Repository: fmi-faim/faim-ipa (language: python)

Issue:
MolecularDevices format to zarr: add z plane parsing

Derive Z-spacing from "Z Projection Step Size" of MIP in parent folder
Currently we compute the z-spacing from the z-position metadata. We might be able to use the "Z Projection Step Size" attribute in the MIP metadata in the parent folder. However, I am not sure what influences this number and if it is reliable. 


Discussion / hints:

Fix patch:
diff --git a/.gitignore b/.gitignore
index 73d56d31..85295861 100644
--- a/.gitignore
+++ b/.gitignore
@@ -82,3 +82,12 @@ venv/
 
 # written by setuptools_scm
 **/_version.py
+
+# zarr storage
+*.zarr
+
+# mobie example project
+mobie-example-project/
+
+# scratchpad
+scratchpad/
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 3ab5151c..54474a3e 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,36 +1,37 @@
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.0.1
+    rev: v4.4.0
     hooks:
       - id: check-docstring-first
       - id: end-of-file-fixer
       - id: trailing-whitespace
   - repo: https://github.com/asottile/setup-cfg-fmt
-    rev: v1.20.0
+    rev: v2.2.0
     hooks:
       - id: setup-cfg-fmt
   - repo: https://github.com/PyCQA/flake8
-    rev: 4.0.1
+    rev: 6.0.0
     hooks:
       - id: flake8
-        args: ["--ignore=E501,W503", ]
-        additional_dependencies: [flake8-typing-imports==1.7.0]
+        args: ["--ignore=E501,W503,E203", ]
+        additional_dependencies: [flake8-typing-imports==1.14.0,
+        flake8-black==0.3.6]
   - repo: https://github.com/myint/autoflake
-    rev: v1.4
+    rev: v2.0.0
     hooks:
       - id: autoflake
         args: ["--in-place", "--remove-all-unused-imports"]
   - repo: https://github.com/PyCQA/isort
-    rev: 5.10.1
+    rev: 5.11.5
     hooks:
       - id: isort
         args: ["--profile", "black"]
   - repo: https://github.com/psf/black
-    rev: 21.11b1
+    rev: 22.12.0
     hooks:
       - id: black
   - repo: https://github.com/asottile/pyupgrade
-    rev: v2.29.1
+    rev: v3.3.1
     hooks:
       - id: pyupgrade
-        args: [--py37-plus, --keep-runtime-typing]
+        args: [--py39-plus, --keep-runtime-typing]
diff --git a/LICENSE b/LICENSE
index 78aa975d..d766e39e 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,4 +1,4 @@
-Copyright (c) 2022, Tim-Oliver Buchholz - FAIM - FMI for biomedical research
+Copyright (c) 2023, Friedrich Miescher Institute for Biomedical Research
 All rights reserved.
 
 Redistribution and use in source and binary forms, with or without
@@ -11,7 +11,7 @@ modification, are permitted provided that the following conditions are met:
   this list of conditions and the following disclaimer in the documentation
   and/or other materials provided with the distribution.
 
-* Neither the name of napari-psf-measures nor the names of its
+* Neither the name of faim-hcs nor the names of its
   contributors may be used to endorse or promote products derived from
   this software without specific prior written permission.
 
diff --git a/MANIFEST.in b/MANIFEST.in
index 04393845..a70cdae0 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -4,3 +4,7 @@ include requirements.txt
 
 recursive-exclude * __pycache__
 recursive-exclude * *.py[co]
+recursive-exclude tests *
+recursive-exclude scratchpad *
+recursive-exclude resources *
+recursive-exclude examples *
diff --git a/README.md b/README.md
index 59856aff..cd1bbea8 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,6 @@
 # FAIM-HCS
 
-Package to organize high content screening experiments.
+Tools used at FMI-FAIM to handle HCS data.
+
+## MoBIE
+Follow the [install instructions](https://github.com/mobie/mobie-utils-python#installation) to get mobie-utils-python.
diff --git a/examples/Create MoBIE Project.ipynb b/examples/Create MoBIE Project.ipynb
new file mode 100644
index 00000000..676383ea
--- /dev/null
+++ b/examples/Create MoBIE Project.ipynb
@@ -0,0 +1,368 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "69678ada",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ome_zarr.io import parse_url\n",
+    "import zarr\n",
+    "\n",
+    "from os.path import join\n",
+    "\n",
+    "import mobie\n",
+    "from mobie.metadata.project_metadata import create_project_metadata, add_dataset\n",
+    "from mobie.metadata.dataset_metadata import create_dataset_structure, create_dataset_metadata\n",
+    "\n",
+    "from faim_hcs.mobie import add_wells_to_project\n",
+    "\n",
+    "from os.path import exists\n",
+    "import shutil"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ef8c617",
+   "metadata": {},
+   "source": [
+    "# MoBIE Project\n",
+    "\n",
+    "MoBIE is a Fiji viewer to visualize large data like ome-zarr HCS plates. A MoBIE project can hold multiple datasets and each dataset consists of views of different image sources, which can be linked to tabular data. For more information check out the [MoBIE Tutorials]().\n",
+    "\n",
+    "Citation: [Pape, Constantin, et al. \"MoBIE: a Fiji plugin for sharing and exploration of multi-modal cloud-hosted big image data.\" Nature Methods (2023): 1-2.](https://www.nature.com/articles/s41592-023-01776-4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "4140329d-ca5b-4d44-8b34-07e044a8fe48",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The MoBIE project folder and name of the plate/dataset\n",
+    "mobie_project_folder = \"./mobie-example-project\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "02fe3b23",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "if exists(mobie_project_folder):\n",
+    "    # Remove if it already exists.\n",
+    "    shutil.rmtree(mobie_project_folder)\n",
+    "\n",
+    "# Create a new empty MoBIE project\n",
+    "create_project_metadata(mobie_project_folder)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "570bccbb",
+   "metadata": {},
+   "source": [
+    "## Add 2D OME-Zarr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "05b1ab95",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset_name = \"Single-Plane\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "fa6db36a-4c17-412c-a6a2-b5800709873b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'./mobie-example-project/Single-Plane'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create a new dataset\n",
+    "create_dataset_structure(mobie_project_folder, dataset_name, file_formats=[\"ome.zarr\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "5f940ebd-2d8b-4357-8562-3127001b58ff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Fill in dataset metadata\n",
+    "create_dataset_metadata(\n",
+    "    dataset_folder=join(mobie_project_folder, dataset_name), \n",
+    "    description=\"A 2D example dataset.\",\n",
+    "    is2d=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "47da13ae-4fdd-482c-9b0e-6511ffe407ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Update MoBIE project by adding the new dataset.\n",
+    "add_dataset(mobie_project_folder, dataset_name, is_default=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "162844bc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# OME-Zarr plate we want to add to a MoBIE project\n",
+    "plate_path = \"./zarr-files/Single-Plane.zarr/\"\n",
+    "store = parse_url(plate_path, mode=\"r\").store\n",
+    "    \n",
