读取中国气象数据网的一个降水产品的二进制文件数据
一个名为中国自动站与CMORPH降水产品融合的逐时降水量网格数据集（1.0版）的数据产品，官网提供了用GrADS读取的.ctl文件，但是我没装也不会GrADS，就有了这次实践。
分析虽然不会GrADS，但根据提供的.ctl文件：
DSET ^SEVP_CLI_CHN_MERGE_FY2_PRE_HOUR_GRID_0.10-%y4%m2%d2%h2.grd*UNDEF -999.0*OPTIONS little_endian template*TITLE China Hourly Merged Precipitation Analysis*xdef 700 linear 70.05 0.10*ydef 440 linear 15.05 0.10 *ZDEF 1 LEVELS 1 *TDEF 9999 LINEAR 00Z01Aug2010 1hr *VARS 2 crain 1 00 CH01 combined analysis (mm/Hour)gsamp 1 00 CH02 gauge numbersENDVARS可直接获得信息：
存储方式是小端存储空间范围为：70.05°E~139.95°E，15.05°N~58.95°N，间隔0.1°有两个变量：降雨量crain和雨量计数量gsamp可推测而得的信息：
结合文件的大小为2464000字节，可推测每个数据为2464000/(700*440*2)=4字节结合示例图图例可知数据应该是浮点数（文档中没有找到数据类型的描述） 实现读取该产品数据的函数如下：
import numpy as npdef read(filename): """ Args: filename: 文件名 Returns: crain: 降水量mm <numpy.ndarray> gsamp: 雨量计数量 <numpy.ndarray> """ data = np.fromfile(filename, dtype="<f4") data = data.reshape((880, 700), order='C')[::-1] gsamp = data[:440] crain = data[440:] return crain, gsamp需要注意的是：
经过尝试，数组存储顺序是右侧为先（C-like）数据的存储顺序是自西向东、自南向北、自crain向gsamp，直接读取时矩阵为上南下北，为了方便绘图等操作已经按习惯调整为上北下南将读取的绘制出来与官网提供的gif对比如下：
Python的标准库struct就可以读取二进制数据，而当数据是整齐的矩阵时，numpy的.fromfile()方法可以直接读取成<numpy.ndarray>，方便后续的处理。
其实出图花了我一天的时间研究matplotlib很多细节，好想整理出来啊，但是我好~懒~啊~，下次吧。这次先贴下出图的源码：
import matplotlib.pyplot as pltimport matplotlib.transforms as mtransformsimport matplotlib as mplimport cartopy.crs as ccrsimport cartopy.io.shapereader as shpreaderfrom cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatterfrom matplotlib.font_manager import FontPropertiesfilename = (r"..dataSURF_CLI_CHN_MERGE_CMP_PRE_HOUR_GRID_0.10" r"surf_cli_chn_merge_cmp_pre_hour_grid_0.10" r"SURF_CLI_CHN_MERGE_CMP_PRE_HOUR_GRID_0.10-2018081707.grd")crain, gsamp = read(filename)crain[crain==-999] = np.nanshpname = r"..datamapChina_province"provinces_records = list(shpreader.Reader(shpname).records())provinces_geometrys = [x.geometry for x in provinces_records]font = FontProperties(fname=r"C:WindowsFontssimsun.ttc")PlateCarree = ccrs.PlateCarree()fig = plt.figure("Demo", figsize=(9, 6), dpi=100, constrained_layout=True )ax = plt.axes(projection=PlateCarree)ax.add_geometries(provinces_geometrys, PlateCarree, edgecolor="gray", facecolor='None')extent=[70.05, 139.95, 15.05, 59.95]cmap = mpl.colors.ListedColormap(["cyan", "darkcyan", "green", "lime", "yellow", "gold", "goldenrod", "darkred", "brown", "red", "darkviolet"])cmap.set_over("indigo")cmap.set_under('white')bounds = [0.1, 0.5, 1, 2, 3, 4, 5, 6, 8, 10, 20, 40]norm = mpl.colors.BoundaryNorm(bounds, cmap.N)im = ax.imshow(crain, transform=PlateCarree, origin='upper', extent=[70, 139.95, 15, 59.95], cmap=cmap, norm=norm, aspect=1.2)cb = fig.colorbar(im, ax=ax, extend="both", extendrect=True, ticks=bounds, extendfrac="auto", pad=0.01, shrink=0.8, aspect=40, fraction=0.01)cb.set_label("mm")ax.set_xlim((72, 135))ax.set_ylim((18, 55))ax.set_xticks(list(range(75, 136, 5)), PlateCarree)ax.set_yticks(list(range(20, 56, 5)), PlateCarree)lon_formatter = LongitudeFormatter(zero_direction_label=True)lat_formatter = LatitudeFormatter()ax.xaxis.set_major_formatter(lon_formatter)ax.yaxis.set_major_formatter(lat_formatter)ax.set_title("中国自动站与CMORPH降水产品融合的逐小时降水量", fontproperties=font, fontsize=23)plt.text(1, -0.07, "公众号 碎积云", fontproperties=font, transform=ax.transAxes, horizontalalignment="right", verticalalignment="top", fontsize=14)plt.text(0.5, 0.98, "2018-08-17 07:00(GMT)", fontproperties=font, transform=ax.transAxes, horizontalalignment="center", verticalalignment="top", fontsize=15)southsea = plt.imread(r"..datamap南海诸岛插图.tif")y, x = southsea.shape[:2]ax.imshow(southsea, origin='upper', extent=[135-10, 135, 18, 18+10/x*y], aspect=1.2)plt.show()plt.savefig(r"..dataSURF_CLI_CHN_MERGE_CMP_PRE_HOUR_GRID_0.10demo.png")喜欢的话请关注吧，天晓得还会更新什么好玩儿的东西：