def walkplot(cons):
"""
Interactive function to walk through the metabolites in the media. It prints
a growth plot of the consortium for each four metabolites.
INPUT -> cons: MMODES consortium object already simulated
"""
# Call always after cons.runn()!
if not hasattr(cons, "outplot"):
print("Consortium must run before analyzing the output of the run!")
return
global plt
if not plt:
import matplotlib.pyplot as plt
# keep original inputs
original_metplots = dcp(cons.mets_to_plot)
original_outplot = dcp(cons.outplot)
cons.outplot = "tmp.png"
mets = [k for k in cons.media]
# loop over metabolites, plot them and let the user close the window
for m in range(0, len(mets), 4):
cons.mets_to_plot = [mets[m], mets[m + 1], mets[m + 2], mets[m + 3]]
plot_comm(cons)
plt.show()
print("Image number", m / 4)
# clean temporary files, return to orginal parameters
plt.close("all")
os.remove(cons.outplot)
cons.mets_to_plot = original_metplots
cons.outplot = original_outplot
return
def main(input, trainset, testset):
whole = pd.read_csv(input, sep="\t")
whole["Mitarbeiter ID"] = whole["Mitarbeiter ID"].apply(json.loads)
whole.loc[:, "Tag"] = whole["Tag"].apply(datetime.fromisoformat)
# whole = pd.DataFrame(whole[np.logical_and(
# whole.Tag >= datetime(2019,1,1),
# whole.Tag < datetime(2019,4,1)
# )])
train, test = train_test_split(whole, random_state=341223)
all_train_ma_ids = sorted(
list(set([it for ll in train["Mitarbeiter ID"] for it in ll])))
should_not_belong_to_test = []
for irow, row in tqdm(
test.iterrows(),
total=len(test),
desc="check test set for rows that don't occur in training set"):
for ma_id in row["Mitarbeiter ID"]:
if not is_in(all_train_ma_ids, ma_id):
should_not_belong_to_test.append(irow)
train = pd.concat([train, dcp(test.loc[should_not_belong_to_test])])
test = dcp(test.loc[~test.index.isin(should_not_belong_to_test)])
train.to_csv(trainset, index=False, sep="\t")
test.to_csv(testset, index=False, sep="\t")
def __init__(self, _genome=None, _other=None, _isolated=False):
import cortex.network as cn
self.ID = 0
# Increment the global species ID
if not _isolated:
Species.ID += 1
self.ID = Species.ID
Species.Populations[self.ID] = self
# Species champion
self.champion = None
# Overall species fitness computed from the fitness
# of networks belonging to this species
self.fitness = Fitness()
# Set of networks belonging to this species
self.nets = set()
# Species genome (list of layer definitions)
self.genome = []
if _other is None:
# print("Creating genome from initial layer definitions")
self.genome = dcp(
cn.Net.Init.Layers) if _genome is None else dcp(_genome)
else:
# print("Copying genome from species", _other.ID)
self.genome = dcp(_other.genome)
print(">>> Species", self.ID, "created")
def __init__(self, data, model, keyList):
self.helper = helper.Helper()
self.data = dcp(data)
self.initialModel = dcp(model)
for key in keyList.keys():
setattr(self, key, keyList[key])
self.initialStuntingTrend = -0.0 # percentage decrease in stunting prevalence per year
self.initialStuntingTrend = (
self.initialStuntingTrend / 100.0 * self.timestep
) # fractional decrease in stunting prevalence per timestep
self.referenceMortality = {}
self.probStuntedIfPrevStunted = {}
self.fracStuntedIfDiarrhea = {}
self.probStuntedIfCovered = {}
self.probCorrectlyBreastfedIfCovered = {}
self.probStuntedComplementaryFeeding = {}
self.probStuntedAtBirth = {}
self.stuntingUpdateAfterInterventions = {}
for ageName in self.ages:
self.stuntingUpdateAfterInterventions[ageName] = 1.0
self.setReferenceMortality()
self.setProbStuntingProgression()
self.setProbStuntedAtBirth()
def oneModelRunWithOutput(self, allocationDictionary):
import costcov
import data
from copy import deepcopy as dcp
costCov = costcov.Costcov()
spreadsheetData = data.readSpreadsheet(self.dataSpreadsheetName, self.helper.keyList)
model, derived, params = self.helper.setupModelConstantsParameters(spreadsheetData)
costCoverageInfo = self.getCostCoverageInfo()
# run the model
modelList = []
timestepsPre = 12
for t in range(timestepsPre):
model.moveOneTimeStep()
modelThisTimeStep = dcp(model)
modelList.append(modelThisTimeStep)
# update coverages after 1 year
targetPopSize = getTargetPopSizeFromModelInstance(self.dataSpreadsheetName, self.helper.keyList, model)
newCoverages = {}
for i in range(0, len(spreadsheetData.interventionList)):
intervention = spreadsheetData.interventionList[i]
newCoverages[intervention] = costCov.function(allocationDictionary[intervention], costCoverageInfo[intervention], targetPopSize[intervention]) / targetPopSize[intervention]
model.updateCoverages(newCoverages)
for t in range(self.numModelSteps - timestepsPre):
model.moveOneTimeStep()
modelThisTimeStep = dcp(model)
modelList.append(modelThisTimeStep)
return modelList
def connect_detec_point(self, A, EM, x, y, visual=0):
Xk_1 = A * 0
Xk = dcp(Xk_1)
Xk[x, y] = 255
if visual:
winName = 'find connect'
cv2.namedWindow(winName, cv2.WINDOW_NORMAL)
while (Xk != Xk_1).any():
if visual:
cv2.imshow(winName, Xk)
cv2.waitKey(1)
# |cv2.destroyWindow(winName)
Xk_1 = Xk
temp = self.dilation(Xk_1, EM)
Xk = cv2.bitwise_and(dcp(temp), dcp(A))
X, Y = np.where(Xk == 255)
mx = np.mean(X)
my = np.mean(Y)
area = len(X)
data = [X, Y, area, mx, my]
statics = pd.DataFrame([data], columns=['X', 'Y', 'Area', 'mx', 'my'])
if visual:
cv2.waitKey(1)
cv2.destroyWindow(winName)
return (statics, Xk)
def __init__(self, data, model, keyList):
self.helper = helper.Helper()
self.data = dcp(data)
self.initialModel = dcp(model)
for key in keyList.keys():
setattr(self, key, keyList[key])
self.initialStuntingTrend = -0. # percentage decrease in stunting prevalence per year
self.initialStuntingTrend = self.initialStuntingTrend / 100. * self.timestep # fractional decrease in stunting prevalence per timestep
self.referenceMortality = {}
self.probStuntedIfPrevStunted = {}
self.fracStuntedIfDiarrhea = {}
self.probStuntedIfCovered = {}
self.probCorrectlyBreastfedIfCovered = {}
self.probStuntedComplementaryFeeding = {}
self.probStuntedAtBirth = {}
self.stuntingUpdateAfterInterventions = {}
for ageName in self.ages:
self.stuntingUpdateAfterInterventions[ageName] = 1.
