def compute_bundle(self, name, tdb, tdb2=0.0):
        """[DEPRECATED] Return a tuple of coefficients and parameters for `tdb`.

        The return value is a tuple that bundles together the
        coefficients and other Chebyshev intermediate values that are
        needed for the computation of either the position or velocity.
        The bundle can then be passed to either `position_from_bundle()`
        or `velocity_from_bundle()` to finish the computation.  See the
        package-level documentation for details; most users will simply
        call `position()` or `position_and_velocity()` instead.

        The barycentric dynamical time `tdb` argument should be a float.
        If there are many dates you want computed, then make `tdb` an
        array, which is more efficient than calling this method multiple
        times; the return values will be arrays providing a value for
        each time in `tdb`.

        For extra precision, the time can be split into two floats; a
        popular choice is to use `tdb` for the integer or half-integer
        date, and `tdb2` to hold the remaining fraction.

        Consult the `names` attribute of this ephemeris for the values
        of `name` it supports, such as ``'mars'`` or ``'earthmoon'``.

        """
        input_was_scalar = getattr(tdb, 'shape', ()) == ()
        if input_was_scalar:
            tdb = np.array((tdb,))
        # no need to deal with tdb2; numpy broadcast will add fine below.

        coefficient_sets = self.load(name)
        number_of_sets, axis_count, coefficient_count = coefficient_sets.shape

        jalpha, jomega = self.jalpha, self.jomega
        days_per_set = (jomega - jalpha) / number_of_sets
        # to keep precision, first subtract, then add
        index, offset = divmod((tdb - jalpha) + tdb2, days_per_set)
        index = index.astype(int)

        if (index < 0).any() or (number_of_sets < index).any():
            raise DateError('ephemeris %s only covers dates %.1f through %.1f'
                            % (self.name, jalpha, jomega))

        omegas = (index == number_of_sets)
        index[omegas] -= 1
        offset[omegas] += days_per_set

        coefficients = np.rollaxis(coefficient_sets[index], 1)

        # Chebyshev recurrence:

        T = np.empty((coefficient_count, len(index)))
        T[0] = 1.0
        T[1] = t1 = 2.0 * offset / days_per_set - 1.0
        twot1 = t1 + t1
        for i in range(2, coefficient_count):
            T[i] = twot1 * T[i-1] - T[i-2]

        bundle = coefficients, days_per_set, T, twot1
        return bundle
def compute_bundle(self, name, tdb, tdb2=0.0):
        """[DEPRECATED] Return a tuple of coefficients and parameters for `tdb`.

        The return value is a tuple that bundles together the
        coefficients and other Chebyshev intermediate values that are
        needed for the computation of either the position or velocity.
        The bundle can then be passed to either `position_from_bundle()`
        or `velocity_from_bundle()` to finish the computation.  See the
        package-level documentation for details; most users will simply
        call `position()` or `position_and_velocity()` instead.

        The barycentric dynamical time `tdb` argument should be a float.
        If there are many dates you want computed, then make `tdb` an
        array, which is more efficient than calling this method multiple
        times; the return values will be arrays providing a value for
        each time in `tdb`.

        For extra precision, the time can be split into two floats; a
        popular choice is to use `tdb` for the integer or half-integer
        date, and `tdb2` to hold the remaining fraction.

        Consult the `names` attribute of this ephemeris for the values
        of `name` it supports, such as ``'mars'`` or ``'earthmoon'``.

        """
        input_was_scalar = getattr(tdb, 'shape', ()) == ()
        if input_was_scalar:
            tdb = np.array((tdb,))
        # no need to deal with tdb2; numpy broadcast will add fine below.

        coefficient_sets = self.load(name)
        number_of_sets, axis_count, coefficient_count = coefficient_sets.shape

        jalpha, jomega = self.jalpha, self.jomega
        days_per_set = (jomega - jalpha) / number_of_sets
        # to keep precision, first subtract, then add
        index, offset = divmod((tdb - jalpha) + tdb2, days_per_set)
        index = index.astype(int)

        if (index < 0).any() or (number_of_sets < index).any():
            raise DateError('ephemeris %s only covers dates %.1f through %.1f'
                            % (self.name, jalpha, jomega))

        omegas = (index == number_of_sets)
        index[omegas] -= 1
        offset[omegas] += days_per_set

        coefficients = np.rollaxis(coefficient_sets[index], 1)

        # Chebyshev recurrence:

        T = np.empty((coefficient_count, len(index)))
        T[0] = 1.0
        T[1] = t1 = 2.0 * offset / days_per_set - 1.0
        twot1 = t1 + t1
        for i in range(2, coefficient_count):
            T[i] = twot1 * T[i-1] - T[i-2]

        bundle = coefficients, days_per_set, T, twot1
        return bundle
def compute_bundle ( self , name , tdb , tdb2 = 0.0 ) : input_was_scalar = getattr ( tdb , 'shape' , ( ) ) == ( ) if input_was_scalar : tdb = np . array ( ( tdb , ) ) # no need to deal with tdb2; numpy broadcast will add fine below. coefficient_sets = self . load ( name ) number_of_sets , axis_count , coefficient_count = coefficient_sets . shape jalpha , jomega = self . jalpha , self . jomega days_per_set = ( jomega - jalpha ) / number_of_sets # to keep precision, first subtract, then add index , offset = divmod ( ( tdb - jalpha ) + tdb2 , days_per_set ) index = index . astype ( int ) if ( index < 0 ) . any ( ) or ( number_of_sets < index ) . any ( ) : raise DateError ( 'ephemeris %s only covers dates %.1f through %.1f' % ( self . name , jalpha , jomega ) ) omegas = ( index == number_of_sets ) index [ omegas ] -= 1 offset [ omegas ] += days_per_set coefficients = np . rollaxis ( coefficient_sets [ index ] , 1 ) # Chebyshev recurrence: T = np . empty ( ( coefficient_count , len ( index ) ) ) T [ 0 ] = 1.0 T [ 1 ] = t1 = 2.0 * offset / days_per_set - 1.0 twot1 = t1 + t1 for i in range ( 2 , coefficient_count ) : T [ i ] = twot1 * T [ i - 1 ] - T [ i - 2 ] bundle = coefficients , days_per_set , T , twot1 return bundle
[DEPRECATED] Return a tuple of coefficients and parameters for `tdb`.

        The return value is a tuple that bundles together the
        coefficients and other Chebyshev intermediate values that are
        needed for the computation of either the position or velocity.
        The bundle can then be passed to either `position_from_bundle()`
        or `velocity_from_bundle()` to finish the computation.  See the
        package-level documentation for details; most users will simply
        call `position()` or `position_and_velocity()` instead.

        The barycentric dynamical time `tdb` argument should be a float.
        If there are many dates you want computed, then make `tdb` an
        array, which is more efficient than calling this method multiple
        times; the return values will be arrays providing a value for
        each time in `tdb`.

        For extra precision, the time can be split into two floats; a
        popular choice is to use `tdb` for the integer or half-integer
        date, and `tdb2` to hold the remaining fraction.

        Consult the `names` attribute of this ephemeris for the values
        of `name` it supports, such as ``'mars'`` or ``'earthmoon'``.
[ DEPRECATED ] Return a tuple of coefficients and parameters for tdb .