Querying the HUC Dataset¶
As input to the query, we require either a “HUC” or a “Region” identifier. Optionally, you can include a parameter “exact = True” that tells us you want to search for exactly the string you’ve entered. You may also include “exact = False” to search for partial matches, however it defaults to this search option automatically.
Below are some examples I’ve put together to display how you can interact with the HUC portion of the Feature Translation Service.
Getting a list of all partially matched regions:
import requests
import json
###################
# Mimicing a user querying partial matches with California
REGION = "California"
###################
# Query Feature Translation Service and parse JSON response
r = requests.get("https://g6zl7z2x7j.execute-api.us-west-2.amazonaws.com/prod/region/{}".format(REGION))
# Example query from above is:
# https://g6zl7z2x7j.execute-api.us-west-2.amazonaws.com/prod/region/California
# Load response from FTS
response = json.loads(r.text)
# Print all elements in HUC database that partially match California
print(response['results'].keys())
You should see something that looks like:
dict_keys(['California Region', 'California Creek-Frontal Semiahmoo Bay', 'California Creek', 'California Creek-Verdigris River', 'California Valley', 'California Wash', 'California Bayou-Richard Bayou Frontal', 'California Spring', 'California Gulch-North Platte River', 'California Slough-Beaverhead River', 'California Gulch', 'California Draw', 'California Creek-Amistad Reservoir', 'California River', 'California Creek-Kuskokwim River', 'California Creek-Titna River', 'California Creek-Brazos River', 'California Tank-Beals Creek', 'California Cove-Frontal Carroll Inlet'])
Query HUC database by HUC and exact matches:
import requests
import json
###################
# Mimicing a user querying exact matches with HUC "180500030105"
HUC = "180500030105"
EXACT = True
###################
# Query Feature Translation Service and parse JSON response
r = requests.get("https://g6zl7z2x7j.execute-api.us-west-2.amazonaws.com/prod/huc/{}?exact={}".format(HUC, EXACT))
# Load response from FTS
response = json.loads(r.text)
# Print all elements in HUC database that exactly match HUC "180500030105"
print(json.dumps(response, indent = 4))
The URL represents the endpoint used to when querying that specific HUC. You could as easily have done:
curl https://g6zl7z2x7j.execute-api.us-west-2.amazonaws.com/prod/huc/180500030105?exact=True
in this case, however the code above makes the output look nicer. Below is what should appear after querying this HUC.
Click to expand output:
{
"status": "200 OK",
"hits": 1,
"time": "1.679 ms.",
"search on": {
"parameter": "HUC",
"exact": true
},
"results": {
"180500030105": {
"Region Name": "Anderson Lake-Coyote Creek",
"Bounding Box": "-121.63767546330098,37.04979849040802,-121.48073155937789,37.23308885991514",
"Convex Hull Polygon": "-121.48860388957405,37.04979849040802,-121.48101806979412,37.05967403310103,-121.480896433336,37.05992186539231,-121.48073155937789,37.060387359141544,-121.48100425416914,37.06816868308783,-121.48213506562576,37.0885239403479,-121.48371335416493,37.09426448304731,-121.48877537915712,37.11173951947853,-121.48900143124007,37.11211648406129,-121.49726712810224,37.12520530591598,-121.52221073848017,37.16180481731749,-121.52397479785247,37.16422715585537,-121.57056402903015,37.221908025557525,-121.57872251026748,37.22856819429717,-121.58057172693128,37.22974378804537,-121.58138176547169,37.23020550575296,-121.58521716442408,37.231498325542645,-121.5939973664938,37.23308885991514,-121.59545081961653,37.23232609533301,-121.59595191753237,37.232037934916775,-121.60244874252231,37.227485860965544,-121.63618427996994,37.177165120418636,-121.63767546330098,37.17474510271404,-121.6283990435237,37.164057801688955,-121.62825781644057,37.163915506897524,-121.53388744367038,37.07483921953582,-121.53347970512937,37.07447522891135,-121.53329459471297,37.07431360078664,-121.5306923582587,37.07216369141497,-121.52077347494077,37.06646913829877,-121.49243878852639,37.051171511239204,-121.49057642498764,37.050229850823996,-121.48978976978054,37.049978487282715,-121.48860388957405,37.04979849040802",
