8952545656

Committed 04 May 2024 06:42PM UTC coverage: 88.832% (-0.2%) from 89.052%

Build # 8952545656

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Pull #84

github

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web-flow

Commit Message

Merge 37d30c822 into ee244e28b

Pull Request Pull Request #84: Parallelisation framework updated

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87.47

/src/ismn/filehandlers.py

# The MIT License (MIT)
#
# Copyright (c) 2021 TU Wien
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.

import os
import traceback

import pandas as pd
import warnings
import numpy as np
from tempfile import gettempdir, TemporaryDirectory
from pathlib import Path
from typing import Tuple, Union
import logging

#warnings.simplefilter(action="ignore", category=UserWarning)
from ismn.base import IsmnRoot
from ismn import const
from ismn.const import IsmnFileError, ismnlog
from ismn.meta import MetaVar, MetaData, Depth


class IsmnFile(object):
    """
    General base class for data and static metadata files (station csv file)
    in ismn archive.

    Attributes
    ----------
    root : IsmnRoot
        Data access object
    file_path : Path
        File subpath in root archive
    temp_root : Path
        Temporary directory
    metadata : MetaData
        File MetaData collection
    verify_filepath : bool
        Switch to activate file path verification
    verify_temp_root : bool
        Switch to activate temp root verification
    """

    def __init__(self, root, file_path, temp_root=gettempdir(),
                 verify_filepath=True, verify_temp_root=True):
        """
        Parameters
        ----------
        root: IsmnRoot or str
            Base archive that contains the file to read
        file_path : Path or str
            Path to the file in the archive.
        temp_root : Path or str, optional (default : gettempdir())
            Root directory where a separate subdir for temporary files
            will be created (and deleted).
        verify_filepath: bool, optional (default: True)
            Check if subpath is a valid path and adapt to archive format and os
        verify_temp_root: bool, optional (default: True)
            Check if temp_root is a valid path and create if if necessary
        """
        if not isinstance(root, IsmnRoot):
            root = IsmnRoot(root)

        self.root = root

        if verify_filepath:
            self.file_path = self.root.clean_subpath(file_path)
            if self.file_path not in self.root:
                raise IOError(
                    f"Archive does not contain file: {self.file_path}")
        else:
            self.file_path = file_path

        if verify_temp_root:
            if not os.path.exists(temp_root):
                os.makedirs(temp_root, exist_ok=True)

        self.temp_root = temp_root

        self.metadata = MetaData()

    def __repr__(self):
        return f"{self.__class__.__name__}({self.root.path / self.file_path})"

    def __getitem__(self, item: int):
        return [self.root, self.file_path][item]

    def check_metadata(
        self,
        variable=None,
        allowed_depth=None,
        filter_meta_dict=None,
        check_only_sensor_depth_from=False,
    ) -> bool:
        """
        Evaluate whether the file complies with the passed metadata requirements

        Parameters
        ----------
        variable : str or list[str], optional (default: None)
            Name of the required variable(s) measured, e.g. soil_moisture
        allowed_depth : Depth, optional (default: None)
            Depth range that is allowed, depth in metadata must be within this
            range.
        filter_meta_dict: dict, optional (default: None)
            Additional metadata keys and values for which the file list is filtered
            e.g. {'station': 'stationname'} to filter for a station name.
        check_only_sensor_depth_from : bool, optional (default: False)
            Ignores the sensors depth_to value and only checks if depth_from of
            the sensor is in the passed depth (e.g. for cosmic ray probes).

        Returns
        -------
        valid : bool
            Whether the metadata complies with the passed conditions or not.
        """

        if variable is not None:
            variable = np.atleast_1d(variable)
            if not (self.metadata["variable"].val in variable):
                return False

        if allowed_depth is not None:
            try:
                sensor_depth = self.metadata["instrument"].depth
            except AttributeError:
                sensor_depth = self.metadata["variable"].depth

            if check_only_sensor_depth_from:
                sensor_depth = Depth(sensor_depth.start, sensor_depth.start)

            if not allowed_depth.encloses(sensor_depth):
                return False

        if filter_meta_dict:
            fil_lc_cl = [True]
            for k in filter_meta_dict.keys():
                vs = self.metadata[k]
                if isinstance(vs, MetaVar):
                    vs = [vs]

                eval_status = False
                ref_list = np.atleast_1d(filter_meta_dict[k]).tolist()
                for v in vs:
                    eval_status = v.val in ref_list
                    if eval_status is True:
                        break
                fil_lc_cl.append(eval_status)

            if not all(fil_lc_cl):
                return False

        return True

    def close(self):
        self.root.close()

    def open(self):
        self.root.open()


class StaticMetaFile(IsmnFile):
    """
    Represents a csv file containing site specific static variables.
    These attributes shall be assigned to all sensors at that site.

