feat: 将模拟平台从西班牙 Demo 数据迁移至中国多省份场景
提交正文：
- simulator.py: 新增 SyntheticWeatherDataProvider，基于河南、黑龙江、湖北、
新疆、四川五省气候模板生成合成日气象数据
- simulator.py: 引入国内农事日历 CROP_CALENDAR_CN 与省份作物配置 PROVINCE_CROPS
- simulator.py: 移除对 GridWeatherDataProvider / AgroManagementDataProvider /
fetch_sitedata 的西班牙 Demo 数据依赖
- app.py: 侧边栏支持省份选择，年份范围扩展为 2019-2023
- app.py: 全面移除自定义 CSS，改用 Streamlit 原生组件（st.metric / st.info /
st.success / st.divider 等）简化界面
- app.py: 图表回归 Plotly 原生 add_vline，移除 hex_to_rgba / add_milestone_line
辅助函数
This commit is contained in:
zhenghu
392
simulator.py
392
simulator.py
@@ -1,37 +1,27 @@
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"""
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麦麦智农 - PCSE 模拟引擎封装
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麦麦智农 - PCSE 模拟引擎封装（国内场景版）
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基于 WOFOST 7.2 潜在生产（PP）与水分限制生产（WLP）模型
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"""
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import datetime
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import math
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import sqlite3
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from collections import namedtuple
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from typing import Literal
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import numpy as np
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import pandas as pd
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import pcse
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from pcse.models import Wofost72_PP, Wofost72_WLP_CWB
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from pcse.settings import settings
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from pcse.base import ParameterProvider
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from pcse.tests.db_input import (
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GridWeatherDataProvider,
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fetch_soildata,
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fetch_sitedata,
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fetch_cropdata,
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AgroManagementDataProvider,
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)
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from pcse.base import ParameterProvider, WeatherDataContainer, WeatherDataProvider
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from pcse.tests.db_input import fetch_soildata, fetch_cropdata
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from pcse.util import reference_ET
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def namedtuple_factory(cursor, row):
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fields = [column[0] for column in cursor.description]
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cls = namedtuple("Row", fields)
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return cls._make(row)
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# Demo 数据库配置
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# ─── 常量配置 ────────────────────────────────────────────────────────────────
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DB_PATH = settings.PCSE_USER_HOME + "/pcse.db"
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# 作物映射（crop_no -> 中文名/元数据）
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CROP_META = {
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1: {"name": "冬小麦", "emoji": "🌾", "color": "#c69c5d", "en": "WINTER WHEAT"},
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2: {"name": "玉米", "emoji": "🌽", "color": "#e8a93f", "en": "GRAIN MAIZE"},
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@@ -46,6 +36,129 @@ MODE_LABELS = {
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"wlp": "水分限制生产",
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}
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# 省份支持作物（黑龙江太冷，去掉越冬作物）
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PROVINCE_CROPS = {
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"河南": [1, 2, 3, 7, 10, 11],
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"黑龙江": [2, 3, 7, 11],
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"湖北": [1, 2, 3, 7, 10, 11],
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"新疆": [1, 2, 3, 7, 10, 11],
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"四川": [1, 2, 3, 7, 10, 11],
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}
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# 国内农事日历（播种月日 -> 收获月日）
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CROP_CALENDAR_CN = {
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"冬小麦": {
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"河南": {"start": (10, 15), "end": (5, 25)},
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"湖北": {"start": (10, 25), "end": (5, 15)},
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"新疆": {"start": (9, 20), "end": (6, 25)},
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"四川": {"start": (10, 20), "end": (5, 10)},
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},
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"玉米": {
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"河南": {"start": (6, 5), "end": (9, 25)},
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"黑龙江": {"start": (5, 1), "end": (9, 20)},
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"湖北": {"start": (3, 25), "end": (7, 20)},
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"新疆": {"start": (4, 15), "end": (9, 10)},
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"四川": {"start": (3, 20), "end": (7, 15)},
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},
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"春大麦": {
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"河南": {"start": (3, 1), "end": (5, 25)},
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"黑龙江": {"start": (4, 10), "end": (7, 15)},
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"湖北": {"start": (2, 15), "end": (5, 20)},
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"新疆": {"start": (3, 25), "end": (7, 10)},
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"四川": {"start": (2, 20), "end": (5, 15)},
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},
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"马铃薯": {
