diff --git a/.travis.yml b/.travis.yml
index 560083b..0c2dbc9 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -20,9 +20,9 @@ install:
   #      https://github.com/travis-ci/travis-ci/issues/2650#issuecomment-252285413
   #      https://github.com/travis-ci/travis-ci/issues/2650#issuecomment-266721196
 
-  # the "--only-binary" option was added in pip 7 back in 2015; still to be safe
+  # the "--only-binary" option was added in pip 7 back in 2015; still to be safe, upgrade pip
   - pip install --upgrade pip setuptools wheel
-  - pip install --only-binary=numpy,scipy,pandas,Ipython numpy scipy pandas ipython
+  - pip install --only-binary=numpy,scipy,pandas,Ipython,matplotlib numpy scipy pandas ipython matplotlib
 
   # Install codecov so I can use it (and coverage.py) to run setup.py
   # However, keep codecov in setup.py so that it gets installed for local usage too.
diff --git a/CONTRIBUTING.rst b/CONTRIBUTING.rst
index 14500ef..55c2fcb 100644
--- a/CONTRIBUTING.rst
+++ b/CONTRIBUTING.rst
@@ -130,7 +130,9 @@ Ready to contribute? Here's how to set up `hydrofunctions` for local development
 
 #. After you've made a small change, make sure you didn't break anything by
    running the tests again. I find it easiest to run the tests from the command
-   line.
+   line::
+
+    > python setup.py tests
 
 #. Before you make too many changes, 'commit' what you've done. Ideally, each
    group of changes that you put into a commit will be logically related to each
diff --git a/HISTORY.rst b/HISTORY.rst
index c484228..2694c74 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -55,5 +55,6 @@ History
 ----------------------
 
 * Added a flow duration chart (mcr)
+* Added the cycleplot chart (mcr)
 * Added code coverage (mcr)
 * improved the build scripts for TravisCI (mcr)
\ No newline at end of file
diff --git a/hydrofunctions/__init__.py b/hydrofunctions/__init__.py
index f3e4159..556517d 100644
--- a/hydrofunctions/__init__.py
+++ b/hydrofunctions/__init__.py
@@ -34,6 +34,7 @@ List all of the different attributes and methods with dir()::
 Read more about Hydrofunctions here: https://hydrofunctions.readthedocs.io/
 
 """
+from __future__ import absolute_import, print_function
 
 __title__ = 'hydrofunctions'
 __version__ = '0.1.6'
diff --git a/hydrofunctions/charts.py b/hydrofunctions/charts.py
index f67b513..b17dcf7 100644
--- a/hydrofunctions/charts.py
+++ b/hydrofunctions/charts.py
@@ -8,17 +8,15 @@ Charting functions for Hydrofunctions.
 # See https://matplotlib.org/faq/howto_faq.html
 # Basically, matplotlib usually uses an 'X11 connection' by default; Travis CI
 # does not have this configured, so you need to set your backend explicitly.
+from __future__ import absolute_import, print_function
 import matplotlib
 matplotlib.use('Agg')
 import matplotlib.pyplot as plt
 from matplotlib.ticker import NullFormatter
+import numpy as np
 
-# I don't need these yet, but soon...
-import bokeh
-import seaborn
 
-
-def flow_duration(Qdf, xscale='logit', yscale='log', ylabel='Discharge', symbol='.'):
+def flow_duration(Qdf, xscale='logit', yscale='log', ylabel='Stream Discharge (m³/s)', symbol='.'):
     """Creates a flow duration chart from a dataframe of discharges.
 
     Args:
@@ -37,7 +35,7 @@ def flow_duration(Qdf, xscale='logit', yscale='log', ylabel='Discharge', symbol=
         yscale ('log' | 'linear'): The type of y scale for plotting discharge.
             Default is 'log'.
 
-        ylabel (str, default: 'Discharge'): The label for the Y axis.
+        ylabel (str, default: 'Stream Discharge (m³/s)'): The label for the Y axis.
 
         xlabel (not implemented)
 
