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AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
tpictr
def tpictr(sample, lenout=_default_len_out, lenerr=_default_len_out): """ Given a sample time string, create a time format picture suitable for use by the routine timout. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/tpictr_c.html :param sample: A sample time string. :type sample: str :param lenout: The length for the output picture string. :type lenout: int :param lenerr: The length for the output error string. :type lenerr: int :return: A format picture that describes sample, Flag indicating whether sample parsed successfully, Diagnostic returned if sample cannot be parsed :rtype: tuple """ sample = stypes.stringToCharP(sample) pictur = stypes.stringToCharP(lenout) errmsg = stypes.stringToCharP(lenerr) lenout = ctypes.c_int(lenout) lenerr = ctypes.c_int(lenerr) ok = ctypes.c_int() libspice.tpictr_c(sample, lenout, lenerr, pictur, ctypes.byref(ok), errmsg) return stypes.toPythonString(pictur), ok.value, stypes.toPythonString( errmsg)
python
def tpictr(sample, lenout=_default_len_out, lenerr=_default_len_out): """ Given a sample time string, create a time format picture suitable for use by the routine timout. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/tpictr_c.html :param sample: A sample time string. :type sample: str :param lenout: The length for the output picture string. :type lenout: int :param lenerr: The length for the output error string. :type lenerr: int :return: A format picture that describes sample, Flag indicating whether sample parsed successfully, Diagnostic returned if sample cannot be parsed :rtype: tuple """ sample = stypes.stringToCharP(sample) pictur = stypes.stringToCharP(lenout) errmsg = stypes.stringToCharP(lenerr) lenout = ctypes.c_int(lenout) lenerr = ctypes.c_int(lenerr) ok = ctypes.c_int() libspice.tpictr_c(sample, lenout, lenerr, pictur, ctypes.byref(ok), errmsg) return stypes.toPythonString(pictur), ok.value, stypes.toPythonString( errmsg)
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Given a sample time string, create a time format picture suitable for use by the routine timout. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/tpictr_c.html :param sample: A sample time string. :type sample: str :param lenout: The length for the output picture string. :type lenout: int :param lenerr: The length for the output error string. :type lenerr: int :return: A format picture that describes sample, Flag indicating whether sample parsed successfully, Diagnostic returned if sample cannot be parsed :rtype: tuple
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L13986-L14013
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
trcdep
def trcdep(): """ Return the number of modules in the traceback representation. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/trcdep_c.html :return: The number of modules in the traceback. :rtype: int """ depth = ctypes.c_int() libspice.trcdep_c(ctypes.byref(depth)) return depth.value
python
def trcdep(): """ Return the number of modules in the traceback representation. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/trcdep_c.html :return: The number of modules in the traceback. :rtype: int """ depth = ctypes.c_int() libspice.trcdep_c(ctypes.byref(depth)) return depth.value
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Return the number of modules in the traceback representation. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/trcdep_c.html :return: The number of modules in the traceback. :rtype: int
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L14033-L14044
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
trcnam
def trcnam(index, namlen=_default_len_out): """ Return the name of the module having the specified position in the trace representation. The first module to check in is at index 0. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/trcnam_c.html :param index: The position of the requested module name. :type index: int :param namlen: Available space in output name string. :type namlen: int :return: The name at position index in the traceback. :rtype: str """ index = ctypes.c_int(index) name = stypes.stringToCharP(namlen) namlen = ctypes.c_int(namlen) libspice.trcnam_c(index, namlen, name) return stypes.toPythonString(name)
python
def trcnam(index, namlen=_default_len_out): """ Return the name of the module having the specified position in the trace representation. The first module to check in is at index 0. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/trcnam_c.html :param index: The position of the requested module name. :type index: int :param namlen: Available space in output name string. :type namlen: int :return: The name at position index in the traceback. :rtype: str """ index = ctypes.c_int(index) name = stypes.stringToCharP(namlen) namlen = ctypes.c_int(namlen) libspice.trcnam_c(index, namlen, name) return stypes.toPythonString(name)
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Return the name of the module having the specified position in the trace representation. The first module to check in is at index 0. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/trcnam_c.html :param index: The position of the requested module name. :type index: int :param namlen: Available space in output name string. :type namlen: int :return: The name at position index in the traceback. :rtype: str
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L14048-L14067
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
twovec
def twovec(axdef, indexa, plndef, indexp): """ Find the transformation to the right-handed frame having a given vector as a specified axis and having a second given vector lying in a specified coordinate plane. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/twovec_c.html :param axdef: Vector defining a principal axis. :type axdef: 3-Element Array of floats :param indexa: Principal axis number of axdef (X=1, Y=2, Z=3). :type indexa: int :param plndef: Vector defining (with axdef) a principal plane. :type plndef: 3-Element Array of floats :param indexp: Second axis number (with indexa) of principal plane. :type indexp: int :return: Output rotation matrix. :rtype: 3x3-Element Array of floats """ axdef = stypes.toDoubleVector(axdef) indexa = ctypes.c_int(indexa) plndef = stypes.toDoubleVector(plndef) indexp = ctypes.c_int(indexp) mout = stypes.emptyDoubleMatrix() libspice.twovec_c(axdef, indexa, plndef, indexp, mout) return stypes.cMatrixToNumpy(mout)
python
def twovec(axdef, indexa, plndef, indexp): """ Find the transformation to the right-handed frame having a given vector as a specified axis and having a second given vector lying in a specified coordinate plane. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/twovec_c.html :param axdef: Vector defining a principal axis. :type axdef: 3-Element Array of floats :param indexa: Principal axis number of axdef (X=1, Y=2, Z=3). :type indexa: int :param plndef: Vector defining (with axdef) a principal plane. :type plndef: 3-Element Array of floats :param indexp: Second axis number (with indexa) of principal plane. :type indexp: int :return: Output rotation matrix. :rtype: 3x3-Element Array of floats """ axdef = stypes.toDoubleVector(axdef) indexa = ctypes.c_int(indexa) plndef = stypes.toDoubleVector(plndef) indexp = ctypes.c_int(indexp) mout = stypes.emptyDoubleMatrix() libspice.twovec_c(axdef, indexa, plndef, indexp, mout) return stypes.cMatrixToNumpy(mout)
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Find the transformation to the right-handed frame having a given vector as a specified axis and having a second given vector lying in a specified coordinate plane. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/twovec_c.html :param axdef: Vector defining a principal axis. :type axdef: 3-Element Array of floats :param indexa: Principal axis number of axdef (X=1, Y=2, Z=3). :type indexa: int :param plndef: Vector defining (with axdef) a principal plane. :type plndef: 3-Element Array of floats :param indexp: Second axis number (with indexa) of principal plane. :type indexp: int :return: Output rotation matrix. :rtype: 3x3-Element Array of floats
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L14112-L14137
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
txtopn
def txtopn(fname): """ Internal undocumented command for opening a new text file for subsequent write access. https://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/ftncls_c.html#Files https://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/ftncls_c.html#Examples :param fname: name of the new text file to be opened. :type fname: str :return: FORTRAN logical unit of opened file :rtype: int """ fnameP = stypes.stringToCharP(fname) unit_out = ctypes.c_int() fname_len = ctypes.c_int(len(fname)) libspice.txtopn_(fnameP, ctypes.byref(unit_out), fname_len) return unit_out.value
python
def txtopn(fname): """ Internal undocumented command for opening a new text file for subsequent write access. https://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/ftncls_c.html#Files https://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/ftncls_c.html#Examples :param fname: name of the new text file to be opened. :type fname: str :return: FORTRAN logical unit of opened file :rtype: int """ fnameP = stypes.stringToCharP(fname) unit_out = ctypes.c_int() fname_len = ctypes.c_int(len(fname)) libspice.txtopn_(fnameP, ctypes.byref(unit_out), fname_len) return unit_out.value
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Internal undocumented command for opening a new text file for subsequent write access. https://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/ftncls_c.html#Files https://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/ftncls_c.html#Examples :param fname: name of the new text file to be opened. :type fname: str :return: FORTRAN logical unit of opened file :rtype: int
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L14141-L14158
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
ucase
def ucase(inchar, lenout=None): """ Convert the characters in a string to uppercase. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/ucase_c.html :param inchar: Input string. :type inchar: str :param lenout: Optional Maximum length of output string. :type lenout: int :return: Output string, all uppercase. :rtype: str """ if lenout is None: lenout = len(inchar) + 1 inchar = stypes.stringToCharP(inchar) outchar = stypes.stringToCharP(" " * lenout) lenout = ctypes.c_int(lenout) libspice.ucase_c(inchar, lenout, outchar) return stypes.toPythonString(outchar)
python
def ucase(inchar, lenout=None): """ Convert the characters in a string to uppercase. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/ucase_c.html :param inchar: Input string. :type inchar: str :param lenout: Optional Maximum length of output string. :type lenout: int :return: Output string, all uppercase. :rtype: str """ if lenout is None: lenout = len(inchar) + 1 inchar = stypes.stringToCharP(inchar) outchar = stypes.stringToCharP(" " * lenout) lenout = ctypes.c_int(lenout) libspice.ucase_c(inchar, lenout, outchar) return stypes.toPythonString(outchar)
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Convert the characters in a string to uppercase. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/ucase_c.html :param inchar: Input string. :type inchar: str :param lenout: Optional Maximum length of output string. :type lenout: int :return: Output string, all uppercase. :rtype: str
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L14178-L14197
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
ucrss
def ucrss(v1, v2): """ Compute the normalized cross product of two 3-vectors. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/ucrss_c.html :param v1: Left vector for cross product. :type v1: 3-Element Array of floats :param v2: Right vector for cross product. :type v2: 3-Element Array of floats :return: Normalized cross product v1xv2 / abs(v1xv2). :rtype: Array of floats """ v1 = stypes.toDoubleVector(v1) v2 = stypes.toDoubleVector(v2) vout = stypes.emptyDoubleVector(3) libspice.ucrss_c(v1, v2, vout) return stypes.cVectorToPython(vout)
python
def ucrss(v1, v2): """ Compute the normalized cross product of two 3-vectors. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/ucrss_c.html :param v1: Left vector for cross product. :type v1: 3-Element Array of floats :param v2: Right vector for cross product. :type v2: 3-Element Array of floats :return: Normalized cross product v1xv2 / abs(v1xv2). :rtype: Array of floats """ v1 = stypes.toDoubleVector(v1) v2 = stypes.toDoubleVector(v2) vout = stypes.emptyDoubleVector(3) libspice.ucrss_c(v1, v2, vout) return stypes.cVectorToPython(vout)
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L14201-L14218
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
uddc
def uddc(udfunc, x, dx): """ SPICE private routine intended solely for the support of SPICE routines. Users should not call this routine directly due to the volatile nature of this routine. This routine calculates the derivative of 'udfunc' with respect to time for 'et', then determines if the derivative has a negative value. Use the @spiceypy.utils.callbacks.SpiceUDFUNS dectorator to wrap a given python function that takes one parameter (float) and returns a float. For example:: @spiceypy.utils.callbacks.SpiceUDFUNS def udfunc(et_in): pos, new_et = spice.spkpos("MERCURY", et_in, "J2000", "LT+S", "MOON") return new_et deriv = spice.uddf(udfunc, et, 1.0) https://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/uddc_c.html :param udfunc: Name of the routine that computes the scalar value of interest. :type udfunc: ctypes.CFunctionType :param x: Independent variable of 'udfunc'. :type x: float :param dx: Interval from 'x' for derivative calculation. :type dx: float :return: Boolean indicating if the derivative is negative. :rtype: bool """ x = ctypes.c_double(x) dx = ctypes.c_double(dx) isdescr = ctypes.c_int() libspice.uddc_c(udfunc, x, dx, ctypes.byref(isdescr)) return bool(isdescr.value)
python
def uddc(udfunc, x, dx): """ SPICE private routine intended solely for the support of SPICE routines. Users should not call this routine directly due to the volatile nature of this routine. This routine calculates the derivative of 'udfunc' with respect to time for 'et', then determines if the derivative has a negative value. Use the @spiceypy.utils.callbacks.SpiceUDFUNS dectorator to wrap a given python function that takes one parameter (float) and returns a float. For example:: @spiceypy.utils.callbacks.SpiceUDFUNS def udfunc(et_in): pos, new_et = spice.spkpos("MERCURY", et_in, "J2000", "LT+S", "MOON") return new_et deriv = spice.uddf(udfunc, et, 1.0) https://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/uddc_c.html :param udfunc: Name of the routine that computes the scalar value of interest. :type udfunc: ctypes.CFunctionType :param x: Independent variable of 'udfunc'. :type x: float :param dx: Interval from 'x' for derivative calculation. :type dx: float :return: Boolean indicating if the derivative is negative. :rtype: bool """ x = ctypes.c_double(x) dx = ctypes.c_double(dx) isdescr = ctypes.c_int() libspice.uddc_c(udfunc, x, dx, ctypes.byref(isdescr)) return bool(isdescr.value)
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SPICE private routine intended solely for the support of SPICE routines. Users should not call this routine directly due to the volatile nature of this routine. This routine calculates the derivative of 'udfunc' with respect to time for 'et', then determines if the derivative has a negative value. Use the @spiceypy.utils.callbacks.SpiceUDFUNS dectorator to wrap a given python function that takes one parameter (float) and returns a float. For example:: @spiceypy.utils.callbacks.SpiceUDFUNS def udfunc(et_in): pos, new_et = spice.spkpos("MERCURY", et_in, "J2000", "LT+S", "MOON") return new_et deriv = spice.uddf(udfunc, et, 1.0) https://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/uddc_c.html :param udfunc: Name of the routine that computes the scalar value of interest. :type udfunc: ctypes.CFunctionType :param x: Independent variable of 'udfunc'. :type x: float :param dx: Interval from 'x' for derivative calculation. :type dx: float :return: Boolean indicating if the derivative is negative. :rtype: bool
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L14221-L14257
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
uddf
def uddf(udfunc, x, dx): """ Routine to calculate the first derivative of a caller-specified function using a three-point estimation. Use the @spiceypy.utils.callbacks.SpiceUDFUNS dectorator to wrap a given python function that takes one parameter (float) and returns a float. For example:: @spiceypy.utils.callbacks.SpiceUDFUNS def udfunc(et_in): pos, new_et = spice.spkpos("MERCURY", et_in, "J2000", "LT+S", "MOON") return new_et deriv = spice.uddf(udfunc, et, 1.0) https://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/uddf_c.html :param udfunc: Name of the routine that computes the scalar value of interest. :type udfunc: ctypes.CFunctionType :param x: Independent variable of 'udfunc'. :type x: float :param dx: Interval from 'x' for derivative calculation. :type dx: float :return: Approximate derivative of 'udfunc' at 'x' :rtype: float """ x = ctypes.c_double(x) dx = ctypes.c_double(dx) deriv = ctypes.c_double() libspice.uddf_c(udfunc, x, dx, ctypes.byref(deriv)) return deriv.value
python
def uddf(udfunc, x, dx): """ Routine to calculate the first derivative of a caller-specified function using a three-point estimation. Use the @spiceypy.utils.callbacks.SpiceUDFUNS dectorator to wrap a given python function that takes one parameter (float) and returns a float. For example:: @spiceypy.utils.callbacks.SpiceUDFUNS def udfunc(et_in): pos, new_et = spice.spkpos("MERCURY", et_in, "J2000", "LT+S", "MOON") return new_et deriv = spice.uddf(udfunc, et, 1.0) https://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/uddf_c.html :param udfunc: Name of the routine that computes the scalar value of interest. :type udfunc: ctypes.CFunctionType :param x: Independent variable of 'udfunc'. :type x: float :param dx: Interval from 'x' for derivative calculation. :type dx: float :return: Approximate derivative of 'udfunc' at 'x' :rtype: float """ x = ctypes.c_double(x) dx = ctypes.c_double(dx) deriv = ctypes.c_double() libspice.uddf_c(udfunc, x, dx, ctypes.byref(deriv)) return deriv.value
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Routine to calculate the first derivative of a caller-specified function using a three-point estimation. Use the @spiceypy.utils.callbacks.SpiceUDFUNS dectorator to wrap a given python function that takes one parameter (float) and returns a float. For example:: @spiceypy.utils.callbacks.SpiceUDFUNS def udfunc(et_in): pos, new_et = spice.spkpos("MERCURY", et_in, "J2000", "LT+S", "MOON") return new_et deriv = spice.uddf(udfunc, et, 1.0) https://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/uddf_c.html :param udfunc: Name of the routine that computes the scalar value of interest. :type udfunc: ctypes.CFunctionType :param x: Independent variable of 'udfunc'. :type x: float :param dx: Interval from 'x' for derivative calculation. :type dx: float :return: Approximate derivative of 'udfunc' at 'x' :rtype: float
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L14261-L14292
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
udf
def udf(x): """ No-op routine for with an argument signature matching udfuns. Allways returns 0.0 . https://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/udf_c.html :param x: Double precision value, unused. :type x: float :return: Double precision value, unused. :rtype: float """ x = ctypes.c_double(x) value = ctypes.c_double() libspice.udf_c(x, ctypes.byref(value)) return value.value
python
def udf(x): """ No-op routine for with an argument signature matching udfuns. Allways returns 0.0 . https://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/udf_c.html :param x: Double precision value, unused. :type x: float :return: Double precision value, unused. :rtype: float """ x = ctypes.c_double(x) value = ctypes.c_double() libspice.udf_c(x, ctypes.byref(value)) return value.value
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No-op routine for with an argument signature matching udfuns. Allways returns 0.0 . https://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/udf_c.html :param x: Double precision value, unused. :type x: float :return: Double precision value, unused. :rtype: float
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L14295-L14310
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
unitim
def unitim(epoch, insys, outsys): """ Transform time from one uniform scale to another. The uniform time scales are TAI, TDT, TDB, ET, JED, JDTDB, JDTDT. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/unitim_c.html :param epoch: An epoch to be converted. :type epoch: float :param insys: The time scale associated with the input epoch. :type insys: str :param outsys: The time scale associated with the function value. :type outsys: str :return: The float in outsys that is equivalent to the epoch on the insys time scale. :rtype: float """ epoch = ctypes.c_double(epoch) insys = stypes.stringToCharP(insys) outsys = stypes.stringToCharP(outsys) return libspice.unitim_c(epoch, insys, outsys)
python
def unitim(epoch, insys, outsys): """ Transform time from one uniform scale to another. The uniform time scales are TAI, TDT, TDB, ET, JED, JDTDB, JDTDT. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/unitim_c.html :param epoch: An epoch to be converted. :type epoch: float :param insys: The time scale associated with the input epoch. :type insys: str :param outsys: The time scale associated with the function value. :type outsys: str :return: The float in outsys that is equivalent to the epoch on the insys time scale. :rtype: float """ epoch = ctypes.c_double(epoch) insys = stypes.stringToCharP(insys) outsys = stypes.stringToCharP(outsys) return libspice.unitim_c(epoch, insys, outsys)
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Transform time from one uniform scale to another. The uniform time scales are TAI, TDT, TDB, ET, JED, JDTDB, JDTDT. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/unitim_c.html :param epoch: An epoch to be converted. :type epoch: float :param insys: The time scale associated with the input epoch. :type insys: str :param outsys: The time scale associated with the function value. :type outsys: str :return: The float in outsys that is equivalent to the epoch on the insys time scale. :rtype: float
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L14345-L14366
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
unload
def unload(filename): """ Unload a SPICE kernel. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/unload_c.html :param filename: The name of a kernel to unload. :type filename: str """ if isinstance(filename, list): for f in filename: libspice.unload_c(stypes.stringToCharP(f)) return filename = stypes.stringToCharP(filename) libspice.unload_c(filename)
python
def unload(filename): """ Unload a SPICE kernel. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/unload_c.html :param filename: The name of a kernel to unload. :type filename: str """ if isinstance(filename, list): for f in filename: libspice.unload_c(stypes.stringToCharP(f)) return filename = stypes.stringToCharP(filename) libspice.unload_c(filename)
