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train | get_re_experiment | Returns an experiment that uses the Roth-Erev learning method. | examples/pyreto/thesis/ex5_1.py | def get_re_experiment(case, minor=1):
""" Returns an experiment that uses the Roth-Erev learning method.
"""
gen = case.generators
profile = array([1.0])
maxSteps = len(profile)
experimentation = 0.55
recency = 0.3
tau = 100.0
decay = 0.99#9995
market = pyreto.SmartMarket(case,... | def get_re_experiment(case, minor=1):
""" Returns an experiment that uses the Roth-Erev learning method.
"""
gen = case.generators
profile = array([1.0])
maxSteps = len(profile)
experimentation = 0.55
recency = 0.3
tau = 100.0
decay = 0.99#9995
market = pyreto.SmartMarket(case,... | [
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train | get_q_experiment | Returns an experiment that uses Q-learning. | examples/pyreto/thesis/ex5_1.py | def get_q_experiment(case, minor=1):
""" Returns an experiment that uses Q-learning.
"""
gen = case.generators
profile = array([1.0])
maxSteps = len(profile)
if minor == 1:
alpha = 0.3 # Learning rate.
gamma = 0.99 # Discount factor
# The closer epsilon gets to 0, the m... | def get_q_experiment(case, minor=1):
""" Returns an experiment that uses Q-learning.
"""
gen = case.generators
profile = array([1.0])
maxSteps = len(profile)
if minor == 1:
alpha = 0.3 # Learning rate.
gamma = 0.99 # Discount factor
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train | Generator.q_limited | Is the machine at it's limit of reactive power? | pylon/generator.py | def q_limited(self):
""" Is the machine at it's limit of reactive power?
"""
if (self.q >= self.q_max) or (self.q <= self.q_min):
return True
else:
return False | def q_limited(self):
""" Is the machine at it's limit of reactive power?
"""
if (self.q >= self.q_max) or (self.q <= self.q_min):
return True
else:
return False | [
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train | Generator.total_cost | Computes total cost for the generator at the given output level. | pylon/generator.py | def total_cost(self, p=None, p_cost=None, pcost_model=None):
""" Computes total cost for the generator at the given output level.
"""
p = self.p if p is None else p
p_cost = self.p_cost if p_cost is None else p_cost
pcost_model = self.pcost_model if pcost_model is None else pcost... | def total_cost(self, p=None, p_cost=None, pcost_model=None):
""" Computes total cost for the generator at the given output level.
"""
p = self.p if p is None else p
p_cost = self.p_cost if p_cost is None else p_cost
pcost_model = self.pcost_model if pcost_model is None else pcost... | [
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train | Generator.pwl_to_poly | Converts the first segment of the pwl cost to linear quadratic.
FIXME: Curve-fit for all segments. | pylon/generator.py | def pwl_to_poly(self):
""" Converts the first segment of the pwl cost to linear quadratic.
FIXME: Curve-fit for all segments.
"""
if self.pcost_model == PW_LINEAR:
x0 = self.p_cost[0][0]
y0 = self.p_cost[0][1]
x1 = self.p_cost[1][0]
y1 = se... | def pwl_to_poly(self):
""" Converts the first segment of the pwl cost to linear quadratic.
FIXME: Curve-fit for all segments.
"""
if self.pcost_model == PW_LINEAR:
x0 = self.p_cost[0][0]
y0 = self.p_cost[0][1]
x1 = self.p_cost[1][0]
y1 = se... | [
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train | Generator.poly_to_pwl | Sets the piece-wise linear cost attribute, converting the
polynomial cost variable by evaluating at zero and then at n_points
evenly spaced points between p_min and p_max. | pylon/generator.py | def poly_to_pwl(self, n_points=4):
""" Sets the piece-wise linear cost attribute, converting the
polynomial cost variable by evaluating at zero and then at n_points
evenly spaced points between p_min and p_max.
"""
assert self.pcost_model == POLYNOMIAL
p_min = self.p_min
... | def poly_to_pwl(self, n_points=4):
""" Sets the piece-wise linear cost attribute, converting the
polynomial cost variable by evaluating at zero and then at n_points
evenly spaced points between p_min and p_max.
"""
assert self.pcost_model == POLYNOMIAL
p_min = self.p_min
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train | Generator.get_offers | Returns quantity and price offers created from the cost function. | pylon/generator.py | def get_offers(self, n_points=6):
""" Returns quantity and price offers created from the cost function.
"""
from pyreto.smart_market import Offer
qtyprc = self._get_qtyprc(n_points)
return [Offer(self, qty, prc) for qty, prc in qtyprc] | def get_offers(self, n_points=6):
""" Returns quantity and price offers created from the cost function.
"""
from pyreto.smart_market import Offer
qtyprc = self._get_qtyprc(n_points)
return [Offer(self, qty, prc) for qty, prc in qtyprc] | [
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train | Generator.get_bids | Returns quantity and price bids created from the cost function. | pylon/generator.py | def get_bids(self, n_points=6):
""" Returns quantity and price bids created from the cost function.
