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486 lines
14 KiB
Python
486 lines
14 KiB
Python
"""
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Data package in WAFO Toolbox.
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Contents
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--------
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atlantic - Significant wave-height data recorded in the Atlantic Ocean
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gfaks89 - Surface elevation measured at Gullfaks C 24.12.1989
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gfaksr89 - Reconstructed surface elevation measured at Gullfaks C 24.12.1989.
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japansea - coastline map of The Japan Sea
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northsea - coastline map of The Nortsea
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sea - Surface elevation dataset used in WAT version 1.1.
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sfa89 - Wind measurements at Statfjord A 24.12.1989
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sn - Fatigue experiment, constant-amplitude loading.
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yura87 - Surface elevation measured off the coast of Yura
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This module gives gives detailed information and easy access to all datasets
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included in WAFO
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"""
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import numpy as np
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import os
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__path2data = os.path.dirname(os.path.realpath(__file__))
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__all__ = ['atlantic', 'gfaks89', 'gfaksr89', 'japansea', 'northsea', 'sea',
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'sfa89', 'sn', 'yura87']
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_NANS = set(['nan', 'NaN', '-1.#IND00+00', '1.#IND00+00', '-1.#INF00+00'])
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def _tofloat(x):
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return np.nan if x in _NANS else float(x or 0)
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_MYCONVERTER = {}
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for i in range(2):
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_MYCONVERTER[i] = _tofloat
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def _load(file): # @ReservedAssignment
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""" local load function
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"""
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return np.loadtxt(os.path.join(__path2data, file))
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def _loadnan(file): # @ReservedAssignment
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""" local load function accepting nan's
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"""
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return np.loadtxt(os.path.join(__path2data, file), converters=_MYCONVERTER)
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def atlantic():
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"""
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Return Significant wave-height data recorded in the Atlantic Ocean
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Data summary
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------------
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Size : 582 X 1
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Sampling Rate : ~ 14 times a month
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Device :
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Source :
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Format : ascii
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Description
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------------
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atlantic.dat contains average significant wave-height data recorded
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approximately 14 times a month in December-February during 7 years and
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at 2 locations in the Atlantic Ocean
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Example
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--------
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>>> import pylab
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>>> import wafo
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>>> Hs = wafo.data.atlantic()
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>>> np.allclose(Hs[:3], [ 5.48296296, 4.3615 , 5.26023256])
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True
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h = pylab.plot(Hs)
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Acknowledgement:
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---------------
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This dataset were made available by Dr. David Carter
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and Dr. David Cotton, Satellite Observing Systems, UK.
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"""
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return _load('atlantic.dat')
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def gfaks89():
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"""
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Return Surface elevation measured at Gullfaks C 24.12.1989
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Data summary
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------------
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Size : 39000 X 2
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Sampling Rate : 2.5 Hz
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Device : EMI laser
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Source : STATOIL
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Format : ascii, c1: time c2: surface elevation
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Description
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------------
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The wave data was measured 24th December 1989 at the Gullfaks C platform
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in the North Sea from 17.00 to 21.20. The period from 20.00 to 20.20
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is missing and contains NaNs. The water depth of 218 m is
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regarded as deep water for the most important wave components.
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There are two EMI laser sensors named 219 and 220. This data set is
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obtained from sensor 219, which is located in the Northwest
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corner approximately two platform leg diameters away from
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the closest leg.
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Thus the wave elevation is not expected to be significantly
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affected by diffraction effects for incoming waves in the western sector.
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The wind direction for this period is from the south.
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Some difficulties in calibration of the instruments have been reported
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resulting in several consecutive measured values being equal or almost
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equal in the observed data set.
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This dataset is for non-commercial use only.
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Hm0 = 6.8m, Tm02 = 8s, Tp = 10.5
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Example
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-------
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>>> import pylab
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>>> import wafo
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>>> x = wafo.data.gfaks89()
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>>> np.allclose(x[:3, 1], [-0.19667949, -0.46667949, -0.38667949])
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True
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h = pylab.plot(x[:,0],x[:,1])
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Acknowledgement:
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---------------
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This dataset were prepared and made available by Dr. S. Haver,
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STATOIL, Norway
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See also
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--------
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gfaksr89, northsea
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"""
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return _loadnan('gfaks89.dat')
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def gfaksr89():
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"""
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Return a reconstruction of surface elevation measured at Gullfaks C
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24.12.1989.
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Data summary
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------------
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Size : 39000 X 2
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Sampling Rate : 2.5 Hz
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Device : EMI laser
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Source : STATOIL
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Format : ascii, c1: time c2: surface elevation
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Description
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-----------
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This is a reconstructed version of the data in the GFAKS89.DAT file.
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The following calls were made to reconstruct the data:
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inds = findoutliers(gfaks89,.02,2,1.23);
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gfaksr89 = reconstruct(gfaks89,inds,6);
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The wave data was measured 24th December 1989 at the Gullfaks C platform
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in the North Sea from 17.00 to 21.20. The period from 20.00 to 20.20
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is missing in the original data. The water depth of 218 m is
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regarded as deep water for the most important wave components.
