"""
The MIT License (MIT)
Copyright (c) [2015-2022] [Andrew Annex]
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
The MIT License (MIT)
Copyright (c) 2013 Philipp Rasch
Permission is hereby granted, free of charge, to any person obtaining a copy of
this software and associated documentation files (the "Software"), to deal in
the Software without restriction, including without limitation the rights to
use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
the Software, and to permit persons to whom the Software is furnished to do so,
subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
"""
import collections.abc as collections_abc
from array import array
from ctypes import (
c_char_p,
c_int,
c_double,
c_char,
c_void_p,
sizeof,
Array,
create_string_buffer,
cast,
Structure,
string_at,
POINTER,
)
import numpy
from numpy import ctypeslib as numpc
# Collection of supporting functions for wrapper functions
__author__ = "AndrewAnnex"
[docs]def to_double_vector(x):
return DoubleArray.from_param(param=x)
[docs]def to_double_matrix(x):
return DoubleMatrix.from_param(param=x)
[docs]def to_int_vector(x):
return IntArray.from_param(param=x)
[docs]def to_int_matrix(x):
return IntMatrix.from_param(param=x)
[docs]def is_iterable(i) -> bool:
"""
From stackoverflow
https://stackoverflow.com/questions/1055360/how-to-tell-a-variable-is-iterable-but-not-a-string/44328500#44328500
:param i: input collection
:return: if the input is iterable but not a string
"""
return isinstance(i, collections_abc.Iterable) and not isinstance(i, str)
[docs]def to_python_string(in_string):
if isinstance(in_string, c_char_p):
return to_python_string(in_string.value)
else:
return bytes.decode(string_at(in_string), errors="ignore").rstrip()
[docs]def empty_char_array(x_len=None, y_len=None):
if not y_len:
y_len = 1
if not x_len:
x_len = 1
if isinstance(x_len, c_int):
x_len = x_len.value
if isinstance(y_len, c_int):
y_len = y_len.value
return ((c_char * x_len) * y_len)()
[docs]def empty_double_matrix(x=3, y=3):
if isinstance(x, c_int):
x = x.value
if isinstance(y, c_int):
y = y.value
return ((c_double * x) * y)()
[docs]def empty_double_vector(n):
if isinstance(n, c_int):
n = n.value
assert isinstance(n, int)
return (c_double * n)()
[docs]def empty_int_matrix(x=3, y=3):
if isinstance(x, c_int):
x = x.value
if isinstance(y, c_int):
y = y.value
return ((c_int * x) * y)()
[docs]def empty_int_vector(n):
if isinstance(n, c_int):
n = n.value
assert isinstance(n, int)
return (c_int * n)()
[docs]def c_vector_to_python(x):
"""
Convert the c vector data into the correct python data type
(numpy arrays or strings)
:param x: ctypes array
:return: Iterable
"""
if isinstance(x[0], bool):
return numpy.frombuffer(x, dtype=numpy.bool).copy()
elif isinstance(x[0], int):
return numpy.frombuffer(x, dtype=numpy.int32).copy()
elif isinstance(x[0], float):
return numpy.frombuffer(x, dtype=numpy.float64).copy()
elif isinstance(x[0].value, bytes):
return [to_python_string(y) for y in x]
[docs]def c_int_vector_to_bool_python(x):
return numpc.as_array(x).copy().astype(bool)
[docs]def c_matrix_to_numpy(x):
"""
Convert a ctypes 2d array (or matrix) into a numpy array for python use
:param x: thing to convert
:return: numpy.ndarray
"""
return numpc.as_array(x).copy()
[docs]def string_to_char_p(inobject, inlen=None):
"""
