Prepare 1.9.8, stop ignoring citation errors (scverse#2831)

docs/conf.py

@@ -6,7 +6,7 @@
 from typing import TYPE_CHECKING
 
 import matplotlib # noqa
-from packaging.version import parse as parse_version
+from packaging.version import Version
 
 # Don’t use tkinter agg when importing scanpy → … → matplotlib
 matplotlib.use("agg")
@@ -25,7 +25,6 @@
 nitpicky = True # Warn about broken links. This is here for a reason: Do not change.
 needs_sphinx = "4.0" # Nicer param docs
 suppress_warnings = [
- "ref.citation",
  "myst.header", # https://github.com/executablebooks/MyST-Parser/issues/262
 ]
 
@@ -37,8 +36,8 @@
 version = scanpy.__version__.replace(".dirty", "")
 
 # Bumping the version updates all docs, so don't do that
-if parse_version(version).is_devrelease:
- parsed = parse_version(version)
+if Version(version).is_devrelease:
+ parsed = Version(version)
  version = f"{parsed.major}.{parsed.minor}.{parsed.micro}.dev"
 
 release = version

docs/references.rst

@@ -37,11 +37,6 @@ References
  *Single cell RNA-seq denoising using a deep count autoencoder*,
  `bioRxiv <https://doi.org/10.1101/300681>`__.
 
-.. [Ester96] Ester *et al.* (1996),
- *A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise*,
- `Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining,
- Portland, OR <https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.121.9220>`__.
-
 .. [Eulenberg17] Eulenberg *et al.* (2017),
  *Reconstructing cell cycle and disease progression using deep learning*
  `Nature Communications <https://doi.org/10.1101/081364>`__.
@@ -58,15 +53,6 @@ References
  *Construction of a genetic toggle switch in Escherichia coli*,
  `Nature <https://doi.org/10.1038/35002131>`__.
 
-.. [Hagberg08] Hagberg *et al.* (2008),
- *Exploring Network Structure, Dynamics, and Function using NetworkX*,
- `Scipy Conference <https://conference.scipy.org/proceedings/SciPy2008/paper_2/>`__.
-
-.. [Hastie09]
- Hastie *et al.* (2009),
- *The Elements of Statistical Learning*,
- `Springer <https://web.stanford.edu/~hastie/ElemStatLearn/>`__.
-
 .. [Haghverdi15] Haghverdi *et al.* (2015),
  *Diffusion maps for high-dimensional single-cell analysis of differentiation data*,
  `Bioinformatics <https://doi.org/10.1093/bioinformatics/btv325>`__.
@@ -83,10 +69,6 @@ References
  *Efficient integration of heterogeneous single-cell transcriptomes using Scanorama*,
  `Nature Biotechnology <https://doi.org/10.1038/s41587-019-0113-3>`__.
 
-.. [Huber15] Huber *et al.* (2015),
- *Orchestrating high-throughput genomic analysis with Bioconductor*,
- `Nature Methods <https://doi.org/10.1038/nmeth.3252>`__.
-
 .. [Islam11] Islam *et al.* (2011),
  *Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq*,
  `Genome Research <https://doi.org/10.1101/gr.110882.110>`__.
@@ -107,10 +89,6 @@ References
  *Fast, sensitive and accurate integration of single-cell data with Harmony*,
  `Nature Methods <https://doi.org/10.1038/s41592-019-0619-0>` __.
 
-.. [Krumsiek10] Krumsiek *et al.* (2010),
- *Odefy – From discrete to continuous models*,
- `BMC Bioinformatics <https://doi.org/10.1186/1471-2105-11-233>`__.
-
 .. [Krumsiek11] Krumsiek *et al.* (2011),
  *Hierarchical Differentiation of Myeloid Progenitors Is Encoded in the Transcription Factor Network*,
  `PLoS ONE <https://doi.org/10.1371/journal.pone.0022649>`__.
@@ -131,10 +109,6 @@ References
  *Data-Driven Phenotypic Dissection of AML Reveals Progenitor--like Cells that Correlate with Prognosis*,
  `Cell <https://doi.org/10.1016/j.cell.2015.05.047>`__.
 
-.. [Lopez18] Lopez *et al.* (2018),
- *Deep generative modeling for single-cell transcriptomics*
- `Nature Methods <https://doi.org/10.1038/s41592-018-0229-2>`__.
-
 .. [Maaten08] Maaten & Hinton (2008),
  *Visualizing data using t-SNE*,
  `JMLR <https://www.jmlr.org/papers/v9/vandermaaten08a.html>`__.
@@ -163,11 +137,6 @@ References
  *Decoding the regulatory network of early blood development from single-cell gene expression measurements*,
  `Nature Biotechnology <https://doi.org/10.1038/nbt.3154>`__.
 
-.. [Murphy12]
- Murphy (2012,
- *Machine Learning: A Probabilisitc Perspective*,
- `MIT Press <https://mitpress.mit.edu/books/machine-learning-0>`__.
-
 .. [Ntranos18] Ntranos *et al.* (2018),
  *Identification of transcriptional signatures for cell types from single-cell RNA-Seq*,
  `bioRxiv <https://doi.org/10.1101/258566>`__.
@@ -208,10 +177,6 @@ References
  *Comprehensive integration of single-cell data*
  `Cell <https://www.sciencedirect.com/science/article/pii/S0092867419305598>`__.
 
-.. [Svensson20] Svensson *et al.* (2020),
- *Interpretable factor models of single-cell RNA-seq via variational autoencoders*
- `Bioinformatics <https://doi.org/10.1093/bioinformatics/btaa169>`__.
-
 .. [Nowotschin18i] Nowotschin *et al.* (2018),
  *The emergent landscape of the mouse gut endoderm at single-cell resolution*
  `Nature <https://www.nature.com/articles/s41586-019-1127-1>`__.

docs/release-notes/1.9.8.md

@@ -0,0 +1,4 @@
+### 1.9.8 {small}`the future`
+
+```{rubric} Bug fixes
+```

docs/release-notes/index.md

@@ -9,6 +9,9 @@
 
 ## Version 1.9
 
+```{include} /release-notes/1.9.8.md
+```
+
 ```{include} /release-notes/1.9.7.md
 ```