def update_accuracy_in_paramters_on_end_then_save_to_json(parameters, history):
# append function name to the call sequence
calling_sequence.append("[update_paramters]==>>")
print(" ==============================================")
print(" [INFO] Entering function[update_paramters] in core.py")
# save parameters in result sheet
# get max validation accuracy and the train accuracy according to it.
max_validation_acc = np.max(history.history['val_acc'])
index = history.history['val_acc'].index(np.max(
history.history['val_acc']))
train_acc_according_to_max_validation_acc = history.history['acc'][index]
parameters['train_acc_according_to_max_validation_acc'] = round(
train_acc_according_to_max_validation_acc, 4)
parameters['max_validation_acc'] = round(max_validation_acc, 4)
parameters['best_accuracy_epoch'] = index + 1
parameters['overall_train_accuracy'] = round(history.history['acc'][-1], 4)
parameters['overall_validation_accuracy'] = round(
history.history['val_acc'][-1], 4)
# print(parameters)
write_json_parameters(parameters)
print(" [INFO] Leaving function[update_paramters] in core.py")
print(" ==============================================")
def save_train_result(history, parameters, initiate_new_result_sheet=True):
# append function name to the call sequence
calling_sequence.append("[save_train_result]==>>")
print(" ==============================================")
print(
" [INFO] Entering function[save_train_result] in core.py INTENT TO CREATE NEW RESULT SHEET = ",
initiate_new_result_sheet)
# save parameters in result sheet
# get max validation accuracy and the train accuracy according to it.
max_validation_acc = np.max(history.history['val_acc'])
index = history.history['val_acc'].index(np.max(
history.history['val_acc']))
train_acc_according_to_max_validation_acc = history.history['acc'][index]
parameters['train_acc_according_to_max_validation_acc'] = round(
train_acc_according_to_max_validation_acc, 4)
parameters['max_validation_acc'] = round(max_validation_acc, 4)
parameters['best_accuracy_epoch'] = index + 1
parameters['overall_train_accuracy'] = round(
history.history['acc'][len(history.history['acc']) - 1], 4)
parameters['overall_validation_accuracy'] = round(
history.history['val_acc'][len(history.history['val_acc']) - 1], 4)
# get the paramters format that will be written in csv file
result_sheet = get_result_parameters(parameters)
# convert python dict to pandas data frame
result_sheet = pd.DataFrame([result_sheet])
# CHECK IF CREATING NEW .CSV FILE DESIRED OR APPEND TO CURENT ONE
if (initiate_new_result_sheet):
result_sheet.to_csv(parameters['result_sheet'], index=False)
else:
previous_result_sheet = pd.read_csv(parameters['result_sheet'])
result_sheet = pd.concat([previous_result_sheet, result_sheet],
sort=False,
ignore_index=True)
result_sheet.to_csv(parameters['result_sheet'], index=False)
print('===============================================')
print(" [INFO] save result at " + str(parameters['model_save_path']))
print('===================SAVED=======================')
print(" [INFO] Leaving function[save_train_result] in core.py")
print(" ==============================================")
img_t1 = load_tiff_image(root_path + 'images/18_08_2017_image' +
'.tif').astype(np.float32)
img_t1 = img_t1.transpose((1, 2, 0))
img_t2 = load_tiff_image(root_path + 'images/21_08_2018_image' +
'.tif').astype(np.float32)
img_t2 = img_t2.transpose((1, 2, 0))
# Concatenation of images
image_array1 = np.concatenate((img_t1, img_t2), axis=-1).astype(np.float32)
h_, w_, channels = image_array1.shape
print(image_array1.shape)
# Normalization
type_norm = 1
image_array = normalization(image_array1, type_norm)
print(np.min(image_array), np.max(image_array))
# Load reference
image_ref1 = load_tiff_image(root_path + 'images/REFERENCE_2018_EPSG4674' +
'.tif')
image_ref = image_ref1[:1700, :1440]
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
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