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)
class Demo(HasTraits):
'''Demo class for response functions'''
fiber_tt_2p = Instance(SPIRRIDLAB)
def _fiber_tt_2p_default(self):
return fiber_tt_2p.create_demo_object()
fiber_tt_5p = Instance(SPIRRIDLAB)
def _fiber_tt_5p_default(self):
return fiber_tt_5p.create_demo_object()
fiber_po_8p = Instance(SPIRRIDLAB)
def _fiber_po_8p_default(self):
return fiber_po_8p.create_demo_object()
fiber_cb_8p = Button()
def _fiber_cb_8p_fired(self):
return fiber_cb_8p.create_demo_object()
mask_arr_b = Button()
def _mask_arr_b_fired(self):
return mask_arr.main()
numexpr_b = Button()
def _numexpr_b_fired(self):
return numexpr_test.main()
script_b = Button()
def _script_b_fired(self):
return script.main()
traits_view = View(Item('fiber_tt_2p', show_label=False),
Item('fiber_tt_5p', show_label=False),
Item('fiber_po_8p', show_label=False),
Item('fiber_cb_8p', show_label=False),
Item('mask_arr_b', show_label=False),
Item('numexpr_b', show_label=False),
Item('script_b', show_label=False),
width=0.2,
height=0.3,
buttons=['OK', 'Cancel'])
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'])
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
)
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 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 SPIRRIDLAB(HasTraits):
'''Class used for elementary parametric studies of spirrid.
'''
s = Instance(SPIRRID)
evars = DelegatesTo('s')
tvars = DelegatesTo('s')
q = DelegatesTo('s')
exact_arr = Array('float')
dpi = Int
plot_mode = Enum(['subplots', 'figures'])
fig_output_dir = Directory('fig')
@on_trait_change('fig_output_dir')
def _check_dir(self):
if os.access(self.fig_output_dir, os.F_OK) == False:
os.mkdir(self.fig_output_dir)
e_arr = Property
def _get_e_arr(self):
return self.s.evar_lst[0]
hostname = Property
def _get_hostname(self):
return gethostname()
qname = Str
def get_qname(self):
if self.qname == '':
if isinstance(self.q, types.FunctionType):
qname = self.q.__name__
else: # if isinstance(self.q, types.ClassType):
qname = self.q.__class__.__name__
else:
qname = self.qname
return qname
show_output = False
save_output = True
plot_sampling_idx = Array(value=[0, 1], dtype=int)
def _plot_sampling(self,
i,
n_col,
sampling_type,
p=p,
ylim=None,
xlim=None):
'''Construct a spirrid object, run the calculation
plot the mu_q / e curve and save it in the subdirectory.
'''
s = self.s
s.sampling_type = sampling_type
plot_idx = self.plot_sampling_idx
qname = self.get_qname()
# get n randomly selected realizations from the sampling
theta = s.sampling.get_samples(500)
tvar_x = s.tvar_lst[plot_idx[0]]
tvar_y = s.tvar_lst[plot_idx[1]]
min_x, max_x, d_x = s.sampling.get_theta_range(tvar_x)
min_y, max_y, d_y = s.sampling.get_theta_range(tvar_y)
# for vectorized execution add a dimension for control variable
theta_args = [t[:, np.newaxis] for t in theta]
q_arr = s.q(self.e_arr[None, :], *theta_args)
if self.plot_mode == 'figures':
f = p.figure(figsize=(7., 6.))
