def test_stop_validation():
def raiser(x):
raise StopValidation({'something': 'bad'})
lst = ListType(StringType(), validators=[raiser])
with pytest.raises(ValidationError) as exc:
lst.validate('*****@*****.**')
assert exc.value.messages == {'something': 'bad'}
def setUp(self):
self.listtype = ListType(IntType())
class TestModel(Model):
the_list = self.listtype
class Options:
roles = {
'owner': wholelist(),
}
self.Testmodel = TestModel
self.testmodel = TestModel()
class TestSetGetSingleScalarData(unittest.TestCase):
def setUp(self):
self.listtype = ListType(IntType())
class TestModel(Model):
the_list = self.listtype
class Options:
roles = {
'owner': wholelist(),
}
self.Testmodel = TestModel
self.testmodel = TestModel()
def test_good_value_for_python(self):
self.testmodel.the_list = [2]
self.assertEqual(self.testmodel.the_list, [2])
def test_single_bad_value_for_python(self):
self.testmodel.the_list = 2
# since no validation happens, nothing should yell at us
self.assertEqual(self.testmodel.the_list, 2)
def test_collection_good_values_for_python(self):
self.testmodel.the_list = [2,2,2,2,2,2]
self.assertEqual(self.testmodel.the_list, [2,2,2,2,2,2])
def test_collection_bad_values_for_python(self):
expected = self.testmodel.the_list = ["2","2","2","2","2","2"]
actual = self.testmodel.the_list
# since no validation happens, nothing should yell at us
self.assertEqual(actual, expected)
def test_good_value_for_json(self):
expected = self.testmodel.the_list = [2]
actual = self.listtype.for_json(self.testmodel.the_list)
self.assertEqual(actual, expected)
def test_good_values_for_json(self):
expected = self.testmodel.the_list = [2,2,2,2,2,2]
actual = self.listtype.for_json(self.testmodel.the_list)
self.assertEqual(actual, expected)
def test_good_values_into_json(self):
self.testmodel.the_list = [2,2,2,2,2,2]
actual = make_safe_json(self.Testmodel, self.testmodel, 'owner')
expected = json.dumps({"the_list":[2,2,2,2,2,2]})
self.assertEqual(actual, expected)
def test_good_value_into_json(self):
self.testmodel.the_list = [2]
actual = make_safe_json(self.Testmodel, self.testmodel, 'owner')
expected = json.dumps({"the_list":[2]})
self.assertEqual(actual, expected)
def test_good_value_validates(self):
self.testmodel.the_list = [2,2,2,2,2,2]
result = validate_instance(self.testmodel)
self.assertEqual(result.tag, 'OK')
def test_coerceible_value_passes_validation(self):
self.testmodel.the_list = ["2","2","2","2","2","2"]
result = validate_instance(self.testmodel)
self.assertEqual(result.tag, 'OK')
def test_uncoerceible_value_passes_validation(self):
self.testmodel.the_list = ["2","2","2","2","horse","2"]
result = validate_instance(self.testmodel)
self.assertNotEqual(result.tag, 'OK')
def test_validation_converts_value(self):
self.testmodel.the_list = ["2","2","2","2","2","2"]
result = validate_instance(self.testmodel)
self.assertEqual(result.tag, 'OK')
converted_data = result.value
new_list = converted_data['the_list']
self.assertEqual(new_list, [2,2,2,2,2,2])
class QuestionResources(Model):
pictures = ListType(ModelType(QuestionResource))
class PlayerInfo(Model):
categories = ListType(ModelType(CategoryStatsInfo),
default=lambda: [],
serialize_when_none=True)
class Card(Model):
users = ListType(ModelType(User), min_size=1, required=True)
class User(Model):
ids = ListType(StringType(required=True))
class UpdateExecutionReport(BaseExecutionReport):
instruction_reports = ListType(ModelType(UpdateInstructionReport))
class CancelExecutionReport(BaseExecutionReport):
instruction_reports = ListType(ModelType(CancelInstructionReport))
class SystemStorage(Model):
temp_unit = StringType(required=True, min_length=1, max_length=1)
temp_stop = FloatType(required=True)
temp_fans_on = FloatType(required=True)
temp_reduce = FloatType(required=True)
fans_off_delay = IntType(required=True)
lcd_contrast = IntType(required=True)
lcd_brightness = IntType(required=True)
beep_type_key = IntType(required=True)
beep_type_hint = IntType(required=True)
beep_type_alarm = IntType(required=True)
beep_type_done = IntType(required=True)
beep_enabled_key = BooleanType(required=True)
beep_enabled_hint = BooleanType(required=True)
beep_enabled_alarm = BooleanType(required=True)
beep_enabled_done = BooleanType(required=True)
beep_volume_key = IntType(required=True, min_value=0, max_value=10)
beep_volume_hint = IntType(required=True, min_value=0, max_value=10)
beep_volume_alarm = IntType(required=True, min_value=0, max_value=10)
beep_volume_done = IntType(required=True, min_value=0, max_value=10)
calibration = IntType(required=True)
selected_input_source = IntType(required=True)
dc_input_low_voltage = FloatType(required=True, min_value=9, max_value=48)
dc_input_over_voltage = FloatType(required=True)
dc_input_current_limit = FloatType(required=True,
min_value=1,
max_value=65)
batt_input_low_voltage = FloatType(required=True,
min_value=9,
max_value=48)
batt_input_over_voltage = FloatType(required=True)
batt_input_current_limit = FloatType(required=True,
min_value=1,
max_value=65)
regenerative_enable = IntType(required=True)
regenerative_volt_limit = FloatType(required=True,
min_value=9,
max_value=48)
regenerative_current_limit = FloatType(required=True,
min_value=1,
max_value=65)
power_priority = IntType(required=True)
charge_power = ListType(IntType(min_value=5, max_value=800))
discharge_power = ListType(IntType(min_value=5, max_value=80))
monitor_log_interval = ListType(IntType)