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"河南": {"start": (2, 25), "end": (5, 30)},
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"黑龙江": {"start": (4, 20), "end": (8, 15)},
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"湖北": {"start": (1, 15), "end": (5, 10)},
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"新疆": {"start": (4, 1), "end": (7, 25)},
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"四川": {"start": (1, 20), "end": (5, 5)},
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},
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"冬油菜": {
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"河南": {"start": (9, 15), "end": (5, 15)},
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"湖北": {"start": (9, 20), "end": (5, 5)},
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"新疆": {"start": (8, 25), "end": (6, 15)},
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"四川": {"start": (9, 10), "end": (4, 25)},
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},
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"向日葵": {
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"河南": {"start": (5, 25), "end": (8, 30)},
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"黑龙江": {"start": (5, 15), "end": (9, 10)},
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"湖北": {"start": (4, 5), "end": (8, 5)},
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"新疆": {"start": (4, 20), "end": (8, 20)},
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"四川": {"start": (3, 25), "end": (7, 25)},
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},
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}
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# 省份气候模板（每月 [TMAX, TMIN, RAIN(mm/day), IRRAD(kJ/m2/day)]）
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PROVINCES = {
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"河南": {
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"lat": 34.76, "lon": 113.65, "elev": 110,
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"rh": 0.65, "wind_base": 2.0,
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"climate": [
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[5, -4, 0.5, 9000], [9, -1, 0.6, 12000], [15, 4, 1.0, 16000],
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[22, 10, 1.5, 19000], [28, 16, 2.0, 22000], [33, 21, 2.5, 24000],
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[32, 23, 4.0, 21000], [31, 22, 3.5, 19000], [27, 17, 2.0, 16000],
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[22, 11, 1.2, 13000], [14, 3, 0.8, 9000], [7, -2, 0.4, 8000],
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],
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},
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"黑龙江": {
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"lat": 45.80, "lon": 126.53, "elev": 150,
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"rh": 0.60, "wind_base": 3.0,
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"climate": [
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[-13, -24, 0.2, 6000], [-8, -20, 0.2, 9000], [2, -9, 0.4, 14000],
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[13, 1, 1.0, 18000], [21, 8, 1.8, 22000], [26, 15, 3.0, 24000],
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[28, 18, 4.5, 22000], [26, 16, 4.0, 19000], [20, 9, 1.5, 15000],
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[11, 1, 0.8, 11000], [-1, -10, 0.4, 7000], [-11, -21, 0.3, 5000],
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],
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},
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"湖北": {
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"lat": 30.59, "lon": 114.31, "elev": 30,
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"rh": 0.75, "wind_base": 1.8,
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"climate": [
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[8, 0, 1.5, 8000], [11, 3, 1.8, 10000], [16, 7, 2.5, 13000],
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[22, 13, 3.0, 16000], [27, 18, 3.5, 19000], [30, 22, 4.5, 20000],
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[33, 25, 5.5, 22000], [33, 25, 4.0, 21000], [29, 21, 3.0, 17000],
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[24, 15, 2.0, 14000], [17, 8, 1.5, 10000], [11, 2, 1.0, 8000],
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],
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},
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"新疆": {
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"lat": 44.30, "lon": 87.60, "elev": 450,
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"rh": 0.45, "wind_base": 2.5,
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"climate": [
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[-8, -16, 0.1, 7000], [-4, -12, 0.2, 11000], [6, -2, 0.3, 16000],
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[18, 7, 0.5, 20000], [25, 13, 0.8, 24000], [30, 18, 1.0, 26000],
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[32, 20, 1.2, 27000], [30, 18, 0.8, 25000], [24, 12, 0.5, 20000],
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[15, 4, 0.3, 15000], [3, -4, 0.2, 9000], [-5, -13, 0.1, 6000],
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],
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},
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"四川": {
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"lat": 30.67, "lon": 104.07, "elev": 500,
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"rh": 0.80, "wind_base": 1.2,
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"climate": [
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[10, 3, 0.6, 6000], [13, 5, 0.8, 8000], [17, 9, 1.2, 11000],
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[23, 14, 2.0, 13000], [27, 18, 3.0, 15000], [29, 21, 4.0, 16000],
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[31, 23, 5.0, 17000], [31, 23, 4.5, 16000], [27, 19, 3.0, 12000],
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[22, 15, 1.5, 9000], [16, 10, 1.0, 7000], [11, 5, 0.5, 6000],
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],
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},
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}
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SITEDATA = {
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"IFUNRN": 0.0,
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"SSMAX": 0.0,
