Cleanup

docs/ADCP_Example.ipynb

@@ -3962,7 +3962,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "TKE esimations are generally more complete than those of dissipation rates because they are found directly from the along-beam velocity measurements. Missing TKE estimations exist whenever the noise calculated by the function `doppler_noise_level` is greater than the calculated TKE, as TKE can't be less than zero. Noise levels are affected by the instrument's processor and working frequency, water waves and other sources of \"interference\", instrument motion, current speed, intricacies in the spectra calculation, the ability to see the noise floor in the spectra, etc.\n",
+        "TKE esimations are generally more complete than those of dissipation rates because they are found directly from the along-beam velocity measurements. Missing TKE estimations exist whenever the noise calculated by the function `calc_doppler_noise` is greater than the calculated TKE, as TKE can't be less than zero. Noise levels are affected by the instrument's processor and working frequency, water waves and other sources of \"interference\", instrument motion, current speed, intricacies in the spectra calculation, the ability to see the noise floor in the spectra, etc.\n",
         "\n",
         "You may also note that high TI doesn't always correlate with high TKE. TI is the ratio of flow speed standard devation to the mean, which is naturally lower when flow speeds are higher. When flow speeds are higher, they also have greater kinetic energy and thereby greater TKE.\n",
         "\n",

dolfyn/adp/turbulence.py

@@ -637,8 +637,9 @@ def calc_dissipation_LT83(self, psd, U_mag, freq_range=[0.2, 0.4], noise=None):
           The range over which to integrate/average the spectrum, in units
           of the psd frequency vector (Hz or rad/s)
         noise : float or array-like
-          A vector of the noise levels of the velocity data with
-          the same first dimension as the velocity vector (time).
+          Instrument noise level in same units as velocity. Typically
+          found from `adp.turbulence.calc_doppler_noise`. 
+          Default: None.
 
         Returns
         -------
@@ -673,6 +674,8 @@ def calc_dissipation_LT83(self, psd, U_mag, freq_range=[0.2, 0.4], noise=None):
             raise Exception('PSD should be 2-dimensional (time, frequency)')
         if len(U_mag.shape) != 1:
             raise Exception('U_mag should be 1-dimensional (time)')
+        if not hasattr(freq_range, "__iter__") or len(freq_range) != 2:
+            raise ValueError("`freq_range` must be an iterable of length 2.")
         if noise is not None:
             if np.shape(noise)[0] != np.shape(psd)[0]:
                 raise Exception(

dolfyn/adv/turbulence.py

@@ -21,8 +21,10 @@ class ADVBinner(VelBinner):
       The length of the FFT for computing spectra (must be <= n_bin)
     n_fft_coh : int (optional, default: `n_fft_coh`=`n_fft`)
       Number of data points to use for coherence and cross-spectra ffts
-    noise : float, list or numpy.ndarray
-      Instrument's doppler noise in same units as velocity
+    noise : float or array-like
+      Instrument noise level in same units as velocity. Typically
+      found from `adv.turbulence.calc_doppler_noise`. 
+      Default: None.
     """
 
     def __call__(self, ds, freq_units='rad/s', window='hann'):
@@ -309,8 +311,9 @@ def calc_epsilon_LT83(self, psd, U_mag, freq_range=[6.28, 12.57], noise=None):
           The range over which to integrate/average the spectrum, in units 
           of the psd frequency vector (Hz or rad/s)
         noise : float or array-like
-          A vector of the noise levels of the velocity data with
-          the same first dimension as the velocity vector.
+          Instrument noise level in same units as velocity. Typically
+          found from `adv.turbulence.calc_doppler_noise`. 
+          Default: None.
 