"Visvalingam Polygon": "-121.58806250921134,37.2316227890841,-121.59264420712088,37.231896370333686,-121.5939973664938,37.23308885991514,-121.59595191753237,37.232037934916775,-121.59977078419314,37.22788898596491,-121.60261379668873,37.22716914846603,-121.6016108123153,37.22436869847036,-121.60295853106317,37.222389815140104,-121.60427384043618,37.21782753181384,-121.60890233417899,37.21653934327418,-121.6074674029312,37.21283977973826,-121.61021511021858,37.20773161724617,-121.61252820084002,37.20713216620544,-121.61107104250891,37.20551669016629,-121.60773546334747,37.19999182975823,-121.60794556126376,37.19899689955139,-121.61327484771385,37.197780694345,-121.61262822271487,37.19380909226783,-121.61292882167271,37.19135747039661,-121.61591523416809,37.1897428516491,-121.6205293497859,37.19089637456398,-121.6237499779059,37.18869607352576,-121.6256988122779,37.18611010373809,-121.62806206748257,37.18534469019761,-121.62843133310696,37.18171738811992,-121.63272383935032,37.17853183291652,-121.63506391018001,37.17814962354208,-121.63767546330098,37.17474510271404,-121.63232243518428,37.17154535271902,-121.62990595393802,37.168205528765895,-121.63065950289518,37.16717847043412,-121.62781469664964,37.16368020689788,-121.62424781540517,37.163012267315594,-121.62345255498974,37.1614234131514,-121.62101453624348,37.15998701002866,-121.62030911749457,37.15827049857296,-121.6139672820878,37.15624052253446,-121.61068506855122,37.15368323191342,-121.60975016438596,37.15182316629131,-121.60710791855678,37.15002665691907,-121.60441556335257,37.14965349233631,-121.59720055919712,37.14566015380086,-121.59576041544938,37.142763677763696,-121.59054217274911,37.141855636098455,-121.58537960921547,37.13896283818627,-121.58381942380123,37.13726589235557,-121.5793186081832,37.13485330277598,-121.5744490227741,37.130106875699994,-121.5691209592407,37.12823199861958,-121.56748776965992,37.12852001945248,-121.56716054986873,37.126001953831405,-121.56513314674692,37.123240200710654,-121.564640198831,37.11854930592631,-121.56278191237556,37.1162589975965,-121.55929466238098,37.11373619343374,-121.5570859998844,37.109406602815454,-121.5572637946758,37.10681280698617,-121.5538499103061,37.10462442365622,-121.55139001760159,37.10194934136871,-121.55133699780998,37.099308187206134,-121.55012572906185,37.09842683720751,-121.5475199790659,37.09287281534114,-121.54585853740184,37.092235915342144,-121.53824969366366,37.081910515358175,-121.53531102075152,37.08016952473588,-121.53405755929515,37.07504359974382,-121.53000371450975,37.07179637787385,-121.52741861868043,37.07169844766565,-121.52369238951957,37.072768854955655,-121.5220949384804,37.07010230912647,-121.52235906868827,37.06834881329587,-121.52077347494077,37.06646913829877,-121.51757171140406,37.06750525704717,-121.51332047495231,37.06593807163296,-121.51227115620395,37.06363009246985,-121.51002244579075,37.06150839768151,-121.50594653850544,37.06111527893211,-121.50390213850864,37.05977303310084,-121.49910575101603,37.061136444557064,-121.49413868019042,37.05470632685871,-121.49243878852639,37.051171511239204,-121.48862659999065,37.04997845499105,-121.49025179582145,37.05302723102801,-121.48742278957587,37.05348996748563,-121.48641130207744,37.05643856852271,-121.48181507291793,37.05871785393583,-121.48073155937789,37.060387359141544,-121.48137940208522,37.06496260080115,-121.48103537708579,37.0685587518372,-121.48321114062406,37.0725354414144,-121.48313336770752,37.075025113285506,-121.48713180311796,37.079890366402935,-121.48668403124367,37.08186417994159,-121.48483490728819,37.083866848688444,-121.