def do_proj(self):
cons_view = View(Item('axis'),
Item('field', editor=EnumEditor(name='field_list')),
Item('weight_field',
editor=EnumEditor(name='none_field_list')),
buttons=OKCancelButtons,
title="Projector: %s" % self.pf)
ps = ProjPlotSpec(pf=self.pf)
hand = PlotCreationHandler(main_window=mw,
pnode=self,
model=ps,
plot_type=ProjPlotTab,
format="Proj: %s")
ps.edit_traits(cons_view, handler=hand)
def _get_table_editor ( self, names ):
""" Returns a table editor to use for editing the filter.
"""
from enthought.traits.ui.api import TableEditor
names = self._object.editable_traits()
name_editor = EnumEditor( values = names )
if len( self.rules ) == 0:
self.rules = [ GenericTableFilterRule(
filter = self,
name_editor = name_editor ).set(
name = name )
for name in names ]
for rule in self.rules:
rule.enabled = False
return TableEditor( columns = menu_table_filter_rule_columns,
orientation = 'vertical',
deletable = True,
sortable = False,
configurable = False,
auto_size = False,
auto_add = True,
row_factory = GenericTableFilterRule,
row_factory_kw = {
'filter': self,
'name_editor': name_editor } )
def default_traits_view(self):
if self.model is None:
g = Group()
g.content.append(Label('No Model Selected'))
else:
#g = Group(label=self.modelname,show_border=False,orientation='horizontal',layout='flow')
g = Group(label=self.modelname,show_border=True,orientation='vertical')
hg = HGroup(Item('fittype',label='Fit Technique',
editor=EnumEditor(name='fittypes')))
g.content.append(hg)
gp = HGroup(scrollable=True)
for p in self.model.params:
gi = Group(orientation='horizontal',label=p)
self.add_trait(p,Float)
setattr(self,p,getattr(self.model,p))
self.on_trait_change(self._param_change_handler,p)
gi.content.append(Item(p,show_label=False))
ffp = 'fixfit_'+p
self.add_trait(ffp,Bool)
#default to fixed if the paramtere is a class-level fixed model
setattr(self,ffp,p in self.model.__class__.fixedpars)
self.on_trait_change(self._param_change_handler,ffp)
gi.content.append(Item(ffp,label='Fix?'))
gp.content.append(gi)
g.content.append(gp)
return View(g,buttons=['Apply','Revert','OK','Cancel'])
class NewModelSelector(HasTraits):
modelnames = List
selectedname = Str('No Model')
modelargnum = Int(2)
selectedmodelclass = Property
isvarargmodel = Property(depends_on='modelnames')
traits_view = View(Item('selectedname',label='Model Name:',editor=EnumEditor(name='modelnames')),
Item('modelargnum',label='Extra Parameters:',enabled_when='isvarargmodel'),
buttons=['OK','Cancel'])
def __init__(self,include_models=None,exclude_models=None,**traits):
super(NewModelSelector,self).__init__(**traits)
self.modelnames = list_models(include_models,exclude_models,FunctionModel1D)
self.modelnames.insert(0,'No Model')
self.modelnames.sort()
def _get_selectedmodelclass(self):
n = self.selectedname
if n == 'No Model':
return None
else:
return get_model_class(n)
def _get_isvarargmodel(self):
cls = self.selectedmodelclass
if cls is None:
return False
else:
return cls.isVarnumModel()
class DataFitterPanel(DataFitter):
"""Fitter panel object, for data fitting with gui for fit function selection
>>> import numpy
>>> x = numpy.linspace(0,100)
>>> y = numpy.linspace(0,100) + numpy.random.randn(50)
>>> data = FitData(x = x, y = y)
>>> t = DataFitter(data = data)
>>> p,c = t.fit()
"""
category = Enum(list(CATEGORIES.keys()))
function_names = Property(List(Str), depends_on='category')
function_name = Str
description = DelegatesTo('function')
view = View(Group('category',
Item(name='function_name',
editor=EnumEditor(name='function_names'),
id='function_name_edit'),
Item('description', style='custom'),
label='Fit Function'),
data_fitter_group,
resizable=False)
def _function_name_changed(self, name):
self.function.function = getattr(CATEGORIES[self.category], name)
def _get_function_names(self):
function_names = CATEGORIES[self.category].FUNCTIONS
self.function_name = function_names[0]
return function_names
class RemapDemo(HasTraits):
surf_func = Str()
func_list = List([
"np.sqrt(8- x**2 - y**2)",
"np.sin(6*np.sqrt(x**2+y**2))",
"np.sin(6*x)",
"np.sin(6*y)",
"np.sin(np.sqrt(x**2+y**2))/np.sqrt(x**2+y**2)",
])
range = Range(1.0, 100.0)
view_height = Range(1.0, 50.0, 10.0)
grid = Bool(True)
view = View(Item("surf_func",
label="曲面函数",
editor=EnumEditor(name="func_list",
auto_set=False,
evaluate=lambda x: x)),
Item("range", label="曲面范围"),
Item("view_height", label="视点高度"),
Item("grid", label="显示网格"),
title="Remap Demo控制面板")
def __init__(self, *args, **kwargs):
super(RemapDemo, self).__init__(*args, **kwargs)
self.img = cv.imread("lena.jpg")
self.size = self.img.size()
self.w, self.h = self.size.width, self.size.height
self.dstimg = cv.Mat()
self.map1 = cv.Mat(self.size, cv.CV_32FC1)
self.map2 = cv.Mat(self.size, cv.CV_32FC1)
self.gridimg = self.make_grid_img()
self.on_trait_change(self.redraw, "surf_func,range,view_height,grid")
def redraw(self):
def func(x, y):
return eval(self.surf_func, globals(), locals())
try:
self.map1[:], self.map2[:] = make_surf_map(func, self.range,
self.w, self.h,
self.view_height)
except SyntaxError:
return
if self.grid:
img = self.gridimg
else:
img = self.img
cv.remap(img, self.dstimg, self.map1, self.map2, cv.INTER_LINEAR)
cv.imshow("Remap Demo", self.dstimg)
def make_grid_img(self):
img = self.img.clone()
for i in range(0, self.w, 30):
cv.line(img, cv.Point(i, 0), cv.Point(i, self.h),
cv.CV_RGB(0, 0, 0), 1)
for i in range(0, self.h, 30):
cv.line(img, cv.Point(0, i), cv.Point(self.w, i),
cv.CV_RGB(0, 0, 0), 1)
return img
class TestEnumEditor(HasTraits):
#---------------------------------------------------------------------------
# Trait definitions:
#---------------------------------------------------------------------------
value = Trait(1,
enum,
range,
editor=EnumEditor(values=values, evaluate=int))
def create_editor ( self):
#from enthought.traits.ui.api import EnumEditor
#print dict(self.wrdict.items())
ed = EnumEditor( values = self,
cols = self.cols or 3,
evaluate = self.evaluate,
mode = self.mode or 'radio' )
return ed
class Equalizers(HasTraits):
eqs = List(Equalizer, [Equalizer()])
h = Array(dtype=np.complex, transient=True)
# Equalizer列表eqs的编辑器定义
table_editor = TableEditor(columns=[
ObjectColumn(name="freq", width=0.4, style="readonly"),
ObjectColumn(name="Q", width=0.3, style="readonly"),
ObjectColumn(name="gain", width=0.3, style="readonly"),
],
deletable=True,
sortable=True,
auto_size=False,
show_toolbar=True,
edit_on_first_click=False,
orientation='vertical',
edit_view=View(Group(
Item("freq",
editor=EnumEditor(values=EQ_FREQS)),
Item("freq", editor=scrubber(1.0)),
Item("Q", editor=scrubber(0.01)),
Item("gain", editor=scrubber(0.1)),
show_border=True,
),
resizable=True),
row_factory=Equalizer)
