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Bleach_compare.py

@@ -0,0 +1,72 @@
+# This script is used to compare the bleaching effects established in the 
+# Bleaching Analysis and bleaching_bline scripts
+
+
+# import necessary packages
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import pandas as pd
+import scipy as sp
+
+# import functions from the simulation and bleaching libraries
+from s_functions import simulate_neuron, simulate_nm_conc, simulate_flourescence_signal
+from b_factory import bleach_1, bleach_2, bleach_3, bleach_4
+
+
+# ANALYSIS 1: comparing the different contributions at one timescale for the bleach factor
+
+# choose the timescale for the bleach factor
+chosen_tau = 10e4
+
+# generate an input nm conc array from firing neuron
+firing_neuron = simulate_neuron(n_timesteps=70000,firing_rate=1)
+nm_conc, nm_b_conc, nm_r_conc = simulate_nm_conc(firing_neuron,nm_conc0=0,k_b=0.6, k_r=0.4,gamma=0.004)
+
+# 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)
+
+b1 = bleach_1(K_D = 1000, tau=chosen_tau, F_max = 45, F_min = 10, nm_conc=nm_conc, bline_len=5000)
+b2 = bleach_2(K_D = 1000, tau=chosen_tau, F_max = 45, F_min = 10, nm_conc=nm_conc, bline_len=5000)
+b3 = bleach_3(K_D = 1000, tau=chosen_tau, F_max = 45, F_min = 10, nm_conc=nm_conc, bline_len=5000)
+b4 = bleach_4(K_D = 1000, tau=chosen_tau, F_max = 45, F_min = 10, nm_conc=nm_conc, bline_len=5000)
+
+
+# create a fit for the initial values of the bleach 2 and 4 result and subtract it -- revise this later
+# it should be a fit of the f value
+poly2 = np.polyfit(t,b2,2)
+fit2 = np.polyval(poly2,t)
+
+poly4 = np.polyfit(t,b4,2)
+fit4 = np.polyval(poly4,t)
+
+
+
+plt.plot(t,b1,label='bleach 1')
+#plt.plot(t,b2,label='bleach 2')
+# plt.plot(fit2, label='fit2')
+plt.plot(b2-fit2,label='subtracted 2')
+
+
+plt.plot(t,b3,label='bleach 3')
+#plt.plot(t,b4,label='bleach 4')
+plt.plot(b4-fit4,label='subtracted 4')
+
+plt.xlabel('time(ms)')
+plt.ylabel('Delta F/F0')
+plt.title('Flourescence intensity signal over time')
+plt.legend()
+
+plt.show()
+
+
+# ANALYSIS 2: 
+
+
+
+
+# ANALYSIS 3:
+# check this bleaching effect 1 for different values of tau -- different bleach factor
+#different_taus = np.array([10e6,10e5,10e4,10e3,10e2,10e1,10e0])

Bleaching Analysis.py

@@ -25,7 +25,7 @@ def bleach_1(K_D, tau, F_max, F_min, nm_conc):
     bleach = np.exp(-t/tau)
 
     # calculate bleached f values: derived in part from eq 2 in Neher/Augustine
-    f_b = bg_tissue + (K_D*F_min + bleach*nm_conc*F_max)/(K_D + bleach*nm_conc)
+    f_b = bg_tissue + (K_D*F_min + bleach*nm_conc*F_max)/(K_D + nm_conc)
 
     # calculate normalized signal: (assume f0 is the initial f value)
     delta_ft_f0 = (f_b-f_b[0])/f_b[0]
@@ -37,12 +37,6 @@ def bleach_1(K_D, tau, F_max, F_min, nm_conc):
     plt.ylabel('Delta F/F0')
     plt.title('Flourescence intensity signal over time (bleach 1)')
     plt.legend()
-    #plt.show()
-
-    # print the bleach factor
-    # plt.plot(bleach)
-    # plt.xlabel('timesteps(ms)')
-    # plt.title('Bleach factor as function of time')
 
