Source code for argopy.stores.filesystems

import os
import types
import numpy as np
import xarray as xr
import pandas as pd
import fsspec
import aiohttp
import shutil
import pickle  # nosec B403 only used with internal files/assets
import json
import tempfile
import warnings
import logging
from packaging import version

import concurrent.futures
import multiprocessing

from ..options import OPTIONS
from ..errors import FileSystemHasNoCache, CacheFileNotFound, DataNotFound, \
    InvalidMethod
from abc import ABC, abstractmethod
from ..utilities import Registry

log = logging.getLogger("argopy.stores")

try:
    from tqdm import tqdm
except ModuleNotFoundError:
    log.debug("argopy needs tqdm installed to display progress bars")

    def tqdm(fct, **kw):
        return fct


try:
    import distributed
    has_distributed = True
except ModuleNotFoundError:
    log.debug("argopy needs distributed to use Dask cluster/client")
    has_distributed = False


def new_fs(protocol: str = '', cache: bool = False, cachedir: str = OPTIONS['cachedir'], **kwargs):
    """ Create a new fsspec file system

    Parameters
    ----------
    protocol: str (optional)
    cache: bool (optional)
        Use a filecache system on top of the protocol. Default: False
    cachedir: str
        Define path to cache directory.
    **kwargs: (optional)
        Other arguments passed to :class:`fsspec.filesystem`

    """
    default_filesystem_kwargs = {'simple_links': True, "block_size": 0}
    if protocol == 'http':
        default_filesystem_kwargs = {**default_filesystem_kwargs,
                                     **{"client_kwargs": {"trust_env": OPTIONS['trust_env']}}}
    elif protocol == 'ftp':
        default_filesystem_kwargs = {**default_filesystem_kwargs,
                                     **{"block_size": 1000 * (2 ** 20)}}
    filesystem_kwargs = {**default_filesystem_kwargs, **kwargs}

    if not cache:
        fs = fsspec.filesystem(protocol, **filesystem_kwargs)
        cache_registry = None
        log_msg = "Opening a fsspec [file] system for '%s' protocol with options: %s" % \
                  (protocol, str(filesystem_kwargs))
    else:
        # https://filesystem-spec.readthedocs.io/en/latest/_modules/fsspec/implementations/cached.html#WholeFileCacheFileSystem
        fs = fsspec.filesystem("filecache",
                               target_protocol=protocol,
                               target_options={**filesystem_kwargs},
                               cache_storage=cachedir,
                               expiry_time=86400, cache_check=10)
        # We use a refresh rate for cache of 1 day,
        # since this is the update frequency of the Ifremer erddap
        cache_registry = Registry(name='Cache')  # Will hold uri cached by this store instance
        log_msg = "Opening a fsspec [filecache, storage='%s'] system for '%s' protocol with options: %s" % \
                  (cachedir, protocol, str(filesystem_kwargs))

    if protocol == 'file' and os.path.sep != fs.sep:
        # For some reasons (see https://github.com/fsspec/filesystem_spec/issues/937), the property fs.sep is
        # not '\' under Windows. So, using this dirty fix to overwrite it:
        fs.sep = os.path.sep
        # fsspec folks recommend to use posix internally. But I don't see how to handle this. So keeping this fix
        # because it solves issues with failing tests under Windows. Enough at this time.
        #todo: Revisit this choice in a while

    # log_msg = "%s\n[sys sep=%s] vs [fs sep=%s]" % (log_msg, os.path.sep, fs.sep)
    # log.warning(log_msg)
    log.debug(log_msg)
    return fs, cache_registry


class argo_store_proto(ABC):
    """ Argo Abstract File System

        Provide a prototype for Argo file systems

        Should this class inherits from fsspec.spec.AbstractFileSystem ?
    """
    protocol = ''
    """str: File system name, one in fsspec.registry.known_implementations"""

    def __init__(self,
                 cache: bool = False,
                 cachedir: str = "",
                 **kwargs):
        """ Create a file storage system for Argo data

            Parameters
            ----------
            cache: bool (False)
            cachedir: str (from OPTIONS)
            **kwargs: (optional)
                Other arguments passed to :class:`fsspec.filesystem`

