class AnalyticalFunction( HasTraits ):
expression = Expression('x**2', auto_set = False, enter_set = True )
refresh = Button('redraw')
def _refresh_fired(self):
xdata = linspace(0.001,10,10000)
fneval = frompyfunc( lambda x: eval( self.expression ), 1, 1 )
ydata = fneval( xdata )
self.mfn.set( xdata = xdata, ydata = ydata )
self.mfn.data_changed = True
mfn = Instance( MFnLineArray )
def _mfn_default( self ):
return MFnLineArray()
@on_trait_change('expression' )
def update_mfn(self):
self._refresh_fired()
view_mpl = View( HGroup( Item( 'expression' ), Item('refresh' ) ),
Item( 'mfn', editor = MFnMatplotlibEditor( adapter = a ),
show_label = False ),
resizable = True,
scrollable = True,
height = 0.5, width = 0.5
)
view_chaco = View( HGroup( Item( 'expression' ), Item('refresh' ) ),
Item( 'mfn', editor = MFnChacoEditor( adapter = a ),
resizable = True, show_label = False ),
resizable = True,
scrollable = True,
height = 0.3, width = 0.3
)
def default_traits_view(self):
'''
Generates the view from the param items.
'''
#rf_param_items = [ Item( 'model.' + name, format_str = '%g' ) for name in self.model.param_keys ]
D2_plot_param_items = [
VGroup(Item('max_x', label='max x value'),
Item('x_points', label='No of plot points'))
]
if hasattr(self.rf, 'get_q_x'):
D3_plot_param_items = [
VGroup(Item('min_x', label='min x value'),
Item('max_x', label='max x value'),
Item('min_y', label='min y value'),
Item('max_y', label='max y value'))
]
else:
D3_plot_param_items = []
control_items = [
Item('show', show_label=False),
Item('clear', show_label=False),
]
view = View(
HSplit(
VGroup(Item('@rf', show_label=False),
label='Function parameters',
id='stats.spirrid_bak.rf_model_view.rf_params',
scrollable=True),
VGroup(HGroup(*D2_plot_param_items),
label='plot parameters',
id='stats.spirrid_bak.rf_model_view.2Dplot_params'),
# VGroup( HGroup( *D3_plot_param_items ),
# label = '3D plot parameters',
# id = 'stats.spirrid_bak.rf_model_view.3Dplot_params' ),
VGroup(
Item('model.comment', show_label=False, style='readonly'),
label='Comment',
id='stats.spirrid_bak.rf_model_view.comment',
scrollable=True,
),
VGroup(HGroup(*control_items),
Item('figure',
editor=MPLFigureEditor(),
resizable=True,
show_label=False),
label='Plot',
id='stats.spirrid_bak.rf_model_view.plot'),
dock='tab',
id='stats.spirrid_bak.rf_model_view.split'),
kind='modal',
resizable=True,
dock='tab',
buttons=[OKButton],
id='stats.spirrid_bak.rf_model_view')
return view
class RVModelView(ModelView):
'''
ModelView class for displaying the table of parameters and
set the distribution parameters of random variables
'''
title = Str('randomization setup')
model = Instance(SPIRRID)
rv_list = List(RIDVariable)
@on_trait_change('model.rf')
def get_rv_list(self):
self.rv_list = [
RIDVariable(s=self.model,
rf=self.model.rf,
varname=nm,
trait_value=st) for nm, st in
zip(self.model.rf.param_keys, self.model.rf.param_values)
]
selected_var = Instance(RIDVariable)
def _selected_var_default(self):
return self.rv_list[0]
title = Str('random variable editor')
selected_var = Instance(RIDVariable)
traits_view = View(VSplit(
HGroup(
Item('rv_list', editor=rv_list_editor, show_label=False),
id='rid.tview.randomization.rv',
label='Model variables',
),
HGroup(
Item('selected_var@', show_label=False, resizable=True),
id='rid.tview.randomization.distr',
label='Distribution',
),
scrollable=True,
id='rid.tview.tabs',
dock='tab',
),
title='RANDOM VARIABLES',
id='rid.ridview',
dock='tab',
resizable=True,
height=1.0,
width=1.0)
class ECBLCalibStateModelView(ModelView):
'''Model in a viewable window.
'''
model = Instance(ECBLCalibState)
def _model_default(self):
return ECBLCalibState()
cs_state = Property(Instance(ECBCrossSectionState), depends_on = 'model')
@cached_property
def _get_cs_state(self):
return self.model.cs_state
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)
def default_traits_view( self ):
return View( HGroup( Item( 'n_int', visible_when = 'random', label = 'NIP',
),
Spring(),
show_border = True,
label = 'Variable name: %s' % self.varname
),
Item( 'pd@', show_label = False ),
resizable = True,
id = 'rid_variable',
height = 800 )
class ConstitutiveLawModelView(ModelView):
model = Instance(CLBase)
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.plot(ax)
self.data_changed = True
clear = Button()
def _clear_fired(self):
self.figure.clear()
self.data_changed = True
traits_view = View(HSplit(
Group(
Item('model', style='custom', show_label=False, resizable=True),
scrollable=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,
resizable=True,
buttons=['OK', 'Cancel'])
def default_traits_view(self):
'''
Generates the view from the param items.
'''
rf_param_items = [
Item('model.' + name, format_str='%g')
for name in self.model.param_keys
]
plot_param_items = [
Item('eps_max'),
Item('n_eps'),
Item('x_name', label='x-axis'),
Item('y_name', label='y-axis')
]
control_items = [
Item('show', show_label=False),
Item('clear', show_label=False),
]
view = View(HSplit(
VGroup(*rf_param_items,
label='Function Parameters',
id='stats.spirrid_bak.rf_model_view.rf_params',
scrollable=True),
VGroup(*plot_param_items,
label='Plot Parameters',
id='stats.spirrid_bak.rf_model_view.plot_params'),
VGroup(
Item('model.comment', show_label=False, style='readonly'),
label='Comment',
id='stats.spirrid_bak.rf_model_view.comment',
scrollable=True,
),
VGroup(HGroup(*control_items),
Item('figure',
editor=MPLFigureEditor(),
resizable=True,
show_label=False),
label='Plot',
id='stats.spirrid_bak.rf_model_view.plot'),
dock='tab',
id='stats.spirrid_bak.rf_model_view.split'),
kind='modal',
resizable=True,
dock='tab',
buttons=['Ok', 'Cancel'],
id='stats.spirrid_bak.rf_model_view')
return view
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 ECBMatrixCrossSection(ECBCrossSectionComponent):
'''Cross section characteristics needed for tensile specimens.
'''
n_cj = Float(30, auto_set=False, enter_set=True, geo_input=True)
'''Number of integration points.
