Cloned DMOJ

judge/performance_points.py

@@ -0,0 +1,78 @@
+from collections import namedtuple
+
+from django.conf import settings
+from django.db import connection
+
+from judge.models import Submission
+from judge.timezone import from_database_time
+
+PP_WEIGHT_TABLE = [pow(settings.DMOJ_PP_STEP, i) for i in range(settings.DMOJ_PP_ENTRIES)]
+
+PPBreakdown = namedtuple('PPBreakdown', 'points weight scaled_points problem_name problem_code '
+                                        'sub_id sub_date sub_points sub_total sub_result_class '
+                                        'sub_short_status sub_long_status sub_lang')
+
+
+def get_pp_breakdown(user, start=0, end=settings.DMOJ_PP_ENTRIES):
+    with connection.cursor() as cursor:
+        cursor.execute('''
+            SELECT max_points_table.problem_code,
+                   max_points_table.problem_name,
+                   max_points_table.max_points,
+                   judge_submission.id,
+                   judge_submission.date,
+                   judge_submission.case_points,
+                   judge_submission.case_total,
+                   judge_submission.result,
+                   judge_language.short_name,
+                   judge_language.key
+            FROM judge_submission
+            JOIN (SELECT judge_problem.id problem_id,
+                         judge_problem.name problem_name,
+                         judge_problem.code problem_code,
+                         MAX(judge_submission.points) AS max_points
+                FROM judge_problem
+                INNER JOIN judge_submission ON (judge_problem.id = judge_submission.problem_id)
+                WHERE (judge_problem.is_public = True AND
+                       judge_problem.is_organization_private = False AND
+                       judge_submission.points IS NOT NULL AND
+                       judge_submission.user_id = %s)
+                GROUP BY judge_problem.id
+                HAVING MAX(judge_submission.points) > 0.0) AS max_points_table
+            ON (judge_submission.problem_id = max_points_table.problem_id AND
+                judge_submission.points = max_points_table.max_points AND
+                judge_submission.user_id = %s)
+            JOIN judge_language
+            ON judge_submission.language_id = judge_language.id
+            GROUP BY max_points_table.problem_id
+            ORDER BY max_points DESC, judge_submission.date DESC
+            LIMIT %s OFFSET %s
+        ''', (user.id, user.id, end - start + 1, start))
+        data = cursor.fetchall()
+
+    breakdown = []
+    for weight, contrib in zip(PP_WEIGHT_TABLE[start:end], data):
+        code, name, points, id, date, case_points, case_total, result, lang_short_name, lang_key = contrib
+
+        # Replicates a lot of the logic usually done on Submission objects
+        lang_short_display_name = lang_short_name or lang_key
+        result_class = Submission.result_class_from_code(result, case_points, case_total)
+        long_status = Submission.USER_DISPLAY_CODES.get(result, '')
+
+        breakdown.append(PPBreakdown(
+            points=points,
+            weight=weight * 100,
+            scaled_points=points * weight,
+            problem_name=name,
+            problem_code=code,
+            sub_id=id,
+            sub_date=from_database_time(date),
+            sub_points=case_points,
+            sub_total=case_total,
+            sub_short_status=result,
+            sub_long_status=long_status,
+            sub_result_class=result_class,
+            sub_lang=lang_short_display_name,
+        ))
+    has_more = end < min(len(PP_WEIGHT_TABLE), start + len(data))
+    return breakdown, has_more