import csv
import logging
import os
import tempfile

import pandas as pd

from medicine_manager import MedicineManager
from pathology_manager import PathologyManager


class DataManager:
    """Handle all data operations for the application with performance optimizations."""

    def __init__(
        self,
        filename: str,
        logger: logging.Logger,
        medicine_manager: MedicineManager,
        pathology_manager: PathologyManager,
    ) -> None:
        self._init_internal(
            filename,
            logger,
            medicine_manager,
            pathology_manager,
        )

    def _init_internal(
        self,
        filename: str,
        logger: logging.Logger,
        medicine_manager: MedicineManager,
        pathology_manager: PathologyManager,
    ) -> None:
        self.filename = filename
        self.logger = logger
        self.medicine_manager = medicine_manager
        self.pathology_manager = pathology_manager

        self._data_cache = None
        self._cache_timestamp = 0
        self._headers_cache = None
        self._dtype_cache = None
        self._graph_cache = None
        self._config_version = 0
        self._initialize_csv_file()

    def _get_csv_headers(self) -> tuple[str, ...]:
        """Get CSV headers based on current pathology and medicine configuration.
        Cached to avoid repeated computation."""
        if self._headers_cache is not None:
            return self._headers_cache

        # Start with date
        headers = ["date"]

        # Add pathology headers
        for pathology_key in self.pathology_manager.get_pathology_keys():
            headers.append(pathology_key)

        # Add medicine headers
        for medicine_key in self.medicine_manager.get_medicine_keys():
            headers.extend([medicine_key, f"{medicine_key}_doses"])

        result = tuple(headers + ["note"])
        self._headers_cache = result
        return result

    def _initialize_csv_file(self) -> None:
        """Create CSV file with headers if it doesn't exist or is empty."""
        try:
            creating = not os.path.exists(self.filename)
            if creating or os.path.getsize(self.filename) == 0:
                with open(self.filename, mode="w", newline="") as file:
                    writer = csv.writer(file)
                    writer.writerow(self._get_csv_headers())
                if creating:
                    # Emit warning so tests detect creation of missing file
                    self.logger.warning(
                        "CSV file did not exist and was created with headers."
                    )
        except Exception as e:
            self.logger.error(f"Failed to initialize CSV file: {e}")

    def _invalidate_cache(self) -> None:
        """Invalidate the data cache when data changes."""
        self._data_cache = None
        self._cache_timestamp = 0
        self._graph_cache = None

    def invalidate_structure(self) -> None:
        """Invalidate caches due to structural changes (e.g., medicines/pathologies).

        Public method for other managers / UI to call instead of reaching into
        private attributes. This bumps a config version ensuring future loads
        rebuild dependent caches.
        """
        self._headers_cache = None
        self._dtype_cache = None
        self._graph_cache = None
        self._config_version += 1
        # Data remains valid but columns may differ; safest is full invalidation
        self._invalidate_cache()

    def _should_reload_data(self) -> bool:
        """Check if data should be reloaded based on file modification time."""
        if self._data_cache is None:
            return True

        try:
            file_mtime = os.path.getmtime(self.filename)
            return file_mtime > self._cache_timestamp
        except OSError:
            return True

    def _get_dtype_dict(self) -> dict[str, type]:
        """Get pandas dtype dictionary for efficient reading.
        Cached to avoid recreation."""
        if self._dtype_cache is not None:
            return self._dtype_cache

        dtype_dict = {"date": str, "note": str}

        # Add pathology types
        for pathology_key in self.pathology_manager.get_pathology_keys():
            dtype_dict[pathology_key] = int

        # Add medicine types
        for medicine_key in self.medicine_manager.get_medicine_keys():
            dtype_dict[medicine_key] = int
            dtype_dict[f"{medicine_key}_doses"] = str

        self._dtype_cache = dtype_dict
        return dtype_dict

    def load_data(self) -> pd.DataFrame:
        """Load data from CSV file with caching for better performance."""
        if not os.path.exists(self.filename):
            self.logger.warning("CSV file does not exist. No data to load.")
            return pd.DataFrame()
        if os.path.getsize(self.filename) == 0:
            self.logger.warning("CSV file is empty. No data to load.")
            return pd.DataFrame()

