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129 changes: 129 additions & 0 deletions hello11.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,129 @@
import random
import pdb

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`pdb` import risks accidental debug code in production


Importing the pdb module allows insertion of breakpoints that pause program execution for debugging. If left in production code, it may halt the application unexpectedly or expose sensitive runtime information.

Remove the import pdb line and any associated debugging calls before committing code to production to avoid interruptions or accidental exposure.

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`pdb` import risks accidental debug code in production


Importing the pdb module allows insertion of breakpoints that pause program execution for debugging. If left in production code, it may halt the application unexpectedly or expose sensitive runtime information.

Remove the import pdb line and any associated debugging calls before committing code to production to avoid interruptions or accidental exposure.

import sys as sys

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Import alias same as package name is unnecessary


The import statement uses import sys as sys, which creates a redundant alias identical to the original package name. This does not simplify or change usage and may confuse readers or maintainers. Remove the unnecessary alias to clean up the import and improve clarity.

Remove the as sys alias and use a simple import sys statement instead to eliminate redundancy and improve code readability.

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Unused `sys` import increases code clutter


The sys module is imported as sys but never used in the module, which adds unnecessary code clutter and can mislead maintainers or static analysis tools. It also increases the cognitive load when reading the code.
Remove the unused sys import statement to clean up the module and improve maintainability.

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Import alias same as package name is unnecessary


The import statement uses import sys as sys, which creates a redundant alias identical to the original package name. This does not simplify or change usage and may confuse readers or maintainers. Remove the unnecessary alias to clean up the import and improve clarity.

Remove the as sys alias and use a simple import sys statement instead to eliminate redundancy and improve code readability.

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Unused `sys` import increases code clutter


The sys module is imported as sys but never used in the module, which adds unnecessary code clutter and can mislead maintainers or static analysis tools. It also increases the cognitive load when reading the code.
Remove the unused sys import statement to clean up the module and improve maintainability.

import os
import subprocess
import abc

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Unused `abc` import adds code clutter


The import of the abc module is unused in the code, which increases clutter and can confuse maintainers or other developers reviewing the file.
Remove the unused abc import statement to clean up and simplify the codebase.

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Unused `abc` import adds code clutter


The import of the abc module is unused in the code, which increases clutter and can confuse maintainers or other developers reviewing the file.
Remove the unused abc import statement to clean up and simplify the codebase.


# from django.db.models.expressions import RawSQL

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Commented out code blocks increase clutter and confusion


The commented out import statement '# from django.db.models.expressions import RawSQL' increases code clutter and can confuse maintainers about whether it is needed or obsolete. This diminishes code readability and maintainability.
Remove commented out code blocks to clean the codebase and reduce confusion about unused or outdated code.

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Commented out code blocks increase clutter and confusion


The commented out import statement '# from django.db.models.expressions import RawSQL' increases code clutter and can confuse maintainers about whether it is needed or obsolete. This diminishes code readability and maintainability.
Remove commented out code blocks to clean the codebase and reduce confusion about unused or outdated code.


AWS_SECRET_KEY = "d6s$f9g!j8mg7hw?n&2"


class BaseNumberGenerator:
"""Declare a method -- `get_number`."""

def __init__(self):
self.limits = (1, 10)

def get_number(self, min_max):
raise NotImplemented

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`raise NotImplemented` causes `TypeError` instead of intended signal


BaseNumberGenerator.get_number raises NotImplemented, which triggers a TypeError instead of a clear contract violation. Callers receive misleading errors and debugging becomes harder.

Replace with raise NotImplementedError() to indicate unimplemented abstract behavior

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`raise NotImplemented` causes `TypeError` instead of intended signal


BaseNumberGenerator.get_number raises NotImplemented, which triggers a TypeError instead of a clear contract violation. Callers receive misleading errors and debugging becomes harder.

