2DofArm_simulation_testing_learned_readouts-B-STP_OFF.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing the coefficients in a real simulation where the feedback is generated by the readout."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Makes possible to show the output from matplotlib inline\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Makes the figures in the PNG format:\n",
    "# For more information see %config InlineBackend\n",
    "%config InlineBackend.figure_formats=set([u'png'])\n",
    "\n",
    "# plt.rcParams['figure.figsize'] = 5, 10\n",
    "\n",
    "import numpy\n",
    "import sys\n",
    "import os\n",
    "\n",
    "import save_load_file as slf\n",
    "\n",
    "from generates_lsm_start import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Main functions to the 2 joint arm simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from simulation_2DoF_Arm_physics import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Start of the simulation!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Experiment identifier\n",
    "sim_sets = [\"set_A\", \"set_B\", \"set_C\", \"set_D\"]\n",
    "\n",
    "sim_set = sim_sets[1]\n",
    "\n",
    "base_dir = \"2DofArm_simulation_data\"\n",
    "\n",
    "trajectories = [[[0.75,0.25],[0.0,0.5]],\\\n",
    "                [[0.25,0.60],[-0.25,0.60]],\\\n",
    "                [[-0.10,0.75],[-0.10,0.25]],\\\n",
    "                [[-0.75,0.50],[-0.40,0.00]]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# First the arm parametres according to Joshi/Maass paper\n",
    "aparams = {\n",
    "    'l1' : 0.5, # metres\n",
    "    'l2' : 0.5,\n",
    "    'lc1' : 0.25,\n",
    "    'lc2' : 0.25,\n",
    "    'm1' : 1.0, # kg\n",
    "    'm2' : 1.0,\n",
    "    'i1' : 0.03, # kg*m*m\n",
    "    'i2' : 0.03\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Total time spent during the movement (in seconds)\n",
    "MT = 0.5\n",
    "# Simulation time step (in seconds)\n",
    "time_step = 2/1000.0\n",
    "# Number of neurons at each input layer (defines the resolution of the system)\n",
    "Ninput = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "t_mov=numpy.arange(0, MT, time_step) # t starts in 0s and steps time_step(s) until reaches MT(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Reads all the experiments to check which range of torques is necessary to control the arm\n",
    "# These values are based ONLY in the 4 trajectories used!\n",
    "torques_all_1 = numpy.array([]);\n",
    "torques_all_2 = numpy.array([]);\n",
    "number_of_trajectories = len(trajectories)\n",
    "for i in range(1,number_of_trajectories+1):\n",
    "    states,torques = slf.load_from_file_gz(\"./\"+base_dir+\"/\"+sim_set+\"/States_Torques_movement\"+str(i)+\".gzpickle\")\n",
    "    torques_all_1 = numpy.concatenate((torques_all_1,torques[:,0])) # torques joint 1\n",
    "    torques_all_2 = numpy.concatenate((torques_all_2,torques[:,1])) # torques joint 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Creates the 50 possible discrete values of torques for each joint (probably will be different)\n",
    "torques_values_1=numpy.linspace(torques_all_1.min(),torques_all_1.max(),num=50)\n",
    "torques_values_2=numpy.linspace(torques_all_2.min(),torques_all_2.max(),num=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Creates the 50 possible discrete values of angles for each joint\n",
    "teta1_values=numpy.linspace(-numpy.pi/6,numpy.pi,num=50)\n",
    "teta2_values=numpy.linspace(0,numpy.pi,num=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# These are the 50 possible values that the input neurons will represent (x,y) \n",
    "x_values=numpy.linspace(-1,1,num=50)\n",
    "y_values=numpy.linspace(-1,1,num=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loads the coefficients generated by the linear regression:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "[c_tau1,r_tau1] = slf.load_from_file(\"./\"+base_dir+\"/\"+sim_set+\"/coefficients_residues_tau1_08-05-15.pickle\")\n",
    "[c_tau2,r_tau2] = slf.load_from_file(\"./\"+base_dir+\"/\"+sim_set+\"/coefficients_residues_tau2_08-05-15.pickle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
