diff --git a/Simulation.ipynb b/Simulation.ipynb index d0a92f4..592be05 100644 --- a/Simulation.ipynb +++ b/Simulation.ipynb @@ -829,47 +829,39 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 117, "metadata": {}, "outputs": [], "source": [ - "def simulate_f_signal_bline2(K_D, tau_nm, tau_tissue, F_max, F_min, nm_conc, bline_len):\n", + "def simulate_f_signal_bline2(tau_dnm, tau_tissue, nm_conc=nm_conc_input, K_D = 1000, F_max = 45, F_min = 10, bline_len=5000):\n", "\n", " # autofluorescence\n", " f_tissue = 0.02\n", "\n", - " # create timesteps array for the plot\n", + " # create timesteps \n", " n_timesteps = nm_conc.size\n", " t = np.linspace(0,n_timesteps-1,n_timesteps)\n", "\n", - " # bleaching factor -- starts off as 1 then exponentially decreases \n", - " # we set tau to be a very large constant so this is a slow decrease\n", - " bleach_nm = np.exp(-t/tau_nm)\n", + " # define bleach factors for the autofluorescence and fluorescence from dye + nm\n", + " bleach_dnm = np.exp(-t/tau_dnm)\n", " bleach_tissue = np.exp(-t/tau_tissue)\n", " \n", " # calculate F: derived from eq 2 in Neher/Augsutine\n", - " f = bleach_tissue*f_tissue + bleach_nm*(K_D*F_min + nm_conc*F_max)/(K_D + nm_conc)\n", - "\n", - "\n", - " # fit a polynmial to f and subtract it from f \n", - " # comes after during the processing\n", - " # poly = np.polyfit(t,f,5)\n", - " # fit = np.polyval(poly,t)\n", - " # f = f-fit\n", + " f = bleach_tissue*f_tissue + bleach_dnm*(K_D*F_min + nm_conc*F_max)/(K_D + nm_conc)\n", "\n", + " # fitting polynomial comes later\n", "\n", " # calculate f0 by getting the median value of the bottom 70% of previous f values\n", " percentile_mark = np.percentile(f,70)\n", " f0 = np.median(f[f" ] @@ -951,33 +957,14 @@ } ], "source": [ - "bline_f_signal2 = simulate_f_signal_bline2(K_D = 1000, tau_nm=10e7, tau_tissue=10e7, F_max = 45, F_min = 10, nm_conc=nm_conc_input,bline_len=5000)" + "# simulate the fluorescence signal and plot \n", + "f , df , df_f_ave, df_f_med = simulate_f_signal_bline2(tau_dnm=10e7, tau_tissue=10e1)\n", + "plot_f_signal(f,df,df_f_ave, df_f_med)" ] }, { "cell_type": "code", - "execution_count": 84, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([-1, 0, 1, 2, 3])" - ] - }, - "execution_count": 84, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "a = np.array([0,1,2,3,4])\n", - "a-1" - ] - }, - { - "cell_type": "code", - "execution_count": 67, + "execution_count": 123, "metadata": {}, "outputs": [], "source": [ @@ -986,14 +973,13 @@ "\n", "def bleach_nm(tau_values):\n", "\n", - "\n", " # create an array to store the snr values\n", " snr = np.zeros((tau_values.size, tau_values.size))\n", "\n", " # find way to do it that's more effecient -- this is O(n^2)\n", " for i in range(len(tau_values)):\n", " for j in range(len(tau_values)):\n", - " signal = simulate_f_signal_bline2(K_D = 1000, tau_nm=tau_values[j], tau_tissue=tau_values[i], F_max = 45, F_min = 10, nm_conc=nm_conc_input, bline_len=5000)\n", + " signal = simulate_f_signal_bline2(tau_dnm=tau_values[i], tau_tissue=tau_values[j])\n", " snr[i,j] = np.mean(signal)/np.std(signal)\n", " \n", "\n", @@ -1007,19 +993,25 @@ " return snr\n", "\n", "\n", - "# get values of SNR for increasing bleach factor ([NM] version) at different values of bleach factor (tissue version)\n", - "\n", - "\n" + "# get values of SNR for increasing bleach factor ([NM] version) at different values of bleach factor (tissue version)\n" ] }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 124, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/brian.gitahi/anaconda3/lib/python3.11/site-packages/numpy/core/_methods.py:239: RuntimeWarning: overflow encountered in multiply\n", + " x = um.multiply(x, x, out=x)\n" + ] + }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1030,21 +1022,21 @@ { "data": { "text/plain": [ - "array([[-0.5687582 , -0.57212555, -0.5759453 , -0.60719916, -0.26226205,\n", - " -0.08417809],\n", - " [-0.5687582 , -0.57212555, -0.5759453 , -0.60719916, -0.26226205,\n", - " -0.08417809],\n", - " [-0.56875824, -0.57212557, -0.57594534, -0.60719916, -0.26226205,\n", - " -0.08417809],\n", - " [-0.56897775, -0.57242487, -0.57658732, -0.60710139, -0.26231368,\n", - " -0.08426454],\n", - " [-0.5725434 , -0.57722315, -0.5905801 , -0.60504598, -0.2622618 ,\n", - " -0.08508396],\n", - " [-0.56861604, -0.57213062, -0.57714874, -0.60604502, -0.262184 ,\n", - " -0.08417809]])" + "array([[0.5782468 , 0.57825064, 0.57833589, 0.57842891, 0.57826899,\n", + " 0.57824959],\n", + " [0.5849869 , 0.58499872, 0.58510661, 0.58521028, 0.58502173,\n", + " 0.5850031 ],\n", + " [0.64110212, 0.64110992, 0.64118773, 0.64131602, 0.64120789,\n", + " 0.64120482],\n", + " [0.3255303 , 0.32510132, 0.32165726, 0.31488084, 0.31471493,\n", + " 0.31476297],\n", + " [0.08714467, 0.08712359, 0.08700881, 0.08612622, 0.08486242,\n", + " 0.08484845],\n", + " [0.02598925, 0.02627133, 0.02871999, 0.03143645, 0.02716971,\n", + " 0. ]])" ] }, - "execution_count": 68, + "execution_count": 124, "metadata": {}, "output_type": "execute_result" } @@ -1053,6 +1045,7 @@ "# input tau values\n", "different_taus = np.array([10e6,10e5,10e4,10e3,10e2,10e1])\n", "\n", + "# run the simulation with different bleach factors on the different sources of fluorescence\n", "bleach_nm(different_taus)" ] }