This page is meant to prepare test fixtures for use in hydrofunctions.
Table of Contents:
In [1]:
import hydrofunctions as hf
print("Hydrofunctions version: ", hf.__version__)
import numpy as np
print("Numpy version: ", np.__version__)
import pandas as pd
print("Pandas version: ", pd.__version__)
import requests
print("Requests version: ", requests.__version__)
import matplotlib as plt
%matplotlib inline
Hydrofunctions version: 0.1.8dev
Numpy version: 1.16.1
Pandas version: 0.24.1
Requests version: 2.21.0
The cells below will generate the output a user would expect to get from successful requests by hf.NWIS.json(). In other words, these are not the raw JSON returned by the NWIS, but rather the output from calling the .json() method on a Requests object. It actually isn't json, but rather a Python dict. You can tell because the true and false in the original json have been coverted to True and False for Python.
Each of the fixtures will represent the response to a different type of request to the NWIS. These will be used to test whether hydrofunctions is parsing the JSON correctly. Note that I need to call these directly from the NWIS using Requests, not hydrofunctions!
NOTE: within the response from NWIS, there is a field called 'queryURL', which contains the query used to generate this response... except with one tiny modification. The 'queryURL' doesn't contain the '?' neccessary between the static part of the URL and the requested parameters. Is this a bug?
Request Name | NWIS request URL | Description |
---|---|---|
JSON15min2month | http://nwis.waterservices.usgs.gov/nwis/iv/?format=json%2C1.1&sites=03213700¶meterCd=00060&startDT=2016-09-01&endDT=2017-11-01 | A fairly long request for discharge from one site. |
two_sites_two_params_iv | http://waterservices.usgs.gov/nwis/iv/?format=json&sites=01541000,01541200&siteStatus=all | Returns stage & discharge for two sites, along with qualifier flags. |
nothing_avail | http://waterservices.usgs.gov/nwis/iv/?format=json&indent=on&stateCd=al¶meterCd=00001&siteStatus=all | This is basically an empty dataset. NWIS returns this in response to filters that filter too much stuff, requests for parameters that don't exist, or don't exist at this station... |
mult_flags | http://waterservices.usgs.gov/nwis/iv/?format=json&sites=01542500&startDT=2019-01-24&endDT=2019-01-28¶meterCd=00060 | NWIS returns data for discharge at one site, but with 3 _qualifier flags: 'e':estimated, 'P': provisional data, 'Ice'. |
diff_freq | https://waterservices.usgs.gov/nwis/iv/?format=json&sites=01570500,01541000&startDT=2018-06-01&endDT=2018-06-01 | The 01541000 site collects every 15 minutes; the 01570500 collects every 30 minutes. The 01570500 site also collects other parameters like precipitation; these have no data for these dates, but NWIS reports them as if there is a data series, but it just delivers an empty series. If you request just the parameter with no data, the NWIS doesn't return any series, so this requires a test during parsing. |
startDST | https://waterservices.usgs.gov/nwis/iv/?format=json&sites=01541000&startDT=2018-03-10&endDT=2018-03-12¶meterCd=00060 | If handled improperly, this request will have a gap as Daylight Savings jumps forward. |
endDST | https://waterservices.usgs.gov/nwis/iv/?format=json&sites=01541000&startDT=2018-11-03&endDT=2018-11-05¶meterCd=00060 | If handled impropery, this request will have an hour of duplicates as Daylight Savings ends and time goes back to normal. |
In [2]:
header = {'Accept-encoding': 'gzip','max-age': '120'}
JSON15min2day_req = requests.get("https://waterservices.usgs.gov/nwis/iv/?format=json%2C1.1&sites=03213700¶meterCd=00060&startDT=2016-09-01&endDT=2016-09-02", headers=header)
two_sites_two_params_iv_req = requests.get("https://waterservices.usgs.gov/nwis/iv/?format=json&sites=01541000,01541200&period=P1D", headers=header)
nothing_avail_req = requests.get("https://waterservices.usgs.gov/nwis/iv/?format=json&indent=on&stateCd=al¶meterCd=00001", headers=header)
mult_flags_req = requests.get("https://waterservices.usgs.gov/nwis/iv/?format=json&sites=01542500&startDT=2019-01-24&endDT=2019-01-28¶meterCd=00060", headers=header)
diff_freq = requests.get("https://waterservices.usgs.gov/nwis/iv/?format=json&sites=01570500,01541000&startDT=2018-06-01&endDT=2018-06-01", headers=header)
startDST_req = requests.get("https://waterservices.usgs.gov/nwis/iv/?format=json&sites=01541000&startDT=2018-03-10&endDT=2018-03-12¶meterCd=00060", headers=header)
endDST_req = requests.get("https://waterservices.usgs.gov/nwis/iv/?format=json&sites=01541000&startDT=2018-11-03&endDT=2018-11-05¶meterCd=00060", headers=header)
In [3]:
JSON15min2day_req.json()
Out[3]:
{'name': 'ns1:timeSeriesResponseType',
'declaredType': 'org.cuahsi.waterml.TimeSeriesResponseType',
'scope': 'javax.xml.bind.JAXBElement$GlobalScope',
'value': {'queryInfo': {'queryURL': 'http://nwis.waterservices.usgs.gov/nwis/iv/format=json%2C1.1&sites=03213700¶meterCd=00060&startDT=2016-09-01&endDT=2016-09-02',
'criteria': {'locationParam': '[ALL:03213700]',
'variableParam': '[00060]',
'timeParam': {'beginDateTime': '2016-09-01T00:00:00.000',
'endDateTime': '2016-09-02T23:59:59.000'},
'parameter': []},
'note': [{'value': '[ALL:03213700]', 'title': 'filter:sites'},
{'value': '[mode=RANGE, modifiedSince=null] interval={INTERVAL[2016-09-01T00:00:00.000-04:00/2016-09-02T23:59:59.000Z]}',
'title': 'filter:timeRange'},
