Source code for hydrofunctions.hydrofunctions


This module contains the main functions used in an interactive session.

from __future__ import absolute_import, print_function, division, unicode_literals
import requests
import numpy as np
import pandas as pd
import json
import pyarrow as pa
import pyarrow.parquet as pq
from pandas.tseries.frequencies import to_offset
import logging

# Change to relative import: from . import exceptions
from . import exceptions
import warnings
from . import typing
from . import helpers


[docs]def select_data(nwis_df): """Create a boolean array of columns that contain data. Args: nwis_df: A pandas dataframe created by ``extract_nwis_df``. Returns: an array of Boolean values corresponding to the columns in the original dataframe. Example: >>> my_dataframe[:, select_data(my_dataframe)] returns a dataframe with only the data columns; the qualifier columns do not show. """ data_regex = r"[0-9]$" return nwis_df.columns.str.contains(data_regex)
[docs]def calc_freq(index): # Method 0: calc_freq() was called, but we haven't done anything yet. method = 0 if isinstance(index, pd.DataFrame): index = index.index try: # Method 1: Try the direct approach first. Maybe freq has already been set. freq = index.freq method = 1 except AttributeError: # index.freq does not exist, so let's keep trying. freq = None if freq is None: # Method 2: Use the built-in pd.infer_freq(). It raises ValueError # when it fails, so catch ValueErrors and keep trying. try: freq = to_offset(pd.infer_freq(index)) method = 2 except ValueError: pass if freq is None: # Method 3: divide the length of time by the number of observations. freq = (index.max() - index.min()) / len(index) if pd.Timedelta("13 minutes") < freq < pd.Timedelta("17 minutes"): freq = to_offset("15min") elif pd.Timedelta("27 minutes") < freq < pd.Timedelta("33 minutes"): freq = to_offset("30min") elif pd.Timedelta("55 minutes") < freq < pd.Timedelta("65 minutes"): freq = to_offset("60min") else: freq = None method = 3 if freq is None: # Method 4: Subtract two adjacent values and use the difference! if len(index) > 3: freq = to_offset(abs(index[2] - index[3])) method = 4 logging.debug( "calc_freq4:" + str(freq) + "= index[2]:" + str(index[3]) + "- index [3]:" + str(index[2]) ) if freq is None: # Method 5: If all else fails, freq is 0 minutes! warnings.warn( "It is not possible to determine the frequency " "for one of the datasets in this request. " "This dataset will be set to a frequency of " "0 minutes", exceptions.HydroUserWarning, ) freq = to_offset("0min") method = 5 debug_msg = "Calc_freq method:" + str(method) + "freq:" + str(freq) logging.debug(debug_msg) return pd.Timedelta(freq)
[docs]def get_nwis( site, service="dv", start_date=None, end_date=None, stateCd=None, countyCd=None, bBox=None, parameterCd="all", period=None, ): """Request stream gauge data from the USGS NWIS. Args: site (str or list of strings): a valid site is '01585200' or ['01585200', '01646502']. site should be `None` if stateCd or countyCd are not `None`. service (str): can either be 'iv' or 'dv' for instantaneous or daily data. - 'dv'(default): daily values. Mean value for an entire day. - 'iv': instantaneous value measured at this time. Also known\ as 'Real-time data'. Can be measured as often as every\ five minutes by the USGS. 15 minutes is more typical. start_date (str): should take on the form yyyy-mm-dd end_date (str): should take on the form yyyy-mm-dd stateCd (str): a valid two-letter state postal abbreviation. Default is `None`. countyCd (str or list of strings): a valid county abbreviation. Default is `None`. bBox (str, list, or tuple): a set of coordinates that defines a bounding box. * Coordinates are in decimal degrees * Longitude values are negative (west of the prime meridian). * Latitude values are positive (north of the equator). * comma-delimited, no spaces, if provided as a string. * The order of the boundaries should be: "West,South,East,North" * Example: "-83.000000,36.500000,-81.000000,38.500000" parameterCd (str or list of strings): NWIS parameter code. Usually a five digit code. Default is 'all'.