264 lines
12 KiB
Python
264 lines
12 KiB
Python
import json
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import os
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import matplotlib
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import pandas as pd
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import solara
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from mesa.visualization import SolaraViz, make_plot_component
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from simulation_model import SimulationModel
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# Configure matplotlib to support Chinese labels.
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matplotlib.rcParams["font.family"] = ["Microsoft YaHei", "SimHei", "sans-serif"]
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matplotlib.rcParams["axes.unicode_minus"] = False
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# set year
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year = json.load(open('year.json', 'r', encoding='utf-8'))['year']
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filename = f"{year}"
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# Load default parameters from data/model_params.json
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with open(f"data/{filename}/model_params.json", "r", encoding="utf-8") as f:
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_cfg = json.load(f)
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def _load_factory_name_map():
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try:
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with open(f"data/{filename}/factory_mapping.json", "r", encoding="utf-8") as f:
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data = json.load(f)
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except FileNotFoundError:
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return {}
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# data is Chinese name -> code; invert it
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return {v: k for k, v in data.items()}
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FACTORY_NAME_MAP = _load_factory_name_map()
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def _load_product_catalog():
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try:
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try:
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df = pd.read_csv(f"data/{filename}/product.csv", encoding="utf-8")
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except UnicodeDecodeError:
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df = pd.read_csv(f"data/{filename}/product.csv", encoding="gbk")
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except FileNotFoundError:
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return pd.DataFrame()
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df.columns = [c.strip() for c in df.columns]
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rename_map = {
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df.columns[0]: "平台",
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df.columns[1]: "标准机型",
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df.columns[2]: "叶片型号",
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}
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df = df.rename(columns=rename_map)
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df["平台"] = df["平台"].astype(str).str.strip()
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df["标准机型"] = df["标准机型"].astype(str).str.strip()
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df["叶片型号"] = df["叶片型号"].astype(str).str.strip()
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return df
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PRODUCT_DF = _load_product_catalog()
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ALL_PLATFORMS = sorted(PRODUCT_DF["平台"].dropna().unique()) if not PRODUCT_DF.empty else []
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ALL_STANDARDS = sorted(PRODUCT_DF["标准机型"].dropna().unique()) if not PRODUCT_DF.empty else []
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def _platform_options():
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return ["全部"] + ALL_PLATFORMS
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def _standard_options(platform):
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if PRODUCT_DF.empty:
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return ["全部"]
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if platform and platform != "全部":
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stds = sorted(PRODUCT_DF.loc[PRODUCT_DF["平台"] == platform, "标准机型"].dropna().unique())
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else:
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stds = ALL_STANDARDS
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return ["全部"] + [s for s in stds if s]
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def _product_set_for_selection(platform, standard):
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if PRODUCT_DF.empty:
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return []
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df = PRODUCT_DF
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if platform and platform != "全部":
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df = df[df["平台"] == platform]
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if standard and standard != "全部":
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df = df[df["标准机型"] == standard]
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return sorted(df["叶片型号"].dropna().unique())
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def _legend_postprocess(label_map, ylabel=None):
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def _post(ax):
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handles, labels = ax.get_legend_handles_labels()
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if labels:
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mapped = [label_map.get(label, label) for label in labels]
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ax.legend(handles, mapped, loc="best")
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if ylabel:
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ax.set_ylabel(ylabel)
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return _post
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def _split_product_set(text: str):
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return [p.strip() for p in text.split(",") if p.strip()]
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@solara.component
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def Page():
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platform_selected = solara.use_reactive("全部")
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standard_selected = solara.use_reactive("全部")
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initial_product_set_text = ",".join(_cfg.get("product_set", []))
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product_set_value = solara.use_reactive(initial_product_set_text)
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def sync_product_set(platform_val, standard_val):