    Attributes
    ----------
    See Parent Class (IsmnFile)
    """

    def __init__(self,
                 root,
                 file_path,
                 load_metadata=True,
                 temp_root=gettempdir()):
        """
        Parameters
        ----------
        root: IsmnRoot or str
            Archive that contains the file to read
        file_path : Path or str
            Subpath to the file in the root. No leading slash!
        load_metadata : bool, optional (default: True)
            Load metadata during initialisation.
        temp_root : Path or str, optional (default : gettempdir())
            Root directory where a separate subdir for temporary files
            will be created (and deleted).
        """
        super(StaticMetaFile, self).__init__(root, file_path, temp_root)

        if self.file_path.suffix.lower() != ".csv":
            raise IsmnFileError(f"CSV file expected for StaticMetaFile object")

        if load_metadata:
            self.metadata = self.read_metadata()

    @staticmethod
    def __read_field(data: pd.DataFrame,
                     fieldname: str,
                     new_name=None) -> np.array:
        """
        Extract a field from the loaded csv metadata
        """
        field_vars = []

        if fieldname in data.index:
            froms = np.atleast_1d(data.loc[fieldname]["depth_from[m]"]).astype(float)
            tos = np.atleast_1d(data.loc[fieldname]["depth_to[m]"]).astype(float)
            vals = np.atleast_1d(data.loc[fieldname]["value"])

            for d_from, d_to, val in zip(froms, tos, vals):
                d = Depth(d_from, d_to)
                name = new_name if new_name is not None else fieldname
                try:
                    val = float(val)
                except ValueError:
                    pass  # value is actually a string, that's ok
                field_vars.append(MetaVar(name, val, d))

        return field_vars

    @staticmethod
    def __read_csv(csvfile: Path) -> pd.DataFrame:
        """Load static metadata data frame from csv"""
        try:
            data = pd.read_csv(csvfile, delimiter=";")
            data.set_index("quantity_name", inplace=True)
        except Exception:
            # set columns manually
            ismnlog.info("no header: {}".format(csvfile))
            data = pd.read_csv(csvfile, delimiter=";", header=None)
            cols = list(data.columns)
            cols[:len(const.CSV_COLS)] = const.CSV_COLS  # todo: not safe
            data.columns = cols
            data.set_index("quantity_name", inplace=True)

        return data

    def read_metadata(self) -> MetaData:
        """
        Read csv file containing static variables into data frame.

        Returns
        -------
        metadata : MetaData
            Static metadata read from csv file.
        """
        if self.root.zip:
            if not self.root.isopen:
                self.root.open()
            with TemporaryDirectory(
                    prefix="ismn", dir=self.temp_root) as tempdir:
                extracted = self.root.extract_file(self.file_path, tempdir)
                data = self.__read_csv(extracted)
        else:
            data = self.__read_csv(self.root.path / self.file_path)

        # read landcover classifications
        lc = data.loc[["land cover classification"
                      ]][["value", "quantity_source_name"]]

        lc_dict = {
            "CCI_landcover_2000": const.CSV_META_TEMPLATE["lc_2000"],
            "CCI_landcover_2005": const.CSV_META_TEMPLATE["lc_2005"],
            "CCI_landcover_2010": const.CSV_META_TEMPLATE["lc_2010"],
            "insitu": const.CSV_META_TEMPLATE["lc_insitu"],
        }

        cl_dict = {
            "koeppen_geiger_2007": const.CSV_META_TEMPLATE["climate_KG"],
            "insitu": const.CSV_META_TEMPLATE["climate_insitu"],
        }

        for key in lc_dict.keys():
            if key in lc["quantity_source_name"].values:
                if key != "insitu":
                    lc_dict[key] = np.int32(lc.loc[lc["quantity_source_name"]
                                                   == key]["value"].values[0])
                else:
                    lc_dict[key] = lc.loc[lc["quantity_source_name"] ==
                                          key]["value"].values[0]
                    ismnlog.info(
                        f"insitu land cover classification available: {self.file_path}"
                    )

