Package schrodinger :: Package protein :: Module assignment

Module assignment

Module for optimizing hydroxyl, thiol and water orientiations, Chi-flips of asparagine, glutamine and histidine, and protonation states of aspartic acid, glutamic acid, and histidine.

Usage: ProtAssign(st)

Version: 0.14.0rc1

Classes

[hide private]

PropKaException

ProtAssign

Functions

[hide private]

measure(ct, atom1=None, atom2=None, atom3=None, atom4=None, use_xtal=False, max_dist=10.0)

rand(d0, d1, dn, ...)
Random values in a given shape.

randn(d0, d1, dn, ...)
Return a sample (or samples) from the "standard normal" distribution.

report(message_level=1, message='')

Variables

[hide private]

ALLOW_THREADS = 1

BUFSIZE = 8192

CLIP = 0
hash(x)

DEFAULT_LOG_LEVEL = 1

ERR_CALL = 3
hash(x)

ERR_DEFAULT = 0
hash(x)

ERR_DEFAULT2 = 521

ERR_IGNORE = 0
hash(x)

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

LOG_BASIC = 1

LOG_DEBUG = 3
hash(x)

LOG_EXTRA = 2

LOG_FULL_DEBUG = 4

LOG_NONE = 0
hash(x)

LOG_SCORE_DEBUG = 5

MAXDIMS = 32

NAN = nan

NINF = -inf

NZERO = -0.0

NaN = nan

PINF = inf

PZERO = 0.0

RAISE = 2

SHIFT_DIVIDEBYZERO = 0
hash(x)

SHIFT_INVALID = 9

SHIFT_OVERFLOW = 3
hash(x)

SHIFT_UNDERFLOW = 6

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

True_ = True

UFUNC_BUFSIZE_DEFAULT = 8192

UFUNC_PYVALS_NAME = 'UFUNC_PYVALS'

WRAP = 1

__package__ = 'schrodinger.protein'

absolute = <ufunc 'absolute'>

add = <ufunc 'add'>

arccosh = <ufunc 'arccosh'>

arcsinh = <ufunc 'arcsinh'>

arctan = <ufunc 'arctan'>

arctan2 = <ufunc 'arctan2'>

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 at 0x7f597582b250>

cast = {<type 'numpy.void'>: <function <lambda> at 0x7f5976590...

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 at ...

inf = inf

infty = inf

invert = <ufunc 'invert'>

isfinite = <ufunc 'isfinite'>

isinf = <ufunc 'isinf'>

isnan = <ufunc 'isnan'>

label_color = 13

ldexp = <ufunc 'ldexp'>

left_shift = <ufunc 'left_shift'>

less = <ufunc 'less'>

less_equal = <ufunc 'less_equal'>

little_endian = True
hash(x)

log1p = <ufunc 'log1p'>

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 at 0x7f5975823fd0>

minimum = <ufunc 'minimum'>

mod = <ufunc 'remainder'>

modf = <ufunc 'modf'>

multiply = <ufunc 'multiply'>

nan = nan

nbytes = {<type 'numpy.void'>: 0, <type 'numpy.uint64'>: 8, <t...

negative = <ufunc 'negative'>

newaxis = None
hash(x)

nextafter = <ufunc 'nextafter'>

not_equal = <ufunc 'not_equal'>

ogrid = <numpy.lib.index_tricks.nd_grid object at 0x7f597582b110>

pH_high = 'high'

pH_low = 'low'

pH_neutral = 'neutral'

pH_vlow = 'very_low'

pi = 3.14159265359

pka_property = 'r_pa_PropKa_pKa'

r_ = <numpy.lib.index_tricks.RClass object at 0x7f597582b190>

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 at 0x7f597...

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'>

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]

rand(d0, d1, dn, ...)

Random values in a given shape.

Create an array of the given shape and propagate it with
random samples from a uniform distribution
over ``[0, 1)``.

Parameters
----------
d0, d1, ..., dn : int, optional
    The dimensions of the returned array, should all be positive.
    If no argument is given a single Python float is returned.

