# LiberTEM/LiberTEM

src/libertem/io/dataset/base/tiling_scheme.py

### Summary

C
7 hrs

#### Use of assert detected. The enclosed code will be removed when compiling to optimised byte code. Open

``        assert len(factors) == len(base_shape)``

#### Use of assert detected. The enclosed code will be removed when compiling to optimised byte code. Open

``        assert len(shape) == len(containing_shape)``

#### Cyclomatic complexity is too high in method get_scheme. (14) Open

``````    def get_scheme(
self,
udfs: Sequence[UDFProtocol],
dataset,

## Cyclomatic Complexity

Cyclomatic Complexity corresponds to the number of decisions a block of code contains plus 1. This number (also called McCabe number) is equal to the number of linearly independent paths through the code. This number can be used as a guide when testing conditional logic in blocks.

Radon analyzes the AST tree of a Python program to compute Cyclomatic Complexity. Statements have the following effects on Cyclomatic Complexity:

Construct Effect on CC Reasoning
if +1 An if statement is a single decision.
elif +1 The elif statement adds another decision.
else +0 The else statement does not cause a new decision. The decision is at the if.
for +1 There is a decision at the start of the loop.
while +1 There is a decision at the while statement.
except +1 Each except branch adds a new conditional path of execution.
finally +0 The finally block is unconditionally executed.
with +1 The with statement roughly corresponds to a try/except block (see PEP 343 for details).
assert +1 The assert statement internally roughly equals a conditional statement.
Comprehension +1 A list/set/dict comprehension of generator expression is equivalent to a for loop.
Boolean Operator +1 Every boolean operator (and, or) adds a decision point.

#### Cyclomatic complexity is too high in method validate. (12) Open

``````    def validate(
self,
shape: Tuple[int, ...],
ds_sig_shape: Tuple[int, ...],
size: int,``````

## Cyclomatic Complexity

Cyclomatic Complexity corresponds to the number of decisions a block of code contains plus 1. This number (also called McCabe number) is equal to the number of linearly independent paths through the code. This number can be used as a guide when testing conditional logic in blocks.

Radon analyzes the AST tree of a Python program to compute Cyclomatic Complexity. Statements have the following effects on Cyclomatic Complexity:

Construct Effect on CC Reasoning
if +1 An if statement is a single decision.
elif +1 The elif statement adds another decision.
else +0 The else statement does not cause a new decision. The decision is at the if.
for +1 There is a decision at the start of the loop.
while +1 There is a decision at the while statement.
except +1 Each except branch adds a new conditional path of execution.
finally +0 The finally block is unconditionally executed.
with +1 The with statement roughly corresponds to a try/except block (see PEP 343 for details).
assert +1 The assert statement internally roughly equals a conditional statement.
Comprehension +1 A list/set/dict comprehension of generator expression is equivalent to a for loop.
Boolean Operator +1 Every boolean operator (and, or) adds a decision point.

#### File `tiling_scheme.py` has 423 lines of code (exceeds 400 allowed). Consider refactoring. Open

``````import math
import logging
import warnings
from typing import List, TYPE_CHECKING, Optional, Tuple, Union, Sequence
from typing_extensions import Literal``````
Found in src/libertem/io/dataset/base/tiling_scheme.py - About 2 hrs to fix

#### Function `get_scheme` has a Cognitive Complexity of 17 (exceeds 5 allowed). Consider refactoring. Open

``````    def get_scheme(
self,
udfs: Sequence[UDFProtocol],
dataset,
Found in src/libertem/io/dataset/base/tiling_scheme.py - About 2 hrs to fix

# Cognitive Complexity

Cognitive Complexity is a measure of how difficult a unit of code is to intuitively understand. Unlike Cyclomatic Complexity, which determines how difficult your code will be to test, Cognitive Complexity tells you how difficult your code will be to read and comprehend.

### A method's cognitive complexity is based on a few simple rules:

• Code is not considered more complex when it uses shorthand that the language provides for collapsing multiple statements into one
• Code is considered more complex for each "break in the linear flow of the code"
• Code is considered more complex when "flow breaking structures are nested"

#### Cyclomatic complexity is too high in method _get_scale_factors. (8) Open

``````    def _get_scale_factors(self, shape, containing_shape, size, min_factors=None):
"""
Generate scaling factors to scale `shape` up to `size` elements,
while being constrained to `containing_shape`.
"""``````

## Cyclomatic Complexity

Cyclomatic Complexity corresponds to the number of decisions a block of code contains plus 1. This number (also called McCabe number) is equal to the number of linearly independent paths through the code. This number can be used as a guide when testing conditional logic in blocks.

Radon analyzes the AST tree of a Python program to compute Cyclomatic Complexity. Statements have the following effects on Cyclomatic Complexity:

Construct Effect on CC Reasoning
if +1 An if statement is a single decision.
elif +1 The elif statement adds another decision.
else +0 The else statement does not cause a new decision. The decision is at the if.
for +1 There is a decision at the start of the loop.
while +1 There is a decision at the while statement.
except +1 Each except branch adds a new conditional path of execution.
finally +0 The finally block is unconditionally executed.
with +1 The with statement roughly corresponds to a try/except block (see PEP 343 for details).
assert +1 The assert statement internally roughly equals a conditional statement.
Comprehension +1 A list/set/dict comprehension of generator expression is equivalent to a for loop.
Boolean Operator +1 Every boolean operator (and, or) adds a decision point.

#### Cyclomatic complexity is too high in method _get_intent. (7) Open

``````    def _get_intent(self, udfs: Sequence[UDFProtocol]) -> TilingIntent:
udf_methods = tuple(udf.get_method() for udf in udfs)
if any(m not in tuple(UDFMethod) for m in udf_methods):
raise UDFException('A UDF declared an invalid processing method')
if UDFMethod.PARTITION in udf_methods:``````

## Cyclomatic Complexity

Cyclomatic Complexity corresponds to the number of decisions a block of code contains plus 1. This number (also called McCabe number) is equal to the number of linearly independent paths through the code. This number can be used as a guide when testing conditional logic in blocks.

