def draw(self, label_fmt=None):
"Visualize points with interactive scatter plot browser."
if not self.points:
print('[warn] ConvexHull is empty.')
return
import scipy.spatial
points = np.array([(p.x, p.y) for p in self.points])
hull = scipy.spatial.ConvexHull(points)
for simplex in hull.simplices:
pl.plot(points[simplex, 0],
points[simplex, 1],
'k-',
zorder=-1,
alpha=0.5,
lw=.5)
for p in self:
pl.scatter(p.x, p.y, c='r', alpha=0.5, zorder=-1)
pl.box(False)
pl.xticks([p.x for p in self], rotation='vertical')
pl.yticks([p.y for p in self])
for p in self:
pl.text(x=p.x,
y=p.y,
s=str(p.d) if label_fmt is None else label_fmt(p))
def fitzpatrick(input,wavelength=True,plot=False):
if wavelength:
x = (input/ 10**4.)**-1. # convert from angstroms to micrometers
else:
x = input
''' for R_v = 3.1 '''
wavelength = [0.000,0.377,0.820,1.667,1.828,2.141,2.433,3.704,3.846]
ratio = [0.000,0.265,0.829,2.688,3.055,3.806,4.315,6.265,6.591]
#wavelength = [1.667,1.828,2.141,2.433]
#ratio = [2.688,3.055,3.806,4.315]
import scipy
from scipy import interpolate
fitzSpline = scipy.interpolate.interp1d(wavelength,ratio,
kind='cubic') #, bounds_error=False)
if plot:
import pylab, scipy
pylab.clf()
x_range = scipy.arange(wavelength[0],wavelength[-1],0.01)
pylab.plot(x_range,fitzSpline(x_range))
pylab.scatter(wavelength,ratio)
pylab.box()
pylab.xlim([0,4])
pylab.savefig('/Users/pkelly/Dropbox/spline.png')
''' normalized so that A_1 um = 1 mag '''
A = fitzSpline(x) / fitzSpline(1.)
return A
def gui_repr(self):
"""Generate a GUI to represent the sentence alignments
"""
if __pylab_loaded__:
fig_width = max(len(self.sent1), len(self.sent2)) + 1
fig_height = 3
pylab.figure(figsize=(fig_width*0.8, fig_height*0.8), facecolor='w')
pylab.box(on=False)
pylab.subplots_adjust(left=0, right=1, bottom=0, top=1)
pylab.xlim(-1, fig_width - 1)
pylab.ylim(0, fig_height)
pylab.xticks([])
pylab.yticks([])
for i in xrange(len(self.sent1)):
pylab.text(i, 2.5, self.sent1[i], ha = 'center', va = 'center',
rotation=30)
if len(self.sent1_index[i]) > 0:
pylab.arrow(i, 2.5, 0, -0.5, color='r', alpha=0.3, lw=2)
for j in self.sent1_index[i]:
pylab.arrow(i, 2, j - i, -1, color='r')
for i in xrange(len(self.sent2)):
pylab.text(i, 0.5, self.sent2[i], ha = 'center', va = 'center',
rotation=30)
if len(self.sent2_index[i]) > 0:
pylab.arrow(i, 0.5, 0, 0.5, color='r', alpha=0.3, lw=2)
pylab.draw()
def gui_repr(self):
"""Generate a GUI to represent the sentence alignments
"""
if __pylab_loaded__:
fig_width = max(len(self.text_e), len(self.text_f)) + 1
fig_height = 3
pylab.figure(figsize=(fig_width*0.8, fig_height*0.8), facecolor='w')
pylab.box(on=False)
pylab.subplots_adjust(left=0, right=1, bottom=0, top=1)
pylab.xlim(-1, fig_width - 1)
pylab.ylim(0, fig_height)
pylab.xticks([])
pylab.yticks([])
e = [0 for _ in xrange(len(self.text_e))]
f = [0 for _ in xrange(len(self.text_f))]
for (i, j) in self.align:
e[i] = 1
f[j] = 1
# draw the middle line
pylab.arrow(i, 2, j - i, -1, color='r')
for i in xrange(len(e)):
# draw e side line
pylab.text(i, 2.5, self.text_e[i], ha = 'center', va = 'center',
rotation=30)
if e[i] == 1:
pylab.arrow(i, 2.5, 0, -0.5, color='r', alpha=0.3, lw=2)
for i in xrange(len(f)):
# draw f side line
pylab.text(i, 0.5, self.text_f[i], ha = 'center', va = 'center',
rotation=30)
if f[i] == 1:
pylab.arrow(i, 0.5, 0, 0.5, color='r', alpha=0.3, lw=2)
pylab.draw()
def PlotNumberOfTags(corpus):
word_tag_dict = defaultdict(set)
for (word, tag) in corpus:
word_tag_dict[word].add(tag)
# using Counter for efficiency (leaner than FreqDist)
C = Counter(len(val) for val in word_tag_dict.itervalues())
pylab.subplot(211)
pylab.plot(C.keys(), C.values(), '-go', label='Linear Scale')
pylab.suptitle('Word Ambiguity:')
pylab.title('Number of Words by Possible Tag Number')
pylab.box('off') # for better appearance
pylab.grid('on') # for better appearance
pylab.ylabel('Words With This Number of Tags (Linear)')
pylab.legend(loc=0)
# add value tags
for x,y in zip(C.keys(), C.values()):
pylab.annotate(str(y), (x,y + 0.5))
pylab.subplot(212)
pylab.plot(C.keys(), C.values(), '-bo', label='Logarithmic Scale')
pylab.yscale('log') # to make the graph more readable, for the log graph version
pylab.box('off') # for better appearance
pylab.grid('on') # for better appearance
pylab.xlabel('Number of Tags per Word')
pylab.ylabel('Words With This Number of Tags (Log)')
pylab.legend(loc=0)
# add value tags
for x,y in zip(C.keys(), C.values()):
pylab.annotate(str(y), (x,y + 0.5))
pylab.show()
def vis_result(images, image_t_syn, flow, filename='./result.png'):
'''
Visualize estimated results
images: input images to deep-voxel-flow, should be stack of 2 or 3 images shape(2or3, h, w, 3)
image_t_syn: synthesized image at time slice t, shape(h, w, 3)
flow: sub-estimated optical flow, shape(h, w, 2)
'''
if len(images) == 3:
num_contents = 6
is_triplet = True
elif len(images) == 2:
num_contents = 4
is_triplet = False
else:
raise ValueError('Invalid number of images')
n_cols = num_contents / 2
tick_config = {
'labelbottom': False,
'bottom': False,
'labelleft': False,
'left': False
}
fig = plt.figure(figsize=(8 * 2, 8 * (n_cols / 2)))
for i, image in enumerate(images):
plt.subplot(2, n_cols, i + 1)
plt.imshow(image)
plt.title(f'Image at time slice {["0", "t", "1"][i]}')
plt.tick_params(**tick_config)
plt.xticks([])
box(False)
plt.subplot(2, n_cols, n_cols + 1)
plt.imshow(vis_flow(flow))
plt.title('Estimated optical flow')
plt.tick_params(**tick_config)
plt.xticks([])
box(False)
plt.subplot(2, n_cols, n_cols + 2)
plt.imshow(image_t_syn)
plt.title('Synthesized image at time slice t')
plt.tick_params(**tick_config)
plt.xticks([])
box(False)
if is_triplet:
image_diff = np.mean(np.abs(images[1] - image_t_syn), axis=-1)
plt.subplot(2, n_cols, n_cols + 3)
plt.imshow(image_diff)
plt.title('Diff-map between GT and synthesized frame')
plt.tick_params(**tick_config)
plt.xticks([])
box(False)
plt.tight_layout()
plt.savefig(filename, bbox_inches='tight', pad_inches=0.1)
plt.close()
def show_graph(src):
img = plt.imread(src)
xpixels, ypixels = img.shape[0], img.shape[1]
dpi = 100
margin = 0.01
figsize = (1 + margin) * ypixels / dpi, (1 + margin) * xpixels / dpi
fig = plt.figure(figsize=figsize, dpi=dpi)
ax = fig.add_axes([margin, margin, 1 - 2 * margin, 1 - 2 * margin])
ax.tick_params(labelbottom="off", bottom="off")
ax.tick_params(labelleft="off", left="off")
ax.imshow(img, interpolation='none')
box("off")
plt.show()
def computeInputResistance(segment, Irange, dur, delay, dt=0.005, plot=False):
if plot:
import pylab as p
stim = makeIclamp(segment, dur, 0, delay)
rec = makeRecorders(segment, {'v': '_ref_v'})
ap = makeAPcount(segment)
I = []
V = []
if plot:
p.figure()
p.subplot(1,2,1)
for k,i in enumerate(np.arange(Irange[0],Irange[1],Irange[2])):
ap.n = 0
stim.amp = i
run(2*delay+dur, dt)
t = np.array(rec['t'])
v = np.array(rec['v'])
if ap.n == 0:
idx = np.intersect1d(np.nonzero(t > delay+0.75*dur)[0], np.nonzero(t < delay+dur)[0])
I.append(i)
V.append(np.mean(v[idx]))
else:
print('The neuron emitted spikes at I = %g pA' % (stim.amp*1e3))
if plot:
p.plot(1e-3*t,v)
V = np.array(V)*1e-3
I = np.array(I)*1e-9
poly = np.polyfit(I,V,1)
if plot:
ymin,ymax = p.ylim()
p.plot([1e-3*(delay+0.75*dur),1e-3*(delay+0.75*dur)],[ymin,ymax],'r--')
p.plot([1e-3*(delay+dur),1e-3*(delay+dur)],[ymin,ymax],'r--')
p.xlabel('t (s)')
p.ylabel('V (mV)')
p.box(True)
p.grid(False)
p.subplot(1,2,2)
x = np.linspace(I[0],I[-1],100)
y = np.polyval(poly,x)
p.plot(1e12*x,1e3*y,'k--')
p.plot(1e12*I,1e3*V,'bo')
p.xlabel('I (pA)')
p.ylabel('V (mV)')
p.show()
return poly[0]
def NumberLine(gt, pred, path, label="auto"):
"""
発生年数がどうなってるかを確認したくって
Args
gt: 真値t list[nk,tnk,tk]
pred: 予測値t [perticles,cells]
"""
for cell in np.arange(3):
#pdb.set_trace()
# predict year [perticles,]
x = gt[cell]
xhat = pred[:, cell]
y = [0] * 1 # y = 0
yhat = [0] * xhat.shape[0]
# 数直線 ---------------------------------------------------------------
fig, ax = plt2.subplots(figsize=(10, 10)) #画像サイズ
fig.set_figheight(1) #高さ調整
ax.tick_params(labelbottom=True, bottom=False) #x軸設定
ax.tick_params(labelleft=False, left=False) #y軸設定
# ---------------------------------------------------------------------
# グラフの体裁 -----------------------------------------------------------
#xMin, xMax = np.min(np.append(x,xhat)), np.max(np.append(x,xhat))
xMin, xMax = 0, 1400
plt2.tight_layout() #グラフの自動調整
plt2.hlines(y=0, xmin=xMin, xmax=xMax, color="silver") #横軸
pylab.box(False) #枠を消す
# -----------------------------------------------------------------
# 散布図 -----------------------------------------------------------
plt2.scatter(xhat, yhat, c='skyblue') # 予測値
plt2.scatter(x, y[0], c='coral') # 真値
plt2.title(f"min:{int(np.min(xhat))} max:{int(np.max(xhat))}")
# -----------------------------------------------------------------
myData.isDirectory(path)
plt2.savefig(os.path.join(path, f"{label}_{cellname[cell]}.png"),
bbox_inches="tight")
plt2.close()
def vis_flow_pyramid(flow_pyramid,
flow_gt=None,
images=None,
filename='./flow.png'):
num_contents = len(flow_pyramid) + int(
flow_gt is not None) + int(images is not None) * 2
fig = plt.figure(figsize=(12, 15 * num_contents))
fig_id = 1
if images is not None:
plt.subplot(1, num_contents, fig_id)
plt.imshow(images[0])
plt.tick_params(labelbottom=False, bottom=False)
plt.tick_params(labelleft=False, left=False)
plt.xticks([])
box(False)
fig_id += 1
plt.subplot(1, num_contents, num_contents)
plt.imshow(images[1])
plt.tick_params(labelbottom=False, bottom=False)
plt.tick_params(labelleft=False, left=False)
plt.xticks([])
box(False)
for flow in flow_pyramid:
plt.subplot(1, num_contents, fig_id)
plt.imshow(vis_flow(flow))
plt.tick_params(labelbottom=False, bottom=False)
plt.tick_params(labelleft=False, left=False)
plt.xticks([])
box(False)
fig_id += 1
if flow_gt is not None:
plt.subplot(1, num_contents, fig_id)
plt.imshow(vis_flow(flow_gt))
plt.tick_params(labelbottom=False, bottom=False)
plt.tick_params(labelleft=False, left=False)
plt.xticks([])
box(False)
plt.tight_layout()
plt.savefig(filename, bbox_inches='tight', pad_inches=0.1)
plt.close()
def vis_flow_pyramid(flow_pyramid, flow_gt = None, images = None, filename = './flow.png'):
num_contents = len(flow_pyramid) + int(flow_gt is not None) + int(images is not None)*2
fig = plt.figure(figsize = (12, 15*num_contents))
fig_id = 1
if images is not None:
plt.subplot(1, num_contents, fig_id)
plt.imshow(images[0])
plt.tick_params(labelbottom = False, bottom = False)
plt.tick_params(labelleft = False, left = False)
plt.xticks([])
box(False)
fig_id += 1
plt.subplot(1, num_contents, num_contents)
plt.imshow(images[1])
plt.tick_params(labelbottom = False, bottom = False)
plt.tick_params(labelleft = False, left = False)
plt.xticks([])
box(False)
for flow in flow_pyramid:
plt.subplot(1, num_contents, fig_id)
plt.imshow(vis_flow(flow))
plt.tick_params(labelbottom = False, bottom = False)
plt.tick_params(labelleft = False, left = False)
plt.xticks([])
box(False)
fig_id += 1
if flow_gt is not None:
plt.subplot(1, num_contents, fig_id)
plt.imshow(vis_flow(flow_gt))
plt.tick_params(labelbottom = False, bottom = False)
plt.tick_params(labelleft = False, left = False)
plt.xticks([])
box(False)
plt.tight_layout()
plt.savefig(filename, bbox_inches = 'tight', pad_inches = 0.1)
plt.close()
def adv_convergence(width=2e-2,
delta=1e-2,
relp=1,
Nadapt=10,
use_adapt=True,
problem=3,
outname='',
use_reform=False,
CGorderL=[2, 3],
noplot=False,
Lx=3.):
### SETUP SOLUTION
sy = Symbol('sy')
width_ = Symbol('ww')
if problem == 3:
stepfunc = 0.5 + 165. / 104. / width_ * sy - 20. / 13. / width_**3 * sy**3 - 102. / 13. / width_**5 * sy**5 + 240. / 13. / width_**7 * sy**7
elif problem == 2:
stepfunc = 0.5 + 15. / 8. / width_ * sy - 5. / width_**3 * sy**3 + 6. / width_**5 * sy**5
elif problem == 1:
stepfunc = 0.5 + 1.5 / width_ * sy - 2 / width_**3 * sy**3
stepfunc = str(stepfunc).replace('sy',
'x[1]').replace('x[1]**2', '(x[1]*x[1])')
#REPLACE ** with pow
stepfunc = stepfunc.replace('x[1]**3', 'pow(x[1],3.)')
stepfunc = stepfunc.replace('x[1]**5', 'pow(x[1],5.)')
stepfunc = stepfunc.replace('x[1]**7', 'pow(x[1],7.)')
testsol = '(-ww/2 < x[1] && x[1] < ww/2 ? ' + stepfunc + ' : 0) + (ww/2<x[1] ? 1 : 0)'
testsol = testsol.replace('ww**2', '(ww*ww)').replace(
'ww**3',
'pow(ww,3.)').replace('ww**5',
'pow(ww,5.)').replace('ww**7', 'pow(ww,7.)')
testsol = testsol.replace('ww', str(width))
dp = Constant(1.)
fac = Constant(1 + 2. * delta)
delta = Constant(delta)
def left(x, on_boundary):
return x[0] + Lx / 2. < DOLFIN_EPS
def right(x, on_boundary):
return x[0] - Lx / 2. > -DOLFIN_EPS
def top_bottom(x, on_boundary):
return x[1] - 0.5 > -DOLFIN_EPS or x[1] + 0.5 < DOLFIN_EPS
class Inletbnd(SubDomain):
def inside(self, x, on_boundary):
return x[0] + Lx / 2. < DOLFIN_EPS
for CGorder in [2]: #CGorderL:
dofs = []
L2errors = []
for eta in 0.04 * pyexp2(-array(range(9)) * pylog(2) / 2):
### SETUP MESH
meshsz = int(
round(80 * 0.005 / (eta * (bool(use_adapt) == False) + 0.05 *
(bool(use_adapt) == True))))
if not bool(use_adapt) and meshsz > 80:
continue
mesh = RectangleMesh(-Lx / 2., -0.5, Lx / 2., 0.5, meshsz, meshsz,
"left/right")
# PERFORM TEN ADAPTATION ITERATIONS
for iii in range(Nadapt):
V = VectorFunctionSpace(mesh, "CG", CGorder)
Q = FunctionSpace(mesh, "CG", CGorder - 1)
W = V * Q
(u, p) = TrialFunctions(W)
(v, q) = TestFunctions(W)
alpha = Expression(
"-0.25<x[0] && x[0]<0.25 && 0. < x[1] ? 1e4 : 0")
boundaries = FacetFunction("size_t", mesh)
#outletbnd = Outletbnd()
inletbnd = Inletbnd()
boundaries.set_all(0)
#outletbnd.mark(boundaries, 1)
inletbnd.mark(boundaries, 1)
ds = Measure("ds")[boundaries]
bc0 = DirichletBC(W.sub(0), Constant((0., 0.)), top_bottom)
bc1 = DirichletBC(W.sub(1), dp, left)
bc2 = DirichletBC(W.sub(1), Constant(0), right)
bc3 = DirichletBC(W.sub(0).sub(1), Constant(0.), left)
bc4 = DirichletBC(W.sub(0).sub(1), Constant(0.), right)
bcs = [bc0, bc1, bc2, bc3, bc4]
bndterm = dp * dot(v, Constant((-1., 0.))) * ds(1)
a = eta * inner(grad(u), grad(
v)) * dx - div(v) * p * dx + q * div(u) * dx + alpha * dot(
u, v) * dx #+bndterm
L = inner(Constant((0., 0.)), v) * dx - bndterm
U = Function(W)
solve(a == L, U, bcs)
u, ps = U.split()
#SOLVE CONCENTRATION
mm = mesh_metric2(mesh)
vdir = u / sqrt(inner(u, u) + DOLFIN_EPS)
if iii == 0 or use_reform == False:
Q2 = FunctionSpace(mesh, 'CG', 2)
c = Function(Q2)
q = TestFunction(Q2)
p = TrialFunction(Q2)
newq = (q + dot(vdir, dot(mm, vdir)) * inner(grad(q), vdir)
) #SUPG
if use_reform:
F = newq * (fac / (
(1 + exp(-c))**2) * exp(-c)) * inner(grad(c), u) * dx
J = derivative(F, c)
bc = DirichletBC(
Q2,
Expression("-log(" + str(float(fac)) + "/(" + testsol +
"+" + str(float(delta)) + ")-1)"), left)
# bc = DirichletBC(Q, -ln(fac/(Expression(testsol)+delta)-1), left)
problem = NonlinearVariationalProblem(F, c, bc, J)
solver = NonlinearVariationalSolver(problem)
solver.parameters["newton_solver"][
"relaxation_parameter"] = relp
solver.solve()
else:
a2 = newq * inner(grad(p), u) * dx
bc = DirichletBC(Q2, Expression(testsol), left)
L2 = Constant(0.) * q * dx
solve(a2 == L2, c, bc)
if (not bool(use_adapt)) or iii == Nadapt - 1:
break
um = project(sqrt(inner(u, u)), FunctionSpace(mesh, 'CG', 2))
H = metric_pnorm(um,
eta,
max_edge_ratio=1 + 49 * (use_adapt != 2),
p=2)
H2 = metric_pnorm(c,
eta,
max_edge_ratio=1 + 49 * (use_adapt != 2),
p=2)
#H3 = metric_pnorm(ps , eta, max_edge_ratio=1+49*(use_adapt!=2), p=2)
H4 = metric_ellipse(H, H2)
#H5 = metric_ellipse(H3,H4,mesh)
mesh = adapt(H4)
if use_reform:
Q2 = FunctionSpace(mesh, 'CG', 2)
c = interpolate(c, Q2)
if use_reform:
c = project(fac / (1 + exp(-c)) - delta,
FunctionSpace(mesh, 'CG', 2))
L2error = bnderror(c, Expression(testsol), ds)
dofs.append(len(c.vector().array()) + len(U.vector().array()))
L2errors.append(L2error)
# fid = open("DOFS_L2errors_mesh_c_CG"+str(CGorder)+outname+".mpy",'w')
# pickle.dump([dofs[0],L2errors[0],c.vector().array().min(),c.vector().array().max()-1,mesh.cells(),mesh.coordinates(),c.vector().array()],fid)
# fid.close();
log(
INFO + 1,
"%1dX ADAPT<->SOLVE complete: DOF=%5d, error=%0.0e, min(c)=%0.0e,max(c)-1=%0.0e"
% (Nadapt, dofs[len(dofs) - 1], L2error,
c.vector().array().min(), c.vector().array().max() - 1))
# PLOT MESH + solution
figure()
testf = interpolate(c, FunctionSpace(mesh, 'CG', 1))
testfe = interpolate(Expression(testsol), FunctionSpace(mesh, 'CG', 1))
vtx2dof = vertex_to_dof_map(FunctionSpace(mesh, "CG", 1))
zz = testf.vector().array()[vtx2dof]
zz[zz == 1] -= 1e-16
hh = tricontourf(mesh.coordinates()[:, 0],
mesh.coordinates()[:, 1],
mesh.cells(),
zz,
100,
cmap=get_cmap('binary'))
colorbar(hh)
hold('on')
triplot(mesh.coordinates()[:, 0],
mesh.coordinates()[:, 1],
mesh.cells(),
color='r',
linewidth=0.5)
hold('off')
axis('equal')
box('off')
# savefig(outname+'final_mesh_CG2.png',dpi=300) #; savefig('outname+final_mesh_CG2.eps',dpi=300)
#PLOT ERROR
figure()
xe = interpolate(Expression("x[0]"),
FunctionSpace(mesh, 'CG', 1)).vector().array()
ye = interpolate(Expression("x[1]"),
FunctionSpace(mesh, 'CG', 1)).vector().array()
I = xe - Lx / 2 > -DOLFIN_EPS
I2 = ye[I].argsort()
pyplot(ye[I][I2],
testf.vector().array()[I][I2] - testfe.vector().array()[I][I2],
'-b')
ylabel('error')
# PLOT L2error graph
figure()
pyloglog(dofs, L2errors, '-b.', linewidth=2, markersize=16)
xlabel('Degree of freedoms')
ylabel('L2 error')
# SAVE SOLUTION
dofs = array(dofs)
L2errors = array(L2errors)
fid = open("DOFS_L2errors_CG" + str(CGorder) + outname + ".mpy", 'w')
pickle.dump([dofs, L2errors], fid)
fid.close()
# #show()
# #LOAD SAVED SOLUTIONS
# fid = open("DOFS_L2errors_CG2"+outname+".mpy",'r')
# [dofs,L2errors] = pickle.load(fid)
# fid.close()
#
# PERFORM FITS ON LAST THREE POINTS
NfitP = 5
I = array(range(len(dofs) - NfitP, len(dofs)))
slope, ints = polyfit(pylog(dofs[I]), pylog(L2errors[I]), 1)
fid = open("DOFS_L2errors_CG2_fit" + outname + ".mpy", 'w')
pickle.dump([dofs, L2errors, slope, ints], fid)
fid.close()
#PLOT THEM TOGETHER
if CGorderL != [2]:
fid = open("DOFS_L2errors_CG3.mpy", 'r')
[dofs_old, L2errors_old] = pickle.load(fid)
fid.close()
slope2, ints2 = polyfit(pylog(dofs_old[I]), pylog(L2errors_old[I]), 1)
figure()
pyloglog(dofs,
L2errors,
'-b.',
dofs_old,
L2errors_old,
'--b.',
linewidth=2,
markersize=16)
hold('on')
pyloglog(dofs,
pyexp2(ints) * dofs**slope,
'-r',
dofs_old,
pyexp2(ints2) * dofs_old**slope2,
'--r',
linewidth=1)
hold('off')
xlabel('Degree of freedoms')
ylabel('L2 error')
legend([
'CG2', 'CG3',
"%0.2f*log(DOFs)" % slope,
"%0.2f*log(DOFs)" % slope2
]) #legend(['new data','old_data'])
# savefig('comparison.png',dpi=300) #savefig('comparison.eps');
if not noplot:
show()
def __call__(self, inputs):
from pylab import box, gca
box(self.get_input('on'))
gca().get_figure().canvas.draw()
return self.get_input('axes')
def makeOmniPlot(koiList,name,npix,useHardCodedFigSize=False, useHalfBox=2.0,ncols=4, scalebarLabel="", noLabels=False, figWidth=12, useFilts=["J","Ks"], useInstr= ["ARIES","PHARO"], plotExt=".pdf", plotContourList=False, extraScalebar=False):
if (npix % ncols) == 0:
nrows = npix/ncols
else:
nrows = npix / ncols +1
if ncols > 3:
fig=pylab.figure(0,figsize=(figWidth,3.5*nrows))
elif ncols ==3:
fig=pylab.figure(0,figsize=(figWidth,4.25*nrows))
elif useHardCodedFigSize != False:
fig=pylab.figure(0,figsize=(useHardCodedFigSize[0],useHardCodedFigSize[1]))
else:
fig=pylab.figure(0,figsize=(4,2.5*nrows))
ii=0
for koi in koiList:
if koi == "blank":
print "leaving blank square"
ii = ii + 1
elif koi == "scalebar":
ax=pylab.subplot(nrows,ncols,ii+1)
pylab.plot([0,1],[1,1],color="k",linewidth=2.0)
pylab.text(0.5,1.05, scalebarLabel, color="k", fontsize=18, horizontalalignment='center')
pylab.xlim(xmin=0,xmax=1)
pylab.ylim(ymin=0,ymax=1.15)
pylab.box(on=False)
ax.set_xticks([])
ax.set_yticks([])
# pylab.title(scalebarLabel)
ii = ii + 1
else:
for filt in useFilts:
for instr in useInstr:
# print "Looking for object:", ao.koiFilterDir(koi,filt,instr)
if os.path.isdir(ao.koiFilterDir(koi,filt,instr)):
print koi, instr, filt, ii
myKoi = koiPlusFilter.koiPlusFilter(koi,filt,instr)
if plotContourList == False:
useContours = False
else:
useContours = plotContourList[ii]
if ii == 0: ## only plot the (optional) scale bar on the first image
finderPlots.zoomedInSubPlot(myKoi,nrows,ncols,ii+1,useColorMap=colorScheme,plotContours=useContours,plotLowerScalebar=extraScalebar,scalebarLabel=scalebarLabel)
else:
finderPlots.zoomedInSubPlot(myKoi,nrows,ncols,ii+1,useColorMap=colorScheme,plotContours=useContours,plotLowerScalebar=False)
if noLabels == False:
if (len(useFilts)==1 & (len(useInstr)==1)):
pylab.title(koi)
else:
# pylab.title(koi+"\n"+instr+" "+filt)
pylab.title(koi+" "+" "+filt+" ("+scalebarLabel+")")
ii = ii + 1
# Finish and export
pylab.subplots_adjust(wspace=0.1,hspace=0.1)
binaryOutfile = ao.plotDir+"allBinaries_"+name+plotExt
if plotExt == ".eps":
pylab.savefig(binaryOutfile, format='eps')
else:
pylab.savefig(binaryOutfile)
pylab.close()
def skewtlogp(olga, si):
"""
Get directory of script for saving arrays
"""
tmpPath = os.path.dirname(os.path.realpath(__file__)) + '/tmp/'
if (not os.path.isdir(tmpPath)):
os.mkdir(tmpPath)
"""
Settings for high and low sounding top
"""
if (si.stype == 0):
pbottom = 105000. # highest pressure in diagram (bottom)
ptop = 20000. # lowest pressue in diagram (top)
ptop_thd = 40000 # top of dry adiabats
ptop_mxr = 60000 # top of mixing ratio lines
pbottom_thw = pbottom # bottom of moist adiabats
dp = 100. # pressure interval used in some calculations
Tleft = -35. # lowest temperature (@pb) in diagram (left)
Tright = 35. # highest temperature (@pb) in diagram (right)
dp_label = 7000
isotherms = np.arange(-100, 30.001, 10)
isobars = np.array([
1050., 1000., 850., 700., 500., 400., 300., 250., 200., 150., 100.,
50
]) * 100.
