def generate_cycle(length,*args,peep=None):
startpos = np.random.randint(0,len(args[0]))
delta = (length - startpos)
nzeroes = delta//len(args[0])+1
zpos = np.random.randint(2,delta//10,size=nzeroes)
res = [None]*len(args)
vrandom = [np.random.uniform(0.95,1.05) if np.random.random() > 0.01 else np.random.uniform(0.1,0.2) for i in range(nzeroes+1)]
for i in range(len(args)):
rarr = args[i][-startpos:]
# mode = stats.mode(args[i])[0]
mode = peep if (not peep is None) and (i==len(args)-1) else 0
for j in range(nzeroes):
aux = vrandom[j+1]*args[i] + (1-vrandom[j+1])*mode
# aux = 0.0
# if vrandom[j+1] < 0.9: aux = np.array([ vrandom[j+1]*value if value > mode/vrandom[j+1] else value for value in args[i]])
# else: aux = np.array([vrandom[j+1]*value if value > mode/vrandom[j+1] else value for value in args[i]])
rarr = np.concatenate((rarr,np.ones(zpos[j])*mode,aux))
res[i] = rarr[:length]
return res
def conv2d(inputs, num_outputs, kernel_shape, mask_type, scope="conv2d"):
with tf.compat.v1.variable_scope(scope):
WEIGHT_INITIALIZER = tf.compat.v1.keras.initializers.VarianceScaling(
scale=1.0, mode="fan_avg", distribution="uniform")
batch_size, height, width, channel = inputs.get_shape().as_list()
kernel_h, kernel_w = kernel_shape
center_h = kernel_h // 2
center_w = kernel_w // 2
weights_shape = [kernel_h, kernel_w, channel, num_outputs]
weights = tf.compat.v1.get_variable("weights", weights_shape,
tf.float32, WEIGHT_INITIALIZER,
None)
mask = np.ones((kernel_h, kernel_w, channel, num_outputs),
dtype=np.float32)
mask[center_h, center_w + 1:, :, :] = 0.0
mask[center_h + 1:, :, :, :] = 0.0
if mask_type == 'a':
mask[center_h, center_w, :, :] = 0.0
weights = weights * tf.constant(mask, dtype=tf.float32)
outputs = tf.nn.conv2d(input=inputs,
filters=weights,
strides=[1, 1, 1, 1],
padding="SAME",
name='outputs')
biases = tf.compat.v1.get_variable("biases", [
num_outputs,
], tf.float32, tf.compat.v1.zeros_initializer(), None)
outputs = tf.nn.bias_add(outputs, biases, name='outputs_plus_b')
return outputs
past_ref1 = load_tiff_image(root_path +
'images/PAST_REFERENCE_FOR_2018_EPSG4674' + '.tif')
unique, counts = np.unique(past_ref1, return_counts=True)
counts_dict = dict(zip(unique, counts))
print('=' * 50)
print(counts_dict)
past_ref = past_ref1[:1700, :1440]
# Creation of buffer
buffer = 2
final_mask = mask_no_considered(image_ref, buffer, past_ref)
#plt.imshow(final_mask)
# Mask with tiles
tile_number = np.ones((340, 480))
mask_c_1 = np.concatenate((tile_number, 2 * tile_number, 3 * tile_number),
axis=1)
mask_c_2 = np.concatenate((4 * tile_number, 5 * tile_number, 6 * tile_number),
axis=1)
mask_c_3 = np.concatenate((7 * tile_number, 8 * tile_number, 9 * tile_number),
axis=1)
mask_c_4 = np.concatenate(
(10 * tile_number, 11 * tile_number, 12 * tile_number), axis=1)
mask_c_5 = np.concatenate(
(13 * tile_number, 14 * tile_number, 15 * tile_number), axis=1)
mask_tiles = np.concatenate((mask_c_1, mask_c_2, mask_c_3, mask_c_4, mask_c_5),
axis=0)
mask_tr_val = np.zeros((mask_tiles.shape))
tr1 = 1
def solve_model(header_params,params,header_features,features,debugmsg):
#Extracts each parameter
fs = params[header_params.index('Fs')]
rvent = params[header_params.index('Rvent')]
c = params[header_params.index('C')]
