def calc_pore_size_by_slope():
(fmax_arr, k1_arr, k2_arr, conc_list) = \
titration_fits.plot_two_exp_fits(bgsub_norm_wells, layout, plot=True)
titration_fits.plot_fmax_curve(fmax_arr, conc_list)
titration_fits.plot_k1_curve(k1_arr[:,1:], conc_list[1:])
titration_fits.plot_k2_curve(k2_arr[:,1:], conc_list[1:])
conc_lipos = 5.16
fmax_means = np.mean(fmax_arr, axis=0)
fmax_stds = np.std(fmax_arr, axis=0)
ratios = conc_list / conc_lipos
derivs = np.zeros(len(fmax_means)-1)
deriv_sds = np.zeros(len(fmax_means)-1)
for i in np.arange(1, len(fmax_means)):
if i == 1:
prev_mean = 0
prev_sd = 0
else:
prev_mean = fmax_means[i-1]
prev_sd = fmax_stds[i-1]
fmax_diff = fmax_means[i] - prev_mean
conc_diff = conc_list[i] - conc_list[i-1]
derivs[i-1] = fmax_diff / float(conc_diff)
deriv_sds[i-1] = np.sqrt(fmax_stds[i] ** 2 + prev_sd ** 2)
plt.figure()
#plt.errorbar(conc_list[1:], derivs, yerr=deriv_sds, marker='o', )
plt.errorbar(conc_list[1:], derivs, marker='o', )
plt.title('d[Fmax]/d[Bax]')
# Fit with exp dist?
exp_lambda = fitting.Parameter(1/20.)
def exp_dist(x):
return exp_lambda() * np.exp(-exp_lambda() * x)
(residuals, result) = fitting.fit(exp_dist, [exp_lambda], derivs,
conc_list[1:])
plt.plot(conc_list[1:], exp_dist(conc_list[1:]), color='g')
print "exp_lambda: %f" % exp_lambda()
# Fit with exp decay
exp_c0 = fitting.Parameter(0.005)
exp_k = fitting.Parameter(1/50.)
def exp_decay(x):
return exp_c0() * np.exp(-exp_k() * x)
(residuals, result) = fitting.fit(exp_decay, [exp_c0, exp_k],
derivs, conc_list[1:])
plt.plot(conc_list[1:], exp_decay(conc_list[1:]), color='r')
print "exp_c0: %f" % exp_c0()
print "exp_k: %f" % exp_k()
# We want to estimate Bax per pore, or pores per Bax, if Bax
# holes didn't exist. Basically we want to know the fraction release
# we would get if the amount of Bax approached 0. In the case of
# the exponential decay, this is the value of exp_c0.
# We convert exp_c0 from % release to number of pores:
# The derivative dfmax/dBax tells us how much more permeabilization we get
# per Bax. At low concentrations, we get a certain amount of release
# per Bax that is relatively unaffected by the Bax hole phenomenon.
print "exp_c0 * conc_lipos: %f" % (exp_c0() * conc_lipos)
def plot_normalized_by_fmax():
(fmax_arr, k1_arr, k2_arr, conc_list) = \
titration_fits.plot_two_exp_fits(bgsub_norm_wells, layout, plot=False)
data = bgsub_norm_wells
k1_arr = np.zeros((3, len(layout.keys())))
conc_list = np.zeros(len(layout.keys()))
plt.figure()
for i, conc_str in enumerate(layout.keys()):
# The list of wells for this concentration
wells = layout[conc_str]
well_conc = float(conc_str.split(' ')[1])
conc_list[i] = well_conc
if well_conc == 0.0:
continue
for j, well in enumerate(wells):
well_data = data[well]
time = np.array(well_data[TIME][:])
y = np.array(well_data[VALUE][:])
fmax = fmax_arr[j, i]
norm_data = y / float(fmax)
plt.plot(norm_data)
def calc_pore_size_by_poisson():
set_fig_params_for_publication()
(fmax_arr, k1_arr, k2_arr, conc_list) = \
titration_fits.plot_two_exp_fits(bgsub_norm_wells, layout, plot=False)
conc_lipos = 2.8
fmax_means = np.mean(fmax_arr, axis=0)
fmax_stds = np.std(fmax_arr, axis=0)
ratios = conc_list / conc_lipos
