def plotScatter():
import plot
import plot_mcc_ener
high_mcc_diff = h5py.File('/ddnB/work/jaydy/dat/output/linr_out/08ff_all_decoy.h5')['/gaussian_nb/high/high_mcc_diff'][()]
low_mcc_diff = h5py.File('/ddnB/work/jaydy/dat/output/linr_out/08ff_all_decoy.h5')['/gaussian_nb/low/low_mcc_diff'][()]
high_mcc_diff = plot_mcc_ener.sampleMccTotalByMcc(high_mcc_diff, 2000)
low_mcc_diff = plot_mcc_ener.sampleMccTotalByMcc(low_mcc_diff, 2000)
x1 = high_mcc_diff[:, 0]
y1 = high_mcc_diff[:, 1]
x2 = low_mcc_diff[:, 0]
y2 = low_mcc_diff[:, 1]
plot.two_scatter(x1, y1, x2, y2, ofn='08_gaussian_mcc_diff_scatter.pdf')
def sampleMcc():
from myh5 import myh5
h5_path = "/ddnB/work/jaydy/dat/output/linr_out/08_lnr.h5"
dt_path = "weighted_high"
f = myh5(h5_path)
high_mcc_total = f.loadDt(dt_path)
dt_path = "weighted_low"
low_mcc_total = f.loadDt(dt_path)
sample_sz = 2000
high_mcc_total = sampleMccTotalByMcc(high_mcc_total, sample_sz)
low_mcc_total = sampleMccTotalByMcc(low_mcc_total, sample_sz)
return high_mcc_total, low_mcc_total
def plotMccEnerRelation(complx_id):
# loading
weight_fn = "/work/jaydy/dat/08ff_opt"
weight = np.loadtxt(weight_fn)
# lnr_ff = Lnr_ff('08ff_all_decoy.h5', 'all_set')
lnr_ff = Lnr_ff("all_decoy.h5", "all_set")
all_set = lnr_ff.loadH5()
mcc = all_set[:, 0]
ener = all_set[:, 1:]
total_ener = np.dot(ener, weight)
mcc_total = np.column_stack((mcc, total_ener))
# sampling
sample_sz = 2000
if sample_sz <= mcc_total.shape[0]:
mcc_total = sortMccTotalByMcc(mcc_total)
sampled_mcc_total = np.vstack(row for row in sampleMccTotalByMcc(mcc_total, sample_sz))
mcc_total = sampled_mcc_total
mcc, ener = mcc_total[:, 0], mcc_total[:, 1]
# scatter_ofn = 'weighted_lnr' + '_scatter.pdf'
scatter_ofn = complx_id + "_scatter.pdf"
plot.scatter(mcc, ener, ofn=scatter_ofn, x_label="mcc", y_label="diff")
# line_ofn = 'weighted_lnr' + '_line.pdf'
line_ofn = complx_id + "_line.pdf"
plot.two_scales(ener, mcc, ofn=line_ofn, right_label="ener", left_label="diff")
