# 3.4 Traits：创建交互对话
In [10]:
```
%matplotlib inline
import numpy as np
```
> 作者 : _Didrik Pinte_
Traits项目允许你可以向Python项目属性方便的添加验证、初始化、委托、通知和图形化界面。
在这个教程中，我们将研究Traits工具包并且学习如何动态减少你所写的锅炉片代码，进行快速的GUI应用开发，以及理解Enthought工具箱中其他部分的想法。
Traits和Enthought工具箱是基于BSD-style证书的开源项目。
**目标受众**
## Python中高级程序员
**要求**
* [wxPython](http://www.wxpython.org/)、[PyQt](https://riverbankcomputing.com/software/pyqt/intro)或[PySide](https://pyside.github.io/docs/pyside/)之一
* Numpy和Scipy
* [Enthought工具箱](http://code.enthought.com/projects)
* 所有需要的软件都可以通过安装[EPD免费版](https://store.enthought.com/)来获得
**教程内容**
* 介绍
* 例子
* Traits是什么
* 初始化
* 验证
* 文档
* 可视化: 打开一个对话框
* 推迟
* 通知
* 一些更高级的特征
## 3.4.1 介绍
Enthought工具箱可以构建用于数据分析、2D绘图和3D可视化的精密应用框架。这些强力可重用的组块是在BSD-style证书下发布的。
Enthought工具箱主要的包是：
* Traits - 基于组块的方式构建我们的应用。
* Kiva - 2D原生支持基于路径的rendering、affine转化、alpha混合及其它。
* Enable - 基于对象的2D绘图画布。
* Chaco - 绘图工具箱，用于构建复杂的交互2D图像。
* Mayavi -基于VTK的3D科学数据可视化
* Envisage - 应用插件框架，用于构建脚本化可扩展的应用
在这篇教程中，我们将关注Traits。
## 3.4.2 例子
在整个这篇教程中，我们将使用基于水资源管理简单案例的一个样例。我们将试着建模一个水坝和水库系统。水库和水坝有下列参数：
* 名称
* 水库的最小和最大容量 [$hm^3$]
* 水坝的高度和宽度[$m$]
* 蓄水面积[$km^2$]
* 水压头[$m$]
* 涡轮的动力[$MW$]
* 最小和最大放水量[$m^3/s$]
* 涡轮的效率
水库有一个已知的运转情况。一部分是与基于放水量有关的能量产生。估算水力发电机电力生产的简单公式是$P = \rho hrgk$, 其中
* P 以瓦特为单位的功率,
* \rho 是水的密度 ($~1000 kg/m^3$),
* h 是水的高度,
* r 是以每秒立方米为单位的流动率,
* g 重力加速度，9.8 $m/s^2$,
* k 是效率系数，范围从0到1。
年度的电能生产取决于可用的水供给。在一些设施中，水流率在一年中可能差10倍。
运行状态的第二个部分是蓄水量，蓄水量(storage)依赖于控制和非控制参数：
$storage_{t+1} = storage_t + inflows - release - spillage - irrigation$
本教程中使用的数据不是真实的，可能甚至在现实中没有意义。
## 3.4.3 Traits是什么
trait是可以用于常规Python对象属性的类型定义，给出属性的一些额外特性:
* 标准化:
* 初始化
* 验证
* 推迟
* 通知
* 可视化
* 文档
类可以自由混合基于trait的属性与通用Python属性，或者选择允许在这个类中只使用固定的或开放的trait属性集。类定义的Trait属性自动继承自由这个类衍生的其他子类。
创建一个traits类的常用方式是通过扩展**HasTraits**基础类，并且定义类的traits :
In [1]:
```
from traits.api import HasTraits, Str, Float
class Reservoir(HasTraits):
name = Str
max_storage = Float
```
对Traits 3.x用户来说
如果使用Traits 3.x, 你需要调整traits包的命名空间:
* traits.api应该为enthought.traits.api
* traitsui.api应该为enthought.traits.ui.api
像这样使用traits类和使用其他Python类一样简单。注意，trait值通过关键词参数传递:
In [2]:
```
reservoir = Reservoir(name='Lac de Vouglans', max_storage=605)
```
### 3.4.3.1 初始化
所有的traits都有一个默认值来初始化变量。例如，基础python类型有如下的trait等价物:
| Trait | Python类型 | 内置默认值 |
| --- | --- | --- |
| Bool | Boolean | False |
| Complex | Complex number | 0+0j |
| Float | Floating point number | 0.0 |
| Int | Plain integer | 0 |
| Long | Long integer | 0L |
| Str | String | '' |
| Unicode | Unicode | u'' |
存在很多其他预定义的trait类型: Array, Enum, Range, Event, Dict, List, Color, Set, Expression, Code, Callable, Type, Tuple, etc。
自定义默认值可以在代码中定义:
In [3]:
```
from traits.api import HasTraits, Str, Float
class Reservoir(HasTraits):
name = Str
max_storage = Float(100)
reservoir = Reservoir(name='Lac de Vouglans')
```
复杂初始化
当一个trait需要复杂的初始化时，可以实施_XXX_默认魔法方法。当调用XXX trait时，它会被懒惰的调用。例如：
In [4]:
```
def _name_default(self):
""" Complex initialisation of the reservoir name. """
return 'Undefined'
```
### 3.4.3.2 验证
当用户试图设置trait的内容时，每一个trait都会被验证:
In [5]:
```
reservoir = Reservoir(name='Lac de Vouglans', max_storage=605)
reservoir.max_storage = '230'
```
```