Returns
-------
out : ndarray, shape ``(d0, d1, ..., dn)``
    Random values.

See Also
--------
random

Notes
-----
This is a convenience function. If you want an interface that
takes a shape-tuple as the first argument, refer to
np.random.random_sample .

Examples
--------
>>> np.random.rand(3,2)
array([[ 0.14022471,  0.96360618],  #random
       [ 0.37601032,  0.25528411],  #random
       [ 0.49313049,  0.94909878]]) #random

randn(d0, d1, dn, ...)

Return a sample (or samples) from the "standard normal" distribution.

If positive, int_like or int-convertible arguments are provided,
`randn` generates an array of shape ``(d0, d1, ..., dn)``, filled
with random floats sampled from a univariate "normal" (Gaussian)
distribution of mean 0 and variance 1 (if any of the :math:`d_i` are
floats, they are first converted to integers by truncation). A single
float randomly sampled from the distribution is returned if no
argument is provided.

This is a convenience function.  If you want an interface that takes a
tuple as the first argument, use `numpy.random.standard_normal` instead.

Parameters
----------
d0, d1, ..., dn : int, optional
    The dimensions of the returned array, should be all positive.
    If no argument is given a single Python float is returned.

Returns
-------
Z : ndarray or float
    A ``(d0, d1, ..., dn)``-shaped array of floating-point samples from
    the standard normal distribution, or a single such float if
    no parameters were supplied.

See Also
--------
random.standard_normal : Similar, but takes a tuple as its argument.

Notes
-----
For random samples from :math:`N(\mu, \sigma^2)`, use:

``sigma * np.random.randn(...) + mu``

Examples
--------
>>> np.random.randn()
2.1923875335537315 #random

Two-by-four array of samples from N(3, 6.25):

>>> 2.5 * np.random.randn(2, 4) + 3
array([[-4.49401501,  4.00950034, -1.81814867,  7.29718677],  #random
       [ 0.39924804,  4.68456316,  4.99394529,  4.84057254]]) #random

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.void'>: <function <lambda> at 0x7f59765905f0>, <type 'nu
mpy.uint64'>: <function <lambda> at 0x7f5976590668>, <type 'numpy.comp
lex128'>: <function <lambda> at 0x7f59765906e0>, <type 'numpy.int64'>:
 <function <lambda> at 0x7f5976590758>, <type 'numpy.float64'>: <funct
ion <lambda> at 0x7f59765907d0>, <type 'numpy.unicode_'>: <function <l
ambda> at 0x7f5976590848>, <type 'numpy.uint32'>: <function <lambda> a
t 0x7f59765908c0>, <type 'numpy.complex64'>: <function <lambda> at 0x7
f5976590938>, <type 'numpy.int32'>: <function <lambda> at 0x7f59765909
...

index_exp

Value:

<numpy.lib.index_tricks.IndexExpression object at 0x7f597582b310>

nbytes

Value:

{<type 'numpy.void'>: 0, <type 'numpy.uint64'>: 8, <type 'numpy.comple
x128'>: 16, <type 'numpy.int64'>: 8, <type 'numpy.float64'>: 8, <type 
'numpy.unicode_'>: 0, <type 'numpy.uint32'>: 4, <type 'numpy.complex64
'>: 8, <type 'numpy.int32'>: 4, <type 'numpy.float32'>: 4, <type 'nump
y.string_'>: 0, <type 'numpy.uint16'>: 2, <type 'numpy.timedelta64'>: 
8, <type 'numpy.int16'>: 2, <type 'numpy.float16'>: 2, <type 'numpy.bo
ol_'>: 1, <type 'numpy.uint8'>: 1, <type 'numpy.datetime64'>: 8, <type
 'numpy.int8'>: 1, <type 'numpy.uint64'>: 8, <type 'numpy.complex256'>
...

s_

Value:

<numpy.lib.index_tricks.IndexExpression object at 0x7f597582b390>

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',
...