steinbock.io
img_dtype
mask_dtype
list_data_files(data_dir, base_files=None)
Source code in steinbock/io.py
def list_data_files(
data_dir: Union[str, PathLike],
base_files: Optional[Sequence[Union[str, PathLike]]] = None,
) -> List[Path]:
if base_files is not None:
return _list_related_files(base_files, data_dir, ".csv")
return sorted(Path(data_dir).rglob("*.csv"))
list_image_files(img_dir, base_files=None)
Source code in steinbock/io.py
def list_image_files(
img_dir: Union[str, PathLike],
base_files: Optional[Sequence[Union[str, PathLike]]] = None,
) -> List[Path]:
if base_files is not None:
return _list_related_files(base_files, img_dir, ".tiff")
return sorted(Path(img_dir).rglob("*.tiff"))
list_mask_files(mask_dir, base_files=None)
Source code in steinbock/io.py
def list_mask_files(
mask_dir: Union[str, PathLike],
base_files: Optional[Sequence[Union[str, PathLike]]] = None,
) -> List[Path]:
if base_files is not None:
return _list_related_files(base_files, mask_dir, ".tiff")
return sorted(Path(mask_dir).rglob("*.tiff"))
list_neighbors_files(neighbors_dir, base_files=None)
Source code in steinbock/io.py
def list_neighbors_files(
neighbors_dir: Union[str, PathLike],
base_files: Optional[Sequence[Union[str, PathLike]]] = None,
) -> List[Path]:
if base_files is not None:
return _list_related_files(base_files, neighbors_dir, ".csv")
return sorted(Path(neighbors_dir).rglob("*.csv"))
read_data(data_stem)
Source code in steinbock/io.py
def read_data(data_stem: Union[str, PathLike]) -> pd.DataFrame:
data_file = _as_path_with_suffix(data_stem, ".csv")
return pd.read_csv(
data_file, sep=",|;", index_col="Object", engine="python"
)
read_image(img_file, keep_suffix=False, use_imageio=False, native_dtype=False)
Source code in steinbock/io.py
def read_image(
img_file: Union[str, PathLike],
keep_suffix: bool = False,
use_imageio: bool = False,
native_dtype: bool = False,
) -> np.ndarray:
if not keep_suffix:
img_file = _as_path_with_suffix(
img_file, ".tiff", replace_ome_suffix=False
)
if use_imageio:
img = imageio.volread(img_file)
else:
img = tifffile.imread(img_file)
while img.ndim > 3 and img.shape[0] == 1:
img = img.sqeeze(axis=0)
while img.ndim > 3 and img.shape[-1] == 1:
img = img.sqeeze(axis=img.ndim - 1)
if img.ndim == 2:
img = img[np.newaxis, :, :]
elif img.ndim != 3:
raise ValueError(f"Unsupported number of image dimensions: {img_file}")
if not native_dtype:
img = _to_dtype(img, img_dtype)
return img
read_image_info(image_info_stem)
Source code in steinbock/io.py
def read_image_info(image_info_stem: Union[str, PathLike]) -> pd.DataFrame:
image_info_file = _as_path_with_suffix(image_info_stem, ".csv")
image_info = pd.read_csv(
image_info_file,
sep=",|;",
dtype={
"image": pd.StringDtype(),
"width_px": pd.UInt16Dtype(),
"height_px": pd.UInt16Dtype(),
"num_channels": pd.UInt8Dtype(),
},
engine="python",
)
for required_col in ("image", "width_px", "height_px", "num_channels"):
if required_col not in image_info:
raise ValueError(
f"Missing '{required_col}' column in {image_info_file}"
)
for notnan_col in ("image", "width_px", "height_px", "num_channels"):
if notnan_col in image_info and image_info[notnan_col].isna().any():
raise ValueError(
f"Missing values for '{notnan_col}' in {image_info_file}"
)
for unique_col in ("image",):
if unique_col in image_info:
if image_info[unique_col].dropna().duplicated().any():
raise ValueError(
f"Duplicated values for '{unique_col}'"
f" in {image_info_file}"
)
return image_info
read_mask(mask_stem)
Source code in steinbock/io.py
def read_mask(mask_stem: Union[str, PathLike]) -> np.ndarray:
mask_file = _as_path_with_suffix(mask_stem, ".tiff")
mask = tifffile.imread(mask_file)
while mask.ndim > 2 and mask.shape[0] == 1:
mask = mask.sqeeze(axis=0)
while mask.ndim > 2 and mask.shape[-1] == 1:
mask = mask.sqeeze(axis=mask.ndim - 1)
if mask.ndim != 2:
raise ValueError(f"Unsupported number of mask dimensions: {mask_file}")
return _to_dtype(mask, mask_dtype)
read_neighbors(neighbors_stem)
Source code in steinbock/io.py
def read_neighbors(neighbors_stem: Union[str, PathLike]) -> pd.DataFrame:
neighbors_file = _as_path_with_suffix(neighbors_stem, ".csv")
return pd.read_csv(
neighbors_file,
sep=",|;",
usecols=["Object", "Neighbor", "Distance"],
dtype={
"Object": mask_dtype,
"Neighbor": mask_dtype,
"Distance": np.float32,
},
engine="python",
)
read_panel(panel_stem, kept_only=True)
Source code in steinbock/io.py
def read_panel(
panel_stem: Union[str, PathLike], kept_only: bool = True
) -> pd.DataFrame:
panel_file = _as_path_with_suffix(panel_stem, ".csv")
panel = pd.read_csv(
panel_file,
sep=",|;",
dtype={
"channel": pd.StringDtype(),
"name": pd.StringDtype(),
"keep": pd.BooleanDtype(),
},
engine="python",
true_values=["1"],
false_values=["0"],
)
for required_col in ("channel", "name"):
if required_col not in panel:
raise ValueError(
f"Missing '{required_col}' column in {panel_file}"
)
for notnan_col in ("channel", "keep"):
if notnan_col in panel and panel[notnan_col].isna().any():
raise ValueError(
f"Missing values for '{notnan_col}' in {panel_file}"
)
for unique_col in ("channel", "name"):
if unique_col in panel:
if panel[unique_col].dropna().duplicated().any():
raise ValueError(
f"Duplicated values for '{unique_col}' in {panel_file}"
)
if kept_only and "keep" in panel:
panel = panel.loc[panel["keep"].astype(bool), :]
return panel
write_data(data, data_stem)
Source code in steinbock/io.py
def write_data(data: pd.DataFrame, data_stem: Union[str, PathLike]) -> Path:
data_file = _as_path_with_suffix(data_stem, ".csv")
data = data.reset_index()
data.to_csv(data_file, index=False)
return data_file
write_image(img, img_stem, ignore_dtype=False)
Source code in steinbock/io.py
def write_image(
img: np.ndarray, img_stem: Union[str, PathLike], ignore_dtype: bool = False
) -> Path:
if not ignore_dtype:
img = _to_dtype(img, img_dtype)
img_file = _as_path_with_suffix(img_stem, ".tiff")
tifffile.imwrite(img_file, data=img, imagej=True)
return img_file
write_image_info(image_info, image_info_stem)
Source code in steinbock/io.py
def write_image_info(
image_info: pd.DataFrame, image_info_stem: Union[str, PathLike]
) -> Path:
image_info_file = _as_path_with_suffix(image_info_stem, ".csv")
image_info.to_csv(image_info_file, index=False)
return image_info_file
write_mask(mask, mask_stem)
Source code in steinbock/io.py
def write_mask(mask: np.ndarray, mask_stem: Union[str, PathLike]) -> Path:
mask = _to_dtype(mask, mask_dtype)
mask_file = _as_path_with_suffix(mask_stem, ".tiff")
tifffile.imwrite(mask_file, data=mask, imagej=True)
return mask_file
write_neighbors(neighbors, neighbors_stem)
Source code in steinbock/io.py
def write_neighbors(
neighbors: pd.DataFrame, neighbors_stem: Union[str, PathLike]
) -> Path:
neighbors_file = _as_path_with_suffix(neighbors_stem, ".csv")
neighbors = neighbors.loc[:, ["Object", "Neighbor", "Distance"]].astype(
{
"Object": mask_dtype,
"Neighbor": mask_dtype,
"Distance": np.float32,
}
)
neighbors.to_csv(neighbors_file, index=False)
return neighbors_file
write_panel(panel, panel_stem)
Source code in steinbock/io.py
def write_panel(panel: pd.DataFrame, panel_stem: Union[str, PathLike]) -> Path:
panel_file = _as_path_with_suffix(panel_stem, ".csv")
panel = panel.copy()
for col in panel.columns:
if panel[col].convert_dtypes().dtype == pd.BooleanDtype():
panel[col] = panel[col].astype(pd.UInt8Dtype())
panel.to_csv(panel_file, index=False)
return panel_file