Package qubx :: Module model_constraints

Module model_constraints

System of linear constraints for constrained optimization.

There are more informative comments in the source code.

QUB Express is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

QUB Express is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License, named LICENSE.txt, in the QUB Express program directory. If not, see <http://www.gnu.org/licenses/>.

Version: 1.11.0

Functions

[hide private]

AllConstraints(K0, K1, K2, L, V, P, constraints)
Returns combined AutoConstraints(...) and UserConstraints(...)

source code

AutoConstraints(K0, K1, K2, L, V, P)
Returns the constraints (Ain, Bin) [11] which fix unconnected k0 and unsensitive k1.

source code

CalcStdOfK(Acns, Ascal, HessInv)
Returns array([error limit of k for k in K]).

source code

K012toK(K0, K1, K2)
Returns a flat array of the useful (non-diagonal) numbers in log(K0) and K1; first all the K0s, then all the K1s, in canonical (K_index) order.

source code

K01toK(K0, K1)
Returns a flat array of the useful (non-diagonal) numbers in log(K0) and K1; first all the K0s, then all the K1s, in canonical (K_index) order.

source code

KToPars(rates, A, Ainv, B)

source code

K_index(N)
Returns all pairs (a,b) such that (0 <= a,b < N) and (a != b); in canonical order.

source code

KtoK01(K, last_K0)
Returns matrices K0, and K1 filled with numbers from K (see K01toK).

source code

KtoK012(K, last_K0)
Returns matrices K0, K1, K2 filled with numbers from K (see K01toK).

source code

ParsToK(P, A, Ainv, B)

source code

UserConstraints(K0, K1, K2, L, V, P, constraints)
Returns the constraints (Ain, Bin) [11] corresponding to the tuples (name, states) in constraints where name is in ["FixRate", "FixExp", "FixPress", "ScaleRate", "ScaleExp", "ScalePress", "BalanceLoop", "ImbalanceLoop"] and states is a sequence of state indices.

source code

adjusted_svd(A)
Returns U, W, V, Winv, Ainv, Nsv, such that A = U*W*V.T = Ainv.I, and A has rank Nsv.

source code

linear_constraints(Ain, Bin, K0)
Returns A, B, A.I, P0 [12].

source code

parsToK(pars, A, Ainv, B)

source code

reduce_constraints(A, B)
Returns constraint system (A, B) with linearly dependent rows deleted.

source code

start_at_ones(P0)
Returns As, Bs, As.I, S0 [32].

source code

Variables

[hide private]

ALLOW_THREADS = 1

BUFSIZE = 8192

CLIP = 0

ERR_CALL = 3

ERR_DEFAULT = 521

ERR_IGNORE = 0

ERR_LOG = 5

ERR_PRINT = 4

ERR_RAISE = 2

ERR_WARN = 1

FLOATING_POINT_SUPPORT = 1

FPE_DIVIDEBYZERO = 1

FPE_INVALID = 8

FPE_OVERFLOW = 2

FPE_UNDERFLOW = 4

False_ = False

Inf = inf

Infinity = inf

MAXDIMS = 32

MAY_SHARE_BOUNDS = 0

MAY_SHARE_EXACT = -1

NAN = nan

NINF = -inf

NZERO = -0.0

NaN = nan

PINF = inf

PZERO = 0.0

RAISE = 2

SHIFT_DIVIDEBYZERO = 0

SHIFT_INVALID = 9

SHIFT_OVERFLOW = 3

SHIFT_UNDERFLOW = 6

ScalarType = (<type 'int'>, <type 'float'>, <type 'complex'>, ...

True_ = True

UFUNC_BUFSIZE_DEFAULT = 8192

UFUNC_PYVALS_NAME = 'UFUNC_PYVALS'

WRAP = 1

__package__ = 'qubx'

absolute = <ufunc 'absolute'>

add = <ufunc 'add'>

arccos = <ufunc 'arccos'>

arccosh = <ufunc 'arccosh'>

arcsin = <ufunc 'arcsin'>

arcsinh = <ufunc 'arcsinh'>

arctan = <ufunc 'arctan'>

arctan2 = <ufunc 'arctan2'>

arctanh = <ufunc 'arctanh'>

bitwise_and = <ufunc 'bitwise_and'>

bitwise_not = <ufunc 'invert'>

bitwise_or = <ufunc 'bitwise_or'>

bitwise_xor = <ufunc 'bitwise_xor'>

c_ = <numpy.lib.index_tricks.CClass object>

cast = {<type 'numpy.bool_'>: <function <lambda> at 0x7fd1396e...

