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

import datetime
import glob
import os
from logging import info

import numpy as np
import pandas as pd

from .....utils import path
from .utils import find_valid_file




def fetch(ref_dir, ref_file, input_interval, target_dir, tracer=None, **kwargs):

    # Inputs:
    # ---------
    # ref_dir: directory where the original files are found
    # ref_file: (template) name of the original files
    # input_interval: list of the periods to simulate, each item is the list of the dates of the period
    # target_dir: directory where the links to the orginal files are created
    #
    # Ouputs:
    # ---------
    # 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="1h")
    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
        )

        if os.path.isfile(files_3d[0]):
            list_dates[dd] = [[dd, dd + datetime.timedelta(hours=1)]]
            list_files[dd] = [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