def from_netcdf(cls, path):
"""
Construct a fuel moisture model from data stored in a netCDF file.
:param path: the path to the netCDF4 file
"""
import netCDF4
logging.info("reading from netCDF file " + path)
d = netCDF4.Dataset(path)
ncfmc = d.variables['FMC_GC'][:, :, :]
d0, d1, k = ncfmc.shape
P = d.variables['FMC_COV'][:, :, :, :]
logging.info(
'fuel_moisture_model.from_netcdf: reading FMC_GC %s FMC_COV %s' %
(inq(ncfmc), inq(P)))
Tk = np.array([1.0, 10.0, 100.0]) * 3600
fm = cls(ncfmc[:, :, :k - 2], Tk)
fm.m_ext[:, :, k - 2:] = ncfmc[:, :, k - 2:]
fm.P[:, :, :, :] = P
logging.info(
'fuel_moisture_model.from_netcdf: err %s' %
np.max(np.abs(ncfmc[:, :, :k - 2] - fm.m_ext[:, :, :k - 2])))
logging.info(
'fuel_moisture_model.from_netcdf: extended state fmc %d fields + 2 parameters'
% (k - 2))
return fm
def to_netcdf(self, path, data_vars):
"""
Store the model in a netCDF file that attempts to be displayable
using standard tools and loosely follows the WRF 'standard'.
:param path: the path where to store the model
:param data_vars: dictionary of additional variables to store for visualization
"""
import netCDF4
d = netCDF4.Dataset(path, 'w', format='NETCDF4')
d0, d1, k = self.m_ext.shape
d.createDimension('fuel_moisture_classes_stag', k)
d.createDimension('south_north', d0)
d.createDimension('west_east', d1)
ncfmc = d.createVariable(
'FMC_GC', 'f4',
('south_north', 'west_east', 'fuel_moisture_classes_stag'))
ncfmc[:, :, :] = self.m_ext
logging.info(
'fuel_moisture_model.to_netcdf: writing extended state as FMC_GC %s covariance as FMC_COV %s'
% (inq(self.m_ext), inq(self.P)))
ncfmc_cov = d.createVariable(
'FMC_COV', 'f4',
('south_north', 'west_east', 'fuel_moisture_classes_stag',
'fuel_moisture_classes_stag'))
ncfmc_cov[:, :, :, :] = self.P
for v in data_vars:
d.createVariable(v, 'f4',
('south_north', 'west_east'))[:, :] = data_vars[v]
d.close()
def write_geogrid_var(path_dir, varname, array, description, index, bits=32):
"""
write geogrid dataset and index
"""
path_dir = osp.abspath(path_dir)
logging.info('write_geogrid_var path_dir=%s varname=%s array=%s index=%s' %
(path_dir, varname, inq(array), str(index)))
if not osp.exists(path_dir):
os.makedirs(path_dir)
# write geogrid dataset
geogrid_ds_path = osp.join(path_dir, varname)
index['description'] = addquotes(description)
index['tile_bdr'] = 0
write_geogrid(geogrid_ds_path, array, index, bits=bits)
# write also the index as json entry to modify later
index_json_path = osp.join(path_dir, 'index.json')
try:
index_json = json.load(open(index_json_path, 'r'))
except:
index_json = {}
index_json[varname] = index
json.dump(index_json,
open(index_json_path, 'w'),
indent=4,
separators=(',', ': '))
geogrid_tbl_var = {
'name': varname,
'dest_type': 'continuous',
'interp_option': 'default:average_gcell(4.0)+four_pt+average_4pt',
'abs_path': geogrid_ds_path,
'priority': 1
}
# write a segment of GEOGRID.TBL
geogrid_tbl_path = osp.join(path_dir, 'GEOGRID.TBL')
write_table(geogrid_tbl_path,
geogrid_tbl_var,
mode='a',
divider_after=True)
# write also as json
geogrid_tbl_json_path = osp.join(path_dir, 'geogrid_tbl.json')
try:
geogrid_tbl = json.load(open(geogrid_tbl_json_path, 'r'))
except:
geogrid_tbl = {}
geogrid_tbl[varname] = geogrid_tbl_var
json.dump(index,
open(geogrid_tbl_json_path, 'w'),
indent=4,
separators=(',', ': '))
json.dump(geogrid_tbl,
open(geogrid_tbl_json_path, 'w'),
indent=4,
separators=(',', ': '))
def __init__(self, m0, Tk=None, P0=None):
"""
Initialize the model with given position and moisture levels.
