PythonImageAnalysis/resources.html at main · HenriquesLab/PythonImageAnalysis · GitHub

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    <title>Resources | So You Want To Do Image Analysis With Python</title>
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      content="Central course resources for notebooks, setup materials, and quick links."
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          <section class="page-hero">
            <p class="page-kicker">Resources</p>
            <h1>The “where did that file go?” page</h1>
            <p class="lead">
              This page gathers the useful bits in one place so you do not have
              to play hide-and-seek with notebooks, setup links, or the next step.
            </p>
          </section>

          <section class="panel">
            <h2>Sample notebooks</h2>
            <ul class="resource-list">
              <li>
                <a href="notebooks/cell_measurements_intro.ipynb">Cell measurements intro</a>
                for grouped summaries and plotting.
              </li>
              <li>
                <a href="notebooks/image_qc_exercises.ipynb">Image QC exercises</a>
                for simple image-like arrays and practice tasks.
              </li>
            </ul>
          </section>

          <section class="panel">
            <h2>Recommended course path</h2>
            <ol class="step-list">
              <li>Install Python locally.</li>
              <li>Create one environment with <code>venv</code> or conda.</li>
              <li>Learn enough Python basics to read and edit beginner code.</li>
              <li>Meet the main scientific Python libraries you will keep seeing.</li>
              <li>Move into Jupyter notebooks and examples.</li>
              <li>Use pandas and matplotlib for tables and plotting.</li>
              <li>Install napari and try the <code>napari-mAIcrobe</code> plugin.</li>
              <li>Create a small package once your workflow becomes reusable.</li>
              <li>Continue into segmentation, classification, QC, neural networks, and pretrained models.</li>
            </ol>
          </section>

          <section class="panel">
            <h2>Recommended pacing</h2>
            <ol class="step-list">
              <li>Day 1: install Python and get comfortable with the terminal.</li>
              <li>Day 2: create and activate environments a few times.</li>
              <li>Day 3: work slowly through Python basics and type the examples.</li>
              <li>Day 4: meet NumPy, pandas, matplotlib, and the rest of the scientific stack.</li>
              <li>Day 5: open Jupyter and complete the first notebook exercises.</li>
              <li>Day 6: work through pandas and matplotlib with real tables and plots.</li>
              <li>Day 7: install napari and explore one viewer-based workflow.</li>
              <li>Day 8: scaffold a package and add one simple function.</li>
              <li>Day 9 and beyond: move into segmentation, QC, classifiers, and pretrained models.</li>
            </ol>
          </section>

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            <h2>Useful repo pages</h2>
            <ul class="resource-list">
              <li><a href="tutorials/install-python.html">Install Python</a></li>
              <li><a href="tutorials/virtual-environments.html">Virtual environments</a></li>
              <li><a href="tutorials/python-basics.html">Python basics</a></li>
              <li><a href="tutorials/scientific-python-libraries.html">Scientific Python libraries</a></li>
              <li><a href="tutorials/jupyter-notebooks.html">Jupyter notebooks</a></li>
              <li><a href="tutorials/pandas-and-matplotlib.html">pandas and matplotlib</a></li>
              <li><a href="tutorials/napari-maicrobe.html">napari + mAIcrobe</a></li>
              <li><a href="tutorials/create-python-package.html">Create a package</a></li>
              <li><a href="tutorials/advanced-ml-segmentation.html">ML segmentation</a></li>
              <li><a href="tutorials/advanced-single-cell-classification.html">Single-cell classification</a></li>
              <li><a href="tutorials/advanced-segmentation-qc.html">Segmentation QC</a></li>
              <li><a href="tutorials/advanced-neural-networks.html">Neural networks</a></li>
              <li><a href="tutorials/advanced-deep-learning-tools.html">Pretrained models and accessible DL</a></li>
              <li><a href="glossary.html">Glossary</a></li>
              <li><a href="troubleshooting.html">Troubleshooting</a></li>
            </ul>
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            <h3>Why this page exists</h3>
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              Every course eventually needs one dependable page for links,
              notebooks, and “where was I again?” moments. This is that page.
            </p>
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          <section class="panel">
            <h2>Quick start</h2>
            <div class="quick-links">
              <a href="tutorials/install-python.html">
                <strong>Begin with setup</strong><br />
                Start with local Python installation.
              </a>
              <a href="tutorials/jupyter-notebooks.html">
                <strong>Jump to notebooks</strong><br />
                Open examples and exercises.
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