def command_lookup(ns):
"""
Command to lookup the given descriptors from command line
"""
table_group = TableGroupCacheManager.get_table_group(
ns.tables_root_directory, ns.master_table_number,
ns.originating_centre, ns.originating_subcentre,
ns.master_table_version, ns.local_table_version)
flat_text_render = FlatTextRenderer()
table_group.B.load_code_and_flag(
) # load the code and flag tables for additional details
descriptors = table_group.descriptors_from_ids(
*[d.strip() for d in ns.descriptors.split(',')])
for descriptor in descriptors:
if isinstance(descriptor, ElementDescriptor):
print('{}, {}, {}, {}, {}'.format(
flat_text_render.render(descriptor), descriptor.unit,
descriptor.scale, descriptor.refval, descriptor.nbits))
if ns.code_and_flag and descriptor.unit in (
UNITS_FLAG_TABLE, UNITS_CODE_TABLE,
UNITS_COMMON_CODE_TABLE_C1):
code_and_flag = table_group.B.code_and_flag_for_descriptor(
descriptor)
if code_and_flag:
for v, description in code_and_flag:
output = u'{:8d} {}'.format(v, description)
# With Python 2, some terminal utilities, e.g. more, redirect to file,
# cause errors when unicode string is printed. The fix is to encode
# them before print.
if six.PY2:
output = output.encode('utf-8', 'ignore')
print(output)
else:
print(flat_text_render.render(descriptor))
def command_info(ns):
"""
Command to show metadata information of given files from command line.
"""
flat_text_render = FlatTextRenderer()
decoder = Decoder(definitions_dir=ns.definitions_directory,
tables_root_dir=ns.tables_root_directory)
def show_message_info(m):
bufr_template, table_group = m.build_template(
ns.tables_root_directory, normalize=1)
print(flat_text_render.render(m))
if ns.template:
print(flat_text_render.render(bufr_template))
for filename in ns.filenames:
with open(filename, 'rb') as ins:
s = ins.read()
if ns.multiple_messages:
for bufr_message in generate_bufr_message(decoder, s,
file_path=filename, info_only=True):
show_message_info(bufr_message)
elif ns.count_only:
count = 0
for _ in generate_bufr_message(decoder, s, info_only=True):
count += 1
print('{}: {}'.format(filename, count))
else:
bufr_message = decoder.process(s, file_path=filename, info_only=True)
show_message_info(bufr_message)
def command_query(ns):
"""
Command to query given BUFR files.
"""
decoder = Decoder(definitions_dir=ns.definitions_directory,
tables_root_dir=ns.tables_root_directory,
compiled_template_cache_max=ns.compiled_template_cache_max)
for filename in ns.filenames:
with open(filename, 'rb') as ins:
s = ins.read()
if ns.query_string.strip()[0] == '%':
bufr_message = decoder.process(s, file_path=filename, info_only=True)
from pybufrkit.mdquery import MetadataExprParser, MetadataQuerent
querent = MetadataQuerent(MetadataExprParser())
value = querent.query(bufr_message, ns.query_string)
print(filename)
print(value)
else:
bufr_message = decoder.process(s, file_path=filename, wire_template_data=True,
ignore_value_expectation=ns.ignore_value_expectation)
from pybufrkit.dataquery import NodePathParser, DataQuerent
querent = DataQuerent(NodePathParser())
query_result = querent.query(bufr_message, ns.query_string)
if ns.json:
if ns.nested:
print(json.dumps(NestedJsonRenderer().render(query_result), **JSON_DUMPS_KWARGS))
else:
print(json.dumps(FlatJsonRenderer().render(query_result), **JSON_DUMPS_KWARGS))
else:
print(filename)
print(FlatTextRenderer().render(query_result))
class TablesTests(unittest.TestCase):
def setUp(self):
self.table_group = TableGroupCacheManager.get_table_group(master_table_version=29)
self.flat_text_renderer = FlatTextRenderer()
def tearDown(self):
pass
def test_table_group_01(self):
template = self.table_group.lookup(340009)
assert self.flat_text_renderer.render(template) == table_group_01_cmp
assert flat_member_ids(template) == [
1007, 1031, 2019, 2020, 4001, 4002, 4003, 4004, 4005, 4006, 5040,
201136, 5041, 201000, 25071, 5001, 5001, 6001, 6001,
107064, 106032, 8012, 8013, 8065, 8072, 13039, 40015]
def test_table_group_02(self):
template = self.table_group.lookup(340008)
assert self.flat_text_renderer.render(template) == table_group_02_cmp
def show_message(m):
if ns.attributed:
m.wire()
if ns.json:
print(json.dumps(NestedJsonRenderer().render(m), **JSON_DUMPS_KWARGS))
else:
print(NestedTextRenderer().render(m))
else:
if ns.json:
print(json.dumps(FlatJsonRenderer().render(m), **JSON_DUMPS_KWARGS))
else:
print(FlatTextRenderer().render(m))
def test(self):
output = []
with open(os.path.join(DATA_DIR, 'prepbufr.bufr'), 'rb') as ins:
for bufr_message in generate_bufr_message(self.decoder,
ins.read()):
output.append(FlatTextRenderer().render(bufr_message))
lines = [
line for line in ('\n'.join(output)).splitlines(True)
if not line.startswith('TableGroupKey')
and not line.startswith('stop_signature')
]
assert ''.join(lines).endswith(compare)
def read_one_bufr_tc(self, file, id_no=None, fcast_rep=None):
""" Read a single BUFR TC track file.