monitor_save_to_sd = ListType(BooleanType)
servo_type = LongType(required=True)
servo_user_center = LongType(required=True)
server_user_rate = LongType(required=True)
server_user_op_angle = LongType(required=True)
modbus_mode = LongType(required=True)
modbus_serial_addr = LongType(required=True)
modbus_serial_baud_rate = LongType(required=True)
modbus_serial_parity = LongType(required=True)
@staticmethod
def modbus(ds1=None, ds2=None, ds3=None):
if ds1 is not None and ds2 is not None and ds3 is not None:
storage = SystemStorage()
storage.set_from_modbus_data(ds1, ds2, ds3)
return storage
return None
def set_from_modbus_data(self, ds1, ds2, ds3):
dummy1 = None
(self.temp_unit, self.temp_stop, self.temp_fans_on, self.temp_reduce,
dummy1, self.fans_off_delay, self.lcd_contrast, self.lcd_brightness,
dummy1, self.beep_type_key, self.beep_type_hint, self.beep_type_alarm,
self.beep_type_done, self.beep_enabled_key, self.beep_enabled_hint,
self.beep_enabled_alarm, self.beep_enabled_done, self.beep_volume_key,
self.beep_volume_hint, self.beep_volume_alarm,
self.beep_volume_done) = ds1.data
(dummy1, self.calibration, dummy1, self.selected_input_source,
self.dc_input_low_voltage, self.dc_input_over_voltage,
self.dc_input_current_limit, self.batt_input_low_voltage,
self.batt_input_over_voltage, self.batt_input_current_limit,
self.regenerative_enable, self.regenerative_volt_limit,
self.regenerative_current_limit) = ds2.data
self.charge_power = [0, 0]
self.discharge_power = [0, 0]
self.monitor_log_interval = [0, 0]
self.monitor_save_to_sd = [False, False]
(self.charge_power[0], self.charge_power[1], self.discharge_power[0],
self.discharge_power[1], self.power_priority,
self.monitor_log_interval[0], self.monitor_log_interval[1],
self.monitor_save_to_sd[0], self.monitor_save_to_sd[1],
self.servo_type, self.servo_user_center, self.server_user_rate,
self.server_user_op_angle, self.modbus_mode, self.modbus_serial_addr,
self.modbus_serial_baud_rate, self.modbus_serial_parity) = ds3.data
self.temp_unit = "C" if self.temp_unit is 0 else "F"
self.temp_stop /= 10.0
self.temp_fans_on /= 10.0
self.dc_input_low_voltage /= 10.0
self.dc_input_current_limit /= 10.0
self.dc_input_over_voltage /= 10.0
self.batt_input_low_voltage /= 10.0
self.batt_input_over_voltage /= 10.0
self.batt_input_current_limit /= 10.0
self.regenerative_volt_limit /= 10.0
self.regenerative_current_limit /= 10.0
def to_modbus_data(self):
s1 = (
0 if self.temp_unit == "C" else 1,
self.temp_stop * 10,
self.temp_fans_on * 10,
self.temp_reduce,
)
# Note, the trailing 0 is required, else the lcd_brightness isn't set correctly.
s2 = (
self.fans_off_delay,
self.lcd_contrast,
self.lcd_brightness,
0,
)
s3 = (
self.beep_type_key,
self.beep_type_hint,
self.beep_type_alarm,
self.beep_type_done,
self.beep_enabled_key,
self.beep_enabled_hint,
self.beep_enabled_alarm,
self.beep_enabled_done,
self.beep_volume_key,
self.beep_volume_hint,
self.beep_volume_alarm,
self.beep_volume_done,
)
s4 = (self.calibration, )
# Note, the trailing 0 is required, else the regenerative_current_limit isn't set correctly.
s5 = (
self.selected_input_source,
self.dc_input_low_voltage * 10,
self.dc_input_over_voltage * 10,
self.dc_input_current_limit * 10,
self.batt_input_low_voltage * 10,
self.batt_input_over_voltage * 10,
self.batt_input_current_limit * 10,
self.regenerative_enable,
self.regenerative_volt_limit * 10,
self.regenerative_current_limit * 10,
0,
)
# Note, the trailing 0 is required, else the modbus_serial_parity isn't set correctly.
s6 = (self.charge_power[0], self.charge_power[1],
self.discharge_power[0], self.discharge_power[1],
self.power_priority, self.monitor_log_interval[0],
self.monitor_log_interval[1], self.monitor_save_to_sd[0],
self.monitor_save_to_sd[1], self.servo_type,
self.servo_user_center, self.server_user_rate,
self.server_user_op_angle, self.modbus_mode,
self.modbus_serial_addr, self.modbus_serial_baud_rate,
self.modbus_serial_parity, 0)
return s1, s2, s3, s4, s5, s6
@serializable
def selected_input_source_type(self):
return "dc" if self.selected_input_source == 0 else "battery"
@serializable
def power_priority_description(self):
if self.power_priority == 0:
return "average"
if self.power_priority == 1:
return "ch1 priority"
if self.power_priority == 2:
return "ch2 priority"
class ChannelStatus(Model):
channel = IntType(required=True, min_value=0, max_value=1, default=0)
timestamp = LongType(required=True, default=0)
curr_out_power = FloatType(required=True, default=0)
curr_out_amps = FloatType(required=True, default=0)
curr_inp_volts = FloatType(required=True, default=0)
curr_out_volts = FloatType(required=True, default=0)
curr_out_capacity = FloatType(required=True, default=0)
curr_int_temp = FloatType(required=True, default=0)
curr_ext_temp = FloatType(required=True, default=0)
cells = ListType(ModelType(CellStatus), default=[])
cell_total_ir = FloatType(required=True, default=0)
cell_total_voltage = FloatType(required=True, default=0)
cell_count_with_voltage_values = FloatType(required=True, default=0)
cycle_count = IntType(required=True, default=0)
control_status = IntType(required=True, default=0)
run_status = IntType(required=True, default=0)
run_error = IntType(required=True, default=0)
dlg_box_id = IntType(required=True, default=0)
line_intern_resistance = FloatType(required=True, default=0)
# Optionally added to the response, when a channel status is requested
status = ModelType(DeviceInfoStatus)
