For memory sake, make unreadable stacks that discard test pulses

.github/workflows/python-package-conda.yml

@@ -0,0 +1,38 @@
+name: Python Package using Conda
+
+on: [push]
+
+jobs:
+  build-linux:
+    runs-on: ubuntu-latest
+    strategy:
+      max-parallel: 5
+
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Python 3.10
+      uses: actions/setup-python@v3
+      with:
+        python-version: '3.10'
+    - name: Add conda to system path
+      run: |
+        # $CONDA is an environment variable pointing to the root of the miniconda directory
+        echo $CONDA/bin >> $GITHUB_PATH
+    - name: Download test data
+      run: |
+        git submodule init test_data
+        git submodule update test_data
+    - name: Install dependencies
+      run: |
+        # conda env update --file environment.yml --name base
+    - name: Lint with flake8
+      run: |
+        conda install flake8
+        # stop the build if there are Python syntax errors or undefined names
+        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+    - name: Test with pytest
+      run: |
+        conda install pytest
+        pytest

.gitignore

@@ -1,2 +1,4 @@
 *.pyc
 neuroanalysis.egg-info
+build/
+dist/

LICENSE.txt

@@ -1,4 +1,4 @@
-Copyright (c) 2016 Allen Institute for Brain Science
+Copyright (c) 2016- Allen Institute for Brain Science
 
 The MIT License
 Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

examples/release_model.py

@@ -38,8 +38,8 @@
 dynamics_types = ['Dep', 'Fac', 'UR', 'SMR', 'DSR']
 model.Dynamics = {k:0 for k in dynamics_types}
 
-print "Initial parameters:", model.dict_params
-print "Bounds:", model.dict_bounds
+print("Initial parameters:", model.dict_params)
+print("Bounds:", model.dict_bounds)
 
 
 # Fit the model 5 times. Each time we enable another gating mechanism.
@@ -48,10 +48,10 @@
     model.Dynamics[k] = 1
     fit_params.append(model.run_fit(spike_sets))
 
-print "----- fit complete -----"
+print("----- fit complete -----")
 
 for j,params in enumerate(fit_params):
-    print params
+    print(params)
     for i,spikes in enumerate(spike_sets):
         x, y = spikes
         output = model.eval(x, params.values())

examples/test_event_detection.py

@@ -1,5 +1,5 @@
 import pyqtgraph as pg
-from pyqtgraph.Qt import QtGui, QtCore
+from pyqtgraph.Qt import QtWidgets, QtCore
 import numpy as np
 from neuroanalysis.data import TSeries
 from neuroanalysis.ui.event_detection import EventDetector
@@ -15,7 +15,7 @@
 evd = EventDetector()
 evd.params['threshold'] = 5e-10
 
-hs = QtGui.QSplitter(QtCore.Qt.Horizontal)
+hs = QtWidgets.QSplitter(QtCore.Qt.Horizontal)
 pt = pg.parametertree.ParameterTree(showHeader=False)
 
 params = pg.parametertree.Parameter.create(name='params', type='group', children=[

examples/test_psp_train_fit.py

@@ -1,5 +1,6 @@
-import pyqtgraph as pg
 import numpy as np
+
+import pyqtgraph as pg
 from neuroanalysis.fitting import PspTrain
 from neuroanalysis.ui.fitting import FitExplorer
 
@@ -15,18 +16,18 @@
 args = {
     'yoffset': (0, 'fixed'),
     'xoffset': (2e-3, -1e-3, 5e-3),
-    'rise_time': (rise_time, rise_time*0.5, rise_time*2),
-    'decay_tau': (decay_tau, decay_tau*0.5, decay_tau*2),
+    'rise_time': (rise_time, rise_time * 0.5, rise_time * 2),
+    'decay_tau': (decay_tau, decay_tau * 0.5, decay_tau * 2),
     'rise_power': (2, 'fixed'),
 }
 for i in range(n_psp):
-    args['xoffset%d'%i] = (25e-3*i, 'fixed')
-    args['amp%d'%i] = (250e-6, 0, 10e-3)
+    args['xoffset%d' % i] = (25e-3 * i, 'fixed')
+    args['amp%d' % i] = (250e-6, 0, 10e-3)
 