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blades = _product_set_for_selection(platform_val, standard_val)
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if blades:
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product_set_value.set(",".join(blades))
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elif PRODUCT_DF.empty:
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product_set_value.set(initial_product_set_text)
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else:
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product_set_value.set("")
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def on_platform_change(val):
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platform_selected.set(val)
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std_opts = _standard_options(val)
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if standard_selected.value not in std_opts:
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standard_selected.set("全部")
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active_std = "全部"
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else:
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active_std = standard_selected.value
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sync_product_set(val, active_std)
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def on_standard_change(val):
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standard_selected.set(val)
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sync_product_set(platform_selected.value, val)
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# Build model params fresh each render so product_set value stays in sync
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model_params = {
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"product_set": {"type": "InputText", "value": product_set_value, "label": "产品集合（逗号分隔）"},
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"is_within_region_allocation_only": {
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"type": "Select",
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"value": bool(_cfg.get("is_within_region_allocation_only", False)),
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"values": [False, True],
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"label": "仅区域内调拨？（布尔）",
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},
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"month1": {"type": "InputText", "value": _cfg.get("month1", 220), "label": "第1个月效率（小时/支）"},
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"month2": {"type": "InputText", "value": _cfg.get("month2", 100), "label": "第2个月效率（小时/支）"},
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"month3": {"type": "InputText", "value": _cfg.get("month3", 45), "label": "第3个月效率（小时/支）"},
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"month4": {"type": "InputText", "value": _cfg.get("month4", 36), "label": "第4个月及以后效率（小时/支）"},
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"holiday_days_1": {"type": "InputText", "value": _cfg.get("holiday_days_1", 2), "label": "1月假日天数（天）"},
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"holiday_days_2": {"type": "InputText", "value": _cfg.get("holiday_days_2", 2), "label": "2月假日天数（天）"},
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"holiday_days_3": {"type": "InputText", "value": _cfg.get("holiday_days_3", 2), "label": "3月假日天数（天）"},
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"holiday_days_4": {"type": "InputText", "value": _cfg.get("holiday_days_4", 2), "label": "4月假日天数（天）"},
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"holiday_days_5": {"type": "InputText", "value": _cfg.get("holiday_days_5", 2), "label": "5月假日天数（天）"},
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"holiday_days_6": {"type": "InputText", "value": _cfg.get("holiday_days_6", 2), "label": "6月假日天数（天）"},
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"holiday_days_7": {"type": "InputText", "value": _cfg.get("holiday_days_7", 2), "label": "7月假日天数（天）"},
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"holiday_days_8": {"type": "InputText", "value": _cfg.get("holiday_days_8", 2), "label": "8月假日天数（天）"},
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"holiday_days_9": {"type": "InputText", "value": _cfg.get("holiday_days_9", 2), "label": "9月假日天数（天）"},
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"holiday_days_10": {"type": "InputText", "value": _cfg.get("holiday_days_10", 2), "label": "10月假日天数（天）"},
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"holiday_days_11": {"type": "InputText", "value": _cfg.get("holiday_days_11", 2), "label": "11月假日天数（天）"},
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"holiday_days_12": {"type": "InputText", "value": _cfg.get("holiday_days_12", 2), "label": "12月假日天数（天）"},
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}
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for key, val in _cfg.items():
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if key.startswith("factor_"):
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suffix = key.replace("factor_", "")
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label_name = FACTORY_NAME_MAP.get(suffix, suffix)
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model_params[key] = {"type": "InputText", "value": val, "label": f"{label_name} 磨合系数（倍率）"}
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active_model = solara.use_memo(
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lambda: SimulationModel(product_set=_split_product_set(product_set_value.value)),
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dependencies=[product_set_value.value],
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)
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production_label_map = {region: f"{region}-产出[套]" for region in active_model.region_names}
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demand_label_map = {f"demand_{region}": f"{region}-需求[套]" for region in active_model.demand_regions}
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inventory_label_map = {f"inventory_{region}": f"{region}-库存[套]" for region in active_model.region_names}
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fulfill_pct_label_map = {f"fulfill_pct_{region}": f"{region}-满足率[%]" for region in active_model.demand_regions}
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unmet_label_map = {f"unmet_{region}": f"{region}-未满足需求[套]" for region in active_model.demand_regions}
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overall_fulfill_label_map = {"fulfill_pct_overall": "总体满足率[%]"}
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transport_cost_label_map = {"transport_cost": "运输成本[万元]"}
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fulfill_month_label_map = {f"fulfill_month_{region}": f"{region}-当月满足率[%]" for region in active_model.demand_regions}
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fulfill_cum_label_map = {f"fulfill_cum_{region}": f"{region}-累计满足率[%]" for region in active_model.demand_regions}
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transport_units_label_map = {f"transport_units_{region}": f"{region}-运输量[套]" for region in active_model.demand_regions}
