def add(self, device_id, device_class, pool, miner, algo, region, hashrate,
gross_mbtc, net_mbtc, device_watts, total_watts):
if not os.path.exists(self.log_file):
with open(self.log_file, "w") as f:
f.write(
"timestamp,device_id,device_class,pool,miner,algorithm,region,hashrate,gross_mbtc,net_mbtc,device_watts,total_watts\n"
)
with open(self.log_file, "a+") as f:
if net_mbtc == 0:
net_s = ""
else:
net_s = "%.2f" % (net_mbtc)
if '_' in algo:
hashrate_s = Units().hashrate_str(hashrate)
else:
hashrate_s = Units().hashrate_str(hashrate[0])
f.write("%s,%d,%s,%s,%s,%s,%s,%s,%.2f,%s,%.2f,%.2f\n" %
(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
device_id, device_class, pool, miner, algo, region,
hashrate_s, gross_mbtc, net_s, device_watts, total_watts))
if os.path.getsize(self.log_file) >= (
Config().get('stats.max_size_mb') * 1024 * 1024):
self.rotate_logs(self.log_file)
def __init__(self, sim, config):
self.sim = sim
self.config = config # Note: this is passed in, and is located at sim.pod.forces.aero in the config file
# @see http://www.softschools.com/formulas/physics/air_resistance_formula/85/
self.air_resistance_k = Units.SI(
self.config.air_density) * self.config.drag_coefficient * Units.SI(
self.config.drag_area) / 2
def test_str_and_repr(self):
"""
String representations correctly created.
"""
num = Units(value=0, units='ok')
expected = '0ok'
actual = str(num)
self.assertEqual(expected, actual)
expected = "Units(value=0, units='ok')"
actual = num.__repr__()
self.assertEqual(expected, actual)
exec('expected = ' + num.__repr__())
self.assertEqual(expected, num)
def get_power_draw_average_time_periods(self):
data = {}
for seconds in Config().get('live_data.power_draw_averages'):
data[seconds] = Units().seconds_to_string(seconds)
return data
def __init__(self, sim, config):
PollingSensor.__init__(self, sim, config)
self.logger = logging.getLogger("Accelerometer")
self.logger.info("Initializing Accelerometer {}".format(self.config.id))
self.data = namedtuple('AccelerometerData', ['t_usec', 'raw_x', 'raw_y', 'raw_z', 'real_x', 'real_y', 'real_z'])
# Quantities for converting to raw values
raw_min = self.config.raw_min
raw_max = self.config.raw_max
real_min = Units.SI(self.config.real_min)
real_max = Units.SI(self.config.real_max)
self.sensor_input_range = (real_min, real_max)
self.sensor_output_range = (raw_min, raw_max)
def get_miner_hashrate_for_algorithm_on_device(self, miner_name, algorithm, device_class):
calibrated = self.get('%s.%s.%s' % (device_class, miner_name, algorithm))
if calibrated:
return Units().hashrate_str(calibrated['hashrate'])
return " "
class JoyStick:
def __init__(self):
self.converter = Units()
def moveUp(self, amount):
return "PR ,{0}".format(self.converter.El_to_encoder(amount,False),)
def moveDown(self, amount):
return "PR ,{0}".format(-1*self.converter.El_to_encoder(amount,False),)
def moveRight(self, amount):
return "PR {0},".format(self.converter.Az_to_encoder(amount,False),)
def moveLeft(self, amount):
return "PR {0},".format(-1*self.converter.Az_to_encoder(amount,False),)
def moveAbs(self, Az, El):
return "PA {0},{1}".format(self.converter.Az_to_encoder(Az), self.converter.El_to_encoder(El))
def calibrate(self, wanted_Az, wanted_El, current_Az, current_El):
self.converter.setOffset(wanted_Az, wanted_El, current_Az, current_El)
def __init__(self, sim, config):
self.sim = sim
self.config = config
self.name = "F_lateral_stability"
self.step_listeners = []
self.data = namedtuple(self.name, ['x', 'y', 'z'])
self.damping_coefficient = Units.SI(self.config.damping_coefficient)
def realnew(klass, h, M, L, K): self = object.__new__(klass) self.M = M self.L = L self.K = K self.h = h self.units = Units(h) self.U = self.units self.DH = self.units.C / self.units.H0 self.tH = 1.0 / self.units.H0 self._cosmology = _cosmology.Cosmology(self.M, self.K, self.L, self.h) return self
def check_hashrate(self):
hashrates = Calibration().get_hashrates(self)
if len(self.algos) == 0:
return
miner = self.algos[0]['miner']
algo = self.algos[0]['algo']
if not miner in hashrates.keys() or not algo in hashrates[miner].keys(
):
self.log(
'warning', 'running uncalibrated algorithm %s with miner %s' %
(algo, miner))
return
if self.algos[0]['algo'] in Config().get('algorithms.single'):
baseline = float(hashrates[miner][algo]['hashrate'])
else:
baseline = float(hashrates[miner][algo]['hashrate'][0])
threshold_pc_value = (
baseline / 100) * Config().get('hashrate_alert_threshold_percent')
hr_s = Units().hashrate_str(self.algos[0]['hashrate'][0])
bl_s = Units().hashrate_str(baseline)
if self.algos[0]['hashrate'][0] < baseline and (
baseline - self.algos[0]['hashrate'][0]) >= threshold_pc_value:
self.log(
'warning', 'hashrate %d%% below calibrated rate [%s < %s]' %
(Config().get('hashrate_alert_threshold_percent'), hr_s, bl_s))
if self.algos[0]['hashrate'][0] > baseline and (
self.algos[0]['hashrate'][0] - baseline) >= threshold_pc_value:
self.log(
'warning', 'hashrate %d%% above calibrated rate [%s > %s]' %
(Config().get('hashrate_alert_threshold_percent'), hr_s, bl_s))
def __init__(self, sim, config):
PollingSensor.__init__(self, sim, config)
self.logger = logging.getLogger("LaserOptoSensor")
self.logger.info("Initializing Laser Opto Sensor {}".format(
self.config.id))
# Get model info (min/max, raw values, etc.)
self.model = self.sim.config.sensors.models[self.config.model]
# Set ranges for mapping real to raw and vice versa
self.real_range = [
Units.SI(self.model.real_min),
Units.SI(self.model.real_max)
]
self.raw_range = [self.model.raw_min, self.model.raw_max]
