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