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

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<!DOCTYPE html>
<html lang="en">
  <head>
    <meta charset="UTF-8" />
    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
    <title>So You Want to Do Image Analysis with Python.</title>
    <meta
      name="description"
      content="A GitHub Pages course for biological researchers learning local Python, programming basics, environments, Jupyter notebooks, napari, and package creation."
    />
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  <body>
    <a class="skip-link" href="#main-content">Skip to content</a>
    <header class="site-header">
      <div class="wrap">
        <div class="header-shell">
          <a class="brand" href="index.html">SYWTDIAWP</a>
          <nav class="site-nav" aria-label="Primary">
            <a class="nav-link" href="index.html">Home</a>
            <a class="nav-link" href="#modules">Modules</a>
            <a class="nav-link" href="#audience">Audience</a>
            <a class="nav-link" href="resources.html">Resources</a>
            <a class="nav-cta" href="tutorials/install-python.html">Start here</a>
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        </div>
      </div>
    </header>

    <main id="main-content">
      <section class="hero">
        <div class="wrap hero-grid">
          <div class="hero-copy">
            <p class="eyebrow">Biology-first course</p>
            <h1>So You Want to Do Image Analysis with Python.</h1>
            <p class="lead">
              Excellent. This course is here to get biological researchers from
              “I have data and good intentions” to “I can actually run this
              workflow” without pretending setup, notebooks, and tooling are
              somehow obvious on the first try.
            </p>
            <div class="meta-row">
              <span class="meta">13 modules</span>
              <span class="meta">Beginner friendly</span>
              <span class="meta">Multi-day course</span>
              <span class="meta">GitHub Pages ready</span>
            </div>
            <div class="hero-actions">
              <a class="button button-primary" href="tutorials/install-python.html"
                >Begin Module 1</a
              >
              <a class="button button-secondary" href="#modules">Browse course map</a>
            </div>
          </div>

          <aside class="hero-card">
            <p class="eyebrow">What learners will do</p>
            <h2>From setup to a small reusable tool</h2>
            <ul>
              <li>Install Python on Windows, macOS, or Linux</li>
              <li>Practice core Python ideas like variables, types, and functions</li>
              <li>Use pandas and matplotlib to inspect, summarize, and plot data</li>
              <li>Get oriented around the scientific Python ecosystem</li>
              <li>Create clean project environments with <code>venv</code> and conda</li>
              <li>Run and edit Jupyter notebooks with biology-flavoured examples</li>
              <li>Install napari and launch the <code>napari-mAIcrobe</code> plugin</li>
              <li>Keep going into segmentation, classification, and pretrained models</li>
            </ul>
          </aside>
        </div>
      </section>

      <section class="section" id="audience">
        <div class="wrap split-grid">
          <div>
            <p class="eyebrow">Who this is for</p>
            <h2>Researchers first, programmers second</h2>
            <p>
              The course assumes you know your biological question and your data
              better than you know terminals, environments, or package managers.
              Each module explains why a tool matters in a lab workflow instead
              of treating setup as a disconnected computing exercise.
            </p>
            <div class="quick-links">
              <a href="tutorials/jupyter-notebooks.html">
                <strong>Notebook examples</strong><br />
                Move from commands to real exploratory analysis.
              </a>
              <a href="tutorials/napari-maicrobe.html">
                <strong>Microscopy workflow</strong><br />
                Launch napari and explore a plugin-driven analysis path.
              </a>
              <a href="resources.html">
                <strong>Course resources</strong><br />
                Find notebooks, supporting files, and the shortest route back in.
              </a>
              <a href="tutorials/scientific-python-libraries.html">
                <strong>Scientific Python tour</strong><br />
                See which libraries people keep talking about and what they are for.
              </a>
              <a href="tutorials/pandas-and-matplotlib.html">
                <strong>Pandas and plotting</strong><br />
                Work with tables and make plots before the image-analysis side gets heavier.
              </a>
              <a href="tutorials/advanced-ml-segmentation.html">
                <strong>Later-course analysis</strong><br />
                Continue into segmentation, classification, and pretrained models.
              </a>
            </div>
          </div>

