from pyopus.evaluator.auxfunc import listParamDesc, paramDict
from pyopus.evaluator.performance import PerformanceEvaluator
# If MPI is imported an application not using MPI will behave correctly
# (i.e. only slot 0 will run the program) even when started with mpirun
from pyopus.parallel.mpi import MPI
from pyopus.parallel.cooperative import cOS
import numpy as np
if __name__=='__main__':
# Prepare statistical parameters dictionary with nominal values (0)
nominalStat=paramDict(np.zeros(len(statParams)), statParams.keys())
# Prepare operating parameters dictionary with nominal values
names=opParams.keys()
nominalOp=paramDict(listParamDesc(opParams, names, "init"), names)
# Prepare nominal design parameters dictionaries
names=designParams.keys()
nominalDesign=paramDict(listParamDesc(designParams, names, "init"), names)
# Prepare one corner, module 'tm', nominal op parameters
corners={
'nom': {
'params': nominalOp,
'modules': ['tm']
}
}
# Prepare parallel environment
cOS.setVM(MPI(mirrorMap={'*':'.'}))
},
{
'measure': 'mirr_area',
'norm': Nbelow(800e-12, 100e-12),
'shape': Slinear2(1.0,0.001)
}
]
# Performance and cost evaluators
pe=PerformanceEvaluator(heads, analyses, measures, corners, variables=variables, debug=0)
# Input parameter order in vector x is defined by inOrder.
ce=Aggregator(pe, costDefinition, inOrder, debug=0)
# Initial, low, and high value of input parameters.
xlow=listParamDesc(costInput, inOrder, "lo")
xhi=listParamDesc(costInput, inOrder, "hi")
xinit=listParamDesc(costInput, inOrder, "init")
# Optimizer (Hooke-Jeeves). xlo and xhi must be numpy arrays.
opt=optimizerClass("HookeJeeves")(ce, xlo=xlow, xhi=xhi, maxiter=1000)
# Set initial point. Must be a numpy array; xinit is a python list.
opt.reset(xinit)
# Install reporter plugin.
# Print cost. Print performance every time cost is decreased.
opt.installPlugin(ce.getReporter())
# Install stopper plugin.
# Stop when all requirements are satisfied (all cost contributions are 0).
from definitions import *
from pyopus.evaluator.auxfunc import listParamDesc, paramDict
from pyopus.evaluator.performance import PerformanceEvaluator
# If MPI is imported an application not using MPI will behave correctly
# (i.e. only slot 0 will run the program) even when started with mpirun
from pyopus.parallel.mpi import MPI
from pyopus.parallel.cooperative import cOS
import numpy as np
if __name__ == '__main__':
# Prepare statistical parameters dictionary with nominal values (0)
nominalStat = paramDict(np.zeros(len(statParams)), statParams.keys())
# Prepare operating parameters dictionary with nominal values
names = opParams.keys()
nominalOp = paramDict(listParamDesc(opParams, names, "init"), names)
# Prepare nominal design parameters dictionaries
names = designParams.keys()
nominalDesign = paramDict(listParamDesc(designParams, names, "init"),
names)
# Prepare one corner, module 'tm', nominal op parameters
corners = {'nom': {'params': nominalOp, 'modules': ['tm']}}
# Prepare parallel environment
cOS.setVM(MPI(mirrorMap={'*': '.'}))
# Measures have no corners listed - they are evaluated across all specified corners
pe = PerformanceEvaluator(heads,
analyses,
from pyopus.evaluator.auxfunc import listParamDesc, paramDict
from pyopus.design.cbd import CornerBasedDesign, generateCorners
from pyopus.evaluator.aggregate import formatParameters
# If MPI is imported an application not using MPI will behave correctly
# (i.e. only slot 0 will run the program) even when started with mpirun
from pyopus.parallel.mpi import MPI
from pyopus.parallel.cooperative import cOS
import numpy as np
if __name__ == '__main__':
# Prepare statistical parameters dictionary with nominal values (0)
nominalStat = paramDict(np.zeros(len(statParams)), statParams.keys())
# Prepare nominal design parameters dictionaries
names = designParams.keys()
nominalDesign = paramDict(listParamDesc(designParams, names, "init"),
names)
loDesign = paramDict(listParamDesc(designParams, names, "lo"), names)
# Prepare one corner, module 'tm', nominal op parameters
corners = generateCorners(
paramSpec={
'vdd': [1.7, 1.8, 2.0],
'temperature': [0.0, 25, 100.0]
},
modelSpec={'mos': ['tm', 'wp', 'ws', 'wo', 'wz']})
# Add nominal corner
nominalCorner = {
'nom': {
'params': {
'vdd': 1.8,
pe = PerformanceEvaluator(heads,
analyses,
measures,
corners,
variables=variables,
debug=0)
# Plotter
plotter = WxMplPlotter(visualisation, pe)
# Input parameter order in vector x is defined by inOrder.
ce = Aggregator(pe, costDefinition, inOrder, debug=0)
# Initial, low, high, and step value of input parameters.
# Initial, low, and high value of input parameters.
xlow = listParamDesc(costInput, inOrder, "lo")
xhi = listParamDesc(costInput, inOrder, "hi")
xinit = listParamDesc(costInput, inOrder, "init")
# Optimizer (Hooke-Jeeves). xlo and xhi must be numpy arrays.
opt = optimizerClass("HookeJeeves")(ce,
xlo=xlow,
xhi=xhi,
maxiter=1 + 1 * 1000)
# Set initial point. Must be a numpy array; xinit is a Python list.
opt.reset(xinit)
# Install reporter plugin.
# Print cost. Print performance every time cost is decreased.
opt.installPlugin(ce.getReporter())
from pyopus.design.cbd import CornerBasedDesign, generateCorners
from pyopus.evaluator.aggregate import formatParameters
# If MPI is imported an application not using MPI will behave correctly
# (i.e. only slot 0 will run the program) even when started with mpirun
from pyopus.parallel.mpi import MPI
from pyopus.parallel.cooperative import cOS
import numpy as np
if __name__=='__main__':
# Prepare statistical parameters dictionary with nominal values (0)
nominalStat=paramDict(np.zeros(len(statParams)), statParams.keys())
# Prepare nominal design parameters dictionaries
names=designParams.keys()
nominalDesign=paramDict(listParamDesc(designParams, names, "init"), names)
loDesign=paramDict(listParamDesc(designParams, names, "lo"), names)
# Prepare one corner, module 'tm', nominal op parameters
corners=generateCorners(
paramSpec={
'vdd': [1.7, 1.8, 2.0],
'temperature': [0.0, 25, 100.0]
},
modelSpec={
'mos': ['tm', 'wp', 'ws', 'wo', 'wz']
}
)
# Add nominal corner
nominalCorner={
'nom': {