# Get the height offset for this sensor?
self.he_height_offset = Units.SI(self.config.he_height_offset)
self.measurement_offset = self.he_height_offset - Units.SI(
self.model.internal_offset)
# Data types
self.data = namedtuple('LaserOptoSensorData', ('t_usec', 'height'))
def parseFile(self, file):
self.results = {}
file = open(file,"r")
try:
for entry in file:
entry = entry.strip()
cols = entry.split("^")
id = cols[0].strip("~")
unit = cols[1].strip("~")
unit = Units.getUnitForLabel(unit)
label = cols[3].strip("~")
val = Nutrient(label, unit, id)
self.results[id] = val
except:
return None
return self.results
def __init__(self, sim, config):
Sensor.__init__(self, sim, config)
self.logger = logging.getLogger("PollingSensor")
# Configuration
# Grab the fixed timestep from the sim. If we wanted to use a variable timestep we would need to do the next calculations in the lerp function
self.sampling_rate = Units.SI(self.config.sampling_rate) # Hz
# @see self._add_noise() and numpy.random.normal
self.noise_center = self.config.noise.center or 0.0
self.noise_scale = self.config.noise.scale or 0.0
# Volatile
#self.buffer = RingBuffer(config.buffer_size, config.dtype) # @todo: How to specify dtype in configuration? There should be a string rep of dtype I think
self.buffer = None # Note: we depend on our listeners to handle any buffering/saving/sending of sensor values. Buffer is cleard after each step.
self.sample_times = None
self.next_start = 0.0
self.step_lerp_pcts = None # Set during step
def new_controller(self):
'''
Inits controller. Usage: New Simulation
'''
self.universe = Universe()
# Holds the startpoint universe
self.copy_universe = None
# Folder where to save files and Default file name
self.file_name = "test.xml"
self.folder = "files/"
self.filePath = None
self.units = Units()
self.pref_dict = {}
self.timer_time = 10
self.steps_between_paint = 1
def set(self, **kwargs):
"""
set() [public]
Purpose: Sets station plot variables (such as temperature, dewpoint, etc.)
Parameters: Key-value pairs (station variable to value, e.g. tmpf=50.
Returns: [nothing]
"""
re_value_string = re.compile("^(\\-?[\\d]+\\.?[\\d]*)[\\s]*([\\w\\d\\- ]+)$")
for attr, value in kwargs.iteritems():
if attr in self._valid_attrs:
re_value_match = re_value_string.match(value)
if re_value_match is not None:
obs_value, obs_units = re_value_match.groups()
obs_value = Units.convert(float(obs_value), obs_units, HootPyStationPlot._internal_units[attr])
setattr(self, "_%s" % attr, obs_value)
else:
setattr(self, "_%s" % attr, value)
else:
fatalError("Attribute %s is not a valid attribute for this type of station plot." % attr)
return
def create_step_samples(self, dt_usec):
# Pod acceleration (x)
pod_start_accel = self.sim.pod.last_acceleration
pod_end_accel = self.sim.pod.acceleration
# Create the samples (lerp between the times)
sample_data = self._lerp(pod_start_accel, pod_end_accel)
sample_times = sample_times = self._get_sample_times(dt_usec)
# @todo: apply a rotation matrix to simulate the actual
# NOTE: Accel is mounted such that it is rotated 90 degrees in the horizontal plane such that:
# +y accel = +x pod reference frame, +x accel = -y pod reference frame
# ^ This is the format for the data.
# @todo: move this to config somehow (tbd)
# Map real values to sample values
samples = []
for i, t in enumerate(sample_times):
real_x = 0
real_y = sample_data[i]
real_z = 9.81 # Accel due to gravity
# @todo: Apply a rotation matrix?
# Map
xyz = np.array((real_x, real_y, real_z))
# Add some noise (in G's)
xyz += np.random.normal(self.noise_center, Units.SI(self.noise_scale), 3)
xyz = np.interp(xyz, self.sensor_input_range, self.sensor_output_range)
xyz = xyz.astype(int)
samples.append(self.data(t, xyz[0], xyz[1], xyz[2], real_x, real_y, real_z))
#samples += self._get_gaussian_noise(samples, self.noise_center, self.noise_scale)
return samples
def __init__(self, sim, config):
self.sim = sim
self.config = config
# Physical
# NOTE: @see diagram in http://confluence.rloop.org/display/SD/2.+Determine+Pod+Kinematics
# For the track/tube reference frame, x=0 at the start of the track, and x = length at the end.
# For the pod, x=0 is ahead and below the pod, with x+ being toward the read of the pod.
self.length = Units.SI(
config.length
) # meters -- Length from pod start position to end of track
# Reference for the reflective stripes
self.reflective_strip_width = Units.SI(
config.reflective_strip_width) # meters (4 inches)
self.reflective_pattern_interval = Units.SI(
config.reflective_pattern_interval) # meters (100 feet)
self.reflective_pattern_spacing = Units.SI(
config.reflective_pattern_spacing
) # meters -- distance between stripes in a pattern
self.track_gap_width = Units.SI(
config.track_gap_width
) # Width of the gap in the subtrack in meters (probably worst scenario, more realistic 1-2 mm)
self.track_gap_interval = Units.SI(
config.track_gap_interval
) # Interval between the gaps in the subtrack in meters (Length of the aluminium plate)
# Initialize
self.reflective_strips = None # Will be a numpy array after initialization
self._init_reflective_strips(config.enable_strip_patterns)
self.reflective_strip_ends = self.reflective_strips + self.reflective_strip_width # Pre-calc the ends for use in LaserContrastSensor
self.track_gaps = []
self._init_track_gaps()
def handle_device_update(self, device):
if (datetime.datetime.now() - device.changed).seconds < (Config().get('calibration.update_calibration_data_after_mins') * 60):
return
for algo in device.algos:
# Max update once a minute
if algo['calibration_updated_at'] and \
(time.time() - algo['calibration_updated_at']) < 60:
continue
if len(algo['hashrate_readings']) >0:
readings_a = []
readings_b = []
for reading in algo['hashrate_readings']:
readings_a.append(reading[0])
readings_b.append(reading[1])
if '_' in algo['algo']:
variance, n = self.get_variance(readings_a, readings_b)
else:
variance, n = self.get_variance(readings_a)
if self.is_stable(variance, n):
