From 0b077eea5feefd4503635d61455c48246a19a44c Mon Sep 17 00:00:00 2001
From: Your Name <you@example.com>
Date: Fri, 27 Mar 2026 14:03:10 +0000
Subject: [PATCH] expresso_graph

---
 usr/bin/expresso_graph | 96 ++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 96 insertions(+)
 create mode 100755 usr/bin/expresso_graph

diff --git a/usr/bin/expresso_graph b/usr/bin/expresso_graph
new file mode 100755
index 0000000..e94c4c1
--- /dev/null
+++ b/usr/bin/expresso_graph
@@ -0,0 +1,96 @@
+#!/usr/bin/env python3
+import sys
+import subprocess
+
+# === AUTO INSTALL ===
+def ensure(pkg):
+    try:
+        __import__(pkg)
+    except ImportError:
+        subprocess.check_call([sys.executable, "-m", "pip", "install", pkg])
+
+ensure("pandas")
+ensure("matplotlib")
+
+import pandas as pd
+import matplotlib.pyplot as plt
+
+# === ARGS ===
+if len(sys.argv) < 2:
+    print("Usage: python graph.py data.csv")
+    sys.exit(1)
+
+FILE = sys.argv[1]
+
+# === LOAD ===
+df = pd.read_csv(FILE)
+
+if "datetime" not in df.columns:
+    print("Missing datetime column")
+    sys.exit(1)
+
+df["datetime"] = pd.to_datetime(df["datetime"])
+df = df.sort_values("datetime")
+
+# === CHECK REQUIRED COLS ===
+required = ["profit", "buy_in", "category"]
+for col in required:
+    if col not in df.columns:
+        print(f"Missing column: {col}")
+        sys.exit(1)
+
+# === 1. CUMULATIVE PROFIT ===
+df["cum_profit"] = df["profit"].cumsum()
+
+plt.figure()
+plt.plot(df["datetime"], df["cum_profit"])
+plt.title("Cumulative Profit")
+plt.xlabel("Time")
+plt.ylabel("€")
+plt.xticks(rotation=45)
+plt.tight_layout()
+
+# === 2. PROFIT PER GAME (SCATTER) ===
+plt.figure()
+plt.scatter(range(len(df)), df["profit"])
+plt.title("Profit per Game")
+plt.xlabel("Game #")
+plt.ylabel("€")
+plt.tight_layout()
+
+# === 3. ROI BY CATEGORY ===
+roi = df.groupby("category").apply(
+    lambda x: x["profit"].sum() / x["buy_in"].sum()
+)
+
+plt.figure()
+roi.plot(kind="bar")
+plt.title("ROI by Game Type")
+plt.xlabel("Category")
+plt.ylabel("ROI")
+plt.tight_layout()
+
+# === 4. SESSION PERFORMANCE ===
+if "session" in df.columns:
+    session_profit = df.groupby("session")["profit"].sum()
+
+    plt.figure()
+    session_profit.plot(kind="bar")
+    plt.title("Profit by Session")
+    plt.xlabel("Session")
+    plt.ylabel("€")
+    plt.tight_layout()
+
+# === 5. WIN DISTRIBUTION ===
+if "position" in df.columns:
+    pos_counts = df["position"].value_counts()
+
+    plt.figure()
+    pos_counts.plot(kind="bar")
+    plt.title("Position Distribution")
+    plt.xlabel("Position")
+    plt.ylabel("Count")
+    plt.tight_layout()
+
+# === SHOW ALL ===
+plt.show()