dryadiabats = np.arange(-30, 50.001, 10)
moistadiabats = np.array([28., 24., 20., 16., 12., 8., 4., 0.])
mixrat = np.array([20., 12., 8., 5., 3., 2., 1.])
elif (si.stype == 1):
pbottom = 105000. # highest pressure in diagram (bottom)
ptop = 50000. # lowest pressue in diagram (top)
ptop_thd = 70000 # top of dry adiabats
ptop_mxr = 70000 # top of mixing ratio lines
pbottom_thw = pbottom # bottom of moist adiabats
dp = 100. # pressure interval used in some calculations
Tleft = -11. # lowest temperature (@pb) in diagram (left)
Tright = 35. # highest temperature (@pb) in diagram (right)
dp_label = 2500 # spacing (in pressure coords) of some label placement
isotherms = np.arange(-100, 60.001, 10)
isobars = np.array([1050., 1000., 900., 850., 700., 600., 500.]) * 100.
dryadiabats = np.arange(-10, 30.001, 5)
moistadiabats = np.array([28., 24., 20., 16., 12., 8., 4., 0.])
mixrat = np.array([20., 12., 8., 5., 3., 2., 1.])
else:
sys.exit('stype=%i not supported!' % stype)
"""
Setup figure
"""
if (olga != -1):
fig = pl.figure(figsize=(olga.fig_width_px / float(olga.fig_dpi),
olga.fig_width_px / float(olga.fig_dpi)))
else:
fig = pl.figure(figsize=(8, 8))
# L B R T ws hs
fig.subplots_adjust(0.08, 0.10, 1.0, 0.93, 0.2, 0.08)
pl.subplot(111)
# Calculate bounds in figure coordinates
y00 = skewty(pbottom)
y11 = skewty(ptop)
x00 = skewtx(Tleft, y00)
x11 = skewtx(Tright, y00)
# Spacing for some labels
hs = np.abs(x11 - x00) / 200.
vs = np.abs(y11 - y00) / 200.
"""
1. Create isotherms
"""
for T in isotherms:
y = np.array([skewty(pbottom), skewty(ptop)])
x = np.array([skewtx(T, y[0]), skewtx(T, y[1])])
# Check if partially out of bounds
lloc = 0
if (x[0] < x00):
x[0] = x00
y[0] = iskewtxT(x00, T)
if (x[1] > x11):
x[1] = x11
y[1] = iskewtxT(x11, T)
if (x[1] >= x00 and y[1] >= y00):
pl.plot(x, y, color=c2, alpha=a1)
if (x[0] > x00 and x[0] < x11):
pl.text(x[0],
y[0] - 2 * hs,
int(T),
ha='center',
va='top',
color=c2)
else:
pl.text(x[1] - 5 * hs,
y[1] - 5 * vs,
int(T),
ha='center',
va='center',
color=c2,
alpha=a2)
"""
2. Create isobars
"""
for p in isobars:
# Check if out of bounds
if ((p >= ptop) and (p <= pbottom)):
y = skewty(p)
pl.plot([x00, x11], [y, y], color=c2, alpha=a1)
pl.text(x00 - hs,
y,
int(p / 100.),
ha='right',
va='center',
color=c2)
"""
3. Create dry adiabats
"""
# Try loading the data from the tmp directory. If not available, calculate
# and save the arrays
try:
p = np.load(tmpPath + 'theta_p_%i.arr' % si.stype)
y = np.load(tmpPath + 'theta_y_%i.arr' % si.stype)
x = np.load(tmpPath + 'theta_x_%i.arr' % si.stype)
except:
p = np.arange(pbottom, (np.max(([ptop, ptop_thd])) - dp), -dp)
x = np.zeros((dryadiabats.size, p.size))
y = np.zeros(p.size)
for k in range(p.size):
y[k] = skewty(p[k])
for i in range(dryadiabats.size):
x[i, :] = 0.
for k in range(p.size):
xtmp = skewtx(((dryadiabats[i] + T0) * exner(p[k])) - T0, y[k])
if (xtmp >= x00 and xtmp <= x11):
x[i, k] = xtmp
else:
x[i, k] = -9999
p.dump(tmpPath + 'theta_p_%i.arr' % si.stype)
y.dump(tmpPath + 'theta_y_%i.arr' % si.stype)
x.dump(tmpPath + 'theta_x_%i.arr' % si.stype)
# Plot the dry adiabats
for i in range(dryadiabats.size):
doPlot = np.where(x[i, :] != -9999)[0]
if (doPlot[0].size > 0):
lloc = max(0, np.size(x[i, doPlot]) - int(5000. / dp))
pl.plot(x[i, doPlot], y[doPlot], color=c1)
pl.text(x[i, doPlot][lloc],
y[doPlot][lloc],
int(dryadiabats[i]),
color=c1,
ha='center',
va='center',
backgroundcolor='w')
"""
4. Create moist adiabats
"""
# Try loading the data from the tmp directory. If not available, calculate
# and save the arrays
try:
p = np.load(tmpPath + 'thetam_p_%i.arr' % si.stype)
y = np.load(tmpPath + 'thetam_y_%i.arr' % si.stype)
x = np.load(tmpPath + 'thetam_x_%i.arr' % si.stype)
except:
p = np.arange(np.min(([pbottom, pbottom_thw])), ptop - dp, -dp)
x = np.zeros((moistadiabats.size, p.size))
y = np.zeros(p.size)
for k in range(p.size):
y[k] = skewty(p[k])
for i in range(moistadiabats.size):
for k in range(p.size):
thw = dsatlftskewt(moistadiabats[i], p[k])
xtmp = skewtx(thw, y[k])
if (xtmp >= x00 and xtmp <= x11):
x[i, k] = xtmp
else:
x[i, k] = -9999
p.dump(tmpPath + 'thetam_p_%i.arr' % si.stype)
y.dump(tmpPath + 'thetam_y_%i.arr' % si.stype)
x.dump(tmpPath + 'thetam_x_%i.arr' % si.stype)
# Plot the moist adiabats
for i in range(moistadiabats.size):
doPlot = np.where(x[i, :] != -9999)[0]
if (doPlot[0].size > 0):
lloc = max(0, np.size(x[i, doPlot]) - int(8000. / dp))
pl.plot(x[i, doPlot], y[doPlot], '--', color=c3)
pl.text(x[i, doPlot][lloc],
y[doPlot][lloc],
int(moistadiabats[i]),
color=c3,
ha='center',
va='center',
backgroundcolor='w')
"""
5. Create isohumes / mixing ratio lines
"""
# Try loading the data from the tmp directory. If not available, calculate
# and save the arrays
try:
p = np.load(tmpPath + 'mixr_p_%i.arr' % si.stype)
y = np.load(tmpPath + 'mixr_y_%i.arr' % si.stype)
x = np.load(tmpPath + 'mixr_x_%i.arr' % si.stype)
except:
p = np.arange(pbottom, (np.max(([ptop, ptop_mxr])) - dp), -dp)
x = np.zeros((mixrat.size, p.size))
y = np.zeros(p.size)
for k in range(p.size):
y[k] = skewty(p[k])
for i in range(mixrat.size):
for k in range(p.size):
mix = Td(mixrat[i] / 1000., p[k]) - T0
xtmp = skewtx(mix, y[k])
if (xtmp >= x00 and xtmp <= x11):
x[i, k] = xtmp
else:
x[i, k] = -9999
p.dump(tmpPath + 'mixr_p_%i.arr' % si.stype)
y.dump(tmpPath + 'mixr_y_%i.arr' % si.stype)
x.dump(tmpPath + 'mixr_x_%i.arr' % si.stype)
# Plot the mixing ratio lines
for i in range(mixrat.size):
doPlot = np.where(x[i, :] != -9999)[0]
if (doPlot[0].size > 0):
pl.plot(x[i, doPlot], y[doPlot], color=c5, dashes=[3, 2])
pl.text(x[i, doPlot][-1],
y[doPlot][-1] + vs,
int(mixrat[i]),
color=c5,
ha='center',
va='bottom',
backgroundcolor='w')
"""
6. Add sounding data
"""
# 6.1 Temperature and dewpoint temperature
if (si.T.size > 0):
y = skewty(si.p)
x1 = skewtx(si.T - T0, y)
x2 = skewtx(si.Td - T0, y)
pl.plot(x1, y, '-', color=cT, linewidth=2)
pl.plot(x2, y, '-', color=cTd, linewidth=2)
# 6.2 Add height labels to axis
if (si.z.size > 0 and si.z.size == si.p.size):
y = skewty(si.p)
p_last = 1e9
for k in range(si.z.size):
if (y[k] <= y11 and np.abs(si.p[k] - p_last) > dp_label):
pl.text(x11 + hs,
y[k],
str(int(si.z[k])) + 'm',
color=c2,
ha='right',
va='center',
size=7,
backgroundcolor='w')
p_last = si.p[k]
# 6.2 Wind barbs
if (si.u.size > 0):
y = skewty(si.p)
u = si.u * 1.95
v = si.v * 1.95
xb = x11 + 9 * hs
p_last = 1e9
for k in range(si.z.size):
if (y[k] <= y11 and np.abs(si.p[k] - p_last) > dp_label):
pl.barbs(xb,
y[k],
u[k],
v[k],
length=5.2,
linewidth=0.5,
pivot='middle')
p_last = si.p[k]
"""
6.1 :) Try to add measured data
"""
# 6.1 Temperature and dewpoint temperature
if (si.Tm.size > 0):
y = skewty(si.pm)
x1 = skewtx(si.Tm, y)
x2 = skewtx(si.Tdm, y)
pl.plot(x1, y, '--', color=cT, linewidth=1)
pl.plot(x2, y, '--', color=cTd, linewidth=1)
"""
7. Lauch parcel
"""
if (si.parcel == True):
# Plot starting position:
p0s = skewty(si.ps)
T0s = skewtx(si.Ts - T0, p0s)
Td0s = skewtx(Td(si.rs, si.ps) - T0, p0s)
pl.scatter(T0s, p0s, facecolor='none')
pl.scatter(Td0s, p0s, facecolor='none')
# Lists to hold parcel pressure, temp and dewpoint temp during ascent
pp = [si.ps]
Tp = [si.Ts]
Tdp = [Td(si.rs, si.ps)]
# Launch parcel
n = 0
while (Tp[-1] > Tdp[-1] and n < 1000):
n += 1
dp2 = max(1, (Tp[-1] - Tdp[-1]) * 300) # bit weird, but fast
pp.append(pp[-1] - dp2)
Tp.append(si.Ts * exner(pp[-1], si.ps))
Tdp.append(Td(si.rs, pp[-1]))
# Plot lines from surface --> LCL
ps = skewty(np.array(pp))
Tps = skewtx(np.array(Tp) - T0, ps)
Tdps = skewtx(np.array(Tdp) - T0, ps)
pl.plot(Tps, ps, 'k', linewidth=1.5, dashes=[4, 2])
pl.plot(Tdps, ps, 'k', linewidth=1.5, dashes=[4, 2])
pl.scatter(Tps[-1], ps[-1], facecolor='none')
# Iteratively find the moist adiabat starting at p0, which goes through the temperature at LCL
Ths = si.Ts / exner(si.ps, p0) # potential temperature surface (@p0)
ThwsLCL = dsatlftskewt(
Ths - T0, pp[-1]) + T0 # Temp moist adiabat at plcl, through Ths
thw0 = Ths - (ThwsLCL - Tp[-1]
) # First estimate of moist adiabat passing through Tlcl
ThwLCL = dsatlftskewt(thw0 - T0, pp[-1]) + T0
while (np.abs(ThwLCL - Tp[-1]) > 0.1):
thw0 -= ThwLCL - Tp[-1]
ThwLCL = dsatlftskewt(thw0 - T0, pp[-1]) + T0
# Plot moist adiabat from LCL upwards
p = np.arange(pp[-1], ptop, -dp)
x = np.zeros(p.size)
y = np.zeros(p.size)
for k in range(p.size):
thw = dsatlftskewt(thw0 - T0, p[k])
y[k] = skewty(p[k])
x[k] = skewtx(thw, y[k])
pl.plot(x, y, 'k', linewidth=1.5, dashes=[4, 2])
"""
Add info from TEMF boundary layer scheme
"""
if (si.Tu.size > 0):
# Cloud fraction
dw = (x11 - x00) # width of diagram
y = skewty(si.p)
x = x00 + si.cfru * 0.2 * (x11 - x00)
pl.plot(x, y, '-', linewidth=1.5, color=c6) # cloud cover
cfr_pos = np.where(si.cfru > 0.001)[0]
if (np.size(cfr_pos) > 1):
pl.text(x00,
y[cfr_pos[0] - 1],
'- %im' % (si.z[cfr_pos[0] - 1]),
ha='left',
va='center',
size=8,
color=c6)
pl.text(x00,
y[cfr_pos[-1]],
'- %im' % (si.z[cfr_pos[-1]]),
ha='left',
va='center',
size=8,
color=c6)
kmax = np.where(si.cfru == si.cfru.max())[0][0]
pl.text(x.max(),
y[kmax],
'- %i%%' % (si.cfru[kmax] * 100.),
ha='left',
va='center',
size=8,
color=c6)
"""
6. Finish diagram
"""
pl.xticks([])
pl.yticks([])
pl.box('off')
pl.xlim(x00 - 0.1, x11 + 16 * hs)
pl.ylim(y00 - 0.1, y11 + 0.1)
# draw axis
pl.plot([x00, x11], [y00, y00], 'k-')
pl.plot([x00, x00], [y00, y11], 'k-')
pl.figtext(0.5, 0.05, 'Temperature (C)', ha='center')
pl.figtext(0.015, 0.52, 'Pressure (hPa)', rotation=90)
label = 'Skew-T log-P, %s, %s UTC' % (si.name, si.time)
pl.figtext(0.5, 0.97, label, ha='center')
if (olga != -1):
img = image.imread(olga.olgaRoot + 'include/olga_left.png')
pl.figimage(img, 7, 5)
#pl.figimage(img,10,olga.fig_width_px-45)
pl.figtext(0.99, 0.011, '%s' % olga.map_desc[0], size=7, ha='right')
return fig
def minimal_example(width=2e-2, Nadapt=10, eta=0.01):
### CONSTANTS
meshsz = 40
hd = Constant(width)
### SETUP MESH
mesh = RectangleMesh(Point(-0.5, -0.5), Point(0.5, 0.5), 1 * meshsz,
1 * meshsz, "left/right")
### DERIVE FORCING TERM
angle = pi / 8 #rand*pi/2
sx = Symbol('sx')
sy = Symbol('sy')
width_ = Symbol('ww')
aa = Symbol('aa')
testsol = pytanh((sx * pycos(aa) + sy * pysin(aa)) / width_)
ddtestsol = str(diff(testsol, sx, sx) + diff(testsol, sy, sy)).replace(
'sx', 'x[0]').replace('sy', 'x[1]')
#replace ** with pow
ddtestsol = ddtestsol.replace(
'tanh((x[0]*sin(aa) + x[1]*cos(aa))/ww)**2',
'pow(tanh((x[0]*sin(aa) + x[1]*cos(aa))/ww),2.)')
ddtestsol = ddtestsol.replace('cos(aa)**2', 'pow(cos(aa),2.)').replace(
'sin(aa)**2', 'pow(sin(aa),2.)').replace('ww**2', '(ww*ww)')
#insert vaulues
ddtestsol = ddtestsol.replace('aa', str(angle)).replace('ww', str(width))
testsol = str(testsol).replace('sx', 'x[0]').replace('sy', 'x[1]').replace(
'aa', str(angle)).replace('ww', str(width))
ddtestsol = "-(" + ddtestsol + ")"
def boundary(x):
return x[0]-mesh.coordinates()[:,0].min() < DOLFIN_EPS or mesh.coordinates()[:,0].max()-x[0] < DOLFIN_EPS \
or mesh.coordinates()[:,1].min()+0.5 < DOLFIN_EPS or mesh.coordinates()[:,1].max()-x[1] < DOLFIN_EPS
# PERFORM TEN ADAPTATION ITERATIONS
for iii in range(Nadapt):
V = FunctionSpace(mesh, "CG", 2)
dis = TrialFunction(V)
dus = TestFunction(V)
u = Function(V)
a = inner(grad(dis), grad(dus)) * dx
L = Expression(ddtestsol) * dus * dx
bc = DirichletBC(V, Expression(testsol), boundary)
solve(a == L, u, bc)
startTime = time()
H = metric_pnorm(u, eta, max_edge_length=3., max_edge_ratio=None)
H = logproject(H)
if iii != Nadapt - 1:
mesh = adapt(H)
L2error = errornorm(Expression(testsol),
u,
degree_rise=4,
norm_type='L2')
log(
INFO + 1,
"total (adapt+metric) time was %0.1fs, L2error=%0.0e, nodes: %0.0f"
% (time() - startTime, L2error, mesh.num_vertices()))
# # PLOT MESH
# figure()
coords = mesh.coordinates().transpose()
# triplot(coords[0],coords[1],mesh.cells(),linewidth=0.1)
# #savefig('mesh.png',dpi=300) #savefig('mesh.eps');
figure() #solution
testf = interpolate(Expression(testsol), FunctionSpace(mesh, 'CG', 1))
vtx2dof = vertex_to_dof_map(FunctionSpace(mesh, "CG", 1))
zz = testf.vector().array()[vtx2dof]
hh = tricontourf(coords[0], coords[1], mesh.cells(), zz, 100)
colorbar(hh)
# savefig('solution.png',dpi=300) #savefig('solution.eps');
figure() #analytical solution
testfe = interpolate(u, FunctionSpace(mesh, 'CG', 1))
zz = testfe.vector().array()[vtx2dof]
hh = tricontourf(coords[0], coords[1], mesh.cells(), zz, 100)
colorbar(hh)
#savefig('analyt.png',dpi=300) #savefig('analyt.eps');
figure() #error
zz -= testf.vector().array()[vtx2dof]
zz[zz == 1] -= 1e-16
hh = tricontourf(mesh.coordinates()[:, 0],
mesh.coordinates()[:, 1],
mesh.cells(),
zz,
100,
cmap=get_cmap('binary'))
colorbar(hh)
hold('on')
triplot(mesh.coordinates()[:, 0],
mesh.coordinates()[:, 1],
mesh.cells(),
color='r',
linewidth=0.5)
hold('off')
axis('equal')
box('off')
title('error')
show()
def adv_convergence(width=2e-2, delta=1e-2, relp=1, Nadapt=10, use_adapt=True, problem=3, outname='', use_reform=False, CGorderL = [2, 3], noplot=False, Lx=3.):
### SETUP SOLUTION
sy = Symbol('sy'); width_ = Symbol('ww')
if problem == 3:
stepfunc = 0.5+165./104./width_*sy-20./13./width_**3*sy**3-102./13./width_**5*sy**5+240./13./width_**7*sy**7
elif problem == 2:
stepfunc = 0.5+15./8./width_*sy-5./width_**3*sy**3+6./width_**5*sy**5
elif problem == 1:
stepfunc = 0.5+1.5/width_*sy-2/width_**3*sy**3
stepfunc = str(stepfunc).replace('sy','x[1]').replace('x[1]**2','(x[1]*x[1])')
#REPLACE ** with pow
stepfunc = stepfunc.replace('x[1]**3','pow(x[1],3.)')
