def plot_data(self):
plt.clf() # clear the figure
plt.title("Parent incomes vs. student grade")
plt.plot(self.incomes,
self.grades,
color='orange',
marker='o',
linestyle='')
def displayRetireWRate(month, rates, terms):
plt.figure('retireRate')
plt.clf()
for rate in rates:
xvals, yvals = retire(month, rate, terms)
plt.plot(xvals,
yvals,
label='monthly: ' + str(month) + ' rate of: ' +
str(int(rate * 100)))
plt.legend(loc='upper left')
def displayRetireWMonthlies(monthlies, rate, terms):
plt.figure('retireMonth')
plt.clf()
for monthly in monthlies:
xvals, yvals = retire(
monthly, rate,
terms) # using base and savings list as x and y values
plt.plot(xvals,
yvals,
label='retire with monthly inst of ' + str(monthly))
plt.legend()
def convert_all_to_png(vis_path, out_dir="maps_png", size=None):
units = {
'gas_density': 'Gas Density [g/cm$^3$]',
'Tm': 'Temperature [K]',
'Tew': 'Temperature [K]',
'S': 'Entropy []',
'dm': 'DM Density [g/cm$^3$]',
'v': 'Velocity [km/s]'
}
log_list = ['gas_density']
for vis_file in os.listdir(vis_path):
if ".dat" not in vis_file:
continue
print "converting %s" % vis_file
map_type = re.search('sigma_(.*)_[xyz]', vis_file).group(1)
(image, pixel_size,
axis_values) = read_visualization_data(vis_path + "/" + vis_file,
size)
print "image width in Mpc/h: ", axis_values[-1] * 2.0
x, y = np.meshgrid(axis_values, axis_values)
cmap_max = image.max()
cmap_min = image.min()
''' plotting '''
plt.figure(figsize=(5, 4))
if map_type in log_list:
plt.pcolor(x, y, image, norm=LogNorm(vmax=cmap_max, vmin=cmap_min))
else:
plt.pcolor(x, y, image, vmax=cmap_max, vmin=cmap_min)
cbar = plt.colorbar()
if map_type in units.keys():
cbar.ax.set_ylabel(units[map_type])
plt.axis(
[axis_values[0], axis_values[-1], axis_values[0], axis_values[-1]])
del image
plt.xlabel(r"$Mpc/h$", fontsize=18)
plt.ylabel(r"$Mpc/h$", fontsize=18)
out_file = vis_file.replace("dat", "png")
plt.savefig(out_dir + "/" + out_file, dpi=150)
plt.close()
plt.clf()
def displayRetireWMonthsandRates(monthlies, rates, terms):
plt.figure('retire both')
plt.clf()
plt.xlim(30 * 12,
40 * 12) # focusing only on the last 10 years of investment
for monthly in monthlies:
for rate in rates:
xvals, yvals = retire(monthly, rate, terms)
plt.plot(xvals,
yvals,
label='retire with ' + str(monthly) + ":" +
str(int(rate * 100)))
plt.legend(loc='upper left')
def convert_all_to_png(vis_path, out_dir = "maps_png", size = None) :
units = { 'gas_density' : 'Gas Density [g/cm$^3$]',
'Tm' : 'Temperature [K]',
'Tew' : 'Temperature [K]',
'S' : 'Entropy []',
'dm' : 'DM Density [g/cm$^3$]',
'v' : 'Velocity [km/s]' }
log_list = ['gas_density']
for vis_file in os.listdir(vis_path) :
if ".dat" not in vis_file :
continue
print "converting %s" % vis_file
map_type = re.search('sigma_(.*)_[xyz]', vis_file).group(1)
(image, pixel_size, axis_values) = read_visualization_data(vis_path+"/"+vis_file, size)
print "image width in Mpc/h: ", axis_values[-1]*2.0
x, y = np.meshgrid( axis_values, axis_values )
cmap_max = image.max()
cmap_min = image.min()
''' plotting '''
plt.figure(figsize=(5,4))
if map_type in log_list:
plt.pcolor(x,y,image, norm=LogNorm(vmax=cmap_max, vmin=cmap_min))
else :
plt.pcolor(x,y,image, vmax=cmap_max, vmin=cmap_min)
cbar = plt.colorbar()
if map_type in units.keys() :
cbar.ax.set_ylabel(units[map_type])
plt.axis([axis_values[0], axis_values[-1],axis_values[0], axis_values[-1]])
del image
plt.xlabel(r"$Mpc/h$", fontsize=18)
plt.ylabel(r"$Mpc/h$", fontsize=18)
out_file = vis_file.replace("dat", "png")
plt.savefig(out_dir+"/"+out_file, dpi=150 )
plt.close()
plt.clf()
def drawAdoptionNetworkMPL(G, fnum=1, show=False, writeFile=None):
"""Draws the network to matplotlib, coloring the nodes based on adoption.
