yi = np.loadtxt(yi_path, delimiter=',')
lami_path = f'{path}/img/txt/lam{i}_best.txt'
lami = np.loadtxt(lami_path, dtype=int)
wi_path = f'{path}/img/txt/W{i}_best.txt'
wi = np.loadtxt(wi_path)
zi_path = f'{path}/img/txt/Z{i}_best.txt'
zi = np.loadtxt(zi_path)
# Plot
plt.figure(figsize=(6, 6))
plot_yz.plot_y_centroids(yi,
lami,
wi,
vlim=(-3, 3),
cm=blue2red.cm(6),
population=population,
Zi=zi,
fs_xlabel=16,
fs_ylabel=16,
fs_xticks=16,
fs_yticks=16)
outpath = f'{path}/img/y{i}_centroid.pdf'
plt.savefig(outpath, bbox_inches="tight")
plt.close()
# Plot Z estimate.
plt.figure(figsize=(6, 6))
plot_yz.plot_Z(Z_mean=zi,
wi_mean=wi,
lami_est=lami,
w_thresh=0.0,
for i in (1, 2):
# Read data
yi_path = f'{path}/img/txt/y{i}_mean.csv'
yi = np.loadtxt(yi_path, delimiter=',')
lami_path = f'{path}/img/txt/lam{i}_best.txt'
lami = np.loadtxt(lami_path, dtype=int)
wi_path = f'{path}/img/txt/W{i}_best.txt'
wi = np.loadtxt(wi_path)
zi_path = f'{path}/img/txt/Z{i}_best.txt'
Zi = np.loadtxt(zi_path)
# Plot y centroids
plt.figure(figsize=(6, 6))
plot_yz.plot_y_centroids(yi,
lami,
wi,
vlim=(-3, 3),
cm=blue2red.cm(6),
population=population,
Zi=Zi,
fs_xlabel=16,
fs_ylabel=16,
fs_xticks=14,
fs_yticks=16,
rotation=45,
markernames=markernames,
ha="right")
outpath = f'{path}/img/y{i}_centroid.pdf'
plt.savefig(outpath, bbox_inches="tight")
plt.close()