updates to ome_zarr

_includes/ome_zarr/ome_zarr_creation.md

@@ -2,7 +2,7 @@
 
 Create OME-Zarr data from data stored in monolithic file formats.
 
-- Download the tiff [image series](https://zenodo.org/records/14641498/files/tiff_series.zip?download=1) and extract them to a convenient local directory. \
+- Download the [tiff_series](https://zenodo.org/records/14641498/files/tiff_series.zip?download=1) dataset and extract them to a convenient local directory. \
 For example: `~/image_data_course/data/tiff/tiff_series`
 
 - Open the files

_includes/ome_zarr/ome_zarr_creation_BatchConvert.md

@@ -1,6 +1,8 @@
-**Perform parallelised conversion of image data collections to OME-Zarr using BatchConvert**
+#### Perform parallelised conversion of image data collections to OME-Zarr using BatchConvert
 
-As input, use the `tiff_series` dataset (`~/image_data_course/data/tiff/tiff_series`).
+**Important note: BatchConvert is currently only supported on unix-based systems**
+
+As input, use the `tiff_series` dataset.
 
 **Perform parallelised, independent conversion:**
 ```bash
@@ -55,3 +57,13 @@ Check the content of the `zarr_series_concatenated_rechunked` folder:
 ls /path/to/zarr_series_concatenated_rechunked
 ```
 Optional: Inspect the created OME-Zarr. Compare it to the one created earlier.
+
+
+
+```bash
+batchconvert omezarr input_path output_path 
+```
+
+```bash
+bfconvert input_path output_path 
+```

_includes/ome_zarr/ome_zarr_inspection_ome-zarr-py.md

@@ -1,6 +1,6 @@
 **Use the ome_zarr tool for the inspecting and downloading OME-Zarrs from s3:**
 
-Remote OME-Zarr data stored in a public s3 bucket can be inspected and downloaded using 
+Remote OME-Zarr data stored in a **public** s3 bucket can be inspected and downloaded using 
 the `ome-zarr-py` tool. 
 
 Inspect 3 different remote datasets:
@@ -20,8 +20,12 @@ ome_zarr info https://s3.embl.de/i2k-2020/platy-raw.ome.zarr
 **Optional:** Download the dataset `6001240.zarr` from s3 to a local path:
 
 ```bash
-# cd ~/ome_zarr_course/data/zarr
+# cd ~/image_data_formats/day1/ome_zarr
 cd /path/to/local/zarr # where you want to keep the data on your system
 ome_zarr download https://uk1s3.embassy.ebi.ac.uk/idr/zarr/v0.4/idr0062A/6001240.zarr
 ```
 
+**Optional:** Inspect the local dataset in the same way:
+```bash
+ome_zarr info /path/to/local/zarr/6001240.zarr
+```

_includes/ome_zarr/ome_zarr_visualisation_napari.md

@@ -1,20 +1,44 @@
-**Visualise the remote data using Napari together with the napari-ome-zarr plugin.**
+#### Visualise the remote data using Napari together with the napari-ome-zarr plugin.
 
-```
-napari --plugin napari-ome-zarr https://s3.embl.de/ome-zarr-course/data/ZARR/$USER/xyzct_8bit__mitosis.ome.zarr
+**Use command line:**
+``` bash
+napari --plugin napari-ome-zarr https://uk1s3.embassy.ebi.ac.uk/idr/zarr/v0.4/idr0062A/6001240.zarr
 ```
 
-```
-napari --plugin napari-ome-zarr https://s3.embl.de/ome-zarr-course/data/ZARR/$USER/xyz_8bit_calibrated__fib_sem_crop.ome.zarr
-```
+**Use Python code:**
 
-**Optional: visualise local OME-Zarr data in the same way:**
-```
-napari --plugin napari-ome-zarr ~/data/ZARR/xyzct_8bit__mitosis.ome.zarr
-```
+Approach 1: Open the full OME-Zarr from the top level url:
+```python
+import napari
+import zarr, dask.array as da
+
+v = napari.Viewer()
+v.open("https://uk1s3.embassy.ebi.ac.uk/idr/zarr/v0.4/idr0062A/6001240.zarr",
+       plugin = 'napari-ome-zarr'
+       )
+napari.run()
+```
+Note that this approach automates a lot of tasks for the user,
+discovering look-up tables, pixel scalings and units from the OME-Zarr metadata.
 
-**Optional: visualise big remote OME-Zarr data:**
-```
-napari --plugin napari-ome-zarr https://s3.embl.de/i2k-2020/platy-raw.ome.zarr
+Approach 2: Read arrays and open them individually:
+```python
+import napari
+import zarr, dask.array as da
+
+url = "https://uk1s3.embassy.ebi.ac.uk/idr/zarr/v0.4/idr0062A/6001240.zarr"
+gr = zarr.open_group(url, mode = 'r')
+#array0 = da.from_zarr(gr[0])
+array2 = da.from_zarr(gr[2])
+#label_array0 = da.from_zarr(gr.labels['0'][0])
+label_array2 = da.from_zarr(gr.labels['0'][2])
+v = napari.Viewer()
+#v.add_image(array0, contrast_limits = (0, 2000), colormap = 'yellow')
+v.add_image(array2, contrast_limits = (0, 2000), colormap = 'red')
+#v.add_labels(label_array0)
+v.add_labels(label_array2)
+napari.run()
 ```
-Note that compared to BigDataViewer, there are more delays with Napari.
+
+Note that approach 2 is flexible but does not use any metadata. You have to
+specify the metadata to the viewer manually.

