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csj
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from osgeo import gdal
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import numpy as np
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def read_grib_file(file_path):
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# 打开 grib 文件
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grib_dataset = gdal.Open(file_path)
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# 获取数据集的基本信息
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raster_count = grib_dataset.RasterCount
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x_size = grib_dataset.RasterXSize
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y_size = grib_dataset.RasterYSize
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# 读取温度数据并计算每个时间步的平均温度
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temperature_data = []
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mean_temperature_per_time_step = []
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for i in range(raster_count):
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band = grib_dataset.GetRasterBand(i + 1)
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temperature_data.append(band.ReadAsArray())
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mean_temperature_per_time_step.append(np.mean(temperature_data[-1]))
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# 关闭数据集
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grib_dataset = None
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return raster_count, x_size, y_size, temperature_data, mean_temperature_per_time_step
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# 定义文件路径
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file_path = r"D:\图层\11111.grib"
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# 读取 grib 文件并获取基本信息以及温度数据
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raster_count, x_size, y_size, temperature_data, mean_temperature_per_time_step = read_grib_file(file_path)
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# 输出结果
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print("Raster Count:", raster_count)
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print("X Size:", x_size)
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print("Y Size:", y_size)
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print("Mean Temperature per Time Step:", mean_temperature_per_time_step)
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from osgeo import gdal
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# gdal.UseExceptions()
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# gdal.PushErrorHandler('CPLQuietErrorHandler')
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import numpy as np
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from collections import deque
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import math, os, re
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# 日标准：UTC 0：00-24：00；即北京时间 8:00 - 第二天8:00
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# 降水/潜在蒸发/蒸发：m -> mm ：m * 1000 = mm ; sum
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# 气温：k -> ℃ : k - 273.15 = ℃ ; mean max
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# 风速: mean
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def get_ori_values(grib, end, start = 1, mode = 't', day = 365):
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"""
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读取数据，初步处理
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"""
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if grib.RasterCount != day * 24:
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print("大小错误")
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return None
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out = {}
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for i in range(start, end):
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band = grib.GetRasterBand(i)
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if mode == 'k':
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out[band.GetMetadata()['GRIB_VALID_TIME']] = band.ReadAsArray() * 1000
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elif mode == "t":
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out[band.GetMetadata()['GRIB_VALID_TIME']] = band.ReadAsArray() - 273.15
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elif mode == "s":
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out[band.GetMetadata()['GRIB_VALID_TIME']] = band.ReadAsArray()
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# 这个是为了检验数据排序是否出现了问题
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if not list(out.keys()) == sorted(out):
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print("顺序错误")
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return list(out.values())
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"""
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组合形式
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降雨量/蒸散发/潜在蒸散发 - k - sum
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2mu风速/2mv风速 - s - mean
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2m温度/2m露点温度 - t - mean
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2m最大温度 - t - max
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"""
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year = # 年份：2022
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day = # 当年天数： 365
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key = # 提取值的时候用的mode 't'温度相关；'k'降雨蒸发相关; 's'风速相关
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file_name = # 读取的文件名 eg. "降雨量"
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total_mode = # 统计用的，'mean'平均， 'max'最大, 'sum'求和
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"""目录结构
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2022
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2022_降雨量.grib
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2022_蒸散发.grib
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...
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2021
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2022_降雨量.grib
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2022_蒸散发.grib
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...
