def bundle_of_tubes(shape: List[int], spacing: int):
r"""
Create a 3D image of a bundle of tubes, in the form of a rectangular
plate with randomly sized holes through it.
Parameters
----------
shape : list
The size the image, with the 3rd dimension indicating the plate
thickness. If the 3rd dimension is not given then a thickness of
1 voxel is assumed.
spacing : scalar
The center to center distance of the holes. The hole sizes will be
randomly distributed between this values down to 3 voxels.
Returns
-------
image : ND-array
A boolean array with ``True`` values denoting the pore space
"""
shape = sp.array(shape)
if sp.size(shape) == 1:
shape = sp.full((3, ), int(shape))
if sp.size(shape) == 2:
shape = sp.hstack((shape, [1]))
temp = sp.zeros(shape=shape[:2])
Xi = sp.ceil(sp.linspace(spacing/2,
shape[0]-(spacing/2)-1,
int(shape[0]/spacing)))
Xi = sp.array(Xi, dtype=int)
Yi = sp.ceil(sp.linspace(spacing/2,
shape[1]-(spacing/2)-1,
int(shape[1]/spacing)))
Yi = sp.array(Yi, dtype=int)
temp[tuple(sp.meshgrid(Xi, Yi))] = 1
inds = sp.where(temp)
for i in range(len(inds[0])):
r = sp.random.randint(1, (spacing/2))
try:
s1 = slice(inds[0][i]-r, inds[0][i]+r+1)
s2 = slice(inds[1][i]-r, inds[1][i]+r+1)
temp[s1, s2] = ps_disk(r)
except ValueError:
odd_shape = sp.shape(temp[s1, s2])
temp[s1, s2] = ps_disk(r)[:odd_shape[0], :odd_shape[1]]
im = sp.broadcast_to(array=sp.atleast_3d(temp), shape=shape)
return im
forest.var_lss = interp1d(ll, vlss, fill_value='extrapolate', kind='nearest')
forest.fudge = interp1d(ll, fudge, fill_value='extrapolate', kind='nearest')
ll_st,st,wst = prep_del.stack(data)
### Save iter_out_prefix
res = fitsio.FITS(args.iter_out_prefix+".fits.gz",'rw',clobber=True)
hd = {}
hd["NSIDE"] = healpy_nside
hd["PIXORDER"] = healpy_pix_ordering
hd["FITORDER"] = args.order
res.write([ll_st,st,wst],names=['loglam','stack','weight'],header=hd,extname='STACK')
res.write([ll,eta,vlss,fudge,nb_pixels],names=['loglam','eta','var_lss','fudge','nb_pixels'],extname='WEIGHT')
res.write([ll_rest,forest.mean_cont(ll_rest),wmc],names=['loglam_rest','mean_cont','weight'],extname='CONT')
var = sp.broadcast_to(var.reshape(1,-1),var_del.shape)
res.write([var,var_del,var2_del,count,nqsos,chi2],names=['var_pipe','var_del','var2_del','count','nqsos','chi2'],extname='VAR')
res.close()
### Save delta
st = interp1d(ll_st[wst>0.],st[wst>0.],kind="nearest",fill_value="extrapolate")
deltas = {}
data_bad_cont = []
for p in sorted(list(data.keys())):
deltas[p] = [delta.from_forest(d,st,forest.var_lss,forest.eta,forest.fudge, args.use_mock_continuum) for d in data[p] if d.bad_cont is None]
data_bad_cont = data_bad_cont + [d for d in data[p] if d.bad_cont is not None]
for d in data_bad_cont:
log.write("rejected {} due to {}\n".format(d.thid,d.bad_cont))
log.close()