misc local modifications

scripting/plugins/BomGeneration/optimize.py

@@ -0,0 +1,73 @@
+import pandas as pd
+import numpy as np
+
+score_method = "qty"
+columns = [
+    "mpn",
+    "value",
+    "voltage_min",
+    "voltage_max",
+    "tolerance-",
+    "tolerance+",
+]
+parts = {
+    "C1": (None, 100e-9, 10, np.inf, None, None),
+    "C1a": (None, 100e-9, 10, 10, None, None),
+    "C2": (None, 100e-9, 16, np.inf, 0.20, 0.30),
+    "C2a": (None, 100e-9, 16, np.inf, None, None),
+    "C2b": (None, 100e-9, 12, np.inf, None, None),
+    "C3": (None, 100e-9, 0, np.inf, None, None),
+    "C4": (None, 10e-9, 10, np.inf, None, None),
+    "C5": (None, 10e-9, 12, np.inf, None, None),
+}
+
+nominal = pd.DataFrame(
+    [v for _, v in parts.items()],
+    index=parts.keys(),
+    columns=columns,
+)
+actual = nominal.copy(deep=True)
+remaining = nominal.copy(deep=True)
+
+# add temporary columns
+remaining.insert(1, "matches", None, True)
+remaining.insert(2, "score", -np.inf, True)
+remaining.insert(3, "match_mpn", None, True)
+
+# determine matches
+for idx, row in remaining.iterrows():
+    matches = []
+    for ii, rr in remaining.iterrows():
+        if (ii == idx) or (
+            (rr["value"] == row["value"])
+            and (rr["voltage_min"] <= row["voltage_min"])
+            and (rr["voltage_max"] >= row["voltage_max"])
+        ):
+            matches.append(ii)
+    remaining["matches"][idx] = matches
+
+n = 0
+while len(remaining.index) > 0:
+    for idx, row in remaining.iterrows():
+        if score_method == "qty":
+            # score by qty
+            remaining["score"][idx] = len(remaining["matches"][idx])
+        else:
+            # score by price reduction
+            pass
+
+    best_idx = remaining["score"].idxmax()
+    best_score = remaining["score"].max()
+
+    print(f"Step {n}: {best_idx} matched {len(remaining['matches'][best_idx]):d} parts (score: {best_score})")
+
+    for cmp in remaining["matches"][best_idx]:
+        if cmp in remaining.index:
+            actual.loc[cmp] = nominal.loc[best_idx]
+
+    # remove matched parts
+    remaining.drop(remaining["matches"][best_idx], inplace=True)
+    for idx in remaining.index:
+        remaining["matches"].loc[idx] = set(remaining["matches"][idx]).intersection(remaining.index)
+
+    n += 1