def test_reward(self):
model_keys = ["000000100", "000210100", "020112001"]
model_probs = ["112111011", "211001011", "101000120"]
model = Model(load_model=False)
for i, k in enumerate(model_keys):
model.states[k] = self.to_int_list(model_probs[i])
ai_keys = ["001000000", "001012000", "001112020"]
ai_boards = [
BoardState.from_board(self.to_int_list(s)) for s in ai_keys
]
ai_moves = [5, 7, 1]
progress = list(zip(ai_boards, ai_moves))
model.reward(progress, Result.X_WINS)
target_probs = ["110110211", "110100102", "110000101"]
target_probs = [
list(map(float, self.to_int_list(p))) for p in target_probs
]
self.assertEqual(len(model.states), len(model_keys),
"Too much state entries in model.")
for i, k in enumerate(ai_keys):
model_probs = model.states[k]
self.assertEqual(model_probs, target_probs[i])
def on_run_regex(self):
""" Passes clinician note file to Model """
self.phrases = self.regex_text.get(1.0, 'end-1c').strip()
# GETS FILE NAMe, passes global path to ReadRPDR
file_loc = self.data_model.input_fname
self.dirname = os.path.dirname(file_loc)
opts = {
'r_encoding': 'utf-8',
'preserve_header': True,
'patient_id': self.patient_id_entry.get(),
'note_key': self.note_key_entry.get(),
'rpdr': self.rpdr_checkbox.var.get()
}
phrases = [p.strip() for p in self.phrases.split(",")]
self.model = Model(options_=opts,
file_location_=file_loc,
keywords_=phrases)
if self.checkvar:
self.num_notes = self.model.get_num_notes_positive()
else:
self.num_notes = self.model.get_num_notes()
first_note, index = self.model.first(self.checkvar)
self.display_output_note(first_note, index)
def main():
f = open("iris.dataset")
dataset = [[(float(x)) for x in line.split(" ")] for line in f]
dataset = dataset[0:150]
f.close()
model = Model(0.2, [4, 10, 3])
# k-fold cross validation
k = 5
fold = []
fold_len = int(len(dataset) / k)
# print(dataset)
for i in range(0, k):
r = {
'validation_data': dataset[0:fold_len],
'training_data': dataset[fold_len:len(dataset)]
}
fold.append(r)
dataset = shift(dataset, -fold_len)
print("init weights:", model.layer_weights)
for j in range(0, k):
print(fold[j]['training_data'])
model.train(fold[j]['training_data'], fold[j]['validation_data'], 100)
print("final weights:", model.layer_weights)
plt.plot(model.error)
plt.plot(model.accuracy)
plt.xlabel('epoch')
plt.yscale('log')
plt.legend(['Error', 'Accuracy'], loc='upper left')
plt.show()
def main(config) -> None:
logger.info(f"\n{config.pretty()}")
pl.seed_everything(config.hparams.seed)
datamodule = MNISTDataModule(
data_dir=config.dataset.path,
batch_size=config.hparams.batch_size,
num_workers=config.hparams.num_workers,
)
trainer = pl.Trainer(
**config.trainer,
checkpoint_callback=ModelCheckpoint(**config.model_checkpoint),
callbacks=[EarlyStopping(**config.early_stopping)]
+ [hydra.utils.instantiate(i) for i in config.callbacks],
logger=[hydra.utils.instantiate(i) for i in config.loggers],
auto_lr_find=config.hparams.lr == 0,
)
model = Model(**config.hparams)
trainer.tune(model, datamodule=datamodule)
assert model.hparams.lr > 0, f'model.hparams.lr > 0={model.hparams.lr > 0}'
config.hparams.lr = model.hparams.lr
# 更新したhparamsをlogに出力するには↓がいる
model = Model(**config.hparams)
trainer.fit(model, datamodule=datamodule)
trainer.test()
def calc_operating_income(new_trucks: int, num_customers: float) -> float:
# initialize the model
model = Model()
model.inputs.num_customers = num_customers
# get total trucks, demand, and the trucks needed to serve it
model.inputs.trucks_total = (model.operations.productivity.avg_num_trucks +
new_trucks)
trucks_utilized = min([model.trucks_required(), model.inputs.trucks_total])
available_capacity_per_truck = (
model.inputs.lifts_per_truck_day *
model.operations.avg_vol_per_lift() *
model.operations.productivity.working_days_per_year)
# what demand is met
served_demand = model.demand_served()
