# ============================================================================= # # KONFIGURACJA ANALIZY – EDYTUJ TĘ SEKCJĘ, ZAPISZ (Ctrl+S), URUCHOM SKRYPT # # ============================================================================= # --- Plik danych wejściowych (MissionLog) ------------------------------------ # Wpisz sciezke do pliku CSV z danymi symulacji. # Mozesz uzyc ukosnikow (/) lub podwojnych odwrotnych ukosnikow (\\). # Przyklady: # "MissionLog.csv" (plik w tym samym folderze) # "C:/dane/MissionLog.csv" INPUT_FILE = "PKG_AS_1_3_10.csv" # --- Plik typow pojazdow (opcjonalny) ---------------------------------------- # Format: kolumna 1 = numer vehType, kolumna 2 = nazwa pojazdu. # Wpisz None aby pomin. VEHICLES_FILE = "vehicles.csv" # --- Jednostka czasu wyswietlanego w tabelach -------------------------------- # 1 -> sekundy symulacji (np. 3600) # 2 -> zegar 24h HH:MM (symulacja startuje o 00:00) TIME_UNIT = 2 # --- Zakres danych do analizy ------------------------------------------------ # Dla TIME_UNIT=1 (sekundy): podaj liczbe, np. 0 lub 86400 # Dla TIME_UNIT=2 (HH:MM): podaj string, np. "00:00" lub "24:00" # Wpisz None aby uzyc pelnego zakresu. ANALYSE_FROM = None ANALYSE_UNTIL = None # --- Szerokosc przedzialu czasowego na profilu CO2 --------------------------- # Zawsze w sekundach. # Przyklady: 3600 = 1h, 1800 = 30 min, 7200 = 2h, 86400 = 1 doba TIME_BIN_INTERVAL = 3600 # ============================================================================= # KONIEC KONFIGURACJI – ponizej nie ma potrzeby niczego zmieniac # ============================================================================= import sys, warnings, io, os import pandas as pd import numpy as np from openpyxl import Workbook from openpyxl.styles import Font, PatternFill, Alignment, Border, Side from openpyxl.utils import get_column_letter warnings.filterwarnings("ignore") # --------------------------------------------------------------------------- # Column definitions – 18 columns matching new MissionLog CSV format # (last two columns: energyEquiv[kWh] and energyEquiv[MJ] pre-computed) # --------------------------------------------------------------------------- COLS = [ "row", "startTime", "endTime", "distance", "fuelUsed_l", "fuelUsed_kg", "energyUsed_kWh", "CO2Emiss_kg", "numberOfNodesVisited", "missionType", "LMD_mode", "vehicle", "vehType", "noOfPackages", "listPushTime", "listPullTime", "energyEquiv_kWh", "energyEquiv_MJ", ] NUMERIC_COLS = [ "row", "startTime", "endTime", "distance", "fuelUsed_l", "fuelUsed_kg", "energyUsed_kWh", "CO2Emiss_kg", "missionType", "LMD_mode", "vehType", "noOfPackages", "listPushTime", "listPullTime", "energyEquiv_kWh", "energyEquiv_MJ", ] LMD_LABELS = { 1: "Food delivery (restaurants)", 2: "Direct package delivery (couriers)", 3: "Delivery to parcel lockers", 4: "PL pickup by clients", 5: "Drone delivery from PLs", 6: "Direct drone delivery", 7: "Food-box delivery to clients", } # --------------------------------------------------------------------------- # Styles # --------------------------------------------------------------------------- HDR_FONT = Font(bold=True, color="FFFFFF", name="Arial", size=10) HDR_FILL = PatternFill("solid", start_color="1F4E79") SEC_FILL = PatternFill("solid", start_color="2E75B6") ALT_FILL = PatternFill("solid", start_color="D6E4F0") WHT_FILL = PatternFill("solid", start_color="FFFFFF") NORM_FONT = Font(name="Arial", size=10) BOLD_FONT = Font(name="Arial", size=10, bold=True) TITL_FONT = Font(bold=True, name="Arial", size=14, color="1F4E79") SEC_FONT = Font(bold=True, color="FFFFFF", name="Arial", size=10) CENTER = Alignment(horizontal="center", vertical="center") LEFT = Alignment(horizontal="left", vertical="center") RIGHT = Alignment(horizontal="right", vertical="center") def _bd(): s = Side(border_style="thin", color="AAAAAA") return Border(left=s, right=s, top=s, bottom=s) def _hdr(cell, fill=HDR_FILL): cell.font = HDR_FONT; cell.fill = fill cell.alignment = CENTER; cell.border = _bd() def _data(cell, alt=False, bold=False, align=RIGHT): cell.font = BOLD_FONT if bold else NORM_FONT cell.fill = ALT_FILL if alt else WHT_FILL cell.alignment = align; cell.border = _bd() def _lbl(cell, alt=False, bold=False): _data(cell, alt=alt, bold=bold, align=LEFT) def _sec_hdr(ws, row, text, ncols, fill=SEC_FILL): ws.merge_cells(start_row=row, start_column=1, end_row=row, end_column=ncols) c = ws.cell(row=row, column=1, value=text) c.font = SEC_FONT; c.fill = fill c.alignment = CENTER; c.border = _bd() return row + 1 def _auto_width(ws): for col in ws.columns: w = 10 for cell in col: try: if cell.value: w = max(w, len(str(cell.value)) + 2) except Exception: pass ws.column_dimensions[ get_column_letter(col[0].column)].width = min(w, 52) # --------------------------------------------------------------------------- # Time helpers # --------------------------------------------------------------------------- def sec_to_hhmm(s): if pd.isna(s): return "" s = int(s) return f"{s // 3600:02d}:{(s % 3600) // 60:02d}" def fmt_time(s, use_clock): return sec_to_hhmm(s) if use_clock else f"{s:.0f}" def fmt_range(s1, s2, use_clock): return f"{fmt_time(s1, use_clock)} – {fmt_time(s2, use_clock)}" def parse_time_cfg(val, use_clock): if val is None: return None s = str(val).strip() if ":" in s: h, m = s.split(":") return int(h) * 3600 + int(m) * 60 return float(s) # --------------------------------------------------------------------------- # Config validation # --------------------------------------------------------------------------- def validate_config(): errors = [] if not INPUT_FILE: errors.append("INPUT_FILE is empty.") if TIME_UNIT not in (1, 2): errors.append("TIME_UNIT must be 1 or 2.") if not isinstance(TIME_BIN_INTERVAL, (int, float)) or TIME_BIN_INTERVAL <= 0: errors.append("TIME_BIN_INTERVAL must be a positive number.") if errors: print("\n [CONFIG ERROR]") for e in errors: print(f" - {e}") sys.exit(1) # --------------------------------------------------------------------------- # Data loading # --------------------------------------------------------------------------- def _detect_sep(path): with open(path, "r", encoding="utf-8-sig") as fh: sample = fh.read(3000) tabs = sample.count("\t") semis = sample.count(";") commas = sample.count(",") if tabs >= semis and tabs >= commas // 4: return "\t" if semis >= tabs: return ";" return "," def load_mission(path): sep = _detect_sep(path) with open(path, "r", encoding="utf-8-sig") as fh: raw = fh.read().replace(",", ".") df = pd.read_csv( io.StringIO(raw), sep=sep, header=0, names=COLS, dtype=str, on_bad_lines="skip", ) df = df.dropna(how="all") for col in NUMERIC_COLS: if col in df.columns: df[col] = pd.to_numeric(df[col], errors="coerce") else: df[col] = np.nan return df def load_vehicles(path): if not path or not os.path.exists(path): return {} sep = _detect_sep(path) with open(path, "r", encoding="utf-8-sig") as fh: raw = fh.read() try: df = pd.read_csv(io.StringIO(raw), sep=sep, header=0, dtype=str, on_bad_lines="skip") if df.shape[1] < 2: return {} mapping = {} for _, row in df.iterrows(): try: vt = int(float(str(row.iloc[0]).strip())) name = str(row.iloc[1]).strip() if name and name.lower() != "nan": mapping[vt] = name except Exception: pass return mapping except Exception as e: print(f" [WARN] vehicles file error: {e}") return {} def apply_span(df, t0, t1): return df[(df["startTime"] >= t0) & (df["startTime"] <= t1)].copy() # --------------------------------------------------------------------------- # Filter helpers # --------------------------------------------------------------------------- def svc(df): """Service deliveries: missionType==2 with at least 1 package.""" return df[(df["missionType"] == 2) & (df["noOfPackages"] >= 1)].copy() # --------------------------------------------------------------------------- # KPI computations # --------------------------------------------------------------------------- # 1. Per LMD_mode aggregates (service deliveries) def compute_per_lmd(df): sv = svc(df) g = sv.groupby("LMD_mode").agg( trips =("row", "count"), packages =("noOfPackages", "sum"), energy_kWh =("energyEquiv_kWh", "sum"), energy_MJ =("energyEquiv_MJ", "sum"), co2_kg =("CO2Emiss_kg", "sum"), ).reset_index() p = g["packages"].replace(0, np.nan) g["energy_kWh_per_pkg"] = g["energy_kWh"] / p g["energy_MJ_per_pkg"] = g["energy_MJ"] / p g["co2_per_pkg"] = g["co2_kg"] / p g["lmd_label"] = g["LMD_mode"].astype(int).map(LMD_LABELS).fillna("") return g # 2. Per LMD_mode × vehType matrix (service deliveries) def compute_vehtype_lmd(df): sv = svc(df) g = sv.groupby(["LMD_mode", "vehType"]).agg( trips =("row", "count"), packages =("noOfPackages", "sum"), energy_kWh =("energyEquiv_kWh", "sum"), energy_MJ =("energyEquiv_MJ", "sum"), co2_kg =("CO2Emiss_kg", "sum"), ).reset_index() p = g["packages"].replace(0, np.nan) g["energy_kWh_per_pkg"] = g["energy_kWh"] / p g["energy_MJ_per_pkg"] = g["energy_MJ"] / p g["co2_per_pkg"] = g["co2_kg"] / p return g.sort_values(["LMD_mode", "vehType"]).reset_index(drop=True) # 3. Transfer vs Service split per LMD_mode (all rows, not just deliveries) def compute_split(df): rows = [] for lmd in sorted(df["LMD_mode"].dropna().unique().astype(int)): sub = df[df["LMD_mode"] == lmd] tr = sub[sub["missionType"] == 1] sv = sub[sub["missionType"] == 2] d_tr = tr["distance"].sum() d_sv = sv["distance"].sum() d_tot = d_tr + d_sv rows.append({ "LMD_mode" : lmd, "lmd_label" : LMD_LABELS.get(lmd, ""), "dist_transfer" : d_tr, "dist_service" : d_sv, "dist_total" : d_tot, "dist_transfer_pct" : 100.0 * d_tr / d_tot if d_tot > 0 else 0.0, "dist_service_pct" : 100.0 * d_sv / d_tot if d_tot > 0 else 0.0, "co2_transfer" : tr["CO2Emiss_kg"].sum(), "co2_service" : sv["CO2Emiss_kg"].sum(), "energy_kWh_transfer" : tr["energyEquiv_kWh"].sum(), "energy_kWh_service" : sv["energyEquiv_kWh"].sum(), "energy_MJ_transfer" : tr["energyEquiv_MJ"].sum(), "energy_MJ_service" : sv["energyEquiv_MJ"].sum(), }) return pd.DataFrame(rows) # 4. Average delivery time per LMD_mode def compute_delivery_time(df): sv = svc(df) mask = (sv["listPushTime"] > 0) & sv["listPushTime"].notna() sv_t = sv[mask].copy() sv_t["delivery_s"] = sv_t["endTime"] - sv_t["listPushTime"] g = sv_t.groupby("LMD_mode").agg( count =("delivery_s", "count"), avg_delivery_s =("delivery_s", "mean"), ).reset_index() g["avg_delivery_min"] = g["avg_delivery_s"] / 60.0 g["lmd_label"] = g["LMD_mode"].astype(int).map(LMD_LABELS).fillna("") return g # 5. CO2 emission profile per LMD_mode (all rows, by startTime bin) def compute_co2_profile(df, interval, span_start, span_end): bins = np.arange(span_start, span_end + interval, interval) results = {} for lmd in sorted(df["LMD_mode"].dropna().unique().astype(int)): sub = df[df["LMD_mode"] == lmd].copy() sub["time_bin"] = pd.cut(sub["startTime"], bins=bins, labels=bins[:-1], right=False) g = sub.groupby("time_bin", observed=False).agg( co2_total =("CO2Emiss_kg", "sum"), trips =("row", "count"), ).reset_index() g["time_bin_s"] = g["time_bin"].astype(float) g["time_bin_end_s"] = g["time_bin_s"] + interval g["co2_avg_per_trip"] = g["co2_total"] / g["trips"].replace(0, np.nan) results[lmd] = g return results # --------------------------------------------------------------------------- # Excel writer # --------------------------------------------------------------------------- def _rv(v): """Round float or return empty string for NaN.""" if isinstance(v, float) and np.isnan(v): return "" return v def write_analysis_sheet(wb, df_full, per_lmd, veh_lmd, split, delivery_time, co2_profile, interval, veh_map, use_clock, span_start, span_end): if "Analysis" in wb.sheetnames: del wb["Analysis"] ws = wb.create_sheet("Analysis") ws.sheet_view.showGridLines = False r = 1 MAX_COLS = 14 # enough for all sections # Title row ws.merge_cells(start_row=r, start_column=1, end_row=r, end_column=MAX_COLS) c = ws.cell(row=r, column=1, value="FlexSim Simulation – Analysis Results") c.font = TITL_FONT; c.alignment = CENTER r += 1 tu_str = "24-h clock HH:MM" if use_clock else "simulation seconds" span_str = fmt_range(span_start, span_end, use_clock) ws.merge_cells(start_row=r, start_column=1, end_row=r, end_column=MAX_COLS) c2 = ws.cell( row=r, column=1, value=(f"Span: {span_str} | Time unit: {tu_str} | " f"Bin: {interval:.0f} s = {interval / 3600:.2f} h")) c2.font = Font(name="Arial", size=10, italic=True, color="595959") c2.alignment = CENTER r += 2 # ── Section 1: KPIs per LMD_mode ───────────────────────────────────────── N1 = 10 r = _sec_hdr(ws, r, "1. KPIs per LMD_mode (service deliveries: missionType=2, noOfPackages>=1)", N1) hdrs1 = ["LMD_mode", "Description", "Trips", "Total packages", "Total energy [kWh]", "Total energy [MJ]", "Total CO2 [kg]", "Avg energy/pkg [kWh]", "Avg energy/pkg [MJ]", "Avg CO2/pkg [kg]"] for ci, h in enumerate(hdrs1, 1): _hdr(ws.cell(row=r, column=ci, value=h)) r += 1 for i, rd in per_lmd.iterrows(): alt = i % 2 == 0 vals = [ int(rd["LMD_mode"]), rd["lmd_label"], int(rd["trips"]), int(rd["packages"]), round(rd["energy_kWh"], 4), round(rd["energy_MJ"], 4), round(rd["co2_kg"], 4), round(rd["energy_kWh_per_pkg"], 6) if not pd.isna(rd["energy_kWh_per_pkg"]) else "", round(rd["energy_MJ_per_pkg"], 6) if not pd.isna(rd["energy_MJ_per_pkg"]) else "", round(rd["co2_per_pkg"], 6) if not pd.isna(rd["co2_per_pkg"]) else "", ] for ci, v in enumerate(vals, 1): (_lbl if ci <= 2 else _data)(ws.cell(row=r, column=ci, value=v), alt=alt) r += 1 r += 1 # ── Section 2: KPIs per LMD_mode × vehType ─────────────────────────────── N2 = 10 r = _sec_hdr(ws, r, "2. KPIs per LMD_mode x vehicle type (service deliveries)", N2) hdrs2 = ["LMD_mode", "Description", "vehType", "Vehicle name", "Trips", "Total packages", "Avg energy/pkg [kWh]", "Avg energy/pkg [MJ]", "Avg CO2/pkg [kg]", "Total CO2 [kg]"] for ci, h in enumerate(hdrs2, 1): _hdr(ws.cell(row=r, column=ci, value=h)) r += 1 for i, rd in veh_lmd.iterrows(): alt = i % 2 == 0 lmd = int(rd["LMD_mode"]); vt = int(rd["vehType"]) vals = [ lmd, LMD_LABELS.get(lmd, ""), vt, veh_map.get(vt, ""), int(rd["trips"]), int(rd["packages"]), round(rd["energy_kWh_per_pkg"], 6) if not