     return delta_ft_f0
 
@@ -137,4 +131,44 @@ def bleach_3(K_D, tau, F_max, F_min, nm_conc):
 # plot bleached signal with different time constants for the bleach factor
 for i in range(len(different_taus)):
    bleach_3(K_D = 1000, tau=different_taus[i], F_max = 45, F_min = 10, nm_conc=nm_conc)
-plt.show()
+plt.show()
+
+
+# ANALYSIS 4: bleaching factor acting on all the contributions 
+def bleach_4(K_D, tau, F_max, F_min, nm_conc):
+
+    # create timesteps array for the plot
+    n_timesteps = nm_conc.size
+    t = np.linspace(0,n_timesteps-1,n_timesteps)
+
+    # bleaching factor -- starts off as 1 then exponentially decreases 
+    # we set tau to be a very large constant so this is a slow decrease
+    bleach = np.exp(-t/tau)
+
+    # calculate bleached f values: derived in part from eq 2 in Neher/Augustine
+    f_b= bleach*(bg_tissue + (K_D*F_min + nm_conc*F_max)/(K_D + nm_conc))
+
+    # calculate normalized signal: (assume f0 is the initial f value)
+    delta_ft_f0 = (f_b-f_b[0])/f_b[0]
+
+    # plot the normalized signal delta f/f0 at the different t
+    plt.plot(t,delta_ft_f0, label = tau)
+    plt.xlabel('time(ms)')
+    plt.ylabel('Delta F/F0')
+    plt.title('Flourescence intensity signal over time (bleach 4)')
+    plt.legend()
+
+    return delta_ft_f0
+
+
+# check this bleaching effect 1 for different values of tau -- different bleach factor
+different_taus = np.array([10e6,10e5,10e4,10e3,10e2,10e1,10e0])
+
+# generate an input nm conc array from firing neuron
+firing_neuron = simulate_neuron(n_timesteps=70000,firing_rate=1)
+nm_conc, nm_b_conc, nm_r_conc = simulate_nm_conc(firing_neuron,nm_conc0=0,k_b=0.6, k_r=0.4,gamma=0.004)
+
+# plot bleached signal with different time constants for the bleach factor
+for i in range(len(different_taus)):
+   bleach_4(K_D = 1000, tau=different_taus[i], F_max = 45, F_min = 10, nm_conc=nm_conc)
+plt.show()