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Unload a SPICE kernel. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/unload_c.html :param filename: The name of a kernel to unload. :type filename: str
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L14370-L14384
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
unorm
def unorm(v1): """ Normalize a double precision 3-vector and return its magnitude. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/unorm_c.html :param v1: Vector to be normalized. :type v1: 3-Element Array of floats :return: Unit vector of v1, Magnitude of v1. :rtype: tuple """ v1 = stypes.toDoubleVector(v1) vout = stypes.emptyDoubleVector(3) vmag = ctypes.c_double() libspice.unorm_c(v1, vout, ctypes.byref(vmag)) return stypes.cVectorToPython(vout), vmag.value
python
def unorm(v1): """ Normalize a double precision 3-vector and return its magnitude. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/unorm_c.html :param v1: Vector to be normalized. :type v1: 3-Element Array of floats :return: Unit vector of v1, Magnitude of v1. :rtype: tuple """ v1 = stypes.toDoubleVector(v1) vout = stypes.emptyDoubleVector(3) vmag = ctypes.c_double() libspice.unorm_c(v1, vout, ctypes.byref(vmag)) return stypes.cVectorToPython(vout), vmag.value
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Normalize a double precision 3-vector and return its magnitude. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/unorm_c.html :param v1: Vector to be normalized. :type v1: 3-Element Array of floats :return: Unit vector of v1, Magnitude of v1. :rtype: tuple
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L14388-L14403
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
unormg
def unormg(v1, ndim): """ Normalize a double precision vector of arbitrary dimension and return its magnitude. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/unormg_c.html :param v1: Vector to be normalized. :type v1: Array of floats :param ndim: This is the dimension of v1 and vout. :type ndim: int :return: Unit vector of v1, Magnitude of v1. :rtype: tuple """ v1 = stypes.toDoubleVector(v1) vout = stypes.emptyDoubleVector(ndim) vmag = ctypes.c_double() ndim = ctypes.c_int(ndim) libspice.unormg_c(v1, ndim, vout, ctypes.byref(vmag)) return stypes.cVectorToPython(vout), vmag.value
python
def unormg(v1, ndim): """ Normalize a double precision vector of arbitrary dimension and return its magnitude. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/unormg_c.html :param v1: Vector to be normalized. :type v1: Array of floats :param ndim: This is the dimension of v1 and vout. :type ndim: int :return: Unit vector of v1, Magnitude of v1. :rtype: tuple """ v1 = stypes.toDoubleVector(v1) vout = stypes.emptyDoubleVector(ndim) vmag = ctypes.c_double() ndim = ctypes.c_int(ndim) libspice.unormg_c(v1, ndim, vout, ctypes.byref(vmag)) return stypes.cVectorToPython(vout), vmag.value
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Normalize a double precision vector of arbitrary dimension and return its magnitude. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/unormg_c.html :param v1: Vector to be normalized. :type v1: Array of floats :param ndim: This is the dimension of v1 and vout. :type ndim: int :return: Unit vector of v1, Magnitude of v1. :rtype: tuple
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L14407-L14426
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
utc2et
def utc2et(utcstr): """ Convert an input time from Calendar or Julian Date format, UTC, to ephemeris seconds past J2000. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/utc2et_c.html :param utcstr: Input time string, UTC. :type utcstr: str :return: Output epoch, ephemeris seconds past J2000. :rtype: float """ utcstr = stypes.stringToCharP(utcstr) et = ctypes.c_double() libspice.utc2et_c(utcstr, ctypes.byref(et)) return et.value
python
def utc2et(utcstr): """ Convert an input time from Calendar or Julian Date format, UTC, to ephemeris seconds past J2000. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/utc2et_c.html :param utcstr: Input time string, UTC. :type utcstr: str :return: Output epoch, ephemeris seconds past J2000. :rtype: float """ utcstr = stypes.stringToCharP(utcstr) et = ctypes.c_double() libspice.utc2et_c(utcstr, ctypes.byref(et)) return et.value
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Convert an input time from Calendar or Julian Date format, UTC, to ephemeris seconds past J2000. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/utc2et_c.html :param utcstr: Input time string, UTC. :type utcstr: str :return: Output epoch, ephemeris seconds past J2000. :rtype: float
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L14430-L14445
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
valid
def valid(insize, n, inset): """ Create a valid CSPICE set from a CSPICE Cell of any data type. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/valid_c.html :param insize: Size (maximum cardinality) of the set. :type insize: int :param n: Initial no. of (possibly non-distinct) elements. :type n: int :param inset: Set to be validated. :return: validated set :rtype: spiceypy.utils.support_types.SpiceCell """ assert isinstance(inset, stypes.SpiceCell) insize = ctypes.c_int(insize) n = ctypes.c_int(n) libspice.valid_c(insize, n, inset) return inset
python
def valid(insize, n, inset): """ Create a valid CSPICE set from a CSPICE Cell of any data type. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/valid_c.html :param insize: Size (maximum cardinality) of the set. :type insize: int :param n: Initial no. of (possibly non-distinct) elements. :type n: int :param inset: Set to be validated. :return: validated set :rtype: spiceypy.utils.support_types.SpiceCell """ assert isinstance(inset, stypes.SpiceCell) insize = ctypes.c_int(insize) n = ctypes.c_int(n) libspice.valid_c(insize, n, inset) return inset
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L14494-L14512
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
vcrss
def vcrss(v1, v2): """ Compute the cross product of two 3-dimensional vectors. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vcrss_c.html :param v1: Left hand vector for cross product. :type v1: 3-Element Array of floats :param v2: Right hand vector for cross product. :type v2: 3-Element Array of floats :return: Cross product v1 x v2. :rtype: 3-Element Array of floats """ v1 = stypes.toDoubleVector(v1) v2 = stypes.toDoubleVector(v2) vout = stypes.emptyDoubleVector(3) libspice.vcrss_c(v1, v2, vout) return stypes.cVectorToPython(vout)
python
def vcrss(v1, v2): """ Compute the cross product of two 3-dimensional vectors. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vcrss_c.html :param v1: Left hand vector for cross product. :type v1: 3-Element Array of floats :param v2: Right hand vector for cross product. :type v2: 3-Element Array of floats :return: Cross product v1 x v2. :rtype: 3-Element Array of floats """ v1 = stypes.toDoubleVector(v1) v2 = stypes.toDoubleVector(v2) vout = stypes.emptyDoubleVector(3) libspice.vcrss_c(v1, v2, vout) return stypes.cVectorToPython(vout)
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Compute the cross product of two 3-dimensional vectors. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vcrss_c.html :param v1: Left hand vector for cross product. :type v1: 3-Element Array of floats :param v2: Right hand vector for cross product. :type v2: 3-Element Array of floats :return: Cross product v1 x v2. :rtype: 3-Element Array of floats
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L14516-L14533
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
vdist
def vdist(v1, v2): """ Return the distance between two three-dimensional vectors. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vdist_c.html :param v1: First vector in the dot product. :type v1: 3-Element Array of floats :param v2: Second vector in the dot product. :type v2: 3-Element Array of floats :return: the distance between v1 and v2 :rtype: float """ v1 = stypes.toDoubleVector(v1) v2 = stypes.toDoubleVector(v2) return libspice.vdist_c(v1, v2)
python
def vdist(v1, v2): """ Return the distance between two three-dimensional vectors. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vdist_c.html :param v1: First vector in the dot product. :type v1: 3-Element Array of floats :param v2: Second vector in the dot product. :type v2: 3-Element Array of floats :return: the distance between v1 and v2 :rtype: float """ v1 = stypes.toDoubleVector(v1) v2 = stypes.toDoubleVector(v2) return libspice.vdist_c(v1, v2)
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Return the distance between two three-dimensional vectors. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vdist_c.html :param v1: First vector in the dot product. :type v1: 3-Element Array of floats :param v2: Second vector in the dot product. :type v2: 3-Element Array of floats :return: the distance between v1 and v2 :rtype: float
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L14537-L14552
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
vdistg
def vdistg(v1, v2, ndim): """ Return the distance between two vectors of arbitrary dimension. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vdistg_c.html :param v1: ndim-dimensional double precision vector. :type v1: list[ndim] :param v2: ndim-dimensional double precision vector. :type v2: list[ndim] :param ndim: Dimension of v1 and v2. :type ndim: int :return: the distance between v1 and v2 :rtype: float """ v1 = stypes.toDoubleVector(v1) v2 = stypes.toDoubleVector(v2) ndim = ctypes.c_int(ndim) return libspice.vdistg_c(v1, v2, ndim)
python
def vdistg(v1, v2, ndim): """ Return the distance between two vectors of arbitrary dimension. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vdistg_c.html :param v1: ndim-dimensional double precision vector. :type v1: list[ndim] :param v2: ndim-dimensional double precision vector. :type v2: list[ndim] :param ndim: Dimension of v1 and v2. :type ndim: int :return: the distance between v1 and v2 :rtype: float """ v1 = stypes.toDoubleVector(v1) v2 = stypes.toDoubleVector(v2) ndim = ctypes.c_int(ndim) return libspice.vdistg_c(v1, v2, ndim)
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Return the distance between two vectors of arbitrary dimension. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vdistg_c.html :param v1: ndim-dimensional double precision vector. :type v1: list[ndim] :param v2: ndim-dimensional double precision vector. :type v2: list[ndim] :param ndim: Dimension of v1 and v2. :type ndim: int :return: the distance between v1 and v2 :rtype: float
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L14556-L14574
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
vdot
def vdot(v1, v2): """ Compute the dot product of two double precision, 3-dimensional vectors. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vdot_c.html :param v1: First vector in the dot product. :type v1: 3-Element Array of floats :param v2: Second vector in the dot product. :type v2: 3-Element Array of floats :return: dot product of v1 and v2. :rtype: float """ v1 = stypes.toDoubleVector(v1) v2 = stypes.toDoubleVector(v2) return libspice.vdot_c(v1, v2)
python
def vdot(v1, v2): """ Compute the dot product of two double precision, 3-dimensional vectors. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vdot_c.html :param v1: First vector in the dot product. :type v1: 3-Element Array of floats :param v2: Second vector in the dot product. :type v2: 3-Element Array of floats :return: dot product of v1 and v2. :rtype: float """ v1 = stypes.toDoubleVector(v1) v2 = stypes.toDoubleVector(v2) return libspice.vdot_c(v1, v2)
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Compute the dot product of two double precision, 3-dimensional vectors. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vdot_c.html :param v1: First vector in the dot product. :type v1: 3-Element Array of floats :param v2: Second vector in the dot product. :type v2: 3-Element Array of floats :return: dot product of v1 and v2. :rtype: float
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L14578-L14593
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
vdotg
def vdotg(v1, v2, ndim): """ Compute the dot product of two double precision vectors of arbitrary dimension. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vdotg_c.html :param v1: First vector in the dot product. :type v1: list[ndim] :param v2: Second vector in the dot product. :type v2: list[ndim] :param ndim: Dimension of v1 and v2. :type ndim: int :return: dot product of v1 and v2. :rtype: float """ v1 = stypes.toDoubleVector(v1) v2 = stypes.toDoubleVector(v2) ndim = ctypes.c_int(ndim) return libspice.vdotg_c(v1, v2, ndim)
python
def vdotg(v1, v2, ndim): """ Compute the dot product of two double precision vectors of arbitrary dimension. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vdotg_c.html :param v1: First vector in the dot product. :type v1: list[ndim] :param v2: Second vector in the dot product. :type v2: list[ndim] :param ndim: Dimension of v1 and v2. :type ndim: int :return: dot product of v1 and v2. :rtype: float """ v1 = stypes.toDoubleVector(v1) v2 = stypes.toDoubleVector(v2) ndim = ctypes.c_int(ndim) return libspice.vdotg_c(v1, v2, ndim)
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Compute the dot product of two double precision vectors of arbitrary dimension. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vdotg_c.html :param v1: First vector in the dot product. :type v1: list[ndim] :param v2: Second vector in the dot product. :type v2: list[ndim] :param ndim: Dimension of v1 and v2. :type ndim: int :return: dot product of v1 and v2. :rtype: float
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L14597-L14616
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
vequ
def vequ(v1): """ Make one double precision 3-dimensional vector equal to another. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vequ_c.html :param v1: 3-dimensional double precision vector. :type v1: 3-Element Array of floats :return: 3-dimensional double precision vector set equal to vin. :rtype: 3-Element Array of floats """ v1 = stypes.toDoubleVector(v1) vout = stypes.emptyDoubleVector(3) libspice.vequ_c(v1, vout) return stypes.cVectorToPython(vout)
python
def vequ(v1): """ Make one double precision 3-dimensional vector equal to another. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vequ_c.html :param v1: 3-dimensional double precision vector. :type v1: 3-Element Array of floats :return: 3-dimensional double precision vector set equal to vin. :rtype: 3-Element Array of floats """ v1 = stypes.toDoubleVector(v1) vout = stypes.emptyDoubleVector(3) libspice.vequ_c(v1, vout) return stypes.cVectorToPython(vout)
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Make one double precision 3-dimensional vector equal to another. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vequ_c.html :param v1: 3-dimensional double precision vector. :type v1: 3-Element Array of floats :return: 3-dimensional double precision vector set equal to vin. :rtype: 3-Element Array of floats
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L14620-L14634
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
vequg
def vequg(v1, ndim): """ Make one double precision vector of arbitrary dimension equal to another. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vequg_c.html :param v1: ndim-dimensional double precision vector. :type v1: list[ndim] :param ndim: Dimension of vin (and also vout). :type ndim: int :return: ndim-dimensional double precision vector set equal to vin. :rtype: list[ndim] """ v1 = stypes.toDoubleVector(v1) vout = stypes.emptyDoubleVector(ndim) ndim = ctypes.c_int(ndim) libspice.vequg_c(v1, ndim, vout) return stypes.cVectorToPython(vout)
python
def vequg(v1, ndim): """ Make one double precision vector of arbitrary dimension equal to another. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vequg_c.html :param v1: ndim-dimensional double precision vector. :type v1: list[ndim] :param ndim: Dimension of vin (and also vout). :type ndim: int :return: ndim-dimensional double precision vector set equal to vin. :rtype: list[ndim] """ v1 = stypes.toDoubleVector(v1) vout = stypes.emptyDoubleVector(ndim) ndim = ctypes.c_int(ndim) libspice.vequg_c(v1, ndim, vout) return stypes.cVectorToPython(vout)
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Make one double precision vector of arbitrary dimension equal to another. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vequg_c.html :param v1: ndim-dimensional double precision vector. :type v1: list[ndim] :param ndim: Dimension of vin (and also vout). :type ndim: int :return: ndim-dimensional double precision vector set equal to vin. :rtype: list[ndim]
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L14638-L14655
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
vhat
def vhat(v1): """ Find the unit vector along a double precision 3-dimensional vector. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vhat_c.html :param v1: Vector to be unitized. :type v1: 3-Element Array of floats :return: Unit vector v / abs(v). :rtype: 3-Element Array of floats """ v1 = stypes.toDoubleVector(v1) vout = stypes.emptyDoubleVector(3) libspice.vhat_c(v1, vout) return stypes.cVectorToPython(vout)
python
def vhat(v1): """ Find the unit vector along a double precision 3-dimensional vector. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vhat_c.html :param v1: Vector to be unitized. :type v1: 3-Element Array of floats :return: Unit vector v / abs(v). :rtype: 3-Element Array of floats """ v1 = stypes.toDoubleVector(v1) vout = stypes.emptyDoubleVector(3) libspice.vhat_c(v1, vout) return stypes.cVectorToPython(vout)
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Find the unit vector along a double precision 3-dimensional vector. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vhat_c.html :param v1: Vector to be unitized. :type v1: 3-Element Array of floats :return: Unit vector v / abs(v). :rtype: 3-Element Array of floats
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L14659-L14673
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
vhatg
def vhatg(v1, ndim): """ Find the unit vector along a double precision vector of arbitrary dimension. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vhatg_c.html :param v1: Vector to be normalized. :type v1: list[ndim] :param ndim: Dimension of v1 (and also vout). :type ndim: int :return: Unit vector v / abs(v). :rtype: list[ndim] """ v1 = stypes.toDoubleVector(v1) vout = stypes.emptyDoubleVector(ndim) ndim = ctypes.c_int(ndim) libspice.vhatg_c(v1, ndim, vout) return stypes.cVectorToPython(vout)
python
def vhatg(v1, ndim): """ Find the unit vector along a double precision vector of arbitrary dimension. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vhatg_c.html :param v1: Vector to be normalized. :type v1: list[ndim] :param ndim: Dimension of v1 (and also vout). :type ndim: int :return: Unit vector v / abs(v). :rtype: list[ndim] """ v1 = stypes.toDoubleVector(v1) vout = stypes.emptyDoubleVector(ndim) ndim = ctypes.c_int(ndim) libspice.vhatg_c(v1, ndim, vout) return stypes.cVectorToPython(vout)
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Find the unit vector along a double precision vector of arbitrary dimension. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vhatg_c.html :param v1: Vector to be normalized. :type v1: list[ndim] :param ndim: Dimension of v1 (and also vout). :type ndim: int :return: Unit vector v / abs(v). :rtype: list[ndim]
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L14677-L14694
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
vlcom
def vlcom(a, v1, b, v2): """ Compute a vector linear combination of two double precision, 3-dimensional vectors. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vlcom_c.html :param a: Coefficient of v1 :type a: float :param v1: Vector in 3-space :type v1: 3-Element Array of floats :param b: Coefficient of v2 :type b: float :param v2: Vector in 3-space :type v2: 3-Element Array of floats :return: Linear Vector Combination a*v1 + b*v2. :rtype: 3-Element Array of floats """ v1 = stypes.toDoubleVector(v1) v2 = stypes.toDoubleVector(v2) sumv = stypes.emptyDoubleVector(3) a = ctypes.c_double(a) b = ctypes.c_double(b) libspice.vlcom_c(a, v1, b, v2, sumv) return stypes.cVectorToPython(sumv)
python
def vlcom(a, v1, b, v2): """ Compute a vector linear combination of two double precision, 3-dimensional vectors. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vlcom_c.html :param a: Coefficient of v1 :type a: float :param v1: Vector in 3-space :type v1: 3-Element Array of floats :param b: Coefficient of v2 :type b: float :param v2: Vector in 3-space :type v2: 3-Element Array of floats :return: Linear Vector Combination a*v1 + b*v2. :rtype: 3-Element Array of floats """ v1 = stypes.toDoubleVector(v1) v2 = stypes.toDoubleVector(v2) sumv = stypes.emptyDoubleVector(3) a = ctypes.c_double(a) b = ctypes.c_double(b) libspice.vlcom_c(a, v1, b, v2, sumv) return stypes.cVectorToPython(sumv)
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Compute a vector linear combination of two double precision, 3-dimensional vectors. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vlcom_c.html :param a: Coefficient of v1 :type a: float :param v1: Vector in 3-space :type v1: 3-Element Array of floats :param b: Coefficient of v2 :type b: float :param v2: Vector in 3-space :type v2: 3-Element Array of floats :return: Linear Vector Combination a*v1 + b*v2. :rtype: 3-Element Array of floats
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L14698-L14722
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
vlcomg
def vlcomg(n, a, v1, b, v2): """ Compute a vector linear combination of two double precision vectors of arbitrary dimension. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vlcomg_c.html :param n: Dimension of vector space :type n: int :param a: Coefficient of v1 :type a: float :param v1: Vector in n-space :type v1: list[n] :param b: Coefficient of v2 :type b: float :param v2: Vector in n-space :type v2: list[n] :return: Linear Vector Combination a*v1 + b*v2 :rtype: list[n] """ v1 = stypes.toDoubleVector(v1) v2 = stypes.toDoubleVector(v2) sumv = stypes.emptyDoubleVector(n) a = ctypes.c_double(a) b = ctypes.c_double(b) n = ctypes.c_int(n) libspice.vlcomg_c(n, a, v1, b, v2, sumv) return stypes.cVectorToPython(sumv)
python
def vlcomg(n, a, v1, b, v2): """ Compute a vector linear combination of two double precision vectors of arbitrary dimension. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vlcomg_c.html :param n: Dimension of vector space :type n: int :param a: Coefficient of v1 :type a: float :param v1: Vector in n-space :type v1: list[n] :param b: Coefficient of v2 :type b: float :param v2: Vector in n-space :type v2: list[n] :return: Linear Vector Combination a*v1 + b*v2 :rtype: list[n] """ v1 = stypes.toDoubleVector(v1) v2 = stypes.toDoubleVector(v2) sumv = stypes.emptyDoubleVector(n) a = ctypes.c_double(a) b = ctypes.c_double(b) n = ctypes.c_int(n) libspice.vlcomg_c(n, a, v1, b, v2, sumv) return stypes.cVectorToPython(sumv)