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m/K5JvmMrac6DqGOh+Vof91AdP2eY7tWjujDuHJiZ6czZo7purLq1\nLeE9tO53icvnt80e01Njc1q95vNUjrQqXO5Ki27Jel6zqOtIkpWXlVLSayxJXaSRe0/xbxLec9LS\naGu3950WvrPb6mF5Un6df1zSFEk/GpMGHirdBx4qSG8/r5FjXHUu537fa8QOGq8DdYKX9ev4+a2H\nV99TvyFvcuN7Vjm3re1pjb6eJiTtWSL9y7y06jKlz2u8jEE6Ssd+xibpolSvh1I529z9qf6+m69b\nh+vCzd6vWom39GQPp85PkO2Wp7OfxloeGm1ZrRdVT7OltjoyW93TIO8TxqbVCtFaN8iFfyh95ivS\nRQOlv9/6pLTpFGnlDC/4dNEkRU7QLuV3Ysj6GmnVbPbc5KulqbI1X2qo5aXygX+z/9qlvZIeLr4f\nbjWvbFnfr8WaqWkqaLEttS2SpCWSdn37sHatGWtsotqycuSg9K7NZqvCWyr5td58y3Ej5zRpfkhW\nVrbW2p31JObOOWcnTjms6ScXNLFjsWZpj6SZWqWBqo3r9ESqdQ6yam2TJE0+8Gwa13xcvsn6HNYS\nn7ZF26Qn3+t/pxwMDY27joL3ig/+BencG8xW3VD7GNfv9Vh572mp91LbNNba7X+npoem5fMYdFZ5\nuX/BDKkg6U9LPTK18YD0H9dKw5c0dowrzuXsC6Rr75WkIUmvLG6WoJytP4S59vnPqsyqvqeOH5Du\n/IHm77jMltoNkqQjk8+Rm3GcJGnP9MPa88bH00tb5fUUNODueq409OrOCWnrwdjGkwaukUbqt3mW\n53tcnODYez8NpzqFSKmcbXAqigR5p9Vn9KzzWk8GnDo1f0bx5NvUDfJmum9tP0099Db6UFPvhpNe\nl+Dw94guXDtznkoF4qknlIajlX1eweyqLVEFpjTy3tbnNIr/nvUL623D1WPvX/q4tPb3vXMUBJ9C\nqw/osrPbMYwnixtLZwrITq1iU1uaAV9J0oX+vxv0cFyLRWUlKNTqMSTppOIvwivbJVJZjkzc3Mq1\n3tnAYDrzjNVLc90h2fMmO+36u/M6+bBfVemZp2VatWNAmzVHF8oLYLbDYGF3m/bcwc+Nyjf5msur\nXHzayhuKsh0a6l9HB6VrCt6w8qv8XLhrlnTnMemCydEP/nH39VJAre7nBtfD3Njeoom0597Z+Llp\n6SE3vgdjKronoBUc97uWSyv3Sytnlh//bXdIuqP5enywr1bSF33/Snb+syyzKud/3fF2zdXmUr47\nJmnU++/61w5pz4aT0ktb3Arba08vDe8blkIrbFdd12fNWaZVo4nLiawDAOnI3z2udF6eOkc687jS\nbw4fkqY9Ecy55m2bftCvqbp84vI1pWf0oLE5HMSVpD3TF3v3zfSf63o04NTJStLIcOzkW4fnLPPm\n1ZHqVy6ae+ht9EGs3g3HOefsFTPO0OyTpEnHflY2Z48tnOv9cvTILvfywVc1kr7KfZe/06nzFBSI\nK2eUbhzhz4t/GE6nFSLme84fuUyzBs7T0SmvlptXCoLttXGNuhul4vmqGHu/7Q7JzDvn4eBT1ATt\nafbMig3Y3ShlW2GsusEsOH6m9HKiv02zpan1Y5DnySSTd/2uLJfK53Fr9lrvTGAwaX5IVvbWSrNf\nLs3eNcNvzQ54rdobjt2rXWsfSat8TPTQG9VLDolE55s0J6JPW/20pR4Eb9rEzdKvvFm6ItQL7usH\npBufkr55dvSDf+37eqKPDa6Hlq+D9B/Kmj03uX3IbXNAqxnR1+S7JD10UPqtMWnrZ6VNa5Ic/4Ym\nHLfdE2qyETtJHb/2HrJbKCTqnmpL40bvPCXpv0t6xO/ZeNUNrc9fGrF8vC47W7pI0n+VtHWrH1AM\n/qr8ui5cIOneiL3X1j3B1ijZN0oEivlwzuhrNfTYHO8SekLSK7wNRhcMae+7ZoXL3faVh+2pyzf6\nzB+bt6Tt7sfuguqpPDQkbZzZjoBhTwacOllJcq7GcIDCa4ekDf4N9Orx0sTOxb9WUEC29tDb2INY\n3c+Yd+SnuubgfEnTy97/0sBzjafNE5npi0N1Nh2od55au6GFL/yL5M19dLGCz6v3MNxqvonLj5qr\nG7SqsFR6tPwPbpt9UKNjEZX+0E24rGt8EHxKe4L24Gb6vRne5OSvlrRe1QE7/zMyrTAGaR2YIZm0\nZ4a0frv3K9s1oSlPPKbBwu70JmGN0eIxiLuZ5KFC4qWt/nVYtiLYke0HNbVwg6Qbkl7rtT+/M13Q\nk37XemVv7TQnGZK9NcX7WBtbIg/PWeb9Z7S5v29w0uTc7d9XeW7q1UXK81jtgH4tzdwfk9eT8hAE\nf2pY+vt/ly4P3aODBpbfWhf14J9OkLsFpSDVMi0Z+e3i6oijZ0p716eUljycm87pfFA2rswsDpv7\nqLT2zcmOfwMTjttzLa0g1co9srnnpzTvLUmfZ4Yk/YlUXGQijXtZ3ArbK2dInx2XXvrD+g0KTchh\nsDWpZvJLkvpsc9e6nw+Hds7Q9d+t/vV6J+3tTDnZaGBIRyaf4/Xgi3q/Uvw10nivu84FDHsy4ORJ\n90ZcK/NrfuFBfXHgeE0tlCb3OzT7XI2eMbu03dPbpE8skC6JnV+n2ZtE6g9iccMMWhl+EFughlvx\na52nxm5oZQXaEknj98zS0Z+Z0J7zJmmv/HmPvM8rFQyVK8JdtdnsKqVTmQnlx7nvPaj5O2+QtCxy\n08h5RcoLmPA5DwefGi7kEqX5huVeS8FFku6W9C3//18/kmYgt94NptZNStL1pW7uv+dV6PcGhefK\n+6VHu2gS31qr7lVIfzhDdJ4sSnAdlqW1IAVzUCS+1mPStl+LtUTTii2eBR3WgB5vX9AtwXedr/Wa\nu3OBpM1lLbEV4+4j9x5cqzWHZDd/H6u6XpZIOnRNQT+9Qtp7m1KtWBReO6S9Nq4vfe/HVfeJBEGd\nVs5fkopptq3Etc5h3MpM1RW/mnWQph/42rdqW5xmgufFNMx56+0amhj0GxFmabDw2zryuYM6WDhR\nL5aXLZ1seAzSX1YeT+j1+lVNkfdkvKK4OuJ6k/amk5a0z00Wivn6rDnL6gesY/N57LQI9epuyeoV\nlQ9jpWFzja2mW7ths7ivyQeiG7EPz1nWruEu8WlNOlVHOg+tyZ9nfup/TnoPyZXnszxofehBzd9W\nmk9KKj1b7HzruEa/MxjZM22zpImBN9miKS82MgF8dxkZlj5xs/TDGYnm0ktSn5339JmaMeXNZaM/\nbNeEDjw9UTsdaz8iKboRz3ZPSLVXpW3k/lS/7tFAZ5CBiZcVnq+t/P0yta+ROr3uQg0httS2VOXh\nNt4nezbglP6NuMZ8By+44apVsmzRldKq2yRFzLuTfhSx3vfLQw+JovLu6ct09j3P6cjJBR18+ZDb\nGfc9GryhzdUCzQsXaAVJj0pTn5NscL9GP6jyfDEyLJ24Tbr2bOniSSre7NPpAVCWH+e9vLNmN/2I\nwF5UARP8v/p36Qw9KqW5cJv0rRnSL6rUtfgXJd38UNXxj/pOEwNvslecskN73vB4otaJuIemGjcp\n90KQ1m/cJl00Q9ok6RK1q/CMvZ4KWtzqvpsOILfSi6Pe0JH9/iTiQdBiYpkkk0bPkPbubKAc2xqU\nycknEQ+n7cKy33jzTLWtFTBBmZMwEBhfMdG2Wq3ZLd3HItM2Jt364IT2alLT10Y4j+yYLR0+N8gH\nD2q0kEFgN39zSIRVnMPyfD/30GLNfvc06TXy8sbLkl4jjZ4n7X1Xxfmp8T3nj1ymaaHrUk6a9HBB\nh8eCiUeSpM3vPRuVV5dJuvMl6YXW6ytNB89HhjV310F/NcPQPbogbdACvbg14jpJseExumxcZifb\nre4Ft7rqYeScwR9JhYiJ8NNuQc7HHIRRkvVS8PN1YdeM+sOR4srkFuYITFSvqLc4RPzxL+vxGzR8\nHnt5kqYddhrYP0uDocaK+dquF/d9VfsKA9qg3RofOEFHzylNuTC6YEh613ntLtuS3HfKz+1V8udU\n8ybXnvML4zrxe+XfTUr0vGEn261aogXFv5vQ66O3POSkjUeld0xNt543EtOA+6Q/KqEyrxSk2/59\nTKNuduy9/FcLA1LhlWpgAvgsNfq86B2nhbdJ5/1+xFx6zeXVecdMqx6tbIibpA3VjXOlvBjkw5jp\nNOy5/dJY7XIyrjz4QkSwt05QrKxjQL1j2mBHj/i8nqDXnVeP9htCpLI83Maesj0bcPKkeSOu3zoR\nv3143p2M5kLIW5fN0oPjkFQYknYoqiAJNN1aeWHlG2PSlwae1egdPwznC7/ArFgRzinojVTWqiE1\nGajz8+NgYWFjf1dKY5LfNR2wiDQyLN3k93IKVup4xWHpA0568q8rClKvd0z4mG+WdFJhQO7ASRp6\ndJ6mPHWCLbUt0cevwaBiOFJ/hu3QEk1T4bJpGj9BKhwtyP3sgMYeP6g9baiIx19Pe9PYfey5q37o\nWRZ/PJsU/oxghbvjtLj0ff2Wekm6dcmE9tZuMSoXlMkzNuU5SCClPDQ7XDEZf0CaOS7JSeN6oyYX\nosu9cJDPH5poS+2Gls+1PbdfaqBisVnSdh3TBh0svlfQYbmB3XrpFUu0d4V5vUL3ptwrNOkDa0W5\nMXSd9Ip/Lui4sfJyO+XGlca6/Mfk+9kXSNffq+L1FFh/fETX/xrl41zdoFVjsyv2M1sbIiq4VSrr\nSbGBrWulRbeYXZXJikTO1VvNsLq+l2rD46ikX656d0hfmHNF5FydsT3Cf1KQZqRW92vlft9QHk7Q\noNHovI+eiqHFlfe4wuAKO3XqhI5zTmfN2Sf3HSf3MxNy8yZ5810+2+bhk0kWh6ixomHZhPMFafOj\nQV4yVeblDZJe3HmheyEYcmsmnV65DHqHhk7619P8nZdV1X8lVS9q4eStmnyZNPc/DuqaiGDrutDq\nt2Hhsjlutd0dQ9JhvwO27ZrQ2JMvSTc9LV26XPqjA9Iyv1dNINQ7voF8XrMB98jkmyKHPenwhLRm\nTPsP/bs2aJIOzT5XE7NnS5OkaTvkTT7eRZp6Xtz2Uelvr5PecWLnhzBX9hQ+LXqzVlalLbx2SLqu\nPNhbJyjm5bt3nWW2qv7Qtj3ari/5AeZjL0/SzDFp0qGDmghdMwnqMFX11qTzwU0+8Ky05rR2xiV6\nOuCU5EactCBq9OEjqlXT+83NBWml/68uVY3Wx7aq7FYX2KPtmhvzNw1Ngt4O1Tf+pnqaDBZ2Szvf\np/lab0ut9LdLJB16dp/2vHVmsWth0BupUhOBuiA/qgNT8aZbmV30Kelr66SRHd4Eivc9LD30aLFi\nX693zIWSNu+VtDfUKl0dKGnqAT+I1G/WkPf/CUk7JWlA+q70pYGd7qfJjkVVXgpWbyguvysVh7As\nidnJhI6LfD9yHHaTqnv5rEgtcFwZoPVXuPPLiJOqtk/SYhQSugaUl4kma0uph0S4YlLeU2uwWKHe\nqYMyPVD8TVmQLzw0sakUlNSqWMQ9VB7Vdre9sju5mTT/cen1bekV6okKflTOh1hsXT8mXT5Zc77h\n/F4w5eX2F1u7BqvKh7MHTtDRKa/Wnp+bpL23+m/GfPfS0MvfLushOPhYzKdVByVqlY/xE+vWF/2A\nFTeMaKG1HChOMFdF7H3ddELS7xE1l1zDAdvy62GZFLFSXdlcnQmOxeCuXdKOVMu5yus4+QN2jRV0\nj5++VXOmziu+fdwc6bDqLB4TzKHyf2doznb/M39T0t8orkdsMV/vPf12rV8xqGk/kK4+4v1ys6RX\njkujMi9AEzyw+fNe+vNdNlJ3iMhb0cPIi/XdYJLwNWNx+yx7L8nCC3MiP7FqX6k2ejSoeK8uWyVO\noe9zbK9mVvR2to37NXTyDE0bi362nKlpDdeng3vl+tdKT29ReJoE6cl3es9Y+26Trl8TXy65u6TJ\n/pQZwTWxe8K7j7xqXVTQqfI4+AHv6GFPkwvPS8M/1K5j73M7nfNGuFz3j9L503TGX6iZicS7jXec\nzlpTzKs1erkVnzFrNKDGPodGqrxnzZX0fPVmLa1KW6OOGvc9wkHZwgWqlQ/cC251McB8xgnLdfV3\nJW8e5ekK6t6N9JyXP8Q86Xxwg4Xd0vAP29lTtqcDTlKSB+9GuuNXPHzMH4mO/BcrNFGtmnfLm6fh\n9VOkn2xs5bu17EJJm8Pd6iRJy7Rfh/VlvaQBPa4jA+doQC/r6Dmv1p6fG5JuLatYtWOoXvxFtGhb\naEhOUNGOjsTfXrtJIfZvNx+Tptxb0NAKb0jCYOHUZr5Dnc8NfV7ol0FvkpQnsq2l6vzt12LN1LTi\n/DiSF4jb8cxLGtALmn7yQk3dNcvvpeXdSMLBvVpTMCQKlEQEFRuZ46Gy4B8snJq4B1BV4Mxfgney\nG9LsnSdpdIE/zOWhGyVVX/eSdEAHdeuSweoutlvjnixLm9WaS6Khm2/y/dY9JkGlO+7Yh1qMkp6n\n0vZ5WP2qtlR7SMQJrosNesD92F0QvB0b5Kuh4lxHP0jVqFg0UmZ7xyaiV2jqw4WC8uDjkqZI2nFU\n+nBFr4lgxbLLztakQ4eliMBvxFwIDalq9fWHaa8/QV6/xhrfvexvQz0Evxwdn44PSrRv5Zvw/2MD\nW2atz9WSZK6KuEaMXV7kNcn3iJ9LLnFKy64H/3qsrmsUhywkOBabJU0tHKcljQ81akzSem2NXnMn\nDPyprjlUHWCruXiMv785B5eXT9q70/vn1iUTmrL1kOZVfP8lkl7ct0dP75inM+eMSUdKD0hePdVT\nmR+mjQ1qiTbbybZdZfMtheY+q9njKGKfgWIgsWyS8OK5beew+nLpXfONTsQcG8wuDsmp6O08Ze+z\nuqawVJsrFhyqJzQ3VWxjXrGnRmV5tG9lcfL2oQ3LNedpFYcUB71c52u7XixOmRHscZK0cYH04T+u\nl7xiuVJz2NPOiqD99x+WXl4eOaSpUhPTITR6LuvMVaZEQdi6ngrNVRvq5VZ+jZXvO4UG1Kp71t4T\nxnXb4H4dN/6fZRs282wVpH3aA9LZR0sBtMp6eeT3CM9b2sj3OPAVSQMJ/qJGOb/1LmnNumKvu5Lo\nhhNJjaxE3YyeDDgleoiW6kwKWGPyzDnnfFknbpulQV1RduMKbAhvX9maH0y8/P2Hc9OaX32hDGmD\n7vWWTDS/NDv9Pmm9f9FUduVP0GIRFKhHJp+jyRPzpEKNi6nWRTRyR6JhkqZxeU8nyW2W95x89cSA\nNn9XkmY3u+hSIjG9Sdr4idWiWuOKlYnQw+6GY/dKMq3aMaDqbuDeMLJwxbCeYLsDeoMttdJRPkeH\n5e7ZrZ3nHNPohF+pWNTAHA9KpwdQ2T72Srq3coWL6IDTLP2nHj19akUX+PulR98abBJ745+txVoV\nEaH1tl8AACAASURBVGDYoHCFpMbNIkYrw2mDSndc60xZBcy/bl+eMSPZCoGJeyzWrvwcmXxOsxPG\nJpPfOVKqhM91zWsxrYrFtmHpyL3Spp9vR+CwvCJ5wQwvcPAn06sn1w5WFFqzTpMPRQ+7aql1s4aI\nB6Hir+pOxn+koKiK5dTCE3E9KTozSXT0Q24qPS4aXZQkXBZvTlIJ77C48x+U2eMDJ+jIvMWauutx\nDRZO9euM5eVxC70W7WS7VdMnXyQ3oxS9PEvST39jikafV616bZ1ec40vHjNf6zXrnlmymKqXPbdf\ns/VqrYoION42Oqo9a8ZiJ8wOlN/fp8u/v5fmW6q4NuJ6HAX/1qrjhYbnBn8VethsbFh9+PNq9HCq\nVO+arxFIOFVzVR4cPHvgBO087Ry50we9Hmjy7tH2zGtt4dx3lvXmnvv9xXrFLqepMb22K9lz++uW\nsXE9wMuHK0XXrSKGgpf1PrJFn9LcZzZodXGIU6mX6zot1tn379aR6yQtVvGaSHHKheig/bQvyF4+\nTuWVk+q/bSrY3ODchXH59dbj3yRJWvVyqWxtcgxGWV6tvI5L1+1QsVe314DQVJlec1GU0WkPanS8\nofkka96rL5SkQ1L4+yQus4OVhrfNiPx11eiHkWENDuxSVKNMkNZisPFclUZNRZXz+1YGve6Kfxvb\ncJLeyJg4PRlwSv4QLUn+DfKoPyY8WP1kn9ZLWl0dRT5XmnOX86P4NQXjN72/DU2opxXHpI35as3f\nLGmHpGnFd95kS21U5+iwxnS3NyFnja78lcciCCQEQb4gEjymxzXjnBOLXaKjzB95R9wEqP4E7fEP\nS0E6Dmpy0KjWkHBFICqAUvq5aihik930y9/PI+87xz/0TwzMVO2G52je8Z2sC8sCKENS4SR9cdsO\njRb8QtN/+Bk9Y7nWr5CfH8Z03JhTveBL1Pnar8U6TioLPkv1WyZDK1zUHsJSZyn7RiqqwbGv7OFU\noytyU5WXqDx5aPa5OvjsbJ1xgTTtoerP3iFpwC8nJC9YeNS/4Zd1fT/ymOYeWqm5B5fZwrml6v3C\nyYc09u3Htas0VCG616G8Y7Nfh7UrVEIFZcuYnLT652pVvFpZTjtcqU103Lz3T62Yq6LJFsPa6g4P\nCc5V0HvSS9v2dANC9jZp7X3tW5o9CH7ctVxauV9aOdNruPmmpItV6rrvr1iWcq/UuooPQhHziATz\nNsTdRyZNNDEWrvEAaHOTwMY95DbW46JslcmZmqYDmq6vaEJuYJLcTGlcTgP7H9W+wvbYnYTLnDsk\nHVNB0v7wSpX1jkHbxMyJ5l5wq73vXpAKu3b5D+M182ZDKyQF287VMq06NlQVObl1aEKjG4/Un1Q5\nxV5zc7VAqwpLtflQ9e82S/5Qq+jhHcGwpCbnuPQ0eG0Edbzw9blTB3VoyTSNLpikuQ9MRA7PjSrz\nS/uYWffzGlbje9WqT6xS+bHcXJDs3oI0U7p6LEjLJO3QHE07Kk3dN6QTv3WSxqdJg2PS1ceSB+i8\nAEM91XPJbJZXvzjll6doaHydYo+fG5Q+WJC2HqwK7vgTjaswKfq+NlPTtKqw1OtpF3owiJhyoU5P\nIO9+f2j2uZpYNtuvhz4UlD/l9YxzJf14TKOjk7X+LZOL25bvrwUtrAwYzoNTDw1IkxN/av0yKsFc\ntaVe3fsVV4ev95wUtyjK+m8VtLeJOfLCvWrLy4PoOZAOz/HrWqELIqJertNHbtKUK6ZoYEr0NlOO\nzQ7X3f168BMKB5wqn2PKhvO/V3Erh0fWWzN8/sw04GRmF0v6O3kRzs875/4yYpsbJf2SpIOSVjvn\nHqrcxttu1Za686tUOjxnmY7TMl0zOhQKgHhzUWyQH6mPiiIvnagZMCmKW/L45qckXWp21Q0dm3yz\nOpNV95R4pcJR6AGVejst0IuxvRFKDzjlLZFBIKG6p8yLu1/W+rfMCwWTygpt78KvmAB1s2Zrl64I\nJocuPvA7v+tu+GboTew5uazg2HGc5I7+OLqnRYNKwctSYbdOi23RlBfLWhklac/0w7I9+/TKQ6UB\nxUHwIOmKHY1WQAM1evY1HJRYpaGqikfxgytaKMofcqVGe+RsVvmwg2B1l5dOc3r6WvMmKJ77rM66\n99T6yyireujoZAV5pLw3Ub0Jv8PzFdUstINr5bvLpa2hJWIlyUkLY+YwiRNuEQ8f+1Z6cvmBrGJQ\nJCJPmi26UgumbtDqeyeVXUvBNeYF8oNywk9D9SpokkxDc67TtS9U3rSHtEFPaNfwV4sV6aj5XS6U\n9MXJh9zTR+dHfRWvF2YQ8IipeDW1xG5JXOUl7jqypbal7jwezag+b/HDQ8L5Y4M/VNd7QF0QFSxv\neuhl0KvhyBynSZN+VjZnjy2c65V9dVemrM855+zEKYc1/ZSCJu/eo/GfmS53wiTpcWn04ooJagvl\nx6D8uHuLCzRYJsYOFQ32PW1sUGecXNDEjhma/pMVGp8tjfux68lH4r+YVy5NanRYdVmrfjiYEy6/\nKgMxVau2ygvezNWbbLFdHH0soh9yG+5xEeTRUsNfMZHSXum22fs0UnhN3QeE8vLOK3c62Su4sswP\n7q+Tx5zOOLngDTX3hndVT4DsD+mrVw400hu13tyJtme/dNOj0qXLayx+cqqW6DmN3zNLR8q/QyvD\nuGP9iqbXXp13Z0tzXNZsHIhT3dP8AW0/faq0frleMceb/yTqQTJuX1G98KJ6c1Tuc5f2xl33TX2v\nOFdPDGjzWGWa/ePgX5PaG30WagXMKns3hRuxj06SJlTQpJjbrRf88p4ZKh/4g8nCR8+YLr00Lrmb\ngl+VB101pM2JbufxaZZq9RJepqDRZv+h/9CuJf9Fev565/aG5uGtfF500uifHdbo8+PV21Z8h7C4\nRtFW5zwNK+b7g9Hn1MuzpYYq//PrlVGhkT0t1XpqBNiD+kvMlAEtzpFXXR7sU9zcfXttXF/63o/9\nRq6hqr/frCFN1pB+eVzaPF4aRVO++ESxl2aCdPnPAuHh/BdJQ0ulOfOkSQ8fKVssZb62S1od3k3H\nV6UPySzgZGYDktZKepu82b0eMLM7nXOPhbZZKWmRc+5VZvbzkj4jKXos5Bk7V3i9k546QXGtSJXZ\nf7Ibqr96QMQKOFMfr31BB6sLna7FOu6yaV7A3qRxkw7/vTTj8InSwPk6OjW83Gl4fpjUlR4ig8rz\nvyjRw7r/fbREmysrJSposeY1GEyQpD2zPqg9f+AXkhsPSB++3rm9w3ay3VqzIFkVnhzaV/mAFffA\ntX56QU9f4t9cHm7srlQe7XaK6h47XXP1q8emVLcynjxLU17a3fSQpmYqoOF0x/fsa034WE8UvFbn\noFKhid3Fh50jx71RX5gqTdk3s/YwygpVww4K0m27D2nv648rTlD88mrp1ge8IVuHCyf4vV9mKq6L\nblxlKUnLpFQ2X1HdQKEt+pQ09QvSe6dK7wh9j40HNHDHdtXoLltXVOW1GXWHdowMy2Z7Fe64ay78\nc1C5PO7RSTpnSCocnKIp4zdoj07V1JiWR0llw7uSzO9SIVHFq4EldgMNznlQ/F1s3ggqcOGeWsc0\nRZN1f9l+w59Tfa0uiw1A1lOvLGli6KV/jK7QqoI/n0do7o71rx3SnutmtnJPK56DeVrmD+c9rayx\nZ/23CpFDcDbL66kwMXm6ri4GMMP3jwbKxBrLtnv7mi7tkDZrhi48JK/r/UuldNRSXblNFkAJFryo\nDDjW20/ldXyhBrRZJ0k6qXj9ztWbbKldoXN0WAM7H9cerbeTrTy/BxNxHyz8VVUQI+kcOYHxsYmq\nOY2S9gjcr8XFIOIBTdcM/3p2SnSPr+p9FQgF7IoPeJUPQGVB5R1SkAfLV+JqvmdjZbC0gZWKJPk9\nTp70AoPxi5/4vWAKKvsOnVy9ONR6ryXaXAyeFnRYprkKpkYIHtbqKLbu1x3OGpuWZTrzO04TJ0lT\nD5TqBJUNPLXydVQQ2etBXT1kPhRkrnVeMx0RkfDYFwXHqlgHnZCkgWIw6alB6bjpkg5Lk47+f/bu\nPd7Oqr73/WesS+5XSAADQoAAIdQSu7FFtzZQrwWLVgRrT6tgPbVFS23d3dJ6Trfdr1df+1Tt2VuL\n9uVLyUV7uMSe4FaItNSToFu8lAoWREJIDMolFyBrJVm5rTXXOH8885nzuYznOp9nzrnW+r5fr/mC\ntTLXnM91PGP8xhi/4f5bXyhZ+K8/5C1e0JQVdK1CcCqY/9zdPP5AqKOsJTrqCGKL7ES5nrv+cdsW\nuV4mebW5xKwMdwpUsJBS0j4HniNF7qfsWQARjoBuK3k4EA4qNt8xkvC3fme1l9ct/NxKS6+TPIsg\nacQdjMx9iJHGa1jDNhKmqbVmzQTviSx5BohAc9aFGWDp0Ulu+NYAwWmkkKscryMPc5JejnD6ZeAp\na+0eAGPMncDbgGBy3WuATQDW2u8bY5YYY0631u6LfdqND4A/OinthIYKs4QoftDLIlPu5vwAzphM\nb+j5KzFsA66cpFkR93gFyRK2NZaEKs57H4ZT7m5w/NA1wJY8F0GRylKL39N/YvYAdyyGgSMkTofy\nj80ClrV76QKVkjsHG6WmUiUN4S778PAaFxl+YRC+si401cZ1U0cDaNGH310cA0dCxKScURnz2gvf\n7IHKWe7VBaOCqw0mJXgOnoPcicCblYovLZnD/nmrOeXARSw8+SInz13I4RcHuOM4TMyBxqmTzH38\nOK7jmGb42G74+9WtBMWjG2DUW62kVclMmZ/c4sqf4PdMplWsHOcx8fy9jD2c+NmzLN52OpNrZ4em\nhppG4kpLsWs/ui05hiLnXoXHJTCf3NqsZcgj2xUawXAIvLFk69jMKMubZaarp3hNYFRA0fwuNPd3\nDStbq3/5yUJXmGs6emBmBWDcI5jWOoPmwQrc04HE4MaYIqOncl3fUX4jyv375G1OEepZTux0cIw0\nK/P516c07oZaU5Xa/GP9pSXjTJ44CePFcvnFdDClNyjcCM0MGKc+35cyx3lckp4PWY2N8P3bHt0M\np/Ol5tSB2JLOjYRpO6nfFLcQ42xw3TnYrFykdFTMG1zG6Y3BePkTaNz6Dfp2Y2N1bIRg+G/B3/cy\ngZfgSlxZxyJPubENmvfYutB7Xec0+H1DjbNYs+uPOLHnaOozJ0mwfnRi8CJmN3a0fp9HNOji5e9x\n34uuUeP+qMzNsWeZK5jpqg8l1yfTOm2up9kRfcAbgVhUQhC5tQhEUudNcORppP4XeqYHywOvrj+H\nOrkayq6Amn9dBO+ZxsBrIdBe8j9r77A3qin6WS6hHGlP9VEqkngeRHfn11OfhP13x1bF9uUpm9tm\nERgN4xptmriQUtYI/ixJ91PyPXgW69nXvFbdHcFHOM5mHoFAOex1fHhlwVKa+aWa5bkvbXBBdLR/\nJ53wSSMWAdgJ/PJCuG4bz39nNV86OsqskeQyrgBn5waOYz/7Ccs562Do2QbRaX/Nel8olYgr2JaW\nO7ZivQw4nQkE5/w+A/xKjvecRav7MCBvA7noe1tz0h93vz+5JyO7Ihp68BwCWMS+wd8x5y99O4sH\nF3B9wzVVKT6doh0N9/k3UzyvS7Snv10pa7DccWOl3XB2wWAraFeg9yNrSH4h/jG8YzEklafbgDmP\nwDnrwitXwB7HqlDrUvfHJMyFSvp91rz2Mgmd3ZWz/CZXLGTxT9ax5BCcGIc7hsFMTgKWsQYc4AiW\nebyrObE7WtHIOteNSxez+MBCfve5AeCM8IiES+HpeT9gzeMnyGo4RwvXocZZnPuNwxx8/QJG/r8h\n50iWvD0DLsGKVbChEvzsqNTzN/4c149fEJoaCovYnLBsyRGOAxQaHRB9z+Tga1nMa0NlR3R/Tgxf\nxHDjNJiMV6ajI4nqSLScVjlI0gqSWmJTtFrn4BCtZP+wjsnB15rzl74dgEEzy7lazNH5rzZLzz7J\nkqPNYzHZYMAew4wdYyg90WfCfnn7k7PRXUvlOX7thoM2wR5Cb2h3fJvD2x8fXZGncyCQ86zwPswb\nfjPXj2dPQfIbwS6NSxdjDuSb/n4kNCqlzct3dYO3bPuHNjHaHpXLmkM3Ey3DkgLGBXo3ncG26PM9\nuxMg+nzI39iIbuOwTSuP0keFlnUlsOG1g4ys9EaxJnVQ+HWQpM+AcIPfK6vbjY2iwbEqhO9PfzRY\nernhLjPbOx69p0NBqkbm6n4uoYBHQmA8sXPDdb0HO+XaDcvUSQbOaTXRAFTw+LW/053LDrJHHOXh\nb/9mGowx2RxZV27EcZ4k8tFpXsFRN0XqCHnq6VnvdQTUWgG+KDt5GGcXX8JiCa7t8BvVeTuSOmmT\nFZD8XIt3qIcCQdHzVeLejH9fjrxl7eAOuOrF2/BGBRv+tfW7g+xJHNmUXY/wyif/GeY6L+PssT+2\nN4Q60qKpXXpRRkcFt2HvQIPjawYZWW7hq15n7EHg4D1jnHvtUThZfLEqT768wOFO/kH4FtzFOH4a\nGb9cnSCejsMdbOssEFlALwNOeSuh0Up/8t9FG4wAewfhyKmw9wSYMbyzQKEKYOjzo5V2nzf/+qv2\nudbNU/xBdiV4vXmOJKO+rOkURRtzOUZMJLPFj6NfsfeH5M9ueMGfDpd+z7yc3j1Ks+HfHnLYPC6h\nnmS/kPQSm4LhaCginOfB1br2gKHG6tjv22pJJJxZYTDPHGHJ2Yu48WfBv/Ea3Ps5CjyC5dWxv8t9\nrm0z35EjOWizEcpBrmEzMDr0GvbOG4YjDQYnjwR67OLzoWExjMOmfzvESDxJK6T0DBThGE5cKf/B\nHx25EMz+VaSCCIHj1Ig/oKMNLz83k6shXtdKXlGOe6EZHD+LzTzgJeU8e5F3Xy+DkZWLYeO61FVY\nfMFjQXNUxl0cdb7XnjeLxcANrbjwIF6jaHGpB3H0vCXvZ3YONl+Roc2uofCueyDaQ5j2WaGpBLm2\nwhPMeVZU0tRKiAXDkj8kpRyKCo5KCWrtr3NUrns1pegIBleAIW21tbRgXp7z5pK0OlSStJHg4f0r\nP623/ZnJQSt/FGtiB0X18drCigTh/GO3nuOlQ3Wu4xYMNEW/y/ubjqZfJzauo9dqNKl7u+OqXaeN\nTrcqcD3n6vkfIZxzMmnEUTB41X4Wx0dibAP2N0coB70b8BrEg7n2I2mKdBWBEHcH2VmhAEN0RGRa\n56G/P+EgQXKQqOiKfQOTJ8gzwt2bjjcYqrvnac+0vz/tXBMa7VhiCpHzmb0GOPDN48HFUJzb6R/b\n4RL35tGlr27NUuCdwMO7rd2XXhgOD1sefxtg4Lz7CZ38dr3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fja5SN84eiNTZ2ipdFCRP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+PDH3b87tFe73TZA1DnhTFdLsai29tPBUf+HqMy+arKJ8bVq55rT6+0Y9mdIGie79F57b/zVu8+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\nlKURTiIiI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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a267d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(20,10))\n",