f.subplots_adjust(left=0.15, right=0.97, bottom=0.15, top=0.92)
if self.plot_mode == 'subplots':
if i == 0:
f = p.figure()
p.subplot('2%i%i' % (n_col, (i + 1)))
p.plot(theta[plot_idx[0]], theta[plot_idx[1]], 'o', color='grey')
p.xlabel('$\lambda$')
p.ylabel('$\\xi$')
p.xlim(min_x, max_x)
p.ylim(min_y, max_y)
p.title(s.sampling_type)
if self.save_output:
fname = os.path.join(
self.fig_output_dir,
qname + '_sampling_' + s.sampling_type + '.png')
p.savefig(fname, dpi=self.dpi)
if self.plot_mode == 'figures':
f = p.figure(figsize=(7., 5))
f.subplots_adjust(left=0.15, right=0.97, bottom=0.18, top=0.91)
elif self.plot_mode == 'subplots':
p.subplot('2%i%i' % (n_col, (i + 5)))
p.plot(self.e_arr, q_arr.T, color='grey')
if len(self.exact_arr) > 0:
p.plot(self.e_arr,
self.exact_arr,
label='exact solution',
color='black',
linestyle='--',
linewidth=2)
# numerically obtained result
p.plot(self.e_arr,
s.mu_q_arr,
label='numerical integration',
linewidth=3,
color='black')
p.title(s.sampling_type)
p.xlabel('$\\varepsilon$ [-]')
p.ylabel(r'$q(\varepsilon;\, \lambda,\, \xi)$')
if ylim:
p.ylim(0.0, ylim)
if xlim:
p.xlim(0.0, xlim)
p.xticks(position=(0, -.015))
p.legend(loc=2)
if self.save_output:
fname = os.path.join(self.fig_output_dir,
qname + '_' + s.sampling_type + '.png')
p.savefig(fname, dpi=self.dpi)
sampling_structure_btn = Button(label='compare sampling structure')
@on_trait_change('sampling_structure_btn')
def sampling_structure(self, **kw):
'''Plot the response into the file in the fig subdirectory.
'''
if self.plot_mode == 'subplots':
p.rcdefaults()
else:
fsize = 28
p.rcParams['font.size'] = fsize
rc('legend', fontsize=fsize - 8)
rc('axes', titlesize=fsize)
rc('axes', labelsize=fsize + 6)
rc('xtick', labelsize=fsize - 8)
rc('ytick', labelsize=fsize - 8)
rc('xtick.major', pad=8)
s_lst = ['TGrid', 'PGrid', 'MCS', 'LHS']
for i, s in enumerate(s_lst):
self._plot_sampling(i, len(s_lst), sampling_type=s, **kw)
if self.show_output:
p.show()
n_int_range = Array()
#===========================================================================
# Output file names for sampling efficiency
#===========================================================================
fname_sampling_efficiency_time_nint = Property
def _get_fname_sampling_efficiency_time_nint(self):
return self.get_qname(
) + '_' + '%s' % self.hostname + '_time_nint' + '.png'
fname_sampling_efficiency_error_nint = Property
def _get_fname_sampling_efficiency_error_nint(self):
return self.get_qname(
) + '_' + '%s' % self.hostname + '_error_nint' + '.png'
fname_sampling_efficiency_error_time = Property
def _get_fname_sampling_efficiency_error_time(self):
return self.get_qname(
) + '_' + '%s' % self.hostname + '_error_time' + '.png'
fnames_sampling_efficiency = Property
def _get_fnames_sampling_efficiency(self):
fnames = [self.fname_sampling_efficiency_time_nint]
if len(self.exact_arr) > 0:
fnames += [
self.fname_sampling_efficiency_error_nint,
self.fname_sampling_efficiency_error_time
]
return fnames
#===========================================================================
# Run sampling efficiency studies
#===========================================================================
sampling_types = Array(value=['TGrid', 'PGrid', 'MCS', 'LHS'], dtype=str)
sampling_efficiency_btn = Button(label='compare sampling efficiency')
@on_trait_change('sampling_efficiency_btn')
def sampling_efficiency(self):
'''
Run the code for all available sampling types.
Plot the results.
