def __init__(self, exp_path, agents=None):
"""
Constructor
"""
self.params = parameters.Params()
self.params.load(os.path.join(exp_path, '_params.json'))
self.exp_path = exp_path
self.agents = agents
self.bd_extractor = BehaviorDescriptor(self.params)
self.pca = None
self.traj_to_obs = registered_envs[self.params.env_name]['traj_to_obs']
self.grid_params = registered_envs[self.params.env_name]['grid']
if not os.path.exists(os.path.join(self.exp_path, 'analyzed_data')):
os.mkdir(os.path.join(self.exp_path, 'analyzed_data'))
# GT_BD is the ground truth bd that is used to calculate the CVG
if self.params.env_name == 'Dummy':
self.gt_bd_extractor = dummy_gt_bd
self.goals = 0
elif self.params.env_name == 'Walker2D':
self.gt_bd_extractor = dummy_gt_bd
self.goals = 0
elif self.params.env_name == 'CollectBall':
self.gt_bd_extractor = collect_ball_gt_bd
self.goals = 0
elif self.params.env_name == 'NDofArm':
self.gt_bd_extractor = red_arm_gt_bd
self.goals = 3
elif self.params.env_name == 'AntMaze':
self.gt_bd_extractor = ant_maze_gt_bd
self.goals = 2
elif self.params.env_name == 'HardMaze':
self.gt_bd_extractor = hard_maze_gt_bd
self.goals = 2
elif self.params.env_name == 'Curling':
self.gt_bd_extractor = curling_gt_bd
self.goals = 0
else:
print("No GT BD given for: {}".format(self.params.env_name))
raise ValueError
def __init__(self, exp_path, multip=False, agents=None):
"""
Constructor
"""
self.params = parameters.Params()
self.params.load(os.path.join(exp_path, '_params.json'))
self.exp_path = exp_path
self.agents = agents
self.bd_extractor = BehaviorDescriptor(self.params)
self.traj_to_obs = registered_envs[self.params.env_name]['traj_to_obs']
self.mp = multip
if self.mp:
global main_pool
main_pool = mp.Pool(initializer=self.init_process,
processes=int(os.cpu_count()))
else:
self.evaluator = Evaluator(self.params)
if not os.path.exists(os.path.join(self.exp_path, 'analyzed_data')):
os.mkdir(os.path.join(self.exp_path, 'analyzed_data'))
def setupModelSpecificPopsizes(self, inputData, agePopSizes):
import model
import derived
import parameters
# gaussianise stunting in *data*
ages = self.keyList['ages']
for iAge in range(len(ages)):
ageName = ages[iAge]
probStunting = self.sumStuntedComponents(
inputData.stuntingDistribution[ageName])
inputData.stuntingDistribution[ageName] = self.restratify(
probStunting)
listOfAgeCompartments = self.makeAgeCompartments(
agePopSizes, inputData)
pregnantWomen = self.makePregnantWomen(inputData)
model = model.Model(pregnantWomen, listOfAgeCompartments, self.keyList)
derived = derived.Derived(inputData, model, self.keyList)
model.setDerived(derived)
parameters = parameters.Params(inputData, derived, self.keyList)
model.setParams(parameters)
model.updateMortalityRate()
return model, derived, parameters
# file.writerow(['x', 'y'])
# file.writerow([1.0, 2.0])
# file.close()
# out = csv.writer(open('natural_shape_points.csv', 'w'), delimiter=',', quoting=csv.QUOTE_ALL)
# out.writerow(['x','y'])
# out.writerow([1.0,2.0])
# out.close()
# writer = csv.DictWriter(out, fieldnames=fieldnames)
# writer.writeheader()
# writer.writerow({'first_name': 'Baked', 'last_name': 'Beans'})
# writer.writerow({'first_name': 'Lovely', 'last_name': 'Spam'})
# writer.writerow({'first_name': 'Wonderful', 'last_name': 'Spam'})
params = parameters.Params()
''' Shape Definition '''
pointsNaturalShape = []
circle_sections = 50
for i in range(0, circle_sections):
theta = (np.pi / (1.7 * circle_sections)) * i
# pointsNaturalShape.append([params.r_base * (1.0 - np.cos(np.pi / 1.7 - theta)), params.r_base * np.sin(np.pi / 1.7 - theta)])
pointsNaturalShape.append(
[params.r_base * (1.0 - np.cos(theta)), params.r_base * np.sin(theta)])
## Transition region
pointsNaturalShape.append([params.r_base + 1.4, params.r_base - 0.3])
.... ....
linearly interpolate between times
Note: this is different from Wang et al (2020), which assumes
piecewise constant values for R_zero
"""
r_zero_array = np.zeros([6, 2])
r_zero_array[0, :] = [0.0, 3.0] # t=0 days R_zero = 3.0
r_zero_array[1, :] = [20.0, 2.6] # t = 60 days R_zero = 2.6
r_zero_array[2, :] = [70.0, 1.9] # t = 70 days R_zero = 1.9
r_zero_array[3, :] = [84.0, 1.0] # t = 84 days R_zero = 1.0
r_zero_array[4, :] = [90.0, .50] # t = 90 days R_zero = .50
r_zero_array[5, :] = [1000, .50] # t = 1000 days R_zero =.50
params = parameters.Params(c, N, sigma, gamma, r_zero_array)
t_0 = 0
tspan = np.linspace(t_0, 181, 180) # time in days
y_init = np.zeros(4)
y_init[0] = S_0
y_init[1] = E_0
y_init[2] = I_0
y_init[3] = R_0
def seir_with_params(t, y):
return seir_function(t, y, params)
Y_tout_snr, snr_diff_dec = dt.gen_signaux_snr(objet_principal, source_gal,
source_halo, bruit_pose, SNR,
simu)
Y = Y_tout_snr[:, :, :, :, 0]
Y_src = Y.mean(axis=3)
empty_cube = snr_diff_dec * st.norm.rvs(loc=0, scale=1, size=(S, S, W))
# to change if the object center is not the subcube center :
centre = np.array([int(S / 2), int(S / 2)])
# creating the "parameter" object :
params = parameters.Params(Y,
centre,
pfa_bright=pfa_bright,
pfa_faint=pfa_faint,
taille_f=11,
FWHM=FWHM)
#%%
# Actual detection
Xe1, ve1, vem1, Xi, vi = sdp.detection_strategy(params)
#%% isolating some regions for averaging spectra
reg_init = ma.masked_array(
Y_src, np.tile((Xi == 0)[:, :, np.newaxis], (1, 1, Y_src.shape[2])))
sp_init = ma.mean(ma.mean(reg_init, axis=0), axis=0)
reg_ext = ma.masked_array(
Y_src,
import threading
import generator
import parameters
import controller
import webGui
import os
threadLock = threading.Lock()
threads = []
Parameters = parameters.Params(50, 44100, 10 * 4410, 500)
controller.initialize(Parameters, threadLock)
webGui.initialize(Parameters)
generatorThread = generator.Generator(Parameters, threadLock)
controllerThread = controller.Controller(Parameters, threadLock)
generatorThread.start()
controllerThread.start()
webGui.run()
# Add threads to thread list
threads.append(generatorThread)
threads.append(controllerThread)
# Wait for all threads to complete
for t in threads:
t.join()
generatorThread.stop()
print('Generator and controller stoped')