Speedup 02: 3x speed up for prep_data_for_correlation with custom copy and trace-selection

CHANGES.md

@@ -9,6 +9,7 @@
 * core.match_filter.template
   - new quick_group_templates function for 50x quicker template grouping.
 * utils.pre_processing
+  - `_prep_data_for_correlation`: 3x speedup for filling NaN-traces in templates
   - New function ``quick_trace_select` for a very efficient selection of trace
     by seed ID without wildcards (4x speedup).
 * utils.catalog_to_dd._prepare_stream

eqcorrscan/core/match_filter/matched_filter.py

@@ -25,7 +25,7 @@
 from eqcorrscan.utils.correlate import get_stream_xcorr
 from eqcorrscan.utils.findpeaks import multi_find_peaks
 from eqcorrscan.utils.pre_processing import (
-    dayproc, shortproc, _prep_data_for_correlation)
+    dayproc, shortproc, _prep_data_for_correlation, _quick_copy_stream)
 
 Logger = logging.getLogger(__name__)
 
@@ -339,8 +339,8 @@ def _group_process(template_group, parallel, cores, stream, daylong,
             kwargs.update({'endtime': _endtime})
         else:
             _endtime = kwargs['starttime'] + 86400
-        chunk_stream = stream.slice(starttime=kwargs['starttime'],
-                                    endtime=_endtime).copy()
+        chunk_stream = _quick_copy_stream(
+            stream.slice(starttime=kwargs['starttime'], endtime=_endtime))
         Logger.debug(f"Processing chunk {i} between {kwargs['starttime']} "
                      f"and {_endtime}")
         if len(chunk_stream) == 0:
@@ -688,8 +688,8 @@ def match_filter(template_names, template_list, st, threshold,
     if copy_data:
         # Copy the stream here because we will muck about with it
         Logger.info("Copying data to keep your input safe")
-        stream = st.copy()
-        templates = [t.copy() for t in template_list]
+        stream = _quick_copy_stream(st)
+        templates = [_quick_copy_stream(t) for t in template_list]
         _template_names = template_names.copy()  # This can be a shallow copy
     else:
         stream, templates, _template_names = st, template_list, template_names

eqcorrscan/utils/pre_processing.py

@@ -12,6 +12,7 @@
 import numpy as np
 import logging
 import datetime as dt
+import copy
 
 from collections import Counter, defaultdict
 from multiprocessing import Pool, cpu_count
@@ -728,6 +729,77 @@ def _fill_gaps(tr):
     return gaps, tr
 
 
+def _quick_copy_trace(trace, deepcopy_data=True):
+    """
+    Function to quickly copy a trace. Sets values in the traces' and trace
+    header's dict directly, circumventing obspy's init functions.
+    Speedup: from 37 us to 12 us per trace - 3x faster
+
+    :type trace: :class:`obspy.core.trace.Trace`
+    :param trace: Stream to quickly copy
+    :type deepcopy_data: bool
+    :param deepcopy_data:
+        Whether to deepcopy trace data (with `deepcopy_data=False` expect up to
+        20 % speedup, but use only when you know that data trace contents will
+        not change or affect results). Warning: do not use this option to copy
+        traces with processing history or response information.
+    :rtype: :class:`obspy.core.trace.Trace`
+    return: trace
+    """
+    new_trace = Trace()
+    for key, value in trace.__dict__.items():
+        if key == 'stats':
+            new_stats = new_trace.stats
+            for key_2, value_2 in value.__dict__.items():
+                if isinstance(value_2, UTCDateTime):
+                    new_stats.__dict__[key_2] = UTCDateTime(
+                        ns=value_2.__dict__['_UTCDateTime__ns'])
+                else:
+                    new_stats.__dict__[key_2] = value_2
+        elif deepcopy_data:
+            # data needs to be deepcopied (and anything else, to be safe)
+            new_trace.__dict__[key] = copy.deepcopy(value)
+        else:  # No deepcopy, e.g. for NaN-traces with no effect on results
+            new_trace.__dict__[key] = value
+    return new_trace
+
+
+def _quick_copy_stream(stream, deepcopy_data=True):
+    """
+    Function to quickly copy a stream.
+    Speedup for simple trace:
+        from 112 us to 44 (35) us per 3-trace stream - 2.8x (3.2x) faster
+
+    Warning: use `deepcopy_data=False` (saves extra ~20 % time) only when the
+             changing the data in the stream later does not change results
+             (e.g., for NaN-trace or when data array will not be changed).
+
+    This is what takes longest (1 empty trace, total time to copy 27 us):
+    copy header: 18 us (vs create new empty header: 683 ns)
+    Two points that can speed up copying / creation:
+        1. circumvent trace.__init__ and trace.__set_attr__ by setting value
+           directly in trace's __dict__
+        2. when setting trace header, circumvent that Stats(header) is called
+           when header is already a Stats instance
+
+    :type stream: :class:`obspy.core.stream.Stream`
+    :param stream: Stream to quickly copy
+    :type deepcopy_data: bool
+    :param deepcopy_data:
+        Whether to deepcopy data (with `deepcopy_data=False` expect up to 20 %
+        speedup, but use only when you know that data trace contents will not
+        change or affect results).
+
+    :rtype: :class:`obspy.core.stream.Stream`
+    return: stream
+    """
+    new_traces = list()
+    for trace in stream:
+        new_traces.append(
+            _quick_copy_trace(trace, deepcopy_data=deepcopy_data))
+    return Stream(new_traces)
+
+
 def _stream_quick_select(stream, seed_id):
     """
     4x quicker selection of traces in stream by full Seed-ID. Does not support
@@ -849,12 +921,13 @@ def _prep_data_for_correlation(stream, templates, template_names=None,
 