Looks for the node attribute 'adopted'. If the attribute is True, colors
the node a different color, showing adoption visually. This function assumes
that the node attributes have been pre-populated.
:param networkx.Graph G: Any NetworkX Graph object.
:param int fnum: The matplotlib figure number. Defaults to 1.
:param bool show:
:param str writeFile: A filename/path to save the figure image. If not
specified, no output file is written.
"""
Gclean = G.subgraph([n for n in G.nodes() if n not in nx.isolates(G)])
plt.figure(num=fnum, figsize=(6,6))
# clear figure
plt.clf()
# Blue ('b') node color for adopters, red ('r') for non-adopters.
nodecolors = ['b' if Gclean.node[n]['adopted'] else 'r' \
for n in Gclean.nodes()]
layout = nx.spring_layout(Gclean)
nx.draw_networkx_nodes(Gclean, layout, node_size=80,
nodelist=Gclean.nodes(),
node_color=nodecolors)
nx.draw_networkx_edges(Gclean, layout, alpha=0.5) # width=4
# TODO: Draw labels of Ii values. Maybe vary size of node.
# TODO: Color edges blue based on influences from neighbors
influenceEdges = []
for a in Gclean.nodes():
for n in Gclean.node[a]['influence']:
influenceEdges.append((a,n))
if len(influenceEdges)>0:
nx.draw_networkx_edges(Gclean, layout, alpha=0.5, width=5,
edgelist=influenceEdges,
edge_color=['b']*len(influenceEdges))
#some extra space around figure
plt.xlim(-0.05,1.05)
plt.ylim(-0.05,1.05)
plt.axis('off')
if writeFile != None:
plt.savefig(writeFile)
if show:
plt.show()
def dynamic_img_show(img,title_str='',fig_size=[14,8],hide_axes=True):
'''Show image <img>. If called repeatedly within a cycle will dynamically redraw image.
#DEMO
import time
for i in range(10):
img = np.zeros([50,50])
img[:i*5]=1
dynamic_img_show(img,'iter=%s'%i)
time.sleep(0.1)
'''
plt.clf()
plt.title(title_str)
plt.imshow(img)
plt.xticks([]); plt.yticks([]);
plt.gcf().set_size_inches(fig_size)
display.display(plt.gcf())
display.clear_output(wait=True)
def displayRetireWMonthsandRates2(monthlies, rates, terms):
plt.figure('retire better')
plt.clf()
plt.xlim(30 * 12, 40 * 12)
monthLabels = ['r', 'b', 'g', 'k']
rateLabels = ['-', 'o', '^']
for i in range(len(monthlies)):
monthly = monthlies[i]
monthLabel = monthLabels[i % len(
monthLabels
)] # using remainder to pick new label for each new month choice
for j in range(len(rates)):
rate = rates[j]
rateLable = rateLabels[j % len(
rateLabels)] # if more months, cycles back to the beginning
xvals, yvals = retire(monthly, rate, terms)
plt.plot(xvals,
yvals,
monthLabel + rateLable,
label='retire: ' + str(monthly) + " : " +
str(int(rate * 100)))
plt.legend(loc="upper left")
xx = xx.astype(np.float)
yy = yy.astype(np.float)
dimx = float(dimx)
dimy=float(dimy)
nTimesInX = np.floor(xx / M).max() + 1
seg_cpu = np.floor(yy / M) * nTimesInX + np.floor(xx / M)
seg_cpu = seg_cpu.astype(np.int32)
return seg_cpu
def random_permute_seg(seg):
p=np.random.permutation(seg.max()+1)
seg2 = np.zeros_like(seg)
for c in range(seg.max()+1):
seg2[seg==c]=p[c]
return seg2.astype(np.int32)
if __name__ == "__main__":
tic = time.clock()