'''
f_ck = Float(55.7, auto_set=False, enter_set=True, cc_input=True)
'''Ultimate compression stress [MPa]
'''
eps_c_u = Float(0.0033, auto_set=False, enter_set=True, cc_input=True)
'''Strain at failure of the matrix in compression [-]
'''
height = Float(0.4, auto_set=False, enter_set=True, geo_input=True)
'''height of the cross section [m]
'''
width = Float(0.20, auto_set=False, enter_set=True, geo_input=True)
'''width of the cross section [m]
'''
x = Property(depends_on=ECB_COMPONENT_AND_EPS_CHANGE)
'''Height of the compressive zone
'''
@cached_property
def _get_x(self):
eps_lo = self.state.eps_lo
eps_up = self.state.eps_up
if eps_up == eps_lo:
# @todo: explain
return (abs(eps_up) / (abs(eps_up - eps_lo * 1e-9)) * self.height)
else:
return (abs(eps_up) / (abs(eps_up - eps_lo)) * self.height)
z_ti_arr = Property(depends_on=ECB_COMPONENT_AND_EPS_CHANGE)
'''Discretizaton of the compressive zone
'''
@cached_property
def _get_z_ti_arr(self):
if self.state.eps_up <= 0: # bending
zx = min(self.height, self.x)
return np.linspace(0, zx, self.n_cj)
elif self.state.eps_lo <= 0: # bending
return np.linspace(self.x, self.height, self.n_cj)
else: # no compression
return np.array([0], dtype='f')
eps_ti_arr = Property(depends_on=ECB_COMPONENT_AND_EPS_CHANGE)
'''Compressive strain at each integration layer of the compressive zone [-]:
'''
@cached_property
def _get_eps_ti_arr(self):
# for calibration us measured compressive strain
# @todo: use mapped traits instead
#
height = self.height
eps_up = self.state.eps_up
eps_lo = self.state.eps_lo
eps_j_arr = (eps_up + (eps_lo - eps_up) * self.z_ti_arr / height)
return (-np.fabs(eps_j_arr) + eps_j_arr) / 2.0
zz_ti_arr = Property(depends_on=ECB_COMPONENT_AND_EPS_CHANGE)
'''Distance of reinforcement layers from the bottom
'''
@cached_property
def _get_zz_ti_arr(self):
return self.height - self.z_ti_arr
#===========================================================================
# Compressive concrete constitutive law
#===========================================================================
cc_law_type = Trait('constant',
dict(constant=CCLawBlock,
linear=CCLawLinear,
quadratic=CCLawQuadratic,
quad=CCLawQuad),
cc_input=True)
'''Selector of the concrete compression law type
['constant', 'linear', 'quadratic', 'quad']'''
cc_law = Property(Instance(CCLawBase), depends_on='+cc_input')
'''Compressive concrete law corresponding to cc_law_type'''
@cached_property
def _get_cc_law(self):
return self.cc_law_type_(f_ck=self.f_ck, eps_c_u=self.eps_c_u, cs=self)
show_cc_law = Button
'''Button launching a separate view of the compression law.
'''
def _show_cc_law_fired(self):
cc_law_mw = ConstitutiveLawModelView(model=self.cc_law)
cc_law_mw.edit_traits(kind='live')
return
cc_modified = Event
#===========================================================================
# Calculation of compressive stresses and forces
#===========================================================================
sig_ti_arr = Property(depends_on=ECB_COMPONENT_AND_EPS_CHANGE)
'''Stresses at the j-th integration point.
'''
@cached_property
def _get_sig_ti_arr(self):
return -self.cc_law.mfn_vct(-self.eps_ti_arr)
f_ti_arr = Property(depends_on=ECB_COMPONENT_AND_EPS_CHANGE)
'''Layer force corresponding to the j-th integration point.
'''
@cached_property
def _get_f_ti_arr(self):
return self.width * self.sig_ti_arr * self.unit_conversion_factor
def _get_N(self):
return np.trapz(self.f_ti_arr, self.z_ti_arr)
def _get_M(self):
return np.trapz(self.f_ti_arr * self.z_ti_arr, self.z_ti_arr)
modified = Event
@on_trait_change('+geo_input')
def set_modified(self):
self.modified = True
view = View(HGroup(
Group(Item('height', springy=True),
Item('width'),
Item('n_layers'),
Item('n_rovings'),
Item('A_roving'),
label='Geometry',
springy=True),
springy=True,
),
resizable=True,
buttons=['OK', 'Cancel'])
class SPIRRIDModelView(ModelView):
title = Str('spirrid exec ctrl')
model = Instance(SPIRRID)
ins = Instance(NoOfFibers)
def _ins_default(self):
return NoOfFibers()
eval = Button
def _eval_fired(self):
Specimen_Volume = self.ins.Lx * self.ins.Ly * self.ins.Lz
self.no_of_fibers_in_specimen = (
Specimen_Volume * self.ins.Fiber_volume_fraction / 100) / (
pi *
(self.ins.Fiber_diameter / 20)**2 * self.ins.Fiber_Length / 10)
prob_crackbridging_fiber = (self.ins.Fiber_Length /
(10 * 2)) / self.ins.Lx
self.mean_parallel_links = prob_crackbridging_fiber * self.no_of_fibers_in_specimen
self.stdev_parallel_links = (prob_crackbridging_fiber *
self.no_of_fibers_in_specimen *
(1 - prob_crackbridging_fiber))**0.5
run = Button(desc='Run the computation')
def _run_fired(self):
self.evaluate()
run_legend = Str('mean response',
desc='Legend to be added to the plot of the results')
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')
plot_title = Str('response', desc='diagram title')
label_x = Str('epsilon', desc='label of the horizontal axis')
label_y = Str('sigma', desc='label of the vertical axis')
stdev = Bool(True)
mean_parallel_links = Float(1.,
desc='mean number of parallel links (fibers)')
stdev_parallel_links = Float(
0., desc='stdev of number of parallel links (fibers)')
no_of_fibers_in_specimen = Float(
0.,
desc='Number of Fibers in the specimen',
)
data_changed = Event(True)
def evaluate(self):
self.model.set(
min_eps=0.00,
max_eps=self.max_eps,
n_eps=self.n_eps,
)
# evaluate the mean curve
self.model.mean_curve
# evaluate the variance if the stdev bool is True
if self.stdev:
self.model.var_curve
self.data_changed = True
traits_view = View(VGroup(
HGroup(
Item('run_legend',
resizable=False,
label='Run label',
width=80,
springy=False), Item('run', show_label=False,
resizable=False)),
Tabbed(
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),
HGroup(Item('stdev', label='plot standard deviation'), ),
HSplit(
HGroup(
VGroup(
Item('mean_parallel_links',
label='mean No of fibers'),
Item('stdev_parallel_links',
label='stdev No of fibers'),
)),
VGroup(
Item('@ins',
label='evaluate No of fibers',
show_label=False),
VGroup(
HGroup(
Item('eval',
show_label=False,
resizable=False,
label='Evaluate No of Fibers'),
Item('no_of_fibers_in_specimen',
label='No of Fibers in specimen',
style='readonly')))),
label='number of parralel fibers',
show_border=True,
scrollable=True,
),
VGroup(
Item('plot_title',
label='title',
resizable=False,
springy=False),
Item('label_x', label='x', resizable=False, springy=False),
Item('label_y', label='y', resizable=False, springy=False),
label='title and axes labels',
show_border=True,
scrollable=True,
),
label='Execution control',
id='spirrid.tview.view_params',
dock='tab',
),
scrollable=True,
id='spirrid.tview.tabs',
dock='tab',
),
),
title='SPIRRID',
id='spirrid.viewmodel',
dock='tab',
resizable=True,
height=1.0,
width=1.0)
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 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 RunTable(SimDBClass):
'''Manage the combinations of exec configurations and randomization patterns.
