Processing EK60 Data to compute MVBS#
Step 1: Setting Up 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, StorageType, 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 == 5:
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)
Checking Configuration
Configuration Check Completed
Checking Connection to Prefect Server
Starting the Pipeline
Dataset Configuration Loaded For This Run
--------------------------------------------------
{'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': 's3://echodataflow-workground/test-flow-9-1-24', 'retention': False, 'overwrite': True, 'storage_options': {'block_name': 'echodataflow-aws-credentials', 'type': 'AWS'}}}
Pipeline Configuration Loaded For This Run
--------------------------------------------------
{'active_recipe': 'standard', 'use_local_dask': True, 'n_workers': 3, 'pipeline': [{'recipe_name': 'standard', '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_combine_echodata', 'module': 'echodataflow.stages.subflows.combine_echodata', 'options': {'use_offline': True}}, {'name': 'echodataflow_compute_Sv', 'module': 'echodataflow.stages.subflows.compute_Sv', 'options': {'use_offline': True}}, {'name': 'echodataflow_compute_MVBS', 'module': 'echodataflow.stages.subflows.compute_MVBS', 'options': {'use_offline': True}, 'external_params': {'range_meter_bin': '20m', 'ping_time_bin': '20S'}}]}]}
Initiliazing Singleton Object
Reading Configurations
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's3://ncei-wcsd-archive/data/raw/Bell_M._Shimada/SH1707/EK60/Summer2017-D20170913-T173715.raw', 's3://ncei-wcsd-archive/data/raw/Bell_M._Shimada/SH1707/EK60/Summer2017-D20170913-T180733.raw', 's3://ncei-wcsd-archive/data/raw/Bell_M._Shimada/SH1707/EK60/Winter2017-D20170615-T002629.raw']
[{'instrument': 'EK60', 'file_path': 's3://ncei-wcsd-archive/data/raw/Bell_M._Shimada/SH1707/EK60/Summer2017-D20170615-T190214.raw', 'transect_num': 2017, 'month': 6, 'year': 2017, 'jday': 166, 'datetime': '2017-06-15T19:02:14'}, {'instrument': 'EK60', 'file_path': 's3://ncei-wcsd-archive/data/raw/Bell_M._Shimada/SH1707/EK60/Summer2017-D20170615-T190843.raw', 'transect_num': 2017, 'month': 6, 'year': 2017, 'jday': 166, 'datetime': '2017-06-15T19:08:43'}, {'instrument': 'EK60', 'file_path': 's3://ncei-wcsd-archive/data/raw/Bell_M._Shimada/SH1707/EK60/Summer2017-D20170615-T212409.raw', 'transect_num': 2017, 'month': 6, 'year': 2017, 'jday': 166, 'datetime': '2017-06-15T21:24:09'}, {'instrument': 'EK60', 'file_path': 's3://ncei-wcsd-archive/data/raw/Bell_M._Shimada/SH1707/EK60/Summer2017-D20170615-T212933.raw', 'transect_num': 2017, 'month': 6, 'year': 2017, 'jday': 166, 'datetime': '2017-06-15T21:29:33'}, {'instrument': 'EK60', 'file_path': 's3://ncei-wcsd-archive/data/raw/Bell_M._Shimada/SH1707/EK60/Summer2017-D20170615-T213153.raw', 'transect_num': 2017, 'month': 6, 'year': 2017, 'jday': 166, 'datetime': '2017-06-15T21:31:53'}]
Storing JSON Metadata
Out path is s3://echodataflow-workground/test-flow-9-1-24/json_metadata/Bell_M._Shimada-SH1707-EK60.json
<Client: 'tcp://127.0.0.1:60614' processes=3 threads=18, memory=15.63 GiB>
Scheduler at : tcp://127.0.0.1:60614
--------------------------------------------------
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
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
--------------------------------------------------
Storing JSON Metadata
Out path is s3://echodataflow-workground/test-flow-9-1-24/json_metadata/echodataflow_open_raw_output.json
