Version 1.1#

Sigima Version 1.1.4#

🛠️ Bug Fixes since version 1.1.3#

  • Ellipse/circle contour detection: Fixed regression in axis orientation after the v1.1.3 coordinate swap fix. This closes Issue #27.

    • The sign correction applied in v1.1.3 for the (row, col) → (x, y) conversion introduced a regression in ellipse axis orientation, causing the semi-major and semi-minor axes to be transposed

    • Added ground-truth unit tests for fit_circle_model and fit_ellipse_model verifying all output parameters (center coordinates, radius/semi-axes, and rotation angle)

Sigima Version 1.1.3#

🛠️ Bug Fixes since version 1.1.2#

  • Ellipse/circle contour detection: Fixed swapped X/Y coordinates when using scikit-image ≥ 0.26.0. This closes Issue #21.

    • The new EllipseModel/CircleModel API returns center coordinates as (row, col) but the code was treating them as (x, y), causing detected ellipses and circles to appear at wrong positions on the image

    • Semi-axis lengths were also swapped for ellipses, resulting in incorrect aspect ratios

    • This bug only affected scikit-image ≥ 0.26.0; older versions were handled correctly

  • Ellipse visualization: Fixed incorrect minor axis direction in ellipse_to_diameters coordinate conversion

    • The minor axis endpoints were computed with a wrong sign, making the minor axis non-perpendicular to the major axis for rotated ellipses (e.g., at θ=45° both axes pointed in the same direction). This caused distorted ellipse overlays in DataLab when displaying detected or annotated ellipses at non-trivial rotation angles

  • Circle/ellipse detection with calibrated images: Fixed radius and semi-axes not being converted from pixel units to physical units

    • When an image had non-default pixel calibration (e.g., dx=2 mm/pixel), center coordinates were correctly converted to physical units but the radius and semi-axes remained in pixels, causing values to be wrong by a factor equal to the pixel size

  • Custom signal XY preservation: Fixed user-edited XY values being silently discarded when regenerating a custom signal from its creation parameters. generate_1d_data() now initializes the XY array only on first use and preserves user-edited contents on subsequent calls. This closes Issue #25.

🔧 Other changes since version 1.1.2#

  • Debug environment variable: Renamed the test debug environment variable from DEBUG to SIGIMA_DEBUG to avoid collisions with unrelated tools and frameworks that commonly export a bare DEBUG variable. This closes Issue #19.

  • Remote control client: Added get_current_object_uuid() method to SimpleRemoteProxy and SimpleAbstractDLControl, allowing users to retrieve the UUID of the currently selected object in DataLab

🧪 Tests#

  • Non-uniform image coordinates: Strengthened test coverage for images with non-uniform (irregularly spaced) X/Y coordinates, closing the remaining gaps in coordinate-mode switching, geometry operations and coordinated-text reading. This closes Issue #24.

Sigima Version 1.1.2 (2026-04-20)#

💥 Breaking changes since version 1.1.1#

  • Removed deprecated sigima.tests.vistools module: The backward-compatibility shim introduced in version 1.1.0 has been removed

    • Update imports from from sigima.tests import vistools to from sigima import viz

    • All visualization helpers (view_curves, view_images, create_curve, create_image, etc.) remain available under their new home in sigima.viz

🛠️ Bug Fixes since version 1.1.1#

  • CSV header parsing: Fixed whitespace and unit extraction issues when reading CSV column headers

    • Leading and trailing whitespace in column titles is now properly trimmed (e.g. "  Padded NoUnit  " → "Padded NoUnit")

    • Nested parentheses in units are now correctly handled (e.g. "Signal (a.u. (norm))" → label "Signal", unit "a.u.(norm)" instead of incorrectly splitting at the last parenthesis)

    • Exotic whitespace characters (tabs, non-breaking spaces) in headers are normalized before parsing

  • Pandas 3.0 compatibility: Fixed datetime CSV parsing issue with pandas 3.0+

    • Replaced iloc assignment with column name assignment to handle dtype conversion correctly

