Initial commit
This commit is contained in:
Zhongwei Li
252
skills/competenties/scripts/hboi.py
Executable file
252
skills/competenties/scripts/hboi.py
Executable file
@@ -0,0 +1,252 @@
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#!/usr/bin/env python3
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"""
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HBO-I Professional Tasks Query Tool
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This module provides a command-line interface for querying professional tasks
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from the Open-ICT competency framework for HBO-I (Higher Professional Education
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in Information Technology). The framework organizes tasks by architecture layers,
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activities, and proficiency levels.
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The data structure follows a three-dimensional matrix:
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- Architecture layers (e.g., Software, Infrastructure)
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- Activities (e.g., Analyseren, Ontwerpen, Realiseren)
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- Proficiency levels (1-4, representing increasing complexity)
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Usage:
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python hboi.py [--layer LAYER] [--activity ACTIVITY] [--level LEVEL]
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Exit Codes:
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0: Success
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1: Error (invalid input, missing data file, or no results found)
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"""
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import json
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import sys
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import argparse
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from pathlib import Path
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# Directory paths for locating data files relative to script location
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SCRIPT_DIR = Path(__file__).parent
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DATA_DIR = SCRIPT_DIR.parent / "data"
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# Valid architecture layers as defined in the Open-ICT framework
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# These represent different technical domains in ICT systems
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VALID_LAYERS = [
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"Gebruikersinteractie", # User interaction layer
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"Organisatieprocessen", # Organizational processes layer
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"Infrastructuur", # Infrastructure layer
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"Software", # Software layer
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"Hardwareinterfacing" # Hardware interfacing layer
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]
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# Valid professional activities as defined in the Open-ICT framework
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# These represent different phases of the development lifecycle
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VALID_ACTIVITIES = [
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"Analyseren", # Analysis phase
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"Adviseren", # Advisory phase
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"Ontwerpen", # Design phase
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"Realiseren", # Implementation phase
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"Manage & Control" # Management and control phase
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]
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def load_data():
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"""
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Load professional tasks data from the JSON data file.
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Reads the hboi-nl.json file containing the complete HBO-I competency
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framework data. The file is expected to be in the data directory relative
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to this script's location.
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Returns:
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dict: Nested dictionary structure containing professional tasks organized by:
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{layer: {activity: {level: task_description}}}
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Raises:
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SystemExit: If the data file is not found, contains invalid JSON,
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or cannot be read for any other reason.
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Note:
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All errors are written to stderr before exiting with status code 1.
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"""
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data_file = DATA_DIR / "hboi-nl.json"
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# Verify data file exists before attempting to read
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if not data_file.exists():
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print(f"Error: Data file not found: {data_file}", file=sys.stderr)
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sys.exit(1)
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try:
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# Open with explicit UTF-8 encoding to handle Dutch characters
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with open(data_file, 'r', encoding='utf-8') as f:
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return json.load(f)
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except json.JSONDecodeError as e:
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# Provide specific error for malformed JSON
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print(f"Error: Invalid JSON in data file: {e}", file=sys.stderr)
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sys.exit(1)
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except Exception as e:
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# Catch-all for other file reading errors (permissions, I/O, etc.)
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print(f"Error: {e}", file=sys.stderr)
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sys.exit(1)
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def filter_tasks(data, layer=None, activity=None, level=None):
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"""
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Filter professional tasks based on specified criteria.
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Applies one or more filters to the competency framework data to narrow down
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the results. Filters can be combined and are applied in the following order:
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1. Architecture layer (reduces dataset to single layer)
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2. Activity (filters activities within remaining layers)
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3. Level (filters specific proficiency levels within remaining activities)
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Args:
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data (dict): Complete competency framework data structure
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layer (str, optional): Architecture layer to filter by (e.g., "Software")
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activity (str, optional): Activity type to filter by (e.g., "Ontwerpen")
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level (str, optional): Proficiency level to filter by ("1", "2", "3", or "4")
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Returns:
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dict: Filtered data structure maintaining the same nested format as input,
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but containing only entries matching all specified filters
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Raises:
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SystemExit: If the specified layer is not found in the data, or if no
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tasks match the specified filter combination
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Note:
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When no filters are specified, returns the complete dataset unchanged.
