Processing EK60 Data to Extract Target Strength

Step 1: Fetch Configuration Files

We begin by importing the required libraries and specifying the paths for the dataset and pipeline configuration files. These files contain the necessary information for data processing.

from pathlib import Path
from echodataflow import echodataflow_start, glob_url

dataset_config = Path("./datastore.yaml").resolve()
pipeline_config = Path("./pipeline.yaml").resolve()

Step 1.1: Configuration Files

Familiarize yourself with the configuration options by exploring the documentation for:

• Pipeline Configuration: Learn about configuration settings for the pipeline by referring to Pipeline Configuration.

• Datastore Configuration: Understand the various configuration options related to data storage by reading Datastore Configuration.

These documents provide detailed information on the configurations used during the setup process.

Step 2: Getting Data

Next, we’ll use the glob_url function to retrieve a list of URLs matching a specific pattern. In this case, we’re targeting raw EK60 data files from the SH1707 survey.

all_files = glob_url("s3://ncei-wcsd-archive/data/raw/Bell_M._Shimada/SH1707/EK60/*.raw", {'anon':True})

c:\Users\soham\anaconda3\envs\echodataflow\lib\site-packages\fsspec\registry.py:273: UserWarning: Your installed version of s3fs is very old and known to cause
severe performance issues, see also https://github.com/dask/dask/issues/10276

To fix, you should specify a lower version bound on s3fs, or
update the current installation.

  warnings.warn(s3_msg)

Step 3: Preparing Files

We’ll now extract the file names from the URLs and create a file listing for the transect. This will help us organize and work with the data effectively.

files = []
for file in all_files:
    f = file.split(".r")[0]
    files.append(f.split("/")[-1])

transect = open('EK60_SH1707_Shimada.txt','w')
i = 0
for f in files:
    if i == 20:
        break
    transect.write(f+".raw\n")
    i = i + 1
transect.close()

Step 4: Processing with echodataflow

Now, we’re ready to kick off the data processing using echodataflow. We’ll provide the dataset and pipeline configurations, along with additional options.

options = {"storage_options_override": False}
data  = echodataflow_start(dataset_config=dataset_config, pipeline_config=pipeline_config, options=options)

C:\Users\soham\Desktop\Soham\Projects\echodataflow\echodataflow_books\EchodataflowLocalDemo\datastore.yaml
C:\Users\soham\Desktop\Soham\Projects\echodataflow\echodataflow_books\EchodataflowLocalDemo\pipeline.yaml
{'name': 'Bell_M._Shimada-SH1707-EK60', 'sonar_model': 'EK60', 'raw_regex': '(.*)-?D(?P<date>\\w{1,8})-T(?P<time>\\w{1,6})', 'args': {'urlpath': 's3://ncei-wcsd-archive/data/raw/{{ ship_name }}/{{ survey_name }}/{{ sonar_model }}/*.raw', 'parameters': {'ship_name': 'Bell_M._Shimada', 'survey_name': 'SH1707', 'sonar_model': 'EK60'}, 'storage_options': {'anon': True}, 'transect': {'file': './EK60_SH1707_Shimada.txt'}, 'default_transect_num': 2017, 'json_export': True}, 'output': {'urlpath': './echodataflow-output', 'retention': True, 'overwrite': True}}
{'active_recipe': 'target_strength', 'use_local_dask': True, 'n_workers': 5, 'pipeline': [{'recipe_name': 'target_strength', 'stages': [{'name': 'echodataflow_open_raw', 'module': 'echodataflow.stages.subflows.open_raw', 'options': {'save_raw_file': True, 'use_raw_offline': True, 'use_offline': True}}, {'name': 'echodataflow_compute_TS', 'module': 'echodataflow.stages.subflows.compute_TS', 'options': {'use_offline': True}}]}]}
<Client: 'tcp://127.0.0.1:64712' processes=5 threads=20, memory=15.63 GiB>
--------------------------------------------------

