Updates to spectral() behavior

pyleoclim/core/geoseries.py

@@ -713,11 +713,11 @@ def dashboard(self, figsize=[11, 8], gs=None, plt_kwargs=None, histplt_kwargs=No
         if 'method' in spectral_kwargs.keys():
             pass
         else:
-            spectral_kwargs.update({'method': 'lomb_scargle'})
-        if 'freq_method' in spectral_kwargs.keys():
+            spectral_kwargs.update({'method': 'lomb_scargle'}) # unneeded as it is already the default 
+        if 'freq' in spectral_kwargs.keys():
             pass
         else:
-            spectral_kwargs.update({'freq_method': 'lomb_scargle'})
+            spectral_kwargs.update({'freq': 'lomb_scargle'})
 
         ax['spec'] = fig.add_subplot(gs[-1, -2:])
         spectralfig_kwargs = {} if spectralfig_kwargs is None else spectralfig_kwargs.copy()

pyleoclim/core/lipdseries.py

@@ -871,11 +871,11 @@ def dashboard(self, figsize=[11, 8], plt_kwargs=None, histplt_kwargs=None, spect
             pass
         else:
             spectral_kwargs.update({'method': 'lomb_scargle'})
-        if 'freq_method' in spectral_kwargs.keys():
+        if 'freq' in spectral_kwargs.keys():
             pass
         else:
             if ensemble == False:
-                spectral_kwargs.update({'freq_method': 'lomb_scargle'})
+                spectral_kwargs.update({'freq': 'lomb_scargle'})
             elif ensemble == True:
                 pass
 

pyleoclim/core/multipleseries.py

@@ -1303,7 +1303,7 @@ def detrend(self,method='emd',**kwargs):
             ms.series_list[idx]=s
         return ms
 
-    def spectral(self, method='lomb_scargle', settings=None, mute_pbar=False, freq_method='log', 
+    def spectral(self, method='lomb_scargle', freq=None, settings=None, mute_pbar=False, 
                 freq_kwargs=None, label=None, verbose=False, scalogram_list=None):
         ''' Perform spectral analysis on the timeseries
 
@@ -1312,7 +1312,14 @@ def spectral(self, method='lomb_scargle', settings=None, mute_pbar=False, freq_m
 
         method : str; {'wwz', 'mtm', 'lomb_scargle', 'welch', 'periodogram', 'cwt'}
 
-        freq_method : str; {'log','scale', 'nfft', 'lomb_scargle', 'welch'}
+        freq : str or array, optional
+           Information to produce the frequency vector. 
+           This can be 'log','scale', 'nfft', 'lomb_scargle' or 'welch' or a NumPy array.
+           If a string, will use make_freq_vector with the specified frequency-generating method.
+           If an array, this will be passed directly to the spectral method.
+           If None (default), will use 'log' for WWZ and 'lomb_scargle' for Lomb-Scargle. 
+           This parameter is highly consequential for the WWZ and Lomb-Scargle methods, 
+           but is otherwise ignored, as other spectral methods generate their frequency vector internally.
 
         freq_kwargs : dict
 
@@ -1375,14 +1382,12 @@ def spectral(self, method='lomb_scargle', settings=None, mute_pbar=False, freq_m
 
         .. jupyter-execute::
 
-            import pyleoclim as pyleo
-            url = 'http://wiki.linked.earth/wiki/index.php/Special:WTLiPD?op=export&lipdid=MD982176.Stott.2004'
-            data = pyleo.Lipd(usr_path = url)
-            tslist = data.to_LipdSeriesList()
-            tslist = tslist[2:] # drop the first two series which only concerns age and depth
-            ms = pyleo.MultipleSeries(tslist)
-            ms_psd = ms.spectral()
-
+            soi = pyleo.utils.load_dataset('SOI')
+            nino = pyleo.utils.load_dataset('NINO3')
+            ms = soi & nino
+            ms_psd = ms.spectral(method='mtm')
+            ms_psd.plot()
+
         '''
         settings = {} if settings is None else settings.copy()
 
