{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true, "deletable": true, "editable": true }, "source": [ "# Replicates Fig 4" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Importing useful functions" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "###### to run, run: exec(open(\"./p_PAPER_graphs_spkAmplDecay_Python3.py\").read())\n", "import numpy as np\n", "from matplotlib import pyplot as plt\n", "from matplotlib import rc\n", "from matplotlib.pylab import *\n", "import pickle\n", "from scipy.integrate import odeint\n", "from scipy.optimize import curve_fit\n", "from copy import copy\n", "\n", "global resol, thresh" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "thresh=0 #mV\n", "resol=0.01#ms (min resolution of integration)\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "######Importing useful functions\n", "exec(open(\"./functions/f_stimulations_simulations.py\").read())\n", "exec(open(\"./functions/f_plots.py\").read())\n", "exec(open(\"./functions/f_saving_plots.py\").read())\n", "exec(open(\"./functions/f_post_simulation_analysis.py\").read())\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "####### This part is needed just to give a neuron structure to the generation of sim_exp (the specific model is completely irrelevant)\n", "#Model to Analyze\n", "s_model_abrev=\"MTM_W_sPNaS_sICD\"\n", "s_model=\"neuron_\"+s_model_abrev\n", "exec(open(\"./cfg/m_\"+s_model_abrev+\".py\").read())\n", "d_Pars=eval(\"Pars_\"+s_model_abrev)\n", "# #######Importing model to be used########\n", "identifier = getattr(sys.modules[__name__], s_model)\n", "# ############ Creating neuron\n", "d_Pars_copy=copy(d_Pars)\n", "neuron=identifier(d_Pars_copy)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "## Path of the folder where the pickled dictionaries will be loaded from..\n", "import pickle\n", "dir_exps='./Data/Recordings/Ko_Dependent_Irregularity/'\n", "\n", "i_s_cell='KData_close2thres_16_06_18'\n", "cell=str(i_s_cell)\n", "f=open(dir_exps+cell+'.pk1', 'rb')\n", "u = pickle._Unpickler(f)\n", "u.encoding = 'latin1'\n", "d_Cell = u.load()\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "5.2" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "matplotlib.rcParams['lines.linewidth']=2.0\n", "fig_wide=matplotlib.rcParams[\"figure.figsize\"][0]\n", "fig_height=matplotlib.rcParams[\"figure.figsize\"][1]\n", "fig_wide" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/python3/dist-packages/ipykernel_launcher.py:663: DeprecationWarning: object of type cannot be safely interpreted as an integer.\n", "/usr/lib/python3/dist-packages/ipykernel_launcher.py:56: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n", "/usr/lib/python3/dist-packages/ipykernel_launcher.py:41: RuntimeWarning: invalid value encountered in greater\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "A180615C\n", "Baseline\n", "0.2625\n", "-0.009375\n", "A180615C\n", "Baseline\n", "0.265625\n", "0.0\n", "A180615C\n", "High\n", "0.125\n", "0.0\n", "A180615C\n", "High\n", "0.109375\n", "0.0\n", "A180615C\n", "Wash\n", "0.25\n", "-0.003125\n", "A180615C\n", "Wash\n", "0.24375000000000002\n", "0.0\n", "A180615C\n", "Wash\n", "0.24062499999999998\n", "0.