'''
name = Str(simdb=True)
memsize = Float(1e4, simdb=True)
s = Property(Instance(SPIRRID), depends_on='rf')
@cached_property
def _get_s(self):
return SPIRRID(rf=self.rf,
min_eps=0.00,
max_eps=1.0,
n_eps=20,
compiler_verbose=0)
rf = Instance(IRF, simdb=True)
config_list = List(config=True)
def _config_list_default(self):
return ['I', 'IV']
config_dict = Property(depends_on='config_list')
@cached_property
def _get_config_dict(self):
cd = {}
for config_idx in self.config_list:
cd[config_idx] = config_dict[config_idx]
return cd
rand_list = List(rand=True)
run_arr = Property(Array, depends_on='+rand,+config')
@cached_property
def _get_run_arr(self):
# generate the runs to be performed
run_table = [[
SingleRun(run_table=self, config=config, rand_idx_arr=rand_idx_arr)
for rand_idx_arr in self.rand_list
] for config in self.config_dict.items()]
return array(run_table)
exec_time_arr = Array
n_int_arr = Array
real_memsize_arr = Array
calculate = Button()
def _calculate_fired(self):
s = self.run_arr.shape
self.exec_time_arr = array(
[run.exec_time for run in self.run_arr.flatten()]).reshape(s)
self.n_int_arr = array([run.n_int
for run in self.run_arr.flatten()]).reshape(s)
self.real_memsize_arr = array(
[run.real_memsize for run in self.run_arr.flatten()]).reshape(s)
self.save()
self._redraw_fired()
clear = Button()
def _clear_fired(self):
figure = self.figure
figure.clear()
self.data_changed = True
figure = Instance(Figure, transient=True)
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)
normalized_numpy = Bool(True)
c_code = Bool(False)
redraw = Button()
def _redraw_fired(self):
figure = self.figure
axes = figure.gca()
self.plot(axes)
self.data_changed = True
redraw_in_window = Button()
def _redraw_in_window_fired(self):
figure = plt.figure(0)
axes = figure.gca()
self.plot(axes)
plt.show()
def plot(self, ax):
exec_time_arr = self.exec_time_arr
n_int_arr = self.n_int_arr[0, :]
real_memsize_arr = self.real_memsize_arr[0, :]
rand_arr = arange(len(self.rand_list)) + 1
width = 0.45
if exec_time_arr.shape[0] == 1:
shift = width / 2.0
ax.bar(rand_arr - shift,
exec_time_arr[0, :],
width,
color='lightgrey')
elif self.exec_time_arr.shape[0] == 2:
max_exec_time = nmax(exec_time_arr)
ax.set_ylabel('$\mathrm{execution \, time \, [sec]}$', size=20)
ax.set_xlabel(
'$n_{\mathrm{rnd}} \;-\; \mathrm{number \, of \, random \, parameters}$',
size=20)
ax.bar(rand_arr - width,
exec_time_arr[0, :],
width,
hatch='/',
color='white',
label='C') # , color = 'lightgrey' )
ax.bar(rand_arr,
exec_time_arr[1, :],
width,
color='lightgrey',
label='numpy')
yscale = 1.25
ax_xlim = rand_arr[-1] + 1
ax_ylim = max_exec_time * yscale
ax.set_xlim(0, ax_xlim)
ax.set_ylim(0, ax_ylim)
ax2 = ax.twinx()
ydata = exec_time_arr[1, :] / exec_time_arr[0, :]
ax2.plot(rand_arr,
ydata,
'-o',
color='black',
linewidth=1,
label='numpy/C')
ax2.plot([rand_arr[0] - 1, rand_arr[-1] + 1], [1, 1], '-')
ax2.set_ylabel(
'$\mathrm{time}( \mathsf{numpy} ) / \mathrm{ time }(\mathsf{C}) \; [-]$',
size=20)
ax2_ylim = nmax(ydata) * yscale
ax2_xlim = rand_arr[-1] + 1
ax2.set_ylim(0, ax2_ylim)
ax2.set_xlim(0, ax2_xlim)
ax.set_xticks(rand_arr)
ax.set_xticklabels(rand_arr, size=14)
xticks = ['%.2g' % n_int for n_int in n_int_arr]
ax3 = ax.twiny()
ax3.set_xlim(0, rand_arr[-1] + 1)
ax3.set_xticks(rand_arr)
ax3.set_xlabel('$n_{\mathrm{int}}$', size=20)
ax3.set_xticklabels(xticks, rotation=30)
'set the tick label size of the lower X axis'
X_lower_tick = 14
xt = ax.get_xticklabels()
for t in xt:
t.set_fontsize(X_lower_tick)
'set the tick label size of the upper X axis'
X_upper_tick = 12
xt = ax3.get_xticklabels()
for t in xt:
t.set_fontsize(X_upper_tick)
'set the tick label size of the Y axes'
Y_tick = 14
yt = ax2.get_yticklabels() + ax.get_yticklabels()
for t in yt:
t.set_fontsize(Y_tick)
'set the legend position and font size'
leg_fontsize = 16
leg = ax.legend(loc=(0.02, 0.83))
for t in leg.get_texts():
t.set_fontsize(leg_fontsize)
leg = ax2.legend(loc=(0.705, 0.90))
for t in leg.get_texts():
t.set_fontsize(leg_fontsize)
traits_view = View(Item('name'),
Item('memsize'),
Item('rf'),
Item('config_dict'),
Item('rand_list'),
HGroup(
Item('calculate', show_label=False),
Item('redraw', show_label=False),
Item('clear', show_label=False),
Item('redraw_in_window', show_label=False),
),
Item('figure',
editor=MPLFigureEditor(),
resizable=True,
show_label=False),
buttons=['OK', 'Cancel'])
class LS(HasTraits):
'''Limit state class
'''
# backward link to the info shell to access the
# input data when calculating
# the limit-state-specific values
#
ls_table = WeakRef
# parameters of the limit state
#
dir = Enum(DIRLIST)
stress_res = Enum(SRLIST)
#-------------------------------
# ls columns
#-------------------------------
# defined in the subclasses
#
ls_columns = List
show_ls_columns = Bool(True)
#-------------------------------
# sr columns
#-------------------------------
# stress resultant columns - for ULS this is defined in the subclasses
#
sr_columns = List(['m', 'n'])
show_sr_columns = Bool(True)
# stress resultant columns - generated from the parameter combination
# dir and stress_res - one of MX, NX, MY, NY
#
m_varname = Property(Str)
def _get_m_varname(self):
# e.g. mx_N
appendix = self.dir + '_' + self.stress_res
return 'm' + appendix
n_varname = Property(Str)
def _get_n_varname(self):
# e.g. nx_N
appendix = self.dir + '_' + self.stress_res
return 'n' + appendix
n = Property(Float)
def _get_n(self):
return getattr(self.ls_table, self.n_varname)
m = Property(Float)
def _get_m(self):
return getattr(self.ls_table, self.m_varname)
#-------------------------------
# geo columns form info shell
#-------------------------------
geo_columns = List(['elem_no', 'X', 'Y', 'Z', 'D_elem'])
show_geo_columns = Bool(True)
elem_no = Property(Float)
def _get_elem_no(self):
return self.ls_table.elem_no
X = Property(Float)
def _get_X(self):
return self.ls_table.X
Y = Property(Float)
def _get_Y(self):
return self.ls_table.Y
Z = Property(Float)
def _get_Z(self):
return self.ls_table.Z
D_elem = Property(Float)
def _get_D_elem(self):
return self.ls_table.D_elem
#-------------------------------
# state columns form info shell
#-------------------------------
# state_columns = List( ['mx', 'my', 'mxy', 'nx', 'ny', 'nxy' ] )
state_columns = List([
'mx',
'my',
'mxy',
'nx',
'ny',
'nxy',
'sigx_lo',
'sigy_lo',
'sigxy_lo',
'sig1_lo',
'sig2_lo',
'alpha_sig_lo',
'sigx_up',
'sigy_up',
'sigxy_up',
'sig1_up',
'sig2_up',
'alpha_sig_up',
])
show_state_columns = Bool(True)
mx = Property(Float)
def _get_mx(self):
return self.ls_table.mx
my = Property(Float)
def _get_my(self):
return self.ls_table.my
mxy = Property(Float)
def _get_mxy(self):
return self.ls_table.mxy
nx = Property(Float)
def _get_nx(self):
return self.ls_table.nx
ny = Property(Float)
def _get_ny(self):
return self.ls_table.ny
nxy = Property(Float)
def _get_nxy(self):
return self.ls_table.nxy
# evaluate principal stresses
# upper face:
#
sigx_up = Property(Float)
def _get_sigx_up(self):
return self.ls_table.sigx_up
sigy_up = Property(Float)
def _get_sigy_up(self):
return self.ls_table.sigy_up
sigxy_up = Property(Float)
def _get_sigxy_up(self):
return self.ls_table.sigxy_up
sig1_up = Property(Float)
def _get_sig1_up(self):
return self.ls_table.sig1_up
sig2_up = Property(Float)
def _get_sig2_up(self):
return self.ls_table.sig2_up
alpha_sig_up = Property(Float)
def _get_alpha_sig_up(self):
return self.ls_table.alpha_sig_up
# lower face:
#
sigx_lo = Property(Float)
def _get_sigx_lo(self):
return self.ls_table.sigx_lo
sigy_lo = Property(Float)
def _get_sigy_lo(self):
return self.ls_table.sigy_lo
sigxy_lo = Property(Float)
def _get_sigxy_lo(self):
return self.ls_table.sigxy_lo
sig1_lo = Property(Float)
def _get_sig1_lo(self):
return self.ls_table.sig1_lo
sig2_lo = Property(Float)
def _get_sig2_lo(self):
return self.ls_table.sig2_lo
alpha_sig_lo = Property(Float)
def _get_alpha_sig_lo(self):
return self.ls_table.alpha_sig_lo
#-------------------------------
# ls table
#-------------------------------
# all columns associated with the limit state including the corresponding
# stress resultants
#
columns = Property(List,
