Figure_2C.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import my packages\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import math\n",
    "from mpl_toolkits import mplot3d\n",
    "from matplotlib import animation\n",
    "from scipy.fft import fft, ifft, ifft2, fftfreq, fft2, fftshift\n",
    "from scipy import signal\n",
    "#from figure_1 import gauss_FT,time_filter,time_filter_der, gratings, contrast_fourier, filtered_contrast_f, filtered_contrast_f_dev, g_vm_cal_calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here I run all my functions from fig one\n",
    "%run fig1_functions_latest.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "###### parameters\n",
    "t = np.arange(0, 12, 1/240)\n",
    "x = np.arange(-180, 180, 0.5)\n",
    "x_range = x\n",
    "t_range = t\n",
    "E_inh = -30 \n",
    "E_exc = 60\n",
    "c_o = 0.5\n",
    "f=1\n",
    "omega = 2*math.pi*f\n",
    "k = (2*math.pi)/45\n",
    "tau = 0.15 # ms\n",
    "time_step = 1/240\n",
    "x_initial = -180\n",
    "which_stimulus = 'FTB_OFF' # choose the stimulus\n",
    "FWHM = 5.7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# THIS WORKS\n",
    "h,h_shifted, sample_freq_x = gauss_FT(FWHM, x_range)\n",
    "###### Run my simulation and then basicaly just play with data\n",
    "sample_freq_shifted,f_shifted = time_filter(tau,t,time_step)\n",
    "sample_freq_shifted,f_dev_shifted = time_filter_der(tau,t,time_step)\n",
    "\n",
    "f = np.linspace(0,32) #num=16\n",
    "#omega = 2*math.pi*np.linspace(0,32,num=15)\n",
    "k = np.linspace(-math.pi/90,math.pi/90) # ,num=16 k = (-1/30,-1/60,-1/120,0, 1/120, 1/60, 1/30)\n",
    "c = np.empty([len(x),len(t),len(k),len(f)])\n",
    "v_mem = np.empty([len(x),len(t),len(k), len(f)])\n",
    "cal = np.empty([len(x),len(t),len(k), len(f)])\n",
    "calcium_try= np.empty([len(x),len(t),len(k), len(f)])\n",
    "calcium_new_try =np.empty([len(k), len(f)])\n",
    "for i, kapa_value in enumerate(k):\n",
    "    for j, f_value in enumerate(f):\n",
    "        #print(f_value)\n",
    "        contrast = gratings(c_o, f_value, kapa_value, t_range, x_range)\n",
    "        c[:,:,i,j] = contrast\n",
    "np.save(r'D:\\b_Module_Fernanda\\contrasts\\gratings_for_spatiotemporal_tuning',c)\n",
    "#c=np.load(r'D:\\b_Module_Fernanda\\contrasts\\gratings_for_spatiotemporal_tuning.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(720, 1920, 50, 50)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(720, 1920)\n"
     ]
    }
   ],
   "source": [
    "# it works\n",
    "for i, kapa_value in enumerate(k):\n",
    "    for j, f_value in enumerate(f):\n",
    "        c_shifted = contrast_fourier(c[:,:,i,j],x_range,t_range)\n",
    "        filtered_c_f = filtered_contrast_f(sample_freq_x, sample_freq_shifted,c_shifted, f_shifted,h_shifted)\n",
    "        filtered_c_dev = filtered_contrast_f_dev(sample_freq_x, sample_freq_shifted, c_shifted,f_dev_shifted,h_shifted)\n",
    "        g1_div_g_l, g2_div_g_l, g3_div_g_l, V_m , calcium = g_vm_cal_calculation(filtered_c_f,filtered_c_dev)\n",
    "        calcium_try[:,:,i,j] = (ramp(V_m))**2\n",
    "        #calcium_new_try[i,j] = np.mean(calcium[:,100]) # axis=0\n",
    "        #v_mem[:,:,i,j] = V_m\n",
    "        cal[:,:,i,j] = calcium\n",
    "#np.save(r'D:\\b_Module_Fernanda\\contrasts\\calcium_for_spatiotemporal_tuning',cal)\n",
    "#calcium=np.load(r'D:\\b_Module_Fernanda\\contrasts\\calcium_for_spatiotemporal_tuning.npy')\n",
    "#print(calcium.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(r'D:\\b_Module_Fernanda\\contrasts\\calcium_for_spatiotemporal_tuning',calcium_try)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_values = np.max(calcium_try, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x2766b121210>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i,x_value in enumerate(x):\n",
    "    plt.imshow(max_values[i,:,:].T,cmap='Reds',extent=[k[0],k[-1],f[0],f[-1]],aspect = 'auto',origin='lower', norm = 'linear') #  vmin=0, vmax=50, extent=[ -0.3,0.3,0.5, 32,], aspect = 'auto', ) #,vmin=0, vmax=50 # extent is [x_min, x_max, y_min, y_max]\n",
    "plt.xlabel('spatial frequency (1/o)')\n",
    "plt.ylabel('temporal frequency (Hz)')\n",
    "plt.title('Spatiotemporal tuning of T4')\n",
    "plt.colorbar()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "lif",
   "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}