self.setReferenceMortality()
self.setProbStuntingProgression()
self.setProbStuntedAtBirth()
def negamaxsearch(cfirst_player_pieces,
csecond_player_pieces,
cturn,
depth,
possibal_piece=0):
global all_pieces
first_player_pieces = dcp(cfirst_player_pieces)
second_player_pieces = dcp(csecond_player_pieces)
turn = dcp(cturn)
if depth <= 0:
return evaluation(first_player_pieces, second_player_pieces,
turn), possibal_piece
else:
best = -50
best_move = 0
all_possible_pieces = set(all_pieces) - (set(first_player_pieces)
| set(second_player_pieces))
for possible_piece in all_possible_pieces:
v_first_player_pieces, v_second_player_pieces, v_turn = game_move(
first_player_pieces, second_player_pieces, turn,
possible_piece)
value, best_piece = negamaxsearch(v_first_player_pieces,
v_second_player_pieces, v_turn,
depth - 1, possible_piece)
value *= -1
if value > best:
best = value
best_move = possible_piece
return best, best_move
def oneModelRunWithOutput(self, allocationDictionary):
import costcov
import data
from copy import deepcopy as dcp
costCov = costcov.Costcov()
spreadsheetData = data.readSpreadsheet(self.dataSpreadsheetName,
self.helper.keyList)
model, derived, params = self.helper.setupModelConstantsParameters(
spreadsheetData)
costCoverageInfo = self.getCostCoverageInfo()
# run the model
modelList = []
timestepsPre = 12
for t in range(timestepsPre):
model.moveOneTimeStep()
modelThisTimeStep = dcp(model)
modelList.append(modelThisTimeStep)
# update coverages after 1 year
targetPopSize = getTargetPopSizeFromModelInstance(
self.dataSpreadsheetName, self.helper.keyList, model)
newCoverages = {}
for i in range(0, len(spreadsheetData.interventionList)):
intervention = spreadsheetData.interventionList[i]
newCoverages[intervention] = costCov.function(
allocationDictionary[intervention],
costCoverageInfo[intervention],
targetPopSize[intervention]) / targetPopSize[intervention]
model.updateCoverages(newCoverages)
for t in range(self.numModelSteps - timestepsPre):
model.moveOneTimeStep()
modelThisTimeStep = dcp(model)
modelList.append(modelThisTimeStep)
return modelList
def __init__(self,
name,
directory,
out_irq_num = 0,
in_irq_num = 0,
mmio_num = 0,
pio_num = 0,
mmio = None,
pio = None,
**qomd_kw
):
super(SysBusDeviceType, self).__init__(name, directory, **qomd_kw)
self.out_irq_num = out_irq_num
self.in_irq_num = in_irq_num
self.mmio_num = mmio_num
self.pio_num = pio_num
self.mmio_size_macros = []
# Qemu requires that any RAM, ROM or ROM device has unique name. Some
# devices has such MMIOs. Also, different names simplify understanding
# of `info mtree` HMP command output during debugging.
self.mmio_name_macros = []
self.pio_size_macros = []
self.pio_address_macros = []
self.mmio = {} if mmio is None else dcp(mmio)
self.pio = {} if pio is None else dcp(pio)
self.mmio_names = {}
self.add_state_field_h("SysBusDevice", "parent_obj", save = False)
for irqN in range(0, self.out_irq_num):
self.add_state_field_h("qemu_irq", self.get_Ith_irq_name(irqN),
save = False
)
for mmioN in range(0, self.mmio_num):
self.add_state_field_h("MemoryRegion",
self.get_Ith_mmio_name(mmioN),
save = False
)
self.add_fields_for_mmio(self.mmio.get(mmioN, list()))
for ioN in range(0, self.pio_num):
self.add_state_field_h("MemoryRegion",
self.get_Ith_io_name(ioN),
save = False
)
self.add_fields_for_regs(self.pio.get(ioN, list()))
self.timer_declare_fields()
self.char_declare_fields()
self.block_declare_fields()
def getCostCoverageInfo(self):
import data
from copy import deepcopy as dcp
spreadsheetData = data.readSpreadsheet(self.dataSpreadsheetName, self.helper.keyList)
costCoverageInfo = {}
for intervention in spreadsheetData.interventionList:
costCoverageInfo[intervention] = {}
costCoverageInfo[intervention]['unitcost'] = dcp(spreadsheetData.costSaturation[intervention]["unit cost"])
costCoverageInfo[intervention]['saturation'] = dcp(spreadsheetData.costSaturation[intervention]["saturation coverage"])
return costCoverageInfo
def get_target_grasp_pose(source_pcd):
# copy the source pcd and transform into robot frame
source_pcd_original = dcp(source_pcd)
source_pcd_original.paint_uniform_color([0, 0, 1])
source_pcd_original.transform(transform_cam_to_base_hand_calibrated)
# copy one to be processed, and crop
source_pcd_to_process = dcp(source_pcd_original)
source_pcd_to_process.crop(workspace_bounding_box)
# pre-processing for registration
# transform the source pcd to an arbitrary pose far away from the target
source_pcd_to_process.transform(
init_transformation_for_global_registration)
# compute fpfh
source_down, source_fpfh = preprocess_point_cloud(source_pcd_to_process)
# global
result_fast = execute_fast_global_registration(source_down, target_down,
source_fpfh, target_fpfh)
# icp refinement
result_refine = refine_registration(source_down, target_down,
result_fast.transformation)
# final transformation should includes the initial transformation
transform_source_to_target = np.matmul(
result_refine.transformation,
init_transformation_for_global_registration)
# visualizing result
new_grasp_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(
size=0.2)
transform_target_to_source = np.linalg.inv(transform_source_to_target)
transform_target_grasp = np.matmul(transform_target_to_source,
transform_base_to_reference_grasp)
new_grasp_frame.transform(transform_target_grasp)
print('[INFO] Visualizing the found grasping pose')
o3d.visualization.draw_geometries(
[robot_frame, new_grasp_frame, source_pcd_original, target],
window_name='Grasping pose proposal',
width=1200,
height=960)
# convert transformation matrix to coordinate and quaternion
rotation_quat = quat.from_rotation_matrix(
transform_target_grasp[:-1, :-1]) # this is already normalized
rotation_quat = quat.as_float_array(rotation_quat).tolist() # wxyz
translate_matrix = transform_target_grasp[:-1, -1].tolist() # xyz
# construct and return a PoseStamped msg
pose = dcp(pose_msg)
pose.pose.position.x = translate_matrix[0]
pose.pose.position.y = translate_matrix[1]
pose.pose.position.z = translate_matrix[2]
pose.pose.orientation.w = rotation_quat[0]
pose.pose.orientation.x = rotation_quat[1]
pose.pose.orientation.y = rotation_quat[2]
pose.pose.orientation.z = rotation_quat[3]
return pose
def execute_body(body, s, env, stack):
for stmt in body:
if type(stmt) == AxiomCall:
if type(s) == set:
for f in env[stmt.ax]:
if type(f) == Val:
f = f.val
lasts = dcp(s)
s = {f(x, env, stack) for x in s}
if s != lasts:
break
elif type(s) == str:
for f in env[stmt.ax]:
if type(f) == Val:
f = f.val
lasts = dcp(s)
s = f(s, env, stack)
if s != lasts:
break
elif type(stmt) == FixpointCall:
last = None
if type(s) == set:
for f in env[stmt.ax]:
if type(f) == Val:
f = f.val
lasts = dcp(s)
s = {f(x, env, stack) for x in s}
if s != lasts:
while last != s:
last = s
s = {f(x, env, stack) for x in s}
break
continue
elif type(s) == str:
for f in env[stmt.ax]:
if type(f) == Val:
f = f.val
lasts = dcp(s)
s = f(s, env, stack)
if lasts != s:
while last != s:
last = s
s = f(s, env, stack)
elif type(stmt) == Sect:
if stmt.var == 'str':
s = env[stmt.args[0]][0].val
elif stmt.var == 'strs':
s = {env[a][0].val for a in stmt.args}
continue
return s
def init_V(S, goal, g = 1.0, ng = None):
V, V_ = {}, {}
for state in S:
s = tuple(state)
if s not in V:
V[s], V_[s] = 0.0, 0.0
if s in goal:
V[s], V_[s] = g, g
if ng != None and s in ng:
V[s], V_[s] = 0.0, 0.0
return dcp(V), dcp(V_)
def make_pipelines_mp(self):
'''
Core algorithm, coming soon.