calibrated_data = self.get('%s.%s.%s' % (device.dclass, algo['miner'], algo['algo']))
if '_' in algo['algo']:
nominal = [self.get_nominal_hashrate_from_range(readings_a), self.get_nominal_hashrate_from_range(readings_b)]
else:
nominal = self.get_nominal_hashrate_from_range(readings_a)
if self.within_update_threshold(nominal, calibrated_data['hashrate']) and \
self.hashrate_is_visibly_different(nominal, calibrated_data['hashrate']):
device.log('info', '%s[%s]: new calibrated rate: %s' % (algo['algo'], algo['miner'], Units().hashrate_str(nominal)))
calibrated_data['hashrate'] = nominal
self.update_calibration_data(device.dclass, algo['miner'], algo['algo'], nominal, calibrated_data['power_limit'])
self.set("%s.%s.%s" % (device.dclass, algo['miner'], algo['algo']), calibrated_data)
algo['calibration_updated_at'] = time.time()
def __init__(self, sim, config):
ForceExerter.__init__(self, sim, config)
self.name = 'F_lateral_stability'
self.damping_coefficient = Units.SI(self.config.damping_coefficient)
class controller(object):
'''
Controlls universe
'''
def __init__(self):
self.new_controller()
def new_controller(self):
'''
Inits controller. Usage: New Simulation
'''
self.universe = Universe()
# Holds the startpoint universe
self.copy_universe = None
# Folder where to save files and Default file name
self.file_name = "test.xml"
self.folder = "files/"
self.filePath = None
self.units = Units()
self.pref_dict = {}
self.timer_time = 10
self.steps_between_paint = 1
def create_object(self, name, mass, radius):
'''
Creates Object and adds it to the current universe
'''
uni_object = Uni_Object(name, mass, radius)
self.universe.add_object(uni_object)
return uni_object
def delete_force(self, uni_object, delete_force_vector):
'''
delete force from object
'''
for i, force_vector in enumerate(uni_object.force_vector_list):
if delete_force_vector == force_vector:
uni_object.force_vector_list.pop(i)
return True, 'Force removed'
return False, 'Force not found'
def delete_object(self, delete_uni_object):
'''
delete object from universe
'''
for i, uni_object in enumerate(self.universe.object_list):
if delete_uni_object == uni_object:
self.universe.object_list.pop(i)
return True, 'Object removed'
return False, 'Object not found'
def validate_input(self, input_type, text, no_null = False):
'''
If input text is invalid, returns default value
'''
if input_type == 'string':
if text == "":
text = 'NONAME'
elif input_type == 'int':
if self.is_int(text):
text = int(text)
if no_null:
if text == 0:
text = 1
else:
if no_null:
text = 1
else:
text = 0
elif input_type == 'float':
if self.is_float(text):
text = float(text)
if no_null:
if text == 0:
text = 1.1
else:
if no_null:
text = 1.0
else:
text = 0.0
return text
def is_int(self, s):
'''
Tests that given parameter is integer
@return: Bool (True, if param is integer)
'''
try:
int(s)
return True
except ValueError:
return False
def is_float(self, s):
'''
Tests that given parameter is float
@return: Bool (True, if param is float)
'''
try:
float(s)
return True
except ValueError:
return False
def create_angle_force(self, uni_object, force, angle2d, angle3d):
'''
Creates force vector from force and it's angles
force,angle_xy,angle,xz => x,y,z (starting from the object location)
'''
( x, y, z ) = self.universe.maths.get_vector_xyz(force, angle2d, angle3d)
force = Force_Vector(x, y, z)
uni_object.add_force_vector(force)
return force
def set_object_angle_speed(self, uni_object, speed, angle2d, angle3d):
'''
Sets object speed vector by transforming
speed,angle_xy,angle,xz => x,y,z (starting from the object location)
'''
( x, y, z ) = self.universe.maths.get_vector_xyz(speed, angle2d, angle3d)
uni_object.set_speed(x, y, z)
def set_force_angle(self, force_vector, force, angle2d, angle3d):
'''
Sets force vector by transforming
speed,angle_xy,angle,xz => x,y,z (starting from the object location)
'''
( x, y, z ) = self.universe.maths.get_vector_xyz(force, angle2d, angle3d)
force_vector.set_force(x, y, z)
def get_object_angle_speed(self, uni_object):
'''
Get speed vector in angle form
'''
return self.universe.maths.xyz_to_angle(uni_object.speed_x, uni_object.speed_y, uni_object.speed_z)
def get_object_force_speed(self, uni_object):
'''
Get object force vector in angle form
'''
return self.universe.maths.xyz_to_angle(uni_object.force_x, uni_object.force_y, uni_object.force_z)
def get_force_angle(self, force_vector):
'''
Get force vector in angle form
'''
return self.universe.maths.xyz_to_angle(force_vector.x, force_vector.y, force_vector.z)
def animate_step(self):
'''
Simulates universe one step
'''
self.universe.move_objects()
def animate(self, obj_num, time):
'''
Simulation is executed one year and it returns x-axis values divided by
initial x-axis value from given object per month.
Also step size is needed
'''
running = True
self.universe.maths.time = time
status, message = self.set_startpoint()
hour = round(3600.0 / self.universe.maths.time)
day = round(hour*24.0)
month = round(day*30.0)
year = round(day*365.0)
run_day = 0
message += "\n============="
while running:
if (self.universe.step % day) == 0:
run_day += 1
os.system("clear")
print str(round((100 * self.universe.step / year),2)) + " % "
if (self.universe.step % month) == 0:
#print "month: " + str(self.controller.universe.step)
x = self.universe.object_list[obj_num].x / self.copy_universe.object_list[obj_num].x
x = str(x).replace(".",",")
message += "\n" + x
if self.universe.step > year:
running = False
self.animate_step()
message += "\n============="
if status is True:
message += "\nSimulation done. x-position monthly, over a year"
return status, message
def set_startpoint(self):
'''
Sets startpoint for universe. It copies current in
copy_universe
'''
self.copy_universe = copy.deepcopy(self.universe)
self.copy_universe.step = 0
self.copy_universe.calc_time = 0
return True, "Startpoint set"
def reverse_startpoint(self):
'''
Copies startpoint-universe as the current one.
If no startpoint-universe is set, then nothing is done.
