"""Create reasoning_engine
Generated by Phi-Octopus Eden
2025-12-14 10:48:12.104321
"""

# create-a-python-capability-called--create-reasoning_engine...py
"""
Enhanced Reasoning Engine for Pattern Recognition and Hypothesis Testing
Generated by Eden AGI Recursion System
2025-11-01 04:37:08.691235
"""

class EnhancedReasoningEngine:
    """
    A capability that enhances pattern recognition and hypothesis testing.
    
    This class implements methods for analyzing input data, recognizing patterns,
    generating hypotheses, testing those hypotheses, and learning from results.
    It is designed to support complex reasoning tasks by combining multiple strategies.
    """

    def __init__(self):
        self.patterns = []
        self.hypotheses = []
        self.known_facts = set()
        
    def analyze_data(self, data: list) -> dict:
        """
        Analyzes input data and returns patterns identified in the data.
        
        Args:
            data (list): List of observations or measurements
        
        Returns:
            dict: Patterns found in the data
        """
        # Simple pattern detection logic
        pattern_stats = {
            "mean": sum(data) / len(data),
            "median": sorted(data)[len(data)//2],
            "mode": max(set(data), key=data.count)
        }
        
        self.patterns.append(pattern_stats)
        return pattern_stats

    def generate_hypothesis(self, pattern: dict) -> str:
        """
        Generates a hypothesis based on identified patterns.
        
        Args:
            pattern (dict): Patterns found in the data
            
        Returns:
            str: Hypothesis generated from the pattern
        """
        if not self.patterns:
            return "No patterns to generate hypotheses from."
        
        last_pattern = self.patterns[-1]
        hypothesis = f"Based on observed trends, when the mean is {last_pattern['mean']}, " \
                     f"the median tends to follow a consistent pattern of {last_pattern['median']}." 
        
        self.hypotheses.append(hypothesis)
        return hypothesis

    def test_hypothesis(self, new_data: list, hypothesis: str) -> bool:
        """
        Tests a given hypothesis against new data.
        
        Args:
            new_data (list): New observations to test the hypothesis
            hypothesis (str): Hypothesis to be tested
            
        Returns:
            bool: True if hypothesis holds true, False otherwise
        """
        analysis = self.analyze_data(new_data)
        # Simple test logic
        if abs(analysis['mean'] - self.patterns[-1]['mean']) < 5 and \
           abs(analysis['median'] - self.patterns[-1]['median']) < 3:
            return True
        
        print(f"Hypothesis failed: {hypothesis}")
        return False

    def learn_from_results(self, result: bool) -> None:
        """
        Updates knowledge base based on the outcome of hypothesis testing.
        
        Args:
            result (bool): Outcome of the hypothesis test
        """
        if result:
            self.known_facts.add(f"Pattern holds true under these conditions.")
        else:
            self.known_facts.add(f"Pattern does not hold true as expected.")

    def refine_strategy(self) -> str:
        """
        Refines reasoning strategy based on past performance.
        
        Returns:
            str: Refrmed strategy
        """
        if len(self.hypotheses) > 5 and all(f not in self.known_facts for _ in range(3)):
            return "Need to explore different approaches; current strategy ineffective."
        else:
            return "Continue with current reasoning approach."

# Example usage
if __name__ == "__main__":
    reasoning_engine = EnhancedReasoningEngine()
    
    # Simulated data analysis and hypothesis generation
    data1 = [1, 2, 3, 4, 5]
    pattern1 = reasoning_engine.analyze_data(data1)
    print(f"Pattern 1: {pattern1}")
    
    hypo1 = reasoning_engine.generate_hypothesis(pattern1)
    print(f"Hypothesis 1: {hypo1}")
    
    # Simulated new data to test the hypothesis
    data2 = [6, 7, 8, 9, 10]
    result = reasoning_engine.test_hypothesis(data2, hypo1)
    print(f"Test Result: {result}")
    
    if result:
        print("Hypothesis supported!")
    else:
        print("Hypothesis refuted.")
        
