Merge branch 'dev' into 'master'

Dev

Closes #234

See merge request it/syncopy!90

syncopy/examples/ex_specest.py

@@ -33,9 +33,24 @@
 
 if __name__ == "__main__":
 
+    data = spy.load('/mnt/hpx/it/dev/testdata.spy/')
+
+    cfg = spy.get_defaults(spy.freqanalysis)
+    cfg.method = 'mtmfft'
+    cfg.taper = 'dpss'
+    cfg.output = 'pow'
+    cfg.tapsmofrq = 20
+    cfg.keeptrials = False
+    cfg.keeptapers = False
+    cfg.pad = 'nextpow2'
+    cfg.select = {"toilim": [-0.25, 0]}
+
+    baselineSpectrum = spy.freqanalysis(cfg, data)
+
+    sys.exit()
+
     client = spy.esi_cluster_setup(n_jobs=10, partition="DEV", mem_per_job="2GB")
 
-    data = spy.load('/mnt/hpx/it/dev/testdata.spy/')
 
     cfg = spy.get_defaults(spy.freqanalysis)
     cfg.method = 'mtmfft'

syncopy/specest/freqanalysis.py

@@ -4,7 +4,7 @@
 # 
 # Created: 2019-01-22 09:07:47
 # Last modified by: Stefan Fuertinger [[email protected]]
-# Last modification time: <2019-10-09 12:09:31>
+# Last modification time: <2019-10-10 11:10:29>
 
 # Builtin/3rd party package imports
 import numpy as np
@@ -217,7 +217,7 @@ def freqanalysis(data, method='mtmfft', output='fourier',
     # Construct array of maximally attainable frequencies
     minTrialLength = minSampleNum/data.samplerate
     nFreq = int(np.floor(minSampleNum / 2) + 1)
-    freqs = np.arange(nFreq)
+    freqs = np.linspace(0, data.samplerate / 2, nFreq)
 
     # Match desired frequencies as close as possible to actually attainable freqs
     if foi is not None:
@@ -230,7 +230,12 @@ def freqanalysis(data, method='mtmfft', output='fourier',
         foi.sort()
         foi = foi[foi <= freqs.max()]
         foi = foi[foi >= freqs.min()]
-        foi = freqs[np.unique(np.searchsorted(freqs, foi, side="right") - 1)]
+        fidx = np.searchsorted(freqs, foi, side="left")
+        for k, fid in enumerate(fidx):
+            if np.abs(freqs[fid - 1] - foi[k]) < np.abs(freqs[fid] - foi[k]):
+                fidx[k] = fid -1
+        fidx = np.unique(fidx)
+        foi = freqs[fidx]
     else:
         foi = freqs
 
@@ -312,7 +317,8 @@ def freqanalysis(data, method='mtmfft', output='fourier',
         log_dct["nTaper"] = nTaper
 
         # Set up compute-kernel
-        specestMethod = MultiTaperFFT(nTaper=nTaper, 
+        specestMethod = MultiTaperFFT(1 / data.samplerate,
+                                      nTaper=nTaper, 
                                       timeAxis=timeAxis, 
                                       taper=taper, 
                                       taperopt=taperopt,

syncopy/specest/mtmfft.py

@@ -4,7 +4,7 @@
 # 
 # Created: 2019-09-02 14:25:34
 # Last modified by: Stefan Fuertinger [[email protected]]
-# Last modification time: <2019-10-09 12:12:19>
+# Last modification time: <2019-10-10 11:11:12>
 
 # Builtin/3rd party package imports
 import numpy as np
@@ -19,7 +19,7 @@
 
 # Local workhorse that performs the computational heavy lifting
 @unwrap_io
-def mtmfft(trl_dat, nTaper=1, timeAxis=0,
+def mtmfft(trl_dat, dt, nTaper=1, timeAxis=0,
            taper=spwin.hann, taperopt={}, tapsmofrq=None,
            pad="nextpow2", padtype="zero", padlength=None, foi=None,
            keeptapers=True, polyorder=None, output_fmt="pow",
@@ -30,6 +30,8 @@ def mtmfft(trl_dat, nTaper=1, timeAxis=0,
     ----------
     trl_dat : 2D :class:`numpy.ndarray`
         Multi-channel uniformly sampled time-series 
+    dt : float
+        sampling interval (between 0 and 1)
     nTaper : int
         number of filter windows to use
     timeAxis : int
@@ -88,16 +90,17 @@ def mtmfft(trl_dat, nTaper=1, timeAxis=0,
     nFreq = int(np.floor(nSamples / 2) + 1)
     fidx = slice(None)
     if foi is not None:
-        freqs = np.arange(nFreq)
+        freqs = np.linspace(0, 1 /(2 * dt), nFreq)
         foi = foi[foi <= freqs.max()]
         foi = foi[foi >= freqs.min()]
-        fidx = np.searchsorted(freqs, foi, side="right") - 1
+        fidx = np.searchsorted(freqs, foi, side="left")
         for k, fid in enumerate(fidx):
             if np.abs(freqs[fid - 1] - foi[k]) < np.abs(freqs[fid] - foi[k]):
                 fidx[k] = fid -1
+        fidx = np.unique(fidx)
         nFreq = fidx.size
     outShape = (1, max(1, nTaper * keeptapers), nFreq, nChannels)
-
+    
     # For initialization of computational routine, just return output shape and dtype
     if noCompute:
         return outShape, freq.spectralDTypes[output_fmt]

syncopy/statistics/timelockanalysis.py

@@ -9,7 +9,7 @@
 import os
 import numpy as np
 import syncopy as spy
-from tqdm import tqdm
+from tqdm.auto import tqdm
 from syncopy.shared.parsers import data_parser
 import syncopy as spy