    "plt.plot(c_tau1,'v',label=\"Tau1\")\n",
    "plt.plot(c_tau2,'s',label=\"Tau2\")\n",
    "plt.title(\"Coefficients - \" + sim_set)\n",
    "plt.xlabel(\"Readout index\")\n",
    "plt.ylabel(\"Weight value\")\n",
    "plt.legend()\n",
    "plt.savefig(os.getcwd()+\"/\"+base_dir+\"/\"+\"coefficients_\"+sim_set+\".pdf\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DON'T FORGET TO SET PROPERLY THE SIMULATION'S VARIABLES!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def new_experiment():\n",
    "    global run_number\n",
    "    run_number+=1\n",
    "    #####DEBUG\n",
    "    #####Reads exactly the same input spikes using during the training to check if the system is reproducing the same simulation!\n",
    "    # inputs_training = slf.load_from_file_gz(\"./\"+base_dir+\"/\"+sim_set+\"/States_Torques_movement\"+str(tji)+\"_INPUTS_LSM_1.gzpickle\")\n",
    "    # tau_temp = []\n",
    "    # teta_temp = []\n",
    "    # torque_idx = []\n",
    "    #####END_DEBUG\n",
    "\n",
    "    import numpy\n",
    "    import time\n",
    "    import save_load_file as slf\n",
    "\n",
    "    import step_by_step_brian\n",
    "    reload(sys.modules['step_by_step_brian']) # Makes sure the interpreter is going to reload the module\n",
    "    step_by_step_brian_sim = step_by_step_brian.step_by_step_brian_sim\n",
    "\n",
    "\n",
    "    #\n",
    "    # It's necessary to uncomment the import statement according to which parameters should be used\n",
    "    #\n",
    "\n",
    "    # Simulator based on Joshi/Maass 2006 parameters\n",
    "    import simulation_2DoF_Arm_md \n",
    "    reload(sys.modules['simulation_2DoF_Arm_md']) # Makes sure the interpreter is going to reload the module\n",
    "    simulation_2DoF_Arm = simulation_2DoF_Arm_md.simulation_2DoF_Arm\n",
    "\n",
    "    import membrane_lowpass_md\n",
    "    reload(sys.modules['membrane_lowpass_md']) # Makes sure the interpreter is going to reload the module\n",
    "    membrane_lowpass = membrane_lowpass_md.membrane_lowpass\n",
    "\n",
    "\n",
    "    # Calculates the initial teta1 and teta2 angles according to the start position\n",
    "    teta1_0, teta2_0 = hand_to_joints(numpy.array([[xstart,ystart]]),aparams,ang_error=0.01)[0]\n",
    "\n",
    "    step_size = 2\n",
    "    \n",
    "#     noiselevel=0\n",
    "    noiselevellist = [1.0,2.0,10.0]\n",
    "\n",
    "    ################## Simulation's variables\n",
    "    simulation_2DoF_Arm.func_globals['my_seed']=93200         # This seed is important to always repeat the liquid's structure\n",
    "    simulation_2DoF_Arm.func_globals['input_gain']=70.0    # Base value used within the input weights\n",
    "    simulation_2DoF_Arm.func_globals['noisy']=1            # Controls if the liquid is going to use the noisy currents/input weights\n",
    "    simulation_2DoF_Arm.func_globals['gaussian_pop']=1         # Controls which type of input weight configuration is used (1=>gaussian distributed)\n",
    "    simulation_2DoF_Arm.func_globals['w_SD']=3.0           # The width of the gaussian used above.\n",
    "    simulation_2DoF_Arm.func_globals['stratification']=1   # Type of connections: stratified or 30% random\n",
    "    simulation_2DoF_Arm.func_globals['STP_OFF']=True      # Uses or not STP (False means it uses STP)\n",
    "\n",
    "    simulation_2DoF_Arm.func_globals['i_noiseA'] = noiselevellist[noiselevel]*1.0/1     # amplitude (dimensionless) of the random currents (updated every time step).\n",
    "    simulation_2DoF_Arm.func_globals['offset_noise'] = (13.5/1,14.5/1) \n",
    "                              # Range of the uniform distribution used to generate the constant offset current.\n",
    "                              # Maass 2002, should be drawn from a uniform distr [14.975nA,15.025nA]\n",
    "                              # Joshi/Maass 2005 does [13.5nA,14.5nA]. Maybe it is to avoid too many spikes without inputs...\n",
    "\n",
    "    simulation_2DoF_Arm.func_globals['initial_volt'] = (13.5,14.9) \n",
    "                              # Range of the uniform distribution used to generate the initial membrane voltage.\n",
    "                              # Maass 2002, should be drawn from a uniform distr [13.5mV,15.0mV]\n",
    "                              # Joshi/Maass 2005 does [13.5mV,14.9mV], this way it's impossible to generate spikes (14.9mV is below threshold)\n",
