def get_results(flight, config="", group="All quantities"):
"""
This function loads the retrieval results from a given flight
Results are automatically augmented with the retrieved iwc.
Args:
flight: The flight name, i.e. b984, c159 or c161
config: Name of retrieval configuration that defines from which sub-folder
(if any) the data is loaded.
group: The NetCDF4 group containing the results.
"""
flight = flight.lower()
path = os.path.join(joint_flight.PATH, "data", "old")
if config != "":
path = os.path.join(path, config)
pattern = re.compile(f"output_{flight}_([\w-]*).nc")
results = {}
psd = D14NDmIce()
for f in glob.glob(os.path.join(path, "*")):
match = re.match(pattern, os.path.basename(f))
if match is None:
continue
shape = match.group(1)
data = xr.load_dataset(os.path.join(path, f), group=group)
dm = data["ice_dm"]
n0 = data["ice_n0"]
psd.mass_weighted_diameter = dm
psd.intercept_parameter = n0
wc = psd.get_mass_density()
k = np.ones((5, 1)) / 5.0
wc_s = convolve(wc, k, mode="same")
nd = psd.get_moment(0)
nd.data[wc < 5e-6] = 0.0
data["ice_water_content"] = (dm.dims, wc.data)
data["ice_water_content_smooth"] = (dm.dims, wc_s.data)
data["number_density"] = (dm.dims, nd.data)
results[shape] = data
return results
def load_nc(path, time):
"""Load the netCDF dataset corresponding to the given time
This function finds files matching the AERIS :data:`nc_filename` formatted for the
given time
Args:
path (str): The directory containing GOES netCDF files
time (datetime.datetime): The time of the file to load
Returns:
xarray.dataset: The loaded netCDF file with values filtered where the
coordinates are NaN
Raises:
FileNotFoundError: If the netCDF is not present
FileExistsError: If multiple matching netCDF files are found with the same name
"""
filename = nc_filename.format(
year=time.year,
day=time.timetuple().tm_yday,
hour=time.hour,
minute=time.minute,
something="*",
)
# Find matching filenames in the GOES folder
file_path = list(pathlib.Path(path).rglob(filename))
if len(file_path) == 0:
raise FileNotFoundError("No GOES data found in {} for {}".format(
path, time))
elif len(file_path) > 1:
raise FileExistsError("More than one file found in {} for {}".format(
path, time))
dataset = xr.load_dataset(str(file_path[0]))
# Remove values where the coordinates are NaNs
dataset = dataset.where(~dataset.longitude.isnull(), drop=True)
return dataset
def load_workflow_result(datasets,
loader=xarray.load_dataset,
sortby='location'):
if isinstance(datasets, str):
if isdir(datasets):
datasets = glob(join(datasets, "*.nc"))
else:
datasets = glob(datasets)
if len(datasets) == 1:
ds = xarray.load_dataset(datasets[0]).sortby('locations')
else:
ds = xarray.concat(map(loader, datasets), dim="location")
if sortby is not None:
ds = ds.sortby(sortby)
return ds
def read_and_concat_smoothFinals(rinexpath, solution='Final'):
import xarray as xr
from aux_gps import save_ncfile
from aux_gps import path_glob
years = [x.as_posix().split('/')[-1] for x in path_glob(rinexpath, '*/')]
years = [x for x in years if x.isnumeric()]
for year in years:
