GotPsi / src /processors /users_processor.py

instNoeticSciences

claude opus 4.6

Flatten history: re-sanitized (128-bit hash) GotPsi public dataset

9deebf2 5 months ago

26.4 kB

"""
Users dataset processor for Psi survey data.
Processes two types of user survey files:
1. users14.dat (2000-2015): Fixed format with variable commas in username/location fields
2. questions.dat (2014-2022): Similar format with email field and different timestamp format
Common schema:
- Username, Timestamp, Email, City, State, Coordinates, Country
- Psi1-15: Psi test responses (15 questions)
- Hemi1-10: Hemispheric dominance questions (10 questions)
- HowFind: How user found the survey (text field)
- na_count_Psi_and_Hemi: Count of NAs in Psi and Hemi columns
Data cleaning rules:
- Usernames and location fields can contain commas (dirty data before Psi1)
- Remove duplicate usernames: keep row with fewest NAs, then prefer oldest date
- Column 32 (HowFind) format changed in Sept 2002 from yes/no to descriptive text
"""

from pathlib import Path
from typing import List, Dict, Any, Optional
from datetime import datetime
import pandas as pd
import re
import csv
from io import StringIO

from ..core.base_classes import BaseProcessor
from ..core.config import Config
from ..core.exceptions import ProcessorError
from ..cleaners.encoding_cleaner import EncodingCleaner
from ..cleaners.delimiter_cleaner import DelimiterCleaner
from ..cleaners.temporal_parser import TemporalParser
from ..cleaners.location_parser import LocationParser


class UsersProcessor(BaseProcessor):
    """
    Processes user survey data from two time periods.
    Handles:
    - users14.dat: 2000-2015 data with basic timestamp format
    - questions.dat: 2014-2022 data with Wed Jan 1 format and email field
    Both formats have extremely dirty user-entered data before Psi1 column.
    Username and location fields can contain commas.
    """

    BATCH_DELIMITER_CLEAN = False  # dirty comma fields, no delimiter standardization

    # File discovery patterns
    FILE_PATTERNS = ['users14*.dat', 'questions*.dat']

    # Column definitions for unified schema
    COLUMNS_UNIFIED = [
        'username', 'timestamp', 'email', 'city', 'state',
        'coordinates', 'country'
    ] + [f'psi_{i:02d}' for i in range(1, 16)] + [  # Psi1-15
        f'hemi_{i:02d}' for i in range(1, 11)  # Hemi1-10
    ] + ['how_find', 'na_count_psi_and_hemi', 'source_file', 'source_row_number', 'file_type']

    # Psi and Hemi column names
    PSI_COLUMNS = [f'psi_{i:02d}' for i in range(1, 16)]
    HEMI_COLUMNS = [f'hemi_{i:02d}' for i in range(1, 11)]

    def __init__(self, config: Config):
        """Initialize Users processor.
        Args:
            config: Configuration object
        """
        super().__init__(config, 'users')

        # Initialize cleaners
        self.encoding_cleaner = EncodingCleaner(config, self.errata_logger)
        self.delimiter_cleaner = DelimiterCleaner(config, self.errata_logger)
        self.temporal_parser = TemporalParser(config, self.errata_logger)
        self.location_parser = LocationParser(config, self.errata_logger)

        # Statistics
        self.stats = {
            'files_processed': 0,
            'files_failed': 0,
            'rows_total': 0,
            'rows_valid': 0,
            'rows_invalid': 0,
            'users14_rows': 0,
            'questions_rows': 0,
            'duplicates_removed': 0,
            'na_usernames_count': 0
        }

    def detect_file_type(self, file_path: Path) -> str:
        """
        Detect whether file is users14 or questions format.
        Args:
            file_path: Path to file
        Returns:
            'users14' or 'questions'
        """
        if 'users14' in file_path.name.lower():
            return 'users14'
        elif 'questions' in file_path.name.lower():
            return 'questions'
        else:
            # Try to detect from first line
            with open(file_path, 'r', encoding='utf-8', errors='replace') as f:
                first_line = f.readline()
                # questions.dat has "Wed Jan" style dates
                if 'Wed ' in first_line or 'Mon ' in first_line or 'Tue ' in first_line:
                    return 'questions'
                else:
                    return 'users14'

    def parse_dirty_csv_line(self, line: str, expected_clean_cols: int = 25) -> tuple:
        """
        Parse CSV line by finding clean numeric data working backwards.
        Strategy:
        1. Split by comma
        2. Work backwards to find 25 contiguous numeric columns (Psi1-15, Hemi1-10)
        3. Everything before = dirty user data (username, timestamp, location fields)
        4. Everything after = HowFind + na_count (for users14 only)
        The Psi and Hemi columns should only contain values 1-5 or be empty.
        We use this as an anchor to identify where the clean data starts.
        Args:
            line: Raw CSV line
            expected_clean_cols: Number of expected columns from Psi1 onwards (default 25)
        Returns:
            Tuple of (dirty_part, clean_part, tail_part)
            - dirty_part: List of user-entered fields before Psi1
            - clean_part: List of 25 Psi/Hemi columns
            - tail_part: List of fields after Hemi10 (HowFind, na_count)
        """
        def is_valid_response(val: str) -> bool:
            """Check if value is valid Psi/Hemi response (1-5 or empty)."""
            val = val.strip()
            return val == '' or val in ['1', '2', '3', '4', '5']

        # Split by comma
        parts = line.split(',')

        # Need at least 25 columns for Psi/Hemi
        if len(parts) < expected_clean_cols:
            return parts, [], []

        # Search backwards for 25 contiguous valid response columns
        # Start from a reasonable position (need at least some dirty fields)
        min_dirty_fields = 2  # At minimum: username, timestamp

        for i in range(len(parts) - expected_clean_cols, min_dirty_fields - 1, -1):
            window = parts[i:i + expected_clean_cols]

