diff --git a/.gitignore b/.gitignore index 4f5f1bf..685e910 100644 --- a/.gitignore +++ b/.gitignore @@ -84,4 +84,9 @@ latex/*.synctex.gz # Misc # ====================== *.swp -*.tmp \ No newline at end of file +*.tmp + +# ====================== +# logs +# ====================== +var/ diff --git a/results/iis.ilp b/results/iis.ilp new file mode 100644 index 0000000..c3f3a38 --- /dev/null +++ b/results/iis.ilp @@ -0,0 +1,44 @@ +\ Model unknown_copy +\ LP format - for model browsing. Use MPS format to capture full model detail. +\ Signature: 0x7ecab9ecbc4d4aff +Minimize + +Subject To + delivery_tolerance(B3_202548_Fr): 1000 k(Nochten_B3_202548_Fr_F) + + 1000 k(Nochten_B3_202548_Fr_N) + 1000 k(Nochten_B3_202548_Fr_S) + + 1000 k(Reichwalde_B3_202548_Fr_F) + 1000 k(Reichwalde_B3_202548_Fr_N) + + 1000 k(Reichwalde_B3_202548_Fr_S) + 1000 k(Welzow_B3_202548_Fr_F) + + 1000 k(Welzow_B3_202548_Fr_N) + 1000 k(Welzow_B3_202548_Fr_S) + + Rgdelivery_tolerance(B3_202548_Fr) = 25567 + mix_upper(Nochten_B3_202548_Fr): 350 k(Nochten_B3_202548_Fr_F) + + 350 k(Nochten_B3_202548_Fr_N) + 350 k(Nochten_B3_202548_Fr_S) + - 650 k(Reichwalde_B3_202548_Fr_F) - 650 k(Reichwalde_B3_202548_Fr_N) + - 650 k(Reichwalde_B3_202548_Fr_S) - 650 k(Welzow_B3_202548_Fr_F) + - 650 k(Welzow_B3_202548_Fr_N) - 650 k(Welzow_B3_202548_Fr_S) <= 0 + mix_upper(Reichwalde_B3_202548_Fr): - 350 k(Nochten_B3_202548_Fr_F) + - 350 k(Nochten_B3_202548_Fr_N) - 350 k(Nochten_B3_202548_Fr_S) + + 650 k(Reichwalde_B3_202548_Fr_F) + 650 k(Reichwalde_B3_202548_Fr_N) + + 650 k(Reichwalde_B3_202548_Fr_S) - 350 k(Welzow_B3_202548_Fr_F) + - 350 k(Welzow_B3_202548_Fr_N) - 350 k(Welzow_B3_202548_Fr_S) <= 0 + mix_upper(Welzow_B3_202548_Fr): - 30 k(Nochten_B3_202548_Fr_F) + - 30 k(Nochten_B3_202548_Fr_N) - 30 k(Nochten_B3_202548_Fr_S) + - 30 k(Reichwalde_B3_202548_Fr_F) - 30 k(Reichwalde_B3_202548_Fr_N) + - 30 k(Reichwalde_B3_202548_Fr_S) + 970 k(Welzow_B3_202548_Fr_F) + + 970 k(Welzow_B3_202548_Fr_N) + 970 k(Welzow_B3_202548_Fr_S) <= 0 +Bounds + k(Nochten_B3_202548_Fr_F) free + k(Nochten_B3_202548_Fr_N) free + k(Nochten_B3_202548_Fr_S) free + k(Reichwalde_B3_202548_Fr_F) free + k(Reichwalde_B3_202548_Fr_N) free + k(Reichwalde_B3_202548_Fr_S) free + k(Welzow_B3_202548_Fr_F) free + k(Welzow_B3_202548_Fr_N) free + k(Welzow_B3_202548_Fr_S) free + Rgdelivery_tolerance(B3_202548_Fr) <= 2000 +Generals + k(Nochten_B3_202548_Fr_F) k(Nochten_B3_202548_Fr_N) + k(Nochten_B3_202548_Fr_S) k(Reichwalde_B3_202548_Fr_F) + k(Reichwalde_B3_202548_Fr_N) k(Reichwalde_B3_202548_Fr_S) + k(Welzow_B3_202548_Fr_F) k(Welzow_B3_202548_Fr_N) k(Welzow_B3_202548_Fr_S) +End diff --git a/setup.py b/setup.py new file mode 100644 index 0000000..4d5bb73 --- /dev/null +++ b/setup.py @@ -0,0 +1,36 @@ +""" +Setup-Skript für POC1 Installation +Verwendung: pip install -e . +""" + +from setuptools import setup, find_packages + +setup( + name='braunkohle-supply-chain-poc1', + version='0.1.0', + description='POC1: Braunkohle Supply Chain Modellierung mit Pyomo', + author='LEAG', + packages=find_packages(), + python_requires='>=3.8', + install_requires=[ + 'pyomo>=6.7', + 'pandas>=2.0', + 'numpy>=1.24', + 'openpyxl>=3.10', + 'matplotlib>=3.7', + ], + extras_require={ + 'dev': [ + 'jupyter>=1.0', + 'ipython>=8.12', + 'pytest>=7.3', + 'black>=23.0', + 'flake8>=6.0', + ] + }, + entry_points={ + 'console_scripts': [ + 'poc1=main:main', + ], + }, +) diff --git a/uv.lock b/uv.lock new file mode 100644 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0000000..2c4e77c --- /dev/null +++ b/webapp/backend/main.py @@ -0,0 +1,975 @@ +from __future__ import annotations + +import json +import os +import shutil +import subprocess +import sys +import threading +import time +import uuid +from pathlib import Path + +from fastapi import FastAPI, File, Form, HTTPException, UploadFile +from fastapi.middleware.cors import CORSMiddleware +from fastapi.responses import FileResponse, PlainTextResponse, StreamingResponse + +PROJECT_ROOT = Path(__file__).resolve().parents[2] +VAR_DIR = PROJECT_ROOT / "var" +JOBS_DIR = VAR_DIR / "jobs" +JOBS_DIR.mkdir(parents=True, exist_ok=True) +ACTIVE_JOBS: dict[str, dict[str, object]] = {} +ACTIVE_JOBS_LOCK = threading.Lock() + +app = FastAPI() +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], + allow_methods=["*"], + allow_headers=["*"], +) + + +def _get_solver_availability() -> dict[str, bool]: + try: + import pyomo.environ as pyo + except Exception: + return {"highs": False, "gurobi": False} + + availability: dict[str, bool] = {} + for solver_name in ("highs", "gurobi", "scip"): + try: + solver = pyo.SolverFactory(solver_name) + availability[solver_name] = bool(solver) and bool( + solver.available(exception_flag=False) + ) + except Exception: + availability[solver_name] = False + return availability + + +class JobCancelledError(RuntimeError): + pass + + +def _set_active_process(job_id: str, process) -> None: + with ACTIVE_JOBS_LOCK: + runtime = ACTIVE_JOBS.get(job_id) + if runtime is not None: + runtime["process"] = process + + +def _clear_active_process(job_id: str) -> None: + with ACTIVE_JOBS_LOCK: + runtime = ACTIVE_JOBS.get(job_id) + if runtime is not None: + runtime["process"] = None + + +def _run_step( + cmd: list[str], + log_path: Path, + env: dict[str, str] | None = None, + *, + job_id: str, + cancel_event: threading.Event, +) -> None: + log_path.parent.mkdir(parents=True, exist_ok=True) + with log_path.open("w", encoding="utf-8") as log_file: + process = subprocess.Popen( + cmd, + cwd=PROJECT_ROOT, + env=env, + stdout=log_file, + stderr=subprocess.STDOUT, + text=True, + ) + _set_active_process(job_id, process) + try: + while True: + if cancel_event.is_set(): + process.terminate() + try: + process.wait(timeout=5) + except subprocess.TimeoutExpired: + process.kill() + process.wait(timeout=5) + raise JobCancelledError("Job cancelled by user") + result_code = process.poll() + if result_code is not None: + break + time.sleep(0.2) + finally: + _clear_active_process(job_id) + + if result_code != 0: + raise RuntimeError(f"Command failed: {' '.join(cmd)}") + + +def _run_job( + job_dir: Path, + input_path: Path, + processed_dir: Path, + output_dir: Path, + solver_name: str, + step_size_tonnes: int, + mip_gap: float, + time_limit_seconds: int, + cancel_event: threading.Event, +) -> None: + logs_dir = job_dir / "logs" + logs_dir.mkdir(parents=True, exist_ok=True) + + env = os.environ.copy() + path_entries = env.get("PATH", "").split(os.pathsep) + for extra_path in ["/usr/local/bin", "/opt/homebrew/bin"]: + if extra_path not in path_entries: + path_entries.insert(0, extra_path) + env["PATH"] = os.pathsep.join(path_entries) + env["POC1_INPUT_XLSX"] = str(input_path) + env["POC1_OUTPUT_DIR"] = str(processed_dir) + + try: + _write_status(job_dir, "processing") + _run_step( + [sys.executable, "src/preprocessing/exploration_preprocess.py"], + logs_dir / "preprocess.log", + env=env, + job_id=job_dir.name, + cancel_event=cancel_event, + ) + output_path = output_dir / "output.xlsx" + _run_step( + [ + sys.executable, + "src/optimization/run_optimization.py", + "--data-dir", + str(processed_dir), + "--solver", + solver_name, + "--step-size-tonnes", + str(step_size_tonnes), + "--mip-gap", + str(mip_gap), + "--time-limit", + str(time_limit_seconds), + "--output-xlsx", + str(output_path), + ], + logs_dir / "optimization.log", + env=env, + job_id=job_dir.name, + cancel_event=cancel_event, + ) + _write_status(job_dir, "completed", {"output": str(output_path)}) + except JobCancelledError: + _write_status(job_dir, "cancelled") + except Exception as exc: + _write_status(job_dir, "failed", {"error": str(exc)}) + finally: + with ACTIVE_JOBS_LOCK: + ACTIVE_JOBS.pop(job_dir.name, None) + + +def _write_status(job_dir: Path, status: str, extra: dict[str, str] | None = None) -> None: + payload = {"status": status} + if extra: + payload.update(extra) + (job_dir / "job.json").write_text(json.dumps(payload, indent=2), encoding="utf-8") + + +def _read_monthly_flow_rows(output_path: Path) -> dict[str, object]: + try: + import pandas as pd + except Exception as exc: # pragma: no cover + raise HTTPException(status_code=500, detail=f"Pandas not available: {exc}") from exc + + try: + df = pd.read_excel( + output_path, + sheet_name="Sheet1", + header=[0, 1, 2], + index_col=[0, 1, 2], + ) + except Exception as exc: + raise HTTPException(status_code=500, detail=f"Could not read output workbook: {exc}") from exc + + source_names = {"Reichwalde", "Nochten", "Welzow"} + shift_names = {"F", "S", "N"} + + flow_cols = [] + for col in df.columns: + if not isinstance(col, tuple) or len(col) != 3: + continue + target, source, shift = col + if str(source) in source_names and str(shift) in shift_names: + flow_cols.append(col) + + if not flow_cols: + return {"rows": [], "targets": [], "sources": sorted(source_names)} + + flows = df[flow_cols].copy() + raw_dates = pd.to_datetime(flows.index.get_level_values(0), errors="coerce") + flows = flows.loc[~raw_dates.isna()].copy() + raw_dates = raw_dates[~raw_dates.isna()] + flows.index = pd.MultiIndex.from_arrays( + [ + raw_dates, + flows.index.get_level_values(1), + flows.index.get_level_values(2), + ], + names=flows.index.names, + ) + + monthly_by_shift = flows.groupby(pd.Grouper(level=0, freq="MS")).sum() + monthly = monthly_by_shift.T.groupby(level=[0, 1]).sum().T + + target_labels = { + "J": "Jänschwalde", + "SP": "Schwarze Pumpe", + "B3": "Boxberg Werk 3", + "B4": "Boxberg Werk 4", + "V": "Veredlung", + } + + rows: list[dict[str, object]] = [] + for month, values in monthly.iterrows(): + for (target, source), amount in values.items(): + amount_value = float(amount) if amount is not None else 0.0 + rows.append( + { + "month": month.strftime("%Y-%m"), + "target": str(target), + "target_label": target_labels.get(str(target), str(target)), + "source": str(source), + "amount_tonnes": amount_value, + } + ) + + targets = sorted({str(col[0]) for col in monthly.columns}) + sources = sorted({str(col[1]) for col in monthly.columns}) + return {"rows": rows, "targets": targets, "sources": sources} + + +def _read_flow_long_rows(output_path: Path): + try: + import pandas as pd + except Exception as exc: # pragma: no cover + raise HTTPException(status_code=500, detail=f"Pandas not available: {exc}") from exc + + try: + df = pd.read_excel( + output_path, + sheet_name="Sheet1", + header=[0, 1, 2], + index_col=[0, 1, 2], + ) + except Exception as exc: + raise HTTPException(status_code=500, detail=f"Could not read output workbook: {exc}") from exc + + source_names = {"Reichwalde", "Nochten", "Welzow"} + shift_names = {"F", "S", "N"} + flow_cols = [ + col + for col in df.columns + if isinstance(col, tuple) + and len(col) == 3 + and str(col[1]) in source_names + and str(col[2]) in shift_names + ] + if not flow_cols: + return pd.DataFrame(columns=["date", "month", "target", "source", "shift", "amount_tonnes"]) + + rows = [] + for (date_raw, _week, _day), values in df[flow_cols].iterrows(): + date = pd.to_datetime(date_raw, errors="coerce") + if pd.isna(date): + continue + date = date.normalize() + for (target, source, shift), amount in values.items(): + rows.append( + { + "date": date, + "month": date.strftime("%Y-%m"), + "target": str(target), + "source": str(source), + "shift": str(shift), + "amount_tonnes": float(amount) if amount is not None else 0.0, + } + ) + return pd.DataFrame(rows) + + +def _read_capacity_timeseries(job_dir: Path) -> dict[str, object]: + try: + import math + import pandas as pd + except Exception as exc: # pragma: no cover + raise HTTPException(status_code=500, detail=f"Pandas not available: {exc}") from exc + + output_path = job_dir / "output" / "output.xlsx" + processed_dir = job_dir / "processed" + if not output_path.exists(): + raise HTTPException(status_code=404, detail="Output not found") + + flows = _read_flow_long_rows(output_path) + if flows.empty: + return {"groups": []} + + def _point_x(date_val, shift=None, granularity="day"): + if granularity == "month": + return f"{date_val}-01" + if granularity == "shift": + hour_map = {"F": 6, "S": 14, "N": 22} + hour = hour_map.get(shift, 0) + return f"{date_val.strftime('%Y-%m-%d')}T{hour:02d}:00:00" + return date_val.strftime("%Y-%m-%d") + + def _point_label(date_val, shift=None, granularity="day"): + if granularity == "month": + return str(date_val) + if granularity == "shift": + return f"{date_val.strftime('%Y-%m-%d')} {shift}" + return date_val.strftime("%Y-%m-%d") + + def _series_from_dataframe( + *, + series_id: str, + label: str, + df_usage, + granularity: str, + limit_value: float | None = None, + limit_map: dict[tuple, float] | None = None, + ) -> dict[str, object]: + points: list[dict[str, object]] = [] + for row in df_usage.itertuples(index=False): + key = row.key + usage = float(row.usage_tonnes) + if granularity == "month": + x = _point_x(key, granularity="month") + point_label = _point_label(key, granularity="month") + limit = limit_value + util = (100 * usage / limit) if limit and limit > 0 else None + elif granularity == "day": + x = _point_x(key, granularity="day") + point_label = _point_label(key, granularity="day") + if limit_map is not None: + limit = limit_map.get(key) + else: + limit = limit_value + util = (100 * usage / limit) if limit and limit > 0 else None + else: + date_val, shift = key + x = _point_x(date_val, shift=shift, granularity="shift") + point_label = _point_label(date_val, shift=shift, granularity="shift") + if limit_map is not None: + limit = limit_map.get((date_val, shift)) + else: + limit = limit_value + util = (100 * usage / limit) if limit and limit > 0 else None + + points.append( + { + "x": x, + "label": point_label, + "usage_tonnes": usage, + "limit_tonnes": float(limit) if limit is not None else None, + "utilization_pct": float(util) if util is not None else None, + } + ) + return {"id": series_id, "label": label, "granularity": granularity, "points": points} + + def _aggregate_usage( + *, + sources: set[str] | None = None, + targets: set[str] | None = None, + granularity: str = "day", + ): + df = flows + if sources is not None: + df = df[df["source"].isin(sources)] + if targets is not None: + df = df[df["target"].isin(targets)] + if granularity == "month": + agg = df.groupby("month", as_index=False)["amount_tonnes"].sum() + agg = agg.rename(columns={"month": "key", "amount_tonnes": "usage_tonnes"}) + agg["key"] = agg["key"].astype(str) + return agg.sort_values("key") + if granularity == "day": + agg = df.groupby("date", as_index=False)["amount_tonnes"].sum() + agg = agg.rename(columns={"date": "key", "amount_tonnes": "usage_tonnes"}) + return agg.sort_values("key") + agg = df.groupby(["date", "shift"], as_index=False)["amount_tonnes"].sum() + agg["_shift_sort"] = pd.Categorical(agg["shift"], categories=["F", "S", "N"], ordered=True) + agg = agg.sort_values(["date", "_shift_sort"]) + agg["key"] = list(zip(agg["date"], agg["shift"])) + agg = agg.rename(columns={"amount_tonnes": "usage_tonnes"}) + return agg[["key", "usage_tonnes"]] + + groups: list[dict[str, object]] = [] + + # Foerderkapazitaeten (kumuliert je Monat auf Tagesbasis) + foerder_file = processed_dir / "foerderkapaz.parquet" + if foerder_file.exists(): + cap_df = pd.read_parquet(foerder_file) + source_map = { + "Reichwalde (RW)": "Reichwalde", + "Nochten (NO)": "Nochten", + "Welzow-Süd": "Welzow", + } + label_map = { + "Reichwalde": "Förderung TB Reichwalde (kumuliert im Monat)", + "Nochten": "Förderung TB Nochten (kumuliert im Monat)", + "Welzow": "Förderung TB Welzow-Süd (kumuliert im Monat)", + } + limit_by_source: dict[str, float] = {} + for _, row in cap_df[cap_df["zeitraum"] == "pro Monat"].iterrows(): + source = source_map.get(str(row["tagebau"])) + if source: + limit_by_source[source] = float(row["maximal"]) + + foerder_series: list[dict[str, object]] = [] + + def _aggregate_cumulative_daily_in_month(*, sources: set[str]): + usage_df = _aggregate_usage(sources=sources, granularity="day") + if usage_df.empty: + return usage_df, {} + usage_df = usage_df.copy() + usage_df["month"] = usage_df["key"].dt.strftime("%Y-%m") + usage_df["usage_tonnes"] = usage_df.groupby("month")["usage_tonnes"].cumsum() + + limit_map: dict[object, float] = {} + for day in usage_df["key"]: + month_start = pd.Timestamp(day).replace(day=1) + month_days = int(month_start.days_in_month) + month_key = month_start.strftime("%Y-%m") + if sources == {"Reichwalde", "Nochten"}: + monthly_limit = 3_000_000.0 + else: + source = next(iter(sources)) + monthly_limit = limit_by_source.get(source) + if monthly_limit is None: + continue + # monthly cap line (constant during month) for cumulative comparison + limit_map[day] = float(monthly_limit) + return usage_df[["key", "usage_tonnes"]], limit_map + + for source in ["Reichwalde", "Nochten", "Welzow"]: + usage_df, limit_map = _aggregate_cumulative_daily_in_month(sources={source}) + if usage_df.empty: + continue + foerder_series.append( + _series_from_dataframe( + series_id=f"foerder_{source.lower()}_cum_month", + label=label_map[source], + df_usage=usage_df, + granularity="day", + limit_map=limit_map, + ) + ) + + rwno_usage_df, rwno_limit_map = _aggregate_cumulative_daily_in_month( + sources={"Reichwalde", "Nochten"} + ) + if not rwno_usage_df.empty: + foerder_series.append( + _series_from_dataframe( + series_id="foerder_rw_nochten_cum_month", + label="Förderung TB Reichwalde + Nochten (kumuliert im Monat, gemeinsame Grenze)", + df_usage=rwno_usage_df, + granularity="day", + limit_map=rwno_limit_map, + ) + ) + + if foerder_series: + groups.append({"key": "foerder", "label": "Förderkapazitäten", "series": foerder_series}) + + # Verladungskapazitaeten (static + availability) + verladung_series: list[dict[str, object]] = [] + verladung_file = processed_dir / "verladungskap.parquet" + if verladung_file.exists(): + verladung_cap = pd.read_parquet(verladung_file) + + def _cap_lookup(verladung_label: str, zeitraum_label: str): + row = verladung_cap[ + (verladung_cap["verladung"] == verladung_label) & (verladung_cap["zeitraum"] == zeitraum_label) + ] + if row.empty: + return None + return float(row.iloc[0]["maximal"]) + + boxberg_targets = {"J", "SP", "B3", "V"} + welzow_targets = {"J", "SP", "B3", "V"} + boxberg_sources = {"Reichwalde", "Nochten"} + welzow_sources = {"Welzow"} + + for series_id, label, sources, targets, granularity, cap_label, cap_period in [ + ( + "verladung_boxberg_shift", + "Verladung Boxberg (RW+NO) pro Schicht", + boxberg_sources, + boxberg_targets, + "shift", + "Boxberg (RW+NO)", + "pro Schicht", + ), + ( + "verladung_boxberg_day", + "Verladung Boxberg (RW+NO) pro Tag", + boxberg_sources, + boxberg_targets, + "day", + "Boxberg (RW+NO)", + "pro Tag", + ), + ( + "verladung_welzow_shift", + "Verladung Welzow-Süd pro Schicht", + welzow_sources, + welzow_targets, + "shift", + "Welzow-Süd", + "pro Schicht", + ), + ( + "verladung_welzow_day", + "Verladung Welzow-Süd pro Tag", + welzow_sources, + welzow_targets, + "day", + "Welzow-Süd", + "pro Tag", + ), + ]: + usage_df = _aggregate_usage(sources=sources, targets=targets, granularity=granularity) + if usage_df.empty: + continue + cap = _cap_lookup(cap_label, cap_period) + verladung_series.append( + _series_from_dataframe( + series_id=series_id, + label=label, + df_usage=usage_df, + granularity=granularity, + limit_value=cap, + ) + ) + + avail_file = processed_dir / "Verfuegbarkeiten.parquet" + if avail_file.exists(): + avail = pd.read_parquet(avail_file) + shift_map = {"S1": "F", "S2": "S", "S3": "N"} + + for scope, label, cols, sources in [ + ( + "welzow", + "Verfügbarkeit Welzow-Süd (pro Schicht)", + ["Welzow_Sued_S1_t", "Welzow_Sued_S2_t", "Welzow_Sued_S3_t"], + {"Welzow"}, + ), + ( + "rw_no", + "Verfügbarkeit Boxberg RW+NO (pro Schicht)", + ["Boxberg_NO_RW_S1_t", "Boxberg_NO_RW_S2_t", "Boxberg_NO_RW_S3_t"], + {"Reichwalde", "Nochten"}, + ), + ]: + limit_map: dict[tuple, float] = {} + for _, row in avail.iterrows(): + date = pd.to_datetime(row["datum"], errors="coerce") + if pd.isna(date): + continue + date = date.normalize() + for col in cols: + val = row.get(col) + if val is None or (isinstance(val, float) and math.isnan(val)): + continue + shift = shift_map[col.split("_")[-2]] + limit_map[(date, shift)] = float(val) + + if not limit_map: + continue + + usage_df = _aggregate_usage(sources=sources, granularity="shift") + if usage_df.empty: + continue + verladung_series.append( + _series_from_dataframe( + series_id=f"availability_{scope}_shift", + label=label, + df_usage=usage_df, + granularity="shift", + limit_map=limit_map, + ) + ) + + if verladung_series: + groups.append({"key": "verladung", "label": "Verladungskapazitäten", "series": verladung_series}) + + # Zugdurchlasskapazitaeten (per shift) + zug_file = processed_dir / "zugdurchlass.parquet" + zug_series: list[dict[str, object]] = [] + if zug_file.exists(): + zug = pd.read_parquet(zug_file).copy() + + def _zug_cap(start_exact: str, ziel_exact: str): + s = zug["start"].astype(str).str.strip() + z = zug["ziel"].astype(str).str.strip() + row = zug[(s == start_exact) & (z == ziel_exact)] + if row.empty: + return None + return float(row.iloc[0]["maximal"]) + + def _add_zug_series(series_id: str, label: str, limit: float | None, terms: list[tuple[str, str]]): + if limit is None: + return + mask = None + for source, target in terms: + part = (flows["source"] == source) & (flows["target"] == target) + mask = part if mask is None else (mask | part) + if mask is None: + return + df = flows[mask].copy() + if df.empty: + return + usage_df = df.groupby(["date", "shift"], as_index=False)["amount_tonnes"].sum() + usage_df["_shift_sort"] = pd.Categorical( + usage_df["shift"], categories=["F", "S", "N"], ordered=True + ) + usage_df = usage_df.sort_values(["date", "_shift_sort"]) + usage_df["key"] = list(zip(usage_df["date"], usage_df["shift"])) + usage_df = usage_df.rename(columns={"amount_tonnes": "usage_tonnes"})[["key", "usage_tonnes"]] + zug_series.append( + _series_from_dataframe( + series_id=series_id, + label=label, + df_usage=usage_df, + granularity="shift", + limit_value=limit, + ) + ) + + _add_zug_series( + "zug_rwno_to_j", + "Zugdurchlass KLP: RW+NO -> KW Jänschwalde", + _zug_cap("Verladung Boxberg (KLP)", "KW Jänschwalde"), + [("Reichwalde", "J"), ("Nochten", "J")], + ) + _add_zug_series( + "zug_rwno_to_sp", + "Zugdurchlass KLP: RW+NO -> KW Schwarze Pumpe", + _zug_cap("Verladung Boxberg (KLP)", "KW Schwarze Pumpe"), + [("Reichwalde", "SP"), ("Nochten", "SP")], + ) + _add_zug_series( + "zug_rwno_to_v", + "Zugdurchlass KLP: RW+NO -> Veredlung", + _zug_cap("Verladung Boxberg (KLP)", "Veredlung ISP"), + [("Reichwalde", "V"), ("Nochten", "V")], + ) + _add_zug_series( + "zug_rwno_to_b3", + "Zugdurchlass KLP: RW+NO -> KW Boxberg Werk 3", + _zug_cap("Verladung Boxberg (KLP)", "KW Boxberg Werk 3"), + [("Reichwalde", "B3"), ("Nochten", "B3")], + ) + _add_zug_series( + "zug_w_to_j", + "Zugdurchlass KUP: Welzow -> KW Jänschwalde", + _zug_cap("Verladung Welzow-Süd (KUP)", "KW Jänschwalde"), + [("Welzow", "J")], + ) + _add_zug_series( + "zug_w_to_sp", + "Zugdurchlass KUP: Welzow -> KW Schwarze Pumpe", + _zug_cap("Verladung Welzow-Süd (KUP)", "KW Schwarze Pumpe"), + [("Welzow", "SP")], + ) + _add_zug_series( + "zug_w_to_v", + "Zugdurchlass KUP: Welzow -> Veredlung", + _zug_cap("Verladung Welzow-Süd (KUP)", "Veredlung ISP"), + [("Welzow", "V")], + ) + _add_zug_series( + "zug_w_to_b3", + "Zugdurchlass KUP: Welzow -> KW Boxberg Werk 3", + _zug_cap("Verladung Welzow-Süd (KUP)", "KW Boxberg Werk 3"), + [("Welzow", "B3")], + ) + _add_zug_series( + "zug_rwno_to_sp_v", + "Zugdurchlass KLP kombiniert: RW+NO -> SP + Veredlung", + _zug_cap("Verladung Boxberg (KLP)", "KW SP + V ISP"), + [("Reichwalde", "SP"), ("Nochten", "SP"), ("Reichwalde", "V"), ("Nochten", "V")], + ) + _add_zug_series( + "zug_rwno_to_all", + "Zugdurchlass KLP kombiniert: RW+NO -> JW + SP + V + B3", + _zug_cap("Verladung Boxberg (KLP)", "KW JW + KW SP + V ISP + KW B3"), + [ + ("Reichwalde", "J"), + ("Reichwalde", "SP"), + ("Reichwalde", "V"), + ("Reichwalde", "B3"), + ("Nochten", "J"), + ("Nochten", "SP"), + ("Nochten", "V"), + ("Nochten", "B3"), + ], + ) + _add_zug_series( + "zug_w_to_sp_v", + "Zugdurchlass KUP kombiniert: Welzow -> SP + Veredlung", + _zug_cap("Verladung Welzow-Süd (KUP)", "KW SP + V ISP"), + [("Welzow", "SP"), ("Welzow", "V")], + ) + _add_zug_series( + "zug_w_to_sp_v_b3", + "Zugdurchlass KUP kombiniert: Welzow -> SP + V + B3", + _zug_cap("Verladung Welzow-Süd (KUP)", "KW SP + V ISP + KW B3"), + [("Welzow", "SP"), ("Welzow", "V"), ("Welzow", "B3")], + ) + _add_zug_series( + "zug_all_to_j", + "Zugdurchlass KUP+KLP: gesamt -> KW Jänschwalde", + _zug_cap("KUP + KLP", "KW Jänschwalde"), + [("Reichwalde", "J"), ("Nochten", "J"), ("Welzow", "J")], + ) + _add_zug_series( + "zug_all_to_b3", + "Zugdurchlass KUP+KLP: gesamt -> KW Boxberg Werk 3", + _zug_cap("KUP + KLP", "KW Boxberg Werk 3"), + [("Reichwalde", "B3"), ("Nochten", "B3"), ("Welzow", "B3")], + ) + _add_zug_series( + "zug_rwno_j_and_w_b3", + "Zugdurchlass kombiniert: KLP->JW + KUP->B3", + _zug_cap("KLP zum KW JW", "KUP zum KW B3"), + [("Reichwalde", "J"), ("Nochten", "J"), ("Welzow", "B3")], + ) + _add_zug_series( + "zug_rwno_jspv_and_w_b3", + "Zugdurchlass kombiniert: KLP->JW+SP+V + KUP->B3", + _zug_cap("KLP zum KW JW + KW SP + V ISP", "KUP zum KW B3"), + [ + ("Reichwalde", "J"), + ("Nochten", "J"), + ("Reichwalde", "SP"), + ("Nochten", "SP"), + ("Reichwalde", "V"), + ("Nochten", "V"), + ("Welzow", "B3"), + ], + ) + + kvb_file = processed_dir / "zugdurchlass_kvb_nord.parquet" + if kvb_file.exists(): + kvb = pd.read_parquet(kvb_file) + limit_map: dict[tuple, float] = {} + shift_cols = [("KVB_Nord_S1_t", "F"), ("KVB_Nord_S2_t", "S"), ("KVB_Nord_S3_t", "N")] + for _, row in kvb.iterrows(): + date = pd.to_datetime(row["datum"], errors="coerce") + if pd.isna(date): + continue + date = date.normalize() + for col, shift in shift_cols: + val = row.get(col) + if val is None or pd.isna(val): + continue + limit_map[(date, shift)] = float(val) + + usage_df = _aggregate_usage(targets={"J"}, granularity="shift") + if limit_map and not usage_df.empty: + zug_series.append( + _series_from_dataframe( + series_id="zug_kvb_nord_dynamic_j", + label="Zugdurchlass KVB Nord (dynamisch) -> KW Jänschwalde", + df_usage=usage_df, + granularity="shift", + limit_map=limit_map, + ) + ) + + if zug_series: + groups.append({"key": "zugdurchlass", "label": "Zugdurchlasskapazitäten", "series": zug_series}) + + return {"groups": groups} + + +@app.get("/api/health") +def health() -> dict[str, object]: + return {"status": "ok", "solvers": _get_solver_availability()} + + +@app.post("/api/run") +async def run( + file: UploadFile = File(...), + solver: str = Form("highs"), + step_size_tonnes: int = Form(1000), + mip_gap_pct: float = Form(5.0), + max_runtime_minutes: float = Form(10.0), +) -> dict[str, str]: + solver = solver.lower().strip() + if solver not in {"highs", "gurobi", "scip"}: + raise HTTPException(status_code=400, detail="Unsupported solver") + availability = _get_solver_availability() + if not availability.get(solver, False): + raise HTTPException(status_code=400, detail=f"Solver not available: {solver}") + if step_size_tonnes not in {960, 1000}: + raise HTTPException(status_code=400, detail="Unsupported step size") + if not (0 <= float(mip_gap_pct) <= 100): + raise HTTPException(status_code=400, detail="mip_gap_pct must be between 0 and 100") + if not (0 < float(max_runtime_minutes) <= 24 * 60): + raise HTTPException(status_code=400, detail="max_runtime_minutes must be between 0 and 1440") + mip_gap = float(mip_gap_pct) / 100.0 + time_limit_seconds = max(1, int(round(float(max_runtime_minutes) * 60))) + job_id = uuid.uuid4().hex + job_dir = JOBS_DIR / job_id + input_dir = job_dir / "input" + processed_dir = job_dir / "processed" + output_dir = job_dir / "output" + + input_dir.mkdir(parents=True, exist_ok=True) + processed_dir.mkdir(parents=True, exist_ok=True) + output_dir.mkdir(parents=True, exist_ok=True) + + input_path = input_dir / "PoC1_Rohkohleverteilung_Input_Parameter.xlsx" + with input_path.open("wb") as buffer: + shutil.copyfileobj(file.file, buffer) + + cancel_event = threading.Event() + with ACTIVE_JOBS_LOCK: + ACTIVE_JOBS[job_id] = {"cancel_event": cancel_event, "process": None} + + _write_status( + job_dir, + "queued", + { + "solver": solver, + "step_size_tonnes": str(step_size_tonnes), + "mip_gap_pct": str(float(mip_gap_pct)), + "max_runtime_minutes": str(float(max_runtime_minutes)), + }, + ) + thread = threading.Thread( + target=_run_job, + args=( + job_dir, + input_path, + processed_dir, + output_dir, + solver, + step_size_tonnes, + mip_gap, + time_limit_seconds, + cancel_event, + ), + daemon=True, + ) + thread.start() + + return { + "job_id": job_id, + "download_url": f"/api/jobs/{job_id}/output", + "solver": solver, + "step_size_tonnes": str(step_size_tonnes), + "mip_gap_pct": str(float(mip_gap_pct)), + "max_runtime_minutes": str(float(max_runtime_minutes)), + } + + +@app.get("/api/jobs/{job_id}") +def job_status(job_id: str) -> dict[str, str]: + job_dir = JOBS_DIR / job_id + status_path = job_dir / "job.json" + if not status_path.exists(): + raise HTTPException(status_code=404, detail="Job not found") + return json.loads(status_path.read_text(encoding="utf-8")) + + +@app.post("/api/jobs/{job_id}/cancel") +def cancel_job(job_id: str) -> dict[str, str]: + job_dir = JOBS_DIR / job_id + status_path = job_dir / "job.json" + if not status_path.exists(): + raise HTTPException(status_code=404, detail="Job not found") + + payload = json.loads(status_path.read_text(encoding="utf-8")) + if payload.get("status") in {"completed", "failed", "cancelled"}: + return {"status": str(payload.get("status"))} + + with ACTIVE_JOBS_LOCK: + runtime = ACTIVE_JOBS.get(job_id) + if runtime is None: + _write_status(job_dir, "cancelled") + return {"status": "cancelled"} + cancel_event = runtime.get("cancel_event") + process = runtime.get("process") + if isinstance(cancel_event, threading.Event): + cancel_event.set() + if process is not None and getattr(process, "poll", lambda: None)() is None: + try: + process.terminate() + except Exception: + pass + + _write_status(job_dir, "cancelling") + return {"status": "cancelling"} + + +@app.get("/api/jobs/{job_id}/output") +def job_output(job_id: str) -> FileResponse: + output_path = JOBS_DIR / job_id / "output" / "output.xlsx" + if not output_path.exists(): + raise HTTPException(status_code=404, detail="Output not found") + return FileResponse(output_path, filename="output.xlsx") + + +@app.get("/api/jobs/{job_id}/monthly-flows") +def job_monthly_flows(job_id: str) -> dict[str, object]: + output_path = JOBS_DIR / job_id / "output" / "output.xlsx" + if not output_path.exists(): + raise HTTPException(status_code=404, detail="Output not found") + return _read_monthly_flow_rows(output_path) + + +@app.get("/api/jobs/{job_id}/capacity-timeseries") +def job_capacity_timeseries(job_id: str) -> dict[str, object]: + job_dir = JOBS_DIR / job_id + if not job_dir.exists(): + raise HTTPException(status_code=404, detail="Job not found") + return _read_capacity_timeseries(job_dir) + + +@app.get("/api/jobs/{job_id}/logs/{log_name}") +def job_log(job_id: str, log_name: str) -> PlainTextResponse: + if log_name not in {"preprocess", "optimization"}: + raise HTTPException(status_code=400, detail="Unknown log name") + log_path = JOBS_DIR / job_id / "logs" / f"{log_name}.log" + if not log_path.exists(): + raise HTTPException(status_code=404, detail="Log not found") + return PlainTextResponse(log_path.read_text(encoding="utf-8")) + + +@app.get("/api/jobs/{job_id}/logs/{log_name}/stream") +def job_log_stream(job_id: str, log_name: str) -> StreamingResponse: + if log_name not in {"preprocess", "optimization"}: + raise HTTPException(status_code=400, detail="Unknown log name") + log_path = JOBS_DIR / job_id / "logs" / f"{log_name}.log" + if not log_path.exists(): + raise HTTPException(status_code=404, detail="Log not found") + + def event_stream(): + with log_path.open("r", encoding="utf-8") as log_file: + for line in log_file: + yield f"data: {line.rstrip()}\n\n" + while True: + line = log_file.readline() + if line: + yield f"data: {line.rstrip()}\n\n" + else: + time.sleep(0.5) + + return StreamingResponse(event_stream(), media_type="text/event-stream") diff --git a/webapp/frontend/index.html b/webapp/frontend/index.html new file mode 100644 index 0000000..86f5257 --- /dev/null +++ b/webapp/frontend/index.html @@ -0,0 +1,12 @@ + + + + + + POC1 Optimierung + + +
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@@ -0,0 +1,394 @@ +:root { + --ink: #1a1a1a; + --slate: #4a4a4a; + --sand: #f4f0e6; + --clay: #d0b58a; + --ember: #b34a2a; + --paper: #ffffff; + --shadow: 0 20px 50px rgba(0, 0, 0, 0.12); + --panel: #fffdf8; + --panel-soft: #fffaf1; + --line: #eadfcb; + --line-soft: #eee3cf; + --log-bg: #f6efe4; + --log-ink: #2f2a25; +} + +:root[data-theme="dark"] { + --ink: #e8edf2; + --slate: #b7c0c9; + --clay: #9a845f; + --ember: #d97b59; + --paper: #161b20; + --shadow: 0 20px 50px rgba(0, 0, 0, 0.35); + --panel: #1b2127; + --panel-soft: #222a31; + --line: #36404a; + --line-soft: #414b56; + --log-bg: #222930; + --log-ink: #d8e0e8; +} + +* { + box-sizing: border-box; +} + +body { + margin: 0; + font-family: "Palatino Linotype", "Book Antiqua", Palatino, serif; + color: var(--ink); + background: radial-gradient(circle at top, #f8f5ed 0%, #efe6d5 40%, #e0d0b2 100%); + min-height: 100vh; +} + +:root[data-theme="dark"] body { + background: radial-gradient(circle at top, #20272e 0%, #171c22 40%, #11161b 100%); +} + +.page { + display: flex; + flex-direction: column; + gap: 32px; + padding: 40px 24px 72px; +} + +.hero { + position: relative; + padding: 32px 24px; + border-radius: 24px; + background: linear-gradient(120deg, #ffffff 0%, #f1e4cc 100%); + box-shadow: var(--shadow); + overflow: hidden; + animation: reveal 600ms ease-out; +} + +:root[data-theme="dark"] .hero { + background: linear-gradient(120deg, #20262d 