1. Echogram and EchoStats: Plotting echogram and statistics from echo data generated by a moored echosounder

1.1. Introduction

1.1.1. Goals

• Illustrate how to connect work between echopype and echoshader.

• Illustrate examples for plotting echogram using HoloViz ecosystem.

• Illustrate a common workflow for plotting echogram using echoshader.

• Illustrate a common workflow for plotting statistics from echogram interactively using echoshader

1.1.2. Description

This notebook uses EK60 echosounder data from the U.S. Ocean Observatories Initiative (OOI). The raw data is converted, combined, calibrated using echopype. Calibrated MVBS data from echopype procession is stored in form of .nc files locally or cloudly. After loading Calibrated data, echoshader can load these files to plot echogram and statistics. This lazy load mechanism can avoid memory overflow resulted by processing all raw data at the same time.There are some examples in this jupyterbook to teach users how to get a echogram with panel control widgets. Echoshader leverages two of APIs: Reactive functions and Parameterized class. Users can also add their own widgets.

Data are from the OOI Oregon Offshore Cabled Shallow Profiler Mooring collected on August 1-31, 2017.This was the day before and of a solar eclipse, during which the reduced sunlight affected the regular diel vertical migration (DVM) patterns of marine life. This change was directly observed using the upward-looking echosounder mounted on this mooring platform that happened to be within the totality zone.

1.1.3. Outline

1. Establish file system connection and Process raw files with echopype

2. Load converted MVBS files from Google Drive, and plot echogram simply with HoloViz ecosystem.

3. Plot echogram and control widgets with echoshader.

4. Plot statistics from echogram interactively with echoshader

1.1.4. Warning

When Image dimension ping_time is not evenly sampled to relative tolerance of 0.001. There will be an error.

Use echogram.erase_error() to get rid of them.

1.1.5. Note

We encourage importing echopype as ep for consistency.

from pathlib import Path
import itertools as it
import datetime as dt
from dateutil import parser as dtparser

import fsspec
import gc
from urllib import request

import xarray as xr
import echopype as ep
from echopype import qc

import panel
import pandas
import param
import hvplot.xarray  # noqa
import holoviews as hv

from echoshader.echogram import Echogram
from echoshader.echostats import EchoStats, simple_hist

hv.extension("bokeh")

1.2. Establish file system connection and Process raw files with echopype

base_dpath = Path('./exports')
base_dpath.mkdir(exist_ok=True)

calibrated_dpath = (base_dpath / 'OOI_30_Days')
calibrated_dpath.mkdir(exist_ok=True)

fs = fsspec.filesystem('https')

ooi_raw_url = (
    "https://rawdata.oceanobservatories.org/files/"
    "CE04OSPS/PC01B/ZPLSCB102_10.33.10.143/2017/08"
)

start_datetime = dt.datetime(2017, 8, 1, 0, 0)
end_datetime = dt.datetime(2017, 8, 31, 23, 59)

desired_day_urls = [f"{ooi_raw_url}/{str(day).zfill(2)}" for day in range(start_datetime.day, end_datetime.day + 1)]

all_raw_file_urls = it.chain.from_iterable([fs.glob(f"{day_url}/*.raw") for day_url in desired_day_urls])

def in_range(raw_file: str, start: dt.datetime, end: dt.datetime) -> bool:
    """Check if file url is in datetime range"""
    file_name = Path(raw_file).name
    file_datetime = dtparser.parse(file_name, fuzzy=True)
    return file_datetime >= start and file_datetime <= end

desired_raw_file_urls = list(filter(
    lambda raw_file: in_range(
        raw_file, 
        start_datetime,
        end_datetime
    ), 
    all_raw_file_urls
))

print(f"There are {len(desired_raw_file_urls)} raw files within the specified datetime range.")

for desired_raw_file_url in desired_raw_file_urls:
    raw_fpath = Path(desired_raw_file_url)
    
    try_times = 0
    while True:
        try_times += 1
        
#         if try_times > 2:
#             print(f"Failed to process raw file {raw_fpath.name}: {e}.")
#             break
        
        try:
            # Access file directly from S3 to create a converted EchoData object in memory
            ed = ep.open_raw(f"{desired_raw_file_url}", sonar_model='ek60')

