def cluster_points(r, z):
R, Z = geom.pointloop(r, z)
dX = norm([r[1:] - r[:-1], z[1:] - z[:-1]], axis=0)
dx_median = sp.median(dX)
cluster, i = OrderedDict(), count(0)
for r, z in zip(R, Z):
dx = []
for cl in cluster:
rc, zc = cluster[cl]['r'], cluster[cl]['z']
dx.append(np.min(norm([r - rc, z - zc], axis=0)))
if len(dx) == 0 or np.min(dx) > 2 * dx_median: # new
cl = 'group{:1.0f}'.format(next(i))
cluster[cl] = {}
cluster[cl] = {'r': [r], 'z': [z]}
else:
icl = np.argmin(dx)
cl = list(cluster.keys())[icl]
cluster[cl]['r'] = np.append(cluster[cl]['r'], r)
cluster[cl]['z'] = np.append(cluster[cl]['z'], z)
for cl in cluster:
r, z = cluster[cl]['r'], cluster[cl]['z']
dx = norm([r[:1] - r[:-1], z[:1] - z[:-1]], axis=0)
imax = np.argmax(dx) + 1
r = np.append(r[imax:], r[:imax])
z = np.append(z[imax:], z[:imax])
cluster[cl]['r'], cluster[cl]['z'] = r, z
return cluster
def process(self):
for part in self.parts:
x = {
'in': {
'r': np.array([]),
'z': np.array([])
},
'out': {
'r': np.array([]),
'z': np.array([])
},
'ports': {
'r': np.array([]),
'z': np.array([])
},
'r': np.array([]),
'z': np.array([])
}
for loop, side in zip(self.parts[part], ['out', 'in', 'ports']):
r, z = geom.read_loop(self.parts[part], loop)
x[side]['r'], x[side]['z'] = r, z
if part in ['TF_Coil', 'Vessel', 'Blanket']:
if side != 'out':
x['r'], x['z'] = geom.polyloop(x['in'], x['out'])
else:
x['r'], x['z'] = geom.pointloop(x['out']['r'],
x['out']['z'])
lines = cutcorners(x['r'], x['z']) # select halfs
for seg, side in zip(lines, ['in', 'out']):
x[side] = {'r': lines[seg]['r'], 'z': lines[seg]['z']}
for key in x:
self.parts[part][key] = x[key]
def cutcorners(r, z):
r, z = geom.pointloop(r, z, ref='min') # form loop from closest neighbour
n = len(r) - 1
dR = np.array([r[1:] - r[:-1], z[1:] - z[:-1]])
dR = np.array([np.gradient(r), np.gradient(z)])
dL = norm(dR, axis=0)
k, kflag = count(0), False
lines = OrderedDict()
segment = add_segment(lines, k)
for i in range(n):
kink = np.arccos(np.dot(dR[:, i] / dL[i],
dR[:, i + 1] / dL[i + 1])) * 180 / np.pi
append_value(lines, segment, r[i + 1], z[i + 1])
if abs(kink) > 40 and kflag == False: # angle, deg
segment = add_segment(lines, k)
append_value(lines, segment, r[i + 1], z[i + 1])
kflag = True
else:
kflag = False
segments = list(lines.keys())
l = np.zeros(len(segments))
for i, seg in enumerate(segments):
l[i] = len(lines[seg]['r'])
seg = np.argsort(l)[-2:] # select loops (in/out)
rmax = np.zeros(len(seg))
for i, s in enumerate(seg):
rmax[i] = np.max(lines[segments[s]]['r'])
seg = seg[np.argsort(rmax)]
lines_sort = OrderedDict()
for s in seg[:2]:
lines_sort[segments[s]] = lines[segments[s]]
return lines_sort
rb.trim_sol()
filename = trim_dir('../../Data/') + 'CATIA_FW.xlsx'
wb = load_workbook(filename=filename, read_only=True, data_only=True)
ws = wb[wb.get_sheet_names()[0]]
FW = {}
for col, var in zip([5, 6], ['r', 'z']):
row = ws.columns[col]
FW[var] = np.zeros(len(row) - 1)
for i, r in enumerate(row[1:]):
try:
FW[var][i] = 1e-3 * float(r.value) # m
except:
break
r, z = geom.pointloop(FW['r'], FW['z'], ref='min')
pl.plot(r[:-1], z[:-1])
'''
self.parts = OrderedDict() # component parts
part,loop = [],[]
for row in ws.columns:
new_part = get_label(row[0].value,part)
if new_part:
self.parts[part[-1]] = OrderedDict()
if len(part) == 0:
continue
new_loop = get_label(row[1].value,loop,force=new_part,part=part[-1])
p,l = part[-1],loop[-1]
if new_loop:
self.parts[p][l] = OrderedDict()
'''