'''
def run_estimation(n_int, sampling_type):
# instantiate spirrid with samplingetization methods
print 'running', sampling_type, n_int
self.s.set(n_int=n_int, sampling_type=sampling_type)
n_sim = self.s.sampling.n_sim
exec_time = np.sum(self.s.exec_time)
return self.s.mu_q_arr, exec_time, n_sim
# vectorize the estimation to accept arrays
run_estimation_vct = np.vectorize(run_estimation, [object, float, int])
#===========================================================================
# Generate the inspected domain of input parameters using broadcasting
#===========================================================================
run_estimation_vct([5], ['PGrid'])
sampling_types = self.sampling_types
sampling_colors = np.array(
['grey', 'black', 'grey', 'black'],
dtype=str) # 'blue', 'green', 'red', 'magenta'
sampling_linestyle = np.array(['--', '--', '-', '-'], dtype=str)
# run the estimation on all combinations of n_int and sampling_types
mu_q, exec_time, n_sim_range = run_estimation_vct(
self.n_int_range[:, None], sampling_types[None, :])
p.rcdefaults()
f = p.figure(figsize=(12, 6))
f.subplots_adjust(left=0.06, right=0.94)
#===========================================================================
# Plot the results
#===========================================================================
p.subplot(1, 2, 1)
p.title('response for %d $n_\mathrm{sim}$' % n_sim_range[-1, -1])
for i, (sampling, color, linestyle) in enumerate(
zip(sampling_types, sampling_colors, sampling_linestyle)):
p.plot(self.e_arr,
mu_q[-1, i],
color=color,
label=sampling,
linestyle=linestyle)
if len(self.exact_arr) > 0:
p.plot(self.e_arr,
self.exact_arr,
color='black',
label='Exact solution')
p.legend(loc=1)
p.xlabel('e', fontsize=18)
p.ylabel('q', fontsize=18)
# @todo: get n_sim - x-axis
p.subplot(1, 2, 2)
for i, (sampling, color, linestyle) in enumerate(
zip(sampling_types, sampling_colors, sampling_linestyle)):
p.loglog(n_sim_range[:, i],
exec_time[:, i],
color=color,
label=sampling,
linestyle=linestyle)
p.legend(loc=2)
p.xlabel('$n_\mathrm{sim}$', fontsize=18)
p.ylabel('$t$ [s]', fontsize=18)
if self.save_output:
basename = self.fname_sampling_efficiency_time_nint
fname = os.path.join(self.fig_output_dir, basename)
p.savefig(fname, dpi=self.dpi)
#===========================================================================
# Evaluate the error
#===========================================================================
if len(self.exact_arr) > 0:
er = ErrorEval(exact_arr=self.exact_arr)
def eval_error(mu_q, error_measure):
return error_measure(mu_q)
eval_error_vct = np.vectorize(eval_error)
error_measures = np.array(
[er.eval_error_max, er.eval_error_energy, er.eval_error_rms])
error_table = eval_error_vct(mu_q[:, :, None],
error_measures[None, None, :])
f = p.figure(figsize=(14, 6))
f.subplots_adjust(left=0.07, right=0.97, wspace=0.26)
p.subplot(1, 2, 1)
p.title('max rel. lack of fit')
for i, (sampling, color, linestyle) in enumerate(
zip(sampling_types, sampling_colors, sampling_linestyle)):
p.loglog(n_sim_range[:, i],
error_table[:, i, 0],
color=color,
label=sampling,
linestyle=linestyle)
#p.ylim( 0, 10 )
p.legend()
p.xlabel('$n_\mathrm{sim}$', fontsize=18)