            # Use the EchoData object "ed" to generate calibrate Sv
            ds_Sv = ep.calibrate.compute_Sv(ed)
                        
            # Check and correct reversed ping_time to allow compute_MVBS,
            # which does not allow non-monotonic ping_time
            qc.coerce_increasing_time(ds_Sv)

            # Computed MVBS and save to netcdf
            ds_MVBS = ep.preprocess.compute_MVBS(
                ds_Sv,
                range_meter_bin=1,  # in meters
                ping_time_bin='1800s'  # in seconds
            )

            ds_MVBS.to_netcdf(calibrated_dpath / f"MVBS_{raw_fpath.stem}.nc")
            
            # release to avoid memory overflow
            del ed
            del ds_Sv
            del ds_MVBS
            
            gc.collect()

        except Exception as e:
            print(f"Failed to process raw file {raw_fpath.name}: {e}. Execute again")
            
            if try_times > 2:
                continue
            else:
                break
            
        break

1.3 Load converted MVBS files from Google Drive, and plot echogram simply with HoloViz ecosystem

# Calibrated data is stored in Google Drive
url = 'https://drive.google.com/uc?export=download&id=1zHSleP3Ox_NjKrZfZ_sVvkc5g9r4K7tc'

def urllib_download():
    request.urlretrieve(url, 'test_Echogram_EchoStats.nc')

urllib_download()

MVBS_ds = xr.open_mfdataset(
    paths = 'test_Echogram_EchoStats.nc', 
    data_vars = 'minimal', coords='minimal',
    combine = 'by_coords'
)

To visualize, invert the echo_range axis since the echosounder is upward-looking from a platform at approximately 200 m water depth.

MVBS_ds["echo_range"] = MVBS_ds["echo_range"].values[::-1]

HvPlot provides one API to explore data of many different types including pandas and xarray.

We can use hvplot.image to plot xarray type data (gridded data) directly.

MVBS_ds["Sv"].hvplot.image(
    x = 'ping_time', 
    y = 'echo_range', 
    color = 'Sv', 
    rasterize = True, 
    cmap = 'jet',
    clim=(-90, -30),
    xlabel = 'Time (UTC)',
    ylabel = 'Depth (m)',
    clabel = 'Sv(dB)'
).options(width = 800, invert_yaxis = True)

WARNING:param.Image00855: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.
WARNING:param.Image00855: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.

A simple hist is provided in echostats for holoview-made echogram.

But user should turn off rasterize mode.

gram = MVBS_ds["Sv"].sel(channel = 'GPT  38 kHz 00907208dd13 5-1 OOI.38|200').hvplot.image(
    x = 'ping_time', 
    y = 'echo_range', 
    color = 'Sv', 
    cmap = 'jet',
    clim=(-90, -30),
    xlabel = 'Time (UTC)',
    ylabel = 'Depth (m)',
    clabel = 'Sv(dB)'
).options(width = 800, invert_yaxis = True)

simple_hist(gram)

WARNING:param.Image01160: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.
WARNING:param.Image01160: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.
WARNING:param.Image01160: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.
WARNING:param.Image01160: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.

The image can automatically apply a groupby along a particular dimension(‘channel’ in this example), allowing us to explore the data as images along that dimension with a slider.

We also can use panel lib to create widgets to control the echogram.

We can supply both regular values, widgets and parameters as arguments to methods on the .interactive accessor in hvplot. Here, we can use widgets from the Panel library. The repr of the resulting object will contain a layout of the widget and a view of the resulting output.

MVBS_ds.Sv.interactive.sel(channel = panel.widgets.Select).hvplot(
        kind = 'image',
        x = 'ping_time', 
        y = 'echo_range', 
        color = 'Sv', 
        cmap = 'jet',
        clim=(-90, -30),
        xlabel = 'Time (UTC)',
        ylabel = 'Depth (m)',
        clabel = 'Sv(dB)'
        ).options(width=800, invert_yaxis=True)

WARNING:param.Image01450: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.
WARNING:param.Image01450: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.
WARNING:param.Image01525: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.
WARNING:param.Image01525: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.
WARNING:param.Image01525: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.
WARNING:param.Image01613: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.
WARNING:param.Image01613: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.
WARNING:param.Image01613: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.