view = View(Item("eqs", show_label=False, editor=table_editor),
width=0.25,
height=0.5,
resizable=True)
@on_trait_change("eqs.h")
def recalculate_h(self):
'''计算多组均衡器级联时的频率响应'''
try:
tmp = np.array([
eq.h for eq in self.eqs if eq.h != None and len(eq.h) == len(W)
])
self.h = np.prod(tmp, axis=0)
except:
pass
def export(self, path):
'''将均衡器的系数输出为C语言文件'''
f = file(path, "w")
f.write("double EQ_PARS[][5] = {\n")
f.write("//b0,b1,b2,a1,a2 // frequency, Q, gain\n")
for eq in self.eqs:
eq.export_parameters(f)
f.write("};\n")
f.close()
class EnumExample(HasTraits):
priority = Enum('Medium', 'Highest', 'High', 'Medium', 'Low', 'Lowest')
view = View(
Item(name='priority',
editor=EnumEditor(
values={
'Highest': '1:Highest',
'High': '2:High',
'Medium': '3:Medium',
'Low': '4:Low',
'Lowest': '5:Lowest',
})))
def do_slice(self):
cons_view = View(Item('axis'),
Item('center'),
Item('field', editor=EnumEditor(name='field_list')),
buttons=OKCancelButtons,
title="Slicer: %s" % self.pf)
ps = SlicePlotSpec(pf=self.pf)
hand = PlotCreationHandler(main_window=mw,
pnode=self,
model=ps,
plot_type=SlicePlotTab,
format="Slice: %s")
ps.edit_traits(cons_view, handler=hand)
class TableTest(HasStrictTraits):
#---------------------------------------------------------------------------
# Trait definitions:
#---------------------------------------------------------------------------
#people = Instance( Person )
people = List(Person)
#---------------------------------------------------------------------------
# Traits view definitions:
#---------------------------------------------------------------------------
_valid_states = List(["AL", "AR", "AZ", "AK"])
_state_editor = EnumEditor(name="_valid_states",
evaluate=evaluate_value,
object='table_editor_object')
table_editor = TableEditor(columns=[
ObjectColumn(name='name'),
ObjectColumn(name='age'),
ObjectColumn(name='phone'),
ObjectColumn(name='state', editor=_state_editor),
],
editable=True,
deletable=True,
sortable=True,
sort_model=True,
show_lines=True,
orientation='vertical',
show_column_labels=True,
edit_view=View(
['name', 'age', 'phone', 'state', '|[]'],
resizable=True),
filter=None,
filters=filters,
row_factory=Person)
traits_view = View(
[Item('people', id='people', editor=table_editor), '|[]<>'],
title='Table Editor Test',
id='enthought.traits.ui.tests.table_editor_test',
dock='horizontal',
width=.4,
height=.3,
resizable=True,
buttons=NoButtons,
kind='live')
def default_traits_view(self):
view = View(
VGroup(
HGroup(
Item("current_map", label=u"颜色映射", editor=EnumEditor(name="object.color_maps")),
Item("reverse_map", label=u"反转颜色"),
Item("position", label=u"位置", style="readonly"),
),
Item("plot", show_label=False, editor=ComponentEditor()),
),
resizable = True,
width = 550, height = 300,
title = u"Mandelbrot观察器"
)
return view
def createView(self):
"""Set up a view for the traits."""
indexItems = [
Item('index0',
editor=EnumEditor(name='index0_enum'),
visible_when=self.runtimeElementCondition +
self.numDimensionsString + '>=1'),
Item('index1',
editor=EnumEditor(name='index1_enum'),
visible_when=self.runtimeElementCondition +
self.numDimensionsString + '>=2'),
Item('index2',
editor=EnumEditor(name='index2_enum'),
visible_when=self.runtimeElementCondition +
self.numDimensionsString + '>=3'),
]
items = [Item('item')]
#items = [Item('item')] + indexItems[:self.getMaxDimensions()]
self.view = View(Group(
*items, **dict(orientation='horizontal', show_labels=False)),
buttons=NoButtons,
handler=SelectorHandler)
def _get_table_editor ( self, names ):
""" Returns a table editor to use for editing the filter.
"""
from enthought.traits.ui.api import TableEditor
return TableEditor( columns = generic_table_filter_rule_columns,
orientation = 'vertical',
deletable = True,
sortable = False,
configurable = False,
auto_size = False,
auto_add = True,
row_factory = GenericTableFilterRule,
row_factory_kw = {
'filter': self,
'name_editor': EnumEditor( values = names ) } )
class Address(HasTraits):
""" Demo class for demonstrating dynamic redefinition of valid trait values.
"""
street_address = Str
st = Enum(cities.keys()[0], cities.keys())
city = Str
view = View(Item(name='street_address'),
Item(name='st', label='State'),
Item(name='city',
editor=EnumEditor(name='handler.cities'),
id='cityedit'),
title='Address Information',
buttons=['OK'],
resizable=True,
handler=AddressHandler)
class Constraint(HasTraits):
name = String
runcase = Any
constraint_variables_available = Property(List(String), depends_on='runcase.constraint_variables')
@cached_property
def _get_constraint_variables_available(self):
return self.runcase.constraint_variables.keys()
#constraint = Instance(ConstraintVariable)
constraint_name = String
value = Float
pattern = String
cmd = String
editor = TableEditor(
auto_size=False,
columns=[ ObjectColumn(name='name', editable=False, label='Parameter'),
ObjectColumn(name='constraint_name', label='Constraint', editor=EnumEditor(name='constraint_variables_available')),
ObjectColumn(name='value', label='Value', editor=TextEditor(evaluate=float, enter_set=True, auto_set=False))
])
class TrimCaseConfig(RunOptionsConfig):
trimcase = Instance(TrimCase, TrimCase())
#runcase = DelegatesTo('trimcase')
varying_param = String('velocity')
varying_expr = Expression('100')
param_name_list = Property(List(String), depends_on='trimcase.parameters')
@cached_property
def _get_param_name_list(self):
return sorted(self.trimcase.parameters.keys(), key=lambda x: x.lower())
@on_trait_change('trimcase.parameter_view.value,trimcase.type')
def on_trimcase_changed(self, object, name, old, new):
print 'trimcase_changed'
if name == 'value':
self.trimcase.runcase.update_trim_case(self.trimcase, object)
self.trimcase.update_parameters_from_avl()
elif name == 'type':
self.trimcase.update_parameters_from_avl()
self.param_name_list[0]
# send changed value of trimcase to avl, parameter is Parameter instance
def update_trim_case(self, trimcase, parameter):
print 'update_trim_case'
self.get_parameters_info_from_avl(self.avl)
self.avl.sendline('oper')
self.avl.sendline(trimcase.type_)
self.avl.expect(AVL.patterns['/oper/m'])
#print self.parameters.keys()
#for p, v in trimcase.parameters.iteritems():
self.avl.sendline('%s %f' % (parameter.cmd, parameter.value))
AVL.goto_state(self.avl)
custom_group = Group(
Item('object.trimcase.type'),
Group(Item('object.trimcase.parameter_view',
editor=Parameter.editor,
show_label=False,
height=0.4),
label='Parameters',
scrollable=True),
#Item('trimcase', style='custom', show_label=False),
Group(Item('varying_param', editor=EnumEditor(name='param_name_list')),
Item('varying_expr', label='Expression')))
class AnimatedGIFDemo(HasTraits):
# The animated GIF file to display:
gif_file = File(files[0])