revenue = served_demand * model.inputs.revenue_per_m3
disposal_cost = model.new_disposal_cost()
# depot related costs
depot_overhead = model.depot_overhead_cost(
model.inputs.trucks_total) # assume each depot incurs OH
depot_labor = model.depot_labor_cost(
trucks_utilized) # but only "active" ones incur labor cost
# driver costs
driver_labor = model.driver_labor_cost(trucks_utilized)
# fuel and maintenance
fuel = model.fuel_cost(trucks_utilized)
maintenance = model.maintenance_cost(trucks_utilized)
return (revenue - disposal_cost - depot_overhead - depot_labor -
driver_labor - fuel - maintenance)
def test_tensor_size_valid(self) -> None:
t1: torch.Tensor = torch.tensor([1, 2])
same_size: bool = Model._tensor_size_valid(t1, [2])
diff_size: bool = Model._tensor_size_valid(t1, [3])
self.assertTrue(same_size)
self.assertFalse(diff_size)
def __init__(self, loop):
self.loop = loop
controller = Controller()
self.model = Model(controller=controller)
view = View(controller=controller)
controller.segundo_init(model=self.model, view=view)
self.model.segundo_init()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--mel-path')
args = parser.parse_args()
print('Loading model')
model = Model(FITTED_MODEL_PATH)
print('Successfully loaded model')
preds = model.predict(args.mel_path)
print(preds)
def test_model_predict():
"""Test if model can extract entities and accompanying sentence"""
with open("tests/processed_html_text.txt", "r", encoding="utf-8") as f:
data = f.read()
model = Model()
output = model.predict(data)
assert len(output) > 0, "model did not return any prediction"
assert len(output[0]) == 2, "model did not return entity and sentence"
def __init__(self, parent=None):
super().__init__(parent)
self.view = Ui_View()
self.mainwindow = QMainWindow()
self.model = Model()
self.view.setupUi(self.mainwindow)
self.setFixedSize(620, 250)
self.setup_signals()
def test_discrete_line(self):
self.assertSequenceEqual(Model.discrete_line(Point(1, 2), Point(3, 6)),
[Point(1, 2), Point(1, 3), Point(2, 3), Point(2, 4), Point(2, 5), Point(3, 5),
Point(3, 6)])
self.assertSequenceEqual(Model.discrete_line(Point(1, 1), Point(4, 4)),
[Point(1, 1), Point(2, 2), Point(3, 3), Point(4, 4)])
self.assertSequenceEqual(Model.discrete_line(Point(1, 1), Point(1, 4)),
[Point(1, 1), Point(1, 2), Point(1, 3), Point(1, 4)])
self.assertSequenceEqual(Model.discrete_line(Point(1, 4), Point(1, 1)),
[Point(1, 4), Point(1, 3), Point(1, 2), Point(1, 1)])
def main(config):
config_proto = tf.ConfigProto()
config_proto.gpu_options.allow_growth = True
sess = tf.InteractiveSession(config=config_proto)
dataloader = Dataloader(sess, config, prefetch_buffer_size=10)
model = Model(sess, dataloader, config)
model.build_model()
logger = Logger(sess, config)
trainer = Trainer(sess, model, dataloader, logger, config)
trainer.train()
def __init__(self):
self.iteration_number = 0
self.solution_type = ""
self.solution_name = ""
self.model = Model()
self.solver_data = SolverData()
self.data = DataStore()
self.iteration_results = IterationResults()
self.sequence_list = SequenceList()
self.current_sequence = Sequence()
self.iteration_saved = False
def main(model_dir, data_dir):
best_train_result_path = os.path.join(model_dir, "best03.pth")
checkpoint = torch.load(best_train_result_path)
model = Model().cuda()
model.load_state_dict(checkpoint["model"])
input = torch.randn(1, 3, 96, 96, device='cuda')
torch.onnx.export(model, input, './model.onnx')
def get(self):
'''
Import the model
write prediction module
outputs datetime, price, trade amount as JSON
pull data from poloniex
use model.prediction
return model.prediction output
'''
m = Model(symbols=['BTC'])
return m.predict_for_api(5900, self.funds)
def search_t(mock_key, test_key):
mock_probs = self.mock_state_probs(mock_key)
model = Model(load_model=False)
