Notebook Demo

Notebook Demo#

We can use Markdown to easily write and style text or make it look like code.
Using LaTeX equations can be done within $$.

Nearfield example#

For a calibration or integration of acoustic data, we should consider ranges at least 3 times (according to Medwin and Clay 1998) the nearfield range ($r_{nf}$): $$r_{nf} = \frac{\pi d_{t}^2}{4\lambda}$$ with wavelength $\lambda=\frac{c}{f}$ (m) and $d_t$ (m) is the largest distance across the active elements in a circular piston projector. This recommendataion is about one third more conservative than recommendations found in Simmonds and MacLennan 2005. With $\frac{\theta_{-3~dB}}{2}\simeq sin^{-1}(\frac{3.2}{k~d_t})$ with the wave number $k=\frac{2\pi}{\lambda}$.
Luckily we have already created the uwa package which computes the nearfield of a round transducer at a given frequency, ambient soundspeed and 3 dB beamwidth for us
First let’s check if the uwa package is working as we thing it should:

import uwa #import the uwa package

# create an acoustic wave object
# with an ambient soundspeed of 1470 m/s, 
# at 38 kHz with a 3 dB beamwidth of 7°
uw = uwa.AcousticWave(speed =1470, frequency=38e3, bw=7) 

#let's see all the results
uw.__dict__

{'frequency': 38000.0,
 'speed': 1470,
 'bw': 7,
 'wl': 0.038684210526315786,
 'k': 162.4224773284519,
 'ar': 0.32272199776928523,
 'rnf': 2.114527228518027}

This is looking good!
Let’s generate a range of values for various frequencies and beamwidths and put them all in a pandas DataFrame:

#import packages
import pandas as pd

#loop over frequencies between 0.1 and 100 kHz and beam widths [3,  20[ °
#NOTE: range in Python includes the starting value but excludes the stop values e.g. range(0,10) is 0,1,2,3,4,5,6,7,8,9
nfs = pd.DataFrame([
    uwa.AcousticWave(speed =1470, frequency=f * 100, bw=b).__dict__ 
    for f in range(1,1001) 
    for b in range(3,20)
])

#let's see the Dataframe
nfs

	frequency	speed	bw	wl	k	ar	rnf
0	100	1470	3	14.7000	0.427428	286.001578	4370.281434
1	100	1470	4	14.7000	0.427428	214.520243	2458.720194
2	100	1470	5	14.7000	0.427428	171.635802	1573.940508
3	100	1470	6	14.7000	0.427428	143.049809	1093.319537
4	100	1470	7	14.7000	0.427428	122.634359	803.520347
...	...	...	...	...	...	...	...
16995	100000	1470	15	0.0147	427.427572	0.057357	0.175773
16996	100000	1470	16	0.0147	427.427572	0.053794	0.154610
16997	100000	1470	17	0.0147	427.427572	0.050651	0.137070
16998	100000	1470	18	0.0147	427.427572	0.047858	0.122372
16999	100000	1470	19	0.0147	427.427572	0.045361	0.109933

17000 rows × 7 columns

Plotting#

We can plot the results interactively, such that we can zoom and get information when hoovering over some points.

import plotly.express as px
import plotly.graph_objects as go

fig = go.Figure()
fig.add_trace(
    go.Heatmap()
)
fig.add_trace(
    go.Contour(
        z=nfs.pivot(index='bw', columns='frequency', values='rnf'),
              x=nfs['frequency'].unique() / 1e3,
              y=nfs['bw'].unique(),
        contours=dict(
            coloring ='heatmap',#fill contours like a heatmap
            showlabels = True, # show labels on contours
            labelfont = dict( # label font properties
                size = 12,
                color = 'white',
            )),
        colorbar=dict(
            title=dict(
                text='Nearfield (m)', # colorbar title
                side='right',
                font=dict(
                    size=14,
                    family='Arial, sans-serif')
            )
        ),
        hovertemplate = #stylise the information received when hoovering
        "Nearfield: %{z:.2f} m <br>" +
        "Frequency: %{x} kHz<br>" +
        "3 dB beam width: %{y} °<br>" 
    )
)
fig.update_traces(colorscale="RdYlBu_r", zmin=1, zmax=10, name="Nearfield (m)")
fig.update_layout(yaxis=dict(title=dict( text="3 dB Beam width (°)" )  ),
                 xaxis=dict(title=dict( text="Frequency (kHz)" )  ))

fig

Notebook Demo

Contents

Notebook Demo#

Nearfield example#

Plotting#

Dead Zone example#

Echogram#