:return:
'''
ret_list = []
for k1, v1 in self.process_dict['preprocessing'].items():
steps = []
parameters = {}
k1_list = []
if k1 != 'do_nothing':
steps.append((k1, v1))
if k1 in self.parameters_dict:
for k, v in self.parameters_dict[k1].items():
key = k1 + "__" + k
parameters[key] = v
k1_list.append(key)
for k2, v2 in self.process_dict['decomposition'].items():
k2_list = []
if k2 != 'do_nothing':
steps.append((k2, v2))
if k2 in self.parameters_dict:
for k, v in self.parameters_dict[k2].items():
key = k2 + "__" + k
parameters[key] = v
k2_list.append(key)
for k3, v3 in self.process_dict['model'].items():
k3_list = []
if k3 in self.parameters_dict:
for k, v in self.parameters_dict[k3].items():
key = k3 + "__" + k
parameters[key] = v
k3_list.append(key)
steps.append((k3, v3))
# r_key = ((k1 + "+") if k1 != 'do_nothing' else "") + ((k2 + "+") if k2 != 'do_nothing' else "") + k3
r_key = k1 + "+" + k2 + "+" + k3
self.result[r_key] = {}
ret_list.append(
[r_key,
Pipeline(steps=dcp(steps)),
dcp(parameters)])
steps.remove((k3, v3))
for key in k3_list:
del parameters[key]
if k2 != 'do_nothing':
steps.remove((k2, v2))
for key in k2_list:
del parameters[key]
if k1 != 'do_nothing':
steps.remove((k1, v1))
for key in k1_list:
del parameters[key]
return ret_list
def makePregnantWomen(self, inputData):
import model
annualPregnancies = dcp(inputData.demographics['number of pregnant women'])
annualBirths = dcp(inputData.demographics['number of live births'])
populationSize = annualPregnancies
birthRate = annualBirths / annualPregnancies
projectedBirths = dcp(inputData.projectedBirths)
baseBirths = float(projectedBirths[0])
numYears = len(projectedBirths)-1
annualGrowth = (projectedBirths[numYears]-baseBirths)/float(numYears)/baseBirths
pregnantWomen = model.PregnantWomen(birthRate, populationSize, annualGrowth)
return pregnantWomen
def __init__(self, gli_dict=None, **OGS_Config):
super(GLI, self).__init__(**OGS_Config)
self.file_ext = ".gli"
self.force_writing = True
if gli_dict is None:
self.__dict = dcp(EMPTY_GLI)
elif check_gli_dict(gli_dict):
self.__dict = gli_dict
else:
print("given gli_dict is not valid.. will set default")
self.__dict = dcp(EMPTY_GLI)
def reset(self):
self.P_h_g = dcp(self.P_g)
self.ctrl_belief_P1 = dcp(self.init_ctrl_belief)
self.decoys = dcp(self.init_decoys)
for goals in self.decoys:
if self.decoys[goals][0] in self.true_goal:
self.ctrl_belief_P1[goals] = 1.0
else:
self.ctrl_belief_P1[goals] = 0.0
self.record = []
self.true_record = []
self.total_KL = 0
def cell_copy(cell):
copy = {'secs': {}, 'secLists': {}, 'globals': {}}
for sec in cell['secs']:
copy['secs'][sec] = {'geom': dcp(cell['secs'][sec]['geom']),
'topol': dcp(cell['secs'][sec]['topol']),
'mechs': {}, 'ions': {} }
for mech in cell['secs'][sec]['mechs']:
copy['secs'][sec]['mechs'][mech] = dcp(cell['secs'][sec]['mechs'][mech])
for ion in cell['secs'][sec]['ions']:
copy['secs'][sec]['ions'][ion] = dcp(cell['secs'][sec]['ions'][ion])
return copy
def get_mnist_dataloaders(
labeled_sample_num=10,
unlabeled_class_sample_nums=None,
train_transform=None,
val_transform=None,
dataloader_params={},
):
train_set = MNIST(root=DATA_PATH, train=True, download=True)
val_set = MNIST(root=DATA_PATH, train=False, download=True, transform=val_transform)
val_set_index = _draw_equal_dataset(
val_set.targets, num_samples=4000, allowed_classes=[0, 1, 2, 3, 4]
)
val_set = Subset(val_set, val_set_index)
labeled_index = _draw_equal_dataset(
train_set.targets, labeled_sample_num, allowed_classes=[0, 1, 2, 3, 4]
)
labeled_set = Subset(dcp(train_set), labeled_index)
_override_transformation(labeled_set, train_transform)
unlabeled_index = _draw_inequal_dataset(
train_set.targets,
class_sample_nums=unlabeled_class_sample_nums,
excluded_index=labeled_index.tolist(),
)
unlabeled_set = Subset(dcp(train_set), unlabeled_index)
_override_transformation(unlabeled_set, train_transform)
assert set(labeled_index.tolist()) & set(unlabeled_index.tolist()) == set()
del train_set
show_dataset(labeled_set)
show_dataset(unlabeled_set)
show_dataset(val_set)
labeled_loader = DataLoader(
labeled_set,
sampler=RandomSampler(
data_source=labeled_set, replacement=True, num_samples=int(1e5)
),
**dataloader_params
)
unlabeled_loader = DataLoader(
unlabeled_set,
sampler=RandomSampler(
data_source=unlabeled_set, replacement=True, num_samples=int(1e5)
),
**dataloader_params
)
val_loader = DataLoader(val_set, num_workers=1, batch_size=16)
return labeled_loader, unlabeled_loader, val_loader
def __init__(self,
group2index,
partion2index,
group_sample_num=4,
partition_sample_num=1,
shuffle=False) -> None:
self._group2index, self._partition2index = dcp(group2index), dcp(
partion2index)
assert 1 <= group_sample_num <= len(
self._group2index.keys()), group_sample_num
self._group_sample_num = group_sample_num
self._partition_sample_num = partition_sample_num
self._shuffle = shuffle
def getCostCoverageInfo(self):
import data
from copy import deepcopy as dcp
spreadsheetData = data.readSpreadsheet(self.dataSpreadsheetName,
self.helper.keyList)
costCoverageInfo = {}
for intervention in spreadsheetData.interventionList:
costCoverageInfo[intervention] = {}
costCoverageInfo[intervention]['unitcost'] = dcp(
spreadsheetData.costSaturation[intervention]["unit cost"])
costCoverageInfo[intervention]['saturation'] = dcp(
spreadsheetData.costSaturation[intervention]
["saturation coverage"])
return costCoverageInfo
def _renew_goals(self):
# this function refreshes goals
block_r_pos = self.sim.data.get_site_xpos('block_r')
block_b_pos = self.sim.data.get_site_xpos('block_b')
block_r_ground_pos = dcp(block_r_pos)
block_r_ground_pos[2] = self.block_ground_pos_z
block_b_ground_pos = dcp(block_b_pos)
block_b_ground_pos[2] = self.block_ground_pos_z
# absolute position of blue block on top of red block
block_b_pos_on_red = dcp(block_r_pos)
block_b_pos_on_red[2] += self.block_height
sub_goals = {
"pick_blue":
np.concatenate([
# gripper state & position
self.grasping_gripper_state,
(block_b_ground_pos + GRASPING_HEIGHT_OFFSET).ravel(),
# absolute positions of blocks
block_r_ground_pos.ravel(),
block_b_ground_pos.ravel(),
]),
"blue_on_red":
np.concatenate([
# gripper state & position
self.grasping_gripper_state,
