diff --git a/Bleach_compare.py b/Bleach_compare.py
index 530e518..102a716 100644
--- a/Bleach_compare.py
+++ b/Bleach_compare.py
@@ -26,12 +26,12 @@
 # plot bleached signal for the bleach factor acting on different components of f
 
 # create timesteps array for the plot
-t = np.linspace(0,nm_conc.size-1,nm_conc.size)
+# t = np.linspace(0,nm_conc.size-1,nm_conc.size)
 
-b1 = bleach_nm(K_D = 1000, tau=chosen_tau, F_max = 45, F_min = 10, nm_conc=nm_conc, bline_len=5000)
-b2 = bleach_dnm(K_D = 1000, tau=chosen_tau, F_max = 45, F_min = 10, nm_conc=nm_conc, bline_len=5000)
-b3 = bleach_t(K_D = 1000, tau=chosen_tau, F_max = 45, F_min = 10, nm_conc=nm_conc, bline_len=5000)
-b4 = bleach_all(K_D = 1000, tau=chosen_tau, F_max = 45, F_min = 10, nm_conc=nm_conc, bline_len=5000)
+# b1 = bleach_nm(K_D = 1000, tau=chosen_tau, F_max = 45, F_min = 10, nm_conc=nm_conc, bline_len=5000)
+# b2 = bleach_dnm(K_D = 1000, tau=chosen_tau, F_max = 45, F_min = 10, nm_conc=nm_conc, bline_len=5000)
+# b3 = bleach_t(K_D = 1000, tau=chosen_tau, F_max = 45, F_min = 10, nm_conc=nm_conc, bline_len=5000)
+# b4 = bleach_all(K_D = 1000, tau=chosen_tau, F_max = 45, F_min = 10, nm_conc=nm_conc, bline_len=5000)
 
 
 
@@ -52,9 +52,10 @@
 
 # for different variances, get the heatmap
 plt.figure()
-for i in range(len(var_v)):
+for i in range(len(var_values)):
     plt.subplot(3,2,i+1)
-    bleach_dnm_heat(specific_taus,nm_conc_input=nm_conc, var = var_v[i])
+    bleach_dnm_heat(specific_taus,nm_conc_input=nm_conc, var = var_values[i])
+    print('Generated heatmap {}'.format(i))
 
 plt.suptitle('SNR vs bleach strength at different variance for ftissue', size = 16)
 plt.tight_layout()
diff --git a/Simulation.ipynb b/Simulation.ipynb
index 8a50875..e402b9a 100644
--- a/Simulation.ipynb
+++ b/Simulation.ipynb
@@ -128,7 +128,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Ok, look's like the neuron is firing as we expect it to over number of timesteps. This section of code produced figure 1 on the interal note document.\n",
+    "Ok, look's like the neuron is firing as we expect it to over number of timesteps. (This section of code produced figure 1 on the interal note document.)\n",
     "\n",
     "Bonus: run this function several times and histogram the n_spikes to see if it's actually a poisson w mean of n_timesteps spikes.\n"
    ]
@@ -423,11 +423,11 @@
     "Now, let's simulate the flourescence signal produced by the changes in the neuromodulator concentration, $\\ \\Delta$ [NM]. This is described by eq. 3 in the paper by Neher/Augustine -- 1992: \n",
     "\n",
     "$\\begin{equation}\n",
-    "    \\frac{\\Delta F(t)}{F(0)} = \\frac{(K'_D + [NM]_{i,t})/(K_D + [NM]_{i,t})}{(K'_D + [NM]_{i,0})/(K_D + [NM]_{i,0})} - 1\n",
+    "    \\frac{\\Delta F(t)}{F(0)} = \\frac{(K'_D + [NM]_{t})/(K_D + [NM]_{t})}{(K'_D + [NM]_{0})/(K_D + [NM]_{0})} - 1\n",
     "\\end{equation}\n",
     "$\n",
     "\n",
-    "where $K_D$ is the dissociation constant for the NM binding to the sensor, $\\ [NM]_{i,0}\\ $ is the initial (resting) [NM], $\\ [NM]_{i,t}\\ $ is the [NM] for the current timestep, and $K'_D$ is defined as:\n",
+    "where $K_D$ is the dissociation constant for the NM binding to the sensor, $\\ [NM]_{0}\\ $ is the initial (resting) [NM], $\\ [NM]_{t}\\ $ is the [NM] for the current timestep, and $K'_D$ is defined as:\n",
     "\n",
     "$\\begin{equation} \\tag{2}\n",
     "    K'_D = K_D\\ (F_{min}/F_{max})\n",
diff --git a/__pycache__/s_functions.cpython-311.pyc b/__pycache__/s_functions.cpython-311.pyc
index 35d6310..18bb740 100644
Binary files a/__pycache__/s_functions.cpython-311.pyc and b/__pycache__/s_functions.cpython-311.pyc differ
diff --git a/f_rate2.py b/f_rate2.py
index 34de8cd..1269baa 100644
--- a/f_rate2.py
+++ b/f_rate2.py
@@ -112,29 +112,5 @@ def plot_dff_v_activity(firing_rates, dff, fit):
 # plt.show()
 
 
-# Test 2:
-
-# check the effect of changing the variance of the gaussian noise on ftissue on the snr 
-var_values = np.array([0.0001,0.001,0.01,0.1,1,3])
-var_v = np.array([1,3,10])
-
-# bleach time constants for heatmap
-specific_taus = np.logspace(5,7,20)
-
-# generate a firing neuron
-neuron = simulate_neuron(n_timesteps=70000,firing_rate=13)
-
-# generate nm_conc 
-nm_conc, nm_b_conc, nm_r_conc = simulate_nm_conc(neuron,nm_conc0=0,k_b=0.6, k_r=0.4,gamma=0.004)
-
-# for different variances, get the heatmap
-plt.figure()
-for i in range(len(var_v)):
-    plt.subplot(3,2,i+1)
-    bleach_dnm_heat(specific_taus,nm_conc_input=nm_conc, var = var_v[i])
-
-plt.suptitle('SNR vs bleach strength at different variance for ftissue', size = 16)
-plt.tight_layout()
-plt.show()