---------------------------------------------------------------------------
TraitError Traceback (most recent call last)
<ipython-input-5-cbed071af0b9> in <module>()
1 reservoir = Reservoir(name='Lac de Vouglans', max_storage=605)
2
----> 3 reservoir.max_storage = '230'
/Library/Python/2.7/site-packages/traits/trait_handlers.pyc in error(self, object, name, value)
170 """
171 raise TraitError( object, name, self.full_info( object, name, value ),
--> 172 value ) 173
174 def full_info ( self, object, name, value ):
TraitError: The 'max_storage' trait of a Reservoir instance must be a float, but a value of '230' <type 'str'> was specified.
```
### 3.4.3.3 文档
从本质上说，所有的traits都提供关于模型自身的文档。创建类的声明方式使它是自解释的:
In [6]:
```
from traits.api import HasTraits, Str, Float
class Reservoir(HasTraits):
name = Str
max_storage = Float(100)
```
trait的**desc**元数据可以用来提供关于trait更多的描述信息:
In [7]:
```
from traits.api import HasTraits, Str, Float
class Reservoir(HasTraits):
name = Str
max_storage = Float(100, desc='Maximal storage [hm3]')
```
现在让我们来定义完整的reservoir类:
In [8]:
```
from traits.api import HasTraits, Str, Float, Range
class Reservoir(HasTraits):
name = Str
max_storage = Float(1e6, desc='Maximal storage [hm3]')
max_release = Float(10, desc='Maximal release [m3/s]')
head = Float(10, desc='Hydraulic head [m]')
efficiency = Range(0, 1.)
def energy_production(self, release):
''' Returns the energy production [Wh] for the given release [m3/s]
'''
power = 1000 * 9.81 * self.head * release * self.efficiency
return power * 3600
if __name__ == '__main__':
reservoir = Reservoir(
name = 'Project A',
max_storage = 30,
max_release = 100.0,
head = 60,
efficiency = 0.8
)
release = 80
print 'Releasing {} m3/s produces {} kWh'.format(
release, reservoir.energy_production(release)
)
```
```
Releasing 80 m3/s produces 1.3561344e+11 kWh
```
### 3.4.3.4 可视化: 打开一个对话框
Traits库也关注用户界面，可以弹出一个Reservoir类的默认视图:
In [ ]:
```
reservoir1 = Reservoir()
reservoir1.edit_traits()
```
TraitsUI简化了创建用户界面的方式。HasTraits类上的每一个trait都有一个默认的编辑器，将管理trait在屏幕上显示的方式 (即Range trait显示为一个滑块等)。
与Traits声明方式来创建类的相同渠道，TraitsUI提供了声明的界面来构建用户界面代码:
In [ ]:
```
from traits.api import HasTraits, Str, Float, Range
from traitsui.api import View
class Reservoir(HasTraits):
name = Str
max_storage = Float(1e6, desc='Maximal storage [hm3]')
max_release = Float(10, desc='Maximal release [m3/s]')
head = Float(10, desc='Hydraulic head [m]')
efficiency = Range(0, 1.)
traits_view = View(
'name', 'max_storage', 'max_release', 'head', 'efficiency',
title = 'Reservoir',
resizable = True,
)
def energy_production(self, release):
''' Returns the energy production [Wh] for the given release [m3/s]
'''
power = 1000 * 9.81 * self.head * release * self.efficiency
return power * 3600
if __name__ == '__main__':
reservoir = Reservoir(
name = 'Project A',
max_storage = 30,
max_release = 100.0,
head = 60,
efficiency = 0.8
)
reservoir.configure_traits()
```
### 3.4.3.5 推迟
可以将trait定义和它的值推送给另一个对象是Traits的有用的功能。
In [ ]:
```
from traits.api import HasTraits, Instance, DelegatesTo, Float, Range
from reservoir import Reservoir
class ReservoirState(HasTraits):
"""Keeps track of the reservoir state given the initial storage.