{'value': 'methodIds=[ALL]', 'title': 'filter:methodId'},
{'value': '2019-02-25T04:03:19.835Z', 'title': 'requestDT'},
{'value': '49671fa0-38b2-11e9-a754-3440b59d3362', 'title': 'requestId'},
{'value': 'Provisional data are subject to revision. Go to http://waterdata.usgs.gov/nwis/help/?provisional for more information.',
'title': 'disclaimer'},
{'value': 'nadww02', 'title': 'server'}]},
'timeSeries': [{'sourceInfo': {'siteName': 'TUG FORK AT WILLIAMSON, WV',
'siteCode': [{'value': '03213700',
'network': 'NWIS',
'agencyCode': 'USGS'}],
'timeZoneInfo': {'defaultTimeZone': {'zoneOffset': '-05:00',
'zoneAbbreviation': 'EST'},
'daylightSavingsTimeZone': {'zoneOffset': '-04:00',
'zoneAbbreviation': 'EDT'},
'siteUsesDaylightSavingsTime': True},
'geoLocation': {'geogLocation': {'srs': 'EPSG:4326',
'latitude': 37.67315699,
'longitude': -82.2801408},
'localSiteXY': []},
'note': [],
'siteType': [],
'siteProperty': [{'value': 'ST', 'name': 'siteTypeCd'},
{'value': '05070201', 'name': 'hucCd'},
{'value': '21', 'name': 'stateCd'},
{'value': '21195', 'name': 'countyCd'}]},
'variable': {'variableCode': [{'value': '00060',
'network': 'NWIS',
'vocabulary': 'NWIS:UnitValues',
'variableID': 45807197,
'default': True}],
'variableName': 'Streamflow, ft³/s',
'variableDescription': 'Discharge, cubic feet per second',
'valueType': 'Derived Value',
'unit': {'unitCode': 'ft3/s'},
'options': {'option': [{'name': 'Statistic', 'optionCode': '00000'}]},
'note': [],
'noDataValue': -999999.0,
'variableProperty': [],
'oid': '45807197'},
'values': [{'value': [{'value': '366',
'qualifiers': ['A'],
'dateTime': '2016-09-01T00:00:00.000-04:00'},
{'value': '363',
'qualifiers': ['A'],
'dateTime': '2016-09-01T00:15:00.000-04:00'},
{'value': '363',
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'dateTime': '2016-09-01T00:30:00.000-04:00'},
{'value': '363',
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'dateTime': '2016-09-01T00:45:00.000-04:00'},
{'value': '363',
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{'value': '363',
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{'value': '290',
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{'value': '290',
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{'value': '290',
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{'value': '290',
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{'value': '290',
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'dateTime': '2016-09-02T08:45:00.000-04:00'},
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{'value': '286',
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{'value': '286',
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'typeSubstituted': False}
In [4]:
two_sites_two_params_iv_req.json()
Out[4]:
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'note': [{'value': '[ALL:01541000, ALL:01541200]', 'title': 'filter:sites'},
{'value': '[mode=PERIOD, period=P1D, modifiedSince=null]',
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{'value': 'methodIds=[ALL]', 'title': 'filter:methodId'},
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{'value': '49bf51c0-38b2-11e9-9463-6cae8b6642f6', 'title': 'requestId'},
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{'sourceInfo': {'siteName': 'West Branch Susquehanna River at Bower, PA',
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mult_flags_req.json()
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'dateTime': '2019-01-28T21:45:00.000-05:00'},
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'dateTime': '2019-01-28T22:00:00.000-05:00'},
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In [7]:
diff_freq.json()
Out[7]:
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{'sourceInfo': {'siteName': 'West Branch Susquehanna River at Bower, PA',
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In [8]:
startDST_req.json()
Out[8]:
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'nil': False,
'globalScope': True,
'typeSubstituted': False}
In [9]:
endDST_req.json()
Out[9]:
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{'value': '[mode=RANGE, modifiedSince=null] interval={INTERVAL[2018-11-03T00:00:00.000-04:00/2018-11-05T23:59:59.000Z]}',
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{'value': 'methodIds=[ALL]', 'title': 'filter:methodId'},
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{'value': '4c207340-38b2-11e9-9463-6cae8b6642f6', 'title': 'requestId'},
{'value': 'Provisional data are subject to revision. Go to http://waterdata.usgs.gov/nwis/help/?provisional for more information.',
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{'value': 'caas01', 'title': 'server'}]},
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{'value': '758',
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'dateTime': '2018-11-04T17:15:00.000-05:00'},
{'value': '758',
'qualifiers': ['P'],
'dateTime': '2018-11-04T17:30:00.000-05:00'},
{'value': '752',
'qualifiers': ['P'],
'dateTime': '2018-11-04T17:45:00.000-05:00'},
{'value': '752',
'qualifiers': ['P'],
'dateTime': '2018-11-04T18:00:00.000-05:00'},
{'value': '752',
'qualifiers': ['P'],
'dateTime': '2018-11-04T18:15:00.000-05:00'},
{'value': '752',
'qualifiers': ['P'],
'dateTime': '2018-11-04T18:30:00.000-05:00'},
{'value': '752',
'qualifiers': ['P'],
'dateTime': '2018-11-04T18:45:00.000-05:00'},
{'value': '746',
'qualifiers': ['P'],
'dateTime': '2018-11-04T19:00:00.000-05:00'},