seg= get_init_seg(500, 500,17,True)
# seg= get_init_seg(512, 512,50,False)
toc = time.clock()
print toc-tic
print 'k = ', seg.max()+1
plt.figure(1)
plt.clf()
plt.imshow(seg,interpolation="Nearest")
plt.axis('scaled')
yy = yy.astype(np.float)
dimx = float(dimx)
dimy = float(dimy)
nTimesInX = np.floor(xx / M).max() + 1
seg_cpu = np.floor(yy / M) * nTimesInX + np.floor(xx / M)
seg_cpu = seg_cpu.astype(np.int32)
return seg_cpu
def random_permute_seg(seg):
p = np.random.permutation(seg.max() + 1)
seg2 = np.zeros_like(seg)
for c in range(seg.max() + 1):
seg2[seg == c] = p[c]
return seg2.astype(np.int32)
if __name__ == "__main__":
tic = time.clock()
seg = get_init_seg(500, 500, 17, True)
# seg= get_init_seg(512, 512,50,False)
toc = time.clock()
print(toc - tic)
print('k = ', seg.max() + 1)
plt.figure(1)
plt.clf()
plt.imshow(seg, interpolation="Nearest")
plt.axis('scaled')
def fit(self, use_prior=False, proposal_scale=0.001, use_local=False):
nLevels = self.nLevels
tw = self.tw
sigma_lm = self.sigma_lm
self.use_local = use_local
inference_record = Bunch()
inference_record.nLevels = nLevels
inference_record.tw_args = tw.args
inference_record.steps = []
inference_record.use_prior = use_prior
inference_record.proposal_scale = proposal_scale
inference_record.sigma_lm = sigma_lm
try:
run_lengths = self.run_lengths
except AttributeError:
self.set_run_lengths([500] * self.nLevels)
run_lengths = self.run_lengths
# raise Exception("self.set_run_lengths was not called yet")
for i in range(nLevels):
# for i in range(nLevels+5):
if i < nLevels:
level = i
if level == 0:
theta = tw.ms.L_cpa_space[level].get_zeros_theta()
else:
theta_fine = tw.ms.L_cpa_space[level].get_zeros_theta()
tw.ms.propogate_theta_coarse2fine(theta_coarse=theta,
theta_fine=theta_fine)
theta = theta_fine
_sigma_lm = sigma_lm
else:
_sigma_lm *= 0.9
print '-' * 10, 'level', level, '-' * 10
cpa_space = tw.ms.L_cpa_space[level]
print cpa_space
data = {
'src': self.src,
'dst': self.dst,
'transformed': self.transformed
}
if use_prior:
lp_func = LP(ms=tw.ms,
msp=tw.msp,
level=level,
SDLP=SDLP,
required={})
else:
lp_func = None
sampler = Metropolis(
ll_func=LL(
ms=tw.ms,
level=level,
SDLL=SDLL,
data=data,
required={
'sigma_lm': _sigma_lm,
'params_flow_int': tw.params_flow_int_coarse
},
),
proposal=Proposal(ms=tw.ms,
msp=tw.msp,
level=level,
scale=proposal_scale,
use_local=use_local),
# proposal=ProposalSphericalGaussian(ms=tw.ms,
# level=level,
# scale=0.1 / (1.2**level)
# scale=0.01,
# scale=0.1 / (1.1**level)
# scale=0.1
# ) ,
lp_func=lp_func,
wlp=self.wlp)
sampler.set_theta(theta)
run_length = run_lengths[level]
sampler.run(run_length)
theta = sampler.theta_current # prepare for next iteration
inference_record.steps.append(sampler.get_record())
if i >= nLevels - 1:
plt.figure(i + 1)
plt.clf()
self.disp(sampler=sampler)
inference_record.theta = theta.copy()
steps = inference_record.steps
nAccepted = [step.nAccepted for step in steps]
run_lengths = [step.N for step in steps]
times = [step.runs[0].time for step in steps]
total_time = sum(times)
print "run_lengths:", run_lengths