            # Check if all values in window are valid responses
            if len(window) == expected_clean_cols and all(is_valid_response(v) for v in window):
                # Found the clean section!
                dirty_part = parts[:i]  # Everything before Psi1
                clean_part = parts[i:i + expected_clean_cols]  # Psi1-15 + Hemi1-10
                tail_part = parts[i + expected_clean_cols:]  # HowFind + na_count

                return dirty_part, clean_part, tail_part

        # Fallback: couldn't find clean section, return original split
        # This will be caught by validation later
        return parts, [], []

    def process_users14_file(self, file_path: Path, pre_cleaned_text: Optional[str] = None) -> Optional[pd.DataFrame]:
        """
        Process users14.dat format file (2000-2015).
        Format: Username, Timestamp, City, State, Coordinates, Country,
                Psi1-15, Hemi1-10, HowFind, na_count
        Args:
            file_path: Path to file
            pre_cleaned_text: Pre-cleaned text from parallel batch cleaning.
        Returns:
            DataFrame or None
        """
        try:
            # Use pre-cleaned text if available, otherwise clean inline
            if pre_cleaned_text is not None:
                text = pre_cleaned_text
            else:
                text = self.encoding_cleaner.clean(file_path)

            # Parse lines manually due to dirty commas
            lines = text.strip().split('\n')

            parsed_rows = []
            for line_num, line in enumerate(lines, 1):
                if not line.strip():
                    continue

                # Parse with dirty comma handling
                # Expected: variable dirty fields + 25 clean Psi/Hemi fields + tail fields
                dirty_part, clean_part, tail_part = self.parse_dirty_csv_line(line, expected_clean_cols=25)

                # Validate we found the clean section
                if len(clean_part) != 25:
                    self.errata_logger.log_error(
                        'parse_failed',
                        f'Line {line_num}: Could not identify clean Psi/Hemi section',
                        file_path=str(file_path),
                        line_number=line_num
                    )
                    continue

                # Map dirty fields to schema
                # Expected dirty fields for users14: Username, Timestamp, Location
                # Location is a single comma-separated string containing city, state, coords, country
                username = dirty_part[0].strip() if len(dirty_part) > 0 else None
                timestamp = dirty_part[1].strip() if len(dirty_part) > 1 else None

                # Everything after timestamp is the location string
                location_str = ','.join(dirty_part[2:]) if len(dirty_part) > 2 else None

                # Parse location string
                location = self.location_parser.parse_location(location_str) if location_str else {
                    'city': None, 'state': None, 'coordinates': None, 'country': None
                }

                row = {
                    'username': username,
                    'timestamp': timestamp,
                    'email': None,  # Not in users14
                    'city': location['city'],
                    'state': location['state'],
                    'coordinates': location['coordinates'],
                    'country': location['country'],
                }

                # Psi1-15 (first 15 of clean_part)
                for i in range(15):
                    row[f'psi_{i+1:02d}'] = clean_part[i].strip() if clean_part[i].strip() else None

                # Hemi1-10 (next 10 of clean_part)
                for i in range(10):
                    row[f'hemi_{i+1:02d}'] = clean_part[15 + i].strip() if clean_part[15 + i].strip() else None

                # HowFind and na_count from tail
                row['how_find'] = tail_part[0].strip() if len(tail_part) > 0 else None
                row['na_count_psi_and_hemi'] = tail_part[1].strip() if len(tail_part) > 1 else None

                # If HowFind has multiple parts (due to commas in text), join them
                if len(tail_part) > 2:
                    row['how_find'] = ','.join([t.strip() for t in tail_part[:-1]])
                    row['na_count_psi_and_hemi'] = tail_part[-1].strip()

                row['source_file'] = file_path.name
                row['source_row_number'] = line_num
                row['file_type'] = 'users14'

                parsed_rows.append(row)

            if not parsed_rows:
                self.errata_logger.log_error(
                    'empty_file',
                    'No valid rows parsed',
                    file_path=str(file_path)
                )
                return None

            df = pd.DataFrame(parsed_rows)
            return df

        except Exception as e:
            import traceback
            self.errata_logger.log_error(
                'file_processing_failed',
                f'{type(e).__name__}: {e} - entire file omitted',
                file_path=str(file_path),
                scope='file',
                context={'traceback': traceback.format_exc()},
            )
            return None

    def process_questions_file(self, file_path: Path, pre_cleaned_text: Optional[str] = None) -> Optional[pd.DataFrame]:
        """
        Process questions.dat format file (2014-2022).
        Format: Username, Timestamp, Email, City, State, Coordinates, Country,
                Psi1-15, Hemi1-10, HowFind
        Timestamp format: "Wed Jan  1 00:33:44 2014"
        Args:
            file_path: Path to file
            pre_cleaned_text: Pre-cleaned text from parallel batch cleaning.
        Returns:
            DataFrame or None
        """
        try:
            # Use pre-cleaned text if available, otherwise clean inline
            if pre_cleaned_text is not None:
                text = pre_cleaned_text
            else:
                text = self.encoding_cleaner.clean(file_path)

            # Parse lines manually due to dirty commas
            lines = text.strip().split('\n')

            parsed_rows = []
            for line_num, line in enumerate(lines, 1):
                if not line.strip():
                    continue

                # Parse with dirty comma handling
                # Expected: variable dirty fields + 25 clean Psi/Hemi fields + tail fields
                dirty_part, clean_part, tail_part = self.parse_dirty_csv_line(line, expected_clean_cols=25)

                # Validate we found the clean section
                if len(clean_part) != 25:
                    self.errata_logger.log_error(
                        'parse_failed',
                        f'Line {line_num}: Could not identify clean Psi/Hemi section',
                        file_path=str(file_path),
                        line_number=line_num
                    )
                    continue