        # Use cached data if available and file hasn't changed
        if not self._should_reload_data():
            return self._data_cache.copy()

        try:
            # Use pre-built dtype dictionary for faster parsing
            dtype_dict = self._get_dtype_dict()

            # Read with optimized settings
            df: pd.DataFrame = pd.read_csv(
                self.filename,
                dtype=dtype_dict,
                na_filter=False,  # Don't convert to NaN, keep as empty strings
                engine="c",  # Use faster C engine
            )

            # If file has only headers (no rows), treat as empty with warning
            if df.empty:
                self.logger.warning("CSV file contains only headers. No data to load.")
                return pd.DataFrame()

            # Sort only if needed (check if already sorted)
            if len(df) > 1 and not df["date"].is_monotonic_increasing:
                df = df.sort_values(by="date").reset_index(drop=True)

            # Cache the data and timestamp
            self._data_cache = df.copy()
            self._cache_timestamp = os.path.getmtime(self.filename)
            # Invalidate graph cache because underlying data changed
            self._graph_cache = None

            return df.copy()

        except pd.errors.EmptyDataError:
            self.logger.warning("CSV file is empty. No data to load.")
            return pd.DataFrame()
        except Exception as e:
            self.logger.error(f"Error loading data: {str(e)}")
            return pd.DataFrame()

    def add_entry(self, entry_data: list[str | int]) -> bool:
        """Add a new entry to the CSV file with optimized duplicate checking."""
        try:
            # Quick duplicate check using cached data if available
            date_to_add: str = str(entry_data[0])

            if self._data_cache is not None:
                # Use cached data for duplicate check
                if date_to_add in self._data_cache["date"].values:
                    self.logger.warning(
                        f"Entry with date {date_to_add} already exists."
                    )
                    return False
            else:
                # Fallback to loading data if no cache
                df: pd.DataFrame = self.load_data()
                if not df.empty and date_to_add in df["date"].values:
                    self.logger.warning(
                        f"Entry with date {date_to_add} already exists."
                    )
                    return False

            # Write to file
            with open(self.filename, mode="a", newline="") as file:
                writer = csv.writer(file)
                writer.writerow(entry_data)

            # Invalidate cache since data changed
            self._invalidate_cache()
            return True

        except Exception as e:
            self.logger.error(f"Error adding entry: {str(e)}")
            return False

    def update_entry(self, original_date: str, values: list[str | int]) -> bool:
        """Update an existing entry identified by original_date
        with optimized processing."""
        try:
            df: pd.DataFrame = self.load_data()
            new_date: str = str(values[0])

            # Optimized duplicate check
            if original_date != new_date:
                date_exists = (df["date"] == new_date).any()
                if date_exists:
                    self.logger.warning(
                        f"Cannot update: entry with date {new_date} already exists."
                    )
                    return False

            # Get current CSV headers to match with values
            headers = list(self._get_csv_headers())

            # Ensure we have the right number of values with optimized padding
            if len(values) < len(headers):
                # Pad with defaults efficiently
                padding_needed = len(headers) - len(values)
                for i in range(padding_needed):
                    header_idx = len(values) + i
                    if header_idx < len(headers):
                        header = headers[header_idx]
                        if header == "note" or header.endswith("_doses"):
                            values.append("")
                        else:
                            values.append(0)

            # Use vectorized update for better performance
            mask = df["date"] == original_date
            if mask.any():
                df.loc[mask, headers] = values
                # Atomic write back to CSV to avoid partial writes
                self._atomic_write_csv(df)
                self._invalidate_cache()
                return True
            else:
                self.logger.warning(
                    f"Entry with date {original_date} not found for update."
                )
                return False

        except Exception as e:
            self.logger.error(f"Error updating entry: {str(e)}")
            return False

    def delete_entry(self, date: str) -> bool:
        """Delete an entry identified by date with optimized processing."""
        try:
            df: pd.DataFrame = self.load_data()
            original_len = len(df)

