class Nirvana(HasTraits):
xcontrol = Instance(XControl)
csetting = Instance(XSetting)
figure = Instance(Figure) # 控制绘图控件的Figure对象
view = View(
HSplit( #
Item("figure", editor=MPLFigureEditor(), show_label=False,width=0.85),
Group(
Item('xcontrol',style='custom',show_label=False),
Item('csetting',style='custom',show_label=False),
show_labels = False, # 组中的所有控件都不显示标签
layout = 'tabbed',
),
show_labels = False # 组中的所有控件都不显示标签
),
resizable=True,
height=0.95,
width=0.99,
buttons=[OKButton,]
)
def _figure_default(self):
figure = Figure()
figure.add_axes([0.05, 0.04, 0.9, 0.92])
return figure
def _xcontrol_default(self):
return XControl(figure=self.figure)
def _csetting_default(self):
cs = XSetting(figure=self.figure)
cs.generate_candidate([base_infos,index_infos,custom_infos])
return cs
class Test(HasTraits):
figure = Instance(Figure, ())
view = View(Item('figure', editor=MPLFigureEditor(), show_label=False),
width=400,
height=300,
resizable=True)
def __init__(self):
super(Test, self).__init__()
axes = self.figure.add_subplot(111)
t = linspace(0, 2 * pi, 200)
axes.plot(
sin(t) * (1 + 0.5 * cos(11 * t)),
cos(t) * (1 + 0.5 * cos(11 * t)))
class MainWindow(HasTraits):
""" The main window, here go the instructions to create and destroy the application. """
figure = Instance(Figure)
panel = Instance(ControlPanel)
def _figure_default(self):
figure = Figure()
figure.add_axes([0.05, 0.04, 0.9, 0.92])
return figure
def _panel_default(self):
return ControlPanel(figure=self.figure)
view = View(HSplit(
Item('figure', editor=MPLFigureEditor(), dock='vertical'),
Item('panel', style="custom"),
show_labels=False,
),
resizable=True,
height=0.75,
width=0.75,
handler=MainWindowHandler(),
buttons=NoButtons)
class Mainwindow(HasTraits):
# panel = Instance(ControlPanel)
mats_eval = Instance(MATSEval)
fets_eval = Instance(FETS1D52ULRH)
time_stepper = Instance(TStepper)
time_loop = Instance(TLoop)
t_record = Array
U_record = Array
F_record = Array
sf_record = Array
eps_record = List
sig_record = List
figure = Instance(Figure)
def _figure_default(self):
figure = Figure()
return figure
plot = Button()
def _plot_fired(self):
self.draw()
self.figure.canvas.draw()
sigma_y = Range(0.2, 1.2)
E_b = Range(0.05, 0.35)
K_bar = Range(-0.01, 0.05)
@on_trait_change('sigma_y, E_b, K_bar')
def plot(self):
self.mats_eval.sigma_y = self.sigma_y
self.mats_eval.E_b = self.E_b
self.mats_eval.K_bar = self.K_bar
self.draw()
self.figure.canvas.draw()
L_x = Range(5., 15., value=15.)
@on_trait_change('L_x')
def plot1(self):
self.time_stepper.L_x = self.L_x
self.draw()
self.figure.canvas.draw()
ax1 = Property()
@cached_property
def _get_ax1(self):
return self.figure.add_subplot(231)
ax2 = Property()
@cached_property
def _get_ax2(self):
return self.figure.add_subplot(232)
ax3 = Property()
@cached_property
def _get_ax3(self):
return self.figure.add_subplot(234)
ax4 = Property()
@cached_property
def _get_ax4(self):
return self.figure.add_subplot(235)
ax5 = Property()
@cached_property
def _get_ax5(self):
return self.figure.add_subplot(233)
ax6 = Property()
@cached_property
def _get_ax6(self):
return self.figure.add_subplot(236)
def draw(self):
self.U_record, self.F_record, self.sf_record, self.t_record, self.eps_record, self.sig_record = self.time_loop.eval(
)
n_dof = 2 * self.time_stepper.domain.n_active_elems + 1
slip, bond = self.time_stepper.mats_eval.get_bond_slip()
self.ax1.cla()
l_bs, = self.ax1.plot(slip, bond)
self.ax1.set_title('bond-slip law')
self.ax2.cla()
l_po, = self.ax2.plot(self.U_record[:, n_dof], self.F_record[:, n_dof])
marker_po, = self.ax2.plot(self.U_record[-1, n_dof],
self.F_record[-1, n_dof], 'ro')
self.ax2.set_title('pull-out force-displacement curve')
self.ax3.cla()
X = np.linspace(0, self.time_stepper.L_x, self.time_stepper.n_e_x + 1)
X_ip = np.repeat(X, 2)[1:-1]
l_sf, = self.ax3.plot(X_ip, self.sf_record[-1, :])
self.ax3.set_title('shear flow in the bond interface')
self.ax4.cla()
U = np.reshape(self.U_record[-1, :], (-1, 2)).T
l_u0, = self.ax4.plot(X, U[0])
l_u1, = self.ax4.plot(X, U[1])
l_us, = self.ax4.plot(X, U[1] - U[0])
self.ax4.set_title('displacement and slip')
self.ax5.cla()
l_eps0, = self.ax5.plot(X_ip, self.eps_record[-1][:, :, 0].flatten())
l_eps1, = self.ax5.plot(X_ip, self.eps_record[-1][:, :, 2].flatten())
self.ax5.set_title('strain')
self.ax6.cla()
l_sig0, = self.ax6.plot(X_ip, self.sig_record[-1][:, :, 0].flatten())
l_sig1, = self.ax6.plot(X_ip, self.sig_record[-1][:, :, 2].flatten())
self.ax6.set_title('stress')
time = Range(0.00, 1.02, value=1.02)
@on_trait_change('time')
def draw_t(self):
idx = (np.abs(self.time - self.t_record)).argmin()
n_dof = 2 * self.time_stepper.domain.n_active_elems + 1
self.ax2.cla()
l_po, = self.ax2.plot(self.U_record[:, n_dof], self.F_record[:, n_dof])
marker_po, = self.ax2.plot(self.U_record[idx, n_dof],
self.F_record[idx, n_dof], 'ro')
self.ax2.set_title('pull-out force-displacement curve')
self.ax3.cla()
X = np.linspace(0, self.time_stepper.L_x, self.time_stepper.n_e_x + 1)
X_ip = np.repeat(X, 2)[1:-1]
l_sf, = self.ax3.plot(X_ip, self.sf_record[idx, :])
self.ax3.set_title('shear flow in the bond interface')
self.ax4.cla()
U = np.reshape(self.U_record[idx, :], (-1, 2)).T
