iframe-proxy | Sunbelt Computer Software

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# coding: utf-8
# Copyright 2020-2022 IFREMER (Brest, FRANCE), all rights reserved.
# contact -- mailto:nicolas.raillard@ifremer.fr
# This file is part of Resourcecode.
# Written by Logilab SA (contact@logilab.fr)
# Resourcecode is free software: you can redistribute it and/or modify it under
# the terms of the GNU General Public License as published by the Free
# Software Foundation, either version 3.0 of the License, or any later version.
# Resourcecode is distributed in the hope that it will be useful, but WITHOUT
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
# FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
# You should have received a copy of the GNU General Public License along
# with Resourcecode. If not, see <https://www.gnu.org/licenses/>.
import json
from typing import TYPE_CHECKING, Union
from pathlib import Path
import xarray
import pandas as pd
import scipy
from resourcecode.data import DATA_DIR
if TYPE_CHECKING:
        from dask.delayed import Delayed
    except ImportError:
        Delayed = None
with open(DATA_DIR / "netcdf_description.json") as fobj:
    NETCFD_DESCRIPTION = json.load(fobj)
def to_netcdf(dataframe: pd.DataFrame, path: Union[str, Path, None] = None) -> Union[bytes, "Delayed", None]:
    """Write dataframe contents to a netCFD file.
    Parameters
    ----------
    path: str, Path or file-like, optional
        Path to which to save this dataset. File-like objects are only supported
        by the scipy engine. If no path is provided, this function returns the
        resulting netCDF file as bytes; in this case, we need to use scipy,
        which does not support netCDF version 4 (the default format becomes
        NETCDF3_64BIT).
    xr = dataframe.to_xarray()
    for variable in xr:
        variable_attrs = NETCFD_DESCRIPTION.get(variable, {})
        if not variable_attrs:
            continue
        scale_factor = variable_attrs.get("scale_factor", 1)
        add_offset = variable_attrs.get("add_offset", 0)
        xr[variable] = (xr[variable] - add_offset) / scale_factor
        xr[variable].attrs.update(variable_attrs)
    return xr.to_netcdf(path)
def to_mat(
    dataframe: pd.DataFrame,
    path: Union[str, Path] = "data.mat",
    name: Union[str, None] = "rscd",
) -> Union[bytes, "Delayed", None]:
    """Write dataframe contents to a MATLAB file.
    Parameters
    ----------
    path: str, Path or file-like, optional
        Path to which to save this dataset. File-like objects are only supported
        by the scipy engine.
    name: str
        The name of the structure containing the data in the MATLAB file.
    df = dataframe.reset_index(names="time")  # convert the pandas index to a proper variable
    df.time = 719529 + pd.to_numeric(df.time) / (
    )  # 1970-01-01 + time in fractional days from nanoseconds
    scipy.io.savemat(path, {name: df.to_dict("list")})
    return df.to_mat(path)
def read_netcdf(filename_or_obj: Union[str, Path]) -> pd.DataFrame:
    """Open and decode a dataframe from a file or file-like object.
    Parameters
    ----------
    filename_or_obj: str, Path, file-like
        Strings and Path objects are interpreted as a path to a netCDF file
        or an OpenDAP URL and opened with python-netCDF4, unless the filename
        ends with .gz, in which case the file is gunzipped and opened with
        scipy.io.netcdf (only netCDF3 supported). Byte-strings or file-like
        objects are opened by scipy.io.netcdf (netCDF3) or h5py (netCDF4/HDF).
    Returns
    -------
    dataframe: pd.DataFrame
        The newly created dataset.
    `read_netcdf` is a simple helper to load netcdf files. Please refer to
    :py:func:`xarray.open_dataset` for more parameters if needs be.
    xr = xarray.open_dataset(filename_or_obj=filename_or_obj, mask_and_scale=True)
    return xr.to_dataframe()
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