def __init__(self):
super(ChannelStatus, self).__init__()
self.device_id = None
@staticmethod
def modbus(device_id=None,
channel=0,
header=None,
cell_v=None,
cell_b=None,
cell_i=None,
footer=None):
status = ChannelStatus()
if header is not None and cell_v is not None and cell_b is not None and cell_i is not None and footer is not None:
status.set_from_modbus_data(device_id, channel, header, cell_v,
cell_b, cell_i, footer)
return status
def set_from_modbus_data(self, device_id, channel, data, cell_v, cell_b,
cell_i, footer):
self.device_id = device_id
self.channel = channel
self.timestamp = data[0] / 1000.0
self.curr_out_power = data[1] / 1000.0
self.curr_out_amps = data[2] / 100.0
self.curr_inp_volts = data[3] / 1000.0
self.curr_out_volts = data[4] / 1000.0
self.curr_out_capacity = data[5] # mAh sent or taken from batt
self.curr_int_temp = data[6] / 10.0
self.curr_ext_temp = data[7] / 10.0
self.cell_count_with_voltage_values = 0
self.cell_total_voltage = 0
cells = []
for x in range(0, 10):
if cell_v[x] == 1024:
continue
c = CellStatus(x, cell_v[x], cell_b[x], cell_i[x])
cells.append(c)
self.cell_total_voltage += c.voltage
if c.voltage > 0:
self.cell_count_with_voltage_values += 1
self.cells = cells
self.cell_total_ir = footer[0] / 10.0
self.line_intern_resistance = footer[1] / 10.0
self.cycle_count = footer[2]
self.control_status = footer[3]
self.run_status = footer[4]
self.run_error = footer[5]
self.dlg_box_id = footer[6]
# print "channel: {0}, len cels: {3}, cell_total_volt: {1}, curr_out_volt is: {2}".format(channel,
# self.cell_total_voltage,
# self.curr_out_volts,
# self.cell_count_with_voltage_values)
# If the charger isn't doing anything, make sure timestamp shows 00:00
if self.run_status == 0:
self.timestamp = 0
def max_charger_input_voltage(self):
return 0
@serializable
def battery_plugged_in(self):
plugged_in = False
if self.device_id:
max_voltage = 0
# With this, we can work out if the main battery lead is plugged in
if self.device_id is DEVICEID_406_DUO:
max_voltage = 22 # because 3.75 * n gives the max voltage?
elif self.device_id is DEVICEID_308_DUO:
max_voltage = 30
elif self.device_id is DEVICEID_4010_DUO:
max_voltage = 38
# if there are no balance leads plugged in, zero out the curr_out_volts
if self.cell_count_with_voltage_values == 0:
if self.curr_out_volts > max_voltage:
plugged_in = False
else:
plugged_in = True
pcnt_diff = 0
if self.cell_count_with_voltage_values > 0:
pcnt_diff = abs(100.0 - (
(self.curr_out_volts / self.cell_total_voltage) * 100.0))
# if the difference is too great - then we think that curr_out_volts is total lies
if pcnt_diff < 3.0:
plugged_in = True
# if self.channel == 0:
# logger.info("channel: {0}, plugged_in: {1}, cells: {2}, pcnt_diff: {3}, curr_out_volts: {4}, cell_total_voltage: {5}".format(
# self.channel,
# plugged_in,
# self.cell_count_with_voltage_values,
# pcnt_diff,
# self.curr_out_volts,
# self.cell_total_voltage))
return plugged_in
@serializable
def balance_leads_plugged_in(self):
return self.cell_total_voltage > 0
class MultipleSpecEntity(Model):
dim_xs = ListType(IntType, required=True, min_size=1)
problem_names = ListType(StringType, required=True, min_size=1)
method_optimizations = ListType(StringType, required=True, min_size=1)
choose_noises = ListType(BooleanType, required=True, min_size=1)
bounds_domain_xs = ListType(ListType(ModelType(BoundsEntity),
min_size=1,
required=True),
required=True,
min_size=1)
number_points_each_dimensions = ListType(ListType(IntType),
min_size=1,
required=True)
training_names = ListType(StringType, required=False, min_size=1)
bounds_domains = ListType(ListType(ListType(FloatType)),
min_size=1,
required=True)
type_boundss = ListType(ListType(IntType, min_size=1),
min_size=1,
required=True)
n_trainings = ListType(IntType, min_size=1, required=True)
pointss = ListType(ListType(ListType(FloatType)), required=False)
noises = ListType(BooleanType, required=False)
n_sampless = ListType(IntType, required=False)
random_seeds = ListType(IntType, required=True)
parallels = ListType(BooleanType, required=False)
# New parameters due to the GP model
name_models = ListType(StringType, required=False)
type_kernels = ListType(ListType(StringType), required=True, min_size=1)
dimensionss = ListType(ListType(IntType), required=True, min_size=1)
mles = ListType(BooleanType, required=False)
thinnings = ListType(IntType, required=False)
n_burnings = ListType(IntType, required=False)
max_steps_outs = ListType(IntType, required=False)
training_datas = ListType(BaseType())
# New parameters due Bayesian quadrature
x_domains = ListType(ListType(IntType), required=False)
distributions = ListType(StringType, required=False)
# parameters_distributions = ListType(DictType(ListType(FloatType)), required=False)
parameters_distributions = NetlocType(required=False)
minimizes = ListType(BooleanType, required=False)
n_iterationss = ListType(IntType, required=False)
kernel_valuess = ListType(ListType(FloatType), required=False)
mean_values = ListType(ListType(FloatType), required=False)
var_noise_values = ListType(ListType(FloatType), required=False)
caches = ListType(BooleanType, required=False)
debugs = ListType(BooleanType, required=False)
same_correlations = ListType(BooleanType, required=False)
number_points_each_dimension_debugs = ListType(ListType(IntType),
required=False)
monte_carlo_sbos = ListType(BooleanType, required=False)
n_samples_mcs = ListType(IntType, required=False)
