misc.calculus

Package misc :: Module calculus |
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# Module calculus

source code

Version: 0.6.0

Functions | [hide private]
construct_4_blocks_matrix(blocks) construct block matrix line by line input : [[A1,A2],[A3,A4]] | source code
rand(...) Return an array of the given dimensions which is initialized to random numbers from a uniform distribution in the range [0,1). | source code
randn(...) Returns zero-mean, unit-variance Gaussian random numbers in an array of shape (d0, d1, ..., dn). | source code
second_order_taylorisation(expr, shocks, covariances) | source code

Variables | [hide private]
| ALLOW_THREADS = `1`
| BUFSIZE = `10000`
| C = `<sympy.core.basic.ClassesRegistry instance at 0x9fab6cc>`
| CLIP = `0`
| Catalan = `Catalan`
| E = `E`
| ERR_CALL = `3`
| ERR_DEFAULT = `0`
| ERR_DEFAULT2 = `2084`
| ERR_IGNORE = `0`
| ERR_LOG = `5`
| ERR_PRINT = `4`
| ERR_RAISE = `2`
| ERR_WARN = `1`
| EulerGamma = `EulerGamma`
| FLOATING_POINT_SUPPORT = `1`
| FPE_DIVIDEBYZERO = `1`
| FPE_INVALID = `8`
| FPE_OVERFLOW = `2`
| FPE_UNDERFLOW = `4`
| False_ = `False`
| GoldenRatio = `GoldenRatio`
| I = `I`
| Inf = `inf`
| Infinity = `inf`
| MAXDIMS = `32`
| NAN = `nan`
| NINF = `-inf`
| NZERO = `-0.0`
| NaN = `nan`
| PINF = `inf`
| PZERO = `0.0`
| RAISE = `2`
| S = `S`
| SHIFT_DIVIDEBYZERO = `0`
| SHIFT_INVALID = `9`
| SHIFT_OVERFLOW = `3`
| SHIFT_UNDERFLOW = `6`
| ScalarType = ``(`<type 'int'>`,` <type 'float'>`,` <type 'complex'>`,` `...``
| True_ = `True`
| UFUNC_BUFSIZE_DEFAULT = `10000`
| UFUNC_PYVALS_NAME = ``'``UFUNC_PYVALS``'``
| WRAP = `1`
| 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 0xa3b50ec>`
| cast = `{<type 'numpy.int64'>: <function <lambda> at 0xa28b3ac>`...``
| ceil = `<ufunc 'ceil'>`
| conj = `<ufunc 'conjugate'>`
| conjugate = `<ufunc 'conjugate'>`
| cos = `<ufunc 'cos'>`
| cosh = `<ufunc 'cosh'>`
| degrees = `<ufunc 'degrees'>`
| divide = `<ufunc 'divide'>`
| e = `2.71828182846`
| equal = `<ufunc 'equal'>`
| exp = `<ufunc 'exp'>`
| expm1 = `<ufunc 'expm1'>`
| fabs = `<ufunc 'fabs'>`
| floor = `<ufunc 'floor'>`
| floor_divide = `<ufunc 'floor_divide'>`
| 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'>`
| ldexp = `<ufunc 'ldexp'>`
| left_shift = `<ufunc 'left_shift'>`
| less = `<ufunc 'less'>`
| less_equal = `<ufunc 'less_equal'>`
| little_endian = `True`
| log1p = `<ufunc 'log1p'>`
| 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 0xa3affcc>`
| minimum = `<ufunc 'minimum'>`
| mod = `<ufunc 'remainder'>`
| modf = `<ufunc 'modf'>`
| multiply = `<ufunc 'multiply'>`
| nan = `nan`
| nbytes = `{<type 'numpy.int64'>: 8, <type 'numpy.int16'>: 2, <t`...``
| negative = `<ufunc 'negative'>`
| newaxis = `None`
| not_equal = `<ufunc 'not_equal'>`
| ogrid = `<numpy.lib.index_tricks.nd_grid object at 0xa3affec>`
| ones_like = `<ufunc 'ones_like'>`
| oo = `oo`
| pi = `3.14159265359`
| pkgload = `<numpy._import_tools.PackageLoader instance at 0xa51`...``
| r_ = `<numpy.lib.index_tricks.RClass object at 0xa3b508c>`
| 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 0xa3b52ac>`
| sctypeDict = ``{`0`:` <type 'numpy.bool_'>`,` 1`:` <type 'numpy.int8'>`,``...``
| sctypeNA = ``{``'``?``'``:` `'``Bool``'``,` `'``B``'``:` `'``UInt8``'``,` `'``Bool``'``:` <type 'numpy.bo`...``
| sctypes = ``{``'``complex``'``:` `[`<type 'numpy.complex64'>`,` <type 'numpy.`...``
| sieve = `<Sieve with 6 primes sieved: 2, 3, 5, ... 11, 13>`
| sign = `<ufunc 'sign'>`
| signbit = `<ufunc 'signbit'>`
| sin = `<ufunc 'sin'>`
| sinh = `<ufunc 'sinh'>`
| square = `<ufunc 'square'>`
| subtract = `<ufunc 'subtract'>`
| tan = `<ufunc 'tan'>`
| tanh = `<ufunc 'tanh'>`
| true_divide = `<ufunc 'true_divide'>`
| typeDict = ``{`0`:` <type 'numpy.bool_'>`,` 1`:` <type 'numpy.int8'>`,` 2`...``
| typeNA = ``{``'``?``'``:` `'``Bool``'``,` `'``B``'``:` `'``UInt8``'``,` `'``Bool``'``:` <type 'numpy.bool`...``
| typecodes = ``{``'``All``'``:` `'``?bhilqpBHILQPfdgFDGSUVO``'``,` `'``AllFloat``'``:` `'``fd``...``
| zoo = `zoo`