convert a python string to a char_p
:param inobject: input string, int for getting null string of length of int
:param inlen: optional parameter, length of a given string can be specified
:return:
"""
if inlen and isinstance(inobject, str):
return create_string_buffer(inobject.encode(encoding="UTF-8"), inlen)
if isinstance(inobject, bytes):
return inobject
if isinstance(inobject, c_int):
return string_to_char_p(" " * inobject.value)
if isinstance(inobject, int):
return string_to_char_p(" " * inobject)
if isinstance(inobject, numpy.str_):
return c_char_p(inobject.encode(encoding="utf-8"))
return c_char_p(inobject.encode(encoding="UTF-8"))
[docs]def list_to_char_array(arg, x_len=None, y_len=None):
assert is_iterable(arg)
if not y_len:
y_len = len(arg)
if not x_len:
x_len = max(len(s) for s in arg) + 1
if isinstance(x_len, c_int):
x_len = x_len.value
if isinstance(y_len, c_int):
y_len = y_len.value
return ((c_char * x_len) * y_len)(*[string_to_char_p(l, inlen=x_len) for l in arg])
[docs]def list_to_char_array_ptr(input, x_len=None, y_len=None):
return cast(list_to_char_array(input, x_len=x_len, y_len=y_len), c_char_p)
[docs]class DoubleArrayType:
"""
Class type that will handle all double vectors,
inspiration from python cookbook 3rd edition
"""
[docs] def from_param(self, param):
if hasattr(param, "__array_interface__"):
return self.from_ndarray(param)
elif isinstance(param, Array):
return param
elif isinstance(param, (list, tuple)):
return self.from_list(param)
elif isinstance(param, array):
if param.typecode != "d":
raise TypeError("must be an array of doubles")
return self.from_list(param)
else:
raise TypeError(f"Can't convert {type(param)}")
# Cast from lists/tuples
[docs] def from_list(self, param):
val = ((c_double) * len(param))(*param)
return val
# Cast from a numpy array,
[docs] def from_ndarray(self, param):
# return param.data_as(POINTER(c_double))
# the above older method does not work with
# functions which take vectors of known size
return numpc.as_ctypes(
param.astype(numpy.float64, casting="same_kind", copy=False)
)
[docs]class DoubleMatrixType:
"""
Class type that will handle all 2d double arrays,
inspiration from python cookbook 3rd edition
"""
[docs] def from_param(self, param):
if hasattr(param, "__array_interface__"):
return self.from_ndarray(param)
elif isinstance(param, Array):
return param
elif isinstance(param, (list, tuple)):
return self.from_list(param)
else:
raise TypeError(f"Can't convert {type(param)}")
# Cast from lists/tuples
[docs] def from_list(self, param):
val = ((c_double * len(param[0])) * len(param))(
*[DoubleArray.from_param(x) for x in param]
)
return val
# Cast from a numpy array
[docs] def from_ndarray(self, param):
return numpc.as_ctypes(
param.astype(numpy.float64, casting="same_kind", copy=False)
)
[docs]class IntArrayType:
"""
Class type that will handle all int vectors,
inspiration from python cookbook 3rd edition
"""
[docs] def from_param(self, param):
if hasattr(param, "__array_interface__"):
return self.from_ndarray(param)
elif isinstance(param, Array):
return param
elif isinstance(param, (list, tuple)):
return self.from_list(param)
elif isinstance(param, array):
if param.typecode != "i":
raise TypeError("must be an array of ints")
return self.from_list(param)
else:
raise TypeError(f"Can't convert {type(param)}")
