kevinyamauchi · minnerbe · May 26, 2023 · May 26, 2023 · Jun 1, 2023 · Jun 1, 2023
diff --git a/.DS_Store b/.DS_Store
diff --git a/0.1/index.bs b/0.1/index.bs
@@ -501,8 +501,7 @@ Projects which support reading and/or writing OME-NGFF data include:
 
 </dl>
 
-<img src="https://downloads.openmicroscopy.org/presentations/2020/Dundee/Workshops/NGFF/zarr_diagram/images/zarr-ome-diagram.png"
-    alt="Diagram of related projects"/>
+<img src="https://downloads.openmicroscopy.org/presentations/2020/Dundee/Workshops/NGFF/zarr_diagram/images/zarr-ome-diagram.png" alt="Diagram of related projects"></img>
 
 All implementations prevent an equivalent representation of a dataset which can be downloaded or uploaded freely. An interactive
 version of this diagram is available from the [OME2020 Workshop](https://downloads.openmicroscopy.org/presentations/2020/Dundee/Workshops/NGFF/zarr_diagram/).

diff --git a/0.2/index.bs b/0.2/index.bs
@@ -559,8 +559,7 @@ Projects which support reading and/or writing OME-NGFF data include:
 
 </dl>
 
-<img src="https://downloads.openmicroscopy.org/presentations/2020/Dundee/Workshops/NGFF/zarr_diagram/images/zarr-ome-diagram.png"
-    alt="Diagram of related projects"/>
+<img src="https://downloads.openmicroscopy.org/presentations/2020/Dundee/Workshops/NGFF/zarr_diagram/images/zarr-ome-diagram.png" alt="Diagram of related projects"></img>
 
 All implementations prevent an equivalent representation of a dataset which can be downloaded or uploaded freely. An interactive
 version of this diagram is available from the [OME2020 Workshop](https://downloads.openmicroscopy.org/presentations/2020/Dundee/Workshops/NGFF/zarr_diagram/).

diff --git a/0.3/index.bs b/0.3/index.bs
@@ -569,8 +569,7 @@ Projects which support reading and/or writing OME-NGFF data include:
 
 </dl>
 
-<img src="https://downloads.openmicroscopy.org/presentations/2020/Dundee/Workshops/NGFF/zarr_diagram/images/zarr-ome-diagram.png"
-    alt="Diagram of related projects"/>
+<img src="https://downloads.openmicroscopy.org/presentations/2020/Dundee/Workshops/NGFF/zarr_diagram/images/zarr-ome-diagram.png" alt="Diagram of related projects"></img>
 
 All implementations prevent an equivalent representation of a dataset which can be downloaded or uploaded freely. An interactive
 version of this diagram is available from the [OME2020 Workshop](https://downloads.openmicroscopy.org/presentations/2020/Dundee/Workshops/NGFF/zarr_diagram/).

diff --git a/0.4/index.bs b/0.4/index.bs
@@ -578,8 +578,7 @@ Projects which support reading and/or writing OME-NGFF data include:
 
 </dl>
 
-<img src="https://downloads.openmicroscopy.org/presentations/2020/Dundee/Workshops/NGFF/zarr_diagram/images/zarr-ome-diagram.png"
-    alt="Diagram of related projects"/>
+<img src="https://downloads.openmicroscopy.org/presentations/2020/Dundee/Workshops/NGFF/zarr_diagram/images/zarr-ome-diagram.png" alt="Diagram of related projects"></img>
 
 All implementations prevent an equivalent representation of a dataset which can be downloaded or uploaded freely. An interactive
 version of this diagram is available from the [OME2020 Workshop](https://downloads.openmicroscopy.org/presentations/2020/Dundee/Workshops/NGFF/zarr_diagram/).