l_u0, = self.ax4.plot(X, U[0])
l_u1, = self.ax4.plot(X, U[1])
l_us, = self.ax4.plot(X, U[1] - U[0])
self.ax4.set_title('displacement and slip')
self.ax5.cla()
l_eps0, = self.ax5.plot(X_ip, self.eps_record[idx][:, :, 0].flatten())
l_eps1, = self.ax5.plot(X_ip, self.eps_record[idx][:, :, 2].flatten())
self.ax5.set_title('strain')
self.ax6.cla()
l_sig0, = self.ax6.plot(X_ip, self.sig_record[idx][:, :, 0].flatten())
l_sig1, = self.ax6.plot(X_ip, self.sig_record[idx][:, :, 2].flatten())
self.ax6.set_title('stress')
self.figure.canvas.draw()
view = View(
HSplit(Item('figure',
editor=MPLFigureEditor(),
dock='vertical',
width=0.7,
height=0.9),
Group(Item('mats_eval'), Item('fets_eval'),
Item('time_stepper'), Item('time_loop'), Item('sigma_y'),
Item('E_b'), Item('K_bar'), Item('L_x'), Item('time')),
show_labels=False),
resizable=True,
height=0.9,
width=1.0,
)
class MainWindow(HasTraits):
panel = Instance(ControlPanel, ())
df = DelegatesTo('panel')
plot_fourier_series = Bool(True)
plot_data = Bool(True)
plot_type = Trait('0_plot_xy', {'0_plot_xy':0,
'1_plot_n_coeff':1,
'2_plot_freq_coeff':2,
'3_plot_freq_coeff_abs':3,
'4_plot_freq_energy':4})
plot_title = Str(enter_set=True, auto_set=False, changed=True)
label_fsize = Float(15, enter_set=True, auto_set=False, changed=True)
tick_fsize = Float(15, enter_set=True, auto_set=False, changed=True)
title_fsize = Float(15, enter_set=True, auto_set=False, changed=True)
label_default = Bool(True)
x_label = Str('x', changed=True)
x_limit_on = Bool(False)
x_limit = Tuple((0., 1.), changed=True)
y_label = Str('y', changed=True)
y_limit_on = Bool(False)
y_limit = Tuple((0., 1.), changed=True)
figure = Instance(Figure)
def _figure_default(self):
figure = Figure(tight_layout=True)
figure.add_subplot(111)
# figure.add_axes([0.15, 0.15, 0.75, 0.75])
return figure
@on_trait_change('+changed')
def _redraw(self):
self._draw_fired()
draw = Button
def _draw_fired(self):
figure = self.figure
axes = figure.axes[0]
axes.clear()
# self.x_limit = (axes.axis()[0], axes.axis()[1])
# self.y_limit = (axes.axis()[2], axes.axis()[3])
df = self.df
label_fsize = self.label_fsize
tick_fsize = self.tick_fsize
title_fsize = self.title_fsize
if self.plot_type_ == 0:
if self.plot_data and self.plot_fourier_series == False:
axes.plot(df.x, df.y, color='blue', label='data')
elif self.plot_fourier_series and self.plot_data == False:
axes.plot(df.x, df.y_fourier, color='green', label='fourier')
else:
axes.plot(df.x, df.y, color='blue', label='data')
axes.plot(df.x, df.y_fourier, color='green', label='fourier')
axes.legend(loc='best')
axes.grid()
if self.label_default:
self.x_label = 'x'
self.y_label = 'y'
axes.set_xlabel(self.x_label, fontsize=label_fsize)
axes.set_ylabel(self.y_label, fontsize=label_fsize)
axes.set_title(self.plot_title, fontsize=title_fsize)
if self.x_limit_on:
axes.set_xlim(self.x_limit)
if self.y_limit_on:
axes.set_ylim(self.y_limit)
p.setp(axes.get_xticklabels(), fontsize=tick_fsize, position=(0, -.01)) # position - posun od osy x
p.setp(axes.get_yticklabels(), fontsize=tick_fsize)
if self.plot_type_ == 1:
axes.vlines(df.N_arr - 0.05, [0], df.cos_coeff, color='blue', label='cos')
axes.vlines(df.N_arr + 0.05, [0], df.sin_coeff, color='green', label='sin')
axes.legend(loc='best')
axes.grid()
if self.label_default:
self.x_label = 'n'
self.y_label = 'coeff'
axes.set_title(self.plot_title, fontsize=title_fsize)
axes.set_xlabel(self.x_label, fontsize=label_fsize)
axes.set_ylabel(self.y_label, fontsize=label_fsize)
if self.x_limit_on:
axes.set_xlim(self.x_limit)
if self.y_limit_on:
axes.set_ylim(self.y_limit)
p.setp(axes.get_xticklabels(), fontsize=tick_fsize, position=(0, -.01)) # position - posun od osy x
p.setp(axes.get_yticklabels(), fontsize=tick_fsize)
if self.plot_type_ == 2:
axes.vlines(df.freq, [0], df.cos_coeff, color='blue', label='cos')
axes.vlines(df.freq, [0], df.sin_coeff, color='green', label='sin')
axes.legend(loc='best')
axes.grid()
if self.label_default:
self.x_label = 'freq'
self.y_label = 'coeff'
axes.set_title(self.plot_title, fontsize=title_fsize)
axes.set_xlabel(self.x_label, fontsize=label_fsize)
axes.set_ylabel(self.y_label, fontsize=label_fsize)
if self.x_limit_on:
axes.set_xlim(self.x_limit)
if self.y_limit_on:
axes.set_ylim(self.y_limit)
p.setp(axes.get_xticklabels(), fontsize=tick_fsize, position=(0, -.01)) # position - posun od osy x
p.setp(axes.get_yticklabels(), fontsize=tick_fsize)
if self.plot_type_ == 3:
axes.vlines(df.freq, [0], np.abs(df.cos_coeff), color='blue', label='cos')
axes.vlines(df.freq, [0], np.abs(df.sin_coeff), color='green', label='sin')
axes.legend(loc='best')
axes.set_title(self.plot_title, fontsize=title_fsize)
if self.label_default:
self.x_label = 'freq'
self.y_label = 'coeff'
axes.set_xlabel(self.x_label, fontsize=label_fsize)
axes.set_ylabel(self.y_label, fontsize=label_fsize)
y_val = np.abs(np.hstack((df.cos_coeff, df.sin_coeff))).max()
axes.set_ybound((0, y_val * 1.05))
if self.x_limit_on:
axes.set_xlim(self.x_limit)
if self.y_limit_on:
axes.set_ylim(self.y_limit)
p.setp(axes.get_xticklabels(), fontsize=tick_fsize, position=(0, -.01)) # position - posun od osy x
p.setp(axes.get_yticklabels(), fontsize=tick_fsize)