n_restarts_mcs = ListType(IntType, required=False)
factr_mcs = ListType(FloatType, required=False)
maxiter_mcs = ListType(IntType, required=False)
use_only_training_pointss = ListType(BooleanType, required=False)
n_restartss = ListType(IntType, required=False)
n_best_restartss = ListType(IntType, required=False)
n_best_restarts_mcs = ListType(IntType, required=False)
n_samples_parameterss = ListType(IntType, required=False)
n_restarts_means = ListType(IntType, required=False)
n_best_restarts_means = ListType(IntType, required=False)
method_opt_mcs = ListType(StringType, required=False)
maxepochs = ListType(IntType, required=False)
n_samples_parameters_means = ListType(IntType, required=False)
maxepoch_means = ListType(IntType, required=False)
threshold_sbos = ListType(FloatType, required=False)
parallel_trainings = ListType(BooleanType, required=False)
optimize_only_posterior_means = ListType(BooleanType, required=False)
start_optimize_posterior_means = ListType(IntType, required=False)
simplex_domain = ListType(FloatType, required=False)
# TODO - Complete all the other needed params
@classmethod
def from_json(cls, specfile):
"""
:param specfile:
:return: MultipleSpecEntity
"""
with open(path.join(MULTIPLESPECS_DIR, specfile)) as specfile:
spec = ujson.load(specfile)
return cls.from_dictionary(spec)
@classmethod
def from_dictionary(cls, spec):
"""
:param spec: dict
:return: MultipleSpecEntity
"""
entry = {}
dim_xs = spec['dim_xs']
choose_noises = spec['choose_noises']
bounds_domain_xs_list = spec['bounds_domain_xs']
bounds_domain_xs = []
for bound in bounds_domain_xs_list:
bounds_domain_xs.append(BoundsEntity.to_bounds_entity(bound))
method_optimizations = spec['method_optimizations']
problem_names = spec['problem_names']
number_points_each_dimensions = spec.get(
'number_points_each_dimensions')
n_specs = len(dim_xs)
training_names = spec.get('training_names')
bounds_domains = spec.get('bounds_domains')
simplex_domain = spec.get('simplex_domain', n_specs * [None])
n_trainings = spec.get('n_trainings', n_specs * [10])
optimize_only_posterior_means = spec.get(
'optimize_only_posterior_means', n_specs * [False])
start_optimize_posterior_means = spec.get(
'start_optimize_posterior_means', n_specs * [0])
type_boundss = spec.get('type_boundss',
[len(bd) * [0] for bd in bounds_domains])
pointss = spec.get('pointss', None)
noises = spec.get('noises', n_specs * [False])
n_sampless = spec.get('n_sampless', n_specs * [0])
random_seeds = spec.get('random_seeds',
n_specs * [DEFAULT_RANDOM_SEED])
parallels = spec.get('parallels', n_specs * [True])
name_models = spec.get('name_models',
n_specs * ['gp_fitting_gaussian'])
type_kernels = spec.get('type_kernels')
dimensionss = spec.get('dimensionss')
mles = spec.get('mles', n_specs * [True])
thinnings = spec.get('thinnings', n_specs * [0])
n_burnings = spec.get('n_burnings', n_specs * [0])
max_steps_outs = spec.get('max_steps_outs', n_specs * [1])
training_datas = spec.get('training_datas')
x_domains = spec.get('x_domains')
distributions = spec.get('distributions')
parameters_distributions = spec.get('parameters_distributions',
n_specs * [[]])
minimizes = spec.get('minimizes', n_specs * [False])
n_iterationss = spec.get('n_iterationss', n_specs * [5])
kernel_valuess = spec.get('kernel_valuess')
mean_values = spec.get('mean_values')
var_noise_values = spec.get('var_noise_values')
caches = spec.get('caches', n_specs * [True])
debugs = spec.get('debugs', n_specs * [False])
same_correlations = spec.get('same_correlations', n_specs * [True])
number_points_each_dimension_debugs = spec.get(
'number_points_each_dimension_debugs')
monte_carlo_sbos = spec.get('monte_carlo_sbos', n_specs * [True])
n_samples_mcs = spec.get('n_samples_mcs', n_specs * [5])
n_restarts_mcs = spec.get('n_restarts_mcs', n_specs * [5])
factr_mcs = spec.get('factr_mcs', n_specs * [1e12])
maxiter_mcs = spec.get('maxiter_mcs', n_specs * [1000])
use_only_training_pointss = spec.get('use_only_training_pointss',
n_specs * [True])
n_restartss = spec.get('n_restartss', n_specs * [10])
n_best_restartss = spec.get('n_best_restartss', n_specs * [0])
n_best_restarts_mcs = spec.get('n_best_restarts_mcs', n_specs * [0])
n_samples_parameterss = spec.get('n_samples_parameterss',
n_specs * [0])
n_restarts_means = spec.get('n_restarts_means', n_specs * [100])
n_best_restarts_means = spec.get('n_best_restarts_means',
n_specs * [10])
method_opt_mcs = spec.get('method_opt_mcs', n_specs * [DOGLEG])
maxepochs = spec.get('maxepochs', n_specs * [10])
n_samples_parameters_means = spec.get('n_samples_parameters_means',
n_specs * [20])
maxepoch_means = spec.get('maxepoch_means', n_specs * [20])
threshold_sbos = spec.get('threshold_sbos', n_specs * [[]])
parallel_trainings = spec.get('parallel_trainings', n_specs * [True])
entry.update({
'caches': caches,
'debugs': debugs,
'same_correlations': same_correlations,
'number_points_each_dimension_debugs':
number_points_each_dimension_debugs,
'monte_carlo_sbos': monte_carlo_sbos,
'n_samples_mcs': n_samples_mcs,
'n_restarts_mcs': n_restarts_mcs,
'factr_mcs': factr_mcs,
'maxiter_mcs': maxiter_mcs,
'use_only_training_pointss': use_only_training_pointss,
'n_restartss': n_restartss,
'n_best_restartss': n_best_restartss,
'n_best_restarts_mcs': n_best_restarts_mcs,
'n_samples_parameterss': n_samples_parameterss,
'n_restarts_means': n_restarts_means,
'n_best_restarts_means': n_best_restarts_means,