+    "plate = zarr.group(store=store)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "e057207d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add CYX wells from OME-Zarr to MoBIE project\n",
+    "add_wells_to_project(\n",
+    "    plate=plate,\n",
+    "    dataset_folder=join(mobie_project_folder, dataset_name),\n",
+    "    well_group=\"0\",\n",
+    "    view_name=\"default\",\n",
+    "    gap=50\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "97fce82e",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Check sources for dataset Single-Plane: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9/9 [00:00<00:00, 186.40it/s]\n",
+      "Check views for dataset Single-Plane: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:00<00:00, 66.15it/s]\n",
+      "Check view files for dataset Single-Plane: 0it [00:00, ?it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The project at ./mobie-example-project is a valid MoBIE project.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Validate MoBIE project integrety\n",
+    "mobie.validation.validate_project(mobie_project_folder)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ba390db",
+   "metadata": {},
+   "source": [
+    "## Add 3D OME-Zarr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "40ef8c8a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset_name = \"Projection-Mix\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "aa00abb8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'./mobie-example-project/Projection-Mix'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create a new dataset\n",
+    "create_dataset_structure(mobie_project_folder, dataset_name, file_formats=[\"ome.zarr\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "ff80691f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Fill in dataset metadata\n",
+    "create_dataset_metadata(\n",
+    "    dataset_folder=join(mobie_project_folder, dataset_name), \n",
+    "    description=\"A 3D projection mix example dataset.\",\n",
+    "    is2d=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "67486c85",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Update MoBIE project by adding the new dataset.\n",
+    "add_dataset(mobie_project_folder, dataset_name, is_default=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "a83fecb0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# OME-Zarr plate we want to add to a MoBIE project\n",
+    "plate_path = \"./zarr-files/Projection-Mix.zarr/\"\n",
+    "store = parse_url(plate_path, mode=\"r\").store\n",
+    "    \n",
+    "plate = zarr.group(store=store)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "43ffff1b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add CZYX wells from OME-Zarr to MoBIE project\n",
+    "add_wells_to_project(\n",
+    "    plate=plate,\n",
+    "    dataset_folder=join(mobie_project_folder, dataset_name),\n",
+    "    well_group=\"0\",\n",
+    "    view_name=\"default\",\n",
+    "    gap=50\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "6acd8ae2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add CYX well projections from OME-Zarr to MoBIE project\n",
+    "add_wells_to_project(\n",
+    "    plate=plate,\n",
+    "    dataset_folder=join(mobie_project_folder, dataset_name),\n",
+    "    well_group=\"0/projections\",\n",
+    "    view_name=\"Projections\",\n",
+    "    gap=50\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "cdbf872c",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Check sources for dataset Single-Plane: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9/9 [00:00<00:00, 201.01it/s]\n",
+      "Check views for dataset Single-Plane: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:00<00:00, 71.89it/s]\n",
+      "Check view files for dataset Single-Plane: 0it [00:00, ?it/s]\n",
+      "Check sources for dataset Projection-Mix: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 17/17 [00:00<00:00, 206.55it/s]\n",
+      "Check views for dataset Projection-Mix: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 26/26 [00:00<00:00, 55.53it/s]\n",
+      "Check view files for dataset Projection-Mix: 0it [00:00, ?it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The project at ./mobie-example-project is a valid MoBIE project.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Validate MoBIE project integrety\n",
+    "mobie.validation.validate_project(mobie_project_folder)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/Create ome-zarr from Single-Plane Multi-Field Acquisition.ipynb b/examples/Create ome-zarr from Single-Plane Multi-Field Acquisition.ipynb
new file mode 100644
index 00000000..e3cbed57
--- /dev/null
+++ b/examples/Create ome-zarr from Single-Plane Multi-Field Acquisition.ipynb
@@ -0,0 +1,416 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "38bc0937",
+   "metadata": {},
+   "source": [
+    "# Create ome-zarr from Single-Plane Multi-Field Acquisition\n",
+    "\n",
+    "This example builds an ome-zarr (NGFF) high-content screening file from a `Molecular Devices ImageXpress` acquistion.\n",
+    "\n",
+    "The example data is located in `../resources/Projection-Mix` and has the following form:\n",
+    "```\n",
+    "Storage hierarchy on disk for 2 wells with 2 fields and 3 channels:\n",
+    "    Projection-Mix --> {name}\n",
+    "    └── 2023-02-21 --> {date}\n",
+    "        └── 1334 --> {acquisition id}\n",
+    "            ├── Projection-Mix_E07_s1_w1E94C24BD-45E4-450A-9919-257C714278F7.tif\n",
+    "            ├── Projection-Mix_E07_s1_w1_thumb4BFD4018-E675-475E-B5AB-2E959E6B6DA1.tif\n",
+    "            ├── Projection-Mix_E07_s1_w2B14915F6-0679-4494-82D1-F80894B32A66.tif\n",
+    "            ├── Projection-Mix_E07_s1_w2_thumbE5A6D08C-AF44-4CA9-A6E8-7DC9BC9565F2.tif\n",
+    "            ├── Projection-Mix_E07_s1_w3BB87F860-FC67-4B3A-A740-A9EACF8A8F5F.tif\n",
+    "            ├── Projection-Mix_E07_s1_w3_thumbD739A948-8CA4-4559-910C-79D42CEC98A0.tif\n",
+    "            ├── Projection-Mix_E07_s2_w1DCFD1526-D063-4F8B-9E51-F1BD2EBD9F1A.tif\n",
+    "            ├── Projection-Mix_E07_s2_w1_thumbE4903920-91AA-405E-A39B-6EB284EA48E5.tif\n",
+    "            ├── Projection-Mix_E07_s2_w2607EE13F-AB5E-4E8C-BC4B-52E1118E7723.tif\n",
+    "            ├── Projection-Mix_E07_s2_w2_thumb15675A28-B45B-42E9-802C-833005E72657.tif\n",
+    "            ├── Projection-Mix_E07_s2_w3B0A47337-5945-4B26-9F5F-4EBA468CDBA9.tif\n",
+    "            ├── Projection-Mix_E07_s2_w3_thumbE3E91A92-3DD7-4EF5-BC32-8F5EF95B2CCA.tif\n",
+    "            ├── Projection-Mix_E08_s1_w117654C10-92F1-4DFD-98AA-6A01BBD77557.tif\n",
+    "            ├── Projection-Mix_E08_s1_w1_thumbD59B8F66-7743-4113-B6A2-8899FD53C3C8.tif\n",
+    "            ├── Projection-Mix_E08_s1_w281928711-999D-41F6-B88C-999513D4C092.tif\n",
+    "            ├── Projection-Mix_E08_s1_w2_thumb6D866DBC-F5F2-41D3-BEDD-28BEEB281FDD.tif\n",
+    "            ├── Projection-Mix_E08_s1_w3DD77D22D-07CB-4529-A1F5-DCC5473786FA.tif\n",
+    "            ├── Projection-Mix_E08_s1_w3_thumb615741CD-A218-4C30-BC79-E62A9D675DF5.tif\n",
+    "            ├── Projection-Mix_E08_s2_w1B38C01F5-0D36-4A29-9F5A-BE62B6F7F73F.tif\n",
+    "            ├── Projection-Mix_E08_s2_w1_thumbC7F53295-5DB8-4989-9E25-DD1068A2E266.tif\n",
+    "            ├── Projection-Mix_E08_s2_w266923EBB-9960-4952-8955-D1721D112EE2.tif\n",
