1 files changed, 0 insertions, 86 deletions
diff --git a/fw/hid-dials/tools/dsss_demod_test_waveform_gen.py b/fw/hid-dials/tools/dsss_demod_test_waveform_gen.py
deleted file mode 100644
index 414c553..0000000
--- a/fw/hid-dials/tools/dsss_demod_test_waveform_gen.py
+++ /dev/null
@@ -1,86 +0,0 @@
-
-from os import path
-import json
-import functools
-
-import numpy as np
-import numbers
-import math
-from scipy import signal as sig
-import scipy.fftpack
-
-sampling_rate = 10 # sp/s
-
-# From https://github.com/mubeta06/python/blob/master/signal_processing/sp/gold.py
-preferred_pairs = {5:[[2],[1,2,3]], 6:[[5],[1,4,5]], 7:[[4],[4,5,6]],
-                        8:[[1,2,3,6,7],[1,2,7]], 9:[[5],[3,5,6]], 
-                        10:[[2,5,9],[3,4,6,8,9]], 11:[[9],[3,6,9]]}
-
-def gen_gold(seq1, seq2):
-    gold = [seq1, seq2]
-    for shift in range(len(seq1)):
-        gold.append(seq1 ^ np.roll(seq2, -shift))
-    return gold
-
-def gold(n):
-    n = int(n)
-    if not n in preferred_pairs:
-        raise KeyError('preferred pairs for %s bits unknown' % str(n))
-    t0, t1 = preferred_pairs[n]
-    (seq0, _st0), (seq1, _st1) = sig.max_len_seq(n, taps=t0), sig.max_len_seq(n, taps=t1)
-    return gen_gold(seq0, seq1)
-
-def modulate(data, nbits=5):
-    # 0, 1 -> -1, 1
-    mask = np.array(gold(nbits))*2 - 1
-    
-    sel = mask[data>>1]
-    data_lsb_centered = ((data&1)*2 - 1)
-
-    signal = (np.multiply(sel, np.tile(data_lsb_centered, (2**nbits-1, 1)).T).flatten() + 1) // 2
-    return np.hstack([ np.zeros(len(mask)), signal, np.zeros(len(mask)) ])
-
-def load_noise_meas_params(capture_file):
-    with open(capture_file, 'rb') as f:
-        meas_data = np.copy(np.frombuffer(f.read(), dtype='float32'))
-        meas_data -= np.mean(meas_data)
-        return (meas_data,)
-
-def mains_noise_measured(seed, n, meas_data):
-    last_valid = len(meas_data) - n
-    st = np.random.RandomState(seed)
-    start = st.randint(last_valid)
-    return meas_data[start:start+n] + 50.00
-
-def load_noise_synth_params(specfile):
-    with open(specfile) as f:
-        d = json.load(f)
-        return {'spl_x': np.linspace(*d['x_spec']),
-                'spl_N': d['x_spec'][2],
-                'psd_spl': (d['t'], d['c'], d['k']) }
-
-def mains_noise_synthetic(seed, n, psd_spl, spl_N, spl_x):
-    st = np.random.RandomState(seed)
-    noise = st.normal(size=spl_N) * 2
-    spec = scipy.fftpack.fft(noise) **2
-
-    spec *= np.exp(scipy.interpolate.splev(spl_x, psd_spl))
-
-    spec **= 1/2
-    
-    renoise = scipy.fftpack.ifft(spec)
-    return renoise[10000:][:n] + 50.00
-
-@functools.lru_cache()
-def load_noise_gen(url):
-    schema, refpath = url.split('://')
-    if not path.isabs(refpath):
-        refpath = path.abspath(path.join(path.dirname(__file__), refpath))
-
-    if schema == 'meas':
-        return mains_noise_measured, load_noise_meas_params(refpath)
-    elif schema == 'synth':
-        return mains_noise_synthetic, load_noise_synth_params(refpath)
-    else:
-        raise ValueError('Invalid schema', schema)
-