Parameters:
file (str, filelike): Path object, string, or file-like object
id_no (int): Numerical ID; optional. Else use date + random int.
fcast_rep (int): Of the form 1xx000, indicating the delayed
replicator containing the forecast values; optional.
"""
decoder = pybufrkit.decoder.Decoder()
if hasattr(file, 'read'):
bufr = decoder.process(file.read())
elif hasattr(file, 'read_bytes'):
bufr = decoder.process(file.read_bytes())
elif os.path.isfile(file):
with open(file, 'rb') as i:
bufr = decoder.process(i.read())
else:
raise FileNotFoundError('Check file argument')
text_data = FlatTextRenderer().render(bufr)
# setup parsers and querents
#npparser = pybufrkit.dataquery.NodePathParser()
#data_query = pybufrkit.dataquery.DataQuerent(npparser).query
meparser = pybufrkit.mdquery.MetadataExprParser()
meta_query = pybufrkit.mdquery.MetadataQuerent(meparser).query
timestamp_origin = dt.datetime(
meta_query(bufr, '%year'), meta_query(bufr, '%month'),
meta_query(bufr, '%day'), meta_query(bufr, '%hour'),
meta_query(bufr, '%minute'),
)
timestamp_origin = np.datetime64(timestamp_origin)
orig_centre = meta_query(bufr, '%originating_centre')
if orig_centre == 98:
provider = 'ECMWF'
else:
provider = 'BUFR code ' + str(orig_centre)
list1=[]
with StringIO(text_data) as input_data:
# Skips text before the beginning of the interesting block:
for line in input_data:
if line.startswith('<<<<<< section 4 >>>>>>'): # Or whatever test is needed
break
# Reads text until the end of the block:
for line in input_data: # This keeps reading the file
if line.startswith('<<<<<< section 5 >>>>>>'):
break
list1.append(line)
list1=[li for li in list1 if li.startswith(" ") or li.startswith("##") ]
list2=[]
for items in list1:
if items.startswith("######"):
list2.append([0,items.split()[1],items.split()[2]])
else:
list2.append([int(items.split()[0]),items.split()[1],items.split()[-1]])
df_ = pd.DataFrame(list2,columns=['id','code','Data'])
def label_en (row,co):
if row['code'] == co :
return int(row['Data'])
return np.nan
df_['subset'] = df_.apply (lambda row: label_en(row,co='subset'), axis=1)
df_['subset'] =df_['subset'].fillna(method='ffill')
df_['model_sgn'] = df_.apply (lambda row: label_en(row,co='008005'), axis=1)
df_['model_sgn'] =df_['model_sgn'].fillna(method='ffill')
df_['model_sgn'] =df_['model_sgn'].fillna(method='bfill')
for names, group in df_.groupby("subset"):
pcen = list(group.query('code in ["010051"]')['Data'].values)
latc = list(group.query('code in ["005002"] and model_sgn in [1]')['Data'].values)
lonc = list(group.query('code in ["006002"] and model_sgn in [1]')['Data'].values)
latm = list(group.query('code in ["005002"] and model_sgn in [3]')['Data'].values)
lonm = list(group.query('code in ["006002"] and model_sgn in [3]')['Data'].values)
wind = list(group.query('code in ["011012"]')['Data'].values)
vhr = list(group.query('code in ["004024"]')['Data'].values)
wind=[np.nan if value=='None' else float(value) for value in wind]
pre=[np.nan if value=='None' else float(value)/100 for value in pcen]
lonm=[np.nan if value=='None' else float(value) for value in lonm]
lonc=[np.nan if value=='None' else float(value) for value in lonc]
latm=[np.nan if value=='None' else float(value) for value in latm]
latc=[np.nan if value=='None' else float(value) for value in latc]
vhr=[np.nan if value=='None' else int(value) for value in vhr]
timestep_int = np.array(vhr).squeeze() #np.array(msg['timestamp'].get_values(index)).squeeze()
