Python >=3.9 compat, update packaging

docs/ext/nbsplit.py

@@ -23,7 +23,7 @@ def is_hv_init(cell):
 def split_and_parse(app):
     for src_nb, out_dir in app.config.nbsplit_dict.items():
         if not os.path.exists(src_nb):
-            warnings.warn("notebook not found: {}".format(src_nb), RuntimeWarning)
+            warnings.warn(f"notebook not found: {src_nb}", RuntimeWarning)
             continue
         srcpath = os.path.split(src_nb)[0]
         outpath = os.path.join(os.path.abspath(app.srcdir), out_dir)
@@ -33,7 +33,7 @@ def split_and_parse(app):
         if os.path.exists(img_path):
             shutil.copytree(img_path, os.path.join(outpath, "img"))
         # read source notebook
-        with open(src_nb, mode="r") as notebook:
+        with open(src_nb) as notebook:
             jnote = json.load(notebook)
         cells = jnote["cells"]
         # split and process cells
@@ -46,7 +46,7 @@ def split_and_parse(app):
         hv_init["execution_count"] = None
         # write notebooks
         for isplit, start in enumerate(split):
-            fname = "notebook_{}.ipynb".format(isplit)
+            fname = f"notebook_{isplit}.ipynb"
             meta = jnote["metadata"].copy()
             meta["name"] = fname
             try:

docs/source/conf.py

@@ -33,7 +33,7 @@ def _as_gufunc(pyfunc):
 # -- Project information -----------------------------------------------------
 
 project = "MiniAn"
-copyright = "2018-%s, MiniAn Developers" % datetime.datetime.now().year
+copyright = f"2018-{datetime.datetime.now().year}, MiniAn Developers"
 
 
 # -- General configuration ---------------------------------------------------

minian/__init__.py

@@ -1,8 +1,12 @@
 import dask as da
 import os
+from importlib.metadata import version
 from .utilities import custom_arr_optimize, custom_delay_optimize
 
-__version__ = "1.2.1"
+try:
+    __version__ = version("minian")
+except:
+    __version__ = "0.0.0"
 
 da.config.set(
     array_optimize=custom_arr_optimize, delayed_optimize=custom_delay_optimize
@@ -28,3 +32,4 @@
 # os.environ["LD_PRELOAD"] = "~/.conda/envs/minian-dev/lib/libjemalloc.so"
 # alternatively one can limit the malloc pool, which is the default for minian
 os.environ["MALLOC_MMAP_THRESHOLD_"] = "16384"
+

minian/cnmf.py

@@ -1,7 +1,7 @@
 import functools as fct
 import os
 import warnings
-from typing import List, Optional, Tuple, Union
+from typing import Optional, Union
 