        """
        self.cache = cache
        self.cachedir = OPTIONS['cachedir'] if cachedir == '' else cachedir
        self._filesystem_kwargs = {**kwargs}
        self.fs, self.cache_registry = new_fs(self.protocol,
                                              self.cache,
                                              self.cachedir,
                                              **self._filesystem_kwargs)

    def open(self, path, *args, **kwargs):
        self.register(path)
        # log.debug("Opening path: %s" % path)
        return self.fs.open(path, *args, **kwargs)

    def glob(self, path, **kwargs):
        return self.fs.glob(path, **kwargs)

    def exists(self, path, *args):
        return self.fs.exists(path, *args)

    def expand_path(self, path):
        if self.protocol != "http" and self.protocol != "https":
            return self.fs.expand_path(path)
        else:
            return [path]

    def store_path(self, uri):
        path = uri
        path = self.expand_path(path)[0]
        if not path.startswith(self.fs.target_protocol) and version.parse(fsspec.__version__) <= version.parse("0.8.3"):
            path = self.fs.target_protocol + "://" + path
        return path

    def register(self, uri):
        """ Keep track of files open with this instance """
        if self.cache:
            path = self.store_path(uri)
            if path not in self.cache_registry:
                self.cache_registry.commit(path)

    def cachepath(self, uri: str, errors: str = 'raise'):
        """ Return path to cached file for a given URI """
        if not self.cache:
            if errors == 'raise':
                raise FileSystemHasNoCache("%s has no cache system" % type(self.fs))
        elif uri is not None:
            store_path = self.store_path(uri)
            self.fs.load_cache()  # Read set of stored blocks from file and populate self.fs.cached_files
            if store_path in self.fs.cached_files[-1]:
                return os.path.sep.join([self.cachedir, self.fs.cached_files[-1][store_path]['fn']])
            elif errors == 'raise':
                raise CacheFileNotFound("No cached file found in %s for: \n%s" % (self.fs.storage[-1], uri))
        else:
            raise CacheFileNotFound("No cached file found in %s for: \n%s" % (self.fs.storage[-1], uri))

    def _clear_cache_item(self, uri):
        """ Open fsspec cache registry (pickle file) and remove entry for uri

        """
        # See the "save_cache()" method in:
        # https://filesystem-spec.readthedocs.io/en/latest/_modules/fsspec/implementations/cached.html#WholeFileCacheFileSystem
        fn = os.path.join(self.fs.storage[-1], "cache")
        self.fs.load_cache()  # Read set of stored blocks from file and populate self.fs.cached_files
        cache = self.fs.cached_files[-1]
        if os.path.exists(fn):
            with open(fn, "rb") as f:
                cached_files = pickle.load(f)  # nosec B301 because files controlled internally
        else:
            cached_files = cache
        cache = {}
        for k, v in cached_files.items():
            if k != uri:
                cache[k] = v.copy()
            else:
                os.remove(os.path.join(self.fs.storage[-1], v['fn']))
                # log.debug("Removed %s -> %s" % (uri, v['fn']))
        with tempfile.NamedTemporaryFile(mode="wb", delete=False) as f:
            pickle.dump(cache, f)
        shutil.move(f.name, fn)

    def clear_cache(self):
        """ Remove cache files and entry from uri open with this store instance """
        if self.cache:
            for uri in self.cache_registry:
                # log.debug("Removing from cache %s" % uri)
                self._clear_cache_item(uri)
            self.cache_registry.clear()  # Reset registry

    @abstractmethod
    def open_dataset(self, *args, **kwargs):
        raise NotImplementedError("Not implemented")

    @abstractmethod
    def read_csv(self):
        raise NotImplementedError("Not implemented")


