Welcome to metocean-stats’s documentation!

metocean-stats is a Python tool for comprehensive statistics and visualization of metocean data (wind, waves, ocean currents, tide levels, air and water temperature, sea ice, and more). The input data is conveniently provided within the tool as a Pandas DataFrame (time series of metocean variables) from a single position.

The tool is designed with cross-platform compatibility in mind. It can smoothly operate on various operating systems, including Linux, Windows, and MacOS. It is also compatible with WEkEO Jupyter Lab, allowing seamless integration and use. For instructions on how to install metocean-stats in WEkEO, please refer here

The tool contains functions that generate:

• general (e.g., seasonal, annual) statistics,

• extreme statistics,

• marine operation statistics.

Installing metocean-stats

Alternative 1: Using Miniforge (alternative to Miniconda)

1. Install miniforge (download)

2. Set up a Python 3 environment for metocean-stats and install metocean-stats

$ mamba create -n metocean-stats python=3 metocean-stats
$ conda activate metocean-stats

Alternative 2: Using Miniforge (alternative to Miniconda) and Git

1. Install miniforge (download)

2. Clone metocean-stats:

$ git clone https://github.com/MET-OM/metocean-stats.git
$ cd metocean-stats/

3. Create environment with the required dependencies and install metocean-stats

$ mamba env create -f environment.yml
$ conda activate metocean-stats
$ pip install --no-deps -e .

This installs the metocean-stats as an editable package. Therefore, you can directly change the repository or fetch the newest changes with git pull.

To update the local conda environment in case new dependencies are added to environment.yml:

$ mamba env update -f environment.yml

Metocean Statistics

Import plots and tables from metocean-stats:

from metocean_stats import plots, tables

Scatter Diagram

tables.scatter_diagram(
    df,
    var1='HS',
    step_var1=1,
    var2='TP',
    step_var2=1,
    output_file='Hs_Tp_scatter.csv'
)

Scatter diagram

HS/TP	2-3 | 0.00%	3-4 | 0.21%	4-5 | 1.96%	5-6 | 6.74%	6-7 | 12.62%	7-8 | 15.56%	8-9 | 16.86%	9-10 | 14.14%	10-11 | 12.11%	11-12 | 8.53%	12-13 | 5.42%	13-14 | 2.69%	14-15 | 1.52%	15-16 | 0.93%	16-17 | 0.39%	17-18 | 0.18%	18-19 | 0.12%	19-20 | 0.01%	20-21 | 0.01%	21-22 | 0.00%	22-23 | 0.00%
13.0-14.0 | 0.00%	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
12.0-13.0 | 0.01%	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
11.0-12.0 | 0.01%	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
10.0-11.0 | 0.04%	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.01	0.01	0.01	0.01	0.0	0.0	0.0	0.0	0.0	0.0	0.0
09.0-10.0 | 0.12%	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.01	0.03	0.04	0.02	0.01	0.01	0.0	0.0	0.0	0.0	0.0	0.0
08.0-09.0 | 0.28%	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.01	0.05	0.11	0.07	0.02	0.02	0.0	0.0	0.0	0.0	0.0	0.0	0.0
07.0-08.0 | 0.74%	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.07	0.23	0.26	0.11	0.04	0.02	0.0	0.0	0.0	0.0	0.0	0.0	0.0
06.0-07.0 | 1.78%	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.09	0.39	0.71	0.36	0.13	0.06	0.04	0.01	0.01	0.0	0.0	0.0	0.0	0.0
05.0-06.0 | 3.54%	0.0	0.0	0.0	0.0	0.0	0.0	0.11	0.59	1.27	0.9	0.33	0.15	0.09	0.06	0.02	0.01	0.01	0.0	0.0	0.0	0.0
04.0-05.0 | 7.40%	0.0	0.0	0.0	0.0	0.0	0.08	1.14	2.23	1.94	0.9	0.51	0.3	0.14	0.09	0.03	0.02	0.01	0.0	0.0	0.0	0.0
03.0-04.0 | 14.13%	0.0	0.0	0.0	0.0	0.26	1.65	3.97	3.08	1.87	1.3	1.02	0.46	0.23	0.14	0.07	0.04	0.02	0.0	0.0	0.0	0.0
02.0-03.0 | 24.66%	0.0	0.0	0.01	0.41	3.14	5.47	4.62	3.11	3.02	2.29	1.21	0.55	0.38	0.24	0.12	0.05	0.04	0.0	0.0	0.0	0.0
01.0-02.0 | 35.42%	0.0	0.03	0.92	4.09	6.78	6.61	5.8	4.11	2.76	1.56	1.19	0.73	0.41	0.25	0.09	0.04	0.02	0.0	0.0	0.0	0.0
00.0-01.0 | 11.87%	0.0	0.18	1.03	2.23	2.43	1.74	1.23	0.94	0.76	0.58	0.38	0.15	0.1	0.06	0.03	0.01	0.01	0.0	0.0	0.0	0.0

tables.scatter_diagram(
    df,
    var1='HS',
    step_var1=1,
    var2='TP',
    step_var2=1,
    output_file='Hs_Tp_scatter.png'
)

Wind Profile Return Values Table

tables.table_profile_return_values(
    df,
    var=['W10', 'W50', 'W80', 'W100', 'W150'],
    z=[10, 50 , 80, 100, 150],
    periods=[1, 10, 100, 10000],
    output_file='RVE_wind_profile.csv'
)

z	Return period 1 [years]	Return period 10 [years]	Return period 100 [years]	Return period 10000 [years]
m	m/s	m/s	m/s	m/s
10	27.83	30.63	33.8	39.34
50	31.84	35.15	38.89	45.46
80	32.74	36.14	39.98	46.74
100	33.11	36.54	40.43	47.26
150	34.8	38.47	42.64	49.96

Wind Profile Return Values Plot

plots.plot_profile_return_values(
    df,
    var=['W10', 'W50', 'W80', 'W100', 'W150'],
    z=[10, 50, 80, 100, 150],
    periods=[1, 10, 100, 10000],
    reverse_yaxis=False,
    title='Return Periods over z',
    units='m/s',
    distribution='Weibull3P',
    method='default',
    threshold='default',
    output_file='RVE_wind_profile.png'
)

Wind Profile Statistics Plot

plots.plot_profile_stats(
    df,
    var=['W10', 'W50', 'W80', 'W100', 'W150'],
    z=[10, 50, 80, 100, 150],
    reverse_yaxis=False,
    output_file='wind_stats_profile.png'
)

Directional Wind Return Periods (NORSOK Adjustment)

plots.plot_directional_return_periods(
    df,
    var='W10',
    var_dir='D10',
    periods=[1, 10, 100, 1000],
    distribution='Weibull3P_MOM',
    adjustment='NORSOK',
    units='m/s',
    output_file='Wind.dir_extremes_weibull_norsok.png'
)

Directional Wind Return Periods (No Adjustment)

plots.plot_directional_return_periods(
    df,
    var='W10',
    var_dir='D10',
    periods=[1, 10, 100, 1000],
    distribution='Weibull3P_MOM',
    adjustment=None,
    units='m/s',
    output_file='Wind.dir_extremes_weibull.png'
)

Monthly Non-Exceedance Table

tables.table_monthly_non_exceedance(
    df,
    var='HS',
    step_var=0.5,
    output_file='Hs_table_monthly_non_exceedance.csv'
)

HS-level	JAN	FEB	MAR	MAY	APR	JUN	JUL	AUG	SEP	OCT	NOV	DEC	Year
<0.5	0.16	0.27	0.55	1.24	2.55	3.37	4.5	4.17	1.32	0.52	0.35	0.28	1.61
<1.0	2.95	4.93	6.07	15.51	25.77	31.47	37.0	33.01	14.42	6.62	4.01	3.47	15.44
<1.5	10.18	16.18	18.05	37.1	52.27	60.63	66.85	61.75	36.99	20.61	13.32	12.16	33.84
<2.0	22.44	29.75	33.28	56.46	71.95	78.33	83.95	80.12	56.52	37.77	28.18	23.6	50.2
<2.5	35.63	43.85	49.52	70.79	83.89	88.6	92.28	90.3	70.65	54.52	44.09	37.49	63.47
<3.0	48.4	56.88	62.79	81.4	90.81	94.27	96.32	94.88	80.99	67.79	58.74	51.28	73.71
<3.5	59.88	67.65	73.53	88.1	95.14	97.12	98.38	97.46	88.38	77.55	69.9	62.9	81.33
<4.0	70.04	76.41	81.75	92.49	97.58	98.62	99.24	98.82	93.19	84.89	78.71	72.43	87.01
<4.5	78.06	82.99	87.7	95.56	98.7	99.31	99.65	99.45	96.03	90.38	85.15	80.0	91.08
<5.0	84.06	88.19	91.79	97.25	99.4	99.71	99.86	99.73	97.49	94.09	89.97	85.42	93.91
<5.5	88.58	92.04	94.53	98.24	99.67	99.84	99.94	99.84	98.37	96.32	93.36	89.86	95.88
<6.0	91.88	94.74	96.5	98.8	99.8	99.97	99.98	99.88	98.99	97.75	95.74	93.04	97.26
<6.5	94.4	96.7	97.86	99.26	99.86	99.98	100.0	99.92	99.4	98.68	97.42	95.5	98.25
<7.0	96.22	98.07	98.68	99.53	99.93	99.99	100.0	99.96	99.65	99.23	98.39	97.1	98.9
<7.5	97.44	98.82	99.27	99.69	99.98	99.99	100.0	99.97	99.78	99.54	99.12	98.16	99.31
<8.0	98.37	99.35	99.6	99.8	99.99	100.0	100.0	99.99	99.87	99.69	99.47	98.83	99.58
<8.5	98.93	99.58	99.8	99.88	100.0	100.0	100.0	99.99	99.91	99.76	99.74	99.26	99.74
<9.0	99.32	99.74	99.95	99.92	100.0	100.0	100.0	100.0	99.94	99.84	99.86	99.49	99.84
<9.5	99.6	99.84	100.0	99.96	100.0	100.0	100.0	100.0	99.96	99.88	99.94	99.64	99.9
<10.0	99.78	99.93	100.0	100.0	100.0	100.0	100.0	100.0	99.98	99.92	99.95	99.79	99.95
<10.5	99.87	99.98	100.0	100.0	100.0	100.0	100.0	100.0	99.98	99.96	99.97	99.88	99.97
<11.0	99.93	100.0	100.0	100.0	100.0	100.0	100.0	100.0	99.99	99.99	99.99	99.91	99.98
<11.5	99.96	100.0	100.0	100.0	100.0	100.0	100.0	100.0	99.99	99.99	99.99	99.94	99.99
<12.0	99.97	100.0	100.0	100.0	100.0	100.0	100.0	100.0	99.99	100.0	99.99	99.96	99.99
<12.5	99.99	100.0	100.0	100.0	100.0	100.0	100.0	100.0	99.99	100.0	100.0	99.97	100.0
<13.0	99.99	100.0	100.0	100.0	100.0	100.0	100.0	100.0	99.99	100.0	100.0	100.0	100.0
<13.5	99.99	100.0	100.0	100.0	100.0	100.0	100.0	100.0	100.0	100.0	100.0	100.0	100.0
<14.0	100.0	100.0	100.0	100.0	100.0	100.0	100.0	100.0	100.0	100.0	100.0	100.0	100.0
Minimum	0.3	0.3	0.3	0.3	0.2	0.2	0.2	0.2	0.2	0.3	0.3	0.3	0.2
Mean	3.42	3.07	2.85	2.15	1.71	1.54	1.41	1.5	2.14	2.67	3.03	3.3	2.4
P50	3.1	2.8	2.6	1.9	1.5	1.3	1.2	1.3	1.9	2.4	2.7	3.0	2.0
P75	4.3	4.0	3.6	2.7	2.1	1.9	1.8	1.9	2.7	3.4	3.8	4.2	3.1
P95	6.7	6.1	5.7	4.4	3.5	3.2	2.9	3.1	4.3	5.2	5.9	6.4	5.3
P99	8.6	7.7	7.3	6.2	4.7	4.3	3.8	4.1	6.1	6.8	7.4	8.28	7.2
Maximum	13.9	10.8	9.5	10.0	8.4	7.8	6.4	8.7	13.4	11.8	12.4	13.0	13.9

Monthly Statistics Plot

plots.plot_monthly_stats(
    df,
    var='HS',
    show=['Maximum','P99','Mean'],
    title='Hs[m]',
    output_file='Hs_monthly_stats.png'
)

Directional Non-Exceedance Table

tables.table_directional_non_exceedance(
    df,
    var='HS',
    step_var=0.5,
    var_dir='DIRM',
    output_file='table_directional_non_exceedance.csv'
)

HS-level	0°	30°	60°	90°	120°	150°	180°	210°	240°	270°	300°	330°	Omni
<0.5	0.31	0.02	0.01	0.0	0.01	0.04	0.13	0.16	0.19	0.27	0.23	0.25	1.62
<1.0	3.58	0.29	0.09	0.07	0.11	0.49	1.29	1.37	1.42	1.98	2.06	2.74	15.5
<1.5	8.11	0.55	0.18	0.14	0.25	1.34	3.54	3.02	2.94	3.9	4.07	5.9	33.94
<2.0	11.99	0.68	0.24	0.2	0.36	2.26	5.97	4.59	4.43	5.56	5.61	8.43	50.3
<2.5	14.69	0.76	0.27	0.24	0.42	3.13	8.06	5.94	5.77	7.07	6.83	10.39	63.56
<3.0	16.5	0.79	0.28	0.25	0.45	3.89	9.81	7.03	6.92	8.25	7.75	11.87	73.78
<3.5	17.63	0.8	0.29	0.25	0.46	4.54	11.18	7.93	7.84	9.18	8.39	12.89	81.39
<4.0	18.37	0.8	0.29	0.26	0.48	5.11	12.22	8.66	8.56	9.88	8.84	13.59	87.05
<4.5	18.79	0.81	0.29	0.26	0.48	5.56	13.01	9.16	9.09	10.4	9.15	14.11	91.11
<5.0	19.04	0.81	0.29	0.26	0.48	5.92	13.64	9.49	9.43	10.73	9.38	14.46	93.93
<5.5	19.19	0.81	0.29	0.26	0.48	6.17	14.07	9.72	9.67	10.99	9.53	14.72	95.89
<6.0	19.29	0.81	0.29	0.26	0.48	6.37	14.34	9.9	9.83	11.15	9.63	14.91	97.26
<6.5	19.34	0.81	0.29	0.26	0.48	6.52	14.53	10.03	9.94	11.29	9.71	15.06	98.25
<7.0	19.38	0.81	0.29	0.26	0.48	6.62	14.63	10.11	10.0	11.39	9.76	15.16	98.9
<7.5	19.41	0.81	0.29	0.26	0.48	6.69	14.71	10.15	10.04	11.45	9.79	15.23	99.31
<8.0	19.42	0.81	0.29	0.26	0.48	6.74	14.75	10.17	10.07	11.49	9.82	15.28	99.58
<8.5	19.44	0.81	0.29	0.26	0.48	6.77	14.78	10.18	10.08	11.51	9.84	15.31	99.74
<9.0	19.44	0.81	0.29	0.26	0.48	6.79	14.8	10.18	10.1	11.52	9.85	15.32	99.84
<9.5	19.44	0.81	0.29	0.26	0.48	6.81	14.81	10.18	10.1	11.53	9.86	15.33	99.9
<10.0	19.45	0.81	0.29	0.26	0.48	6.82	14.81	10.18	10.11	11.54	9.86	15.35	99.95
<10.5	19.45	0.81	0.29	0.26	0.48	6.82	14.82	10.18	10.11	11.54	9.86	15.35	99.97
<11.0	19.45	0.81	0.29	0.26	0.48	6.82	14.82	10.18	10.11	11.54	9.86	15.35	99.98
<11.5	19.45	0.81	0.29	0.26	0.48	6.82	14.82	10.18	10.11	11.54	9.86	15.36	99.99
<12.0	19.45	0.81	0.29	0.26	0.48	6.82	14.82	10.18	10.11	11.54	9.86	15.36	99.99
<12.5	19.45	0.81	0.29	0.26	0.48	6.82	14.82	10.18	10.11	11.54	9.87	15.36	100.0
<13.0	19.45	0.81	0.29	0.26	0.48	6.82	14.82	10.18	10.11	11.55	9.87	15.36	100.0
<13.5	19.45	0.81	0.29	0.26	0.48	6.82	14.82	10.18	10.11	11.55	9.87	15.36	100.0
<14.0	19.45	0.81	0.29	0.26	0.48	6.82	14.82	10.18	10.11	11.55	9.87	15.36	100.0
Mean	2.0	1.38	1.48	1.57	1.69	3.08	2.7	2.54	2.56	2.46	2.2	2.28	2.4
P50	1.7	1.2	1.3	1.5	1.5	2.7	2.4	2.2	2.3	2.1	1.8	1.9	2.0
P75	2.5	1.7	1.8	2.0	2.1	4.1	3.5	3.4	3.4	3.3	2.8	2.9	3.1
P95	4.2	2.6	2.8	2.8	3.4	6.4	5.6	5.4	5.4	5.5	5.0	5.3	5.3
P99	5.9	3.4	3.8	3.9	4.2	8.2	7.3	6.8	7.1	7.4	7.2	7.4	7.2
Maximum	11.8	4.8	4.7	4.9	6.1	10.9	10.3	12.5	11.3	13.9	13.4	13.0	13.9

Directional Statistics Plot

plots.plot_directional_stats(
    df,
    var='HS',
    step_var=0.5,
    var_dir='DIRM',
    title='$H_s$[m]',
    output_file='directional_stats.png'
)

Prob. of Non-Exceedance (fitted)

plots.plot_prob_non_exceedance_fitted_3p_weibull(df,
                                                 var='HS',
                                                 output_file='prob_non_exceedance_fitted_3p_weibull.png')

Joint Distribution Hs-Tp Plot

plots.plot_joint_distribution_Hs_Tp(
    df,
    var_hs='HS',
    var_tp='TP',
    periods=[1,10,100,1000],
    title='Hs-Tp joint distribution',
    output_file='Hs.Tp.joint.distribution.png',
    density_plot=True
)

Joint 2D distribution contours

plots.plot_joint_2D_contour(
    df,
    var1='HS',
    var2='TP',
    return_periods=[1,10,100,1000],
)

Joint 3D distribution contour

plots.plot_joint_3D_contour(
    df,
    var1 = 'W10',
    var2 = 'HS',
    var3 = 'TP',
    return_period=100
)

Joint 3D distribution contour cross-sections

plots.plot_joint_3D_contour_slices(
   df,
   var1 = 'W10',
   var2 = 'HS',
   var3 = 'TP',
   slice_values = [5,10,15,20,25]
)

Monthly Joint Distribution Hs-Tp Parameter Table

tables.table_monthly_joint_distribution_Hs_Tp_param(
    df,
    var_hs='HS',
    var_tp='TP',
    periods=[1,10,100,10000],
    output_file='monthly_Hs_Tp_joint_param.csv'
)

Month	a1	a2	a3	b1	b2	b3
Jan	1.839	0.316	0.36	0.005	0.142	0.422
Feb	1.819	0.329	0.36	0.005	0.153	0.439
Mar	1.775	0.343	0.36	0.005	0.161	0.493
Apr	1.637	0.436	0.36	0.005	0.122	0.411
May	1.524	0.462	0.36	0.005	0.13	0.552
Jun	1.591	0.39	0.36	0.005	0.138	0.771
Jul	1.461	0.46	0.36	0.005	0.116	0.72
Aug	1.509	0.442	0.36	0.005	0.128	0.717
Sept	1.55	0.455	0.36	0.005	0.117	0.454
Oct	1.605	0.447	0.36	0.005	0.125	0.421
Nov	1.779	0.348	0.36	0.005	0.124	0.413
Dec	1.79	0.352	0.36	0.005	0.141	0.422
Year	1.526	0.48	0.36	0.005	0.122	0.401

Directional Joint Distribution Hs-Tp Parameter Table

tables.table_directional_joint_distribution_Hs_Tp_param(
    df,
    var_hs='HS',
    var_tp='TP',
    var_dir='DIRM',
    periods=[1,10,100],
    output_file='dir_Hs_Tp_joint_param.csv'
)

Direction	a1	a2	a3	b1	b2	b3
0°	1.396	0.574	0.36	0.005	0.08	0.313
30°	1.133	0.84	0.36	0.005	0.056	0.265
60°	1.189	0.776	0.36	0.005	0.01	-0.713
90°	1.112	0.83	0.36	0.005	0.055	0.136
120°	1.363	0.607	0.36	0.005	0.058	0.065
150°	1.453	0.486	0.36	0.005	0.109	0.527
180°	1.332	0.537	0.36	0.005	0.118	0.603
210°	1.369	0.533	0.36	0.005	0.221	0.695
240°	1.625	0.413	0.36	0.005	0.258	0.641
270°	1.78	0.359	0.36	0.005	0.188	0.497
300°	1.742	0.407	0.36	0.005	0.11	0.393
330°	1.548	0.519	0.36	0.005	0.085	0.394
Omni	1.522	0.483	0.36	0.005	0.121	0.389

Joint Distribution Hs-Tp Multi Plot with binned Var3

plots.plots.plot_multi_joint_distribution_Hs_Tp_var3(
   df,
   var_hs='HS',
   var_tp='TP',
   var3='W10',
   var3_units='m/s',
   periods=[100],
   var3_bin=5,
   threshold_min=100,
   output_file='Hs.Tp.joint.distribution.multi.binned.var3.png'
   )

Monthly Weather Window Multiple Variables Plot (Waiting time in Days)

plots.plot_monthly_weather_window(
    df,
    var=['W10','HS','TP'],
    threshold=[12.35,2,8],
    window_size=62,
    timestep=3,
    add_table=True,
    output_file='NORA10_monthly_weather_window_mv_plot.png'
)

Number Of Hours Per Year Below A Threshold Plot

plots.plot_nb_hours_below_threshold(
    df,
    var='HS',
    thr_arr=(np.arange(0.05,20.05,0.05)).tolist(),
    output_file='number_hours_per_year.png'
)

Number Of Hours Per Year Below A Threshold Table

tables.table_nb_hours_below_threshold(
    df,
    var='HS',
    threshold=[1,2,3,4,5,6,7,8,9,10],
    output_file='number_hours_per_year.csv'
)

HS	Minimum	Mean	Maximum
<1	771	1227	1812
<2	3783	4454	5025
<3	6036	6582	7086
<4	7260	7717	8055
<5	7971	8279	8472
<6	8397	8538	8670
<7	8583	8676	8745
<8	8694	8734	8778
<9	8736	8756	8784
<10	8742	8763	8784

All-Year Round Weather Window For Hs Under A Threshold Table

tables.table_weather_window_thresholds(
    df,
    var='HS',
    threshold=[0.5,1,2],
    op_duration=[6,12,24,48],
    output_file='weather_window_thresholds.csv'
)

Operation duration [h]	HS<0.5m	HS<1m	HS<2m
6	130.66	16.63	2.2
12	185.78	20.07	3.26
24	433.91	31.09	5.73
48	702.67	68.74	11.06

Characteristic durations for custom sequence of operation limits

import pandas as pd
limits = pd.DataFrame({
    "HS":[2.5]*24+[3.0]*48,
    "TP":[20]*24+[25]*48
})
plots.plot_characteristic_durations(data,limits)

Empirical weather window probability in different time periods in the year

import pandas as pd
limits = pd.DataFrame({
    "HS":[2.5]*24+[3.0]*48,
    "TP":[20]*24+[25]*48
})
plots.plot_weather_window_probability(data,limits)

Monthly Return Periods Table

tables.table_monthly_return_periods(
    df,
    var='HS',
    periods=[1, 10, 100, 10000],
    distribution='Weibull3P_MOM',
    units='m',
    output_file='HS_monthly_extremes_Weibull.csv'
)

	Month	Annual prob.	Shape	Scale	Location	Return period: 1 [years]	Return period: 10 [years]	Return period: 100 [years]	Return period: 10000 [years]
0		%		m	m	m	m	m	m
1	Jan	8.33	1.542	2.864	0.84	9.52	11.72	13.7	17.24
2	Feb	8.33	1.608	2.756	0.6	8.5	10.44	12.17	15.23
3	Mar	8.33	1.547	2.431	0.66	8.0	9.85	11.52	14.49
4	Apr	8.33	1.217	1.56	0.68	7.01	9.12	11.12	14.89
5	May	8.33	1.247	1.236	0.56	5.43	7.0	8.48	11.26
6	Jun	8.33	1.248	1.096	0.52	4.81	6.2	7.51	9.96
7	Jul	8.33	1.219	0.952	0.52	4.39	5.67	6.88	9.17
8	Aug	8.33	1.161	0.984	0.57	4.86	6.36	7.79	10.54
9	Sept	8.33	1.226	1.536	0.7	6.87	8.9	10.83	14.46
10	Oct	8.33	1.424	2.098	0.77	7.73	9.66	11.43	14.64
11	Nov	8.33	1.544	2.469	0.8	8.25	10.14	11.85	14.89
12	Dec	8.33	1.541	2.769	0.81	9.21	11.34	13.26	16.68
13	Year	100.0	1.293	2.037	0.51	10.66	12.86	14.95	18.88

Directional Return Periods Table

tables.table_directional_return_periods(
    df,
    var='HS',
    periods=[1, 10, 100, 10000],
    units='m',
    var_dir='DIRM',
    distribution='Weibull3P_MOM',
    adjustment='NORSOK',
    output_file='directional_extremes_weibull.csv'
)

	Direction sector	Sector prob.	Shape	Scale	Location	Return period: 1 [years]	Return period: 10 [years]	Return period: 100 [years]	Return period: 10000 [years]
0		%		m	m	m	m	m	m
1	0°	19.45	1.155	1.38	0.69	10.66	12.86	14.95	18.88
2	30°	0.81	1.362	0.954	0.51	6.76	7.56	8.32	9.78
3	60°	0.29	1.356	1.051	0.51	7.05	7.96	8.83	10.48
4	90°	0.26	1.481	1.151	0.53	6.63	7.41	8.15	9.53
5	120°	0.48	1.347	1.204	0.59	8.39	9.44	10.45	12.37
6	150°	6.82	1.589	2.976	0.41	10.66	12.86	14.95	18.88
7	180°	14.82	1.499	2.424	0.52	10.66	12.86	14.95	18.88
8	210°	10.18	1.552	2.466	0.32	10.66	12.86	14.95	18.88
9	240°	10.11	1.472	2.388	0.4	10.66	12.86	14.95	18.88
10	270°	11.55	1.319	2.206	0.43	10.66	12.86	14.95	18.88
11	300°	9.87	1.149	1.731	0.55	10.66	12.86	14.95	18.88
12	330°	15.36	1.149	1.782	0.59	10.66	12.86	14.95	18.88
13	Omni	100.0	1.293	2.037	0.51	10.66	12.86	14.95	18.88

Monthly Return Periods Plot

plots.plot_monthly_return_periods(
    df,
    var='HS',
    periods=[1, 10, 100],
    distribution='Weibull3P_MOM',
    units='m',
    output_file='HS_monthly_extremes.png'
)

Directional Return Periods Plot (GUM)

plots.plot_directional_return_periods(
    df,
    var='HS',
    var_dir='DIRM',
    periods=[1, 10, 100, 10000],
    distribution='GUM',
    units='m',
    output_file='dir_extremes_GUM.png'
)

Directional Return Periods Plot (Weibull)

plots.plot_directional_return_periods(
    df,
    var='HS',
    var_dir='DIRM',
    periods=[1, 10, 100, 10000],
    distribution='Weibull3P_MOM',
    units='m',
    adjustment='NORSOK',
    output_file='dir_extremes_Weibull_norsok.png'
)

Monthly Joint Distribution Hs-Tp Return Values Table

tables.table_monthly_joint_distribution_Hs_Tp_return_values(
    df,
    var_hs='HS',
    var_tp='TP',
    periods=[1,10,100,10000],
    model='lonowe' # change to 'hs_tp' for DNV model
    output_file='monthly_Hs_Tp_joint_return_values.csv'
)

Month	Annual prob.[%]	H_s [1 years]	T_p [1 years]	H_s [10 years]	T_p [10 years]	H_s [100 years]	T_p [100 years]	H_s [10000 years]	T_p [10000 years]
Jan	8.33	9.97	13.11	11.69	13.69	13.67	14.31	17.22	15.37
Feb	8.33	8.89	12.89	10.41	13.47	12.15	14.09	15.22	15.11
Mar	8.33	8.37	12.49	9.83	13.07	11.49	13.69	14.47	14.71
Apr	8.33	7.43	12.77	9.09	13.67	11.09	14.71	14.87	16.53
May	8.33	5.73	11.11	6.97	11.85	8.45	12.69	11.23	14.15
Jun	8.33	5.07	9.97	6.17	10.47	7.49	11.05	9.93	12.03
Jul	8.33	4.63	9.75	5.65	10.35	6.85	11.01	9.15	12.19
Aug	8.33	5.15	10.17	6.33	10.81	7.77	11.55	10.51	12.83
Sept	8.33	7.27	12.03	8.89	12.91	10.81	13.87	14.43	15.61
Oct	8.33	8.11	12.93	9.65	13.73	11.41	14.61	14.61	16.13
Nov	8.33	8.63	12.71	10.11	13.29	11.83	13.91	14.87	14.97
Dec	8.33	9.63	13.39	11.31	14.05	13.23	14.77	16.66	15.97
Year	100.0	11.09	14.57	12.83	15.51	14.93	16.61	18.86	18.59

Directional Joint Distribution Hs-Tp Return Values Table

tables.table_directional_joint_distribution_Hs_Tp_return_values(
    df,
    var_hs='HS',
    var_tp='TP',
    var_dir='DIRM',
    periods=[1,10,100,1000],
    adjustment='NORSOK',
    model='lonowe' # change to 'hs_tp' for DNV model
    output_file='directional_Hs_Tp_joint_return_values.csv'
)

Direction	Sector prob.[%]	Hs[m] [1 years]	Tp[s] [1 years]	Hs[m] [10 years]	Tp[s] [10 years]	Hs[m] [100 years]	Tp[s] [100 years]	Hs[m] [1000 years]	Tp[s] [1000 years]
0°	19.45	9.13	14.57	11.17	15.49	13.15	16.59	15.1	17.61
30°	0.81	3.57	11.87	4.57	13.43	5.49	14.81	6.35	16.09
60°	0.29	3.39	10.75	4.55	11.37	5.61	10.55	6.59	7.95
90°	0.26	3.35	11.11	4.43	12.79	5.37	14.19	6.23	15.43
120°	0.48	4.21	10.75	5.53	11.95	6.73	12.97	7.87	13.91
150°	6.82	10.59	13.41	12.57	14.43	14.37	15.31	16.04	16.13
180°	14.82	10.09	13.13	11.87	14.15	13.53	15.09	15.1	15.93
210°	10.18	9.31	13.13	11.01	14.15	12.59	15.07	14.05	15.93
240°	10.11	9.75	13.09	11.61	13.93	13.35	14.67	14.99	15.35
270°	11.55	10.69	13.87	12.83	14.75	14.93	15.53	16.94	16.23
300°	9.87	10.47	14.57	12.83	15.49	14.93	16.59	16.94	17.61
330°	15.36	11.09	14.57	12.83	15.49	14.93	16.59	16.94	17.61
Omni	100.0	11.09	14.57	12.83	15.49	14.93	16.59	16.94	17.61

Hs-Tp Return Values Table

tables.table_Hs_Tpl_Tph_return_values(
    df,
    var_hs='HS',
    var_tp='TP',
    periods=[1,10,100,10000],
    output_file='hs_tpl_tph_return_values.csv'
)

HS [1 years]	TpL [1 years]	TpH [1 years]	HS [10 years]	TpL [10 years]	TpH [10 years]	HS [100 years]	TpL [100 years]	TpH [100 years]	HS [10000 years]	TpL [10000 years]	TpH [10000 years]
11.1	14.1	14.6	12.8	14.8	15.7	14.9	15.9	16.8	18.9	18.0	18.6
10.1	12.1	15.7	11.8	13.0	16.6	13.9	14.0	17.8	17.9	15.9	19.9
9.1	10.9	16.0	10.8	11.8	16.9	12.9	12.9	18.1	16.9	14.8	20.3
8.1	9.8	16.3	9.8	10.8	17.1	11.9	11.9	18.2	15.9	13.8	20.4
7.1	8.7	16.6	8.8	9.8	17.3	10.9	11.0	18.3	14.9	13.0	20.5
6.1	7.6	17.0	7.8	8.8	17.6	9.9	10.1	18.4	13.9	12.2	20.5
5.1	6.5	17.3	6.8	7.8	17.9	8.9	9.2	18.6	12.9	11.4	20.5
4.1	5.5	17.9	5.8	6.7	18.4	7.9	8.3	18.9	11.9	10.6	20.5
3.1	4.4	18.8	4.8	5.7	19.0	6.9	7.3	19.4	10.9	9.9	20.5
2.1	3.3	19.9	3.8	4.7	19.9	5.9	6.3	20.0	9.9	9.1	20.6

Tp for Given Hs Plot

plots.plot_tp_for_given_hs(
    df,
    var_hs='HS',
    var_tp='TP',
    output_file='tp_for_given_hs.png'
)

Tp for Given Hs Table

tables.table_tp_for_given_hs(
    df,
    var_hs='HS',
    var_tp='TP',
    max_hs=20,
    output_file='tp_for_given_hs.csv'
)

Hs[m]	Tp(P5-model) [s]	Tp(Mean-model) [s]	Tp(P95-model) [s]
1.0	3.91	7.77	11.62
2.0	5.06	8.8	12.53
3.0	6.1	9.61	13.12
4.0	7.07	10.32	13.58
5.0	7.96	10.98	14.0
6.0	8.8	11.6	14.4
7.0	9.58	12.19	14.8
8.0	10.31	12.76	15.22
9.0	11.0	13.32	15.64
10.0	11.64	13.86	16.09
11.0	12.24	14.39	16.55
12.0	12.81	14.92	17.02
13.0	13.35	15.43	17.51
14.0	13.87	15.94	18.01
15.0	14.36	16.44	18.52
16.0	14.84	16.94	19.03
17.0	15.3	17.43	19.55
18.0	15.76	17.91	20.07
19.0	16.2	18.4	20.59
20.0	16.64	18.87	21.11

Tp for RV Hs Table

tables.table_tp_for_rv_hs(
    df,
    var_hs='HS',
    var_tp='TP',
    periods=[1,10,100,10000],
    output_file='tp_for_rv_hs.csv'
)

Return period [years]	Hs[m]	Tp(P5-model) [s]	Tp(Mean-model) [s]	Tp(P95-model) [s]
1	11.09	12.27	14.47	16.66
10	12.83	13.25	15.37	17.49
100	14.93	14.33	16.44	18.54
10000	18.86	16.16	18.37	20.57

Wave-Induced Current (JONSWAP) Table

tables.table_wave_induced_current(
    df,
    var_hs='HS',
    var_tp='TP',
    depth=200,
    ref_depth=200,
    spectrum='JONSWAP',
    output_file='JONSWAP_wave_induced_current_depth200.csv'
)

Hs[m]	Tp(P5-model) [s]	Us(P5) [m/s]	Tu(P5-model) [s]	Tp(Mean-model) [s]	Us(Mean) [m/s]	Tu(Mean-model) [s]	Tp(P95-model) [s]	Us(P95) [m/s]	Tu(P95-model) [s]
1.0	3.91	0.0	7.3	7.76	0.0	11.51	11.6	0.0	14.55
2.0	5.03	0.0	8.62	8.8	0.0	12.48	12.58	0.01	15.11
3.0	6.04	0.0	9.74	9.62	0.0	13.27	13.21	0.02	15.59
4.0	6.98	0.0	10.72	10.35	0.01	13.93	13.72	0.03	15.82
5.0	7.86	0.0	11.6	11.01	0.01	14.53	14.16	0.05	16.06
6.0	8.69	0.0	12.37	11.63	0.02	15.03	14.58	0.07	16.77
7.0	9.47	0.0	13.04	12.23	0.04	15.46	14.99	0.1	17.56
8.0	10.21	0.01	13.59	12.81	0.05	15.88	15.41	0.12	18.02
9.0	10.9	0.02	14.08	13.37	0.07	16.28	15.84	0.15	18.38
10.0	11.56	0.03	14.54	13.92	0.1	16.65	16.28	0.19	18.71
11.0	12.17	0.05	14.86	14.45	0.13	17.05	16.73	0.23	19.04
12.0	12.76	0.07	15.31	14.98	0.16	17.45	17.2	0.27	19.39
13.0	13.32	0.1	15.7	15.5	0.2	17.78	17.68	0.32	19.76
14.0	13.85	0.13	15.95	16.01	0.25	18.05	18.17	0.37	20.16
15.0	14.37	0.16	16.32	16.52	0.3	18.34	18.67	0.43	20.54
16.0	14.86	0.2	16.82	17.02	0.35	18.73	19.18	0.49	20.9
17.0	15.34	0.25	17.25	17.52	0.4	19.21	19.69	0.56	21.24
18.0	15.81	0.31	17.5	18.01	0.46	19.71	20.21	0.63	21.56
19.0	16.26	0.37	17.66	18.5	0.54	20.15	20.73	0.7	21.88
20.0	16.71	0.43	17.9	18.98	0.63	20.49	21.26	0.77	22.23

Wave-Induced Current (TORSEHAUGEN) Table

tables.table_wave_induced_current(
    df,
    var_hs='HS',
    var_tp='TP',
    depth=200,
    ref_depth=200,
    spectrum='TORSEHAUGEN',
    output_file='TORSEHAUGEN_wave_induced_current_depth200.csv'
)

Hs[m]	Tp(P5-model) [s]	Us(P5) [m/s]	Tu(P5-model) [s]	Tp(Mean-model) [s]	Us(Mean) [m/s]	Tu(Mean-model) [s]	Tp(P95-model) [s]	Us(P95) [m/s]	Tu(P95-model) [s]
1.0	3.92	0.0	12.7	7.76	0.0	11.87	11.61	0.0	15.19
2.0	5.01	0.0	14.29	8.81	0.0	12.89	12.6	0.01	15.83
3.0	6.0	0.01	15.34	9.63	0.0	13.64	13.25	0.02	16.25
4.0	6.92	0.02	16.13	10.35	0.01	14.31	13.77	0.03	16.49
5.0	7.79	0.03	16.82	11.01	0.02	14.9	14.22	0.05	16.74
6.0	8.61	0.04	17.37	11.63	0.03	15.39	14.64	0.06	17.07
7.0	9.39	0.05	17.78	12.22	0.04	15.82	15.05	0.08	17.4
8.0	10.12	0.06	18.07	12.79	0.06	16.25	15.47	0.11	17.66
9.0	10.81	0.08	18.27	13.35	0.08	16.63	15.89	0.14	17.82
10.0	11.46	0.09	18.36	13.89	0.1	16.98	16.32	0.17	17.95
11.0	12.08	0.11	18.36	14.42	0.13	17.35	16.76	0.2	18.17
12.0	12.67	0.13	18.35	14.95	0.16	17.72	17.22	0.23	18.53
13.0	13.23	0.15	18.31	15.46	0.2	18.05	17.69	0.26	19.02
14.0	13.77	0.18	18.25	15.97	0.25	18.31	18.17	0.3	19.53
15.0	14.29	0.21	18.27	16.47	0.3	18.53	18.66	0.35	19.89
16.0	14.78	0.24	18.38	16.97	0.34	18.83	19.16	0.43	20.07
17.0	15.26	0.28	18.49	17.46	0.39	19.23	19.66	0.5	20.15
18.0	15.73	0.32	18.58	17.95	0.44	19.7	20.17	0.55	20.22
19.0	16.19	0.37	18.67	18.43	0.5	20.11	20.68	0.56	20.33
20.0	16.63	0.42	18.86	18.91	0.58	20.34	21.19	0.57	20.54

Hs for Given Wind Table

tables.table_hs_for_given_wind(
    df,
    var_hs='HS',
    var_wind='W10',
    bin_width=2,
    max_wind=42,
    output_file='table_perc_hs_for_wind.csv'
)

U[m/s]	Hs(P5-model) [m]	Hs(Mean-model) [m]	Hs(P95-model) [m]
1.0	0.1	1.14	2.18
3.0	0.17	1.23	2.29
5.0	0.33	1.43	2.54
7.0	0.58	1.75	2.93
9.0	0.92	2.19	3.46
11.0	1.36	2.74	4.12
13.0	1.88	3.39	4.89
15.0	2.49	4.13	5.76
17.0	3.18	4.96	6.73
19.0	3.95	5.86	7.77
21.0	4.77	6.82	8.88
23.0	5.66	7.84	10.03
25.0	6.59	8.91	11.23
27.0	7.57	10.01	12.45
29.0	8.58	11.13	13.69
31.0	9.61	12.28	14.94
33.0	10.67	13.43	16.19
35.0	11.74	14.58	17.42
37.0	12.82	15.73	18.64
39.0	13.89	16.86	19.84
41.0	14.96	17.98	21.0

Hs for Given Wind Plot

plots.plot_hs_for_given_wind(
    df,
    var_hs='HS',
    var_wind='W10',
    output_file='hs_for_given_wind.png'
)

Hs for RV Wind Table

tables.table_hs_for_rv_wind(
    df,
    var_wind='W10',
    var_hs='HS',
    periods=[1,10,100,10000],
    output_file='hs_for_rv_wind.csv'
)

Return period [years]	U[m/s]	Hs(P5-model) [m]	Hs(Mean-model) [m]	Hs(P95-model) [m]
1	26.43	7.28	9.69	12.1
10	29.74	8.96	11.56	14.16
100	32.67	10.5	13.24	15.98
10000	37.75	13.22	16.16	19.09

Hmax Return Values Table

tables.table_Hmax_crest_return_periods(
    df,
    var_hs='HS',
    var_tp='TP',
    depth=200,
    periods=[1, 10, 100, 10000],
    sea_state='long-crested'
)

Directional Hmax Return Values Table

tables.table_directional_Hmax_return_periods(
    df,
    var_hs='HS',
    var_tp='TP',
    var_dir='DIRM',
    periods=[10, 100, 10000],
    adjustment='NORSOK',
    output_file='table_dir_Hmax_return_values.csv'
)

Direction	Hmax[m] [10 years]	Hmax[m] [100 years]	Hmax[m] [10000 years]	T_Hmax(P5-model) [s] [10 years]	T_Hmax(Mean-model) [s] [10 years]	T_Hmax(P95-model) [s] [10 years]	T_Hmax(P5-model) [s] [100 years]	T_Hmax(Mean-model) [s] [100 years]	T_Hmax(P95-model) [s] [100 years]	T_Hmax(P5-model) [s] [10000 years]	T_Hmax(Mean-model) [s] [10000 years]	T_Hmax(P95-model) [s] [10000 years]
0°	21.51	25.21	32.31	10.81	13.02	15.22	11.84	13.92	16.01	13.6	15.62	17.64
30°	8.88	10.6	13.71	6.57	9.65	12.74	7.25	10.18	13.1	8.41	11.07	13.72
60°	8.92	11.03	15.36	6.55	9.64	12.73	7.34	10.24	13.15	8.64	11.24	13.85
90°	8.63	10.39	13.46	6.46	9.57	12.68	7.16	10.11	13.06	8.32	11.0	13.67
120°	10.84	13.12	17.27	7.28	10.2	13.12	8.12	10.84	13.56	9.53	11.95	14.37
150°	24.3	27.67	33.71	11.55	13.66	15.77	12.43	14.47	16.52	13.87	15.9	17.92
180°	22.98	26.08	31.73	11.19	13.34	15.49	12.02	14.1	16.17	13.42	15.44	17.47
210°	21.32	24.28	29.57	10.72	12.94	15.16	11.56	13.67	15.78	12.92	14.95	16.97
240°	22.5	25.78	31.76	11.05	13.22	15.39	11.94	14.01	16.09	13.41	15.43	17.45
270°	24.78	28.74	36.09	11.68	13.78	15.88	12.69	14.72	16.75	14.38	16.43	18.47
300°	24.71	28.62	35.89	11.68	13.78	15.88	12.69	14.72	16.75	14.38	16.43	18.47
330°	24.71	28.62	35.89	11.68	13.78	15.88	12.69	14.72	16.75	14.38	16.43	18.47
Omni	24.71	28.62	35.89	11.68	13.78	15.88	12.69	14.72	16.75	14.38	16.43	18.47

T2m Monthly Return Periods Plot (Negative)

plots.plot_monthly_return_periods(
    df,
    var='T2m',
    periods=[1, 10, 100],
    distribution='GUM_L',
    method='minimum',
    units='°C',
    output_file='T2m_monthly_extremes_neg.png'
)

T2m Monthly Return Periods Table (Negative)

tables.table_monthly_return_periods(
    df,
    var='T2m',
    periods=[1, 10, 100],
    distribution='GUM_L',
    method='minimum',
    units='°C',
    output_file='T2m_monthly_extremes_neg.csv'
)

	Month	Annual prob.	Shape	Scale	Location	Return period: 1 [years]	Return period: 10 [years]	Return period: 100 [years]
0		%		°C	°C	°C	°C	°C
1	Jan	8.33		1.868	1.71	4.38	-2.53	-6.91
2	Feb	8.33		1.682	1.26	3.67	-2.55	-6.5
3	Mar	8.33		1.549	1.87	4.08	-1.64	-5.28
4	Apr	8.33		1.21	3.04	4.77	0.3	-2.55
5	May	8.33		1.05	5.32	6.82	2.94	0.47
6	Jun	8.33		0.984	8.37	9.78	6.14	3.83
7	Jul	8.33		0.775	10.67	11.77	8.91	7.09
8	Aug	8.33		1.036	11.63	13.12	9.29	6.85
9	Sept	8.33		1.33	9.64	11.54	6.63	3.5
10	Oct	8.33		1.379	6.78	8.75	3.66	0.42
11	Nov	8.33		1.473	4.2	6.3	0.86	-2.6
12	Dec	8.33		1.843	2.46	5.1	-1.72	-6.05
13	Year	100.0		1.775	-0.12	2.42	-4.14	-8.31

T2m Monthly Return Periods Plot (Positive)

plots.plot_monthly_return_periods(
    df,
    var='T2m',
    periods=[1, 10, 100],
    distribution='GUM',
    method='maximum',
    units='°C',
    output_file='T2m_monthly_extremes_pos.png'
)

T2m Monthly Return Periods Table (Positive)

tables.table_monthly_return_periods(
    df,
    var='T2m',
    periods=[1, 10, 100],
    distribution='GUM',
    method='maximum',
    units='°C',
    output_file='T2m_monthly_extremes_pos.csv'
)

	Month	Annual prob.	Shape	Scale	Location	Return period: 1 [years]	Return period: 10 [years]	Return period: 100 [years]
0		%		°C	°C	°C	°C	°C
1	Jan	8.33		0.772	8.25	7.15	10.0	11.82
2	Feb	8.33		1.132	7.5	5.88	10.06	12.72
3	Mar	8.33		0.796	7.47	6.33	9.28	11.15
4	Apr	8.33		0.816	8.14	6.98	9.99	11.91
5	May	8.33		0.903	10.59	9.3	12.64	14.76
6	Jun	8.33		1.039	12.8	11.31	15.15	17.59
7	Jul	8.33		1.326	14.95	13.05	17.96	21.07
8	Aug	8.33		1.383	15.5	13.52	18.64	21.89
9	Sept	8.33		1.195	14.15	12.44	16.86	19.67
10	Oct	8.33		0.781	12.28	11.16	14.05	15.88
11	Nov	8.33		0.77	10.35	9.25	12.09	13.9
12	Dec	8.33		0.827	9.15	7.97	11.03	12.97
13	Year	100.0		1.44	15.78	13.72	19.05	22.43

T2m Monthly Statistics Plot

plots.plot_monthly_stats(
    df,
    var='T2m',
    show=['Minimum','Mean','Maximum'],
    title='T2m',
    output_file='T2m_monthly_stats.png'
)

T2m Monthly Non-Exceedance Table

tables.table_monthly_non_exceedance(
    df,
    var='T2m',
    step_var=0.5,
    output_file='T2m_table_monthly_non_exceedance.csv'
)

Current Speed Monthly Return Periods Plot

plots.plot_monthly_return_periods(
    df,
    var='current_speed_0m',
    periods=[1, 10, 100],
    distribution='Weibull3P_MOM',
    method='POT',
    threshold='P99',
    units='m/s',
    output_file='csp0m_monthly_extremes.png'
)

Current Speed Monthly Rose Plot

plots.var_rose(
    df,
    'current_direction_0m',
    'current_speed_0m',
    max_perc=30,
    decimal_places=2,
    units='m/s',
    method='monthly',
    output_file='monthly_rose.png'
)

Current Speed Overall Rose Plot

plots.var_rose(
    df,
    'current_direction_0m',
    'current_speed_0m',
    max_perc=30,
    decimal_places=2,
    units='m/s',
    method='overall',
    output_file='overall_rose.png'
)

Current Speed Monthly Statistics Plot

plots.plot_monthly_stats(
    df,
    var='current_speed_0m',
    show=['Mean', 'P99', 'Maximum'],
    title='Current[m/s], depth:0m',
    output_file='current_0m_monthly_stats.png'
)

Hs Monthly Statistics with shaded band

plots.plot_monthly_stats(
   ds,
   var="HS",
   show=["10%", "mean", "90%"],
   fill_between=["25%", "75%"],
   fill_color_like="mean",
   output_file="")

Hs Daily Statistics with shaded band

plots.plot_daily_stats(
   ds,
   var="HS",
   show=["5%", "mean", "95%"],
   fill_between=["25%", "75%"],
   fill_color_like="mean")

Hs Hourly Statistics with shaded band

plots.plot_hourly_stats(
   ds,
   var="HS",
   show=["5%", "mean", "95%"],
   fill_between=["25%", "75%"],
   fill_color_like="mean",
   title="HS[m]")

Hs Daily percentile table

tables.table_daily_percentile(
   ds,
   var="HS",
   percentiles=["5%", "10%", "15%", "20%","mean", "99%", "max"],
   divide_months=True)

Current Speed Directional Monthly Statistics

tables.monthly_directional_percentiles(
     data=ds,
     var="W10",
     var_dir="D10",
     percentiles=["5%", "25%", "mean", "75%", "99%", "max"],
     nsectors=16,
     compass_point_names=True)

Current Speed Directional Statistics Plot

plots.plot_directional_stats(
    df,
    var='current_speed_0m',
    var_dir='current_direction_0m',
    step_var=0.05,
    show=['Mean', 'P99', 'Maximum'],
    title='Current[m/s], depth:0m',
    output_file='current_0m_dir_stats.png'
)

Current Speed Directional Return Periods Table

tables.table_directional_return_periods(
    df,
    var='current_speed_0m',
    periods=[1, 10, 100, 10000],
    units='m/s',
    var_dir='current_direction_0m',
    distribution='Weibull3P_MOM',
    adjustment='NORSOK',
    output_file='directional_extremes_weibull_current_0m.csv'
)

	Direction sector	Sector prob.	Shape	Scale	Location	Return period: 1 [years]	Return period: 10 [years]	Return period: 100 [years]	Return period: 10000 [years]
0		%		m/s	m/s	m/s	m/s	m/s	m/s
1	0°	19.33	2.103	0.235	-0.0	0.63	0.73	0.81	0.96
2	30°	9.99	1.958	0.176	0.01	0.63	0.73	0.81	0.93
3	60°	5.0	1.667	0.141	0.01	0.63	0.73	0.81	0.96
4	90°	3.29	1.268	0.121	0.02	0.63	0.73	0.81	0.96
5	120°	2.22	1.375	0.129	0.0	0.63	0.73	0.81	0.96
6	150°	2.21	1.37	0.112	0.01	0.63	0.73	0.81	0.96
7	180°	2.91	1.501	0.151	0.0	0.63	0.73	0.81	0.96
8	210°	4.69	1.376	0.164	0.01	0.63	0.73	0.81	0.96
9	240°	6.58	1.485	0.159	0.01	0.63	0.73	0.81	0.96
10	270°	9.44	1.632	0.145	0.03	0.63	0.73	0.81	0.96
11	300°	14.14	1.821	0.179	0.02	0.63	0.73	0.81	0.96
12	330°	20.2	2.073	0.224	0.0	0.63	0.73	0.81	0.96
13	Omni	100.0	1.775	0.189	0.01	0.63	0.73	0.81	0.96

Current Speed Monthly Return Periods Table

tables.table_monthly_return_periods(
    df,
    var='current_speed_0m',
    periods=[1, 10, 100, 10000],
    units='m/s',
    distribution='Weibull3P_MOM',
    method='POT',
    threshold='P99',
    output_file='monthly_extremes_weibull_current_0m.csv'
)

	Month	Annual prob.	Shape	Scale	Location	Threshold	Events	Return period: 1 [years]	Return period: 10 [years]	Return period: 100 [years]	Return period: 10000 [years]
0		%		m/s	m/s	m/s	1/year	m/s	m/s	m/s	m/s
1	Jan	8.33	2.449	0.097	0.47	0.5	11.1	0.68	0.71	0.73	0.77
2	Feb	8.33	3.602	0.072	0.43	0.45	7.5	0.55	0.56	0.57	0.58
3	Mar	8.33	1.892	0.106	0.46	0.51	9.2	0.74	0.79	0.83	0.9
4	Apr	8.33	3.602	0.09	0.35	0.39	9.3	0.51	0.52	0.53	0.55
5	May	8.33	3.602	0.018	0.36	0.35	7.4	0.39	0.39	0.39	0.4
6	Jun	8.33	0.674	0.0	0.41	0.35	8.3	0.41	0.41	0.41	0.41
7	Jul	8.33	3.602	0.036	0.33	0.31	10.2	0.39	0.39	0.4	0.41
8	Aug	8.33	0.674	0.0	0.59	0.48	7.4	0.59	0.59	0.59	0.59
9	Sept	8.33	0.674	0.0	0.52	0.44	8.3	0.52	0.52	0.52	0.52
10	Oct	8.33	3.602	0.18	0.33	0.43	9.2	0.63	0.66	0.68	0.71
11	Nov	8.33	1.969	0.066	0.41	0.43	7.4	0.58	0.61	0.63	0.67
12	Dec	8.33	3.602	0.018	0.38	0.38	10.2	0.41	0.41	0.41	0.42
13	Year	100.0	1.751	0.087	0.45	0.45	45.7	0.74	0.79	0.83	0.9

Current Speed Profile Return Values Plot

plots.plot_profile_return_values(
    df,
    var=['current_speed_' + d for d in depth],
    z=[float(d[:-1]) for d in depth],
    periods=[1, 10, 100, 10000],
    reverse_yaxis=True,
    output_file='RVE_current_profile.png'
)

Current Speed for Given Wind Table

tables.table_current_for_given_wind(
    ds_all,
    var_curr='current_speed_0m',
    var_wind='W10',
    bin_width=2,
    max_wind=42,
    output_file='table_perc_current_for_wind.csv'
)

U[m/s]	Uc(P5-model) [m/s]	Uc(Mean-model) [m/s]	Uc(P95-model) [m/s]
1.0	0.02	0.06	0.1
3.0	0.11	0.15	0.2
5.0	0.2	0.25	0.3
7.0	0.3	0.35	0.39
9.0	0.4	0.45	0.49
11.0	0.5	0.54	0.59
13.0	0.6	0.64	0.69
15.0	0.7	0.74	0.79
17.0	0.8	0.84	0.89
19.0	0.89	0.94	0.99
21.0	0.99	1.04	1.09
23.0	1.09	1.14	1.19
25.0	1.19	1.24	1.28
27.0	1.29	1.33	1.38
29.0	1.38	1.43	1.48
31.0	1.48	1.53	1.57
33.0	1.57	1.62	1.67
35.0	1.67	1.72	1.76
37.0	1.76	1.81	1.86
39.0	1.86	1.9	1.95
41.0	1.95	2.0	2.04

Current Speed for Given Wind Plot

plots.plot_current_for_given_wind(
    ds_all,
    var_curr='current_speed_0m',
    var_wind='W10',
    output_file='curr_for_given_wind.png'
)

Current Speed for Given Hs Table

tables.table_current_for_given_hs(
    ds_all,
    var_curr='current_speed_0m',
    var_hs='HS',
    bin_width=2,
    max_hs=20,
    output_file='table_perc_current_for_Hs.csv'
)

Hs[m]	Uc(P5-model) [m/s]	Uc(Mean-model) [m/s]	Uc(P95-model) [m/s]
1.0	0.02	0.27	0.52
3.0	0.31	0.53	0.76
5.0	0.51	0.73	0.94
7.0	0.68	0.89	1.1
9.0	0.82	1.03	1.24
11.0	0.95	1.16	1.36
13.0	1.08	1.28	1.48
15.0	1.19	1.39	1.59
17.0	1.3	1.49	1.69
19.0	1.4	1.59	1.79

Current Speed for Given Wind Table

tables.table_current_for_given_wind(
    df,
    var_curr='current_speed_0m',
    var_wind='W10',
    bin_width=2,
    max_wind=42,
    output_file='table_perc_current_for_wind.csv'
)

U[m/s]	Uc(P5-model) [m/s]	Uc(Mean-model) [m/s]	Uc(P95-model) [m/s]
1.0	0.02	0.06	0.1
3.0	0.11	0.15	0.2
5.0	0.2	0.25	0.3
7.0	0.3	0.35	0.39
9.0	0.4	0.45	0.49
11.0	0.5	0.54	0.59
13.0	0.6	0.64	0.69
15.0	0.7	0.74	0.79
17.0	0.8	0.84	0.89
19.0	0.89	0.94	0.99
21.0	0.99	1.04	1.09
23.0	1.09	1.14	1.19
25.0	1.19	1.24	1.28
27.0	1.29	1.33	1.38
29.0	1.38	1.43	1.48
31.0	1.48	1.53	1.57
33.0	1.57	1.62	1.67
35.0	1.67	1.72	1.76
37.0	1.76	1.81	1.86
39.0	1.86	1.9	1.95
41.0	1.95	2.0	2.04

Current Speed for Given Hs Table

tables.table_current_for_given_hs(
    df,
    var_curr='current_speed_0m',
    var_hs='HS',
    bin_width=2,
    max_hs=20,
    output_file='table_perc_current_for_Hs.csv'
)

Hs[m]	Uc(P5-model) [m/s]	Uc(Mean-model) [m/s]	Uc(P95-model) [m/s]
1.0	0.02	0.27	0.52
3.0	0.31	0.53	0.76
5.0	0.51	0.73	0.94
7.0	0.68	0.89	1.1
9.0	0.82	1.03	1.24
11.0	0.95	1.16	1.36
13.0	1.08	1.28	1.48
15.0	1.19	1.39	1.59
17.0	1.3	1.49	1.69
19.0	1.4	1.59	1.79

Current Speed for Given Hs Plot

plots.plot_current_for_given_hs(
    ds_all,
    var_curr='current_speed_0m',
    var_hs='HS',
    max_hs=20,
    output_file='curr_for_given_hs.png'
)

Extreme Current Profile Return Values Table

tables.table_extreme_current_profile_rv(
    df,
    var=['current_speed_' + d for d in depth],
    z=[float(d[:-1]) for d in depth],
    periods=[1, 100, 1000],
    percentile=95,
    output_file='table_extreme_current_profile_rv.png'
)

Profile Statistics Table

tables.table_profile_stats(
    df,
    var=['current_speed_' + d for d in depth],
    z=[float(d[:-1]) for d in depth],
    var_dir=['current_direction_' + d for d in depth],
    output_file='table_profile_stats.csv'
)

z	Mean	Std.dev	P5	P10	P50	P90	P95	P99	Max	Dir. Max Event	Time Max Event
[m]	[m/s]	[m/s]	[m/s]	[m/s]	[m/s]	[m/s]	[m/s]	[m/s]	[m/s]	[°]	YYYY-MM-DD hh:mm:ss
0.0	0.17	0.1	0.04	0.06	0.16	0.31	0.36	0.45	0.63	328.43	2018-03-24 12:00:00
1.0	0.17	0.1	0.04	0.06	0.16	0.31	0.36	0.45	0.63	328.43	2018-03-24 12:00:00
2.5	0.17	0.1	0.04	0.06	0.16	0.31	0.36	0.45	0.63	328.58	2018-03-24 12:00:00
5.0	0.17	0.1	0.04	0.06	0.16	0.31	0.36	0.45	0.62	329.07	2018-03-24 12:00:00
10.0	0.17	0.1	0.04	0.06	0.16	0.3	0.35	0.44	0.62	329.71	2018-03-24 12:00:00
15.0	0.17	0.09	0.04	0.06	0.15	0.3	0.35	0.44	0.61	330.17	2018-03-24 12:00:00
20.0	0.17	0.09	0.04	0.06	0.15	0.3	0.35	0.44	0.61	330.65	2018-03-24 12:00:00
25.0	0.17	0.09	0.04	0.06	0.15	0.29	0.34	0.43	0.6	331.02	2018-03-24 12:00:00
30.0	0.16	0.09	0.04	0.06	0.15	0.29	0.34	0.43	0.6	331.48	2018-03-24 12:00:00
40.0	0.16	0.09	0.04	0.05	0.14	0.28	0.33	0.43	0.58	332.67	2018-03-24 12:00:00
50.0	0.15	0.09	0.04	0.05	0.14	0.27	0.32	0.41	0.55	333.73	2018-03-24 12:00:00
75.0	0.14	0.08	0.03	0.05	0.12	0.25	0.3	0.39	0.53	14.3	2017-08-12 08:00:00
100.0	0.13	0.08	0.03	0.04	0.11	0.24	0.29	0.37	0.53	13.79	2017-08-12 08:00:00
150.0	0.13	0.08	0.03	0.04	0.11	0.23	0.28	0.35	0.5	13.88	2017-08-12 08:00:00
200.0	0.12	0.07	0.03	0.04	0.11	0.22	0.26	0.33	0.46	13.45	2017-08-12 08:00:00

Profile Statistics Plot

plots.plot_profile_stats(
    df,
    var=['current_speed_' + d for d in depth],
    z=[float(d[:-1]) for d in depth],
    reverse_yaxis=True,
    output_file='stats_current_profile.png'
)

Current Speed for RV Wind Table

tables.table_current_for_rv_wind(
    ds_all,
    var_curr='current_speed_0m',
    var_wind='W10',
    periods=[1, 10, 100, 10000],
    output_file='Uc_for_rv_wind.csv'
)

Return period [years]	U[m/s]	Uc(P5-model) [m/s]	Uc(Mean-model) [m/s]	Uc(P95-model) [m/s]
1	27.55	0.0	0.27	0.62
10	31.23	0.0	0.32	1.0
100	34.51	0.0	0.39	1.85
10000	40.27	0.0	0.57	7.56

Current Speed for RV Hs Table

tables.table_current_for_rv_hs(
    ds_all,
    var_curr='current_speed_0m',
    var_hs='HS',
    periods=[1, 10, 100, 10000],
    output_file='Uc_for_rv_hs.csv'
)

Return period [years]	Hs[m]	Uc(P5-model) [m/s]	Uc(Mean-model) [m/s]	Uc(P95-model) [m/s]
1	11.55	0.02	0.23	0.45
10	14.19	0.02	0.25	0.48
100	16.75	0.01	0.26	0.51
10000	21.68	0.0	0.28	0.56

Sea Temperature Profile Monthly Stats Table (Mean)

tables.table_profile_monthly_stats(
    df,
    var=['temp_' + d for d in depth],
    z=[float(d[:-1]) for d in depth],
    method='mean',
    output_file='table_mean_temp_profile_monthly_stats.png'
)

Sea Temperature Profile Monthly Stats Table (Standard Deviation)

tables.table_profile_monthly_stats(
    df,
    var=['temp_' + d for d in depth],
    z=[float(d[:-1]) for d in depth],
    method='std.dev',
    output_file='table_std_temp_profile_monthly_stats.png'
)

Sea Temperature Profile Monthly Stats Table (Minimum)

tables.table_profile_monthly_stats(
    df,
    var=['temp_' + d for d in depth],
    z=[float(d[:-1]) for d in depth],
    method='minimum',
    output_file='table_min_temp_profile_monthly_stats.png'
)

Sea Temperature Profile Monthly Stats Table (Maximum)

tables.table_profile_monthly_stats(
    df,
    var=['temp_' + d for d in depth],
    z=[float(d[:-1]) for d in depth],
    method='maximum',
    output_file='table_max_temp_profile_monthly_stats.png'
)

Mean Sea Temperature Profile Monthly Stats Plot

plots.plot_profile_monthly_stats(
    df,
    var=['temp_' + d for d in depth],
    z=[float(d[:-1]) for d in depth],
    method='mean',
    title='Mean Sea Temperature [°C]',
    output_file='plot_mean_temp_profile_monthly_stats.png'
)

Sea Temperature Profile Monthly Stats Plot (Minimum)

plots.plot_profile_monthly_stats(
    df,
    var=['temp_' + d for d in depth],
    z=[float(d[:-1]) for d in depth],
    method='minimum',
    title='Min. Sea Temperature [°C]',
    output_file='plot_min_temp_profile_monthly_stats.png'
)

Sea Temperature Profile Monthly Stats Plot (Maximum)

plots.plot_profile_monthly_stats(
    df,
    var=['temp_' + d for d in depth],
    z=[float(d[:-1]) for d in depth],
    method='maximum',
    title='Max. Sea Temperature [°C]',
    output_file='plot_max_temp_profile_monthly_stats.png'
)

Mean Sea Temperature Profile Monthly Stats Plot

plots.plot_profile_monthly_stats(
    df,
    var=['temp_' + d for d in depth],
    z=[float(d[:-1]) for d in depth],
    method='mean',
    title='Mean Sea Temperature [°C]',
    output_file='plot_mean_temp_profile_monthly_stats.png'
)

Min. Sea Temperature Profile Monthly Stats Plot

plots.plot_profile_monthly_stats(
    df,
    var=['temp_' + d for d in depth],
    z=[float(d[:-1]) for d in depth],
    method='minimum',
    title='Min. Sea Temperature [°C]',
    output_file='plot_min_temp_profile_monthly_stats.png'
)

Max. Sea Temperature Profile Monthly Stats Plot

plots.plot_profile_monthly_stats(
    df,
    var=['temp_' + d for d in depth],
    z=[float(d[:-1]) for d in depth],
    method='maximum',
    title='Max. Sea Temperature [°C]',
    output_file='plot_max_temp_profile_monthly_stats.png'
)

Mean Salinity Profile Monthly Stats Table

tables.table_profile_monthly_stats(
    df,
    var=['salt_' + d for d in depth],
    z=[float(d[:-1]) for d in depth],
    method='mean',
    output_file='table_mean_sal_profile_monthly_stats.png'
)

Standard Deviation Salinity Profile Monthly Stats Table

tables.table_profile_monthly_stats(
    df,
    var=['salt_' + d for d in depth],
    z=[float(d[:-1]) for d in depth],
    method='std.dev',
    output_file='table_std_sal_profile_monthly_stats.png'
)

Min. Salinity Profile Monthly Stats Table

tables.table_profile_monthly_stats(
    df,
    var=['salt_' + d for d in depth],
    z=[float(d[:-1]) for d in depth],
    method='minimum',
    output_file='table_min_sal_profile_monthly_stats.png'
)

Max. Salinity Profile Monthly Stats Table

tables.table_profile_monthly_stats(
    df,
    var=['salt_' + d for d in depth],
    z=[float(d[:-1]) for d in depth],
    method='maximum',
    output_file='table_max_sal_profile_monthly_stats.png'
)

Mean Salinity Profile Monthly Stats Plot

plots.plot_profile_monthly_stats(
    df,
    var=['salt_' + d for d in depth],
    z=[float(d[:-1]) for d in depth],
    method='mean',
    title='Mean Salinity [PSU]',
    output_file='plot_mean_sal_profile_monthly_stats.png'
)

Standard Deviation Salinity Profile Monthly Stats Plot

plots.plot_profile_monthly_stats(
    df,
    var=['salt_' + d for d in depth],
    z=[float(d[:-1]) for d in depth],
    method='std.dev',
    title='St.Dev Salinity [PSU]',
    output_file='plot_std_sal_profile_monthly_stats.png'
)

Min. Salinity Profile Monthly Stats Plot

plots.plot_profile_monthly_stats(
    df,
    var=['salt_' + d for d in depth],
    z=[float(d[:-1]) for d in depth],
    method='minimum',
    title='Min. Salinity [PSU]',
    output_file='plot_min_sal_profile_monthly_stats.png'
)

Max. Salinity Profile Monthly Stats Plot

plots.plot_profile_monthly_stats(
    df,
    var=['salt_' + d for d in depth],
    z=[float(d[:-1]) for d in depth],
    method='maximum',
    title='Max. Salinity [PSU]',
    output_file='plot_max_sal_profile_monthly_stats.png'
)

CCA profiles Plot

plots.plot_cca_profiles(
    df,
    var='current_speed_',
    month=None,
    return_period=10,
    output_file='plot_cca_profiles.png'
)

CCA profiles Table

tables.table_cca_profiles(
    df,
    var='current_speed_',
    month=None,
    return_period=10,
    output_file='table_cca_profiles.csv'
)

Depth [m]	0.0	1.0	2.5	5.0	10.0	15.0	20.0	25.0	30.0	40.0	50.0	75.0	100.0	150.0	200.0	250.0	300.0	350.0	400.0	450.0	500.0	750.0	1000.0	1250.0	1500.0	Worst case
0.0	0.96	0.96	0.96	0.96	0.95	0.94	0.94	0.93	0.91	0.88	0.83	0.7	0.65	0.62	0.6	0.58	0.55	0.51	0.47	0.43	0.37	0.24	0.22	0.22	0.21	0.96
1.0	0.96	0.96	0.96	0.96	0.95	0.94	0.94	0.93	0.91	0.88	0.83	0.7	0.65	0.62	0.6	0.58	0.55	0.51	0.47	0.43	0.37	0.24	0.22	0.22	0.21	0.96
2.5	0.96	0.96	0.96	0.96	0.95	0.94	0.94	0.93	0.91	0.88	0.83	0.7	0.65	0.62	0.6	0.58	0.55	0.51	0.47	0.42	0.37	0.24	0.22	0.22	0.21	0.96
5.0	0.95	0.95	0.95	0.95	0.94	0.94	0.93	0.92	0.91	0.88	0.83	0.7	0.65	0.62	0.6	0.58	0.55	0.51	0.47	0.42	0.36	0.24	0.22	0.22	0.21	0.95
10.0	0.93	0.93	0.93	0.93	0.93	0.93	0.93	0.92	0.91	0.88	0.83	0.7	0.65	0.62	0.6	0.58	0.55	0.51	0.47	0.42	0.36	0.24	0.22	0.21	0.21	0.93
15.0	0.91	0.91	0.92	0.92	0.92	0.92	0.92	0.92	0.91	0.88	0.83	0.7	0.65	0.62	0.6	0.57	0.55	0.51	0.47	0.42	0.36	0.24	0.22	0.21	0.21	0.92
20.0	0.9	0.9	0.9	0.91	0.91	0.91	0.92	0.91	0.9	0.87	0.83	0.7	0.65	0.61	0.59	0.57	0.55	0.51	0.47	0.42	0.36	0.24	0.22	0.21	0.21	0.92
25.0	0.88	0.88	0.89	0.89	0.9	0.9	0.91	0.91	0.9	0.87	0.83	0.7	0.65	0.61	0.59	0.57	0.55	0.51	0.47	0.42	0.36	0.23	0.21	0.21	0.2	0.91
30.0	0.87	0.87	0.87	0.88	0.89	0.89	0.89	0.9	0.9	0.88	0.83	0.71	0.65	0.62	0.6	0.58	0.55	0.51	0.47	0.43	0.36	0.23	0.21	0.21	0.2	0.9
40.0	0.82	0.82	0.82	0.83	0.84	0.84	0.85	0.85	0.86	0.88	0.85	0.72	0.66	0.63	0.61	0.58	0.56	0.52	0.48	0.43	0.37	0.23	0.21	0.2	0.2	0.88
50.0	0.76	0.76	0.76	0.77	0.78	0.78	0.79	0.79	0.8	0.83	0.85	0.74	0.68	0.64	0.62	0.6	0.57	0.53	0.49	0.44	0.38	0.23	0.21	0.21	0.2	0.85
75.0	0.61	0.61	0.61	0.62	0.63	0.63	0.64	0.64	0.65	0.67	0.7	0.77	0.73	0.69	0.67	0.65	0.61	0.57	0.52	0.46	0.39	0.25	0.23	0.23	0.23	0.77
100.0	0.54	0.54	0.54	0.55	0.56	0.56	0.56	0.57	0.58	0.6	0.62	0.7	0.73	0.71	0.69	0.67	0.63	0.58	0.52	0.46	0.37	0.23	0.22	0.22	0.22	0.73
150.0	0.48	0.48	0.49	0.49	0.5	0.5	0.5	0.51	0.51	0.53	0.56	0.63	0.67	0.72	0.71	0.69	0.65	0.59	0.53	0.45	0.37	0.22	0.22	0.22	0.22	0.72
200.0	0.44	0.44	0.45	0.45	0.45	0.46	0.46	0.46	0.47	0.48	0.51	0.57	0.61	0.67	0.69	0.68	0.64	0.58	0.52	0.45	0.36	0.21	0.21	0.21	0.21	0.69
250.0	0.4	0.4	0.4	0.41	0.41	0.41	0.42	0.42	0.42	0.44	0.46	0.52	0.56	0.61	0.64	0.67	0.64	0.58	0.51	0.44	0.36	0.21	0.2	0.2	0.2	0.67
300.0	0.36	0.36	0.36	0.36	0.36	0.37	0.37	0.37	0.37	0.39	0.41	0.46	0.49	0.53	0.56	0.59	0.62	0.58	0.52	0.44	0.36	0.2	0.19	0.19	0.19	0.62
350.0	0.31	0.31	0.31	0.31	0.31	0.31	0.32	0.32	0.32	0.33	0.35	0.39	0.41	0.45	0.47	0.5	0.54	0.57	0.54	0.46	0.37	0.2	0.19	0.19	0.18	0.57
400.0	0.26	0.26	0.26	0.26	0.26	0.26	0.26	0.27	0.27	0.28	0.29	0.32	0.34	0.36	0.38	0.4	0.43	0.48	0.52	0.48	0.4	0.22	0.19	0.18	0.18	0.52
450.0	0.21	0.21	0.21	0.21	0.22	0.22	0.22	0.22	0.22	0.23	0.24	0.27	0.27	0.29	0.3	0.31	0.34	0.38	0.44	0.48	0.44	0.24	0.2	0.19	0.18	0.48
500.0	0.18	0.18	0.18	0.18	0.18	0.18	0.18	0.18	0.19	0.19	0.2	0.22	0.22	0.23	0.24	0.25	0.27	0.3	0.35	0.43	0.45	0.27	0.21	0.2	0.19	0.45
750.0	0.1	0.1	0.1	0.1	0.1	0.1	0.1	0.1	0.1	0.1	0.1	0.12	0.11	0.11	0.11	0.12	0.12	0.13	0.16	0.19	0.22	0.37	0.32	0.3	0.29	0.37
1000.0	0.09	0.09	0.09	0.09	0.09	0.09	0.09	0.09	0.09	0.09	0.1	0.11	0.12	0.12	0.12	0.12	0.12	0.13	0.14	0.16	0.18	0.34	0.39	0.38	0.37	0.39
1250.0	0.09	0.09	0.09	0.09	0.09	0.09	0.09	0.09	0.09	0.09	0.1	0.12	0.12	0.13	0.12	0.13	0.13	0.13	0.15	0.16	0.18	0.33	0.4	0.41	0.4	0.41
1500.0	0.09	0.09	0.09	0.09	0.09	0.09	0.09	0.09	0.09	0.09	0.1	0.12	0.12	0.13	0.13	0.13	0.13	0.14	0.15	0.16	0.18	0.33	0.4	0.41	0.42	0.42

Wave Spectrum Plots

In all the spectra functions, the data can be filtered using the selected start_time and end_time. If start_time and end_time is set to None the full dataset will be used.

Monthly Mean 1D Wave Spectrum

plots.plot_spectra_1d(
   data,
   var = 'SPEC',
   period = None,
   month = None,
   method = 'mean',
   output_file  = 'wave_spectrum_1d_months.png'
)

1D Spectrum Mean for a Specific Month Across Several Years

plots.plot_spectra_1d(
   data,
   var = 'SPEC',
   period = None,
   month = 1,
   method = 'mean',
   output_file  = 'wave_spectrum_1d_month.png'
)

2D Wave Spectrum at Time of Maximum Hm0 in Selected Period

plots.plot_spectrum_2d(
   data,
   var = 'SPEC',
   period = ['2021-01-01T00','2024-12-31T23'],
   month = None,
   method = 'hm0_max',
   plot_type = 'pcolormesh',
   output_file  = 'wave_spectrum_2d.png'
)

Diana Wave Spectrum with Swell and Windsea Partitions, Averaged Over Times with Hm0 ≥ 99th Percentile

The function takes an xarray Dataset as input. If the wind data are provided in a CSV file, they must first be converted to an xarray Dataset. This can be done as follows:

df = pd.read_csv('NORA3_wind_wave.csv', comment="#", index_col=0, parse_dates=True)
df1=df[['wind_speed_10m','wind_direction_10m']]
df1.columns=['wind_speed','wind_direction']
NORA3_wind_sub=df1.to_xarray()

Partitioning requires that the wave spectra data (NORA3_wave_spec) are merged with the wind data (NORA3_wind_sub) beforehand, using:

spec_funcs.merge_spec_wind(NORA3_wave_spec, NORA3_wind_sub)

The plotting function can also be used without combining with wind data. However, in that case, partitioning will not be available. Once the datasets are combined, the following code can be used to generate the plot:

plots.plot_diana_spectrum(
   data,
   var = 'SPEC',
   period = ['2022-01-01T00', '2022-07-31T23'],
   month = 1,
   method = 'top_1_percent_mean',
   partition = True,
   plot_type = 'pcolormesh',
   freq_mask = True,
   bar = 'hm0',
   arrow_dir = 'pdir',
   output_file  = 'wave_spectrum_diana.png'
)

Wind Sea and Swell Wave Peak Direction-Frequency Occurrences

This function requires the wave spectra data (NORA3_wave_spec) to be merged with the wind data (NORA3_wind_sub) beforehand. Refer to the Diana section for instructions.

plots.plot_partition_peak_dir_freq_2d(
   data,
   var = 'SPEC',
   period = time,
   month=month,
   hm0_threshold = 1,
   windsea_freq_mask_percentile=None,
   swell_freq_mask_percentile=99,
   output_file = 'wave_partition_peak_dir_freq_2d.png'
)

2D Monthly Mean Wave Spectra

plots.plot_spectra_2d(
   data,
   var = 'SPEC',
   method = 'mean',
   plot_type = 'contour',
   cbar = 'multiple',
   output_file  = 'wave_spectra_2d_monthly_mean.png'
)

Directional Wave Spectra Averaged by Peak Wave Direction in 30° Sectors

plots.plot_spectra_2d(
   data,
   var = 'SPEC',
   method = 'direction'
   plot_type = 'contour',
   cbar = 'single',
   output_file  = 'wave_spectra_2d_direction.png'
)

Tidal Levels Table

tables.table_tidal_levels(
    ds_tide,
    var='tide',
    output_file='tidal_levels.csv'
)

Tidal Level	[m]
HAT	0.29
MSL	-0.0
LAT	-0.24

Tidal Levels Plot

plots.plot_tidal_levels(
    ds_all,
    var='tide',
    start_time='1980-01-01',
    end_time='2014-12-31',
    output_file='tidal_levels.png'
)

Tidal Type

stats.tidal_type(
    df,
    var='tide'
)

‘Tidal type = semi-diurnal’

Storm Surge for Given Hs Table

tables.table_storm_surge_for_given_hs(
    ds_all,
    var_surge='zeta_0m',
    var_hs='HS',
    bin_width=1,
    max_hs=20,
    output_file='table_perc_surge_for_Hs.csv'
)

Hs[m]	S(P5-model) [m]	S(Mean-model) [m]	S(P95-model) [m]
0.5	-0.97	-0.32	0.32
1.5	-0.99	-0.32	0.35
2.5	-0.97	-0.29	0.39
3.5	-0.94	-0.25	0.44
4.5	-0.9	-0.21	0.48
5.5	-0.86	-0.16	0.53
6.5	-0.82	-0.12	0.58
7.5	-0.78	-0.07	0.63
8.5	-0.73	-0.03	0.68
9.5	-0.68	0.02	0.72
10.5	-0.64	0.07	0.77
11.5	-0.59	0.12	0.82
12.5	-0.54	0.17	0.87
13.5	-0.49	0.21	0.92
14.5	-0.45	0.26	0.97
15.5	-0.4	0.31	1.02
16.5	-0.35	0.36	1.07
17.5	-0.3	0.41	1.12
18.5	-0.25	0.46	1.17
19.5	-0.2	0.51	1.22

Storm Surge for Given Hs Plot

plots.plot_storm_surge_for_given_hs(
    ds_all,
    var_surge='zeta_0m',
    var_hs='HS',
    max_hs=20,
    output_file='surge_for_given_hs.png'
)

Extreme Total Water Level Table

tables.table_extreme_total_water_level(
    ds_all,
    var_hs='HS',
    var_tp='TP',
    var_surge='zeta_0m',
    var_tide='tide',
    periods=[100,10000],
    output_file='table_extreme_total_water_level.csv'
)

