errorcodexero · AarushPalve · Jun 14, 2025 · Jun 25, 2025
diff --git a/2025_Data_Fixer/2025 DCMP - Match Data.csv b/2025_Data_Fixer/2025 DCMP - Match Data.csv
diff --git a/2025_Data_Fixer/2025 WCMP - Raw WL.csv b/2025_Data_Fixer/2025 WCMP - Raw WL.csv
diff --git a/2025_Data_Fixer/2025 WCMP - Raw match.csv b/2025_Data_Fixer/2025 WCMP - Raw match.csv
diff --git a/2025_Data_Fixer/2025 WCMP Data accuracy - 1425 dat.csv b/2025_Data_Fixer/2025 WCMP Data accuracy - 1425 dat.csv
diff --git a/2025_Data_Fixer/2025 WCMP Data accuracy - 7034 dat.csv b/2025_Data_Fixer/2025 WCMP Data accuracy - 7034 dat.csv
diff --git a/2025_Data_Fixer/2025_Data_Fixer.code-workspace b/2025_Data_Fixer/2025_Data_Fixer.code-workspace
@@ -0,0 +1,9 @@
+{
+	"folders": [
+		{
+			"name": "2025_Data_Fixer",
+			"path": "."
+		}
+	],
+	"settings": {}
+}
diff --git a/2025_Data_Fixer/7034.csv b/2025_Data_Fixer/7034.csv
diff --git a/2025_Data_Fixer/Fixed_data.csv b/2025_Data_Fixer/Fixed_data.csv
diff --git a/2025_Data_Fixer/junk/auto_fixed.csv b/2025_Data_Fixer/junk/auto_fixed.csv
diff --git a/2025_Data_Fixer/junk/cell_level_coral_merger.ipynb b/2025_Data_Fixer/junk/cell_level_coral_merger.ipynb
@@ -0,0 +1,269 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0e4ffcbb",
+   "metadata": {},
+   "source": [
+    "# 🧠 Smart Coral Cell-Level Mixer (DCMP + 7034)\n",
+    "This notebook matches the most accurate per-cell coral values (L1–L4) from DCMP and 7034 for each match and alliance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f18489b9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "import pandas as pd\n",
+    "from itertools import combinations, product\n",
+    "\n",
+    "# Load files\n",
+    "dcmp_df = pd.read_csv(\"2025 DCMP - Match Data.csv\")\n",
+    "csv7034_df = pd.read_csv(\"7034.csv\")\n",
+    "\n",
+    "# Normalize DCMP\n",
+    "dcmp_df['team_number'] = dcmp_df['team_key'].str.extract(r'(\\d+)$').astype(float)\n",
+    "dcmp_df['match_key'] = dcmp_df['match_number'].astype(str) + '_' + dcmp_df['comp_level']\n",
+    "\n",
+    "# Normalize 7034\n",
+    "csv7034_df = csv7034_df.dropna(subset=['Match #'])\n",
+    "csv7034_df['match_number'] = csv7034_df['Match #'].astype(int)\n",
+    "csv7034_df['team_number'] = csv7034_df['Team #']\n",
+    "csv7034_df['match_key'] = csv7034_df['match_number'].astype(str) + '_qm'\n",
+    "csv7034_df['alliance'] = csv7034_df['Robot'].str[0].map({'R': 'red', 'B': 'blue'})\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c37c8e65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def extract_alliance_teams(df):\n",
+    "    alliances = {}\n",
+    "    for _, row in df.iterrows():\n",
+    "        key = f\"{row['match_number']}_{row['comp_level']}\"\n",
+    "        if key not in alliances:\n",
+    "            alliances[key] = {\n",
+    "                'red': [row['r1'], row['r2'], row['r3']],\n",
+    "                'blue': [row['b1'], row['b2'], row['b3']],\n",
+    "            }\n",
+    "    return alliances\n",
+    "\n",
+    "match_alliances = extract_alliance_teams(dcmp_df)\n",
+    "\n",
+    "def assign_alliance(row):\n",
+    "    key = row['match_key']\n",
+    "    team_key = row['team_key']\n",
+    "    alliance = match_alliances.get(key, {})\n",
+    "    if 'red' in alliance and team_key in alliance['red']:\n",
+    "        return 'red'\n",
+    "    elif 'blue' in alliance and team_key in alliance['blue']:\n",
+    "        return 'blue'\n",
+    "    return None\n",
+    "\n",
