logbookgenerator.computation package¶

Submodules¶

logbookgenerator.computation.code_processing module¶

code_processing.py: Contains the functions for processing the code files.

logbookgenerator.computation.code_processing.process_block_comment(comment_lines: list[str], code_lines: list[str], task_comments: dict[str, list[tuple[str, str]]]) → tuple[list[str], list[str], dict[str, list[tuple[str, str]]]]¶

Process a block comment to extract answer comments and related code.

Parameters:

comment_lines (list[str]) – The comment lines.
code_lines (list[str]) – The code lines.
task_comments (dict[str, list[tuple[str, str]]]) – A dictionary where keys are task identifiers and values are lists of tuples containing comments and associated code.

Returns:

The updated comment lines, code lines, and task comments.

Return type:

tuple[list[str], list[str], dict[str, list[tuple[str, str]]]]

Raises:

ValueError – If the block comment is empty.

logbookgenerator.computation.code_processing.process_code_comments(code_lines: list[str], remove_comments: bool = False) → tuple[dict[str, list[tuple[str, str]]] | str, str | None]¶

Process the C++ code to extract answer comments and related code.

Parameters:

code_lines (list[str]) – The code lines.
remove_comments (bool, optional) – Whether to remove comments from the code, by default False

Returns:

The task comments, or the code as a string, and any remaining code lines, if remove_comments is True.

Return type:

tuple[dict[str, list[tuple[str, str]]] | str, str | None]

Notes

The resulting dictionary is in the form: {

“task_{task_number}_{subtask_number}”: [
(“The comment”, “The associated code”), …

}

logbookgenerator.computation.code_processing.process_inline_comment(comment_line: str, code_lines: list[str], task_comments: dict[str, list[tuple[str, str]]]) → tuple[str, list[str], dict[str, list[tuple[str, str]]]]¶

Process an inline comment to extract answer comments and related code.

Parameters:

comment_line (str) – The comment line.
code_lines (list[str]) – The code lines.
task_comments (dict[str, list[tuple[str, str]]]) – A dictionary where keys are task identifiers and values are lists of tuples containing comments and associated code.

Returns:

The updated comment line, code lines, and task comments.

Return type:

tuple[str, list[str], dict[str, list[tuple[str, str]]]]

logbookgenerator.computation.comment_extraction module¶

comment_extraction.py: Contains the functions for extracting comments from the code files.

logbookgenerator.computation.comment_extraction.extract_block_comment(code_lines: list[str], line_number: int) → tuple[list[str], int]¶

Extract a block comment from the code lines.

Parameters:

code_lines (list[str]) – The code lines.
line_number (int) – The line number of the block comment start.

Returns:

list[str] – The block comment lines.
int – The line number of the last line of the block comment.

logbookgenerator.computation.comment_extraction.extract_comment_code(comment_lines: list[str], line_number: int) → tuple[str, int]¶

Extract the comment and code from the comment lines.

Parameters:

comment_lines (list[str]) – The comment lines.
line_number (int) – The line number of the comment start.

Returns:

str – The comment content.
int – The line number of the last line of the comment.

logbookgenerator.computation.comment_extraction.extract_comment_content(comment_lines: list[str]) → str¶

Extract the comment content from the comment lines.

Parameters:: comment_lines (list[str]) – The comment lines.
Returns:: The comment content.
Return type:: str

logbookgenerator.computation.comment_extraction.extract_comment_id(comment_line: str) → str¶

Extract the comment identifier from the comment line.

Parameters:: comment_line (str) – The comment line.
Returns:: The comment identifier.
Return type:: str

logbookgenerator.computation.config_generation module¶

config_generation.py: Generate the configuration file for the logbook.

logbookgenerator.computation.config_generation.build_config_file() → Path¶

Builds the YAML config file with user input.

Returns:: The path to the configuration file.
Return type:: Path

Notes

The generated YAML file will have the following structure: ``` — module:

code: <module_code> name: <module_name> semester: <module_semester> year: <module_year>