Bleaching w:out_bline.py

@@ -0,0 +1,178 @@
+# This script was the original analysis script and
+# has since been updated -- cf b_compare or analysis
+
+# it has the bleaching analysis with the df/f computed without the 
+# moving baseline calculation of f0
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import pandas as pd
+import scipy as sp
+
+# import the necessary functions
+from s_functions import simulate_neuron, simulate_nm_conc
+
+
+# define the background fluorescence due to the tissue
+bg_tissue = 1.5
+
+# ANALYSIS 1: bleaching factor acting on the [NM] contribution to F
+def bleach_1(K_D, tau, F_max, F_min, nm_conc):
+
+    # create timesteps array for the plot
+    n_timesteps = nm_conc.size
+    t = np.linspace(0,n_timesteps-1,n_timesteps)
+
+    # bleaching factor -- starts off as 1 then exponentially decreases 
+    # we set tau to be a very large constant so this is a slow decrease
+    bleach = np.exp(-t/tau)
+
+    # calculate bleached f values: derived in part from eq 2 in Neher/Augustine
+    f_b = bg_tissue + (K_D*F_min + bleach*nm_conc*F_max)/(K_D + nm_conc)
+
+    # calculate normalized signal: (assume f0 is the initial f value)
+    delta_ft_f0 = (f_b-f_b[0])/f_b[0]
+
+
+    # plot the normalized signal delta f/f0 at the different t
+    plt.plot(t,delta_ft_f0, label = tau)
+    plt.xlabel('time(ms)')
+    plt.ylabel('Delta F/F0')
+    plt.title('Flourescence intensity signal over time (bleach 1)')
+    plt.legend()
+
+    return delta_ft_f0
+
+
+# check this bleaching effect 1 for different values of tau -- different bleach factor
+different_taus = np.array([10e6,10e5,10e4,10e3,10e2,10e1,10e0])
+
+# generate an input nm conc array from firing neuron
+firing_neuron = simulate_neuron(n_timesteps=70000,firing_rate=1)
+nm_conc, nm_b_conc, nm_r_conc = simulate_nm_conc(firing_neuron,nm_conc0=0,k_b=0.6, k_r=0.4,gamma=0.004)
+
+# plot bleached signal with different time constants for the bleach factor
+for i in range(len(different_taus)):
+   bleach_1(K_D = 1000, tau=different_taus[i], F_max = 45, F_min = 10, nm_conc=nm_conc)
+plt.show()
+
+
+# ANALYSIS 2: bleaching factor acting on the contributions of the dye, F0 and [NM]
+def bleach_2(K_D, tau, F_max, F_min, nm_conc):
+
+    # create timesteps array for the plot
+    n_timesteps = nm_conc.size
+    t = np.linspace(0,n_timesteps-1,n_timesteps)
+
+    # bleaching factor -- starts off as 1 then exponentially decreases 
+    # we set tau to be a very large constant so this is a slow decrease
+    bleach = np.exp(-t/tau)
+
+    # calculate bleached f values: derived in part from eq 2 in Neher/Augustine
+    f_b= bg_tissue+ bleach*(K_D*F_min + nm_conc*F_max)/(K_D + nm_conc)
+
+    # calculate normalized signal: (assume f0 is the initial f value)
+    delta_ft_f0 = (f_b-f_b[0])/f_b[0]
+
+    # plot the normalized signal delta f/f0 at the different t
+    plt.plot(t,delta_ft_f0, label = tau)
+    plt.xlabel('time(ms)')
+    plt.ylabel('Delta F/F0')
+    plt.title('Flourescence intensity signal over time (bleach 2)')
+    plt.legend()
+
+    return delta_ft_f0
+
+
+# check this bleaching effect 1 for different values of tau -- different bleach factor
+different_taus = np.array([10e6,10e5,10e4,10e3,10e2,10e1,10e0])
+
+# generate an input nm conc array from firing neuron
+firing_neuron = simulate_neuron(n_timesteps=70000,firing_rate=1)
+nm_conc, nm_b_conc, nm_r_conc = simulate_nm_conc(firing_neuron,nm_conc0=0,k_b=0.6, k_r=0.4,gamma=0.004)
+
+# plot bleached signal with different time constants for the bleach factor
+for i in range(len(different_taus)):
+   bleach_2(K_D = 1000, tau=different_taus[i], F_max = 45, F_min = 10, nm_conc=nm_conc)
+plt.show()
+
+
+# ANALYIS 3: bleaching factor acting on the background fluorescence 
+def bleach_3(K_D, tau, F_max, F_min, nm_conc):
+
+    # create timesteps array for the plot
+    n_timesteps = nm_conc.size
+    t = np.linspace(0,n_timesteps-1,n_timesteps)
+
+    # bleaching factor -- starts off as 1 then exponentially decreases 
+    # we set tau to be a very large constant so this is a slow decrease
+    bleach = np.exp(-t/tau)
+
+    # calculate bleached f values: derived in part from eq 2 in Neher/Augustine
+    f_b= bleach*bg_tissue + (K_D*F_min + nm_conc*F_max)/(K_D + nm_conc)
+
+    # calculate normalized signal: (assume f0 is the initial f value)
+    delta_ft_f0 = (f_b-f_b[0])/f_b[0]
+
+    # plot the normalized signal delta f/f0 at the different t
+    plt.plot(t,delta_ft_f0, label = tau)
+    plt.xlabel('time(ms)')
+    plt.ylabel('Delta F/F0')
+    plt.title('Flourescence intensity signal over time (bleach 3)')
+    plt.legend()
+
+    return delta_ft_f0
+
+
+# check this bleaching effect 1 for different values of tau -- different bleach factor
+different_taus = np.array([10e6,10e5,10e4,10e3,10e2,10e1,10e0])
+
+# generate an input nm conc array from firing neuron
+firing_neuron = simulate_neuron(n_timesteps=70000,firing_rate=1)
+nm_conc, nm_b_conc, nm_r_conc = simulate_nm_conc(firing_neuron,nm_conc0=0,k_b=0.6, k_r=0.4,gamma=0.004)
+
+# plot bleached signal with different time constants for the bleach factor
+for i in range(len(different_taus)):
+   bleach_3(K_D = 1000, tau=different_taus[i], F_max = 45, F_min = 10, nm_conc=nm_conc)
+plt.show()
+
+
+# ANALYSIS 4: bleaching factor acting on all the contributions 
+def bleach_4(K_D, tau, F_max, F_min, nm_conc):
+
+    # create timesteps array for the plot
+    n_timesteps = nm_conc.size
+    t = np.linspace(0,n_timesteps-1,n_timesteps)
+
+    # bleaching factor -- starts off as 1 then exponentially decreases 
+    # we set tau to be a very large constant so this is a slow decrease
+    bleach = np.exp(-t/tau)
+
+    # calculate bleached f values: derived in part from eq 2 in Neher/Augustine
+    f_b= bleach*(bg_tissue + (K_D*F_min + nm_conc*F_max)/(K_D + nm_conc))
+
+    # calculate normalized signal: (assume f0 is the initial f value)
+    delta_ft_f0 = (f_b-f_b[0])/f_b[0]
+
+    # plot the normalized signal delta f/f0 at the different t
+    plt.plot(t,delta_ft_f0, label = tau)
+    plt.xlabel('time(ms)')
+    plt.ylabel('Delta F/F0')
+    plt.title('Flourescence intensity signal over time (bleach 4)')
+    plt.legend()
+
+    return delta_ft_f0
+
+
+# check this bleaching effect 1 for different values of tau -- different bleach factor
+different_taus = np.array([10e6,10e5,10e4,10e3,10e2,10e1,10e0])
+
+# generate an input nm conc array from firing neuron
+firing_neuron = simulate_neuron(n_timesteps=70000,firing_rate=1)
+nm_conc, nm_b_conc, nm_r_conc = simulate_nm_conc(firing_neuron,nm_conc0=0,k_b=0.6, k_r=0.4,gamma=0.004)
+
+# plot bleached signal with different time constants for the bleach factor
+for i in range(len(different_taus)):
+   bleach_4(K_D = 1000, tau=different_taus[i], F_max = 45, F_min = 10, nm_conc=nm_conc)
+plt.show()