Executing stage :  name='echodataflow_open_raw' module='echodataflow.stages.subflows.open_raw' external_params=None options={'save_raw_file': True, 'use_raw_offline': True, 'use_offline': True} prefect_config=None
{'out_path': 'C:\\Users\\soham\\Desktop\\Soham\\Projects\\echodataflow\\echodataflow_books\\EchodataflowLocalDemo\\echodataflow-output\\echodataflow_open_raw\\2017\\Summer2017-D20170615-T190214.zarr', 'transect': 2017, 'file_name': 'Summer2017-D20170615-T190214.raw', 'error': False}
{'out_path': 'C:\\Users\\soham\\Desktop\\Soham\\Projects\\echodataflow\\echodataflow_books\\EchodataflowLocalDemo\\echodataflow-output\\echodataflow_open_raw\\2017\\Summer2017-D20170615-T190843.zarr', 'transect': 2017, 'file_name': 'Summer2017-D20170615-T190843.raw', 'error': False}
{'out_path': 'C:\\Users\\soham\\Desktop\\Soham\\Projects\\echodataflow\\echodataflow_books\\EchodataflowLocalDemo\\echodataflow-output\\echodataflow_open_raw\\2017\\Summer2017-D20170615-T212409.zarr', 'transect': 2017, 'file_name': 'Summer2017-D20170615-T212409.raw', 'error': False}
{'out_path': 'C:\\Users\\soham\\Desktop\\Soham\\Projects\\echodataflow\\echodataflow_books\\EchodataflowLocalDemo\\echodataflow-output\\echodataflow_open_raw\\2017\\Summer2017-D20170615-T212933.zarr', 'transect': 2017, 'file_name': 'Summer2017-D20170615-T212933.raw', 'error': False}
{'out_path': 'C:\\Users\\soham\\Desktop\\Soham\\Projects\\echodataflow\\echodataflow_books\\EchodataflowLocalDemo\\echodataflow-output\\echodataflow_open_raw\\2017\\Summer2017-D20170615-T213153.zarr', 'transect': 2017, 'file_name': 'Summer2017-D20170615-T213153.raw', 'error': False}
{'out_path': 'C:\\Users\\soham\\Desktop\\Soham\\Projects\\echodataflow\\echodataflow_books\\EchodataflowLocalDemo\\echodataflow-output\\echodataflow_open_raw\\2017\\Summer2017-D20170615-T214606.zarr', 'transect': 2017, 'file_name': 'Summer2017-D20170615-T214606.raw', 'error': False}
{'out_path': 'C:\\Users\\soham\\Desktop\\Soham\\Projects\\echodataflow\\echodataflow_books\\EchodataflowLocalDemo\\echodataflow-output\\echodataflow_open_raw\\2017\\Summer2017-D20170615-T214712.zarr', 'transect': 2017, 'file_name': 'Summer2017-D20170615-T214712.raw', 'error': False}
{'out_path': 'C:\\Users\\soham\\Desktop\\Soham\\Projects\\echodataflow\\echodataflow_books\\EchodataflowLocalDemo\\echodataflow-output\\echodataflow_open_raw\\2017\\Summer2017-D20170615-T214734.zarr', 'transect': 2017, 'file_name': 'Summer2017-D20170615-T214734.raw', 'error': False}
{'out_path': 'C:\\Users\\soham\\Desktop\\Soham\\Projects\\echodataflow\\echodataflow_books\\EchodataflowLocalDemo\\echodataflow-output\\echodataflow_open_raw\\2017\\Summer2017-D20170619-T225736.zarr', 'transect': 2017, 'file_name': 'Summer2017-D20170619-T225736.raw', 'error': False}
{'out_path': 'C:\\Users\\soham\\Desktop\\Soham\\Projects\\echodataflow\\echodataflow_books\\EchodataflowLocalDemo\\echodataflow-output\\echodataflow_open_raw\\2017\\Summer2017-D20170619-T231647.zarr', 'transect': 2017, 'file_name': 'Summer2017-D20170619-T231647.raw', 'error': False}