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Compute a vector linear combination of two double precision vectors of arbitrary dimension. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vlcomg_c.html :param n: Dimension of vector space :type n: int :param a: Coefficient of v1 :type a: float :param v1: Vector in n-space :type v1: list[n] :param b: Coefficient of v2 :type b: float :param v2: Vector in n-space :type v2: list[n] :return: Linear Vector Combination a*v1 + b*v2 :rtype: list[n]
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L14760-L14787
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
vminug
def vminug(vin, ndim): """ Negate a double precision vector of arbitrary dimension. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vminug_c.html :param vin: ndim-dimensional double precision vector to be negated. :type vin: Array of floats :param ndim: Dimension of vin. :type ndim: int :return: ndim-dimensional double precision vector equal to -vin. :rtype: list[ndim] """ vin = stypes.toDoubleVector(vin) vout = stypes.emptyDoubleVector(ndim) ndim = ctypes.c_int(ndim) libspice.vminug_c(vin, ndim, vout) return stypes.cVectorToPython(vout)
python
def vminug(vin, ndim): """ Negate a double precision vector of arbitrary dimension. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vminug_c.html :param vin: ndim-dimensional double precision vector to be negated. :type vin: Array of floats :param ndim: Dimension of vin. :type ndim: int :return: ndim-dimensional double precision vector equal to -vin. :rtype: list[ndim] """ vin = stypes.toDoubleVector(vin) vout = stypes.emptyDoubleVector(ndim) ndim = ctypes.c_int(ndim) libspice.vminug_c(vin, ndim, vout) return stypes.cVectorToPython(vout)
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Negate a double precision vector of arbitrary dimension. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vminug_c.html :param vin: ndim-dimensional double precision vector to be negated. :type vin: Array of floats :param ndim: Dimension of vin. :type ndim: int :return: ndim-dimensional double precision vector equal to -vin. :rtype: list[ndim]
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L14791-L14808
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
vminus
def vminus(vin): """ Negate a double precision 3-dimensional vector. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vminus_c.html :param vin: Vector to be negated. :type vin: 3-Element Array of floats :return: Negated vector -v1. :rtype: 3-Element Array of floats """ vin = stypes.toDoubleVector(vin) vout = stypes.emptyDoubleVector(3) libspice.vminus_c(vin, vout) return stypes.cVectorToPython(vout)
python
def vminus(vin): """ Negate a double precision 3-dimensional vector. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vminus_c.html :param vin: Vector to be negated. :type vin: 3-Element Array of floats :return: Negated vector -v1. :rtype: 3-Element Array of floats """ vin = stypes.toDoubleVector(vin) vout = stypes.emptyDoubleVector(3) libspice.vminus_c(vin, vout) return stypes.cVectorToPython(vout)
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Negate a double precision 3-dimensional vector. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vminus_c.html :param vin: Vector to be negated. :type vin: 3-Element Array of floats :return: Negated vector -v1. :rtype: 3-Element Array of floats
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L14812-L14826
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
vnormg
def vnormg(v, ndim): """ Compute the magnitude of a double precision vector of arbitrary dimension. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vnormg_c.html :param v: Vector whose magnitude is to be found. :type v: Array of floats :param ndim: Dimension of v :type ndim: int :return: magnitude of v calculated in a numerically stable way :rtype: float """ v = stypes.toDoubleVector(v) ndim = ctypes.c_int(ndim) return libspice.vnormg_c(v, ndim)
python
def vnormg(v, ndim): """ Compute the magnitude of a double precision vector of arbitrary dimension. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vnormg_c.html :param v: Vector whose magnitude is to be found. :type v: Array of floats :param ndim: Dimension of v :type ndim: int :return: magnitude of v calculated in a numerically stable way :rtype: float """ v = stypes.toDoubleVector(v) ndim = ctypes.c_int(ndim) return libspice.vnormg_c(v, ndim)
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Compute the magnitude of a double precision vector of arbitrary dimension. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vnormg_c.html :param v: Vector whose magnitude is to be found. :type v: Array of floats :param ndim: Dimension of v :type ndim: int :return: magnitude of v calculated in a numerically stable way :rtype: float
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L14846-L14861
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
vpack
def vpack(x, y, z): """ Pack three scalar components into a vector. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vpack_c.html :param x: first scalar component :type x: float :param y: second scalar component :type y: float :param z: third scalar component :type z: float :return: Equivalent 3-vector. :rtype: 3-Element Array of floats """ x = ctypes.c_double(x) y = ctypes.c_double(y) z = ctypes.c_double(z) vout = stypes.emptyDoubleVector(3) libspice.vpack_c(x, y, z, vout) return stypes.cVectorToPython(vout)
python
def vpack(x, y, z): """ Pack three scalar components into a vector. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vpack_c.html :param x: first scalar component :type x: float :param y: second scalar component :type y: float :param z: third scalar component :type z: float :return: Equivalent 3-vector. :rtype: 3-Element Array of floats """ x = ctypes.c_double(x) y = ctypes.c_double(y) z = ctypes.c_double(z) vout = stypes.emptyDoubleVector(3) libspice.vpack_c(x, y, z, vout) return stypes.cVectorToPython(vout)
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Pack three scalar components into a vector. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vpack_c.html :param x: first scalar component :type x: float :param y: second scalar component :type y: float :param z: third scalar component :type z: float :return: Equivalent 3-vector. :rtype: 3-Element Array of floats
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L14865-L14885
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
vperp
def vperp(a, b): """ Find the component of a vector that is perpendicular to a second vector. All vectors are 3-dimensional. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vperp_c.html :param a: The vector whose orthogonal component is sought. :type a: 3-Element Array of floats :param b: The vector used as the orthogonal reference. :type b: 3-Element Array of floats :return: The component of a orthogonal to b. :rtype: 3-Element Array of floats """ a = stypes.toDoubleVector(a) b = stypes.toDoubleVector(b) vout = stypes.emptyDoubleVector(3) libspice.vperp_c(a, b, vout) return stypes.cVectorToPython(vout)
python
def vperp(a, b): """ Find the component of a vector that is perpendicular to a second vector. All vectors are 3-dimensional. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vperp_c.html :param a: The vector whose orthogonal component is sought. :type a: 3-Element Array of floats :param b: The vector used as the orthogonal reference. :type b: 3-Element Array of floats :return: The component of a orthogonal to b. :rtype: 3-Element Array of floats """ a = stypes.toDoubleVector(a) b = stypes.toDoubleVector(b) vout = stypes.emptyDoubleVector(3) libspice.vperp_c(a, b, vout) return stypes.cVectorToPython(vout)
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Find the component of a vector that is perpendicular to a second vector. All vectors are 3-dimensional. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vperp_c.html :param a: The vector whose orthogonal component is sought. :type a: 3-Element Array of floats :param b: The vector used as the orthogonal reference. :type b: 3-Element Array of floats :return: The component of a orthogonal to b. :rtype: 3-Element Array of floats
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L14889-L14907
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
vprjp
def vprjp(vin, plane): """ Project a vector onto a specified plane, orthogonally. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vprjp_c.html :param vin: The projected vector. :type vin: 3-Element Array of floats :param plane: Plane containing vin. :type plane: spiceypy.utils.support_types.Plane :return: Vector resulting from projection. :rtype: 3-Element Array of floats """ vin = stypes.toDoubleVector(vin) vout = stypes.emptyDoubleVector(3) libspice.vprjp_c(vin, ctypes.byref(plane), vout) return stypes.cVectorToPython(vout)
python
def vprjp(vin, plane): """ Project a vector onto a specified plane, orthogonally. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vprjp_c.html :param vin: The projected vector. :type vin: 3-Element Array of floats :param plane: Plane containing vin. :type plane: spiceypy.utils.support_types.Plane :return: Vector resulting from projection. :rtype: 3-Element Array of floats """ vin = stypes.toDoubleVector(vin) vout = stypes.emptyDoubleVector(3) libspice.vprjp_c(vin, ctypes.byref(plane), vout) return stypes.cVectorToPython(vout)
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Project a vector onto a specified plane, orthogonally. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vprjp_c.html :param vin: The projected vector. :type vin: 3-Element Array of floats :param plane: Plane containing vin. :type plane: spiceypy.utils.support_types.Plane :return: Vector resulting from projection. :rtype: 3-Element Array of floats
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L14911-L14927
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
vprjpi
def vprjpi(vin, projpl, invpl): """ Find the vector in a specified plane that maps to a specified vector in another plane under orthogonal projection. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vprjpi_c.html :param vin: The projected vector. :type vin: 3-Element Array of floats :param projpl: Plane containing vin. :type projpl: spiceypy.utils.support_types.Plane :param invpl: Plane containing inverse image of vin. :type invpl: spiceypy.utils.support_types.Plane :return: Inverse projection of vin. :rtype: list """ vin = stypes.toDoubleVector(vin) vout = stypes.emptyDoubleVector(3) found = ctypes.c_int() libspice.vprjpi_c(vin, ctypes.byref(projpl), ctypes.byref(invpl), vout, ctypes.byref(found)) return stypes.cVectorToPython(vout), bool(found.value)
python
def vprjpi(vin, projpl, invpl): """ Find the vector in a specified plane that maps to a specified vector in another plane under orthogonal projection. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vprjpi_c.html :param vin: The projected vector. :type vin: 3-Element Array of floats :param projpl: Plane containing vin. :type projpl: spiceypy.utils.support_types.Plane :param invpl: Plane containing inverse image of vin. :type invpl: spiceypy.utils.support_types.Plane :return: Inverse projection of vin. :rtype: list """ vin = stypes.toDoubleVector(vin) vout = stypes.emptyDoubleVector(3) found = ctypes.c_int() libspice.vprjpi_c(vin, ctypes.byref(projpl), ctypes.byref(invpl), vout, ctypes.byref(found)) return stypes.cVectorToPython(vout), bool(found.value)
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Find the vector in a specified plane that maps to a specified vector in another plane under orthogonal projection. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vprjpi_c.html :param vin: The projected vector. :type vin: 3-Element Array of floats :param projpl: Plane containing vin. :type projpl: spiceypy.utils.support_types.Plane :param invpl: Plane containing inverse image of vin. :type invpl: spiceypy.utils.support_types.Plane :return: Inverse projection of vin. :rtype: list
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L14932-L14953
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
vproj
def vproj(a, b): """ Find the projection of one vector onto another vector. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vproj_c.html :param a: The vector to be projected. :type a: 3-Element Array of floats :param b: The vector onto which a is to be projected. :type b: 3-Element Array of floats :return: The projection of a onto b. :rtype: 3-Element Array of floats """ a = stypes.toDoubleVector(a) b = stypes.toDoubleVector(b) vout = stypes.emptyDoubleVector(3) libspice.vproj_c(a, b, vout) return stypes.cVectorToPython(vout)
python
def vproj(a, b): """ Find the projection of one vector onto another vector. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vproj_c.html :param a: The vector to be projected. :type a: 3-Element Array of floats :param b: The vector onto which a is to be projected. :type b: 3-Element Array of floats :return: The projection of a onto b. :rtype: 3-Element Array of floats """ a = stypes.toDoubleVector(a) b = stypes.toDoubleVector(b) vout = stypes.emptyDoubleVector(3) libspice.vproj_c(a, b, vout) return stypes.cVectorToPython(vout)
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Find the projection of one vector onto another vector. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vproj_c.html :param a: The vector to be projected. :type a: 3-Element Array of floats :param b: The vector onto which a is to be projected. :type b: 3-Element Array of floats :return: The projection of a onto b. :rtype: 3-Element Array of floats
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L14957-L14974
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
vrel
def vrel(v1, v2): """ Return the relative difference between two 3-dimensional vectors. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vrel_c.html :param v1: First vector :type v1: 3-Element Array of floats :param v2: Second vector :type v2: 3-Element Array of floats :return: the relative difference between v1 and v2. :rtype: float """ v1 = stypes.toDoubleVector(v1) v2 = stypes.toDoubleVector(v2) return libspice.vrel_c(v1, v2)
python
def vrel(v1, v2): """ Return the relative difference between two 3-dimensional vectors. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vrel_c.html :param v1: First vector :type v1: 3-Element Array of floats :param v2: Second vector :type v2: 3-Element Array of floats :return: the relative difference between v1 and v2. :rtype: float """ v1 = stypes.toDoubleVector(v1) v2 = stypes.toDoubleVector(v2) return libspice.vrel_c(v1, v2)
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Return the relative difference between two 3-dimensional vectors. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vrel_c.html :param v1: First vector :type v1: 3-Element Array of floats :param v2: Second vector :type v2: 3-Element Array of floats :return: the relative difference between v1 and v2. :rtype: float
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L14978-L14993
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
vrelg
def vrelg(v1, v2, ndim): """ Return the relative difference between two vectors of general dimension. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vrelg_c.html :param v1: First vector :type v1: Array of floats :param v2: Second vector :type v2: Array of floats :param ndim: Dimension of v1 and v2. :type ndim: int :return: the relative difference between v1 and v2. :rtype: float """ v1 = stypes.toDoubleVector(v1) v2 = stypes.toDoubleVector(v2) ndim = ctypes.c_int(ndim) return libspice.vrelg_c(v1, v2, ndim)
python
def vrelg(v1, v2, ndim): """ Return the relative difference between two vectors of general dimension. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vrelg_c.html :param v1: First vector :type v1: Array of floats :param v2: Second vector :type v2: Array of floats :param ndim: Dimension of v1 and v2. :type ndim: int :return: the relative difference between v1 and v2. :rtype: float """ v1 = stypes.toDoubleVector(v1) v2 = stypes.toDoubleVector(v2) ndim = ctypes.c_int(ndim) return libspice.vrelg_c(v1, v2, ndim)
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Return the relative difference between two vectors of general dimension. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vrelg_c.html :param v1: First vector :type v1: Array of floats :param v2: Second vector :type v2: Array of floats :param ndim: Dimension of v1 and v2. :type ndim: int :return: the relative difference between v1 and v2. :rtype: float
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L14997-L15015
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
vrotv
def vrotv(v, axis, theta): """ Rotate a vector about a specified axis vector by a specified angle and return the rotated vector. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vrotv_c.html :param v: Vector to be rotated. :type v: 3-Element Array of floats :param axis: Axis of the rotation. :type axis: 3-Element Array of floats :param theta: Angle of rotation (radians). :type theta: float :return: Result of rotating v about axis by theta :rtype: 3-Element Array of floats """ v = stypes.toDoubleVector(v) axis = stypes.toDoubleVector(axis) theta = ctypes.c_double(theta) r = stypes.emptyDoubleVector(3) libspice.vrotv_c(v, axis, theta, r) return stypes.cVectorToPython(r)
python
def vrotv(v, axis, theta): """ Rotate a vector about a specified axis vector by a specified angle and return the rotated vector. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vrotv_c.html :param v: Vector to be rotated. :type v: 3-Element Array of floats :param axis: Axis of the rotation. :type axis: 3-Element Array of floats :param theta: Angle of rotation (radians). :type theta: float :return: Result of rotating v about axis by theta :rtype: 3-Element Array of floats """ v = stypes.toDoubleVector(v) axis = stypes.toDoubleVector(axis) theta = ctypes.c_double(theta) r = stypes.emptyDoubleVector(3) libspice.vrotv_c(v, axis, theta, r) return stypes.cVectorToPython(r)
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Rotate a vector about a specified axis vector by a specified angle and return the rotated vector. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vrotv_c.html :param v: Vector to be rotated. :type v: 3-Element Array of floats :param axis: Axis of the rotation. :type axis: 3-Element Array of floats :param theta: Angle of rotation (radians). :type theta: float :return: Result of rotating v about axis by theta :rtype: 3-Element Array of floats
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L15019-L15040
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
vscl
def vscl(s, v1): """ Multiply a scalar and a 3-dimensional double precision vector. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vscl_c.html :param s: Scalar to multiply a vector :type s: float :param v1: Vector to be multiplied :type v1: 3-Element Array of floats :return: Product vector, s*v1. :rtype: 3-Element Array of floats """ s = ctypes.c_double(s) v1 = stypes.toDoubleVector(v1) vout = stypes.emptyDoubleVector(3) libspice.vscl_c(s, v1, vout) return stypes.cVectorToPython(vout)
python
def vscl(s, v1): """ Multiply a scalar and a 3-dimensional double precision vector. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vscl_c.html :param s: Scalar to multiply a vector :type s: float :param v1: Vector to be multiplied :type v1: 3-Element Array of floats :return: Product vector, s*v1. :rtype: 3-Element Array of floats """ s = ctypes.c_double(s) v1 = stypes.toDoubleVector(v1) vout = stypes.emptyDoubleVector(3) libspice.vscl_c(s, v1, vout) return stypes.cVectorToPython(vout)
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Multiply a scalar and a 3-dimensional double precision vector. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vscl_c.html :param s: Scalar to multiply a vector :type s: float :param v1: Vector to be multiplied :type v1: 3-Element Array of floats :return: Product vector, s*v1. :rtype: 3-Element Array of floats
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L15044-L15061
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
vsclg
def vsclg(s, v1, ndim): """ Multiply a scalar and a double precision vector of arbitrary dimension. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vsclg_c.html :param s: Scalar to multiply a vector :type s: float :param v1: Vector to be multiplied :type v1: Array of floats :param ndim: Dimension of v1 :type ndim: int :return: Product vector, s*v1. :rtype: Array of floats """ s = ctypes.c_double(s) v1 = stypes.toDoubleVector(v1) vout = stypes.emptyDoubleVector(ndim) ndim = ctypes.c_int(ndim) libspice.vsclg_c(s, v1, ndim, vout) return stypes.cVectorToPython(vout)
python
def vsclg(s, v1, ndim): """ Multiply a scalar and a double precision vector of arbitrary dimension. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vsclg_c.html :param s: Scalar to multiply a vector :type s: float :param v1: Vector to be multiplied :type v1: Array of floats :param ndim: Dimension of v1 :type ndim: int :return: Product vector, s*v1. :rtype: Array of floats """ s = ctypes.c_double(s) v1 = stypes.toDoubleVector(v1) vout = stypes.emptyDoubleVector(ndim) ndim = ctypes.c_int(ndim) libspice.vsclg_c(s, v1, ndim, vout) return stypes.cVectorToPython(vout)
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Multiply a scalar and a double precision vector of arbitrary dimension. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vsclg_c.html :param s: Scalar to multiply a vector :type s: float :param v1: Vector to be multiplied :type v1: Array of floats :param ndim: Dimension of v1 :type ndim: int :return: Product vector, s*v1. :rtype: Array of floats
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L15065-L15085
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
vsep
def vsep(v1, v2): """ Find the separation angle in radians between two double precision, 3-dimensional vectors. This angle is defined as zero if either vector is zero. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vsep_c.html :param v1: First vector :type v1: 3-Element Array of floats :param v2: Second vector :type v2: 3-Element Array of floats :return: separation angle in radians :rtype: float """ v1 = stypes.toDoubleVector(v1) v2 = stypes.toDoubleVector(v2) return libspice.vsep_c(v1, v2)
python
def vsep(v1, v2): """ Find the separation angle in radians between two double precision, 3-dimensional vectors. This angle is defined as zero if either vector is zero. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vsep_c.html :param v1: First vector :type v1: 3-Element Array of floats :param v2: Second vector :type v2: 3-Element Array of floats :return: separation angle in radians :rtype: float """ v1 = stypes.toDoubleVector(v1) v2 = stypes.toDoubleVector(v2) return libspice.vsep_c(v1, v2)
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Find the separation angle in radians between two double precision, 3-dimensional vectors. This angle is defined as zero if either vector is zero. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vsep_c.html :param v1: First vector :type v1: 3-Element Array of floats :param v2: Second vector :type v2: 3-Element Array of floats :return: separation angle in radians :rtype: float
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L15089-L15106
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
vsepg
