diff --git a/.github/workflows/build-test-publish.yaml b/.github/workflows/build-test-publish.yaml
deleted file mode 100644
index e896e7b..0000000
--- a/.github/workflows/build-test-publish.yaml
+++ /dev/null
@@ -1,146 +0,0 @@
-name: Build, Test, and Publish
-
-on:
-  pull_request:
-    types: [opened, synchronize]
-  push:
-    branches:
-      - main
-  workflow_dispatch:
-    inputs:
-      publish_to_main:
-        description: Publish to PyPI if tests successful
-        required: true
-        type: boolean
-        default: false
-
-permissions:
-  contents: write
-
-jobs:
-  
-  check-version-txt:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v3
-        with:
-          fetch-depth: 0
-      - name: Tag with the release version
-        run: |
-          git tag $(cat version.txt)
-
-  lint-format-and-static-code-checks:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v3
-      - name: Set up Python 3.8
-        uses: actions/setup-python@v3
-        with:
-          python-version: 3.8
-      - name: Install pre-commit
-        run: |
-          pip install pre-commit
-      - name: Lint, Format, and other static code quality checks
-        run: |
-          /bin/bash -x run.sh lint:ci
-
-  build-wheel-and-sdist:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v3
-      - name: Set up Python 3.8
-        uses: actions/setup-python@v3
-        with:
-          python-version: 3.8
-      - name: Install build CLI
-        run: |
-          pip install build
-      - name: Build python package
-        run: |
-          /bin/bash -x run.sh build
-      - name: Upload wheel and sdist
-        uses: actions/upload-artifact@v3
-        with:
-          name: wheel-and-sdist
-          path: ./dist/*
-
-  execute-tests:
-    needs:
-      - build-wheel-and-sdist
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v3
-      - name: Set up Python 3.8
-        uses: actions/setup-python@v3
-        with:
-          python-version: 3.8
-      - name: Download wheel and sdist
-        uses: actions/download-artifact@v3
-        with:
-          name: wheel-and-sdist
-          path: ./dist/
-      - name: Install test dependencies
-        run: |
-          pip install pytest pytest-cov ./dist/*.whl
-      - name: Lint, Format, and other static code quality checks
-        run: |
-          /bin/bash -x run.sh test:ci
-
-  publish:
-    needs:
-      - execute-tests
-      - build-wheel-and-sdist
-      - lint-format-and-static-code-checks
-      - check-version-txt
-    runs-on: ubuntu-latest
-    if: github.event_name == 'push' && github.ref == 'refs/heads/main'
-    steps:
-      - uses: actions/checkout@v3
-      - name: Set up Python 3.8
-        uses: actions/setup-python@v3
-        with:
-          python-version: 3.8
-      - name: Download wheel and sdist
-        uses: actions/download-artifact@v3
-        with:
-          name: wheel-and-sdist
-          path: ./dist/
-      - name: Install twine
-        run: |
-          pip install twine
-      # - name: Publish to TestPyPI
-      #   run: |
-      #     /bin/bash -x run.sh publish:test
-      #   env:
-      #     TEST_PYPI_TOKEN: ${{ secrets.TEST_PYPI_TOKEN }}
-      # - name: Publish to Prod PyPI
-      #   run: |
-      #     /bin/bash -x run.sh publish:prod
-      #   env:
-      #     PROD_PYPI_TOKEN: ${{ secrets.PROD_PYPI_TOKEN }}
-      - name: Push tags
-        run: |
-          git tag $(cat version.txt)
-          git push origin --tags
-
-  # https://docs.github.com/en/actions/learn-github-actions/contexts#example-printing-context-information-to-the-log
-  dump-contexts-to-log:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Dump GitHub context
-        id: github_context_step
-        run: echo '${{ toJSON(github) }}'
-      - name: Dump job context
-        run: echo '${{ toJSON(job) }}'
-      - name: Dump steps context
-        run: echo '${{ toJSON(steps) }}'
-      - name: Dump runner context
-        run: echo '${{ toJSON(runner) }}'
-      - name: Dump strategy context
-        run: echo '${{ toJSON(strategy) }}'
-      - name: Dump matrix context
-        run: echo '${{ toJSON(matrix) }}'
-      - name: Dump secrets
-        run: echo '${{ toJSON(secrets) }}'
-      - name: Dump vars
-        run: echo '${{ toJSON(vars) }}'
diff --git a/.github/workflows/ci-test.yaml b/.github/workflows/ci-test.yaml
new file mode 100644
index 0000000..de8bf7f
--- /dev/null
+++ b/.github/workflows/ci-test.yaml
@@ -0,0 +1,42 @@
+name: CI
+
+on:
+    push:
+        branches:
+            - main
+    pull_request:
+        branches:
+            - main
+    workflow_dispatch: {}
+
+permissions:
+    contents: read
+
+jobs:
+    ci:
+        runs-on: ubuntu-latest
+
+        steps:
+            - name: Checkout repository
+              uses: actions/checkout@v4
+
+            - name: Install uv
+              uses: astral-sh/setup-uv@v7
+              with:
+                  version: "0.7.19"
+                  enable-cache: true
+                  cache-dependency-glob: |
+                      pyproject.toml
+                      uv.lock
+
+            - name: Install Python
+              run: uv python install
+
+            - name: Install the project
+              run: uv sync --locked --all-extras --dev
+
+            - name: Lint (run.sh lint:ci)
+              run: uv run /bin/bash -x run.sh lint:ci
+
+            - name: Tests (run.sh test:ci)
+              run: uv run /bin/bash -x run.sh test:ci
diff --git a/.gitignore b/.gitignore
index 9a63d91..b804181 100644
--- a/.gitignore
+++ b/.gitignore
@@ -208,9 +208,7 @@ __marimo__/
 
 .DS_Store
 
-tests/*
 test-reports/*
-tests/data/*
 !tests/data/.gitkeep   # leave a marker so the empty dir is in the repo
 
 src/cosinor_lite/*.egg-info/
@@ -218,4 +216,4 @@ src/cosinor_lite/states_info.py
 src/cosinor_lite/cities.json
 "README 2.md"
 
-.DS_Store
\ No newline at end of file
+.DS_Store
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 3f07032..e15e5e0 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,106 +1,40 @@
-# See https://pre-commit.com for more information
-# See https://pre-commit.com/hooks.html for more hooks
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
+    rev: v6.0.0
     hooks:
-      # Fails if there are any ">>>>>" lines in files due to merge conflicts.
-      - id: check-merge-conflict
-      # Trims trailing whitespace. Allow a single space on the end of .md lines for hard line breaks.
       - id: trailing-whitespace
-        args: [--markdown-linebreak-ext=md]
-      # Makes sure files end in a newline and only a newline;
       - id: end-of-file-fixer
-      # Attempts to load all TOML files to verify syntax.
-      - id: check-toml
-      # Attempts to load all yaml files to verify syntax; unsafe: only check syntax, do not load yaml
       - id: check-yaml
-        args: ["--unsafe"]
-      # Check for symlinks that do not point to anything.
-      - id: check-symlinks
-      # Fail if staged files are above a certain size.
-      # To add a large file, use 'git lfs track <file>; git add <file> to track large files with
-      # git-lfs rather than commiting them directly to the git history
+      - id: check-toml
       - id: check-added-large-files
-        args: ["--maxkb=500"]
-
-        # HALT! Before you exclude a large file and commit it, forever
-        # bloating our repo size, did you:
-        # (1) use a CLI tool like imageoptim to compress them if they are images
-        # (2) think hard about whether using DVC or git-lfs is more appropriate
-        #     for the file--such as in the case of CSV files or other data
-        # This can be confusing. Reach out for help in our chat to help decide
-        # how to deal adding these large files you have :)
-        exclude: |
-          (?x)(
-              ^example/large/file.csv|
-              ^example/large/sklearn-model.pkl
-          )
-      # Sort requirements in requirements.txt files.
-      - id: requirements-txt-fixer
-      # Prevent addition of new git submodules.
-      - id: forbid-new-submodules
-      # Prevent committing directly to trunk (since Bitbucket wants us to pay for this feature)
-      - id: no-commit-to-branch
-        args: ["--branch=main"]
-      # Detects the presence of private keys
-      - id: detect-private-key
 
-  - repo: https://github.com/charliermarsh/ruff-pre-commit
-    rev: "v0.0.261"
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.14.6
     hooks:
       - id: ruff
-        args: [--exit-non-zero-on-fix, --config=./pyproject.toml]
-
-  # - repo: https://github.com/pre-commit/mirrors-mypy
-  #   rev: "v1.2.0"
-  #   hooks:
-  #     - id: mypy
-  #       args:
-  #         [
-  #           --no-strict-optional,
-  #           --ignore-missing-imports,
-  #           --config-file=./pyproject.toml,
-  #         ]
-
-  # - repo: https://github.com/psf/black
-  #   rev: 23.1.0
-  #   hooks:
-  #     - id: black
-  #       args:
-  #         - --config=./pyproject.toml
+        args: [--fix]
+        exclude: ^app\.py$
+      - id: ruff-format
 
-  # - repo: https://github.com/PyCQA/pylint
-  #   rev: v2.16.3
-  #   hooks:
-  #     - id: pylint
-  #       args:
-  #         - --rcfile=./pyproject.toml
+  - repo: https://github.com/pre-commit/mirrors-mypy
+    rev: v1.18.2
+    hooks:
+      - id: mypy
+        exclude: "^(app\\.py$|tests/)"
 
-  # - repo: https://github.com/PyCQA/flake8
-  #   rev: 6.0.0
-  #   hooks:
-  #     - id: flake8
-  #       args:
-  #         - --toml-config=./pyproject.toml
-  #       additional_dependencies:
-  #         - radon
-  #         - flake8-docstrings
-  #         - Flake8-pyproject
+  - repo: https://github.com/kynan/nbstripout
+    rev: 0.8.2
+    hooks:
+      - id: nbstripout
 
-  # - repo: https://github.com/pycqa/isort
-  #   rev: 5.12.0
-  #   hooks:
-  #     - id: isort
-  #       args:
-  #         - --settings-path=./pyproject.toml
+  - repo: https://github.com/Yelp/detect-secrets
+    rev: v1.5.0
+    hooks:
+      - id: detect-secrets
 
-  # - repo: https://github.com/PyCQA/autoflake
-  #   rev: v2.0.1
-  #   hooks:
-  #     - id: autoflake
-  #       args:
-  #         - --in-place
-  #         - --remove-all-unused-imports
-  #         - --remove-unused-variable
-  #         - --ignore-init-module-imports
+  - repo: https://github.com/econchick/interrogate
+    rev: 1.7.0
+    hooks:
+      - id: interrogate
+        args: ["--fail-under=70", "-vvv"]
+        files: "^src/"
diff --git a/.vscode/settings.json b/.vscode/settings.json
index 7c2ca5c..399162f 100644
--- a/.vscode/settings.json
+++ b/.vscode/settings.json
@@ -9,14 +9,23 @@
     "python.defaultInterpreterPath": "${workspaceFolder}/.venv/bin/python",
     // ─── Ruff: formatter, linter, import sorter ────────
     // Formatting + code actions on save
+    // "[python]": {
+    //     "editor.defaultFormatter": "charliermarsh.ruff", // Ruff formats & sorts imports
+    //     "editor.formatOnSave": true,
+    //     "editor.codeActionsOnSave": {
+    //         "source.organizeImports": "explicit", // Ruff handles import‑sorting:contentReference[oaicite:0]{index=0}
+    //         "source.fixAll": "explicit" // Ruff auto‑fixes lint on save:contentReference[oaicite:1]{index=1}
+    //     }
+    // },
     "[python]": {
-        "editor.defaultFormatter": "charliermarsh.ruff", // Ruff formats & sorts imports
+        "editor.defaultFormatter": "charliermarsh.ruff",
         "editor.formatOnSave": true,
         "editor.codeActionsOnSave": {
-            "source.organizeImports": "explicit", // Ruff handles import‑sorting:contentReference[oaicite:0]{index=0}
-            "source.fixAll": "explicit" // Ruff auto‑fixes lint on save:contentReference[oaicite:1]{index=1}
+            "source.fixAll": true,
+            "source.fixAll.ruff": true,
+            "source.organizeImports": true
         }
-    },
+    }
     // ─── Ty: super‑fast type checking / language server ─
     // After installing the “ty” VS Code extension:
     // "python.languageServer": "None",          // disable Pylance’s LS to avoid clashing:contentReference[oaicite:2]{index=2}
@@ -38,4 +47,4 @@
             "color": "lightgreen"
         }
     ]
-}
\ No newline at end of file
+}
diff --git a/Makefile b/Makefile
index c6733ef..0cad278 100644
--- a/Makefile
+++ b/Makefile
@@ -1,5 +1,3 @@
-# Execute the "targets" in this file with `make <target>` e.g., `make test`.
-#
 # You can also run multiple in sequence, e.g. `make clean lint test serve-coverage-report`
 
 build:
@@ -20,18 +18,6 @@ lint:
 lint-ci:
 	bash run.sh lint:ci
 
-publish-prod:
-	bash run.sh publish:prod
-
-publish-test:
-	bash run.sh publish:test
-
-release-prod:
-	bash run.sh release:prod
-
-release-test:
-	bash run.sh release:test
-
 serve-coverage-report:
 	bash run.sh serve-coverage-report
 
@@ -43,6 +29,3 @@ test-quick:
 
 test:
 	bash run.sh run-tests
-
-test-wheel-locally:
-	bash run.sh test:wheel-locally
diff --git a/README 2.md b/README 2.md
deleted file mode 100644
index 041130f..0000000
--- a/README 2.md	
+++ /dev/null
@@ -1,26 +0,0 @@
-# cosinor-lite
-
-## Quick start
-
-```bash
-pip install cosinor-lite
-```
-
-```python
-from cosinor_lite import ...
-```
-
-## Contributing
-
-```bash
-# clone the repo
-git clone https://github.com/nick-e-p/cosinor-lite.git
-
-# install the dev dependencies
-make install
-
-# run the tests
-make test
-```
-
-
diff --git a/README.md b/README.md
index 7dc5482..55f4b31 100644
--- a/README.md
+++ b/README.md
@@ -1,12 +1,147 @@
 # cosinor-lite
-A simplified package for cosinor analysis of circadian bioluminescent, fluorescent and omics datasets
 
-## Installing
+`cosinor-lite` is a lightweight toolkit for exploring circadian oscillations across multiomics and live-cell imaging experiments. It relies on widely used statistical models and offers two modes of use.
+
+- **Interactive Gradio dashboard** to drag & drop your data files.
+- **Python package** for fine-grained customisation beyond the settings offered in the Gradio UI.
+
+🚀 Try the interactive analysis pipeline on
+**[Hugging Face Spaces → cosinor-lite](https://huggingface.co/spaces/nick-e-p/cosinor-lite)**.
+
+## Why `cosinor-lite?`
+
+- **Unified workflow for live cell data**: Perform detrending and fit cosinor models with either a fixed 24 h, free-period or damped oscillation with consistent APIs across data modalities (i.e. bioluminescence, cytokines etc.).
+- **Differential rhythmicity across any omics type**: Compare two conditions for differential rhythmicity for any type of omics data using BIC model-selection strategies with widespread use in the circadian literature.
+- **Create publication-ready analysis with a simple UI**: Launch a browser-based analysis console for visual quality control, plots, and parameter export.
+
+## Extracting circadian parameters from live-cell data
+
+For live-cell data (see tutorial in [`notebooks/live_cell_tutorial`](./notebooks/live_cell_tutorial.ipynb)), `cosinor-lite` offers the choice between three different cosinor models:
+
+- a) Fixed 24-h period
+
+- b) Free period
+
+- c) Free period with damped amplitude
+
+<div style="text-align: center;">
+  <img src="./images/live_cell_fitting-01.png" alt="Model selection" style="width:100%;"/>
+</div>
+
+Once a model is chosen, it is fitted independently to each sample. The fitted parameters can be exported for downstream statistical analysis.
+
+## Differential rhytmicity analysis of omics datasets
+
+`cosinor-lite` includes a toolbox for performing differential rhytmicity analysis of omics datasets (ee [`notebooks/omics_tutorial`](./notebooks/omics_tutorial.ipynb)) for tutorial. The details of the method are nicely explained in the article:
+
+> Pelikan A, Herzel H, Kramer A, Ananthasubramaniam B. 2022. Venn diagram analysis overestimates the extent of circadian rhythm reprogramming. The FEBS Journal 289:6605–6621. doi:10.1111/febs.16095
+
+Here is an adaptation of their figure explaining the methdology:
+
+<div style="text-align: center;">
+  <img src="./images/model_selection.png" alt="Model selection" style="width:60%;"/>
+</div>
+
+For condition 1 (i.e. alpha cells) and condition 2 (i.e. beta cells), we fit 5 different models:
+
+- Model 1) Arrhythmic in alpha and beta cells
+
+- Model 2) Rhythmic in beta cells only
+
+- Model 3) Rhythmic in alpha cells only
+
+- Model 4) Rhythmic in alpha and beta cells with the same rhythmic parameters (i.e. phase and amplitude)
+
+- Model 5) Rhythmic in both but with differential rhythmicity in alpha vs beta cells
+
+## Installation
 
 ```bash
-# clone the repo
-git clone https://github.com/nick-e-p/cosinor-lite.git && cd cosinor-lite
+# clone the repository
+git clone https://github.com/nick-e-p/cosinor-lite.git
+cd cosinor-lite
 
-# install the dev dependencies
+# create and populate the local uv environment
 uv sync
 
+# activate the environment
+source .venv/bin/activate
+```
+
+> **Tip:** The project targets Python 3.11+. Using `uv` keeps package resolution reproducible via `uv.lock`.
+
+## Quick Start
+
+### Launch the interactive app
+
+```bash
+uv run python app.py
+```
+
+Open the printed local URL to explore:
+
+- **Live cell**: Upload CSVs with participant, replicate, and time-series data, then compare detrending strategies or cosinor fits across groups.
+- **Omics**: Load expression matrices, build time vectors automatically or manually, compute differential rhythmicity, and download publication-ready plots.
+
+### Use the Python API
+
+```python
+from cosinor_lite.livecell_dataset import LiveCellDataset
+from cosinor_lite.livecell_cosinor_analysis import CosinorAnalysis
+
+dataset = LiveCellDataset(
+    ids=["mouse_a", "mouse_b"],
+    group=["treated", "control"],
+    replicate=[1, 1],
+    time_series=my_expression_matrix,
+    time=my_timepoints,
+)
+
+analysis = CosinorAnalysis(dataset=dataset)
+fit = analysis.fit(group="treated", model="damped")
+print(fit.parameters)
+```
+
+## Repository Layout
+
+```
+src/cosinor_lite/        # Core library modules and models
+app.py                   # Gradio application entry point
+tests/                   # Pytest suites (unit, integration, fixtures)
+data/                    # Example datasets for the app
+images/                  # Illustration assets used in the UI
+```
+
+## Development Workflow
+
+```bash
+# run style, type, and security hooks
+pre-commit run --all-files
+
+# execute unit tests
+uv run pytest
+```
+
+## Sample Datasets
+
+The `data/` directory includes curated examples to help you get started:
+
+- `bioluminescence_example.csv`: Bioluminescence time series for live-cell analysis.
+- `cytokine_example.csv`: An alternative CSV with the same schema for cosinor fitting using cytokine data.
+- `GSE95156_Alpha_Beta.txt`: RNA-seq data used in the omics differential rhythmicity workflow.
+
+Each file is formatted to drag & drop into `app.py` as well as the library APIs.
+
+## Contributing
+
+1. Fork the repository and create a feature branch.
+2. Install dependencies with `uv sync` and activate the environment.
+3. Implement your changes with tests.
+4. Verify via `pre-commit run --all-files` and `uv run pytest`.
+5. Open a pull request describing the motivation and results.
+
+Bug reports and feature proposals are welcome through GitHub issues. Please include reproducible examples whenever possible.
+
+## License
+
+Licensed under the Apache License 2.0. See `LICENSE` for details.
diff --git a/app.py b/app.py
new file mode 100644
index 0000000..75a866b
--- /dev/null
+++ b/app.py
@@ -0,0 +1,869 @@
+from __future__ import annotations
+
+import re
+import tempfile
+from collections.abc import Sequence
+from pathlib import Path
+
+import gradio as gr
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+from cosinor_lite.livecell_cosinor_analysis import CosinorAnalysis
+from cosinor_lite.livecell_dataset import LiveCellDataset
+from cosinor_lite.omics_dataset import OmicsDataset
+from cosinor_lite.omics_differential_rhytmicity import DifferentialRhythmicity, OmicsHeatmap
+
+plt.rcParams.update(
+    {
+        "font.size": 8,
+        "axes.titlesize": 8,
+        "axes.labelsize": 8,
+        "xtick.labelsize": 8,
+        "ytick.labelsize": 8,
+        "legend.fontsize": 8,
+        "figure.titlesize": 8,
+        "pdf.fonttype": 42,
+    },
+)
+plt.style.use("seaborn-v0_8-ticks")
+
+# --- (your rcParams / styles as-is) ---
+
+APP_DIR = Path(__file__).parent
+bioluminescence_file = str(APP_DIR / "data" / "bioluminescence_example.csv")
+cytokine_file = str(APP_DIR / "data" / "cytokine_example.csv")
+omics_example = str(APP_DIR / "data" / "GSE95156_Alpha_Beta.txt")
+method_img = str(APP_DIR / "images" / "live_cell_fitting-01.png")
+model_selection_img = str(APP_DIR / "images" / "model_selection.png")
+
+with gr.Blocks(title="Cosinor Analysis — Live Cell & Omics") as demo:
+    gr.Markdown(
+        """
+    # Cosinor Analysis App
+
+    A simple app for circadian cosinor analysis & biostatistics.
+
+    Choose between:
+    - inferring rhythmic properties of live cell data using three different cosinor models
+    - differential rhythmicity analysis of omics datasets
+
+    """,
+    )
+
+    with gr.Tabs() as tabs:
+        with gr.Tab("Live cell", id=0):
+            gr.Image(
+                value=method_img,
+                label="Choosing a cosinor model and fitting parameters",
+                interactive=False,
+                show_label=True,
+                height=600,  # adjust as needed; or remove to use natural size
+            )
+
+            gr.Markdown(
+                """
+            # Fitting live cell data
+
+            This section allows the use to infer parameters describing circadian oscillations in live cell data.
+            Once inferred, the extracted parameters can be compared between groups in downstream analyses. There are three types of cosinor model to choose from:
+            - 24h period cosinor
+            - Free period (constrained within 20-28h) cosinor
+            - Damped cosinor (equivalent to Chronostar analysis), with an additional dampening coefficient
+
+            There are many valid ways to organise the underlying live cell data file. Here we assume a specific format to facilitate data processing
+
+            - Row 1: contains a unique identifier for the participant, mouse etc.
+            - Row 2: replicate number. If there's only one replicate per unique ID, this can just be a row of 1's
+            - Row 3: the group to which each measurement belongs
+            - Left column; the left column contains the time (going down)
+
+            """,
+            )
+
+            file = gr.File(label="Upload CSV", file_types=[".csv"], type="filepath")
+
+            gr.Examples(
+                examples=[bioluminescence_file, cytokine_file],
+                inputs=file,
+                label="Example input",
+            )
+
+            status = gr.Textbox(label="CSV status", interactive=False)
+
+            with gr.Row():
+                group1_label = gr.Textbox(label="Group 1 label", value="Group 1")
+                group2_label = gr.Textbox(label="Group 2 label", value="Group 2")
+
+            # State (values will be pandas / numpy objects, not components)
+            st_participant_id = gr.State()
+            st_replicate = gr.State()
+            st_group = gr.State()
+            st_time = gr.State()
+            st_time_rows = gr.State()
+
+            def process_csv(
+                fpath: str | Path,
+            ) -> tuple[
+                str,
+                pd.Series,
+                pd.Series,
+                pd.Series,
+                np.ndarray,
+                pd.DataFrame,
+            ]:
+                # If needed, normalize FileData dict -> str path here
+                df_data = pd.read_csv(fpath, index_col=0, header=None)
+
+                participant_id = df_data.iloc[0, :].astype(str)
+                replicate = df_data.iloc[1, :].astype(int)
+                group = df_data.iloc[2, :].astype(str)
+
+                time_index = df_data.index[3:]
+                try:
+                    time = time_index.astype(float).to_numpy()
+                except (TypeError, ValueError) as error:
+                    msg = f"Time index not numeric from row 4 onward: {list(time_index)}"
+                    raise ValueError(msg) from error
+
+                time_rows = df_data.iloc[3:].apply(pd.to_numeric, errors="raise")
+
+                shape_info = (
+                    f"Loaded shape: {df_data.shape} | participants: {len(participant_id)} | time points: {len(time)}"
+                )
+                return shape_info, participant_id, replicate, group, time, time_rows
+
+            # Trigger on both upload and change (Examples sets the value via change)
+            for evt in (file.upload, file.change):
+                evt(
+                    process_csv,
+                    inputs=file,
+                    outputs=[
+                        status,
+                        st_participant_id,
+                        st_replicate,
+                        st_group,
+                        st_time,
+                        st_time_rows,
+                    ],
+                )
+
+            group_choice = gr.Radio(
+                choices=[("Group 1", "group1"), ("Group 2", "group2")],
+                value="group1",
+                label="Select group to analyse",
+            )
+            m_slider = gr.Slider(
+                1,
+                10,
+                value=5,
+                step=1,
+                label="Number of columns for plot",
+            )
+
+            def make_plot(  # noqa: PLR0913
+                ids_series: pd.Series | None,
+                group_series: pd.Series | None,
+                repl_series: pd.Series | None,
+                time_array: np.ndarray | None,
+                time_rows_df: pd.DataFrame | None,
+                g1: str,
+                g2: str,
+                which_group: str,
+                method: str,
+                window: float,
+                m: float,
+                plot_style: str,
+            ) -> tuple[plt.Figure | None, str | None]:
+                if (
+                    ids_series is None
+                    or group_series is None
+                    or repl_series is None
+                    or time_array is None
+                    or time_rows_df is None
+                ):
+                    return None, None
+                ds = LiveCellDataset(
+                    ids=list(ids_series),
+                    group=list(group_series),
+                    replicate=[int(x) for x in list(repl_series)],
+                    time_series=time_rows_df.to_numpy(dtype=float),
+                    time=np.asarray(time_array, dtype=float),
+                    group1_label=g1,
+                    group2_label=g2,
+                )
+                if method == "moving_average":
+                    fig, pdf_path = ds.plot_group_data(
+                        which_group,
+                        method=method,
+                        m=int(m),
+                        window=int(window),
+                        plot_style=plot_style,
+                    )
+                else:
+                    fig, pdf_path = ds.plot_group_data(
+                        which_group,
+                        method=method,
+                        m=int(m),
+                        plot_style=plot_style,
+                    )
+                return fig, pdf_path
+
+            method_choice = gr.Radio(
+                choices=[
+                    ("None (no detrending)", "none"),
+                    ("Linear", "linear"),
+                    ("Linear + quadratic", "poly2"),
+                    ("Moving average", "moving_average"),
+                ],
+                value="none",
+                label="Detrending method",
+            )
+
+            window_slider = gr.Slider(
+                1,
+                1000,
+                value=144,
+                step=1,
+                label="Window size for moving average (if used)",
+            )
+            gr.Markdown(
+                "⚠️ Note: Window size is in **data points** (measurement intervals), not hours.",
+            )
+
+            plot_style_choice = gr.Radio(
+                choices=[("Line", "line"), ("Scatter", "scatter")],
+                value="line",
+                label="Plot style for raw data",
+            )
+
+            build_btn = gr.Button("Plot raw data and trend", variant="primary")
+            plot = gr.Plot(label="Preview")
+            download = gr.File(label="Download plot")
+
+            build_btn.click(
+                make_plot,
+                inputs=[
+                    st_participant_id,
+                    st_group,
+                    st_replicate,
+                    st_time,
+                    st_time_rows,
+                    group1_label,
+                    group2_label,
+                    group_choice,
+                    method_choice,
+                    window_slider,
+                    m_slider,
+                    plot_style_choice,
+                ],
+                outputs=[plot, download],
+            )
+
+            cosinor_model = gr.Radio(
+                choices=[
+                    ("24h period cosinor", "cosinor_24"),
+                    ("Free period (20-28h) cosinor", "cosinor_free_period"),
+                    ("Damped cosinor (Chronostar)", "cosinor_damped"),
+                ],
+                value="cosinor_24",  # must match one of the VALUES above
+                label="Cosinor model",
+            )
+
+            t_lower_slider = gr.Slider(
+                0,
+                1000,
+                value=0,
+                step=1,
+                label="Remove data below this time limit (hours)",
+            )
+            t_upper_slider = gr.Slider(
+                0,
+                1000,
+                value=1000,
+                step=1,
+                label="Remove data above this time limit (hours)",
+            )
+
+            build_btn_cosinor = gr.Button("Build cosinor fits", variant="primary")
+            plot_cosinor = gr.Plot(label="Model fit preview")
+            pdf_export = gr.File(label="Download figure")
+            table_export = gr.Dataframe()
+            download_export = gr.File(label="Download fitted parameters")
+
+            def make_cosinor_fits(  # noqa: PLR0913
+                ids_series: pd.Series | None,
+                group_series: pd.Series | None,
+                repl_series: pd.Series | None,
+                time_array: np.ndarray | None,
+                time_rows_df: pd.DataFrame | None,
+                g1: str,
+                g2: str,
+                which_group: str,
+                method: str,
+                window: float,
+                cosinor_model: str,
+                t_lower: float,
+                t_upper: float,
+                m: float,
+                plot_style: str,
+            ) -> tuple[
+                plt.Figure | None,
+                str | None,
+                pd.DataFrame | None,
+                str | None,
+            ]:
+                if (
+                    ids_series is None
+                    or group_series is None
+                    or repl_series is None
+                    or time_array is None
+                    or time_rows_df is None
+                ):
+                    return None, None, None, None
+                ds = CosinorAnalysis(
+                    ids=list(ids_series),
+                    group=list(group_series),
+                    replicate=[int(x) for x in list(repl_series)],
+                    time_series=time_rows_df.to_numpy(dtype=float),
+                    time=np.asarray(time_array, dtype=float),
+                    group1_label=g1,
+                    group2_label=g2,
+                    t_lower=t_lower,
+                    t_upper=t_upper,
+                )
+                if method == "moving_average":
+                    df_export, csv_path, fig, pdf_path = ds.fit_cosinor(
+                        which_group,
+                        method=method,
+                        window=int(window),
+                        cosinor_model=cosinor_model,
+                        m=int(m),
+                        plot_style=plot_style,
+                    )
+                else:
+                    df_export, csv_path, fig, pdf_path = ds.fit_cosinor(
+                        which_group,
+                        method=method,
+                        cosinor_model=cosinor_model,
+                        m=int(m),
+                        plot_style=plot_style,
+                    )
+                return fig, pdf_path, df_export, csv_path
+
+            build_btn_cosinor.click(
+                make_cosinor_fits,
+                inputs=[
+                    st_participant_id,
+                    st_group,
+                    st_replicate,
+                    st_time,
+                    st_time_rows,
+                    group1_label,
+                    group2_label,
+                    group_choice,
+                    method_choice,
+                    window_slider,
+                    cosinor_model,
+                    t_lower_slider,
+                    t_upper_slider,
+                    m_slider,
+                    plot_style_choice,  # <-- add
+                ],
+                outputs=[plot_cosinor, pdf_export, table_export, download_export],
+            )
+        with gr.Tab("Omics", id=1):
+            gr.Markdown(
+                """
+            # Differential rhytmicity analysis of omics datasets
+
+            Here we perform differential rhythmicity analysis on omics data using a model selection approach.
+            The example data includes published RNA-seq data, but in theory any types of omics data (RNA-seq, proteomics, metabolomics, lipidomics) could be used. The dataset is from the following publication:
+
+            Petrenko V, Saini C, Giovannoni L, Gobet C, Sage D, Unser M, Heddad Masson M, Gu G, Bosco D, Gachon F, Philippe J, Dibner C. 2017. Pancreatic alpha- and beta-cellular clocks have distinct molecular properties and impact on islet hormone secretion and gene expression. Genes Dev 31:383-398. doi:10.1101/gad.290379.116.
+                """,
+            )
+
+            gr.Image(
+                value=model_selection_img,
+                label="Differential rhytmicity analysis with model selection",
+                interactive=False,
+                show_label=True,
+                height=600,  # adjust as needed; or remove to use natural size
+            )
+
+            gr.Markdown(
+                """
+            ## How does the method actually work?
+
+            The details of the method are nicely explained in the article:
+
+            Pelikan A, Herzel H, Kramer A, Ananthasubramaniam B. 2022. Venn diagram analysis overestimates the extent of circadian rhythm reprogramming. The FEBS Journal 289:6605-6621. doi:10.1111/febs.16095
+
+            See the above adaptation of their figure explaining the methodology.
+
+            For condition 1 (i.e. alpha cells) and condition 2 (i.e. beta cells), we fit five different models:
+
+            - Model 1) Arrhythmic in alpha and beta cells
+            - Model 2) Rhythmic in beta cells only
+            - Model 3) Rhythmic in alpha cells only
+            - Model 4) Rhythmic in alpha and beta cells with the same rhythmic parameters (i.e. phase and amplitude)
+            - Model 5) Rhythmic in both but with differential rhythmicity in alpha vs beta cells
+
+            A degree of confidence is calculated for each model (called model weight, which sums to 1 across all models), and a model is chosen if the model weight exceeds a threshold (for this tutorial we will use 0.5). If no model exceeds this threshold, then the model is unclassified, which we define as Model 0.
+
+            - Model 0) unclassified
+
+                """,
+            )
+
+            # File input + example
+            omics_file = gr.File(
+                label="Upload Omics TXT/TSV",
+                file_types=[".txt", ".tsv", ".csv"],
+                type="filepath",
+            )
+            gr.Examples(
+                examples=[omics_example],
+                inputs=omics_file,
+                label="Example input",
+            )
+
+            omics_status = gr.Textbox(label="File status", interactive=False)
+
+            # Multiselects for choosing columns by header names
+            columns_cond1_dd = gr.Dropdown(
+                choices=[],
+                multiselect=True,
+                label="Condition A columns (e.g., ZT_*_a_*)",
+            )
+            columns_cond2_dd = gr.Dropdown(
+                choices=[],
+                multiselect=True,
+                label="Condition B columns (e.g., ZT_*_b_*)",
+            )
+
+            # Optional manual time vectors
+            override_time = gr.Checkbox(
+                label="Override time vectors manually?",
+                value=False,
+            )
+            t_cond1_tb = gr.Textbox(label="t_cond1 (comma-separated)", visible=False)
+            t_cond2_tb = gr.Textbox(label="t_cond2 (comma-separated)", visible=False)
+
+            # Hidden state to stash the DataFrame
+            st_df_rna = gr.State()
+
+            # Preview planned inputs for your class
+            omics_preview = gr.Code(label="Planned class inputs", language="python")
+            build_omics_btn = gr.Button("Build Omics inputs", variant="primary")
+
+            # Sample dropdowns for replicate scatterplot
+            sample1_dd = gr.Dropdown(choices=[], label="Sample 1 (x-axis)")
+            sample2_dd = gr.Dropdown(choices=[], label="Sample 2 (y-axis)")
+            scatter_btn = gr.Button(
+                "Generate replicate scatterplot",
+                variant="secondary",
+            )
+            scatter_plot = gr.Plot(label="Replicate scatterplot")
+            scatter_download = gr.File(label="Download scatterplot")
+
+            # Histogram outputs
+            hist_btn = gr.Button("Generate histogram", variant="primary")
+            omics_plot = gr.Plot(label="Expression histogram")
+            omics_download = gr.File(label="Download histogram")
+
+            # ---------- helpers ----------
+            def _guess_cols(cols: Sequence[str]) -> tuple[list[str], list[str]]:
+                cols = [str(c) for c in cols]
+                a_guess = [c for c in cols if re.search(r"_a_", str(c))]
+                b_guess = [c for c in cols if re.search(r"_b_", str(c))]
+
+                def zt_key(col: str) -> int:
+                    m = re.search(r"ZT_(\d+)", str(col))
+                    return int(m.group(1)) if m else 0
+
+                a_guess = sorted(a_guess, key=zt_key)
+                b_guess = sorted(b_guess, key=zt_key)
+                return a_guess, b_guess
+
+            def _pick_default_samples(
+                cols: Sequence[str],
+            ) -> tuple[str | None, str | None]:
+                # Try ZT_0 ... _1 and ZT_0 ... _2 as a sensible default pair
+                s1 = next((c for c in cols if re.search(r"ZT_0_.*_1$", str(c))), None)
+                s2 = next((c for c in cols if re.search(r"ZT_0_.*_2$", str(c))), None)
+                if s1 and s2 and s1 != s2:
+                    return s1, s2
+                cols = [str(c) for c in cols]
+                return (cols[0] if cols else None, cols[1] if len(cols) > 1 else None)
+
+            def _build_time_vec(n_cols: int, manual_text: str | None) -> list[float]:
+                if manual_text:
+                    try:
+                        return [float(x.strip()) for x in manual_text.split(",") if x.strip()]
+                    except (AttributeError, ValueError):
+                        pass
+                base = [0, 4, 8, 12, 16, 20]
+                reps = max(1, (n_cols + len(base) - 1) // len(base))
+                return [float(value) for value in (base * reps)[:n_cols]]
+
+            # ---------- loaders & toggles ----------
+            def load_omics(
+                fpath: str | Path,
+            ) -> tuple[object, object, object, pd.DataFrame, object, object, object, object]:
+                # Read TSV (tab-separated). Pandas also handles CSV if present.
+                dataframe = pd.read_csv(fpath, sep="\t")
+                # Drop first column by index (your pipeline)
+                if dataframe.shape[1] > 0:
+                    dataframe = dataframe.drop(dataframe.columns[0], axis=1)
+                # Create Genes column from gene_name if present
+                if "gene_name" in dataframe.columns:
+                    dataframe["Genes"] = dataframe["gene_name"].astype(str).str.split("|").str[1]
+
+                status = f"Loaded {dataframe.shape[0]} rows x {dataframe.shape[1]} columns."
+                choices = dataframe.columns.tolist()
+                a_guess, b_guess = _guess_cols(choices)
+                s1, s2 = _pick_default_samples(choices)
+                default_cycle = "0,4,8,12,16,20"
+
+                return (
+                    status,
+                    gr.update(choices=choices, value=a_guess),
+                    gr.update(choices=choices, value=b_guess),
+                    dataframe,
+                    gr.update(value=default_cycle),
+                    gr.update(value=default_cycle),
+                    gr.update(choices=choices, value=s1),
+                    gr.update(choices=choices, value=s2),
+                )
+
+            for evt in (omics_file.upload, omics_file.change):
+                evt(
+                    load_omics,
+                    inputs=omics_file,
+                    outputs=[
+                        omics_status,
+                        columns_cond1_dd,
+                        columns_cond2_dd,
+                        st_df_rna,
+                        t_cond1_tb,
+                        t_cond2_tb,
+                        sample1_dd,
+                        sample2_dd,
+                    ],
+                )
+
+            def toggle_time_fields(checked: object) -> tuple[object, object]:
+                is_checked = bool(checked)
+                return gr.update(visible=is_checked), gr.update(visible=is_checked)
+
+            override_time.change(
+                toggle_time_fields,
+                inputs=override_time,
+                outputs=[t_cond1_tb, t_cond2_tb],
+            )
+
+            # ---------- preview inputs for your future class ----------
+            def build_omics_inputs(  # noqa: PLR0913
+                df: pd.DataFrame | None,
+                cols_a: Sequence[str] | None,
+                cols_b: Sequence[str] | None,
+                use_manual_time: object,
+                t_a_text: str | None,
+                t_b_text: str | None,
+            ) -> str:
+                if df is None:
+                    return "# Upload or select an example file first."
+
+                cols_a = list(cols_a or [])
+                cols_b = list(cols_b or [])
+
+                manual_flag = bool(use_manual_time)
+
+                t_a = _build_time_vec(
+                    len(cols_a),
+                    t_a_text if manual_flag else None,
+                )
+                t_b = _build_time_vec(
+                    len(cols_b),
+                    t_b_text if manual_flag else None,
+                )
+
+                snippet = f"""# Planned inputs for your Omics class
+        columns_cond1 = {cols_a}
+        columns_cond2 = {cols_b}
+        t_cond1 = {t_a}
+        t_cond2 = {t_b}
+
+        # Example construction (later):
+        # rna_data = OmicsDataset(
+        #     df=df_rna,
+        #     columns_cond1=columns_cond1,
+        #     columns_cond2=columns_cond2,
+        #     t_cond1=t_cond1,
+        #     t_cond2=t_cond2,
+        #     deduplicate_on_init=True,
+        # )
+        """
+                return snippet
+
+            build_omics_btn.click(
+                build_omics_inputs,
+                inputs=[
+                    st_df_rna,
+                    columns_cond1_dd,
+                    columns_cond2_dd,
+                    override_time,
+                    t_cond1_tb,
+                    t_cond2_tb,
+                ],
+                outputs=omics_preview,
+            )
+
+            # ---------- histogram ----------
+            def run_histogram(  # noqa: PLR0913
+                df: pd.DataFrame | None,
+                cols_a: Sequence[str] | None,
+                cols_b: Sequence[str] | None,
+                use_manual_time: object,
+                t_a_text: str | None,
+                t_b_text: str | None,
+            ) -> tuple[plt.Figure | None, str | None]:
+                if df is None:
+                    return None, None
+
+                cols_a = list(cols_a or [])
+                cols_b = list(cols_b or [])
+                manual_flag = bool(use_manual_time)
+
+                t_a = _build_time_vec(len(cols_a), t_a_text if manual_flag else None)
+                t_b = _build_time_vec(len(cols_b), t_b_text if manual_flag else None)
+
+                t_a_array = np.asarray(t_a, dtype=float)
+                t_b_array = np.asarray(t_b, dtype=float)
+
+                rna_data = OmicsDataset(
+                    df=df,
+                    columns_cond1=cols_a,
+                    columns_cond2=cols_b,
+                    t_cond1=t_a_array,
+                    t_cond2=t_b_array,
+                    deduplicate_on_init=True,
+                )
+
+                fig = rna_data.expression_histogram()
+
+                with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmpfile:
+                    fig.savefig(tmpfile.name)
+                    tmp_path = tmpfile.name
+                plt.close(fig)
+                return fig, tmp_path
+
+            hist_btn.click(
+                run_histogram,
+                inputs=[
+                    st_df_rna,
+                    columns_cond1_dd,
+                    columns_cond2_dd,
+                    override_time,
+                    t_cond1_tb,
+                    t_cond2_tb,
+                ],
+                outputs=[omics_plot, omics_download],
+            )
+
+            # ---------- replicate scatterplot ----------
+            def run_replicate_scatter(  # noqa: PLR0913
+                df: pd.DataFrame | None,
+                cols_a: Sequence[str] | None,
+                cols_b: Sequence[str] | None,
+                use_manual_time: object,
+                t_a_text: str | None,
+                t_b_text: str | None,
+                sample1: str | None,
+                sample2: str | None,
+            ) -> tuple[plt.Figure | None, str | None]:
+                if df is None or not sample1 or not sample2:
+                    return None, None
+
+                cols_a = list(cols_a or [])
+                cols_b = list(cols_b or [])
+                manual_flag = bool(use_manual_time)
+
+                t_a = _build_time_vec(len(cols_a), t_a_text if manual_flag else None)
+                t_b = _build_time_vec(len(cols_b), t_b_text if manual_flag else None)
+
+                t_a_array = np.asarray(t_a, dtype=float)
+                t_b_array = np.asarray(t_b, dtype=float)
+
+                rna_data = OmicsDataset(
+                    df=df,
+                    columns_cond1=cols_a,
+                    columns_cond2=cols_b,
+                    t_cond1=t_a_array,
+                    t_cond2=t_b_array,
+                    deduplicate_on_init=True,
+                )
+
+                fig = rna_data.replicate_scatterplot(sample1=sample1, sample2=sample2)
+
+                with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp:
+                    fig.savefig(tmp.name)
+                    tmp_path = tmp.name
+                plt.close(fig)
+                return fig, tmp_path
+
+            scatter_btn.click(
+                run_replicate_scatter,
+                inputs=[
+                    st_df_rna,
+                    columns_cond1_dd,
+                    columns_cond2_dd,
+                    override_time,
+                    t_cond1_tb,
+                    t_cond2_tb,
+                    sample1_dd,
+                    sample2_dd,
+                ],
+                outputs=[scatter_plot, scatter_download],
+            )
+
+            # ------------------------------------------------------------
+            # Differential rhythmicity + heatmap
+            # (Paste this inside your existing `with gr.Tab("Omics", id=1):` block)
+            # Re-uses helpers: _build_time_vec, and states/components you already created.
