docs update

puyuanOT · Dec 26, 2022 · cba1cf8 · cba1cf8
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diff --git a/deeplake/api/tests/test_nifti.py b/deeplake/api/tests/test_nifti.py
@@ -4,6 +4,7 @@
 import numpy as np
 
 import deeplake
+import pytest
 import os
 
 
@@ -76,3 +77,16 @@ def test_nifti_2(memory_ds):
         ds.nifti2.append(sample)
         np.testing.assert_array_equal(ds.nifti2.numpy()[0], img.get_fdata())
         assert ds.nifti2.shape == (1, *sample.shape)
+
+
+def test_nifti_raw_compress(memory_ds):
+    with memory_ds as ds:
+        ds.create_tensor("abc", htype="nifti", sample_compression="nii.gz")
+
+        with pytest.raises(NotImplementedError):
+            ds.abc.append(np.ones((40, 40, 10)))
+
+        ds.create_tensor("xyz", htype="nifti", sample_compression=None)
+        ds.xyz.append(np.ones((40, 40, 10)))
+
+        np.testing.assert_array_equal(ds.xyz[0].numpy(), np.ones((40, 40, 10)))
diff --git a/deeplake/core/compression.py b/deeplake/core/compression.py
@@ -237,6 +237,11 @@ def compress_array(array: np.ndarray, compression: Optional[str]) -> bytes:
         raise NotImplementedError(
             "In order to store mesh data, you should use `deeplake.read(path_to_file)`. Compressing raw data is not yet supported."
         )
+    elif compr_type == NIFTI_COMPRESSION:
+        raise NotImplementedError(
+            "In order to store nifti data, you should use `deeplake.read(path_to_file)` or use a None compression. "
+            "Compressing raw data is not yet supported."
+        )
     if compression == "apng":
         return _compress_apng(array)
     try:

diff --git a/docs/source/Htypes.rst b/docs/source/Htypes.rst
@@ -553,6 +553,37 @@ Appending polygons with numpy arrays
 >>> sample = [poly1, poly2, poly3]
 >>> ds.polygons.append(sample)
 
+.. _nifti-htype:
+
+Nifti Htype
+~~~~~~~~~~~
+
+- :bluebold:`Sample dimensions:` ``(# height, # width, # slices)`` or ``(# height, # width, # slices, # time unit)`` in case of time-series data.
+
+:blue:`Creating a nifti tensor`
+-------------------------------
+
+A nifti tensor can be created using 
+
+>>> ds.create_tensor("patients", htype="nifti", sample_compression="nii.gz")
+
+- Supported compressions:
+
+>>> ["nii.gz", "nii", None]
+
+:blue:`Appending nifti data`
+----------------------------
+
+- Nifti samples can be of type ``np.ndarray`` or :class:`~deeplake.core.sample.Sample` which is returned by :meth:`deeplake.read`.
+- Deep Lake does not support compression of raw nifti data. Therefore, array of raw frames can only be appended to tensors with
+  ``None`` compression.
+
+:bluebold:`Examples`
+
+>>> ds.patients.append(deeplake.read("data/patient0.nii.gz"))
+
+>>> ds.patients.extend([deeplake.read(f"data/patient{i}.nii.gz") for i in range(10)])
+
 .. _point_cloud-htype:
 
 Point Cloud Htype

diff --git a/docs/source/Random-Split.rst b/docs/source/Random-Split.rst
@@ -1,7 +1,7 @@
 .. currentmodule:: deeplake.core.dataset
 
 Random Split
-=======
+============
 
 Splits the dataset into non overlapping new datasets of given lengths.
 The resulting datasets are generated in such a way that when creating a dataloader from the view and training on it,

diff --git a/docs/source/_static/csv/htypes.csv b/docs/source/_static/csv/htypes.csv
@@ -16,6 +16,7 @@ text, str, None
 json, Any, None
 list, List, None
 dicom, None, dcm
+:ref:`nifti <nifti-htype>`,None,Required arg
 :ref:`point_cloud <point_cloud-htype>`, None, las
 :ref:`mesh <mesh-htype>`, None, ply
 instance_label, uint32, None