class YMBCrossCorrel(HasTraits):
data = Instance(IYMBData)
# convert the dictionary keys to an ordered list.
var_name_list = Property(List)
@cached_property
def _get_var_name_list(self):
return sorted(var_dict.keys())
# list of data arrays in the order of the var_name_list
var_arr_list = Property(List, depends_on='data.input_change')
@cached_property
def _get_var_arr_list(self):
return [
getattr(self.data, var_dict[var_name]).flatten()[:, None]
for var_name in self.var_name_list
]
corr_arr = Property(Array, depends_on='data.input_change')
@cached_property
def _get_corr_arr(self):
print 'redrawing cross correl'
# get the list of names and sort them alphabetically
corr_data = ma.hstack(self.var_arr_list)
# @kelidas: return small differences between ma and numpy corrcoef
# return ma.corrcoef( corr_data, rowvar = False, allow_masked = True )
return MatSpearman(corr_data)
figure = Instance(Figure)
def _figure_default(self):
figure = Figure()
figure.add_axes([0.1, 0.1, 0.8, 0.8])
return figure
data_changed = Event(True)
@on_trait_change('data, data.input_change')
def _redraw(self):
figure = self.figure
figure.clear()
var_data = self.corr_arr
figure.add_axes([0.1, 0.1, 0.8, 0.8])
axes = figure.axes[0]
axes.clear()
x_coor = arange(var_data.shape[1])
axes.grid()
for i in range(0, var_data.shape[1]):
axes.plot(x_coor[i:] - x_coor[i], var_data[i, (i):], '-x')
axes.set_xlabel('$\mathrm{x}\, [\mu\mathrm{m}]$', fontsize=16)
axes.set_ylabel('$\mathrm{correlation}$', fontsize=16)
axes.set_ylim(-1, 1)
self.data_changed = True
traits_view_mpl = View(
Group(
# Group( Item( 'figure', style = 'custom',
# editor = MPLFigureEditor(),
# show_label = False )
# , id = 'figure.view' ),
Item('corr_arr',
show_label=False,
style='readonly',
editor=TabularEditor(adapter=ArrayAdapter()))),
resizable=True,
)
traits_view = View(Item('corr_arr',
editor=tabular_editor,
show_label=False),
resizable=True,
scrollable=True,
buttons=['OK', 'Cancel'],
width=1.0,
height=0.5)
class RFView3D(ModelView):
model = Instance(IRF)
scalar_arr = Property(depends_on='var_enum')
@cached_property
def _get_scalar_arr(self):
return getattr(self.data, self.var_enum_)
color_map = Str('blue-red')
scene = Instance(MlabSceneModel, ())
plot = Instance(PipelineBase)
# When the scene is activated or parameters change the scene is updated
@on_trait_change('model.')
def update_plot(self):
x_arrr, y_arrr, z_arrr = self.data.cut_data[0:3]
scalar_arrr = self.scalar_arr
mask = y_arrr > -1
x = x_arrr[mask]
y = y_arrr[mask]
z = z_arrr[mask]
scalar = scalar_arrr[mask]
connections = -ones_like(x_arrr)
mesk = x_arrr.filled() > -1
connections[mesk] = list(range(0, len(connections[mesk])))
connections = connections[self.start_fib:self.end_fib + 1, :].filled()
connection = connections.astype(int).copy()
connection = connection.tolist()
# TODO: better
for i in range(0, self.data.n_cols + 1):
for item in connection:
try:
item.remove(-1)
except:
pass
if self.plot is None:
print('plot 3d -- 1')
#self.scene.parallel_projection = False
pts = self.scene.mlab.pipeline.scalar_scatter(
array(x), array(y), array(z), array(scalar))
pts.mlab_source.dataset.lines = connection
self.plot = self.scene.mlab.pipeline.surface(
self.scene.mlab.pipeline.tube(
# fig.scene.mlab.pipeline.stripper(
pts,
figure=self.scene.mayavi_scene,
# ),
tube_sides=10,
tube_radius=0.015,
), )
self.plot.actor.mapper.interpolate_scalars_before_mapping = True
self.plot.module_manager.scalar_lut_manager.show_scalar_bar = True
self.plot.module_manager.scalar_lut_manager.show_legend = True
self.plot.module_manager.scalar_lut_manager.shadow = True
self.plot.module_manager.scalar_lut_manager.label_text_property.italic = False
self.plot.module_manager.scalar_lut_manager.scalar_bar.orientation = 'horizontal'
self.plot.module_manager.scalar_lut_manager.scalar_bar_representation.position2 = array(
[0.61775334, 0.17])
self.plot.module_manager.scalar_lut_manager.scalar_bar_representation.position = array(
[0.18606834, 0.08273163])
self.plot.module_manager.scalar_lut_manager.scalar_bar.width = 0.17000000000000004
self.plot.module_manager.scalar_lut_manager.lut_mode = self.color_map #'black-white'
self.plot.module_manager.scalar_lut_manager.data_name = self.var_enum
self.plot.module_manager.scalar_lut_manager.label_text_property.font_family = 'times'
self.plot.module_manager.scalar_lut_manager.label_text_property.shadow = True
self.plot.module_manager.scalar_lut_manager.title_text_property.color = (
0.0, 0.0, 0.0)
self.plot.module_manager.scalar_lut_manager.label_text_property.color = (
0.0, 0.0, 0.0)
self.plot.module_manager.scalar_lut_manager.title_text_property.font_family = 'times'
self.plot.module_manager.scalar_lut_manager.title_text_property.shadow = True
#fig.scene.parallel_projection = True
self.scene.scene.background = (1.0, 1.0, 1.0)
self.scene.scene.camera.position = [
16.319534155794827, 10.477447863842627, 6.1717943847883232
]
self.scene.scene.camera.focal_point = [
3.8980860486356859, 2.4731178194274621, 0.14856957086692035
]
self.scene.scene.camera.view_angle = 30.0
self.scene.scene.camera.view_up = [
-0.27676100729835512, -0.26547169369097656, 0.92354107904740446
]
self.scene.scene.camera.clipping_range = [
7.7372124315754673, 26.343575352248056
]
self.scene.scene.camera.compute_view_plane_normal()
#fig.scene.reset_zoom()
axes = Axes()
self.scene.engine.add_filter(axes, self.plot)
axes.label_text_property.font_family = 'times'
axes.label_text_property.shadow = True
axes.title_text_property.font_family = 'times'
axes.title_text_property.shadow = True
axes.property.color = (0.0, 0.0, 0.0)
axes.title_text_property.color = (0.0, 0.0, 0.0)
axes.label_text_property.color = (0.0, 0.0, 0.0)
axes.axes.corner_offset = .1
axes.axes.x_label = 'x'
axes.axes.y_label = 'y'
axes.axes.z_label = 'z'
else:
print('plot 3d -- 2')
#self.plot.mlab_source.dataset.reset()
#self.plot.mlab_source.set( x = x, y = y, z = z, scalars = scalar )
#self.plot.mlab_source.dataset.points = array( [x, y, z] ).T
self.plot.mlab_source.scalars = scalar
self.plot.mlab_source.dataset.lines = connection
self.plot.module_manager.scalar_lut_manager.data_name = self.var_enum
# The layout of the dialog created
view = View(
Item('scene',
editor=SceneEditor(scene_class=MayaviScene),
height=250,
width=300,
show_label=False),
Group(
'_',
'start_fib',
'end_fib',
'var_enum',
),
resizable=True,
)
class SimCrackLoc(IBVModel):
'''Model assembling the components for studying the restrained crack localization.
'''
geo_transform = Instance(FlawCenteredGeoTransform)
def _geo_transform_default(self):
return FlawCenteredGeoTransform()
shape = Int(10,
desc='Number of finite elements',
ps_levsls=(10, 40, 4),
input=True)
length = Float(1000,
desc='Length of the simulated region',
unit='mm',
input=True)
flaw_position = Float(500, input=True, unit='mm')
flaw_radius = Float(100, input=True, unit='mm')
reduction_factor = Float(0.9, input=True)
elastic_fraction = Float(0.9, input=True)
avg_radius = Float(400, input=True, unit='mm')
# tensile strength of concrete
f_m_t = Float(3.0, input=True, unit='MPa')
epsilon_0 = Property(unit='-')
def _get_epsilon_0(self):
return self.f_m_t / self.E_m
epsilon_f = Float(10, input=True, unit='-')
h_m = Float(10, input=True, unit='mm')
b_m = Float(8, input=True, unit='mm')
A_m = Property(unit='m^2')
def _get_A_m(self):
return self.b_m * self.h_m
E_m = Float(30.0e5, input=True, unit='MPa')
E_f = Float(70.0e6, input=True, unit='MPa')
A_f = Float(1.0, input=True, unit='mm^2')
s_crit = Float(0.009, input=True, unit='mm')
P_f = Property(depends_on='+input')
@cached_property
def _get_P_f(self):
return sqrt(4 * self.A_f * pi)
K_b = Property(depends_on='+input')
@cached_property
def _get_K_b(self):
return self.T_max / self.s_crit
tau_max = Float(0.0, input=True, unit='MPa')
T_max = Property(depends_on='+input', unit='N/mm')
@cached_property
def _get_T_max(self):
return self.tau_max * self.P_f
rho = Property(depends_on='+input')
@cached_property
def _get_rho(self):
return self.A_f / (self.A_f + self.A_m)
#-------------------------------------------------------
# Material model for the matrix
#-------------------------------------------------------
mats_m = Property(Instance(MATS1DDamageWithFlaw), depends_on='+input')
@cached_property
def _get_mats_m(self):
mats_m = MATS1DDamageWithFlaw(E=self.E_m * self.A_m,
flaw_position=self.flaw_position,
flaw_radius=self.flaw_radius,
reduction_factor=self.reduction_factor,
epsilon_0=self.epsilon_0,
epsilon_f=self.epsilon_f)
return mats_m
mats_f = Instance(MATS1DElastic)
def _mats_f_default(self):
mats_f = MATS1DElastic(E=self.E_f * self.A_f)
return mats_f
mats_b = Instance(MATS1DEval)
def _mats_b_default(self):
mats_b = MATS1DElastic(E=self.K_b)
mats_b = MATS1DPlastic(E=self.K_b,
sigma_y=self.T_max,
K_bar=0.,
H_bar=0.) # plastic function of slip
return mats_b
mats_fb = Property(Instance(MATS1D5Bond), depends_on='+input')
@cached_property
def _get_mats_fb(self):
# Material model construction
return MATS1D5Bond(
mats_phase1=MATS1DElastic(E=0),
mats_phase2=self.mats_f,
mats_ifslip=self.mats_b,
mats_ifopen=MATS1DElastic(
E=0) # elastic function of open - inactive
)
#-------------------------------------------------------
# Finite element type
#-------------------------------------------------------
fets_m = Property(depends_on='+input')
@cached_property
def _get_fets_m(self):
fets_eval = FETS1D2L(mats_eval=self.mats_m)
#fets_eval = FETS1D2L3U( mats_eval = self.mats_m )
return fets_eval
fets_fb = Property(depends_on='+input')
@cached_property
def _get_fets_fb(self):
return FETS1D52L4ULRH(mats_eval=self.mats_fb)
#return FETS1D52L6ULRH( mats_eval = self.mats_fb )
#return FETS1D52L8ULRH( mats_eval = self.mats_fb )
#--------------------------------------------------------------------------------------
# Mesh integrator
#--------------------------------------------------------------------------------------
fe_domain_structure = Property(depends_on='+input')
@cached_property
def _get_fe_domain_structure(self):
'''Root of the domain hierarchy
'''
elem_length = self.length / float(self.shape)
fe_domain = FEDomain()
fe_m_level = FERefinementGrid(name='matrix domain',
domain=fe_domain,
fets_eval=self.fets_m)
fe_grid_m = FEGrid(name='matrix grid',
coord_max=(self.length, ),
shape=(self.shape, ),
level=fe_m_level,
fets_eval=self.fets_m,
geo_transform=self.geo_transform)
fe_fb_level = FERefinementGrid(name='fiber bond domain',
domain=fe_domain,
fets_eval=self.fets_fb)
fe_grid_fb = FEGrid(coord_min=(0., length / 5.),
coord_max=(length, 0.),
shape=(self.shape, 1),
level=fe_fb_level,
fets_eval=self.fets_fb,
geo_transform=self.geo_transform)
return fe_domain, fe_grid_m, fe_grid_fb, fe_m_level, fe_fb_level
fe_domain = Property
def _get_fe_domain(self):
return self.fe_domain_structure[0]
fe_grid_m = Property
def _get_fe_grid_m(self):
return self.fe_domain_structure[1]
fe_grid_fb = Property
def _get_fe_grid_fb(self):
return self.fe_domain_structure[2]
fe_m_level = Property
def _get_fe_m_level(self):
return self.fe_domain_structure[3]
fe_fb_level = Property
def _get_fe_fb_level(self):
return self.fe_domain_structure[4]
#---------------------------------------------------------------------------
# Load scaling adapted to the elastic and inelastic regime
#---------------------------------------------------------------------------
final_displ = Property(depends_on='+input')
@cached_property
def _get_final_displ(self):
damage_onset_displ = self.mats_m.epsilon_0 * self.length
return damage_onset_displ / self.elastic_fraction
step_size = Property(depends_on='+input')
@cached_property
def _get_step_size(self):
n_steps = self.n_steps
return 1.0 / float(n_steps)
time_function = Property(depends_on='+input')
@cached_property
def _get_time_function(self):
'''Get the time function so that the elastic regime
is skipped in a single step.