 import cv2
 import cvxpy as cvx
@@ -235,7 +235,7 @@ def update_spatial(
     normalize=True,
     size_thres=(9, None),
     in_memory=False,
-) -> Tuple[xr.DataArray, xr.DataArray, xr.DataArray, xr.DataArray]:
+) -> tuple[xr.DataArray, xr.DataArray, xr.DataArray, xr.DataArray]:
     """
     Update spatial components given the input data and temporal dynamic for each
     cell.
@@ -387,7 +387,7 @@ def update_spatial(
                     f=f_in,
                 )
             else:
-                cur_blk = darr.array(sparse.zeros((cur_sub.shape)))
+                cur_blk = darr.array(sparse.zeros(cur_sub.shape))
             A_new[hblk, wblk, 0] = cur_blk
         A_new = darr.block(A_new.tolist())
     else:
@@ -437,7 +437,7 @@ def update_spatial(
         )
     else:
         mask = (A_new.sum(["height", "width"]) > 0).compute()
-    print("{} out of {} units dropped".format(len(mask) - mask.sum().values, len(mask)))
+    print(f"{len(mask) - mask.sum().values} out of {len(mask)} units dropped")
     A_new = A_new.sel(unit_id=mask)
     if normalize:
         norm_fac = A_new.max(["height", "width"]).compute()
@@ -673,7 +673,7 @@ def update_temporal(
     post_scal=False,
     scs_fallback=False,
     concurrent_update=False,
-) -> Tuple[
+) -> tuple[
     xr.DataArray, xr.DataArray, xr.DataArray, xr.DataArray, xr.DataArray, xr.DataArray
 ]:
     """
@@ -889,10 +889,10 @@ def update_temporal(
         # issue a warning if expected memory demand is larger than 1G
         if mem_demand > 1e3:
             warnings.warn(
-                "{} cells will be updated togeter, "
-                "which takes roughly {} MB of memory. "
+                f"{cur_YrA.shape[0]} cells will be updated togeter, "
+                f"which takes roughly {mem_demand} MB of memory. "
                 "Consider merging the units "
-                "or changing jac_thres".format(cur_YrA.shape[0], mem_demand)
+                "or changing jac_thres"
             )
         if not warm_start:
             cur_C = None
@@ -992,7 +992,7 @@ def update_temporal(
         int_ds["g"],
     )
     mask = (S_new.sum("frame") > 0).compute()
-    print("{} out of {} units dropped".format((~mask).sum().values, len(Ymask)))
+    print(f"{(~mask).sum().values} out of {len(Ymask)} units dropped")
     C_new, S_new, b0_new, c0_new, g = (
         C_new[mask],
         S_new[mask],
@@ -1098,7 +1098,7 @@ def update_temporal_block(
     med_wd=None,
     concurrent=False,
     **kwargs
-) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
+) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
     """
     Update temporal components given residule traces of a group of cells.
 
@@ -1203,7 +1203,7 @@ def update_temporal_block(
 
 def update_temporal_cvxpy(
     y: np.ndarray, g: np.ndarray, sn: np.ndarray, A=None, bseg=None, **kwargs
-) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
+) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
     """
     Solve the temporal update optimization problem using `cvxpy`
 
@@ -1378,7 +1378,7 @@ def update_temporal_cvxpy(
                     raise ValueError
             except (cvx.SolverError, ValueError):
                 warnings.warn(
-                    "problem status is {}, returning zero".format(prob.status),
+                    f"problem status is {prob.status}, returning zero",
                     RuntimeWarning,
                 )
                 return [np.zeros(c.shape, dtype=float)] * 4
@@ -1395,10 +1395,10 @@ def update_temporal_cvxpy(
 def unit_merge(
     A: xr.DataArray,
     C: xr.DataArray,
-    add_list: Optional[List[xr.DataArray]] = None,
+    add_list: Optional[list[xr.DataArray]] = None,
     thres_corr=0.9,
     noise_freq: Optional[float] = None,
-) -> Tuple[xr.DataArray, xr.DataArray, Optional[List[xr.DataArray]]]:
+) -> tuple[xr.DataArray, xr.DataArray, Optional[list[xr.DataArray]]]:
     """
     Merge cells given spatial footprints and temporal components
 
@@ -1416,8 +1416,8 @@ def unit_merge(
         Spatial footprints of the cells.
     C : xr.DataArray
         Temporal component of cells.
-    add_list : List[xr.DataArray], optional
-        List of additional variables to be merged. By default `None`.
+    add_list : list[xr.DataArray], optional
+        list of additional variables to be merged. By default `None`.
     thres_corr : float, optional
         The threshold of correlation. Any pair of spatially overlapping cells
         with correlation higher than this threshold will be transitively grouped
@@ -1433,8 +1433,8 @@ def unit_merge(
         Merged spatial footprints of cells.
     C_merge : xr.DataArray
         Merged temporal components of cells.
-    add_list : List[xr.DataArray], optional
-        List of additional merged variables. Only returned if input `add_list`
+    add_list : list[xr.DataArray], optional
+        list of additional merged variables. Only returned if input `add_list`
         is not `None`.
     """
     print("computing spatial overlap")
@@ -1799,7 +1799,7 @@ def construct_comput(edf, pxs):
             npxs.append(len(pixels))
             pixels = set()
             eg_ls = []
-    print("pixel recompute ratio: {}".format(sum(npxs) / G.number_of_nodes()))
+    print(f"pixel recompute ratio: {sum(npxs) / G.number_of_nodes()}")
     print("computing correlations")
     corr_ls = da.compute(corr_ls)[0]
     corr = pd.Series(np.concatenate(corr_ls), index=np.concatenate(idx_ls), name="corr")
@@ -1847,7 +1847,7 @@ def adj_corr(
     )
 
