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    Module contents

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    Module contentshydrotools.nwm_client subpackage diff --git a/objects.inv b/objects.inv index 661d2883..6097c479 100644 Binary files a/objects.inv and b/objects.inv differ diff --git a/py-modindex.html b/py-modindex.html index 19a5fbaa..5befba81 100644 --- a/py-modindex.html +++ b/py-modindex.html @@ -368,6 +368,12 @@

    Python Module Index

    hydrotools.nwis_client.iv + + +     + hydrotools.nwm_client + + diff --git a/python/nwm_client/build/lib/hydrotools/nwm_client/AzureFileCatalog.py b/python/nwm_client/build/lib/hydrotools/nwm_client/AzureFileCatalog.py new file mode 100644 index 00000000..98cd3ea1 --- /dev/null +++ b/python/nwm_client/build/lib/hydrotools/nwm_client/AzureFileCatalog.py @@ -0,0 +1,94 @@ +""" +============================================= +NWM Azure Blob Storage Container File Catalog +============================================= +Concrete implementation of a National Water Model file client for discovering +files on Microsoft Azure. + +https://planetarycomputer.microsoft.com/dataset/storage/noaa-nwm + +Classes +------- +AzureFileCatalog +""" +from .NWMFileCatalog import NWMFileCatalog +from hydrotools._restclient.urllib import Url +import azure.storage.blob +import planetary_computer +import adlfs +from typing import List + +class AzureFileCatalog(NWMFileCatalog): + """An Azure Cloud client class for NWM data. + This AzureFileCatalog class provides various methods for discovering NWM + files on Azure Blob Storage. + """ + + def __init__( + self, + server: str = 'https://noaanwm.blob.core.windows.net/' + ) -> None: + """Initialize catalog of NWM data source on Azure Blob Storage. + + Parameters + ---------- + server : str, required + Fully qualified path to Azure Cloud endpoint. + + Returns + ------- + None + """ + super().__init__() + self.server = server + + def list_blobs( + self, + configuration: str, + reference_time: str, + must_contain: str = 'channel_rt' + ) -> List[str]: + """List available blobs with provided parameters. + + Parameters + ---------- + configuration : str, required + Particular model simulation or forecast configuration. For a list + of available configurations see NWMDataService.configurations + reference_time : str, required + Model simulation or forecast issuance/reference time in + YYYYmmddTHHZ format. + must_contain : str, optional, default 'channel_rt' + Optional substring found in each blob name. + + Returns + ------- + A list of blob names that satisfy the criteria set by the parameters. + """ + # Validate configuration + self.raise_invalid_configuration(configuration) + + # Break-up reference time + issue_date, issue_time = self.separate_datetime(reference_time) + + # Get list of blobs + fs = adlfs.AzureBlobFileSystem( + "noaanwm", credential=planetary_computer.sas.get_token("noaanwm", "nwm").token + ) + blobs = fs.glob(f"nwm/nwm.{issue_date}/{configuration}/nwm.t{issue_time}*") + + # Return blob URLs + return [ + str(self.server / suffix) + for suffix in list(blobs) + if must_contain in suffix + ] + + @property + def server(self) -> str: + return self._server + + @server.setter + def server(self, server: str) -> None: + self._server = Url(server) + \ No newline at end of file diff --git a/python/nwm_client/build/lib/hydrotools/nwm_client/FileDownloader.py b/python/nwm_client/build/lib/hydrotools/nwm_client/FileDownloader.py new file mode 100644 index 00000000..565ff9dd --- /dev/null +++ b/python/nwm_client/build/lib/hydrotools/nwm_client/FileDownloader.py @@ -0,0 +1,209 @@ +""" +=============== +File Downloader +=============== +Tool for downloading files asynchronously. + +Classes +------- +FileDownloader +""" +import asyncio +import ssl +import aiohttp +import aiofiles +from pathlib import Path +from typing import List, Tuple, Union +import warnings +from http import HTTPStatus + +class FileDownloader: + """Provides a convenient interface to download a list of files + asynchronously using HTTP. + """ + + def __init__( + self, + output_directory: Union[str, Path] = Path("."), + create_directory: bool = False, + ssl_context: ssl.SSLContext = ssl.create_default_context(), + limit: int = 10 + ) -> None: + """Initialize File Downloader object with specified output directory. + + Parameters + ---------- + output_directory: str, pathlib.Path, optional, default "." + Output directory where files are written. + create_directory: bool, options, default False + Indicates whether to create the output directory if it does not + exist. + ssl_context : ssl.SSLContext, optional, default context + SSL configuration context. + limit: int, optional, default 10 + Number of simultaneous connections. + + Returns + ------- + None + """ + # Set output directory + self.output_directory = output_directory + + # Set directory creation + self.create_directory = create_directory + + # Setup SSL context + self.ssl_context = ssl_context + + # Set limit + self.limit = limit + + async def get_file( + self, + url: str, + filename: str, + session: aiohttp.ClientSession + ) -> None: + """Download a single file. + + Parameters + ---------- + url: str, required + URL path to file. + filename: str, required + Local filename used to write downloaded file. This will save the file + to self.output_directory/filename + session: aiohttp.ClientSession, required + Session object used for retrieval. + + Returns + ------- + None + """ + # Retrieve a single file + async with session.get(url, ssl=self.ssl_context, timeout=900) as response: + # Warn if unable to locate file + if response.status != HTTPStatus.OK: + status = HTTPStatus(response.status_code) + message = ( + f"HTTP Status: {status.value}" + + f" - {status.phrase}" + + f" - {status.description}\n" + + f"{response.url}" + ) + warnings.warn(message, RuntimeWarning) + return + + # Construct output file path + output_file = self.output_directory / filename + + # Stream download + async with aiofiles.open(output_file, 'wb') as fo: + while True: + chunk = await response.content.read(1024) + if not chunk: + break + await fo.write(chunk) + + async def get_files(self, src_dst_list: List[Tuple[str,str]]) -> None: + """Asynchronously download multiple files. + + Parameters + ---------- + src_dst_list: List[Tuple[str,str]], required + List of tuples containing two strings. The first string is the + source URL from which to retrieve a file, the second string is the + local filename where the file will be saved. + + Returns + ------- + None + """ + # Retrieve each file + connector = aiohttp.TCPConnector(limit=self.limit) + async with aiohttp.ClientSession(connector=connector) as session: + await asyncio.gather(*[self.get_file(url, filename, session) for url, filename in src_dst_list]) + + def get(self, src_dst_list: List[Tuple[str,str]], overwrite: bool = False) -> None: + """Setup event loop and asynchronously download multiple files. If + self.create_directory is True, an output directory will be + created if needed. + + Parameters + ---------- + src_dst_list: List[Tuple[str,str]], required + List of tuples containing two strings. The first string is the + source URL from which to retrieve a file, the second string is the + local filename where the file will be saved. + overwrite: bool, optional, default False + If True will overwrite destination file, if it exists. If False, + download of this file is skipped. + + Returns + ------- + None + + Examples + -------- + >>> from nwm_client.FileDownloader import FileDownloader + >>> downloader = FileDownloader(output_directory="my_output") + + >>> # This will download the pandas homepage and save it to "my_output/index.html" + >>> downloader.get( + >>> [("https://pandas.pydata.org/docs/user_guide/index.html","index.html")] + >>> ) + """ + # Shorten list to files that do not exist + if not overwrite: + short = [] + for src, dst in src_dst_list: + if (self.output_directory / dst).exists(): + message = f"File exists, skipping download of {self.output_directory / dst}" + warnings.warn(message, UserWarning) + continue + short.append((src, dst)) + src_dst_list = short + + # Check output directory, optionally create + if not self.output_directory.exists(): + if self.create_directory: + self.output_directory.mkdir(parents=True) + else: + message = f"{self.output_directory} does not exist." + raise FileNotFoundError(message) + + # Start event loop to retrieve files + asyncio.run(self.get_files(src_dst_list)) + + @property + def output_directory(self) -> Path: + return self._output_directory + + @output_directory.setter + def output_directory(self, output_directory: Union[str, Path]): + self._output_directory = Path(output_directory).expanduser().resolve() + + @property + def create_directory(self) -> bool: + return self._create_directory + + @create_directory.setter + def create_directory(self, create_directory: bool): + self._create_directory = bool(create_directory) + + @property + def ssl_context(self) -> ssl.SSLContext: + return self._ssl_context + + @ssl_context.setter + def ssl_context(self, ssl_context: ssl.SSLContext) -> None: + self._ssl_context = ssl_context + + @property + def limit(self) -> int: + return self._limit + + @limit.setter + def limit(self, limit: int) -> None: + self._limit = limit diff --git a/python/nwm_client/build/lib/hydrotools/nwm_client/GCPFileCatalog.py b/python/nwm_client/build/lib/hydrotools/nwm_client/GCPFileCatalog.py new file mode 100644 index 00000000..232152c2 --- /dev/null +++ b/python/nwm_client/build/lib/hydrotools/nwm_client/GCPFileCatalog.py @@ -0,0 +1,91 @@ +""" +====================================== +NWM Google Cloud Platform File Catalog +====================================== +Concrete implementation of a National Water Model file client for discovering +files on Google Cloud Platform (GCP). + +GCP -- https://console.cloud.google.com/marketplace/details/noaa-public/national-water-model + +Classes +------- +GCPFileCatalog +""" +from .NWMFileCatalog import NWMFileCatalog +from google.cloud import storage +from typing import List + +class GCPFileCatalog(NWMFileCatalog): + """A Google Cloud client class for NWM data. + This GCPFileCatalog class provides various methods for discovering NWM + files on Google Cloud Platform. + """ + + def __init__( + self, + bucket_name: str = 'national-water-model' + ) -> None: + """Initialize catalog of NWM data source on Google Cloud Platform. + + Parameters + ---------- + bucket_name : str, required, default 'national-water-model' + Name of Google Cloud Bucket + + Returns + ------- + None + """ + super().__init__() + self.bucket_name = bucket_name + + def list_blobs( + self, + configuration: str, + reference_time: str, + must_contain: str = 'channel_rt' + ) -> List[str]: + """List available blobs with provided parameters. + + Parameters + ---------- + configuration : str, required + Particular model simulation or forecast configuration. For a list + of available configurations see NWMDataService.configurations + reference_time : str, required + Model simulation or forecast issuance/reference time in + YYYYmmddTHHZ format. + must_contain : str, optional, default 'channel_rt' + Optional substring found in each blob name. + + Returns + ------- + A list of blob names that satisfy the criteria set by the parameters. + """ + # Validate configuration + self.raise_invalid_configuration(configuration) + + # Break-up reference time + issue_date, issue_time = self.separate_datetime(reference_time) + + # Connect to bucket with anonymous client + client = storage.Client.create_anonymous_client() + bucket = client.bucket(self.bucket_name) + + # Get list of blobs + blobs = client.list_blobs( + bucket, + prefix=f'nwm.