# Cast from lists/tuples
[docs] def from_list(self, param):
val = ((c_int) * len(param))(*param)
return val
# Cast from a numpy array
[docs] def from_ndarray(self, param):
# cspice always uses a int size half as big as the float, ie int32 if a float64 system default
return numpc.as_ctypes(
param.astype(numpy.int32, casting="same_kind", copy=False)
)
[docs]class IntMatrixType:
"""
Class type that will handle all 2d int arrays,
inspiration from python cookbook 3rd edition
"""
[docs] def from_param(self, param):
if hasattr(param, "__array_interface__"):
return self.from_ndarray(param)
elif isinstance(param, Array):
return param
elif isinstance(param, (list, tuple)):
return self.from_list(param)
else:
raise TypeError(f"Can't convert {type(param)}")
# Cast from lists/tuples
[docs] def from_list(self, param):
val = ((c_int * len(param[0])) * len(param))(
*[IntArray.from_param(x) for x in param]
)
return val
# Cast from a numpy array
[docs] def from_ndarray(self, param):
# cspice always uses a int size half as big as the float, ie int32 if a float64 system default
return numpc.as_ctypes(
param.astype(numpy.int32, casting="same_kind", copy=False)
)
DoubleArray = DoubleArrayType()
DoubleMatrix = DoubleMatrixType()
IntArray = IntArrayType()
IntMatrix = IntMatrixType()
[docs]class Plane(Structure):
_fields_ = [("_normal", c_double * 3), ("_constant", c_double)]
@property
def normal(self):
return c_vector_to_python(self._normal)
@property
def constant(self):
return self._constant
def __str__(self) -> str:
return f"<SpicePlane: normal={', '.join([str(x) for x in self._normal])}; constant={self._constant}>"
[docs]class Ellipse(Structure):
_fields_ = [
("_center", c_double * 3),
("_semi_major", c_double * 3),
("_semi_minor", c_double * 3),
]
@property
def center(self):
return c_vector_to_python(self._center)
@property
def semi_major(self):
return c_vector_to_python(self._semi_major)
@property
def semi_minor(self):
return c_vector_to_python(self._semi_minor)
def __str__(self) -> str:
return f"<SpiceEllipse: center = {self.center} semi_major = {self.semi_major}, semi_minor = {self.semi_minor}>"
[docs]class DataType(object):
SPICE_CHR = 0
SPICE_DP = 1
SPICE_INT = 2
SPICE_TIME = 3
SPICE_BOOL = 4
CHR = 0
DP = 1
INT = 2
TIME = 3
BOOL = 4
def __init__(self) -> None:
pass
[docs]class SpiceDSKDescr(Structure):
_fields_ = [
("_surfce", c_int),
("_center", c_int),
("_dclass", c_int),
("_dtype", c_int),
("_frmcde", c_int),
("_corsys", c_int),
("_corpar", c_double * 10),
("_co1min", c_double),
("_co1max", c_double),
("_co2min", c_double),
("_co2max", c_double),
("_co3min", c_double),
("_co3max", c_double),
("_start", c_double),
("_stop", c_double),
]
@property
def surfce(self):
return self._surfce
@property
def center(self):
return self._center
@property
def dclass(self):
return self._dclass
@property
def dtype(self):
return self._dtype
@property
def frmcde(self):
return self._frmcde
@property
def corsys(self):
return self._corsys
@property
def corpar(self):
return c_vector_to_python(self._corpar)
@property
def co1min(self):
return self._co1min
@property
def co1max(self):
return self._co1max
@property
def co2min(self):
return self._co2min
@property
def co2max(self):
return self._co2max
@property
def co3min(self):
return self._co3min
@property
def co3max(self):
return self._co3max