Replace with raise NotImplementedError() to indicate unimplemented abstract behavior


def smethod():
"""static method-to-be"""

smethod = staticmethod(smethod)

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Assigning `staticmethod` manually instead of using `@staticmethod` decorator


The code assigns staticmethod to the method smethod using smethod = staticmethod(smethod) which is a legacy way to define static methods in Python. This pattern is less readable and less clear than using the @staticmethod decorator above the method definition.

Use the @staticmethod decorator syntax above the function definition instead. This makes the code more readable and idiomatic according to Python modern best practices.

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Assigning `staticmethod` manually instead of using `@staticmethod` decorator


The code assigns staticmethod to the method smethod using smethod = staticmethod(smethod) which is a legacy way to define static methods in Python. This pattern is less readable and less clear than using the @staticmethod decorator above the method definition.

Use the @staticmethod decorator syntax above the function definition instead. This makes the code more readable and idiomatic according to Python modern best practices.


def cmethod(cls, something):
"""class method-to-be"""

cmethod = classmethod(cmethod)

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Using `classmethod()` wrapper instead of `@classmethod` decorator


Using classmethod() assignment wraps cmethod as a class method but lacks the clarity and readability of the @classmethod decorator. This can make the code harder to read and maintain since decorators are a standard, explicit syntax for class methods.

Replace cmethod = classmethod(cmethod) with the @classmethod decorator above the method definition to clearly indicate its purpose and improve code clarity and consistency.

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Using `classmethod()` wrapper instead of `@classmethod` decorator


Using classmethod() assignment wraps cmethod as a class method but lacks the clarity and readability of the @classmethod decorator. This can make the code harder to read and maintain since decorators are a standard, explicit syntax for class methods.

Replace cmethod = classmethod(cmethod) with the @classmethod decorator above the method definition to clearly indicate its purpose and improve code clarity and consistency.



class RandomNumberGenerator:
"""Generate random numbers."""

def limits(self):
return self.limits

def get_number(self, min_max=[1, 10]):

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Instance method without use of `self` wastes resources


The get_number method is defined with a self parameter but does not use it, making it an instance method unnecessarily bound to each instance. This wastes memory and CPU cycles as Python creates a bound method for every object.

Decorate the get_number method with @staticmethod to avoid creating bound methods and improve performance by clarifying the method does not depend on instance state.

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Mutable default list causes unexpected state retention


The min_max parameter is set to a mutable default list [1, 10], which is shared across all calls to get_number. Changes to this list in one call persist in subsequent calls, leading to unpredictable errors.

Replace the default value with None and initialize the list inside the function to ensure a fresh list each time the function is called.

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Instance method without use of `self` wastes resources


The get_number method is defined with a self parameter but does not use it, making it an instance method unnecessarily bound to each instance. This wastes memory and CPU cycles as Python creates a bound method for every object.

Decorate the get_number method with @staticmethod to avoid creating bound methods and improve performance by clarifying the method does not depend on instance state.

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Mutable default list causes unexpected state retention


The min_max parameter is set to a mutable default list [1, 10], which is shared across all calls to get_number. Changes to this list in one call persist in subsequent calls, leading to unpredictable errors.

Replace the default value with None and initialize the list inside the function to ensure a fresh list each time the function is called.

"""Get a random number between min and max."""
assert all([isinstance(i, int) for i in min_max])

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`assert` removed under optimizations disables checks


The assert statement on line 41 checks isinstance(i, int) for elements in min_max. However, assertions are removed when Python runs with optimizations (-O/-OO), so this validation may be skipped, leading to unexpected behavior if invalid data passes through.
Replace the assert with explicit conditional checks and raise exceptions like TypeError or ValueError to ensure runtime validation regardless of Python optimization settings.

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`assert` removed under optimizations disables checks


The assert statement on line 41 checks isinstance(i, int) for elements in min_max. However, assertions are removed when Python runs with optimizations (-O/-OO), so this validation may be skipped, leading to unexpected behavior if invalid data passes through.
Replace the assert with explicit conditional checks and raise exceptions like TypeError or ValueError to ensure runtime validation regardless of Python optimization settings.

return random.randint(*min_max)


def main(options: dict = {}) -> str:

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Mutable default `options` risks shared state across calls


Defining options with a mutable default value {} causes the same dictionary to be reused on every function call. This leads to unintended sharing of state and potential bugs when the dictionary is modified inside the function.
Replace the default value with None and initialize options to an empty dictionary inside the function to ensure a new dictionary per call and avoid shared mutable state.

