Merge pull request #40 from team4099/separate-scouting-accuracy

Separate scouting accuracy

Author: 00magikarp

src/page_managers/scouting_accuracy_manager.py

@@ -1,3 +1,5 @@
+# TODO: Debug teleop and maybe auto later
+
 """Creates the `ScoutingAccuracyManager` class used to set up the Scouting Accuracy page and generate its table."""
 import pandas as pd
 import streamlit as st
@@ -8,7 +10,8 @@
     Queries,
     retrieve_scouting_data,
     retrieve_match_schedule,
-    retrieve_match_data
+    retrieve_match_data,
+    Criteria
 )
 import requests
 import os
@@ -45,6 +48,9 @@ def generate_accuracy_table(self, member_name) -> DataFrame:
         accuracy_dict = {
             'ScoutersNames': [],
             'CumulativeAccuracy': [],
+            'AutoAccuracy': [],
+            'TeleopAccuracy': [],
+            'EndgameAccuracy': [],
             'NumberOfScoutedMatches': []
         }
 
@@ -65,10 +71,16 @@ def generate_accuracy_table(self, member_name) -> DataFrame:
                 if (match["comp_level"] + str(match["match_number"])) == match_key:
                     red_total_score = match["score_breakdown"]["red"]["totalPoints"]
                     red_foul_score = match["score_breakdown"]["red"]["foulPoints"]
+                    red_auto_score = match["score_breakdown"]["red"]["autoPoints"]
+                    red_teleop_score = match["score_breakdown"]["red"]["teleopPoints"]
+                    red_endgame_score = match["score_breakdown"]["red"]["endGameBargePoints"]
                     red_calculated_score = red_total_score - red_foul_score
                     break
 
             red_scouting_alliance_score = 0
+            red_scouting_auto_score = 0
+            red_scouting_teleop_score = 0
+            red_scouting_endgame_score = 0
 
             scouters_names_list_r = []
 
@@ -78,22 +90,73 @@ def generate_accuracy_table(self, member_name) -> DataFrame:
                 match_index_list = scouting_team_filter.index[scouting_team_filter[Queries.MATCH_KEY] == match_key].tolist()
                 if len(match_index_list) != 0:
                     match_index = match_index_list[0]
+
+                    # Auto Accuracy Retrieval
+                    auto_coral_per_match = [
+                        self.calculated_stats.cycles_by_structure_per_match(int(team_key), Queries.AUTO_CORAL_L1).values,
+                        self.calculated_stats.cycles_by_structure_per_match(int(team_key), Queries.AUTO_CORAL_L2).values,
+                        self.calculated_stats.cycles_by_structure_per_match(int(team_key), Queries.AUTO_CORAL_L3).values,
+                        self.calculated_stats.cycles_by_structure_per_match(int(team_key), Queries.AUTO_CORAL_L4).values
+                    ]
+                    for i in range(len(auto_coral_per_match)):
+                        red_scouting_auto_score += auto_coral_per_match[i][match_index] * Criteria.AUTO_CORAL_POINTAGE[i + 1]
+                    auto_algae_per_match = [
+                        self.calculated_stats.cycles_by_structure_per_match(int(team_key), Queries.AUTO_BARGE).values,
+                        self.calculated_stats.cycles_by_structure_per_match(int(team_key), Queries.AUTO_PROCESSOR).values
+                    ]
+                    for i in range(len(auto_algae_per_match)):
+                        red_scouting_auto_score += auto_algae_per_match[i][match_index] * Criteria.ALGAE_POINTAGE[i + 1]
+                    leave_points = self.calculated_stats.stat_per_match(int(team_key), Queries.LEFT_STARTING_ZONE, Criteria.BOOLEAN_CRITERIA).values
+                    red_scouting_auto_score += (leave_points[match_index] * 2)
+
+                    # Teleop Accuracy Retrieval
+                    teleop_coral_per_match = [
+                        self.calculated_stats.cycles_by_structure_per_match(int(team_key), Queries.TELEOP_CORAL_L1).values,
+                        self.calculated_stats.cycles_by_structure_per_match(int(team_key), Queries.TELEOP_CORAL_L2).values,
+                        self.calculated_stats.cycles_by_structure_per_match(int(team_key), Queries.TELEOP_CORAL_L3).values,
+                        self.calculated_stats.cycles_by_structure_per_match(int(team_key), Queries.TELEOP_CORAL_L4).values
+                    ]
+                    for i in range(len(teleop_coral_per_match)):
+                        red_scouting_teleop_score += teleop_coral_per_match[i][match_index] * Criteria.TELEOP_CORAL_POINTAGE[i + 1]
+                    teleop_algae_per_match = [
+                        self.calculated_stats.cycles_by_structure_per_match(int(team_key), Queries.TELEOP_BARGE).values,
+                        self.calculated_stats.cycles_by_structure_per_match(int(team_key), Queries.TELEOP_PROCESSOR).values
+                    ]
+                    for i in range(len(teleop_algae_per_match)):
+                        red_scouting_teleop_score += teleop_algae_per_match[i][match_index] * Criteria.ALGAE_POINTAGE[i + 1]
+
+                    # Endgame Accuracy Retrieval
+                    park_points = self.calculated_stats.stat_per_match(int(team_key), Queries.PARKED_UNDER_BARGE, Criteria.BOOLEAN_CRITERIA).values
+                    climb_points = self.calculated_stats.stat_per_match(int(team_key), Queries.CLIMBED_CAGE, Criteria.CLIMBING_POINTAGE).values
+                    red_scouting_endgame_score = park_points[match_index] * 2 + climb_points[match_index]
+
+                    # Cumulative Accuracy Retrieval
                     points_per_match = self.calculated_stats.points_contributed_by_match(int(team_key)).values
                     red_scouting_alliance_score += points_per_match[match_index]
+
                     scout_name = scouting_team_filter.iloc[match_index][Queries.SCOUT_ID]
                     scouters_names_list_r.append(scout_name.title().replace(" ", ""))
 