depends_on='show_geo_columns, show_state_columns,\
show_sr_columns, show_ls_columns')
@cached_property
def _get_columns(self):
columns = []
if self.show_geo_columns:
columns += self.geo_columns
if self.show_state_columns:
columns += self.state_columns
if self.show_sr_columns:
columns += self.sr_columns
if self.show_ls_columns:
columns += self.ls_columns
return columns
# select column used for sorting the data in selected sorting order
#
sort_column = Enum(values='columns')
def _sort_column_default(self):
return self.columns[-1]
sort_order = Enum('descending', 'ascending', 'unsorted')
#-------------------------------------------------------
# get the maximum value of the selected variable
# 'max_in_column' of the current sheet (only one sheet)
#-------------------------------------------------------
# get the maximum value of the chosen column
#
max_in_column = Enum(values='columns')
def _max_in_column_default(self):
return self.columns[-1]
max_value = Property(depends_on='max_in_column')
def _get_max_value(self):
col = getattr(self, self.max_in_column)[:, 0]
return max(col)
#-------------------------------------------------------
# get the maximum value and the corresponding case of
# the selected variable 'max_in_column' in all (!) sheets
#-------------------------------------------------------
max_value_all = Property(depends_on='max_in_column')
def _get_max_value_all(self):
return self.ls_table.max_value_and_case[
self.max_in_column]['max_value']
max_case = Property(depends_on='max_in_column')
def _get_max_case(self):
return self.ls_table.max_value_and_case[self.max_in_column]['max_case']
#-------------------------------------------------------
# get ls_table for View
#-------------------------------------------------------
# stack columns together for table used by TabularEditor
#
ls_array = Property(
Array,
depends_on='sort_column, sort_order, show_geo_columns, \
show_state_columns, show_sr_columns, show_ls_columns'
)
@cached_property
def _get_ls_array(self):
arr_list = [getattr(self, col) for col in self.columns]
# get the array currently selected by the sort_column enumeration
#
sort_arr = getattr(self, self.sort_column)[:, 0]
sort_idx = argsort(sort_arr)
ls_array = hstack(arr_list)
if self.sort_order == 'descending':
return ls_array[sort_idx[::-1]]
if self.sort_order == 'ascending':
return ls_array[sort_idx]
if self.sort_order == 'unsorted':
return ls_array
#---------------------------------
# plot outputs in mlab-window
#---------------------------------
plot_column = Enum(values='columns')
plot = Button
def _plot_fired(self):
X = self.ls_table.X[:, 0]
Y = self.ls_table.Y[:, 0]
Z = self.ls_table.Z[:, 0]
plot_col = getattr(self, self.plot_column)[:, 0]
if self.plot_column == 'n_tex':
plot_col = where(plot_col < 0, 0, plot_col)
mlab.figure(figure="SFB532Demo",
bgcolor=(1.0, 1.0, 1.0),
fgcolor=(0.0, 0.0, 0.0))
mlab.points3d(
X,
Y,
(-1.0) * Z,
plot_col,
# colormap = "gist_rainbow",
# colormap = "Reds",
colormap="YlOrBr",
mode="cube",
scale_factor=0.15)
mlab.scalarbar(title=self.plot_column, orientation='vertical')
mlab.show
# name of the trait that is used to assess the evaluated design
#
assess_name = Str('')
#-------------------------------
# ls group
#-------------------------------
# @todo: the dynamic selection of the columns to be displayed
# does not work in connection with the LSArrayAdapter
ls_group = VGroup(
HGroup( #Item( 'assess_name' ),
Item('max_in_column'),
Item('max_value', style='readonly', format_str='%6.2f'),
Item('max_value_all', style='readonly', format_str='%6.2f'),
Item('max_case', style='readonly', label='found in case: '),
),
HGroup(
Item('sort_column'),
Item('sort_order'),
Item('show_geo_columns', label='show geo'),
Item('show_state_columns', label='show state'),
Item('show_sr_columns', label='show sr'),
Item('plot_column'),
Item('plot'),
),
)
class ECBReinfTexUniform(ECBReinfComponent):
'''Cross section characteristics needed for tensile specimens
'''
height = DelegatesTo('matrix_cs')
'''height of reinforced cross section
'''
n_layers = Int(12, auto_set=False, enter_set=True, geo_input=True)
'''total number of reinforcement layers [-]
'''
n_rovings = Int(23, auto_set=False, enter_set=True, geo_input=True)
'''number of rovings in 0-direction of one composite layer of the
bending test [-]:
'''
A_roving = Float(0.461, auto_set=False, enter_set=True, geo_input=True)
'''cross section of one roving [mm**2]'''
def convert_eps_tex_u_2_lo(self, eps_tex_u):
'''Convert the strain in the lowest reinforcement layer at failure
to the strain at the bottom of the cross section'''
eps_up = self.state.eps_up
return eps_up + (eps_tex_u - eps_up) / self.z_ti_arr[0] * self.height
def convert_eps_lo_2_tex_u(self, eps_lo):
'''Convert the strain at the bottom of the cross section to the strain
in the lowest reinforcement layer at failure'''
eps_up = self.state.eps_up
return (eps_up + (eps_lo - eps_up) / self.height * self.z_ti_arr[0])
'''Convert the MN to kN
'''
#===========================================================================
# material properties
#===========================================================================
sig_tex_u = Float(1216., auto_set=False, enter_set=True, tt_input=True)
'''Ultimate textile stress measured in the tensile test [MPa]
'''
#===========================================================================
# Distribution of reinforcement
#===========================================================================
s_tex_z = Property(depends_on=ECB_COMPONENT_AND_EPS_CHANGE)
'''spacing between the layers [m]'''
@cached_property
def _get_s_tex_z(self):
return self.height / (self.n_layers + 1)
z_ti_arr = Property(depends_on=ECB_COMPONENT_AND_EPS_CHANGE)
'''property: distance of each reinforcement layer from the top [m]:
'''
@cached_property
def _get_z_ti_arr(self):
return np.array([
self.height - (i + 1) * self.s_tex_z for i in range(self.n_layers)
],
dtype=float)
zz_ti_arr = Property
'''property: distance of reinforcement layers from the bottom
'''
def _get_zz_ti_arr(self):
return self.height - self.z_ti_arr
#===========================================================================
# Discretization conform to the tex layers
#===========================================================================
eps_i_arr = Property(depends_on=ECB_COMPONENT_AND_EPS_CHANGE)
'''Strain at the level of the i-th reinforcement layer
'''
@cached_property
def _get_eps_i_arr(self):
# ------------------------------------------------------------------------
# geometric params independent from the value for 'eps_t'
# ------------------------------------------------------------------------
height = self.height
eps_lo = self.state.eps_lo
eps_up = self.state.eps_up
# strain at the height of each reinforcement layer [-]:
#
return eps_up + (eps_lo - eps_up) * self.z_ti_arr / height
eps_ti_arr = Property(depends_on=ECB_COMPONENT_AND_EPS_CHANGE)
'''Tension strain at the level of the i-th layer of the fabrics
'''
@cached_property
def _get_eps_ti_arr(self):
return (np.fabs(self.eps_i_arr) + self.eps_i_arr) / 2.0
eps_ci_arr = Property(depends_on=ECB_COMPONENT_AND_EPS_CHANGE)
'''Compression strain at the level of the i-th layer.
'''
@cached_property
def _get_eps_ci_arr(self):
return (-np.fabs(self.eps_i_arr) + self.eps_i_arr) / 2.0
#===========================================================================
# Effective crack bridge law
#===========================================================================
ecb_law_type = Trait('fbm',
dict(fbm=ECBLFBM,
cubic=ECBLCubic,
linear=ECBLLinear,
bilinear=ECBLBilinear),
tt_input=True)
'''Selector of the effective crack bridge law type
['fbm', 'cubic', 'linear', 'bilinear']'''
ecb_law = Property(Instance(ECBLBase), depends_on='+tt_input')
'''Effective crack bridge law corresponding to ecb_law_type'''
@cached_property
def _get_ecb_law(self):
return self.ecb_law_type_(sig_tex_u=self.sig_tex_u, cs=self)
show_ecb_law = Button
'''Button launching a separate view of the effective crack bridge law.