        # read climate classifications
        try:
            cl = data.loc[["climate classification"
                          ]][["value", "quantity_source_name"]]
            for key in cl_dict.keys():
                if key in cl["quantity_source_name"].values:
                    cl_dict[key] = cl.loc[cl["quantity_source_name"] ==
                                          key]["value"].values[0]
                    if key == "insitu":
                        ismnlog.info(
                            f"insitu climate classification available: {self.file_path}"
                        )
        except KeyError:
            ismnlog.info(f"No climate metadata found for {self.file_path}")

        metavars = [
            MetaVar("lc_2000", lc_dict["CCI_landcover_2000"]),
            MetaVar("lc_2005", lc_dict["CCI_landcover_2005"]),
            MetaVar("lc_2010", lc_dict["CCI_landcover_2010"]),
            MetaVar("lc_insitu", lc_dict["insitu"]),
            MetaVar("climate_KG", cl_dict["koeppen_geiger_2007"]),
            MetaVar("climate_insitu", cl_dict["insitu"]),
        ]

        static_meta = {
            "saturation":
                self.__read_field(data, "saturation"),
            "clay_fraction":
                self.__read_field(data, "clay fraction",
                                  const.VARIABLE_LUT["cl_h"]),
            "sand_fraction":
                self.__read_field(data, "sand fraction",
                                  const.VARIABLE_LUT["sa_h"]),
            "silt_fraction":
                self.__read_field(data, "silt fraction",
                                  const.VARIABLE_LUT["si_h"]),
            "organic_carbon":
                self.__read_field(data, "organic carbon",
                                  const.VARIABLE_LUT["oc_h"]),
        }

        for name, v in static_meta.items():
            if len(v) > 0:
                metavars += v
            else:
                metavars.append(MetaVar(name, const.CSV_META_TEMPLATE[name]))

        metadata = MetaData(metavars)

        return metadata


class DataFile(IsmnFile):
    """
    IsmnFile class represents a single ISMN data file.
    This represents only .stm data files not metadata csv files.

    Attributes
    ----------
    See :class:`ismn.filehandlers.IsmnFile`
    file_type : str
        File type information (e.g. ceop).
    """

    def __init__(self,
                 root,
                 file_path,
                 load_metadata=True,
                 temp_root=gettempdir(),
                 *args,
                 **kwargs):
        """
        Parameters
        ----------
        root : IsmnRoot or str
            Archive to the downloaded data.
        file_path : str or Path
            Path in the archive to the ismn file. No leading slash!
        load_metadata : bool, optional (default: True)
            Load metadata during initialisation.
        temp_root : Path or str, optional (default : gettempdir())
            Root directory where a separate subdir for temporary files
            will be created (and deleted).
        verify_filepath: bool, optional (default: True)
            Check if subpath is a valid path and adapt to archive format and os
        verify_temp_root: bool, optional (default: True)
            Check if temp_root is a valid path and create if if necessary
        """

        super(DataFile, self).__init__(root, file_path, temp_root,
                                       *args, **kwargs)

        self.file_type = "undefined"
        self.posix_path = file_path

        self.metadata = None

        if load_metadata:
            self.metadata = self.read_metadata(best_meta_for_sensor=True)

    @staticmethod
    def __read_lines(filename: Path) -> Tuple[list, list, list]:
        """
        Read fist and last line from file as list, skips empty lines.
        """
        with filename.open(mode="rb", newline=None) as f:
            lines = f.read().splitlines()
            headr = lines[0].split()

            last, scnd = [], []
            i = 1
            while (not last) or (not scnd):
                if not last:
                    last = lines[-i].split()
                if not scnd:
                    scnd = lines[i].split()
                i += 1

        headr = [s.decode("ascii") for s in headr]
        scnd = [s.decode("ascii") for s in scnd]
        last = [s.decode("ascii") for s in last]

        return headr, scnd, last

    @staticmethod
    def __get_parent_path(filepath: Union[str, Path]):
        """
        returns the parent directory of a full path
        """
        normalized_path = os.path.normpath(filepath)
        path_components = normalized_path.split(os.sep)

        return path_components[0]

    def get_metadata_ceop_sep(self, elements=None):
        """
        Get metadata in the file format called CEOP in separate files.