{'out_path': 'C:\\Users\\soham\\Desktop\\Soham\\Projects\\echodataflow\\echodataflow_books\\EchodataflowLocalDemo\\echodataflow-output\\echodataflow_open_raw\\2017\\Summer2017-D20170619-T233922.zarr', 'transect': 2017, 'file_name': 'Summer2017-D20170619-T233922.raw', 'error': False}
{'out_path': 'C:\\Users\\soham\\Desktop\\Soham\\Projects\\echodataflow\\echodataflow_books\\EchodataflowLocalDemo\\echodataflow-output\\echodataflow_open_raw\\2017\\Summer2017-D20170620-T002659.zarr', 'transect': 2017, 'file_name': 'Summer2017-D20170620-T002659.raw', 'error': False}
{'out_path': 'C:\\Users\\soham\\Desktop\\Soham\\Projects\\echodataflow\\echodataflow_books\\EchodataflowLocalDemo\\echodataflow-output\\echodataflow_open_raw\\2017\\Summer2017-D20170620-T005939.zarr', 'transect': 2017, 'file_name': 'Summer2017-D20170620-T005939.raw', 'error': False}
{'out_path': 'C:\\Users\\soham\\Desktop\\Soham\\Projects\\echodataflow\\echodataflow_books\\EchodataflowLocalDemo\\echodataflow-output\\echodataflow_open_raw\\2017\\Summer2017-D20170620-T011027.zarr', 'transect': 2017, 'file_name': 'Summer2017-D20170620-T011027.raw', 'error': False}
{'out_path': 'C:\\Users\\soham\\Desktop\\Soham\\Projects\\echodataflow\\echodataflow_books\\EchodataflowLocalDemo\\echodataflow-output\\echodataflow_open_raw\\2017\\Summer2017-D20170620-T014302.zarr', 'transect': 2017, 'file_name': 'Summer2017-D20170620-T014302.raw', 'error': False}
{'out_path': 'C:\\Users\\soham\\Desktop\\Soham\\Projects\\echodataflow\\echodataflow_books\\EchodataflowLocalDemo\\echodataflow-output\\echodataflow_open_raw\\2017\\Summer2017-D20170620-T021537.zarr', 'transect': 2017, 'file_name': 'Summer2017-D20170620-T021537.raw', 'error': False}
{'out_path': 'C:\\Users\\soham\\Desktop\\Soham\\Projects\\echodataflow\\echodataflow_books\\EchodataflowLocalDemo\\echodataflow-output\\echodataflow_open_raw\\2017\\Summer2017-D20170620-T024811.zarr', 'transect': 2017, 'file_name': 'Summer2017-D20170620-T024811.raw', 'error': False}
{'out_path': 'C:\\Users\\soham\\Desktop\\Soham\\Projects\\echodataflow\\echodataflow_books\\EchodataflowLocalDemo\\echodataflow-output\\echodataflow_open_raw\\2017\\Summer2017-D20170620-T032047.zarr', 'transect': 2017, 'file_name': 'Summer2017-D20170620-T032047.raw', 'error': False}
{'out_path': 'C:\\Users\\soham\\Desktop\\Soham\\Projects\\echodataflow\\echodataflow_books\\EchodataflowLocalDemo\\echodataflow-output\\echodataflow_open_raw\\2017\\Summer2017-D20170620-T043221.zarr', 'transect': 2017, 'file_name': 'Summer2017-D20170620-T043221.raw', 'error': False}
{'out_path': 'C:\\Users\\soham\\Desktop\\Soham\\Projects\\echodataflow\\echodataflow_books\\EchodataflowLocalDemo\\echodataflow-output\\echodataflow_open_raw\\2017\\Summer2017-D20170620-T061742.zarr', 'transect': 2017, 'file_name': 'Summer2017-D20170620-T061742.raw', 'error': False}

