import numpy as np
import xarray as xr
from .binned import TimeBinner
from .time import dt642epoch, dt642date
from .rotate.api import rotate2, set_declination, set_inst2head_rotmat
from .io.api import save
from .tools.misc import slice1d_along_axis, convert_degrees
[docs]@xr.register_dataset_accessor('velds') # 'vel dataset'
class Velocity():
"""
All ADCP and ADV xarray datasets wrap this base class.
The turbulence-related attributes defined within this class
assume that the ``'tke_vec'`` and ``'stress_vec'`` data entries are
included in the dataset. These are typically calculated using a
:class:`VelBinner` tool, but the method for calculating these
variables can depend on the details of the measurement
(instrument, it's configuration, orientation, etc.).
See Also
========
:class:`VelBinner`
"""
########
# Major components of the dolfyn-API
[docs] def rotate2(self, out_frame='earth', inplace=True):
"""
Rotate the dataset to a new coordinate system.
Parameters
----------
out_frame : string {'beam', 'inst', 'earth', 'principal'}
The coordinate system to rotate the data into.
inplace : bool
When True the existing data object is modified. When False
a copy is returned. Default = True
Returns
-------
ds : xarray.Dataset or None
Returns the rotated dataset **when ``inplace=False``**, otherwise
returns None.
Notes
-----
- This function rotates all variables in ``ds.attrs['rotate_vars']``.
- To rotate to the 'principal' frame, a value of
``ds.attrs['principal_heading']`` must exist. The function
:func:`calc_principal_heading <dolfyn.calc_principal_heading>`
is recommended for this purpose, e.g.::
ds.attrs['principal_heading'] = dolfyn.calc_principal_heading(ds['vel'].mean(range))
where here we are using the depth-averaged velocity to calculate
the principal direction.
"""
return rotate2(self.ds, out_frame, inplace)
[docs] def set_declination(self, declin, inplace=True):
"""
Set the magnetic declination
Parameters
----------
declination : float
The value of the magnetic declination in degrees (positive
values specify that Magnetic North is clockwise from True North)
inplace : bool
When True the existing data object is modified. When False
a copy is returned. Default = True
Returns
-------
ds : xarray.Dataset or None
Returns the rotated dataset **when ``inplace=False``**, otherwise
returns None.
Notes
-----
This method modifies the data object in the following ways:
- If the dataset is in the *earth* reference frame at the time of
setting declination, it will be rotated into the "*True-East*,
*True-North*, Up" (hereafter, ETU) coordinate system
- ``dat['orientmat']`` is modified to be an ETU to
instrument (XYZ) rotation matrix (rather than the magnetic-ENU to
XYZ rotation matrix). Therefore, all rotations to/from the 'earth'
frame will now be to/from this ETU coordinate system.
- The value of the specified declination will be stored in
``dat.attrs['declination']``
- ``dat['heading']`` is adjusted for declination
(i.e., it is relative to True North).
- If ``dat.attrs['principal_heading']`` is set, it is
adjusted to account for the orientation of the new 'True'
earth coordinate system (i.e., calling set_declination on a
data object in the principal coordinate system, then calling
dat.rotate2('earth') will yield a data object in the new
'True' earth coordinate system)
"""
return set_declination(self.ds, declin, inplace)
[docs] def set_inst2head_rotmat(self, rotmat, inplace=True):
"""
Set the instrument to head rotation matrix for the Nortek ADV if it
hasn't already been set through a '.userdata.json' file.
Parameters
----------
rotmat : float
3x3 rotation matrix
inplace : bool
When True the existing data object is rotated. When False
a copy is returned that is rotated. Default = True
Returns
-------
ds : xarray.Dataset or None
Returns the rotated dataset **when ``inplace=False``**, otherwise
returns None.
Notes
-----
If the data object is in earth or principal coords, it is first
rotated to 'inst' before assigning inst2head_rotmat, it is then
rotated back to the coordinate system in which it was input. This
way the inst2head_rotmat gets applied correctly (in inst
coordinate system).
"""
return set_inst2head_rotmat(self.ds, rotmat, inplace)
[docs] def save(self, filename, **kwargs):
"""
Save the data object (underlying xarray dataset) as netCDF (.nc).
Parameters
----------
filename : str
Filename and/or path with the '.nc' extension
**kwargs : dict
These are passed directly to :func:`xarray.Dataset.to_netcdf`.
Notes
-----
See DOLfYN's :func:`save <dolfyn.io.api.save>` function for
additional details.