{'value': '746',
'qualifiers': ['P'],
'dateTime': '2018-11-04T19:15:00.000-05:00'},
{'value': '746',
'qualifiers': ['P'],
'dateTime': '2018-11-04T19:30:00.000-05:00'},
{'value': '746',
'qualifiers': ['P'],
'dateTime': '2018-11-04T19:45:00.000-05:00'},
{'value': '746',
'qualifiers': ['P'],
'dateTime': '2018-11-04T20:00:00.000-05:00'},
{'value': '741',
'qualifiers': ['P'],
'dateTime': '2018-11-04T20:15:00.000-05:00'},
{'value': '741',
'qualifiers': ['P'],
'dateTime': '2018-11-04T20:30:00.000-05:00'},
{'value': '741',
'qualifiers': ['P'],
'dateTime': '2018-11-04T20:45:00.000-05:00'},
{'value': '741',
'qualifiers': ['P'],
'dateTime': '2018-11-04T21:00:00.000-05:00'},
{'value': '735',
'qualifiers': ['P'],
'dateTime': '2018-11-04T21:15:00.000-05:00'},
{'value': '735',
'qualifiers': ['P'],
'dateTime': '2018-11-04T21:30:00.000-05:00'},
{'value': '735',
'qualifiers': ['P'],
'dateTime': '2018-11-04T21:45:00.000-05:00'},
{'value': '735',
'qualifiers': ['P'],
'dateTime': '2018-11-04T22:00:00.000-05:00'},
{'value': '735',
'qualifiers': ['P'],
'dateTime': '2018-11-04T22:15:00.000-05:00'},
{'value': '735',
'qualifiers': ['P'],
'dateTime': '2018-11-04T22:30:00.000-05:00'},
{'value': '729',
'qualifiers': ['P'],
'dateTime': '2018-11-04T22:45:00.000-05:00'},
{'value': '729',
'qualifiers': ['P'],
'dateTime': '2018-11-04T23:00:00.000-05:00'},
{'value': '729',
'qualifiers': ['P'],
'dateTime': '2018-11-04T23:15:00.000-05:00'},
{'value': '729',
'qualifiers': ['P'],
'dateTime': '2018-11-04T23:30:00.000-05:00'},
{'value': '729',
'qualifiers': ['P'],
'dateTime': '2018-11-04T23:45:00.000-05:00'},
{'value': '729',
'qualifiers': ['P'],
'dateTime': '2018-11-05T00:00:00.000-05:00'},
{'value': '724',
'qualifiers': ['P'],
'dateTime': '2018-11-05T00:15:00.000-05:00'},
{'value': '724',
'qualifiers': ['P'],
'dateTime': '2018-11-05T00:30:00.000-05:00'},
{'value': '724',
'qualifiers': ['P'],
'dateTime': '2018-11-05T00:45:00.000-05:00'},
{'value': '724',
'qualifiers': ['P'],
'dateTime': '2018-11-05T01:00:00.000-05:00'},
{'value': '724',
'qualifiers': ['P'],
'dateTime': '2018-11-05T01:15:00.000-05:00'},
{'value': '718',
'qualifiers': ['P'],
'dateTime': '2018-11-05T01:30:00.000-05:00'},
{'value': '718',
'qualifiers': ['P'],
'dateTime': '2018-11-05T01:45:00.000-05:00'},
{'value': '718',
'qualifiers': ['P'],
'dateTime': '2018-11-05T02:00:00.000-05:00'},
{'value': '718',
'qualifiers': ['P'],
'dateTime': '2018-11-05T02:15:00.000-05:00'},
{'value': '718',
'qualifiers': ['P'],
'dateTime': '2018-11-05T02:30:00.000-05:00'},
{'value': '718',
'qualifiers': ['P'],
'dateTime': '2018-11-05T02:45:00.000-05:00'},
{'value': '718',
'qualifiers': ['P'],
'dateTime': '2018-11-05T03:00:00.000-05:00'},
{'value': '713',
'qualifiers': ['P'],
'dateTime': '2018-11-05T03:15:00.000-05:00'},
{'value': '713',
'qualifiers': ['P'],
'dateTime': '2018-11-05T03:30:00.000-05:00'},
{'value': '713',
'qualifiers': ['P'],
'dateTime': '2018-11-05T03:45:00.000-05:00'},
{'value': '713',
'qualifiers': ['P'],
'dateTime': '2018-11-05T04:00:00.000-05:00'},
{'value': '713',
'qualifiers': ['P'],
'dateTime': '2018-11-05T04:15:00.000-05:00'},
{'value': '713',
'qualifiers': ['P'],
'dateTime': '2018-11-05T04:30:00.000-05:00'},
{'value': '713',
'qualifiers': ['P'],
'dateTime': '2018-11-05T04:45:00.000-05:00'},
{'value': '713',
'qualifiers': ['P'],
'dateTime': '2018-11-05T05:00:00.000-05:00'},
{'value': '713',
'qualifiers': ['P'],
'dateTime': '2018-11-05T05:15:00.000-05:00'},
{'value': '713',
'qualifiers': ['P'],
'dateTime': '2018-11-05T05:30:00.000-05:00'},
{'value': '718',
'qualifiers': ['P'],
'dateTime': '2018-11-05T05:45:00.000-05:00'},
{'value': '718',
'qualifiers': ['P'],
'dateTime': '2018-11-05T06:00:00.000-05:00'},
{'value': '724',
'qualifiers': ['P'],
'dateTime': '2018-11-05T06:15:00.000-05:00'},
{'value': '724',
'qualifiers': ['P'],
'dateTime': '2018-11-05T06:30:00.000-05:00'},
{'value': '724',
'qualifiers': ['P'],
'dateTime': '2018-11-05T06:45:00.000-05:00'},
{'value': '729',
'qualifiers': ['P'],
'dateTime': '2018-11-05T07:00:00.000-05:00'},
{'value': '729',
'qualifiers': ['P'],
'dateTime': '2018-11-05T07:15:00.000-05:00'},
{'value': '735',
'qualifiers': ['P'],
'dateTime': '2018-11-05T07:30:00.000-05:00'},
{'value': '735',
'qualifiers': ['P'],
'dateTime': '2018-11-05T07:45:00.000-05:00'},
{'value': '741',
'qualifiers': ['P'],
'dateTime': '2018-11-05T08:00:00.000-05:00'},
{'value': '741',
'qualifiers': ['P'],
'dateTime': '2018-11-05T08:15:00.000-05:00'},
{'value': '746',
'qualifiers': ['P'],
'dateTime': '2018-11-05T08:30:00.000-05:00'},
{'value': '746',
'qualifiers': ['P'],
'dateTime': '2018-11-05T08:45:00.000-05:00'},
{'value': '752',
'qualifiers': ['P'],
'dateTime': '2018-11-05T09:00:00.000-05:00'},
{'value': '752',
'qualifiers': ['P'],
'dateTime': '2018-11-05T09:15:00.000-05:00'},
{'value': '758',
'qualifiers': ['P'],
'dateTime': '2018-11-05T09:30:00.000-05:00'},
{'value': '758',
'qualifiers': ['P'],
'dateTime': '2018-11-05T09:45:00.000-05:00'},
{'value': '763',
'qualifiers': ['P'],
'dateTime': '2018-11-05T10:00:00.000-05:00'},
{'value': '769',
'qualifiers': ['P'],
'dateTime': '2018-11-05T10:15:00.000-05:00'},
{'value': '769',
'qualifiers': ['P'],
'dateTime': '2018-11-05T10:30:00.000-05:00'},
{'value': '775',
'qualifiers': ['P'],
'dateTime': '2018-11-05T10:45:00.000-05:00'},
{'value': '781',
'qualifiers': ['P'],
'dateTime': '2018-11-05T11:00:00.000-05:00'},
{'value': '787',
'qualifiers': ['P'],