"""
from pyreto.smart_market import Bid
qtyprc = self._get_qtyprc(n_points)
return [Bid(self, qty, prc) for qty, prc in qtyprc] | def get_bids(self, n_points=6):
""" Returns quantity and price bids created from the cost function.
"""
from pyreto.smart_market import Bid
qtyprc = self._get_qtyprc(n_points)
return [Bid(self, qty, prc) for qty, prc in qtyprc] | [
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train | Generator._get_qtyprc | Returns a list of tuples of the form (qty, prc) created from the
cost function. If the cost function is polynomial it will be converted
to piece-wise linear using poly_to_pwl(n_points). | pylon/generator.py | def _get_qtyprc(self, n_points=6):
""" Returns a list of tuples of the form (qty, prc) created from the
cost function. If the cost function is polynomial it will be converted
to piece-wise linear using poly_to_pwl(n_points).
"""
if self.pcost_model == POLYNOMIAL:
# C... | def _get_qtyprc(self, n_points=6):
""" Returns a list of tuples of the form (qty, prc) created from the
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train | Generator.offers_to_pwl | Updates the piece-wise linear total cost function using the given
offer blocks.
Based on off2case.m from MATPOWER by Ray Zimmerman, developed at PSERC
Cornell. See U{http://www.pserc.cornell.edu/matpower/} for more info. | pylon/generator.py | def offers_to_pwl(self, offers):
""" Updates the piece-wise linear total cost function using the given
offer blocks.
Based on off2case.m from MATPOWER by Ray Zimmerman, developed at PSERC
Cornell. See U{http://www.pserc.cornell.edu/matpower/} for more info.
"""
assert no... | def offers_to_pwl(self, offers):
""" Updates the piece-wise linear total cost function using the given
offer blocks.
Based on off2case.m from MATPOWER by Ray Zimmerman, developed at PSERC
Cornell. See U{http://www.pserc.cornell.edu/matpower/} for more info.
"""
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train | Generator.bids_to_pwl | Updates the piece-wise linear total cost function using the given
bid blocks.
Based on off2case.m from MATPOWER by Ray Zimmerman, developed at PSERC
Cornell. See U{http://www.pserc.cornell.edu/matpower/} for more info. | pylon/generator.py | def bids_to_pwl(self, bids):
""" Updates the piece-wise linear total cost function using the given
bid blocks.
Based on off2case.m from MATPOWER by Ray Zimmerman, developed at PSERC
Cornell. See U{http://www.pserc.cornell.edu/matpower/} for more info.
"""
assert self.is_... | def bids_to_pwl(self, bids):
""" Updates the piece-wise linear total cost function using the given
bid blocks.
Based on off2case.m from MATPOWER by Ray Zimmerman, developed at PSERC
Cornell. See U{http://www.pserc.cornell.edu/matpower/} for more info.
"""
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train | Generator._offbids_to_points | Returns a list of points for a piece-wise linear function from the
given offer/bid blocks. | pylon/generator.py | def _offbids_to_points(self, offbids, arebids=False):
""" Returns a list of points for a piece-wise linear function from the
given offer/bid blocks.
"""
# Sort offers/bids by price in ascending order.
offbids.sort(key=lambda x: x.price, reverse=arebids)
points = [(0.0, o... | def _offbids_to_points(self, offbids, arebids=False):
""" Returns a list of points for a piece-wise linear function from the
given offer/bid blocks.
"""
# Sort offers/bids by price in ascending order.
offbids.sort(key=lambda x: x.price, reverse=arebids)
points = [(0.0, o... | [
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train | Generator._adjust_limits | Sets the active power limits, 'p_max' and 'p_min', according to
the pwl cost function points. | pylon/generator.py | def _adjust_limits(self):
""" Sets the active power limits, 'p_max' and 'p_min', according to
the pwl cost function points.
"""
if not self.is_load:
# self.p_min = min([point[0] for point in self.p_cost])
self.p_max = max([point[0] for point in self.p_cost])
... | def _adjust_limits(self):
""" Sets the active power limits, 'p_max' and 'p_min', according to
the pwl cost function points.
"""
if not self.is_load:
# self.p_min = min([point[0] for point in self.p_cost])
self.p_max = max([point[0] for point in self.p_cost])
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train | MarketEnvironment.getSensors | Returns the currently visible state of the world as a numpy array
of doubles. | pyreto/continuous/environment.py | def getSensors(self):
""" Returns the currently visible state of the world as a numpy array
of doubles.
"""
sensors = array([])
sensors = r_[sensors, self._getTotalDemandSensor()]
# sensors = r_[sensors, self._getDemandSensor()]
# sensors = r_[sensors, self._get... | def getSensors(self):
""" Returns the currently visible state of the world as a numpy array
of doubles.
"""
sensors = array([])
sensors = r_[sensors, self._getTotalDemandSensor()]
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train | MarketEnvironment.performAction | Performs an action on the world that changes it's internal state.
@param action: an action that should be executed in the Environment
@type action: array: [ g1_prc, g2_prc, g1_qty, g2_qty, ... ] | pyreto/continuous/environment.py | def performAction(self, action):
""" Performs an action on the world that changes it's internal state.
