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],