--- /srv/reproducible-results/rbuild-debian/r-b-build.L9fwudf8/b1/bmtk_1.1.1+ds-1_amd64.changes
+++ /srv/reproducible-results/rbuild-debian/r-b-build.L9fwudf8/b2/bmtk_1.1.1+ds-1_amd64.changes
├── Files
│ @@ -1,4 +1,4 @@
│  
│ - 86783eb90b9e529b99a6e532933d42a0 52888280 doc optional python3-bmtk-doc_1.1.1+ds-1_all.deb
│ + 82fd11dfc56e0a15d7e56390f35e5fcd 52888276 doc optional python3-bmtk-doc_1.1.1+ds-1_all.deb
│   ec83cd091b9988cecfca923391c4b301 31053704 python optional python3-bmtk-examples_1.1.1+ds-1_all.deb
│   bdfb44383e388e0e44f3cb2653244574 532536 python optional python3-bmtk_1.1.1+ds-1_amd64.deb
├── python3-bmtk-doc_1.1.1+ds-1_all.deb
│ ├── file list
│ │ @@ -1,3 +1,3 @@
│ │  -rw-r--r--   0        0        0        4 2024-07-17 20:34:48.000000 debian-binary
│ │  -rw-r--r--   0        0        0    14928 2024-07-17 20:34:48.000000 control.tar.xz
│ │ --rw-r--r--   0        0        0 52873160 2024-07-17 20:34:48.000000 data.tar.xz
│ │ +-rw-r--r--   0        0        0 52873156 2024-07-17 20:34:48.000000 data.tar.xz
│ ├── control.tar.xz
│ │ ├── control.tar
│ │ │ ├── ./md5sums
│ │ │ │ ├── ./md5sums
│ │ │ │ │┄ Files differ
│ ├── data.tar.xz
│ │ ├── data.tar
│ │ │ ├── ./usr/share/doc/python3-bmtk-doc/html/tutorial_auditory_filternet.html
│ │ │ │ @@ -865,15 +865,15 @@
│ │ │ │  <span class="w">   </span><span class="p">}</span>
│ │ │ │  <span class="p">}</span>
│ │ │ │  </pre></div>
│ │ │ │  </div>
│ │ │ │  <p>To change the stimulus to a WAV file of your choice, point to the relative path of the file under “data_file”.</p>
│ │ │ │  <p>The filter carrier consists of a sinusoidal modulation in 2D akin to a plane wave. This carrier is multiplied by a Gaussian envelope in the spectral axis and an asymmetric scaled gamma distribution function in the temporal axis to allow for faster onset of responses and a slower tail decay.</p>
│ │ │ │  <p>Filters with very little spectral modulation have a “vertical” appearance and respond preferentially to broadband temporal edges such as sound onsets. Filters with very little temporal modulation have a “horizontal” appearance and respond preferentially to sustained spectral edges. If the nodes are ordered by their center frequencies, we can construct different “views” of the stimulus (speech in this case) through these different types of filters.</p>
│ │ │ │ -<div><p><img alt="ff571d629e4141d7966fa056b5573a0f" class="no-scaled-link" src="_images/Filters_sp_t_mod.png" style="width: 800px;" /></p>
│ │ │ │ +<div><p><img alt="94aedb37a12946ed8b603c7fda3d450b" class="no-scaled-link" src="_images/Filters_sp_t_mod.png" style="width: 800px;" /></p>
│ │ │ │  </div><img alt="Responses" src="_images/Responses_to_diff_scales_and_orientations.png" />
│ │ │ │  <p>Now we will build a larger bank of filters with randomly assigned properties. Beware that if n_filts is set larger, the code could take a long time to run in the notebook.</p>
│ │ │ │  <div class="nbinput docutils container">
│ │ │ │  <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
│ │ │ │  </pre></div>
│ │ │ │  </div>
│ │ │ │  <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">n_filts</span> <span class="o">=</span> <span class="mi">100</span>
│ │ │ │ ├── html2text {}
│ │ │ │ │ @@ -138,15 +138,15 @@
│ │ │ │ │  allow for faster onset of responses and a slower tail decay.
│ │ │ │ │  Filters with very little spectral modulation have a “vertical” appearance and
│ │ │ │ │  respond preferentially to broadband temporal edges such as sound onsets.
│ │ │ │ │  Filters with very little temporal modulation have a “horizontal” appearance and
│ │ │ │ │  respond preferentially to sustained spectral edges. If the nodes are ordered by
│ │ │ │ │  their center frequencies, we can construct different “views” of the stimulus
│ │ │ │ │  (speech in this case) through these different types of filters.