dsl = []
# doys = [x.as_posix().split('/')[-1] for x in path_glob(rinexpath/year, '*/')]
for doypath in path_glob(rinexpath/year, '*/'):
file = doypath / 'dr' / solution / 'smoothFinal.nc'
if file.is_file():
dsl.append(xr.load_dataset(file))
print('found smoothFinal.nc in {}'.format(doypath))
if dsl:
ds = xr.concat(dsl, 'time')
ds = ds.sortby('time')
save_ncfile(ds, rinexpath, 'smoothFinal_{}.nc'.format(year))
return ds
def process_month(self, year, month, output_path):
files = {
gn: fs
for gn, fs in self.validation_files.items()
if fs["date"].year == year and fs["date"].month == month
}
parts = []
for gn, fs in files.items():
match_up_file = fs["match_up_file"]
gprof_file = fs["gprof_file"]
qprof_file = fs["qprof_file"]
match_up_data = xr.load_dataset(match_up_file)
gprof_data = RetrievalFile(
gprof_file, has_sensitivity=True).to_xarray_dataset()
qprof_data = RetrievalFile(qprof_file).to_xarray_dataset()
match_up_data = match_up_data.stack(samples=("scans", "pixels"))
gprof_data = gprof_data.stack(samples=("scans", "pixels"))
qprof_data = qprof_data.stack(samples=("scans", "pixels"))
valid = np.isfinite(match_up_data["surface_precipitation"])
valid *= gprof_data["surface_precip"] >= 0.0
match_up_data = match_up_data.isel(samples=valid).rename(
{"surface_precipitation": "surface_precip_mrms"})
gprof_data = gprof_data.isel(samples=valid)
gprof_data = gprof_data[["surface_precip"]].rename(
{"surface_precip": "surface_precip_gprof"})
qprof_data = qprof_data.isel(samples=valid)
qprof_data = qprof_data[["surface_precip"]].rename(
{"surface_precip": "surface_precip_qprof"})
merged = xr.merge([match_up_data, gprof_data, qprof_data])
parts.append(merged.reset_index("samples"))
results = xr.concat(parts, dim="samples")
output_path = Path(output_path)
if output_path.is_dir():
output_path = output_path = f"validation_results_{year}_{month:02}.nc"
results.to_netcdf(output_path)
def test_goes_mag():
fname = 'dn_magn-l2-hires_g17_d20211219_v1-0-1.nc'
url = (
"https://lasp.colorado.edu/maven/sdc/public/data/sdc/web/cdflib_testing/dn_magn-l2-hires_g17_d20211219_v1-0-1.nc")
if not os.path.exists(fname):
urllib.request.urlretrieve(url, fname)
c = xr.load_dataset("dn_magn-l2-hires_g17_d20211219_v1-0-1.nc")
for var in c:
c[var].attrs['VAR_TYPE'] = 'data'
c['coordinate'].attrs['VAR_TYPE'] = 'support_data'
c['time'].attrs['VAR_TYPE'] = 'support_data'
c['time_orbit'].attrs['VAR_TYPE'] = 'support_data'
cdflib.xarray_to_cdf(c, 'dn_magn-l2-hires_g17_d20211219_v1-0-1-created-from-netcdf-input.cdf')
d = cdflib.cdf_to_xarray('dn_magn-l2-hires_g17_d20211219_v1-0-1-created-from-netcdf-input.cdf', to_unixtime=True,
fillval_to_nan=True)
os.remove('dn_magn-l2-hires_g17_d20211219_v1-0-1-created-from-netcdf-input.cdf')
os.remove('dn_magn-l2-hires_g17_d20211219_v1-0-1.nc')
def read_evo(filename):
"""Load an `evo` post-processed Enlil file into an Evolution object.
Parameters
----------
filename : str
netcdf Enlil post-processed output file
Example: evo.earth.nc
Returns
-------
enlil.Evolution
An Evolution class representing the loaded file.