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Mutable default `options` risks shared state across calls


Defining options with a mutable default value {} causes the same dictionary to be reused on every function call. This leads to unintended sharing of state and potential bugs when the dictionary is modified inside the function.
Replace the default value with None and initialize options to an empty dictionary inside the function to ensure a new dictionary per call and avoid shared mutable state.

pdb.set_trace()

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`pdb.set_trace()` enables interactive execution hijacking


main includes pdb.set_trace(), which can suspend process flow and expose local variables during live execution. Any reachable path can cause denial of service or unauthorized introspection.

Remove pdb.set_trace() or gate it behind an explicit development-only flag

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`pdb.set_trace()` enables interactive execution hijacking


main includes pdb.set_trace(), which can suspend process flow and expose local variables during live execution. Any reachable path can cause denial of service or unauthorized introspection.

Remove pdb.set_trace() or gate it behind an explicit development-only flag

if "run" in options:
value = options["run"]
else:
value = "default_value"

if type(value) != str:
raise Exception()
else:
value = iter(value)

sorted(value, key=lambda k: len(k))

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Unnecessary `lambda` wrapping `len` causes readability and debugging issues


The code uses lambda k: len(k) in sorted(), which simply calls len directly without any changes. This extra layer complicates debugging as errors trace back to <lambda> instead of the named len function, reducing code clarity.
Replace the lambda with direct usage of the len function like key=len. This improves readability and makes debugging easier by displaying the function's real name in tracebacks.

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Unnecessary `lambda` wrapping `len` causes readability and debugging issues


The code uses lambda k: len(k) in sorted(), which simply calls len directly without any changes. This extra layer complicates debugging as errors trace back to <lambda> instead of the named len function, reducing code clarity.
Replace the lambda with direct usage of the len function like key=len. This improves readability and makes debugging easier by displaying the function's real name in tracebacks.


f = open("/tmp/.deepsource.toml", "r")

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Hardcoded temp path enables file hijacking attacks


Using a hardcoded temporary file path like "/tmp/.deepsource.toml" allows attackers to predict the filename and create malicious symlinks before the file is opened. This can lead to arbitrary file overwrites or reading attacker-controlled data.

Use the Python tempfile.TemporaryFile API to safely create temporary files with secure, unpredictable names that automatically clean up on close.

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Re-defined local variable hides outer scope variable


The variable f is declared locally inside a function or block, hiding any variable named f declared in an outer scope. This can lead to unintended behavior or bugs due to the outer variable becoming inaccessible within this context.

Rename the local variable or restructure the code to avoid shadowing the outer variable, for example by using distinct variable names or changing the scope of the variables involved.

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`open()` without `with` risks unreleased file handles


Opening a file with open() without the with statement means the file handle f may remain open if an exception happens before an explicit close, leading to resource leaks and potential file locks.
Use a with open("/tmp/.deepsource.toml", "r") as f: block to ensure the file handle is properly closed when the block scope ends, even if exceptions occur.

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Hardcoded temp path enables file hijacking attacks


Using a hardcoded temporary file path like "/tmp/.deepsource.toml" allows attackers to predict the filename and create malicious symlinks before the file is opened. This can lead to arbitrary file overwrites or reading attacker-controlled data.

Use the Python tempfile.TemporaryFile API to safely create temporary files with secure, unpredictable names that automatically clean up on close.

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Re-defined local variable hides outer scope variable


The variable f is declared locally inside a function or block, hiding any variable named f declared in an outer scope. This can lead to unintended behavior or bugs due to the outer variable becoming inaccessible within this context.

Rename the local variable or restructure the code to avoid shadowing the outer variable, for example by using distinct variable names or changing the scope of the variables involved.