+    "            ├── Projection-Mix_E08_s2_w2_thumb49A20B6B-1B86-47F1-B5FA-C22B47D2590D.tif\n",
+    "            ├── Projection-Mix_E08_s2_w3CCE83D85-0912-429E-9F18-716A085BB5BC.tif\n",
+    "            └── Projection-Mix_E08_s2_w3_thumb4D88636E-181E-4AF6-BC53-E7A435959C8F.tif\n",
+    "\n",
+    "```\n",
+    "\n",
+    "* Image data is stored in `{name}_{well}_{field}_w{channel}{md_id}.tif`.\n",
+    "* The `*_thumb*.tif` files are used by Molecular Devices as preview.\n",
+    "\n",
+    "__Note:__ For this example we ignore the `ZStep_#` data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "919ac7a2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from faim_hcs.io.MolecularDevicesImageXpress import parse_single_plane_multi_fields\n",
+    "from faim_hcs.Zarr import build_zarr_scaffold, write_cyx_image_to_well, PlateLayout\n",
+    "from faim_hcs.MetaSeriesUtils import get_well_image_CYX, montage_grid_image_YX\n",
+    "from faim_hcs.UIntHistogram import UIntHistogram\n",
+    "import shutil\n",
+    "from tqdm.notebook import tqdm\n",
+    "from os.path import join, exists"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a5472407",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "acquisition_dir = '../resources/Projection-Mix/'\n",
+    "zarr_root = './zarr-files'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9c4b5bd6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "files = parse_single_plane_multi_fields(acquisition_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "fe2936b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>acq_id</th>\n",
+       "      <th>name</th>\n",
+       "      <th>well</th>\n",
+       "      <th>field</th>\n",
+       "      <th>channel</th>\n",
+       "      <th>md_id</th>\n",
+       "      <th>ext</th>\n",
+       "      <th>path</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2023-02-21</td>\n",
+       "      <td>1334</td>\n",
+       "      <td>Projection-Mix</td>\n",
+       "      <td>E08</td>\n",
+       "      <td>s2</td>\n",
+       "      <td>w2</td>\n",
+       "      <td>66923EBB-9960-4952-8955-D1721D112EE2</td>\n",
+       "      <td>.tif</td>\n",
+       "      <td>../resources/Projection-Mix/2023-02-21/1334/Pr...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2023-02-21</td>\n",
+       "      <td>1334</td>\n",
+       "      <td>Projection-Mix</td>\n",
+       "      <td>E07</td>\n",
+       "      <td>s2</td>\n",
+       "      <td>w1</td>\n",
+       "      <td>DCFD1526-D063-4F8B-9E51-F1BD2EBD9F1A</td>\n",
+       "      <td>.tif</td>\n",
+       "      <td>../resources/Projection-Mix/2023-02-21/1334/Pr...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2023-02-21</td>\n",
+       "      <td>1334</td>\n",
+       "      <td>Projection-Mix</td>\n",
+       "      <td>E07</td>\n",
+       "      <td>s1</td>\n",
+       "      <td>w1</td>\n",
+       "      <td>E94C24BD-45E4-450A-9919-257C714278F7</td>\n",
+       "      <td>.tif</td>\n",
+       "      <td>../resources/Projection-Mix/2023-02-21/1334/Pr...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2023-02-21</td>\n",
+       "      <td>1334</td>\n",
+       "      <td>Projection-Mix</td>\n",
+       "      <td>E07</td>\n",
+       "      <td>s1</td>\n",
+       "      <td>w2</td>\n",
+       "      <td>B14915F6-0679-4494-82D1-F80894B32A66</td>\n",
+       "      <td>.tif</td>\n",
+       "      <td>../resources/Projection-Mix/2023-02-21/1334/Pr...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2023-02-21</td>\n",
+       "      <td>1334</td>\n",
+       "      <td>Projection-Mix</td>\n",
+       "      <td>E08</td>\n",
+       "      <td>s1</td>\n",
+       "      <td>w3</td>\n",
+       "      <td>DD77D22D-07CB-4529-A1F5-DCC5473786FA</td>\n",
+       "      <td>.tif</td>\n",
+       "      <td>../resources/Projection-Mix/2023-02-21/1334/Pr...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2023-02-21</td>\n",
+       "      <td>1334</td>\n",
+       "      <td>Projection-Mix</td>\n",
+       "      <td>E08</td>\n",
+       "      <td>s2</td>\n",
+       "      <td>w3</td>\n",
+       "      <td>CCE83D85-0912-429E-9F18-716A085BB5BC</td>\n",
+       "      <td>.tif</td>\n",
+       "      <td>../resources/Projection-Mix/2023-02-21/1334/Pr...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2023-02-21</td>\n",
+       "      <td>1334</td>\n",
+       "      <td>Projection-Mix</td>\n",
+       "      <td>E07</td>\n",
+       "      <td>s1</td>\n",
+       "      <td>w3</td>\n",
+       "      <td>BB87F860-FC67-4B3A-A740-A9EACF8A8F5F</td>\n",
+       "      <td>.tif</td>\n",
+       "      <td>../resources/Projection-Mix/2023-02-21/1334/Pr...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2023-02-21</td>\n",
+       "      <td>1334</td>\n",
+       "      <td>Projection-Mix</td>\n",
+       "      <td>E08</td>\n",
+       "      <td>s1</td>\n",
+       "      <td>w1</td>\n",
+       "      <td>17654C10-92F1-4DFD-98AA-6A01BBD77557</td>\n",
+       "      <td>.tif</td>\n",
+       "      <td>../resources/Projection-Mix/2023-02-21/1334/Pr...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>2023-02-21</td>\n",
+       "      <td>1334</td>\n",
+       "      <td>Projection-Mix</td>\n",
+       "      <td>E07</td>\n",
+       "      <td>s2</td>\n",
+       "      <td>w3</td>\n",
+       "      <td>B0A47337-5945-4B26-9F5F-4EBA468CDBA9</td>\n",
+       "      <td>.tif</td>\n",
+       "      <td>../resources/Projection-Mix/2023-02-21/1334/Pr...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>2023-02-21</td>\n",
+       "      <td>1334</td>\n",
+       "      <td>Projection-Mix</td>\n",
+       "      <td>E07</td>\n",
+       "      <td>s2</td>\n",
+       "      <td>w2</td>\n",
+       "      <td>607EE13F-AB5E-4E8C-BC4B-52E1118E7723</td>\n",
+       "      <td>.tif</td>\n",
+       "      <td>../resources/Projection-Mix/2023-02-21/1334/Pr...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>2023-02-21</td>\n",
+       "      <td>1334</td>\n",
+       "      <td>Projection-Mix</td>\n",
+       "      <td>E08</td>\n",
+       "      <td>s1</td>\n",
+       "      <td>w2</td>\n",
+       "      <td>81928711-999D-41F6-B88C-999513D4C092</td>\n",
+       "      <td>.tif</td>\n",
+       "      <td>../resources/Projection-Mix/2023-02-21/1334/Pr...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>2023-02-21</td>\n",
+       "      <td>1334</td>\n",
+       "      <td>Projection-Mix</td>\n",
+       "      <td>E08</td>\n",
+       "      <td>s2</td>\n",
+       "      <td>w1</td>\n",
+       "      <td>B38C01F5-0D36-4A29-9F5A-BE62B6F7F73F</td>\n",
+       "      <td>.tif</td>\n",
+       "      <td>../resources/Projection-Mix/2023-02-21/1334/Pr...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          date acq_id            name well field channel  \\\n",
+       "0   2023-02-21   1334  Projection-Mix  E08    s2      w2   \n",
+       "1   2023-02-21   1334  Projection-Mix  E07    s2      w1   \n",
+       "2   2023-02-21   1334  Projection-Mix  E07    s1      w1   \n",
+       "3   2023-02-21   1334  Projection-Mix  E07    s1      w2   \n",
+       "4   2023-02-21   1334  Projection-Mix  E08    s1      w3   \n",
+       "5   2023-02-21   1334  Projection-Mix  E08    s2      w3   \n",