    • Ensures compatibility with both pandas < 3.0 and pandas 3.0+ versions

    • Affected functionality: CSV file reading with datetime columns and signal data import from CSV files

    • All related tests pass after the fix

    • This closes Issue #12 - Fix pandas 3.0 compatibility issue in datetime CSV parsing

  • HDF5 serialization of TableResult: Fixed DataLab workspace save failure when table results contain enum values or callable formatters

    • Enum subclasses (e.g. SignalShape) stored in table data are now converted to plain strings before serialization, preventing dtype('<U4') errors

    • Non-serializable entries (e.g. callable column formatters) in TableResult.attrs are now sanitized before HDF5/JSON export

  • Datetime signal compatibility: Fixed datetime column handling for compatibility with various NumPy and pandas versions

    • Allowed any datetime64 resolution (not just ns) to support newer NumPy/pandas combinations

  • Signal uncertainty (dx/dy): Fixed phantom error bars displayed when only one of dx or dy was set on a fresh signal

    • When dy was set first (or dx first), the deferred uncertainty row for the other dimension was previously filled with zeros, which downstream tools interpreted as a real all-zero uncertainty array (e.g. PlotPy drew zero-width error bars)

    • Deferred rows are now initialized with NaN, so a missing uncertainty is properly reported as absent — no more spurious error bars

  • ROI extraction with uncertainty data: Fixed extract_roi and extract_rois losing the input signal’s dx / dy uncertainty arrays

    • Uncertainty samples are now sliced and propagated to the extracted signal exactly like the X/Y data

    • Affects both single-ROI and multi-ROI extraction workflows

  • Zero-crossing detection: Fixed find_zero_crossings over-reporting on signals containing exact-zero samples

    • Samples exactly equal to zero used to count as two crossings (e.g. [-1, 0, 1] reported two crossings instead of one) and “touch-and-bounce” patterns like [1, 0, 1] were wrongly flagged as crossings

    • Sign changes are now detected between consecutive non-zero samples, with contiguous zero runs collapsed to a single crossing only when they actually bridge opposite signs

    • Downstream features benefit from the fix: find_x_axis_crossings, find_x_values_at_y, find_bandwidth_coordinates, and pulse FWHM / rise / fall measurements

  • Contrast computation: Fixed contrast returning inf / nan warnings on signals with negative or all-zero values

    • Denominator now uses absolute values ((max - min) / (|max| + |min|)), keeping the result bounded in [0, 1] for signals with negative samples while matching the standard Michelson contrast for non-negative signals

    • Returns nan cleanly (without a division-by-zero warning) when both max and min are zero

  • Datetime conversion accuracy: Fixed datetime64_to_seconds returning wrong Unix timestamps for coarse datetime64 resolutions

    • The previous implementation only recognized ns, us and ms and silently used a nanosecond divisor for any other resolution, producing incorrect timestamps for datetime64[s], datetime64[m], datetime64[h] or datetime64[D]

    • Inputs are now normalized to datetime64[ns] before conversion, ensuring correct results for any source resolution and for pandas DatetimeIndex objects

  • DICOM image origin and spacing: Fixed ImageObj.dicom_template falling back to inconsistent default coordinates when DICOM tags were missing

    • When ImagePositionPatient or PixelSpacing was absent from the DICOM template, only the Y origin and Y pixel spacing received the fallback while the X origin and X pixel spacing were left as bare scalars, due to operator precedence

    • Both X and Y origin (x0, y0) and pixel spacing (dx, dy) are now consistently filled with the intended (0.0, 0.0) and (1.0, 1.0) defaults

✨ Enhancements since version 1.1.1#

  • Legend value formatting: Improved numeric formatting in analysis result legends

    • Short values are displayed as plain numbers, while long values use scientific notation adapted to fit within the display width

    • Pulse features table now uses adaptive scientific notation showing only the significant digits needed for exact round-trip representation