@param action: an action that should be executed in the Environment
@type action: array: [ g1_prc, g2_prc, g1_qty, g2_qty, ... ]
"""
self._lastAction = []
n = s... | def performAction(self, action):
""" Performs an action on the world that changes it's internal state.
@param action: an action that should be executed in the Environment
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train | MarketEnvironment.indim | The number of action values that the environment accepts. | pyreto/continuous/environment.py | def indim(self):
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train | MarketEnvironment._getBusVoltageLambdaSensor | Returns an array of length nb where each value is the sum of the
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multipliers on the lower voltage limits. | pyreto/continuous/environment.py | def _getBusVoltageLambdaSensor(self):
""" Returns an array of length nb where each value is the sum of the
Lagrangian multipliers on the upper and the negative of the Lagrangian
multipliers on the lower voltage limits. """
muVmin = array([b.mu_vmin for b in self.market.case.connected_bus... | def _getBusVoltageLambdaSensor(self):
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train | flatten_component_refs | Flattens component refs in a tree
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:param instance_prefix: prefix for instance
:return: flattened expression | src/pymoca/tree.py | def flatten_component_refs(
container: ast.Class,
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Flattens component refs in ... | def flatten_component_refs(
container: ast.Class,
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train | fully_scope_function_calls | Turns the function references in this expression into fully scoped
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:param node: collection for performing symbol lookup etc.
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"""
Turns the function references in this expression into fully scoped
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Turns the function references in this expression into fully scoped
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train | modify_symbol | Apply a modification to a symbol if the scope matches (or is None)
:param sym: symbol to apply modifications for
:param scope: scope of modification | src/pymoca/tree.py | def modify_symbol(sym: ast.Symbol, scope: ast.InstanceClass) -> None:
"""
Apply a modification to a symbol if the scope matches (or is None)
:param sym: symbol to apply modifications for
:param scope: scope of modification
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Apply a modification to a symbol if the scope matches (or is None)
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train | annotate_states | Finds all derivative expressions and annotates all differentiated
symbols as states by adding state the prefix list
:param node: node of tree to walk
:return: | src/pymoca/tree.py | def annotate_states(node: ast.Node) -> None:
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Finds all derivative expressions and annotates all differentiated
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"""
w = TreeWalker()
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Finds all derivative expressions and annotates all differentiated
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train | flatten | This function takes a Tree and flattens it so that all subclasses instances
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:param root: The Tree to flatten
:param class_name: The class that we want to create a flat model for
:return: flat_class, a Cla... | src/pymoca/tree.py | def flatten(root: ast.Tree, class_name: ast.ComponentRef) -> ast.Class:
"""
This function takes a Tree and flattens it so that all subclasses instances
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train | TreeWalker.skip_child | Skip certain childs in the tree walking. By default it prevents
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"""
Skip certain childs in the tree walking. By default it prevents
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Skip certain childs in the tree walking. By default it prevents
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train | TreeWalker.walk | Walks an AST tree recursively
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"""
Handles tree walking, has to account for dictionaries and lists
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train | DoxyfileParse | Parse a Doxygen source file and return a dictionary of all the values.
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Parse a Doxygen source file and return a dictionary of all the values.
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train | DoxySourceScan | Doxygen Doxyfile source scanner. This should scan the Doxygen file and add
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Doxygen Doxyfile source scanner. This should scan the Doxygen file and add
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Doxygen Doxyfile source scanner. This should scan the Doxygen file and add
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train | DoxyEmitter | Doxygen Doxyfile emitter | site_scons/site_tools/doxygen.py | def DoxyEmitter(source, target, env):
"""Doxygen Doxyfile emitter"""
# possible output formats and their default values and output locations
output_formats = {
"HTML": ("YES", "html"),
"LATEX": ("YES", "latex"),
"RTF": ("NO", "rtf"),
"MAN": ("YES", "man"),
"XML": ("NO", "xml"),
... | def DoxyEmitter(source, target, env):
"""Doxygen Doxyfile emitter"""
# possible output formats and their default values and output locations
output_formats = {
"HTML": ("YES", "html"),
"LATEX": ("YES", "latex"),
"RTF": ("NO", "rtf"),
"MAN": ("YES", "man"),
"XML": ("NO", "xml"),
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train | generate | Add builders and construction variables for the
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"""
Add builders and construction variables for the
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doxyfile_scanner = env.Scanner(
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Add builders and construction variables for the
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train | PosMetric.reset | Reset metric counter. | metrics/position.py | def reset(self):
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self._positions = []
self._line = 1
self._curr = None # current scope we are analyzing
self._scope = 0
self.language = None | def reset(self):
"""Reset metric counter."""
self._positions = []
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train | PosMetric.add_scope | we identified a scope and add it to positions. | metrics/position.py | def add_scope(self, scope_type, scope_name, scope_start, is_method=False):
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train | PosMetric.process_token | count lines and track position of classes and functions | metrics/position.py | def process_token(self, tok):
"""count lines and track position of classes and functions"""
if tok[0] == Token.Text:
count = tok[1].count('\n')
if count:
self._line += count # adjust linecount
if self._detector.process(tok):
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train | _Solver._unpack_model | Returns data from the OPF model. | pylon/solver.py | def _unpack_model(self, om):
""" Returns data from the OPF model.
