Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Gvanhoy rtd changes #218

Merged
merged 3 commits into from
Sep 23, 2023
Merged
Show file tree
Hide file tree
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
114 changes: 114 additions & 0 deletions docs/00_dataset_tutorials/00_Sig53DatasetTutorial.ipynb
Original file line number Diff line number Diff line change
@@ -0,0 +1,114 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "ff639336-c092-4882-b08e-78f8b82f741e",
"metadata": {
"tags": []
},
"source": [
"## Sig53 Dataset\n",
"\n",
"last updated: 2023-07-29\n",
"\n",
"TODO: This notebook is just a placeholder for now."
]
},
{
"cell_type": "markdown",
"id": "d2d7597b-7f82-4493-b3b1-d4ef7b9c24b8",
"metadata": {},
"source": [
"### Modulation Family Background\n",
"\n",
"TODO: Describe and plot the modulation families present in Sig53. Should resemble Figure 3 from [Large Scale Radio Frequency Signal Classification](https://arxiv.org/pdf/2207.09918.pdf)."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7888b4a8-0b66-4d29-bf40-6bce2b83a288",
"metadata": {},
"outputs": [],
"source": [
"#TODO: Replace this code block\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"num_samples = 4096\n",
"x = np.exp(2j * np.pi * 2.0 / num_samples * np.arange(num_samples))\n",
"\n",
"plt.figure()\n",
"plt.plot(x.real, c='b')\n",
"plt.plot(x.imag, c='r')\n",
"plt.title('Test plot')\n",
"plt.xlabel('samples')\n",
"plt.ylabel('amplitude')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "2e3151e0-813b-4c07-bc86-72c72f92f6ee",
"metadata": {},
"source": [
"### Instantiate the Sig53 Dataset\n",
"\n",
"TODO: Explain details of the Sig53 dataset and show code on how to instantiate the datasets."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "462b7078-f064-4a2b-ba18-8c8b320e35ef",
"metadata": {},
"outputs": [],
"source": [
"#TODO: Replace this code block\n",
"\n",
"from torchsig.transforms import AddNoise\n",
"\n",
"t = AddNoise(noise_power_db=-20)\n",
"\n",
"y = t(x)\n",
"\n",
"plt.figure()\n",
"plt.plot(y.real, c='b')\n",
"plt.plot(y.imag, c='r')\n",
"plt.title('Test plot')\n",
"plt.xlabel('samples')\n",
"plt.ylabel('amplitude')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b2166e37-6a60-4088-9c60-d2bb62d7834b",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
114 changes: 114 additions & 0 deletions docs/00_dataset_tutorials/01_WidebandSig53DatasetTutorial.ipynb
Original file line number Diff line number Diff line change
@@ -0,0 +1,114 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "ff639336-c092-4882-b08e-78f8b82f741e",
"metadata": {
"tags": []
},
"source": [
"## WidebandSig53 Dataset\n",
"\n",
"last updated: 2023-07-29\n",
"\n",
"TODO: This notebook is just a placeholder for now."
]
},
{
"cell_type": "markdown",
"id": "d2d7597b-7f82-4493-b3b1-d4ef7b9c24b8",
"metadata": {},
"source": [
"### Dataset Background\n",
"\n",
"TODO: Describe and plot a few examples from WidebandSig53 (via the `WidebandModulations` class such that we are not asking sphinx to generate the full dataset). Should resemble Figure 1 from [Large Scale Wideband Radio Frequency Signal Detection & Recognition](https://arxiv.org/pdf/2211.10335.pdf)."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7888b4a8-0b66-4d29-bf40-6bce2b83a288",
"metadata": {},
"outputs": [],
"source": [
"#TODO: Replace this code block\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"num_samples = 4096\n",
"x = np.exp(2j * np.pi * 2.0 / num_samples * np.arange(num_samples))\n",
"\n",
"plt.figure()\n",
"plt.plot(x.real, c='b')\n",
"plt.plot(x.imag, c='r')\n",
"plt.title('Test plot')\n",
"plt.xlabel('samples')\n",
"plt.ylabel('amplitude')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "2e3151e0-813b-4c07-bc86-72c72f92f6ee",
"metadata": {},
"source": [
"### Instantiate the WidebandSig53 Dataset\n",
"\n",
"TODO: Explain details of the WidebandSig53 dataset and show code on how to instantiate the datasets."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "462b7078-f064-4a2b-ba18-8c8b320e35ef",
"metadata": {},
"outputs": [],
"source": [
"#TODO: Replace this code block\n",
"\n",
"from torchsig.transforms import AddNoise\n",
"\n",
"t = AddNoise(noise_power_db=-20)\n",
"\n",
"y = t(x)\n",
"\n",
"plt.figure()\n",
"plt.plot(y.real, c='b')\n",
"plt.plot(y.imag, c='r')\n",
"plt.title('Test plot')\n",
"plt.xlabel('samples')\n",
"plt.ylabel('amplitude')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b2166e37-6a60-4088-9c60-d2bb62d7834b",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
80 changes: 80 additions & 0 deletions docs/00_dataset_tutorials/02_RadioMLDatasetTutorial.ipynb
Original file line number Diff line number Diff line change
@@ -0,0 +1,80 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "ff639336-c092-4882-b08e-78f8b82f741e",
"metadata": {
"tags": []
},
"source": [
"## RadioML Dataset\n",
"\n",
"last updated: 2023-07-29\n",
"\n",
"TODO: This notebook is just a placeholder for now."
]
},
{
"cell_type": "markdown",
"id": "d2d7597b-7f82-4493-b3b1-d4ef7b9c24b8",
"metadata": {},
"source": [
"### Dataset Background\n",
"\n",
"TODO: Describe and plot a few examples from RadioML"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7888b4a8-0b66-4d29-bf40-6bce2b83a288",
"metadata": {},
"outputs": [],
"source": [
"#TODO: Replace this code block\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"num_samples = 4096\n",
"x = np.exp(2j * np.pi * 2.0 / num_samples * np.arange(num_samples))\n",
"\n",
"plt.figure()\n",
"plt.plot(x.real, c='b')\n",
"plt.plot(x.imag, c='r')\n",
"plt.title('Test plot')\n",
"plt.xlabel('samples')\n",
"plt.ylabel('amplitude')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b2166e37-6a60-4088-9c60-d2bb62d7834b",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Loading