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Tools: Web: FilterReview: fix nan in penultimate frequency

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Iampete1 2023-05-14 23:47:59 +01:00 committed by Andrew Tridgell
parent c1aa398977
commit 114f9b1311
1 changed files with 18 additions and 18 deletions
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36
Tools/autotest/web-firmware/Tools/FilterReview/FilterReview.js
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@ -16,21 +16,24 @@ function window_correction_factors(w) {
}
}
// Frequency bins for given fft length and sample period
function fft_freq(len, d) {
// Length of real half of fft of len points
function real_length(len) {
return math.floor(len / 2) + 1
}
// Frequency bins for given fft length and sample period (real only)
function rfft_freq(len, d) {
const real_len = real_length(len)
freq = []
for (var i=0;i<len;i++) {
freq[i] = i
if (i >= len/2) {
freq[i] -= len
}
freq[i] /= len * d
for (var i=0;i<real_len;i++) {
freq[i] = i / (len * d)
}
return freq
}
function run_fft(data, window_size, window_spacing, windowing_function, positive_index) {
function run_fft(data, window_size, window_spacing, windowing_function) {
const num_points = data.x.length
const real_len = real_length(window_size)
var fft_x = []
var fft_y = []
@ -47,10 +50,11 @@ function run_fft(data, window_size, window_spacing, windowing_function, positive
// normalize all points by the window size
var scale = []
scale[0] = 1 / window_size
for (var j=1;j<positive_index-1;j++) {
for (var j=1;j<real_len-1;j++) {
scale[j] = 2 / window_size
}
scale[positive_index] = 1 / window_size
scale[real_len-1] = 1 / window_size
const tf_scale = tf.tensor1d(scale)
const num_windows = math.floor((num_points-window_size)/window_spacing) + 1
for (var i=0;i<num_windows;i++) {
@ -70,7 +74,7 @@ function run_fft(data, window_size, window_spacing, windowing_function, positive
const p1 = p2.abs()
// Apply scale and convert back to js array
const result = tf.mul(p1, scale).arraySync()
const result = tf.mul(p1, tf_scale).arraySync()
fft_x[i] = result[0]
fft_y[i] = result[1]
@ -113,11 +117,7 @@ function run_batch_fft(data_set) {
const window_correction = window_correction_factors(windowing_function)
// Get bins
var bins = fft_freq(window_size, sample_time)
// discard negative spectrum
const positive_index = math.floor(window_size/2)
bins = bins.slice(0, positive_index)
var bins = rfft_freq(window_size, sample_time)
var x = []
var y = []
@ -126,7 +126,7 @@ function run_batch_fft(data_set) {
var time = []
for (let i=0;i<num_batch;i++) {
var ret = run_fft(data_set[i], window_size, window_spacing, windowing_function, positive_index)
var ret = run_fft(data_set[i], window_size, window_spacing, windowing_function)
time.push(...math.add(data_set[i].sample_time, math.dotMultiply(sample_time, ret.center)))
x.push(...ret.x)