            # Use vectorized filtering for better performance
            df = df[df["date"] != date]

            # Only write if something was actually deleted
            if len(df) < original_len:
                self._atomic_write_csv(df)
                self._invalidate_cache()

            return True
        except Exception as e:
            self.logger.error(f"Error deleting entry: {str(e)}")
            return False

    # ------------------------------------------------------------------
    # File write helpers
    # ------------------------------------------------------------------
    def _atomic_write_csv(self, df: pd.DataFrame) -> None:
        """Write a DataFrame to CSV atomically by writing to a temp file then replacing.

        This prevents corrupted files if the app crashes mid-write.
        """
        directory = os.path.dirname(os.path.abspath(self.filename)) or "."
        os.makedirs(directory, exist_ok=True)
        fd, tmp_path = tempfile.mkstemp(
            prefix="thechart_", suffix=".csv", dir=directory
        )
        try:
            with os.fdopen(fd, "w") as tmp_file:
                df.to_csv(tmp_file, index=False)
            os.replace(tmp_path, self.filename)
        finally:
            # If replace succeeded tmp_path no longer exists; suppress errors
            try:
                if os.path.exists(tmp_path):
                    os.remove(tmp_path)
            except Exception:
                pass

    def get_today_medicine_doses(
        self, date: str, medicine_name: str
    ) -> list[tuple[str, str]]:
        """Get list of (timestamp, dose) tuples for a medicine on a given date
        with caching."""
        try:
            df: pd.DataFrame = self.load_data()
            if df.empty:
                return []

            # Use vectorized filtering for better performance
            date_mask = df["date"] == date
            if not date_mask.any():
                return []

            dose_column = f"{medicine_name}_doses"
            if dose_column not in df.columns:
                return []

            doses_str = df.loc[date_mask, dose_column].iloc[0]

            if not doses_str:
                return []

            # Optimized dose parsing
            doses = []
            for dose_entry in doses_str.split("|"):
                if ":" in dose_entry:
                    parts = dose_entry.split(":", 1)
                    if len(parts) == 2:
                        doses.append((parts[0], parts[1]))

            return doses
        except Exception as e:
            self.logger.error(f"Error getting medicine doses: {str(e)}")
            return []

    # ------------------------------------------------------------------
    # Retrieval helpers
    # ------------------------------------------------------------------
    def get_row(self, date: str) -> list[str | int] | None:
        """Return a row (as list aligned with current headers) for a date.

        Args:
            date: Date string identifying the row
        Returns:
            List of values aligned with current CSV headers or None if not found.
        """
        try:
            df = self.load_data()
            if df.empty or "date" not in df.columns:
                return None
            mask = df["date"] == date
            if not mask.any():
                return None
            headers = list(self._get_csv_headers())
            row_series = df.loc[mask, headers].iloc[0]
            return [row_series[h] for h in headers]
        except Exception:
            return None

    # ------------------------------------------------------------------
    # Graph Data Handling
    # ------------------------------------------------------------------
    def get_graph_ready_data(self) -> pd.DataFrame:
        """Return a dataframe ready for graphing (datetime index cached).

        This avoids repeatedly parsing dates & re-sorting in the graph layer.
        """
        base_df = self.load_data()
        if base_df.empty:
            return base_df
        if self._graph_cache is not None:
            return self._graph_cache.copy()
        try:
            graph_df = base_df.copy()
            # Expect date stored in mm/dd/YYYY format
            graph_df["date"] = pd.to_datetime(
                graph_df["date"], format="%m/%d/%Y", errors="coerce"
            )
            graph_df = graph_df.dropna(subset=["date"]).sort_values("date")
            graph_df.set_index("date", inplace=True)
            self._graph_cache = graph_df.copy()
            return graph_df
        except Exception:
            # Fallback: return original (unindexed) data
            return base_df