statement:: text: <statement_text>
student:: id: <student_id> name: <student_name>
university:: department: <university_department> name: <university_name> start: <university_start>

```

logbookgenerator.computation.context_generation module¶

context_generation.py: Contains the functions for generating the context for the logbook.

logbookgenerator.computation.context_generation.generate_coursework_context(coursework_files: dict[str, str]) → tuple[dict[str, Any], dict[str, str]]¶

Generate the coursework context.

Parameters:: coursework_files (dict[str, str]) – The coursework files.
Returns:: The coursework context and clean codes.
Return type:: tuple[dict[str, Any], dict[str, str]]

Notes

The clean code is the code without any comments. The coursework context is a dictionary in the form: {

“file_name”: {

“task_{task_number}_{subtask_number}”: [
(“The comment”, “The associated code”), …

}

logbookgenerator.computation.context_generation.generate_logbook_contexts(config: dict[str, Any], weekly_files: list[dict[str, dict[str, str] | str]], coursework_files: dict[str, str], references: list[dict[str, str]]) → tuple[dict[str, Any], dict[str, Any] | None, dict[str, str] | None]¶

Generate the contexts for the logbook.

Parameters:

config (dict[str, Any]) – The configuration file.
weekly_files (list[dict[str, dict[str, str] | str]]) – The weekly files, containing the CPP files and reflections.
coursework_files (dict[str, str]) – The coursework files.
references (list[dict[str, str]]) – The references.

Returns:

The logbook contexts, coursework context, and clean coursework code.

Return type:

tuple[dict[str, Any], dict[str, Any] | None, dict[str, str] | None]

logbookgenerator.computation.context_generation.generate_tasks_context(cpp_files: dict[str, str]) → dict[str, Any]¶

Generate the tasks context.

Parameters:: cpp_files (dict[str, str]) – The CPP files.
Returns:: The tasks context.
Return type:: dict[str, Any]

logbookgenerator.computation.context_generation.generate_week_context(week_number: int, week_start_date: datetime, week_end_date: datetime, weekly_file: dict[str, dict[str, str] | str]) → dict[str, Any]¶

Generate the week context.

Parameters:

week_number (int) – The week number.
week_start_date (datetime) – The start date of the week.
week_end_date (datetime) – The end date of the week.
weekly_file (dict[str, dict[str, str] | str]) – The weekly file, containing the CPP files and reflections.

Returns:

The week context.

Return type:

dict[str, Any]

logbookgenerator.computation.context_generation.generate_weeks_context(weekly_files: list[dict[str, dict[str, str] | str]], start_date: datetime) → dict[str, Any]¶

Generate the weeks context.

Parameters:

weekly_files (list[dict[str, dict[str, str] | str]]) – The weekly files, containing the CPP files and reflections.
start_date (datetime) – The start date of the university.

Returns:

The weeks context.

Return type:

dict[str, Any]

logbookgenerator.computation.parsing module¶

parsing.py: Contains the functions for parsing the input directory.

logbookgenerator.computation.parsing.parse_input_directory(input_directory: Path) → tuple[list[dict[str, dict[str, str] | str]], dict[str, str], list[dict[str, str]]]¶

Parse the input directory.

Parameters:: input_directory (Path) – Path to the input directory.
Returns:: The weekly files (code and reflections), coursework files, and references.
Return type:: tuple[list[dict[str, dict[str, str] | str]], dict[str, str], list[dict[str, str]]]

logbookgenerator.computation.parsing.parse_weekly_directories(input_directory: Path) → tuple[list[dict[str, dict[str, str] | str]], dict[str, str]]¶

Parse the weeks directory.

Parameters:: input_directory (Path) – Path to the input directory.
Returns:: The CPP files and reflections for each week, given as a list of weeks, where each week has keys “cpp” and “reflection”, each containing a dictionary of files or a string respectively. Also returns the coursework files.
Return type:: tuple[list[dict[str, dict[str, str] | str]], dict[str, str]]

logbookgenerator.computation.render_context module¶

render_context.py: Contains the logic for rendering the context into a logbook.

logbookgenerator.computation.render_context.create_coursework(coursework_context: dict[str, Any]) → str¶

Create the coursework from the context.

Parameters:: coursework_context (dict) – The contexts to render into the coursework.

Notes

This function renders each part of the coursework and then combines them into the final markdown coursework.

logbookgenerator.computation.render_context.create_logbook(logbook_contexts: dict[str, Any]) → str¶

Create the logbook from the contexts.

Parameters:: logbook_contexts (dict) – The contexts to render into the logbook.

Notes

This function renders each part of the logbook and then combines them into the final markdown logbook.

logbookgenerator.computation.render_context.render_template(template_path: Path, context: dict[str, Any]) → str¶

Render the template with the context.

Parameters:

template_path (Path) – Path to the template.
context (dict[str, Any]) – Context to render the template.

Returns:

Rendered template.

Return type:

str

Raises:

FileNotFoundError – If the template does not exist.
jinja2.exceptions.TemplateSyntaxError – If there is a syntax error in the template.

Module contents¶

Computation package for the project.

logbookgenerator.computation package¶

Submodules¶

logbookgenerator.computation.code_processing module¶

logbookgenerator.computation.comment_extraction module¶

logbookgenerator.computation.config_generation module¶

logbookgenerator.computation.context_generation module¶

logbookgenerator.computation.parsing module¶

logbookgenerator.computation.render_context module¶

Module contents¶

logbookgenerator

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