model.states[mock_key] = mock_probs
searched_board = self.to_int_list(test_key)
searched_board_state = BoardState.from_board(searched_board)
for _ in range(0, 100):
picked_move = model.pick_move(searched_board_state, 1)
self.assertEqual(
searched_board[picked_move], 0,
"Search Error with keys " + mock_key + " and " + test_key)
def solve_data_set(self):
self.model = Model()
self.model.set_data(self.solver_data.data_set_number, self.data)
self.annealing.set_data_set_number(self.solver_data.data_set_number,
self.data)
self.annealing.start_sequence1()
self.annealing.sequence.set_sequences()
self.model.current_sequence = self.annealing.next_sequence()
self.model.set_sequence()
# calculate sequence
#self.model.current_sequence = self.current_sequence
self.model.solve()
def prepare(
test_path=params["common"]["test-path"],
train_path=params["common"]["train-path"],
data_path=params["common"]["all-data-path"],
indices_path=params["common"]["indices-path"],
output_path=params["train"]["output-path"],
model_output_path=params["train"]["model-output-path"],
lowercase=params["train"]["lowercase"],
seq_length=params["train"]["seq-length"],
num_words=params["train"]["num-words"],
oov_token=params["train"]["oov-token"],
padding_style=params["train"]["padding-style"],
trunc_style=params["train"]["trunc-style"],
test_prop=params["prepare"]["test-prop"],
):
for path in [
output_path,
model_output_path,
os.path.split(data_path)[0],
os.path.split(test_path)[0],
os.path.split(train_path)[0],
]:
os.makedirs(path, exist_ok=True)
model = Model(
output_path=output_path,
model_output_path=model_output_path,
seq_length=int(seq_length),
)
# Load the data from disk
model.load_data(data_path, test_prop)
# Prepare the data with tokenisation, padding, etc.
model.prep_data(
oov_token=oov_token,
trunc_style=trunc_style,
padding_style=padding_style,
num_words=int(num_words),
lowercase=lowercase,
)
# Save the intermediate objects to disk
model.save_indices(indices_path)
# Save split data to disk as np arrays
model.save_train_test_data(test_path, train_path)
def _loadFromFile(self):
"""
It opens a dialog to load a xml file, which is then parsed and shown
in the main window.
"""
fileName = QFileDialog.getOpenFileName(self, 'Open file',
'../puzzles/')[0]
# continue only if some file was really loaded
if len(fileName) > 0:
self.model = Model(fileName)
self._showModel()
self.rawHeuristicAction.setEnabled(True)
self.solveAction.setEnabled(True)
def main():
alpha = 1.5
mu = 0.8
model = Model(alpha, mu)
curr = (1.5, 1.5)
values = [curr[0]]
count = 100
while curr[0] > 0 and count > 0:
curr = model.next(curr)
values.append(curr[0])
count -= 1
points = list(enumerate(values))
write_to_files('../files/time_series', points)
def main(config) -> None:
all_done = False
try:
logger.info('\n' + OmegaConf.to_yaml(config))
shutil.copytree(
Path(hydra.utils.get_original_cwd()) / 'src',
Path.cwd() / 'copied' / 'src')
pl.seed_everything(config.hparams.seed)
datamodule = hydra.utils.instantiate(config.data.datamodule)
trainer = pl.Trainer(
**config.trainer,
checkpoint_callback=ModelCheckpoint(**config.model_checkpoint),
callbacks=[EarlyStopping(**config.early_stopping)] +
[hydra.utils.instantiate(i) for i in config.callbacks],
logger=[hydra.utils.instantiate(i) for i in config.loggers],
auto_lr_find=config.hparams.lr == 0,
)
model = Model(**config.hparams)
torchsummary.summary(model)
trainer.tune(model, datamodule=datamodule)
if config.debug == True:
# fast_dev_runモードではauto_lr_findが失敗し，model.hparams.lrにNoneが代入される
assert model.hparams.lr is None
model.hparams.lr = 1
assert model.hparams.lr > 0, f'model.hparams.lr > 0={model.hparams.lr > 0}'
config.hparams.lr = model.hparams.lr
# 更新したhparamsをロガーに出力するには↓がいる
model = Model(**config.hparams)
trainer.fit(model, datamodule=datamodule)
trainer.test()
logger.info('All done.')