+       "6   2023-02-21   1334  Projection-Mix  E07    s1      w3   \n",
+       "7   2023-02-21   1334  Projection-Mix  E08    s1      w1   \n",
+       "8   2023-02-21   1334  Projection-Mix  E07    s2      w3   \n",
+       "9   2023-02-21   1334  Projection-Mix  E07    s2      w2   \n",
+       "10  2023-02-21   1334  Projection-Mix  E08    s1      w2   \n",
+       "11  2023-02-21   1334  Projection-Mix  E08    s2      w1   \n",
+       "\n",
+       "                                   md_id   ext  \\\n",
+       "0   66923EBB-9960-4952-8955-D1721D112EE2  .tif   \n",
+       "1   DCFD1526-D063-4F8B-9E51-F1BD2EBD9F1A  .tif   \n",
+       "2   E94C24BD-45E4-450A-9919-257C714278F7  .tif   \n",
+       "3   B14915F6-0679-4494-82D1-F80894B32A66  .tif   \n",
+       "4   DD77D22D-07CB-4529-A1F5-DCC5473786FA  .tif   \n",
+       "5   CCE83D85-0912-429E-9F18-716A085BB5BC  .tif   \n",
+       "6   BB87F860-FC67-4B3A-A740-A9EACF8A8F5F  .tif   \n",
+       "7   17654C10-92F1-4DFD-98AA-6A01BBD77557  .tif   \n",
+       "8   B0A47337-5945-4B26-9F5F-4EBA468CDBA9  .tif   \n",
+       "9   607EE13F-AB5E-4E8C-BC4B-52E1118E7723  .tif   \n",
+       "10  81928711-999D-41F6-B88C-999513D4C092  .tif   \n",
+       "11  B38C01F5-0D36-4A29-9F5A-BE62B6F7F73F  .tif   \n",
+       "\n",
+       "                                                 path  \n",
+       "0   ../resources/Projection-Mix/2023-02-21/1334/Pr...  \n",
+       "1   ../resources/Projection-Mix/2023-02-21/1334/Pr...  \n",
+       "2   ../resources/Projection-Mix/2023-02-21/1334/Pr...  \n",
+       "3   ../resources/Projection-Mix/2023-02-21/1334/Pr...  \n",
+       "4   ../resources/Projection-Mix/2023-02-21/1334/Pr...  \n",
+       "5   ../resources/Projection-Mix/2023-02-21/1334/Pr...  \n",
+       "6   ../resources/Projection-Mix/2023-02-21/1334/Pr...  \n",
+       "7   ../resources/Projection-Mix/2023-02-21/1334/Pr...  \n",
+       "8   ../resources/Projection-Mix/2023-02-21/1334/Pr...  \n",
+       "9   ../resources/Projection-Mix/2023-02-21/1334/Pr...  \n",
+       "10  ../resources/Projection-Mix/2023-02-21/1334/Pr...  \n",
+       "11  ../resources/Projection-Mix/2023-02-21/1334/Pr...  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "948904be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if exists(join(zarr_root, \"Single-Plane.zarr\")):\n",
+    "    # Remove zarr if it already exists.\n",
+    "    shutil.rmtree(join(zarr_root, \"Single-Plane.zarr\"))\n",
+    "\n",
+    "# Build empty zarr plate scaffold.\n",
+    "plate = build_zarr_scaffold(root_dir=zarr_root,\n",
+    "                            files=files,\n",
+    "                            name='Single-Plane',\n",
+    "                            layout=96,\n",
+    "                            order_name=\"example-order\",\n",
+    "                            barcode=\"example-barcode\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "37f65e50",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d39b97703a4f4214b3c1618843c8f481",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "channels = ['w1', 'w2', 'w3', 'w4']\n",
+    "# Add image data to wells\n",
+    "for well in tqdm(files['well'].unique()):\n",
+    "    well_files = files[files['well'] == well]\n",
+    "    \n",
+    "    img, hists, ch_metadata, metadta = get_well_image_CYX(\n",
+    "        well_files=well_files,\n",
+    "        channels=channels,\n",
+    "        assemble_fn=montage_grid_image_YX,\n",
+    "    )\n",
+    "    \n",
+    "    well_group = plate[well[0]][str(int(well[1:]))][0]\n",
+    "    write_cyx_image_to_well(img, hists, ch_metadata, metadta, well_group)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f5a8f42",
+   "metadata": {},
+   "source": [
+    "# Inspect ome-zarr plate\n",
+    "The data can be opened with the [ome-zarr Napari plugin](https://www.napari-hub.org/plugins/napari-ome-zarr)."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/Create ome-zarr from Z-Stack Multi-Field Acquisition.ipynb b/examples/Create ome-zarr from Z-Stack Multi-Field Acquisition.ipynb
new file mode 100644
index 00000000..3a3f64da
--- /dev/null
+++ b/examples/Create ome-zarr from Z-Stack Multi-Field Acquisition.ipynb
@@ -0,0 +1,625 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "38bc0937",
+   "metadata": {},
+   "source": [
+    "# Create ome-zarr from Z-Stack Multi-Field Acquisition\n",
+    "\n",
+    "This example builds an ome-zarr (NGFF) high-content screening file from a `Molecular Devices ImageXpress` acquistion.\n",
+    "\n",
+    "The example data is located in `../resources/Projection-Mix` and has the following form:\n",
+    "```\n",
+    "Storage hierarchy on disk for z-stacks of 2 wells with 2 fields and 3 channels:\n",
+    "    Projection-Mix --> {name}\n",
+    "    └── 2023-02-21 --> {date}\n",
+    "        └── 1334 --> {acquisition id}\n",
+    "            ├── Projection-Mix_E07_s1_w1E94C24BD-45E4-450A-9919-257C714278F7.tif\n",
+    "            ├── Projection-Mix_E07_s1_w1_thumb4BFD4018-E675-475E-B5AB-2E959E6B6DA1.tif\n",
+    "            ├── Projection-Mix_E07_s1_w2B14915F6-0679-4494-82D1-F80894B32A66.tif\n",
+    "            ├── Projection-Mix_E07_s1_w2_thumbE5A6D08C-AF44-4CA9-A6E8-7DC9BC9565F2.tif\n",
+    "            ├── Projection-Mix_E07_s1_w3BB87F860-FC67-4B3A-A740-A9EACF8A8F5F.tif\n",
+    "            ├── Projection-Mix_E07_s1_w3_thumbD739A948-8CA4-4559-910C-79D42CEC98A0.tif\n",
+    "            ├── Projection-Mix_E07_s2_w1DCFD1526-D063-4F8B-9E51-F1BD2EBD9F1A.tif\n",
+    "            ├── Projection-Mix_E07_s2_w1_thumbE4903920-91AA-405E-A39B-6EB284EA48E5.tif\n",
+    "            ├── Projection-Mix_E07_s2_w2607EE13F-AB5E-4E8C-BC4B-52E1118E7723.tif\n",
+    "            ├── Projection-Mix_E07_s2_w2_thumb15675A28-B45B-42E9-802C-833005E72657.tif\n",
+    "            ├── Projection-Mix_E07_s2_w3B0A47337-5945-4B26-9F5F-4EBA468CDBA9.tif\n",
+    "            ├── Projection-Mix_E07_s2_w3_thumbE3E91A92-3DD7-4EF5-BC32-8F5EF95B2CCA.tif\n",
+    "            ├── Projection-Mix_E08_s1_w117654C10-92F1-4DFD-98AA-6A01BBD77557.tif\n",
+    "            ├── Projection-Mix_E08_s1_w1_thumbD59B8F66-7743-4113-B6A2-8899FD53C3C8.tif\n",
+    "            ├── Projection-Mix_E08_s1_w281928711-999D-41F6-B88C-999513D4C092.tif\n",
+    "            ├── Projection-Mix_E08_s1_w2_thumb6D866DBC-F5F2-41D3-BEDD-28BEEB281FDD.tif\n",
+    "            ├── Projection-Mix_E08_s1_w3DD77D22D-07CB-4529-A1F5-DCC5473786FA.tif\n",
+    "            ├── Projection-Mix_E08_s1_w3_thumb615741CD-A218-4C30-BC79-E62A9D675DF5.tif\n",
+    "            ├── Projection-Mix_E08_s2_w1B38C01F5-0D36-4A29-9F5A-BE62B6F7F73F.tif\n",
+    "            ├── Projection-Mix_E08_s2_w1_thumbC7F53295-5DB8-4989-9E25-DD1068A2E266.tif\n",
+    "            ├── Projection-Mix_E08_s2_w266923EBB-9960-4952-8955-D1721D112EE2.tif\n",
+    "            ├── Projection-Mix_E08_s2_w2_thumb49A20B6B-1B86-47F1-B5FA-C22B47D2590D.tif\n",
+    "            ├── Projection-Mix_E08_s2_w3CCE83D85-0912-429E-9F18-716A085BB5BC.tif\n",
+    "            ├── Projection-Mix_E08_s2_w3_thumb4D88636E-181E-4AF6-BC53-E7A435959C8F.tif\n",
+    "            ├── ZStep_1\n",
+    "            │   ├── Projection-Mix_E07_s1_w1E78EB128-BD0D-4D94-A6AD-3FF28BB1B105.tif\n",
+    "            │   ├── ...\n",
+    "            │   └── Projection-Mix_E08_s2_w4_thumbD2785594-4F49-464F-9F80-1B82E30A560A.tif\n",