if self.plot_type_ == 4:
axes.plot(df.freq, df.energy, 'k-', label='energ')
axes.legend(loc='best')
axes.set_title(self.plot_title, fontsize=title_fsize)
if self.label_default:
self.x_label = 'freq'
self.y_label = 'energy'
axes.set_xlabel(self.x_label, fontsize=label_fsize)
axes.set_ylabel(self.y_label, fontsize=label_fsize)
y_val = np.abs(np.hstack((df.cos_coeff, df.sin_coeff))).max()
axes.set_ybound((0, y_val * 1.05))
if self.x_limit_on:
axes.set_xlim(self.x_limit)
if self.y_limit_on:
axes.set_ylim(self.y_limit)
p.setp(axes.get_xticklabels(), fontsize=tick_fsize, position=(0, -.01)) # position - posun od osy x
p.setp(axes.get_yticklabels(), fontsize=tick_fsize)
self.figure.canvas.draw()
traits_view = View(HSplit(
Tabbed(
Group(
Item('panel@', show_label=False, id='fourier.panel'),
Group(
Item('plot_data'),
Item('plot_fourier_series'),
'_',
Item('plot_type'),
label='plot options',
show_border=True,
id='fourier.plot_options'
),
UItem('draw', label='calculate and draw'),
label='fourier',
id='fourier.fourier'
),
Group(
Item('plot_title', label='title'),
Item('title_fsize', label='title fontsize'),
Item('label_fsize', label='label fontsize'),
Item('tick_fsize', label='tick fontsize'),
Item('_'),
Item('x_limit_on'),
Item('x_limit', label='x limit - plot', enabled_when='x_limit_on'),
Item('_'),
Item('y_limit_on'),
Item('y_limit', label='y limit - plot', enabled_when='y_limit_on'),
Item('_'),
Item('label_default'),
Item('x_label', enabled_when='label_default==False'),
Item('y_label', enabled_when='label_default==False'),
label='plot settings',
show_border=True,
id='fourier.plot_settings',
dock='tab',
),
),
VGroup(
Item('figure', editor=MPLFigureEditor(),
resizable=True, show_label=False),
label='Plot sheet',
id='fourier.figure',
dock='tab',
),
),
title='Fourier series',
id='main_window.view',
resizable=True,
width=0.7,
height=0.7,
buttons=[OKButton]
)
class Graph(HasTraits):
"""
绘图组件，包括左边的数据选择控件和右边的绘图控件
"""
name = Str # 绘图名，显示在标签页标题和绘图标题中
data_source = Instance(DataSource) # 保存数据的数据源
figure = Instance(Figure) # 控制绘图控件的Figure对象
selected_xaxis = Str # X轴所用的数据名
selected_items = List # Y轴所用的数据列表
clear_button = Button(u"清除") # 快速清除Y轴的所有选择的数据
view = View(
HSplit( # HSplit分为左右两个区域，中间有可调节宽度比例的调节手柄
# 左边为一个组
VGroup(
Item("name"), # 绘图名编辑框
Item("clear_button"), # 清除按钮
Heading(u"X轴数据"), # 静态文本
# X轴选择器，用EnumEditor编辑器，即ComboBox控件，控件中的候选数据从
# data_source的names属性得到
Item("selected_xaxis", editor=
EnumEditor(name="object.data_source.names", format_str=u"%s")),
Heading(u"Y轴数据"), # 静态文本
# Y轴选择器，由于Y轴可以多选，因此用CheckBox列表编辑，按两列显示
Item("selected_items", style="custom",
editor=CheckListEditor(name="object.data_source.names",
cols=2, format_str=u"%s")),
show_border = True, # 显示组的边框
scrollable = True, # 组中的控件过多时，采用滚动条
show_labels = False # 组中的所有控件都不显示标签
),
# 右边绘图控件
Item("figure", editor=MPLFigureEditor(), show_label=False, width=600)
)
)
def _name_changed(self):
"""
当绘图名发生变化时，更新绘图的标题
"""
axe = self.figure.axes[0]
axe.set_title(self.name)
self.figure.canvas.draw()
def _clear_button_fired(self):
"""
清除按钮的事件处理
"""
self.selected_items = []
self.update()
def _figure_default(self):
"""
figure属性的缺省值，直接创建一个Figure对象
"""
figure = Figure()
figure.add_axes([0.1, 0.1, 0.85, 0.80]) #添加绘图区域，四周留有边距
return figure
def _selected_items_changed(self):
"""
Y轴数据选择更新
"""
self.update()
def _selected_xaxis_changed(self):
"""
X轴数据选择更新
"""
self.update()
def update(self):
"""
重新绘制所有的曲线
"""
axe = self.figure.axes[0]
axe.clear()
try:
xdata = self.data_source.data[self.selected_xaxis]
except:
return
for field in self.selected_items:
axe.plot(xdata, self.data_source.data[field], label=field)
axe.set_xlabel(self.selected_xaxis)
axe.set_title(self.name)
axe.legend()
self.figure.canvas.draw()
ax = self.figure.add_subplot(111)
ax.imshow(self.imgarray, cmap = cm.gist_gray)
self.figure.canvas.draw()
def do_threshold(self):
"""
threshold image
"""
thrsh = threshold_otsu(self.imgarray)
self.imgarray = self.imgarray>thrsh
def do_swirl(self):
"""
swirl image
"""
swirled = swirl(self.imgarray, rotation=0, strength=10, radius=150, order=2)
self.imgarray = swirled
def do_rotate(self):
""" if you rotate after thresholding, then the image looks weird"""
rotated = ndimage.rotate(self.imgarray, 90)
self.imgarray=rotated
def do_undo(self):
""" 1 step undo """
self.imgarray = self.prev_array
## configure gui appearance
view1 = View(Item('query',width = -250,resizable=True,label = "Query String",full_size=True), Item('refresh',resizable = True, label = "Refresh Image"), Item('url',width = -250, resizable = True, padding = 2, label = "Image URL",full_size=True), Item('figure', show_label = False, width = 600, height = 600, resizable = True, editor = MPLFigureEditor()), Item('thresh', label = 'Threshold',show_label=False), Item('swirled',resizable=False,label = 'Swirl',show_label=False), Item('rotate',resizable=False,label="Rotate 90",show_label=False),Item('undo',resizable=False,label="UNDO",show_label=False))
i = ImgManip(); i.configure_traits(view = view1)
class MPLPlot(Viewer):
"""
A plot, cointains code to display using a Matplotlib figure and to update itself
dynamically from a Variables instance (which must be passed in on initialisation).