Also, we can use four APIs provided from panel to link widgets and echogram.Each of these APIs has its own benefits and drawbacks. I recommend using Reactive functions and Parameterized class to accomplish our goals. See more details about them in APIs.

Here, we use panel.bind to declare that function’s input values should come from the widgets’ value parameters..It is a form of Reactive functions and requires the programmer to select and configure widgets explicitly and to lay out components explicitly, without relying on inference of widget types and ranges and without any default layouts.Once widgets have been bound to a reactive function, you can lay out the bound function and the widgets in any order or combination you like, including across Jupyter notebook cells if desired.

If users are writing code specifically for building an app, and do not wish to keep domain and GUI code separate, the functionality of pn.bind is also available as a decorator @pn.depends.

channel_select = panel.widgets.Select(
            name = 'Channel Select',
            options = MVBS_ds.channel.values.tolist())

lower_time = pandas.to_datetime(MVBS_ds.ping_time.data[0])

upper_time = pandas.to_datetime(MVBS_ds.ping_time.data[-1])

datetime_range_input = panel.widgets.DatetimeRangeInput(
            name = 'Datetime Range Input',
            start = lower_time,
            end = upper_time,
            value = (lower_time, upper_time))

def create_echogram(channel_select, datetime_range_input):
        
    lower_time = datetime_range_input[0]
    
    upper_time = datetime_range_input[-1]

    image = MVBS_ds.Sv.sel(
        channel = channel_select, 
        ping_time=slice(lower_time,upper_time)).hvplot(
            kind = 'image',
            x='ping_time', 
            y='echo_range', 
            color='Sv', 
            cmap='jet',
            clim=(-90, -30),
            xlabel='Time (UTC)',
            ylabel='Depth (m)'
            ).options(invert_yaxis=True)
    
    return image

bind_gram = panel.bind(create_echogram, channel_select = channel_select, datetime_range_input = datetime_range_input)

panel.Column(channel_select,datetime_range_input, bind_gram)

WARNING:param.Image01717: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.
WARNING:param.Image01717: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.
WARNING:param.Image01717: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.

1.4 Plot echogram and control widgets with echoshader

In echoshader, we combine Parameterized Classes and Reactive Functions APIs.

The Param library allows expressing the parameters of a class (or a hierarchy of classes) completely independently of a GUI implementation. Panel and other libraries can then take those parameter declarations and turn them into a GUI to control the parameters. This approach allows the parameters controlling some computation to be captured specifically and explicitly (but as abstract parameters, not as widgets).

Although it is a aim for Parameterized Classes that declaring way of expressing parameters and dependencies between parameters and computation and the resulting code is not tied to any particular GUI framework and can be used in other contexts as well. In echoshader, we don’t use param to get a widget, but still use panel.widgets. This allows us to get more powerful and flexible widgets with Parameterized Classes class structure. Values of panel.widgets rather than param are as input for @param.depends decorator.

echogram = Echogram(MVBS_ds)

Below shows how to get control widgets and echoram.

Notice: No add parentheses after ‘view_gram’ to accomplish links, although it is a function

panel.Row(echogram.widgets , echogram.view_gram) 

WARNING:param.Image01844: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.
WARNING:param.Image01844: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.

Below shows how to get MVBS data in select box.

echogram.get_box_data()

<xarray.Dataset>
Dimensions:            (echo_range: 205, ping_time: 1642)
Coordinates:
  * echo_range         (echo_range) float64 204.0 203.0 202.0 ... 2.0 1.0 0.0
  * ping_time          (ping_time) datetime64[ns] 2017-08-01 ... 2017-08-31T2...
    channel            <U39 'GPT  38 kHz 00907208dd13 5-1 OOI.38|200'
Data variables:
    Sv                 (ping_time, echo_range) float64 dask.array<chunksize=(1642, 205), meta=np.ndarray>
    frequency_nominal  float64 dask.array<chunksize=(), meta=np.ndarray>
Attributes:
    processing_software_name:     echopype
    processing_software_version:  0.6.0
    processing_time:              2022-08-19T22:36:57Z
    processing_function:          preprocess.compute_MVBS

xarray.Dataset

• Dimensions:
  • echo_range: 205
  • ping_time: 1642
• Coordinates: (3)
  • echo_range
    (echo_range)
    float64
    204.0 203.0 202.0 ... 2.0 1.0 0.0

    array([204., 203., 202., ...,   2.,   1.,   0.])