+            # ------------------------------------------------------------
+
+            # Labels for the heatmap and expressed-threshold
+            with gr.Row():
+                cond1_label_tb = gr.Textbox(
+                    label="Condition 1 label",
+                    value="Alpha cells",
+                )
+                cond2_label_tb = gr.Textbox(
+                    label="Condition 2 label",
+                    value="Beta cells",
+                )
+                mean_min_num = gr.Number(
+                    label="mean_min (for is_expressed)",
+                    value=0,
+                    precision=0,
+                )
+
+            compute_dr_btn = gr.Button(
+                "Compute differential rhythmicity & heatmap",
+                variant="primary",
+            )
+
+            # Outputs
+            heatmap_plot = gr.Plot(label="Heatmap preview")
+            heatmap_download = gr.File(label="Download heatmap (PDF)")
+            params_preview = gr.Dataframe(
+                label="Rhythmic parameters (preview)",
+                interactive=False,
+            )
+            params_download = gr.File(label="Download rhythmic parameters (CSV)")
+
+            def run_dr_and_heatmap(  # noqa: PLR0913
+                df: pd.DataFrame | None,
+                cols_a: Sequence[str] | None,
+                cols_b: Sequence[str] | None,
+                use_manual_time: object,
+                t_a_text: str | None,
+                t_b_text: str | None,
+                cond1_label: str,
+                cond2_label: str,
+                mean_min: float | None,
+            ) -> tuple[plt.Figure | None, str | None, pd.DataFrame | None, str | None]:
+                if df is None or not cols_a or not cols_b:
+                    # Nothing to do yet
+                    return None, None, None, None
+
+                cols_a = list(cols_a or [])
+                cols_b = list(cols_b or [])
+                manual_flag = bool(use_manual_time)
+
+                # Build time vectors (reuse your helper)
+                t_a = _build_time_vec(len(cols_a), t_a_text if manual_flag else None)
+                t_b = _build_time_vec(len(cols_b), t_b_text if manual_flag else None)
+
+                t_a_array = np.asarray(t_a, dtype=float)
+                t_b_array = np.asarray(t_b, dtype=float)
+
+                # Construct dataset
+                rna_data = OmicsDataset(
+                    df=df,
+                    columns_cond1=cols_a,
+                    columns_cond2=cols_b,
+                    t_cond1=t_a_array,
+                    t_cond2=t_b_array,
+                    deduplicate_on_init=True,
+                )
+
+                # Mark expressed genes
+                try:
+                    mean_min_value = float(mean_min) if mean_min is not None else 0.0
+                except (TypeError, ValueError):
+                    mean_min_value = 0.0
+                rna_data.add_is_expressed(mean_min=mean_min_value)
+
+                # Differential rhythmicity
+                dr = DifferentialRhythmicity(dataset=rna_data)
+                rhythmic_all = dr.extract_all_circadian_params()  # pandas DataFrame
+
+                # Build heatmap
+                heatmap = OmicsHeatmap(
+                    df=rhythmic_all,
+                    columns_cond1=cols_a,
+                    columns_cond2=cols_b,
+                    t_cond1=t_a_array,
+                    t_cond2=t_b_array,
+                    cond1_label=cond1_label or "Condition 1",
+                    cond2_label=cond2_label or "Condition 2",
+                    show_unexpressed=False,
+                )
+                fig = heatmap.plot_heatmap()  # should return a Matplotlib Figure
+
+                # Save outputs for download
+
+                # Heatmap PDF
+                with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp_pdf:
+                    fig.savefig(tmp_pdf.name)
+                    tmp_pdf_path = tmp_pdf.name
+
+                # Rhythmic params CSV
+                with tempfile.NamedTemporaryFile(delete=False, suffix=".csv") as tmp_csv:
+                    rhythmic_all.to_csv(tmp_csv.name, index=False)
+                    tmp_csv_path = tmp_csv.name
+
+                # For preview table, show up to 200 rows to keep UI snappy
+                preview_df = rhythmic_all.head(20)
+
+                return fig, tmp_pdf_path, preview_df, tmp_csv_path
+
+            compute_dr_btn.click(
+                run_dr_and_heatmap,
+                inputs=[
+                    st_df_rna,
+                    columns_cond1_dd,
+                    columns_cond2_dd,
+                    override_time,
+                    t_cond1_tb,
+                    t_cond2_tb,
+                    cond1_label_tb,
+                    cond2_label_tb,
+                    mean_min_num,
+                ],
+                outputs=[
+                    heatmap_plot,
+                    heatmap_download,
+                    params_preview,
+                    params_download,
+                ],
+            )
+
+
+if __name__ == "__main__":
+    demo.launch()
diff --git a/data/bioluminescence_example.csv b/data/bioluminescence_example.csv
new file mode 100644
index 0000000..6104417
--- /dev/null
+++ b/data/bioluminescence_example.csv
@@ -0,0 +1,583 @@
+Participant ID,1,1,1,2,3,3,4,4,5,5,6,6,7,7,7,8,8,8,9,9,10,10,11,11,11,12,12,12,12,13,13,13,14,14,14,15,15,15,16,16,17,17,18,18,18,19,19,19,19,20,20,20,21,21,22,22,23,23,24,24
+Replicate,1,2,3,1,1,2,1,2,1,2,1,2,1,2,3,1,2,3,1,2,1,2,1,2,3,1,2,3,4,1,2,3,1,2,3,1,2,3,1,2,1,2,1,2,3,1,2,3,4,1,2,3,1,2,1,2,1,2,1,2
+Group,Group 1,Group 1,Group 1,Group 1,Group 1,Group 1,Group 1,Group 1,Group 1,Group 1,Group 1,Group 1,Group 1,Group 1,Group 1,Group 1,Group 1,Group 1,Group 1,Group 1,Group 1,Group 1,Group 1,Group 1,Group 1,Group 1,Group 1,Group 1,Group 1,Group 2,Group 2,Group 2,Group 2,Group 2,Group 2,Group 2,Group 2,Group 2,Group 2,Group 2,Group 2,Group 2,Group 2,Group 2,Group 2,Group 2,Group 2,Group 2,Group 2,Group 2,Group 2,Group 2,Group 2,Group 2,Group 2,Group 2,Group 1,Group 1,Group 2,Group 2
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diff --git a/data/cytokine_example.csv b/data/cytokine_example.csv
new file mode 100644
index 0000000..53999fb
--- /dev/null
+++ b/data/cytokine_example.csv
@@ -0,0 +1,13 @@
+Participant ID,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15
+Replicate,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+Group,Group 1,Group 1,Group 1,Group 1,Group 1,Group 1,Group 1,Group 2,Group 2,Group 2,Group 2,Group 2,Group 2,Group 2,Group 2
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diff --git a/images/live_cell_fitting-01.png b/images/live_cell_fitting-01.png
new file mode 100644
index 0000000..d42d750
Binary files /dev/null and b/images/live_cell_fitting-01.png differ
diff --git a/notebooks/live_cell_tutorial.ipynb b/notebooks/live_cell_tutorial.ipynb
new file mode 100644
index 0000000..cf7b888
--- /dev/null
+++ b/notebooks/live_cell_tutorial.ipynb
@@ -0,0 +1,214 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0",
+   "metadata": {},
+   "source": [
+    "# Extracting circadian parameters from live-cell data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1",
+   "metadata": {},
+   "source": [
+    "The aim of this tutorial is to use the cosinor_lite Python package to extract circadian parameters from live-cell data. Here we will use bioluminescence data, but in theory any data (e.g. qPCR, cytokine) could be used."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2",
+   "metadata": {},
+   "source": [
+    "## Core methodology - three types of cosinor models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3",
+   "metadata": {},
+   "source": [
+    "The core of the methodology is the possibility to choose between three different cosinor models:\n",
+    "\n",
+    "- a) Fixed 24-h period\n",
+    "\n",
+    "- b) Free period\n",
+    "\n",
+    "- c) Free period with damped amplitude\n",
+    "\n",
+    "<div style=\"text-align: center;\">\n",
+    "  <img src=\"../images/live_cell_fitting-01.png\" alt=\"Model selection\" style=\"width:100%;\"/>\n",
+    "</div>\n",
+    "\n",
+    "Once a model is chosen, it is fitted independently to each sample. The fitted parameters can be exported for downstream statistical analysis."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4",
+   "metadata": {},
+   "source": [
+    "## Loading data and creating a LiveCellDataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5",
+   "metadata": {},
+   "source": [
+    "We first load the data, where we assume it's in a standardised format (see data folder for examples). The data is assumed to have the following format:\n",
+    "- participant_id (row 1)\n",
+    "- replicate (row 2)\n",
+    "- group (row 3)\n",
+    "- time (col 1)\n",
+    "- time series (all the rest of the data from row 4, col 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "import pandas as pd\n",
+    "\n",
+    "from cosinor_lite.livecell_cosinor_analysis import CosinorAnalysis\n",
+    "from cosinor_lite.livecell_dataset import LiveCellDataset\n",
+    "\n",
+    "file: Path = Path.cwd().parent / \"data\" / \"bioluminescence_example.csv\"\n",
+    "\n",
+    "df_data = pd.read_csv(file, header=None)\n",
+    "\n",
+    "participant_id = df_data.iloc[0, 1:].astype(str)\n",
+    "replicate = df_data.iloc[1, 1:].astype(int)\n",
+    "group = df_data.iloc[2, 1:].astype(str)\n",
+    "time = df_data.iloc[3:, 0].to_numpy(dtype=float)\n",
+    "\n",
+    "time_rows = df_data.iloc[3:, 1:].apply(pd.to_numeric)\n",
+    "time_series = time_rows.to_numpy(dtype=float)\n",
+    "\n",
+    "dataset = LiveCellDataset(\n",
+    "    ids=participant_id.tolist(),\n",
+    "    group=group.tolist(),\n",
+    "    replicate=replicate.tolist(),\n",
+    "    time_series=time_series,\n",
+    "    time=time,\n",
+    "    group1_label=\"Group 1\",\n",
+    "    group2_label=\"Group 2\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7",
+   "metadata": {},
+   "source": [
+    "We can now plot the data and perform detrending using either:\n",
+    "- none\n",
+    "- linear\n",
+    "- poly2 (linear + quadratic terms)\n",
+    "- moving_average (need to provide number of window steps)\n",
+    "\n",
+    "As measurements are every 10 mins, we'll use a moving_average window of 240 (24 hour moving average window)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, tmp_path = dataset.plot_group_data(\"group1\", method=\"moving_average\", window=240, plot_style=\"line\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9",
+   "metadata": {},
+   "source": [
+    "## Fitting cosinor parameters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "10",
+   "metadata": {},
+   "source": [
+    "We then create a CosinorAnalysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "11",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cosinor_analysis = CosinorAnalysis(\n",
+    "    ids=participant_id.tolist(),\n",
+    "    group=group.tolist(),\n",
+    "    replicate=replicate.tolist(),\n",
+    "    time_series=time_rows.to_numpy(dtype=float),\n",
+    "    time=time,\n",
+    "    group1_label=\"Group 1\",\n",
+    "    group2_label=\"Group 2\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12",
+   "metadata": {},
+   "source": [
+    "We can then fit our chosen cosinor model from , plot the results and export the parameters as a pandas dataframe df_export, which can then be saved as a .csv file for downstream analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "13",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_export, tmp1_path, fig, tmp2_path = cosinor_analysis.fit_cosinor(\n",
+    "    \"group1\",\n",
+    "    method=\"poly2\",\n",
+    "    cosinor_model=\"cosinor_damped\",\n",
+    "    plot_style=\"line\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "cosinor-lite",
+   "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": "3.11.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/omics_demo.ipynb b/notebooks/omics_demo.ipynb
deleted file mode 100644
index 5b57a84..0000000
--- a/notebooks/omics_demo.ipynb
+++ /dev/null
@@ -1,431 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "03149a0b",
-   "metadata": {},
-   "source": [
-    "# Differential rhytmicity analysis of omics datasets"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8e5abd58",
-   "metadata": {},
-   "source": [
-    "The aim of this tutorial is to use the cosinor_lite Python package to perform differential rhythmicity analysis on omics data. Here we will use published RNA-seq data, but in theory any types of omics data (RNA-seq, proteomics, metabolomics, lipidomics) could be used. The dataset is from the following publication:\n",
-    "\n",
-    "Petrenko V, Saini C, Giovannoni L, Gobet C, Sage D, Unser M, Heddad Masson M, Gu G, Bosco D, Gachon F, Philippe J, Dibner C. 2017. Pancreatic α- and β-cellular clocks have distinct molecular properties and impact on islet hormone secretion and gene expression. Genes Dev 31:383–398. doi:10.1101/gad.290379.116\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2f2a8fe9",
-   "metadata": {},
-   "source": [
-    "### How does the method actually work?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1a0d84d7",
-   "metadata": {},
-   "source": [
-    "The details of the method are nicely explained in the article: \n",
-    "\n",
-    "Pelikan A, Herzel H, Kramer A, Ananthasubramaniam B. 2022. Venn diagram analysis overestimates the extent of circadian rhythm reprogramming. The FEBS Journal 289:6605–6621. doi:10.1111/febs.16095\n",
-    "\n",
-    "Here is an adaptation of their figure explaining the methdology:\n",
-    "\n",
-    "<div style=\"text-align: center;\">\n",
-    "  <img src=\"../images/model_selection.png\" alt=\"Model selection\" style=\"width:60%;\"/>\n",
-    "</div>\n",
-    "\n",
-    "For condition 1 (i.e. alpha cells) and condition 2 (i.e. beta cells), we fit 5 different models:\n",
-    "\n",
-    "- Model 1) Arrhythmic in alpha and beta cells\n",
-    "\n",
-    "- Model 2) Rhythmic in beta cells only\n",
-    "\n",
-    "- Model 3) Rhythmic in alpha cells only\n",
-    "\n",
-    "- Model 4) Rhythmic in alpha and beta cells with the same rhythmic parameters (i.e. phase and amplitude)\n",
-    "\n",
-    "- Model 5) Rhythmic in both but with differential rhythmicity in alpha vs beta cells\n",
-    "\n",
-    "A degree of confidence is calculated for each model (called model weight, which sums to 1 across all models), and a model is chosen if the model weight exceeds a threshold (for this tutorial we will use 0.5). If no model exceeds this threshold, then the model is unclassified, which we define as Model 0\n",
-    "\n",
-    "- Model 0) unclassified\n",
-    "\n",
-    "Let's start running the code to make things more concrete! First we need to load the required packages."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "ea8f55b1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from cosinor_lite.datasets import OmicsDataset\n",
-    "from cosinor_lite.differential_rhytmicity_omics import DifferentialRhythmicity, OmicsHeatmap, TimeSeriesExample\n",
-    "from pathlib import Path\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "plt.rcParams.update(\n",
-    "    {\n",
-    "        \"font.size\": 8,\n",
-    "        \"axes.titlesize\": 8,\n",
-    "        \"axes.labelsize\": 8,\n",
-    "        \"xtick.labelsize\": 8,\n",
-    "        \"ytick.labelsize\": 8,\n",
-    "        \"legend.fontsize\": 8,\n",
-    "        \"figure.titlesize\": 8,\n",
-    "        \"pdf.fonttype\": 42,\n",
-    "    },\n",
-    ")\n",
-    "plt.style.use(\"seaborn-v0_8-ticks\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7ae0da39",
-   "metadata": {},
-   "source": [
-    "## Load data\n",
-    "\n",
-    "Next, we load in the omics dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "85e7d834",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "file: Path = Path.cwd().parent / \"data\" / \"GSE95156_Alpha_Beta.txt\"\n",
-    "\n",
-    "df_rna: pd.DataFrame = pd.read_csv(file, sep=\"\\t\")\n",
-    "df_rna: pd.DataFrame = df_rna.drop(df_rna.columns[0], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f58de732",
-   "metadata": {},
-   "source": [
-    "## Defining sample conditions and times\n",
-    "\n",
-    "Typically in omics datasets, each row represents a different entity (gene/protein/lipid etc.), and each samples are stored as separate column.\n",
-    "\n",
-    "We need to tell the algorithm the names of the samples corresponding to condition 1 and 2, as well as the corresponding times."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "89e5fe76",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_rna[\"Genes\"] = df_rna[\"gene_name\"].str.split(\"|\").str[1]\n",
-    "\n",
-    "columns_cond1: list[str] = [\n",
-    "    \"ZT_0_a_1\",\n",
-    "    \"ZT_4_a_1\",\n",
-    "    \"ZT_8_a_1\",\n",
-    "    \"ZT_12_a_1\",\n",
-    "    \"ZT_16_a_1\",\n",
-    "    \"ZT_20_a_1\",\n",
-    "    \"ZT_0_a_2\",\n",
-    "    \"ZT_4_a_2\",\n",
-    "    \"ZT_8_a_2\",\n",
-    "    \"ZT_12_a_2\",\n",
-    "    \"ZT_16_a_2\",\n",
-    "    \"ZT_20_a_2\",\n",
-    "]\n",
-    "\n",
-    "columns_cond2: list[str] = [\n",
-    "    \"ZT_0_b_1\",\n",
-    "    \"ZT_4_b_1\",\n",
-    "    \"ZT_8_b_1\",\n",
-    "    \"ZT_12_b_1\",\n",
-    "    \"ZT_16_b_1\",\n",
-    "    \"ZT_20_b_1\",\n",
-    "    \"ZT_0_b_2\",\n",
-    "    \"ZT_4_b_2\",\n",
-    "    \"ZT_8_b_2\",\n",
-    "    \"ZT_12_b_2\",\n",
-    "    \"ZT_16_b_2\",\n",
-    "    \"ZT_20_b_2\",\n",
-    "]\n",
-    "\n",
-    "t_cond1: np.ndarray = np.array(\n",
-    "    [\n",
-    "        0,\n",
-    "        4,\n",
-    "        8,\n",
-    "        12,\n",
-    "        16,\n",
-    "        20,\n",
-    "        0,\n",
-    "        4,\n",
-    "        8,\n",
-    "        12,\n",
-    "        16,\n",
-    "        20,\n",
-    "    ],\n",
-    ")\n",
-    "\n",
-    "t_cond2: np.ndarray = t_cond1.copy()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5ad6104c",
-   "metadata": {},
-   "source": [
-    "## Processing the data and visualising the distribution of expression\n",
-    "\n",
-    "We next load the data and create a plot of the distribution of all expression values.\n",
-    "\n",
-    "In some cases there will be a bimodal distribution, with one large peak at high values and a second, typically smaller peak at very low values. This can be a sign that the small peak at low values represents noise and can be considered as unexpressed."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "ec92dddd",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "8.0\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 236.22x472.441 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "rna_data: OmicsDataset = OmicsDataset(\n",
-    "    df=df_rna,\n",
-    "    columns_cond1=columns_cond1,\n",
-    "    columns_cond2=columns_cond2,\n",
-    "    t_cond1=t_cond1,\n",
-    "    t_cond2=t_cond2,\n",
-    "    deduplicate_on_init=True,\n",
-    ")\n",
-    "rna_data.expression_histogram()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b639ef92",
-   "metadata": {},
-   "source": [
-    "## Quality control\n",
-    "\n",
-    "Another basic technique for quality control is to create a scatterplot of technical replicates"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "22d636f8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 314.961x314.961 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "rna_data.replicate_scatterplot(sample1=\"ZT_0_a_1\", sample2=\"ZT_0_a_2\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f7c75c45",
-   "metadata": {},
-   "source": [
-    "In some cases this may reveal that measurements below a certain threshold look visually less correlated between samples. Here we will defines genes as non-expressed using the threshold of <0, as used in the original 2017 article."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "3d59d590",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rna_data.add_is_expressed(mean_min=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f7794808",
-   "metadata": {},
-   "source": [
-    "## Fitting rhythmic models to the omics dataset\n",
-    "\n",
-    "Now things get exciting! We can finally start fitting the rhythmic models to the data. The output of calling DifferentialRhythmicity().extract_all_circadian_params() is a dataframe that contains the model weights across the 5 different models, as well as the amplitude and phase from the selected model for each condition. This can take some time (~5 mins), so go and get a coffee!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "0de5e8df",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Fitting models to genes expressed in both conditions: 100%|██████████| 11147/11147 [07:37<00:00, 24.39it/s]\n",
-      "Fitting models to genes expressed in cond1 only: 100%|██████████| 760/760 [00:07<00:00, 104.93it/s]\n",
-      "Fitting models to genes expressed in cond2 only: 100%|██████████| 478/478 [00:04<00:00, 105.43it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "dr = DifferentialRhythmicity(dataset=rna_data)\n",
-    "rhythmic_all = dr.extract_all_circadian_params()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e135eb6c",
-   "metadata": {},
-   "source": [
-    "## Heatmap of model selection\n",
-    "\n",
-    "We can display the results as a heatmap, where we'll visualise models 2-5 using z-scores for each sample.\n",
-    "\n",
-    "Note that genes expressed that are only expressed in one condition are given the subclass \"a\", while genes that are expressed in both conditions are given the subclass \"b\"."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "cbb9bcab",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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By+Vy4fN44OhRnVQ9qkLoy/E5ZC6AY8ekOqpU0bm8Xi8lPZDB5XLh83ohM1MnVf/MZcrIVAcP2WQugOhoqY6yds33xOvxYPl8AV0bloErMTERjhxh8cUXy5yj/U/KXABPPKFz2dd9r5MBD89tLfU90uIzmSv56achVjfk8HQkJiZCejqLhwzRSS+4QOcC6eDKGZsayVwAb7wh1bHwDs39kfz00wFfG5aBCyDNX4Fb03TB5K3a2sCV7X5A5opZsULmAqhRQxu4aNFC5yrhIZJ5xMfDLbfofMo3LcD34DiZq2MlmQqAF17Q+mQrc78/4EvDNnBVinfx1i2rdcKag3UuIObYPplrQ7d7ZC6A9+6Q6rj1N+Er7IkTUKGCzpcfHg/s2KHziQO2cpdgf7T2RSsnR6qD1qKfLyoq4EvDNnCRnQ1Llsh0bWfpVkAA33yjcyWKc0+Xv/e7VrheuI2l/IsriOxsWLRI50tI0LlAelDqrClTAVC9utb39f4GEk8wZ+nhG7jKlYORI2W6++vLVABEvfGizFXl559lLoBJbd+R+sqWPVfmyvC/RJzMVgBlykD//jrfpk06F8CsWTLV3gHdZS4IaketSOjUIbCEicISTNJJ+AYukP4NX97tuMwFwDDdNujrF02XuQC2iJ9xL16xXOaaHJkDiNPCToPLa+f71GoyX+uPx8tcALRqJVOpqwqqVtX6PH67VhgAYRu4fH4bqYdjZL7vv9e5AC7zeGSuYQkfyFwAo6OvkfqOn3eRzOV3RodE1b/DAbVrC4XNmgllSFd4CZfKVABkZGh9qqPQYB5pYRu4jh+HV1/V+cRJU/DGCZnq6CXaQJIk/laW26o7yI/wuII6hC62z2H5qOjWJfhQq5bOBfDDDzJVxYoyFQA1xWdqCxZoPNnZgV8btoGrQpybJwdtkPmenKB9o3zgHd05UPmrtHv4WdctlPo2CLPC3LYoQiEh3oedfei2Cqt9/rnMBcDw4TJVzKrFMhdAVlay1KcqO7QFUccdtoEr7YSTW1/SBZO3XtNt3QF4OEvmcowdK3MBJByT6mj2+NUyl/PYQUhMlPnyw26HapWE39mBA3UugPXrdS5xK4uffpLquOoqjccRRIOesA1c5crB/ffrfD9u1LZFajnxep1MvA/6ofDvDaCf8O2cMWN0rgLweODXHbrvbIN162QuQLsdW7euzoX4bBI4eFDjCbDbExDGgcvhd1HviLAAWfW3e5JtD70vc9V7UlsR3Lnza1LfyM90yRmHM6OpWPJJhWRnw1df6XwN1DncyuLqoUN1LqCWsPwOdN26TDo84HdEcbxhW5mvXJKm1uEUt3TVub4+X/ukHXrWXKkv49reMpd6myc/YmKgRw+h8Dvx9qgy9U5eVK4943r7bY0nPT3wa8M2cB09CuPH63zP7xigkwFfPz5KJ6t7n84FLN6kSxoAKLtZ53K7QyKpEI8Htm3T+Rqo+xRlZelcZcvqXECcuIJd9ePZgygXC9vAVbGsh+dv/1UnXCBcAgGkpclUXR/WbaUBLHlru9T3+aY6Mlcw+/jFSazDQ+86wohd60KdC+AsXfISq4VHEkCjRtreiM89p/EEs0gO28B18KiDa8ZremwBfGB7ROYC4A7dudOS2tqWT3M3N5f6+iTp6pleiPIBJd+JwBfh4FAl3TiOyqk/ylwA7N6tcynn/gGrD0h1dOyo8QRTMRG2gatSJXhNecZ/p/hhNGmSTPX5OcKsO/TnQNWq6bYmXV47USUft8jKgjlzdL6ba+p2CAA4fFjnEvdgyhQulAGuEfUfWBxEOVzYBq6ICO1e8Pbxuiw/gDo/zZC5+swUDhwEpudMkfoGDdK5gtnHL07i4uBqXfkav+7RFrE3mDdPJxMfWqonhIciYRu4jh2DZ5/V+R7YO0InA9KfeFnmenff5TIXwAfp2qGcqVG6kTTBdAcoTo4cgXG6WYs8n6j9O6WO7txStiQ5yTsIV5PA469relqZlk/kZm9t0HV84vf/6AIJwHrdqDGGHxA+4UDa2RugxralMpfdkwP2ki/kiowU97zrpis5AGDmTJ3r9dd1LuCzJtpONqru9yarkNz+Wn366HznntAmMExcpEtg6HcsReYC+P2mx6S+c1N1Y01CBa9XW6MrH9ubmqpzPfqozgVc5hUmngDPfXy2xBNMjXrYBq6oKO2L+ysLtZlwqmp2AF7YKpTpJ7ySWV7nigiFoSa5W5bx8UKhOOPG9cpbMlfU3cNkLkCcdZa7OldgmuwCjsxjNJip21dfEKs7JwG4ZoBLJ9sr6rJ5kqgsbXrxHa81lbnSTjipVEmmy5dy5WCE8Fh20pxbdTJg6AO66efUF48/VxZXA6mpZSQerzfwa8M2cJ2ISODlOF0wueE6mSoX5VuXaq7BSYaOEa6AgEnX6bYKk38OjQnIaWnwr3/pfP/+t84FwAlh95XMTJ0Lcg/whdxxhyZwrVkT+LVhG7hiYqBbN53vzTd1LoD7779H5rJdqNyXhF6jxVsv5f95W4XR0XCRsCFKMA+louB85Spo8mSdC8i+V5ucESJf2T8RtoFLjXhRgm2FMKHAsnQutJnMAIsP6LYKs31OYmS2/FFn3d6je88CIL1WP5kr/sUXZS6AmCN7pL6aNTXts4I5SwvbwOXAQyN0JeaNbNopqBktdd0sWh/7VuYC2HyW9oyr6bFfZK4nbKGxVahusquenSls5Um8spIbtC1PAFvnzhqRJ/DBpkEFrtTUVJKTkxkwYABPPvn/iQ9paWl07NiRYcOG0bVrVx577DGysrKIiYnhkUceoWHDhowZM4Y2bdowYICoi7plSVNwr16ubYv00VmfyVzvvXeZzAXwww7tGdf5ypqfzEz5xNzTERurHWuybJnOBdCunVAmHiQp7awAuG7SbN1bkYEPNg16xVW+fHmWL1+Ox+PBcXLW8vz580lISADg/vvvZ/z48bRu3ZqVK1dy//33M0f8hgDkzoFu2FCm699fpsrljTdkqtaTk2QuQP56PpKJMtdhktH0ISiYsmVBOfhZfQasSuEGOCdDWDMGuQf4QjZt0niCWHAFH7hiY2Np0qQJK1eupPPJJeSCBQvo3r07fr+fIUOG0Lp1btv9Ro0asW/f/3feXrZsGdOmTcPtdnPHHXfQq1evYPUBc+CgjcsG6f6CP3tHu721vc2XMledUdqtkONvfiT1TbxVt6WcvNYDBP5mWVy4XLB2rc5Xq5bOBfDDDzrX5XWCyOMuCs7WFASfomWk5jDUSeDZkmf0XtKzZ0/mz59P586d2bNnD9HR0VQ6WZxyxRVX5F33wgsv0O0PqX1ZWVl8/PHHHD58mMsvv5zWrVtTuZj6iVSJz+Kzm3WTdN+fq21pc71T+HBv00bnAoaJkwpfflk33sNvdxAKSVoxUX66tzku8w0bU07mAvF056aiuR+nmDVLqrOaNtOInM6ALz2jwNWlSxcmTJiA2+1m3rx59OrVi7179+b9e5/PxxNPPMHGjRt599138/55v379iIyMpEqVKrRo0YKff/75T4GtSHG5pK9dG7PEvdiiRet34N3a2lljo0dLdVT06gYcRVihMY8LCK7HTiFZsUKmAuD1l4LYdyokVmQ9mQvA1lv7rLF9+olGFEQ93BkFrpiYGJKSkli5ciWLFi1i8uTJTJ06FQC3283dd99NVlYW7733HnF/mC1i/0MXRcuy8s7IigXLCm6kZiG5W1ioD0CsTviuNjeDGxNmS32Ha+hSp/02e0isuLDZgnrDLSyttUN7WfyNbjtW2n4NiFGObAEYP17jCSIenPERZq9evZg0aRKJiYnE/6Hp2bhx43A4HLz11lt/CUxffPEFPXv2ZO/evWzYsIHHHiu+ZqrZMRWY3/WZYvvz/5eeAzvJXACfDP9a5hqiHcelHaSGtgYvVMaa4PXCH3ZJipt69bSrko8/1rmSI3TTBQBo3FjrU92PQdwcZxy4OnbsyJgxYxg8eHDeP0tJSWHu3Lmce+65fzrrmnky3TguLo4BAwbg9Xp59NFHqVChwpnq/5YjR+Chh4rtj/8LPYffpJMBVyJavgNUL76/p9NxoGmy1BehW5jj94fGMEkPDn7164KJ9MwJ+M9/hDLpfBiCa+pXFKiyeFyB918NKnDVqFGDJUtyB0E5nU7W/uEHGnGyY+dzzz132v92woQJwagKzVmRB1h8tqhmDKDHqzoX8PNBXS+2F16QqQCYcvtqqe/n6LYyl88XGoHL4c6kwXrdy0/bZ6+UuQBWr9CdcW1L0bZ6qVejhtQnG7MRxCTpsO2cQZkyoCp2Bl6ZIWzqCQwXFiCD+JDr+++lulrX6wKXsr6oQCIi4GTtpQLhcRoQXDFrYTlxQqbKRT3b7NAhjcfnC/jSULmNihx/bBzH++hatr+szRhn+BsJMteUF3Rp0wAs09aplFs1X+aKcGXLC0hPS1QUJOkKyy++WKYCtFuF6vINdu7U+lRzeIqr5VNpIuLgfsoN7CnzXXqp7uEHSDMKXNHaGhxPsi7LD2DhQp0ry3oWcT/m05OTA998I9MtX679O737bp1LnEukT9FUtShTJGeEPGXLwm23yXTb3pGpAMhootveilNPeH3hdanuvPN0LvWWWb54PLBVN9laPRpD6YtyaqcnyH+Z5oxLhzsylg11dWczn4/VJhTwo26YXPNV2kDy8yfTpL46zUSdAYBIvxso+ehllS1H+u33yXwTxFmFn36qc0VGamsckj/8UOqjoqi7ptkqzM0YVW4FNzsmnBEBPLVHd343apRMlcvJzFUZyq2XECnkstm0W1zHjulcAPv361zqbHjOPVfrS0nReIJI8w/bwHX8OCjnu/Wep21EO/rEYZ1MPSVzvjZrKr16A5nLinQQEqErOxvbEt0Mud37tLV5yun26llj8nqK9u01niBWkmEbuKrFpbPwqrdkvg8+vVXmArimfopOpmxDANx31nSpr+9POpfLFRLjuOQt0d5+W6YCtIt2YY4LAJ369tUKvxUNki2uAuTShCc6ns0ddMGkt7gmcNL082WuQWN1LoBnWmnbA/04SLfNqz5XzxenE1q0kOlGint5KgOXcjwMQKcvn/z7i4qSl17SeD4KfOJF2AYucXN4Gq19XycDKlW6Xuaaps2VYNh330l9LYdfI3M5jx0sgb2l0yBusquumVUiLypXJUucYpvoxc6ccUGc7zjX7dM12UW8fL/8G9026EcVtNugA27V3pgzZwi3JotrjE+wuN2wfr1Mt3SptrPM9br3OrKydC5AWjgO6DJdTOCCvZnlaD9Tl+77bcTpezQWF7/2vlfmuvQsmQqAqxPF3baX6OZSkR0inTMiIqAYm1z/L8pAAuLu8Nq8E/jpJ61PVc393/8GfGnYBq7qVf0sniVsVbRJO5SnwfDuMtfjnYStJYBOnbpIfceFX5Ms64nQ6JwRESHNh48PvA1dkSDMO9Ej7DEJQGqqxmNWXHrajxSljJ7khRd0wWTswc9lLgA26eZEAdL5Rs9H5AAhkFbodsPGjTLdWysayVwAd92lcy1frnMBuk4Wp9i3T+MxTXaBo0fhEd3I+W+n/UvmAmDRIpnqg7JDZS6AVbrnKQBDL9S53BHRIdA34yR+3RapMIERgA0bdC554smuTVrfJZdoPIoJyCGPzyctnx/9pnYmT+/eOt81O7Vphdc8oW3I+uWq+L+/qIgI4qWyeBH3KlwjXkQr0+/VdVzyFVfI1HD8P2EbuA7ZEhkSpcsWmxL/uMwF8G3kWJlr6Vm69lIAXXb8LPVdvElX9PO0+zjEaLvtn5Zy5aRP95vEi4RVq3Sugwd1LgBqi9fsb76p8aSnB3xp2AauyvE5TLlBl5325He6QAIQu0bnuuMOnQuAKx6S6nwzZ+tkc+fqXAVx/LiusBTY2/gBmQu0wx1V46ryUFc8q0agX3ttwJeGbeAiKgou0GX67dZNQQegeXOdyzFyhE4G8hZT9g+EW6EZGSUwwOk0iAuQf/tNpgKgrW7qD4t1LR9zUd78oGtDkp0d8KXhG7j27JGeCI9esFnmApg1SyhTvXGd5OgJbRPRX2rptkJzIqeEQk5h7mHbYWGjZnGzB2UfWvkRUBBJDEVCmTIaTxC/yPANXE4nNG0q1SlR9mK764J1OhkQ2VA74VX5dh4SDXYh9yEhLIRWZxUuWKBzVa+ucwH6vUlVEo+p4yL3l6CaIwNMmCBTATB5ss61O0cbSM72HpX6Dh0rL3P5fPqpFKfFsqTp8MLuUgCMGaNz3atrYpNLUoLWpxprZFZc5C6nGzaU6V7sre0uwbFaMtWwidpu7R076gIJQNeuUl1oIO6coZzVCbBJmMWo3HEFglqZFAmqTJcgXqTCN3BFRUn3J279WNeCCaBuXZ3r9X+JK4JXrtT6nFfJVHabHwiBuhjLkk5b7NAyU+YCeORZ0bkM+mbt8n5WtWppPEG02Q/fwJWZCTNnynSN+wt7zAD3NNWt8FYc0wblKl11Z5MAx4QZby5PBFFROl+++HzSAqQZC3SBBGCssDpl8GCdC4B27bS+Tz/VeMyKC7LKVePzUbo81Xv2TpK5AOhwg051p3asych43cgW0HYut9l0rgIpW1ZaoHf5lAdlLoBrPn1C5urVS6bKRZ3GqNpSDuLmsFmWZRXjRykRmjVrhs/no2pV3Qwg+QPp2DGZKt2eIHOBdAcLgIoRumSQfVlZ2B0ONiib6f0PzZo1w+fxUE259CuvPbfM8egyYIJo+FAkqJvDO9yabd59x49jj4hgwy+//O21YbniioqKwu12h87bbXEg/PbqOvmVFLqHaqTXi1NdO/E/REVF4bbZSqDlg45oYeZmyJQ4FBcOzTZvZFZWwPdGWK64DAaDwRC+hEB6k8FgMBgMgWMCl8FgMBhKFSZwGQwGg6FUYQKXwWAwGEoVJnAZDAaDoVRhApfBYDAYShUmcBkMBoOhVGECl8FgMBhKFSZwGQwGg6FUYQKXwWAwGEoVJnAZDAaDoVRhApfBYDAYShUmcBkMBoOhVBGWY02SkpJwu91Urly5pD+KwfAnDh06hNPpZO3atSX2Gcz9YQhFgrk3wjJwuVwufF6vdiKhevhXVpZMdSCnnMwFEBsr1REfmS1zeT0eSnqSkMvlwufzlehnMBj+F6/XG/C9EZaBKzExEXJyWPz44zqpcCIxALt3y1TTEobLXADTp0t1zJtyQOZKvvpqsAunHJ6GxMREABYvWlSin8Ng+CPJ3boFfG1YBi4AoqPxXNBJpnN8+L7MBcC6dTLV1+Ln7F13aX2HI6vIXH6b3RwsGwyFJHwDFxChfEKozywaNJCp7ughUwEwdarWF+C08CLB7f4HjHo3GIqZsA1cOTnw7bc6Xwf1QfcB3fZWhQoyFaA9mgSoV0/ncjh0LoMhXAnbwBUdZdHhfN2hO/ZknQtg1iyZ6pwjP8pcADt2tJT6fv1V53K7ISZG5zMYwpGwDVy4XPDDDzrf+efrXABvvqlznTzMV3HJJVIdc+fqXFlZJnAZDIUlfANXdDRceKHOt2aNzgXQuLHOtWSJzgWsW3e91HfBBTrXihU6l8EQroRt4MrJgeUrdLVVF+1NkbkA2LRJ5+rfX+cCOuZIdaSl6Vx+v85lMIQrYRu4bDaIVP506owC5X7azJk6F7Dj2Gip76yzdC5ppqvBEKYE9WhPTU0lOTmZAQMG8OSTT+b987S0NDp27MiwYcNISkri6aefxuv1ctZZZ/HUU09Rrpy28wLkHnEpM9Tbx3l1MoCvvtK53ntP5wJu2SHVSeNyCTfNMBjCgqDXJOXLl2f58uV4PB4cJ3N758+fT0JCAj6fj9GjRzN16lRq167Ns88+y9tvv83IkSOL/IP/HT6fdguIaHHgUp5xzZmjcwE7YvtJfWXL6lxmxWUwFJ6gA1dsbCxNmjRh5cqVdO7cGYAFCxbQvXt37HY7ixcvxuFw4Ha7OXDgAA1OFsquWbOGiRMn4nK5OH78OPfeey+XXnppkf4wf8Ruh0qViu2P/yu9ewtlwMSJOtf69ToXsL+WNnAlJOhcJnAZDIXnjE6Bevbsyfz58+ncuTN79uwhOjqaSiejhMPhYPPmzdx00004HI681da0adN47LHHqFevHt999x3/+c9/ij1wKbO4P/uumk4GXKbsjSiumq1eXaojLk7nkp67GgxhyhndRl26dGHChAm43W7mzZtHr1692Lt3b96/b9SoEd9++y3Tp0/nnnvu4cMPP+SZZ55h6dKlfPXVV/z8889kZmYW2Q9xOjIytM1ax4/XuQBo2FDnqlVL5wKST3wm9f2QcJnUZzAYCscZBa6YmBiSkpJYuXIlixYtYvLkyUydOpWsrCyWLFlC165dAejfvz/PPPMMAIMGDaJNmza0a9eO9u3bM2rUqKL7KU5DmTJwxRXFqvgTNWroXAAj99wrc03MFnbZB0hPl+qa9tIFLmVfRIMhXDnjjYtevXoxadIkEhMTiY+PB3K3CceOHcuUKVNo0KABX3zxBUlJSRw7doyUlBSmT59OVFQUL7/8smQekLJmpvKJ7ToZsGdPHZ3s+uLb0j0t72s77Uel6Ho+2bwe07DQYCgkZxy4OnbsyJgxYxg8eHDeP3M4HEycOJExY8bg9/upWrUqjz/+OAkJCVx55ZVceumlxMXF0bx5c3JycsjMzKRMmTJF8XP8hbgYHzd221Msf/bpeGqaMJAg7sK0YIFQBjzyiNb37rs6V3a2CVwGQyGxWSU9jrUYSE5OxuWC559fLHPmiLs9fCY8Bho3TucCKD9BW4CsHOyYPH8+VKjA4sW67+ZfPkNybkNoM0jSEEqcGiQZyL0RtjlODgfUr6/z/fabzgXaYyBpPRxQXnzGpZxtZtIKDYbCE7Z3UYTNolxk8WYu/pHq1YtnyzM/lK0Ka9fWuQCopi0toGpVnUu4ujMYwpWwDVxZ2TbmLtUFk5O12DJuu03nUpYVAFyvTtH86COdKyMjN+XVYDCcMWEbuGJjoWdPnU+5AgLt9l1UlM4FwI4NWl/z5jpXSorOZTCEKWEbuPB6se/6XabLyDhX5gLwClsjyo9l1Fl3ytYZpueTwVBowjdwRURIm9C1r+GRuQC++kr3cM/IkKlyUSdnJCXpXCY5w2AoNOF7F9lsuVOQVezapXMBGRm6urHYWJkql7Zttb6NG3Uut9uccRkMhSR8A1d6Orz9tky3s89wmQugbl2dS73iOtz7Rqmv4t3X62TKPV6DIUwJywLkZs2a4fN6qVa+vE7qculcwDESZC6PdheUyvHiam7hmdq+gwex2+1s2CBOQPkDzZo1w+fzUU1ddmAwFMC+ffsCvjfCcsUVFRWF22aDmBidVOkCYdgqCYRbvGIiIyNxlnCn3aioKNxud4l+BoPhfwnm3gjLFZfBYDAYwheTm2swGAyGUoUJXAaDwWAoVZjAZTAYDIZShQlcBoPBYChVmMBlMBgMhlKFCVwGg8FgKFWYwGUwGAyGUoUJXAaDwWAoVZjAZTAYDIZShQlcBoPBYChVmMBlMBgMhlKFCVwGg8FgKFWYwGUwGAyGUkVYjjVJSkrC7XZTuXLlkv4oBsOfOHToEE6nk7Vr15bYZzD3hyEUCebeCMvA5XK58Pl84PfrpMeO6VwAwplOaTlxMhdIfzQAysbpvider5eSniTkcrnweTxw5IhOGql91By3yspc5eJ8MheAD7vUd/y4xuN2e/F4Ars3wjJwJSYmQlYWi++9Vyfdtk3nEjPuyD1SX40aUh316+tcY8cmE13CczITExPJzITb7lksc0aIDyWuzHlf5jrQ43qZC+C776Q6+k3qJfEk77QBgb1EhmXgArBiYslO7i3zxRzV3SgAnHuuTPXYObtlLoB6Xc+W+rbN/PtR4UVFdIQbKNkJyABlMg5w9fR+Mt/G/8yWuQAemaELJo2/kakAGDBA64OhGs2ECQFfGraBy+bKIWbtcp3wggt0LoCxY2Wqn8d8IHMBXHSRVMe3Gc1kLpflJEpmy580exWGVdcFk4a6xR0AD48Vbt89+qjOBdDxDq1v716Nx+sN+NKwDVxER+Npp3sCOpZ8KXMB0jO1rVtlKkC6mASgUiWdS71llh8VK8Izz+h8ccvm6mQAI+bJVI9Ve03mAsh+UarjiUGi5+j06QFfGr6By+/HkSU6VQTYskXnAjjvPJmqa1eZCoBFi7Q+ZYKfywUOh86XH7b0E8RNfUXm6zpzuMwFsGTIUZnrDs0RUB4VNwl3koDvszWBy2UFvoUevoHL64Vdu3S+O+/UuQB69JCplMmZoM8qrF5d5xIn1+XLwZyyXPm1LpgsGbdU5spFl+FTMWOnzAXAmjVS3dpYUeByBX5tiNxGRU+2z8nig7qzi+SbhshcAPTtK1OV1WUWAxAfr/XZtdnFIUF8PNx2m1DYsKFQBpfdUU3m+uwq7Rmwa7gwWxoY9tBoiedTb+DlGWEbuCIixA/cXuL9gg90N8uxq0bIXACZmVIdVaroXKESJGOsLLq7hOdOj+nOnAASEnTnTl9Xv0bmAui0/nupb/WApyQe18rA9+zDNnBFOS1aNxY+Abfk6FwA55wjU1Wp4JG5AFq00B4CPf20znX8uDYZJF8sS/qG8Pt92gSGKZXSZa57HtJuEexv11rqu3quprQg6sShgK8N28Dl9tjY+HsZma9prVoyF6DNYEhJ0bmAr76qJ/WNGaNzDdfmKORLdkQZFla4WuarLT4nVZ4DPX/8vzIXwI3z3pH6rh6t2Spk2LCALw3bwGVZkJ0tFM6cKZQBHTroXOLsjEsukeqCycItNBkZUKGCzpcfkZHaDiV1Zj2nkwEZQ3XnQHHt2slcAHv7S3XwpajUJ4gHdtgGrshIkC6Cbr5ZKAP+9S+d66abdC4gfaFUJ32Ah0pWod2uzaakt66LDUDc84/pZMLSFICcHF3HE4DfB2heArxzAj8HDZHbqOjx+7U9RCv7tI02GThQ53r9dZ0LmCmu+XnrLZ3r0091roKIOHGMci/pHu4Pe8fJXACPNBY2oExI0LnQN+lRveAEk7gUtoHLboeqVYXCstq3rmD2gwvNJ5/oXECnw1IdP/2kc7lcobHqcscm8GNvXTBJeUGmAmDzQN35XSPndpkL4Klt4maFWx+RaGwed8DXhsAtVDz4fLB/v85XbsNKnQyge3edS9zKYu/eG6W+OnV0rhKeaJKH03LR0qtLq371VW0mXNyrmhRuAGrW1LmAXjna8/S7RK0Ks33F1DkjNTWV5ORkBgwYwJNPPpn3z9PS0ujYsSPDhg0jKSmJp59+Gq/Xy1lnncVTTz1FuXLluP766xk+fDht27YNRnnG2O2QmChRAXC4kjBZAqh466062aZNOhfQp6JUx25h83t1F5ICEX6Y1tq4xeZp3WSu9Prny1wA87LelvpooTmffNoZ+HFL0Cuu8uXLs3z5cjweD46TTdfmz59PQkICPp+P0aNHM3XqVGrXrs2zzz7L22+/zciRI4PVFBqPB377TedrfULc/nrwYJ3rFV1PO4AfjmgLnuOEczJDZcXliYhic1nNSyTAtdfKVABscOqCyd5VMhUAF//yi1ao6sMaxMTKoANXbGwsTZo0YeXKlXTu3BmABQsW0L17d+x2O4sXL8bhcOB2uzlw4AANGjTI+28//vhjJkyYgN/v54EHHqBdMaaRulywSviFap0jHsWuPCgRZ011ltpgubBnaagELr8fTpzQ+cZmPaiTAUTq5nHdOaGRzAVw8UN9pD5Z6c26dQFfekZPv549ezJ//nw6d+7Mnj17iI6OptLJdgAOh4PNmzdz00034XA4/rTaiomJ4bPPPmPLli0MHTqUhQsX4iymjqpRUdCmTbH80acnVlx8NE/YQkf5ZAfWRHWS+qKEA7JsNp2rINSBS9npBQhqtlNhWTpImJYKdH9CeEwALJwbRPfbwhDEW90ZBa4uXbowYcIE3G438+bNo1evXuz9w7CxRo0a8e233zJ9+nTuuecePvzwQwAGnBzd2bBhQypUqMCOHTtoWEzNN7OztTkF7Zvu0MkAsrJ0rqZNdS6ghbhA99dfda5QCVxuN6xfr/N1vlM0RfckjtuETa9btdK5CGpQcJEwZbrmzS49I/Cb44wCV0xMDElJSaxcuZJFixYxefJkpk6dSlZWFkuWLKHryQFO/fv355k/TKuz/yFR37IsIotxuys2VtpAHaitlGk7CE+bpnMBDNKlMgP8/LPO5Q4847dYKefI4t76n8t8z7+i3d66R9jN4ud22qD86YdSHU9W0nQ9meYvxjOuU/Tq1YtJkyaRmJhI/Mk5FA6Hg7FjxzJlyhQaNGjAF198QVJSUt5/8/nnn9O8eXM2bNhARkYG5xTj9oHXq22xV7Vdc50MqPKisJtFbW1QVo5RA21j/40bda6CsGJiyeiiCyb32MQTkA/phro1r61r6AswcJa2qe+Ts0RdT2bPDvjSMw5cHTt2ZMyYMQz+Q3abw+Fg4sSJjBkzBr/fT9WqVXn88cfz/n1OTg79+/cnIiKC5557Li8rsThQb4X026vtLpH+oq7RZvwduoNugCTpShm2btW5QiU54/hxeP55nW8cP+pkwPu1dMXV1z+t7Qqyee5gqW/hjgZ/f1ERkO0NPB4EFbhq1KjBkiVLAHA6naz9w8zzESP+P4X5s88++8t/+/777wejKjQ2G0RH63xzzxZ2sgB69+uqkz38sM4FNG+sHaNy5IhujEqozONKKONh3MBtMt/Xe7UP9+vLCgPlxz/pXMDIV4UV88Bll2k8wZz/hm3nDLsdqumGoPKadtwQvf/9b51M2RMJ5L3fugiTQaIj3IBuGytf7HZpm/oUca1Tp3nCDAbxZIjsO6U6LorWdFiJtgWevRi2gSvO6ea6FroDhenTtZl376Ymy1w3VhD1fDmFco8X5HVqIYFlQY5u+KlQBcCMqz6SuS7v2F7mAnhd3Kl58RZN25Nsf+DZi2EbuPyRTg5X0wWT++6TqQB4+WWd68bntO2smDhR61Ou8LxeKKbaxaCw2/FVPUumE4+skpbCzBj1rU4GXD7wIqkvWXQ/PhFhVlxEZGVQcb4ujfuhSdfJXACPPiqUqScfdtP1mQNCp7hKyIkT8OabOt+whkt1MqD5oOKpDz0t0psRbm2obQjwVq1DGlEQ5VFhG7iIjYWePWW628TZYsoVV5d/idtZSSeAoh2S5XLlfjdLGKcTGjfW+TwXdNHJgBuFAwamV9auoCc+LdXBdNEZXhBNFcI3cFmWtPt1feHcOghqynWh8XTUnacBOMaOlvr4Q61hsbNQPN45H6Kdfjqdd1Tme+Ch8jIXgHJ4Am2eEMogvpv2TC31E81WqO+DjwO+NnwDV1aWNNtn7Kfa6vlnn9W5lIXcAPXUI16V7bNCpZArKwvm6oqCly3T1gKOGqVz/X6wjE4GnHuTsPkAUGPPaonH7jVnXBAfD7fcItO91FGmAuAdXf2x/GC93gJxlwVlr7kQGcjljY5jZ0ddMPnWpm0b9txU3ZlzWppMBcCTg8Q3pOphk5ER8KXhG7hcLlirO5upVFs32whgzhyd65nhO3UyAKe4dYYyOUM5jqYAMjPh48B3ZgrNfX21kyTvrRv4UMJCo5xGDrxcfYnUd/vTohYrlwR+JBEad1Ex4CKKb/26YNK+knYLaMAA3cP21xztSIoGjXUjKQC4XriNdexYSCRnlIvK4b72K2S+H7O0JRUNhc2MY3zCIIm+UbNqglIwO/ZhG7iibG7ax22Q+VavaSZzgXZ74uBBnQuggT9VK6wjbKGjbEVfEF4vbN8u07X8VTg/Drhymy5h4hNlXQFwtfZIjc2bNR7T8gnk+b71lUP5gNtu07me0CZNcdH52joV6byxTZt0rgI45o3j8d26nPFOusoUAD55TLgKyqiqcwE1umjnf6X/N/DJxIUhmF308A1cGRnwwQcyXVpbbQHykSM6l7w+t0oVrS8mRucKkTMuu137a/7qK50L4KIVumKn43c8IHMBlLtI2zmjqiguB9OAOjTuouIgKyuo+S6F5UQjbeBSbt81144ag5OTsmV8843OFRGhcxVAfOZ+bv1MN4js+orarULG6r5D8kRRZS0MkLpF4/EGcbRts6xQKSwpOpo1a4bP46FalGbkNCBvi+T26h6AJ8TboGXEe/gxXt0gwH0nTmB3ONiwQXf++r/k3R/CX/QhT4LMBVC5ss6lLAMEiHXpCscBrARN8fj+/fuw2+0B3RthueKKiorCbbNBpUol/VGKDWWf1jD+NZ5EN1E2MicHZwk32c27P4TNhYVxRI48STRW24VEdVIQGRkZ8L0Rlisug8FgMIQvobHhbjAYDAZDgJjAZTAYDIZShQlcBoPBYChVmMBlMBgMhlKFCVwGg8FgKFWYwGUwGAyGUoUJXAaDwWAoVZjAZTAYDIZShQlcBoPBYChVmMBlMBgMhlKFCVwGg8FgKFWYwGUwGAyGUoUJXAaDwWAoVYTlWJOkpCTcbjeVlUN5DIYAOHToEE6nk7Vr15bYZzD3hyEUCebeCMvA5XK58Hm9kJ2tkwYzvrMIsOJ0M6T27ZOpAEhM1PoiPbrvidfjoaQnCblcLnwej3ZCqNutcwGZMbohcuIfTT7/K8qhGfHs9XoDvjfCMnAlJiaCx8Pit9/WSWfO1LkAOnaUqZrf3l7mAvh6XqbUd9yrmwTcv38yESW8QZ+YmAh+P4tnzdJJ775b5wJm958ic336qUwFwPvviV98liyRaJLHjQv42rAMXABYlnamtugv9xSHbxktc/XqJVMB8NFcXSABuLq3LlBG2Cx0M2ULwOuFXbtkutW36wIJQL/pd8lcK6q+KHMBvD9N+/253iv6ngSxaxW+gcvhgLp1dT7hQwBg2TKdq0cPnQsgI0Prs2KFgdIWAkELwOeDvXtlurbuFJkLkK7wbtRuEHDttVrf9ROqakSRgYej8A1cR47Agw/qfGvW6FzA5bOnyVxPrblO5gJIS5Pq6NPDpZNZVmgEr8hIqFVL51uwQOcCOHhQplqccbPMBXDeeVIdtGql8TidAV8avoGrQgV4/HGdb948nQvgwgtlqqypMlXJMH68znX0aO53s6SJjISaNXW+Y8d0LpD+bIP6ylQAfPih1if7vtrtAV8avoHL59O+uvs1mTd5rFghU1Wteq7MBbBwoVQHI4R7od98o3MVREYGTNOt2mVv7afYskWmenyZTAXAM89ofdM+ckg8wRwRhG/gysiAjz/W+S64QOcC6VZIxn6ZCoCKFbU+6d9dVJTOVQBWXDzpA2+V+eLf0SYwBLPtVFi6dZOpAJg4Ueub+Z/NEs+UKE/A14Zv4HI6oUkTmc53wUUyF4B9QD+Zq+7gy2UuyD2eVPL7Xl0w8fpswZxBFxs2j5v4337UCfeL336EGQyJ4uSMw4e1PtlWYRB1IiFwCxUTliUtQLY/+5TMBcCVV8pUKSkyVYmgrMNV7yjnhxsnGyJbynzNVt8rcwHSVNgjOTIVoF/hpcdWkXisCHPGlVsToHziXnqpzgXSg6CbbpKpAHj/fa3vjTd0rhMnoJKuqUO+OP05NDvytU7Yp4/OBUG9vReW6tVlKkCfBzbufk3WrY3AC6vDN3BFR0OHDjrfl1/qXCAtdlLfKEePan3KN9gfhbtzBWK3a5+46nT4TZtkqjfWd5K5AKZOler4YKZmKz0jM/AykfANXBERkJBQ0p8iLFCW+4C2uBogKUnnKul2T3nYbNoPU7u2zgXSe3/UKJkKgCuu0Pp+/OqQxDN5ki/ga8M3cFlWbkp8uCJ8W1bHf/WZWv36OpdDk1n89/j92hYlO3boXCCt40pNlakA6NJF6/tkmWaKQGaOOeMCpxOrSVOZzvbFFzIXIL1b4nWN6AEoW1br+ycmZ+BwQMOGOp+63UPjxjJVp6baF+Rbbgn8AV8UTOw+X+J5IyLwZLrwDVweD7YtmvoDIKg+W0WCcJtnvuZ7m0eNGlqfckxEKHR7Ajh23MaTE3VlAA8sfEvmAuD552WqH37SBpIJE6Q6aNNG4wmixjFsA5fbcrDR10jma+qfK3MB0lf3a66RqQC4/nqtr6qohygE1dWmWEkoZ/HA8HSd8FhrnQuk+9vnJwrn/gG33BIj9V3eLfTCROh9oiLC64Xff9f5mqr38AcMkKluu02mAuCdd7S+cvt/lbkifB6IKPmDLrfHxs87dHvAzdXTFn/6Sedq0ULnAqpVO0fqO5BTTuLxWaYAmViy6OOfrROqi52EB+vTxwm3XIEG7XQrZYAVKxrIXL4IB6Gw6MrJ0bZNbC5MTwfgkktkqm/3agOJekdCVXcYzG5E2AYuIiOhTh2dr4x2+CFz5shUjzkfk7kAunaV6qi8canMZffkgD1a5suPsrFeRvTfrRPWvVvnAli/XqZq315777+/XFifiq4kLpj6zfANXE4nnga6rELHlg0yFwC//CJTjZuhHRV+5VXaDIYNlXT5xe6IaHTtXwvA59P2D1ROIwfYt0+mumO6NpA0by7V0b+/xrN8eeDXhm/gcrtxbBS2KWiqC5KAfoUnJEfc+02ZEBoqWYU4ndoU9d9+07kAKmtqjwBGD5KpABg4UOsbOkiTxGOPMC2fcguQlUUzc8VZhb1761zih87evfWkPmUdbsjUcdlsWE5dOrxN3dK8WTOZ6hy/MAsMyMjQzsez4kRJPEG81YVv4IqI0FayqlsiCJvsHu1xtcwF0tpRQNv5IJSaudi8gc8/KjSZ4tkf27bJVLubC18i0Tf1VY3+C+beCNvA5YtwkBqje3OvsW+ZzAXAubq3rvLR2jqVlBRtnYpy8frKKzpXgXg82t5a6nT4q66Sqc4+8rPMBXDsmPaQSzUfL5jdiLANXHavixq7v9UJ1Q3LhO0lft+vDSQXXyzV8fjjOtfRo1C+vM6XHzk+B8v3617sLlq1SuYCYJDw4ElZwY68bEzW8i2YZkBhG7jkvdjUgWvNGpnq3AHa84lffqko9SlHqYVKk93oCDcXJQgzYS+8UOcCbcaNuPnApk2awY6nUDVy8ASxcx2+gevYMW1TL+HWBJA7b0zEtiPaQKLOnG4krHcOlcCV7XMyP1WXwNCzeYrMBcANN8hUXz8qHMgJDB0q1cm6Z5kVF+T+FpRtzcXbBezaJVPV82o7Z8TGajtnCBevuFxB9RItNqKjoXNnoXCqeEdC2HxAfet/+qnWV62axmOSMyA3o3DECJ1PPeF15kyZ6vtRH8lcAE2aSHXExelcoTJI0oZFTIRmJDugH+rWp49M1cCzUeYCWLRIWzP6wgsaT0wQR+lhG7iyXRF8+Z2mOSTAxYc0U0LzEBYEOcWtHoL5AhcFyjpc9e8yX3w+7blsjx46F0jHEs+/aorMBfpeB/bUnRqR1xvwpWEbuGJixD3vWmlrnXj3XZlKOEwW0K9KtmzRuTyeEAleERHaVdDYsToX6GZIof/7PPtsrW/pDk0T4Rxv4OEobAMXXi+O/cImosr2CwBJSTJVea92NfnVV7p2PQAPPqhzhco8LssWQXasLukmRnqghrQAObmztqp87Fjtl+ippzSeYPLNwjpwSVsVqTsDJCbKVJ4EbSBRH3ZvFB5RuN3ShNB8sblyiFkbRFfTwlK/vs4F0hZsP67XBpKHHpLquPNOjSctLfBrwzZw5RDNCoeu63eHL4bJXIC0nZVwggqg60Z9ithYnStU0uGtqGiyky6S+WIefUDmAqSBUv2iNXWq1tetm8bzYxA90cM2cNls4kP+Xr2EMuC992Sqy+/WjYgAePlTUf7tSYI4Ey40brc++SQkUC8zhQezyukwoJuPdYoXHzwg8bz2cuBbrmEbuOx2qFBBKJygCyQAGx/+ROZquuwDmQvgt9+ukfruuEPn+uwznasgbG4XMetX64TqwCVsidaygijr7iRXXKGduHzlcE2njoOHA99yDdvA5XLBunU637niOpWmH42TuRZ31k5AvuUWqY7vv9e5XK7Q2C7MsaL4OqetzNfp0+EyFwDt2slUczdoA4m6O/yYMRrPXXcFfm3YBq7oaGiruy9hl3gWh3A0uXI6DMAH2gWedJdX2UKvIKKjoYNycK/4xS61TieZq/ce4coVmHtI+WCDjz/WeILJbwuR26josdv81IgRNodV7jeBdAxq65qaPe5TPL1N20RU2YIpVCYgu93abMrmHTvqZECNJ3T34wcXvSZzgXYaDehWXMG0XgvbwEVGBkyfrvMNHqxzgbap74kTOhewd682cP1TkdaUqVNFhTnj1zQVdtkHRn6va44MsGyZxpOTE/i1YRu4rPiypA/W9SqMnz5J5gKke/gsWqRzAU6nbk7UPxW7HSoqm/5PE/fyrFRJpvo2QxtI1E2aVWdqwbxIhW3gsmERT7pO2KqVzgW6zpcA77yjcwExn0t10pY9obJV6PNp07irCXtrAtL7sX0NYYce4NGftD2fRo7UeII5/w3bwIXHAzuFaaqrtQe0slkDwJSp2qetqsXMKZRxOT1dvNLJB6c/h5YZws4Zw8VZhc8+q3Mpm10CZctqA5eqAUEwc/jCN3BFRmq7Ud58s84FcO+9MtWQDrq+bwAXDtZuFX75pc7Vt6/OVRAuWzSrnbrOGW2nvyVzAdq3A3E7K3XB880DNe3spv/XCvja8A1cERFYZXVjTWwzZ8hcAOzWbU8sTdUGEnUd10fCcWMZGdr5pvkR5bRoe162TujUNYUG4IsvdK69e3UuICdHWzf2a2oZicfjDXxnJ2wDl88He/bofDWE3doBacunLtV/lbkAbp3eQOobNEjnCoXiYwCvz8buNF3vqbNXrpS5AGgknKJdu7bORW4pg5IGFTTTIRwRpuUTIM6+Wafd55YWdIqrZitrm9Hz0086l8sVGt3hI7NOcPbsV3TCSy/VuUCavLQ88XKZC/SVBW/P0dyQ6Vmm5RPZ2dqzi+uOac+BrKm6QZK2V16WuQC2bdOVMQA8cfXPMtfcmW4gBCZJli0Lw4QTDebN07lAupVevrxMBcCRI1qfqnY8mN2IsA1ccU431zUJok9+YVkgTL0HbI/r+gfObanriwjQ/LhUx5R1zWWudJeT+BCIW1iWdM9pQ60+MhdAs0NPy1xNo7QvrQkJ2jNnVSWDFXhuRvgGLnlPG2UnC4CZM2Wq3s21dSoffaRN91UmSwRzcxYrNpu0gK1ZLe2LnXRIlrhGTT3WRNXLMyIi8GvDNnD5YuLY1+16ma/aKnFW4datWp+QuDitT5k5HczNWZz4/XD4mK7nU8VvtN1XaCxsei2eJClOYqRFC40nmPeosA1cXi/s2qXzVVOn+mRk6Fzi9vDqsQ3/RCIixH+t330nlCGtrfLE6spuQNrNCtCt8DyewK8N28DldOreFABYkyaUIe2cke3U3pi//y7Vcd55OleorLiOHdN2KBkr7egLpOnuR+UEbQB15U3LpkFElEJgVlzkZhUuXKjz9VZvPAtfu4SjvwBopu1ZSsOGOleo1HHJUQeuYF7fC0lMlnB8EhAdre0Ztn2X5ksbzAtA2AYudccnaSIIwNixWp8QdZ3TwYM6l88XGsFLviOxrYJQhnZpqxy1Dvz+e3ep77ffNB6zVQg4HRbNG7p0QvHoD4YMkanaDtHub83br70xlceFoVKAHB0NF14oFP4iPgOOjdW5unbVuYDNT0h1PPKIxmO2CgG/ZeN4jq51Rrnd4hWXcCtEeZ4G8invHDumc6mne+SHuIxL3hZJevAUzATEIiAtTdM78BSVUzX1sHZf4F/IsA1clqX9PpVTNkYEbfFRGe2NomzBBHDllTrXV1/pXAVhs4lXfrVqCWXAww/LVMd7XSNzgT45Y6OjpcTjDqKjTNgGLq8XUlJ0virK13bQNmIMlVS4YkLZG1Gdo5Af6kGS5b75RicD6KPr1FEubbvMBZCaWkfqKydKKjYFyOQ+19smBd5tuNBE1tW5QNuIUcyJE1qf8owrlLYKgxncV2hq1hTK0KbCSn+R8p7XIUnY/gq8Xvh9l+719tymTWUuQLsPumGDzgWULaudN6QsxA2VxavTYdGyrrAN035xOxThGdf2WG39hvJFC+DsRE2SW6TdDJLEZhOnHavruJR7TlWq6FxIW+gBkJqqc/mEmwAF4bdsHHbrzkkrqpfRwsBVp6pmQvApoqO1Z86/79UcS3h9ZpAkHo/2jKvGD+I9fOW4cPH+lrpXYabwuRMqW4UeD+zYofNVVN6MAImJOpe43VtsrDZwqZJ4bIHHrfANXOoJyPIhOconoDiVOUY3mBfQJryFyvlElJVDa9eKkv4YxUfv3jqXODHroYe0A8AGDNB4gvk1hshtVPTExECPHjqf7wpRld5J7AOFOdzibdA9ezpJfcot5WDeKouV6GisCzvIdLZt2plV0oYAyiAJ3HmnVMdbb2k8wcw1DdvA5fNJ+2xS/hXdYEcAxozRucQjVA4dkuqk9UyhErhcLm3D9vYVxC2flNstykNSIC7uXKlPdcQdzLF9WAeuAwd0vnrduulkAK+8onO99JLOBUR9LNXRvLGuC4k68SQ/oqwc2nuX64R1tLVH0gM8Maqtu1PcdpvGE8xCI2wDl92uHRDID9oiRGVU3n5Q2KUD6NtXquPt93R7henp2qYn+RIdDRdcoPPNmaNzgXbaojIRBNgxV6qTjf3ZuTPwa8M2cHm92u9uo8Pa0QbK1vfqdN/pqdqsKeWw3FCp43K54Ns1upKK9uofXFnsJA5c6uHnU6dqPME0VwnbwOV2a3veJaem6GQArVrpXKq5BidJSGgu9ulcoRK4LEs8AFFd5yisqdidoc3yU09ADkXCNnCVK2tx723CzgA5D+pcAI8+qnOddZbOBaxdqw1cyrox9bTc/IiMFPe9zRK+aIG05dPZvy2VuQC2bOki9aWLHqNW4I0zwjdwWdhIR3eYEJ8lruNSPgHFnWHFrd+oUUPnCpU6Lr9fW3p4trKjL2jPCaQTOaFqValOVqAfTGlqiNxGRY983pC4n590FSReJqi305QP8FDpnCFPXlIGEoDOnXWuJUt0LiA+/nKpb9UqjSeY9qthG7gyM7WJTEP2/KSTQe7pugpl9gLQTNuz9B95xpWTA6tX63w11C92yoxJZZdm9INWVVmFZgIyuW+U0kPM/eJBS8pMJmUlN7B9u7bm59JLda5QmcdVpoy47KCpbrAjAC+8oHNde63OhbYHK+huf7NVSO4DQroXvFVcWarMib3pJp0L7ZkTwJYtOpfbHRpFyEeOSIcE88ShZ3UygFtu0bnE+7+tW0t1XHihxhNMB5uwDVxRdi+tKwiLgrt21blA+0YZG6tzIe+go94JDQmcTvGWbIUrhDJg2jSdq39/nQvYvVuq4/ffNR5PEA1swjZwuXyRfH9Et+XUeuMUmQvQdmxXj11H22RX2ckiVM64nE5o2FAo3HhQKEOb6SfuDq/eKgyVhKI/EraBy+GAunWFwlY3CGXAJZfoXKpmZSdR3yiNGulcobBNCLm/Y+UcMnnV7A8/6Fzive1g6p2KAlX2qWmyC0TgpzxHdcLp4gZid92lc6nyYU/Sv7823ff223WuQ4egWjWdLz8iI8Wf49uNQhnaWTXiwirpShmYNUvjCeZFKmwDV447gq/X61qxdBK3RVIWqe28/UmZC2DhU1IdH36oc10hPurJj6wsmD9f5xuuHmut9Im30mvW1HaWqVxZ4zErLvTzuKheXSgDli2Tqc6pqd2baNZMO7RK+dzJyZHnupyWMmXgSuEsUl4Td85Qzv8Sv43UFM5RA109bDAdc8I2cMXGwsUX63wHMofqZEAV5TwucQ+m6Ghtd3jlWU+oHHTbM45TZfrzOuH99+tcAG+8oXOJM27UWbeqc9lghqyGbeDyeLTZN013C/ddALp317lWrNC5gHXrhG8cQFKSVBcS5ESV4+tW98h8nd57XeYCtE0hxZ3vV63SHpKq3jk2bw782rANXHa7buQ0ABuOCWVo582LW9qoj0OUdTGh0h1ePtZEXVWu3P8VN9lVd19RvQOYFRdg97qo/Nu3Mt+3ta6RuQDaf/WVTqYcyod8iopsbEMo4XTqetABkCJuaR5Mx9bCIs4qVDfaN+nwQo7nRPH8d+1lvnuOjJO5AO1bnnjFlZ0t1dGunc41V1w1kR8ej7bVVeUl83QygKZNda4wf7FT7UgEswMQtoGrbFltu7Klax/TyYAuE/voZOJtnqystlKfctc1mO2Q4sThENcD5Qi7tQO8+aZMlXGtNjFLjaqzjNkqBGw+L/HHdIcXjRufLXMB2kZz4jfKdeukOnr00PpCAZtNnAynPuMSvo3EbdAdSQDExel2kgB++knjCaY0NWwDF5YlTeOu4hV1ojyFskW0ck8JbTcrgJde0rmOH9dOpMmPnBxtQ5Q+R9boZCAt4lyaow0kyq1t0D1qoqICvzZ8A1dkJNSqpfUpeeYZnau99sbcKl5xKWcOqhus5Id8Xl3TjkIZ0u7wyu8PaDu9AFx3ncYTTJeusA1cOS4bS1cFEcILSReWylyAdhX06qs6F1BD3Ej83HN1LvX7TX5YlrRrmP4HF+6DRqXtkbkAIiK02Rn79mk8Pl/g14bIbVT0RDt8dGl8QCfcITzhB12OKvDzem1Gwfr1Up20w0qoBK7sbFi0SOfrdNUxnQygWzed6733dC7grLMekPqCCSiFIZiu9yFyGxU96Vl2Js3SVSAPLbtE5gKkQ6SaO4MoaS8CWrUSzhlBW4CsegiEHJMna33K6aA3aEcaOYRnsqArUzN1XOTuFCj7bLJf2dEXaNJE5xIfzFSvrg1cy5frXNnZEBOj8+WHwwH16wuFqeIC5I7CMzVxcd7q1cruyBCVcVjisVmBN/IM28AVG6vNTlu6doROBnQZ2VIn+/hjnQvI0o7/opNw4LKwqX+BREZCzZpC4X7xo0bYZDfjPy/KXABVxJs7245ojiU8vsDPJcM2cLnd2vyFLg1FJ5inCONeheqxH8rviccTGisuddItvym3P4AjR2SquO0/y1wA0dHaeVxffqnxBNMxJ2wDl8MBdesKhWvEGQXKGUCqCsSTvPaatjv8xIk616ef6lwF4fHA1q06n7/rzToZcO4C4XaaeEJ49erawHX++RpPMGn+YRu40tLgX//S+aaXET+RhPtbx9tpA8lj2u5Z0nN85UT5glBPQJ7ISJ0MYOBAnUu6dIWIZVKdrDWYqeMCEsvmMH3o1zphK+FrO8Dw4TKVvb+oAvEkyhUQwAPC7GK3O7gOAcVFTAxcdJFQWPtGoQxYI+zUIZ5Xd6KsaAl0EtW5bDAN/cM2cB3NjmbcIt2q5LHdukp9QFpgGXdI287qxhuFFcFIj0NCZgKyfKzJPOF8LNAOdRswQOcCvntcqpP9eMHUOIZt4Cof5+GxQbr6o6NVtauS8oc1KaqA/GAmvdp9Up/yx8vICJ3kDGlWYYcOQhkwfbrOJe2dpe91qWp67XIFfm3YBq70HAdvrdDVA936+4MyFwD16ulc1avrXMBuca9CZZ5LqPQqdLu1E+ebq1uG1K6tcyl/kcCJE8KRRkBSksZjmuySuyVz4oRQeJ92lcDgwTqXsGEpgPs7qY5+TbfLXC85vYTCbefzSRuowz5tyrjsaQu6fPESQrXCM50zyJ035HQKhTNnCmVoW1J/LUxyAbKyekt9P2fUkbnc/kiUX8v88Pl0zVMBOHRIKAP2CBvfiqcntBK3DVNl+5utQnKjt3QvOPWYUAb8rHuD3TZgtMwFcHaKVPePxOOBX38VCu8QpqcD3H23ziXtnaU/LhSONQyYsA1cNpu2uYR8wuv+/TJVvR3arZAPj2rrxpTd6N1u8U5APkRHix+AP/wglKHtDp+aqnMBb2sHLnPvvRqP2SqkBG7MSPHY3hde0LkefVTnArziXoXly+tcwiqGv0X6WaRFY8Btt+lcgwbpXEBlcYKPKq/GFsT0pLANXD4fHBQOJKyY6NXJAJo21bnEI1dr1x4i9a1cqXPl5Oh7MZ4Oea9C5ewY0GZmiffu3AukOtlWoZnHRe4evjL1uNEWcYGl8uknTof375XqKFNG5wqVFZffry28JkucnNFIOBrnG/G9z2VSm+pUwkxABmKjfPRJEqZNVdJmwkkb34r38CPFZ0DKMhy3W+cqCLtdXDebIe4OH8zre2EJpldRESA9u0e3c2UCF+D22flxfzWZr+VBcZ2KqvMlaCctApsTtJ3ElfO4QqUA2eeDY8eEwrLi/VFlMBF3zkhJkeoYKeqPHEyzk7ANXOoC5HdTtKMGbnxNePj83HM6F9AliLk8RYFy/I2yE1FBROHifO9qndApnoCsmjcPuuyFk6gTmN98U+NJTw/82rANXOpebMpJ4QC8I9wvOOssnQuolinV8cwzOtexY/IX9NPitUexvVJbma9OJfFfqnJG1lVX6VzIdyZlLdGC6UIfvoHLm8O5e4TjBmpqq+e5UjgoTzy2YVuctju8cuvFK04+zQ+XS3tMWidqqU4G0K6dzvWdtkdZ7drCxBNMk10p7ohoNpTTpak2S9H1uwNgyRKd6667dC5g109SHcnJOtcHH+hcIcXRo1pf16461wJtfvpB8YpL9Q5gBkkCmZnacRXNOuzQyUDbHX6vOD9djHKb97PPdK6CKBPj5/LOwtE4CdoiXcaO1bnELZ/ShO+soGv7GMxuRNgGrpgY6N5d59t5tlAGnKM6MQX5jSnNdvuH4vJE8ENKRZnv/AraYaTSxtCqtLuTdBCXVASzhVcYTAEyubNdmgsT/eI3CTO0QFvJquz7BtRYJNVJM/0yMoKbO1Rc+Hza8rzzm2qL2GmrSzxh6lSdC7iwp3aE0ltvaTzB1DiGbeCy2bTTu2ncWChDm+mnPE8DIiK0LZ+U3a+VdbEFERWlzV/QDv9C2+1F3D79o4+kOtmv0jTZJXe/dIfw2KlOVopOBtrOsOK9u/W7pDpatNC5VBlaf4d8Xp1TXIDcqpXOpdy2B44pxzWha4tqkjPQdwbYVraZTgbUmycsQH7iCZ0LOC9BqqOCsBuReoJ9fkTkZFF+5Vyd8PzzdS7QnsuK6xwrxEt1rBadggRTnxYit1HRExXp4/waB2S+lz+uInMB1FN2h//lF50LiIjvIvX9E7FiYknvpOuvGe8/LnMBMGyYziUuF9nwlFTHuHEaz/ffB35t2AauLJedz9fogsmIfQ/KXIA0OWNz8nCZCyAxRaqTjr/x+3Wugjh8GO6/X+d7/VnxoyaYA5PCouwtB8yYIdUxbZrGY5IzKIE9/J49hTLg2WdlqkaxO2UugNTIc6Q+5Vmox6NzFUREBCQkCIXqPVJlyqTyPA34+GOpjusHar60wSRnhm3gioyE2rWFwgU/CWXAHXfoXPPm6VzAkQrCbR7+mS2foqOhTRuhUJx5x7nCtmHiSOKO094fL78RRNZEIQhm4Rq2gevAARgwQOfbMFn5FEDbaE48dr2NcLAjwNVNNspcycPcgHjg2Gmw2cSTmJX7kiAt4jwwQBtIOosLkCdO1HiC2Y0I28BVoQL8+98l/SmKEWVtlTJfHNgqajFzire+1iW6HMlwSrMY88PlgjVrdL6LlTsEAO+8I1NVWSg6BDrJ84euk/oGD9Z41q4N/NqwDVyRkXD22Trf4jRhpT6QrDzlF3e/LttG+7uspps3GjLp8AkJMGaMUDhPXJx33nlanxDxXFfZGB6TnEHuslM5bfbypr/qZAA9euhc4qrZXeKZg8rjl1DJKnS5tH+tbdXLTOUoHmVvOfR5YJs2aTwmcJFbgLxvn87n6dtAJwMcyu7XvXX1PqCdSAzaQnVllnZByM+4IoSDTwE26s4teVBbCrNV2w9A9o4cTF/ksA1cDgc0Es5bE7fz42LlU2f3bp0LWCc+fFb27FPVxPwdXi/sFFY5NGuQoJOBthZGmXoP5OQI272hy842LZ/QHz4/0OpLnQygb1+dSzwB2ZUg1UkLkH0+nasgLAuys4VC5TQD0M40Ug7+AzIytO3lVNMMbLbArw3bwBUZCbVqCYXSak60tVWtW+tcQL3KUh3ffKNz5eTkzooraSwruN5whUZ9uPfDDzpXr146F3DbJVIdkyZpPKaOi9ydgoYNdb6jtbSZcOXXC1PClAVxwLpVUp10AvLixTpXQfj9cFzZPjBafMZVVZjhs2WLzgVsPCbVyRqDLApiDl/4Bq5IPy1rC+/MyZN1LtAeloi3CsUvsNImJEePhsaKy27XlgEsTxHWpgAXKVNFVSOCTxJdS6qjZk2Nx8zjArJyIpi7vJzMd/6ge2UugGqDhB3UVaXzJxEfGQT1pldYLr1U5yoIr1fb6uryVr/rZGqkveW0E1sAli3TeMxYE3JTfS++WOdz7NU2opXug4qn115wgVTHDTfoXIcOQaJ4EGBIIB6Nw+2361wvvKBzAXWvkeqYNUvjCSZxKWwD14kT8MYbOl/VqtqO5lcqtyd2abse9OyhTb9P66vbxhL/KvMlJga6dhUK94sL2CZM0LkeekjnQl42xsCBGk8wSVJhG7icTt3IaYAuHcV5zrua6Fzilk8vZ90s9SlL4ixL5yoInw+OHNH59rXQtnuopqyFefppnQto1kyU5ncS1ffErLiAjAz45BOdr8uS8ToZwKBBOleNGjoXMKKvssAInnpJly0RKnVckZG6Q3eAalXFEVvZ7WXkSJ0LOKbNc5Em8QRK2AYup1PbOSNjyGM6GRD33CM62YEDOhfwddWrpb6zztK5QqXJrs8He/fqfPVqiQeRBTOVsLBcoi2sqiJqensKVWMQyYorNTWV5ORkBgwYwJNPPpn3z9PS0ujYsSPDhg1jxIgRAGzatImrrrqKjcL+YTabtnRElXlzit7KUSOvvKJzoe0dCNC/v84lnLZRIPICfWUKI2gzfDZs0LmAz8Td4f/weC9WgskmLtT7X/ny5Vm+fDkejwfHyUZT8+fPJ+EPXSSys7N59NFH8Yhnlqu3Qi5up6zmBF4W3izNtC1mxM3opWRni5vb5oPLFdz8o8Ly1ZF6Ohlw6/zROpk0ywUeuUyqk3frCoRCBa7Y2FiaNGnCypUr6dy5MwALFiyg+x/6hE2YMIHBgwfz448/FuqDBktmpi6NE6BmTV3NGECjesIHwcyZOhdwhbDxPUD16jqXuoFEfkRHaxcl1XLEdVw5wjpH5Z4r8PHHUp1sASCt4+rZsyfz58+nc+fO7Nmzh+joaCqdnDy2ePFicnJyuES8Bwy5bwnlhU2UG8WK67j279e56tTRudDXOSm379LToZz2Hee0HD+urSt/ZtAxnQzg5Iu0BHE6/CCtTlbCEcz5b6EDV5cuXZgwYQJut5t58+bRq1cv9u7dy6FDh3j99deZqjwk/QPlysE99+h87y/Q1nFdr2xpI20jrt9KU6aFh0pWYU6OuJNXK20/P+n+1k036Vxoj0BA1z88mEk0hQ5cMTExJCUlsXLlShYtWsTkyZOZOnUqy5Ytw+l0cu211+Zd269fP95//33Kli1bWO3f4vdrGz4ot5sAOB6vc33/vc4FzJ0r1cmaiAIsXapzFUSVKvDRRzrfU1O17R5GR7+ok61erXMB6+P6SH1eUUJoMGkQRZKc26tXLyZNmkRiYiLx8bkP1CuvvDIvqxCgQYMGzJ49uyh0AeH3a0ey//abzgWQrEwJE6f5qVclqgmvAK+9pnMVhN+vXWkmJelcAHwonIDcr5/OBXRpekjqu2m0Zs5QRkbg1xZJ4OrYsSNjxoxh8ODBRfHHFQmHD8Odd+p88gfSDmGWpnIoH3DdheKD/FjdCIwImwUEMTGvmEhP157tPXP7dp0MoE0bnUvcWWZ7krbOsa1oYlMw1VJnHLhq1KjBkpPz6p1OJ2v/kFv7x5XWKX799dczVZ0RcXFw3XU6n2D3888o97eeeELnAnz3PyD1TZ+uc2Vk2oiL0/nyw7K0K9tvD2oTfNo32qeTKYe6AnUitYlgKXU15/fS5IxQRd0ZIDNT5wI4UFmXDFJFmaEF2MdqA1fCBaIKS0KnJsbp1JbnHT6scwGwShhMKmlbWSzfpU0EU72UB3NvhG3gUtdxPdZDW85+uNpFOtmoUToXwIcfSnV9Guu2sV5wegmF2y4nJ7hu3IXl0Ud1LgDKCZv6igurLnJrx2h/sj9Z4gkmCaTk76BiolIl7WSD59YIAwlwhTAltmLvbjoZyCcuH/bqCqv8EZGEwqIrPl6bxb1jh84FEN1Ydz9W/uoWmQuAxx+X6vwLpLqACNvAlZ4OU6bofEOH6lygrT8+5xbxjfmIsIEwUFHoC5XkDI8HtghLq/r21bkANm3SuTqpRxLPmSPVRURfL/UFQtgGLvUE5DJldC6AL77QudpWEFcE79YOkjyQpauJ8/lt2MUzFU+Hugn1AvFbe69eQlllTbp4Hju1yRkJoqxCc8aF/saMitK5QNwebcsqoQztLCWgSgVdaUEoBC3ITc5o2VLn++wznQu0q8nK6nEG4gFZqnF8JquQ3OhdoYLOp9yWBOjQQSjb20IoQ9u2HLSB0u8PidTCo0dh3Did7803dS7QBi7atxfK0LYEAhpV0Mzjc0QEXp8RtoHL49Huc6uK9E4h/e4ma7KK8vj6a63vp590LpcLYnQTl/PDboequrprWaPWU0gbNavPuKZNk+qy79SMiLEiAt+OCNvAFe/IYWh9XWO4YR8LxyggbqEzf75QhrxTx681dIHZE/kEDpktf8qV03aWeeklnQu0ySANxOUb0r84ICZ1m8Rj8wa+ZR+2gYvISKhdW6ZT726NGSOUfegXygBx67AGiz6XuRyeLHCU/CRJR4SPRmX3yHwZGWfJXBDcbKdCo15BS384yK6hmf1nRQb+She2gSvbE8ni33QV5sq2QQDvv69zjf33/ToZaPvMgbYdvTqLJx+yXHZmr9UFE3W5yCFlH1rlYSHIawuOfaWZtu4P4v04bANXVpa2c8bLYzUHmKfIyqqik61Zo3OBfL6RNFc7O5tQaFYYGwv9+loy3/Yd2to1ZfvAy2pokyXU6feq3SSXK/BrwzZwVagAT+pa0PHKVGEgQTxpRJ3uu0HzhpeHcjkQzLS8YsTlgtVrdMFk3TqZChAvbMUtn7jsMqmuT0PNGdcLDnPGhc3nJe6orpD1yy/PlrkAbrtNKFu0SCgDrtaObZCmoIVIIVeUN5O2uz6R+RYevlLmguC2nQrNoEFCGfDee1JdaozmjMsXYc64ciuQg6loKyTKLh0AX32lc/W5QlsQzNSpWp+yyChEAhcxMdIJmrvF7z79+wtl6ro88dRaVVcgWxAbAGEbuHzY2Yeuwrycrk8rIE4sEmcxyYebKVNCXa6QSNCwbBGkR+i+tOo6LmWNmnZ5B5yt3d0pn/KjxBPhdQd8bdgGrhMntFOJawq7tQNcdZVQlhP4F6pIqF5d61MXIIdA4PJ4ICVF55s/TTuQq2W3ijLXj4+mylyAviGAqttBEJNNwzZwxcZqG22q67jWr9e5undQtiFA3hmAkSN1rv/+V+cqgMhI7cvWzym6QAJw77061+rEPjoZ0PYaXX0qQMZ6zUwaKzrwOVRhG7iicNHe+YPM99j882UugOuVkwbUre9btND6/oFEuHMo95Outdb4FzrJXAA33KBztU2doZOBvFzE0lVNBEzYBq4cK4rlWbpg8vzzMhWgLa36/PemOhnQZ8G/pD7pDPsgtkOKk6PZ0YxbpAsm6ia7yh2J7Asu18mAmKe1jVHjhwyReGx+s1VIVJT2xX3mTJ0L4Ndfda4n/iN+5XpdvMKrKNzGCpGswqgobYOSA9r6fGYIF0HJEbqeqIC+qe+JExpPEEkuYRu4/H5tMlydOjoXwKefCmXKU3zQbxVu365zeTwhkZyhZpumhjWPBg2EMnVWoXisCZUqaTxBlBWEbeBSjyZv107ngtzkExnqLD/xBGRpN3phbWFBREdDx446n/rdZ+FCoSxZ2zVHXZ5yCE2LKR9mrAkOB5x7rs63Q5N4k4e0c5B67rr4HCg9STeSxoqKRtu17/TYbNpdS3WNrrIhwLSftGfA1+3fL/VVjs2UeOwRgR9JhG3gstv81ChzVOZ7f2l5mQsgU/NdyqVWLaEMOEfX1R8gfq3ujMLmysld7pQwNpt2God6Qrhy+vnYiq/rZKBvMbV3r8bj9QZ8adgGLq8/gt0ZumCibvYg7dSh7mZ+5IjWpzp8Bv15SD5YVm6jehXC0XiAuC/0gAFCGXDddVqfqrYgiJ5PYRu4bDbtdpo6cGVkCGXqjubqX2Z6us4VIoHL79eu2ps317kA5swRytQvWm5tJ5uMipodEMseeDgK28Blt/mpEn1c5tsaqW1WeMUVOteT07S90R4QNxGlQwedS33Ykw92u3Y7bf58nQvgvPOEsg8/FMqQZ4KphnIGc7QdtoHL44tg815dMFEfWygzJgcP1rkAWCB+g1U+wUMkcIH2o6hXXN99p3NdP0o5Ywi45Rap7tzofRJPpM0UIGOzaYPJqlU6F2j7zKl3Qs45SzdSHtC2IXG5QiI5IyMDPvhA51M2JwG44AKd6/0l2u/r9dJzAnRvyR4zSBKvV1s7cnPSzzoZcMck3StsPc0cuZJD2UJC3WIlH+LKWFx3ZRCz0gvJ1Tdoi66VfZPVuRLMUc5sQbcjYQqQ9QWWS7/R7oXs06zeAejQQLTJfYr4eK1PedgdIh1L/ZaNwxm6YCJtCg1s3apzqatFqijH8ADZ9TXPNssReBJY2AaurCyYN0/n63PeTp0MeL+8rtZp4wFN5fwpmip7BwJs3KhzeTzaAqp8iIjQHu398ovOBdoGJVVOiPtZiWsLYryarFsbpgCZWFs2fZxfynyPvy8s1Uc7aaTp9+Lq0Wq6ydUArttGyFzW7Dkh0TmDnBxsy3SF16mpuu4koA3K2v5raA/wAH4WHYO4At+6DtvApW5/HSN8aQdxKUfnzkIZ8M47Ul3UrI9kLltWpn6+2emIjsbqrAsmVztkKgDeflsoU2eKKlMmAUaN0niCaD4dvoErIgIrQdc5Q92HVtkZ4OcTwqaPQHN19+tU4ej1INraFCdut3aHVDmGB8TnTuIzJ3knGzPWRIfPB8pelOqWNsqax8duE3drT0jQ+hITda4QmcfltPtoVlWXdHOwlvacVNo5o684y099f4TIRIM/EnqfqIiIiND+/aoXCcrdpsOx2s4ZFdWt9rt107lCJHD5sMvGVYB+kSDtUqZuUSYeayLbkQiidUbYBi6b30fMMV3OeEqKNqFA2V6vYpp4n0ddgKx88IRI54zsbFi0SOe7pq9ynAFERgrf7KSZIMDZ2hdJ2VaoSc4Aj9/OtgxdMOnTW1ufM2GCMDdNvTWhHKQG2jMKtzskJiBHRkKNGjrf7EXahJSqyt07dWsZdS2g6uczZ1zgcGdS76dPZL7HP7pS5gLxmdquXUIZ+kBZv77Ope60nw/RNhcXxf4g89005XyZC8QtptSBS53gc9VVGk8QB5NhG7jUrTP26kpiAGjcWChTJi8AfPqp1qfcKhRPd84PX2QU+6rrgkmnTjIVoM1idJ3XWicDolavlvp49FGNJ4hU6fANXC6XtHlqz559ZC7Qljodvk07kbhipUpS3z8VZbKYeqGpzF+IOijOuv1S11gBgCef1HiGDQv40vANXE6ndFmyRdw7VbmHXzFLfGMq66pAO48rRLIK7T43lQ/oCrl27Woqc4E48U7dOUPd9Vo1kMuccYHLF8kPx+rIfPfdLkzzAy6+QteI1pWozWKKUldzr12rc7lcIZGcgcMB5+hW0urdZuVkCPlUa2WHbQDVDojpDp+L9AxTPE5becalnjXWRRlIAG66SedyiHsf5YPXZ2P7Qd3Lj3qRoLwdf9ilLa4+f9MmqU82P84WeKZ02AauqEgfbWvrOgP8fkL75VW+5Iljcu6UQyXKupgQ2SqMtFvUqaGbx7Vpk3aV+fXXOtfQNj/qZABNmmh9qixfs+Iid7klTOOeu0obuJTb6up6YNq10/qWClNCc3JCIyXeZsMToQsmLVrIVABMny6UqQuQlY1Kgew4zbPNigj8pS58A5eYH8P4paupTzvdWd7SpkcPnesTXW1hgWRk4Pj0A5lu7IJrZC6ApCShTH2AN2iQVBczRzM9wZYVeHeVsA1c2f4ovkzT1alcdplMBWh30370a6c7t0TYQRhg4kSd6/hx3WF3QcTFwRVXyHQv9ZKpAPj8c51ryofawaBDJj4l9aWv2iDxWJPeDPjasA1cMREuLq7wvcy3+IS2CPHpp3Wu5R+Ls5jUs9CVW5Pr1+tcBeHzwcGDMt1r72n3m5XNLJ4ZdUAnA/hGuZyE+K2aDis2t+lVmJu9JeyLFLVZpgLguut0rklztA2Eh86aJfUxZozOFSJNdvH5pE/39eu1geuWW3Suw5FVdDKgojr9XlWpbrIKyX1ACFv57BbX6Cozxl96SecCYJOwdyDA1q06l8ejSy8uCLtdejYjbVEGKDPG1RsE8sCluj88noAvDd/A5fHAb7/JdIcPN5K5AAYO1Llee03nArhXnX+vzPIL4q2yWPH7pdlplSppVyVK5HMWpdXVQN++Gs/rrwd8afgGLq9X+hfscGgD14wZOtcDD+hcALwknjqo7NQRInVcREZKlwrqCocnntC5ht2kq4cDoLK29Ia9ezUeM0iS3Jk1wjf3JUtkKgDeflvnUgZJgBulM1vQjq8Oke7w2GxYTl0d18qVMhWg7UafekhbXF3j8GGpT7YyN70KyU3OEM5Z6txZpgLg/vt1ruee07kAGCnOvBswQOcKlRWX14tt106Zzm7XThhQnjnL30XUHQFU2admxQXpOQ4mfaPbvrvoIpkK0M5aFGZNA3COcpgSBDUyvNCop9fmhzh5aViP7TIXQPfbdQ22e4lr1M6pIj4vVCXxBPFSF7aBKz7OYuggXcf2r9fpGpaCthxI3dHmHGHXcgAOCOtwQmWrEKSp+Z+s0wUSgEsu0bnKldO5APjuO63v7rs1HtOr8CTCdJ8TJ2QqQDuySr0NymxtJwLpmJFQySq02aSBq317mQqAadN0LmVNJaDPv1c93MwZF/gtG0dzdA/Ali1lKgDmzdO5VqzQuQCSK1bUCpVbk0HUqhQn2Tk2Fq/R7RKoR6xdcIHOpX5praLcIQDYKToLDWIOVdgGroisDMov0DURvWG6tono5Mk6l7xLkfIAD7Styxcv1rkKICbCRXLcapnvwffbylwADRroXOopPKjPuFQ+c8ZF7jahcM5SW+19KS0Kvv12nQuAubpxNID2KRcqyRk2m7Twunx5mQrQzpCTBy5lMhGIJ/IGRvgGLodDOnZ1cC2ZCoA779S5xON/qHZINwAU0BZ0hkg6vDfCye5Kuv3tkSNlKkDbq7B3b50LgHRd0hkANWpoPGbFhbwAeZuuuxSgzSeIEzeyQH3GpWyh4/Vq//LyIdLn4uzUb2W+R97RZmco8xfkc0GFL+SA6VWoxO2P5Mc03VZhlwu0y/d583QPv3XrZCoAzlb3KlTOwAiVdHiHAxo2lOlqi1/slN9Z+U6aegskO1vjCWIbPWwDlxM3LR0bZb63pzWVuQD27NG5+vUQfXFPsVq84qpaVeeSd2TNB7cbfvlFpjt+vIPMBdpgom7WTh1tTZxp+aTE6cTTQBdMTiyUqQC4+GKd68ct2rqqlhs0E1fzaK0dAhoS2GzSLcvkZJkKgG3bdC511m21n37SClUH6kGkSodv4MrOxrFMF01uuaW7zAUwfbrOpZ7HNaVNG63wn4jfL90ivUPYWxPg5Zd1rqZVxMlE6uFmqtZgpnMG8j18ZScLgC+/1Lmef17nAmCscLAjQI8eOleIZBWqexUOGiRTAfDWWzrXmDHaMSPVlGeyAN9/r/EEkeYftoErxxvJ0t+EyRlNtW9drVrpbpbly2UqAM5RnwMpx+W63SGRVWg5o3C10mX6JYmHPivPuNQ15dcJzyYB3fc1iHZoYRu4oqIgKUkoTNNWIX7/vS5wqfvMUbOm1ifs2RcqvQptrhyiVi2V+VIzushcoK1weGqsuK7qI23y0sYYzRmw2xZ4gAzbwGXzuInfqcsqzK6jzSpUbnN/qyv3ASBZvRWifMNRBsmCcDrhvPNkur2fylSGIqZpOc1wM2eE6VWYm3YsHLi2f79MBWjb+ckzuNUTXnNydK5Qafnk9cIuXWutTZu0q4QrrtC51CONOkWL911VE8JNOjy5hZ7CaLJklbYZ20bdYpL33tO5AHhIXKeiPBAJlcDl8eg6IgC1amnHJ+zYoXPFxupcgH5Anur+MAXIyANXpUq6acugXSQoi50BalSqpBVmZelcoRK4xFm37hSZCtDugFx7rc4FwAfi9HvVCs8kZ5AbuIRnJXvF4+3btdO5fvhB5wKooXxdBujbV+cKlc4Z4l6e6g7qdevqXMpEEIAa6pHLqlqfINqhhchdVPT4Y8pwuPPlMt9VMlMuY8fqXOp6R3mt09q1OpfLBTHiCc+nIyoKXytdx5CRwkACumnzoH2JBGDvXq1P9QBwOAK+NGwDV4Qrm4o/6QosJv2m7WmjbK+nnusobyLar5/ONWeOzlUQ2dnYF+mq2NdGCHuUAYmJOpdypxnQbd2dQvXLNGNNyM1QET4Ab7tNpgLgkkt0rofHiAfXiSPlxoqdZC53RDTqKRinxWaDMmVkuu90JWOANqtQPT2hpTpwqQr0g9i6Dt/AVaYMvv66rcITx2QqQDuJQ5YOe4pLL5Xqmn7ysMzlzDwKTvE44NPhcEi7jLcTDxhQHpOqpn7koR60GoKEb+BCO25AmeUH0EE4JWLGd7p6OIDL5woP8AA6dtS5Qik5Q1gGoD7W++47nevKK3UuAL7UrZSBEpjb8veEyF1UDFgWDq/uVeirr7R35vHjOtfl/cXDD9cIDyhAml0XMng80mLAbft1fUNB2j+YdHHHJzp31vpWrNB4gnj7D9/A5fNJt7hatdLemMuW6VzLV2mz/C5StyG56CKdK1S6w9tsEK/r+FBWnMCgbL5So4bOBcDHwu4DoDvAnzEj4EvDN3B5vbBzp0xnS9AGLuXqXb5ToO7nJ2wNFjJbhRBU3UxhUR+TKks41O9ZzZTbLQBxcRqPKUAGjz2abVV0B0FN934tcwEkJeky4RYskKmAEujFpsx0EQaLAhF3zohOkakA7e6v/F0kM1PrUwVK06sQHHY/9RKFbybVlTNUYNFEnWvUKJ0LgGniJZ5TmKAeImNNsCxpAVKrVjIVAMOH61wjbhGnFQq7+gNBFQYXCrPiIveXIHwVOpSlzfTp1k3nmj9f5wK4SF3xrExBc7n0BaSnw+uV9iqaPOtcmQugd2+d62iONjGrvGrr7hSq70kQWa5hG7jcHhsbf9cFk6bnaFOLMjJ0B+tVqshUuRwRP9hPnNC5QiS1OIdoVjh0wx1fvF/bqfnGB3XnlupGL+W3bNEKk0VdgUznjNzfgfLF/bBbO5NHWWB50006FwDviTsWN2mic4VIVqG4/hh2iRq1niQrSxe4hNNhADhX2e8NdBXWZqxJLlGBT4IuNBX92mr2Vq0qy1yffy5TAdBenYKmDFzK87QCsHtdVNura/v/+rq2MhdAr146l7rzvbzJrurIxZxx5WZUK4sQ2av99q5ZowtcyoNuAB4Ubt0BVmfdlllInG8BfkcUR2ufL/MNa6WdQ3bf/bokGGFyZi7qQZK//abxeDwBXxq2gct25DBRo0bIfKNjX5a5AGrX1rlatNC5ABg5Uqqz/fSjTuZ2h8SqKyIznfIfT5L5XnYPlbkAdu3SuZ65fbtOBhArbFEGUL++xhPEfRG2gYsKFeDpp2W6kdpFgnQV9O23OhdAhyVLtMKzhcXjOTkhEbiIiYEePWS6buJenspM2N8jlIeFcO66N6Q+2ffVZBWS+0tQTe4E3NH1ZC6QPnOYPVvnAuiwX7yHr6wtUNXE/B02G8TGynS7xAkMXYS7v+oJyOcqyzcA7rhD4wkiKSFsA5cvwsGBsrpgkiNecSkTi5QZjIC++duiRTpXdnZoTEDOzIRPPpHpvj+mPSgVtmGUbtsD+pHk1atrPCYdXl5/TD226WTAzE26oCxv9nBM3ItN+Xr+tbY1WL6UKSOdx1FvmUwlZ80are8c1WDHU6jeXM1WIUTYLCo6dUXBGdW0W4Wq81KAWbN0LgA6tNT6lId4OTnSycP54vdLezRu2qStYq8nvB3VU0Z4XbzdnCgaM2RWXCcRLrn2aesr+eYbnUtZEwPA6s1aX7VqOleodIf3+6UdQ9SZqcpmKOoXu+HqlmiqCmuTDg9+yybtIaauCVRucwt7seZy9KjWpwxc/1CStD2o+fRTnUsZJAFtZwXQJfGYAuRclGOdOnXQjqtYtEjXOqhSJZkqF3W6uLKgUz1rLD+cTunbT1zgxxdFwvr1OtfAgToXAL+KD51VA8eCGPkTtoHL7YZ163S+Lv5lOhlwwQWixpeUwBmXOk1L1YsNgurHVpxY2MiO1KXebREP7b3qKp1LnStxsTr/XrU1GcRLXdgGrshIqFlTKNylfZNWbnOrt0GpLq5WVfbsCZEzLpvlJ8aty96MjCwnc4H2DPjaa3UuAD4Wv/zUravxBFHjGBp3UTEQeeIIdd5+UObr98sTMhfAO+/oXOPH61wArBPVjZyia1ed68UXda6COH4cXnhBppvrf1jmAm2dY7p2ohHceafWt2yZxhPEzkfYBi7i42HIEJluvLhD9K236lyPPqpzAfpzIGFrMI4dg/Lldb78iI+HwYNlujvFsw9XrNC5FizQuQDaL9T2RbVWrtKIXnst4EvDN3BFREhb2rQse0DmAjh6VFcXox6UJ+2QCtChg861eLHOVRDiOq6DWefIXKBthtK/v84FwLqKUp1t/z6NyCRnkLt+nzJFpvv1yrEyF2jrYjIzdS5Anl+cfsHFMpcV/TTqRiSnJSsL5s6V6V47qC0qV6bfqzoi5aHc2gY4KBrsagIXuW+UwieuertAOWtMng5/3nlSXfzPun0lmysnNGZyRUdLV5q1hBm+kHv7q/jpJ50LoNEh7dBaVE19Xa6ALw3fwOVwQK1aMl1jcQa38mZRTxk5P0Fc9LNZ2KnD4wmNwJWdLf2L/WqNsB8kMGqUztW96gadDKC6bocA0G3vfPhhwJeGb+CKjpY2EeteQfsWlJamm4A8c6ZMlUsNcWGMsoI0FGZxQe75b+/eMt0Y8e7WvHk6V+3bm+lkQJ27tL9M13zNC45lM3Vcue1DhG+2v2foAgno2ocBXHCBzgXAFnGPKeXcFo9H37LndERFQatWMl1jXR4IABuFBc/vvadzATxyjjbRJerDdyUeW2bgqdnhG7gyMqTfqPf842QuyC3DUfHwLXt0MoCEiVrftGk6V4h0ziAnB1avlukWpggzN9G+2CmrKQDIEd8ft92m8QTRFDVsA5crJoFvu+mCycP7P5O5AJYmXCZzXTPqLJkL4IMLtHUqxImLjEIAnyOa1Fq6YNK5lkwFwB7hu9acOToXwNVTB2mFqgnITwTexCFsA5fTKR6lsF6bE7teOL1bPUVBXsfVqZPOFcTMoeLE7s6mxq+6mrKn1up6awI0aaJzHT6scwHatiCgS2E2vQpzCaIsoPBIGyOCU5heLJ+ArC6M+eEHncvlkhbG50tUlPTN7n5xcsbNN+tcAwboXID2nADMIEkltox04j56W+a7Z6PwTgGev073sH0s7XyZC8ht7S/EN1bYR0/Z/bUgcnJglaiVD7CxVh+ZC7SLku3bdS4AxMkZpIqm5HoDL4MJ28DlLxPP8St0weT5ztpv71tLdMFEvXNHfW2vQvvEZ3Sy48ehnLZT+mmx2aSd6pXJEqBdtJcpo3MBcEDbXk7WgcCsuCDi2BHKPX6fzHfjQeHDD22T3UaNdC4AynTT+pS50yFyxqUuF6ktPpZZuVLnkt8fZ5+t9aneOjyegC8N28BF2bK6bBjgVnHG+NKlOte55+pcAB1mPqYV/utfOleoBC5x54y0jtrOGcpgotpJy6N+fa1PteIyyRlAZCSeGronbof938pcADOOtJe5lH3fAG2WH2j3sTweiInR+fIjLg5uuEGmyxI3Q1F+Z9VJfrL5WKfo21fjCeIvLXwDl8eDI2WbzifOFNu/X+cStnzMRV1XpZxKrJ41lh8ul7QA+bcD9WQu0H6FKlTQuQBo2lTryxANGzSBi9ytkC+/lOmG/TJc5gLtuHD5AmFtmtannIERKluFXq+0SrfbJTIVEFSCWqFRTwi/+paGWqHqZcskZ5D7ynX99TLd615tFeLnq3TD5GbMkKkASN4ubkd/9906Vyh0hofcVaYw9U79Yz/0kM41dKjOBcD2FK1PvuXy94Rv4LKs3FoVFeLtLeWBsPLXCMD54rqxIMYpFJrMzNBoMRUZKX0gKbe2AXr00LnU87h6bxMPN1MVqpvkDHLnzQfR+6qwzO76oswF2ptlxAidC4Ad4sClfKN0OHSugnC5dAMCgZ+cF8lcoP0rFQ/slnb1B3KnySswZ1zkVgVeeaVM5xXXBCoT4Vryo04G+rEf6kGSoTDWxOeDI7pZI5cLKw4A3tY1zdFn3bauo/WpzmWD6C0XtoHLY4/m18q67teXoxv/DrB3gO5nu296S5kL4JkI4dYdQDPtIMCQoFw5GK5LKKosKgU6RceOOtdLL+lcAOz/XOu76iqtLwDCNnA5fDk0OKQLJtnna+cNJQmT0+QTkAc31voyM3WuUJnH5fPB0aMy3cT3tKNxxozRuWbN0rkAqCtOh1edSwTRozRsAxdZWfDFFzLdD2gDlzKYjB2rcwHwjXAiMWhT0D7+WOcqCJ8P9u2T6e6/X/uwvekmnUt95CSfM6TaUjZbheQO5Gqse3NX1zopR7aop4wcuOMRqa/KddfoZAcP6sZEFIRl5SZoiEgTl+atX69zTblpuU4GHD9PV+YDUG7661JfIIR34BLOGzo/YbfMBZDSUddoc+RImQqA+aN0Aw4B7dRBdZv0/HA6pQ391Jl3ynT491O0GZPXv6RLOgPgzjs1Hqcz4EvDNnBZDieu+rpDd3VmkXK6s3yQpHoW+gUX6Fyhkg5vWdK5Z9XEDc1Vz1rQtfI7xfU3CDNPAPbu1XiC2EYK28Bl83qI2qXrVZheVduLbepUnattW50LAKe4a6mymlvZi6ggfD7p/l1cWe34hBem6JJB5GdcbdpofXPnajwmOYPcB0RKikwXL6yJAfjwQ100WScu1GdFC62vdm2da/ZsnasgLEvXPBXkLdSVBci//KJzAfDRR1qfqqfV118HfGn4Bi67XXuzqGbWnOTRR3WuQYN0LoDP+4u2Jk6h7p8VCtuFXq90tPXGzdrmwseO6VzVqulcAFSurPWp7o8gdiPCN3CB9ODp2xTtt1c5HUOYnJnLCm0xNwMG6Fyh0h3e6ZTucTX98jmZC+CTE/fKXMoyQACGCr+vANtERy5B1DiGb+DKyoJ582S6ZTSXuQAaCicbPDU+WycD5i6eIvX1XniXTpadLZ/ddjr8jiiO1m0t85X/5huZC7R9jINIhisa1Hv3IfB9/V/CNnC5YhL4occDMt8Dm6fJXAAz/NfJXDf9S1uk9k5X7e+SunV1rlWrdK4CiPB7KZ8hLOHo1k3nAjoKJxooMxgBOE94NgkmcCmJOnGI81/Vlc+/3+UdmQtgUH+dSz0p3HO1LigDOD4SBspQmYB8/DhMnKjTjX9e5gL49HGdS97Kr3NnrW/tWo3HdIcntzt8v34yXY92MhUAd9yhcylrxgAcM8VZUweErf2VLU8KIjISatSQ6cq5D8lcAM88pJtc2SApXuYCuLe3uGi0g6id3RtvBHxp+AYujwd26HreJYqLECvqBiDTq5fOBTBlydVS35DtwrcAj0fnKojYWO1frKoW6CRv+4fIXPI6rpo1tT5V+/sgyjPCN3Dl5IDwQNjWVNtEtGLF7jKXendLXBIHTz2lcwl3AQrk6FFpTcUDtT6QuQCevO13mevIkXNlLkCfDaLqnxXEy43NskJlzkLR0axZM3weD9WEA/t85cUDh4Qc0u7yULWK+CspbDa778gR7JGRbNiwQeb8X/LuD+Whe/nyOhdI21kdPKYNJOpcibiILIln37Fj2CMi2BBARXdYrriioqJw22zSouAQqc4pFsRNDwBxc8Ro3XlIpMOBU54//Wfy7g91MFEi/B2HQrP/4kUTKSMzMwO+N8JyxWUwGAyG8CVEcnMNBoPBYAgME7gMBoPBUKowgctgMBgMpQoTuAwGg8FQqjCBy2AwGAylChO4DAaDwVCqMIHLYDAYDKUKE7gMBoPBUKowgctgMBgMpQoTuAwGg8FQqjCBy2AwGAylChO4DAaDwVCqMIHLYDAYDKWKsBxrkpSUhNvtpnLlyiX9UQyGP3Ho0CGcTidr164tsc9g7g9DKBLMvRGWgcvlcuHzeODEiZL+KMVHmTIy1cHD2mljwvFYAJSN8chcXq+Xkp4klHd/pKfrpDbtjLVse5zOlS1TAfoByHF+zXPU63Zjeb0BXRuWgSsxMRFyclg8YYJOqn6DrldPpvo6vrfMBTB+vFTH4q90gSv5kktkrvxITEyEQ4dY3LSpTjpsmM4FPL+itcy1Y4dMBcDLjx7VCo8ckWiSb7op4GvDMnAB4HBA/fo638MP61wATz4pU+3eJlMBMHy41vf+hw6ZKyMD4nSLgfw/R0xlPuj+jsx3TfU9MhfAPUfG6WSVtI/RpT9pnzXLlmkmZR9ND/z3GL6By+eD/ft1vhYtdC6AjRtlqgbntZW5ANavl+r47Tedy+3WuQoizp7NNZUW6oRj3tO5gM8GvC9z7dolUwEQJ17hPdJ9hcSzYlFOwNeGb+Dy+2VLXAAuukjnAulBUOsTi2UugKcXJEt97drpXHbtcWH+HDkCDz4o071y4/cyF0CTBJ0rQegCaK3bBc3l93IaT0TgSe5hG7iOu6J5bl0Xme/exb1kLgAGDZKpdne6TuYCuO02qU66dffZZzpXgSQkwH33yXTDvR/JXABDP7xa5qpQQaYCoItDswLKQ/W2FUTS0hkHrtTUVJKTkxkwYABP/uG8JS0tjY4dOzJs2DBatGjBqFGjqFq1KgCNGzf+07XFSXw8BHHWV3gGvS2UAcJDfnd7beDau1eqk2ZpeTwQFaXz5YvdLn3ivrxR9xIJcPvtOtcnn+hcAMyYofV16KDxBJhRCIVccZUvX57ly5fj8XhwOHIPuOfPn0/CybXz+vXrGTZsGIMHDy6M5oxwu+Gnn3S+Lmun6WQAAwbIVHVO/ChzAXy6v6XUFxurc/n9OldBuGzRfButCyYjFvWTuQBoMUqmajmirswFcN/E56W+SxI0nuyI1wK+tlCBKzY2liZNmrBy5Uo6d+4MwIIFC+jevTsAGzZsICcnh1mzZlGtWjUefvjhvNWXAocuWQxatRLKgFde0bmkS1dtMijAunU6l8+ncxWEy6Wt4Piq1WydDHg4RZecwTTtS+szwrNJgM1Z50g8kUFEo0KfcfXs2ZP58+fTuXNn9uzZQ3R0NJUqVQKgXLlyDBo0iI4dOzJ9+nTuvfde/vvf/xZWGRCWBZmZElUuSS2EMrRVuiu0e+pbd10j9SkTQhcKE/kKIioK2rTR+dom/KqTAdwpDCavBb5SKAoenKQJJKfo1EnjCWKnsPCBq0uXLkyYMAG32828efPo1asXe08eUjz11FN51w0aNIiJEyeSnp5OfHx8YbV/i2VBTuDZlYXn2DGhDChbVucSrpIBRl4h1XGd8AgvM1Pa9CRfojwZtN3xgU44a5bOBTBnjkz18xbtoWVL7U667L01mOd1oQNXTEwMSUlJrFy5kkWLFjF58mSmTp2Ky+Vi0qRJDB06NO9ay7KIDGY9WAi8XkhJkahy6ShOLapeXedSHhYCjpPbzioSE3XtiH4VLzzyI9sex/wE3cq25wBxP++HHpKpmvfpI3MB+OuKkiVOMm+exiNdcQH06tWLSZMmkZiYmLeaioqKYubMmdSrV4+uXbsyY8YMWrRoQUxMTFEo/5YTJ2DyZIkKgLvuELdD6NFD5xIHLlaulOoefVT3IBDm1BRIejq8J6wJ7jpVl54OEKU8KH3iCZ0LaDlVu+S6+WbNFsHWrYFfWySBq2PHjowZM+Yv2YMTJ05k/PjxPPfcc1SsWPFPW4fFTZkyoHwRevs9ZSYItGjRXuY6PzaIb1RR8O67Ul35sWfLXBF+L0SUfPlkYiU/H7xyWOYbcntFmQvg9tt1D/fIMdp8+PNHafs+drjgAokn2psR8LVnfAfVqFGDJUuWAPylFf2IESPy/vcn8iKHXMqX1zZrVSb5gXY1ef6YzjoZ6PfTFi3SubKzc4sMS5jM7AhmLNMFk2rVZCoADh7Uufr8pk1Pp7ymd2AeqmbMQRRUlvyrXzFi8+tyj0fcom1C1+kSzZYrwO4IbRbT2dKsGqBGDZ1LdMb7d2RlafMl3h++WicDtlcS9tf8Stw8sLd2WsPcPZrVa5bXBK7cN9tly2S6DYna/nrKFO6vvtK5AG6WFuCR27JdRYhUIJctC3feqfP1eVTbqPnzqbptUM/El2UuAMfwoX9/URHS+ypNmHjeFvhgs7ANXF5HDDvr6oJJs/3aN8qaNXUPgn37ZKpclPnpAI8/rnOFSD680wkNG+p8n98gPjKYo3sZEb9m8WLjSVLfxg81nrTMwJNcwjZwRWanc84iYf/Arl11LqB2bZ3rjTd0LgBuTND6lH93v/+ucxWALTOD+FnC7hLCptAAVw7UteFXJvgC3FVV27CYRM0uQfL6wF82bFZJzxEvBpo1a4bP46GasruEeLaBx6+7MZXTYQAqahPQiHTpWqzsO34ce2QkGzZskDn/l7z7Q5gkkuMUFsyjnXsWTP1RUVAhOksr9GgmhO/LyMAeEcGGTZv+9tqwXHFFRUXhttmCfgJalpXX2cNmK/6i1ML4HGcQt87UV6WKznWmFMoXGfzW3Zn6IrOycCrb0Z+GvPsjyO4rhfkdn8krZKF8QQpL1feV4LtCK3++M7433O7A7w3LkEd6erpVv359Kz093fhKkeuf4AsFwvl3HM4/m9qncIn7sBgMBoPBUDhM4DIYDAZDqcIELoPBYDCUKkzgMhgMBkOpwgQug8FgMJQqTOAyGAwGQ6nCBK4/4HQ6GT58uKzOJpx94fyzlYQvFAjn33E4/2xqn8IVlp0zDAaDwRC+mBWXwWAwGEoVJnAZDAaDoVRhApfBYDAYShUmcBkMBoOhVGEC10nmz5/PpZdeSo8ePXjllVdk3qeeeooxY8YUu2f27NlceumlXHrppTz11FPF4sjIyKBPnz6kpqYC8Pnnn9OnTx/69OnDv/71L44fP16svh9//JGrrrqKSy+9lJEjR+IuwtkWU6ZMoXfv3vTu3ZsHHngAt9vN1q1bueqqq7jkkku48847ycoSj5sQYu6PwmPujyK8P4qtfW8p4uDBg1bnzp2tw4cPW26327rxxhutb775pti9q1atstq2bWuNHj26WD1ZWVlW69atrbS0NMvj8VhXXHGFtXLlyiJ1rFu3zurdu7fVpEkTa/fu3db+/futiy66yDp8+LBlWZY1ceJE67HHHis2X3p6unXhhRdamzdvtizLsu655x5r2rRpReL6+eefrd69e1uZmZmW3++3Ro0aZU2ZMsXq27evtXr1asuyLOuFF16wnn322SLxhRrm/ig85v4o2vvDrLiAlStX0q5dOypUqIDD4aB///7MmzevWJ3Hjh3j+eef5/bbby9WD4DP58Pv95OTk4PP58Pn8xEVFVWkjo8//piHH36YxMREAGw2G48++igVKlQAoFGjRuzbt6/YfCtXrqRFixY0PDmPfuzYsXTv3r1IXGXLlmXcuHHExsZis9lo2LAhv/76K+np6bRp0waAK6+8sti/MyWFuT8Kj7k/ivb+CMtBksFy8ODBvL9ggMTERA4cOFCszoceeoh77rmnSL+s+REXF8ddd91Fz549iYmJoXXr1rRq1apIHU8++eSf/n9iYmLe7zQ7O5s333yT66+/vth8O3fupEyZMowYMYKUlBTOP/98HnjggSJx1apVi1q1agFw+PBh/vvf/zJw4EBSUlLyrlF8Z0oKc38UHnN/FO13xqy4AL/f/5d/VpxTQj/55BOqVatG+/bti83xR7Zs2cKMGTNYunQpy5cvJyIigrffflviPnr0KDfffDONGzfmsssuKzaPz+fj66+/5r777mPWrFnk5OTw5ptvFqkjNTWVG264gSuvvJKkpKS//HvF5NySwNwfxYe5P84ME7iAqlWrcujQobz/f/DgQapWrVpsvnnz5rFy5Ur69evHSy+9xJIlS3j88ceLzbdixQrat29PxYoVcTqdDBgwgDVr1hSb7xR79uzhmmuuoWXLljz22GPF6qpUqRLnnXceNWvWxG6307NnT9avX19kf/7mzZu55pprGDhwIMOGDfvLd+bQoUPF+p0pScz9UTyY++PMMYELaN++Pd999x1paWl4PB7mzJlD586di803ZcoU5s6dy+zZs7nzzjvp2rUrY8eOLTZfw4YNWbFiBZmZmViWxZIlS2jSpEmx+QBcLhc333wzAwcO5L777iv21UiHDh3YtGkTe/bsAeDrr7+mcePGRfJnHzlyhFtuuYVx48blbedUr16dmJiYvAfcjBkz6NSpU5H4Qg1zfxQ95v4oHOaMC6hSpQr33XcfQ4YMwe1207Vr1yI7uAwFTn1pBwwYgNPppGnTptx2223F6pw9eza7du3is88+47PPPgOgcePGf9l7LyqqVavG448/zrBhw3C73TRo0IBRo0YVyZ/97rvvkpGRwauvvsqrr74KQOfOnXnuuecYN24c6enp1KhRg+eee65IfKGGuT+KHnN/FA7TZNdgMBgMpQqzVWgwGAyGUoUJXAaDwWAoVZjAZTAYDIZShQlcBoPBYChVmMBlMBgMhlKFCVwGg8FgKFWYwGUwGAyGUoUpQA5j/v3vf7Nx40Y8Hg+7du2iTp06QG5vtm+++YYqVaoU2jFx4kSSk5PZvn07a9asYcKECX/69xkZGTzwwAO8+OKLRESY9yRD6GDuj9KLCVxhzH/+8x/g/5tfzp49u0j//K1bt5KSkkLz5s3Zvn37aa+Ji4ujTZs2fPDBB1x77bVF6jcYCoO5P0ovJnD9A+natSvvvfcea9asYenSpRw6dIj9+/dzxRVXcPz4cb777jvKlSvH5MmTiY6OZvbs2bz77rv4fD7q1q3LI488QlxcHG+//Tb9+/fP+3N37drFDTfcwP79+2nQoAHPPfccTqeT3r17c+WVV3LNNdeYt0pDyGPuj9DH/Jb+4axfv57Jkyfz3//+l5dffpmOHTvy+eefY7PZWLFiBdu3b+fDDz9k+vTpzJ49m7p16/Lqq69iWRZLly7NGxQHud2uX3jhBRYsWMDevXtZtWoVAOXLlyc+Pp4tW7aU1I9pMJwR5v4ITcyK6x9OUlIScXFxxMXFAeTNQDrrrLM4ceIE3377LTt37uTqq68GwOv1cvbZZ3P06FEsy8r77wBat26dN9G1bt26HD16NO/fVa9enZ07dxZZR2qDQYG5P0ITE7j+4Tgcjj/9/8jIP38lfD4fvXr1yhsrkZWVhdvtxrKsv1z7x/9vs9n4Y//myMhI7HZ7UX98g6FYMfdHaGK2Cg0F0rZtWxYuXEhaWhqQOxL8tddeo3z58liWRWZmZkB/TmpqKjVr1izOj2owyDH3R8lgApehQBo2bMjw4cMZMmQIl156KcePH+euu+4CcmfurF69+m//jOPHj5ORkUHDhg2L++MaDFLM/VEymHlchjNm69atvPTSS7zyyisFXjd16lScTieDBg0SfTKDoeQx90fxYVZchjOmfv36nHPOOfz000/5XpORkcHq1avzDq8Nhn8K5v4oPsyKy2AwGAylCrPiMhgMBkOpwgQug8FgMJQqTOAyGAwGQ6nCBC6DwWAwlCpM4DIYDAZDqcIELoPBYDCUKkzgMhgMBkOpwgQug8FgMJQq/g/1lasWtmPXGwAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 472.441x708.661 with 12 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "heatmap: OmicsHeatmap = OmicsHeatmap(\n",
-    "    df=rhythmic_all,\n",
-    "    columns_cond1=columns_cond1,\n",
-    "    columns_cond2=columns_cond2,\n",
-    "    t_cond1=t_cond1,\n",
-    "    t_cond2=t_cond2,\n",
-    "    cond1_label=\"Alpha cells\",\n",
-    "    cond2_label=\"Beta cells\",\n",
-    "    show_unexpressed=False,\n",
-    ")\n",
-    "heatmap.plot_heatmap()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "52787ba5",
-   "metadata": {},
-   "source": [
-    "## Showing individual examples\n",
-    "\n",