"""
save(self.ds, filename, **kwargs)
########
# Magic methods of the API
def __init__(self, ds, *args, **kwargs):
self.ds = ds
def __getitem__(self, key):
return self.ds[key]
def __contains__(self, val):
return val in self.ds
def __repr__(self, ):
time_string = '{:.2f} {} (started: {})'
if ('time' not in self or dt642epoch(self['time'][0]) < 1):
time_string = '-->No Time Information!<--'
else:
tm = self['time'][[0, -1]].values
dt = dt642date(tm[0])[0]
delta = (dt642epoch(tm[-1]) -
dt642epoch(tm[0])) / (3600 * 24) # days
if delta > 1:
units = 'days'
elif delta * 24 > 1:
units = 'hours'
delta *= 24
elif delta * 24 * 60 > 1:
delta *= 24 * 60
units = 'minutes'
else:
delta *= 24 * 3600
units = 'seconds'
try:
time_string = time_string.format(delta, units,
dt.strftime('%b %d, %Y %H:%M'))
except AttributeError:
time_string = '-->Error in time info<--'
p = self.ds.attrs
t_shape = self['time'].shape
if len(t_shape) > 1:
shape_string = '({} bins, {} pings @ {}Hz)'.format(
t_shape[0], t_shape, p.get('fs'))
else:
shape_string = '({} pings @ {}Hz)'.format(
t_shape[0], p.get('fs', '??'))
_header = ("<%s data object>: "
" %s %s\n"
" . %s\n"
" . %s-frame\n"
" . %s\n" %
(p.get('inst_type'),
self.ds.attrs['inst_make'], self.ds.attrs['inst_model'],
time_string,
p.get('coord_sys'),
shape_string))
_vars = ' Variables:\n'
# Specify which variable show up in this view here.
# * indicates a wildcard
# This list also sets the display order.
# Only the first 12 matches are displayed.
show_vars = ['time*', 'vel*', 'range', 'range_echo',
'orientmat', 'heading', 'pitch', 'roll',
'temp', 'press*', 'amp*', 'corr*',
'accel', 'angrt', 'mag', 'echo',
]
n = 0
for v in show_vars:
if n > 12:
break
if v.endswith('*'):
v = v[:-1] # Drop the '*'
for nm in self.variables:
if n > 12:
break
if nm.startswith(v):
n += 1
_vars += ' - {} {}\n'.format(nm, self.ds[nm].dims)
elif v in self.ds:
_vars += ' - {} {}\n'.format(v, self.ds[v].dims)
if n < len(self.variables):
_vars += ' ... and others (see `<obj>.variables`)\n'
return _header + _vars
######
# Duplicate valuable xarray properties here.
@property
def variables(self, ):
"""A sorted list of the variable names in the dataset."""
return sorted(self.ds.variables)
@property
def attrs(self, ):
"""The attributes in the dataset."""
return self.ds.attrs
@property
def coords(self, ):
"""The coordinates in the dataset."""
return self.ds.coords
######
# A bunch of DOLfYN specific properties
@property
def u(self,):
"""
The first velocity component.
This is simply a shortcut to self['vel'][0]. Therefore,
depending on the coordinate system of the data object
(self.attrs['coord_sys']), it is:
- beam: beam1
- inst: x
- earth: east
- principal: streamwise
"""
return self.ds['vel'][0].drop('dir')
@property
def v(self,):
"""
The second velocity component.
This is simply a shortcut to self['vel'][1]. Therefore,
depending on the coordinate system of the data object
(self.attrs['coord_sys']), it is:
- beam: beam2
- inst: y
- earth: north
- principal: cross-stream
"""
return self.ds['vel'][1].drop('dir')
@property
def w(self,):
"""
The third velocity component.
This is simply a shortcut to self['vel'][2]. Therefore,
depending on the coordinate system of the data object
(self.attrs['coord_sys']), it is:
- beam: beam3
- inst: z
- earth: up
- principal: up
"""
return self.ds['vel'][2].drop('dir')
@property
def U(self,):
"""Horizontal velocity as a complex quantity"""
return xr.DataArray(
(self.u + self.v * 1j).astype('complex64'),
attrs={'units': 'm s-1',
'long_name': 'Horizontal Water Velocity'})
@property
def U_mag(self,):
"""Horizontal velocity magnitude"""
return xr.DataArray(
np.abs(self.U).astype('float32'),
attrs={'units': 'm s-1',
'long_name': 'Water Speed',
'standard_name': 'sea_water_speed'})
@property
def U_dir(self,):
"""
Angle of horizontal velocity vector. Direction is 'to',
as opposed to 'from'. This function calculates angle as
"degrees CCW from X/East/streamwise" and then converts it to
"degrees CW from X/North/streamwise".