# Is the animation playing or not?
playing = Bool(True)
# The traits view:
view = View(VGroup(
HGroup(
Item('gif_file',
editor=AnimatedGIFEditor(playing='playing'),
show_label=False),
Item('playing'),
), '_',
Item('gif_file', label='GIF File', editor=EnumEditor(values=files))),
title='Animated GIF Demo',
buttons=['OK'])
class Sp4ArrayFileSource(ArraySource):
file_name = Str
component = Enum('Amp', 'Phase', 'Real', 'Imag')
set_component = Str
f = Instance(sp4.Sp4File)
view = View(Group(Item(name='transpose_input_array'),
Item(name='file_name', editor=TextEditor(auto_set=False,\
enter_set=True)),
Item(name='component', editor=EnumEditor(values=component)),
Item(name='scalar_name'),
Item(name='vector_name'),
Item(name='spacing'),
Item(name='origin'),
Item(name='update_image_data', show_label=False),
show_labels=True)
)
def __init__(self, **kw_args):
super(Sp4ArrayFileSource, self).__init__(**kw_args)
fn = kw_args.pop('file_name', None)
if fn is not None:
self.file_name = fn
self._open(self.file_name)
self.component = "Amp"
self._component_changed('Amp')
def _open(self, fn):
self.f = sp4.Sp4File(self.file_name)
def _component_changed(self, info):
if info == "Amp":
self.scalar_data = numpy.abs(self.f.GetArray())
if info == "Phase":
self.scalar_data = numpy.angle(self.f.GetArray())
self.update()
def _file_name_changed(self, info):
print self.file_name
class ConstraintConfig(HasTraits):
constraint = Instance(Constraint)
expr = Expression()
def __init__(self, *args, **kwargs):
super(ConstraintConfig, self).__init__(*args, **kwargs)
self.expr = str(self.constraint.value)
constraint_variables_available = DelegatesTo('constraint')
editor = TableEditor(
auto_size=False,
columns=[
ObjectColumn(name='constraint.name',
editable=False,
label='Parameter'),
ObjectColumn(
name='constraint.constraint_name',
label='Constraint',
editor=EnumEditor(name='constraint_variables_available')),
ObjectColumn(name='expr', label='Expression')
])
class HierarchyImporter(HasTraits):
pf = Any
min_grid_level = Int(0)
max_level = Int(1)
number_of_levels = Range(0, 13)
max_import_levels = Property(depends_on='min_grid_level')
field = Str("Density")
field_list = List
center_on_max = Bool(True)
center = CArray(shape=(3, ), dtype='float64')
cache = Bool(True)
smoothed = Bool(True)
show_grids = Bool(True)
def _field_list_default(self):
fl = self.pf.h.field_list
df = self.pf.h.derived_field_list
fl.sort()
df.sort()
return fl + df
default_view = View(
Item('min_grid_level',
editor=RangeEditor(low=0, high_name='max_level')),
Item('number_of_levels',
editor=RangeEditor(low=1, high_name='max_import_levels')),
Item('field', editor=EnumEditor(name='field_list')),
Item('center_on_max'),
Item('center', enabled_when='not object.center_on_max'),
Item('smoothed'),
Item('cache', label='Pre-load data'),
Item('show_grids'),
buttons=OKCancelButtons)
def _center_default(self):
return [0.5, 0.5, 0.5]
@cached_property
def _get_max_import_levels(self):
return min(13, self.pf.h.max_level - self.min_grid_level + 1)
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()
# -*- coding: utf-8 -*-
class TVTKClassChooser(HasTraits):
# The selected object, is None if no valid class_name was made.
object = Property
# The TVTK class name to choose.
class_name = Str('', desc='class name of TVTK class (case sensitive)')
# The string to search for in the class docs -- the search supports
# 'and' and 'or' keywords.
search = Str('', desc='string to search in TVTK class documentation '\
'supports the "and" and "or" keywords. '\
'press <Enter> to start search. '\
'This is case insensitive.')
clear_search = Button
# The class documentation.
doc = Str(_search_help_doc)
# Completions for the choice of class.
completions = List(Str)
# List of available class names as strings.
available = List(TVTK_CLASSES)
########################################
# Private traits.
finder = Instance(DocSearch)
n_completion = Int(25)
########################################
# View related traits.
view = View(Group(Item(name='class_name',
editor=EnumEditor(name='available')),
Item(name='class_name',
has_focus=True
),
Item(name='search',
editor=TextEditor(enter_set=True,
auto_set=False)
),
Item(name='clear_search',
show_label=False),
Item('_'),
Item(name='completions',
editor=ListEditor(columns=3),
style='readonly'
),
Item(name='doc',
resizable=True,
label='Documentation',
style='custom')
),
id='tvtk_doc',
resizable=True,
width=800,
height=600,
title='TVTK class chooser',
buttons = ["OK", "Cancel"]
)
######################################################################
# `object` interface.
######################################################################
def __init__(self, **traits):
super(TVTKClassChooser, self).__init__(**traits)
self._orig_available = list(self.available)
