Clean up docstrings

cesium/features/lomb_scargle.py

@@ -4,9 +4,10 @@
 
 
 def lomb_scargle_model(time, signal, error, sys_err=0.05, nharm=8, nfreq=3, tone_control=5.0, opt_normalize=False):
-    """Simultaneous fit of a sum of sinusoids by weighted least squares:
-           y(t) = Sum_k Ck*t^k + Sum_i Sum_j A_ij sin(2*pi*j*fi*(t-t0)+phi_j),
-           i=[1,nfreq], j=[1,nharm]
+    """Simultaneous fit of a sum of sinusoids by weighted least squares.
+
+    y(t) = Sum_k Ck*t^k + Sum_i Sum_j A_ij sin(2*pi*j*fi*(t-t0)+phi_j),
+    i=[1,nfreq], j=[1,nharm]
 
     Parameters
     ----------
@@ -19,11 +20,20 @@ def lomb_scargle_model(time, signal, error, sys_err=0.05, nharm=8, nfreq=3, tone
     error : array_like
         Array containing measurement error values.
 
-    nharm : int
-        Number of harmonics to fit for each frequency.
+    sys_err : float, optional
+        Defaults to 0.05
+
+    nharm : int, optional
+        Number of harmonics to fit for each frequency. Defaults to 8.
+
+    nfreq : int, optional
+        Number of frequencies to fit. Defaults to 3.
 
-    nfreq : int
-        Number of frequencies to fit.
+    tone_control : float, optional
+        Defaults to 5.0
+
+    opt_normalize : boolean, optional
+        Defaults to False.
 
     Returns
     -------
@@ -94,17 +104,17 @@ def lomb_scargle_model(time, signal, error, sys_err=0.05, nharm=8, nfreq=3, tone
 
 
 def rescale_lomb_model(norm_model, mmean, mscale):
-    """ Rescale Lomb-Scargle model if compiled on normalized data.
+    """Rescale Lomb-Scargle model if compiled on normalized data.
 
     Parameters
     ----------
         norm_model : dict
             Lomb-Scargle model computed on normalized data
 
-        mmean      : float64, 
+        mmean : float64
             mean of input_signal
 
-        mscale     : float64, 
+        mscale : float64
             standard-deviation of input_signal
 
     Returns
@@ -148,7 +158,8 @@ def fit_lomb_scargle(time, signal, error, f0, df, numf, nharm=8, psdmin=6., detr
     lomb_scargle_model in order to produce a fit with multiple distinct
     frequencies.
 
-    Inputs:
+    Parameters
+    ----------
     time : array_like
         Array containing time values.
 
@@ -306,21 +317,21 @@ def fit_lomb_scargle(time, signal, error, f0, df, numf, nharm=8, psdmin=6., detr
     vA0, vB0 = err2[0:nharm], err2[nharm:]
     covA0B0 = hat_hat[(ii, nharm + ii)]
 
-    vmodl = vcn/s0 + np.dot((hat_matr/wth0).T, np.dot(hat_hat, hat_matr/wth0))
-    vmodl0 = vcn/s0 + np.dot((hat_matr0/wth0).T, np.dot(hat_hat, hat_matr0/wth0))
+    vmodl = vcn / s0 + np.dot((hat_matr / wth0).T, np.dot(hat_hat, hat_matr / wth0))
+    vmodl0 = vcn / s0 + np.dot((hat_matr0 / wth0).T, np.dot(hat_hat, hat_matr0 / wth0))
     out_dict['model_error'] = np.sqrt(np.diag(vmodl))
     out_dict['trend_error'] = np.sqrt(np.diag(vmodl0))
 
     amp = np.sqrt(A0**2 + B0**2)
     damp = np.sqrt(A0**2 * vA0 + B0**2 * vB0 + 2. * A0 * B0 * covA0B0) / amp
     phase = np.arctan2(B0, A0)
-    rel_phase = phase - phase[0]*(1.+ii)
+    rel_phase = phase - phase[0] * (1. + ii)
     rel_phase = np.arctan2(np.sin(rel_phase), np.cos(rel_phase))
-    dphase = 0.*rel_phase
+    dphase = 0. * rel_phase
     for i in range(nharm - 1):
         j = i + 1
         v = np.array([-A0[0] * (1. + j) / amp[0]**2, B0[0] * (1. + j) / amp[0]**2, A0[j] / amp[j]**2, -B0[j] / amp[j]**2])
-        jj = np.array([0, nharm, j, j+nharm])
+        jj = np.array([0, nharm, j, j + nharm])
         m = hat_hat[np.ix_(jj, jj)]
         dphase[j] = np.sqrt(np.dot(np.dot(v, m), v))
 
@@ -334,39 +345,39 @@ def fit_lomb_scargle(time, signal, error, f0, df, numf, nharm=8, psdmin=6., detr
     out_dict['trend_coef'] = coef / ncp
     out_dict['y_offset'] = out_dict['trend_coef'][0] - cn0
 
-    prob = stats.f.sf(0.5 * (ntime - 1. - detrend_order) * (1. -out_dict['chi2'] / out_dict['chi0']), 2, ntime - 1 - detrend_order)
+    prob = stats.f.sf(0.5 * (ntime - 1. - detrend_order) * (1. - out_dict['chi2'] / out_dict['chi0']), 2, ntime - 1 - detrend_order)
     out_dict['signif'] = lprob2sigma(np.log(prob))
 
     return out_dict
 
 
 def get_lomb_frequency(lomb_model, i):
     """Get the ith frequency from a fitted Lomb-Scargle model."""
-    return lomb_model['freq_fits'][i-1]['freq']
+    return lomb_model['freq_fits'][i - 1]['freq']
 
 
 def get_lomb_amplitude(lomb_model, i, j):
     """
     Get the amplitude of the jth harmonic of the ith frequency from a fitted
     Lomb-Scargle model.
     """
-    return lomb_model['freq_fits'][i-1]['amplitude'][j-1]
+    return lomb_model['freq_fits'][i - 1]['amplitude'][j - 1]
 
 
 def get_lomb_rel_phase(lomb_model, i, j):
     """
     Get the relative phase of the jth harmonic of the ith frequency from a
     fitted Lomb-Scargle model.
     """
-    return lomb_model['freq_fits'][i-1]['rel_phase'][j-1]
+    return lomb_model['freq_fits'][i - 1]['rel_phase'][j - 1]
 
 
 def get_lomb_amplitude_ratio(lomb_model, i):
     """
     Get the ratio of the amplitudes of the first harmonic for the ith and first
     frequencies from a fitted Lomb-Scargle model.
     """
-    return (lomb_model['freq_fits'][i-1]['amplitude'][0] /
+    return (lomb_model['freq_fits'][i - 1]['amplitude'][0] /
             lomb_model['freq_fits'][0]['amplitude'][0])
 
 
@@ -375,7 +386,7 @@ def get_lomb_frequency_ratio(lomb_model, i):
     Get the ratio of the ith and first frequencies from a fitted Lomb-Scargle
     model.
     """
-    return (lomb_model['freq_fits'][i-1]['freq'] /
+    return (lomb_model['freq_fits'][i - 1]['freq'] /
             lomb_model['freq_fits'][0]['freq'])