{issue_date}/{configuration}/nwm.t{issue_time}' + ) + + # Return blob names + return [b.public_url for b in list(blobs) if must_contain in b.name] + + @property + def bucket_name(self) -> str: + return self._bucket_name + + @bucket_name.setter + def bucket_name(self, bucket_name: str) -> None: + self._bucket_name = bucket_name + \ No newline at end of file diff --git a/python/nwm_client/build/lib/hydrotools/nwm_client/HTTPFileCatalog.py b/python/nwm_client/build/lib/hydrotools/nwm_client/HTTPFileCatalog.py new file mode 100644 index 00000000..2a64f2dc --- /dev/null +++ b/python/nwm_client/build/lib/hydrotools/nwm_client/HTTPFileCatalog.py @@ -0,0 +1,151 @@ +""" +===================== +NWM HTTP File Catalog +===================== +Concrete implementation of a National Water Model file client for discovering +files on generic HTTP servers, for example: + +NOMADS -- https://nomads.ncep.noaa.gov/pub/data/nccf/com/nwm/prod/ + +Classes +------- +HTTPFileCatalog +""" +from .NWMFileCatalog import NWMFileCatalog +import asyncio +import aiohttp +import ssl +from bs4 import BeautifulSoup +from typing import List + +class HTTPFileCatalog(NWMFileCatalog): + """An HTTP client class for NWM data. + This HTTPFileCatalog class provides various methods for discovering NWM + files on generic web servers. + """ + + def __init__( + self, + server: str, + ssl_context: ssl.SSLContext = ssl.create_default_context() + ) -> None: + """Initialize HTTP File Catalog of NWM data source. + + Parameters + ---------- + server : str, required + Fully qualified path to web server endpoint. Example: + "https://nomads.ncep.noaa.gov/pub/data/nccf/com/nwm/prod/" + ssl_context : ssl.SSLContext, optional, default context + SSL configuration context. + + Returns + ------- + None + """ + super().__init__() + # Server path + self.server = server + + # Setup SSL context + self.ssl_context = ssl_context + + @staticmethod + async def get_html( + url: str, + ssl_context: ssl.SSLContext = ssl.create_default_context() + ) -> str: + """Retrieve an HTML document. + + Parameters + ---------- + url : str, required + Path to HTML document + ssl_context : ssl.SSLContext, optional, default context + SSL configuration context. + + Returns + ------- + HTML document retrieved from url. + """ + async with aiohttp.ClientSession() as session: + async with session.get(url, ssl=ssl_context) as response: + # Get html content + html_doc = await response.text() + + # Raise for no results found + if response.status >= 400: + raise FileNotFoundError(html_doc) + + # Otherwise return response content + return html_doc + + def list_blobs( + self, + configuration: str, + reference_time: str, + must_contain: str = 'channel_rt' + ) -> List[str]: + """List available blobs with provided parameters. + + Parameters + ---------- + configuration : str, required + Particular model simulation or forecast configuration. For a list + of available configurations see NWMDataService.configurations + reference_time : str, required + Model simulation or forecast issuance/reference time in + %Y%m%dT%HZ format. + must_contain : str, optional, default 'channel_rt' + Optional substring that must be found in each blob name. + + Returns + ------- + A list of blob names that satisfy the criteria set by the parameters. + """ + # Validate configuration + self.raise_invalid_configuration(configuration) + + # Break-up reference time + issue_date, issue_time = NWMFileCatalog.separate_datetime(reference_time) + + # Set prefix + prefix = f"nwm.{issue_date}/{configuration}/" + + # Generate url + directory = self.server + prefix + + # Get directory listing + html_doc = asyncio.run(self.get_html(directory, self.ssl_context)) + + # Parse content + soup = BeautifulSoup(html_doc, 'html.parser') + + # Get links + elements = soup.select("a[href]") + + # Generate list + blob_list = [] + for e in elements: + filename = e.get("href") + if filename.startswith(f"nwm.t{issue_time}"): + full_path = directory + filename + blob_list.append(full_path) + + return [b for b in blob_list if must_contain in b] + + @property + def server(self) -> str: + return self._server + + @server.setter + def server(self, server: str) -> None: + self._server = server + + @property + def ssl_context(self) -> ssl.SSLContext: + return self._ssl_context + + @ssl_context.setter + def ssl_context(self, ssl_context: ssl.SSLContext) -> None: + self._ssl_context = ssl_context \ No newline at end of file diff --git a/python/nwm_client/build/lib/hydrotools/nwm_client/NWMClient.py b/python/nwm_client/build/lib/hydrotools/nwm_client/NWMClient.py new file mode 100644 index 00000000..2d7d0ca7 --- /dev/null +++ b/python/nwm_client/build/lib/hydrotools/nwm_client/NWMClient.py @@ -0,0 +1,61 @@ +""" +================ +NWM Client Tools +================ +Client tools for retrieving National Water Model data from various sources. + +Classes +------- +NWMClient +""" + +from abc import ABC, abstractmethod +import pandas as pd +import dask.dataframe as dd +from typing import List, Union +import numpy.typing as npt +from .NWMClientDefaults import _NWMClientDefault + +class StoreNotFoundError(Exception): + """Exception raised by methods that require a dataframe store.""" + pass + +class QueryError(Exception): + """Exception raised when a combination of configuration and/or reference + times does not return any results.""" + pass + +class NWMClient(ABC): + """Abstract base class by building National Water Model output data clients.""" + + @abstractmethod + def get( + self, + configurations: List[str], + reference_times: npt.ArrayLike, + variables: List[str] = ["streamflow"], + nwm_feature_ids: npt.ArrayLike = _NWMClientDefault.CROSSWALK.index, + compute: bool = True + ) -> Union[pd.DataFrame, dd.DataFrame]: + """Abstract method to retrieve National Water Model data as a + DataFrame. + + Parameters + ---------- + configurations: List[str], required + List of NWM configurations. + reference_times: array-like, required + array-like of reference times. Should be compatible with pandas.Timestamp. + variables: List[str], optional, default ['streamflow'] + List of variables to retrieve from NWM files. + nwm_feature_ids: array-like, optional + array-like of NWM feature IDs to return. Defaults to channel features + with a known USGS mapping. + compute: bool, optional, default True + When True returns a pandas.DataFrame. When False returns a dask.dataframe.DataFrame. + + Returns + ------- + dask.dataframe.DataFrame of NWM data or a pandas.DataFrame in canonical + format. + """ diff --git a/python/nwm_client/build/lib/hydrotools/nwm_client/NWMClientDefaults.py b/python/nwm_client/build/lib/hydrotools/nwm_client/NWMClientDefaults.py new file mode 100644 index 00000000..47a2ab22 --- /dev/null +++ b/python/nwm_client/build/lib/hydrotools/nwm_client/NWMClientDefaults.py @@ -0,0 +1,112 @@ +""" +=================== +NWM Client Defaults +=================== +Manages default options to build National Water Model clients. + +Classes +------- +NWMClientDefaults +""" +from dataclasses import dataclass, field +import pandas as pd +from .ParquetStore import ParquetStore +from .NWMFileCatalog import NWMFileCatalog +from .GCPFileCatalog import GCPFileCatalog +from .UnitHandler import UnitHandler +import ssl +import dask.dataframe as dd +from tempfile import TemporaryDirectory +from .FileDownloader import FileDownloader +from pathlib import Path +from typing import List, Dict +from enum import Enum +from pint import UnitRegistry + +class MeasurementUnitSystem(Enum): + """A system of units to define measurements. + Options are SI or US. + """ + SI = "SI" + US = "US" + +@dataclass +class NWMClientDefaults: + """Stores application default options. + + STORE: Configured ParquetStore instance. Default is class level object, not instance object. + CATALOG: Concrete NWM data source instance. + SSL_CONTEXT: ssl context instance. Default is class level object, not instance object. + ROUTELINK_URL: URL string path that points at an HDF5 file containing a + pandas.DataFrame with NWM crosswalk data. + VARIABLES: default list of variables to retrieve from channel_rt files. + NWM_TO_SI_UNIT_MAPPING: mapping from NWM units to pint compatible metric unit symbols + SI_TO_US_UNIT_MAPPING: mapping from SI units to default US standard conversion units + CROSSWALK: A property that generates a pandas.DataFrame that maps between + point feature data source identifiers (i.e. USGS gage id -> NWM feature + ID). + NWM_TO_US_UNIT_CONVERSION: Propertying mapping NWM units to US standard units and conversion factors + DOWNLOAD_DIRECTORY: Local path to save downloaded NWM files. Default is class level object, not instance object. + UNIT_SYSTEM: Default system of measurements. + """ + STORE: ParquetStore = field( + default_factory=lambda: ParquetStore( + "hydrotools_data/nwm_store.parquet", + write_index=False, + compression="snappy" + )) + CATALOG: NWMFileCatalog = field(default_factory=GCPFileCatalog) + SSL_CONTEXT: ssl.SSLContext = field(default_factory=ssl.create_default_context) + ROUTELINK_URL: str = "https://www.hydroshare.org/resource/e9fe66730d184bdfbaea19639bd7cb55/data/contents/RouteLink.h5" + VARIABLES: List[str] = field(default_factory=lambda: ["streamflow"]) + NWM_TO_SI_UNIT_MAPPING: Dict[str, str] = field(default_factory=lambda: {"m": "m", "m s-1": "m/s", "m3 s-1": "m^3/s"}) + SI_TO_US_UNIT_MAPPING: Dict[str, str] = field(default_factory=lambda: {"m": "ft", "m/s": "ft/s", "m^3/s": "ft^3/s"}) + DOWNLOAD_DIRECTORY: Path = Path("hydrotools_data/NWMFileClient_NetCDF_files") + UNIT_SYSTEM: MeasurementUnitSystem = field(default_factory=lambda: MeasurementUnitSystem.SI) + + def _download_and_read_routelink_file(self) -> dd.DataFrame: + """Retrieve NWM RouteLink data from URL and return a + dask.dataframe.DataFrame. + + Returns + ------- + df: dask.dataframe.DataFrame + DataFrame containing associated location metadata. + """ + with TemporaryDirectory() as td: + # Setup downloader + downloader = FileDownloader( + output_directory=td, + create_directory=False, + ssl_context=self.SSL_CONTEXT + ) + + # Download files + downloader.get([(self.ROUTELINK_URL, "RouteLink.h5")]) + return dd.from_pandas(pd.read_hdf(Path(td)/"RouteLink.h5"), + npartitions=1) + + @property + def CROSSWALK(self) -> pd.DataFrame: + """Retrieve and store a default crosswalk for use by a NWM client.""" + key = "CROSSWALK" + if key not in self.STORE: + self.STORE[key] = self._download_and_read_routelink_file() + return self.STORE[key].compute()[["nwm_feature_id", "usgs_site_code"]].set_index( + "nwm_feature_id") + + @property + def NWM_TO_US_UNIT_CONVERSION(self) -> Dict[str, Dict[str, float]]: + """Mapping from NWM units to US standard units and respective conversion factors.""" + # Set up unit handler + unit_handler = UnitHandler(unit_registry=UnitRegistry(cache_folder=Path("hydrotools/pint_cache"))) + + # Build conversion from NWM to US + nwm_to_us_mapping = {key: self.SI_TO_US_UNIT_MAPPING[val] for key, val in self.NWM_TO_SI_UNIT_MAPPING.items()} + si_to_us_conversion = {key: unit_handler.conversion_factor(key, val) for key, val in self.SI_TO_US_UNIT_MAPPING.items()} + nwm_to_us_conversion = {key: si_to_us_conversion[val] for key, val in