def plot_points(pts, img, name='', s=10, c='r', cmap=None,
vmin=None, vmax=None, cbar=False):
global snapshot_num, imprefix
fig, ax = plt.subplots(figsize=(8, 8))
# dpi = 300 gives 2.675 pixels for each image pixel, or 112.14 real
# pixels per inch. This may be unreliable, but assume that many image
# pixels per inch, and use integer multiples of that for dpi
# PPI = 112.14 if figsize (8, 6)
PPI = 84.638 # if figsize (8, 8)
dpi = 4*PPI
axim = ax.imshow(img, cmap=cmap, vmin=vmin, vmax=vmax,
interpolation='nearest')
if cbar:
fig.tight_layout()
cb_height = 4
cax = fig.add_axes(np.array([10, 99-cb_height, 80, cb_height])/100)
fig.colorbar(axim, cax=cax, orientation='horizontal')
xl, yl = ax.get_xlim(), ax.get_ylim()
s = abs(s)
helpy.draw_circles(helpy.consecutive_fields_view(pts, 'xy')[:, ::-1], s,
ax, lw=max(s/10, .5), color=c, fill=False, zorder=2)
if s > 3:
ax.scatter(pts['y'], pts['x'], s, c, '+')
ax.set_xlim(xl)
ax.set_ylim(yl)
ax.set_xticks([])
ax.set_yticks([])
if args.save:
savename = '{}_{:02d}_{}.png'.format(imprefix, snapshot_num, name)
fig.savefig(savename, dpi=dpi, bbox_inches='tight', pad_inches=0)
snapshot_num += 1
plt.close(fig)
def vv_autocorr(vs, normalize=False):
normalize = normalize and 1
fields = helpy.vel_dtype.names
vvs = [corr.autocorr(helpy.consecutive_fields_view(tv, fields),
norm=normalize, cumulant=True)
for pvs in vs.itervalues() for tv in pvs.itervalues()]
vvs, vv, dvv = helpy.avg_uneven(vvs, weight=True)
return [np.array(a, order='C').astype('f4').view(helpy.vel_dtype).squeeze()
for a in vvs, vv, dvv]
def convol_track_config(self, pdata, odata, startframe):
pfsets = helpy.load_framesets(pdata)
pftrees = {f: KDTree(helpy.consecutive_fields_view(pfset, 'xy'),
leafsize=32) for f, pfset in pfsets.iteritems()}
trackids = tracks.find_tracks(pdata, maxdist= self.max_dist, giveup = 10, n = 0, stub = 20, \
pftrees = pftrees, pfsets = pfsets, startframe = startframe)
trackids = tracks.remove_duplicates(trackids, data = pdata)
return_data = helpy.initialize_tdata(pdata, trackids, odata)
return_data = helpy.add_self_view(return_data, ('x','y'),'xy')
return return_data
def vv_autocorr(vs, normalize=False):
normalize = normalize and 1
fields = helpy.vel_dtype.names
vvs = [
corr.autocorr(helpy.consecutive_fields_view(tv, fields),
norm=normalize,
cumulant=True) for pvs in vs.itervalues()
for tv in pvs.itervalues()
]
vvs, vv, dvv = helpy.avg_uneven(vvs, weight=True)
return [
np.array(a, order='C').astype('f4').view(helpy.vel_dtype).squeeze()
for a in vvs, vv, dvv
]
def plot_points(pts,
img,
name='',
s=10,
c='r',
cmap=None,
vmin=None,
vmax=None,
cbar=False):
global snapshot_num, imprefix
fig, ax = plt.subplots(figsize=(8, 8))
# dpi = 300 gives 2.675 pixels for each image pixel, or 112.14 real
# pixels per inch. This may be unreliable, but assume that many image
# pixels per inch, and use integer multiples of that for dpi
# PPI = 112.14 if figsize (8, 6)
PPI = 84.638 # if figsize (8, 8)
dpi = 4 * PPI
axim = ax.imshow(img,
cmap=cmap,
vmin=vmin,
vmax=vmax,
interpolation='nearest')
if cbar:
fig.tight_layout()
cb_height = 4
cax = fig.add_axes(
np.array([10, 99 - cb_height, 80, cb_height]) / 100)
fig.colorbar(axim, cax=cax, orientation='horizontal')
xl, yl = ax.get_xlim(), ax.get_ylim()
s = abs(s)
helpy.draw_circles(helpy.consecutive_fields_view(pts, 'xy')[:, ::-1],
s,
ax,
lw=max(s / 10, .5),
