2DofArm_simulation_testing_learned_readouts-D-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[3]\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": [],
   "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]*1.0/100     # amplitude (dimensionless) of the random currents (updated every time step).\n",
    "    simulation_2DoF_Arm.func_globals['offset_noise'] = (13.5/100,14.5/100) \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,
    "scrolled": true
   },
   "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<15): #trials for each situation\n",
    "#                 new_experiment()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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D64C7geuBVcCjwCeTxr8K/IJYIOhe4NkOx7d9/3fA2vif4YC132JmHwD/DjxMLLD1ArFS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cJumTwH1SvZKracXFMZV60i7Ocs5gRGlC2/k5U0sKRsfjCAKczIJ3OCVV+SJEmSJGlL0Rvu8zcP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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8637f90>"
      ]
     },
     "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",
    "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": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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oAgBoILoAABqILgCABqILAKCB6AIAaCC6AAAaiC4AgAaiCwCggegCAGggugAAGoguAIAG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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6c1b950>"
      ]
     },
     "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": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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/P7ZKc84+AAAAfLzx672/8ejZf+2H99S2ertZa+0Pt9b+6O3//4iIfLuI/J8i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\n41WvplxmPy35eM25pW3PmJF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      "text/plain": [
       "<matplotlib.figure.Figure at 0x77ea650>"
      ]
     },
     "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
}