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"NOTINF": 0.0,
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"SSI": 0.0,
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"WAV": 100.0,
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"SMLIM": 0.0,
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}
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# ─── 数据库辅助 ───────────────────────────────────────────────────────────────
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def namedtuple_factory(cursor, row):
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fields = [column[0] for column in cursor.description]
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cls = namedtuple("Row", fields)
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return cls._make(row)
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def get_db_conn():
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conn = sqlite3.connect(DB_PATH)
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@@ -53,68 +166,172 @@ def get_db_conn():
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return conn
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def list_available_years(grid_no: int = 31031) -> list[int]:
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"""返回 Demo 数据库中指定 grid 有气象数据的年份列表。"""
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conn = get_db_conn()
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c = conn.cursor()
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rows = c.execute(
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"SELECT DISTINCT strftime('%Y', day) as year FROM grid_weather WHERE grid_no=? ORDER BY year",
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(grid_no,),
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).fetchall()
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conn.close()
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return [int(r.year) for r in rows]
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# ─── 气象数据生成器 ───────────────────────────────────────────────────────────
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class SyntheticWeatherDataProvider(WeatherDataProvider):
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"""基于省份气候模板和年份生成合成日气象数据。"""
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angstA = 0.29
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angstB = 0.49
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def __init__(self, province: str, start_date: datetime.date, end_date: datetime.date, seed: int):
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WeatherDataProvider.__init__(self)
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self.province = PROVINCES[province]
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self.latitude = self.province["lat"]
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self.longitude = self.province["lon"]
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self.elevation = self.province["elev"]
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self.description = f"Synthetic weather for {province}"
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rng = np.random.default_rng(seed)
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days = (end_date - start_date).days + 1
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days_in_month = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
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if (start_date.year % 4 == 0 and start_date.year % 100 != 0) or (start_date.year % 400 == 0):
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days_in_month[1] = 29
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for i in range(days):
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day = start_date + datetime.timedelta(days=i)
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month = day.month - 1
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next_month = (month + 1) % 12
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d = day.day - 1
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frac = (d + 0.5) / days_in_month[month]
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w2 = 0.5 - 0.5 * math.cos(math.pi * frac)
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w1 = 1 - w2
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c = self.province["climate"][month]
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c_next = self.province["climate"][next_month]
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tmax_base = c[0] * w1 + c_next[0] * w2
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tmin_base = c[1] * w1 + c_next[1] * w2
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rain_mean = c[2] * w1 + c_next[2] * w2
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irrad_base = (c[3] * w1 + c_next[3] * w2) * 1000.0 # kJ -> J
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tmax = tmax_base + rng.normal(0, 1.5)
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tmin = tmin_base + rng.normal(0, 1.5)
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tmin = min(tmin, tmax - 1.0)
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irrad = max(0, irrad_base * (1 + rng.normal(0, 0.1)))
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p_rain = min(0.8, max(0.05, rain_mean / 5.0))
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if rng.random() < p_rain:
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scale = rain_mean / (2 * p_rain)
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rain = rng.gamma(2, scale) if scale > 0 else 0.0
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else:
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rain = 0.0
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rain = max(0.0, rain)
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tmean = (tmax + tmin) / 2.0
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es = 0.6108 * math.exp(17.27 * tmean / (tmean + 237.3))
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rh = self.province["rh"] + rng.normal(0, 0.05)
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rh = max(0.2, min(0.95, rh))
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vap = es * rh * 10.0
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wind = max(0.5, self.province["wind_base"] + rng.normal(0, 0.8))
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e0, es0, et0 = reference_ET(