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There are two EMI laser sensors named 219 and 220. This data set is
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obtained from sensor 219, which is located in the Northwest
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corner approximately two platform leg diameters away from
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the closest leg.
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Thus the wave elevation is not expected to be significantly
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affected by diffraction effects for incoming waves in the western sector.
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The wind direction for this period is from the south.
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Some difficulties in calibration of the instruments have been reported
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resulting in several consecutive measured values being equal or almost
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equal in the observed data set.
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Hm0 = 6.8m, Tm02 = 8s, Tp = 10.5
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Example
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-------
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>>> import pylab
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>>> import wafo
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>>> x = wafo.data.gfaksr89()
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h = pylab.plot(x[:,0],x[:,1])
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See also
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--------
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gfaks89
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"""
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return _loadnan('gfaksr89.dat')
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def japansea():
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"""
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Return coastline map of The Japan Sea
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Data summary
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------------
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Size : 692 X 2
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Sampling Rate :
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Device :
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Source : http://crusty.er.usgs.gov/coast/getcoast.html
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Format : ascii, c1: longitude c2: latitude
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Description
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-----------
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JAPANSEA.DAT contains data for plotting a map of The Japan Sea.
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The data is obtained from USGS coastline extractor.
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Example:
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-------
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#the map is seen by
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>>> import pylab
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>>> import wafo
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>>> map1 = wafo.data.japansea()
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>>> np.allclose(map1[1:4, 0], [ 141.960057, 142.058624, 142.103214])
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True
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h = pylab.plot(map1[:,0],map1[:,1])
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lon_loc = [131,132,132,135,139.5,139]
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lat_loc = [46, 43, 40, 35, 38.3, 35.7]
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loc = ['China','Vladivostok','Japan Sea', 'Japan', 'Yura','Tokyo']
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algn = 'right'
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for lon, lat, name in zip(lon_loc,lat_loc,loc):
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pylab.text(lon,lat,name,horizontalalignment=algn)
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# If you have the m_map toolbox (see http://www.ocgy.ubc.ca/~rich/):
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m_proj('lambert','long',[130 148],'lat',[30 48]);
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m_line(map(:,1),map(:,2));
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m_grid('box','fancy','tickdir','out');
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m_text(131,46,'China');
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m_text(132,43,'Vladivostok');
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m_text(132,40,'Japan Sea');
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m_text(135,35,'Japan');
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m_text(139.5,38.3,'Yura');
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m_text(139,35.7,'Tokyo');
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"""
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return _loadnan('japansea.dat')
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def northsea():
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"""
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NORTHSEA coastline map of The Nortsea
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Data summary
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-------------
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Size : 60646 X 2
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Sampling Rate :
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Device :
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Source : http://crusty.er.usgs.gov/coast/getcoast.html
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Format : ascii, c1: longitude c2: latitude
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Description
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-----------
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NORTHSEA.DAT contains data for plotting a map of The Northsea.
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The data is obtained from USGS coastline extractor.
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Example
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-------
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# the map is seen by
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>>> import pylab
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>>> import wafo
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>>> map1 = wafo.data.northsea()
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>>> np.allclose(map1[1:4, 0], [ 1.261996, 1.264064, 1.268171])
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True
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h = pylab.plot(map1[:,0],map1[:,1])
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lon_pltfrm = [1.8, 2.3, 2., 1.9, 2.6]
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lat_pltfrm = [61.2, 61.2, 59.9, 58.4, 57.7]
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pltfrm = ['Statfjord A', 'Gullfaks C', 'Frigg', 'Sleipner', 'Draupner']
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h = pylab.scatter(lon_pltfrm,lat_pltfrm);
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algn = 'right'
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for lon, lat, name in zip(lon_pltfrm,lat_pltfrm,pltfrm):
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pylab.text(lon,lat,name,horizontalalignment=algn); algn = 'left'
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lon_city = [10.8, 10.8, 5.52, 5.2]
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lat_city = [59.85, 63.4, 58.9, 60.3]
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city = ['Oslo','Trondheim','Stavanger', 'Bergen']
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h = pylab.scatter(lon_city,lat_city);
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algn = 'right'
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for lon, lat, name in zip(lon_city,lat_city,city):
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pylab.text(lon,lat,name,horizontalalignment=algn)
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# If you have the mpl_toolkits.basemap installed
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from mpl_toolkits.basemap import Basemap
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import matplotlib.pyplot as plt
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# setup Lambert Conformal basemap.
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m = Basemap(width=1200000,height=900000,projection='lcc',
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resolution='f',lat_1=56.,lat_2=64,lat_0=58,lon_0=5.)