          <div class="course-outline">
            <div class="mini-card">
              <p class="mini-label">Before you begin</p>
              <p>Plan for a few short sessions and keep one terminal window open.</p>
            </div>
            <div class="mini-card">
              <p class="mini-label">Recommended outcome</p>
              <p>Finish with one working environment and one small analysis package.</p>
            </div>
            <div class="mini-card">
              <p class="mini-label">Materials</p>
              <p>Local computer access, a browser, and permission to install software.</p>
            </div>
          </div>
        </div>
      </section>

      <section class="section">
        <div class="wrap split-grid">
          <div>
            <p class="eyebrow">Pacing</p>
            <h2>Built for researchers who also have experiments to run</h2>
            <p>
              The lessons are meant to be worked through over several days, not
              inhaled in one heroic sitting. Each module leaves more room for
              explanation, checkpoints, and sensible stopping points.
            </p>
          </div>
          <div class="quick-links">
            <a href="resources.html">
              <strong>Plan your study path</strong><br />
              Use the resources page to spread the course across several sessions.
            </a>
            <a href="tutorials/python-basics.html">
              <strong>Slow down in basics</strong><br />
              Spend extra time here before notebooks if code still feels new.
            </a>
          </div>
        </div>
      </section>

      <section class="section" id="modules">
        <div class="wrap course-layout">
          <aside class="course-menu">
            <h2>On this path</h2>
            <ol>
              <li>
                <a href="tutorials/install-python.html">Install Python</a>
                <p>Get a working local interpreter and verify your terminal setup.</p>
              </li>
              <li>
                <a href="tutorials/virtual-environments.html">Virtual environments</a>
                <p>Compare <code>venv</code> and conda, then create an isolated workspace.</p>
              </li>
              <li>
                <a href="tutorials/python-basics.html">Python basics</a>
                <p>Learn variables, data types, loops, and functions with exercises.</p>
              </li>
              <li>
                <a href="tutorials/scientific-python-libraries.html">Scientific Python libraries</a>
                <p>Get oriented around NumPy, pandas, matplotlib, scikit-image, scikit-learn, and more.</p>
              </li>
              <li>
                <a href="tutorials/jupyter-notebooks.html">Jupyter notebooks</a>
                <p>Launch notebooks, run examples, and work through short exercises.</p>
              </li>
              <li>
                <a href="tutorials/pandas-and-matplotlib.html">pandas and matplotlib</a>
                <p>Load tables, summarize measurements, and build plots you can actually interpret.</p>
              </li>
              <li>
                <a href="tutorials/napari-maicrobe.html">napari + mAIcrobe</a>
                <p>Install the viewer, add the plugin, and try a biology-relevant workflow.</p>
              </li>
              <li>
                <a href="tutorials/create-python-package.html">Create a package</a>
                <p>Generate a package template you can grow into a real analysis project.</p>
              </li>
              <li>
                <a href="tutorials/advanced-ml-segmentation.html">ML segmentation</a>
                <p>Use classical machine learning for clustering, features, and segmentation quality.</p>
              </li>
              <li>
                <a href="tutorials/advanced-single-cell-classification.html">Cell classification</a>
                <p>Train classifiers on morphology-derived single-cell measurements.</p>
              </li>
              <li>
                <a href="tutorials/advanced-segmentation-qc.html">Segmentation QC</a>
                <p>Measure objects and filter out bad segmentations automatically.</p>
              </li>
              <li>
                <a href="tutorials/advanced-neural-networks.html">Neural networks</a>
                <p>Build intuition for network structure before you touch a bigger DL toolchain.</p>
              </li>
              <li>
                <a href="tutorials/advanced-deep-learning-tools.html">Pretrained models</a>
                <p>Explore Cellpose, BioImage Model Zoo, and accessible deep-learning workflows.</p>
              </li>
            </ol>
          </aside>

          <div>
            <div class="section-heading">
              <p class="eyebrow">Modules</p>
            <h2>A guided path, not a software scavenger hunt</h2>
              <p>
                Each module includes goals, copy-paste commands, common mistakes,
                and a clear next step so learners do not have to infer the
                workflow on their own.
              </p>
            </div>

            <div class="module-grid">
              <article class="module-card">
                <p class="module-index">Module 01</p>
                <h3>Install Python locally</h3>
                <p>
                  Choose a straightforward installation route, verify your
                  version, and learn the few terminal actions you need to get
                  unstuck.
                </p>
                <a class="module-link" href="tutorials/install-python.html">Open module</a>
              </article>