######################################################################
# Non-public interface.
######################################################################
def _get_object(self):
o = None
if len(self.class_name) > 0:
try:
o = getattr(tvtk, self.class_name)()
except (AttributeError, TypeError):
pass
return o
def _class_name_changed(self, value):
av = self.available
comp = [x for x in av if x.startswith(value)]
self.completions = comp[:self.n_completion]
if len(comp) == 1 and value != comp[0]:
self.class_name = comp[0]
o = self.object
if o is not None:
self.doc = get_tvtk_class_doc(o)
else:
self.doc = _search_help_doc
def _finder_default(self):
return DocSearch()
def _clear_search_fired(self):
self.search = ''
def _search_changed(self, value):
if len(value) < 3:
self.available = self._orig_available
return
f = self.finder
result = f.search(value)
if len(result) == 0:
self.available = self._orig_available
elif len(result) == 1:
self.class_name = result[0]
else:
self.available = result
self.completions = result[:self.n_completion]
class IFSDesigner(HasTraits):
plot = Instance(Plot)
clear = Bool(False)
draw = Bool(False)
timer = Instance(Timer)
ifs_names = List()
ifs_points = List()
current_name = Str()
save_button = Button(u"保存当前IFS")
unsave_button = Button(u"删除当前IFS")
view = View(
HGroup(
Item("current_name", editor = EnumEditor(name="object.ifs_names")),
Item("save_button"),
Item("unsave_button"),
show_labels=False
),
Item("plot", editor=ComponentEditor(),show_label=False),
resizable=True,
width = 500,
height = 500,
title = u"IFS图形设计器"
)
def __init__(self):
self.data = ArrayPlotData()
self.set_empty_data()
self.plot = Plot(self.data, padding=10)
scatter = self.plot.plot(("x","y", "c"), type="cmap_scatter",
marker_size=1, color_mapper=make_color_map(), line_width=0)[0]
self.plot.x_grid.visible = False
self.plot.y_grid.visible = False
self.plot.x_axis.visible = False
self.plot.y_axis.visible = False
self.tool = TrianglesTool(self.plot)
self.plot.overlays.append(self.tool)
try:
with file("ifs_chaco.data","rb") as f:
tmp = pickle.load(f)
self.ifs_names = [x[0] for x in tmp]
self.ifs_points = [np.array(x[1]) for x in tmp]
if len(self.ifs_names) > 0:
self.current_name = self.ifs_names[-1]
except:
pass
self.tool.on_trait_change(self.triangle_changed, 'changed')
self.timer = Timer(10, self.ifs_calculate)
def set_empty_data(self):
self.data["x"] = np.array([])
self.data["y"] = np.array([])
self.data["c"] = np.array([])
def triangle_changed(self):
count = len(self.tool.points)
if count % 3 == 0:
self.set_empty_data()
if count < 9:
self.draw = False
if count >= 9 and count % 3 == 0:
self.clear = True
def ifs_calculate(self):
if self.clear == True:
self.clear = False
self.initpos = [0, 0]
# 不绘制迭代的初始100个点
x, y, c = ifs( self.tool.get_areas(), self.tool.get_eqs(), self.initpos, 100)
self.initpos = [x[-1], y[-1]]
self.draw = True
if self.draw and len(self.data["x"]) < ITER_COUNT * ITER_TIMES:
x, y, c = ifs( self.tool.get_areas(), self.tool.get_eqs(), self.initpos, ITER_COUNT)
ox, oy, oc = self.data["x"], self.data["y"], self.data["c"]
if np.max(np.abs(x)) < 1000000 and np.max(np.abs(y)) < 1000000:
self.initpos = [x[-1], y[-1]]
x, y, z = np.hstack((ox, x)), np.hstack((oy, y)), np.hstack((oc, c))
self.data["x"], self.data["y"], self.data["c"] = x, y, z
# 调整绘图范围，保持X-Y轴的比例为1:1
xmin, xmax = np.min(x), np.max(x)
ymin, ymax = np.min(y), np.max(y)
xptp, yptp = xmax - xmin, ymax-ymin
xcenter, ycenter =(xmax + xmin) / 2.0 , (ymax + ymin) / 2.0
w, h = float(self.plot.width), float(self.plot.height)
scale = max(xptp/w , yptp/h)
self.plot.index_range.low = xcenter - 0.5*scale*w
self.plot.index_range.high = xcenter + 0.5*scale*w
self.plot.value_range.low = ycenter - 0.5*scale*h
self.plot.value_range.high = ycenter + 0.5*scale
def _current_name_changed(self):
index = self.ifs_names.index(self.current_name)
self.tool.points = list(self.ifs_points[index])
self.tool.changed = True
self.clear = True
def _save_button_fired(self):
"""
保存按钮处理
"""
ask = AskName(name = self.current_name)
if ask.configure_traits():
if ask.name not in self.ifs_names:
self.ifs_names.append( ask.name )
self.ifs_points.append( self.tool.points[:] )
else:
index = self.ifs_names.index(ask.name)
self.ifs_names[index] = ask.name
self.ifs_points[index] = self.tool.points[:]
self.save_data()
self.current_name = ask.name
def _unsave_button_fired(self):
index = self.ifs_names.index(self.current_name)
del self.ifs_names[index]
del self.ifs_points[index]
if index >= self.ifs_names[index]: index -= 1
self.current_name = self.ifs_names[index]
self.save_data()
def save_data(self):
with file("IFS_chaco.data", "wb") as f:
pickle.dump(zip(self.ifs_names, self.ifs_points), f)
def make_editor(trait=None):
return EnumEditor(name=trait.values_name)
class ParticleArrayHelper(HasTraits):
"""
This class manages a particle array and sets up the necessary
plotting related information for it.
"""
# The particle array we manage.
particle_array = Instance(ParticleArray)
# The name of the particle array.
name = Str
# The active scalar to view.
scalar = Str('rho', desc='name of the active scalar to view')
# The mlab plot for this particle array.
plot = Instance(PipelineBase)
# List of available scalars in the particle array.
scalar_list = List(Str)
scene = Instance(MlabSceneModel)
# Sync'd trait with the scalar lut manager.
show_legend = Bool(False, desc='if the scalar legend is to be displayed')
# Sync'd trait with the dataset to turn on/off visibility.
visible = Bool(True, desc='if the particle array is to be displayed')
########################################
# View related code.
view = View(
Item(name='name', show_label=False, editor=TitleEditor()),
Group(
Item(name='visible'),
Item(name='scalar', editor=EnumEditor(name='scalar_list')),
Item(name='show_legend'),
),
)
######################################################################
# Private interface.
######################################################################
def _particle_array_changed(self, pa):
self.name = pa.name
# Setup the scalars.
self.scalar_list = sorted(pa.properties.keys())
# Update the plot.
x, y, z, u, v, w = pa.x, pa.y, pa.z, pa.u, pa.v, pa.w
s = getattr(pa, self.scalar)
p = self.plot
mlab = self.scene.mlab
if p is None:
src = mlab.pipeline.vector_scatter(x, y, z, u, v, w, scalars=s)
p = mlab.pipeline.glyph(src, mode='point', scale_mode='none')
p.actor.property.point_size = 3
p.mlab_source.dataset.point_data.scalars.name = self.scalar
scm = p.module_manager.scalar_lut_manager
scm.set(show_legend=self.show_legend,
use_default_name=False,
data_name=self.scalar)
self.sync_trait('visible', p.mlab_source.m_data, mutual=True)
self.sync_trait('show_legend', scm, mutual=True)
#set_arrays(p.mlab_source.m_data, pa)
self.plot = p
else:
if len(x) == len(p.mlab_source.x):
p.mlab_source.set(x=x, y=y, z=z, scalars=s, u=u, v=v, w=w)
else:
p.mlab_source.reset(x=x, y=y, z=z, scalars=s, u=u, v=v, w=w)
def _scalar_changed(self, value):
p = self.plot
if p is not None:
p.mlab_source.scalars = getattr(self.particle_array, value)
p.module_manager.scalar_lut_manager.data_name = value
class DataSourceWizardView(DataSourceWizard):
#----------------------------------------------------------------------
# Private traits
#----------------------------------------------------------------------
_top_label = Str('Describe your data')
_info_text = Str('Array size do not match')
_array_label = Str('Available arrays')
_data_type_text = Str("What does your data represents?" )
_lines_text = Str("Connect the points with lines" )
_scalar_data_text = Str("Array giving the value of the scalars")
_optional_scalar_data_text = Str("Associate scalars with the data points")
_connectivity_text = Str("Array giving the triangles")
_vector_data_text = Str("Associate vector components")
_position_text = Property(depends_on="position_type_")
_position_text_dict = {'explicit':
'Coordinnates of the data points:',
'orthogonal grid':
'Position of the layers along each axis:',
}
def _get__position_text(self):
return self._position_text_dict.get(self.position_type_, "")
_shown_help_text = Str
_data_sources_wrappers = Property(depends_on='data_sources')
def _get__data_sources_wrappers(self):
return [
ArrayColumnWrapper(name=name,
shape=repr(self.data_sources[name].shape))
for name in self._data_sources_names
]
# A traits pointing to the object, to play well with traitsUI
_self = Instance(DataSourceWizard)
_suitable_traits_view = Property(depends_on="data_type_")
def _get__suitable_traits_view(self):
return "_%s_data_view" % self.data_type_
ui = Any(False)
_preview_button = Button(label='Preview structure')
def __preview_button_fired(self):
if self.ui:
self.build_data_source()
self.preview()
_ok_button = Button(label='OK')
def __ok_button_fired(self):
if self.ui:
self.ui.dispose()
self.build_data_source()
_cancel_button = Button(label='Cancel')
def __cancel_button_fired(self):
if self.ui:
self.ui.dispose()
_is_ok = Bool
_is_not_ok = Bool
def _anytrait_changed(self):
""" Validates if the OK button is enabled.
