How to use the symengine.cos function in symengine

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github neurophysik / jitcode / tests / test_sympy_input.py View on Github external
"""
Tests whether things works independent of where symbols are imported from.
"""

import jitcode
import jitcode.sympy_symbols
import sympy
import symengine

symengine_manually = [
		symengine.Symbol("t",real=True),
		symengine.Function("y",real=True),
		symengine.cos,
	]

sympy_manually = [
		sympy.Symbol("t",real=True),
		sympy.Function("y",real=True),
		sympy.cos,
	]

jitcode_provisions = [
		jitcode.t,
		jitcode.y,
		symengine.cos,
	]

jitcode_sympy_provisions = [
		jitcode.sympy_symbols.t,
github neurophysik / jitcdde / tests / test_sympy_input.py View on Github external
sympy_manually = [
		sympy_t,
		sympy_y,
		sympy.cos,
	]

jitcdde_provisions = [
		jitcdde.t,
		jitcdde.y,
		symengine.cos,
	]

jitcdde_sympy_provisions = [
		jitcdde.sympy_symbols.t,
		jitcdde.sympy_symbols.y,
		symengine.cos,
	]

mixed = [
		jitcdde.sympy_symbols.t,
		jitcdde.y,
		sympy.cos,
	]

results = set()

for t,y,cos in [
			symengine_manually,
			sympy_manually,
			jitcdde_provisions,
			jitcdde_sympy_provisions,
			mixed,
github exa-analytics / exatomic / exatomic / algorithms / basis.py View on Github external
der = (_x ** 2 - 1) ** L
        den = 2 ** L * facts[L]
        for _ in range(L):
            der = der.diff(_x)
        for m in range(L + 1):
            pol = (1 - _x ** 2) ** (m/2)
            if m: der = der.diff(_x)
            leg = phase[m] / den * (pol * der).subs({_x: _z / _r})
            if not m:
                sh[L][m] = rac * leg
                continue
            N = 2 ** 0.5 * phase[m]
            facs = facts[L - m] / facts[L + m]
            norm = facs ** 0.5
            phi = (m * _x).subs({_x: 'arctan2(_y, _x)'})
            fun = cos(phi)
            sh[L][m] = N * rac * norm * leg * fun
            fun = sin(phi)
            sh[L][-m] = N * rac * norm * leg * fun
    return sh