:param m0: the initial condition for the entire grid (shape: grid0 x grid1 x num_fuels)
:param Tk: drying/wetting time constants of simulated fuels (one per fuel), default [1 10 100 1000]
:param P0: initial state error covariance
"""
logging.info('FuelMoistureModel.__init__ m0 %s, Tk %s, P0 %s' %
(inq(m0), inq(Tk), inq(P0)))
self.Tk = np.array([1.0, 10.0, 100.0]) * 3600 # nominal fuel delays
self.r0 = 0.05 # threshold rainfall [mm/h]
self.rk = 8.0 # saturation rain intensity [mm/h]
self.Trk = 14.0 * 3600 # time constant for wetting model [s]
self.S = 2.5 # saturation intensity [dimensionless]
s0, s1, k = m0.shape
if Tk is not None:
self.Tk = Tk
dim = k + 2
assert k == len(self.Tk)
self.m_ext = np.zeros((s0, s1, dim))
self.m_ext[:, :, :k] = m0
# note: the moisture advance will proceed by fuel moisture types
# thus we only need space for one class at a time
self.m_i = np.zeros((s0, s1))
#self.mn_i = np.zeros((s0,s1))
self.rlag = np.zeros((s0, s1))
self.equi = np.zeros((s0, s1))
self.model_ids = np.zeros((s0, s1))
self.EdA = np.zeros((s0, s1))
self.EwA = np.zeros((s0, s1))
# state covariance current and forecasted
self.P = np.zeros((s0, s1, dim, dim))
self.P2 = np.zeros((dim, dim))
for s in np.ndindex((s0, s1)):
self.P[s[0], s[1], :, :] = P0
# fill out the fixed parts of the jacobian
self.J = np.zeros((s0, s1, dim, dim))
self.Jii = np.zeros((s0, s1))
# note: the observation operator H is common for all dims
self.H = np.zeros((k, dim))
def execute_da_step(model, model_time, covariates, covariates_names, fm10):
"""
Execute a single DA step from the current state/extended parameters and covariance matrix using
the <covariates> and observations <fm10>. Assimilation time window is fixed at 60 mins.
:param model: a FuelMoistureModel
:param model_time: the current model time
:param covariates: the covariate fields to take into account to model the spatial structure of the FM field
:param covariates_names: strings with the names of the covariates
:param fm10: the 10-hr fuel moisture observations
"""
valid_times = [
z for z in fm10.keys() if abs((z - model_time).total_seconds()) < 1800
]
if len(valid_times) > 0:
# retrieve all observations for current time
obs_valid_now = []
for z in valid_times:
obs_valid_now.extend(fm10[z])
logging.info('FMDA found %d valid observations at model time %s' %
(len(obs_valid_now), str(model_time)))
fmc_gc = model.get_state()
dom_shape = fmc_gc.shape[:2]
logging.info('execute_da_step: model state fmc_gc %s' % inq(fmc_gc))
# construct covariate storage
Xd3 = min(len(covariates) + 1, len(obs_valid_now))
logging.info('FMDA is using %d covariates: %s' %
(Xd3, ','.join(['fmc_gc[:,:,1]'] + covariates_names)))
X = np.zeros((dom_shape[0], dom_shape[1], Xd3))
X[:, :, 0] = fmc_gc[:, :, 1]
for i, c in zip(list(range(Xd3 - 1)), covariates):
X[:, :, i + 1] = covariates[i]
# run the trend surface model (clamp output to [0.0 - 2.5] to be safe)
Kf_fn, Vf_fn = fit_tsm(obs_valid_now, X)
Kf_fn[Kf_fn < 0.0] = 0.0
Kf_fn[Kf_fn > 2.5] = 2.5
Kg = np.zeros((dom_shape[0], dom_shape[1], fmc_gc.shape[2]))
# run the data assimilation step now
logging.info(
"FMDA mean Kf: %g Vf: %g state[0]: %g state[1]: %g state[2]: %g" %
(np.mean(Kf_fn), np.mean(Vf_fn), np.mean(fmc_gc[:, :, 0]),
np.mean(fmc_gc[:, :, 1]), np.mean(fmc_gc[:, :, 2])))
model.kalman_update_single2(Kf_fn[:, :, np.newaxis],
Vf_fn[:, :, np.newaxis, np.newaxis], 1, Kg)
logging.info(
"FMDA mean Kf: %g Vf: %g state[0]: %g state[1]: %g state[2]: %g" %
(np.mean(Kf_fn), np.mean(Vf_fn), np.mean(fmc_gc[:, :, 0]),
np.mean(fmc_gc[:, :, 1]), np.mean(fmc_gc[:, :, 2])))
else:
logging.warning(
'FMDA no valid observations found, skipping data assimilation.')