pd.isna(rd["energy_kWh_per_pkg"]) else "", round(rd["energy_MJ_per_pkg"], 6) if not pd.isna(rd["energy_MJ_per_pkg"]) else "", round(rd["co2_per_pkg"], 6) if not pd.isna(rd["co2_per_pkg"]) else "", round(rd["co2_kg"], 4), ] for ci, v in enumerate(vals, 1): (_lbl if ci <= 4 else _data)(ws.cell(row=r, column=ci, value=v), alt=alt) r += 1 r += 1 # ── Section 3: Transfer vs Service split ───────────────────────────────── N3 = 13 r = _sec_hdr(ws, r, "3. Transfer (empty-run, missionType=1) vs Service (missionType=2) split per LMD_mode", N3) hdrs3 = [ "LMD_mode", "Description", "Dist Transfer [m]", "Dist Service [m]", "Total dist [m]", "Transfer dist [%]", "Service dist [%]", "CO2 Transfer [kg]", "CO2 Service [kg]", "Energy Transfer [kWh]", "Energy Service [kWh]", "Energy Transfer [MJ]", "Energy Service [MJ]", ] for ci, h in enumerate(hdrs3, 1): _hdr(ws.cell(row=r, column=ci, value=h)) r += 1 for i, rd in split.iterrows(): alt = i % 2 == 0 vals = [ int(rd["LMD_mode"]), rd["lmd_label"], round(rd["dist_transfer"], 2), round(rd["dist_service"], 2), round(rd["dist_total"], 2), round(rd["dist_transfer_pct"], 2), round(rd["dist_service_pct"], 2), round(rd["co2_transfer"], 4), round(rd["co2_service"], 4), round(rd["energy_kWh_transfer"], 4), round(rd["energy_kWh_service"], 4), round(rd["energy_MJ_transfer"], 4), round(rd["energy_MJ_service"], 4), ] for ci, v in enumerate(vals, 1): (_lbl if ci <= 2 else _data)(ws.cell(row=r, column=ci, value=v), alt=alt) r += 1 r += 1 # ── Section 4: Average delivery time ───────────────────────────────────── N4 = 5 r = _sec_hdr(ws, r, "4. Average delivery time per LMD_mode (endTime - listPushTime, service deliveries with listPushTime > 0)", N4) hdrs4 = ["LMD_mode", "Description", "Deliveries counted", "Avg time [s]", "Avg time [min]"] for ci, h in enumerate(hdrs4, 1): _hdr(ws.cell(row=r, column=ci, value=h)) r += 1 for i, rd in delivery_time.iterrows(): alt = i % 2 == 0 vals = [ int(rd["LMD_mode"]), rd["lmd_label"], int(rd["count"]), round(rd["avg_delivery_s"], 2) if not pd.isna(rd["avg_delivery_s"]) else "", round(rd["avg_delivery_min"], 2) if not pd.isna(rd["avg_delivery_min"]) else "", ] for ci, v in enumerate(vals, 1): (_lbl if ci <= 2 else _data)(ws.cell(row=r, column=ci, value=v), alt=alt) r += 1 r += 1 # ── Section 5: CO2 profile per LMD_mode ────────────────────────────────── lmd_ids = sorted(co2_profile.keys()) N5 = max(3, 1 + len(lmd_ids) * 2) r = _sec_hdr( ws, r, f"5. CO2 emission profile per LMD_mode " f"(all trips, bin = {interval:.0f} s = {interval / 3600:.2f} h)", N5, ) t_col = "Time range (HH:MM)" if use_clock else "Time range (s)" _hdr(ws.cell(row=r, column=1, value=t_col)) for li, lmd in enumerate(lmd_ids): base = 2 + li * 2 _hdr(ws.cell(row=r, column=base, value=f"LMD {lmd} – CO2 total [kg]")) _hdr(ws.cell(row=r, column=base + 1, value=f"LMD {lmd} – CO2 avg/trip [kg]")) r += 1 if lmd_ids: ref = co2_profile[lmd_ids[0]] for i, trow in ref.iterrows(): alt = i % 2 == 0 t1 = trow["time_bin_s"] t2 = trow["time_bin_end_s"] _lbl(ws.cell(row=r, column=1, value=fmt_range(t1, t2, use_clock)), alt=alt) for li, lmd in enumerate(lmd_ids): base = 2 + li * 2 profile = co2_profile[lmd] match = profile[profile["time_bin_s"] == t1] if not match.empty: total = float(match["co2_total"].iloc[0]) avg = match["co2_avg_per_trip"].iloc[0] avg_v = round(float(avg), 6) if not pd.isna(avg) else "" else: total, avg_v = 0.0, "" _data(ws.cell(row=r, column=base, value=round(total, 6)), alt=alt) _data(ws.cell(row=r, column=base + 1, value=avg_v), alt=alt) r += 1 _auto_width(ws) ws.freeze_panes = "A4" def write_rawdata_sheet(wb, df_full): ws = wb.active ws.title = "RawData" for ci, col in enumerate(COLS, 1): _hdr(ws.cell(row=1, column=ci, value=col)) for ri, row_vals in enumerate(df_full[COLS].values, 2): for ci, v in enumerate(row_vals, 1): ws.cell( row=ri, column=ci, value=None if (isinstance(v, float) and np.isnan(v)) else v, ) for col in ws.columns: ws.column_dimensions[ get_column_letter(col[0].column)].width = 14 ws.freeze_panes = "A2" # --------------------------------------------------------------------------- # Main # --------------------------------------------------------------------------- def main(): validate_config() use_clock = (TIME_UNIT == 2) interval = float(TIME_BIN_INTERVAL) script_dir = os.path.dirname(os.path.abspath(__file__)) input_path = (INPUT_FILE if os.path.isabs(INPUT_FILE) else os.path.join(script_dir, INPUT_FILE)) if not os.path.exists(input_path): print(f"\n [ERROR] Input file not found: {input_path}") print(" Check the INPUT_FILE setting at the top of the script.") sys.exit(1) veh_path = None if VEHICLES_FILE: veh_path = (VEHICLES_FILE if os.path.isabs(VEHICLES_FILE) else os.path.join(script_dir, VEHICLES_FILE)) print(f"\n Loading: {input_path}") df_full = load_mission(input_path) t_min = float(df_full["startTime"].min()) t_max = float(df_full["endTime"].dropna().max()) print(f" Rows : {len(df_full):,}") print(f" Span : {fmt_range(t_min, t_max, use_clock)}") print(f" LMDs : {sorted(df_full['LMD_mode'].dropna().unique().astype(int))}") print(f" Types : {sorted(df_full['vehType'].dropna().unique().astype(int))}") span_start_raw = parse_time_cfg(ANALYSE_FROM, use_clock) span_end_raw = parse_time_cfg(ANALYSE_UNTIL, use_clock) span_start = (max(span_start_raw, t_min) if span_start_raw is not None else t_min) span_end = (min(span_end_raw, t_max) if span_end_raw is not None else t_max) print(f"\n Analysing span : {fmt_range(span_start, span_end, use_clock)}") print(f" Bin interval : {interval:.0f} s = {interval / 3600:.2f} h") df = apply_span(df_full, span_start, span_end) print(f"\n Loading vehicles: {veh_path or '(not set)'}") veh_map = load_vehicles(veh_path) if veh_path else {} if veh_map: for vt, name in sorted(veh_map.items()): print(f" vehType {vt:>3} -> {name}") else: print(" (no vehicle names loaded)") print("\n Computing KPIs ...") per_lmd = compute_per_lmd(df) veh_lmd = compute_vehtype_lmd(df) split = compute_split(df) delivery_time = compute_delivery_time(df) co2_profile = compute_co2_profile(df, interval, span_start, span_end) DIV = "─" * 64 print(f"\n {DIV}") print(" RESULTS (service deliveries, missionType=2, noOfPackages>=1)") print(f" {DIV}") for _, rd in per_lmd.iterrows(): print(f" LMD {int(rd['LMD_mode']):>2} | pkgs: {int(rd['packages']):>6} |" f" kWh/pkg: {rd['energy_kWh_per_pkg']:.6f} |" f" MJ/pkg: {rd['energy_MJ_per_pkg']:.6f} |" f" CO2/pkg: {rd['co2_per_pkg']:.6f} kg") print(f"\n Transfer vs Service distance split per LMD_mode:") for _, rd in split.iterrows(): print(f" LMD {int(rd['LMD_mode']):>2} | transfer: {rd['dist_transfer_pct']:.1f}%" f" | service: {rd['dist_service_pct']:.1f}%") print(f" {DIV}") base = os.path.splitext(input_path)[0] xlsx_out = base + "_analysis.xlsx" print(f"\n Writing: {xlsx_out}") wb = Workbook() write_rawdata_sheet(wb, df_full) write_analysis_sheet( wb, df_full, per_lmd, veh_lmd, split, delivery_time, co2_profile, interval, veh_map, use_clock, span_start, span_end, ) wb.save(xlsx_out) print(f"\n Saved: {xlsx_out}") print(" Done.\n") if __name__ == "__main__": main()