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-rw-r--r--lab-windows/scratch.ipynb348
1 files changed, 304 insertions, 44 deletions
diff --git a/lab-windows/scratch.ipynb b/lab-windows/scratch.ipynb
index da795fd..d31ef92 100644
--- a/lab-windows/scratch.ipynb
+++ b/lab-windows/scratch.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
@@ -10,6 +10,7 @@
"import csv\n",
"import re\n",
"import math\n",
+ "import struct\n",
"\n",
"import numpy as np\n",
"from matplotlib import pyplot as plt\n",
@@ -20,7 +21,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
@@ -29,7 +30,7 @@
},
{
"cell_type": "code",
- "execution_count": 51,
+ "execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
@@ -46,21 +47,13 @@
},
{
"cell_type": "code",
- "execution_count": 64,
+ "execution_count": 12,
"metadata": {},
"outputs": [
{
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "<ipython-input-64-bdef8329a3e8>:1: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
- " fig, axs = plt.subplots(2, 2, figsize=(15, 9))\n"
- ]
- },
- {
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "c040d7b8285a4444abc23ec0ef8c0d45",
+ "model_id": "57f40afcd2214d0392a5fc7577843454",
"version_major": 2,
"version_minor": 0
},
@@ -97,7 +90,146 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "8b2507e2a7a24129acddcdbb4356f8df",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, axs = plt.subplots(2, 2, figsize=(15, 9))\n",
+ "ax1, ax2, ax3, ax4 = axs.flatten()\n",
+ "\n",
+ "freqs_mod, freqs_clean = read_freq_log('data/meas_sig_audio_test.log'), read_freq_log('/mnt/c/Users/jaseg/shared/dsss_test_50hz_clean_improved.log')\n",
+ "\n",
+ "ax1.plot(freqs_mod)\n",
+ "ax1.grid()\n",
+ "\n",
+ "ax2.plot(freqs_clean)\n",
+ "ax2.grid()\n",
+ "\n",
+ "w = 512\n",
+ "\n",
+ "ax3.psd(freqs_mod[:80000], w, 100/128 * 10)\n",
+ "ax4.psd(freqs_clean[:80000], w, 100/128 * 10)\n",
+ "None"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "with open('data/dsss_test_demod_fixed_03.bin', 'wb') as f:\n",
+ " for freq in read_freq_log('data/dsss_test_demod_fixed_03.log'):\n",
+ " f.write(struct.pack('f', freq))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "d3a944613a184c44be006ef957770e31",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x7f49ad047130>]"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots()\n",
+ "ax.plot(read_freq_log('data/dsss_test_demod_fixed_03.log'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "5d366527c29840da9404ec7b1e1e0665",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "(84250, 86000)"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "fig, axs = plt.subplots(2, 1, figsize=(15, 6))\n",
+ "ax1, ax2 = axs.flatten()\n",
+ "\n",
+ "freqs_mod = read_freq_log('data/meas_sig_audio_test.log')\n",
+ "\n",
+ "with open('data/meas_sig_audio_test.bin', 'wb') as f:\n",
+ " for freq in freqs_mod:\n",
+ " f.write(struct.pack('f', freq))\n",
+ " \n",
+ "with open('data/ref_sig_audio_test.bin', 'rb') as f:\n",
+ " freqs_ref = np.array(list(struct.iter_unpack('f', f.read())))\n",
+ "\n",
+ "ax1.plot(freqs_mod)\n",
+ "ax1.set_title('measured')\n",
+ "ax1.grid()\n",
+ "ax2.plot(freqs_ref)\n",
+ "ax2.set_title('reference')\n",
+ "ax2.grid()\n",
+ "\n",
+ "ax1.set_xlim([84250+47, 86000+47])\n",
+ "ax2.set_xlim([84250, 86000])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
@@ -109,13 +241,13 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "f5f37f4970ae4d0fb5677c55045c6ebf",
+ "model_id": "bc4efa3809dd423482f1ef34974192c0",
"version_major": 2,
"version_minor": 0
},
@@ -125,41 +257,73 @@
},
"metadata": {},
"output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "<ipython-input-19-55f36f700399>:6: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