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/python3/dist-packages/matplotlib/font_manager.py:1241: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n", " (prop.get_family(), self.defaultFamily[fontext]))\n", "/usr/lib/python3/dist-packages/matplotlib/font_manager.py:1241: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n", " (prop.get_family(), self.defaultFamily[fontext]))\n", "/usr/lib/python3/dist-packages/matplotlib/font_manager.py:1241: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n", " (prop.get_family(), self.defaultFamily[fontext]))\n" ] }, { "data": { "image/png": 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\n", 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\n", 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\n", 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i4wKAF154QXK7u+++OyRNavs5c+ZE3E4Jn332GT744APZ9b/4xS+iKjdW9H7q5BwmAHjwwQfR0tIime/IkSMCW1wuV4gjtGXLFsHyP//5T8GynIivW7dO0BbWrFmjzPgEQSIeBVpV5MrKSkEl3bJlCyorK3Hw4EF88cUXaG1txaeffopbb70VAPCrX/0KAHDbbbcBGLgR/OY3v8F7772H++67DwDwk5/8BAD4p4Q1a9agq6sLP/7xjwGAf0IAgGeeeQZ33HEHL57Bwzw3bNjAp//pT38S2L1r1y7+9+rVqwEMCPP27dv59N7eXqxatYpfLisr439/+umnWLNmDWbPns17Txs2bMCCBQsE+/F4PIIbAwCcOHGCL3/p0qWIhT//+c8hadxNVsyRI0dC0sTnBQj19I8dOxaldcYjKSkprvtrbm42xEgxqZu1kSARjwLGmOYVOtyNQRwn5DpYvV4venp6JDtcufJWr16Nrq4ugfcuzid3LPFstIwxQzRYgjAbJOJhCCesWgpctGVx9unxiKvHjYpQjlHPv1E78Y16vuIBibjJ0aviGqGxRtMwjWC3FhhRlIxmTzBGPF/xgkQ8DHKVIt7hFDn0rLRGaBTR2pBouwczg+UmOZggEY8CrQUu1rLCNaxobTWCiA9ljCiWVB+MCYm4yYnUsMwsxlJCZkRx0wMzX7dEMJTPF4l4FBglnKLnfvVsFEqPdSg3TMB4nm9SUpJhb6JDua6QiEeBmcIpkdi6dSsaGxsly9SzUSgtW5xvqDZUgpCDRDwMYnE0oheihahxb3kGYwTPJprzbcRrFA1GPQ41dhn1GAYbJOJBqHnMT9S+5bbROixiBBEHhq7nbZTzH4xW9ujRzox4vuIFibgEXGWQe5Q3Qzgl+EUgcfla3oT09Lb06Ng0k3cYrl6Y6TjigVQ9j/dNJ1GQiIdB7uKpfT1cSSWI5jHV6/XK3nB27NgRdsrPSPsOBAKavo6v1dMAN/ujEaYK0HP/RhSOeHdsJsq7TnQdUguJeBRUVVWhurpacp24kgdXiM7OTvz+978PyadFpeHKCy4rlrmZt2zZIpgsK1GYrUFpxWAID2hhv9KnEa2f2ox4E5WDRFwCKUEMTgeAnp4e1Y9vnZ2d+Mc//iFbplRapMok9o60/PpQuEmzlKSHOw6peV/Eec6dO8fPWqjGjnAEXyOjh2vC1adECbwWx6eHuG/evHlQzRipBhLxb5CqWFp1wKgJpwTboSZkwKV9+eWXIeUqKVNqnZLGFqn/QOk2Uvnr6uqwZMmSiDYMRozoCRr5yaCpqQl79uzRpWwjHzdAIq6KaDoI1QhhLOjx0o4RhCSc7eJJ/wFj2KwFg+U4CP0hEQ9D8CfUAOGXbyIJY3Nzc8iXavRAi84mue0T7YEkev+JxmjHb+Q3NocyJOIaINXYjh49yoc2pCq+1sP+9PDmEy0ijDHDze4YTwbLccSLoXq+SMSjRKm4qI0NGwmj2qUV4foWEs2hQ4fw/vvvJ9qMEIzsiRvl2sUbEnGTE+4Rl/Nko63ciW4USUlJCbchURw+fBgbN25MtBkChuq1MDok4lGipkJr9bIP95V6NdsrEXEje1dGtk1vhrpoGuXaG8UOOUjEdSbaIYNS7Ny5UzLd6JUsngyWc2HEl33UdmzG+1rodb6Mdh3EkIibHC3j6kbr2NQDs4i8Fu8eGBUz225EUiJlWLlyJaqqqnD69GlkZGTg1KlTyMzMhN1ux6OPPhoPGw1JIsIp4fLq4SEpOcah8mpzIhiMN9F4olVfkHiosdEIK+Jz5sxBUVFRyKviALB+/XoUFxdj+fLluhlndpQ+EsfSWCM94optOHfuHHp7ezF8+HBBHrlt5fYZjZ1qywp3XIN9AiwjMlQmwDIbsuGUtrY2lJWV4fbbb5dcn5+fj+XLl6O9vV034+KNXhVUap4QrfctV9nFDWHHjh34+9//HtO+CP2hp5QB1Ij4UBV8WRFPT08XvKEoxwUXXKCpQUZAbWWIdaIivV67N7sQDNVGaVQ+/PBDvP3224k2Q5ahWl/CdmzW19cjNzcXpaWliuemHoqomXVQbb5I6Pnavd4Y5QtJRsVootTe3o7W1lbF+WO1f+vWrWEntdKz89dM9SisiM+bNw91dXWw2WyYP38+Zs2ahf/93/+Nl22GR+nMfXJT2wavk7sRxNoJqSauqDSfllPRKilXS8wyFW2k62aGjyXIPckrLefMmTMhs3JGa8tgRtEQw/z8fFRWVqKiogINDQ3IzMzU2y7Do6Uwyo0lV9N5J/d1Hqn8WnYKKp2KNniec27d1q1bsX///rD71lrYY/lQBmEcD7W7uxstLS2JNsMQKB4nvmrVKuTl5aGmpgZut1tPmwyFks7IRFbscOGURYsWKZp8SyvEH5HYvHkz/3vXrl0h+c+dOyeZHivhjm/Hjh2a708PjCKWwQwbNkz1pwmlUNLXFolt27bh5ZdfFqQNVe88rIjv2LEDRUVFmDBhAhoaGlBZWYmPP/4YM2fOjJd9CUHpuNBYww9q86i1Re7LPEr3LZfe2dkpuY779JwaIp3DeDdMI40J7uzsTLQJAhIxFW20Q1CHEmHHib/yyitwOp3Iz8+Plz2GI9rpULltI8VgAf1Gpwwbps8Lufv378exY8cwfvx4Xco3C9u3b0djYyMuu+wyzcs2okAZaT5xI89AGW/CtvLKykqBgLe3t/N/hDEI17C4dZHi1ZFuLtu3b49oh95j7JXS1tYW10+6dXV16VKuUcQymESIuBHPg9GI+No9ABQVFaGtrQ3p6ekABi5mRUWFroYZBS1e0FHzso+WlVZpp2Mkm9SuU4PW3lNnZ6cuHV5KO17Fy9GGZ4woXvHwdGOJlw9VT1yRiGdkZKCyslJvWwxLpEc3OQ9F6XzYXJ5oxpiHK18unCInPNHMv6Kn2JhxPnHxU4ue5yfer6UbKZwSCATw1VdfCdLMVle0QpGI5+Tk4NVXX4XVauXTZsyYoZtRRkKvOLaWAhUpnBLOBq33qSXh+iO0GCapJq+cR20UUYsHRhJxAGhsbAy7PtqnILPdDBT1fLlcLrS0tKChoYH/0xK/34/y8nJ4PB74fD5Ny46VaCqt0piz0jxaf5lHrec/WKmvr8eBAwdiKsMofQHxwOyjU5SO9mltbcW6detUlZ1IFHniNptN184it9sNh8MBi8WCkpISlJWV6bYvrVASTpHKFymPFNEK/LBhw2Lq2IwHkd5KVHsDU3sssXbSDzURjzfz5s3D448/LrlOPGY9kn3ci2WROHHiBCoqKgw9T0wwikTc6/XizjvvFIRTtJyCtra2FvPnzwcASU/c7XZr8oJAOBhjkg1ai/kX9J7FUG5bLiYebeM7efKk6n1qSTTDO/XObxQOHz6MpqYmjBs3Lq77jde0AhzhvGezXjutUSTi8fSM/X5/SJrD4cDx48d13e/x48dx33334cyZM5qWG66iBgIB9Pb26ubhqB2FIm4UGzduxPTp08NuoyeMMcHr+kq30TN/vIhk16lTp7Bx40bMnj07ThZpG07RolNWbIva8pR65kZHkYhPnDhRVyPy8vLg9/thsVhgsVh03Zcc3d3dOHv2bEi62BvkxgVrIbzvvfceRo4ciaeeeirqMpSME5dKjxWtnhwS3YlklAmvoik3OTlZl33LobWIJ7qMweLlh/0oRHFxsewUtAcPHkRxcbEmL/44HA5UVlbC4/GgtLQ05vKi4d1331WU7/Tp0yFpkWYdlEvr6urC3/72N9k8alDzBpsWMXE97I0mT6wYVcSVEG8R12ruFL3Qqr4YbRROJGQ98fT0dCxZsgSLFy/G9u3b+Xj4qVOn4Pf7kZWVhSVLlmjyUQiLxQKHwxFzOXqgplMxmleBv/7667CiGs3YcaX7joSeIioVwon04gwhJBGeuFYYwROPd7l6ETacwgk5MOB5+/1+WK1W/s3NoUw84thKPOlwXsOwYcMUzUnNbR+Nhx7NiBs9572Id0ycwinRYWQRBxIf5lODopg4AFx++eV62mFYlHTARKpMg3GceDy8lWiOe7DM7WFUETdSOIU88QH0meZuEBHrI74RxolH2iYRMXG9GCyeuBKGuicuxkzes5aQiOtIvBq4XlMDRLNPtST67VGj3oyUoNdUw3IYTcRjHWIoh9luBmFrwYYNG+Jlh2HRQiBjfdknWs9Z6QRYsaBlWWqOM1Ex9HgRS3+EXhhdxLXETEJOX7tXQCwjP6IdShecFq2Yyb22bqTX7sPZEK8XihK5vRxKYs9m9sS1gGLiA9DX7iOg5wW1WCyyn7pTs1+1Twt6vOyjxXmKdt5zre0w8v6CGeqeuBgzec9aomh0Sn5+Pv+Fn6VLlyIzMxOnTp3S1TCjo3SYH5DY0Smx7DvWY1KD1CggM3jiicTMomXkcIrZzit97T4CWo5OkSorXMhDaSxdrtLp+bith4gnohyjhlMGe0xcD7QK+Rr5GKWgr93riJKRF3J55F7AERNOxOU8W6Xp3/ve98LuV2sS4QEZVcSVYOaYuB7nbeXKlZqVJa6L3d3dmpWtNfS1+wiEE1ep3+K84QQ2nCeuBcFlKxmvLj6G66+/Xhe75PadiIm5zOZ1BWNmT9zI4RQpjPSSk5iwIj6Uv6spR3DF0bMRqZlGNtZwihGETKvRKUYMp0Rzgxrs4RQtyhELq5712AhtRA562ScC4gYYfDGVXthYwinBeSKVIUYs4kYdH86hxeiUeBNrp3Us25g5nKIF8eyPMNJxiyERj0C4cIqUAIvFN1zoQkncOzifWoJfuw8XTomHxxsNKSmKp/YxNHqd38HmiRs5FEYibnKkRFrvmfjkbgDnzp2TzCtFPMeJq/WilYy8AdTPD2JEISARD0WPmLiRbjDxhEQ8AuKLF00HR7hx2HLTwcqFU06fPq248YrnKufK/PLLLxXZF+7TaOHsjUSwTRUVFfz2ZhwnnshwSrxFXG5CNa1QW7aStmikm45ekIirRMoTl7vAJ0+ejDhOnPsfKaTCkZqaqjguv3r1av53cPklJSWCfHL7fumll2TL5j6iHGvl3rlzpyblcMTbEz969Kgu+1AiUEeOHFFdbiyY0ROPZj9S7YFE3MTE8si2du3asNt2dHTA6/VKiriasd/hbgBerxdNTU2KbVaKVpOjBXfOqTk2o4jJd77znYh59Hq7Od4T1Bk9Ji514+vv71dn2Ddw39INV7ZRIBFXiZqKdsUVVwi2kYrvHjhwIKIQB6+LpjL5fD6BHWLk0n/84x/Lltna2qraDim4cyIXTpEL6ag9FjliFSUlIhHug7xyKLnOZv4ohFIRHz58uGwZF110kWBZqiO8o6NDtW09PT0haSTiJiYWT/x73/ueQJjCTQ2rNL4pVZminSkxEllZWbLrpD4YHQ0zZswIu16tUIWL40vxxhtvqMovRkrExddI3BmtBCVvCHJOQjyJ5lhi4aGHHpJd9/DDDwuWpUQ8GntHjx4dkmbkcMrgGMOlAT/60Y9w9uzZiPmkhhjKPWYqFRTGmOIxv1IvOMQ6EiSajk2tRHzkyJG8DVKeuNo50Xt7e1XtP9ZhjFLnqKOjA+effz6/HI0nrkTEw91k9WDixIlhPWM1SDkjUtdUjfMhdcOPxhNXcmM2EuSJR0BJ3M3r9QIYaNDB+bkGLiWiwY9snIB5vV4cPXoU+/fv59dt3LhRsK/+/n5+f2pQ0hjE5YaL5XLhlAMHDgBQ56n09/fzIZ7gc6RmdIrcOrWNTRz7VIvUo7fYGYhGSJTcXOItLD/60Y8wbdo0TcpSGqsOVwfEN/g9e/aE5InmBjpixIiQtFjriZ6QiH9DOC81WNyC84kv7Lp16/D3v/+dXxZ7abW1tfzvr776CgBgs9kEZX722WeCDlFxp2QsnrgccvFtTnykPD6/3y/Io6YDac+ePfj9738P4NsRFr29vXj33XdD7FYbBho3bpyq/LE2ztTU1JC0M2fOCJajeaT/wQ9+ENHrjbbTLlqSk5M126dUOVL19NixY7JlKHl61coTj3f/gxpIxL9B6tF9ypQpguXRo0cLwim7d++WLIfD5/MJQi1iAU5NTcXFF18sK1ZSlTqWcepyYn7BBRdIpnP7Sk9PD1nX3t4uWFbTuIM9He78xnRrAAAU0ElEQVRm0Nrayg83DN6/XOPRyhOPdXa6Dz/8MCRNfG6i8QaVPNnE2xNPSUlR3ecgh1Q5Usf83e9+V7YMKc9bTDTnXsq2aMqJFyTi38AYCwlx7Nq1S/BofPfdd4d9YxMQegff//73BfnEvemBQIB/gUIqvs410uDKLRZLpWGMcB6tXMMMJxKc+EYqQwouDg4A1157LQAgLS1Ncv9qhxiq9RRj9cQ/+eSTkDSxJx6NN6iERIi4np64FOGGx15zzTURt49GfKVsM/I8PiTi38AYE8SiA4EAjh07JkhTMk5WPO45OH9KSopAwPr7+5GcnCwr4lKVSarjLtYKJteguGPx+/0hxy0WbTWNW8peuY4utR2bakUmVk9c6iYgjolHE05REkqKt4gnJydr5okrDadYrVbZMpQ4MGJnQwlStml13HpAIh7Eddddx//mLlpwgwwWWK5jToxYdMSiLO6wEos48K33wFWm4HVqRDwvLw9A5HBKpAo6atSoiHnUPCEovTlxTypqiLcnLiXQWnjiSkQ83jHxpKQk1NfXa1KW0nBKrKOHbr75ZtXbSNmmdtRTPCER/wZxo+EuZHCDPHjwIP8JqEsvvVSynHCv2ff19YXES8XhlM7OTj7WLtVIxaMhwonlAw88IHlsYuSEjCs7PT09YiWOVsS5RirVcOIRTonVE5cSaLGIR/NI39bWFtGDT8Swt2BHJxaUXqdwHYpKbvDReNBS25AnbgKuueYaXHbZZfwyV8k4TzwQCOCKK67gRz+0tbUJtueG2gWPVhA3sv7+fowdO5ZfPv/887Fp0yaByGZmZvLeg5SAcWKqZN4Vbv8HDhyQFKt9+/YBkI5HBx9LampqRBE/77zzBMvhGmlw4+MEz2KxhORra2sLOc8csYh48HWJ9eUVqXcLtBidMnLkyIgiJe4I1hvGmGbTAysNp4QTcbl6G0w0HvTVV1+tSTnxgkT8GwoLCzFx4kQAA8P+xOGU06dPY/jw4Xx6amoqrrvuOr7idXR0oLi4GMnJydi7dy+ee+45AEBjYyO6urqwZ88e3HfffZg1axYAIDs7G0uXLsXcuXMBANu3bwcAvPDCC5g0aRLq6+sxfvx4ZGRkwOv1oru7G0888QR6enpw5ZVX8o3giy++wOOPPy45UoYbopaVlYWvv/4aPT09+N3vfsev5zxE7iYg/hwbNzIgLS1NUImXLVsWsq/LL79c8O3VQCCAyZMn88vcuQWA22+/HQCwYsUKfuSL1NuHaWlpIaMTfvjDH/K/paYFuOWWW/DTn/40JJ3jhhtu4K/hww8/LHuTEBNsP8fcuXMlG7cW4ZSenh5kZGTIrn/11Vfxgx/8QHW5saDmpTQAyMnJkUx/5JFHFHu2ciK+YsWKkPMs5sEHHwy7H6lrCoTWqyeeeII8cTPAdTICwg4cTsSbm5uRmprKhzN++ctf4ssvvxQ00OPHj2P06NEYOXIkxo8fD+Bbcbroootw/vnn86/0njp1CklJSUhJSUFSUhJfoZKTk5GUlIS8vDz09/fjhhtuwOTJk9Hb24vRo0ejp6cHY8eO5cUjIyMD7e3tuOaaayRHs3DHduWVV6K3txfJyclYsWKFYD33QpGcRx983ID0a8kA8D//8z+S5xMQPo4Gd+JecsklkjYDA6EO8egEbngiY9Ify0hPTw/roQaPsLjyyiuxZs0awfrs7GzZbcXInYdgccnIyJAcSx6J7u7usJ7m8OHD4/7RjHDhLTXIjXKRup5SzgmHXEiTQ8kTpBRiO6ItJ16QiH+DWMTF4RROxMUX88SJE/zvnTt3oqenhxdoYKCxdXV18V4V9+JQS0sLvx3n4XBeDmMMw4cPR0tLCw4ePIgRI0agq6uL94jT0tJ4UeWGLaampvIhE+7Gwr141Nvbi87OTsm3CwHgxhtv5I9RipSUFMFxc/0C4RCL+K5du/jfwf0C4eYj7+npCfHQz549i0AgAMaY7DjhqVOnytpVW1vLX9Nhw4Zh06ZNgvVyIsU9KQUzatQoybzBIv6LX/wCd999t6w9cnDXWQ7xjTUeRNPRLEV3d7eg/nNI1QGxt9zS0qL4uKVG0wSH0uRGmzHGcNVVV/HL586d41/OMyIk4t8QCASwZcsWAAPDkjgh5P43Nzejp6cnpHc+eE7ngwcPhlSwiooK7N69G16vF9XV1byHzpX7l7/8BTNnzoTL5UIgEIDH48GwYcOwb98+LFu2DA0NDejr60N3dzfeeust9PT0YOvWrbyocqLzxRdf8OLEzfXNvazU29uLzZs3o6mpCXV1dTh8+LDAxpSUFDQ1NcnOjX348GGBwAf3HQADlf6vf/2rIM3v96OmpkayvObmZkydOhWnT5+WHE65f/9+nD59GseOHQvxYpuamtDV1YUjR47IdhiG61QNvhmNHj06xPPmGn5bWxt/nuRuWpmZmfiv//qvkPTgYW0jRoyIqvO0vr4+bLjggw8+iPukTGrCKYwx2ZEsGRkZIX0ocuzdu1ewfOGFF2L48OFhP33IUVlZiYaGBgDftjfuWgQCAXi93pA6lJSUhDvvvBM33XQTnzZu3LiIXn8iGbIiLm4Aa9aswa233goA2LZtG7+e89Sam5vx29/+Fq+++qpguyVLlgiW33zzTQDAO++8w1cYThyXLl2K8ePHY926dQAGRoV88sknghtBW1sb6urq0NLSgnfeeQd9fX3YsGEDVq9ejWPHjsHj8eDiiy/mbxbvvPMOL9azZ88G8K2Ic3CeYEpKClatWoXFixcL1vf19eGWW24BMNAJGuyt/OMf/8D27duRlpaG/v5+fPbZZyguLg7ZXixmDz74IP9727Zt/O/+/n5Mnz4dY8eOxa9//WtUV1cDGLgJcVx11VXIyMiAz+fjJ5Lq7u7G66+/jvb2dnR1deHaa68NiTUnJSVh165dIeEiDu56cKNxli1bJhCC7u5ubNu2DQ0NDbBYLPj+978PYCDeL8XRo0cFNzRu+oLgaQqWL1+uaFheb28vkpKS8PrrrwMYCOtw/SVStLS0YM6cOZLrkpKSJM/Biy++GNP87IFAIGx4g4NzROR47bXXsHXr1pD08vJywXJpaSnfJsU4nU6sX7+eX5bqjGxtbUVmZiYAoKioCMC313769OkABq55R0cHTpw4ITg3VVVVAIDi4mJ0d3djwYIFsseTcJhJeOGFFzQtjzt0APzf66+/zv9esGABA8CuvvpqlpKSIsin9O/BBx9Uvc1NN93ERo0aJbnusssuY//93//NALCnnnqKAWB33nlnSL63336bAWA2m02QHnwcS5cu5X9PnTpVcn/Tp09nVquVZWZmsvXr14es59JaWlr4NPE5DV5ub2/nf8+dO5cBYL/97W9ZZ2cnnx4IBATbL1++PKTML7/8kv/d2trKGGMhx9DR0SFZHgD2yiuv8GX+5je/4evE4sWLGQC2du1aZrVaBdvbbLaQ+gOAjRs3LiTtb3/7G2OMsc2bN/PXg1tfV1cnsKWqqoo9/PDDgrQdO3bw5z9c/eXObyAQYNu2bWPjxo1jF1xwAb/ukUceYQBYX18fe+mllwTnmOOpp55ir732mqA8MU6nk1+vpB1yeVNTU/m0/v5+1tPTw44fPy7Y1xVXXBFyffr7+/lyysvLGWOM/epXvwrJ9/rrrzPGGLvnnnsYAFZYWMj8fr8gz5QpU/hrmJSUxCZNmsSvmzRpEquurpZti8HHAoDt3LlTcx3SgiEl4osWLWIbN25kjDHJi3bHHXcwAGzixIkMACsqKmIA2OOPP84AsE2bNkluV15eLlsRuL+8vDzBcmpqatj8nA3iyi2XnxOC1atX89uOHTs2JN/evXvZNddcI0gbPny4YHnz5s2CfXE3tGAB4JbtdjubMGECA8BuvfVWQTkzZsxgn376KQMgEJETJ06wefPmRTxfK1asYHa7nf35z3/mhfTJJ59k9957L5/v5Zdf5n8vX76c/x2cR/x3ww03sAsvvJAB394Mg/+Cy+T+Vq5cyXp6egRpM2fOZADY559/zqdt3bqVjR07lv3Hf/yHIO8f//jHiHVE/Ld8+fKw1xwAO3LkSEgad85j+Zs9ezbbsmULKykpEaT//Oc/Z4FAgG3YsCHs9pWVlWzlypWst7dX1pmZM2eOZPqLL77I/542bZpg3UcffcT/zs3NZbt27Qq5Htyf2MHJz88XLE+fPp3/vWHDBsYYY4cOHQq5Vh6Ph/9NIh4DkU5esHchhVxle/PNN9mzzz7Ldu/ezb7zne8w4FtPiGu027dvZ8C3d2Yuff/+/YKyuPWdnZ3s7NmzDBjwPjn7enp62IgRI1h6ejp76623+O1mzJjBvF4vv3z48GHW29vLrr/+egaAXXLJJayzs1P2OB544AH2z3/+kz8eAKympkaQh1sfXEZbWxt/4wqu8OJz1tzcLLuO815PnjzJnz+/388YY+zmm29mANi8efNYWloaA0JvAuJzF5wnOG3u3LmMMcY/iQDgywfA3njjjZBtPv74Y7Zhwwa2ePFift2+ffv49WIBr6ioEHilO3fuDLFNzmZg4CYr9vzF9nDXUY6ysjI2c+ZMVllZGVYkxX/PPfccW7NmDdu9ezdjjLF3332Xtba28nX0mWeeYQBYb2+v5PYrVqwQ1Enx39q1a1lxcbHs+rvuuov//de//pUxxtgDDzzAp914443sd7/7Hevu7hbcmK677jrW3NzMzp49G3I+g8/lypUrZdv0vHnzBMtSeU6dOsXfeM6cOcP6+/v5m77L5ZItu76+XrBMIh4DwRWR+9uyZQt744032OTJkwUn2ev1sttvv50Bwjs3MPBIde2117K+vj7JfeTk5DDGGPvZz37GGGNs4cKFkvb09PTwv7nHv1jp7+8PsUu8fPLkScYYY59//jkLBAKS+x41ahRjjLFJkyaxPXv28CEHjjFjxjCv18sv33zzzYwxxtasWcP+9V//lU93u92ssbGRMcYkxbWpqYmdPXuWVVVVMcYY7wlxrFmzhl/u6+tjTU1Ngu05oaqrqxOkBWO32wVlBgIBNmHCBMYY4x/NxUg1Zg5OQNLS0iTzcsLR3d0t2O7pp5+WTAcGwlziNKkbkVqkbmpS66Olr68vovMjt08ldZ4TSc6REZfzl7/8JSSdcwo4Vq1axdcvOVuClydOnBiSZ8eOHYyxb6+teL3Ssk+cOEEiHgtqvBKpv0WLFkXch9oKTQwttLpZK2Xfvn2yAsYYYwcPHox7nfV4PKyrq0tR3p6eHvbRRx/pbFF8MaKIJzEW53FKUfLCCy9g4cKFiTaDIIghzIIFCww3UsU0QwyNPJ8vQRBEojCNiBMEQRChkIgTBEGYGBJxgiAIExPfadCCcLvdyM3Nhc/ng81mg9vthtVqRXZ2dthPMhEEQRDfkhBP3OPx8IKdnZ0Nt9sNh8OBwsJCuFyuRJhEEARhShIi4tXV1fD5fPB4PPD5fKitreW/7CL17Uq3263Zt/0IgiAGEwkLp+Tm5iI7OxsFBQWCT3NJfZ3a4XDg+PHj8TSPIAjCFOgq4h6PR7BssVhgs9kEU3UCA19l9/v9sFgskt9aJAiCIKTRVcQLCwsl0x0OByorK+Hz+VBSUoLc3FxUVlYiIyMDpaWleppEEAQxqEhIOMViscDhcAjSxMsEQRBEZGicOEEQhIkhEScIgjAxJOIEQRAmhkScIAjCxJCIEwRBmBgScYIgCBNDIk4QBGFiSMQJgiBMDIk4QRCEiSERJwiCMDEk4gRBECaGRJwgCMLEJGw+cbXU1NQk2oRBS319PXJychJtxqCEzq1+JOLcHj16NK77U0ISY4wl2ggl5Obmoq6uLtFmDEro3OoHnVv9oHM7QPKCBQsWJNoIpZBHox90bvWDzq1+0Lk1kSdOEARBhEIdmwRBECaGRJwgCMLEGH50it/vh9vthtVqRXZ2NqxWa6JNGlS43W7k5ubC5/PJfhOVUI7f70dNTQ18Ph/mz58Pv9+Puro6+Hw+WK1W2Gy2RJtoWsTnFhCOWhuq59bwnrjb7YbD4UBhYSFcLleizRlUeDwe/uaYnZ2daHMGBRaLRXAuKysrkZubC4fDgbKysgRaZn7E59bj8SAjIwM2mw0+ny+BliUWw4t4bW0tLBYLAAzpC6UH1dXV8Pl88Hg8dG51wuFwwGKxwOv10o1SY2w2Gx577DE4nU4UFRUl2pyEYXgRD8bv9yfahEFHbm4uCgsLyUvUmYqKCjrHGuPz+VBWVgaLxYLFixcn2pyEYXgRz8vL48Wb88gJbcjKykq0CUMCj8eD0tJSetrRmJqaGthstiF/czT8OHG/34/KykpkZGTw8VtCG4LPrcViGbIdQ1rjdrtRXV2NsrIygbdotVqHvODESvC59fv9fIdxa2vrkK2/hhdxgiAIQh7Dh1MIgiAIeUjECYIgTAyJOEEQhIkhEScIgjAxJOIEQRAmhkScGJJE++IYvXBGGA0SccKwBL8c4/V6UVJSokm5fr8/prKcTqcmdhCEFtA4ccKw2O12VFVVaV5uSUkJnE5n1DNicjPpORwOjS0jCPWQJ04YkvLyctTU1MDpdMLv9/OeuNfrRUFBAUpKSpCTkwO32w2n0wm73c5va7fbYbfb4Xa7Jcv2er28gNfU1MBut8PpdMLr9Upuz5XPeeA2m02XmwtBRAOJOGFI5s+fj9zcXLhcLsk5c8rKymCz2eD3++FyufhYdXl5OWbNmoWqqirU19eHbMe9ps1RVVWF0tJSuFwuZGdnh2zvdruRk5ODqqoqwVTIra2tOhw1QajH8B+FIAgxnAhnZmbyvy0WC/x+PxoaGtDQ0CCYwjgYccckF1oBAJfLFbJ9fX29ZAw8IyND68MiiKggEScGFTk5OcjIyJD9ShE3WVLwcnV1NWpqauByuUK2Ly8vR11dXcjEa+SJE0aBRJwwNHa7XdXMfw6HA06nE9XV1WhtbQ2JXXMeO0d5eTlqa2vh9/tRVlaG7OzskO2DY+5ceeSJE0aBRqcQQw6n04mSkhIanUIMCqhjkxhylJWVxTSvd1VVFQk4YRjIEyeGJH6/P6ovRUW7HUHoBYk4QRCEiaFwCkEQhIkhEScIgjAxJOIEQRAmhkScIAjCxJCIEwRBmBgScYIgCBPz/ybeZLibUyK+AAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "useful_cell={}\n", "useful_cell['High']=['A180615B','A180529A','A180615D','A180615C','A180529C','A180615A']\n", "useful_cell['Baseline']=['A180615B','A180529A','A180615D','A180615C','A180529C','A180615A']\n", "useful_cell['Wash']=['A180615B','A180529A','A180615D','A180615C','A180529C','A180615A']\n", "useful_traces={}\n", "useful_traces['High']=[[0,1,2],[0,2],[0,1],[0,1],[0,1,2],[0,1]]\n", "useful_traces['Baseline']=[[0,1],[0,1,2],[0,1,2],[0,1],[0,1,2],[0,1]]\n", "useful_traces['Wash']=[[0,1,2],[0,1,2],[0,1,2,3],[0,1,2],[0,1,2],[0,1,2]]\n", "useful_times={}\n", "useful_times['High']=[[4500,[],[]],[[],10000],[5000,5000],[[],[]],[[],[],20000],[[],4000]]\n", "useful_times['Baseline']=[[[],[]],[[],[],[]],[[],[],[]],[[],[],[]],[[],[],[]],[[],[]]]\n", "useful_times['Wash']=[[[],[],[]],[[],[],[]],[[],[],[],[]],[[],[],[]],[[],20000,[]],[[],[],[]]]\n", "\n", "#### Only considering spikes after adatation..