timestamp = timestamp_origin + timestep_int.astype('timedelta64[h]')
year = list(group.query('code in ["004001"]')['Data'].values)
month = list(group.query('code in ["004002"]')['Data'].values)
day = list(group.query('code in ["004003"]')['Data'].values)
hour = list(group.query('code in ["004004"]')['Data'].values)
#forecs_agency_id = list(group.query('code in ["001033"]')['Data'].values)
storm_name = list(group.query('code in ["001027"]')['Data'].values)
storm_id = list(group.query('code in ["001025"]')['Data'].values)
frcst_type = list(group.query('code in ["001092"]')['Data'].values)
max_radius=np.sqrt(np.square(np.array(latc)-np.array(latm))+np.square(np.array(lonc)-np.array(lonm)))*111
date_object ='%04d%02d%02d%02d'%(int(year[0]),int(month[0]),int(day[0]),int(hour[0]))
date_object=dt.datetime.strptime(date_object, "%Y%m%d%H")
#timestamp=[(date_object + dt.timedelta(hours=int(value))).strftime("%Y%m%d%H") for value in vhr]
#timestamp=[dt.datetime.strptime(value, "%Y%m%d%H") for value in timestamp]
track = xr.Dataset(
data_vars={
'max_sustained_wind': ('time', wind[1:]),
'central_pressure': ('time', pre[1:]),
'ts_int': ('time', timestep_int),
'max_radius': ('time', max_radius[1:]),
'lat': ('time', latc[1:]),
'lon': ('time', lonc[1:]),
'environmental_pressure':('time', np.full_like(timestamp, DEF_ENV_PRESSURE, dtype=float)),
'radius_max_wind':('time', np.full_like(timestamp, np.nan, dtype=float)),
},
coords={'time': timestamp,
},
attrs={
'max_sustained_wind_unit': 'm/s',
'central_pressure_unit': 'mb',
'name': storm_name[0].strip("'"),
'sid': storm_id[0].split("'")[1],
'orig_event_flag': False,
'data_provider': provider,
'id_no': 'NA',
'ensemble_number': int(names),
'is_ensemble': ['TRUE' if frcst_type[0]!='0' else 'False'][0],
'forecast_time': date_object,
})
track = track.set_coords(['lat', 'lon'])
track['time_step'] = track.ts_int - track.ts_int.shift({'time': 1}, fill_value=0)
#track = track.drop('ts_int')
track.attrs['basin'] = BASINS[storm_id[0].split("'")[1][2].upper()]
cat_name = CAT_NAMES[set_category(
max_sus_wind=track.max_sustained_wind.values,
wind_unit=track.max_sustained_wind_unit,
saffir_scale=SAFFIR_MS_CAT)]
track.attrs['category'] = cat_name
if track.sizes['time'] == 0:
track= None
if track is not None:
self.append(track)
else:
LOGGER.debug('Dropping empty track, subset %s', names)
'jaso_214.bufr',
'mpco_217.bufr',
'profiler_european.bufr',
'rado_250.bufr',
'uegabe.bufr',
)
def read_bufr_file(file_name):
with open(os.path.join(DATA_DIR, file_name), 'rb') as ins:
s = ins.read()
return s
decoder = Decoder()
flat_text_renderer = FlatTextRenderer()
nested_text_renderer = NestedTextRenderer()
flat_json_renderer = FlatJsonRenderer()
nested_json_renderer = NestedJsonRenderer()
def test_nested_json_to_flat_json():
def func(filename):
s = read_bufr_file(filename)
bufr_message = decoder.process(s)
nested = nested_json_renderer.render(bufr_message)
flat = flat_json_renderer.render(bufr_message)
assert flat == nested_json_to_flat_json(nested)
for filename in FILES:
func(filename)
sys.exit("No named storms, exiting")
composite_storm_files = [named_storm_files[x:x+2] for x in range(0, len(named_storm_files),2)]
for storm in composite_storm_files:
ens_path = storm[0]
det_path = storm[1]
# Decode ensemble bufr file
decoder = Decoder()
with open(ens_path, 'rb') as ins:
bufr_message = decoder.process(ins.read())
text_data = FlatTextRenderer().render(bufr_message)