"""
reservoir = Instance(Reservoir, ())
min_storage = Float
max_storage = DelegatesTo('reservoir')
min_release = Float
max_release = DelegatesTo('reservoir')
# state attributes
storage = Range(low='min_storage', high='max_storage')
# control attributes
inflows = Float(desc='Inflows [hm3]')
release = Range(low='min_release', high='max_release')
spillage = Float(desc='Spillage [hm3]')
def print_state(self):
print 'Storage\tRelease\tInflows\tSpillage'
str_format = '\t'.join(['{:7.2f}'for i in range(4)])
print str_format.format(self.storage, self.release, self.inflows,
self.spillage)
print '-' * 79
if __name__ == '__main__':
projectA = Reservoir(
name = 'Project A',
max_storage = 30,
max_release = 100.0,
hydraulic_head = 60,
efficiency = 0.8
)
state = ReservoirState(reservoir=projectA, storage=10)
state.release = 90
state.inflows = 0
state.print_state()
print 'How do we update the current storage ?'
```
特殊的trait允许用魔法_xxxx_fired方法管理事件和触发器函数:
In [ ]:
```
from traits.api import HasTraits, Instance, DelegatesTo, Float, Range, Event
from reservoir import Reservoir
class ReservoirState(HasTraits):
"""Keeps track of the reservoir state given the initial storage.
For the simplicity of the example, the release is considered in
hm3/timestep and not in m3/s.
"""
reservoir = Instance(Reservoir, ())
min_storage = Float
max_storage = DelegatesTo('reservoir')
min_release = Float
max_release = DelegatesTo('reservoir')
# state attributes
storage = Range(low='min_storage', high='max_storage')
# control attributes
inflows = Float(desc='Inflows [hm3]')
release = Range(low='min_release', high='max_release')
spillage = Float(desc='Spillage [hm3]')
update_storage = Event(desc='Updates the storage to the next time step')
def _update_storage_fired(self):
# update storage state
new_storage = self.storage - self.release + self.inflows
self.storage = min(new_storage, self.max_storage)
overflow = new_storage - self.max_storage
self.spillage = max(overflow, 0)
def print_state(self):
print 'Storage\tRelease\tInflows\tSpillage'
str_format = '\t'.join(['{:7.2f}'for i in range(4)])
print str_format.format(self.storage, self.release, self.inflows,
self.spillage)
print '-' * 79
if __name__ == '__main__':
projectA = Reservoir(
name = 'Project A',
max_storage = 30,
max_release = 5.0,
hydraulic_head = 60,
efficiency = 0.8
)
state = ReservoirState(reservoir=projectA, storage=15)
state.release = 5
state.inflows = 0
# release the maximum amount of water during 3 time steps
state.update_storage = True
state.print_state()
state.update_storage = True
state.print_state()
state.update_storage = True
state.print_state()
```
对象间的依赖可以自动使用trait**Property**完成。**depends_on**属性表示property其他traits的依赖性。当其他traits改变了,property是无效的。此外，Traits为属性使用魔法函数的名字:
* _get_XXX 来获得XXX属性的trait
* _set_XXX 来设置XXX属性的trait
In [ ]:
```
from traits.api import HasTraits, Instance, DelegatesTo, Float, Range
from traits.api import Property
from reservoir import Reservoir
class ReservoirState(HasTraits):
"""Keeps track of the reservoir state given the initial storage.
For the simplicity of the example, the release is considered in
hm3/timestep and not in m3/s.
