python/docs/TRACEBACKS.md

# Reading and Troubleshooting Stack Traces in Python

## Frame Object

When a function is called, a frame object is created to hold the local variables and arguments passed to a function.
When the function is returns, the frame object is destroyed.
When function `B` is called within function `A`, function `B`'s values are put into a frame object, which is then placed on top of function `A`'s frame object on the call stack.

## Call Stack

The call stack is a collection of frame objects for the currently active functions.
If function `A` has called function `B` and function `B` has called function `C`, then the frame objects for all three functions will be on the call stack.
Once function `C` returns, then its frame object will be popped off the stack and only the frame objects for functions `A` and `B` will remain on the call stack.

## Traceback

A Traceback is a report of all the frame objects on the stack at a specific time.
When a Python program encounters an unhandled exception, it will print the exception message and a Traceback.
The Traceback will show where the exception was raised and what functions were called leading up to it.

## How to Read a Traceback

`ValueError` is a common exception.

The following is an example of `ValueError` resulting from trying to assign two variables on the left from only one value on the right:

```python
>>> first, second = [1]
Traceback (most recent call last):
File <stdin>, line 1, in <module>
    first, second = [1]
ValueError: not enough values to unpack (expected 2, got 1)

```

Tracebacks are organized with the most recent call is last, so the place to start reading the Traceback is with the exception at the bottom.
By reading upwards from there, we can see how that statement was reached.
If we place the offending line in a function and then call the function we see a longer trace:

```python
>>> def my_func():
...     first, second = [1]
...
>>> my_func()
Traceback (most recent call last):
  File <stdin>, line 5, in <module>
    my_func()
  File <stdin>, line 2, in my_func
    first, second = [1]
ValueError: not enough values to unpack (expected 2, got 1)

```

Working backwards from the bottom, we see that the call where the exception happened is on line 2 in `my_func`.
We got there by calling `my_func` on line 5.

## Using the `print` function

Sometimes an error is not being raised, but a value is not what is expected.
This can be especially perplexing if the value is the result of a chain of calculations.
In such a situation it can be helpful to look at the value at each step to see which step is the one that isn't behaving as expected.
The [print][print] function can be used for printing the value to the console.
The following is an example of a function that doesn't return the value expected:

```python
# the intent is to pass an integer to this function and get an integer back
def halve_and_quadruple(num):
    return (num / 2) * 4
```

When the function is passed `5`, the expected value is `8`, but it it returns `10.0`.
To troubleshoot, the calculating is broken up so that the value can be inspected at every step.

```python
# the intent is to pass an integer to this function and get an integer back
def halve_and_quadruple(num):
    # verify the number in is what is expected
    # prints 5
    print(num)
    # we want the int divided by an integer to be an integer
    # but this prints 2.5! We've found our mistake.
    print(num / 2)
    # this makes sense, since 2.5 x 4 = 10.0
    print((num / 2) * 4)
    return (num / 2) * 4

What the `print` calls revealed is that we used `/` when we should have used `//`, the [floor divison operator][floor divison operator].

## Logging

[Logging][logging] can be used similarly to `print`, but it is more powerful.
What is logged can be configured by the logging severity (e.g., 'DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'.)
A call to the `logging.error` function can pass `True` to the `exc_info` parameter, which will additionally log the stack trace.
By configuring multiple handlers, logging can write to more than one place with the same logging function.

Following is an example of logging printed to the console:

```python

>>> import logging
>>>
>>> # configures minimum logging level as INFO
>>> logging.basicConfig(level=logging.INFO)
>>>
>>> def halve_and_quadruple(num):
...     # prints INFO:root: num == 5
...     logging.info(f" num == {num}")
...     return (num // 2) * 4
...
>>> print(halve_and_quadruple(5))

```

The level is configured as `INFO` because the default level is `WARNING`.
For a persistent log, the logger can be configured to write to a file, like so:

```python
>>> import logging
...
>>> # configures the output file name to example.log, and the minimum logging level as INFO
>>> logging.basicConfig(filename='example.log', level=logging.INFO)
...
... def halve_and_quadruple(num):
...     # prints INFO:root: num == 5 to the example.log file
...     logging.info(f" num == {num}")
...     return (num // 2) * 4
...
>>> print(halve_and_quadruple(5))
```

## assert

[`assert`][assert] is a statement which should always evaluate to `True` unless there is a bug in the program.
When an `assert` evaluates to `False` it will raise an [`AssertionError`][AssertionError].
The Traceback for the `AssertionError` can include an optional message that is part of the `assert` statement.
Although a message is optional, it is good practice to always include one in the `assert` definition.

The following is an example of using `assert`:

```python
>>> def int_division(dividend, divisor):
...     assert divisor != 0, "divisor must not be 0"
...     return dividend // divisor
...
>>> print(int_division(2, 1))
2
>>> print(int_division(2, 0))
Traceback (most recent call last):
  File <stdin>, line 7, in <module>
    print(int_division(2, 0))
          ^^^^^^^^^^^^^^^^^^
  File <stdin>, line 2, in int_division
    assert divisor != 0, "divisor must not be 0"
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AssertionError: divisor must not be 0

```

If we start reading the Traceback at the bottom (as we should) we quickly see the problem is that `0` should not be passsed as the `divisor`.

`assert` can also be used to test that a value is of the expected type:

```python
>>> import numbers
...
...
... def int_division(dividend, divisor):
...     assert divisor != 0, "divisor must not be 0"
...     assert isinstance(divisor, numbers.Number), "divisor must be a number"
...     return dividend // divisor
...
>>> print(int_division(2, 1))
2
>>> print(int_division(2, '0'))
Traceback (most recent call last):
  File <stdin>, line 11, in <module>
    print(int_division(2, '0'))
          ^^^^^^^^^^^^^^^^^^^^
  File <stdin>, line 6, in int_division
    assert isinstance(divisor, numbers.Number), "divisor must be a number"
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AssertionError: divisor must be a number
```

Once a bug is identified, consider replacing the `assert` with error handling.
This is because all `assert` statements can be disabled through running Python with the `-O` or `-OO` options, or from setting the `PYTHONOPTIMIZE` environment variable to `1` or `2`.
Setting `PYTHONOPTIMIZE` to `1` is equivalent to running Python with the `-O` option, which disables assertions.
Setting `PYTHONOPTIMIZE` to `2` is equivalent to running Python with the `-OO` option, which both disables assertions and removes docstrings from the bytcode.
Reducing bytecode is one way to make the code run faster.

## Python Debugger

Python has a built in debugger, [pdb][pdb]. It can be used to step through code and inspect variables. You can also set breakpoints with it.
To get started you have to first import pdb and then call pdb.set_trace() where you want to start debugging.

```python
import pdb

def add(num1, num2):
    return num1 + num2

pdb.set_trace()
sum = add(1,5)
print(sum)
```

Running this code will give you a prompt where you can type in commands. Write `help` to get a list of commands.
The most common onces are `step` which steps into a function called at that line. `next` which steps over a function call and move to the next line. `where` which tells you which line you are on. Some other usefull commands are `whatis <variable>` which tells you the type of a variable and `print(<variable>)` which prints the value of a variable. You can also just use `<variable>` to print the value of a variable. Another command is `jump <line number>` which jumps to a specific line number.

Here are an example on how to use the debugger based on the code earlier:

```python
>>> python pdb.py
... > c:\pdb.py(7)<module>()
... -> sum = add(1,5)
... (Pdb)
>>> step
... > c:\pdb.py(3)add()
... -> def add(num1, num2):
... (Pdb)
>>> whatis num1
... <class 'int'>
>>> print(num2)
... 5
>>> next
... > c:\pdb.py(4)add()
... -> return num1 + num2
... (Pdb)
>>> jump 3
... > c:\pdb.py(3)add()
... -> def add(num1, num2):
... (Pdb)
```

Breakpoints is setup by `break <filename>:<line number> <condition>` where condition is an optional condition that has to be true for the breakpoint to be hit. You can simply write `break` to get a list of the breakpoints you have set. To disable a breakpoint you can write `disable <breakpoint number>`. To enable a breakpoint you can write `enable <breakpoint number>`. To delete a breakpoint you can write `clear <breakpoint number>`. To continue execution you can write `continue` or `c`. To exit the debugger you can write `quit` or `q`.

Here are an example on how to use the debugger based on the code earlier:

```python
>>> python pdb.py
... > c:\pdb.py(7)<module>()
... -> sum = add(1,5)
... (Pdb)
>>> break
...
>>> break pdb:4
... Breakpoint 1 at c:\pdb.py:4
>>> break
... Num Type         Disp Enb   Where
... 2   breakpoint   keep yes   at c:\pdb.py:4
>>> c # continue
... > c:\pdn.py(4)add()
... -> return num1 + num2
>>> disable break 1
... Disabled breakpoint 1 at c:\pdb.py:4
>>> break
... Num Type         Disp Enb   Where
... 1   breakpoint   keep no    at c:\pdb.py:4
...         breakpoint already hit 1 time
>>> clear break 1
... Deleted breakpoint 1 at c:\pdb.py:4
>>> break
...
```

In python 3.7 and newer there is an easier way to create breakpoints You can simply write `breakpoint()` where you want to create a breakpoint.

```python
import pdb

def add(num1, num2):
    breakpoint()
    return num1 + num2

pdb.set_trace()
sum = add(1,5)
print(sum)
```

```python
>>> python pdb.py
... > c:\pdb.py(7)<module>()
... -> sum = add(1,5)
... (Pdb)
>>> c # continue
... > c:\pdb.py(5)add()
... -> return num1 + num2
```

[assert]: https://realpython.com/python-assert-statement/
[AssertionError]: https://www.geeksforgeeks.org/python-assertion-error/
[floor divison operator]: https://www.codingem.com/python-floor-division
[logging]: https://docs.python.org/3/howto/logging.html
[print]: https://www.w3schools.com/python/ref_func_print.asp
[pdb]: https://www.geeksforgeeks.org/python-debugger-python-pdb/