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Add a test for beamformer corresponding to ground truth for two rx si…
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…mulated data
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@misko
misko committed Dec 29, 2023
1 parent b2fbd94 commit f93bd39
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Showing 4 changed files with 194 additions and 48 deletions.
34 changes: 21 additions & 13 deletions spf/notebooks/load_real_data_session.ipynb
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Expand Up @@ -106,7 +106,7 @@
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x28b2d4f10>"
"<matplotlib.legend.Legend at 0x17c3ce190>"
]
},
"execution_count": 7,
Expand All @@ -127,19 +127,11 @@
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"def get_peaks(beam_former_output):\n",
" first_peak=np.argmax(beam_former_output)\n",
"\n",
" pivot=16\n",
" if first_peak>32:\n",
" pivot=32+16\n",
"\n",
" d=np.abs(first_peak-pivot)\n",
" return pivot+d, pivot-d\n",
"from spf.rf import get_peaks_for_2rx\n",
"\n",
"idx=490\n",
"bf_out=session['beam_former_outputs_at_t'][idx]\n",
"peaks=get_peaks(bf_out)\n",
"peaks=get_peaks_for_2rx(bf_out)\n",
"plt.plot(bf_out,color='red',label=\"beamformer\")\n",
"for peak in peaks:\n",
" plt.axvline(x=peak,label=\"Peak\")\n",
Expand Down Expand Up @@ -195,8 +187,8 @@
"axs[1,1].set_title(\"Ground truth theta\")\n",
"\n",
"#plot the beamformer output\n",
"peaks_at_t=np.array([ get_peaks(bf_out) for bf_out in session[\"beam_former_outputs_at_t\"]])\n",
"peaks_at_t_in_radians=2*(peaks_at_t/65-0.5)*np.pi\n",
"peaks_at_t=np.array([ get_peaks_for_2rx(bf_out) for bf_out in session[\"beam_former_outputs_at_t\"]])\n",
"peaks_at_t_in_radians=2*(peaks_at_t/session[\"beam_former_outputs_at_t\"].shape[1]-0.5)*np.pi\n",
"peaks_at_t_in_radians_adjusted=(peaks_at_t_in_radians+20*session[\"detector_orientation_at_t\"]) #% (np.pi*2)\n",
"\n",
"axs[0,1].plot(peaks_at_t_in_radians_adjusted[:,0])\n",
Expand All @@ -206,6 +198,22 @@
"\n",
"f.tight_layout()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "45e737e2",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "b2e53138",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
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129 changes: 100 additions & 29 deletions spf/notebooks/load_simulated_data_session.ipynb
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Expand Up @@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 48,
"id": "59584a7c-1e24-4e41-9952-23e7298bc583",
"metadata": {},
"outputs": [],
Expand All @@ -16,18 +16,12 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 49,
"id": "9eda4979",
"metadata": {},
"outputs": [],
"source": [
"class dotdict(dict):\n",
" \"\"\"dot.notation access to dictionary attributes\"\"\"\n",
"\n",
" __getattr__ = dict.get\n",
" __setattr__ = dict.__setitem__\n",
" __delattr__ = dict.__delitem__\n",
"\n",
"from spf.utils import dotdict\n",
"\n",
"args=dotdict(\n",
" {\n",
Expand Down Expand Up @@ -62,7 +56,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 50,
"id": "3c05faba",
"metadata": {},
"outputs": [],
Expand All @@ -78,7 +72,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 51,
"id": "f76fd39b",
"metadata": {},
"outputs": [],
Expand All @@ -89,7 +83,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 52,
"id": "7084912d-c62f-4383-a25b-79a6a99583f5",
"metadata": {},
"outputs": [],
Expand All @@ -101,17 +95,25 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": null,
"id": "3552f89f",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 53,
"id": "21858d41-83af-4adb-b5cb-55e33b81c247",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x1431dd110>"
"<matplotlib.legend.Legend at 0x15353f3d0>"
]
},
"execution_count": 6,
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
},