0%, #182027 100%); +} + +.hero::after { + content: ""; + position: absolute; + inset: -40% -20% auto auto; + width: 200px; + height: 200px; + background: radial-gradient(circle, rgba(179, 74, 42, 0.35), transparent 70%); +} + +.hero-content { + position: relative; + z-index: 1; +} + +.hero-top { + display: flex; + justify-content: space-between; + align-items: flex-start; + gap: 16px; +} + +.eyebrow { + text-transform: uppercase; + letter-spacing: 0.2em; + font-size: 0.7rem; + color: var(--ember); + margin: 0 0 8px; +} + +h1 { + margin: 0 0 12px; + font-size: clamp(2rem, 3vw, 3rem); +} + +.subtitle { + margin: 0; + color: var(--slate); + max-width: 520px; +} + +.card { + background: var(--paper); + border-radius: 20px; + padding: 32px; + box-shadow: var(--shadow); + animation: reveal 700ms ease-out; +} + +.form { + display: flex; + flex-direction: column; + gap: 16px; + max-width: 720px; +} + +.file-card { + display: flex; + flex-direction: column; + gap: 12px; + padding: 16px; + border: 2px dashed var(--clay); + border-radius: 16px; + background: var(--panel-soft); + cursor: pointer; +} + +.file-card input { + display: none; +} + +.file-card.disabled { + opacity: 0.55; + cursor: not-allowed; +} + +.form-actions { + display: flex; + gap: 12px; + flex-wrap: wrap; + width: fit-content; +} + +.theme-toggle { + background: transparent; + color: var(--ink); + border: 1px solid var(--line); + box-shadow: none; + white-space: nowrap; +} + +.theme-toggle:hover { + transform: none; + box-shadow: none; + background: var(--panel-soft); +} + +button { + border: none; + border-radius: 12px; + padding: 12px 16px; + font-size: 1rem; + background: var(--ember); + color: white; + cursor: pointer; + transition: transform 150ms ease, box-shadow 150ms ease; +} + +button:hover { + transform: translateY(-2px); + box-shadow: 0 12px 24px rgba(179, 74, 42, 0.25); +} + +button:disabled { + opacity: 0.5; + cursor: not-allowed; + transform: none; + box-shadow: none; +} + +button.secondary { + background: var(--panel-soft); + color: var(--ink); +} + +.status { + margin-top: 24px; + display: flex; + flex-direction: column; + gap: 8px; +} + +.progress-panel { + margin-top: 4px; + padding: 10px 12px; + border: 1px solid var(--line); + border-radius: 12px; + background: var(--panel-soft); + display: flex; + flex-direction: column; + gap: 8px; +} + +.progress-panel-row { + display: flex; + align-items: center; + justify-content: space-between; + gap: 12px; + color: var(--slate); + font-size: 0.9rem; +} + +.progress-panel-row strong { + color: var(--ink); +} + +.progress-metrics { + display: flex; + flex-wrap: wrap; + gap: 8px 14px; + font-size: 0.85rem; + color: var(--ink); +} + +.progress-metrics.terminal-like { + font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", + "Courier New", monospace; + background: var(--log-bg); + border-radius: 8px; + padding: 8px 10px; + border: 1px solid var(--line); +} + +.muted { + color: var(--slate); + font-size: 0.9rem; +} + +.download { + display: inline-block; + padding: 10px 14px; + border-radius: 12px; + background: var(--clay); + color: var(--ink); + text-decoration: none; + width: fit-content; +} + +.log { + margin-top: 16px; + padding: 12px; + background: var(--log-bg); + border-radius: 12px; + max-height: 240px; + overflow: auto; + font-size: 0.85rem; + color: var(--log-ink); +} + +.chart-panel { + margin-top: 28px; + padding-top: 20px; + border-top: 1px solid var(--line); + display: flex; + flex-direction: column; + gap: 16px; +} + +.charts-stack { + display: flex; + flex-direction: column; + gap: 20px; +} + +.chart-card { + padding: 14px; + border: 1px solid var(--line-soft); + border-radius: 16px; + background: var(--panel); + box-shadow: 0 8px 24px rgba(0, 0, 0, 0.05); +} + +.chart-card-title { + display: flex; + flex-wrap: wrap; + justify-content: space-between; + gap: 8px 16px; + align-items: baseline; + margin-bottom: 10px; +} + +.chart-card-title strong { + font-size: 1rem; +} + +.chart-card-title span { + color: var(--slate); + font-size: 0.9rem; +} + +.chart-panel h2 { + margin: 0 0 6px; + font-size: 1.25rem; +} + +.chart-panel-header { + display: flex; + justify-content: space-between; + align-items: flex-start; + gap: 16px; + flex-wrap: wrap; +} + +.target-select { + display: flex; + flex-direction: column; + gap: 6px; + min-width: 220px; + max-width: 280px; + font-size: 0.9rem; + color: var(--slate); +} + +.target-select select { + border: 1px solid var(--line); + border-radius: 10px; + padding: 10px 12px; + background: var(--panel-soft); + color: var(--ink); + font: inherit; +} + +.target-select input { + border: 1px solid var(--line); + border-radius: 10px; + padding: 10px 12px; + background: var(--panel-soft); + color: var(--ink); + font: inherit; +} + +.target-select.compact { + min-width: auto; + margin-bottom: 10px; +} + +.plot { + border-radius: 14px; + overflow: hidden; + background: var(--panel-soft); + border: 1px solid var(--line-soft); +} + +@keyframes reveal { + from { + opacity: 0; + transform: translateY(12px); + } + to { + opacity: 1; + transform: translateY(0); + } +} + +@media (max-width: 720px) { + .card { + padding: 24px; + } + + .hero { + padding: 24px 20px; + } + + .target-select { + min-width: 100%; + } + + .chart-card-title { + flex-direction: column; + align-items: flex-start; + } + + .hero-top { + flex-direction: column; + align-items: stretch; + } +} diff --git a/webapp/frontend/src/App.jsx b/webapp/frontend/src/App.jsx new file mode 100644 index 0000000..c32927b --- /dev/null +++ b/webapp/frontend/src/App.jsx @@ -0,0 +1,1048 @@ +import { useEffect, useState } from "react"; +import Plot from "react-plotly.js"; + +const API_BASE = import.meta.env.VITE_API_BASE || ""; +const THEME_STORAGE_KEY = "poc1-theme"; +const SOURCE_ORDER = ["Reichwalde", "Nochten", "Welzow"]; +const SOURCE_COLORS = { + Reichwalde: "#e4dc41", + Nochten: "#f6bd1f", + Welzow: "#ef6c00", +}; +const TARGET_ORDER = ["J", "SP", "B3", "B4", "V"]; +const TARGET_COLORS = { + J: "#439c6b", + SP: "#5aaed6", + B3: "#ef6c00", + B4: "#f6bd1f", + V: "#e4dc41", +}; +const TARGET_LABELS = { + J: "KW Jänschwalde", + SP: "KW Schwarze Pumpe", + B3: "KW Boxberg Werk 3", + B4: "KW Boxberg Werk 4", + V: "Veredlung", +}; +const SOURCE_LABELS = { + Reichwalde: "TB Reichwalde", + Nochten: "TB Nochten", + Welzow: "TB Welzow-Süd", +}; + +function toNumber(value) { + return typeof value === "number" ? value : Number(value || 0); +} + +function tonnesToKt(valueTonnes) { + return toNumber(valueTonnes) / 1000; +} + +function formatKt(valueTonnes) { + return Math.round(tonnesToKt(valueTonnes)).toLocaleString("de-DE"); +} + +function formatPct(part, total) { + if (!total) return "0%"; + return `${Math.round((100 * part) / total)}%`; +} + +function monthLabel(monthKey) { + if (!monthKey) return ""; + const [year, month] = monthKey.split("-").map(Number); + const date = new Date(year, (month || 1) - 1, 1); + return date.toLocaleDateString("de-DE", { month: "long", year: "numeric" }); +} + +function extractOptimizationLog(fullLogText) { + if (!fullLogText) return ""; + const marker = "=== Optimierung ==="; + const idx = fullLogText.indexOf(marker); + if (idx === -1) return fullLogText; + return fullLogText.slice(idx + marker.length).trim(); +} + +function parseSolverProgress(logText, solverName) { + const optimizationLog = extractOptimizationLog(logText); + if (!optimizationLog) { + return {}; + } + + const metrics = {}; + const lines = optimizationLog.split("\n").map((line) => line.trimEnd()); + const reversed = [...lines].reverse(); + const text = optimizationLog; + const solver = (solverName || "").toLowerCase(); + + const assignIfNumber = (key, value) => { + const num = Number(value); + if (Number.isFinite(num)) { + metrics[key] = num; + } + }; + + if (solver === "scip") { + for (const line of reversed) { + if (!metrics.gapPct) { + const m = line.match(/gap[^0-9-]*([0-9]+(?:\.[0-9]+)?)\s*%/i); + if (m) assignIfNumber("gapPct", m[1]); + } + if (!metrics.nodes) { + const m = line.match(/Solving Nodes\s*:\s*([0-9]+)/i); + if (m) assignIfNumber("nodes", m[1]); + } + if (!metrics.bestPrimal) { + const m = line.match(/Primal Bound\s*:\s*([+-]?[0-9.eE]+)/i); + if (m) assignIfNumber("bestPrimal", m[1]); + } + if (!metrics.bestDual) { + const m = line.match(/Dual Bound\s*:\s*([+-]?[0-9.eE]+)/i); + if (m) assignIfNumber("bestDual", m[1]); + } + + // Best-effort parsing of SCIP progress table rows: time|node|...