'method_opt_mcs': method_opt_mcs,
'maxepochs': maxepochs,
'n_samples_parameters_means': n_samples_parameters_means,
'maxepoch_means': maxepoch_means,
'problem_names': problem_names,
'dim_xs': dim_xs,
'choose_noises': choose_noises,
'bounds_domain_xs': bounds_domain_xs,
'number_points_each_dimensions': number_points_each_dimensions,
'method_optimizations': method_optimizations,
'training_names': training_names,
'bounds_domains': bounds_domains,
'n_trainings': n_trainings,
'pointss': pointss,
'noises': noises,
'n_sampless': n_sampless,
'random_seeds': random_seeds,
'parallels': parallels,
'type_boundss': type_boundss,
'name_models': name_models,
'type_kernels': type_kernels,
'dimensionss': dimensionss,
'mles': mles,
'thinnings': thinnings,
'n_burnings': n_burnings,
'max_steps_outs': max_steps_outs,
'training_datas': training_datas,
'distributions': distributions,
'x_domains': x_domains,
'parameters_distributions': parameters_distributions,
'n_iterationss': n_iterationss,
'minimizes': minimizes,
'kernel_valuess': kernel_valuess,
'mean_values': mean_values,
'var_noise_values': var_noise_values,
'threshold_sbos': threshold_sbos,
'parallel_trainings': parallel_trainings,
'optimize_only_posterior_means': optimize_only_posterior_means,
'start_optimize_posterior_means': start_optimize_posterior_means,
'simplex_domain': simplex_domain,
})
return cls(entry)
class RunSpecEntity(Model):
problem_name = StringType(required=True)
method_optimization = StringType(required=True)
dim_x = IntType(required=True)
choose_noise = BooleanType(
required=True) #I think that we should remove it
bounds_domain_x = ListType(ModelType(BoundsEntity),
min_size=1,
required=True)
number_points_each_dimension = ListType(IntType) # Only for x
training_name = StringType(required=True)
bounds_domain = ListType(ListType(FloatType), min_size=1, required=True)
type_bounds = ListType(IntType, required=True)
n_training = IntType(required=True)
points = ListType(ListType(FloatType), required=False)
noise = BooleanType(required=False)
n_samples = IntType(required=False)
random_seed = IntType(required=True)
parallel = BooleanType(required=False)
simplex_domain = IntType(required=False)
name_model = StringType(required=False)
type_kernel = ListType(StringType, required=True)
dimensions = ListType(IntType, required=True)
mle = BooleanType(required=False)
thinning = IntType(required=False)
n_burning = IntType(required=False)
max_steps_out = IntType(required=False)
training_data = BaseType()
x_domain = ListType(IntType, required=False)
distribution = StringType(required=False)
parameters_distribution = NetlocType(required=False)
minimize = BooleanType(required=True)
n_iterations = IntType(required=True)
kernel_values = ListType(FloatType)
mean_value = ListType(FloatType)
var_noise_value = ListType(FloatType)
same_correlation = BooleanType(
required=False) # Used only for the task kernel
cache = BooleanType(required=False)
debug = BooleanType(required=False)
number_points_each_dimension_debug = ListType(IntType) #Used to debug.
# Parameter to estimate sbo by MC
monte_carlo_sbo = BooleanType(required=False)
n_samples_mc = IntType(required=False)
n_restarts_mc = IntType(required=False)
factr_mc = FloatType(required=False)
maxiter_mc = IntType(required=False)
n_best_restarts_mc = IntType(required=False)
#Acquistion function parameters
n_restarts = IntType(required=False)
n_best_restarts = IntType(required=False)
# We use only training points when reading GP model from cache
use_only_training_points = BooleanType(required=False)
# Computes everything using samples of the parameters if n_samples_parameters > 0
n_samples_parameters = IntType(required=False)
n_restarts_mean = IntType(required=False)
n_best_restarts_mean = IntType(required=False)
method_opt_mc = StringType(required=False)
maxepoch = IntType(required=False)
n_samples_parameters_mean = IntType(required=False)
maxepoch_mean = IntType(required=False)
threshold_sbo = FloatType(required=False)
parallel_training = BooleanType(required=False)
optimize_only_posterior_mean = BooleanType(required=False)
start_optimize_posterior_mean = IntType(required=False)
@classmethod
def from_json(cls, specfile):
"""
:param specfile: (str)
:return: RunSpecEntity
"""
with open(path.join(SPECS_DIR, specfile)) as specfile:
spec = ujson.load(specfile)
return cls.from_dictionary(spec)
@classmethod
def from_dictionary(cls, spec):
"""
Create from dict
:param spec: dict
:return: RunSpecEntity
"""
entry = {}
dim_x = int(spec['dim_x'])
choose_noise = spec.get('choose_noise', True)
bounds_domain_x = spec['bounds_domain_x']
bounds_domain_x = BoundsEntity.to_bounds_entity(bounds_domain_x)
method_optimization = spec.get('method_optimization', 'SBO')
problem_name = spec['problem_name']
number_points_each_dimension = spec.get('number_points_each_dimension')
training_name = spec.get('training_name')
bounds_domain = spec.get('bounds_domain')
n_training = spec.get('n_training', 10)
type_bounds = spec.get('type_bounds', len(bounds_domain) * [0])
points = spec.get('points', None)
noise = spec.get('noise', False)
n_samples = spec.get('n_samples', 0)
random_seed = spec.get('random_seed', DEFAULT_RANDOM_SEED)
parallel = spec.get('parallel', True)
name_model = spec.get('name_model', 'gp_fitting_gaussian')
type_kernel = spec.get('type_kernel')
dimensions = spec.get('dimensions')
mle = spec.get('mle', True)
thinning = spec.get('thinning', 0)
n_burning = spec.get('n_burning', 0)