isf_pore_sizes = []
k_max = 20
for i, ratio in enumerate(ratios):
if i == 0:
continue
# Probability of permeabilization is
# 1 - poisson.cdf(pore_size, ratio)
pore_sizes = np.arange(1, k_max)
probs = [1 - stats.poisson.cdf(pore_size, ratio)
for pore_size in pore_sizes]
isf_pore_size = stats.poisson.isf(fmax_means[i], ratio) + 1
isf_pore_sizes.append(isf_pore_size)
#plt.figure()
#plt.plot(pore_sizes, probs, marker='o')
#plt.xlabel('Pore size')
#plt.ylim([-0.05, 1.05])
#plt.title('Ratio of %f, isf pore size: %f' % (ratio, isf_pore_size))
#sd_lines = [fmax_means[i] + fmax_stds[i]*num_sds
# for num_sds in np.arange(-2, 3)]
#plt.hlines(sd_lines, 0, k_max, color='r')
# Plot min pore size vs. Bax, with linear fit
fig = plt.figure(figsize=(1.5, 1.5), dpi=300)
ax = fig.gca()
ax.plot(ratios[1:], isf_pore_sizes, marker='o', markersize=2,
linestyle='') # Skip the 0 Bax pt
ax.set_xlabel('[Bax]/[Lipo]')
ax.set_ylabel('Predicted pore size')
ax.set_xscale('log')
ax.set_yscale('log')
lbound = 0.1
ubound = 200
ax.set_xlim(lbound, 1000)
#ax.set_ylim(1, 200)
lin_fit = stats.linregress(ratios[1:], isf_pore_sizes)
slope = lin_fit[0]
intercept = lin_fit[1]
ax.plot(ratios[1:], slope*ratios[1:] + intercept, color='r')
interp_range = np.linspace(lbound, ratios[1])
ax.plot(interp_range, slope*interp_range + intercept, color='r',
linestyle='--', dashes=(2, 2))
ax.plot(interp_range, [slope*ratios[1] + intercept] * len(interp_range),
color='r', linestyle='--', dashes=(2, 2))
#ax.set_title('Slope %f, intercept %f' % (slope, intercept),
# fontsize=fontsize)
fig.subplots_adjust(left=0.22, bottom=0.19)
format_axis(ax)
figure()
plot_all(reset_norm_subset, legend_position=0.7)
title("Norm., Avg., Reset to t = 0")
figure()
plot_all(pores)
title("Avg. pores per liposome")
if __name__ == '__main__':
#plot_data()
plt.ion()
bax63_layout = extract(bax63_wells, layout)
(fmax_arr, k1_arr, k2_arr, conc_list) = \
titration_fits.plot_two_exp_fits(norm_wells, bax63_layout,
num_reps=1, conc_str_index=4,
plot=True)
plt.figure()
plt.semilogx(conc_list, fmax_arr[0], marker='o')
plt.xlabel('[cBid] (nM)')
plt.ylabel('$F_{max}$')
plt.figure()
plt.semilogx(conc_list, k1_arr[0], marker='o')
plt.xlabel('[cBid] (nM)')
plt.ylabel('$k_1$')
plt.figure()
plt.semilogx(conc_list, k2_arr[0], marker='o')
plt.xlabel('[cBid] (nM)')
plt.ylabel('$k_2$')
def get_twoexp_fmax_arr():
(fmax_arr, k1_arr, k2_arr, conc_list) = \
titration_fits.plot_two_exp_fits(bgsub_norm_wells, layout, plot=False)
return (fmax_arr, conc_list)
k_decay_sd = np.sqrt(cov_matrix[1, 1])
bg_bg_sd = np.sqrt(cov_matrix[2, 2])
"""
def get_twoexp_fmax_arr():
(fmax_arr, k1_arr, k2_arr, conc_list) = \
titration_fits.plot_two_exp_fits(bgsub_norm_wells, layout, plot=False)
return (fmax_arr, conc_list)
if __name__ == '__main__':
plt.ion()
plt.close('all')
(fmax_arr, k1_arr, k2_arr, conc_list) = \
titration_fits.plot_two_exp_fits(bgsub_norm_wells, layout, plot=False)
plot_k_curves(fmax_arr, conc_list)
calc_pore_size_by_poisson()
sys.exit()
(k1_arr, k2_arr, tau_arr, conc_list) = plot_pore_fits()
plot_k_curves(k1_arr, conc_list)
plot_k_curves(k2_arr, conc_list)
plot_k_curves(tau_arr, conc_list)
#plot_normalized_to_ref_curve()
#plot_normalized_by_fmax()