+    "dcmp_df['alliance'] = dcmp_df.apply(assign_alliance, axis=1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a0690693",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def find_best_cell_level_combo(dcmp_rows, csv_rows, target, prefix):\n",
+    "    team_data = {}\n",
+    "    for row in dcmp_rows + csv_rows:\n",
+    "        team_number = row['team_number']\n",
+    "        if team_number not in team_data:\n",
+    "            team_data[team_number] = {}\n",
+    "        if f'{prefix}_L1' in row:\n",
+    "            team_data[team_number]['dcmp'] = row\n",
+    "        else:\n",
+    "            team_data[team_number]['7034'] = row\n",
+    "\n",
+    "    best_diff = float('inf')\n",
+    "    best_combo = None\n",
+    "\n",
+    "    for team_set in combinations(team_data.keys(), 3):\n",
+    "        robot_mix_options = list(product(['dcmp', '7034'], repeat=4))\n",
+    "        all_mix_combos = product(robot_mix_options, repeat=3)\n",
+    "\n",
+    "        for robot_cell_choices in all_mix_combos:\n",
+    "            combo_rows = []\n",
+    "            valid = True\n",
+    "            total = 0\n",
+    "\n",
+    "            for team, mix in zip(team_set, robot_cell_choices):\n",
+    "                sources = team_data[team]\n",
+    "                row = {}\n",
+    "                for i, lvl in enumerate(['L1', 'L2', 'L3', 'L4']):\n",
+    "                    dcmp_col = f'{prefix}_L{lvl[-1]}'\n",
+    "                    alt_col = f'{prefix}L{lvl[-1]}'\n",
+    "                    source = mix[i]\n",
+    "                    if source in sources:\n",
+    "                        src_row = sources[source]\n",
+    "                        val = src_row.get(dcmp_col) if source == 'dcmp' else src_row.get(alt_col, 0)\n",
+    "                        row[dcmp_col] = val\n",
+    "                        total += val\n",
+    "                    else:\n",
+    "                        valid = False\n",
+    "                        break\n",
+    "                row['team_number'] = team\n",
+    "                if not valid:\n",
+    "                    break\n",
+    "                combo_rows.append(row)\n",
+    "\n",
+    "            if valid and abs(total - target) < best_diff:\n",
+    "                best_diff = abs(total - target)\n",
+    "                best_combo = combo_rows\n",
+    "\n",
+    "    return best_combo\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "19e9d70e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done! Saved to smart_cell_level_corals.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "results = []\n",
+    "\n",
+    "for match_key in dcmp_df['match_key'].unique():\n",
+    "    for alliance in ['red', 'blue']:\n",
+    "        dcmp_rows = list(dcmp_df[(dcmp_df['match_key'] == match_key) & (dcmp_df['alliance'] == alliance)].to_dict('records'))\n",
+    "        csv_rows = list(csv7034_df[(csv7034_df['match_key'] == match_key) & (csv7034_df['alliance'] == alliance)].to_dict('records'))\n",
+    "        if not dcmp_rows:\n",
+    "            continue\n",
+    "\n",
+    "        target_auto = next((r['ba_autoCoralCount'] for r in dcmp_rows if pd.notna(r.get('ba_autoCoralCount'))), 0)\n",
+    "        target_tele = next((r['ba_teleopCoralCount'] for r in dcmp_rows if pd.notna(r.get('ba_teleopCoralCount'))), 0)\n",
+    "\n",
+    "        team_info = {r['team_number']: r for r in dcmp_rows}\n",
+    "        best_auto = find_best_cell_level_combo(dcmp_rows, csv_rows, target_auto, 'a') or []\n",
+    "        best_tele = find_best_cell_level_combo(dcmp_rows, csv_rows, target_tele, 't') or []\n",