"""
def convert_to_CW(angle):
if self.ds.coord_sys == 'earth':
# Convert "deg CCW from East" to "deg CW from North" [0, 360]
angle = convert_degrees(angle, tidal_mode=False)
relative_to = self.ds.dir[1].values
else:
# Switch to clockwise and from [-180, 180] to [0, 360]
angle *= -1
angle[angle < 0] += 360
relative_to = self.ds.dir[0].values
return angle, relative_to
# Convert from radians to degrees
angle, rel = convert_to_CW(np.angle(self.U)*(180/np.pi))
return xr.DataArray(
angle.astype('float32'),
dims=self.U.dims,
coords=self.U.coords,
attrs={'units': 'degrees_CW_from_' + str(rel),
'long_name': 'Water Direction',
'standard_name': 'sea_water_to_direction'})
@property
def E_coh(self,):
"""
Coherent turbulent energy
Niel Kelley's 'coherent turbulence energy', which is the RMS
of the Reynold's stresses.
See: NREL Technical Report TP-500-52353
"""
E_coh = (self.upwp_**2 + self.upvp_**2 + self.vpwp_**2) ** (0.5)
return xr.DataArray(
E_coh.astype('float32'),
coords={'time': self.ds['stress_vec'].time},
dims=['time'],
attrs={'units': self.ds['stress_vec'].units,
'long_name': 'Coherent Turbulence Energy'})
@property
def I_tke(self, thresh=0):
"""
Turbulent kinetic energy intensity.
Ratio of sqrt(tke) to horizontal velocity magnitude.
"""
I_tke = np.ma.masked_where(self.U_mag < thresh,
np.sqrt(2 * self.tke) / self.U_mag)
return xr.DataArray(
I_tke.data.astype('float32'),
coords=self.U_mag.coords,
dims=self.U_mag.dims,
attrs={'units': '% [0,1]',
'long_name': 'TKE Intensity'})
@property
def I(self, thresh=0):
"""
Turbulence intensity.
Ratio of standard deviation of horizontal velocity
to horizontal velocity magnitude.
"""
I = np.ma.masked_where(self.U_mag < thresh,
self.ds['U_std'] / self.U_mag)
return xr.DataArray(
I.data.astype('float32'),
coords=self.U_mag.coords,
dims=self.U_mag.dims,
attrs={'units': '% [0,1]',
'long_name': 'Turbulence Intensity'})
@property
def tke(self,):
"""Turbulent kinetic energy (sum of the three components)
"""
tke = self.ds['tke_vec'].sum('tke') / 2
tke.name = 'TKE'
tke.attrs['units'] = self.ds['tke_vec'].units
tke.attrs['long_name'] = 'TKE'
tke.attrs['standard_name'] = 'specific_turbulent_kinetic_energy_of_sea_water'
return tke
@property
def upvp_(self,):
"""u'v'bar Reynolds stress"""
return self.ds['stress_vec'].sel(tau="upvp_").drop('tau')
@property
def upwp_(self,):
"""u'w'bar Reynolds stress"""
return self.ds['stress_vec'].sel(tau="upwp_").drop('tau')
@property
def vpwp_(self,):
"""v'w'bar Reynolds stress"""
return self.ds['stress_vec'].sel(tau="vpwp_").drop('tau')
@property
def upup_(self,):
"""u'u'bar component of the tke"""
return self.ds['tke_vec'].sel(tke="upup_").drop('tke')
@property
def vpvp_(self,):
"""v'v'bar component of the tke"""
return self.ds['tke_vec'].sel(tke="vpvp_").drop('tke')
@property
def wpwp_(self,):
"""w'w'bar component of the tke"""
return self.ds['tke_vec'].sel(tke="wpwp_").drop('tke')
[docs]class VelBinner(TimeBinner):
"""
This is the base binning (averaging) tool.
All DOLfYN binning tools derive from this base class.