"""
ds = xr.load_dataset(filename)
# Change the dimension to time
# Depending on which version, the key could be rundate or refdate
try:
t0 = np.datetime64(ds.attrs['rundate_cal'], 's')
except KeyError:
t0 = np.datetime64(ds.attrs['refdate_cal'], 's')
time = t0 + np.array(ds['TIME'], np.timedelta64)
ds = ds.rename({'nevo': 'earth_t'}).assign_coords({'earth_t': time})
ds = ds.drop(['TIME', 'DT', 'NSTEP'])
for var in _variables_evo:
if var not in ds:
continue
da = ds[var]
# Update the name to be consistent across data sets
name = _variables_evo[var]
da.name = name
# Unit conversions
da = _transform_variable(da)
ds[name] = da
ds = ds.drop(var)
ds.attrs['name'] = ds.label
return Evolution(ds)
def get_infos(subd):
files = glob.glob(f"{dirgrid}/{subd:02}/*nc")
tiles = sorted([int(f.split(".")[1]) for f in files])
ntiles = len(files)
grdfile = files[0]
ds = xr.load_dataset(grdfile)
headersize = 29400
stripes = {"size": 177848320, "roundingsize": 1024}
dims = {"chunk": ntiles // 100 + 1, "tile": 100}
attrs = ds.attrs
varlist = list(ds.variables)
attrs.pop("_NCProperties")
attrs["partition_ucla"] = [0, 10000, 1, 1]
varlist.remove("spherical")
variables = {}
for name in varlist:
var = ds.variables[name]
variables[name] = {"shape": list(var.shape), "dtype": var.dtype.name}
vattrs = var.attrs
for key, value in var.attrs.items():
if isinstance(value, np.float64):
vattrs[key] = float(value)
if isinstance(value, np.int32):
vattrs[key] = int(value)
variables[name].update(vattrs)
infos = {}
infos["headersize"] = headersize
infos["stripes"] = stripes
infos["dimensions"] = dims
infos["variables"] = variables
infos["attrs"] = attrs
infos["tiles"] = tiles
return infos
def test_saber():
fname = 'SABER_L2B_2021020_103692_02.07.nc'
url = (
"https://lasp.colorado.edu/maven/sdc/public/data/sdc/web/cdflib_testing/SABER_L2B_2021020_103692_02.07.nc")
if not os.path.exists(fname):
urllib.request.urlretrieve(url, fname)
c = xr.load_dataset("SABER_L2B_2021020_103692_02.07.nc")
for var in c:
c[var].attrs['VAR_TYPE'] = 'data'
c['event'].attrs['VAR_TYPE'] = 'support_data'
c['sclatitude'].attrs['VAR_TYPE'] = 'support_data'
c['sclongitude'].attrs['VAR_TYPE'] = 'support_data'
c['scaltitude'].attrs['VAR_TYPE'] = 'support_data'
cdflib.xarray_to_cdf(c, 'SABER_L2B_2021020_103692_02.07-created-from-netcdf-input.cdf')
d = cdflib.cdf_to_xarray('SABER_L2B_2021020_103692_02.07-created-from-netcdf-input.cdf', to_unixtime=True,
fillval_to_nan=True)
os.remove('SABER_L2B_2021020_103692_02.07-created-from-netcdf-input.cdf')
os.remove('SABER_L2B_2021020_103692_02.07.nc')
def convert_to_rcmip_nc(fname):
ds = xr.load_dataset(fname)
# convert years to datetimes
ds["time"] = np.array([dt.datetime(y, 1, 1) for y in ds["time"][:]],
dtype="datetime64[s]").astype("float")
for v in ds.variables:
ds_variable = ds.variables[v]
if v not in ["time", "ensemble_member"]:
# rename units
ds_variable.attrs["unit"] = ds_variable.attrs["units"]
del ds_variable.attrs["units"]
# Add scenario and model info
ds_variable.attrs["scenario"] = ds.attrs["scenario"]
ds_variable.attrs["model"] = ds.attrs["model"]
# Write out as rcmipII-{sce}-converted.nc
ds.to_netcdf(os.path.join(root_dir, "{}-converted.nc".format(fname[:-3])))
def load_data(self, **kwargs):
"""Load data to xarray dataset
:returns: (xarray.Dataset) simulation output data
"""
# load z and zi
with netcdf.netcdf_file(self.data, 'r', mmap=False) as ncfile:
nc_z = ncfile.variables['z']
nc_zi = ncfile.variables['zi']
z = xr.DataArray(nc_z[0, :, 0, 0],
dims=('z'),
coords={'z': nc_z[0, :, 0, 0]},
attrs={
'long_name': nc_z.long_name.decode(),
'units': nc_z.units.decode()
})
zi = xr.DataArray(nc_zi[0, :, 0, 0],
dims=('zi'),
coords={'zi': nc_zi[0, :, 0, 0]},
attrs={
'long_name': nc_zi.long_name.decode(),
'units': nc_zi.units.decode()
})
# load other variables
out = xr.load_dataset(
self.data,
drop_variables=['z', 'zi'],
**kwargs,
)
out = out.assign_coords({
'z': z,
'zi': zi,
})
for var in out.data_vars:
if 'z' in out.data_vars[var].dims:
out.data_vars[var].assign_coords({'z': z})
elif 'zi' in out.data_vars[var].dims:
out.data_vars[var].assign_coords({'zi': zi})
# return a reorderd view
return out.transpose('z', 'zi', 'time', 'lon', 'lat')
def read_input_data(metadata: list, dim: str = 'dataset'):
"""Load data from metadata.