-    "Finally, we can manually select genes of interest to show the underlying data (as points) and the fitted cosinor models from the selected model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "1944f9e9",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fitting model 2\n",
-      "(<cosinor_lite.differential_rhytmicity_omics.M1OneCondition object at 0x13243f4d0>, <cosinor_lite.differential_rhytmicity_omics.MOscOneCondition object at 0x13243f510>)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 472.441x236.22 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "example = TimeSeriesExample(\n",
-    "    df=rhythmic_all,\n",
-    "    columns_cond1=columns_cond1,\n",
-    "    columns_cond2=columns_cond2,\n",
-    "    t_cond1=t_cond1,\n",
-    "    t_cond2=t_cond2,\n",
-    "    cond1_label=\"Alpha cells\",\n",
-    "    cond2_label=\"Beta cells\",\n",
-    ")\n",
-    "\n",
-    "# example.plot_time_series(\"Scg2\", xticks=np.arange(0, 25, 4))\n",
-    "# example.plot_time_series(\"Upk3a\", xticks=np.arange(0, 25, 4))\n",
-    "# example.plot_time_series(\"Kcnk3\", xticks=np.arange(0, 25, 4))\n",
-    "\n",
-    "example.plot_time_series(\"Ntrk1\", xticks=np.arange(0, 25, 4))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "cosinor-lite",
-   "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": "3.11.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/notebooks/omics_tutorial.ipynb b/notebooks/omics_tutorial.ipynb
new file mode 100644
index 0000000..6739159
--- /dev/null
+++ b/notebooks/omics_tutorial.ipynb
@@ -0,0 +1,364 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0",
+   "metadata": {},
+   "source": [
+    "# Differential rhytmicity analysis of omics datasets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1",
+   "metadata": {},
+   "source": [
+    "The aim of this tutorial is to use the cosinor_lite Python package to perform differential rhythmicity analysis on omics data. Here we will use published RNA-seq data, but in theory any types of omics data (RNA-seq, proteomics, metabolomics, lipidomics) could be used. The dataset is from the following publication:\n",
+    "\n",
+    "Petrenko V, Saini C, Giovannoni L, Gobet C, Sage D, Unser M, Heddad Masson M, Gu G, Bosco D, Gachon F, Philippe J, Dibner C. 2017. Pancreatic α- and β-cellular clocks have distinct molecular properties and impact on islet hormone secretion and gene expression. Genes Dev 31:383–398. doi:10.1101/gad.290379.116\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2",
+   "metadata": {},
+   "source": [
+    "### How does the method actually work?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3",
+   "metadata": {},
+   "source": [
+    "The details of the method are nicely explained in the article: \n",
+    "\n",
+    "Pelikan A, Herzel H, Kramer A, Ananthasubramaniam B. 2022. Venn diagram analysis overestimates the extent of circadian rhythm reprogramming. The FEBS Journal 289:6605–6621. doi:10.1111/febs.16095\n",
+    "\n",
+    "Here is an adaptation of their figure explaining the methdology:\n",
+    "\n",
+    "<div style=\"text-align: center;\">\n",
+    "  <img src=\"../images/model_selection.png\" alt=\"Model selection\" style=\"width:60%;\"/>\n",
+    "</div>\n",
+    "\n",
+    "For condition 1 (i.e. alpha cells) and condition 2 (i.e. beta cells), we fit 5 different models:\n",
+    "\n",
+    "- Model 1) Arrhythmic in alpha and beta cells\n",
+    "\n",
+    "- Model 2) Rhythmic in beta cells only\n",
+    "\n",
+    "- Model 3) Rhythmic in alpha cells only\n",
+    "\n",
+    "- Model 4) Rhythmic in alpha and beta cells with the same rhythmic parameters (i.e. phase and amplitude)\n",
+    "\n",
+    "- Model 5) Rhythmic in both but with differential rhythmicity in alpha vs beta cells\n",
+    "\n",
+    "A degree of confidence is calculated for each model (called model weight, which sums to 1 across all models), and a model is chosen if the model weight exceeds a threshold (for this tutorial we will use 0.5). If no model exceeds this threshold, then the model is unclassified, which we define as Model 0\n",
+    "\n",
+    "- Model 0) unclassified\n",
+    "\n",
+    "Let's start running the code to make things more concrete! First we need to load the required packages."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from cosinor_lite.datasets import OmicsDataset\n",
+    "from cosinor_lite.differential_rhytmicity_omics import DifferentialRhythmicity, OmicsHeatmap, TimeSeriesExample\n",
+    "\n",
+    "plt.rcParams.update(\n",
+    "    {\n",
+    "        \"font.size\": 8,\n",
+    "        \"axes.titlesize\": 8,\n",
+    "        \"axes.labelsize\": 8,\n",
+    "        \"xtick.labelsize\": 8,\n",
+    "        \"ytick.labelsize\": 8,\n",
+    "        \"legend.fontsize\": 8,\n",
+    "        \"figure.titlesize\": 8,\n",
+    "        \"pdf.fonttype\": 42,\n",
+    "    },\n",
+    ")\n",
+    "plt.style.use(\"seaborn-v0_8-ticks\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5",
+   "metadata": {},
+   "source": [
+    "## Load data\n",
+    "\n",
+    "Next, we load in the omics dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "file: Path = Path.cwd().parent / \"data\" / \"GSE95156_Alpha_Beta.txt\"\n",
+    "\n",
+    "df_rna: pd.DataFrame = pd.read_csv(file, sep=\"\\t\")\n",
+    "df_rna: pd.DataFrame = df_rna.drop(df_rna.columns[0], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7",
+   "metadata": {},
+   "source": [
+    "## Defining sample conditions and times\n",
+    "\n",
+    "Typically in omics datasets, each row represents a different entity (gene/protein/lipid etc.), and each samples are stored as separate column.\n",
+    "\n",
+    "We need to tell the algorithm the names of the samples corresponding to condition 1 and 2, as well as the corresponding times."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_rna[\"Genes\"] = df_rna[\"gene_name\"].str.split(\"|\").str[1]\n",
+    "\n",
+    "columns_cond1: list[str] = [\n",
+    "    \"ZT_0_a_1\",\n",
+    "    \"ZT_4_a_1\",\n",
+    "    \"ZT_8_a_1\",\n",
+    "    \"ZT_12_a_1\",\n",
+    "    \"ZT_16_a_1\",\n",
+    "    \"ZT_20_a_1\",\n",
+    "    \"ZT_0_a_2\",\n",
+    "    \"ZT_4_a_2\",\n",
+    "    \"ZT_8_a_2\",\n",
+    "    \"ZT_12_a_2\",\n",
+    "    \"ZT_16_a_2\",\n",
+    "    \"ZT_20_a_2\",\n",
+    "]\n",
+    "\n",
+    "columns_cond2: list[str] = [\n",
+    "    \"ZT_0_b_1\",\n",
+    "    \"ZT_4_b_1\",\n",
+    "    \"ZT_8_b_1\",\n",
+    "    \"ZT_12_b_1\",\n",
+    "    \"ZT_16_b_1\",\n",
+    "    \"ZT_20_b_1\",\n",
+    "    \"ZT_0_b_2\",\n",
+    "    \"ZT_4_b_2\",\n",
+    "    \"ZT_8_b_2\",\n",
+    "    \"ZT_12_b_2\",\n",
+    "    \"ZT_16_b_2\",\n",
+    "    \"ZT_20_b_2\",\n",
+    "]\n",
+    "\n",
+    "t_cond1: np.ndarray = np.array(\n",
+    "    [\n",
+    "        0,\n",
+    "        4,\n",
+    "        8,\n",
+    "        12,\n",
+    "        16,\n",
+    "        20,\n",
+    "        0,\n",
+    "        4,\n",
+    "        8,\n",
+    "        12,\n",
+    "        16,\n",
+    "        20,\n",
+    "    ],\n",
+    ")\n",
+    "\n",
+    "t_cond2: np.ndarray = t_cond1.copy()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9",
+   "metadata": {},
+   "source": [
+    "## Processing the data and visualising the distribution of expression\n",
+    "\n",
+    "We next load the data and create a plot of the distribution of all expression values.\n",
+    "\n",
+    "In some cases there will be a bimodal distribution, with one large peak at high values and a second, typically smaller peak at very low values. This can be a sign that the small peak at low values represents noise and can be considered as unexpressed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rna_data: OmicsDataset = OmicsDataset(\n",
+    "    df=df_rna,\n",
+    "    columns_cond1=columns_cond1,\n",
+    "    columns_cond2=columns_cond2,\n",
+    "    t_cond1=t_cond1,\n",
+    "    t_cond2=t_cond2,\n",
+    "    deduplicate_on_init=True,\n",
+    ")\n",
+    "rna_data.expression_histogram()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11",
+   "metadata": {},
+   "source": [
+    "## Quality control\n",
+    "\n",
+    "Another basic technique for quality control is to create a scatterplot of technical replicates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "12",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rna_data.replicate_scatterplot(sample1=\"ZT_0_a_1\", sample2=\"ZT_0_a_2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "13",
+   "metadata": {},
+   "source": [
+    "In some cases this may reveal that measurements below a certain threshold look visually less correlated between samples. Here we will defines genes as non-expressed using the threshold of <0, as used in the original 2017 article."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "14",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rna_data.add_is_expressed(mean_min=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "15",
+   "metadata": {},
+   "source": [
+    "## Fitting rhythmic models to the omics dataset\n",
+    "\n",
+    "Now things get exciting! We can finally start fitting the rhythmic models to the data. The output of calling DifferentialRhythmicity().extract_all_circadian_params() is a dataframe that contains the model weights across the 5 different models, as well as the amplitude and phase from the selected model for each condition. This can take some time (~5 mins), so go and get a coffee!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "16",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dr = DifferentialRhythmicity(dataset=rna_data)\n",
+    "rhythmic_all = dr.extract_all_circadian_params()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17",
+   "metadata": {},
+   "source": [
+    "## Heatmap of model selection\n",
+    "\n",
+    "We can display the results as a heatmap, where we'll visualise models 2-5 using z-scores for each sample.\n",
+    "\n",
+    "Note that genes expressed that are only expressed in one condition are given the subclass \"a\", while genes that are expressed in both conditions are given the subclass \"b\"."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "18",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "heatmap: OmicsHeatmap = OmicsHeatmap(\n",
+    "    df=rhythmic_all,\n",
+    "    columns_cond1=columns_cond1,\n",
+    "    columns_cond2=columns_cond2,\n",
+    "    t_cond1=t_cond1,\n",
+    "    t_cond2=t_cond2,\n",
+    "    cond1_label=\"Alpha cells\",\n",
+    "    cond2_label=\"Beta cells\",\n",
+    "    show_unexpressed=False,\n",
+    ")\n",
+    "heatmap.plot_heatmap()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19",
+   "metadata": {},
+   "source": [
+    "## Showing individual examples\n",
+    "\n",
+    "Finally, we can manually select genes of interest to show the underlying data (as points) and the fitted cosinor models from the selected model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "20",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "example = TimeSeriesExample(\n",
+    "    df=rhythmic_all,\n",
+    "    columns_cond1=columns_cond1,\n",
+    "    columns_cond2=columns_cond2,\n",
+    "    t_cond1=t_cond1,\n",
+    "    t_cond2=t_cond2,\n",
+    "    cond1_label=\"Alpha cells\",\n",
+    "    cond2_label=\"Beta cells\",\n",
+    ")\n",
+    "\n",
+    "# example.plot_time_series(\"Scg2\", xticks=np.arange(0, 25, 4))\n",
+    "# example.plot_time_series(\"Upk3a\", xticks=np.arange(0, 25, 4))\n",
+    "# example.plot_time_series(\"Kcnk3\", xticks=np.arange(0, 25, 4))\n",
+    "\n",
+    "example.plot_time_series(\"Ntrk1\", xticks=np.arange(0, 25, 4))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "cosinor-lite",
+   "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": "3.11.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/setup_notebook.py b/notebooks/setup_notebook.py
deleted file mode 100644
index 48d42c3..0000000
--- a/notebooks/setup_notebook.py
+++ /dev/null
@@ -1,15 +0,0 @@
-import matplotlib.pyplot as plt
-
-plt.rcParams.update(
-    {
-        "font.size": 8,
-        "axes.titlesize": 8,
-        "axes.labelsize": 8,
-        "xtick.labelsize": 8,
-        "ytick.labelsize": 8,
-        "legend.fontsize": 8,
-        "figure.titlesize": 8,
-        "pdf.fonttype": 42,
-    },
-)
-plt.style.use("seaborn-v0_8-ticks")
diff --git a/pyproject.toml b/pyproject.toml
index 65d034f..0727e15 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -20,6 +20,8 @@ dependencies = [
     "pingouin",
     "pydantic>=2.11.9",
     "tqdm>=4.67.1",
+    "openpyxl>=3.1.5",
+    "gradio>=5.50.0",
 ]
 name = "cosinor-lite"
 version = "0.0.0"
@@ -114,8 +116,6 @@ lint.ignore = [
     "FA102",   # Flake8-future-annotations - Missing `from __future__ import annotations`, but uses PEP 604 union
 ]
 
-# Avoid trying to fix flake8-bugbear (`B`) violations.
-#unfixable = ["B"]
 line-length = 119
 
 # Ignore `E402` (import violations) in all `__init__.py` files, and in `path/to/file.py`.
@@ -126,8 +126,5 @@ line-length = 119
 [tool.ty.environment]
 root = "./src/cosinor_lite"
 
-# [tool.ty.rules]
-# invalid-type-form = "ignore"
-
-# [tool.uv.sources]
-# cosinor-lite = { workspace = true }
+[tool.interrogate]
+exclude = ["tests", "app.py"]
diff --git a/run.sh b/run.sh
index 6610aea..9d38ce5 100755
--- a/run.sh
+++ b/run.sh
@@ -4,22 +4,19 @@ set -e
 
 THIS_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
 
-# install core and development Python dependencies into the currently activated venv
+# install core and development Python dependencies using uv
 function install {
-    python -m pip install --upgrade pip
-    python -m pip install --editable "$THIS_DIR/[dev]"
+    uv sync --extra dev
 }
 
 # run linting, formatting, and other static code quality tools
 function lint {
-    pre-commit run --all-files
+    uv run pre-commit run --all-files
 }
 
 # same as `lint` but with any special considerations for CI
 function lint:ci {
-    # We skip no-commit-to-branch since that blocks commits to `main`.
-    # All merged PRs are commits to `main` so this must be disabled.
-    SKIP=no-commit-to-branch pre-commit run --all-files
+    SKIP=no-commit-to-branch uv run pre-commit run --all-files
 }
 
 # execute tests that are not marked as `slow`
@@ -34,10 +31,9 @@ function test:ci {
     COVERAGE_DIR="$INSTALLED_PKG_DIR" run-tests
 }
 
-# (example) ./run.sh test tests/test_states_info.py::test__slow_add
 function run-tests {
     PYTEST_EXIT_STATUS=0
-    python -m pytest ${@:-"$THIS_DIR/tests/"} \
+    uv run python -m pytest ${@:-"$THIS_DIR/tests/"} \
         --cov "${COVERAGE_DIR:-$THIS_DIR/src}" \
         --cov-report html \
         --cov-report term \
@@ -50,56 +46,12 @@ function run-tests {
     return $PYTEST_EXIT_STATUS
 }
 
-function test:wheel-locally {
-    deactivate || true
-    rm -rf test-env || true
-    python -m venv test-env
-    source test-env/bin/activate
-    clean || true
-    pip install build
-    build
-    pip install ./dist/*.whl pytest pytest-cov
-    test:ci
-    deactivate || true
-}
-
-# serve the html test coverage report on localhost:8000
 function serve-coverage-report {
-    python -m http.server --directory "$THIS_DIR/test-reports/htmlcov/" 8000
-}
-
-# build a wheel and sdist from the Python source code
-function build {
-    python -m build --sdist --wheel "$THIS_DIR/"
+    python -m http.server 8000 \
+        --bind 127.0.0.1 \
+        --directory "$THIS_DIR/test-reports/htmlcov/"
 }
 
-function release:test {
-    lint
-    clean
-    build
-    publish:test
-}
-
-function release:prod {
-    release:test
-    publish:prod
-}
-
-function publish:test {
-    try-load-dotenv || true
-    twine upload dist/* \
-        --repository testpypi \
-        --username=__token__ \
-        --password="$TEST_PYPI_TOKEN"
-}
-
-function publish:prod {
-    try-load-dotenv || true
-    twine upload dist/* \
-        --repository pypi \
-        --username=__token__ \
-        --password="$PROD_PYPI_TOKEN"
-}
 
 # remove all files generated by tests, builds, or operating this codebase
 function clean {
@@ -122,17 +74,6 @@ function clean {
       -exec rm {} +
 }
 
-# export the contents of .env as environment variables
-function try-load-dotenv {
-    if [ ! -f "$THIS_DIR/.env" ]; then
-        echo "no .env file found"
-        return 1
-    fi
-
-    while read -r line; do
-        export "$line"
-    done < <(grep -v '^#' "$THIS_DIR/.env" | grep -v '^$')
-}
 
 # print all functions in this file
 function help {
diff --git a/src/cosinor_lite/base_models.py b/src/cosinor_lite/base_models.py
deleted file mode 100644
index a2e56df..0000000
--- a/src/cosinor_lite/base_models.py
+++ /dev/null
@@ -1,73 +0,0 @@
-from __future__ import annotations
-
-from abc import ABC, abstractmethod
-
-import pandas as pd
-import statsmodels.formula.api as smf
-from pydantic.dataclasses import dataclass
-
-
-@dataclass
-class ModelResult:
-    name: int
-    llf: float
-    bic: float
-    alpha_phase: float
-    alpha_amp: float
-    beta_phase: float
-    beta_amp: float
-
-
-@dataclass
-class ModelResultOneCondition:
-    name: int
-    llf: float
-    bic: float
-    phase: float
-    amp: float
-
-
-class BaseModel(ABC):
-    name: str
-    k: int
-    formula: str
-
-    def fit(self, df: pd.DataFrame) -> ModelResult:
-        # df must already contain: y, constant, cos_wt, sin_wt, is_alpha, is_beta
-        model = smf.ols(self.formula, data=df).fit()
-        n = len(df)
-        alpha_phase, alpha_amp, beta_phase, beta_amp = self.extract(model.params)
-        return ModelResult(
-            name=self.name,
-            llf=model.llf,
-            bic=bic(model.llf, self.k, n),
-            alpha_phase=alpha_phase,
-            alpha_amp=alpha_amp,
-            beta_phase=beta_phase,
-            beta_amp=beta_amp,
-        )
-
-    @abstractmethod
-    def extract(self, params: pd.Series) -> tuple[float, float, float, float]: ...
-
-
-class BaseModelOneCondition(ABC):
-    name: str
-    k: int
-    formula: str
-
-    def fit(self, df: pd.DataFrame) -> ModelResultOneCondition:
-        # df must already contain: y, constant, cos_wt, sin_wt, is_alpha, is_beta
-        model = smf.ols(self.formula, data=df).fit()
-        n = len(df)
-        phase, amp = self.extract(model.params)
-        return ModelResultOneCondition(
-            name=self.name,
-            llf=model.llf,
-            bic=bic(model.llf, self.k, n),
-            phase=phase,
-            amp=amp,
-        )
-
-    @abstractmethod
-    def extract(self, params: pd.Series) -> tuple[float, float]: ...
diff --git a/src/cosinor_lite/cities.json b/src/cosinor_lite/cities.json
deleted file mode 100644
index c37c112..0000000
--- a/src/cosinor_lite/cities.json
+++ /dev/null
@@ -1,233 +0,0 @@
-[
-    {
-        "city": "Montgomery",
-        "state": "Alabama",
-        "lat": 32.3792233,
-        "lng": -86.3077368,
-        "capital": true
-    },
-    {
-        "city": "Juneau",
-        "state": "Alaska",
-        "lat": 58.3019444,
-        "lng": -134.4197222,
-        "capital": true
-    },
-    {
-        "city": "Phoenix",
-        "state": "Arizona",
-        "lat": 33.4483771,
-        "lng": -112.0740373,
-        "capital": true
-    },
-    {
-        "city": "Little Rock",
-        "state": "Arkansas",
-        "lat": 34.7464809,
-        "lng": -92.2895948,
-        "capital": true
-    },
-    {
-        "city": "Sacramento",
-        "state": "California",
-        "lat": 38.5757647,
-        "lng": -121.4788515,
-        "capital": true
-    },
-    {
-        "city": "Denver",
-        "state": "Colorado",
-        "lat": 39.7392364,
-        "lng": -104.9848623,
-        "capital": true
-    },
-    {
-        "city": "Hartford",
-        "state": "Connecticut",
-        "lat": 41.7658043,
-        "lng": -72.6733723,
-        "capital": true
-    },
-    {
-        "city": "Dover",
-        "state": "Delaware",
-        "lat": 39.158168,
-        "lng": -75.5243682,
-        "capital": true
-    },
-    {
-        "city": "Tallahassee",
-        "state": "Florida",
-        "lat": 30.4382559,
-        "lng": -84.2807329,
-        "capital": true
-    },
-    {
-        "city": "Atlanta",
-        "state": "Georgia",
-        "lat": 33.7489954,
-        "lng": -84.3879824,
-        "capital": true
-    },
-    {
-        "city": "Honolulu",
-        "state": "Hawaii",
-        "lat": 21.3069444,
-        "lng": -157.8583333,
-        "capital": true
-    },
-    {
-        "city": "Boise",
-        "state": "Idaho",
-        "lat": 43.6150186,
-        "lng": -116.2023137,
-        "capital": true
-    },
-    {
-        "city": "Springfield",
-        "state": "Illinois",
-        "lat": 39.7983633,
-        "lng": -89.6549613,
-        "capital": true
-    },
-    {
-        "city": "Indianapolis",
-        "state": "Indiana",
-        "lat": 39.7683331,
-        "lng": -86.1583502,
-        "capital": true
-    },
-    {
-        "city": "Des Moines",
-        "state": "Iowa",
-        "lat": 41.6005448,
-        "lng": -93.6091064,
-        "capital": true
-    },
-    {
-        "city": "Topeka",
-        "state": "Kansas",
-        "lat": 39.0558243,
-        "lng": -95.6890185,
-        "capital": true
-    },
-    {
-        "city": "Frankfort",
-        "state": "Kentucky",
-        "lat": 38.2009055,
-        "lng": -84.8732835,
-        "capital": true
-    },
-    {
-        "city": "Baton Rouge",
-        "state": "Louisiana",
-        "lat": 30.4582829,
-        "lng": -91.1403196,
-        "capital": true
-    },
-    {
-        "city": "Augusta",
-        "state": "Maine",
-        "lat": 44.3106241,
-        "lng": -69.7794897,
-        "capital": true
-    },
-    {
-        "city": "Annapolis",
-        "state": "Maryland",
-        "lat": 38.9784453,
-        "lng": -76.4921829,
-        "capital": true
-    },
-    {
-        "city": "Boston",
-        "state": "Massachusetts",
-        "lat": 42.3600825,
-        "lng": -71.0588801,
-        "capital": true
-    },
-    {
-        "city": "Lansing",
-        "state": "Michigan",
-        "lat": 42.732535,
-        "lng": -84.5555347,
-        "capital": true
-    },
-    {
-        "city": "Oklahoma City",
-        "state": "Oklahoma",
-        "lat": 42.732535,
-        "lng": -84.5555347,
-        "capital": true
-    },
-    {
-        "city": "Salem",
-        "state": "Oregon",
-        "lat": 42.732535,
-        "lng": -84.5555347,
-        "capital": true
-    },
-    {
-        "city": "St. Paul",
-        "state": "Minnesota",
-        "lat": 44.9537029,
-        "lng": -93.0899578,
-        "capital": true
-    },
-    {
-        "city": "Jackson",
-        "state": "Mississippi",
-        "lat": 32.2987573,
-        "lng": -90.1848103,
-        "capital": true
-    },
-    {
-        "city": "Jefferson City",
-        "state": "Missouri",
-        "lat": 38.5767017,
-        "lng": -92.1735164,
-        "capital": true
-    },
-    {
-        "city": "Helena",
-        "state": "Montana",
-        "lat": 46.5958056,
-        "lng": -112.0270318,
-        "capital": true
-    },
-    {
-        "city": "Lincoln",
-        "state": "Nebraska",
-        "lat": 40.813616,
-        "lng": -96.7025955,
-        "capital": true
-    },
-    {
-        "city": "Carson City",
-        "state": "Nevada",
-        "lat": 39.1637984,
-        "lng": -119.7674034,
-        "capital": true
-    },
-    {
-        "city": "Concord",
-        "state": "New Hampshire",
-        "lat": 43.2081366,
-        "lng": -71.5375718,
-        "capital": true
-    },
-    {
-        "city": "Trenton",
-        "state": "New Jersey",
-        "lat": 40.2205824,
-        "lng": -74.759717,
-        "capital": true
-    },
-    {
-        "city": "Santa Fe",
-        "state": "New Mexico",
-        "lat": 35.6869752,
-        "lng": -105.937799,
-        "capital": true
-    }
-]
diff --git a/src/cosinor_lite/livecell_cosinor_analysis.py b/src/cosinor_lite/livecell_cosinor_analysis.py
new file mode 100644
index 0000000..c80b55b
--- /dev/null
+++ b/src/cosinor_lite/livecell_cosinor_analysis.py
@@ -0,0 +1,727 @@
+from __future__ import annotations
+
+import os
+import tempfile
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+from numpy.typing import NDArray
+from scipy.optimize import curve_fit
+from scipy.stats import f as f_dist
+
+from cosinor_lite.livecell_dataset import LiveCellDataset
+
+plt.rcParams.update(
+    {
+        "font.size": 8,
+        "axes.titlesize": 8,
+        "axes.labelsize": 8,
+        "xtick.labelsize": 8,
+        "ytick.labelsize": 8,
+        "legend.fontsize": 8,
+        "figure.titlesize": 8,
+        "pdf.fonttype": 42,
+    },
+)
+plt.style.use("seaborn-v0_8-ticks")
+
+
+def constant_model(x: NDArray[np.float64], mesor: float) -> NDArray[np.float64]:
+    """
+    Evaluate a constant model at the supplied mesor.
+
+    Parameters
+    ----------
+    x : NDArray[np.float64]
+        Time values at which the constant model is evaluated.
+    mesor : float
+        The mesor (mean level) used as the constant prediction.
+
+    Returns
+    -------
+    NDArray[np.float64]
+        The mesor value supplied by the caller broadcast over ``x``.
+
+    """
+    return np.full_like(x, mesor)
+
+
+def cosine_model_24(x: NDArray[np.float64], amplitude: float, acrophase: float, mesor: float) -> NDArray[np.float64]:
+    """
+    Evaluate a 24 h cosine model for the provided time points.
+
+    Parameters
+    ----------
+    x : NDArray[np.float64]
+        Time values at which to evaluate the curve.
+    amplitude : float
+        Cosine amplitude.
+    acrophase : float
+        Phase shift (in hours) applied to the cosine.
+    mesor : float
+        Baseline offset of the curve.
+
+    Returns
+    -------
+    NDArray[np.float64]
+        The cosine values with a fixed 24 h period.
+
+    """
+    period = 24.0
+    return amplitude * np.cos(2 * np.pi * (x - acrophase) / period) + mesor
+
+
+def cosine_model_free_period(
+    x: NDArray[np.float64],
+    amplitude: float,
+    acrophase: float,
+    period: float,
+    mesor: float,
+) -> NDArray[np.float64]:
+    """
+    Evaluate a cosine model allowing the period to vary.
+
+    Parameters
+    ----------
+    x : NDArray[np.float64]
+        Time values at which to evaluate the cosine.
+    amplitude : float
+        Cosine amplitude.
+    acrophase : float
+        Phase shift (in hours) applied to the cosine.
+    period : float
+        Oscillation period in hours.
+    mesor : float
+        Baseline offset for the model.
+
+    Returns
+    -------
+    NDArray[np.float64]
+        The cosine values with the requested period.
+
+    """
+    return amplitude * np.cos(2 * np.pi * (x - acrophase) / period) + mesor
+
+
+def cosine_model_damped(  # noqa: PLR0913
+    x: NDArray[np.float64],
+    amplitude: float,
+    damp: float,
+    acrophase: float,
+    period: float,
+    mesor: float,
+) -> NDArray[np.float64]:
+    """
+    Evaluate a damped cosine model that decays exponentially over time.
+
+    Parameters
+    ----------
+    x : NDArray[np.float64]
+        Time values at which to evaluate the cosine.
+    amplitude : float
+        Initial amplitude of the oscillation.
+    damp : float
+        Exponential damping coefficient (positive values decay).
+    acrophase : float
+        Phase shift (in hours) applied to the cosine.
+    period : float
+        Oscillation period in hours.
+    mesor : float
+        Baseline offset for the model.
+
+    Returns
+    -------
+    NDArray[np.float64]
+        The damped cosine evaluated at ``x``.
+
+    """
+    return amplitude * np.exp(-damp * x) * np.cos(2 * np.pi * (x - acrophase) / period) + mesor
+
+
+def _metrics(y_true: NDArray[np.float64], y_pred: NDArray[np.float64], p: int) -> tuple[float, float, float]:
+    """
+    Compute residual sum of squares and R-squared metrics.
+
+    Parameters
+    ----------
+    y_true : NDArray[np.float64]
+        Observed values.
+    y_pred : NDArray[np.float64]
+        Predicted values from a model.
+    p : int
+        Number of model parameters used in the fit.
+
+    Returns
+    -------
+    tuple[float, float, float]
+        Residual sum of squares, R-squared, and adjusted R-squared.
+
+    """
+    y_true = np.asarray(y_true, float)
+    y_pred = np.asarray(y_pred, float)
+    n: int = y_true.size
+    rss: float = np.sum((y_true - y_pred) ** 2)
+    sst: float = np.sum((y_true - np.mean(y_true)) ** 2)
+    r2 = np.nan
+    r2_adj = np.nan
+    if sst > 0:
+        r2 = 1.0 - (rss / sst)
+        if n > p and n > 1:
+            r2_adj = 1.0 - (rss / (n - p)) / (sst / (n - 1))
+    return rss, r2, r2_adj
+
+
+def _sanitize_xy(
+    x: NDArray[np.float64],
+    y: NDArray[np.float64],
+    min_points: int = 4,
+) -> tuple[NDArray[np.float64], NDArray[np.float64]]:
+    """
+    Remove non-finite values, sort by time, and validate point count.
+
+    Parameters
+    ----------
+    x : NDArray[np.float64]
+        Candidate time points.
+    y : NDArray[np.float64]
+        Candidate measurements aligned with ``x``.
+    min_points : int, optional
+        Minimum required number of finite points after cleaning, by default 4.
+
+    Returns
+    -------
+    tuple[NDArray[np.float64], NDArray[np.float64]]
+        Cleaned and sorted ``x`` and ``y`` arrays.
+
+    Raises
+    ------
+    ValueError
+        If fewer than ``min_points`` observations remain after cleaning.
+
+    """
+    x = np.asarray(x, float)
+    y = np.asarray(y, float)
+    ok = np.isfinite(x) & np.isfinite(y)
+    x, y = x[ok], y[ok]
+    order: NDArray[np.float64] = np.argsort(x)
+    x, y = x[order], y[order]
+    if y.size < min_points:
+        msg = f"Not enough valid points after cleaning (need ≥{min_points}, got {y.size})."
+        raise ValueError(
+            msg,
+        )
+    return x, y
+
+
+class CosinorAnalysis(LiveCellDataset):
+    """
+    Extend ``LiveCellDataset`` with cosinor model fitting utilities.
+
+    Parameters
+    ----------
+    *args
+        Positional arguments forwarded to ``LiveCellDataset``.
+    period : float, optional
+        Default oscillation period for fits, by default 24.0.
+    method : str, optional
+        Default detrending method, by default ``"ols"``.
+    t_lower : float, optional
+        Lower bound on time window (hours) used when fitting, by default 0.0.
+    t_upper : float, optional
+        Upper bound on time window (hours) used when fitting, by default 720.0.
+    **kwargs
+        Keyword arguments forwarded to ``LiveCellDataset``.
+
+    """
+
+    def __init__(
+        self,
+        *args,  # noqa: ANN002
+        period: float = 24.0,
+        method: str = "ols",
+        t_lower: float = 0.0,
+        t_upper: float = 720.0,
+        **kwargs,  # noqa: ANN003
+    ) -> None:
+        """
+        Initialize the cosinor analysis object with dataset metadata.
+
+        Parameters
+        ----------
+        *args
+            Positional arguments passed to ``LiveCellDataset``.
+        period : float, optional
+            Default oscillation period (hours), by default 24.0.
+        method : str, optional
+            Default detrending method, by default ``"ols"``.
+        t_lower : float, optional
+            Lower time bound (hours), by default 0.0.
+        t_upper : float, optional
+            Upper time bound (hours), by default 720.0.
+        **kwargs
+            Keyword arguments forwarded to ``LiveCellDataset``.
+
+        """
+        super().__init__(*args, **kwargs)
+
+        self.t_lower = t_lower
+        self.t_upper = t_upper
+        self.period = period
+        self.method = method
+
+    def fit_cosinor_24(
+        self,
+        x: NDArray[np.float64],
+        y: NDArray[np.float64],
+    ) -> tuple[dict, NDArray[np.float64], NDArray[np.float64]]:
+        """
+        Fit a fixed 24 h cosinor model to the supplied data.
+
+        Parameters
+        ----------
+        x : NDArray[np.float64]
+            Time values (hours).
+        y : NDArray[np.float64]
+            Measurements aligned with ``x``.
+
+        Returns
+        -------
+        tuple[dict, NDArray[np.float64], NDArray[np.float64]]
+            Fit summary metrics, high-resolution prediction times, and predictions.
+
+        """
+        x, y = _sanitize_xy(x, y)
+
+        p0_const: list[float] = [float(np.mean(y))]
+        params_const, _ = curve_fit(constant_model, x, y, p0=p0_const)
+        yhat_const: float = constant_model(x, *params_const)
+
+        p0_cos: list[float] = [
+            float(np.std(y)),
+            0.0,
+            float(np.mean(y)),
+        ]
+        params_cos, _ = curve_fit(cosine_model_24, x, y, p0=p0_cos)
+        amp_fit, acro_fit, mesor_fit = params_cos
+        yhat_cos: NDArray[np.float64] = cosine_model_24(x, amp_fit, acro_fit, mesor_fit)
+
+        rss_cos, r2, r2_adj = _metrics(y, yhat_cos, p=3)
+        rss_const, _, _ = _metrics(y, np.full_like(x, yhat_const), p=1)
+
+        n = len(y)
+        p1, p2 = 1, 3
+        num = max(rss_const - rss_cos, 0.0)
+        den = max(rss_cos, 1e-12)
+        f_stat = (num / (p2 - p1)) / (den / max(n - p2, 1))
+        p_val = np.nan
+        if n > p2:
+            p_val = f_dist.sf(f_stat, p2 - p1, n - p2)
+
+        t_test_acro = np.linspace(0.0, 24.0, 1440)
+        y_test_acro = cosine_model_24(t_test_acro, amp_fit, acro_fit, mesor_fit)
+        amplitude = abs(amp_fit)
+        acrophase = t_test_acro[int(np.argmax(y_test_acro))]
+        mesor = mesor_fit
+
+        t_test: NDArray[np.float64] = np.linspace(float(x[0]), float(x[-1]), 1440)
+        y_test = cosine_model_24(t_test, amp_fit, acro_fit, mesor_fit)
+
+        results = {
+            "mesor": mesor,
+            "amplitude": amplitude,
+            "acrophase": acrophase,
+            "p-val osc": p_val,
+            "r2": r2,
+            "r2_adj": r2_adj,
+        }
+        return results, t_test, y_test
+
+    def fit_cosinor_free_period(
+        self,
+        x: NDArray[np.float64],
+        y: NDArray[np.float64],
+    ) -> tuple[dict, NDArray[np.float64], NDArray[np.float64]]:
+        """
+        Fit a cosinor model with a free (bounded) period parameter.
