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-rw-r--r--analyze_dab_nonlinearity.ipynb451
-rw-r--r--src/dab_util.py39
-rw-r--r--src/dab_util_test.py36
-rw-r--r--src/gen_source.py6
4 files changed, 71 insertions, 461 deletions
diff --git a/analyze_dab_nonlinearity.ipynb b/analyze_dab_nonlinearity.ipynb
deleted file mode 100644
index 0bd874a..0000000
--- a/analyze_dab_nonlinearity.ipynb
+++ /dev/null
@@ -1,451 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "deletable": true,
- "editable": true
- },
- "outputs": [],
- "source": [
- "%matplotlib inline\n",
- "import matplotlib.pyplot as plt\n",
- "import numpy as np\n",
- "import src.dab_util as du"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
- "source": [
- "# Align Original / Recored Files\n",
- "\n",
- "Using Txgain 81"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "deletable": true,
- "editable": true
- },
- "outputs": [],
- "source": [
- "#path = \"../test_dat/out.iq\"\n",
- "path = \"./recored.dat\"\n",
- "frame = 96*8192\n",
- "offset = 5 * frame + 8192\n",
- "length = 2 * frame"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "deletable": true,
- "editable": true
- },
- "outputs": [],
- "source": [
- "sig_rec = np.fromfile(path, dtype=np.complex64, count=offset + length)[offset:]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "deletable": true,
- "editable": true
- },
- "outputs": [],
- "source": [
- "#sig_rec = sig_rec[30370+0*frame:30370+1*frame]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "deletable": true,
- "editable": true
- },
- "outputs": [],
- "source": [
- "i = 0\n",
- "l = length\n",
- "plt.plot(np.abs(sig_rec[i:i + l]))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "deletable": true,
- "editable": true
- },
- "outputs": [],
- "source": [
- "n = sig_rec.shape[0]\n",
- "max_mean = np.mean(np.sort(np.abs(sig_rec))[int(n*0.90):])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "deletable": true,
- "editable": true
- },
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "deletable": true,
- "editable": true
- },
- "outputs": [],
- "source": [
- "sym = int(frame / 76.0)\n",
- "mask = np.ones((int(4*sym)))\n",
- "mask[int(2*sym) : int(3*sym)] = -1"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "deletable": true,
- "editable": true
- },
- "outputs": [],
- "source": [
- "import scipy.signal as ss"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "deletable": true,
- "editable": true
- },
- "outputs": [],
- "source": [
- "c = ss.correlate(np.abs(sig_rec), mask, mode='same')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "deletable": true,
- "editable": true
- },
- "outputs": [],
- "source": [
- "max1 = np.argmax(c[:frame])\n",
- "max2 = frame + np.argmax(c[frame:])\n",
- "max2 - max1 - frame < sym / 100"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "deletable": true,
- "editable": true
- },
- "outputs": [],
- "source": [
- "plt.plot(c)\n",
- "plt.plot((max1, max1), (0, 1200))\n",
- "plt.plot((max2, max2), (0, 1200))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "deletable": true,
- "editable": true
- },
- "outputs": [],
- "source": [
- "i = max2\n",
- "l = 2*sym\n",
- "plt.plot(np.abs(sig_rec[i:i + l]))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "deletable": true,
- "editable": true
- },
- "outputs": [],
- "source": [
- "sig_rec.tofile(\"./orig_frame_8.iq\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "deletable": true,
- "editable": true
- },
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "deletable": true,
- "editable": true
- },
- "outputs": [],
- "source": [
- "path = \"../test_dat/out.iq\"\n",
- "frame = 96*8192\n",
- "offset = 8 * frame + 8192 + 10500 + 72\n",
- "length = 1 * frame"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "deletable": true,
- "editable": true