'dateTime': '2018-11-05T11:15:00.000-05:00'},
{'value': '792',
'qualifiers': ['P'],
'dateTime': '2018-11-05T11:30:00.000-05:00'},
{'value': '798',
'qualifiers': ['P'],
'dateTime': '2018-11-05T11:45:00.000-05:00'},
{'value': '804',
'qualifiers': ['P'],
'dateTime': '2018-11-05T12:00:00.000-05:00'},
{'value': '810',
'qualifiers': ['P'],
'dateTime': '2018-11-05T12:15:00.000-05:00'},
{'value': '816',
'qualifiers': ['P'],
'dateTime': '2018-11-05T12:30:00.000-05:00'},
{'value': '822',
'qualifiers': ['P'],
'dateTime': '2018-11-05T12:45:00.000-05:00'},
{'value': '828',
'qualifiers': ['P'],
'dateTime': '2018-11-05T13:00:00.000-05:00'},
{'value': '834',
'qualifiers': ['P'],
'dateTime': '2018-11-05T13:15:00.000-05:00'},
{'value': '846',
'qualifiers': ['P'],
'dateTime': '2018-11-05T13:30:00.000-05:00'},
{'value': '852',
'qualifiers': ['P'],
'dateTime': '2018-11-05T13:45:00.000-05:00'},
{'value': '858',
'qualifiers': ['P'],
'dateTime': '2018-11-05T14:00:00.000-05:00'},
{'value': '865',
'qualifiers': ['P'],
'dateTime': '2018-11-05T14:15:00.000-05:00'},
{'value': '871',
'qualifiers': ['P'],
'dateTime': '2018-11-05T14:30:00.000-05:00'},
{'value': '877',
'qualifiers': ['P'],
'dateTime': '2018-11-05T14:45:00.000-05:00'},
{'value': '890',
'qualifiers': ['P'],
'dateTime': '2018-11-05T15:00:00.000-05:00'},
{'value': '896',
'qualifiers': ['P'],
'dateTime': '2018-11-05T15:15:00.000-05:00'},
{'value': '902',
'qualifiers': ['P'],
'dateTime': '2018-11-05T15:30:00.000-05:00'},
{'value': '902',
'qualifiers': ['P'],
'dateTime': '2018-11-05T15:45:00.000-05:00'},
{'value': '909',
'qualifiers': ['P'],
'dateTime': '2018-11-05T16:00:00.000-05:00'},
{'value': '915',
'qualifiers': ['P'],
'dateTime': '2018-11-05T16:15:00.000-05:00'},
{'value': '921',
'qualifiers': ['P'],
'dateTime': '2018-11-05T16:30:00.000-05:00'},
{'value': '928',
'qualifiers': ['P'],
'dateTime': '2018-11-05T16:45:00.000-05:00'},
{'value': '934',
'qualifiers': ['P'],
'dateTime': '2018-11-05T17:00:00.000-05:00'},
{'value': '941',
'qualifiers': ['P'],
'dateTime': '2018-11-05T17:15:00.000-05:00'},
{'value': '947',
'qualifiers': ['P'],
'dateTime': '2018-11-05T17:30:00.000-05:00'},
{'value': '954',
'qualifiers': ['P'],
'dateTime': '2018-11-05T17:45:00.000-05:00'},
{'value': '960',
'qualifiers': ['P'],
'dateTime': '2018-11-05T18:00:00.000-05:00'},
{'value': '974',
'qualifiers': ['P'],
'dateTime': '2018-11-05T18:15:00.000-05:00'},
{'value': '980',
'qualifiers': ['P'],
'dateTime': '2018-11-05T18:30:00.000-05:00'},
{'value': '987',
'qualifiers': ['P'],
'dateTime': '2018-11-05T18:45:00.000-05:00'},
{'value': '1000',
'qualifiers': ['P'],
'dateTime': '2018-11-05T19:00:00.000-05:00'},
{'value': '1010',
'qualifiers': ['P'],
'dateTime': '2018-11-05T19:15:00.000-05:00'},
{'value': '1020',
'qualifiers': ['P'],
'dateTime': '2018-11-05T19:30:00.000-05:00'},
{'value': '1030',
'qualifiers': ['P'],
'dateTime': '2018-11-05T19:45:00.000-05:00'},
{'value': '1040',
'qualifiers': ['P'],
'dateTime': '2018-11-05T20:00:00.000-05:00'},
{'value': '1060',
'qualifiers': ['P'],
'dateTime': '2018-11-05T20:15:00.000-05:00'},
{'value': '1060',
'qualifiers': ['P'],
'dateTime': '2018-11-05T20:30:00.000-05:00'},
{'value': '1080',
'qualifiers': ['P'],
'dateTime': '2018-11-05T20:45:00.000-05:00'},
{'value': '1080',
'qualifiers': ['P'],
'dateTime': '2018-11-05T21:00:00.000-05:00'},
{'value': '1090',
'qualifiers': ['P'],
'dateTime': '2018-11-05T21:15:00.000-05:00'},
{'value': '1100',
'qualifiers': ['P'],
'dateTime': '2018-11-05T21:30:00.000-05:00'},
{'value': '1110',
'qualifiers': ['P'],
'dateTime': '2018-11-05T21:45:00.000-05:00'},
{'value': '1110',
'qualifiers': ['P'],
'dateTime': '2018-11-05T22:00:00.000-05:00'},
{'value': '1110',
'qualifiers': ['P'],
'dateTime': '2018-11-05T22:15:00.000-05:00'},
{'value': '1110',
'qualifiers': ['P'],
'dateTime': '2018-11-05T22:30:00.000-05:00'},
{'value': '1120',
'qualifiers': ['P'],
'dateTime': '2018-11-05T22:45:00.000-05:00'},
{'value': '1110',
'qualifiers': ['P'],
'dateTime': '2018-11-05T23:00:00.000-05:00'},
{'value': '1120',
'qualifiers': ['P'],
'dateTime': '2018-11-05T23:15:00.000-05:00'},
{'value': '1120',
'qualifiers': ['P'],
'dateTime': '2018-11-05T23:30:00.000-05:00'},
{'value': '1110',
'qualifiers': ['P'],
'dateTime': '2018-11-05T23:45:00.000-05:00'}],
'qualifier': [{'qualifierCode': 'P',
'qualifierDescription': 'Provisional data subject to revision.',
'qualifierID': 0,
'network': 'NWIS',
'vocabulary': 'uv_rmk_cd'}],
'qualityControlLevel': [],
'method': [{'methodDescription': '', 'methodID': 121813}],
'source': [],
'offset': [],
'sample': [],
'censorCode': []}],
'name': 'USGS:01541000:00060:00000'}]},
'nil': False,
'globalScope': True,
'typeSubstituted': False}
Several properties of the test dataframes can be checked, including:
In [10]:
# Import the 'JSON' dicts from our test_data module.
from tests.test_data import JSON15min2day, two_sites_two_params_iv, nothing_avail, mult_flags, diff_freq, startDST, endDST
In [11]:
json = hf.extract_nwis_df(JSON15min2day, interpolate=False)
print("json shape: ", json.shape)
json shape: (192, 2)
In [12]:
two = hf.extract_nwis_df(two_sites_two_params_iv, interpolate=False)
print("two shape: ", two.shape)
two shape: (93, 8)
In [13]:
try:
nothing = hf.extract_nwis_df(nothing_avail, interpolate=False)
print("nothing shape: ", nothing.shape)
except hf.HydroNoDataError as err:
print(err)
The NWIS reports that it does not have any data for this request.