Examples
========
The VelBinner class is used to compute averages and turbulence
statistics from 'raw' (not averaged) ADV or ADP measurements, for
example::
# First read or load some data.
rawdat = dolfyn.read_example('BenchFile01.ad2cp')
# Now initialize the averaging tool:
binner = dolfyn.VelBinner(n_bin=600, fs=rawdat.fs)
# This computes the basic averages
avg = binner.bin_average(rawdat)
"""
# This defines how cross-spectra and stresses are computed.
_cross_pairs = [(0, 1), (0, 2), (1, 2)]
tke = xr.DataArray(["upup_", "vpvp_", "wpwp_"],
dims=['tke'],
name='tke',
attrs={'units': '1',
'long_name': 'Turbulent Kinetic Energy Vector Components',
'coverage_content_type': 'coordinate'})
tau = xr.DataArray(["upvp_", "upwp_", "vpwp_"],
dims=['tau'],
name='tau',
attrs={'units': '1',
'long_name': 'Reynolds Stress Vector Components',
'coverage_content_type': 'coordinate'})
S = xr.DataArray(['Sxx', 'Syy', 'Szz'],
dims=['S'],
name='S',
attrs={'units': '1',
'long_name': 'Power Spectral Density Vector Components',
'coverage_content_type': 'coordinate'})
C = xr.DataArray(['Cxy', 'Cxz', 'Cyz'],
dims=['C'],
name='C',
attrs={'units': '1',
'long_name': 'Cross-Spectral Density Vector Components',
'coverage_content_type': 'coordinate'})
[docs] def bin_average(self, raw_ds, out_ds=None, names=None):
"""
Bin the dataset and calculate the ensemble averages of each
variable.
Parameters
----------
raw_ds : xarray.Dataset
The raw data structure to be binned
out_ds : xarray.Dataset
The bin'd (output) data object to which averaged data is added.
names : list of strings
The names of variables to be averaged. If `names` is None,
all data in `raw_ds` will be binned.
Returns
-------
out_ds : xarray.Dataset
The new (or updated when out_ds is not None) dataset
with the averages of all the variables in raw_ds.
Raises
------
AttributeError : when out_ds is supplied as input (not None)
and the values in out_ds.attrs are inconsistent with
raw_ds.attrs or the properties of this VelBinner (n_bin,
n_fft, fs, etc.)
Notes
-----
raw_ds.attrs are copied to out_ds.attrs. Inconsistencies
between the two (when out_ds is specified as input) raise an
AttributeError.
"""
out_ds = self._check_ds(raw_ds, out_ds)
if names is None:
names = raw_ds.data_vars
for ky in names:
# set up dimensions and coordinates for Dataset
dims_list = raw_ds[ky].dims
coords_dict = {}
for nm in dims_list:
if 'time' in nm:
coords_dict[nm] = self.mean(raw_ds[ky][nm].values)
else:
coords_dict[nm] = raw_ds[ky][nm].values
# create Dataset
if 'ensemble' not in ky:
try: # variables with time coordinate
out_ds[ky] = xr.DataArray(self.mean(raw_ds[ky].values),
coords=coords_dict,
dims=dims_list,
attrs=raw_ds[ky].attrs
).astype('float32')
except: # variables not needing averaging
pass
# Add standard deviation
std = self.standard_deviation(raw_ds.velds.U_mag.values)
out_ds['U_std'] = xr.DataArray(
std.astype('float32'),
dims=raw_ds.vel.dims[1:],
attrs={'units': 'm s-1',
'long_name': 'Water Velocity Standard Deviation'})
return out_ds
[docs] def bin_variance(self, raw_ds, out_ds=None, names=None, suffix='_var'):
"""
Bin the dataset and calculate the ensemble variances of each
variable. Complementary to `bin_average()`.
Parameters
----------
raw_ds : xarray.Dataset
The raw data structure to be binned.
out_ds : xarray.Dataset
The binned (output) dataset to which variance data is added,
nominally dataset output from `bin_average()`
names : list of strings
The names of variables of which to calculate variance. If
`names` is None, all data in `raw_ds` will be binned.
Returns
-------
out_ds : xarray.Dataset
The new (or updated when out_ds is not None) dataset
with the variance of all the variables in raw_ds.
Raises
------
AttributeError : when out_ds is supplied as input (not None)
and the values in out_ds.attrs are inconsistent with
raw_ds.attrs or the properties of this VelBinner (n_bin,
n_fft, fs, etc.)
Notes
-----
raw_ds.attrs are copied to out_ds.attrs. Inconsistencies
between the two (when out_ds is specified as input) raise an
AttributeError.