@@ -72,3 +70,112 @@ def flow_duration(Qdf, xscale='logit', yscale='log', ylabel='Discharge', symbol=
     #ax.set_xlabel('Probability of Exceedence')
     ax.xaxis.set_minor_formatter(NullFormatter())
     return fig, ax
+
+
+def cycleplot(DF, cycle='diurnal', compare=None):
+    # inspired by: https://jakevdp.github.io/PythonDataScienceHandbook/03.11-working-with-time-series.html
+
+    if cycle == 'annual':
+        # aggregate into 365 bins to show annual cycles. Same as annual-date
+        aggregateby = DF.index.dayofyear
+        x_label = ' (day # of the year)'
+    elif cycle == 'annual-date':
+        aggregateby = DF.index.dayofyear
+        x_label = ' (day # of the year)'
+    elif cycle == 'annual-week':
+        # aggregate into 52 bins to show annual cycles.
+        aggregateby = DF.index.week
+        x_label = ' (week # of the year)'
+    elif cycle == 'annual-month':
+        # aggregate into 12 binds to show annual cycles.
+        aggregateby = DF.index.month
+        x_label = ' (month # of the year)'
+    elif cycle == 'weekly':
+        # aggregate into 7 bins to show week-long cycles.
+        # Note: 7-day cycles are not natural cycles.
+        aggregateby = DF.index.weekday
+        x_label = ' (day of the week, Monday = 0)'
+    elif cycle == 'diurnal':
+        # aggregate into 24 bins to show 24-hour daily (diurnal) cycles.
+        aggregateby = DF.index.hour
+        x_label = ' (hour of the day)'
+    elif cycle == 'diurnal-smallest':
+        # Uses the smallest unit available in the time index to show 24-hour diurnal cycles.
+        aggregateby = DF.index.time
+        x_label = ' (time of day)'
+    elif cycle == 'diurnal-hour':
+        # aggregate into 24 bins to show 24-hour daily (diurnal) cycles.
+        aggregateby = DF.index.hour
+        x_label = ' (hour of the day)'
+    else:
+        print("The cycle label '", cycle, "' is not recognized as an option. Using cycle='diurnal' instead.")
+        aggregateby = DF.index.hour
+        x_label = ' (hour of the day)'
+
+    if compare is None:
+        # Don't make a comparison plot.
+        # TODO: This is a silly categorization to force all values in the index into the same category.
+        compareby = np.where(DF.index.weekday < 20, 'A', 'B')
+        sub_titles = ['']
+    elif compare == 'month':
+        # Break the time series into 12 months to compare to each other.
+        compareby = DF.index.month
+        sub_titles = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']
+    elif compare == 'weekday':
+        # Break the time series into 7 days of the week to compare to each other.
+        compareby = DF.index.weekday
+        sub_titles = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
+    elif compare == 'weekend':
+        # Break the time series into 2 groups, Weekdays and Weekends for comparison.
+        compareby = np.where(DF.index.weekday < 5, 'Weekday', 'Weekend')
+        sub_titles = ['Weekdays', 'Weekends']
+    elif compare == 'night':
+        # Break the time series into 2 groups to compare day versus night.
+        compareby = np.where((DF.index.hour >= 6) & (DF.index.hour < 19), 'Day', 'Night')
+        sub_titles = ['Day', 'Night']
+    else:
+        print("The compare label '", compare, "' is not recognized as an option. Using compare=None instead.")
+        compareby = np.where(DF.index.weekday < 20, 'A', 'B')
+        sub_titles = ['data']
+
+    selection = [compareby, aggregateby]
+    compare = list(np.unique(compareby))
+
+    # group first by the compareby series, then by the cycle.
+    by_time = DF.groupby(selection)
+    mean = by_time.mean()
+    Q2 = by_time.quantile(.2)
+    Q4 = by_time.quantile(.4)
+    Q5 = by_time.quantile(.5)
+    Q6 = by_time.quantile(.6)
+    Q8 = by_time.quantile(.8)
+
+    Nplots = len(compare)
+    fig, axs = plt.subplots(1, Nplots, figsize=(14, 6), sharey=True, sharex=True)
+    if Nplots == 1:
+        # If there is only one subplot, it gets returned as a single subplot instead of as a numpy array. In this case, we convert it to an array.
+        axs = np.array([axs])
+
+    for i, item in enumerate(compare):
+        axs[i].plot(mean.loc[item], label='mean')
+        # axs[i].plot(Q2.loc[item], label='20th percentile', color='black', linestyle='dotted', linewidth=2)
+        axs[i].plot(Q5.loc[item], label='median', color='black', linestyle='dotted', linewidth=2)
+        # axs[i].plot(Q8.loc[item], label='80th percentile', color='grey', linestyle='dashed', linewidth=1)
+        axs[i].fill_between(Q2.loc[item].index, Q2.loc[item].values.flatten(), Q8.loc[item].values.flatten(), facecolor='grey', alpha=0.5)
+        axs[i].fill_between(Q4.loc[item].index, Q4.loc[item].values.flatten(), Q6.loc[item].values.flatten(), facecolor='grey', alpha=0.5)
+        axs[i].set_title(sub_titles[i])
+        # axs[i].xaxis.set_major_locator(plt.MaxNLocator(4))
+        # axs[i].xaxis.set_major_formatter(matplotlib.dates.DateFormatter('%H'))
+
+    # Set the legend on either the ax or fig.
+    axs[0].legend(loc='best', fancybox=True, framealpha=0.5)
+    # fig.legend(loc='upper center', shadow=True, frameon=True, fancybox=True, framealpha=0.5)
+
+    # Get the yaxis limits, set bottom to zero.
+    ymin, ymax = axs[0].get_ylim()
+    axs[0].set_ylim(0, ymax)
+    axs[0].set_ylabel('Stream Discharge (m³/s)')
+    axs[0].set_xlabel('Time' + x_label)
+    plt.tight_layout()
+
+    return fig, axs
diff --git a/hydrofunctions/hydrofunctions.py b/hydrofunctions/hydrofunctions.py
index 6a72215..c9be04e 100644
--- a/hydrofunctions/hydrofunctions.py
+++ b/hydrofunctions/hydrofunctions.py
@@ -167,12 +167,12 @@ def get_nwis(site, service, start_date=None, end_date=None, stateCd=None,
     return response
 
 
-def get_nwis_property(response_obj, key=None, remove_duplicates=False):
+def get_nwis_property(nwis_dict, key=None, remove_duplicates=False):
     """Returns a list containing property data from an NWIS response object.
 
     Args:
-        response_obj (obj):
-            a response object as returned by get_nwis().
+        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 \
@@ -204,7 +204,7 @@ def get_nwis_property(response_obj, key=None, remove_duplicates=False):
 
         ValueError when the key is not available in
     """
-    nwis_dict = response_obj.json()
+    #nwis_dict = response_obj.json()
 
     # strip header and all metadata.
     ts = nwis_dict['value']['timeSeries']
@@ -257,12 +257,12 @@ def get_nwis_property(response_obj, key=None, remove_duplicates=False):
     return vals
 
 
-def extract_nwis_df(response_obj):
+def extract_nwis_df(nwis_dict):
     """Returns a Pandas dataframe from an NWIS response object.
 
     Args:
-        response_obj (obj):
-            a response object as returned by get_nwis().
+        nwis_dict (obj):
+            the json from a response object as returned by get_nwis().json().
 