p.ylabel('$\mathrm{e}_{\max}$ [-]', fontsize=18)
p.subplot(1, 2, 2)
p.title('rel. root mean square error')
for i, (sampling, color, linestyle) in enumerate(
zip(sampling_types, sampling_colors, sampling_linestyle)):
p.loglog(n_sim_range[:, i],
error_table[:, i, 2],
color=color,
label=sampling,
linestyle=linestyle)
p.legend()
p.xlabel('$n_{\mathrm{sim}}$', fontsize=18)
p.ylabel('$\mathrm{e}_{\mathrm{rms}}$ [-]', fontsize=18)
if self.save_output:
basename = self.fname_sampling_efficiency_error_nint
fname = os.path.join(self.fig_output_dir, basename)
p.savefig(fname, dpi=self.dpi)
f = p.figure(figsize=(14, 6))
f.subplots_adjust(left=0.07, right=0.97, wspace=0.26)
p.subplot(1, 2, 1)
p.title('rel. max lack of fit')
for i, (sampling, color, linestyle) in enumerate(
zip(sampling_types, sampling_colors, sampling_linestyle)):
p.loglog(exec_time[:, i],
error_table[:, i, 0],
color=color,
label=sampling,
linestyle=linestyle)
p.legend()
p.xlabel('time [s]', fontsize=18)
p.ylabel('$\mathrm{e}_{\max}$ [-]', fontsize=18)
p.subplot(1, 2, 2)
p.title('rel. root mean square error')
for i, (sampling, color, linestyle) in enumerate(
zip(sampling_types, sampling_colors, sampling_linestyle)):
p.loglog(exec_time[:, i],
error_table[:, i, 2],
color=color,
label=sampling,
linestyle=linestyle)
p.legend()
p.xlabel('time [s]', fontsize=18)
p.ylabel('$\mathrm{e}_{\mathrm{rms}}$ [-]', fontsize=18)
if self.save_output:
basename = self.fname_sampling_efficiency_error_time
fname = os.path.join(self.fig_output_dir, basename)
p.savefig(fname, dpi=self.dpi)
if self.show_output:
p.show()
#===========================================================================
# Efficiency of numpy versus C code
#===========================================================================
run_lst_detailed_config = Property(List)
def _get_run_lst_detailed_config(self):
run_lst = []
if hasattr(self.q, 'c_code'):
run_lst += [
# ('c',
# {'cached_dG' : True,
# 'compiled_eps_loop' : True },
# 'go-',
# '$\mathsf{C}_{\\varepsilon} \{\, \mathsf{C}_{\\theta} \{\, q(\\varepsilon,\\theta) \cdot G[\\theta] \,\}\,\} $ - %4.2f sec',
# ),
# ('c',
# {'cached_dG' : True,
# 'compiled_eps_loop' : False },
# 'r-2',
# '$\mathsf{Python} _{\\varepsilon} \{\, \mathsf{C}_{\\theta} \{\, q(\\varepsilon,\\theta) \cdot G[\\theta] \,\}\,\} $ - %4.2f sec'
# ),
# ('c',
# {'cached_dG' : False,
# 'compiled_eps_loop' : True },
# 'r-2',
# '$\mathsf{C}_{\\varepsilon} \{\, \mathsf{C}_{\\theta} \{\, q(\\varepsilon,\\theta) \cdot g[\\theta_1] \cdot \ldots \cdot g[\\theta_m] \,\}\,\} $ - %4.2f sec'
# ),
(
'c',
{
'cached_dG': False,
'compiled_eps_loop': False
},
'bx-',
'$\mathsf{Python} _{\\varepsilon} \{\, \mathsf{C}_{\\theta} \{\, q(\\varepsilon,\\theta) \cdot g[\\theta_1] \cdot \ldots \cdot g[\\theta_m] \,\} \,\} $ - %4.2f sec',
)
]
if hasattr(self.q, 'cython_code'):
run_lst += [