def vsepg(v1, v2, ndim): """ Find the separation angle in radians between two double precision vectors of arbitrary dimension. This angle is defined as zero if either vector is zero. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vsepg_c.html :param v1: First vector :type v1: Array of floats :param v2: Second vector :type v2: Array of floats :param ndim: The number of elements in v1 and v2. :type ndim: int :return: separation angle in radians :rtype: float """ v1 = stypes.toDoubleVector(v1) v2 = stypes.toDoubleVector(v2) ndim = ctypes.c_int(ndim) return libspice.vsepg_c(v1, v2, ndim)
python
def vsepg(v1, v2, ndim): """ Find the separation angle in radians between two double precision vectors of arbitrary dimension. This angle is defined as zero if either vector is zero. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vsepg_c.html :param v1: First vector :type v1: Array of floats :param v2: Second vector :type v2: Array of floats :param ndim: The number of elements in v1 and v2. :type ndim: int :return: separation angle in radians :rtype: float """ v1 = stypes.toDoubleVector(v1) v2 = stypes.toDoubleVector(v2) ndim = ctypes.c_int(ndim) return libspice.vsepg_c(v1, v2, ndim)
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Find the separation angle in radians between two double precision vectors of arbitrary dimension. This angle is defined as zero if either vector is zero. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vsepg_c.html :param v1: First vector :type v1: Array of floats :param v2: Second vector :type v2: Array of floats :param ndim: The number of elements in v1 and v2. :type ndim: int :return: separation angle in radians :rtype: float
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L15110-L15130
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
vsub
def vsub(v1, v2): """ Compute the difference between two 3-dimensional, double precision vectors. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vsub_c.html :param v1: First vector (minuend). :type v1: 3-Element Array of floats :param v2: Second vector (subtrahend). :type v2: 3-Element Array of floats :return: Difference vector, v1 - v2. :rtype: 3-Element Array of floats """ v1 = stypes.toDoubleVector(v1) v2 = stypes.toDoubleVector(v2) vout = stypes.emptyDoubleVector(3) libspice.vsub_c(v1, v2, vout) return stypes.cVectorToPython(vout)
python
def vsub(v1, v2): """ Compute the difference between two 3-dimensional, double precision vectors. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vsub_c.html :param v1: First vector (minuend). :type v1: 3-Element Array of floats :param v2: Second vector (subtrahend). :type v2: 3-Element Array of floats :return: Difference vector, v1 - v2. :rtype: 3-Element Array of floats """ v1 = stypes.toDoubleVector(v1) v2 = stypes.toDoubleVector(v2) vout = stypes.emptyDoubleVector(3) libspice.vsub_c(v1, v2, vout) return stypes.cVectorToPython(vout)
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Compute the difference between two 3-dimensional, double precision vectors. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vsub_c.html :param v1: First vector (minuend). :type v1: 3-Element Array of floats :param v2: Second vector (subtrahend). :type v2: 3-Element Array of floats :return: Difference vector, v1 - v2. :rtype: 3-Element Array of floats
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L15134-L15152
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
vsubg
def vsubg(v1, v2, ndim): """ Compute the difference between two double precision vectors of arbitrary dimension. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vsubg_c.html :param v1: First vector (minuend). :type v1: Array of floats :param v2: Second vector (subtrahend). :type v2: Array of floats :param ndim: Dimension of v1, v2, and vout. :type ndim: int :return: Difference vector, v1 - v2. :rtype: Array of floats """ v1 = stypes.toDoubleVector(v1) v2 = stypes.toDoubleVector(v2) vout = stypes.emptyDoubleVector(ndim) ndim = ctypes.c_int(ndim) libspice.vsubg_c(v1, v2, ndim, vout) return stypes.cVectorToPython(vout)
python
def vsubg(v1, v2, ndim): """ Compute the difference between two double precision vectors of arbitrary dimension. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vsubg_c.html :param v1: First vector (minuend). :type v1: Array of floats :param v2: Second vector (subtrahend). :type v2: Array of floats :param ndim: Dimension of v1, v2, and vout. :type ndim: int :return: Difference vector, v1 - v2. :rtype: Array of floats """ v1 = stypes.toDoubleVector(v1) v2 = stypes.toDoubleVector(v2) vout = stypes.emptyDoubleVector(ndim) ndim = ctypes.c_int(ndim) libspice.vsubg_c(v1, v2, ndim, vout) return stypes.cVectorToPython(vout)
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Compute the difference between two double precision vectors of arbitrary dimension. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vsubg_c.html :param v1: First vector (minuend). :type v1: Array of floats :param v2: Second vector (subtrahend). :type v2: Array of floats :param ndim: Dimension of v1, v2, and vout. :type ndim: int :return: Difference vector, v1 - v2. :rtype: Array of floats
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L15156-L15177
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
vtmv
def vtmv(v1, matrix, v2): """ Multiply the transpose of a 3-dimensional column vector a 3x3 matrix, and a 3-dimensional column vector. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vtmv_c.html :param v1: 3 dimensional double precision column vector. :type v1: 3-Element Array of floats :param matrix: 3x3 double precision matrix. :type matrix: 3x3-Element Array of floats :param v2: 3 dimensional double precision column vector. :type v2: 3-Element Array of floats :return: the result of (v1**t * matrix * v2 ). :rtype: float """ v1 = stypes.toDoubleVector(v1) matrix = stypes.toDoubleMatrix(matrix) v2 = stypes.toDoubleVector(v2) return libspice.vtmv_c(v1, matrix, v2)
python
def vtmv(v1, matrix, v2): """ Multiply the transpose of a 3-dimensional column vector a 3x3 matrix, and a 3-dimensional column vector. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vtmv_c.html :param v1: 3 dimensional double precision column vector. :type v1: 3-Element Array of floats :param matrix: 3x3 double precision matrix. :type matrix: 3x3-Element Array of floats :param v2: 3 dimensional double precision column vector. :type v2: 3-Element Array of floats :return: the result of (v1**t * matrix * v2 ). :rtype: float """ v1 = stypes.toDoubleVector(v1) matrix = stypes.toDoubleMatrix(matrix) v2 = stypes.toDoubleVector(v2) return libspice.vtmv_c(v1, matrix, v2)
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Multiply the transpose of a 3-dimensional column vector a 3x3 matrix, and a 3-dimensional column vector. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vtmv_c.html :param v1: 3 dimensional double precision column vector. :type v1: 3-Element Array of floats :param matrix: 3x3 double precision matrix. :type matrix: 3x3-Element Array of floats :param v2: 3 dimensional double precision column vector. :type v2: 3-Element Array of floats :return: the result of (v1**t * matrix * v2 ). :rtype: float
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L15181-L15200
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
vtmvg
def vtmvg(v1, matrix, v2, nrow, ncol): """ Multiply the transpose of a n-dimensional column vector a nxm matrix, and a m-dimensional column vector. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vtmvg_c.html :param v1: n-dimensional double precision column vector. :type v1: Array of floats :param matrix: nxm double precision matrix. :type matrix: NxM-Element Array of floats :param v2: m-dimensional double porecision column vector. :type v2: Array of floats :param nrow: Number of rows in matrix (number of rows in v1.) :type nrow: int :param ncol: Number of columns in matrix (number of rows in v2.) :type ncol: int :return: the result of (v1**t * matrix * v2 ) :rtype: float """ v1 = stypes.toDoubleVector(v1) matrix = stypes.toDoubleMatrix(matrix) v2 = stypes.toDoubleVector(v2) nrow = ctypes.c_int(nrow) ncol = ctypes.c_int(ncol) return libspice.vtmvg_c(v1, matrix, v2, nrow, ncol)
python
def vtmvg(v1, matrix, v2, nrow, ncol): """ Multiply the transpose of a n-dimensional column vector a nxm matrix, and a m-dimensional column vector. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vtmvg_c.html :param v1: n-dimensional double precision column vector. :type v1: Array of floats :param matrix: nxm double precision matrix. :type matrix: NxM-Element Array of floats :param v2: m-dimensional double porecision column vector. :type v2: Array of floats :param nrow: Number of rows in matrix (number of rows in v1.) :type nrow: int :param ncol: Number of columns in matrix (number of rows in v2.) :type ncol: int :return: the result of (v1**t * matrix * v2 ) :rtype: float """ v1 = stypes.toDoubleVector(v1) matrix = stypes.toDoubleMatrix(matrix) v2 = stypes.toDoubleVector(v2) nrow = ctypes.c_int(nrow) ncol = ctypes.c_int(ncol) return libspice.vtmvg_c(v1, matrix, v2, nrow, ncol)
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L15204-L15230
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
vupack
def vupack(v): """ Unpack three scalar components from a vector. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vupack_c.html :param v: Vector :type v: 3-Element Array of floats :return: (x, y, z) :rtype: tuple """ v1 = stypes.toDoubleVector(v) x = ctypes.c_double() y = ctypes.c_double() z = ctypes.c_double() libspice.vupack_c(v1, ctypes.byref(x), ctypes.byref(y), ctypes.byref(z)) return x.value, y.value, z.value
python
def vupack(v): """ Unpack three scalar components from a vector. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vupack_c.html :param v: Vector :type v: 3-Element Array of floats :return: (x, y, z) :rtype: tuple """ v1 = stypes.toDoubleVector(v) x = ctypes.c_double() y = ctypes.c_double() z = ctypes.c_double() libspice.vupack_c(v1, ctypes.byref(x), ctypes.byref(y), ctypes.byref(z)) return x.value, y.value, z.value
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Unpack three scalar components from a vector. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vupack_c.html :param v: Vector :type v: 3-Element Array of floats :return: (x, y, z) :rtype: tuple
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L15234-L15250
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
vzero
def vzero(v): """ Indicate whether a 3-vector is the zero vector. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vzero_c.html :param v: Vector to be tested :type v: 3-Element Array of floats :return: true if and only if v is the zero vector :rtype: bool """ v = stypes.toDoubleVector(v) return bool(libspice.vzero_c(v))
python
def vzero(v): """ Indicate whether a 3-vector is the zero vector. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vzero_c.html :param v: Vector to be tested :type v: 3-Element Array of floats :return: true if and only if v is the zero vector :rtype: bool """ v = stypes.toDoubleVector(v) return bool(libspice.vzero_c(v))
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Indicate whether a 3-vector is the zero vector. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vzero_c.html :param v: Vector to be tested :type v: 3-Element Array of floats :return: true if and only if v is the zero vector :rtype: bool
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L15254-L15266
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
vzerog
def vzerog(v, ndim): """ Indicate whether a general-dimensional vector is the zero vector. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vzerog_c.html :param v: Vector to be tested :type v: Array of floats :param ndim: Dimension of v :type ndim: int :return: true if and only if v is the zero vector :rtype: bool """ v = stypes.toDoubleVector(v) ndim = ctypes.c_int(ndim) return bool(libspice.vzerog_c(v, ndim))
python
def vzerog(v, ndim): """ Indicate whether a general-dimensional vector is the zero vector. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vzerog_c.html :param v: Vector to be tested :type v: Array of floats :param ndim: Dimension of v :type ndim: int :return: true if and only if v is the zero vector :rtype: bool """ v = stypes.toDoubleVector(v) ndim = ctypes.c_int(ndim) return bool(libspice.vzerog_c(v, ndim))
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Indicate whether a general-dimensional vector is the zero vector. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/vzerog_c.html :param v: Vector to be tested :type v: Array of floats :param ndim: Dimension of v :type ndim: int :return: true if and only if v is the zero vector :rtype: bool
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L15270-L15285
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
wncomd
def wncomd(left, right, window): """ Determine the complement of a double precision window with respect to a specified interval. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/wncomd_c.html :param left: left endpoints of complement interval. :type left: float :param right: right endpoints of complement interval. :type right: float :param window: Input window :type window: spiceypy.utils.support_types.SpiceCell :return: Complement of window with respect to left and right. :rtype: spiceypy.utils.support_types.SpiceCell """ assert isinstance(window, stypes.SpiceCell) assert window.dtype == 1 left = ctypes.c_double(left) right = ctypes.c_double(right) result = stypes.SpiceCell.double(window.size) libspice.wncomd_c(left, right, ctypes.byref(window), result) return result
python
def wncomd(left, right, window): """ Determine the complement of a double precision window with respect to a specified interval. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/wncomd_c.html :param left: left endpoints of complement interval. :type left: float :param right: right endpoints of complement interval. :type right: float :param window: Input window :type window: spiceypy.utils.support_types.SpiceCell :return: Complement of window with respect to left and right. :rtype: spiceypy.utils.support_types.SpiceCell """ assert isinstance(window, stypes.SpiceCell) assert window.dtype == 1 left = ctypes.c_double(left) right = ctypes.c_double(right) result = stypes.SpiceCell.double(window.size) libspice.wncomd_c(left, right, ctypes.byref(window), result) return result
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Determine the complement of a double precision window with respect to a specified interval. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/wncomd_c.html :param left: left endpoints of complement interval. :type left: float :param right: right endpoints of complement interval. :type right: float :param window: Input window :type window: spiceypy.utils.support_types.SpiceCell :return: Complement of window with respect to left and right. :rtype: spiceypy.utils.support_types.SpiceCell
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L15310-L15332
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
wncond
def wncond(left, right, window): """ Contract each of the intervals of a double precision window. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/wncond_c.html :param left: Amount added to each left endpoint. :type left: float :param right: Amount subtracted from each right endpoint. :type right: float :param window: Window to be contracted :type window: spiceypy.utils.support_types.SpiceCell :return: Contracted Window. :rtype: spiceypy.utils.support_types.SpiceCell """ assert isinstance(window, stypes.SpiceCell) assert window.dtype == 1 left = ctypes.c_double(left) right = ctypes.c_double(right) libspice.wncond_c(left, right, ctypes.byref(window)) return window
python
def wncond(left, right, window): """ Contract each of the intervals of a double precision window. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/wncond_c.html :param left: Amount added to each left endpoint. :type left: float :param right: Amount subtracted from each right endpoint. :type right: float :param window: Window to be contracted :type window: spiceypy.utils.support_types.SpiceCell :return: Contracted Window. :rtype: spiceypy.utils.support_types.SpiceCell """ assert isinstance(window, stypes.SpiceCell) assert window.dtype == 1 left = ctypes.c_double(left) right = ctypes.c_double(right) libspice.wncond_c(left, right, ctypes.byref(window)) return window
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Contract each of the intervals of a double precision window. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/wncond_c.html :param left: Amount added to each left endpoint. :type left: float :param right: Amount subtracted from each right endpoint. :type right: float :param window: Window to be contracted :type window: spiceypy.utils.support_types.SpiceCell :return: Contracted Window. :rtype: spiceypy.utils.support_types.SpiceCell
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L15336-L15356
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
wndifd
def wndifd(a, b): """ Place the difference of two double precision windows into a third window. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/wndifd_c.html :param a: Input window A. :type a: spiceypy.utils.support_types.SpiceCell :param b: Input window B. :type b: spiceypy.utils.support_types.SpiceCell :return: Difference of a and b. :rtype: spiceypy.utils.support_types.SpiceCell """ assert isinstance(a, stypes.SpiceCell) assert isinstance(b, stypes.SpiceCell) assert a.dtype == 1 assert b.dtype == 1 c = stypes.SpiceCell.double(a.size + b.size) libspice.wndifd_c(ctypes.byref(a), ctypes.byref(b), ctypes.byref(c)) return c
python
def wndifd(a, b): """ Place the difference of two double precision windows into a third window. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/wndifd_c.html :param a: Input window A. :type a: spiceypy.utils.support_types.SpiceCell :param b: Input window B. :type b: spiceypy.utils.support_types.SpiceCell :return: Difference of a and b. :rtype: spiceypy.utils.support_types.SpiceCell """ assert isinstance(a, stypes.SpiceCell) assert isinstance(b, stypes.SpiceCell) assert a.dtype == 1 assert b.dtype == 1 c = stypes.SpiceCell.double(a.size + b.size) libspice.wndifd_c(ctypes.byref(a), ctypes.byref(b), ctypes.byref(c)) return c
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Place the difference of two double precision windows into a third window. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/wndifd_c.html :param a: Input window A. :type a: spiceypy.utils.support_types.SpiceCell :param b: Input window B. :type b: spiceypy.utils.support_types.SpiceCell :return: Difference of a and b. :rtype: spiceypy.utils.support_types.SpiceCell
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L15360-L15380
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
wnelmd
def wnelmd(point, window): """ Determine whether a point is an element of a double precision window. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/wnelmd_c.html :param point: Input point. :type point: float :param window: Input window :type window: spiceypy.utils.support_types.SpiceCell :return: returns True if point is an element of window. :rtype: bool """ assert isinstance(window, stypes.SpiceCell) assert window.dtype == 1 point = ctypes.c_double(point) return bool(libspice.wnelmd_c(point, ctypes.byref(window)))
python
def wnelmd(point, window): """ Determine whether a point is an element of a double precision window. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/wnelmd_c.html :param point: Input point. :type point: float :param window: Input window :type window: spiceypy.utils.support_types.SpiceCell :return: returns True if point is an element of window. :rtype: bool """ assert isinstance(window, stypes.SpiceCell) assert window.dtype == 1 point = ctypes.c_double(point) return bool(libspice.wnelmd_c(point, ctypes.byref(window)))
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Determine whether a point is an element of a double precision window. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/wnelmd_c.html :param point: Input point. :type point: float :param window: Input window :type window: spiceypy.utils.support_types.SpiceCell :return: returns True if point is an element of window. :rtype: bool
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L15384-L15401
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
wnexpd
def wnexpd(left, right, window): """ Expand each of the intervals of a double precision window. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/wnexpd_c.html :param left: Amount subtracted from each left endpoint. :type left: float :param right: Amount added to each right endpoint. :type right: float :param window: Window to be expanded. :type window: spiceypy.utils.support_types.SpiceCell :return: Expanded Window. :rtype: spiceypy.utils.support_types.SpiceCell """ assert isinstance(window, stypes.SpiceCell) assert window.dtype == 1 left = ctypes.c_double(left) right = ctypes.c_double(right) libspice.wnexpd_c(left, right, ctypes.byref(window)) return window
python
def wnexpd(left, right, window): """ Expand each of the intervals of a double precision window. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/wnexpd_c.html :param left: Amount subtracted from each left endpoint. :type left: float :param right: Amount added to each right endpoint. :type right: float :param window: Window to be expanded. :type window: spiceypy.utils.support_types.SpiceCell :return: Expanded Window. :rtype: spiceypy.utils.support_types.SpiceCell """ assert isinstance(window, stypes.SpiceCell) assert window.dtype == 1 left = ctypes.c_double(left) right = ctypes.c_double(right) libspice.wnexpd_c(left, right, ctypes.byref(window)) return window
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Expand each of the intervals of a double precision window. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/wnexpd_c.html :param left: Amount subtracted from each left endpoint. :type left: float :param right: Amount added to each right endpoint. :type right: float :param window: Window to be expanded. :type window: spiceypy.utils.support_types.SpiceCell :return: Expanded Window. :rtype: spiceypy.utils.support_types.SpiceCell
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L15405-L15425
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
wnextd
def wnextd(side, window): """ Extract the left or right endpoints from a double precision window. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/wnextd_c.html :param side: Extract left "L" or right "R" endpoints. :type side: str :param window: Window to be extracted. :type window: spiceypy.utils.support_types.SpiceCell :return: Extracted Window. :rtype: spiceypy.utils.support_types.SpiceCell """ assert isinstance(window, stypes.SpiceCell) assert window.dtype == 1 assert side == 'L' or side == 'R' side = ctypes.c_char(side.encode(encoding='UTF-8')) libspice.wnextd_c(side, ctypes.byref(window)) return window
python
def wnextd(side, window): """ Extract the left or right endpoints from a double precision window. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/wnextd_c.html :param side: Extract left "L" or right "R" endpoints. :type side: str :param window: Window to be extracted. :type window: spiceypy.utils.support_types.SpiceCell :return: Extracted Window. :rtype: spiceypy.utils.support_types.SpiceCell """ assert isinstance(window, stypes.SpiceCell) assert window.dtype == 1 assert side == 'L' or side == 'R' side = ctypes.c_char(side.encode(encoding='UTF-8')) libspice.wnextd_c(side, ctypes.byref(window)) return window
