diff --git a/enstools/feature/identification/aew_vortices/configuration.py b/enstools/feature/identification/aew_vortices/configuration.py
index 8c80480199f607c69715cf73bd8e41f438e11825..dffeab69425b4673549a2d629edb8d417132e88c 100644
--- a/enstools/feature/identification/aew_vortices/configuration.py
+++ b/enstools/feature/identification/aew_vortices/configuration.py
@@ -36,8 +36,8 @@ base_threshold = {'cv': 3e-5, 'cva': 1e-5, 'rv': 3e-5}
# time of interest, if None all
# june-oct is AEW season
-start_date = '2021-08-23T00:00' # '2022-08-01T00:00' # None # '2008-08-01T00:00' # # '2008-08-01T00:00'
-end_date = '2021-09-03T00:00' # '2022-08-15T00:00' # None # '2008-08-15T00:00' # None # '2008-08-03T00:00'
+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'
radius = 500000 # 500 km
prominence_radius = 400000 # 300 km
diff --git a/enstools/feature/identification/aew_vortices/identification.py b/enstools/feature/identification/aew_vortices/identification.py
index 6eeacecf61490f48a3ad9fa54da182f65b600402..af753f46319dfbccdde70d392727ad4cebb9fb0e 100644
--- a/enstools/feature/identification/aew_vortices/identification.py
+++ b/enstools/feature/identification/aew_vortices/identification.py
@@ -1,3 +1,4 @@
+import IMTreatment.vortex_detection.vortex_detection
import skimage.morphology
from enstools.feature.identification import IdentificationStrategy
@@ -22,6 +23,12 @@ from skimage.draw import line
from IMTreatment.vortex_criterions import get_lambda2, get_gamma
from IMTreatment.core import VectorField, ScalarField, SpatialScalarFields, SpatialVectorFields
+def round_off_rating(number):
+ """Round a number to the closest half integer.
+ """
+ return round(number * 2) / 2
+
+
class AEWVortexIdentification(IdentificationStrategy):
def __init__(self, **kwargs):
@@ -93,12 +100,14 @@ class AEWVortexIdentification(IdentificationStrategy):
# BPF (optional) TODO
if self.bpf:
+ print(dataset)
dataset = bpf(dataset, u_name, v_name, min_days=2, max_days=6)
self.config.u_dim = 'u_bpf'
self.config.v_dim = 'v_bpf'
u_name = 'u_bpf'
v_name = 'v_bpf'
+ print(dataset)
self.json_desc = interpolate_wavetroughs(self.json_desc, african_easterly_waves_pb2, self.pb_dataset)
# create field to use
@@ -106,34 +115,45 @@ class AEWVortexIdentification(IdentificationStrategy):
dataset = dataset.transpose(..., lat_str, lon_str)
data_field = dataset[self.field]
- dataset['vortices'] = xr.zeros_like(dataset[self.field], dtype=int)
+ # dataset['vortices'] = xr.zeros_like(dataset[self.field], dtype=int)
+ dataset['saddle'] = xr.zeros_like(dataset[self.field], dtype=int)
+ 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:
- print(time)
- dt = dataset.sel(level=850, time=time)
-
- sx = ScalarField()
- sx.import_from_arrays(dt.latitude.values.tolist(), dt.longitude.values.tolist(), dt[u_name].data)
- sy = ScalarField()
- sy.import_from_arrays(dt.latitude.values.tolist(), dt.longitude.values.tolist(), dt[v_name].data)
-
- v = VectorField()
- v.import_from_sfs(sx, sy)
-
- cur_l2 = get_gamma(v)
- print(cur_l2)
- l2.loc[dict(level=850, time=time)] = cur_l2.values
- print(l2)
- dataset['lambda2'] = l2
- dataset['lambda2'].attrs['long_name'] = 'lambda2'
- dataset['lambda2'].attrs['standard_name'] = 'l2'
- dataset['lambda2'].attrs['units'] = 'dunno'
- """
+ for lev in dataset.level.values:
+ print(time)
+ print(lev)
+ dt = dataset.sel(level=lev, time=time)
+ cp = dt.saddle
+ cp_c = dt.foci_c
+
+ sx = ScalarField()
+ sx.import_from_arrays(dt.latitude.values.tolist(), dt.longitude.values.tolist(), dt[u_name].data)
+ sy = ScalarField()
+ sy.import_from_arrays(dt.latitude.values.tolist(), dt.longitude.values.tolist(), dt[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
+
+ 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)
+ """
# search local max.
neighborhood = generate_binary_structure(2, 2)
# TODO depending on num dimensions. here 4d, with latlon back.
@@ -145,6 +165,7 @@ class AEWVortexIdentification(IdentificationStrategy):
# max_lon = find_peaks(data_field.values, np.greater, axis=-1) # scipy.signal.find_peaks
dataset['vortices'].data = max_mask
+ """
# out: set data to 1, fakes to 2
dataset['wt_rea'] = xr.where(dataset.wt_rea > 0, 1, dataset.wt_rea)
@@ -156,48 +177,24 @@ class AEWVortexIdentification(IdentificationStrategy):
thr = self.config.base_threshold[self.field]
if self.bpf:
thr = thr / 2 # TODO?