'''
step_size = self.step_size
elastic_value = self.elastic_fraction * 0.98 * self.reduction_factor
inelastic_value = 1.0 - elastic_value
def ls(t):
if t <= step_size:
return (elastic_value / step_size) * t
else:
return elastic_value + (t - step_size) * (inelastic_value) / (
1 - step_size)
return ls
def plot_time_function(self, p):
'''Plot the time function.
'''
n_steps = self.n_steps
mats = self.mats
step_size = self.step_size
ls_t = linspace(0, step_size * n_steps, n_steps + 1)
ls_fn = frompyfunc(self.time_function, 1, 1)
ls_v = ls_fn(ls_t)
p.subplot(321)
p.plot(ls_t, ls_v, 'ro-')
final_epsilon = self.final_displ / self.length
kappa = linspace(mats.epsilon_0, final_epsilon, 10)
omega_fn = frompyfunc(lambda kappa: mats._get_omega(None, kappa), 1, 1)
omega = omega_fn(kappa)
kappa_scaled = (step_size + (1 - step_size) *
(kappa - mats.epsilon_0) /
(final_epsilon - mats.epsilon_0))
xdata = hstack([array([0.0], dtype=float), kappa_scaled])
ydata = hstack([array([0.0], dtype=float), omega])
p.plot(xdata, ydata, 'g')
p.xlabel('regular time [-]')
p.ylabel('scaled time [-]')
run = Button
@on_trait_change('run')
def peval(self):
'''Evaluation procedure.
'''
#mv = MATS1DDamageView( model = mats_eval )
#mv.configure_traits()
right_dof_m = self.fe_grid_m[-1, -1].dofs[0, 0, 0]
right_dof_fb = self.fe_grid_fb[-1, -1, -1, -1].dofs[0, 0, 0]
# Response tracers
A = self.A_m + self.A_f
self.sig_eps_m = RTraceGraph(name='F_u_m',
var_y='F_int',
idx_y=right_dof_m,
var_x='U_k',
idx_x=right_dof_m,
transform_y='y / %g' % A)
# Response tracers
self.sig_eps_f = RTraceGraph(name='F_u_f',
var_y='F_int',
idx_y=right_dof_fb,
var_x='U_k',
idx_x=right_dof_fb,
transform_y='y / %g' % A)
self.eps_m_field = RTraceDomainListField(name='eps_m',
position='int_pnts',
var='eps_app',
warp=False)
self.eps_f_field = RTraceDomainListField(name='eps_f',
position='int_pnts',
var='mats_phase2_eps_app',
warp=False)
# Response tracers
self.sig_m_field = RTraceDomainListField(name='sig_m',
position='int_pnts',
var='sig_app')
self.sig_f_field = RTraceDomainListField(name='sig_f',
position='int_pnts',
var='mats_phase2_sig_app')
self.omega_m_field = RTraceDomainListField(name='omega_m',
position='int_pnts',
var='omega',
warp=False)
self.shear_flow_field = RTraceDomainListField(name='shear flow',
position='int_pnts',
var='shear_flow',
warp=False)
self.slip_field = RTraceDomainListField(name='slip',
position='int_pnts',
var='slip',
warp=False)
avg_processor = None
if self.avg_radius > 0.0:
n_dofs = self.fe_domain.n_dofs
avg_processor = RTUAvg(sd=self.fe_m_level,
n_dofs=n_dofs,
avg_fn=QuarticAF(radius=self.avg_radius))
ts = TStepper(
u_processor=avg_processor,
dof_resultants=True,
sdomain=self.fe_domain,
bcond_list=[ # define the left clamping
BCSlice(var='u',
value=0.,
dims=[0],
slice=self.fe_grid_fb[0, 0, 0, :]),
# BCSlice( var = 'u', value = 0., dims = [0], slice = self.fe_grid_m[ 0, 0 ] ),
# loading at the right edge
# BCSlice( var = 'f', value = 1, dims = [0], slice = domain[-1, -1, -1, 0],
# time_function = ls ),
BCSlice(var='u',
value=self.final_displ,
dims=[0],
slice=self.fe_grid_fb[-1, -1, -1, :],
time_function=self.time_function),
# BCSlice( var = 'u', value = self.final_displ, dims = [0], slice = self.fe_grid_m[-1, -1],
# time_function = self.time_function ),
# fix horizontal displacement in the top layer
# BCSlice( var = 'u', value = 0., dims = [0], slice = domain[:, -1, :, -1] ),
# fix the vertical displacement all over the domain
BCSlice(var='u',
value=0.,
dims=[1],
slice=self.fe_grid_fb[:, :, :, :]),
# # Connect bond and matrix domains
BCDofGroup(var='u',
value=0.,
dims=[0],
get_link_dof_method=self.fe_grid_fb.get_bottom_dofs,
get_dof_method=self.fe_grid_m.get_all_dofs,
link_coeffs=[1.])
],
rtrace_list=[
self.sig_eps_m,
self.sig_eps_f,
self.eps_m_field,
self.eps_f_field,
self.sig_m_field,
self.sig_f_field,
self.omega_m_field,
self.shear_flow_field,
self.slip_field,
])
# Add the time-loop control
tloop = TLoop(tstepper=ts,
KMAX=300,
tolerance=1e-5,
debug=False,
verbose_iteration=True,
verbose_time=False,
tline=TLine(min=0.0, step=self.step_size, max=1.0))
tloop.on_accept_time_step = self.plot
U = tloop.eval()
self.sig_eps_f.refresh()
max_sig_m = max(self.sig_eps_m.trace.ydata)
return array([U[right_dof_m], max_sig_m], dtype='float_')
#--------------------------------------------------------------------------------------
# Tracers
#--------------------------------------------------------------------------------------
def plot_sig_eps(self, p):
p.set_xlabel('control displacement [mm]')
p.set_ylabel('stress [MPa]')
self.sig_eps_m.refresh()
self.sig_eps_m.trace.plot(p, 'o-')
self.sig_eps_f.refresh()
self.sig_eps_f.trace.plot(p, 'o-')
p.plot(self.sig_eps_m.trace.xdata,
self.sig_eps_m.trace.ydata + self.sig_eps_f.trace.ydata, 'o-')
def plot_eps(self, p):
eps_m = self.eps_m_field.subfields[0]
xdata = eps_m.vtk_X[:, 0]
ydata = eps_m.field_arr[:, 0, 0]
idata = argsort(xdata)
p.plot(xdata[idata], ydata[idata], 'o-')
eps_f = self.eps_f_field.subfields[1]
xdata = eps_f.vtk_X[:, 0]
ydata = eps_f.field_arr[:, 0, 0]
idata = argsort(xdata)
p.plot(xdata[idata], ydata[idata], 'o-')
p.set_ylim(ymin=0)
p.set_xlabel('bar axis [mm]')
p.set_ylabel('strain [-]')
def plot_omega(self, p):
omega_m = self.omega_m_field.subfields[0]
xdata = omega_m.vtk_X[:, 0]
ydata = omega_m.field_arr[:]
idata = argsort(xdata)
p.fill(xdata[idata], ydata[idata], facecolor='gray', alpha=0.2)
print 'max omega', max(ydata[idata])
p.set_ylim(ymin=0, ymax=1.0)
p.set_xlabel('bar axis [mm]')
p.set_ylabel('omega [-]')
def plot_sig(self, p):
sig_m = self.sig_m_field.subfields[0]
xdata = sig_m.vtk_X[:, 0]
ydata = sig_m.field_arr[:, 0, 0]
idata = argsort(xdata)
ymax = max(ydata)
p.plot(xdata[idata], ydata[idata], 'o-')
sig_f = self.sig_f_field.subfields[1]
xdata = sig_f.vtk_X[:, 0]
ydata = sig_f.field_arr[:, 0, 0]
idata = argsort(xdata)
p.plot(xdata[idata], ydata[idata], 'o-')
xdata = sig_f.vtk_X[:, 0]
ydata = sig_f.field_arr[:, 0, 0] + sig_m.field_arr[:, 0, 0]
p.plot(xdata[idata], ydata[idata], 'ro-')
p.set_ylim(ymin=0) # , ymax = 1.2 * ymax )
p.set_xlabel('bar axis [mm]')
p.set_ylabel('stress [MPa]')
def plot_shear_flow(self, p):
shear_flow = self.shear_flow_field.subfields[1]
xdata = shear_flow.vtk_X[:, 0]
ydata = shear_flow.field_arr[:, 0] / self.P_f
idata = argsort(xdata)
ymax = max(ydata)
p.plot(xdata[idata], ydata[idata], 'o-')
p.set_xlabel('bar axis [mm]')
p.set_ylabel('shear flow [N/m]')
def plot_slip(self, p):
slip = self.slip_field.subfields[1]
xdata = slip.vtk_X[:, 0]
ydata = slip.field_arr[:, 0]
idata = argsort(xdata)
ymax = max(ydata)
p.plot(xdata[idata], ydata[idata], 'ro-')
p.set_xlabel('bar axis [mm]')
p.set_ylabel('slip [N/m]')
def plot_tracers(self, p=p):
p.subplot(221)
self.plot_sig_eps(p)
p.subplot(223)
self.plot_eps(p)
p.subplot(224)
self.plot_sig(p)
#---------------------------------------------------------------
# PLOT OBJECT
#-------------------------------------------------------------------
figure_ld = Instance(Figure)
def _figure_ld_default(self):
figure = Figure(facecolor='white')
figure.add_axes([0.12, 0.13, 0.85, 0.74])
return figure
#---------------------------------------------------------------
# PLOT OBJECT
#-------------------------------------------------------------------
figure_eps = Instance(Figure)
def _figure_eps_default(self):
figure = Figure(facecolor='white')
figure.add_axes([0.12, 0.13, 0.85, 0.74])
return figure
#---------------------------------------------------------------
# PLOT OBJECT
#-------------------------------------------------------------------
figure_shear_flow = Instance(Figure)
def _figure_shear_flow_default(self):
figure = Figure(facecolor='white')
figure.add_axes([0.12, 0.13, 0.85, 0.74])
return figure
#---------------------------------------------------------------
# PLOT OBJECT
#-------------------------------------------------------------------
figure_sig = Instance(Figure)
def _figure_sig_default(self):
figure = Figure(facecolor='white')
figure.add_axes([0.12, 0.13, 0.85, 0.74])
return figure
#---------------------------------------------------------------
# PLOT OBJECT
#-------------------------------------------------------------------
figure_shear_flow = Instance(Figure)
def _figure_shear_flow_default(self):
figure = Figure(facecolor='white')
figure.add_axes([0.12, 0.13, 0.85, 0.74])
return figure
def plot(self):
self.figure_ld.clear()
ax = self.figure_ld.gca()
self.plot_sig_eps(ax)
self.figure_eps.clear()
ax = self.figure_eps.gca()
self.plot_eps(ax)
ax2 = ax.twinx()
self.plot_omega(ax2)
self.figure_sig.clear()
ax = self.figure_sig.gca()
self.plot_sig(ax)
ax2 = ax.twinx()
self.plot_omega(ax2)
self.figure_shear_flow.clear()
ax = self.figure_shear_flow.gca()
self.plot_shear_flow(ax)
ax2 = ax.twinx()
self.plot_slip(ax2)
self.data_changed = True
def get_sim_outputs(self):
'''
Specifies the results and their order returned by the model
evaluation.