cbrt = <ufunc 'cbrt'>

ceil = <ufunc 'ceil'>

conj = <ufunc 'conjugate'>

conjugate = <ufunc 'conjugate'>

copysign = <ufunc 'copysign'>

cos = <ufunc 'cos'>

cosh = <ufunc 'cosh'>

deg2rad = <ufunc 'deg2rad'>

degrees = <ufunc 'degrees'>

divide = <ufunc 'divide'>

e = 2.71828182846

equal = <ufunc 'equal'>

euler_gamma = 0.577215664902

exp = <ufunc 'exp'>

exp2 = <ufunc 'exp2'>

expm1 = <ufunc 'expm1'>

fabs = <ufunc 'fabs'>

floor = <ufunc 'floor'>

floor_divide = <ufunc 'floor_divide'>

fmax = <ufunc 'fmax'>

fmin = <ufunc 'fmin'>

fmod = <ufunc 'fmod'>

frexp = <ufunc 'frexp'>

greater = <ufunc 'greater'>

greater_equal = <ufunc 'greater_equal'>

hypot = <ufunc 'hypot'>

index_exp = <numpy.lib.index_tricks.IndexExpression object>

inf = inf

infty = inf

invert = <ufunc 'invert'>

isfinite = <ufunc 'isfinite'>

isinf = <ufunc 'isinf'>

isnan = <ufunc 'isnan'>

ldexp = <ufunc 'ldexp'>

left_shift = <ufunc 'left_shift'>

less = <ufunc 'less'>

less_equal = <ufunc 'less_equal'>

little_endian = True

log = <ufunc 'log'>

log10 = <ufunc 'log10'>

log1p = <ufunc 'log1p'>

log2 = <ufunc 'log2'>

logaddexp = <ufunc 'logaddexp'>

logaddexp2 = <ufunc 'logaddexp2'>

logical_and = <ufunc 'logical_and'>

logical_not = <ufunc 'logical_not'>

logical_or = <ufunc 'logical_or'>

logical_xor = <ufunc 'logical_xor'>

maximum = <ufunc 'maximum'>

mgrid = <numpy.lib.index_tricks.nd_grid object>

minimum = <ufunc 'minimum'>

mod = <ufunc 'remainder'>

modf = <ufunc 'modf'>

multiply = <ufunc 'multiply'>

nan = nan

nbytes = {<type 'numpy.bool_'>: 1, <type 'numpy.timedelta64'>:...

negative = <ufunc 'negative'>

newaxis = None
hash(x)

nextafter = <ufunc 'nextafter'>

not_equal = <ufunc 'not_equal'>

ogrid = <numpy.lib.index_tricks.nd_grid object>

pi = 3.14159265359

power = <ufunc 'power'>

r_ = <numpy.lib.index_tricks.RClass object>

rad2deg = <ufunc 'rad2deg'>

radians = <ufunc 'radians'>

reciprocal = <ufunc 'reciprocal'>

remainder = <ufunc 'remainder'>

right_shift = <ufunc 'right_shift'>

rint = <ufunc 'rint'>

s_ = <numpy.lib.index_tricks.IndexExpression object>

sctypeDict = {0: <type 'numpy.bool_'>, 1: <type 'numpy.int8'>,...

sctypeNA = {'?': 'Bool', 'B': 'UInt8', 'Bool': <type 'numpy.bo...

sctypes = {'complex': [<type 'numpy.complex64'>, <type 'numpy....

sign = <ufunc 'sign'>

signbit = <ufunc 'signbit'>

sin = <ufunc 'sin'>

sinh = <ufunc 'sinh'>

spacing = <ufunc 'spacing'>

sqrt = <ufunc 'sqrt'>

square = <ufunc 'square'>

subtract = <ufunc 'subtract'>

tan = <ufunc 'tan'>

tanh = <ufunc 'tanh'>

true_divide = <ufunc 'true_divide'>

trunc = <ufunc 'trunc'>

typeDict = {0: <type 'numpy.bool_'>, 1: <type 'numpy.int8'>, 2...

typeNA = {'?': 'Bool', 'B': 'UInt8', 'Bool': <type 'numpy.bool...

typecodes = {'All': '?bhilqpBHILQPefdgFDGSUVOMm', 'AllFloat': ...

Function Details

[hide private]

CalcStdOfK(Acns, Ascal, HessInv)

source code

Returns array([error limit of k for k in K]).

Parameters:

Acns - the Nk x Np matrix from linear_constraints; K = Acns*P + B
Ascal - the Np x Np matrix from start_at_ones
HessInv - inverse Hessian matrix, as returned by qubx.optimize

KtoK01(K, last_K0)

source code

Returns matrices K0, and K1 filled with numbers from K (see K01toK). You must provide last_K0 to disambiguate between K[i] == 0 == log(1) vs. K0[i] == 0 == unconnected.

KtoK012(K, last_K0)

source code

Returns matrices K0, K1, K2 filled with numbers from K (see K01toK). You must provide last_K0 to disambiguate between K[i] == 0 == log(1) vs. K0[i] == 0 == unconnected.

