'Bax 116 nM, NBD-Bax 96 nM',
'Bax 78 nM, NBD-Bax 96 nM',
'Bax 52 nM, NBD-Bax 96 nM',
'Bax 35 nM, NBD-Bax 96 nM',
'Bax 23 nM, NBD-Bax 96 nM',
'Bax 15 nM, NBD-Bax 96 nM',
'Bax 10 nM, NBD-Bax 96 nM',
'Bax 0 nM, NBD-Bax 96 nM',]
nbd_layout = extract(nbd_conditions, layout)
nbd_wells = extract(nbd_conditions, timecourse_averages)
#nbd_wells = extract([layout[cond][0] for cond in nbd_conditions],
# timecourse_wells)
# Background subtracted
bgsub_wells = subtract_background_set(nbd_wells, bax_bg_wells)
# Background subtracted, averaged
#(bgsub_averages, bgsub_stds) = averages(bgsub_wells, layout)
# First timepoint shifted to 0 (better for fitting)
#reset_bgsub_means = reset_first_timepoint_to_zero(bgsub_norm_averages)
#Timecourses normalized, BG-subtracted, averaged, then with first point
#shifted to t = 0.
#reset_bgsub_sds = reset_first_timepoint_to_zero(bgsub_norm_stds)
# Get the time vector
time = bgsub_wells['Bax 0 nM, NBD-Bax 96 nM'][TIME]
# Initialize numpy data matrix
data_to_fit = np.zeros((len(bgsub_wells.keys()), 1, len(time)))
# Initialize matrix of experimental error values
'Bax 185 nM, Lipos 0.25 mg/ml',
'Bax 185 nM, Lipos 0.125 mg/ml',
'Bax 185 nM, Lipos 0.063 mg/ml',
'Bax 185 nM, Lipos 0.031 mg/ml',
'Bax 185 nM, Lipos 0.016 mg/ml',
'Bax 185 nM, Lipos 0.008 mg/ml',
'Bax 185 nM, Lipos 0.004 mg/ml',
'Bax 185 nM, Lipos 0.002 mg/ml',
'Bax 185 nM, Lipos 0.001 mg/ml',
'Bax 185 nM, Lipos 0 mg/ml', ]
bax_lipo_layout = extract(bax_lipo_conditions, layout)
bax_lipo_wells = extract([layout[cond][0] for cond in bax_lipo_conditions],
timecourse_wells)
# Normalized and background subtracted
bgsub_wells = subtract_background_set(bax_lipo_wells, lipo_bg_wells)
(bgsub_averages, bgsub_sds) = averages(bgsub_wells, bax_lipo_layout)
# First timepoint shifted to 0 (better for fitting)
#reset_bgsub_means = reset_first_timepoint_to_zero(bgsub_norm_averages)
"""Timecourses normalized, BG-subtracted, averaged, then with first point
shifted to t = 0."""
#reset_bgsub_sds = reset_first_timepoint_to_zero(bgsub_norm_stds)
lipo_conc_conv_factor = 15.502 # 1 mg/ml ~= 15.502 nM liposomes
bg_tc = bgsub_averages['Bax 185 nM, Lipos 0 mg/ml'][VALUE]
bg_time = bgsub_averages['Bax 185 nM, Lipos 0 mg/ml'][TIME]
lipo_concs_to_fit = []
lipo_mgs_to_fit = [1., 0.5, 0.25, 0.125, 0.063, 0.031, 0.016, 0.008]
# Initialize numpy data matrix