  • ping_time
    (ping_time)
    datetime64[ns]
    2017-08-01 ... 2017-08-31T23:30:00

    long_name :

    Ping time

    standard_name :

    time

    axis :

    T

    array(['2017-08-01T00:00:00.000000000', '2017-08-01T00:30:00.000000000',
           '2017-08-01T01:00:00.000000000', ..., '2017-08-31T23:00:00.000000000',
           '2017-08-31T23:00:00.000000000', '2017-08-31T23:30:00.000000000'],
          dtype='datetime64[ns]')

  • channel
    ()
    <U39
    'GPT 38 kHz 00907208dd13 5-1 OO...

    long_name :

    Vendor channel ID

    array('GPT  38 kHz 00907208dd13 5-1 OOI.38|200', dtype='<U39')

• Data variables: (2)
  • Sv
    (ping_time, echo_range)
    float64
    dask.array<chunksize=(1642, 205), meta=np.ndarray>

    long_name :

    Mean volume backscattering strength (MVBS, mean Sv re 1 m-1)

    units :

    dB

    actual_range :

    [-89.5 22.29]

    cell_methods :

    ping_time: mean (interval: 30 minute comment: ping_time is the interval start) echo_range: mean (interval: 1 meter comment: echo_range is the interval start)

    binning_mode :

    physical units

    range_meter_interval :

    1m

    ping_time_interval :

    1800s

    Array Chunk Bytes 2.57 MiB 2.57 MiB Shape (1642, 205) (1642, 205) Count 3 Graph Layers 1 Chunks Type float64 numpy.ndarray

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

    units :

    Hz

    long_name :

    Transducer frequency

    valid_min :

    0.0

    standard_name :

    sound_frequency

    Array Chunk Bytes 8 B 8 B Shape () () Count 3 Graph Layers 1 Chunks Type float64 numpy.ndarray

• Attributes: (4)

  processing_software_name :

  echopype

  processing_software_version :

  0.6.0

  processing_time :

  2022-08-19T22:36:57Z

  processing_function :

  preprocess.compute_MVBS

Below shows how to get echograms with all frequencies at the same time.

panel.Row(echogram.widgets , echogram.view_all_gram)

WARNING:param.Image02349: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.
WARNING:param.Image02349: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.
WARNING:param.Image02413: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.
WARNING:param.Image02413: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.
WARNING:param.Image02479: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.
WARNING:param.Image02479: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.

Below shows how to get MVBS data with all frequencies in select box.

echogram.get_all_box_data()

<xarray.Dataset>
Dimensions:            (echo_range: 205, ping_time: 1642, channel: 3)
Coordinates:
  * echo_range         (echo_range) float64 204.0 203.0 202.0 ... 2.0 1.0 0.0
  * ping_time          (ping_time) datetime64[ns] 2017-08-01 ... 2017-08-31T2...
  * channel            (channel) object 'GPT  38 kHz 00907208dd13 5-1 OOI.38|...
Data variables:
    Sv                 (channel, ping_time, echo_range) float64 dask.array<chunksize=(3, 1642, 205), meta=np.ndarray>
    frequency_nominal  (channel) float64 dask.array<chunksize=(3,), meta=np.ndarray>
Attributes:
    processing_software_name:     echopype
    processing_software_version:  0.6.0
    processing_time:              2022-08-19T22:36:57Z
    processing_function:          preprocess.compute_MVBS

xarray.Dataset

• Dimensions:
  • echo_range: 205
  • ping_time: 1642
  • channel: 3
• Coordinates: (3)
  • echo_range
    (echo_range)
    float64
    204.0 203.0 202.0 ... 2.0 1.0 0.0

    array([204., 203., 202., ...,   2.,   1.,   0.])

