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.

[1]:

from tsgettoolbox import tsgettoolbox
from tstoolbox import tstoolbox
from tstoolbox.toolbox_utils.src.toolbox_utils import tsutils

[2]:

ock_flow = tsgettoolbox.nwis_dv(
    sites="02240000", startDT="2008-01-01", endDT="2016-01-01", parameterCd="00060"
)

[3]:

ock_flow.head()

[3]:

	USGS_02240000_22614_00060_00003
Datetime
2008-01-01	541
2008-01-02	533
2008-01-03	520
2008-01-04	508
2008-01-05	516

[4]:

ock_stage = tsgettoolbox.nwis_dv(
    sites="02240000", startDT="2008-01-01", endDT="2016-01-01", parameterCd="00065"
)

[5]:

ock_stage.head()

[5]:

	USGS_02240000_22615_00065_00003
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.

[6]:

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"
)

[7]:

a_ock_stage

[7]:

	USGS_02240000_22615_00065_00003::rolling.1.mean_max	USGS_02240000_22615_00065_00003.001::rolling.14.mean_max	USGS_02240000_22615_00065_00003.002::rolling.30.mean_max	USGS_02240000_22615_00065_00003.003::rolling.60.mean_max	USGS_02240000_22615_00065_00003.004::rolling.90.mean_max	USGS_02240000_22615_00065_00003.005::rolling.120.mean_max	USGS_02240000_22615_00065_00003.006::rolling.180.mean_max	USGS_02240000_22615_00065_00003.007::rolling.274.mean_max	USGS_02240000_22615_00065_00003.008::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>

[9]:

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

[10]:

q_max_avg_obs

[10]:

	USGS_02240000_22615_00065_00003::rolling.1.mean_max	USGS_02240000_22615_00065_00003.001::rolling.14.mean_max	USGS_02240000_22615_00065_00003.002::rolling.30.mean_max	USGS_02240000_22615_00065_00003.003::rolling.60.mean_max	USGS_02240000_22615_00065_00003.004::rolling.90.mean_max	USGS_02240000_22615_00065_00003.005::rolling.120.mean_max	USGS_02240000_22615_00065_00003.006::rolling.180.mean_max	USGS_02240000_22615_00065_00003.007::rolling.274.mean_max	USGS_02240000_22615_00065_00003.008::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>

[13]:

tstoolbox.plot(
    input_ts=q_max_avg_obs,
    ofilename="q_max_avg.png",
    type="norm_xaxis",
    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"],
)

[13]:

<module 'matplotlib.pyplot' from '/var/home/timcera/miniconda3/envs/default314/lib/python3.14/site-packages/matplotlib/pyplot.py'>

[14]:

combined = ock_flow.join(ock_stage)
tstoolbox.plot(
    input_ts=combined,
    type="xy",
    ofilename="stage_flow.png",
    legend=False,
    title="Plot of Daily Stage vs Flow for 02240000",
    xtitle="Flow (cfs)",
    ytitle="Stage (ft)",
)

[14]:

<module 'matplotlib.pyplot' from '/var/home/timcera/miniconda3/envs/default314/lib/python3.14/site-packages/matplotlib/pyplot.py'>

Note the signficant effect of tail-water elevation.

[ ]:

tstoolbox: Statistics¶

Tim Cera

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