@@ -1395,20 +1400,20 @@ def spectral(self, method='lomb_scargle', settings=None, mute_pbar=False, freq_m
             #OR if the scalogram list is longer than the series list we use as many scalograms from the scalogram list as we need
             if scalogram_list_len >= series_len:
                 for idx, s in enumerate(tqdm(self.series_list, desc='Performing spectral analysis on individual series', position=0, leave=True, disable=mute_pbar)):
-                    psd_tmp = s.spectral(method=method, settings=settings, freq_method=freq_method, freq_kwargs=freq_kwargs, label=label, verbose=verbose,scalogram = scalogram_list.scalogram_list[idx])
+                    psd_tmp = s.spectral(method=method, settings=settings, freq=freq, freq_kwargs=freq_kwargs, label=label, verbose=verbose,scalogram = scalogram_list.scalogram_list[idx])
                     psd_list.append(psd_tmp)
             #If the scalogram list isn't as long as the series list, we re-use all the scalograms we can and then calculate the rest
             elif scalogram_list_len < series_len:
                 for idx, s in enumerate(tqdm(self.series_list, desc='Performing spectral analysis on individual series', position=0, leave=True, disable=mute_pbar)):
                     if idx < scalogram_list_len:
-                        psd_tmp = s.spectral(method=method, settings=settings, freq_method=freq_method, freq_kwargs=freq_kwargs, label=label, verbose=verbose,scalogram = scalogram_list.scalogram_list[idx])
+                        psd_tmp = s.spectral(method=method, settings=settings, freq=freq, freq_kwargs=freq_kwargs, label=label, verbose=verbose,scalogram = scalogram_list.scalogram_list[idx])
                         psd_list.append(psd_tmp)
                     else:
-                        psd_tmp = s.spectral(method=method, settings=settings, freq_method=freq_method, freq_kwargs=freq_kwargs, label=label, verbose=verbose)
+                        psd_tmp = s.spectral(method=method, settings=settings, freq=freq, freq_kwargs=freq_kwargs, label=label, verbose=verbose)
                         psd_list.append(psd_tmp)
         else:
             for s in tqdm(self.series_list, desc='Performing spectral analysis on individual series', position=0, leave=True, disable=mute_pbar):
-                psd_tmp = s.spectral(method=method, settings=settings, freq_method=freq_method, freq_kwargs=freq_kwargs, label=label, verbose=verbose)
+                psd_tmp = s.spectral(method=method, settings=settings, freq=freq, freq_kwargs=freq_kwargs, label=label, verbose=verbose)
                 psd_list.append(psd_tmp)
 
         psds = MultiplePSD(psd_list=psd_list)

pyleoclim/core/psds.py

@@ -249,14 +249,11 @@ def signif_test(self, method='ar1sim', number=None, seed=None, qs=[0.95],
         '''
         from ..core.surrogateseries import SurrogateSeries
 
-        if self.spec_method == 'wwz' and method == 'ar1asym':
+        if self.spec_method in ['wwz','lomb_scargle'] and method == 'ar1asym':
             raise ValueError('Asymptotic solution is not supported for the wwz method')
 
-        if self.spec_method == 'lomb_scargle' and method == 'ar1asym':
-            raise ValueError('Asymptotic solution is not supported for the Lomb-Scargle method')
-
         if method not in ['ar1sim', 'uar1','ar1asym']:
-                raise ValueError("The available methods are 'ar1sim' and 'ar1asym'")
+                raise ValueError("The available methods are 'ar1sim', 'uar1' and 'ar1asym'")
 
         if method in ['ar1sim', 'uar1']:
             signif_scals = None
@@ -310,7 +307,7 @@ def signif_test(self, method='ar1sim', number=None, seed=None, qs=[0.95],
                                                        self.spec_args['param'],
                                                        qs=qs, **settings)
             else:
-                # hard code Mortlet values to obtain the spectrum
+                # hard code Morlet values to obtain the spectrum
                 param = 6
                 fourier_factor = 4 * np.pi / (param + np.sqrt(2 + param**2))
                 scale = 1/(fourier_factor*self.frequency)

pyleoclim/core/series.py

@@ -2757,20 +2757,28 @@ def detrend(self, method='emd', keep_log=False, preserve_mean = False, **kwargs)
             new.log += ({len(new.log): 'detrend','method': method, 'args': kwargs, 'previous_trend': trend},)
         return new
 
-    def spectral(self, method='lomb_scargle', freq_method='log', freq_kwargs=None, settings=None, label=None, scalogram=None, verbose=False):
+    def spectral(self, method='lomb_scargle', freq=None, freq_kwargs=None, settings=None, label=None, scalogram=None, verbose=False):
         ''' Perform spectral analysis on the timeseries
 