"""
buses = om.case.connected_buses
branches = om.case.online_branches
gens = om.case.online_generators
cp = om.get_cost_params()
# Bf = om._Bf
# Pfinj = om._Pfinj
return buses, bra... | def _unpack_model(self, om):
""" Returns data from the OPF model.
"""
buses = om.case.connected_buses
branches = om.case.online_branches
gens = om.case.online_generators
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train | _Solver._dimension_data | Returns the problem dimensions. | pylon/solver.py | def _dimension_data(self, buses, branches, generators):
""" Returns the problem dimensions.
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ipol = [i for i, g in enumerate(generators)
if g.pcost_model == POLYNOMIAL]
ipwl = [i for i, g in enumerate(generators)
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train | _Solver._linear_constraints | Returns the linear problem constraints. | pylon/solver.py | def _linear_constraints(self, om):
""" Returns the linear problem constraints.
"""
A, l, u = om.linear_constraints() # l <= A*x <= u
# Indexes for equality, greater than (unbounded above), less than
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# ieq = flatnonze... | def _linear_constraints(self, om):
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train | _Solver._var_bounds | Returns bounds on the optimisation variables. | pylon/solver.py | def _var_bounds(self):
""" Returns bounds on the optimisation variables.
"""
x0 = array([])
xmin = array([])
xmax = array([])
for var in self.om.vars:
x0 = r_[x0, var.v0]
xmin = r_[xmin, var.vl]
xmax = r_[xmax, var.vu]
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train | _Solver._initial_interior_point | Selects an interior initial point for interior point solver. | pylon/solver.py | def _initial_interior_point(self, buses, generators, xmin, xmax, ny):
""" Selects an interior initial point for interior point solver.
"""
Va = self.om.get_var("Va")
va_refs = [b.v_angle * pi / 180.0 for b in buses
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x0 = (xmin + xmax) / 2... | def _initial_interior_point(self, buses, generators, xmin, xmax, ny):
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"""
Va = self.om.get_var("Va")
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train | DCOPFSolver.solve | Solves DC optimal power flow and returns a results dict. | pylon/solver.py | def solve(self):
""" Solves DC optimal power flow and returns a results dict.
"""
base_mva = self.om.case.base_mva
Bf = self.om._Bf
Pfinj = self.om._Pfinj
# Unpack the OPF model.
bs, ln, gn, cp = self._unpack_model(self.om)
# Compute problem dimensions.
... | def solve(self):
""" Solves DC optimal power flow and returns a results dict.
"""
base_mva = self.om.case.base_mva
Bf = self.om._Bf
Pfinj = self.om._Pfinj
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train | DCOPFSolver._pwl_costs | Returns the piece-wise linear components of the objective function. | pylon/solver.py | def _pwl_costs(self, ny, nxyz, ipwl):
""" Returns the piece-wise linear components of the objective function.
"""
any_pwl = int(ny > 0)
if any_pwl:
y = self.om.get_var("y")
# Sum of y vars.
Npwl = csr_matrix((ones(ny), (zeros(ny), array(ipwl) + y.i1)))... | def _pwl_costs(self, ny, nxyz, ipwl):
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any_pwl = int(ny > 0)
if any_pwl:
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train | DCOPFSolver._quadratic_costs | Returns the quadratic cost components of the objective function. | pylon/solver.py | def _quadratic_costs(self, generators, ipol, nxyz, base_mva):
""" Returns the quadratic cost components of the objective function.
"""
npol = len(ipol)
rnpol = range(npol)
gpol = [g for g in generators if g.pcost_model == POLYNOMIAL]
if [g for g in gpol if len(g.p_cost) ... | def _quadratic_costs(self, generators, ipol, nxyz, base_mva):
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npol = len(ipol)
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train | DCOPFSolver._combine_costs | Combines pwl, polynomial and user-defined costs. | pylon/solver.py | def _combine_costs(self, Npwl, Hpwl, Cpwl, fparm_pwl, any_pwl,
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NN = vstack([n for n in [Npwl, Npol] if n is not None], "csr")
if (Hpwl is not None) and (Hpol is not No... | def _combine_costs(self, Npwl, Hpwl, Cpwl, fparm_pwl, any_pwl,
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""" Combines pwl, polynomial and user-defined costs.
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train | DCOPFSolver._transform_coefficients | Transforms quadratic coefficients for w into coefficients for x. | pylon/solver.py | def _transform_coefficients(self, NN, HHw, CCw, ffparm, polycf,
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"""
nnw = any_pwl + npol + nw
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train | DCOPFSolver._run_opf | Solves the either quadratic or linear program. | pylon/solver.py | def _run_opf(self, HH, CC, AA, ll, uu, xmin, xmax, x0, opt):
""" Solves the either quadratic or linear program.
"""
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solution = qps_pips(None, CC, AA, ll, uu, ... | def _run_opf(self, HH, CC, AA, ll, uu, xmin, xmax, x0, opt):
""" Solves the either quadratic or linear program.