'''
return [
SimOut(name='right end displacement', unit='m'),
SimOut(name='peak load', uni='MPa')
]
data_changed = Event
toolbar = ToolBar(Action(name="Run",
tooltip='Start computation',
image=ImageResource('kt-start'),
action="start_study"),
Action(name="Pause",
tooltip='Pause computation',
image=ImageResource('kt-pause'),
action="pause_study"),
Action(name="Stop",
tooltip='Stop computation',
image=ImageResource('kt-stop'),
action="stop_study"),
image_size=(32, 32),
show_tool_names=False,
show_divider=True,
name='view_toolbar'),
traits_view = View(HSplit(
VSplit(
Item('run', show_label=False),
VGroup(
Item('shape'),
Item('n_steps'),
Item('length'),
label='parameters',
id='crackloc.viewmodel.factor.geometry',
dock='tab',
scrollable=True,
),
VGroup(Item('E_m'),
Item('f_m_t'),
Item('avg_radius'),
Item('h_m'),
Item('b_m'),
Item('A_m', style='readonly'),
Item('mats_m', show_label=False),
label='Matrix',
dock='tab',
id='crackloc.viewmodel.factor.matrix',
scrollable=True),
VGroup(Item('E_f'),
Item('A_f'),
Item('mats_f', show_label=False),
label='Fiber',
dock='tab',
id='crackloc.viewmodel.factor.fiber',
scrollable=True),
VGroup(Group(
Item('tau_max'),
Item('s_crit'),
Item('P_f', style='readonly', show_label=True),
Item('K_b', style='readonly', show_label=True),
Item('T_max', style='readonly', show_label=True),
),
Item('mats_b', show_label=False),
label='Bond',
dock='tab',
id='crackloc.viewmodel.factor.bond',
scrollable=True),
VGroup(Item('rho', style='readonly', show_label=True),
label='Composite',
dock='tab',
id='crackloc.viewmodel.factor.jcomposite',
scrollable=True),
id='crackloc.viewmodel.left',
label='studied factors',
layout='tabbed',
dock='tab',
),
VSplit(
VGroup(
Item('figure_ld',
editor=MPLFigureEditor(),
resizable=True,
show_label=False),
label='stress-strain',
id='crackloc.viewmode.figure_ld_window',
dock='tab',
),
VGroup(
Item('figure_eps',
editor=MPLFigureEditor(),
resizable=True,
show_label=False),
label='strains profile',
id='crackloc.viewmode.figure_eps_window',
dock='tab',
),
VGroup(
Item('figure_sig',
editor=MPLFigureEditor(),
resizable=True,
show_label=False),
label='stress profile',
id='crackloc.viewmode.figure_sig_window',
dock='tab',
),
VGroup(
Item('figure_shear_flow',
editor=MPLFigureEditor(),
resizable=True,
show_label=False),
label='bond shear and slip profiles',
id='crackloc.viewmode.figure_shear_flow_window',
dock='tab',
),
id='crackloc.viewmodel.right',
),
id='crackloc.viewmodel.splitter',
),
title='SimVisage Component: Crack localization',
id='crackloc.viewmodel',
dock='tab',
resizable=True,
height=0.8,
width=0.8,
buttons=[OKButton])
class CBView(ModelView):
def __init__(self, **kw):
super(CBView, self).__init__(**kw)
self.on_trait_change(self.refresh, 'model.+params')
self.refresh()
model = Instance(Model)
figure = Instance(Figure)
def _figure_default(self):
figure = Figure(facecolor='white')
return figure
figure2 = Instance(Figure)
def _figure2_default(self):
figure = Figure(facecolor='white')
return figure
data_changed = Event
def plot(self, fig, fig2):
figure = fig
figure.clear()
axes = figure.gca()
# plot PDF
axes.plot(self.model.w, self.model.model_rand, lw=2.0, color='blue', \
label='model')
axes.plot(self.model.w, self.model.interpolate_experiment(self.model.w), lw=1.0, color='black', \
label='experiment')
axes.legend(loc='best')
# figure2 = fig2
# figure2.clear()
# axes = figure2.gca()
# # plot PDF
# axes.plot(self.model.w2, self.model.model_extrapolate, lw=2.0, color='red', \
# label='model')
# axes.legend()
def refresh(self):
self.plot(self.figure, self.figure2)
self.data_changed = True
traits_view = View(HSplit(VGroup(
Group(
Item('model.tau_scale'),
Item('model.tau_shape'),
Item('model.tau_loc'),
Item('model.m'),
Item('model.sV0'),
Item('model.Ef'),
Item('model.w_min'),
Item('model.w_max'),
Item('model.w_pts'),
Item('model.n_int'),
Item('model.w2_min'),
Item('model.w2_max'),
Item('model.w2_pts'),
Item('model.sigmamu'),
),
id='pdistrib.distr_type.pltctrls',
label='Distribution parameters',
scrollable=True,
),
Tabbed(
Group(
Item('figure',
editor=MPLFigureEditor(),
show_label=False,
resizable=True),
scrollable=True,
label='Plot',
),
label='Plot',
id='pdistrib.figure.params',
dock='tab',
),
Tabbed(
Group(
Item('figure2',
editor=MPLFigureEditor(),
show_label=False,
resizable=True),
scrollable=True,
label='Plot',
),
label='Plot',
id='pdistrib.figure2',
dock='tab',
),
dock='tab',
id='pdistrib.figure.view'),
id='pdistrib.view',
dock='tab',
title='Statistical distribution',
buttons=[OKButton, CancelButton],
scrollable=True,
resizable=True,
width=600,
height=400)
class PDistrib(HasTraits):
implements = IPDistrib
def __init__(self, **kw):
super(PDistrib, self).__init__(**kw)
self.on_trait_change(self.refresh,
'distr_type.changed,quantile,n_segments')
self.refresh()
# puts all chosen continuous distributions distributions defined
# in the scipy.stats.distributions module as a list of strings
# into the Enum trait
# distr_choice = Enum(distr_enum)
distr_choice = Enum('sin2x', 'weibull_min', 'sin_distr', 'uniform', 'norm',
'piecewise_uniform', 'gamma')
distr_dict = {
'sin2x': sin2x,
'uniform': uniform,
'norm': norm,
'weibull_min': weibull_min,
'sin_distr': sin_distr,
'piecewise_uniform': piecewise_uniform,
'gamma': gamma
}
# instantiating the continuous distributions
distr_type = Property(Instance(Distribution), depends_on='distr_choice')
@cached_property
def _get_distr_type(self):
return Distribution(self.distr_dict[self.distr_choice])
# change monitor - accumulate the changes in a single event trait
changed = Event
@on_trait_change('distr_choice, distr_type.changed, quantile, n_segments')
def _set_changed(self):
self.changed = True
#------------------------------------------------------------------------
# Methods setting the statistical modments
#------------------------------------------------------------------------
mean = Property
def _get_mean(self):
return self.distr_type.mean
def _set_mean(self, value):
self.distr_type.mean = value
variance = Property
def _get_variance(self):
return self.distr_type.mean
def _set_variance(self, value):
self.distr_type.mean = value
#------------------------------------------------------------------------
# Methods preparing visualization
#------------------------------------------------------------------------
quantile = Float(1e-14, auto_set=False, enter_set=True)
range = Property(Tuple(Float), depends_on=\
'distr_type.changed, quantile')
@cached_property
def _get_range(self):
return (self.distr_type.distr.ppf(self.quantile),
self.distr_type.distr.ppf(1 - self.quantile))
n_segments = Int(500, auto_set=False, enter_set=True)
dx = Property(Float, depends_on=\
'distr_type.changed, quantile, n_segments')
@cached_property
def _get_dx(self):
range_length = self.range[1] - self.range[0]
return range_length / self.n_segments
#-------------------------------------------------------------------------
# Discretization of the distribution domain
#-------------------------------------------------------------------------
x_array = Property(Array('float_'), depends_on=\
'distr_type.changed,'\
'quantile, n_segments')
@cached_property
def _get_x_array(self):
'''Get the intrinsic discretization of the distribution
respecting its bounds.