color=c,
fill=False,
zorder=2)
if s > 3:
ax.scatter(pts['y'], pts['x'], s, c, '+')
ax.set_xlim(xl)
ax.set_ylim(yl)
ax.set_xticks([])
ax.set_yticks([])
if args.save:
savename = '{}_{:02d}_{}.png'.format(imprefix, snapshot_num, name)
fig.savefig(savename, dpi=dpi, bbox_inches='tight', pad_inches=0)
snapshot_num += 1
plt.close(fig)
def trackmsd(trackset, dt0, dtau):
""" finds the mean squared displacement as a function of tau,
averaged over t0, for one track (particle)
parameters
----------
trackset : a subset of the data for a given track
dt0 : spacing stepsize for values of t0, gives the number of starting
points averaged over in `t0avg`
dtau : spacing stepsize for values of tau, gives the spacing of the
points in time at which the msd is evaluated
For dt0, dtau: Small values will take longer to calculate without
adding to the statistics. Large values calculate faster but give
worse statistics. For the special case dt0 = dtau = 1, a
correlation is used for a signicant speedup
returns
-------
a list of tuples (tau, msd(tau)) the value of tau and the mean squared
displacement for a single track at that value of tau
"""
if dt0 == dtau == 1:
xy = helpy.consecutive_fields_view(trackset, 'xy')
return corr.msd(xy, ret_taus=True)
trackbegin, trackend = trackset['f'][[0,-1]]
tracklen = trackend - trackbegin + 1
if verbose:
print "length {} from {} to {}".format(tracklen, trackbegin, trackend)
if isinstance(dtau, float):
taus = helpy.farange(dt0, tracklen, dtau)
elif isinstance(dtau, int):
taus = xrange(dtau, tracklen, dtau)
tmsd = []
for tau in taus:
avg = t0avg(trackset, tracklen, tau)
if avg > 0 and not np.isnan(avg):
tmsd.append([tau,avg[0]])
if verbose:
print "\t...actually", len(tmsd)
return tmsd
def fill_gaps(tracksets, max_gap=5, interp=['xy','o'], inplace=True, verbose=False):
if not inplace:
tracksets = {t: s.copy() for t,s in tracksets.iteritems()}
if verbose:
print 'filling gaps with nans'
if interp:
print 'and interpolating nans in', ', '.join(interp)
for t, tset in tracksets.items():
if verbose: print "\ttrack {:4d}:".format(t),
fs = tset['f']
gaps = np.diff(fs) - 1
mx = gaps.max()
if not mx:
if verbose: print "not any gaps"
if 'o' in interp:
interp_nans(tset['o'], tset['f'], inplace=True)
continue
elif mx > max_gap:
if verbose:
print "dropped, gap too big: {} > {}".format(mx, max_gap)
tracksets.pop(t)
continue
gapi = gaps.nonzero()[0]
gaps = gaps[gapi]
gapi = np.repeat(gapi, gaps)
missing = np.full(len(gapi), np.nan, tset.dtype)
if verbose:
print ("missing {:3} frames in {:2} gaps (biggest {})"
).format(len(gapi), len(gaps), mx)
missing['f'] = np.concatenate(map(range, gaps)) + fs[gapi] + 1
missing['t'] = t
tset = np.insert(tset, gapi+1, missing)
if interp:
for field in interp:
view = helpy.consecutive_fields_view(tset, field, careful=False)
interp_nans(view, inplace=True)
tracksets[t] = tset
return tracksets
def animate_detection(imstack, fsets, fcsets, fosets=None, rc=0, side=15, verbose=False):
from matplotlib.patches import Circle
def advance(event):
key = event.key
if verbose:
print 'pressed', key, 'next frame:',
global f_display
if key=='left':
if f_display>=1:
f_display -= 1
elif key=='right':
f_display += 1
else:
plt.close()
f_display = -1
if verbose:
print f_display
sys.stdout.flush()
plt_text = np.vectorize(plt.text)
def draw_circles(ax, centers, r, *args, **kwargs):