                # Map dirty fields to schema
                # Expected dirty fields for questions: Username, Timestamp, Email, Location
                # Location is a single comma-separated string containing city, state, coords, country
                username = dirty_part[0].strip() if len(dirty_part) > 0 else None
                timestamp = dirty_part[1].strip() if len(dirty_part) > 1 else None
                email = dirty_part[2].strip() if len(dirty_part) > 2 else None

                # Everything after email is the location string
                location_str = ','.join(dirty_part[3:]) if len(dirty_part) > 3 else None

                # Parse location string
                location = self.location_parser.parse_location(location_str) if location_str else {
                    'city': None, 'state': None, 'coordinates': None, 'country': None
                }

                row = {
                    'username': username,
                    'timestamp': timestamp,
                    'email': email,
                    'city': location['city'],
                    'state': location['state'],
                    'coordinates': location['coordinates'],
                    'country': location['country'],
                }

                # Psi1-15 (first 15 of clean_part)
                for i in range(15):
                    row[f'psi_{i+1:02d}'] = clean_part[i].strip() if clean_part[i].strip() else None

                # Hemi1-10 (next 10 of clean_part)
                for i in range(10):
                    row[f'hemi_{i+1:02d}'] = clean_part[15 + i].strip() if clean_part[15 + i].strip() else None

                # HowFind from tail (can contain commas, so join all tail parts)
                row['how_find'] = ','.join([t.strip() for t in tail_part]) if tail_part else None

                # na_count not in questions.dat - will be calculated later
                row['na_count_psi_and_hemi'] = None

                row['source_file'] = file_path.name
                row['source_row_number'] = line_num
                row['file_type'] = 'questions'

                parsed_rows.append(row)

            if not parsed_rows:
                self.errata_logger.log_error(
                    'empty_file',
                    'No valid rows parsed',
                    file_path=str(file_path)
                )
                return None

            df = pd.DataFrame(parsed_rows)
            return df

        except Exception as e:
            import traceback
            self.errata_logger.log_error(
                'file_processing_failed',
                f'{type(e).__name__}: {e} - entire file omitted',
                file_path=str(file_path),
                scope='file',
                context={'traceback': traceback.format_exc()},
            )
            return None

    def process_file(self, file_path: Path, pre_cleaned_text: Optional[str] = None) -> Optional[pd.DataFrame]:
        """
        Process a single user data file.
        Args:
            file_path: Path to file
            pre_cleaned_text: Pre-cleaned text from parallel batch cleaning.
                If None, cleans the file inline (backward compat).
        Returns:
            DataFrame or None
        """
        file_type = self.detect_file_type(file_path)

        if file_type == 'users14':
            df = self.process_users14_file(file_path, pre_cleaned_text=pre_cleaned_text)
        else:
            df = self.process_questions_file(file_path, pre_cleaned_text=pre_cleaned_text)

        if df is None or df.empty:
            self.stats['files_failed'] += 1
            return None

        # Validate and clean
        df = self.validate_dataframe(df, file_path)

        if df.empty:
            self.stats['files_failed'] += 1
            return None

        # Track stats
        self.stats['files_processed'] += 1
        if file_type == 'users14':
            self.stats['users14_rows'] += len(df)
        else:
            self.stats['questions_rows'] += len(df)

        return df

    def validate_dataframe(self, df: pd.DataFrame, file_path: Path) -> pd.DataFrame:
        """
        Validate and clean DataFrame.
        Args:
            df: Input DataFrame
            file_path: Source file path
        Returns:
            Cleaned DataFrame
        """
        initial_count = len(df)

        # Track NA usernames (June 18 2025: these are now kept, not removed)
        if 'username' in df.columns:
            na_usernames = df['username'].isna() | (df['username'].str.lower().str.strip() == 'na')
            self.stats['na_usernames_count'] += na_usernames.sum()

        # Parse timestamps
        if 'timestamp' in df.columns:
            df['timestamp'] = self.temporal_parser.parse_dates_vectorized(
                df['timestamp'], file_path=str(file_path)
            )

        # Convert Psi columns to numeric
        for col in self.PSI_COLUMNS:
            if col in df.columns:
                df[col] = self.to_numeric_logged(df[col], col, str(file_path))

        # Convert Hemi columns to numeric
        for col in self.HEMI_COLUMNS:
            if col in df.columns:
                df[col] = self.to_numeric_logged(df[col], col, str(file_path))

        # Calculate na_count_psi_and_hemi if not present
        if 'na_count_psi_and_hemi' not in df.columns or df['na_count_psi_and_hemi'].isna().all():
            psi_hemi_cols = self.PSI_COLUMNS + self.HEMI_COLUMNS
            existing_cols = [col for col in psi_hemi_cols if col in df.columns]
            df['na_count_psi_and_hemi'] = df[existing_cols].isna().sum(axis=1)
        else:
            df['na_count_psi_and_hemi'] = self.to_numeric_logged(df['na_count_psi_and_hemi'], 'na_count_psi_and_hemi', str(file_path))

        # Ensure all unified columns exist
        for col in self.COLUMNS_UNIFIED:
            if col not in df.columns:
                df[col] = None

        # Reorder to unified schema
        df = df[self.COLUMNS_UNIFIED]

        invalid_count = initial_count - len(df)
        if invalid_count > 0:
            self.errata_logger.log_file_summary(
                str(file_path),
                'partial',
                initial_count,
                len(df),
                invalid_count
            )

        return df

    def remove_duplicates(self, df: pd.DataFrame) -> pd.DataFrame:
        """
        Remove duplicate usernames using deduplication rules:
        1. Keep row with fewest NAs in Psi and Hemi columns
        2. If tied on NAs, keep oldest timestamp
        Args:
            df: Input DataFrame
        Returns:
            Deduplicated DataFrame
        """
        initial_count = len(df)