In [14]:
mult = hf.extract_nwis_df(mult_flags, interpolate=False)
# This has missing observations that get replaced.
print("mult shape: ", mult.shape, "orig json length series1: ", len(mult_flags['value']['timeSeries'][0]['values'][0]['value']))
# This version gets missing observations replaced with interpolated values.
mult_interp = hf.extract_nwis_df(mult_flags, interpolate=True)
print("mult_interp shape: ", mult_interp.shape, "orig json length series1: ", len(mult_flags['value']['timeSeries'][0]['values'][0]['value']))
mult shape: (480, 2) orig json length series1: 438
mult_interp shape: (480, 2) orig json length series1: 438
In [15]:
diff = hf.extract_nwis_df(diff_freq, interpolate=False)
print("diff shape: ", diff.shape)
diff_interp = hf.extract_nwis_df(diff_freq, interpolate=True)
print("diff_interp shape: ", diff_interp.shape)
diff shape: (96, 8)
diff_interp shape: (96, 8)
c:\users\marty\google drive\pydev\src\hydrofunctions\hydrofunctions\hydrofunctions.py:430: HydroUserWarning: One or more datasets in this request is going to be 'upsampled' to 15T because the data were collected at a lower frequency of 30T
exceptions.HydroUserWarning)
In [16]:
diff
Out[16]:
USGS:01541000:00060:00000_qualifiers
USGS:01541000:00060:00000
USGS:01541000:00065:00000_qualifiers
USGS:01541000:00065:00000
USGS:01570500:00060:00000_qualifiers
USGS:01570500:00060:00000
USGS:01570500:00065:00000_qualifiers
USGS:01570500:00065:00000
datetime
2018-06-01 04:00:00+00:00
P
2540.0
P
8.25
A
42200.0
A
5.25
2018-06-01 04:15:00+00:00
P
2620.0
P
8.33
hf.upsampled
NaN
hf.upsampled
NaN
2018-06-01 04:30:00+00:00
P
2700.0
P
8.40
A
42200.0
A
5.25
2018-06-01 04:45:00+00:00
P
2780.0
P
8.47
hf.upsampled
NaN
hf.upsampled
NaN
2018-06-01 05:00:00+00:00
P
2860.0
P
8.54
A
41900.0
A
5.24
2018-06-01 05:15:00+00:00
P
2920.0
P
8.59
hf.upsampled
NaN
hf.upsampled
NaN
2018-06-01 05:30:00+00:00
P
2960.0
P
8.63
A
41900.0
A
5.24
2018-06-01 05:45:00+00:00
P
2960.0
P
8.63
hf.upsampled
NaN
hf.upsampled
NaN
2018-06-01 06:00:00+00:00
P
3020.0
P
8.68
A
41900.0
A
5.24
2018-06-01 06:15:00+00:00
P
3050.0
P
8.70
hf.upsampled
NaN
hf.upsampled
NaN
2018-06-01 06:30:00+00:00
P
3060.0
P
8.71
A
41900.0
A
5.24
2018-06-01 06:45:00+00:00
P
3050.0
P
8.70
hf.upsampled
NaN
hf.upsampled
NaN
2018-06-01 07:00:00+00:00
P
3030.0
P
8.69
A
41900.0
A
5.24
2018-06-01 07:15:00+00:00
P
3010.0
P
8.67
hf.upsampled
NaN
hf.upsampled
NaN
2018-06-01 07:30:00+00:00
P
2990.0
P
8.65
A
41900.0
A
5.24
2018-06-01 07:45:00+00:00
P
2960.0
P
8.63
hf.upsampled
NaN
hf.upsampled
NaN
2018-06-01 08:00:00+00:00
P
2920.0
P
8.59
A
41900.0
A
5.24
2018-06-01 08:15:00+00:00
P
2860.0
P
8.54
hf.upsampled
NaN
hf.upsampled
NaN
2018-06-01 08:30:00+00:00
P
2860.0
P
8.54
A
42200.0
A
5.25
2018-06-01 08:45:00+00:00
P
2770.0
P
8.46
hf.upsampled
NaN
hf.upsampled
NaN
2018-06-01 09:00:00+00:00
P
2710.0
P
8.41
A
42700.0
A
5.27
2018-06-01 09:15:00+00:00
P
2650.0
P
8.35
hf.upsampled
NaN
hf.upsampled
NaN
2018-06-01 09:30:00+00:00
P
2590.0
P
8.30
A
42700.0
A
5.27
2018-06-01 09:45:00+00:00
P
2530.0
P
8.24
hf.upsampled
NaN
hf.upsampled
NaN
2018-06-01 10:00:00+00:00
P
2470.0
P
8.19
A
42700.0
A
5.27
2018-06-01 10:15:00+00:00
P
2410.0
P
8.13
hf.upsampled
NaN
hf.upsampled
NaN
2018-06-01 10:30:00+00:00
P
2360.0
P
8.09
A
42700.0
A
5.27
2018-06-01 10:45:00+00:00
P
2300.0
P
8.03
hf.upsampled
NaN
hf.upsampled
NaN
2018-06-01 11:00:00+00:00
P
2300.0
P
8.03
A
42700.0
A
5.27
2018-06-01 11:15:00+00:00
P
2200.0
P
7.93
hf.upsampled
NaN
hf.upsampled
NaN
...
...
...
...
...
...
...
...
...