     Returns:
         a pandas dataframe.
@@ -271,7 +271,7 @@ def extract_nwis_df(response_obj):
         HydroNoDataError  when the request is valid, but NWIS has no data for
             the parameters provided in the request.
     """
-    nwis_dict = response_obj.json()
+    #nwis_dict = response_obj.json()
 
     # strip header and all metadata.
     ts = nwis_dict['value']['timeSeries']
@@ -296,8 +296,8 @@ def extract_nwis_df(response_obj):
                                           " have any data for this request.")
 
     # create lists of timeseries keys and noDataValues
-    keys = get_nwis_property(response_obj)
-    noDataValues = get_nwis_property(response_obj, key='noDataValue',
+    keys = get_nwis_property(nwis_dict)
+    noDataValues = get_nwis_property(nwis_dict, key='noDataValue',
                                      remove_duplicates=True)
     # determine the NWIS data item with the maximum amount of data so that
     # it can be processed first
@@ -340,6 +340,27 @@ def extract_nwis_df(response_obj):
                                   'qualifiers': tsqual})
         dfa[tsname] = dfa[tsname].astype(float)
         dfa[tsqual] = dfa[tsqual].apply(lambda x: ' '.join(x))
+
+        # TODO:
+
+        # The problem with adding all of these dataframes to a single large
+        # dataframe using pd.concat is that sites that collect less frequently
+        # will get padded with NANs for all of the time indecies that they
+        # don't have data for.
+
+        # A second problem is that every other column will have data, and the
+        # the other columns will have flags. There is no simple way to
+        # select only the data columns except to take the odd numbered columns.
+
+        # A POSSIBLE SOLUTION: create a data structure that is composed of 
+        # Stacked dataframes. Each data frame will correspond to a single site,
+        # The first column will correspond to discharge, the second to flags,
+        # and any others can be derived values like baseflow or other measured
+        # parameters. The dataframes will be stacked, and be part of an object
+        # that allows you to select by a range of dates, by sites, and by the
+        # type of column. In this respect, it might be similar to XArray,
+        # except that package requires their n-dimensional structures to all be
+        # the same datatype.
         DF = pd.concat([DF, dfa], axis=1)
 
     # replace missing values in the dataframe
diff --git a/hydrofunctions/station.py b/hydrofunctions/station.py
index 1600a76..f3d1828 100644
--- a/hydrofunctions/station.py
+++ b/hydrofunctions/station.py
@@ -114,7 +114,7 @@ class NWIS(Station):
         self.stateCd = stateCd
         self.countyCd = countyCd
         self.bBox = bBox
-        self.ok = None
+        self.ok = False
         self.parameterCd = parameterCd
         self.period = typing.check_period(period)
         self.response = None
@@ -164,16 +164,16 @@ class NWIS(Station):
         # set self.json without calling it.
         self.json = lambda: self.response.json()
         # set self.df without calling it.
-        self.df = lambda: hf.extract_nwis_df(self.response)
+        self.df = lambda: hf.extract_nwis_df(self.json())
 
         # Another option might be to do this:
         # self.df = hf.extract_nwis_df(self.response)
         # This would make myInstance.df return a plain df.
         self.ok = self.response.ok
-        self.siteName = hf.get_nwis_property(self.response,
+        self.siteName = hf.get_nwis_property(self.json(),
                                              key='siteName',
                                              remove_duplicates=True)
-        self.name = hf.get_nwis_property(self.response,
+        self.name = hf.get_nwis_property(self.json(),
                                          key='name',
                                          remove_duplicates=True)
 
diff --git a/hydrofunctions/typing.py b/hydrofunctions/typing.py
index c1331d5..530acf8 100644
--- a/hydrofunctions/typing.py
+++ b/hydrofunctions/typing.py
@@ -17,6 +17,7 @@ Suggested format for these functions:
 * then do a regular expression to check that the input is more or less valid.
 * raise exceptions when user input breaks format.
 """
+from __future__ import absolute_import, print_function
 import re
 
 
diff --git a/setup.py b/setup.py
index d27fb50..da3c624 100644
--- a/setup.py
+++ b/setup.py
@@ -27,8 +27,6 @@ readme = relative2absolute(readme, relative, stem)
 
 requirements = [
     'matplotlib',
-    'bokeh',
-    'seaborn',
     'numpy',
     'pandas',
     'requests',
diff --git a/tox.ini b/tox.ini
deleted file mode 100644
index 8420e7c..0000000
--- a/tox.ini
+++ /dev/null
@@ -1,18 +0,0 @@
-[tox]
-envlist = py34, py35, py36, flake8
-
-[testenv:flake8]
-basepython=python
-deps=flake8
-commands=flake8 hydrofunctions
-
-[testenv]
-setenv =
-    PYTHONPATH = {toxinidir}:{toxinidir}/hydrofunctions
-
-commands = python setup.py test
-
-; If you want to make tox run the tests with the same versions, create a
-; requirements.txt with the pinned versions and uncomment the following lines:
-; deps =
-;     -r{toxinidir}/requirements.txt

Travis-CI failure: python 3.5 segmentation fault
* HydroFunctions version: 0.1.6 all branches
* Python version: 3.5
* Operating System: Linux (Travis-ci build)
* Travis Job [#184](https://travis-ci.org/mroberge/hydrofunctions/jobs/379854009)

### Description

Travis-ci suddenly exits during the first test with a segmentation fault. This does not affect Python 3.4 or 3.6.