_includes/ome_zarr/open_ome_zarr_ome-zarr-py.md

@@ -2,20 +2,39 @@
 
 ```python
 import ome_zarr, zarr, pprint, os
+from ome_zarr import utils
 from ome_zarr.reader import Reader
 from ome_zarr.io import parse_url
 ```
+
+**Use the utils module to inspect, download or validate the remote data**
+
+Inspect:
+```python
+list(utils.info("https://uk1s3.embassy.ebi.ac.uk/EuBI/anna_steyer0/20160112_C.elegans_std_fullhead.zarr"))
+```
+
+Download:
+```python
+utils.download(input_path = "https://uk1s3.embassy.ebi.ac.uk/idr/zarr/v0.4/idr0062A/6001240.zarr",
+               output_dir = "/path/to/local/zarr") # eg: "/home/oezdemir/image_data_formats/day1/ome_zarr"
+```
+
+Validate:
+```python
+utils.view(input_path = "https://uk1s3.embassy.ebi.ac.uk/idr/zarr/v0.4/idr0062A/6001240.zarr")
+```
+
+
 **Read remote OME-Zarr:**
 ```python
 # local_path = f"{os.path.expanduser('~')}/image_data_course/data/zarr/6001240.zarr"
 remote_path = f"https://uk1s3.embassy.ebi.ac.uk/idr/zarr/v0.4/idr0062A/6001240.zarr"
 reader = Reader(parse_url(remote_path))
 # Note here that 'parse_url' can parse both remote and local urls.
-# No need for explicit use of s3fs.
 ```
 
-
-Note that ome-zarr-py uses the term 'node' for different zarr groups \
+Note that ome-zarr-py uses the term 'node' for different zarr groups
 and reads them in a flat list. 
 
 **Print the node information per resolution level:**

_includes/ome_zarr/save_ome_zarr_ome-zarr-py.md

@@ -22,7 +22,7 @@ url as input to the `parse_url` function.
 
 ```python
 # Specify the path where you want to write
-# output_path = f"{os.path.expanduser('~')}/ome_zarr_course/data/zarr/outputs/astronaut.zarr"
+# output_path = f"{os.path.expanduser('~')}/image_data_formats/day2/astronaut.zarr"
 output_path = "/path/to/astronaut.zarr"
 # Parse the url as a zarr store. Note that "mode = 'w'" enables writing to this store.
 store = parse_url(output_path, mode = 'w').store 
@@ -35,8 +35,8 @@ In order to create an image pyramid, one has to instantiate a scaler.
 This scaler requires the parameters: scale factor, number of resolution
 layers and downscaling method.
 ```python
-scaler = scale.Scaler(downscale=2, # Downscaling factor fox x and y axes
-                      max_layer=4, # Number of downscalings
+scaler = scale.Scaler(downscale=2, # Downscaling factor for x and y axes
+                      max_layer=4, # Number of downscalings = 5
                       method = 'nearest' # downscaling method
                       )
 ```

_includes/ome_zarr/update_rendering_metadata.md

@@ -2,14 +2,16 @@
 
 ```python
 import zarr, os
-from ome_zarr.io import parse_url
+from ome_zarr.io import parse_url, utils
 import matplotlib.colors as mcolors
 ```
 
 **At this stage inspect the image using the OME-Zarr validator:**
 
-```bash
-ome_zarr view ~/ome_zarr_course/data/zarr/outputs/astronaut.zarr
+```python
+# path = f"{os.path.expanduser('~')}/image_data_formats/day2/astronaut.zarr"
+path = "/path/to/astronaut.zarr"
+utils.view(path) 
 ```
 
 **Define a utility function to get the hex color code by simple color names**
@@ -26,7 +28,6 @@ def get_color_code(color_name):
 **Now add rendering metadata**
 
 ```python
-path = f"{os.path.expanduser('~')}/ome_zarr_course/data/zarr/outputs/astronaut.zarr"
 store = parse_url(path, mode = 'w').store # Create a zarr store to save the data. Note that this can also be an s3 object store.
 root = zarr.open_group(store=store)
 root.attrs["omero"] = {
@@ -55,7 +56,8 @@ root.attrs["omero"] = {
 
 It is important to know here that not all OME-Zarr readers recognize each of these settings. \
 Apply the validator again to the data to see the changes:
-```bash
-ome_zarr view ~/ome_zarr_course/data/zarr/outputs/astronaut.zarr
+```python
+path = "/path/to/astronaut.zarr"
+utils.view(path) 
 ```
 As the data looks valid, now visualize using different viewers to see if the rendering is working.

_includes/ome_zarr/validate_ome_zarr_ome-zarr-validator.md

@@ -13,19 +13,11 @@ of the OME-Zarr dataset.
 
 **Now do the same but with remote data:**
 
-```bash
-ome_zarr view https://uk1s3.embassy.ebi.ac.uk/idr/zarr/v0.4/idr0062A/6001240.zarr
-```
-Note that with the remote url it is possible to copy the link from your browser and share it with 
-your colleagues.
-
-**Take ome-zarr-py out of the loop and use the web browser directly:**
-
 Enter the following into your browser: `https://ome.github.io/ome-ngff-validator/?source=`
 
 Then paste the dataset url after the 'equal' sign, constructing the following link: 
 
 [https://ome.github.io/ome-ngff-validator/?source=https://uk1s3.embassy.ebi.ac.uk/idr/zarr/v0.4/idr0062A/6001240.zarr](https://ome.github.io/ome-ngff-validator/?source=https://uk1s3.embassy.ebi.ac.uk/idr/zarr/v0.4/idr0062A/6001240.zarr) 
 
-
-
+Note that with the remote url it is possible to copy the link from your browser and share it with 
+your colleagues.