def inner_prepare(array):
if jacknife_size:
return rm_nans(np.random.choice(array,
size=(jacknife_size,),
replace=True))
else:
return rm_nans(array)
def get_t_distro_outlier_bound_estimation(array, background_std):
narray = rm_nans(array)
low, up = t.interval(0.95, narray.shape[0]-1, np.mean(narray), np.sqrt(np.var(narray)+background_std**2))
up, low = (up-np.mean(narray), np.mean(narray)-low)
return max(up, low)
def pull_breakpoints(contingency_list):
"""
A method to extract breakpoints separating np.array regions with the same value.
:param contingency_list: np.array containing regions of identical values
:return: list of breakpoint indexes
"""
no_nans_parsed = rm_nans(contingency_list)
contingency = np.lib.pad(
no_nans_parsed[:-1] == no_nans_parsed[1:], (1, 0), "constant", constant_values=(True, True)
)
nans_contingency = np.zeros(contingency_list.shape).astype(np.bool)
nans_contingency[np.logical_not(np.isnan(contingency_list))] = contingency
breakpoints = np.nonzero(np.logical_not(nans_contingency))[0].tolist()
return breakpoints
def logistic_regression(TF, T0, concentrations, background_std):
def get_1p_bounds(mean, std, dof):
return t.interval(0.99, dof, mean, std)
mask = concentrations == 0.0
vals_at_0 = rm_nans(TF[:, mask, :])
max_capacity = get_1p_bounds(np.mean(vals_at_0),
np.sqrt(np.var(vals_at_0) + background_std**2),
vals_at_0.shape[0])[1]*1.05
compensation_T0 = -np.log2(max_capacity/T0-1)[:, :, np.newaxis]
compensation_TF = -np.log2(max_capacity/TF-1)
alphas = compensation_TF - compensation_T0
return alphas
def p_stabilize(array, percentile):
p_low = np.percentile(rm_nans(array), percentile)
p_high = np.percentile(rm_nans(array), 100-percentile)
array[array < p_low] = p_low
array[array > p_high] = p_high
return array