Completed stage name='echodataflow_open_raw' module='echodataflow.stages.subflows.open_raw' external_params=None options={'save_raw_file': True, 'use_raw_offline': True, 'use_offline': True} prefect_config=None
--------------------------------------------------
<Client: 'tcp://127.0.0.1:64712' processes=5 threads=20, memory=15.63 GiB>
--------------------------------------------------

Executing stage :  name='echodataflow_compute_TS' module='echodataflow.stages.subflows.compute_TS' external_params=None options={'use_offline': True} prefect_config=None
{'out_path': 'C:\\Users\\soham\\Desktop\\Soham\\Projects\\echodataflow\\echodataflow_books\\EchodataflowLocalDemo\\echodataflow-output\\echodataflow_compute_TS\\2017\\Summer2017-D20170615-T190214_TS.zarr', 'transect': '2017', 'file_name': 'Summer2017-D20170615-T190214_TS.zarr', 'error': False}
{'out_path': 'C:\\Users\\soham\\Desktop\\Soham\\Projects\\echodataflow\\echodataflow_books\\EchodataflowLocalDemo\\echodataflow-output\\echodataflow_compute_TS\\2017\\Summer2017-D20170615-T190843_TS.zarr', 'transect': '2017', 'file_name': 'Summer2017-D20170615-T190843_TS.zarr', 'error': False}
{'out_path': 'C:\\Users\\soham\\Desktop\\Soham\\Projects\\echodataflow\\echodataflow_books\\EchodataflowLocalDemo\\echodataflow-output\\echodataflow_compute_TS\\2017\\Summer2017-D20170615-T212409_TS.zarr', 'transect': '2017', 'file_name': 'Summer2017-D20170615-T212409_TS.zarr', 'error': False}
{'out_path': 'C:\\Users\\soham\\Desktop\\Soham\\Projects\\echodataflow\\echodataflow_books\\EchodataflowLocalDemo\\echodataflow-output\\echodataflow_compute_TS\\2017\\Summer2017-D20170615-T212933_TS.zarr', 'transect': '2017', 'file_name': 'Summer2017-D20170615-T212933_TS.zarr', 'error': False}
{'out_path': 'C:\\Users\\soham\\Desktop\\Soham\\Projects\\echodataflow\\echodataflow_books\\EchodataflowLocalDemo\\echodataflow-output\\echodataflow_compute_TS\\2017\\Summer2017-D20170615-T213153_TS.zarr', 'transect': '2017', 'file_name': 'Summer2017-D20170615-T213153_TS.zarr', 'error': False}
{'out_path': 'C:\\Users\\soham\\Desktop\\Soham\\Projects\\echodataflow\\echodataflow_books\\EchodataflowLocalDemo\\echodataflow-output\\echodataflow_compute_TS\\2017\\Summer2017-D20170615-T214606_TS.zarr', 'transect': '2017', 'file_name': 'Summer2017-D20170615-T214606_TS.zarr', 'error': False}
{'out_path': 'C:\\Users\\soham\\Desktop\\Soham\\Projects\\echodataflow\\echodataflow_books\\EchodataflowLocalDemo\\echodataflow-output\\echodataflow_compute_TS\\2017\\Summer2017-D20170615-T214712_TS.zarr', 'transect': '2017', 'file_name': 'Summer2017-D20170615-T214712_TS.zarr', 'error': False}
{'out_path': 'C:\\Users\\soham\\Desktop\\Soham\\Projects\\echodataflow\\echodataflow_books\\EchodataflowLocalDemo\\echodataflow-output\\echodataflow_compute_TS\\2017\\Summer2017-D20170615-T214734_TS.zarr', 'transect': '2017', 'file_name': 'Summer2017-D20170615-T214734_TS.zarr', 'error': False}
{'out_path': 'C:\\Users\\soham\\Desktop\\Soham\\Projects\\echodataflow\\echodataflow_books\\EchodataflowLocalDemo\\echodataflow-output\\echodataflow_compute_TS\\2017\\Summer2017-D20170619-T225736_TS.zarr', 'transect': '2017', 'file_name': 'Summer2017-D20170619-T225736_TS.zarr', 'error': False}