\ A valid code can also be given as a list: ``parameterCd=['00060','00065']`` * if value of 'all' is submitted, then NWIS will return every \ parameter collected at this site. (default option) * stage: '00065' * discharge: '00060' * not all sites collect all parameters! * See for full list period (str): NWIS period code. Default is `None`. * Format is "PxxD", where xx is the number of days before today. * Either use start_date or period, but not both. Returns: a response object. This function will always return the response, even if the NWIS returns a status_code that indicates a problem. * response.url: the url we used to request data * response.json: the content translated as json * response.status_code: the internet status code - '200': is a good request - non-200 codes will be reported as a warning. - '400': is a 'Bad Request'-- the parameters did not make sense - see <> for more codes and meaning. * response.ok: `True` when we get a '200' status_code Raises: ConnectionError: due to connection problems like refused connection or DNS Error. SyntaxWarning: when NWIS returns a response code that is not 200. **Example:** >>> import hydrofunctions as hf >>> response = hf.get_nwis('01585200', 'dv', '2012-06-01', '2012-07-01') >>> response <response [200]> >>> response.json() *JSON ensues* >>> hf.extract_nwis_df(response) *a Pandas dataframe appears* Other Valid Ways to Make a Request:: >>> sites = ['07180500', '03380475', '06926000'] # Request a list of sites. >>> service = 'iv' # Request real-time data >>> days = 'P10D' # Request the last 10 days. >>> stage = '00065' # Sites that collect discharge usually collect water depth too. >>> response2 = hf.get_nwis(sites, service, period=days, parameterCd=stage) Request Data By Location:: >>> # Request the most recent daily data for every site in Maine >>> response3 = hf.get_nwis(None, 'dv', stateCd='ME') >>> response3 <Response [200]> The specification for the USGS NWIS IV service is located here: """ service = typing.check_NWIS_service(service) if parameterCd == "all": parameterCd = None header = {"Accept-encoding": "gzip", "max-age": "120"} values = { # specify version of nwis json. Based on WaterML1.1 # json,1.1 works; json%2C works; json1.1 DOES NOT WORK "format": "json,1.1", "sites": typing.check_parameter_string(site, "site"), "stateCd": stateCd, "countyCd": typing.check_parameter_string(countyCd, "county"), "bBox": typing.check_NWIS_bBox(bBox), "parameterCd": typing.check_parameter_string(parameterCd, "parameterCd"), "period": period, "startDT": start_date, "endDT": end_date, } # Check that site selection parameters are exclusive! total = helpers.count_number_of_truthy([site, stateCd, countyCd, bBox]) if total == 1: pass elif total > 1: raise ValueError( "Select sites using either site, stateCd, " "countyCd, or bBox, but not more than one." ) elif total < 1: raise ValueError( "Select sites using at least one of the following " "arguments: site, stateCd, countyCd or bBox." ) # Check that time parameters are not both set. # If neither is set, then NWIS will return the most recent observation. if start_date and period: raise ValueError( "Use either start_date or period, or neither, " "but not both." ) if not (start_date or period): # User didn't specify time; must be requesting most recent data. # See issue #49. pass url = "" url = url + service + "/?" response = requests.get(url, params=values, headers=header) print("Requested data from", response.url) # requests will raise a 'ConnectionError' if the connection is refused # or if we are disconnected from the internet. # .get_nwis() will always return the response. # Higher-level code that calls get_nwis() may decide to handle or # report status codes that indicate something went wrong. # Issue warnings for bad status codes nwis_custom_status_codes(response) if not response.text: raise exceptions.HydroNoDataError( "The NWIS has returned an empty string for this request." ) return response
