Merge branch 'theislab:main' into fix/print_versions

.github/workflows/release.yml

@@ -20,7 +20,7 @@ jobs:
             - name: Install dependencies
               run: |
                   python -m pip install --upgrade pip
-                  python -m pip build
+                  python -m pip install --upgrade build
 
             - name: Build package
               run: |

pyproject.toml

@@ -1,11 +1,10 @@
 [build-system]
-build-backend = "hatchling.build"
-requires = ["hatchling"]
-
+requires = ["setuptools>=61", "setuptools-scm[toml]>=6.2"]
+build-backend = "setuptools.build_meta"
 
 [project]
 name = "scvelo"
-version = "0.3.1"
+dynamic = ["version"]
 description = "RNA velocity generalized through dynamical modeling"
 readme = {file = "README.md", content-type="text/markdown"}
 requires-python = ">=3.8"
@@ -46,7 +45,7 @@ dependencies = [
     "loompy>=2.0.12",
     "umap-learn>=0.3.10",
     "numba>=0.41.0",
-    "numpy>=1.17",
+    "numpy>=1.17, <2.0.0",
     "pandas>=1.1.1, !=1.4.0",
     "scipy>=1.4.1",
     "scikit-learn>=0.21.2",
@@ -98,8 +97,10 @@ docs = [
     "nbsphinx>=0.7,<0.8.7"
 ]
 
-[tool.hatch.build.targets.wheel]
-packages = ["scvelo"]
+[tool.setuptools]
+include-package-data = true
+
+[tool.setuptools_scm]
 
 [tool.coverage.run]
 source = ["scvelo"]

scvelo/core/_anndata.py

@@ -1,4 +1,5 @@
 import re
+import warnings
 from typing import List, Literal, Optional, Union
 
 import numpy as np
@@ -178,6 +179,13 @@ def get_df(
     :class:`pd.DataFrame`
         A dataframe.
     """
+    warnings.warn(
+        "`get_df` is deprecated since scvelo==0.4.0 and will be removed in a future version "
+        "of scVelo. Please `AnnData::get_df` or Scanpy's `scanpy.get.obs_df` or `scanpy.get.var_df`.",
+        DeprecationWarning,
+        stacklevel=2,
+    )
+
     if precision is not None:
         pd.set_option("display.precision", precision)
 
@@ -188,8 +196,6 @@ def get_df(
         keys, key_add = (
             keys.split("/") if isinstance(keys, str) and "/" in keys else (keys, None)
         )
-        keys = [keys] if isinstance(keys, str) else keys
-        key = keys[0]
 
         s_keys = ["obs", "var", "obsm", "varm", "uns", "layers"]
         d_keys = [
@@ -207,62 +213,68 @@ def get_df(
 
