def myplot(frame, fig):
ax = fig.add_subplot(111)
plot.cells(frame, ax)
plot.solidarea(frame, ax)
plot.shape(frame, ax)
plot.velocity(frame, ax)
plot.pressure_force(frame, ax)
plot.walls(frame, ax)
ax.axes.set_aspect('equal', adjustable='box')
ax.set_xlim([0, frame.parameters['Size'][0]-1])
ax.set_ylim([0, frame.parameters['Size'][1]-1])
ax.axis('off')
def myplot(frame, fig):
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)
plot.cells(frame, ax1)
plot.shape(frame, ax1)
plot.velocity_field(frame, avg=5, cbar=False, engine=ax2)
for ax in [ax1, ax2]:
ax.axes.set_aspect('equal', adjustable='box')
ax.set_xlim([0, frame.parameters['Size'][0] - 1])
ax.set_ylim([0, frame.parameters['Size'][1] - 1])
ax.axis('off')
def myplot(frame, fig):
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)
plot.cells(frame, ax1)
plot.solidarea(frame, ax1)
plot.shape(frame, ax1)
plot.velocity(frame, ax1)
plot.pressure_force(frame, ax1)
plot.interfaces(frame, ax2)
for ax in [ax1, ax2]:
ax.axes.set_aspect('equal', adjustable='box')
ax.set_xlim([0, frame.parameters['Size'][0] - 1])
ax.set_ylim([0, frame.parameters['Size'][1] - 1])
ax.axis('off')
for c_dir in dirs:
if not os.path.exists(c_dir):
os.makedirs(c_dir)
# Go for quick generation of the biophysical data (if false take ~5 min)
FAST = False
# Generate the biophysical if not existing
try:
hdf = h5py.File("%s/data.hdf5" % DATA_FOLDER, "r")
except IOError:
print("Need to generate a set of data")
model, stim = lib_phy.generate_data(short=FAST)
print("Draw model stimulated points")
FIG_NAME = "%sFig%dD%s" % (FIG_FOLDER, 1, FIG_SUF)
plot.shape(model, save=FIG_NAME)
# Load data into namespace
biophy = ["soma_v", "dend_v",
"vtrace_c", "exp_c", "meas_c",
"vtrace_s", "exp_s", "meas_s"]
hyp = ["soma_v_h", "dend_v_h", "vtrace_c_h", "exp_c_h",
"meas_c_h", "vtrace_s_h", "exp_s_h", "meas_s_h"]
t = ["time_v", "time_em"]
data_load = biophy + hyp + t
with h5py.File("%s/data.hdf5" % DATA_FOLDER, "r") as hdf:
for name in data_load:
globals()[name] = np.array(hdf[name])
# Data visually extracted from Jia et al 2011 article
data = [[1, 0.1, 0, 0.8, 0.12, 0, 0, 0.25],