The function plotted is calculated using 'expr' which should also be set on init
and can be any python expression using the variables in the pool.
"""
name = Str('MPL Plot')
figure = Instance(Figure, ())
expr = Str
x_max = Float
x_max_auto = Bool(True)
x_min = Float
x_min_auto = Bool(True)
y_max = Float
y_max_auto = Bool(True)
y_min = Float
y_min_auto = Bool(True)
scroll = Bool(True)
scroll_width = Float(300)
legend = Bool(False)
legend_pos = Enum('upper left', 'upper right', 'lower left', 'lower right',
'right', 'center left', 'center right', 'lower center',
'upper center', 'center', 'best')
traits_view = View(Item(name='name', label='Plot name'),
Item(name='expr',
label='Expression(s)',
editor=TextEditor(enter_set=True, auto_set=False)),
Item(label='Use commas\nfor multi-line plots.'),
HGroup(
Item(name='legend', label='Show legend'),
Item(name='legend_pos', show_label=False),
),
VGroup(HGroup(Item(name='x_max', label='Max'),
Item(name='x_max_auto', label='Auto')),
HGroup(Item(name='x_min', label='Min'),
Item(name='x_min_auto', label='Auto')),
HGroup(
Item(name='scroll', label='Scroll'),
Item(name='scroll_width',
label='Scroll width'),
),
label='X',
show_border=True),
VGroup(HGroup(Item(name='y_max', label='Max'),
Item(name='y_max_auto', label='Auto')),
HGroup(Item(name='y_min', label='Min'),
Item(name='y_min_auto', label='Auto')),
label='Y',
show_border=True),
title='Plot settings',
resizable=True)
view = View(Item(name='figure', editor=MPLFigureEditor(),
show_label=False),
width=400,
height=300,
resizable=True)
legend_prop = matplotlib.font_manager.FontProperties(size=8)
def start(self):
# Init code creates an empty plot to be updated later.
axes = self.figure.add_subplot(111)
axes.plot([0], [0])
def update(self):
"""
Update the plot from the Variables instance and make a call to wx to
redraw the figure.
"""
axes = self.figure.gca()
lines = axes.get_lines()
if lines:
exprs = self.get_exprs()
if len(exprs) > len(lines):
for i in range(len(exprs) - len(lines)):
axes.plot([0], [0])
lines = axes.get_lines()
max_xs = max_ys = min_xs = min_ys = 0
for n, expr in enumerate(exprs):
first = 0
last = None
if self.scroll and self.x_min_auto and self.x_max_auto:
first = -self.scroll_width
if not self.x_min_auto:
first = int(self.x_min)
if not self.x_max_auto:
last = int(self.x_max) + 1
ys = self.variables.new_expression(expr).get_array(first, last)
if len(ys) != 0:
xs = self.variables.new_expression('sample_num').get_array(
first, last)
else:
xs = [0]
ys = [0]
if len(xs) != len(ys):
print "MPL Plot: x and y arrays different sizes!!! Ignoring (but fix me soon)."