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RegionPlot = make_plot_component(
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{region: f"C{idx}" for idx, region in enumerate(active_model.region_names)},
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post_process=_legend_postprocess(production_label_map, ylabel="产出[套]"),
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)
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DemandPlot = make_plot_component(
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{f"demand_{region}": f"C{idx}" for idx, region in enumerate(active_model.demand_regions)},
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post_process=_legend_postprocess(demand_label_map, ylabel="需求量[套]"),
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)
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InventoryPlot = make_plot_component(
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{f"inventory_{region}": f"C{idx}" for idx, region in enumerate(active_model.region_names)},
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post_process=_legend_postprocess(inventory_label_map, ylabel="库存量[套]"),
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)
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FulfillPlot = make_plot_component(
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{f"fulfill_pct_{region}": f"C{idx}" for idx, region in enumerate(active_model.demand_regions)},
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post_process=_legend_postprocess(fulfill_pct_label_map, ylabel="满足率[%]"),
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)
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UnmetPlot = make_plot_component(
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{f"unmet_{region}": f"C{idx}" for idx, region in enumerate(active_model.demand_regions)},
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post_process=_legend_postprocess(unmet_label_map, ylabel="未满足需求[套]"),
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)
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OverallFulfillPlot = make_plot_component(
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{"fulfill_pct_overall": "tab:purple"},
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post_process=_legend_postprocess(overall_fulfill_label_map, ylabel="总体满足率[%]"),
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)
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TransportCostPlot = make_plot_component(
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{"transport_cost": "tab:brown"},
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post_process=_legend_postprocess(transport_cost_label_map, ylabel="运输成本[万元]"),
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)
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FulfillMonthPlot = make_plot_component(
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{f"fulfill_month_{region}": f"C{idx}" for idx, region in enumerate(active_model.demand_regions)},
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post_process=_legend_postprocess(fulfill_month_label_map, ylabel="当月满足率[%]"),
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)
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FulfillCumPlot = make_plot_component(
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{f"fulfill_cum_{region}": f"C{idx}" for idx, region in enumerate(active_model.demand_regions)},
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post_process=_legend_postprocess(fulfill_cum_label_map, ylabel="累计满足率[%]"),
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)
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TransportUnitsPlot = make_plot_component(
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{f"transport_units_{region}": f"C{idx}" for idx, region in enumerate(active_model.demand_regions)},
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post_process=_legend_postprocess(transport_units_label_map, ylabel="运输量[套]"),
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)
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def get_total_production(model):
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return solara.Markdown(f"**累计产量（机组）：{model.cumulative_production:.2f}**")
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def get_mean_abs_error(model):
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return solara.Markdown(f"**工厂年产量均值绝对误差：{model.mean_abs_error:.2f}**")
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with solara.Column() as main:
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with solara.Card("产品筛选"):
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solara.Select(
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label="平台",
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value=platform_selected,
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values=_platform_options(),
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on_value=on_platform_change,
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)
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solara.Select(
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label="标准机型",
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value=standard_selected,
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values=_standard_options(platform_selected.value),
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on_value=on_standard_change,
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)
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solara.Text(f"叶片型号数：{len(_split_product_set(product_set_value.value))}")
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SolaraViz(
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active_model,
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renderer=None,
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components=[
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RegionPlot,
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DemandPlot,
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InventoryPlot,
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FulfillPlot,
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UnmetPlot,
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OverallFulfillPlot,
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TransportCostPlot,
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FulfillMonthPlot,
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FulfillCumPlot,
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TransportUnitsPlot,
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get_total_production,
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get_mean_abs_error,
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],
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model_params=model_params,
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name="产线生产与需求可视化",
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show_parameter_values=True,
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)
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return main
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page = Page()
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