+    "            ├── ...\n",
+    "            └── ZStep_10\n",
+    "                ├── Projection-Mix_E07_s1_w11D01380B-E7DA-4C09-A343-EDA3D235D808.tif\n",
+    "                ├── Projection-Mix_E07_s1_w1_thumb16F6E9D3-12E4-426F-A4BF-9AD5D23A000F.tif\n",
+    "                ├── Projection-Mix_E07_s1_w27FF63331-0307-4A31-89AC-B363F8BCED7A.tif\n",
+    "                ├── Projection-Mix_E07_s1_w2_thumb55E0167A-3CFE-4E67-AC0B-A1A900FC9CF6.tif\n",
+    "                ├── Projection-Mix_E07_s2_w12BFD0E01-D7F8-4596-8904-3209AD741437.tif\n",
+    "                ├── Projection-Mix_E07_s2_w1_thumb6F13EC7B-89B8-4720-9E33-5D56699B2ED0.tif\n",
+    "                ├── Projection-Mix_E07_s2_w20DB41161-451B-4A8B-9DA6-8E9C7D56D30E.tif\n",
+    "                ├── Projection-Mix_E07_s2_w2_thumbAF5EB66F-8476-4520-865B-D74FE5AE5CE2.tif\n",
+    "                ├── Projection-Mix_E08_s1_w1F11777E5-3795-4B08-BB89-F854F3414F88.tif\n",
+    "                ├── Projection-Mix_E08_s1_w1_thumbE57B9E91-5584-4A7A-8D1B-9E4C0F263742.tif\n",
+    "                ├── Projection-Mix_E08_s1_w2D6F71AC1-8418-4C24-94A2-A273E706891B.tif\n",
+    "                ├── Projection-Mix_E08_s1_w2_thumb5F0241E5-6AE0-45A8-A464-65E6B71F2FB3.tif\n",
+    "                ├── Projection-Mix_E08_s2_w1E84FADEE-6DCE-4646-8329-F841B2D45415.tif\n",
+    "                ├── Projection-Mix_E08_s2_w1_thumbED8582E8-6F5A-4639-86F1-E98C869814C2.tif\n",
+    "                ├── Projection-Mix_E08_s2_w2BB584250-EC8C-49E1-98F3-93113F70C320.tif\n",
+    "                └── Projection-Mix_E08_s2_w2_thumb912362B1-19FE-40C6-A7DC-DAE2CC83872B.tif\n",
+    "\n",
+    "```\n",
+    "\n",
+    "\n",
+    "* Z-Stack data is stored in `ZStep_{z}/{name}_{well}_{field}_w{channel}{md_id}.tif`\n",
+    "* Projection data is stored in `{name}_{well}_{field}_w{channel}{md_id}.tif`.\n",
+    "* The `*_thumb*.tif` files are used by Molecular Devices as preview.\n",
+    "\n",
+    "## Acquisition Protocol\n",
+    "\n",
+    "In total four channels were acquired:\n",
+    "* Z-stacks were only acquired for for channels 1 and 2. \n",
+    "    * For channels 1 and 2 the maximum and best-focus projections were saved by the microscope as well. \n",
+    "* For channel 3 only a maximum projection was saved. \n",
+    "* For channel 4 only a single z-plane was acquired and saved."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "919ac7a2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from faim_hcs.io.MolecularDevicesImageXpress import parse_files\n",
+    "from faim_hcs.Zarr import build_zarr_scaffold, write_czyx_image_to_well, write_cyx_image_to_well\n",
+    "from faim_hcs.MetaSeriesUtils import get_well_image_CYX, get_well_image_CZYX, montage_grid_image_YX\n",
+    "from faim_hcs.UIntHistogram import UIntHistogram\n",
+    "import shutil\n",
+    "from tqdm.notebook import tqdm\n",
+    "from os.path import join, exists\n",
+    "\n",
+    "import zarr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a5472407",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "acquisition_dir = '../resources/Projection-Mix/'\n",
+    "zarr_root = './zarr-files'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9c4b5bd6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "files = parse_files(acquisition_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "fe2936b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>acq_id</th>\n",
+       "      <th>z</th>\n",
+       "      <th>name</th>\n",
+       "      <th>well</th>\n",
+       "      <th>field</th>\n",
+       "      <th>channel</th>\n",
+       "      <th>md_id</th>\n",
+       "      <th>ext</th>\n",
+       "      <th>path</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2023-02-21</td>\n",
+       "      <td>1334</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Projection-Mix</td>\n",
+       "      <td>E08</td>\n",
+       "      <td>s2</td>\n",
+       "      <td>w2</td>\n",
+       "      <td>66923EBB-9960-4952-8955-D1721D112EE2</td>\n",
+       "      <td>.tif</td>\n",
+       "      <td>../resources/Projection-Mix/2023-02-21/1334/Pr...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2023-02-21</td>\n",
+       "      <td>1334</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Projection-Mix</td>\n",
+       "      <td>E07</td>\n",
+       "      <td>s2</td>\n",
+       "      <td>w1</td>\n",
+       "      <td>DCFD1526-D063-4F8B-9E51-F1BD2EBD9F1A</td>\n",
+       "      <td>.tif</td>\n",
+       "      <td>../resources/Projection-Mix/2023-02-21/1334/Pr...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2023-02-21</td>\n",
+       "      <td>1334</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Projection-Mix</td>\n",
+       "      <td>E07</td>\n",
+       "      <td>s1</td>\n",
+       "      <td>w1</td>\n",
+       "      <td>E94C24BD-45E4-450A-9919-257C714278F7</td>\n",
+       "      <td>.tif</td>\n",
+       "      <td>../resources/Projection-Mix/2023-02-21/1334/Pr...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2023-02-21</td>\n",
+       "      <td>1334</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Projection-Mix</td>\n",
+       "      <td>E07</td>\n",
+       "      <td>s1</td>\n",
+       "      <td>w2</td>\n",
+       "      <td>B14915F6-0679-4494-82D1-F80894B32A66</td>\n",
+       "      <td>.tif</td>\n",
+       "      <td>../resources/Projection-Mix/2023-02-21/1334/Pr...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2023-02-21</td>\n",
+       "      <td>1334</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Projection-Mix</td>\n",
+       "      <td>E08</td>\n",
+       "      <td>s1</td>\n",
+       "      <td>w3</td>\n",
+       "      <td>DD77D22D-07CB-4529-A1F5-DCC5473786FA</td>\n",
+       "      <td>.tif</td>\n",
+       "      <td>../resources/Projection-Mix/2023-02-21/1334/Pr...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>91</th>\n",
+       "      <td>2023-02-21</td>\n",
+       "      <td>1334</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Projection-Mix</td>\n",
+       "      <td>E07</td>\n",
+       "      <td>s2</td>\n",
+       "      <td>w2</td>\n",
+       "      <td>D7E0C2D6-CE27-4EC4-8E7B-90D3F351B9C3</td>\n",
+       "      <td>.tif</td>\n",
+       "      <td>../resources/Projection-Mix/2023-02-21/1334/ZS...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>92</th>\n",
+       "      <td>2023-02-21</td>\n",
+       "      <td>1334</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Projection-Mix</td>\n",
+       "      <td>E08</td>\n",
+       "      <td>s1</td>\n",
+       "      <td>w1</td>\n",
+       "      <td>1C103277-03CC-448A-9994-016D0DA8E40D</td>\n",
+       "      <td>.tif</td>\n",
+       "      <td>../resources/Projection-Mix/2023-02-21/1334/ZS...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>93</th>\n",
+       "      <td>2023-02-21</td>\n",
+       "      <td>1334</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Projection-Mix</td>\n",
+       "      <td>E07</td>\n",
+       "      <td>s2</td>\n",
+       "      <td>w1</td>\n",
+       "      <td>3F8FCF31-E226-4345-BE10-362D859537BE</td>\n",
+       "      <td>.tif</td>\n",
+       "      <td>../resources/Projection-Mix/2023-02-21/1334/ZS...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>94</th>\n",
+       "      <td>2023-02-21</td>\n",