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DAY=day, LAT=self.latitude, ELEV=self.elevation,
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TMIN=float(tmin), TMAX=float(tmax), IRRAD=float(irrad),
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VAP=float(vap), WIND=float(wind),
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ANGSTA=self.angstA, ANGSTB=self.angstB,
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)
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t = {
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"DAY": day,
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"LAT": self.latitude,
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"LON": self.longitude,
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"ELEV": self.elevation,
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"TMAX": float(tmax),
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"TMIN": float(tmin),
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"VAP": float(vap),
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"WIND": float(wind),
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"RAIN": float(rain) / 10.0,
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"IRRAD": float(irrad),
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"E0": float(e0) / 10.0,
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"ES0": float(es0) / 10.0,
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"ET0": float(et0) / 10.0,
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"SNOWDEPTH": 0.0,
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}
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wdc = WeatherDataContainer(**t)
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self._store_WeatherDataContainer(wdc, day)
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def list_available_crops(grid_no: int = 31031, year: int = 2000) -> list[dict]:
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"""返回指定 grid 与年份有作物日历的作物列表。"""
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conn = get_db_conn()
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c = conn.cursor()
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rows = c.execute(
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"SELECT crop_no FROM crop_calendar WHERE grid_no=? AND year=? ORDER BY crop_no",
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(grid_no, year),
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).fetchall()
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conn.close()
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result = []
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for r in rows:
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meta = CROP_META.get(r.crop_no)
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if meta:
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result.append({"crop_no": r.crop_no, **meta})
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return result
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# ─── 农事管理构造 ───────────────────────────────────────────────────────────
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def build_agromanagement(crop_no: int, province: str, year: int):
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meta = CROP_META[crop_no]
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crop_name = meta["en"]
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cal = CROP_CALENDAR_CN[meta["name"]][province]
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sm, sd = cal["start"]
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em, ed = cal["end"]
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start_date = datetime.date(year, sm, sd)
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end_date = datetime.date(year, em, ed)
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if end_date < start_date:
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end_date = datetime.date(year + 1, em, ed)
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campaign_start = datetime.date(year, 1, 1)
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else:
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campaign_start = datetime.date(year, 1, 1)
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return [{
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campaign_start: {
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"CropCalendar": {
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"crop_name": crop_name,
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"variety_name": f"{crop_name}_{province}_{year}",
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"crop_start_date": start_date,
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"crop_start_type": "emergence",
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"crop_end_date": end_date,
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"crop_end_type": "earliest",
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"max_duration": 365,
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},
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"TimedEvents": None,
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"StateEvents": None,
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}
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}]
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# ─── 公开接口 ───────────────────────────────────────────────────────────────
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def list_available_provinces() -> list[str]:
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return list(PROVINCES.keys())
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def list_available_crops(province: str) -> list[dict]:
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return [{"crop_no": c, **CROP_META[c]} for c in PROVINCE_CROPS.get(province, [])]
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def run_wofost(
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grid_no: int = 31031,
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crop_no: int = 1,
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year: int = 2000,
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province: str,
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crop_no: int,
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year: int,
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mode: Literal["pp", "wlp"] = "wlp",
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) -> dict:
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"""
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运行 WOFOST 模拟，返回包含输出时间序列与关键指标的字典。
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"""
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agromanagement = build_agromanagement(crop_no, province, year)
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# 确定气象起止日期（覆盖整个生育期 + 前后缓冲）
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campaign = agromanagement[0]
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campaign_date = list(campaign.keys())[0]
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cc = campaign[campaign_date]["CropCalendar"]
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start = campaign_date - datetime.timedelta(days=30)
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end = cc["crop_end_date"] + datetime.timedelta(days=30)
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seed = year * 1000 + hash(province) % 100000
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wdp = SyntheticWeatherDataProvider(province, start, end, seed)
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conn = get_db_conn()
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try:
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agromanagement = AgroManagementDataProvider(conn, grid_no, crop_no, year)
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sited = fetch_sitedata(conn, grid_no, year)
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cropd = fetch_cropdata(conn, grid_no, year, crop_no)
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soild = fetch_soildata(conn, grid_no)
|
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parvalues = ParameterProvider(sitedata=sited, soildata=soild, cropdata=cropd)
|
||||
wdp = GridWeatherDataProvider(conn, grid_no=grid_no)
|
||||
|
||||
if mode == "pp":
|
||||
wofsim = Wofost72_PP(parvalues, wdp, agromanagement)
|
||||
else:
|
||||
wofsim = Wofost72_WLP_CWB(parvalues, wdp, agromanagement)
|
||||
|
||||
wofsim.run_till_terminate()
|
||||
output = wofsim.get_output()
|
||||
# 作物/土壤参数复用 PCSE Demo DB（生理参数通用）
|
||||
cropd = fetch_cropdata(conn, 31031, 2000, crop_no)
|
||||
soild = fetch_soildata(conn, 31031)
|
||||
parvalues = ParameterProvider(sitedata=SITEDATA, soildata=soild, cropdata=cropd)
|
||||
finally:
|
||||
conn.close()
|
||||
|
||||
if mode == "pp":
|
||||
wofsim = Wofost72_PP(parvalues, wdp, agromanagement)
|
||||
else:
|
||||
wofsim = Wofost72_WLP_CWB(parvalues, wdp, agromanagement)
|
||||
|
||||
wofsim.run_till_terminate()
|
||||
output = wofsim.get_output()
|
||||
df = pd.DataFrame(output)
|
||||
if not df.empty and "day" in df.columns:
|
||||
df = df.set_index("day")
|
||||
|
||||
# 关键指标
|
||||
if not df.empty:
|
||||
last = df.iloc[-1]
|
||||
max_lai = df["LAI"].max() if "LAI" in df.columns else None
|
||||
@@ -126,28 +343,27 @@ def run_wofost(
|
||||
else:
|
||||
max_lai = tagp = twso = sm_end = dvs_end = duration = None
|
||||
|
||||
# 生育期里程碑（基于 DVS）
|
||||
milestones = {}
|
||||
crop_start = cc["crop_start_date"]
|
||||
crop_end = cc["crop_end_date"]
|
||||
if not df.empty and "DVS" in df.columns:
|
||||
dvs_series = df["DVS"]
|
||||
# 出苗/开始生长 DVS ~0（实际在 calendar 开始日）
|
||||
# 开花 DVS ~1.0
|
||||
# 成熟 DVS ~2.0
|
||||
flowering = dvs_series[dvs_series >= 1.0]
|
||||
if not flowering.empty:
|
||||
milestones["flowering"] = flowering.index[0]
|
||||
maturity = dvs_series[dvs_series >= 2.0]
|
||||
if not maturity.empty:
|
||||
milestones["maturity"] = maturity.index[0]
|
||||
# 开始日
|
||||
milestones["start"] = df.index[0]
|
||||
milestones["end"] = df.index[-1]
|
||||
milestones["start"] = crop_start
|
||||
milestones["end"] = df.index[-1] if df.index[-1] <= crop_end else crop_end
|
||||
|
||||
actual_duration = (milestones.get("end", crop_end) - crop_start).days + 1 if milestones else None
|
||||
|
||||
meta = CROP_META.get(crop_no, {"name": "未知作物", "emoji": "🌱", "color": "#888"})
|
||||
|
||||
return {
|
||||
"meta": {
|
||||
"grid_no": grid_no,
|
||||
"province": province,
|
||||
"crop_no": crop_no,
|
||||
"year": year,
|
||||
"mode": mode,
|
||||
@@ -156,30 +372,27 @@ def run_wofost(
|
||||
},
|
||||
"df": df,
|
||||
"summary": {
|
||||
"tagp": tagp, # 总地上生物量 kg/ha
|
||||
"twso": twso, # 经济产量 kg/ha
|
||||
"tagp": tagp,
|
||||
"twso": twso,
|
||||
"max_lai": max_lai,
|
||||
"sm_end": sm_end,
|
||||
"dvs_end": dvs_end,
|
||||
"duration": duration,
|
||||
"duration": actual_duration,
|
||||
"milestones": milestones,
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
def run_multi_crop(
|
||||
grid_no: int = 31031,
|
||||
year: int = 2000,
|
||||
province: str,
|
||||
year: int,
|
||||
mode: Literal["pp", "wlp"] = "wlp",
|
||||
) -> list[dict]:
|
||||
"""
|
||||
对指定年份所有可用作物运行模拟，返回各作物汇总结果列表（按 TWSO 排序）。
|
||||
"""
|
||||
crops = list_available_crops(grid_no, year)
|
||||
crops = list_available_crops(province)
|
||||
results = []
|
||||
for c in crops:
|
||||
try:
|
||||
res = run_wofost(grid_no=grid_no, crop_no=c["crop_no"], year=year, mode=mode)
|
||||
res = run_wofost(province=province, crop_no=c["crop_no"], year=year, mode=mode)
|
||||
results.append({
|
||||
"crop_no": c["crop_no"],
|
||||
"name": c["name"],
|
||||
@@ -191,26 +404,23 @@ def run_multi_crop(
|
||||
"duration": res["summary"]["duration"],
|
||||
})
|
||||
except Exception:
|
||||
# 某些作物可能因数据缺失导致模拟失败，静默跳过
|
||||
continue
|
||||
results.sort(key=lambda x: (x["twso"] if x["twso"] is not None else -1), reverse=True)
|
||||
return results
|
||||
|
||||
|
||||
def run_multi_year(
|
||||
grid_no: int = 31031,
|
||||
crop_no: int = 1,
|
||||
province: str,
|
||||
crop_no: int,
|
||||
mode: Literal["pp", "wlp"] = "wlp",
|
||||
years: list[int] | None = None,
|
||||
) -> list[dict]:
|
||||
"""
|
||||
对指定作物在所有可用年份运行模拟，返回各年份汇总结果列表。
|
||||
"""
|
||||
years = list_available_years(grid_no)
|
||||
if years is None:
|
||||
years = list(range(2019, 2024))
|
||||
results = []
|
||||
meta = CROP_META.get(crop_no, {"name": "未知作物", "emoji": "🌱", "color": "#888"})
|
||||
for year in years:
|
||||
try:
|
||||
res = run_wofost(grid_no=grid_no, crop_no=crop_no, year=year, mode=mode)
|
||||
res = run_wofost(province=province, crop_no=crop_no, year=year, mode=mode)
|
||||
results.append({
|
||||
"year": year,
|
||||
"tagp": res["summary"]["tagp"],
|
||||
|
||||
Reference in New Issue
Block a user