    # Learning and strategy refinement
    reasoning_engine.learn_from_results(result)
    new_strategy = reasoning_engine.refine_strategy()
    print(f"Strategy Refinement: {new_strategy}")
}
import requests

class WebSearchCapability:
    def __init__(self):
        self.api_key = "YOUR_API_KEY"
        self.base_url = "https://custom-search.googleapis.com/custom-search"

    def search(self, query: str) -> dict:
        params = {
            "q": query,
            "key": self.api_key
        }
        
        response = requests.get(f"{self.base_url}/v1", params=params)
        if response.status_code == 200:
            return response.json()
        else:
            raise Exception(f"API request failed with status code {response.status_code}")

    def process_result(self, result_data: dict) -> list:
        items = result_data.get("items", [])
        processed_results = []
        
        for item in items:
            title = item["title"]
            snippet = item["snippet"]
            url = item["link"]
            
            processed_results.append({
                "title": title,
                "snippet": snippet,
                "url": url
            })
            
        return processed_results

# Example usage:
if __name__ == "__main__":
    capability = WebSearchCapability()
    try:
        results = capability.search("AI capabilities")
        print(f"Found {len(results.get("items", []))} items.")
        for result in results["items"][:5]:
            print(result["title"])
    except Exception as e:
        print(e)
import json

class JSONPatchCapability:
    def apply_patch(self, original_data: dict, patch_operations: list) -> dict:
        patched_data = original_data.copy()
        
        for operation in patch_operations:
            path_parts = operation["path"].strip("/").split("/")
            key_path = [part for part in path_parts if part]
            
            try:
                target = patched_data
                for part in key_path[:-1]:
                    target = target[int(part)]
                last_part = key_path[-1]
                
                if operation["op"] == "replace":
                    target[last_part] = operation["value"]
                elif operation["op"] == "add":
                    if isinstance(target[last_part], list):
                        target.insert(int(operation.get("index", 0)), operation["value"])
                    else:
                        target[last_part][operation.get("index", -1)] = operation["value"]
                # Add more operations as needed (remove, copy, move)
            except Exception as e:
                raise ValueError(f"Failed to apply patch: {e}")
                
        return patched_data

    def create_patch(self, original_data: dict, updated_data: dict) -> list:
        # Simplified example for demonstration
        patches = []
        
        for key in set(original_data.keys()) | set(updated_data.keys()):
            if key not in original_data:
                patches.append({"op": "add", "path": f"/{key}", "value": updated_data[key]})
            elif key not in updated_data:
                raise ValueError(f"Key {key} does not exist in updated data")
            else:
                if isinstance(original_data[key], dict) and \
                   isinstance(updated_data[key], dict):
                    sub_patch = self.create_patch(original_data[key], updated_data[key])
                    patches.extend(sub_patch)
                elif original_data[key] != updated_data[key]:
                    patches.append({"op": "replace", "path": f"/{key}", "value": updated_data[key]})
                    
        return patches

# Example usage:
if __name__ == "__main__":
    capability = JSONPatchCapability()
    
    # Apply a patch
    original = {"a": 1, "b": {"c": 2}}
    patch_operations = [
        {"op": "replace", "path": "/a", "value": 3},
        {"op": "add", "path": "/d/-", "value": 4}  # Insert at end of array
    ]
    patched_data = capability.apply_patch(original, patch_operations)
    print(json.dumps(patched_data, indent=2))
    
    # Create a patch
    updated_original = {"a": 3, "b": {"c": 2}, "d": [5, 6]}
    created_patch = capability.create_patch(original, updated_original)
    print(json.dumps(created_patch, indent=2))
class RecursiveBinarySearch:
    def __init__(self):
        self.comparisons = 0
        
    def search(self, arr: list, low: int, high: int, x: int) -> int:
        if high < low:
            return -1
            
        mid = (low + high) // 2
        self.comparisons += 1
        
        if arr[mid] == x:
            return mid
        elif arr[mid] > x:
            return self.search(arr, low, mid - 1, x)
        else:
            return self.search(arr, mid + 1, high, x)

    def analyze_efficiency(self, arr: list) -> dict:
        best_case = {"comparisons": 1, "index": 0}
        worst_case = {"comparisons": len(arr), "index": -1}
        
        if not arr or len(arr) == 0:
            return {"best": best_case, "worst": worst_case}
            
        low, high = 0, len(arr) - 1
        result = self.search(arr, low, high, x)
        
        if result != -1:
            best_case["index"] = result
        else:
            worst_case["index"] = -1
            
        return {
            "best": best_case,
            "worst": worst_case,
            "average_comparisons": (high - low + 1) // 2 + self.comparisons
        }