"""
N = self._nieq
if HH.nnz > 0:
solution = qps_pips(HH, CC, AA, ll, uu, xmin, xmax, x0, opt)
else:
solution = qps_pips(None, CC, AA, ll, uu, ... | [
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train | DCOPFSolver._update_solution_data | Returns the voltage angle and generator set-point vectors. | pylon/solver.py | def _update_solution_data(self, s, HH, CC, C0):
""" Returns the voltage angle and generator set-point vectors.
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x = s["x"]
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Va = x[Va_v.i1:Va_v.iN + 1]
Pg = x[Pg_v.i1:Pg_v.iN + 1]
# f = 0.5 * dot(... | def _update_solution_data(self, s, HH, CC, C0):
""" Returns the voltage angle and generator set-point vectors.
"""
x = s["x"]
Va_v = self.om.get_var("Va")
Pg_v = self.om.get_var("Pg")
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Pg = x[Pg_v.i1:Pg_v.iN + 1]
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train | DCOPFSolver._update_case | Calculates the result attribute values. | pylon/solver.py | def _update_case(self, bs, ln, gn, base_mva, Bf, Pfinj, Va, Pg, lmbda):
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"""
Pmis = self.om.get_lin_constraint("Pmis")
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train | PIPSSolver._ref_bus_angle_constraint | Adds a constraint on the reference bus angles. | pylon/solver.py | def _ref_bus_angle_constraint(self, buses, Va, xmin, xmax):
""" Adds a constraint on the reference bus angles.
"""
refs = [bus._i for bus in buses if bus.type == REFERENCE]
Varefs = array([b.v_angle for b in buses if b.type == REFERENCE])
xmin[Va.i1 - 1 + refs] = Varefs
... | def _ref_bus_angle_constraint(self, buses, Va, xmin, xmax):
""" Adds a constraint on the reference bus angles.
"""
refs = [bus._i for bus in buses if bus.type == REFERENCE]
Varefs = array([b.v_angle for b in buses if b.type == REFERENCE])
xmin[Va.i1 - 1 + refs] = Varefs
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train | PIPSSolver.solve | Solves AC optimal power flow. | pylon/solver.py | def solve(self):
""" Solves AC optimal power flow.
"""
case = self.om.case
self._base_mva = case.base_mva
# TODO: Find an explanation for this value.
self.opt["cost_mult"] = 1e-4
# Unpack the OPF model.
self._bs, self._ln, self._gn, _ = self._unpack_model... | def solve(self):
""" Solves AC optimal power flow.
"""
case = self.om.case
self._base_mva = case.base_mva
# TODO: Find an explanation for this value.
self.opt["cost_mult"] = 1e-4
# Unpack the OPF model.
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train | PIPSSolver._solve | Solves using Python Interior Point Solver (PIPS). | pylon/solver.py | def _solve(self, x0, A, l, u, xmin, xmax):
""" Solves using Python Interior Point Solver (PIPS).
"""
s = pips(self._costfcn, x0, A, l, u, xmin, xmax,
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return s | def _solve(self, x0, A, l, u, xmin, xmax):
""" Solves using Python Interior Point Solver (PIPS).
"""
s = pips(self._costfcn, x0, A, l, u, xmin, xmax,
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train | PIPSSolver._f | Evaluates the objective function. | pylon/solver.py | def _f(self, x, user_data=None):
""" Evaluates the objective function.
"""
p_gen = x[self._Pg.i1:self._Pg.iN + 1] # Active generation in p.u.
q_gen = x[self._Qg.i1:self._Qg.iN + 1] # Reactive generation in p.u.
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xx = r_[p_gen, q_gen] * self._... | def _f(self, x, user_data=None):
""" Evaluates the objective function.
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p_gen = x[self._Pg.i1:self._Pg.iN + 1] # Active generation in p.u.
q_gen = x[self._Qg.i1:self._Qg.iN + 1] # Reactive generation in p.u.
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xx = r_[p_gen, q_gen] * self._... | [
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train | PIPSSolver._df | Evaluates the cost gradient. | pylon/solver.py | def _df(self, x, user_data=None):
""" Evaluates the cost gradient.
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p_gen = x[self._Pg.i1:self._Pg.iN + 1] # Active generation in p.u.
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train | PIPSSolver._d2f | Evaluates the cost Hessian. | pylon/solver.py | def _d2f(self, x):
""" Evaluates the cost Hessian.
"""
d2f_dPg2 = lil_matrix((self._ng, 1)) # w.r.t p.u. Pg
d2f_dQg2 = lil_matrix((self._ng, 1)) # w.r.t p.u. Qg]
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d2f_dPg2[i, 0] = polyval(polyder(p_cos... | def _d2f(self, x):
""" Evaluates the cost Hessian.
"""
d2f_dPg2 = lil_matrix((self._ng, 1)) # w.r.t p.u. Pg
d2f_dQg2 = lil_matrix((self._ng, 1)) # w.r.t p.u. Qg]
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train | PIPSSolver._gh | Evaluates the constraint function values. | pylon/solver.py | def _gh(self, x):
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"""
Pgen = x[self._Pg.i1:self._Pg.iN + 1] # Active generation in p.u.
Qgen = x[self._Qg.i1:self._Qg.iN + 1] # Reactive generation in p.u.
