# -*- coding: utf-8 -*-
"""
Module for a command line interface to convert the ERA Interim data into a
time series format using the repurpose package
"""
import os
import sys
import argparse
import numpy as np
from pygeogrids import BasicGrid
from repurpose.img2ts import Img2Ts
from ecmwf_models.erainterim.interface import ERAIntGrbDs, ERAIntNcDs
from ecmwf_models.utils import mkdate, parse_filetype
from datetime import time, datetime
[docs]def reshuffle(
input_root,
outputpath,
startdate,
enddate,
variables,
mask_seapoints=False,
h_steps=(0, 6, 12, 18),
imgbuffer=50,
):
"""
Reshuffle method applied to ERA images for conversion into netcdf time
series format.
Parameters
----------
input_root: str
Input path where ERA image data was downloaded to.
outputpath : str
Output path, where the reshuffled netcdf time series are stored.
startdate : datetime
Start date, from which images are read and time series are generated.
enddate : datetime
End date, from which images are read and time series are generated.
variables: list or str or tuple
Variables to read from the passed images and convert into time
series format.
mask_seapoints: bool, optional (default: False)
Mask points over sea, replace them with nan.
h_steps: tuple, optional (default: (0,6,12,18))
Full hours for which images are available.
imgbuffer: int, optional (default: 50)
How many images to read at once before writing time series. This number
affects how many images are stored in memory and should be chosen according
to the available amount of memory and the size of a single image.
"""
filetype = parse_filetype(input_root)
if filetype == "grib":
input_dataset = ERAIntGrbDs(
root_path=input_root,
parameter=variables,
subgrid=None,
array_1D=True,
mask_seapoints=mask_seapoints,
h_steps=h_steps,
)
elif filetype == "netcdf":
input_dataset = ERAIntNcDs(
root_path=input_root,
parameter=variables,
subgrid=None,
array_1D=True,
mask_seapoints=mask_seapoints,
h_steps=h_steps,
)
else:
raise Exception("Unknown file format")
if not os.path.exists(outputpath):
os.makedirs(outputpath)
global_attr = {"product": "ERA Interim (from {})".format(filetype)}
# get time series attributes from first day of data.
first_date_time = datetime.combine(startdate.date(), time(h_steps[0], 0))
data = input_dataset.read(first_date_time)
ts_attributes = data.metadata
grid = BasicGrid(data.lon, data.lat)
reshuffler = Img2Ts(
input_dataset=input_dataset,
outputpath=outputpath,
startdate=startdate,
enddate=enddate,
input_grid=grid,
imgbuffer=imgbuffer,
cellsize_lat=5.0,
cellsize_lon=5.0,
ts_dtypes=np.dtype("float32"),
global_attr=global_attr,
zlib=True,
unlim_chunksize=1000,
ts_attributes=ts_attributes,
)
reshuffler.calc()
[docs]def parse_args(args):
"""
Parse command line parameters for conversion from image to time series.
Parameters
----------
args: list
command line parameters as list of strings
Returns
----------
args : argparse.Namespace
Parsed command line parameters
"""
parser = argparse.ArgumentParser(
description="Convert downloaded ERA image data into time series "
"format."
)
parser.add_argument(
"dataset_root",
help="Root of local filesystem where the image data is stored.",
)
parser.add_argument(
"timeseries_root",
help="Root of local filesystem where the time series "
"should be stored.",
)
parser.add_argument(
"start", type=mkdate, help=("Startdate in format YYYY-MM-DD")
)
parser.add_argument(
"end", type=mkdate, help=("Enddate in format YYYY-MM-DD")
)
parser.add_argument(
"variables",
metavar="variables",
nargs="+",
help=(
"Short name of variables as stored in the images, "
"which are reshuffled. "
"See documentation on image download for resp. ERA products, "
"for more information on variable names of the product. "
),
)
parser.add_argument(
"--mask_seapoints",
type=bool,
default=False,
help=(
"Replace points over water with nan. This option needs the "
"LandSeaMask (lsm) variable in the image data (will use mask "
"from first available file). "
"To use a dynamic LSM, reshuffle the LSM variable to time series."
),
)
parser.add_argument(
"--h_steps",
type=int,
default=None,
nargs="+",
help=(
"Time steps (full hours) of images that will be reshuffled "
"(must be in the images). "
"By default 6H images (starting at 0:00 UTC) will be reshuffled."
),
)
parser.add_argument(
"--imgbuffer",
type=int,
default=50,
help=(
"How many images to read at once. Bigger numbers make the "
"conversion faster but consume more memory. "
"Choose this according to your system and the size of a "
"single image."
),
)
args = parser.parse_args(args)
print("ERA Interim data is deprecated. Use ERA5 instead.")
print(
"Converting data from {} to {} into {}.".format(
args.start.isoformat(), args.end.isoformat(), args.timeseries_root
)
)
return args
[docs]def main(args):
args = parse_args(args)
reshuffle(
input_root=args.dataset_root,
outputpath=args.timeseries_root,
startdate=args.start,
enddate=args.end,
variables=args.variables,
mask_seapoints=args.mask_seapoints,
h_steps=args.h_steps,
imgbuffer=args.imgbuffer,
)
[docs]def run():
main(sys.argv[1:])