UserConstraints(K0, K1, K2, L, V, P, constraints)

source code

Returns the constraints (Ain, Bin) [11] corresponding to the tuples (name, states) in constraints where name is in ["FixRate", "FixExp", "FixPress", "ScaleRate", "ScaleExp", "ScalePress", "BalanceLoop", "ImbalanceLoop"] and states is a sequence of state indices. The first four names require 2,2,4,4 states respectively; the last two require at least 3.

Variables Details

[hide private]

ScalarType

Value:

(<type 'int'>,
 <type 'float'>,
 <type 'complex'>,
 <type 'long'>,
 <type 'bool'>,
 <type 'str'>,
 <type 'unicode'>,
 <type 'buffer'>,
...

cast

Value:

{<type 'numpy.bool_'>: <function <lambda> at 0x7fd1396ed758>, <type 'n
umpy.timedelta64'>: <function <lambda> at 0x7fd1396ed7d0>, <type 'nump
y.float16'>: <function <lambda> at 0x7fd1396ed848>, <type 'numpy.uint8
'>: <function <lambda> at 0x7fd1396ed8c0>, <type 'numpy.int8'>: <funct
ion <lambda> at 0x7fd1396ed938>, <type 'numpy.object_'>: <function <la
mbda> at 0x7fd1396ed9b0>, <type 'numpy.datetime64'>: <function <lambda
> at 0x7fd1396eda28>, <type 'numpy.uint64'>: <function <lambda> at 0x7
fd1396edaa0>, <type 'numpy.int64'>: <function <lambda> at 0x7fd1396edb
...

nbytes

Value:

{<type 'numpy.bool_'>: 1, <type 'numpy.timedelta64'>: 8, <type 'numpy.
float16'>: 2, <type 'numpy.uint8'>: 1, <type 'numpy.int8'>: 1, <type '
numpy.object_'>: 8, <type 'numpy.datetime64'>: 8, <type 'numpy.uint64'
>: 8, <type 'numpy.int64'>: 8, <type 'numpy.void'>: 0, <type 'numpy.co
mplex256'>: 32, <type 'numpy.float128'>: 16, <type 'numpy.uint64'>: 8,
 <type 'numpy.int64'>: 8, <type 'numpy.unicode_'>: 0, <type 'numpy.com
plex128'>: 16, <type 'numpy.float64'>: 8, <type 'numpy.uint32'>: 4, <t
ype 'numpy.int32'>: 4, <type 'numpy.string_'>: 0, <type 'numpy.complex
...

sctypeDict

Value:

{0: <type 'numpy.bool_'>,
 1: <type 'numpy.int8'>,
 2: <type 'numpy.uint8'>,
 3: <type 'numpy.int16'>,
 4: <type 'numpy.uint16'>,
 5: <type 'numpy.int32'>,
 6: <type 'numpy.uint32'>,
 7: <type 'numpy.int64'>,
...

sctypeNA

Value:

{'?': 'Bool',
 'B': 'UInt8',
 'Bool': <type 'numpy.bool_'>,
 'Complex128': <type 'numpy.complex256'>,
 'Complex32': <type 'numpy.complex64'>,
 'Complex64': <type 'numpy.complex128'>,
 'D': 'Complex64',
 'Datetime64': <type 'numpy.datetime64'>,
...

sctypes

Value:

{'complex': [<type 'numpy.complex64'>,
             <type 'numpy.complex128'>,
             <type 'numpy.complex256'>],
 'float': [<type 'numpy.float16'>,
           <type 'numpy.float32'>,
           <type 'numpy.float64'>,
           <type 'numpy.float128'>],
 'int': [<type 'numpy.int8'>, <type 'numpy.int16'>, <type 'numpy.int32
...

typeDict

Value:

{0: <type 'numpy.bool_'>,
 1: <type 'numpy.int8'>,
 2: <type 'numpy.uint8'>,
 3: <type 'numpy.int16'>,
 4: <type 'numpy.uint16'>,
 5: <type 'numpy.int32'>,
 6: <type 'numpy.uint32'>,
 7: <type 'numpy.int64'>,
...

typeNA

Value:

{'?': 'Bool',
 'B': 'UInt8',
 'Bool': <type 'numpy.bool_'>,
 'Complex128': <type 'numpy.complex256'>,
 'Complex32': <type 'numpy.complex64'>,
 'Complex64': <type 'numpy.complex128'>,
 'D': 'Complex64',
 'Datetime64': <type 'numpy.datetime64'>,
...

typecodes

Value:

{'All': '?bhilqpBHILQPefdgFDGSUVOMm',
 'AllFloat': 'efdgFDG',
 'AllInteger': 'bBhHiIlLqQpP',
 'Character': 'c',
 'Complex': 'FDG',
 'Datetime': 'Mm',
 'Float': 'efdg',
 'Integer': 'bhilqp',
...