         Parameters
         ----------
 
         method : str;
             {'wwz', 'mtm', 'lomb_scargle', 'welch', 'periodogram', 'cwt'}
-
-        freq_method : str
-            {'log','scale', 'nfft', 'lomb_scargle', 'welch'}
-
+            Default is Lomb-Scargle, because it can handle unevenly spaced series, and is fast. 
+            However, for evenly spaced data mtm would almost surely be a better choice. 
+
+        freq : str or array, optional
+           Information to produce the frequency vector. 
+           This can be 'log','scale', 'nfft', 'lomb_scargle' or 'welch' or a NumPy array.
+           If a string, will use make_freq_vector with the specified frequency-generating method.
+           If an array, this will be passed directly to the spectral method.
+           If None (default), will use 'log' for WWZ and 'lomb_scargle' for Lomb-Scargle. 
+           This parameter is highly consequential for the WWZ and Lomb-Scargle methods, 
+           but is otherwise ignored, as other spectral methods generate their frequency vector internally.
+
         freq_kwargs : dict
-            Arguments for frequency vector
+            Arguments for frequency vector 
 
         settings : dict
             Arguments for the specific spectral method
@@ -2829,28 +2837,33 @@ def spectral(self, method='lomb_scargle', freq_method='log', freq_kwargs=None, s
         .. jupyter-execute::
 
             psd_ls = ts_std.spectral(method='lomb_scargle')
-            psd_ls_signif = psd_ls.signif_test(number=20) #in practice, need more AR1 simulations
+            psd_ls_signif = psd_ls.signif_test(number=20) #in practice, need more AR(1) simulations
             fig, ax = psd_ls_signif.plot(title='PSD using Lomb-Scargle method')
 
         We may pass in method-specific arguments via "settings", which is a dictionary.
         For instance, to adjust the number of overlapping segment for Lomb-Scargle, we may specify the method-specific argument "n50";
-        to adjust the frequency vector, we may modify the "freq_method" or modify the method-specific argument "freq".
+        to adjust the frequency vector, we may modify the "freq" or modify the method-specific argument "freq".
 
         .. jupyter-execute::
 
             import numpy as np
             psd_LS_n50 = ts_std.spectral(method='lomb_scargle', settings={'n50': 4})  # c=1e-2 yields lower frequency resolution
-            psd_LS_freq = ts_std.spectral(method='lomb_scargle', settings={'freq': np.linspace(1/20, 1/0.2, 51)})
-            psd_LS_LS = ts_std.spectral(method='lomb_scargle', freq_method='lomb_scargle')  # with frequency vector generated using REDFIT method
+            psd_LS_freq = ts_std.spectral(method='lomb_scargle',freq=np.linspace(1/20, 1/0.2, 51))
+            psd_LS_log = ts_std.spectral(method='lomb_scargle', freq='log')  # with frequency vector generated using REDFIT method
             fig, ax = psd_LS_n50.plot(
-                title='PSD using Lomb-Scargle method with 4 overlapping segments',
+                title='Lomb-Scargle PSD with 4 overlapping segments',
                 label='settings={"n50": 4}')
-            psd_ls.plot(ax=ax, label='settings={"n50": 3}', marker='o')
+            psd_ls.plot(ax=ax, label='settings={"n50": 3}', marker='o', alpha=.2)
 
             fig, ax = psd_LS_freq.plot(
-                title='PSD using Lomb-Scargle method with different frequency vectors',
+                title='Lomb-Scargle PSD with specified frequency vector',
                 label='freq=np.linspace(1/20, 1/0.2, 51)', marker='o')
-            psd_ls.plot(ax=ax, label='freq_method="log"', marker='o')
+            psd_ls.plot(ax=ax, label='Lomb-Scargle frequency vector', marker='o', alpha=.2)
+
+            fig, ax = psd_LS_log.plot(
+                title='Lomb-Scargle PSD with different frequency vectors',
+                label='log frequency method', marker='o')
+            psd_ls.plot(ax=ax, label='Lomb-Scargle frequency vector', marker='o', alpha=.2)
 
         You may notice the differences in the PSD curves regarding smoothness and the locations of the analyzed period points.
 