'''
def _show_ecb_law_fired(self):
ecb_law_mw = ConstitutiveLawModelView(model=self.ecb_law)
ecb_law_mw.edit_traits(kind='live')
return
tt_modified = Event
sig_ti_arr = Property(depends_on=ECB_COMPONENT_AND_EPS_CHANGE)
'''Stresses at the i-th fabric layer.
'''
@cached_property
def _get_sig_ti_arr(self):
return self.ecb_law.mfn_vct(self.eps_ti_arr)
f_ti_arr = Property(depends_on=ECB_COMPONENT_AND_EPS_CHANGE)
'''force at the height of each reinforcement layer [kN]:
'''
@cached_property
def _get_f_ti_arr(self):
sig_ti_arr = self.sig_ti_arr
n_rovings = self.n_rovings
A_roving = self.A_roving
return sig_ti_arr * n_rovings * A_roving / self.unit_conversion_factor
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
replot = Button
def _replot_fired(self):
self.figure.clear()
ax = self.figure.add_subplot(2, 2, 1)
self.plot_eps(ax)
ax = self.figure.add_subplot(2, 2, 2)
self.plot_sig(ax)
ax = self.figure.add_subplot(2, 2, 3)
self.cc_law.plot(ax)
ax = self.figure.add_subplot(2, 2, 4)
self.ecb_law.plot(ax)
self.data_changed = True
def plot_eps(self, ax):
#ax = self.figure.gca()
d = self.height
# eps ti
ax.plot([-self.eps_lo, -self.eps_up], [0, self.height], color='black')
ax.hlines(self.zz_ti_arr, [0], -self.eps_ti_arr, lw=4, color='red')
# eps cj
ec = np.hstack([self.eps_cj_arr] + [0, 0])
zz = np.hstack([self.zz_cj_arr] + [0, self.height])
ax.fill(-ec, zz, color='blue')
# reinforcement layers
eps_range = np.array([max(0.0, self.eps_lo),
min(0.0, self.eps_up)],
dtype='float')
z_ti_arr = np.ones_like(eps_range)[:, None] * self.z_ti_arr[None, :]
ax.plot(-eps_range, z_ti_arr, 'k--', color='black')
# neutral axis
ax.plot(-eps_range, [d, d], 'k--', color='green', lw=2)
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')
def plot_sig(self, ax):
d = self.height
# f ti
ax.hlines(self.zz_ti_arr, [0], -self.f_ti_arr, lw=4, color='red')
# f cj
f_c = np.hstack([self.f_cj_arr] + [0, 0])
zz = np.hstack([self.zz_cj_arr] + [0, self.height])
ax.fill(-f_c, zz, color='blue')
f_range = np.array(
[np.max(self.f_ti_arr), np.min(f_c)], dtype='float_')
# neutral axis
ax.plot(-f_range, [d, d], 'k--', color='green', lw=2)
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')
view = View(HSplit(
Group(
HGroup(
Group(Item('height', springy=True),
Item('width'),
Item('n_layers'),
Item('n_rovings'),
Item('A_roving'),
label='Geometry',
springy=True),
Group(Item('eps_up', label='Upper strain', springy=True),
Item('eps_lo', label='Lower strain'),
label='Strain',
springy=True),
springy=True,
),
HGroup(
Group(VGroup(Item('cc_law_type',
show_label=False,
springy=True),
Item('cc_law',
label='Edit',
show_label=False,
springy=True),
Item('show_cc_law',
label='Show',
show_label=False,
springy=True),
springy=True),
Item('f_ck', label='Compressive strength'),
Item('n_cj', label='Discretization'),
label='Concrete',
springy=True),
Group(VGroup(
Item('ecb_law_type', show_label=False, springy=True),
Item('ecb_law',
label='Edit',
show_label=False,
springy=True),
Item('show_ecb_law',
label='Show',
show_label=False,
springy=True),
springy=True,
),
label='Reinforcement',
springy=True),
springy=True,
),
Group(
Item('s_tex_z', label='vertical spacing', style='readonly'),
label='Layout',
),
Group(HGroup(
Item('M', springy=True, style='readonly'),
Item('N', springy=True, style='readonly'),
),
label='Stress resultants'),
scrollable=True,
),
Group(
Item('replot', 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,
resizable=True,
buttons=['OK', 'Cancel'])
class ULS(LS):
'''Ultimate limit state
'''
#--------------------------------------------------------
# ULS: material parameters (Inputs)
#--------------------------------------------------------
# gamma-factor
gamma = Float(1.5, input=True)
# long term reduction factor
beta = Float(1.0, input=True)
# INDEX l: longitudinal direction of the textile (MAG-02-02-06a)
# characteristic tensile strength of the tensile specimen [N/mm2]
f_tk_l = Float(537, input=True)
# design value of the tensile strength of the tensile specimen [N/mm2]
# containing a gamma-factor of 1.5 and d long term reduction factor of 0.7
# f_td_l = 251
f_td_l = Property(Float, depends_on='+input')
def _get_f_td_l(self):
return self.beta * self.f_tk_l / self.gamma
# cross sectional area of the reinforcement [mm2/m]
a_t_l = Float(71.65, input=True)
# INDEX q: orthogonal direction of the textile (MAG-02-02-06a)
# characteristic tensile strength of the tensile specimen [N/mm2]
f_tk_q = Float(511, input=True)
# design value of the tensile strength of the tensile specimen [kN/m]
# f_td_q = 238
f_td_q = Property(Float, depends_on='+input')
def _get_f_td_q(self):
return self.beta * self.f_tk_q / self.gamma
# cross sectional area of the reinforcement [mm2/m]
a_t_q = Float(53.31, input=True)
# tensile strength of the textile reinforcement [kN/m]
f_Rtex_l = Property(Float, depends_on='+input')
def _get_f_Rtex_l(self):
return self.a_t_l * self.f_td_l / 1000.
# tensile strength of the textile reinforcement [kN/m]
f_Rtex_q = Property(Float)
def _get_f_Rtex_q(self, depends_on='+input'):
return self.a_t_q * self.f_td_q / 1000.
# tensile strength of the textile reinforcement [kN/m]
# as simplification the lower value of 0- and 90-direction is taken
#
# sig_composite,exp = 103.4 kN/14cm/6cm = 12.3 MPa
# f_tex,exp = 103.4 kN/14cm/12layers = 61.5 kN/m
# f_tex,k = f_tex,exp * 0,81 (EN-DIN 1990) = 0.82*61.5 kN/m = 50.4 kN/m
# f_tex,d = f_tex,k / 1,5 = 33.6 kN/m
#
f_Rtex_l = f_Rtex_q = 34.3
print 'NOTE: f_Rtex_l = f_Rtex_q = set to %g kN/m !' % (f_Rtex_l)
k_fl = 7.95 / 6.87
print 'NOTE: k_fl = set to %g [-] !' % (k_fl)
# ------------------------------------------------------------
# ULS - derived params:
# ------------------------------------------------------------
# Parameters for the cracked state (GdT):
# assumptions!
# (resultierende statische Nutzhoehe)
#
d = Property(Float)
def _get_d(self):
return 0.75 * self.ls_table.D_elem
# (Abstand Schwereachse zur resultierende Bewehrungslage)
# chose the same amount of reinforcement at the top as at the bottom
# i.e. zs = zs1 = zs2
#
zs = Property(Float)
def _get_zs(self):
return self.d - self.ls_table.D_elem / 2.