{'out_path': 'C:\\Users\\soham\\Desktop\\Soham\\Projects\\echodataflow\\echodataflow_books\\EchodataflowLocalDemo\\echodataflow-output\\echodataflow_compute_TS\\2017\\Summer2017-D20170619-T231647_TS.zarr', 'transect': '2017', 'file_name': 'Summer2017-D20170619-T231647_TS.zarr', 'error': False}
{'out_path': 'C:\\Users\\soham\\Desktop\\Soham\\Projects\\echodataflow\\echodataflow_books\\EchodataflowLocalDemo\\echodataflow-output\\echodataflow_compute_TS\\2017\\Summer2017-D20170619-T233922_TS.zarr', 'transect': '2017', 'file_name': 'Summer2017-D20170619-T233922_TS.zarr', 'error': False}
{'out_path': 'C:\\Users\\soham\\Desktop\\Soham\\Projects\\echodataflow\\echodataflow_books\\EchodataflowLocalDemo\\echodataflow-output\\echodataflow_compute_TS\\2017\\Summer2017-D20170620-T002659_TS.zarr', 'transect': '2017', 'file_name': 'Summer2017-D20170620-T002659_TS.zarr', 'error': False}
{'out_path': 'C:\\Users\\soham\\Desktop\\Soham\\Projects\\echodataflow\\echodataflow_books\\EchodataflowLocalDemo\\echodataflow-output\\echodataflow_compute_TS\\2017\\Summer2017-D20170620-T005939_TS.zarr', 'transect': '2017', 'file_name': 'Summer2017-D20170620-T005939_TS.zarr', 'error': False}
{'out_path': 'C:\\Users\\soham\\Desktop\\Soham\\Projects\\echodataflow\\echodataflow_books\\EchodataflowLocalDemo\\echodataflow-output\\echodataflow_compute_TS\\2017\\Summer2017-D20170620-T011027_TS.zarr', 'transect': '2017', 'file_name': 'Summer2017-D20170620-T011027_TS.zarr', 'error': False}
{'out_path': 'C:\\Users\\soham\\Desktop\\Soham\\Projects\\echodataflow\\echodataflow_books\\EchodataflowLocalDemo\\echodataflow-output\\echodataflow_compute_TS\\2017\\Summer2017-D20170620-T014302_TS.zarr', 'transect': '2017', 'file_name': 'Summer2017-D20170620-T014302_TS.zarr', 'error': False}
{'out_path': 'C:\\Users\\soham\\Desktop\\Soham\\Projects\\echodataflow\\echodataflow_books\\EchodataflowLocalDemo\\echodataflow-output\\echodataflow_compute_TS\\2017\\Summer2017-D20170620-T021537_TS.zarr', 'transect': '2017', 'file_name': 'Summer2017-D20170620-T021537_TS.zarr', 'error': False}
{'out_path': 'C:\\Users\\soham\\Desktop\\Soham\\Projects\\echodataflow\\echodataflow_books\\EchodataflowLocalDemo\\echodataflow-output\\echodataflow_compute_TS\\2017\\Summer2017-D20170620-T024811_TS.zarr', 'transect': '2017', 'file_name': 'Summer2017-D20170620-T024811_TS.zarr', 'error': False}
{'out_path': 'C:\\Users\\soham\\Desktop\\Soham\\Projects\\echodataflow\\echodataflow_books\\EchodataflowLocalDemo\\echodataflow-output\\echodataflow_compute_TS\\2017\\Summer2017-D20170620-T032047_TS.zarr', 'transect': '2017', 'file_name': 'Summer2017-D20170620-T032047_TS.zarr', 'error': False}
{'out_path': 'C:\\Users\\soham\\Desktop\\Soham\\Projects\\echodataflow\\echodataflow_books\\EchodataflowLocalDemo\\echodataflow-output\\echodataflow_compute_TS\\2017\\Summer2017-D20170620-T043221_TS.zarr', 'transect': '2017', 'file_name': 'Summer2017-D20170620-T043221_TS.zarr', 'error': False}
{'out_path': 'C:\\Users\\soham\\Desktop\\Soham\\Projects\\echodataflow\\echodataflow_books\\EchodataflowLocalDemo\\echodataflow-output\\echodataflow_compute_TS\\2017\\Summer2017-D20170620-T061742_TS.zarr', 'transect': '2017', 'file_name': 'Summer2017-D20170620-T061742_TS.zarr', 'error': False}