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"""
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result = {}
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# First-level filter: narrow down to specific architecture layer if requested
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if layer:
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if layer not in data:
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print(f"Error: Architecture layer '{layer}' not found", file=sys.stderr)
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print(f"Valid layers: {', '.join(VALID_LAYERS)}", file=sys.stderr)
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sys.exit(1)
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# Reduce dataset to only the requested layer
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data = {layer: data[layer]}
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# Second and third level filters: iterate through remaining layers
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# and apply activity and/or level filters
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for layer_name, activities in data.items():
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filtered_activities = {}
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for activity_name, levels in activities.items():
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# Skip activities that don't match the activity filter
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if activity and activity_name != activity:
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continue
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# Apply level filter if specified
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if level:
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# Use dictionary comprehension to filter levels efficiently
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filtered_levels = {lvl: content for lvl, content in levels.items() if lvl == level}
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# Only include activity if it has matching levels
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if filtered_levels:
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filtered_activities[activity_name] = filtered_levels
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else:
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# No level filter: include all levels for this activity
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filtered_activities[activity_name] = levels
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# Only include layer if it has matching activities after filtering
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if filtered_activities:
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result[layer_name] = filtered_activities
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# Validate that at least one task matches the filter criteria
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if not result:
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print("No professional tasks found with the specified filters", file=sys.stderr)
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sys.exit(1)
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return result
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def main():
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"""
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Main entry point for the command-line interface.
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Parses command-line arguments, validates user input, loads the competency
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framework data, applies filters, and outputs the results as formatted JSON.
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The function handles all user interaction and orchestrates the data loading
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and filtering pipeline. All errors are reported to stderr with helpful
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messages, and the program exits with appropriate status codes.
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Returns:
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None: Outputs results to stdout and exits with status code 0 on success,
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or exits with status code 1 on error
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Side Effects:
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- Reads from data file system
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- Writes to stdout (JSON results)
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- Writes to stderr (error messages)
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- Calls sys.exit() on errors or successful completion
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"""
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# Configure argument parser with detailed help text
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# RawDescriptionHelpFormatter preserves formatting in epilog
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parser = argparse.ArgumentParser(
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description="Retrieve HBO-I professional tasks from the Open-ICT competency framework.",
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formatter_class=argparse.RawDescriptionHelpFormatter,
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epilog=f"""
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Architecture layers:
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{chr(10).join(' - ' + layer for layer in VALID_LAYERS)}
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Activities:
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{chr(10).join(' - ' + activity for activity in VALID_ACTIVITIES)}
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Examples:
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%(prog)s
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%(prog)s --layer "Software"
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%(prog)s --activity "Ontwerpen"
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%(prog)s --layer "Software" --activity "Realiseren"
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%(prog)s --layer "Software" --level 2
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%(prog)s --level 3
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"""
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)
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# Define command-line arguments
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# All arguments are optional, allowing flexible querying
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parser.add_argument(
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'--layer',
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help='Filter by architecture layer'
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)
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parser.add_argument(
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'--activity',
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help='Filter by activity'
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)
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# Level argument uses choices to restrict valid values at parse time
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parser.add_argument(
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'--level',
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type=str,
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choices=['1', '2', '3', '4'],
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help='Filter by level (1-4)'
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)
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# Parse command-line arguments (exits with error message if invalid)
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args = parser.parse_args()
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# Additional validation: argparse handles level choices, but we validate
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# layer and activity against our constants to ensure consistency
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# This provides better error messages than relying on filter_tasks()
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if args.layer and args.layer not in VALID_LAYERS:
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print(f"Error: Invalid architecture layer: {args.layer}", file=sys.stderr)
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print(f"Valid layers: {', '.join(VALID_LAYERS)}", file=sys.stderr)
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sys.exit(1)
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if args.activity and args.activity not in VALID_ACTIVITIES:
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print(f"Error: Invalid activity: {args.activity}", file=sys.stderr)
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print(f"Valid activities: {', '.join(VALID_ACTIVITIES)}", file=sys.stderr)
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sys.exit(1)
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# Execute the main workflow: load data, apply filters, output results
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data = load_data()
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result = filter_tasks(data, args.layer, args.activity, args.level)
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# Output filtered results as formatted JSON
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# ensure_ascii=False preserves Dutch characters (e.g., é, ë)
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# indent=2 provides human-readable formatting for debugging
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print(json.dumps(result, ensure_ascii=False, indent=2))
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# Entry point: only execute main() when script is run directly (not when imported)
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if __name__ == "__main__":
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main()
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216
skills/competenties/scripts/vaardigheden.py
Executable file
216
skills/competenties/scripts/vaardigheden.py
Executable file
@@ -0,0 +1,216 @@
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#!/usr/bin/env python3
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"""
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Student Skills Query Tool
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This module provides a command-line interface for querying student skills
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from the Open-ICT competency framework. The framework organizes skills by
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skill name and proficiency levels, representing different competencies that
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students should develop during their ICT education.