Read the input data from the list of given data sets. `metadata` is
a list of metadata containing the filenames to load. The datasets
are stacked along the `dim` dimension. Returns an xarray.DataArray.
"""
identifiers = []
datasets = []
for info in metadata:
filename = info['filename']
dataset = xr.load_dataset(filename)
datasets.append(dataset)
identifier = info[dim]
identifiers.append(identifier)
stacked_datasets = xr.concat(datasets, dim=dim)
stacked_datasets[dim] = identifiers
return stacked_datasets
def deserialize_dataset(data):
"""
Read xarray dataset from byte stream containing the
dataset in NetCDF format.
Args:
data: The bytes object containing the binary data of the
NetCDf file.
Returns:
The deserialized xarray dataset.
"""
_, filename = mkstemp()
try:
with open(filename, "wb") as file:
buffer = file.write(data)
dataset = xr.load_dataset(filename)
finally:
Path(filename).unlink()
return dataset
def test_MGITM_model():
fname = 'MGITM_LS180_F130_150615.nc'
url = (
"https://lasp.colorado.edu/maven/sdc/public/data/sdc/web/cdflib_testing/MGITM_LS180_F130_150615.nc")
if not os.path.exists(fname):
urllib.request.urlretrieve(url, fname)
c = xr.load_dataset("MGITM_LS180_F130_150615.nc")
for var in c:
c[var].attrs['VAR_TYPE'] = 'data'
c = c.rename({'Latitude': 'latitude', 'Longitude': 'longitude'})
c['longitude'].attrs['VAR_TYPE'] = 'support_data'
c['latitude'].attrs['VAR_TYPE'] = 'support_data'
c['altitude'].attrs['VAR_TYPE'] = 'support_data'
cdflib.xarray_to_cdf(c, 'MGITM_LS180_F130_150615-created-from-netcdf-input.cdf')
d = cdflib.cdf_to_xarray('MGITM_LS180_F130_150615-created-from-netcdf-input.cdf', to_unixtime=True,
fillval_to_nan=True)
os.remove('MGITM_LS180_F130_150615-created-from-netcdf-input.cdf')
os.remove('MGITM_LS180_F130_150615.nc')
def interpolate_vertical(ml_file, inter_file, new_vertical_axis):
"""
Linearly interpolate all 4D variables of ml_file to the levels of new_vertical_axis and save it in inter_file
"""
with xr.load_dataset(inter_file) as interpolated:
reference = [variable for variable in interpolated.variables if len(interpolated[variable].shape) == 4][0]
with xr.open_dataset(ml_file) as ml:
for variable in [variable for variable in ml.variables if variable not in interpolated.variables
and len(ml[variable].dims) == 4
and "lev_2" in ml[variable].dims]:
try:
x = np.array(ml[new_vertical_axis].data)
y = np.array(ml[variable].data)
interpolated_data = interpolate_1d(interpolated["lev"].data, x, y, axis=1)
attributes = ml[variable].attrs
interpolated[variable] = interpolated[reference].copy(data=interpolated_data)