             red_alliance_accuracy = (1 - abs((red_scouting_alliance_score-red_calculated_score)/red_calculated_score)) * 100
+            red_auto_accuracy = (1 - abs((red_scouting_auto_score - red_auto_score)/red_auto_score)) * 100
+            red_teleop_accuracy = (1 - abs((red_scouting_teleop_score - red_teleop_score)/red_teleop_score)) * 100
+            red_endgame_accuracy = (1 - abs((red_scouting_endgame_score - red_endgame_score)/red_endgame_score)) * 100
             scouters_names = ", ".join(scouters_names_list_r)
 
             if member_name.replace(" ", "").lower() in scouters_names.lower():
                 if scouters_names not in accuracy_dict['ScoutersNames']:
                     accuracy_dict['ScoutersNames'].append(scouters_names)
                     accuracy_dict['CumulativeAccuracy'].append(red_alliance_accuracy)
+                    accuracy_dict['AutoAccuracy'].append(red_auto_accuracy)
+                    accuracy_dict['TeleopAccuracy'].append(red_teleop_accuracy)
+                    accuracy_dict['EndgameAccuracy'].append(red_endgame_accuracy)
                     accuracy_dict['NumberOfScoutedMatches'].append(1)
                 else:
                     accuracy_scouts_index = accuracy_dict['ScoutersNames'].index(scouters_names)
                     accuracy_dict['CumulativeAccuracy'][accuracy_scouts_index] += red_alliance_accuracy
+                    accuracy_dict['AutoAccuracy'][accuracy_scouts_index] += red_auto_accuracy
+                    accuracy_dict['TeleopAccuracy'][accuracy_scouts_index] += red_teleop_accuracy
+                    accuracy_dict['EndgameAccuracy'][accuracy_scouts_index] += red_endgame_accuracy
                     accuracy_dict['NumberOfScoutedMatches'][accuracy_scouts_index] += 1
 
             # Blue Alliance score from TBA
@@ -103,10 +166,16 @@ def generate_accuracy_table(self, member_name) -> DataFrame:
                 if (match["comp_level"] + str(match["match_number"])) == match_key:
                     blue_total_score = match["score_breakdown"]["blue"]["totalPoints"]
                     blue_foul_score = match["score_breakdown"]["blue"]["foulPoints"]
+                    blue_auto_score = match["score_breakdown"]["blue"]["autoPoints"]
+                    blue_teleop_score = match["score_breakdown"]["blue"]["teleopPoints"]
+                    blue_endgame_score = match["score_breakdown"]["blue"]["endGameBargePoints"]
                     blue_calculated_score = blue_total_score - blue_foul_score
                     break
 
             blue_scouting_alliance_score = 0
+            blue_scouting_auto_score = 0
+            blue_scouting_teleop_score = 0
+            blue_scouting_endgame_score = 0
 
             scouters_names_list_b = []
 