Completed stage name='echodataflow_compute_TS' module='echodataflow.stages.subflows.compute_TS' external_params=None options={'use_offline': True} prefect_config=None
--------------------------------------------------
Local Client has been closed

Step 5: Results

Finally, let’s take a look at the first entry from the processed data.

data[0][0]

<xarray.Dataset>
Dimensions:                        (channel: 3, ping_time: 19,
                                    range_sample: 3888, filenames: 1)
Coordinates:
  * channel                        (channel) <U37 'GPT  18 kHz 009072058c8d 1...
  * filenames                      (filenames) int64 0
  * ping_time                      (ping_time) datetime64[ns] 2017-06-15T19:0...
  * range_sample                   (range_sample) int64 0 1 2 ... 3885 3886 3887
Data variables: (12/16)
    TS                             (channel, ping_time, range_sample) float64 dask.array<chunksize=(2, 10, 1944), meta=np.ndarray>
    angle_offset_alongship         (channel) float64 dask.array<chunksize=(3,), meta=np.ndarray>
    angle_offset_athwartship       (channel) float64 dask.array<chunksize=(3,), meta=np.ndarray>
    angle_sensitivity_alongship    (channel) float64 dask.array<chunksize=(3,), meta=np.ndarray>
    angle_sensitivity_athwartship  (channel) float64 dask.array<chunksize=(3,), meta=np.ndarray>
    beamwidth_alongship            (channel) float64 dask.array<chunksize=(3,), meta=np.ndarray>
    ...                             ...
    gain_correction                (ping_time, channel) float64 dask.array<chunksize=(19, 3), meta=np.ndarray>
    sa_correction                  (ping_time, channel) float64 dask.array<chunksize=(19, 3), meta=np.ndarray>
    sound_absorption               (channel, ping_time) float64 dask.array<chunksize=(3, 19), meta=np.ndarray>
    sound_speed                    (channel, ping_time) float64 dask.array<chunksize=(3, 19), meta=np.ndarray>
    source_filenames               (filenames) <U126 dask.array<chunksize=(1,), meta=np.ndarray>
    water_level                    float64 ...
Attributes:
    processing_function:          calibrate.compute_TS
    processing_software_name:     echopype
    processing_software_version:  0.7.2.dev51+gb45c942
    processing_time:              2023-08-29T19:38:55Z

xarray.Dataset

• Dimensions:
  • channel: 3
  • ping_time: 19
  • range_sample: 3888
  • filenames: 1
• Coordinates: (4)
  • channel
    (channel)
    <U37
    'GPT 18 kHz 009072058c8d 1-1 ES...

    long_name :

    Vendor channel ID

    array(['GPT  18 kHz 009072058c8d 1-1 ES18-11',
           'GPT  38 kHz 009072058146 2-1 ES38B',
           'GPT 120 kHz 00907205a6d0 4-1 ES120-7C'], dtype='<U37')

  • filenames
    (filenames)
    int64
    0

    long_name :

    Index for data and metadata source filenames

    array([0])

  • ping_time
    (ping_time)
    datetime64[ns]
    2017-06-15T19:02:14.206856192 .....

    array(['2017-06-15T19:02:14.206856192', '2017-06-15T19:02:16.784002048',
           '2017-06-15T19:02:19.356149760', '2017-06-15T19:02:21.927298048',
           '2017-06-15T19:02:24.495442944', '2017-06-15T19:02:27.073590784',
           '2017-06-15T19:02:29.644739072', '2017-06-15T19:02:32.213883904',
           '2017-06-15T19:02:34.794033152', '2017-06-15T19:02:37.374179840',
           '2017-06-15T19:02:39.951328256', '2017-06-15T19:02:42.524474880',
           '2017-06-15T19:02:45.094622208', '2017-06-15T19:02:47.672770048',
           '2017-06-15T19:02:50.241916928', '2017-06-15T19:02:52.814064128',
           '2017-06-15T19:02:55.394345984', '2017-06-15T19:02:57.974493184',
           '2017-06-15T19:03:00.543640064'], dtype='datetime64[ns]')

  • range_sample
    (range_sample)
    int64
    0 1 2 3 4 ... 3884 3885 3886 3887

    long_name :

    Along-range sample number, base 0

    array([   0,    1,    2, ..., 3885, 3886, 3887])

• Data variables: (16)
  • TS
    (channel, ping_time, range_sample)
    float64
    dask.array<chunksize=(2, 10, 1944), meta=np.ndarray>

    actual_range :