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m.drawcoastlines()
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h = m.scatter(lon_pltfrm,lat_pltfrm);
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algn = 'right'
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for lon, lat, name in zip(lon_pltfrm,lat_pltfrm,pltfrm):
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m.text(lon,lat,name,horizontalalignment=algn); algn = 'left'
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m.scatter(lon_city,lat_city)
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algn = 'right'
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for lon, lat, name in zip(lon_city,lat_city,city):
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m.text(lon,lat,name,horizontalalignment=algn)
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"""
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return _loadnan('northsea.dat')
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def sea():
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"""
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Return Surface elevation dataset used in WAT version 1.1.
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Data summary
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------------
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Size : 9524 X 2
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Sampling Rate : 4.0 Hz
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Device : unknown
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Source : unknown
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Format : ascii, c1: time c2: surface elevation
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Description
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-----------
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The wave data was used in one of WAFO predecessors, i.e. the Wave
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Analysis Toolbox version 1.1 (WAT)
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Hm0 = 1.9m, Tm02 = 4.0s, Tp2 = 11.5s Tp1=5.6s
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Example
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-------
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>>> import pylab
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>>> import wafo
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>>> x = wafo.data.sea()
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>>> np.allclose(x[:3,1],[-1.2004945 , -1.0904945 , -0.79049454])
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True
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h = pylab.plot(x[:,0],x[:,1])
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"""
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return _load('sea.dat')
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def sfa89():
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"""
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Return Wind measurements at Statfjord A 24.12.1989
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Data summary
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------------
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Size : 144 X 3
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Sampling Rate : 1/600 Hz
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Device :
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Source : DNMI (The Norwegian Meteorological Institute)
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Format : ascii, c1: time (hours)
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c2: velocity (m/s)
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c3: direction (degrees)
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Description
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-----------
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The registration of wind speeds at the Gullfaks field
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started up on Statfjord A in 1978 and continued until 1990.
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The dataregistration was transferred to Gullfaks C in Nov 1989.
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Due to some difficulties of the windregistration on Gullfaks C in
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the beginning, they continued to use the registered data from
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Statfjord A.
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The windspeed is measured in (meter/second), 110 m above mean water
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level (MWL) and the wind direction is given in degrees for the data.
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The data are a mean value of every 10 minutes.
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Wind directions are defined in the meteorological convention, i.e.,
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0 degrees = wind approaching from North, 90 degrees = wind from East, etc.
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This dataset is for non-commercial use only.
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Example
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-------
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>>> import pylab
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>>> import wafo
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>>> x = wafo.data.sfa89()
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h = pylab.plot(x[:,0],x[:,1])
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Acknowledgement
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----------------
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These data are made available by Knut A. Iden, DNMI.
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See also
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--------
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northsea
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"""
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return _load('sfa89.dat')
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def sn():
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"""
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Return SN Fatigue experiment, constant-amplitude loading.
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Data summary
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------------
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Size : 40 X 2
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Source : unknown
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Format : ascii, c1: Amplitude MPa c2: Number of cycles
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Description
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-----------
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A fatigue experiment with constant amplitudes at five levels:
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10,15,20,25 and 30 MPa. For each level is related 8 observations of
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the number of cycles to failure.
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The origin of the data is unknown.
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Example
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-------
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>>> import pylab
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>>> import wafo
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>>> x = wafo.data.sn()
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h = pylab.plot(x[:,0],x[:,1])
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See also
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--------
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The same data appear in the directory wdemos/itmkurs/
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as SN.mat.
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"""
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return _load('sn.dat')
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def yura87():
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"""
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Return Surface elevation measured off the coast of Yura.
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Data summary
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-----------
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Size : 85547 X 4
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Sampling Rate : 1 Hz
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Device : ultrasonic wave gauges
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Source : SRI, Ministry of Transport, Japan
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Format : ascii, c1: time (sec) c2-4: surface elevation (m)
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Description
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-----------
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The wave data was measured at the Poseidon platform
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in the Japan Sea from 24th November 1987 08.12 hours to 25th November
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1987 07.57 hours. Poseidon was located 3 km off the coast of Yura
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in the Yamagata prefecture, in the Japan Sea during the measurements.
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The most important wave components are to some extent influenced by the
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water depth of 42 m. The data are measured with three ultrasonic wave
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gauges located at the sea floor and the relative coordinates of the
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gauges are as follows (x-axis points to the East, y-axis points to
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the North):
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X (m) Y (m)
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c2: -4.93, 25.02
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c3: 5.80, 92.12
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c4: 0.00, 0.00
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This dataset is for non-commercial use only.
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Hm0 = 5.1m, Tm02 = 7.7s, Tp = 12.8s
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Example
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-------
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>>> import pylab
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>>> import wafo
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>>> x = wafo.data.yura87()
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h = pylab.plot(x[:,0],x[:,1])
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Acknowledgement:
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-----------------
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This dataset were prepared and made available by Dr. Sc. H. Tomita,
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Ship Research Institute, Ministry of Transport, Japan.
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See also
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--------
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japansea
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"""
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return _load('yura87.dat')
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if __name__ == '__main__':
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import doctest
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doctest.testmod()
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