max_steps_out = spec.get('max_steps_out', 1)
training_data = spec.get('training_data')
x_domain = spec.get('x_domain')
distribution = spec.get('distribution')
parameters_distribution = spec.get('parameters_distribution')
minimize = spec.get('minimize', False)
n_iterations = spec.get('n_iterations', 5)
kernel_values = spec.get('kernel_values')
mean_value = spec.get('mean_value')
var_noise_value = spec.get('var_noise_value')
cache = spec.get('cache', True)
debug = spec.get('debug', False)
optimize_only_posterior_mean = spec.get('optimize_only_posterior_mean',
False)
same_correlation = spec.get('same_correlation', False)
number_points_each_dimension_debug = spec.get(
'number_points_each_dimension_debug')
monte_carlo_sbo = spec.get('monte_carlo_sbo', False)
n_samples_mc = spec.get('n_samples_mc', 100)
n_restarts_mc = spec.get('n_restarts_mc', 100)
factr_mc = spec.get('factr_mc', 1e12)
maxiter_mc = spec.get('maxiter_mc', 1000)
use_only_training_points = spec.get('use_only_training_points', True)
n_restarts = spec.get('n_restarts', 10)
n_best_restarts = spec.get('n_best_restarts', 10)
n_best_restarts_mc = spec.get('n_best_restarts_mc', 10)
n_samples_parameters = spec.get('n_samples_parameters', 0)
n_restarts_mean = spec.get('n_restarts_mean', 1000)
n_best_restarts_mean = spec.get('n_best_restarts_mean', 100)
method_opt_mc = spec.get('method_opt_mc', LBFGS_NAME)
maxepoch = spec.get('maxepoch', 10)
n_samples_parameters_mean = spec.get('n_samples_parameters_mean', 15)
maxepoch_mean = spec.get('maxepoch_mean', 15)
threshold_sbo = spec.get('threshold_sbo')
parallel_training = spec.get('parallel_training', True)
start_optimize_posterior_mean = spec.get(
'start_optimize_posterior_mean', 0)
entry.update({
'problem_name':
problem_name,
'dim_x':
dim_x,
'choose_noise':
choose_noise,
'bounds_domain_x':
bounds_domain_x,
'number_points_each_dimension':
number_points_each_dimension,
'method_optimization':
method_optimization,
'training_name':
training_name,
'bounds_domain':
bounds_domain,
'n_training':
n_training,
'points':
points,
'noise':
noise,
'n_samples':
n_samples,
'random_seed':
random_seed,
'parallel':
parallel,
'type_bounds':
type_bounds,
'name_model':
name_model,
'type_kernel':
type_kernel,
'dimensions':
dimensions,
'mle':
mle,
'optimize_only_posterior_mean':
optimize_only_posterior_mean,
'thinning':
thinning,
'n_burning':
n_burning,
'max_steps_out':
max_steps_out,
'training_data':
training_data,
'x_domain':
x_domain,
'distribution':
distribution,
'parameters_distribution':
parameters_distribution,
'minimize':
minimize,
'n_iterations':
n_iterations,
'kernel_values':
kernel_values,
'mean_value':
mean_value,
'var_noise_value':
var_noise_value,
'cache':
cache,
'debug':
debug,
'same_correlation':
same_correlation,
'number_points_each_dimension_debug':
number_points_each_dimension_debug,
'monte_carlo_sbo':
monte_carlo_sbo,
'n_samples_mc':
n_samples_mc,
'n_restarts_mc':
n_restarts_mc,
'factr_mc':
factr_mc,
'maxiter_mc':
maxiter_mc,
'use_only_training_points':
use_only_training_points,
'n_restarts':
n_restarts,
'n_best_restarts_mc':
n_best_restarts_mc,
'n_best_restarts':
n_best_restarts,
'n_samples_parameters':
n_samples_parameters,
'n_best_restarts_mean':
n_best_restarts_mean,
'n_restarts_mean':
n_restarts_mean,
'method_opt_mc':
method_opt_mc,
'maxepoch':
maxepoch,
'n_samples_parameters_mean':
n_samples_parameters_mean,
'maxepoch_mean':
maxepoch_mean,
'threshold_sbo':
threshold_sbo,
'parallel_training':
parallel_training,
'start_optimize_posterior_mean':
start_optimize_posterior_mean,
})
return cls(entry)
class BaseLot(BaseResourceItem):
class Options:
roles = {
'create': create_role,
'plain': plain_role,
'edit': edit_role,
'view': view_role,
'listing': listing_role,
'default': schematics_default_role,
'Administrator': Administrator_role,
# Draft role
'draft': view_role,
'edit_draft': whitelist('status'),
# Pending role
'pending': view_role,
'edit_pending': edit_role,
# Pending.deleted role
'pending.deleted': view_role,
'edit_pending.deleted': whitelist(),
# Deleted role
'deleted': view_role,
'edit_deleted': whitelist(),
# Verification role
'verification': view_role,
'edit_verification': whitelist(),
# Recomposed role
'recomposed': view_role,
'edit_recomposed': whitelist(),
# Active.salable role
'active.salable': view_role,
'edit_active.salable': whitelist('status'),
# pending.dissolution role
'pending.dissolution': view_role,
'edit_pending.dissolution': whitelist('status'),
# Dissolved role
'dissolved': view_role,
'edit_dissolved': whitelist(),
# Active.awaiting role
'active.awaiting': view_role,
'edit_active.awaiting': whitelist(),
# Active.auction role
'active.auction': view_role,
'edit_active.auction': edit_role,
# pending.sold role
'pending.sold': view_role,
'edit.pending.sold': whitelist(),
# Sold role
'sold': view_role,
'concierge': whitelist('status'),
'convoy': whitelist('status', 'auctions'),
'extract_credentials': whitelist('owner', 'id')
}
# lotID should always be the same as the OCID. It is included to make the flattened data structure more convenient.
lotID = StringType()
date = IsoDateTimeType()
title = StringType(required=True)
title_en = StringType()
title_ru = StringType()
description = StringType()
description_en = StringType()
description_ru = StringType()
lotCustodian = ModelType(Organization, required=True)
documents = ListType(ModelType(Document), default=list()) # All documents and attachments related to the lot.
