added ipython to rtd requirments file

.devcontainer/postCreateCommand.sh

@@ -1,5 +1,5 @@
 #! /usr/bin/env bash
 python -m pip install poetry
 poetry config virtualenvs.in-project true --local
-poetry install --all-extras
+poetry install --with cell_locations,collate,dev
 poetry run pre-commit install --install-hooks

.github/workflows/pytest.yml

@@ -55,7 +55,7 @@ jobs:
       #----------------------------------------------
       - name: Install dependencies
         if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true'
-        run: poetry install --no-interaction --no-root  --all-extras
+        run: poetry install --no-interaction --no-root --all-extras --without docs
       #----------------------------------------------
       # install pycytominer
       #----------------------------------------------

.readthedocs.yml

@@ -4,11 +4,21 @@ build:
   os: ubuntu-22.04
   tools:
     python: "3.11"
+  jobs:
+    post_create_environment:
+      # Install poetry
+      # https://python-poetry.org/docs/#installing-manually
+      - pip install poetry
+      # Tell poetry to not use a virtual environment
+      - poetry config virtualenvs.create false
+    post_install:
+      # Install dependencies with 'docs' dependency group
+      # https://python-poetry.org/docs/managing-dependencies/#dependency-groups
+      - poetry install --with docs
 
 python:
   install:
     - requirements: docs/requirements.txt
-    - requirements: requirements.txt
 
 sphinx:
   builder: html

docs/requirements.txt

@@ -1,6 +1,7 @@
 sphinx
 sphinx-autobuild
 sphinx-autoapi
+ipython
 groundwork-sphinx-theme
 sphinx_copybutton
 nbsphinx

pyproject.toml

@@ -49,6 +49,13 @@ codecov = ">=2.1.12"
 pytest-cov = "^4.1.0"
 pre-commit = ">=3.3.2"
 
+[tool.poetry.group.docs]
+optional = true
+
+[tool.poetry.group.docs.dependencies]
+mkdocs = "*"
+sphinx = "~7.1"
+
 [build-system]
 requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"

walkthroughs/nbconverted/single_cell_usage.py

@@ -78,9 +78,9 @@
 #
 # Currently, the NF1 morphology `SQLite` dataset was generated by using [CellProfiler's](https://github.com/CellProfiler/CellProfiler) [ExportToDatabase](https://cellprofiler-manual.s3.amazonaws.com/CellProfiler-4.2.5/modules/fileprocessing.html?highlight=exporttodatabase#module-cellprofiler.modules.exporttodatabase) function, where each table represents a different compartment, such as Image, Cell, Nucleus, and Cytoplasm.
 #
-# To achieve this, we will utilize the SingleCells class, which offers a range of functionalities specifically designed for single-cell analysis. You can find detailed documentation on these functionalities [here](https://pycytominer.readthedocs.io/en/latest/pycytominer.cyto_utils.html#pycytominer.cyto_utils.cells.SingleCells).
+# To achieve this, we will utilize the `SingleCells` class, which offers a range of functionalities specifically designed for single-cell analysis. You can find detailed documentation on these functionalities [here](https://pycytominer.readthedocs.io/en/latest/pycytominer.cyto_utils.html#pycytominer.cyto_utils.cells.SingleCells).
 #
-# However, for our purpose in this walkthrough, we will focus on using the SingleCells class to merge all the tables within the NF1 sqlite file into a merged single-cell morphology dataset.
+# However, for our purpose in this walkthrough, we will focus on using the `SingleCells` class to merge all the tables within the NF1 sqlite file into a merged single-cell morphology dataset.
 #
 # ### Updating defaults
 # Before we proceed further, it is important to update the default parameters in the `SingleCells`class to accommodate the table name changes in our NF1 dataset.
@@ -136,7 +136,7 @@
 
 # Now that we have created our merged single-cell profile, let's move on to the next step: loading our `platemaps`.
 #
-# Platemaps provide us with additional information that is crucial for our analysis. They contain details such as well positions, genotypes, gene names, perturbation types, and more. In other words, platemaps serve as a valuable source of metadata for our single-cell morphology profile.
+# `Platemaps` provide us with additional information that is crucial for our analysis. They contain details such as well positions, genotypes, gene names, perturbation types, and more. In other words, platemaps serve as a valuable source of metadata for our single-cell morphology profile.
 #
 
 # In[5]:

walkthroughs/single_cell_usage.ipynb

@@ -100,9 +100,9 @@
     "\n",
     "Currently, the NF1 morphology `SQLite` dataset was generated by using [CellProfiler's](https://github.com/CellProfiler/CellProfiler) [ExportToDatabase](https://cellprofiler-manual.s3.amazonaws.com/CellProfiler-4.2.5/modules/fileprocessing.html?highlight=exporttodatabase#module-cellprofiler.modules.exporttodatabase) function, where each table represents a different compartment, such as Image, Cell, Nucleus, and Cytoplasm.\n",
     "\n",
-    "To achieve this, we will utilize the SingleCells class, which offers a range of functionalities specifically designed for single-cell analysis. You can find detailed documentation on these functionalities [here](https://pycytominer.readthedocs.io/en/latest/pycytominer.cyto_utils.html#pycytominer.cyto_utils.cells.SingleCells).\n",
+    "To achieve this, we will utilize the `SingleCells` class, which offers a range of functionalities specifically designed for single-cell analysis. You can find detailed documentation on these functionalities [here](https://pycytominer.readthedocs.io/en/latest/pycytominer.cyto_utils.html#pycytominer.cyto_utils.cells.SingleCells).\n",
     "\n",
-    "However, for our purpose in this walkthrough, we will focus on using the SingleCells class to merge all the tables within the NF1 sqlite file into a merged single-cell morphology dataset.\n",
+    "However, for our purpose in this walkthrough, we will focus on using the `SingleCells` class to merge all the tables within the NF1 sqlite file into a merged single-cell morphology dataset.\n",
     "\n",
     "### Updating defaults\n",
     "Before we proceed further, it is important to update the default parameters in the `SingleCells`class to accommodate the table name changes in our NF1 dataset.\n",
@@ -187,7 +187,7 @@
    "source": [
     "Now that we have created our merged single-cell profile, let's move on to the next step: loading our `platemaps`. \n",
     "\n",
-    "Platemaps provide us with additional information that is crucial for our analysis. They contain details such as well positions, genotypes, gene names, perturbation types, and more. In other words, platemaps serve as a valuable source of metadata for our single-cell morphology profile.\n"
+    "`Platemaps` provide us with additional information that is crucial for our analysis. They contain details such as well positions, genotypes, gene names, perturbation types, and more. In other words, platemaps serve as a valuable source of metadata for our single-cell morphology profile.\n"
    ]
   },
   {