interpolated[variable].attrs = ml[variable].attrs
except Exception as e:
print(variable, e)
interpolated.to_netcdf(inter_file)
def download(self):
outfile = self.datasets_root / 'MOS_ANMN-WA_AETVZ_WATR20_FV01_WATR20-1909-Continental-194_currents.nc'
get_current_timeseries(outfile=outfile)
# made in previous notebook
xd = xr.load_dataset(outfile)
df = xd.to_dataframe()
df = df[[
'VCUR', 'UCUR', 'WCUR', 'TEMP', 'DEPTH', 'M2', 'S2', 'N2', 'K2',
'K1', 'O1', 'P1', 'Q1', 'M4', 'M6', 'S4', 'MK3', 'MM', 'SSA', 'SA',
'SPD'
]]
df.dropna(subset=self.columns_target, inplace=True)
# Only keep parts with at most 5 nans in last 48 periods
has_past = df.SPD.isna().rolling(48).sum() < 5
df = df[has_past]
df = df.resample('30T').mean()
return df
def sample_function(sample,
baseFolder=baseFolder,
destination=destination,
overWrite=False):
filename = ''.join(['pore_affiliation_', sample, '.nc'])
path = os.path.join(destination, filename)
if not os.path.exists(path) or overWrite:
try:
# metadata = xr.load_dataset(os.path.join(destination, ''.join(['pore_props_',sample,'.nc'])))
metadata = xr.load_dataset(
os.path.join(destination, ''.join(['dyn_data_', sample,
'.nc'])))
relevant_pores = metadata['label'].data
pore_affiliation, labels = track_pore_affiliation(
sample,
relevant_pores,
baseFolder,
label_im=metadata['label_matrix'].data)
data = xr.Dataset(
{'pore_affiliation': ('label', pore_affiliation)},
coords={'label': labels})
# attrs={'explanation': '1 - top yarn, 2 - bottom yarn, 3 - interlace, 0 - not in contact'})
data.attrs = metadata.attrs
data.attrs[
'explanation'] = '1 - top yarn, 2 - bottom yarn, 3 - interlace, 0 - not in contact'
filename = ''.join(
['pore_affiliation_small_interface', sample, '.nc'])
path = os.path.join(destination, filename)
data.to_netcdf(path)
return 'completed'
except Exception: # as e:
# return
traceback.print_exc()
else:
return 'already done'
def fudge_intervals_cbound(file):
"""make data consistent for given bound"""
values_changed = 0
ds = xr.load_dataset(file)
for cbound in ["pf_lower", "pf_upper"]:
for i in range(1, len(ds.interval)):
higher_arr = ds[cbound][i, ...].values
lower_arr = ds[cbound][i - 1, ...].values
diff = higher_arr - lower_arr
mask = diff <= 0
num = np.count_nonzero(mask)
if num > 0:
print(f" {num} value(s) changed", flush=True)
values_changed += num
higher_arr[mask] = lower_arr[mask] * 1.001
ds[cbound][i, ...] = higher_arr
ds.close()
ds.to_netcdf(file)
return values_changed
def test_save_netcdf(self):
"""
Testing for saver done here since we have a model to integrate so it's
easy.