+ " fig.tight_layout()\n"
+ ]
}
],
"source": [
- "fig, axs = plt.subplots(2, 2, figsize=(15, 9))\n",
+ "# We see artifacts of the 128sp processing window in the analog readings if we process a buffer immediately after capture. We can reduce these artifacts by deferring the sampling a few milliseconds into the capture period.\n",
+ "# The reason for this is presumably a suboptimal power layout on the cheap f407 devboard leading to CPU load changes dumping noise into our ADC supply.\n",
+ "# In time domain plots we can see frequent high spikes in ADC counts that would be in accordance with this conjecture.\n",
+ "\n",
+ "fig, axs = plt.subplots(2, 2, figsize=(15, 9), sharey='row', gridspec_kw={'hspace': 0.2, 'wspace': 0.05})\n",
+ "fig.tight_layout()\n",
"ax1, ax2, ax3, ax4 = axs.flatten()\n",
"\n",
- "raw_50hz, raw_silence = read_raw_log('/mnt/c/Users/jaseg/shared/rawlog_50hz_clean.log'), read_raw_log('/mnt/c/Users/jaseg/shared/rawlog_silence_clean3.log')\n",
+ "raw_left, raw_right = read_raw_log('data/rawlog_silence_clean.log'), read_raw_log('data/rawlog_silence_clean3.log')\n",
+ "raw_left = raw_left[26000:-10000].astype(float)\n",
"\n",
- "ax1.plot(raw_50hz)\n",
+ "raw_right = raw_right[26000:]\n",
+ "raw_right = raw_right[:len(raw_left)].astype(float)\n",
+ "\n",
+ "raw_left -= np.mean(raw_left)\n",
+ "raw_right -= np.mean(raw_right)\n",
+ "\n",
+ "ax1.set_title('Immediate processing')\n",
+ "ax1.plot(raw_left)\n",
"ax1.grid()\n",
- "#for x in range(0, len(raw_50hz), 128):\n",
- "# ax1.axvline(x, color='red', alpha=0.3)\n",
+ "for x in range(0, len(raw_left), 128):\n",
+ " ax1.axvline(x, color='red', alpha=0.3)\n",
"\n",
- "ax2.plot(raw_silence)\n",
+ "ax2.set_title('Deferred processing')\n",
+ "ax2.plot(raw_right)\n",
"ax2.grid()\n",
- "#for x in range(0, len(raw_silence), 128):\n",
- "# ax2.axvline(x, color='red', alpha=0.3)\n",
+ "for x in range(0, len(raw_right), 128):\n",
+ " ax2.axvline(x, color='red', alpha=0.3)\n",
"\n",
- "w = 16384\n",
+ "ax1.set_xlim([0, 1000])\n",
+ "ax1.set_ylim([-200, 200])\n",
+ "ax2.set_xlim([0, 1000])\n",
+ "ax2.set_ylim([-200, 200])\n",
+ "ax1.set_xlabel('t [ms]')\n",
+ "ax2.set_xlabel('t [ms]')\n",
+ "ax1.set_ylabel('ADC reading [counts]')\n",
"\n",
- "ax3.psd(raw_50hz, w, 1e3)\n",
+ "w = 2048\n",
"\n",
- "ax4.psd(raw_silence, w, 1e3)\n",
+ "ax3.psd(raw_left, w, 1e3)\n",
+ "ax4.psd(raw_right, w, 1e3)\n",
+ "ax3.set_ylim([-10, 26])\n",
+ "ax4.set_ylim([-10, 26])\n",
+ "ax4.set_ylabel(None)\n",
"None"
]
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "eba72dea09bc45ff9de68cb22b352fb3",
+ "model_id": "291774359d674902aad182268ec9c469",
"version_major": 2,
"version_minor": 0
},
@@ -174,8 +338,8 @@
"source": [
"fig, ax = plt.subplots(figsize=(12, 6))\n",
"\n",
- "raw_silence = read_raw_log('/mnt/c/Users/jaseg/shared/rawlog_silence_clean.log')\n",
- "raw_silence2 = read_raw_log('/mnt/c/Users/jaseg/shared/rawlog_silence_clean3.log')\n",
+ "raw_silence = read_raw_log('data/rawlog_silence_clean.log')\n",
+ "raw_silence2 = read_raw_log('data/rawlog_silence_clean3.log')\n",
"\n",
"raw_silence = raw_silence.reshape([-1, 128])\n",
"le_mean = raw_silence.mean(axis=0)\n",
@@ -189,21 +353,21 @@
},
{
"cell_type": "code",
- "execution_count": 90,
+ "execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
- "<ipython-input-90-5a220009d359>:1: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
- " fig, axs = plt.subplots(2, 2, figsize=(15, 9))\n"
+ "<ipython-input-40-132a47f1202f>:1: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+ " fig, axs = plt.subplots(4, 1, figsize=(15, 9), sharex=True)\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "d9143b226974466aaec05a70592ac69e",