'''
return linspace(self.range[0], self.range[1], self.n_segments + 1)
#===========================================================================
# Access function to the scipy distribution
#===========================================================================
def pdf(self, x):
return self.distr_type.distr.pdf(x)
def cdf(self, x):
return self.distr_type.distr.cdf(x)
def rvs(self, n):
return self.distr_type.distr.rvs(n)
def ppf(self, e):
return self.distr_type.distr.ppf(e)
#===========================================================================
# PDF - permanent array
#===========================================================================
pdf_array = Property(Array('float_'), depends_on=\
'distr_type.changed,'\
'quantile, n_segments')
@cached_property
def _get_pdf_array(self):
'''Get pdf values in intrinsic positions'''
return self.distr_type.distr.pdf(self.x_array)
def get_pdf_array(self, x_array):
'''Get pdf values in externally specified positions'''
return self.distr_type.distr.pdf(x_array)
#===========================================================================
# CDF permanent array
#===========================================================================
cdf_array = Property(Array('float_'), depends_on=\
'distr_type.changed,'\
'quantile, n_segments')
@cached_property
def _get_cdf_array(self):
'''Get cdf values in intrinsic positions'''
return self.distr_type.distr.cdf(self.x_array)
def get_cdf_array(self, x_array):
'''Get cdf values in externally specified positions'''
return self.distr_type.distr.cdf(x_array)
#-------------------------------------------------------------------------
# Randomization
#-------------------------------------------------------------------------
def get_rvs_array(self, n_samples):
return self.distr_type.distr.rvs(n_samples)
figure = Instance(Figure)
def _figure_default(self):
figure = Figure(facecolor='white')
return figure
data_changed = Event
def plot(self, fig):
figure = fig
figure.clear()
axes = figure.gca()
# plot PDF
axes.plot(self.x_array, self.pdf_array, lw=1.0, color='blue', \
label='PDF')
axes2 = axes.twinx()
# plot CDF on a separate axis (tick labels left)
axes2.plot(self.x_array, self.cdf_array, lw=2, color='red', \
label='CDF')
# fill the unity area given by integrating PDF along the X-axis
axes.fill_between(self.x_array,
0,
self.pdf_array,
color='lightblue',
alpha=0.8,
linewidth=2)
# plot mean
mean = self.distr_type.distr.stats('m')
axes.plot([mean, mean], [0.0, self.distr_type.distr.pdf(mean)],
lw=1.5,
color='black',
linestyle='-')
# plot stdev
stdev = sqrt(self.distr_type.distr.stats('v'))
axes.plot([mean - stdev, mean - stdev],
[0.0, self.distr_type.distr.pdf(mean - stdev)],
lw=1.5,
color='black',
linestyle='--')
axes.plot([mean + stdev, mean + stdev],
[0.0, self.distr_type.distr.pdf(mean + stdev)],
lw=1.5,
color='black',
linestyle='--')
axes.legend(loc='center left')
axes2.legend(loc='center right')
axes.ticklabel_format(scilimits=(-3., 4.))
axes2.ticklabel_format(scilimits=(-3., 4.))
# plot limits on X and Y axes
axes.set_ylim(0.0, max(self.pdf_array) * 1.15)
axes2.set_ylim(0.0, 1.15)
range = self.range[1] - self.range[0]
axes.set_xlim(self.x_array[0] - 0.05 * range,
self.x_array[-1] + 0.05 * range)
axes2.set_xlim(self.x_array[0] - 0.05 * range,
self.x_array[-1] + 0.05 * range)
def refresh(self):
self.plot(self.figure)
self.data_changed = True
icon = Property(Instance(ImageResource),
depends_on='distr_type.changed,quantile,n_segments')
@cached_property
def _get_icon(self):
fig = plt.figure(figsize=(4, 4), facecolor='white')
self.plot(fig)
tf_handle, tf_name = tempfile.mkstemp('.png')
fig.savefig(tf_name, dpi=35)
return ImageResource(name=tf_name)
traits_view = View(HSplit(VGroup(
Group(
Item('distr_choice', show_label=False),
Item('@distr_type', show_label=False),
),
id='pdistrib.distr_type.pltctrls',
label='Distribution parameters',
scrollable=True,
),
Tabbed(
Group(
Item('figure',
editor=MPLFigureEditor(),
show_label=False,
resizable=True),
scrollable=True,
label='Plot',
),
Group(Item('quantile', label='quantile'),
Item('n_segments',
label='plot points'),
label='Plot parameters'),
label='Plot',
id='pdistrib.figure.params',
dock='tab',
),
dock='tab',
id='pdistrib.figure.view'),
id='pdistrib.view',
dock='tab',
title='Statistical distribution',
buttons=['Ok', 'Cancel'],
scrollable=True,
resizable=True,
width=600,
height=400)
class ECBLMNDiagram(HasTraits):
# calibrator supplying the effective material law
calib = Instance(ECBLCalib)
def _calib_default(self):
return ECBLCalib(notify_change=self.set_modified)
def _calib_changed(self):
self.calib.notify_change = self.set_modified
modified = Event
def set_modified(self):
print 'MN:set_modifeid'
self.modified = True
# cross section
cs = DelegatesTo('calib')
calibrated_ecb_law = Property(depends_on='modified')
@cached_property
def _get_calibrated_ecb_law(self):
print 'NEW CALIBRATION'
return self.calib.calibrated_ecb_law
eps_cu = Property()
def _get_eps_cu(self):
return -self.cs.cc_law.eps_c_u
eps_tu = Property()
def _get_eps_tu(self):
return self.calibrated_ecb_law.eps_tex_u
n_eps = Int(5, auto_set=False, enter_set=True)
eps_range = Property(depends_on='n_eps')
@cached_property
def _get_eps_range(self):
eps_c_space = np.linspace(self.eps_cu, 0, self.n_eps)
eps_t_space = np.linspace(0, self.eps_tu, self.n_eps)
eps_ccu = 0.8 * self.eps_cu
#eps_cc = self.eps_cu * np.ones_like(eps_c_space)
eps_cc = np.linspace(eps_ccu, self.eps_cu, self.n_eps)
eps_ct = self.eps_cu * np.ones_like(eps_t_space)
eps_tc = self.eps_tu * np.ones_like(eps_c_space)
eps_tt = self.eps_tu * np.ones_like(eps_t_space)
eps1 = np.vstack([eps_c_space, eps_cc])
eps2 = np.vstack([eps_t_space, eps_ct])
eps3 = np.vstack([eps_tc, eps_c_space])
eps4 = np.vstack([eps_tt, eps_t_space])
return np.hstack([eps1, eps2, eps3, eps4])
n_eps_range = Property(depends_on='n_eps')
@cached_property
def _get_n_eps_range(self):
return self.eps_range.shape[1]
#===========================================================================
# MN Diagram
#===========================================================================
def _get_MN_fn(self, eps_lo, eps_up):
self.cs.set(eps_lo=eps_lo, eps_up=eps_up)
return (self.cs.M, self.cs.N)
MN_vct = Property(depends_on='modified')
def _get_MN_vct(self):
return np.vectorize(self._get_MN_fn)
MN_arr = Property(depends_on='modified')
@cached_property
def _get_MN_arr(self):
return self.MN_vct(self.eps_range[0, :], self.eps_range[1, :])
#===========================================================================
# f_eps Diagram
#===========================================================================
current_eps_idx = Int(0) # , auto_set = False, enter_set = True)
def _current_eps_idx_changed(self):
self._clear_fired()
self._replot_fired()
current_eps = Property(depends_on='current_eps_idx')
@cached_property
def _get_current_eps(self):
return self.eps_range[(0, 1), self.current_eps_idx]
current_MN = Property(depends_on='current_eps_idx')
@cached_property
def _get_current_MN(self):
return self._get_MN_fn(*self.current_eps)
#===========================================================================
# Plotting
#===========================================================================
figure = Instance(Figure)
def _figure_default(self):
figure = Figure(facecolor='white')
figure.add_axes([0.08, 0.13, 0.85, 0.74])
return figure
data_changed = Event
clear = Button
def _clear_fired(self):
self.figure.clear()
self.data_changed = True
replot = Button
def _replot_fired(self):
ax = self.figure.add_subplot(2, 2, 1)
ax.plot(-self.eps_range, [0, 0.06], color='black')
ax.plot(-self.current_eps, [0, 0.06], lw=3, color='red')
ax.spines['left'].set_position('zero')
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.spines['left'].set_smart_bounds(True)
ax.spines['bottom'].set_smart_bounds(True)
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
ax = self.figure.add_subplot(2, 2, 2)
ax.plot(self.MN_arr[0], -self.MN_arr[1], lw=2, color='blue')
ax.plot(self.current_MN[0],
-self.current_MN[1],
'g.',
markersize=20.0,
color='red')
ax.spines['left'].set_position('zero')
ax.spines['bottom'].set_position('zero')
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.spines['left'].set_smart_bounds(True)
ax.spines['bottom'].set_smart_bounds(True)
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
ax.grid(b=None, which='major')
self.cs.set(eps_lo=self.current_eps[0], eps_up=self.current_eps[1])
ax = self.figure.add_subplot(2, 2, 3)
self.cs.plot_eps(ax)
ax = self.figure.add_subplot(2, 2, 4)
self.cs.plot_sig(ax)
self.data_changed = True
view = View(HSplit(
Group(
HGroup(
Group(Item('n_eps', springy=True),
label='Discretization',
springy=True),
springy=True,
),
HGroup(
Group(VGroup(
Item(
'cs',
label='Cross section',
show_label=False,
springy=True,
editor=InstanceEditor(kind='live'),
),
Item(
'calib',
label='Calibration',
show_label=False,
springy=True,
editor=InstanceEditor(kind='live'),
),
springy=True,
),
label='Cross sectoin',
springy=True),
springy=True,
),
scrollable=True,
),
Group(
HGroup(
Item('replot', show_label=False),
Item('clear', show_label=False),
),
Item(
'current_eps_idx',
editor=RangeEditor(
low=0,
high_name='n_eps_range',
format='(%s)',
mode='slider',
auto_set=False,
enter_set=False,
),
show_label=False,
),
Item('figure',
editor=MPLFigureEditor(),
resizable=True,
show_label=False),
id='simexdb.plot_sheet',
label='plot sheet',
dock='tab',
),
),
width=1.0,
height=0.8,
resizable=True,
buttons=['OK', 'Cancel'])
class YMBView2D(HasTraits):
data = Instance(YMBData)
zero = Constant(0)
slider_max = Property()
def _get_slider_max(self):
return self.data.n_cuts - 1
var_enum = Trait('radius', var_dict, modified=True)
cut_slider = Range('zero',
'slider_max',
mode='slider',
auto_set=False,
enter_set=True,
modified=True)
circle_diameter = Float(20, enter_set=True, auto_set=False, modified=True)
underlay = Bool(False, modified=True)
variable = Property(Array, depends_on='var_enum')
@cached_property
def _get_variable(self):
return getattr(self.data, self.var_enum_)
figure = Instance(Figure)
def _figure_default(self):
figure = Figure()
figure.add_axes([0.1, 0.1, 0.8, 0.8])
return figure
data_changed = Event(True)
@on_trait_change('+modified, data.input_changed')
def _redraw(self):
# TODO: set correct ranges, fix axis range (axes.xlim)
self.figure.clear()
self.figure.add_axes([0.1, 0.1, 0.8, 0.8])
figure = self.figure
axes = figure.axes[0]
axes.clear()
y_arr, z_arr = self.data.cut_data[1:3]
y_raw_arr, z_raw_arr = self.data.cut_raw_data[0:2]