return
lines[n].set_xdata(xs)
lines[n].set_ydata(ys)
max_xs = max_xs if (max(xs) < max_xs) else max(xs)
max_ys = max_ys if (max(ys) < max_ys) else max(ys)
min_xs = min_xs if (min(xs) > min_xs) else min(xs)
min_ys = min_ys if (min(ys) > min_ys) else min(ys)
if self.x_max_auto:
self.x_max = max_xs
if self.x_min_auto:
if self.scroll and self.x_max_auto:
scroll_x_min = self.x_max - self.scroll_width
self.x_min = scroll_x_min if (scroll_x_min >= 0) else 0
else:
self.x_min = min_xs
if self.y_max_auto:
self.y_max = max_ys
if self.y_min_auto:
self.y_min = min_ys
axes.set_xbound(upper=self.x_max, lower=self.x_min)
axes.set_ybound(upper=self.y_max * 1.1, lower=self.y_min * 1.1)
self.draw_plot()
def get_exprs(self):
return self.expr.split(',')
def add_expr(self, expr):
if self.expr == '' or self.expr[:-1] == ',':
self.expr += expr
else:
self.expr += ',' + expr
def draw_plot(self):
if self.figure.canvas:
CallAfter(self.figure.canvas.draw)
@on_trait_change('legend_pos')
def update_legend_pos(self, old, new):
""" Move the legend, calls update_legend """
self.update_legend(None, None)
@on_trait_change('legend')
def update_legend(self, old, new):
""" Called when we change the legend display """
axes = self.figure.gca()
lines = axes.get_lines()
exprs = self.get_exprs()
if len(exprs) >= 1 and self.legend:
axes.legend(lines[:len(exprs)],
exprs,
loc=self.legend_pos,
prop=self.legend_prop)
else:
axes.legend_ = None
self.draw_plot()
class MainWindow(HasTraits):
def _pck_(self, action, f=mainpck):
if action == 'load':
try:
fpck = open(f, "rb")
print 'Loading panel from %s' % mainpck
self.seqs = pickle.load(fpck)
except:
print "Loading Fail"
return
if action == 'save':
print 'Saving panel to %s' % mainpck
fpck = open(f, "w+b")
pickle.dump(self.seqs, fpck)
fpck.close()
figure = Figure()
seqs = List([sequence(name='S0', file=default_file)])
add = Button("Add Sequence")
plot = Button("Plot")
data_digi = List()
data_analog = List()
data_digi_time = List()
data_digi_names = List()
data_analog_time = List()
data_analog_names = List()
autorangeY = Bool(True, label="Autorange in Y?")
plot_rangeY_max = Float(10)
plot_rangeY_min = Float(0)
autorange = Bool(True, label="Autorange?")
plot_range_max = Float(10000)
plot_range_min = Float()
def _figure_default(self):
self.figure = Figure()
self.figure.add_axes([0.05, 0.04, 0.9, 0.92])
control_group = Group(
Item('plot', show_label=False),
VGroup(
Item('seqs',
style='custom',
editor=ListEditor(use_notebook=True,
deletable=True,
dock_style='tab',
page_name='.name')),
Item('add', show_label=False),
show_labels=False,
show_border=True,
label='seqs',
))
view = View(
HSplit(
control_group,
VGroup(
Item('figure',
editor=MPLFigureEditor(),
dock='vertical',
show_label=False,
width=700),
HGroup('autorange', 'plot_range_min', spring,
'plot_range_max'),
HGroup('autorangeY', 'plot_rangeY_min', spring,
'plot_rangeY_max'))),
width=1,
height=0.95,
resizable=True,
handler=MainWindowHandler(),
)
def _add_fired(self):
self.seqs.append(
sequence(name='S%d' % len(self.seqs), file=default_file))
def _plot_fired(self):
self.get_data()
self.image_show()
#~ def _plot_range_max_changed(self):
#~ sleep()
#~ self.image_show()
#~ def _plot_range_min_changed(self):
#~ self.image_show()
def get_data(self):
self.data_digi = []
self.data_analog = []
self.data_digi_time = []
self.data_digi_names = []
self.data_analog_time = []
self.data_analog_names = []
digi_counter = 1
for seq in self.seqs:
for waveform in seq.waveforms:
for i, channel in enumerate(waveform.channels):
if channel in waveform.select_channels:
if waveform.name == 'Digital':
digi_counter = digi_counter + 1
digi_counter_2 = 1
for seq in self.seqs:
for waveform in seq.waveforms:
for i, channel in enumerate(waveform.channels):
if channel in waveform.select_channels:
if waveform.name == 'Digital':
self.data_digi_time.append(waveform.time)
self.data_digi_names.append(seq.name + '_' +
waveform.name + '_' +
channel)
length = len(waveform.select_channels)
height = 10. / (digi_counter - 1)
self.data_digi.append([
i * height * 0.8 - digi_counter_2 * height +
height * 0.1 for i in waveform.data[i]
])
digi_counter_2 = digi_counter_2 + 1
else:
self.data_analog_time.append(waveform.time)
self.data_analog_names.append(seq.name + '_' +
waveform.name + '_' +
channel)
self.data_analog.append(waveform.data[i])
def image_clear(self):
""" Clears canvas
"""
self.figure.clf()
wx.CallAfter(self.figure.canvas.draw)
def image_show(self):
""" Plots an image on the canvas
"""
self.image_clear()
self.figure.add_axes([0.08, 0.5, 0.7, 0.4])
analog_axis = self.figure.axes[0]
self.figure.add_axes([0.08, 0.05, 0.7, 0.4])
self.figure.axes[1].set_yticks([])
digi_axis = self.figure.axes[1].twinx()
digi_ticks = []
for i, name in enumerate(self.data_digi_names):
digi_axis.step(self.data_digi_time[i],
self.data_digi[i],
where='post',
label=name)
digi_ticks.append(-(i + 0.5) * 10. / len(self.data_digi_names))
digi_axis.axhline(-(i + 1) * 10. / len(self.data_digi_names),
color='grey',
lw=1.5)
for i, name in enumerate(self.data_analog_names):
#~ print name,len(self.data_analog_time[i]),len(self.data_analog[i])
analog_axis.step(self.data_analog_time[i],
self.data_analog[i],
where='post',
label=name)
analog_axis.axhline(0, color='black', lw=2)
