Welcome to metocean-stats’s documentation!

metocean-stats is a Python package (under developement) for metocean analysis of NORA3 (wind and wave) hindcast.

The package contains functions that:

  • generate statistics (tables, diagrams etc)

Installing metocean-stats

  1. Install anaconda3 or miniconda3

  2. Clone metocean-stats:

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

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

$ conda config --add channels conda-forge
$ conda env create -f environment.yml
$ conda activate metocean-stats
$ pip install -e .

To update the enviroment using a new environment.yml, run:

$ conda env update --file environment.yml --prune

Download metocean data

Use metocean-api to download some sample data:

from metocean_api import ts
ds = ts.TimeSeries(lon=1.320, lat=53.324,
                   start_time='2000-01-01',
                   end_time='2000-12-31',
                   product='NORA3_wind_wave')

Import data from server and save it as csv:

ds.import_data(save_csv=True)

Or import data from a local csv-file:

ds.load_data(local_file=ts.datafile)

General Statistics

To generate general/basic stastistics, import general_stats module:

from metocean_stats.stats import general_stats

Create scatter Hs-Tp diagram:

general_stats.scatter_diagram(data=ts.data,
                  var1='hs', step_var1=1,
                  var2='tp', step_var2=2,
                  output_file='scatter_hs_tp_diagram.png')
_images/scatter_hs_tp_diagram.png

Create table with monthly percentiles:

general_stats.table_monthly_percentile(data=ts.data, var='hs', output_file='hs_monthly_perc.csv')

Month

5%

50%

Mean

95%

99%

jan

0.5

1.3

1.4

2.7

3.7

feb

0.4

1.2

1.4

2.8

3.8

mar

0.4

1.1

1.2

2.5

3.4

apr

0.3

0.9

1.0

2.1

3.2

mai

0.3

0.9

0.9

1.9

2.5

jun

0.3

0.7

0.8

1.7

2.5

jul

0.2

0.7

0.8

1.6

2.2

aug

0.3

0.7

0.8

1.8

2.5

sep

0.3

0.9

1.1

2.3

3.2

okt

0.4

1.2

1.3

2.5

3.2

nov

0.5

1.3

1.4

2.7

3.6

des

0.4

1.3

1.4

2.8

3.5

Annual

0.4

1.3

1.4

2.8

3.5

Create table with monthly min, mean, and max values:

general_stats.table_monthly_min_mean_max(data=ts.data, var='hs',output_file='hs_montly_min_mean_max.csv')

Month

Minimum

Mean

Maximum

jan

2.4

3.7

5.13

feb

2.62

3.7

5.53

mar

2.02

3.3

4.52

apr

1.95

3.0

4.67

mai

1.6

2.5

3.29

jun

1.46

2.4

3.32

jul

1.29

2.2

3.57

aug

1.7

2.5

4.42

sep

1.8

3.0

5.61

okt

2.04

3.2

4.43

nov

2.3

3.7

5.48

des

2.46

3.6

5.14

Annual

3.63

4.6

5.61

Create table with sorted statistics by Hs:

general_stats.table_var_sorted_by_hs(data=ts.data, var='tp', var_hs='hs', output_file='tp_sorted_by_hs.csv')

Hs

Entries

Min

5%

Mean

95%

Max

0-1

140613

1.8

3.2

6.0

11.2

21.8

1-2

111055

3.6

4.3

6.8

12.3

21.8

2-3

24175

4.7

5.7

7.8

12.3

18.0

3-4

4058

6.3

6.9

9.8

13.5

18.0

4-5

571

7.6

9.2

11.6

14.9

18.0

5-6

40

9.2

10.2

11.8

14.9

14.9

6-7

0

0-7

280512

1.8

3.6

6.5

11.2

21.8

Directional Statistics

To generate directional stastistics, import dir_stats module:

from metocean_stats.stats import dir_stats

Create rose for overall data (method=’overall’) or for each month (method=’monthly’):

dir_stats.var_rose(ts.data, 'thq','hs','waverose.png',method='overall')
_images/waverose.png 
dir_stats.var_rose(ts.data, 'thq','hs','waverose.png',method='monthly')
_images/monthly_waveroses.png

Create table with min, mean, and maximum values as a function direction:

dir_stats.directional_min_mean_max(ts.data,'thq','hs','hs_dir_min_mean_max.csv')

Direction

Minimum

Mean

Maximum

345-15

2.2

2.8

3.7

15-45

2.2

3.0

4.0

45-75

2.4

3.3

4.3

75-105

2.5

3.3

4.1

105-135

2.3

3.4

4.2

135-165

2.8

4.1

5.5

165-195

3.0

4.2

5.6

195-225

1.9

3.4

5.5

225-255

1.9

3.1

4.8

255-285

2.0

2.9

4.4

285-315

2.0

2.7

4.2

315-345

2.0

2.9

4.1

Annual

3.6

4.6

5.6

Extreme Statistics

To generate extreme stastistics, import extreme_stats module:

from metocean_stats.stats import extreme_stats

Create time series of Hs with return levels using POT and Annual Maximum(GEV):

rl_pot = extreme_stats.return_levels_pot(data=ds.data, var='hs',
         periods=[20,50,100,1000],
         output_file='return_levels_POT.png')
_images/return_levels_POT.png 
rl_am = extreme_stats.return_levels_annual_max(data=ds.data, var='hs',
        periods=[20,50,100,1000],method='GEV',
        output_file='return_levels_GEV.png')
_images/return_levels_GEV.png

Profile Statistics

To generate profile stastistics, import profile_stats module:

from metocean_stats.stats import profile_stats

Estimate and plot mean wind profile:

mean_prof = profile_stats.mean_profile(data = ds.data, vars =
             ['wind_speed_10m','wind_speed_20m','wind_speed_50m',
             'wind_speed_100m','wind_speed_250m','wind_speed_500m',
             'wind_speed_750m'],
             height_levels=[10,20,50,100,250,500,750],
             perc=[25,75],
             output_file='wind_profile.png')
_images/wind_profile.png