text_array = np.array(text_data.splitlines())
for line in text_array:
if "WMO LONG STORM NAME" in line:
storm_name = line.split()[-1][:-1]
section4 = text_array[np.where(text_array=="<<<<<< section 4 >>>>>>")[0][0]:np.where(text_array=="<<<<<< section 5 >>>>>>")[0][0]]
list = []
ens_subset = 0
attribute = None
tplus_hour = None
def setUp(self):
self.table_group = TableGroupCacheManager.get_table_group(master_table_version=29)
self.flat_text_renderer = FlatTextRenderer()
def setUp(self):
self.table_group = get_table_group()
self.flat_text_renderer = FlatTextRenderer()
def setUp(self):
self.table_group = get_table_group(master_table_version=29)
self.flat_text_renderer = FlatTextRenderer()
def ecmwf_data_process(Input_folder,filepatern):
"""
preprocess ecmwf forecast data downloaded above
"""
#ecmwf_data_download(Input_folder,filepatern)
path_ecmwf=os.path.join(Input_folder,'ecmwf/')
decoder = Decoder()
#1=Storm Centre 4 = Location of the storm in the perturbed analysis
#5 = Location of the storm in the analysis #3=Location of maximum wind
ecmwf_files = [f for f in listdir(path_ecmwf) if isfile(join(path_ecmwf, f))]
#ecmwf_files = [file_name for file_name in ecmwf_files if file_name.startswith('A_JSXX02ECEP')]
list_df=[]
for ecmwf_file in ecmwf_files:
ecmwf_file=ecmwf_files[0]
f_name='ECMWF_'+ ecmwf_file.split('_')[1]+'_'+ecmwf_file.split('_')[4]
model_name=ecmwf_file.split('_')[1][6:10]
typhoon_name=ecmwf_file.split('_')[-4]
with open(os.path.join(path_ecmwf,ecmwf_file), 'rb') as bin_file:
bufr_message = decoder.process(bin_file.read())
text_data = FlatTextRenderer().render(bufr_message)
STORMNAME=typhoon_name #ecmwf_file.split('_')[8]
list1=[]
with StringIO(text_data) as input_data:
# Skips text before the beginning of the interesting block:
for line in input_data:
if line.startswith('<<<<<< section 4 >>>>>>'): # Or whatever test is needed
break
# Reads text until the end of the block:
for line in input_data: # This keeps reading the file
if line.startswith('<<<<<< section 5 >>>>>>'):
break
list1.append(line)
list_var=["004024","004001","004002","004003","004004","004005","001092","011012","010051","005002","006002","001091",'001092',"008005"]
list2=[[int(li.split()[0]),li.split()[1],li.split()[-1]] for li in list1 if li.startswith(" ") and li.split()[1] in list_var]
df = pd.DataFrame(list2,columns=['id','code','Data'])
def label_en (row,co):
if row['code'] == co :
return int(row['Data'])
return np.nan
df['model_sgn'] = df.apply (lambda row: label_en(row,co='008005'), axis=1)
df['ensamble_num'] = df.apply (lambda row: label_en(row,co='001091'), axis=1)
df['frcst_type'] = df.apply (lambda row: label_en(row,co='001092'), axis=1)
df['frcst_type'] =df['frcst_type'].fillna(method='ffill')
df['frcst_type'] =df['frcst_type'].fillna(method='bfill')
df['ensamble_num'] =df['ensamble_num'].fillna(method='ffill')
df['model_sgn'] =df['model_sgn'].fillna(method='ffill')
df['model_sgn'] =df['model_sgn'].fillna(method='bfill')
df_time = df.query('code in ["004001","004002","004003","004004","004005"]')
date_object ='%04d%02d%02d%02d'%(int(df_time['Data'].to_list()[0]),
int(df_time['Data'].to_list()[1]),
int(df_time['Data'].to_list()[2]),
int(df_time['Data'].to_list()[3]))
date_object=datetime.strptime(date_object, "%Y%m%d%H")
#(date_object + timedelta(hours=x)).strftime("%Y%m%d%H%M")
df_center = df.query('code in ["010051","005002","006002"] and model_sgn in [1]')
df_center2 = df.query('code in ["010051","005002","006002"] and model_sgn in [4,5]')