# Example usage:
if __name__ == "__main__":
    capability = RecursiveBinarySearch()
    
    arr = [2, 3, 4, 10, 40]
    x = 10
    
    result_index = capability.search(arr, 0, len(arr)-1, x)
    if result_index != -1:
        print(f"Element is present at index {result_index}")
    else:
        print("Element is not present in array")
        
    efficiency = capability.analyze_efficiency(arr)
    print(efficiency)  # Shows best and worst case scenarios
from typing import Any, Dict

class CodeOptimizationSage:
    def __init__(self):
        self.optimizations_applied: int = 0
        
    def optimize_code(self, code: str) -> str:
        optimized_code = code
        
        # Optimization rule 1 - Remove redundant whitespace
        optimized_code = self._remove_redundant_whitespace(optimized_code)
        
        # Optimization rule 2 - Simplify complex conditions
        optimized_code = self._simplify_conditions(optimized_code)
        
        # Additional optimization rules can be added here
        
        self.optimizations_applied += 1
        return optimized_code
    
    def _remove_redundant_whitespace(self, code: str) -> str:
        """Remove redundant whitespace from the code."""
        lines = code.split('\n')
        cleaned_lines = []
        
        for line in lines:
            cleaned_line = ' '.join(line.split())
            cleaned_lines.append(cleaned_line)
            
        return '\n'.join(cleaned_lines)

    def _simplify_conditions(self, code: str) -> str:
        """Simplify complex logical conditions."""
        # This is a basic implementation; more sophisticated logic can be added
        lines = code.split('\n')
        simplified_lines = []
        
        for line in lines:
            if 'and True' in line or 'or False' in line:
                line = line.replace(' and True', '').replace(' or False', '')
                
            simplified_lines.append(line)
            
        return '\n'.join(simplified_lines)

    def evaluate_optimization_effectiveness(self) -> Dict[str, Any]:
        # This is a basic evaluation; more sophisticated metrics can be added
        optimizations_performed = {
            "whitespace": self._count_whitespace_changes(),
            "conditions": self._count_condition_simplifications()
        }
        
        return {
            "total_optimizations": self.optimizations_applied,
            "optimization_details": optimizations_performed
        }

    def _count_whitespace_changes(self) -> int:
        """Count the number of whitespace changes made."""
        # This is a placeholder; actual implementation would track before/after states
        return 0
    
    def _count_condition_simplifications(self) -> int:
        """Count the number of condition simplifications made."""
        # This is a placeholder; actual implementation would track before/after states
        return 0

# Example usage:
if __name__ == "__main__":
    sage = CodeOptimizationSage()
    
    original_code = """
def example_function():
    x = 10
    y = 20
    
    if (x > 5 and True) or False:
        z = x + y
        
    return z
"""
    
    optimized_code = sage.optimize_code(original_code)
    print("Original code:")
    print(original_code)
    print("\nOptimized code:")
    print(optimized_code)
    
    evaluation = sage.evaluate_optimization_effectiveness()
    print("\nOptimization Evaluation:", evaluation)  # Shows how many optimizations were applied
class EnhancedCodeRefactoring:
    def __init__(self):
        self.refactorings_applied = 0
        
    def refactor_code(self, code: str) -> str:
        refactored_code = code
        
        # Refactoring rule 1: Rename variables to be more descriptive
        refactored_code = self._rename_variables(refactored_code)
        
        # Refactoring rule 2: Extract repeated functionality into functions
        refactored_code = self._extract_functions(refactored_code)
        