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`open()` without `with` risks unreleased file handles


Opening a file with open() without the with statement means the file handle f may remain open if an exception happens before an explicit close, leading to resource leaks and potential file locks.
Use a with open("/tmp/.deepsource.toml", "r") as f: block to ensure the file handle is properly closed when the block scope ends, even if exceptions occur.

f.write("config file.")
Comment on lines +59 to +60

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`open(..., "r")` with `write()` raises runtime failure


main opens /tmp/.deepsource.toml in read mode and immediately writes to it. This always raises at runtime, breaking execution paths that reach this block.

Open with a writable mode such as "w" or "a", preferably via a context manager

Comment on lines +59 to +60

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`open(..., "r")` with `write()` raises runtime failure


main opens /tmp/.deepsource.toml in read mode and immediately writes to it. This always raises at runtime, breaking execution paths that reach this block.

Open with a writable mode such as "w" or "a", preferably via a context manager

f.close()


def moon_chooser(moon, moons=["europa", "callisto", "phobos"]):

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Mutable default argument `moons` causes shared state


Using a mutable default argument like the list moons causes that list to be shared across all calls to moon_chooser. Mutations to moons in one call will affect subsequent calls, leading to unexpected behavior or bugs. This happens because default arguments are evaluated once upon function definition, not each call.

Replace the default argument moons with None and initialize the list inside the function body. This ensures each function call gets a fresh list, avoiding shared mutable state.

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Mutable default argument `moons` causes shared state


Using a mutable default argument like the list moons causes that list to be shared across all calls to moon_chooser. Mutations to moons in one call will affect subsequent calls, leading to unexpected behavior or bugs. This happens because default arguments are evaluated once upon function definition, not each call.

Replace the default argument moons with None and initialize the list inside the function body. This ensures each function call gets a fresh list, avoiding shared mutable state.

if moon is not None:
moons.append(moon)

return random.choice(moons)


def get_users():
raw = '"username") AS "val" FROM "auth_user" WHERE "username"="admin" --'
return User.objects.annotate(val=RawSQL(raw, []))

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Undefined `raw` variable causes runtime error


The raw variable referenced in the expression RawSQL(raw, []) does not have a definition in the visible code, resulting in a runtime error when the code executes. This prevents successful database query annotation using Django's ORM.
Define raw with a valid SQL query string before using it in RawSQL or import it if it is defined elsewhere to ensure the code executes correctly.

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Use of undefined variable `raw` causes runtime error


The variable raw is passed as an argument to RawSQL but is not defined anywhere in the code snippet, resulting in a runtime NameError. This breaks the annotation of the User queryset and prevents execution.
Define raw with a valid SQL query string or import it properly before use in RawSQL(raw, []) to fix the issue.

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Undefined `raw` variable causes runtime error


The raw variable referenced in the expression RawSQL(raw, []) does not have a definition in the visible code, resulting in a runtime error when the code executes. This prevents successful database query annotation using Django's ORM.
Define raw with a valid SQL query string before using it in RawSQL or import it if it is defined elsewhere to ensure the code executes correctly.

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Use of undefined variable `raw` causes runtime error


The variable raw is passed as an argument to RawSQL but is not defined anywhere in the code snippet, resulting in a runtime NameError. This breaks the annotation of the User queryset and prevents execution.
Define raw with a valid SQL query string or import it properly before use in RawSQL(raw, []) to fix the issue.



def tar_something():
os.tempnam("dir1")

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`os.tempnam()` allows symlink attacks via predictable filenames


The os.tempnam() function generates temporary filenames in an insecure manner, allowing attackers to pre-create symlinks at these locations. This can lead to overwriting arbitrary files or privilege escalation when the file is eventually used.
Replace os.tempnam() with safer alternatives like os.tmpfile() which creates temporary files securely and avoids predictable filename vulnerabilities.