+    "\n",
+    "        merged = {}\n",
+    "        for row in best_auto:\n",
+    "            tn = row['team_number']\n",
+    "            if tn not in merged:\n",
+    "                if tn in team_info:\n",
+    "                    merged[tn] = team_info[tn].copy()\n",
+    "                else:\n",
+    "                    # fallback to 7034\n",
+    "                    fallback_row = next((r for r in csv_rows if r['team_number'] == tn), {})\n",
+    "                    merged[tn] = {\n",
+    "                        'match_key': match_key,\n",
+    "                        'alliance': alliance,\n",
+    "                        'team_number': tn,\n",
+    "                        'team_key': f\"frc{int(tn)}\"\n",
+    "                    }\n",
+    "                    merged[tn].update(fallback_row)\n",
+    "            for k, v in row.items():\n",
+    "                if k.startswith('a_'):\n",
+    "                    merged[tn][k] = v\n",
+    "            merged[tn]['selected_for_auto'] = 'auto'\n",
+    "\n",
+    "        for row in best_tele:\n",
+    "            tn = row['team_number']\n",
+    "            if tn not in merged:\n",
+    "                if tn in team_info:\n",
+    "                    merged[tn] = team_info[tn].copy()\n",
+    "                else:\n",
+    "                    # fallback: use full 7034 row if available\n",
+    "                    fallback_row = next((r for r in csv_rows if r['team_number'] == tn), {})\n",
+    "                    merged[tn] = fallback_row.copy()\n",
+    "                    # Ensure required metadata is present\n",
+    "                    merged[tn]['match_key'] = match_key\n",
+    "                    merged[tn]['alliance'] = alliance\n",
+    "                    merged[tn]['team_number'] = tn\n",
+    "                    merged[tn]['team_key'] = merged[tn].get('team_key', f\"frc{int(tn)}\")\n",
+    "\n",
+    "            for k, v in row.items():\n",
+    "                if k.startswith('t_'):\n",
+    "                    merged[tn][k] = v\n",
+    "            merged[tn]['selected_for_teleop'] = 'teleop'\n",
+    "\n",
+    "        for row in merged.values():\n",
+    "            row['match_key'] = match_key\n",
+    "            row['alliance'] = alliance\n",
+    "            results.append(row)\n",
+    "\n",
+    "df_final = pd.DataFrame(results)\n",
+    "df_final = df_final.sort_values(by=['match_key', 'alliance', 'team_number'])\n",
+    "df_final.to_csv(\"smart_cell_level_corals2.csv\", index=False)\n",
+    "print(\"Done! Saved to smart_cell_level_corals2.csv\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "4c105145",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Sorted and saved to smart_cell_level_corals_full_sorted.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Load the merged output\n",
+    "df = pd.read_csv(\"smart_cell_level_corals.csv\")\n",
+    "\n",
+    "# Format and sort\n",
+    "df['match_number'] = pd.to_numeric(df['match_number'], errors='coerce').fillna(-1).astype(int)\n",
+    "df['alliance'] = pd.Categorical(df['alliance'], categories=['red', 'blue'], ordered=True)\n",
+    "\n",
+    "# Sort by match number, red then blue\n",
+    "df_sorted = df.sort_values(by=['match_number', 'alliance', 'team_number'])\n",
+    "\n",
+    "# Save result\n",
+    "df_sorted.to_csv(\"smart_cell_level_corals_full_sorted.csv\", index=False)\n",
+    "print(\"✅ Sorted and saved to smart_cell_level_corals_full_sorted.csv\")\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}