@@ -2879,7 +2892,7 @@ def spectral(self, method='lomb_scargle', freq_method='log', freq_kwargs=None, s
 
             ts_interp = ts_std.interp()
             psd_perio = ts_interp.spectral(method='periodogram')
-            psd_perio_signif = psd_perio.signif_test(number=20, method='ar1sim') #in practice, need more AR1 simulations
+            psd_perio_signif = psd_perio.signif_test(number=20, method='ar1sim') #in practice, need more AR(1) simulations
             fig, ax = psd_perio_signif.plot(title='PSD using Periodogram method')
 
 
@@ -2888,7 +2901,7 @@ def spectral(self, method='lomb_scargle', freq_method='log', freq_kwargs=None, s
         .. jupyter-execute::
 
             psd_welch = ts_interp.spectral(method='welch')
-            psd_welch_signif = psd_welch.signif_test(number=20, method='ar1sim') #in practice, need more AR1 simulations
+            psd_welch_signif = psd_welch.signif_test(number=20, method='ar1sim') #in practice, need more AR(1) simulations
             fig, ax = psd_welch_signif.plot(title='PSD using Welch method')
 
 
@@ -2897,7 +2910,7 @@ def spectral(self, method='lomb_scargle', freq_method='log', freq_kwargs=None, s
         .. jupyter-execute::
 
             psd_mtm = ts_interp.spectral(method='mtm', label='MTM, NW=4')
-            psd_mtm_signif = psd_mtm.signif_test(number=20, method='ar1sim') #in practice, need more AR1 simulations
+            psd_mtm_signif = psd_mtm.signif_test(number=20, method='ar1sim') #in practice, need more AR(1) simulations
             fig, ax = psd_mtm_signif.plot(title='PSD using the multitaper method')
 
 
@@ -2909,7 +2922,6 @@ def spectral(self, method='lomb_scargle', freq_method='log', freq_kwargs=None, s
             psd_mtm2 = ts_interp.spectral(method='mtm', settings={'NW':2}, label='MTM, NW=2')
             fig, ax = psd_mtm2.plot(title='MTM with NW=2')
 
-
         - Continuous Wavelet Transform
 
         .. jupyter-execute::
@@ -2919,7 +2931,6 @@ def spectral(self, method='lomb_scargle', freq_method='log', freq_kwargs=None, s
             psd_cwt_signif = psd_cwt.signif_test(number=20)
             fig, ax = psd_cwt_signif.plot(title='PSD using the CWT method')
 
-
         '''
         if not verbose:
             warnings.simplefilter('ignore')
@@ -2935,15 +2946,35 @@ def spectral(self, method='lomb_scargle', freq_method='log', freq_kwargs=None, s
         }
         args = {}
         freq_kwargs = {} if freq_kwargs is None else freq_kwargs.copy()
-        freq = specutils.make_freq_vector(self.time, method=freq_method, **freq_kwargs)
-
-        args['wwz'] = {'freq': freq}
-        args['cwt'] = {'freq': freq}
-        args['mtm'] = {}
-        args['lomb_scargle'] = {'freq': freq}
-        args['welch'] = {}
-        args['periodogram'] = {}
-        args[method].update(settings)
+
+        if 'freq' in settings.keys():
+            freq_vec = settings['freq']
+        else:
+            if freq is None: # assign the frequency method automatically based on context
+                if method in ['wwz','cwt']:
+                    freq_vec = specutils.make_freq_vector(self.time, method='log', **freq_kwargs) 
+                elif method=='lomb_scargle':
+                    freq_vec = specutils.make_freq_vector(self.time, method='lomb_scargle', **freq_kwargs) 
+                else:
+                    warnings.warn(f'freq argument ignored; it is determined automatically by {method}')
+            elif isinstance(freq, str):   # apply the specified method     
+                freq_vec = specutils.make_freq_vector(self.time, method=freq, **freq_kwargs) 
+            elif isinstance(freq,np.ndarray): # use the specified vector if dimensions check out
+                freq_vec = np.squeeze(freq)
+                if freq.ndim != 1:
+                    raise ValueError("freq should be a 1-dimensional array")
+
+        # pass frequency
+        if method in ['wwz','cwt','lomb_scargle']:
+            args[method] = {'freq': freq_vec}
+        else:
+            args[method] = {}
+
+        # args['mtm'] = {'freq': freq_vec}
+        # args['welch'] = {}
+        # args['periodogram'] = {}
+
+        args[method].update(settings) # this overrides the frequency vector
 
         if method == 'wwz' and scalogram is not None:
             args['wwz'].update(
@@ -3006,9 +3037,9 @@ def wavelet(self, method='cwt', settings=None, freq_method='log', freq_kwargs=No
                 is appropriate for unevenly-spaced series.
             Default is cwt, returning an error if the Series is unevenly-spaced.
 
-        freq_method : str
-            {'log', 'scale', 'nfft', 'lomb_scargle', 'welch'}
-
+        freq_method : str, optional
+            Can be one of 'log', 'scale', 'nfft', 'lomb_scargle', 'welch'. 
+            
         freq_kwargs : dict
             Arguments for the frequency vector