async-retriever 0.17.1

Description:

asyncretriever 0.17.1

Package
Description
Status



PyNHD
Navigate and subset NHDPlus (MR and HR) using web services


Py3DEP
Access topographic data through National Map’s 3DEP web service


PyGeoHydro
Access NWIS, NID, WQP, eHydro, NLCD, CAMELS, and SSEBop databases


PyDaymet
Access daily, monthly, and annual climate data via Daymet


PyGridMET
Access daily climate data via GridMET


PyNLDAS2
Access hourly NLDAS-2 data via web services


HydroSignatures
A collection of tools for computing hydrological signatures


AsyncRetriever
High-level API for asynchronous requests with persistent caching


PyGeoOGC
Send queries to any ArcGIS RESTful-, WMS-, and WFS-based services


PyGeoUtils
Utilities for manipulating geospatial, (Geo)JSON, and (Geo)TIFF data





AsyncRetriever: Asynchronous requests with persistent caching



































Features
AsyncRetriever is a part of HyRiver software stack that
is designed to aid in hydroclimate analysis through web services. This package serves as HyRiver’s
engine for asynchronously sending requests and retrieving responses as text, binary, or
json objects. It uses persistent caching using
aiohttp-client-cache to speed up the retrieval
even further. Moreover, thanks to nest_asyncio
you can use this package in Jupyter notebooks. Although this package is part of the HyRiver
software stack, it can be used for any web calls. There are three functions that you can
use to make web calls:

retrieve_text: Get responses as text objects.
retrieve_binary: Get responses as binary objects.
retrieve_json: Get responses as json objects.
stream_write: Stream responses and write them to disk in chunks.

You can also use the general-purpose retrieve function to get responses as any
of the three types. All responses are returned as a list that has the same order as the
input list of requests. Moreover, there is another function called delete_url_cache
for removing all requests from a cache file that contains a given URL.
You can control the request/response caching behavior and verbosity of the package
by setting the following environment variables:

HYRIVER_CACHE_NAME: Path to the caching SQLite database. It defaults to
./cache/aiohttp_cache.sqlite
HYRIVER_CACHE_EXPIRE: Expiration time for cached requests in seconds. It defaults to
one week.
HYRIVER_CACHE_DISABLE: Disable reading/writing from/to the cache. The default is false.
HYRIVER_SSL_CERT: Path to a SSL certificate file.

For example, in your code before making any requests you can do:
import os

os.environ["HYRIVER_CACHE_NAME"] = "path/to/file.sqlite"
os.environ["HYRIVER_CACHE_EXPIRE"] = "3600"
os.environ["HYRIVER_CACHE_DISABLE"] = "true"
os.environ["HYRIVER_SSL_CERT"] = "path/to/cert.pem"
You can find some example notebooks here.
You can also try using AsyncRetriever without installing
it on your system by clicking on the binder badge. A Jupyter Lab
instance with the HyRiver stack pre-installed will be launched in your web browser, and you
can start coding!
Moreover, requests for additional functionalities can be submitted via
issue tracker.


Citation
If you use any of HyRiver packages in your research, we appreciate citations:
@article{Chegini_2021,
author = {Chegini, Taher and Li, Hong-Yi and Leung, L. Ruby},
doi = {10.21105/joss.03175},
journal = {Journal of Open Source Software},
month = {10},
number = {66},
pages = {1--3},
title = {{HyRiver: Hydroclimate Data Retriever}},
volume = {6},
year = {2021}
}


Installation
You can install async-retriever using pip:
$ pip install async-retriever
Alternatively, async-retriever can be installed from the conda-forge repository
using Conda:
$ conda install -c conda-forge async-retriever