stepfunc = stepfunc.replace('x[1]**5','pow(x[1],5.)')
stepfunc = stepfunc.replace('x[1]**7','pow(x[1],7.)')
testsol = '(-ww/2 < x[1] && x[1] < ww/2 ? ' + stepfunc +' : 0) + (ww/2<x[1] ? 1 : 0)'
testsol = testsol.replace('ww**2','(ww*ww)').replace('ww**3','pow(ww,3.)').replace('ww**5','pow(ww,5.)').replace('ww**7','pow(ww,7.)')
testsol = testsol.replace('ww',str(width))
dp = Constant(1.)
fac = Constant(1+2.*delta)
delta = Constant(delta)
def left(x, on_boundary):
return x[0] + Lx/2. < DOLFIN_EPS
def right(x, on_boundary):
return x[0] - Lx/2. > -DOLFIN_EPS
def top_bottom(x, on_boundary):
return x[1] - 0.5 > -DOLFIN_EPS or x[1] + 0.5 < DOLFIN_EPS
class Inletbnd(SubDomain):
def inside(self, x, on_boundary):
return x[0] + Lx/2. < DOLFIN_EPS
for CGorder in [2]: #CGorderL:
dofs = []
L2errors = []
for eta in 0.04*pyexp2(-array(range(9))*pylog(2)/2):
### SETUP MESH
meshsz = int(round(80*0.005/(eta*(bool(use_adapt)==False)+0.05*(bool(use_adapt)==True))))
if not bool(use_adapt) and meshsz > 80:
continue
mesh = RectangleMesh(-Lx/2.,-0.5,Lx/2.,0.5,meshsz,meshsz,"left/right")
# PERFORM TEN ADAPTATION ITERATIONS
for iii in range(Nadapt):
V = VectorFunctionSpace(mesh, "CG", CGorder); Q = FunctionSpace(mesh, "CG", CGorder-1)
W = V*Q
(u, p) = TrialFunctions(W)
(v, q) = TestFunctions(W)
alpha = Expression("-0.25<x[0] && x[0]<0.25 && 0. < x[1] ? 1e4 : 0")
boundaries = FacetFunction("size_t",mesh)
#outletbnd = Outletbnd()
inletbnd = Inletbnd()
boundaries.set_all(0)
#outletbnd.mark(boundaries, 1)
inletbnd.mark(boundaries, 1)
ds = Measure("ds")[boundaries]
bc0 = DirichletBC(W.sub(0), Constant((0., 0.)), top_bottom)
bc1 = DirichletBC(W.sub(1), dp , left)
bc2 = DirichletBC(W.sub(1), Constant(0), right)
bc3 = DirichletBC(W.sub(0).sub(1), Constant(0.), left)
bc4 = DirichletBC(W.sub(0).sub(1), Constant(0.), right)
bcs = [bc0,bc1,bc2,bc3,bc4]
bndterm = dp*dot(v,Constant((-1.,0.)))*ds(1)
a = eta*inner(grad(u), grad(v))*dx - div(v)*p*dx + q*div(u)*dx+alpha*dot(u,v)*dx#+bndterm
L = inner(Constant((0.,0.)), v)*dx -bndterm
U = Function(W)
solve(a == L, U, bcs)
u, ps = U.split()
#SOLVE CONCENTRATION
mm = mesh_metric2(mesh)
vdir = u/sqrt(inner(u,u)+DOLFIN_EPS)
if iii == 0 or use_reform == False:
Q2 = FunctionSpace(mesh,'CG',2); c = Function(Q2)
q = TestFunction(Q2); p = TrialFunction(Q2)
newq = (q+dot(vdir,dot(mm,vdir))*inner(grad(q),vdir)) #SUPG
if use_reform:
F = newq*(fac/((1+exp(-c))**2)*exp(-c))*inner(grad(c),u)*dx
J = derivative(F,c)
bc = DirichletBC(Q2, Expression("-log("+str(float(fac)) +"/("+testsol+"+"+str(float(delta))+")-1)"), left)
# bc = DirichletBC(Q, -ln(fac/(Expression(testsol)+delta)-1), left)
problem = NonlinearVariationalProblem(F,c,bc,J)
solver = NonlinearVariationalSolver(problem)
solver.parameters["newton_solver"]["relaxation_parameter"] = relp
solver.solve()
else:
a2 = newq*inner(grad(p),u)*dx
bc = DirichletBC(Q2, Expression(testsol), left)
L2 = Constant(0.)*q*dx
solve(a2 == L2, c, bc)
if (not bool(use_adapt)) or iii == Nadapt-1:
break
um = project(sqrt(inner(u,u)),FunctionSpace(mesh,'CG',2))
H = metric_pnorm(um, eta, max_edge_ratio=1+49*(use_adapt!=2), p=2)
H2 = metric_pnorm(c, eta, max_edge_ratio=1+49*(use_adapt!=2), p=2)
#H3 = metric_pnorm(ps , eta, max_edge_ratio=1+49*(use_adapt!=2), p=2)
H4 = metric_ellipse(H,H2)
#H5 = metric_ellipse(H3,H4,mesh)
mesh = adapt(H4)
if use_reform:
Q2 = FunctionSpace(mesh,'CG',2)
c = interpolate(c,Q2)
if use_reform:
c = project(fac/(1+exp(-c))-delta,FunctionSpace(mesh,'CG',2))
L2error = bnderror(c,Expression(testsol),ds)
dofs.append(len(c.vector().array())+len(U.vector().array()))
L2errors.append(L2error)
# fid = open("DOFS_L2errors_mesh_c_CG"+str(CGorder)+outname+".mpy",'w')
# pickle.dump([dofs[0],L2errors[0],c.vector().array().min(),c.vector().array().max()-1,mesh.cells(),mesh.coordinates(),c.vector().array()],fid)
# fid.close();
log(INFO+1,"%1dX ADAPT<->SOLVE complete: DOF=%5d, error=%0.0e, min(c)=%0.0e,max(c)-1=%0.0e" % (Nadapt, dofs[len(dofs)-1], L2error,c.vector().array().min(),c.vector().array().max()-1))
# PLOT MESH + solution
figure()
testf = interpolate(c ,FunctionSpace(mesh,'CG',1))
testfe = interpolate(Expression(testsol),FunctionSpace(mesh,'CG',1))
vtx2dof = vertex_to_dof_map(FunctionSpace(mesh, "CG" ,1))
zz = testf.vector().array()[vtx2dof]; zz[zz==1] -= 1e-16
hh=tricontourf(mesh.coordinates()[:,0],mesh.coordinates()[:,1],mesh.cells(),zz,100,cmap=get_cmap('binary'))
colorbar(hh)
hold('on'); triplot(mesh.coordinates()[:,0],mesh.coordinates()[:,1],mesh.cells(),color='r',linewidth=0.5); hold('off')
axis('equal'); box('off')
# savefig(outname+'final_mesh_CG2.png',dpi=300) #; savefig('outname+final_mesh_CG2.eps',dpi=300)
#PLOT ERROR
figure()
xe = interpolate(Expression("x[0]"),FunctionSpace(mesh,'CG',1)).vector().array()
ye = interpolate(Expression("x[1]"),FunctionSpace(mesh,'CG',1)).vector().array()
I = xe - Lx/2 > -DOLFIN_EPS; I2 = ye[I].argsort()
pyplot(ye[I][I2],testf.vector().array()[I][I2]-testfe.vector().array()[I][I2],'-b'); ylabel('error')
# PLOT L2error graph
figure()
pyloglog(dofs,L2errors,'-b.',linewidth=2,markersize=16); xlabel('Degree of freedoms'); ylabel('L2 error')
# SAVE SOLUTION
dofs = array(dofs); L2errors = array(L2errors)
fid = open("DOFS_L2errors_CG"+str(CGorder)+outname+".mpy",'w')
pickle.dump([dofs,L2errors],fid)
fid.close();
# #show()
# #LOAD SAVED SOLUTIONS
# fid = open("DOFS_L2errors_CG2"+outname+".mpy",'r')
# [dofs,L2errors] = pickle.load(fid)
# fid.close()
#
# PERFORM FITS ON LAST THREE POINTS
NfitP = 5
I = array(range(len(dofs)-NfitP,len(dofs)))
slope,ints = polyfit(pylog(dofs[I]), pylog(L2errors[I]), 1)
fid = open("DOFS_L2errors_CG2_fit"+outname+".mpy",'w')
pickle.dump([dofs,L2errors,slope,ints],fid)
fid.close()
#PLOT THEM TOGETHER
if CGorderL != [2]:
fid = open("DOFS_L2errors_CG3.mpy",'r')
[dofs_old,L2errors_old] = pickle.load(fid)
fid.close()
slope2,ints2 = polyfit(pylog(dofs_old[I]), pylog(L2errors_old[I]), 1)
figure()
pyloglog(dofs,L2errors,'-b.',dofs_old,L2errors_old,'--b.',linewidth=2,markersize=16)
hold('on'); pyloglog(dofs,pyexp2(ints)*dofs**slope,'-r',dofs_old,pyexp2(ints2)*dofs_old**slope2,'--r',linewidth=1); hold('off')
xlabel('Degree of freedoms'); ylabel('L2 error')
legend(['CG2','CG3',"%0.2f*log(DOFs)" % slope, "%0.2f*log(DOFs)" % slope2]) #legend(['new data','old_data'])
# savefig('comparison.png',dpi=300) #savefig('comparison.eps');
if not noplot:
show()
pylab.hold('on')
# xi = np.linspace(x.min(), x.max(), 10)
xi = np.linspace(lim[0], lim[1], 10)
yi = slope * xi + intercept
pylab.plot(xi, yi, 'k-')
pylab.title(titles[i])
pylab.text(xi.mean() * 1.05,
yi.mean() * 0.6,
'$R^2$ = ' + str.format('{0:.2f}', np.round(r**2, 2)),
fontsize=fontSize + 1)
# pylab.xlabel(r'MODIS $\rho_t$')
# pylab.ylabel(r'DMC $\rho_t$')
pylab.xlabel(r'MODIS surface reflectance')
pylab.ylabel(r'DMC surface reflectance')
pylab.grid('off')
pylab.box('on')
# for j in range(asterFnF.__len__()):
# pylab.text(x[j]+.2, y[j], asterFnF[j])
pylab.tight_layout()
pylab.axis(lim + lim)
# Hide the right and top spines
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
# Only show ticks on the left and bottom spines
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
pyplot.locator_params(
axis='y',
def main():
usage = "Usage: %prog [options] <images>\n"
parser = OptionParser(usage=usage)
parser.add_option('-o',
'--out',
default='mosaic',
dest='outroot',
help='Root name for output')
parser.add_option('-t',
'--threshold',
dest='threshold',
default=0.05,
type='float',
help='Beam threshold [default=%default]')
parser.add_option('-v',
'--verbose',
action="store_true",
dest="verbose",
default=False,
help="Increase verbosity of output")
(options, args) = parser.parse_args()
images = args
newbeam = [
re.sub('_regrid(\S+).fits', '_beamI_regrid\\1.fits', i) for i in images
]
ff0 = pyfits.open('MWATS_201404_I_MOL.fits')
mask = ff0[1].data <= 2
f = pyfits.open(images[0])
D = numpy.zeros(f[0].data.shape)
W = numpy.zeros(f[0].data.shape)
N = numpy.zeros(f[0].data.shape)
pylab.clf()
f = pylab.gcf()
f.set_figwidth(12)
f.set_figheight(6)
for i in xrange(len(images)):
#imagelist=' '.join(images[:i+1])
#command='python /localhome/kaplan/python/mosaic_images.py -i %s -t %f -o %s_%03d.fits %s' % (
#images[0],
# options.threshold,
# options.outroot,
# i,
# imagelist)
#print command
fi = pyfits.open(images[i])
try:
fb = pyfits.open(newbeam[i])
except:
#newbeam[i]=newbeam[i].replace('-v_','-i_')
newbeam[i] = newbeam[i].replace(Vstring, Istring)
fb = pyfits.open(newbeam[i])
beam = (fb[0].data)
beam[0, 0][numpy.isnan(fi[0].data[0, 0])] = 0
beam[0, 0][beam[0, 0] <= options.threshold * beam[0, 0].max()] = 0
fi[0].data[0, 0][numpy.isnan(fi[0].data[0, 0])] = 0
D[0, 0] += fi[0].data[0, 0] * beam[0, 0]**2
W[0, 0] += beam[0, 0]**2
N[0, 0] += beam[0, 0]
DD = D / W
DD[0, 0][W[0, 0] == 0] = numpy.nan
DD[mask] = numpy.nan
fi[0].data = DD
if os.path.exists('temp.fits'):
os.remove('temp.fits')
fi.writeto('temp.fits')
w = wcs.WCS(fi[0].header, naxis=(1, 2))
f.clf()
gc = aplpy.FITSFigure('temp.fits', figure=f)
gc.show_colorscale(vmin=-0.1, vmax=0.1, cmap=pylab.cm.copper)
gc.recenter(14 * 15, -22.5, width=160, height=95)
gc.axis_labels.hide_x()
gc.axis_labels.hide_y()
gc.tick_labels.hide()
gc.add_label(0.1, 0.9, '%03d' % i, relative=True, size=16)
ra = numpy.linspace(8, 20, 50) * 15
for dec in xrange(-80, 30, 10):
x, y = w.wcs_world2pix(ra, ra * 0 + dec, 0)
pylab.plot(x, y, 'k')
if dec < 20 and dec > -80:
gc.add_label(15 * 16,
dec,
'$\\delta=%d^\\ocirc$' % dec,
size=12,
verticalalignment='bottom')
dec = numpy.linspace(-80, 20, 50)
for ra in xrange(8, 22, 2):
ra_show = ra
if ra_show < 0:
ra_show += 24
x, y = w.wcs_world2pix(0 * dec + ra_show * 15, dec, 0)
pylab.plot(x, y, 'k')
gc.add_label(ra * 15,
-45,
'$\\alpha=%d^h$' % ra_show,
size=12,
verticalalignment='bottom',
horizontalalignment='left')
ax = pylab.gca()
ax.axis('off')
pylab.box('off')
pylab.savefig('MWATS_201404_I_MOL_%03d.png' % i)
print 'MWATS_201404_I_MOL_%03d.png' % i
if os.path.exists('temp.fits'):
os.remove('temp.fits')
def plotEvent3(clump, rawData=None, fitRes=None):
from scipy import ndimage
dt = 18.27
pl.figure()
pl.subplot(211)
c = clump
if not fitRes is None:
t0 = fitRes['t0']
else:
t0 = 0
i1 = abs(c['fitError_Ag']) < 500
t = c['t'][i1]
I = c['Ag'][i1]
t = np.hstack([t[0] - 1, t])
I = np.hstack([0, I])
pl.plot((t - t0) * dt, ndimage.median_filter(I, 3), lw=4)
pl.plot([0, 0], pl.ylim(), 'k:')
pl.yticks([])
pl.xlim(-20 * dt, 100 * dt)
pl.xticks([])
#pl.axes()
pl.box()
pl.subplot(212)
i2 = (abs(c['fitError_Ar'] / c['fitResults_Ar']) <
2) * (c['fitResults_sigmag'] > 100)
pl.plot((c['t'][i2] - t0) * dt,
ndimage.median_filter(c['Ar'][i2], 3),
'r',
lw=4)
pl.xlim(-20 * dt, 100 * dt)
pl.yticks([])
pl.plot([0, 0], pl.ylim(), 'k:')
ysb = .2 * np.mean(pl.ylim())
pl.plot([1500, 1700], [ysb, ysb], 'k', lw=8)
pl.text(1500, ysb + .6 * ysb, '200 ms')
pl.xticks([])
#pl.axes()
pl.box()
pl.figure()
tvals = np.array([-20, -5, 1, 5, 10, 20, 50, 100]) + int(t0)
vs = 1e3 * clump.pipeline.mdh['voxelsize.x']
xp = int(clump['x'].mean() / vs)
yp = int(clump['y'].mean() / vs)
xp1 = int((clump['x'].mean() - clump.pipeline.mdh['chroma.dx'](0, 0)) / vs)
yp1 = int(
(clump['y'].mean() - clump.pipeline.mdh['chroma.dy'](0, 0)) / vs) + 256
lMax = 1.0 * rawData[max(xp - 10, 0):(xp + 10),
max(yp - 10, 0):(yp + 10), tvals[3]].max()
lMin = 1.0 * rawData[max(xp - 10, 0):(xp + 10),
max(yp - 10, 0):(yp + 10), tvals[0]].min()
cMax = 1.0 * rawData[max(xp1 - 10, 0):(xp1 + 10),
max(yp1 - 10, 0):(yp1 + 10), tvals[4]].max()
cMin = 1.0 * rawData[max(xp1 - 10, 0):(xp1 + 10),
max(yp1 - 10, 0):(yp1 + 10), tvals[0]].min()
for i, t_i in enumerate(tvals):
pl.subplot(2, 8, i + 1)
frame = rawData[max(xp - 10, 0):(xp + 10),
max(yp - 10, 0):(yp + 10), t_i].squeeze()
#pl.imshow(frame, interpolation='nearest', cmap='hot', clim=(lMin, lMax))
fr = 1.0 - np.clip(((frame - lMin) / float(lMax - lMin)), 0,
1)[:, :, None] * np.array([1, 1, .5])[None, None, :]
pl.imshow(fr, interpolation='nearest')
pl.xticks([])
pl.yticks([])
pl.subplot(2, 8, i + 8 + 1)
frame = rawData[max(xp1 - 10, 0):(xp1 + 10),
max(yp1 - 10, 0):(yp1 + 10), t_i].squeeze()
#pl.imshow(frame, interpolation='nearest', cmap='hot', clim=(cMin, cMax))
fr = 1.0 - np.clip(((frame - cMin) / float(cMax - cMin)), 0,
1)[:, :, None] * np.array([.5, 1, 1])[None, None, :]
pl.imshow(fr, interpolation='nearest')
pl.xticks([])
pl.yticks([])
pl.tight_layout(pad=.5)
def skewtlogp(olga, si):
"""
Get directory of script for saving arrays
"""
tmpPath = os.path.dirname(os.path.realpath(__file__))+'/tmp/'
if(not os.path.isdir(tmpPath)):
os.mkdir(tmpPath)
"""
Settings for high and low sounding top
"""
if(si.stype==0):
pbottom = 105000. # highest pressure in diagram (bottom)
ptop = 20000. # lowest pressue in diagram (top)
ptop_thd = 40000 # top of dry adiabats
ptop_mxr = 60000 # top of mixing ratio lines
pbottom_thw = pbottom # bottom of moist adiabats
dp = 100. # pressure interval used in some calculations
Tleft = -35. # lowest temperature (@pb) in diagram (left)
Tright = 35. # highest temperature (@pb) in diagram (right)
dp_label = 7000
isotherms = np.arange(-100,30.001,10)
isobars = np.array([1050.,1000.,850.,700.,500.,400.,300.,250.,200.,150.,100.,50])*100.
dryadiabats = np.arange(-30,50.001,10)
moistadiabats = np.array([28.,24.,20.,16.,12.,8.,4.,0.])
mixrat = np.array([20.,12.,8.,5.,3.,2.,1.])
elif(si.stype==1):
pbottom = 105000. # highest pressure in diagram (bottom)
ptop = 50000. # lowest pressue in diagram (top)
ptop_thd = 70000 # top of dry adiabats
ptop_mxr = 70000 # top of mixing ratio lines
pbottom_thw = pbottom # bottom of moist adiabats
dp = 100. # pressure interval used in some calculations
Tleft = -11. # lowest temperature (@pb) in diagram (left)
Tright = 35. # highest temperature (@pb) in diagram (right)
dp_label = 2500 # spacing (in pressure coords) of some label placement
isotherms = np.arange(-100,60.001,10)
isobars = np.array([1050.,1000.,900.,850.,700.,600.,500.])*100.
dryadiabats = np.arange(-10,30.001,5)
moistadiabats = np.array([28.,24.,20.,16.,12.,8.,4.,0.])
mixrat = np.array([20.,12.,8.,5.,3.,2.,1.])
else:
sys.exit('stype=%i not supported!'%stype)
"""
Setup figure
"""
if(olga != -1):
fig = pl.figure(figsize=(olga.fig_width_px/float(olga.fig_dpi), olga.fig_width_px/float(olga.fig_dpi)))
else:
fig = pl.figure(figsize=(8,8))
# L B R T ws hs
fig.subplots_adjust(0.08,0.10,1.0,0.93,0.2,0.08)
pl.subplot(111)
# Calculate bounds in figure coordinates
y00 = skewty(pbottom)
y11 = skewty(ptop)
x00 = skewtx(Tleft,y00)
x11 = skewtx(Tright,y00)
# Spacing for some labels
hs = np.abs(x11-x00)/200.
vs = np.abs(y11-y00)/200.