analog_axis.legend(bbox_to_anchor=(1.01, 1.01),
loc=2,
prop={'size': 10})
#digi_axis.legend(bbox_to_anchor=(1.01, 0.5),loc=2,prop={'size':10})
analog_axis.set_xlabel('Time(ms)')
self.figure.axes[1].set_ylabel('TTL')
analog_axis.set_ylabel('Voltage(V)')
analog_axis.grid(True)
if not self.autorangeY:
analog_axis.set_ylim(self.plot_rangeY_min, self.plot_rangeY_max)
else:
axismin = min(analog_axis.get_ylim())
axismax = max(analog_axis.get_ylim())
analog_axis.set_ylim(axismin, axismax)
self.plot_rangeY_max = axismax
self.plot_rangeY_min = axismin
if not self.autorange:
analog_axis.set_xlim(self.plot_range_min, self.plot_range_max)
digi_axis.set_xlim(self.plot_range_min, self.plot_range_max)
else:
axismin = min(analog_axis.get_xlim() + digi_axis.get_xlim())
axismax = max(analog_axis.get_xlim() + digi_axis.get_xlim())
analog_axis.set_xlim(axismin, axismax)
digi_axis.set_xlim(axismin, axismax)
#~ analog_axis.set_ylim(bottom=0,top=11)
self.plot_range_max = axismax
self.plot_range_min = axismin
digi_axis.set_ylim(bottom=-11, top=0)
digi_axis.set_yticks(digi_ticks)
digi_axis.set_yticklabels(self.data_digi_names)
wx.CallAfter(self.figure.canvas.draw)
class MainWindow(HasTraits):
#This functio takes care of loading or saving the pck
def _pck_(self, action, f=mainpck):
if action == 'load':
try:
try:
fpck = open(f, "rb")
print 'Loading panel from %s ... ' % mainpck
self.seqs = pickle.load(fpck)
self.autorangeY = pickle.load(fpck)
self.plot_rangeY_max = pickle.load(fpck)
self.plot_rangeY_min = pickle.load(fpck)
self.autorange = pickle.load(fpck)
self.plot_range_max = pickle.load(fpck)
self.plot_range_min = pickle.load(fpck)
fpck.close()
except:
fpck = open(f, "rb")
print 'Loading panel from %s ... ' % mainpck
self.seqs = pickle.load(fpck)
fpck.close()
except:
print "Loading Fail"
return
if action == 'save':
print 'Saving panel to %s ...' % mainpck
fpck = open(f, "w+b")
pickle.dump(self.seqs, fpck)
pickle.dump(self.autorangeY, fpck)
pickle.dump(self.plot_rangeY_max, fpck)
pickle.dump(self.plot_rangeY_min, fpck)
pickle.dump(self.autorange, fpck)
pickle.dump(self.plot_range_max, fpck)
pickle.dump(self.plot_range_min, fpck)
fpck.close()
#Here are the elements of the main window
figure = Figure()
global seqct
seqct = seqct + 1 #increment seq counter
seqs = List([sequence(name='S%d' % seqct, txtfile=default_file)])
selectedseq = Instance(sequence)
index = Int
add = Button("Add Sequence")
plot = Button("Plot")
data_digi = List()
data_digi_time = List()
data_digi_names = List()
data_analog = List()
data_analog_time = List()
data_analog_names = List()
autorangeY = Bool(True, label="Autorange in Y?")
plot_rangeY_max = Float(10)
plot_rangeY_min = Float(0)
autorange = Bool(True, label="Autorange?")
plot_range_max = Float(10000)
plot_range_min = Float()
def _figure_default(self):
self.figure = Figure()
self.figure.add_axes([0.05, 0.04, 0.9, 0.92])
#This group contains the View of the control buttons
#and the waveforms
control_group = Group(
Item('plot', show_label=False),
VGroup(
Item('seqs',
style='custom',
editor=ListEditor(use_notebook=True,
selected='selectedseq',
deletable=True,
dock_style='tab',
page_name='.name')),
Item('add', show_label=False),
show_labels=False,
show_border=True,
label='seqs',
))
#This is the view of the Main Window, including the
#control group
view = View(
HSplit(
control_group,
VGroup(
Item('figure',
editor=MPLFigureEditor(),
dock='vertical',
show_label=False,
width=700),
HGroup('autorange', 'plot_range_min', spring,
'plot_range_max'),
HGroup('autorangeY', 'plot_rangeY_min', spring,
'plot_rangeY_max'))),
title='Display Sequence',
width=1,
height=0.95,
resizable=True,
handler=MainWindowHandler(),
)
def _selectedseq_changed(self, selectedseq):
self.index = self.seqs.index(selectedseq)
#Define action when a new sequence is added
def _add_fired(self):
global seqct
seqct = seqct + 1
new = copy.deepcopy(self.seqs[self.index])
new.name = 'S%d' % seqct
self.seqs.append(new)
#Define action when the plot button is pressed
def _plot_fired(self):
self.get_data()
self.image_show()
#~ def _plot_range_max_changed(self):
#~ sleep()
#~ self.image_show()
#~ def _plot_range_min_changed(self):
#~ self.image_show()
#Here the data
def get_data(self):
self.data_digi = []
self.data_digi_time = []
self.data_digi_names = []
self.data_analog = []
self.data_analog_time = []
self.data_analog_names = []
self.data_physical = []
self.data_physical_time = []
self.data_physical_names = []
#Find out how many digital channels will be plotted
digi_counter = 1
for seq in self.seqs:
if seq.plotme == True:
for waveform in seq.waveforms:
for i, channel in enumerate(waveform.channels):
if channel in waveform.select_channels:
if waveform.name == 'Digital':
digi_counter = digi_counter + 1
#Setup all the digital & analog channels from the various
#seqs for plotting
digi_counter_2 = 1
for seq in self.seqs:
if seq.plotme == True:
#Prepare the physical quantities calculator class
physical = physics.calc(seq.seq.wfms)
print "\n-------- GETTING DATA TO PRODUCE PLOT --------"
for waveform in seq.waveforms:
for i, channel in enumerate(waveform.channels):
if channel in waveform.select_channels:
if waveform.name == 'Digital':
self.data_digi_time.append(waveform.time)
self.data_digi_names.append(seq.name + '_' +
waveform.name +
'_' + channel)