patches = [Circle(cent, r, *args, **kwargs) for cent in centers]
map(ax.add_patch, patches)
ax.figure.canvas.draw()
return patches
if side==1:
side = 15
txtoff = max(rc, side, 10)
fig = plt.figure(figsize=(12, 12))
p = plt.imshow(imstack[0], cmap='gray')
h, w = imstack[0].shape
ax = p.axes
global f_display
f_display = repeat = f_old = 0
lengths = map(len, [imstack, fsets, fcsets])
f_max = min(lengths)
assert f_max, 'Lengths imstack: {}, fsets: {}, fcsets: {}'.format(*lengths)
while 0 <= f_display < f_max:
if repeat > 5:
if verbose:
print 'stuck on frame', f_display
break
f_display %= f_max
if f_display==f_old:
repeat += 1
else:
repeat = 0
f_old = f_display
if verbose:
print 'starting loop with f_display =', f_display
xyo = helpy.consecutive_fields_view(fsets[f_display], 'xyo', False)
xyc = helpy.consecutive_fields_view(fcsets[f_display], 'xy', False)
x, y, o = xyo.T
omask = np.isfinite(o)
xo, yo, oo = xyo[omask].T
p.set_data(imstack[f_display])
remove = []
if rc > 0:
patches = draw_circles(ax, xyo[:, 1::-1], rc,
color='g', fill=False, zorder=.5)
remove.extend(patches)
q = plt.quiver(yo, xo, np.sin(oo), np.cos(oo), angles='xy',
units='xy', width=side/8, scale_units='xy', scale=1/side)
ps = plt.scatter(y, x, c='r')#c=np.where(omask, 'r', 'b'))
cs = plt.scatter(xyc[:,1], xyc[:,0], c='g', s=8)
if fosets is not None:
oc = helpy.quick_field_view(fosets[f_display], 'corner').reshape(-1, 2)
ocs = plt.scatter(oc[:,1], oc[:,0], c='orange', s=8)
remove.append(ocs)
remove.extend([q, ps, cs])
tstr = fsets[f_display]['t'].astype('S')
txt = plt_text(y+txtoff, x+txtoff, tstr, color='r')
remove.extend(txt)
plt.xlim(0, w)
plt.ylim(0, h)
plt.title("frame {}\n{} orientations / {} particles detected".format(
f_display, np.count_nonzero(omask), len(o)))
fig.canvas.draw()
plt.waitforbuttonpress()
fig.canvas.mpl_connect('key_press_event', advance)
for rem in remove:
rem.remove()
if verbose:
print 'ending frame', f_display
sys.stdout.flush()
if verbose:
print 'loop broken'
def phase_detection(self):
self.solids = []
self.detect = []
self.liquid_density = list()
self.solid_density = list()
self.solid_fraction = list()
self.liquid_fraction = list()
self.solid_molecular_order = list()
self.liquid_molecular_order = list()
self.solid_local_bond4 = list()
self.solid_local_bond6 = list()
self.solid_local_mole4 = list()
self.solid_polar = list()
self.solid_polar_fraction = list()
self.radi_polar = list()
self.radi_polar_fraction = list()
self.xys = dict()
self.final_id = dict()
self.vor_area = list()
self.vornoi_history = list()
self.inter_boundary_number = list()
#self.liquid_vor_area = np.empty(0)
plot_number = 0
sorted_keys = sorted(self.config_vdata.keys())
for startframe in sorted_keys:
#pids = v_data.keys()
v_data = self.config_vdata[startframe]
qualify_id, order_para_mean, vr_mean, vomega_list = self.single_config_detect(v_data)
self.detect.append(len(qualify_id))
qualify_id_set = set(qualify_id)
order_mask, vr_mask, vomega_mask = self.solid_criteria(order_para_mean, vr_mean, vomega_list)
fdata = helpy.load_framesets(v_data)
# find the frame where contains all the qualified particles
count = 0
while (count < 49) & (not set(fdata[startframe+count]['t']).issuperset(qualify_id_set)):
count+=1
if count == 49:
break
startframe += count
# for the idtentified frame, make TRUE if t in qualified_id
fdata_track = fdata[startframe]['t']