offset = hstack([0, self.data.cut_raw_data[5]])
scalar_arr = self.variable
mask = y_arr[:, self.cut_slider] > -1
axes.scatter(
y_raw_arr[offset[self.cut_slider]:offset[self.cut_slider + 1]],
z_raw_arr[offset[self.cut_slider]:offset[self.cut_slider + 1]],
s=self.circle_diameter,
color='k',
marker='x',
label='identified filament in cut')
scat = axes.scatter(y_arr[:, self.cut_slider][mask],
z_arr[:, self.cut_slider][mask],
s=self.circle_diameter,
c=scalar_arr[:, self.cut_slider][mask],
cmap=my_cmap_lin,
label='connected filaments')
axes.set_xlabel('$y\, [\mathrm{mm}]$', fontsize=16)
axes.set_ylabel('$z\, [\mathrm{mm}]$', fontsize=16)
axes.set_xlim([0, ceil(max(y_arr))])
axes.set_ylim([0, ceil(max(z_arr))])
axes.legend()
figure.colorbar(scat)
if self.underlay == True:
axes.text(axes.get_xlim()[0],
axes.get_ylim()[0],
'That\'s all at this moment :-)',
color='red',
fontsize=20)
self.data_changed = True
traits_view = View(
Group(
Item('var_enum'),
Item('cut_slider', springy=True),
Item('circle_diameter', springy=True),
Item('underlay', springy=True),
),
Item('figure',
style='custom',
editor=MPLFigureEditor(),
show_label=False),
resizable=True,
)
class YMBAutoCorrelView(HasTraits):
correl_data = Instance(YMBAutoCorrel)
axes_adjust = List([0.1, 0.1, 0.8, 0.8])
data = Property
def _get_data(self):
return self.correl_data.data
zero = Constant(0)
slider_max = Property()
def _get_slider_max(self):
return self.data.n_cuts - 1
cut_slider = Range('zero', 'slider_max', mode='slider', auto_set=False, enter_set=True, modified=True)
vcut_slider = Range('zero', 'slider_max', mode='slider', auto_set=False, enter_set=True, modified=True)
cut_slider_on = Bool(False, modified=True)
color = Str('blue')
figure = Instance(Figure)
def _figure_default(self):
figure = Figure()
figure.add_axes(self.axes_adjust)
return figure
data_changed = Event(True)
@on_trait_change('correl_data.input_change, +modified')
def _redraw(self):
# TODO: set correct ranges, fix axis range (axes.xlim)
print 'redrawing xxxx'
figure = self.figure
figure.clear()
var_data = self.correl_data.corr_arr
id = self.cut_slider
if self.cut_slider_on == True:
i = self.cut_slider
j = self.vcut_slider
plot_data = getattr(self.data, self.correl_data.var_enum_)
# plot_data = vstack( [plot_data[:, i], plot_data[:, j]] ).T
# plot only values > -1
# plot_data = plot_data[prod( plot_data >= 0, axis = 1, dtype = bool )]
plot_data_x = plot_data[:, i]
plot_data_y = plot_data[:, j]
plot_data_corr = min(corrcoef(plot_data_x, plot_data_y))
plot_data_corr_spear = spearmanr(plot_data_x, plot_data_y)[0]
left, width = 0.1, 0.65
bottom, height = 0.1, 0.65
bottom_h = left_h = left + width + 0.02
rect_scatter = [left, bottom, width, height]
rect_histx = [left, bottom_h, width, 0.2]
rect_histy = [left_h, bottom, 0.2, height]
axScatter = figure.add_axes(rect_scatter)
axHistx = figure.add_axes(rect_histx)
axHisty = figure.add_axes(rect_histy)
axScatter.clear()
axHistx.clear()
axHisty.clear()
from matplotlib.ticker import NullFormatter
axHistx.xaxis.set_major_formatter(NullFormatter())
axHisty.yaxis.set_major_formatter(NullFormatter())
axScatter.scatter(plot_data_x,
plot_data_y)
# binwidth = 0.25
# xymax = max( [max( abs( self.data.cf[:, j] ) ), max( abs( self.data.cf[:, i] ) )] )
# lim = ( int( xymax / binwidth ) + 1 ) * binwidth
# axScatter.set_xlim( ( -lim, lim ) )
# axScatter.set_ylim( ( -lim, lim ) )
# bins = arange( -lim, lim + binwidth, binwidth )
axHistx.hist(plot_data_x.compressed(), bins=40)
axHisty.hist(plot_data_y.compressed(), bins=40, orientation='horizontal')
axHistx.set_xlim(axScatter.get_xlim())
axHisty.set_ylim(axScatter.get_ylim())
axScatter.set_xlabel('$\mathrm{cut\, %i}$' % self.cut_slider, fontsize=16)
axScatter.set_ylabel('$\mathrm{cut\, %i}$' % self.vcut_slider, fontsize=16)
axScatter.text(axScatter.get_xlim()[0], axScatter.get_ylim()[0],
'actual set correlation %.3f (Pearson), %.3f (Spearman)' % (plot_data_corr, plot_data_corr_spear), color='r')
if self.cut_slider_on == False:
figure.add_axes(self.axes_adjust)
axes = figure.axes[0]
axes.clear()
x_coor = self.data.x_coord
axes.grid()
for i in range(0, var_data.shape[1]):
axes.plot(x_coor[i:] - x_coor[i],
var_data[i, (i):], '-x', color=self.color)
# approximate by the polynomial (of the i-th order)
# axes.plot( x_coor, self.correl_data.peval( x_coor, self.correl_data.fit_correl ), 'b', linewidth = 3 )
setp(axes.get_xticklabels(), position=(0, -.025))
axes.set_xlabel('$x \, [\mathrm{mm}]$', fontsize=15)
axes.set_ylabel('$\mathrm{correlation}$', fontsize=15)
axes.set_ylim(-1, 1)
self.data_changed = True
traits_view = View(Group(Item('correl_data', show_label=False, style='custom'),
HGroup(
Item('cut_slider_on', label='Scatter'),
Item('cut_slider', show_label=False, springy=True, enabled_when='cut_slider_on == True'),
Item('vcut_slider', show_label=False, springy=True, enabled_when='cut_slider_on == True'),
),
Group(Item('figure', style='custom',
editor=MPLFigureEditor(),
show_label=False)
, id='figure.view'),
),
resizable=True,
)
class SPIRRIDModelView(ModelView):
'''
Size effect depending on the yarn length
'''
model = Instance(SPIRRID)
def _model_changed(self):
self.model.rf = self.rf
rf_values = List(IRF)
def _rf_values_default(self):
return [ Filament() ]
rf = Enum(values = 'rf_values')
def _rf_default(self):
return self.rf_values[0]
def _rf_changed(self):
# reset the rf in the spirrid model and in the rf_modelview
self.model.rf = self.rf
self.rf_model_view = RFModelView(model = self.rf)
# actually, the view should be reusable but the model switch
# did not work for whatever reason
# the binding of the view generated by edit_traits does not
# react to the change in the 'model' attribute.
#
# Remember - we are implementing a handler here
# that has an associated view.
#
# self.rf.model_view.model = self.rf
rf_model_view = Instance(RFModelView)
def _rf_model_view_default(self):
return RFModelView(model = self.rf)
rv_list = Property(List(RIDVariable), depends_on = 'rf')
@cached_property
def _get_rv_list(self):
return [ RIDVariable(spirrid = self.model, rf = self.rf,
varname = nm, trait_value = st)
for nm, st in zip(self.rf.param_keys, self.rf.param_values) ]
selected_var = Instance(RIDVariable)
def _selected_var_default(self):
return self.rv_list[0]
run = Button(desc = 'Run the computation')
def _run_fired(self):
self._redraw()
sample = Button(desc = 'Show samples')
def _sample_fired(self):
n_samples = 50
self.model.set(
min_eps = 0.00, max_eps = self.max_eps, n_eps = self.n_eps,
)
# get the parameter combinations for plotting
rvs_theta_arr = self.model.get_rvs_theta_arr(n_samples)
eps_arr = self.model.eps_arr
figure = self.figure
axes = figure.gca()
for theta_arr in rvs_theta_arr.T:
q_arr = self.rf(eps_arr, *theta_arr)
axes.plot(eps_arr, q_arr, color = 'grey')
self.data_changed = True
run_legend = Str('',
desc = 'Legend to be added to the plot of the results')
clear = Button
def _clear_fired(self):
axes = self.figure.axes[0]
axes.clear()
self.data_changed = True
min_eps = Float(0.0,
desc = 'minimum value of the control variable')
max_eps = Float(1.0,
desc = 'maximum value of the control variable')
n_eps = Int(100,
desc = 'resolution of the control variable')
label_eps = Str('epsilon',
desc = 'label of the horizontal axis')
label_sig = Str('sigma',
desc = 'label of the vertical axis')
figure = Instance(Figure)
def _figure_default(self):
figure = Figure(facecolor = 'white')
#figure.add_axes( [0.08, 0.13, 0.85, 0.74] )
return figure
data_changed = Event(True)
def _redraw(self):
figure = self.figure
axes = figure.gca()
self.model.set(
min_eps = 0.00, max_eps = self.max_eps, n_eps = self.n_eps,
)
mc = self.model.mean_curve
axes.plot(mc.xdata, mc.ydata,
linewidth = 2, label = self.run_legend)
axes.set_xlabel(self.label_eps)
axes.set_ylabel(self.label_sig)
axes.legend(loc = 'best')
self.data_changed = True
traits_view_tabbed = View(
VGroup(
HGroup(
Item('run_legend', resizable = False, label = 'Run label',
width = 80, springy = False),
Item('run', show_label = False, resizable = False),
Item('sample', show_label = False, resizable = False),
Item('clear', show_label = False,
resizable = False, springy = False)
),
Tabbed(
VGroup(
Item('rf', show_label = False),
Item('rf_model_view@', show_label = False, resizable = True),
label = 'Deterministic model',
id = 'spirrid.tview.model',
),
Group(
Item('rv_list', editor = rv_list_editor, show_label = False),
id = 'spirrid.tview.randomization.rv',
label = 'Model variables',
),
Group(
Item('selected_var@', show_label = False, resizable = True),
id = 'spirrid.tview.randomization.distr',
label = 'Distribution',
),
VGroup(
Item('model.cached_dG' , label = 'Cached weight factors',
resizable = False,
springy = False),
Item('model.compiled_QdG_loop' , label = 'Compiled loop over the integration product',
springy = False),
Item('model.compiled_eps_loop' ,
enabled_when = 'model.compiled_QdG_loop',
label = 'Compiled loop over the control variable',
springy = False),
scrollable = True,
label = 'Execution configuration',
id = 'spirrid.tview.exec_params',
dock = 'tab',
),
VGroup(
HGroup(
Item('min_eps' , label = 'Min',
springy = False, resizable = False),
Item('max_eps' , label = 'Max',
springy = False, resizable = False),
Item('n_eps' , label = 'N',
springy = False, resizable = False),
label = 'Simulation range',
show_border = True
),
VGroup(
Item('label_eps' , label = 'x', resizable = False,
springy = False),
Item('label_sig' , label = 'y', resizable = False,
springy = False),
label = 'Axes labels',
show_border = True,
scrollable = True,
),
label = 'Execution control',
id = 'spirrid.tview.view_params',
dock = 'tab',
),
VGroup(
Item('figure', editor = MPLFigureEditor(),
resizable = True, show_label = False),
label = 'Plot sheet',
id = 'spirrid.tview.figure_window',
dock = 'tab',
scrollable = True,
),
scrollable = True,
id = 'spirrid.tview.tabs',
dock = 'tab',
),
),
title = 'SPIRRID',
id = 'spirrid.viewmodel',
dock = 'tab',
resizable = True,
height = 1.0, width = 1.0
)
class ImageProcessing(HasTraits):
def __init__(self, **kw):
super(ImageProcessing, self).__init__(**kw)
self.on_trait_change(self.refresh, '+params')
self.refresh()
image_path = Str
def rgb2gray(self, rgb):
return np.dot(rgb[..., :3], [0.299, 0.587, 0.144])
filter = Bool(False, params=True)
block_size = Range(1, 100, params=True)
offset = Range(1, 20, params=True)
denoise = Bool(False, params=True)
denoise_spatial = Range(1, 100, params=True)
processed_image = Property
def _get_processed_image(self):
# read image
image = mpimg.imread(self.image_path)
mask = image[:, :, 1] > 150.
image[mask] = 255.