2018-06-01 20:30:00+00:00
P
1320.0
P
6.97
A
38800.0
A
5.10
2018-06-01 20:45:00+00:00
P
1310.0
P
6.96
hf.upsampled
NaN
hf.upsampled
NaN
2018-06-01 21:00:00+00:00
P
1300.0
P
6.94
A
38500.0
A
5.09
2018-06-01 21:15:00+00:00
P
1290.0
P
6.93
hf.upsampled
NaN
hf.upsampled
NaN
2018-06-01 21:30:00+00:00
P
1280.0
P
6.92
A
38500.0
A
5.09
2018-06-01 21:45:00+00:00
P
1270.0
P
6.91
hf.upsampled
NaN
hf.upsampled
NaN
2018-06-01 22:00:00+00:00
P
1260.0
P
6.90
A
38300.0
A
5.08
2018-06-01 22:15:00+00:00
P
1250.0
P
6.88
hf.upsampled
NaN
hf.upsampled
NaN
2018-06-01 22:30:00+00:00
P
1250.0
P
6.88
A
38300.0
A
5.08
2018-06-01 22:45:00+00:00
P
1230.0
P
6.86
hf.upsampled
NaN
hf.upsampled
NaN
2018-06-01 23:00:00+00:00
P
1220.0
P
6.85
A
38100.0
A
5.07
2018-06-01 23:15:00+00:00
P
1220.0
P
6.84
hf.upsampled
NaN
hf.upsampled
NaN
2018-06-01 23:30:00+00:00
P
1210.0
P
6.83
A
38100.0
A
5.07
2018-06-01 23:45:00+00:00
P
1200.0
P
6.82
hf.upsampled
NaN
hf.upsampled
NaN
2018-06-02 00:00:00+00:00
P
1190.0
P
6.81
A
37800.0
A
5.06
2018-06-02 00:15:00+00:00
P
1190.0
P
6.80
hf.upsampled
NaN
hf.upsampled
NaN
2018-06-02 00:30:00+00:00
P
1180.0
P
6.79
A
37600.0
A
5.05
2018-06-02 00:45:00+00:00
P
1170.0
P
6.78
hf.upsampled
NaN
hf.upsampled
NaN
2018-06-02 01:00:00+00:00
P
1160.0
P
6.77
A
37600.0
A
5.05
2018-06-02 01:15:00+00:00
P
1150.0
P
6.76
hf.upsampled
NaN
hf.upsampled
NaN
2018-06-02 01:30:00+00:00
P
1150.0
P
6.75
A
37400.0
A
5.04
2018-06-02 01:45:00+00:00
P
1140.0
P
6.74
hf.upsampled
NaN
hf.upsampled
NaN
2018-06-02 02:00:00+00:00
P
1130.0
P
6.73
A
37400.0
A
5.04
2018-06-02 02:15:00+00:00
P
1130.0
P
6.73
hf.upsampled
NaN
hf.upsampled
NaN
2018-06-02 02:30:00+00:00
P
1120.0
P
6.72
A
37200.0
A
5.03
2018-06-02 02:45:00+00:00
P
1120.0
P
6.71
hf.upsampled
NaN
hf.upsampled
NaN
2018-06-02 03:00:00+00:00
P
1110.0
P
6.70
A
36900.0
A
5.02
2018-06-02 03:15:00+00:00
P
1100.0
P
6.69
hf.upsampled
NaN
hf.upsampled
NaN
2018-06-02 03:30:00+00:00
P
1090.0
P
6.68
A
36900.0
A
5.02
2018-06-02 03:45:00+00:00
P
1090.0
P
6.67
hf.upsampled
NaN
hf.upsampled
NaN
96 rows × 8 columns
In [17]:
mult['2019-01-24 16-05:00']
Out[17]:
USGS:01542500:00060:00000_qualifiers
USGS:01542500:00060:00000
datetime
2019-01-24 16:00:00+00:00
hf.missing
NaN
2019-01-24 16:15:00+00:00
hf.missing
NaN
2019-01-24 16:30:00+00:00
hf.missing
NaN
2019-01-24 16:45:00+00:00
hf.missing
NaN
In [18]:
mult_interp['2019-01-24 16-05:00']
Out[18]:
USGS:01542500:00060:00000_qualifiers
USGS:01542500:00060:00000
datetime
2019-01-24 16:00:00+00:00
hf.missing
4067.50
2019-01-24 16:15:00+00:00
hf.missing
4081.25
2019-01-24 16:30:00+00:00
hf.missing
4095.00
2019-01-24 16:45:00+00:00
hf.missing
4108.75
In [19]:
shapes = {'two':two.shape, 'json':json.shape, 'mult':mult.shape, 'diff':diff.shape}
shapes
Out[19]:
{'two': (93, 8), 'json': (192, 2), 'mult': (480, 2), 'diff': (96, 8)}
In [20]:
json.index.is_unique
Out[20]:
True
In [21]:
two.index.is_monotonic
Out[21]:
True
In [22]:
mult.loc['2019-01-24T10:30', 'USGS:01542500:00060:00000_qualifiers']
Out[22]:
'P'
In [23]:
mult.loc['2019-01-28T16:00:00.000-05:00', 'USGS:01542500:00060:00000_qualifiers']
Out[23]:
'P,Ice'
In [24]:
mult.loc['2019-01-28T16:00:00.000-05:00', 'USGS:01542500:00060:00000']
Out[24]:
nan
In [25]:
type(mult.loc['2019-01-28T16:00:00.000-05:00', 'USGS:01542500:00060:00000'])
Out[25]:
numpy.float64
In [26]:
mult.plot()
C:\Users\Marty\Anaconda3\envs\py37hfdev\lib\site-packages\pandas\core\arrays\datetimes.py:1172: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.
"will drop timezone information.", UserWarning)
Out[26]:
<matplotlib.axes._subplots.AxesSubplot at 0x1dada9ad438>
In [27]:
mult_interp.plot()
Out[27]:
<matplotlib.axes._subplots.AxesSubplot at 0x1dadcaa1978>
In [28]:
start = mult.index.min()
stop = mult.index.max()
(stop-start)/pd.Timedelta('15 minutes')
Out[28]:
479.0
In [29]:
# The length is the same as calculated above. No missing index values.
mult.shape
Out[29]:
(480, 2)
In [30]:
# Missing index values from Jan. 24th were filled in
mult['2019-01-24']
Out[30]:
USGS:01542500:00060:00000_qualifiers
USGS:01542500:00060:00000
datetime
2019-01-24 05:00:00+00:00
P
2230.0
2019-01-24 05:15:00+00:00
P
2210.0
2019-01-24 05:30:00+00:00
P
2220.0
2019-01-24 05:45:00+00:00
P
2250.0
2019-01-24 06:00:00+00:00
P
2280.0
2019-01-24 06:15:00+00:00
P
2290.0
2019-01-24 06:30:00+00:00
P
2300.0
2019-01-24 06:45:00+00:00
P
2320.0
2019-01-24 07:00:00+00:00
P
2330.0
2019-01-24 07:15:00+00:00
P
2350.0
2019-01-24 07:30:00+00:00
P
2380.0
2019-01-24 07:45:00+00:00
P
2430.0
2019-01-24 08:00:00+00:00
P
2430.0
2019-01-24 08:15:00+00:00
P
2500.0
2019-01-24 08:30:00+00:00
P
2530.0
2019-01-24 08:45:00+00:00
P
2550.0
2019-01-24 09:00:00+00:00
P
2570.0
2019-01-24 09:15:00+00:00
P
2630.0
2019-01-24 09:30:00+00:00
P
2640.0
2019-01-24 09:45:00+00:00
P
2700.0
2019-01-24 10:00:00+00:00
P
2730.0
2019-01-24 10:15:00+00:00
P
2750.0
2019-01-24 10:30:00+00:00
P
2780.0
2019-01-24 10:45:00+00:00
P
2810.0
2019-01-24 11:00:00+00:00
P
2850.0
2019-01-24 11:15:00+00:00
P
2930.0
2019-01-24 11:30:00+00:00
P
2990.0
2019-01-24 11:45:00+00:00
P
3060.0
2019-01-24 12:00:00+00:00
P
3110.0
2019-01-24 12:15:00+00:00
P
3150.0
...
...
...