(block_b_pos_on_red + GRASPING_HEIGHT_OFFSET).ravel(),
# absolute positions of blocks
block_r_ground_pos.ravel(),
block_b_pos_on_red.ravel(),
]),
"ending_br":
np.concatenate([
# gripper state & position
ending_gripper_state,
GRIP_END_POS.ravel(),
# absolute positions of blocks
block_r_ground_pos.ravel(),
block_b_pos_on_red.ravel(),
]),
}
# a final goal is a binary vector which represents the completion of subgoals
final_goals = {}
gripper_target_positions = {}
object_target_positions = {}
_ = np.zeros(len(sub_goals))
for i, (k, v) in enumerate(sub_goals.items()):
final_goals[k] = _.copy()
final_goals[k][i] += 1.0
gripper_target_positions[k] = v[2:5].copy()
object_target_positions[k] = v[-6:].copy()
if not self.binary_final_goal:
final_goals = dcp(sub_goals)
assert len(sub_goals) == len(final_goals) == len(
gripper_target_positions) == len(object_target_positions)
return dcp(sub_goals), dcp(final_goals), dcp(
gripper_target_positions), dcp(object_target_positions)
def makePregnantWomen(self, inputData):
import model
annualPregnancies = dcp(
inputData.demographics['number of pregnant women'])
annualBirths = dcp(inputData.demographics['number of live births'])
populationSize = annualPregnancies
birthRate = annualBirths / annualPregnancies
projectedBirths = dcp(inputData.projectedBirths)
baseBirths = float(projectedBirths[0])
numYears = len(projectedBirths) - 1
annualGrowth = (projectedBirths[numYears] -
baseBirths) / float(numYears) / baseBirths
pregnantWomen = model.PregnantWomen(birthRate, populationSize,
annualGrowth)
return pregnantWomen
def creating_session(self):
if self.round_number == 1:
# if you want to create a new ordering file
# gets executed if 'create_ques_order' SESSION_CONFIG is True
if self.session.config['create_ques_order']:
# create question number 0 to 6 (7 questions)
ques = [i for i in range(7)]
# open writable file in folder 'storage' in otree root directory
with open('storage/questOrder.csv', 'w',
newline='') as csvfile:
# create a csv write object (tab delimited)
storewriter = csv.writer(csvfile, delimiter='\t')
# get player objects
for p in self.get_players():
# make deep copy to avoid resetting values for all players
randomOrder = dcp(ques)
# randomize question list for each player
shuffle(randomOrder)
# assign order to player's participants vars
p.participant.vars['questOrder'] = randomOrder
# make a deep copy from the quest list
line = dcp(randomOrder)
# add player ID to front of order list
line.insert(0, p.id)
# write each line from the list above to the csvfile
storewriter.writerow(line)
# closing is done automatically
''' Now every player has a questions order in their participant
vars 'questOrder'. You can use this list item and condition
in each number in views.py or a template'''
else:
# of create_ques_order is False, open file for reading
with open('storage/questOrder.csv', 'r') as csvfile:
# create reader object
storereader = csv.reader(csvfile, delimiter='\t')
# read row
addrow = []
for row in storereader:
# create list x list object
addrow.append(row)
j = 0
# add vars to participant
for p in self.get_players():
# assign to stored questOrder to player
p.participant.vars['questOrder'] = addrow[j][1:]
j = 1 + j
def get_graph_for_feasible_solution(g: Graph):
fg = dcp(g)
b = 0
neg = 0
for n in fg.node_list:
b += n.value
if n.value < 0:
neg += n.value
if b != 0:
return None
s = Node(-neg)
t = Node(neg)
for n in fg.node_list:
if n.value > 0:
a = Arc(s, n, 0, n.value)
s.outList.append(a)
n.inList.append(a)
fg.arc_list.append(a)
elif n.value < 0:
a = Arc(n, t, 0, -n.value)
n.outList.append(a)
t.inList.append(a)
fg.arc_list.append(a)
fg.node_list.insert(0, s)
fg.node_list.append(t)
fg.number()
fg.s = s
fg.t = t
return fg
def set_P(S, A, epsilon): #wall_cord,
P = {}
for state in S:
s = tuple(state)
explore = []
for act in A.keys():
temp = tuple(np.array(s) + np.array(A[act]))
explore.append(temp)
# print s, explore
for a in A.keys():
P[s, a] = {}
P[s, a][s] = 0
s_ = tuple(np.array(s) + np.array(A[a]))
unit = epsilon / 3
if list(s_) in S:
P[s, a][s_] = 1 - epsilon
for _s_ in explore:
if tuple(_s_) != s_:
if list(_s_) in S:
P[s, a][tuple(_s_)] = unit
else:
P[s, a][s] += unit
else:
P[s, a][s] = 1 - epsilon
for _s_ in explore:
if _s_ != s_:
if list(_s_) in S:
P[s, a][tuple(_s_)] = unit
else:
P[s, a][s] += unit
return dcp(P)
def randomWander(self, fnum):
for _it in range(self.max_iter): # 最外层循环（周游次数）
self.probs = (self.phm**self.a) * self.prefix
for k in range(self.city): # 周游循环
for i in range(self.ant_num): # 对每个蚂蚁进行循环
if k == self.city - 1:
self.ants[i].back2Start()
cost, path = self.ants[i].calculate(self.adjs)
if cost < self.cost:
self.cost = cost
self.shortest = dcp(path)
else:
pos, choice_vec = self.choiceForAnt(i)
# print(pos, choice_vec)
next_pos = rd.choices(pos, choice_vec, k=1)[0]
self.ants[i].updatePos(next_pos)
self.costs.append(self.cost)
self.updateSecretion()
for i in range(self.ant_num):
self.ants[i].reset()
print("Iter %d / %d" % (_it, self.max_iter))
self.shortest = self.exchange(self.shortest)
print("Result(%d):" % (fnum), self.shortest)
print("Random wader(%d ants) for %d times completed." %
(self.ant_num, self.max_iter))
self.draw(fnum)
def getZa(self, incidence, breastfeedingDistribution):
bfDistribution = dcp(breastfeedingDistribution)
Za = {}
for ageName in self.ages:
riskSum = self.getDiarrheaRiskSum(ageName, bfDistribution)
Za[ageName] = incidence[ageName] / riskSum
return Za
def getZa(self, incidence, breastfeedingDistribution):
bfDistribution = dcp(breastfeedingDistribution)
Za = {}
for ageName in self.ages:
riskSum = self.getDiarrheaRiskSum(ageName, bfDistribution)
Za[ageName] = incidence[ageName] / riskSum
return Za
def list_to_osm(data, destination_file_name, lane_widths=None):
writer = osmwriter.OSMWriter(destination_file_name)
version = int(1)
uuid = 1
for k in range(len(data)):
way_ = data[k]
way_ids = []
way_node0_uuid = None
print 'First point in way: ', utm.from_latlon(way_[0][0], way_[0][1])
for kk in range(len(way_)):
pt = way_[kk]
# lat, lon
if lane_widths:
tags = {'width': str(lane_widths[k][kk])}
else:
tags = None
writer.node(uuid, pt[0], pt[1], tags=tags, version=version)
way_ids.append(uuid)
if len(way_ids) == 1:
# this is the first node in the way. We want the way to be closed since
# the track is circular, so save this uuid for later to append it to the
# end of the way's uuid's.
way_node0_uuid = dcp(uuid)
uuid += 1
# add the first node to the end since we know this is a nice perfect loop
way_ids.append(way_node0_uuid)
writer.way(uuid, {}, way_ids, version=version)
uuid += 1
writer.close()
def SemiSupervisedParallelDataLoaders(
self,
labeled_transforms: List[Callable[[Image.Image], Tensor]],
unlabeled_transforms: List[Callable[[Image.Image], Tensor]],
val_transforms: List[Callable[[Image.Image], Tensor]],
test_transforms: List[Callable[[Image.Image], Tensor]],
target_transform: Callable[[Tensor], Tensor] = None,
) -> Tuple[DataLoader, DataLoader, DataLoader, DataLoader]:
_dataloader_params = dcp(self.dataloader_params)
def _override_transforms(dataset, img_transform_list, target_transform_list):
# here deep copying the datasets are needed.
return [
self.override_transforms(dcp(dataset), img_trans, target_trans)
for img_trans, target_trans in zip(
img_transform_list, target_transform_list
)
]
(
labeled_set,
unlabeled_set,
val_set,
test_set,
) = self._init_labeled_unlabled_val_and_test_sets()
target_transform_list = repeat(target_transform)
labeled_sets = _override_transforms(
labeled_set, labeled_transforms, target_transform_list
)
unlabeled_sets = _override_transforms(
unlabeled_set, unlabeled_transforms, target_transform_list
)
val_sets = _override_transforms(val_set, val_transforms, target_transform_list)
test_sets = _override_transforms(
test_set, test_transforms, target_transform_list
)
labeled_set = CombineDataset(*labeled_sets)
unlabeled_set = CombineDataset(*unlabeled_sets)
val_set = CombineDataset(*val_sets)
test_set = CombineDataset(*test_sets)
if self._if_use_indiv_bz:
_dataloader_params.update(
{"batch_size": self.batch_params.get("labeled_batch_size")}
)
labeled_loader = DataLoader(labeled_set, **_dataloader_params)
if self._if_use_indiv_bz:
_dataloader_params.update(
{"batch_size": self.batch_params.get("unlabeled_batch_size")}
)
unlabeled_loader = DataLoader(unlabeled_set, **_dataloader_params)
_dataloader_params.update({"shuffle": False, "drop_last": False})
if self._if_use_indiv_bz:
_dataloader_params.update(
{"batch_size": self.batch_params.get("val_batch_size")}
)
val_loader = DataLoader(val_set, **_dataloader_params)
test_loader = DataLoader(test_set, **_dataloader_params)
return labeled_loader, unlabeled_loader, val_loader, test_loader
def _grouped_dataloader(
self,
dataset: MedicalImageSegmentationDataset,
use_infinite_sampler: bool = False,
**dataloader_params: Dict[str, Union[int, float, bool]],
) -> DataLoader:
"""
return a dataloader that requires to be grouped based on the reg of patient's pattern.
:param dataset:
:param shuffle:
:return:
"""
dataloader_params = dcp(dataloader_params)
batch_sampler = PatientSampler(
dataset=dataset,
grp_regex=dataset._re_pattern,
shuffle=dataloader_params.get("shuffle", False),
verbose=self.verbose,
infinite_sampler=True if use_infinite_sampler else False,
)
# having a batch_sampler cannot accept batch_size > 1
dataloader_params["batch_size"] = 1
dataloader_params["shuffle"] = False
dataloader_params["drop_last"] = False
return DataLoader(dataset,
batch_sampler=batch_sampler,
**dataloader_params)
def _generate_goal(self):
block_pos, _ = self._p.getBasePositionAndOrientation(self.object_bodies['block'])
block_pos = np.array(block_pos)
end_effector_tip_initial_position = self.robot.end_effector_tip_initial_position.copy()
block_target_position = end_effector_tip_initial_position + \
self.np_random.uniform(-self.obj_range, self.obj_range, size=3)
if self.target_one_table:
block_target_position = self.object_initial_pos['block'][2]
picking_grip_pos = block_pos.copy()
picking_grip_pos[-1] += self.gripper_tip_offset
placing_grip_pos = block_target_position.copy()
placing_grip_pos[-1] += self.gripper_tip_offset
sub_goals = {
"pick": np.concatenate([
# gripper state & position
picking_grip_pos, [0.03],
# absolute positions of blocks
block_pos,
]),
"place": np.concatenate([
# gripper state & position
placing_grip_pos, [0.03],
# absolute positions of blocks
block_target_position,
]),
}
final_goals = dcp(sub_goals)
goal_images = {
"pick": self._generate_goal_image(0.55, picking_grip_pos, block_pos),
"place": self._generate_goal_image(0.55, placing_grip_pos, block_target_position),
}
return sub_goals, final_goals, goal_images
def reduce(self, decoy):
self.P_h_g.pop(decoy)
self.decoys.pop(decoy)
self.ctrl_belief_P1.pop(decoy)
temp_sum = sum(self.P_h_g.values())
flag = False
for goal in self.P_h_g.keys():
if self.P_h_g[goal] < 0.01:
flag = True
break
normalize_flag = True
normalized_p = {}
##do the normalization
if flag:
for goal in self.decoys:
self.P_h_g[goal] = 1.0 / len(self.P_h_g)
else:
for goal in self.decoys:
normalized_p[goal] = self.P_h_g[goal] / temp_sum
if normalized_p[goal] > 0.99:
new_temp_sum = temp_sum - self.P_h_g[goal]
self.P_h_g[goal] = 0.99
for g in self.P_h_g:
if not g == goal:
self.P_h_g[g] = (
1 - 0.99) * self.P_h_g[g] / new_temp_sum
normalize_flag = False
break
if normalize_flag:
self.P_h_g = dcp(normalized_p)
def diffCost(p1:np.array, p2:np.array, col = True):
r, c = p1.shape
_p1 = dcp(p1)
_p2 = dcp(p2)
_p1 = _p1.astype(float)
_p2 = _p2.astype(float)
if col: # 以图片的列作为讨论依据
pic1 = np.zeros((1, r))
pic2 = np.zeros((1, r))
for i in range(c):
pic1 += i**2 / (c**3) * (_p1[:, i])
pic2 += (1 - i**2 / c**2) / c * (_p2[:, i])
else: # 以行为讨论依据
pic1 = p1[-1, :]
pic2 = p2[0, :]
return np.linalg.norm((pic1 - pic2))
def get_dataloader(name: str = None,
aug: bool = False,
DataLoader_DICT: dict = {}):
DataLoader_DICT = dcp(DataLoader_DICT)
assert name in ("cifar10", "svhn")
print(f"data aug: {bool(aug)}.")