_internal_type = None
if SANDBOX_MODE:
sandboxParameters = StringType()
def __local_roles__(self):
roles = dict([('{}_{}'.format(self.owner, self.owner_token), 'lot_owner')])
return roles
def get_role(self):
root = self.__parent__
request = root.request
if request.authenticated_role == 'Administrator':
role = 'Administrator'
elif request.authenticated_role == 'concierge':
role = 'concierge'
elif request.authenticated_role == 'convoy':
role = 'convoy'
else:
role = 'edit_{}'.format(request.context.status)
return role
def __acl__(self):
acl = [
(Allow, '{}_{}'.format(self.owner, self.owner_token), 'edit_lot'),
(Allow, '{}_{}'.format(self.owner, self.owner_token), 'upload_lot_documents'),
]
return acl
def validate_sandboxParameters(self, *args, **kw):
if self.mode and self.mode == 'test' and self.sandboxParameters and self.sandboxParameters != '':
raise ValidationError(u"procurementMethodDetails should be used with mode test")
class PostValidator(Model):
title = StringType(max_length=255, min_length=5, required=True)
markdown_body = StringType(required=True)
tags = ListType(StringType)
class Contract(BaseContract):
items = ListType(ModelType(Item))
class Tender(SchematicsDocument, Model):
"""Data regarding tender process - publicly inviting prospective contractors
to submit bids for evaluation and selecting a winner or winners.
"""
class Options:
roles = {
'plain': plain_role,
'create': create_role,
'edit': edit_role,
'edit_draft': draft_role,
'edit_active': edit_role,
'edit_active.awarded': whitelist(),
'edit_complete': whitelist(),
'edit_unsuccessful': whitelist(),
'edit_cancelled': whitelist(),
'view': view_role,
'listing': listing_role,
'draft': enquiries_role,
'active': enquiries_role,
'active.awarded': view_role,
'complete': view_role,
'unsuccessful': view_role,
'cancelled': view_role,
'Administrator': Administrator_role,
'chronograph': chronograph_role, # remove after chronograph fix
'chronograph_view':
chronograph_view_role, # remove after chronograph fix
'default': schematics_default_role,
'contracting': whitelist('doc_id', 'owner'),
}
title = StringType(required=True)
title_en = StringType()
title_ru = StringType()
description = StringType()
description_en = StringType()
description_ru = StringType()
date = IsoDateTimeType()
tenderID = StringType(
) # TenderID should always be the same as the OCID. It is included to make the flattened data structure more convenient.
items = ListType(
ModelType(Item),
required=True,
min_size=1,
validators=[validate_cpv_group, validate_items_uniq]
) # The goods and services to be purchased, broken into line items wherever possible. Items should not be duplicated, but a quantity of 2 specified instead.
value = ModelType(
Value, required=True) # The total estimated value of the procurement.
procurementMethod = StringType(
choices=['open', 'selective', 'limited'], default='limited'
) # Specify tendering method as per GPA definitions of Open, Selective, Limited (http://www.wto.org/english/docs_e/legal_e/rev-gpr-94_01_e.htm)
procurementMethodRationale = StringType(
) # Justification of procurement method, especially in the case of Limited tendering.
procurementMethodRationale_en = StringType()
procurementMethodRationale_ru = StringType()
procurementMethodType = StringType(default="reporting")
procuringEntity = ModelType(
ProcuringEntity, required=True
) # The entity managing the procurement, which may be different from the buyer who is paying / using the items being procured.
documents = ListType(
ModelType(Document),
default=list()) # All documents and attachments related to the tender.
awards = ListType(ModelType(Award), default=list())
contracts = ListType(ModelType(Contract), default=list())
revisions = ListType(ModelType(Revision), default=list())
status = StringType(
choices=['draft', 'active', 'complete', 'cancelled', 'unsuccessful'],
default='active')
mode = StringType(choices=['test'])
cancellations = ListType(ModelType(Cancellation), default=list())
_attachments = DictType(DictType(BaseType),
default=dict()) # couchdb attachments
dateModified = IsoDateTimeType()
owner_token = StringType()
owner = StringType()
if SANDBOX_MODE:
procurementMethodDetails = StringType()
create_accreditation = 1
edit_accreditation = 2
procuring_entity_kinds = ['general', 'special', 'defense', 'other']
block_complaint_status = OpenUATender.block_complaint_status
__parent__ = None
__name__ = ''
def __local_roles__(self):
return dict([('{}_{}'.format(self.owner,
self.owner_token), 'tender_owner')])
def get_role(self):
root = self.__parent__
request = root.request
if request.authenticated_role == 'Administrator':
role = 'Administrator'
elif request.authenticated_role == 'chronograph':
role = 'chronograph'
elif request.authenticated_role == 'contracting':
role = 'contracting'
else:
role = 'edit_{}'.format(request.context.status)
return role
def __acl__(self):
return [
(Allow, 'g:brokers', 'create_award_complaint'),
(Allow, '{}_{}'.format(self.owner,
self.owner_token), 'edit_tender'),
(Allow, '{}_{}'.format(self.owner, self.owner_token),
'upload_tender_documents'),
(Allow, '{}_{}'.format(self.owner,
self.owner_token), 'edit_complaint'),
]
def initialize(self):
self.date = get_now()
def validate_procurementMethodDetails(self, *args, **kw):
if self.mode and self.mode == 'test' and self.procurementMethodDetails and self.procurementMethodDetails != '':
raise ValidationError(
u"procurementMethodDetails should be used with mode test")
def __repr__(self):
return '<%s:%r@%r>' % (type(self).__name__, self.id, self.rev)
@serializable(serialized_name='id')
def doc_id(self):
"""A property that is serialized by schematics exports."""
return self._id
def import_data(self, raw_data, **kw):
"""
Converts and imports the raw data into the instance of the model
according to the fields in the model.