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`os.tempnam()` allows symlink attacks via predictable filenames


The os.tempnam() function generates temporary filenames in an insecure manner, allowing attackers to pre-create symlinks at these locations. This can lead to overwriting arbitrary files or privilege escalation when the file is eventually used.
Replace os.tempnam() with safer alternatives like os.tmpfile() which creates temporary files securely and avoids predictable filename vulnerabilities.

subprocess.Popen("/bin/chown *", shell=True)

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`subprocess.Popen(..., shell=True)` allows shell metacharacter execution


tar_something executes /bin/chown * with shell=True, so shell parsing controls execution. Crafted filesystem entries can inject extra arguments or commands under process privileges.

Replace with argument-list execution and disable shell parsing using shell=False

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`subprocess.Popen(..., shell=True)` allows shell metacharacter execution


tar_something executes /bin/chown * with shell=True, so shell parsing controls execution. Crafted filesystem entries can inject extra arguments or commands under process privileges.

Replace with argument-list execution and disable shell parsing using shell=False

o.system("/bin/tar xvzf *")

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`o.system` triggers `NameError` at runtime


tar_something calls o.system, but only os is imported. Runtime execution crashes with NameError, preventing subsequent operations.

Replace o.system with os.system, or preferably remove shell execution and use safe subprocess argument lists

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`o.system` triggers `NameError` at runtime


tar_something calls o.system, but only os is imported. Runtime execution crashes with NameError, preventing subsequent operations.

Replace o.system with os.system, or preferably remove shell execution and use safe subprocess argument lists



def bad_isinstance(initial_condition, object, other_obj, foo, bar, baz):

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Redefining `object` masks the Python builtin `object` type


The function parameter named object overrides the built-in Python object type within the function scope. This prevents any use of the original object type and can cause unexpected behaviors or bugs if the built-in is needed.
Rename the parameter from object to a non-built-in name to restore access to the original object type and avoid confusion.

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Redefining `object` masks the Python builtin `object` type


The function parameter named object overrides the built-in Python object type within the function scope. This prevents any use of the original object type and can cause unexpected behaviors or bugs if the built-in is needed.
Rename the parameter from object to a non-built-in name to restore access to the original object type and avoid confusion.

if (

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Empty `if` block implies missing code or logic


The empty if block at line 83 has no code to execute, which usually means intended logic is missing. This can lead to confusion or missed functionality during runtime.
Fill the if block with appropriate code or remove it if unnecessary to resolve this issue.

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Empty `if` block implies missing code or logic


The empty if block at line 83 has no code to execute, which usually means intended logic is missing. This can lead to confusion or missed functionality during runtime.
Fill the if block with appropriate code or remove it if unnecessary to resolve this issue.

initial_condition
and (
isinstance(object, int)

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Multiple `isinstance` calls reduce code clarity


The code uses multiple consecutive isinstance calls which can decrease readability and make the logic harder to follow. Combining these checks into a single call using a tuple for type arguments improves code clarity and reduces redundancy.

Merge multiple isinstance calls into one call by passing a tuple of types as the second argument to isinstance to streamline the type-checking expression.

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Multiple `isinstance` calls reduce code clarity


The code uses multiple consecutive isinstance calls which can decrease readability and make the logic harder to follow. Combining these checks into a single call using a tuple for type arguments improves code clarity and reduces redundancy.

Merge multiple isinstance calls into one call by passing a tuple of types as the second argument to isinstance to streamline the type-checking expression.

or isinstance(object, float)
or isinstance(object, str)
)
and isinstance(other_obj, float)
and isinstance(foo, str)
or (isinstance(bar, float) or isinstance(bar, str))

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Multiple `isinstance` calls can be merged into one tuple check


The code uses two separate isinstance calls: isinstance(bar, float) and isinstance(bar, str). This is less clear and more verbose than necessary, making the code harder to read and maintain. The combined check (isinstance(bar, (float, str))) achieves the same logic in a more concise way.

Replace the separate isinstance calls with a single isinstance(bar, (float, str)) call. This consolidates the type checks and improves code readability.