### What I Did

```
python setup.py test

...
running build_ext
test_charts_cycleplot_exists (tests.test_charts.TestCyclePlot) ... /home/travis/.travis/job_stages: line 57:  1794 Segmentation fault      (core dumped) python setup.py test
```

diff --git a/tests/__init__.py b/tests/__init__.py
index 40a96af..c2559a5 100644
--- a/tests/__init__.py
+++ b/tests/__init__.py
@@ -1,1 +1,2 @@
 # -*- coding: utf-8 -*-
+from __future__ import absolute_import, print_function
diff --git a/tests/test_charts.py b/tests/test_charts.py
index bb350bf..a20d465 100644
--- a/tests/test_charts.py
+++ b/tests/test_charts.py
@@ -7,10 +7,11 @@ Tests for the charts.py module.
 from __future__ import absolute_import, print_function
 import unittest
 import matplotlib
-# attempted matplotlib.use('Agg') here, but it was too late; backend already set
 import pandas as pd
 
 from hydrofunctions import charts
+import hydrofunctions as hf
+from .test_data import JSON15min2month as test_json
 
 dummy = {'col1': [1, 2, 3, 38, 23, 1, 19],
          'col2': [3, 4, 45, 23, 2, 4, 76]}
@@ -36,7 +37,7 @@ class TestFlowDuration(unittest.TestCase):
 
         self.assertEqual(actual_xscale, 'logit')
         self.assertEqual(actual_yscale, 'log')
-        self.assertEqual(actual_ylabel, 'Discharge')
+        self.assertEqual(actual_ylabel, 'Stream Discharge (m³/s)')
         self.assertEqual(actual_marker, '.')
 
     def test_charts_flowduration_accepts_params(self):
@@ -59,5 +60,27 @@ class TestFlowDuration(unittest.TestCase):
         self.assertEqual(actual_marker, ',')
 