|gap + if ((!metrics.gapPct || !metrics.nodes) && line.includes("|")) { + if (!metrics.nodes) { + const nodeCol = line.match(/^\s*[^|]+\|\s*([0-9]+)\s*\|/); + if (nodeCol) assignIfNumber("nodes", nodeCol[1]); + } + if (!metrics.gapPct) { + const percents = [...line.matchAll(/([0-9]+(?:\.[0-9]+)?)\s*%/g)]; + if (percents.length > 0) { + assignIfNumber("gapPct", percents[percents.length - 1][1]); + } + } + } + + if (metrics.gapPct != null && metrics.nodes != null && metrics.bestPrimal != null) { + break; + } + } + } else if (solver === "gurobi") { + for (const line of reversed) { + if (!metrics.gapPct) { + const m = line.match(/\bgap\s+([0-9]+(?:\.[0-9]+)?)%/i); + if (m) assignIfNumber("gapPct", m[1]); + } + if (!metrics.nodes) { + const m = line.match(/Explored\s+([0-9]+)\s+nodes/i); + if (m) assignIfNumber("nodes", m[1]); + } + if (metrics.gapPct != null && metrics.nodes != null) break; + } + } else if (solver === "highs") { + for (const line of reversed) { + if (!metrics.gapPct) { + const m = line.match(/\bgap[^0-9-]*([0-9]+(?:\.[0-9]+)?)\s*%/i); + if (m) assignIfNumber("gapPct", m[1]); + } + if (metrics.gapPct != null) break; + } + } + + const statusPatterns = [ + /SCIP Status\s*:\s*(.+)/i, + /Model status\s*:\s*(.+)/i, + /Status\s*:\s*(.+)/i, + ]; + for (const line of reversed) { + for (const pattern of statusPatterns) { + const m = line.match(pattern); + if (m) { + metrics.solverStatus = m[1].trim(); + return metrics; + } + } + } + + return metrics; +} + +export default function App() { + const [file, setFile] = useState(null); + const [status, setStatus] = useState("Bereit für Upload"); + const [downloadUrl, setDownloadUrl] = useState(""); + const [jobId, setJobId] = useState(""); + const [logText, setLogText] = useState(""); + const [jobState, setJobState] = useState(""); + const [monthlyFlows, setMonthlyFlows] = useState(null); + const [monthlyFlowError, setMonthlyFlowError] = useState(""); + const [selectedMonth, setSelectedMonth] = useState(""); + const [capacityData, setCapacityData] = useState(null); + const [capacityError, setCapacityError] = useState(""); + const [selectedCapacitySeries, setSelectedCapacitySeries] = useState({}); + const [solver, setSolver] = useState("highs"); + const [stepSizeTonnes, setStepSizeTonnes] = useState("1000"); + const [mipGapPct, setMipGapPct] = useState("5"); + const [maxRuntimeMinutes, setMaxRuntimeMinutes] = useState("10"); + const [availableSolvers, setAvailableSolvers] = useState({ + highs: true, + gurobi: false, + scip: false, + }); + const [theme, setTheme] = useState("dark"); + const [jobSolver, setJobSolver] = useState(""); + const [jobStepSizeTonnes, setJobStepSizeTonnes] = useState(""); + const [jobMipGapPct, setJobMipGapPct] = useState(""); + const [jobMaxRuntimeMinutes, setJobMaxRuntimeMinutes] = useState(""); + const [elapsedSeconds, setElapsedSeconds] = useState(0); + const [timerRunning, setTimerRunning] = useState(false); + const [cancelPending, setCancelPending] = useState(false); + + const handleSubmit = async (event) => { + event.preventDefault(); + if (!file) { + setStatus("Bitte zuerst eine Excel-Datei wählen."); + return; + } + + setStatus("Upload läuft…"); + setDownloadUrl(""); + setJobId(""); + setLogText(""); + setJobState(""); + setMonthlyFlows(null); + setMonthlyFlowError(""); + setSelectedMonth(""); + setCapacityData(null); + setCapacityError(""); + setSelectedCapacitySeries({}); + setJobSolver(""); + setJobStepSizeTonnes(""); + setJobMipGapPct(""); + setJobMaxRuntimeMinutes(""); + setElapsedSeconds(0); + setTimerRunning(true); + setCancelPending(false); + + const formData = new FormData(); + formData.append("file", file); + formData.append("solver", solver); + formData.append("step_size_tonnes", stepSizeTonnes); + formData.append("mip_gap_pct", mipGapPct); + formData.append("max_runtime_minutes", maxRuntimeMinutes); + + try { + const response = await fetch(`${API_BASE}/api/run`, { + method: "POST", + body: formData, + }); + + if (!response.ok) { + throw new Error("Serverfehler beim Starten des Jobs"); + } + + const data = await response.json(); + setJobId(data.job_id); + setJobSolver(data.solver || solver); + setJobStepSizeTonnes(String(data.step_size_tonnes || stepSizeTonnes)); + setJobMipGapPct(String(data.mip_gap_pct || mipGapPct)); + setJobMaxRuntimeMinutes(String(data.max_runtime_minutes || maxRuntimeMinutes)); + setStatus("Job gestartet. Warte auf Ergebnis…"); + } catch (error) { + setStatus("Fehler: Job konnte nicht abgeschlossen werden."); + setTimerRunning(false); + } + }; + + const handleCancel = async () => { + if (!jobId || cancelPending) { + return; + } + setCancelPending(true); + try { + const response = await fetch(`${API_BASE}/api/jobs/${jobId}/cancel`, { + method: "POST", + }); + if (!response.ok) { + throw new Error("Abbrechen fehlgeschlagen"); + } + const data = await response.json(); + setJobState(data.status || "cancelling"); + setStatus("Abbruch angefordert…"); + } catch { + setStatus("Fehler beim Abbrechen des Jobs."); + setCancelPending(false); + } + }; + + const pollStatus = async (id) => { + try { + const response = await fetch(`${API_BASE}/api/jobs/${id}`); + if (!response.ok) { + return; + } + const data = await response.json(); + setJobState(data.status); + setStatus(`Status: ${data.status}`); + if (data.solver) { + setJobSolver(data.solver); + } + if (data.step_size_tonnes) { + setJobStepSizeTonnes(String(data.step_size_tonnes)); + } + if (data.mip_gap_pct) { + setJobMipGapPct(String(data.mip_gap_pct)); + } + if (data.max_runtime_minutes) { + setJobMaxRuntimeMinutes(String(data.max_runtime_minutes)); + } + if (data.status === "completed") { + setDownloadUrl(`${API_BASE}/api/jobs/${id}/output`); + setStatus("Fertig. Ergebnis steht zum Download bereit."); + setTimerRunning(false); + setCancelPending(false); + } + if (data.status === "failed") { + setStatus(`Fehler: ${data.error || "Job fehlgeschlagen"}`); + setTimerRunning(false); + setCancelPending(false); + } + if (data.status === "cancelled") { + setStatus("Job wurde abgebrochen."); + setTimerRunning(false); + setCancelPending(false); + } + } catch { + setStatus("Fehler beim Status-Abruf."); + } + }; + + const loadMonthlyFlows = async (id) => { + try { + const response = await fetch(`${API_BASE}/api/jobs/${id}/monthly-flows`); + if (!response.ok) { + throw new Error("Monatsdaten konnten nicht geladen werden"); + } + const data = await response.json(); + setMonthlyFlows(data); + setMonthlyFlowError(""); + const months = Array.from(new Set((data.rows || []).map((row) => row.month))).sort(); + if (months.length > 0) { + setSelectedMonth(months[months.length - 1]); + } + } catch { + setMonthlyFlowError("Monatsdaten für Plot konnten nicht geladen werden."); + } + }; + + const loadCapacityData = async (id) => { + try { + const response = await fetch(`${API_BASE}/api/jobs/${id}/capacity-timeseries`); + if (!response.ok) { + throw new Error("Kapazitätsdaten konnten nicht geladen werden"); + } + const data = await response.json(); + setCapacityData(data); + setCapacityError(""); + const defaults = {}; + for (const group of data.groups || []) { + if (group.series?.length) { + defaults[group.key] = group.series[0].id; + } + } + setSelectedCapacitySeries(defaults); + } catch { + setCapacityError("Kapazitäts-Zeitreihen konnten nicht geladen werden."); + } + }; + + const loadBackendCapabilities = async () => { + try { + const response = await fetch(`${API_BASE}/api/health`); + if (!response.ok) { + return; + } + const data = await response.json(); + const reported = data?.solvers || {}; + const solvers = { + highs: reported.highs !== false, + gurobi: reported.gurobi === true, + scip: reported.scip === true, + }; + setAvailableSolvers(solvers); + if ( + (solver === "gurobi" && !solvers.gurobi) || + (solver === "scip" && !solvers.scip) + ) { + setSolver("highs"); + } + } catch { + // Keep defaults (HiGHS enabled, Gurobi hidden). + } + }; + + const fetchJobLogs = async (id) => { + const fetchOne = async (name) => { + try { + const response = await fetch(`${API_BASE}/api/jobs/${id}/logs/${name}`); + if (!response.ok) { + return ""; + } + return await response.text(); + } catch { + return ""; + } + }; + + const [preprocessLog, optimizationLog] = await Promise.all([ + fetchOne("preprocess"), + fetchOne("optimization"), + ]); + + const sections = []; + if (preprocessLog.trim()) { + sections.push(`=== Preprocessing ===\n${preprocessLog.trimEnd()}`); + } + if (optimizationLog.trim()) { + sections.push(`=== Optimierung ===\n${optimizationLog.trimEnd()}`); + } + setLogText(sections.join("\n\n")); + }; + + useEffect(() => { + try { + const saved = window.localStorage.getItem(THEME_STORAGE_KEY); + if (saved === "light" || saved === "dark") { + setTheme(saved); + } + } catch { + // ignore + } + }, []); + + useEffect(() => { + document.documentElement.dataset.theme = theme; + try { + window.localStorage.setItem(THEME_STORAGE_KEY, theme); + } catch { + // ignore + } + }, [theme]); + + useEffect(() => { + loadBackendCapabilities(); + }, []); + + useEffect(() => { + if ( + !jobId || + downloadUrl || + jobState === "failed" || + jobState === "completed" || + jobState === "cancelled" + ) { + return undefined; + } + const interval = setInterval(() => { + pollStatus(jobId); + }, 3000); + return () => clearInterval(interval); + }, [jobId, downloadUrl, jobState]); + + useEffect(() => { + if (!timerRunning) { + return undefined; + } + const interval = setInterval(() => { + setElapsedSeconds((prev) => prev + 1); + }, 1000); + return () => clearInterval(interval); + }, [timerRunning]); + + useEffect(() => { + if (jobId && jobState === "completed") { + loadMonthlyFlows(jobId); + loadCapacityData(jobId); + } + }, [jobId, jobState]); + + useEffect(() => { + if (!jobId) { + return undefined; + } + let cancelled = false; + + const refreshLogs = async () => { + await fetchJobLogs(jobId); + }; + + refreshLogs(); + + if (jobState === "failed" || jobState === "completed" || jobState === "cancelled") { + return () => { + cancelled = true; + }; + } + + const interval = setInterval(() => { + if (!cancelled) { + refreshLogs(); + } + }, 1500); + + return () => { + cancelled = true; + clearInterval(interval); + }; + }, [jobId, jobState]); + + const isDark = theme === "dark"; + const plotTheme = { + paper: isDark ? "#1a1f24" : "#ffffff", + plot: isDark ? "#20262c" : "#ffffff", + grid: isDark ? "#36404a" : "#f0e6d2", + text: isDark ? "#e8edf2" : "#1a1a1a", + axis: isDark ? "#c9d3dc" : "#4a4a4a", + usage: isDark ? "#7fd0e0" : "#2f6f7e", + limit: isDark ? "#ff8f70" : "#b34a2a", + annotation: isDark ? "#cfe7ff" : "#1f4f6d", + }; + + const allRows = monthlyFlows?.rows || []; + const monthOptions = Array.from(new Set(allRows.map((row) => row.month))).sort(); + const activeMonth = + selectedMonth && monthOptions.includes(selectedMonth) + ? selectedMonth + : monthOptions[monthOptions.length - 1] || ""; + const monthRows = allRows.filter((row) => row.month === activeMonth); + + const matrix = {}; + for (const target of TARGET_ORDER) { + matrix[target] = {}; + for (const source of SOURCE_ORDER) { + matrix[target][source] = 0; + } + } + for (const row of monthRows) { + const target = row.target; + const source = row.source; + if (!matrix[target]) { + matrix[target] = {}; + } + matrix[target][source] = toNumber(row.amount_tonnes); + } + + const targetTotals = {}; + for (const target of TARGET_ORDER) { + targetTotals[target] = SOURCE_ORDER.reduce( + (sum, source) => sum + toNumber(matrix[target]?.[source]), + 0 + ); + } + const sourceTotals = {}; + for (const source of SOURCE_ORDER) { + sourceTotals[source] = TARGET_ORDER.reduce( + (sum, target) => sum + toNumber(matrix[target]?.[source]), + 0 + ); + } + const grandTotal = TARGET_ORDER.reduce((sum, target) => sum + targetTotals[target], 0); + + const presentTargets = TARGET_ORDER.filter((target) => targetTotals[target] > 0); + const presentSources = SOURCE_ORDER.filter((source) => sourceTotals[source] > 0); + const demandBarY = ["Rohkohlebedarf [kt]"]; + + const demandDistributionData = presentTargets.map((target) => ({ + type: "bar", + orientation: "h", + name: TARGET_LABELS[target], + x: [tonnesToKt(targetTotals[target])], + y: demandBarY, + marker: { color: TARGET_COLORS[target] }, + text: [`${formatKt(targetTotals[target])} (${formatPct(targetTotals[target], grandTotal)})`], + textposition: "inside", + insidetextanchor: "middle", + hovertemplate: + `${TARGET_LABELS[target]}
%{x:,.0f} kt`.replace(",", "."), + })); + + const demandDistributionAnnotations = + grandTotal > 0 + ? [ + { + x: tonnesToKt(grandTotal), + y: demandBarY[0], + xshift: 50, + showarrow: false, + text: `${formatKt(grandTotal)} kt`, + font: { size: 14, color: plotTheme.annotation }, + }, + ] + : []; + + const targetBySourceData = presentSources.map((source) => ({ + type: "bar", + orientation: "h", + name: SOURCE_LABELS[source], + y: presentTargets.map((target) => TARGET_LABELS[target]), + x: presentTargets.map((target) => tonnesToKt(matrix[target]?.[source] || 0)), + marker: { color: SOURCE_COLORS[source] }, + text: presentTargets.map((target) => { + const value = matrix[target]?.[source] || 0; + if (value <= 0) return ""; + return `${formatKt(value)} (${formatPct(value, targetTotals[target])})`; + }), + textposition: "inside", + insidetextanchor: "middle", + hovertemplate: `${SOURCE_LABELS[source]}
%{y}: %{x:,.0f} kt`, + })); + + const targetBySourceAnnotations = presentTargets + .filter((target) => targetTotals[target] > 0) + .map((target) => ({ + x: tonnesToKt(targetTotals[target]), + y: TARGET_LABELS[target], + xshift: 42, + showarrow: false, + text: `${formatKt(targetTotals[target])}`, + font: { size: 14, color: plotTheme.annotation }, + })); + + const sourceToTargetData = presentTargets.map((target) => ({ + type: "bar", + name: TARGET_LABELS[target], + x: presentSources.map((source) => SOURCE_LABELS[source]), + y: presentSources.map((source) => tonnesToKt(matrix[target]?.[source] || 0)), + marker: { color: TARGET_COLORS[target] }, + text: presentSources.map((source) => { + const value = matrix[target]?.[source] || 0; + if (value <= 0) return ""; + return `${formatKt(value)} (${formatPct(value, sourceTotals[source])})`; + }), + textposition: "inside", + insidetextanchor: "middle", + hovertemplate: `${TARGET_LABELS[target]}
%{x}: %{y:,.0f} kt`, + })); + + const sourceToTargetAnnotations = presentSources + .filter((source) => sourceTotals[source] > 0) + .map((source) => ({ + x: SOURCE_LABELS[source], + y: tonnesToKt(sourceTotals[source]), + yshift: 16, + showarrow: false, + text: `${formatKt(sourceTotals[source])} kt`, + font: { size: 14, color: plotTheme.annotation }, + })); + + const runtimeLabel = (() => { + const minutes = Math.floor(elapsedSeconds / 60); + const seconds = elapsedSeconds % 60; + return `${String(minutes).padStart(2, "0")}:${String(seconds).padStart(2, "0")}`; + })(); + + const capacityGroups = capacityData?.groups || []; + const getCapacityGroup = (key) => capacityGroups.find((group) => group.key === key); + const buildCapacityPlot = (groupKey) => { + const group = getCapacityGroup(groupKey); + if (!group || !group.series?.length) { + return null; + } + const selectedId = selectedCapacitySeries[groupKey] || group.series[0].id; + const series = group.series.find((item) => item.id === selectedId) || group.series[0]; + const points = series.points || []; + const usageTrace = { + type: "scatter", + mode: "lines+markers", + name: "Nutzung", + x: points.map((p) => p.x), + y: points.map((p) => tonnesToKt(p.usage_tonnes)), + line: { color: plotTheme.usage, width: 3 }, + marker: { size: 6, color: plotTheme.usage }, + customdata: points.map((p) => [p.label, p.utilization_pct]), + hovertemplate: + "%{customdata[0]}
Nutzung: %{y:.1f} kt
Auslastung: %{customdata[1]:.1f}%", + }; + const limitTrace = { + type: "scatter", + mode: "lines", + name: "Grenze", + x: points.map((p) => p.x), + y: points.map((p) => + p.limit_tonnes == null || Number.isNaN(p.limit_tonnes) ? null : tonnesToKt(p.limit_tonnes) + ), + line: { color: plotTheme.limit, width: 2, dash: "dash" }, + hovertemplate: "%{x}
Grenze: %{y:.1f} kt", + }; + return { + group, + series, + data: [usageTrace, limitTrace], + layout: { + paper_bgcolor: plotTheme.paper, + plot_bgcolor: plotTheme.plot, + font: { color: plotTheme.text }, + margin: { l: 60, r: 20, t: 30, b: 70 }, + xaxis: { + title: series.granularity === "month" ? "Monat" : "Zeit", + automargin: true, + gridcolor: plotTheme.grid, + color: plotTheme.axis, + }, + yaxis: { title: "kt", gridcolor: plotTheme.grid, color: plotTheme.axis }, + legend: { orientation: "h", y: 1.15, x: 0 }, + }, + }; + }; + + const jobInProgress = + Boolean(jobId) && + !["completed", "failed", "cancelled"].includes(jobState || ""); + const activeSolver = (jobSolver || solver || "highs").toLowerCase(); + const solverProgress = parseSolverProgress(logText, activeSolver); + const foerderPlot = buildCapacityPlot("foerder"); + const verladungPlot = buildCapacityPlot("verladung"); + const zugPlot = buildCapacityPlot("zugdurchlass"); + + return ( +
+
+
+
+
+

