def test_to_hdf(self):
c = columnfile.columnfile("test.col")
columnfile.colfile_to_hdf(c, "testcolfile.hdf")
h = columnfile.columnfile("testcolfile.hdf")
for t in c.titles:
assert ((c.getcolumn(t) == h.getcolumn(t)).all())
assert t in h.titles
def main():
colf = columnfile.columnfile(sys.argv[1])
colf.filter(colf.Number_of_pixels > 3)
print("Read", colf.nrows, "peaks")
p = parameters.read_par_file(sys.argv[2])
# input a sample radius to use for computing ranges
radius = int(sys.argv[3])
omegastep = guess_omega_step(colf)
print("Guess omega step as", omegastep)
colf.updateGeometry(p)
for i, t in enumerate(colf.titles):
print("\n", t, end=" ")
for j in (0, 1, 2, colf.nrows // 2, colf.nrows // 2 + 1,
colf.nrows // 2 + 2, colf.nrows - 3, colf.nrows - 2,
colf.nrows - 1):
print(colf.bigarray[i, j], end=" ")
print()
modXL = np.sqrt(colf.xl * colf.xl + colf.yl * colf.yl + colf.zl * colf.zl)
modXLmean = modXL.mean()
dangle = np.degrees(np.arctan2(radius, modXLmean))
print("Angle precision is about ", dangle, "for average distance",
modXLmean, "and sample size ", radius)
mergecolf = findpairs_2d(colf, dangle, omegastep)
columnfile.colfile_to_hdf(mergecolf, sys.argv[4])
raise
c.updateGeometry(p)
if titles is None:
titles = [t for t in c.titles]
for told, tnew in zip(titles, c.titles):
assert told == tnew, "titles do not match %s %s %s" % (f, told,
tnew)
ars.append(c.bigarray)
c.bigarray = concatenate(ars, axis=1)
c.nrows = c.bigarray.shape[1]
c.set_attributes()
return c
if __name__ == "__main__":
try:
p = read_par_file(sys.argv[1])
motor = sys.argv[2]
out = sys.argv[3]
if os.path.exists(out):
print(help)
print("File exists already, try again")
except:
print(help)
raise
c = merge_flts(p, sorted(sys.argv[4:]), motor)
colfile_to_hdf(c, out)
import sys
"""
Script for repairing use of incorrect spline file
"""
import h5py, tqdm, numpy as np
from ImageD11 import columnfile, blobcorrector
spline = sys.argv[1]
cor = blobcorrector.correctorclass(spline)
h5in = sys.argv[2]
pks = sys.argv[3]
outname = sys.argv[4]
with h5py.File(h5in, 'r') as hin:
print(list(hin), pks)
g = hin[pks]
cd = {name: g[name][:] for name in list(g)}
cd['sc'] = np.zeros_like(cd['s_raw'])
cd['fc'] = np.zeros_like(cd['f_raw'])
inc = columnfile.colfile_from_dict(cd)
for i in tqdm.tqdm(range(inc.nrows)):
inc.sc[i], inc.fc[i] = cor.correct(inc.s_raw[i], inc.f_raw[i])
columnfile.colfile_to_hdf(inc, outname)