# ('cython',
# {'cached_dG' : True,
# 'compiled_eps_loop' : True },
# 'go-',
# '$\mathsf{Cython}_{\\varepsilon} \{\, \mathsf{Cython}_{\\theta} \{\, q(\\varepsilon,\\theta) \cdot G[\\theta] \,\}\,\} $ - %4.2f sec',
# ),
# ('cython',
# {'cached_dG' : True,
# 'compiled_eps_loop' : False },
# 'r-2',
# '$\mathsf{Python} _{\\varepsilon} \{\, \mathsf{Cython}_{\\theta} \{\, q(\\varepsilon,\\theta) \cdot G[\\theta] \,\}\,\} $ - %4.2f sec'
# ),
# ('cython',
# {'cached_dG' : False,
# 'compiled_eps_loop' : True },
# 'r-2',
# '$\mathsf{Cython}_{\\varepsilon} \{\, \mathsf{Cython}_{\\theta} \{\, q(\\varepsilon,\\theta) \cdot g[\\theta_1] \cdot \ldots \cdot g[\\theta_m] \,\}\,\} $ - %4.2f sec'
# ),
# ('cython',
# {'cached_dG' : False,
# 'compiled_eps_loop' : False },
# 'bx-',
# '$\mathsf{Python} _{\\varepsilon} \{\, \mathsf{Cython}_{\\theta} \{\, q(\\varepsilon,\\theta) \cdot g[\\theta_1] \cdot \ldots \cdot g[\\theta_m] \,\} \,\} $ - %4.2f sec',
# )
]
if hasattr(self.q, '__call__'):
run_lst += [
# ('numpy',
# {},
# 'y--',
# '$\mathsf{Python}_{\\varepsilon} \{\, \mathsf{Numpy}_{\\theta} \{\, q(\\varepsilon,\\theta) \cdot G[\\theta] \,\} \,\} $ - %4.2f sec'
# )
]
return run_lst
# number of recalculations to get new time.
n_recalc = Int(2)
def codegen_efficiency(self):
# define a tables with the run configurations to start in a batch
basenames = []
qname = self.get_qname()
s = self.s
legend = []
legend_lst = []
time_lst = []
p.figure()
for idx, run in enumerate(self.run_lst_detailed_config):
code, run_options, plot_options, legend_string = run
s.codegen_type = code
s.codegen.set(**run_options)
print 'run', idx, run_options
for i in range(self.n_recalc):
s.recalc = True # automatically proagated within spirrid
print 'execution time', s.exec_time
p.plot(s.evar_lst[0], s.mu_q_arr, plot_options)
# @todo: this is not portable!!
#legend.append(legend_string % s.exec_time)
#legend_lst.append(legend_string[:-12])
time_lst.append(s.exec_time)
p.xlabel('strain [-]')
p.ylabel('stress')
#p.legend(legend, loc = 2)
p.title(qname)
if self.save_output:
print 'saving codegen_efficiency'
basename = qname + '_' + 'codegen_efficiency' + '.png'
basenames.append(basename)
fname = os.path.join(self.fig_output_dir, basename)
p.savefig(fname, dpi=self.dpi)
self._bar_plot(legend_lst, time_lst)
p.title('%s' % s.sampling_type)
if self.save_output:
basename = qname + '_' + 'codegen_efficiency_%s' % s.sampling_type + '.png'
basenames.append(basename)
fname = os.path.join(self.fig_output_dir, basename)
p.savefig(fname, dpi=self.dpi)
if self.show_output:
p.show()
return basenames
#===========================================================================
# Efficiency of numpy versus C code
#===========================================================================
run_lst_language_config = Property(List)
def _get_run_lst_language_config(self):
run_lst = []
if hasattr(self.q, 'c_code'):
run_lst += [(
'c',
{
'cached_dG': False,
'compiled_eps_loop': False
},
'bx-',
'$\mathsf{Python} _{\\varepsilon} \{\, \mathsf{C}_{\\theta} \{\, q(\\varepsilon,\\theta) \cdot g[\\theta_1] \cdot \ldots \cdot g[\\theta_m] \,\} \,\} $ - %4.2f sec',
)]
if hasattr(self.q, 'cython_code'):
run_lst += [(
'cython',
{
'cached_dG': False,
'compiled_eps_loop': False
},
'bx-',
'$\mathsf{Python} _{\\varepsilon} \{\, \mathsf{Cython}_{\\theta} \{\, q(\\varepsilon,\\theta) \cdot g[\\theta_1] \cdot \ldots \cdot g[\\theta_m] \,\} \,\} $ - %4.2f sec',
)]
if hasattr(self.q, '__call__'):
run_lst += [(
'numpy', {}, 'y--',
'$\mathsf{Python}_{\\varepsilon} \{\, \mathsf{Numpy}_{\\theta} \{\, q(\\varepsilon,\\theta) \cdot G[\\theta] \,\} \,\} $ - %4.2f sec'
