Python code debugging artifact: pysnopper

Wang Yibai 2020-11-14 22:33:05
python code debugging artifact pysnopper

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For every programmer , Debugging is almost a must .

The code is stuck in the middle of writing , I don't know what the result of this function is ? Debug it and see

The code runs half way and reports an error , What circumstance ? How is it different from what I expected ? Debug it and see

There are many ways to debug , Different debugging methods are suitable for different scenarios and people .

  • If you're a little new to programming , I'm not very proficient in many tools , that print and log Dafa is good
  • If you're here (Win perhaps Mac) Development on computer , that IDE Graphical interface debugging is undoubtedly the most suitable ;
  • If you check on the server BUG, So use PDB Debugging without graphical interface should be the first choice ;
  • If you're going to develop locally , But the project needs to rely on a complex server environment , So we can understand PyCharm Remote debugging

In addition to the above , Today Mingo will introduce you a very useful debugging tool , It can be used in some scenarios , Greatly improve the debugging efficiency , That's it PySnooper, It's in Github It's been received on 13k Of star, Get everybody's unanimous praise .

With this tool , Even xiaomengxin can start without any threshold , From now on with print say goodbye ~

1. Fast installation

Execute the following commands to install PySnooper

$ python3 -m pip install pysnooper
# perhaps
$ conda install -c conda-forge pysnooper
# perhaps
$ yay -S python-pysnooper

2. A simple case

Here's the code , Defined a demo_func Function of , Generate a profile The dictionary variable of , And then update it , Finally back to .

The code itself has no practical significance , But to demonstrate PySnooper Enough already .

import pysnooper
def demo_func():
profile = {}
profile["name"] = " Mingo who wrote the code "
profile["age"] = 27
profile["gender"] = "male"
return profile
def main():
profile = demo_func()

Now I use the terminal command line to run it

[[email protected] ~]# python3
Source path:...
17:52:49.624943 call 4 def demo_func():
17:52:49.625124 line 5 profile = {}
New var:....... profile = {}
17:52:49.625156 line 6 profile["name"] = " Mingo who wrote the code "
Modified var:.. profile = {'name': ' Mingo who wrote the code '}
17:52:49.625207 line 7 profile["age"] = 27
Modified var:.. profile = {'name': ' Mingo who wrote the code ', 'age': 27}
17:52:49.625254 line 8 profile["gender"] = "male"
Modified var:.. profile = {'name': ' Mingo who wrote the code ', 'age': 27, 'gender': 'male'}
17:52:49.625306 line 10 return profile
17:52:49.625344 return 10 return profile
Return value:.. {'name': ' Mingo who wrote the code ', 'age': 27, 'gender': 'male'}
Elapsed time: 00:00:00.000486

You can see PySnooper Record the whole process of function running , Include :

  • Snippets of code 、 Line number and other information , And when each line of code is called ?
  • How the value of a local variable in a function changes ? When a variable was added , When the variable was modified .
  • What is the return value of the function ?
  • How much time does it take to run the function ?

And as a developer , To get this detailed debugging information , What you need to do is very simple , Just put a hat on the function you want to debug ( Decorator ) -- @pysnooper.snoop() that will do .

3. Detailed use

2.1 Redirect to log file

@pysnooper.snoop() When no parameters are added , The debug information will be output to standard output by default .

For a single debugging can solve BUG , There's no problem with that , But there are some BUG It only appears in specific scenarios , It takes you to put the program in the back and run for a while to reproduce .

In this case , You can redirect the debug information to a log file , It is convenient to trace and check .

def demo_func():

2.2 Tracking nonlocal variable values

PySnooper Is a unit of function debugging , By default, it only tracks local variables within the function body , If you want to track global variables , You can give @pysnooper.snoop() add watch Parameters

out = {"foo": "bar"}
def demo_func():

In this way ,PySnooper Will be in out["foo"] When the value changes , And print it out

watch Parameters , Receive an iteratable object ( It can be list perhaps tuple), The elements inside are string expressions , What does that mean ? Take a look at the following example

@pysnooper.snoop(watch=('out["foo"]', '', '["bar"]'))
def demo_func():

and watch Relative ,pysnooper.snoop() You can also receive a function watch_explode, Indicates that all global variables except these parameters are monitored .