"""
out_ds = self._check_ds(raw_ds, out_ds)
if names is None:
names = raw_ds.data_vars
for ky in names:
# set up dimensions and coordinates for dataarray
dims_list = raw_ds[ky].dims
coords_dict = {}
for nm in dims_list:
if 'time' in nm:
coords_dict[nm] = self.mean(raw_ds[ky][nm].values)
else:
coords_dict[nm] = raw_ds[ky][nm].values
# create Dataset
if 'ensemble' not in ky:
try: # variables with time coordinate
out_ds[ky+suffix] = xr.DataArray(self.variance(raw_ds[ky].values),
coords=coords_dict,
dims=dims_list,
attrs=raw_ds[ky].attrs
).astype('float32')
except: # variables not needing averaging
pass
return out_ds
[docs] def autocovariance(self, veldat, n_bin=None):
"""
Calculate the auto-covariance of the raw-signal `veldat`
Parameters
----------
veldat : xarray.DataArray
The raw dataArray of which to calculate auto-covariance
n_bin : float
Number of data elements to use
Returns
-------
da : xarray.DataArray
The auto-covariance of veldat
Notes
-----
As opposed to cross-covariance, which returns the full
cross-covariance between two arrays, this function only
returns a quarter of the full auto-covariance. It computes the
auto-covariance over half of the range, then averages the two
sides (to return a 'quartered' covariance).
This has the advantage that the 0 index is actually zero-lag.
"""
indat = veldat.values
n_bin = self._parse_nbin(n_bin)
out = np.empty(self._outshape(indat.shape, n_bin=n_bin)[:-1] +
[int(n_bin // 4)], dtype=indat.dtype)
dt1 = self.reshape(indat, n_pad=n_bin / 2 - 2)
# Here we de-mean only on the 'valid' range:
dt1 = dt1 - dt1[..., :, int(n_bin // 4):
int(-n_bin // 4)].mean(-1)[..., None]
dt2 = self.demean(indat)
se = slice(int(n_bin // 4) - 1, None, 1)
sb = slice(int(n_bin // 4) - 1, None, -1)
for slc in slice1d_along_axis(dt1.shape, -1):
tmp = np.correlate(dt1[slc], dt2[slc], 'valid')
# The zero-padding in reshape means we compute coherence
# from one-sided time-series for first and last points.
if slc[-2] == 0:
out[slc] = tmp[se]
elif slc[-2] == dt2.shape[-2] - 1:
out[slc] = tmp[sb]
else:
# For the others we take the average of the two sides.
out[slc] = (tmp[se] + tmp[sb]) / 2
dims_list, coords_dict = self._new_coords(veldat)
# tack on new coordinate
dims_list.append('lag')
coords_dict['lag'] = np.arange(n_bin//4)
da = xr.DataArray(out.astype('float32'),
coords=coords_dict,
dims=dims_list,)
da['lag'].attrs['units'] = 'timestep'
return da
[docs] def turbulent_kinetic_energy(self, veldat, noise=None, detrend=True):
"""
Calculate the turbulent kinetic energy (TKE) (variances
of u,v,w).
Parameters
----------
veldat : xarray.DataArray
Velocity data array from ADV or single beam from ADCP.
The last dimension is assumed to be time.
noise : float or array-like
A vector of the noise levels of the velocity data with
the same first dimension as the velocity vector.
detrend : bool (default: False)
Detrend the velocity data (True), or simply de-mean it
(False), prior to computing tke. Note: the psd routines
use detrend, so if you want to have the same amount of
variance here as there use ``detrend=True``.
Returns
-------
tke_vec : xarray.DataArray
dataArray containing u'u'_, v'v'_ and w'w'_
"""
if 'xarray' in type(veldat).__module__:
vel = veldat.values
if 'xarray' in type(noise).__module__:
noise = noise.values
if len(np.shape(vel)) > 2:
raise ValueError("This function is only valid for calculating TKE using "
"velocity from an ADV or a single ADCP beam.")