    "\n",
    "    simulation_2DoF_Arm.func_globals['random_reset'] = (13.8,14.5)    # Range of the uniform distribution used to generate the constant reset voltages.\n",
    "    simulation_2DoF_Arm.func_globals['Net_shape'] = (20,5,6)          # Defines the shape of the network (liquid state machine)\n",
    "    simulation_2DoF_Arm.func_globals['Number_of_input_layers'] = 6    # It means I will have 6 input layers...\n",
    "    simulation_2DoF_Arm.func_globals['Number_of_neurons_inputs'] = 50 # ...with 50 neurons each.\n",
    "    simulation_2DoF_Arm.func_globals['lbd_value'] = 1.2               # lbd controls the connection probability\n",
    "\n",
    "    simulation_2DoF_Arm.func_globals['noisy_currents'] = True # Injects noisy currents the neurons\n",
    "    simulation_2DoF_Arm.func_globals['noisy_input_weights'] = False # Injects noise into the input weights\n",
    "    simulation_2DoF_Arm.func_globals['noisy_input'] = True # Ignores the random seed for the input noise\n",
    "    simulation_2DoF_Arm.func_globals['input_noise_level'] = 0 # 1/SNR\n",
    "\n",
    "    # STP parameters\n",
    "    simulation_2DoF_Arm.func_globals['AII']=47.0  # Joshi/Maass2006=>47.0 / Maass2002=>2.8\n",
    "    simulation_2DoF_Arm.func_globals['AIE']=47.0  # Joshi/Maass2006=>47.0 / Maass2002=>3.0\n",
    "    simulation_2DoF_Arm.func_globals['AEI']=150.0 # Joshi/Maass2006=>150.0 / Maass2002=>1.6\n",
    "    simulation_2DoF_Arm.func_globals['AEE']=70.0  # Joshi/Maass2006=>70.0 / Maass2002=>1.2\n",
    "    \n",
    "\n",
    "    indices2kill=numpy.arange(600)\n",
    "    numpy.random.shuffle(indices2kill)\n",
    "#     decimationlevel=0\n",
    "    decimationlevellist=[0,6,60]\n",
    "    simulation_2DoF_Arm.func_globals['killed_neurons']=indices2kill[:decimationlevellist[decimationlevel]] #Sets the indices of the neurons I whant to 'kill'\n",
    "    \n",
    "    \n",
    "\n",
    "    cheat_feedback = 0 #Uses the torque values from the mechanical simulation instead of the calculated ones by the LSM\n",
    "    using_spikes = 0 #Uses only the spikes without the membrane low-pass filtering\n",
    "    membrane_time_constant = 30E-3 #time constant used with the membrane low-pass filter\n",
    "\n",
    "    delta_t = 200 # Time delay imposed to the joint angles - proprioceptive (in ms)\n",
    "    ################## END OF Simulation's variables\n",
    "\n",
    "    # Starting the simulation object!\n",
    "    # Initializing the simulation...\n",
    "    s = step_by_step_brian_sim(simulation_2DoF_Arm, step_size)\n",
    "\n",
    "    init_t = time.time()\n",
    "\n",
    "\n",
    "    # The input (one big neurongroup with 300 neurons) will be divided like this:\n",
    "    # 6 groups of 50 neurons.\n",
    "    # - Group 1: xdest => offset:0\n",
    "    # - Group 2: ydest => offset:50\n",
    "    # - Group 3: teta1 => offset:100\n",
    "    # - Group 4: teta2 => offset:150\n",
    "    # - Group 5: tau1  => offset:200\n",
    "    # - Group 6: tau2  => offset:250\n",
    "    input_spikes = [x_lsm, y_lsm] # At the first step the system only receives the X,Y destination position\n",
    "\n",
    "    delta_t_idx = int(delta_t/(1000*time_step)) # Number of steps the delay (delta_t) takes\n",
    "\n",
    "    m_v=membrane_lowpass(600,membrane_time_constant) # Initialize the membrane\n",
    "\n",
    "    teta = [] # Used to save the calculated teta1 and teta2 angles\n",
    "    torques = [] # Used to save the calculated tau1 and tau2 torques\n",
    "    velocities = [] # Used to save the calculated joint velocities\n",
    "    OutputSpks_save = []\n",
    "    OutputSpks_Filtered_save = []\n",
    "\n",
    "\n",
    "    tau1 = 0 # Initial values of the torques (because of the type of velocity curve, it's always zero at the beginning)\n",
    "    tau2 = 0\n",
    "\n",
    "    teta1 = teta1_0 # Initial values of the joint angles\n",
    "    teta2 = teta2_0\n",
    "\n",
    "    if delta_t==0:\n",
    "        teta.append([teta1_0,teta2_0]) # otherwise the generation of joint_states_idx will raise an error.\n",
    "\n",
    "    # Runs the simulation going step-by-step (250 steps = 500ms)\n",
    "    for step_i in range(250):\n",
    "\n",
    "        # Because the LSM accepts only indexes, I have to convert the real values to indexes\n",
    "        # torques_values_1 and torques_values_2 are numpy.arrays with all the possible torque values.\n",
    "        if cheat_feedback:\n",
    "            torques_normalized_idx = [abs(torques_values_1-torques_all_1[(tji-1)*250:(tji+1)*250][step_i]).argmin(), abs(torques_values_2-torques_all_2[(tji-1)*250:(tji+1)*250][step_i]).argmin()]\n",
    "        else:\n",
    "            torques_normalized_idx = [abs(torques_values_1-tau1).argmin(), abs(torques_values_2-tau2).argmin()]\n",