"""
if self.ui:
self._is_ok = self.check_arrays()
self._is_not_ok = not self._is_ok
_preview_window = Instance(PreviewWindow, ())
_info_image = Instance(ImageResource,
ImageLibrary.image_resource('@std:alert16',))
#----------------------------------------------------------------------
# TraitsUI views
#----------------------------------------------------------------------
_coordinates_group = \
HGroup(
Item('position_x', label='x',
editor=EnumEditor(name='_data_sources_names',
invalid='_is_not_ok')),
Item('position_y', label='y',
editor=EnumEditor(name='_data_sources_names',
invalid='_is_not_ok')),
Item('position_z', label='z',
editor=EnumEditor(name='_data_sources_names',
invalid='_is_not_ok')),
)
_position_group = \
Group(
Item('position_type'),
Group(
Item('_position_text', style='readonly',
resizable=False,
show_label=False),
_coordinates_group,
visible_when='not position_type_=="image data"',
),
Group(
Item('grid_shape_source_',
label='Grid shape',
editor=EnumEditor(
name='_grid_shape_source_labels',
invalid='_is_not_ok')),
HGroup(
spring,
Item('grid_shape', style='custom',
editor=ArrayEditor(width=-60),
show_label=False),
enabled_when='grid_shape_source==""',
),
visible_when='position_type_=="image data"',
),
label='Position of the data points',
show_border=True,
show_labels=False,
),
_connectivity_group = \
Group(
HGroup(
Item('_connectivity_text', style='readonly',
resizable=False),
spring,
Item('connectivity_triangles',
editor=EnumEditor(name='_data_sources_names'),
show_label=False,
),
show_labels=False,
),
label='Connectivity information',
show_border=True,
show_labels=False,
enabled_when='position_type_=="explicit"',
),
_scalar_data_group = \
Group(
Item('_scalar_data_text', style='readonly',
resizable=False,
show_label=False),
HGroup(
spring,
Item('scalar_data',
editor=EnumEditor(name='_data_sources_names',
invalid='_is_not_ok')),
show_labels=False,
),
label='Scalar value',
show_border=True,
show_labels=False,
)
_optional_scalar_data_group = \
Group(
HGroup(
'has_scalar_data',
Item('_optional_scalar_data_text',
resizable=False,
style='readonly'),
show_labels=False,
),
Item('_scalar_data_text', style='readonly',
resizable=False,
enabled_when='has_scalar_data',
show_label=False),
HGroup(
spring,
Item('scalar_data',
editor=EnumEditor(name='_data_sources_names',
invalid='_is_not_ok'),
enabled_when='has_scalar_data'),
show_labels=False,
),
label='Scalar data',
show_border=True,
show_labels=False,
),
_vector_data_group = \
VGroup(
HGroup(
Item('vector_u', label='u',
editor=EnumEditor(name='_data_sources_names',
invalid='_is_not_ok')),
Item('vector_v', label='v',
editor=EnumEditor(name='_data_sources_names',
invalid='_is_not_ok')),
Item('vector_w', label='w',
editor=EnumEditor(name='_data_sources_names',
invalid='_is_not_ok')),
),
label='Vector data',
show_border=True,
),
_optional_vector_data_group = \
VGroup(
HGroup(
Item('has_vector_data', show_label=False),
Item('_vector_data_text', style='readonly',
resizable=False,
show_label=False),
),
HGroup(
Item('vector_u', label='u',
editor=EnumEditor(name='_data_sources_names',
invalid='_is_not_ok')),
Item('vector_v', label='v',
editor=EnumEditor(name='_data_sources_names',
invalid='_is_not_ok')),
Item('vector_w', label='w',
editor=EnumEditor(name='_data_sources_names',
invalid='_is_not_ok')),
enabled_when='has_vector_data',
),
label='Vector data',
show_border=True,
),
_array_view = \
View(
Item('_array_label', editor=TitleEditor(),
show_label=False),
Group(
Item('_data_sources_wrappers',
editor=TabularEditor(
adapter = ArrayColumnAdapter(),
),
),
show_border=True,
show_labels=False
))
_questions_view = View(
Item('_top_label', editor=TitleEditor(),
show_label=False),
HGroup(
Item('_data_type_text', style='readonly',
resizable=False),
spring,
'data_type',
spring,
show_border=True,
show_labels=False,
),
HGroup(
Item('_self', style='custom',
editor=InstanceEditor(
view_name='_suitable_traits_view'),
),
Group(
# FIXME: Giving up on context sensitive help
# because of lack of time.
#Group(
# Item('_shown_help_text', editor=HTMLEditor(),
# width=300,
# label='Help',
# ),
# show_labels=False,
# label='Help',
#),
#Group(
Item('_preview_button',
enabled_when='_is_ok'),
Item('_preview_window', style='custom',
label='Preview structure'),
show_labels=False,
#label='Preview structure',
#),
#layout='tabbed',
#dock='tab',
),
show_labels=False,
show_border=True,
),
)
_point_data_view = \
View(Group(
Group(_coordinates_group,
label='Position of the data points',
show_border=True,
),
HGroup(
'lines',
Item('_lines_text', style='readonly',
resizable=False),
label='Lines',
show_labels=False,
show_border=True,
),
_optional_scalar_data_group,
_optional_vector_data_group,
# XXX: hack to have more vertical space
Label('\n'),
Label('\n'),
Label('\n'),
))
_surface_data_view = \
View(Group(
_position_group,
_connectivity_group,
_optional_scalar_data_group,
_optional_vector_data_group,
))
_vector_data_view = \
View(Group(
_vector_data_group,
_position_group,
_optional_scalar_data_group,
))
_volumetric_data_view = \
View(Group(
_scalar_data_group,
_position_group,
_optional_vector_data_group,
))
_wizard_view = View(
Group(
HGroup(
Item('_self', style='custom', show_label=False,
editor=InstanceEditor(view='_array_view'),
width=0.17,
),
'_',
Item('_self', style='custom', show_label=False,
editor=InstanceEditor(view='_questions_view'),
),
),
HGroup(
Item('_info_image', editor=ImageEditor(),
visible_when="_is_not_ok"),
Item('_info_text', style='readonly', resizable=False,
visible_when="_is_not_ok"),
spring,
'_cancel_button',
Item('_ok_button', enabled_when='_is_ok'),
show_labels=False,
),
),
title='Import arrays',
resizable=True,
)
#----------------------------------------------------------------------
# Public interface
#----------------------------------------------------------------------
def __init__(self, **traits):
DataSourceFactory.__init__(self, **traits)
self._self = self
def view_wizard(self):
""" Pops up the view of the wizard, and keeps the reference it to
be able to close it.
"""
# FIXME: Workaround for traits bug in enabled_when
self.position_type_
self.data_type_
self._suitable_traits_view
self.grid_shape_source
self._is_ok
self.ui = self.edit_traits(view='_wizard_view')
def preview(self):
""" Display a preview of the data structure in the preview
window.