all_done = True
finally:
if all_done == False:
path = Path.cwd()
if 'outputs' in path.parts or 'multirun' in path.parts:
logger.info(
f'Rename directory name. "{path}" -> "{path}__interrupted"'
)
path.rename(path.parent / (path.name + '__interrupted__'))
def create_model(args, device, logger, storage, storage_test):
start_time = time.time()
model = Model(args, logger, storage, storage_test, model_type=args.model_type)
logger.info(model)
logger.info('Trainable parameters:')
for n, p in model.named_parameters():
logger.info('{} - {}'.format(n, p.shape))
logger.info("Number of trainable parameters: {}".format(utils.count_parameters(model)))
logger.info("Estimated size (under fp32): {:.3f} MB".format(utils.count_parameters(model) * 4. / 10**6))
logger.info('Model init {:.3f}s'.format(time.time() - start_time))
return model
def gen_data() -> pd.DataFrame:
model = Model()
# assume their performance slips
model.inputs.lifts_per_truck_day = 10
current_customers = model.inputs.num_customers
customers_3x = current_customers * 3
n_customers = range(current_customers, customers_3x, 500)
roics = []
for n in n_customers:
model.inputs.num_customers = n
model.set_trucks()
r = model.new_roic()
roics.append(r)
return pd.DataFrame({"customers": n_customers, "ROIC": roics})
def __init__(self, list_players, start_money):
QObject.__init__(self)
self.gui = ViewMainFrame(list_players, start_money)
self.gui.show()
self.m = Model(list_players, start_money)
# Signals
self.gui.central_widget.sig_player_bank = self.sig_bank_player
self.sig_bank_player.connect(self.player_bank)
self.gui.central_widget.sig_trade = self.sig_trade
self.sig_trade.connect(self.player_trade)
self.list_players = list_players
def generate(self, solution: Solution[Model]) -> Solution[Model]:
model = solution.getvalue()
connection: torch.Tensor = model.connection
weight: torch.Tensor = model.weight
self.device = model.device
connection, weight = self._regulate_neural(connection,
weight,
removable=model.input_dim +
model.output_dim)
flip_mask = torch.ones_like(connection) * self.possibility_of_invert
flip_mask = torch.bernoulli(flip_mask).to(torch.int8)
new_connection = connection.to(torch.int8).__xor__(flip_mask)
uniform_from_distance = torch.empty_like(weight).uniform_(
-1 * self.distance, self.distance)
new_weight = weight + uniform_from_distance
new_model = Model(
model.activation, model.input_dim, model.output_dim,
connection.size()[0] - model.input_dim - model.output_dim,
new_connection, new_weight, model.delay, model.device)
return Solution(new_model, solution.get_generator(),
solution.get_evaluator())
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--model", default="assets/model/model.h5")
parser.add_argument("--total-samples", type=int, default=10)
args = parser.parse_args()
from src.app import D3DScreenshot, MSSScreenshot
bounding_box = (0, 0, 400, 500)
d3d_screenshotter = D3DScreenshot()
mss_screenshotter = MSSScreenshot()
print("Taking {:d} samples".format(args.total_samples))
dt = profile_time(d3d_screenshotter.get_screen_shot,
args.total_samples)(bounding_box)
print("D3D took {:.02f}ms".format(dt * 1000))
dt = profile_time(mss_screenshotter.get_screen_shot,
args.total_samples)(bounding_box)
print("MSS took {:.02f}ms".format(dt * 1000))
from src.model import Model
model = Model.load(args.model)
test_image = np.full((1, 160, 227, 3), 125) / 255