         if keys is None:
             df = data.to_df()
-        elif key in data.var_names:
-            df = obs_df(data, keys, layer=layer)
-        elif key in data.obs_names:
-            df = var_df(data, keys, layer=layer)
         else:
-            if keys_split is not None:
-                keys = [
-                    k
-                    for k in list(data.obs.keys()) + list(data.var.keys())
-                    if key in k and keys_split in k
-                ]
-                key = keys[0]
-            s_key = [s for (s, d_key) in zip(s_keys, d_keys) if key in d_key]
-            if len(s_key) == 0:
-                raise ValueError(f"'{key}' not found in any of {', '.join(s_keys)}.")
-            if len(s_key) > 1:
-                logg.warn(f"'{key}' found multiple times in {', '.join(s_key)}.")
-
-            s_key = s_key[-1]
-            df = getattr(data, s_key)[keys if len(keys) > 1 else key]
-            if key_add is not None:
-                df = df[key_add]
-            if index is None:
-                index = (
-                    data.var_names
-                    if s_key == "varm"
-                    else data.obs_names
-                    if s_key in {"obsm", "layers"}
-                    else None
-                )
-                if index is None and s_key == "uns" and hasattr(df, "shape"):
-                    key_cats = np.array(
-                        [
-                            key
-                            for key in data.obs.keys()
-                            if is_categorical_dtype(data.obs[key])
-                        ]
-                    )
-                    num_cats = [
-                        len(data.obs[key].cat.categories) == df.shape[0]
-                        for key in key_cats
+            keys = [keys] if isinstance(keys, str) else keys
+            key = keys[0]
+
+            if key in data.var_names:
+                df = obs_df(data, keys, layer=layer)
+            elif key in data.obs_names:
+                df = var_df(data, keys, layer=layer)
+            else:
+                if keys_split is not None:
+                    keys = [
+                        k
+                        for k in list(data.obs.keys()) + list(data.var.keys())
+                        if key in k and keys_split in k
                     ]
-                    if np.sum(num_cats) == 1:
-                        index = data.obs[key_cats[num_cats][0]].cat.categories
-                        if (
-                            columns is None
-                            and len(df.shape) > 1
-                            and df.shape[0] == df.shape[1]
-                        ):
-                            columns = index
-            elif isinstance(index, str) and index in data.obs.keys():
-                index = pd.Categorical(data.obs[index]).categories
-            if columns is None and s_key == "layers":
-                columns = data.var_names
-            elif isinstance(columns, str) and columns in data.obs.keys():
-                columns = pd.Categorical(data.obs[columns]).categories
+                    key = keys[0]
+                s_key = [s for (s, d_key) in zip(s_keys, d_keys) if key in d_key]
+                if len(s_key) == 0:
+                    raise ValueError(
+                        f"'{key}' not found in any of {', '.join(s_keys)}."
+                    )
+                if len(s_key) > 1:
+                    logg.warn(f"'{key}' found multiple times in {', '.join(s_key)}.")
+
+                s_key = s_key[-1]
+                df = getattr(data, s_key)[keys if len(keys) > 1 else key]
+                if key_add is not None:
+                    df = df[key_add]
+                if index is None:
+                    index = (
+                        data.var_names
+                        if s_key == "varm"
+                        else data.obs_names
+                        if s_key in {"obsm", "layers"}
+                        else None
+                    )
+                    if index is None and s_key == "uns" and hasattr(df, "shape"):
+                        key_cats = np.array(
+                            [
+                                key
+                                for key in data.obs.keys()
+                                if is_categorical_dtype(data.obs[key])
+                            ]
+                        )
+                        num_cats = [
+                            len(data.obs[key].cat.categories) == df.shape[0]
+                            for key in key_cats
+                        ]
+                        if np.sum(num_cats) == 1:
+                            index = data.obs[key_cats[num_cats][0]].cat.categories
+                            if (
+                                columns is None
+                                and len(df.shape) > 1
+                                and df.shape[0] == df.shape[1]
+                            ):
+                                columns = index
+                elif isinstance(index, str) and index in data.obs.keys():
+                    index = pd.Categorical(data.obs[index]).categories
+                if columns is None and s_key == "layers":
+                    columns = data.var_names
+                elif isinstance(columns, str) and columns in data.obs.keys():
+                    columns = pd.Categorical(data.obs[columns]).categories
     elif isinstance(data, pd.DataFrame):
         if isinstance(keys, str) and "*" in keys:
             keys, keys_split = keys.split("*")

scvelo/plotting/utils.py

@@ -5,7 +5,6 @@
 
 import numpy as np
 import pandas as pd
-from numpy.core._exceptions import UFuncTypeError
 from pandas import Index
 from scipy import stats
 from scipy.sparse import issparse
@@ -24,6 +23,12 @@
 from scvelo.tools.utils import strings_to_categoricals
 from . import palettes
 
+try:
+    from numpy.core._exceptions import UFuncTypeError
+except ModuleNotFoundError:
+    from numpy._core._exceptions import UFuncTypeError
+
+
 """helper functions"""
 
 
@@ -1460,6 +1465,7 @@ def hist(
     ax=None,
     dpi=None,
     show=True,
+    save=None,
     **kwargs,
 ):
     """Plot a histogram.
@@ -1530,6 +1536,9 @@ def hist(
         Figure dpi.
     show: `bool`, optional (default: `None`)
         Show the plot, do not return axis.
+    save: `bool` or `str`, optional (default: `None`)
+        If `True` or a `str`, save the figure. A string is appended to the default filename.
+        Infer the filetype if ending on {'.pdf', '.png', '.svg'}.
 
     Returns
     -------
@@ -1658,10 +1667,9 @@ def log_fmt(x, pos):
     if rcParams["savefig.transparent"]:
         ax.patch.set_alpha(0)
 
-    if not show:
+    savefig_or_show(dpi=dpi, save=save, show=show)
+    if show is False:
         return ax
-    else:
-        pl.show()
 
 
 # TODO: Add docstrings

scvelo/preprocessing/moments.py

@@ -1,3 +1,5 @@
+import warnings
+
 import numpy as np
 from scipy.sparse import csr_matrix, issparse
 
