def generate_numerical_precision(): # pragma: no cover
"""
Generate expected data with infinite numerical precision using SymPy.
:return: dataframe of expected values
"""
if symbols is NotImplemented:
LOGGER.critical("SymPy is required to generate expected data.")
raise ImportError("could not import sympy")
il, io, rs, rsh, nnsvt, vd = symbols('il, io, rs, rsh, nnsvt, vd')
a = sy_exp(vd / nnsvt)
b = 1.0 / rsh
i = il - io * (a - 1.0) - vd * b
v = vd - i * rs
c = io * a / nnsvt
grad_i = - c - b # di/dvd
grad_v = 1.0 - grad_i * rs # dv/dvd
# dp/dv = d(iv)/dv = v * di/dv + i
grad = grad_i / grad_v # di/dv
p = i * v
grad_p = v * grad + i # dp/dv
grad2i = -c / nnsvt
grad2v = -grad2i * rs
grad2p = (
grad_v * grad + v * (grad2i/grad_v - grad_i*grad2v/grad_v**2) + grad_i
)
# generate exact values
data = dict(zip((il, io, rs, rsh, nnsvt), ARGS))
vdtest = np.linspace(0, estimate_voc(IL, I0, NNSVTH), IVCURVE_NPTS)
expected = []
for test in vdtest:
data[vd] = test
test_data = {
'i': np.float64(i.evalf(subs=data)),
'v': np.float64(v.evalf(subs=data)),
'p': np.float64(p.evalf(subs=data)),
'grad_i': np.float64(grad_i.evalf(subs=data)),
'grad_v': np.float64(grad_v.evalf(subs=data)),
'grad': np.float64(grad.evalf(subs=data)),
'grad_p': np.float64(grad_p.evalf(subs=data)),
'grad2p': np.float64(grad2p.evalf(subs=data))
}
LOGGER.debug(test_data)
expected.append(test_data)
return pd.DataFrame(expected, index=vdtest)
def generate_numerical_precision():
"""
Generate expected data with infinite numerical precision using SymPy.
:return: dataframe of expected values
"""
if symbols is NotImplemented:
LOGGER.critical("SymPy is required to generate expected data.")
raise ImportError("could not import sympy")
il, io, rs, rsh, nnsvt, vd = symbols('il, io, rs, rsh, nnsvt, vd')
a = sy_exp(vd / nnsvt)
b = 1.0 / rsh
i = il - io * (a - 1.0) - vd * b
v = vd - i * rs
c = io * a / nnsvt
grad_i = - c - b # di/dvd
grad_v = 1.0 - grad_i * rs # dv/dvd
# dp/dv = d(iv)/dv = v * di/dv + i
grad = grad_i / grad_v # di/dv
p = i * v
grad_p = v * grad + i # dp/dv
grad2i = -c / nnsvt
grad2v = -grad2i * rs
grad2p = (
grad_v * grad + v * (grad2i/grad_v - grad_i*grad2v/grad_v**2) + grad_i
)
# generate exact values
data = dict(zip((il, io, rs, rsh, nnsvt), ARGS))
vdtest = np.linspace(0, estimate_voc(IL, I0, NNSVTH), IVCURVE_NPTS)
expected = []
for test in vdtest:
data[vd] = test
test_data = {
'i': np.float64(i.evalf(subs=data)),
'v': np.float64(v.evalf(subs=data)),
'p': np.float64(p.evalf(subs=data)),
'grad_i': np.float64(grad_i.evalf(subs=data)),
'grad_v': np.float64(grad_v.evalf(subs=data)),
'grad': np.float64(grad.evalf(subs=data)),
'grad_p': np.float64(grad_p.evalf(subs=data)),
'grad2p': np.float64(grad2p.evalf(subs=data))
}
LOGGER.debug(test_data)
expected.append(test_data)
return pd.DataFrame(expected, index=vdtest)