rins = params[header_params.index('Rins')]
rexp = rins # params[4]
peep = params[header_params.index('PEEP')]
sp = params[header_params.index('SP')]
trigger_type = features[header_features.index('Triggertype')]
trigger_arg = params[header_params.index('Triggerarg')]
rise_type = features[header_features.index('Risetype')]
rise_time = params[header_params.index('Risetime')]
cycle_off = params[header_params.index('Cycleoff')]
rr = params[header_params.index('RR')]
pmus_type = features[header_features.index('Pmustype')]
pp = params[header_params.index('Pp')]
tp = params[header_params.index('Tp')]
tf = params[header_params.index('Tf')]
noise = params[header_params.index('Noise')]
e2 = params[header_params.index('E2')]
model = features[header_features.index('Model')]
expected_len = int(np.floor(180.0 / np.min(RR) * np.max(Fs)) + 1)
#Assings pmus profile
pmus = pmus_profile(fs, rr, pmus_type, pp, tp, tf)
pmus = pmus + peep #adjusts PEEP
pmus = np.concatenate((np.array([0]), pmus)) #sets the first value to zero
#Unit conversion from cmH2O.s/L to cmH2O.s/mL
rins = rins / 1000.0
rexp = rexp / 1000.0
rvent = rvent / 1000.0
#Generates time, flow, volume, insex and paw waveforms
time = np.arange(0, np.floor(60.0 / rr * fs) + 1, 1) / fs
time = np.concatenate((np.array([0]), time))
flow = np.zeros(len(time))
volume = np.zeros(len(time))
insex = np.zeros(len(time))
paw = np.zeros(len(time)) + peep #adjusts PEEP
len_time = len(time)
#Peak flow detection
peak_flow = flow[0]
detect_peak_flow = False
#Support detection
detect_support = False
time_support = -1
#Expiration detection
detect_exp = False
time_exp = -1
if trigger_type == 'flow':
# units conversion from L/min to mL/s
trigger_arg = trigger_arg / 60.0 * 1000.0
for i in range(1, len(time)):
# period until the respiratory effort beginning
if (((trigger_type == 'flow' and flow[i] < trigger_arg) or
(trigger_type == 'pressure' and paw[i] > trigger_arg + peep) or
(trigger_type == 'delay' and time[i] < trigger_arg)) and
(not detect_support) and (not detect_exp)):
paw[i] = peep
y0 = volume[i - 1]
tspan = [time[i - 1], time[i]]
args = (paw[i], pmus[i], model, c, e2, rins)
sol = odeint(flow_model, y0, tspan, args=args)
volume[i] = sol[-1]
flow[i] = flow_model(volume[i], time[i], paw[i], pmus[i], model, c, e2, rins)
if debugmsg:
print('volume[i]= {:.2f}, flow[i]= {:.2f}, paw[i]= {:.2f}, waiting'.format(volume[i], flow[i], paw[i]))
if (((trigger_type == 'flow' and flow[i] >= trigger_arg) or
(trigger_type == 'pressure' and paw[i] <= trigger_arg + peep) or
(trigger_type == 'delay' and time[i] >= trigger_arg))):
detect_support = True
time_support = time[i+1]
continue
# detection of inspiratory effort
# ventilator starts to support the patient
elif (detect_support and (not detect_exp)):
if rise_type == 'step':
paw[i] = sp + peep
elif rise_type == 'exp':
rise_type = rise_type if np.random.random() > 0.01 else 'linear'
if paw[i] < sp + peep:
paw[i] = (1.0 - np.exp(-(time[i] - time_support) / rise_time )) * sp + peep
if paw[i] >= sp + peep:
paw[i] = sp + peep
elif rise_type == 'linear':
rise_type = rise_type if np.random.random() > 0.01 else 'exp'
if paw[i] < sp + peep:
paw[i] = (time[i] - time_support) / rise_time * sp + peep
if paw[i] >= sp + peep:
paw[i] = sp + peep
