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‎docs/source/basic/write_image.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "255d97a8",
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"metadata": {},
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"source": [
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"# Write OME-ZARR images\n",
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"(basic:write)=\n",
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"\n",
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"Writing ome-zarr images is primarily exposed through the {py:class}`ome_zarr.image.NgffImage` and {py:class}`ome_zarr.image.NgffMultiscales` classes, which provide a high-level API for creating and manipulating OME-ZARR images and pyramids."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "21e12529",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from ome_zarr.image import NgffImage, NgffMultiscales"
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]
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},
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{
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"cell_type": "markdown",
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"id": "33774809",
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"metadata": {},
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"source": [
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"Let's first create some random data to write:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "4cdd0f46",
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"metadata": {},
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"outputs": [],
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"source": [
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"path = \"test_ngff.ome.zarr\"\n",
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"\n",
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"# create some random data to write\n",
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"size_xy = 128\n",
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"size_z = 10\n",
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"rng = np.random.default_rng(0)\n",
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"data = rng.poisson(lam=10, size=(size_z, size_xy, size_xy)).astype(np.uint8)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "ce122c48",
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"metadata": {},
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"outputs": [
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"cells": [
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{
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"data": {
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"text/plain": [
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"[]"
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"cell_type": "markdown",
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"id": "255d97a8",
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"metadata": {},
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"source": [
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"# Write OME-ZARR images\n",
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"(basic:write)=\n",
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"\n",
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"Writing ome-zarr images is primarily exposed through the {py:class}`ome_zarr.image.NgffImage` and {py:class}`ome_zarr.image.NgffMultiscales` classes, which provide a high-level API for creating and manipulating OME-ZARR images and pyramids."
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"ngff_image = NgffImage(\n",
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" data=data,\n",
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" axes=\"zyx\",\n",
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" scale={\"z\": 0.5, \"y\": 0.1, \"x\": 0.1},\n",
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")\n",
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"\n",
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"ngff_multiscales = NgffMultiscales(image=ngff_image)\n",
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"ngff_multiscales.to_ome_zarr(path)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9015365c",
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"metadata": {},
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"source": [
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"## Write OME-ZARR images: Legacy API\n",
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"\n",
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"\n",
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"In previous versions, the principle entry-point for writing OME-ZARR images was {py:func}`ome_zarr.writer.write_image`.\n",
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"This takes an n-dimensional `numpy` array or `dask` array and writes it to the specified `zarr group` according to the OME-ZARR specification.\n",
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"By default, a pyramid of resolution levels will be created by down-sampling the data by a factor\n",
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"of 2 in the X and Y dimensions.\n",
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"For more custom control over the pyramid, see the more in-depth example on [scaling functions and scale factors](advanced:pyramid)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f87d36fa",
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"metadata": {},
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"outputs": [
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},
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{
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"data": {
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"text/plain": [
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"[]"
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"cell_type": "code",
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"execution_count": 1,
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"id": "21e12529",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from ome_zarr.image import NgffImage, NgffMultiscales"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import numpy as np\n",
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"\n",
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"from ome_zarr.writer import write_image\n",
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"\n",
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"path = \"test_ngff_image.ome.zarr\"\n",
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"write_image(data, path, axes=\"zyx\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "03f9f06a",
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"metadata": {},
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"source": [
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"Alternatively, the {py:func}`ome_zarr.writer.write_multiscale` can be used,\n",
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"which takes a \"pyramid\" of pre-computed `numpy` arrays.\n",
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"\n",
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"The default version of OME-NGFF is v0.5, which is based on Zarr v3.\n",
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"A zarr v3 group and store is created by `zarr.open_group()` below.\n",
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"To write OME-NGFF v0.4 (Zarr v2), add the `zarr_format=2` argument."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "cfb1d49f",
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"metadata": {},
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"outputs": [
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},
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{
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"cell_type": "markdown",
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"id": "33774809",
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"metadata": {},
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"source": [
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"Let's first create some random data to write:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "4cdd0f46",
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"metadata": {},
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"outputs": [],
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"source": [
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"path = \"test_ngff.ome.zarr\"\n",
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"\n",
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"# create some random data to write\n",
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"size_xy = 128\n",
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"size_z = 10\n",
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"rng = np.random.default_rng(0)\n",
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"data = rng.poisson(lam=10, size=(size_z, size_xy, size_xy)).astype(np.uint8)"
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]
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},
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{
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"data": {
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"text/plain": [
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"[]"
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"cell_type": "code",
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"execution_count": 4,
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"id": "ce122c48",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[]"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"ngff_image = NgffImage(\n",
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" data=data,\n",
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" axes=\"zyx\",\n",
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" scale={\"z\": 0.5, \"y\": 0.1, \"x\": 0.1},\n",
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")\n",
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"\n",
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"ngff_multiscales = NgffMultiscales(image=ngff_image)\n",
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"ngff_multiscales.to_ome_zarr(path)"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"cell_type": "markdown",
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"id": "9015365c",
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"metadata": {},
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"source": [
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"## Write OME-ZARR images: Legacy API\n",
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"\n",
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"\n",
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"In previous versions, the principle entry-point for writing OME-ZARR images was {py:func}`ome_zarr.writer.write_image`.\n",
86+
"This takes an n-dimensional `numpy` array or `dask` array and writes it to the specified `zarr group` according to the OME-ZARR specification.\n",
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"By default, a pyramid of resolution levels will be created by down-sampling the data by a factor\n",
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"of 2 in the X and Y dimensions.\n",
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"For more custom control over the pyramid, see the more in-depth example on [scaling functions and scale factors](advanced:pyramid)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f87d36fa",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[]"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import numpy as np\n",
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"\n",
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"from ome_zarr.writer import write_image\n",
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"\n",
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"path = \"test_ngff_image.ome.zarr\"\n",
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"write_image(data, path, axes=\"zyx\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "03f9f06a",
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"metadata": {},
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"source": [
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"Alternatively, the {py:func}`ome_zarr.writer.write_multiscale` can be used,\n",
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"which takes a \"pyramid\" of pre-computed `numpy` arrays.\n",
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"\n",
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"The default version of OME-NGFF is v0.5, which is based on Zarr v3.\n",
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"A zarr v3 group and store is created by `zarr.open_group()` below.\n",
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"To write OME-NGFF v0.4 (Zarr v2), add the `zarr_format=2` argument."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "cfb1d49f",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[]"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"path = \"test_ngff_image_v2.ome.zarr\"\n",
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"write_image(data, path, axes=\"zyx\", zarr_format=2)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7b9a0138",
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"metadata": {},
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"source": [
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"To view the image, see tutorial on [viewing images](basic:view_images)."
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]
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},
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{
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"cell_type": "markdown",
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"id": "1f8a602d",
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"metadata": {},
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "ome-zarr (3.12.12)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.12"
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}
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],
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"source": [
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"path = \"test_ngff_image_v2.ome.zarr\"\n",
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"write_image(data, path, axes=\"zyx\", zarr_format=2)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7b9a0138",
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"metadata": {},
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"source": [
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"To view the image, see tutorial on [viewing images](basic:view_images)."
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]
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},
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{
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"cell_type": "markdown",
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"id": "1f8a602d",
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"metadata": {},
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "ome-zarr (3.12.12)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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"nbformat": 4,
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"nbformat_minor": 5
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}

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