{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pylab as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from objsimpy.stim.stim_sequence import InputTrace\n",
    "from objsimpy.som_results import show_preferred_stimulus_maps, show_preferred_stimuli_x_y\n",
    "from objsimpy.eval_sim_data import calc_response_strength2_PICKLED\n",
    "from objsimpy.eval_sim_data import calc_response_strength2, calc_preferred_stimuli_from_response\n",
    "from objsimpy.som_results import evaluate_single_sim, calc_response_selectivity\n",
    "from objsimpy.sim_data import SimInfo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "simname = \"som02_example\"\n",
    "from objsimpy.config import SIMDATA_BASE_DIR, OBJSIM_DIR\n",
    "import os\n",
    "data_dir = os.path.join(SIMDATA_BASE_DIR, 'objsim', simname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ls -l $data_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import objsimpy.spike_file as spike_file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spike_file_name = os.path.join(data_dir, \"RepresentationLayerspikes.datTEST3.bin\")\n",
    "spikes = spike_file.read(spike_file_name)\n",
    "spike_times, neuron_numbers = spikes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t1 = 10000\n",
    "t2 = 110000\n",
    "plt.figure(figsize=(16, 5))\n",
    "plt.plot(spike_times[t1:t2], neuron_numbers[t1:t2], '.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from objsimpy.stim.stim_sequence import read_input_file, InputTrace\n",
    "fname_input = os.path.join(data_dir, \"MovieScanInput0.inp.datTEST3\")\n",
    "input_trace = InputTrace(fname_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(input_trace.nparray[t1:t2], '.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "response_strength_pickle_file = os.path.join(data_dir, spike_file_name + \".response.pickle\")\n",
    "response_strength = calc_response_strength2_PICKLED(\n",
    "    response_strength_pickle_file, \n",
    "    spikes, \n",
    "    input_trace,\n",
    "    100, 500,\n",
    "    10000,\n",
    "    #reevaluate=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(response_strength[:,200], '.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim_info = SimInfo(os.path.join(data_dir, \"Sim.SimInfo\"))\n",
    "layer_name = \"RepresentationLayer\"\n",
    "input_name = \"MovieScanInput0\"\n",
    "layer_info = [l for l in sim_info.get_layers() if l.name==layer_name][0]\n",
    "n_neurons = layer_info.LayerDimensions['N']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_mean_response = response_strength.mean()\n",
    "print(total_mean_response)\n",
    "plt.plot(response_strength.argmax(axis=1), '.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "preferred_stim = response_strength.argmax(axis=1)\n",
    "nx = sim_info.get_inputs()[0].NXPara\n",
    "ny = sim_info.get_inputs()[0].NYPara"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pref_stimuli, selectivity = calc_preferred_stimuli_from_response(response_strength)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.colors import Normalize\n",
    "from objsimpy.colormaps import cmap_color_circle\n",
    "norm = Normalize(vmin = 0.)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "preferences = [pref_stimuli // nx, pref_stimuli % nx]\n",
    "plt.imshow(preferences[0], origin = 'lower', interpolation = 'Nearest', cmap = cmap_color_circle(), norm = norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.imshow(preferences[1], origin = 'lower', interpolation = 'Nearest', cmap = cmap_color_circle(), norm = norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "stim_onsets = input_trace.get_stim_onset()\n",
    "stim_numbers = [onset[1] for onset in stim_onsets]\n",
    "plt.hist(stim_numbers, bins=list(range(-2,410)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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