'''
if self.copy_universe is not None:
self.universe = copy.deepcopy(self.copy_universe)
return True, "Startpoint reversed"
return False, "No saved universe"
def set_filePath(self, filePath):
'''
For Qt filepath load/save
'''
self.filePath = filePath
def save(self):
'''
Saves current simulation in file
'''
self.pref_dict['paint'] = {'timer_time' : self.timer_time,
'steps_between_paint' : self.steps_between_paint }
self.pref_dict['units'] = self.units.save_units()
s = Dom_Save(self.pref_dict)
s.add_universe(self.universe)
if self.copy_universe is not None:
s.add_universe(self.copy_universe)
if self.filePath is None:
return s.save_simulation(self.folder + self.file_name)
return s.save_simulation(self.filePath)
def load(self):
'''
Loads simulation from file
'''
l = Dom_Load()
if self.filePath is None:
filePath = self.folder + self.file_name
else:
filePath = self.filePath
(uni_list, self.pref_dict) = l.load_simulation(filePath)
if 'units' in self.pref_dict:
self.units.load_units(self.pref_dict['units'])
if 'paint' in self.pref_dict:
self.timer_time = int(self.pref_dict['paint']['timer_time'])
self.steps_between_paint = int(self.pref_dict['paint']['steps_between_paint'])
if uni_list is not None:
if len(uni_list) == 1:
self.universe = uni_list[0]
self.copy_universe = None
elif len(uni_list) >= 2:
self.universe = uni_list[0]
self.copy_universe = uni_list[1]
else:
return False, "Not enough universe-objects"
return True, "Load ok"
return False, "File not found"
def list_files_in_folder(self):
'''
Returns files from file-folder
'''
files = []
for f in os.listdir(self.folder):
files.append(f)
return files
def not_empty(self):
'''
Finds out if universe is empty and has no objects
@return: Bool (true, if universe is empty)
'''
if len(self.universe.object_list) > 0:
return True
return False
class Army(object):
def __init__(self, units_config):
self.units_config = units_config
self.units = Units(units_config["units"])
self.allies = Allies(units_config["allies"])
self.battalions = Battalions(units_config["battalions"])
self.all = [self.units, self.allies, self.battalions]
def __str__(self):
line = []
for u in self.all:
s = str(u)
if s:
line.append(s)
return "{}: {}".format(self.points(), ", ".join(line))
def __repr__(self):
return str(self.units)
def __len__(self):
return len(self.units) + len(self.allies) + len(self.battalions)
def fullstr(self, rules_config={}):
points = self.points()
if rules_config:
line = [
("Points {} [{} to {}]".format(points,
rules_config["points"] - points,
rules_config["points"]))
]
else:
line = [("Points {}".format(points))]
line.append(
"\tWounds: {}, Models: {}, Bravery/Unit: {:.2f}, Save/Wound: {:.2f}+"
.format(self.wounds(), self.unitsize(),
self.avg_bravery_per_unit(), self.avg_save_per_wound()))
for u in self.all:
s = u.fullstr()
if s:
line.append(s)
line.append("Roles: {}".format(self.sum_roles_str(rules_config)))
line.append("")
return "\n".join(line)
def __getitem__(self, index):
for u in self.all:
if index < len(u):
return u[index]
index = index - len(u)
raise IndexError("index out of range")
def __setitem__(self, index, item):
for u in self.all:
if index < len(u):
u[index] = item
return
index = index - len(u)
raise IndexError("index out of range")
def add(self, name, unittype):
if unittype == "unit":
self.units.add(name)
elif unittype == "ally":
self.allies.add(name)
elif unittype == "battalion":
self.battalions.add(name)
else:
raise KeyError('Invalid unit type')
def points(self):
x = 0
for u in self.all:
x = x + u.points()
return x
def unitsize(self):
x = 0
for u in self.all:
x = x + u.unitsize()
return x
def wounds(self):
x = 0
for u in self.all:
x = x + u.wounds()
return x
def _bravery_sum(self):
x = 0
for u in self.all:
x = x + u._bravery_sum()
return x
def _save_mul_wounds_sum(self):
x = 0
for u in self.all:
x = x + u._save_mul_wounds_sum()
return x
def avg_bravery_per_unit(self):
count = self.unitsize()
if count == 0:
return 0
return self._bravery_sum() / float(count)
def avg_save_per_wound(self):
count = self.wounds()
if count == 0:
return 0
return self._save_mul_wounds_sum() / float(count)
def sum_roles(self, rules_config={}):
r = {}
for rulename, ruleactions in rules_config.get("units", {}).iteritems():
r[rulename] = 0
for u in self.all:
u.sum_roles(r)
return r
def sum_roles_str(self, rules_config={}):
roles = self.sum_roles(rules_config)
line = []
for role, count in roles.iteritems():
rule = rules_config.get("units", {}).get(role, {})
if rule:
if rule["max"] == -1:
line.append("{} {} [{}+]".format(count, role, rule["min"]))
else:
line.append("{} {} [{}->{}]".format(
count, role, rule["min"], rule["max"]))
else:
line.append("{} {}".format(count, role))
return ", ".join(line)
def __check_min_max(self, constraint, current_value, default_min,
default_max, restrict_config, final, showfails):
con_min = restrict_config.get("min_" + constraint, default_min)
con_max = restrict_config.get("max_" + constraint, default_max)
if (current_value > con_max
and con_max != -1) or (final and current_value < con_min):
if showfails.value > PrintOption.SILENT.value:
print "FAIL {}: {} {}->{} : {}".format(constraint,
current_value, con_min,
con_max, self)
return False
return True
def is_valid(self,
rules_config,
restrict_config={},
final=True,
showfails=PrintOption.SILENT):
if not self.__check_min_max(
"points", self.points(), rules_config["points"],
rules_config["points"], restrict_config, final, showfails):
return False
if not self.__check_min_max("wounds", self.wounds(), 0, -1,
restrict_config, final, showfails):
return False
if not self.__check_min_max("models", self.unitsize(), 0, -1,
restrict_config, final, showfails):
return False
if not self.__check_min_max("allies", self.allies.points(), 0,
rules_config["allies"], restrict_config,
final, showfails):
return False
#Default battalions to off until better support added
if not self.__check_min_max("battalions", self.battalions.num(), 0, 0,
restrict_config, final, showfails):
return False
for u in self.all:
if not u.is_valid(restrict_config, final, showfails):
return False
# Check roles
rules_check = self.sum_roles(rules_config)
for role, count in rules_check.iteritems():
# Check role meets min requirements
if final and count < rules_config["units"][role]["min"]:
if showfails.value > PrintOption.SILENT.value:
print "FAIL Role MIN {} {} {} : {}".format(
role, rules_config["units"][role]["min"], count, self)
return False
# Check role meets max requirements
if rules_config["units"][role][