)]
return run_lst
extra_compiler_args = Bool(True)
le_sampling_lst = List(['LHS', 'PGrid'])
le_n_int_lst = List([440, 5000])
#===========================================================================
# Output file names for language efficiency
#===========================================================================
fnames_language_efficiency = Property
def _get_fnames_language_efficiency(self):
return [
'%s_codegen_efficiency_%s_extra_%s.png' %
(self.qname, self.hostname, extra)
for extra in [self.extra_compiler_args]
]
language_efficiency_btn = Button(label='compare language efficiency')
@on_trait_change('language_efficiency_btn')
def codegen_language_efficiency(self):
# define a tables with the run configurations to start in a batch
home_dir = expanduser("~")
# pyxbld_dir = os.path.join(home_dir, '.pyxbld')
# if os.path.exists(pyxbld_dir):
# shutil.rmtree(pyxbld_dir)
python_compiled_dir = os.path.join(home_dir, '.python27_compiled')
if os.path.exists(python_compiled_dir):
shutil.rmtree(python_compiled_dir)
for extra, fname in zip([self.extra_compiler_args],
self.fnames_language_efficiency):
print 'extra compilation args:', extra
legend_lst = []
error_lst = []
n_sim_lst = []
exec_times_sampling = []
meth_lst = zip(self.le_sampling_lst, self.le_n_int_lst)
for item, n_int in meth_lst:
print 'sampling method:', item
s = self.s
s.exec_time # eliminate first load time delay (first column)
s.n_int = n_int
s.sampling_type = item
exec_times_lang = []
for idx, run in enumerate(self.run_lst_language_config):
code, run_options, plot_options, legend_string = run
#os.system('rm -fr ~/.python27_compiled')
s.codegen_type = code
s.codegen.set(**run_options)
if s.codegen_type == 'c':
s.codegen.set(**dict(use_extra=extra))
print 'run', idx, run_options
exec_times_run = []
for i in range(self.n_recalc):
s.recalc = True # automatically propagated
exec_times_run.append(s.exec_time)
print 'execution time', s.exec_time
legend_lst.append(legend_string[:-12])
if s.codegen_type == 'c':
# load weave.inline time from tmp file and fix values in time_arr
#@todo - does not work on windows
import tempfile
tdir = tempfile.gettempdir()
f = open(os.path.join(tdir, 'w_time'), 'r')
value_t = float(f.read())
f.close()
exec_times_run[0][1] = value_t
exec_times_run[0][2] -= value_t
exec_times_lang.append(exec_times_run)
else:
exec_times_lang.append(exec_times_run)
print 'legend_lst', legend_lst
n_sim_lst.append(s.sampling.n_sim)
exec_times_sampling.append(exec_times_lang)
#===========================================================================
# Evaluate the error
#===========================================================================
if len(self.exact_arr) > 0:
er = ErrorEval(exact_arr=self.exact_arr)
error_lst.append((er.eval_error_rms(s.mu_q_arr),
er.eval_error_max(s.mu_q_arr)))
times_arr = np.array(exec_times_sampling, dtype='d')
self._multi_bar_plot(meth_lst, legend_lst, times_arr, error_lst,
n_sim_lst)
if self.save_output:
fname_path = os.path.join(self.fig_output_dir, fname)
p.savefig(fname_path, dpi=self.dpi)
if self.show_output:
p.show()
def combination_efficiency(self, tvars_det, tvars_rand):
'''
Run the code for all available random parameter combinations.
