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[#]: subject: (Use this open source tool to monitor variables in Python)
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[#]: via: (https://opensource.com/article/21/4/monitor-debug-python)
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[#]: author: (Tian Gao https://opensource.com/users/gaogaotiantian)
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[#]: collector: (lujun9972)
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[#]: translator: (geekpi)
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[#]: reviewer: ( )
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[#]: publisher: ( )
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[#]: url: ( )
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Use this open source tool to monitor variables in Python
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======
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Watchpoints is a simple but powerful tool to help you with monitoring
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variables while debugging Python.
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![Looking back with binoculars][1]
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When debugging code, you're often faced with figuring out when a variable changes. Without any advanced tools, you have the option of using print statements to announce the variables when you expect them to change. However, this is a very ineffective way because the variables could change in many places, and constantly printing them to a terminal is noisy, while printing them to a log file becomes unwieldy.
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This is a common issue, but now there is a simple but powerful tool to help you with monitoring variables: [watchpoints][2].
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The [watchpoint concept is common in C and C++ debuggers][3] to monitor memories, but there's a lack of equivalent tools in Python. `watchpoints` fills in the gap.
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### Installing
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To use it, you must first install it by using `pip`:
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```
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`$ python3 -m pip install watchpoints`
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```
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### Using watchpoints in Python
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For any variable you'd like to monitor, use the **watch** function on it.
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```
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from watchpoints import watch
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a = 0
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watch(a)
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a = 1
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```
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As the variable changes, information about its value is printed to **stdout**:
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```
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====== Watchpoints Triggered ======
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Call Stack (most recent call last):
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<module> (my_script.py:5):
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> a = 1
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a:
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0
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->
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1
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```
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The information includes:
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* The line where the variable was changed.
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* The call stack.
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* The previous/current value of the variable.
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It not only works with the variable itself, but it also works with object changes:
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```
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from watchpoints import watch
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a = []
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watch(a)
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a = {} # Trigger
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a["a"] = 2 # Trigger
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```
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The callback is triggered when the variable **a** is reassigned, but also when the object assigned to a is changed.
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What makes it even more interesting is that the monitor is not limited by the scope. You can watch the variable/object anywhere you want, and the callback is triggered no matter what function the program is executing.
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```
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from watchpoints import watch
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def func(var):
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var["a"] = 1
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a = {}
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watch(a)
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func(a)
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```
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For example, this code prints:
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```
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====== Watchpoints Triggered ======
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Call Stack (most recent call last):
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<module> (my_script.py:8):
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> func(a)
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func (my_script.py:4):
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> var["a"] = 1
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a:
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{}
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->
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{'a': 1}
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```
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The **watch** function can monitor more than a variable. It can also monitor the attributes and an element of a dictionary or list.
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```
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from watchpoints import watch
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class MyObj:
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def __init__(self):
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self.a = 0
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obj = MyObj()
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d = {"a": 0}
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watch(obj.a, d["a"]) # Yes you can do this
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obj.a = 1 # Trigger
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d["a"] = 1 # Trigger
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```
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This could help you narrow down to some specific objects that you are interested in.
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If you are not happy about the format of the output, you can customize it. Just define your own callback function:
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```
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watch(a, callback=my_callback)
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# Or set it globally
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watch.config(callback=my_callback)
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```
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You can even bring up **pdb** when the trigger is hit:
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```
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`watch.config(pdb=True)`
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```
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This behaves similarly to **breakpoint()**, giving you a debugger-like experience.
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If you don’t want to import the function in every single file, you can make it global by using **install** function:
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```
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`watch.install() # or watch.install("func_name") and use it as func_name()`
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```
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Personally, I think the coolest thing about watchpoints is its intuitive usage. Are you interested in some data? Just "watch" it, and you'll know when your variable changes.
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### Try watchpoints
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I developed and maintain `watchpoints` on [GitHub][2], and have released it under the licensed under Apache 2.0. Install it and use it, and of course contribution is always welcome.