diff --git a/index.html b/index.html
@@ -1,6 +0,0 @@
-<head>
-  <meta http-equiv="refresh" content="5; URL=https://ngff.openmicroscopy.org/latest/" />
-</head>
-<body>
-  <p>If you are not redirected in five seconds, <a href="https://ngff.openmicroscopy.org/latest/">click here</a>.</p>
-</body>

diff --git a/latest/generate_anndata_example.py b/latest/generate_anndata_example.py
@@ -0,0 +1,149 @@
+import numpy as np
+import pandas as pd
+import anndata as ad
+import zarr
+import numcodecs
+from scipy.sparse import csr_matrix, csc_matrix
+
+def generate_example(n_obs, n_var):
+    # X and layers
+    adata = ad.AnnData(csr_matrix(np.random.poisson(1, size=(n_obs, n_var)), dtype=np.float32))
+    adata.layers["log_transformed"] = np.log1p(adata.X)
+    adata.layers["other_data"] = np.random.poisson(1, size=(n_obs, n_var)) + 1.0
+
+    # obs and var names
+    adata.obs_names = [f"Cell_{i:d}" for i in range(adata.n_obs)]
+    adata.var_names = [f"Gene_{i:d}" for i in range(adata.n_vars)]
+
+    # annotations
+    # the tutorial mentions that string arrays are automatically converted to categoricals if convenient
+    adata.obs["cell_type"] = pd.Categorical(np.random.choice(["B", "T", "Monocyte"], size=(adata.n_obs,)))
+    adata.obsm["X_umap"] = np.random.normal(0, 1, size=(adata.n_obs, 2))
+    adata.varm["gene_stuff"] = np.random.normal(0, 1, size=(adata.n_vars, 5))
+    adata.obsp["pairwise_data"] = csc_matrix(np.random.poisson(1, size=(n_obs, n_obs)), dtype=np.int32)
+
+    # uns
+    adata.uns["random"] = [1, 2, 3]
+
+    return adata
+
+
+def write_anndata(adata, filename, chunks):
+    # root and tables
+    root = zarr.open(filename, mode="w")
+    root.array("some_image", np.array([0]), chunks=(1,))
+    tables = root.create_group("tables")
+    tables.attrs["tables"] = ["/anndata/obs", "/anndata/var", "/anndata/obsm", "/anndata/varm", "/anndata/obsp", "/anndata/varp"]
+    adgroup = tables.create_group("anndata")
+    adgroup.attrs["anndata"] = "0.9.1"
+    adgroup.attrs["other-metadata"] = "metadata describing how the anndata annotates some_image"
+
+    # X/layers
+    X = adgroup.array('X', adata.X.todense(), chunks=chunks)
+    layers = adgroup.create_group("layers")
+    layers.array("log_transformed", adata.layers["log_transformed"].todense(), chunks=chunks)
+    layers.array("other_data", adata.layers["other_data"], chunks=chunks)
+
+    # obs
+    localChunks = (chunks[0],)
+    obs = adgroup.create_group("obs")
+    obs.create_dataset("row_names", data=np.array(adata.obs_names), dtype=object, object_codec=numcodecs.VLenUTF8())
+    obs.create_dataset("cell_type", data=np.array(adata.obs["cell_type"]), chunks=localChunks, object_codec=numcodecs.VLenUTF8())
+    obs.attrs["annotated-data"] = get_annotated_data_map(dimension=0)
+    obs.attrs["column-order"] = ["row_names", "cell_type"]
+    obs.attrs["row-names"] = "row_names"
+
+    # obsm
+    obsm = adgroup.create_group("obsm")
+    obsm.create_dataset("X_umap", data=adata.obsm["X_umap"], chunks=(chunks[0], 2))
+    obsm.attrs["annotated-data"] = get_annotated_data_map(dimension=0)
+    obsm.attrs["column-order"] = ["X_umap"]
+
+    # obsp
+    obsp = adgroup.create_group("obsp")
+    obsp.create_dataset("pairwise_data", data=adata.obsp["pairwise_data"].todense(), chunks=(chunks[0], chunks[0]))
+    obsp.attrs["annotated-data"] = get_annotated_data_map(dimension=0)
+    obsp.attrs["column-order"] = ["pairwise_data"]
+
+    # var
+    var = adgroup.create_group("var")
+    var.create_dataset("row_names", data=np.array(adata.var_names), chunks=(chunks[1],), object_codec=numcodecs.VLenUTF8())
+    var.attrs["annotated-data"] = get_annotated_data_map(dimension=1)
+    var.attrs["column-order"] = ["row_names"]
+    var.attrs["row-names"] = "row_names"