+
+ """
dataset['vortices'] = xr.where(dataset[self.field] > thr, dataset.vortices, 0)
# FILTER by area -> in vicinity of WT
dataset['vortices'] = xr.where(dataset['infl_rea'] != 0, dataset.vortices, 0)
dataset['vortices'] = dataset.vortices.transpose(..., lat_str, lon_str)
-
-
- # self.config.prominence_radius
-
-
-
- # filter vortices somehow, before? prob need after
- # add them to WT descs.
-
- # filter max points by WT mask
- # interpolate_wave_areas() # for gaps in tracks. -> also add json info? maybe create WT area ourself.
- # for each child in track node: if dt > 6h: add inbetween "fake" wt to json
- # put lows in.
- # maybe depending on whether they form a track?
-
-
- #if self.config.cv_name not in dataset.data_vars:
- # print("Curvature Vorticity not found, trying to compute it out of U and V...")
- # dataset = compute_cv(dataset, u_name, v_name, self.config.cv_name)
-
- # make dataset to 2.5 (or same as cv_clim)
- # dataset = dataset.interp({lat_str: cv_clim.coords[lat_str], lon_str: cv_clim.coords[lon_str]})
-
- # make sure that lat and lon are last two dimensions
- # dataset = dataset.transpose(..., lat_str, lon_str)
-
-
- # dataset['wavetroughs'] = wt
+ """
return dataset
def identify(self, data_chunk: xr.Dataset, **kwargs):
objs = []
- v = data_chunk.vortices
+ # v = data_chunk.vortices
+ cp = data_chunk.saddle
ds = data_chunk.stack(z=["latitude", "longitude"])
- ds = ds.where(ds.vortices > 0, drop=True)
+ ds = ds.where(ds.saddle > 0, drop=True)
# TODO maybe create WT+area also in here, so we have direct reference to IDs
# CLUSTER
@@ -205,7 +202,7 @@ class AEWVortexIdentification(IdentificationStrategy):
# -> or discard if any peak within 2deg is higher? or is this the same?
# For each vortex...
for z in range(len(ds.z.data)):
- if ds.vortices[z].item() == 0:
+ if ds.saddle[z].item() == 0:
print("skip?")
continue
lon_vortex = ds.longitude[z].item()
@@ -222,9 +219,9 @@ class AEWVortexIdentification(IdentificationStrategy):
distance_c = circle.sel(longitude=lon_vc, latitude=lat_vc).item()
# if distance in promenance radius and other point has higher vorticity -> delete this point.
- if distance_c < self.config.prominence_radius and data_c > data_vertex: # TODO what happens if two with exactly the same cv?
+ if distance_c < self.config.prominence_radius and data_c > data_vertex:
# vortex in vicinity is higher. discard this.
- v.loc[dict(longitude=lon_vortex, latitude=lat_vortex)] = 0
+ cp.loc[dict(longitude=lon_vortex, latitude=lat_vortex)] = 0
break
# data_chunk.vortices.values = skimage.morphology.binary_dilation(data_chunk.vortices.values)
@@ -235,25 +232,6 @@ class AEWVortexIdentification(IdentificationStrategy):
- """
- id_ = 1
- for path in []:
-
- # get new object, set id
- # o = self.get_new_object()
- # o.id = id_
- # populate it
- # populate_object(o.properties, path, self.config)
-
- # add to objects if keep
- # if not self.keep_wavetrough(o.properties):
- # continue
-
- # objs.append(o)
- # id_ += 1
- break
- """
-
return data_chunk, objs
def postprocess(self, dataset: xr.Dataset, data_desc, **kwargs):
diff --git a/enstools/feature/identification/aew_vortices/plotting.py b/enstools/feature/identification/aew_vortices/plotting.py
index d67d525ce4ab77966e6d14306fb85634e2af2881..5313f1ebfffcd6c89e06dc7b6cd15bfa0c525387 100644
--- a/enstools/feature/identification/aew_vortices/plotting.py
+++ b/enstools/feature/identification/aew_vortices/plotting.py
@@ -131,7 +131,9 @@ 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).vortices.plot.contourf(levels=[-0.5,0.5,99], colors=('#00000000', 'orange'), 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.prec_rate_rea.plot.contourf(levels=levels_rain, extend='max', subplot_kws={'transform_first': True},
# cmap=rain_cm, norm=norm, add_colorbar=False)