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files/environment.yml

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name: dc-image
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channels:
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- conda-forge
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dependencies:
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- python>=3.11
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- jupyterlab
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- numpy
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- matplotlib
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- scikit-image
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- ipympl
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- imageio

md5sum.txt

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"learners/edge-detection.md" "fdbcee7436e104e6587e1cb40cd37d18" "site/built/edge-detection.md" "2023-08-16"
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"learners/prereqs.md" "7ca883d3d01d18c98ce7524ed297e56c" "site/built/prereqs.md" "2023-07-26"
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"learners/reference.md" "7ae9deea84007c6ed1339955ac23f4b7" "site/built/reference.md" "2023-04-26"
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"learners/setup.md" "7edbbef5b2cb020d40d88c9cb0dcd98f" "site/built/setup.md" "2023-12-19"
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"learners/setup.md" "c27b40d65c3c692479a010690dba0c38" "site/built/setup.md" "2025-04-06"
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"profiles/learner-profiles.md" "60b93493cf1da06dfd63255d73854461" "site/built/learner-profiles.md" "2023-04-25"

setup.md

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## Data
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The example images used in this lesson are available on [FigShare](https://figshare.com/).
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The example images and a description of the Python environment used in this lesson are available on [FigShare](https://figshare.com/).
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To download the data, please visit [the dataset page for this workshop][figshare-data]
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and click the "Download all" button.
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Unzip the downloaded file, and save the contents as a folder called `data` somewhere you will easily find it again,
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## Software
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1. Download and install the latest [Anaconda
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distribution](https://www.anaconda.com/download/) for your
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operating system. Make sure to choose the Python 3 version (as
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opposed to the one with Python 2). If you wish to use an existing
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installation, be sure to upgrade your scikit-image to at least 0.19.
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You can upgrade to the latest scikit-image using the shell command that follows.
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1. Download and install the latest [MiniForge distribution of Python](https://conda-forge.org/download/) for your operating system.
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If you already have a Python 3 setup that you are happy with, you can continue to use that (we recommend that you make sure your Python version is current).
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The next step assumes that `conda` is available to manage your Python environment.
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2. Setup an environment to work in during the lesson.
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In a terminal (Linux/Mac) or the MiniForge Prompt application (Windows), navigate to the location where you saved the unzipped data for the lesson and run the following command:
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::::::::::::::::::::::::::::::::::::::::: callout
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## Updating scikit-image in an existing Anaconda distribution
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```shell
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conda upgrade -y scikit-image
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```bash
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conda env create -f environment.yml
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```
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::::::::::::::::::::::::::::::::::::::::::::::::::
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2. This lesson uses Matplotlib features to display images, and some
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interactive features will be valuable. To enable the interactive
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tools in JupyterLab, the `ipympl` package is required. The package
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can be installed with the command
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If prompted, allow `conda` to install the required libraries.
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3. Activate the new environment you just created:
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```shell
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conda install -c conda-forge ipympl
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```bash
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conda activate dc-image
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```
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::::::::::::::::::::::::::::::::::::::::: callout
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## Instructions for Windows
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Launch the Anaconda Prompt program and type `jupyter lab`.
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Launch the MiniForge Prompt program and type `jupyter lab`.
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(Running this command on the standard Command Prompt will return an error:
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`'conda' is not recognized as an internal or external command, operable program or batch file.`)
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`'jupyter' is not recognized as an internal or external command, operable program or batch file.`)
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