"""
system = BackendTestingHelper()
results = system.run(self.DURATION, self.DT, ZeroInput().as_cubic_splines(self.DURATION, self.DT))
results.attrs = self.EXTRA_ATTRS
# save to pickle
nc_name = os.path.join(self.TEST_DIR, "netcdf_test")
save_to_netcdf(results, nc_name)
# actual data
self.assertTrue(os.path.exists(nc_name + ".nc"))
# metadata
self.assertTrue(os.path.exists(nc_name + ".json"))
# load and check
loaded = xr.load_dataset(nc_name + ".nc")
with open(nc_name + ".json", "r") as f:
attrs = json.load(f)
loaded.attrs = attrs
xr.testing.assert_equal(results, loaded)
self.assertDictEqual(loaded.attrs, self.EXTRA_ATTRS)
def process_IMS_data_at_station_on_dsea_foehn_dates(
ims_path=ims_path,
station='SEDOM',
path=des_path,
times=['2014-08-01', '2014-08-31']):
import xarray as xr
import pandas as pd
import numpy as np
# import matplotlib.pyplot as plt
ws = xr.open_dataset(
ims_path / 'IMS_WS_israeli_10mins.nc')[station].sel(time=slice(*times))
wd = xr.open_dataset(
ims_path / 'IMS_WD_israeli_10mins.nc')[station].sel(time=slice(*times))
rh = xr.open_dataset(
ims_path / 'IMS_RH_israeli_10mins.nc')[station].sel(time=slice(*times))
ts = xr.open_dataset(
ims_path / 'IMS_TD_israeli_10mins.nc')[station].sel(time=slice(*times))
ds = xr.Dataset()
ds['WS'] = ws
ds['WD'] = wd
ds['T'] = ts
ds['RH'] = rh
# convert to UTC, since IMS is always UTC+2
new_time = ds['time'] - pd.Timedelta(2, units='H')
new_time = new_time.dt.round('s')
ds['time'] = new_time
ds['PWV'] = xr.load_dataset(path / 'DSEA_PWV_GNSS_2014-08.nc')['pwv-soi']
da8 = ds.sel(time=slice('2014-08-08T13:00:00',
'2014-08-08T19:00:00')).to_array('var')
da8['time'] = np.linspace(13, 19, len(da8['time']))
# da8['time'] = da8['time'].dt.time
da16 = ds.sel(time=slice('2014-08-16T13:00:00',
'2014-08-16T19:00:00')).to_array('var')
da16['time'] = np.linspace(13, 19, len(da16['time']))
# da16['time'] = da16['time'].dt.time
dss = xr.concat([da8, da16], 'date')
# dss['date'] = [pd.to_datetime(x).date() for x in ['2014-08-08', '2014-08-16']]
dss['date'] = ['2014-08-08', '2014-08-16']
return dss
def prepare_for_go(self):
logger.debug('prepare_for_go...')
try:
#input is either going to be a netcdf file or a list of rmc6f files
data = xr.load_dataset(self.input)
assert 'rmcalyse_version' in data.attrs.keys()
logger.info('successfully loaded data from netcdf file {}'.format(
self.input))
self.data = data
except FileNotFoundError as e:
logger.debug('File {} not found: {}'.format(self.input, e))
raise
except (OSError, AssertionError, AttributeError):
logger.debug(
'File {} is not a valid rmcalyse/netcdf4 file. trying rmc6f loader...'
.format(self.input))
try:
self._load_rmc6f_files()
except Exception as e:
logger.error(
'there was an unhandled error in loading {}'.format(
self.input))
def saveDataToFile(self, source):
"""
Saves the given data as database to file.
@ In, source, DataObjects.DataObject, object to write to file
@ Out, None
"""
ds, meta = source.getData()
# we actually just tell the DataSet to write out as netCDF
path = self.get_fullpath()
# TODO set up to use dask for on-disk operations
# convert metadata into writeable
for key, xml in meta.items():
ds.attrs[key] = xmlUtils.prettify(xml.getRoot())
# get rid of "object" types
for var in ds:
if ds[var].dtype == np.dtype(object):
# is it a string?
if mathUtils.isAString(ds[var].values[0]):
ds[var] = ds[var].astype(str)
# is there existing data? Read it in and merge it, if so
# -> we've already wiped the file in initializeDatabase if it's in write mode
if os.path.isfile(path):
exists = xr.load_dataset(path)
if 'RAVEN_sample_ID' in exists:
floor = int(exists['RAVEN_sample_ID'].values[-1]) + 1
new = ds['RAVEN_sample_ID'].values + floor
ds = ds.assign_coords(RAVEN_sample_ID=new)
# NOTE order matters! This preserves the sampling order in which data was inserted
# into this database
ds = xr.concat((exists, ds), 'RAVEN_sample_ID')
# if this is open somewhere else, we can't write to it
# TODO is there a way to check if it's writable? I can't find one ...
try:
ds.to_netcdf(path, engine=self._format)
except PermissionError:
self.raiseAnError(
PermissionError,
f'NetCDF file "{path}" denied RAVEN permission to write! Is it open in another program?'