\n", "adapt_time=2000#Set to 0 to include all\n", "####################################3 Plot of what is useful...\n", "Stats_Cells={}\n", "\n", "for s_Cell in d_Cell.keys():\n", " #### Finding min and max input...\n", " min_I=1000\n", " max_I=0\n", " s_high_or_Low=['Baseline','High','Wash']\n", " for si_high_or_Low in s_high_or_Low:\n", " if s_Cell!='s_name':\n", " for s_iExp in d_Cell[s_Cell].keys():\n", " if si_high_or_Low in s_iExp:\n", " i_Exp=d_Cell[s_Cell][s_iExp]\n", " c_s=0\n", " i_t=[]\n", " i_v=[]\n", " i_I=[]\n", " for s_var in i_Exp['s_vars']:\n", " if 'Time' in s_var or '(s))' in s_var:\n", " i_t.append(c_s)\n", " if 'Voltage' in s_var or 'Vm' in s_var:\n", " i_v.append(c_s)\n", " if 'Current' in s_var or 'Im' in s_var:\n", " i_I.append(c_s)\n", " c_s+=1\n", " m_t=[]\n", " m_V=[]\n", " m_I=[]\n", " for c_s in range(len(i_v)):\n", " m_t.append(i_Exp['a_vars'][:,i_t[0]]*1000)#s To ms\n", " m_V.append(i_Exp['a_vars'][:,i_v[c_s]]*1000)\n", " m_I.append(i_Exp['a_vars'][:,i_I[c_s]]*1e9)\n", " if np.min(m_I)max_I:\n", " max_I=np.max(m_I)\n", "\n", " s_high_or_Low=['Baseline','High','Wash']\n", " for si_high_or_Low in s_high_or_Low:\n", " if s_Cell!='s_name':\n", " for s_iExp in d_Cell[s_Cell].keys():\n", " if si_high_or_Low in s_iExp:\n", " i_Exp=d_Cell[s_Cell][s_iExp]\n", " c_s=0\n", " i_t=[]\n", " i_v=[]\n", " i_I=[]\n", " for s_var in i_Exp['s_vars']:\n", " if 'Time' in s_var or '(s))' in s_var:\n", " i_t.append(c_s)\n", " if 'Voltage' in s_var or 'Vm' in s_var:\n", " i_v.append(c_s)\n", " if 'Current' in s_var or 'Im' in s_var:\n", " i_I.append(c_s)\n", " c_s+=1\n", " m_t=[]\n", " m_V=[]\n", " m_I=[]\n", " for c_s in range(len(i_v)):\n", " m_t.append(i_Exp['a_vars'][:,i_t[0]]*1000)#s To ms\n", " m_V.append(i_Exp['a_vars'][:,i_v[c_s]]*1000)\n", " m_I.append(i_Exp['a_vars'][:,i_I[c_s]]*1e9)\n", " \n", " red_samp=2\n", " pres=3000\n", "# ##################################################\n", "# ############### First Graph ####################3\n", "# #################################################3\n", "\n", " for i_t in range(len(m_t)):\n", " if sum(np.argwhere(np.isnan(m_t[i_t])))==0:\n", " v_Iapp=m_I[i_t]\n", " v_t=m_t[i_t]\n", " v_V=m_V[i_t]\n", " else:\n", " v_Iapp=m_I[i_t][0:np.argwhere(np.isnan(m_t[i_t]))[0][0]]\n", " v_t=m_t[i_t][0:np.argwhere(np.isnan(m_t[i_t]))[0][0]]\n", " v_V=m_V[i_t][0:np.argwhere(np.isnan(m_t[i_t]))[0][0]]\n", " t_st_stim=v_t[0]\n", " time_stim=v_t[-1]\n", " resol=v_t[1]-v_t[0]\n", " neuron.noisy_current=np.empty(len(v_t))\n", " neuron.noisy_current=v_Iapp\n", " sim=organizing_experimentalData(neuron,v_t,v_V)\n", " t_v=[float(i_n) for i_n in range(0,92)]\n", " lt=int(len(sim.a_Results.t)*1)\n", "\n", " if s_Cell=='A180615C':# or s_Cell=='A180615B':\n", " size_axis_font=12\n", " matplotlib.rcParams['lines.linewidth']=2.0*0.2\n", " size_axis_font=matplotlib.rcParams[\"font.size\"]\n", " m_t_str=[0]\n", " f7 = plt.figure(facecolor=\"1\",figsize=(fig_wide,fig_height*0.7))\n", " ax1=[];axp=[];ax0p=[];\n", " ny_length_unit=1+1\n", " for i_t0 in range(len(m_t_str)):\n", " ax1.append(plt.subplot2grid((5,11), (0, 