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gen.p = Pgen[i] * self._base_m... | def _gh(self, x):
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Pgen = x[self._Pg.i1:self._Pg.iN + 1] # Active generation in p.u.
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train | PIPSSolver._costfcn | Evaluates the objective function, gradient and Hessian for OPF. | pylon/solver.py | def _costfcn(self, x):
""" Evaluates the objective function, gradient and Hessian for OPF.
"""
f = self._f(x)
df = self._df(x)
d2f = self._d2f(x)
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train | PIPSSolver._consfcn | Evaluates nonlinear constraints and their Jacobian for OPF. | pylon/solver.py | def _consfcn(self, x):
""" Evaluates nonlinear constraints and their Jacobian for OPF.
"""
h, g = self._gh(x)
dh, dg = self._dgh(x)
return h, g, dh, dg | def _consfcn(self, x):
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h, g = self._gh(x)
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train | PIPSSolver._hessfcn | Evaluates Hessian of Lagrangian for AC OPF. | pylon/solver.py | def _hessfcn(self, x, lmbda):
""" Evaluates Hessian of Lagrangian for AC OPF.
"""
Pgen = x[self._Pg.i1:self._Pg.iN + 1] # Active generation in p.u.
Qgen = x[self._Qg.i1:self._Qg.iN + 1] # Reactive generation in p.u.
for i, g in enumerate(self._gn):
g.p = Pgen[i] * se... | def _hessfcn(self, x, lmbda):
""" Evaluates Hessian of Lagrangian for AC OPF.
"""
Pgen = x[self._Pg.i1:self._Pg.iN + 1] # Active generation in p.u.
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train | PIPSSolver._update_solution_data | Returns the voltage angle and generator set-point vectors. | pylon/solver.py | def _update_solution_data(self, s):
""" Returns the voltage angle and generator set-point vectors.
"""
x = s["x"]
# Va_var = self.om.get_var("Va")
# Vm_var = self.om.get_var("Vm")
# Pg_var = self.om.get_var("Pg")
# Qg_var = self.om.get_var("Qg")
Va = x[self._... | def _update_solution_data(self, s):
""" Returns the voltage angle and generator set-point vectors.
"""
x = s["x"]
# Va_var = self.om.get_var("Va")
# Vm_var = self.om.get_var("Vm")
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train | PIPSSolver._update_case | Calculates the result attribute values. | pylon/solver.py | def _update_case(self, bs, ln, gn, base_mva, Yf, Yt, Va, Vm, Pg, Qg,lmbda):
""" Calculates the result attribute values.
"""
V = Vm * exp(1j * Va)
# Va_var = self.om.get_var("Va")
Vm_var = self._Vm
Pmis = self.om.get_nln_constraint("Pmis")
Qmis = self.om.get_nln_co... | def _update_case(self, bs, ln, gn, base_mva, Yf, Yt, Va, Vm, Pg, Qg,lmbda):
""" Calculates the result attribute values.
"""
V = Vm * exp(1j * Va)
# Va_var = self.om.get_var("Va")
Vm_var = self._Vm
Pmis = self.om.get_nln_constraint("Pmis")
Qmis = self.om.get_nln_co... | [
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Serializes the given serializable object or collection of serializable objects.
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train | sim | Simulates a Dae model.
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Simulates a Dae model.
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train | PickleReader.read | Loads a pickled case. | pylon/io/pickle.py | def read(self, file_or_filename):
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if isinstance(file_or_filename, basestring):
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train | SLOCMetric.process_token | count comments and non-empty lines that contain code | metrics/sloc.py | def process_token(self, tok):
"""count comments and non-empty lines that contain code"""
if(tok[0].__str__() in ('Token.Comment.Multiline', 'Token.Comment',
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train | SLOCMetric.get_metrics | Calculate ratio_comment_to_code and return with the other values | metrics/sloc.py | def get_metrics(self):
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train | CaseEnvironment.getSensors | Returns the currently visible state of the world as a numpy array
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""" Returns the currently visible state of the world as a numpy array
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"""
Pd = array([b.p_demand for b in self.case.buses if b.type == PQ])
logger.info("State: %s" % list(Pd))
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train | CaseEnvironment.performAction | Perform an action on the world that changes it's internal state. | pyreto/rlopf.py | def performAction(self, action):
""" Perform an action on the world that changes it's internal state.
"""
gs = [g for g in self.case.online_generators if g.bus.type !=REFERENCE]
assert len(action) == len(gs)
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""" Perform an action on the world that changes it's internal state.
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gs = [g for g in self.case.online_generators if g.bus.type !=REFERENCE]
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train | CaseEnvironment.reset | Re-initialises the environment. | pyreto/rlopf.py | def reset(self):
""" Re-initialises the environment.
"""
logger.info("Reseting environment.")
self._step = 0
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gs = [g for g in self.case.online_generators if g.bus.type !=REFERENCE]
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logger.info("Reseting environment.")
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train | OPFExperiment.doEpisodes | Does the the given number of episodes. | pyreto/rlopf.py | def doEpisodes(self, number=1):
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train | AutomaticJSONEncoderClassBuilder.get_json_encoders_for_type | Gets the registered JSON encoder for the given type.
