Rename embryo

CITATION.cff

@@ -1,10 +1,10 @@
-cff-version: 1.2.2
+cff-version: 2.0.0
 message: "If you use this software, please cite it as below."
 authors:
 - family-names: "Guignard"
   given-names: "Léo"
   orcid: "https://orcid.org/0000-0002-3686-1385"
 title: "sc3D"
-version: 1.2.2
+version: 2.0.0
 date-released: 2023-07-01
 url: "https://github.com/GuignardLab/sc3D"

notebooks/Embryo-Registration.ipynb

@@ -8,9 +8,7 @@
    "outputs": [],
    "source": [
     "import json\n",
-    "from matplotlib import pyplot as plt\n",
-    "import numpy as np\n",
-    "from sc3D import Embryo\n",
+    "from sc3D import SpatialOmicArray\n",
     "\n",
     "%matplotlib inline"
    ]
@@ -108,7 +106,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "embryo = Embryo(\n",
+    "embryo = SpatialOmicArray(\n",
     "    data_path,\n",
     "    tissues_to_ignore,\n",
     "    corres_tissues,\n",
@@ -163,7 +161,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "sc3D",
    "language": "python",
    "name": "python3"
   },
@@ -177,7 +175,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.13"
+   "version": "3.13.5"
   }
  },
  "nbformat": 4,

pyproject.toml

@@ -15,7 +15,7 @@ profile = "black"
 line_length = 79
 
 [tool.bumpver]
-current_version = "1.2.2"
+current_version = "2.0.0"
 version_pattern = "MAJOR.MINOR.PATCH[-TAG]"
 commit_message = "bump version {old_version} -> {new_version}"
 commit = true

setup.cfg

@@ -1,6 +1,6 @@
 [metadata]
 name = sc-3D
-version = 1.2.2
+version = 2.0.0
 author = Leo Guignard
 author_email = leo.guignard@univ-amu.fr
 url = https://github.com/GuignardLab/sc3D

src/sc3D/__init__.py

@@ -1,4 +1,9 @@
-__version__ = "1.2.2"
+__version__ = "2.0.0"
 
-from .sc3D import Embryo
+from .sc3D import SpatialOmicArray
 from .transformations import transformations
+
+__all__ = [
+    "SpatialOmicArray",
+    "transformations",
+]

src/sc3D/_tests/test_sc3D.py

@@ -1,9 +1,9 @@
-from sc3D import Embryo
+from sc3D import SpatialOmicArray
 import numpy as np
 
 
 def test_sc3D():
-    em = Embryo("data/data_test.h5ad", store_anndata=True)
+    em = SpatialOmicArray("data/data_test.h5ad", store_anndata=True)
     assert len(em.all_cells) == 120
     em.smooth_data()
     em.plot_coverslip(7)
@@ -15,7 +15,7 @@ def test_sc3D():
     em.compute_volumes()
     em.set_zpos()
 
-    em = Embryo(
+    em = SpatialOmicArray(
         "data/DLPFC.h5ad",
         tissue_id="layer_guess",
         pos_id="spatial",

src/sc3D/sc3D.py

@@ -27,9 +27,9 @@
 from sc3D.transformations import transformations as tr
 
 
-class Embryo:
+class SpatialOmicArray:
     """
-    Embryo class to handle samples from 3D spatial
+    SpatialOmicArray class to handle samples from 3D spatial
     single cell omics. It was initially designed with
     a specific dataset in mind but it should work
     for other kinds of datasets.
@@ -187,7 +187,7 @@ def read_anndata(
         elif genes_of_interest == "all":
             genes_of_interest = data.var_names
         self.all_genes = sorted(genes_of_interest)
-        
+
         if 0 < len(genes_of_interest):
             raw_X = self._to_dense(data.raw[:, self.all_genes].X)
             self.gene_expression = dict(zip(ids, np.array(raw_X)))
@@ -695,7 +695,7 @@ def smooth_data(self, inplace=True):
         """
         Smooth the gene expression according to the spatial neighborhood relationship.
         The spatial neighborhood relationship is computed as the Gabriel graph.
-        The smoothed expression (\(s_c \) of the gene $g$ of a cell $c$ which has
+        The smoothed expression ($s_c$) of the gene $g$ of a cell $c$ which has
         a set of neighbors $N_c = \{n_i\}$ is computed as follow:
             $$s_c = \\frac{\sum_{n_i \in N_c} ||n_i - c||.g_{n_i}}{\sum_{n_i \in N_c} ||n_i - c||}$$
         where $||n_i - c||$ is the distance between $n_i$ and $c$ and $g_{n_i}$ is the measured
@@ -789,16 +789,21 @@ def downsample(self, spacing=10, pos_id="pos_3D"):
                     if 0 < len(v)
                 ]
             )
-
-            final_expr = np.array([
-                np.mean(
-                    self._to_dense(self.anndata.raw[
-                        mapping_from_removed[[cells[vi] for vi in v]]
-                    ].X),
-                    axis=0,
-                )
-                for v in mapping if len(v) > 0
-            ])
+
+            final_expr = np.array(
+                [
+                    np.mean(
+                        self._to_dense(
+                            self.anndata.raw[
+                                mapping_from_removed[[cells[vi] for vi in v]]
+                            ].X
+                        ),
+                        axis=0,
+                    )
+                    for v in mapping
+                    if len(v) > 0
+                ]
+            )
 
             nb_cells = np.array([len(v) for v in mapping if 0 < len(v)])
             tissue = [
@@ -1945,7 +1950,7 @@ def neighbs(self, gene, sub_data, cells):
             gene (int): gene id (position in the `self.anndata` array)
             sub_data (ndarray): sliced version of `self.anndata` only containing
                 the beads corresponding to the tissue to analyse
-            cells (ndarray): ids of the cells in `Embryo` ordered similarly to
+            cells (ndarray): ids of the cells in `SpatialOmicArray` ordered similarly to
                 the `self.anndata` array (to do correspondancy)
         Returns:
             avg_nb_neighbs (float): average number of positive neighbors per
@@ -2428,7 +2433,7 @@ def __init__(
         sample_list=None,
     ):
         """
-        Initialize an spatial single cell embryo
+        Initialize an spatial single cell SpatialOmicArray
 
         Args:
             data_path (str): path to the file containing the sc data (h5ad format)