[docs]class filestore(argo_store_proto): """Argo local file system Relies on: https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.implementations.local.LocalFileSystem """ protocol = 'file'
[docs] def open_dataset(self, path, *args, **kwargs): """Return a xarray.dataset from a path. Parameters ---------- path: str Path to resources passed to xarray.open_dataset *args, **kwargs: Other arguments are passed to :func:`xarray.open_dataset` Returns ------- :class:`xarray.DataSet` """ with self.open(path) as of: # log.debug("Opening dataset: '%s'" % path) # Redundant with fsspec logger ds = xr.open_dataset(of, *args, **kwargs) ds.load() if "source" not in ds.encoding: if isinstance(path, str): ds.encoding["source"] = path return ds.copy()
def _mfprocessor(self, url, preprocess=None, *args, **kwargs): # Load data ds = self.open_dataset(url, *args, **kwargs) # Pre-process if isinstance(preprocess, types.FunctionType) or isinstance(preprocess, types.MethodType): ds = preprocess(ds) return ds
[docs] def open_mfdataset(self, # noqa: C901 urls, concat_dim='row', max_workers: int = 112, method: str = 'thread', progress: bool = False, concat: bool = True, preprocess=None, errors: str = 'ignore', *args, **kwargs): """ Open multiple urls as a single xarray dataset. This is a version of the ``open_dataset`` method that is able to handle a list of urls/paths sequentially or in parallel. Use a Threads Pool by default for parallelization. Parameters ---------- urls: list(str) List of url/path to open concat_dim: str Name of the dimension to use to concatenate all datasets (passed to :class:`xarray.concat`) max_workers: int Maximum number of threads or processes method: str The parallelization method to execute calls asynchronously: - ``thread`` (Default): use a pool of at most ``max_workers`` threads - ``process``: use a pool of at most ``max_workers`` processes - (XFAIL) a :class:`distributed.client.Client` object (:class:`distributed.client.Client`) Use 'seq' to simply open data sequentially progress: bool Display a progress bar (True by default) preprocess: callable (optional) If provided, call this function on each dataset prior to concatenation errors: str Should it 'raise' or 'ignore' errors. Default: 'ignore' Returns ------- :class:`xarray.Dataset` """ if not isinstance(urls, list): urls = [urls] results = [] if method in ['thread', 'process']: if method == 'thread': ConcurrentExecutor = concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) else: if max_workers == 112: max_workers = multiprocessing.cpu_count() ConcurrentExecutor = concurrent.futures.ProcessPoolExecutor(max_workers=max_workers) with ConcurrentExecutor as executor: future_to_url = {executor.submit(self._mfprocessor, url, preprocess=preprocess, *args, **kwargs): url for url in urls} futures = concurrent.futures.as_completed(future_to_url) if progress: futures = tqdm(futures, total=len(urls)) for future in futures: data = None # url = future_to_url[future] try: data = future.result() except Exception as e: if errors == 'ignore': log.debug( "Ignored error with this file: %s\nException raised: %s" % (future_to_url[future], str(e.args))) pass else: raise finally: results.append(data) elif has_distributed and isinstance(method, distributed.client.Client): # Use a dask client: if progress: from dask.diagnostics import ProgressBar with ProgressBar(): futures = method.map(self._mfprocessor, urls, preprocess=preprocess, *args, **kwargs) results = method.gather(futures) else: futures = method.map(self._mfprocessor, urls, preprocess=preprocess, *args, **kwargs) results = method.gather(futures) elif method in ['seq', 'sequential']: if progress: urls = tqdm(urls, total=len(urls)) for url in urls: data = None try: data = self._mfprocessor(url, preprocess=preprocess, *args, **kwargs) except Exception as e: if errors == 'ignore': log.debug( "Ignored error with this url: %s\nException raised: %s" % (url, str(e.args))) pass else: raise finally: results.append(data) else: raise InvalidMethod(method) # Post-process results results = [r for r in results if r is not None] # Only keep non-empty results if len(results) > 0: if concat: # ds = xr.concat(results, dim=concat_dim, data_vars='all', coords='all', compat='override') ds = xr.concat(results, dim=concat_dim, data_vars='minimal', coords='minimal', compat='override') return ds else: return results else: raise DataNotFound(urls)
[docs] def read_csv(self, url, **kwargs): """ Return a pandas.dataframe from an url that is a csv resource Parameters ---------- Path: str Path to csv resources passed to pandas.read_csv Returns ------- :class:`pandas.DataFrame` """ log.debug("Reading csv: %s" % url) with self.open(url) as of: df = pd.read_csv(of, **kwargs) return df