Function Details | [hide private]

### rand(...) | source code
    Return an array of the given dimensions which is initialized to
    random numbers from a uniform distribution in the range [0,1).

    rand(d0, d1, ..., dn) -> random values

    Note:  This is a convenience function. If you want an
                interface that takes a tuple as the first argument
                use numpy.random.random_sample(shape_tuple).

### randn(...) | source code
    Returns zero-mean, unit-variance Gaussian random numbers in an
    array of shape (d0, d1, ..., dn).

    randn(d0, d1, ..., dn) -> random values

    Note:  This is a convenience function. If you want an
                interface that takes a tuple as the first argument
                use numpy.random.standard_normal(shape_tuple).

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.int64'>: <function <lambda> at 0xa28b3ac>, <type 'numpy.
      int16'>: <function <lambda> at 0xa28b3e4>, <type 'numpy.float64'>: <fu
      nction <lambda> at 0xa28b614>, <type 'numpy.uint16'>: <function <lambd
      a> at 0xa28b454>, <type 'numpy.int8'>: <function <lambda> at 0xa28b48c
      >, <type 'numpy.string_'>: <function <lambda> at 0xa28b4c4>, <type 'nu
      mpy.float96'>: <function <lambda> at 0xa28b4fc>, <type 'numpy.uint32'>
      : <function <lambda> at 0xa28b534>, <type 'numpy.int32'>: <function <l
      ambda> at 0xa28b41c>, <type 'numpy.complex64'>: <function <lambda> at
      ...

  ### index_exp Value:
      <numpy.lib.index_tricks.IndexExpression object at 0xa3b526c>

  ### nbytes Value:
      {<type 'numpy.int64'>: 8, <type 'numpy.int16'>: 2, <type 'numpy.int32'
      >: 4, <type 'numpy.uint16'>: 2, <type 'numpy.int8'>: 1, <type 'numpy.s
      tring_'>: 0, <type 'numpy.float96'>: 12, <type 'numpy.uint32'>: 4, <ty
      pe 'numpy.unicode_'>: 0, <type 'numpy.complex64'>: 8, <type 'numpy.uin
      t32'>: 4, <type 'numpy.float64'>: 8, <type 'numpy.void'>: 0, <type 'nu
      mpy.int32'>: 4, <type 'numpy.complex128'>: 16, <type 'numpy.uint64'>:
      8, <type 'numpy.complex192'>: 24, <type 'numpy.object_'>: 4, <type 'nu
      mpy.float32'>: 4, <type 'numpy.bool_'>: 1, <type 'numpy.uint8'>: 1}

  ### pkgload Value:
      <numpy._import_tools.PackageLoader instance at 0xa519b6c>

  ### 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.int32'>,
      ...

  ### sctypeNA Value:
      {'?': 'Bool',
       'B': 'UInt8',
       'Bool': <type 'numpy.bool_'>,
       'Complex32': <type 'numpy.complex64'>,
       'Complex64': <type 'numpy.complex128'>,
       'Complex96': <type 'numpy.complex192'>,
       'D': 'Complex64',
       'F': 'Complex32',
      ...

  ### sctypes Value:
      {'complex': [<type 'numpy.complex64'>,
                   <type 'numpy.complex128'>,
                   <type 'numpy.complex192'>],
       'float': [<type 'numpy.float32'>,
                 <type 'numpy.float64'>,
                 <type 'numpy.float96'>],
       'int': [<type 'numpy.int8'>,
               <type 'numpy.int16'>,
      ...

  ### 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.int32'>,
      ...

  ### typeNA Value:
      {'?': 'Bool',
       'B': 'UInt8',
       'Bool': <type 'numpy.bool_'>,
       'Complex32': <type 'numpy.complex64'>,
       'Complex64': <type 'numpy.complex128'>,
       'Complex96': <type 'numpy.complex192'>,
       'D': 'Complex64',
       'F': 'Complex32',
      ...

  ### typecodes Value:
      {'All': '?bhilqpBHILQPfdgFDGSUVO',
       'AllFloat': 'fdgFDG',
       'AllInteger': 'bBhHiIlLqQpP',
       'Character': 'c',
       'Complex': 'FDG',
       'Float': 'fdg',
       'Integer': 'bhilqp',
       'UnsignedInteger': 'BHILQP'}