@@ -60,6 +62,12 @@ def moments(
         normalize_per_cell(adata)
 
     if n_neighbors is not None and n_neighbors > get_n_neighs(adata):
+        warnings.warn(
+            "Automatic neighbor calculation is deprecated since scvelo==0.4.0 and will be removed in a future version "
+            "of scVelo. Please compute neighbors first with Scanpy.",
+            DeprecationWarning,
+            stacklevel=2,
+        )
         neighbors(
             adata,
             n_neighbors=n_neighbors,

scvelo/preprocessing/neighbors.py

@@ -37,7 +37,7 @@ def _get_scanpy_neighbors(adata: AnnData, **kwargs):
     with warnings.catch_warnings():  # ignore numba warning (umap/issues/252)
         warnings.simplefilter("ignore")
         neighbors = Neighbors(adata)
-        neighbors.compute_neighbors(write_knn_indices=True, **kwargs)
+        neighbors.compute_neighbors(**kwargs)
     logg.switch_verbosity("on", module="scanpy")
 
     return neighbors
@@ -124,6 +124,12 @@ def _set_pca(adata, n_pcs: Optional[int], use_highly_variable: bool):
         or n_pcs is not None
         and n_pcs > adata.obsm["X_pca"].shape[1]
     ):
+        warnings.warn(
+            "Automatic computation of PCA is deprecated since scvelo==0.4.0 and will be removed in a future version "
+            "of scVelo. Please compute PCA with Scanpy first.",
+            DeprecationWarning,
+            stacklevel=2,
+        )
         if use_highly_variable and "highly_variable" in adata.var.keys():
             n_vars = np.sum(adata.var["highly_variable"])
         else:
@@ -213,6 +219,12 @@ def neighbors(
     distances : `.obsp`
         Sparse matrix of distances for each pair of neighbors.
     """
+    warnings.warn(
+        "`neighbors` is deprecated since scvelo==0.4.0 and will be removed in a future version "
+        "of scVelo. Please compute neighbors with Scanpy.",
+        DeprecationWarning,
+        stacklevel=2,
+    )
     adata = adata.copy() if copy else adata
 
     use_rep = _get_rep(adata=adata, use_rep=use_rep, n_pcs=n_pcs)

scvelo/tools/_em_model_core.py

@@ -419,6 +419,7 @@ def recover_dynamics(
     copy=False,
     n_jobs=None,
     backend="loky",
+    show_progress_bar: bool = True,
     **kwargs,
 ):
     """Recovers the full splicing kinetics of specified genes.
@@ -476,6 +477,8 @@ def recover_dynamics(
     backend: `str` (default: "loky")
         Backend used for multiprocessing. See :class:`joblib.Parallel` for valid
         options.
+    show_progress_bar
+        Whether to show a progress bar.
 
     Returns
     -------
@@ -557,7 +560,7 @@ def recover_dynamics(
         unit="gene",
         as_array=False,
         backend=backend,
-        show_progress_bar=len(var_names) > 9,
+        show_progress_bar=show_progress_bar,
     )(
         adata=adata,
         use_raw=use_raw,

scvelo/tools/_vi_model.py

@@ -127,7 +127,6 @@ def train(
         max_epochs: Optional[int] = 500,
         lr: float = 1e-2,
         weight_decay: float = 1e-2,
-        use_gpu: Optional[Union[str, int, bool]] = None,
         accelerator: str = "auto",
         devices: Union[int, List[int], str] = "auto",
         train_size: float = 0.9,
@@ -149,9 +148,14 @@ def train(
             Learning rate for optimization
         weight_decay
             Weight decay for optimization
-        use_gpu
-            Use default GPU if available (if None or True), or index of GPU to use (if int),
-            or name of GPU (if str, e.g., `'cuda:0'`), or use CPU (if False).
+        accelerator
+            Supports passing different accelerator types `("cpu", "gpu", "tpu", "ipu", "hpu", "mps, "auto")` as well as
+            custom accelerator instances.
+        devices
+            The devices to use. Can be set to a non-negative index (`int` or `str`), a sequence of device indices
+            (`list` or comma-separated `str`), the value `-1` to indicate all available devices, or `"auto"` for
+            automatic selection based on the chosen `accelerator`. If set to `"auto"` and `accelerator` is not
+            determined to be `"cpu"`, then `devices` will be set to the first available device.
         train_size
             Size of training set in the range [0.0, 1.0].
         validation_size
@@ -195,7 +199,8 @@ def train(
             training_plan=training_plan,
             data_splitter=data_splitter,
             max_epochs=max_epochs,
-            use_gpu=use_gpu,
+            accelerator=accelerator,
+            devices=devices,
             **trainer_kwargs,
         )
         return runner()