#plt.imshow(image)
#plt.show()
# convert to grayscale
image = self.rgb2gray(image)
# crop image
image = image[100:1000, 200:1100]
mask = mask[100:1000, 200:1100]
image = image - np.min(image)
image[mask] *= 255. / np.max(image[mask])
if self.filter == True:
image = denoise_bilateral(image,
sigma_spatial=self.denoise_spatial)
if self.denoise == True:
image = threshold_adaptive(image,
self.block_size,
offset=self.offset)
return image, mask
edge_detection_method = Enum('canny', 'sobel', 'roberts', params=True)
canny_sigma = Range(2.832, 5, params=True)
canny_low = Range(5.92, 100, params=True)
canny_high = Range(0.1, 100, params=True)
edges = Property
def _get_edges(self):
img_edg, mask = self.processed_image
if self.edge_detection_method == 'canny':
img_edg = canny(img_edg,
sigma=self.canny_sigma,
low_threshold=self.canny_low,
high_threshold=self.canny_high)
elif self.edge_detection_method == 'roberts':
img_edg = roberts(img_edg)
elif self.edge_detection_method == 'sobel':
img_edg = sobel(img_edg)
img_edg = img_edg > 0.0
return img_edg
radii = Int(80, params=True)
radius_low = Int(40, params=True)
radius_high = Int(120, params=True)
step = Int(2, params=True)
hough_circles = Property
def _get_hough_circles(self):
hough_radii = np.arange(self.radius_low, self.radius_high,
self.step)[::-1]
hough_res = hough_circle(self.edges, hough_radii)
centers = []
accums = []
radii = [] # For each radius, extract num_peaks circles
num_peaks = 3
for radius, h in zip(hough_radii, hough_res):
peaks = peak_local_max(h, num_peaks=num_peaks)
centers.extend(peaks)
print 'circle centers = ', peaks
accums.extend(h[peaks[:, 0], peaks[:, 1]])
radii.extend([radius] * num_peaks)
im = mpimg.imread(self.image_path)
# crop image
im = im[100:1000, 200:1100]
for idx in np.arange(len(centers)):
center_x, center_y = centers[idx]
radius = radii[idx]
cx, cy = circle_perimeter(center_y, center_x, radius)
mask = (cx < im.shape[0]) * (cy < im.shape[1])
im[cy[mask], cx[mask]] = (220., 20., 20.)
return im
eval_edges = Button
def _eval_edges_fired(self):
edges = self.figure_edges
edges.clear()
axes_edges = edges.gca()
axes_edges.imshow(self.edges, plt.gray())
self.data_changed = True
eval_circles = Button
def _eval_circles_fired(self):
circles = self.figure_circles
circles.clear()
axes_circles = circles.gca()
axes_circles.imshow(self.hough_circles, plt.gray())
self.data_changed = True
figure = Instance(Figure)
def _figure_default(self):
figure = Figure(facecolor='white')
return figure
figure_edges = Instance(Figure)
def _figure_edges_default(self):
figure = Figure(facecolor='white')
return figure
figure_circles = Instance(Figure)
def _figure_circles_default(self):
figure = Figure(facecolor='white')
return figure
data_changed = Event
def plot(self, fig, fig2):
figure = fig
figure.clear()
axes = figure.gca()
img, mask = self.processed_image
axes.imshow(img, plt.gray())
def refresh(self):
self.plot(self.figure, self.figure_edges)
self.data_changed = True
traits_view = View(HGroup(
Group(Item('filter', label='filter'),
Item('block_size'),
Item('offset'),
Item('denoise', label='denoise'),
Item('denoise_spatial'),
label='Filters'),
Group(
Item('figure',
editor=MPLFigureEditor(),
show_label=False,
resizable=True),
scrollable=True,
label='Plot',
),
),
Tabbed(
VGroup(Item('edge_detection_method'),
Item('canny_sigma'),
Item('canny_low'),
Item('canny_high'),
Item('eval_edges', label='Evaluate'),
Item('figure_edges',
editor=MPLFigureEditor(),
show_label=False,
resizable=True),
scrollable=True,
label='Plot_edges'), ),
Tabbed(
VGroup(Item('radii'),
Item('radius_low'),
Item('radius_high'),
Item('step'),
Item('eval_circles'),
Item('figure_circles',
editor=MPLFigureEditor(),
show_label=False,
resizable=True),
scrollable=True,
label='Plot_circles'), ),
id='imview',
dock='tab',
title='Image processing',
scrollable=True,
resizable=True,
width=600,
height=400)
class ECBLCalibModelView(ModelView):
'''Model in a viewable window.
'''
model = Instance(ECBLCalib)
def _model_default(self):
return ECBLCalib()
cs_state = Property(Instance(ECBCrossSection), depends_on='model')
@cached_property
def _get_cs_state(self):
return self.model.cs
data_changed = Event
figure = Instance(Figure)
def _figure_default(self):
figure = Figure(facecolor='white')
return figure
replot = Button()
def _replot_fired(self):
ax = self.figure.add_subplot(1, 1, 1)
self.model.calibrated_ecb_law.plot(ax)
self.data_changed = True
clear = Button()
def _clear_fired(self):
self.figure.clear()
self.data_changed = True
calibrated_ecb_law = Property(Instance(ECBLBase), depends_on='model')
@cached_property
def _get_calibrated_ecb_law(self):
return self.model.calibrated_ecb_law
view = View(HSplit(
VGroup(
Item('cs_state', label='Cross section', show_label=False),
Item('model@', show_label=False),
Item('calibrated_ecb_law@', show_label=False, resizable=True),
),
Group(
HGroup(
Item('replot', show_label=False),
Item('clear', show_label=False),
),
Item('figure',
editor=MPLFigureEditor(),
resizable=True,
show_label=False),
id='simexdb.plot_sheet',
label='plot sheet',
dock='tab',
),
),
width=0.5,
height=0.4,
buttons=['OK', 'Cancel'],
resizable=True)
class YMBHist(HasTraits):
slider = Instance(YMBSlider)
figure = Instance(Figure)
bins = Int(20, auto_set=False, enter_set=True, modified=True)
xlimit_on = Bool(False, modified=True)
ylimit_on = Bool(False, modified=True)
xlimit = Float(100, auto_set=False, enter_set=True, modified=True)
ylimit = Float(100, auto_set=False, enter_set=True, modified=True)
multi_hist_on = Bool(False, modified=True)
stats_on = Bool(False, modified=True)
normed_on = Bool(False, modified=True)
normed_hist = Property(depends_on='+modified, slider.input_change')
@cached_property
def _get_normed_hist(self):
data = self.slider.stat_data
h, b = histogram(data, bins=self.bins, normed=True)
return h, b
range = Property
def _get_range(self):
h, b = self.normed_hist
return (min(b), max(b))
bin_width = Property
def _get_bin_width(self):
return (self.range[1] - self.range[0]) / self.bins
def _figure_default(self):
figure = Figure()
figure.add_axes(self.axes_adjust)
return figure
edge_color = Str(None)
face_color = Str(None)
axes_adjust = List([0.1, 0.1, 0.8, 0.8])
data_changed = Event(True)
@on_trait_change('+modified, slider.input_change')
def _redraw(self):
figure = self.figure
axes = figure.axes[0]
axes.clear()
if self.multi_hist_on == True:
histtype = 'step'
lw = 3
plot_data = getattr(self.slider.data, self.slider.var_enum_)
for i in range(0, plot_data.shape[1]):
axes.hist(plot_data[:, i].compressed(),
bins=self.bins,
histtype=histtype,
color='gray')
if self.multi_hist_on == False:
histtype = 'bar'
lw = 1
var_data = self.slider.stat_data
axes.hist(var_data, bins=self.bins, histtype=histtype, linewidth=lw, \
normed=self.normed_on, edgecolor=self.edge_color, facecolor=self.face_color)
if self.stats_on == True:
xint = axes.xaxis.get_view_interval()
yint = axes.yaxis.get_view_interval()
axes.text(
xint[0], yint[0],
'mean = %e, std = %e' % (mean(var_data), sqrt(var(var_data))))
# redefine xticks labels
# inter = axes.xaxis.get_view_interval()
# axes.set_xticks( linspace( inter[0], inter[1], 5 ) )
axes.set_xlabel(self.slider.var_enum)
axes.set_ylabel('frequency') # , fontsize = 16
setp(axes.get_xticklabels(), position=(0, -.025))
if self.xlimit_on == True:
axes.set_xlim(0, self.xlimit)
if self.ylimit_on == True:
axes.set_ylim(0, self.ylimit)
self.data_changed = True
view = View(
Group(
Item('figure',
style='custom',
editor=MPLFigureEditor(),
show_label=False,
id='figure.view'),
HGroup(
Item('bins'), Item('ylimit_on', label='Y limit'),
Item('ylimit',
enabled_when='ylimit_on == True',
show_label=False), Item('stats_on', label='stats'),
Item('normed_on', label='norm'),
Item('multi_hist_on', label='multi')),
label='histogram',
dock='horizontal',
id='yarn_hist.figure',
),
Group(
Item('slider', style='custom', show_label=False),
label='yarn data',
dock='horizontal',
id='yarn_hist.config',
),
id='yarn_structure_view',
resizable=True,
scrollable=True,
# width = 0.8,
# height = 0.4
)
class ECBLCalibHistModelView(ModelView):
'''Model in a viewable window.