"""
self._preview_window.clear()
self._preview_window.add_source(self.data_source)
data = lambda name: self.data_sources[name]
g = Glyph()
g.glyph.glyph_source.glyph_source = \
g.glyph.glyph_source.glyph_list[0]
g.glyph.scale_mode = 'data_scaling_off'
if not (self.has_vector_data or self.data_type_ == 'vector'):
g.glyph.glyph_source.glyph_source.glyph_type = 'cross'
g.actor.property.representation = 'points'
g.actor.property.point_size = 3.
self._preview_window.add_module(g)
if not self.data_type_ in ('point', 'vector') or self.lines:
s = Surface()
s.actor.property.opacity = 0.3
self._preview_window.add_module(s)
if not self.data_type_ == 'point':
self._preview_window.add_filter(ExtractEdges())
s = Surface()
s.actor.property.opacity = 0.2
self._preview_window.add_module(s)
class FitGui(HasTraits):
"""
This class represents the fitgui application state.
"""
plot = Instance(Plot)
colorbar = Instance(ColorBar)
plotcontainer = Instance(HPlotContainer)
tmodel = Instance(TraitedModel,allow_none=False)
nomodel = Property
newmodel = Button('New Model...')
fitmodel = Button('Fit Model')
showerror = Button('Fit Error')
updatemodelplot = Button('Update Model Plot')
autoupdate = Bool(True)
data = Array(dtype=float,shape=(2,None))
weights = Array
weighttype = Enum(('custom','equal','lin bins','log bins'))
weightsvary = Property(Bool)
weights0rem = Bool(True)
modelselector = NewModelSelector
ytype = Enum(('data and model','residuals'))
zoomtool = Instance(ZoomTool)
pantool = Instance(PanTool)
scattertool = Enum(None,'clicktoggle','clicksingle','clickimmediate','lassoadd','lassoremove','lassoinvert')
selectedi = Property #indecies of the selected objects
weightchangesel = Button('Set Selection To')
weightchangeto = Float(1.0)
delsel = Button('Delete Selected')
unselectonaction = Bool(True)
clearsel = Button('Clear Selections')
lastselaction = Str('None')
datasymb = Button('Data Symbol...')
modline = Button('Model Line...')
savews = Button('Save Weights')
loadws = Button('Load Weights')
_savedws = Array
plotname = Property
updatestats = Event
chi2 = Property(Float,depends_on='updatestats')
chi2r = Property(Float,depends_on='updatestats')
nmod = Int(1024)
#modelpanel = View(Label('empty'),kind='subpanel',title='model editor')
modelpanel = View
panel_view = View(VGroup(
Item('plot', editor=ComponentEditor(),show_label=False),
HGroup(Item('tmodel.modelname',show_label=False,style='readonly'),
Item('nmod',label='Number of model points'),
Item('updatemodelplot',show_label=False,enabled_when='not autoupdate'),
Item('autoupdate',label='Auto?'))
),
title='Model Data Fitter'
)
selection_view = View(Group(
Item('scattertool',label='Selection Mode',
editor=EnumEditor(values={None:'1:No Selection',
'clicktoggle':'3:Toggle Select',
'clicksingle':'2:Single Select',
'clickimmediate':'7:Immediate',
'lassoadd':'4:Add with Lasso',
'lassoremove':'5:Remove with Lasso',
'lassoinvert':'6:Invert with Lasso'})),
Item('unselectonaction',label='Clear Selection on Action?'),
Item('clearsel',show_label=False),
Item('weightchangesel',show_label=False),
Item('weightchangeto',label='Weight'),
Item('delsel',show_label=False)
),title='Selection Options')
traits_view = View(VGroup(
HGroup(Item('object.plot.index_scale',label='x-scaling',
enabled_when='object.plot.index_mapper.range.low>0 or object.plot.index_scale=="log"'),
spring,
Item('ytype',label='y-data'),
Item('object.plot.value_scale',label='y-scaling',
enabled_when='object.plot.value_mapper.range.low>0 or object.plot.value_scale=="log"')
),
Item('plotcontainer', editor=ComponentEditor(),show_label=False),
HGroup(VGroup(HGroup(Item('weighttype',label='Weights:'),
Item('savews',show_label=False),
Item('loadws',enabled_when='_savedws',show_label=False)),
Item('weights0rem',label='Remove 0-weight points for fit?'),
HGroup(Item('newmodel',show_label=False),
Item('fitmodel',show_label=False),
Item('showerror',show_label=False,enabled_when='tmodel.lastfitfailure'),
VGroup(Item('chi2',label='Chi2:',style='readonly',format_str='%6.6g',visible_when='tmodel.model is not None'),
Item('chi2r',label='reduced:',style='readonly',format_str='%6.6g',visible_when='tmodel.model is not None'))
)#Item('selbutton',show_label=False))
,springy=False),spring,
VGroup(HGroup(Item('autoupdate',label='Auto?'),
Item('updatemodelplot',show_label=False,enabled_when='not autoupdate')),
Item('nmod',label='Nmodel'),
HGroup(Item('datasymb',show_label=False),Item('modline',show_label=False)),springy=False),springy=True),
'_',
HGroup(Item('scattertool',label='Selection Mode',
editor=EnumEditor(values={None:'1:No Selection',
'clicktoggle':'3:Toggle Select',
'clicksingle':'2:Single Select',
'clickimmediate':'7:Immediate',
'lassoadd':'4:Add with Lasso',
'lassoremove':'5:Remove with Lasso',
'lassoinvert':'6:Invert with Lasso'})),
Item('unselectonaction',label='Clear Selection on Action?'),
Item('clearsel',show_label=False),
Item('weightchangesel',show_label=False),
Item('weightchangeto',label='Weight'),
Item('delsel',show_label=False),
),#layout='flow'),
Item('tmodel',show_label=False,style='custom',editor=InstanceEditor(kind='subpanel'))
),
handler=FGHandler(),
resizable=True,
title='Data Fitting',
buttons=['OK','Cancel'],
width=700,
height=900
)
def __init__(self,xdata=None,ydata=None,weights=None,model=None,
include_models=None,exclude_models=None,fittype=None,**traits):
"""
:param xdata: the first dimension of the data to be fit
:type xdata: array-like
:param ydata: the second dimension of the data to be fit
:type ydata: array-like
:param weights:
The weights to apply to the data. Statistically interpreted as inverse
errors (*not* inverse variance). May be any of the following forms:
* None for equal weights
* an array of points that must match `ydata`
* a 2-sequence of arrays (xierr,yierr) such that xierr matches the
`xdata` and yierr matches `ydata`
* a function called as f(params) that returns an array of weights
that match one of the above two conditions
:param model: the initial model to use to fit this data
:type model:
None, string, or :class:`pymodelfit.core.FunctionModel1D`
instance.
:param include_models:
With `exclude_models`, specifies which models should be available in
the "new model" dialog (see `models.list_models` for syntax).
:param exclude_models:
With `include_models`, specifies which models should be available in
the "new model" dialog (see `models.list_models` for syntax).
:param fittype:
The fitting technique for the initial fit (see
:class:`pymodelfit.core.FunctionModel`).
:type fittype: string
kwargs are passed in as any additional traits to apply to the
application.