# (Innerer Hebelarm)
#
z = Property(Float)
def _get_z(self):
return 0.9 * self.d
# ------------------------------------------------------------
# ULS: outputs
# ------------------------------------------------------------
ls_columns = List([
'e', 'm_Eds', 'f_t', 'f_t_sig', 'beta_l', 'beta_q', 'f_Rtex',
'k_fl_NM', 'n_tex'
])
sr_columns = ['m', 'n', 'alpha', 'd', 'zs', 'z']
alpha_varname = Property()
def _get_alpha_varname(self):
return 'alpha_' + self.stress_res
alpha = Property
def _get_alpha(self):
return getattr(self.ls_table, self.alpha_varname)
ls_values = Property(depends_on='+input')
@cached_property
def _get_ls_values(self):
'''get the outputs for ULS
'''
#-------------------------------------------------
# VAR 1:use simplified reinforced concrete approach
#-------------------------------------------------
n = self.n
m = self.m
alpha = self.alpha
zs = self.zs
z = self.z
f_Rtex_l = self.f_Rtex_l
f_Rtex_q = self.f_Rtex_q
# (Exzentrizitaet)
e = abs(m / n)
e[n == 0] = 1E9 # if normal force is zero set e to very large value
# moment at the height of the resulting reinforcement layer:
m_Eds = abs(m) - zs * n
# tensile force in the reinforcement for bending and compression
f_t = m_Eds / z + n
# check if the two conditions are true:
cond1 = n > 0
cond2 = e < zs
bool_arr = cond1 * cond2
# in case of pure tension in the cross section:
f_t[bool_arr] = n[bool_arr] * (zs[bool_arr] + e[bool_arr]) / (
zs[bool_arr] + zs[bool_arr])
#-------------------------------------------------
# VAR 2:use principal stresses to calculate the resulting tensile force
#-------------------------------------------------
#
princ_stress_eval = True
# princ_stress_eval = False
f_t_sig = self.sig1_lo * self.D_elem * 1000.
if princ_stress_eval == True:
print "NOTE: the principle tensile stresses are used to evaluate 'n_tex'"
# resulting tensile force of the composite cross section[kN]
# the entire (!) cross section is used!
# as the maximum value of the tensile stresses at the top or the bottom
# i.e. sig1_max = min( 0, max( self.sig1_up, self.sig1_lo ) )
#---------------------------------------------------------
# initialize arrays t be filled by case distinction:
#---------------------------------------------------------
#
f_t_sig = zeros_like(self.sig1_up)
alpha = zeros_like(self.sig1_up)
k_fl_NM = ones_like(self.sig1_up)
# absolute value of the bending stress created by mx or my
sig_b = (abs(self.sig1_lo) + abs(self.sig1_up)) / 2
#---------------------------------------------------------
# conditions for case distinction
#---------------------------------------------------------
cond_t_up = self.sig1_up > 0. # compression stress upper side
cond_t_lo = self.sig1_lo > 0. # compression stress lower side
cond_u_gt_l = abs(self.sig1_up) > abs(
self.sig1_lo
) # absolute value of upper stress greater then lower stress
cond_l_gt_u = abs(self.sig1_lo) > abs(
self.sig1_up
) # absolute value of lower stress greater then lower stress
cond_u_eq_l = abs(self.sig1_up) == abs(
self.sig1_lo
) # absolute values of upper stress equals lower stress
cond_b = self.sig1_up * self.sig1_lo < 0 # bending case
cond_u_gt_0 = self.sig1_up > 0 # value of upper stress greater then 0.
cond_l_gt_0 = self.sig1_lo > 0 # value of lower stress greater then 0.
cond_c_up = self.sig1_up < 0. # compression stress upper side
cond_c_lo = self.sig1_lo < 0. # compression stress lower side
#---------------------------------------------------------
# tension case:
#---------------------------------------------------------
# sig_up > sig_lo
bool_arr = cond_t_up * cond_t_lo * cond_u_gt_l
alpha[bool_arr] = self.alpha_sig_up[bool_arr]
k_fl_NM[bool_arr] = 1.0
f_t_sig[bool_arr] = self.sig1_up[bool_arr] * self.D_elem[
bool_arr] * 1000.
# sig_lo > sig_up
bool_arr = cond_t_up * cond_t_lo * cond_l_gt_u
alpha[bool_arr] = self.alpha_sig_lo[bool_arr]
k_fl_NM[bool_arr] = 1.0
f_t_sig[bool_arr] = self.sig1_lo[bool_arr] * self.D_elem[
bool_arr] * 1000.
# sig_lo = sig_up
bool_arr = cond_t_up * cond_t_lo * cond_u_eq_l
alpha[bool_arr] = (self.alpha_sig_lo[bool_arr] +
self.alpha_sig_up[bool_arr]) / 2.
k_fl_NM[bool_arr] = 1.0
f_t_sig[bool_arr] = self.sig1_lo[bool_arr] * self.D_elem[
bool_arr] * 1000.
#---------------------------------------------------------
# bending case (= different signs)
#---------------------------------------------------------
# bending with tension at the lower side
# AND sig_N > 0 (--> bending and tension; sig1_lo results from bending and tension)
bool_arr = cond_b * cond_l_gt_0 * cond_l_gt_u
alpha[bool_arr] = self.alpha_sig_lo[bool_arr]
k_fl_NM[ bool_arr ] = 1.0 + ( self.k_fl - 1.0 ) * \
( 1.0 - ( sig_b[ bool_arr ] - abs( self.sig1_up[ bool_arr] ) ) / self.sig1_lo[ bool_arr ] )
f_t_sig[bool_arr] = self.sig1_lo[bool_arr] * self.D_elem[
bool_arr] * 1000. / k_fl_NM[bool_arr]
# bending with tension at the lower side
# AND sig_N < 0 (--> bending and compression; sig1_lo results only from bending)
bool_arr = cond_b * cond_l_gt_0 * cond_u_gt_l
alpha[bool_arr] = self.alpha_sig_lo[bool_arr]
k_fl_NM[bool_arr] = self.k_fl
f_t_sig[bool_arr] = self.sig1_lo[bool_arr] * self.D_elem[
bool_arr] * 1000. / k_fl_NM[bool_arr]
# bending with tension at the upper side
# AND sig_N > 0 (--> bending and tension; sig1_up results from bending and tension)
bool_arr = cond_b * cond_u_gt_0 * cond_u_gt_l
alpha[bool_arr] = self.alpha_sig_up[bool_arr]
k_fl_NM[ bool_arr ] = 1.0 + ( self.k_fl - 1.0 ) * \
( 1.0 - ( sig_b[ bool_arr ] - abs( self.sig1_lo[ bool_arr] ) ) / self.sig1_up[ bool_arr ] )
f_t_sig[bool_arr] = self.sig1_up[bool_arr] * self.D_elem[
bool_arr] * 1000. / k_fl_NM[bool_arr]
# bending with tension at the upper side
# AND sig_N < 0 (--> bending and compression; sig1_up results only from bending)
bool_arr = cond_b * cond_u_gt_0 * cond_l_gt_u
alpha[bool_arr] = self.alpha_sig_up[bool_arr]
k_fl_NM[bool_arr] = self.k_fl
f_t_sig[bool_arr] = self.sig1_up[bool_arr] * self.D_elem[
bool_arr] * 1000. / k_fl_NM[bool_arr]
#---------------------------------------------------------
# compression case:
#---------------------------------------------------------
bool_arr = cond_c_up * cond_c_lo
# deflection angle is of minor interest use this simplification
alpha[bool_arr] = (self.alpha_sig_up[bool_arr] +
self.alpha_sig_up[bool_arr]) / 2.
f_t_sig[bool_arr] = 0.