        # Track per-file duplicate stats before deduplication
        print("\nPer-file duplicate analysis:")
        print("-" * 50)

        # Count duplicates within each file type
        if 'file_type' in df.columns:
            for file_type in df['file_type'].unique():
                file_df = df[df['file_type'] == file_type]
                file_dupes = len(file_df) - file_df['username'].nunique()
                print(f"  {file_type}: {len(file_df):,} rows, {file_dupes:,} internal duplicates")

        # Count cross-file duplicates
        total_rows = len(df)
        unique_usernames = df['username'].nunique()
        total_dupes = total_rows - unique_usernames

        print(f"\nCross-file analysis:")
        print(f"  Total rows: {total_rows:,}")
        print(f"  Unique usernames: {unique_usernames:,}")
        print(f"  Total duplicates to remove: {total_dupes:,}")
        print()

        # Sort by username, na_count (ascending), timestamp (ascending)
        df_sorted = df.sort_values(
            by=['username', 'na_count_psi_and_hemi', 'timestamp'],
            ascending=[True, True, True]
        )

        # Keep first row per username (fewest NAs, then oldest)
        # Track which file types are kept vs removed
        kept_indices = df_sorted.groupby('username').head(1).index
        df_dedup = df_sorted.loc[kept_indices].reset_index(drop=True)

        # Analyze what was kept vs removed by file type
        if 'file_type' in df.columns:
            print("Deduplication results by file type:")
            removed_df = df[~df.index.isin(kept_indices)]

            for file_type in df['file_type'].unique():
                kept_count = (df_dedup['file_type'] == file_type).sum()
                removed_count = (removed_df['file_type'] == file_type).sum()
                original_count = (df['file_type'] == file_type).sum()
                print(f"  {file_type}: kept {kept_count:,} / {original_count:,}, removed {removed_count:,}")
            print()

        duplicates_removed = initial_count - len(df_dedup)
        self.stats['duplicates_removed'] = duplicates_removed

        if duplicates_removed > 0:
            self.errata_logger.log_error(
                'duplicates_removed',
                f'Removed {duplicates_removed} duplicate username entries',
                file_path='combined_data'
            )

            # Log detail for a sample of discarded duplicates so the dedup
            # policy can be audited without changing the output schema.
            removed_df = df_sorted[~df_sorted.index.isin(kept_indices)]
            compare_cols = self.PSI_COLUMNS + self.HEMI_COLUMNS + ['timestamp', 'city', 'state', 'country']
            compare_cols = [c for c in compare_cols if c in df.columns]

            sample_users = removed_df['username'].unique()[:50]
            for uname in sample_users:
                kept_row = df_dedup[df_dedup['username'] == uname]
                removed_rows = removed_df[removed_df['username'] == uname]
                if kept_row.empty or removed_rows.empty:
                    continue
                kept_vals = kept_row[compare_cols].iloc[0]
                for _, rem_row in removed_rows.iterrows():
                    diffs = {}
                    for col in compare_cols:
                        k, r = kept_vals[col], rem_row[col]
                        if str(k) != str(r):
                            diffs[col] = {'kept': str(k), 'removed': str(r)}
                    if diffs:
                        self.errata_logger.log_error(
                            'duplicate_detail',
                            f"Discarded row for '{uname}' differs in {len(diffs)} field(s)",
                            file_path='combined_data',
                            scope='row',
                            context={'differing_fields': diffs},
                        )

        return df_dedup

    def process(self, max_files: Optional[int] = None) -> pd.DataFrame:
        """
        Process all user data files.
        Args:
            max_files: Optional limit on number of files to process
        Returns:
            Combined DataFrame
        """
        files = self.get_file_list()

        if max_files:
            files = files[:max_files]

        print(f"Processing {len(files)} user data files...")

        # Parallel batch cleaning (encoding only, no delimiter cleaning)
        cleaned_texts = self._batch_clean_files(files, use_delimiter_cleaner=False)
        print(f"Successfully cleaned {len(cleaned_texts)}/{len(files)} files")

        dfs = []

        try:
            from tqdm import tqdm
            iterator = tqdm(files, desc="Processing user files")
        except ImportError:
            iterator = files

        for file_path in iterator:
            pre_cleaned = cleaned_texts.get(file_path)
            if pre_cleaned is None:
                continue
            df = self.process_file(file_path, pre_cleaned_text=pre_cleaned)
            if df is not None and not df.empty:
                dfs.append(df)

        if not dfs:
            raise ProcessorError("No valid data processed")

        print("Combining files...")
        combined = pd.concat(dfs, ignore_index=True)

        self.stats['rows_total'] = len(combined)

        # Log unique usernames before deduplication
        unique_usernames_before = combined['username'].nunique()
        print(f"\nBefore deduplication:")
        print(f"  Total rows: {len(combined):,}")
        print(f"  Unique usernames: {unique_usernames_before:,}")
        print()

        # Remove duplicates
        print("Removing duplicate usernames...")
        combined = self.remove_duplicates(combined)

        # Log unique usernames after deduplication
        unique_usernames_after = combined['username'].nunique()
        print(f"After deduplication:")
        print(f"  Total rows: {len(combined):,}")
        print(f"  Unique usernames: {unique_usernames_after:,}")
        print()

        self.stats['rows_valid'] = len(combined)

        # Remove audit columns if audit mode is disabled
        audit_mode = self.config.get('processing.audit_mode', False)
        if not audit_mode:
            audit_cols = ['source_file', 'source_row_number']
            cols_to_drop = [col for col in audit_cols if col in combined.columns]
            if cols_to_drop:
                combined = combined.drop(columns=cols_to_drop)
                print(f"Audit mode disabled: Removed columns {cols_to_drop}")
                print()


        # Close errata logger
        self.errata_logger.close()

        return combined

    def get_stats(self) -> Dict[str, Any]:
        """Get processing statistics."""
        return self.stats.copy()

	"""
	Users dataset processor for Psi survey data.