"""
self.modelpanel = View(Label('empty'),kind='subpanel',title='model editor')
self.tmodel = TraitedModel(model)
if model is not None and fittype is not None:
self.tmodel.model.fittype = fittype
if xdata is None or ydata is None:
if not hasattr(self.tmodel.model,'data') or self.tmodel.model.data is None:
raise ValueError('data not provided and no data in model')
if xdata is None:
xdata = self.tmodel.model.data[0]
if ydata is None:
ydata = self.tmodel.model.data[1]
if weights is None:
weights = self.tmodel.model.data[2]
self.on_trait_change(self._paramsChanged,'tmodel.paramchange')
self.modelselector = NewModelSelector(include_models,exclude_models)
self.data = [xdata,ydata]
if weights is None:
self.weights = np.ones_like(xdata)
self.weighttype = 'equal'
else:
self.weights = np.array(weights,copy=True)
self.savews = True
weights1d = self.weights
while len(weights1d.shape)>1:
weights1d = np.sum(weights1d**2,axis=0)
pd = ArrayPlotData(xdata=self.data[0],ydata=self.data[1],weights=weights1d)
self.plot = plot = Plot(pd,resizable='hv')
self.scatter = plot.plot(('xdata','ydata','weights'),name='data',
color_mapper=_cmapblack if self.weights0rem else _cmap,
type='cmap_scatter', marker='circle')[0]
self.errorplots = None
if not isinstance(model,FunctionModel1D):
self.fitmodel = True
self.updatemodelplot = False #force plot update - generates xmod and ymod
plot.plot(('xmod','ymod'),name='model',type='line',line_style='dash',color='black',line_width=2)
del plot.x_mapper.range.sources[-1] #remove the line plot from the x_mapper source so only the data is tied to the scaling
self.on_trait_change(self._rangeChanged,'plot.index_mapper.range.updated')
self.pantool = PanTool(plot,drag_button='left')
plot.tools.append(self.pantool)
self.zoomtool = ZoomTool(plot)
self.zoomtool.prev_state_key = KeySpec('a')
self.zoomtool.next_state_key = KeySpec('s')
plot.overlays.append(self.zoomtool)
self.scattertool = None
self.scatter.overlays.append(ScatterInspectorOverlay(self.scatter,
hover_color = "black",
selection_color="black",
selection_outline_color="red",
selection_line_width=2))
self.colorbar = colorbar = ColorBar(index_mapper=LinearMapper(range=plot.color_mapper.range),
color_mapper=plot.color_mapper.range,
plot=plot,
orientation='v',
resizable='v',
width = 30,
padding = 5)
colorbar.padding_top = plot.padding_top
colorbar.padding_bottom = plot.padding_bottom
colorbar._axis.title = 'Weights'
self.plotcontainer = container = HPlotContainer(use_backbuffer=True)
container.add(plot)
container.add(colorbar)
super(FitGui,self).__init__(**traits)
self.on_trait_change(self._scale_change,'plot.value_scale,plot.index_scale')
if weights is not None and len(weights)==2:
self.weightsChanged() #update error bars
def _weights0rem_changed(self,old,new):
if new:
self.plot.color_mapper = _cmapblack(self.plot.color_mapper.range)
else:
self.plot.color_mapper = _cmap(self.plot.color_mapper.range)
self.plot.request_redraw()
# if old and self.filloverlay in self.plot.overlays:
# self.plot.overlays.remove(self.filloverlay)
# if new:
# self.plot.overlays.append(self.filloverlay)
# self.plot.request_redraw()
def _paramsChanged(self):
self.updatemodelplot = True
def _nmod_changed(self):
self.updatemodelplot = True
def _rangeChanged(self):
self.updatemodelplot = True
#@on_trait_change('object.plot.value_scale,object.plot.index_scale',post_init=True)
def _scale_change(self):
self.plot.request_redraw()
def _updatemodelplot_fired(self,new):
#If the plot has not been generated yet, just skip the update
if self.plot is None:
return
#if False (e.g. button click), update regardless, otherwise check for autoupdate
if new and not self.autoupdate:
return
mod = self.tmodel.model
if self.ytype == 'data and model':
if mod:
#xd = self.data[0]
#xmod = np.linspace(np.min(xd),np.max(xd),self.nmod)
xl = self.plot.index_range.low
xh = self.plot.index_range.high
if self.plot.index_scale=="log":
xmod = np.logspace(np.log10(xl),np.log10(xh),self.nmod)
else:
xmod = np.linspace(xl,xh,self.nmod)
ymod = self.tmodel.model(xmod)
self.plot.data.set_data('xmod',xmod)
self.plot.data.set_data('ymod',ymod)
else:
self.plot.data.set_data('xmod',[])
self.plot.data.set_data('ymod',[])
elif self.ytype == 'residuals':
if mod:
self.plot.data.set_data('xmod',[])
self.plot.data.set_data('ymod',[])
#residuals set the ydata instead of setting the model
res = mod.residuals(*self.data)
self.plot.data.set_data('ydata',res)
else:
self.ytype = 'data and model'
else:
assert True,'invalid Enum'
def _fitmodel_fired(self):
from warnings import warn
preaup = self.autoupdate
try:
self.autoupdate = False
xd,yd = self.data
kwd = {'x':xd,'y':yd}
if self.weights is not None:
w = self.weights
if self.weights0rem:
if xd.shape == w.shape:
m = w!=0
w = w[m]
kwd['x'] = kwd['x'][m]
kwd['y'] = kwd['y'][m]
elif np.any(w==0):
warn("can't remove 0-weighted points if weights don't match data")
kwd['weights'] = w
self.tmodel.fitdata = kwd
finally:
self.autoupdate = preaup
self.updatemodelplot = True
self.updatestats = True
# def _tmodel_changed(self,old,new):
# #old is only None before it is initialized
# if new is not None and old is not None and new.model is not None:
# self.fitmodel = True
def _newmodel_fired(self,newval):
from inspect import isclass
if isinstance(newval,basestring) or isinstance(newval,FunctionModel1D) \
or (isclass(newval) and issubclass(newval,FunctionModel1D)):
self.tmodel = TraitedModel(newval)
else:
if self.modelselector.edit_traits(kind='modal').result:
cls = self.modelselector.selectedmodelclass
if cls is None:
self.tmodel = TraitedModel(None)
elif self.modelselector.isvarargmodel:
self.tmodel = TraitedModel(cls(self.modelselector.modelargnum))
self.fitmodel = True
else:
self.tmodel = TraitedModel(cls())
self.fitmodel = True
else: #cancelled
return
def _showerror_fired(self,evt):
if self.tmodel.lastfitfailure:
ex = self.tmodel.lastfitfailure
dialog = HasTraits(s=ex.__class__.__name__+': '+str(ex))
view = View(Item('s',style='custom',show_label=False),
resizable=True,buttons=['OK'],title='Fitting error message')
dialog.edit_traits(view=view)
@cached_property
def _get_chi2(self):
try:
return self.tmodel.model.chi2Data()[0]
except:
return 0
@cached_property
def _get_chi2r(self):
try:
return self.tmodel.model.chi2Data()[1]
except:
return 0
def _get_nomodel(self):
return self.tmodel.model is None
def _get_weightsvary(self):
w = self.weights
return np.any(w!=w[0])if len(w)>0 else False
def _get_plotname(self):
xlabel = self.plot.x_axis.title
ylabel = self.plot.y_axis.title
if xlabel == '' and ylabel == '':
return ''
else:
return xlabel+' vs '+ylabel
def _set_plotname(self,val):
if isinstance(val,basestring):
val = val.split('vs')
if len(val) ==1:
val = val.split('-')
val = [v.strip() for v in val]
self.x_axis.title = val[0]
self.y_axis.title = val[1]
#selection-related
def _scattertool_changed(self,old,new):