"max"] != -1 and count > rules_config["units"][role]["max"]:
if showfails.value > PrintOption.SILENT.value:
print "FAIL Role MAX {} {} {} : {}".format(
role, rules_config["units"][role]["max"], count, self)
return False
return True
def __init__(self, units_config):
self.units_config = units_config
self.units = Units(units_config["units"])
self.allies = Allies(units_config["allies"])
self.battalions = Battalions(units_config["battalions"])
self.all = [self.units, self.allies, self.battalions]
def start(self,
device,
pool_name,
miner,
algorithm,
region,
quick=False,
force=False):
self.miner = miner
self.device = device
self.default_profile = Config().get('device_profiles.default')
Miners().reload_config()
device.pin = {
'pool_name': pool_name,
'miner_name': miner.name,
'algorithm': algorithm,
'region': region
}
if not Config().get('calibration'):
Log().add('fatal', "calibration config missing from config file")
for key in [
"hashrate_stabilisation_timeout_mins",
"hashrate_stabilisation_tolerance",
"hashrate_stabilisation_consecutive_readings_required",
"algorithm_start_timeout"
]:
if Config().get('calibration.%s' % (key)) == None:
Log().add('fatal',
"missing config option: calibration.%s" % (key))
if Config().get('calibration.power_tuning.enable'):
for key in ["decrement_watts", "acceptable_loss_percent"]:
if not Config().get('calibration.power_tuning.%s' % (key)):
Log().add(
'fatal',
"missing config option: calibration.power_tuning.%s" %
(key))
Config().set('pools.%s.append_device_id_to_worker_name' % (pool_name),
False)
if pool_name == 'miningpoolhub':
if Config().get('pools.miningpoolhub.hub_workers.%s' %
(algorithm))[-5:] != 'CALIB':
Config().set(
'pools.miningpoolhub.hub_workers.%s' % (algorithm),
Config().get('pools.miningpoolhub.hub_workers.%s' %
(algorithm)) + 'CALIB')
else:
if Config().get('pools.%s.worker_name' %
(pool_name))[-5:] != 'CALIB':
Config().set(
'pools.%s.worker_name' % (pool_name),
Config().get('pools.%s.worker_name' % (pool_name)) +
"CALIB")
if quick:
Config().set('calibration.power_tuning.enable', False)
profile = Profiles().get_for_device_algo(device, algorithm)
Log().add('info', ' device id: %d' % (device.id))
Log().add('info', ' pool: %s' % (pool_name))
Log().add('info', ' device class: %s' % (device.dclass))
Log().add('info', 'device profile: %s' % (profile.name))
Log().add('info', ' miner: %s' % (miner.name))
Log().add('info', ' algorithm: %s' % (algorithm))
Log().add('info', ' region: %s' % (region))
Miners().poll()
Miners().get_device_state(device)
if device.state != 'inactive':
if device.state == 'calibrating':
device.log(
'fatal',
'this device is already being used for calibration')
if force:
device.log('info', 'terminating existing worker')
if not device.stop(True):
device.log('fatal', 'failed to stop existing worker')
else:
device.log(
'fatal',
'unable to start calibration - device is in use (use --force to override)'
)
self.set_pin(device.id)
default_power_limit = device.default_power_limit_f
min_power_limit = device.min_power_limit_f
max_power_limit = device.max_power_limit_f
if max_power_limit == 0:
device.log('info', 'device does not support power monitoring')
else:
device.log('info', 'max power limit: %d W' % (max_power_limit))
device.log(
'info', 'stabilisation tolerance: %.2f%%' %
(Config().get('calibration.hashrate_stabilisation_tolerance')))
if Config().get('debug'):
device.log('debug', 'loading default profile')
Nvidia().set_default_profile(device)
if Config().get('debug'):
device.log('debug', 'initialising pools')
Pools()
sample_timeout = Config().get(
'calibration.hashrate_stabilisation_timeout_mins')
if default_power_limit != None and Config().get(
'calibration.power_tuning.enable'):
device.log(
'info',
'starting initial run at max power limit [timeout=%dmins]' %
(sample_timeout))
else:
device.log(
'info',
'starting single run [timeout=%dmins]' % (sample_timeout))
Miners().poll()
Miners().get_device_state(device)
if device.state != 'calibrating':
if not device.start(True):
device.log('fatal', 'worker failed to start')
if Config().get('calibration.power_tuning.enable'):
if not device.power_supported():
device.log('info', 'device does not support power monitoring')
else:
device.set_power_limit(max_power_limit)
initial_hashrate, initial_max_power_draw = self.get_max_hashrate_and_power_draw(
miner, device, sample_timeout * 60)
if initial_hashrate == None:
device.log(
'info',
'skipping algorithm as we failed to get a stable reading')
print ""
return
if initial_max_power_draw != None and initial_max_power_draw > max_power_limit:
initial_max_power_draw = max_power_limit
device.log(
'info', 'benchmark hashrate: %s' %
(Units().hashrate_str(initial_hashrate)))
if initial_max_power_draw != None:
device.log('info',
'max power draw: %.2f' % (initial_max_power_draw))
initial_power_limit = int(math.ceil(initial_max_power_draw))
hashrate = initial_hashrate
if initial_max_power_draw == None:
power_limit = None
elif Config().get('calibration.power_tuning.enable'):
if max_power_limit == 0 or min_power_limit == 0 or default_power_limit == 0:
device.log(
'error',
'device did not give us sane values for its min/max/default power limits'
)
device.log(
'error',
'unable to proceed with power calibration - device may not support changing the power limit'
)
power_limit = default_power_limit
else:
power_limit = initial_power_limit
device.log('info', 'tuning power limit for optimum efficiency')
hashrate, power_limit = self.do_power_calibration(
device, power_limit, miner, hashrate, min_power_limit)
else:
power_limit = default_power_limit
Nvidia().set_default_profile(device)
self.unset_pin(device.id)
device.log('info', 'storing calibration data')
Calibration().update_calibration_data(device.dclass, miner.name,
algorithm, hashrate, power_limit)
if not device.stop(True):
device.log('fatal', "failed to stop the miner process")
print ""
device.log(
'info',
' calibrated hashrate: %s' % (Units().hashrate_str(hashrate)))
if power_limit != None:
device.log('info',
'calibrated power limit: %.2f W' % (power_limit))
print ""
def __init__(self, sim, config):
self.sim = sim
self.config = config
self.logger = logging.getLogger("Brake")
# Limit Switches
# Switch activation: We only want to call the callback when the switch is tripped, not repeatedly
self.retract_sw_activated = False
self.extend_sw_activated = False
# Volatile
#self.gap = Units.SI(self.config.initial_gap) # @todo: make this work
