--- /srv/reproducible-results/rbuild-debian/r-b-build.0nTwehcz/b1/bmtk_1.1.1+ds-2_amd64.changes
+++ /srv/reproducible-results/rbuild-debian/r-b-build.0nTwehcz/b2/bmtk_1.1.1+ds-2_amd64.changes
├── Files
│ @@ -1,4 +1,4 @@
│  
│ - 0ab08bb84d5db7c7c212e97910a6a10b 52887212 doc optional python3-bmtk-doc_1.1.1+ds-2_all.deb
│ + 7a29afc314c76fba0c8ba7697988ea2c 52887216 doc optional python3-bmtk-doc_1.1.1+ds-2_all.deb
│   286a9a506a2779073d90227e6ba63939 30976580 python optional python3-bmtk-examples_1.1.1+ds-2_all.deb
│   6326e0bd8afad7a41afd712b2ab4db82 532324 python optional python3-bmtk_1.1.1+ds-2_amd64.deb
├── python3-bmtk-doc_1.1.1+ds-2_all.deb
│ ├── file list
│ │ @@ -1,3 +1,3 @@
│ │  -rw-r--r--   0        0        0        4 2024-10-17 16:14:53.000000 debian-binary
│ │  -rw-r--r--   0        0        0    14940 2024-10-17 16:14:53.000000 control.tar.xz
│ │ --rw-r--r--   0        0        0 52872080 2024-10-17 16:14:53.000000 data.tar.xz
│ │ +-rw-r--r--   0        0        0 52872084 2024-10-17 16:14:53.000000 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="9b5553340636470a905ae89e74fe8701" class="no-scaled-link" src="_images/Filters_sp_t_mod.png" style="width: 800px;" /></p>
│ │ │ │ +<div><p><img alt="b4e08ea8a0d1467b8879ed9a4412f151" class="no-scaled-link" src="_images/Filters_sp_t_mod.png" style="width: 800px;" /></p>
│ │ │ │  </div><p><img alt="Responses" src="_images/Responses_to_diff_scales_and_orientations.png" /></p>
│ │ │ │  <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.
│ │ │ │ │ -[9b5553340636470a905ae89e74fe8701]
│ │ │ │ │ +[b4e08ea8a0d1467b8879ed9a4412f151]
│ │ │ │ │  [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="4b5f3d1b37a3469d82e3790d92dc63b3" class="no-scaled-link" src="_images/disconnected_sim_figure.png" style="width: 700px;" /></p>
│ │ │ │ +<div><p><img alt="0fd17ffeef0d49f2aa836930d23ebec7" 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.
│ │ │ │ │ -[4b5f3d1b37a3469d82e3790d92dc63b3]
│ │ │ │ │ +[0fd17ffeef0d49f2aa836930d23ebec7]
│ │ │ │ │  [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