@property
def start(self):
return self._start
@property
def stop(self):
return self._stop
[docs]class SpiceDLADescr(Structure):
_fields_ = [
("_bwdptr", c_int),
("_fwdptr", c_int),
("_ibase", c_int),
("_isize", c_int),
("_dbase", c_int),
("_dsize", c_int),
("_cbase", c_int),
("_csize", c_int),
]
@property
def bwdptr(self):
return self._bwdptr
@property
def fwdptr(self):
return self._fwdptr
@property
def ibase(self):
return self._ibase
@property
def isize(self):
return self._isize
@property
def dbase(self):
return self._dbase
@property
def dsize(self):
return self._dsize
@property
def cbase(self):
return self._cbase
@property
def csize(self):
return self._csize
[docs]class SpiceEKDataType(c_int):
_SPICE_CHR = c_int(0)
_SPICE_DP = c_int(1)
_SPICE_INT = c_int(2)
_SPICE_TIME = c_int(3)
_SPICE_BOOL = c_int(4)
_fields_ = [
("SPICE_CHR", _SPICE_CHR),
("SPICE_DP", _SPICE_DP),
("SPICE_INT", _SPICE_INT),
("SPICE_TIME", _SPICE_TIME),
("SPICE_BOOL", _SPICE_BOOL),
]
SPICE_CHR = _SPICE_CHR.value
SPICE_DP = _SPICE_DP.value
SPICE_INT = _SPICE_INT.value
SPICE_TIME = _SPICE_TIME.value
SPICE_BOOL = _SPICE_BOOL.value
[docs]def empty_spice_ek_data_type_vector(n):
if isinstance(n, c_int):
n = n.value
assert isinstance(n, int)
return (SpiceEKDataType * n)()
[docs]class SpiceEKExprClass(c_int):
_SPICE_EK_EXP_COL = c_int(0)
_SPICE_EK_EXP_FUNC = c_int(1)
_SPICE_EK_EXP_EXPR = c_int(2)
_fields_ = [
("SPICE_EK_EXP_COL", _SPICE_EK_EXP_COL),
("SPICE_EK_EXP_FUNC", _SPICE_EK_EXP_FUNC),
("SPICE_EK_EXP_EXPR", _SPICE_EK_EXP_EXPR),
]
SPICE_EK_EXP_COL = _SPICE_EK_EXP_COL.value
SPICE_EK_EXP_FUNC = _SPICE_EK_EXP_FUNC.value
SPICE_EK_EXP_EXPR = _SPICE_EK_EXP_EXPR.value
[docs]class SpiceSPK18Subtype(c_int):
_S18TP0 = c_int(0)
_S18TP1 = c_int(1)
S18TP0 = _S18TP0.value
S18TP1 = _S18TP1.value
_fields_ = [("S18TP0", _S18TP0), ("S18TP1", _S18TP1)]
[docs]def empty_spice_ek_expr_class_vector(n):
if isinstance(n, c_int):
n = n.value
assert isinstance(n, int)
return (SpiceEKExprClass * n)()
[docs]class SpiceEKAttDsc(Structure):
_fields_ = [
("_cclass", c_int),
("_dtype", SpiceEKDataType),
("_strlen", c_int),
("_size", c_int),
("_indexd", c_int),
("_nullok", c_int),
]
@property
def cclass(self):
return self._cclass
@property
def dtype(self):
return self._dtype.value
@property
def strlen(self):
return self._strlen
@property
def size(self):
return self._size
@property
def indexd(self):
return bool(self._indexd)
@property
def nullok(self):
return bool(self._nullok)
def __str__(self) -> str:
return (
f"<SpiceEKAttDsc cclass = {self.cclass}, dtype = {self.dtype}, strlen = {self.strlen}, "
f"size = {self.size}, indexd = {self.indexd}, nullok = {self.nullok} >"
)
[docs]class SpiceEKSegSum(Structure):
_fields_ = [
("_tabnam", c_char * 65),
("_nrows", c_int),
("_ncols", c_int),
("_cnames", (c_char * 100) * 33),
("_cdescrs", SpiceEKAttDsc * 100),
]
@property
def tabnam(self):
return to_python_string(self._tabnam)
@property
def nrows(self):
return self._nrows
@property
def ncols(self):
return self._ncols
@property
def cnames(self):
return c_vector_to_python(self._cnames)[0 : self.ncols]
@property
def cdescrs(self):
return self._cdescrs[0 : self.ncols]
def __str__(self) -> str:
return (
f"<SpiceEKSegSum tabnam = {self.tabnam}, nrows = {self.nrows}, ncols = {self.ncols},"
f" cnames = {self.cnames}, cdescrs = {self.cdescrs} >"
)