<Client: 'tcp://127.0.0.1:60614' processes=3 threads=18, memory=15.63 GiB>
Scheduler at : tcp://127.0.0.1:60614
--------------------------------------------------
Executing stage : name='echodataflow_combine_echodata' module='echodataflow.stages.subflows.combine_echodata' external_params=None options={'use_offline': True} prefect_config=None
Completed stage name='echodataflow_combine_echodata' module='echodataflow.stages.subflows.combine_echodata' external_params=None options={'use_offline': True} prefect_config=None
--------------------------------------------------
Storing JSON Metadata
Out path is s3://echodataflow-workground/test-flow-9-1-24/json_metadata/echodataflow_combine_echodata_output.json
Cleaning : s3://echodataflow-workground/test-flow-9-1-24/echodataflow_open_raw
Cleanup complete
<Client: 'tcp://127.0.0.1:60614' processes=3 threads=18, memory=15.63 GiB>
Scheduler at : tcp://127.0.0.1:60614
--------------------------------------------------
Executing stage : name='echodataflow_compute_Sv' module='echodataflow.stages.subflows.compute_Sv' external_params=None options={'use_offline': True} prefect_config=None
Completed stage name='echodataflow_compute_Sv' module='echodataflow.stages.subflows.compute_Sv' external_params=None options={'use_offline': True} prefect_config=None
--------------------------------------------------
Storing JSON Metadata
Out path is s3://echodataflow-workground/test-flow-9-1-24/json_metadata/echodataflow_compute_Sv_output.json
Cleaning : s3://echodataflow-workground/test-flow-9-1-24/echodataflow_combine_echodata
s3://echodataflow-workground/test-flow-9-1-24/echodataflow_combine_echodata
Failed to cleanup s3://echodataflow-workground/test-flow-9-1-24/echodataflow_combine_echodata
<Client: 'tcp://127.0.0.1:60614' processes=3 threads=18, memory=15.63 GiB>
Scheduler at : tcp://127.0.0.1:60614
--------------------------------------------------
Executing stage : name='echodataflow_compute_MVBS' module='echodataflow.stages.subflows.compute_MVBS' external_params={'range_meter_bin': '20m', 'ping_time_bin': '20S'} options={'use_offline': True} prefect_config=None
Completed stage name='echodataflow_compute_MVBS' module='echodataflow.stages.subflows.compute_MVBS' external_params={'range_meter_bin': '20m', 'ping_time_bin': '20S'} options={'use_offline': True} prefect_config=None
--------------------------------------------------
Storing JSON Metadata
Out path is s3://echodataflow-workground/test-flow-9-1-24/json_metadata/echodataflow_compute_MVBS_output.json
Cleaning : s3://echodataflow-workground/test-flow-9-1-24/echodataflow_compute_Sv
s3://echodataflow-workground/test-flow-9-1-24/echodataflow_compute_Sv
Failed to cleanup s3://echodataflow-workground/test-flow-9-1-24/echodataflow_compute_Sv
Local Client has been closed
Step 5: Results#
Finally, let’s take a look at the first entry from the processed data.
data
[[<xarray.Dataset>
Dimensions: (channel: 3, ping_time: 456, echo_range: 38)
Coordinates:
* channel (channel) <U37 'GPT 18 kHz 009072058c8d 1-1 ES18-11' ...
* echo_range (echo_range) float64 0.0 20.0 40.0 ... 700.0 720.0 740.0
* ping_time (ping_time) datetime64[ns] 2017-06-15T19:02:00 ... 201...
Data variables:
Sv (channel, ping_time, echo_range) float64 dask.array<chunksize=(3, 456, 38), meta=np.ndarray>
frequency_nominal (channel) float64 dask.array<chunksize=(3,), meta=np.ndarray>
water_level float64 ...
Attributes:
processing_function: commongrid.compute_MVBS
processing_software_name: echopype
processing_software_version: 0.8.2
processing_time: 2024-01-09T21:08:09Z]]