"""
reservoir = Instance(Reservoir, ())
max_storage = DelegatesTo('reservoir')
min_release = Float
max_release = DelegatesTo('reservoir')
# state attributes
storage = Property(depends_on='inflows, release')
# control attributes
inflows = Float(desc='Inflows [hm3]')
release = Range(low='min_release', high='max_release')
spillage = Property(
desc='Spillage [hm3]', depends_on=['storage', 'inflows', 'release']
)
### Private traits.
_storage = Float
### Traits property implementation.
def _get_storage(self):
new_storage = self._storage - self.release + self.inflows
return min(new_storage, self.max_storage)
def _set_storage(self, storage_value):
self._storage = storage_value
def _get_spillage(self):
new_storage = self._storage - self.release + self.inflows
overflow = new_storage - self.max_storage
return max(overflow, 0)
def print_state(self):
print 'Storage\tRelease\tInflows\tSpillage'
str_format = '\t'.join(['{:7.2f}'for i in range(4)])
print str_format.format(self.storage, self.release, self.inflows,
self.spillage)
print '-' * 79
if __name__ == '__main__':
projectA = Reservoir(
name = 'Project A',
max_storage = 30,
max_release = 5,
hydraulic_head = 60,
efficiency = 0.8
)
state = ReservoirState(reservoir=projectA, storage=25)
state.release = 4
state.inflows = 0
state.print_state()
```
**注意** 缓存属性 当访问一个输入没有改变的属性时，[[email protected]](/cdn-cgi/l/email-protection)_property修饰器可以用来缓存这个值，并且只有在失效时才会重新计算一次他们。
让我们用ReservoirState的例子来扩展TraitsUI介绍:
In [ ]:
```
from traits.api import HasTraits, Instance, DelegatesTo, Float, Range, Property
from traitsui.api import View, Item, Group, VGroup
from reservoir import Reservoir
class ReservoirState(HasTraits):
"""Keeps track of the reservoir state given the initial storage.
For the simplicity of the example, the release is considered in
hm3/timestep and not in m3/s.
"""
reservoir = Instance(Reservoir, ())
name = DelegatesTo('reservoir')
max_storage = DelegatesTo('reservoir')
max_release = DelegatesTo('reservoir')
min_release = Float
# state attributes
storage = Property(depends_on='inflows, release')
# control attributes
inflows = Float(desc='Inflows [hm3]')
release = Range(low='min_release', high='max_release')
spillage = Property(
desc='Spillage [hm3]', depends_on=['storage', 'inflows', 'release']
)
### Traits view
traits_view = View(
Group(
VGroup(Item('name'), Item('storage'), Item('spillage'),
label = 'State', style = 'readonly'
),
VGroup(Item('inflows'), Item('release'), label='Control'),
)
)
### Private traits.
_storage = Float
### Traits property implementation.
def _get_storage(self):
new_storage = self._storage - self.release + self.inflows
return min(new_storage, self.max_storage)
def _set_storage(self, storage_value):
self._storage = storage_value
def _get_spillage(self):
new_storage = self._storage - self.release + self.inflows
overflow = new_storage - self.max_storage
return max(overflow, 0)
def print_state(self):
print 'Storage\tRelease\tInflows\tSpillage'
str_format = '\t'.join(['{:7.2f}'for i in range(4)])
print str_format.format(self.storage, self.release, self.inflows,
self.spillage)
print '-' * 79
if __name__ == '__main__':
projectA = Reservoir(
name = 'Project A',
max_storage = 30,
max_release = 5,
hydraulic_head = 60,
efficiency = 0.8
)
state = ReservoirState(reservoir=projectA, storage=25)
state.release = 4
state.inflows = 0
state.print_state()
state.configure_traits()
```
Some use cases need the delegation mechanism to be broken by the user when setting the value of the trait. The PrototypeFrom trait implements this behaviour.
In [ ]:
```
TraitsUI simplifies the way user interfaces are created. Every trait on a HasTraits class has a default editor that will manage the way the trait is rendered to the screen (e.g. the Range trait is displayed as a slider, etc.).
In the very same vein as the Traits declarative way of creating classes, TraitsUI provides a declarative interface to build user interfaces code:
```
- 介绍
- 1.1 科学计算工具及流程
- 1.2 Python语言
- 1.3 NumPy：创建和操作数值数据
- 1.4 Matplotlib：绘图
- 1.5 Scipy：高级科学计算
- 1.6 获取帮助及寻找文档
- 2.1 Python高级功能（Constructs）
- 2.2 高级Numpy
- 2.3 代码除错
- 2.4 代码优化
- 2.5 SciPy中稀疏矩阵
- 2.6 使用Numpy和Scipy进行图像操作及处理
- 2.7 数学优化：找到函数的最优解
- 2.8 与C进行交互
- 3.1 Python中的统计学
- 3.2 Sympy：Python中的符号数学
- 3.3 Scikit-image：图像处理
- 3.4 Traits：创建交互对话
- 3.5 使用Mayavi进行3D作图
- 3.6 scikit-learn：Python中的机器学习