if name == "cifar10":
SemiDatasetHandler = Cifar10SemiSupervisedDatasetInterface(
tra_img_transformation=default_cifar10_aug_transformation["train"]
if aug else default_cifar10_transformation["train"],
val_img_transformation=default_cifar10_aug_transformation["val"]
if aug else default_cifar10_transformation["val"],
verbose=True,
)
else:
SemiDatasetHandler = SVHNSemiSupervisedDatasetInterface(
tra_img_transformation=default_svhn_aug_transformation["train"]
if aug else default_svhn_transformation["train"],
val_img_transformation=default_svhn_aug_transformation["val"]
if aug else default_svhn_transformation["val"],
verbose=True,
)
DataLoader_DICT.pop("name", None)
label_loader, unlabel_loader, val_loader = SemiDatasetHandler.SemiSupervisedDataLoaders(
**DataLoader_DICT)
return label_loader, unlabel_loader, val_loader
def getDiarrheaRiskSum(self, ageName, breastfeedingDistribution):
bfDistribution = dcp(breastfeedingDistribution)
riskSum = 0.0
for breastfeedingCat in self.breastfeedingList:
RDa = self.data.RRdiarrhea[ageName][breastfeedingCat]
pab = bfDistribution[ageName][breastfeedingCat]
riskSum += RDa * pab
return riskSum
def publish(self):
pub = rospy.Publisher('survey', Marker, latch=True)
m = dcp(self.marker)
m.id = 0
m.pose.position.x = self.position[0]# - float(self.UTMdatum['E'])
m.pose.position.y = self.position[1]# - float(self.UTMdatum['N'])
m.pose.position.z = self.position[2]
pub.publish(m)
def normalize(self):
'''
Return new normalized vector, i.e. length of 1
'''
protein = self.protein
self.protein = None
v = dcp(self)
v.protein = protein
return v/abs(v)
def __neg__(self):
'''
Returns new Vector = self * -1
'''
protein = self.protein
self.protein = None
v = dcp(self)
v.protein = protein
return v*(-1)
def negamaxsearch(cfirst_player_pieces,csecond_player_pieces,cturn, depth, possibal_piece=0):
global all_pieces
first_player_pieces=dcp(cfirst_player_pieces)
second_player_pieces=dcp(csecond_player_pieces)
turn=dcp(cturn)
if depth<=0:
return evaluation(first_player_pieces,second_player_pieces,turn), possibal_piece
else:
best=-50
best_move=0
all_possible_pieces=set(all_pieces)-(set(first_player_pieces)|set(second_player_pieces))
for possible_piece in all_possible_pieces:
v_first_player_pieces,v_second_player_pieces, v_turn=game_move(first_player_pieces,second_player_pieces,turn, possible_piece)
value, best_piece=negamaxsearch(v_first_player_pieces,v_second_player_pieces,v_turn, depth-1, possible_piece)
value*=-1
if value>best:
best=value
best_move=possible_piece
return best, best_move
def __loop(self):
while True:
cur_ext_ip = get_external_ip()
changed = cur_ext_ip != self.ext_ip
old_ext_ip = dcp(self.ext_ip)
self.ext_ip = cur_ext_ip
with self.lock:
if self.is_kill:
return
if changed:
old_ext_ip_info = dcp(self.ext_ip_info)
self.ext_ip_info = geoip.geolite2.lookup(self.ext_ip).get_info_dict()
print 'IP has changed.'
print self.ext_ip
print self.location_string()
self.menu_item_extip.get_child().set_text(self.ext_ip)
self.alert(old_info=old_ext_ip_info)
self.menu_item_loc.get_child().set_text(self.location_string())
sleep(5.0)
def setProbStuntedComplementaryFeeding(self, stuntingDistributionArg, coverageArg):
coverage = dcp(coverageArg)
stuntingDistribution = dcp(stuntingDistributionArg)
coEffs = self.getComplementaryFeedingQuarticCoefficients(stuntingDistribution, coverage)
baselineProbStuntingComplementaryFeeding = self.getBaselineProbabilityViaQuarticByAge(coEffs)
probStuntedComplementaryFeeding = {}
for ageName in self.ages:
probStuntedComplementaryFeeding[ageName] = {}
p0 = baselineProbStuntingComplementaryFeeding[ageName]
probStuntedComplementaryFeeding[ageName]["Complementary feeding (food secure with promotion)"] = p0
for group in self.data.foodSecurityGroups:
OR = self.data.ORstuntingComplementaryFeeding[ageName][group]
probStuntedComplementaryFeeding[ageName][group] = p0 * OR / (1.0 - p0 + OR * p0)
pi = probStuntedComplementaryFeeding[ageName][group]
if pi < 0.0 or pi > 1.0:
raise ValueError(
"probability of stunting complementary feeding, at outcome %s, age %s, is out of range (%f)"
% (group, ageName, pi)
)
self.probStuntedComplementaryFeeding = probStuntedComplementaryFeeding
def minimizeTimes(self):
"""subtracts the lowest existing timestamp value from all msg times
Designed to work on time-based architectures.
relies on get_tmin function.
"""
from copy import deepcopy as dcp
tmin = self.get_tmin()
for t in self.srcData:
old = dcp(self.srcData[t])
new_t = t - tmin
self.outData[new_t] = old
def getTotalInitialAllocation(data, costCoverageInfo, targetPopSize):
import costcov
costCov = costcov.Costcov()
allocation = []
for intervention in data.interventionList:
coverageFraction = array([dcp(data.interventionCoveragesCurrent[intervention])])
coverageNumber = coverageFraction * targetPopSize[intervention]
if coverageNumber == 0:
spending = array([0.])
else:
spending = costCov.inversefunction(coverageNumber, costCoverageInfo[intervention], targetPopSize[intervention])
allocation.append(spending)
return allocation
def publish_array(self):
pub = rospy.Publisher('survey', MarkerArray, latch=True)
array = MarkerArray()
m_id = 0
for p in self.position:
m = dcp(self.marker)
m.id = m_id
m.pose.position.x = p[0] - float(self.UTMdatum['E'])
m.pose.position.y = p[1] - float(self.UTMdatum['N'])
m.pose.position.z = p[2]
array.markers.append(m)
m_id += 1
pub.publish(array)
def __truediv__(self,value):
'''
Returns new Vector = self / scalar
Vector.desc contains history of operation with index position in protein.atoms
'''
protein = self.protein
self.protein = None
v = dcp(self)
v.protein = protein
v.x = self.x/float(value)
v.y = self.y/float(value)
v.z = self.z/float(value)
v.desc = 'idx:'+str(v.desc)+'/'+str(value)
return v
def __rmul__(self,value):
'''
Returns new Vector = scalar * self
Vector.desc contains history of operation with index position in protein.atoms
'''
protein = self.protein
self.protein = None
v = dcp(self)
v.protein = protein
v.x = self.x*float(value)
v.y = self.y*float(value)
v.z = self.z*float(value)
v.desc = 'idx:'+str(v.desc)+'*'+str(value)
return v
def __mul__(self, value):
"""
Returns new Vector = self * scalar
Vector.desc contains history of operation with index position in protein.atoms
"""
protein = self.protein
self.protein = None
v = dcp(self)
v.protein = protein
v.x = self.x * float(value)
v.y = self.y * float(value)
v.z = self.z * float(value)
v.desc = "idx:" + str(v.desc) + "*" + str(value)
return v
def getTotalInitialAllocation(data, costCoverageInfo, targetPopSize):
import costcov
from copy import deepcopy as dcp
costCov = costcov.Costcov()
allocation = []
for intervention in data.interventionList:
coverageFraction = dcp(data.coverage[intervention])
coverageNumber = coverageFraction * targetPopSize[intervention]
if coverageNumber == 0:
spending = 0.
else:
spending = costCov.inversefunction(coverageNumber, costCoverageInfo[intervention], targetPopSize[intervention])
allocation.append(spending)
return allocation
def pchip(x, y, xnew, deriv = False, method='pchip'):
xnew = [xnew]
sortzip = dcp(sorted(zip(x,y)))
xs = [a for a,b in sortzip]
ys = [b for a,b in sortzip]
x = dcp(xs)
y = dcp(ys)
if not isinstance(xnew,collections.Sequence): # Is this reliable enough...?