# Rail Gap
# Screw pos is the main value from which we calculate the gap and MLP values. [0, 75000]um fully retracted to fully extended (maps to brake gap)
# Note: screw position is updated by the callback from the FCU
self.gap = Units.SI(self.config.gap.initial_gap)
#print "Gap after conversion is {}".format(self.gap)
#exit()
self.retracted_gap = Units.SI(self.config.gap.retracted_gap)
self.extended_gap = Units.SI(self.config.gap.extended_gap)
self.gap_range = [self.retracted_gap, self.extended_gap]
# Screw
self.screw_limit_sw_retract = Units.SI(
self.config.lead_screw.limit_sw_retract)
self.screw_limit_sw_extend = Units.SI(
self.config.lead_screw.limit_sw_extend)
self.screw_range = [
self.screw_limit_sw_retract, self.screw_limit_sw_extend
]
# Calculate initial screw position from the initial gap (note: during processing it's the other way around)
self.screw_pos = np.interp(self.gap, self.gap_range, self.screw_range)
# Linear Position Sensor
# @todo: check this to make sure the min/max are in the correct order -- should be retracted->extended
self.mlp_range = [self.config.mlp.raw_min, self.config.mlp.raw_max]
# Calculate raw MLP value from the screw position
print("{}, {}, {}".format(self.screw_pos, self.screw_range,
self.mlp_range))
self.mlp_raw = np.interp(self.screw_pos, self.screw_range,
self.mlp_range)
# Negator
self.negator_torque = Units.SI(self.config.negator.torque)
# TESTING ONLY
self._gap_target = self.gap # Initialize to current value so we don't move yet
self._gap_close_time = Units.SI(self.config.gap_close_min_time)
self._gap_close_dist = self.retracted_gap - self.extended_gap
self._gap_close_speed = self._gap_close_dist / self._gap_close_time # meters/second -- this is just a guess -- .007 m/s = closing 21mm in 3s
#self.logger.debug("Brake gap close speed: {} m/s".format(self._gap_close_speed))
# /TESTING
self.normal_force = 0.0 # N -- normal against the rail; +normal is away from the rail
self.drag_force = 0.0 # N -- drag on the pod; -drag is toward the back of the pod
self.last_normal_force = 0.0
self.last_drag_force = 0.0
# Lead Screw
# revs per cm=2.5 There are 4 mm per single lead so 2.5 turns move the carriage 1 cm
# Formulas: http://www.nookindustries.com/LinearLibraryItem/Ballscrew_Torque_Calculations
self.screw_pitch = Units.SI(self.config.lead_screw.pitch)
self.drive_efficiency = self.config.lead_screw.drive_efficiency
self.backdrive_efficiency = self.config.lead_screw.backdrive_efficiency
# Lead Screw Precalculated Values
self._drive_torque_multiplier = self.screw_pitch / (
self.drive_efficiency * 2 * 3.14)
self._backdrive_torque_multiplier = (
self.screw_pitch * self.backdrive_efficiency) / (2 * 3.14)
def hashrate_str(self):
if self.algos[0]['algo'] in Config().get('algorithms.single'):
return Units().hashrate_str(self.algos[0]['hashrate'][0])
return Units().hashrate_str(self.algos[0]['hashrate'])
def do_power_calibration(self, device, power_limit, miner,
initial_hashrate, min_power_limit):
sample_timeout = Config().get(
'calibration.hashrate_stabilisation_timeout_mins')
acceptable_loss_pc = Config().get(
'calibration.power_tuning.acceptable_loss_percent')
dial_back = False
dialed_back = False
hashrate = initial_hashrate
if "_" in device.algos[0]['algo']:
initial_value = initial_hashrate[0]
else:
initial_value = initial_hashrate
while True:
if dial_back:
power_limit += Config().get(
'calibration.power_tuning.decrement_watts') / 2
dialed_back = True
else:
power_limit -= Config().get(
'calibration.power_tuning.decrement_watts')
if power_limit < min_power_limit:
power_limit = min_power_limit
device.log('info', 'setting power limit: %d W' % (power_limit))
device.set_power_limit(power_limit)
new_hashrate, max_power_draw = self.get_max_hashrate_and_power_draw(
miner, device, sample_timeout * 60)
if new_hashrate == None:
device.log(
'info',
'skipping algorithm as we failed to get a stable reading')
print ""
return
if "_" in device.algos[0]['algo']:
new_value = new_hashrate[0]
else:
new_value = new_hashrate
device.log(
'info',
'nominal hashrate: %s' % (Units().hashrate_str(new_hashrate)))
if new_value > initial_value:
device.log('info', 'hashrate is higher than before, w00t!')
hashrate = new_hashrate
initial_value = new_value
elif new_value >= (initial_value -
(initial_value / 100) * acceptable_loss_pc):
hashrate = new_hashrate
if power_limit == min_power_limit or dial_back:
device.log(
'info', 'hashrate loss is within acceptable %.2f%%' %
(acceptable_loss_pc))
else:
device.log(
'info',
'hashrate loss is within acceptable %.2f%%, continuing'
% (acceptable_loss_pc))
else:
if dial_back or Config().get(
'calibration.power_tuning.decrement_watts') == 1:
device.log(
'info',
'hashrate still below our acceptable loss level of %.2f%%, stopping'
% (acceptable_loss_pc))
if Config().get(
'calibration.power_tuning.decrement_watts') == 1:
power_limit += 1
else:
power_limit += Config().get(
'calibration.power_tuning.decrement_watts') / 2
break
else:
device.log(
'info',
'hashrate fell below our acceptable loss level of %.2f%%, dialling back %d W'
% (acceptable_loss_pc, Config().get(
'calibration.power_tuning.decrement_watts') / 2))
dial_back = True
if dialed_back:
break
if power_limit == min_power_limit and not dial_back:
device.log('info', 'minimum power limit reached, stopping')
break
return [hashrate, power_limit]
def __init__(self, sim, config):
ForceExerter.__init__(self, sim, config)
self.name = 'F_aero'
# @see http://www.softschools.com/formulas/physics/air_resistance_formula/85/
self.air_resistance_k = Units.SI(self.config.air_density) * self.config.drag_coefficient * Units.SI(self.config.drag_area) / 2
def testElEncToDeg(self): p = Units() self.failUnless(p.encoder_to_El(10)==1110)
def __init__(self): self.converter = Units()
def resistance(self):
"""Return a succinct string representing the resistance of this Resistor.
e.g. '23.6KΩ'
"""
return str(Units.ohms(self.ohms))
def testAzEncToDeg(self): p = Units() self.failUnless(p.encoder_to_Az(10)==28440)
def __init__(self, sim, config):
self.logger = logging.getLogger("Pod")
self.logger.info("Initializing pod")
self.sim = sim
self.config = config
self.name = 'pod_actual'
self.step_listeners = []
self.step_forces = OrderedDict(
) # This will be filled in during each step
# Pod physical properties
# *** NOTE: all distances are in the track/tube reference frame, origin is pod location reference (centerpoint of fwd crossbar). +x is forward, +y is left, +z is up.