    [-131.4, 65.9]

    long_name :

    Target strength (TS re 1 m^2)

    units :

    dB

    Array Chunk Bytes 1.69 MiB 303.75 kiB Shape (3, 19, 3888) (2, 10, 1944) Dask graph 8 chunks in 2 graph layers Data type float64 numpy.ndarray

  • angle_offset_alongship
    (channel)
    float64
    dask.array<chunksize=(3,), meta=np.ndarray>

    comment :

    Introduced in echopype for Simrad echosounders. The alongship angle corresponds to the minor angle in SONAR-netCDF4 vers 2.

    long_name :

    electrical alongship angle offset of the transducer

    Array Chunk Bytes 24 B 24 B Shape (3,) (3,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • angle_offset_athwartship
    (channel)
    float64
    dask.array<chunksize=(3,), meta=np.ndarray>

    comment :

    Introduced in echopype for Simrad echosounders. The athwartship angle corresponds to the major angle in SONAR-netCDF4 vers 2.

    long_name :

    electrical athwartship angle offset of the transducer

    Array Chunk Bytes 24 B 24 B Shape (3,) (3,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • angle_sensitivity_alongship
    (channel)
    float64
    dask.array<chunksize=(3,), meta=np.ndarray>

    comment :

    Introduced in echopype for Simrad echosounders. The alongship angle corresponds to the minor angle in SONAR-netCDF4 vers 2.

    long_name :

    alongship angle sensitivity of the transducer

    Array Chunk Bytes 24 B 24 B Shape (3,) (3,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • angle_sensitivity_athwartship
    (channel)
    float64
    dask.array<chunksize=(3,), meta=np.ndarray>

    comment :

    Introduced in echopype for Simrad echosounders. The athwartship angle corresponds to the major angle in SONAR-netCDF4 vers 2.

    long_name :

    athwartship angle sensitivity of the transducer

    Array Chunk Bytes 24 B 24 B Shape (3,) (3,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • beamwidth_alongship
    (channel)
    float64
    dask.array<chunksize=(3,), meta=np.ndarray>

    comment :

    Introduced in echopype for Simrad echosounders to avoid potential confusion with convention definitions. The alongship angle corresponds to the minor angle in SONAR-netCDF4 vers 2. The convention defines one-way transmit or receive beamwidth (beamwidth_receive_minor and beamwidth_transmit_minor), but Simrad echosounders record two-way beamwidth in the data.

    long_name :

    Half power two-way beam width along alongship axis of beam

    units :

    arc_degree

    valid_range :

    [0.0, 360.0]

    Array Chunk Bytes 24 B 24 B Shape (3,) (3,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • beamwidth_athwartship
    (channel)
    float64
    dask.array<chunksize=(3,), meta=np.ndarray>

    comment :

    Introduced in echopype for Simrad echosounders to avoid potential confusion with convention definitions. The athwartship angle corresponds to the major angle in SONAR-netCDF4 vers 2. The convention defines one-way transmit or receive beamwidth (beamwidth_receive_major and beamwidth_transmit_major), but Simrad echosounders record two-way beamwidth in the data.

    long_name :

    Half power two-way beam width along athwartship axis of beam

    units :

    arc_degree

    valid_range :

    [0.0, 360.0]

    Array Chunk Bytes 24 B 24 B Shape (3,) (3,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • echo_range
    (channel, ping_time, range_sample)
    float64
    dask.array<chunksize=(2, 10, 1944), meta=np.ndarray>

    long_name :

    Range distance

    units :

    m

    Array Chunk Bytes 1.69 MiB 303.75 kiB Shape (3, 19, 3888) (2, 10, 1944) Dask graph 8 chunks in 2 graph layers Data type float64 numpy.ndarray

  • equivalent_beam_angle
    (channel)
    float64
    dask.array<chunksize=(3,), meta=np.ndarray>

    long_name :

    Equivalent beam angle

    units :

    sr

    valid_range :

    [0.0, 12.566370614359172]

    Array Chunk Bytes 24 B 24 B Shape (3,) (3,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • frequency_nominal
    (channel)
    float64
    dask.array<chunksize=(3,), meta=np.ndarray>

    long_name :