	Processes two types of user survey files:
	1. users14.dat (2000-2015): Fixed format with variable commas in username/location fields
	2. questions.dat (2014-2022): Similar format with email field and different timestamp format

	Common schema:
	- Username, Timestamp, Email, City, State, Coordinates, Country
	- Psi1-15: Psi test responses (15 questions)
	- Hemi1-10: Hemispheric dominance questions (10 questions)
	- HowFind: How user found the survey (text field)
	- na_count_Psi_and_Hemi: Count of NAs in Psi and Hemi columns

	Data cleaning rules:
	- Usernames and location fields can contain commas (dirty data before Psi1)
	- Remove duplicate usernames: keep row with fewest NAs, then prefer oldest date
	- Column 32 (HowFind) format changed in Sept 2002 from yes/no to descriptive text
	"""

	from pathlib import Path
	from typing import List, Dict, Any, Optional
	from datetime import datetime
	import pandas as pd
	import re
	import csv
	from io import StringIO

	from ..core.base_classes import BaseProcessor
	from ..core.config import Config
	from ..core.exceptions import ProcessorError
	from ..cleaners.encoding_cleaner import EncodingCleaner
	from ..cleaners.delimiter_cleaner import DelimiterCleaner
	from ..cleaners.temporal_parser import TemporalParser
	from ..cleaners.location_parser import LocationParser


	class UsersProcessor(BaseProcessor):
	"""
	Processes user survey data from two time periods.

	Handles:
	- users14.dat: 2000-2015 data with basic timestamp format
	- questions.dat: 2014-2022 data with Wed Jan 1 format and email field

	Both formats have extremely dirty user-entered data before Psi1 column.
	Username and location fields can contain commas.
	"""

	BATCH_DELIMITER_CLEAN = False # dirty comma fields, no delimiter standardization

	# File discovery patterns
	FILE_PATTERNS = ['users14.dat', 'questions.dat']

	# Column definitions for unified schema
	COLUMNS_UNIFIED = [
	'username', 'timestamp', 'email', 'city', 'state',
	'coordinates', 'country'
	] + [f'psi_{i:02d}' for i in range(1, 16)] + [ # Psi1-15
	f'hemi_{i:02d}' for i in range(1, 11) # Hemi1-10
	] + ['how_find', 'na_count_psi_and_hemi', 'source_file', 'source_row_number', 'file_type']

	# Psi and Hemi column names
	PSI_COLUMNS = [f'psi_{i:02d}' for i in range(1, 16)]
	HEMI_COLUMNS = [f'hemi_{i:02d}' for i in range(1, 11)]

	def __init__(self, config: Config):
	"""Initialize Users processor.

	Args:
	config: Configuration object
	"""
	super().__init__(config, 'users')

	# Initialize cleaners
	self.encoding_cleaner = EncodingCleaner(config, self.errata_logger)
	self.delimiter_cleaner = DelimiterCleaner(config, self.errata_logger)
	self.temporal_parser = TemporalParser(config, self.errata_logger)
	self.location_parser = LocationParser(config, self.errata_logger)

	# Statistics
	self.stats = {
	'files_processed': 0,
	'files_failed': 0,
	'rows_total': 0,
	'rows_valid': 0,
	'rows_invalid': 0,
	'users14_rows': 0,
	'questions_rows': 0,
	'duplicates_removed': 0,
	'na_usernames_count': 0
	}

	def detect_file_type(self, file_path: Path) -> str:
	"""
	Detect whether file is users14 or questions format.

	Args:
	file_path: Path to file

	Returns:
	'users14' or 'questions'
	"""
	if 'users14' in file_path.name.lower():
	return 'users14'
	elif 'questions' in file_path.name.lower():
	return 'questions'
	else:
	# Try to detect from first line
	with open(file_path, 'r', encoding='utf-8', errors='replace') as f:
	first_line = f.readline()
	# questions.dat has "Wed Jan" style dates
	if 'Wed ' in first_line or 'Mon ' in first_line or 'Tue ' in first_line:
	return 'questions'
	else:
	return 'users14'

	def parse_dirty_csv_line(self, line: str, expected_clean_cols: int = 25) -> tuple:
	"""
	Parse CSV line by finding clean numeric data working backwards.

	Strategy:
	1. Split by comma
	2. Work backwards to find 25 contiguous numeric columns (Psi1-15, Hemi1-10)
	3. Everything before = dirty user data (username, timestamp, location fields)
	4. Everything after = HowFind + na_count (for users14 only)

	The Psi and Hemi columns should only contain values 1-5 or be empty.
	We use this as an anchor to identify where the clean data starts.

	Args:
	line: Raw CSV line
	expected_clean_cols: Number of expected columns from Psi1 onwards (default 25)

	Returns:
	Tuple of (dirty_part, clean_part, tail_part)
	- dirty_part: List of user-entered fields before Psi1
	- clean_part: List of 25 Psi/Hemi columns
	- tail_part: List of fields after Hemi10 (HowFind, na_count)
	"""
	def is_valid_response(val: str) -> bool:
	"""Check if value is valid Psi/Hemi response (1-5 or empty)."""
	val = val.strip()
	return val == '' or val in ['1', '2', '3', '4', '5']

	# Split by comma
	parts = line.split(',')

	# Need at least 25 columns for Psi/Hemi
	if len(parts) < expected_clean_cols:
	return parts, [], []

	# Search backwards for 25 contiguous valid response columns
	# Start from a reasonable position (need at least some dirty fields)
	min_dirty_fields = 2 # At minimum: username, timestamp

	for i in range(len(parts) - expected_clean_cols, min_dirty_fields - 1, -1):
	window = parts[i:i + expected_clean_cols]

	# Check if all values in window are valid responses
	if len(window) == expected_clean_cols and all(is_valid_response(v) for v in window):
	# Found the clean section!
	dirty_part = parts[:i] # Everything before Psi1
	clean_part = parts[i:i + expected_clean_cols] # Psi1-15 + Hemi1-10
	tail_part = parts[i + expected_clean_cols:] # HowFind + na_count

	return dirty_part, clean_part, tail_part

	# Fallback: couldn't find clean section, return original split
	# This will be caught by validation later
	return parts, [], []

	def process_users14_file(self, file_path: Path, pre_cleaned_text: Optional[str] = None) -> Optional[pd.DataFrame]:
	"""
	Process users14.dat format file (2000-2015).