"""
1. Create isotherms
"""
for T in isotherms:
y = np.array([skewty(pbottom),skewty(ptop)])
x = np.array([skewtx(T,y[0]),skewtx(T,y[1])])
# Check if partially out of bounds
lloc = 0
if(x[0] < x00):
x[0] = x00
y[0] = iskewtxT(x00,T)
if(x[1] > x11):
x[1] = x11
y[1] = iskewtxT(x11,T)
if(x[1] >= x00 and y[1] >= y00):
pl.plot(x,y,color=c2,alpha=a1)
if(x[0] > x00 and x[0] < x11):
pl.text(x[0],y[0]-2*hs,int(T),ha='center',va='top',color=c2)
else:
pl.text(x[1]-5*hs,y[1]-5*vs,int(T),ha='center',va='center',color=c2,alpha=a2)
"""
2. Create isobars
"""
for p in isobars:
# Check if out of bounds
if((p >= ptop) and (p <= pbottom)):
y = skewty(p)
pl.plot([x00,x11],[y,y],color=c2,alpha=a1)
pl.text(x00-hs,y,int(p/100.),ha='right',va='center',color=c2)
"""
3. Create dry adiabats
"""
# Try loading the data from the tmp directory. If not available, calculate
# and save the arrays
try:
p = np.load(tmpPath+'theta_p_%i.arr'%si.stype)
y = np.load(tmpPath+'theta_y_%i.arr'%si.stype)
x = np.load(tmpPath+'theta_x_%i.arr'%si.stype)
except:
p = np.arange(pbottom,(np.max(([ptop,ptop_thd]))-dp),-dp)
x = np.zeros((dryadiabats.size, p.size))
y = np.zeros(p.size)
for k in range(p.size):
y[k] = skewty(p[k])
for i in range(dryadiabats.size):
x[i,:] = 0.
for k in range(p.size):
xtmp = skewtx(((dryadiabats[i]+T0) * exner(p[k]))-T0, y[k])
if(xtmp >= x00 and xtmp <= x11):
x[i,k] = xtmp
else:
x[i,k] = -9999
p.dump(tmpPath+'theta_p_%i.arr'%si.stype)
y.dump(tmpPath+'theta_y_%i.arr'%si.stype)
x.dump(tmpPath+'theta_x_%i.arr'%si.stype)
# Plot the dry adiabats
for i in range(dryadiabats.size):
doPlot = np.where(x[i,:] != -9999)[0]
if(doPlot[0].size > 0):
lloc = max(0,np.size(x[i,doPlot])-int(5000./dp))
pl.plot(x[i,doPlot],y[doPlot],color=c1)
pl.text(x[i,doPlot][lloc],y[doPlot][lloc],int(dryadiabats[i]),color=c1,ha='center',va='center',backgroundcolor='w')
"""
4. Create moist adiabats
"""
# Try loading the data from the tmp directory. If not available, calculate
# and save the arrays
try:
p = np.load(tmpPath+'thetam_p_%i.arr'%si.stype)
y = np.load(tmpPath+'thetam_y_%i.arr'%si.stype)
x = np.load(tmpPath+'thetam_x_%i.arr'%si.stype)
except:
p = np.arange(np.min(([pbottom,pbottom_thw])),ptop-dp,-dp)
x = np.zeros((moistadiabats.size, p.size))
y = np.zeros(p.size)
for k in range(p.size):
y[k] = skewty(p[k])
for i in range(moistadiabats.size):
for k in range(p.size):
thw = dsatlftskewt(moistadiabats[i], p[k])
xtmp = skewtx(thw, y[k])
if(xtmp >= x00 and xtmp <= x11):
x[i,k] = xtmp
else:
x[i,k] = -9999
p.dump(tmpPath+'thetam_p_%i.arr'%si.stype)
y.dump(tmpPath+'thetam_y_%i.arr'%si.stype)
x.dump(tmpPath+'thetam_x_%i.arr'%si.stype)
# Plot the moist adiabats
for i in range(moistadiabats.size):
doPlot = np.where(x[i,:] != -9999)[0]
if(doPlot[0].size > 0):
lloc = max(0,np.size(x[i,doPlot])-int(8000./dp))
pl.plot(x[i,doPlot],y[doPlot],'--',color=c3)
pl.text(x[i,doPlot][lloc],y[doPlot][lloc],int(moistadiabats[i]),color=c3,ha='center',va='center',backgroundcolor='w')
"""
5. Create isohumes / mixing ratio lines
"""
# Try loading the data from the tmp directory. If not available, calculate
# and save the arrays
try:
p = np.load(tmpPath+'mixr_p_%i.arr'%si.stype)
y = np.load(tmpPath+'mixr_y_%i.arr'%si.stype)
x = np.load(tmpPath+'mixr_x_%i.arr'%si.stype)
except:
p = np.arange(pbottom,(np.max(([ptop,ptop_mxr]))-dp),-dp)
x = np.zeros((mixrat.size, p.size))
y = np.zeros(p.size)
for k in range(p.size):
y[k] = skewty(p[k])
for i in range(mixrat.size):
for k in range(p.size):
mix = Td(mixrat[i]/1000.,p[k])-T0
xtmp = skewtx(mix,y[k])
if(xtmp >= x00 and xtmp <= x11):
x[i,k] = xtmp
else:
x[i,k] = -9999
p.dump(tmpPath+'mixr_p_%i.arr'%si.stype)
y.dump(tmpPath+'mixr_y_%i.arr'%si.stype)
x.dump(tmpPath+'mixr_x_%i.arr'%si.stype)
# Plot the mixing ratio lines
for i in range(mixrat.size):
doPlot = np.where(x[i,:] != -9999)[0]
if(doPlot[0].size > 0):
pl.plot(x[i,doPlot],y[doPlot],color=c5,dashes=[3,2])
pl.text(x[i,doPlot][-1],y[doPlot][-1]+vs,int(mixrat[i]),color=c5,ha='center',va='bottom',backgroundcolor='w')
"""
6. Add sounding data
"""
# 6.1 Temperature and dewpoint temperature
if(si.T.size > 0):
y = skewty(si.p)
x1 = skewtx(si.T-T0,y)
x2 = skewtx(si.Td-T0,y)
pl.plot(x1,y,'-',color=cT,linewidth=2)
pl.plot(x2,y,'-',color=cTd,linewidth=2)
# 6.2 Add height labels to axis
if(si.z.size > 0 and si.z.size==si.p.size):
y = skewty(si.p)
p_last = 1e9
for k in range(si.z.size):
if(y[k] <= y11 and np.abs(si.p[k]-p_last) > dp_label):
pl.text(x11+hs,y[k],str(int(si.z[k]))+'m',color=c2,ha='right',va='center',size=7,backgroundcolor='w')
p_last = si.p[k]
# 6.2 Wind barbs
if(si.u.size > 0):
y = skewty(si.p)
u = si.u * 1.95
v = si.v * 1.95
xb = x11 + 9*hs
p_last = 1e9
for k in range(si.z.size):
if(y[k] <= y11 and np.abs(si.p[k]-p_last) > dp_label):
pl.barbs(xb,y[k],u[k],v[k],length=5.2,linewidth=0.5,pivot='middle')
p_last = si.p[k]
"""
6.1 :) Try to add measured data
"""
# 6.1 Temperature and dewpoint temperature
if(si.Tm.size > 0):
y = skewty(si.pm)
x1 = skewtx(si.Tm,y)
x2 = skewtx(si.Tdm,y)
pl.plot(x1,y,'--',color=cT,linewidth=1)
pl.plot(x2,y,'--',color=cTd,linewidth=1)
"""
7. Lauch parcel
"""
if(si.parcel == True):
# Plot starting position:
p0s = skewty(si.ps)
T0s = skewtx(si.Ts-T0, p0s)
Td0s = skewtx(Td(si.rs,si.ps)-T0, p0s)
pl.scatter(T0s, p0s, facecolor='none')
pl.scatter(Td0s,p0s, facecolor='none')
# Lists to hold parcel pressure, temp and dewpoint temp during ascent
pp = [si.ps]
Tp = [si.Ts]
Tdp = [Td(si.rs, si.ps)]
# Launch parcel
n = 0
while(Tp[-1] > Tdp[-1] and n<1000):
n += 1
dp2 = max(1,(Tp[-1] - Tdp[-1])*300) # bit weird, but fast
pp. append(pp[-1] - dp2)
Tp. append(si.Ts*exner(pp[-1], si.ps))
Tdp.append(Td(si.rs, pp[-1]))
# Plot lines from surface --> LCL
ps = skewty(np.array(pp))
Tps = skewtx(np.array(Tp)-T0, ps)
Tdps = skewtx(np.array(Tdp)-T0, ps)
pl.plot(Tps, ps, 'k', linewidth=1.5, dashes=[4,2])
pl.plot(Tdps, ps, 'k', linewidth=1.5, dashes=[4,2])
pl.scatter(Tps[-1], ps[-1], facecolor='none')
# Iteratively find the moist adiabat starting at p0, which goes through the temperature at LCL
Ths = si.Ts / exner(si.ps, p0) # potential temperature surface (@p0)
ThwsLCL = dsatlftskewt(Ths-T0, pp[-1])+T0 # Temp moist adiabat at plcl, through Ths
thw0 = Ths - (ThwsLCL - Tp[-1]) # First estimate of moist adiabat passing through Tlcl
ThwLCL = dsatlftskewt(thw0-T0, pp[-1])+T0
while(np.abs(ThwLCL-Tp[-1]) > 0.1):
thw0 -= ThwLCL-Tp[-1]
ThwLCL = dsatlftskewt(thw0-T0, pp[-1])+T0
# Plot moist adiabat from LCL upwards
p = np.arange(pp[-1], ptop, -dp)
x = np.zeros(p.size)
y = np.zeros(p.size)
for k in range(p.size):
thw = dsatlftskewt(thw0-T0,p[k])
y[k] = skewty(p[k])
x[k] = skewtx(thw,y[k])
pl.plot(x,y,'k', linewidth=1.5, dashes=[4,2])
"""
Add info from TEMF boundary layer scheme
"""
if(si.Tu.size > 0):
# Cloud fraction
dw = (x11-x00) # width of diagram
y = skewty(si.p)
x = x00 + si.cfru * 0.2 * (x11-x00)
pl.plot(x,y,'-',linewidth=1.5,color=c6) # cloud cover
cfr_pos = np.where(si.cfru > 0.001)[0]
if(np.size(cfr_pos)>1):
pl.text(x00,y[cfr_pos[0]-1],'- %im'%(si.z[cfr_pos[0]-1]),ha='left',va='center',size=8,color=c6)
pl.text(x00,y[cfr_pos[-1]],'- %im'%(si.z[cfr_pos[-1]]),ha='left',va='center',size=8,color=c6)
kmax=np.where(si.cfru == si.cfru.max())[0][0]
pl.text(x.max(),y[kmax],'- %i%%'%(si.cfru[kmax]*100.),ha='left',va='center',size=8,color=c6)
"""
6. Finish diagram
"""
pl.xticks([])
pl.yticks([])
pl.box('off')
pl.xlim(x00-0.1,x11+16*hs)
pl.ylim(y00-0.1,y11+0.1)
# draw axis
pl.plot([x00,x11],[y00,y00],'k-')
pl.plot([x00,x00],[y00,y11],'k-')
pl.figtext(0.5,0.05,'Temperature (C)',ha='center')
pl.figtext(0.015,0.52,'Pressure (hPa)',rotation=90)
label = 'Skew-T log-P, %s, %s UTC'%(si.name,si.time)
pl.figtext(0.5,0.97,label,ha='center')
if(olga != -1):
img = image.imread(olga.olgaRoot+'include/olga_left.png')
pl.figimage(img,7,5)
#pl.figimage(img,10,olga.fig_width_px-45)
pl.figtext(0.99,0.011,'%s'%olga.map_desc[0],size=7,ha='right')
return fig
def plot_vwp(data,
times,
parameters,
fname=None,
add_hodo=False,
fixed=False,
web=False,
archive=False):
img_title = "%s VWP valid ending %s" % (
data[0].rid, times[0].strftime("%d %b %Y %H%M UTC"))
if fname is not None:
img_file_name = fname
else:
img_file_name = "%s_vwp.png" % data[0].rid
sat_age = 6 * 3600
now = datetime.utcnow()
img_age = now - times[0]
age_cstop = min(_total_seconds(img_age) / sat_age, 1) * 0.4
age_color = mpl.cm.get_cmap('hot')(age_cstop)[:-1]
age_str = "Image created on %s (%s old)" % (
now.strftime("%d %b %Y %H%M UTC"), _fmt_timedelta(img_age))
fig_aspect = 2.5714
fig_wid = 24
fig_hght = fig_wid / fig_aspect
pylab.figure(figsize=(fig_wid, fig_hght), dpi=200)
axes_left = 0.01
axes_bot = 0.02
axes_hght = 0.94
axes_wid = axes_hght / fig_aspect
pylab.axes((axes_left, axes_bot, 0.99, axes_hght))
_plot_vwp_background(times)
_plot_vwp_data(data)
#_plot_param_table(parameters, web=web)
pylab.xlim(0, 1.)
pylab.ylim(0, 1.)
pylab.xticks([])
pylab.yticks([])
pylab.box(False)
if not archive:
pylab.title(img_title, color=age_color)
pylab.text(x_start,
1.03,
age_str,
transform=pylab.gca().transAxes,
ha='left',
va='top',
fontsize=9,
color=age_color)
else:
pylab.title(img_title)
if web:
web_brand = "http://www.autumnsky.us/vad/"
pylab.text(1.0,
-0.01,
web_brand,
transform=pylab.gca().transAxes,
ha='right',
va='top',
fontsize=9)
if add_hodo:
inset_ax = inset_axes(pylab.gca(),
width="30%",
height="55%",
loc='upper left',
bbox_to_anchor=(0.63, 0, 0.85, 1),
bbox_transform=pylab.gca().transAxes)
u, v = vec2comp(data[0]['wind_dir'], data[0]['wind_spd'])
if fixed or len(u) == 0:
ctr_u, ctr_v = 20, 20
size = 120
else:
ctr_u = u.mean()
ctr_v = v.mean()
size = max(u.max() - u.min(), v.max() - v.min()) + 20
size = max(120, size)
min_u = ctr_u - size / 2
max_u = ctr_u + size / 2
min_v = ctr_v - size / 2
max_v = ctr_v + size / 2
_plot_background(min_u, max_u, min_v, max_v)
_plot_data(data[0], parameters)
_plot_param_table(parameters, web=web)
inset_ax.set_xlim(min_u, max_u)
inset_ax.set_ylim(min_v, max_v)
inset_ax.set_xticks([])
inset_ax.set_yticks([])
pylab.savefig(img_file_name, dpi=pylab.gcf().dpi)
pylab.close()
if web:
bounds = {
'min_u': min_u,
'max_u': max_u,
'min_v': min_v,
'max_v': max_v
}
print(json.dumps(bounds))
def main():
print("Loading data...")
#args
parser = argparse.ArgumentParser()
parser.add_argument("--input_file", type=str)
parser.add_argument("--epochs", type=int)
parser.add_argument("--load_npz", type=str)
args = parser.parse_args()
task_params = eval(args.input_file.split(".csv")[0] + '_params')
ALL_TIME = time.time()
traindata, valdata, testdata = load_data(
task_params['data_file'],
(task_params['train'], task_params['val'], task_params['test']),
input_name='smiles',
target_name=task_params['target_name'])
x_trains, y_trains = traindata
x_vals, y_vals = valdata
x_tests, y_tests = testdata
x_trains = np.reshape(x_trains, (task_params['train'], 1))
y_trains = np.reshape(y_trains, (task_params['train'], 1))
x_vals = np.reshape(x_vals, (task_params['val'], 1))
y_vals = np.reshape(y_vals, (task_params['val'], 1))
x_tests = np.reshape(x_tests, (task_params['test'], 1))
y_tests = np.reshape(y_tests, (task_params['test'], 1)).astype(np.float32)
def run_conv_experiment():
'''Initialize model'''
NNFP = Main(model_params)
optimizer = optimizers.Adam()
optimizer.setup(NNFP)
#gpu_device = 0
#cuda.get_device(gpu_device).use()
#NNFP.to_gpu(gpu_device)
'''Learn'''
trained_NNFP, conv_training_curve, undo_norm = \
train_nn(NNFP,
x_trains, y_trains,
args.epochs,
validation_smiles=x_vals,
validation_raw_targets=y_vals)
save_name = "input-attention-ecfp-cep-top-remove.npz"
serializers.save_npz(save_name, trained_NNFP)
mse, _ = trained_NNFP.mse(x_tests, y_tests, undo_norm)
return math.sqrt(mse._data[0]), conv_training_curve
def load_model_experiment():
'''Initialize model'''
trained_NNFP = Main(model_params)
serializers.load_npz(args.load_npz, trained_NNFP)
_, undo_norm = normalize_array(y_tests)
mse, input_attention = trained_NNFP.mse(x_tests, y_tests, undo_norm)
return math.sqrt(mse._data[0]), input_attention
print("Starting neural fingerprint experiment...")
if args.load_npz == None:
test_loss_neural, conv_training_curve = run_conv_experiment()
else:
test_loss_neural, input_attention = load_model_experiment()
x_ecfp = input_attention._data[0]
y = [0] * len(x_ecfp)
attentions = np.split(x_ecfp, 5, 1)
xmin, xmax = 0, 1
for i in range(len(attentions)):
fig, ax = plt.subplots(figsize=(10, 10))
fig.set_figheight(1)
plt.tight_layout()
plt.tick_params(labelbottom=True, bottom=False)
plt.tick_params(labelleft=False, left=False)
plt.scatter(attentions[i], y, c="red", marker="o", alpha=0.3)
plt.hlines(y=0, xmin=xmin, xmax=xmax)
plt.vlines(x=[i for i in range(xmin, xmax + 1, 1)],
ymin=-0.04,
ymax=0.04)
plt.vlines(x=[i / 10 for i in range(xmin * 10, xmax * 10 + 1, 1)],
ymin=-0.02,
ymax=0.02)
line_width = 0.1
plt.xticks(np.arange(xmin, xmax + line_width, line_width))
pylab.box(False)
#plt.savefig(args.input_file + '_attention_ecfp_' + str(i) + '.png')
plt.savefig("test.png")
#plt.show()
print("Neural test RMSE", test_loss_neural)
print("time : ", time.time() - ALL_TIME)
pl.title("Right Parietal", fontsize="large")
elif grp == "Occipital":
pl.subplot(3, 4, 11)
pl.title("Right Occipital", fontsize="large")
####Reading the Evoked data structure
for (cfname, l) in zip(condEveFiles, colorList):
evoked = mne.fiff.Evoked(cfname, setno="epochs_TaggedWord", baseline=(None, 0))
# evoked = mne.fiff.Evoked(fname, setno = c , baseline = (None, 0)) ##Use this if you are using condition numbers
badChanSet = set(evoked.info["bads"])
# print c
good_chan = list(set(chan_list))
sel = mne.fiff.pick_types(evoked.info, meg=False, eeg=False, include=good_chan)
print sel
data = evoked.data[sel]
###Computing the MEG RMS from the evoked data for the specified condition
times = evoked.times * 1000
square = np.power(data, 2)
meanSquare = np.mean(square, 0)
rms = np.power(meanSquare, 0.5)
###Plotting the MEG rms value for the current condition
pl.plot(times, rms * 1e13, color=l, linewidth=2)
pl.ylim([ymin, ymax])
pl.xlim([xmin, xmax])
pl.box("off")
pl.tick_params(axis="both", right="off", top="off")
pl.yticks(np.array([0.0, 4.0, 8.0, 12.0, 16.0, 20.0, 24.0, 28.0, 32.0]))
pl.xticks(np.array([0, 200, 400, 600]))
pl.savefig(out_fname)
pl.show()
def check_metric_ellipse(width=2e-2, eta=0.02, Nadapt=6):
set_log_level(WARNING)
parameters["allow_extrapolation"] = True
### CONSTANTS
meshsz = 40
hd = Constant(width)
### SETUP MESH
mesh = RectangleMesh(-0.5, -0.5, 0.5, 0.5, 1 * meshsz, 1 * meshsz,
"left/right")
### SETUP SOLUTION
angle = pi / 8 #rand()*pi/2
#testsol = 'tanh(x[0]/' + str(float(hd)) + ')' #tanh(x[0]/hd)
testsol = 'tanh((' + str(cos(angle)) + '*x[0]+' + str(
sin(angle)) + '*x[1])/' + str(float(hd)) + ')' #tanh(x[0]/hd)
ddtestsol = str(cos(angle) + sin(angle)
) + '*2*' + testsol + '*(1-pow(' + testsol + ',2))/' + str(
float(hd)**2)
#testsol2 = 'tanh(x[1]/' + str(float(hd)) + ')' #tanh(x[0]/hd)
testsol2 = 'tanh((' + str(cos(angle)) + '*x[1]-' + str(
sin(angle)) + '*x[0])/' + str(float(hd)) + ')' #tanh(x[0]/hd)
ddtestsol2 = str(cos(angle) - sin(
angle)) + '*2*' + testsol2 + '*(1-pow(' + testsol2 + ',2))/' + str(
float(hd)**2)
def boundary(x):
return x[0]-mesh.coordinates()[:,0].min() < DOLFIN_EPS or mesh.coordinates()[:,0].max()-x[0] < DOLFIN_EPS \
or mesh.coordinates()[:,1].min()+0.5 < DOLFIN_EPS or mesh.coordinates()[:,1].max()-x[1] < DOLFIN_EPS
# PERFORM ONE ADAPTATION ITERATION
for iii in range(Nadapt):
V = FunctionSpace(mesh, "CG", 2)
dis = TrialFunction(V)
dus = TestFunction(V)
u = Function(V)
u2 = Function(V)
bc = DirichletBC(V, Expression(testsol), boundary)
bc2 = DirichletBC(V, Expression(testsol2), boundary)
R = interpolate(Expression(ddtestsol), V)
R2 = interpolate(Expression(ddtestsol2), V)
a = inner(grad(dis), grad(dus)) * dx
L = R * dus * dx
L2 = R2 * dus * dx
solve(a == L, u, bc)
solve(a == L2, u2, bc2)
H = metric_pnorm(u, eta, max_edge_length=1., max_edge_ratio=50)
#Mp = project(H, TensorFunctionSpace(mesh, "CG", 1))
H2 = metric_pnorm(u2, eta, max_edge_length=1., max_edge_ratio=50)
#Mp2 = project(H2, TensorFunctionSpace(mesh, "CG", 1))
H3 = metric_ellipse(H, H2)
Mp3 = project(H3, TensorFunctionSpace(mesh, "CG", 1))
print("H11: %0.0f, H22: %0.0f, V: %0.0f,E: %0.0f" %
(assemble(abs(H3[0, 0]) * dx), assemble(
abs(H3[1, 1]) * dx), mesh.num_vertices(), mesh.num_cells()))
startTime = time()
if iii != 6:
# mesh2 = Mesh(adapt(Mp2))
mesh = Mesh(adapt(Mp3))
# mesh3 = adapt(Mp)
print("total time was %0.1fs" % (time() - startTime))
# PLOT MESH
figure(1)
triplot(mesh.coordinates()[:, 0],
mesh.coordinates()[:, 1], mesh.cells())
axis('equal')
axis('off')
box('off')
#figure(2); triplot(mesh2.coordinates()[:,0],mesh2.coordinates()[:,1],mesh2.cells()) #mesh = mesh2
#figure(3); triplot(mesh3.coordinates()[:,0],mesh3.coordinates()[:,1],mesh3.cells()) #mesh = mesh3
figure(4)
testf = interpolate(u2, FunctionSpace(mesh, 'CG', 1))
vtx2dof = vertex_to_dof_map(FunctionSpace(mesh, "CG", 1))
zz = testf.vector().array()[vtx2dof]
zz[zz == 1] -= 1e-16
tricontourf(mesh.coordinates()[:, 0],
mesh.coordinates()[:, 1], mesh.cells(), zz, 100)
show()
def main():
usage="Usage: %prog [options] <images>\n"
parser = OptionParser(usage=usage)
parser.add_option('-o','--out',default='mosaic',
dest='outroot',
help='Root name for output')
parser.add_option('-t','--threshold',dest='threshold',default=0.05,
type='float',
help='Beam threshold [default=%default]')
parser.add_option('-v','--verbose',action="store_true",
dest="verbose",default=False,
help="Increase verbosity of output")
(options, args) = parser.parse_args()
images=args
newbeam=[re.sub('_regrid(\S+).fits','_beamI_regrid\\1.fits',i) for i in images]
ff0=pyfits.open('MWATS_201404_I_MOL.fits')
mask=ff0[1].data<=2
f=pyfits.open(images[0])
D=numpy.zeros(f[0].data.shape)
W=numpy.zeros(f[0].data.shape)
N=numpy.zeros(f[0].data.shape)
pylab.clf()
f=pylab.gcf()
f.set_figwidth(12)
f.set_figheight(6)
for i in xrange(len(images)):
#imagelist=' '.join(images[:i+1])
#command='python /localhome/kaplan/python/mosaic_images.py -i %s -t %f -o %s_%03d.fits %s' % (
#images[0],
# options.threshold,
# options.outroot,
# i,
# imagelist)
#print command
fi=pyfits.open(images[i])
try:
fb=pyfits.open(newbeam[i])
except:
#newbeam[i]=newbeam[i].replace('-v_','-i_')
newbeam[i]=newbeam[i].replace(Vstring,Istring)
fb=pyfits.open(newbeam[i])
beam=(fb[0].data)
beam[0,0][numpy.isnan(fi[0].data[0,0])]=0
beam[0,0][beam[0,0]<=options.threshold*beam[0,0].max()]=0
fi[0].data[0,0][numpy.isnan(fi[0].data[0,0])]=0
D[0,0]+=fi[0].data[0,0]*beam[0,0]**2
W[0,0]+=beam[0,0]**2
N[0,0]+=beam[0,0]
DD=D/W
DD[0,0][W[0,0]==0]=numpy.nan
DD[mask]=numpy.nan
fi[0].data=DD
if os.path.exists('temp.fits'):
os.remove('temp.fits')
fi.writeto('temp.fits')
w=wcs.WCS(fi[0].header,naxis=(1,2))
f.clf()
gc=aplpy.FITSFigure('temp.fits',figure=f)
gc.show_colorscale(vmin=-0.1,vmax=0.1,cmap=pylab.cm.copper)
gc.recenter(14*15,-22.5, width=160, height=95)
gc.axis_labels.hide_x()
gc.axis_labels.hide_y()
gc.tick_labels.hide()
gc.add_label(0.1,0.9,'%03d' % i, relative=True,
size=16)
ra=numpy.linspace(8,20,50)*15
for dec in xrange(-80,30,10):
x,y=w.wcs_world2pix(ra,ra*0+dec,0)
pylab.plot(x,y,'k')
if dec < 20 and dec>-80:
gc.add_label(15*16,dec,'$\\delta=%d^\\ocirc$' % dec,
size=12,verticalalignment='bottom')
dec=numpy.linspace(-80,20,50)
for ra in xrange(8,22,2):
ra_show=ra
if ra_show < 0:
ra_show+=24
x,y=w.wcs_world2pix(0*dec+ra_show*15,dec,0)
pylab.plot(x,y,'k')
gc.add_label(ra*15,-45,'$\\alpha=%d^h$' % ra_show,
size=12,verticalalignment='bottom',
horizontalalignment='left')
ax=pylab.gca()
ax.axis('off')
pylab.box('off')
pylab.savefig('MWATS_201404_I_MOL_%03d.png' % i)
print 'MWATS_201404_I_MOL_%03d.png' % i
if os.path.exists('temp.fits'):
os.remove('temp.fits')
#tkin
ax = pl.subplot(projection=cube_wcs.celestial)
ax.set(xlim=(50, 270), ylim=(60, 240))
ax.coords[0].set_ticks_visible(False)
ax.coords[1].set_ticks_visible(False)
ax.coords[0].set_ticklabel_visible(False)
ax.coords[1].set_ticklabel_visible(False)
ax.set_axis_off
pl.imshow(cube_hdu.data[0, :, :],
origin='lower',
cmap='plasma',
vmin=20,
vmax=140)
cbar = pl.colorbar()
#cbar.ax.set_ylabel('K', rotation=0)
pl.box(on=None)
scalebar = ScaleBar(1.53)
pl.gca().add_artist(scalebar)
pl.title('Kinematic Temperature (K)', fontsize=18)
pl.savefig('tkin.pdf')
pl.close()
#tex
ax = pl.subplot(projection=cube_wcs.celestial)
ax.set(xlim=(50, 270), ylim=(60, 240))
ax.coords[0].set_ticks_visible(False)
ax.coords[1].set_ticks_visible(False)
ax.coords[0].set_ticklabel_visible(False)
ax.coords[1].set_ticklabel_visible(False)
ax.set_axis_off
pl.imshow(cube_hdu.data[1, :, :], origin='lower', cmap='bone')
FOS.sort(key=lambda x: len(x), reverse=True)
for element in FOS:
# if len(element) < 4:
# continue
count += 1
for xi in element:
for i in range(size_of_fos):
matrix[i + ((count - 1) * size_of_fos)][xi] = len(FOS) - count
print(count)
current_cmap = pylab.matplotlib.cm.get_cmap()
current_cmap.set_bad(color='white')
pylab.imshow(matrix[:size_of_fos * count])
pylab.box(False)
pylab.xlim(xmin=0, xmax=20)
pylab.xticks([x for x in range(0, 22, 2)])
pylab.tick_params(top='off',
bottom='off',
left='off',
right='off',
labelleft='off',
labelbottom='on')
# pylab.axes().xaxis.set_visible(True)
if full == "":
pylab.title(f"Sparse FOS of CEC 2013 f{problem}\n ")
elif full == "_overlap":
pylab.title(f"Manual FOS of CEC 2013 f{problem}\n ")
else:
pylab.title(f"FOS of FBMP on OSoREB \n ")
def main():
print("Loading data...")
