1 files changed, 86 insertions, 0 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
new file mode 100644
index 0000000..414c553
--- /dev/null
+++ b/fw/hid-dials/tools/dsss_demod_test_waveform_gen.py
@@ -0,0 +1,86 @@
+
+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)
+