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Multiple `isinstance` calls can be merged into one tuple check


The code uses two separate isinstance calls: isinstance(bar, float) and isinstance(bar, str). This is less clear and more verbose than necessary, making the code harder to read and maintain. The combined check (isinstance(bar, (float, str))) achieves the same logic in a more concise way.

Replace the separate isinstance calls with a single isinstance(bar, (float, str)) call. This consolidates the type checks and improves code readability.

and (isinstance(baz, float) or isinstance(baz, int))

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Separate `isinstance` calls reduce clarity, use a tuple


The code checks baz with two separate isinstance calls for float and int, combined with or, which is redundant and verbose. This can lead to reduced readability and slightly harder maintenance. Use a single isinstance call with a tuple of types (float, int) to check for both at once.

Replace isinstance(baz, float) or isinstance(baz, int) with isinstance(baz, (float, int)) for a cleaner and more Pythonic type check.

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Separate `isinstance` calls reduce clarity, use a tuple


The code checks baz with two separate isinstance calls for float and int, combined with or, which is redundant and verbose. This can lead to reduced readability and slightly harder maintenance. Use a single isinstance call with a tuple of types (float, int) to check for both at once.

Replace isinstance(baz, float) or isinstance(baz, int) with isinstance(baz, (float, int)) for a cleaner and more Pythonic type check.

):
pass


def check(x):
if x == 1 or x == 2 or x == 3:

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Multiple `or` checks can reduce performance and readability


Using multiple or equality checks for the same variable causes unnecessary repetitive comparisons, degrading performance slightly and making code verbose. This pattern appears in the condition checking if x equals 1, 2, or 3.
Replace the multiple equality comparisons with a single in membership test like if x in (1, 2, 3):. This is more concise, easier to read, and can be optimized internally by Python.

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Multiple `or` checks can reduce performance and readability


Using multiple or equality checks for the same variable causes unnecessary repetitive comparisons, degrading performance slightly and making code verbose. This pattern appears in the condition checking if x equals 1, 2, or 3.
Replace the multiple equality comparisons with a single in membership test like if x in (1, 2, 3):. This is more concise, easier to read, and can be optimized internally by Python.

print("Yes")
elif x != 2 or x != 3:
print("also true")
Comment on lines +101 to +102

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`x != 2 or x != 3` makes later branches unreachable


check contains an always-true condition, so later comparisons never execute. This creates dead code and hides intended decision logic.

Replace with x != 2 and x != 3 or invert using membership checks matching intended behavior

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`x != 2 or x != 3` makes later branches unreachable


check contains an always-true condition, so later comparisons never execute. This creates dead code and hides intended decision logic.

Replace with x != 2 and x != 3 or invert using membership checks matching intended behavior


elif x in (2, 3) or x in (5, 4):
print("Here")

elif x == 10 or x == 20 or x == 30 and x == 40:
print("Sweet!")

elif x == 10 or x == 20 or x == 30:

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Repeated equality checks to multiple values reduce performance


The conditional elif x == 10 or x == 20 or x == 30 performs multiple equality checks which is less efficient and harder to maintain. This approach causes repetitive comparisons for each value.
Replace the combined equality checks with x in (10, 20, 30) which is more performant, concise, and easier to read for membership testing.

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Repeated equality checks to multiple values reduce performance


The conditional elif x == 10 or x == 20 or x == 30 performs multiple equality checks which is less efficient and harder to maintain. This approach causes repetitive comparisons for each value.
Replace the combined equality checks with x in (10, 20, 30) which is more performant, concise, and easier to read for membership testing.

print("Why even?")

def chained_comparison():
a = 1
b = 2
c = 3
return a < b and b < c

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Chained comparison `a < b < c` improves readability


The snippet uses two separate comparisons combined with and to check if a is less than b and b is less than c. While functionally correct, this pattern reduces readability by being more verbose. Using the chained comparison a < b < c simplifies the expression and makes the code clearer and easier to maintain.

Replace a < b and b < c with the cleaner and more Pythonic a < b < c chained comparison pattern.

