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Optimize get vend cal params power #1285

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merged 4 commits into from
Mar 26, 2024
Merged

Optimize get vend cal params power #1285

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6980fbf
Optimize get vend cal params power (#2)
anujsinha3 Mar 20, 2024
a0823ca
test: update test_get_interp_da() to test on 200 time_coordinates (#3)
anujsinha3 Mar 20, 2024
e2f9687
test: refactor test data (#4)
anujsinha3 Mar 25, 2024
d3328fb
test: refactor test data (#5)
anujsinha3 Mar 25, 2024
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14 changes: 7 additions & 7 deletions echopype/calibrate/cal_params.py
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Original file line number Diff line number Diff line change
Expand Up @@ -286,7 +286,7 @@ def get_vend_cal_params_power(beam: xr.Dataset, vend: xr.Dataset, param: str) ->
raise ValueError(f"{param} does not exist in the Vendor_specific group!")

# Find idx to select the corresponding param value
# by matching beam["transmit_duration_nominal"] with ds_vend["pulse_length"]
# by matching beam["transmit_duration_nominal"] with vend["pulse_length"]
transmit_isnull = beam["transmit_duration_nominal"].isnull()
idxmin = np.abs(beam["transmit_duration_nominal"] - vend["pulse_length"]).idxmin(
dim="pulse_length_bin"
Expand All @@ -297,13 +297,13 @@ def get_vend_cal_params_power(beam: xr.Dataset, vend: xr.Dataset, param: str) ->
idxmin = idxmin.where(~transmit_isnull, 0).astype(int)

# Get param dataarray into correct shape
da_param = (
vend[param]
.expand_dims(dim={"ping_time": idxmin["ping_time"]}) # expand dims for direct indexing
.sortby(idxmin.channel) # sortby in case channel sequence differs in vend and beam
)
da_param = vend[param].transpose("pulse_length_bin", "channel")

if not np.array_equal(da_param.channel.data, idxmin.channel.data):
da_param = da_param.sortby(
da_param.channel, ascending=False
) # sortby because channel sequence differs in vend and beam

# Select corresponding index and clean up the original nan elements
da_param = da_param.sel(pulse_length_bin=idxmin, drop=True)

# Set the nan elements back to nan.
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