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train | SimpleMessageHandler.handleResponse | handles a response by fireing the response event for the response coming in | contrib/public/services/jsonrpc/__init__.py | def handleResponse(self, resp):
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train | SimpleServiceHandler.handleRequest | handles a request by calling the appropriete method the service exposes | contrib/public/services/jsonrpc/__init__.py | def handleRequest(self, req):
"""handles a request by calling the appropriete method the service exposes"""
name = req["method"]
params = req["params"]
id=req["id"]
obj=None
try: #to get a callable obj
obj = getMethodByName(self.service, name)
except ... | def handleRequest(self, req):
"""handles a request by calling the appropriete method the service exposes"""
name = req["method"]
params = req["params"]
id=req["id"]
obj=None
try: #to get a callable obj
obj = getMethodByName(self.service, name)
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train | SimpleServiceHandler.handleNotification | handles a notification request by calling the appropriete method the service exposes | contrib/public/services/jsonrpc/__init__.py | def handleNotification(self, req):
"""handles a notification request by calling the appropriete method the service exposes"""
name = req["method"]
params = req["params"]
try: #to get a callable obj
obj = getMethodByName(self.service, name)
rslt = obj(*params)
... | def handleNotification(self, req):
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name = req["method"]
params = req["params"]
try: #to get a callable obj
obj = getMethodByName(self.service, name)
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train | PSATReader.read | Parses a PSAT data file and returns a case object
file_or_filename: File object or path to PSAT data file
return: Case object | pylon/io/psat.py | def read(self, file_or_filename):
""" Parses a PSAT data file and returns a case object
file_or_filename: File object or path to PSAT data file
return: Case object
"""
self.file_or_filename = file_or_filename
logger.info("Parsing PSAT case file [%s]." % file_or_... | def read(self, file_or_filename):
""" Parses a PSAT data file and returns a case object
file_or_filename: File object or path to PSAT data file
return: Case object
"""
self.file_or_filename = file_or_filename
logger.info("Parsing PSAT case file [%s]." % file_or_... | [
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train | PSATReader._get_bus_array_construct | Returns a construct for an array of bus data. | pylon/io/psat.py | def _get_bus_array_construct(self):
""" Returns a construct for an array of bus data.
"""
bus_no = integer.setResultsName("bus_no")
v_base = real.setResultsName("v_base") # kV
v_magnitude = Optional(real).setResultsName("v_magnitude")
v_angle = Optional(real).setResultsNa... | def _get_bus_array_construct(self):
""" Returns a construct for an array of bus data.
"""
bus_no = integer.setResultsName("bus_no")
v_base = real.setResultsName("v_base") # kV
v_magnitude = Optional(real).setResultsName("v_magnitude")
v_angle = Optional(real).setResultsNa... | [
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train | PSATReader._get_line_array_construct | Returns a construct for an array of line data. | pylon/io/psat.py | def _get_line_array_construct(self):
""" Returns a construct for an array of line data.
"""
from_bus = integer.setResultsName("fbus")
to_bus = integer.setResultsName("tbus")
s_rating = real.setResultsName("s_rating") # MVA
v_rating = real.setResultsName("v_rating") # kV
... | def _get_line_array_construct(self):
""" Returns a construct for an array of line data.
"""
from_bus = integer.setResultsName("fbus")
to_bus = integer.setResultsName("tbus")
s_rating = real.setResultsName("s_rating") # MVA
v_rating = real.setResultsName("v_rating") # kV
... | [
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train | PSATReader._get_slack_array_construct | Returns a construct for an array of slack bus data. | pylon/io/psat.py | def _get_slack_array_construct(self):
""" Returns a construct for an array of slack bus data.
"""
bus_no = integer.setResultsName("bus_no")
s_rating = real.setResultsName("s_rating") # MVA
v_rating = real.setResultsName("v_rating") # kV
v_magnitude = real.setResultsName("... | def _get_slack_array_construct(self):
""" Returns a construct for an array of slack bus data.
"""
bus_no = integer.setResultsName("bus_no")
s_rating = real.setResultsName("s_rating") # MVA
v_rating = real.setResultsName("v_rating") # kV
v_magnitude = real.setResultsName("... | [
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] | rwl/pylon | python | https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/io/psat.py#L163-L190 | [
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train | PSATReader._get_pv_array_construct | Returns a construct for an array of PV generator data. | pylon/io/psat.py | def _get_pv_array_construct(self):
""" Returns a construct for an array of PV generator data.
"""
bus_no = integer.setResultsName("bus_no")
s_rating = real.setResultsName("s_rating") # MVA
v_rating = real.setResultsName("v_rating") # kV
p = real.setResultsName("p") # p.u.... | def _get_pv_array_construct(self):
""" Returns a construct for an array of PV generator data.
"""
bus_no = integer.setResultsName("bus_no")
s_rating = real.setResultsName("s_rating") # MVA
v_rating = real.setResultsName("v_rating") # kV
p = real.setResultsName("p") # p.u.... | [
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train | PSATReader._get_pq_array_construct | Returns a construct for an array of PQ load data. | pylon/io/psat.py | def _get_pq_array_construct(self):
""" Returns a construct for an array of PQ load data.
