import pandas as pd
from utils import get_distinct_colors, calculate_estimated_1rm

class PeopleGraphs:
    def __init__(self, db_connection_method):
        self.execute = db_connection_method

    def get(self, selected_people_ids=None, min_date=None, max_date=None, selected_exercise_ids=None):
        """
        Fetch workout topsets, calculate Estimated1RM in Python,
        then generate weekly workout & PR graphs.
        """
        # Build query (no in-SQL 1RM calculation).
        query = """
        SELECT
            P.person_id   AS "PersonId",
            P.name        AS "PersonName",
            W.workout_id  AS "WorkoutId",
            W.start_date  AS "StartDate",
            T.topset_id   AS "TopSetId",
            E.exercise_id AS "ExerciseId",
            E.name        AS "ExerciseName",
            T.repetitions AS "Repetitions",
            T.weight      AS "Weight"
        FROM Person P
        LEFT JOIN Workout W ON P.person_id = W.person_id
        LEFT JOIN TopSet T ON W.workout_id = T.workout_id
        LEFT JOIN Exercise E ON T.exercise_id = E.exercise_id
        WHERE TRUE
        """
        params = []
        if selected_people_ids:
            query += f" AND P.person_id IN ({', '.join(['%s'] * len(selected_people_ids))})"
            params.extend(selected_people_ids)
        if min_date:
            query += " AND W.start_date >= %s"
            params.append(min_date)
        if max_date:
            query += " AND W.start_date <= %s"
            params.append(max_date)
        if selected_exercise_ids:
            query += f" AND E.exercise_id IN ({', '.join(['%s'] * len(selected_exercise_ids))})"
            params.extend(selected_exercise_ids)

        # Execute and convert to DataFrame
        raw_data = self.execute(query, params)
        if not raw_data:
            # Return empty graphs if no data at all
            return [
                self.get_graph_model("Workouts per week", {}),
                self.get_graph_model("PRs per week", {})
            ]

        df = pd.DataFrame(raw_data)

        # Calculate Estimated1RM in Python
        df['Estimated1RM'] = df.apply(
            lambda row: calculate_estimated_1rm(row["Weight"], row["Repetitions"]), axis=1
        )

        # Build the weekly data models
        weekly_counts   = self.get_workout_counts(df, period='week')
        weekly_pr_counts = self.count_prs_over_time(df, period='week')

        return [
            self.get_graph_model("Workouts per week", weekly_counts),
            self.get_graph_model("PRs per week", weekly_pr_counts)
        ]

    def _prepare_period_column(self, df, period='week'):
        """
        Convert StartDate to datetime and add a Period column 
        based on 'week' or 'month' as needed.
        """
        df['StartDate'] = pd.to_datetime(df['StartDate'], errors='coerce')
        freq = 'W' if period == 'week' else 'M'
        df['Period'] = df['StartDate'].dt.to_period(freq)
        return df

    def get_workout_counts(self, df, period='week'):
        """
        Returns a dictionary:
          {
            person_id: {
              'PersonName': 'Alice',
              'PRCounts': {
                Timestamp('2023-01-02'): 2,
                ...
              }
            },
            ...
          }
        representing how many workouts each person performed per time period.
        """
        # Make a copy and prepare Period column
        df = self._prepare_period_column(df.copy(), period)

        # Count unique workouts per (PersonId, PersonName, Period)
        grp = (
            df.groupby(['PersonId', 'PersonName', 'Period'], as_index=False)['WorkoutId']
              .nunique()
              .rename(columns={'WorkoutId': 'Count'})
        )
        # Convert each Period to its start time
        grp['Period'] = grp['Period'].apply(lambda p: p.start_time)

        return self._pivot_to_graph_dict(
            grp,
            index_col='PersonId',
            name_col='PersonName',
            period_col='Period',
            value_col='Count'
        )

    def count_prs_over_time(self, df, period='week'):
        """
        Returns a dictionary:
          {
            person_id: {
              'PersonName': 'Alice',
              'PRCounts': {
                Timestamp('2023-01-02'): 1,
                ...
              }
            },
            ...
          }
        representing how many PRs each person hit per time period.
        """
        # Make a copy and prepare Period column
        df = self._prepare_period_column(df.copy(), period)