Quick start
AsyncRetriever by default creates and/or uses ./cache/aiohttp_cache.sqlite as the cache
that you can customize by the cache_name argument. Also, by default, the cache doesn’t
have any expiration date and the delete_url_cache function should be used if you know
that a database on a server was updated, and you want to retrieve the latest data.
Alternatively, you can use the expire_after to set the expiration date for the cache.
As an example for retrieving a binary response, let’s use the DAAC server to get
NDVI.
The responses can be directly passed to xarray.open_mfdataset to get the data as
a xarray Dataset. We can also disable SSL certificate verification by setting
ssl=False.
import io
import xarray as xr
import async_retriever as ar
from datetime import datetime

west, south, east, north = (-69.77, 45.07, -69.31, 45.45)
base_url = "https://thredds.daac.ornl.gov/thredds/ncss/ornldaac/1299"
dates_itr = ((datetime(y, 1, 1), datetime(y, 1, 31)) for y in range(2000, 2005))
urls, kwds = zip(
*[
(
f"{base_url}/MCD13.A{s.year}.unaccum.nc4",
{
"params": {
"var": "NDVI",
"north": f"{north}",
"west": f"{west}",
"east": f"{east}",
"south": f"{south}",
"disableProjSubset": "on",
"horizStride": "1",
"time_start": s.strftime("%Y-%m-%dT%H:%M:%SZ"),
"time_end": e.strftime("%Y-%m-%dT%H:%M:%SZ"),
"timeStride": "1",
"addLatLon": "true",
"accept": "netcdf",
}
},
)
for s, e in dates_itr
]
)
resp = ar.retrieve_binary(urls, kwds, max_workers=8, ssl=False)
data = xr.open_mfdataset(io.BytesIO(r) for r in resp)
We can remove these requests and their responses from the cache like so:
ar.delete_url_cache(base_url)



For a json response example, let’s get water level recordings of an NOAA’s water level station,
8534720 (Atlantic City, NJ), during 2012, using CO-OPS API. Note that this CO-OPS product has a
31-day limit for a single request, so we have to break the request down accordingly.
import pandas as pd

station_id = "8534720"
start = pd.to_datetime("2012-01-01")
end = pd.to_datetime("2012-12-31")

s = start
dates = []
for e in pd.date_range(start, end, freq="m"):
dates.append((s.date(), e.date()))
s = e + pd.offsets.MonthBegin()

url = "https://api.tidesandcurrents.noaa.gov/api/prod/datagetter"

urls, kwds = zip(
*[
(
url,
{
"params": {
"product": "water_level",
"application": "web_services",
"begin_date": f'{s.strftime("%Y%m%d")}',
"end_date": f'{e.strftime("%Y%m%d")}',
"datum": "MSL",
"station": f"{station_id}",
"time_zone": "GMT",
"units": "metric",
"format": "json",
}
},
)
for s, e in dates
]
)

resp = ar.retrieve_json(urls, kwds)
wl_list = []
for rjson in resp:
wl = pd.DataFrame.from_dict(rjson["data"])
wl["t"] = pd.to_datetime(wl.t)
wl = wl.set_index(wl.t).drop(columns="t")
wl["v"] = pd.to_numeric(wl.v, errors="coerce")
wl_list.append(wl)
water_level = pd.concat(wl_list).sort_index()
water_level.attrs = rjson["metadata"]



Now, let’s see an example without any payload or headers. Here’s how we can retrieve
harmonic constituents of several NOAA stations from CO-OPS:
stations = [
"8410140",
"8411060",
"8413320",
"8418150",
"8419317",
"8419870",
"8443970",
"8447386",
]

base_url = "https://api.tidesandcurrents.noaa.gov/mdapi/prod/webapi/stations"
urls = [f"{base_url}/{i}/harcon.json?units=metric" for i in stations]
resp = ar.retrieve_json(urls)

amp_list = []
phs_list = []
for rjson in resp:
sid = rjson["self"].rsplit("/", 2)[1]
const = pd.DataFrame.from_dict(rjson["HarmonicConstituents"]).set_index("name")
amp = const.rename(columns={"amplitude": sid})[sid]
phase = const.rename(columns={"phase_GMT": sid})[sid]
amp_list.append(amp)
phs_list.append(phase)

amp = pd.concat(amp_list, axis=1)
phs = pd.concat(phs_list, axis=1)





Contributing
Contributions are appreciated and very welcomed. Please read
CONTRIBUTING.rst
for instructions.