LEAG • POC1

+

Rohkohle-Optimierung

+

+ Excel hochladen, Optimierung starten, Ergebnis als Excel herunterladen. +

+
+ +
+
+
+ +
+
+ + + + + + + + + + +
+ + {jobInProgress && ( + + )} +
+
+ +
+

{status}

+ {(timerRunning || elapsedSeconds > 0) && ( +

+ Laufzeit: {runtimeLabel} + {timerRunning ? " (läuft)" : " (fertig)"} +

+ )} + {jobId &&

Job-ID: {jobId}

} +

Solver: {jobSolver || solver}

+

Schrittweite: {jobStepSizeTonnes || stepSizeTonnes} t

+

MIP Gap: {jobMipGapPct || mipGapPct}%

+

+ Max Laufzeit: {jobMaxRuntimeMinutes || maxRuntimeMinutes} min +

+ {(jobId || logText) && ( +
+
+ Solver-Monitor (aus Log) +
+
+ {solverProgress.gapPct != null && ( + Gap: {solverProgress.gapPct.toFixed(2)}% + )} + {solverProgress.nodes != null && Nodes: {solverProgress.nodes}} + {solverProgress.bestPrimal != null && ( + Primal: {solverProgress.bestPrimal.toExponential(3)} + )} + {solverProgress.bestDual != null && ( + Dual: {solverProgress.bestDual.toExponential(3)} + )} + {solverProgress.solverStatus && ( + Status: {solverProgress.solverStatus} + )} + {solverProgress.gapPct == null && + solverProgress.nodes == null && + solverProgress.bestPrimal == null && + solverProgress.bestDual == null && + !solverProgress.solverStatus && ( + Keine strukturierten Fortschrittsdaten erkannt, siehe Solver-Log unten. + )} +
+
+ )} + {downloadUrl && ( + + Ergebnis herunterladen + + )} + {logText && ( + 
+              {logText}
+
+ )} +
+ + {(monthlyFlows || monthlyFlowError) && ( +
+
+
+

Monatliche Liefermengen

+

+ Visualisierung der Mengenströme Tagebau ↔ Kraftwerk/Veredlung auf Monatsbasis. +

+
+ {monthOptions.length > 0 && ( + + )} +
+ + {monthlyFlowError &&

{monthlyFlowError}

} + {!monthlyFlowError && activeMonth && grandTotal > 0 && ( +
+
+
+ Bedarfsverteilung + {monthLabel(activeMonth)} +
+ +
+ +
+
+ Verteilung je Kraftwerk / Veredlung + nach Tagebau, {monthLabel(activeMonth)} +
+ TARGET_LABELS[target]).reverse(), + automargin: true, + color: plotTheme.axis, + }, + legend: { orientation: "v", x: 1.02, y: 0.5 }, + annotations: targetBySourceAnnotations, + }} + config={{ responsive: true, displaylogo: false }} + style={{ width: "100%", height: "430px" }} + useResizeHandler + /> +
+ +
+
+ Verteilung je Tagebau + nach Kraftwerk / Veredlung, {monthLabel(activeMonth)} +
+ +
+
+ )} + {!monthlyFlowError && activeMonth && grandTotal <= 0 && ( +

Für den ausgewählten Monat sind keine Liefermengen vorhanden.

+ )} +
+ )} + + {(capacityData || capacityError) && ( +
+
+
+

Kapazitätsanalyse (Zeitreihen)

+

+ Nutzung vs. Kapazitätsgrenze für Förder-, Verlade- und Zugdurchlasskapazitäten. +

+
+
+ + {capacityError &&

{capacityError}

} + + {!capacityError && ( +
+ {[foerderPlot, verladungPlot, zugPlot].filter(Boolean).map((plot) => ( +
+
+ {plot.group.label} + + {plot.group.series.length > 1 ? "Serie auswählbar" : plot.series.label} + +
+ {plot.group.series.length > 1 && ( + + )} + +
+ ))} +
+ )} +
+ )} +
+
+ ); +} diff --git a/webapp/frontend/src/main.jsx b/webapp/frontend/src/main.jsx new file mode 100644 index 0000000..c893985 --- /dev/null +++ b/webapp/frontend/src/main.jsx @@ -0,0 +1,10 @@ +import React from "react"; +import { createRoot } from "react-dom/client"; +import App from "./App.jsx"; +import "./App.css"; + +createRoot(document.getElementById("root")).render( + + + +); diff --git a/webapp/frontend/vite.config.js b/webapp/frontend/vite.config.js new file mode 100644 index 0000000..a5152d7 --- /dev/null +++ b/webapp/frontend/vite.config.js @@ -0,0 +1,12 @@ +import { defineConfig } from "vite"; +import react from "@vitejs/plugin-react"; + +export default defineConfig({ + plugins: [react()], + server: { + port: 5173, + proxy: { + "/api": "http://localhost:8000", + }, + }, +});