k_fl_NM[bool_arr] = 1.0
#------------------------------------------------------------
# get angel of deflection of the textile reinforcement
#------------------------------------------------------------
# angel of deflection of the textile reinforcement for dimensioning in x-direction
# distinguished between longitudinal (l) and transversal (q) direction
# # ASSUMPTION: worst case angle used
# # as first step use the worst case reduction due to deflection possible (at 55 degrees)
# beta_l = 55. * pi / 180. * ones_like( alpha )
# beta_q = ( 90. - 55. ) * pi / 180. * ones_like( alpha )
# @todo: as second step use the value for an alternating layup (i.e. deflection angle)
# @todo: get the correct formula for the demonstrator arrangement
# i.e. the RFEM coordinate system orientation
# print "NOTE: deflection angle is used to evaluate 'n_tex'"
beta_l = 90 - abs(alpha) # [degree]
beta_q = abs(alpha) # [degree]
# resulting strength of the bi-directional textile considering the
# deflection of the reinforcement in the loading direction:
f_Rtex = f_Rtex_l * cos( beta_l * pi / 180. ) * ( 1 - beta_l / 90. ) + \
f_Rtex_q * cos( beta_q * pi / 180. ) * ( 1 - beta_q / 90. )
# f_Rtex= 11.65 kN/m corresponds to sig_Rtex = 585 MPa
#
# f_Rtex = 11.65 * ones_like( alpha )
# f_Rtex= 10.9 kN/m corresponds to sig_Rtex = 678 MPa
# with 1/3*( 679 + 690 + 667 ) = 678 MPa
#
# f_Rtex = 10.9 * ones_like( alpha )
# f_Rtex = 10.00 kN/m corresponds to sig_Rtex = 610 MPa
#
# f_Rtex = 10.00 * ones_like( alpha )
# print 'NOTE: f_Rtex set to %g kN/m !' % ( f_Rtex[0] )
if princ_stress_eval == False:
# necessary number of reinforcement layers
n_tex = f_t / f_Rtex
return {
'e': e,
'm_Eds': m_Eds,
'f_t': f_t,
'f_t_sig': f_t_sig,
'beta_l': beta_l,
'beta_q': beta_q,
'f_Rtex': f_Rtex,
'n_tex': n_tex
}
elif princ_stress_eval == True:
# NOTE: as the entire (!) cross section is used to calculate 'f_tex_sig'
# the number of layers is evaluated also directly for the entire (!)
# cross section!
#
n_tex = f_t_sig / f_Rtex
return {
'e': e,
'm_Eds': m_Eds,
'f_t': f_t,
'f_t_sig': f_t_sig,
'beta_l': beta_l,
'beta_q': beta_q,
'f_Rtex': f_Rtex,
'n_tex': n_tex,
'k_fl_NM': k_fl_NM
}
e = Property
def _get_e(self):
return self.ls_values['e']
m_Eds = Property
def _get_m_Eds(self):
return self.ls_values['m_Eds']
f_t = Property
def _get_f_t(self):
return self.ls_values['f_t']
f_t_sig = Property
def _get_f_t_sig(self):
return self.ls_values['f_t_sig']
beta_l = Property
def _get_beta_l(self):
return self.ls_values['beta_l']
beta_q = Property
def _get_beta_q(self):
return self.ls_values['beta_q']
f_Rtex = Property
def _get_f_Rtex(self):
return self.ls_values['f_Rtex']
k_fl_NM = Property
def _get_k_fl_NM(self):
return self.ls_values['k_fl_NM']
n_tex = Property
def _get_n_tex(self):
return self.ls_values['n_tex']
assess_name = 'max_n_tex'
max_n_tex = Property(depends_on='+input')
@cached_property
def _get_max_n_tex(self):
return ndmax(self.n_tex)
# assess_name = 'max_sig1_up'
# max_sig1_up = Property( depends_on = '+input' )
# @cached_property
# def _get_max_sig1_up( self ):
# return ndmax( self.sig1_up )
# assess_name = 'max_sig1_lo'
# max_sig1_lo = Property( depends_on = '+input' )
# @cached_property
# def _get_max_sig1_lo( self ):
# return ndmax( self.sig1_lo )
#-------------------------------
# ls view
#-------------------------------
# @todo: the dynamic selection of the columns to be displayed
# does not work in connection with the LSArrayAdapter
traits_view = View(VGroup(
HGroup(
VGroup(Item(name='gamma',
label='safety factor material [-]: gamma '),
Item(name='beta',
label='reduction long term durability [-]: beta '),
label='safety factors'),
VGroup(Item(name='f_tk_l',
label='characteristic strength textil [MPa]: f_tk_l ',
format_str="%.1f"),
Item(name='f_td_l',
label='design strength textil [MPa]: f_td_l ',
style='readonly',
format_str="%.1f"),
Item(name='a_t_l',
label='cross sectional area textil [mm^2]: a_t_l ',
style='readonly',
format_str="%.1f"),
Item(name='f_Rtex_l',
label='design strength textil [kN/m]: f_Rtex_l ',
style='readonly',
format_str="%.0f"),
label='material properties (longitudinal)'),
VGroup(Item(name='f_tk_q',
label='characteristic strength textil [MPa]: f_tk_q ',
format_str="%.1f"),
Item(name='f_td_q',
label='design strength textil [MPa]: f_td_q ',
style='readonly',
format_str="%.1f"),
Item(name='a_t_q',
label='cross sectional area textil [mm^2]: a_t_q ',
style='readonly',
format_str="%.1f"),
Item(name='f_Rtex_q',
label='design strength textil [kN/m]: f_Rtex_q ',
style='readonly',
format_str="%.0f"),
label='material Properties (transversal)'),
),
VGroup(
Include('ls_group'),
Item('ls_array',
show_label=False,
editor=TabularEditor(adapter=LSArrayAdapter()))),
),
resizable=True,
scrollable=True,
height=1000,
width=1100)
class SLS(LS):
'''Serviceability limit state
'''
# ------------------------------------------------------------
# SLS: material parameters (Inputs)
# ------------------------------------------------------------
# tensile strength [MPa]
f_ctk = Float(4.0, input=True)
# flexural tensile strength [MPa]
f_m = Float(5.0, input=True)
# ------------------------------------------------------------
# SLS - derived params:
# ------------------------------------------------------------
# area
#
A = Property(Float)
def _get_A(self):
return self.ls_table.D_elem * 1.
# moment of inertia
#
W = Property(Float)
def _get_W(self):
return 1. * self.ls_table.D_elem**2 / 6.
# ------------------------------------------------------------
# SLS: outputs
# ------------------------------------------------------------
ls_columns = List([
'sig_n',
'sig_m',
'eta_n',
'eta_m',
'eta_tot',
])
ls_values = Property(depends_on='+input')
@cached_property
def _get_ls_values(self):
'''get the outputs for SLS
'''
n = self.n
m = self.m
A = self.A
W = self.W
f_ctk = self.f_ctk
f_m = self.f_m
sig_n = n / A / 1000.
sig_m = abs(m / W) / 1000.
eta_n = sig_n / f_ctk
eta_m = sig_m / f_m
eta_tot = eta_n + eta_m
return {
'sig_n': sig_n,
'sig_m': sig_m,
'eta_n': eta_n,
'eta_m': eta_m,
'eta_tot': eta_tot
}
sig_n = Property
def _get_sig_n(self):
return self.ls_values['sig_n']
sig_m = Property
def _get_sig_m(self):
return self.ls_values['sig_m']
eta_n = Property
def _get_eta_n(self):
return self.ls_values['eta_n']
eta_m = Property
def _get_eta_m(self):
return self.ls_values['eta_m']
eta_tot = Property
def _get_eta_tot(self):
return self.ls_values['eta_tot']
assess_name = 'max_eta_tot'
# assess_name = 'max_sig1_up'
#
# max_sig1_up = Property( depends_on = '+input' )
# @cached_property
# def _get_max_sig1_up( self ):
# return ndmax( self.sig1_up )
# @todo: make it possible to select the assess value:
#
# assess_name = Enum( values = 'columns' )
# def _assess_name_default( self ):
# return self.columns[-1]
max_eta_tot = Property(depends_on='+input')
@cached_property
def _get_max_eta_tot(self):
return ndmax(self.eta_tot)
#-------------------------------
# ls view
#-------------------------------
# @todo: the dynamic selection of the columns to be displayed
# does not work in connection with the LSArrayAdapter
traits_view = View(
VGroup(
HGroup(
Item(name='f_ctk',
label='Tensile strength concrete [MPa]: f_ctk '),
Item(name='f_m',
label='Flexural tensile trength concrete [MPa]: f_m ')),
VGroup(
Include('ls_group'),
# @todo: currently LSArrayAdapter must be called both
# in SLS and ULS separately to configure columns
# arrangement individually
#
Item('ls_array',
show_label=False,
editor=TabularEditor(adapter=LSArrayAdapter()))),
),
resizable=True,
scrollable=True,
height=1000,
width=1100)
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 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 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 ECBCrossSection(ECBCrossSectionState):
'''Cross section characteristics needed for tensile specimens
'''
matrix_cs = Instance(ECBMatrixCrossSection)
def _matrix_cs_default(self):
return ECBMatrixCrossSection()
reinf = List(ECBReinfComponent)
'''Components of the cross section including the matrix and reinforcement.
