(/1/volume3Val) STRING(-6.9 dB
(/1/volume3) FLOAT(0.6257049)
(/1/volume3Val) STRING(-6.3 dB
(/1/volume3) FLOAT(0.65181774)
(/1/volume3Val) STRING(-5.4 dB
(/1/volume3) FLOAT(0.6605223)
(/1/volume3Val) STRING(-5.1 dB
(/1/volume3) FLOAT(0.67793137)
(/1/volume3Val) STRING(-4.6 dB
(/1/volume3) FLOAT(0.686636)
(/1/volume3Val) STRING(-4.3 dB
(/1/volume3) FLOAT(0.70404506)
(/1/volume3Val) STRING(-3.7 dB
(/1/volume3) FLOAT(0.7214542)
(/1/volume3Val) STRING(-3.1 dB
(/1/volume3) FLOAT(0.7388633)
(/1/volume3Val) STRING(-2.6 dB
(/1/volume3) FLOAT(0.764977)
(/1/volume3Val) STRING(-1.7 dB
(/1/volume3) FLOAT(0.78238606)
(/1/volume3Val) STRING(-1.1 dB
(/1/volume3) FLOAT(0.8172043)
(/1/volume3Val) STRING(0.0 dB)
(/1/volume3) FLOAT(0.843318)
(/1/volume3Val) STRING(0.9 dB)
(/1/volume3) FLOAT(0.8520225)
(/1/volume3Val) STRING(1.1 dB)
(/1/volume3) FLOAT(0.8781362)
(/1/volume3Val) STRING(2.0 dB)
(/1/volume3) FLOAT(0.9477727)
(/1/volume3Val) STRING(4.3 dB)
(/1/volume3) FLOAT(0.9564772)
(/1/volume3Val) STRING(4.6 dB)
(/1/volume3) FLOAT(0.98259085)
(/1/volume3Val) STRING(5.4 dB)
(/1/volume3) FLOAT(1)
(/1/volume3Val) STRING(6.0 dB)

Value    dB
0.008687576    -63.7
0.017375093    -62.4
0.026062608    -61.1
0.034750246    -59.8
0.04343788    -58.5
0.052125454    -57.3
0.06081315    -56.0
0.06950079    -54.8
0.07818848    -53.6
0.086876236    -52.4
0.09556393    -51.2
0.10425174    -50.1
0.1129395    -48.9
0.121627316    -47.8
0.13031518    -46.6
0.147691    -44.4
0.15637898    -43.4
0.16506693    -42.3
0.17375498    -41.2
0.18244304    -40.2
0.19113109    -39.2
0.19981919    -38.2
0.20850737    -37.2
0.2258839    -35.2
0.23457219    -34.3
0.24326053    -33.4
0.25194895    -32.4
0.26063743    -31.5
0.26932594    -30.6
0.27801454    -29.7
0.2867032    -28.9
0.29539183    -28.0
0.30408067    -27.2
0.3127695    -26.4
0.32145846    -25.6
0.33883652    -24.0
0.34752572    -23.2
0.35621497    -22.5
0.36490437    -21.7
0.37359387    -21.0
0.38228348    -20.3
0.39097318    -19.6
0.39966303    -18.9
0.40835303    -18.3
0.41704318    -17.6
0.42573345    -17.0
0.4344239    -16.4
0.44311455    -15.7
0.45180538    -15.2
0.46049643    -14.6
0.46918768    -14.0
0.4778792    -13.5
0.495263    -12.4
0.5039553    -11.9
0.512648    -11.4
0.5213411    -10.9
0.53003454    -10.5
0.5387284    -10.0
0.5474227    -9.6
0.5561176    -9.2
0.5648131    -8.7
0.5735092    -8.4
0.5822061    -8.0
0.5909037    -7.6
0.59960234    -7.3
0.60830194    -6.9
0.6170027    -6.6
0.6257049    -6.3
0.64311314    -5.7
0.6605223    -5.1
0.6692268    -4.9
0.67793137    -4.6
0.686636    -4.3
0.6953405    -4.0
0.70404506    -3.7
0.7214542    -3.1
0.73015875    -2.9
0.7388633    -2.6
0.7562724    -2.0
0.764977    -1.7
0.7736815    -1.4
0.78238606    -1.1
0.7910906    -0.9
0.7997952    -0.6
0.80849975    -0.3
0.8172043    0.0
0.82590884    0.3
0.83461344    0.6
0.843318    0.9
0.8520225    1.1
0.8694316    1.7
0.8781362    2.0
0.88684076    2.3
0.8955453    2.6
0.90424985    2.9
0.91295445    3.1
0.921659    3.4
0.93036354    3.7
0.9390681    4.0
0.9477727    4.3
0.9564772    4.6
0.96518177    4.9
0.9738863    5.1
0.98259085    5.4
0.99129546    5.7
1    6.0

