FY4B卫星L2级产品掌握和python处理

废话不多说，展示二级产品CTT为例：
在这里插入图片描述
抱歉没空了解FY4B产品情况了，直接看代码

# CTT色标配置
bounds_CTT = [180, 200, 220, 240, 260, 280, 300, 320]  # 根据你的数据设定
colors_CTT = [
        (0, 0, 139/255),
        (10/255, 0, 245/255),
        (0, 164/255, 235/255),
        (0, 246/255, 192/255),
        (89/255, 255/255, 5/255),
        (255/255, 122/255, 0),
        (255/255, 68/255, 0),
        (155/255, 14/255, 0)
    ]

def get_fy4b_l2_png(filePaths, pngPath):
        try:
            file_type = filePaths.split(".")[-1]
            dataset = nc.Dataset(filePaths, 'r')
            time = dataset.date_created
            fyTime = datetime.strptime(time, '%Y-%m-%dT%H:%M:%SZ').strftime('%Y-%m-%d %H:%M:%S')
            timeStr = fyTime.replace(' ', '_').replace(':', '')
            file_name = os.path.basename(filePaths)
            type = file_name.split('-')[4].replace('_', '')
            if type == 'QPE':
                data = dataset.variables['Precipitation'][:]
            else:
                data = dataset.variables[type][:]
            # 经纬度32°N-42°N，104°E-125°E
            # 设置经纬度网格，并通过坐标转换转为CTT数据中的行列号来读取数据
            lat = np.arange(3, 55, 0.1)
            lon = np.arange(60, 137, 0.1)
            # 将经纬度转为格点，变为[lon lat]形式，转换为[l c]，目标输出每个经纬度格点（行列号）的CTT数据，维度CTT（lat,lon）=（90，120）
            lon, lat = np.meshgrid(lon, lat)
            line, column = latlontolinecolumn(lat, lon, "4000M")

            l = line[:, :, np.newaxis]
            c = column[:, :, np.newaxis]
            lc = np.concatenate((l, c), axis=2)  # (90*120*2)
            sichun = [[] for i in range(520)]

            i = 0
            for point_l in lc:
                # for point_c in point_l:
                #     CTT_sichun[i].append(CTT[round(point_c[0])][round(point_c[1])])
                # i+=1
                for point_c in point_l:
                    # 添加有效性检查，确保行列号在合理范围内
                    if 0 <= point_c[0] < data.shape[0] - 1 and 0 <= point_c[1] < data.shape[1] - 1:
                        sichun[i].append(data[round(point_c[0])][round(point_c[1])])
                    else:
                        # 如果行列号越界，可以添加一些处理逻辑，比如跳过该点或者使用默认值
                        sichun[i].append(0)  # 这里使用了默认值，你可以根据需要进行调整
                i += 1
            if type == 'CLM':
                bounds = bounds_CLM
                colors = colors_CLM
            elif type == 'CTT':
                bounds = bounds_CTT
                colors = colors_CTT
            else:
                logger.error('数据错误或格式错误')
            # 创建自定义颜色映射
            custom_cmap = ListedColormap(colors)
            norm = BoundaryNorm(bounds, custom_cmap.N, clip=False)
            # # 绘制数据
            fig, ax = plt.subplots(subplot_kw={'projection': ccrs.PlateCarree()})
            # 添加省界
            china = cfeature.ShapelyFeature(Reader(shp_path).geometries(), ccrs.PlateCarree(),
                                            linewidth=0.5, facecolor='none', edgecolor='yellow', alpha=0.7)
            ax.add_feature(china)
            # 绘制数据，使用 'viridis' 颜色映射
            b = ax.contourf(lon, lat, sichun, cmap=custom_cmap, transform=ccrs.PlateCarree())
            # # 添加网格线
            # ax.gridlines(draw_labels=True, linestyle='--', color='gray', alpha=0.5)
            dir_png = pngPath + '/' + type
            if not os.path.exists(dir_png):  # 如果路径不存在
                os.makedirs(dir_png)  # 则创建该目录
            else:
                print("路径已经存在")
            out_path = dir_png + '/' + type + '_' + timeStr + '.png'
            # img = ax.imshow(CTT_sichun, cmap=custom_cmap, norm=norm, transform=ccrs.Mercator())
            plt.savefig(out_path, transparent=True, dpi=600, bbox_inches='tight', pad_inches=0)
            dataset.close()
            logger.debug(out_path + 'Product production successful')
            path = get_path(out_path, pngPath)
            add_fy4b_record(fyTime, type, path)
        except Exception as e:
            # 记录异常信息
            logger.error('绘制FY4B' + type + '时发生错误: ' + str(e))
            # 根据需要抛出 HTTP 异常或其他类型的异常
            # raise HTTPException(status_code=500, detail="处理" + type + "时发生错误")


def get_path(path, path1):
    # 找到path1在path中的结束位置
    end_index = path.find(path1) + len(path1)
    # 截取从path1之后的部分
    sub_path = path[end_index:]
    return sub_path


def latlontolinecolumn(lat, lon, resolution):
    """
    (lat, lon) → (line, column)
    resolution：文件名中的分辨率{'0500M', '1000M', '2000M', '4000M'}
    line, column不是整数
    """
    # 坐标转换函数，author: modabao
    ea = 6378.137  # 地球的半长轴[km]
    eb = 6356.7523  # 地球的短半轴[km]
    h = 42164  # 地心到卫星质心的距离[km]
    λD = np.deg2rad(105.0)  # 卫星星下点所在经度
    # 列偏移
    COFF = {"0500M": 10991.5,
            "1000M": 5495.5,
            "2000M": 2747.5,
            "4000M": 1373.5}
    # 列比例因子2,033,406.58
    CFAC = {"0500M": 81865099,
            "1000M": 40932549,
            "2000M": 20466274,
            "4000M": 10233137}
    LOFF = {"0500M": 10991.5,
            "1000M": 5495.5,
            "2000M": 2747.5,
            "4000M": 1373.5}  # 行偏移
    # LOFF = COFF
    LFAC = {"0500M": 81865099,
            "1000M": 40932549,
            "2000M": 20466274,
            "4000M": 10233137}  # 行比例因子
    # LFAC = CFAC
    in_ = 0.5  # 归一化后的像点亮度，范围为 [0, 1]
    # Step1.检查地理经纬度
    # Step2.将地理经纬度的角度表示转化为弧度表示
    lat = np.deg2rad(lat)
    lon = np.deg2rad(lon)
    # Step3.将地理经纬度转化成地心经纬度
    eb2_ea2 = eb ** 2 / ea ** 2
    λe = lon
    φe = np.arctan(eb2_ea2 * np.tan(lat))
    # Step4.求Re
    cosφe = np.cos(φe)
    re = eb / np.sqrt(1 - (1 - eb2_ea2) * cosφe ** 2)
    # Step5.求r1,r2,r3
    λe_λD = λe - λD
    r1 = h - re * cosφe * np.cos(λe_λD)
    r2 = -re * cosφe * np.sin(λe_λD)
    r3 = re * np.sin(φe)
    # Step6.求rn,x,y
    rn = np.sqrt(r1 ** 2 + r2 ** 2 + r3 ** 2)
    x = np.rad2deg(np.arctan(-r2 / r1))
    y = np.rad2deg(np.arcsin(-r3 / rn))
    # Step7.求c,l
    column = COFF[resolution] + x * 2 ** -16 * CFAC[resolution]
    line = LOFF[resolution] + y * 2 ** -16 * LFAC[resolution]
    return line, column