    "\n",
    "    #####DEBUG\n",
    "    #     tau_temp.append(torques_normalized_idx)\n",
    "    #     torque_idx.append(torques_normalized_idx)\n",
    "    #####END_DEBUG\n",
    "\n",
    "        if step_i>=delta_t_idx: # Tests if the delayed angles can be feed to the LSM\n",
    "            # teta1_values and teta2_values  are numpy.arrays with all the possible joint angles.\n",
    "            joint_states_idx = [abs(teta1_values-teta[step_i-delta_t_idx][0]).argmin(), abs(teta2_values-teta[step_i-delta_t_idx][1]).argmin()]\n",
    "\n",
    "            # Here I start to send the teta1 and teta2 delayed inputs\n",
    "            # It is necessary to convert the values of teta1, teta2, tau1 and tau2 to input indexes!!!\n",
    "            input_spikes = [x_lsm, y_lsm, joint_states_idx[0]+100, joint_states_idx[1]+150, torques_normalized_idx[0]+200, torques_normalized_idx[1]+250]\n",
    "    #####DEBUG\n",
    "    #         teta_temp.append(joint_states_idx)\n",
    "    #####END_DEBUG\n",
    "        else:\n",
    "            # It is necessary to convert the values of teta1, teta2, tau1 and tau2 to input indexes!!!\n",
    "            input_spikes = [x_lsm, y_lsm, torques_normalized_idx[0]+200, torques_normalized_idx[1]+250]\n",
    "\n",
    "    #####DEBUG\n",
    "    #####Replaces the calculated inputs by the ones used during the training!\n",
    "    #     input_spikes = inputs_training[step_i]\n",
    "    #####END_DEBUG\n",
    "\n",
    "        # Here the simulator is receiving the input spikes (and returning the output ones):\n",
    "        r, current_t, received_spikes, OutputSpks, t = s.run_step(input_spikes) # Sends the spikes and reads the output\n",
    "\n",
    "\n",
    "        if OutputSpks!=[]:\n",
    "            OutputSpks_save.append([OutputSpks[0],t_mov[step_i]])\n",
    "        else:\n",
    "            OutputSpks_save.append([[],t_mov[step_i]])\n",
    "\n",
    "        if using_spikes:\n",
    "            Output = numpy.zeros(600); Output[OutputSpks]=1 # Using the output of the liquid directly\n",
    "        else:\n",
    "            m_v.process_spikes(OutputSpks,t_mov[step_i]) # Send the spikes to the membrane filter\n",
    "            OutputSpks_Filtered = m_v.check_values(t_mov[step_i]) # Reads the values of the membranes\n",
    "            OutputSpks_Filtered_save.append(OutputSpks_Filtered)        \n",
    "            Output = OutputSpks_Filtered # Using the low-pass (membrane) filtered version of the liquid's output\n",
    "\n",
    "        torques.append([tau1,tau2])\n",
    "        tau1=(Output.dot(c_tau1)+r_tau1) # Calculates the torques based on the membrane filtered output of the liquid\n",
    "        tau2=(Output.dot(c_tau2)+r_tau2) # These are real numbers, not input indexes!\n",
    "\n",
    "    #####DEBUG\n",
    "    #     tau1=torques_all_1[(tji-1)*250:(tji+1)*250][step_i]\n",
    "    #     tau2=torques_all_2[(tji-1)*250:(tji+1)*250][step_i]\n",
    "    #####END_DEBUG\n",
    "\n",
    "\n",
    "    #####DEBUG    \n",
    "    ##### Moving averaging the torques before sending to the simulator\n",
    "    #     ws=2 #window size\n",
    "    #     temp_tau1_v = numpy.concatenate((numpy.array(torques)[step_i-ws:,0],[tau1[-1]]))\n",
    "    #     temp_tau2_v = numpy.concatenate((numpy.array(torques)[step_i-ws:,1],[tau2[-1]]))\n",
    "    #     tau1[-1]=moving_average(temp_tau1_v, window=len(temp_tau1_v))[-1]\n",
    "    #     tau2[-1]=moving_average(temp_tau2_v, window=len(temp_tau2_v))[-1] \n",
    "    #####END_DEBUG\n",
    "\n",
    "\n",
    "        if step_i>0: # Because the simulator needs two time steps to be able to calculate the next state\n",
    "            state = odeint_arms(twojointarm, state, [t_mov[step_i-1], t_mov[step_i]], aparams, [tau1,tau2])[1]\n",
    "            teta1,teta2 = state[0],state[1] \n",
    "            teta.append([teta1,teta2])\n",
    "            velocities.append([state[2],state[3]])\n",
    "        else:\n",
    "            state = [teta1_0,teta2_0,0,0]\n",
    "            teta.append([teta1_0,teta2_0])        \n",
    "            velocities.append([0,0])\n",
    "\n",
    "    print \"Total simulation time (seconds):\",time.time()-init_t\n",
    "\n",
    "    slf.save_to_file(numpy.array(torques),\"./\"+base_dir+\"/\"+sim_set+\"/torques_\"+str(tji)+\"_noise_level_\"+str(noiselevel)+\"_killed_neurons_\"+str(decimationlevel)+\"_run_\"+str(run_number)+\".pickles\")\n",
    "    slf.save_to_file(numpy.array(teta),\"./\"+base_dir+\"/\"+sim_set+\"/teta_\"+str(tji)+\"_noise_level_\"+str(noiselevel)+\"_killed_neurons_\"+str(decimationlevel)+\"_run_\"+str(run_number)+\".pickles\")\n",
