Remove_python_restriction

.github/workflows/cdci.yml

@@ -28,13 +28,13 @@ jobs:
           pip install ruff
       - name: Lint with ruff  
         run: |
-          ruff check .
+          ruff check src
   test:
     name: Test
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.9", "3.10", "3.11", "3.12"]
+        python-version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
     steps:
       - uses: actions/checkout@v4
       - name: Set up Python ${{ matrix.python-version }}

docs/api_examples/scatter_plot.ipynb

@@ -11,16 +11,16 @@
     "\n",
     "[![Open In Colab][colab_badge]][colab_link]\n",
     "\n",
-    "[VueCore][vuecore_repo] is a Python package for creating interactive and static visualizations of multi-omics data. \n",
+    "[VueCore][vuecore_repo] is a Python package for creating interactive and static visualizations of multi-omics data.\n",
     "It is part of a broader ecosystem of tools—including [ACore][acore_repo] for data processing and [VueGen][vuegen_repo] for automated reporting—that together enable end-to-end workflows for omics analysis.\n",
     "\n",
-    "This notebook demonstrates how to generate scatter plots using plotting functions from VueCore. We showcase basic and \n",
-    "advanced plot configurations, highlighting key customization options such as grouping, color mapping, text annotations, and export \n",
+    "This notebook demonstrates how to generate scatter plots using plotting functions from VueCore. We showcase basic and\n",
+    "advanced plot configurations, highlighting key customization options such as grouping, color mapping, text annotations, and export\n",
     "to multiple file formats.\n",
     "\n",
     "## Notebook structure\n",
     "\n",
-    "First, we will set up the work environment by installing the necessary packages and importing the required libraries. Next, we will create \n",
+    "First, we will set up the work environment by installing the necessary packages and importing the required libraries. Next, we will create\n",
     "basic and advanced scatter plots.\n",
     "\n",
     "0. [Work environment setup](#0-work-environment-setup)\n",
@@ -294,7 +294,8 @@
     "# Imports\n",
     "import pandas as pd\n",
     "import plotly.io as pio\n",
-    "from vuecore.plots.basic.scatter import  create_scatter_plot\n",
+    "\n",
+    "from vuecore.plots.basic.scatter import create_scatter_plot\n",
     "\n",
     "# Set the Plotly renderer based on the environment\n",
     "pio.renderers.default = \"notebook\""
@@ -449,14 +450,25 @@
    ],
    "source": [
     "# Created sample data\n",
-    "sample_df = pd.DataFrame({\n",
-    "    \"gene_expression\": [1.2, 2.5, 3.1, 4.5, 5.2, 6.8, 3.9, 2.1],\n",
-    "    \"log_p_value\": [0.5, 1.5, 2.0, 3.5, 4.0, 5.5, 1.8, 0.9],\n",
-    "    \"regulation\": [\"Up\", \"Up\", \"None\", \"Down\", \"Down\", \"Down\", \"None\", \"Up\"],\n",
-    "    \"significance_score\": [10, 20, 5, 40, 55, 80, 15, 25],\n",
-    "    \"gene_name\": [\"GENE_A\", \"GENE_B\", \"GENE_C\", \"GENE_D\", \"GENE_E\", \"GENE_F\", \"GENE_G\", \"GENE_H\"],\n",
-    "    \"cell_type\": [\"A\", \"B\", \"A\", \"B\", \"A\", \"B\", \"A\", \"B\"],\n",
-    "})\n",
+    "sample_df = pd.DataFrame(\n",
+    "    {\n",
+    "        \"gene_expression\": [1.2, 2.5, 3.1, 4.5, 5.2, 6.8, 3.9, 2.1],\n",
+    "        \"log_p_value\": [0.5, 1.5, 2.0, 3.5, 4.0, 5.5, 1.8, 0.9],\n",
+    "        \"regulation\": [\"Up\", \"Up\", \"None\", \"Down\", \"Down\", \"Down\", \"None\", \"Up\"],\n",
+    "        \"significance_score\": [10, 20, 5, 40, 55, 80, 15, 25],\n",
+    "        \"gene_name\": [\n",
+    "            \"GENE_A\",\n",
+    "            \"GENE_B\",\n",
+    "            \"GENE_C\",\n",
+    "            \"GENE_D\",\n",
+    "            \"GENE_E\",\n",
+    "            \"GENE_F\",\n",
+    "            \"GENE_G\",\n",
+    "            \"GENE_H\",\n",
+    "        ],\n",
+    "        \"cell_type\": [\"A\", \"B\", \"A\", \"B\", \"A\", \"B\", \"A\", \"B\"],\n",
+    "    }\n",
+    ")\n",
     "\n",
     "sample_df"
    ]