Plot the results.
'''
qname = self.get_qname()
s = self.s
s.set(sampling_type='TGrid')
# list of all combinations of response function parameters
rv_comb_lst = list(powerset(s.tvars.keys()))
p.figure()
exec_time_lst = []
for id, rv_comb in enumerate(rv_comb_lst[163:219]): # [1:-1]
s.tvars = tvars_det
print 'Combination', rv_comb
for rv in rv_comb:
s.tvars[rv] = tvars_rand[rv]
#legend = []
#p.figure()
time_lst = []
for idx, run in enumerate(self.run_lst):
code, run_options, plot_options, legend_string = run
print 'run', idx, run_options
s.codegen_type = code
s.codegen.set(**run_options)
#p.plot(s.evar_lst[0], s.mu_q_arr, plot_options)
#print 'integral of the pdf theta', s.eval_i_dG_grid()
print 'execution time', s.exec_time
time_lst.append(s.exec_time)
#legend.append(legend_string % s.exec_time)
exec_time_lst.append(time_lst)
p.plot(np.array((1, 2, 3, 4)), np.array(exec_time_lst).T)
p.xlabel('method')
p.ylabel('time')
if self.save_output:
print 'saving codegen_efficiency'
fname = os.path.join(
self.fig_output_dir,
qname + '_' + 'combination_efficiency' + '.png')
p.savefig(fname, dpi=self.dpi)
if self.show_output:
p.title(s.q.title)
p.show()
def _bar_plot(self, legend_lst, time_lst):
rc('font', size=15)
#rc('font', family = 'serif', style = 'normal', variant = 'normal', stretch = 'normal', size = 15)
fig = p.figure(figsize=(10, 5))
n_tests = len(time_lst)
times = np.array(time_lst)
x_norm = times[1]
xmax = times.max()
rel_xmax = xmax / x_norm
rel_times = times / x_norm
m = int(rel_xmax % 10)
if m < 5:
x_max_plt = int(rel_xmax) - m + 10
else:
x_max_plt = int(rel_xmax) - m + 15
ax1 = fig.add_subplot(111)
p.subplots_adjust(left=0.45, right=0.88)
#fig.canvas.set_window_title('window title')
pos = np.arange(n_tests) + 0.5
rects = ax1.barh(pos,
rel_times,
align='center',
height=0.5,
color='w',
edgecolor='k')
ax1.set_xlabel('normalized execution time [-]')
ax1.axis([0, x_max_plt, 0, n_tests])
ax1.set_yticks(pos)
ax1.set_yticklabels(legend_lst)
for rect, t in zip(rects, rel_times):
width = rect.get_width()
xloc = width + (0.03 * rel_xmax)
clr = 'black'
align = 'left'
yloc = rect.get_y() + rect.get_height() / 2.0
ax1.text(xloc,
yloc,
'%4.2f' % t,
horizontalalignment=align,
verticalalignment='center',
color=clr) #, weight = 'bold')
ax2 = ax1.twinx()
ax1.plot([1, 1], [0, n_tests], 'k--')
ax2.set_yticks([0] + list(pos) + [n_tests])
ax2.set_yticklabels([''] + ['%4.2f s' % s for s in list(times)] + [''])
ax2.set_xticks([0, 1] + range(5, x_max_plt + 1, 5))
ax2.set_xticklabels(
['%i' % s for s in ([0, 1] + range(5, x_max_plt + 1, 5))])
def _multi_bar_plot(self, title_lst, legend_lst, time_arr, error_lst,
n_sim_lst):
'''Plot the results if the code efficiency.
