8. 装饰器 + Cartopy 地图渲染¶

演示如何用 batch_cmaps 装饰器在 cartopy 地图上，用不同色带渲染真实的温度栅格数据。

导入公共模块¶

本 notebook 需要 cartopy 和 rasterio。如果未安装，代码单元格会跳过执行。

In [1]:

import numpy as np
import matplotlib.pyplot as plt
from multicolor import cmap, batch_cmaps, data_ensure

# Check optional dependencies
try:
    import cartopy.crs as ccrs
    import rasterio
    _has_deps = True
except ImportError:
    _has_deps = False
    print('cartopy or rasterio not installed. Install with: pip install cartopy rasterio')

import numpy as np import matplotlib.pyplot as plt from multicolor import cmap, batch_cmaps, data_ensure # Check optional dependencies try: import cartopy.crs as ccrs import rasterio _has_deps = True except ImportError: _has_deps = False print('cartopy or rasterio not installed. Install with: pip install cartopy rasterio')

cartopy or rasterio not installed. Install with: pip install cartopy rasterio

方式一：@batch_cmaps() — 渲染真实温度栅格¶

用不同发散色带渲染同一份温度数据。数据通过 data_ensure() 自动下载。

In [2]:

if not _has_deps:
    print('Skipping: requires cartopy and rasterio')
else:
    # 自动下载并获取本地路径
    tavg_path = data_ensure('tavg_10min')
    print(f'Data file: {tavg_path}')

    # 读取栅格数据和地理范围
    with rasterio.open(tavg_path) as src:
        temperature = src.read(1)
        temperature = np.ma.masked_equal(temperature, src.nodata)
        extent = [src.bounds.left, src.bounds.right, src.bounds.bottom, src.bounds.top]
        print(f'Shape: {temperature.shape}, dtype: {temperature.dtype}')

    # 用不同发散色带渲染
    @batch_cmaps(['Coolwarm', 'RdYlGn', 'RdBu'])
    def show_temperature_raster(cmap_obj, ax=None):
        if ax is None:
            fig = plt.figure(figsize=(10, 5))
            ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())
        im = ax.imshow(
            temperature,
            cmap=cmap_obj,
            extent=extent,
            transform=ccrs.PlateCarree(),
        )
        ax.coastlines(linewidth=0.5)
        ax.set_title(f'Average Temperature ({cmap_obj.name})', fontsize=11)
        plt.colorbar(im, ax=ax, orientation='horizontal', pad=0.05)


    show_temperature_raster()

if not _has_deps: print('Skipping: requires cartopy and rasterio') else: # 自动下载并获取本地路径 tavg_path = data_ensure('tavg_10min') print(f'Data file: {tavg_path}') # 读取栅格数据和地理范围 with rasterio.open(tavg_path) as src: temperature = src.read(1) temperature = np.ma.masked_equal(temperature, src.nodata) extent = [src.bounds.left, src.bounds.right, src.bounds.bottom, src.bounds.top] print(f'Shape: {temperature.shape}, dtype: {temperature.dtype}') # 用不同发散色带渲染 @batch_cmaps(['Coolwarm', 'RdYlGn', 'RdBu']) def show_temperature_raster(cmap_obj, ax=None): if ax is None: fig = plt.figure(figsize=(10, 5)) ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree()) im = ax.imshow( temperature, cmap=cmap_obj, extent=extent, transform=ccrs.PlateCarree(), ) ax.coastlines(linewidth=0.5) ax.set_title(f'Average Temperature ({cmap_obj.name})', fontsize=11) plt.colorbar(im, ax=ax, orientation='horizontal', pad=0.05) show_temperature_raster()

Skipping: requires cartopy and rasterio

方式二：@batch_cmaps() — 按查询 kwargs 渲染¶

直接传入筛选条件，不用手动 cmap.names()。

In [3]:

if not _has_deps:
    print('Skipping: requires cartopy and rasterio')
else:
    # 用 sequential 类型色带渲染最高气温数据
    tmax_path = data_ensure('tmax_10min')

    with rasterio.open(tmax_path) as src:
        tmax_data = src.read(1)
        tmax_data = np.ma.masked_equal(tmax_data, src.nodata)
        extent = [src.bounds.left, src.bounds.right, src.bounds.bottom, src.bounds.top]

    @batch_cmaps(cmap_type='sequential', colorblind_safe='any', limit=3)
    def show_tmax_raster(cmap_obj, ax=None):
        if ax is None:
            fig = plt.figure(figsize=(8, 5))
            ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())
        im = ax.imshow(
            tmax_data,
            cmap=cmap_obj,
            extent=extent,
            transform=ccrs.PlateCarree(),
        )
        ax.coastlines(linewidth=0.5)
        ax.set_title(f'Max Temperature ({cmap_obj.name})', fontsize=11)
        plt.colorbar(im, ax=ax, orientation='horizontal', pad=0.05)


    show_tmax_raster()

if not _has_deps: print('Skipping: requires cartopy and rasterio') else: # 用 sequential 类型色带渲染最高气温数据 tmax_path = data_ensure('tmax_10min') with rasterio.open(tmax_path) as src: tmax_data = src.read(1) tmax_data = np.ma.masked_equal(tmax_data, src.nodata) extent = [src.bounds.left, src.bounds.right, src.bounds.bottom, src.bounds.top] @batch_cmaps(cmap_type='sequential', colorblind_safe='any', limit=3) def show_tmax_raster(cmap_obj, ax=None): if ax is None: fig = plt.figure(figsize=(8, 5)) ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree()) im = ax.imshow( tmax_data, cmap=cmap_obj, extent=extent, transform=ccrs.PlateCarree(), ) ax.coastlines(linewidth=0.5) ax.set_title(f'Max Temperature ({cmap_obj.name})', fontsize=11) plt.colorbar(im, ax=ax, orientation='horizontal', pad=0.05) show_tmax_raster()

Skipping: requires cartopy and rasterio

方式三：@batch_cmaps() — 色盲安全色带对比¶

用 query_func 动态获取色盲安全色带，对比它们在真实数据上的效果。

In [4]:

if not _has_deps:
    print('Skipping: requires cartopy and rasterio')
else:
    # 用最低气温数据
    tmin_path = data_ensure('tmin_10min')

    with rasterio.open(tmin_path) as src:
        tmin_data = src.read(1)
        tmin_data = np.ma.masked_equal(tmin_data, src.nodata)
        extent = [src.bounds.left, src.bounds.right, src.bounds.bottom, src.bounds.top]

    @batch_cmaps(
        query_func=lambda: cmap.names(colorblind_safe='any', is_custom=False, limit=6),
        layout='single',
    )
    def show_cb_safe_raster(cmap_obj):
        fig = plt.figure(figsize=(8, 4))
        ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())
        im = ax.imshow(
            tmin_data,
            cmap=cmap_obj,
            extent=extent,
            transform=ccrs.PlateCarree(),
        )
        ax.coastlines(linewidth=0.3)
        ax.set_title(f'Min Temperature ({cmap_obj.name})', fontsize=10)
        plt.colorbar(im, ax=ax, orientation='horizontal', pad=0.05)


    show_cb_safe_raster()

if not _has_deps: print('Skipping: requires cartopy and rasterio') else: # 用最低气温数据 tmin_path = data_ensure('tmin_10min') with rasterio.open(tmin_path) as src: tmin_data = src.read(1) tmin_data = np.ma.masked_equal(tmin_data, src.nodata) extent = [src.bounds.left, src.bounds.right, src.bounds.bottom, src.bounds.top] @batch_cmaps( query_func=lambda: cmap.names(colorblind_safe='any', is_custom=False, limit=6), layout='single', ) def show_cb_safe_raster(cmap_obj): fig = plt.figure(figsize=(8, 4)) ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree()) im = ax.imshow( tmin_data, cmap=cmap_obj, extent=extent, transform=ccrs.PlateCarree(), ) ax.coastlines(linewidth=0.3) ax.set_title(f'Min Temperature ({cmap_obj.name})', fontsize=10) plt.colorbar(im, ax=ax, orientation='horizontal', pad=0.05) show_cb_safe_raster()