docs/api_examples/scatter_plot.py

@@ -1,59 +1,92 @@
-"""
-Title: Scatter Plot Examples using VueCore
-Description:
-    This script demonstrates how to generate scatter plots using VueCore — a Python package for creating
-    interactive and static visualizations of multi-omics data. It is part of an ecosystem including ACore
-    for data processing and VueGen for automated reporting.
-
-    We showcase basic and advanced plot configurations, highlighting customization options such as grouping,
-    color mapping, annotations, and export to multiple formats.
-
-Script Structure:
-    0. Work environment setup
-    1. Basic scatter plot
-    2. Advanced scatter plot
-
-Authors:
-    Sebastián Ayala-Ruano
-Supervisors:
-    Henry Webel, Alberto Santos (Multiomics Network Analytics Group, DTU Biosustain)
-
-Institution:
-    Multiomics Network Analytics Group (MoNA),
-    Novo Nordisk Foundation Center for Biosustainability (DTU Biosustain)
+# ---
+# jupyter:
+#   jupytext:
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.17.2
+#   kernelspec:
+#     display_name: vuecore-dev
+#     language: python
+#     name: python3
+# ---
+
+# %% [markdown]
+# # Scatter Plot
+#
+# ![VueCore logo][vuecore_logo]
+#
+# [![Open In Colab][colab_badge]][colab_link]
+#
+# [VueCore][vuecore_repo] is a Python package for creating interactive and static visualizations of multi-omics data.
+# It is part of a broader ecosystem of tools—including [ACore][acore_repo] for data processing and [VueGen][vuegen_repo] for automated reporting—that together enable end-to-end workflows for omics analysis.
+#
+# This notebook demonstrates how to generate scatter plots using plotting functions from VueCore. We showcase basic and
+# advanced plot configurations, highlighting key customization options such as grouping, color mapping, text annotations, and export
+# to multiple file formats.
+#
+# ## Notebook structure
+#
+# First, we will set up the work environment by installing the necessary packages and importing the required libraries. Next, we will create
+# basic and advanced scatter plots.
+#
+# 0. [Work environment setup](#0-work-environment-setup)
+# 1. [Basic scatter plot](#1-basic-scatter-plot)
+# 2. [Advanced scatter plot](#2-advanced-scatter-plot)
+#
+# ## Credits and Contributors
+# - This notebook was created by Sebastián Ayala-Ruano under the supervision of Henry Webel and Alberto Santos, head of the [Multiomics Network Analytics Group (MoNA)][Mona] at the [Novo Nordisk Foundation Center for Biosustainability (DTU Biosustain)][Biosustain].
+# - You can find more details about the project in this [GitHub repository][vuecore_repo].
+#
+# [colab_badge]: https://colab.research.google.com/assets/colab-badge.svg
+# [colab_link]: https://colab.research.google.com/github/Multiomics-Analytics-Group/vuecore/blob/main/docs/api_examples/scatter_plot.ipynb
+# [vuecore_logo]: https://raw.githubusercontent.com/Multiomics-Analytics-Group/vuecore/main/docs/images/logo/vuecore_logo.svg
+# [Mona]: https://multiomics-analytics-group.github.io/
+# [Biosustain]: https://www.biosustain.dtu.dk/
+# [vuecore_repo]: https://github.com/Multiomics-Analytics-Group/vuecore
+# [vuegen_repo]: https://github.com/Multiomics-Analytics-Group/vuegen
+# [acore_repo]: https://github.com/Multiomics-Analytics-Group/acore
+
+# %% [markdown]
+# ## 0. Work environment setup
+
+# %% [markdown]
+# ### 0.1. Installing libraries and creating global variables for platform and working directory
+#
+# To run this notebook locally, you should create a virtual environment with the required libraries. If you are running this notebook on Google Colab, everything should be set.
+
+# %% tags=["hide-output"]
+# VueCore library
+# %pip install vuecore
+
+# %% tags=["hide-cell"]
+import os
 
-Project Repository:
-    https://github.com/Multiomics-Analytics-Group/vuecore
+IN_COLAB = "COLAB_GPU" in os.environ
 
-License:
-    MIT License
+# %% tags=["hide-cell"]
+# Create a directory for outputs
+output_dir = "./outputs"
+os.makedirs(output_dir, exist_ok=True)
 
-Created: 2025-06-25
-Last Updated: 2025-06-25
-"""
+# %% [markdown]
+# ### 0.2. Importing libraries
 
 # %%
-# 0. Work environment setup
-# 0.1. Installing libraries and creating global variables for platform and working directory
-# To run this notebook locally, you should create a virtual environment with the required libraries.
-# pip install vuecore
-
-# 0.2. Importing libraries
-import os
+# Imports
 import pandas as pd
 import plotly.io as pio
+
 from vuecore.plots.basic.scatter import create_scatter_plot
 
-# Set the Plotly renderer based on the environment, default to notebook, but you can change it
-# to "browser" if you do not want to use jupyter widgets.
+# Set the Plotly renderer based on the environment
 pio.renderers.default = "notebook"
 
-# 0.3. Create a directory for outputs
-output_dir = "./outputs"
-os.makedirs(output_dir, exist_ok=True)
+# %% [markdown]
+# ## 1. Basic Scatter Plot
 