48249655937519,37.08893382472223,-121.48723527707614,37.10208726845184,-121.49045229061284,37.10569764240455,-121.48987685832208,37.108215367400646,-121.48830673228281,37.10888117156628,-121.48877537915712,37.11173951947853,-121.49426047602356,37.11386677051689,-121.49693434268607,37.11644134447124,-121.498516832267,37.11909555800878,-121.4959494999793,37.11998262571575,-121.49726712810224,37.12520530591598,-121.50248761976081,37.1260039538314,-121.50833411975174,37.12863390486899,-121.50960340308308,37.1284316548693,-121.5132420114108,37.13050363194941,-121.51329699057737,37.13167332673925,-121.51701764473825,37.13653624027336,-121.51966352910915,37.13762921839668,-121.52061257077435,37.1418806590151,-121.522983213479,37.14412354442828,-121.52718232180581,37.146467055882965,-121.53292299158858,37.15066874754308,-121.53196873221503,37.153696339205055,-121.52762230409678,37.15490493503654,-121.52509076451742,37.15791285794853,-121.52393503222754,37.15725941211622,-121.52212906139698,37.15899819648848,-121.52221073848017,37.16180481731749,-121.52591905826608,37.16640231939368,-121.53289038429693,37.17250530792586,-121.53781407803933,37.17272204542553,-121.54040702595194,37.17158501730228,-121.5423534446989,37.17368201104904,-121.54245592594879,37.175444567296324,-121.54565006031879,37.17749450375146,-121.54846312802277,37.178090125625545,-121.55480401759627,37.184714057906945,-121.5540414040558,37.1920149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"USGS Polygon": {
"Object URL": "https://podaac-dev-feature-translation-service.s3-us-west-1.amazonaws.com/180500030105",
"Source": "ftp://rockyftp.cr.usgs.gov/vdelivery/Datasets/Staged/Hydrography/WBD/HU2/Shape/"
}
}
}
}
You’ll notice the service returns everything associated with that HUC back to you.
This service works for partial HUC matches as well. Removing that final “5” in the HUC:
import requests
import json
###################
# Mimicing a user querying partial matches with HUC "1805000301"
# This "partial" match is anything that BEGINS with the HUC specified.
HUC = "1805000301"
EXACT = False
###################
# Query Feature Translation Service and parse JSON response
r = requests.get("https://g6zl7z2x7j.execute-api.us-west-2.amazonaws.com/prod/huc/{}?exact={}".format(HUC, EXACT))
# Load response from FTS
response = json.loads(r.text)
# Print all elements in HUC database that partially matches with HUC 1805000301
print(json.dumps(response, indent = 4))
Will grab all regions that begin with the HUC you specified. So the feature translation service returns:
Click to expand output:
{
"status": "200 OK",
"hits": 6,
"time": "4.491 ms.",
"search on": {
"parameter": "HUC",
"exact": false
},
"results": {
"1805000301": {
"Region Name": "Upper Coyote Creek",
"Bounding Box": "-121.73972838189258,37.01812391649884,-121.37745628974653,37.3505276441079",
"Convex Hull Polygon": "-121.46324080107172,37.01812391649884,-121.41644204489438,37.01938953628854,-121.41604124697835,37.01943259983011,-121.38924813347825,37.029469425856234,-121.38615585639974,37.030891615437326,-121.38523752202616,37.03138961751989,-121.38491633244331,37.03159835918626,-121.37984129182615,37.039406947715804,-121.37973573349302,37.039706119590335,-121.37939454495188,37.04078733729699,-121.37745628974653,37.0485418195766,-121.37748121787149,37.049256088325535,-121.37749731995484,37.04953592374176,-121.37755949495471,37.04997641228272,-121.37817246578709,37.05307921748624,-121.423106010509,37.20536550162484,-121.45433100421053,37.27955529421803,-121.45785790212176,37.287727894205375,-121.4589552948284,37.28933892545285,-121.48963580936407,37.317232384784575,-121.49238281040147,37.3183537254078,-121.72294718504361,37.3505276441079,-121.72359000066763,37.35052033160787,-121.72387703608382,37.35043791702469,-121.7322171048209,37.34707858786322,-121.73972838189258,37.34087807120619,-121.72063677671389,37.23811789011569,-121.71381752672443,37.22899085262986,-121.7052095027795,37.22084106514251,-121.69021425384443,37.2077098745379,-121.53329459471297,37.07431360078664,-121.5306923582587,37.07216369141497,-121.46352466461298,37.01817605399873,-121.46324080107172,37.01812391649884",