2019-01-24 16:30:00+00:00
hf.missing
NaN
2019-01-24 16:45:00+00:00
hf.missing
NaN
2019-01-24 17:00:00+00:00
hf.missing
NaN
2019-01-24 17:15:00+00:00
hf.missing
NaN
2019-01-24 17:30:00+00:00
P,e
4150.0
2019-01-24 17:45:00+00:00
hf.missing
NaN
2019-01-24 18:00:00+00:00
hf.missing
NaN
2019-01-24 18:15:00+00:00
hf.missing
NaN
2019-01-24 18:30:00+00:00
hf.missing
NaN
2019-01-24 18:45:00+00:00
hf.missing
NaN
2019-01-24 19:00:00+00:00
hf.missing
NaN
2019-01-24 19:15:00+00:00
hf.missing
NaN
2019-01-24 19:30:00+00:00
P,e
4620.0
2019-01-24 19:45:00+00:00
hf.missing
NaN
2019-01-24 20:00:00+00:00
hf.missing
NaN
2019-01-24 20:15:00+00:00
hf.missing
NaN
2019-01-24 20:30:00+00:00
hf.missing
NaN
2019-01-24 20:45:00+00:00
hf.missing
NaN
2019-01-24 21:00:00+00:00
hf.missing
NaN
2019-01-24 21:15:00+00:00
hf.missing
NaN
2019-01-24 21:30:00+00:00
P,e
5270.0
2019-01-24 21:45:00+00:00
hf.missing
NaN
2019-01-24 22:00:00+00:00
hf.missing
NaN
2019-01-24 22:15:00+00:00
hf.missing
NaN
2019-01-24 22:30:00+00:00
hf.missing
NaN
2019-01-24 22:45:00+00:00
hf.missing
NaN
2019-01-24 23:00:00+00:00
hf.missing
NaN
2019-01-24 23:15:00+00:00
hf.missing
NaN
2019-01-24 23:30:00+00:00
P,e
5440.0
2019-01-24 23:45:00+00:00
hf.missing
NaN
76 rows × 2 columns
the diff_freq request also has some other strange things about it that require special handling. For example, it reports a precipitation data series, but delivers the series with no data. Normally if you request a paramter with no data, it doesn't report a series.
In [31]:
diff = hf.extract_nwis_df(diff_freq)
In [32]:
diff
Out[32]:
USGS:01541000:00060:00000_qualifiers
USGS:01541000:00060:00000
USGS:01541000:00065:00000_qualifiers
USGS:01541000:00065:00000
USGS:01570500:00060:00000_qualifiers
USGS:01570500:00060:00000
USGS:01570500:00065:00000_qualifiers
USGS:01570500:00065:00000
datetime
2018-06-01 04:00:00+00:00
P
2540.0
P
8.25
A
42200.0
A
5.250
2018-06-01 04:15:00+00:00
P
2620.0
P
8.33
hf.upsampled
42200.0
hf.upsampled
5.250
2018-06-01 04:30:00+00:00
P
2700.0
P
8.40
A
42200.0
A
5.250
2018-06-01 04:45:00+00:00
P
2780.0
P
8.47
hf.upsampled
42050.0
hf.upsampled
5.245
2018-06-01 05:00:00+00:00
P
2860.0
P
8.54
A
41900.0
A
5.240
2018-06-01 05:15:00+00:00
P
2920.0
P
8.59
hf.upsampled
41900.0
hf.upsampled
5.240
2018-06-01 05:30:00+00:00
P
2960.0
P
8.63
A
41900.0
A
5.240
2018-06-01 05:45:00+00:00
P
2960.0
P
8.63
hf.upsampled
41900.0
hf.upsampled
5.240
2018-06-01 06:00:00+00:00
P
3020.0
P
8.68
A
41900.0
A
5.240
2018-06-01 06:15:00+00:00
P
3050.0
P
8.70
hf.upsampled
41900.0
hf.upsampled
5.240
2018-06-01 06:30:00+00:00
P
3060.0
P
8.71
A
41900.0
A
5.240
2018-06-01 06:45:00+00:00
P
3050.0
P
8.70
hf.upsampled
41900.0
hf.upsampled
5.240
2018-06-01 07:00:00+00:00
P
3030.0
P
8.69
A
41900.0
A
5.240
2018-06-01 07:15:00+00:00
P
3010.0
P
8.67
hf.upsampled
41900.0
hf.upsampled
5.240
2018-06-01 07:30:00+00:00
P
2990.0
P
8.65
A
41900.0
A
5.240
2018-06-01 07:45:00+00:00
P
2960.0
P
8.63
hf.upsampled
41900.0
hf.upsampled
5.240
2018-06-01 08:00:00+00:00
P
2920.0
P
8.59
A
41900.0
A
5.240
2018-06-01 08:15:00+00:00
P
2860.0
P
8.54
hf.upsampled
42050.0
hf.upsampled
5.245
2018-06-01 08:30:00+00:00
P
2860.0
P
8.54
A
42200.0
A
5.250
2018-06-01 08:45:00+00:00
P
2770.0
P
8.46
hf.upsampled
42450.0
hf.upsampled
5.260
2018-06-01 09:00:00+00:00
P
2710.0
P
8.41
A
42700.0
A
5.270
2018-06-01 09:15:00+00:00
P
2650.0
P
8.35
hf.upsampled
42700.0
hf.upsampled
5.270
2018-06-01 09:30:00+00:00
P
2590.0
P
8.30
A
42700.0
A
5.270
2018-06-01 09:45:00+00:00
P
2530.0
P
8.24
hf.upsampled
42700.0
hf.upsampled
5.270
2018-06-01 10:00:00+00:00
P
2470.0
P
8.19
A
42700.0
A
5.270
2018-06-01 10:15:00+00:00
P
2410.0
P
8.13
hf.upsampled
42700.0
hf.upsampled
5.270
2018-06-01 10:30:00+00:00
P
2360.0
P
8.09
A
42700.0
A
5.270
2018-06-01 10:45:00+00:00
P
2300.0
P
8.03
hf.upsampled
42700.0
hf.upsampled
5.270
2018-06-01 11:00:00+00:00
P
2300.0
P
8.03
A
42700.0
A
5.270
2018-06-01 11:15:00+00:00
P
2200.0
P
7.93
hf.upsampled
42550.0
hf.upsampled
5.265
...
...
...
...
...
...
...
...
...
