Updated to version v1.6.1

.github/workflows/signaloid-python.yaml

@@ -62,15 +62,15 @@ jobs:
         which python
         pip freeze
 
-    - name: Test
-      run: poetry run pytest
-
     - name: Lint
       run: poetry run flake8 src/
 
     - name: Typecheck
       run: poetry run mypy -p signaloid
 
+    - name: Test
+      run: poetry run pytest
+
     - name: Build
       run: poetry build
 

README.md

@@ -11,7 +11,7 @@ pip install git+https://github.com/signaloid/signaloid-python
 You can construct `DistributionalValue` objects by parsing `Ux` string or `Ux` bytes. You can find more details about the Signaloid `Ux` format [here](https://docs.signaloid.io/docs/hardware-api/ux-data-format/). Following is an example of parsing `Ux` strings and `Ux` bytes.
 
 ```python
-from signaloid.distributional import DistributionalValue
+from signaloid.distributional.distributional import DistributionalValue
 
 ...
 

pyproject.toml

@@ -1,13 +1,17 @@
 [tool.poetry]
 name = "signaloid"
-version = "1.6.0"
+version = "1.6.1"
 description = ""
 authors = [
 	"Signaloid <info@signaloid.com>"
 ]
 
+packages = [
+    { include = "signaloid", from = "src" }
+]
+
 [tool.poetry.group.dev.dependencies]
-wheel = "^0.42.0"
+wheel = "^0.46.2"
 pex = "^2.1.159"
 types-toml = "^0.10.8.7"
 pytest = "^7.4.4"

src/signaloid/__init__.py

@@ -0,0 +1,6 @@
+from .distributional import DistributionalValue, DiracDelta
+
+__all__ = [
+    "DistributionalValue",
+    "DiracDelta",
+]

src/signaloid/distributional/__init__.py

@@ -0,0 +1,4 @@
+from signaloid.distributional.distributional import DistributionalValue
+from signaloid.distributional.dirac_delta import DiracDelta
+
+__all__ = ['DistributionalValue', 'DiracDelta']

src/signaloid/uxdata_toolkit.py

@@ -147,7 +147,7 @@ def command_plot(args: argparse.Namespace) -> None:
         print(f"Mean: {dist_value.mean}")
         print(f"Variance: {dist_value.variance}")
         print(f"Number of Dirac deltas: {dist_value.UR_order}")
-        print(f"Double Precision: { dist_value.double_precision }")
+        print(f"Double Precision: {dist_value.double_precision}")
 
         # Create PlotData object from the distributional value
         print("\nPreparing plot data...")