def to_geogrid(self, path, index, lats=[], lons=[]):
"""
Store model to geogrid files
"""
not_coord = len(lats) == 0 or len(lons) == 0
test_latslons = True
if not_coord:
test_latslons = False
logging.info(
"fmda.fuel_moisture_model.to_geogrid path=%s lats %s lons %s" %
(path, inq(lats), inq(lons)))
logging.info("fmda.fuel_moisture_model.to_geogrid: geogrid_index=" +
str(index))
ensure_dir(path)
xsize, ysize, n = self.m_ext.shape
if n != 5:
logging.error('wrong number of extended state fields, expecting 5')
if not not_coord:
x = int(xsize * 0.5)
y = int(ysize * 0.5)
index.update({
'known_x': float(y),
'known_y': float(x),
'known_lat': float(lats[x - 1, y - 1]),
'known_lon': float(lons[x - 1, y - 1])
})
logging.info(
"fmda.fuel_moisture_model.to_geogrid: geogrid updated=" +
str(index))
FMC_GC = np.zeros((xsize, ysize, 5))
FMC_GC[:, :, :3] = self.m_ext[:, :, :3]
if test_latslons:
logging.info(
"fmda.fuel_moisture_model.to_geogrid: storing lons lats to FMC_GC(:,:,4:5) to test in WRF against XLONG and XLAT"
)
FMC_GC[:, :, 3] = lons
FMC_GC[:, :, 4] = lats
FMEP = self.m_ext[:, :, 3:]
write_geogrid_var(path, 'FMC_GC', FMC_GC, index, bits=32)
write_geogrid_var(path, 'FMEP', FMEP, index, bits=32)
def write_geogrid(path, array, index, bits=32, scale=None, uscale=None):
"""
Write geogrid dataset
:param path: the directory where the dataset is to be stored
:param array: numpy array of real values, 2d or 3d
:param index: json with geogrid index, with geolocation and description already set
:param bits: 16 or 32 (default)
:param scale: numeric scale or None (default)
:param uscale: numeric transform scale to change units or None (default)
:param data_type: 'categorical' or 'continuous' (default)
"""
logging.info('write_geogrid path=%s array=%s index=%s' %
(path, inq(array), str(index)))
if not osp.exists(path):
os.makedirs(path)
# write binary data file
a = np.array(array)
if uscale != None:
a = a * float(uscale)
dims = a.shape
if len(dims) < 3:
dims = dims + (1, )
a = np.reshape(a, dims)
xsize, ysize, zsize = dims
if scale is None:
scale = 2**(np.ceil(np.log2(np.max(np.abs(a)))) - bits + 1)
if scale != 1.:
a = np.round(a / scale)
if bits == 32:
a = np.int32(a)
elif bits == 16:
a = np.int16(a)
else:
print('unsupported word size')
sys.exit(1)
a = a.transpose(2, 0, 1)
logging.info('write_geogrid array min=%f max=%f avg=%f' %
(a.min(), a.max(), a.mean()))
zsize, ysize, xsize = a.shape
data_file = "00001-%05i.00001-%05i" % (xsize, ysize)
data_path = osp.join(path, data_file)
a.flatten().tofile(data_path)
# write index
index.update({
'scale_factor': scale,
'wordsize': bits // 8,
'tile_x': xsize,
'tile_y': ysize,
'tile_z': zsize,
'endian': sys.byteorder
})
index_path = osp.join(path, 'index')
write_table(index_path, index)
def write_geogrid(path, array, index, bits=32):
"""
Write geogrid dataset
:param path: the directory where the dataset is to be stored
:param array: numpy array of real values, 2d or 3d
:param index: json with geogrid index, with geolocation and description already set
:param bits: 16 or 32 (default)
"""
logging.info('write_geogrid_var path=%s array=%s index=%s' %
(path, inq(array), str(index)))
if not osp.exists(path):
os.makedirs(path)
# write binary data file
a = np.array(array)
dims = a.shape
if len(dims) < 3:
dims = dims + (1, )
xsize, ysize, zsize = dims
scale = 2**(np.ceil(np.log2(np.max(np.abs(a)))) - bits + 1)
aa = np.round(a / scale)
if bits == 32:
aa = np.int32(aa)
elif bits == 16:
aa = np.int16(aa)
else:
print('unsupported word size')
sys.exit(1)
a = aa.transpose(2, 0, 1)
zsize, ysize, xsize = a.shape
data_file = "00001-%05i.00001-%05i" % (xsize, ysize)
data_path = osp.join(path, data_file)
a.flatten().tofile(data_path)
# write index
index.update({
'type': 'continuous',
'signed': 'yes',
'scale_factor': scale,
'wordsize': bits // 8,
'tile_x': xsize,
'tile_y': ysize,
'tile_z': zsize,
'endian': sys.byteorder
})
index_path = osp.join(path, 'index')
write_table(index_path, index)
def to_wps_format(self, path, index, lats, lons, time_tag):
"""
Store model to wps format files
"""
test_latslons = True
logging.info(
"fmda.fuel_moisture_model.to_wps_format path=%s lats %s lons %s" %
(path, inq(lats), inq(lons)))
logging.info("fmda.fuel_moisture_model.to_wps_format: index=" +
str(index))
ny, nx, n = self.m_ext.shape
if n != 5:
logging.error('wrong number of extended state fields, expecting 5')
m = 7
var = np.zeros((ny, nx, m))
var[:, :, :3] = self.m_ext[:, :, :3]
var[:, :, 3] = lons
var[:, :, 4] = lats
var[:, :, 5:] = self.m_ext[:, :, 3:]
arrs = [var[:, :, k].swapaxes(0, 1) for k in range(m)]
startloc = "SWCORNER"
startlat = lats[0, 0]
startlon = lons[0, 0]
params = {
'ifv':
5,
'hdate':
"{}:00:00".format(time_tag),
'xfcst':
0.,
'map_source':
"WRF-SFIRE Wildland Fire Information and Forecasting System",
'field':
["FM1H", "FM10H", "FM100H", "FMXLON", "FMXLAT", "FMEP0", "FMEP1"],
'units': ["1", "1", "1", "degrees", "degrees", "1", "1"],
'desc': [
"1h Fuel Moisture Content", "10h Fuel Moisture Content",
"100h Fuel Moisture Content", "Longitude for testing",
"Latitude for testing",
"Drying/Wetting Equilibrium Adjustment",
"Rain Equilibrium Adjustment"
],
'xlvl':
200100.,
'nx':
nx,
'ny':
ny,
'iproj':
3,
'startloc':
startloc,
'startlat':
startlat,
'startlon':
startlon,
'dx':
index['dx'],
'dy':
index['dy'],
'xlonc':
index['stdlon'],
'truelat1':
index['truelat1'],
'truelat2':
index['truelat2'],
'earth_radius':
index['radius'],
'is_wind_earth_rel':
0,
'slab':
arrs
}
WPSFormat.from_params(**params).to_file(
osp.join(path, 'FMDA:{}'.format(time_tag)))
def to_geogrid(self, path, index, lats, lons):
"""
Store model to geogrid files
"""
test_latslons=True
logging.info("fmda.fuel_moisture_model.to_geogrid path=%s lats %s lons %s" % (path, inq(lats), inq(lons)))
logging.info("fmda.fuel_moisture_model.to_geogrid: geogrid_index="+str(index))
ensure_dir(path)
xsize, ysize, n = self.m_ext.shape
if n != 5:
logging.error('wrong number of extended state fields, expecting 5')
x=1
y=1
x=int(xsize*0.5)
y=int(ysize*0.5)
index.update({'known_x':float(y),'known_y':float(x),'known_lat':lats[x-1,y-1],'known_lon':lons[x-1,y-1]})
logging.info("fmda.fuel_moisture_model.to_geogrid: geogrid updated="+str(index))
FMC_GC = np.zeros((xsize, ysize, 5))
FMC_GC[:,:,:3] = self.m_ext[:,:,:3]
if test_latslons:
logging.info("fmda.fuel_moisture_model.to_geogrid: storing lons lats to FMC_GC(:,:,4:5) to test in WRF against XLONG and XLAT")
FMC_GC[:,:,3] = lons
FMC_GC[:,:,4] = lats
FMEP = self.m_ext[:,:,3:]
index['units']=addquotes('1')