Expand All @@ -129,19 +131,11 @@
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"def get_peaks(beam_former_output):\n",
" first_peak=np.argmax(beam_former_output)\n",
"\n",
" pivot=16\n",
" if first_peak>32:\n",
" pivot=32+16\n",
"\n",
" d=np.abs(first_peak-pivot)\n",
" return pivot+d, pivot-d\n",
"from spf.rf import get_peaks_for_2rx\n",
"\n",
"idx=490\n",
"bf_out=session['beam_former_outputs_at_t'][idx]\n",
"peaks=get_peaks(bf_out)\n",
"peaks=get_peaks_for_2rx(bf_out)\n",
"plt.plot(bf_out,color='red',label=\"beamformer\")\n",
"for peak in peaks:\n",
" plt.axvline(x=peak,label=\"Peak\")\n",
Expand All @@ -150,7 +144,15 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": null,
"id": "f700afb8",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 54,
"id": "ab349f95-e442-4b52-b1dc-b18e1748874c",
"metadata": {},
"outputs": [
Expand Down Expand Up @@ -189,8 +191,8 @@
"axs[1,1].set_title(\"Ground truth theta\")\n",
"\n",
"#plot the beamformer output\n",
"peaks_at_t=np.array([ get_peaks(bf_out) for bf_out in session[\"beam_former_outputs_at_t\"]])\n",
"peaks_at_t_in_radians=2*(peaks_at_t/65-0.5)*np.pi\n",
"peaks_at_t=np.array([ get_peaks_for_2rx(bf_out) for bf_out in session[\"beam_former_outputs_at_t\"]])\n",
"peaks_at_t_in_radians=2*(peaks_at_t/args.beam_former_spacing-0.5)*np.pi\n",
"peaks_at_t_in_radians_adjusted=(peaks_at_t_in_radians+20*session[\"detector_orientation_at_t\"]) #% (np.pi*2)\n",
"\n",
"axs[0,1].plot(peaks_at_t_in_radians_adjusted[:,0])\n",
Expand All @@ -203,9 +205,78 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 55,
"id": "cb653ce9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[-0.53165414, -2.65827071],\n",
" [-0.53165414, -2.65827071],\n",
" [-0.43498975, -2.7549351 ],\n",
" ...,\n",
" [-1.01497609, -2.17494876],\n",
" [-1.01497609, -2.17494876],\n",
" [-1.11164048, -2.07828437]])"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"peaks_at_t=np.array([ get_peaks_for_2rx(bf_out) for bf_out in session[\"beam_former_outputs_at_t\"]])\n",
"peaks_at_t_in_radians=2*(peaks_at_t/args.beam_former_spacing-0.5)*np.pi\n",
"peaks_at_t_in_radians_adjusted=(peaks_at_t_in_radians+session[\"detector_orientation_at_t\"]) #% (np.pi*2)\n",
"peaks_at_t_in_radians_adjusted"
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "cd929e75",
"metadata": {},
"outputs": [],
"source": [
"ground_truth=session[\"detector_orientation_at_t\"]+ session[\"source_theta_at_t\"]"
]
},
{
"cell_type": "code",
"execution_count": 57,
"id": "c5ebdfb6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.08651613092957919"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.abs(peaks_at_t_in_radians_adjusted-ground_truth).min(axis=1).mean()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ef694ea5",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "1062621d",
"metadata": {},
"outputs": [],
"source": []
}
Expand Down
13 changes: 12 additions & 1 deletion spf/rf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -73,7 +73,6 @@ def __init__(self, pos, frequency, phase=0, amplitude=1):
self.amplitude = amplitude

def signal(self, sampling_times):
# return np.cos(2*np.pi*sampling_times*self.frequency+self.phase)+np.sin(2*np.pi*sampling_times*self.frequency+self.phase)*1j
return self.amplitude * np.sin(
2 * np.pi * sampling_times * self.frequency + self.phase
) # .reshape(1,-1)
Expand Down Expand Up @@ -378,3 +377,15 @@ def beamformer(
) # this is adjust and sum in one step
steer_dot_signal = np.absolute(phase_adjusted).mean(axis=1) # mean over samples
return thetas, steer_dot_signal, steering_vectors


def get_peaks_for_2rx(beam_former_output):
n = beam_former_output.shape[0]
first_peak = np.argmax(beam_former_output)

pivot = n // 4
if first_peak > n // 2:
pivot = n // 2 + n // 4

d = np.abs(first_peak - pivot)
return pivot + d, pivot - d
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