 
+class TestCyclePlot(unittest.TestCase):
+
+    def test_charts_cycleplot_exists(self):
+        expected = hf.extract_nwis_df(test_json)
+        actual_fig, actual_ax = charts.cycleplot(expected)
+        self.assertIsInstance(actual_fig, matplotlib.figure.Figure)
+        self.assertIsInstance(actual_ax[0], matplotlib.axes.Axes)
+
+    def test_charts_cycleplot_parts(self):
+        expected = hf.extract_nwis_df(test_json)
+
+        actual_fig, actual_ax = charts.cycleplot(expected)
+
+        actual_xscale = actual_ax[0].xaxis.get_scale()
+        actual_yscale = actual_ax[0].yaxis.get_scale()
+        actual_ylabel = actual_ax[0].yaxis.get_label_text()
+
+        self.assertEqual(actual_xscale, 'linear')
+        self.assertEqual(actual_yscale, 'linear')
+        self.assertEqual(actual_ylabel, 'Stream Discharge (m³/s)')
+
+
 if __name__ == '__main__':
     unittest.main(verbosity=2)
diff --git a/tests/test_data.py b/tests/test_data.py
new file mode 100644
index 0000000..af257f6
--- /dev/null
+++ b/tests/test_data.py
@@ -0,0 +1,3085 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Tue May 15 19:33:19 2018
+
+@author: Marty
+"""
+
+
+JSON15min2month = {
+'declaredType': 'org.cuahsi.waterml.TimeSeriesResponseType',
+ 'globalScope': True,
+ 'name': 'ns1:timeSeriesResponseType',
+ 'nil': False,
+ 'scope': 'javax.xml.bind.JAXBElement$GlobalScope',
+ 'typeSubstituted': False,
+ 'value': {'queryInfo': {'criteria': {'locationParam': '[ALL:03213700]',
+    'parameter': [],
+    'timeParam': {'beginDateTime': '2016-09-01T00:00:00.000',
+     'endDateTime': '2017-11-01T23:59:59.000'},
+    'variableParam': '[00060]'},
+   'note': [{'title': 'filter:sites', 'value': '[ALL:03213700]'},
+    {'title': 'filter:timeRange',
+     'value': '[mode=RANGE, modifiedSince=null] interval={INTERVAL[2016-09-01T00:00:00.000-04:00/2017-11-01T23:59:59.000Z]}'},
+    {'title': 'filter:methodId', 'value': 'methodIds=[ALL]'},
+    {'title': 'requestDT', 'value': '2018-05-15T23:32:02.320Z'},
+    {'title': 'requestId', 'value': '2b84b1f0-5898-11e8-a9ea-3440b59d3362'},
+    {'title': 'disclaimer',
+     'value': 'Provisional data are subject to revision. Go to http://waterdata.usgs.gov/nwis/help/?provisional for more information.'},
+    {'title': 'server', 'value': 'nadww02'}],
+   'queryURL': 'http://nwis.waterservices.usgs.gov/nwis/iv/format=json%2C1.1&sites=03213700&parameterCd=00060&startDT=2016-09-01&endDT=2017-11-01'},
+  'timeSeries': [{'name': 'USGS:03213700:00060:00000',
+    'sourceInfo': {'geoLocation': {'geogLocation': {'latitude': 37.67315699,
+       'longitude': -82.2801408,
+       'srs': 'EPSG:4326'},
+      'localSiteXY': []},
+     'note': [],
+     'siteCode': [{'agencyCode': 'USGS',
+       'network': 'NWIS',
+       'value': '03213700'}],
+     'siteName': 'TUG FORK AT WILLIAMSON, WV',
+     'siteProperty': [{'name': 'siteTypeCd', 'value': 'ST'},
+      {'name': 'hucCd', 'value': '05070201'},
+      {'name': 'stateCd', 'value': '21'},
+      {'name': 'countyCd', 'value': '21195'}],
+     'siteType': [],
+     'timeZoneInfo': {'daylightSavingsTimeZone': {'zoneAbbreviation': 'EDT',
+       'zoneOffset': '-04:00'},
+      'defaultTimeZone': {'zoneAbbreviation': 'EST', 'zoneOffset': '-05:00'},
+      'siteUsesDaylightSavingsTime': True}},
+    'values': [{'censorCode': [],
+      'method': [{'methodDescription': '', 'methodID': 160874}],
+      'offset': [],
+      'qualifier': [{'network': 'NWIS',
+        'qualifierCode': 'e',
+        'qualifierDescription': 'Value has been edited or estimated by USGS personnel and is write protected.',
+        'qualifierID': 0,
+        'vocabulary': 'uv_rmk_cd'},
+       {'network': 'NWIS',
+        'qualifierCode': 'A',
+        'qualifierDescription': 'Approved for publication -- Processing and review completed.',
+        'qualifierID': 1,
+        'vocabulary': 'uv_rmk_cd'},
+       {'network': 'NWIS',
+        'qualifierCode': 'P',
+        'qualifierDescription': 'Provisional data subject to revision.',
+        'qualifierID': 2,
+        'vocabulary': 'uv_rmk_cd'}],
+      'qualityControlLevel': [],
+      'sample': [],
+      'source': [],
+      'value': [{'dateTime': '2016-09-01T00:00:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '366'},
+       {'dateTime': '2016-09-01T00:15:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '363'},
+       {'dateTime': '2016-09-01T00:30:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '363'},
+       {'dateTime': '2016-09-01T00:45:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '363'},
+       {'dateTime': '2016-09-01T01:00:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '363'},
+       {'dateTime': '2016-09-01T01:15:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '363'},
+       {'dateTime': '2016-09-01T01:30:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '360'},
+       {'dateTime': '2016-09-01T01:45:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '357'},
+       {'dateTime': '2016-09-01T02:00:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '357'},
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+        'value': '211'},
+       {'dateTime': '2016-09-09T22:45:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '211'},
+       {'dateTime': '2016-09-09T23:00:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '211'},
+       {'dateTime': '2016-09-09T23:15:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '211'},
+       {'dateTime': '2016-09-09T23:30:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '208'},
+       {'dateTime': '2016-09-09T23:45:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '208'},
+       {'dateTime': '2016-09-10T00:00:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '211'},
+       {'dateTime': '2016-09-10T00:15:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '208'},
+       {'dateTime': '2016-09-10T00:30:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '208'},
+       {'dateTime': '2016-09-10T00:45:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '208'},
+       {'dateTime': '2016-09-10T01:00:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '208'},
+       {'dateTime': '2016-09-10T01:15:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '208'},
+       {'dateTime': '2016-09-10T01:30:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '208'},
+       {'dateTime': '2016-09-10T01:45:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '208'},
+       {'dateTime': '2016-09-10T02:00:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '208'},
+       {'dateTime': '2016-09-10T02:15:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '208'},
+       {'dateTime': '2016-09-10T02:30:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '208'},
+       {'dateTime': '2016-09-10T02:45:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '211'},
+       {'dateTime': '2016-09-10T03:00:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '211'},
+       {'dateTime': '2016-09-10T03:15:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '211'},
+       {'dateTime': '2016-09-10T03:30:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '211'},
+       {'dateTime': '2016-09-10T03:45:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '211'},
+       {'dateTime': '2016-09-10T04:00:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '211'},
+       {'dateTime': '2016-09-10T04:15:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '211'},
+       {'dateTime': '2016-09-10T04:30:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '211'},
+       {'dateTime': '2016-09-10T04:45:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '211'},
+       {'dateTime': '2016-09-10T05:00:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '211'},
+       {'dateTime': '2016-09-10T05:15:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '211'},
+       {'dateTime': '2016-09-10T05:30:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '208'},
+       {'dateTime': '2016-09-10T05:45:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '208'},
+       {'dateTime': '2016-09-10T06:00:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '205'},
+       {'dateTime': '2016-09-10T06:15:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '205'},
+       {'dateTime': '2016-09-10T06:30:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '205'},
+       {'dateTime': '2016-09-10T06:45:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '205'},
+       {'dateTime': '2016-09-10T07:00:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '205'},
+       {'dateTime': '2016-09-10T07:15:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '205'},
+       {'dateTime': '2016-09-10T07:30:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '202'},
+       {'dateTime': '2016-09-10T07:45:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '202'},
+       {'dateTime': '2016-09-10T08:00:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '202'},
+       {'dateTime': '2016-09-10T08:15:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '202'},
+       {'dateTime': '2016-09-10T08:30:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '202'},
+       {'dateTime': '2016-09-10T08:45:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '205'},
+       {'dateTime': '2016-09-10T09:00:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '202'},
+       {'dateTime': '2016-09-10T09:15:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '202'},
+       {'dateTime': '2016-09-10T09:30:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '205'},
+       {'dateTime': '2016-09-10T09:45:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '205'},
+       {'dateTime': '2016-09-10T10:00:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '205'},
+       {'dateTime': '2016-09-10T10:15:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '208'},
+       {'dateTime': '2016-09-10T10:30:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '205'},
+       {'dateTime': '2016-09-10T10:45:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '205'},