@@ -116,30 +185,85 @@ def generate_accuracy_table(self, member_name) -> DataFrame:
                 match_index_list = scouting_team_filter.index[scouting_team_filter[Queries.MATCH_KEY] == match_key].tolist()
                 if len(match_index_list) != 0:
                     match_index = match_index_list[0]
+
+                    # Auto Accuracy Retrieval
+                    auto_coral_per_match = [
+                        self.calculated_stats.cycles_by_structure_per_match(int(team_key), Queries.AUTO_CORAL_L1).values,
+                        self.calculated_stats.cycles_by_structure_per_match(int(team_key), Queries.AUTO_CORAL_L2).values,
+                        self.calculated_stats.cycles_by_structure_per_match(int(team_key), Queries.AUTO_CORAL_L3).values,
+                        self.calculated_stats.cycles_by_structure_per_match(int(team_key), Queries.AUTO_CORAL_L4).values
+                    ]
+                    for i in range(len(auto_coral_per_match)):
+                        blue_scouting_auto_score += auto_coral_per_match[i][match_index] * Criteria.AUTO_CORAL_POINTAGE[i + 1]
+                    algae_per_match = [
+                        self.calculated_stats.cycles_by_structure_per_match(int(team_key), Queries.AUTO_BARGE).values,
+                        self.calculated_stats.cycles_by_structure_per_match(int(team_key), Queries.AUTO_PROCESSOR).values
+                    ]
+                    for i in range(len(algae_per_match)):
+                        blue_scouting_auto_score += algae_per_match[i][match_index] * Criteria.ALGAE_POINTAGE[i + 1]
+                    leave_points = self.calculated_stats.stat_per_match(int(team_key), Queries.LEFT_STARTING_ZONE, Criteria.BOOLEAN_CRITERIA).values
+                    blue_scouting_auto_score += (leave_points[match_index] * 2)
+
+                    # Teleop Accuracy Retrieval
+                    teleop_coral_per_match = [
+                        self.calculated_stats.cycles_by_structure_per_match(int(team_key), Queries.TELEOP_CORAL_L1).values,
+                        self.calculated_stats.cycles_by_structure_per_match(int(team_key), Queries.TELEOP_CORAL_L2).values,
+                        self.calculated_stats.cycles_by_structure_per_match(int(team_key), Queries.TELEOP_CORAL_L3).values,
+                        self.calculated_stats.cycles_by_structure_per_match(int(team_key), Queries.TELEOP_CORAL_L4).values
+                    ]
+                    for i in range(len(teleop_coral_per_match)):
+                        blue_scouting_teleop_score += teleop_coral_per_match[i][match_index] * Criteria.TELEOP_CORAL_POINTAGE[i + 1]
+                    teleop_algae_per_match = [
+                        self.calculated_stats.cycles_by_structure_per_match(int(team_key), Queries.TELEOP_BARGE).values,
+                        self.calculated_stats.cycles_by_structure_per_match(int(team_key), Queries.TELEOP_PROCESSOR).values
+                    ]
+                    for i in range(len(teleop_algae_per_match)):
+                        blue_scouting_teleop_score += teleop_algae_per_match[i][match_index] * Criteria.ALGAE_POINTAGE[i + 1]
+
+                    # Endgame Accuracy Retrieval
+                    park_points = self.calculated_stats.stat_per_match(int(team_key), Queries.PARKED_UNDER_BARGE, Criteria.BOOLEAN_CRITERIA).values
+                    climb_points = self.calculated_stats.stat_per_match(int(team_key), Queries.CLIMBED_CAGE, Criteria.CLIMBING_POINTAGE).values
+                    blue_scouting_endgame_score = park_points[match_index] * 2 + climb_points[match_index]
+
+                    # Cumulative Accuracy Retrieval
                     points_per_match = self.calculated_stats.points_contributed_by_match(int(team_key)).values
                     blue_scouting_alliance_score += points_per_match[match_index]
+
                     scout_name = scouting_team_filter.iloc[match_index][Queries.SCOUT_ID]
                     scouters_names_list_b.append(scout_name.title().replace(" ", ""))
 
                 self.calculated_stats.points_contributed_by_match(team_key)
                 blue_scouting_alliance_score += self.calculated_stats.points_contributed_by_match(team_key).sum()
 