	Format: Username, Timestamp, City, State, Coordinates, Country,
	Psi1-15, Hemi1-10, HowFind, na_count

	Args:
	file_path: Path to file
	pre_cleaned_text: Pre-cleaned text from parallel batch cleaning.

	Returns:
	DataFrame or None
	"""
	try:
	# Use pre-cleaned text if available, otherwise clean inline
	if pre_cleaned_text is not None:
	text = pre_cleaned_text
	else:
	text = self.encoding_cleaner.clean(file_path)

	# Parse lines manually due to dirty commas
	lines = text.strip().split('\n')

	parsed_rows = []
	for line_num, line in enumerate(lines, 1):
	if not line.strip():
	continue

	# Parse with dirty comma handling
	# Expected: variable dirty fields + 25 clean Psi/Hemi fields + tail fields
	dirty_part, clean_part, tail_part = self.parse_dirty_csv_line(line, expected_clean_cols=25)

	# Validate we found the clean section
	if len(clean_part) != 25:
	self.errata_logger.log_error(
	'parse_failed',
	f'Line {line_num}: Could not identify clean Psi/Hemi section',
	file_path=str(file_path),
	line_number=line_num
	)
	continue

	# Map dirty fields to schema
	# Expected dirty fields for users14: Username, Timestamp, Location
	# Location is a single comma-separated string containing city, state, coords, country
	username = dirty_part[0].strip() if len(dirty_part) > 0 else None
	timestamp = dirty_part[1].strip() if len(dirty_part) > 1 else None

	# Everything after timestamp is the location string
	location_str = ','.join(dirty_part[2:]) if len(dirty_part) > 2 else None

	# Parse location string
	location = self.location_parser.parse_location(location_str) if location_str else {
	'city': None, 'state': None, 'coordinates': None, 'country': None
	}

	row = {
	'username': username,
	'timestamp': timestamp,
	'email': None, # Not in users14
	'city': location['city'],
	'state': location['state'],
	'coordinates': location['coordinates'],
	'country': location['country'],
	}

	# Psi1-15 (first 15 of clean_part)
	for i in range(15):
	row[f'psi_{i+1:02d}'] = clean_part[i].strip() if clean_part[i].strip() else None

	# Hemi1-10 (next 10 of clean_part)
	for i in range(10):
	row[f'hemi_{i+1:02d}'] = clean_part[15 + i].strip() if clean_part[15 + i].strip() else None

	# HowFind and na_count from tail
	row['how_find'] = tail_part[0].strip() if len(tail_part) > 0 else None
	row['na_count_psi_and_hemi'] = tail_part[1].strip() if len(tail_part) > 1 else None

	# If HowFind has multiple parts (due to commas in text), join them
	if len(tail_part) > 2:
	row['how_find'] = ','.join([t.strip() for t in tail_part[:-1]])
	row['na_count_psi_and_hemi'] = tail_part[-1].strip()

	row['source_file'] = file_path.name
	row['source_row_number'] = line_num
	row['file_type'] = 'users14'

	parsed_rows.append(row)

	if not parsed_rows:
	self.errata_logger.log_error(
	'empty_file',
	'No valid rows parsed',
	file_path=str(file_path)
	)
	return None

	df = pd.DataFrame(parsed_rows)
	return df

	except Exception as e:
	import traceback
	self.errata_logger.log_error(
	'file_processing_failed',
	f'{type(e).__name__}: {e} - entire file omitted',
	file_path=str(file_path),
	scope='file',
	context={'traceback': traceback.format_exc()},
	)
	return None

	def process_questions_file(self, file_path: Path, pre_cleaned_text: Optional[str] = None) -> Optional[pd.DataFrame]:
	"""
	Process questions.dat format file (2014-2022).

	Format: Username, Timestamp, Email, City, State, Coordinates, Country,
	Psi1-15, Hemi1-10, HowFind

	Timestamp format: "Wed Jan 1 00:33:44 2014"

	Args:
	file_path: Path to file
	pre_cleaned_text: Pre-cleaned text from parallel batch cleaning.

	Returns:
	DataFrame or None
	"""
	try:
	# Use pre-cleaned text if available, otherwise clean inline
	if pre_cleaned_text is not None:
	text = pre_cleaned_text
	else:
	text = self.encoding_cleaner.clean(file_path)

	# Parse lines manually due to dirty commas
	lines = text.strip().split('\n')

	parsed_rows = []
	for line_num, line in enumerate(lines, 1):
	if not line.strip():
	continue

	# Parse with dirty comma handling
	# Expected: variable dirty fields + 25 clean Psi/Hemi fields + tail fields
	dirty_part, clean_part, tail_part = self.parse_dirty_csv_line(line, expected_clean_cols=25)

	# Validate we found the clean section
	if len(clean_part) != 25:
	self.errata_logger.log_error(
	'parse_failed',
	f'Line {line_num}: Could not identify clean Psi/Hemi section',
	file_path=str(file_path),
	line_number=line_num
	)
	continue