- },
- "outputs": [],
- "source": [
- "sig_orig = np.fromfile(path, dtype=np.complex64, count=offset + length)[offset:]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "deletable": true,
- "editable": true
- },
- "outputs": [],
- "source": [
- "plt.plot(sig_orig)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "deletable": true,
- "editable": true
- },
- "outputs": [],
- "source": [
- "sig_orig.tofile(\"./orig_frame_8.iq\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "deletable": true,
- "editable": true
- },
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "deletable": true,
- "editable": true
- },
- "outputs": [],
- "source": [
- "sig_rec = sig_rec / sig_rec.std()\n",
- "sig_orig = sig_orig / sig_orig.std()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "deletable": true,
- "editable": true
- },
- "outputs": [],
- "source": [
- "i = 10000\n",
- "l = 400\n",
- "plt.plot(sig_orig[i:i+l], label=\"in\")\n",
- "plt.plot(sig_rec[i:i+l], label=\"out\")\n",
- "plt.legend()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "deletable": true,
- "editable": true
- },
- "outputs": [],
- "source": [
- "from sklearn.linear_model import Ridge\n",
- "from sklearn.preprocessing import PolynomialFeatures\n",
- "from sklearn.pipeline import make_pipeline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
- "source": [
- "# Approximate Amplifier Function"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "deletable": true,
- "editable": true
- },
- "outputs": [],
- "source": [
- "plt.plot(np.abs(sig_orig), np.abs(sig_rec), 'o')\n",
- "plt.xlabel(\"Amplitude In\")\n",
- "plt.ylabel(\"Amplitude Out\")\n",
- "\n",
- "deg = 8\n",
- "t = np.linspace(0,5)\n",
- "model = make_pipeline(PolynomialFeatures(deg), Ridge())\n",
- "model.fit(np.abs(sig_orig.reshape(-1,1)), np.abs(sig_rec.reshape(-1,1)))\n",
- "y = model.predict(t.reshape(-1,1))\n",
- "plt.plot(t, np.abs(y))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
- "source": [
- "# Approximate Inverse Function"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "deletable": true,
- "editable": true
- },
- "outputs": [],
- "source": [
- "plt.plot(np.abs(sig_orig), np.abs(sig_rec), 'o')\n",
- "plt.xlabel(\"Amplitude In\")\n",
- "plt.ylabel(\"Amplitude Out\")\n",
- "\n",
- "deg = 8\n",
- "t = np.linspace(0,3)\n",
- "model = make_pipeline(PolynomialFeatures(deg), Ridge(alpha=100))\n",
- "model.fit(np.abs(sig_rec.reshape(-1,1)),np.abs(sig_orig.reshape(-1,1)) )\n",
- "y = model.predict(t.reshape(-1,1))\n",
- "plt.plot(t, np.abs(y))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "deletable": true,
- "editable": true
- },
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
- "source": [
- "# Phase Error"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "deletable": true,
- "editable": true
- },
- "outputs": [],
- "source": [
- "l = 100000\n",
- "plt.plot(np.abs(sig_orig[i:i+l]), (np.angle(sig_rec[i:i+l], deg = 1) - np.angle(sig_orig[i:i+l], deg = 1)) % 180, 'o')\n",
- "plt.xlabel(\"Amplitude In\")\n",
- "plt.ylabel(\"Phase difference\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "deletable": true,
- "editable": true
- },
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "deletable": true,
- "editable": true
- },
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 2",
- "language": "python",
- "name": "python2"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 2
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython2"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/src/dab_util.py b/src/dab_util.py
index 2b23812..843f8a5 100644
--- a/src/dab_util.py
+++ b/src/dab_util.py
@@ -3,6 +3,7 @@ import scipy
import matplotlib.pyplot as plt
import fftconvolve
import src.dabconst as dabconst
+from scipy import signal
c = {}
c["bw"]=1536000