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Extract the left or right endpoints from a double precision window. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/wnextd_c.html :param side: Extract left "L" or right "R" endpoints. :type side: str :param window: Window to be extracted. :type window: spiceypy.utils.support_types.SpiceCell :return: Extracted Window. :rtype: spiceypy.utils.support_types.SpiceCell
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L15429-L15448
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
wnfetd
def wnfetd(window, n): """ Fetch a particular interval from a double precision window. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/wnfetd_c.html :param window: Input window :type window: spiceypy.utils.support_types.SpiceCell :param n: Index of interval to be fetched. :type n: int :return: Left, right endpoints of the nth interval. :rtype: tuple """ assert isinstance(window, stypes.SpiceCell) assert window.dtype == 1 n = ctypes.c_int(n) left = ctypes.c_double() right = ctypes.c_double() libspice.wnfetd_c(ctypes.byref(window), n, ctypes.byref(left), ctypes.byref(right)) return left.value, right.value
python
def wnfetd(window, n): """ Fetch a particular interval from a double precision window. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/wnfetd_c.html :param window: Input window :type window: spiceypy.utils.support_types.SpiceCell :param n: Index of interval to be fetched. :type n: int :return: Left, right endpoints of the nth interval. :rtype: tuple """ assert isinstance(window, stypes.SpiceCell) assert window.dtype == 1 n = ctypes.c_int(n) left = ctypes.c_double() right = ctypes.c_double() libspice.wnfetd_c(ctypes.byref(window), n, ctypes.byref(left), ctypes.byref(right)) return left.value, right.value
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L15452-L15472
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
wnfild
def wnfild(small, window): """ Fill small gaps between adjacent intervals of a double precision window. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/wnfild_c.html :param small: Limiting measure of small gaps. :type small: float :param window: Window to be filled :type window: spiceypy.utils.support_types.SpiceCell :return: Filled Window. :rtype: spiceypy.utils.support_types.SpiceCell """ assert isinstance(window, stypes.SpiceCell) assert window.dtype == 1 small = ctypes.c_double(small) libspice.wnfild_c(small, ctypes.byref(window)) return window
python
def wnfild(small, window): """ Fill small gaps between adjacent intervals of a double precision window. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/wnfild_c.html :param small: Limiting measure of small gaps. :type small: float :param window: Window to be filled :type window: spiceypy.utils.support_types.SpiceCell :return: Filled Window. :rtype: spiceypy.utils.support_types.SpiceCell """ assert isinstance(window, stypes.SpiceCell) assert window.dtype == 1 small = ctypes.c_double(small) libspice.wnfild_c(small, ctypes.byref(window)) return window
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Fill small gaps between adjacent intervals of a double precision window. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/wnfild_c.html :param small: Limiting measure of small gaps. :type small: float :param window: Window to be filled :type window: spiceypy.utils.support_types.SpiceCell :return: Filled Window. :rtype: spiceypy.utils.support_types.SpiceCell
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L15476-L15493
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
wnincd
def wnincd(left, right, window): """ Determine whether an interval is included in a double precision window. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/wnincd_c.html :param left: Left interval :type left: float :param right: Right interval :type right: float :param window: Input window :type window: spiceypy.utils.support_types.SpiceCell :return: Returns True if the input interval is included in window. :rtype: bool """ assert isinstance(window, stypes.SpiceCell) assert window.dtype == 1 left = ctypes.c_double(left) right = ctypes.c_double(right) return bool(libspice.wnincd_c(left, right, ctypes.byref(window)))
python
def wnincd(left, right, window): """ Determine whether an interval is included in a double precision window. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/wnincd_c.html :param left: Left interval :type left: float :param right: Right interval :type right: float :param window: Input window :type window: spiceypy.utils.support_types.SpiceCell :return: Returns True if the input interval is included in window. :rtype: bool """ assert isinstance(window, stypes.SpiceCell) assert window.dtype == 1 left = ctypes.c_double(left) right = ctypes.c_double(right) return bool(libspice.wnincd_c(left, right, ctypes.byref(window)))
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Determine whether an interval is included in a double precision window. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/wnincd_c.html :param left: Left interval :type left: float :param right: Right interval :type right: float :param window: Input window :type window: spiceypy.utils.support_types.SpiceCell :return: Returns True if the input interval is included in window. :rtype: bool
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L15518-L15537
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
wninsd
def wninsd(left, right, window): """ Insert an interval into a double precision window. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/wninsd_c.html :param left: Left endpoints of new interval. :type left: float :param right: Right endpoints of new interval. :type right: float :param window: Input window. :type window: spiceypy.utils.support_types.SpiceCell """ assert isinstance(window, stypes.SpiceCell) assert window.dtype == 1 left = ctypes.c_double(left) right = ctypes.c_double(right) libspice.wninsd_c(left, right, ctypes.byref(window))
python
def wninsd(left, right, window): """ Insert an interval into a double precision window. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/wninsd_c.html :param left: Left endpoints of new interval. :type left: float :param right: Right endpoints of new interval. :type right: float :param window: Input window. :type window: spiceypy.utils.support_types.SpiceCell """ assert isinstance(window, stypes.SpiceCell) assert window.dtype == 1 left = ctypes.c_double(left) right = ctypes.c_double(right) libspice.wninsd_c(left, right, ctypes.byref(window))
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Insert an interval into a double precision window. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/wninsd_c.html :param left: Left endpoints of new interval. :type left: float :param right: Right endpoints of new interval. :type right: float :param window: Input window. :type window: spiceypy.utils.support_types.SpiceCell
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L15541-L15558
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
wnintd
def wnintd(a, b): """ Place the intersection of two double precision windows into a third window. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/wnintd_c.html :param a: Input window A. :type a: spiceypy.utils.support_types.SpiceCell :param b: Input window B. :type b: spiceypy.utils.support_types.SpiceCell :return: Intersection of a and b. :rtype: spiceypy.utils.support_types.SpiceCell """ assert isinstance(a, stypes.SpiceCell) assert b.dtype == 1 assert isinstance(b, stypes.SpiceCell) assert a.dtype == 1 c = stypes.SpiceCell.double(b.size + a.size) libspice.wnintd_c(ctypes.byref(a), ctypes.byref(b), ctypes.byref(c)) return c
python
def wnintd(a, b): """ Place the intersection of two double precision windows into a third window. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/wnintd_c.html :param a: Input window A. :type a: spiceypy.utils.support_types.SpiceCell :param b: Input window B. :type b: spiceypy.utils.support_types.SpiceCell :return: Intersection of a and b. :rtype: spiceypy.utils.support_types.SpiceCell """ assert isinstance(a, stypes.SpiceCell) assert b.dtype == 1 assert isinstance(b, stypes.SpiceCell) assert a.dtype == 1 c = stypes.SpiceCell.double(b.size + a.size) libspice.wnintd_c(ctypes.byref(a), ctypes.byref(b), ctypes.byref(c)) return c
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Place the intersection of two double precision windows into a third window. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/wnintd_c.html :param a: Input window A. :type a: spiceypy.utils.support_types.SpiceCell :param b: Input window B. :type b: spiceypy.utils.support_types.SpiceCell :return: Intersection of a and b. :rtype: spiceypy.utils.support_types.SpiceCell
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L15562-L15583
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
wnreld
def wnreld(a, op, b): """ Compare two double precision windows. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/wnreld_c.html :param a: First window. :type a: spiceypy.utils.support_types.SpiceCell :param op: Comparison operator. :type op: str :param b: Second window. :type b: spiceypy.utils.support_types.SpiceCell :return: The result of comparison: a (op) b. :rtype: bool """ assert isinstance(a, stypes.SpiceCell) assert b.dtype == 1 assert isinstance(b, stypes.SpiceCell) assert a.dtype == 1 assert isinstance(op, str) op = stypes.stringToCharP(op.encode(encoding='UTF-8')) return bool(libspice.wnreld_c(ctypes.byref(a), op, ctypes.byref(b)))
python
def wnreld(a, op, b): """ Compare two double precision windows. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/wnreld_c.html :param a: First window. :type a: spiceypy.utils.support_types.SpiceCell :param op: Comparison operator. :type op: str :param b: Second window. :type b: spiceypy.utils.support_types.SpiceCell :return: The result of comparison: a (op) b. :rtype: bool """ assert isinstance(a, stypes.SpiceCell) assert b.dtype == 1 assert isinstance(b, stypes.SpiceCell) assert a.dtype == 1 assert isinstance(op, str) op = stypes.stringToCharP(op.encode(encoding='UTF-8')) return bool(libspice.wnreld_c(ctypes.byref(a), op, ctypes.byref(b)))
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L15587-L15608
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
wnsumd
def wnsumd(window): """ Summarize the contents of a double precision window. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/wnsumd_c.html :param window: Window to be summarized. :type window: spiceypy.utils.support_types.SpiceCell :return: Total measure of intervals in window, Average measure, Standard deviation, Location of shortest interval, Location of longest interval. :rtype: tuple """ assert isinstance(window, stypes.SpiceCell) assert window.dtype == 1 meas = ctypes.c_double() avg = ctypes.c_double() stddev = ctypes.c_double() shortest = ctypes.c_int() longest = ctypes.c_int() libspice.wnsumd_c(ctypes.byref(window), ctypes.byref(meas), ctypes.byref(avg), ctypes.byref(stddev), ctypes.byref(shortest), ctypes.byref(longest)) return meas.value, avg.value, stddev.value, shortest.value, longest.value
python
def wnsumd(window): """ Summarize the contents of a double precision window. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/wnsumd_c.html :param window: Window to be summarized. :type window: spiceypy.utils.support_types.SpiceCell :return: Total measure of intervals in window, Average measure, Standard deviation, Location of shortest interval, Location of longest interval. :rtype: tuple """ assert isinstance(window, stypes.SpiceCell) assert window.dtype == 1 meas = ctypes.c_double() avg = ctypes.c_double() stddev = ctypes.c_double() shortest = ctypes.c_int() longest = ctypes.c_int() libspice.wnsumd_c(ctypes.byref(window), ctypes.byref(meas), ctypes.byref(avg), ctypes.byref(stddev), ctypes.byref(shortest), ctypes.byref(longest)) return meas.value, avg.value, stddev.value, shortest.value, longest.value
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L15612-L15637
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
wnunid
def wnunid(a, b): """ Place the union of two double precision windows into a third window. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/wnunid_c.html :param a: Input window A. :type a: spiceypy.utils.support_types.SpiceCell :param b: Input window B. :type b: spiceypy.utils.support_types.SpiceCell :return: Union of a and b. :rtype: spiceypy.utils.support_types.SpiceCell """ assert isinstance(a, stypes.SpiceCell) assert b.dtype == 1 assert isinstance(b, stypes.SpiceCell) assert a.dtype == 1 c = stypes.SpiceCell.double(b.size + a.size) libspice.wnunid_c(ctypes.byref(a), ctypes.byref(b), ctypes.byref(c)) return c
python
def wnunid(a, b): """ Place the union of two double precision windows into a third window. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/wnunid_c.html :param a: Input window A. :type a: spiceypy.utils.support_types.SpiceCell :param b: Input window B. :type b: spiceypy.utils.support_types.SpiceCell :return: Union of a and b. :rtype: spiceypy.utils.support_types.SpiceCell """ assert isinstance(a, stypes.SpiceCell) assert b.dtype == 1 assert isinstance(b, stypes.SpiceCell) assert a.dtype == 1 c = stypes.SpiceCell.double(b.size + a.size) libspice.wnunid_c(ctypes.byref(a), ctypes.byref(b), ctypes.byref(c)) return c
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Place the union of two double precision windows into a third window. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/wnunid_c.html :param a: Input window A. :type a: spiceypy.utils.support_types.SpiceCell :param b: Input window B. :type b: spiceypy.utils.support_types.SpiceCell :return: Union of a and b. :rtype: spiceypy.utils.support_types.SpiceCell
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L15641-L15660
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
wnvald
def wnvald(insize, n, window): """ Form a valid double precision window from the contents of a window array. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/wnvald_c.html :param insize: Size of window. :type insize: int :param n: Original number of endpoints. :type n: int :param window: Input window. :type window: spiceypy.utils.support_types.SpiceCell :return: The union of the intervals in the input cell. :rtype: spiceypy.utils.support_types.SpiceCell """ assert isinstance(window, stypes.SpiceCell) assert window.dtype == 1 insize = ctypes.c_int(insize) n = ctypes.c_int(n) libspice.wnvald_c(insize, n, ctypes.byref(window)) return window
python
def wnvald(insize, n, window): """ Form a valid double precision window from the contents of a window array. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/wnvald_c.html :param insize: Size of window. :type insize: int :param n: Original number of endpoints. :type n: int :param window: Input window. :type window: spiceypy.utils.support_types.SpiceCell :return: The union of the intervals in the input cell. :rtype: spiceypy.utils.support_types.SpiceCell """ assert isinstance(window, stypes.SpiceCell) assert window.dtype == 1 insize = ctypes.c_int(insize) n = ctypes.c_int(n) libspice.wnvald_c(insize, n, ctypes.byref(window)) return window
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Form a valid double precision window from the contents of a window array. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/wnvald_c.html :param insize: Size of window. :type insize: int :param n: Original number of endpoints. :type n: int :param window: Input window. :type window: spiceypy.utils.support_types.SpiceCell :return: The union of the intervals in the input cell. :rtype: spiceypy.utils.support_types.SpiceCell
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L15664-L15685
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
writln
def writln(line, unit): """ Internal undocumented command for writing a text line to a logical unit No URL available; relevant lines from SPICE source: FORTRAN SPICE, writln.f:: C$Procedure WRITLN ( Write a text line to a logical unit ) SUBROUTINE WRITLN ( LINE, UNIT ) CHARACTER*(*) LINE INTEGER UNIT C Variable I/O Description C -------- --- -------------------------------------------------- C LINE I The line which is to be written to UNIT. C UNIT I The Fortran unit number to use for output. CSPICE, writln.c:: /* $Procedure WRITLN ( Write a text line to a logical unit ) */ /* Subroutine */ int writln_(char *line, integer *unit, ftnlen line_len) :param line: The line which is to be written to UNIT. :type line: str :param unit: The Fortran unit number to use for output. :type unit: int """ lineP = stypes.stringToCharP(line) unit = ctypes.c_int(unit) line_len = ctypes.c_int(len(line)) libspice.writln_(lineP, ctypes.byref(unit), line_len)
python
def writln(line, unit): """ Internal undocumented command for writing a text line to a logical unit No URL available; relevant lines from SPICE source: FORTRAN SPICE, writln.f:: C$Procedure WRITLN ( Write a text line to a logical unit ) SUBROUTINE WRITLN ( LINE, UNIT ) CHARACTER*(*) LINE INTEGER UNIT C Variable I/O Description C -------- --- -------------------------------------------------- C LINE I The line which is to be written to UNIT. C UNIT I The Fortran unit number to use for output. CSPICE, writln.c:: /* $Procedure WRITLN ( Write a text line to a logical unit ) */ /* Subroutine */ int writln_(char *line, integer *unit, ftnlen line_len) :param line: The line which is to be written to UNIT. :type line: str :param unit: The Fortran unit number to use for output. :type unit: int """ lineP = stypes.stringToCharP(line) unit = ctypes.c_int(unit) line_len = ctypes.c_int(len(line)) libspice.writln_(lineP, ctypes.byref(unit), line_len)
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Internal undocumented command for writing a text line to a logical unit No URL available; relevant lines from SPICE source: FORTRAN SPICE, writln.f:: C$Procedure WRITLN ( Write a text line to a logical unit ) SUBROUTINE WRITLN ( LINE, UNIT ) CHARACTER*(*) LINE INTEGER UNIT C Variable I/O Description C -------- --- -------------------------------------------------- C LINE I The line which is to be written to UNIT. C UNIT I The Fortran unit number to use for output. CSPICE, writln.c:: /* $Procedure WRITLN ( Write a text line to a logical unit ) */ /* Subroutine */ int writln_(char *line, integer *unit, ftnlen line_len) :param line: The line which is to be written to UNIT. :type line: str :param unit: The Fortran unit number to use for output. :type unit: int
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L15689-L15720
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
xfmsta
def xfmsta(input_state, input_coord_sys, output_coord_sys, body): """ Transform a state between coordinate systems. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/xfmsta_c.html :param input_state: Input state. :type input_state: 6-Element Array of floats :param input_coord_sys: Current (input) coordinate system. :type input_coord_sys: str :param output_coord_sys: Desired (output) coordinate system. :type output_coord_sys: str :param body: Name or NAIF ID of body with which coordinates are associated (if applicable). :type body: str :return: Converted output state :rtype: 6-Element Array of floats """ input_state = stypes.toDoubleVector(input_state) input_coord_sys = stypes.stringToCharP(input_coord_sys) output_coord_sys = stypes.stringToCharP(output_coord_sys) body = stypes.stringToCharP(body) output_state = stypes.emptyDoubleVector(6) libspice.xfmsta_c(input_state, input_coord_sys, output_coord_sys, body, output_state) return stypes.cVectorToPython(output_state)
python
def xfmsta(input_state, input_coord_sys, output_coord_sys, body): """ Transform a state between coordinate systems. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/xfmsta_c.html :param input_state: Input state. :type input_state: 6-Element Array of floats :param input_coord_sys: Current (input) coordinate system. :type input_coord_sys: str :param output_coord_sys: Desired (output) coordinate system. :type output_coord_sys: str :param body: Name or NAIF ID of body with which coordinates are associated (if applicable). :type body: str :return: Converted output state :rtype: 6-Element Array of floats """ input_state = stypes.toDoubleVector(input_state) input_coord_sys = stypes.stringToCharP(input_coord_sys) output_coord_sys = stypes.stringToCharP(output_coord_sys) body = stypes.stringToCharP(body) output_state = stypes.emptyDoubleVector(6) libspice.xfmsta_c(input_state, input_coord_sys, output_coord_sys, body, output_state) return stypes.cVectorToPython(output_state)
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L15774-L15800
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
xpose
def xpose(m): """ Transpose a 3x3 matrix http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/xpose_c.html :param m: Matrix to be transposed :type m: 3x3-Element Array of floats :return: Transposed matrix :rtype: 3x3-Element Array of floats """ m = stypes.toDoubleMatrix(m) mout = stypes.emptyDoubleMatrix(x=3, y=3) libspice.xpose_c(m, mout) return stypes.cMatrixToNumpy(mout)
python
def xpose(m): """ Transpose a 3x3 matrix http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/xpose_c.html :param m: Matrix to be transposed :type m: 3x3-Element Array of floats :return: Transposed matrix :rtype: 3x3-Element Array of floats """ m = stypes.toDoubleMatrix(m) mout = stypes.emptyDoubleMatrix(x=3, y=3) libspice.xpose_c(m, mout) return stypes.cMatrixToNumpy(mout)
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Transpose a 3x3 matrix http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/xpose_c.html :param m: Matrix to be transposed :type m: 3x3-Element Array of floats :return: Transposed matrix :rtype: 3x3-Element Array of floats
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L15804-L15818
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
xpose6
def xpose6(m): """ Transpose a 6x6 matrix http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/xpose6_c.html :param m: Matrix to be transposed :type m: list[6][6] :return: Transposed matrix :rtype: list[6][6] """ m = stypes.toDoubleMatrix(m) mout = stypes.emptyDoubleMatrix(x=6, y=6) libspice.xpose6_c(m, mout) return stypes.cMatrixToNumpy(mout)
python
def xpose6(m): """ Transpose a 6x6 matrix http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/xpose6_c.html :param m: Matrix to be transposed :type m: list[6][6] :return: Transposed matrix :rtype: list[6][6] """ m = stypes.toDoubleMatrix(m) mout = stypes.emptyDoubleMatrix(x=6, y=6) libspice.xpose6_c(m, mout) return stypes.cMatrixToNumpy(mout)
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L15822-L15836
train
AndrewAnnex/SpiceyPy
spiceypy/spiceypy.py
xposeg
def xposeg(matrix, nrow, ncol): """ Transpose a matrix of arbitrary size in place, the matrix need not be square. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/xposeg_c.html :param matrix: Matrix to be transposed :type matrix: NxM-Element Array of floats :param nrow: Number of rows of input matrix. :type nrow: int :param ncol: Number of columns of input matrix :type ncol: int :return: Transposed matrix :rtype: NxM-Element Array of floats """ matrix = stypes.toDoubleMatrix(matrix) mout = stypes.emptyDoubleMatrix(x=ncol, y=nrow) ncol = ctypes.c_int(ncol) nrow = ctypes.c_int(nrow) libspice.xposeg_c(matrix, nrow, ncol, mout) return stypes.cMatrixToNumpy(mout)
python
