Improved treatment of time shifts

.github/workflows/python-app.yaml

@@ -41,6 +41,7 @@ jobs:
          python tests/test_grid_search_mt.py --no_figures
          python tests/test_grid_search_mt_depth.py --no_figures
          python tests/test_greens_SPECFEM3D_SAC.py --no_figures
+         python tests/test_time_shifts.py --no_figures
 
     # unfortunately, these Conda installation tests exceed the resource limits
     # for GitHub workflows

docs/install/issues.rst

@@ -44,7 +44,7 @@ MTUQ installation on Apple M1 and Apple M2 Macs
 
 Installation on Apple M1 and Apple M2 Macs is now possible using the default installation procedure.
 
-For older versions of MTUQ, a modified installation procedure may stil be necessary.  For more information, please see:
+For older versions of MTUQ, a modified installation procedure may still be necessary.  For more information, please see:
 
 `MTUQ installation on ARM64 systems <https://uafgeotools.github.io/mtuq/install/arm64.html>`_
 

mtuq/dataset.py

@@ -207,6 +207,23 @@ def get_stations(self):
         return stations
 
 
+    def get_stats(self):
+        """ Returns trace metadata in nested lists
+
+        .. note ::
+
+          For Datasets created using ``mtuq.io.readers``, SAC header metadata
+          is used to populate the Station attributes
+
+        """ 
+        stats = []
+        for stream in self:
+            stats += [[]]
+            for trace in stream:
+                stats[-1] += [trace.stats]
+        return stats
+
+
     def get_origins(self):
         """ Returns origin metadata from all streams as a `list` of 
         `mtuq.event.Origin` objects
@@ -269,8 +286,12 @@ def __copy__(self):
         return new_ds
 
 
+    def copy(self):
+        return self.__copy__()
+
+
     def write(self, path, format='sac'):
-        """ Writes a Python pickle of current dataset
+        """ Writes dataset to disk
         """
         if format.lower() == 'pickle':
 
@@ -289,5 +310,7 @@ def write(self, path, format='sac'):
                     fullpath = '%s/%s.%s' % (path,filename,'sac')
                     trace.write(fullpath, format='sac')
 
+        else:
+            raise ValueError('Unrecognized file format')
 
 

mtuq/graphics/annotations.py

@@ -49,10 +49,10 @@ def trace_label_writer(axis, dat, syn, total_misfit=1.):
     d = dat.data
 
     # display cross-correlation time shift
-    time_shift = 0.
-    time_shift += _getattr(syn, 'time_shift', np.nan)
-    time_shift += _getattr(dat, 'static_time_shift', 0)
-    axis.text(0.,(1/4.)*ymin, '%.2f' %time_shift, fontsize=11)
+    total_shift = 0.
+    total_shift += _getattr(syn, 'time_shift', np.nan)
+    total_shift += _getattr(dat, 'static_shift', 0)
+    axis.text(0.,(1/4.)*ymin, '%.2f' % total_shift, fontsize=11)
 
     # display maximum cross-correlation coefficient
     Ns = np.dot(s,s)**0.5

mtuq/graphics/attrs.py

@@ -9,9 +9,30 @@
 from mtuq.util import defaults, warn
 
 
-def plot_time_shifts(dirname, attrs, stations, origin, **kwargs):
+def plot_time_shifts(dirname, attrs, stations, origin, key='total_shift', 
+    **kwargs):
+
     """ Plots how time shifts vary by location and component
 
+    By default, total time shifts are plotted. To plot just static or
+    cross-correlation time shifts, use ``key='static_shift'`` or 
+    ``key='time_shift'``, respectively
+
+    .. note ::
+
+        MTUQ distinguishes between the following different types of 
+        time shifts
+
+        - `static_shift` is an initial user-supplied time shift applied during
+          data processing
+
+        - `time_shift` is a subsequent cross-correlation time shift applied 
+          during misfit evaluation
+
+        - `total_shift` is the total correction, or in other words the sum of
+          static and cross-correlation time shifts
+
+
     .. rubric :: Required input arguments
 
     ``dirname`` (`str`):
@@ -37,7 +58,7 @@ def plot_time_shifts(dirname, attrs, stations, origin, **kwargs):
         'label': 'Time shift (s)',
         })
 
-    _plot_attrs(dirname, stations, origin, attrs, 'time_shift', **kwargs)
+    _plot_attrs(dirname, stations, origin, attrs, key, **kwargs)
 
 
 def plot_amplitude_ratios(dirname, attrs, stations, origin, **kwargs):

mtuq/graphics/waveforms.py

@@ -441,8 +441,9 @@ def _plot(axis, dat, syn, label=None):
     """
     t1,t2,nt,dt = _time_stats(dat)
 
-    start = _getattr(syn, 'start', 0)
-    stop = _getattr(syn, 'stop', len(syn.data))
+    # which start and stop indices will correctly align synthetics?
+    start = _getattr(syn, 'idx_start', 0)
+    stop = _getattr(syn, 'idx_stop', len(syn.data))
 
     t = np.linspace(0,t2-t1,nt,dt)
     d = dat.data

mtuq/greens_tensor/FK.py

@@ -48,7 +48,7 @@ def _precompute(self):
 
     def _precompute_mt(self):
         """ Recombines FK time series so they can be used in straightforward
-        liner combination with Mrr,Mtt,Mpp,Mrt,Mrp,Mtp
+        linear combination with Mrr,Mtt,Mpp,Mrt,Mrp,Mtp
         """
 
         array = self._array

mtuq/greens_tensor/base.py

@@ -142,24 +142,27 @@ def _get_shape(self):
         return nc, nr, nt
 
 
-    def _allocate_stream(self):
+    def _allocate_stream(self, stats=None):
         """ Allocates ObsPy stream used by `get_synthetics`
         """
         nc, nr, nt = self._get_shape()
 
