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Add optional parameter frequency_dimension across wave.resource (MHKi…
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…T-Software#336)

* add frequency_dimension parameter across all necessary wave.resource functions

* specify frequency_dimension in surface_elevation summation
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@akeeste
akeeste authored Aug 19, 2024
1 parent e0d59c7 commit efcda0a
Showing 1 changed file with 134 additions and 50 deletions.
184 changes: 134 additions & 50 deletions mhkit/wave/resource.py
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Original file line number Diff line number Diff line change
Expand Up @@ -398,11 +398,8 @@ def surface_elevation(
)

# wave elevation
# eta = xr.DataArray(columns=S.columns, index=time_index)
# for mcol in eta.columns:
C = np.cos(B + phase)
# C = xr.DataArray(data=C, index=time_index, columns=omega.index)
eta[var] = (C * A).sum(axis=1)
eta[var] = (C * A).sum(dim=frequency_dimension)

if to_pandas:
eta = eta.to_dataframe()
Expand Down Expand Up @@ -475,14 +472,19 @@ def frequency_moment(S, N, frequency_bins=None, frequency_dimension="", to_panda
return m


def significant_wave_height(S, frequency_bins=None, to_pandas=True):
def significant_wave_height(
S, frequency_dimension="", frequency_bins=None, to_pandas=True
):
"""
Calculates wave height from spectra
Parameters
------------
S: pandas DataFrame, pandas Series, xarray DataArray, or xarray Dataset
Spectral density [m^2/Hz] indexed by frequency [Hz]
frequency_dimension: string (optional)
Name of the xarray dimension corresponding to frequency. If not supplied,
defaults to the first dimension. Does not affect pandas input.
frequency_bins: numpy array or pandas Series (optional)
Bin widths for frequency of S. Required for unevenly sized bins
to_pandas: bool (optional)
Expand All @@ -498,9 +500,13 @@ def significant_wave_height(S, frequency_bins=None, to_pandas=True):
raise TypeError(f"to_pandas must be of type bool. Got: {type(to_pandas)}")

# Eq 12 in IEC 62600-101
m0 = frequency_moment(S, 0, frequency_bins=frequency_bins, to_pandas=False).rename(
{"m0": "Hm0"}
)
m0 = frequency_moment(
S,
0,
frequency_bins=frequency_bins,
frequency_dimension=frequency_dimension,
to_pandas=False,
).rename({"m0": "Hm0"})
Hm0 = 4 * np.sqrt(m0)

if to_pandas:
Expand All @@ -509,14 +515,19 @@ def significant_wave_height(S, frequency_bins=None, to_pandas=True):
return Hm0


def average_zero_crossing_period(S, frequency_bins=None, to_pandas=True):
def average_zero_crossing_period(
S, frequency_dimension="", frequency_bins=None, to_pandas=True
):
"""
Calculates wave average zero crossing period from spectra
Parameters
------------
S: pandas DataFrame, pandas Series, xarray DataArray, or xarray Dataset
Spectral density [m^2/Hz] indexed by frequency [Hz]
frequency_dimension: string (optional)
Name of the xarray dimension corresponding to frequency. If not supplied,
defaults to the first dimension. Does not affect pandas input.
frequency_bins: numpy array or pandas Series (optional)
Bin widths for frequency of S. Required for unevenly sized bins
to_pandas: bool (optional)
Expand All @@ -532,12 +543,20 @@ def average_zero_crossing_period(S, frequency_bins=None, to_pandas=True):
raise TypeError(f"to_pandas must be of type bool. Got: {type(to_pandas)}")

# Eq 15 in IEC 62600-101
m0 = frequency_moment(S, 0, frequency_bins=frequency_bins, to_pandas=False).rename(
{"m0": "Tz"}
)
m2 = frequency_moment(S, 2, frequency_bins=frequency_bins, to_pandas=False).rename(
{"m2": "Tz"}
)
m0 = frequency_moment(
S,
0,
frequency_bins=frequency_bins,
frequency_dimension=frequency_dimension,
to_pandas=False,
).rename({"m0": "Tz"})
m2 = frequency_moment(
S,
2,
frequency_bins=frequency_bins,
frequency_dimension=frequency_dimension,
to_pandas=False,
).rename({"m2": "Tz"})

Tz = np.sqrt(m0 / m2)

Expand All @@ -547,14 +566,19 @@ def average_zero_crossing_period(S, frequency_bins=None, to_pandas=True):
return Tz


def average_crest_period(S, frequency_bins=None, to_pandas=True):
def average_crest_period(
S, frequency_dimension="", frequency_bins=None, to_pandas=True
):
"""
Calculates wave average crest period from spectra
Parameters
------------
S: pandas DataFrame, pandas Series, xarray DataArray, or xarray Dataset
Spectral density [m^2/Hz] indexed by frequency [Hz]
frequency_dimension: string (optional)
Name of the xarray dimension corresponding to frequency. If not supplied,
defaults to the first dimension. Does not affect pandas input.
frequency_bins: numpy array or pandas Series (optional)
Bin widths for frequency of S. Required for unevenly sized bins
to_pandas: bool (optional)
Expand All @@ -570,12 +594,20 @@ def average_crest_period(S, frequency_bins=None, to_pandas=True):
if not isinstance(to_pandas, bool):
raise TypeError(f"to_pandas must be of type bool. Got: {type(to_pandas)}")

m2 = frequency_moment(S, 2, frequency_bins=frequency_bins, to_pandas=False).rename(
{"m2": "Tavg"}
)
m4 = frequency_moment(S, 4, frequency_bins=frequency_bins, to_pandas=False).rename(
{"m4": "Tavg"}
)
m2 = frequency_moment(
S,
2,
frequency_bins=frequency_bins,
frequency_dimension=frequency_dimension,
to_pandas=False,
).rename({"m2": "Tavg"})
m4 = frequency_moment(
S,
4,
frequency_bins=frequency_bins,
frequency_dimension=frequency_dimension,
to_pandas=False,
).rename({"m4": "Tavg"})

Tavg = np.sqrt(m2 / m4)

Expand All @@ -585,14 +617,17 @@ def average_crest_period(S, frequency_bins=None, to_pandas=True):
return Tavg


def average_wave_period(S, frequency_bins=None, to_pandas=True):
def average_wave_period(S, frequency_dimension="", frequency_bins=None, to_pandas=True):
"""
Calculates mean wave period from spectra
Parameters
------------
S: pandas DataFrame, pandas Series, xarray DataArray, or xarray Dataset
Spectral density [m^2/Hz] indexed by frequency [Hz]
frequency_dimension: string (optional)
Name of the xarray dimension corresponding to frequency. If not supplied,
defaults to the first dimension. Does not affect pandas input.
frequency_bins: numpy array or pandas Series (optional)
Bin widths for frequency of S. Required for unevenly sized bins
to_pandas: bool (optional)
Expand All @@ -607,12 +642,20 @@ def average_wave_period(S, frequency_bins=None, to_pandas=True):
if not isinstance(to_pandas, bool):
raise TypeError(f"to_pandas must be of type bool. Got: {type(to_pandas)}")

m0 = frequency_moment(S, 0, frequency_bins=frequency_bins, to_pandas=False).rename(
{"m0": "Tm"}
)
m1 = frequency_moment(S, 1, frequency_bins=frequency_bins, to_pandas=False).rename(
{"m1": "Tm"}
)
m0 = frequency_moment(
S,
0,
frequency_bins=frequency_bins,
frequency_dimension=frequency_dimension,
to_pandas=False,
).rename({"m0": "Tm"})
m1 = frequency_moment(
S,
1,
frequency_bins=frequency_bins,
frequency_dimension=frequency_dimension,
to_pandas=False,
).rename({"m1": "Tm"})

Tm = np.sqrt(m0 / m1)

Expand Down Expand Up @@ -664,14 +707,17 @@ def peak_period(S, frequency_dimension="", to_pandas=True):
return Tp


def energy_period(S, frequency_bins=None, to_pandas=True):
def energy_period(S, frequency_dimension="", frequency_bins=None, to_pandas=True):
"""
Calculates wave energy period from spectra
Parameters
------------
S: pandas DataFrame, pandas Series, xarray DataArray, or xarray Dataset
Spectral density [m^2/Hz] indexed by frequency [Hz]
frequency_dimension: string (optional)
Name of the xarray dimension corresponding to frequency. If not supplied,
defaults to the first dimension. Does not affect pandas input.
frequency_bins: numpy array or pandas Series (optional)
Bin widths for frequency of S. Required for unevenly sized bins
to_pandas: bool (optional)
Expand All @@ -687,11 +733,19 @@ def energy_period(S, frequency_bins=None, to_pandas=True):
raise TypeError(f"to_pandas must be of type bool. Got: {type(to_pandas)}")

mn1 = frequency_moment(
S, -1, frequency_bins=frequency_bins, to_pandas=False
S,
-1,
frequency_bins=frequency_bins,
frequency_dimension=frequency_dimension,
to_pandas=False,
).rename({"m-1": "Te"})
m0 = frequency_moment(S, 0, frequency_bins=frequency_bins, to_pandas=False).rename(
{"m0": "Te"}
)
m0 = frequency_moment(
S,
0,
frequency_bins=frequency_bins,
frequency_dimension=frequency_dimension,
to_pandas=False,
).rename({"m0": "Te"})

# Eq 13 in IEC 62600-101
Te = mn1 / m0
Expand All @@ -702,14 +756,17 @@ def energy_period(S, frequency_bins=None, to_pandas=True):
return Te


def spectral_bandwidth(S, frequency_bins=None, to_pandas=True):
def spectral_bandwidth(S, frequency_dimension="", frequency_bins=None, to_pandas=True):
"""
Calculates bandwidth from spectra
Parameters
------------
S: pandas DataFrame, pandas Series, xarray DataArray, or xarray Dataset
Spectral density [m^2/Hz] indexed by frequency [Hz]
frequency_dimension: string (optional)
Name of the xarray dimension corresponding to frequency. If not supplied,
defaults to the first dimension. Does not affect pandas input.
frequency_bins: numpy array or pandas Series (optional)
Bin widths for frequency of S. Required for unevenly sized bins
to_pandas: bool (optional)
Expand All @@ -724,15 +781,27 @@ def spectral_bandwidth(S, frequency_bins=None, to_pandas=True):
if not isinstance(to_pandas, bool):
raise TypeError(f"to_pandas must be of type bool. Got: {type(to_pandas)}")

m2 = frequency_moment(S, 2, frequency_bins=frequency_bins, to_pandas=False).rename(
{"m2": "e"}
)
m0 = frequency_moment(S, 0, frequency_bins=frequency_bins, to_pandas=False).rename(
{"m0": "e"}
)
m4 = frequency_moment(S, 4, frequency_bins=frequency_bins, to_pandas=False).rename(
{"m4": "e"}
)
m2 = frequency_moment(
S,
2,
frequency_bins=frequency_bins,
frequency_dimension=frequency_dimension,
to_pandas=False,
).rename({"m2": "e"})
m0 = frequency_moment(
S,
0,
frequency_bins=frequency_bins,
frequency_dimension=frequency_dimension,
to_pandas=False,
).rename({"m0": "e"})
m4 = frequency_moment(
S,
4,
frequency_bins=frequency_bins,
frequency_dimension=frequency_dimension,
to_pandas=False,
).rename({"m4": "e"})

e = np.sqrt(1 - (m2**2) / (m0 / m4))

Expand All @@ -742,14 +811,17 @@ def spectral_bandwidth(S, frequency_bins=None, to_pandas=True):
return e


def spectral_width(S, frequency_bins=None, to_pandas=True):
def spectral_width(S, frequency_dimension="", frequency_bins=None, to_pandas=True):
"""
Calculates wave spectral width from spectra
Parameters
------------
S: pandas DataFrame, pandas Series, xarray DataArray, or xarray Dataset
Spectral density [m^2/Hz] indexed by frequency [Hz]
frequency_dimension: string (optional)
Name of the xarray dimension corresponding to frequency. If not supplied,
defaults to the first dimension. Does not affect pandas input.
frequency_bins: numpy array or pandas Series (optional)
Bin widths for frequency of S. Required for unevenly sized bins
to_pandas: bool (optional)
Expand All @@ -765,13 +837,25 @@ def spectral_width(S, frequency_bins=None, to_pandas=True):
raise TypeError(f"to_pandas must be of type bool. Got: {type(to_pandas)}")

mn2 = frequency_moment(
S, -2, frequency_bins=frequency_bins, to_pandas=False
S,
-2,
frequency_bins=frequency_bins,
frequency_dimension=frequency_dimension,
to_pandas=False,
).rename({"m-2": "v"})
m0 = frequency_moment(S, 0, frequency_bins=frequency_bins, to_pandas=False).rename(
{"m0": "v"}
)
m0 = frequency_moment(
S,
0,
frequency_bins=frequency_bins,
frequency_dimension=frequency_dimension,
to_pandas=False,
).rename({"m0": "v"})
mn1 = frequency_moment(
S, -1, frequency_bins=frequency_bins, to_pandas=False
S,
-1,
frequency_bins=frequency_bins,
frequency_dimension=frequency_dimension,
to_pandas=False,
).rename({"m-1": "v"})

# Eq 16 in IEC 62600-101
Expand Down

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