print "nAccepted:", nAccepted
print 'times:'
print_iterable(times)
print 'time total:', total_time
return theta.copy(), inference_record
def main(data, infernece_params, dispOn):
img1 = data.img1 # src
img2 = data.img2 # dst
if img1.shape != img2.shape:
raise ValueError(img1.shape, img2.shape)
nRows, nCols = img1.shape[:2]
nPts = nRows * nCols
reg = Register(nRows=nRows,
nCols=nCols,
base=inference_params.base,
nLevels=inference_params.nLevels,
tess='I',
zero_v_across_bdry=[False, False],
sigma_signal=inference_params.sigma_signal,
scale_spatial=inference_params.scale_spatial,
scale_value=inference_params.scale_value,
wlp=inference_params.wlp,
ll_type=inference_params.ll_type,
only_local=False,
valid_outside=infernece_params.valid_outside)
reg.set_dense(domain_start=0, domain_end=nPts)
reg.set_data(x=reg.tw.pts_src_dense,
signal_src=img1,
signal_dst=img2,
isbinary=inference_params.isbinary)
print reg
# reg.set_run_lengths([50000]*reg.nLevels)
reg.set_run_lengths([infernece_params.MCMCniters_per_level] * reg.nLevels)
# if inside_spyder():
# reg.set_run_lengths([100]*reg.nLevels)
# # reg.set_run_lengths([50000]*reg.nLevels)
# else:
# reg.set_run_lengths(run_lengths)
theta_est, inference_record = reg.fit(
use_prior=infernece_params.use_prior,
use_local=infernece_params.use_local,
dispOn=dispOn,
#interp_type_for_ll=cv2.INTER_LANCZOS4,
interp_type_for_ll='gpu_linear',
interp_type_during_visualization=cv2.INTER_LANCZOS4)
if dispOn:
plt.figure(1000)
plt.clf()
reg.plot_inference_summary(inference_record)
reg.transformed.gpu2cpu()
reg.signal.transformed.gpu2cpu()
inference_record.transformed = reg.transformed.cpu
inference_record.src = reg.src.cpu
inference_record.signal = Bunch()
inference_record.signal.src = reg.signal.src.cpu
inference_record.signal.dst = reg.signal.dst.cpu
inference_record.signal.transformed = reg.signal.transformed.cpu
# if TF.dump_results:
# Pkl.dump(results_filename,inference_record,create_dir_if_needed=1,override=1)
return reg, inference_record, theta_est
def example(base=[5],
scale_spatial=100,
nLevels=2,
zero_v_across_bdry=[1],
use_local_basis=True):
nPtsDense = 10000
tw = TransformWrapper(nCols=100,
nLevels=nLevels,
base=base,
scale_spatial=scale_spatial,
nPtsDense=nPtsDense,
zero_v_across_bdry=zero_v_across_bdry)
print_iterable(tw.ms.L_cpa_space)
seed=0
np.random.seed(seed)
for level in range(tw.ms.nLevels):
cpa_space = tw.ms.L_cpa_space[level]
Avees = cpa_space.Avees
velTess = cpa_space.zeros_velTess()
if use_local_basis:
if 0:
tw.sample_gaussian_velTess(level,Avees,velTess,mu=None)
Avees*=0.001
velTess*=0.001
else:
if not zero_v_across_bdry[0]:
velTess[:]=10*np.random.standard_normal(velTess.shape)
cpa_space.velTess2Avees(velTess=velTess,Avees=Avees)
else:
velTess[1:-1]=10*np.random.standard_normal(velTess[1:-1].shape)
cpa_space.velTess2Avees(velTess=velTess,Avees=Avees)
cpa_space.velTess2Avees(velTess=velTess,Avees=Avees)
else:
theta= cpa_space.get_zeros_theta()
tw.sample_gaussian(level,Avees,theta,mu=None)
# theta/=10
cpa_space.theta2Avees(theta=theta,Avees=Avees)