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"""
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res = gdal.Open(f".\\ERA5\\原始数据\\{year}\\{year}_{file_name}.grib")
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out = get_ori_values(res, start = 1, end = res.RasterCount + 1, mode = key, day = day)
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out = np.stack(out)
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if file_name == "降雨量":
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"""
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处理降雨量的时候，额外计算年度暴雨量
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暴雨定义：24h > 50
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那么找到所有24h大于50的窗口
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合并这些窗口后，计算在窗口内的累计值即可
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"""
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driver = gdal.GetDriverByName('GTiff')
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dataset = driver.Create(f'{year}_暴雨量.tif', res.RasterXSize, res.RasterYSize, 1, gdal.GDT_Float64)
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dataset.SetGeoTransform(res.GetGeoTransform())
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dataset.SetProjection(res.GetProjection())
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band = dataset.GetRasterBand(1)
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def calculate_annual_rainfall(rainfall):
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# Step 1: 找出所有24小时内降雨量大于50mm的时间段
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windows = []
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current_window = None
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rainfall_queue = deque(maxlen=24)
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rainfall_sum = 0
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for i, hour_rainfall in enumerate(rainfall):
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rainfall_queue.append(hour_rainfall)
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rainfall_sum += hour_rainfall
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if len(rainfall_queue) == 24:
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if rainfall_sum > 50:
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if current_window is None:
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current_window = [i - 23, i + 1]
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else:
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current_window[1] = i + 1
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else:
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if current_window is not None:
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windows.append(current_window)
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current_window = None
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rainfall_sum -= rainfall_queue[0]
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if current_window is not None:
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windows.append(current_window)
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# Step 2: 合并所有重叠的窗口
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merged_windows = []
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for start, end in sorted(windows):
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if merged_windows and start <= merged_windows[-1][1]:
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merged_windows[-1][1] = max(merged_windows[-1][1], end)
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else:
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merged_windows.append([start, end])
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# Step 3: 累计每个合并后的窗口内的降雨量
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annual_rainfall = 0.0
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for start, end in merged_windows:
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annual_rainfall += sum(rainfall[start:end])
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return annual_rainfall
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band.WriteArray(np.apply_along_axis(calculate_annual_rainfall, 0, out))
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del dataset
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driver = gdal.GetDriverByName('GTiff')
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dataset = driver.Create(f'{year}_{file_name}.tif', res.RasterXSize, res.RasterYSize, day, gdal.GDT_Float64)
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dataset.SetGeoTransform(res.GetGeoTransform())
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dataset.SetProjection(res.GetProjection())
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for i in range(day):
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band = dataset.GetRasterBand(i + 1)
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band.SetMetadata({'day': f"{year}{i + 1:0>3d}"})
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if total_mode == "sum":
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band.WriteArray(out[i * 24: i * 24 + 24,:,:].sum(axis = 0))
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elif total_mode == "max":
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band.WriteArray(out[i * 24: i * 24 + 24,:,:].max(axis = 0))
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elif total_mode == "mean":
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band.WriteArray(out[i * 24: i * 24 + 24,:,:].mean(axis = 0))
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del dataset, res, out
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from osgeo import gdal
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import numpy as np
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def process_layer(layer_path):
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# 读取上传的图层数据
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layer_data = gdal.Open(layer_path)
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# 处理后的数据可以存储在某个变量中，以便后续计算水源涵养
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# 示例：假设处理后的数据存储在变量 processed_data 中
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processed_data = ...
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return processed_data
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def calculate_water_conservation(processed_data):
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# 计算后的结果可以存储在某个变量中，例如 result_data
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# 示例：假设计算后的结果存储在变量 result_data 中
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result_data = ...
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return result_data
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def save_layer(data, output_path):
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# 将计算结果保存为GeoTIFF格式的文件
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driver = gdal.GetDriverByName('GTiff')
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dataset = driver.Create(output_path, data.shape[1], data.shape[0], 1, gdal.GDT_Float64)
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dataset.SetGeoTransform(layer_data.GetGeoTransform())
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dataset.SetProjection(layer_data.GetProjection())
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band = dataset.GetRasterBand(1)
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band.WriteArray(data)
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del dataset
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# 上传的图层文件路径
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uploaded_layer_path = "uploaded_layer.tif"
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# 处理上传的图层数据
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processed_data = process_layer(uploaded_layer_path)
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# 计算水源涵养
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water_conservation_data = calculate_water_conservation(processed_data)
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# 保存水源涵养图层
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output_path = "water_conservation_layer.tif"
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save_layer(water_conservation_data, output_path)
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