)
def read_multiple_strain_netcdfs(MyParams, plot_type):
"""
Get all the models (e.g. gpsgridder, geostats, huang, etc.) that have computed plot_type of
strain rate and return them as a single xarray Dataset
Parameters
----------
MyParams: dict - Parameter Dictionary containing strain rate methods/directories in a sub-dict
plot_type: str - The type of strain rate quantity to return. Can be max_shear, dilatation, etc.
Returns
-------
ds_new: xarray Dataset - A dataset containing the plot_type variable from each type of model
"""
building_dict = {}
for k, method in enumerate(MyParams.strain_dict.keys()):
specific_filename = os.path.join(MyParams.strain_dict[method], "{}_strain.nc".format(method))
ds = xr.load_dataset(specific_filename)
building_dict[method] = ds[plot_type];
ds_new = xr.Dataset(building_dict, coords=ds.coords)
return ds_new
def iterate_intervals():
print(" Iterating over intervals...", flush=True)
values_changed = 0
# Since intervals are stored within each file, this process is
# more straightforward than iterating over durations since all the info
# we need to compare interval values are contained within a single file.
# We just have to work on each file in turn.
# Since the first iteration over intervals occurs after the iteration over
# durations, we can also just load from the output directory since the
# durations step would have moved everything into there.
files = glob(os.path.join(out_path, f"*_{data_group}_*.nc"))
for file in files:
print(f" {os.path.basename(file)}", flush=True)
ds = xr.load_dataset(file)
for i in range(1, len(ds.interval)):
higher_arr = ds['pf'][i, ..., ...].values
lower_arr = ds['pf'][i - 1, ..., ...].values
diff = higher_arr - lower_arr
mask = diff <= 0
num = np.count_nonzero(mask)
if num > 0:
print(f" {num} value(s) changed", flush=True)
values_changed += num
higher_arr[mask] = lower_arr[mask] * 1.01
ds['pf'][i, ..., ...] = higher_arr
ds.close()
ds.to_netcdf(file)
return values_changed
def get_weights():
ds = xr.load_dataset("weights.nc")
n_s = ds.dims['n_s']
col = ds['col'].values - 1
row = ds['row'].values - 1
S = ds['S'].values
A = coo_matrix((S, (row, col)))
if args.type == 'csc':
A = A.tocsc()
elif args.type == 'csr':
A = A.tocsr()
elif args.type == 'coo':
pass
else:
raise ValueError
return A
def _set_cache_from_netcdf(cls, ds: DataSetInMem,
xr_path: Optional[str]) -> bool:
import xarray as xr
success = True
if xr_path is not None:
try:
loaded_data = xr.load_dataset(xr_path, engine="h5netcdf")
ds._cache = DataSetCacheInMem(ds)
ds._cache._data = cls._from_xarray_dataset_to_qcodes_raw_data(
loaded_data)
except (
FileNotFoundError,
OSError,
): # older versions of h5py may throw a OSError here
success = False
warnings.warn("Could not load raw data for dataset with guid :"
f"{ds.guid} from location {xr_path}")
else:
warnings.warn(
f"No raw data stored for dataset with guid : {ds.guid}")
success = False
return success
def produce_pwv_from_dsea_axis_station(path=axis_path, ims_path=ims_path):
"""use axis_path = work_yuval/dsea_gispyx for original soi-apn dsea station"""
import xarray as xr
from aux_gps import transform_ds_to_lat_lon_alt
from aux_gps import get_unique_index
ds = xr.load_dataset(path / 'smoothFinal_2014.nc').squeeze()
ds = get_unique_index(ds)