write_geogrid_var(path,'FMC_GC',FMC_GC,'1h, 10h, 100h fuel moisture',index,bits=32)
write_geogrid_var(path,'FMEP',FMEP,'fuel moisture drying/wetting and rain equilibrium adjustments',index,bits=32)
def write_geogrid_var(path_dir,varname,array,index,bits=32,coord=None):
"""
write geogrid dataset and index
"""
path_dir=osp.abspath(path_dir)
logging.info('write_geogrid_var path_dir=%s varname=%s array=%s index=%s' % (path_dir, varname, inq(array), str(index)))
if not osp.exists(path_dir):
os.makedirs(path_dir)
# get information from src/geo/var_wisdom.py
wisdom = get_wisdom(varname).copy()
# write geogrid dataset
geogrid_ds_path = osp.join(path_dir,varname)
index['description'] = addquotes(wisdom.get('description',''))
index['units'] = addquotes(wisdom.get('units',''))
index['type'] = wisdom.get('type','continuous')
index['signed'] = wisdom.get('signed','yes')
bits = wisdom.get('bits',bits)
scale = wisdom.get('scale',None)
# some adds to index
if 'category_range' in wisdom:
index['category_min'] = wisdom['category_range'][0]
index['category_max'] = wisdom['category_range'][1]
if 'missing_value' in wisdom:
index['missing_value'] = wisdom['missing_value']
if 'tile_bdr' in wisdom:
index['tile_bdr'] = wisdom['tile_bdr']
# categorical substitution and interpolation
if index['type'] == 'categorical':
fill = wisdom.get('fill',{})
array = fill_categories(array,fill,coord)
write_geogrid(geogrid_ds_path,array,index,bits=bits,scale=scale)
# write also the index as json entry to modify later
index_json_path = osp.join(path_dir,'index.json')
try:
index_json = json.load(open(index_json_path,'r'))
except:
index_json = {}
index_json[varname]=index
json.dump(index_json,open(index_json_path,'w'), indent=4, separators=(',', ': '))
geogrid_tbl_var = {'name': varname,
'dest_type': wisdom.get('type','continuous'),
'interp_option': wisdom.get('interp_option','default:average_gcell(4.0)+four_pt+average_4pt'),
'abs_path': geogrid_ds_path,
'priority': wisdom.get('priority',1)}
# some adds to geogrid_tbl_var
if 'fill_missing' in wisdom:
geogrid_tbl_var['fill_missing'] = wisdom['fill_missing']
if 'smooth_option' in wisdom:
geogrid_tbl_var['smooth_option'] = wisdom['smooth_option']
if 'subgrid' in wisdom:
geogrid_tbl_var['subgrid'] = wisdom['subgrid']
if 'add_opts' in wisdom:
for key in wisdom['add_opts'].keys():
geogrid_tbl_var[key] = wisdom['add_opts'][key]
# write a segment of GEOGRID.TBL
geogrid_tbl_path=osp.join(path_dir,'GEOGRID.TBL')
write_table(geogrid_tbl_path, geogrid_tbl_var, mode='a', divider_after=True)
# write also as json
geogrid_tbl_json_path = osp.join(path_dir,'geogrid_tbl.json')
try:
geogrid_tbl = json.load(open(geogrid_tbl_json_path,'r'))
except:
geogrid_tbl = {}
geogrid_tbl[varname]=geogrid_tbl_var
json.dump(index,open(geogrid_tbl_json_path,'w'), indent=4, separators=(',', ': '))
json.dump(geogrid_tbl,open(geogrid_tbl_json_path,'w'), indent=4, separators=(',', ': '))
def write_geogrid_var(path_dir, varname, array, index, bits=32, coord=None):
"""
write geogrid dataset and index
"""
path_dir = osp.abspath(path_dir)
logging.info('write_geogrid_var path_dir=%s varname=%s array=%s index=%s' %
(path_dir, varname, inq(array), str(index)))
if not osp.exists(path_dir):
os.makedirs(path_dir)
# get information from src/geo/var_wisdom.py
wisdom = get_wisdom(varname).copy()
