Aggregated Logbook

src/boardlib/api/aurora.py

@@ -4,7 +4,8 @@
 import bs4
 import requests
 import pandas as pd
-
+from datetime import datetime as dt
+from dateutil.relativedelta import relativedelta
 import boardlib.util.grades
 
 HOST_BASES = {
@@ -580,5 +581,115 @@ def logbook_entries(board, username, password, grade_type="font", db_path=None):
 
     return full_logbook_df
 
+def bids_logbook_entries_agg(board, token, user_id, db_path=None):
+    raw_entries = get_bids_logbook(board, token, user_id)
+
+    for raw_entry in raw_entries:
+        yield {
+            "climb_uuid": raw_entry["climb_uuid"],
+            "board": board,
+            "climb_name": get_climb_name_from_db(db_path, raw_entry["climb_uuid"]) if db_path else get_climb_name(board, raw_entry["climb_uuid"]),
+            "angle": raw_entry["angle"],
+            "is_mirror": raw_entry["is_mirror"],
+            "climbed_at": raw_entry["climbed_at"],
+            "bid_count": raw_entry["bid_count"],
+        }
+
+def summarize_bids_agg(bids_df):
+    bids_summary = bids_df.groupby(['climb_uuid', 'climb_name', 'board', bids_df['climbed_at'].dt.date, 'is_mirror', 'angle']).agg({
+        'bid_count': 'sum'
+    }).reset_index().rename(columns={'climbed_at': 'date'})
+    bids_summary['tries'] = bids_summary['bid_count']
+    return bids_summary
+
+def days_to_years_months_days(days):
+    start_date = dt.now()
+    end_date = start_date - pd.Timedelta(days=days)
+    delta = relativedelta(start_date, end_date)
+
+    parts = []
+    if delta.years > 0:
+        parts.append(f"{delta.years} year{'s' if delta.years > 1 else ''}")
+    if delta.months > 0:
+        parts.append(f"{delta.months} month{'s' if delta.months > 1 else ''}")
+    parts.append(f"{delta.days} day{'s' if delta.days > 1 else ''}")
+    return ' and '.join(parts)
+
+def combine_ascents_and_bids_agg(ascents_df, bids_summary):
+    final_logbook = []
+
+    for _, ascent_row in ascents_df.iterrows():
+        ascent_date = ascent_row['date'].date()
+        ascent_climb_uuid = ascent_row['climb_uuid']
+
+        bid_match = bids_summary[
+            (bids_summary['climb_uuid'] == ascent_climb_uuid) &
+            (bids_summary['date'] == ascent_date) &
+            (bids_summary['is_mirror'] == ascent_row['is_mirror']) &
+            (bids_summary['angle'] == ascent_row['angle'])
+        ]
+
+        if not bid_match.empty:
+            bid_row = bid_match.iloc[0]
+            final_logbook.append({
+                'uid': f"{ascent_row['climb_uuid']}-{ascent_row['angle']}",
+                'board': ascent_row['board'],
+                'climb_name': ascent_row['name'],
+                'date': ascent_row['date'],
+                'tries': ascent_row['tries'] + bid_row['tries']
+            })
+            bids_summary = bids_summary.drop(bid_match.index)
+        else:
+            final_logbook.append({
+                'uid': f"{ascent_row['climb_uuid']}-{ascent_row['angle']}",
+                'board': ascent_row['board'],
+                'climb_name': ascent_row['name'],
+                'date': ascent_row['date'],
+                'tries': ascent_row['tries']
+            })
+
+    for _, bid_row in bids_summary.iterrows():
+        final_logbook.append({
+            'uid': f"{bid_row['climb_uuid']}-{bid_row['angle']}",
+            'board': bid_row['board'],
+            'climb_name': bid_row['climb_name'],
+            'date': bid_row['date'],
+            'tries': bid_row['tries']
+        })
+    return final_logbook
+
+def logbook_entries_agg(board, token, user_id, db_path=None, grade_type="font"):
+    bids_entries = list(bids_logbook_entries_agg(board, token, user_id, db_path))
+    raw_ascents_entries = get_logbook(board, token, user_id)
+
+    if not bids_entries and not raw_ascents_entries:
+        return pd.DataFrame(columns=['uid', 'board', 'climb_name'])
+
+    bids_summary = pd.DataFrame(columns=['climb_uuid', 'climb_name', 'board', 'date', 'is_mirror', 'angle', 'tries'])
+    if bids_entries:
+        bids_df = pd.DataFrame(bids_entries)
+        bids_df['climbed_at'] = pd.to_datetime(bids_df['climbed_at'])
+        bids_summary = summarize_bids_agg(bids_df)
+
+    ascents_df = pd.DataFrame(columns=['board', 'angle', 'climb_uuid', 'name', 'date', 'tries', 'is_mirror'])
+    if raw_ascents_entries:
+        grades = get_grades(board)
+        grades_dict = {grade['difficulty']: grade for grade in grades}
+        ascents_entries = process_raw_ascent_entries(raw_ascents_entries, board, db_path, grades_dict, grade_type)
+        ascents_df = pd.DataFrame(ascents_entries)
+
+    final_logbook = combine_ascents_and_bids_agg(ascents_df, bids_summary)
+
+    full_logbook_df = pd.DataFrame(final_logbook)
+    full_logbook_df['date'] = pd.to_datetime(full_logbook_df['date'])
 
+    aggregated_logbook = full_logbook_df.groupby(['uid', 'board', 'climb_name']).agg(
+        date=('date', 'max'),
+        sessions=('uid', 'count'),
+        tries=('tries', 'sum')
+    ).reset_index()
 
+    current_date = dt.now()
+    aggregated_logbook['days'] = aggregated_logbook['date'].apply(lambda x: (current_date - pd.to_datetime(x)).days)
+    aggregated_logbook['time_since'] = aggregated_logbook['days'].apply(days_to_years_months_days)
+    return aggregated_logbook