toto.plugins.wave.wave_spectra

class toto.plugins.wave.wave_spectra.WaveAnalysis(pandas_obj)

Bases: object

ssh_to_wave(sea_level='sea_level', args={'crossing': {'downcrossing': True, 'upcrossing': False}, 'detrend': {'Off': True, 'constante': False, 'linear': False}, 'method': {'spectra': True, 'zero-crossing': False}, 'minimum number of waves per window for zero crossing analysis': 30, 'nfft': 3600, 'overlap': 1800, 'wave period range (min and max) (in s)': [3, 25], 'windows': 3600})

This function transform a timeseries of elevation to Hs using: -Spectra method - zero-crossing method

Parameters

sea_levelstr

Name of the column which contains the sea level.

args: dict

Dictionnary with the folowing keys: units: str

Units of the sea level

windows: int

windows to process in seconds.

overlap: int

overlap in seconds

nfft: int

Length of the signal to calculate the Fourier transform of.

detrend: str

linear, constante or Off to detrend the timeseries before doing the analysis

wave period range (min and max) (in s): list

Calulating wave within this Wave period

method: str

Can be spectra or zero-crossing depending what method to use

minimum number of waves per window for zero crossing analysis: int

Minimum number of waves per window for zero crossing analysis

crossing: str

Can be downcrossing or upcrossing. Method to use if not using the spectra method

Examples:

>>> df=tf['test1']['dataframe'].WaveAnalysis.ssh_to_wave(sea_level='ssh',args={'method'='spectra',windows:3600,'nfft':3600,'overlap':3600)
>>> 

wave_spectra(sea_level='sea_level', args={'detrend': {'Off': True, 'constante': False, 'linear': False}, 'display': {'Off': False, 'On': True}, 'folder out': '/home/docs/checkouts/readthedocs.org/user_builds/totodoc/checkouts/latest/docs', 'nfft': 3600, 'overlap': 1800, 'period (s) min and max for plotting': [10, 1000], 'units': 'm', 'windows': 3600, 'xaxis': {'frequency': False, 'period': True}})

This function estimated the 1D wave spectrum (Power Spectral Density) and plot it

Parameters

sea_levelstr

Name of the column which contains the sea level.

args: dict

Dictionnary with the folowing keys: units: str

Units of the sea level

windows: int

windows to process in seconds.

overlap: int

overlap in seconds

nfft: int

Length of the signal to calculate the Fourier transform of.

detrend: str

linear, constante or Off to detrend the timeseries before doing the analysis

period (s) min and max for plotting: list

X axis limit in seconds

xaxis: str

Can be period or frequency depending what on the type of plot

display: str

On or Off to display image

folder out: str

Path to save the output

Examples:

>>> df=tf['test1']['dataframe'].WaveAnalysis.wave_spectra(sea_level='ssh',args={'windows:3600,'nfft':3600,'overlap':3600)
>>> 

wavelet_analysis(sea_level='sea_level', args={'display': {'Off': False, 'On': True}, 'folder out': '/home/docs/checkouts/readthedocs.org/user_builds/totodoc/checkouts/latest/docs', 'mother wavelet': {'DOG': False, 'Morlet': True, 'Paul': False}, 'number of sub-ocatve per period band': 8, 'units': 'm', 'wave period range (min and max) (in s)': [3, 25]})

This function estimates the wavelet power spectrum of a time series, as well as the scaled-averaged wavelet power time series within a specific period band. The code is based on the wavelet toolbox from Torrence and Compo See https://paos.colorado.edu/research/wavelets/software.html

Parameters

sea_levelstr

Name of the column which contains the sea level.

args: dict

Dictionnary with the folowing keys: units: str

Units of the sea level

mother wavelet: str

Can be Morlet,`Paul` or DOG

wave period range (min and max) (in s): list

Calulating wave within this Wave period

number of sub-ocatve per period band: int

Number of sub-ocatve per period band

display: str

On or Off to display image

folder out: str

Path to save the output

Examples:

>>> df=tf['test1']['dataframe'].WaveAnalysis.wavelet_analysis(sea_level='ssh',args={})
>>>