0.0 => "-oo"
0.00327084 => "-64.5 dB"
0.0065550446 => "-64.0 dB"
0.0098527325 => "-63.5 dB"
0.013164144 => "-63.0 dB"
0.016489396 => "-62.5 dB"
0.01982873 => "-62.0 dB"
0.023182262 => "-61.5 dB"
0.026550174 => "-61.0 dB"
0.029932702 => "-60.5 dB"
0.033330027 => "-60.0 dB"
0.03674227 => "-59.5 dB"
0.040169723 => "-59.0 dB"
0.043612573 => "-58.5 dB"
0.04707099 => "-58.0 dB"
0.050545223 => "-57.5 dB"
0.054035444 => "-57.0 dB"
0.05754195 => "-56.5 dB"
0.06106487 => "-56.0 dB"
0.06460455 => "-55.5 dB"
0.068161175 => "-55.0 dB"
0.07173498 => "-54.5 dB"
0.07532627 => "-54.0 dB"
0.07893522 => "-53.5 dB"
0.08256213 => "-53.0 dB"
0.08620729 => "-52.5 dB"
0.08987096 => "-52.0 dB"
0.09355347 => "-51.5 dB"
0.09725507 => "-51.0 dB"
0.10097601 => "-50.5 dB"
0.104716696 => "-50.0 dB"
0.10847737 => "-49.5 dB"
0.11225845 => "-49.0 dB"
0.11606017 => "-48.5 dB"
0.11988289 => "-48.0 dB"
0.12372703 => "-47.5 dB"
0.12759289 => "-47.0 dB"
0.13148083 => "-46.5 dB"
0.13539125 => "-46.0 dB"
0.13932459 => "-45.5 dB"
0.14328125 => "-45.0 dB"
0.1472616 => "-44.5 dB"
0.15126605 => "-44.0 dB"
0.15529515 => "-43.5 dB"
0.1593493 => "-43.0 dB"
0.16342893 => "-42.5 dB"
0.16753459 => "-42.0 dB"
0.17166677 => "-41.5 dB"
0.175826 => "-41.0 dB"
0.18001278 => "-40.5 dB"
0.18422768 => "-40.0 dB"
0.18847126 => "-39.5 dB"
0.1927442 => "-39.0 dB"
0.19704697 => "-38.5 dB"
0.20138034 => "-38.0 dB"
0.20574492 => "-37.5 dB"
0.21014136 => "-37.0 dB"
0.21457043 => "-36.5 dB"
0.21903287 => "-36.0 dB"
0.22352935 => "-35.5 dB"
0.22806075 => "-35.0 dB"
0.2326279 => "-34.5 dB"
0.2372316 => "-34.0 dB"
0.24187277 => "-33.5 dB"
0.24655232 => "-33.0 dB"
0.2512713 => "-32.5 dB"
0.25603062 => "-32.0 dB"
0.2608314 => "-31.5 dB"
0.2656747 => "-31.0 dB"
0.27056175 => "-30.5 dB"
0.27549365 => "-30.0 dB"
0.28047168 => "-29.5 dB"
0.28549722 => "-29.0 dB"
0.2905716 => "-28.5 dB"
0.29569635 => "-28.0 dB"
0.3008729 => "-27.5 dB"
0.30610293 => "-27.0 dB"
0.31138808 => "-26.5 dB"
0.31673017 => "-26.0 dB"
0.32213104 => "-25.5 dB"
0.3275927 => "-25.0 dB"
0.33311725 => "-24.5 dB"
0.33870688 => "-24.0 dB"
0.344364 => "-23.5 dB"
0.3500911 => "-23.0 dB"
0.3558908 => "-22.5 dB"
0.361766 => "-22.0 dB"
0.36771968 => "-21.5 dB"
0.37375513 => "-21.0 dB"
0.37987575 => "-20.5 dB"
