From 69a5b8a286afb4ef46770eb79d8e1452eefa6e6e Mon Sep 17 00:00:00 2001
From: Chad <Chad.Page@gmail.com>
Date: Sat, 9 Nov 2024 10:44:54 -0800
Subject: [PATCH] Apply Novgorod's supergauss filter.  gets rid of the delay
 buffer flush in the he010 testdata

---
 lddecode/core.py  |  3 ++-
 lddecode/utils.py | 21 +++++++++++++++++++++
 2 files changed, 23 insertions(+), 1 deletion(-)

diff --git a/lddecode/core.py b/lddecode/core.py
index 1c92c6342..8e2991629 100644
--- a/lddecode/core.py
+++ b/lddecode/core.py
@@ -27,7 +27,7 @@
 from .utils import LRUupdate, clb_findbursts, angular_mean_helper, phase_distance
 from .utils import build_hilbert, unwrap_hilbert, emphasis_iir, filtfft
 from .utils import fft_do_slice, fft_determine_slices, StridedCollector, hz_to_output_array
-from .utils import Pulse, nb_std, nb_gt, n_ornotrange, nb_concatenate
+from .utils import Pulse, nb_std, nb_gt, n_ornotrange, nb_concatenate, gen_bpf_supergauss
 
 try:
     # If Anaconda's numpy is installed, mkl will use all threads for fft etc
@@ -446,6 +446,7 @@ def computeefmfilter(self):
         )
 
         self.Filters["Fefm"] = coeffs * 8
+        self.Filters["Fefm"] *= gen_bpf_supergauss(20000, 1600000, 60, 20000000, 32768)
 
     # Lambda-scale functions used to simplify following filter builders
 
diff --git a/lddecode/utils.py b/lddecode/utils.py
index 40bc3673b..70c2c85d3 100644
--- a/lddecode/utils.py
+++ b/lddecode/utils.py
@@ -691,6 +691,27 @@ def calczc(data, _start_offset, target, edge=0, count=10, reverse=False):
     return calczc_do(data, _start_offset, target, edge, count)
 
 
+# copied from vhs-decode
+
+def gen_bpf_supergauss(freq_low, freq_high, order, nyquist_hz, block_len):
+    sg = supergauss(
+        np.linspace(0, nyquist_hz, block_len // 2 + 1),
+        freq_high - freq_low,
+        order,
+        (freq_high + freq_low) / 2.0,
+    )[:-1]
+
+    return np.concatenate([sg, np.flip(sg)])
+
+def supergauss(x, freq, order=1, centerfreq=0):
+    return np.exp(
+        -2
+        * np.power(
+            (2 * (x - centerfreq) * (math.log(2.0) / 2.0) ** (1 / (2 * order))) / freq,
+            2 * order,
+        )
+    )
+
 # Shamelessly based on https://github.com/scipy/scipy/blob/v1.6.0/scipy/signal/signaltools.py#L2264-2267
 # ... and intended for real FFT, but seems fine with complex as well ;)
 def build_hilbert(fft_size):