    "    slf.save_to_file(numpy.array(OutputSpks_save),\"./\"+base_dir+\"/\"+sim_set+\"/OutputSpks_save_\"+str(tji)+\"_noise_level_\"+str(noiselevel)+\"_killed_neurons_\"+str(decimationlevel)+\"_run_\"+str(run_number)+\".pickles\")\n",
    "    slf.save_to_file(numpy.array(OutputSpks_Filtered_save),\"./\"+base_dir+\"/\"+sim_set+\"/OutputSpks_Filtered_save_\"+str(tji)+\"_noise_level_\"+str(noiselevel)+\"_killed_neurons_\"+str(decimationlevel)+\"_run_\"+str(run_number)+\".pickles\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# %%capture\n",
    "\n",
    "# # Here I'm going to run 5 trials for each experiment\n",
    "# for tji in [1,2,3,4]: #[1,2,3,4] are the training set trajectories\n",
    "#     x_lsm,y_lsm,xstart,ystart,xdest,ydest = generates_lsm_start(tji,trajectories,x_values,y_values)\n",
    "    \n",
    "#     for decimationlevel in range(3): #[0,6,60] neurons\n",
    "#         for noiselevel in range(3):  #[1.0,2.0,10.0] multiplication\n",
    "#             run_number=5\n",
    "#             while(run_number<25): #trials for each situation\n",
    "#                 new_experiment()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1+1+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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UJEmSJEmSJElSVTLIkiRJkiRJkiRJUlUyyJIkSZIkSZIkSVJVMsiSJEmSJEmSJElSVTLI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TwLHNr+eWZe2sPDjgb+CdgG2BXYDvj3ek+OiM8B/wH8C7AVsAPwfWByD8/fE7Eez7VeGWRJkiRJ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\nzUL6ALgjpgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x606c310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_number=24\n",
    "tji=3\n",
    "noiselevel=0\n",
    "decimationlevel=0\n",
    "x_lsm,y_lsm,xstart,ystart,xdest,ydest = generates_lsm_start(tji,trajectories,x_values,y_values)\n",
    "\n",
    "torques = slf.load_from_file(\"./\"+base_dir+\"/\"+sim_set+\"/torques_\"+str(tji)+\"_noise_level_\"+str(noiselevel)+\"_killed_neurons_\"+str(decimationlevel)+\"_run_\"+str(run_number)+\".pickles\")\n",
    "\n",
    "ymin = numpy.array([torques_all_1.min(),torques_all_2.min()]).min()\n",
    "ymax = numpy.array([torques_all_1.max(),torques_all_2.max()]).max()\n",
    "plt.figure(figsize =(30,10))\n",
    "plt.subplot(2,1,1)\n",
    "plt.plot(torques[:,0],'v-',label=\"Tau1 Calculated\")\n",
    "plt.plot(torques_all_1[(tji-1)*250:(tji+1)*250],'s-',label=\"Tau1 Original\")\n",
    "plt.title(\"Tau1\")\n",
    "plt.xlim(0,250)\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(2,1,2)\n",
    "plt.plot(torques[:,1],'.-',label=\"Tau2 Calculated\")\n",
    "plt.plot(torques_all_2[(tji-1)*250:(tji+1)*250],'o-',label=\"Tau2 Original\")\n",
    "plt.xlim(0,250)\n",
    "plt.title(\"Tau2\")\n",
    "# plt.ylim(ymin,ymax)\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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uZroAABqILmDXWWIEWlheBHbWviXGq6suOLEeYOFEF7DT7O0CulheBABoILoA9tjbBSyV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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f09f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_number=1\n",
    "tji=1\n",
    "noiselevel=0\n",
    "decimationlevel=0\n",
    "x_lsm,y_lsm,xstart,ystart,xdest,ydest = generates_lsm_start(tji,trajectories,x_values,y_values)\n",
    "\n",
    "teta = slf.load_from_file(\"./\"+base_dir+\"/\"+sim_set+\"/teta_\"+str(tji)+\"_noise_level_\"+str(noiselevel)+\"_killed_neurons_\"+str(decimationlevel)+\"_run_\"+str(run_number)+\".pickles\")\n",
    "\n",
    "H,E = joints_to_hand(teta,aparams)\n",
    "\n",
    "plt.figure(figsize =(10,10))\n",
    "plt.plot(H[:,0],H[:,1],'.',markersize=1)\n",
    "plt.plot([xstart,xdest],[ystart,ydest],'.-')\n",
    "plt.xlim(-1,1)\n",
    "plt.ylim(-1,1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3a6394dbaH738+4+IyPeJyP8pIj8vIp++hH1aRH7u8u+fF5Efaa19U2vt20Tk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\nbuP3nPWyZi43A04iavZcW47XrzxL+VS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      "text/plain": [
       "<matplotlib.figure.Figure at 0x3f6e750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_number=1\n",
    "tji=1\n",
    "noiselevel=0\n",
    "decimationlevel=0\n",
    "x_lsm,y_lsm,xstart,ystart,xdest,ydest = generates_lsm_start(tji,trajectories,x_values,y_values)\n",
    "\n",
    "OutputSpks_save = slf.load_from_file(\"./\"+base_dir+\"/\"+sim_set+\"/OutputSpks_save_\"+str(tji)+\"_noise_level_\"+str(noiselevel)+\"_killed_neurons_\"+str(decimationlevel)+\"_run_\"+str(run_number)+\".pickles\")\n",
    "\n",
    "y = numpy.array([j for i in OutputSpks_save for j in i[0]])\n",
    "x = numpy.array([i[1] for i in OutputSpks_save for j in i[0]])\n",
    "\n",
    "plt.figure(figsize =(20,10))\n",
    "plt.plot(x*1000,y,'.',markersize=1)\n",
    "# plt.xlim(-1,251)\n",
    "plt.ylim(-1,601)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}