Radon analyzes the AST tree of a Python program to compute Cyclomatic Complexity. Statements have the following effects on Cyclomatic Complexity:

Construct Effect on CC Reasoning
if +1 An if statement is a single decision.
elif +1 The elif statement adds another decision.
else +0 The else statement does not cause a new decision. The decision is at the if.
for +1 There is a decision at the start of the loop.
while +1 There is a decision at the while statement.
except +1 Each except branch adds a new conditional path of execution.
finally +0 The finally block is unconditionally executed.
with +1 The with statement roughly corresponds to a try/except block (see PEP 343 for details).
assert +1 The assert statement internally roughly equals a conditional statement.
Comprehension +1 A list/set/dict comprehension of generator expression is equivalent to a for loop.
Boolean Operator +1 Every boolean operator (and, or) adds a decision point.

#### Function `validate` has a Cognitive Complexity of 12 (exceeds 5 allowed). Consider refactoring. Open

``````    def validate(
self,
shape: Tuple[int, ...],
ds_sig_shape: Tuple[int, ...],
size: int,``````
Found in src/libertem/io/dataset/base/tiling_scheme.py - About 1 hr to fix

# Cognitive Complexity

Cognitive Complexity is a measure of how difficult a unit of code is to intuitively understand. Unlike Cyclomatic Complexity, which determines how difficult your code will be to test, Cognitive Complexity tells you how difficult your code will be to read and comprehend.

### A method's cognitive complexity is based on a few simple rules:

• Code is not considered more complex when it uses shorthand that the language provides for collapsing multiple statements into one
• Code is considered more complex for each "break in the linear flow of the code"
• Code is considered more complex when "flow breaking structures are nested"

#### Function `get_scheme` has 28 lines of code (exceeds 25 allowed). Consider refactoring. Open

``````    def get_scheme(
self,
udfs: Sequence[UDFProtocol],
dataset,
Found in src/libertem/io/dataset/base/tiling_scheme.py - About 1 hr to fix

#### Refactor this function to reduce its Cognitive Complexity from 20 to the 15 allowed. Open

``    def get_scheme(``

Cognitive Complexity is a measure of how hard the control flow of a function is to understand. Functions with high Cognitive Complexity will be difficult to maintain.

## See

#### Method "validate" has 8 parameters, which is greater than the 7 authorized. Open

``````        self,
shape: Tuple[int, ...],
ds_sig_shape: Tuple[int, ...],
size: int,
io_max_size: int,``````

A long parameter list can indicate that a new structure should be created to wrap the numerous parameters or that the function is doing too many things.

## Noncompliant Code Example

With a maximum number of 4 parameters:

```def do_something(param1, param2, param3, param4, param5):
...
```

## Compliant Solution

```def do_something(param1, param2, param3, param4):
...
```

#### Take the required action to fix the issue indicated by this "FIXME" comment. Open

``        # FIXME: validate navigation part of the tileshape to be contiguous``

`FIXME` tags are commonly used to mark places where a bug is suspected, but which the developer wants to deal with later.

Sometimes the developer will not have the time or will simply forget to get back to that tag.

This rule is meant to track those tags and to ensure that they do not go unnoticed.

## Noncompliant Code Example

```def divide(numerator, denominator):
return numerator / denominator              # FIXME denominator value might be 0
```

## See

#### Take the required action to fix the issue indicated by this "FIXME" comment. Open

``        # FIXME: adjust size to L3 // number of workers per node``

`FIXME` tags are commonly used to mark places where a bug is suspected, but which the developer wants to deal with later.

Sometimes the developer will not have the time or will simply forget to get back to that tag.

This rule is meant to track those tags and to ensure that they do not go unnoticed.

## Noncompliant Code Example

```def divide(numerator, denominator):
return numerator / denominator              # FIXME denominator value might be 0
```

## See

#### Take the required action to fix the issue indicated by this "FIXME" comment. Open

``        # FIXME: this veto may break if the base shape was adjusted``

`FIXME` tags are commonly used to mark places where a bug is suspected, but which the developer wants to deal with later.

Sometimes the developer will not have the time or will simply forget to get back to that tag.

This rule is meant to track those tags and to ensure that they do not go unnoticed.

## Noncompliant Code Example

```def divide(numerator, denominator):
return numerator / denominator              # FIXME denominator value might be 0
```

## See

#### Take the required action to fix the issue indicated by this "FIXME" comment. Open

``        # FIXME: let the UDF define upper bound for signal size (lower bound, too?)``

`FIXME` tags are commonly used to mark places where a bug is suspected, but which the developer wants to deal with later.

Sometimes the developer will not have the time or will simply forget to get back to that tag.

This rule is meant to track those tags and to ensure that they do not go unnoticed.

## Noncompliant Code Example

```def divide(numerator, denominator):
return numerator / denominator              # FIXME denominator value might be 0
```