#args
parser = argparse.ArgumentParser()
parser.add_argument("--input_file", type=str)
parser.add_argument("--epochs", type=int)
parser.add_argument("--fp_length", type=int)
parser.add_argument("--i", type=int)
parser.add_argument("--load_npz", type=str)
args = parser.parse_args()
model_params['fp_length'] = args.fp_length
task_params = eval(args.input_file.split(".csv")[0] + '_params')
ALL_TIME = time.time()
traindata, valdata, testdata = load_data(
task_params['data_file'],
(task_params['train'], task_params['val'], task_params['test']),
input_name='smiles',
target_name=task_params['target_name'])
x_trains, y_trains = traindata
x_vals, y_vals = valdata
x_tests, y_tests = testdata
x_trains = np.reshape(x_trains, (task_params['train'], 1))
y_trains = np.reshape(y_trains, (task_params['train'], 1))
x_vals = np.reshape(x_vals, (task_params['val'], 1))
y_vals = np.reshape(y_vals, (task_params['val'], 1))
x_tests = np.reshape(x_tests, (task_params['test'], 1))
y_tests = np.reshape(y_tests, (task_params['test'], 1)).astype(np.float32)
def run_conv_experiment():
'''Initialize model'''
NNFP = Main(model_params)
optimizer = optimizers.Adam()
optimizer.setup(NNFP)
'''Learn'''
trained_NNFP, conv_training_curve, undo_norm = \
train_nn(NNFP,
x_trains, y_trains,
args.epochs,
validation_smiles=x_vals,
validation_raw_targets=y_vals)
#save_name = "fp_concat_" + args.input_file + "_fp_length_" + str(args.fp_length) + "_" + str(args.i) + ".npz"
save_name = "test_cep.npz"
serializers.save_npz(save_name, trained_NNFP)
mse, _, _ = trained_NNFP.mse(x_tests, y_tests, undo_norm)
return math.sqrt(mse._data[0]), conv_training_curve
def load_model_experiment():
'''Initialize model'''
trained_NNFP = Main(model_params)
serializers.load_npz(args.load_npz, trained_NNFP)
_, undo_norm = normalize_array(y_tests)
mse, attention_ecfp, attention_fcfp = trained_NNFP.mse(
x_tests, y_tests, undo_norm)
return math.sqrt(mse._data[0]), attention_ecfp, attention_fcfp
print("Starting neural fingerprint experiment...")
if args.load_npz == None:
test_loss_neural, conv_training_curve = run_conv_experiment()
else:
test_loss_neural, attention_ecfp, attention_fcfp = load_model_experiment(
)
x_ecfp = attention_ecfp._data[0]
x_fcfp = attention_fcfp._data[0]
print(x_fcfp, x_ecfp)
np.savetxt('weight_delaney.txt', x_ecfp, delimiter=' ')
y = [0] * len(x_ecfp)
fig, ax = plt.subplots(figsize=(10, 10))
fig.set_figheight(1)
ax.tick_params(labelbottom=True, bottom=False)
ax.tick_params(labelleft=False, left=False)
xmin, xmax = 0, 1
plt.tight_layout()
plt.scatter(x_ecfp,
y,
c="red",
marker="o",
alpha=0.3,
label="Input Representation 1")
plt.scatter(x_fcfp,
y,
c="blue",
marker="o",
alpha=0.3,
label="Input Representation 2")
plt.hlines(y=0, xmin=xmin, xmax=xmax)
plt.vlines(x=[i for i in range(xmin, xmax + 1, 1)],
ymin=-0.04,
ymax=0.04)
plt.vlines(x=[i / 10 for i in range(xmin * 10, xmax * 10 + 1, 1)],
ymin=-0.02,
ymax=0.02)
line_width = 0.1
plt.xticks(np.arange(xmin, xmax + line_width, line_width))
pylab.box(False)
#plt.legend(loc='upper right', bbox_to_anchor=(0.2,1,0.15,0), borderaxespad=0.)
#plt.show()
#plt.savefig("fp_scalar_" + args.input_file + ".png")
#plt.savefig("test.png")
print("Neural test RMSE", test_loss_neural)
print("time : ", time.time() - ALL_TIME)
def check_metric_ellipse(width=2e-2, eta = 0.02, Nadapt=6):
set_log_level(WARNING)
parameters["allow_extrapolation"] = True
### CONSTANTS
meshsz = 40
hd = Constant(width)
### SETUP MESH
mesh = RectangleMesh(Point(-0.5,-0.5),Point(0.5,0.5),1*meshsz,1*meshsz,"left/right")
### SETUP SOLUTION
angle = pi/8#rand()*pi/2
#testsol = 'tanh(x[0]/' + str(float(hd)) + ')' #tanh(x[0]/hd)
testsol = 'tanh((' + str(cos(angle)) + '*x[0]+'+ str(sin(angle)) + '*x[1])/' + str(float(hd)) + ')' #tanh(x[0]/hd)
ddtestsol = str(cos(angle)+sin(angle))+'*2*'+testsol+'*(1-pow('+testsol+',2))/'+str(float(hd)**2)
#testsol2 = 'tanh(x[1]/' + str(float(hd)) + ')' #tanh(x[0]/hd)
testsol2 = 'tanh((' + str(cos(angle)) + '*x[1]-'+ str(sin(angle)) + '*x[0])/' + str(float(hd)) + ')' #tanh(x[0]/hd)
ddtestsol2 = str(cos(angle)-sin(angle))+'*2*'+testsol2+'*(1-pow('+testsol2+',2))/'+str(float(hd)**2)
def boundary(x):
return x[0]-mesh.coordinates()[:,0].min() < DOLFIN_EPS or mesh.coordinates()[:,0].max()-x[0] < DOLFIN_EPS \
or mesh.coordinates()[:,1].min()+0.5 < DOLFIN_EPS or mesh.coordinates()[:,1].max()-x[1] < DOLFIN_EPS
# PERFORM ONE ADAPTATION ITERATION
for iii in range(Nadapt):
V = FunctionSpace(mesh, "CG" ,2); dis = TrialFunction(V); dus = TestFunction(V); u = Function(V); u2 = Function(V)
bc = DirichletBC(V, Expression(testsol), boundary)
bc2 = DirichletBC(V, Expression(testsol2), boundary)
R = interpolate(Expression(ddtestsol),V)
R2 = interpolate(Expression(ddtestsol2),V)
a = inner(grad(dis), grad(dus))*dx
L = R*dus*dx
L2 = R2*dus*dx
solve(a == L, u, bc)
solve(a == L2, u2, bc2)
H = metric_pnorm(u , eta, max_edge_length=1., max_edge_ratio=50); #Mp = project(H, TensorFunctionSpace(mesh, "CG", 1))
H2 = metric_pnorm(u2, eta, max_edge_length=1., max_edge_ratio=50); #Mp2 = project(H2, TensorFunctionSpace(mesh, "CG", 1))
H3 = metric_ellipse(H,H2); Mp3 = project(H3, TensorFunctionSpace(mesh, "CG", 1))
print("H11: %0.0f, H22: %0.0f, V: %0.0f,E: %0.0f" % (assemble(abs(H3[0,0])*dx),assemble(abs(H3[1,1])*dx),mesh.num_vertices(),mesh.num_cells()))
startTime = time()
if iii != 6:
# mesh2 = Mesh(adapt(Mp2))
mesh = Mesh(adapt(Mp3))
# mesh3 = adapt(Mp)
print("total time was %0.1fs" % (time()-startTime))
# PLOT MESH
figure(1); triplot(mesh.coordinates()[:,0],mesh.coordinates()[:,1],mesh.cells()); axis('equal'); axis('off'); box('off')
#figure(2); triplot(mesh2.coordinates()[:,0],mesh2.coordinates()[:,1],mesh2.cells()) #mesh = mesh2
#figure(3); triplot(mesh3.coordinates()[:,0],mesh3.coordinates()[:,1],mesh3.cells()) #mesh = mesh3
figure(4); testf = interpolate(u2,FunctionSpace(mesh,'CG',1))
vtx2dof = vertex_to_dof_map(FunctionSpace(mesh, "CG" ,1))
zz = testf.vector().array()[vtx2dof]; zz[zz==1] -= 1e-16
tricontourf(mesh.coordinates()[:,0],mesh.coordinates()[:,1],mesh.cells(),zz,100)
show()
def vis_flow_image_final(flow_pyramid,
flow_gt_pyramid,
images_list,
gray_images_list,
filename='./flow.png'):
num_contents = len(flow_pyramid) + len(flow_gt_pyramid) + len(
images_list) + len(gray_images_list)
nums_list = [
len(flow_pyramid),
len(flow_gt_pyramid),
len(images_list),
len(gray_images_list)
]
nums_list.sort()
cols = nums_list[-2]
if cols <= 3:
cols = nums_list[-1]
cols = 4
rows = math.ceil(num_contents / cols)
fig_dpi = 200
plt.rcParams['savefig.dpi'] = fig_dpi
plt.rcParams['figure.dpi'] = fig_dpi
fig = plt.figure()
fig_id = 1
for image in images_list:
plt.subplot(rows, cols, fig_id)
plt.imshow(image)
plt.tick_params(labelbottom=False, bottom=False)
plt.tick_params(labelleft=False, left=False)
plt.xticks([])
box(False)
fig_id += 1
for image in gray_images_list:
plt.subplot(rows, cols, fig_id)
plt.imshow(image, cmap='gray')
plt.tick_params(labelbottom=False, bottom=False)
plt.tick_params(labelleft=False, left=False)
plt.xticks([])
box(False)
fig_id += 1
for flow_gt in flow_gt_pyramid:
plt.subplot(rows, cols, fig_id)
plt.imshow(vis_flow(flow_gt))
plt.tick_params(labelbottom=False, bottom=False)
plt.tick_params(labelleft=False, left=False)
plt.xticks([])
box(False)
fig_id += 1
for flow in flow_pyramid:
plt.subplot(rows, cols, fig_id)
plt.imshow(vis_flow(flow))
plt.tick_params(labelbottom=False, bottom=False)
plt.tick_params(labelleft=False, left=False)
plt.xticks([])
box(False)
fig_id += 1
plt.tight_layout()
plt.savefig(filename, bbox_inches='tight', pad_inches=0.1, dpi=fig_dpi)
plt.close()
def maximal_example(eta_list = array([0.001]), Nadapt=5, timet=1., period=2*pi):
### CONSTANTS
### SETUP SOLUTION
#testsol = '0.1*sin(50*x+2*pi*t/T)+atan(-0.1/(2*x - sin(5*y+2*pi*t/T)))';
sx = Symbol('sx'); sy = Symbol('sy'); sT = Symbol('sT'); st = Symbol('st'); spi = Symbol('spi')
testsol = 0.1*pysin(50*sx+2*spi*st/sT)+pyatan(-0.1,2*sx - pysin(5*sy+2*spi*st/sT))
ddtestsol = str(diff(testsol,sx,sx)+diff(testsol,sy,sy)).replace('sx','x[0]').replace('sy','x[1]').replace('spi','pi')
# replacing **P with pow(,P)
ddtestsol = ddtestsol.replace("(2*x[0] - sin(5*x[1] + 2*pi*st/sT))**2","pow(2*x[0] - sin(5*x[1] + 2*pi*st/sT),2.)")
ddtestsol = ddtestsol.replace("cos(5*x[1] + 2*pi*st/sT)**2","pow(cos(5*x[1] + 2*pi*st/sT),2.)")
ddtestsol = ddtestsol.replace("(pow(2*x[0] - sin(5*x[1] + 2*pi*st/sT),2.) + 0.01)**2","pow((pow(2*x[0] - sin(5*x[1] + 2*pi*st/sT),2.) + 0.01),2.)")
ddtestsol = ddtestsol.replace("(1 + 0.01/pow(2*x[0] - sin(5*x[1] + 2*pi*st/sT),2.))**2","pow(1 + 0.01/pow(2*x[0] - sin(5*x[1] + 2*pi*st/sT),2.),2.)")
ddtestsol = ddtestsol.replace("(2*x[0] - sin(5*x[1] + 2*pi*st/sT))**5","pow(2*x[0] - sin(5*x[1] + 2*pi*st/sT),5.)")
#insert values
ddtestsol = ddtestsol.replace('sT',str(period)).replace('st',str(timet))
testsol = str(testsol).replace('sx','x[0]').replace('sy','x[1]').replace('spi','pi').replace('sT',str(period)).replace('st',str(timet))
ddtestsol = "-("+ddtestsol+")"
error_list = []; dof_list = []
for eta in eta_list:
meshsz = 40
### SETUP MESH
# mesh = RectangleMesh(0.4,-0.1,0.6,0.3,1*meshsz,1*meshsz,"left/right") #shock
# mesh = RectangleMesh(-0.75,-0.3,-0.3,0.5,1*meshsz,1*meshsz,"left/right") #waves
mesh = RectangleMesh(-1.5,-0.25,0.5,0.75,1*meshsz,1*meshsz,"left/right") #shock+waves
def boundary(x):
return near(x[0],mesh.coordinates()[:,0].min()) or near(x[0],mesh.coordinates()[:,0].max()) \
or near(x[1],mesh.coordinates()[:,1].min()) or near(x[1],mesh.coordinates()[:,1].max())
# PERFORM ONE ADAPTATION ITERATION
for iii in range(Nadapt):
startTime = time()
V = FunctionSpace(mesh, "CG" ,2); dis = TrialFunction(V); dus = TestFunction(V); u = Function(V)
# R = interpolate(Expression(ddtestsol),V)
a = inner(grad(dis), grad(dus))*dx
L = Expression(ddtestsol)*dus*dx #
bc = DirichletBC(V, Expression(testsol), boundary)
solve(a == L, u, bc)
soltime = time()-startTime
startTime = time()
H = metric_pnorm(u, eta, max_edge_ratio=50, CG0H=3, p=4)
metricTime = time()-startTime
if iii != Nadapt-1:
mesh = adapt(H)
TadaptTime = time()-startTime
L2error = errornorm(Expression(testsol), u, degree_rise=4, norm_type='L2')
printstr = "%5.0f elements, %0.0e L2error, adapt took %0.0f %% of the total time, (%0.0f %% of which was the metric calculation)" \
% (mesh.num_cells(),L2error,TadaptTime/(TadaptTime+soltime)*100,metricTime/TadaptTime*100)
if len(eta_list) == 1:
print(printstr)
else:
error_list.append(L2error); dof_list.append(len(u.vector().array()))
print(printstr)
if len(dof_list) > 1:
dof_list = array(dof_list); error_list = array(error_list)
figure()
loglog(dof_list,error_list,'.b-',linewidth=2,markersize=16); xlabel('Degree of freedoms'); ylabel('L2 error')
# # PLOT MESH
# figure()
coords = mesh.coordinates().transpose()
# triplot(coords[0],coords[1],mesh.cells(),linewidth=0.1)
# #savefig('mesh.png',dpi=300) #savefig('mesh.eps');
figure() #solution
testf = interpolate(Expression(testsol),FunctionSpace(mesh,'CG',1))
vtx2dof = vertex_to_dof_map(FunctionSpace(mesh, "CG" ,1))
zz = testf.vector().array()[vtx2dof]
hh=tricontourf(coords[0],coords[1],mesh.cells(),zz,100)
colorbar(hh)
#savefig('solution.png',dpi=300) #savefig('solution.eps');
figure() #analytical solution
testfe = interpolate(u,FunctionSpace(mesh,'CG',1))
zz = testfe.vector().array()[vtx2dof]
hh=tricontourf(coords[0],coords[1],mesh.cells(),zz,100)
colorbar(hh)
#savefig('analyt.png',dpi=300) #savefig('analyt.eps');
figure() #error
zz -= testf.vector().array()[vtx2dof]; zz[zz==1] -= 1e-16
hh=tricontourf(mesh.coordinates()[:,0],mesh.coordinates()[:,1],mesh.cells(),zz,100,cmap=get_cmap('binary'))
colorbar(hh)
hold('on'); triplot(mesh.coordinates()[:,0],mesh.coordinates()[:,1],mesh.cells(),color='r',linewidth=0.5); hold('off')
axis('equal'); box('off'); title('error')
show()
pl.title("P2")
##pl.title(1.0, 1.0, 'C6', fontsize='medium', verticalalignment='top')
sel = fiff.pick_types(evoked.info,meg=False,eeg=False,include=[chan], exclude ="bads")
print sel
data = evoked.data[sel]*1e6
##Mean
region_mean = np.mean(data,0)
###plotting commands
pl.plot(times,region_mean,color=colorList[c],linewidth=lWidth) #plot the data
#pl.title(chan)
pl.ylim([eegymin,eegymax]) #set the y limits
pl.xlim([eegxmin,eegxmax]) #set the x limits
pl.box('off') # turn off the box frame
pl.axhline(y=0,xmin=0,xmax=1,color='k',linewidth=2) #draw a thicker horizontal line at 0
yfactor = abs(eegymax)+abs(eegymin)
pl.axvline(x=0,ymin=(.5-(vertScaleBar/float(yfactor))),ymax=(.5+(vertScaleBar/float(yfactor))),color='k',linewidth=2) #draw a vertical line at 0 that goes 1/8 of the range in each direction from the middle (e.g., if the range is -8:8, =16, 1/8 of 16=2, so -2:2).