+
+    # varm
+    varm = adgroup.create_group("varm")
+    varm.create_dataset("gene_stuff", data=adata.varm["gene_stuff"], chunks=(chunks[1], 5))
+    varm.attrs["annotated-data"] = get_annotated_data_map(dimension=1)
+    varm.attrs["column-order"] = ["gene_stuff"]
+
+    # varm
+    varp = adgroup.create_group("varp")
+    varp.attrs["annotated-data"] = get_annotated_data_map(dimension=1)
+    varp.attrs["column-order"] = []
+
+    # uns
+    uns = adgroup.create_group("uns")
+    uns.create_dataset("random", data=adata.uns["random"], chunks=(3,))
+
+
+def get_annotated_data_map(*, dimension):
+    return [{"array": "/tables/anndata/X", "dimension": str(dimension)},
+            {"array": "/tables/anndata/layers/log_transformed", "dimension": str(dimension)},
+            {"array": "/tables/anndata/layers/other_data", "dimension": str(dimension)}]
+
+
+def write_anndata_suggestion(adata, filename, chunks):
+    # root and tables
+    root = zarr.open(filename, mode="w")
+    root.array("some_image", np.array([0]), chunks=(1,))
+    tables = root.create_group("tables")
+    tables.attrs["tables"] = ["/anndata/obs", "/anndata/var"]
+    adgroup = tables.create_group("anndata")
+    adgroup.attrs["anndata"] = "0.9.1"
+    adgroup.attrs["other-metadata"] = "metadata describing how the anndata annotates some_image"
+
+    # X and layers are combined into one array, a table is used to name the layers
+    all_together = np.stack([np.array(adata.X.todense()), np.array(adata.layers["log_transformed"].todense()), adata.layers["other_data"]], axis=2)
+    X = adgroup.array('X', all_together, chunks=(*chunks,1))
+    layers = adgroup.create_group("layers")
+    row_names = np.array(["X", "log_transformed", "other_data"])
+    layers.create_dataset("row_names", data=row_names, dtype=object, object_codec=numcodecs.VLenUTF8())
+    layers.attrs["annotated-data"] = [{"array": "/tables/anndata/X", "dimension": "2"}]
+    layers.attrs["row-names"] = "row_names"
+
+    # obs (combines obs, obsm, obsp)
+    localChunks = (chunks[0],)
+    obs = adgroup.create_group("obs")
+    obs.create_dataset("row_names", data=np.array(adata.obs_names), dtype=object, object_codec=numcodecs.VLenUTF8())
+    obs.create_dataset("cell_type", data=np.array(adata.obs["cell_type"]), chunks=localChunks, object_codec=numcodecs.VLenUTF8())
+    obs.create_dataset("X_umap", data=adata.obsm["X_umap"], chunks=(chunks[0], 2))
+    obs.create_dataset("pairwise_data", data=adata.obsp["pairwise_data"].todense(), chunks=(chunks[0], chunks[0]))
+    obs.attrs["annotated-data"] = [{"array": "/tables/anndata/X", "dimension": "0"}]
+    obs.attrs["column-order"] = ["row_names", "cell_type", "X_umap", "pairwise_data"]
+    obs.attrs["row-names"] = "row_names"
+
+    # var (combines var, varm, varp)
+    var = adgroup.create_group("var")
+    var.create_dataset("row_names", data=np.array(adata.var_names), chunks=(chunks[1],), object_codec=numcodecs.VLenUTF8())
+    var.create_dataset("gene_stuff", data=adata.varm["gene_stuff"], chunks=(chunks[1], 5))
+    var.attrs["annotated-data"] = [{"array": "/tables/anndata/X", "dimension": "1"}]
+    var.attrs["column-order"] = ["row_names", "gene_stuff"]
+    var.attrs["row-names"] = "row_names"
+
+    # uns
+    uns = adgroup.create_group("uns")
+    uns.create_dataset("random", data=adata.uns["random"], chunks=(3,))
+
+
+# generate example and store as zarr using the minimal table spec proposal
+n_obs = 10
+n_var = 200
+chunks = (10, 40)
+
+adata = generate_example(n_obs, n_var)
+write_anndata(adata, "example.zarr", chunks)
+
+# store example in an alternative way, exploiting the properties of the suggested minimal table spec a bit more
+write_anndata_suggestion(adata, "example_suggestion.zarr", chunks)