track_mask = list()
for t in fdata_track:
track_mask.append(t in qualify_id)
track_mask = np.asarray(track_mask)
#build KDTree to query the nearest neighbor
xys = helpy.consecutive_fields_view(fdata[startframe][track_mask], 'xy')
ors = helpy.consecutive_fields_view(fdata[startframe][track_mask], 'o')
disp = xys - [self.x0, self.y0] # displacement to the center
radial = np.hypot(*disp.T)
criteria = self.R - 1.4*self.side_len
radial_mask = radial >= criteria
#switch x, y coordinate into the regular orientation
xys = xys[:,::-1]
xys[:,1] = 1024 - xys[:,1]
self.xys[startframe] = xys
ftree = KDTree(xys, leafsize = 16)
#####################################################################
# 1st iteration,
# find at least two particles within 1.5 particle size radius
# 3 of your neighbor must satisfy vr_criteria.
# need to meet vomega criteria
#####################################################################
final_mask = []
for pt_id in range(len(xys)):
if not vr_mask[pt_id]:
final_mask.append(False)
continue
dists, ids = ftree.query(xys[pt_id], self.nnn)
#if np.all(dists < self.side_len * 2.0):
if np.sum(dists < self.side_len*1.5) > 2:
final_mask.append(np.sum(vr_mask[ids]) > 3)
else:
final_mask.append(False)
temp_mask = np.array(final_mask) & np.array(vomega_mask)
##############################################################################
# if you neighbors qualified then you will be solid ,exclude detection error
#
# qualified_id is a True or False mask
##############################################################################
qualified_solid = list()
for pt_id in range(len(xys)):
dists, ids = ftree.query(xys[pt_id], self.nnn)
qualified_solid.append(temp_mask[pt_id] or np.sum(temp_mask[ids[1:]]) >= 3)
self.final_id[startframe] = qualified_solid
solid_number = np.sum(qualified_solid)
self.solids.append(solid_number)
plot_vor = startframe < 550
voronoi = self.density_calculation(solid_number, len(qualify_id), xys, ors, disp,\
qualified_solid, plot_vor, radial_mask)
#print(qualified_solid)
self.liquid_density.append(voronoi.liquid_density)
self.solid_density.append(voronoi.solid_density)
self.solid_local_bond4.append(np.nanmean(voronoi.solid_local_bond4))
self.solid_local_bond6.append(np.nanmean(voronoi.solid_local_bond6))
self.solid_local_mole4.append(np.nanmean(voronoi.solid_local_mole4))
self.solid_polar.append(np.nanmean(voronoi.solid_polar))
self.solid_polar_fraction.append(voronoi.solid_polar_fraction)
self.radi_polar.append(np.nanmean(voronoi.radius_polar))
self.radi_polar_fraction.append(voronoi.R_polar_fraction)
self.inter_boundary_number.append(voronoi.interface_number)
self.vornoi_history.append(voronoi)
if plot_number < self.plot_check:
xs = helpy.consecutive_fields_view(fdata[startframe][track_mask],'x')
ys = helpy.consecutive_fields_view(fdata[startframe][track_mask],'y')
self.plot_check_solid(xs, ys, vr_mean, vr_mask, order_para_mean, \
order_mask,vomega_list, vomega_mask, final_mask,\
qualified_solid)
plot_number += 1
self.save_phase()
return len(qualify_id)
def get_angles_loop(pdata, cdata, pfsets, cfsets, cftrees, nc=3, rc=11, drc=0, do_average=True, verbose=False):
""" get_angles(pdata, cdata, pfsets, cfsets, cftrees, nc=3, rc=11, drc=0, do_average=True)
arguments:
pdata - data array with 'x' and 'y' fields for particle centers
cdata - data array wity 'x' and 'y' fields for corners
