import time, sys from datetime import datetime, timedelta from netCDF4 import Dataset, date2num, num2date import numpy as np day = 20170101 d = datetime.strptime(str(day), '%Y%m%d') f_in = 'tp_%d-%s.nc' % (day, (d + timedelta(days = 1)).strftime('%Y%m%d')) f_out = 'daily-tp_%d.nc' % day time_needed = [] for i in range(1, 25): time_needed.append(d + timedelta(hours = i)) with Dataset(f_in) as ds_src: var_time = ds_src.variables['time'] time_avail = num2date(var_time[:], var_time.units, calendar = var_time.calendar) indices = [] for tm in time_needed: a = np.where(time_avail == tm)[0] if len(a) == 0: sys.stderr.write('Error: precipitation data is missing/incomplete - %s!\n' % tm.strftime('%Y%m%d %H:%M:%S')) sys.exit(200) else: print('Found %s' % tm.strftime('%Y%m%d %H:%M:%S')) indices.append(a[0]) var_tp = ds_src.variables['tp'] tp_values_set = False for idx in indices: if not tp_values_set: data = var_tp[idx, :, :] tp_values_set = True else: data += var_tp[idx, :, :] with Dataset(f_out, mode = 'w', format = 'NETCDF3_64BIT_OFFSET') as ds_dest: # Dimensions for name in ['latitude', 'longitude']: dim_src = ds_src.dimensions[name] ds_dest.createDimension(name, dim_src.size) var_src = ds_src.variables[name] var_dest = ds_dest.createVariable(name, var_src.datatype, (name,)) var_dest[:] = var_src[:] var_dest.setncattr('units', var_src.units) var_dest.setncattr('long_name', var_src.long_name) ds_dest.createDimension('time', None) var = ds_dest.createVariable('time', np.int32, ('time',)) time_units = 'hours since 1900-01-01 00:00:00' time_cal = 'gregorian' var[:] = date2num([d], units = time_units, calendar = time_cal) var.setncattr('units', time_units) var.setncattr('long_name', 'time') var.setncattr('calendar', time_cal) # Variables var = ds_dest.createVariable(var_tp.name, np.double, var_tp.dimensions) var[0, :, :] = data var.setncattr('units', var_tp.units) var.setncattr('long_name', var_tp.long_name) # Attributes ds_dest.setncattr('Conventions', 'CF-1.6') ds_dest.setncattr('history', '%s %s' % (datetime.now().strftime('%Y-%m-%d %H:%M:%S'), ' '.join(time.tzname))) print('Done! Daily total precipitation saved in %s' % f_out)
时间: 2023-06-09 12:04:56 浏览: 161
null是一个表示空值或缺失值的特殊数据类型。在程序中,当一个变量的值为空时,通常会被赋予null值。null与undefined不同,undefined通常表示变量未被定义或未初始化,而null表示该变量的值为空。在许多编程语言中,null也可以被认为是一个对象,它没有任何属性。
相关问题
运行“import datetime time = np.linspace(nc.date2num(datetime.datetime(1950, 1, 1)), nc.date2num(datetime.datetime(2000, 12, 31)), num=nc.shape[0], dtype='float32')”后报错:TypeError: date2num() takes at least 2 positional arguments (1 given)
这个错误提示表明你调用了 `date2num` 函数,但是该函数需要至少两个位置参数,而你只提供了一个参数。你可以尝试以下代码来解决这个问题:
```
import datetime
import numpy as np
import netCDF4 as nc
start_date = datetime.datetime(1950, 1, 1)
end_date = datetime.datetime(2000, 12, 31)
time = nc.date2num([start_date, end_date], units='days since 1950-01-01', calendar='standard')
time = np.linspace(time[0], time[1], num=nc.shape[0], dtype='float32')
```
在这里,我们首先将起始日期和结束日期存储在两个变量中。然后,我们使用 `date2num` 函数将这两个日期转换为数字,并将其存储在 `time` 变量中。最后,我们使用 `linspace` 函数在起始日期和结束日期之间创建一个等间隔的日期时间数组。
python的netCDF4中num2date的用法
num2date是netCDF4中的一个函数,用于将netCDF文件中的时间值转换为Python datetime对象。其用法如下:
```python
from netCDF4 import Dataset, num2date
# 打开netCDF文件
ncfile = Dataset('example.nc', 'r')
# 获取时间变量
time_var = ncfile.variables['time']
# 获取时间值数组
time_vals = time_var[:]
# 获取时间单位
time_unit = time_var.units
# 将时间值数组转换为Python datetime对象
time_dates = num2date(time_vals, units=time_unit, calendar='standard')
# 打印转换后的时间值数组
print(time_dates)
# 关闭netCDF文件
ncfile.close()
```
其中,参数units指定时间单位,calendar指定日历类型,默认为标准日历类型。
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