│ │ │ │ │ -[ff571d629e4141d7966fa056b5573a0f]
│ │ │ │ │ +[94aedb37a12946ed8b603c7fda3d450b]
│ │ │ │ │  [Responses]
│ │ │ │ │  Now we will build a larger bank of filters with randomly assigned properties.
│ │ │ │ │  Beware that if n_filts is set larger, the code could take a long time to run in
│ │ │ │ │  the notebook.
│ │ │ │ │  [3]:
│ │ │ │ │  n_filts = 100
│ │ │ ├── ./usr/share/doc/python3-bmtk-doc/html/tutorial_bionet_disconnected_sims.html
│ │ │ │ @@ -619,15 +619,15 @@
│ │ │ │            <div class="body" role="main">
│ │ │ │              
│ │ │ │    <section id="Replaying-Parts-of-a-Simulation">
│ │ │ │  <h1>Replaying Parts of a Simulation<a class="headerlink" href="#Replaying-Parts-of-a-Simulation" title="Link to this heading">¶</a></h1>
│ │ │ │  <p>When simulating a bio-realistic network, cells will recieve synaptic stimulation from both locally recurrent connections as-well-as feedforward connections from external inputs. Often when analyzing the results of a full network activity we would like to know the contribution of only a subset of the synaptic activity. For example, how much does the feedforward synapses, or only recurrent synapses between specific population of cells, contributed to the simulation results. Certain techniques,
│ │ │ │  like running with only a subset of the full network, or using optogenetic/current-clamping to turn on-off subpoluations, can provide useful insights but also not tell the full story of a network simulation.</p>
│ │ │ │  <p>Instead we can used the BMTK “replay” input module to disentangle subsections of a simulation activity from the full network in BioNet/biophysically realistic simulations. The BMTK “replay” module let’s the user take a previous simulation, and replay a simulation but using only activity for only a subset of the synapses. This can be helpful in parameter tuning and optimization, and for very large networks can provide an efficient manner to replay small subsets of a full network.</p>
│ │ │ │ -<div><p><img alt="9588aefae415454a983c207b12d63d1c" class="no-scaled-link" src="_images/disconnected_sim_figure.png" style="width: 700px;" /></p>
│ │ │ │ +<div><p><img alt="42408ae4b7bc46fba8dfa2181a79a81f" class="no-scaled-link" src="_images/disconnected_sim_figure.png" style="width: 700px;" /></p>
│ │ │ │  </div><div class="nbinput nblast docutils container">
│ │ │ │  <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
│ │ │ │  </pre></div>
│ │ │ │  </div>
│ │ │ │  <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">bmtk.simulator</span> <span class="kn">import</span> <span class="n">bionet</span>
│ │ │ │  <span class="kn">from</span> <span class="nn">bmtk.analyzer.spike_trains</span> <span class="kn">import</span> <span class="n">plot_raster</span>
│ │ │ │  </pre></div>
│ │ │ │ ├── html2text {}
│ │ │ │ │ @@ -14,15 +14,15 @@
│ │ │ │ │  Instead we can used the BMTK “replay” input module to disentangle subsections
│ │ │ │ │  of a simulation activity from the full network in BioNet/biophysically
│ │ │ │ │  realistic simulations. The BMTK “replay” module let’s the user take a previous
│ │ │ │ │  simulation, and replay a simulation but using only activity for only a subset
│ │ │ │ │  of the synapses. This can be helpful in parameter tuning and optimization, and
│ │ │ │ │  for very large networks can provide an efficient manner to replay small subsets
│ │ │ │ │  of a full network.
│ │ │ │ │ -[9588aefae415454a983c207b12d63d1c]
│ │ │ │ │ +[42408ae4b7bc46fba8dfa2181a79a81f]
│ │ │ │ │  [1]:
│ │ │ │ │  from bmtk.simulator import bionet
│ │ │ │ │  from bmtk.analyzer.spike_trains import plot_raster
│ │ │ │ │  ********** IInniittiiaall SSiimmuullaattiioonn ((GGeenneerraattiinngg aa BBaasseelliinnee ffoorr SSyynnaappttiicc AAccttiivviittyy))_?¶ **********
│ │ │ │ │  First step is to take an existing network + simulation or build one from
│ │ │ │ │  scratch. For more information on how to build and run BioNet simulations please
│ │ │ │ │  see existing _t_u_t_o_r_i_a_l_s. For our example we copy the _b_i_o_n_e_t___4_5_0_c_e_l_l_ _e_x_a_m_p_l_e