# Calc TKE
if detrend:
out = np.nanmean(self.detrend(vel)**2, axis=-1)
else:
out = np.nanmean(self.demean(vel)**2, axis=-1)
if 'dir' in veldat.dims:
# Subtract noise
if noise is not None:
if np.shape(noise)[0] != 3:
raise Exception(
'Noise should have same first dimension as velocity')
out[0] -= noise[0] ** 2
out[1] -= noise[1] ** 2
out[2] -= noise[2] ** 2
# Set coords
dims = ['tke', 'time']
coords = {'tke': self.tke,
'time': self.mean(veldat.time.values)}
else:
# Subtract noise
if noise is not None:
if np.shape(noise) > np.shape(vel):
raise Exception(
'Noise should have same or fewer dimensions as velocity')
out -= noise ** 2
# Set coords
dims = veldat.dims
coords = {}
for nm in veldat.dims:
if 'time' in nm:
coords[nm] = self.mean(veldat[nm].values)
else:
coords[nm] = veldat[nm].values
return xr.DataArray(
out.astype('float32'),
dims=dims,
coords=coords,
attrs={'units': 'm2 s-2',
'long_name': 'TKE Vector',
'standard_name': 'specific_turbulent_kinetic_energy_of_sea_water'})
[docs] def power_spectral_density(self, veldat,
freq_units='rad/s',
fs=None,
window='hann',
noise=None,
n_bin=None, n_fft=None, n_pad=None,
step=None):
"""
Calculate the power spectral density of velocity.
Parameters
----------
veldat : xr.DataArray
The raw velocity data (of dims 'dir' and 'time').
freq_units : string
Frequency units of the returned spectra in either Hz or rad/s
(`f` or :math:`\\omega`)
fs : float (optional)
The sample rate. Default is `binner.fs`
window : string or array
Specify the window function.
Options: 1, None, 'hann', 'hamm'
noise : float or array-like
A vector of the noise levels of the velocity data with
the same first dimension as the velocity vector.
Default = 0.
n_bin : int (optional)
The bin-size. Default: from the binner.
n_fft : int (optional)
The fft size. Default: from the binner.
n_pad : int (optional)
The number of values to pad with zero. Default = 0.
step : int (optional)
Controls amount of overlap in fft. Default: the step size is
chosen to maximize data use, minimize nens, and have a
minimum of 50% overlap.
Returns
-------
psd : xarray.DataArray (3, M, N_FFT)
The spectra in the 'u', 'v', and 'w' directions.
"""
fs_in = self._parse_fs(fs)
n_fft = self._parse_nfft(n_fft)
if 'xarray' in type(veldat).__module__:
vel = veldat.values
if 'xarray' in type(noise).__module__:
noise = noise.values
if ('rad' not in freq_units) and ('Hz' not in freq_units):
raise ValueError("`freq_units` should be one of 'Hz' or 'rad/s'")
# Create frequency vector, also checks whether using f or omega
if 'rad' in freq_units:
fs = 2*np.pi*fs_in
freq_units = 'rad s-1'
units = 'm2 s-1 rad-1'
else:
fs = fs_in
freq_units = 'Hz'
units = 'm2 s-2 Hz-1'
freq = xr.DataArray(self._fft_freq(fs=fs_in, units=freq_units, n_fft=n_fft),
dims=['freq'],
name='freq',
attrs={'units': freq_units,
'long_name': 'FFT Frequency Vector',
'coverage_content_type': 'coordinate'}
).astype('float32')
# Spectra, if input is full velocity or a single array
if len(vel.shape) == 2:
assert vel.shape[0] == 3, "Function can only handle 1D or 3D arrays." \
" If ADCP data, please select a specific depth bin."
if (noise is not None) and (np.shape(noise)[0] != 3):
raise Exception(
'Noise should have same first dimension as velocity')
else:
noise = np.array([0, 0, 0])
out = np.empty(self._outshape_fft(vel[:3].shape, n_fft=n_fft, n_bin=n_bin),
dtype=np.float32)
for idx in range(3):
out[idx] = self._psd_base(vel[idx],
fs=fs,
noise=noise[idx],
window=window,
n_bin=n_bin,
n_pad=n_pad,
n_fft=n_fft,
step=step)
coords = {'S': self.S,
'time': self.mean(veldat['time'].values),
'freq': freq}
dims = ['S', 'time', 'freq']
else:
if (noise is not None) and (len(np.shape(noise)) > 1):
raise Exception(
'Noise should have same first dimension as velocity')
else:
noise = np.array(0)
out = self._psd_base(vel,
fs=fs,
noise=noise,
window=window,
n_bin=n_bin,
n_pad=n_pad,
n_fft=n_fft,
step=step)
coords = {veldat.dims[-1]: self.mean(veldat[veldat.dims[-1]].values),
'freq': freq}
dims = [veldat.dims[-1], 'freq']
return xr.DataArray(
out.astype('float32'),
coords=coords,
dims=dims,
attrs={'units': units,
'n_fft': n_fft,
'long_name': 'Power Spectral Density'})