"Visvalingam Polygon": 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"USGS Polygon": {
"Object URL": "https://podaac-dev-feature-translation-service.s3-us-west-1.amazonaws.com/1805000301",
"Source": "ftp://rockyftp.cr.usgs.gov/vdelivery/Datasets/Staged/Hydrography/WBD/HU2/Shape/"
}
},
"180500030101": {
"Region Name": "East Fork Coyote Creek",
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"USGS Polygon": {
"Object URL": "https://podaac-dev-feature-translation-service.s3-us-west-1.amazonaws.com/180500030105",
"Source": "ftp://rockyftp.cr.usgs.gov/vdelivery/Datasets/Staged/Hydrography/WBD/HU2/Shape/"
}
}
}
}
The same can be done when you query by region instead of HUC. Here is an example of querying exactly one region:
import requests
import json
###################
# Mimicing a user querying exact matches with region "Woods Creek-Skykomish River"
REGION = "Woods Creek-Skykomish River"
EXACT = True
###################
# Query Feature Translation Service and parse JSON response
# Note the change in endpoint from "/prod/huc" to "/prod/region"
r = requests.get("https://g6zl7z2x7j.execute-api.us-west-2.amazonaws.com/prod/region/{}?exact={}".format(REGION, EXACT))
# Load response from FTS
response = json.loads(r.text)
# Print all elements in HUC database that exact matches with region "Woods Creek-Skykomish River"
print(json.dumps(response, indent = 4))
Note: I’ve taken care of spaces in the region for you. For example, querying the region above will result in a URL that compensates for the spaces like so:
https://g6zl7z2x7j.execute-api.us-west-2.amazonaws.com/prod/region/Woods%20Creek-Skykomish%20River
with %20 as spaces. I’ve handled this in the Lambda function itself, so when querying there is no need to modify your region of interest whatsoever.
The same can be done for partial matches by name as well. It will return the same JSON with multiple regions:
import requests
import json
###################
# Mimicing a user querying partial matches with region "California R"
# This "partial" match is anything that BEGINS with the region specified.
REGION = "California R"
EXACT = False
###################
# Query Feature Translation Service and parse JSON response
r = requests.get("https://g6zl7z2x7j.execute-api.us-west-2.amazonaws.com/prod/region/{}?exact={}".format(REGION, EXACT))
# Load response from FTS
response = json.loads(r.text)
# Print all elements in HUC database that partially matches with HUC 1805000301
print(json.dumps(response, indent = 4))
Finally, it is important to show that these results can be used to query NASA’s Common Metadata Repository (CMR) - as this was an essential consideration when designing the service. The format of the CMR by polygon or bounding box can be found here.
Below are some examples of querying CMR by polygon and by bounding box.
Query CMR by polygon:
from bs4 import BeautifulSoup
import requests
import json
###################
COLLECTION_ID = "C1522341104-NSIDC_ECS" # SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V002
REGION = "California Creek-Kuskokwim River"
EXACT = True
###################
# Query Feature Translation Service and parse JSON response
r = requests.get("https://g6zl7z2x7j.execute-api.us-west-2.amazonaws.com/prod/region/{}?exact={}".format(REGION, EXACT))
# Load response from FTS
response = json.loads(r.text)
# Obtain convex hull polygon from response
polygon = response['results'][REGION]['Convex Hull Polygon']
#polygon = response['results'][REGION]['Visvalingam Polygon']
# Query CMR
# --------- #
#cmr_response = requests.get("https://cmr.earthdata.nasa.gov/search/granules.json?polygon={}&echo_collection_id=C1522341104-NSIDC_ECS&pretty=True".format(polygon))