[docs]def get_nwis_property(nwis_dict, key=None, remove_duplicates=False): """Returns a list containing property data from an NWIS response object. Args: nwis_dict (dict): the json returned in a response object as produced by ``get_nwis().json()``. key (str): a valid NWIS response property key. Default is `None`. The index is \ returned if key is `None`. Valid keys are: * None * name - constructed name "provider:site:parameterCd:statistic" * siteName * siteCode * timeZoneInfo * geoLocation * siteType * siteProperty * variableCode * variableName * variableDescription * valueType * unit * options * noDataValue remove_duplicates (bool): a flag used to remove duplicate values in the returned list. Returns: a list with the data for the passed key string. Raises: HydroNoDataError when the request is valid, but NWIS has no data for \ the parameters provided in the request. ValueError when the key is not available. """ # nwis_dict = response_obj.json() # strip header and all metadata. ts is the 'timeSeries' element of the # response; it is an array of objects that contain time series data. ts = nwis_dict["value"]["timeSeries"] msg = "The NWIS reports that it does not have any data for this request." if len(ts) < 1: raise exceptions.HydroNoDataError(msg) # This predefines what to expect in the response. # Would it be better to look in the response for the key? # Pseudo code # skip stations with no data # if key in tts['variable']: # v = etc # elif key in tts['sourceInfo']: # v = etc # elif key in tts: # v = etc # else just return index or raise an error later # sourceInfo = [ "siteName", "siteCode", "timeZoneInfo", "geoLocation", "siteType", "siteProperty", ] variable = [ "variableCode", "variableName", "variableDescription", "valueType", "unit", "options", "noDataValue", ] root = ["name"] vals = [] try: for idx, tts in enumerate(ts): d = tts["values"][0]["value"] # skip stations with no data if len(d) < 1: continue if key in variable: v = tts["variable"][key] elif key in sourceInfo: v = tts["sourceInfo"][key] elif key in root: v = tts[key] else: v = idx # just return index if remove_duplicates: if v not in vals: vals.append(v) else: vals.append(v) # Why catch this? If we can't find the key, we already return the index. except: # TODO: dangerous to use bare 'except' clauses. msg = 'The selected key "{}" could not be found'.format(key) raise ValueError(msg) return vals
[docs]def extract_nwis_df(nwis_dict, interpolate=True): """Returns a Pandas dataframe and a metadata dict from the NWIS response object or the json dict of the response. Args: nwis_dict (obj): the json from a response object as returned by get_nwis().json(). Alternatively, you may supply the response object itself. Returns: a pandas dataframe. Raises: HydroNoDataError when the request is valid, but NWIS has no data for the parameters provided in the request. HydroUserWarning when one dataset is sampled at a lower frequency than another dataset in the same request. """ if type(nwis_dict) is not dict: nwis_dict = nwis_dict.json() # strip header and all metadata. ts = nwis_dict["value"]["timeSeries"] if ts == []: # raise a HydroNoDataError if NWIS returns an empty set. # # Ideally, an empty set exception would be raised when the request # is first returned, but I do it here so that the data doesn't get # extracted twice. # TODO: raise this exception earlier?? # # ** Interactive sessions should have an error raised. # # **Automated systems should catch these errors and deal with them. # In this case, if NWIS returns an empty set, then the request # needs to be reconsidered. The request was valid somehow, but # there is no data being collected. raise exceptions.HydroNoDataError( "The NWIS reports that it does not " "have any data for this request." ) # create a list of time series; # set the index, set the data types, replace NaNs, sort, find the first and last collection = [] starts = [] ends = [] freqs = [] meta = {} for series in ts: series_name = series["name"] temp_name = series_name.split(":") agency = str(temp_name[0]) site_id = agency + ":" + str(temp_name[1]) parameter_cd = str(temp_name[2]) stat = str(temp_name[3]) siteName = series["sourceInfo"]["siteName"] siteLatLongSrs = series["sourceInfo"]["geoLocation"]["geogLocation"] noDataValues = series["variable"]["noDataValue"] variableDescription = series["variable"]["variableDescription"] unit = series["variable"]["unit"]["unitCode"] data = series["values"][0]["value"] if data == []: # This parameter has no data. Skip to next series. continue if len(data) == 1: # This parameter only contains the most recent reading. # See Issue #49 pass qualifiers = series_name + "_qualifiers" DF = pd.DataFrame(data=data) DF.index = pd.to_datetime(DF.pop("dateTime"), utc=True) DF["value"] = DF["value"].astype(float) DF = DF.replace(to_replace=noDataValues, value=np.nan) DF["qualifiers"] = DF["qualifiers"].apply(lambda x: ",".join(x)) DF.rename( columns={"qualifiers": qualifiers, "value": series_name}, inplace=True ) DF.sort_index(inplace=True) local_start = DF.index.min() local_end = DF.index.max() starts.append(local_start) ends.append(local_end) local_freq = calc_freq(DF.index) freqs.append(local_freq) if not DF.index.is_unique: print( "Series index for " + series_name + " is not unique. Attempting to drop identical rows." ) DF = DF.drop_duplicates(keep="first") if not DF.index.is_unique: print( "Series index for " + series_name + " is STILL not unique. Dropping first rows with duplicated date." ) DF = DF[~DF.index.duplicated(keep="first")] if local_freq > to_offset("0min"): local_clean_index = pd.date_range( start=local_start, end=local_end, freq=local_freq, tz="UTC" ) # if len(local_clean_index) != len(DF): # This condition happens quite frequently with missing data. # print(str(series_name) + "-- clean index length: "+ str(len(local_clean_index)) + " Series length: " + str(len(DF))) DF = DF.reindex(index=local_clean_index, copy=True) else: # The dataframe DF must contain only the most recent data. pass qual_cols = DF.columns.str.contains("_qualifiers") # # Instead, create a temporary dataframe, fillna, then copy back into original. DFquals = DF.loc[:, qual_cols].fillna("hf.missing") DF.loc[:, qual_cols] = DFquals if local_freq > pd.Timedelta(to_offset("0min")): variableFreq_str = str(to_offset(local_freq)) else: variableFreq_str = str(to_offset("0min")) parameter_info = { "variableFreq": variableFreq_str, "variableUnit": unit, "variableDescription": variableDescription, } site_info = { "siteName": siteName, "siteLatLongSrs": siteLatLongSrs, "timeSeries": {}, } # if site is not in meta keys, add it. if site_id not in meta: meta[site_id] = site_info # Add the variable info to the site dict. meta[site_id]["timeSeries"][parameter_cd] = parameter_info collection.append(DF) if len(collection) < 1: # It seems like this condition should not occur. The NWIS trims the # response and returns an empty nwis_dict['value']['timeSeries'] # if none of the parameters requested have data. # If at least one of the paramters have data, # then the empty series will get delivered, but with no data. # Compare these requests: # empty: # one empty, one full:,00060 raise exceptions.HydroNoDataError( "The NWIS does not have any data for" " the requested combination of sites" ", parameters, and dates." ) startmin = min(starts) endmax = max(ends) # Remove all frequencies of zero from freqs list. zero = to_offset("0min") freqs2 = list(filter(lambda x: x > zero, freqs)) if len(freqs2) > 0: freqmin = min(freqs) freqmax = max(freqs) if freqmin != freqmax: warnings.warn( "One or more datasets in this request is going to be " "'upsampled' to " + str(freqmin) + " because the data " "were collected at a lower frequency of " + str(freqmax), exceptions.HydroUserWarning, ) clean_index = pd.date_range(start=startmin, end=endmax, freq=freqmin, tz="UTC") cleanDF = pd.DataFrame(index=clean_index) for dataset in collection: cleanDF = pd.concat([cleanDF, dataset], axis=1) # Replace lines with missing _qualifier flags with hf.upsampled qual_cols = cleanDF.columns.str.contains("_qualifiers") cleanDFquals = cleanDF.loc[:, qual_cols].fillna("hf.upsampled") cleanDF.loc[:, qual_cols] = cleanDFquals if interpolate: # TODO: mark interpolated values with 'hf.interp' # select data, then replace Nans with interpolated values. data_cols = cleanDF.columns.str.contains(r"[0-9]$") cleanDFdata = cleanDF.loc[:, data_cols].interpolate() cleanDF.loc[:, data_cols] = cleanDFdata else: # If datasets only contain most recent data, then # don't set an index or a freq. Just concat all of the datasets. cleanDF = pd.concat(collection, axis=1) = "datetimeUTC" if not DF.index.is_unique: DF = DF[~DF.index.duplicated(keep="first")] if not DF.index.is_monotonic: DF.sort_index(axis=0, inplace=True) return cleanDF, meta
[docs]def nwis_custom_status_codes(response): """ Raise custom warning messages from the NWIS when it returns a status_code that is not 200. Args: response: a response object as returned by get_nwis(). Returns: * `None` if response.status_code == 200 * `response.status_code` for all other status codes. Raises: SyntaxWarning: when a non-200 status code is returned. Note: To raise an exception, call ``response.raise_for_status()`` This will raise `requests.exceptions.HTTPError` with a helpful message or it will return `None` for status code 200. From: NWIS status_code messages come from: Additional status code documentation: """ nwis_msg = { "200": "OK", "400": "400 Bad Request - " "This often occurs if the URL arguments " "are inconsistent. For example, if you submit a request using " "a startDT and an endDT with the period argument. " "An accompanying error should describe why the request was " "bad." + "\nError message from NWIS: {}".format(response.reason), "403": "403 Access Forbidden - " "This should only occur if for some reason the USGS has " "blocked your Internet Protocol (IP) address from using " "the service. This can happen if we believe that your use " "of the service is so excessive that it is seriously " "impacting others using the service. To get unblocked, " "send us the URL you are using along with the IP using " "this form. We may require changes to your query and " "frequency of use in order to give you access to the " "service again.", "404": "404 Not Found - " "Returned if and only if the query expresses a combination " "of elements where data do not exist. For multi-site " "queries, if any data are found, it is returned for those " "site/parameters/date ranges where there are data.", "503": "500 Internal Server Error - " "If you see this, it means there is a problem with the web " "service itself. It usually means the application server " "is down unexpectedly. This could be caused by a host of " "conditions, but changing your query will not solve this " "problem. The NWIS application support team has to fix it. Most " "of these errors are quickly detected and the support team " "is notified if they occur.", } if response.status_code == 200: return None # All other status codes will raise a warning. else: # Use the status_code as a key, return None if key not in dict msg = ( "The NWIS returned a code of {}.\n".format(response.status_code) + nwis_msg.get(str(response.status_code)) + "\n\nURL used in this request: {}".format(response.url) ) # Warnings will not beak the flow. They just print a message. # However, they are often supressed in some applications. warnings.warn(msg, SyntaxWarning) return response.status_code
[docs]def read_parquet(filename): pa_table = pq.read_table(filename) dataframe = pa_table.to_pandas() meta_dict = pa_table.schema.metadata if b"hydrofunctions_meta" in meta_dict: meta_string = meta_dict[b"hydrofunctions_meta"].decode() meta = json.loads(meta_string, encoding="utf-8") else: meta = None return dataframe, meta
[docs]def save_parquet(filename, dataframe, hf_meta): table = pa.Table.from_pandas(dataframe, preserve_index=True) meta_dict = table.schema.metadata hf_string = json.dumps(hf_meta).encode() meta_dict[b"hydrofunctions_meta"] = hf_string table = table.replace_schema_metadata(meta_dict) pq.write_table(table, filename, compression='gzip')