        Parameters
        ----------
        elements : dict, optional (default: None)
            Previously loaded elements can be passed here to avoid reading the
            file again.

        Returns
        -------
        metadata : MetaData
            Metadata information.
        depth : Depth
            Sensor Depth, generated from file name
        """
        if elements:
            headr = elements["headr"]
            last = elements["last"]
            fname = elements["fname"]
        else:
            headr, _, last, fname = self.get_elements_from_file()

        if len(fname) > 9:
            instr = "_".join(fname[6:len(fname) - 2])
        else:
            instr = fname[6]

        if fname[3] in const.VARIABLE_LUT:
            variable = const.VARIABLE_LUT[fname[3]]
        else:
            variable = fname[3]

        timerange_from = pd.to_datetime(" ".join(headr[:2]))
        timerange_to = pd.to_datetime(" ".join(last[:2]))

        depth = Depth(float(fname[4]), float(fname[5]))

        metadata = MetaData([
            MetaVar("network", fname[1]),
            MetaVar("station", fname[2]),
            MetaVar("variable", variable, depth),
            MetaVar("instrument", instr, depth),
            MetaVar("timerange_from", timerange_from),
            MetaVar("timerange_to", timerange_to),
            MetaVar("latitude", float(headr[7])),
            MetaVar("longitude", float(headr[8])),
            MetaVar("elevation", float(headr[9])),
        ])

        return metadata, depth

    def get_metadata_header_values(self, elements=None):
        """
        Get metadata file in the format called Header Values.

        Parameters
        ----------
        elements : dict, optional (default: None)
            Previously loaded elements can be passed here to avoid reading the
            file again.

        Returns
        -------
        metadata : MetaData
            Metadata information.
        depth : Depth
            Sensor Depth, generated from file name
        """
        if elements:
            headr = elements["headr"]
            scnd = elements["scnd"]
            last = elements["last"]
            fname = elements["fname"]
        else:
            headr, scnd, last, fname = self.get_elements_from_file()

        if len(fname) > 9:
            instrument = "_".join(fname[6:len(fname) - 2])
        else:
            instrument = fname[6]

        if fname[3] in const.VARIABLE_LUT:
            variable = const.VARIABLE_LUT[fname[3]]
        else:
            variable = fname[3]

        timerange_from = pd.to_datetime(" ".join(scnd[:2]))
        timerange_to = pd.to_datetime(" ".join(last[:2]))

        depth = Depth(float(headr[6]), float(headr[7]))

        metadata = MetaData([
            MetaVar("network", fname[1]),
            MetaVar("station", fname[2]),
            MetaVar("variable", variable, depth),
            MetaVar("instrument", instrument, depth),
            MetaVar("timerange_from", timerange_from),
            MetaVar("timerange_to", timerange_to),
            MetaVar("latitude", float(headr[3])),
            MetaVar("longitude", float(headr[4])),
            MetaVar("elevation", float(headr[5])),
        ])

        return metadata, depth

    def get_elements_from_file(self, delim="_", only_basename_elements=False):
        """
        Read first line of file and split filename.
        Information is used to collect metadata information for all
        ISMN formats.

        Parameters
        ----------
        delim : str, optional (default: '_')
            File basename delimiter.
        only_basename_elements : bool, optional (default: False)
            Parse only the filename and not the file contents.

        Returns
        -------
        headr : list[str] or None
            First line of file split into list, None if only_filename is True
        secnd : list[str] or None
            Second line of file split into list, None if only_filename is True
        last : list[str] or None
            Last non empty line elements,  None if only_filename is True
        file_basename_elements : list[str], None if only_filename is True
            File basename without path split by 'delim'
        """
        if only_basename_elements:
            headr = None
            secnd = None
            last = None
        else:
            if self.root.zip:
                if not self.root.isopen:
                    self.root.open()

                with TemporaryDirectory(
                        prefix="ismn", dir=self.temp_root) as tempdir:
                    filename = self.root.extract_file(self.file_path, tempdir)
                    headr, secnd, last = self.__read_lines(filename)

            else:
                filename = self.root.path / self.file_path
                headr, secnd, last = self.__read_lines(filename)

        path, basename = os.path.split(filename)
        file_basename_elements = basename.split(delim)