+       "      <td>1334</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Projection-Mix</td>\n",
+       "      <td>E07</td>\n",
+       "      <td>s1</td>\n",
+       "      <td>w2</td>\n",
+       "      <td>5B80CE92-FF54-4E8F-B208-22AB72686763</td>\n",
+       "      <td>.tif</td>\n",
+       "      <td>../resources/Projection-Mix/2023-02-21/1334/ZS...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>2023-02-21</td>\n",
+       "      <td>1334</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Projection-Mix</td>\n",
+       "      <td>E08</td>\n",
+       "      <td>s2</td>\n",
+       "      <td>w1</td>\n",
+       "      <td>BB405418-05F7-4C37-A3E0-D676A75B9C99</td>\n",
+       "      <td>.tif</td>\n",
+       "      <td>../resources/Projection-Mix/2023-02-21/1334/ZS...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>96 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          date acq_id     z            name well field channel  \\\n",
+       "0   2023-02-21   1334  None  Projection-Mix  E08    s2      w2   \n",
+       "1   2023-02-21   1334  None  Projection-Mix  E07    s2      w1   \n",
+       "2   2023-02-21   1334  None  Projection-Mix  E07    s1      w1   \n",
+       "3   2023-02-21   1334  None  Projection-Mix  E07    s1      w2   \n",
+       "4   2023-02-21   1334  None  Projection-Mix  E08    s1      w3   \n",
+       "..         ...    ...   ...             ...  ...   ...     ...   \n",
+       "91  2023-02-21   1334     3  Projection-Mix  E07    s2      w2   \n",
+       "92  2023-02-21   1334     3  Projection-Mix  E08    s1      w1   \n",
+       "93  2023-02-21   1334     3  Projection-Mix  E07    s2      w1   \n",
+       "94  2023-02-21   1334     3  Projection-Mix  E07    s1      w2   \n",
+       "95  2023-02-21   1334     3  Projection-Mix  E08    s2      w1   \n",
+       "\n",
+       "                                   md_id   ext  \\\n",
+       "0   66923EBB-9960-4952-8955-D1721D112EE2  .tif   \n",
+       "1   DCFD1526-D063-4F8B-9E51-F1BD2EBD9F1A  .tif   \n",
+       "2   E94C24BD-45E4-450A-9919-257C714278F7  .tif   \n",
+       "3   B14915F6-0679-4494-82D1-F80894B32A66  .tif   \n",
+       "4   DD77D22D-07CB-4529-A1F5-DCC5473786FA  .tif   \n",
+       "..                                   ...   ...   \n",
+       "91  D7E0C2D6-CE27-4EC4-8E7B-90D3F351B9C3  .tif   \n",
+       "92  1C103277-03CC-448A-9994-016D0DA8E40D  .tif   \n",
+       "93  3F8FCF31-E226-4345-BE10-362D859537BE  .tif   \n",
+       "94  5B80CE92-FF54-4E8F-B208-22AB72686763  .tif   \n",
+       "95  BB405418-05F7-4C37-A3E0-D676A75B9C99  .tif   \n",
+       "\n",
+       "                                                 path  \n",
+       "0   ../resources/Projection-Mix/2023-02-21/1334/Pr...  \n",
+       "1   ../resources/Projection-Mix/2023-02-21/1334/Pr...  \n",
+       "2   ../resources/Projection-Mix/2023-02-21/1334/Pr...  \n",
+       "3   ../resources/Projection-Mix/2023-02-21/1334/Pr...  \n",
+       "4   ../resources/Projection-Mix/2023-02-21/1334/Pr...  \n",
+       "..                                                ...  \n",
+       "91  ../resources/Projection-Mix/2023-02-21/1334/ZS...  \n",
+       "92  ../resources/Projection-Mix/2023-02-21/1334/ZS...  \n",
+       "93  ../resources/Projection-Mix/2023-02-21/1334/ZS...  \n",
+       "94  ../resources/Projection-Mix/2023-02-21/1334/ZS...  \n",
+       "95  ../resources/Projection-Mix/2023-02-21/1334/ZS...  \n",
+       "\n",
+       "[96 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "files"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5cb9501d",
+   "metadata": {},
+   "source": [
+    "## Zarr Organization\n",
+    "\n",
+    "We store for each channel a ZYX volume in the zarr-file. Channel 3 contains only zeros and channel 4 contains only zeros except for a single plane. We make use of the zarr-option to __not__ write empty chunks to disk. \n",
+    "\n",
+    "The projections for channel 1 and 2 are saved in an extra group `projections` next to the field. While this is not official NGFF ome-zarr spec it follows the logic of how `labels` are described by the NGFF ome-zarr spec.\n",
+    "\n",
+    "__Note:__ We montage the individual fields of the well into a single image and then save this in the zeroth field of the well in the ome-zarr."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "948904be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if exists(join(zarr_root, \"Projection-Mix.zarr\")):\n",
+    "    # Remove zarr if it already exists.\n",
+    "    shutil.rmtree(join(zarr_root, \"Projection-Mix.zarr\"))\n",
+    "\n",
+    "# Build empty zarr plate scaffold.\n",
+    "plate = build_zarr_scaffold(root_dir=zarr_root,\n",
+    "                            files=files,\n",
+    "                            layout=96,\n",
+    "                            order_name=\"example-order\",\n",
+    "                            barcode=\"example-barcode\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "37f65e50",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4a7526e2228d4707b0b62de7792b1d8d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "channels = ['w1', 'w2', 'w3', 'w4']\n",
+    "# Add image data to wells\n",
+    "for well in tqdm(files['well'].unique()):\n",
+    "    well_files = files[files['well'] == well]\n",
+    "    \n",
+    "    projection_files = well_files[well_files['z'].isnull()]\n",
+    "    \n",
+    "    # This also contains the single z-plane for channel 3\n",
+    "    stack_files = well_files[~well_files['z'].isnull()]\n",
+    "    \n",
+    "    \n",
+    "    projection, proj_hists, proj_ch_metadata, proj_metadata = get_well_image_CYX(\n",
+    "        well_files=projection_files,\n",
+    "        channels=channels,\n",
+    "        assemble_fn=montage_grid_image_YX,\n",
+    "    )\n",
+    "    \n",
+    "    stack, stack_hist, stack_ch_metadata, stack_metadata = get_well_image_CZYX(\n",
+    "        well_files=stack_files,\n",
+    "        channels=channels,\n",
+    "        assemble_fn=montage_grid_image_YX,\n",
+    "    )\n",
+    "    \n",
+    "    # Get the zeroth field of the well.\n",
+    "    field: zarr.Group = plate[well[0]][str(int(well[1:]))][0]\n",
+    "        \n",
+    "    # Write CZYX stack into zeroth field.\n",
+    "    write_czyx_image_to_well(stack, stack_hist, stack_ch_metadata, stack_metadata, field, True)\n",
+    "        \n",
+    "    # Create projections group \n",
+    "    projections = field.create_group(\"projections\")\n",
+    "    \n",
+    "    # Write projections\n",
+    "    write_cyx_image_to_well(projection, proj_hists, proj_ch_metadata, proj_metadata, projections, True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f5a8f42",
+   "metadata": {},
+   "source": [
+    "# Inspect ome-zarr plate\n",
+    "The CZYX data can be opened with the [ome-zarr Napari plugin](https://www.napari-hub.org/plugins/napari-ome-zarr).\n",
+    "\n",
+    "The projections will not be discovered by the plugin."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5ee0b7e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib import pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "273e42ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(4, 10, 512, 1024)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# row/column/field/resolution-level\n",