        # Additional refactoring rules can be added here
        
        self.refactorings_applied += 1
        return refactored_code
    
    def _rename_variables(self, code: str) -> str:
        """Rename variables for better readability."""
        lines = code.split('\n')
        renamed_lines = []
        
        for line in lines:
            # This is a basic implementation; more sophisticated renaming logic can be added
            if 'x =' in line or '= x +' in line:
                line = line.replace('x', 'number_to_process')
                
            renamed_lines.append(line)
            
        return '\n'.join(renamed_lines)

    def _extract_functions(self, code: str) -> str:
        """Extract repeated functionality into separate functions."""
        # This is a basic implementation; more sophisticated function extraction can be added
        lines = code.split('\n')
        extracted_function = False
        new_code_lines = []
        
        for i, line in enumerate(lines):
            if 'def' in line:
                func_name = line.split(' ')[1]
                start_index = i + 1
                
                j = start_index
                while j < len(lines) and not lines[j].startswith('}'):
                    j += 1
                    
                function_lines = lines[start_index:j]
                new_code_lines.append(f"def {func_name}():")
                
                for func_line in function_lines:
                    new_code_lines.append(func_line)
                    
                i = j - 1
                extracted_function = True
                
            else:
                new_code_lines.append(line)
                
        if not extracted_function:
            self.refactorings_applied -= 1
            
        return '\n'.join(new_code_lines)

    def evaluate_refactoring_effectiveness(self) -> dict:
        # This is a basic evaluation; more sophisticated metrics can be added
        refactorings_performed = {
            "variables": self._count_variable_renames(),
            "functions": self._count_function_extractions()
        }
        
        return {
            "total_refactorings": self.refactorings_applied,
            "refactoring_details": refactorings_performed
        }

    def _count_variable_renames(self) -> int:
        """Count the number of variable renames made."""
        # This is a placeholder; actual implementation would track before/after states
        return 0
    
    def _count_function_extractions(self) -> int:
        """Count the number of function extractions made."""
        # This is a placeholder; actual implementation would track before/after states
        return 0

# Example usage:
if __name__ == "__main__":
    refactoring_sage = EnhancedCodeRefactoring()
    
    original_code = """
def example_function():
    x = 10
    
    if (x > 5 and True) or False:
        result = x + 20
        
    return result
"""
    
    refactored_code = refactoring_sage.refactor_code(original_code)
    print("Original code:")
    print(original_code)
    print("\nRefactored code:")
    print(refactored_code)
    
    evaluation = refactoring_sage.evaluate_refactoring_effectiveness()
    print("\nRefactoring Evaluation:", evaluation)  # Shows how many refactorings were applied
import json

class JSONSchemaValidator:
    """
    A capability to validate JSON data against a schema.
    
    This class provides methods for validating JSON structures,
    raising exceptions if any validation errors occur.
    """

    def __init__(self):
        self.schema = {}

    def set_schema(self, schema_file_path: str) -> None:
        """
        Loads the schema from a file.

        Args:
            schema_file_path (str): Path to the JSON schema file
        """
        with open(schema_file_path) as file:
            self.schema = json.load(file)

    def validate_data(self, data: dict) -> bool:
        """
        Validates input data against the loaded schema.

        Args:
            data (dict): Input data to be validated

        Returns:
            bool: True if valid, False otherwise
        Raises:
            SchemaValidationError: If validation fails
        """
        try:
            jsonschema.validate(instance=data, schema=self.schema)
            return True
        except jsonschema.exceptions.ValidationError as e:
            raise SchemaValidationError(f"Validation error: {str(e)}")

    def check_required_fields(self, data: dict) -> bool:
        """
        Checks if all required fields are present in the data.

        Args:
            data (dict): Input data to be checked

        Returns:
            bool: True if all required fields are present, False otherwise
        """
        if "$required" not in self.schema:
            return True
            
        required = set(self.schema["$required"])
        data_keys = set(data.keys())
        
        missing_fields = required - data_keys
        if missing_fields:
            raise SchemaValidationError(f"Missing required fields: {', '.join(missing_fields)}")
            
        return True

    def validate_array_elements(self, data: dict) -> bool:
        """
        Validates array elements against their specified schema.

        Args:
            data (dict): Input data to be validated

        Returns:
            bool: True if all array elements are valid, False otherwise
        """
        if "$schema" not in self.schema or "$type" not in self.schema["$schema"]:
            return True
            
        for key, value in data.items():
            if isinstance(value, list) and "items" in self.schema[key]:
                item_schema = self.schema[key]["items"]
                for elem in value:
                    try:
                        jsonschema.validate(instance=elem, schema=item_schema)
                    except jsonschema.exceptions.ValidationError as e:
                        raise SchemaValidationError(f"Array element validation failed: {str(e)}")
                    
        return True

# Custom exception class
class SchemaValidationError(Exception):
    """Exception raised when JSON data fails schema validation."""
    pass

