Sample

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&[DATE] &[TIME] - &[FILENAME]

Admittances for the various elements are simply the inverse of their impedances. The expressions for inductors and capacitors should be expressed in terms of the operating frequency for the circuit. Using the admittance matrix, it is possible to find the resonnant frequency for the network, and the general frequency response. Admittance formulae for capacitors and inductors are shown below

ω Y.L 1 j ω * L * / ≡

c 299792458 m s / * :

j 1 - sqrt :

ω Y.C j ω * C * ≡

MHz Hz 106^* :

μF 106 -^:

μH 106 -^:

Hz 1 :

Ω 1 :

ω A 01 Ω / * 01 Ω / * 01 Ω / * 1 j ω * 100 * μH * / j ω * 10 * μF * 01 Ω / * 01 Ω / * 1 j ω * 200 * μH * / 01 Ω / * 01 Ω / * 01 Ω / * 01 Ω / * 01 Ω / * 150 Ω * / 1 0.5 Ω * / 01 Ω / * 01 Ω / * 01 Ω / * 01 Ω / * 01 Ω / * 01 Ω / * 01 Ω / * 01 Ω / * 01 Ω / * 01 Ω / * 55 mat :

ω AS ω A ω A transpose + :

1 Hz * AS 00010000 i * - 1105 -^* i * 005000 i * - 0005000 i * - 0 0.02 210000 i * - 0 0.02 001105 -^* i * 020055 mat

i 11 Hz * A rows 1 - (range : k 11 Hz * A rows 1 - (range : 21 line

ω i k F ω AS - (i 2 + k 2 + el i k ≠ ω AS i 2 + col sum cases :

ω Y ω 00 F ω 01 F ω 02 F ω 03 F ω 10 F ω 11 F ω 12 F ω 13 F ω 20 F ω 21 F ω 22 F ω 23 F ω 30 F ω 31 F ω 32 F ω 33 F 44 mat :

sanity checked 1 Hz * Y 5000 i * - 5000 i * 005000 i * 2.02 5000 i * - 0.02 - 2 - 0 0.02 - 0.02 10000 i * - 002 - 021105 -^* i * + 44 mat

ω V ω Y 1 -^100041 mat * :

sanity checked 1 Hz * V 49.99999 0.0247 i * - 49.99999 0.0249 i * - 5108 -^* 10105 -^* i * + 49.99999 0.02515 i * - 41 mat

f.min 100 Hz * :

f.max 0.5 MHz * :

r f.max Hz / log10 f.min Hz / log10 - :

sanity checked r 3.699

n 050 range f n 1 + el f.min 10 0.02 r * n *^* eval : for m 1 f rows range G m el 2 π * f m el * V 4 el 2 π * f m el * V 1 el / eval : for j 1 G rows range Bode j el 20 G j el abs log10 * eval : for 31 line

data char size clr plot k 1 data rows range r3 k el char : r4 k el size : r5 k el clr : 31 line for data r3 r4 r5 augment 21 line :

Bode max 13.8

Bode f Bode augment : bode Bode o 5 black plot : 21 line

Bode 3577 20 3577 10 - 2 2 mat bode 3 1 sys

Bode 100 0.01 118.57 0.01 140.59 0.01 166.7 0.02 197.66 0.03 234.37 0.04 277.89 0.05 329.5 0.07 390.69 0.1 463.25 0.15 549.28 0.21 651.29 0.29 772.24 0.41 915.66 0.59 1085.7 0.83 1287.33 1.2 1526.41 1.73 1809.88 2.54 2146 3.8 2544.54 5.91 3017.09 9.75 3577.4 13.84 4241.77 6.16 5029.52 0.41 - 5963.56 5.37 - 252 mat

XY1 Bode 122 range 12 range el : XY2 Bode 2350 range 12 range el : 21 line

x S1 XY1 1 col XY1 2 col x ainterp 100 x ≤ (x 3577.4 ≤ (& S2(x) cases : x S2 XY2 1 col XY2 2 col x ainterp 3577.4 x ≤ (x 5105^* ≤ (& S1(x) cases : 21 line

x S1 x S2 3577.4 20 3577.4 5 - 2 2 mat 3 1 sys

bode_zed bode 1 col bode 2 col augment :

ZED GRAPH REGION [alyles]

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Bode bode_zed 2 1 sys

j 1 G rows range Φ j el 180 π / G j el arg * eval : for

phase f Φ augment : phase_zed phase : 21 line

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phase phase_zed 180 - 3 1 sys

more generally z Φase 180 π / z arg * 3601 z arg 0 ≥ 0 cases * - :

Type conversion error G Φase vectorize #

n 1 G rows range Φase n el 180 π / G n el arg * 3601 G n el arg 0 ≥ 0 cases * - eval : for

Φase 0.3243 - 0.3846 - 0.4562 - 0.5413 - 0.6424 - 0.7626 - 0.9059 - 1.0768 - 1.2812 - 1.5266 - 1.8228 - 2.1828 - 2.6248 - 3.1754 - 3.8756 - 4.7929 - 6.0476 - 7.879 - 10.8377 - 16.5122 - 31.6579 - 94.6632 - 150.7509 - 231 mat

LOG PLOT USING MAXIMA DRAW2D()

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bode_obj grid true ≡ logx true ≡ title Admittance and Phase ≡ xlabel Frequency (Hz) ≡ key Admittance (Y) ≡ xrange auto ≡ yrange 100 - 2021 sys ≡ xaxis true ≡ points_joined true ≡ Bode points yaxis_secondary true ≡ color red ≡ key Phase (deg) ≡ yrange_secondary 180 - 021 sys ≡ ytics_secondary 30 ≡ phase points key Phase (deg) ≡ 171 sys :

INVOKE DRAW2D() using Insert->Maxima->Draw2D()

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