tstoolbox: Statistics

I will use the water level record for USGS site ‘02240000 OCKLAWAHA RIVER NEAR CONNER, FL’. Use tsgettoolbox to download the data we will be using.

%matplotlib inline

# Third party imports
from plottoolbox import plottoolbox
from tsgettoolbox import tsgettoolbox

# First party imports
from tstoolbox import tstoolbox
from tstoolbox.toolbox_utils.src.toolbox_utils import tsutils

ock_flow = tsgettoolbox.wdfn_daily(
    monitoring_location_id="USGS-02240000",
    time="2008-01-01/2016-01-01",
    parameter_code="00060",
)

ock_flow.head()

	USGS-02240000_00060_00003:ft^3/s
Datetime
2008-01-01	541
2008-01-02	533
2008-01-03	520
2008-01-04	508
2008-01-05	516

ock_stage = tsgettoolbox.wdfn_daily(
    monitoring_location_id="USGS-02240000",
    time="2008-01-01/2016-01-01",
    parameter_code="00065",
)

ock_stage.head()

	USGS-02240000_00065_00003:ft
Datetime
2008-01-01	3.67
2008-01-02	3.64
2008-01-03	3.60
2008-01-04	3.58
2008-01-05	3.57

The tstoolbox.rolling_window calculates the average stage over the spans listed in the ‘span’ argument.

r_ock_stage = tstoolbox.rolling_window(
    input_ts=ock_stage, span=[1, 14, 30, 60, 90, 120, 180, 274, 365], statistic="mean"
)
a_ock_stage = tstoolbox.aggregate(
    input_ts=r_ock_stage,
    agg_interval=tsutils.pandas_offset_by_version("YE"),
    statistic="max",
)

a_ock_stage

	USGS-02240000_00065_00003:ft:rolling.1.mean_max	USGS-02240000_00065_00003.001:ft:rolling.14.mean_max	USGS-02240000_00065_00003.002:ft:rolling.30.mean_max	USGS-02240000_00065_00003.003:ft:rolling.60.mean_max	USGS-02240000_00065_00003.004:ft:rolling.90.mean_max	USGS-02240000_00065_00003.005:ft:rolling.120.mean_max	USGS-02240000_00065_00003.006:ft:rolling.180.mean_max	USGS-02240000_00065_00003.007:ft:rolling.274.mean_max	USGS-02240000_00065_00003.008:ft:rolling.365.mean_max
Datetime
2008-12-31	7.73	6.055714	5.638667	4.383333	4.049444	3.958	3.985167	<NA>	<NA>
2009-12-31	5.29	4.949286	4.257667	4.179333	4.272222	4.154833	3.953389	<NA>	<NA>
2010-12-31	7.49	6.492857	5.919667	5.079167	4.78	4.788167	<NA>	<NA>	<NA>
2011-12-31	6.22	5.357143	5.227	5.189333	5.175444	5.147917	4.967722	4.632664	4.345315
2012-12-31	6.12	5.473571	5.334667	5.308	5.233444	5.122083	4.809944	4.554051	4.559014
2013-12-31	5.39	5.248571	5.083	4.775333	4.458444	4.19275	3.812833	3.41062	<NA>
2014-12-31	5.86	5.762857	5.704667	5.614333	5.510222	5.437917	5.213722	3.709526	3.379041
2015-12-31	6.85	5.256429	5.001333	4.884167	4.792333	4.882583	5.126222	5.06073	4.767342
2016-12-31	3.41	3.485	3.549667	3.667167	3.866889	<NA>	<NA>	<NA>	<NA>

q_max_avg_obs = tstoolbox.calculate_fdc(input_ts=a_ock_stage, sort_values="descending")