dt = profile_time(model.predict, args.total_samples)(test_image)
print("Model took {:.02f}ms".format(dt * 1000))
def update_alphas(generic_model, model_name, model_configs, alpha, l1_ratio,
prefix):
refined_alphas = model_configs['update_alphas'] * alpha
refined_l1_ratios = model_configs['update_alphas'] * l1_ratio
if model_name == 'Ridge':
model = generic_model(alphas=refined_alphas,
cv=model_configs['cross_val'])
elif model_name == 'Lasso':
model = generic_model(alphas=refined_alphas,
cv=model_configs['cross_val'],
max_iter=model_configs['max_iter'])
elif model_name == 'ElasticNet':
model = generic_model(l1_ratio=refined_l1_ratios,
alphas=refined_alphas,
cv=model_configs['cross_val'],
max_iter=model_configs['max_iter'])
model = Model(model,
model_name,
prefix,
cross_val=model_configs['cross_val'])
return model
def run_cv(data, target, base_model, params, tags, k=5):
"""
Performing a CV training
Args:
data (pd.DataFrame): DataFrame with the filtered data
target (str): Name of the prediction target.
base_model (sklearn model): Scikit-learn object that will be used as base model for training.
Returns:
model: Trained model
metrics (dict): Dictionary with all the metrics for the model
"""
experiment_name = "StockForecasting_PROD"
print('{:=^80}'.format(' RUN '))
print("Starting RUN on project {}.".format(experiment_name))
# Init model
norm = Normalize()
pipeline = Pipeline([("norm", norm), ("model", base_model)])
model = Model(pipeline)
# Get CV metrics
cv_metrics = cross_validate(model, data, target, k)
print(cv_metrics)
# Log in mlflow with no artifacts
log_mlflow(experiment_name, None, params, cv_metrics, tags)
return cv_metrics
def run(app):
dir_name = os.path.dirname(__file__)
os.environ['QML_IMPORT_PATH'] = os.path.join(dir_name, 'resources')
os.environ['QML2_IMPORT_PATH'] = os.path.join(dir_name, 'resources')
#QCoreApplication.setAttribute(Qt.AA_EnableHighDpiScaling, True)
# Create the application instance.
#app = QGuiApplication(sys.argv)
# Create QML engine
engine = QQmlApplicationEngine()
context = engine.rootContext()
# Testor
manage = ManageThreads()
context.setContextProperty("manage", manage)
# Model
TestorModel = Model()
manage.runtimeSig.connect(TestorModel.addData)
context.setContextProperty("TestorModel", TestorModel)
engine.load(QUrl('src/resources/main.qml'))
engine.quit.connect(app.quit)
sys.exit(app.exec_())
class Controller(QWidget):
"""
MVC Pattern: Represents the controller class
"""
def __init__(self, parent=None):
super().__init__(parent)
self.view = Ui_View()
self.mainwindow = QMainWindow()
self.model = Model()
self.view.setupUi(self.mainwindow)
self.setFixedSize(620, 250)
self.setup_signals()
def setup_signals(self):
self.view.open.clicked.connect(lambda: self.open_clicked())
def open_clicked(self):
fname = QFileDialog.getOpenFileName(self.mainwindow, 'Öffnen ...',
os.getcwd())
csv = self.model.read_csv(fname[0], delimiter=';')
self.view.text_area.clear()
self.view.text_area.insertPlainText(csv)
def main():
# Load train dataset
data = dataLoader(directory='./dataset/captcha', dataset_dir='train',\
dataset_name='train.txt', max_steps=6, image_width=200,\
image_height=64, grd_attn=True, mode='Train')
# Load Model
model = Model(dim_feature=[672, 128],
dim_hidden=128,
n_time_step=8,
alpha_c=1.0,
image_height=64,
image_width=200,
mode='train')
# Load Trainer
trainer = Train(model,