cmr_response = requests.get("https://cmr.earthdata.nasa.gov/search/granules?polygon={}&echo_collection_id={}&pretty=True".format(polygon, COLLECTION_ID))
# --------- #
# Make it look nice
soup = BeautifulSoup(cmr_response.text, features = 'lxml')
print(soup.prettify())
Query CMR by bounding box:
from bs4 import BeautifulSoup
import requests
import json
###################
COLLECTION_ID = "C1522341104-NSIDC_ECS" # SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V002
REGION = "California Creek-Kuskokwim River"
EXACT = False
###################
# Query Feature Translation Service and parse JSON response
r = requests.get("https://g6zl7z2x7j.execute-api.us-west-2.amazonaws.com/prod/region/{}?exact={}".format(REGION, EXACT))
# Load response from FTS
response = json.loads(r.text)
# Obtain bounding box from response
bbox = response['results'][REGION]['Bounding Box']
# Query CMR
# --------- #
#cmr_response = requests.get("https://cmr.earthdata.nasa.gov/search/granules.json?bounding_box={}&echo_collection_id=C1522341104-NSIDC_ECS&pretty=True".format(bbox))
cmr_response = requests.get("https://cmr.earthdata.nasa.gov/search/granules?bounding_box={}&echo_collection_id={}&pretty=True".format(bbox, COLLECTION_ID))
# --------- #
# Make it look nice
soup = BeautifulSoup(cmr_response.text, features = 'lxml')
print(soup.prettify())
These should return you a response from CMR that looks similar to:
Click to expand response:
<?xml version="1.0" encoding="UTF-8"?>
<html>
<body>
<results>
<hits>
36
</hits>
<took>
47
</took>
<references>
<reference>
<name>
SC:SPL2SMAP_S.002:137224366
</name>
<id>
G1547570860-NSIDC_ECS
</id>
<location>
https://cmr.earthdata.nasa.gov:443/search/concepts/G1547570860-NSIDC_ECS/6
</location>
<revision-id>
6
</revision-id>
</reference>
<reference>
<name>
SC:SPL2SMAP_S.002:138549434
</name>
<id>
G1549595081-NSIDC_ECS
</id>
<location>
https://cmr.earthdata.nasa.gov:443/search/concepts/G1549595081-NSIDC_ECS/6
</location>
<revision-id>
6
</revision-id>
</reference>
<reference>
<name>
SC:SPL2SMAP_S.002:137107471
</name>
<id>
G1547458114-NSIDC_ECS
</id>
<location>
https://cmr.earthdata.nasa.gov:443/search/concepts/G1547458114-NSIDC_ECS/6
</location>
<revision-id>
6
</revision-id>
</reference>
<reference>
<name>
SC:SPL2SMAP_S.002:137399181
</name>
<id>
G1547779414-NSIDC_ECS
</id>
<location>
https://cmr.earthdata.nasa.gov:443/search/concepts/G1547779414-NSIDC_ECS/6
</location>
<revision-id>
6
</revision-id>
</reference>
<reference>
<name>
SC:SPL2SMAP_S.002:141348049
</name>
<id>
G1563933600-NSIDC_ECS
</id>
<location>
https://cmr.earthdata.nasa.gov:443/search/concepts/G1563933600-NSIDC_ECS/6
</location>
<revision-id>
6
</revision-id>
</reference>
<reference>
<name>
SC:SPL2SMAP_S.002:138255812
</name>
<id>
G1549146165-NSIDC_ECS
</id>
<location>
https://cmr.earthdata.nasa.gov:443/search/concepts/G1549146165-NSIDC_ECS/6
</location>
<revision-id>
6
</revision-id>
</reference>
<reference>
<name>
SC:SPL2SMAP_S.002:138282912
</name>
<id>
G1549276181-NSIDC_ECS
</id>
<location>
https://cmr.earthdata.nasa.gov:443/search/concepts/G1549276181-NSIDC_ECS/6
</location>
<revision-id>
6
</revision-id>
</reference>
<reference>
<name>
SC:SPL2SMAP_S.002:139027139
</name>
<id>
G1550045647-NSIDC_ECS
</id>
<location>
https://cmr.earthdata.nasa.gov:443/search/concepts/G1550045647-NSIDC_ECS/6
</location>
<revision-id>
6
</revision-id>
</reference>
<reference>
<name>
SC:SPL2SMAP_S.002:139077958
</name>
<id>
G1550136273-NSIDC_ECS
</id>
<location>
https://cmr.earthdata.nasa.gov:443/search/concepts/G1550136273-NSIDC_ECS/6
</location>
<revision-id>
6
</revision-id>
</reference>
<reference>
<name>
SC:SPL2SMAP_S.002:142195213
</name>
<id>
G1569477618-NSIDC_ECS
</id>
<location>
https://cmr.earthdata.nasa.gov:443/search/concepts/G1569477618-NSIDC_ECS/6
</location>
<revision-id>
6
</revision-id>
</reference>
</references>
</results>
</body>
</html>