remove all but one reference to postproc

doc/whats_new.rst

@@ -57,7 +57,7 @@ API
 
 - Replace parameter `T` with `tstop` in :func:`~hnn_core.Network.add_tonic_bias` and :func:`~hnn_core.Cell.create_tonic_bias` to be more consistent with other functions and improve readability, by `Kenneth Loi`_ in `#354 <https://github.com/jonescompneurolab/hnn-core/pull/354>`_
 
-- Set `postproc` argument in :func:`~hnn_core.dipole.simulate_dipole` to `False` by default, whereby user should explicitly smooth and scale resulting dipoles, by `Christopher Bailey`_ in `#372 <https://github.com/jonescompneurolab/hnn-core/pull/372>`_
+- Deprecated `postproc` argument in :func:`~hnn_core.dipole.simulate_dipole`, whereby user should explicitly smooth and scale resulting dipoles, by `Christopher Bailey`_ in `#372 <https://github.com/jonescompneurolab/hnn-core/pull/372>`_
 
 .. _0.1:
 

examples/howto/plot_connectivity.py

@@ -57,9 +57,7 @@
 # Data recorded during simulations are stored under
 # :class:`~hnn_core.Cell_Response`. Spiking activity can be visualized after
 # a simulation is using :meth:`~hnn_core.Cell_Response.plot_spikes_raster`
-# Note that in the current example, we apply smoothing and scaling to the
-# dipoles as defined in the parameter file (``postproc=True``).
-dpl_erp = simulate_dipole(net_erp, n_trials=1, postproc=True)
+dpl_erp = simulate_dipole(net_erp, n_trials=1)
 net_erp.cell_response.plot_spikes_raster()
 
 ###############################################################################
@@ -97,14 +95,14 @@ def get_network(probability=1.0):
 
 
 net_all = get_network()
-dpl_all = simulate_dipole(net_all, n_trials=1, postproc=True)
+dpl_all = simulate_dipole(net_all, n_trials=1)
 
 ###############################################################################
 # We can additionally use the ``probability`` argument to create a sparse
 # connectivity pattern instead of all-to-all. Let's try creating the same
 # network with a 10% chance of cells connecting to each other.
 net_sparse = get_network(probability=0.1)
-dpl_sparse = simulate_dipole(net_sparse, n_trials=1, postproc=True)
+dpl_sparse = simulate_dipole(net_sparse, n_trials=1)
 
 ###############################################################################
 # With the previous connection pattern there appears to be synchronous rhythmic
@@ -138,7 +136,13 @@ def get_network(probability=1.0):
 from hnn_core.viz import plot_dipole
 fig, axes = plt.subplots(2, 1, sharex=True, figsize=(6, 6),
                          constrained_layout=True)
-dpls = [dpl_erp[0], dpl_all[0], dpl_sparse[0]]
+
+window_len = 30  # ms
+scaling_factor = 3000
+dpls = [dpl_erp[0].smooth(window_len).scale(scaling_factor),
+        dpl_all[0].smooth(window_len).scale(scaling_factor),
+        dpl_sparse[0].smooth(window_len).scale(scaling_factor)]
+
 plot_dipole(dpls, ax=axes[0], layer='agg', show=False)
 axes[0].legend(['Default', 'Custom All', 'Custom Sparse'])
 net_erp.cell_response.plot_spikes_hist(

examples/workflows/plot_simulate_gamma.py

@@ -51,9 +51,9 @@
     location='proximal', synaptic_delays=synaptic_delays, seedcore=1079)
 
 ###############################################################################
-# Note that in the current example, we apply smoothing and scaling to the
-# dipoles as defined in the parameter file (``postproc=True``).
-dpls = simulate_dipole(net, postproc=True)
+dpls = simulate_dipole(net)
+scaling_factor = 30000
+dpls = [dpl.scale(scaling_factor) for dpl in dpls]  # scale in place
 
 ###############################################################################
 # Take a look at how different cell types respond to the exogenous drive. Note
@@ -91,7 +91,8 @@
 # dominates over the influence of the Poisson drive. By default, a tonic bias
 # is applied to the entire duration of the simulation.
 net.add_tonic_bias(cell_type='L5_pyramidal', amplitude=6.)
-dpls = simulate_dipole(net, n_trials=1, postproc=True)
+dpls = simulate_dipole(net, n_trials=1)
+dpls = [dpl.scale(scaling_factor) for dpl in dpls]  # scale in place
 
 dpls[trial_idx].plot()
 
@@ -118,7 +119,8 @@
 # refactory period between L5 pyramidal cell spikes and increase the PING
 # frequency from ~50 to ~65 Hz.
 net.cell_types['L5_pyramidal'].p_syn['gabaa']['tau2'] = 2
-dpls = simulate_dipole(net, n_trials=1, postproc=True)
+dpls = simulate_dipole(net, n_trials=1)
+dpls = [dpl.scale(scaling_factor) for dpl in dpls]  # scale in place
 
 fig, axes = plt.subplots(3, 1, sharex=True, figsize=(6, 6),
                          constrained_layout=True)

hnn_core/tests/conftest.py

@@ -74,7 +74,7 @@ def pytest_runtest_setup(item):
 def run_hnn_core_fixture():
     def _run_hnn_core_fixture(backend=None, n_procs=None, n_jobs=1,
                               reduced=False, record_vsoma=False,
-                              record_isoma=False, postproc=True,
+                              record_isoma=False, postproc=False,
                               electrode_array=None):
         hnn_core_root = op.dirname(hnn_core.__file__)
 

hnn_core/tests/test_dipole.py

@@ -26,8 +26,16 @@ def test_dipole(tmpdir, run_hnn_core_fixture):
     dipole = Dipole(times, data)
     dipole._baseline_renormalize(params['N_pyr_x'], params['N_pyr_y'])
     dipole._convert_fAm_to_nAm()
+
+    # test smoothing and scaling
+    dipole_raw = dipole.copy()
     dipole.scale(params['dipole_scalefctr'])
     dipole.smooth(window_len=params['dipole_smooth_win'])
+    with pytest.raises(AssertionError):
+        assert_allclose(dipole.data['agg'], dipole_raw.data['agg'])
+    assert_allclose(dipole.data['agg'],
+                    (params['dipole_scalefctr'] * dipole_raw.smooth(
+                        params['dipole_smooth_win']).data['agg']))
 
     dipole.plot(show=False)
     plot_dipole([dipole, dipole], show=False)
@@ -48,10 +56,10 @@ def test_dipole(tmpdir, run_hnn_core_fixture):
                        "average of 2 trials"):
         dipole_avg = average_dipoles([dipole_avg, dipole_read])
 
-    # test postproc
+    # XXX all below to be deprecated in 0.3
     dpls_raw, net = run_hnn_core_fixture(backend='joblib', n_jobs=1,
                                          reduced=True, record_isoma=True,
-                                         record_vsoma=True, postproc=False)
+                                         record_vsoma=True)
     # test deprecation of postproc
     with pytest.warns(DeprecationWarning,
                       match='The postproc-argument is deprecated'):

hnn_core/tests/test_parallel_backends.py

@@ -157,7 +157,7 @@ def test_compare_hnn_core(self, run_hnn_core_fixture, backend, n_jobs=1):
         dpl_master = loadtxt('dpl.txt')
 
         dpls, net = run_hnn_core_fixture(backend=backend)
-        dpl = dpls[0]
+        dpl = dpls[0].smooth(30).scale(3000)
 
         # write the dipole to a file and compare
         fname = './dpl2.txt'