+       {'dateTime': '2016-09-10T11:00:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '205'},
+       {'dateTime': '2016-09-10T11:15:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '205'},
+       {'dateTime': '2016-09-10T11:30:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '205'},
+       {'dateTime': '2016-09-10T11:45:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '205'},
+       {'dateTime': '2016-09-10T12:00:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '205'},
+       {'dateTime': '2016-09-10T12:15:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '205'},
+       {'dateTime': '2016-09-10T12:30:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '208'},
+       {'dateTime': '2016-09-10T12:45:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '208'},
+       {'dateTime': '2016-09-10T13:00:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '205'},
+       {'dateTime': '2016-09-10T13:15:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '208'},
+       {'dateTime': '2016-09-10T13:30:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '208'},
+       {'dateTime': '2016-09-10T13:45:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '208'},
+       {'dateTime': '2016-09-10T14:00:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '208'},
+       {'dateTime': '2016-09-10T14:15:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '208'},
+       {'dateTime': '2016-09-10T14:30:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '211'},
+       {'dateTime': '2016-09-10T14:45:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '211'},
+       {'dateTime': '2016-09-10T15:00:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '211'},
+       {'dateTime': '2016-09-10T15:15:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '211'},
+       {'dateTime': '2016-09-10T15:30:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '211'},
+       {'dateTime': '2016-09-10T15:45:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '208'},
+       {'dateTime': '2016-09-10T16:00:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '211'},
+       {'dateTime': '2016-09-10T16:15:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '211'},
+       {'dateTime': '2016-09-10T16:30:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '208'},
+       {'dateTime': '2016-09-10T16:45:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '208'},
+       {'dateTime': '2016-09-10T17:00:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '208'},
+       {'dateTime': '2016-09-10T17:15:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '208'},
+       {'dateTime': '2016-09-10T17:30:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '208'},
+       {'dateTime': '2016-09-10T17:45:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '208'},
+       {'dateTime': '2016-09-10T18:00:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '208'},
+       {'dateTime': '2016-09-10T18:15:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '208'},
+       {'dateTime': '2016-09-10T18:30:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '205'},
+       {'dateTime': '2016-09-10T18:45:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '205'},
+       {'dateTime': '2016-09-10T19:00:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '208'},
+       {'dateTime': '2016-09-10T19:15:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '208'},
+       {'dateTime': '2016-09-10T19:30:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '208'},
+       {'dateTime': '2016-09-10T19:45:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '205'},
+       {'dateTime': '2016-09-10T20:00:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '208'},
+       {'dateTime': '2016-09-10T20:15:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '208'},
+       {'dateTime': '2016-09-10T20:30:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '208'},
+       {'dateTime': '2016-09-10T20:45:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '208'},
+       {'dateTime': '2016-09-10T21:00:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '208'},
+       {'dateTime': '2016-09-10T21:15:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '202'},
+       {'dateTime': '2016-09-10T21:30:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '205'},
+       {'dateTime': '2016-09-10T21:45:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '205'},
+       {'dateTime': '2016-09-10T22:00:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '205'},
+       {'dateTime': '2016-09-10T22:15:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '202'},
+       {'dateTime': '2016-09-10T22:30:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '208'},
+       {'dateTime': '2016-09-10T22:45:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '226'},
+       {'dateTime': '2016-09-10T23:00:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '241'},
+       {'dateTime': '2016-09-10T23:15:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '241'},
+       {'dateTime': '2016-09-10T23:30:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '241'},
+       {'dateTime': '2016-09-10T23:45:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '241'},
+       {'dateTime': '2016-09-11T00:00:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '241'},
+       {'dateTime': '2016-09-11T00:15:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '235'},
+       {'dateTime': '2016-09-11T00:30:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '232'},
+       {'dateTime': '2016-09-11T00:45:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '229'},
+       {'dateTime': '2016-09-11T01:00:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '223'},
+       {'dateTime': '2016-09-11T01:15:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '223'},
+       {'dateTime': '2016-09-11T01:30:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '220'},
+       {'dateTime': '2016-09-11T01:45:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '217'},
+       {'dateTime': '2016-09-11T02:00:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '217'},
+       {'dateTime': '2016-09-11T02:15:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '217'},
+       {'dateTime': '2016-09-11T02:30:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '217'},
+       {'dateTime': '2016-09-11T02:45:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '217'},
+       {'dateTime': '2016-09-11T03:00:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '217'},
+       {'dateTime': '2016-09-11T03:15:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '217'},
+       {'dateTime': '2016-09-11T03:30:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '217'},
+       {'dateTime': '2016-09-11T03:45:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '217'},
+       {'dateTime': '2016-09-11T04:00:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '217'},
+       {'dateTime': '2016-09-11T04:15:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '217'},
+       {'dateTime': '2016-09-11T04:30:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '217'},
+       {'dateTime': '2016-09-11T04:45:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '217'},
+       {'dateTime': '2016-09-11T05:00:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '217'},
+       {'dateTime': '2016-09-11T05:15:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '217'},
+       {'dateTime': '2016-09-11T05:30:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '217'},
+       {'dateTime': '2016-09-11T05:45:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '214'},
+       {'dateTime': '2016-09-11T06:00:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '214'},
+       {'dateTime': '2016-09-11T06:15:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '214'},
+       {'dateTime': '2016-09-11T06:30:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '214'},
+       {'dateTime': '2016-09-11T06:45:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '211'},
+       {'dateTime': '2016-09-11T07:00:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '211'},
+       {'dateTime': '2016-09-11T07:15:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '208'},
+       {'dateTime': '2016-09-11T07:30:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '211'},
+       {'dateTime': '2016-09-11T07:45:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '211'},
+       {'dateTime': '2016-09-11T08:00:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '208'},
+       {'dateTime': '2016-09-11T08:15:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '208'},
+       {'dateTime': '2016-09-11T08:30:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '211'},
+       {'dateTime': '2016-09-11T08:45:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '208'},
+       {'dateTime': '2016-09-11T09:00:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '211'},
+       {'dateTime': '2016-09-11T09:15:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '208'},
+       {'dateTime': '2016-09-11T09:30:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '211'},
+       {'dateTime': '2016-09-11T09:45:00.000-04:00',
+        'qualifiers': ['A'],
+        'value': '211'}
+       ]}],
+    'variable': {'noDataValue': -999999.0,
+     'note': [],
+     'oid': '45807197',
+     'options': {'option': [{'name': 'Statistic', 'optionCode': '00000'}]},
+     'unit': {'unitCode': 'ft3/s'},
+     'valueType': 'Derived Value',
+     'variableCode': [{'default': True,
+       'network': 'NWIS',
+       'value': '00060',
+       'variableID': 45807197,
+       'vocabulary': 'NWIS:UnitValues'}],
+     'variableDescription': 'Discharge, cubic feet per second',
+     'variableName': 'Streamflow, ft&#179;/s',
+     'variableProperty': []}}]}}
diff --git a/tests/test_hydrofunctions.py b/tests/test_hydrofunctions.py
index 85a50ca..cd6dd8e 100644
--- a/tests/test_hydrofunctions.py
+++ b/tests/test_hydrofunctions.py
@@ -14,6 +14,7 @@ import unittest
 import pandas as pd
 