self.NWM_TO_SI_UNIT_MAPPING.items()} + + return {"measurement_unit_conversion": nwm_to_us_mapping, "conversion_factor": nwm_to_us_conversion} + +# Initialize defaults +_NWMClientDefault = NWMClientDefaults() diff --git a/python/nwm_client/build/lib/hydrotools/nwm_client/NWMFileCatalog.py b/python/nwm_client/build/lib/hydrotools/nwm_client/NWMFileCatalog.py new file mode 100644 index 00000000..efaa3504 --- /dev/null +++ b/python/nwm_client/build/lib/hydrotools/nwm_client/NWMFileCatalog.py @@ -0,0 +1,123 @@ +""" +================ +NWM File Catalog +================ +Tools for discovering operational NWM NetCDF data from file-based sources. + +Classes +------- +NWMFileCatalog +""" +from abc import ABC, abstractmethod +from typing import List, Tuple +import pandas as pd + +class NWMFileCatalog(ABC): + """Abstract base class for sources of NWM file data.""" + + def raise_invalid_configuration(self, configuration) -> None: + """Raises an error for an invalid configuration. + + Parameters + ---------- + configuration: str, required + Configuration to validate + + Returns + ------- + None + + Raises + ------ + ValueError if the configuration is invalid. + """ + # Validate configuration + if configuration not in self.configurations: + message = (f"Invalid configuration '{configuration}'. " + + f"Valid options: {str(self.configurations)}") + raise ValueError(message) + + @staticmethod + def separate_datetime(reference_time: pd.Timestamp) -> Tuple[str, str]: + """Divide reference time into separate date and time strings. + + Parameters + ---------- + reference_time: pandas.Timestamp, required + pandas.Timestamp compatible datetime object + + Returns + ------- + Two strings: issue_date, issue_time + """ + # Break-up reference time + tokens = reference_time.strftime("%Y%m%dT%HZ").split('T') + issue_date = tokens[0] + issue_time = tokens[1].lower() + return issue_date, issue_time + + @abstractmethod + def list_blobs( + self, + configuration: str, + reference_time: str + ) -> List[str]: + """Abstract method to query for NWM files. + + Parameters + ---------- + configuration : str, required + Particular model simulation or forecast configuration. For a list + of available configurations see NWMDataService.configurations + reference_time : str, required + Model simulation or forecast issuance/reference time in + %Y%m%dT%HZ format. + + Returns + ------- + A list of blob names that satisfy the criteria set by the parameters. + """ + + @property + def configurations(self) -> List[str]: + return [ + 'analysis_assim', + 'analysis_assim_alaska', + 'analysis_assim_alaska_no_da', + 'analysis_assim_extend', + 'analysis_assim_extend_no_da', + 'analysis_assim_extend_alaska', + 'analysis_assim_extend_alaska_no_da', + 'analysis_assim_hawaii', + 'analysis_assim_hawaii_no_da', + 'analysis_assim_no_da', + 'analysis_assim_puertorico', + 'analysis_assim_puertorico_no_da', + 'analysis_assim_long', + 'analysis_assim_long_no_da', + 'long_range_mem1', + 'long_range_mem2', + 'long_range_mem3', + 'long_range_mem4', + 'medium_range_alaska_mem1', + 'medium_range_alaska_mem2', + 'medium_range_alaska_mem3', + 'medium_range_alaska_mem4', + 'medium_range_alaska_mem5', + 'medium_range_alaska_mem6', + 'medium_range_alaska_no_da', + 'medium_range_mem1', + 'medium_range_mem2', + 'medium_range_mem3', + 'medium_range_mem4', + 'medium_range_mem5', + 'medium_range_mem6', + 'medium_range_mem7', + 'medium_range_no_da', + 'short_range', + 'short_range_alaska', + 'short_range_hawaii', + 'short_range_hawaii_no_da', + 'short_range_puertorico', + 'short_range_puertorico_no_da', + ] diff --git a/python/nwm_client/build/lib/hydrotools/nwm_client/NWMFileClient.py b/python/nwm_client/build/lib/hydrotools/nwm_client/NWMFileClient.py new file mode 100644 index 00000000..a52fcf46 --- /dev/null +++ b/python/nwm_client/build/lib/hydrotools/nwm_client/NWMFileClient.py @@ -0,0 +1,374 @@ +""" +===================== +NWM File Client Tools +===================== +Client tools for retrieving National Water Model data from file-based sources + +Classes +------- +NWMFileClient +""" + +from .NWMClient import NWMClient, QueryError, StoreNotFoundError +from typing import Union, List, Dict +from pathlib import Path +from .NWMClientDefaults import _NWMClientDefault, MeasurementUnitSystem +from .ParquetStore import ParquetStore +from .NWMFileCatalog import NWMFileCatalog +import numpy as np +import pandas as pd +import ssl +import dask.dataframe as dd +from .FileDownloader import FileDownloader +from .NWMFileProcessor import NWMFileProcessor +import numpy.typing as npt +import warnings +import shutil + +class NWMFileClient(NWMClient): + def __init__( + self, + file_directory: Union[str, Path] = _NWMClientDefault.DOWNLOAD_DIRECTORY, + dataframe_store: Union[ParquetStore, None] = _NWMClientDefault.STORE, + catalog: NWMFileCatalog = _NWMClientDefault.CATALOG, + location_metadata_mapping: pd.DataFrame = _NWMClientDefault.CROSSWALK, + ssl_context: ssl.SSLContext = _NWMClientDefault.SSL_CONTEXT, + cleanup_files: bool = False, + unit_system: MeasurementUnitSystem = _NWMClientDefault.UNIT_SYSTEM + ) -> None: + """Client class for retrieving data as dataframes from a remote + file-based source of National Water Model data. + + Parameters + ---------- + file_directory: str or pathlib.Path, optional, default "NWMFileClient_NetCDF_files" + Directory to save downloaded NetCDF files. + dataframe_store: ParquetStore, default ParquetStore("nwm_store.parquet") + Local parquet directory used to locally store retrieved dataframes. + catalog: NWMFileCatalog, optional, default GCPFileCatalog() + NWMFileCatalog object used to discover NWM files. + location_metadata_mapping: pandas.DataFrame with nwm_feature_id Index and + columns of corresponding site metadata. Defaults to 7500+ usgs_site_code + used by the NWM for data assimilation. + ssl_context: ssl.SSLContext, optional, default default_context + SSL configuration context. + cleanup_files: bool, default False + Delete downloaded NetCDF files upon program exit. + unit_system: MeasurementUnitSystem, optional, default MeasurementUnitSystem.SI + The default measurement_unit for NWM streamflow data are cubic meter per second, + meter per second, and meter. Setting this option to MeasurementUnitSystem.US will convert the units + to cubic foot per second, foot per second, or foot respectively. + + Returns + ------- + NWMClient object + """ + super().