        return headr, secnd, last, file_basename_elements

    def __read_format_ceop_sep(self) -> pd.DataFrame:
        """
        Read data in the file format called CEOP in separate files.
        """
        var = self.metadata["variable"]
        varname = var.val
        names = [
            "date",
            "time",
            varname,
            varname + "_flag",
            varname + "_orig_flag",
        ]
        usecols = [0, 1, 12, 13, 14]

        return self.__read_csv(names, usecols)

    def __read_format_header_values(self) -> pd.DataFrame:
        """
        Read data file in the format called Header Values.
        """
        var = self.metadata["variable"]
        varname = var.val
        names = [
            "date",
            "time",
            varname,
            varname + "_flag",
            varname + "_orig_flag",
        ]

        return self.__read_csv(
            names=names,
            usecols=[0, 1, 2, 3, 4],
            skiprows=1,
            sep=r'\s+',
            low_memory=False,
        )

    def __read_csv(self, names=None, usecols=None, skiprows=0, **kwargs):
        """
        Read data from csv.

        Parameters
        ----------
        names : list, optional (default: None)
            List of column names to use.
        usecols : list, optional (default: None)
            Return a subset of the columns.
        skiprows : list-like, int or callable, optional (default: 0)
            See pd.read_csv()

        Returns
        -------
        data : pd.DataFrame
            Time series.
        """

        def readf(
            f,
            names=names,
            usecols=usecols,
            skiprows=skiprows,
            parse_dates=[[0, 1]],
            engine="c",
            **kwargs
        ):
            try:
                df = pd.read_csv(
                    filepath_or_buffer=f,
                    skiprows=skiprows,
                    usecols=usecols,
                    names=names,
                    engine=engine,
                    **kwargs
                )
            except pd.errors.ParserError:
                df = pd.read_csv(
                    filepath_or_buffer=f,
                    skiprows=skiprows,
                    usecols=usecols,
                    names=names,
                    sep=r'\s+',
                    engine="c",
                    **kwargs
                )
            for tup in parse_dates:
                c = [df.columns[t] for t in tup]
                df.insert(0, '_'.join(c),
                          pd.to_datetime(df.pop(c[0]) + ' ' + df.pop(c[1])))

            return df

        if self.root.zip:
            with TemporaryDirectory(
                    prefix="ismn", dir=self.temp_root) as tempdir:
                filename = self.root.extract_file(self.file_path, tempdir)
                data = readf(filename, **kwargs)

        else:
            data = readf(self.root.path / self.file_path, **kwargs)

        data.set_index("date_time", inplace=True)

        return data

    def read_data(self) -> pd.DataFrame:
        """
        Read data in file. Load file if necessary.

        Returns
        -------
        data : pd.DataFrame
            File content.
        """

        if not self.root.isopen:
            self.open()

        if self.file_type == "ceop":
            # todo: what is this format, should we support it?
            # self._read_format_ceop()
            raise NotImplementedError(
                "Ceop (old) format is no longer supported")
        elif self.file_type == "ceop_sep":
            return self.__read_format_ceop_sep()
        elif self.file_type == "header_values":
            return self.__read_format_header_values()
        else:
            raise IOError(f"Unknown file format found for: {self.file_path}")

    def read_metadata(self, best_meta_for_sensor=True) -> MetaData:
        """
        Read metadata from file name and first line of file.

        Parameters
        ----------
        best_meta_for_sensor : bool, optional (default: True)
            Compare the sensor depth to metadata that is available in multiple
            depth layers (e.g. static metadata variables). Find the variable
            for which the depth matches best with the sensor depth.
        """
        try:
            headr, scnd, last, fname = self.get_elements_from_file()
        except Exception as e:
            raise IOError(f"Unknown file format found for {self.file_path}:"
                          f"{traceback.format_exc()}")

        elements = dict(headr=headr, scnd=scnd, last=last, fname=fname)

        if (len(headr) == 15) and (len(headr) == len(scnd)):
            metadata, depth = self.get_metadata_ceop_sep(elements)
            self.file_type = "ceop_sep"
        else:
            # the default / fallback format is header_values
            metadata, depth = self.get_metadata_header_values(elements)
            self.file_type = "header_values"

        if best_meta_for_sensor:
            depth = metadata["instrument"].depth
            metadata = metadata.best_meta_for_depth(depth)

        self.metadata = metadata
        self.metadata.replace("network",
                              self.__get_parent_path(self.posix_path))

        return self.metadata

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