def xposeg(matrix, nrow, ncol): """ Transpose a matrix of arbitrary size in place, the matrix need not be square. http://naif.jpl.nasa.gov/pub/naif/toolkit_docs/C/cspice/xposeg_c.html :param matrix: Matrix to be transposed :type matrix: NxM-Element Array of floats :param nrow: Number of rows of input matrix. :type nrow: int :param ncol: Number of columns of input matrix :type ncol: int :return: Transposed matrix :rtype: NxM-Element Array of floats """ matrix = stypes.toDoubleMatrix(matrix) mout = stypes.emptyDoubleMatrix(x=ncol, y=nrow) ncol = ctypes.c_int(ncol) nrow = ctypes.c_int(nrow) libspice.xposeg_c(matrix, nrow, ncol, mout) return stypes.cMatrixToNumpy(mout)
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/spiceypy.py#L15840-L15861
train
AndrewAnnex/SpiceyPy
spiceypy/utils/callbacks.py
CallUDFUNS
def CallUDFUNS(f, x): """ We are given a UDF CFUNCTYPE and want to call it in python :param f: SpiceUDFUNS :type f: CFUNCTYPE :param x: some scalar :type x: float :return: value :rtype: float """ value = c_double() f(x, byref(value)) return value.value
python
def CallUDFUNS(f, x): """ We are given a UDF CFUNCTYPE and want to call it in python :param f: SpiceUDFUNS :type f: CFUNCTYPE :param x: some scalar :type x: float :return: value :rtype: float """ value = c_double() f(x, byref(value)) return value.value
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fc20a9b9de68b58eed5b332f0c051fb343a6e335
https://github.com/AndrewAnnex/SpiceyPy/blob/fc20a9b9de68b58eed5b332f0c051fb343a6e335/spiceypy/utils/callbacks.py#L158-L171
train
mnick/scikit-tensor
sktensor/dedicom.py
Updater.updateD_G
def updateD_G(self, x): """ Compute Gradient for update of D See [2] for derivation of Gradient """ self.precompute(x) g = zeros(len(x)) Ai = zeros(self.A.shape[0]) for i in range(len(g)): Ai = self.A[:, i] g[i] = (self.E * (dot(self.AD, outer(self.R[:, i], Ai)) + dot(outer(Ai, self.R[i, :]), self.ADt))).sum() return -2 * g
python
def updateD_G(self, x): """ Compute Gradient for update of D See [2] for derivation of Gradient """ self.precompute(x) g = zeros(len(x)) Ai = zeros(self.A.shape[0]) for i in range(len(g)): Ai = self.A[:, i] g[i] = (self.E * (dot(self.AD, outer(self.R[:, i], Ai)) + dot(outer(Ai, self.R[i, :]), self.ADt))).sum() return -2 * g
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Compute Gradient for update of D See [2] for derivation of Gradient
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fe517e9661a08164b8d30d2dddf7c96aeeabcf36
https://github.com/mnick/scikit-tensor/blob/fe517e9661a08164b8d30d2dddf7c96aeeabcf36/sktensor/dedicom.py#L226-L239
train
mnick/scikit-tensor
sktensor/dedicom.py
Updater.updateD_H
def updateD_H(self, x): """ Compute Hessian for update of D See [2] for derivation of Hessian """ self.precompute(x) H = zeros((len(x), len(x))) Ai = zeros(self.A.shape[0]) Aj = zeros(Ai.shape) for i in range(len(x)): Ai = self.A[:, i] ti = dot(self.AD, outer(self.R[:, i], Ai)) + dot(outer(Ai, self.R[i, :]), self.ADt) for j in range(i, len(x)): Aj = self.A[:, j] tj = outer(Ai, Aj) H[i, j] = ( self.E * (self.R[i, j] * tj + self.R[j, i] * tj.T) - ti * ( dot(self.AD, outer(self.R[:, j], Aj)) + dot(outer(Aj, self.R[j, :]), self.ADt) ) ).sum() H[j, i] = H[i, j] H *= -2 e = eigvals(H).min() H = H + (eye(H.shape[0]) * e) return H
python
def updateD_H(self, x): """ Compute Hessian for update of D See [2] for derivation of Hessian """ self.precompute(x) H = zeros((len(x), len(x))) Ai = zeros(self.A.shape[0]) Aj = zeros(Ai.shape) for i in range(len(x)): Ai = self.A[:, i] ti = dot(self.AD, outer(self.R[:, i], Ai)) + dot(outer(Ai, self.R[i, :]), self.ADt) for j in range(i, len(x)): Aj = self.A[:, j] tj = outer(Ai, Aj) H[i, j] = ( self.E * (self.R[i, j] * tj + self.R[j, i] * tj.T) - ti * ( dot(self.AD, outer(self.R[:, j], Aj)) + dot(outer(Aj, self.R[j, :]), self.ADt) ) ).sum() H[j, i] = H[i, j] H *= -2 e = eigvals(H).min() H = H + (eye(H.shape[0]) * e) return H
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Compute Hessian for update of D See [2] for derivation of Hessian
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fe517e9661a08164b8d30d2dddf7c96aeeabcf36
https://github.com/mnick/scikit-tensor/blob/fe517e9661a08164b8d30d2dddf7c96aeeabcf36/sktensor/dedicom.py#L241-L269
train
mnick/scikit-tensor
sktensor/pyutils.py
is_sequence
def is_sequence(obj): """ Helper function to determine sequences across Python 2.x and 3.x """ try: from collections import Sequence except ImportError: from operator import isSequenceType return isSequenceType(obj) else: return isinstance(obj, Sequence)
python
def is_sequence(obj): """ Helper function to determine sequences across Python 2.x and 3.x """ try: from collections import Sequence except ImportError: from operator import isSequenceType return isSequenceType(obj) else: return isinstance(obj, Sequence)
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Helper function to determine sequences across Python 2.x and 3.x
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fe517e9661a08164b8d30d2dddf7c96aeeabcf36
https://github.com/mnick/scikit-tensor/blob/fe517e9661a08164b8d30d2dddf7c96aeeabcf36/sktensor/pyutils.py#L8-L19
train
mnick/scikit-tensor
sktensor/pyutils.py
is_number
def is_number(obj): """ Helper function to determine numbers across Python 2.x and 3.x """ try: from numbers import Number except ImportError: from operator import isNumberType return isNumberType(obj) else: return isinstance(obj, Number)
python
def is_number(obj): """ Helper function to determine numbers across Python 2.x and 3.x """ try: from numbers import Number except ImportError: from operator import isNumberType return isNumberType(obj) else: return isinstance(obj, Number)
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Helper function to determine numbers across Python 2.x and 3.x
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fe517e9661a08164b8d30d2dddf7c96aeeabcf36
https://github.com/mnick/scikit-tensor/blob/fe517e9661a08164b8d30d2dddf7c96aeeabcf36/sktensor/pyutils.py#L22-L33
train
mnick/scikit-tensor
sktensor/pyutils.py
func_attr
def func_attr(f, attr): """ Helper function to get the attribute of a function like, name, code, defaults across Python 2.x and 3.x """ if hasattr(f, 'func_%s' % attr): return getattr(f, 'func_%s' % attr) elif hasattr(f, '__%s__' % attr): return getattr(f, '__%s__' % attr) else: raise ValueError('Object %s has no attr' % (str(f), attr))
python
def func_attr(f, attr): """ Helper function to get the attribute of a function like, name, code, defaults across Python 2.x and 3.x """ if hasattr(f, 'func_%s' % attr): return getattr(f, 'func_%s' % attr) elif hasattr(f, '__%s__' % attr): return getattr(f, '__%s__' % attr) else: raise ValueError('Object %s has no attr' % (str(f), attr))
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Helper function to get the attribute of a function like, name, code, defaults across Python 2.x and 3.x
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fe517e9661a08164b8d30d2dddf7c96aeeabcf36
https://github.com/mnick/scikit-tensor/blob/fe517e9661a08164b8d30d2dddf7c96aeeabcf36/sktensor/pyutils.py#L36-L46
train
mnick/scikit-tensor
sktensor/pyutils.py
from_to_without
def from_to_without(frm, to, without, step=1, skip=1, reverse=False, separate=False): """ Helper function to create ranges with missing entries """ if reverse: frm, to = (to - 1), (frm - 1) step *= -1 skip *= -1 a = list(range(frm, without, step)) b = list(range(without + skip, to, step)) if separate: return a, b else: return a + b
python
def from_to_without(frm, to, without, step=1, skip=1, reverse=False, separate=False): """ Helper function to create ranges with missing entries """ if reverse: frm, to = (to - 1), (frm - 1) step *= -1 skip *= -1 a = list(range(frm, without, step)) b = list(range(without + skip, to, step)) if separate: return a, b else: return a + b
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Helper function to create ranges with missing entries
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fe517e9661a08164b8d30d2dddf7c96aeeabcf36
https://github.com/mnick/scikit-tensor/blob/fe517e9661a08164b8d30d2dddf7c96aeeabcf36/sktensor/pyutils.py#L49-L62
train
mnick/scikit-tensor
sktensor/dtensor.py
dtensor.unfold
def unfold(self, mode): """ Unfolds a dense tensor in mode n. Parameters ---------- mode : int Mode in which tensor is unfolded Returns ------- unfolded_dtensor : unfolded_dtensor object Tensor unfolded along mode Examples -------- Create dense tensor from numpy array >>> T = np.zeros((3, 4, 2)) >>> T[:, :, 0] = [[ 1, 4, 7, 10], [ 2, 5, 8, 11], [3, 6, 9, 12]] >>> T[:, :, 1] = [[13, 16, 19, 22], [14, 17, 20, 23], [15, 18, 21, 24]] >>> T = dtensor(T) Unfolding of dense tensors >>> T.unfold(0) array([[ 1., 4., 7., 10., 13., 16., 19., 22.], [ 2., 5., 8., 11., 14., 17., 20., 23.], [ 3., 6., 9., 12., 15., 18., 21., 24.]]) >>> T.unfold(1) array([[ 1., 2., 3., 13., 14., 15.], [ 4., 5., 6., 16., 17., 18.], [ 7., 8., 9., 19., 20., 21.], [ 10., 11., 12., 22., 23., 24.]]) >>> T.unfold(2) array([[ 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12.], [ 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24.]]) """ sz = array(self.shape) N = len(sz) order = ([mode], from_to_without(N - 1, -1, mode, step=-1, skip=-1)) newsz = (sz[order[0]][0], prod(sz[order[1]])) arr = self.transpose(axes=(order[0] + order[1])) arr = arr.reshape(newsz) return unfolded_dtensor(arr, mode, self.shape)
python
def unfold(self, mode): """ Unfolds a dense tensor in mode n. Parameters ---------- mode : int Mode in which tensor is unfolded Returns ------- unfolded_dtensor : unfolded_dtensor object Tensor unfolded along mode Examples -------- Create dense tensor from numpy array >>> T = np.zeros((3, 4, 2)) >>> T[:, :, 0] = [[ 1, 4, 7, 10], [ 2, 5, 8, 11], [3, 6, 9, 12]] >>> T[:, :, 1] = [[13, 16, 19, 22], [14, 17, 20, 23], [15, 18, 21, 24]] >>> T = dtensor(T) Unfolding of dense tensors >>> T.unfold(0) array([[ 1., 4., 7., 10., 13., 16., 19., 22.], [ 2., 5., 8., 11., 14., 17., 20., 23.], [ 3., 6., 9., 12., 15., 18., 21., 24.]]) >>> T.unfold(1) array([[ 1., 2., 3., 13., 14., 15.], [ 4., 5., 6., 16., 17., 18.], [ 7., 8., 9., 19., 20., 21.], [ 10., 11., 12., 22., 23., 24.]]) >>> T.unfold(2) array([[ 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12.], [ 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24.]]) """ sz = array(self.shape) N = len(sz) order = ([mode], from_to_without(N - 1, -1, mode, step=-1, skip=-1)) newsz = (sz[order[0]][0], prod(sz[order[1]])) arr = self.transpose(axes=(order[0] + order[1])) arr = arr.reshape(newsz) return unfolded_dtensor(arr, mode, self.shape)
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Unfolds a dense tensor in mode n. Parameters ---------- mode : int Mode in which tensor is unfolded Returns ------- unfolded_dtensor : unfolded_dtensor object Tensor unfolded along mode Examples -------- Create dense tensor from numpy array >>> T = np.zeros((3, 4, 2)) >>> T[:, :, 0] = [[ 1, 4, 7, 10], [ 2, 5, 8, 11], [3, 6, 9, 12]] >>> T[:, :, 1] = [[13, 16, 19, 22], [14, 17, 20, 23], [15, 18, 21, 24]] >>> T = dtensor(T) Unfolding of dense tensors >>> T.unfold(0) array([[ 1., 4., 7., 10., 13., 16., 19., 22.], [ 2., 5., 8., 11., 14., 17., 20., 23.], [ 3., 6., 9., 12., 15., 18., 21., 24.]]) >>> T.unfold(1) array([[ 1., 2., 3., 13., 14., 15.], [ 4., 5., 6., 16., 17., 18.], [ 7., 8., 9., 19., 20., 21.], [ 10., 11., 12., 22., 23., 24.]]) >>> T.unfold(2) array([[ 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12.], [ 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24.]])
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fe517e9661a08164b8d30d2dddf7c96aeeabcf36
https://github.com/mnick/scikit-tensor/blob/fe517e9661a08164b8d30d2dddf7c96aeeabcf36/sktensor/dtensor.py#L103-L150
train
mnick/scikit-tensor
sktensor/utils.py
accum
def accum(subs, vals, func=np.sum, issorted=False, with_subs=False): """ NumPy implementation for Matlab's accumarray """ # sort accmap for ediff if not sorted if not issorted: sidx = lexsort(subs, axis=0) subs = [sub[sidx] for sub in subs] vals = vals[sidx] idx = np.where(np.diff(subs).any(axis=0))[0] + 1 idx = np.concatenate(([0], idx, [subs[0].shape[0]])) # create values array nvals = np.zeros(len(idx) - 1) for i in range(len(idx) - 1): nvals[i] = func(vals[idx[i]:idx[i + 1]]) # return results if with_subs: return nvals, tuple(sub[idx[:-1]] for sub in subs) else: return nvals
python
def accum(subs, vals, func=np.sum, issorted=False, with_subs=False): """ NumPy implementation for Matlab's accumarray """ # sort accmap for ediff if not sorted if not issorted: sidx = lexsort(subs, axis=0) subs = [sub[sidx] for sub in subs] vals = vals[sidx] idx = np.where(np.diff(subs).any(axis=0))[0] + 1 idx = np.concatenate(([0], idx, [subs[0].shape[0]])) # create values array nvals = np.zeros(len(idx) - 1) for i in range(len(idx) - 1): nvals[i] = func(vals[idx[i]:idx[i + 1]]) # return results if with_subs: return nvals, tuple(sub[idx[:-1]] for sub in subs) else: return nvals
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NumPy implementation for Matlab's accumarray
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fe517e9661a08164b8d30d2dddf7c96aeeabcf36
https://github.com/mnick/scikit-tensor/blob/fe517e9661a08164b8d30d2dddf7c96aeeabcf36/sktensor/utils.py#L5-L26
train
mnick/scikit-tensor
sktensor/tucker.py
hooi
def hooi(X, rank, **kwargs): """ Compute Tucker decomposition of a tensor using Higher-Order Orthogonal Iterations. Parameters ---------- X : tensor_mixin The tensor to be decomposed rank : array_like The rank of the decomposition for each mode of the tensor. The length of ``rank`` must match the number of modes of ``X``. init : {'random', 'nvecs'}, optional The initialization method to use. - random : Factor matrices are initialized randomly. - nvecs : Factor matrices are initialzed via HOSVD. default : 'nvecs' Examples -------- Create dense tensor >>> T = np.zeros((3, 4, 2)) >>> T[:, :, 0] = [[ 1, 4, 7, 10], [ 2, 5, 8, 11], [3, 6, 9, 12]] >>> T[:, :, 1] = [[13, 16, 19, 22], [14, 17, 20, 23], [15, 18, 21, 24]] >>> T = dtensor(T) Compute Tucker decomposition of ``T`` with n-rank [2, 3, 1] via higher-order orthogonal iterations >>> Y = hooi(T, [2, 3, 1], init='nvecs') Shape of the core tensor matches n-rank of the decomposition. >>> Y['core'].shape (2, 3, 1) >>> Y['U'][1].shape (3, 2) References ---------- .. [1] L. De Lathauwer, B. De Moor, J. Vandewalle: On the best rank-1 and rank-(R_1, R_2, \ldots, R_N) approximation of higher order tensors; IEEE Trans. Signal Process. 49 (2001), pp. 2262-2271 """ # init options ainit = kwargs.pop('init', __DEF_INIT) maxIter = kwargs.pop('maxIter', __DEF_MAXITER) conv = kwargs.pop('conv', __DEF_CONV) dtype = kwargs.pop('dtype', X.dtype) if not len(kwargs) == 0: raise ValueError('Unknown keywords (%s)' % (kwargs.keys())) ndims = X.ndim if is_number(rank): rank = rank * ones(ndims) normX = norm(X) U = __init(ainit, X, ndims, rank, dtype) fit = 0 exectimes = [] for itr in range(maxIter): tic = time.clock() fitold = fit for n in range(ndims): Utilde = ttm(X, U, n, transp=True, without=True) U[n] = nvecs(Utilde, n, rank[n]) # compute core tensor to get fit core = ttm(Utilde, U, n, transp=True) # since factors are orthonormal, compute fit on core tensor normresidual = sqrt(normX ** 2 - norm(core) ** 2) # fraction explained by model fit = 1 - (normresidual / normX) fitchange = abs(fitold - fit) exectimes.append(time.clock() - tic) _log.debug( '[%3d] fit: %.5f | delta: %7.1e | secs: %.5f' % (itr, fit, fitchange, exectimes[-1]) ) if itr > 1 and fitchange < conv: break return core, U
python
def hooi(X, rank, **kwargs): """ Compute Tucker decomposition of a tensor using Higher-Order Orthogonal Iterations. Parameters ---------- X : tensor_mixin The tensor to be decomposed rank : array_like The rank of the decomposition for each mode of the tensor. The length of ``rank`` must match the number of modes of ``X``. init : {'random', 'nvecs'}, optional The initialization method to use. - random : Factor matrices are initialized randomly. - nvecs : Factor matrices are initialzed via HOSVD. default : 'nvecs' Examples -------- Create dense tensor >>> T = np.zeros((3, 4, 2)) >>> T[:, :, 0] = [[ 1, 4, 7, 10], [ 2, 5, 8, 11], [3, 6, 9, 12]] >>> T[:, :, 1] = [[13, 16, 19, 22], [14, 17, 20, 23], [15, 18, 21, 24]] >>> T = dtensor(T) Compute Tucker decomposition of ``T`` with n-rank [2, 3, 1] via higher-order orthogonal iterations >>> Y = hooi(T, [2, 3, 1], init='nvecs') Shape of the core tensor matches n-rank of the decomposition. >>> Y['core'].shape (2, 3, 1) >>> Y['U'][1].shape (3, 2) References ---------- .. [1] L. De Lathauwer, B. De Moor, J. Vandewalle: On the best rank-1 and rank-(R_1, R_2, \ldots, R_N) approximation of higher order tensors; IEEE Trans. Signal Process. 49 (2001), pp. 2262-2271 """ # init options ainit = kwargs.pop('init', __DEF_INIT) maxIter = kwargs.pop('maxIter', __DEF_MAXITER) conv = kwargs.pop('conv', __DEF_CONV) dtype = kwargs.pop('dtype', X.dtype) if not len(kwargs) == 0: raise ValueError('Unknown keywords (%s)' % (kwargs.keys())) ndims = X.ndim if is_number(rank): rank = rank * ones(ndims) normX = norm(X) U = __init(ainit, X, ndims, rank, dtype) fit = 0 exectimes = [] for itr in range(maxIter): tic = time.clock() fitold = fit for n in range(ndims): Utilde = ttm(X, U, n, transp=True, without=True) U[n] = nvecs(Utilde, n, rank[n]) # compute core tensor to get fit core = ttm(Utilde, U, n, transp=True) # since factors are orthonormal, compute fit on core tensor normresidual = sqrt(normX ** 2 - norm(core) ** 2) # fraction explained by model fit = 1 - (normresidual / normX) fitchange = abs(fitold - fit) exectimes.append(time.clock() - tic) _log.debug( '[%3d] fit: %.5f | delta: %7.1e | secs: %.5f' % (itr, fit, fitchange, exectimes[-1]) ) if itr > 1 and fitchange < conv: break return core, U
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Compute Tucker decomposition of a tensor using Higher-Order Orthogonal Iterations. Parameters ---------- X : tensor_mixin The tensor to be decomposed rank : array_like The rank of the decomposition for each mode of the tensor. The length of ``rank`` must match the number of modes of ``X``. init : {'random', 'nvecs'}, optional The initialization method to use. - random : Factor matrices are initialized randomly. - nvecs : Factor matrices are initialzed via HOSVD. default : 'nvecs' Examples -------- Create dense tensor >>> T = np.zeros((3, 4, 2)) >>> T[:, :, 0] = [[ 1, 4, 7, 10], [ 2, 5, 8, 11], [3, 6, 9, 12]] >>> T[:, :, 1] = [[13, 16, 19, 22], [14, 17, 20, 23], [15, 18, 21, 24]] >>> T = dtensor(T) Compute Tucker decomposition of ``T`` with n-rank [2, 3, 1] via higher-order orthogonal iterations >>> Y = hooi(T, [2, 3, 1], init='nvecs') Shape of the core tensor matches n-rank of the decomposition. >>> Y['core'].shape (2, 3, 1) >>> Y['U'][1].shape (3, 2) References ---------- .. [1] L. De Lathauwer, B. De Moor, J. Vandewalle: On the best rank-1 and rank-(R_1, R_2, \ldots, R_N) approximation of higher order tensors; IEEE Trans. Signal Process. 49 (2001), pp. 2262-2271
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fe517e9661a08164b8d30d2dddf7c96aeeabcf36
https://github.com/mnick/scikit-tensor/blob/fe517e9661a08164b8d30d2dddf7c96aeeabcf36/sktensor/tucker.py#L36-L123
train
mnick/scikit-tensor
sktensor/ktensor.py
ktensor.uttkrp
def uttkrp(self, U, mode): """ Unfolded tensor times Khatri-Rao product for Kruskal tensors Parameters ---------- X : tensor_mixin Tensor whose unfolding should be multiplied. U : list of array_like Matrices whose Khatri-Rao product should be multiplied. mode : int Mode in which X should be unfolded. See also -------- sktensor.sptensor.uttkrp : Efficient computation of uttkrp for sparse tensors ttensor.uttkrp : Efficient computation of uttkrp for Tucker operators """ N = self.ndim if mode == 1: R = U[1].shape[1] else: R = U[0].shape[1] W = np.tile(self.lmbda, 1, R) for i in range(mode) + range(mode + 1, N): W = W * dot(self.U[i].T, U[i]) return dot(self.U[mode], W)
python
def uttkrp(self, U, mode): """ Unfolded tensor times Khatri-Rao product for Kruskal tensors Parameters ---------- X : tensor_mixin Tensor whose unfolding should be multiplied. U : list of array_like Matrices whose Khatri-Rao product should be multiplied. mode : int Mode in which X should be unfolded. See also -------- sktensor.sptensor.uttkrp : Efficient computation of uttkrp for sparse tensors ttensor.uttkrp : Efficient computation of uttkrp for Tucker operators """ N = self.ndim if mode == 1: R = U[1].shape[1] else: R = U[0].shape[1] W = np.tile(self.lmbda, 1, R) for i in range(mode) + range(mode + 1, N): W = W * dot(self.U[i].T, U[i]) return dot(self.U[mode], W)
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Unfolded tensor times Khatri-Rao product for Kruskal tensors Parameters ---------- X : tensor_mixin Tensor whose unfolding should be multiplied. U : list of array_like Matrices whose Khatri-Rao product should be multiplied. mode : int Mode in which X should be unfolded. See also -------- sktensor.sptensor.uttkrp : Efficient computation of uttkrp for sparse tensors ttensor.uttkrp : Efficient computation of uttkrp for Tucker operators
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fe517e9661a08164b8d30d2dddf7c96aeeabcf36
https://github.com/mnick/scikit-tensor/blob/fe517e9661a08164b8d30d2dddf7c96aeeabcf36/sktensor/ktensor.py#L84-L111
train
mnick/scikit-tensor
sktensor/ktensor.py
ktensor.norm
def norm(self): """ Efficient computation of the Frobenius norm for ktensors Returns ------- norm : float Frobenius norm of the ktensor """ N = len(self.shape) coef = outer(self.lmbda, self.lmbda) for i in range(N): coef = coef * dot(self.U[i].T, self.U[i]) return np.sqrt(coef.sum())
python
def norm(self): """ Efficient computation of the Frobenius norm for ktensors Returns ------- norm : float Frobenius norm of the ktensor """ N = len(self.shape) coef = outer(self.lmbda, self.lmbda) for i in range(N): coef = coef * dot(self.U[i].T, self.U[i]) return np.sqrt(coef.sum())
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Efficient computation of the Frobenius norm for ktensors Returns ------- norm : float Frobenius norm of the ktensor
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fe517e9661a08164b8d30d2dddf7c96aeeabcf36
https://github.com/mnick/scikit-tensor/blob/fe517e9661a08164b8d30d2dddf7c96aeeabcf36/sktensor/ktensor.py#L113-L126
train
mnick/scikit-tensor
sktensor/ktensor.py
ktensor.innerprod
def innerprod(self, X): """ Efficient computation of the inner product of a ktensor with another tensor Parameters ---------- X : tensor_mixin Tensor to compute the inner product with. Returns ------- p : float Inner product between ktensor and X. """ N = len(self.shape) R = len(self.lmbda) res = 0 for r in range(R): vecs = [] for n in range(N): vecs.append(self.U[n][:, r]) res += self.lmbda[r] * X.ttv(tuple(vecs)) return res
python
def innerprod(self, X): """ Efficient computation of the inner product of a ktensor with another tensor Parameters ---------- X : tensor_mixin Tensor to compute the inner product with. Returns ------- p : float Inner product between ktensor and X. """ N = len(self.shape) R = len(self.lmbda) res = 0 for r in range(R): vecs = [] for n in range(N): vecs.append(self.U[n][:, r]) res += self.lmbda[r] * X.ttv(tuple(vecs)) return res
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Efficient computation of the inner product of a ktensor with another tensor Parameters ---------- X : tensor_mixin Tensor to compute the inner product with. Returns ------- p : float Inner product between ktensor and X.