'''
p.rcdefaults()
fsize = 14
fig = p.figure(figsize=(15, 3))
rc('font', size=fsize)
rc('legend', fontsize=fsize - 2)
legend_lst = ['weave', 'cython', 'numpy']
# times are stored in 3d array - dimensions are:
n_sampling, n_lang, n_run, n_times = time_arr.shape
print 'arr', time_arr.shape
times_sum = np.sum(time_arr, axis=n_times)
p.subplots_adjust(left=0.1,
right=0.95,
wspace=0.1,
bottom=0.15,
top=0.8)
for meth_i in range(n_sampling):
ax1 = fig.add_subplot(1, n_sampling, meth_i + 1)
ax1.set_xlabel('execution time [s]')
ytick_pos = np.arange(n_lang) + 1
# ax1.axis([0, x_max_plt, 0, n_lang])
# todo: **2 n_vars
if len(self.exact_arr) > 0:
ax1.set_title(
'%s: $ n_\mathrm{sim} = %s, \mathrm{e}_\mathrm{rms}=%s, \mathrm{e}_\mathrm{max}=%s$'
% (title_lst[meth_i][0],
self._formatSciNotation('%.2e' % n_sim_lst[meth_i]),
self._formatSciNotation('%.2e' % error_lst[meth_i][0]),
self._formatSciNotation('%.2e' % error_lst[meth_i][1])))
else:
ax1.set_title(
'%s: $ n_\mathrm{sim} = %s$' %
(title_lst[meth_i][0],
self._formatSciNotation('%.2e' % n_sim_lst[meth_i])))
ax1.set_yticks(ytick_pos)
if meth_i == 0:
ax1.set_yticklabels(legend_lst, fontsize=fsize + 2)
else:
ax1.set_yticklabels([])
ax1.set_xlim(0, 1.2 * np.max(times_sum[meth_i]))
distance = 0.2
height = 1.0 / n_run - distance
offset = height / 2.0
colors = ['w', 'w', 'w', 'r', 'y', 'b', 'g', 'm']
hatches = ['/', '\\', 'x', '-', '+', '|', 'o', 'O', '.', '*']
label_lst = ['sampling', 'compilation', 'integration']
for i in range(n_run):
pos = np.arange(n_lang) + 1 - offset + i * height
end_bar_pos = np.zeros((n_lang, ), dtype='d')
for j in range(n_times):
if i > 0:
label = label_lst[j]
else:
label = None
bar_lengths = time_arr[meth_i, :, i, j]
rects = ax1.barh(pos,
bar_lengths,
align='center',
height=height,
left=end_bar_pos,
color=colors[j],
edgecolor='k',
hatch=hatches[j],
label=label)
end_bar_pos += bar_lengths
for k in range(n_lang):
x_val = times_sum[meth_i, k,
i] + 0.01 * np.max(times_sum[meth_i])
ax1.text(x_val,
pos[k],
'$%4.2f\,$s' % x_val,
horizontalalignment='left',
verticalalignment='center',
color='black') #, weight = 'bold')
if meth_i == 0:
ax1.text(0.02 * np.max(times_sum[0]),
pos[k],
'$%i.$' % (i + 1),
horizontalalignment='left',
verticalalignment='center',
color='black',
bbox=dict(pad=0., ec="w", fc="w"))
p.legend(loc=0)
def _formatSciNotation(self, s):
# transform 1e+004 into 1e4, for example
tup = s.split('e')
try:
significand = tup[0].rstrip('0').rstrip('.')
sign = tup[1][0].replace('+', '')
exponent = tup[1][1:].lstrip('0')
if significand == '1':
# reformat 1x10^y as 10^y
significand = ''
if exponent:
exponent = '10^{%s%s}' % (sign, exponent)
if significand and exponent:
return r'%s{\cdot}%s' % (significand, exponent)
else:
return r'%s%s' % (significand, exponent)
except IndexError, msg:
return s
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 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 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',
)