# SpiceCell implementation below is inpart from github.com/DaRasch/spiceminer/
# and modified as needed for this author, maybe we should work together?
# helper classes/functions
BITSIZE = {
"char": sizeof(c_char),
"int": sizeof(c_int),
"double": sizeof(c_double),
"bool": sizeof(c_int),
"time": sizeof(c_int),
}
def _char_getter(data_p, index, length):
return to_python_string(
(c_char * length).from_address(data_p + index * length * BITSIZE["char"])
)
def _double_getter(data_p, index, length):
return c_double.from_address(data_p + index * BITSIZE["double"]).value
def _int_getter(data_p, index, length):
return c_int.from_address(data_p + index * BITSIZE["int"]).value
[docs]class SpiceCell(Structure):
# Most written by DaRasch, see included MIT license at file header
DATATYPES_ENUM = {"char": 0, "double": 1, "int": 2, "time": 3, "bool": 4}
DATATYPES_GET = [_char_getter, _double_getter] + [_int_getter] * 3
baseSize = 6
minCharLen = 6
CTRLBLOCK = 6
_fields_ = [
("dtype", c_int),
("length", c_int),
("size", c_int),
("card", c_int),
("isSet", c_int),
("adjust", c_int),
("init", c_int),
("base", c_void_p),
("data", c_void_p),
]
def __init__(
self,
dtype=None,
length=None,
size=None,
card=None,
isSet=None,
base=None,
data=None,
):
super(SpiceCell, self).__init__()
self.dtype = dtype
self.length = length
self.size = size
self.card = card
self.isSet = isSet
self.adjust = 0 # Always False, because not implemented
self.init = 0 # Always False, because this is the constructor
self.base = base # void pointer
self.data = data
def __str__(self) -> str:
return (
f"<SpiceCell dtype = {self.dtype}, length = {self.length}, size = {self.size}, card = {self.card},"
f" is_set = {self.isSet}, adjust = {self.adjust}, init = {self.init}, base = {self.base}, data = {self.data}>"
)
[docs] def is_int(self):
return self.dtype == 2
[docs] def is_double(self):
return self.dtype == 1
[docs] def is_char(self):
return self.dtype == 0
[docs] def is_time(self):
return self.dtype == 3
[docs] def is_bool(self):
return self.dtype == 4
[docs] def is_set(self):
return self.isSet == 1
[docs] @classmethod
def character(cls, size, length):
base = (c_char * ((cls.CTRLBLOCK + size) * length))()
data = (c_char * (size * length)).from_buffer(
base, cls.CTRLBLOCK * BITSIZE["char"] * length
)
instance = cls(
cls.DATATYPES_ENUM["char"],
length,
size,
0,
1,
cast(base, c_void_p),
cast(data, c_void_p),
)
return instance
[docs] @classmethod
def integer(cls, size):
base = (c_int * (cls.CTRLBLOCK + size))()
data = (c_int * size).from_buffer(base, cls.CTRLBLOCK * BITSIZE["int"])
instance = cls(
cls.DATATYPES_ENUM["int"],
0,
size,
0,
1,
cast(base, c_void_p),
cast(data, c_void_p),
)
return instance
[docs] @classmethod
def double(cls, size):
base = (c_double * (cls.CTRLBLOCK + size))()
data = (c_double * size).from_buffer(base, cls.CTRLBLOCK * BITSIZE["double"])
instance = cls(
cls.DATATYPES_ENUM["double"],
0,
size,
0,
1,
cast(base, c_void_p),
cast(data, c_void_p),
)
return instance
[docs] @classmethod
def bool(cls, size):
base = (c_int * (cls.CTRLBLOCK + size))()
data = (c_int * size).from_buffer(base, cls.CTRLBLOCK * BITSIZE["bool"])
instance = cls(
cls.DATATYPES_ENUM["bool"],
0,
size,
0,
1,
cast(base, c_void_p),
cast(data, c_void_p),
)
return instance
[docs] @classmethod
def time(cls, size):
base = (c_int * (cls.CTRLBLOCK + size))()
data = (c_int * size).from_buffer(base, cls.CTRLBLOCK * BITSIZE["time"])
instance = cls(
cls.DATATYPES_ENUM["time"],
0,
size,
0,
1,
cast(base, c_void_p),
cast(data, c_void_p),
)
return instance
def __len__(self):
return self.card
def __iter__(self):
getter = SpiceCell.DATATYPES_GET[self.dtype]
length, card, data = self.length, self.card, self.data
for i in range(card):
yield getter(data, i, length)
def __contains__(self, key):
return key in self.__iter__()
def __getitem__(self, key):
getter = SpiceCell.DATATYPES_GET[self.dtype]
if isinstance(key, slice):
