Modify pyproject.toml to make it publishable on PyPi

README.md

@@ -2,10 +2,10 @@ This script could be used for conversion of a .tiff file to .zarr format with OM
 #### How to run
 1. open command line terminal
 2. install tif_to_zarr
-    ``pip install tif_to_zarr``
+    ``pip install tiff-to-zarr``
 5. run script using cli:
-    ``tif_to_zarr --src=PATH_TO_SOURCE_DIRECTORY/input_file.tif --dest=PATH_TO_DEST_DIRECTORY/output_file.zarr``
+    ``tiff-to-zarr --src=PATH_TO_SOURCE_DIRECTORY/input_file.tif --dest=PATH_TO_DEST_DIRECTORY/output_file.zarr --num_workers=20, --cluster=local``
 6. to convert a tiff file to zarr with custom metadata values, you can run smthg similar to this:
-``tif_to_zarr --src=path_to_source_tif(3d or stack)  --dest=path_to_output_zarr --num_workers=20 --cluster=local(or lsf) --zarr_chunks 13 128 128 --axes x z y --scale 4.0 5.0 6.0 --translation 1.0 2.0 3.0 --units nanometer nanometer nanometer``
+``tiff-to-zarr --src=path_to_source_tif(3d or stack)  --dest=path_to_output_zarr --num_workers=20 --cluster=local(or lsf) --zarr_chunks 13 128 128 --axes x z y --scale 4.0 5.0 6.0 --translation 1.0 2.0 3.0 --units nanometer nanometer nanometer``
 7. To get the list of options, you may run this:
-``tif_to_zarr --help``
+``tiff-to-zarr --help``

pyproject.toml

@@ -1,6 +1,7 @@
 [tool.poetry]
-name = "tif_to_zarr"
-version = "0.6.0"
+packages = [{include = "tiff_to_zarr", from = "src"}]
+name = "tiff-to-zarr"
+version = "0.6.13"
 description = ""
 authors = ["yurii_zubov"]
 license = "BSD 3-Clause License"
@@ -24,7 +25,7 @@ logging = "^0.4.9.6"
 xarray-multiscale = "^2.1.0"
 
 [tool.poetry.scripts]
-tif_to_zarr = 'tif_to_zarr.tif_to_zarr:tif_to_zarr'
+tiff-to-zarr = 'tiff_to_zarr.tiff_to_zarr:cli'
 
 [build-system]
 requires = ["poetry-core"]

src/tiff_stack.py → src/tiff_to_zarr/tiff_stack.py

@@ -7,7 +7,7 @@
 import dask.array as da
 from natsort import natsorted
 from glob import glob
-from tiff_volume import TiffVolume
+from tiff_to_zarr.tiff_volume import TiffVolume
 
 
 class TiffStack(TiffVolume):
@@ -72,7 +72,6 @@ def write_tile_slab_to_zarr(
     # parallel writing of tiff stack into zarr array
     def write_to_zarr(self, zarray: zarr.Array, client: Client):
         chunks_list = np.arange(0, zarray.shape[0], zarray.chunks[0])
-        print(chunks_list)
 
         start = time.time()
         fut = client.map(

src/tiff_to_zarr.py → src/tiff_to_zarr/tiff_to_zarr.py

@@ -6,11 +6,11 @@
 from dask_jobqueue import LSFCluster
 from dask.distributed import Client, LocalCluster
 import time
-from tiff_stack import TiffStack
-from tiff_volume import TiffVolume
+from tiff_to_zarr.tiff_stack import TiffStack
+from tiff_to_zarr.tiff_volume import TiffVolume
 
 
-@click.command()
+@click.command("tiff-to-zarr")
 @click.option(
     "--src",
     "-s",
@@ -101,8 +101,6 @@ def cli(src, dest, num_workers, cluster, zarr_chunks, axes, translation, scale,
     elif src.endswith(".tif") or src.endswith(".tiff"):
         tiff_volume = TiffVolume(src, axes, scale, translation, units)
 
-    print(tiff_volume.shape)
-
     z_store = zarr.NestedDirectoryStore(dest)
     z_root = zarr.open(store=z_store, mode="a")
     z_arr = z_root.require_dataset(
@@ -114,11 +112,9 @@ def cli(src, dest, num_workers, cluster, zarr_chunks, axes, translation, scale,
     )
 
     # write in parallel to zarr using dask
-    start_time = time.time()
     client.cluster.scale(num_workers)
     tiff_volume.write_to_zarr(z_arr, client)
     client.cluster.scale(0)
-    print(time.time() - start_time)
     # populate zarr metadata
     tiff_volume.populate_zarr_attrs(z_root)
 

src/tiff_volume.py → src/tiff_to_zarr/tiff_volume.py

@@ -27,7 +27,6 @@ def __init__(
 
         self.zarr_store = imread(os.path.join(src_path), aszarr=True)
         self.zarr_arr = zarr.open(self.zarr_store)
-        print(type(self.zarr_arr))
 
         self.shape = self.zarr_arr.shape
         self.dtype = self.zarr_arr.dtype
@@ -43,7 +42,6 @@ def __init__(
     # multiprocess writing tiff stack into zarr array
     def write_to_zarr(self, zarray: zarr.Group, client: Client):
         chunks_list = np.arange(0, zarray.shape[0], zarray.chunks[0])
-        print(chunks_list)
 
         src_path = copy.copy(self.src_path)