(these arrays need not have the same length,
but both must have 'f' field for the image frame)
pfsets - slices into pdata, by frame
cfsets - and for cdata
cftrees - dict of KDTrees for corners, by frame
nc - number of corner dots
rc - distance between center and corner dot
drc - tolerance for rc
do_average - whether to average the nc corners to one value for return
returns:
odata - array with fields:
'orient' for orientation of particles
'corner' for particle corner (with 'x' and 'y' sub-fields)
(odata has the same shape as data)
"""
import helpy
if do_average or nc == 1:
dt = [('corner',float,(nc,2)),
('orient',float),
('cdisp',float,(nc,))]
elif nc > 1:
dt = [('corner',float,(nc,2,)),
('orient',float,(nc,)),
('cdisp',float,(nc,))]
odata = np.full(len(pdata), np.nan, dtype=dt)
odata_corner = odata['corner']
odata_orient = odata['orient']
odata_cdisp = odata['cdisp']
full_ids = pdata['id']
id_ok = full_ids[0]==0 and np.all(np.diff(full_ids)==1)
print_freq = len(pfsets)//(100 if verbose>1 else 5) + 1
if verbose:
print 'seeking orientations'
for f in pfsets:
if verbose and not f % print_freq:
print f,
fpdata = pfsets[f]
fcdata = cfsets[f]
tree = cftrees[f]
positions = helpy.consecutive_fields_view(fpdata, 'xy')
cpositions = helpy.consecutive_fields_view(fcdata, 'xy')
frame_ids = helpy.quick_field_view(fpdata, 'id')
for frame_id, posi in izip(frame_ids, positions):
#TODO could probably be sped up by looping through the output of
# ptree.query_ball_tree(ctree)
corner, orient, disp = \
find_corner(posi, cpositions, tree=tree,
nc=nc, rc=rc, drc=drc, do_average=do_average)
if orient is None: continue
full_id = frame_id if id_ok else np.searchsorted(full_ids, frame_id)
odata_corner[full_id] = corner
odata_orient[full_id] = orient
odata_cdisp[full_id] = disp
if do_average or nc == 1:
mask = np.isfinite(odata['orient'])
elif nc > 1:
mask = np.all(np.isfinite(odata['orient']), axis=1)
return odata, mask
def get_angles(pdata,
cdata,
pfsets,
cfsets,
cftrees,
nc,
rc,
drc=None,
ang=None,
dang=None,
do_average=True,
verbose=False):
"""find the orientations of particles given center and corner positions
Parameters
----------
pdata: data array with 'x' and 'y' fields for particle centers
cdata: data array wity 'x' and 'y' fields for corners
pdata and cdata arrays need not have the same length,
but both must have 'f' field for the image frame)
pfsets: slices into pdata, by frame
cfsets: slices into cdata, by frame
cftrees: dict of KDTrees for corners, by frame
the following arguments are passed to find_corner:
nc: number of corner dots
rc: expected distance between center and corner dot
drc: tolerance for rc, defaults to sqrt(rc)
ang: angular separation between corners (if nc > 1)
dang: tolerance for ang (if None, ang is ignored if nfound == nc, but
is uses to choose best nc of nfound if nfound > nc)
do_average: whether to average the nc corners to one value for return
Returns
-------
odata: structured array, same shape as pdata, with fields:
'corner' for particle corner (with 'x' and 'y' sub-fields)
'orient' for orientation of particles
'cdisp' for the corner - center displacement
"""
dt = [('corner', float, (nc, 2)), ('orient', float),
('cdisp', float, (nc, ))]
if nc > 1 and not do_average:
dt[1] += ((nc, ), ) # give the 'orient' field a shape of (nc,)
odata = np.full(len(pdata), np.nan, dtype=dt)
if ang > pi:
ang = np.radians(ang)
if dang:
dang = np.radians(dang)
odata_corner = odata['corner']
odata_orient = odata['orient']
odata_cdisp = odata['cdisp']
full_ids = pdata['id']
id_ok = full_ids[0] == 0 and np.all(np.diff(full_ids) == 1)
print_freq = len(pfsets) // (100 if verbose > 1 else 5) + 1
print 'seeking orientations'
for f in pfsets:
if verbose and not f % print_freq:
print f,
fpdata = pfsets[f]
fcdata = cfsets[f]
cftree = cftrees[f]
positions = helpy.consecutive_fields_view(fpdata, 'xy')
cpositions = helpy.consecutive_fields_view(fcdata, 'xy')
fp_ids = helpy.quick_field_view(fpdata, 'id')
for fp_id, posi in it.izip(fp_ids, positions):
# TODO could probably be sped up by looping through the output of
# ptree.query_ball_tree(ctree)
corner, orient, disp = \
find_corner(posi, cpositions, nc=nc, rc=rc, drc=drc, ang=ang,
dang=dang, tree=cftree, do_average=do_average)
if orient is None:
continue
full_id = fp_id if id_ok else np.searchsorted(full_ids, fp_id)
odata_corner[full_id] = corner
odata_orient[full_id] = orient
odata_cdisp[full_id] = disp
if do_average or nc == 1:
mask = np.isfinite(odata['orient'])
elif nc > 1:
mask = np.all(np.isfinite(odata['orient']), axis=1)
return odata, mask
def get_angles(pdata, cdata, pfsets, cfsets, cftrees, nc, rc, drc=None,
ang=None, dang=None, do_average=True, verbose=False):
"""find the orientations of particles given center and corner positions
Parameters
----------
pdata: data array with 'x' and 'y' fields for particle centers
cdata: data array wity 'x' and 'y' fields for corners
pdata and cdata arrays need not have the same length,
but both must have 'f' field for the image frame)
pfsets: slices into pdata, by frame
cfsets: slices into cdata, by frame
cftrees: dict of KDTrees for corners, by frame
the following arguments are passed to find_corner:
nc: number of corner dots
rc: expected distance between center and corner dot
drc: tolerance for rc, defaults to sqrt(rc)
ang: angular separation between corners (if nc > 1)
dang: tolerance for ang (if None, ang is ignored if nfound == nc, but
is uses to choose best nc of nfound if nfound > nc)
do_average: whether to average the nc corners to one value for return
Returns
-------
odata: structured array, same shape as pdata, with fields:
'corner' for particle corner (with 'x' and 'y' sub-fields)
'orient' for orientation of particles
'cdisp' for the corner - center displacement
"""
dt = [('corner', float, (nc, 2)),
('orient', float),
('cdisp', float, (nc,))]
if nc > 1 and not do_average:
dt[1] += ((nc,),) # give the 'orient' field a shape of (nc,)
odata = np.full(len(pdata), np.nan, dtype=dt)
if ang > pi:
ang = np.radians(ang)
if dang:
dang = np.radians(dang)
odata_corner = odata['corner']
odata_orient = odata['orient']
odata_cdisp = odata['cdisp']
full_ids = pdata['id']
id_ok = full_ids[0] == 0 and np.all(np.diff(full_ids) == 1)
print_freq = len(pfsets)//(100 if verbose > 1 else 5) + 1
print 'seeking orientations'
for f in pfsets:
if verbose and not f % print_freq:
print f,
fpdata = pfsets[f]
fcdata = cfsets[f]
cftree = cftrees[f]
positions = helpy.consecutive_fields_view(fpdata, 'xy')
cpositions = helpy.consecutive_fields_view(fcdata, 'xy')
fp_ids = helpy.quick_field_view(fpdata, 'id')
for fp_id, posi in it.izip(fp_ids, positions):
# TODO could probably be sped up by looping through the output of
# ptree.query_ball_tree(ctree)
corner, orient, disp = \