+ "model_id": "2740bc857e6c4296b9ca74ada34fbb1d",
"version_major": 2,
"version_minor": 0
},
@@ -216,23 +380,119 @@
}
],
"source": [
- "fig, axs = plt.subplots(2, 2, figsize=(15, 9))\n",
+ "fig, axs = plt.subplots(4, 1, figsize=(15, 9), sharex=True)\n",
"ax1, ax2, ax3, ax4 = axs.flatten()\n",
"\n",
- "raw_d, raw_e = read_raw_log('/mnt/c/Users/jaseg/shared/rawlog_test_d.log'), read_raw_log('/mnt/c/Users/jaseg/shared/rawlog_test_e.log')\n",
+ "#with open('data/meas_sig_audio_test_processed.log') as f:\n",
+ "with open('data/meas_sig_audio_test_fixed_03.log') as f:\n",
+ " lines = f.readlines()\n",
+ " data = np.array([ [float(x) for x in line.split(': ')[1].split()] for line in lines if not line.startswith('GROUP:') ])\n",
+ " groups = [ int(line.split(' ')[2]) for line in lines if line.startswith('GROUP:') ]\n",
"\n",
- "ax1.plot(raw_d)\n",
+ "ax1.plot(data[:,0])\n",
"ax1.grid()\n",
+ "ax1.set_xlim([4000, 16000])\n",
+ "ax1.set_ylim([-0.02, 0.1])\n",
"\n",
- "ax2.plot(raw_e)\n",
+ "ax2.plot(data[:,1:-2:2])\n",
"ax2.grid()\n",
+ "ax2.set_ylim([-0.6, 0.6])\n",
"\n",
- "w = 16384\n",
+ "ax3.plot(data[:,2:-2:2])\n",
+ "ax3.grid()\n",
"\n",
- "ax3.psd(raw_d, w, 1e3)\n",
- "ax4.psd(raw_e, w, 1e3)\n",
- "#ax3.psd(raw_silence, w, 1e3)\n",
- "None"
+ "ax4.plot(data[:,-2:])\n",
+ "ax4.grid()\n",
+ "\n",
+ "for x in groups:\n",
+ " ax4.axvline(x, color='red', alpha=0.4)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.lines.Line2D at 0x7f497186d6d0>"
+ ]
+ },
+ "execution_count": 41,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ax4.axvline(12433)\n",
+ "ax4.axvline(12164)\n",
+ "ax4.axvline(12475)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "cff56db4807048cda06ae8b15b0c6344",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, axs = plt.subplots(4, 1, figsize=(15, 9), sharex=True)\n",
+ "ax1, ax2, ax3, ax4 = axs.flatten()\n",
+ "\n",
+ "with open('data/ref_sig_audio_test_processed.log') as f:\n",
+ " lines = f.readlines()\n",
+ " data = np.array([ [float(x) for x in line.split(': ')[1].split()] for line in lines if not line.startswith('GROUP:') ])\n",
+ " groups = [ int(line.split(': ')[1]) for line in lines if line.startswith('GROUP:') ]\n",
+ "\n",
+ "ax1.plot(data[:,0])\n",
+ "ax1.grid()\n",
+ "ax1.set_xlim([82000, 95000])\n",
+ "ax1.set_ylim([-0.02, 0.1])\n",
+ "\n",
+ "ax2.plot(data[:,1:-2:2])\n",
+ "ax2.grid()\n",
+ "ax2.set_ylim([-0.6, 0.6])\n",
+ "\n",
+ "ax3.plot(data[:,2:-2:2])\n",
+ "ax3.grid()\n",
+ "\n",
+ "ax4.plot(data[:,-2:])\n",
+ "ax4.grid()\n",
+ "\n",
+ "for x in groups:\n",
+ " ax4.axvline(x, color='red', alpha=0.4)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 93,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[516, 518, 4402, 4404, 5537, 7238, 12585, 17497, 17724, 34074, 34076, 41628, 44677, 46287, 49371, 49837, 53877, 54554, 56666, 56949, 60923, 67313, 67744, 67746, 68392, 79601, 84707, 84709, 84711, 85017, 85019, 85021, 85327, 85329, 85637, 85947, 85949, 85951, 86257, 86259, 86261, 86567, 86569, 86571, 86877, 86879, 86881, 87187, 87189, 87191, 87498, 87500, 87807, 87809, 87811, 88117, 88119, 88121, 88427, 88429, 88431, 88488, 88737, 88739, 88741, 89047, 89049, 89051, 89357, 89359, 89361, 89668, 89670, 89730, 89978, 89980, 90287, 90289, 90291, 90598, 90600, 90602, 90907, 90909, 90911, 91217, 91219, 91221, 91527, 91529, 91531, 91837, 91839, 91841, 92148, 92150, 92458, 92460, 92768, 92770, 93078, 93080, 93388, 93390, 93392, 93698, 93700, 93702, 94008, 94010, 94318, 94320]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(groups)"
]
},
{
generated by cgit (git 2.34.1) at 2024-11-21 17:16:21 +0000