data,
val_data=None,
n_epochs=1000,
batch_size=64,
update_rule='adam',
learning_rate=0.0001,
print_every=100,
save_every=5,
pretrained_model=None,
model_path='model/lstm1/',
log_path='log1/')
# Begin Training
trainer.train()
def __init__(self, parent=None):
super().__init__(parent)
self.view = Ui_View()
self.mainwindow = QMainWindow()
self.model = Model()
self.view.setupUi(self.mainwindow)
self.setFixedSize(620, 250)
self.setup_signals()
def __init__(self):
self.iteration_number = 0
self.solution_type = ""
self.solution_name = ""
self.model = Model()
self.solver_data = SolverData()
self.data = DataStore()
self.iteration_results = IterationResults()
self.sequence_list = SequenceList()
self.current_sequence = Sequence()
self.iteration_saved = False
def test_fitness(self):
terrain_map = [
[0.1, 0.1, 0.1, 0.1, 0.1],
[0.1, 1, 1, 0.1, 0.1],
[1, 1, 1, 0.1, 1],
[1, 1, 0.1, 0.1, 1],
[1, 1, 0.1, 0.1, 0.1]
]
start = Point(3, 4)
finish = Point(0, 1)
model = Model(terrain_map, n_turns=3, start=start, finish=finish,
weight_segment=1, weight_turn=0)
turns = [Point(*x) for x in [(3, 2), (3, 0), (1, 0)]]
fitness = model.get_fitness(turns)
# self.assertAlmostEqual(fitness, 6 + math.sqrt(2))
self.assertAlmostEqual(fitness, 4.4) #TODO wyliczyc co tu powinno
def solve_data_set(self):
self.model = Model()
self.model.set_data(self.solver_data.data_set_number, self.data)
self.annealing.set_data_set_number(self.solver_data.data_set_number, self.data)
self.annealing.start_sequence1()
self.annealing.sequence.set_sequences()
self.model.current_sequence = self.annealing.next_sequence()
self.model.set_sequence()
# calculate sequence
#self.model.current_sequence = self.current_sequence
self.model.solve()
def test_segments(self):
terrain_map = [
[0, 0, 0, 0, 0],
[0, 1, 1, 0, 0],
[1, 1, 1, 0, 1],
[1, 1, 0, 0, 1],
[1, 1, 0, 0, 0]
]
start = (3, 4)
finish = (0, 1)
model = Model(terrain_map, n_turns=3, start=start, finish=finish,
weight_segment=1, weight_turn=1)
turns = [(3, 2), (3, 0), (1, 0)]
# turns = [Point(*x) for x in turns]
expected = [((3, 4), (3, 2)),
((3, 2), (3, 0)),
((3, 0), (1, 0)),
((1, 0), (0, 1))]
# expected = [(Point(*x), Point(*y)) for x, y in expected]
self.assertSequenceEqual(model._segments(turns), expected)
def solve_simluation(self):
self.model = Model()
self.model.simulator = self.simulator
self.model.simulation_on = True
self.model.set_data(self.solver_data.data_set_number, self.data)
self.simulator.reset()
self.simulator.trucks = self.model.all_trucks
self.simulator.doors = self.model.all_doors
self.simulator.station = self.model.station
self.annealing.set_data_set_number(self.solver_data.data_set_number, self.data)
self.annealing.start_sequence1()
self.annealing.sequence.set_sequences()
self.model.current_sequence = self.annealing.next_sequence()
self.model.set_sequence()
self.model.solve()
class Controller(QWidget):
"""
MVC Pattern: Represents the controller class
"""
def __init__(self, parent=None):
super().__init__(parent)
self.view = Ui_View()
self.mainwindow = QMainWindow()
self.model = Model()
self.view.setupUi(self.mainwindow)
self.setFixedSize(620, 250)
self.setup_signals()
def setup_signals(self):
self.view.open.clicked.connect(lambda: self.open_clicked())
def open_clicked(self):
fname = QFileDialog.getOpenFileName(self.mainwindow, 'Öffnen ...', os.getcwd())
csv = self.model.read_csv(fname[0], delimiter=';')