# TODO Typechecking
if self.card == 0:
return []
else:
start, stop, step = key.indices(self.card)
return [
getter(self.data, i, self.length) for i in range(start, stop, step)
]
elif key in range(-self.card, self.card):
index = key if key >= 0 else self.card - abs(key)
return getter(self.data, index, self.length)
elif not isinstance(key, int):
raise TypeError(
"SpiceCell indices must be integers, not {}".format(type(key))
)
else:
raise IndexError("SpiceCell index out of range")
[docs] def reset(self):
self.card = 0
self.init = 0
def __eq__(self, other):
"""
element wise equality, other can be a list or cell
I think sets should not equal a non set even if
elements are equal... might be a bad idea
:param other:
:return:
"""
if not hasattr(other, "__iter__"):
return False
if len(self) != len(other):
return False
if isinstance(other, SpiceCell):
if other.dtype != self.dtype:
return False
if other.isSet != self.isSet:
return False
for x, y in zip(self, other):
if x != y:
return False
return True
SpiceCellPointer = POINTER(SpiceCell)
[docs]def SPICEDOUBLE_CELL(size: int) -> SpiceCell:
"""
Returns a Double Spice Cell with a given size
:param size: number of elements
:return: empty Spice Cell
"""
return SpiceCell.double(size)
[docs]def SPICEINT_CELL(size: int) -> SpiceCell:
"""
Returns a Int Spice Cell with a given size
:param size: number of elements
:return: empty Spice Cell
"""
return SpiceCell.integer(size)
[docs]def SPICECHAR_CELL(size: int, length: int) -> SpiceCell:
"""
Returns a Char Spice Cell with a given size
:param size: number of elements
:param length: width of elements
:return: empty Spice Cell
"""
return SpiceCell.character(size, length)
[docs]def SPICEBOOL_CELL(size: int) -> SpiceCell:
"""
Returns a Bool Spice Cell with a given size
:param size: number of elements
:return: empty Spice Cell
"""
return SpiceCell.bool(size)
[docs]def SPICETIME_CELL(size: int):
"""
Returns a Time Spice Cell with a given size
:param size: number of elements
:return: empty Spice Cell
"""
return SpiceCell.time(size)
# Spice Cell classes
[docs]class Cell_Time(SpiceCell):
def __init__(self, size: int) -> None:
"""
Init a Time Spice Cell with a given size and length
:param size: number of elements
"""
base = (c_int * (6 + size))()
data = (c_int * size).from_buffer(base, 6 * BITSIZE["time"])
super(Cell_Time, self).__init__(
3, 0, size, 0, 1, cast(base, c_void_p), cast(data, c_void_p)
)
[docs]class Cell_Bool(SpiceCell):
def __init__(self, size: int) -> None:
"""
Init a Bool Spice Cell with a given size and length
:param size: number of elements
"""
base = (c_int * (6 + size))()
data = (c_int * size).from_buffer(base, 6 * BITSIZE["bool"])
super(Cell_Bool, self).__init__(
4, 0, size, 0, 1, cast(base, c_void_p), cast(data, c_void_p)
)
[docs]class Cell_Int(SpiceCell):
def __init__(self, size: int) -> None:
"""
Init a Int Spice Cell with a given size and length
:param size: number of elements
"""
base = (c_int * (6 + size))()
data = (c_int * size).from_buffer(base, 6 * BITSIZE["int"])
super(Cell_Int, self).__init__(
2, 0, size, 0, 1, cast(base, c_void_p), cast(data, c_void_p)
)
[docs]class Cell_Double(SpiceCell):
def __init__(self, size: int) -> None:
"""
Init a Double Spice Cell with a given size and length
:param size: number of elements
"""
base = (c_double * (6 + size))()
data = (c_double * size).from_buffer(base, 6 * BITSIZE["double"])
super(Cell_Double, self).__init__(
1, 0, size, 0, 1, cast(base, c_void_p), cast(data, c_void_p)
)
[docs]class Cell_Char(SpiceCell):
def __init__(self, size: int, length: int) -> None:
"""
Init a Char Spice Cell with a given size and length
:param size: number of elements
:param length: width of elements
"""
base = (c_char * ((6 + size) * length))()
data = (c_char * (size * length)).from_buffer(
base, 6 * BITSIZE["char"] * length
)
super(Cell_Char, self).__init__(
0, length, size, 0, 1, cast(base, c_void_p), cast(data, c_void_p)
)