        # Max 1RM per (Person, Exercise, Period)
        grouped = (
            df.groupby(['PersonId', 'PersonName', 'ExerciseId', 'Period'], as_index=False)['Estimated1RM']
              .max()
              .rename(columns={'Estimated1RM': 'PeriodMax'})
        )

        # Sort so we can track "all-time max" up to that row
        grouped.sort_values(by=['PersonId', 'ExerciseId', 'Period'], inplace=True)

        # For each person & exercise, track the cumulative max (shifted by 1)
        grouped['AllTimeMax'] = grouped.groupby(['PersonId', 'ExerciseId'])['PeriodMax'].cummax().shift(1)
        grouped['IsPR'] = (grouped['PeriodMax'] > grouped['AllTimeMax']).astype(int)

        # Sum PRs across exercises for (Person, Period)
        pr_counts = (
            grouped.groupby(['PersonId', 'PersonName', 'Period'], as_index=False)['IsPR']
                   .sum()
                   .rename(columns={'IsPR': 'Count'})
        )
        pr_counts['Period'] = pr_counts['Period'].apply(lambda p: p.start_time)

        return self._pivot_to_graph_dict(
            pr_counts,
            index_col='PersonId',
            name_col='PersonName',
            period_col='Period',
            value_col='Count'
        )

    def _pivot_to_graph_dict(self, df, index_col, name_col, period_col, value_col):
        """
        Convert [index_col, name_col, period_col, value_col]
        into a nested dictionary for plotting:
           {
             person_id: {
               'PersonName': <...>,
               'PRCounts': {
                 <timestamp>: <value>,
                 ...
               }
             },
             ...
           }
        """
        if df.empty:
            return {}

        pivoted = df.pivot(
            index=[index_col, name_col],
            columns=period_col,
            values=value_col
        ).fillna(0)

        pivoted.reset_index(inplace=True)

        result = {}
        for _, row in pivoted.iterrows():
            pid = row[index_col]
            pname = row[name_col]
            # Remaining columns = date -> count
            period_counts = row.drop([index_col, name_col]).to_dict()
            result[pid] = {
                'PersonName': pname,
                'PRCounts': period_counts
            }

        return result

    def get_graph_model(self, title, data_dict):
        """
        Builds a line-graph model from a dictionary of the form:
           {
             person_id: {
               'PersonName': 'Alice',
               'PRCounts': {
                 Timestamp('2023-01-02'): 2,
                 Timestamp('2023-01-09'): 1,
                 ...
               }
             },
             ...
           }
        """
        if not data_dict:
            return {
                'title': title,
                'vb_width': 200,
                'vb_height': 75,
                'plots': []
            }

        # Gather all dates & values
        all_dates  = []
        all_values = []
        for user_data in data_dict.values():
            all_dates.extend(user_data['PRCounts'].keys())
            all_values.extend(user_data['PRCounts'].values())

        min_date = min(all_dates)
        max_date = max(all_dates)
        date_span = max((max_date - min_date).days, 1)

        max_val = max(all_values)
        min_val = 0
        val_range = max_val - min_val if max_val != min_val else 1

        vb_width, vb_height = 200, 75
        colors = get_distinct_colors(len(data_dict))
        plots = []

        for i, (pid, user_data) in enumerate(data_dict.items()):
            name = user_data['PersonName']
            pr_counts = user_data['PRCounts']
            # Sort by date so points are in chronological order
            sorted_pr = sorted(pr_counts.items(), key=lambda x: x[0])

            points = []
            labels = []
            for d, val in sorted_pr:
                # Scale x,y to fit [0..1], then we multiply y by vb_height
                x = (d - min_date).days / date_span
                y = (val - min_val) / val_range * vb_height
                points.append((y, x))
                labels.append((y, x, f'{val} for {name} at {d.strftime("%d %b %y")}'))

            plots.append({
                'label': name,
                'color': colors[i],
                'points': points,
                'plot_labels': labels
            })

        return {
            'title': title,
            'vb_width': vb_width,
            'vb_height': vb_height,
            'plots': plots
        }