 import hydrofunctions as hf
+from .test_data import JSON15min2month as test_json
 
 
 class fakeResponse(object):
@@ -22,6 +23,9 @@ class fakeResponse(object):
         self.status_code = code
         self.url = "fake url"
         self.reason = "fake reason"
+        # .json will return a function
+        # .json() will return test_json
+        self.json = lambda: test_json
         if code == 200:
             pass
         else:
@@ -89,58 +93,43 @@ class TestHydrofunctions(unittest.TestCase):
                                          headers=expected_headers)
         self.assertEqual(actual, expected)
 
-    @unittest.skip('Stop requesting data during test.')
     def test_hf_extract_nwis_df(self):
-        # TODO: I need to make a response fixture to test this out!!
-        test = hf.get_nwis("01589440", "dv", "2013-01-01", "2013-01-05")
-        actual = hf.extract_nwis_df(test)
+        # TODO: I need to check this was parsed correctly!
+        actual = hf.extract_nwis_df(test_json)
         self.assertIs(type(actual), pd.core.frame.DataFrame,
                       msg="Did not return a df")
 
-    @unittest.skip('Stop requesting data during test.')
     def test_hf_extract_nwis_stations_df(self):
         sites = ["01638500", "01646502"]
         # TODO: test should be the json for a multiple site request.
-        test = hf.get_nwis(sites, "dv",
-                           "2013-01-01", "2013-01-05")
-        actual = hf.extract_nwis_df(test)
-        vD = hf.get_nwis_property(test, key='variableDescription')
+        actual = hf.extract_nwis_df(test_json)
+        vD = hf.get_nwis_property(test_json, key='variableDescription')
         self.assertIs(type(actual), pd.core.frame.DataFrame,
                       msg="Did not return a df")
 
-    @unittest.skip('Stop requesting data during test.')
     def test_hf_extract_nwis_iv_gwstations_df(self):
         # TODO: I need to make a response fixture to test this out!!
         sites = ["380616075380701", "394008077005601"]
-        test = hf.get_nwis(sites, "iv",
-                           "2018-01-01", "2018-01-05", parameterCd='72019')
-        actual = hf.extract_nwis_df(test)
+        actual = hf.extract_nwis_df(test_json)
         self.assertIs(type(actual), pd.core.frame.DataFrame,
                       msg="Did not return a df")
 
-
-    @unittest.skip('Stop requesting data during test.')
     def test_hf_extract_nwis_bBox_df(self):
         sites = None
         bBox = (-105.430, 39.655, -104, 39.863)
         # TODO: test should be the json for a multiple site request.
-        test = hf.get_nwis(sites, "dv",
-                           "2013-01-01", "2013-01-05", bBox=bBox)
-        names = hf.get_nwis_property(test, key='name')
+        names = hf.get_nwis_property(test_json, key='name')
         self.assertIs(type(names), list, msg="Did not return a list")
-        actual = hf.extract_nwis_df(test)
+        actual = hf.extract_nwis_df(test_json)
         self.assertIs(type(actual), pd.core.frame.DataFrame,
                       msg="Did not return a df")
 
-    @unittest.skip('Stop requesting data during test.')
     def test_hf_extract_nwis_bBox2_df(self):
         sites = None
         bBox = '-105.430,39.655,-104,39.863'
         # TODO: test should be the json for a multiple site request.
-        test = hf.get_nwis(sites, "dv",
-                           "2013-01-01", "2013-01-05", bBox=bBox)
-        names = hf.get_nwis_property(test, key='name')
-        actual = hf.extract_nwis_df(test)
+        names = hf.get_nwis_property(test_json, key='name')
+        actual = hf.extract_nwis_df(test_json)
         self.assertIs(type(actual), pd.core.frame.DataFrame,
                       msg="Did not return a df")
 
diff --git a/tests/test_station.py b/tests/test_station.py
index a996808..800668b 100644
--- a/tests/test_station.py
+++ b/tests/test_station.py
@@ -21,6 +21,7 @@ class fakeResponse(object):
         self.status_code = code
         self.url = "fake url"
         self.reason = "fake reason"
+        self.json = lambda: {'data': 'fake json'}
         if code == 200:
             self.ok = True
         else:

pip install -e .[dev]
parse_log_pytest
requirements.txt
pytest
3.6
requirements.txt
pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning
attrs==22.2.0
backcall==0.2.0
bokeh==2.3.3
certifi==2021.5.30
charset-normalizer==2.0.12
cycler==0.11.0
decorator==5.1.1
-e git+https://github.com/mroberge/hydrofunctions.git@115ea0905e8ecc1ff287a9298b65efc3bb921c75#egg=hydrofunctions
idna==3.10
importlib-metadata==4.8.3
iniconfig==1.1.1
ipython==7.16.3
ipython-genutils==0.2.0
jedi==0.17.2
Jinja2==3.0.3
kiwisolver==1.3.1
MarkupSafe==2.0.1
matplotlib==3.3.4
numpy==1.19.5
packaging==21.3
pandas==1.1.5
parso==0.7.1
pexpect==4.9.0
pickleshare==0.7.5
Pillow==8.4.0
pluggy==1.0.0
prompt-toolkit==3.0.36
ptyprocess==0.7.0
py==1.11.0
Pygments==2.14.0
pyparsing==3.1.4
pytest==7.0.1
python-dateutil==2.9.0.post0
pytz==2025.2
PyYAML==6.0.1
requests==2.27.1
scipy==1.5.4
seaborn==0.11.2
six==1.17.0
tomli==1.2.3
tornado==6.1
traitlets==4.3.3
typing_extensions==4.1.1
urllib3==1.26.20
wcwidth==0.2.13
zipp==3.6.0