self.view.text_area.clear()
self.view.text_area.insertPlainText(csv)
def test_angle_metric_order(self):
"""Test that the closer the angle is to straight line, the smaller it costs is"""
terrain_map = [[1]*6 for _ in range(7)]
A = Point(0, 0)
B = Point(1, 2)
C = Point(3, 4)
D = Point(2, 1)
E = Point(3, 5)
F = Point(0, 3)
model = Model(terrain_map, None, None, None, None, None,
turn_penalty=lambda x: x**2)
gentle_angle_weight = model._calculate_turn_penalty([A, B], [B, C])
harsh_angle_weight = model._calculate_turn_penalty([A, B], [B, D])
self.assertLess(gentle_angle_weight, harsh_angle_weight)
zero_angle_weight = model._calculate_turn_penalty([A, B], [B, A])
self.assertLess(harsh_angle_weight, zero_angle_weight)
straight_angle_weight = model._calculate_turn_penalty([A, B], [B, E])
self.assertLess(straight_angle_weight, gentle_angle_weight)
medium_angle_weight = model._calculate_turn_penalty([A, B], [B, F])
self.assertLess(medium_angle_weight, harsh_angle_weight)
import sys
src_directory = '../../../'
sys.path.append(src_directory)
from src.model import Model
from pylab import zeros, linspace, sqrt
from dolfin import project, Function, File, as_vector
nx = 40
ny = 40
nz = 7
model = Model()
model.generate_uniform_mesh(nx,ny,nz,0,1,0,1,deform=False,generate_pbcs=True)
Q = model.Q
U_obs = project(dolfin.as_vector([Function(Q),Function(Q)]))
b_obs = Function(Q)
U_opt = project(as_vector([Function(Q),Function(Q),Function(Q)]))
b_opt = Function(Q)
rcParams['text.usetex']=True
rcParams['font.size'] = 12
rcParams['font.family'] = 'serif'
for L in [10000]:
File('./results/U_obs.xml') >> U_obs
File('./results/U_opt.xml') >> U_opt
File('./results/beta2_obs.xml') >> b_obs
File('./results/beta2_opt.xml') >> b_opt
class Solver(QObject):
def __init__(self, data=DataStore(), solver_data=SolverData()):
QObject.__init__(self)
self.data = data
self.solver_data = solver_data
self.annealing = Annealing(self.data)
self.current_iteration = 0
def solve_data_set(self):
self.model = Model()
self.model.set_data(self.solver_data.data_set_number, self.data)
self.annealing.set_data_set_number(self.solver_data.data_set_number, self.data)
self.annealing.start_sequence1()
self.annealing.sequence.set_sequences()
self.model.current_sequence = self.annealing.next_sequence()
self.model.set_sequence()
# calculate sequence
#self.model.current_sequence = self.current_sequence
self.model.solve()
def solve_simluation(self):
self.model = Model()
self.model.simulator = self.simulator
self.model.simulation_on = True
self.model.set_data(self.solver_data.data_set_number, self.data)
self.simulator.reset()
self.simulator.trucks = self.model.all_trucks
self.simulator.doors = self.model.all_doors
self.simulator.station = self.model.station
self.annealing.set_data_set_number(self.solver_data.data_set_number, self.data)
self.annealing.start_sequence1()
self.annealing.sequence.set_sequences()
self.model.current_sequence = self.annealing.next_sequence()
self.model.set_sequence()
self.model.solve()
def simulation_step(self):
self.model.solve()
def new_model(self):
self.model = Model()
M_max = 0.5
T_min = 238.15
S_T = 1.67e-5
class MassBalance(Expression):
def eval(self,values,x):
values[0] = min(M_max,S_b*(R_el-sqrt(x[0]**2 + x[1]**2)))
class SurfaceTemperature(Expression):
def eval(self,values,x):
values[0] = T_min + S_T*sqrt(x[0]**2 + x[1]**2)
