Added the files.

Grade_Calculator.py

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+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+
+# === Input Data ===
+# Weighted Grade (%): Represents the metal content of ore
+# Net Price ($/unit): Fixed assumed net revenue per unit of recovered metal
+data = {
+    "Weighted Grade (%)": [0.5, 1.0, 1.5, 2.0, 2.5],
+    "Net Price ($/unit)": [100, 100, 100, 100, 100]
+}
+
+df = pd.DataFrame(data)
+
+# === Recovery Calculation (Logistic Model) ===
+# Recovery (%) = R_max / (1 + exp(-k * (Grade - x0)))
+# Models increasing recovery efficiency with grade using an S-curve
+R_max = 100   # Maximum theoretical recovery (%)
+k = 10        # Steepness of the logistic curve
+x_0 = 0.8     # Grade at which recovery rate reaches ~50% of R_max
+
+df["Recovery (%)"] = (R_max / (1 + np.exp(-k * (df["Weighted Grade (%)"] - x_0)))) / 100
+
+# === Processing Cost Estimation (Linear Regression Model) ===
+# Processing Cost ($/t) = A + B * Grade
+# Reflects increasing cost with higher grade due to more intensive processing
+A = 12    # Fixed base processing cost ($/t)
+B = 0.1   # Incremental cost increase per % grade
+df["Processing Cost ($/t)"] = A + B * df["Weighted Grade (%)"]
+
+# === Cut-off Grade Calculation ===
+# Cut-off Grade (%) = 100 * (Processing Cost / (Net Price * Recovery))
+# Represents the minimum grade required to break even
+df["Cut-off Grade (%)"] = 100 * (df["Processing Cost ($/t)"] / (df["Net Price ($/unit)"] * df["Recovery (%)"]))
+
+# === Output Table ===
+print("\nCalculated Mining Metrics:")
+print(df[[
+    "Weighted Grade (%)",
+    "Recovery (%)",
+    "Processing Cost ($/t)",
+    "Cut-off Grade (%)"
+]])
+
+# === Visualization ===
+plt.figure(figsize=(15, 5))
+
+# Plot 1: Recovery vs Grade
+plt.subplot(1, 3, 1)
+plt.plot(df["Weighted Grade (%)"], df["Recovery (%)"], marker='s', color='green')
+plt.title("Recovery vs Weighted Grade")
+plt.xlabel("Weighted Grade (%)")
+plt.ylabel("Recovery (%)")
+plt.grid(True)
+
+# Plot 2: Processing Cost vs Grade
+plt.subplot(1, 3, 2)
+plt.plot(df["Weighted Grade (%)"], df["Processing Cost ($/t)"], marker='^', color='red')
+plt.title("Processing Cost vs Weighted Grade")
+plt.xlabel("Weighted Grade (%)")
+plt.ylabel("Processing Cost ($/t)")
+plt.grid(True)
+
+# Plot 3: Cut-off Grade vs Grade
+plt.subplot(1, 3, 3)
+plt.plot(df["Weighted Grade (%)"], df["Cut-off Grade (%)"], marker='o', color='blue')
+plt.title("Cut-off Grade vs Weighted Grade")
+plt.xlabel("Weighted Grade (%)")
+plt.ylabel("Cut-off Grade (%)")
+plt.grid(True)
+
+plt.tight_layout()
+plt.show()