[docs]class httpstore(argo_store_proto): """Argo http file system Relies on: https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.implementations.http.HTTPFileSystem This store intends to make argopy: safer to failures from http requests and to provide higher levels methods to work with our datasets This store is primarily used by the Erddap/Argovis data/index fetchers """ protocol = "http"
[docs] def open_dataset(self, url, *args, **kwargs): """ Open and decode a xarray dataset from an url Parameters ---------- url: str Returns ------- :class:`xarray.Dataset` """ try: data = self.fs.cat_file(url) except aiohttp.ClientResponseError as e: if e.status == 413: warnings.warn("Server says payload Too Large ! Try to use 'parallel=True' or a smaller " "chunk parameter with your fetcher") log.debug("Error %i (Payload Too Large) raised with %s" % (e.status, url)) raise ds = xr.open_dataset(data, *args, **kwargs) if "source" not in ds.encoding: if isinstance(url, str): ds.encoding["source"] = url self.register(url) return ds
def _mfprocessor_dataset(self, url, preprocess=None, preprocess_opts={}, *args, **kwargs): # Load data ds = self.open_dataset(url, *args, **kwargs) # Pre-process if isinstance(preprocess, types.FunctionType) or isinstance(preprocess, types.MethodType): ds = preprocess(ds, **preprocess_opts) return ds
[docs] def open_mfdataset(self, # noqa: C901 urls, max_workers: int = 112, method: str = 'thread', progress: bool = False, concat: bool = True, concat_dim='row', preprocess=None, preprocess_opts={}, errors: str = 'ignore', *args, **kwargs): """ Open multiple urls as a single xarray dataset. This is a version of the :class:`argopy.stores.httpstore.open_dataset` method that is able to handle a list of urls/paths sequentially or in parallel. Use a Threads Pool by default for parallelization. Parameters ---------- urls: list(str) List of url/path to open max_workers: int, default: 112 Maximum number of threads or processes method: str, default: ``thread`` The parallelization method to execute calls asynchronously: - ``thread`` (default): use a pool of at most ``max_workers`` threads - ``process``: use a pool of at most ``max_workers`` processes - :class:`distributed.client.Client`: Experimental, expect this method to fail ! - ``seq``: open data sequentially, no parallelization applied progress: bool, default: False Display a progress bar concat: bool, default: True Concatenate results in a single :class:`xarray.Dataset` or not (in this case, function will return a list of :class:`xarray.Dataset`) concat_dim: str, default: ``row`` Name of the dimension to use to concatenate all datasets (passed to :class:`xarray.concat`) preprocess: callable (optional) If provided, call this function on each dataset prior to concatenation preprocess_opts: dict (optional) If ``preprocess`` is provided, pass this as options errors: str, default: ``ignore`` Define how to handle errors raised during data URIs fetching: - ``raise``: Raise any error encountered - ``ignore`` (default): Do not stop processing, simply issue a debug message in logging console - ``silent``: Do not stop processing and do not issue log message Other args and kwargs: other options passed to :class:`argopy.stores.httpstore.open_dataset`. Returns ------- output: :class:`xarray.Dataset` or list of :class:`xarray.Dataset` """ strUrl = lambda x: x.replace("https://", "").replace("http://", "") # noqa: E731 def drop_variables_not_in_all_datasets(ds_collection): """Drop variables that are not in all datasets (Lowest common denominator)""" # List all possible data variables: vlist = [] for res in ds_collection: [vlist.append(v) for v in list(res.data_vars)] vlist = np.unique(vlist) # Check if all possible variables are in each datasets: ishere = np.zeros((len(vlist), len(ds_collection))) for ir, res in enumerate(ds_collection): for iv, v in enumerate(res.data_vars): for iu, u in enumerate(vlist): if v == u: ishere[iu, ir] = 1 # List of dataset with missing variables: ir_missing = np.sum(ishere, axis=0) < len(vlist) # List of variables missing in some dataset: iv_missing = np.sum(ishere, axis=1) < len(ds_collection) # List of variables to keep iv_tokeep = np.sum(ishere, axis=1) == len(ds_collection) for ir, res in enumerate(ds_collection): # print("\n", res.attrs['Fetched_uri']) v_to_drop = [] for iv, v in enumerate(res.data_vars): if v not in vlist[iv_tokeep]: v_to_drop.append(v) ds_collection[ir] = ds_collection[ir].drop_vars(v_to_drop) return ds_collection if not isinstance(urls, list): urls = [urls] results = [] failed = [] if method in ['thread', 'process']: if method == 'thread': ConcurrentExecutor = concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) else: if max_workers == 112: max_workers = multiprocessing.cpu_count() ConcurrentExecutor = concurrent.futures.ProcessPoolExecutor(max_workers=max_workers) with ConcurrentExecutor as executor: future_to_url = {executor.submit(self._mfprocessor_dataset, url, preprocess=preprocess, preprocess_opts=preprocess_opts, *args, **kwargs): url for url in urls} futures = concurrent.futures.as_completed(future_to_url) if progress: futures = tqdm(futures, total=len(urls)) for future in futures: data = None try: data = future.result() except Exception: failed.append(future_to_url[future]) if errors == 'ignore': log.debug("Ignored error with this url: %s" % strUrl(future_to_url[future])) # See fsspec.http logger for more pass elif errors == 'silent': pass else: raise finally: results.append(data) elif has_distributed and isinstance(method, distributed.client.Client): # Use a dask client: if progress: from dask.diagnostics import ProgressBar with ProgressBar(): futures = method.map(self._mfprocessor_dataset, urls, preprocess=preprocess, *args, **kwargs) results = method.gather(futures) else: futures = method.map(self._mfprocessor_dataset, urls, preprocess=preprocess, *args, **kwargs) results = method.gather(futures) elif method in ['seq', 'sequential']: if progress: urls = tqdm(urls, total=len(urls)) for url in urls: data = None try: data = self._mfprocessor_dataset(url, preprocess=preprocess, preprocess_opts=preprocess_opts, *args, **kwargs) except Exception: failed.append(url) if errors == 'ignore': log.debug("Ignored error with this url: %s" % strUrl(url)) # See fsspec.http logger for more pass elif errors == 'silent': pass else: raise finally: results.append(data) else: raise InvalidMethod(method) # Post-process results results = [r for r in results if r is not None] # Only keep non-empty results if len(results) > 0: if concat: # ds = xr.concat(results, dim=concat_dim, data_vars='all', coords='all', compat='override') results = drop_variables_not_in_all_datasets(results) ds = xr.concat(results, dim=concat_dim, data_vars='minimal', coords='minimal', compat='override') return ds else: return results elif len(failed) == len(urls): raise ValueError("Errors happened with all URLs, this could be due to an internal impossibility to read returned content.") else: raise DataNotFound(urls)
[docs] def read_csv(self, url, **kwargs): """ Read a comma-separated values (csv) url into Pandas DataFrame. Parameters ---------- url: str **kwargs: Arguments passed to :class:`pandas.read_csv` Returns ------- :class:`pandas.DataFrame` """ log.debug("Opening/reading csv from: %s" % url) with self.open(url) as of: df = pd.read_csv(of, **kwargs) return df
[docs] def open_json(self, url, **kwargs): """ Return a json from an url, or verbose errors Parameters ---------- url: str Returns ------- json """ log.debug("Opening json from: %s" % url) # try: # with self.open(url) as of: # js = json.load(of, **kwargs) # return js # except ClientResponseError: # raise # except json.JSONDecodeError: # raise data = self.fs.cat_file(url) js = json.loads(data, **kwargs) if len(js) == 0: js = None self.register(url) return js
def _mfprocessor_json(self, url, preprocess=None, url_follow=False, *args, **kwargs): # Load data data = self.open_json(url, **kwargs) # Pre-process if data is None: raise DataNotFound(url) elif isinstance(preprocess, types.FunctionType) or isinstance(preprocess, types.MethodType): if url_follow: data = preprocess(data, url=url, **kwargs) else: data = preprocess(data) return data
[docs] def open_mfjson(self, # noqa: C901 urls, max_workers=112, method: str = 'thread', progress: bool = False, preprocess=None, url_follow=False, errors: str = 'ignore', *args, **kwargs): """ Open multiple json urls This is a parallelized version of ``open_json``. Use a Threads Pool by default for parallelization. Parameters ---------- urls: list(str) max_workers: int Maximum number of threads or processes. method: The parallelization method to execute calls asynchronously: - 'thread' (Default): use a pool of at most ``max_workers`` threads - 'process': use a pool of at most ``max_workers`` processes - (XFAIL) Dask client object: use a Dask distributed client object Use 'seq' to simply open data sequentially progress: bool Display a progress bar (True by default, not for dask client method) preprocess: (callable, optional) If provided, call this function on each json set url_follow: bool, False Follow the URL to the preprocess method as ``url`` argument. Returns ------- list() """ strUrl = lambda x: x.replace("https://", "").replace("http://", "") # noqa: E731 if not isinstance(urls, list): urls = [urls] results = [] failed = [] if method in ['thread', 'process']: if method == 'thread': ConcurrentExecutor = concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) else: if max_workers == 112: max_workers = multiprocessing.cpu_count() ConcurrentExecutor = concurrent.futures.ProcessPoolExecutor(max_workers=max_workers) with ConcurrentExecutor as executor: future_to_url = {executor.submit(self._mfprocessor_json, url, preprocess=preprocess, url_follow=url_follow, *args, **kwargs): url for url in urls} futures = concurrent.futures.as_completed(future_to_url) if progress: futures = tqdm(futures, total=len(urls)) for future in futures: data = None try: data = future.result() except Exception: failed.append(future_to_url[future]) if errors == 'ignore': log.debug("Ignored error with this url: %s" % strUrl(future_to_url[future])) # See fsspec.http logger for more pass elif errors == 'silent': pass else: raise finally: results.append(data) # elif type(method) == distributed.client.Client: # # Use a dask client: # futures = method.map(self._mfprocessor_json, urls, preprocess=preprocess, *args, **kwargs) # results = method.gather(futures) elif method in ['seq', 'sequential']: if progress: urls = tqdm(urls, total=len(urls)) for url in urls: data = None try: data = self._mfprocessor_json(url, preprocess=preprocess, url_follow=url_follow, *args, **kwargs) except Exception: failed.append(url) if errors == 'ignore': log.debug("Ignored error with this url: %s" % strUrl(url)) # See fsspec.http logger for more pass elif errors == 'silent': pass else: raise finally: results.append(data) else: raise InvalidMethod(method) # Post-process results results = [r for r in results if r is not None] # Only keep non-empty results if len(results) > 0: return results else: raise DataNotFound(urls)
[docs]class memorystore(filestore): """ Argo in-memory file system (global) Note that this inherits from filestore, not argo_store_proto Relies on: https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.implementations.memory.MemoryFileSystem """ protocol = 'memory'
[docs] def exists(self, path, *args): """ Check if path can be open or not Special handling for memory store The fsspec.exists() will return False even if the path is in cache. Here we bypass this in order to return True if the path is in cache. This assumes that the goal of fs.exists is to determine if we can load the path or not. If the path is in cache, it can be loaded. """ guess = self.fs.exists(path, *args) if not guess: try: self.cachepath(path) return True except CacheFileNotFound: pass except FileSystemHasNoCache: pass return guess
[docs]class ftpstore(httpstore): """ Argo ftp file system Relies on: https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.implementations.ftp.FTPFileSystem """ protocol = 'ftp'
[docs] def open_dataset(self, url, *args, **kwargs): """ Open and decode a xarray dataset from an ftp url Parameters ---------- url: str Returns ------- :class:`xarray.Dataset` """ try: this_url = self.fs._strip_protocol(url) data = self.fs.cat_file(this_url) except Exception: log.debug("Error with: %s" % url) # except aiohttp.ClientResponseError as e: raise ds = xr.open_dataset(data, *args, **kwargs) if "source" not in ds.encoding: if isinstance(url, str): ds.encoding["source"] = url self.register(this_url) self.register(url) return ds
def _mfprocessor_dataset(self, url, preprocess=None, preprocess_opts={}, *args, **kwargs): # Load data ds = self.open_dataset(url, *args, **kwargs) # Pre-process if isinstance(preprocess, types.FunctionType) or isinstance(preprocess, types.MethodType): ds = preprocess(ds, **preprocess_opts) return ds
[docs] def open_mfdataset(self, # noqa: C901 urls, max_workers: int = 112, method: str = 'thread', progress: bool = False, concat: bool = True, concat_dim='row', preprocess=None, preprocess_opts={}, errors: str = 'ignore', *args, **kwargs): """ Open multiple ftp urls as a single xarray dataset. This is a version of the :class:`argopy.stores.ftpstore.open_dataset` method that is able to handle a list of urls/paths sequentially or in parallel. Use a Threads Pool by default for parallelization. Parameters ---------- urls: list(str) List of url/path to open max_workers: int, default: 112 Maximum number of threads or processes method: str, default: ``thread`` The parallelization method to execute calls asynchronously: - ``thread`` (default): use a pool of at most ``max_workers`` threads - ``process``: use a pool of at most ``max_workers`` processes - :class:`distributed.client.Client`: Experimental, expect this method to fail ! - ``seq``: open data sequentially, no parallelization applied progress: bool, default: False Display a progress bar concat: bool, default: True Concatenate results in a single :class:`xarray.Dataset` or not (in this case, function will return a list of :class:`xarray.Dataset`) concat_dim: str, default: ``row`` Name of the dimension to use to concatenate all datasets (passed to :class:`xarray.concat`) preprocess: callable (optional) If provided, call this function on each dataset prior to concatenation preprocess_opts: dict (optional) If ``preprocess`` is provided, pass this as options errors: str, default: ``ignore`` Define how to handle errors raised during data URIs fetching: - ``raise``: Raise any error encountered - ``ignore`` (default): Do not stop processing, simply issue a debug message in logging console - ``silent``: Do not stop processing and do not issue log message Other args and kwargs: other options passed to :class:`argopy.stores.httpstore.open_dataset`. Returns ------- output: :class:`xarray.Dataset` or list of :class:`xarray.Dataset` """ strUrl = lambda x: x.replace("ftps://", "").replace("ftp://", "") # noqa: E731 def drop_variables_not_in_all_datasets(ds_collection): """Drop variables that are not in all datasets (Lowest common denominator)""" # List all possible data variables: vlist = [] for res in ds_collection: [vlist.append(v) for v in list(res.data_vars)] vlist = np.unique(vlist) # Check if all possible variables are in each datasets: ishere = np.zeros((len(vlist), len(ds_collection))) for ir, res in enumerate(ds_collection): for iv, v in enumerate(res.data_vars): for iu, u in enumerate(vlist): if v == u: ishere[iu, ir] = 1 # List of dataset with missing variables: ir_missing = np.sum(ishere, axis=0) < len(vlist) # List of variables missing in some dataset: iv_missing = np.sum(ishere, axis=1) < len(ds_collection) # List of variables to keep iv_tokeep = np.sum(ishere, axis=1) == len(ds_collection) for ir, res in enumerate(ds_collection): # print("\n", res.attrs['Fetched_uri']) v_to_drop = [] for iv, v in enumerate(res.data_vars): if v not in vlist[iv_tokeep]: v_to_drop.append(v) ds_collection[ir] = ds_collection[ir].drop_vars(v_to_drop) return ds_collection if not isinstance(urls, list): urls = [urls] results = [] failed = [] if method in ['thread', 'process']: if method == 'thread': ConcurrentExecutor = concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) else: if max_workers == 112: max_workers = multiprocessing.cpu_count() ConcurrentExecutor = concurrent.futures.ProcessPoolExecutor(max_workers=max_workers) with ConcurrentExecutor as executor: future_to_url = {executor.submit(self._mfprocessor_dataset, url, preprocess=preprocess, preprocess_opts=preprocess_opts, *args, **kwargs): url for url in urls} futures = concurrent.futures.as_completed(future_to_url) if progress: futures = tqdm(futures, total=len(urls)) for future in futures: data = None try: data = future.result() except Exception: failed.append(future_to_url[future]) if errors == 'ignore': log.debug("Ignored error with this url: %s" % strUrl(future_to_url[future])) # See fsspec.http logger for more pass elif errors == 'silent': pass else: raise finally: results.append(data) elif has_distributed and isinstance(method, distributed.client.Client): # Use a dask client: if progress: from dask.diagnostics import ProgressBar with ProgressBar(): futures = method.map(self._mfprocessor_dataset, urls, preprocess=preprocess, *args, **kwargs) results = method.gather(futures) else: futures = method.map(self._mfprocessor_dataset, urls, preprocess=preprocess, *args, **kwargs) results = method.gather(futures) elif method in ['seq', 'sequential']: if progress: urls = tqdm(urls, total=len(urls)) for url in urls: data = None try: data = self._mfprocessor_dataset(url, preprocess=preprocess, preprocess_opts=preprocess_opts, *args, **kwargs) except Exception: failed.append(url) if errors == 'ignore': log.debug("Ignored error with this url: %s" % strUrl(url)) # See fsspec.http logger for more pass elif errors == 'silent': pass else: raise finally: results.append(data) else: raise InvalidMethod(method) # Post-process results results = [r for r in results if r is not None] # Only keep non-empty results if len(results) > 0: if concat: # ds = xr.concat(results, dim=concat_dim, data_vars='all', coords='all', compat='override') results = drop_variables_not_in_all_datasets(results) ds = xr.concat(results, dim=concat_dim, data_vars='minimal', coords='minimal', compat='override') return ds else: return results else: raise DataNotFound(urls)