for index_data in data: grid[(grid > 0) & (grid <= 50)] -= 1 if index_data == []: # could use <if not individual_data.any():> but this is more readable. current_state = grid.copy() output.append(current_state) else: indices = np.unravel_index(index_data, a.shape) for ind in range(len(indices[0])): grid[indices[0][ind]][indices[1][ind]] = 50 current_state = grid.copy() output.append(current_state) return output e = at.ECG(shape=(200, 200), probe_height=3, m=args.mode) # Assuming shape/probe height doesn't change. file_name = args.output #nput("Name of output file: ") #print("Nu Value:") nu = args.nu #float(input()) print(file_name, nu, e.mode, Iterations) # print(nu) h5f = h5py.File('%s.h5' % file_name, 'w') for index in range(Iterations): #nu = (np.random.rand() / 5) + 0.1 start_time1 = time.time() index_grp = h5f.create_group('Index: %s' % index) a = fp.Heart(nu=nu, fakedata=True) crit_position = np.random.randint(40000)
pp = 0 print_counter(pp, number_of_rotors) for i in range(number_of_rotors): # Initialising the Heart structure a = ps.Heart(nu=0.2, delta=0.0, fakedata=True) # Randomises the rotor x,y position cp_x_pos = randint(30, 169) cp_y_pos = randint(0, 199) a.set_circuit(np.ravel_multi_index([cp_y_pos, cp_x_pos], (200, 200))) rotor[i] = (cp_x_pos, cp_y_pos) # Initialising ECG recording (randomises the probe x,y position) current_ecg_x_pos = randint(20, 179) current_ecg_y_pos = randint(0, 199) ecg_processing = at.ECG(centre=(current_ecg_y_pos, current_ecg_x_pos), m='g_single') ecg_start[i] = (current_ecg_x_pos, current_ecg_y_pos) # Animation grids animation_grid = np.zeros(a.shape) # reset time step counter ptr1 = 0 prev_y_vector = None prev_x_vector = None # Loop checking y_short_memory = [] x_short_memory = []
import propagate_singlesource as ps import analysis_theano as at import numpy as np import matplotlib.pyplot as plt from Functions import sampling_convert # import pyqtgraph as pg # import pyqtgraph.ptime as ptime # from pyqtgraph.Qt import QtCore, QtGui a = ps.Heart(fakedata=True) a.set_pulse(220, [[100], [100]]) e = at.ECG(shape=(200, 200), probe_height=3) raw_data = a.propagate(400) converted_data = list() grid = np.zeros(a.shape) sampling_convert(raw_data, converted_data, shape=a.shape, rp=a.rp, animation_grid=grid) ecg = e.solve(converted_data[100:]) probe_positions = e.probe_position print probe_positions print "Plotting..." fig = plt.figure() for index, i in enumerate(ecg): plt.plot(range(len(i)), i,