from data_quality_doctor.data_doctor import DataDoctor

@staticmethod
def clean_column_names(df: pd.DataFrame) -> pd.DataFrame:
    """
    Clean column names by replacing non-alphanumeric characters with underscores and converting to lowercase.
    
    Args:
        df (pd.DataFrame): Dataframe to clean column names.
    
    Returns:
        pd.DataFrame: Dataframe with cleaned column names.
    """

@staticmethod
def read_data_quality_template(excel_file_path: str) -> pd.DataFrame:
    """
    Read the data quality template from an Excel file.
    
    Args:
        excel_file_path (str): Path to the Excel file.
    
    Returns:
        pd.DataFrame: DataFrame containing the data quality template.
    """

@staticmethod
def assess_completeness(df: pd.DataFrame, column_name: str) -> pd.DataFrame:
    """
    Assess completeness for a specific column in the dataframe.
    
    Args:
        df (pd.DataFrame): Dataframe containing the data.
        column_name (str): Name of the column to assess.
    
    Returns:
        pd.DataFrame: DataFrame containing completeness assessment results.
    """

@staticmethod
def similar(a: str, b: str) -> float:
    """
    Calculate similarity ratio between two strings.
    
    Args:
        a (str): First string.
        b (str): Second string.
    
    Returns:
        float: Similarity ratio.
    """

@staticmethod
def is_pii(column_name: str) -> bool:
    """
    Check if a column name indicates personally identifiable information (PII).
    
    Args:
        column_name (str): Column name to check.
    
    Returns:
        bool: True if column name indicates PII, False otherwise.
    """

@staticmethod
def read_all_structured_files(directory_path: str) -> List[Tuple[str, pd.DataFrame]]:
    """
    Read all CSV and Excel files from a directory and return their data as dataframes.
    
    Args:
        directory_path (str): Path to the directory containing files.
    
    Returns:
        List[Tuple[str, pd.DataFrame]]: List of tuples containing file paths and dataframes.
    """

@staticmethod
def find_critical_elements(all_sheets: List[Tuple[str, pd.DataFrame]]) -> Dict[str, List[str]]:
    """
    Find critical elements (columns) that appear in multiple files.
    
    Args:
        all_sheets (List[Tuple[str, pd.DataFrame]]): List of tuples containing file paths and dataframes.
    
    Returns:
        Dict[str, List[str]]: Dictionary with column names as keys and list of file paths as values.
    """

def configure_quality_check(self, csv_file_path: str, excel_file_path: Optional[str] = None) -> None:
    """
    Configure quality check and create an Excel template if it doesn't already exist.
    
    Args:
        csv_file_path (str): Path to the CSV file for which to configure the quality check.
        excel_file_path (Optional[str]): Path to save the Excel template. If None, saves in the current directory.
    """

def evaluate_data_quality(self, data_file_path: str, template_file_path: str) -> pd.DataFrame:
    """
    Evaluate data quality based on a template.
    
    Args:
        data_file_path (str): Path to the data file (.csv or .xlsx).
        template_file_path (str): Path to the template file (.xlsx).
    
    Returns:
        pd.DataFrame: DataFrame containing completeness assessment results.
    """

from data_quality_doctor.data_doctor import DataDoctor
import pandas as pd
import os

# Create a sample DataFrame
df = pd.DataFrame({
    'First Name': ['Alice', 'Bob'],
    'Last-Name': ['Smith', 'Jones'],
    'Date of Birth': ['1990-01-01', '1985-05-12']
})

# Clean the column names
cleaned_df = DataDoctor.clean_column_names(df)
print(cleaned_df.columns)
# Output: Index(['first_name', 'last_name', 'date_of_birth'], dtype='object')

# Path to the Excel file containing the data quality template
excel_file_path = 'path/to/data_quality_template.xlsx'

# Read the template
template_df = DataDoctor.read_data_quality_template(excel_file_path)
print(template_df.head())

# Create a sample DataFrame
df = pd.DataFrame({
    'name': ['Alice', 'Bob', None],
    'age': [25, None, 30]
})

# Assess completeness for the 'name' column
completeness_df = DataDoctor.assess_completeness(df, 'name')
print(completeness_df)

# Calculate similarity between two strings
similarity_ratio = DataDoctor.similar('First Name', 'first_name')
print(similarity_ratio)
# Output: 0.9090909090909091

# Check if a column name indicates PII
is_pii = DataDoctor.is_pii('name')
print(is_pii)
# Output: True

is_pii = DataDoctor.is_pii('age')
print(is_pii)
# Output: True

is_pii = DataDoctor.is_pii('email')
print(is_pii)
# Output: False

# Directory path containing structured files (CSV and Excel)
directory_path = 'path/to/directory'

# Read all structured files
all_sheets = DataDoctor.read_all_structured_files(directory_path)
for file_path, df in all_sheets:
    print(f'File: {file_path}')
    print(df.head())

# Assuming all_sheets is obtained from read_all_structured_files method
all_sheets = DataDoctor.read_all_structured_files(directory_path)

# Find critical elements
critical_elements = DataDoctor.find_critical_elements(all_sheets)
print(critical_elements)

# Path to the CSV file and the Excel template file
csv_file_path = 'path/to/data.csv'
excel_file_path = 'path/to/data_quality_checks_template.xlsx'

# Create an instance of DataDoctor
data_doctor = DataDoctor()

# Configure quality check
data_doctor.configure_quality_check(csv_file_path, excel_file_path)

# Path to the data file and the template file
data_file_path = 'path/to/data.csv'
template_file_path = 'path/to/data_quality_checks_template.xlsx'

# Create an instance of DataDoctor
data_doctor = DataDoctor()

# Evaluate data quality based on the template
completeness_results = data_doctor.evaluate_data_quality(data_file_path, template_file_path)
print(completeness_results)