- add option to skip autoscaling brightness when saving as mp4

- let coords_pandas also deal with polars dataframes

ndbioimage/__init__.py

@@ -418,7 +418,7 @@ class Imread(np.lib.mixins.NDArrayOperatorsMixin, ABC):
             )
             return new
 
-    def __getstate__(self) -> dict[str:Any]:
+    def __getstate__(self) -> dict[str, Any]:
         return {key: value for key, value in self.__dict__.items() if key not in self.do_not_pickle} | {
             "cache_size": self.cache.maxlen
         }
@@ -1100,6 +1100,7 @@ class Imread(np.lib.mixins.NDArrayOperatorsMixin, ABC):
         t: str | int | Sequence[int] = None,  # noqa
         colors: tuple[str] = None,
         brightnesses: tuple[float] = None,
+        no_scale_movie_brightnesses: bool = False,
         scale: int = None,
         bar: bool = True,
         speed: float = None,
@@ -1133,9 +1134,20 @@ class Imread(np.lib.mixins.NDArrayOperatorsMixin, ABC):
             frame = np.dstack([255 * frame * i for i in color])
             return np.clip(np.round(frame), 0, 255).astype("uint8")
 
+        if no_scale_movie_brightnesses and self.dtype.kind != "f":
+            info = np.iinfo(self.dtype)
+            ab = list(zip(*[(info.min, info.max) for _ in range(self.shape["c"])]))
+        elif no_scale_movie_brightnesses:
+            ab = list(zip(*[(0, 1) for _ in range(self.shape["c"])]))
+        else:
             ab = list(zip(*[get_ab(i) for i in self.transpose("cztyx")]))  # type: ignore
         colors = colors or ("r", "g", "b")[: self.shape["c"]] + max(0, self.shape["c"] - 3) * ("w",)
-        brightnesses = brightnesses or (1,) * self.shape["c"]
+        if brightnesses is None:
+            brightnesses = (1,) * self.shape["c"]
+        elif len(brightnesses) == 1:
+            brightnesses = brightnesses * self.shape["c"]
+        elif len(brightnesses) != self.shape["c"]:
+            raise ValueError("brightnesses must have same length as shape[c]")
         scale = scale or 1
         shape_x = 2 * ((self.shape["x"] * scale + 1) // 2)
         shape_y = 2 * ((self.shape["y"] * scale + 1) // 2)
@@ -1538,6 +1550,13 @@ def main() -> None:
     parser.add_argument("-C", "--movie-colors", help="colors for channels in movie", type=str, nargs="*")
     parser.add_argument("-V", "--movie_speed", help="speed of move, default = 25 / 7", type=float, default=None)
     parser.add_argument("-B", "--movie-brightnesses", help="scale brightness of each channel", type=float, nargs="*")
+    parser.add_argument(
+        "-N",
+        "--no-scale-movie-brightnesses",
+        help="do not scale brightness of each channel,"
+        " if image file has an integer data type it's range will be scaled to [0, 1]",
+        action="store_true",
+    )
     parser.add_argument("-S", "--movie-scale", help="upscale movie xy size, int", type=float)
     args = parser.parse_args()
 
@@ -1559,6 +1578,7 @@ def main() -> None:
                         args.time,
                         args.movie_colors,
                         args.movie_brightnesses,
+                        args.no_scale_movie_brightnesses,
                         args.movie_scale,
                         bar=len(args.file) == 1,
                         speed=args.movie_speed,

ndbioimage/transforms.py

@@ -16,9 +16,14 @@ except ImportError:
     sitk = None
 
 try:
-    from pandas import DataFrame, Series, concat
+    import pandas as pd
 except ImportError:
-    DataFrame, Series, concat = None, None, None
+    pd = None
+
+try:
+    import polars as pl
+except ImportError:
+    pl = None
 
 
 if hasattr(yaml, "full_load"):
@@ -126,9 +131,13 @@ class Transforms(dict):
         return inverse
 
     def coords_pandas(self, array, channel_names, columns=None):
-        if isinstance(array, DataFrame):
-            return concat([self.coords_pandas(row, channel_names, columns) for _, row in array.iterrows()], axis=1).T
-        elif isinstance(array, Series):
+        if pd is None:
+            raise ImportError("pandas is not available")
+        if isinstance(array, pd.DataFrame):
+            return pd.concat(
+                [self.coords_pandas(row, channel_names, columns) for _, row in array.iterrows()], axis=1
+            ).T
+        elif isinstance(array, pd.Series):
             key = []
             if "C" in array:
                 key.append(channel_names[int(array["C"])])
@@ -459,14 +468,21 @@ class Transform:
         """
         if self.is_unity():
             return array.copy()
-        elif DataFrame is not None and isinstance(array, (DataFrame, Series)):
+        elif pd is not None and isinstance(array, (pd.DataFrame, pd.Series)):
             columns = columns or ["x", "y"]
             array = array.copy()
-            if isinstance(array, DataFrame):
+            if isinstance(array, pd.DataFrame):
                 array[columns] = self.coords(np.atleast_2d(array[columns].to_numpy()))
-            elif isinstance(array, Series):
+            elif isinstance(array, pd.Series):
                 array[columns] = self.coords(np.atleast_2d(array[columns].to_numpy()))[0]
             return array
+        elif pl is not None and isinstance(array, (pl.DataFrame, pl.LazyFrame)):
+            columns = columns or ["x", "y"]
+            if isinstance(array, pl.DataFrame):
+                xy = self.coords(np.atleast_2d(array.select(columns).to_numpy()))
+            elif isinstance(array, pl.LazyFrame):
+                xy = self.coords(np.atleast_2d(array.select(columns).collect().to_numpy()))
+            return array.with_columns(**{c: i for c, i in zip(columns, xy.T)})
         else:  # somehow we need to use the inverse here to get the same effect as when using self.frame
             return np.array([self.inverse.transform.TransformPoint(i.tolist()) for i in np.asarray(array)])
 

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ndbioimage"
-version = "2025.8.0"
+version = "2025.9.0"
 description = "Bio image reading, metadata and some affine registration."
 authors = [
     { name = "W. Pomp", email = "w.pomp@nki.nl" }