df_max = df.query('code in ["011012","005002","006002","004024"] and model_sgn in [3]')
# 1 storm center and 3 maximum wind speed https://vocabulary-manager.eumetsat.int/vocabularies/BUFR/WMO/6/TABLE_CODE_FLAG/008005
latc,lonc,pcen,frcst_type,ensambles=[],[],[],[],[]
for names, group in df_center.groupby("ensamble_num"):
latc.append(list(group[group.code=="005002"]['Data'].values))
lonc.append(list(group[group.code=="006002"]['Data'].values))
pcen.append(list(group[group.code=="010051"]['Data'].values))
lat,lon,vmax,vhr=[],[],[],[]
for names, group in df_max.groupby("ensamble_num"):
lat.append(list(group[group.code=="005002"]['Data'].values))
lon.append(list(group[group.code=="006002"]['Data'].values))
vmax.append(list(group[group.code=="011012"]['Data'].values))
vhr.append(list(group[group.code=="004024"]['Data'].values))
frcst_type.append(list(np.unique(group.frcst_type.values))[0])
ensambles.append(names)
latc1,lonc1,pcen1=[],[],[]
for names, group in df_center2.groupby("ensamble_num"):
latc1.append(list(group[group.code=="005002"]['Data'].values))
lonc1.append(list(group[group.code=="006002"]['Data'].values))
pcen1.append(list(group[group.code=="010051"]['Data'].values))
for i in range(len(pcen1)):
pcen1[i].extend(pcen[i])
vhr=['0','6', '12', '18', '24', '30', '36', '42', '48', '54', '60', '66', '72', '78', '84', '90', '96', '102', '108']
for i in range(len(ensambles)):
wind=[np.nan if value=='None' else float(value) for value in vmax[i]]
pre=[np.nan if value=='None' else float(value)/100 for value in pcen1[i]]
lon_=[np.nan if value=='None' else float(value) for value in lon[i]]
lat_=[np.nan if value=='None' else float(value) for value in lat[i]]
lon1_=[np.nan if value=='None' else float(value) for value in lonc[i]]
lat1_=[np.nan if value=='None' else float(value) for value in latc[i]]
max_radius=np.sqrt(np.square(np.array(lon_)-np.array(lon1_))+np.square(np.array(lat_)-np.array(lat1_)))*110
timestamp=[(date_object + timedelta(hours=int(value))).strftime("%Y%m%d%H%M") for value in vhr]
timestep_int=[int(value) for value in vhr]
['TRUE' if frcst_type[i]==4 else 'False']
track = xr.Dataset(
data_vars={
'max_sustained_wind': ('time', wind),
'central_pressure': ('time', pre),
'ts_int': ('time', timestep_int),
'max_radius': ('time', max_radius),
'lat': ('time', lat_),
'lon': ('time', lon_),
},
coords={'time': timestamp,
},
attrs={
'max_sustained_wind_unit': 'm/s',
'central_pressure_unit': 'mb',
'name': typhoon_name,
'sid': 'NA',
'orig_event_flag': False,
'data_provider': 'ECMWF',
'id_no': 'NA',
'ensemble_number': ensambles[i],
'is_ensemble': ['TRUE' if frcst_type[i]==4 else 'False'][0],
'forecast_time': date_object.strftime("%Y%m%d%H%M"),
'basin': 'WP',
'category': 'NA',
})
track = track.set_coords(['lat', 'lon'])
list_df.append(track)
#%%
date_object ='%04d%02d%02d%02d'%(int([line.split()[-1] for line in StringIO(text_data) if line[6:17].upper=="004001 YEAR" ][0]),
int([line.split()[-1] for line in StringIO(text_data) if line[6:18].upper=="004002 MONTH" ][0]),
int([line.split()[-1] for line in StringIO(text_data) if line[6:16].upper=="004003 DAY" ][0]),
int([line.split()[-1] for line in StringIO(text_data) if line[6:17].upper=="004004 HOUR" ][0]))
date_object=datetime.strptime(date_object, "%Y%m%d%H%M")
val_t = [int(line.split()[-1]) for num, line in enumerate(StringIO(text_data), 1) if line[6:40].upper=="004024 TIME PERIOD OR DISPLACEMENT"]# and link.endswith(('.html', '.xml'))]