	# Map dirty fields to schema
	# Expected dirty fields for questions: Username, Timestamp, Email, Location
	# Location is a single comma-separated string containing city, state, coords, country
	username = dirty_part[0].strip() if len(dirty_part) > 0 else None
	timestamp = dirty_part[1].strip() if len(dirty_part) > 1 else None
	email = dirty_part[2].strip() if len(dirty_part) > 2 else None

	# Everything after email is the location string
	location_str = ','.join(dirty_part[3:]) if len(dirty_part) > 3 else None

	# Parse location string
	location = self.location_parser.parse_location(location_str) if location_str else {
	'city': None, 'state': None, 'coordinates': None, 'country': None
	}

	row = {
	'username': username,
	'timestamp': timestamp,
	'email': email,
	'city': location['city'],
	'state': location['state'],
	'coordinates': location['coordinates'],
	'country': location['country'],
	}

	# Psi1-15 (first 15 of clean_part)
	for i in range(15):
	row[f'psi_{i+1:02d}'] = clean_part[i].strip() if clean_part[i].strip() else None

	# Hemi1-10 (next 10 of clean_part)
	for i in range(10):
	row[f'hemi_{i+1:02d}'] = clean_part[15 + i].strip() if clean_part[15 + i].strip() else None

	# HowFind from tail (can contain commas, so join all tail parts)
	row['how_find'] = ','.join([t.strip() for t in tail_part]) if tail_part else None

	# na_count not in questions.dat - will be calculated later
	row['na_count_psi_and_hemi'] = None

	row['source_file'] = file_path.name
	row['source_row_number'] = line_num
	row['file_type'] = 'questions'

	parsed_rows.append(row)

	if not parsed_rows:
	self.errata_logger.log_error(
	'empty_file',
	'No valid rows parsed',
	file_path=str(file_path)
	)
	return None

	df = pd.DataFrame(parsed_rows)
	return df

	except Exception as e:
	import traceback
	self.errata_logger.log_error(
	'file_processing_failed',
	f'{type(e).__name__}: {e} - entire file omitted',
	file_path=str(file_path),
	scope='file',
	context={'traceback': traceback.format_exc()},
	)
	return None

	def process_file(self, file_path: Path, pre_cleaned_text: Optional[str] = None) -> Optional[pd.DataFrame]:
	"""
	Process a single user data file.

	Args:
	file_path: Path to file
	pre_cleaned_text: Pre-cleaned text from parallel batch cleaning.
	If None, cleans the file inline (backward compat).

	Returns:
	DataFrame or None
	"""
	file_type = self.detect_file_type(file_path)

	if file_type == 'users14':
	df = self.process_users14_file(file_path, pre_cleaned_text=pre_cleaned_text)
	else:
	df = self.process_questions_file(file_path, pre_cleaned_text=pre_cleaned_text)

	if df is None or df.empty:
	self.stats['files_failed'] += 1
	return None

	# Validate and clean
	df = self.validate_dataframe(df, file_path)

	if df.empty:
	self.stats['files_failed'] += 1
	return None

	# Track stats
	self.stats['files_processed'] += 1
	if file_type == 'users14':
	self.stats['users14_rows'] += len(df)
	else:
	self.stats['questions_rows'] += len(df)

	return df

	def validate_dataframe(self, df: pd.DataFrame, file_path: Path) -> pd.DataFrame:
	"""
	Validate and clean DataFrame.

	Args:
	df: Input DataFrame
	file_path: Source file path

	Returns:
	Cleaned DataFrame
	"""
	initial_count = len(df)

	# Track NA usernames (June 18 2025: these are now kept, not removed)
	if 'username' in df.columns:
	na_usernames = df['username'].isna() \| (df['username'].str.lower().str.strip() == 'na')
	self.stats['na_usernames_count'] += na_usernames.sum()

	# Parse timestamps
	if 'timestamp' in df.columns:
	df['timestamp'] = self.temporal_parser.parse_dates_vectorized(
	df['timestamp'], file_path=str(file_path)
	)

	# Convert Psi columns to numeric
	for col in self.PSI_COLUMNS:
	if col in df.columns:
	df[col] = self.to_numeric_logged(df[col], col, str(file_path))

	# Convert Hemi columns to numeric
	for col in self.HEMI_COLUMNS:
	if col in df.columns:
	df[col] = self.to_numeric_logged(df[col], col, str(file_path))

	# Calculate na_count_psi_and_hemi if not present
	if 'na_count_psi_and_hemi' not in df.columns or df['na_count_psi_and_hemi'].isna().all():
	psi_hemi_cols = self.PSI_COLUMNS + self.HEMI_COLUMNS
	existing_cols = [col for col in psi_hemi_cols if col in df.columns]
	df['na_count_psi_and_hemi'] = df[existing_cols].isna().sum(axis=1)
	else:
	df['na_count_psi_and_hemi'] = self.to_numeric_logged(df['na_count_psi_and_hemi'], 'na_count_psi_and_hemi', str(file_path))

	# Ensure all unified columns exist
	for col in self.COLUMNS_UNIFIED:
	if col not in df.columns:
	df[col] = None

	# Reorder to unified schema
	df = df[self.COLUMNS_UNIFIED]

	invalid_count = initial_count - len(df)
	if invalid_count > 0:
	self.errata_logger.log_file_summary(
	str(file_path),
	'partial',
	initial_count,
	len(df),
	invalid_count
	)

	return df

	def remove_duplicates(self, df: pd.DataFrame) -> pd.DataFrame:
	"""
	Remove duplicate usernames using deduplication rules:
	1. Keep row with fewest NAs in Psi and Hemi columns
	2. If tied on NAs, keep oldest timestamp

	Args:
	df: Input DataFrame

	Returns:
	Deduplicated DataFrame
	"""
	initial_count = len(df)