0.38608524 => "-20.0 dB"
0.39238766 => "-19.5 dB"
0.3987872 => "-19.0 dB"
0.40528864 => "-18.5 dB"
0.4118969 => "-18.0 dB"
0.41861746 => "-17.5 dB"
0.42545626 => "-17.0 dB"
0.43241978 => "-16.5 dB"
0.43951502 => "-16.0 dB"
0.4467498 => "-15.5 dB"
0.45413277 => "-15.0 dB"
0.4616733 => "-14.5 dB"
0.46938202 => "-14.0 dB"
0.47727063 => "-13.5 dB"
0.48535246 => "-13.0 dB"
0.49364233 => "-12.5 dB"
0.5021573 => "-12.0 dB"
0.5109166 => "-11.5 dB"
0.5199427 => "-11.0 dB"
0.5292615 => "-10.5 dB"
0.53890353 => "-10.0 dB"
0.54890496 => "-9.5 dB"
0.5593092 => "-9.0 dB"
0.5701692 => "-8.5 dB"
0.5815507 => "-8.0 dB"
0.5935371 => "-7.5 dB"
0.6062363 => "-7.0 dB"
0.619793 => "-6.5 dB"
0.63440865 => "-6.0 dB"
0.64964163 => "-5.5 dB"
0.6648746 => "-5.0 dB"
0.6801076 => "-4.5 dB"
0.6953405 => "-4.0 dB"
0.71057355 => "-3.5 dB"
0.72580653 => "-3.0 dB"
0.74103945 => "-2.5 dB"
0.7562725 => "-2.0 dB"
0.7715054 => "-1.5 dB"
0.7867384 => "-1.0 dB"
0.8019714 => "-0.5 dB"
0.81720436 => "0.0 dB"
0.83243734 => "0.5 dB"
0.8476703 => "1.0 dB"
0.8629033 => "1.5 dB"
0.8781362 => "2.0 dB"
0.89336926 => "2.5 dB"
0.90860224 => "3.0 dB"
0.92383516 => "3.5 dB"
0.9390682 => "4.0 dB"
0.9543011 => "4.5 dB"
0.9695341 => "5.0 dB"
0.98476714 => "5.5 dB"
1.0 => "6.0 dB"

import numpy as np
import scipy

x_bottom = np.array(
    [
        -65.0,
        -64.5,
...
        -6.5,
        -6.0,
    ]
)

x_top = np.array(
    [
        -6.0,
        -5.5,
...
        5.5,
        6.0,
    ]
)

y_bottom = np.array(
    [
        0.0,
        0.00327084,
...
        0.619793,
        0.63440865,
    ]
)

y_top = np.array(
    [
        0.63440865,
        0.64964163,
...
        0.98476714,
        1.0,
    ]
)

def linear_model(x, a, b):
    return m * x + b

def sqrt_model(x, a, b, c):
    return a - np.sqrt(b - c * x)

params, covariance = scipy.optimize.curve_fit(sqrt_model, x_bottom, y_bottom)
lparams, lcovariance = scipy.optimize.curve_fit(linear_model, x_top, y_top)

if db_float <= -65.0:
    estimate = 0.0
elif db_float <= -6.0:
    estimate = sqrt_model(
        db_float,
        a=0.8074290817975913,
        b=-0.03331873487226907,
        c=0.01054246851655579,
    )
else:
    estimate = linear_model(db_float, a=0.03046595035384614, b=0.8172043564)