:param raw_data:
The data to be imported.
"""
data = self.convert(raw_data, **kw)
del_keys = [
k for k in data.keys()
if data[k] == self.__class__.fields[k].default
or data[k] == getattr(self, k)
]
for k in del_keys:
del data[k]
self._data.update(data)
return self
class PresetIndex(Model):
indexes = ListType(IntType(min_value=0, max_value=255),
required=True,
min_size=0,
max_size=63,
default=[])
@staticmethod
def modbus(count=None, indexes=None):
if count is not None and indexes is not None:
pi = PresetIndex()
pi.set_from_modbus_data(count, indexes)
return pi
return None
def preset_exists_at_index(self, index):
return self.indexes[index] != 255
def index_of_preset_with_memory_slot_number(self, memory_slot_number):
for index in self.range_of_presets():
if self.indexes[index] == memory_slot_number:
return index
return None
@property
def next_available_memory_slot(self):
for slot_number in range(0, 64):
# does any index take this?
memory_slot_number = self.index_of_preset_with_memory_slot_number(
slot_number)
if memory_slot_number is None:
return slot_number
return None
def add_to_index(self, new_preset):
next_memory_slot = self.next_available_memory_slot
if next_memory_slot is None:
return None
next_index = self.first_empty_index_position
if next_index is None:
return None
new_preset.memory_slot = next_memory_slot
new_preset.use_flag = 0x55aa # Used
self.indexes[next_index] = next_memory_slot
return new_preset
def range_of_presets(self):
if self.first_empty_index_position is not None:
return range(0, self.first_empty_index_position)
return range(0, 64)
def is_valid_index(self, index):
return index in self.range_of_presets()
def swap(self, index_one, index_two):
if not self.is_valid_index(index_one):
message = "Index one {0} is invalid".format(index_one)
raise ObjectNotFoundException(message)
if not self.is_valid_index(index_two):
message = "Index two {0} is invalid".format(index_two)
raise ObjectNotFoundException(message)
value1 = self.indexes[index_one]
value2 = self.indexes[index_two]
self.indexes[index_one] = value2
self.indexes[index_two] = value1
@property
def number_of_presets(self):
first_empty_slot = self.first_empty_index_position
if not first_empty_slot:
return len(self.indexes)
return first_empty_slot
@property
def first_empty_index_position(self):
first_empty = None
for i, index in enumerate(self.indexes):
if index == 255:
first_empty = i
break
return first_empty
def delete_item_at_index(self, index, validate=True):
if self.is_valid_index(index):
del self.indexes[index]
if validate:
self._validate_and_fix_index_list()
def _validate_and_fix_index_list(self):
# Add 255's on the end until we have 64 of them
# this also clears out any other 0's and other stuff.
if self.first_empty_index_position is not None:
for index in range(self.first_empty_index_position, 64):
if index < len(self.indexes):
self.indexes[index] = 255
else:
self.indexes.append(255)
# Indexes must be unique
set_of_seen = {}
current_position = 0
while current_position < self.number_of_presets - 1:
index_at_this_position = self.indexes[current_position]
if set_of_seen.get(index_at_this_position, None):
# if we've already seen it, we should delete this one
self.delete_item_at_index(current_position, validate=False)
# Don't inc counter, as we've deleted one
else:
set_of_seen[index_at_this_position] = 1
current_position += 1
def set_indexes(self, new_value):
self.indexes = copy.deepcopy(new_value)
self._validate_and_fix_index_list()
def set_from_modbus_data(self, count, indexes):
self.set_indexes(indexes)
@serializable
def count(self):
# The number of non-255 index positions
return self.number_of_presets
def to_modbus_data(self):
v1 = [
self.number_of_presets,
]
v1.extend(self.indexes[:32])
v2 = self.indexes[32:]
return v1, v2
class ReplaceExecutionReport(BaseExecutionReport):
instruction_reports = ListType(ModelType(ReplaceInstructionReport))
class Group(Model):
users = ListType(ModelType(User))
class AccountStatementReport(BetfairModel):
account_statement = ListType(ModelType(StatementItem))
more_available = BooleanType()
def test_compound_fields():
comments = ListType(ListType, compound_field=StringType)
assert isinstance(comments.field, ListType)
class User(Model):
ids = ListType(IntType)
class TaskDTOs(Model):
""" Describes an array of Task DTOs"""
tasks = ListType(ModelType(TaskDTO))
class PlayerInfo(Model):
categories = ListType(ModelType(CategoryStatsInfo))
class StartingPrices(BetfairModel):
near_price = FloatType()
far_price = FloatType()
back_stake_taken = ListType(ModelType(PriceSize))
lay_liability_taken = ListType(ModelType(PriceSize))
actual_SP = FloatType()
class PlayerInfo(Model):
following = ListType(StringType)
class ExchangePrices(BetfairModel):
available_to_back = ListType(ModelType(PriceSize))
available_to_lay = ListType(ModelType(PriceSize))
traded_volume = ListType(ModelType(PriceSize))
class QuestionPack(Model):
id = StringType()
questions = ListType(ModelType(Question))
class MarketProfitAndLoss(BetfairModel):
market_id = StringType()
commission_applied = FloatType()
profit_and_losses = ListType(ModelType(RunnerProfitAndLoss))
class PriceProjection(BetfairModel):
price_data = ListType(EnumType(constants.PriceData))
ex_best_offers_overrides = ModelType(ExBestOffersOverrides)
virtualise = BooleanType()
rollover_stakes = BooleanType()
class ClearedOrderSummaryReport(BetfairModel):
cleared_orders = ListType(ModelType(ClearedOrderSummary), required=True)
more_available = BooleanType(required=True)
def __init__(self):
ListType.__init__(self, IntType(), min_size=2, max_size=2)
class PostParams(SchemaModel):
role_ids = ListType(IntType(), required=True)