+
+        Parameters
+        ----------
+        x : NDArray[np.float64]
+            Time values (hours).
+        y : NDArray[np.float64]
+            Measurements aligned with ``x``.
+
+        Returns
+        -------
+        tuple[dict, NDArray[np.float64], NDArray[np.float64]]
+            Fit summary metrics, high-resolution prediction times, and predictions.
+
+        """
+        x, y = _sanitize_xy(x, y)
+
+        p0_const = [float(np.mean(y))]
+        params_const, _ = curve_fit(constant_model, x, y, p0=p0_const)
+        yhat_const = constant_model(x, *params_const)
+
+        p0_24 = [float(np.std(y)), 0.0, float(np.mean(y))]
+        params_24, _ = curve_fit(cosine_model_24, x, y, p0=p0_24)
+        amp0, acro0, mesor0 = params_24
+        p0_free = [amp0, acro0, 24.0, mesor0]  # [A, phase, period, mesor]
+
+        bounds = ([-np.inf, -np.inf, 20.0, -np.inf], [np.inf, np.inf, 28.0, np.inf])
+        params_free, _ = curve_fit(
+            cosine_model_free_period,
+            x,
+            y,
+            p0=p0_free,
+            bounds=bounds,
+        )
+        amp_fit, acro_fit, period_fit, mesor_fit = params_free
+        yhat_free = cosine_model_free_period(
+            x,
+            amp_fit,
+            acro_fit,
+            period_fit,
+            mesor_fit,
+        )
+
+        rss_free, r2, r2_adj = _metrics(y, yhat_free, p=4)
+        rss_const, _, _ = _metrics(y, yhat_const, p=1)
+
+        n = len(y)
+        p1, p2 = 1, 4
+        num = max(rss_const - rss_free, 0.0)
+        den = max(rss_free, 1e-12)
+        f_stat = (num / (p2 - p1)) / (den / max(n - p2, 1))
+        p_val = np.nan
+        if n > p2:
+            p_val = f_dist.sf(f_stat, p2 - p1, n - p2)
+
+        t_test_acro = np.linspace(0.0, float(period_fit), 2000)
+        y_test_acro = cosine_model_free_period(
+            t_test_acro,
+            amp_fit,
+            acro_fit,
+            period_fit,
+            mesor_fit,
+        )
+        amplitude = abs(amp_fit)
+        acrophase = t_test_acro[int(np.argmax(y_test_acro))]
+        mesor = mesor_fit
+        period = float(period_fit)
+
+        t_test = np.linspace(float(x[0]), float(x[-1]), 1440)
+        y_test = cosine_model_free_period(
+            t_test,
+            amp_fit,
+            acro_fit,
+            period_fit,
+            mesor_fit,
+        )
+
+        results = {
+            "mesor": mesor,
+            "amplitude": amplitude,
+            "acrophase": acrophase,
+            "period": period,
+            "p-val osc": p_val,
+            "r2": r2,
+            "r2_adj": r2_adj,
+        }
+        return results, t_test, y_test
+
+    def fit_cosinor_damped(
+        self,
+        x: NDArray[np.float64],
+        y: NDArray[np.float64],
+    ) -> tuple[dict, NDArray[np.float64], NDArray[np.float64]]:
+        """
+        Fit a damped cosinor model with exponential decay.
+
+        Parameters
+        ----------
+        x : NDArray[np.float64]
+            Time values (hours).
+        y : NDArray[np.float64]
+            Measurements aligned with ``x``.
+
+        Returns
+        -------
+        tuple[dict, NDArray[np.float64], NDArray[np.float64]]
+            Fit summary metrics, high-resolution prediction times, and predictions.
+
+        """
+        x, y = _sanitize_xy(x, y)
+
+        p0_const = [float(np.mean(y))]
+        params_const, _ = curve_fit(constant_model, x, y, p0=p0_const)
+        yhat_const = constant_model(x, *params_const)
+
+        p0_24 = [float(np.std(y)), 0.0, float(np.mean(y))]
+        params_24, _ = curve_fit(cosine_model_24, x, y, p0=p0_24)
+        amp0, acro0, mesor0 = params_24
+
+        p0_damped = [amp0, 0.01, acro0, 24.0, mesor0]
+        bounds = (
+            [-np.inf, 0.0, -np.inf, 20.0, -np.inf],
+            [np.inf, np.inf, np.inf, 28.0, np.inf],
+        )
+        params_damped, _ = curve_fit(
+            cosine_model_damped,
+            x,
+            y,
+            p0=p0_damped,
+            bounds=bounds,
+        )
+        amp_fit, damp_fit, acro_fit, period_fit, mesor_fit = params_damped
+        yhat_damped = cosine_model_damped(
+            x,
+            amp_fit,
+            damp_fit,
+            acro_fit,
+            period_fit,
+            mesor_fit,
+        )
+
+        rss_damped, r2, r2_adj = _metrics(y, yhat_damped, p=5)
+        rss_const, _, _ = _metrics(y, yhat_const, p=1)
+
+        n = len(y)
+        p1, p2 = 1, 5
+        num = max(rss_const - rss_damped, 0.0)
+        den = max(rss_damped, 1e-12)
+        f_stat = (num / (p2 - p1)) / (den / max(n - p2, 1))
+        p_val = np.nan
+        if n > p2:
+            p_val = f_dist.sf(f_stat, p2 - p1, n - p2)
+
+        t_test_acro = np.linspace(0.0, float(period_fit), 1440)
+        y_test_acro = cosine_model_damped(
+            t_test_acro,
+            amp_fit,
+            damp_fit,
+            acro_fit,
+            period_fit,
+            mesor_fit,
+        )
+        amplitude = abs(amp_fit)
+        acrophase = t_test_acro[int(np.argmax(y_test_acro))]
+        mesor = mesor_fit
+        period = float(period_fit)
+        damp = float(damp_fit)
+
+        t_test = np.linspace(float(x[0]), float(x[-1]), 1440)
+        y_test = cosine_model_damped(
+            t_test,
+            amp_fit,
+            damp_fit,
+            acro_fit,
+            period_fit,
+            mesor_fit,
+        )
+
+        results = {
+            "mesor": mesor,
+            "amplitude": amplitude,
+            "acrophase": acrophase,
+            "period": period,
+            "damp": damp,
+            "p-val osc": p_val,
+            "r2": r2,
+            "r2_adj": r2_adj,
+        }
+        return results, t_test, y_test
+
+    def get_cosinor_fits(
+        self,
+        x: NDArray[np.float64],
+        y: NDArray[np.float64],
+        method: str = "cosinor_24",
+    ) -> tuple[dict, NDArray[np.float64], NDArray[np.float64]]:
+        """
+        Dispatch to the requested cosinor model and return its fit.
+
+        Parameters
+        ----------
+        x : NDArray[np.float64]
+            Time values (hours).
+        y : NDArray[np.float64]
+            Measurements aligned with ``x``.
+        method : str, optional
+            Cosinor model name (``"cosinor_24"``, ``"cosinor_free_period"``, ``"cosinor_damped"``).
+
+        Returns
+        -------
+        tuple[dict, NDArray[np.float64], NDArray[np.float64]]
+            Fit summary metrics, prediction times, and predictions.
+
+        Raises
+        ------
+        ValueError
+            If an unknown model name is supplied.
+
+        """
+        if method == "cosinor_24":
+            results, t_test, model_predictions_cosine = self.fit_cosinor_24(x, y)
+        elif method == "cosinor_free_period":
+            results, t_test, model_predictions_cosine = self.fit_cosinor_free_period(
+                x,
+                y,
+            )
+        elif method == "cosinor_damped":
+            results, t_test, model_predictions_cosine = self.fit_cosinor_damped(x, y)
+        else:
+            msg = f"Unknown cosine model: {method}"
+            raise ValueError(msg)
+
+        return results, t_test, model_predictions_cosine
+
+    def group_selector(
+        self,
+        group: str,
+    ) -> tuple[np.ndarray, np.ndarray, np.ndarray, str, int, str]:
+        """
+        Retrieve group-specific data and plotting metadata.
+
+        Parameters
+        ----------
+        group : str
+            Group name (``"group1"`` or ``"group2"``).
+
+        Returns
+        -------
+        tuple[np.ndarray, np.ndarray, np.ndarray, str, int, str]
+            Tuple containing IDs, replicates, data matrix, color, number of unique IDs,
+            and group label.
+
+        Raises
+        ------
+        ValueError
+            If ``group`` is not ``"group1"`` or ``"group2"``.
+
+        """
+        if group == "group1":
+            ids, replicates, data = self.get_group1_ids_replicates_data()
+            color = self.color_group1
+            group_label = self.group1_label
+        elif group == "group2":
+            ids, replicates, data = self.get_group2_ids_replicates_data()
+            color = self.color_group2
+            group_label = self.group2_label
+        else:
+            msg = "group must be 'group1' or 'group2'"
+            raise ValueError(msg)
+
+        ids = np.asarray(ids)
+        replicates = np.asarray(replicates)
+        data = np.asarray(data)
+
+        n_group = len(np.unique(ids))
+        return ids, replicates, data, color, n_group, group_label
+
+    def fit_cosinor(  # noqa: PLR0913
+        self,
+        group: str,
+        method: str = "linear",
+        window: int = 5,
+        cosinor_model: str = "cosinor_24",
+        m: int = 5,
+        plot_style: str = "scatter",
+    ) -> tuple[pd.DataFrame, str, plt.Figure, str]:
+        """
+        Detrend each replicate in a group and fit the selected cosinor model.
+
+        Parameters
+        ----------
+        group : str
+            Group name (``"group1"`` or ``"group2"``).
+        method : str, optional
+            Detrending method provided to ``get_trend``, by default ``"linear"``.
+        window : int, optional
+            Window size for moving-average detrending, by default 5.
+        cosinor_model : str, optional
+            Name of cosinor model to fit, by default ``"cosinor_24"``.
+        m : int, optional
+            Number of subplot columns, by default 5.
+        plot_style : str, optional
+            Plot style for data (``"scatter"`` or ``"line"``), by default ``"scatter"``.
+
+        Returns
+        -------
+        tuple[pd.DataFrame, str, plt.Figure, str]
+            DataFrame of fit metrics, CSV path, generated figure, and PDF path.
+
+        Raises
+        ------
+        ValueError
+            If ``group`` is not ``"group1"`` or ``"group2"``.
+
+        """
+        ids, _replicates, data, color, n_group, group_label = self.group_selector(group)
+        n = np.ceil(n_group / m).astype(int)
+
+        study_list = np.unique(ids).tolist()
+
+        fig = plt.figure(figsize=(5 * m / 2.54, 5 * n / 2.54))
+
+        to_export_list = []
+
+        for i, id_curr in enumerate(study_list):
+            mask = np.array(ids) == id_curr
+            n_reps = np.sum(mask)
+
+            ax = fig.add_subplot(n, m, i + 1)
+
+            for j in range(n_reps):
+                exp_info = {"id": id_curr, "replicate": j + 1, "group": group_label}
+
+                x = self.time
+                y = data[:, mask][:, j]
+
+                valid_mask = ~np.isnan(y)
+                x_valid = x[valid_mask]
+                y_valid = y[valid_mask]
+
+                range_mask = (x_valid >= self.t_lower) & (x_valid <= self.t_upper)
+                x_fit = x_valid[range_mask]
+                y_fit = y_valid[range_mask]
+
+                x_processed, y_processed = self.get_trend(
+                    x_fit,
+                    y_fit,
+                    method=method,
+                    window=window,
+                )
+                y_detrended = y_fit - y_processed + np.mean(y_fit)
+
+                if plot_style == "scatter":
+                    ax.scatter(x_processed, y_detrended, s=4, alpha=0.8, color=color)
+                else:
+                    ax.plot(x_processed, y_detrended, color=color)
+
+                results, t_test, model_predictions_cosine = self.get_cosinor_fits(
+                    x_processed,
+                    y_detrended,
+                    method=cosinor_model,
+                )
+                to_export_list.append({**exp_info, **results})
+                ax.plot(t_test, model_predictions_cosine, color="k", linestyle="--")
+
+            ax.set_title(f"ID: {id_curr} (n={n_reps}) - {group_label}")
+            ax.set_xlabel("Time (h)")
+            if i % m == 0:
+                ax.set_ylabel("Expression")
+
+        plt.tight_layout()
+
+        df_export = pd.DataFrame(to_export_list)
+        fd, tmp1_path = tempfile.mkstemp(suffix=".csv")
+        os.close(fd)
+        df_export.to_csv(tmp1_path, index=False)
+
+        fd, tmp2_path = tempfile.mkstemp(suffix=".pdf")
+        os.close(fd)
+        fig.savefig(tmp2_path)
+
+        return df_export, tmp1_path, fig, tmp2_path
diff --git a/src/cosinor_lite/livecell_dataset.py b/src/cosinor_lite/livecell_dataset.py
new file mode 100644
index 0000000..2f45d8b
--- /dev/null
+++ b/src/cosinor_lite/livecell_dataset.py
@@ -0,0 +1,395 @@
+from __future__ import annotations
+
+import os
+import tempfile
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import statsmodels.api as sm
+from pydantic import ConfigDict, field_validator, model_validator
+from pydantic.dataclasses import dataclass
+
+plt.rcParams.update(
+    {
+        "font.size": 8,
+        "axes.titlesize": 8,
+        "axes.labelsize": 8,
+        "xtick.labelsize": 8,
+        "ytick.labelsize": 8,
+        "legend.fontsize": 8,
+        "figure.titlesize": 8,
+        "pdf.fonttype": 42,
+    },
+)
+plt.style.use("seaborn-v0_8-ticks")
+
+
+@dataclass(config=ConfigDict(arbitrary_types_allowed=True))
+class LiveCellDataset:
+    """
+    Handles live cell dataset operations: validation, trend analysis, and plotting.
+
+    Attributes:
+        ids (list[str]): Unique identifiers for each sample.
+        group (list[str]): Group labels for each sample.
+        replicate (list[int]): Replicate numbers for each sample.
+        time_series (np.ndarray): 2D array of time series data (shape: [timepoints, samples]).
+        time (np.ndarray): 1D array of timepoints.
+        group1_label (str): Label for group 1 (default: "group1").
+        group2_label (str): Label for group 2 (default: "group2").
+        color_group1 (str): Color for plotting group 1 (default: "tab:blue").
+        color_group2 (str): Color for plotting group 2 (default: "tab:orange").
+
+    Methods:
+        _to_2d_f64(v): Converts input to a 2D numpy array of float64 type.
+        _check_columns(): Validates that ids, group, and replicate lengths match time_series columns.
+        get_group1_ids_replicates_data(): Returns ids, replicates, and data for group 1.
+        get_group2_ids_replicates_data(): Returns ids, replicates, and data for group 2.
+        linear_trend(x, y): Fits a linear trend to the data.
+        poly2_trend(x, y): Fits a second-order polynomial trend to the data.
+        moving_average_trend(x, y, window): Computes a moving average trend.
+        get_trend(x, y, method, window): Applies the specified detrending method.
+        plot_group_data(group, method, window, m, plot_style): Plots time series data for the specified group.
+
+    """
+
+    ids: list[str]
+    group: list[str]
+    replicate: list[int]
+    time_series: np.ndarray
+    time: np.ndarray
+
+    group1_label: str = "group1"
+    group2_label: str = "group2"
+
+    color_group1: str = "tab:blue"
+    color_group2: str = "tab:orange"
+
+    @field_validator("time_series", mode="before")
+    @classmethod
+    def _to_2d_f64(cls, v: object) -> np.ndarray:
+        """
+        Converts the input object to a 2D NumPy array of type float64.
+
+        Parameters
+        ----------
+        v : object
+            The input object to be converted. Should be convertible to a NumPy array.
+
+        Returns
+        -------
+        np.ndarray
+            A 2D NumPy array of dtype float64.
+
+        Raises
+        ------
+        ValueError
+            If the input cannot be converted to a 2D array.
+
+        """
+        a = np.asarray(v, dtype=np.float64)
+        two_d_array_dims = 2
+        if a.ndim != two_d_array_dims:
+            msg = "expected 2D array"
+            raise ValueError(msg)
+        return a
+
+    @model_validator(mode="after")
+    def _check_columns(self) -> LiveCellDataset:
+        """
+        Validates that the lengths of `ids`, `group`, and `replicate` match the number of columns in `time_series`.
+
+        Parameters
+        ----------
+        self : LiveCellDataset
+            The instance of the LiveCellDataset being validated.
+
+        Returns
+        -------
+        LiveCellDataset
+            The validated instance.
+
+        Raises
+        ------
+        ValueError
+            If the length of `ids`, `group`, or `replicate` does not match the number of columns in `time_series`.
+
+        """
+        if len(self.ids) != self.time_series.shape[1]:
+            msg = "Length of ids must match number of columns in time_series"
+            raise ValueError(
+                msg,
+            )
+        if len(self.group) != self.time_series.shape[1]:
+            msg = "Length of group must match number of columns in time_series"
+            raise ValueError(
+                msg,
+            )
+        if len(self.replicate) != self.time_series.shape[1]:
+            msg = "Length of replicate must match number of columns in time_series"
+            raise ValueError(
+                msg,
+            )
+        return self
+
+    def get_group1_ids_replicates_data(self) -> tuple[list[str], list[int], np.ndarray]:
+        """
+        Retrieves the IDs, replicate numbers, and time series data for samples belonging to group1.
+
+        Returns:
+            tuple[list[str], list[int], np.ndarray]:
+                - ids: List of sample IDs in group1.
+                - replicates: List of replicate numbers corresponding to group1 samples.
+                - data: 2D NumPy array of time series data for group1 samples (columns correspond to samples).
+
+        """
+        mask = np.array(self.group) == self.group1_label
+        ids = list(np.array(self.ids)[mask])
+        replicates = list(np.array(self.replicate)[mask])
+        data = self.time_series[:, mask]
+        return ids, replicates, data
+
+    def get_group2_ids_replicates_data(self) -> tuple[list[str], list[int], np.ndarray]:
+        """
+        Retrieves the IDs, replicate numbers, and time series data for samples belonging to group2.
+
+        Returns:
+            tuple[list[str], list[int], np.ndarray]:
+                - ids: List of sample IDs in group2.
+                - replicates: List of replicate numbers corresponding to group2 samples.
+                - data: 2D NumPy array of time series data for group2 samples (columns correspond to samples).
+
+        """
+        mask = np.array(self.group) == self.group2_label
+        ids = list(np.array(self.ids)[mask])
+        replicates = list(np.array(self.replicate)[mask])
+        data = self.time_series[:, mask]
+        return ids, replicates, data
+
+    def linear_trend(self, x: np.ndarray, y: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
+        """
+        Fits a linear regression model to the provided data and returns the input x values along with the predicted linear trend.
+
+        Parameters
+        ----------
+        x : np.ndarray
+            The independent variable values.
+        y : np.ndarray
+            The dependent variable values.
+
+        Returns
+        -------
+        tuple[np.ndarray, np.ndarray]
+            A tuple containing the original x values and the predicted linear fit values.
+
+        """
+        model = sm.OLS(y, sm.add_constant(x)).fit()
+        linear_fit = model.predict(sm.add_constant(x))
+        return x, linear_fit
+
+    def poly2_trend(self, x: np.ndarray, y: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
+        """
+        Fits a second-degree polynomial (quadratic) trend to the given data.
+
+        Args:
+            x (np.ndarray): 1D array of independent variable values.
+            y (np.ndarray): 1D array of dependent variable values.
+
+        Returns:
+            tuple[np.ndarray, np.ndarray]:
+                - x: The input array of independent variable values.
+                - poly_fit: The fitted quadratic values corresponding to x.
+
+        """
+        coeffs = np.polyfit(x, y, 2)
+        poly_fit = np.polyval(coeffs, x)
+        return x, poly_fit
+
+    def moving_average_trend(self, x: np.ndarray, y: np.ndarray, window: int = 5) -> tuple[np.ndarray, np.ndarray]:
+        """
+        Computes the moving average trend of the input data using a specified window size.
+
+        Parameters
+        ----------
+        x : np.ndarray
+            The array of x-values (e.g., time points).
+        y : np.ndarray
+            The array of y-values (e.g., measurements corresponding to x).
+        window : int, optional
+            The size of the moving average window. Must be at least 1. Default is 5.
+
+        Returns
+        -------
+        tuple[np.ndarray, np.ndarray]
+            A tuple containing:
+                - x values corresponding to valid (finite) moving average points.
+                - The moving average values (trend) for the valid points.
+
+        Raises
+        ------
+        ValueError
+            If the window size is less than 1.
+
+        """
+        if window < 1:
+            msg = "Window size must be at least 1."
+            raise ValueError(msg)
+        y_series = pd.Series(y)
+        ma_fit = y_series.rolling(window=window, center=True).mean().to_numpy()
+        good = np.isfinite(x) & np.isfinite(ma_fit)
+        return x[good], ma_fit[good]
+
+    def get_trend(
+        self,
+        x: np.ndarray,
+        y: np.ndarray,
+        method: str = "linear",
+        window: int = 5,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        """
+        Computes the trend of the given data using the specified method.
+
+        Parameters
+        ----------
+        x : np.ndarray
+            The independent variable data (e.g., time points).
+        y : np.ndarray
+            The dependent variable data (e.g., measurements).
+        method : str, optional
+            The detrending method to use. Options are:
+                - "none": No detrending, returns x and zeros for y.
+                - "linear": Applies a linear trend.
+                - "poly2": Applies a quadratic (2nd degree polynomial) trend.
+                - "moving_average": Applies a moving average trend.
+            Default is "linear".
+        window : int, optional
+            The window size for the moving average method. Default is 5.
+
+        Returns
+        -------
+        tuple[np.ndarray, np.ndarray]
+            A tuple containing:
+                - The x values (possibly unchanged).
+                - The trend values corresponding to y.
+
+        Raises
+        ------
+        ValueError
+            If an unknown detrending method is specified.
+
+        """
+        if method == "none":
+            return np.asarray(x, float), np.zeros_like(np.asarray(y, float))
+        if method == "linear":
+            return self.linear_trend(x, y)
+        if method == "poly2":
+            return self.poly2_trend(x, y)
+        if method == "moving_average":
+            return self.moving_average_trend(x, y, window=window)
+        msg = f"Unknown detrending method: {method}"
+        raise ValueError(msg)
+
+    def plot_group_data(
+        self,
+        group: str,
+        method: str = "linear",
+        window: int = 5,
+        m: int = 5,
+        plot_style: str = "scatter",
+    ) -> tuple[plt.Figure, str]:
+        """
+        Plots data for a specified group, with options for trend fitting and plot style.
+
+        Parameters
+        ----------
+        group : str
+            The group to plot, must be either 'group1' or 'group2'.
+        method : str, optional
+            The method used for trend fitting. Default is "linear".
+            Use "none" to skip trend fitting.
+        window : int, optional
+            The window size for trend fitting (if applicable). Default is 5.
+        m : int, optional
+            Number of columns in the subplot grid. Default is 5.
+        plot_style : str, optional
+            The style of the plot, either "scatter" or "line". Default is "scatter".
+
+        Returns
+        -------
+        fig : matplotlib.figure.Figure
+            The matplotlib Figure object containing the plots.
+        tmp_path : str
+            The path to the saved temporary PDF file of the figure.
+
+        Raises
+        ------
+        ValueError
+            If the group argument is not 'group1' or 'group2'.
+
+        """
+        if group == "group1":
+            ids, _replicates, data = self.get_group1_ids_replicates_data()
+            color = self.color_group1
+            group_label = self.group1_label
+        elif group == "group2":
+            ids, _replicates, data = self.get_group2_ids_replicates_data()
+            color = self.color_group2
+            group_label = self.group2_label
+        else:
+            msg = "group must be 'group1' or 'group2'"
+            raise ValueError(msg)
+
+        n_group = len(np.unique(ids))
+        n_cols = m
+        n_rows = int(np.ceil(n_group / n_cols))
+
+        study_list = np.unique(ids).tolist()
+        fig = plt.figure(figsize=(5 * n_cols / 2.54, 5 * n_rows / 2.54))
+
+        for i, id_curr in enumerate(study_list):
+            mask = np.array(ids) == id_curr
+            n_reps = int(np.sum(mask))
+            ax = fig.add_subplot(n_rows, n_cols, i + 1)
+
+            for j in range(n_reps):
+                x = self.time
+                y = data[:, mask][:, j]
+
+                if plot_style == "scatter":
+                    ax.scatter(x, y, s=4, alpha=0.8, color=color)
+                else:
+                    ax.plot(x, y, color=color)
+
+                valid_mask = ~np.isnan(y)
+                x_fit = x[valid_mask]
+                y_fit = y[valid_mask]
+
+                x_processed, y_processed = self.get_trend(
+                    x_fit,
+                    y_fit,
+                    method=method,
+                    window=window,
+                )
+                if method != "none":
+                    ax.plot(
+                        x_processed,
+                        y_processed,
+                        color="black",
+                        linestyle="--",
+                        linewidth=0.8,
+                    )
+
+            ax.set_title(f"ID: {id_curr} (n={n_reps}) - {group_label}")
+            ax.set_xlabel("Time (h)")
+            if i % n_cols == 0:
+                ax.set_ylabel("Expression")
+
+        plt.tight_layout()
+
+        fd, tmp_path = tempfile.mkstemp(suffix=".pdf")
+        os.close(fd)
+
+        fig.savefig(tmp_path)
+
+        return fig, tmp_path
diff --git a/src/cosinor_lite/models.py b/src/cosinor_lite/models.py
deleted file mode 100644
index db8d7dc..0000000
--- a/src/cosinor_lite/models.py
+++ /dev/null
@@ -1,117 +0,0 @@
-from __future__ import annotations
-
-import numpy as np
-import pandas as pd
-from pydantic.dataclasses import dataclass
-
-from cosinor_lite.base_models import BaseModel, BaseModelOneCondition
-from cosinor_lite.utils import amp_from_ab, phase_from_ab
-
-
-@dataclass
-class M0:
-    name: int = 0
-    alpha_phase: float = np.nan
-    alpha_amp: float = np.nan
-    beta_phase: float = np.nan
-    beta_amp: float = np.nan
-    amp: float = np.nan
-    phase: float = np.nan
-    bic: float = np.nan
-
-
-class M1(BaseModel):
-    name: int = 1
-    k: int = 3
-    formula: str = "y ~ is_alpha:constant + is_beta:constant -1"
-
-    def extract(self, params: pd.Series) -> tuple[float, float, float, float]:
-        # No rhythmic terms → phases/amps are NaN
-        return (np.nan, np.nan, np.nan, np.nan)
-
-
-class M1OneCondition(BaseModelOneCondition):
-    name: int = 1
-    k: int = 1
-    formula: str = "y ~ 1"
-
-    def extract(self, params: pd.Series) -> tuple[float, float]:
-        # No rhythmic terms → phases/amps are NaN
-        return (np.nan, np.nan)
-
-
-class M2(BaseModel):
-    name: int = 2
-    k: int = 5
-    formula: str = "y ~ is_alpha:constant + is_beta:constant + is_beta:cos_wt + is_beta:sin_wt -1"
-
-    def extract(self, params: pd.Series) -> tuple[float, float, float, float]:
-        # beta has rhythmic terms, alpha does not
-        a: float = params["is_beta:cos_wt"]
-        b: float = params["is_beta:sin_wt"]
-        beta_phase: float = phase_from_ab(a, b)
-        beta_amp: float = amp_from_ab(a, b)
-        return (np.nan, np.nan, beta_phase, beta_amp)
-
-
-class M3(BaseModel):
-    name: int = 3
-    k: int = 5
-    formula: str = "y ~ is_alpha:constant + is_beta:constant + is_alpha:cos_wt + is_alpha:sin_wt -1"
-
-    def extract(self, params: pd.Series) -> tuple[float, float, float, float]:
-        # alpha has rhythmic terms, beta does not
-        a: float = params["is_alpha:cos_wt"]
-        b: float = params["is_alpha:sin_wt"]
-        alpha_phase: float = phase_from_ab(a, b)
-        alpha_amp: float = amp_from_ab(a, b)
-        return (alpha_phase, alpha_amp, np.nan, np.nan)
-
-
-class MOscOneCondition(BaseModelOneCondition):
-    name: int = 3
-    k: int = 3
-    formula: str = "y ~ 1 + cos_wt + sin_wt"
-
-    def extract(self, params: pd.Series) -> tuple[float, float]:
-        # alpha has rhythmic terms, beta does not
-        a: float = params["cos_wt"]
-        b: float = params["sin_wt"]
-        phase: float = phase_from_ab(a, b)
-        amp: float = amp_from_ab(a, b)
-        return (phase, amp)
-
-
-class M4(BaseModel):
-    name: int = 4
-    k: int = 5
-    formula: str = "y ~ is_alpha:constant + is_beta:constant + cos_wt + sin_wt -1"
-
-    def extract(self, params: pd.Series) -> tuple[float, float, float, float]:
-        # shared rhythmic terms for both alpha and beta
-        a: float = params["cos_wt"]
-        b: float = params["sin_wt"]
-        ph: float = phase_from_ab(a, b)
-        am: float = amp_from_ab(a, b)
-        return (ph, am, ph, am)
-
-
-class M5(BaseModel):
-    name: int = 5
-    k: int = 7
-    formula: str = (
-        "y ~ is_alpha:constant + is_beta:constant + "
-        "is_alpha:cos_wt + is_alpha:sin_wt + is_beta:cos_wt + is_beta:sin_wt -1"
-    )
-
-    def extract(self, params: pd.Series) -> tuple[float, float, float, float]:
-        a_cond1: float = params["is_alpha:cos_wt"]
-        b_cond1: float = params["is_alpha:sin_wt"]
-        a_cond2: float = params["is_beta:cos_wt"]
-        b_cond2: float = params["is_beta:sin_wt"]
-
-        alpha_phase: float = phase_from_ab(a_cond1, b_cond1)
-        alpha_amp: float = amp_from_ab(a_cond1, b_cond1)
-        beta_phase: float = phase_from_ab(a_cond2, b_cond2)
-        beta_amp: float = amp_from_ab(a_cond2, b_cond2)
-        return (alpha_phase, alpha_amp, beta_phase, beta_amp)
diff --git a/src/cosinor_lite/datasets.py b/src/cosinor_lite/omics_dataset.py
similarity index 50%
rename from src/cosinor_lite/datasets.py
rename to src/cosinor_lite/omics_dataset.py
index d2fbfca..57d6aa7 100644
--- a/src/cosinor_lite/datasets.py
+++ b/src/cosinor_lite/omics_dataset.py
@@ -9,9 +9,47 @@
 from pydantic.dataclasses import dataclass
 from scipy.stats import spearmanr
 
+plt.rcParams.update(
+    {
+        "font.size": 8,
+        "axes.titlesize": 8,
+        "axes.labelsize": 8,
+        "xtick.labelsize": 8,
+        "ytick.labelsize": 8,
+        "legend.fontsize": 8,
+        "figure.titlesize": 8,
+        "pdf.fonttype": 42,
+    },
+)
+plt.style.use("seaborn-v0_8-ticks")
+
 
 @dataclass(config=ConfigDict(arbitrary_types_allowed=True))
 class OmicsDataset:
+    """
+    Represent an omics expression table and derived metrics.
+
+    Parameters
+    ----------
+    df : pandas.DataFrame
+        Input table containing gene expression measurements.
+    columns_cond1 : list[str]
+        Column names for condition 1 samples.
+    columns_cond2 : list[str]
+        Column names for condition 2 samples.
+    t_cond1 : numpy.ndarray
+        Time points associated with ``columns_cond1``.
+    t_cond2 : numpy.ndarray
+        Time points associated with ``columns_cond2``.
+    cond1_label : str, optional
+        Display label for condition 1, by default ``"cond1"``.
+    cond2_label : str, optional
+        Display label for condition 2, by default ``"cond2"``.
+    deduplicate_on_init : bool, optional
+        Whether to deduplicate genes immediately after initialization, by default ``False``.
+
+    """
+
     df: pd.DataFrame
 
     columns_cond1: list[str]
@@ -23,10 +61,29 @@ class OmicsDataset:
 
     deduplicate_on_init: bool = False
 
-    # --- validators ---
     @field_validator("t_cond1", "t_cond2", mode="before")
     @classmethod
     def _to_1d_f64(cls, v: object) -> np.ndarray:  # ← return type fixes ANN206
+        """
+        Coerce incoming arrays to one-dimensional ``float64`` np.ndarrays.
+
+        Parameters
+        ----------
+        v : object
+            Sequence-like data to convert.
+
+        Returns
+        -------
+        numpy.ndarray
+            Converted one-dimensional array.
+
+        Raises
+        ------
+        ValueError
+            If the incoming value cannot be reshaped to one dimension.
+
+
+        """
         a = np.asarray(v, dtype=np.float64)
         if a.ndim != 1:
             text: str = "expected 1D array"
@@ -35,6 +92,21 @@ def _to_1d_f64(cls, v: object) -> np.ndarray:  # ← return type fixes ANN206
 
     @model_validator(mode="after")
     def _check_columns(self) -> Self:
+        """
+        Ensure all requested columns are present in the DataFrame.
+
+        Returns
+        -------
+        Self
+            The validated dataset instance.
+
+        Raises
+        ------
+        ValueError
+            If any required columns are missing.
+
+
+        """
         missing = [c for c in (self.columns_cond1 + self.columns_cond2) if c not in self.df.columns]
         if missing:
             text = f"Missing columns: {missing}"  # satisfies EM101 (no string literal in raise)
@@ -42,6 +114,7 @@ def _check_columns(self) -> Self:
         return self
 
     def __post_init__(self) -> None:
+        """Populate derived columns and optionally deduplicate entries."""
         self.add_detected_timepoint_counts()
         self.add_mean_expression()
         self.add_number_detected()
@@ -50,13 +123,23 @@ def __post_init__(self) -> None:
 
     def detected_timepoint_counts(self, cond: str) -> list[int]:
         """
-        Count number of timepoints with detected values for each gene.
+        Count detected time points per gene for the requested condition.
+
+        Parameters
+        ----------
+        cond : str
+            Either ``"cond1"`` or ``"cond2"`` specifying which condition to evaluate.
+
+        Returns
+        -------
+        list[int]
+            Number of distinct time points detected for each gene.
 
-        Args:
-            cond (str): "cond1" or "cond2"
+        Raises
+        ------
+        ValueError
+            If ``cond`` is not a recognized condition label.
 
-        Returns:
-            list[int]: List of counts for each gene.
 
         """
         if cond == "cond1":
@@ -69,37 +152,29 @@ def detected_timepoint_counts(self, cond: str) -> list[int]:
             text = f"Invalid condition: {cond}"  # satisfies EM101 (no string literal in raise)
             raise ValueError(text)
 
-        # y-like frame, boolean mask of non-NaN
-        mask = y.notna()  # shape: (n_rows, n_cols)
+        mask = y.notna()
 
-        # t_beta must be length n_cols and aligned to those columns
-        # Group columns by time, check if ANY non-NaN per group, then count groups per row
-        detected_timepoints = (
-            mask.T.groupby(t)
-            .any()  # (n_rows, n_unique_times) booleans
-            .T.sum(axis=1)  # per-row counts
-            .to_numpy()
-        )
+        detected_timepoints = mask.T.groupby(t).any().T.sum(axis=1).to_numpy()
 
         return detected_timepoints
 
     def add_detected_timepoint_counts(self) -> None:
-        """Add two columns to self.df with counts for cond1 and cond2."""
-        self.df["detected_cond1"] = self.detected_timepoint_counts("cond1")
-        self.df["detected_cond2"] = self.detected_timepoint_counts("cond2")
+        """Augment the DataFrame with detected time-point counts for each condition."""
+        self.df["detected_timepoints_cond1"] = self.detected_timepoint_counts("cond1")
+        self.df["detected_timepoints_cond2"] = self.detected_timepoint_counts("cond2")
 
     def add_mean_expression(self) -> None:
-        """Add two columns to self.df with mean expression for cond1 and cond2."""
+        """Compute mean expression for both conditions and store in the DataFrame."""
         self.df["mean_cond1"] = self.df[self.columns_cond1].mean(axis=1)
         self.df["mean_cond2"] = self.df[self.columns_cond2].mean(axis=1)
 
     def add_number_detected(self) -> None:
-        """Add two columns to self.df with number of detected values for cond1 and cond2."""
+        """Store the count of non-null measurements for each condition."""
         self.df["num_detected_cond1"] = self.df[self.columns_cond1].count(axis=1)
         self.df["num_detected_cond2"] = self.df[self.columns_cond2].count(axis=1)
 
     def deduplicate_genes(self) -> None:
-        """Deduplicate self.df by 'Genes', keeping entry with highest total mean expression."""
+        """Remove duplicate gene entries, keeping the highest combined mean expression."""
         if not {"mean_cond1", "mean_cond2"}.issubset(self.df):
             self.add_mean_expression()
 
@@ -117,9 +192,21 @@ def add_is_expressed(
         mean_min: float | None = None,
         num_detected_min: int | None = None,
     ) -> None:
-        """Add is_expressed_cond1/cond2 based on thresholds."""
-        # Ensure prerequisite columns exist
-        if not {"detected_cond1", "detected_cond2"}.issubset(self.df):
+        """
+        Flag genes as expressed based on configurable thresholds.
+
+        Parameters
+        ----------
+        detected_min : int | None, optional
+            Minimum detected time points required, by default ``None``.
+        mean_min : float | None, optional
+            Minimum mean expression required, by default ``None``.
+        num_detected_min : int | None, optional
+            Minimum number of detected samples required, by default ``None``.
+
+
+        """
+        if not {"detected_timepoints_cond1", "detected_timepoints_cond2"}.issubset(self.df):
             self.add_detected_timepoint_counts()
         if not {"mean_cond1", "mean_cond2"}.issubset(self.df):
             self.add_mean_expression()
@@ -127,7 +214,6 @@ def add_is_expressed(
             self.add_number_detected()
 
         def _mask(which: Literal["cond1", "cond2"]) -> pd.Series:
-            # start with all-True masks
             m_detected = pd.Series(True, index=self.df.index)
             m_mean = pd.Series(True, index=self.df.index)
             m_num = pd.Series(True, index=self.df.index)
@@ -144,10 +230,24 @@ def _mask(which: Literal["cond1", "cond2"]) -> pd.Series:
         self.df["is_expressed_cond1"] = _mask("cond1")
         self.df["is_expressed_cond2"] = _mask("cond2")
 
-    def expression_histogram(self, bins: int = 20) -> None:
-        """Plot histogram of mean expression for cond1 and cond2."""
+    def expression_histogram(self, bins: int = 20) -> plt.Figure:
+        """
+        Plot histograms of mean expression for both conditions.
+
+        Parameters
+        ----------
+        bins : int, optional
+            Number of histogram bins, by default 20.
+
+        Returns
+        -------
+        matplotlib.figure.Figure
+            Figure containing the histogram panels.
+
+
+        """
         print(plt.rcParams["font.size"])
-        plt.figure(figsize=(6 / 2.54, 12 / 2.54))
+        fig = plt.figure(figsize=(6 / 2.54, 12 / 2.54))
         plt.subplot(2, 1, 1)
         plt.hist(self.df["mean_cond1"].to_numpy().flatten(), bins=bins)
         plt.xlabel("Mean Expression")
@@ -161,24 +261,54 @@ def expression_histogram(self, bins: int = 20) -> None:
         plt.title(f"Mean expression ({self.cond2_label})")
         plt.gca().xaxis.set_major_locator(plt.MaxNLocator(nbins=10))
         plt.tight_layout()
-        plt.show()
 
-    def replicate_scatterplot(self, sample1: str, sample2: str) -> None:
-        """Scatterplot of two replicates."""
+        return fig
+
+    def replicate_scatterplot(self, sample1: str, sample2: str) -> plt.Figure:
+        """
+        Create a scatterplot comparing two samples with correlation annotations.
+
+        Parameters
+        ----------
+        sample1 : str
+            Column name of the first sample.
+        sample2 : str
+            Column name of the second sample.
+
+        Returns
+        -------
+        matplotlib.figure.Figure
+            Figure containing the scatter plot.
+
+        Raises
+        ------
+        ValueError
+            If either sample column is missing from the DataFrame.
+
+
+        """
         if sample1 not in self.df.columns or sample2 not in self.df.columns:
             text = f"Samples {sample1} and/or {sample2} not in DataFrame columns."
             raise ValueError(text)
 
-        plt.figure(figsize=(8 / 2.54, 8 / 2.54))
-        x: np.ndarray = self.df[sample1].to_numpy().flatten()
-        y: np.ndarray = self.df[sample2].to_numpy().flatten()
+        fig = plt.figure(figsize=(8 / 2.54, 8 / 2.54))
+        xy = self.df[[sample1, sample2]].dropna()
+        x: np.ndarray = xy[sample1].to_numpy().flatten()
+        y: np.ndarray = xy[sample2].to_numpy().flatten()
         r_pearson: float = np.corrcoef(x, y)[0, 1]
         r_spearman: float = spearmanr(x, y).statistic
         plt.scatter(x, y, alpha=0.1, s=4)
         plt.xlabel(sample1)
         plt.ylabel(sample2)
-        plt.title(f"{sample1} vs {sample2} (Pearson R = {r_pearson:.2f}, Spearman R = {r_spearman:.2f})")
+        plt.title(f"Pearson R = {r_pearson:.2f}, Spearman R = {r_spearman:.2f}")
         plt.axis("equal")
-        plt.plot([x.min(), x.max()], [x.min(), x.max()], color="grey", linestyle="--", alpha=0.8)
+        plt.plot(
+            [x.min(), x.max()],
+            [x.min(), x.max()],
+            color="grey",
+            linestyle="--",
+            alpha=0.8,
+        )
         plt.tight_layout()
-        plt.show()
+
+        return fig
diff --git a/src/cosinor_lite/differential_rhytmicity_omics.py b/src/cosinor_lite/omics_differential_rhytmicity.py
similarity index 50%
rename from src/cosinor_lite/differential_rhytmicity_omics.py
rename to src/cosinor_lite/omics_differential_rhytmicity.py
index 8574dbb..1cba7b8 100644
--- a/src/cosinor_lite/differential_rhytmicity_omics.py
+++ b/src/cosinor_lite/omics_differential_rhytmicity.py
@@ -1,15 +1,21 @@
 from __future__ import annotations
 
 from abc import ABC, abstractmethod
+from collections.abc import Sequence
+from dataclasses import dataclass as std_dataclass
+from typing import Self, cast
 
+import gradio as gr
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 import statsmodels.formula.api as smf
 from pydantic import ConfigDict, field_validator, model_validator
-from pydantic.dataclasses import dataclass
+from pydantic.dataclasses import dataclass as pydantic_dataclass
 from tqdm import tqdm
 
+from cosinor_lite.omics_dataset import OmicsDataset
+
 plt.rcParams.update(
     {
         "font.size": 8,
@@ -22,25 +28,79 @@
         "pdf.fonttype": 42,
     },
 )
+plt.style.use("seaborn-v0_8-ticks")
+
 
 W: float = 2 * np.pi / 24.0
 RAD2H: float = 24.0 / (2.0 * np.pi)
 
 
 def phase_from_ab(a: float, b: float) -> float:
+    """
+    Convert cosine and sine coefficients to a phase value in hours.
+
+    Parameters
+    ----------
+    a : float
+        Cosine-term regression coefficient.
+    b : float
+        Sine-term regression coefficient.
+
+    Returns
+    -------
+    float
+        Phase value constrained to the ``[0, 24)`` hour interval.
+
+    """
     return (np.arctan2(b, a) * RAD2H) % 24.0
 
 
 def amp_from_ab(a: float, b: float) -> float:
+    """
+    Compute the amplitude implied by cosine and sine coefficients.
+
+    Parameters
+    ----------
+    a : float
+        Cosine-term regression coefficient.
+    b : float
+        Sine-term regression coefficient.
+
+    Returns
+    -------
+    float
+        Non-negative amplitude derived from the Euclidean norm.
+
+    """
     return float(np.hypot(a, b))
 
 
 def bic(llf: float, k: int, n: int) -> float:
+    """
+    Evaluate the Bayesian Information Criterion for a fitted model.
+
+    Parameters
+    ----------
+    llf : float
+        Log-likelihood of the fitted model.
+    k : int
+        Number of estimated parameters.
+    n : int
+        Number of observations used during fitting.
+
+    Returns
+    -------
+    float
+        BIC score, where lower values indicate preferred models.
+
+    """
     return k * np.log(n) - 2.0 * llf
 
 
-@dataclass
+@std_dataclass
 class ModelResult:
+    """Store parameter estimates and metrics for dual-condition models."""
+
     name: int
     llf: float
     bic: float
@@ -50,8 +110,10 @@ class ModelResult:
     beta_amp: float
 
 
-@dataclass
+@std_dataclass
 class ModelResultOneCondition:
+    """Store parameter estimates and metrics for single-condition models."""
+
     name: int
     llf: float
     bic: float
@@ -60,14 +122,29 @@ class ModelResultOneCondition:
 
 
 class BaseModel(ABC):
-    name: str
+    """Template for two-condition cosinor models sharable across datasets."""
+
+    name: int
     k: int
     formula: str
 
     def fit(self, df: pd.DataFrame) -> ModelResult:
-        # df must already contain: y, constant, cos_wt, sin_wt, is_alpha, is_beta
+        """
+        Fit the configured statsmodels OLS formula to the supplied design matrix.
+
+        Parameters
+        ----------
+        df : pandas.DataFrame
+            Design matrix containing predictors and response values.
+
+        Returns
+        -------
+        ModelResult
+            Bundle of model metadata, BIC, and derived phase/amplitude values.
+
+        """
         model = smf.ols(self.formula, data=df).fit()
-        n = len(df)
+        n: int = len(df)
         alpha_phase, alpha_amp, beta_phase, beta_amp = self.extract(model.params)
         return ModelResult(
             name=self.name,
@@ -84,12 +161,27 @@ def extract(self, params: pd.Series) -> tuple[float, float, float, float]: ...
 
 
 class BaseModelOneCondition(ABC):
-    name: str
+    """Template for single-condition cosinor models."""
+
+    name: int
     k: int
     formula: str
 
     def fit(self, df: pd.DataFrame) -> ModelResultOneCondition:
-        # df must already contain: y, constant, cos_wt, sin_wt, is_alpha, is_beta
+        """
+        Fit a single-condition OLS cosinor model on the provided design matrix.
+
+        Parameters
+        ----------
+        df : pandas.DataFrame
+            Design matrix containing predictors and response values.
+
+        Returns
+        -------
+        ModelResultOneCondition
+            Bundle of model metadata plus the fitted phase and amplitude.
+
+        """
         model = smf.ols(self.formula, data=df).fit()
         n = len(df)
         phase, amp = self.extract(model.params)
@@ -105,11 +197,10 @@ def fit(self, df: pd.DataFrame) -> ModelResultOneCondition:
     def extract(self, params: pd.Series) -> tuple[float, float]: ...
 
 
-# ----- Five concrete models -----
-
-
-@dataclass
+@std_dataclass
 class M0:
+    """Fallback result used when all cosinor models fall below the weight cutoff."""
+
     name: int = 0
     alpha_phase: float = np.nan
     alpha_amp: float = np.nan
@@ -125,8 +216,7 @@ class M1(BaseModel):
     k: int = 3
     formula: str = "y ~ is_alpha:constant + is_beta:constant -1"
 
-    def extract(self, params: pd.Series) -> tuple[float, float, float, float]:
-        # No rhythmic terms → phases/amps are NaN
+    def extract(self, params: pd.Series) -> tuple[float, float, float, float]:  # noqa: ARG002
         return (np.nan, np.nan, np.nan, np.nan)
 
 
@@ -135,8 +225,7 @@ class M1OneCondition(BaseModelOneCondition):
     k: int = 1
     formula: str = "y ~ 1"
 
-    def extract(self, params: pd.Series) -> tuple[float, float]:
-        # No rhythmic terms → phases/amps are NaN
+    def extract(self, params: pd.Series) -> tuple[float, float]:  # noqa: ARG002
         return (np.nan, np.nan)
 
 
@@ -146,9 +235,8 @@ class M2(BaseModel):
     formula: str = "y ~ is_alpha:constant + is_beta:constant + is_beta:cos_wt + is_beta:sin_wt -1"
 
     def extract(self, params: pd.Series) -> tuple[float, float, float, float]:
-        # beta has rhythmic terms, alpha does not
-        a: float = params["is_beta:cos_wt"]
-        b: float = params["is_beta:sin_wt"]
+        a: float = cast(float, params["is_beta:cos_wt"])
+        b: float = cast(float, params["is_beta:sin_wt"])
         beta_phase: float = phase_from_ab(a, b)
         beta_amp: float = amp_from_ab(a, b)
         return (np.nan, np.nan, beta_phase, beta_amp)
@@ -160,9 +248,8 @@ class M3(BaseModel):
     formula: str = "y ~ is_alpha:constant + is_beta:constant + is_alpha:cos_wt + is_alpha:sin_wt -1"
 
     def extract(self, params: pd.Series) -> tuple[float, float, float, float]:
-        # alpha has rhythmic terms, beta does not
-        a: float = params["is_alpha:cos_wt"]
-        b: float = params["is_alpha:sin_wt"]
+        a: float = cast(float, params["is_alpha:cos_wt"])
+        b: float = cast(float, params["is_alpha:sin_wt"])
         alpha_phase: float = phase_from_ab(a, b)
         alpha_amp: float = amp_from_ab(a, b)
         return (alpha_phase, alpha_amp, np.nan, np.nan)
@@ -174,9 +261,8 @@ class MOscOneCondition(BaseModelOneCondition):
     formula: str = "y ~ 1 + cos_wt + sin_wt"
 
     def extract(self, params: pd.Series) -> tuple[float, float]:
-        # alpha has rhythmic terms, beta does not
-        a: float = params["cos_wt"]
-        b: float = params["sin_wt"]
+        a: float = cast(float, params["cos_wt"])
+        b: float = cast(float, params["sin_wt"])
         phase: float = phase_from_ab(a, b)
         amp: float = amp_from_ab(a, b)
         return (phase, amp)
@@ -188,9 +274,8 @@ class M4(BaseModel):
     formula: str = "y ~ is_alpha:constant + is_beta:constant + cos_wt + sin_wt -1"
 
     def extract(self, params: pd.Series) -> tuple[float, float, float, float]:
-        # shared rhythmic terms for both alpha and beta
-        a: float = params["cos_wt"]
-        b: float = params["sin_wt"]
+        a: float = cast(float, params["cos_wt"])
+        b: float = cast(float, params["sin_wt"])
         ph: float = phase_from_ab(a, b)
         am: float = amp_from_ab(a, b)
         return (ph, am, ph, am)
@@ -205,10 +290,10 @@ class M5(BaseModel):
     )
 
     def extract(self, params: pd.Series) -> tuple[float, float, float, float]:
-        a_cond1: float = params["is_alpha:cos_wt"]
-        b_cond1: float = params["is_alpha:sin_wt"]
-        a_cond2: float = params["is_beta:cos_wt"]
-        b_cond2: float = params["is_beta:sin_wt"]
+        a_cond1: float = cast(float, params["is_alpha:cos_wt"])
+        b_cond1: float = cast(float, params["is_alpha:sin_wt"])
+        a_cond2: float = cast(float, params["is_beta:cos_wt"])
+        b_cond2: float = cast(float, params["is_beta:sin_wt"])
 
         alpha_phase: float = phase_from_ab(a_cond1, b_cond1)
         alpha_amp: float = amp_from_ab(a_cond1, b_cond1)
@@ -217,26 +302,89 @@ def extract(self, params: pd.Series) -> tuple[float, float, float, float]:
         return (alpha_phase, alpha_amp, beta_phase, beta_amp)
 
 
-# ----- Runner utilities -----
-
 MODELS: tuple[BaseModel, ...] = (M1(), M2(), M3(), M4(), M5())
-MODELS_ONE_CONDITION: tuple[BaseModelOneCondition, ...] = (M1OneCondition(), MOscOneCondition())
+MODELS_ONE_CONDITION: tuple[BaseModelOneCondition, ...] = (
+    M1OneCondition(),
+    MOscOneCondition(),
+)
 
 
 def akaike_weights_from_bics(bics: np.ndarray) -> np.ndarray:
+    """
+    Convert BIC scores into normalized Akaike weights.