Skipping: requires cartopy and rasterio

方式四：@batch_cmaps() — 按 ID 渲染站点散点图¶

从数据库获取色带 ID，在地图上渲染模拟站点数据。

In [5]:

if not _has_deps:
    print('Skipping: requires cartopy and rasterio')
else:
    np.random.seed(42)
    n_stations = 50
    station_lons = np.random.uniform(-180, 180, n_stations)
    station_lats = np.random.uniform(-60, 60, n_stations)
    station_values = np.random.uniform(-10, 40, n_stations)

    # 获取前 3 个色带的 ID
    items = cmap.list(limit=3)
    print(f'IDs: {[item[0] for item in items]}')
    print(f'Names: {[item[1] for item in items]}')

    @batch_cmaps([item[0] for item in items])
    def show_station_scatter(cmap_obj, ax=None):
        if ax is None:
            fig = plt.figure(figsize=(10, 5))
            ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())
        sc = ax.scatter(
            station_lons,
            station_lats,
            c=station_values,
            cmap=cmap_obj,
            s=60,
            transform=ccrs.PlateCarree(),
            edgecolors='white',
            linewidth=0.5,
            vmin=-10,
            vmax=40,
        )
        ax.coastlines(linewidth=0.5)
        ax.set_title(f'Station Temperature ({cmap_obj.name})', fontsize=11)
        plt.colorbar(
            sc, ax=ax, orientation='horizontal', pad=0.05, label='Temperature'
        )


    show_station_scatter()

if not _has_deps: print('Skipping: requires cartopy and rasterio') else: np.random.seed(42) n_stations = 50 station_lons = np.random.uniform(-180, 180, n_stations) station_lats = np.random.uniform(-60, 60, n_stations) station_values = np.random.uniform(-10, 40, n_stations) # 获取前 3 个色带的 ID items = cmap.list(limit=3) print(f'IDs: {[item[0] for item in items]}') print(f'Names: {[item[1] for item in items]}') @batch_cmaps([item[0] for item in items]) def show_station_scatter(cmap_obj, ax=None): if ax is None: fig = plt.figure(figsize=(10, 5)) ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree()) sc = ax.scatter( station_lons, station_lats, c=station_values, cmap=cmap_obj, s=60, transform=ccrs.PlateCarree(), edgecolors='white', linewidth=0.5, vmin=-10, vmax=40, ) ax.coastlines(linewidth=0.5) ax.set_title(f'Station Temperature ({cmap_obj.name})', fontsize=11) plt.colorbar( sc, ax=ax, orientation='horizontal', pad=0.05, label='Temperature' ) show_station_scatter()

Skipping: requires cartopy and rasterio

方式五：@batch_cmaps() — grid 模式多子图¶

将不同色带画在一张多子图表格上，方便对比。

In [6]:

if not _has_deps:
    print('Skipping: requires cartopy and rasterio')
else:
    @batch_cmaps(
        ['Viridis', 'Plasma', 'Inferno', 'Cividis'],
        layout='grid',
        cols=2,
    )
    def show_grid_raster(cmap_obj, ax=None):
        im = ax.imshow(
            temperature,
            cmap=cmap_obj,
            extent=extent,
            transform=ccrs.PlateCarree(),
        )
        ax.coastlines(linewidth=0.3)
        ax.set_title(cmap_obj.name, fontsize=10)
        ax.set_global()


    show_grid_raster()

if not _has_deps: print('Skipping: requires cartopy and rasterio') else: @batch_cmaps( ['Viridis', 'Plasma', 'Inferno', 'Cividis'], layout='grid', cols=2, ) def show_grid_raster(cmap_obj, ax=None): im = ax.imshow( temperature, cmap=cmap_obj, extent=extent, transform=ccrs.PlateCarree(), ) ax.coastlines(linewidth=0.3) ax.set_title(cmap_obj.name, fontsize=10) ax.set_global() show_grid_raster()

Skipping: requires cartopy and rasterio