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.. _api_viz:
:mod:`sigima.viz` --- Visualization Tools
=========================================
.. module:: sigima.viz
This module provides visualization utilities for Sigima objects, useful for:
- Interactive testing and debugging
- Data analysis in Jupyter notebooks
- Quick visual inspection of processing results
Backend Selection
-----------------
The module automatically selects between **PlotPy** and **Matplotlib** backends based on availability and configuration settings.
The backend selection follows this priority:
1. Environment variable ``SIGIMA_VIZ_BACKEND`` (if set)
2. Configuration option :attr:`sigima.config.options.viz_backend`
3. Auto-detection (PlotPy preferred, Matplotlib as fallback)
Backend selection logic:
- ``"auto"``: Try PlotPy first, fall back to Matplotlib
- ``"plotpy"``: Use PlotPy (raise :class:`ImportError` if not available)
- ``"matplotlib"``: Use Matplotlib (raise :class:`ImportError` if not available)
.. rubric:: Configuring the Backend
Using environment variable:
.. code-block:: python
import os
os.environ["SIGIMA_VIZ_BACKEND"] = "matplotlib" # Before importing sigima.viz
from sigima import viz
# Now uses Matplotlib backend
Using configuration option:
.. code-block:: python
from sigima.config import options
options.viz_backend.set("plotpy")
from sigima import viz
# Now uses PlotPy backend
Module Attributes
-----------------
.. py:data:: BACKEND_NAME
:type: str
Name of the currently selected backend: ``"plotpy"`` or ``"matplotlib"``.
.. py:data:: BACKEND_SOURCE
:type: str
How the backend was selected: ``"env"``, ``"config"``, or ``"auto"``.
Quick Start
-----------
.. code-block:: python
from sigima import viz
import sigima.proc.signal as sips
from sigima.tests.data import get_test_signal
# Load a test signal
signal = get_test_signal("paracetamol.txt")
# Apply some processing
filtered = sips.moving_average(signal, n=10)
# View the results
viz.view_curves([signal, filtered], title="Signal Processing")
Viewing Functions
-----------------
These functions display Sigima objects (:class:`~sigima.objects.SignalObj` and :class:`~sigima.objects.ImageObj`) in interactive dialogs or plots.
.. autofunction:: view_curves
.. autofunction:: view_images
.. autofunction:: view_images_side_by_side
.. autofunction:: view_curves_and_images
Low-Level Viewing Functions
---------------------------
These functions display plot items (curves, images) rather than Sigima objects.
.. autofunction:: view_curve_items
.. autofunction:: view_image_items
Creation Functions
------------------
These functions create plot items that can be passed to the low-level viewing functions.
Curve and Image Items
~~~~~~~~~~~~~~~~~~~~~
.. autofunction:: create_curve
.. autofunction:: create_image
Annotation Items
~~~~~~~~~~~~~~~~
.. autofunction:: create_contour_shapes
.. autofunction:: create_circle
.. autofunction:: create_segment
.. autofunction:: create_cursor
.. autofunction:: create_range
.. autofunction:: create_label
.. autofunction:: create_marker
Backend Differences
-------------------
The two backends have different capabilities:
.. list-table::
:header-rows: 1
:widths: 40 30 30
* - Feature
- PlotPy
- Matplotlib
* - Interactive zoom/pan
- ✅ Full Qt tools
- ✅ Basic toolbar
* - ROI display
- ✅ Native support
- ✅ Patches overlay
* - Geometry results
- ✅ Shape annotations
- ✅ Markers/lines
* - Linked axes
- ✅ Native
- ✅ via ``sharex``/``sharey``
* - Qt integration
- ✅ Native
- ⚠️ Requires Qt backend
* - Headless/CI
- ⚠️ Needs display
- ✅ ``Agg`` backend
For automated testing and CI environments, Matplotlib with the ``Agg`` backend is recommended. For interactive data exploration, PlotPy provides a richer experience.
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