"""
bus_no = integer.setResultsName("bus_no")
s_rating = real.setResultsName("s_rating") # MVA
v_rating = real.setResultsName("v_rating") # kV
p = real.setResultsName("p") # p.u.
... | def _get_pq_array_construct(self):
""" Returns a construct for an array of PQ load data.
"""
bus_no = integer.setResultsName("bus_no")
s_rating = real.setResultsName("s_rating") # MVA
v_rating = real.setResultsName("v_rating") # kV
p = real.setResultsName("p") # p.u.
... | [
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] | rwl/pylon | python | https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/io/psat.py#L220-L242 | [
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train | PSATReader._get_demand_array_construct | Returns a construct for an array of power demand data. | pylon/io/psat.py | def _get_demand_array_construct(self):
""" Returns a construct for an array of power demand data.
"""
bus_no = integer.setResultsName("bus_no")
s_rating = real.setResultsName("s_rating") # MVA
p_direction = real.setResultsName("p_direction") # p.u.
q_direction = real.setR... | def _get_demand_array_construct(self):
""" Returns a construct for an array of power demand data.
"""
bus_no = integer.setResultsName("bus_no")
s_rating = real.setResultsName("s_rating") # MVA
p_direction = real.setResultsName("p_direction") # p.u.
q_direction = real.setR... | [
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] | rwl/pylon | python | https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/io/psat.py#L245-L278 | [
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train | PSATReader._get_supply_array_construct | Returns a construct for an array of power supply data. | pylon/io/psat.py | def _get_supply_array_construct(self):
""" Returns a construct for an array of power supply data.
"""
bus_no = integer.setResultsName("bus_no")
s_rating = real.setResultsName("s_rating") # MVA
p_direction = real.setResultsName("p_direction") # CPF
p_bid_max = real.setResu... | def _get_supply_array_construct(self):
""" Returns a construct for an array of power supply data.
"""
bus_no = integer.setResultsName("bus_no")
s_rating = real.setResultsName("s_rating") # MVA
p_direction = real.setResultsName("p_direction") # CPF
p_bid_max = real.setResu... | [
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train | PSATReader._get_generator_ramping_construct | Returns a construct for an array of generator ramping data. | pylon/io/psat.py | def _get_generator_ramping_construct(self):
""" Returns a construct for an array of generator ramping data.
"""
supply_no = integer.setResultsName("supply_no")
s_rating = real.setResultsName("s_rating") # MVA
up_rate = real.setResultsName("up_rate") # p.u./h
down_rate = r... | def _get_generator_ramping_construct(self):
""" Returns a construct for an array of generator ramping data.
"""
supply_no = integer.setResultsName("supply_no")
s_rating = real.setResultsName("s_rating") # MVA
up_rate = real.setResultsName("up_rate") # p.u./h
down_rate = r... | [
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] | rwl/pylon | python | https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/io/psat.py#L319-L340 | [
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train | PSATReader._get_load_ramping_construct | Returns a construct for an array of load ramping data. | pylon/io/psat.py | def _get_load_ramping_construct(self):
""" Returns a construct for an array of load ramping data.
"""
bus_no = integer.setResultsName("bus_no")
s_rating = real.setResultsName("s_rating") # MVA
up_rate = real.setResultsName("up_rate") # p.u./h
down_rate = real.setResultsNa... | def _get_load_ramping_construct(self):
""" Returns a construct for an array of load ramping data.
"""
bus_no = integer.setResultsName("bus_no")
s_rating = real.setResultsName("s_rating") # MVA
up_rate = real.setResultsName("up_rate") # p.u./h
down_rate = real.setResultsNa... | [
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] | rwl/pylon | python | https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/io/psat.py#L343-L363 | [
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"setResultsName"... | 916514255db1ae1661406f0283df756baf960d14 |
train | PSATReader.push_bus | Adds a Bus object to the case. | pylon/io/psat.py | def push_bus(self, tokens):
""" Adds a Bus object to the case.
"""
logger.debug("Pushing bus data: %s" % tokens)
bus = Bus()
bus.name = tokens["bus_no"]
bus.v_magnitude = tokens["v_magnitude"]
bus.v_angle = tokens["v_angle"]
bus.v_magnitude = tokens["v_ma... | def push_bus(self, tokens):
""" Adds a Bus object to the case.
"""
logger.debug("Pushing bus data: %s" % tokens)
bus = Bus()
bus.name = tokens["bus_no"]
bus.v_magnitude = tokens["v_magnitude"]
bus.v_angle = tokens["v_angle"]
bus.v_magnitude = tokens["v_ma... | [
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] | rwl/pylon | python | https://github.com/rwl/pylon/blob/916514255db1ae1661406f0283df756baf960d14/pylon/io/psat.py#L369-L381 | [
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train | PSATReader.sort_buses | Sorts bus list according to name (bus_no). | pylon/io/psat.py | def sort_buses(self, tokens):
""" Sorts bus list according to name (bus_no).
"""
self.case.buses.sort(key=lambda obj: obj.name) | def sort_buses(self, tokens):
""" Sorts bus list according to name (bus_no).
"""
self.case.buses.sort(key=lambda obj: obj.name) | [
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