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The data structure follows a two-dimensional matrix:
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- Skill names (e.g., "Samenwerken", "Kritisch oordelen")
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- Proficiency levels (1-4, representing increasing complexity)
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Usage:
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python vaardigheden.py [--skill SKILL] [--level LEVEL]
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Exit Codes:
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0: Success
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1: Error (invalid input, missing data file, or no results found)
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"""
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import json
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import sys
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import argparse
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from pathlib import Path
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# Directory paths for locating data files relative to script location
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SCRIPT_DIR = Path(__file__).parent
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DATA_DIR = SCRIPT_DIR.parent / "data"
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# Valid skill names as defined in the Open-ICT framework
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# These represent core competencies that ICT students should develop
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VALID_SKILLS = [
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"Juiste kennis ontwikkelen", # Developing appropriate knowledge
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"Kwalitatief product maken", # Creating quality products
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"Overzicht creëren", # Creating overview
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"Kritisch oordelen", # Critical judgment
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"Samenwerken", # Collaboration
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"Boodschap delen", # Sharing messages
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"Plannen", # Planning
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"Flexibel opstellen", # Being flexible
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"Pro-actief handelen", # Proactive action
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"Reflecteren" # Reflection
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]
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def load_data():
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"""
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Load student skills data from the JSON data file.
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Reads the vaardigheden-nl.json file containing the complete student skills
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competency framework data. The file is expected to be in the data directory
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relative to this script's location.
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Returns:
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dict: Nested dictionary structure containing student skills organized by:
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{skill_name: {level: skill_description}}
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Raises:
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SystemExit: If the data file is not found, contains invalid JSON,
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or cannot be read for any other reason.
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Note:
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All errors are written to stderr before exiting with status code 1.
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"""
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data_file = DATA_DIR / "vaardigheden-nl.json"
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# Verify data file exists before attempting to read
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if not data_file.exists():
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print(f"Error: Data file not found: {data_file}", file=sys.stderr)
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sys.exit(1)
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try:
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# Open with explicit UTF-8 encoding to handle Dutch characters
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with open(data_file, 'r', encoding='utf-8') as f:
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return json.load(f)
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except json.JSONDecodeError as e:
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# Provide specific error for malformed JSON
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print(f"Error: Invalid JSON in data file: {e}", file=sys.stderr)
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sys.exit(1)
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except Exception as e:
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# Catch-all for other file reading errors (permissions, I/O, etc.)
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print(f"Error: {e}", file=sys.stderr)
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sys.exit(1)
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def filter_skills(data, skill_name=None, level=None):
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"""
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Filter student skills based on specified criteria.
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Applies one or more filters to the competency framework data to narrow down
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the results. Filters can be combined and are applied in the following order:
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1. Skill name (reduces dataset to single skill)
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2. Level (filters specific proficiency levels within remaining skills)
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Args:
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data (dict): Complete competency framework data structure
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skill_name (str, optional): Skill name to filter by (e.g., "Samenwerken")
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level (str, optional): Proficiency level to filter by ("1", "2", "3", or "4")
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Returns:
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dict: Filtered data structure maintaining the same nested format as input,
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but containing only entries matching all specified filters
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Raises:
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SystemExit: If the specified skill name is not found in the data, or if
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no skills match the specified filter combination
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Note:
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When no filters are specified, returns the complete dataset unchanged.