	# Track per-file duplicate stats before deduplication
	print("\nPer-file duplicate analysis:")
	print("-" * 50)

	# Count duplicates within each file type
	if 'file_type' in df.columns:
	for file_type in df['file_type'].unique():
	file_df = df[df['file_type'] == file_type]
	file_dupes = len(file_df) - file_df['username'].nunique()
	print(f" {file_type}: {len(file_df):,} rows, {file_dupes:,} internal duplicates")

	# Count cross-file duplicates
	total_rows = len(df)
	unique_usernames = df['username'].nunique()
	total_dupes = total_rows - unique_usernames

	print(f"\nCross-file analysis:")
	print(f" Total rows: {total_rows:,}")
	print(f" Unique usernames: {unique_usernames:,}")
	print(f" Total duplicates to remove: {total_dupes:,}")
	print()

	# Sort by username, na_count (ascending), timestamp (ascending)
	df_sorted = df.sort_values(
	by=['username', 'na_count_psi_and_hemi', 'timestamp'],
	ascending=[True, True, True]
	)

	# Keep first row per username (fewest NAs, then oldest)
	# Track which file types are kept vs removed
	kept_indices = df_sorted.groupby('username').head(1).index
	df_dedup = df_sorted.loc[kept_indices].reset_index(drop=True)

	# Analyze what was kept vs removed by file type
	if 'file_type' in df.columns:
	print("Deduplication results by file type:")
	removed_df = df[~df.index.isin(kept_indices)]

	for file_type in df['file_type'].unique():
	kept_count = (df_dedup['file_type'] == file_type).sum()
	removed_count = (removed_df['file_type'] == file_type).sum()
	original_count = (df['file_type'] == file_type).sum()
	print(f" {file_type}: kept {kept_count:,} / {original_count:,}, removed {removed_count:,}")
	print()

	duplicates_removed = initial_count - len(df_dedup)
	self.stats['duplicates_removed'] = duplicates_removed

	if duplicates_removed > 0:
	self.errata_logger.log_error(
	'duplicates_removed',
	f'Removed {duplicates_removed} duplicate username entries',
	file_path='combined_data'
	)

	# Log detail for a sample of discarded duplicates so the dedup
	# policy can be audited without changing the output schema.
	removed_df = df_sorted[~df_sorted.index.isin(kept_indices)]
	compare_cols = self.PSI_COLUMNS + self.HEMI_COLUMNS + ['timestamp', 'city', 'state', 'country']
	compare_cols = [c for c in compare_cols if c in df.columns]

	sample_users = removed_df['username'].unique()[:50]
	for uname in sample_users:
	kept_row = df_dedup[df_dedup['username'] == uname]
	removed_rows = removed_df[removed_df['username'] == uname]
	if kept_row.empty or removed_rows.empty:
	continue
	kept_vals = kept_row[compare_cols].iloc[0]
	for _, rem_row in removed_rows.iterrows():
	diffs = {}
	for col in compare_cols:
	k, r = kept_vals[col], rem_row[col]
	if str(k) != str(r):
	diffs[col] = {'kept': str(k), 'removed': str(r)}
	if diffs:
	self.errata_logger.log_error(
	'duplicate_detail',
	f"Discarded row for '{uname}' differs in {len(diffs)} field(s)",
	file_path='combined_data',
	scope='row',
	context={'differing_fields': diffs},
	)

	return df_dedup

	def process(self, max_files: Optional[int] = None) -> pd.DataFrame:
	"""
	Process all user data files.

	Args:
	max_files: Optional limit on number of files to process

	Returns:
	Combined DataFrame
	"""
	files = self.get_file_list()

	if max_files:
	files = files[:max_files]

	print(f"Processing {len(files)} user data files...")

	# Parallel batch cleaning (encoding only, no delimiter cleaning)
	cleaned_texts = self._batch_clean_files(files, use_delimiter_cleaner=False)
	print(f"Successfully cleaned {len(cleaned_texts)}/{len(files)} files")

	dfs = []

	try:
	from tqdm import tqdm
	iterator = tqdm(files, desc="Processing user files")
	except ImportError:
	iterator = files

	for file_path in iterator:
	pre_cleaned = cleaned_texts.get(file_path)
	if pre_cleaned is None:
	continue
	df = self.process_file(file_path, pre_cleaned_text=pre_cleaned)
	if df is not None and not df.empty:
	dfs.append(df)

	if not dfs:
	raise ProcessorError("No valid data processed")

	print("Combining files...")
	combined = pd.concat(dfs, ignore_index=True)

	self.stats['rows_total'] = len(combined)

	# Log unique usernames before deduplication
	unique_usernames_before = combined['username'].nunique()
	print(f"\nBefore deduplication:")
	print(f" Total rows: {len(combined):,}")
	print(f" Unique usernames: {unique_usernames_before:,}")
	print()

	# Remove duplicates
	print("Removing duplicate usernames...")
	combined = self.remove_duplicates(combined)

	# Log unique usernames after deduplication
	unique_usernames_after = combined['username'].nunique()
	print(f"After deduplication:")
	print(f" Total rows: {len(combined):,}")
	print(f" Unique usernames: {unique_usernames_after:,}")
	print()

	self.stats['rows_valid'] = len(combined)

	# Remove audit columns if audit mode is disabled
	audit_mode = self.config.get('processing.audit_mode', False)
	if not audit_mode:
	audit_cols = ['source_file', 'source_row_number']
	cols_to_drop = [col for col in audit_cols if col in combined.columns]
	if cols_to_drop:
	combined = combined.drop(columns=cols_to_drop)
	print(f"Audit mode disabled: Removed columns {cols_to_drop}")
	print()


	# Close errata logger
	self.errata_logger.close()

	return combined

	def get_stats(self) -> Dict[str, Any]:
	"""Get processing statistics."""
	return self.stats.copy()