             blue_alliance_accuracy = (1 - abs((blue_scouting_alliance_score-blue_calculated_score)/blue_calculated_score)) * 100
+            blue_auto_accuracy = (1 - abs((blue_scouting_auto_score - blue_auto_score)/blue_auto_score)) * 100
+            blue_teleop_accuracy = (1 - abs((blue_scouting_teleop_score - blue_teleop_score)/blue_teleop_score)) * 100
+            blue_endgame_accuracy = (1 - abs((blue_scouting_endgame_score - blue_endgame_score)/blue_endgame_score)) * 100
 
             scouters_names = ", ".join(scouters_names_list_b)
+
             if member_name.replace(" ", "").lower() in scouters_names.lower():
                 if scouters_names not in accuracy_dict['ScoutersNames']:
                     accuracy_dict['ScoutersNames'].append(scouters_names)
                     accuracy_dict['CumulativeAccuracy'].append(blue_alliance_accuracy)
+                    accuracy_dict['AutoAccuracy'].append(blue_auto_accuracy)
+                    accuracy_dict['TeleopAccuracy'].append(blue_teleop_accuracy)
+                    accuracy_dict['EndgameAccuracy'].append(blue_endgame_accuracy)
                     accuracy_dict['NumberOfScoutedMatches'].append(1)
                 else:
                     accuracy_scouts_index = accuracy_dict['ScoutersNames'].index(scouters_names)
                     accuracy_dict['CumulativeAccuracy'][accuracy_scouts_index] += blue_alliance_accuracy
+                    accuracy_dict['AutoAccuracy'][accuracy_scouts_index] += blue_auto_accuracy
+                    accuracy_dict['TeleopAccuracy'][accuracy_scouts_index] += blue_teleop_accuracy
+                    accuracy_dict['EndgameAccuracy'][accuracy_scouts_index] += blue_endgame_accuracy
                     accuracy_dict['NumberOfScoutedMatches'][accuracy_scouts_index] += 1
 
         df = pd.DataFrame(data={
             'Scouters': accuracy_dict['ScoutersNames'],
             'Average Accuracy %': [round(accuracy_dict['CumulativeAccuracy'][scouter_set]/accuracy_dict['NumberOfScoutedMatches'][scouter_set], 2) for scouter_set in range(len(accuracy_dict['NumberOfScoutedMatches']))],
+            'Average Auto Accuracy %': [round(accuracy_dict['AutoAccuracy'][scouter_set]/accuracy_dict['NumberOfScoutedMatches'][scouter_set], 2) for scouter_set in range(len(accuracy_dict['NumberOfScoutedMatches']))],
+            'Average Teleop Accuracy %': [round(accuracy_dict['TeleopAccuracy'][scouter_set]/accuracy_dict['NumberOfScoutedMatches'][scouter_set], 2) for scouter_set in range(len(accuracy_dict['NumberOfScoutedMatches']))],
+            'Average Endgame Accuracy %': [round(accuracy_dict['EndgameAccuracy'][scouter_set]/accuracy_dict['NumberOfScoutedMatches'][scouter_set], 2) for scouter_set in range(len(accuracy_dict['NumberOfScoutedMatches']))],
             'NumberOfScoutedMatches': accuracy_dict['NumberOfScoutedMatches']
         })
 

src/utils/constants.py

@@ -53,8 +53,8 @@ class GeneralConstants:
 
 class EventSpecificConstants:
     """Constants specific to an event."""
-    EVENT_CODE = "2025vabla"
-    EVENT_NAME = "Duel on the Delaware"
+    EVENT_CODE = "2025mdbob"
+    EVENT_NAME = "Battle o Baltimore"
     URL = f"https://raw.githubusercontent.com/team4099/ScoutingAppData/main/{EVENT_CODE}_match_data.json"
     NOTE_SCOUTING_URL = f"https://raw.githubusercontent.com/team4099/ScoutingAppData/main/{EVENT_CODE}_qualitative_data.json"
     PIT_SCOUTING_URL = (
@@ -148,6 +148,28 @@ class Criteria:
         True: 1
     }
 
+    # Autogame Criteria
+    AUTO_CORAL_POINTAGE = {
+        1: 2,
+        2: 3,
+        3: 4,
+        4: 5
+    }
+
+    # Teleop Criteria
+    TELEOP_CORAL_POINTAGE = {
+        1: 3,
+        2: 4,
+        3: 6,
+        4: 7
+    }
+
+    # Algae Criteria
+    ALGAE_POINTAGE = {
+        1: 4,
+        2: 6
+    }
+
     # Endgame Criteria
     CLIMBING_POINTAGE = {
         "No Climb": 2,