pl.yticks(np.array([])) #turn off the y tick labels
pl.xticks(np.array([])) #turn off the x tick labels
pl.tick_params(axis='both',right='off',left='off',bottom='off',top='off') #turn off all the tick marks
#draw vertical lines every hundred ms
pl.axvline(x=100,ymin=.48, ymax=.52, color='k',linewidth=2)
pl.axvline(x=200,ymin=.48, ymax=.52, color='k',linewidth=2)
pl.axvline(x=300,ymin=.48, ymax=.52, color='k',linewidth=2)
pl.axvline(x=400,ymin=.48, ymax=.52, color='k',linewidth=2)
pl.axvline(x=500,ymin=.48, ymax=.52, color='k',linewidth=2)
pl.axvline(x=600,ymin=.48, ymax=.52, color='k',linewidth=2)
def simanalyze(
project=None,
image=None,
# if image==False:
imagename=None,
skymodel=None,
# else:
vis=None, modelimage=None, imsize=None, imdirection=None, cell=None,
interactive=None, niter=None, threshold=None,
weighting=None, mask=None, outertaper=None, pbcor=None, stokes=None,
featherimage=None,
# endif
analyze=None,
showuv=None, showpsf=None, showmodel=None,
showconvolved=None, showclean=None, showresidual=None, showdifference=None,
showfidelity=None,
graphics=None,
verbose=None,
overwrite=None,
dryrun=False,
logfile=None
):
#def simanalyze(project='sim', image=True, imagename='default', skymodel='', vis='default', modelimage='', imsize=[128, 128], imdirection='', cell='', interactive=False, niter=0, threshold='0.1mJy', weighting='natural', mask=[], outertaper=[''], stokes='I', featherimage='', analyze=False, showuv=True, showpsf=True, showmodel=True, showconvolved=False, showclean=True, showresidual=False, showdifference=True, showfidelity=True, graphics='both', verbose=False, overwrite=True, dryrun=False):
# Collect a list of parameter values to save inputs
in_params = locals()
import re
import glob
casalog.origin('simanalyze')
if verbose: casalog.filter(level="DEBUG2")
a = inspect.stack()
stacklevel = 0
for k in range(len(a)):
if (string.find(a[k][1], 'ipython console') > 0):
stacklevel = k
myf = sys._getframe(stacklevel).f_globals
# create the utility object:
myutil = simutil()
if logfile:
myutil.reportfile=logfile
myutil.openreport()
if verbose: myutil.verbose = True
msg = myutil.msg
from simutil import is_array_type
# put output in directory called "project"
fileroot = project
if not os.path.exists(fileroot):
msg(fileroot+" directory doesn't exist - the task expects to find results from creating the datasets there, like the skymodel.",priority="error")
# msg should raise an exception for priority=error
saveinputs = myf['saveinputs']
saveinputs('simanalyze',fileroot+"/"+project+".simanalyze.last")
# myparams=in_params)
if (not image) and (not analyze):
casalog.post("No operation to be done. Exiting from task.", "WARN")
return True
grscreen = False
grfile = False
if graphics == "both":
grscreen = True
grfile = True
if graphics == "screen":
grscreen = True
if graphics == "file":
grfile = True
try:
# Predefined parameters
pbcoeff = 1.13 ## PB defined as pbcoeff*lambda/d
# handle '$project' in modelimage
modelimage = modelimage.replace('$project',project)
featherimage = featherimage.replace('$project',project)
#=========================
# things we need: model_cell, model_direction if user doesn't specify -
# so find those first, and get information using util.modifymodel
# with skymodel=newmodel
# we need to parse either the mslist or imagename (if image=False)
# first, so that we can pick the appropriate skymodel,
# if there are several.
skymodel_searchstring="NOT SPECIFIED"
if not (image or dryrun):
user_imagename=imagename
if user_imagename=="default" or len(user_imagename)<=0:
images= glob.glob(fileroot+"/*image")
if len(images)<1:
msg("can't find any image in project directory",priority="error")
return False
if len(images)>1:
msg("found multiple images in project directory",priority="warn")
msg("using "+images[0],priority="warn")
imagename=images[0]
# trim .image suffix:
imagename= imagename.replace(".image","")
# if the user hasn't specified a sky model image, we can try to
# see if their imagename contains something like the project and
# configuration, as it would if simobserve created it.
user_skymodel=skymodel
if not os.path.exists(user_skymodel):
if os.path.exists(fileroot+"/"+user_skymodel):
user_skymodel=fileroot+"/"+user_skymodel
elif len(user_skymodel)>0:
raise Exception,"Can't find your specified skymodel "+user_skymodel
# try to strip a searchable identifier
tmpstring=user_skymodel.split("/")[-1]
skymodel_searchstring=tmpstring.replace(".image","")
if image:
# check for default measurement sets:
default_mslist = glob.glob(fileroot+"/*ms")
n_default=len(default_mslist)
# is the user requesting this ms?
default_requested=[]
for i in range(n_default): default_requested.append(False)
# parse ms parameter and check for existance;
# initial ms list
if vis=="default" or len(vis)==0:
mslist0=default_mslist
else:
mslist0 = vis.split(',')
# verified found ms list
mslist = []
mstype = []
mstoimage=[]
tpmstoimage=None
for ms0 in mslist0:
if not len(ms0): continue
ms1 = ms0.replace('$project',project)
# MSes in fileroot/ have priority
if os.path.exists(fileroot+"/"+ms1):
ms1 = fileroot + "/" + ms1
if os.path.exists(ms1)or dryrun:
mslist.append(ms1)
# mark as requested
if default_mslist.count(ms1):
i=default_mslist.index(ms1)
default_requested[i]=True
# check if noisy in name
if re.search('noisy.',ms1):
ms1_raw=str.join("",re.split('noisy.',ms1))
if default_mslist.count(ms1_raw):
i=default_mslist.index(ms1_raw)
default_requested[i]=True
else: # not noisy
if ms1.endswith(".sd.ms"):
ms1_noisy=re.split('.sd.ms',ms1)[0]+'.noisy.sd.ms'
else:
ms1_noisy=re.split('.ms',ms1)[0]+'.noisy.ms'
if default_mslist.count(ms1_noisy):
i=default_mslist.index(ms1_noisy)
default_requested[i]=True
if vis == "default": continue
msg("You requested "+ms1+" but there is a noisy version of the ms in your project directory - if your intent is to model noisy data you may want to check inputs",priority="warn")
# check if the ms is tp data or not.
if dryrun:
# HACK
mstype.append('INT')
mstoimage.append(ms1)
elif myutil.ismstp(ms1,halt=False):
mstype.append('TP')
tpmstoimage = ms1
# XXX TODO more than one TP ms will not be handled
# correctly
msg("Found a total power measurement set, %s." % ms1,origin='simanalyze')
else:
mstype.append('INT')
mstoimage.append(ms1)
msg("Found a synthesis measurement set, %s." % ms1,origin='simanalyze')
else:
msg("measurement set "+ms1+" not found -- removing from imaging list")
# check default mslist for unrequested ms:
for i in range(n_default):
if not default_requested[i]:
msg("Project directory contains "+default_mslist[i]+" but you have not requested to include it in your simulated image.")
if not mstoimage and len(tpmstoimage) == 0:
raise Exception,"No MS found to image"
# now try to parse the mslist for an identifier string that
# we can use to find the right skymodel if there are several
if len(mstoimage) == 0 and len(tpmstoimage) > 0:
tmpstring = tpmstoimage.split("/")[-1]
else:
tmpstring=(mstoimage[0]).split("/")[-1]
skymodel_searchstring=tmpstring.replace(".ms","")
# more than one to image?
if len(mstoimage) > 1:
msg("Multiple interferometric ms found:",priority="info",origin='simanalyze')
for i in range(len(mstoimage)):
msg(" "+mstoimage[i],priority="info",origin='simanalyze')
msg(" will be concated and simultaneously deconvolved; if something else is desired, please specify vis, or image manually and use image=F",priority="info",origin='simanalyze')
concatms=project+"/"+project+".concat.ms"
from concat import concat
weights = get_concatweights(mstoimage)
msg(" concat("+str(mstoimage)+",concatvis='"+concatms+"',visweightscale="+str(weights)+")",origin='simanalyze')
if not dryrun:
concat(mstoimage,concatvis=concatms,visweightscale=weights)
mstoimage=[concatms]
#========================================================
# now we can search for skymodel, and if there are several,
# pick the one that is closest to either the imagename,
# or the first MS if there are several MS to image.
components_only=False
# first look for skymodel, if not then compskymodel
skymodels=glob.glob(fileroot+"/"+project+"*.skymodel")+glob.glob(fileroot+"/"+project+"*.newmodel")
nmodels=len(skymodels)
skymodel_index=0
if nmodels>1:
msg("Found %i sky model images:" % nmodels,origin='simanalyze')
# use the skymodel_searchstring to try to pick the right one
# print them out for the user while we're at it.
for i in range(nmodels):
msg(" "+skymodels[i])
if skymodels[i].count(skymodel_searchstring)>0:
skymodel_index=i
msg("Using skymodel "+skymodels[skymodel_index],origin='simanalyze')
if nmodels>=1:
skymodel=skymodels[skymodel_index]
else:
skymodel=""
if os.path.exists(skymodel):
msg("Sky model image "+skymodel+" found.",origin='simanalyze')
else:
skymodels=glob.glob(fileroot+"/"+project+"*.compskymodel")
nmodels=len(skymodels)
if nmodels>1:
msg("Found %i sky model images:" % nmodels,origin='simanalyze')
for ff in skymodels:
msg(" "+ff)
msg("Using "+skymodels[0],origin='simanalyze')
if nmodels>=1:
skymodel=skymodels[0]
else:
skymodel=""
if os.path.exists(skymodel):
msg("Sky model image "+skymodel+" found.",origin='simanalyze')
components_only=True
elif not dryrun:
msg("Can't find a model image in your project directory, named skymodel or compskymodel - output image will be created, but comparison with the input model is not possible.",priority="warn",origin='simanalyze')
analyze=False
modelflat = skymodel+".flat"
if os.path.exists(skymodel):
if not (os.path.exists(modelflat) or dryrun):
myutil.flatimage(skymodel,verbose=verbose)
# modifymodel just collects info if skymodel==newmodel
(model_refdir,model_cell,model_size,
model_nchan,model_center,model_width,
model_stokes) = myutil.modifymodel(skymodel,skymodel,
"","","","","",-1,
flatimage=False)
cell_asec=qa.convert(model_cell[0],'arcsec')['value']
#####################################################################
# clean if desired, use noisy image for further calculation if present
# todo suggest a cell size from psf?
#####################################################################
if image:
# make sure cell is defined
if is_array_type(cell):
if len(cell) > 0:
cell0 = cell[0]
else:
cell0 = ""
else:
cell0 = cell
if len(cell0)<=0:
cell = model_cell
if is_array_type(cell):
if len(cell) == 1:
cell = [cell[0],cell[0]]
else:
cell = [cell,cell]
# cells are positive by convention
cell = [qa.abs(cell[0]),qa.abs(cell[1])]
# and imsize
if is_array_type(imsize):
if len(imsize) > 0:
imsize0 = imsize[0]
if len(imsize) > 1:
imsize1 = imsize[1]
else:
imsize1 = imsize0
else:
imsize0 = -1
else:
imsize0 = imsize
if imsize0 <= 0:
imsize = [int(pl.ceil(qa.convert(qa.div(model_size[0],cell[0]),"")['value'])),
int(pl.ceil(qa.convert(qa.div(model_size[1],cell[1]),"")['value']))]
else:
imsize=[imsize0,imsize1]
if len(mstoimage) == 0:
if tpmstoimage:
sd_only = True
else:
msg("no measurement sets found to image",priority="error",origin='simanalyze')
else:
sd_only = False
# get some quantities from the interferometric ms
# TODO use something like aU.baselineStats for this, and the 90% baseline
maxbase=0.
if len(mstoimage)>1 and dryrun:
msg("imaging multiple ms not possible in dryrun mode",priority="warn",origin="simanalyze")
# TODO make work better for multiple MS
for msfile in mstoimage:
if os.path.exists(msfile):
tb.open(msfile)
rawdata = tb.getcol("UVW")
tb.done()
maxbase = max([max(rawdata[0,]),max(rawdata[1,])]) # in m
psfsize = 0.3/qa.convert(qa.quantity(model_center),'GHz')['value']/maxbase*3600.*180/pl.pi # lambda/b converted to arcsec
minimsize = 8* int(psfsize/cell_asec)
elif dryrun:
minimsize = min(imsize)
psfsize = qa.mul(cell[0],3) # HACK
else:
raise Exception,mstoimage+" not found."
if imsize[0] < minimsize:
msg("The number of image pixel in x-axis, %d, is small to cover 8 x PSF. Setting x pixel number, %d." % (imsize[0], minimsize), priority='warn',origin='simanalyze')
imsize[0] = minimsize
if imsize[1] < minimsize:
msg("The number of image pixel in y-axis, %d, is small to cover 8 x PSF. Setting y pixel number, %d" % (imsize[1], minimsize), priority='warn',origin='simanalyze')
imsize[1] = minimsize
tpimage=None
# Do single dish imaging first if tpmstoimage exists.
if tpmstoimage and os.path.exists(tpmstoimage):
msg('creating image from ms: '+tpmstoimage,origin='simanalyze')
#if len(mstoimage):
# tpimage = project + '.sd.image'
#else:
# tpimage = project + '.image'
tpimage = project + '.sd.image'
tpimage = fileroot + "/" + tpimage
if len(mstoimage):
if len(modelimage) and tpimage != modelimage and \
tpimage != fileroot+"/"+modelimage:
msg("modelimage parameter set to "+modelimage+" but also creating a new total power image "+tpimage,priority="warn",origin='simanalyze')
msg("assuming you know what you want, and using modelimage="+modelimage+" in deconvolution",priority="warn",origin='simanalyze')
elif len(featherimage) and tpimage != featherimage and \
tpimage != fileroot+"/"+featherimage:
msg("featherimage parameter set to "+featherimage+" but also creating a new total power image "+tpimage,priority="warn",origin='simanalyze')
msg("assuming you know what you want, and using featherimage="+featherimage+" in feather",priority="warn",origin='simanalyze')
# Get PB size of TP Antenna
# !! aveant will only be set if modifymodel or setpointings and in
# any case it will the the aveant of the INTERFM array - we want the SD
if os.path.exists(tpmstoimage):
# antenna diameter
tb.open(tpmstoimage+"/ANTENNA")
diams = tb.getcol("DISH_DIAMETER")
tb.close()
aveant = pl.mean(diams)
# theoretical antenna beam size
import sdbeamutil
pb_asec = sdbeamutil.primaryBeamArcsec(qa.tos(qa.convert(qa.quantity(model_center),'GHz')),aveant,(0.75 if aveant==12.0 else 0.0),10.0)
elif dryrun:
aveant = 12.0
pb_asec = pbcoeff*0.29979/qa.convert(qa.quantity(model_center),'GHz')['value']/aveant*3600.*180/pl.pi
else:
raise Exception, tpmstoimage+" not found."
# default PSF from PB of antenna
imbeam = {'major': qa.quantity(pb_asec,'arcsec'),
'minor': qa.quantity(pb_asec,'arcsec'),
'positionangle': qa.quantity(0.0,'deg')}
# Common imaging parameters
sdim_param = dict(infiles=[tpmstoimage], overwrite=overwrite,
phasecenter=model_refdir, mode='channel',
nchan=model_nchan, start=0, width=1)
if True: #SF gridding
msg("Generating TP image using 'SF' kernel.",origin='simanalyze')
beamsamp = 9
sfcell_asec = pb_asec/beamsamp
sfcell = qa.tos(qa.quantity(sfcell_asec, "arcsec"))
cell_asec = [qa.convert(cell[0],"arcsec")['value'],
qa.convert(cell[1],"arcsec")['value']]
if cell_asec[0] > sfcell_asec or \
cell_asec[1] > sfcell_asec:
# imregrid() may not work properly for regrid of
# small to large cell
msg("The requested cell size is too large to invoke SF gridding. Please set cell size <= %f arcsec or grid TP MS '%s' manually" % (sfcell_asec, tpmstoimage),priority="error",origin='simanalyze')
sfsupport = 6
temp_out = tpimage+"0"
temp_cell = [sfcell, sfcell]
# too small - is imsize too small to start with?
# needs to cover all pointings.
temp_imsize = [int(pl.ceil(cell_asec[0]/sfcell_asec*imsize[0])),
int(pl.ceil(cell_asec[1]/sfcell_asec*imsize[1]))]
msg("Using predefined algorithm to define grid parameters.",origin='simanalyze')
msg("SF gridding summary",origin='simanalyze')
msg("- Antenna primary beam: %f arcsec" % pb_asec,origin='simanalyze')
msg("- Image pixels per antenna PB (predefined): %f" % beamsamp,origin='simanalyze')
msg("- Cell size (arcsec): [%s, %s]" % (temp_cell[0], temp_cell[1]),origin='simanalyze')
msg("- Imsize to cover final TP image area: [%d, %d] (type: %s)" % (temp_imsize[0], temp_imsize[1], type(temp_imsize[0])),origin='simanalyze')
msg("- convolution support: %d" % sfsupport,origin='simanalyze')
# kernel specific imaging parameters
sdim_param['gridfunction'] = 'SF'
sdim_param['convsupport'] = sfsupport
sdim_param['outfile'] = temp_out
sdim_param['imsize'] = temp_imsize
sdim_param['cell'] = temp_cell
msg(get_taskstr('sdimaging', sdim_param), priority="info")
if not dryrun:
sdimaging(**sdim_param)
if not os.path.exists(temp_out):
raise RuntimeError, "TP imaging failed."
# Scale image by convolved beam / antenna primary beam
ia.open(temp_out)
imbeam = ia.restoringbeam()
ia.close()
beam_area_ratio = qa.getvalue(qa.convert(imbeam['major'], "arcsec")) \
* qa.getvalue(qa.convert(imbeam['minor'], "arcsec")) \
/ pb_asec**2
msg("Scaling TP image intensity by %f." % (beam_area_ratio),origin='simanalyze')
temp_in = temp_out
temp_out = temp_out + ".scaled"
immath(imagename=temp_in, mode='evalexpr', expr="IM0*%f" % (beam_area_ratio),
outfile=temp_out)
if not os.path.exists(temp_out):
raise RuntimeError, "TP image scaling failed."
# Regrid TP image to final resolution
msg("Regridding TP image to final resolution",origin='simanalyze')
msg("- cell size (arecsec): [%s, %s]" % (cell[0], cell[1]),origin='simanalyze')
msg("- imsize: [%d, %d]" % (imsize[0], imsize[1]),origin='simanalyze')
if not dryrun:
ia.open(temp_out)
newcsys = ia.coordsys()
ia.close()
dir_idx = newcsys.findcoordinate("direction")['world']
newcsys.setreferencepixel([imsize[0]/2., imsize[1]/2.],
type="direction")
incr = newcsys.increment(type='direction')['numeric']
newincr = [incr[0]*cell_asec[0]/sfcell_asec,
incr[1]*cell_asec[1]/sfcell_asec,]
newcsys.setincrement(newincr, type="direction")
#
sdtemplate = imregrid(imagename=temp_out, template="get")
sdtemplate['csys'] = newcsys.torecord()
for idx in range(len(dir_idx)):
sdtemplate['shap'][ dir_idx[idx] ] = imsize[idx]
imregrid(imagename=temp_out, interpolation="cubic",
template=sdtemplate, output=tpimage,
overwrite=overwrite)
del newcsys, sdtemplate, incr, newincr, dir_idx
del temp_out, temp_cell, temp_imsize, sfcell_asec, cell_asec
else: #PB grid
msg("Generating TP image using 'PB' kernel.",origin='simanalyze')
# Final TP cell and image size.
# imsize and cell are already int and quantum arrays
sdimsize = imsize
sdcell = [qa.tos(cell[0]), qa.tos(cell[1])]
### TODO: need to set phasecenter properly based on imdirection
# kernel specific imaging parameters
sdim_param['gridfunction'] = 'PB'
sdim_param['outfile'] = tpimage
sdim_param['imsize'] = sdimsize
sdim_param['cell'] = sdcell
msg(get_taskstr('sdimaging', sdim_param), priority="info")
if not dryrun:
sdimaging(**sdim_param)
del sdimsize, sdcell
# TODO: Define PSF of image here
# for now use default
# get image beam size form TP image
if os.path.exists(tpimage):
ia.open(tpimage)
beam = ia.restoringbeam()
ia.close()
if sd_only:
bmarea = beam['major']['value']*beam['minor']['value']*1.1331 #arcsec2
bmarea = bmarea/(cell[0]['value']*cell[1]['value']) # bm area in pix
else: del beam
#del beam
msg('generation of total power image '+tpimage+' complete.',origin='simanalyze')
# update TP ms name the for following steps
sdmsfile = tpmstoimage
sd_any = True
imagename = re.split('.image$',tpimage)[0]
# End of single dish imaging part
outflat_current = False
convsky_current = False
if image and len(mstoimage) > 0:
# for reruns
foo=mstoimage[0]
foo=foo.replace(".ms","")
foo=foo.replace(project,"")
foo=foo.replace("/","")
project=project+foo
imagename = fileroot + "/" + project
# get nfld, sourcefieldlist, from (interfm) ms if it was not just created
# TODO make work better for multiple mstoimage (figures below)
if os.path.exists(mstoimage[0]):
tb.open(mstoimage[0]+"/SOURCE")
code = tb.getcol("CODE")
sourcefieldlist = pl.where(code=='OBJ')[0]
nfld = len(sourcefieldlist)
tb.done()
elif dryrun:
nfld=1 # HACK
msfile = mstoimage[0]
# set cleanmode automatically (for interfm)
if nfld == 1:
cleanmode = "csclean"
else:
cleanmode = "mosaic"
# clean insists on using an existing model if its present
if os.path.exists(imagename+".image"): shutil.rmtree(imagename+".image")
if os.path.exists(imagename+".model"): shutil.rmtree(imagename+".model")
# An image in fileroot/ has priority
if len(modelimage) > 0 and os.path.exists(fileroot+"/"+modelimage):
modelimage = fileroot + "/" + modelimage
msg("Found modelimage, %s." % modelimage,origin='simanalyze')
# in simdata we use imdirection instead of model_refdir
if not myutil.isdirection(imdirection,halt=False):
imdirection=model_refdir
myutil.imclean(mstoimage,imagename,
cleanmode,cell,imsize,imdirection,
interactive,niter,threshold,weighting,
outertaper,pbcor,stokes, #sourcefieldlist=sourcefieldlist,
modelimage=modelimage,mask=mask,dryrun=dryrun)
# create imagename.flat and imagename.residual.flat:
if not dryrun:
myutil.flatimage(imagename+".image",verbose=verbose)
myutil.flatimage(imagename+".residual",verbose=verbose)
outflat_current = True
# feather
if featherimage:
if not os.path.exists(featherimage):
raise Exception,"Could not find featherimage "+featherimage
else:
featherimage=""
if tpimage:
# if you set modelimage, then it won't force tpimage into
# featherimage. this could be hard to explain
# to the user.
if os.path.exists(tpimage) and not os.path.exists(modelimage):
featherimage=tpimage
if os.path.exists(featherimage):
msg("feathering the interfermetric image "+imagename+".image with "+featherimage,origin='simanalyze',priority="info")