val_wind = [line.split()[-1] for num, line in enumerate(StringIO(text_data), 1) if line[6:12].upper=="011012" ]#and num > ind_x[0]]# and link.endswith(('.html', '.xml'))]
val_pre = [line.split()[-1] for num, line in enumerate(StringIO(text_data), 1) if line[6:12].upper=="010051" ]#and num > ind_x[0]]# and link.endswith(('.html', '.xml'))]
val_lat = [line.split()[-1] for num, line in enumerate(StringIO(text_data), 1) if line[6:12].upper=="005002" ]#and num > ind_x[0]]# and link.endswith(('.html', '.xml'))]
val_lon = [line.split()[-1] for num, line in enumerate(StringIO(text_data), 1) if line[6:12].upper=="006002" ]#and num > ind_x[0]]# and link.endswith(('.html', '.xml'))]
val_ens = [line.split()[-1] for num, line in enumerate(StringIO(text_data), 1) if line[6:12].upper=="001091" ]#and num > ind_x[0]]# and link.endswith(('.html', '.xml'))]
val_dis = [line.split()[-1] for num, line in enumerate(StringIO(text_data), 1) if line[6:12].upper=="008005" ]#and num > ind_x[0]]# and link.endswith(('.html', '.xml'))]
if len(val_ens) >1:
val_t=val_t[0:int(len(val_t)/len(val_ens))]
val_t.insert(0, 0)
val_ensamble=duplicate(val_ens, int(len(val_wind)/len(val_ens)))
val_time=val_t* len(val_ens) #52
else:
val_ensamble='NA'
val_t.insert(0, 0)
val_time=val_t
ecmwf_df=pd.DataFrame({'lon': val_lon,'lat': val_lat,'met_dis': val_dis })
ecmwf_center=ecmwf_df[ecmwf_df['met_dis']=='1']
ecmwf_df2=pd.DataFrame({'STORMNAME':STORMNAME,
'time':val_time,
'lon':ecmwf_center['lon'].values,
'lat':ecmwf_center['lat'].values,
'windsped':val_wind,
'pressure':val_pre,
'ens': val_ensamble})
ecmwf_df2['YYYYMMDDHH']=ecmwf_df2['time'].apply(lambda x: (date_object + timedelta(hours=x)).strftime("%Y%m%d%H%M") )
dict1=[]
ecmwf_df2=ecmwf_df2.replace(['None'],np.nan)
typhoon_df=pd.DataFrame()
typhoon_df[['YYYYMMDDHH','LAT','LON','VMAX','PRESSURE','STORMNAME','ENSAMBLE']]=ecmwf_df2[['YYYYMMDDHH','lat','lon','windsped','pressure','STORMNAME','ens']]
typhoon_df[['LAT','LON','VMAX']] = typhoon_df[['LAT','LON','VMAX']].apply(pd.to_numeric)
typhoon_df['VMAX'] = typhoon_df['VMAX'].apply(lambda x: x*1.94384449*1.05) #convert to knotes
typhoon_df.to_csv(os.path.join(Input_folder,'ECMWF_%s_%s_%s.csv'%(Input_folder.split('/')[-3],STORMNAME,model_name)),index=False)
#%%
Input_folder='C:/Users/ATeklesadik/OneDrive - Rode Kruis/Documents/documents/Typhoon-Impact-based-forecasting-model/temp/'
#date_object ='%04d%02d%02d%02d'%(int(
[line.split()[-1] for line in StringIO(text_data) if line[6:17].upper=="004001 YEAR" ]
path_ecmwf=os.path.join(Input_folder,'ecmwf/')
#1=Storm Centre 4 = Location of the storm in the perturbed analysis
#5 = Location of the storm in the analysis #3=Location of maximum wind
ecmwf_files = [f for f in listdir(path_ecmwf) if isfile(join(path_ecmwf, f))]
#ecmwf_files = [file_name for file_name in ecmwf_files if file_name.startswith('A_JSXX02ECEP')]
list_df=[]
ecmwf_file=ecmwf_files[1]
with open(os.path.join(path_ecmwf,ecmwf_file), 'rb') as bin_file:
bufr = decoder.process(bin_file.read())
text_data = FlatTextRenderer().render(bufr)
# Convert the BUFR message to JSON
#
#%%
list1=[]
with StringIO(text_data) as input_data:
# Skips text before the beginning of the interesting block:
for line in input_data:
if line.startswith('<<<<<< section 4 >>>>>>'): # Or whatever test is needed
break
# Reads text until the end of the block:
for line in input_data: # This keeps reading the file
if line.startswith('<<<<<< section 5 >>>>>>'):
break