     # Initialize nan template for speed.
     nan_channel = np.full(template_length, np.nan, dtype=np.float32)
+    nan_channel = np.require(nan_channel, requirements=['C_CONTIGUOUS'])
     nan_template = Stream()
     for _seed_id in seed_ids:
         net, sta, loc, chan = _seed_id[0].split('.')
         nan_template += Trace(header=Stats({
             'network': net, 'station': sta, 'location': loc,
-            'channel': chan, 'starttime': UTCDateTime(),
+            'channel': chan, 'starttime': UTCDateTime(ns=0),
             'npts': template_length, 'sampling_rate': samp_rate}))
 
     # Remove templates with no matching channels
@@ -889,8 +962,8 @@ def _prep_data_for_correlation(stream, templates, template_names=None,
                 net, sta, loc, chan = earliest_templ_trace_id.split('.')
                 nan_template += Trace(header=Stats({
                     'network': net, 'station': sta, 'location': loc,
-                    'channel': chan, 'starttime': UTCDateTime(),
-                    'npts': template_length, 'sampling_rate': samp_rate}))
+                    'channel': chan, 'starttime': UTCDateTime(ns=0),
+                    'sampling_rate': samp_rate}))
                 stream_nan_data = np.full(
                     stream_length, np.nan, dtype=np.float32)
                 out_stream += Trace(
@@ -909,7 +982,7 @@ def _prep_data_for_correlation(stream, templates, template_names=None,
     for template_name in incomplete_templates:
         template = _out[template_name]
         template_starttime = min(tr.stats.starttime for tr in template)
-        out_template = nan_template.copy()
+        out_template = _quick_copy_stream(nan_template, deepcopy_data=False)
 
         # Select traces very quickly: assume that trace order does not change,
         # make dict of trace-ids and list of indices and use indices to select
@@ -925,9 +998,17 @@ def _prep_data_for_correlation(stream, templates, template_names=None,
             template_channel = Stream([
                 template.traces[idx] for idx in stream_trace_id_dict[seed_id]])
             if len(template_channel) <= channel_index:
-                out_template[channel_number].data = nan_channel
-                out_template[channel_number].stats.starttime = \
+                # out_template[channel_number].data = nan_channel  # quicker:
+                out_template[channel_number].__dict__['data'] = copy.deepcopy(
+                    nan_channel)
+                out_template[channel_number].stats.__dict__['npts'] = \
+                    template_length
+                out_template[channel_number].stats.__dict__['starttime'] = \
                     template_starttime
+                out_template[channel_number].stats.__dict__['endtime'] = \
+                    UTCDateTime(ns=int(
+                        round(template_starttime.ns
+                              + (template_length * samp_rate) * 1e9)))
             else:
                 out_template[channel_number] = template_channel[channel_index]
         # If a template-trace matches a NaN-trace in the stream , then set