 
-def adj_list(G: nx.Graph) -> List[np.ndarray]:
+def adj_list(G: nx.Graph) -> list[np.ndarray]:
     """
     Generate adjacency list representation from graph.
 
@@ -1858,7 +1858,7 @@ def adj_list(G: nx.Graph) -> List[np.ndarray]:
 
     Returns
     -------
-    adj_ls : List[np.ndarray]
+    adj_ls : list[np.ndarray]
         The adjacency list representation of graph.
     """
     gdict = nx.to_dict_of_dicts(G)
@@ -1936,7 +1936,7 @@ def idx_corr(X: np.ndarray, ridx: np.ndarray, cidx: np.ndarray) -> np.ndarray:
 
 def update_background(
     Y: xr.DataArray, A: xr.DataArray, C: xr.DataArray, b: xr.DataArray = None
-) -> Tuple[xr.DataArray, xr.DataArray]:
+) -> tuple[xr.DataArray, xr.DataArray]:
     """
     Update background terms given spatial and temporal components of cells.
 

minian/cross_registration.py

@@ -1,5 +1,5 @@
 import itertools as itt
-from typing import Iterable
+from collections.abc import Iterable
 
 import dask as da
 import networkx as nx

minian/initialization.py

@@ -1,7 +1,7 @@
 import functools as fct
 import itertools as itt
 import os
-from typing import Optional, Tuple, Union
+from typing import Optional, Union
 
 import cv2
 import dask as da
@@ -192,7 +192,7 @@ def gmm_refine(
     n_components=2,
     valid_components=1,
     mean_mask=True,
-) -> Tuple[pd.DataFrame, xr.DataArray, GaussianMixture]:
+) -> tuple[pd.DataFrame, xr.DataArray, GaussianMixture]:
     """
     Filter seeds by fitting a GMM to peak-to-peak values.
 
@@ -282,7 +282,7 @@ def pnr_refine(
     thres: Union[float, str] = 1.5,
     q=(0.1, 99.9),
     med_wnd: Optional[int] = None,
-) -> Tuple[pd.DataFrame, xr.DataArray, Optional[GaussianMixture]]:
+) -> tuple[pd.DataFrame, xr.DataArray, Optional[GaussianMixture]]:
     """
     Filter seeds by thresholding peak-to-noise ratio.
 
@@ -389,7 +389,7 @@ def ptp_q(a: np.ndarray, q: tuple) -> float:
     a : np.ndarray
         Input array.
     q : tuple
-        Tuple specifying low and high percentile values.
+        tuple specifying low and high percentile values.
 
     Returns
     -------

minian/install.py

@@ -1,6 +1,7 @@
 import argparse
 import os
 import requests
+from importlib.metadata import version
 
 NOTEBOOK_FILES = [
     "pipeline.ipynb",
@@ -13,7 +14,10 @@
 DEMO_FILES = [f"demo_movies/msCam{i}.avi" for i in range(1, 11)] + [
     f"demo_data/session{i}/minian.nc" for i in range(1, 3)
 ]
-VERSION = "1.2.1"
+try:
+    VERSION = version("minian")
+except:
+    VERSION = "0.0.0"
 
 
 def _get_file(filename: str, version: str):
@@ -57,7 +61,7 @@ def main():
         "-v",
         action="store",
         default=VERSION,
-        help="Git repo branch or tag name, default {}".format(VERSION),
+        help=f"Git repo branch or tag name, default {VERSION}",
     )
     args = parser.parse_args()