0), colspan=11,rowspan=1))\n", " if i_t0>0:\n", " axp.append(plt.subplot2grid((5,11), (1, 0), colspan=11,rowspan=4,sharex=ax1[-1],sharey = axp[0]))\n", " else:\n", " axp.append(plt.subplot2grid((5,11), (1, 0), colspan=11,rowspan=4,sharex=ax1[-1]))\n", "\n", " if sum(np.argwhere(np.isnan(m_t[i_t])))==0:\n", " v_Iapp=m_I[i_t]\n", " v_t=m_t[i_t]\n", " v_V=m_V[i_t]\n", " else:\n", " v_Iapp=m_I[i_t][0:np.argwhere(np.isnan(m_t[i_t]))[0][0]]\n", " v_t=m_t[i_t][0:np.argwhere(np.isnan(m_t[i_t]))[0][0]]\n", " v_V=m_V[i_t][0:np.argwhere(np.isnan(m_t[i_t]))[0][0]]\n", " t_st_stim=v_t[0]\n", " time_stim=v_t[-1]\n", " resol=v_t[1]-v_t[0]\n", " neuron.noisy_current=np.empty(len(v_t))\n", " neuron.noisy_current=v_Iapp\n", " sim=organizing_experimentalData(neuron,v_t,v_V)\n", " t_v=[float(i_n) for i_n in range(0,92)]\n", " lt=int(len(sim.a_Results.t)*1)\n", " ####\n", " #### Plot complete trace\n", " b=[]\n", " b=np.nonzero((sim.a_Results.t[0:lt]>t_st_stim) & (sim.a_Results.t[0:lt]<20000))\n", " ax1[0].plot(sim.a_Results.t[0:lt][b][::red_samp]/1000.0, sim.c_neuron.noisy_current[0:lt][b][::red_samp],color='black')\n", " print(s_Cell)\n", " print(si_high_or_Low)\n", " print(max(np.array(v_Iapp)))\n", " print(min(np.array(v_Iapp)))\n", " ax1[0].set_ylim([np.nanmin(m_I)-0.1,np.nanmax(m_I)+0.1])\n", " ax1[0].set_ylim([min_I-0.1,max_I+0.1])\n", " ax1[0].get_xaxis().set_visible(False)\n", " ax1[0].get_yaxis().set_visible(False)\n", "\n", " ax1[0].set_title('input', loc='left')\n", " locatory1 = MaxNLocator(nbins=2) # with 3 bins you will have 4 ticks\n", " ax1[0].yaxis.set_major_locator(locatory1)\n", " ax1[0].spines['bottom'].set_color('white')\n", " ax1[0].spines['top'].set_color('white')\n", " ax1[0].spines['left'].set_color('white')\n", " ax1[0].spines['right'].set_color('white')\n", " for t in ax1[0].xaxis.get_ticklines(): t.set_color('white')\n", " \n", " axp[0].plot((sim.a_Results.t[0:lt][b][::red_samp]-t_st_stim)/1000.0,sim.a_Results.V[0:lt][b][::red_samp],color=[0,0,0])\n", " axp[0].set_ylabel(r'V (mV)',labelpad=5)\n", "\n", " axp[0].set_xlim([-10/1000.0,max(sim.a_Results.t[0:lt][b])/1000.0])\n", " axp[0].set_ylim([-80,80])\n", " if i_t==0:\n", " axp[i_t].set_xlabel('time (sec)',labelpad=5)\n", " axp[0].set_xlabel('time (sec)',labelpad=5)\n", " locatory21 = MaxNLocator(nbins=3) # with 3 bins you will have 4 ticks\n", " axp[0].yaxis.set_major_locator(locatory21)\n", " locatory22 = MaxNLocator(nbins=4) # with 3 bins you will have 4 ticks\n", " axp[0].xaxis.set_major_locator(locatory22)\n", " \n", " add_scale=[0.1,'0.1nA']\n", " box1 =ax1[0].get_position()\n", " xxpos=1.12\n", " yypos=0.7\n", " ob = AnchoredHScaleBarY(ax=ax1[0],size=add_scale[0], label=add_scale[1],bbox_to_anchor=(xxpos,yypos), bbox_transform=ax1[0].transAxes, loc=\"upper right\", frameon=False,\n", " pad=0.6,sep=4, linekw=dict(color=\"k\", linewidth=0.8))\n", "\n", " ax1[0].add_artist(ob)\n", " \n", " \n", " f7.suptitle(si_high_or_Low+r' $[K^{+}]_o$',fontsize=matplotlib.rcParams[\"font.size\"])\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }