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 09:04:56 浏览: 58
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
import argparse import time import numpy as np import data import models import os import utils from datetime import datetime
As an AI language model, I cannot perform these operations as it requires access to specific files and dependencies. However, I can explain what this code does.
This code imports several Python libraries such as argparse, time, numpy, and datetime. It also imports specific modules from the data, models, os, and utils packages.
The argparse library is used to parse command-line arguments. The time library is used to measure the runtime of code snippets. The numpy library is used for numerical computations. The datetime library is used to get the current date and time.
The data module likely contains code to load and preprocess data for a machine learning task. The models module likely contains code to define and train machine learning models. The utils module likely contains various utility functions used throughout the code.
Overall, this code likely sets up a machine learning pipeline that loads and preprocesses data, trains a model, and saves the trained model for future use.
import numpy as np import datetime import time import sys
这段代码导入了`numpy`、`datetime`、`time`和`sys`模块。
`numpy`是一个用于进行科学计算的Python库,提供了高性能的多维数组对象和用于处理这些数组的函数。
`datetime`是Python的内置模块,提供了处理日期和时间的功能。
`time`也是Python的内置模块,提供了与时间相关的函数,例如延时、获取当前时间等。
`sys`也是Python的内置模块,提供了与Python解释器和运行时环境交互的功能,例如访问命令行参数、退出程序等。
通过导入这些模块,您可以在代码中使用它们提供的函数和类来进行各种操作,例如数值计算、日期时间处理、延时等。
请注意,这是一个基于上下文的假设回答,因为您在问题中提供的代码片段不够完整,无法得知这些模块在代码中的具体用途。如果您有更多关于这些模块的问题,或需要进一步讨论相关内容,请随时提问。