+    "e07_stack = plate['E/7/0/0']\n",
+    "print(e07_stack.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "cff7325c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.subplot(4, 1, 1)\n",
+    "plt.imshow(e07_stack[0, ..., :100, 512])\n",
+    "plt.title(\"C01, ZY-slice\");\n",
+    "plt.subplot(4, 1, 2)\n",
+    "plt.imshow(e07_stack[1, ..., :100, 512])\n",
+    "plt.title(\"C02, ZY-slice\");\n",
+    "plt.subplot(4, 1, 3)\n",
+    "plt.imshow(e07_stack[2, ..., :100, 512])\n",
+    "plt.title(\"C03, ZY-slice\");\n",
+    "plt.subplot(4, 1, 4)\n",
+    "plt.imshow(e07_stack[3, ..., :100, 512])\n",
+    "plt.title(\"C04, ZY-slice\");\n",
+    "\n",
+    "plt.suptitle(\"Slices of CZYX stacks\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "303802a5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# row/column/field/projections/resolution-level\n",
+    "e07_projections = plate['E/7/0/projections/0']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "79ceae37",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(4, 512, 1024)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "e07_projections.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "ab2bbfdc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,6))\n",
+    "plt.subplot(2, 2, 1)\n",
+    "plt.imshow(e07_projections[0])\n",
+    "plt.title(\"C01 - Maximum Projection\");\n",
+    "plt.subplot(2, 2, 2)\n",
+    "plt.imshow(e07_projections[1])\n",
+    "plt.title(\"C02 - Best-focus Projection\");\n",
+    "plt.subplot(2, 2, 3)\n",
+    "plt.imshow(e07_projections[2])\n",
+    "plt.title(\"C03 - Maxium Projection\");\n",
+    "plt.subplot(2, 2, 4)\n",
+    "plt.imshow(e07_projections[3])\n",
+    "plt.title(\"C04 - Empty - No Projection Acquired\");\n",
+    "plt.suptitle(\"Projections\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "03b3b9aa",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/Inspect Zarr.ipynb b/examples/Inspect Zarr.ipynb
new file mode 100644
index 00000000..faf0b252
--- /dev/null
+++ b/examples/Inspect Zarr.ipynb
@@ -0,0 +1,588 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2cf7dca3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ome_zarr.io import parse_url"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8d7d75a4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import zarr\n",
+    "from matplotlib import pyplot as plt\n",
+    "from faim_hcs.UIntHistogram import UIntHistogram\n",
+    "from os.path import join"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "45671e23",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "storage = parse_url(path=\"./zarr-files/MIP-2P-2sub.zarr/\", mode=\"w\").store"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "bea83d0d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plate = zarr.group(store=storage)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "0911c50c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"zarr-info\"><tbody><tr><th style=\"text-align: left\">Name</th><td style=\"text-align: left\">/</td></tr><tr><th style=\"text-align: left\">Type</th><td style=\"text-align: left\">zarr.hierarchy.Group</td></tr><tr><th style=\"text-align: left\">Read-only</th><td style=\"text-align: left\">False</td></tr><tr><th style=\"text-align: left\">Store type</th><td style=\"text-align: left\">zarr.storage.FSStore</td></tr><tr><th style=\"text-align: left\">No. members</th><td style=\"text-align: left\">1</td></tr><tr><th style=\"text-align: left\">No. arrays</th><td style=\"text-align: left\">0</td></tr><tr><th style=\"text-align: left\">No. groups</th><td style=\"text-align: left\">1</td></tr><tr><th style=\"text-align: left\">Groups</th><td style=\"text-align: left\">C</td></tr></tbody></table>"
+      ],
+      "text/plain": [
+       "Name        : /\n",
+       "Type        : zarr.hierarchy.Group\n",
+       "Read-only   : False\n",
+       "Store type  : zarr.storage.FSStore\n",
+       "No. members : 1\n",
+       "No. arrays  : 0\n",
+       "No. groups  : 1\n",
+       "Groups      : C"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plate.info"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b46da748",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/home/tibuch/Gitrepos/faim-hcs/examples/zarr-files/MIP-2P-2sub.zarr/'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plate.store.dir_path()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "b5a1c208",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plate_attrs = plate.attrs.asdict()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "2b8425d6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'barcode': 'example-barcode',\n",
+       " 'order_name': 'example-order',\n",
+       " 'plate': {'columns': [{'name': '1'},\n",
+       "   {'name': '2'},\n",
+       "   {'name': '3'},\n",
+       "   {'name': '4'},\n",
+       "   {'name': '5'},\n",
+       "   {'name': '6'},\n",
+       "   {'name': '7'},\n",
+       "   {'name': '8'},\n",
+       "   {'name': '9'},\n",
+       "   {'name': '10'},\n",
+       "   {'name': '11'},\n",
+       "   {'name': '12'}],\n",
+       "  'field_count': 1,\n",
+       "  'name': 'MIP-2P-2sub',\n",
+       "  'rows': [{'name': 'A'},\n",
+       "   {'name': 'B'},\n",
+       "   {'name': 'C'},\n",
+       "   {'name': 'D'},\n",
+       "   {'name': 'E'},\n",
+       "   {'name': 'F'},\n",
+       "   {'name': 'G'},\n",
+       "   {'name': 'H'}],\n",
+       "  'version': '0.4',\n",
+       "  'wells': [{'columnIndex': 4, 'path': 'C/5', 'rowIndex': 2},\n",
+       "   {'columnIndex': 5, 'path': 'C/6', 'rowIndex': 2}]}}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plate_attrs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "9c08b046",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'columnIndex': 4, 'path': 'C/5', 'rowIndex': 2},\n",
+       " {'columnIndex': 5, 'path': 'C/6', 'rowIndex': 2}]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plate_attrs['plate']['wells']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "c2245b18",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"zarr-info\"><tbody><tr><th style=\"text-align: left\">Name</th><td style=\"text-align: left\">/C</td></tr><tr><th style=\"text-align: left\">Type</th><td style=\"text-align: left\">zarr.hierarchy.Group</td></tr><tr><th style=\"text-align: left\">Read-only</th><td style=\"text-align: left\">False</td></tr><tr><th style=\"text-align: left\">Store type</th><td style=\"text-align: left\">zarr.storage.FSStore</td></tr><tr><th style=\"text-align: left\">No. members</th><td style=\"text-align: left\">2</td></tr><tr><th style=\"text-align: left\">No. arrays</th><td style=\"text-align: left\">0</td></tr><tr><th style=\"text-align: left\">No. groups</th><td style=\"text-align: left\">2</td></tr><tr><th style=\"text-align: left\">Groups</th><td style=\"text-align: left\">5, 6</td></tr></tbody></table>"
+      ],
+      "text/plain": [
+       "Name        : /C\n",
+       "Type        : zarr.hierarchy.Group\n",
+       "Read-only   : False\n",
+       "Store type  : zarr.storage.FSStore\n",
+       "No. members : 2\n",
+       "No. arrays  : 0\n",
+       "No. groups  : 2\n",