-fit_kws = {'xtol': 1e-4, 'maxfev': 1000, 'nan_policy': 'omit'}                
+fit_kws = {'xtol': 1e-4, 'maxfev': 1000, 'nan_policy': 'omit'}
 model = PspTrain(n_psp)
 
-args2 = {k:(v[0] if isinstance(v, tuple) else v) for k,v in args.items()}
+args2 = {k: (v[0] if isinstance(v, tuple) else v) for k, v in args.items()}
 y = np.random.normal(size=len(t), scale=30e-6) + model.eval(x=t, **args2)
 
 fit = model.fit(y, x=t, params=args, fit_kws=fit_kws, method='leastsq')

neuroanalysis/__init__.py

@@ -1 +1 @@
-__version__ = "1.0.0"
+__version__ = "0.0.5"

neuroanalysis/analyzers/analyzer.py

@@ -0,0 +1,19 @@
+
+
+class Analyzer(object):
+    """Base class for attaching analysis results to a data object.
+    """
+    @classmethod
+    def get(cls, obj):
+        """Get the analyzer attached to a recording, or create a new one.
+        """
+        analyzer = getattr(obj, '_' + cls.__name__, None)
+        if analyzer is None:
+            analyzer = cls(obj)
+        return analyzer
+
+    def _attach(self, obj):
+        attr = '_' + self.__class__.__name__
+        if hasattr(obj, attr):
+            raise TypeError("Object %s already has attached %s" % (obj, self.__class__.__name__))
+        setattr(obj, attr, self)

neuroanalysis/analyzers/baseline.py

@@ -0,0 +1,61 @@
+from neuroanalysis.analyzers.analyzer import Analyzer
+
+class BaselineAnalyzer(Analyzer):
+
+    _settle_time = None ## (float) The amount of time (in seconds) to allow the cell to settle back
+                        ## to baseline after the end of a stimulus. 
+
+    def __init__(self, sync_rec):
+        self._attach(sync_rec)
+        self.sync_rec = sync_rec
+
+        self._baseline_regions = None
+
+    @property
+    def settle_time(self):
+        if self._settle_time is None:
+            raise Exception("""%s._settle_time must be defined. 
+                Should be a float specifying the amount of time (in seconds) 
+                to allow the cell to settle back to baseline after the end of a stimulus.""" % self.__class__.__name__)
+        return self._settle_time
+
+    @property
+    def baseline_regions(self):
+        """A list of (start,stop) time pairs where the recordings in this 
+        sync_rec can be expected to be in a quiescent state.
+
+        """
+        raise Exception("Must be implemented in subclass.")
+
+
+class BaselineDistributor(Analyzer):
+    """Used to find baseline regions in a trace and distribute them on request.
+    """
+    def __init__(self, rec):
+        self._attach(rec)
+        self.rec = rec
+        self.baselines = [list(r) for r in rec.baseline_regions]
+
+    def get_baseline_chunk(self, duration=20e-3):
+        """Return the (start, stop) indices of a chunk of unused baseline with the
+        given duration.
+        """
+        for i, baseline_rgn in enumerate(self.baselines):
+            rgn_start, rgn_stop = baseline_rgn
+            if rgn_stop - rgn_start < duration:
+                continue
+            chunk_stop = rgn_start + duration
+            baseline_rgn[0] = chunk_stop
+            return rgn_start, chunk_stop
+
+        # coundn't find any baseline data of the requested length
+        return None
+
+    def baseline_chunks(self, duration=20e-3):
+        """Iterator yielding (start, stop) indices of baseline chunks.
+        """
+        while True:
+            chunk = self.get_baseline_chunk(duration)
+            if chunk is None:
+                break
+            yield chunk