# @see http://confluence.rloop.org/display/SD/2.+Determine+Pod+Kinematics
self.mass = Units.SI(self.config.mass)
self.pusher_plate_offset = Units.SI(
self.config.physical.pusher_plate_offset)
self.pusher_pin_travel = Units.SI(
self.config.physical.pusher_pin_travel)
# Forces that can act on the pod (note: these are cleared at the end of each step)
self.net_force = np.array(
(0.0, 0.0, 0.0)
) # Newtons; (x, y, z). +x pushes the pod forward, +z force lifts the pod, y is not currently used.
# Initialize actual physical values (volatile variables). All refer to action in the x dimension only.
self.acceleration = Units.SI(
self.config.acceleration) or 0.0 # meters per second ^2
self.velocity = Units.SI(
self.config.velocity) or 0.0 # meters per second
self.position = Units.SI(
self.config.position
) or 0.0 # meters. Position relative to the track; start position is 0m
# @todo: this is just a sketch, for use with the hover engine calculations. Maybe switch accel, velocity, and position to coordinates? hmmmm...
self.z_acceleration = 0.0
self.z_velocity = 0.0
self.he_height = Units.SI(
self.config.landing_gear.initial_height
) # This should be gotten from the starting height of the lift mechanism
self._initial_he_height = self.he_height # @todo: Remove this -- it's only used as a temporary block from going through the floor until the landing gear is implemented
self.elapsed_time_usec = 0
# Values from the previous step (for lerping and whatnot)
self.last_acceleration = 0.0
self.last_velocity = 0.0
self.last_position = 0.0
self.last_he_height = 0.0
# Handle forces that act on the pod
"""
self.forces = []
self.forces.append( AeroForce(self.sim, self.config.forces.aero) )
self.forces.append( BrakeForce(self.sim, self.config.forces.brakes) )
self.forces.append( GimbalForce(self.sim, self.config.forces.gimbals) )
self.forces.append( HoverEngineForce(self.sim, self.config.forces.hover_engines) )
self.forces.append( LateralStabilityForce(self.sim, self.config.forces.lateral_stability) )
self.forces.append( LandingGearForce(self.sim, self.config.forces.landing_gear) )
"""
self.force_exerters = OrderedDict()
self.force_exerters['aero'] = AeroForce(self.sim,
self.config.forces.aero)
self.force_exerters['brakes'] = BrakeForce(self.sim,
self.config.forces.brakes)
self.force_exerters['gimbals'] = GimbalForce(
self.sim, self.config.forces.gimbals)
self.force_exerters['hover_engines'] = HoverEngineForce(
self.sim, self.config.forces.hover_engines)
self.force_exerters['lateral_stability'] = LateralStabilityForce(
self.sim, self.config.forces.lateral_stability)
self.force_exerters['landing_gear'] = LandingGearForce(
self.sim, self.config.forces.landing_gear)
# Forces applied during the step (for recording purposes)
# @todo: Maybe initialize these to ForceExerter.data(0,0,0)?
self.step_forces = OrderedDict()
self.step_forces['aero'] = ForceExerter.data(0, 0, 0)
self.step_forces['brakes'] = ForceExerter.data(0, 0, 0)
self.step_forces['gimbals'] = ForceExerter.data(0, 0, 0)
self.step_forces['hover_engines'] = ForceExerter.data(0, 0, 0)
self.step_forces['lateral_stability'] = ForceExerter.data(0, 0, 0)
self.step_forces['landing_gear'] = ForceExerter.data(0, 0, 0)
# Pre-calculated values
# HE Drag
"""
Drag for a single hover engine (keep in mind that we have 8):
Constants:
w: Comes from solving 3d maxwell's equations (@whiplash) and using other fancy math -- provided by @whiplash -- not dependent on velocity, just depends on magnetic characteristics
mu_0: Absolute permeability of the vacuum (constant) -- permeability constant, magnetic constant, etc. -- permeability of free space
m: Magnetic dipole strength (constant, but depends on how magnets are arranged)
h: thickness of conducting material (rail in this case)
rho: density of conducting material (aluminum in this case)
Variables:
z_0: Distance between magnet and conducting surface
v: Velocity
Calculated:
w = 2 * rho / (mu_0 * h)
F_lift / F_drag = v / w
F_lift = ((3 * mu_0 * m**2) / (32 * 3.14159 * z_0**4)) * (1 - w * (v**2 + w**2)**(-1/2))
Values (from @whiplash):
mu_0: 4 pi x10e-7
rho: 2.7 g/cubic centimeter
h: 0.5 in
m: (@whiplash will need to calculate this -- it's a vector quantity, need to draw a magnetic circuit)
"""
# Brakes
#self.brakes = []
#for brake_config in self.config.brakes:
# self.brakes.append(Brake(self.sim, brake_config))
self.brakes = Brakes(self.sim, self.config.brakes)
""" Sketch:
args_cli = pytools.parse_args()
var = args_cli.var
#confs = [ "gam3.ini", ]
confs = [
args_cli.conf_filename,
]
for conf_filename in confs:
conf = Configuration(conf_filename, do_print=True)
# restructure path to point to this dir
fdir = conf.outdir
print(fdir)
units = Units(conf)
#--------------------------------------------------
# read data
flds_Epar = []
flds_Eperp = []
flds_Bpar = []
flds_Bperp = []
flds_Jpar = []
flds_Jperp = []
flds_time = []
flds_tot = []
def get_local_data(self, minimal=False):
data = []
devices = nvidia.Nvidia().get_nvidia_devices(1, True)
Miners().poll()
if not minimal and os.path.exists(
self.power_file) and os.path.getsize(self.power_file) > 0:
power_draw_readings = pickle.loads(open(self.power_file).read())
else:
power_draw_readings = []
if not minimal and os.path.exists(
self.profitability_file) and os.path.getsize(
self.profitability_file) > 0:
profitability_readings = pickle.loads(
open(self.profitability_file).read())
else:
profitability_readings = []
Calibration().load()
total_mbtc = 0
total_power = 0
total_power_limit = 0
power_values = {}
power_limit_values = {}
for device in devices:
device.algos = []
Miners().get_device_state(device)
mbtc_per_day_values = [0]
algos = device.algos
if len(algos) == 0:
algos = ['IDLE']
for algo_i in range(0, len(algos)):
algo = algos[algo_i]
if algo_i == 0:
omit_fields = False
else:
omit_fields = True
if algo == 'IDLE':
algo = "IDLE"
algo1 = "IDLE"
rate_s = "-"
mbtc_per_day = 0
miner = '-'
region = '-'
pool = '-'
_time = '-'
else:
algo1 = algo['algo']
miner = algo['miner']
region = algo['region']
pool = algo['pool']
_pool = Pools().pools[pool]
if os.path.exists("/tmp/.minotaur.%d" % (device.id)):
_time = Units().to_timestr(
int(time.time() - os.stat("/tmp/.minotaur.%d" %
(device.id)).st_mtime))
else:
_time = '-'
rate_a, rate_b = algo['hashrate']
if algo['algo'] in Config().get('algorithms')['double']:
rate_s = Units().hashrate_str(rate_a)
rate_s2 = Units().hashrate_str(rate_b)
benchmarks = {algo['algo']: [rate_a, rate_b]}
algo1, algo2 = algo['algo'].split("_")
mbtc_per_day_values = [
_pool.mbtc_per_day(
benchmarks,
'cached')[algo['region']][algo['algo']]
]
else:
rate_s = Units().hashrate_str(rate_a)
benchmarks = {algo['algo']: rate_a}
if algo['algo'] in _pool.mbtc_per_day(
benchmarks, 'cached')[algo['region']].keys():
mbtc_per_day_values = [
_pool.mbtc_per_day(
benchmarks,
'cached')[algo['region']][algo['algo']]
]
else:
mbtc_per_day_values = [0]
if pool != '-':
pool = _pool.shortened(region)
metrics = device.to_hash()
metrics.pop('changed', None)
metrics['miner'] = miner
metrics['region'] = region
metrics['pool'] = pool
metrics['time'] = _time
metrics['omit_fields'] = omit_fields
if metrics['fan']:
metrics['fan'] = str(metrics['fan']).rjust(3) + " %"
if metrics['miner']:
metrics['miner'] = metrics['miner'][0:5]
else:
metrics['miner'] = '-'
if algo == "IDLE":
if metrics['gpu_u_i'] and metrics['gpu_u_i'] >= 90:
algo = ".pending."
algo1 = ".pending."
rate_s = ".."
mbtc_per_day = mbtc_per_day_values[0]
metrics['host'] = 'local'
metrics['id'] = device.id
metrics[" algo"] = algo1
metrics["rate"] = rate_s
metrics[" mBTC/day"] = ("%.2f" % (mbtc_per_day)).rjust(5)
if not metrics['region']:
metrics['region'] = '-'
total_mbtc += sum(mbtc_per_day_values)
if metrics['power_f']:
power_values[metrics['id']] = metrics['power_f']
margin = self.calculate_profit_margin_for_card(
sum(mbtc_per_day_values), metrics['power_f'])
net_mbtc = (mbtc_per_day / 100) * margin
if margin < 0:
margin = 0
margin_s = "%d%%" % (int(margin))
metrics[" mBTC/day"] += "/"
metrics[" mBTC/day"] += ("%.2f" % (net_mbtc)).rjust(5)
metrics[" mBTC/day"] += " %s" % (margin_s.rjust(4))
else:
margin = 0
if margin > 0:
metrics["margin"] = "%.1f%%" % (margin)
else:
metrics["margin"] = "-"
if metrics['limit_f']:
power_limit_values[metrics['id']] = metrics['limit_f']
if device.state == 'calibrating':
metrics[' algo'] = '*' + metrics[' algo']
elif device.get_pin() and 'algorithm' in device.get_pin().keys(
) and device.get_pin()['algorithm'] == metrics[' algo']:
metrics[' algo'] = '+' + metrics[' algo']
elif device.get_pin() and 'algorithm' in device.get_pin().keys(
) and '_' in device.get_pin()['algorithm'] and metrics[
' algo'] in device.get_pin()['algorithm'].split('_'):
metrics[' algo'] = '+' + metrics[' algo']
else:
metrics[' algo'] = ' ' + metrics[' algo']
if metrics['gpu_f']:
match = re.match("^([\d]+)", metrics['gpu_f'])
if match:
metrics['gpu_f'] = match.group(1)
else:
metrics['gpu_f'] = '-'
else:
metrics['gpu_f'] = '-'
if metrics['mem_f']:
match = re.match("^([\d]+)", metrics['mem_f'])
if match:
metrics['mem_f'] = match.group(1)
else:
metrics['mem_f'] = '-'
else:
metrics['mem_f'] = '-'
metrics['ps clocks'] = "%s %s/%s MHz" % (
metrics['ps'], metrics['gpu_f'], metrics['mem_f'])
power = re.match("^([\d]+)", metrics['power'])
limit = re.match("^([\d]+)", metrics['limit'])
if power and limit:
metrics['power'] = "%s/%s W" % (power.group(1),
limit.group(1))
else:
metrics['power'] = '-'
data.append(metrics)
if algo not in [
'IDLE', '.pending.'
] and algo['algo'] in Config().get('algorithms.double'):
mbtc_per_day = mbtc_per_day_values[1]
metrics2 = metrics.copy()
metrics2[" algo"] = algo2
metrics2["rate"] = rate_s2
metrics2[" mBTC/day"] = ("%.2f" % (mbtc_per_day)).rjust(5)
net_mbtc = (mbtc_per_day / 100) * margin
metrics2[" mBTC/day"] += "/"
metrics2[" mBTC/day"] += ("%.2f" % (net_mbtc)).rjust(5)
metrics2[" mBTC/day"] += " %s" % (margin_s.rjust(4))
if device.state == 'calibrating':
metrics2[" algo"] = '*' + metrics2[' algo']
elif device.get_pin() and '_' in device.get_pin(
)['algorithm'] and metrics2[' algo'] in device.get_pin(
)['algorithm'].split('_'):
metrics2[" algo"] = '+' + metrics2[' algo']
else:
metrics2[" algo"] = ' ' + metrics2[' algo']
data.append(metrics2)
total_power = sum(power_values.values())
total_power_limit = sum(power_limit_values.values())
electric_cost_mbtc = self.get_electricity_cost(
total_power + Config().get('system_draw_watts'), True,
Config().get('electricity_per_kwh'))
net_mbtc = total_mbtc - electric_cost_mbtc
return [
data, total_mbtc, net_mbtc, total_power, total_power_limit,
power_draw_readings, profitability_readings
]