         Returns
         -------
@@ -344,6 +347,9 @@ def calc_epsilon_LT83(self, psd, U_mag, freq_range=[6.28, 12.57], noise=None):
         # Ensure time has been averaged
         if len(psd.time) != len(U_mag.time):
             raise Exception("`U_mag` should be from ensembled-averaged dataset")
+        if not hasattr(freq_range, "__iter__") or len(freq_range) != 2:
+            raise ValueError("`freq_range` must be an iterable of length 2.")
+
         if noise is not None:
             if np.shape(noise)[0] != 3:
                 raise Exception(

dolfyn/tests/test_analysis.py

@@ -140,13 +140,14 @@ def test_adcp_turbulence(make_data=False):
     bnr = apm.ADPBinner(n_bin=20.0, fs=dat.fs, diff_style='centered')
     tdat = bnr.do_avg(dat)
 
-    tdat['dudz'] = bnr.calc_dudz(tdat.vel)
-    tdat['dvdz'] = bnr.calc_dvdz(tdat.vel)
-    tdat['dwdz'] = bnr.calc_dwdz(tdat.vel)
-    tdat['tau2'] = bnr.calc_shear2(tdat.vel)
+    tdat['dudz'] = bnr.calc_dudz(tdat["vel"])
+    tdat['dvdz'] = bnr.calc_dvdz(tdat["vel"])
+    tdat['dwdz'] = bnr.calc_dwdz(tdat["vel"])
+    tdat['tau2'] = bnr.calc_shear2(tdat["vel"])
     tdat['I'] = tdat.velds.I
     tdat['ti'] = bnr.calc_ti(dat.velds.U_mag, detrend=False)
     dat.velds.rotate2('beam')
+
     tdat['psd'] = bnr.calc_psd(dat['vel'].isel(
         dir=2, range=len(dat.range)//2), freq_units='Hz')
     tdat['noise'] = bnr.calc_doppler_noise(tdat['psd'], pct_fN=0.8)
@@ -156,6 +157,7 @@ def test_adcp_turbulence(make_data=False):
         dat, noise=tdat['noise'], orientation='up', beam_angle=25, tke_only=False)
     tdat['tke'] = bnr.calc_total_tke(
         dat, noise=tdat['noise'], orientation='up', beam_angle=25)
+    tdat['ti_noise'] = bnr.calc_ti(dat.velds.U_mag, detrend=False, noise=tdat['noise'])
     # This is "negative" for this code check
     tdat['wpwp'] = bnr.calc_tke(dat['vel_b5'], noise=tdat['noise'])
     tdat['dissipation_rate_LT83'] = bnr.calc_dissipation_LT83(

dolfyn/tools/psd.py

@@ -30,14 +30,23 @@ def psd_freq(nfft, fs, full=False):
 
 
 def _getwindow(window, nfft):
-    if 'hann' in window:
-        window = np.hanning(nfft)
-    elif 'hamm' in window:
-        window = np.hamming(nfft)
-    elif window is None or np.sum(window == 1):
+    if window is None:
         window = np.ones(nfft)
-    if len(window) != nfft:
-        raise ValueError("Custom window length must be equal to nfft")
+    elif isinstance(window, (int, float)) and window == 1:
+        window = np.ones(nfft)
+    elif isinstance(window, str):
+        if "hann" in window:
+            window = np.hanning(nfft)
+        elif "hamm" in window:
+            window = np.hamming(nfft)
+        else:
+            raise ValueError("Unsupported window type: {}".format(window))
+    elif isinstance(window, np.ndarray):
+        if len(window) != nfft:
+            raise ValueError("Custom window length must be equal to nfft")
+    else:
+        raise ValueError("Invalid window parameter")
+
     return window
 
 

dolfyn/velocity.py

@@ -938,6 +938,8 @@ def calc_ti(self, U_mag, noise=0, thresh=0, detrend=False):
 
         if 'xarray' in type(U_mag).__module__:
             U = U_mag.values
+        if "xarray" in type(noise).__module__:
+            noise = noise.values
 
         if detrend:
             up = self.detrend(U)
@@ -1064,7 +1066,8 @@ def calc_psd(self, veldat,
           Specify the window function.
           Options: 1, None, 'hann', 'hamm'
         noise : numeric
-          The noise level in the same units as velocity
+          Instrument noise level in same units as velocity. 
+          Default: 0 (ADCP) or [0, 0, 0] (ADV).
         n_bin : int (optional)
           The bin-size (default: from the binner).
         n_fft : int (optional)
@@ -1088,6 +1091,8 @@ def calc_psd(self, veldat,
             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: