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Bug/transient attenuation mask type #93

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merged 2 commits into from
Oct 16, 2023
Merged

Bug/transient attenuation mask type #93

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aa96228
Refactor _fielding function for consistent return type
simedroniraluca Oct 16, 2023
aee2f2f
Refactor signal attenuation functions for consistent return type
simedroniraluca Oct 16, 2023
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4 changes: 3 additions & 1 deletion echopype/clean/signal_attenuation.py
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Original file line number Diff line number Diff line change
@@ -1,3 +1,5 @@
import warnings

import numpy as np
import xarray as xr
from skimage.measure import label
Expand Down Expand Up @@ -65,9 +67,9 @@ def _ryan(source_Sv: xr.DataArray, desired_channel: str, parameters=DEFAULT_RYAN

# return empty mask if searching range is outside the echosounder range
if (r0 > r[-1]) or (r1 < r[0]):
warnings.warn("Searching range is outside the echosounder range. Returning empty mask.")
mask = np.zeros_like(Sv, dtype=bool)
mask_ = np.zeros_like(Sv, dtype=bool)
return mask, mask_

# turn layer boundaries into arrays with length = Sv.shape[1]
r0 = np.ones(Sv.shape[1]) * r0
Expand Down
14 changes: 13 additions & 1 deletion echopype/clean/transient_noise.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,6 +14,7 @@
"Mihai Boldeanu", # adapted the mask transient noise algorithms to echopype
]

import warnings

import numpy as np
import xarray as xr
Expand Down Expand Up @@ -189,9 +190,20 @@ def _fielding(

# return empty mask if searching range is outside the echosounder range
if (r0 > r[-1]) or (r1 < r[0]):
# Raise a warning to inform the user
warnings.warn(
"The searching range is outside the echosounder range. "
"A default mask with all False values is returned, "
"which won't mask any data points in the dataset."
)
mask = np.zeros_like(Sv, dtype=bool)
mask_ = np.zeros_like(Sv, dtype=bool)
return mask, mask_
combined_mask = mask
return xr.DataArray(
combined_mask,
dims=("ping_time", "range_sample"),
coords={"ping_time": source_Sv.ping_time, "range_sample": source_Sv.range_sample},
)

# get upper and lower range indexes
up = np.argmin(abs(r - r0))
Expand Down