Exception('Error: Values to interpolate for with PCHIP have not been given in sequence form (e.g. list or array)!')
xnew = dcp(sorted(xnew))
# print x
# print y
if method=='pchip': # WARNING, need to rename this function something else...
m = pchip_slopes(x, y) # Compute slopes used by piecewise cubic Hermite interpolator.
ynew = pchip_eval(x, y, m, xnew, deriv) # Use these slopes (along with the Hermite basis function) to interpolate.
elif method=='smoothinterp':
from utils import smoothinterp
ynew = smoothinterp(xnew, x, y)
if deriv:
if len(xnew)==1:
print('WARNING, length 1 smooth interpolation derivative not implemented')
ynew = [0.0] # WARNING, temp
else:
ynew = (diff(ynew)/diff(xnew)).tolist() # Calculate derivative explicitly
ynew.append(ynew[-1]) # Duplicate the last element so the right length
else:
raise Exception('Interpolation method "%s" not understood' % method)
if type(y)==type(array([])): ynew = array(ynew) # Try to preserve original type
return ynew[0]
def getComplementaryFeedingQuarticCoefficients(self, stuntingDistribution, coverageArg):
coverage = dcp(coverageArg)
coEffs = {}
for iAge in range(len(self.ages)):
ageName = self.ages[iAge]
OR = [1.0] * 4
OR[0] = 1.0
OR[1] = self.data.ORstuntingComplementaryFeeding[ageName][
"Complementary feeding (food secure without promotion)"
]
OR[2] = self.data.ORstuntingComplementaryFeeding[ageName][
"Complementary feeding (food insecure with promotion and supplementation)"
]
OR[3] = self.data.ORstuntingComplementaryFeeding[ageName][
"Complementary feeding (food insecure with neither promotion nor supplementation)"
]
FracSecure = 1.0 - self.data.demographics["fraction food insecure"]
FracCoveredEduc = coverage["Complementary feeding (education)"]
FracCoveredSupp = coverage["Complementary feeding (supplementation)"]
Frac = [0.0] * 4
Frac[0] = FracSecure * FracCoveredEduc
Frac[1] = FracSecure * (1 - FracCoveredEduc)
Frac[2] = (1 - FracSecure) * FracCoveredSupp
Frac[3] = (1 - FracSecure) * (1 - FracCoveredSupp)
FracStunted = self.helper.sumStuntedComponents(stuntingDistribution[ageName])
# [i] will refer to the three non-baseline birth outcomes
A = Frac[0] * (OR[1] - 1.0) * (OR[2] - 1.0) * (OR[3] - 1.0)
B = (
(OR[1] - 1.0)
* (OR[2] - 1.0)
* (OR[3] - 1.0)
* (
sum(Frac[0] / (OR[i] - 1.0) for i in (1, 2, 3))
+ sum(OR[i] * Frac[i] / (OR[i] - 1.0) for i in (1, 2, 3))
- FracStunted
)
)
C = (
sum(Frac[0] * (OR[i] - 1.0) for i in (1, 2, 3))
+ sum(
OR[i] * Frac[i] * ((OR[1] - 1.0) + (OR[2] - 1.0) + (OR[3] - 1.0) - (OR[i] - 1.0)) for i in (1, 2, 3)
)
- sum(FracStunted * (OR[1] - 1.0) * (OR[2] - 1.0) * (OR[3] - 1.0) / (OR[i] - 1.0) for i in (1, 2, 3))
)
D = Frac[0] + sum(OR[i] * Frac[i] for i in (1, 2, 3)) - sum(FracStunted * (OR[i] - 1.0) for i in (1, 2, 3))
E = -FracStunted
coEffs[ageName] = [A, B, C, D, E]
return coEffs
def __mul__(self,other): ''' Returns new Vector with old properties, but transformed by transformation matrix ''' protein = other.protein other.protein = None # must add to avoid deepcopy recursive ultra madness transformed_Vector = dcp(other) transformed_Vector.protein = protein transformed_Vector = transformed_Vector.translateBy(-self.source) new_x = (self.a*transformed_Vector.x + self.b*transformed_Vector.y + self.c*transformed_Vector.z) new_y = (self.d*transformed_Vector.x + self.e*transformed_Vector.y + self.f*transformed_Vector.z) new_z = (self.g*transformed_Vector.x + self.h*transformed_Vector.y + self.i*transformed_Vector.z) transformed_Vector.x = round(new_x,7) transformed_Vector.y = round(new_y,7) transformed_Vector.z = round(new_z,7) transformed_Vector = transformed_Vector.translateBy(self.target) transformed_Vector.desc += 'Transformed' return transformed_Vector
def onTgtUpdate(self, msg):
# if self.DEBUG: print('In ErrorNode::onRefUpdate')
self.tgt_pose = dcp(msg)
self.time_sync()
# read data into list, with each item corresponding to a different lane center file
#
data = []
for center_file_name in lane_center_file_names:
pts = []
with open(center_file_name, 'r') as file:
for line in file:
pts.append([float(val) for val in line.split()])
data.append(pts)
writer = osmwriter.OSMWriter(destination_file_name)
version = int(1)
uuid = 1
for way_ in data:
way_ids = []
way_node0_uuid = None
for pt in way_:
# lat, lon
writer.node(uuid, pt[0], pt[1], version=version)
way_ids.append(uuid)
if len(way_ids) == 1:
# this is the first node in the way. We want the way to be closed since
# the track is circular, so save this uuid for later to append it to the
# end of the way's uuid's.
way_node0_uuid = dcp(uuid)
uuid += 1
# add the first node to the end since we know this is a nice perfect loop
way_ids.append(way_node0_uuid)
writer.way(uuid, {}, way_ids, version=version)
uuid += 1
writer.close()
# initialise
newCoverages={}
for intervention in inputData.interventionList:
newCoverages[intervention] = inputData.coverage[intervention]
# allocation of funding
investment = array([investmentIncrease])
# calculate coverage (%)
targetPopSize = {}
targetPopSize[chosenIntervention] = 0.
