_ds(ds, ob)
     print(ds)
+    if 'u' in ds.data_vars:
+        ds = ds.drop_vars(['u'])
+    if 'v' in ds.data_vars:
+        ds = ds.drop_vars(['v'])
+    if 'cv' in ds.data_vars:
+        ds = ds.drop_vars(['cv'])
     
     pipeline.set_data(ds)