'''
model = Instance(ECBLCalibHist)
def _model_default(self):
return ECBLCalibHist()
cs_states = Property(List(Instance(ECBCrossSectionState)),
depends_on='model')
@cached_property
def _get_cs_states(self):
return self.model.cs_states
current_cs_state = Instance(ECBCrossSectionState)
def _current_cs_default(self):
self.model.cs_states[0]
def _current_cs_state_changed(self):
self._replot_fired()
eps_range = Property
def _get_eps_range(self):
eps_arr = [[cs_state.eps_up, cs_state.eps_lo]
for cs_state in self.cs_states]
eps_range = np.asarray(eps_arr)
print 'eps_range', eps_range
return (-np.max(eps_range[:, 1]), -np.min(eps_range[:, 0]))
f_range = Property
def _get_f_range(self):
f_arr = [[np.max(cs_state.f_ti_arr),
np.min(cs_state.f_cj_arr)] for cs_state in self.cs_states]
f_range = np.asarray(f_arr)
print 'eps_range', f_range
return (-np.max(f_range[:, 0]), -np.min(f_range[:, 1]))
data_changed = Event
figure = Instance(Figure)
def _figure_default(self):
figure = Figure(facecolor='white')
return figure
replot = Button()
def _replot_fired(self):
if self.current_cs_state:
cs = self.current_cs_state
else:
cs = self.cs_states[0]
cs.plot(self.figure, eps_range=self.eps_range, f_range=self.f_range)
self.data_changed = True
clear = Button()
def _clear_fired(self):
self.figure.clear()
self.data_changed = True
view = View(HSplit(
VGroup(
Item('cs_states',
editor=cs_states_editor,
label='Cross section',
show_label=False),
Item('model@', show_label=False),
),
Group(
HGroup(
Item('replot', show_label=False),
Item('clear', show_label=False),
),
Item('figure',
editor=MPLFigureEditor(),
resizable=True,
show_label=False),
id='simexdb.plot_sheet',
label='plot sheet',
dock='tab',
),
),
width=0.8,
height=0.7,
buttons=['OK', 'Cancel'],
resizable=True)
class YMBReport(HasTraits):
body_tex = Str()
data = Instance(YMBData, changed=True)
yarn = Property(Str, depends_on='+changed')
@cached_property
def _get_yarn(self):
return self.data.source.yarn_type
data_dir = Property(Str, depends_on='+changed')
def _get_data_dir(self):
return join(simdb.exdata_dir, 'trc', 'yarn_structure', self.yarn,
'raw_data')
tex_dir = Property(Str, depends_on='+changed')
def _get_tex_dir(self):
return join(simdb.exdata_dir, 'trc', 'yarn_structure', self.yarn,
'report')
fig_dir = Property(Str, depends_on='+changed')
def _get_fig_dir(self):
return join(simdb.exdata_dir, 'trc', 'yarn_structure', self.yarn,
'report', 'figs')
# select data for report
plot_3d_on = Bool(True)
hist_rad_on = Bool(True)
hist_cf_on = Bool(True)
hist_bfl_on = Bool(True)
hist_slack_on = Bool(True)
corr_plot_rad_on = Bool(True)
corr_plot_cf_on = Bool(True)
corr_plot_bfl_on = Bool(True)
corr_plot_slack_on = Bool(True)
spirrid_on = Bool(False)
hist_axes_adjust = List([0.12, 0.17, 0.68, 0.68])
corr_axes_adjust = List([0.15, 0.17, 0.75, 0.68])
n_bins = Int(40)
#################################
# BUILD REPORT
#################################
build = Button(label='build report')
def _build_fired(self):
self._directory_test()
# get the yarn type
yt = self.data.source.yarn_type
if self.plot_3d_on == True:
self._save_plot3d_fired()
if self.hist_rad_on == True:
self._save_rad_fired()
if self.hist_cf_on == True:
self._save_cf_fired()
if self.hist_bfl_on == True:
self._save_bfl_fired()
if self.hist_slack_on == True:
self._save_slack_fired()
if self.corr_plot_rad_on == True:
self._save_corr_plot_rad_fired()
if self.corr_plot_cf_on == True:
self._save_corr_plot_cf_fired()
if self.corr_plot_bfl_on == True:
self._save_corr_plot_bfl_fired()
if self.corr_plot_slack_on == True:
self._save_corr_plot_slack_fired()
print '================'
print 'Figure(s) saved'
print '================'
#################################
# BUILD REPORT
#################################
filename = 'ymb_report_' + yt
texfile = join(self.tex_dir, filename + '.tex')
pdffile = join(self.tex_dir, filename + '.pdf')
bodyfile = join(self.tex_dir, texfile)
body_out = open(bodyfile, 'w')
self.body_tex += 'Yarn with contact fraction limit = %s\n' % self.data.cf_limit
if self.plot_3d_on == True:
self.body_tex += self.plot3d_tex
if self.hist_rad_on == True:
self.body_tex += self.rad_tex
if self.hist_cf_on == True:
self.body_tex += self.cf_tex
if self.hist_bfl_on == True:
self.body_tex += self.bfl_tex
if self.hist_slack_on == True:
self.body_tex += self.slack_tex
if self.corr_plot_rad_on == True:
self.body_tex += self.corr_plot_rad_tex
if self.corr_plot_cf_on == True:
self.body_tex += self.corr_plot_cf_tex
if self.corr_plot_bfl_on == True:
self.body_tex += self.corr_plot_bfl_tex
if self.corr_plot_slack_on == True:
self.body_tex += self.corr_plot_slack_tex
body_out.write(start_tex)
body_out.write('\section*{ Yarn %s }' % (self.yarn))
body_out.write(self.body_tex)
body_out.write(end_tex)
body_out.close()
os.system('cd ' + self.tex_dir + ';pdflatex -shell-escape ' + texfile)
print '=============================='
print 'Report written to %s', texfile
print '=============================='
os.system('acroread ' + pdffile + ' &')
#################################
# 3d PLOT
#################################
plot3d = Property(Instance(YMBView3D))
@cached_property
def _get_plot3d(self):
plot3d = YMBView3D(data=self.data, color_map='binary') # black-white
return plot3d
save_plot3d = Button(label='save plot3d figure')
def _save_plot3d_fired(self):
self._directory_test()
filename = join(self.fig_dir, 'plot3d.png')
self.plot3d.scene.save(filename, (1000, 800))
plot3d_tex = Property(Str)
@cached_property
def _get_plot3d_tex(self):
filename = 'plot3d.png'
if file_test(join(self.fig_dir, filename)) == True:
return fig_tex % (filename, '3D yarn plot')
else:
self._save_plot3d_fired()
return fig_tex % (filename, '3D yarn plot')
#################################
# HISTOGRAM PLOT AND SAVE
#################################
hist_rad = Property(Instance(YMBHist)) # Instance(Figure )
@cached_property
def _get_hist_rad(self):
histog = YMBHist()
histog.set(edge_color='black',
face_color='0.75',
axes_adjust=self.hist_axes_adjust)
slider = YMBSlider(var_enum='radius', data=self.data)
return histog.set(slider=slider, bins=self.n_bins, normed_on=True)
save_rad = Button(label='save histogram of radius')
def _save_rad_fired(self):
self._directory_test()
filename = join(self.fig_dir, 'radius.pdf')
self.hist_rad.figure.savefig(filename, format='pdf')
rad_tex = Property(Str)
@cached_property
def _get_rad_tex(self):
filename = 'radius.pdf'
if file_test(join(self.fig_dir, filename)) == True:
return fig_tex % (filename, 'Histogram of filament radius')
else:
self._save_rad_fired()
return fig_tex % (filename, 'Histogram of filament radius')
hist_cf = Property(Instance(YMBHist))
@cached_property
def _get_hist_cf(self):
histog = YMBHist()
histog.set(edge_color='black',
face_color='0.75',
axes_adjust=self.hist_axes_adjust)
slider = YMBSlider(var_enum='contact fraction', data=self.data)
return histog.set(slider=slider, bins=self.n_bins, normed_on=True)
save_cf = Button(label='save histogram of contact fraction')
def _save_cf_fired(self):
self._directory_test()
filename = join(self.fig_dir, 'contact_fraction.pdf')
self.hist_cf.figure.savefig(filename, format='pdf')
cf_tex = Property(Str)
@cached_property
def _get_cf_tex(self):
filename = 'contact_fraction.pdf'
if file_test(join(self.fig_dir, filename)) == True:
return fig_tex % (filename, 'Histogram of contact fraction')
else:
self._save_cf_fired()
return fig_tex % (filename, 'Histogram of contact fraction')
hist_bfl = Property(Instance(YMBHist))
@cached_property
def _get_hist_bfl(self):
histog = YMBHist()
histog.set(edge_color='black',
face_color='0.75',
axes_adjust=self.hist_axes_adjust)
slider = YMBSlider(var_enum='bond free length', data=self.data)
return histog.set(slider=slider, bins=self.n_bins, normed_on=True)
save_bfl = Button(label='save histogram of bond free length')
def _save_bfl_fired(self):
self._directory_test()
filename = 'bond_free_length.pdf'
self.hist_bfl.figure.savefig(join(self.fig_dir, filename),
format='pdf')
bfl_tex = Property(Str)
@cached_property
def _get_bfl_tex(self):
filename = 'bond_free_length.pdf'
if file_test(join(self.fig_dir, filename)) == True:
return fig_tex % (filename, 'Histogram of bond free length')
else:
self._save_bfl_fired()
return fig_tex % (filename, 'Histogram of bond free length')
hist_slack = Property(Instance(YMBHist))
@cached_property
def _get_hist_slack(self):
histog = YMBHist()
histog.set(edge_color='black',
face_color='0.75',
axes_adjust=self.hist_axes_adjust)
slider = YMBSlider(var_enum='slack', data=self.data)
return histog.set(slider=slider,
bins=self.n_bins,
normed_on=True,
xlimit_on=True,
xlimit=0.03)
save_slack = Button(label='save histogram of slack')
def _save_slack_fired(self):
self._directory_test()
filename = join(self.fig_dir, 'slack.pdf')
self.hist_slack.figure.savefig(filename, format='pdf')
slack_tex = Property(Str)
@cached_property
def _get_slack_tex(self):
filename = 'slack.pdf'
if file_test(join(self.fig_dir, filename)) == True:
return fig_tex % (filename, 'Histogram of slack')
else:
self._save_slack_fired()
return fig_tex % (filename, 'Histogram of slack')
corr_plot_rad = Property(Instance(YMBAutoCorrel))
@cached_property
def _get_corr_plot_rad(self):
plot = YMBAutoCorrelView()
plot.set(color='black', axes_adjust=self.corr_axes_adjust)
return plot.set(
correl_data=YMBAutoCorrel(data=data, var_enum='radius'))
save_corr_plot_rad = Button(label='save correlation plot of radius')
def _save_corr_plot_rad_fired(self):
self._directory_test()
filename = join(self.fig_dir, 'corr_plot_rad.pdf')
self.corr_plot_rad.figure.savefig(filename, format='pdf')
corr_plot_rad_tex = Property(Str)
@cached_property
def _get_corr_plot_rad_tex(self):
filename = 'corr_plot_rad.pdf'
if file_test(join(self.fig_dir, filename)) == True:
return fig_tex % (filename, 'Autocorrelation of radius')
else:
self._save_corr_plot_rad_fired()
return fig_tex % (filename, 'Autocorrelation of radius')
corr_plot_cf = Property(Instance(YMBAutoCorrel))
@cached_property
def _get_corr_plot_cf(self):
plot = YMBAutoCorrelView()
plot.set(color='black', axes_adjust=self.corr_axes_adjust)
return plot.set(
correl_data=YMBAutoCorrel(data=data, var_enum='contact fraction'))
save_corr_plot_cf = Button(
label='save correlation plot of contact fraction')
def _save_corr_plot_cf_fired(self):
self._directory_test()
filename = join(self.fig_dir, 'corr_plot_cf.pdf')
self.corr_plot_cf.figure.savefig(filename, format='pdf')
corr_plot_cf_tex = Property(Str)
@cached_property
def _get_corr_plot_cf_tex(self):
filename = 'corr_plot_cf.pdf'
if file_test(join(self.fig_dir, filename)) == True:
return fig_tex % (filename, 'Autocorrelation of contact fraction')
else:
self._save_corr_plot_cf_fired()
return fig_tex % (filename, 'Autocorrelation of contact fraction')
corr_plot_bfl = Property(Instance(YMBAutoCorrel))
@cached_property
def _get_corr_plot_bfl(self):
plot = YMBAutoCorrelView()
plot.set(color='black', axes_adjust=self.corr_axes_adjust)
return plot.set(
correl_data=YMBAutoCorrel(data=data, var_enum='bond free length'))
save_corr_plot_bfl = Button(label='save corr plot of bond free length')
def _save_corr_plot_bfl_fired(self):
self._directory_test()
filename = join(self.fig_dir, 'corr_plot_bfl.pdf')
self.corr_plot_bfl.figure.savefig(filename, format='pdf')
corr_plot_bfl_tex = Property(Str)
@cached_property
def _get_corr_plot_bfl_tex(self):
filename = 'corr_plot_bfl.pdf'
if file_test(join(self.fig_dir, filename)) == True:
return fig_tex % (filename, 'Autocorrelation of bond free length')
else:
self._save_corr_plot_bfl_fired()
return fig_tex % (filename, 'Autocorrelation of bond free length')
corr_plot_slack = Property(Instance(YMBAutoCorrel))
@cached_property
def _get_corr_plot_slack(self):
plot = YMBAutoCorrelView()
plot.set(color='black', axes_adjust=self.corr_axes_adjust)
return plot.set(correl_data=YMBAutoCorrel(data=data, var_enum='slack'))
save_corr_plot_slack = Button(label='save corr plot of slack')
def _save_corr_plot_slack_fired(self):
self._directory_test()
filename = join(self.fig_dir, 'corr_plot_slack.pdf')
self.corr_plot_slack.figure.savefig(filename, format='pdf')
corr_plot_slack_tex = Property(Str)
@cached_property
def _get_corr_plot_slack_tex(self):
filename = 'corr_plot_slack.pdf'
if file_test(join(self.fig_dir, filename)) == True:
return fig_tex % (filename, 'Autocorrelation of slack')
else:
self._save_corr_plot_slack_fired()
return fig_tex % (filename, 'Autocorrelation of slack')
#################################
# CORRELATION PLOT AND TABLE
#################################
def corr_plot(self):
return 0
#################################
# SPIRRID PLOT
#################################
spirrid_plot = Property(Instance(YMBPullOut))
@cached_property
def _get_spirrid_plot(self):
return YMBPullOut(data=self.data)
pdf_theta = Property(Instance(IPDistrib))
def _get_pdf_theta(self):
theta = self.spirrid_plot.pdf_theta
# theta.set( face_color = '0.75', edge_color = 'black', axes_adjust = [0.13, 0.18, 0.8, 0.7] )
return theta
pdf_l = Property(Instance(IPDistrib))
def _get_pdf_l(self):
return self.spirrid_plot.pdf_l
pdf_phi = Property(Instance(IPDistrib))
def _get_pdf_phi(self):
return self.spirrid_plot.pdf_phi
pdf_xi = Property(Instance(IPDistrib))
def _get_pdf_xi(self):
return self.spirrid_plot.pdf_xi.figure
def _directory_test(self):
if os.access(self.tex_dir, os.F_OK) == False:
os.mkdir(self.tex_dir)
if os.access(self.fig_dir, os.F_OK) == False:
os.mkdir(self.fig_dir)
traits_view = View(
HSplit(
Group(
Item('data@', show_label=False),
HGroup(
VGrid(
Item(
'plot_3d_on',
label='plot3d',
),
Item('save_plot3d',
show_label=False,
visible_when='plot_3d_on == True'),
Item('_'),
Item('hist_rad_on', label='radius_hist'),
Item('save_rad',
show_label=False,
visible_when='hist_rad_on == True'),
Item('hist_cf_on', label='cf_hist'),
Item('save_cf',
show_label=False,
visible_when='hist_cf_on == True'),
Item('hist_bfl_on', label='bfl_hist'),
Item('save_bfl',
show_label=False,
visible_when='hist_bfl_on == True'),
Item('hist_slack_on', label='slack_hist'),
Item('save_slack',
show_label=False,
visible_when='hist_slack_on == True'),
Item('_'),
Item('corr_plot_rad_on', label='corr_plot_rad'),
Item('save_corr_plot_rad',
show_label=False,
visible_when='hist_slack_on == True'),
Item('corr_plot_cf_on', label='corr_plot_cf'),
Item('save_corr_plot_cf',
show_label=False,
visible_when='hist_slack_on == True'),
Item('corr_plot_bfl_on', label='corr_plot_bfl'),
Item('save_corr_plot_bfl',
show_label=False,
visible_when='hist_slack_on == True'),
Item('corr_plot_slack_on', label='corr_plot_slack'),
Item('save_corr_plot_slack',
show_label=False,
visible_when='hist_slack_on == True'),
),
VGrid(Item('spirrid_on'), ),
),
Item('build'),
label='report',
id='report.bool',
), ),
VGroup(
HGroup(
Item('hist_rad@', show_label=False),
Item('hist_cf@', show_label=False),
),
HGroup(
Item('hist_bfl@', show_label=False),
Item('hist_slack@', show_label=False),
),
label='histograms',
id='report.hist',
),
VGroup(
HGroup(
Item('corr_plot_rad@', show_label=False),
Item('corr_plot_cf@', show_label=False),
),
HGroup(
Item('corr_plot_bfl@', show_label=False),
Item('corr_plot_slack@', show_label=False),
),
label='correlation plot',
id='report.corr_plot',
),
# HGroup(
# Group(
# Item( 'pdf_theta@', show_label = False ),
# Item( 'pdf_l@', show_label = False ),
# ),
# Group(
# Item( 'pdf_phi@', show_label = False ),
# Item( 'pdf_xi@', show_label = False ),
# ),
# label = 'pdf',
# id = 'report.pdf',
# #scrollable = True,
# ),
Group(Item('plot3d@', show_label=False),
label='plot3d',
id='report.plot3d'),
resizable=True,
title=u"Yarn name",
handler=TitleHandler(),
id='report.main',
)
class YarnPullOut(HasTraits):
'''Idealization of the double sided pullout using the SPIRRID
statistical integration tool.
'''
rf = Instance(DoublePulloutSym)
def _rf_default(self):
return DoublePulloutSym(tau_fr=2.6,
l=0.0,
d=25.5e-3,
E_mod=72.0e3,
theta=0.0,
xi=0.0179,
phi=1.,
L=30.0,
free_fiber_end=True)
# return DoublePulloutSym( tau_fr = 2.5, l = 0.01, d = 25.5e-3, E_mod = 70.0e3,
# theta = 0.01, xi = 0.0179, phi = 1., n_f = 1723 )
figure = Instance(Figure)
def _figure_default(self):
figure = Figure(facecolor='white')
figure.add_axes([0.08, 0.13, 0.85, 0.74])
return figure
pdf_theta_on = Bool(True)
pdf_l_on = Bool(True)
pdf_phi_on = Bool(True)
pdf_xi_on = Bool(True)
pdf_xi = Instance(IPDistrib)
def _pdf_xi_default(self):
pd = PDistrib(distr_choice='weibull_min', n_segments=30)
pd.distr_type.set(shape=4.54, scale=0.017)
return pd
n_f = Float(1,
auto_set=False,
enter_set=True,
desc='Number of filaments in the yarn')
pdf_theta = Instance(IPDistrib)
pdf_l = Instance(IPDistrib)
pdf_phi = Instance(IPDistrib)
run = Button
def _run_fired(self):
self._redraw()
clear = Button
def _clear_fired(self):
axes = self.figure.axes[0]
axes.clear()
self.data_changed = True
w_max = Float(1.0, enter_set=True, auto_set=False)
n_w_pts = Int(100, enter_set=True, auto_set=False)
n_G_ipts = Int(30, enter_set=True, auto_set=False)
e_arr = Property(Array, depends_on='w_max, n_w_pts')
def _get_e_arr(self):
return linspace(0.00, self.w_max, self.n_w_pts)
lab = Str(' ', enter_set=True, auto_set=False)
data_changed = Event(True)
def _redraw(self):
s = SPIRRID(
q=self.rf,
sampling_type='LHS',
e_arr=self.e_arr,
n_int=self.n_G_ipts,
theta_vars=dict(tau_fr=2.6,
l=0.0,
d=25.5e-3,
E_mod=72.0e3,
theta=0.0,
xi=0.0179,
phi=1.,
L=30.0),
# codegen_type='weave'
)
# construct the random variables
if self.pdf_xi_on:
s.theta_vars['xi'] = RV(
'weibull_min', shape=4.54, scale=0.017
) # RV( pd = self.pdf_xi, name = 'xi', n_int = self.n_G_ipts )
print self.pdf_theta.interp_ppf([0.01, 0.02])
print YMB_RV('theta', distr=self.pdf_theta,
n_int=self.n_G_ipts).distr.interp_ppf([0.01, 0.02])
if self.pdf_theta_on:
s.theta_vars['theta'] = YMB_RV('theta',
distr=self.pdf_theta,
n_int=self.n_G_ipts)
if self.pdf_l_on:
s.theta_vars['l'] = YMB_RV('l',
distr=self.pdf_l,
n_int=self.n_G_ipts)
if self.pdf_phi_on:
s.theta_vars['phi'] = YMB_RV('phi',
distr=self.pdf_phi,
n_int=self.n_G_ipts)
# print 'checking unity', s.mu_q_arr()
mu = s.mu_q_arr
axes = self.figure.axes[0]
# TODO:
axes.plot(self.e_arr, mu * self.n_f, linewidth=2, label=self.lab)
axes.set_xlabel('crack opening w[mm]')
axes.set_ylabel('force P[N]')
axes.legend(loc='best')
self.data_changed = True
view = View(HSplit(
Group(Item('rf@', show_label=False), label='Response function'),
Tabbed(
Group(
Item('pdf_theta_on', show_label=False),
Item('pdf_theta@', show_label=False),
label='Slack',
),
Group(
Item('pdf_l_on', show_label=False),
Item('pdf_l@', show_label=False),
label='Contact free length',
),
Group(
Item('pdf_phi_on', show_label=False),
Item('pdf_phi@', show_label=False),
label='Contact fraction',
),
Group(
Item('pdf_xi_on', show_label=False),
Item('pdf_xi@', show_label=False),
label='Strength',
),
label='yarn data',
scrollable=True,
id='ymb.pullout.dist',
dock='tab',
),
Group(
HGroup(Item('run', show_label=False, springy=True),
Item('clear', show_label=False, springy=True)),
HGroup(
Item('w_max',
show_label=True,
springy=True,
tooltip='maximum crack-opening displacement'),
Item('n_w_pts',
show_label=True,
springy=True,
tooltip='number of points for crack-opening'),
Item('n_G_ipts',
show_label=True,
springy=True,
tooltip=
'number of integration points for the random variables'),
Item('lab',
show_label=True,
springy=True,
tooltip='label of pull-out curve'),
),
Item('figure',
style='custom',
editor=MPLFigureEditor(),
show_label=False),
label='Pull-out response',
id='ymb.pullout.figure',
dock='tab',
),
id='ymb.pullout.split',
dock='tab',
),
id='ymb.pullout',
resizable=True,
scrollable=True,
dock='tab',
width=0.8,
height=0.4)