              <article class="module-card">
                <p class="module-index">Module 02</p>
                <h3>Virtual environments with <code>venv</code> and conda</h3>
                <p>
                  Learn why environments matter for reproducibility and compare
                  the simplest standard-library route with the conda ecosystem.
                </p>
                <a class="module-link" href="tutorials/virtual-environments.html"
                  >Open module</a
                >
              </article>

              <article class="module-card">
                <p class="module-index">Module 03</p>
                <h3>Python programming basics</h3>
                <p>
                  Build confidence with variables, strings, numbers, lists,
                  dictionaries, loops, and simple reusable functions.
                </p>
                <a class="module-link" href="tutorials/python-basics.html"
                  >Open module</a
                >
              </article>

              <article class="module-card">
                <p class="module-index">Module 04</p>
                <h3>Scientific Python libraries</h3>
                <p>
                  Get a practical overview of the libraries that show up again
                  and again in scientific Python workflows.
                </p>
                <a class="module-link" href="tutorials/scientific-python-libraries.html"
                  >Open module</a
                >
              </article>

              <article class="module-card">
                <p class="module-index">Module 05</p>
                <h3>Run Jupyter notebooks</h3>
                <p>
                  Install the notebook tools, open example notebooks, and try
                  beginner exercises using tabular and image data.
                </p>
                <a class="module-link" href="tutorials/jupyter-notebooks.html"
                  >Open module</a
                >
              </article>

              <article class="module-card">
                <p class="module-index">Module 06</p>
                <h3>pandas and matplotlib</h3>
                <p>
                  Work with tabular measurements, clean simple data, and make
                  plots that help you think before you model.
                </p>
                <a class="module-link" href="tutorials/pandas-and-matplotlib.html"
                  >Open module</a
                >
              </article>

              <article class="module-card">
                <p class="module-index">Module 07</p>
                <h3>Install napari and launch mAIcrobe</h3>
                <p>
                  Set up a viewer environment, install the plugin, and orient
                  learners around a practical microbial image-analysis workflow.
                </p>
                <a class="module-link" href="tutorials/napari-maicrobe.html"
                  >Open module</a
                >
              </article>

              <article class="module-card">
                <p class="module-index">Module 08</p>
                <h3>Create your own Python package</h3>
                <p>
                  Use the HenriquesLab cookiecutter template to generate a clean
                  project you can adapt for your own scripts and analysis logic.
                </p>
                <a class="module-link" href="tutorials/create-python-package.html"
                  >Open module</a
                >
              </article>

              <article class="module-card">
                <p class="module-index">Module 09</p>
                <h3>Segment with machine learning</h3>
                <p>
                  Move into classical ML segmentation with normalization,
                  clustering, IoU, feature engineering, and random forests.
                </p>
                <a class="module-link" href="tutorials/advanced-ml-segmentation.html"
                  >Open module</a
                >
              </article>

              <article class="module-card">
                <p class="module-index">Module 10</p>
                <h3>Classify single cells</h3>
                <p>
                  Use morphology-derived measurements, normalization, and
                  scikit-learn classifiers to infer cell states.
                </p>
                <a class="module-link" href="tutorials/advanced-single-cell-classification.html"
                  >Open module</a
                >
              </article>

              <article class="module-card">
                <p class="module-index">Module 11</p>
                <h3>Filter bad segmentations</h3>
                <p>
                  Measure objects, inspect failures, and use classification to
                  reduce manual cleanup.
                </p>
                <a class="module-link" href="tutorials/advanced-segmentation-qc.html"
                  >Open module</a
                >
              </article>

              <article class="module-card">
                <p class="module-index">Module 12</p>
                <h3>Understand neural networks</h3>
                <p>
                  Build intuition with tiny NumPy examples before jumping into
                  more opaque deep-learning workflows.
                </p>
                <a class="module-link" href="tutorials/advanced-neural-networks.html"
                  >Open module</a
                >
              </article>

              <article class="module-card">
                <p class="module-index">Module 13</p>
                <h3>Try pretrained models</h3>
                <p>
                  Explore Cellpose, BioImage Model Zoo, and accessible
                  deep-learning tools in a practical research context.
                </p>
                <a class="module-link" href="tutorials/advanced-deep-learning-tools.html"
                  >Open module</a
                >
              </article>
            </div>
          </div>
        </div>
      </section>
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