+        if not stats:
+            stats = []
+            for component in self.components:
+                stats += [self[0].stats.copy()]
+                stats[-1].update({'npts': nt, 'channel': component})
+
         stream = Stream()
-        for component in self.components:
-            # add stats object
-            stats = self.station.copy()
-            stats.update({'npts': nt, 'channel': component})
+        for _i, component in enumerate(self.components):
             # add trace object
-            stream += Trace(np.zeros(nt), stats)
+            stream += Trace(np.zeros(nt), stats[_i])
 
         return stream
 
 
 
-    def get_synthetics(self, source, components=None, inplace=False):
+    def get_synthetics(self, source, components=None, stats=None, inplace=False):
         """ Generates synthetics through a linear combination of time series
 
         Returns an ObsPy stream
@@ -190,7 +193,7 @@ def get_synthetics(self, source, components=None, inplace=False):
         if inplace:
             synthetics = self._synthetics
         else:
-            synthetics = self._allocate_stream()
+            synthetics = self._allocate_stream(stats)
 
         for _i, component in enumerate(self.components):
             # Even with careful attention to index order, np.dot is very slow.
@@ -284,7 +287,7 @@ def select(self, selector):
         return selected
 
 
-    def get_synthetics(self, source, components=None, mode='apply', **kwargs):
+    def get_synthetics(self, source, components=None, stats=None, mode='apply', **kwargs):
         """ Generates synthetics through a linear combination of time series
 
         Returns an MTUQ `Dataset`
@@ -302,15 +305,15 @@ def get_synthetics(self, source, components=None, mode='apply', **kwargs):
         if mode=='map':
             synthetics = Dataset()
             for _i, tensor in enumerate(self):
-                synthetics.append(
-                    tensor.get_synthetics(source, components=components[_i], **kwargs))
+                synthetics.append(tensor.get_synthetics(
+                    source, components=components[_i], stats=stats[_i], **kwargs))
             return synthetics
 
         elif mode=='apply':
             synthetics = Dataset()
             for tensor in self:
-                synthetics.append(
-                    tensor.get_synthetics(source, components=components, **kwargs))
+                synthetics.append(tensor.get_synthetics(
+                    source, components=components, stats=stats, **kwargs))
             return synthetics
 
         else:
@@ -335,8 +338,8 @@ def apply(self, function, *args, **kwargs):
         """
         processed = []
         for tensor in self:
-            processed +=\
-                [function(tensor, *args, **kwargs)]
+            processed += [function(tensor, *args, **kwargs)]
+
         return self.__class__(processed)
 
 
@@ -359,8 +362,8 @@ def map(self, function, *sequences):
         processed = []
         for _i, tensor in enumerate(self):
             args = [sequence[_i] for sequence in sequences]
-            processed +=\
-                [function(tensor, *args)]
+            processed += [function(tensor, *args)]
+
         return self.__class__(processed)
 
 
@@ -416,7 +419,6 @@ def parallel_map(self, function, *sequences):
             return self.__class__(final_list)
 
 
-
     def convolve(self, wavelet):
         """ Convolves time series with given wavelet
 
@@ -486,6 +488,9 @@ def __copy__(self):
         return new_ds
 
 
+    def copy(self):
+        return self.__copy__()
+
 
     def write(self, filename):
        """ Writes a Python pickle of current `GreensTensorList`

mtuq/misfit/waveform/__init__.py

@@ -132,6 +132,9 @@ def __init__(self,
         assert norm in ['L1', 'L2', 'hybrid'],\
             ValueError("Bad input argument: norm")
 
+        assert time_shift_min <= 0.,\
+            ValueError("Bad input argument: time_shift_min")
+
         assert time_shift_max >= 0.,\
             ValueError("Bad input argument: time_shift_max")
 
@@ -176,7 +179,7 @@ def __call__(self, data, greens, sources, progress_handle=Null(),
             warn("Empty data set. No misfit evaluations will be carried out")
             return np.zeros((len(sources), 1))
 
-        # checks that the container legnths are consistent
+        # checks that the container lengths are consistent
         if len(data) != len(greens):
             raise Exception("Inconsistent container lengths\n\n  "+
                 "len(data): %d\n  len(greens): %d\n" %
@@ -219,7 +222,8 @@ def collect_attributes(self, data, greens, source):
         check_padding(greens, self.time_shift_min, self.time_shift_max)
 
         synthetics = greens.get_synthetics(
-            source, components=data.get_components(), mode='map', inplace=True)
+            source, components=data.get_components(), stats=data.get_stats(),
+            mode='map', inplace=True)
 
         # attaches attributes to synthetics
         _ = level0.misfit(
@@ -254,7 +258,8 @@ def collect_synthetics(self, data, greens, source):
         check_padding(greens, self.time_shift_min, self.time_shift_max)
 
         synthetics = greens.get_synthetics(
-            source, components=data.get_components(), mode='map', inplace=True)
+            source, components=data.get_components(), stats=data.get_stats(),
+            mode='map', inplace=True)
 
         # attaches attributes to synthetics
         _ = level0.misfit(