# This step is important and must be done
# before are trying to "use" the new values of
# the (vectorized) A's.
tw.update_pat_from_Avees(Avees,level)
pts_src = tw.x_dense
tw.calc_v(level=level,pts=pts_src,v=tw.v_dense)
tw.v_dense.gpu2cpu()
pts_fwd = CpuGpuArray.zeros_like(pts_src) # Create a buffer for the output
tw.calc_T_fwd(pts_src,pts_fwd,level=level)
pts_fwd.gpu2cpu()
pts_inv = CpuGpuArray.zeros_like(pts_src) # Create a buffer for the output
tw.calc_T_inv(pts_src,pts_inv,level=level)
pts_inv.gpu2cpu()
plt.figure(level)
plt.clf()
interval = pts_src.cpu # interval doesn't have to be pts_src.cpu
Visualize.simple(tw.x_dense,tw.v_dense,interval,pts_src,
transformed_fwd=pts_fwd,transformed_inv=pts_inv,
cpa_space=cpa_space)
return tw
def main(data,infernece_params,dispOn ):
img1=data.img1 # src
img2=data.img2 # dst
if img1.shape != img2.shape:
raise ValueError(img1.shape,img2.shape)
nRows,nCols = img1.shape[:2]
nPts = nRows * nCols
reg = Register(nRows=nRows,
nCols=nCols,
base=inference_params.base,
nLevels=inference_params.nLevels, tess='I',
zero_v_across_bdry=[False,False],
sigma_signal=inference_params.sigma_signal ,
scale_spatial=inference_params.scale_spatial,
scale_value=inference_params.scale_value,
wlp=inference_params.wlp,
ll_type=inference_params.ll_type,
only_local=False,
valid_outside=infernece_params.valid_outside)
reg.set_dense(domain_start=0,domain_end=nPts)
reg.set_data(x=reg.tw.pts_src_dense,
signal_src=img1,signal_dst=img2,
isbinary=inference_params.isbinary)
print reg
# reg.set_run_lengths([50000]*reg.nLevels)
reg.set_run_lengths([infernece_params.MCMCniters_per_level]*reg.nLevels)
# if inside_spyder():
# reg.set_run_lengths([100]*reg.nLevels)
# # reg.set_run_lengths([50000]*reg.nLevels)
# else:
# reg.set_run_lengths(run_lengths)
theta_est,inference_record = reg.fit(use_prior=infernece_params.use_prior,
use_local=infernece_params.use_local,
dispOn=dispOn,
#interp_type_for_ll=cv2.INTER_LANCZOS4,
interp_type_for_ll= 'gpu_linear',
interp_type_during_visualization=cv2.INTER_LANCZOS4)
if dispOn:
plt.figure(1000)
plt.clf()
reg.plot_inference_summary(inference_record)
reg.transformed.gpu2cpu()
reg.signal.transformed.gpu2cpu()
inference_record.transformed = reg.transformed.cpu
inference_record.src = reg.src.cpu
inference_record.signal = Bunch()
inference_record.signal.src = reg.signal.src.cpu
inference_record.signal.dst = reg.signal.dst.cpu
inference_record.signal.transformed = reg.signal.transformed.cpu
# if TF.dump_results:
# Pkl.dump(results_filename,inference_record,create_dir_if_needed=1,override=1)
return reg,inference_record,theta_est
transformed.gpu2cpu()
v_dense.gpu2cpu()
if 0:
plt.figure(17)
for c,A in enumerate(As):
_x = np.ones((2,100))
m=cpa_space.cells_verts[c,0,0]
M=cpa_space.cells_verts[c,1,0]
_x[0] = np.linspace(m,M,100)
_v = A.dot(_x).flatten()
plt.plot(_x[0],_v)
if 1:
# plt.figure()
plt.figure(1);plt.clf()
plt.subplot(231)
plt.plot(interval,src.cpu)
plt.title('src')
plt.subplot(232)
# plt.plot(interval[1:],np.diff(src)/(interval[1]-interval[0]))
dx=interval[1]-interval[0]
plt.plot(interval[1:],np.diff(src.cpu.ravel())/dx)
plt.title(" d/dx src")
plt.ylim(0,.5)
plt.subplot(233)
plt.plot(np.linspace(cpa_space.XMINS[0],cpa_space.XMAXS[0],
interval.size),v_dense.cpu.ravel())
plt.ylim(-1,1)
plt.title('velocity')
plt.subplot(234)
def fit(self,use_prior=False,proposal_scale=0.001,use_local=True,dispOn=True,
interp_type_for_ll=None,
interp_type_during_visualization=None,
scale_local_proposal=None):
nLevels=self.nLevels
tw = self.tw
sigma_signal = self.sigma_signal
self.use_local = use_local
inference_record = Bunch()
inference_record.tw_args = tw.args
inference_record.steps = []
inference_record.use_prior = use_prior
inference_record.proposal_scale= proposal_scale
inference_record.sigma_signal = sigma_signal
try:
run_lengths = self.run_lengths
except AttributeError:
self.set_run_lengths([500]*self.nLevels)
run_lengths = self.run_lengths
# raise Exception("self.set_run_lengths was not called yet")
wlp=self.wlp
for i in range(nLevels):
# for i in range(nLevels+5):
if i<nLevels:
level=i
if level == 0:
theta = tw.ms.L_cpa_space[level].get_zeros_theta()
else:
theta_fine = tw.ms.L_cpa_space[level].get_zeros_theta()
tw.ms.propogate_theta_coarse2fine(theta_coarse=theta,theta_fine=theta_fine)
theta = theta_fine
_sigma_signal = sigma_signal
else:
_sigma_signal *= 0.9
print '-'*10 ,'level',level, '-'*10
cpa_space = tw.ms.L_cpa_space[level]
print cpa_space
data = {'src':self.src,'transformed':self.transformed,
'signal':self.signal}
if use_prior:
lp_func = LP(ms=tw.ms,msp=tw.msp,level=level,SDLP=SDLP,
required={})
else:
lp_func = None
sampler = Metropolis(ll_func= LL(
ms=tw.ms,level=level,SDLL=self.SDLL,
data=data,
required={'sigma_signal':_sigma_signal,
'params_flow_int':tw.params_flow_int_coarse,
'interp_type_for_ll':interp_type_for_ll} ),
proposal=Proposal(ms=tw.ms,msp=tw.msp,level=level,
scale=proposal_scale,
use_local=use_local,
scale_local_proposal=scale_local_proposal),
# proposal=ProposalSphericalGaussian(ms=tw.ms,
# level=level,
# scale=0.1 / (1.2**level)
# scale=0.01,
# scale=0.1 / (1.1**level)
# scale=0.1
# ) ,
lp_func=lp_func,
wlp=wlp
)
sampler.set_theta(theta)
run_length = run_lengths[level]
sampler.run(run_length)
theta = sampler.theta_current # prepare for next iteration
inference_record.steps.append(sampler.get_record())
if dispOn:
if i >= nLevels-1 or 1:
plt.figure(i+1)
plt.clf()
self.disp(sampler=sampler,
interp_type_during_visualization=interp_type_during_visualization)
inference_record.theta = theta.copy()
steps = inference_record.steps
nAccepted = [ step.nAccepted for step in steps]
run_lengths = [ step.N for step in steps]
times = [ step.runs[0].time for step in steps]
total_time = sum(times)
print "run_lengths:",run_lengths
print "nAccepted:",nAccepted
print 'times:'
print_iterable(times)
print 'time total:',total_time
return theta.copy(),inference_record
def example(base=[5],
scale_spatial=100,
nLevels=2,
zero_v_across_bdry=[1],
use_local_basis=True):
nPtsDense = 10000
tw = TransformWrapper(nCols=100,
nLevels=nLevels,
base=base,
scale_spatial=scale_spatial,
nPtsDense=nPtsDense,
zero_v_across_bdry=zero_v_across_bdry)
print_iterable(tw.ms.L_cpa_space)
seed = 0
np.random.seed(seed)
for level in range(tw.ms.nLevels):
cpa_space = tw.ms.L_cpa_space[level]
Avees = cpa_space.Avees
velTess = cpa_space.zeros_velTess()
if use_local_basis:
if 0:
tw.sample_gaussian_velTess(level, Avees, velTess, mu=None)
Avees *= 0.001
velTess *= 0.001
else:
if not zero_v_across_bdry[0]:
velTess[:] = 10 * np.random.standard_normal(velTess.shape)
cpa_space.velTess2Avees(velTess=velTess, Avees=Avees)
else:
velTess[1:-1] = 10 * np.random.standard_normal(
velTess[1:-1].shape)
cpa_space.velTess2Avees(velTess=velTess, Avees=Avees)
cpa_space.velTess2Avees(velTess=velTess, Avees=Avees)
else:
theta = cpa_space.get_zeros_theta()
tw.sample_gaussian(level, Avees, theta, mu=None)
# theta/=10
cpa_space.theta2Avees(theta=theta, Avees=Avees)
# This step is important and must be done
# before are trying to "use" the new values of
# the (vectorized) A's.
tw.update_pat_from_Avees(Avees, level)
pts_src = tw.x_dense
tw.calc_v(level=level, pts=pts_src, v=tw.v_dense)
tw.v_dense.gpu2cpu()
pts_fwd = CpuGpuArray.zeros_like(
pts_src) # Create a buffer for the output
tw.calc_T_fwd(pts_src, pts_fwd, level=level)
pts_fwd.gpu2cpu()
pts_inv = CpuGpuArray.zeros_like(
pts_src) # Create a buffer for the output
tw.calc_T_inv(pts_src, pts_inv, level=level)
pts_inv.gpu2cpu()
plt.figure(level)
plt.clf()
interval = pts_src.cpu # interval doesn't have to be pts_src.cpu
Visualize.simple(tw.x_dense,
tw.v_dense,
interval,
pts_src,
transformed_fwd=pts_fwd,
transformed_inv=pts_inv,
cpa_space=cpa_space)
return tw