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--------------------------------------------------------------------------------
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via: https://opensource.com/article/21/4/monitor-debug-python
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作者：[Tian Gao][a]
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选题：[lujun9972][b]
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译者：[译者ID](https://github.com/译者ID)
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校对：[校对者ID](https://github.com/校对者ID)
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本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
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[a]: https://opensource.com/users/gaogaotiantian
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[b]: https://github.com/lujun9972
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[1]: https://opensource.com/sites/default/files/styles/image-full-size/public/lead-images/look-binoculars-sight-see-review.png?itok=NOw2cm39 (Looking back with binoculars)
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[2]: https://github.com/gaogaotiantian/watchpoints
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[3]: https://opensource.com/article/21/3/debug-code-gdb
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@ -0,0 +1,179 @@
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[#]: subject: (Use this open source tool to monitor variables in Python)
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[#]: via: (https://opensource.com/article/21/4/monitor-debug-python)
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[#]: author: (Tian Gao https://opensource.com/users/gaogaotiantian)
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[#]: collector: (lujun9972)
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[#]: translator: (geekpi)
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[#]: reviewer: ( )
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[#]: publisher: ( )
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[#]: url: ( )
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使用这个开源工具来监控 Python 中的变量
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======
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Watchpoints 是一个简单但功能强大的工具，可以帮助你在调试 Python 时监控变量。
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![Looking back with binoculars][1]
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在调试代码时，你经常面临着要弄清楚一个变量何时发生变化。如果没有任何高级工具，那么可以选择使用打印语句在期望它们更改时输出变量。然而，这是一种非常低效的方法，因为变量可能在很多地方发生变化，并且不断地将其打印到终端上会产生很大的干扰，而将它们打印到日志文件中则变得很麻烦。
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这是一个常见的问题，但现在有一个简单而强大的工具可以帮助你监控变量：[watchpoints][2]。
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[watchpoint 的概念在 C 和 C++ 调试器中很常见][3]，用于监控内存，但在 Python 中缺乏相应的工具。`watchpoints` 填补了这个空白。
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### 安装
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要使用它，你必须先用 `pip` 安装它：
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```
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`$ python3 -m pip install watchpoints`
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```
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### 在Python中使用 watchpoints
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对于任何一个你想监控的变量，使用 **watch** 函数对其进行监控。
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```
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from watchpoints import watch
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a = 0
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watch(a)
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a = 1
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```
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当变量发生变化时，它的值就会被打印到**标准输出**：
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```
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====== Watchpoints Triggered ======
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Call Stack (most recent call last):
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<module> (my_script.py:5):
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> a = 1
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a:
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0
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->
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1
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```
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信息包括：
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* 变量被改变的行。
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* 调用栈。
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* 变量的先前值/当前值。
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它不仅适用于变量本身，也适用于对象的变化：
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```
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from watchpoints import watch
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a = []
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watch(a)
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a = {} # Trigger
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a["a"] = 2 # Trigger
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```
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当变量 **a** 被重新分配时，回调会被触发，同时当分配给 a 的对象发生变化时也会被触发。
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更有趣的是，监控不受作用域的限制。你可以在任何地方观察变量/对象，而且无论程序在执行什么函数，回调都会被触发。
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```
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from watchpoints import watch
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def func(var):
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var["a"] = 1
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a = {}
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watch(a)
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func(a)
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```
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例如，这段代码打印：
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```
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====== Watchpoints Triggered ======
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Call Stack (most recent call last):
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<module> (my_script.py:8):
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> func(a)
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func (my_script.py:4):
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> var["a"] = 1
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a:
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{}
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->
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{'a': 1}
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```
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**watch** 函数不仅可以监视一个变量，它也可以监视一个字典或列表的属性和元素。
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```
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from watchpoints import watch
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class MyObj:
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def __init__(self):
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self.a = 0
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obj = MyObj()
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d = {"a": 0}
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watch(obj.a, d["a"]) # Yes you can do this
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obj.a = 1 # Trigger
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d["a"] = 1 # Trigger
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```
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这可以帮助你缩小到一些你感兴趣的特定对象。
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如果你对输出格式不满意，你可以自定义它。只需定义你自己的回调函数：
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```
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watch(a, callback=my_callback)
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# 或者全局设置
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watch.config(callback=my_callback)
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```
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当触发时，你甚至可以使用 **pdb**：
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```
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`watch.config(pdb=True)`
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```
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这与 **breakpoint()** 的行为类似，会给你带来类似调试器的体验。
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如果你不想在每个文件中都导入这个函数，你可以通过 **install** 函数使其成为全局：
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```
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`watch.install() # or watch.install("func_name") and use it as func_name()`
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```
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我个人认为，watchpoints 最酷的地方就是使用直观。你对一些数据感兴趣吗？只要”观察“它，你就会知道你的变量何时发生变化。
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### 尝试 watchpoints
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我在 [GitHub][2] 上开发维护了 `watchpoints`，并在 Apache 2.0 许可下发布了它。安装并使用它，当然也欢迎大家做出贡献。
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--------------------------------------------------------------------------------
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via: https://opensource.com/article/21/4/monitor-debug-python
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作者：[Tian Gao][a]
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选题：[lujun9972][b]
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译者：[geekpi](https://github.com/geekpi)
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||||
校对：[校对者ID](https://github.com/校对者ID)
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||||
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||||
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
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||||
|
||||
[a]: https://opensource.com/users/gaogaotiantian
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||||
[b]: https://github.com/lujun9972
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[1]: https://opensource.com/sites/default/files/styles/image-full-size/public/lead-images/look-binoculars-sight-see-review.png?itok=NOw2cm39 (Looking back with binoculars)
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[2]: https://github.com/gaogaotiantian/watchpoints
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[3]: https://opensource.com/article/21/3/debug-code-gdb
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