def main():
parser = argparse.ArgumentParser()
parser.add_argument('--config', **config.SECTIONS['general']['config'])
sino_params = ('consumer', 'data-tags')
reco_params = ('dependencies', 'pvs')
tomo_params = config.ALL_PARAMS
gui_params = tomo_params
print(tomo_params)
cmd_parsers = [
('init', init, (), "Create configuration file"),
('update', update, tomo_params, "Update configuration file"),
]
subparsers = parser.add_subparsers(title="Commands", metavar='')
for cmd, func, sections, text in cmd_parsers:
cmd_params = config.Params(sections=sections)
cmd_parser = subparsers.add_parser(cmd, help=text, formatter_class=argparse.ArgumentDefaultsHelpFormatter)
cmd_parser = cmd_params.add_arguments(cmd_parser)
cmd_parser.set_defaults(_func=func)
args = config.parse_known_args(parser, subparser=True)
log_level = logging.DEBUG if args.verbose else logging.INFO
LOG.setLevel(log_level)
stream_handler = logging.StreamHandler(sys.stdout)
stream_handler.setFormatter(logging.Formatter('%(levelname)s: %(message)s'))
LOG.addHandler(stream_handler)
if args.log:
file_handler = logging.FileHandler(args.log)
file_handler.setFormatter(logging.Formatter('%(name)s:%(levelname)s: %(message)s'))
LOG.addHandler(file_handler)
try:
config.log_values(args)
args._func(args)
except RuntimeError as e:
LOG.error(str(e))
sys.exit(1)
def plot_single_var(yy,
var_name,
tt,
num_samples=c.num_samples,
on_off=c.on_off,
days_on=c.days_on):
# Removes first three cycles
tt_mask = np.where(tt >= 84, True, False)
tt = tt[tt_mask]
yy = yy[tt_mask]
yy = get_variable(var_name, yy, tt, c.Params(), days_on)
title, ylabel, ylim = get_plotting_params(var_name)
if on_off:
on_off_mask = np.where((tt % 28) >= days_on, True, False)
on_tt = tt[~on_off_mask]
off_tt = tt[on_off_mask]
on_yy = yy[~on_off_mask]
off_yy = yy[on_off_mask]
plt.plot(on_tt, on_yy, '.')
plt.plot(off_tt, off_yy, '.')
plt.title(title)
plt.ylabel(ylabel)
plt.xlabel('time (days)')
plt.ylim((0, ylim))
plt.show()
else:
plt.plot(tt, yy)
plt.title(title)
plt.ylabel(ylabel)
plt.xlabel('time (days)')
plt.ylim((0, ylim))
plt.show()
def solve(days_on=c.days_on,
lam_up=c.lam_up,
lam_down=c.lam_down,
func_form=c.func_form,
e_dose=None,
p_dose=None,
missed_start=c.missed_start,
days_missed=c.days_missed):
"""
Params:
days_on (float) number of days taking hormonal birth control
lam_up (float) if func_form is "soft_step", lam_up is lam_up param for soft_step()
lam_down (float) if func_form is "soft_step", lam_down is lam_down param for soft_step()
if func_form is "exp_decay", lam_down is the lam param for exp_decay()
if func_form is "lin_decay", lam_down is the intercept param for lin_decay()
func_form (string) indicates which functional form is being used to represent hormone dosing
e_dose (float) default value is None, if e_dose = None, e_dose = c.e_dose
p_dose (float) default value is None, if p_dose = None, p_dose = c.p_dose
Returns:
tt (ndarray)
yy (ndarray)
"""
params = c.Params()
print(f"missed days: {days_missed}")
print(f"missed start: {missed_start}")
if e_dose is not None:
params.e_dose = e_dose
if p_dose is not None:
params.p_dose = p_dose
tt = np.linspace(0, c.upper_bound, c.num_samples)
yy = ddeint(derivs,
initial_conditions,
tt,