+
+    Parameters
+    ----------
+    bics : numpy.ndarray
+        Array of BIC values where lower entries indicate better fits.
+
+    Returns
+    -------
+    numpy.ndarray
+        Non-negative weights that sum to one after exponentiation and scaling.
+
+    """
     d: np.ndarray = bics - np.nanmin(bics)
     w: np.ndarray = np.exp(-0.5 * d)
     return w / np.nansum(w)
 
 
+def _weight_mapping(model_ids: Sequence[int], weights: np.ndarray) -> dict[str, float]:
+    """
+    Align Akaike weights with ``w_model{n}`` keys expected in result tables.
+
+    Parameters
+    ----------
+    model_ids : collections.abc.Sequence[int]
+        Sequence of model identifiers that produced ``weights``.
+    weights : numpy.ndarray
+        Akaike weights in positional order matching ``model_ids``.
+
+    Returns
+    -------
+    dict[str, float]
+        Mapping of weight column names to values (or ``nan`` when absent).
+
+    """
+    mapping: dict[str, float] = {f"w_model{i}": np.nan for i in range(1, len(MODELS) + 1)}
+    for idx, model_id in enumerate(model_ids):
+        if idx < len(weights):
+            mapping[f"w_model{model_id}"] = float(weights[idx])
+    return mapping
+
+
 def build_design(
     alpha_vals: np.ndarray,
     beta_vals: np.ndarray,
     t_cond1: np.ndarray,
     t_cond2: np.ndarray,
 ) -> pd.DataFrame:
-    df_cond1: pd.DataFrame = pd.DataFrame({"y": alpha_vals, "time": t_cond1, "dataset": "alpha"}).dropna()
-    df_cond2: pd.DataFrame = pd.DataFrame({"y": beta_vals, "time": t_cond2, "dataset": "beta"}).dropna()
+    """
+    Construct the full design matrix for simultaneously fitting both conditions.
+
+    Parameters
+    ----------
+    alpha_vals : numpy.ndarray
+        Expression values for condition 1 samples.
+    beta_vals : numpy.ndarray
+        Expression values for condition 2 samples.
+    t_cond1 : numpy.ndarray
+        Zeitgeber time points corresponding to ``alpha_vals``.
+    t_cond2 : numpy.ndarray
+        Zeitgeber time points corresponding to ``beta_vals``.
+
+    Returns
+    -------
+    pandas.DataFrame
+        Design matrix including indicator, cosine, and sine predictors.
+
+    """
+    df_cond1: pd.DataFrame = pd.DataFrame(
+        {"y": alpha_vals, "time": t_cond1, "dataset": "alpha"},
+    ).dropna()
+    df_cond2: pd.DataFrame = pd.DataFrame(
+        {"y": beta_vals, "time": t_cond2, "dataset": "beta"},
+    ).dropna()
     df: pd.DataFrame = pd.concat([df_cond1, df_cond2], ignore_index=True)
     df["constant"] = 1.0
     df["cos_wt"] = np.cos(W * df["time"].to_numpy().astype(float))
@@ -250,7 +398,25 @@ def build_design_cond1(
     alpha_vals: np.ndarray,
     t_cond1: np.ndarray,
 ) -> pd.DataFrame:
-    df_cond1: pd.DataFrame = pd.DataFrame({"y": alpha_vals, "time": t_cond1, "dataset": "alpha"}).dropna()
+    """
+    Construct the design matrix for condition 1 only.
+
+    Parameters
+    ----------
+    alpha_vals : numpy.ndarray
+        Expression values for condition 1 samples.
+    t_cond1 : numpy.ndarray
+        Zeitgeber time points associated with ``alpha_vals``.
+
+    Returns
+    -------
+    pandas.DataFrame
+        Single-condition design matrix with cosine and sine predictors.
+
+    """
+    df_cond1: pd.DataFrame = pd.DataFrame(
+        {"y": alpha_vals, "time": t_cond1, "dataset": "alpha"},
+    ).dropna()
     df: pd.DataFrame = pd.concat([df_cond1], ignore_index=True)
     df["constant"] = 1.0
     df["cos_wt"] = np.cos(W * df["time"].to_numpy().astype(float))
@@ -262,7 +428,25 @@ def build_design_cond2(
     beta_vals: np.ndarray,
     t_cond2: np.ndarray,
 ) -> pd.DataFrame:
-    df_cond2: pd.DataFrame = pd.DataFrame({"y": beta_vals, "time": t_cond2, "dataset": "beta"}).dropna()
+    """
+    Construct the design matrix for condition 2 only.
+
+    Parameters
+    ----------
+    beta_vals : numpy.ndarray
+        Expression values for condition 2 samples.
+    t_cond2 : numpy.ndarray
+        Zeitgeber time points associated with ``beta_vals``.
+
+    Returns
+    -------
+    pandas.DataFrame
+        Single-condition design matrix with cosine and sine predictors.
+
+    """
+    df_cond2: pd.DataFrame = pd.DataFrame(
+        {"y": beta_vals, "time": t_cond2, "dataset": "beta"},
+    ).dropna()
     df: pd.DataFrame = pd.concat([df_cond2], ignore_index=True)
     df["constant"] = 1.0
     df["cos_wt"] = np.cos(W * df["time"].to_numpy().astype(float))
@@ -270,76 +454,118 @@ def build_design_cond2(
     return df
 
 
-@dataclass(config=ConfigDict(arbitrary_types_allowed=True))
+@pydantic_dataclass(config=ConfigDict(arbitrary_types_allowed=True))
 class DifferentialRhythmicity:
+    """
+    Coordinate cosinor model selection across an :class:`OmicsDataset`.
+
+    Parameters
+    ----------
+    dataset : OmicsDataset
+        Dataset containing expression values and metadata required for fitting.
+    BIC_cutoff : float, optional
+        Minimum Akaike weight threshold for accepting a rhythmic model, by default ``0.5``.
+
+    """
+
     dataset: OmicsDataset
     BIC_cutoff: float = 0.5
 
     @property
     def df(self) -> pd.DataFrame:
+        """pandas.DataFrame: Alias to the underlying dataset table."""
         return self.dataset.df
 
     @property
     def columns_cond1(self) -> list[str]:
+        """list[str]: Column names belonging to condition 1."""
         return self.dataset.columns_cond1
 
     @property
     def columns_cond2(self) -> list[str]:
+        """list[str]: Column names belonging to condition 2."""
         return self.dataset.columns_cond2
 
     @property
     def t_cond1(self) -> np.ndarray:
+        """numpy.ndarray: Zeitgeber time points for condition 1."""
         return self.dataset.t_cond1
 
     @property
     def t_cond2(self) -> np.ndarray:
+        """numpy.ndarray: Zeitgeber time points for condition 2."""
         return self.dataset.t_cond2
 
     @property
     def cond1_label(self) -> str:
+        """str: Human-readable label for condition 1."""
         return self.dataset.cond1_label
 
     @property
     def cond2_label(self) -> str:
+        """str: Human-readable label for condition 2."""
         return self.dataset.cond2_label
 
-    def rhythmic_genes_expressed_both(self) -> pd.DataFrame:
+    def rhythmic_genes_expressed_both(self, progress: gr.Progress | None = None) -> pd.DataFrame:
+        """
+        Evaluate rhythmicity for genes expressed in both conditions.
+
+        Parameters
+        ----------
+        progress : gradio.Progress | None, optional
+            Progress reporter used when running inside a Gradio app, by default ``None``.
+
+        Returns
+        -------
+        pandas.DataFrame
+            Per-gene summaries containing model identity, weights, and fitted parameters.
+
+        """
         df: pd.DataFrame = self.df
         mask: pd.Series = (df["is_expressed_cond1"]) & (df["is_expressed_cond2"])
         df_to_analyse: pd.DataFrame = df[mask].reset_index(drop=True)
 
+        progress_manager = progress or gr.Progress()
+        model_ids: list[int] = [model.name for model in MODELS]
         rows: list[dict] = []
-        for gene, row in tqdm(
+        for gene, row in progress_manager.tqdm(
             df_to_analyse.set_index("Genes").iterrows(),
             total=len(df_to_analyse),
             desc="Fitting models to genes expressed in both conditions",
         ):
             alpha_vec: np.ndarray = row[self.columns_cond1].to_numpy(float)
             beta_vec: np.ndarray = row[self.columns_cond2].to_numpy(float)
-            design: pd.DataFrame = build_design(alpha_vec, beta_vec, self.t_cond1, self.t_cond2)
-            results: list[BaseModel] = [m.fit(design) for m in MODELS]
-            bics: np.ndarray = np.array([r.bic for r in results])
+            design: pd.DataFrame = build_design(
+                alpha_vec,
+                beta_vec,
+                self.t_cond1,
+                self.t_cond2,
+            )
+            results: list[ModelResult] = [model.fit(design) for model in MODELS]
+            bics: np.ndarray = np.array([result.bic for result in results], dtype=float)
             weights: np.ndarray = akaike_weights_from_bics(bics)
-            if np.max(weights) < self.BIC_cutoff:
-                best: M0 = M0()
+            best_result: ModelResult | M0
+            if float(np.nanmax(weights)) < self.BIC_cutoff:
+                best_result = M0()
                 chosen_model_biw: float = np.nan
                 model: int = 0
             else:
-                pick: int = int(np.argmax(weights))
-                best: BaseModel = results[pick]
-                chosen_model_biw: np.ndarray = weights[pick]
-                model: int = best.name
+                pick: int = int(np.nanargmax(weights))
+                best_result = results[pick]
+                chosen_model_biw = float(weights[pick])
+                model = int(best_result.name)
+            weight_columns = _weight_mapping(model_ids, weights)
             rows.append(
                 {
                     "gene": gene,
                     "model": model,
                     "chosen_model_bicw": chosen_model_biw,
                     # "weight": weights[pick],
-                    **{f"w_model{i}": weights[i - 1] for i in range(1, 6)},
-                    "alpha_phase": best.alpha_phase,
-                    "alpha_amp": best.alpha_amp,
-                    "beta_phase": best.beta_phase,
-                    "beta_amp": best.beta_amp,
+                    **weight_columns,
+                    "alpha_phase": best_result.alpha_phase,
+                    "alpha_amp": best_result.alpha_amp,
+                    "beta_phase": best_result.beta_phase,
+                    "beta_amp": best_result.beta_amp,
                 },
             )
 
@@ -349,10 +575,20 @@ def rhythmic_genes_expressed_both(self) -> pd.DataFrame:
         return df_results
 
     def rhythmic_genes_expressed_cond1(self) -> pd.DataFrame:
+        """
+        Evaluate rhythmicity for genes uniquely expressed in condition 1.
+
+        Returns
+        -------
+        pandas.DataFrame
+            Per-gene summaries containing model identity, weights, and fitted parameters.
+
+        """
         df: pd.DataFrame = self.df
         mask: pd.Series = (df["is_expressed_cond1"]) & ~(df["is_expressed_cond2"])
         df_to_analyse: pd.DataFrame = df[mask].reset_index(drop=True)
 
+        model_ids: list[int] = [model.name for model in MODELS_ONE_CONDITION]
         rows: list[dict] = []
         for gene, row in tqdm(
             df_to_analyse.set_index("Genes").iterrows(),
@@ -361,27 +597,29 @@ def rhythmic_genes_expressed_cond1(self) -> pd.DataFrame:
         ):
             alpha_vec: np.ndarray = row[self.columns_cond1].to_numpy(float)
             design: pd.DataFrame = build_design_cond1(alpha_vec, self.t_cond1)
-            results: list[BaseModelOneCondition] = [m.fit(design) for m in MODELS_ONE_CONDITION]
-            bics: np.ndarray = np.array([r.bic for r in results])
+            results: list[ModelResultOneCondition] = [model.fit(design) for model in MODELS_ONE_CONDITION]
+            bics: np.ndarray = np.array([result.bic for result in results], dtype=float)
             weights: np.ndarray = akaike_weights_from_bics(bics)
-            if np.max(weights) < self.BIC_cutoff:
-                best: M0 = M0()
+            best_result: ModelResultOneCondition | M0
+            if float(np.nanmax(weights)) < self.BIC_cutoff:
+                best_result = M0()
                 chosen_model_biw: float = np.nan
                 model: int = 0
             else:
-                pick: int = int(np.argmax(weights))
-                best: BaseModel = results[pick]
-                chosen_model_biw: np.ndarray = weights[pick]
-                model: int = [1, 3][pick]
+                pick: int = int(np.nanargmax(weights))
+                best_result = results[pick]
+                chosen_model_biw = float(weights[pick])
+                model = int(best_result.name)
+            weight_columns = _weight_mapping(model_ids, weights)
             rows.append(
                 {
                     "gene": gene,
                     "model": model,
                     "chosen_model_bicw": chosen_model_biw,
                     # "weight": weights[pick],
-                    **{f"w_model{model}": weights[i - 1] for i, model in enumerate([1, 3])},
-                    "alpha_phase": best.phase,
-                    "alpha_amp": best.amp,
+                    **weight_columns,
+                    "alpha_phase": getattr(best_result, "phase", np.nan),
+                    "alpha_amp": getattr(best_result, "amp", np.nan),
                     "beta_phase": np.nan,
                     "beta_amp": np.nan,
                 },
@@ -393,10 +631,20 @@ def rhythmic_genes_expressed_cond1(self) -> pd.DataFrame:
         return df_results
 
     def rhythmic_genes_expressed_cond2(self) -> pd.DataFrame:
+        """
+        Evaluate rhythmicity for genes uniquely expressed in condition 2.
+
+        Returns
+        -------
+        pandas.DataFrame
+            Per-gene summaries containing model identity, weights, and fitted parameters.
+
+        """
         df: pd.DataFrame = self.df
         mask: pd.Series = ~(df["is_expressed_cond1"]) & (df["is_expressed_cond2"])
         df_to_analyse: pd.DataFrame = df[mask].reset_index(drop=True)
 
+        model_ids: list[int] = [model.name for model in MODELS_ONE_CONDITION]
         rows: list[dict] = []
         for gene, row in tqdm(
             df_to_analyse.set_index("Genes").iterrows(),
@@ -405,29 +653,31 @@ def rhythmic_genes_expressed_cond2(self) -> pd.DataFrame:
         ):
             beta_vec: np.ndarray = row[self.columns_cond2].to_numpy(float)
             design: pd.DataFrame = build_design_cond2(beta_vec, self.t_cond2)
-            results: list[BaseModelOneCondition] = [m.fit(design) for m in MODELS_ONE_CONDITION]
-            bics: np.ndarray = np.array([r.bic for r in results])
+            results: list[ModelResultOneCondition] = [model.fit(design) for model in MODELS_ONE_CONDITION]
+            bics: np.ndarray = np.array([result.bic for result in results], dtype=float)
             weights: np.ndarray = akaike_weights_from_bics(bics)
-            if np.max(weights) < self.BIC_cutoff:
-                best: M0 = M0()
+            best_result: ModelResultOneCondition | M0
+            if float(np.nanmax(weights)) < self.BIC_cutoff:
+                best_result = M0()
                 chosen_model_biw: float = np.nan
                 model: int = 0
             else:
-                pick: int = int(np.argmax(weights))
-                best: BaseModel = results[pick]
-                chosen_model_biw: np.ndarray = weights[pick]
-                model: int = [1, 2][pick]
+                pick: int = int(np.nanargmax(weights))
+                best_result = results[pick]
+                chosen_model_biw = float(weights[pick])
+                model = int(best_result.name)
+            weight_columns = _weight_mapping(model_ids, weights)
             rows.append(
                 {
                     "gene": gene,
                     "model": model,
                     "chosen_model_bicw": chosen_model_biw,
                     # "weight": weights[pick],
-                    **{f"w_model{model}": weights[i - 1] for i, model in enumerate([1, 2])},
+                    **weight_columns,
                     "alpha_phase": np.nan,
                     "alpha_amp": np.nan,
-                    "beta_phase": best.phase,
-                    "beta_amp": best.amp,
+                    "beta_phase": getattr(best_result, "phase", np.nan),
+                    "beta_amp": getattr(best_result, "amp", np.nan),
                 },
             )
 
@@ -437,14 +687,34 @@ def rhythmic_genes_expressed_cond2(self) -> pd.DataFrame:
         return df_results
 
     def extract_all_circadian_params(self) -> pd.DataFrame:
+        """
+        Combine rhythmicity results from all subsets into a single table.
+
+        Returns
+        -------
+        pandas.DataFrame
+            Dataset joined with cosinor parameters and model weights.
+
+        """
         rhythmic_analysis_expressed_both = self.rhythmic_genes_expressed_both()
         rhythmic_analysis_cond1 = self.rhythmic_genes_expressed_cond1()
 
         rhythmic_analysis_cond2 = self.rhythmic_genes_expressed_cond2()
 
-        results_total = pd.concat([rhythmic_analysis_expressed_both, rhythmic_analysis_cond1, rhythmic_analysis_cond2])
+        results_total = pd.concat(
+            [
+                rhythmic_analysis_expressed_both,
+                rhythmic_analysis_cond1,
+                rhythmic_analysis_cond2,
+            ],
+        )
 
-        df_pre_export = self.df.merge(results_total, left_on="Genes", right_on="gene", how="right")
+        df_pre_export = self.df.merge(
+            results_total,
+            left_on="Genes",
+            right_on="gene",
+            how="right",
+        )
         column_list: list = [
             "Genes",
             *self.columns_cond1,
@@ -470,8 +740,32 @@ def extract_all_circadian_params(self) -> pd.DataFrame:
         return df_export
 
 
-@dataclass(config=ConfigDict(arbitrary_types_allowed=True))
+@pydantic_dataclass(config=ConfigDict(arbitrary_types_allowed=True))
 class OmicsHeatmap:
+    """
+    Generate clustered heatmaps highlighting rhythmic expression patterns.
+
+    Parameters
+    ----------
+    df : pandas.DataFrame
+        Source table with model classifications and expression data.
+    columns_cond1 : list[str]
+        Column names for condition 1 replicates.
+    columns_cond2 : list[str]
+        Column names for condition 2 replicates.
+    t_cond1 : numpy.ndarray
+        Zeitgeber time points for condition 1 samples.
+    t_cond2 : numpy.ndarray
+        Zeitgeber time points for condition 2 samples.
+    cond1_label : str, optional
+        Human-readable label for condition 1, by default ``"cond1"``.
+    cond2_label : str, optional
+        Human-readable label for condition 2, by default ``"cond2"``.
+    show_unexpressed : bool, optional
+        Whether to include non-expressed entries when plotting, by default ``True``.
+
+    """
+
     df: pd.DataFrame
 
     columns_cond1: list[str]
@@ -483,10 +777,28 @@ class OmicsHeatmap:
 
     show_unexpressed: bool = True
 
-    # --- validators ---
     @field_validator("t_cond1", "t_cond2", mode="before")
     @classmethod
-    def _to_1d_f64(cls, v: object) -> np.ndarray:  # ← return type fixes ANN206
+    def _to_1d_f64(cls, v: object) -> np.ndarray:
+        """
+        Coerce input to a one-dimensional ``float64`` NumPy array.
+
+        Parameters
+        ----------
+        v : object
+            Sequence-like structure representing time points.
+
+        Returns
+        -------
+        numpy.ndarray
+            Flattened array in ``float64`` precision.
+
+        Raises
+        ------
+        ValueError
+            If ``v`` cannot be interpreted as a one-dimensional array.
+
+        """
         a = np.asarray(v, dtype=np.float64)
         if a.ndim != 1:
             text: str = "expected 1D array"
@@ -495,23 +807,53 @@ def _to_1d_f64(cls, v: object) -> np.ndarray:  # ← return type fixes ANN206
 
     @model_validator(mode="after")
     def _check_columns(self) -> Self:
+        """
+        Confirm that required expression columns exist in the DataFrame.
+
+        Returns
+        -------
+        Self
+            The validated :class:`OmicsHeatmap` instance.
+
+        Raises
+        ------
+        ValueError
+            If any specified column names are absent.
+
+        """
         missing = [c for c in (self.columns_cond1 + self.columns_cond2) if c not in self.df.columns]
         if missing:
-            text = f"Missing columns: {missing}"  # satisfies EM101 (no string literal in raise)
+            text = f"Missing columns: {missing}"
             raise ValueError(text)
         return self
 
-    def timepoint_means(self, df: pd.DataFrame, columns: list[str], times: np.ndarray) -> np.ndarray:
+    def timepoint_means(
+        self,
+        df: pd.DataFrame,
+        columns: list[str],
+        times: np.ndarray,
+    ) -> np.ndarray:
         """
-        Compute mean expression across columns at each unique timepoint.
-
-        Args:
-            df: DataFrame with expression values.
-            columns: Subset of column names to use.
-            times: 1D array of timepoints, length must equal len(columns).
-
-        Returns:
-            np.ndarray of shape (n_genes, n_unique_times).
+        Average replicates at identical time points for a collection of genes.
+
+        Parameters
+        ----------
+        df : pandas.DataFrame
+            Table containing the expression measurements.
+        columns : list[str]
+            Subset of column names whose values should be aggregated.
+        times : numpy.ndarray
+            Zeitgeber time labels corresponding to ``columns``.
+
+        Returns
+        -------
+        numpy.ndarray
+            Array where each column represents the mean expression at a unique time.
+
+        Raises
+        ------
+        ValueError
+            If the number of columns differs from the number of time labels provided.
 
         """
         if len(columns) != len(times):
@@ -528,41 +870,76 @@ def timepoint_means(self, df: pd.DataFrame, columns: list[str], times: np.ndarra
         return result
 
     def get_z_score(self, arr: np.ndarray) -> np.ndarray:
-        """Compute z-score normalization for each row in a 2D array."""
-        arr: np.ndarray = (arr - np.mean(arr, axis=1).reshape(-1, 1)) / np.where(
-            np.std(arr, axis=1).reshape(-1, 1) == 0,
-            1,
-            np.std(arr, axis=1).reshape(-1, 1),
-        )
-        return arr
+        """
+        Z-score normalize each gene profile independently.
+
+        Parameters
+        ----------
+        arr : numpy.ndarray
+            Matrix of expression values where rows correspond to genes.
 
-    def plot_heatmap(self, cmap: str = "bwr") -> None:
+        Returns
+        -------
+        numpy.ndarray
+            Z-score normalized matrix preserving the original shape.
+
+        """
+        means: np.ndarray = np.mean(arr, axis=1, keepdims=True)
+        stds: np.ndarray = np.std(arr, axis=1, keepdims=True)
+        safe_stds: np.ndarray = np.where(stds == 0, 1, stds)
+        return (arr - means) / safe_stds
+
+    def plot_heatmap(self, cmap: str = "bwr") -> plt.Figure:  # noqa: PLR0915
+        """
+        Render the full alpha/beta heatmap split by rhythmic subclasses.
+
+        Parameters
+        ----------
+        cmap : str, optional
+            Matplotlib colormap name used for heat intensity, by default ``"bwr"``.
+
+        Returns
+        -------
+        matplotlib.figure.Figure
+            Figure containing the subplot grid of heatmaps.
+
+        """
         df: pd.DataFrame = self.df
-        df: pd.DataFrame = df.sort_values(by=["alpha_phase", "beta_phase"]).reset_index(drop=True)
+        df_sorted: pd.DataFrame = df.sort_values(by=["alpha_phase", "beta_phase"]).reset_index(
+            drop=True,
+        )
 
         t_unique: np.ndarray = np.unique(self.t_cond1).astype(int)
 
-        mean_cond1: np.ndarray = self.timepoint_means(df, self.columns_cond1, self.t_cond1)
-        mean_cond2: np.ndarray = self.timepoint_means(df, self.columns_cond2, self.t_cond2)
+        mean_cond1: np.ndarray = self.timepoint_means(
+            df_sorted,
+            self.columns_cond1,
+            self.t_cond1,
+        )
+        mean_cond2: np.ndarray = self.timepoint_means(
+            df_sorted,
+            self.columns_cond2,
+            self.t_cond2,
+        )
 
         z_cond1: np.ndarray = self.get_z_score(mean_cond1)
         z_cond2: np.ndarray = self.get_z_score(mean_cond2)
 
-        total_rows: int = (df["model"].isin([2, 3, 4, 5])).sum()
+        total_rows: int = (df_sorted["model"].isin([2, 3, 4, 5])).sum()
 
         m2: int = 2
         m3: int = 3
         m4: int = 4
         m5: int = 5
 
-        n_m2a: int = ((df["model"] == m2) & (df["subclass"] == "b")).sum()
-        n_m2b: int = ((df["model"] == m2) & (df["subclass"] == "c")).sum()
-        n_m3a: int = ((df["model"] == m3) & (df["subclass"] == "a")).sum()
-        n_m3b: int = ((df["model"] == m3) & (df["subclass"] == "c")).sum()
-        n_m4: int = (df["model"] == m4).sum()
-        n_m5: int = (df["model"] == m5).sum()
+        n_m2a: int = ((df_sorted["model"] == m2) & (df_sorted["subclass"] == "b")).sum()
+        n_m2b: int = ((df_sorted["model"] == m2) & (df_sorted["subclass"] == "c")).sum()
+        n_m3a: int = ((df_sorted["model"] == m3) & (df_sorted["subclass"] == "a")).sum()
+        n_m3b: int = ((df_sorted["model"] == m3) & (df_sorted["subclass"] == "c")).sum()
+        n_m4: int = (df_sorted["model"] == m4).sum()
+        n_m5: int = (df_sorted["model"] == m5).sum()
 
-        _fig, axes = plt.subplots(
+        fig, axes = plt.subplots(
             nrows=6,
             ncols=2,
             gridspec_kw={
@@ -574,19 +951,19 @@ def plot_heatmap(self, cmap: str = "bwr") -> None:
                     n_m4 / total_rows,
                     n_m5 / total_rows,
                 ],
-                "width_ratios": [1, 1],  # Equal column widths
+                "width_ratios": [1, 1],
             },
-            figsize=(12 / 2.54, 18 / 2.54),  # Adjust figure size as needed
+            figsize=(12 / 2.54, 18 / 2.54),
         )
 
         vmin_global = -2.5
         vmax_global = 2.5
 
-        mask = (df["model"] == m2) & (df["subclass"] == "b")
+        mask = (df_sorted["model"] == m2) & (df_sorted["subclass"] == "b")
         z_cond1_filtered = z_cond1[mask.to_numpy()].copy()
         if not self.show_unexpressed:
             z_cond1_filtered[:] = 0
-        im1 = axes[0, 0].imshow(
+        axes[0, 0].imshow(
             z_cond1_filtered,
             aspect="auto",
             cmap=cmap,
@@ -600,21 +977,20 @@ def plot_heatmap(self, cmap: str = "bwr") -> None:
         axes[0, 0].set_xticks([])
         axes[0, 0].set_yticks([])
 
-        # Add text to the left of the y-axis
         axes[0, 0].text(
             -0.2,
             0.5,
-            "M2a",  # Coordinates: (x, y)
-            transform=axes[0, 0].transAxes,  # Use axis-relative coordinates
+            "M2a",
+            transform=axes[0, 0].transAxes,
             ha="center",
-            va="center",  # Align text
-            rotation=0,  # Rotate text vertically
-            fontsize=8,  # Adjust font size as needed
+            va="center",
+            rotation=0,
+            fontsize=8,
         )
 
-        mask = (df["model"] == m2) & (df["subclass"] == "c")
+        mask = (df_sorted["model"] == m2) & (df_sorted["subclass"] == "c")
         z_cond1_filtered = z_cond1[mask.to_numpy()]
-        im2 = axes[1, 0].imshow(
+        axes[1, 0].imshow(
             z_cond1_filtered,
             aspect="auto",
             cmap=cmap,
@@ -626,20 +1002,19 @@ def plot_heatmap(self, cmap: str = "bwr") -> None:
         axes[1, 0].set_xticks([])
         axes[1, 0].set_yticks([])
 
-        # Add text to the left of the y-axis
         axes[1, 0].text(
             -0.2,
             0.5,
-            "M2b",  # Coordinates: (x, y)
-            transform=axes[1, 0].transAxes,  # Use axis-relative coordinates
+            "M2b",
+            transform=axes[1, 0].transAxes,
             ha="center",
-            va="center",  # Align text
-            rotation=0,  # Rotate text vertically
-            fontsize=8,  # Adjust font size as needed
+            va="center",
+            rotation=0,
+            fontsize=8,
         )
-        mask = (df["model"] == m3) & (df["subclass"] == "a")
+        mask = (df_sorted["model"] == m3) & (df_sorted["subclass"] == "a")
         z_cond1_filtered = z_cond1[mask.to_numpy()]
-        im2 = axes[2, 0].imshow(
+        axes[2, 0].imshow(
             z_cond1_filtered,
             aspect="auto",
             cmap=cmap,
@@ -651,20 +1026,19 @@ def plot_heatmap(self, cmap: str = "bwr") -> None:
         axes[2, 0].set_xticks([])
         axes[2, 0].set_yticks([])
 
-        # Add text to the left of the y-axis
         axes[2, 0].text(
             -0.2,
             0.5,
-            "M3a",  # Coordinates: (x, y)
-            transform=axes[2, 0].transAxes,  # Use axis-relative coordinates
+            "M3a",
+            transform=axes[2, 0].transAxes,
             ha="center",
-            va="center",  # Align text
-            rotation=0,  # Rotate text vertically
-            fontsize=8,  # Adjust font size as needed
+            va="center",
+            rotation=0,
+            fontsize=8,
         )
-        mask = (df["model"] == m3) & (df["subclass"] == "c")
+        mask = (df_sorted["model"] == m3) & (df_sorted["subclass"] == "c")
         z_cond1_filtered = z_cond1[mask.to_numpy()]
-        im3 = axes[3, 0].imshow(
+        axes[3, 0].imshow(
             z_cond1_filtered,
             aspect="auto",
             cmap=cmap,
@@ -675,21 +1049,21 @@ def plot_heatmap(self, cmap: str = "bwr") -> None:
         )
         axes[3, 0].set_xticks([])
         axes[3, 0].set_yticks([])
-        # Add text to the left of the y-axis
+
         axes[3, 0].text(
             -0.2,
             0.5,
-            "M3b",  # Coordinates: (x, y)
-            transform=axes[3, 0].transAxes,  # Use axis-relative coordinates
+            "M3b",
+            transform=axes[3, 0].transAxes,
             ha="center",
-            va="center",  # Align text
-            rotation=0,  # Rotate text vertically
-            fontsize=8,  # Adjust font size as needed
+            va="center",
+            rotation=0,
+            fontsize=8,
         )
 
-        mask = df["model"] == m4
+        mask = df_sorted["model"] == m4
         z_cond1_filtered = z_cond1[mask.to_numpy()]
-        im4 = axes[4, 0].imshow(
+        axes[4, 0].imshow(
             z_cond1_filtered,
             aspect="auto",
             cmap=cmap,
@@ -704,17 +1078,17 @@ def plot_heatmap(self, cmap: str = "bwr") -> None:
         axes[4, 0].text(
             -0.2,
             0.5,
-            "M4",  # Coordinates: (x, y)
-            transform=axes[4, 0].transAxes,  # Use axis-relative coordinates
+            "M4",
+            transform=axes[4, 0].transAxes,
             ha="center",
-            va="center",  # Align text
-            rotation=0,  # Rotate text vertically
-            fontsize=8,  # Adjust font size as needed
+            va="center",
+            rotation=0,
+            fontsize=8,
         )
 
-        mask = df["model"] == m5
+        mask = df_sorted["model"] == m5
         z_cond1_filtered = z_cond1[mask.to_numpy()]
-        im5 = axes[5, 0].imshow(
+        axes[5, 0].imshow(
             z_cond1_filtered,
             aspect="auto",
             cmap=cmap,
@@ -731,17 +1105,17 @@ def plot_heatmap(self, cmap: str = "bwr") -> None:
         axes[5, 0].text(
             -0.2,
             0.5,
-            "M5",  # Coordinates: (x, y)
-            transform=axes[5, 0].transAxes,  # Use axis-relative coordinates
+            "M5",
+            transform=axes[5, 0].transAxes,
             ha="center",
-            va="center",  # Align text
-            rotation=0,  # Rotate text vertically
-            fontsize=8,  # Adjust font size as needed
+            va="center",
+            rotation=0,
+            fontsize=8,
         )
 
-        mask = (df["model"] == m2) & (df["subclass"] == "b")
+        mask = (df_sorted["model"] == m2) & (df_sorted["subclass"] == "b")
         z_cond2_filtered = z_cond2[mask.to_numpy()]
-        im6 = axes[0, 1].imshow(
+        axes[0, 1].imshow(
             z_cond2_filtered,
             aspect="auto",
             cmap=cmap,
@@ -755,9 +1129,9 @@ def plot_heatmap(self, cmap: str = "bwr") -> None:
         axes[0, 1].set_xticks([])
         axes[0, 1].set_yticks([])
 
-        mask = (df["model"] == m2) & (df["subclass"] == "c")
+        mask = (df_sorted["model"] == m2) & (df_sorted["subclass"] == "c")
         z_cond2_filtered = z_cond2[mask.to_numpy()]
-        im7 = axes[1, 1].imshow(
+        axes[1, 1].imshow(
             z_cond2_filtered,
             aspect="auto",
             cmap=cmap,
@@ -770,11 +1144,11 @@ def plot_heatmap(self, cmap: str = "bwr") -> None:
         axes[1, 1].set_xticks([])
         axes[1, 1].set_yticks([])
 
-        mask = (df["model"] == m3) & (df["subclass"] == "a")
+        mask = (df_sorted["model"] == m3) & (df_sorted["subclass"] == "a")
         z_cond2_filtered = z_cond2[mask.to_numpy()].copy()
         if not self.show_unexpressed:
             z_cond2_filtered[:] = 0
-        im8 = axes[2, 1].imshow(
+        axes[2, 1].imshow(
             z_cond2_filtered,
             aspect="auto",
             cmap=cmap,
@@ -787,9 +1161,9 @@ def plot_heatmap(self, cmap: str = "bwr") -> None:
         axes[2, 1].set_xticks([])
         axes[2, 1].set_yticks([])
 
-        mask = (df["model"] == m3) & (df["subclass"] == "c")
+        mask = (df_sorted["model"] == m3) & (df_sorted["subclass"] == "c")
         z_cond2_filtered = z_cond2[mask.to_numpy()]
-        im9 = axes[3, 1].imshow(
+        axes[3, 1].imshow(
             z_cond2_filtered,
             aspect="auto",
             cmap=cmap,
@@ -802,9 +1176,9 @@ def plot_heatmap(self, cmap: str = "bwr") -> None:
         axes[3, 1].set_xticks([])
         axes[3, 1].set_yticks([])
 
-        mask = df["model"] == m4
+        mask = df_sorted["model"] == m4
         z_cond2_filtered = z_cond2[mask.to_numpy()]
-        im10 = axes[4, 1].imshow(
+        axes[4, 1].imshow(
             z_cond2_filtered,
             aspect="auto",
             cmap=cmap,
@@ -817,9 +1191,9 @@ def plot_heatmap(self, cmap: str = "bwr") -> None:
         axes[4, 1].set_xticks([])
         axes[4, 1].set_yticks([])
 
-        mask = df["model"] == m5
+        mask = df_sorted["model"] == m5
         z_cond2_filtered = z_cond2[mask.to_numpy()]
-        im11 = axes[5, 1].imshow(
+        axes[5, 1].imshow(
             z_cond2_filtered,
             aspect="auto",
             cmap=cmap,
@@ -833,9 +1207,35 @@ def plot_heatmap(self, cmap: str = "bwr") -> None:
         axes[5, 1].set_yticks([])
         axes[5, 1].set_xlabel("Time(h)")
 
+        return fig
 
-@dataclass(config=ConfigDict(arbitrary_types_allowed=True))
+
+@pydantic_dataclass(config=ConfigDict(arbitrary_types_allowed=True))
 class TimeSeriesExample:
+    """
+    Plot raw observations alongside cosinor model predictions for genes.
+
+    Parameters
+    ----------
+    df : pandas.DataFrame
+        Table containing expression measurements and model metadata.
+    columns_cond1 : list[str]
+        Column names for condition 1 samples.
+    columns_cond2 : list[str]
+        Column names for condition 2 samples.
+    t_cond1 : numpy.ndarray
+        Zeitgeber times corresponding to condition 1 measurements.
+    t_cond2 : numpy.ndarray
+        Zeitgeber times corresponding to condition 2 measurements.
+    cond1_label : str, optional
+        Display label for condition 1, by default ``"cond1"``.
+    cond2_label : str, optional
+        Display label for condition 2, by default ``"cond2"``.
+    deduplicate_on_init : bool, optional
+        Whether the underlying dataset should deduplicate genes immediately, by default ``False``.
+
+    """
+
     df: pd.DataFrame
 
     columns_cond1: list[str]
@@ -847,10 +1247,28 @@ class TimeSeriesExample:
 
     deduplicate_on_init: bool = False
 
-    # --- validators ---
     @field_validator("t_cond1", "t_cond2", mode="before")
     @classmethod
-    def _to_1d_f64(cls, v: object) -> np.ndarray:  # ← return type fixes ANN206
+    def _to_1d_f64(cls, v: object) -> np.ndarray:
+        """
+        Convert an iterable of time points into a one-dimensional ``float64`` array.
+
+        Parameters
+        ----------
+        v : object
+            Sequence-like input representing time points.
+
+        Returns
+        -------
+        numpy.ndarray
+            Flattened array of times in ``float64`` precision.
+
+        Raises
+        ------
+        ValueError
+            If the supplied object cannot be interpreted as one-dimensional.
+
+        """
         a = np.asarray(v, dtype=np.float64)
         if a.ndim != 1:
             text: str = "expected 1D array"
@@ -859,13 +1277,43 @@ def _to_1d_f64(cls, v: object) -> np.ndarray:  # ← return type fixes ANN206
 
     @model_validator(mode="after")
     def _check_columns(self) -> Self:
+        """
+        Ensure both condition-specific column sets appear in the DataFrame.
+
+        Returns
+        -------
+        Self
+            The validated :class:`TimeSeriesExample` instance.
+
+        Raises
+        ------
+        ValueError
+            If any required column is missing from ``df``.
+
+        """
         missing = [c for c in (self.columns_cond1 + self.columns_cond2) if c not in self.df.columns]
         if missing:
-            text = f"Missing columns: {missing}"  # satisfies EM101 (no string literal in raise)
+            text = f"Missing columns: {missing}"
             raise ValueError(text)
         return self
 
     def plot_time_series(self, gene: str, xticks: np.ndarray | None = None) -> None:
+        """
+        Visualize observed expression values with corresponding model fits.
+
+        Parameters
+        ----------
+        gene : str
+            Gene identifier to visualize.
+        xticks : numpy.ndarray | None, optional
+            Custom x-axis tick positions, by default ``None`` for automatic values.
+
+        Raises
+        ------
+        ValueError
+            If the requested gene is absent from the DataFrame.
+
+        """
         if xticks is None:
             xticks = np.unique(np.concatenate((self.t_cond1, self.t_cond2))).astype(int)
         df: pd.DataFrame = self.df
@@ -910,7 +1358,7 @@ def plot_time_series(self, gene: str, xticks: np.ndarray | None = None) -> None:
         plt.plot(t_test, y_test_cond1)
         plt.ylabel("Expression Level")
         plt.xticks(xticks)
-        # plt.xlim(xticks[0], xticks[-1])
+
         plt.title(f"Time Series for {gene}")
         plt.xlabel("Time (h)")
         plt.subplot(1, 2, 2)
@@ -920,7 +1368,10 @@ def plot_time_series(self, gene: str, xticks: np.ndarray | None = None) -> None:
         plt.xlabel("Time (h)")
 
         plt.xticks(xticks)
-        plt.xlim(xticks[0] - 0.05 * (xticks[-1] - xticks[0]), xticks[-1] + 0.05 * (xticks[-1] - xticks[0]))
+        plt.xlim(
+            xticks[0] - 0.05 * (xticks[-1] - xticks[0]),
+            xticks[-1] + 0.05 * (xticks[-1] - xticks[0]),
+        )
         plt.tight_layout()
         plt.show()
 
@@ -932,8 +1383,32 @@ def get_test_function_expressed_both(
         t_cond2: np.ndarray,
         model: int,
     ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+        """
+        Produce fitted curves for genes expressed in both conditions.
+
+        Parameters
+        ----------
+        alpha_vec : numpy.ndarray
+            Expression values for condition 1 samples.
+        beta_vec : numpy.ndarray
+            Expression values for condition 2 samples.
+        t_cond1 : numpy.ndarray
+            Zeitgeber times for ``alpha_vec``.
+        t_cond2 : numpy.ndarray
+            Zeitgeber times for ``beta_vec``.
+        model : int
+            Model identifier selected for the gene.
+
+        Returns
+        -------
+        tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]
+            Common time grid plus fitted values for condition 1 and condition 2.
+
+        """
         design: pd.DataFrame = build_design(alpha_vec, beta_vec, t_cond1, t_cond2)
         t_test: np.ndarray = np.linspace(0, 24, 100)
+        y_test_cond1: np.ndarray
+        y_test_cond2: np.ndarray
         if model == 0:
             print("No model selected")
             y_test_cond1 = np.full_like(t_test, np.nan)
@@ -941,8 +1416,12 @@ def get_test_function_expressed_both(
         else:
             model_formula: str = MODELS[model - 1].formula
             res = smf.ols(model_formula, data=design).fit()
-            df_cond1: pd.DataFrame = pd.DataFrame({"time": t_test, "dataset": "alpha"}).dropna()
-            df_cond2: pd.DataFrame = pd.DataFrame({"time": t_test, "dataset": "beta"}).dropna()
+            df_cond1: pd.DataFrame = pd.DataFrame(
+                {"time": t_test, "dataset": "alpha"},
+            ).dropna()
+            df_cond2: pd.DataFrame = pd.DataFrame(
+                {"time": t_test, "dataset": "beta"},
+            ).dropna()
             df_test: pd.DataFrame = pd.concat([df_cond1, df_cond2], ignore_index=True)
             df_test["constant"] = 1.0
             df_test["cos_wt"] = np.cos(W * df_test["time"].to_numpy().astype(float))
@@ -950,8 +1429,8 @@ def get_test_function_expressed_both(
             df_test["is_alpha"] = (df_test["dataset"] == "alpha").astype(int)
             df_test["is_beta"] = (df_test["dataset"] == "beta").astype(int)
             y_test = res.predict(exog=df_test)
-            y_test_cond1: np.ndarray = y_test[df_test["dataset"] == "alpha"].to_numpy()
-            y_test_cond2: np.ndarray = y_test[df_test["dataset"] == "beta"].to_numpy()
+            y_test_cond1 = y_test[df_test["dataset"] == "alpha"].to_numpy()
+            y_test_cond2 = y_test[df_test["dataset"] == "beta"].to_numpy()
         return t_test, y_test_cond1, y_test_cond2
 
     def get_test_function_expressed_cond1(
@@ -960,8 +1439,28 @@ def get_test_function_expressed_cond1(
         t_cond1: np.ndarray,
         model: int,
     ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+        """
+        Produce fitted curves for genes expressed only in condition 1.
+
+        Parameters
+        ----------
+        alpha_vec : numpy.ndarray
+            Expression values for condition 1 samples.
+        t_cond1 : numpy.ndarray
+            Zeitgeber times for ``alpha_vec``.
+        model : int
+            Model identifier selected for the gene.
+
+        Returns
+        -------
+        tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]
+            Common time grid, fitted values for condition 1, and ``nan`` placeholders for condition 2.
+
+        """
         design: pd.DataFrame = build_design_cond1(alpha_vec, t_cond1)
         t_test: np.ndarray = np.linspace(0, 24, 100)
+        y_test_cond1: np.ndarray
+        y_test_cond2: np.ndarray
         if model == 0:
             print("No model selected")
             y_test_cond1 = np.full_like(t_test, np.nan)
@@ -969,18 +1468,18 @@ def get_test_function_expressed_cond1(
         else:
             print(f"Fitting model {model}")
             print(MODELS_ONE_CONDITION)
-            if model == 1:
-                model_formula: str = MODELS_ONE_CONDITION[0].formula
-            else:
-                model_formula: str = MODELS_ONE_CONDITION[1].formula
+            model_index: int = 0 if model == 1 else 1
+            model_formula: str = MODELS_ONE_CONDITION[model_index].formula
             res = smf.ols(model_formula, data=design).fit()
-            df_test: pd.DataFrame = pd.DataFrame({"time": t_test, "dataset": "alpha"}).dropna()
+            df_test: pd.DataFrame = pd.DataFrame(
+                {"time": t_test, "dataset": "alpha"},
+            ).dropna()
             df_test["constant"] = 1.0
             df_test["cos_wt"] = np.cos(W * df_test["time"].to_numpy().astype(float))
             df_test["sin_wt"] = np.sin(W * df_test["time"].to_numpy().astype(float))
-            y_test = res.predict(exog=df_test)
-            y_test_cond1: np.ndarray = y_test.to_numpy()
-            y_test_cond2: np.ndarray = np.full_like(t_test, np.nan)
+            y_test = res.predict(exog=df_test).to_numpy()
+            y_test_cond1 = y_test
+            y_test_cond2 = np.full_like(t_test, np.nan)
         return t_test, y_test_cond1, y_test_cond2
 
     def get_test_function_expressed_cond2(
@@ -989,8 +1488,28 @@ def get_test_function_expressed_cond2(
         t_cond2: np.ndarray,
         model: int,
     ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+        """
+        Produce fitted curves for genes expressed only in condition 2.
+
+        Parameters
+        ----------
+        beta_vec : numpy.ndarray
+            Expression values for condition 2 samples.
+        t_cond2 : numpy.ndarray
+            Zeitgeber times for ``beta_vec``.
+        model : int
+            Model identifier selected for the gene.
+
+        Returns
+        -------
+        tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]
+            Common time grid, ``nan`` placeholders for condition 1, and fitted values for condition 2.
+
+        """
         design: pd.DataFrame = build_design_cond2(beta_vec, t_cond2)
         t_test: np.ndarray = np.linspace(0, 24, 100)
+        y_test_cond1: np.ndarray
+        y_test_cond2: np.ndarray
         if model == 0:
             print("No model selected")
             y_test_cond1 = np.full_like(t_test, np.nan)
@@ -998,17 +1517,17 @@ def get_test_function_expressed_cond2(
         else:
             print(f"Fitting model {model}")
             print(MODELS_ONE_CONDITION)
-            if model == 1:
-                model_formula: str = MODELS_ONE_CONDITION[0].formula
-            else:
-                model_formula: str = MODELS_ONE_CONDITION[1].formula
+            model_index: int = 0 if model == 1 else 1
+            model_formula: str = MODELS_ONE_CONDITION[model_index].formula
             res = smf.ols(model_formula, data=design).fit()
-            df_test: pd.DataFrame = pd.DataFrame({"time": t_test, "dataset": "alpha"}).dropna()
+            df_test: pd.DataFrame = pd.DataFrame(
+                {"time": t_test, "dataset": "alpha"},
+            ).dropna()
             df_test["constant"] = 1.0
             df_test["cos_wt"] = np.cos(W * df_test["time"].to_numpy().astype(float))
             df_test["sin_wt"] = np.sin(W * df_test["time"].to_numpy().astype(float))
-            y_test = res.predict(exog=df_test)
-            y_test_cond1: np.ndarray = np.full_like(t_test, np.nan)
-            y_test_cond2: np.ndarray = y_test.to_numpy()
+            y_test = res.predict(exog=df_test).to_numpy()
+            y_test_cond1 = np.full_like(t_test, np.nan)
+            y_test_cond2 = y_test
 
         return t_test, y_test_cond1, y_test_cond2
diff --git a/src/cosinor_lite/states_info.py b/src/cosinor_lite/states_info.py
deleted file mode 100644
index 63acc75..0000000
--- a/src/cosinor_lite/states_info.py
+++ /dev/null
@@ -1,25 +0,0 @@
-"""Utility functions for working with cities and states."""
-
-import json
-from pathlib import Path
-from typing import List
-
-THIS_DIR = Path(__file__).parent
-CITIES_JSON_FPATH = THIS_DIR / "cities.json"
-
-
-def is_city_capitol_of_state(city_name: str, state: str) -> bool:
-    """Return True if `city_name` is the capitol of `state`."""
-    cities_json_contents = CITIES_JSON_FPATH.read_text()
-    cities: List[dict] = json.loads(cities_json_contents)
-    matching_cities: List[dict] = [city for city in cities if city["city"] == city_name]
-    if len(matching_cities) == 0:
-        return False
-    matched_city = matching_cities[0]
-    return matched_city["state"] == state
-
-
-# pylint: disable=invalid-name
-def slow_add(a: int, b: int) -> int:
-    """Return the sum of `a` and `b`."""