  • Table column display formats: Added per-column format control for TableResult

    • New get_column_formats() / set_column_formats() methods on TableResult

    • TableResultBuilder supports set_column_formats() for specifying format strings (e.g. {"x0": ".2e"}) or callable formatters

    • Supports "*" wildcard key as a per-table default formatter

📦 Dependencies changes since version 1.1.1#

  • Pandas 3.0 support: Officially support pandas 3.0.x after validation — upper bound raised from < 3.0 to < 3.1

  • Python 3.14 support: Added Python 3.14 classifier

🔧 Other changes since version 1.1.1#

  • Remote control client: Added set_object method to SimpleRemoteProxy and SimpleAbstractDLControl, allowing users to push modified signal/image objects back to DataLab after retrieving them with get_object (see DataLab Issue #305)

  • Stub server: Added set_object support to DataLabStubServer with proper UUID-based object lookup, and registered add_object/set_object as XML-RPC functions

  • Stub server: Signal and image objects now carry their UUID in metadata, enabling round-trip get_object → modify → set_object workflows

  • Improved development environment setup: new run_with_env.py script supporting multiple Python environment contexts (WinPython, venv, etc.)

  • CI: gated PyPI deployment on test suite passing

  • Test coverage: Substantially expanded the unit-test suite to exercise previously untested branches across sigima.objects, sigima.io, sigima.tools, sigima.proc and sigima.viz (26 new test modules), and excluded the GUI visualization layer from coverage reports for more meaningful metrics

Sigima Version 1.1.1 (2026-02-02)#

🛠️ Bug Fixes since version 1.1.0#

  • Stub server: Added missing Web API control methods to DataLabStubServer for testing DataLab’s REST API integration

    • Added start_webapi_server() stub (returns dummy URL and token)

    • Added stop_webapi_server() stub

    • Added get_webapi_status() stub (returns server status dictionary)

🔧 Other changes since version 1.1.0#

  • Updated development status classifier to “Production/Stable”

  • Added “Try it Online” section with notebook.link integration in documentation

Sigima Version 1.1.0 (2026-01-31)#

✨ New features and enhancements:

  • Stub server: Added missing methods to DataLabStubServer to support new DataLab features

    • Added remove_object() method (removes an object from DataLab)

    • Added call_method() method (simulates calling a method on DataLab’s main interface or panels)

  • Test infrastructure: Refactored visualization code in test suite for better maintainability

    • Centralized PlotPy visualization utilities in sigima.viz module

    • Added standardized helper functions: create_curve(), create_image(), create_circle(), create_segment(), create_cursor(), create_marker(), and create_contour_shapes()

    • Updated all interactive test functions to use the new unified API instead of direct PlotPy builder calls

    • Improved code consistency across signal and image test modules

  • Visualization backend flexibility: Added support for Matplotlib as an alternative to PlotPy for test visualizations

    • New configuration option viz_backend to select visualization library (“auto”, “plotpy”, or “matplotlib”)

    • Environment variable SIGIMA_VIZ_BACKEND can override configuration

    • Automatic fallback from PlotPy to Matplotlib when PlotPy is not available

    • Backend information exposed via sigima.viz.BACKEND_NAME and BACKEND_SOURCE

    • Matplotlib backend provides stubs for unsupported functions (raises NotImplementedError)

    • Unit test ensures API compatibility between backends

  • Jupyter notebook HTML representation: Added rich HTML display for Sigima objects in Jupyter notebooks

    • SignalObj displays shape, dtype, X/Y ranges, axis labels, and title in a formatted table

    • ImageObj displays shape, dtype, value range, origin, pixel spacing, extent, axis labels, and title

    • ROI objects (BaseSingleROI, BaseROI) display their geometric properties

    • BaseCoordinates and derived classes display point coordinates

    • TableResult and GeometryResult display result data with source object information

    • Centralized CSS styling in HTML_TABLE_CSS constant for consistent appearance