fargs=(params, days_on, lam_up, lam_down, func_form,
missed_start, days_missed))
return tt, yy
import numpy as np
import matplotlib.pyplot as plt
import config as c
import sympy as sp
p = c.Params()
# E_2 = p.e_0 + p.e_1*GrF + p.e_2*DomF + p.e_3*Lut_4 + p.e_dose
# P_4 = p.p_0 + p.p_1*Lut_3 + p.p_2*Lut_4 + p.p_dose
# P_app = ((p.p_0 + p.p_1*Lut_3 + p.p_2*Lut_4 + p.p_dose)/2)*(1 + ((p.e_0 + p.e_1*GrF + p.e_2*DomF + p.e_3*Lut_4 + p.e_dose)**p.mu / (p.K_mPapp**p.mu + (p.e_0 + p.e_1*GrF + p.e_2*DomF + p.e_3*Lut_4 + p.e_dose)**p.mu)))
# InhA = p.h_0 + p.h_1*DomF + p.h_2*Lut_2 + p.h_3*Lut_3
def create_matrix(e_dose, p_dose):
RP_LH, LH, RP_FSH, FSH, RcF, GrF, DomF, Sc_1, Sc_2, Lut_1, Lut_2, Lut_3, Lut_4 = sp.symbols(
'RP_LH LH RP_FSH FSH RcF GrF DomF Sc_1 Sc_2 Lut_1 Lut_2 Lut_3 Lut_4')
print(f'edose before: {p.e_dose}')
p.e_dose = e_dose
print(f'edose after: {p.e_dose}')
p.p_dose = p_dose
# Pituitary and hypothaalamus axis
matrix = sp.Matrix([
# RP_LH
((p.V_0LH + (p.V_1LH*((p.e_0 + p.e_1*GrF + p.e_2*DomF + p.e_3*Lut_4 + p.e_dose)**8)/(p.K_mLH**8 + (p.e_0 + p.e_1*GrF + p.e_2*DomF + p.e_3*Lut_4 + p.e_dose)**8)))/(1+((((p.p_0 + p.p_1*Lut_3 + p.p_2*Lut_4 + p.p_dose)/2)*(1 + ((p.e_0 + p.e_1*GrF + p.e_2*DomF + p.e_3*Lut_4 + p.e_dose)**p.mu / (p.K_mPapp**p.mu + (p.e_0 + p.e_1*GrF + p.e_2*DomF + p.e_3*Lut_4 + p.e_dose)**p.mu))))/p.K_iLHP))) - \
((p.k_LH*(1 + p.c_LHP*(((p.p_0 + p.p_1*Lut_3 + p.p_2*Lut_4 + p.p_dose)/2)*(1 + ((p.e_0 + p.e_1*GrF + p.e_2*DomF + p.e_3*Lut_4 + p.e_dose)**p.mu / (p.K_mPapp**p.mu + (p.e_0 + p.e_1*GrF + p.e_2*DomF + p.e_3*Lut_4 + p.e_dose)**p.mu)))))*RP_LH)/(1+p.c_LHE*(p.e_0 + p.e_1*GrF + p.e_2*DomF + p.e_3*Lut_4 + p.e_dose))),
# LH
(p.k_LH*(1+p.c_LHP*(((p.p_0 + p.p_1*Lut_3 + p.p_2*Lut_4 + p.p_dose)/2)*(1 + ((p.e_0 + p.e_1*GrF + p.e_2*DomF + p.e_3*Lut_4 + p.e_dose)**p.mu / (p.K_mPapp**p.mu + (p.e_0 + p.e_1*GrF + p.e_2*DomF + p.e_3*Lut_4 + p.e_dose)**p.mu)))))*RP_LH)/(p.v*(1+p.c_LHE*(p.e_0 + p.e_1*GrF + p.e_2*DomF + p.e_3*Lut_4 + p.e_dose))) - p.a_LH*LH,
# RP_FSH
(p.v_FSH/(1+(p.h_0 + p.h_1*DomF + p.h_2*Lut_2 + p.h_3*Lut_3)/p.K_iFSH_InhA)) - \
import data_exploration
import utilities
import supervised
import unsupervised
import personalized_autoencoder
# workspace
print("Actual workspace: ", os.getcwd())
# logger
# sys.stdout = config.Logger("log/log.txt")
# loading data and params and elementary exploration
loader = config.Loader()
params = config.Params()
# elementary exploration
data_exploration.summary(loader)
# manage missing
starting_data = data_exploration.manage_missing(loader.data)
# variable distribution per class and linear correlation
data_exploration.variable_distribution(starting_data)
data_exploration.linear_correlation(starting_data)
# shuffle
starting_data = shuffle(starting_data).reset_index(drop=True)
# Supervised - naive attempt
def plot_four_variables_red(yy,
var_names,
tt,
num_samples=c.num_samples,
on_off=c.on_off,
days_on=c.days_on,
lam_up=c.lam_up,
lam_down=c.lam_down,
func_form=c.func_form,
missed_start=c.missed_start,
days_missed=c.days_missed):
# Removes first 3 cycles
tt_mask = np.where(tt >= 84, True, False)
tt = tt[tt_mask]
yy = yy[tt_mask]
dosing = calc_e_dose(np.asarray(tt),
1,
func_form,
lam_up=lam_up,
lam_down=lam_down,