length = len(waveform.select_channels)
height = 10. / (digi_counter - 1)
self.data_digi.append( [ i*height*0.8 - digi_counter_2*height + height*0.1 \
for i in waveform.data[i] ] )
digi_counter_2 = digi_counter_2 + 1
elif waveform.name == 'Analog':
self.data_analog_names.append(seq.name + '_' +
waveform.name +
'_' + channel)
self.data_analog_time.append(waveform.time[i])
self.data_analog.append(waveform.data[i])
elif waveform.name == 'Physical':
self.data_physical_names.append(seq.name +
'_' +
waveform.name +
'_' + channel)
if channel in seq.calcwfms.keys(
) and not seq.recalculate:
print "\n...Reusing Physical: %s" % channel
dat = seq.calcwfms[channel]
else:
print "\n...Calculating Physical: %s" % channel
dat = physical.calculate(channel)
seq.calcwfms[channel] = dat
self.data_physical_time.append(dat[0])
self.data_physical.append(dat[1])
seq.recalculate = False
def image_clear(self):
""" Clears canvas
"""
self.figure.clf()
wx.CallAfter(self.figure.canvas.draw)
def image_show(self):
""" Plots an image on the canvas
"""
self.image_clear()
analog_axis = self.figure.add_axes([0.08, 0.5, 0.7, 0.4])
digi_axis_left = self.figure.add_axes([0.08, 0.05, 0.7, 0.4])
digi_axis_left.set_yticks([])
digi_axis = digi_axis_left.twinx()
digi_ticks = []
#physical_axis = analog_axis.twinx()
#Makes the digital plot
for i, name in enumerate(self.data_digi_names):
digi_axis.step(self.data_digi_time[i],
self.data_digi[i],
lw='2.0',
where='post',
label=name)
digi_ticks.append(-(i + 0.5) * 10. / len(self.data_digi_names))
digi_axis.axhline(-(i + 1) * 10. / len(self.data_digi_names),
color='grey',
lw=1.5)
#digi_axis.legend(bbox_to_anchor=(1.01, 0.5),loc=2,prop={'size':10})
digi_axis_left.set_ylabel('TTL')
#Label the digital waveforms using the ticklabels on the plot
digi_axis.set_ylim(bottom=-11, top=0)
digi_axis.set_yticks(digi_ticks)
digi_axis.set_yticklabels(self.data_digi_names)
digi_axis_left.get_xaxis().set_minor_locator(
matplotlib.ticker.AutoMinorLocator())
digi_axis_left.grid(True, which='both')
#Makes the analog plot
for i, name in enumerate(self.data_analog_names):
analog_axis.step(self.data_analog_time[i],
self.data_analog[i],
where='post',
label=name)
#Makes the physical plot
for i, name in enumerate(self.data_physical_names):
analog_axis.step(self.data_physical_time[i],
self.data_physical[i],
ls='-',
lw=1.75,
where='post',
label=name)
analog_axis.axhline(0, color='black', lw=2)
analog_axis.legend(bbox_to_anchor=(1.01, 1.01),
loc=2,
prop={'size': 10})
analog_axis.set_xlabel('Time(ms)')
analog_axis.set_ylabel('Voltage(V) / Physical(?)')
analog_axis.get_xaxis().set_minor_locator(
matplotlib.ticker.AutoMinorLocator())
analog_axis.grid(True, which='both')
#Take care of the Yaxis range of the analog plot
if not self.autorangeY:
analog_axis.set_ylim(self.plot_rangeY_min, self.plot_rangeY_max)
else:
axismin = min(analog_axis.get_ylim())
axismax = max(analog_axis.get_ylim())
analog_axis.set_ylim(axismin, axismax)
self.plot_rangeY_max = axismax
self.plot_rangeY_min = axismin
#Take care of the Xaxis(time) range for both plots
if not self.autorange:
analog_axis.set_xlim(self.plot_range_min, self.plot_range_max)
digi_axis.set_xlim(self.plot_range_min, self.plot_range_max)
else:
axismin = min(analog_axis.get_xlim() + digi_axis.get_xlim())
axismax = max(analog_axis.get_xlim() + digi_axis.get_xlim())
analog_axis.set_xlim(axismin, axismax)
digi_axis.set_xlim(axismin, axismax)
#~ analog_axis.set_ylim(bottom=0,top=11)
self.plot_range_max = axismax
self.plot_range_min = axismin
wx.CallAfter(self.figure.canvas.draw)
class MplPlot(BasePlot, HasTraitsGroup):
figure = Instance(Figure, ())
_draw_pending = Bool(False)
scale = Enum('linear', 'log', 'sqrt')('linear')
scale_values = [
'linear', 'log', 'sqrt'
] # There's probably a way to exract this from the Enum trait but I don't know how
azimuth = Range(-90, 90, -70)
elevation = Range(0, 90, 30)
quality = Range(1, MAX_QUALITY, 1)
flip_order = Bool(False)
x_lower = Float(0.0)
x_upper = Float
x_label = Str('Angle (2$\Theta$)')
y_label = Str('Dataset')
z_lower = Float(0.0)
z_upper = Float
z_label = Str
z_labels = {} # A dictionary to hold edited labels for each scaling type
group = VGroup(
HGroup(
VGroup(
Item('azimuth',
editor=DefaultOverride(mode='slider',
auto_set=False,
enter_set=True)),
Item('elevation',
editor=DefaultOverride(mode='slider',
auto_set=False,
enter_set=True)),
Item('quality'),
Item('flip_order'),
),
VGroup(
HGroup(
Item('x_label',
editor=DefaultOverride(auto_set=False,
enter_set=True)),
Item('x_lower',
editor=DefaultOverride(auto_set=False,
enter_set=True)),
Item('x_upper',
editor=DefaultOverride(auto_set=False,
enter_set=True)),
),
HGroup(Item('y_label'), ),
HGroup(
Item('z_label',
editor=DefaultOverride(auto_set=False,
enter_set=True)),
Item('z_lower',
editor=DefaultOverride(auto_set=False,
enter_set=True)),
Item('z_upper',
editor=DefaultOverride(auto_set=False,
enter_set=True)),
),
),
),
UItem('figure', editor=MPLFigureEditor()),
)
def __init__(self, callback_obj=None, *args, **kws):
super(MplPlot, self).__init__(*args, **kws)
self.figure = plt.figure()
self.figure.subplots_adjust(bottom=0.05, left=0, top=1, right=0.95)
self.ax = None
for s in self.scale_values:
self.z_labels[s] = 'Intensity - ' + get_value_scale_label(s,
mpl=True)