-    return a + b
diff --git a/src/cosinor_lite/utils.py b/src/cosinor_lite/utils.py
deleted file mode 100644
index a71df0d..0000000
--- a/src/cosinor_lite/utils.py
+++ /dev/null
@@ -1,64 +0,0 @@
-import numpy as np
-import pandas as pd
-
-W: float = 2 * np.pi / 24.0
-RAD2H: float = 24.0 / (2.0 * np.pi)
-
-
-def phase_from_ab(a: float, b: float) -> float:
-    return (np.arctan2(b, a) * RAD2H) % 24.0
-
-
-def amp_from_ab(a: float, b: float) -> float:
-    return float(np.hypot(a, b))
-
-
-def bic(llf: float, k: int, n: int) -> float:
-    return k * np.log(n) - 2.0 * llf
-
-
-def akaike_weights_from_bics(bics: np.ndarray) -> np.ndarray:
-    d: np.ndarray = bics - np.nanmin(bics)
-    w: np.ndarray = np.exp(-0.5 * d)
-    return w / np.nansum(w)
-
-
-def build_design(
-    alpha_vals: np.ndarray,
-    beta_vals: np.ndarray,
-    t_cond1: np.ndarray,
-    t_cond2: np.ndarray,
-) -> pd.DataFrame:
-    df_cond1: pd.DataFrame = pd.DataFrame({"y": alpha_vals, "time": t_cond1, "dataset": "alpha"}).dropna()
-    df_cond2: pd.DataFrame = pd.DataFrame({"y": beta_vals, "time": t_cond2, "dataset": "beta"}).dropna()
-    df: pd.DataFrame = pd.concat([df_cond1, df_cond2], ignore_index=True)
-    df["constant"] = 1.0
-    df["cos_wt"] = np.cos(W * df["time"].to_numpy().astype(float))
-    df["sin_wt"] = np.sin(W * df["time"].to_numpy().astype(float))
-    df["is_alpha"] = (df["dataset"] == "alpha").astype(int)
-    df["is_beta"] = (df["dataset"] == "beta").astype(int)
-    return df
-
-
-def build_design_cond1(
-    alpha_vals: np.ndarray,
-    t_cond1: np.ndarray,
-) -> pd.DataFrame:
-    df_cond1: pd.DataFrame = pd.DataFrame({"y": alpha_vals, "time": t_cond1, "dataset": "alpha"}).dropna()
-    df: pd.DataFrame = pd.concat([df_cond1], ignore_index=True)
-    df["constant"] = 1.0
-    df["cos_wt"] = np.cos(W * df["time"].to_numpy().astype(float))
-    df["sin_wt"] = np.sin(W * df["time"].to_numpy().astype(float))
-    return df
-
-
-def build_design_cond2(
-    beta_vals: np.ndarray,
-    t_cond2: np.ndarray,
-) -> pd.DataFrame:
-    df_cond2: pd.DataFrame = pd.DataFrame({"y": beta_vals, "time": t_cond2, "dataset": "beta"}).dropna()
-    df: pd.DataFrame = pd.concat([df_cond2], ignore_index=True)
-    df["constant"] = 1.0
-    df["cos_wt"] = np.cos(W * df["time"].to_numpy().astype(float))
-    df["sin_wt"] = np.sin(W * df["time"].to_numpy().astype(float))
-    return df
diff --git a/src/cosinor_lite/visualisation.py b/src/cosinor_lite/visualisation.py
deleted file mode 100644
index 996f9af..0000000
--- a/src/cosinor_lite/visualisation.py
+++ /dev/null
@@ -1,553 +0,0 @@
-from __future__ import annotations
-
-from typing import Self
-
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-import statsmodels.formula.api as smf
-from pydantic import ConfigDict, field_validator, model_validator
-from pydantic.dataclasses import dataclass
-
-
-@dataclass(config=ConfigDict(arbitrary_types_allowed=True))
-class OmicsHeatmap:
-    df: pd.DataFrame
-
-    columns_cond1: list[str]
-    columns_cond2: list[str]
-    t_cond1: np.ndarray
-    t_cond2: np.ndarray
-    cond1_label: str = "cond1"
-    cond2_label: str = "cond2"
-
-    deduplicate_on_init: bool = False
-
-    # --- validators ---
-    @field_validator("t_cond1", "t_cond2", mode="before")
-    @classmethod
-    def _to_1d_f64(cls, v: object) -> np.ndarray:  # ← return type fixes ANN206
-        a = np.asarray(v, dtype=np.float64)
-        if a.ndim != 1:
-            text: str = "expected 1D array"
-            raise ValueError(text)
-        return a
-
-    @model_validator(mode="after")
-    def _check_columns(self) -> Self:
-        missing = [c for c in (self.columns_cond1 + self.columns_cond2) if c not in self.df.columns]
-        if missing:
-            text = f"Missing columns: {missing}"  # satisfies EM101 (no string literal in raise)
-            raise ValueError(text)
-        return self
-
-    def timepoint_means(self, df: pd.DataFrame, columns: list[str], times: np.ndarray) -> np.ndarray:
-        """
-        Compute mean expression across columns at each unique timepoint.
-
-        Args:
-            df: DataFrame with expression values.
-            columns: Subset of column names to use.
-            times: 1D array of timepoints, length must equal len(columns).
-
-        Returns:
-            np.ndarray of shape (n_genes, n_unique_times).
-
-        """
-        if len(columns) != len(times):
-            text: str = f"Length of columns ({len(columns)}) must match length of times ({len(times)})"
-            raise ValueError(text)
-
-        values: np.ndarray = df[columns].to_numpy()
-        unique_times: np.ndarray = np.unique(times)
-
-        result: np.ndarray = np.column_stack(
-            [values[:, times == t].mean(axis=1) for t in unique_times],
-        )
-
-        return result
-
-    def get_z_score(self, arr: np.ndarray) -> np.ndarray:
-        """Compute z-score normalization for each row in a 2D array."""
-        arr: np.ndarray = (arr - np.mean(arr, axis=1).reshape(-1, 1)) / np.where(
-            np.std(arr, axis=1).reshape(-1, 1) == 0,
-            1,
-            np.std(arr, axis=1).reshape(-1, 1),
-        )
-        return arr
-
-    def plot_heatmap(self, cmap: str = "bwr") -> None:
-        df: pd.DataFrame = self.df
-        df: pd.DataFrame = df.sort_values(by=["alpha_phase", "beta_phase"]).reset_index(drop=True)
-
-        t_unique: np.ndarray = np.unique(self.t_cond1).astype(int)
-
-        mean_cond1: np.ndarray = self.timepoint_means(df, self.columns_cond1, self.t_cond1)
-        mean_cond2: np.ndarray = self.timepoint_means(df, self.columns_cond2, self.t_cond2)
-
-        z_cond1: np.ndarray = self.get_z_score(mean_cond1)
-        z_cond2: np.ndarray = self.get_z_score(mean_cond2)
-
-        total_rows: int = (df["model"].isin([2, 3, 4, 5])).sum()
-
-        m2: int = 2
-        m3: int = 3
-        m4: int = 4
-        m5: int = 5
-
-        n_m2a: int = ((df["model"] == m2) & (df["subclass"] == "b")).sum()
-        n_m2b: int = ((df["model"] == m2) & (df["subclass"] == "c")).sum()
-        n_m3a: int = ((df["model"] == m3) & (df["subclass"] == "a")).sum()
-        n_m3b: int = ((df["model"] == m3) & (df["subclass"] == "c")).sum()
-        n_m4: int = (df["model"] == m4).sum()
-        n_m5: int = (df["model"] == m5).sum()
-
-        _fig, axes = plt.subplots(
-            nrows=6,
-            ncols=2,
-            gridspec_kw={
-                "height_ratios": [
-                    n_m2a / total_rows,
-                    n_m2b / total_rows,
-                    n_m3a / total_rows,
-                    n_m3b / total_rows,
-                    n_m4 / total_rows,
-                    n_m5 / total_rows,
-                ],
-                "width_ratios": [1, 1],  # Equal column widths
-            },
-            figsize=(12 / 2.54, 18 / 2.54),  # Adjust figure size as needed
-        )
-
-        vmin_global = -2.5
-        vmax_global = 2.5
-
-        mask = (df["model"] == m2) & (df["subclass"] == "b")
-        z_cond1_filtered = z_cond1[mask.to_numpy()]
-        im1 = axes[0, 0].imshow(
-            z_cond1_filtered,
-            aspect="auto",
-            cmap=cmap,
-            vmin=vmin_global,
-            vmax=vmax_global,
-            rasterized=False,
-            interpolation="none",
-        )
-
-        axes[0, 0].set_title("Alpha cells")
-        axes[0, 0].set_xticks([])
-        axes[0, 0].set_yticks([])
-
-        # Add text to the left of the y-axis
-        axes[0, 0].text(
-            -0.2,
-            0.5,
-            "M2a",  # Coordinates: (x, y)
-            transform=axes[0, 0].transAxes,  # Use axis-relative coordinates
-            ha="center",
-            va="center",  # Align text
-            rotation=0,  # Rotate text vertically
-            fontsize=8,  # Adjust font size as needed
-        )
-
-        mask = (df["model"] == m2) & (df["subclass"] == "c")
-        z_cond1_filtered = z_cond1[mask.to_numpy()]
-        im2 = axes[1, 0].imshow(
-            z_cond1_filtered,
-            aspect="auto",
-            cmap=cmap,
-            vmin=vmin_global,
-            vmax=vmax_global,
-            rasterized=False,
-            interpolation="none",
-        )
-        axes[1, 0].set_xticks([])
-        axes[1, 0].set_yticks([])
-
-        # Add text to the left of the y-axis
-        axes[1, 0].text(
-            -0.2,
-            0.5,
-            "M2b",  # Coordinates: (x, y)
-            transform=axes[1, 0].transAxes,  # Use axis-relative coordinates
-            ha="center",
-            va="center",  # Align text
-            rotation=0,  # Rotate text vertically
-            fontsize=8,  # Adjust font size as needed
-        )
-        mask = (df["model"] == m3) & (df["subclass"] == "a")
-        z_cond1_filtered = z_cond1[mask.to_numpy()]
-        im2 = axes[2, 0].imshow(
-            z_cond1_filtered,
-            aspect="auto",
-            cmap=cmap,
-            vmin=vmin_global,
-            vmax=vmax_global,
-            rasterized=False,
-            interpolation="none",
-        )
-        axes[2, 0].set_xticks([])
-        axes[2, 0].set_yticks([])
-
-        # Add text to the left of the y-axis
-        axes[2, 0].text(
-            -0.2,
-            0.5,
-            "M3a",  # Coordinates: (x, y)
-            transform=axes[2, 0].transAxes,  # Use axis-relative coordinates
-            ha="center",
-            va="center",  # Align text
-            rotation=0,  # Rotate text vertically
-            fontsize=8,  # Adjust font size as needed
-        )
-        mask = (df["model"] == m3) & (df["subclass"] == "c")
-        z_cond1_filtered = z_cond1[mask.to_numpy()]
-        im3 = axes[3, 0].imshow(
-            z_cond1_filtered,
-            aspect="auto",
-            cmap=cmap,
-            vmin=vmin_global,
-            vmax=vmax_global,
-            rasterized=False,
-            interpolation="none",
-        )
-        axes[3, 0].set_xticks([])
-        axes[3, 0].set_yticks([])
-        # Add text to the left of the y-axis
-        axes[3, 0].text(
-            -0.2,
-            0.5,
-            "M3b",  # Coordinates: (x, y)
-            transform=axes[3, 0].transAxes,  # Use axis-relative coordinates
-            ha="center",
-            va="center",  # Align text
-            rotation=0,  # Rotate text vertically
-            fontsize=8,  # Adjust font size as needed
-        )
-
-        mask = df["model"] == m4
-        z_cond1_filtered = z_cond1[mask.to_numpy()]
-        im4 = axes[4, 0].imshow(
-            z_cond1_filtered,
-            aspect="auto",
-            cmap=cmap,
-            vmin=vmin_global,
-            vmax=vmax_global,
-            rasterized=False,
-            interpolation="none",
-        )
-        axes[4, 0].set_xticks([])
-        axes[4, 0].set_yticks([])
-
-        axes[4, 0].text(
-            -0.2,
-            0.5,
-            "M4",  # Coordinates: (x, y)
-            transform=axes[4, 0].transAxes,  # Use axis-relative coordinates
-            ha="center",
-            va="center",  # Align text
-            rotation=0,  # Rotate text vertically
-            fontsize=8,  # Adjust font size as needed
-        )
-
-        mask = df["model"] == m5
-        z_cond1_filtered = z_cond1[mask.to_numpy()]
-        im5 = axes[5, 0].imshow(
-            z_cond1_filtered,
-            aspect="auto",
-            cmap=cmap,
-            vmin=vmin_global,
-            vmax=vmax_global,
-            rasterized=False,
-            interpolation="none",
-        )
-
-        axes[5, 0].set_xticks(range(len(t_unique)), t_unique)
-        axes[5, 0].set_yticks([])
-        axes[5, 0].set_xlabel("Time(h)")
-
-        axes[5, 0].text(
-            -0.2,
-            0.5,
-            "M5",  # Coordinates: (x, y)
-            transform=axes[5, 0].transAxes,  # Use axis-relative coordinates
-            ha="center",
-            va="center",  # Align text
-            rotation=0,  # Rotate text vertically
-            fontsize=8,  # Adjust font size as needed
-        )
-
-        mask = (df["model"] == m2) & (df["subclass"] == "b")
-        z_cond2_filtered = z_cond2[mask.to_numpy()]
-        im6 = axes[0, 1].imshow(
-            z_cond2_filtered,
-            aspect="auto",
-            cmap=cmap,
-            vmin=vmin_global,
-            vmax=vmax_global,
-            rasterized=False,
-            interpolation="none",
-        )
-
-        axes[0, 1].set_title("Beta cells")
-        axes[0, 1].set_xticks([])
-        axes[0, 1].set_yticks([])
-
-        mask = (df["model"] == m2) & (df["subclass"] == "c")
-        z_cond2_filtered = z_cond2[mask.to_numpy()]
-        im7 = axes[1, 1].imshow(
-            z_cond2_filtered,
-            aspect="auto",
-            cmap=cmap,
-            vmin=vmin_global,
-            vmax=vmax_global,
-            rasterized=False,
-            interpolation="none",
-        )
-
-        axes[1, 1].set_xticks([])
-        axes[1, 1].set_yticks([])
-
-        mask = (df["model"] == m3) & (df["subclass"] == "a")
-        z_cond2_filtered = z_cond2[mask.to_numpy()]
-        im8 = axes[2, 1].imshow(
-            z_cond2_filtered,
-            aspect="auto",
-            cmap=cmap,
-            vmin=vmin_global,
-            vmax=vmax_global,
-            rasterized=False,
-            interpolation="none",
-        )
-
-        axes[2, 1].set_xticks([])
-        axes[2, 1].set_yticks([])
-
-        mask = (df["model"] == m3) & (df["subclass"] == "c")
-        z_cond2_filtered = z_cond2[mask.to_numpy()]
-        im9 = axes[3, 1].imshow(
-            z_cond2_filtered,
-            aspect="auto",
-            cmap=cmap,
-            vmin=vmin_global,
-            vmax=vmax_global,
-            rasterized=False,
-            interpolation="none",
-        )
-
-        axes[3, 1].set_xticks([])
-        axes[3, 1].set_yticks([])
-
-        mask = df["model"] == m4
-        z_cond2_filtered = z_cond2[mask.to_numpy()]
-        im10 = axes[4, 1].imshow(
-            z_cond2_filtered,
-            aspect="auto",
-            cmap=cmap,
-            vmin=vmin_global,
-            vmax=vmax_global,
-            rasterized=False,
-            interpolation="none",
-        )
-
-        axes[4, 1].set_xticks([])
-        axes[4, 1].set_yticks([])
-
-        mask = df["model"] == m5
-        z_cond2_filtered = z_cond2[mask.to_numpy()]
-        im11 = axes[5, 1].imshow(
-            z_cond2_filtered,
-            aspect="auto",
-            cmap=cmap,
-            vmin=vmin_global,
-            vmax=vmax_global,
-            rasterized=False,
-            interpolation="none",
-        )
-
-        axes[5, 1].set_xticks(range(len(t_unique)), t_unique)
-        axes[5, 1].set_yticks([])
-        axes[5, 1].set_xlabel("Time(h)")
-
-
-@dataclass(config=ConfigDict(arbitrary_types_allowed=True))
-class TimeSeriesExample:
-    df: pd.DataFrame
-
-    columns_cond1: list[str]
-    columns_cond2: list[str]
-    t_cond1: np.ndarray
-    t_cond2: np.ndarray
-    cond1_label: str = "cond1"
-    cond2_label: str = "cond2"
-
-    deduplicate_on_init: bool = False
-
-    # --- validators ---
-    @field_validator("t_cond1", "t_cond2", mode="before")
-    @classmethod
-    def _to_1d_f64(cls, v: object) -> np.ndarray:  # ← return type fixes ANN206
-        a = np.asarray(v, dtype=np.float64)
-        if a.ndim != 1:
-            text: str = "expected 1D array"
-            raise ValueError(text)
-        return a
-
-    @model_validator(mode="after")
-    def _check_columns(self) -> Self:
-        missing = [c for c in (self.columns_cond1 + self.columns_cond2) if c not in self.df.columns]
-        if missing:
-            text = f"Missing columns: {missing}"  # satisfies EM101 (no string literal in raise)
-            raise ValueError(text)
-        return self
-
-    def plot_time_series(self, gene: str, xticks: np.ndarray | None = None) -> None:
-        if xticks is None:
-            xticks = np.unique(np.concatenate((self.t_cond1, self.t_cond2))).astype(int)
-        df: pd.DataFrame = self.df
-        df_curr = df[df["Genes"] == gene]
-        if df_curr.empty:
-            msg = f"Gene '{gene}' not found in the DataFrame."
-            raise ValueError(msg)
-        if len(df_curr) > 1:
-            df_curr = df_curr.loc[[df_curr["mean_cond2"].idxmax()]]
-
-        alpha_vec: np.ndarray = df_curr[self.columns_cond1].to_numpy(float).flatten()
-        beta_vec: np.ndarray = df_curr[self.columns_cond2].to_numpy(float).flatten()
-
-        is_expressed_cond1: bool = df_curr["is_expressed_cond1"].to_numpy()[0]
-        is_expressed_cond2: bool = df_curr["is_expressed_cond2"].to_numpy()[0]
-
-        model: int = df_curr["model"].to_numpy()[0]
-
-        if is_expressed_cond1 and is_expressed_cond2:
-            t_test, y_test_cond1, y_test_cond2 = self.get_test_function_expressed_both(
-                alpha_vec,
-                beta_vec,
-                self.t_cond1,
-                self.t_cond2,
-                model,
-            )
-        elif is_expressed_cond1 and not is_expressed_cond2:
-            t_test, y_test_cond1, y_test_cond2 = self.get_test_function_expressed_cond1(
-                alpha_vec,
-                self.t_cond1,
-                model,
-            )
-        elif not is_expressed_cond1 and is_expressed_cond2:
-            t_test, y_test_cond1, y_test_cond2 = self.get_test_function_expressed_cond2(
-                beta_vec,
-                self.t_cond2,
-                model,
-            )
-        plt.figure(figsize=(12 / 2.54, 6 / 2.54))
-        plt.subplot(1, 2, 1)
-        plt.scatter(self.t_cond1, alpha_vec, s=4)
-        plt.plot(t_test, y_test_cond1)
-        plt.ylabel("Expression Level")
-        plt.xticks(xticks)
-        # plt.xlim(xticks[0], xticks[-1])
-        plt.title(f"Time Series for {gene}")
-        plt.xlabel("Time (h)")
-        plt.subplot(1, 2, 2)
-        plt.scatter(self.t_cond2, beta_vec, s=4, color="r")
-        plt.plot(t_test, y_test_cond2, color="r")
-        plt.title(f"Time Series for {gene}")
-        plt.xlabel("Time (h)")
-
-        plt.xticks(xticks)
-        plt.xlim(xticks[0] - 0.05 * (xticks[-1] - xticks[0]), xticks[-1] + 0.05 * (xticks[-1] - xticks[0]))
-        plt.tight_layout()
-        plt.show()
-
-    def get_test_function_expressed_both(
-        self,
-        alpha_vec: np.ndarray,
-        beta_vec: np.ndarray,
-        t_cond1: np.ndarray,
-        t_cond2: np.ndarray,
-        model: int,
-    ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-        design: pd.DataFrame = build_design(alpha_vec, beta_vec, t_cond1, t_cond2)
-        t_test: np.ndarray = np.linspace(0, 24, 100)
-        if model == 0:
-            print("No model selected")
-            y_test_cond1 = np.full_like(t_test, np.nan)
-            y_test_cond2 = np.full_like(t_test, np.nan)
-        else:
-            model_formula: str = MODELS[model - 1].formula
-            res = smf.ols(model_formula, data=design).fit()
-            df_cond1: pd.DataFrame = pd.DataFrame({"time": t_test, "dataset": "alpha"}).dropna()
-            df_cond2: pd.DataFrame = pd.DataFrame({"time": t_test, "dataset": "beta"}).dropna()
-            df_test: pd.DataFrame = pd.concat([df_cond1, df_cond2], ignore_index=True)
-            df_test["constant"] = 1.0
-            df_test["cos_wt"] = np.cos(W * df_test["time"].to_numpy().astype(float))
-            df_test["sin_wt"] = np.sin(W * df_test["time"].to_numpy().astype(float))
-            df_test["is_alpha"] = (df_test["dataset"] == "alpha").astype(int)
-            df_test["is_beta"] = (df_test["dataset"] == "beta").astype(int)
-            y_test = res.predict(exog=df_test)
-            print(y_test)
-            y_test_cond1: np.ndarray = y_test[df_test["dataset"] == "alpha"].to_numpy()
-            y_test_cond2: np.ndarray = y_test[df_test["dataset"] == "beta"].to_numpy()
-        return t_test, y_test_cond1, y_test_cond2
-
-    def get_test_function_expressed_cond1(
-        self,
-        alpha_vec: np.ndarray,
-        t_cond1: np.ndarray,
-        model: int,
-    ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-        design: pd.DataFrame = build_design_cond1(alpha_vec, t_cond1)
-        t_test: np.ndarray = np.linspace(0, 24, 100)
-        if model == 0:
-            print("No model selected")
-            y_test_cond1 = np.full_like(t_test, np.nan)
-            y_test_cond2 = np.full_like(t_test, np.nan)
-        else:
-            print(f"Fitting model {model}")
-            print(MODELS_ONE_CONDITION)
-            if model == 1:
-                model_formula: str = MODELS_ONE_CONDITION[0].formula
-            else:
-                model_formula: str = MODELS_ONE_CONDITION[1].formula
-            res = smf.ols(model_formula, data=design).fit()
-            df_test: pd.DataFrame = pd.DataFrame({"time": t_test, "dataset": "alpha"}).dropna()
-            df_test["constant"] = 1.0
-            df_test["cos_wt"] = np.cos(W * df_test["time"].to_numpy().astype(float))
-            df_test["sin_wt"] = np.sin(W * df_test["time"].to_numpy().astype(float))
-            y_test = res.predict(exog=df_test)
-            print(y_test)
-            y_test_cond1: np.ndarray = y_test.to_numpy()
-            y_test_cond2: np.ndarray = np.full_like(t_test, np.nan)
-        return t_test, y_test_cond1, y_test_cond2
-
-    def get_test_function_expressed_cond2(
-        self,
-        beta_vec: np.ndarray,
-        t_cond2: np.ndarray,
-        model: int,
-    ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-        design: pd.DataFrame = build_design_cond2(beta_vec, t_cond2)
-        t_test: np.ndarray = np.linspace(0, 24, 100)
-        if model == 0:
-            print("No model selected")
-            y_test_cond1 = np.full_like(t_test, np.nan)
-            y_test_cond2 = np.full_like(t_test, np.nan)
-        else:
-            print(f"Fitting model {model}")
-            print(MODELS_ONE_CONDITION)
-            if model == 1:
-                model_formula: str = MODELS_ONE_CONDITION[0].formula
-            else:
-                model_formula: str = MODELS_ONE_CONDITION[1].formula
-            res = smf.ols(model_formula, data=design).fit()
-            df_test: pd.DataFrame = pd.DataFrame({"time": t_test, "dataset": "alpha"}).dropna()
-            df_test["constant"] = 1.0
-            df_test["cos_wt"] = np.cos(W * df_test["time"].to_numpy().astype(float))
-            df_test["sin_wt"] = np.sin(W * df_test["time"].to_numpy().astype(float))
-            y_test = res.predict(exog=df_test)
-            print(y_test)
-            y_test_cond1: np.ndarray = np.full_like(t_test, np.nan)
-            y_test_cond2: np.ndarray = y_test.to_numpy()
-
-        return t_test, y_test_cond1, y_test_cond2
diff --git a/test-reports/report.xml b/test-reports/report.xml
deleted file mode 100644
index 422ef87..0000000
--- a/test-reports/report.xml
+++ /dev/null
@@ -1 +0,0 @@
-<?xml version="1.0" encoding="utf-8"?><testsuites name="pytest tests"><testsuite name="pytest" errors="0" failures="0" skipped="0" tests="7" time="0.525" timestamp="2025-07-10T11:28:53.533818+02:00" hostname="SV-34M-019.local"><testcase classname="tests.unit_tests.test_data_loading" name="test_sample_csv_exists" time="0.001" /><testcase classname="tests.unit_tests.test_data_loading" name="test_bioluminescence_csv_can_be_loaded" time="0.023" /><testcase classname="tests.unit_tests.test_states_info" name="test__is_city_capitol_of_state[Montgomery-Alabama-True]" time="0.001" /><testcase classname="tests.unit_tests.test_states_info" name="test__is_city_capitol_of_state[Oklahoma City-Oklahoma-True]" time="0.001" /><testcase classname="tests.unit_tests.test_states_info" name="test__is_city_capitol_of_state[Salem-Oregon-True]" time="0.001" /><testcase classname="tests.unit_tests.test_states_info" name="test__is_city_capitol_of_state[Salt Lake City-Alabama-False]" time="0.001" /><testcase classname="tests.unit_tests.test_states_info" name="test__slow_add" time="0.001" /></testsuite></testsuites>
\ No newline at end of file
diff --git a/tests/conftest.py b/tests/conftest.py
index fd33401..dffafdb 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -1,22 +1,8 @@
-"""
-Register pytest plugins, fixtures, and hooks to be used during test execution.
-
-Docs: https://stackoverflow.com/questions/34466027/in-pytest-what-is-the-use-of-conftest-py-files
-"""
-
-
-import sys
-from pathlib import Path
-
-THIS_DIR = Path(__file__).parent
-TESTS_DIR_PARENT = (THIS_DIR / "..").resolve()
-
-# add the parent directory of tests/ to PYTHONPATH
-# so that we can use "from tests.<module> import ..." in our tests and fixtures
-sys.path.insert(0, str(TESTS_DIR_PARENT))
+"""Register pytest plugins, fixtures, and hooks to be used during test execution."""
 
 # module import paths to python files containing fixtures
 pytest_plugins = [
-    # e.g. "tests/fixtures/example_fixture.py" should be registered as:
-    "tests.fixtures.example_fixture",
+    "tests.fixtures.livecell_dataset_fixture",
+    "tests.fixtures.cosinor_analysis_fixture",
+    "tests.fixtures.omics_dataset_fixture",
 ]
diff --git a/tests/data/Bioluminescence_test_data_1.csv b/tests/data/Bioluminescence_test_data_1.csv
new file mode 100644
index 0000000..3b168e5
--- /dev/null
+++ b/tests/data/Bioluminescence_test_data_1.csv
@@ -0,0 +1,583 @@
+Participant ID,1,1,1,2,3,3,4,4,5,5,6,6,7,7,7,8,8,8,9,9,10,10,11,11,11,12,12,12,12,13,13,13,14,14,14,15,15,15,16,16,17,17,18,18,18,19,19,19,19,20,20,20,21,21,22,22,23,23,24,24
+Replicate,1,2,3,1,1,2,1,2,1,2,1,2,1,2,3,1,2,3,1,2,1,2,1,2,3,1,2,3,4,1,2,3,1,2,3,1,2,3,1,2,1,2,1,2,3,1,2,3,4,1,2,3,1,2,1,2,1,2,1,2
+Group,group_1,group_1,group_1,group_1,group_1,group_1,group_1,group_1,group_1,group_1,group_1,group_1,group_1,group_1,group_1,group_1,group_1,group_1,group_1,group_1,group_1,group_1,group_1,group_1,group_1,group_1,group_1,group_1,group_1,group_2,group_2,group_2,group_2,group_2,group_2,group_2,group_2,group_2,group_2,group_2,group_2,group_2,group_2,group_2,group_2,group_2,group_2,group_2,group_2,group_2,group_2,group_2,group_2,group_2,group_2,group_2,group_1,group_1,group_2,group_2
+0,919.123,834.821,1227.032,152.91,186,131,540,406,307,189,140,282,564.071,298.783,506.62,295.221,342.331,293.212,564.754,578.906,193.281,287.944,441.721,479.457,558.449,133.5,390.942,407.189,406.213,94.713,124.118,73.916,1112.212,747.835,1086.323,595.233,491.795,408.985,800.394,1221.799,258.223,352.672,95.439,102.981,73.726,1480.701,526.498,574.276,1155.453,345.919,358.793,505.006,106,161,747,444,704,538,159.523,158.988
+0.1668,920.718,838.938,1222.663,156.901,203,121,685,510,288,193,148,302,563.009,303.439,506.763,294.169,343.17,294.523,581.508,582.121,203.778,314.142,445.127,483.091,563.356,123.5,397.305,409.621,412.28,94.812,127.433,75.856,1134.266,756.197,1097.5,597.067,488.03,405.678,826.948,1302.709,282.085,355.342,94.344,103.773,72.361,1479.018,528.973,583.327,1166.273,351.159,357.759,504.341,37,210,725,482,727,537,161.031,157.595
+0.3336,926.536,829.957,1225.876,157.411,203,122,742,519,282,187,153,308,573.158,309.183,509.595,295.857,341.492,293.326,600.822,571.641,218.427,343.226,448.68,487.388,561.829,176.5,402.113,409.225,410.455,99.068,128.184,77.698,1141.821,772.681,1107.573,592.63,484.517,405.846,851.039,1328.244,297.13,359.474,94.557,102.584,71.28,1498.607,528.648,585.689,1170.658,346.882,361.077,505.984,103,250,704,472,696,509,161.23,156.396
+0.5004,922.005,832.11,1218.498,162.01,201,125,763,506,274,179,157,325,576.119,315.781,520.51,295.43,346.911,293.589,626.038,569.885,225.011,372.495,448.767,493.572,568.015,185.5,404.248,416.833,419.251,102.236,127.844,80.446,1150.813,779.686,1113.342,592.573,485.683,405.848,884.628,1349.927,311.976,365.494,97.5,103.264,71.707,1515.381,536.766,590.626,1176.598,352.448,363.184,514.34,104,267,691,456,667,489,162.415,156.561
+0.6672,917.688,836.875,1213.635,165.166,198,127,779,499,266,172,161,329,580.757,323.214,527.845,299.789,343.682,298.941,642.972,563.413,239.726,405.118,462.399,492.359,563.074,185,404.403,420.142,420.241,103.862,131.612,82.953,1171.562,799.399,1122.771,588.15,479.08,398.337,906.121,1358.807,328.748,365.732,96.69,100.899,71.565,1515.961,538.351,593.341,1176.725,353.099,365.362,516.903,107,263,690,444,646,486,165.882,159.958
+0.834,914.059,838.414,1203.028,168.563,187,127,795,495,262,170,165,336,585.023,325.576,531.391,298.163,342.744,296.591,663.599,558.696,249.968,423.106,456.528,499.031,559.504,185.5,408.179,424.271,425.854,110.055,132.093,85.815,1183.079,806.467,1135.094,589.473,475.982,400.34,942.986,1355.804,340.594,367.998,99.491,100.616,73.342,1528.492,540.119,592.676,1179.172,358.779,366.139,521.024,115,256,684,429,646,488,167.077,156.236
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+1.1676,903.943,820.438,1207.446,172.37,171,128,830,484,250,160,171,356,594.352,342.274,545.283,301.93,347.238,303.913,695.492,556.459,270.14,471.92,471.965,512.072,570.423,185,427.58,432.345,427.283,113.562,132.249,87.982,1205.764,827.091,1165.166,585.433,469.054,396.772,995.489,1323.429,371.419,385.206,99.477,99.88,73.072,1548.803,545.111,591.701,1183.5,362.957,372.446,527.043,133,237,676,406,682,484,166.323,155.67
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diff --git a/src/cosinor_lite/live_cell_dataset.py b/tests/fixtures/__init__.py
similarity index 100%
rename from src/cosinor_lite/live_cell_dataset.py
rename to tests/fixtures/__init__.py
diff --git a/tests/fixtures/cosinor_analysis_fixture.py b/tests/fixtures/cosinor_analysis_fixture.py
new file mode 100644
index 0000000..ee770ca
--- /dev/null
+++ b/tests/fixtures/cosinor_analysis_fixture.py
@@ -0,0 +1,24 @@
+"""Unit tests for cosinor analysis utilities."""
+
+from __future__ import annotations
+
+import pytest
+
+from cosinor_lite.livecell_cosinor_analysis import CosinorAnalysis
+from cosinor_lite.livecell_dataset import LiveCellDataset
+
+
+@pytest.fixture(scope="session")
+def cosinor_analysis(bioluminescence_dataset: LiveCellDataset) -> CosinorAnalysis:
+    """Return a ``CosinorAnalysis`` instance seeded from the dataset fixture."""
+    return CosinorAnalysis(
+        ids=bioluminescence_dataset.ids,
+        group=bioluminescence_dataset.group,
+        replicate=bioluminescence_dataset.replicate,
+        time_series=bioluminescence_dataset.time_series,
+        time=bioluminescence_dataset.time,
+        group1_label=bioluminescence_dataset.group1_label,
+        group2_label=bioluminescence_dataset.group2_label,
+        color_group1=bioluminescence_dataset.color_group1,
+        color_group2=bioluminescence_dataset.color_group2,
+    )
diff --git a/tests/fixtures/example_fixture.py b/tests/fixtures/example_fixture.py
deleted file mode 100644
index db837b5..0000000
--- a/tests/fixtures/example_fixture.py
+++ /dev/null
@@ -1,14 +0,0 @@
-"""Example pytest fixture."""
-
-from uuid import uuid4
-
-import pytest
-
-from tests.consts import PROJECT_DIR
-
-
-@pytest.fixture(scope="session")
-def test_session_id() -> str:
-    """Demonstrate how pytest fixtures are used."""
-    test_session_id = str(PROJECT_DIR.name) + str(uuid4())[:6]
-    return test_session_id
diff --git a/tests/fixtures/livecell_dataset_fixture.py b/tests/fixtures/livecell_dataset_fixture.py
new file mode 100644
index 0000000..7bb8734
--- /dev/null
+++ b/tests/fixtures/livecell_dataset_fixture.py
@@ -0,0 +1,38 @@
+# tests/fixtures/livecell_dataset_fixture.py
+import pandas as pd
+import pytest
+
+from cosinor_lite.livecell_dataset import LiveCellDataset
+from tests.consts import PROJECT_DIR
+
+
+@pytest.fixture(
+    scope="session",
+    params=[
+        "bioluminescence_example.csv",
+        "qpcr_example.csv",
+        "single_cell_example.csv",
+    ],
+)
+def bioluminescence_dataset(request: pytest.FixtureRequest) -> LiveCellDataset:
+    """Return a LiveCellDataset built from several example files."""
+    filename = request.param
+    file = PROJECT_DIR / "tests" / "test_data" / filename
+
+    df_data: pd.DataFrame = pd.read_csv(file, index_col=0, header=None)
+
+    participant_id: pd.Series[str] = df_data.iloc[0, :].astype(str)
+    replicate: pd.Series[int] = df_data.iloc[1, :].astype(int)
+    group: pd.Series[str] = df_data.iloc[2, :].astype(str)
+    time = df_data.index[3:].to_numpy(dtype=float)
+    time_rows = df_data.iloc[3:].apply(pd.to_numeric)
+
+    return LiveCellDataset(
+        ids=participant_id.tolist(),
+        group=group.tolist(),
+        replicate=replicate.tolist(),
+        time_series=time_rows.to_numpy(dtype=float),
+        time=time,
+        group1_label="Group 1",
+        group2_label="Group 2",
+    )
diff --git a/tests/fixtures/omics_dataset_fixture.py b/tests/fixtures/omics_dataset_fixture.py
new file mode 100644
index 0000000..df7c475
--- /dev/null
+++ b/tests/fixtures/omics_dataset_fixture.py
@@ -0,0 +1,39 @@
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from cosinor_lite.omics_dataset import OmicsDataset
+from tests.consts import PROJECT_DIR
+
+
+def _columns_for_condition(data: pd.DataFrame, suffix: str) -> list[str]:
+    """Return expression columns that contain the given condition suffix."""
+    return [column for column in data.columns if f"_{suffix}_" in column]
+
+
+def _extract_timepoints(columns: list[str]) -> np.ndarray:
+    """Extract zeitgeber time values from column names."""
+    return np.asarray([float(column.split("_")[1]) for column in columns], dtype=float)
+
+
+@pytest.fixture(scope="session")
+def omics_dataset() -> OmicsDataset:
+    """Return an ``OmicsDataset`` populated with alpha/beta expression data."""
+    file = PROJECT_DIR / "tests" / "test_data" / "GSE95156_Alpha_Beta.csv"
+    dataframe = pd.read_csv(file)
+    columns_cond1 = _columns_for_condition(dataframe, "a")
+    columns_cond2 = _columns_for_condition(dataframe, "b")
+
+    dataset = OmicsDataset(
+        df=dataframe.copy(),
+        columns_cond1=columns_cond1,
+        columns_cond2=columns_cond2,
+        t_cond1=_extract_timepoints(columns_cond1),
+        t_cond2=_extract_timepoints(columns_cond2),
+        cond1_label="Alpha",
+        cond2_label="Beta",
+    )
+
+    return dataset
diff --git a/tests/test_data/GSE95156_Alpha_Beta.csv b/tests/test_data/GSE95156_Alpha_Beta.csv
new file mode 100644
index 0000000..0472301
--- /dev/null
+++ b/tests/test_data/GSE95156_Alpha_Beta.csv
@@ -0,0 +1,101 @@
+Unnamed: 0,gene_name,ZT_0_a_1,ZT_4_a_1,ZT_8_a_1,ZT_12_a_1,ZT_16_a_1,ZT_20_a_1,ZT_0_a_2,ZT_4_a_2,ZT_8_a_2,ZT_12_a_2,ZT_16_a_2,ZT_20_a_2,ZT_0_b_1,ZT_4_b_1,ZT_8_b_1,ZT_12_b_1,ZT_16_b_1,ZT_20_b_1,ZT_0_b_2,ZT_4_b_2,ZT_8_b_2,ZT_12_b_2,ZT_16_b_2,ZT_20_b_2
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+9,ENSMUSG00000000078|Klf6,3.45052755317535,3.92419940080187,2.96958632525943,3.42643774618294,3.44393251441376,3.25190169585205,3.06007738011164,3.22679199686806,4.67318247594152,3.42688921913147,3.46655927425982,3.53742623359347,2.98828290120308,2.86490141996643,2.3816294107304,2.92315222702633,2.66317223916545,3.03294856413769,2.72770136779904,3.00926617847123,3.33950619121033,2.95448703729902,3.0395483642056,2.96392740595873
+10,ENSMUSG00000000085|Scmh1,1.57962331040779,1.85560955487359,1.93410159818831,1.5660023398482,2.41342424263999,2.57879197969704,2.12126940790305,2.23718983073347,1.78287788915067,1.86483901018183,2.33725017374482,1.93506118091017,1.61870220495618,1.68409197169127,1.51979037716061,1.51412855012253,2.31759916822578,1.94265664737031,2.31897726573636,1.64618510383373,1.68406226442009,1.57336549916379,2.13587612326563,1.84227409720804
+11,ENSMUSG00000000088|Cox5a,2.86412798280212,2.17099315916554,2.65628458196535,2.73995423852567,2.38567319865963,2.36727736819482,2.39163994065855,1.77290940090742,2.78487786074305,2.59828484816663,2.29428075932595,2.66423517467467,4.42667872093675,3.74680990225155,4.19677684022746,3.74012882663465,4.09175776010394,4.17106273033875,3.878060077512,4.17229712400695,3.99796100833238,3.5894100323968,3.90264529780137,3.628166802118
+17,ENSMUSG00000000126|Wnt9a,-1.07445246263388,-1.12522600957192,-3.05760839064152,-2.45473165692201,-1.59825916083339,-1.514850314138,-1.61987677395046,-0.85010562613462,-1.17435221636264,-1.13366483216283,-2.05342685669244,-1.04041491739353,-2.68227031767142,-1.66772226042967,-1.93726088597099,-2.56792435442991,-2.30957918008361,-3.46635804867177,-2.63947684499869,-2.16436784883777,-3.08696943604839,-2.52592835590483,-3.50137829685505,-2.68282420468274
+18,ENSMUSG00000000127|Fer,2.5783470558356,2.54409991614559,2.56227689437104,2.66749846632705,2.78836480118853,3.00838276685555,2.82848354729497,2.52720998783662,2.62346304976729,2.59465818688886,2.9119462124926,2.59746713690314,3.10060375149211,3.05776608910531,3.0861309517684,3.16405542822872,2.88539465825404,3.03906014893335,3.11476863315555,2.97870965987139,3.01609042292402,3.05931497160821,3.05237391652703,2.92180046321939
+19,ENSMUSG00000000131|Xpo6,2.53862844930509,2.81089832929782,2.39113849560221,2.73962660684193,3.15639805253471,3.2294066911843,3.06773675995538,2.43562644290909,2.60245642936183,2.53515706956956,3.10603818794571,2.82838441761589,2.30416230061411,2.55308166760272,2.39701225248966,2.57753819011324,2.99891912003699,2.71531792957745,3.09286630380301,2.21422711299259,2.19230810488467,2.35032774941965,2.94269156295404,2.79380151958307
+20,ENSMUSG00000000134|Tfe3,2.23691992665498,2.26134725038941,2.20772786875758,2.25693643503227,2.53009306073133,2.60087783222744,2.39349837807574,2.39613957075211,2.29120897394093,2.24923921156251,2.43521235908308,2.27937065318909,1.79148621622192,1.86985901192119,1.56351165217702,1.82629924985702,1.99133846571604,1.89353555059168,2.16455699698237,1.68550754346153,1.90574357145025,1.8237602872103,1.93729828646817,1.88923213878654
+21,ENSMUSG00000000142|Axin2,1.96136344552499,2.07766391583331,1.98399671832236,1.83661016902198,1.9274650484208,2.06238721071578,2.01892739594965,2.35229858261109,2.19982478320383,2.06955625754926,2.07609715931281,2.01723823745374,0.787335612978814,1.43848991223447,0.841937144560321,1.15250587737013,1.1703128699124,1.08786252992825,1.23465960089924,1.34677666128258,0.847110029942101,0.793567571998347,1.36882035016422,1.00262079134518
+22,ENSMUSG00000000148|Baat1,1.89268705925366,2.14180097692119,2.05892525984495,2.06073517130217,2.06208591039638,1.97305529060852,2.03919250309341,1.92771671836064,1.98395139952956,2.05644269559712,1.86413234132662,2.16875759553323,1.59489858232944,1.9512420814664,1.70495319636702,2.14554556054997,1.97143874078467,1.86945250360989,1.90025647636098,2.01449927286771,2.03006634610123,2.15463374237028,1.84856791805016,2.07872575234159
+23,ENSMUSG00000000149|Gna12,4.31685197439924,4.0722930211003,4.36209257950553,4.17222958552504,4.05227591633601,4.19422270313622,3.93050240502269,4.1821976205975,4.32268359551859,4.06497302177396,4.12843653601751,4.0049896168959,3.76945505249837,3.79995396899633,3.95901466165163,3.87235564874572,4.10256752120301,4.0869577645581,3.87333880669834,3.87365985719779,3.81091809327304,3.92459471499942,4.01242725875374,3.86610776737802
+26,ENSMUSG00000000159|Igsf5,0.531910439631897,0.637834534796141,0.726099187720544,0.576303600880935,1.17749580531095,1.15395448529125,0.956265504355142,0.247087204646029,0.624766288148233,0.820647804194528,1.17548811065175,0.965646496734435,1.8152374959766,1.33860632557959,1.63730364174945,1.46150927689056,1.93525524877289,1.78867114165518,1.99814989514604,1.81339891585337,1.70719127508097,1.6142364157938,1.50328201012016,1.63859559813395
+27,ENSMUSG00000000167|Pih1d2,0.488075974456279,0.663459521109446,0.577142096909311,0.412226261018731,0.439249461012919,0.390640053269273,0.670009204680771,0.196531198829268,0.576876843213114,0.690687004247133,0.311892106543746,0.416471046225547,0.992114668023578,1.23929374866542,0.861167504281203,0.888581622325146,0.703959099548835,0.945470077506158,0.815266660178901,0.834796814843115,1.11906498327401,1.07973819461052,0.790231147898943,0.934455582051924
+28,ENSMUSG00000000168|Dlat,3.8421603289725,3.57988770473422,3.87563173597222,3.684829306016,3.25966592160009,3.44663550495193,3.33504540319275,3.86589542527787,3.79661960480359,3.55069835450955,3.39229892102821,3.60747692163857,4.07769936701538,3.76781534296572,3.94539546155654,3.9097837626277,3.47109456395668,3.79165463715172,3.55073230927102,3.91787438479611,3.88851813812012,3.90955189949127,3.55252363021511,3.68414482414596
+29,ENSMUSG00000000171|Sdhd,6.50485074501025,5.98009531022001,6.61917463037837,6.37617513132435,5.97585373416307,6.01039039390759,6.07269415637311,6.36047648787428,6.40563341021582,6.52900852140011,6.1082667243001,6.30848015879321,6.69535057708853,6.16783684007409,6.55546589873215,6.21814326089532,6.21317138013679,6.50782650781597,5.96225203442394,6.67012292367864,6.48829976938199,6.33778474115713,6.13608323781498,6.24426449027007
+32,ENSMUSG00000000184|Ccnd2,8.03333306982066,7.37094597239797,7.36831488991027,7.19007903703072,8.05736163026849,8.17844947668328,7.56813307892259,7.88102102432574,7.45553804582145,7.1887273440116,7.92676650787817,7.82243588084652,7.94343740644314,7.73492481835371,7.93130158104977,7.67402354442793,8.07436479441417,8.05135473985728,7.83607900698722,7.93524608321815,7.83932856816766,7.90623157277275,8.11357756667067,7.87255687064515
+33,ENSMUSG00000000194|Gpr107,2.87020329245912,2.71835837455958,2.74820779675855,2.72478468276757,2.94805575418808,2.88745020986727,2.94223239479963,2.83529355488001,2.85958364893243,2.65558561171121,2.94616871336367,2.79773764884554,2.82605017107096,2.94254817285187,2.74335618282622,2.75554198331486,2.97921549580747,2.76967650035694,2.90799611808394,2.832742012175,2.8103280538858,2.83964375639611,2.86607639411755,2.83938361927746
+34,ENSMUSG00000000197|Nalcn,4.06985259816499,4.12775178004953,4.05678504970849,3.95859094262613,3.98280080453338,3.98366472179812,4.01636311068752,4.13941797830698,4.25828851260849,4.06902103360186,3.93967687183428,4.04334112214246,3.74274562286731,3.69950628791843,3.89071183665768,3.6363652711876,3.5222545399765,3.54993119274229,3.46869131769733,3.70877955714089,3.52711776791052,3.65031742962404,3.49434068024645,3.56451407564774
+35,ENSMUSG00000000202|Btbd17,2.14930009485253,2.22794474490532,2.56590101246647,2.19121591153401,2.40973575408415,2.50161513260947,2.44209012272214,2.3261535187685,2.2035189593061,2.22816400887918,2.48233425501343,2.25709330770677,3.21408719451722,3.18647107573551,3.38734428673799,2.87033835586227,3.12772327881706,3.07989227697727,3.10218140090971,3.0735202363475,3.18001617648586,2.99476233233904,3.05712635464912,2.95417383685538
+37,ENSMUSG00000000214|Th,-0.737677858742417,0.360543789496933,-1.08010386135554,-0.808257336560244,0.0014189435512563,-0.213920890940867,-1.12601793898773,-0.676022428642562,-1.36964214343902,-1.31465453863324,-0.168404857947126,-0.904901918098516,4.47821975836014,4.25780853441154,4.1780832781334,4.35118341904784,4.63554986324527,4.59448173519772,4.87032898707162,3.96909482559072,4.0992598796511,4.42292456184233,4.75395994793853,4.42922234790169
+38,ENSMUSG00000000215|Ins2,9.53498577729123,11.8313443618587,9.35471847346014,9.66258803425741,11.2988628275883,9.61082425389079,9.86349716275945,9.81027221558471,9.10004852255162,8.1432515125213,9.9411973393443,10.2447410345874,17.6652650557318,17.2898204209166,17.7428753846158,17.3288936436212,16.9875018848449,17.5899447284651,16.9549401619341,17.8704672189037,17.6089103444224,17.4368496510221,16.9991874940784,17.071348027902
+39,ENSMUSG00000000216|Scnn1g,-0.224901688227111,0.0627343071883959,0.0366212317411356,-0.405687269916003,0.0470885110361814,-0.422107202470325,-0.237448539813626,0.335559275703055,0.148654242912187,0.0181044790960394,-0.568575423643255,0.106623722628726,1.90635622981002,2.59823286733592,2.92084184361292,2.94669033939085,2.052084914132,1.694493363485,1.73138071667012,2.51310036172042,2.94716266444733,3.09176670847704,2.1101328684597,2.13618710272165
+44,ENSMUSG00000000253|Gmpr,4.86438014748725,4.80251074610717,5.18418083991172,4.91162746475035,4.44753484269072,4.39260072665192,4.52264848472204,5.32147244841622,5.11179127857755,4.89694368520287,4.63088805260137,4.46772225818755,5.84230358834843,5.87844183104832,6.37567881354449,6.12723200572936,5.62799040357004,5.80119034034362,5.67741926219813,6.31998504126554,6.06322422570091,6.16952414668312,5.71042732690614,5.51723959261591
+46,ENSMUSG00000000266|Mid2,2.34260733991893,2.45040405534336,2.57356895112769,2.44749731023773,2.30240503049185,2.31299396960339,1.9059482355495,2.68030808454191,2.36287807318296,2.31847205420355,2.11084511844703,2.26881855928785,3.3003502996364,3.35133932411031,3.44973710446816,3.28993132552909,3.10399332707838,3.21826763276939,2.98459311371196,3.47633466475986,3.46215325935519,3.17014788242161,2.84860792169826,3.003661239436
+47,ENSMUSG00000000275|Trim25,3.38340505517817,3.60767100450468,3.75854471942133,3.2457743787337,3.53512952224855,3.4274723985848,3.15249516585913,3.9157274582564,3.39130002080326,3.35238011763182,3.35443870936466,3.34546007360848,3.41904974930284,3.82642617570451,3.84345362170865,3.58724878767259,3.68903489292739,3.66722470219263,3.69614827164162,3.9684393961038,3.52742856100812,3.64702987158856,3.74040094402897,3.66643879135975