# write geogrid dataset
geogrid_ds_path = osp.join(path_dir, varname)
index['description'] = addquotes(wisdom.get('description', ''))
index['units'] = addquotes(wisdom.get('units', ''))
index['type'] = wisdom.get('type', 'continuous')
index['signed'] = wisdom.get('signed', 'yes')
bits = wisdom.get('bits', bits)
scale = wisdom.get('scale', None)
uscale = wisdom.get('unit_scale', None)
# some adds to index
if 'category_range' in wisdom:
index['category_min'] = wisdom['category_range'][0]
index['category_max'] = wisdom['category_range'][1]
if 'missing_value' in wisdom:
index['missing_value'] = wisdom['missing_value']
if 'tile_bdr' in wisdom:
index['tile_bdr'] = wisdom['tile_bdr']
# categorical substitution and interpolation
if index['type'] == 'categorical':
fill = wisdom.get('fill', {})
if isinstance(fill, str):
fill_str = fill
fill = {}
if fill_str == 'from_file':
fill_path = 'etc/vtables/fill.json'
try:
fill_vars = json.load(open(fill_path, 'r'))
except:
logging.warning(
'write_geogrid_var fail reading fill file {}'.format(
fill_path))
fill_file = fill_vars.get(varname, '')
if osp.exists(fill_file):
try:
df = pd.read_csv(fill_file,
names=['from', 'to'],
index_col=False)
cfrom = np.array(df.loc[1:, 'from'])
cto = np.array(df.loc[1:, 'to'])
rest_val = df.loc[0, 'from']
unique = np.unique(array)
rest_ind = np.array(
[u for u in unique if u not in cfrom])
fill = Dict({tuple(rest_ind): rest_val})
for k, key in enumerate(cfrom):
fill.update({key: cto[k]})
except Exception as e:
logging.warning(
'write_geogrid_var fail reading fill CSV file {}'.
format(fill_file))
logging.warning('with exception {}'.format(e))
array = fill_categories(array, fill, coord)
write_geogrid(geogrid_ds_path,
array,
index,
bits=bits,
scale=scale,
uscale=uscale)
# write also the index as json entry to modify later
index_json_path = osp.join(path_dir, 'index.json')
try:
index_json = json.load(open(index_json_path, 'r'))
except:
index_json = {}
index_json[varname] = index
json.dump(index_json,
open(index_json_path, 'w'),
indent=4,
separators=(',', ': '))
geogrid_tbl_var = {
'name':
varname,
'dest_type':
wisdom.get('type', 'continuous'),
'interp_option':
wisdom.get('interp_option',
'default:average_gcell(4.0)+four_pt+average_4pt'),
'abs_path':
geogrid_ds_path,
'priority':
wisdom.get('priority', 1)
}
# some adds to geogrid_tbl_var
if 'fill_missing' in wisdom:
geogrid_tbl_var['fill_missing'] = wisdom['fill_missing']
if 'smooth_option' in wisdom:
geogrid_tbl_var['smooth_option'] = wisdom['smooth_option']
if 'subgrid' in wisdom:
geogrid_tbl_var['subgrid'] = wisdom['subgrid']
if 'add_opts' in wisdom:
for key in wisdom['add_opts'].keys():
geogrid_tbl_var[key] = wisdom['add_opts'][key]
# write a segment of GEOGRID.TBL
geogrid_tbl_path = osp.join(path_dir, 'GEOGRID.TBL')
write_table(geogrid_tbl_path,
geogrid_tbl_var,
mode='a',
divider_after=True)
# write also as json
geogrid_tbl_json_path = osp.join(path_dir, 'geogrid_tbl.json')
try:
geogrid_tbl = json.load(open(geogrid_tbl_json_path, 'r'))
except:
geogrid_tbl = {}
geogrid_tbl[varname] = geogrid_tbl_var
json.dump(index,
open(geogrid_tbl_json_path, 'w'),
indent=4,
separators=(',', ': '))
json.dump(geogrid_tbl,
open(geogrid_tbl_json_path, 'w'),
indent=4,
separators=(',', ': '))