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"""
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result = {}
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# First-level filter: narrow down to specific skill if requested
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if skill_name:
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if skill_name not in data:
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print(f"Error: Skill '{skill_name}' not found", file=sys.stderr)
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print(f"Valid skills: {', '.join(VALID_SKILLS)}", file=sys.stderr)
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sys.exit(1)
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# Reduce dataset to only the requested skill
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data = {skill_name: data[skill_name]}
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# Second-level filter: apply level filter if specified
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if level:
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# Iterate through all skills in the dataset (may be filtered or complete)
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for skill, levels in data.items():
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# Use dictionary comprehension to filter levels efficiently
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filtered_levels = {lvl: content for lvl, content in levels.items() if lvl == level}
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# Only include skill if it has matching levels
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if filtered_levels:
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result[skill] = filtered_levels
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else:
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# No level filter: include all skills and levels
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result = data
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# Validate that at least one skill matches the filter criteria
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if not result:
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print("No skills found with the specified filters", file=sys.stderr)
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sys.exit(1)
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return result
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def main():
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"""
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Main entry point for the command-line interface.
|
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|
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Parses command-line arguments, validates user input, loads the competency
|
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framework data, applies filters, and outputs the results as formatted JSON.
|
||||
|
||||
The function handles all user interaction and orchestrates the data loading
|
||||
and filtering pipeline. All errors are reported to stderr with helpful
|
||||
messages, and the program exits with appropriate status codes.
|
||||
|
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Returns:
|
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None: Outputs results to stdout and exits with status code 0 on success,
|
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or exits with status code 1 on error
|
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|
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Side Effects:
|
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- Reads from data file system
|
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- Writes to stdout (JSON results)
|
||||
- Writes to stderr (error messages)
|
||||
- Calls sys.exit() on errors or successful completion
|
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"""
|
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# Configure argument parser with detailed help text
|
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# RawDescriptionHelpFormatter preserves formatting in epilog
|
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parser = argparse.ArgumentParser(
|
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description="Retrieve student skills from the Open-ICT competency framework.",
|
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formatter_class=argparse.RawDescriptionHelpFormatter,
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epilog=f"""
|
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Valid skills:
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{chr(10).join(' - ' + skill for skill in VALID_SKILLS)}
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|
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Examples:
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%(prog)s
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%(prog)s --skill "Samenwerken"
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%(prog)s --skill "Samenwerken" --level 2
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%(prog)s --level 3
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"""
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)
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# Define command-line arguments
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# All arguments are optional, allowing flexible querying
|
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parser.add_argument(
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'--skill',
|
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help='Filter by skill name'
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)
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# Level argument uses choices to restrict valid values at parse time
|
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parser.add_argument(
|
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'--level',
|
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type=str,
|
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choices=['1', '2', '3', '4'],
|
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help='Filter by level (1-4)'
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)
|
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|
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# Parse command-line arguments (exits with error message if invalid)
|
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args = parser.parse_args()
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|
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# Additional validation: validate skill name against our constants
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# This provides better error messages than relying on filter_skills()
|
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if args.skill and args.skill not in VALID_SKILLS:
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print(f"Error: Invalid skill: {args.skill}", file=sys.stderr)
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print(f"Valid skills: {', '.join(VALID_SKILLS)}", file=sys.stderr)
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sys.exit(1)
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# Execute the main workflow: load data, apply filters, output results
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data = load_data()
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result = filter_skills(data, args.skill, args.level)
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|
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# Output filtered results as formatted JSON
|
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# ensure_ascii=False preserves Dutch characters (e.g., é, ë)
|
||||
# indent=2 provides human-readable formatting for debugging
|
||||
print(json.dumps(result, ensure_ascii=False, indent=2))
|
||||
|
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# Entry point: only execute main() when script is run directly (not when imported)
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
||||
Reference in New Issue
Block a user