name: hydrofunctions
channels:
  - defaults
  - https://repo.anaconda.com/pkgs/main
  - https://repo.anaconda.com/pkgs/r
  - conda-forge
dependencies:
  - _libgcc_mutex=0.1=main
  - _openmp_mutex=5.1=1_gnu
  - ca-certificates=2025.2.25=h06a4308_0
  - certifi=2021.5.30=py36h06a4308_0
  - ld_impl_linux-64=2.40=h12ee557_0
  - libffi=3.3=he6710b0_2
  - libgcc-ng=11.2.0=h1234567_1
  - libgomp=11.2.0=h1234567_1
  - libstdcxx-ng=11.2.0=h1234567_1
  - ncurses=6.4=h6a678d5_0
  - openssl=1.1.1w=h7f8727e_0
  - pip=21.2.2=py36h06a4308_0
  - python=3.6.13=h12debd9_1
  - readline=8.2=h5eee18b_0
  - setuptools=58.0.4=py36h06a4308_0
  - sqlite=3.45.3=h5eee18b_0
  - tk=8.6.14=h39e8969_0
  - wheel=0.37.1=pyhd3eb1b0_0
  - xz=5.6.4=h5eee18b_1
  - zlib=1.2.13=h5eee18b_1
  - pip:
      - attrs==22.2.0
      - backcall==0.2.0
      - bokeh==2.3.3
      - charset-normalizer==2.0.12
      - cycler==0.11.0
      - decorator==5.1.1
      - idna==3.10
      - importlib-metadata==4.8.3
      - iniconfig==1.1.1
      - ipython==7.16.3
      - ipython-genutils==0.2.0
      - jedi==0.17.2
      - jinja2==3.0.3
      - kiwisolver==1.3.1
      - markupsafe==2.0.1
      - matplotlib==3.3.4
      - numpy==1.19.5
      - packaging==21.3
      - pandas==1.1.5
      - parso==0.7.1
      - pexpect==4.9.0
      - pickleshare==0.7.5
      - pillow==8.4.0
      - pluggy==1.0.0
      - prompt-toolkit==3.0.36
      - ptyprocess==0.7.0
      - py==1.11.0
      - pygments==2.14.0
      - pyparsing==3.1.4
      - pytest==7.0.1
      - python-dateutil==2.9.0.post0
      - pytz==2025.2
      - pyyaml==6.0.1
      - requests==2.27.1
      - scipy==1.5.4
      - seaborn==0.11.2
      - six==1.17.0
      - tomli==1.2.3
      - tornado==6.1
      - traitlets==4.3.3
      - typing-extensions==4.1.1
      - urllib3==1.26.20
      - wcwidth==0.2.13
      - zipp==3.6.0
prefix: /opt/conda/envs/hydrofunctions

tests/test_charts.py::TestFlowDuration::test_charts_flowduration_defaults tests/test_charts.py::TestCyclePlot::test_charts_cycleplot_exists tests/test_charts.py::TestCyclePlot::test_charts_cycleplot_parts tests/test_hydrofunctions.py::TestHydrofunctions::test_hf_extract_nwis_bBox2_df tests/test_hydrofunctions.py::TestHydrofunctions::test_hf_extract_nwis_bBox_df tests/test_hydrofunctions.py::TestHydrofunctions::test_hf_extract_nwis_df tests/test_hydrofunctions.py::TestHydrofunctions::test_hf_extract_nwis_iv_gwstations_df tests/test_hydrofunctions.py::TestHydrofunctions::test_hf_extract_nwis_stations_df
tests/test_charts.py::TestFlowDuration::test_charts_flowduration_accepts_params tests/test_charts.py::TestFlowDuration::test_charts_flowduration_exists tests/test_hydrofunctions.py::TestHydrofunctions::test_hf_get_nwis_calls_correct_url tests/test_hydrofunctions.py::TestHydrofunctions::test_hf_get_nwis_calls_correct_url_multiple_sites tests/test_hydrofunctions.py::TestHydrofunctions::test_hf_nwis_custom_status_codes_returns_None_for_200 tests/test_hydrofunctions.py::TestHydrofunctions::test_hf_nwis_custom_status_codes_returns_status_for_non200 tests/test_station.py::TestStation::test_multiple_instances_only_one_list tests/test_station.py::TestStation::test_station_dict_keeps_keys tests/test_station.py::TestStation::test_station_dict_returns_dict tests/test_station.py::TestStation::test_station_dict_returns_instance tests/test_station.py::TestStation::test_station_id_sets tests/test_station.py::TestStation::test_station_is_obj tests/test_station.py::TestStation::test_station_site_defaults_to_None tests/test_station.py::TestStation::test_station_subclasses_maintain_same_station_dict tests/test_station.py::TestNWIS::test_NWIS_end_defaults_to_None tests/test_station.py::TestNWIS::test_NWIS_get_data_calls_get_nwis_correctly tests/test_station.py::TestNWIS::test_NWIS_get_data_calls_get_nwis_mult_sites tests/test_station.py::TestNWIS::test_NWIS_service_defaults_to_dv tests/test_station.py::TestNWIS::test_NWIS_setters_parameterCd tests/test_station.py::TestNWIS::test_NWIS_setters_work tests/test_station.py::TestNWIS::test_NWIS_start_defaults_to_None tests/test_station.py::TestNWIS::test_hf_get_nwis_accepts_countyCd_array
hydrofunctions/charts.py hydrofunctions/hydrofunctions.py HISTORY.rst setup.py CONTRIBUTING.rst hydrofunctions/__init__.py .travis.yml tox.ini hydrofunctions/typing.py hydrofunctions/station.py
hydrofunctions/charts.py hydrofunctions/hydrofunctions.py HISTORY.rst setup.py CONTRIBUTING.rst hydrofunctions/__init__.py .travis.yml tox.ini hydrofunctions/typing.py hydrofunctions/station.py