if new == 'No Selection':
self.plot.tools[0].drag_button='left'
else:
self.plot.tools[0].drag_button='right'
if old is not None and 'lasso' in old:
if new is not None and 'lasso' in new:
#connect correct callbacks
self.lassomode = new.replace('lasso','')
return
else:
#TODO:test
self.scatter.tools[-1].on_trait_change(self._lasso_handler,
'selection_changed',remove=True)
del self.scatter.overlays[-1]
del self.lassomode
elif old == 'clickimmediate':
self.scatter.index.on_trait_change(self._immediate_handler,
'metadata_changed',remove=True)
self.scatter.tools = []
if new is None:
pass
elif 'click' in new:
smodemap = {'clickimmediate':'single','clicksingle':'single',
'clicktoggle':'toggle'}
self.scatter.tools.append(ScatterInspector(self.scatter,
selection_mode=smodemap[new]))
if new == 'clickimmediate':
self.clearsel = True
self.scatter.index.on_trait_change(self._immediate_handler,
'metadata_changed')
elif 'lasso' in new:
lasso_selection = LassoSelection(component=self.scatter,
selection_datasource=self.scatter.index)
self.scatter.tools.append(lasso_selection)
lasso_overlay = LassoOverlay(lasso_selection=lasso_selection,
component=self.scatter)
self.scatter.overlays.append(lasso_overlay)
self.lassomode = new.replace('lasso','')
lasso_selection.on_trait_change(self._lasso_handler,
'selection_changed')
lasso_selection.on_trait_change(self._lasso_handler,
'selection_completed')
lasso_selection.on_trait_change(self._lasso_handler,
'updated')
else:
raise TraitsError('invalid scattertool value')
def _weightchangesel_fired(self):
self.weights[self.selectedi] = self.weightchangeto
if self.unselectonaction:
self.clearsel = True
self._sel_alter_weights()
self.lastselaction = 'weightchangesel'
def _delsel_fired(self):
self.weights[self.selectedi] = 0
if self.unselectonaction:
self.clearsel = True
self._sel_alter_weights()
self.lastselaction = 'delsel'
def _sel_alter_weights(self):
if self.weighttype != 'custom':
self._customweights = self.weights
self.weighttype = 'custom'
self.weightsChanged()
def _clearsel_fired(self,event):
if isinstance(event,list):
self.scatter.index.metadata['selections'] = event
else:
self.scatter.index.metadata['selections'] = list()
def _lasso_handler(self,name,new):
if name == 'selection_changed':
lassomask = self.scatter.index.metadata['selection'].astype(int)
clickmask = np.zeros_like(lassomask)
clickmask[self.scatter.index.metadata['selections']] = 1
if self.lassomode == 'add':
mask = clickmask | lassomask
elif self.lassomode == 'remove':
mask = clickmask & ~lassomask
elif self.lassomode == 'invert':
mask = np.logical_xor(clickmask,lassomask)
else:
raise TraitsError('lassomode is in invalid state')
self.scatter.index.metadata['selections'] = list(np.where(mask)[0])
elif name == 'selection_completed':
self.scatter.overlays[-1].visible = False
elif name == 'updated':
self.scatter.overlays[-1].visible = True
else:
raise ValueError('traits event name %s invalid'%name)
def _immediate_handler(self):
sel = self.selectedi
if len(sel) > 1:
self.clearsel = True
raise TraitsError('selection error in immediate mode - more than 1 selection')
elif len(sel)==1:
if self.lastselaction != 'None':
setattr(self,self.lastselaction,True)
del sel[0]
def _savews_fired(self):
self._savedws = self.weights.copy()
def _loadws_fired(self):
self.weights = self._savedws
self._savews_fired()
def _get_selectedi(self):
return self.scatter.index.metadata['selections']
@on_trait_change('data,ytype',post_init=True)
def dataChanged(self):
"""
Updates the application state if the fit data are altered - the GUI will
know if you give it a new data array, but not if the data is changed
in-place.
"""
pd = self.plot.data
#TODO:make set_data apply to both simultaneously?
pd.set_data('xdata',self.data[0])
pd.set_data('ydata',self.data[1])
self.updatemodelplot = False
@on_trait_change('weights',post_init=True)
def weightsChanged(self):
"""
Updates the application state if the weights/error bars for this model
are changed - the GUI will automatically do this if you give it a new
set of weights array, but not if they are changed in-place.
"""
weights = self.weights
if 'errorplots' in self.trait_names():
#TODO:switch this to updating error bar data/visibility changing
if self.errorplots is not None:
self.plot.remove(self.errorplots[0])
self.plot.remove(self.errorplots[1])
self.errorbarplots = None
if len(weights.shape)==2 and weights.shape[0]==2:
xerr,yerr = 1/weights
high = ArrayDataSource(self.scatter.index.get_data()+xerr)
low = ArrayDataSource(self.scatter.index.get_data()-xerr)
ebpx = ErrorBarPlot(orientation='v',
value_high = high,
value_low = low,
index = self.scatter.value,
value = self.scatter.index,
index_mapper = self.scatter.value_mapper,
value_mapper = self.scatter.index_mapper
)
self.plot.add(ebpx)
high = ArrayDataSource(self.scatter.value.get_data()+yerr)
low = ArrayDataSource(self.scatter.value.get_data()-yerr)
ebpy = ErrorBarPlot(value_high = high,
value_low = low,
index = self.scatter.index,
value = self.scatter.value,
index_mapper = self.scatter.index_mapper,
value_mapper = self.scatter.value_mapper
)
self.plot.add(ebpy)
self.errorplots = (ebpx,ebpy)
while len(weights.shape)>1:
weights = np.sum(weights**2,axis=0)
self.plot.data.set_data('weights',weights)
self.plot.plots['data'][0].color_mapper.range.refresh()
if self.weightsvary:
if self.colorbar not in self.plotcontainer.components:
self.plotcontainer.add(self.colorbar)
self.plotcontainer.request_redraw()
elif self.colorbar in self.plotcontainer.components:
self.plotcontainer.remove(self.colorbar)
self.plotcontainer.request_redraw()
def _weighttype_changed(self, name, old, new):
if old == 'custom':
self._customweights = self.weights
if new == 'custom':
self.weights = self._customweights #if hasattr(self,'_customweights') else np.ones_like(self.data[0])
elif new == 'equal':
self.weights = np.ones_like(self.data[0])
elif new == 'lin bins':
self.weights = binned_weights(self.data[0],10,False)
elif new == 'log bins':
self.weights = binned_weights(self.data[0],10,True)
else:
raise TraitError('Invalid Enum value on weighttype')
def getModelInitStr(self):
"""
Generates a python code string that can be used to generate a model with
parameters matching the model in this :class:`FitGui`.
:returns: initializer string
"""
mod = self.tmodel.model
if mod is None:
return 'None'
else:
parstrs = []
for p,v in mod.pardict.iteritems():
parstrs.append(p+'='+str(v))
if mod.__class__._pars is None: #varargs need to have the first argument give the right number
varcount = len(mod.params)-len(mod.__class__._statargs)
parstrs.insert(0,str(varcount))
return '%s(%s)'%(mod.__class__.__name__,','.join(parstrs))
def getModelObject(self):
"""
Gets the underlying object representing the model for this fit.
:returns: The :class:`pymodelfit.core.FunctionModel1D` object.
"""
return self.tmodel.model