for iAge in range(numAgeGroups):
ageName = helper.keyList['ages'][iAge]
targetPopSize[chosenIntervention] += inputData.targetPopulation[chosenIntervention][ageName] * modelX.listOfAgeCompartments[iAge].getTotalPopulation()
targetPopSize[chosenIntervention] += inputData.targetPopulation[chosenIntervention]['pregnant women'] * modelX.pregnantWomen.populationSize
costCovParams = {}
costCovParams['unitcost'] = array([dcp(inputData.costSaturation[chosenIntervention]["unit cost"])])
costCovParams['saturation'] = array([dcp(inputData.costSaturation[chosenIntervention]["saturation coverage"])])
additionalPeopleCovered = costCov.function(investment, costCovParams, targetPopSize[chosenIntervention]) # function from HIV
additionalFractionCovered = additionalPeopleCovered / targetPopSize[chosenIntervention]
print "additional coverage: %g"%(additionalFractionCovered)
newCoverages[chosenIntervention] += additionalFractionCovered
print "new coverage: %g"%(newCoverages[chosenIntervention])
# scale up intervention
modelX.updateCoverages(newCoverages)
# Run model
for t in range(numsteps-1):
modelX.moveOneTimeStep()
pickle.dump(modelX, outfile)
outfile.close()
def __init__(self, data, derived, keyList):
self.derived = derived
for key in keyList.keys():
setattr(self, key, keyList[key])
self.causesOfDeath = dcp(data.causesOfDeath)
self.conditions = dcp(data.conditions)
self.demographics = dcp(data.demographics)
#self.rawMortality = dcp(data.rawMortality)
self.causeOfDeathDist = dcp(data.causeOfDeathDist)
self.stuntingDistribution = dcp(data.stuntingDistribution)
self.wastingDistribution = dcp(data.wastingDistribution)
self.breastfeedingDistribution = dcp(data.breastfeedingDistribution)
self.RRdeathStunting = dcp(data.RRdeathStunting)
self.RRdeathWasting = dcp(data.RRdeathWasting)
self.RRdeathBreastfeeding = dcp(data.RRdeathBreastfeeding)
self.RRdeathByBirthOutcome = dcp(data.RRdeathByBirthOutcome)
#self.ORstuntingProgression = dcp(data.ORstuntingProgression)
self.incidences = dcp(data.incidences)
#self.RRdiarrhea = dcp(data.RRdiarrhea)
#self.ORstuntingCondition = dcp(data.ORstuntingCondition)
#self.ORstuntingBirthOutcome = dcp(data.ORstuntingBirthOutcome)
self.birthOutcomeDist = dcp(data.birthOutcomeDist)
#self.ORstuntingIntervention = dcp(data.ORstuntingIntervention)
#self.ORappropriatebfIntervention = dcp(data.ORappropriatebfIntervention)
self.ageAppropriateBreastfeeding = dcp(data.ageAppropriateBreastfeeding)
self.coverage = dcp(data.coverage)
self.effectivenessMortality = dcp(data.effectivenessMortality)
self.affectedFraction = dcp(data.affectedFraction)
self.effectivenessIncidence = dcp(data.effectivenessIncidence)
self.interventionsMaternal = dcp(data.interventionsMaternal)
self.foodSecurityGroups = dcp(data.foodSecurityGroups)
def __init__(self, name, dictOfBoxes, agingRate, keyList):
self.name = name
self.dictOfBoxes = dcp(dictOfBoxes)
self.agingRate = agingRate
for key in keyList.keys():
setattr(self, key, keyList[key])
def updateCoverages(self, newCoverageArg):
#newCoverage is a dictionary of coverages by intervention
newCoverage = dcp(newCoverageArg)
# call initialisation of probabilities related to interventions
self.derived.setProbStuntedIfCovered(self.params.coverage, self.params.stuntingDistribution)
self.derived.setProbCorrectlyBreastfedIfCovered(self.params.coverage, self.params.breastfeedingDistribution)
self.derived.setProbStuntedIfDiarrhea(self.params.incidences, self.params.breastfeedingDistribution, self.params.stuntingDistribution)
self.derived.setProbStuntedComplementaryFeeding(self.params.stuntingDistribution, self.params.coverage)
# get combined reductions from all interventions
mortalityUpdate = self.params.getMortalityUpdate(newCoverage)
stuntingUpdate = self.params.getStuntingUpdate(newCoverage)
incidenceUpdate = self.params.getIncidenceUpdate(newCoverage)
birthUpdate = self.params.getBirthOutcomeUpdate(newCoverage)
newFracCorrectlyBreastfed = self.params.getAppropriateBFNew(newCoverage)
stuntingUpdateComplementaryFeeding = self.params.getStuntingUpdateComplementaryFeeding(newCoverage)
# MORTALITY
for ageGroup in self.listOfAgeCompartments:
ageName = ageGroup.name
#update mortality
for cause in self.params.causesOfDeath:
self.derived.referenceMortality[ageName][cause] *= mortalityUpdate[ageName][cause]
# BREASTFEEDING
for ageGroup in self.listOfAgeCompartments:
ageName = ageGroup.name
SumBefore = self.derived.getDiarrheaRiskSum(ageName, self.params.breastfeedingDistribution)
correctPractice = self.params.ageAppropriateBreastfeeding[ageName]
agePop = ageGroup.getTotalPopulation()
numCorrectBefore = ageGroup.getNumberCorrectlyBreastfed(correctPractice)
numCorrectAfter = agePop * newFracCorrectlyBreastfed[ageName]
numShifting = numCorrectAfter - numCorrectBefore
numIncorrectBefore = agePop - numCorrectBefore
fracCorrecting = 0.
if numIncorrectBefore > 0.01:
fracCorrecting = numShifting / numIncorrectBefore
self.params.breastfeedingDistribution[ageName][correctPractice] = newFracCorrectlyBreastfed[ageName] # update breastfeeding distribution
incorrectPractices = [practice for practice in self.breastfeedingList if practice!=correctPractice]
for practice in incorrectPractices:
self.params.breastfeedingDistribution[ageName][practice] *= 1. - fracCorrecting
ageGroup.distribute(self.params.stuntingDistribution, self.params.wastingDistribution, self.params.breastfeedingDistribution)
SumAfter = self.derived.getDiarrheaRiskSum(ageName, self.params.breastfeedingDistribution)
self.params.incidences[ageName]['Diarrhea'] *= SumAfter / SumBefore # update incidence of diarrhea
beta = self.derived.getFracDiarrheaFixedZ()
stuntingUpdateDueToBreastfeeding = self.params.getStuntingUpdateDueToIncidence(beta)
# INCIDENCE
incidencesBefore = {}
incidencesAfter = {}
for ageGroup in self.listOfAgeCompartments:
ageName = ageGroup.name
#update incidence
incidencesBefore[ageName] = self.params.incidences[ageName]['Diarrhea']
self.params.incidences[ageName]['Diarrhea'] *= incidenceUpdate[ageName]['Diarrhea']
incidencesAfter[ageName] = self.params.incidences[ageName]['Diarrhea']
# get flow on effect to stunting due to changing incidence
Z0 = self.derived.getZa(incidencesBefore, self.params.breastfeedingDistribution)
Zt = self.derived.getZa(incidencesAfter, self.params.breastfeedingDistribution)
beta = self.derived.getFracDiarrhea(Z0, Zt)
self.derived.updateProbStuntedIfDiarrheaNewZa(Zt)
stuntingUpdateDueToIncidence = self.params.getStuntingUpdateDueToIncidence(beta)
# STUNTING
for ageGroup in self.listOfAgeCompartments:
ageName = ageGroup.name
totalUpdate = stuntingUpdate[ageName] * stuntingUpdateDueToIncidence[ageName] * stuntingUpdateComplementaryFeeding[ageName] *stuntingUpdateDueToBreastfeeding[ageName]
#save total stunting update for use in apply births and apply aging
self.derived.stuntingUpdateAfterInterventions[ageName] *= totalUpdate
#update stunting
oldProbStunting = ageGroup.getStuntedFraction()
newProbStunting = oldProbStunting * totalUpdate
self.params.stuntingDistribution[ageName] = self.helper.restratify(newProbStunting)
ageGroup.distribute(self.params.stuntingDistribution, self.params.wastingDistribution, self.params.breastfeedingDistribution)
# BIRTH OUTCOME
for outcome in self.birthOutcomes:
self.params.birthOutcomeDist[outcome] *= birthUpdate[outcome]
self.params.birthOutcomeDist['Term AGA'] = 1 - (self.params.birthOutcomeDist['Pre-term SGA'] + self.params.birthOutcomeDist['Pre-term AGA'] + self.params.birthOutcomeDist['Term SGA'])
# UPDATE MORTALITY AFTER HAVING CHANGED: underlyingMortality and birthOutcomeDist
self.updateMortalityRate()
# set newCoverages as the coverages in interventions
self.params.coverage = newCoverage