@@ -47,27 +48,45 @@ def crop_signal(signal, n_window = 1000, n_zeros = 120000, debug = False):
signal = signal[max(0,idx_start - n_zeros): min(idx_end + n_zeros, signal.shape[0] -1)]
return signal
-#def fftlag(signal_original, signal_rec):
+#def fftlag(sig_orig, sig_rec):
# """
# Efficient way to find lag between two signals
# Args:
-# signal_original: The signal that has been sent
-# signal_rec: The signal that has been recored
+# sig_orig: The signal that has been sent
+# sig_rec: The signal that has been recored
# """
-# c = np.flipud(scipy.signal.fftconvolve(signal_original,np.flipud(signal_rec)))
+# c = np.flipud(scipy.signal.fftconvolve(sig_orig,np.flipud(sig_rec)))
# #plt.plot(c)
-# return np.argmax(c) - signal_original.shape[0] + 1
+# return np.argmax(c) - sig_orig.shape[0] + 1
-def fftlag(signal_original, signal_rec, n_upsampling = 1):
+def lag(sig_orig, sig_rec):
+ """
+ Find lag between two signals
+ Args:
+ sig_orig: The signal that has been sent
+ sig_rec: The signal that has been recored
+ """
+ off = sig_rec.shape[0]
+ c = signal.correlate(sig_orig, sig_rec)
+ return np.argmax(c) - off + 1
+
+def lag_upsampling(sig_orig, sig_rec, n_up):
+ sig_orig_up = signal.resample(sig_orig, sig_orig.shape[0] * n_up)
+ sig_rec_up = signal.resample(sig_rec, sig_rec.shape[0] * n_up)
+ l = lag(sig_orig_up, sig_rec_up)
+ l_orig = float(l) / n_up
+ return l_orig
+
+def fftlag(sig_orig, sig_rec, n_upsampling = 1):
"""
Efficient way to find lag between two signals
Args:
- signal_original: The signal that has been sent
- signal_rec: The signal that has been recored
+ sig_orig: The signal that has been sent
+ sig_rec: The signal that has been recored
"""
- c = np.flipud(fftconvolve.fftconvolve(signal_original,np.flipud(signal_rec), n_upsampling))
+ c = np.flipud(fftconvolve.fftconvolve(sig_orig,np.flipud(sig_rec), n_upsampling))
#plt.plot(c)
- return (np.argmax(c) - signal_original.shape[0] + 1)
+ return (np.argmax(c) - sig_orig.shape[0] + 1)
def get_amp_ratio(ampl_1, ampl_2, a_out_abs, a_in_abs):
idxs = (a_in_abs > ampl_1) & (a_in_abs < ampl_2)
diff --git a/src/dab_util_test.py b/src/dab_util_test.py
new file mode 100644
index 0000000..83813a9
--- /dev/null
+++ b/src/dab_util_test.py
@@ -0,0 +1,36 @@
+from scipy import signal
+import numpy as np
+import src.gen_source as gs
+reload(gs)
+import src.dab_util as du
+reload(du)
+
+def gen_test_signals(oversampling=4, sample_offset_float=0):
+ off = int(sample_offset_float)
+ phi_samples = sample_offset_float - off
+ phi = phi_samples*360/oversampling
+
+ s1 = np.zeros((1024))
+ s1[256:768] = gs.gen_sin(512, oversampling, 0)
+ s2 = np.zeros((1024))
+ s2[256+off:768+off] = gs.gen_sin(512, oversampling, phi)
+
+ return s1, s2
+
+def test_phase_offset(lag_function, tol):
+ def r():
+ return np.random.rand(1)*100-50
+ res = []
+ for i in range(100):
+ off = r()
+ s1, s2 = gen_test_signals(
+ oversampling=4, sample_offset_float=off)
+
+ off_meas = lag_function(s2, s1)
+ res.append(np.abs(off-off_meas)<tol)
+ return np.mean(res)
+
+for n_up in [1, 2, 3, 4, 7, 8, 16]:
+ correct_ratio = test_phase_offset(lambda x,y: du.lag_upsampling(x,y,n_up), tol=1./n_up)
+ print("%.1f%% of the tested offsets were measured within tolerance %.4f for n_up = %d" % (correct_ratio * 100, 1./n_up, n_up))
+
diff --git a/src/gen_source.py b/src/gen_source.py
index 7620bc3..c8509ed 100644
--- a/src/gen_source.py
+++ b/src/gen_source.py
@@ -97,3 +97,9 @@ def gen_file_i(frequency_0, frequency_1, x1 = 0, x2 = 0, x3 = 0, x4 = 0, samp_ra
assert(np.isclose(a_load, two_tone).all()), "Inconsistent stored file"
return path
+
+def gen_sin(samples, oversampling, phi):
+ t = np.arange(samples, dtype=np.float)
+ sig = np.sin(((2*np.pi)/oversampling) * t - np.pi*phi/180.)
+ return sig
+
generated by cgit v1.2.3 (git 2.39.1) at 2024-11-14 23:01:55 +0000