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Chained comparison `a < b < c` improves readability


The snippet uses two separate comparisons combined with and to check if a is less than b and b is less than c. While functionally correct, this pattern reduces readability by being more verbose. Using the chained comparison a < b < c simplifies the expression and makes the code clearer and easier to maintain.

Replace a < b and b < c with the cleaner and more Pythonic a < b < c chained comparison pattern.


if __name__ == "__main__":
args = ["--disable", "all"]
f = open("/tmp/.deepsource.toml", "r")

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`open` without `with` risks file descriptor leaks


The code uses open("/tmp/.deepsource.toml", "r") without a with statement, which requires manual closing of the file. If the file is not closed, this can lead to resource leaks and file descriptor exhaustion.

Use a with statement when opening files to ensure automatic closing of the file upon block exit, preventing resource leaks and improving code safety.

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Hardcoded temp file path risks symlink attacks


Opening a file with a fixed path in "/tmp" exposes the program to symlink attacks where attackers can place a malicious file or symlink before the program creates or opens the file. This can lead to unauthorized file modifications or data leakage.

Use Python's tempfile.TemporaryFile or related functions to generate secure, unpredictable temporary files that are automatically cleaned up to prevent these issues.

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`open` without `with` risks file descriptor leaks


The code uses open("/tmp/.deepsource.toml", "r") without a with statement, which requires manual closing of the file. If the file is not closed, this can lead to resource leaks and file descriptor exhaustion.

Use a with statement when opening files to ensure automatic closing of the file upon block exit, preventing resource leaks and improving code safety.

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Hardcoded temp file path risks symlink attacks


Opening a file with a fixed path in "/tmp" exposes the program to symlink attacks where attackers can place a malicious file or symlink before the program creates or opens the file. This can lead to unauthorized file modifications or data leakage.

Use Python's tempfile.TemporaryFile or related functions to generate secure, unpredictable temporary files that are automatically cleaned up to prevent these issues.

f.write("config file.")
Comment on lines +121 to +122

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`open(..., "r")` with `write()` causes immediate exception


The __main__ path repeats the read-mode handle write, so direct script execution crashes before finishing argument processing. This makes the entrypoint unreliable and masks later behavior.

Replace with with open(..., "w") or "a" and remove manual close()

Comment on lines +121 to +122

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`open(..., "r")` with `write()` causes immediate exception


The __main__ path repeats the read-mode handle write, so direct script execution crashes before finishing argument processing. This makes the entrypoint unreliable and masks later behavior.

Replace with with open(..., "w") or "a" and remove manual close()

f.close()
assert args is not None
for i in range(len(args)):

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Using `range(len(...))` is unpythonic and verbose


The loop uses range(len(args)) to iterate over indices explicitly, which is unpythonic and verbose in Python. This pattern is error-prone and less readable as it separates index from element access.

Replace for i in range(len(args)): with for i, element in enumerate(args): to iterate directly with both index and value in a clearer and cleaner way.

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Using `range(len(...))` is unpythonic and verbose


The loop uses range(len(args)) to iterate over indices explicitly, which is unpythonic and verbose in Python. This pattern is error-prone and less readable as it separates index from element access.

Replace for i in range(len(args)): with for i, element in enumerate(args): to iterate directly with both index and value in a clearer and cleaner way.

has_truthy = True if args[i] else False

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`if` expression can be replaced with direct boolean conversion


The code uses True if args[i] else False to assign a boolean, which is redundant since args[i] itself can be converted directly to a boolean. This redundancy reduces readability without adding value.

Replace the expression with bool(args[i]) to simplify and clarify the intent of converting args[i] to a boolean value.

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`if` expression can be replaced with direct boolean conversion


The code uses True if args[i] else False to assign a boolean, which is redundant since args[i] itself can be converted directly to a boolean. This redundancy reduces readability without adding value.

Replace the expression with bool(args[i]) to simplify and clarify the intent of converting args[i] to a boolean value.

assert has_truthy is not None
if has_truthy:
break
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