find_corner(posi, cpositions, nc=nc, rc=rc, drc=drc, ang=ang,
dang=dang, tree=cftree, do_average=do_average)
if orient is None:
continue
full_id = fp_id if id_ok else np.searchsorted(full_ids, fp_id)
odata_corner[full_id] = corner
odata_orient[full_id] = orient
odata_cdisp[full_id] = disp
if do_average or nc == 1:
mask = np.isfinite(odata['orient'])
elif nc > 1:
mask = np.all(np.isfinite(odata['orient']), axis=1)
return odata, mask
def phase_detection(self):
self.solids = []
self.detect = []
self.liquid_density = list()
self.solid_fraction = list()
self.xys = dict()
self.final_id = dict()
self.solid_vor_area = np.empty(0)
self.liquid_vor_area = np.empty(0)
plot_number = 0
sorted_keys = sorted(self.config_vdata.keys())
for startframe in sorted_keys:
#pids = v_data.keys()
v_data = self.config_vdata[startframe]
qualify_id, order_para_mean, vr_mean, vomega_list = self.single_config_detect(
v_data)
self.detect.append(len(qualify_id))
qualify_id_set = set(qualify_id)
order_mask, vr_mask, vomega_mask = self.solid_criteria(
order_para_mean, vr_mean, vomega_list)
fdata = helpy.load_framesets(v_data)
# find the frame where contains all the qualified particles
count = 0
while (count < 49) & (not set(fdata[startframe + count]
['t']).issuperset(qualify_id_set)):
count += 1
if count == 49:
break
startframe += count
# for the idtentified frame, make TRUE if t in qualified_id
fdata_track = fdata[startframe]['t']
track_mask = list()
for t in fdata_track:
track_mask.append(t in qualify_id)
track_mask = np.asarray(track_mask)
#build KDTree to query the nearest neighbor
xys = helpy.consecutive_fields_view(fdata[startframe][track_mask],
'xy')
disp = xys - [self.x0, self.y0]
radial = np.hypot(*disp.T)
criteria = self.R - 1.4 * self.side_len
radial_mask = radial >= criteria
#switch x, y coordinate into the regular orientation
xys = xys[:, ::-1]
xys[:, 1] = 1024 - xys[:, 1]
self.xys[startframe] = xys
ftree = KDTree(xys, leafsize=16)
final_mask = []
for pt_id in range(len(xys)):
if not vr_mask[pt_id]:
final_mask.append(False)
continue
dists, ids = ftree.query(xys[pt_id], self.nnn)
#if np.all(dists < self.side_len * 2.0):
if np.sum(dists < self.side_len * 1.5) > 2:
final_mask.append(np.sum(vr_mask[ids]) > 3)
else:
final_mask.append(False)
temp_mask = np.array(final_mask) & np.array(vomega_mask)
# if you neighbors qualified then you will be solid ,exclude detection error
qualified_solid = list()
for pt_id in range(len(xys)):
dists, ids = ftree.query(xys[pt_id], self.nnn)
qualified_solid.append(temp_mask[pt_id]
or np.sum(temp_mask[ids[1:]]) >= 3)
self.final_id[startframe] = qualified_solid
solid_number = np.sum(qualified_solid)
self.solids.append(solid_number)
plot_vor = startframe < 550
rho_liquid, rho_solid = self.density_calculation(
solid_number, len(qualify_id), xys, qualified_solid, plot_vor,
radial_mask)
self.liquid_density.append(rho_liquid)
self.solid_fraction.append(float(solid_number) / len(qualify_id))
self.solid_density.append(rho_solid)
if plot_number < self.plot_check:
xs = helpy.consecutive_fields_view(
fdata[startframe][track_mask], 'x')
ys = helpy.consecutive_fields_view(
fdata[startframe][track_mask], 'y')
self.plot_check_solid(xs, ys, vr_mean, vr_mask, order_para_mean, \
order_mask,vomega_list, vomega_mask, final_mask,\
qualified_solid)
plot_number += 1
return len(qualify_id)