 # %%
-# 1. Basic Scatter Plot
 # Created sample data
 sample_df = pd.DataFrame(
     {
@@ -75,6 +108,9 @@
     }
 )
 
+sample_df
+
+# %%
 # Define output path
 file_path_png = os.path.join(output_dir, "scatter_basic.png")
 
@@ -83,13 +119,16 @@
     data=sample_df,
     x="gene_expression",
     y="log_p_value",
+    title="Basic Gene Expression Scatter Plot",
     file_path=file_path_png,
 )
 
 fig.show()
 
+# %% [markdown]
+# ## 2. Advanced Scatter Plot
+
 # %%
-# 2. Advanced Scatter Plot
 # Define output path
 file_path_adv_html = os.path.join(output_dir, "scatter_advanced.html")
 
@@ -101,7 +140,7 @@
     group="regulation",
     size="significance_score",
     text="gene_name",
-    title="Advanced Gene Expression Plot",
+    title="Advanced Gene Expression Scatter Plot",
     x_title="Log2 Fold Change",
     y_title="-Log10(P-value)",
     colors={"Up": "#FF5733", "Down": "#3380FF", "None": "#33FF57"},

pyproject.toml

@@ -1,44 +1,46 @@
 [project]
-authors = [{ name = "Alberto Santos Delgado", email = "[email protected]" },
-           { name = "Henry Webel", email = "[email protected]"}]
+authors = [
+  { name = "Alberto Santos Delgado", email = "[email protected]" },
+  { name = "Henry Webel", email = "[email protected]" },
+]
 name = "vuecore"
 dynamic = ["version"]
 description = "A Python package for plotting related to multimodal molecular data. Works with acore."
 license = { text = "GNU General Public License v3" }
 readme = "README.md"
-requires-python = ">=3.9,<3.13"
+requires-python = ">=3.9"
 classifiers = [
-    "Development Status :: 2 - Pre-Alpha",
-    "Intended Audience :: Developers",
-    "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
-    "Natural Language :: English",
-    "Programming Language :: Python :: 3.9",
-    "Programming Language :: Python :: 3.10",
-    "Programming Language :: Python :: 3.11",
+  "Development Status :: 2 - Pre-Alpha",
+  "Intended Audience :: Developers",
+  "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
+  "Natural Language :: English",
+  "Programming Language :: Python :: 3.9",
+  "Programming Language :: Python :: 3.10",
+  "Programming Language :: Python :: 3.11",
 ]
 
 dependencies = [
-    "numpy",
-    "pandas",
-    # "dsp-pandas", # has only pandas related dependencies
-    "scipy",
-    "plotly",
-    # "biopython",
-    "beautifulsoup4",
-    "requests",
-    "dash", # from dash import html
-    "networkx",
-    "matplotlib",
-    "kaleido",
-    "pyvis",
-    "wordcloud",
-    "cyjupyter",
-    "nltk",
-    "webweb",
-    "acore",
-    "dash-cytoscape",
-    "pydantic",
-    "nbformat>=4.2.0",
+  "numpy",
+  "pandas",
+  # "dsp-pandas", # has only pandas related dependencies
+  "scipy",
+  "plotly",
+  # "biopython",
+  "beautifulsoup4",
+  "requests",
+  "dash",            # from dash import html
+  "networkx",
+  "matplotlib",
+  "kaleido",
+  "pyvis",
+  "wordcloud",
+  "cyjupyter",
+  "nltk",
+  "webweb",
+  "acore",
+  "dash-cytoscape",
+  "pydantic",
+  "nbformat>=4.2.0",
 ]
 
 [project.optional-dependencies]

src/vuecore/engines/plotly/scatter.py

@@ -1,8 +1,10 @@
 import pandas as pd
 import plotly.express as px
 import plotly.graph_objects as go
-from vuecore.schemas.distribution.scatter import ScatterConfig
+
+from vuecore.schemas.basic.scatter import ScatterConfig
 from vuecore.utils.statistics import get_density
+
 from .theming import apply_scatter_theme
 
 

src/vuecore/engines/plotly/theming.py

@@ -1,5 +1,6 @@
 import plotly.graph_objects as go
-from vuecore.schemas.distribution.scatter import ScatterConfig
+
+from vuecore.schemas.basic.scatter import ScatterConfig
 
 
 def apply_scatter_theme(fig: go.Figure, config: ScatterConfig) -> go.Figure:

src/vuecore/plots/basic/scatter.py

@@ -1,10 +1,10 @@
-import pandas as pd
 from typing import Any
 
-from vuecore.schemas.distribution.scatter import ScatterConfig
+import pandas as pd
 
-from vuecore.engines import get_builder, get_saver
 from vuecore import EngineType
+from vuecore.engines import get_builder, get_saver
+from vuecore.schemas.basic.scatter import ScatterConfig
 
 
 def create_scatter_plot(