y0 = volume[i - 1]
tspan = [time[i - 1], time[i]]
args = (paw[i], pmus[i], model, c, e2, rins)
sol = odeint(flow_model, y0, tspan, args=args)
volume[i] = sol[-1]
flow[i] = flow_model(volume[i], time[i], paw[i], pmus[i], model, c, e2, rins)
if debugmsg:
print('volume[i]= {:.2f}, flow[i]= {:.2f}, paw[i]= {:.2f}, supporting'.format(volume[i], flow[i], paw[i]))
if flow[i] >= flow[i - 1]:
peak_flow = flow[i]
detect_peak_flow = False
elif flow[i] < flow[i - 1]:
detect_peak_flow = True
if (flow[i] <= cycle_off * peak_flow) and detect_peak_flow and i<len_time:
detect_exp = True
time_exp = i+1
try:
paw[i + 1] = paw[i]
except IndexError:
pass
elif detect_exp:
if rise_type == 'step':
paw[i] = peep
elif rise_type == 'exp':
if paw[i - 1] > peep:
paw[i] = sp * (np.exp(-(time[i] - time[time_exp-1]) / rise_time )) + peep
if paw[i - 1] <= peep:
paw[i] = peep
elif rise_type == 'linear':
rise_type = rise_type if np.random.random() > 0.01 else 'exp'
if paw[i - 1] > peep:
paw[i] = sp * (1 - (time[i] - time[time_exp-1]) / rise_time) + peep
if paw[i - 1] <= peep:
paw[i] = peep
y0 = volume[i - 1]
tspan = [time[i - 1], time[i]]
args = (paw[i], pmus[i], model, c, e2, rexp + rvent)
sol = odeint(flow_model, y0, tspan, args=args)
volume[i] = sol[-1]
flow[i] = flow_model(volume[i], time[i], paw[i], pmus[i], model, c, e2, rexp + rvent)
if debugmsg:
print('volume[i]= {:.2f}, flow[i]= {:.2f}, paw[i]= {:.2f}, exhaling'.format(volume[i], flow[i], paw[i]))
#Generates InsEx trace
if time_exp > -1:
insex = np.concatenate((np.ones(time_exp), np.zeros(len(time) - time_exp)))
#Drops the first element
flow = flow[1:] / 1000.0 * 60.0 # converts back to L/min
volume = volume[1:]
paw = paw[1:]
pmus = pmus[1:] - peep #reajust peep again
insex = insex[1:]
flow,volume,pmus,insex,paw = generate_cycle(expected_len,flow,volume,pmus,insex,paw,peep=peep)
# paw = generate_cycle(expected_len,paw,peep=peep)[0]
flow,volume,paw,pmus,insex = generate_noise(noise,flow,volume,paw,pmus,insex)
# plt.plot(flow)
# plt.plot(volume)
# plt.plot(paw)
# plt.plot(pmus)
# plt.show()
return flow, volume, paw, pmus, insex, rins,rexp, c
# Creation of buffer
buffer = 2
final_mask = mask_no_considered(image_ref, buffer, past_ref_sum)
final_mask[img_mask_ref == -99] = -1
unique, counts = np.unique(final_mask, return_counts=True)
counts_dict = dict(zip(unique, counts))
print(f'Pixels of final mask: {counts_dict}')
total_pixels = counts_dict[0] + counts_dict[1] + counts_dict[2]
weight0 = total_pixels / counts_dict[0]
weight1 = total_pixels / counts_dict[1]
final_mask[img_mask_ref == -99] = 0
# Mask with tiles
# Divide tiles in 5 rows and 3 columns. Total = 15 tiles
tile_number = np.ones((1220, 2200))
mask_c_1 = np.concatenate((tile_number, 2 * tile_number, 3 * tile_number),
axis=1)
mask_c_2 = np.concatenate((4 * tile_number, 5 * tile_number, 6 * tile_number),
axis=1)
mask_c_3 = np.concatenate((7 * tile_number, 8 * tile_number, 9 * tile_number),
axis=1)
mask_c_4 = np.concatenate(
(10 * tile_number, 11 * tile_number, 12 * tile_number), axis=1)
mask_c_5 = np.concatenate(
(13 * tile_number, 14 * tile_number, 15 * tile_number), axis=1)
mask_tiles = np.concatenate((mask_c_1, mask_c_2, mask_c_3, mask_c_4, mask_c_5),
axis=0)
mask_tr_val = np.zeros((mask_tiles.shape))
tr1 = 5