+48,ENSMUSG00000000276|Dgke,2.4383038095857,2.45179292144365,2.70156950189099,2.16875135919748,2.09389608240597,2.42773790113019,2.13791761907578,2.24210450771775,2.31330927182629,2.19294771282911,1.87833914559868,2.01056836806698,3.34502839698466,3.64504658481448,3.52061165436538,3.26656426437729,2.9967658670453,3.11936551706921,3.20029666279083,3.51761477791758,3.51757552844963,3.329184135425,2.87909421393606,3.20540320760405
+49,ENSMUSG00000000278|Scpep1,4.07299574620218,4.12584802559617,4.46162804064246,4.13550255997706,3.68954980395395,3.70518393897264,3.73376610679503,4.3472270128815,4.18495495390461,4.17801118626592,3.66030035089433,3.86942345708954,5.31293615383676,5.02476382944532,5.19498282633408,5.06257531979466,4.87238826114201,5.30582769462638,4.85487407031805,5.498401799582,5.0632263478527,5.01774233111886,4.87002741243199,5.11957975846339
+50,ENSMUSG00000000282|Mnt,2.2200787546497,2.60531295378026,2.36881808869937,2.44060024770943,2.84009532542508,2.68194929327307,2.74648825265108,3.06925959297619,2.892826551257,2.32882418897683,3.00555388292988,2.70572441291183,2.27132560935384,2.39687710794743,2.35150884738953,2.35499210586512,3.10040120503558,2.85842432319347,3.03822697946273,2.17605881789419,2.36621732690228,2.39572751559873,3.05481167130747,2.82532633808552
+52,ENSMUSG00000000295|Hddc2,-0.705677279422106,-0.437370067781449,0.327074481025879,-0.439248232557568,-1.01786630454343,-0.591153119642253,-0.718027632973893,-0.0819143352007092,-0.348775337136391,-0.0427019390545849,-0.998383307751489,-0.59043433865013,0.950968859802968,1.01980660627383,1.35323122873359,0.603112531458655,-0.0702227697191595,0.538747694998539,0.357884470872713,1.18472420305265,1.40071446068535,1.21579218573848,0.213884169317731,1.16327298573775
+53,ENSMUSG00000000296|Tpd52l1,1.72555368650394,1.9770590954454,3.04489314405024,2.17562503826881,1.24342389776969,1.46334221130699,1.76766491874837,1.83124001886432,2.0905574643425,2.30032141516512,0.731408653089597,1.66627414614176,2.99942339169629,2.88974214001557,3.60551950423814,2.99470233940207,2.62674134394301,2.88524737289552,2.50942507128031,2.92473325766142,3.24659855467247,3.28709699092832,1.91115134366721,2.57220731311591
+54,ENSMUSG00000000301|Pemt,-0.0234903792219094,0.0295147249995635,0.220935559799394,-0.0077160476414681,-0.357313596341573,-0.350590932783309,-0.581294566467501,-0.421096998922594,0.547709249503465,0.226658998652542,-0.767636832575248,0.352570044649819,0.564242131069295,-0.0092146687005076,0.360042200552573,0.18721899328239,-0.651746853476118,0.442504099290194,0.289874081044081,0.425195215975816,0.337941259849557,-0.283840813251859,-0.159509410949567,0.022904572004536
+55,ENSMUSG00000000303|Cdh1,4.90563014520806,5.03483779039472,5.04535487924895,4.58627850943662,5.07948665297514,5.14246740235959,4.76007763999096,5.40748060837022,4.96036322333107,4.66003775215667,4.9121967754976,4.83917414174812,6.36021739954426,6.51121017109428,6.75415740350216,6.24570053534056,6.49427344091117,6.5951226268458,6.56336659275913,6.70101722824036,6.21189323478049,6.27837204342654,6.49149481077394,6.36553851991091
+56,ENSMUSG00000000305|Cdh4,-0.416265237872681,0.317924565358003,0.364247470658537,-0.561323087962475,0.254624894330564,0.505417856999781,-0.0900294469149663,0.163850186669207,0.0406321240212337,-0.0815109515537049,-0.115842844914742,0.10578836891061,1.05511559618909,2.15753612821361,1.73852800738966,1.59447561005163,1.57397401672848,1.70448172447725,1.66476803446779,0.901219749135597,1.63251648963663,1.22487083778951,1.80241917564783,2.17769797974471
+57,ENSMUSG00000000308|Ckmt1,3.11598438935244,2.53698714506095,3.16323126114605,2.83871050761754,2.4636171111008,2.66377160231801,2.52138514879681,2.90098478415011,2.99950187167958,2.7687506500237,2.28551013941071,2.51385811408886,3.56666827943282,3.25899752070715,3.63173285218232,3.47888233256168,2.93442727669791,3.17038105279374,3.0822999802173,3.55026836107239,3.39852379232033,3.53109486524048,2.83271697362508,2.77077279655441
+58,ENSMUSG00000000317|Bcl6b,2.12816044841067,1.70076239658285,1.44828173493159,1.65951664599185,2.31245418747117,2.15759686343792,2.23276941840297,1.5916559391197,1.78751949325324,1.34584063753256,2.03985113013036,2.0174231158378,2.23246651428502,1.51415304387441,2.31502689867195,1.99918124855406,2.43635835832253,2.51777566031082,2.15918969868789,1.65392561902325,2.62049071172326,1.76675795028878,2.54845257479191,2.0207463419693
+61,ENSMUSG00000000325|Arvcf,2.97439793973105,3.27036339409972,3.06783180859699,3.1271152921968,3.23885211134973,3.28411534104207,3.41840515059227,2.95195354403197,3.06840474567289,3.06870117048128,3.26924542627604,3.2599898540356,1.46495336195934,1.91366385421083,1.32757110870846,1.71666494243304,1.9969967321028,1.70213665291719,2.11790410868835,1.39825406495277,1.46449095900774,1.76159868020795,1.98096893841649,1.96806826742971
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+63,ENSMUSG00000000339|Rtcd1,1.37866856637044,1.16739496847792,1.73146459043424,1.55597312050017,1.37503602678205,1.5298955495579,1.53293244497684,1.27754337988505,1.57146749286377,1.46434148748605,1.37077960551951,1.51164230966185,1.59855841405052,1.5675434653896,1.6667738328707,1.61223181158957,1.45941207946935,1.60818555751548,1.54512619488574,1.24793973593256,1.43935269888081,1.64249233059542,1.42071275991843,1.27527930908556
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+66,ENSMUSG00000000355|Mcts1,3.48275207430104,3.1691194559159,3.35733581901992,3.29130150055005,3.51991176562987,3.15290964970629,3.49586317846141,3.19845441409635,3.43579087421792,3.54471053161232,3.33138021910481,3.55991650523129,3.56221567255587,3.44961666184843,3.01775551090718,3.14422532383198,3.42614903629971,3.42059735060615,3.15643225137055,3.22239058938448,3.40656005967849,3.00998951760657,3.36553124901627,3.48371028015838
+68,ENSMUSG00000000365|Rnf17,-1.73195578782186,-0.833641302711147,-1.03937799883968,-1.5088236949858,-1.26472843234838,-1.23407148767921,-2.07681056887002,-1.18740513757154,-1.31177567046159,-1.98304511426662,-1.57991555567756,-1.95799437960914,-2.83741595451427,-1.69962638385251,-3.17819526198749,-2.50000888370617,-3.76827666520307,-4.75400304608708,-3.01968729027104,-2.44871484746795,-2.93305262514974,-3.18343168109281,-3.41384008239741,-2.6402022981942
+69,ENSMUSG00000000374|Trappc10,4.07222885217912,3.93067248188072,3.89907149741956,3.96976097330632,4.05327026526907,4.08026937627148,4.11038068779701,3.90190662008546,3.96258789052179,3.83408433459974,3.95979503777,3.89386223509016,4.00202689633661,4.46995731135851,4.2792544283492,4.51603220777308,4.63617518063567,4.3334734079852,4.69855183729673,4.13071565900884,4.02400263603252,4.45245096094294,4.74354922932204,4.6123230704432
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+72,ENSMUSG00000000384|Tbrg4,1.91404903325457,2.15222798315405,1.75108899361342,1.99486759805679,2.2239777426706,2.21786305054456,2.33905976372085,1.52239289320978,1.88738407239539,2.01375958007903,2.41711739390817,2.07084407737054,1.78913007458511,1.78016730948512,1.61544435871077,1.80383481873893,1.99183483070664,1.80502043437483,2.43819527292389,1.20589446634709,1.71693753628481,2.13627291153801,2.11184466042953,2.1223447675788
+73,ENSMUSG00000000385|Tmprss2,-1.89916982769554,-1.43167278046048,-2.38429072147743,-2.03222234623788,-0.792840927126234,-0.324109412283786,-0.869246005612903,-2.27314433792082,-2.22702417445121,-3.04199861974381,-2.14300310406243,-1.32313981966786,2.39475796932334,1.82485345009335,1.98794766903398,1.77782589271112,2.63019411737576,2.47542162918351,2.60472915962531,1.63918533696512,1.92854714206641,1.79075280580697,2.46847710685281,2.42175130646346
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+77,ENSMUSG00000000399|Ndufa9,5.25795223930864,4.82862356043443,4.9740375738648,5.15910378916182,4.83903574202863,4.85761935151245,4.79519238487083,5.13217370934737,5.22169382610957,5.045249519733,4.73682204172723,4.87924929279851,5.35178864569931,4.90585258432075,5.31756057185148,5.12997342012164,4.89031268727062,5.24335146997145,4.55102309175414,5.35831212683903,5.20023226782436,5.07850579700026,4.94546562026263,4.92019829887794
+79,ENSMUSG00000000409|Lck,1.10725388164219,1.38525440641549,1.33278963798912,1.3641023576248,1.91598767734505,1.29376531589567,1.48370228475709,1.21579904287159,1.17133804035654,1.60113495276151,1.64051440409482,1.62033290559596,-2.22465476798745,-2.93047462346776,-3.91181904363821,-3.91181904363821,-1.61279702011673,-2.5095602436748,-1.94501183500968,-3.26728215262629,-3.91181904363821,-2.92259891487598,-2.30660973172708,-2.49814352742185
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+82,ENSMUSG00000000420|Galnt1,3.87750286946204,3.72325443711448,4.02331702580475,3.67006709316167,3.71867177874383,3.80209236477073,3.61488900231656,3.80967869604117,3.8955599683388,3.72876021432439,3.65259065783797,3.75574485224449,4.15828216256881,4.00360856062167,4.27015350172661,4.13077290271345,3.79169834232143,4.16505939132087,3.76057415721957,4.29322160306929,4.06978136480591,3.98746278372266,3.77174195265905,3.80452285203737
+84,ENSMUSG00000000439|Mkrn2,3.29903172223036,2.9734343497657,3.545619131425,3.21115289007277,3.13956085699458,3.1715917977322,3.04204438168817,3.15326423657932,3.48512733513419,3.27840252049183,3.09933348076909,3.13548770697058,3.63591782632037,3.48221719522824,3.81014940067018,3.7681236573219,3.48553987772629,3.6931312806667,3.60490082241252,3.81068317866928,3.66888350813287,3.80231143548564,3.58728306295374,3.64618645460716
+85,ENSMUSG00000000440|Pparg,-2.84493663103183,-2.41451068308577,-2.73881668319464,-2.16567709458471,-2.40122806358238,-2.76618470014132,-3.34982287614572,-3.34982287614572,-3.34982287614572,-3.34982287614572,-2.43007986977689,-2.80650490565029,2.77589366967234,1.95341073372888,2.76590359996003,2.46507538152061,2.86979599400108,2.7797144119445,2.27339267490167,2.85294009676482,2.31265382111757,2.27282843132524,2.92797516235035,2.95788009279252
+86,ENSMUSG00000000441|Raf1,3.35198049866846,3.63021290693732,3.17801694493349,3.58005899851263,3.83240208943393,3.69419517122546,3.83451462703715,3.24558782949338,3.48377596724705,3.54508167469046,3.91316237404067,3.66971637864304,2.96460207408516,3.25004256327007,2.94164416915235,3.23425971024014,3.48292622049342,3.01124833173954,3.43397489713944,2.84132491046563,3.09145671881871,3.16138738124127,3.4133373883136,3.40556397345392
+88,ENSMUSG00000000489|Pdgfb,-2.09324830209404,-2.19236022938054,-2.04379244354486,-2.08077756572716,-1.53205685330578,-1.10244367729394,-1.42976778672808,-1.96905789840563,-3.40943405268001,-2.34965727846081,-1.86331786947006,-2.00184049608144,-0.123318697559025,-0.852014071442162,-1.01141263605316,-0.709725717345249,0.154085763322741,-0.465583943554023,0.0519975636731065,-0.582761487516509,-1.3055792217025,-0.603924779467522,-0.792191012486044,-0.124140893741863
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+93,ENSMUSG00000000544|Gpa33,1.02406331736955,0.728801299081272,0.578307769781276,0.908325249595576,0.837554929662087,0.794256321880987,-0.1120492004735,0.984762864623332,0.438428635496799,0.949703637665294,0.418748017668069,0.584041403320486,1.43844100841801,1.25543191321719,1.10079699860328,2.12165246418197,1.70987596142302,1.77143532625711,1.78944560595694,1.04937191424159,1.31033590051985,1.76208202123058,1.65588006781304,1.30593905934642
+94,ENSMUSG00000000552|Zfp385a,-1.06961229018149,-2.31344106857459,-0.319031744426201,-1.28929400251373,-0.402783815445934,-0.560867257610191,-0.880195831846457,-0.462040263512145,-1.76353369600901,-2.0006548538476,-0.614716726066206,-1.0788816769672,-2.14315890583583,-1.99735391076224,-2.24369684057438,-2.85398985714512,-1.25836628554204,-1.88343943236786,-1.21465468267243,-2.4689245476805,-2.5478580242128,-1.76545797508154,-1.95217899715238,-2.14371279284716
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+98,ENSMUSG00000000562|Adora3,-3.1922365156275,-2.45317552025246,-3.08611656779031,-3.18707631404326,-2.74852794817806,-3.11348458473699,-1.89368588480481,-1.9493883111565,-2.83123011925237,-3.69712276074139,-2.77737975437257,-2.45104258275866,0.19783514832907,-0.408950369971349,-1.12361276153683,-1.44312859836048,0.523639184674583,-0.33098403199574,1.15720201038213,0.0460930438660498,-0.773752800691223,-1.25537310822813,-0.14590280489536,0.397686469014347
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+102,ENSMUSG00000000579|Dynlt1c,-0.19778630896144,0.149413628112235,-0.45255050465541,0.0185563580040324,-0.422175514202534,0.109878773777373,0.0500334906686064,-0.27027503267054,-0.373499289533847,-0.148882775092591,-0.186781825291686,-0.312013645170805,-1.8349223544367,-0.920270634287593,-1.04302409797874,-1.6872861953253,-1.21647362085321,-0.991257424198387,-1.28196469330951,-0.535435829591574,-1.09182718581439,-1.39049765779,-1.55256224162971,-1.34194746202227
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+106,ENSMUSG00000000605|Clcn4-2,4.04696788492332,3.98774018692977,3.72442948098902,3.87592114856395,4.10486593621599,3.80822878390063,4.05627251090746,3.74115551652904,3.9524105649929,3.93078759451083,3.958477400389,3.92912447208946,4.50304284020256,4.34595414159997,4.66289826364813,4.4831646413066,4.4489047976456,4.46446231797204,4.39097850441652,4.65176724339473,4.41970619946552,4.45457989705896,4.50093315137013,4.50512087724321
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+120,ENSMUSG00000000708|Kat2b,3.62301342327287,3.81318302898246,3.36967602158673,3.7185204072777,3.85437498575588,3.72683719494952,3.85201172169755,3.69543660226851,3.85509586130105,3.69231464941114,3.84373646100644,3.84552617284871,4.42341678922159,4.52695843504437,4.27946340820969,4.37931866600839,4.05305999381938,4.18948208293908,4.32139684950354,4.56301206004627,4.52688418508959,4.47007439883258,4.07897181888708,4.21248488109475
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+128,ENSMUSG00000000740|Rpl13,5.75189233706554,5.15063390950399,5.96618135854788,5.67828209641101,5.33362143168711,5.46277805053073,5.42666867026901,5.74901097421949,5.71465661326477,5.67962795315579,5.45892029058988,5.5241268124995,4.86102048146791,4.55215161780652,4.72414533899211,4.61264077475506,4.24649976743149,4.74523226264062,4.24798792927734,4.75337605379078,4.75455737962983,4.37749913285239,4.22273277484458,4.30875291898651
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diff --git a/tests/test_data/bioluminescence_example.csv b/tests/test_data/bioluminescence_example.csv
new file mode 100644
index 0000000..6104417
--- /dev/null
+++ b/tests/test_data/bioluminescence_example.csv
@@ -0,0 +1,583 @@
+Participant ID,1,1,1,2,3,3,4,4,5,5,6,6,7,7,7,8,8,8,9,9,10,10,11,11,11,12,12,12,12,13,13,13,14,14,14,15,15,15,16,16,17,17,18,18,18,19,19,19,19,20,20,20,21,21,22,22,23,23,24,24
+Replicate,1,2,3,1,1,2,1,2,1,2,1,2,1,2,3,1,2,3,1,2,1,2,1,2,3,1,2,3,4,1,2,3,1,2,3,1,2,3,1,2,1,2,1,2,3,1,2,3,4,1,2,3,1,2,1,2,1,2,1,2
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diff --git a/tests/test_data/qpcr_example.csv b/tests/test_data/qpcr_example.csv
new file mode 100644
index 0000000..5c5f5ce
--- /dev/null
+++ b/tests/test_data/qpcr_example.csv
@@ -0,0 +1,10 @@
+Participant ID,1,1,2,2,3,3,4,5,5,6,6,7,7,8,8,9,10,11
+Replicate,1,2,1,2,1,2,1,1,2,1,2,1,2,1,2,1,1,1
+Group,Group 1,Group 1,Group 1,Group 1,Group 1,Group 1,Group 1,Group 1,Group 1,Group 2,Group 2,Group 2,Group 2,Group 2,Group 2,Group 2,Group 1,Group 2
+12,0.1521,0.1157,0.2137,0.2373,,0.1287,0.2434,0.3844,0.5509,0.1456,0.1192,0.2279,0.2544,0.1478,0.165,0.2433,0.0757,0.169
+16,0.2682,0.235,0.522,0.5362,0.2034,0.1663,0.5834,0.7298,0.5026,0.1357,0.1508,0.357,0.3393,0.2616,0.2432,0.2816,0.1228,0.2285
+20,0.2328,0.2364,0.7227,0.6,0.2074,0.1887,0.6815,0.6507,0.7113,0.1493,0.1641,0.4117,,0.3647,0.2158,0.2677,0.1582,0.2619
+24,0.1691,0.1394,0.4334,0.4078,0.1752,0.2141,0.4122,0.5008,0.5088,0.1237,0.1432,0.3894,,0.2779,0.2997,0.2772,0.1534,0.3343
+28,0.078,0.065,0.1708,0.1662,0.1165,0.145,0.1788,0.2429,0.2548,0.0888,0.0764,0.1327,0.1392,0.1387,0.103,0.1846,0.0791,
+32,0.0546,0.062,0.1118,0.111,0.1157,0.088,0.1201,0.1236,0.115,0.0656,0.0867,0.1163,0.1133,0.0948,0.1096,0.1236,0.0582,0.1021
+36 ,0.0784,0.0719,0.1551,0.1334,0.1091,0.1146,0.1724,0.1765,0.2084,0.1214,,0.1048,0.1442,0.2728,0.2212,0.2097,0.072,0.1603
diff --git a/tests/test_data/single_cell_example.csv b/tests/test_data/single_cell_example.csv
new file mode 100644
index 0000000..d28599a
--- /dev/null
+++ b/tests/test_data/single_cell_example.csv
@@ -0,0 +1,220 @@
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+Replicate,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
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diff --git a/tests/unit_tests/test_data_loading.py b/tests/unit_tests/test_data_loading.py
deleted file mode 100644
index fc66ff7..0000000
--- a/tests/unit_tests/test_data_loading.py
+++ /dev/null
@@ -1,23 +0,0 @@
-# %%
-from pathlib import Path
-
-import pandas as pd
-from pandas import DataFrame
-
-DATA_DIR = Path(__file__).parent.parent / "data"
-sample_csv = DATA_DIR / "Bioluminescence_test_data_1.csv"
-
-
-def test_sample_csv_exists() -> None:
-    if not sample_csv.exists():
-        raise FileNotFoundError(sample_csv)
-
-
-def test_bioluminescence_csv_can_be_loaded() -> None:
-    df_bioluminescence: DataFrame = pd.read_csv(sample_csv, header=None)
-    if df_bioluminescence.empty:
-        msg = "Loaded DataFrame is empty"
-        raise ValueError(msg)
-    if df_bioluminescence.shape[1] <= 0:
-        msg = "Loaded DataFrame has no columns"
-        raise ValueError(msg)
diff --git a/tests/unit_tests/test_livecell_cosinor_analysis.py b/tests/unit_tests/test_livecell_cosinor_analysis.py
new file mode 100644
index 0000000..91b56a9
--- /dev/null
+++ b/tests/unit_tests/test_livecell_cosinor_analysis.py
@@ -0,0 +1,113 @@
+"""Unit tests for cosinor analysis."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pytest
+
+from cosinor_lite.livecell_cosinor_analysis import (
+    CosinorAnalysis,
+    _sanitize_xy,
+    constant_model,
+    cosine_model_24,
+    cosine_model_damped,
+    cosine_model_free_period,
+)
+
+MIN_VALID_POINTS = 4
+PERIOD_LOWER_BOUND = 20.0
+PERIOD_UPPER_BOUND = 28.0
+
+
+def test_constant_model_returns_mesor() -> None:
+    x = np.linspace(0.0, 1.0, 5, dtype=np.float64)
+    mesor = 1.23
+    result = constant_model(x, mesor)
+    np.testing.assert_allclose(result, np.full_like(x, mesor))
+
+
+def test_cosine_model_implementations_match_formulas() -> None:
+    x = np.linspace(0.0, 24.0, 5, dtype=np.float64)
+    amplitude = 2.5
+    acrophase = 3.0
+    mesor = 1.1
+    period = 26.0
+    damp = 0.02
+
+    direct_24 = amplitude * np.cos(2 * np.pi * (x - acrophase) / 24.0) + mesor
+    direct_free = amplitude * np.cos(2 * np.pi * (x - acrophase) / period) + mesor
+    direct_damped = amplitude * np.exp(-damp * x) * np.cos(2 * np.pi * (x - acrophase) / period) + mesor
+
+    np.testing.assert_allclose(cosine_model_24(x, amplitude, acrophase, mesor), direct_24)
+    np.testing.assert_allclose(cosine_model_free_period(x, amplitude, acrophase, period, mesor), direct_free)
+    np.testing.assert_allclose(
+        cosine_model_damped(x, amplitude, damp, acrophase, period, mesor),
+        direct_damped,
+    )
+
+
+def test_sanitize_xy_enforces_minimum_points() -> None:
+    x = np.array([0.0, 1.0, np.nan], dtype=np.float64)
+    y = np.array([np.nan, 2.0, 3.0], dtype=np.float64)
+    with pytest.raises(ValueError, match="Not enough valid points"):
+        _sanitize_xy(x, y, min_points=3)
+
+
+def _first_valid_trace(analysis: CosinorAnalysis) -> tuple[np.ndarray, np.ndarray]:
+    for column in analysis.time_series.T:
+        mask = np.isfinite(column)
+        if np.count_nonzero(mask) >= MIN_VALID_POINTS:
+            return analysis.time[mask], column[mask]
+    msg = "No trace has sufficient finite observations for cosinor fitting."
+    pytest.skip(msg)
+    raise AssertionError
+
+
+def test_fit_cosinor_24_returns_expected_structure(cosinor_analysis: CosinorAnalysis) -> None:
+    x, y = _first_valid_trace(cosinor_analysis)
+    results, t_test, y_test = cosinor_analysis.fit_cosinor_24(x, y)
+
+    expected_keys = {"mesor", "amplitude", "acrophase", "p-val osc", "r2", "r2_adj"}
+    assert expected_keys.issubset(results.keys())  # noqa: S101
+    assert t_test.shape == y_test.shape  # noqa: S101
+    assert t_test.ndim == 1  # noqa: S101
+    assert np.all(np.isfinite(y_test))  # noqa: S101
+
+
+def test_fit_cosinor_free_period_period_within_bounds(cosinor_analysis: CosinorAnalysis) -> None:
+    x, y = _first_valid_trace(cosinor_analysis)
+    results, _, _ = cosinor_analysis.fit_cosinor_free_period(x, y)
+    period = results["period"]
+    assert PERIOD_LOWER_BOUND <= period <= PERIOD_UPPER_BOUND  # noqa: S101
+
+
+def test_fit_cosinor_damped_returns_non_negative_damp(cosinor_analysis: CosinorAnalysis) -> None:
+    x, y = _first_valid_trace(cosinor_analysis)
+    results, _, _ = cosinor_analysis.fit_cosinor_damped(x, y)
+    damp = results["damp"]
+    assert damp >= 0.0  # noqa: S101
+
+
+def test_get_cosinor_fits_invalid_method_raises(cosinor_analysis: CosinorAnalysis) -> None:
+    x, y = _first_valid_trace(cosinor_analysis)
+    with pytest.raises(ValueError, match="Unknown cosine model"):
+        cosinor_analysis.get_cosinor_fits(x, y, method="invalid")
+
+
+def test_fit_cosinor_exports_results(cosinor_analysis: CosinorAnalysis) -> None:
+    df_export, csv_path, fig, pdf_path = cosinor_analysis.fit_cosinor(
+        group="group1",
+        cosinor_model="cosinor_24",
+    )
+
+    try:
+        assert not df_export.empty  # noqa: S101
+        assert Path(csv_path).is_file()  # noqa: S101
+        assert Path(pdf_path).is_file()  # noqa: S101
+    finally:
+        Path(csv_path).unlink(missing_ok=True)
+        Path(pdf_path).unlink(missing_ok=True)
+        plt.close(fig)
diff --git a/tests/unit_tests/test_livecell_dataset.py b/tests/unit_tests/test_livecell_dataset.py
new file mode 100644
index 0000000..f5f5191
--- /dev/null
+++ b/tests/unit_tests/test_livecell_dataset.py
@@ -0,0 +1,144 @@
+import numpy as np
+import pytest
+
+from cosinor_lite.livecell_dataset import LiveCellDataset
+
+
+def test_to_2d_f64_valid(bioluminescence_dataset: LiveCellDataset) -> None:
+    arr = bioluminescence_dataset.time_series
+    result = LiveCellDataset._to_2d_f64(arr)  # noqa: SLF001
+    assert result.shape == arr.shape  # noqa: S101
+    assert result.dtype == np.float64  # noqa: S101
+
+
+def test_to_2d_f64_invalid() -> None:
+    arr = np.ones(3)
+    with pytest.raises(ValueError, match="expected 2D array"):
+        LiveCellDataset._to_2d_f64(arr)  # noqa: SLF001
+
+
+def test_check_columns_valid(bioluminescence_dataset: LiveCellDataset) -> None:
+    ds = bioluminescence_dataset
+    assert ds.time_series.shape[1] == len(ds.ids)  # noqa: S101
+    assert ds.time_series.shape[1] == len(ds.group)  # noqa: S101
+    assert ds.time_series.shape[1] == len(ds.replicate)  # noqa: S101
+
+
+def test_check_columns_invalid() -> None:
+    ids = ["A", "B"]
+    group = ["group1", "group2"]
+    replicate = [1, 2]
+    time_series = np.ones((10, 3))
+    time = np.arange(10)
+    with pytest.raises(ValueError, match="Length of ids must match number of columns in time_series"):
+        LiveCellDataset(
+            ids=ids,
+            group=group,
+            replicate=replicate,
+            time_series=time_series,
+            time=time,
+        )
+
+
+def test_get_group1_ids_replicates_data(bioluminescence_dataset: LiveCellDataset) -> None:
+    ds = bioluminescence_dataset
+    ids, replicates, data = ds.get_group1_ids_replicates_data()
+    assert set(ids).issubset(set(ds.ids))  # noqa: S101
+    group_values = set(np.array(ds.group)[np.isin(ds.ids, ids)])
+    assert group_values <= {ds.group1_label}  # noqa: S101
+    assert data.shape[1] == len(ids)  # noqa: S101
+    assert len(replicates) == len(ids)  # noqa: S101
+
+
+def test_get_group2_ids_replicates_data(bioluminescence_dataset: LiveCellDataset) -> None:
+    ds = bioluminescence_dataset
+    ids, replicates, data = ds.get_group2_ids_replicates_data()
+    assert set(ids).issubset(set(ds.ids))  # noqa: S101
+    group_values = set(np.array(ds.group)[np.isin(ds.ids, ids)])
+    assert group_values <= {ds.group2_label}  # noqa: S101
+    assert data.shape[1] == len(ids)  # noqa: S101
+    assert len(replicates) == len(ids)  # noqa: S101
+
+
+def test_linear_trend(bioluminescence_dataset: LiveCellDataset) -> None:
+    ds = bioluminescence_dataset
+    x = ds.time
+    y = ds.time_series[:, 0]
+    mask = np.isfinite(x) & np.isfinite(y)
+    x_valid = x[mask]
+    y_valid = y[mask]
+    x_fit, y_fit = ds.linear_trend(x_valid, y_valid)
+    assert x_fit.shape == y_fit.shape == x_valid.shape  # noqa: S101
+    assert np.all(np.isfinite(y_fit))  # noqa: S101
+
+
+def test_poly2_trend(bioluminescence_dataset: LiveCellDataset) -> None:
+    ds = bioluminescence_dataset
+    x = ds.time
+    y = ds.time_series[:, 0]
+    mask = np.isfinite(x) & np.isfinite(y)
+    x_valid = x[mask]
+    y_valid = y[mask]
+    x_fit, y_fit = ds.poly2_trend(x_valid, y_valid)
+    expected = np.polyval(np.polyfit(x_valid, y_valid, 2), x_valid)
+    assert x_fit.shape == y_fit.shape == x_valid.shape  # noqa: S101
+    assert np.allclose(y_fit, expected)  # noqa: S101
+
+
+def test_moving_average_trend(bioluminescence_dataset: LiveCellDataset) -> None:
+    ds = bioluminescence_dataset
+    x = ds.time
+    y = ds.time_series[:, 0]
+    mask = np.isfinite(x) & np.isfinite(y)
+    x_ma, y_ma = ds.moving_average_trend(x[mask], y[mask], window=3)
+    assert len(x_ma) == len(y_ma)  # noqa: S101
+    assert len(x_ma) > 0  # noqa: S101
+    assert np.all(np.isfinite(y_ma))  # noqa: S101
+
+
+def test_moving_average_trend_invalid_window(bioluminescence_dataset: LiveCellDataset) -> None:
+    ds = bioluminescence_dataset
+    x = ds.time
+    y = ds.time_series[:, 0]
+    with pytest.raises(ValueError, match="Window size must be at least 1"):
+        ds.moving_average_trend(x, y, window=0)
+
+
+@pytest.mark.parametrize("method", ["none", "linear", "poly2", "moving_average"])
+def test_get_trend_methods(method: str, bioluminescence_dataset: LiveCellDataset) -> None:
+    ds = bioluminescence_dataset
+    x = ds.time
+    y = ds.time_series[:, 0]
+    mask = np.isfinite(x) & np.isfinite(y)
+    x_valid = x[mask]
+    y_valid = y[mask]
+    x_out, y_out = ds.get_trend(x_valid, y_valid, method=method, window=3)
+    assert isinstance(x_out, np.ndarray)  # noqa: S101
+    assert isinstance(y_out, np.ndarray)  # noqa: S101
+    assert x_out.shape == y_out.shape  # noqa: S101
+    if method != "moving_average":
+        assert x_out.shape == x_valid.shape  # noqa: S101
+    else:
+        assert x_out.size <= x_valid.size  # noqa: S101
+
+
+def test_get_trend_invalid_method(bioluminescence_dataset: LiveCellDataset) -> None:
+    ds = bioluminescence_dataset
+    x = ds.time
+    y = ds.time_series[:, 0]
+    mask = np.isfinite(x) & np.isfinite(y)
+    with pytest.raises(ValueError, match="Unknown detrending method"):
+        ds.get_trend(x[mask], y[mask], method="unknown")
+
+
+@pytest.mark.parametrize("group", ["group1", "group2"])
+def test_plot_group_data(bioluminescence_dataset: LiveCellDataset, group: str) -> None:
+    fig, tmp_path = bioluminescence_dataset.plot_group_data(group=group, method="linear", plot_style="scatter")
+    assert hasattr(fig, "savefig")  # noqa: S101
+    assert tmp_path.endswith(".pdf")  # noqa: S101
+
+
+def test_plot_group_data_invalid_group(bioluminescence_dataset: LiveCellDataset) -> None:
+    ds = bioluminescence_dataset
+    with pytest.raises(ValueError, match="group must be 'group1' or 'group2'"):
+        ds.plot_group_data("invalid_group")
diff --git a/tests/unit_tests/test_omics_dataset.py b/tests/unit_tests/test_omics_dataset.py
new file mode 100644
index 0000000..c28e7a5
--- /dev/null
+++ b/tests/unit_tests/test_omics_dataset.py
@@ -0,0 +1,85 @@
+from __future__ import annotations
+
+import numpy as np
+import pytest
+from matplotlib import pyplot as plt
+from matplotlib.figure import Figure
+from scipy.stats import spearmanr
+
+from cosinor_lite.omics_dataset import OmicsDataset
+
+
+def test_detected_timepoint_counts_cond1_matches_manual(omics_dataset: OmicsDataset) -> None:
+    dataset = omics_dataset
+    manual = dataset.df[dataset.columns_cond1].notna().T.groupby(dataset.t_cond1).any().T.sum(axis=1).to_numpy()
+
+    result = dataset.detected_timepoint_counts("cond1")
+
+    assert np.array_equal(result, manual)  # noqa: S101
+
+
+def test_detected_timepoint_counts_cond2_matches_manual(omics_dataset: OmicsDataset) -> None:
+    dataset = omics_dataset
+    manual = dataset.df[dataset.columns_cond2].notna().T.groupby(dataset.t_cond2).any().T.sum(axis=1).to_numpy()
+
+    result = dataset.detected_timepoint_counts("cond2")
+
+    assert np.array_equal(result, manual)  # noqa: S101
+
+
+def test_detected_timepoint_counts_invalid_condition(omics_dataset: OmicsDataset) -> None:
+    with pytest.raises(ValueError, match="Invalid condition: bad"):
+        omics_dataset.detected_timepoint_counts("bad")
+
+
+def test_mean_expression_columns_initialised(omics_dataset: OmicsDataset) -> None:
+    dataset = omics_dataset
+    mean_cond1 = dataset.df[dataset.columns_cond1].mean(axis=1)
+    mean_cond2 = dataset.df[dataset.columns_cond2].mean(axis=1)
+
+    assert np.allclose(dataset.df["mean_cond1"].to_numpy(), mean_cond1.to_numpy())  # noqa: S101
+    assert np.allclose(dataset.df["mean_cond2"].to_numpy(), mean_cond2.to_numpy())  # noqa: S101
+
+
+def test_number_detected_columns_initialised(omics_dataset: OmicsDataset) -> None:
+    dataset = omics_dataset
+    count_cond1 = dataset.df[dataset.columns_cond1].count(axis=1)
+    count_cond2 = dataset.df[dataset.columns_cond2].count(axis=1)
+
+    assert np.array_equal(  # noqa: S101
+        dataset.df["num_detected_cond1"].to_numpy(),
+        count_cond1.to_numpy(),
+    )
+    assert np.array_equal(  # noqa: S101
+        dataset.df["num_detected_cond2"].to_numpy(),
+        count_cond2.to_numpy(),
+    )
+
+
+def test_expression_histogram_returns_two_panels(omics_dataset: OmicsDataset) -> None:
+    figure = omics_dataset.expression_histogram(bins=15)
+    expected_axis_count = 2
+
+    try:
+        assert isinstance(figure, Figure)  # noqa: S101
+        assert len(figure.axes) == expected_axis_count  # noqa: S101
+        assert figure.axes[0].get_title() == "Mean expression (Alpha)"  # noqa: S101
+        assert figure.axes[1].get_title() == "Mean expression (Beta)"  # noqa: S101
+    finally:
+        plt.close(figure)
+
+
+def test_replicate_scatterplot_title_matches_correlations(omics_dataset: OmicsDataset) -> None:
+    dataset = omics_dataset
+    sample1, sample2 = dataset.columns_cond1[:2]
+    figure = dataset.replicate_scatterplot(sample1, sample2)
+
+    try:
+        filtered = dataset.df[[sample1, sample2]].dropna()
+        x = filtered[sample1].to_numpy().flatten()
+        y = filtered[sample2].to_numpy().flatten()
+        expected_title = f"Pearson R = {np.corrcoef(x, y)[0, 1]:.2f}, Spearman R = {spearmanr(x, y).statistic:.2f}"
+        assert isinstance(figure, Figure)  # noqa: S101
+        assert figure.axes[0].get_title() == expected_title  # noqa: S101
+    finally:
+        plt.close(figure)
diff --git a/tests/unit_tests/test_omics_differential_rhytmicity.py b/tests/unit_tests/test_omics_differential_rhytmicity.py
new file mode 100644
index 0000000..b0f0623
--- /dev/null
+++ b/tests/unit_tests/test_omics_differential_rhytmicity.py
@@ -0,0 +1,329 @@
+from __future__ import annotations
+
+from collections.abc import Iterable
+from typing import TypeVar
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import pytest
+
+from cosinor_lite.omics_dataset import OmicsDataset
+from cosinor_lite.omics_differential_rhytmicity import (
+    OmicsHeatmap,
+    TimeSeriesExample,
+    W,
+    _weight_mapping,
+    akaike_weights_from_bics,
+    amp_from_ab,
+    bic,
+    build_design,
+    build_design_cond1,
+    build_design_cond2,
+    phase_from_ab,
+)
+
+_T = TypeVar("_T")
+
+
+def test_phase_from_ab_returns_expected_hours() -> None:
+    result_zero = phase_from_ab(1.0, 0.0)
+    result_quarter = phase_from_ab(0.0, 1.0)
+
+    assert result_zero == pytest.approx(0.0)  # noqa: S101
+    assert result_quarter == pytest.approx(6.0)  # noqa: S101
+
+
+def test_amp_from_ab_matches_euclidean_norm() -> None:
+    result = amp_from_ab(3.0, 4.0)
+
+    assert result == pytest.approx(5.0)  # noqa: S101
+
+
+def test_bic_prefers_lower_log_likelihood_penalized() -> None:
+    base = bic(llf=-50.0, k=4, n=100)
+    more_params = bic(llf=-49.0, k=10, n=100)
+
+    assert base < more_params  # noqa: S101
+
+
+def test_akaike_weights_are_normalized_and_ranked() -> None:
+    weights = akaike_weights_from_bics(np.array([100.0, 102.0, 105.0]))
+
+    assert weights.sum() == pytest.approx(1.0)  # noqa: S101
+    assert weights[0] > weights[1] > weights[2]  # noqa: S101
+
+
+def test_weight_mapping_populates_model_columns() -> None:
+    mapping = _weight_mapping(model_ids=(2, 4), weights=np.array([0.7, 0.2]))
+
+    assert mapping["w_model2"] == pytest.approx(0.7)  # noqa: S101
+    assert mapping["w_model4"] == pytest.approx(0.2)  # noqa: S101
+    assert np.isnan(mapping["w_model1"])  # noqa: S101
+
+
+def test_build_design_includes_all_expected_columns(omics_dataset: OmicsDataset) -> None:
+    row = omics_dataset.df.iloc[0]
+    alpha_vals = row[omics_dataset.columns_cond1].to_numpy(float)
+    beta_vals = row[omics_dataset.columns_cond2].to_numpy(float)
+
+    design = build_design(alpha_vals, beta_vals, omics_dataset.t_cond1, omics_dataset.t_cond2)
+
+    expected_columns = {
+        "y",
+        "time",
+        "dataset",
+        "constant",
+        "cos_wt",
+        "sin_wt",
+        "is_alpha",
+        "is_beta",
+    }
+
+    assert set(design.columns) == expected_columns  # noqa: S101
+    assert set(design["dataset"]) == {"alpha", "beta"}  # noqa: S101
+
+    alpha_mask = design["dataset"] == "alpha"
+    beta_mask = design["dataset"] == "beta"
+
+    assert np.allclose(  # noqa: S101
+        np.cos(W * design.loc[alpha_mask, "time"].to_numpy(float)),
+        design.loc[alpha_mask, "cos_wt"].to_numpy(float),
+    )
+    assert np.allclose(  # noqa: S101
+        np.sin(W * design.loc[beta_mask, "time"].to_numpy(float)),
+        design.loc[beta_mask, "sin_wt"].to_numpy(float),
+    )
+
+
+def test_build_design_cond1_only_contains_alpha_rows(omics_dataset: OmicsDataset) -> None:
+    row = omics_dataset.df.iloc[0]
+    alpha_vals = row[omics_dataset.columns_cond1].to_numpy(float)
+
+    design = build_design_cond1(alpha_vals, omics_dataset.t_cond1)
+
+    assert set(design["dataset"]) == {"alpha"}  # noqa: S101
+    assert np.allclose(  # noqa: S101
+        np.cos(W * design["time"].to_numpy(float)),
+        design["cos_wt"].to_numpy(float),
+    )
+
+
+def test_build_design_cond2_only_contains_beta_rows(omics_dataset: OmicsDataset) -> None:
+    row = omics_dataset.df.iloc[0]
+    beta_vals = row[omics_dataset.columns_cond2].to_numpy(float)
+
+    design = build_design_cond2(beta_vals, omics_dataset.t_cond2)
+
+    assert set(design["dataset"]) == {"beta"}  # noqa: S101
+    assert np.allclose(  # noqa: S101
+        np.sin(W * design["time"].to_numpy(float)),
+        design["sin_wt"].to_numpy(float),
+    )
+
+
+class _ProgressStub:
+    def tqdm(
+        self,
+        iterable: Iterable[_T],
+        total: int | None = None,
+        desc: str | None = None,
+    ) -> Iterable[_T]:
+        del total, desc
+        return iterable
+
+
+@pytest.fixture
+def synthetic_omics_dataset() -> OmicsDataset:
+    times = np.array([0.0, 8.0, 16.0], dtype=float)
+    dataset_frame = pd.DataFrame(
+        {
+            "Genes": ["gene_both", "gene_cond1", "gene_cond2"],
+            "alpha_0": [1.0, 1.5, np.nan],
+            "alpha_8": [1.2, 1.7, np.nan],
+            "alpha_16": [1.1, 1.6, np.nan],
+            "beta_0": [0.9, np.nan, 1.0],
+            "beta_8": [1.1, np.nan, 1.2],
+            "beta_16": [1.0, np.nan, 1.3],
+        },
+    )
+    dataset = OmicsDataset(
+        df=dataset_frame.copy(),
+        columns_cond1=["alpha_0", "alpha_8", "alpha_16"],
+        columns_cond2=["beta_0", "beta_8", "beta_16"],
+        t_cond1=times,
+        t_cond2=times,
+        cond1_label="Alpha",
+        cond2_label="Beta",
+    )
+    dataset.df["is_expressed_cond1"] = [True, True, False]
+    dataset.df["is_expressed_cond2"] = [True, False, True]
+    return dataset
+
+
+@pytest.fixture
+def heatmap_dataframe() -> pd.DataFrame:
+    data = {
+        "Genes": [
+            "gene_m2b",
+            "gene_m2c",
+            "gene_m3a",
+            "gene_m3c",
+            "gene_m4",
+            "gene_m5",
+        ],
+        "model": [2, 2, 3, 3, 4, 5],
+        "subclass": ["b", "c", "a", "c", "c", "c"],
+        "alpha_phase": [np.nan, 1.0, 2.0, 3.0, 4.0, 5.0],
+        "beta_phase": [0.0, 1.5, np.nan, 3.0, 4.0, 5.0],
+        "alpha_amp": [np.nan, 0.6, 1.2, 0.8, 1.1, 1.0],
+        "beta_amp": [0.9, 1.1, np.nan, 0.7, 0.6, 0.5],
+        "w_model1": [np.nan, 0.0, 0.0, 0.0, 0.0, 0.0],
+        "w_model2": [0.7, 0.2, 0.1, 0.1, 0.1, 0.1],
+        "w_model3": [np.nan, 0.1, 0.6, 0.3, 0.2, 0.2],
+        "w_model4": [np.nan, 0.1, np.nan, 0.3, 0.5, 0.3],
+        "w_model5": [np.nan, 0.6, np.nan, 0.2, 0.2, 0.4],
+        "alpha_0": [np.nan, 1.2, 2.0, 1.1, 0.9, 1.0],
+        "alpha_8": [np.nan, 1.0, 2.2, 1.3, 1.0, 1.2],
+        "alpha_16": [np.nan, 1.1, 2.4, 1.4, 1.1, 1.3],
+        "beta_0": [1.0, 1.0, np.nan, 0.9, 1.2, 1.3],
+        "beta_8": [1.1, 0.9, np.nan, 1.2, 0.8, 1.5],
+        "beta_16": [1.2, 0.8, np.nan, 1.1, 0.9, 1.4],
+        "is_expressed_cond1": [False, True, True, True, True, True],
+        "is_expressed_cond2": [True, True, False, True, True, True],
+    }
+    heatmap_frame = pd.DataFrame(data)
+    heatmap_frame["mean_cond1"] = heatmap_frame[["alpha_0", "alpha_8", "alpha_16"]].mean(axis=1)
+    heatmap_frame["mean_cond2"] = heatmap_frame[["beta_0", "beta_8", "beta_16"]].mean(axis=1)
+    return heatmap_frame
+
+
+@pytest.fixture
+def omics_heatmap(heatmap_dataframe: pd.DataFrame) -> OmicsHeatmap:
+    times = np.array([0.0, 8.0, 16.0], dtype=float)
+    return OmicsHeatmap(
+        df=heatmap_dataframe,
+        columns_cond1=["alpha_0", "alpha_8", "alpha_16"],
+        columns_cond2=["beta_0", "beta_8", "beta_16"],
+        t_cond1=times,
+        t_cond2=times,
+        cond1_label="Alpha",
+        cond2_label="Beta",
+    )
+
+
+def test_omics_heatmap_timepoint_means_shape(omics_heatmap: OmicsHeatmap) -> None:
+    means_cond1 = omics_heatmap.timepoint_means(
+        omics_heatmap.df,
+        omics_heatmap.columns_cond1,
+        omics_heatmap.t_cond1,
+    )
+    means_cond2 = omics_heatmap.timepoint_means(
+        omics_heatmap.df,
+        omics_heatmap.columns_cond2,
+        omics_heatmap.t_cond2,
+    )
+
+    assert means_cond1.shape == (len(omics_heatmap.df), len(np.unique(omics_heatmap.t_cond1)))  # noqa: S101
+    assert means_cond2.shape == (len(omics_heatmap.df), len(np.unique(omics_heatmap.t_cond2)))  # noqa: S101
+
+
+def test_omics_heatmap_get_z_score_standardizes_rows(omics_heatmap: OmicsHeatmap) -> None:
+    values = np.array([[1.0, 2.0, 3.0], [2.0, 2.0, 2.0]], dtype=float)
+    z_scores = omics_heatmap.get_z_score(values)
+
+    assert np.allclose(z_scores[0].mean(), 0.0)  # noqa: S101
+    assert np.allclose(z_scores[0].std(ddof=0), 1.0)  # noqa: S101
+    assert np.all(z_scores[1] == 0.0)  # noqa: S101
+
+
+def test_omics_heatmap_plot_heatmap_returns_figure(omics_heatmap: OmicsHeatmap) -> None:
+    fig = omics_heatmap.plot_heatmap()
+
+    assert fig.axes  # noqa: S101
+    plt.close(fig)
+
+
+@pytest.fixture
+def time_series_dataframe() -> pd.DataFrame:
+    data = {
+        "Genes": ["gene_both", "gene_cond1", "gene_cond2"],
+        "alpha_0": [1.0, 1.5, np.nan],
+        "alpha_8": [1.2, 1.7, np.nan],
+        "alpha_16": [1.1, 1.6, np.nan],
+        "beta_0": [1.0, np.nan, 1.1],
+        "beta_8": [1.2, np.nan, 1.3],
+        "beta_16": [1.1, np.nan, 1.2],
+        "model": [1, 1, 1],
+        "subclass": ["c", "a", "b"],
+        "is_expressed_cond1": [True, True, False],
+        "is_expressed_cond2": [True, False, True],
+    }
+    series_frame = pd.DataFrame(data)
+    series_frame["mean_cond1"] = series_frame[["alpha_0", "alpha_8", "alpha_16"]].mean(axis=1)
+    series_frame["mean_cond2"] = series_frame[["beta_0", "beta_8", "beta_16"]].mean(axis=1)
+    return series_frame
+
+
+@pytest.fixture
+def time_series_example(time_series_dataframe: pd.DataFrame) -> TimeSeriesExample:
+    times = np.array([0.0, 8.0, 16.0], dtype=float)
+    return TimeSeriesExample(
+        df=time_series_dataframe,
+        columns_cond1=["alpha_0", "alpha_8", "alpha_16"],
+        columns_cond2=["beta_0", "beta_8", "beta_16"],
+        t_cond1=times,
+        t_cond2=times,
+        cond1_label="Alpha",
+        cond2_label="Beta",
+    )
+
+
+def test_time_series_example_get_test_function_expressed_both(time_series_example: TimeSeriesExample) -> None:
+    row = time_series_example.df[time_series_example.df["Genes"] == "gene_both"].iloc[0]
+    alpha_vec = row[time_series_example.columns_cond1].to_numpy(float)
+    beta_vec = row[time_series_example.columns_cond2].to_numpy(float)
+
+    t_test, cond1_pred, cond2_pred = time_series_example.get_test_function_expressed_both(
+        alpha_vec,
+        beta_vec,
+        time_series_example.t_cond1,
+        time_series_example.t_cond2,
+        model=1,
+    )
+
+    assert t_test.shape == (100,)  # noqa: S101
+    assert cond1_pred.shape == (100,)  # noqa: S101
+    assert cond2_pred.shape == (100,)  # noqa: S101
+    assert np.isfinite(cond1_pred).all()  # noqa: S101
+    assert np.isfinite(cond2_pred).all()  # noqa: S101
+
+
+def test_time_series_example_conditional_predictions(time_series_example: TimeSeriesExample) -> None:
+    row_cond1 = time_series_example.df[time_series_example.df["Genes"] == "gene_cond1"].iloc[0]
+    alpha_vec = row_cond1[time_series_example.columns_cond1].to_numpy(float)
+
+    _, cond1_only_pred, cond1_only_placeholder = time_series_example.get_test_function_expressed_cond1(
+        alpha_vec,
+        time_series_example.t_cond1,
+        model=1,
+    )
+
+    row_cond2 = time_series_example.df[time_series_example.df["Genes"] == "gene_cond2"].iloc[0]
+    beta_vec = row_cond2[time_series_example.columns_cond2].to_numpy(float)
+
+    _, cond2_placeholder, cond2_only_pred = time_series_example.get_test_function_expressed_cond2(
+        beta_vec,
+        time_series_example.t_cond2,
+        model=1,
+    )
+
+    assert np.isfinite(cond1_only_pred).all()  # noqa: S101
+    assert np.isnan(cond1_only_placeholder).all()  # noqa: S101
+    assert np.isnan(cond2_placeholder).all()  # noqa: S101
+    assert np.isfinite(cond2_only_pred).all()  # noqa: S101
+
+
+def test_time_series_example_plot_time_series_missing_gene(time_series_example: TimeSeriesExample) -> None:
+    with pytest.raises(ValueError, match="not found"):
+        time_series_example.plot_time_series("missing_gene")
diff --git a/tests/unit_tests/test_states_info.py b/tests/unit_tests/test_states_info.py
deleted file mode 100644
index ce83135..0000000
--- a/tests/unit_tests/test_states_info.py
+++ /dev/null
@@ -1,28 +0,0 @@
-"""Tests for `cosinor_lite.states_info`."""
-
-
-import pytest
-from cosinor_lite.states_info import (
-    is_city_capitol_of_state,
-    slow_add,
-)
-
-
-@pytest.mark.parametrize(
-    argnames=("city_name", "state", "is_capitol"),
-    argvalues=[
-        ("Montgomery", "Alabama", True),
-        ("Oklahoma City", "Oklahoma", True),
-        ("Salem", "Oregon", True),
-        ("Salt Lake City", "Alabama", False),
-    ],
-)
-def test__is_city_capitol_of_state(city_name: str, state: str, is_capitol: bool):
-    """Assert `is_city_captio_of_state()` returns correct answer for city-state pairs."""
-    assert is_city_capitol_of_state(city_name=city_name, state=state) == is_capitol
-
-
-@pytest.mark.slow
-def test__slow_add():
-    """Test `slow_add()`."""
-    assert slow_add(1, 2) == 3
diff --git a/uv.lock b/uv.lock
index e9a0a40..fdba46f 100644
--- a/uv.lock
+++ b/uv.lock
@@ -2,10 +2,29 @@ version = 1
 revision = 2
 requires-python = ">=3.11"
 resolution-markers = [
-    "python_full_version >= '3.12'",
+    "python_full_version >= '3.13'",
+    "python_full_version == '3.12.*'",
     "python_full_version < '3.12'",
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+[[package]]
+name = "annotated-doc"
+version = "0.0.4"
+source = { registry = "https://pypi.org/simple" }
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 [[package]]
 name = "annotated-types"
 version = "0.7.0"
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 ]
 
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