--- /srv/reproducible-results/rbuild-debian/r-b-build.ZSvD07cj/b1/bmtk_1.1.0+ds-1_amd64.changes
+++ /srv/reproducible-results/rbuild-debian/r-b-build.ZSvD07cj/b2/bmtk_1.1.0+ds-1_amd64.changes
├── Files
│ @@ -1,4 +1,4 @@
│  
│ - 86d1992c00439f2d06df4742384b1bcc 52874072 doc optional python3-bmtk-doc_1.1.0+ds-1_all.deb
│ + 077adc1a2ee7b94444bd4fb5c257ec5c 52874068 doc optional python3-bmtk-doc_1.1.0+ds-1_all.deb
│   7cf5f6169622db4baff75a2330044c1c 30914904 python optional python3-bmtk-examples_1.1.0+ds-1_all.deb
│   fc451527050a2ae77e7bd80fe747d470 527648 python optional python3-bmtk_1.1.0+ds-1_amd64.deb
├── python3-bmtk-doc_1.1.0+ds-1_all.deb
│ ├── file list
│ │ @@ -1,3 +1,3 @@
│ │  -rw-r--r--   0        0        0        4 2024-04-24 10:15:57.000000 debian-binary
│ │  -rw-r--r--   0        0        0    14672 2024-04-24 10:15:57.000000 control.tar.xz
│ │ --rw-r--r--   0        0        0 52859208 2024-04-24 10:15:57.000000 data.tar.xz
│ │ +-rw-r--r--   0        0        0 52859204 2024-04-24 10:15:57.000000 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_bionet_disconnected_sims.html
│ │ │ │ @@ -875,15 +875,15 @@
│ │ │ │  }
│ │ │ │  </style>
│ │ │ │  <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="2c82ebb7e9174a1b97bce5f9a5ee4b52" class="no-scaled-link" src="_images/disconnected_sim_figure.png" style="width: 700px;" /></p>
│ │ │ │ +<div><p><img alt="820cffb9c914441cac7fb65ef78615f7" 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>from bmtk.simulator import bionet
│ │ │ │  from bmtk.analyzer.spike_trains import plot_raster
│ │ │ │  </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.
│ │ │ │ │ -[2c82ebb7e9174a1b97bce5f9a5ee4b52]
│ │ │ │ │ +[820cffb9c914441cac7fb65ef78615f7]
│ │ │ │ │  [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