missed_start=missed_start,
days_missed=days_missed)
print(np.asarray(tt))
print(dosing)
# print(tt)
# dosing = np.where((tt%28)>=days_on, 0, 1)
y = []
titles = []
ylabels = []
ylims = []
print('\nStarting to get variables')
for var in var_names:
print(f'Getting {var}')
y.append(
get_variable(var,
yy,
tt,
c.Params(),
days_on,
lam_up=lam_up,
lam_down=lam_down,
func_form=func_form,
missed_start=missed_start,
days_missed=days_missed))
title, ylabel, ylim = get_plotting_params(var)
titles.append(title)
ylabels.append(ylabel)
ylims.append(ylim)
fig, axs = plt.subplots(2, 2)
if on_off:
ax1 = axs[0, 0]
ax2 = ax1.twinx()
ax1.plot(tt, y[0])
ax2.plot(tt, dosing, c='r')
ax2.set(ylim=(-3, 1.5))
else:
axs[0, 0].plot(tt, y[0])
axs[0, 0].set_title(var_names[0])
if on_off:
ax1 = axs[0, 1]
ax2 = ax1.twinx()
ax1.plot(tt, y[1])
ax2.plot(tt, dosing, c='r')
ax2.set(ylim=(-3, 1.5))
else:
axs[0, 1].plot(tt, y[1])
axs[0, 1].set_title(var_names[1])
if on_off:
ax1 = axs[1, 1]
ax2 = ax1.twinx()
ax1.plot(tt, y[3])
ax2.plot(tt, dosing, c='r')
ax2.set(ylim=(-3, 1.5))
else:
axs[1, 1].plot(tt, y[3])
axs[1, 1].set_title(var_names[3])
if on_off:
ax1 = axs[1, 0]
ax2 = ax1.twinx()
ax1.plot(tt, y[2])
ax2.plot(tt, dosing, c='r')
ax2.set(ylim=(-3, 1.5))
else:
axs[1, 0].plot(tt, y[2])
axs[1, 0].set_title(var_names[2])
i = 0
for ax in axs.flat:
ax.set(xlabel='Time (days)', ylabel=ylabels[i], ylim=(0, ylims[i]))
i += 1
fig.suptitle('Dose: ' + c.dosing)
plt.tight_layout()
plt.show()
def plot_four_variables(yy,
var_names,
tt,
num_samples=c.num_samples,
on_off=c.on_off,
days_on=c.days_on,
lam_up=c.lam_up,
lam_down=c.lam_down,
func_form=c.func_form):
# Removes first 3 cycles
tt_mask = np.where(tt >= 84, True, False)
tt = tt[tt_mask]
yy = yy[tt_mask]
if on_off:
on_off_mask = np.where((tt % 28) >= days_on, True, False)
on_tt = tt[~on_off_mask]
off_tt = tt[on_off_mask]
y = []
titles = []
ylabels = []
ylims = []
for var in var_names:
y.append(
get_variable(var, yy, tt, c.Params(), days_on, lam_up, lam_down))
title, ylabel, ylim = get_plotting_params(var)
titles.append(title)
ylabels.append(ylabel)
ylims.append(ylim)
fig, axs = plt.subplots(2, 2)
if on_off:
on_yy = y[0][~on_off_mask]
off_yy = y[0][on_off_mask]
axs[0, 0].plot(on_tt, on_yy, '.', markersize=1)
axs[0, 0].plot(off_tt, off_yy, '.', markersize=1)
else:
axs[0, 0].plot(tt, y[0])
axs[0, 0].set_title(var_names[0])
if on_off:
on_yy = y[1][~on_off_mask]
off_yy = y[1][on_off_mask]
axs[0, 1].plot(on_tt, on_yy, '.', markersize=1)
axs[0, 1].plot(off_tt, off_yy, '.', markersize=1)
else:
axs[0, 1].plot(tt, y[1])
axs[0, 1].set_title(var_names[1])
if on_off:
on_yy = y[3][~on_off_mask]
off_yy = y[3][on_off_mask]
axs[1, 1].plot(on_tt, on_yy, '.', markersize=1)
axs[1, 1].plot(off_tt, off_yy, '.', markersize=1)
else:
axs[1, 1].plot(tt, y[3])
axs[1, 1].set_title(var_names[3])
if on_off:
on_yy = y[2][~on_off_mask]
off_yy = y[2][on_off_mask]
axs[1, 0].plot(on_tt, on_yy, '.', markersize=1)
axs[1, 0].plot(off_tt, off_yy, '.', markersize=1)
else:
axs[1, 0].plot(tt, y[2])
axs[1, 0].set_title(var_names[2])
i = 0
for ax in axs.flat:
ax.set(xlabel='Time (days)', ylabel=ylabels[i], ylim=(0, ylims[i]))
i += 1
fig.suptitle('Dose: ' + c.dosing)
plt.tight_layout()
plt.show()