tiskitpy.Compliance

class tiskitpy.Compliance(freqs, values, uncertainties, water_depth, noise_channel, gravity_corrected=False, coherence=None)

Bases: object

Seafloor compliance class

freqs

Frequencies (Hz)

Type:: numpy.ndarray

values

Normalized compliance values (1/Pa)

Type:: numpy.ndarray

uncertainties

Normalized compliance uncertainties (1/Pa)

Type:: numpy.ndarray

water_depth

water depth in meters

Type:: float

noise_channel

If a str, compliance comes from data and this is the channel on which noise was assumed to dominate

Type:: str or None

gravity_corrected

Has data-estimated compliance been corrected for gravitational attraction terms?

Type:: bool

Seafloor compliance data class

Parameters:

freqs (numpy.ndarray) – Frequencies (Hz)
values (numpy.ndarray) – Normalized compliance values at each frequency (1/Pa)
uncertainties (numpy.ndarray) – Normalized compliance uncertainties at each frequency (1/Pa)
water_depth (float) – water depth in meters
noise_channel (str) – (str or None): If a str, compliance comes from data and this is the channel on which noise was assumed to dominate
gravity_corrected (bool) – Has data-estimated compliance been corrected for gravitational attraction terms?
coherence (numpy.ndarray or None) – coherences at each frequency

static calc_compliance(wdepth, freq, model)

Return compliance of a 1D model

Parameters:

wdepth (float) – water depth (m)
freq (numpy.nparray) – frequencies (1/s)
model (EarthModel1D) – 1D earth model

static calc_norm_compliance(wdepth, freq, model)

Return normalized compliance of a 1D model

norm compliance == k(omega) * compliance

Parameters:

wdepth (float) – water depth (m)
freq (numpy.nparray) – frequencies (1/s)
model (EarthModel1D) – 1D earth model

>>> wdepth = 2000
>>> freqs = np.array([0.001, 0.003, 0.005, 0.01, 0.03])
>>> model = EarthModel1D([[1000, 3000, 3000, 1600],
...                      [1000, 3000, 4000, 2300],
...                      [1000, 3000, 5000, 2800],
...                      [3000, 3000, 7500, 4300],
...                      [3000, 3000, 8200, 4700]])
>>> np.set_printoptions(precision=1)
>>> calc_norm_compliance(wdepth, freqs, model)
array([-1.4e-11, -2.0e-11, -2.6e-11, -4.2e-11, -9.0e-11])

correct_gravity_terms(): NOT YET IMPLEMENTED

classmethod from_earth_model_1D(water_depth, freqs, earth_model, limit_freqs=True)

Create object with the compliance of a 1D earth model

Parameters:

water_depth (float) – water depth (m)
freqs (list or numpy.ndarray) – frequencies
earth_model (tiskitpy.compliance.EarthModel1D) – 1D earth model
limit_freqs (bool) – limit frequencies to below Compliance.max_freq()?

classmethod from_file(filename): NOT YET IMPLEMENTED

classmethod from_response_functions(rfs, wdepth, max_freq=None, z_str='*Z')

Extracts compliance from ResponseFunctions object

Parameters:

rfs (tiskitpy.ResponseFunctions) – z/p transfer function(s). The input_channel should be the pressure channel
wdepth (float) – water depth (m)
max_freq (float) – maximum frequency to save. If None, use sqrt(g/(2*pi*wdepth))
z_str (str) – channel_id to use for z channel (may include ‘*’ wildcard)

classmethod from_seafloor_synthetic(obj, max_freq=True)

Create object with the compliance of a SeafloorSynthetic object

Uses objs earth_model, IG_Pa_seafloor.freqs and water_depth attributes

Parameters:

obj (tiskitpy.SeafloorSynthetic) – the object
max_freq (bool or float) – limit frequencies to: True: below Compliance.max_freq() float: below the value False: no limit applied

static gravd(W, h)

Return linear ocean surface gravity wave wavenumbers

Parameters:

W (numpy.ndarray) – angular frequencies (rad/s)
h (float) – water depth (m)

Returns:

wavenumbers (rad/m)

Return type:

K (numpy.ndarray)

>>> f = np.array([0.0001, 0.001, 0.01, 0.1, 1, 10])
>>> K = gravd(2 * np.pi * f, 2000)
>>> wlen = 2 * np.pi * np.power(K, -1)
>>> np.set_printoptions(precision=1)
>>> print(f'{K=} rad/m')
K=array([4.5e-06, 4.5e-05, 5.2e-04, 4.0e-02, 4.0e+00, 4.0e+02]) rad/m
>>> print(f'{wlen=} m')
wlen=array([1.4e+06, 1.4e+05, 1.2e+04, 1.6e+02, 1.6e+00, 1.6e-02]) m
>>> print(f'c={f*wlen} m/s')
c=[140.  139.8 121.1  15.6   1.6   0.2] m/s

static max_freq(water_depth)

Return estimated maximum compliance frequency for the given water depth

About one wavelength, from Janiszewski et al. (2019?)

plot(errorbars=True, show=True, outfile=None)

Plot the compliance

Parameters:

errorbars (bool) – plot error bars
show (bool) – show on the screen
outfile (str) – save figure to this filename

Returns:

axis pair amplitude, phase

Return type:

axa, axp

static raydep(P, om, d, ro, vp2, vs2)

Propagator matrix solutionn for P-SV waves, minor vector method

Parameters:

P (float) – surface wave slowness (s/m)
om (float) – surface wave angular frequency (radians/sec)
d (numpy.ndarray) – layer thicknesses (meters?)
rho (numpy.ndarray) – layer densities (kg/m^3)
vp2 (numpy.ndarray) – layer P velocities squared (m/s)^2
vs2 (numpy.ndarray) – layer shear velocities squared (m/s)^2

Returns:

Parameters, each value is at layer top: v (numpy.ndarray): vertical velocity (m/s?) u (numpy.ndarray): horizontal velocity (m/s?) zz (numpy.ndarray): vertical stress (Pa?) zx (numpy.ndarray): horizontal stress (Pa?)

Return type:

(list)

Notes

d, rho, vp2 and vs2 have one value for each layer (top to bottom),: must be same length

(Normalized compliance = -k*v/(omega*sigzz) )

>>> P = 1/140    # Corresponds to 2000m depth, low freqs
>>> om = 2 * np.pi * 0.005
>>> d = np.array([1000, 1000, 1000, 3000, 3000])
>>> rho = np.array([3000, 3000, 3000, 3000, 3000])
>>> vp2 = np.array([3000**2, 4000**2, 5000**2, 7500**2, 8200**2])
>>> vs2 = np.array([1600**2, 2300**2, 2800**2, 4300**2, 4700**2])
>>> np.set_printoptions(precision=1)
>>> raydep(P, om, d, rho, vp2, vs2)
(array([1. , 0.7, 0.5, 0.4, 0.3]), array([ 1.8e-01,  6.0e-02,  1.2e-02,  5.2e-05, -5.8e-02]), array([-2.8e+08, -2.8e+08, -2.7e+08, -2.6e+08, -1.9e+08]), array([-0.0e+00,  3.1e+07,  5.4e+07,  7.6e+07,  1.0e+08]))

write(base_name, units='1/Pa', out_dir=None)

Save compliance to a CSV file

Parameters:

base_name (str) – base filename. “_{units}.csv” will be appended
units (str) – units in which to save compliance. One of ‘1/Pa’, ‘m/Pa’, ‘m/s/Pa’, ‘m/s^2/Pa’ (‘1/Pa’ is normalized compliance)
out_dir (str or Path) – output directory (None: save to working directory)

write_counts(base_name, z_response, p_response, out_dir=None)

Save compliance IN COUNTS to a CSV file

Only useful for creating compliance values for people who don’t know how to use inventories

Parameters:

base_name (str) – base filename. “_{units}.csv” will be appended
z_reponse (obspy.core.inventory.Response or None) – response of the z channel. If None, expects p_response to be None too and assumes compliance and uncertainty were already in counts/counts
p_reponse (obspy.core.inventory.Response or None) – response of the p channel
out_dir (str or Path) – output directory (None: save to working directory)