{"diffoscope-json-version": 1, "source1": "/srv/reproducible-results/rbuild-debian/r-b-build.ZSvD07cj/b1/bmtk_1.1.0+ds-1_amd64.changes", "source2": "/srv/reproducible-results/rbuild-debian/r-b-build.ZSvD07cj/b2/bmtk_1.1.0+ds-1_amd64.changes", "unified_diff": null, "details": [{"source1": "Files", "source2": "Files", "unified_diff": "@@ -1,4 +1,4 @@\n \n- 86d1992c00439f2d06df4742384b1bcc 52874072 doc optional python3-bmtk-doc_1.1.0+ds-1_all.deb\n+ 077adc1a2ee7b94444bd4fb5c257ec5c 52874068 doc optional python3-bmtk-doc_1.1.0+ds-1_all.deb\n  7cf5f6169622db4baff75a2330044c1c 30914904 python optional python3-bmtk-examples_1.1.0+ds-1_all.deb\n  fc451527050a2ae77e7bd80fe747d470 527648 python optional python3-bmtk_1.1.0+ds-1_amd64.deb\n"}, {"source1": "python3-bmtk-doc_1.1.0+ds-1_all.deb", "source2": "python3-bmtk-doc_1.1.0+ds-1_all.deb", "unified_diff": null, "details": [{"source1": "file list", "source2": "file list", "unified_diff": "@@ -1,3 +1,3 @@\n -rw-r--r--   0        0        0        4 2024-04-24 10:15:57.000000 debian-binary\n -rw-r--r--   0        0        0    14672 2024-04-24 10:15:57.000000 control.tar.xz\n--rw-r--r--   0        0        0 52859208 2024-04-24 10:15:57.000000 data.tar.xz\n+-rw-r--r--   0        0        0 52859204 2024-04-24 10:15:57.000000 data.tar.xz\n"}, {"source1": "control.tar.xz", "source2": "control.tar.xz", "unified_diff": null, "details": [{"source1": "control.tar", "source2": "control.tar", "unified_diff": null, "details": [{"source1": "./md5sums", "source2": "./md5sums", "unified_diff": null, "details": [{"source1": "./md5sums", "source2": "./md5sums", "comments": ["Files differ"], "unified_diff": null}]}]}]}, {"source1": "data.tar.xz", "source2": "data.tar.xz", "unified_diff": null, "details": [{"source1": "data.tar", "source2": "data.tar", "unified_diff": null, "details": [{"source1": "./usr/share/doc/python3-bmtk-doc/html/tutorial_bionet_disconnected_sims.html", "source2": "./usr/share/doc/python3-bmtk-doc/html/tutorial_bionet_disconnected_sims.html", "unified_diff": "@@ -875,15 +875,15 @@\n }\n </style>\n <section id=\"Replaying-Parts-of-a-Simulation\">\n <h1>Replaying Parts of a Simulation<a class=\"headerlink\" href=\"#Replaying-Parts-of-a-Simulation\" title=\"Link to this heading\">\u00b6</a></h1>\n <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,\n 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>\n <p>Instead we can used the BMTK \u201creplay\u201d input module to disentangle subsections of a simulation activity from the full network in BioNet/biophysically realistic simulations. The BMTK \u201creplay\u201d module let\u2019s 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>\n-<div><p><img alt=\"2c82ebb7e9174a1b97bce5f9a5ee4b52\" class=\"no-scaled-link\" src=\"_images/disconnected_sim_figure.png\" style=\"width: 700px;\" /></p>\n+<div><p><img alt=\"820cffb9c914441cac7fb65ef78615f7\" class=\"no-scaled-link\" src=\"_images/disconnected_sim_figure.png\" style=\"width: 700px;\" /></p>\n </div><div class=\"nbinput nblast docutils container\">\n <div class=\"prompt highlight-none notranslate\"><div class=\"highlight\"><pre><span></span>[1]:\n </pre></div>\n </div>\n <div class=\"input_area highlight-ipython3 notranslate\"><div class=\"highlight\"><pre><span></span>from bmtk.simulator import bionet\n from bmtk.analyzer.spike_trains import plot_raster\n </pre></div>\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -14,15 +14,15 @@\n Instead we can used the BMTK \u201creplay\u201d input module to disentangle subsections\n of a simulation activity from the full network in BioNet/biophysically\n realistic simulations. The BMTK \u201creplay\u201d module let\u2019s the user take a previous\n simulation, and replay a simulation but using only activity for only a subset\n of the synapses. This can be helpful in parameter tuning and optimization, and\n for very large networks can provide an efficient manner to replay small subsets\n of a full network.\n-[2c82ebb7e9174a1b97bce5f9a5ee4b52]\n+[820cffb9c914441cac7fb65ef78615f7]\n [1]:\n from bmtk.simulator import bionet\n from bmtk.analyzer.spike_trains import plot_raster\n *\b**\b**\b**\b**\b* I\bIn\bni\bit\bti\bia\bal\bl S\bSi\bim\bmu\bul\bla\bat\bti\bio\bon\bn (\b(G\bGe\ben\bne\ber\bra\bat\bti\bin\bng\bg a\ba B\bBa\bas\bse\bel\bli\bin\bne\be f\bfo\bor\br S\bSy\byn\bna\bap\bpt\bti\bic\bc A\bAc\bct\bti\biv\bvi\bit\bty\by)\b)_\b?\b\u00b6 *\b**\b**\b**\b**\b*\n First step is to take an existing network + simulation or build one from\n scratch. For more information on how to build and run BioNet simulations please\n see existing _\bt_\bu_\bt_\bo_\br_\bi_\ba_\bl_\bs. For our example we copy the _\bb_\bi_\bo_\bn_\be_\bt_\b__\b4_\b5_\b0_\bc_\be_\bl_\bl_\b _\be_\bx_\ba_\bm_\bp_\bl_\be\n"}]}]}]}]}]}