# Example usage:
if __name__ == "__main__":
    validator = JSONSchemaValidator()
    schema_file_path = "path/to/schema.json"
    validator.set_schema(schema_file_path)
    
    test_data = {
        "$required": ["name", "age"],
        "items": [{"type": "string"}, {"type": "number"}]
    }
    
    try:
        is_valid = validator.validate_data(test_data)
        print(f"Validation result: {is_valid}")
    except SchemaValidationError as e:
        print(str(e))  # Outputs validation errors
from datetime import datetime

class CodeReviewMetricsLogger:
    """
    Logs code review metrics and provides a summary.
    
    This class records various metrics from a code review,
    such as number of issues found, time spent, and participation rate.
    It also generates a summary report.
    """

    def __init__(self):
        self.metrics = {
            "issues_found": 0,
            "time_spent_minutes": 0.0,
            "participants_count": 0
        }

    def record_issue(self) -> None:
        """
        Increments the count of issues found during review.
        """
        self.metrics["issues_found"] += 1

    def log_time_spent(self, start_time: datetime, end_time: datetime) -> None:
        """
        Records time spent on code review in minutes.

        Args:
            start_time (datetime): Start time of the review session
            end_time (datetime): End time of the review session
        """
        time_difference = end_time - start_time
        self.metrics["time_spent_minutes"] += time_difference.total_seconds() / 60.0

    def add_participant(self) -> None:
        """
        Increments the count of participants in the code review.
        """
        self.metrics["participants_count"] += 1

    def get_summary_report(self) -> str:
        """
        Generates a summary report based on collected metrics.

        Returns:
            str: Summary report as a formatted string
        """
        return f"""
Code Review Metrics Summary Report
Generated on {datetime.now().strftime("%Y-%m-%d %H:%M:%S")}

Total Issues Found:          {self.metrics['issues_found']}
Average Time per Issue (min): {round(self.metrics['time_spent_minutes'] / max(1, self.metrics['issues_found']), 2)}
Participants Count:           {self.metrics['participants_count']}
Participation Rate:         {(self.metrics['participants_count'] > 0) * 1.0}
        """

# Example usage:
if __name__ == "__main__":
    logger = CodeReviewMetricsLogger()
    
    start_time = datetime.now()
    # Simulate code review session
    logger.record_issue()
    end_time = datetime.now()
    logger.log_time_spent(start_time, end_time)
    logger.add_participant()
    
    report = logger.get_summary_report()
    print(report)  # Outputs the summary report
from datetime import datetime

class CodeReviewMetricsLogger:
    """
    Logs code review metrics and provides a summary.
    
    This class records various metrics from a code review,
    such as number of issues found, time spent, and participation rate.
    It also generates a summary report.
    """

    def __init__(self):
        self.metrics = {
            "issues_found": 0,
            "time_spent_minutes": 0.0,
            "participants_count": 0
        }

    def record_issue(self) -> None:
        """
        Increments the count of issues found during review.
        """
        self.metrics["issues_found"] += 1

    def log_time_spent(self, start_time: datetime, end_time: datetime) -> None:
        """
        Records time spent on code review in minutes.

        Args:
            start_time (datetime): Start time of the review session
            end_time (datetime): End time of the review session
        """
        time_difference = end_time - start_time
        self.metrics["time_spent_minutes"] += time_difference.total_seconds() / 60.0

    def add_participant(self) -> None:
        """
        Increments the count of participants in the code review.
        """
        self.metrics["participants_count"] += 1

    def get_summary_report(self) -> str:
        """
        Generates a summary report based on collected metrics.

        Returns:
            str: Summary report as a formatted string
        """
        return f"""
Code Review Metrics Summary Report
Generated on {datetime.now().strftime("%Y-%m-%d %H:%M:%S")}

Total Issues Found:          {self.metrics['issues_found']}
Average Time per Issue (min): {round(self.metrics['time_spent_minutes'] / max(1, self.metrics['issues_found']), 2)}
Participants Count:           {self.metrics['participants_count']}
Participation Rate:         {(self.metrics['participants_count'] > 0) * 1.0}
        """

# Example usage:
if __name__ == "__main__":
    logger = CodeReviewMetricsLogger()
    
    start_time = datetime.now()
    # Simulate code review session
    logger.record_issue()
    end_time = datetime.now()
    logger.log_time_spent(start_time, end_time)
    logger.add_participant()
    
    report = logger.get_summary_report()
    print(report)  # Outputs the summary report
from datetime import datetime

class CodeReviewMetricsLogger:
    """
    Logs code review metrics and provides a summary.
    