from feather import feather
# TODO call with params?
msg("feather('"+imagename+".feather.image','"+imagename+".image','"+featherimage+"')",priority="info")
if not dryrun:
feather(imagename+".feather.image",imagename+".image",featherimage)
# copy residual flat image
shutil.copytree(imagename+".residual.flat",imagename+".feather.residual.flat")
imagename=imagename+".feather"
# but replace combined flat image
myutil.flatimage(imagename+".image",verbose=verbose)
if verbose:
msg(" ")
msg("done inverting and cleaning",origin='simanalyze')
if not is_array_type(cell):
cell = [cell,cell]
if len(cell) <= 1:
cell = [qa.quantity(cell[0]),qa.quantity(cell[0])]
else:
cell = [qa.quantity(cell[0]),qa.quantity(cell[1])]
cell = [qa.abs(cell[0]),qa.abs(cell[0])]
# get beam from output clean image
if verbose: msg("getting beam from "+imagename+".image",origin='simanalyze')
if os.path.exists(imagename+".image"):
ia.open(imagename+".image")
beam = ia.restoringbeam()
ia.close()
# model has units of Jy/pix - calculate beam area from clean image
# (even if we are not plotting graphics)
bmarea = beam['major']['value']*beam['minor']['value']*1.1331 #arcsec2
bmarea = bmarea/(cell[0]['value']*cell[1]['value']) # bm area in pix
msg("synthesized beam area in output pixels = %f" % bmarea,origin='simanalyze')
if image:
# show model, convolved model, clean image, and residual
if grfile:
file = fileroot + "/" + project + ".image.png"
else:
file = ""
else:
mslist=[]
if dryrun:
grscreen=False
grfile=False
analyze=False
if image and len(mstoimage) > 0:
if grscreen or grfile:
myutil.newfig(multi=[2,2,1],show=grscreen)
# create regridded and convolved sky model image
myutil.convimage(modelflat,imagename+".image.flat")
convsky_current = True # don't remake this for analysis in this run
disprange = [] # passing empty list causes return of disprange
# original sky regridded to output pixels but not convolved with beam
discard = myutil.statim(modelflat+".regrid",disprange=disprange,showstats=False)
myutil.nextfig()
# convolved sky model - units of Jy/bm
disprange = []
discard = myutil.statim(modelflat+".regrid.conv",disprange=disprange)
myutil.nextfig()
# clean image - also in Jy/beam
# although because of DC offset, better to reset disprange
disprange = []
discard = myutil.statim(imagename+".image.flat",disprange=disprange)
myutil.nextfig()
if len(mstoimage) > 0:
myutil.nextfig()
# clean residual image - Jy/bm
discard = myutil.statim(imagename+".residual.flat",disprange=disprange)
myutil.endfig(show=grscreen,filename=file)
#####################################################################
# analysis
if analyze:
if not os.path.exists(imagename+".image"):
if os.path.exists(fileroot+"/"+imagename+".image"):
imagename=fileroot+"/"+imagename
else:
msg("Can't find a simulated image - expecting "+imagename,priority="error")
return False
# we should have skymodel.flat created above
if not image:
if not os.path.exists(imagename+".image"):
msg("you must image before analyzing.",priority="error")
return False
# get beam from output clean image
if verbose: msg("getting beam from "+imagename+".image",origin="analysis")
ia.open(imagename+".image")
beam = ia.restoringbeam()
ia.close()
# model has units of Jy/pix - calculate beam area from clean image
cell = myutil.cellsize(imagename+".image")
cell= [ qa.convert(cell[0],'arcsec'),
qa.convert(cell[1],'arcsec') ]
# (even if we are not plotting graphics)
bmarea = beam['major']['value']*beam['minor']['value']*1.1331 #arcsec2
bmarea = bmarea/(cell[0]['value']*cell[1]['value']) # bm area in pix
msg("synthesized beam area in output pixels = %f" % bmarea)
# flat output:? if the user manually cleaned, this may not exist
outflat = imagename + ".image.flat"
if (not outflat_current) or (not os.path.exists(outflat)):
# create imagename.flat and imagename.residual.flat
myutil.flatimage(imagename+".image",verbose=verbose)
if os.path.exists(imagename+".residual"):
myutil.flatimage(imagename+".residual",verbose=verbose)
else:
if showresidual:
msg(imagename+".residual not found -- residual will not be plotted",priority="warn")
showresidual = False
outflat_current = True
# regridded and convolved input:?
if not convsky_current:
myutil.convimage(modelflat,imagename+".image.flat")
convsky_current = True
# now should have all the flat, convolved etc even if didn't run "image"
# make difference image.
# immath does Jy/bm if image but only if ia.setbrightnessunit("Jy/beam") in convimage()
convolved = modelflat + ".regrid.conv"
difference = imagename + '.diff'
diff_ia = ia.imagecalc(difference, "'%s' - '%s'" % (convolved, outflat), overwrite=True)
diff_ia.setbrightnessunit("Jy/beam")
# get rms of difference image for fidelity calculation
#ia.open(difference)
diffstats = diff_ia.statistics(robust=True, verbose=False,list=False)
diff_ia.close()
del diff_ia
maxdiff = diffstats['medabsdevmed']
if maxdiff != maxdiff: maxdiff = 0.
if type(maxdiff) != type(0.):
if maxdiff.__len__() > 0:
maxdiff = maxdiff[0]
else:
maxdiff = 0.
# Make fidelity image.
absdiff = imagename + '.absdiff'
calc_ia = ia.imagecalc(absdiff, "max(abs('%s'), %f)" % (difference,
maxdiff/pl.sqrt(2.0)), overwrite=True)
calc_ia.close()
fidelityim = imagename + '.fidelity'
calc_ia = ia.imagecalc(fidelityim, "abs('%s') / '%s'" % (convolved, absdiff), overwrite=True)
calc_ia.close()
msg("fidelity image calculated",origin="analysis")
# scalar fidelity
absconv = imagename + '.absconv'
calc_ia = ia.imagecalc(absconv, "abs('%s')" % convolved, overwrite=True)
if ia.isopen(): ia.close() #probably not necessary
calc_ia.close()
del calc_ia
ia.open(absconv)
modelstats = ia.statistics(robust=True, verbose=False,list=False)
maxmodel = modelstats['max']
if maxmodel != maxmodel: maxmodel = 0.
if type(maxmodel) != type(0.):
if maxmodel.__len__() > 0:
maxmodel = maxmodel[0]
else:
maxmodel = 0.
ia.close()
scalarfidel = maxmodel/maxdiff
msg("fidelity range (max model / rms difference) = "+str(scalarfidel),origin="analysis")
# now, what does the user want to actually display?
# need MS for showuv and showpsf
if not image:
msfile = fileroot + "/" + project + ".ms"
elif sd_only:
# imaged and single dish only
msfile = tpmstoimage
# psf is not available for SD only sim
if os.path.exists(msfile) and myutil.ismstp(msfile,halt=False):
if showpsf: msg("single dish simulation -- psf will not be plotted",priority='warn')
showpsf = False
if (not image) and (not os.path.exists(msfile)):
if showpsf or showuv:
msg("No image is generated in this run. Default MS, '%s', does not exist -- uv and psf will not be plotted" % msfile,priority='warn')
showpsf = False
showuv = False
# if the order in the task input changes, change it here too
figs = [showuv,showpsf,showmodel,showconvolved,showclean,showresidual,showdifference,showfidelity]
nfig = figs.count(True)
if nfig > 6:
msg("only displaying first 6 selected panels in graphic output",priority="warn")
if nfig <= 0:
return True
if nfig < 4:
multi = [1,nfig,1]
else:
if nfig == 4:
multi = [2,2,1]
else:
multi = [2,3,1]
if grfile:
file = fileroot + "/" + project + ".analysis.png"
else:
file = ""
if grscreen or grfile:
myutil.newfig(multi=multi,show=grscreen)
# if order in task parameters changes, change here too
if showuv:
# TODO loop over all ms - show all UV including zero
if len(mslist)>1:
msg("Using only "+msfile+" for uv plot",priority="warn",origin='simanalyze')
tb.open(msfile)
rawdata = tb.getcol("UVW")
tb.done()
pl.box()
maxbase = max([max(rawdata[0,]),max(rawdata[1,])]) # in m
klam_m = 300/qa.convert(model_center,'GHz')['value']
pl.plot(rawdata[0,]/klam_m,rawdata[1,]/klam_m,'b,')
pl.plot(-rawdata[0,]/klam_m,-rawdata[1,]/klam_m,'b,')
ax = pl.gca()
ax.yaxis.LABELPAD = -4
pl.xlabel('u[klambda]',fontsize='x-small')
pl.ylabel('v[klambda]',fontsize='x-small')
pl.axis('equal')
# Add zero-spacing (single dish) if not yet plotted
# TODO make this a check over all ms
# if predict_sd and not myutil.ismstp(msfile,halt=False):
# pl.plot([0.],[0.],'r,')
myutil.nextfig()
if showpsf:
if image:
psfim = imagename + ".psf"
else:
psfim = project + ".quick.psf"
if not os.path.exists(psfim):
if len(mslist)>1:
msg("Using only "+msfile+" for psf generation",priority="warn")
im.open(msfile)
# TODO spectral parms
im.defineimage(cellx=qa.tos(model_cell[0]),nx=max([minimsize,128]))
if os.path.exists(psfim):
shutil.rmtree(psfim)
im.approximatepsf(psf=psfim)
# beam is set above (even in "analyze" only)
# note that if image, beam has fields 'major' whereas if not, it
# has fields like 'bmaj'.
# beam=im.fitpsf(psf=psfim)
im.done()
ia.open(psfim)
beamcs = ia.coordsys()
beam_array = ia.getchunk(axes=[beamcs.findcoordinate("spectral")['pixel'][0],beamcs.findcoordinate("stokes")['pixel'][0]],dropdeg=True)
nn = beam_array.shape
xextent = nn[0]*cell_asec*0.5
xextent = [xextent,-xextent]
yextent = nn[1]*cell_asec*0.5
yextent = [-yextent,yextent]
flipped_array = beam_array.transpose()
ttrans_array = flipped_array.tolist()
ttrans_array.reverse()
pl.imshow(ttrans_array,interpolation='bilinear',cmap=pl.cm.jet,extent=xextent+yextent,origin="bottom")
psfim.replace(project+"/","")
pl.title(psfim,fontsize="x-small")
b = qa.convert(beam['major'],'arcsec')['value']
pl.xlim([-3*b,3*b])
pl.ylim([-3*b,3*b])
ax = pl.gca()
pl.text(0.05,0.95,"bmaj=%7.1e\nbmin=%7.1e" % (beam['major']['value'],beam['minor']['value']),transform = ax.transAxes,bbox=dict(facecolor='white', alpha=0.7),size="x-small",verticalalignment="top")
ia.close()
myutil.nextfig()
disprange = [] # first plot will define range
if showmodel:
discard = myutil.statim(modelflat+".regrid",incell=cell,disprange=disprange,showstats=False)
myutil.nextfig()
disprange = []
if showconvolved:
discard = myutil.statim(modelflat+".regrid.conv")
# if disprange gets set here, it'll be Jy/bm
myutil.nextfig()
if showclean:
# own scaling because of DC/zero spacing offset
discard = myutil.statim(imagename+".image.flat")
myutil.nextfig()
if showresidual:
# it gets its own scaling
discard = myutil.statim(imagename+".residual.flat")
myutil.nextfig()
if showdifference:
# it gets its own scaling.
discard = myutil.statim(imagename+".diff")
myutil.nextfig()
if showfidelity:
# it gets its own scaling.
discard = myutil.statim(imagename+".fidelity",showstats=False)
myutil.nextfig()
myutil.endfig(show=grscreen,filename=file)
sim_min,sim_max,sim_rms,sim_units = myutil.statim(imagename+".image.flat",plot=False)
# if not displaying still print stats:
# 20100505 ia.stats changed to return Jy/bm:
msg('Simulation rms: '+str(sim_rms/bmarea)+" Jy/pix = "+
str(sim_rms)+" Jy/bm",origin="analysis")
msg('Simulation max: '+str(sim_max/bmarea)+" Jy/pix = "+
str(sim_max)+" Jy/bm",origin="analysis")
#msg('Simulation rms: '+str(sim_rms)+" Jy/pix = "+
# str(sim_rms*bmarea)+" Jy/bm",origin="analysis")
#msg('Simulation max: '+str(sim_max)+" Jy/pix = "+
# str(sim_max*bmarea)+" Jy/bm",origin="analysis")
msg('Beam bmaj: '+str(beam['major']['value'])+' bmin: '+str(beam['minor']['value'])+' bpa: '+str(beam['positionangle']['value']),origin="analysis")
# cleanup - delete newmodel, newmodel.flat etc
# flat kept by user request CAS-5509
# if os.path.exists(imagename+".image.flat"):
# shutil.rmtree(imagename+".image.flat")
if os.path.exists(imagename+".residual.flat"):
shutil.rmtree(imagename+".residual.flat")
# .flux.pbcoverage is nessesary for feather.
#if os.path.exists(imagename+".flux.pbcoverage"):
# shutil.rmtree(imagename+".flux.pbcoverage")
absdiff = imagename + '.absdiff'
if os.path.exists(absdiff):
shutil.rmtree(absdiff)
absconv = imagename + '.absconv'
if os.path.exists(absconv):
shutil.rmtree(absconv)
# if os.path.exists(imagename+".diff"):
# shutil.rmtree(imagename+".diff")
if os.path.exists(imagename+".quick.psf") and os.path.exists(imagename+".psf"):
shutil.rmtree(imagename+".quick.psf")
finalize_tools()
if myutil.isreport():
myutil.closereport()
except TypeError, e:
finalize_tools()
#msg("simanalyze -- TypeError: %s" % e,priority="error")
casalog.post("simanalyze -- TypeError: %s" % e, priority="ERROR")
raise TypeError, e
return
# Taken from PySparse
# Made by Dominique Orban
import pylab
# original PySparse logo
#irow = [0, 1, 1, 1, 2, 2, 3, 3, 0]
#jcol = [2, 0, 1, 3, 1, 2, 0, 2, 3]
irow = [0, 0, 0, 1, 2, 2, 3, 3]
jcol = [0, 1, 3, 2, 0, 3, 1, 3]
nrow = ncol = 4
ms = 60
fig = pylab.figure()
ax = fig.gca()
ax.plot(jcol, irow, 'ks', markersize=ms, linestyle='None')
ax.xaxis.set_ticks_position('none')
ax.yaxis.set_ticks_position('none')
ax.set_xticks([])
ax.set_yticks([])
ax.set_xlim(xmin=-1, xmax=ncol)
ax.set_ylim(ymin=nrow, ymax=-1)
ax.set_aspect('equal')
pylab.box(on=False)
pylab.show()
def ellipse_convergence(asp=2,width=1e-2, Nadapt=10, eta_list=0.04*pyexp2(-array(range(15))*pylog(2)/2), \
use_adapt=True, problem=2, outname='', CGorderL = [2, 3], noplot=False, octaveimpl=False):
### SETUP SOLUTION
sx = Symbol('sx'); sy = Symbol('sy'); width_ = Symbol('ww'); asp_ = Symbol('a')
rrpy = pysqrt(sx*sx/asp_+sy*sy*asp_)
if problem == 2:
stepfunc = 0.5+165./104./width_*(rrpy-0.25)-20./13./width_**3*(rrpy-0.25)**3-102./13./width_**5*(rrpy-0.25)**5+240./13./width_**7*(rrpy-0.25)**7
elif problem == 1:
stepfunc = 0.5+15./8./width_*(rrpy-0.25)-5./width_**3*(rrpy-0.25)**3+6./width_**5*(rrpy-0.25)**5
else:
stepfunc = 0.5+1.5/width_*(rrpy-0.25)-2/width_**3*(rrpy-0.25)**3
ddstepfunc = str(diff(stepfunc,sx,sx)+diff(stepfunc,sy,sy)).replace('sx','x[0]').replace('sy','x[1]').replace('x[0]**2','(x[0]*x[0])').replace('x[1]**2','(x[1]*x[1])')
stepfunc = str(stepfunc).replace('sx','x[0]').replace('sy','x[1]').replace('x[0]**2','(x[0]*x[0])').replace('x[1]**2','(x[1]*x[1])')
#REPLACE ** with pow
stepfunc = stepfunc.replace('(a*(x[1]*x[1]) + (x[0]*x[0])/a)**(1/2)','sqrt(a*(x[1]*x[1]) + (x[0]*x[0])/a)')
ddstepfunc = ddstepfunc.replace('(a*(x[1]*x[1]) + (x[0]*x[0])/a)**(1/2)','sqrt(a*(x[1]*x[1]) + (x[0]*x[0])/a)')
ddstepfunc = ddstepfunc.replace('(a*(x[1]*x[1]) + (x[0]*x[0])/a)**(3/2)','pow(a*x[1]*x[1]+x[0]*x[0]/a,1.5)')
ddstepfunc = ddstepfunc.replace('(sqrt(a*(x[1]*x[1]) + (x[0]*x[0])/a) - 0.25)**2','pow(sqrt(a*x[1]*x[1]+x[0]*x[0]/a) - 0.25,2.)')
ddstepfunc = ddstepfunc.replace('(sqrt(a*(x[1]*x[1]) + (x[0]*x[0])/a) - 0.25)**3','pow(sqrt(a*x[1]*x[1]+x[0]*x[0]/a) - 0.25,3.)')
ddstepfunc = ddstepfunc.replace('(sqrt(a*(x[1]*x[1]) + (x[0]*x[0])/a) - 0.25)**4','pow(sqrt(a*x[1]*x[1]+x[0]*x[0]/a) - 0.25,4.)')
ddstepfunc = ddstepfunc.replace('(sqrt(a*(x[1]*x[1]) + (x[0]*x[0])/a) - 0.25)**5','pow(sqrt(a*x[1]*x[1]+x[0]*x[0]/a) - 0.25,5.)')
ddstepfunc = ddstepfunc.replace('(sqrt(a*(x[1]*x[1]) + (x[0]*x[0])/a) - 0.25)**6','pow(sqrt(a*x[1]*x[1]+x[0]*x[0]/a) - 0.25,6.)')
stepfunc = stepfunc.replace('(sqrt(a*(x[1]*x[1]) + (x[0]*x[0])/a) - 0.25)**3','pow(sqrt(a*x[1]*x[1] + x[0]*x[0]/a) - 0.25,3.)')
stepfunc = stepfunc.replace('(sqrt(a*(x[1]*x[1]) + (x[0]*x[0])/a) - 0.25)**5','pow(sqrt(a*x[1]*x[1] + x[0]*x[0]/a) - 0.25,5.)')
stepfunc = stepfunc.replace('(sqrt(a*(x[1]*x[1]) + (x[0]*x[0])/a) - 0.25)**7','pow(sqrt(a*x[1]*x[1] + x[0]*x[0]/a) - 0.25,7.)')
testsol = '(0.25-ww/2<sqrt(x[0]*x[0]/a+x[1]*x[1]*a) && sqrt(x[0]*x[0]/a+x[1]*x[1]*a) < 0.25+ww/2 ? (' + stepfunc + ') : 0) + (0.25+ww/2<sqrt(x[0]*x[0]/a+x[1]*x[1]*a) ? 1 : 0)'
# testsol = '(0.25-ww/2<sqrt(x[0]*x[0]+x[1]*x[1]) && sqrt(x[0]*x[0]+x[1]*x[1]) < 0.25+ww/2 ? (' + stepfunc + ')) : (0.25<sqrt(x[0]*x[0]+x[1]*x[1]) ? 1 : 0)'
ddtestsol = '0.25-ww/2<sqrt(x[0]*x[0]/a+x[1]*x[1]*a) && sqrt(x[0]*x[0]/a+x[1]*x[1]*a) < 0.25+ww/2 ? (' + ddstepfunc + ') : 0'
# A = array([[0.5,0.5**3,0.5**5],[1,3*0.5**2,5*0.5**4],[0,6*0.5,20*0.5**3]]); b = array([0.5,0,0])
# from numpy.linalg import solve as pysolve #15/8,-5,6
# X = pysolve(A,b); from numpy import linspace; xx = linspace(-0.5,0.5,100)
# from pylab import plot as pyplot; pyplot(xx,X[0]*xx+X[1]*xx**3+X[2]*xx**5,'-b')
# rrpy = pysqrt(sx*sx+sy*sy)
#
# ddstepfunc = str(diff(stepfunc,sx,sx)+diff(stepfunc,sy,sy)).replace('sx','x[0]').replace('sy','x[1]').replace('x[0]**2','(x[0]*x[0])').replace('x[1]**2','(x[1]*x[1])')
# stepfunc = str(stepfunc).replace('sx','x[0]').replace('sy','x[1]').replace('x[0]**2','(x[0]*x[0])').replace('x[1]**2','(x[1]*x[1])')
# #REPLACE ** with pow
# ddstepfunc = ddstepfunc.replace('(a*(x[1]*x[1]) + (x[0]*x[0])/a)**(3/2)','pow(a*x[1]*x[1]+x[0]*x[0]/a,1.5)')
# ddstepfunc = ddstepfunc.replace('(sqrt(a*(x[1]*x[1]) + (x[0]*x[0])/a) - 0.25)**2','pow(sqrt(a*x[1]*x[1]+x[0]*x[0]/a) - 0.25,2.)')
# ddstepfunc = ddstepfunc.replace('(sqrt(a*(x[1]*x[1]) + (x[0]*x[0])/a) - 0.25)**3','pow(sqrt(a*x[1]*x[1]+x[0]*x[0]/a) - 0.25,3.)')
# ddstepfunc = ddstepfunc.replace('(sqrt(a*(x[1]*x[1]) + (x[0]*x[0])/a) - 0.25)**4','pow(sqrt(a*x[1]*x[1]+x[0]*x[0]/a) - 0.25,4.)')
# stepfunc = stepfunc.replace('(sqrt(a*(x[1]*x[1]) + (x[0]*x[0])/a) - 0.25)**3','pow(sqrt(a*x[1]*x[1]+x[0]*x[0]/a) - 0.25,3.)')
# stepfunc = stepfunc.replace('(sqrt(a*(x[1]*x[1]) + (x[0]*x[0])/a) - 0.25)**5','pow(sqrt(a*x[1]*x[1]+x[0]*x[0]/a) - 0.25,5.)')
# testsol = '(0.25-ww/2<sqrt(x[0]*x[0]+x[1]*x[1]) && sqrt(x[0]*x[0]+x[1]*x[1]) < 0.25+ww/2 ? (' + stepfunc + ') : 0) + (0.25+ww/2<sqrt(x[0]*x[0]+x[1]*x[1]) ? 1 : 0)'
## testsol = '(0.25-ww/2<sqrt(x[0]*x[0]+x[1]*x[1]) && sqrt(x[0]*x[0]+x[1]*x[1]) < 0.25+ww/2 ? (' + stepfunc + ')) : (0.25<sqrt(x[0]*x[0]+x[1]*x[1]) ? 1 : 0)'
# ddtestsol = '0.25-ww/2<sqrt(x[0]*x[0]+x[1]*x[1]) && sqrt(x[0]*x[0]+x[1]*x[1]) < 0.25+ww/2 ? (' + ddstepfunc + ') : 0'
# else: # problem == 0:
# rrpy = pysqrt(sx*sx+sy*sy)
# #'if(t<2*WeMax,0,if(t<4*WeMax,0.5+3/2/(2*WeMax)*(t-3*WeMax)-2/(2*WeMax)^3*(t-3*WeMax)^3,1))'; %0.5+3/2/dx*(x-xc)-2/dx^3*(x-xc)^3
# ddstepfunc = str(diff(stepfunc,sx,sx)+diff(stepfunc,sy,sy)).replace('sx','x[0]').replace('sy','x[1]').replace('x[0]**2','(x[0]*x[0])').replace('x[1]**2','(x[1]*x[1])')
# stepfunc = str(stepfunc).replace('sx','x[0]').replace('sy','x[1]').replace('x[0]**2','(x[0]*x[0])').replace('x[1]**2','(x[1]*x[1])')
# #REPLACE ** with pow
# ddstepfunc = ddstepfunc.replace('(sqrt(a*(x[1]*x[1]) + (x[0]*x[0])/a) - 0.25)**2','pow(sqrt(a*(x[1]*x[1]) + (x[0]*x[0])/a) - 0.25,2.)')
