Source code for pycif.plugins.datastreams.fluxes.CAMSREG_nc.fetch

import datetime
import glob
import os
import pandas as pd

import numpy as np

from pycif.utils import path
from .utils import find_valid_file


[docs] def fetch(ref_dir, ref_file, input_interval, target_dir, tracer=None, **kwargs): """ :param ref_dir: directory where the original files are found :param ref_file: (template) name of the original files :param input_interval: list of the periods to simulate, each item is the list of the dates of the period :param target_dir: directory where the links to the orginal files are created :param tracer: :param kwargs: :return: list_files: for each date that begins a period, an array containing the names of the files that are available for the dates within this period list_dates: for each date that begins a period, an array containing the names of the dates mathcin the files listed in list_files """ list_period_dates = pd.date_range(input_interval[0], input_interval[1], freq="1D") list_dates = {} list_files = {} for dd in list_period_dates: dir_dd = dd.strftime(ref_dir) dir_dd_next = (dd + datetime.timedelta(hours=1)).strftime(ref_dir) dir_dd_previous = (dd - datetime.timedelta(hours=1)).strftime(ref_dir) files_3d, dates_3d = find_valid_file(dir_dd, ref_file, dd, dir_dd_next, ref_dir_previous=dir_dd_previous) list_hours = pd.date_range(dd, dd + datetime.timedelta(hours=23), freq="1H") if os.path.isfile(files_3d[0]): # list_dates[dd] = [[dd, dd + datetime.timedelta(hours=1)]] # list_files[dd] = [files_3d]* len(list_dates[dd]) list_dates[dd] = [[hh, hh + datetime.timedelta(hours=1)] for hh in list_hours] list_files[dd] = len(list_hours) * [files_3d] # the to fetch is a forecast local_files = [] target_file = "{}/{}".format(target_dir, dd.strftime(ref_file)) path.link(files_3d[0], target_file) local_files.append(target_file) return list_files, list_dates