    This class records various metrics from a code review,
    such as number of issues found, time spent, and participation rate.
    It also generates a summary report.
    """

    def __init__(self):
        self.metrics = {
            "issues_found": 0,
            "time_spent_minutes": 0.0,
            "participants_count": 0
        }

    def record_issue(self) -> None:
        """
        Increments the count of issues found during review.
        """
        self.metrics["issues_found"] += 1

    def log_time_spent(self, start_time: datetime, end_time: datetime) -> None:
        """
        Records time spent on code review in minutes.

        Args:
            start_time (datetime): Start time of the review session
            end_time (datetime): End time of the review session
        """
        time_difference = end_time - start_time
        self.metrics["time_spent_minutes"] += time_difference.total_seconds() / 60.0

    def add_participant(self) -> None:
        """
        Increments the count of participants in the code review.
        """
        self.metrics["participants_count"] += 1

    def get_summary_report(self) -> str:
        """
        Generates a summary report based on collected metrics.

        Returns:
            str: Summary report as a formatted string
        """
        return f"""
Code Review Metrics Summary Report
Generated on {datetime.now().strftime("%Y-%m-%d %H:%M:%S")}

Total Issues Found:          {self.metrics['issues_found']}
Average Time per Issue (min): {round(self.metrics['time_spent_minutes'] / max(1, self.metrics['issues_found']), 2)}
Participants Count:           {self.metrics['participants_count']}
Participation Rate:         {(self.metrics['participants_count'] > 0) * 1.0}
        """

# Example usage:
if __name__ == "__main__":
    logger = CodeReviewMetricsLogger()
    
    start_time = datetime.now()
    # Simulate code review session
    logger.record_issue()
    end_time = datetime.now()
    logger.log_time_spent(start_time, end_time)
    logger.add_participant()
    
    report = logger.get_summary_report()
    print(report)  # Outputs the summary report
from datetime import datetime

class CodeReviewMetricsLogger:
    """
    Logs code review metrics and provides a summary.
    
    This class records various metrics from a code review,
    such as number of issues found, time spent, and participation rate.
    It also generates a summary report.
    """

    def __init__(self):
        self.metrics = {
            "issues_found": 0,
            "time_spent_minutes": 0.0,
            "participants_count": 0
        }

    def record_issue(self) -> None:
        """
        Increments the count of issues found during review.
        """
        self.metrics["issues_found"] += 1

    def log_time_spent(self, start_time: datetime, end_time: datetime) -> None:
        """
        Records time spent on code review in minutes.

        Args:
            start_time (datetime): Start time of the review session
            end_time (datetime): End time of the review session
        """
        time_difference = end_time - start_time
        self.metrics["time_spent_minutes"] += time_difference.total_seconds() / 60.0

    def add_participant(self) -> None:
        """
        Increments the count of participants in the code review.
        """
        self.metrics["participants_count"] += 1

    def get_summary_report(self) -> str:
        """
        Generates a summary report based on collected metrics.

        Returns:
            str: Summary report as a formatted string
        """
        return f"""
Code Review Metrics Summary Report
Generated on {datetime.now().strftime("%Y-%m-%d %H:%M:%S")}

Total Issues Found:          {self.metrics['issues_found']}
Average Time per Issue (min): {round(self.metrics['time_spent_minutes'] / max(1, self.metrics['issues_found']), 2)}
Participants Count:           {self.metrics['participants_count']}
Participation Rate:         {(self.metrics['participants_count'] > 0) * 1.0}
        """

# Example usage:
if __name__ == "__main__":
    logger = CodeReviewMetricsLogger()
    
    start_time = datetime.now()
    # Simulate code review session
    logger.record_issue()
    end_time = datetime.now()
    logger.log_time_spent(start_time, end_time)
    logger.add_participant()
    
    report = logger.get_summary_report()
    print(report)  # Outputs the summary report<|fim_middle|>        self.metrics["participants_count"] += 1