# ddstepfunc = ddstepfunc.replace('(a*(x[1]*x[1]) + (x[0]*x[0])/a)**(3/2)','pow(a*(x[1]*x[1]) + (x[0]*x[0])/a,1.5)')
# stepfunc = stepfunc.replace('(sqrt(a*(x[1]*x[1]) + (x[0]*x[0])/a) - 0.25)**3','pow(sqrt(a*(x[1]*x[1]) + (x[0]*x[0])/a) - 0.25,3.)')
# testsol = '0.25-ww/2<sqrt(x[0]*x[0]+x[1]*x[1]) && sqrt(x[0]*x[0]+x[1]*x[1]) < 0.25+ww/2 ? (' + stepfunc + ') : (0.25<sqrt(x[0]*x[0]+x[1]*x[1]) ? 1 : 0)'
# ddtestsol = '0.25-ww/2<sqrt(x[0]*x[0]+x[1]*x[1]) && sqrt(x[0]*x[0]+x[1]*x[1]) < 0.25+ww/2 ? (' + ddstepfunc + ') : 0'
ddtestsol = ddtestsol.replace('a**2','(a*a)').replace('ww**2','(ww*ww)').replace('ww**3','pow(ww,3.)').replace('ww**4','pow(ww,4.)').replace('ww**5','pow(ww,5.)').replace('ww**6','pow(ww,6.)').replace('ww**7','pow(ww,7.)')
testsol = testsol.replace('ww**2','(ww*ww)').replace('ww**3','pow(ww,3.)').replace('ww**5','pow(ww,5.)').replace('ww**7','pow(ww,7.)')
ddtestsol = ddtestsol.replace('ww',str(width)).replace('a',str(asp))
testsol = testsol.replace('ww',str(width)).replace('a',str(asp))
ddtestsol = '-('+ddtestsol+')'
def boundary(x):
return x[0]+0.5 < DOLFIN_EPS or 0.5-x[0] < DOLFIN_EPS or x[1]+0.5 < DOLFIN_EPS or 0.5-x[1] < DOLFIN_EPS
for CGorder in CGorderL:
dofs = []
L2errors = []
#for eta in [0.16, 0.08, 0.04, 0.02, 0.01, 0.005, 0.0025] #, 0.0025/2, 0.0025/4, 0.0025/8]: #
for eta in eta_list:
### SETUP MESH
meshsz = int(round(80*0.005/(eta*(bool(use_adapt)==False)+0.05*(bool(use_adapt)==True))))
if (not bool(use_adapt)) and meshsz > 80:
continue
mesh = RectangleMesh(-0.0,-0.0,0.5*sqrt(asp),0.5/sqrt(asp),meshsz,meshsz,"left/right")
# PERFORM TEN ADAPTATION ITERATIONS
for iii in range(Nadapt):
V = FunctionSpace(mesh, "CG", CGorder); dis = TrialFunction(V); dus = TestFunction(V); u = Function(V)
#V2 = FunctionSpace(mesh, "CG", CGorder+2)
R = Expression(ddtestsol) #interpolate(Expression(ddtestsol),V2)
a = inner(grad(dis), grad(dus))*dx
L = R*dus*dx
bc = DirichletBC(V, Expression(testsol), boundary) #Constant(0.)
solve(a == L, u, bc)
if not bool(use_adapt):
break
H = metric_pnorm(u, eta, max_edge_ratio=1+49*(use_adapt!=2), p=2)
H = logproject(H)
if iii != Nadapt-1:
mesh = adapt(H, octaveimpl=octaveimpl, debugon=False)
L2error = errornorm(Expression(testsol), u, degree_rise=CGorder+2, norm_type='L2')
dofs.append(len(u.vector().array()))
L2errors.append(L2error)
log(INFO+1,"%1dX ADAPT<->SOLVE complete: DOF=%5d, error=%0.0e" % (Nadapt, dofs[len(dofs)-1], L2error))
# PLOT MESH + solution
figure()
testf = interpolate(u ,FunctionSpace(mesh,'CG',1))
testfe = interpolate(Expression(testsol),FunctionSpace(mesh,'CG',1))
vtx2dof = vertex_to_dof_map(FunctionSpace(mesh, "CG" ,1))
zz = testf.vector().array()[vtx2dof]; zz[zz==1] -= 1e-16
hh=tricontourf(mesh.coordinates()[:,0],mesh.coordinates()[:,1],mesh.cells(),zz,100,cmap=get_cmap('binary'))
colorbar(hh)
axis('equal'); axis('off'); box('off'); xlim([0,0.5*sqrt(asp)]); ylim([0, 0.5/sqrt(asp)]); savefig('solution.png',dpi=300)
figure()
hold('on'); triplot(mesh.coordinates()[:,0],mesh.coordinates()[:,1],mesh.cells(),color='r',linewidth=0.5); hold('off')
axis('equal'); box('off')
axis('off'); xlim([0,0.5*sqrt(asp)]); ylim([0, 0.5/sqrt(asp)])
savefig(outname+'final_mesh_CG2.png',dpi=300) #; savefig('outname+final_mesh_CG2.eps',dpi=300)
#PLOT ERROR
figure()
zz = pyabs(testf.vector().array()-testfe.vector().array())[vtx2dof]; zz[zz==1] -= 1e-16
hh=tricontourf(mesh.coordinates()[:,0],mesh.coordinates()[:,1],mesh.cells(),zz,100,cmap=get_cmap('binary'))
colorbar(hh); axis('equal'); box('off'); title('error')
# PLOT L2error graph
figure()
pyloglog(dofs,L2errors,'-b.',linewidth=2,markersize=16); xlabel('Degree of freedoms'); ylabel('L2 error')
# SAVE SOLUTION
dofs = array(dofs); L2errors = array(L2errors)
fid = open("DOFS_L2errors_CG"+str(CGorder)+outname+".mpy",'w')
pickle.dump([dofs,L2errors],fid)
fid.close();
#LOAD SAVED SOLUTIONS
fid = open("DOFS_L2errors_CG2"+outname+".mpy",'r')
[dofs,L2errors] = pickle.load(fid)
fid.close()
# PERFORM FITS ON LAST THREE POINTS
NfitP = 9
I = array(range(len(dofs)-NfitP,len(dofs)))
slope,ints = polyfit(pylog(dofs[I]), pylog(L2errors[I]), 1)
if slope < -0.7:
fid = open("DOFS_L2errors_CG2_fit"+outname+".mpy",'w')
pickle.dump([dofs,L2errors,slope,ints],fid)
fid.close()
log(INFO+1,'succes')
else:
os.system('rm '+outname+'.lock')
log(INFO+1,'fail')
#PLOT THEM TOGETHER
if CGorderL != [2]:
fid = open("DOFS_L2errors_CG3.mpy",'r')
[dofs_old,L2errors_old] = pickle.load(fid)
fid.close()
slope2,ints2 = polyfit(pylog(dofs_old[I]), pylog(L2errors_old[I]), 1)
figure()
pyloglog(dofs,L2errors,'-b.',dofs_old,L2errors_old,'--b.',linewidth=2,markersize=16)
hold('on'); pyloglog(dofs,pyexp2(ints)*dofs**slope,'-r',dofs_old,pyexp2(ints2)*dofs_old**slope2,'--r',linewidth=1); hold('off')
xlabel('Degree of freedoms'); ylabel('L2 error')
legend(['CG2','CG3',"%0.2f*log(DOFs)" % slope, "%0.2f*log(DOFs)" % slope2]) #legend(['new data','old_data'])
# savefig('comparison.png',dpi=300) #savefig('comparison.eps');
if not noplot:
show()
elif grp == 'Occipital':
pl.subplot(3,4,11)
pl.title('Right Occipital', fontsize = 'large')
####Reading the Evoked data structure
for (c,l) in zip(condName, colorList):
evoked = mne.fiff.Evoked(fname, setno = 'epochs_'+c , baseline = (None, 0))
#evoked = mne.fiff.Evoked(fname, setno = c , baseline = (None, 0)) ##Use this if you are using condition numbers
badChanSet = set(evoked.info['bads'])
print c
good_chan=list(set(chan_list))
sel = mne.fiff.pick_types(evoked.info,meg=False, eeg=False, include = good_chan)
data = evoked.data[sel]
###Computing the MEG RMS from the evoked data for the specified condition
times = evoked.times*1000
square = np.power(data, 2)
meanSquare = np.mean(square, 0)
rms = np.power(meanSquare, 0.5)
###Plotting the MEG rms value for the current condition
pl.plot(times, rms*1e13, color = l, linewidth=2)
pl.ylim([ymin,ymax])
pl.xlim([xmin,xmax])
pl.box('off')
pl.tick_params(axis='both',right='off',top='off')
pl.yticks(np.array([0.,4.,8.,12.,16.,20.,24.,28.,32.]))
pl.xticks(np.array([0, 200, 400, 600]))
pl.savefig(out_fname)
pl.show()
def __call__(self, inputs):
from pylab import box, gca
box(self.get_input('on'))
gca().get_figure().canvas.draw()
return self.get_input('axes')
def minimal_example(width=2e-2, Nadapt=10, eta = 0.01):
### CONSTANTS
meshsz = 40
hd = Constant(width)
### SETUP MESH
mesh = RectangleMesh(Point(-0.5,-0.5),Point(0.5,0.5),1*meshsz,1*meshsz,"left/right")
### DERIVE FORCING TERM
angle = pi/8 #rand*pi/2
sx = Symbol('sx'); sy = Symbol('sy'); width_ = Symbol('ww'); aa = Symbol('aa')
testsol = pytanh((sx*pycos(aa)+sy*pysin(aa))/width_)
ddtestsol = str(diff(testsol,sx,sx)+diff(testsol,sy,sy)).replace('sx','x[0]').replace('sy','x[1]')
#replace ** with pow
ddtestsol = ddtestsol.replace('tanh((x[0]*sin(aa) + x[1]*cos(aa))/ww)**2','pow(tanh((x[0]*sin(aa) + x[1]*cos(aa))/ww),2.)')
ddtestsol = ddtestsol.replace('cos(aa)**2','pow(cos(aa),2.)').replace('sin(aa)**2','pow(sin(aa),2.)').replace('ww**2','(ww*ww)')
#insert vaulues
ddtestsol = ddtestsol.replace('aa',str(angle)).replace('ww',str(width))
testsol = str(testsol).replace('sx','x[0]').replace('sy','x[1]').replace('aa',str(angle)).replace('ww',str(width))
ddtestsol = "-("+ddtestsol+")"
def boundary(x):
return x[0]-mesh.coordinates()[:,0].min() < DOLFIN_EPS or mesh.coordinates()[:,0].max()-x[0] < DOLFIN_EPS \
or mesh.coordinates()[:,1].min()+0.5 < DOLFIN_EPS or mesh.coordinates()[:,1].max()-x[1] < DOLFIN_EPS
# PERFORM TEN ADAPTATION ITERATIONS
for iii in range(Nadapt):
V = FunctionSpace(mesh, "CG" ,2); dis = TrialFunction(V); dus = TestFunction(V); u = Function(V)
a = inner(grad(dis), grad(dus))*dx
L = Expression(ddtestsol)*dus*dx
bc = DirichletBC(V, Expression(testsol), boundary)
solve(a == L, u, bc)
startTime = time()
H = metric_pnorm(u, eta, max_edge_length=3., max_edge_ratio=None)
H = logproject(H)
if iii != Nadapt-1:
mesh = adapt(H)
L2error = errornorm(Expression(testsol), u, degree_rise=4, norm_type='L2')
log(INFO+1,"total (adapt+metric) time was %0.1fs, L2error=%0.0e, nodes: %0.0f" % (time()-startTime,L2error,mesh.num_vertices()))
# # PLOT MESH
# figure()
coords = mesh.coordinates().transpose()
# triplot(coords[0],coords[1],mesh.cells(),linewidth=0.1)
# #savefig('mesh.png',dpi=300) #savefig('mesh.eps');
figure() #solution
testf = interpolate(Expression(testsol),FunctionSpace(mesh,'CG',1))
vtx2dof = vertex_to_dof_map(FunctionSpace(mesh, "CG" ,1))
zz = testf.vector().array()[vtx2dof]
hh=tricontourf(coords[0],coords[1],mesh.cells(),zz,100)
colorbar(hh)
# savefig('solution.png',dpi=300) #savefig('solution.eps');
figure() #analytical solution
testfe = interpolate(u,FunctionSpace(mesh,'CG',1))
zz = testfe.vector().array()[vtx2dof]
hh=tricontourf(coords[0],coords[1],mesh.cells(),zz,100)
colorbar(hh)
#savefig('analyt.png',dpi=300) #savefig('analyt.eps');
figure() #error
zz -= testf.vector().array()[vtx2dof]; zz[zz==1] -= 1e-16
hh=tricontourf(mesh.coordinates()[:,0],mesh.coordinates()[:,1],mesh.cells(),zz,100,cmap=get_cmap('binary'))
colorbar(hh)
hold('on'); triplot(mesh.coordinates()[:,0],mesh.coordinates()[:,1],mesh.cells(),color='r',linewidth=0.5); hold('off')
axis('equal'); box('off'); title('error')
show()
def maximal_example(eta_list=array([0.001]), Nadapt=5, timet=1.,
period=2 * pi):
### CONSTANTS
### SETUP SOLUTION
#testsol = '0.1*sin(50*x+2*pi*t/T)+atan(-0.1/(2*x - sin(5*y+2*pi*t/T)))';
sx = Symbol('sx')
sy = Symbol('sy')
sT = Symbol('sT')
st = Symbol('st')
spi = Symbol('spi')
testsol = 0.1 * pysin(50 * sx + 2 * spi * st / sT) + pyatan(
-0.1, 2 * sx - pysin(5 * sy + 2 * spi * st / sT))
ddtestsol = str(diff(testsol, sx, sx) + diff(testsol, sy, sy)).replace(
'sx', 'x[0]').replace('sy', 'x[1]').replace('spi', 'pi')
# replacing **P with pow(,P)
ddtestsol = ddtestsol.replace("(2*x[0] - sin(5*x[1] + 2*pi*st/sT))**2",
"pow(2*x[0] - sin(5*x[1] + 2*pi*st/sT),2.)")
ddtestsol = ddtestsol.replace("cos(5*x[1] + 2*pi*st/sT)**2",
"pow(cos(5*x[1] + 2*pi*st/sT),2.)")
ddtestsol = ddtestsol.replace(
"(pow(2*x[0] - sin(5*x[1] + 2*pi*st/sT),2.) + 0.01)**2",
"pow((pow(2*x[0] - sin(5*x[1] + 2*pi*st/sT),2.) + 0.01),2.)")
ddtestsol = ddtestsol.replace(
"(1 + 0.01/pow(2*x[0] - sin(5*x[1] + 2*pi*st/sT),2.))**2",
"pow(1 + 0.01/pow(2*x[0] - sin(5*x[1] + 2*pi*st/sT),2.),2.)")
ddtestsol = ddtestsol.replace("(2*x[0] - sin(5*x[1] + 2*pi*st/sT))**5",
"pow(2*x[0] - sin(5*x[1] + 2*pi*st/sT),5.)")
#insert values
ddtestsol = ddtestsol.replace('sT', str(period)).replace('st', str(timet))
testsol = str(testsol).replace('sx', 'x[0]').replace('sy', 'x[1]').replace(
'spi', 'pi').replace('sT', str(period)).replace('st', str(timet))
ddtestsol = "-(" + ddtestsol + ")"
error_list = []
dof_list = []
for eta in eta_list:
meshsz = 40
### SETUP MESH
# mesh = RectangleMesh(0.4,-0.1,0.6,0.3,1*meshsz,1*meshsz,"left/right") #shock
# mesh = RectangleMesh(-0.75,-0.3,-0.3,0.5,1*meshsz,1*meshsz,"left/right") #waves
mesh = RectangleMesh(-1.5, -0.25, 0.5, 0.75, 1 * meshsz, 1 * meshsz,
"left/right") #shock+waves
def boundary(x):
return near(x[0],mesh.coordinates()[:,0].min()) or near(x[0],mesh.coordinates()[:,0].max()) \
or near(x[1],mesh.coordinates()[:,1].min()) or near(x[1],mesh.coordinates()[:,1].max())
# PERFORM ONE ADAPTATION ITERATION
for iii in range(Nadapt):
startTime = time()
V = FunctionSpace(mesh, "CG", 2)
dis = TrialFunction(V)
dus = TestFunction(V)
u = Function(V)
# R = interpolate(Expression(ddtestsol),V)
a = inner(grad(dis), grad(dus)) * dx
L = Expression(ddtestsol) * dus * dx #
bc = DirichletBC(V, Expression(testsol), boundary)
solve(a == L, u, bc)
soltime = time() - startTime
startTime = time()
H = metric_pnorm(u, eta, max_edge_ratio=50, CG0H=3, p=4)
metricTime = time() - startTime
if iii != Nadapt - 1:
mesh = adapt(H)
TadaptTime = time() - startTime
L2error = errornorm(Expression(testsol),
u,
degree_rise=4,
norm_type='L2')
printstr = "%5.0f elements, %0.0e L2error, adapt took %0.0f %% of the total time, (%0.0f %% of which was the metric calculation)" \
% (mesh.num_cells(),L2error,TadaptTime/(TadaptTime+soltime)*100,metricTime/TadaptTime*100)
if len(eta_list) == 1:
print(printstr)
else:
error_list.append(L2error)
dof_list.append(len(u.vector().array()))
print(printstr)
if len(dof_list) > 1:
dof_list = array(dof_list)
error_list = array(error_list)
figure()
loglog(dof_list, error_list, '.b-', linewidth=2, markersize=16)
xlabel('Degree of freedoms')
ylabel('L2 error')
# # PLOT MESH
# figure()
coords = mesh.coordinates().transpose()
# triplot(coords[0],coords[1],mesh.cells(),linewidth=0.1)
# #savefig('mesh.png',dpi=300) #savefig('mesh.eps');
figure() #solution
testf = interpolate(Expression(testsol), FunctionSpace(mesh, 'CG', 1))
vtx2dof = vertex_to_dof_map(FunctionSpace(mesh, "CG", 1))
zz = testf.vector().array()[vtx2dof]
hh = tricontourf(coords[0], coords[1], mesh.cells(), zz, 100)
colorbar(hh)
#savefig('solution.png',dpi=300) #savefig('solution.eps');
figure() #analytical solution
testfe = interpolate(u, FunctionSpace(mesh, 'CG', 1))
zz = testfe.vector().array()[vtx2dof]
hh = tricontourf(coords[0], coords[1], mesh.cells(), zz, 100)
colorbar(hh)
#savefig('analyt.png',dpi=300) #savefig('analyt.eps');
figure() #error
zz -= testf.vector().array()[vtx2dof]
zz[zz == 1] -= 1e-16
hh = tricontourf(mesh.coordinates()[:, 0],
mesh.coordinates()[:, 1],
mesh.cells(),
zz,
100,
cmap=get_cmap('binary'))
colorbar(hh)
hold('on')
triplot(mesh.coordinates()[:, 0],
mesh.coordinates()[:, 1],
mesh.cells(),
color='r',
linewidth=0.5)
hold('off')
axis('equal')
box('off')
title('error')
show()
def main():
with serial.Serial('COM6', 115200, timeout=0) as ser:
# 初期化
i = 0
x = np.zeros(50)
y = np.zeros(50)
ratio_array = np.zeros(50)
eeg_array = np.zeros(50)
status = False
sampling_data_set = 50
int_data = 0
# 数直線
fig, ax = plt.subplots(figsize=(10, 10)) # 画像サイズ
fig.set_figheight(1) # 高さ調整
ax.tick_params(labelbottom=True, bottom=False) # x軸設定
ax.tick_params(labelleft=False, left=False) # y軸設定
# 数直線上の数値を表示
while True:
try:
rri_data = ser.read_all()
rri_data_str = rri_data.decode('utf-8')
if rri_data_str != '':
int_data = int(rri_data_str)
i = i + 1
if (50 < int_data) and (int_data < 300):
# 配列をキューと見たてて要素を追加・削除
x = np.append(x, i)
x = np.delete(x, 0)
y = np.append(y, int_data)
y = np.delete(y, 0)
if i > 50:
sdnn_sigma = 0
rmssd_sigma = 0
sdnn = 0
rmssd = 0
ratio = 0
# RRIの平均・分散を計算
s = sum(y)
N = len(y)
ave_rri = s / N
for index in range(sampling_data_set):
sdnn_sigma += (y[index] - ave_rri)**2
for index in range(sampling_data_set - 1):
rmssd_sigma += (y[index] - y[index + 1])**2
sdnn = math.sqrt(sdnn_sigma / 50)
rmssd = math.sqrt(rmssd_sigma / (50 - 1))
ratio = sdnn / rmssd
ratio_array = np.append(ratio_array, ratio)
ratio_array = np.delete(ratio_array, 0)
xmin = 0 # 数直線の最小値
xmax = max(ratio_array) # 数直線の最大値
plt.tight_layout() # グラフの自動調整
plt.scatter(ratio_array, eeg_array, s=10, c='r') # 散布図
#plt.hlines(y=0, xmin=xmin, xmax=xmax) # 横軸
#plt.vlines(x=[i for i in range(xmin, xmax + 1, 1)], ymin=-0.04, ymax=0.04) # 目盛り線（大）
#plt.vlines(x=[i / 10 for i in range(xmin * 10, xmax * 10 + 1, 1)], ymin=-0.02,
# ymax=0.02) # 目盛り線（小）
line_width = 10 # 目盛り数値の刻み幅
plt.xticks(
np.arange(xmin, xmax + line_width,
line_width)) # 目盛り数値
pylab.box(False) # 枠を消す
plt.pause(.01)
if rmssd < 150:
print('y:', y)
print('s:', s)
print('N:', N)
print('ave:', ave_rri)
print('rmssd_sigma:', rmssd_sigma)
print('rmssd:', rmssd)
print('-------------')
elif i == 5:
print('しばらくお待ち下さい')
elif i == 40:
print('残り数ステップです')
elif (45 < i) and (i <= 50):
print('残り', (51 - i), 'ステップです')
except KeyboardInterrupt:
plt.close()
pylab.close()
ser.close()
# In[]
from pylab import box
def show_graph(src):
img = plt.imread(src)
xpixels,ypixels = img.shape[0],img.shape[1]
dpi = 100
margin = 0.01
figsize = (1+margin)*ypixels / dpi, (1+margin)*xpixels / dpi
fig = plt.figure(figsize=figsize, dpi=dpi)
` ax = fig.add_axes([margin,margin,1-2*margin,1-2*margin])
ax.tick_params(labelbottom='off',bottom='off')
ax.tick_params(labelleft='off',left='off')
ax.imshow(img, interpolation='none')
box('off')
plt.show()
show_graph('result/loss.png')
show_graph('result/accuracy.png')
# In[]
# モデルを利用して予測をする関数を定義
def predict(model, X):
# データ数が1の場合は、バッチサイズ分の次元を追加
if len(X.shape) == 1:
pred = model.predictor(X[None, ...]).data.argmax()
# データ数が2以上の場合はそのまま
else:
pred = model.predictor(X).data.argmax(axis=1)
return pred