    Transducer frequency

    standard_name :

    sound_frequency

    units :

    Hz

    valid_min :

    0.0

    Array Chunk Bytes 24 B 24 B Shape (3,) (3,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • gain_correction
    (ping_time, channel)
    float64
    dask.array<chunksize=(19, 3), meta=np.ndarray>

    Array Chunk Bytes 456 B 456 B Shape (19, 3) (19, 3) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • sa_correction
    (ping_time, channel)
    float64
    dask.array<chunksize=(19, 3), meta=np.ndarray>

    Array Chunk Bytes 456 B 456 B Shape (19, 3) (19, 3) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • sound_absorption
    (channel, ping_time)
    float64
    dask.array<chunksize=(3, 19), meta=np.ndarray>

    long_name :

    Indicative acoustic absorption

    units :

    dB/m

    valid_min :

    0.0

    Array Chunk Bytes 456 B 456 B Shape (3, 19) (3, 19) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • sound_speed
    (channel, ping_time)
    float64
    dask.array<chunksize=(3, 19), meta=np.ndarray>

    long_name :

    Indicative sound speed

    standard_name :

    speed_of_sound_in_sea_water

    units :

    m/s

    valid_min :

    0.0

    Array Chunk Bytes 456 B 456 B Shape (3, 19) (3, 19) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • source_filenames
    (filenames)
    <U126
    dask.array<chunksize=(1,), meta=np.ndarray>

    long_name :

    Source filenames

    Array Chunk Bytes 504 B 504 B Shape (1,) (1,) Dask graph 1 chunks in 2 graph layers Data type

  • water_level
    ()
    float64
    ...

    long_name :

    Distance from the platform coordinate system origin to the nominal water level along the z-axis

    units :

    m

    [1 values with dtype=float64]

• Indexes: (4)
  • channel
    PandasIndex

    PandasIndex(Index(['GPT  18 kHz 009072058c8d 1-1 ES18-11',
           'GPT  38 kHz 009072058146 2-1 ES38B',
           'GPT 120 kHz 00907205a6d0 4-1 ES120-7C'],
          dtype='object', name='channel'))

  • filenames
    PandasIndex

    PandasIndex(Int64Index([0], dtype='int64', name='filenames'))

  • ping_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2017-06-15 19:02:14.206856192',
                   '2017-06-15 19:02:16.784002048',
                   '2017-06-15 19:02:19.356149760',
                   '2017-06-15 19:02:21.927298048',
                   '2017-06-15 19:02:24.495442944',
                   '2017-06-15 19:02:27.073590784',
                   '2017-06-15 19:02:29.644739072',
                   '2017-06-15 19:02:32.213883904',
                   '2017-06-15 19:02:34.794033152',
                   '2017-06-15 19:02:37.374179840',
                   '2017-06-15 19:02:39.951328256',
                   '2017-06-15 19:02:42.524474880',
                   '2017-06-15 19:02:45.094622208',
                   '2017-06-15 19:02:47.672770048',
                   '2017-06-15 19:02:50.241916928',
                   '2017-06-15 19:02:52.814064128',
                   '2017-06-15 19:02:55.394345984',
                   '2017-06-15 19:02:57.974493184',
                   '2017-06-15 19:03:00.543640064'],
                  dtype='datetime64[ns]', name='ping_time', freq=None))

  • range_sample
    PandasIndex

    PandasIndex(Int64Index([   0,    1,    2,    3,    4,    5,    6,    7,    8,    9,
                ...
                3878, 3879, 3880, 3881, 3882, 3883, 3884, 3885, 3886, 3887],
               dtype='int64', name='range_sample', length=3888))

• Attributes: (4)

  processing_function :

  calibrate.compute_TS

  processing_software_name :

  echopype

  processing_software_version :

  0.7.2.dev51+gb45c942

  processing_time :

  2023-08-29T19:38:55Z

Congratulations! You’ve successfully processed EK60 data using echodataflow. This notebook provides a simplified overview, and you can explore the capabilities of echodataflow for more advanced processing tasks.

Feel free to modify the parameters, paths, and configurations as needed to adapt to your data and requirements.