From 5f1d8a11cabcb9ee24d1bbae770d61ea605e8b2f Mon Sep 17 00:00:00 2001 From: "Adam J. Stewart" Date: Wed, 18 Dec 2024 17:25:23 -0500 Subject: [PATCH] Add consistent author and copyright info to all tutorials --- docs/tutorials/basic_usage.rst | 2 +- docs/tutorials/contribute_datamodule.ipynb | 10 ++++++++++ docs/tutorials/custom_raster_dataset.ipynb | 15 +++++++++------ docs/tutorials/earth_surface_water.ipynb | 10 ++++++++++ docs/tutorials/indices.ipynb | 17 ++++++++++------- docs/tutorials/trainers.ipynb | 15 +++++++++------ 6 files changed, 49 insertions(+), 20 deletions(-) diff --git a/docs/tutorials/basic_usage.rst b/docs/tutorials/basic_usage.rst index 10fcb97b42..57848bede1 100644 --- a/docs/tutorials/basic_usage.rst +++ b/docs/tutorials/basic_usage.rst @@ -4,7 +4,7 @@ Basic Usage The following tutorials introduce the basic concepts and components of TorchGeo: * `Transforms `_: Preprocessing and data augmentation transforms for geospatial data -* `Indices `_: Spectral indices +* `Spectral Indices `_: Visualizing and appending spectral indices * `Pretrained Weights `_: Models and pretrained weights * `Lightning Trainers `_: PyTorch Lightning data modules and trainers diff --git a/docs/tutorials/contribute_datamodule.ipynb b/docs/tutorials/contribute_datamodule.ipynb index 1bbb728e0e..3182213c51 100644 --- a/docs/tutorials/contribute_datamodule.ipynb +++ b/docs/tutorials/contribute_datamodule.ipynb @@ -1,5 +1,15 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Copyright (c) Microsoft Corporation. All rights reserved.\n", + "# Licensed under the MIT License." + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/docs/tutorials/custom_raster_dataset.ipynb b/docs/tutorials/custom_raster_dataset.ipynb index 0e2785bb7f..f171c31363 100644 --- a/docs/tutorials/custom_raster_dataset.ipynb +++ b/docs/tutorials/custom_raster_dataset.ipynb @@ -1,14 +1,15 @@ { "cells": [ { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": { "id": "iiqWbXISOEAQ" }, + "outputs": [], "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." + "# Copyright (c) Microsoft Corporation. All rights reserved.\n", + "# Licensed under the MIT License." ] }, { @@ -19,6 +20,8 @@ "source": [ "# Custom Raster Datasets\n", "\n", + "_Written by: Ritwik Gupta_\n", + "\n", "In this tutorial, we'll describe how to write a custom dataset in TorchGeo. There are many types of datasets that you may encounter, from image data, to segmentation masks, to point labels. We'll focus on the most common type of dataset: a raster file containing an image or mask. Let's get started!" ] }, @@ -562,9 +565,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.13.0" } }, "nbformat": 4, - "nbformat_minor": 1 + "nbformat_minor": 4 } diff --git a/docs/tutorials/earth_surface_water.ipynb b/docs/tutorials/earth_surface_water.ipynb index e44d1e385f..f65aedbf72 100644 --- a/docs/tutorials/earth_surface_water.ipynb +++ b/docs/tutorials/earth_surface_water.ipynb @@ -1,5 +1,15 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Copyright (c) Microsoft Corporation. All rights reserved.\n", + "# Licensed under the MIT License." + ] + }, { "cell_type": "markdown", "metadata": { diff --git a/docs/tutorials/indices.ipynb b/docs/tutorials/indices.ipynb index 30576609ac..5695cd51b0 100644 --- a/docs/tutorials/indices.ipynb +++ b/docs/tutorials/indices.ipynb @@ -1,14 +1,15 @@ { "cells": [ { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": { "id": "DYndcZst_kdr" }, + "outputs": [], "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." + "# Copyright (c) Microsoft Corporation. All rights reserved.\n", + "# Licensed under the MIT License." ] }, { @@ -17,7 +18,9 @@ "id": "ZKIkyiLScf9P" }, "source": [ - "# Indices" + "# Spectral Indices\n", + "\n", + "_Written by: Isaac A. Corley_" ] }, { @@ -374,9 +377,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.13.0" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 4 } diff --git a/docs/tutorials/trainers.ipynb b/docs/tutorials/trainers.ipynb index 5de3937a02..0394f3faeb 100644 --- a/docs/tutorials/trainers.ipynb +++ b/docs/tutorials/trainers.ipynb @@ -1,15 +1,16 @@ { "cells": [ { - "cell_type": "markdown", - "id": "b13c2251", + "cell_type": "code", + "execution_count": null, + "id": "16421d50-8d7a-4972-b06f-160fd890cc86", "metadata": { "id": "b13c2251" }, + "outputs": [], "source": [ - "Copyright (c) Microsoft Corporation. All rights reserved.\n", - "\n", - "Licensed under the MIT License." + "# Copyright (c) Microsoft Corporation. All rights reserved.\n", + "# Licensed under the MIT License." ] }, { @@ -21,6 +22,8 @@ "source": [ "# Lightning Trainers\n", "\n", + "_Written by: Caleb Robinson_\n", + "\n", "In this tutorial, we demonstrate TorchGeo trainers to train and test a model. We will use the [EuroSAT](https://torchgeo.readthedocs.io/en/stable/api/datasets.html#eurosat) dataset throughout this tutorial. Specifically, a subset containing only 100 images. We will train models to predict land cover classes.\n", "\n", "It's recommended to run this notebook on Google Colab if you don't have your own GPU. Click the \"Open in Colab\" button above to get started." @@ -328,7 +331,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.13.0" } }, "nbformat": 4,