q_max_avg_obs

	USGS-02240000_00065_00003:ft:rolling.1.mean_max	USGS-02240000_00065_00003.001:ft:rolling.14.mean_max	USGS-02240000_00065_00003.002:ft:rolling.30.mean_max	USGS-02240000_00065_00003.003:ft:rolling.60.mean_max	USGS-02240000_00065_00003.004:ft:rolling.90.mean_max	USGS-02240000_00065_00003.005:ft:rolling.120.mean_max	USGS-02240000_00065_00003.006:ft:rolling.180.mean_max	USGS-02240000_00065_00003.007:ft:rolling.274.mean_max	USGS-02240000_00065_00003.008:ft:rolling.365.mean_max
Plotting_position
10.000000	7.73	6.492857	5.919667	5.614333	5.510222	<NA>	<NA>	<NA>	<NA>
11.111111	<NA>	<NA>	<NA>	<NA>	<NA>	5.437917	<NA>	<NA>	<NA>
12.500000	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	5.213722	<NA>	<NA>
16.666667	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	5.06073	<NA>
20.000000	7.49	6.055714	5.704667	5.308	5.233444	<NA>	<NA>	<NA>	4.767342
22.222222	<NA>	<NA>	<NA>	<NA>	<NA>	5.147917	<NA>	<NA>	<NA>
25.000000	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	5.126222	<NA>	<NA>
30.000000	6.85	5.762857	5.638667	5.189333	5.175444	<NA>	<NA>	<NA>	<NA>
33.333333	<NA>	<NA>	<NA>	<NA>	<NA>	5.122083	<NA>	4.632664	<NA>
37.500000	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	4.967722	<NA>	<NA>
40.000000	6.22	5.473571	5.334667	5.079167	4.792333	<NA>	<NA>	<NA>	4.559014
44.444444	<NA>	<NA>	<NA>	<NA>	<NA>	4.882583	<NA>	<NA>	<NA>
50.000000	6.12	5.357143	5.227	4.884167	4.78	<NA>	4.809944	4.554051	<NA>
55.555556	<NA>	<NA>	<NA>	<NA>	<NA>	4.788167	<NA>	<NA>	<NA>
60.000000	5.86	5.256429	5.083	4.775333	4.458444	<NA>	<NA>	<NA>	4.345315
62.500000	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	3.985167	<NA>	<NA>
66.666667	<NA>	<NA>	<NA>	<NA>	<NA>	4.19275	<NA>	3.709526	<NA>
70.000000	5.39	5.248571	5.001333	4.383333	4.272222	<NA>	<NA>	<NA>	<NA>
75.000000	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	3.953389	<NA>	<NA>
77.777778	<NA>	<NA>	<NA>	<NA>	<NA>	4.154833	<NA>	<NA>	<NA>
80.000000	5.29	4.949286	4.257667	4.179333	4.049444	<NA>	<NA>	<NA>	3.379041
83.333333	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	3.41062	<NA>
87.500000	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	3.812833	<NA>	<NA>
88.888889	<NA>	<NA>	<NA>	<NA>	<NA>	3.958	<NA>	<NA>	<NA>
90.000000	3.41	3.485	3.549667	3.667167	3.866889	<NA>	<NA>	<NA>	<NA>

plottoolbox.norm_xaxis(
    input_ts=q_max_avg_obs,
    ofilename="q_max_avg.png",
    xtitle="Annual Exceedence Probability (%)",
    ytitle="Stage (feet)",
    title="N-day Maximum Rolling Window Average Stage for 0224000",
    legend_names=["1", "14", "30", "60", "90", "120", "180", "274", "365"],
)

<module 'matplotlib.pyplot' from '/var/home/timcera/miniconda3/envs/default311/lib/python3.11/site-packages/matplotlib/pyplot.py'>

combined = ock_flow.join(ock_stage)
combined.head()

	USGS-02240000_00060_00003:ft^3/s	USGS-02240000_00065_00003:ft
Datetime
2008-01-01	541	3.67
2008-01-02	533	3.64
2008-01-03	520	3.60
2008-01-04	508	3.58
2008-01-05	516	3.57

plottoolbox.xy(
    input_ts=combined,
    ofilename="stage_flow.png",
    legend=False,
    title="Plot of Daily Stage vs Flow for 02240000",
    xtitle="Flow (cfs)",
    ytitle="Stage (ft)",
    linestyles="",
    markerstyles=".",
)

<module 'matplotlib.pyplot' from '/var/home/timcera/miniconda3/envs/default311/lib/python3.11/site-packages/matplotlib/pyplot.py'>

Note the signficant impact of tail-water elevation.