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fe517e9661a08164b8d30d2dddf7c96aeeabcf36
https://github.com/mnick/scikit-tensor/blob/fe517e9661a08164b8d30d2dddf7c96aeeabcf36/sktensor/ktensor.py#L128-L150
train
mnick/scikit-tensor
sktensor/ktensor.py
ktensor.toarray
def toarray(self): """ Converts a ktensor into a dense multidimensional ndarray Returns ------- arr : np.ndarray Fully computed multidimensional array whose shape matches the original ktensor. """ A = dot(self.lmbda, khatrirao(tuple(self.U)).T) return A.reshape(self.shape)
python
def toarray(self): """ Converts a ktensor into a dense multidimensional ndarray Returns ------- arr : np.ndarray Fully computed multidimensional array whose shape matches the original ktensor. """ A = dot(self.lmbda, khatrirao(tuple(self.U)).T) return A.reshape(self.shape)
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Converts a ktensor into a dense multidimensional ndarray Returns ------- arr : np.ndarray Fully computed multidimensional array whose shape matches the original ktensor.
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fe517e9661a08164b8d30d2dddf7c96aeeabcf36
https://github.com/mnick/scikit-tensor/blob/fe517e9661a08164b8d30d2dddf7c96aeeabcf36/sktensor/ktensor.py#L152-L163
train
mnick/scikit-tensor
sktensor/sptensor.py
fromarray
def fromarray(A): """Create a sptensor from a dense numpy array""" subs = np.nonzero(A) vals = A[subs] return sptensor(subs, vals, shape=A.shape, dtype=A.dtype)
python
def fromarray(A): """Create a sptensor from a dense numpy array""" subs = np.nonzero(A) vals = A[subs] return sptensor(subs, vals, shape=A.shape, dtype=A.dtype)
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Create a sptensor from a dense numpy array
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fe517e9661a08164b8d30d2dddf7c96aeeabcf36
https://github.com/mnick/scikit-tensor/blob/fe517e9661a08164b8d30d2dddf7c96aeeabcf36/sktensor/sptensor.py#L362-L366
train
mnick/scikit-tensor
sktensor/sptensor.py
sptensor._ttm_me_compute
def _ttm_me_compute(self, V, edims, sdims, transp): """ Assume Y = T x_i V_i for i = 1...n can fit into memory """ shapeY = np.copy(self.shape) # Determine size of Y for n in np.union1d(edims, sdims): shapeY[n] = V[n].shape[1] if transp else V[n].shape[0] # Allocate Y (final result) and v (vectors for elementwise computations) Y = zeros(shapeY) shapeY = array(shapeY) v = [None for _ in range(len(edims))] for i in range(np.prod(shapeY[edims])): rsubs = unravel_index(shapeY[edims], i)
python
def _ttm_me_compute(self, V, edims, sdims, transp): """ Assume Y = T x_i V_i for i = 1...n can fit into memory """ shapeY = np.copy(self.shape) # Determine size of Y for n in np.union1d(edims, sdims): shapeY[n] = V[n].shape[1] if transp else V[n].shape[0] # Allocate Y (final result) and v (vectors for elementwise computations) Y = zeros(shapeY) shapeY = array(shapeY) v = [None for _ in range(len(edims))] for i in range(np.prod(shapeY[edims])): rsubs = unravel_index(shapeY[edims], i)
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Assume Y = T x_i V_i for i = 1...n can fit into memory
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fe517e9661a08164b8d30d2dddf7c96aeeabcf36
https://github.com/mnick/scikit-tensor/blob/fe517e9661a08164b8d30d2dddf7c96aeeabcf36/sktensor/sptensor.py#L179-L195
train
mnick/scikit-tensor
sktensor/sptensor.py
sptensor.transpose
def transpose(self, axes=None): """ Compute transpose of sparse tensors. Parameters ---------- axes : array_like of ints, optional Permute the axes according to the values given. Returns ------- d : dtensor dtensor with axes permuted. """ if axes is None: raise NotImplementedError( 'Sparse tensor transposition without axes argument is not supported' ) nsubs = tuple([self.subs[idx] for idx in axes]) nshape = [self.shape[idx] for idx in axes] return sptensor(nsubs, self.vals, nshape)
python
def transpose(self, axes=None): """ Compute transpose of sparse tensors. Parameters ---------- axes : array_like of ints, optional Permute the axes according to the values given. Returns ------- d : dtensor dtensor with axes permuted. """ if axes is None: raise NotImplementedError( 'Sparse tensor transposition without axes argument is not supported' ) nsubs = tuple([self.subs[idx] for idx in axes]) nshape = [self.shape[idx] for idx in axes] return sptensor(nsubs, self.vals, nshape)
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Compute transpose of sparse tensors. Parameters ---------- axes : array_like of ints, optional Permute the axes according to the values given. Returns ------- d : dtensor dtensor with axes permuted.
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fe517e9661a08164b8d30d2dddf7c96aeeabcf36
https://github.com/mnick/scikit-tensor/blob/fe517e9661a08164b8d30d2dddf7c96aeeabcf36/sktensor/sptensor.py#L232-L252
train
mnick/scikit-tensor
sktensor/sptensor.py
sptensor.concatenate
def concatenate(self, tpl, axis=None): """ Concatenates sparse tensors. Parameters ---------- tpl : tuple of sparse tensors Tensors to be concatenated. axis : int, optional Axis along which concatenation should take place """ if axis is None: raise NotImplementedError( 'Sparse tensor concatenation without axis argument is not supported' ) T = self for i in range(1, len(tpl)): T = _single_concatenate(T, tpl[i], axis=axis) return T
python
def concatenate(self, tpl, axis=None): """ Concatenates sparse tensors. Parameters ---------- tpl : tuple of sparse tensors Tensors to be concatenated. axis : int, optional Axis along which concatenation should take place """ if axis is None: raise NotImplementedError( 'Sparse tensor concatenation without axis argument is not supported' ) T = self for i in range(1, len(tpl)): T = _single_concatenate(T, tpl[i], axis=axis) return T
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Concatenates sparse tensors. Parameters ---------- tpl : tuple of sparse tensors Tensors to be concatenated. axis : int, optional Axis along which concatenation should take place
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fe517e9661a08164b8d30d2dddf7c96aeeabcf36
https://github.com/mnick/scikit-tensor/blob/fe517e9661a08164b8d30d2dddf7c96aeeabcf36/sktensor/sptensor.py#L254-L272
train
mnick/scikit-tensor
sktensor/sptensor.py
unfolded_sptensor.fold
def fold(self): """ Recreate original tensor by folding unfolded_sptensor according toc ``ten_shape``. Returns ------- T : sptensor Sparse tensor that is created by refolding according to ``ten_shape``. """ nsubs = zeros((len(self.data), len(self.ten_shape)), dtype=np.int) if len(self.rdims) > 0: nidx = unravel_index(self.row, self.ten_shape[self.rdims]) for i in range(len(self.rdims)): nsubs[:, self.rdims[i]] = nidx[i] if len(self.cdims) > 0: nidx = unravel_index(self.col, self.ten_shape[self.cdims]) for i in range(len(self.cdims)): nsubs[:, self.cdims[i]] = nidx[i] nsubs = [z.flatten() for z in hsplit(nsubs, len(self.ten_shape))] return sptensor(tuple(nsubs), self.data, self.ten_shape)
python
def fold(self): """ Recreate original tensor by folding unfolded_sptensor according toc ``ten_shape``. Returns ------- T : sptensor Sparse tensor that is created by refolding according to ``ten_shape``. """ nsubs = zeros((len(self.data), len(self.ten_shape)), dtype=np.int) if len(self.rdims) > 0: nidx = unravel_index(self.row, self.ten_shape[self.rdims]) for i in range(len(self.rdims)): nsubs[:, self.rdims[i]] = nidx[i] if len(self.cdims) > 0: nidx = unravel_index(self.col, self.ten_shape[self.cdims]) for i in range(len(self.cdims)): nsubs[:, self.cdims[i]] = nidx[i] nsubs = [z.flatten() for z in hsplit(nsubs, len(self.ten_shape))] return sptensor(tuple(nsubs), self.data, self.ten_shape)
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Recreate original tensor by folding unfolded_sptensor according toc ``ten_shape``. Returns ------- T : sptensor Sparse tensor that is created by refolding according to ``ten_shape``.
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fe517e9661a08164b8d30d2dddf7c96aeeabcf36
https://github.com/mnick/scikit-tensor/blob/fe517e9661a08164b8d30d2dddf7c96aeeabcf36/sktensor/sptensor.py#L339-L359
train
mnick/scikit-tensor
sktensor/rescal.py
_updateA
def _updateA(X, A, R, P, Z, lmbdaA, orthogonalize): """Update step for A""" n, rank = A.shape F = zeros((n, rank), dtype=A.dtype) E = zeros((rank, rank), dtype=A.dtype) AtA = dot(A.T, A) for i in range(len(X)): F += X[i].dot(dot(A, R[i].T)) + X[i].T.dot(dot(A, R[i])) E += dot(R[i], dot(AtA, R[i].T)) + dot(R[i].T, dot(AtA, R[i])) # regularization I = lmbdaA * eye(rank, dtype=A.dtype) # attributes for i in range(len(Z)): F += P[i].dot(Z[i].T) E += dot(Z[i], Z[i].T) # finally compute update for A A = solve(I + E.T, F.T).T return orth(A) if orthogonalize else A
python
def _updateA(X, A, R, P, Z, lmbdaA, orthogonalize): """Update step for A""" n, rank = A.shape F = zeros((n, rank), dtype=A.dtype) E = zeros((rank, rank), dtype=A.dtype) AtA = dot(A.T, A) for i in range(len(X)): F += X[i].dot(dot(A, R[i].T)) + X[i].T.dot(dot(A, R[i])) E += dot(R[i], dot(AtA, R[i].T)) + dot(R[i].T, dot(AtA, R[i])) # regularization I = lmbdaA * eye(rank, dtype=A.dtype) # attributes for i in range(len(Z)): F += P[i].dot(Z[i].T) E += dot(Z[i], Z[i].T) # finally compute update for A A = solve(I + E.T, F.T).T return orth(A) if orthogonalize else A
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Update step for A
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fe517e9661a08164b8d30d2dddf7c96aeeabcf36
https://github.com/mnick/scikit-tensor/blob/fe517e9661a08164b8d30d2dddf7c96aeeabcf36/sktensor/rescal.py#L212-L234
train
mnick/scikit-tensor
sktensor/rescal.py
_compute_fval
def _compute_fval(X, A, R, P, Z, lmbdaA, lmbdaR, lmbdaZ, normX): """Compute fit for full slices""" f = lmbdaA * norm(A) ** 2 for i in range(len(X)): ARAt = dot(A, dot(R[i], A.T)) f += (norm(X[i] - ARAt) ** 2) / normX[i] + lmbdaR * norm(R[i]) ** 2 return f
python
def _compute_fval(X, A, R, P, Z, lmbdaA, lmbdaR, lmbdaZ, normX): """Compute fit for full slices""" f = lmbdaA * norm(A) ** 2 for i in range(len(X)): ARAt = dot(A, dot(R[i], A.T)) f += (norm(X[i] - ARAt) ** 2) / normX[i] + lmbdaR * norm(R[i]) ** 2 return f
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Compute fit for full slices
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fe517e9661a08164b8d30d2dddf7c96aeeabcf36
https://github.com/mnick/scikit-tensor/blob/fe517e9661a08164b8d30d2dddf7c96aeeabcf36/sktensor/rescal.py#L268-L274
train
mnick/scikit-tensor
sktensor/cp.py
als
def als(X, rank, **kwargs): """ Alternating least-sqaures algorithm to compute the CP decomposition. Parameters ---------- X : tensor_mixin The tensor to be decomposed. rank : int Tensor rank of the decomposition. init : {'random', 'nvecs'}, optional The initialization method to use. - random : Factor matrices are initialized randomly. - nvecs : Factor matrices are initialzed via HOSVD. (default 'nvecs') max_iter : int, optional Maximium number of iterations of the ALS algorithm. (default 500) fit_method : {'full', None} The method to compute the fit of the factorization - 'full' : Compute least-squares fit of the dense approximation of. X and X. - None : Do not compute the fit of the factorization, but iterate until ``max_iter`` (Useful for large-scale tensors). (default 'full') conv : float Convergence tolerance on difference of fit between iterations (default 1e-5) Returns ------- P : ktensor Rank ``rank`` factorization of X. ``P.U[i]`` corresponds to the factor matrix for the i-th mode. ``P.lambda[i]`` corresponds to the weight of the i-th mode. fit : float Fit of the factorization compared to ``X`` itr : int Number of iterations that were needed until convergence exectimes : ndarray of floats Time needed for each single iteration Examples -------- Create random dense tensor >>> from sktensor import dtensor, ktensor >>> U = [np.random.rand(i,3) for i in (20, 10, 14)] >>> T = dtensor(ktensor(U).toarray()) Compute rank-3 CP decomposition of ``T`` with ALS >>> P, fit, itr, _ = als(T, 3) Result is a decomposed tensor stored as a Kruskal operator >>> type(P) <class 'sktensor.ktensor.ktensor'> Factorization should be close to original data >>> np.allclose(T, P.totensor()) True References ---------- .. [1] Kolda, T. G. & Bader, B. W. Tensor Decompositions and Applications. SIAM Rev. 51, 455–500 (2009). .. [2] Harshman, R. A. Foundations of the PARAFAC procedure: models and conditions for an 'explanatory' multimodal factor analysis. UCLA Working Papers in Phonetics 16, (1970). .. [3] Carroll, J. D., Chang, J. J. Analysis of individual differences in multidimensional scaling via an N-way generalization of 'Eckart-Young' decomposition. Psychometrika 35, 283–319 (1970). """ # init options ainit = kwargs.pop('init', _DEF_INIT) maxiter = kwargs.pop('max_iter', _DEF_MAXITER) fit_method = kwargs.pop('fit_method', _DEF_FIT_METHOD) conv = kwargs.pop('conv', _DEF_CONV) dtype = kwargs.pop('dtype', _DEF_TYPE) if not len(kwargs) == 0: raise ValueError('Unknown keywords (%s)' % (kwargs.keys())) N = X.ndim normX = norm(X) U = _init(ainit, X, N, rank, dtype) fit = 0 exectimes = [] for itr in range(maxiter): tic = time.clock() fitold = fit for n in range(N): Unew = X.uttkrp(U, n) Y = ones((rank, rank), dtype=dtype) for i in (list(range(n)) + list(range(n + 1, N))): Y = Y * dot(U[i].T, U[i]) Unew = Unew.dot(pinv(Y)) # Normalize if itr == 0: lmbda = sqrt((Unew ** 2).sum(axis=0)) else: lmbda = Unew.max(axis=0) lmbda[lmbda < 1] = 1 U[n] = Unew / lmbda P = ktensor(U, lmbda) if fit_method == 'full': normresidual = normX ** 2 + P.norm() ** 2 - 2 * P.innerprod(X) fit = 1 - (normresidual / normX ** 2) else: fit = itr fitchange = abs(fitold - fit) exectimes.append(time.clock() - tic) _log.debug( '[%3d] fit: %.5f | delta: %7.1e | secs: %.5f' % (itr, fit, fitchange, exectimes[-1]) ) if itr > 0 and fitchange < conv: break return P, fit, itr, array(exectimes)
python
def als(X, rank, **kwargs): """ Alternating least-sqaures algorithm to compute the CP decomposition. Parameters ---------- X : tensor_mixin The tensor to be decomposed. rank : int Tensor rank of the decomposition. init : {'random', 'nvecs'}, optional The initialization method to use. - random : Factor matrices are initialized randomly. - nvecs : Factor matrices are initialzed via HOSVD. (default 'nvecs') max_iter : int, optional Maximium number of iterations of the ALS algorithm. (default 500) fit_method : {'full', None} The method to compute the fit of the factorization - 'full' : Compute least-squares fit of the dense approximation of. X and X. - None : Do not compute the fit of the factorization, but iterate until ``max_iter`` (Useful for large-scale tensors). (default 'full') conv : float Convergence tolerance on difference of fit between iterations (default 1e-5) Returns ------- P : ktensor Rank ``rank`` factorization of X. ``P.U[i]`` corresponds to the factor matrix for the i-th mode. ``P.lambda[i]`` corresponds to the weight of the i-th mode. fit : float Fit of the factorization compared to ``X`` itr : int Number of iterations that were needed until convergence exectimes : ndarray of floats Time needed for each single iteration Examples -------- Create random dense tensor >>> from sktensor import dtensor, ktensor >>> U = [np.random.rand(i,3) for i in (20, 10, 14)] >>> T = dtensor(ktensor(U).toarray()) Compute rank-3 CP decomposition of ``T`` with ALS >>> P, fit, itr, _ = als(T, 3) Result is a decomposed tensor stored as a Kruskal operator >>> type(P) <class 'sktensor.ktensor.ktensor'> Factorization should be close to original data >>> np.allclose(T, P.totensor()) True References ---------- .. [1] Kolda, T. G. & Bader, B. W. Tensor Decompositions and Applications. SIAM Rev. 51, 455–500 (2009). .. [2] Harshman, R. A. Foundations of the PARAFAC procedure: models and conditions for an 'explanatory' multimodal factor analysis. UCLA Working Papers in Phonetics 16, (1970). .. [3] Carroll, J. D., Chang, J. J. Analysis of individual differences in multidimensional scaling via an N-way generalization of 'Eckart-Young' decomposition. Psychometrika 35, 283–319 (1970). """ # init options ainit = kwargs.pop('init', _DEF_INIT) maxiter = kwargs.pop('max_iter', _DEF_MAXITER) fit_method = kwargs.pop('fit_method', _DEF_FIT_METHOD) conv = kwargs.pop('conv', _DEF_CONV) dtype = kwargs.pop('dtype', _DEF_TYPE) if not len(kwargs) == 0: raise ValueError('Unknown keywords (%s)' % (kwargs.keys())) N = X.ndim normX = norm(X) U = _init(ainit, X, N, rank, dtype) fit = 0 exectimes = [] for itr in range(maxiter): tic = time.clock() fitold = fit for n in range(N): Unew = X.uttkrp(U, n) Y = ones((rank, rank), dtype=dtype) for i in (list(range(n)) + list(range(n + 1, N))): Y = Y * dot(U[i].T, U[i]) Unew = Unew.dot(pinv(Y)) # Normalize if itr == 0: lmbda = sqrt((Unew ** 2).sum(axis=0)) else: lmbda = Unew.max(axis=0) lmbda[lmbda < 1] = 1 U[n] = Unew / lmbda P = ktensor(U, lmbda) if fit_method == 'full': normresidual = normX ** 2 + P.norm() ** 2 - 2 * P.innerprod(X) fit = 1 - (normresidual / normX ** 2) else: fit = itr fitchange = abs(fitold - fit) exectimes.append(time.clock() - tic) _log.debug( '[%3d] fit: %.5f | delta: %7.1e | secs: %.5f' % (itr, fit, fitchange, exectimes[-1]) ) if itr > 0 and fitchange < conv: break return P, fit, itr, array(exectimes)
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Alternating least-sqaures algorithm to compute the CP decomposition. Parameters ---------- X : tensor_mixin The tensor to be decomposed. rank : int Tensor rank of the decomposition. init : {'random', 'nvecs'}, optional The initialization method to use. - random : Factor matrices are initialized randomly. - nvecs : Factor matrices are initialzed via HOSVD. (default 'nvecs') max_iter : int, optional Maximium number of iterations of the ALS algorithm. (default 500) fit_method : {'full', None} The method to compute the fit of the factorization - 'full' : Compute least-squares fit of the dense approximation of. X and X. - None : Do not compute the fit of the factorization, but iterate until ``max_iter`` (Useful for large-scale tensors). (default 'full') conv : float Convergence tolerance on difference of fit between iterations (default 1e-5) Returns ------- P : ktensor Rank ``rank`` factorization of X. ``P.U[i]`` corresponds to the factor matrix for the i-th mode. ``P.lambda[i]`` corresponds to the weight of the i-th mode. fit : float Fit of the factorization compared to ``X`` itr : int Number of iterations that were needed until convergence exectimes : ndarray of floats Time needed for each single iteration Examples -------- Create random dense tensor >>> from sktensor import dtensor, ktensor >>> U = [np.random.rand(i,3) for i in (20, 10, 14)] >>> T = dtensor(ktensor(U).toarray()) Compute rank-3 CP decomposition of ``T`` with ALS >>> P, fit, itr, _ = als(T, 3) Result is a decomposed tensor stored as a Kruskal operator >>> type(P) <class 'sktensor.ktensor.ktensor'> Factorization should be close to original data >>> np.allclose(T, P.totensor()) True References ---------- .. [1] Kolda, T. G. & Bader, B. W. Tensor Decompositions and Applications. SIAM Rev. 51, 455–500 (2009). .. [2] Harshman, R. A. Foundations of the PARAFAC procedure: models and conditions for an 'explanatory' multimodal factor analysis. UCLA Working Papers in Phonetics 16, (1970). .. [3] Carroll, J. D., Chang, J. J. Analysis of individual differences in multidimensional scaling via an N-way generalization of 'Eckart-Young' decomposition. Psychometrika 35, 283–319 (1970).