# This must be a weak reference, otherwise the entire app will
# hang on exit.
from weakref import proxy
if callback_obj:
self._callback_object = proxy(callback_obj)
else:
self._callback_object = lambda *args, **kw: None
def close(self):
del self._callback_object
plt.close()
def __del__(self):
plt.close()
@on_trait_change('azimuth, elevation')
def _perspective_changed(self):
if self.ax:
self.ax.view_init(azim=self.azimuth, elev=self.elevation)
self.redraw()
def _quality_changed(self):
self.redraw(replot=True)
@on_trait_change(
'x_label, y_label, x_lower, x_upper, z_lower, z_upper, flip_order')
def _trigger_redraw(self):
self.quality = 1
self.redraw(replot=True)
def _z_label_changed(self):
self.z_labels[self.scale] = self.z_label
self._trigger_redraw()
def redraw(self, replot=False, now=False):
if not now and self._draw_pending:
self._redraw_timer.Restart()
return
#import wx
canvas = self.figure.canvas
if canvas is None:
return
def _draw():
self._callback_object._on_redraw(drawing=True)
if replot:
self._plot(self.x, self.y, self.z, self.scale)
else:
canvas.draw()
self._draw_pending = False
self._callback_object._on_redraw(drawing=False)
if now:
_draw()
else:
_draw()
#self._redraw_timer = wx.CallLater(250, _draw)
#self._draw_pending = True
#self._redraw_timer.Start()
# def _prepare_data(self, datasets):
def _prepare_data(self, stack):
# stack = stack_datasets(datasets)
x = stack[:, :, 0]
z = stack[:, :, 1]
# y = array([ [i]*z.shape[1] for i in range(1, len(datasets) + 1) ])
y = array([[i] * z.shape[1] for i in range(1, stack.shape[1] + 1)])
if x[0, 0] < x[0, -1]:
self.x_lower = x[0, 0]
self.x_upper = x[0, -1]
else:
self.x_lower = x[0, -1]
self.x_upper = x[0, 0]
self.z_upper = z.max()
return x, y, z
def _plot(self, x, y, z, scale='linear'):
self.x, self.y, self.z = x, y, z
x, y, z = x.copy(), y.copy(), z.copy()
if self.flip_order:
z = z[::-1]
self.scale = scale
self.figure.clear()
self.figure.set_facecolor('white')
ax = self.ax = self.figure.add_subplot(111, projection='3d')
ax.set_xlabel(self.x_label)
ax.set_ylabel(self.y_label)
self.z_label = self.z_labels[self.scale]
ax.set_zlabel(self.z_label)
y_rows = z.shape[0]
ax.locator_params(axis='y', nbins=10, integer=True)
ax.view_init(azim=self.azimuth, elev=self.elevation)
if self.quality != MAX_QUALITY:
# map quality from 1->5 to 0.05->0.5 to approx. no. of samples
samples = int(z.shape[1] * ((self.quality - 1) * (0.5 - 0.05) /
(5 - 1) + 0.05))
z, truncate_at, bins = rebin_preserving_peaks(z, samples / 2)
# Take the x's from the original x's to maintain visual x-spacing
# We need to calculate the x's for the rebinned data
x0_row = x[0, :truncate_at]
old_xs = np.linspace(x0_row.min(), x0_row.max(), bins * 2)
new_xs = np.interp(
old_xs, np.linspace(x0_row.min(), x0_row.max(), len(x0_row)),
x0_row)
x = np.tile(new_xs, (y.shape[0], 1))
# Set values to inf to avoid rendering by matplotlib
x[(x < self.x_lower) | (x > self.x_upper)] = np.inf
z[(z < self.z_lower) | (z > self.z_upper)] = np.inf
# separate series with open lines
ys = y[:, 0]
points = []
for x_row, z_row in zip(x, z):
points.append(zip(x_row, z_row))
lines = LineCollection(points)
ax.add_collection3d(lines, zs=ys, zdir='y')
ax.set_xlim3d(self.x_lower, self.x_upper)
ax.set_ylim3d(1, y_rows)
ax.set_zlim3d(self.z_lower, self.z_upper)
self.figure.canvas.draw()
return None
def copy_to_clipboard(self):
self.figure.canvas.Copy_to_Clipboard()
def save_as(self, filename):
self.figure.canvas.print_figure(filename)
logger.logger.info('Saved plot {}'.format(filename))
def _reset_view(self):
self.azimuth = -70
self.elevation = 30
class DataPlotEditorBase(HasTraits):
figure = Instance(Figure, (),transient=True)
axs = List([],transient=True)
nplots = Int(1)
layout = Enum('vertical',['horizontal','vertical'])
view = View(Item('figure', editor=MPLFigureEditor(),
show_label=False,
height=400,
width=400,
style='custom',
springy=True),
handler=MPLInitHandler,
resizable=True,
kind='live',
)
def __init__(self):
super(DataPlotEditorBase, self).__init__()
self.figure.patch.set_facecolor('none')
#self.add_subplots(self.nplots)
def mpl_setup(self):
self.figure.patch.set_facecolor('none')
self.add_subplots(self.nplots)
def remove_subplots(self):
self.figure.clf(keep_observers=True)
self.axs = []
#self.display.figure.canvas.draw()
def remove_figure(self):
plt.close(self.figure)
def add_subplots(self, num):
self.figure.patch.set_facecolor('none')
self.axs = []
for n in range(1, num + 1):
if self.layout == 'vertical':
self.axs.append(self.figure.add_subplot(num, 1, n, facecolor='#F4EAEA',zorder=2))
elif self.layout == 'horizontal':
self.axs.append(self.figure.add_subplot(1, num,n , facecolor='#F4EAEA',zorder=2)) #FFFFCC
return self.axs
def add_common_labels(self,xlabel=None,ylabel=None):
ax = self.figure.add_subplot(111, facecolor='none', frameon=False,zorder=1)
ax.tick_params(labelcolor='none', top='off', bottom='off', left='off', right='off')
ax.grid(b=False)
if xlabel is not None:
ax.set_xlabel(xlabel)
if ylabel is not None:
ax.set_ylabel(ylabel)
self.figure.sca(self.axs[0])
def clear_plots(self):
for ax in self.axs:
ax.cla(keep_observers=True)
self.figure.canvas.draw()
def set_title(self, title=' ', size=13,y=0.98):
self.figure.suptitle(title,fontsize=size,y=y)