mesh = MeshFactory.get_circle()
model = Model()
model.set_mesh(mesh, deform=False)
Surface = Expression('S_0', S_0 = S_0, element = model.Q.ufl_element())
Bed = Expression('0.0', element = model.Q.ufl_element())
model.set_geometry(Surface, Bed)
model.deform_mesh_to_geometry()
model.set_parameters(IceParameters())
model.initialize_variables()
nonlin_solver_params = default_nonlin_solver_params()
nonlin_solver_params['newton_solver']['relaxation_parameter'] = 1.0
nonlin_solver_params['newton_solver']['absolute_tolerance'] = 1.0
nonlin_solver_params['newton_solver']['linear_solver'] = 'gmres'
class GeneralSolver(object):
def __init__(self):
self.iteration_number = 0
self.solution_type = ""
self.solution_name = ""
self.model = Model()
self.solver_data = SolverData()
self.data = DataStore()
self.iteration_results = IterationResults()
self.sequence_list = SequenceList()
self.current_sequence = Sequence()
self.iteration_saved = False
def new_model(self):
self.model = Model()
def set_data(self, solver_data=SolverData(), data=DataStore()):
self.new_model()
self.solver_data = solver_data
self.data = data
self.model.set_data(solver_data, data)
self.sequence_algorithms = SequenceAlgorithm(data)
self.sequence_algorithms.set_data_set_number(solver_data.data_set_number,data)
def set_sequence(self, sequence=Sequence()):
self.current_sequence = sequence
self.sequence_list.add_sequence(self.current_sequence)
self.model.set_sequence(self.current_sequence)
# gets a sequence and sets model
def solve_iteration(self):
while not self.iteration_saved:
if not self.model.check_done():
self.model.next_time()
#print(self.model.current_time)
else:
self.iteration_saved = True
self.save_results(self.model)
def start_iteration(self):
self.model.reset_model()
# start model solve
def step_forward(self):
"""
moves the model forward
"""
if not self.model.check_done():
self.model.next_time()
return True
else:
self.save_results(self.model)
return False
def save_results(self, model):
pass
def _angle_tester(self, A, B, P, expected):
ang = Model._angle((A, P), (P, B))
self.assertAlmostEqual(ang, expected)
'adjoint' :
{
'alpha' : None,
'beta' : None,
'max_fun' : None,
'objective_function' : 'logarithmic',
'animate' : False
}}
nx = 50
ny = 50
nz = 10
model = Model()
model.generate_uniform_mesh(nx, ny, nz, xmin=0, xmax=L, ymin=0, ymax=L,
generate_pbcs=True)
Surface = Expression('- x[0] * tan(alpha)', alpha=alpha,
element=model.Q.ufl_element())
Bed = Expression( '- x[0] * tan(alpha) - 1000.0 + 500.0 * ' \
+ ' sin(2*pi*x[0]/L) * sin(2*pi*x[1]/L)',
alpha=alpha, L=L, element=model.Q.ufl_element())
model.set_geometry(Surface, Bed, deform=True)
model.set_parameters(IceParameters())
model.initialize_variables()
F = SteadySolver(model, config)
F.solve()
'T_ma' : None,
'T_ju' : None,
'beta_w' : None,
'sigma' : None,
'precip' : None
},
'adjoint' :
{
'alpha' : None,
'beta' : None,
'max_fun' : None,
'objective_function' : 'logarithmic',
'animate' : False
}}
model = Model()
model.set_geometry(Surface(), Bed())
mesh = MeshFactory.get_circle()
flat_mesh = MeshFactory.get_circle()
model.set_mesh(mesh, flat_mesh=flat_mesh, deform=True)
model.mesh.coordinates()[:,2] = model.mesh.coordinates()[:,2]/1000.0
model.set_parameters(IceParameters())
model.initialize_variables()
F = SteadySolver(model,config)
F.solve()
T = TransientSolver(model,config)
T.solve()