# for now cut:
if 'axis' in path.as_posix():
ds = ds.sel(time=slice(None, '2014-08-12'))
ds = transform_ds_to_lat_lon_alt(ds)
axis_zwd = ds['WetZ']
ts = xr.open_dataset(ims_path / 'IMS_TD_israeli_10mins.nc')['SEDOM']
axis_pwv = produce_pwv_from_zwd_with_ts_tm_from_deserve(ts=ts,
zwd=axis_zwd)
if 'axis' in path.as_posix():
axis_pwv.name = 'AXIS-DSEA'
else:
axis_pwv.name = 'SOI-DSEA'
axis_pwv.attrs['lat'] = ds['lat'].values[0]
axis_pwv.attrs['lon'] = ds['lon'].values[0]
axis_pwv.attrs['alt'] = ds['alt'].values[0]
return axis_pwv
def __init__(self, path):
# waiting time statistics
self.delta_t_025 = np.array([])
self.delta_t_100 = np.array([])
self.delta_t_300 = np.array([])
self.delta_t_all = np.array([])
print('Reading the statistics dataset at {}'.format(path))
stats_dataset = xr.load_dataset(path)
for key in list(stats_dataset.coords):
if not key[-2:] == '_t': continue
if key[3:6] == '025':
self.delta_t_025 = np.concatenate(
[self.delta_t_025, stats_dataset[key].data])
if key[3:6] == '100':
self.delta_t_100 = np.concatenate(
[self.delta_t_100, stats_dataset[key].data])
if key[3:6] == '300':
self.delta_t_300 = np.concatenate(
[self.delta_t_300, stats_dataset[key].data])
self.delta_t_all = stats_dataset['deltatall'].data
def get_pressure_lapse_rate(path=ims_path, model='LR', plot=False):
from aux_gps import linear_fit_using_scipy_da_ts
import matplotlib.pyplot as plt
import xarray as xr
from aux_gps import keep_iqr
bp = xr.load_dataset(ims_path / 'IMS_BP_israeli_10mins.nc')
bps = [keep_iqr(bp[x]) for x in bp]
bp = xr.merge(bps)
mean_p = bp.mean('time').to_array('alt')
mean_p.name = 'mean_pressure'
alts = [bp[x].attrs['station_alt'] for x in bp.data_vars]
mean_p['alt'] = alts
_, results = linear_fit_using_scipy_da_ts(mean_p,
model=model,
slope_factor=1,
not_time=True)
slope = results['slope']
inter = results['intercept']
modeled_var = slope * mean_p['alt'] + inter
if plot:
fig, ax = plt.subplots()
modeled_var.plot(ax=ax, color='r')
mean_p.plot.line(linewidth=0., marker='o', ax=ax, color='b')
# lr = 1000 * abs(slope)
textstr = 'Pressure lapse rate: {:.1f} hPa/km'.format(1000 * slope)
props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
# place a text box in upper left in axes coords
ax.text(0.5,
0.95,
textstr,
transform=ax.transAxes,
fontsize=12,
verticalalignment='top',
bbox=props)
ax.set_xlabel('Height a.s.l [m]')
ax.set_ylabel('Mean Pressure [hPa]')
return results
def perform_pwv_filling_last_decade(path=work_yuval,
fyear='2009',
lyear='2019',
drop=['slom', 'elro']):
import xarray as xr
from aux_gps import save_ncfile
pw = xr.load_dataset(path / 'GNSS_PW_monthly_thresh_50.nc')
pw = pw.sel(time=slice(fyear, lyear))
pw = pw.drop_vars(drop)
prepare_pwv_for_climatol(freq='monthly',
first_year=fyear,
last_year=lyear,
pwv_ds=pw)
# then run these two lines in R:
# homogen('PWV',2009,2019, na.strings="-999.9",dz.max=7,std=2)
# dahstat('PWV',2009,2019,stat='series',long=TRUE)
ds, ds_flag = read_climatol_results(first_year=fyear, last_year=lyear)
filename = 'GNSS_PW_monthly_homogenized_filled_{}-{}.nc'.format(
fyear, lyear)
save_ncfile(ds, path, filename)
filename = 'GNSS_PW_monthly_homogenized_filled_flags_{}-{}.nc'.format(
fyear, lyear)
save_ncfile(ds_flag, path, filename)
return