2018-06-01 20:30:00+00:00
P
1320.0
P
6.97
A
38800.0
A
5.100
2018-06-01 20:45:00+00:00
P
1310.0
P
6.96
hf.upsampled
38650.0
hf.upsampled
5.095
2018-06-01 21:00:00+00:00
P
1300.0
P
6.94
A
38500.0
A
5.090
2018-06-01 21:15:00+00:00
P
1290.0
P
6.93
hf.upsampled
38500.0
hf.upsampled
5.090
2018-06-01 21:30:00+00:00
P
1280.0
P
6.92
A
38500.0
A
5.090
2018-06-01 21:45:00+00:00
P
1270.0
P
6.91
hf.upsampled
38400.0
hf.upsampled
5.085
2018-06-01 22:00:00+00:00
P
1260.0
P
6.90
A
38300.0
A
5.080
2018-06-01 22:15:00+00:00
P
1250.0
P
6.88
hf.upsampled
38300.0
hf.upsampled
5.080
2018-06-01 22:30:00+00:00
P
1250.0
P
6.88
A
38300.0
A
5.080
2018-06-01 22:45:00+00:00
P
1230.0
P
6.86
hf.upsampled
38200.0
hf.upsampled
5.075
2018-06-01 23:00:00+00:00
P
1220.0
P
6.85
A
38100.0
A
5.070
2018-06-01 23:15:00+00:00
P
1220.0
P
6.84
hf.upsampled
38100.0
hf.upsampled
5.070
2018-06-01 23:30:00+00:00
P
1210.0
P
6.83
A
38100.0
A
5.070
2018-06-01 23:45:00+00:00
P
1200.0
P
6.82
hf.upsampled
37950.0
hf.upsampled
5.065
2018-06-02 00:00:00+00:00
P
1190.0
P
6.81
A
37800.0
A
5.060
2018-06-02 00:15:00+00:00
P
1190.0
P
6.80
hf.upsampled
37700.0
hf.upsampled
5.055
2018-06-02 00:30:00+00:00
P
1180.0
P
6.79
A
37600.0
A
5.050
2018-06-02 00:45:00+00:00
P
1170.0
P
6.78
hf.upsampled
37600.0
hf.upsampled
5.050
2018-06-02 01:00:00+00:00
P
1160.0
P
6.77
A
37600.0
A
5.050
2018-06-02 01:15:00+00:00
P
1150.0
P
6.76
hf.upsampled
37500.0
hf.upsampled
5.045
2018-06-02 01:30:00+00:00
P
1150.0
P
6.75
A
37400.0
A
5.040
2018-06-02 01:45:00+00:00
P
1140.0
P
6.74
hf.upsampled
37400.0
hf.upsampled
5.040
2018-06-02 02:00:00+00:00
P
1130.0
P
6.73
A
37400.0
A
5.040
2018-06-02 02:15:00+00:00
P
1130.0
P
6.73
hf.upsampled
37300.0
hf.upsampled
5.035
2018-06-02 02:30:00+00:00
P
1120.0
P
6.72
A
37200.0
A
5.030
2018-06-02 02:45:00+00:00
P
1120.0
P
6.71
hf.upsampled
37050.0
hf.upsampled
5.025
2018-06-02 03:00:00+00:00
P
1110.0
P
6.70
A
36900.0
A
5.020
2018-06-02 03:15:00+00:00
P
1100.0
P
6.69
hf.upsampled
36900.0
hf.upsampled
5.020
2018-06-02 03:30:00+00:00
P
1090.0
P
6.68
A
36900.0
A
5.020
2018-06-02 03:45:00+00:00
P
1090.0
P
6.67
hf.upsampled
36900.0
hf.upsampled
5.020
96 rows × 8 columns
In [33]:
diff['USGS:01570500:00060:00000'].plot()
Out[33]:
<matplotlib.axes._subplots.AxesSubplot at 0x1dadcb4e0b8>
In [34]:
startDSTdf = hf.extract_nwis_df(startDST, interpolate=False)
# Three days at the start of DST should be 3 * 24 * 4 = 288, minus an hour * 4 = 284.
print(startDSTdf.shape)
startDSTdf.plot()
(284, 2)
C:\Users\Marty\Anaconda3\envs\py37hfdev\lib\site-packages\pandas\core\arrays\datetimes.py:1172: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.
"will drop timezone information.", UserWarning)
Out[34]:
<matplotlib.axes._subplots.AxesSubplot at 0x1dadcb9a710>
In [35]:
endDSTdf = hf.extract_nwis_df(endDST, interpolate=False)
# Three days at the end of DST should be 3 * 24 * 4 = 288 long, plus an extra hour *4 = 292.
print(endDSTdf.shape)
endDSTdf.plot()
(292, 2)
C:\Users\Marty\Anaconda3\envs\py37hfdev\lib\site-packages\pandas\core\arrays\datetimes.py:1172: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.
"will drop timezone information.", UserWarning)
Out[35]:
<matplotlib.axes._subplots.AxesSubplot at 0x1daddbd18d0>
In [39]:
startDSTdf['2018-03-11 20':'2018-03-12 05']
Out[39]:
USGS:01541000:00060:00000_qualifiers
USGS:01541000:00060:00000
datetime
2018-03-11 20:00:00+00:00
A
551.0
2018-03-11 20:15:00+00:00
A
551.0
2018-03-11 20:30:00+00:00
A
556.0
2018-03-11 20:45:00+00:00
A
551.0
2018-03-11 21:00:00+00:00
A
551.0
2018-03-11 21:15:00+00:00
A
551.0
2018-03-11 21:30:00+00:00
A
551.0
2018-03-11 21:45:00+00:00
A
551.0
2018-03-11 22:00:00+00:00
A
546.0
2018-03-11 22:15:00+00:00
A
546.0
2018-03-11 22:30:00+00:00
A
546.0
2018-03-11 22:45:00+00:00
A
546.0
2018-03-11 23:00:00+00:00
A
546.0
2018-03-11 23:15:00+00:00
A
546.0
2018-03-11 23:30:00+00:00
A
546.0
2018-03-11 23:45:00+00:00
A
546.0
2018-03-12 00:00:00+00:00
A
546.0
2018-03-12 00:15:00+00:00
A
546.0
2018-03-12 00:30:00+00:00
A
546.0
2018-03-12 00:45:00+00:00
A
546.0
2018-03-12 01:00:00+00:00
A
551.0
2018-03-12 01:15:00+00:00
A
546.0
2018-03-12 01:30:00+00:00
A
546.0
2018-03-12 01:45:00+00:00
A
551.0
2018-03-12 02:00:00+00:00
A
551.0
2018-03-12 02:15:00+00:00
A
546.0
2018-03-12 02:30:00+00:00
A
546.0
2018-03-12 02:45:00+00:00
A
546.0
2018-03-12 03:00:00+00:00
A
546.0
2018-03-12 03:15:00+00:00
A
546.0
2018-03-12 03:30:00+00:00
A
546.0
2018-03-12 03:45:00+00:00
A
551.0
2018-03-12 04:00:00+00:00
A
546.0
2018-03-12 04:15:00+00:00
A
551.0
2018-03-12 04:30:00+00:00
A
551.0
2018-03-12 04:45:00+00:00
A
551.0
2018-03-12 05:00:00+00:00
A
551.0
2018-03-12 05:15:00+00:00
A
551.0
2018-03-12 05:30:00+00:00
A
551.0
2018-03-12 05:45:00+00:00
A
551.0
In [ ]:
startDSTdf['USGS:01541000:00060:00000_qualifiers'].describe()
In [ ]:
endDSTdf['USGS:01541000:00060:00000_qualifiers'].describe()
Content source: mroberge/hydrofunctions
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