'''
matrix_cs_with_state = Property(depends_on='matrix_cs')
@cached_property
def _get_matrix_cs_with_state(self):
self.matrix_cs.state = self
return self.matrix_cs
reinf_components_with_state = Property(depends_on='reinf')
'''Components linked to the strain state of the cross section
'''
@cached_property
def _get_reinf_components_with_state(self):
for r in self.reinf:
r.state = self
r.matrix_cs = self.matrix_cs
return self.reinf
height = DelegatesTo('matrix_cs')
unit_conversion_factor = Constant(1000.0)
'''Convert the MN to kN
'''
#===========================================================================
# State management
#===========================================================================
changed = Event
'''Notifier of a changed in some component of a cross section
'''
@on_trait_change('+eps_input')
def _notify_eps_change(self):
self.changed = True
self.matrix_cs.eps_changed = True
for c in self.reinf:
c.eps_changed = True
#===========================================================================
# Cross-sectional stress resultants
#===========================================================================
N = Property(depends_on='changed')
'''Get the resulting normal force.
'''
@cached_property
def _get_N(self):
N_matrix = self.matrix_cs_with_state.N
return N_matrix + np.sum([c.N for c in self.reinf_components_with_state])
M = Property(depends_on='changed')
'''Get the resulting moment.
'''
@cached_property
def _get_M(self):
M_matrix = self.matrix_cs_with_state.M
M = M_matrix + np.sum([c.M for c in self.reinf_components_with_state])
return M - self.N * self.height / 2.
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
replot = Button
def _replot_fired(self):
self.figure.clear()
ax = self.figure.add_subplot(2, 2, 1)
self.plot_eps(ax)
ax = self.figure.add_subplot(2, 2, 2)
self.plot_sig(ax)
ax = self.figure.add_subplot(2, 2, 3)
self.cc_law.plot(ax)
ax = self.figure.add_subplot(2, 2, 4)
self.ecb_law.plot(ax)
self.data_changed = True
def plot_eps(self, ax):
# ax = self.figure.gca()
d = self.thickness
# eps ti
ax.plot([-self.eps_lo, -self.eps_up], [0, self.thickness], color='black')
ax.hlines(self.zz_ti_arr, [0], -self.eps_ti_arr, lw=4, color='red')
# eps cj
ec = np.hstack([self.eps_cj_arr] + [0, 0])
zz = np.hstack([self.zz_cj_arr] + [0, self.thickness ])
ax.fill(-ec, zz, color='blue')
# reinforcement layers
eps_range = np.array([max(0.0, self.eps_lo),
min(0.0, self.eps_up)], dtype='float')
z_ti_arr = np.ones_like(eps_range)[:, None] * self.z_ti_arr[None, :]
ax.plot(-eps_range, z_ti_arr, 'k--', color='black')
# neutral axis
ax.plot(-eps_range, [d, d], 'k--', color='green', lw=2)
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')
def plot_sig(self, ax):
d = self.thickness
# f ti
ax.hlines(self.zz_ti_arr, [0], -self.f_ti_arr, lw=4, color='red')
# f cj
f_c = np.hstack([self.f_cj_arr] + [0, 0])
zz = np.hstack([self.zz_cj_arr] + [0, self.thickness ])
ax.fill(-f_c, zz, color='blue')
f_range = np.array([np.max(self.f_ti_arr), np.min(f_c)], dtype='float_')
# neutral axis
ax.plot(-f_range, [d, d], 'k--', color='green', lw=2)
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')
view = View(HSplit(Group(
HGroup(
Group(Item('thickness', springy=True),
Item('width'),
Item('n_layers'),
Item('n_rovings'),
Item('A_roving'),
label='Geometry',
springy=True
),
Group(Item('eps_up', label='Upper strain', springy=True),
Item('eps_lo', label='Lower strain'),
label='Strain',
springy=True
),
springy=True,
),
HGroup(
Group(VGroup(
Item('cc_law_type', show_label=False, springy=True),
Item('cc_law', label='Edit', show_label=False, springy=True),
Item('show_cc_law', label='Show', show_label=False, springy=True),
springy=True
),
Item('f_ck', label='Compressive strength'),
Item('n_cj', label='Discretization'),
label='Concrete',
springy=True
),
Group(VGroup(
Item('ecb_law_type', show_label=False, springy=True),
Item('ecb_law', label='Edit', show_label=False, springy=True),
Item('show_ecb_law', label='Show', show_label=False, springy=True),
springy=True,
),
label='Reinforcement',
springy=True
),
springy=True,
),
Group(Item('s_tex_z', label='vertical spacing', style='readonly'),
label='Layout',
),
Group(
HGroup(Item('M', springy=True, style='readonly'),
Item('N', springy=True, style='readonly'),
),
label='Stress resultants'
),
scrollable=True,
),
Group(Item('replot', 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,
resizable=True,
buttons=['OK', 'Cancel'])
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 ResultView(HasTraits):
spirrid_view = Instance(SPIRRIDModelView)
title = Str('result plot')
n_samples = Int(10)
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)
clear = Button
def _clear_fired(self):
axes = self.figure.axes[0]
axes.clear()
self.data_changed = True
def get_rvs_theta_arr(self, n_samples):
rvs_theta_arr = array([
repeat(value, n_samples)
for value in self.spirrid_view.model.rf.param_values
])
for idx, name in enumerate(self.spirrid_view.model.rf.param_keys):
rv = self.spirrid_view.model.rv_dict.get(name, None)
if rv:
rvs_theta_arr[idx, :] = rv.get_rvs_theta_arr(n_samples)
return rvs_theta_arr
sample = Button(desc='Show samples')
def _sample_fired(self):
n_samples = 20
self.spirrid_view.model.set(
min_eps=0.00,
max_eps=self.spirrid_view.max_eps,
n_eps=self.spirrid_view.n_eps,
)
# get the parameter combinations for plotting
rvs_theta_arr = self.get_rvs_theta_arr(n_samples)
eps_arr = self.spirrid_view.model.eps_arr
figure = self.figure
axes = figure.gca()
for theta_arr in rvs_theta_arr.T:
q_arr = self.spirrid_view.model.rf(eps_arr, *theta_arr)
axes.plot(eps_arr, q_arr, color='grey')
self.data_changed = True
@on_trait_change('spirrid_view.data_changed')
def _redraw(self):
figure = self.figure
axes = figure.gca()
mc = self.spirrid_view.model.mean_curve
xdata = mc.xdata
mean_per_fiber = mc.ydata
# total expectation for independent variables = product of marginal expectations
mean = mean_per_fiber * self.spirrid_view.mean_parallel_links
axes.set_title(self.spirrid_view.plot_title, weight='bold')
axes.plot(xdata, mean, linewidth=2, label=self.spirrid_view.run_legend)
if self.spirrid_view.stdev:
# get the variance at x from SPIRRID
variance = self.spirrid_view.model.var_curve.ydata
# evaluate variance for the given mean and variance of parallel links
# law of total variance D[xy] = E[x]*D[y] + D[x]*[E[y]]**2
variance = self.spirrid_view.mean_parallel_links * variance + \
self.spirrid_view.stdev_parallel_links ** 2 * mean_per_fiber ** 2
stdev = sqrt(variance)
axes.plot(xdata,
mean + stdev,
linewidth=2,
color='black',
ls='dashed',
label='stdev')
axes.plot(xdata,
mean - stdev,
linewidth=2,
ls='dashed',
color='black')
axes.fill_between(xdata,
mean + stdev,
mean - stdev,
color='lightgrey')
axes.set_xlabel(self.spirrid_view.label_x, weight='semibold')
axes.set_ylabel(self.spirrid_view.label_y, weight='semibold')
axes.legend(loc='best')
if xdata.any() == 0.:
self.figure.clear()
self.data_changed = True
traits_view = View(
HGroup(Item('n_samples', label='No of samples'),
Item('sample', show_label=False, resizable=False),
Item('clear', show_label=False, resizable=False,
springy=False)),
Item('figure', show_label=False, editor=MPLFigureEditor()))
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)