__init__() + + # Set file output directory + self.file_directory = file_directory + + # Set dataframe store + if dataframe_store: + self.dataframe_store = dataframe_store + else: + self.dataframe_store = None + + # Set file catalog + self.catalog = catalog + + # Set crosswalk + self.crosswalk = location_metadata_mapping + + # Set CA bundle + self.ssl_context = ssl_context + + # Set cleanup flag + self.cleanup_files = cleanup_files + + # Set unit system + self.unit_system = unit_system + + def get_files( + self, + configuration: str, + reference_time: pd.Timestamp, + group_size: int = 20 + ) -> Dict[str, List[Path]]: + """Download files for a single National Water Model cycle. + + Parameters + ---------- + configuration: str, required + NWM configuration cycle. + reference_time: datetime-like, required + pandas.Timestamp compatible datetime object + group_size: int, optional, default 20 + Files are downloaded in groups of 20 by default. This is to accomodate the + xarray, dask, and HDF5 backends that may struggle with opening too many files + at once. This setting is mostly relevant to retrieving medium range forecasts. + + Returns + ------- + dict mapping a group string key to a list of local file paths. + """ + # Validate reference times + reference_time = pd.Timestamp(reference_time) + + # Generate list of urls + urls = self.catalog.list_blobs( + configuration=configuration, + reference_time=reference_time + ) + + # Check urls + if len(urls) == 0: + message = (f"No data found for configuration '{configuration}' and " + + f"reference time '{reference_time}'") + raise QueryError(message) + + # Generate local filenames + filenames = [f"timestep_{idx}.nc" for idx, _ in enumerate(urls)] + + # Output subdirectory + subdirectory = self.file_directory / configuration / reference_time.strftime("RT%Y%m%dT%HZ") + + # Setup downloader + downloader = FileDownloader( + output_directory=subdirectory, + create_directory=True, + ssl_context=self.ssl_context + ) + + # Download files + downloader.get(zip(urls,filenames)) + + # Return nested list of files + files = sorted(list(subdirectory.glob("*.nc"))) + num_groups = len(files) // group_size + 1 + file_groups = np.array_split(files, num_groups) + return {f"group_{idx}": fg for idx, fg in enumerate(file_groups)} + + def get( + self, + configurations: List[str], + reference_times: npt.ArrayLike, + nwm_feature_ids: npt.ArrayLike = _NWMClientDefault.CROSSWALK.index, + variables: List[str] = _NWMClientDefault.VARIABLES, + compute: bool = True + ) -> Union[pd.DataFrame, dd.DataFrame]: + """Abstract method to retrieve National Water Model data as a + DataFrame. + + Parameters + ---------- + configurations: List[str], required + List of NWM configurations. + reference_times: array-like, required + array-like of reference times. Should be compatible with pandas.Timestamp. + nwm_feature_ids: array-like, optional + array-like of NWM feature IDs to return. Defaults to channel features + with a known USGS mapping. + variables: List[str], optional, default ['streamflow'] + List of variables to retrieve from NWM files. + compute: bool, optional, default True + When True returns a pandas.DataFrame. When False returns a dask.dataframe.DataFrame. + + Returns + ------- + dask.dataframe.DataFrame of NWM data or a pandas.DataFrame in canonical + format. + """ + # Check store + if not self.dataframe_store: + raise StoreNotFoundError("get requires a dataframe store") + + # Validate reference times + reference_times = [pd.Timestamp(rft) for rft in reference_times] + + # Put features in array + nwm_feature_ids = np.array(nwm_feature_ids) + + # Collect dataframes + dfs = [] + for cfg in configurations: + for rft in reference_times: + # Build datetime string + rft_str = rft.strftime("%Y%m%dT%HZ") + + # Retrieve or build and store dataframes + for var in variables: + # Generate key + key = f"{cfg}_{rft_str}_{var}" + + # Check store + if key in self.dataframe_store: + # Load data + df = self.dataframe_store[key] + + # Check for features + missing = ~np.isin(nwm_feature_ids, df["nwm_feature_id"].compute()) + missing_ids = nwm_feature_ids[missing] + + # Load + dfs.append(df[df["nwm_feature_id"].isin(nwm_feature_ids)]) + + # If no IDs are missing, continue + if not np.any(missing): + continue + else: + missing_ids = nwm_feature_ids + + # Process data + # NOTE This may be parallizable. This funky mess exists because dask and xarray + # got fussy with the medium range data. + keyed_files = self.get_files(cfg, rft) + for group, files in keyed_files.items(): + # Build subkey + subkey = f"{key}/{group}" + + # Open dataset + ds = NWMFileProcessor.get_dataset(files, missing_ids, ["reference_time"]+variables) + + # Warn for no features + if ds.feature_id.size == 0: + message = f"These filter IDs returned no data: {missing_ids}" + warnings.warn(message) + continue + + # Convert to dask + df = NWMFileProcessor.convert_to_dask_dataframe(ds) + + # Canonicalize + df = df[["reference_time", "feature_id", "time", var]].rename(columns={ + "feature_id": "nwm_feature_id", + "time": "value_time", + var: "value" + }) + + # Add required columns + df["measurement_unit"] = ds[var].attrs["units"] + df["variable_name"] = var + df["configuration"] = ds.attrs["model_configuration"] + + # Address ambigious "No-DA" cycle names + if cfg.endswith("no_da"): + df["configuration"] = df["configuration"] + "_no_da" + + # Map crosswalk + for col in self.crosswalk: + df[col] = df["nwm_feature_id"].map(self.crosswalk[col]) + + # Save + self.dataframe_store.append(subkey, df) + + # Append all parts + df = self.dataframe_store[subkey] + dfs.append(df[df["nwm_feature_id"].isin(missing_ids)]) + + # Close dataset + ds.close() + + # Check for empty data + if not dfs: + message = ( + f"Query returned no data for configurations {configurations}\n" + + f"reference_times {reference_times}\n" + + f"variables {variables}\n" + + f"nwm_feature_ids {nwm_feature_ids}\n" + ) + raise QueryError(message) + + # Clean-up NetCDF files + if self.cleanup_files: + try: + shutil.rmtree(self.file_directory) + except OSError: + message = (f"Unable to delete {self.file_directory}") + warnings.warn(message, RuntimeWarning) + + # Concatenate + # data = dd.multi.concat(dfs) + data = dd.concat(dfs) + + # Convert units + if self.unit_system == MeasurementUnitSystem.US: + # Get conversions + c = _NWMClientDefault.NWM_TO_US_UNIT_CONVERSION + + # Conversion factors + factors = data["measurement_unit"].map(c["conversion_factor"]) + labels = data["measurement_unit"].map(c["measurement_unit_conversion"]) + + # Convert + data["value"] = data["value"].mul(factors) + data["measurement_unit"] = labels + + # Return pandas.DataFrame + if compute: + # Convert to pandas + df = data.compute() + + # Optimize memory + df["nwm_feature_id"] = pd.to_numeric(df["nwm_feature_id"], downcast="integer") + df["value"] = pd.to_numeric(df["value"], downcast="float") + for cat in ["measurement_unit", "usgs_site_code", "variable_name", "configuration"]: + df[cat] = df[cat].astype("category") + + # Reset to unique index + return df.reset_index(drop=True) + + # Return dask dataframe + return data + + @property + def file_directory(self) -> Path: + return self._file_directory + + @file_directory.setter + def file_directory(self, file_directory: Union[str, Path]) -> None: + self._file_directory = Path(file_directory).expanduser().resolve() + self._file_directory.mkdir(exist_ok=True, parents=True) + + @property + def dataframe_store(self) -> ParquetStore: + return self._dataframe_store + + @dataframe_store.setter + def dataframe_store(self, + dataframe_store: Union[ParquetStore, None]) -> None: + self._dataframe_store = dataframe_store + + @property + def catalog(self) -> NWMFileCatalog: + return self._catalog + + @catalog.setter + def catalog(self, + catalog: NWMFileCatalog) -> None: + self._catalog = catalog + + @property + def crosswalk(self) -> pd.DataFrame: + return self._crosswalk + + @crosswalk.setter + def crosswalk(self, + crosswalk: pd.DataFrame) -> None: + self._crosswalk = crosswalk + + @property + def ssl_context(self) -> ssl.SSLContext: + return self._ssl_context + + @ssl_context.setter + def ssl_context(self, ssl_context: ssl.SSLContext) -> None: + self._ssl_context = ssl_context + + @property + def cleanup_files(self) -> bool: + return self._cleanup_files + + @cleanup_files.setter + def cleanup_files(self, cleanup_files: bool) -> None: + self._cleanup_files = cleanup_files diff --git a/python/nwm_client/build/lib/hydrotools/nwm_client/NWMFileProcessor.py b/python/nwm_client/build/lib/hydrotools/nwm_client/NWMFileProcessor.py new file mode 100644 index 00000000..4bc27e22 --- /dev/null +++ b/python/nwm_client/build/lib/hydrotools/nwm_client/NWMFileProcessor.py @@ -0,0 +1,134 @@ +""" +================== +NWM File Processor +================== +Tools for processing NWM data in NetCDF (.nc) format. + +Classes +------- +NWMFileProcessor +""" + +import xarray as xr +import dask.dataframe as dd +import pandas as pd +import numpy as np +import numpy.typing as npt +from typing import List, Union +import warnings +from .NWMClientDefaults import _NWMClientDefault + +class NWMFileProcessor: + """Provides a concrete interface for methods used to process National Water + Model data from NetCDF (.nc) format to xarray.Dataset, + dask.dataframe.Dataframe, or pandas.DataFrame. + """ + + @classmethod + def get_dataset( + cls, + paths: Union[str, npt.ArrayLike], + feature_id_filter: npt.ArrayLike = _NWMClientDefault.CROSSWALK.index, + variables: List[str] = _NWMClientDefault.VARIABLES + ) -> xr.Dataset: + """Generate an xarray.Dataset from an input directory of NWM .nc files. + + Parameters + ---------- + paths: str or array-like of paths, required + Glob string or array-like of paths passed directly to xarray.open_mfdataset + feature_id_filter: array-like, optional + Subset of feature IDs to return. Defaults to USGS assimilation locations. + variables: list of str, optional, default ["streamflow"] + List of variables to retrieve from source files. Options include: + 'streamflow', 'nudge', 'velocity', 'qSfcLatRunoff', 'qBucket', 'qBtmVertRunoff' + + Returns + ------- + xarray.Dataset of paths lazily loaded. + """ + # Minimum coordinates + coordinates = [] + for c in ["feature_id", "time", "reference_time"]: + if c not in variables: + coordinates.append(c) + + # Open dataset + ds = xr.open_mfdataset(paths, engine="netcdf4") + + # Prepare feature_id filter + if len(feature_id_filter) != 0: + # Convert to integer array + feature_id_filter = np.asarray(feature_id_filter, dtype=int) + + # Subset by feature ID and variable + try: + return ds.sel(feature_id=feature_id_filter)[coordinates+variables] + except KeyError: + # Validate filter IDs + check = np.isin(feature_id_filter, ds.feature_id) + + # Note invalid feature IDs + missing = feature_id_filter[~check] + + # Warn + message = f"These filter IDs were not in the index: {missing}" + warnings.warn(message) + + # Subset by valid feature ID and variable + return ds.sel(feature_id=feature_id_filter[check])[coordinates+variables] + + # Subset by variable only + return ds[coordinates+variables] + + @classmethod + def convert_to_dask_dataframe( + cls, + ds: xr.Dataset + ) -> dd.DataFrame: + """Generate a dask.dataframe.DataFrame from an xarray.Dataset. + + Parameters + ---------- + ds: xarray.Dataset, required + xarray.Dataset containing National Water Model data. + + Returns + ------- + dask.dataframe.DataFrame of NWM data. + """ + # Convert to dask dataframe + df = ds.to_dask_dataframe() + + # Compute number of partitions + # Best practice is ~100 MB per partition + npartitions = 1 + len(df.index) // 2_400_000 + + # Sort by feature ID + # Most applications will benefit from this + df = df.sort_values(by="feature_id") + + # Reset index + df = df.set_index("feature_id").reset_index() + + # Repartition + return df.repartition(npartitions=npartitions) + + @classmethod + def convert_to_dataframe( + cls, + ds: xr.Dataset + ) -> pd.DataFrame: + """Generate a pandas.DataFrame from an xarray.Dataset. + + Parameters + ---------- + ds: xarray.Dataset, required + xarray.Dataset containing National Water Model data. + + Returns + ------- + pandas.DataFrame of NWM data. + """ + # Compute pandas dataframe + return cls.convert_to_dask_dataframe(ds).compute() diff --git a/python/nwm_client/build/lib/hydrotools/nwm_client/ParquetStore.py b/python/nwm_client/build/lib/hydrotools/nwm_client/ParquetStore.py new file mode 100644 index 00000000..b53cdc2e --- /dev/null +++ b/python/nwm_client/build/lib/hydrotools/nwm_client/ParquetStore.py @@ -0,0 +1,155 @@ +""" +============================== +Parquet-backed DataFrame Store +============================== +Store dask dataframes using a key-value interface. + +Classes +--------- + - ParquetStore + +""" + +import dask.dataframe as dd +from typing import Union, Iterator +from pathlib import Path +from collections.abc import MutableMapping + +class ParquetStore(MutableMapping): + def __init__(self, root: Union[str, Path], **kwargs) -> None: + """ + Interface for storing dask.dataframe.DataFrame. + + Parameters + ---------- + root: str or pathlib.Path, required + Path to parquet store root directory. + **kwargs + Additional arguments passed to dask.dataframe.DataFrame.to_parquet + + Notes + ----- + ParquetStore is really a collection of conveniene methods for common + data access patterns wrapped around a parquet file. This module + was not specifically designed for parallel workflows and should not + be considered thread-safe. For more complicated file-io patterns + see the dask.dataframe.DataFrame documentation. + + Examples + -------- + >>> import pandas as pd + >>> import dask.dataframe as dd + >>> from time import sleep + >>> from hydrotools.nwm_client.ParquetStore import ParquetStore + >>> def my_long_running_process(data: dict) -> dd.DataFrame: + >>> # Some long running process + >>> sleep(0.2) + >>> return dd.from_pandas(pd.DataFrame(data), npartitions=1) + >>> # Use context manager to setup store + >>> with ParquetStore( + >>> "my_store.parquet", + >>> write_index=False, + >>> compression="snappy" + >>> ) as store: + >>> # Fabricate some data to feed the function + >>> my_data = {"A": [1, 2, 3]} + >>> # Check store before running process + >>> if "my_dataframe" in store: + >>> df = store["my_dataframe"] + >>> else: + >>> df = my_long_running_process(my_data) + >>> store["my_dataframe"] = df + """ + super().__init__() + self.root = root + self.parameters = kwargs + + def __enter__(self): + return self + + def __exit__(self, exc_type, exc_value, exc_traceback): + pass + + def __del__(self): + pass + + def __str__(self) -> str: + # Return root directory string + return str(self.root) + + def __bool__(self) -> bool: + return bool(str(self)) + + def __getitem__(self, subdirectory: str) -> dd.DataFrame: + # Set path + filepath = self.root / subdirectory + + # Check filepath and return dataframe if available + if filepath.exists(): + return dd.read_parquet(filepath) + + # Raise if filepath does not exist + raise KeyError(f"{subdirectory}") + + def __setitem__(self, subdirectory: str, df: dd.DataFrame) -> None: + # Set path + filepath = self.root / subdirectory + + # Save dataframe + df.to_parquet(filepath, **self.parameters) + + def __delitem__(self, subdirectory: str) -> None: + # Set path + filepath = self.root / subdirectory + + # Delete partitions and filepath + if filepath.exists(): + for f in filepath.glob("*"): + f.unlink() + filepath.rmdir() + + def __iter__(self) -> Iterator[str]: + # Iterator of subdirectory names + return iter([f.name for f in self.root.glob("*")]) + + def __len__(self) -> int: + # Number of subdirectories under root + return len([f for f in self.root.glob("*")]) + + def append(self, subdirectory: str, df: dd.DataFrame) -> None: + """Append data to a parquet file. + + Parameters + ---------- + subdirectory: str, required + Key-path under ParquetStore.root where existing dataframe is stored. + df: dask.dataframe.DataFrame, required + Data to append. + + Returns + ------- + None + + """ + # Set path + filepath = self.root / subdirectory + + # Save dataframe + df.to_parquet(filepath, append=True, **self.parameters) + + @property + def root(self) -> Path: + return self._directory + + @root.setter + def root(self, root) -> None: + self._directory = Path(root).resolve().expanduser() + + @property + def parameters(self) -> dict: + return self._parameters + + @parameters.setter + def parameters(self, parameters) -> None: + 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