def plot_range(fpath, mz0, mz1, ax, col, size=20, in_ppm=False):
"""Plot peak scatter within mz0 and mz1
"""
ts = mx.get_triplets_range(fpath, mz0, mz1)
if len(ts) == 0:
return
h = np.array([t.height for t in ts])
t = np.array([t.index for t in ts])
mz = np.array([t.mz for t in ts])
if in_ppm:
x_m = np.mean(mz)
x = (mz - x_m) / x_m * 1e6
ax.scatter(x, t, size * h / np.max(h), c=col, alpha=0.5)
else:
ax.scatter(mz, t, size * h / np.max(h), c=col, alpha=0.5)
fig.savefig(fpath_scatter + '/mz_{}.png'.format(int(mz_i)), dpi=200)
ax.cla()
# ---------- compute mass dispersions around mean spectrum ----------
from msiwarp.util.warp import dispersion_triplets
import pandas as pd
dispersion_raw = np.zeros(len(mz_ref))
dispersion_warped = np.zeros(len(mz_ref))
for i, mz_i in enumerate(mz_ref):
d = mass_tolerance * mz_i / 1e6 # -+ 350 ppm around reference mass
mz0 = mz_i - d
mz1 = mz_i + d
ts_raw = mx.get_triplets_range(fpath_triplets_raw, mz0, mz1)
ts_warped = mx.get_triplets_range(fpath_triplets_warped, mz0, mz1)
q = 0.25 # remove background signal
if len(ts_raw) > 3:
dispersion_raw[i] = dispersion_triplets(ts_raw, q)
if len(ts_warped) > 3:
dispersion_warped[i] = dispersion_triplets(ts_warped, q)
d = {
'mz': mz_ref,
'dispersion raw [ppm]': dispersion_raw,
'dispersion warped [ppm]': dispersion_warped
}
df = pd.DataFrame(d)