def get_r2c_sim(coords):
dims = [
'est',
'sim',
'r2',
'sig2',
'snr',
'ms',
'ns',
]
res = xr.DataArray(np.zeros(tuple(len(c) for c in coords)),
coords=coords,
dims=dims)
res = res.stack(c=dims[2:])
for i in range(res.shape[-1]):
[r2, sig2, snr, m, n] = res[:, :, i].coords['c'].values.tolist()
mu2y = SNR_to_mu2(snr, sig2, m, n)
theta = [r2, sig2, mu2y, mu2y, m, n]
x, y = rc.pds_n2m_r2c(theta, n_exps, ddof=1)
x = x.squeeze()[np.newaxis, np.newaxis]
r2c, r2 = rc.r2c_n2m(x, y)
res[..., i] = np.array([r2, r2c]).squeeze()
res = res.unstack()
return res
def r2c_n2m_ci_from_post(trace,
n,
m,
r2c_hat_obs,
alpha_targ=0.10,
nr2cs=50,
nr2c_hat=1000):
sig2s = trace[0]
mu2ys = trace[1] * m
sample_inds = np.random.choice(len(sig2s), size=nr2c_hat, replace=False)
n_exps = 1
ress = []
r2s = np.linspace(0, 1, nr2cs)
for r2 in r2s:
res = []
for i in range(nr2c_hat):
i = sample_inds[i]
theta = [r2, sig2s[i], mu2ys[i], mu2ys[i], m, n]
x, y = rc.pds_n2m_r2c(theta, n_exps, ddof=1)
res.append(rc.r2c_n2m(x.squeeze(), y)[0])
ress.append(np.array(res).squeeze())
ress = np.array(ress)
tol = alpha_targ
alpha_obs = np.mean(ress > r2c_hat_obs, 1)
ll_dif = np.abs(alpha_obs - alpha_targ / 2)
ll_ind = np.argmin(ll_dif)
if ll_dif[ll_ind] < tol:
ll = r2s[ll_ind]
else:
ll = 0
ul_dif = np.abs(alpha_obs - (1 - alpha_targ / 2))
ul_ind = np.argmin(ul_dif)
if ul_dif[ul_ind] < tol:
ul = r2s[ul_ind]
else:
ul = 1
return ll, ul, alpha_obs
'H&C_their_deriv',
'Y&D',
'H&T',
'S&L',
'S&S',
'Zyl'],
snrs, r2sims, ms, range(n_exps)])
da.attrs = {'n':n, 'm':m, 'mu2x':mu2x, 'sig2':sig2,
'n_exps':n_exps}
for k, mu2y in enumerate(mu2ys):
r2cs = []
r2s = []
for l, m in enumerate(ms):
for j, r2 in enumerate(r2sims):
theta = [r2, sig2, mu2y, mu2y, m, n]
[x, y] = rc.pds_n2m_r2c(theta, n_exps, ddof=1)
res = []
for i in range(y.shape[0]):
a_y = y[i]
mod = np.zeros((len(x), 2))
mod[:,0] = 1
mod[:, 1] = x.squeeze()
beta = np.linalg.lstsq(mod, a_y.mean(0), rcond=-1)[0]
y_hat = np.dot(beta[np.newaxis], mod.T).squeeze()
r2c, r2 = rc.r2c_n2m(x.T, a_y)
r2_pc = rc.r2_SE_corrected(x.squeeze(), a_y)
r2_upsilon = rc.upsilon(y_hat, a_y)
r2_hsu = rc.cc_norm_split(x.squeeze(), a_y)**2
r2_yd = rc.r2_SB_normed(x.squeeze(), a_y)
r2_sl = rc.normalized_spe(y_hat, a_y)