@pysnooper.snoop(watch_explode=('foo', 'bar'))
def demo_func():

2.3 Set the depth of the trace function

When you use PySnooper When debugging a function , If other functions are called in the function ,PySnooper You won't be foolishly following in .

If you want to continue to track other functions that are called in this function , You can specify depth Parameter to set the trace depth ( If not specified, the default is 1).

def demo_func():

2.4 Set the prefix of debug log

When you are using PySnooper When tracking multiple functions , The debug log will look messy , Inconvenient to view .

under these circumstances ,PySnooper A parameter is provided , It is convenient for you to set different flags for different functions , It is convenient for you to distinguish when you view the log .

@pysnooper.snoop(output="/var/log/debug.log", prefix="demo_func: ")
def demo_func():

The effect is as follows

2.5 Set the maximum output length

By default ,PySnooper Output variables and exception information , If exceeded 100 Characters , Will be truncated to 100 Characters .

Of course, you can also specify parameters by Make changes

def demo_func():

You can also use the max_variable_length=None It never cuts them off .

def demo_func():

2.6 Support multithreading debugging mode

PySnooper It also supports multithreading debugging , By setting parameters thread_info=True, It will print out in the log which thread the variable was modified .

def demo_func():

The effect is as follows

2.7 Custom object format output

pysnooper.snoop() The function has an argument of custom_repr, It receives a tuple object .

In this tuple , You can specify specific types of objects to output in a specific format .

Here's an example .

If I want to follow person This Person Object of type , Because it's not conventional Python The base type ,PySnooper It can't output its information normally .

So I am pysnooper.snoop() Function is set to custom_repr Parameters , The first element of the parameter is Person, The second element is print_persion_obj function .

PySnooper When printing debugging information for an object , We will judge whether it is Person Object of type , if , Just pass in the object print_persion_obj Function , It's up to this function to decide how to display the information of this object .

class Person:pass
def print_person_obj(obj):
return f"<Person {} {obj.age} {obj.gender}>"
@pysnooper.snoop(custom_repr=(Person, print_person_obj))
def demo_func():

The complete code is as follows

import pysnooper
class Person:pass
def print_person_obj(obj):
return f"<Person {} {obj.age} {obj.gender}>"
@pysnooper.snoop(custom_repr=(Person, print_person_obj))
def demo_func():
person = Person() = " Mingo who wrote the code "
person.age = 27
person.gender = "male"
return person
def main():
profile = demo_func()

Run it , Observe the effect .

If you want to customize the format, there are many types of output , that custom_repr The value of the parameter can be written as follows

@pysnooper.snoop(custom_repr=((Person, print_person_obj), (numpy.ndarray, print_ndarray)))
def demo_func():

There's one more thing I'd like to remind you of , The first element of a tuple can be a type ( Such as class name Person Or other basic types list etc. ), It can also be a function to determine the type of object .

in other words , The following three expressions are equivalent .

# 【 The first way to write it 】
@pysnooper.snoop(custom_repr=(Person, print_persion_obj))
def demo_func():
# 【 The second way 】
def is_persion_obj(obj):
return isinstance(obj, Person)
@pysnooper.snoop(custom_repr=(is_persion_obj, print_persion_obj))
def demo_func():
# 【 The third way 】
@pysnooper.snoop(custom_repr=(lambda obj: isinstance(obj, Person), print_persion_obj))
def demo_func():

The above is a debugging artifact introduced by Mingge today (PySnooper) A detailed user manual for , Don't you think it's good ?

本文为[Wang Yibai]所创,转载请带上原文链接,感谢

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