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fe517e9661a08164b8d30d2dddf7c96aeeabcf36
https://github.com/mnick/scikit-tensor/blob/fe517e9661a08164b8d30d2dddf7c96aeeabcf36/sktensor/cp.py#L46-L171
train
mnick/scikit-tensor
sktensor/cp.py
_init
def _init(init, X, N, rank, dtype): """ Initialization for CP models """ Uinit = [None for _ in range(N)] if isinstance(init, list): Uinit = init elif init == 'random': for n in range(1, N): Uinit[n] = array(rand(X.shape[n], rank), dtype=dtype) elif init == 'nvecs': for n in range(1, N): Uinit[n] = array(nvecs(X, n, rank), dtype=dtype) else: raise 'Unknown option (init=%s)' % str(init) return Uinit
python
def _init(init, X, N, rank, dtype): """ Initialization for CP models """ Uinit = [None for _ in range(N)] if isinstance(init, list): Uinit = init elif init == 'random': for n in range(1, N): Uinit[n] = array(rand(X.shape[n], rank), dtype=dtype) elif init == 'nvecs': for n in range(1, N): Uinit[n] = array(nvecs(X, n, rank), dtype=dtype) else: raise 'Unknown option (init=%s)' % str(init) return Uinit
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Initialization for CP models
[ "Initialization", "for", "CP", "models" ]
fe517e9661a08164b8d30d2dddf7c96aeeabcf36
https://github.com/mnick/scikit-tensor/blob/fe517e9661a08164b8d30d2dddf7c96aeeabcf36/sktensor/cp.py#L190-L205
train
mnick/scikit-tensor
sktensor/core.py
nvecs
def nvecs(X, n, rank, do_flipsign=True, dtype=np.float): """ Eigendecomposition of mode-n unfolding of a tensor """ Xn = X.unfold(n) if issparse_mat(Xn): Xn = csr_matrix(Xn, dtype=dtype) Y = Xn.dot(Xn.T) _, U = eigsh(Y, rank, which='LM') else: Y = Xn.dot(Xn.T) N = Y.shape[0] _, U = eigh(Y, eigvals=(N - rank, N - 1)) #_, U = eigsh(Y, rank, which='LM') # reverse order of eigenvectors such that eigenvalues are decreasing U = array(U[:, ::-1]) # flip sign if do_flipsign: U = flipsign(U) return U
python
def nvecs(X, n, rank, do_flipsign=True, dtype=np.float): """ Eigendecomposition of mode-n unfolding of a tensor """ Xn = X.unfold(n) if issparse_mat(Xn): Xn = csr_matrix(Xn, dtype=dtype) Y = Xn.dot(Xn.T) _, U = eigsh(Y, rank, which='LM') else: Y = Xn.dot(Xn.T) N = Y.shape[0] _, U = eigh(Y, eigvals=(N - rank, N - 1)) #_, U = eigsh(Y, rank, which='LM') # reverse order of eigenvectors such that eigenvalues are decreasing U = array(U[:, ::-1]) # flip sign if do_flipsign: U = flipsign(U) return U
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Eigendecomposition of mode-n unfolding of a tensor
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fe517e9661a08164b8d30d2dddf7c96aeeabcf36
https://github.com/mnick/scikit-tensor/blob/fe517e9661a08164b8d30d2dddf7c96aeeabcf36/sktensor/core.py#L275-L294
train
mnick/scikit-tensor
sktensor/core.py
flipsign
def flipsign(U): """ Flip sign of factor matrices such that largest magnitude element will be positive """ midx = abs(U).argmax(axis=0) for i in range(U.shape[1]): if U[midx[i], i] < 0: U[:, i] = -U[:, i] return U
python
def flipsign(U): """ Flip sign of factor matrices such that largest magnitude element will be positive """ midx = abs(U).argmax(axis=0) for i in range(U.shape[1]): if U[midx[i], i] < 0: U[:, i] = -U[:, i] return U
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Flip sign of factor matrices such that largest magnitude element will be positive
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fe517e9661a08164b8d30d2dddf7c96aeeabcf36
https://github.com/mnick/scikit-tensor/blob/fe517e9661a08164b8d30d2dddf7c96aeeabcf36/sktensor/core.py#L297-L306
train
mnick/scikit-tensor
sktensor/core.py
khatrirao
def khatrirao(A, reverse=False): """ Compute the columnwise Khatri-Rao product. Parameters ---------- A : tuple of ndarrays Matrices for which the columnwise Khatri-Rao product should be computed reverse : boolean Compute Khatri-Rao product in reverse order Examples -------- >>> A = np.random.randn(5, 2) >>> B = np.random.randn(4, 2) >>> C = khatrirao((A, B)) >>> C.shape (20, 2) >>> (C[:, 0] == np.kron(A[:, 0], B[:, 0])).all() true >>> (C[:, 1] == np.kron(A[:, 1], B[:, 1])).all() true """ if not isinstance(A, tuple): raise ValueError('A must be a tuple of array likes') N = A[0].shape[1] M = 1 for i in range(len(A)): if A[i].ndim != 2: raise ValueError('A must be a tuple of matrices (A[%d].ndim = %d)' % (i, A[i].ndim)) elif N != A[i].shape[1]: raise ValueError('All matrices must have same number of columns') M *= A[i].shape[0] matorder = arange(len(A)) if reverse: matorder = matorder[::-1] # preallocate P = np.zeros((M, N), dtype=A[0].dtype) for n in range(N): ab = A[matorder[0]][:, n] for j in range(1, len(matorder)): ab = np.kron(ab, A[matorder[j]][:, n]) P[:, n] = ab return P
python
def khatrirao(A, reverse=False): """ Compute the columnwise Khatri-Rao product. Parameters ---------- A : tuple of ndarrays Matrices for which the columnwise Khatri-Rao product should be computed reverse : boolean Compute Khatri-Rao product in reverse order Examples -------- >>> A = np.random.randn(5, 2) >>> B = np.random.randn(4, 2) >>> C = khatrirao((A, B)) >>> C.shape (20, 2) >>> (C[:, 0] == np.kron(A[:, 0], B[:, 0])).all() true >>> (C[:, 1] == np.kron(A[:, 1], B[:, 1])).all() true """ if not isinstance(A, tuple): raise ValueError('A must be a tuple of array likes') N = A[0].shape[1] M = 1 for i in range(len(A)): if A[i].ndim != 2: raise ValueError('A must be a tuple of matrices (A[%d].ndim = %d)' % (i, A[i].ndim)) elif N != A[i].shape[1]: raise ValueError('All matrices must have same number of columns') M *= A[i].shape[0] matorder = arange(len(A)) if reverse: matorder = matorder[::-1] # preallocate P = np.zeros((M, N), dtype=A[0].dtype) for n in range(N): ab = A[matorder[0]][:, n] for j in range(1, len(matorder)): ab = np.kron(ab, A[matorder[j]][:, n]) P[:, n] = ab return P
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Compute the columnwise Khatri-Rao product. Parameters ---------- A : tuple of ndarrays Matrices for which the columnwise Khatri-Rao product should be computed reverse : boolean Compute Khatri-Rao product in reverse order Examples -------- >>> A = np.random.randn(5, 2) >>> B = np.random.randn(4, 2) >>> C = khatrirao((A, B)) >>> C.shape (20, 2) >>> (C[:, 0] == np.kron(A[:, 0], B[:, 0])).all() true >>> (C[:, 1] == np.kron(A[:, 1], B[:, 1])).all() true
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fe517e9661a08164b8d30d2dddf7c96aeeabcf36
https://github.com/mnick/scikit-tensor/blob/fe517e9661a08164b8d30d2dddf7c96aeeabcf36/sktensor/core.py#L333-L378
train
mnick/scikit-tensor
sktensor/core.py
teneye
def teneye(dim, order): """ Create tensor with superdiagonal all one, rest zeros """ I = zeros(dim ** order) for f in range(dim): idd = f for i in range(1, order): idd = idd + dim ** (i - 1) * (f - 1) I[idd] = 1 return I.reshape(ones(order) * dim)
python
def teneye(dim, order): """ Create tensor with superdiagonal all one, rest zeros """ I = zeros(dim ** order) for f in range(dim): idd = f for i in range(1, order): idd = idd + dim ** (i - 1) * (f - 1) I[idd] = 1 return I.reshape(ones(order) * dim)
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Create tensor with superdiagonal all one, rest zeros
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fe517e9661a08164b8d30d2dddf7c96aeeabcf36
https://github.com/mnick/scikit-tensor/blob/fe517e9661a08164b8d30d2dddf7c96aeeabcf36/sktensor/core.py#L381-L391
train
mnick/scikit-tensor
sktensor/core.py
tensor_mixin.ttm
def ttm(self, V, mode=None, transp=False, without=False): """ Tensor times matrix product Parameters ---------- V : M x N array_like or list of M_i x N_i array_likes Matrix or list of matrices for which the tensor times matrix products should be performed mode : int or list of int's, optional Modes along which the tensor times matrix products should be performed transp: boolean, optional If True, tensor times matrix products are computed with transpositions of matrices without: boolean, optional It True, tensor times matrix products are performed along all modes **except** the modes specified via parameter ``mode`` Examples -------- Create dense tensor >>> T = zeros((3, 4, 2)) >>> T[:, :, 0] = [[ 1, 4, 7, 10], [ 2, 5, 8, 11], [3, 6, 9, 12]] >>> T[:, :, 1] = [[13, 16, 19, 22], [14, 17, 20, 23], [15, 18, 21, 24]] >>> T = dtensor(T) Create matrix >>> V = array([[1, 3, 5], [2, 4, 6]]) Multiply tensor with matrix along mode 0 >>> Y = T.ttm(V, 0) >>> Y[:, :, 0] array([[ 22., 49., 76., 103.], [ 28., 64., 100., 136.]]) >>> Y[:, :, 1] array([[ 130., 157., 184., 211.], [ 172., 208., 244., 280.]]) """ if mode is None: mode = range(self.ndim) if isinstance(V, np.ndarray): Y = self._ttm_compute(V, mode, transp) elif is_sequence(V): dims, vidx = check_multiplication_dims(mode, self.ndim, len(V), vidx=True, without=without) Y = self._ttm_compute(V[vidx[0]], dims[0], transp) for i in xrange(1, len(dims)): Y = Y._ttm_compute(V[vidx[i]], dims[i], transp) return Y
python
def ttm(self, V, mode=None, transp=False, without=False): """ Tensor times matrix product Parameters ---------- V : M x N array_like or list of M_i x N_i array_likes Matrix or list of matrices for which the tensor times matrix products should be performed mode : int or list of int's, optional Modes along which the tensor times matrix products should be performed transp: boolean, optional If True, tensor times matrix products are computed with transpositions of matrices without: boolean, optional It True, tensor times matrix products are performed along all modes **except** the modes specified via parameter ``mode`` Examples -------- Create dense tensor >>> T = zeros((3, 4, 2)) >>> T[:, :, 0] = [[ 1, 4, 7, 10], [ 2, 5, 8, 11], [3, 6, 9, 12]] >>> T[:, :, 1] = [[13, 16, 19, 22], [14, 17, 20, 23], [15, 18, 21, 24]] >>> T = dtensor(T) Create matrix >>> V = array([[1, 3, 5], [2, 4, 6]]) Multiply tensor with matrix along mode 0 >>> Y = T.ttm(V, 0) >>> Y[:, :, 0] array([[ 22., 49., 76., 103.], [ 28., 64., 100., 136.]]) >>> Y[:, :, 1] array([[ 130., 157., 184., 211.], [ 172., 208., 244., 280.]]) """ if mode is None: mode = range(self.ndim) if isinstance(V, np.ndarray): Y = self._ttm_compute(V, mode, transp) elif is_sequence(V): dims, vidx = check_multiplication_dims(mode, self.ndim, len(V), vidx=True, without=without) Y = self._ttm_compute(V[vidx[0]], dims[0], transp) for i in xrange(1, len(dims)): Y = Y._ttm_compute(V[vidx[i]], dims[i], transp) return Y
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Tensor times matrix product Parameters ---------- V : M x N array_like or list of M_i x N_i array_likes Matrix or list of matrices for which the tensor times matrix products should be performed mode : int or list of int's, optional Modes along which the tensor times matrix products should be performed transp: boolean, optional If True, tensor times matrix products are computed with transpositions of matrices without: boolean, optional It True, tensor times matrix products are performed along all modes **except** the modes specified via parameter ``mode`` Examples -------- Create dense tensor >>> T = zeros((3, 4, 2)) >>> T[:, :, 0] = [[ 1, 4, 7, 10], [ 2, 5, 8, 11], [3, 6, 9, 12]] >>> T[:, :, 1] = [[13, 16, 19, 22], [14, 17, 20, 23], [15, 18, 21, 24]] >>> T = dtensor(T) Create matrix >>> V = array([[1, 3, 5], [2, 4, 6]]) Multiply tensor with matrix along mode 0 >>> Y = T.ttm(V, 0) >>> Y[:, :, 0] array([[ 22., 49., 76., 103.], [ 28., 64., 100., 136.]]) >>> Y[:, :, 1] array([[ 130., 157., 184., 211.], [ 172., 208., 244., 280.]])
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fe517e9661a08164b8d30d2dddf7c96aeeabcf36
https://github.com/mnick/scikit-tensor/blob/fe517e9661a08164b8d30d2dddf7c96aeeabcf36/sktensor/core.py#L50-L103
train
callowayproject/django-categories
categories/registration.py
_process_registry
def _process_registry(registry, call_func): """ Given a dictionary, and a registration function, process the registry """ from django.core.exceptions import ImproperlyConfigured from django.apps import apps for key, value in list(registry.items()): model = apps.get_model(*key.split('.')) if model is None: raise ImproperlyConfigured(_('%(key)s is not a model') % {'key': key}) if isinstance(value, (tuple, list)): for item in value: if isinstance(item, str): call_func(model, item) elif isinstance(item, dict): field_name = item.pop('name') call_func(model, field_name, extra_params=item) else: raise ImproperlyConfigured(_("%(settings)s doesn't recognize the value of %(key)s") % {'settings': 'CATEGORY_SETTINGS', 'key': key}) elif isinstance(value, str): call_func(model, value) elif isinstance(value, dict): field_name = value.pop('name') call_func(model, field_name, extra_params=value) else: raise ImproperlyConfigured(_("%(settings)s doesn't recognize the value of %(key)s") % {'settings': 'CATEGORY_SETTINGS', 'key': key})
python
def _process_registry(registry, call_func): """ Given a dictionary, and a registration function, process the registry """ from django.core.exceptions import ImproperlyConfigured from django.apps import apps for key, value in list(registry.items()): model = apps.get_model(*key.split('.')) if model is None: raise ImproperlyConfigured(_('%(key)s is not a model') % {'key': key}) if isinstance(value, (tuple, list)): for item in value: if isinstance(item, str): call_func(model, item) elif isinstance(item, dict): field_name = item.pop('name') call_func(model, field_name, extra_params=item) else: raise ImproperlyConfigured(_("%(settings)s doesn't recognize the value of %(key)s") % {'settings': 'CATEGORY_SETTINGS', 'key': key}) elif isinstance(value, str): call_func(model, value) elif isinstance(value, dict): field_name = value.pop('name') call_func(model, field_name, extra_params=value) else: raise ImproperlyConfigured(_("%(settings)s doesn't recognize the value of %(key)s") % {'settings': 'CATEGORY_SETTINGS', 'key': key})
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Given a dictionary, and a registration function, process the registry
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3765851320a79b12c6d3306f3784a2302ea64812
https://github.com/callowayproject/django-categories/blob/3765851320a79b12c6d3306f3784a2302ea64812/categories/registration.py#L118-L146
train
callowayproject/django-categories
categories/migration.py
field_exists
def field_exists(app_name, model_name, field_name): """ Does the FK or M2M table exist in the database already? """ model = apps.get_model(app_name, model_name) table_name = model._meta.db_table cursor = connection.cursor() field_info = connection.introspection.get_table_description(cursor, table_name) field_names = [f.name for f in field_info] return field_name in field_names
python
def field_exists(app_name, model_name, field_name): """ Does the FK or M2M table exist in the database already? """ model = apps.get_model(app_name, model_name) table_name = model._meta.db_table cursor = connection.cursor() field_info = connection.introspection.get_table_description(cursor, table_name) field_names = [f.name for f in field_info] return field_name in field_names
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Does the FK or M2M table exist in the database already?
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3765851320a79b12c6d3306f3784a2302ea64812
https://github.com/callowayproject/django-categories/blob/3765851320a79b12c6d3306f3784a2302ea64812/categories/migration.py#L16-L25
train
callowayproject/django-categories
categories/migration.py
drop_field
def drop_field(app_name, model_name, field_name): """ Drop the given field from the app's model """ app_config = apps.get_app_config(app_name) model = app_config.get_model(model_name) field = model._meta.get_field(field_name) with connection.schema_editor() as schema_editor: schema_editor.remove_field(model, field)
python
def drop_field(app_name, model_name, field_name): """ Drop the given field from the app's model """ app_config = apps.get_app_config(app_name) model = app_config.get_model(model_name) field = model._meta.get_field(field_name) with connection.schema_editor() as schema_editor: schema_editor.remove_field(model, field)
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Drop the given field from the app's model
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3765851320a79b12c6d3306f3784a2302ea64812
https://github.com/callowayproject/django-categories/blob/3765851320a79b12c6d3306f3784a2302ea64812/categories/migration.py#L28-L36
train