import methods import methods.outward as outward import numpy as np import matplotlib import matplotlib.pyplot as pl # # Fit all outward current experiments (or used cached result) # force = '--force' in sys.argv table, cells = outward.fit_all(force=force) # # Run simulations with each cell (or use cached data) # aps = outward.simulate_tailored_aps(table, cells, force=force) # # Plot 2APs # # # Order cells by APD at -65mV # tmin = 50 * 1e-3 vapd = -65 * 1e-3 order = np.array([aps['membrane.V', i] for i in xrange(len(outward.ORDER))]) offset = np.where(aps.time() >= tmin)[0][0] order = order[:, offset:] order = [np.where(v < vapd)[0][:1] for v in order] order = [v[0] if v else 2000 for v in order] order = np.argsort(np.argsort(order))
#
# Run simulations with tailored models
#
if TAILORED:
apds = []
stars = []
nmodels = []
for concentration in detailed:
print('Running for drug: ' + drug + ' ' + str(concentration) +
' [uM]')
# Run simulations
sim = outward.simulate_tailored_aps(table,
cells,
drug=drug,
concentration=concentration,
cl=cl,
force=force)
# Normalise and get apds
group = []
t = sim.time() * 1e3
for i, long_id in enumerate(outward.ORDER):
filename = None
if concentration > 0 and drug != 'paracetamol':
filename = os.path.join(
apd.AP_FIGURES, 'sim-' + long_id + '-' + drug + '-' +
str(concentration) + '.png')
ap = apd.normalise(t, sim['membrane.V', i], filename=filename)
if concentration > 0 and drug != 'paracetamol':
filename = os.path.join(
import matplotlib
#matplotlib.use('Agg')
import matplotlib.pyplot as pl
# Don't cache
force = "--force" in sys.argv
#
# Fit all outward current experiments (or used cached result)
#
table, cells = outward.fit_all()
#
# Run simulations with each cell (or use cached data)
#
sim = outward.simulate_tailored_aps(table, cells, force=force, beats=100, cl=1,
stimulate=False)
# Normalise and calculate apd
n = len(outward.ORDER)
sim_time = sim.time() * 1e3 - 50
sim_aps = []
sim_apds = []
for i, long_id in enumerate(outward.ORDER):
filename = os.path.join(apd.AP_FIGURES, 'sim-base-' + long_id + '.png')
#sim_aps.append(apd.normalise(sim_time, sim['membrane.V', i],
# filename=filename))
sim_aps.append(sim['membrane.V', i])
filename = os.path.join(apd.APD_FIGURES, 'sim-base-' + long_id + '.png')
#sim_apds.append(apd.calculate_apd(sim_time, sim_aps[-1],
# filename=filename)[0])
sim_aps = np.array(sim_aps)
import methods
import methods.outward as outward
import numpy as np
import matplotlib
import matplotlib.pyplot as pl
#
# Fit all outward current experiments (or used cached result)
#
force = '--force' in sys.argv
table, cells = outward.fit_all(force=force)
#
# Run simulations with each cell (or use cached data)
#
aps = outward.simulate_tailored_aps(table, cells, store_currents=True,
force=force)
#
# Plot 2APs
#
#
# Order cells by APD at -65mV
#
tmin = 50 * 1e-3
vapd = -65 * 1e-3
order = np.array([aps['membrane.V', i] for i in xrange(len(outward.ORDER))])
offset = np.where(aps.time() >= tmin)[0][0]
order = order[:, offset:]
order = [np.where(v < vapd)[0][:1] for v in order]
order = [v[0] if v else 2000 for v in order]
order = np.argsort(np.argsort(order))