  • ping_time
    (ping_time)
    datetime64[ns]
    2017-08-01 ... 2017-08-31T23:30:00

    long_name :

    Ping time

    standard_name :

    time

    axis :

    T

    array(['2017-08-01T00:00:00.000000000', '2017-08-01T00:30:00.000000000',
           '2017-08-01T01:00:00.000000000', ..., '2017-08-31T23:00:00.000000000',
           '2017-08-31T23:00:00.000000000', '2017-08-31T23:30:00.000000000'],
          dtype='datetime64[ns]')

  • channel
    (channel)
    object
    'GPT 38 kHz 00907208dd13 5-1 OO...

    long_name :

    Vendor channel ID

    array(['GPT  38 kHz 00907208dd13 5-1 OOI.38|200',
           'GPT 120 kHz 00907208a0b1 3-1 ES120-7CD',
           'GPT 200 kHz 00907208dd13 5-2 OOI38|200'], dtype=object)

• Data variables: (2)
  • Sv
    (channel, ping_time, echo_range)
    float64
    dask.array<chunksize=(3, 1642, 205), meta=np.ndarray>

    long_name :

    Mean volume backscattering strength (MVBS, mean Sv re 1 m-1)

    units :

    dB

    actual_range :

    [-89.5 22.29]

    cell_methods :

    ping_time: mean (interval: 30 minute comment: ping_time is the interval start) echo_range: mean (interval: 1 meter comment: echo_range is the interval start)

    binning_mode :

    physical units

    range_meter_interval :

    1m

    ping_time_interval :

    1800s

    Array Chunk Bytes 7.70 MiB 7.70 MiB Shape (3, 1642, 205) (3, 1642, 205) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

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

    units :

    Hz

    long_name :

    Transducer frequency

    valid_min :

    0.0

    standard_name :

    sound_frequency

    Array Chunk Bytes 24 B 24 B Shape (3,) (3,) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

• Attributes: (4)

  processing_software_name :

  echopype

  processing_software_version :

  0.6.0

  processing_time :

  2022-08-19T22:36:57Z

  processing_function :

  preprocess.compute_MVBS

To do more operations about layouts and customization,Use echogram.Echogram.__doc__ to look for relevant attributes

1.5 Plot statistics from echogram interactively with echoshader

Users can just process data from select box.

Also, plotting statistics directly is provided in echoshader.

There using EchoStats, wchich is a subclass of Echogram.

echostats = EchoStats(MVBS_ds)

Below shows how to show a table describing stats info about selsect box Sv data with a specific frequency.

panel.Row(echostats.widgets , panel.Column(echostats.view_gram, echostats.view_table))

WARNING:param.Image04012: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.
WARNING:param.Image04012: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.

Below shows how to show a histogram describing stats info about selsect box Sv data with a specific frequency.

panel.Row(echostats.widgets , panel.Column(echostats.view_gram, echostats.view_hist))

WARNING:param.Image04556: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.
WARNING:param.Image04556: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.

Below shows how to show a table describing stats info about selsect box Sv data with all frequencies.

panel.Row(echostats.widgets , panel.Column(echostats.view_all_gram, echostats.view_all_table))

WARNING:param.Image05190: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.
WARNING:param.Image05190: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.
WARNING:param.Image05254: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.
WARNING:param.Image05254: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.
WARNING:param.Image05320: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.
WARNING:param.Image05320: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.

Below shows how to show a overlay hist stats info about selsect box Sv data with all frequencies.

A Overlay is A collection of HoloViews objects to be displayed overlaid on one another with the same axes.

panel.Row(echostats.widgets , panel.Column(echostats.view_all_gram, echostats.view_overlay_hist))

WARNING:param.Image06857: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.
WARNING:param.Image06857: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.
WARNING:param.Image06921: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.
WARNING:param.Image06921: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.
WARNING:param.Image06987: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.
WARNING:param.Image06987: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.

Below shows how to show a layout hist stats info about selsect box Sv data with all frequencies.

A Layout is a collection of any HoloViews objects to be displayed side by side.

panel.Row(echostats.widgets , panel.Column(echostats.view_all_gram, echostats.view_layout_hist))

WARNING:param.Image08706: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.
WARNING:param.Image08706: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.
WARNING:param.Image08770: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.
WARNING:param.Image08770: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.
WARNING:param.Image08836: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.
WARNING:param.Image08836: Image dimension ping_time is  not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.