Return period [years]	Storm surge[m]	Tidal level(HAT)[m]	Crest heigh[m]	Total water level[m]
100	0.91	0.27	16.08	17.26
10000	0.93	0.27	20.22	21.42

Storm Surge for Return Values Hs Table

tables.table_storm_surge_for_rv_hs(
    ds_all,
    var_hs='HS',
    var_tp='TP',
    var_surge='zeta_0m',
    var_tide='tide',
    periods=[1,10,100,10000],
    depth=200,
    output_file='table_storm_surge_for_rv_hs.csv'
)

Return period [years]	Hs[m]	Crest heigh[m]	Tidal level(HAT)[m]	S(P5-model) [m]	S(Mean-model) [m]	S(P95-model) [m]	Total water level[m]
1	10.31	11.99	0.27	-0.65	0.06	0.76	13.03
10	11.97	13.86	0.27	-0.57	0.14	0.85	14.97
100	13.97	16.07	0.27	-0.47	0.24	0.95	17.28
10000	17.76	20.2	0.27	-0.28	0.42	1.13	21.6

Map Statistics

from metocean_stats import maps

Plot map with points of interest:

maps.plot_points_on_map(lon=[3.35,3.10],
                   lat=[60.40,60.90],
                   label=['NORA3','NORKYST800'],
                   bathymetry='NORA3')

Hexbin plot of extreme significant wave height based on NORA3 data:

maps.plot_extreme_wave_map(return_period=100,
                       product='NORA3',
                       title='100-Year Return Values of Significant Wave Height (NORA3)',
                       set_extent = [0,30,52,73],
                       output_file='wave_100yrs_hexbin.png',
                       method='hexbin'
                       )

Contour plot of extreme significant wave height based on NORA3 data:

maps.plot_extreme_wave_map(return_period=100,
                       product='NORA3',
                       title='100-Year Return Values of Significant Wave Height (NORA3)',
                       set_extent = [0,30,52,73],
                       output_file='wave_100yrs_contour.png',
                       method='contour',
                       percentile_contour=45
                       )

Hexbin plot of extreme wind at 100m height based on NORA3 data:

plot_extreme_wind_map(return_period=100,
                      z=100,
                      product='NORA3',
                      title='100-yr return values Wind at 100 m (NORA3)',
                      set_extent = [0,30,52,73],
                      land_mask = True
                      method = 'hexbin'
                      )

Contour plot of extreme wind at 100m height based on NORA3 data:

plot_extreme_wind_map(return_period=100,
                      z=100,
                      product='NORA3',
                      title='100-yr return values Wind at 100 m (NORA3)',
                      set_extent = [0,30,52,73],
                      land_mask = False
                      method = 'contour',
                      percentile_contour = 50
                      )

Hexbin plot of extreme surface current speed based on NORA3 (Norkyst2400) data:

plot_extreme_current_map(return_period=100,
                      z='surface',
                      distribution='gumbel',
                      product='NORA3',
                      title='100-yr return values Current at surface (NORA3)',
                      set_extent = [0,30,52,73],
                      output_file='current_100yrs_surface_contour.png',
                      method='hexbin'
                      )

Contour plot of extreme surface current speed based on NORA3 (Norkyst2400) data:

plot_extreme_current_map(return_period=100,
                      z='surface',
                      distribution='gumbel',
                      product='NORA3',
                      title='100-yr return values Current at surface (NORA3)',
                      set_extent = [0,30,52,73],
                      output_file='current_100yrs_surface_contour.png',
                      method='contour',
                      percentile_contour=60
                      )

Plot mean 2-m air temperature based on NORA3 data:

plot_mean_air_temperature_map(product='NORA3',
                              title='Mean 2-m air temperature 1991-2020 (NORA3)',
                              set_extent=[-25,-10,60.5,68],
                              unit='degC')

Plot bathymetry based on NORKYST800 url:

plot_bathymetry_cross_section(product = "NORKYST800",
                              lon=4,
                              lat=60.5)

Plot bathymetry with a variable (magnitude.v,u,temperature or salinity) based on NORKYST800 url and csv file:

plot_bathymetry_cross_section_with_variable_from_NORKYST800_overall(product='https://thredds.met.no/thredds/dodsC/fou-hi/norkyst800m-1h/NorKyst-800m_ZDEPTHS_his.an.2025020700.nc',
                                                                    df='NORKYST800_test.csv',
                                                                    lon=4,
                                                                    lat=60.5,
                                                                    var="salinity")

Verification Functions

from plots import verification
from tables import verification

Scatter Plot

plots.plot_scatter(
   df,
   var1='W10',
   var2='W100',
   var1_units='m/s',
   var2_units='m/s',
   title='Scatter',
   regression_line='effective-variance',
   qqplot=False,
   density=True,
   output_file='scatter_plot.png')

Taylor diagram

plots.taylor_diagram(
   df,
   var_ref=['HS'],
   var_comp= ['HS.1','HS.2'],
   norm_std=True,
   output_file='Taylor_diagram.png')

Comparison tables between variables

For the comparison between two variables

tables.table_error_metric(
   df,
   var_ref='HS',var_comp='HS.1',
   error_metric=['bias','mae','rmse','scatter_index','corr'],
   output_file='table_error_metric.csv')

bias	mae	rmse	corr	scatter_index
-0.862	0.862	1.094	0.895	0.481

For the comparison between three variables or more

tables.table_error_metric_multiple(
   df,
   var_ref='TP',var_comp=['TP.1','TP.2'],
   error_metric=['scatter_index','rmse','bias','mae','corr'],
   output_file='table_error_metric_multiple.csv')

TP	bias	mae	rmse	corr	scatter_index
TP.1	-3.712	3.766	5.182	0.15	0.586
TP.2	0.757	0.909	1.647	0.833	0.186

Comparison table between 2 variables as a function of another variable

tables.table_binned_error_metric(
   df,
   var_bin='TP',var_bin_size=0.5,var_ref='HS',var_comp='HS.1',
   threshold_min=0,
   error_metric=['bias','scatter_index'],
   output_file='table_binned_error_metric.csv')

TP_bin	bias	scatter_index	nb_val
0.25			0
0.75			0
1.25			0
1.75			0
2.25			0
2.75	-0.167	0.306	3
3.25	-0.175	0.286	44
3.75	-0.206	0.288	156
4.25	-0.264	0.332	661
4.75	-0.307	0.338	1179
5.25	-0.376	0.372	2104
5.75	-0.42	0.376	2984
6.25	-0.482	0.383	4009
6.75	-0.552	0.385	5043
7.25	-0.635	0.397	5091
7.75	-0.688	0.404	4832
8.25	-0.802	0.426	5412
8.75	-0.873	0.434	5496
9.25	-0.931	0.437	4941
9.75	-1.049	0.444	3597
10.25	-1.073	0.438	3902
10.75	-1.19	0.464	3203
11.25	-1.222	0.464	2808
11.75	-1.301	0.491	2153
12.25	-1.376	0.508	1705
12.75	-1.419	0.541	1514
13.25	-1.517	0.593	876
13.75	-1.528	0.573	782
14.25	-1.525	0.595	473
14.75	-1.555	0.612	363
15.25	-1.496	0.637	368
15.75	-1.46	0.687	172
16.25	-1.744	0.709	112
16.75	-1.472	0.659	103
17.25	-1.738	0.747	87
17.75	-1.471	0.751	34
18.25	-1.388	0.519	33
18.75	-1.467	0.582	21
19.25	-2.14	0.764	5
19.75	-0.8	0.295	2
20.25	-0.5	0.446	5
20.75	-0.775	0.581	4
21.25	-0.65	0.58	2
21.75	-0.8	0.889	1

Comparison plots between variables as a function of another variable

For the comparison between two variables

plots.plot_binned_error_metric(
   df,
   var_bin='W10',var_bin_size=0.5,var_bin_unit='m/s',
   var_ref='HS',var_comp=['HS.1'],var_comp_unit='m',
   threshold_min=100,
   error_metric='bias',
   output_file='plot_binned_error_metric_2var.png')

For the comparison between three variables

plots.plot_binned_error_metric(
   df,
   var_bin='TP',var_bin_size=0.5,var_bin_unit='s',
   var_ref='HS',var_comp=['HS.1','HS.2'],var_comp_unit='m',
   threshold_min=100,
   error_metric='corr',
   output_file='plot_binned_error_metric_3var.png')

Climate and trends

from plots import climate
from tables import climate

Yearly stripes

plots.plot_yearly_stripes(
   df,
   var_name='HS',
   method= 'mean',
   ylabel='Hs [m]',
   output_file='Yearly_stripes.png')

Monthly and yearly heatmap

plots.plot_heatmap_monthly_yearly(
   df,
   var_name='T2m',
   method= 'mean',
   cb_label='2-m temperature [°C]',
   output_file='Heatmap_monthly_yearly.png')

Yearly vertical profiles

plots.plot_yearly_vertical_profiles(
   df,
   rad_colname='current_speed_',
   method= 'mean',
   yaxis_direction='down',
   xlabel='Current speed [m/s]',
   output_file='Yearly_vertical_profiles_current.png')

plots.plot_yearly_vertical_profiles(
   df,
   rad_colname='W',
   method= 'mean',
   yaxis_direction='up',
   xlabel='Wind speed [m/s]',
   output_file='Yearly_vertical_profiles_wind.png')

plots.plot_heatmap_profiles_yearly(
   df,
   rad_colname='W',
   cb_label='Wind speed [m/s]',
   yaxis_direction='up',
   method='P80',
   output_file='Heatmap_yearly_profiles_wind.png')

Linear regression Plot and Table

plots.plot_linear_regression(
   df,
   var='HS',
   time='Year',
   stat='P70',
   method=['Least-Squares','Theil-Sen'],
   confidence_interval=0.95,
   ylabel='Hs [m]',
   output_figure='Linear_regression.png')

tables.table_linear_regression(
   df,
   var='HS',
   stat='mean',
   method=['Least-Squares','Theil-Sen','Kendall-tau'],
   confidence_interval=0.95,
   intercept=True,
   output_file='table_linear_regression.csv'
)

index	LS_slope	LS_intercept	LS_r_square	TS_slope	TS_intercept	TS_slope_lower	TS_slope_upper
Jan	0.086	-159.001	0.132	0.09	-167.611	-0.012	0.195
Feb	-0.052	111.875	0.068	-0.061	129.943	-0.133	0.05
Mar	0.06	-107.96	0.077	0.067	-122.741	-0.022	0.165
Apr	-0.006	19.059	0.001	-0.006	20.198	-0.11	0.094
May	-0.062	128.803	0.186	-0.062	128.233	-0.131	0.001
Jun	0.009	-10.109	0.007	0.016	-24.417	-0.049	0.067
Jul	-0.029	63.998	0.064	-0.02	45.572	-0.079	0.031
Aug	0.016	-24.113	0.014	0.022	-35.765	-0.048	0.079
Sep	0.085	-159.443	0.255	0.095	-178.202	0.003	0.159
Oct	0.041	-70.81	0.044	0.037	-64.575	-0.056	0.121
Nov	0.055	-99.15	0.112	0.044	-77.101	0.007	0.121
Dec	0.02	-27.884	0.012	0.03	-48.163	-0.057	0.124
Year	0.019	-28.557	0.13	0.028	-47.411	-0.003	0.047

Auxiliary Functions

from metocean_stats.stats.aux_funcs import *

Convert lat/lon coordinates from degrees/minutes/seconds to decimals:

lat = degminsec_to_decimals(60,30,00)

returns lat = 60.5

Estimate total depth of wave influence in meters:

depth = depth_of_wave_influence(Hs=12, Tp=15, ref_depth=1000,spectrum='JONSWAP', theshold=0.01)

returns depth = 692.5

Estimate wind speed at a different height and time period:

wind_speed2 = estimate_wind_speed(height1=10, wind_speed1=10, time1=600, height2=10, time2=60)

returns wind_speed2 = 11.08 m/s