+       "Groups      : 5, 6"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plate['C'].info"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e9d14dfa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "c5 = plate['C']['5']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "7174a751",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# works as well\n",
+    "c5 = plate['C/5']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "adccf1d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'well': {'images': [{'path': '0'}], 'version': '0.4'}}"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c5.attrs.asdict()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "aa7bf9a9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Field 0, currently we have everything in field 0\n",
+    "c5_f0 = c5['0']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "dfd59114",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'acquisition_metadata': {'channels': [{'channel-name': 'DAPI',\n",
+       "    'display-color': '0034ff',\n",
+       "    'exposure-time': 20.0,\n",
+       "    'exposure-time-unit': 'ms',\n",
+       "    'objective': '20X Plan Apo Lambda',\n",
+       "    'objective-NA': 0.75,\n",
+       "    'power': 100.0,\n",
+       "    'shading-correction': False,\n",
+       "    'wavelength': 'violet'},\n",
+       "   {'channel-name': 'FITC',\n",
+       "    'display-color': '73ff00',\n",
+       "    'exposure-time': 250.0,\n",
+       "    'exposure-time-unit': 'ms',\n",
+       "    'objective': '20X Plan Apo Lambda',\n",
+       "    'objective-NA': 0.75,\n",
+       "    'power': 100.0,\n",
+       "    'shading-correction': False,\n",
+       "    'wavelength': 'cyan'}]},\n",
+       " 'histograms': {'DAPI': 'DAPI_histogram.npz', 'FITC': 'FITC_histogram.npz'},\n",
+       " 'multiscales': [{'axes': [{'name': 'c', 'type': 'channel'},\n",
+       "    {'name': 'y', 'type': 'space', 'unit': 'micrometer'},\n",
+       "    {'name': 'x', 'type': 'space', 'unit': 'micrometer'}],\n",
+       "   'datasets': [{'coordinateTransformations': [{'scale': [1.0, 0.3417, 0.3417],\n",
+       "       'type': 'scale'}],\n",
+       "     'path': '0'},\n",
+       "    {'coordinateTransformations': [{'scale': [1.0, 0.6834, 0.6834],\n",
+       "       'type': 'scale'}],\n",
+       "     'path': '1'},\n",
+       "    {'coordinateTransformations': [{'scale': [1.0, 1.3668, 1.3668],\n",
+       "       'type': 'scale'}],\n",
+       "     'path': '2'}],\n",
+       "   'name': '/C/5/0',\n",
+       "   'version': '0.4'}],\n",
+       " 'omero': {'channels': [{'active': True,\n",
+       "    'coefficient': 1,\n",
+       "    'color': '0034ff',\n",
+       "    'family': 'linear',\n",
+       "    'inverted': False,\n",
+       "    'label': 'DAPI',\n",
+       "    'wavelength_id': 'C01',\n",
+       "    'window': {'end': 7663, 'max': 65535, 'min': 0, 'start': 118}},\n",
+       "   {'active': True,\n",
+       "    'coefficient': 1,\n",
+       "    'color': '73ff00',\n",
+       "    'family': 'linear',\n",
+       "    'inverted': False,\n",
+       "    'label': 'FITC',\n",
+       "    'wavelength_id': 'C02',\n",
+       "    'window': {'end': 753, 'max': 65535, 'min': 0, 'start': 166}}]}}"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c5_f0.attrs.asdict()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "7b8225c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Resolution level 0 i.e. highest resolution.\n",
+    "c5_f0_res0 = c5_f0['0']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "d68995ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2, 2048, 4096)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c5_f0_res0.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "9c07cad6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('uint16')"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c5_f0_res0.dtype"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "9c393421",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f0ec187d040>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(c5_f0_res0[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "fc6e2d09",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"zarr-info\"><tbody><tr><th style=\"text-align: left\">Name</th><td style=\"text-align: left\">/C/5/0</td></tr><tr><th style=\"text-align: left\">Type</th><td style=\"text-align: left\">zarr.hierarchy.Group</td></tr><tr><th style=\"text-align: left\">Read-only</th><td style=\"text-align: left\">False</td></tr><tr><th style=\"text-align: left\">Store type</th><td style=\"text-align: left\">zarr.storage.FSStore</td></tr><tr><th style=\"text-align: left\">No. members</th><td style=\"text-align: left\">3</td></tr><tr><th style=\"text-align: left\">No. arrays</th><td style=\"text-align: left\">3</td></tr><tr><th style=\"text-align: left\">No. groups</th><td style=\"text-align: left\">0</td></tr><tr><th style=\"text-align: left\">Arrays</th><td style=\"text-align: left\">0, 1, 2</td></tr></tbody></table>"
+      ],
+      "text/plain": [
+       "Name        : /C/5/0\n",
+       "Type        : zarr.hierarchy.Group\n",
+       "Read-only   : False\n",
+       "Store type  : zarr.storage.FSStore\n",
+       "No. members : 3\n",
+       "No. arrays  : 3\n",
+       "No. groups  : 0\n",
+       "Arrays      : 0, 1, 2"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c5_f0.info"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "ab11dddc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dapi_histogram = UIntHistogram.load(join(c5_f0.store.dir_path(), \n",
+    "                                         c5_f0.path, \n",
+    "                                         c5_f0.attrs.asdict()['histograms']['DAPI']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "cae9f851",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dapi_histogram.plot(width=100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "0b07661e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "107\n",
+      "746.8684959411621\n",
+      "16454\n",
+      "1628.5330680038232\n",
+      "147\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(dapi_histogram.min())\n",
+    "print(dapi_histogram.mean())\n",
+    "print(dapi_histogram.max())\n",
+    "print(dapi_histogram.std())\n",
+    "print(dapi_histogram.quantile(0.5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "4f1a0977",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fitc_histogram = UIntHistogram.load(join(c5_f0.store.dir_path(), \n",
+    "                                         c5_f0.path, \n",
+    "                                         c5_f0.attrs.asdict()['histograms']['FITC']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "811e7601",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "joint_hist = UIntHistogram()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "1bf9aa90",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "joint_hist.combine(dapi_histogram)\n",
+    "joint_hist.combine(fitc_histogram)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "11e79fcf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "joint_hist.plot(width=100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c5f63d29",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/src/faim_hcs/hcs/__init__.py b/examples/zarr-files/.gitkeep
similarity index 100%
rename from src/faim_hcs/hcs/__init__.py
rename to examples/zarr-files/.gitkeep
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diff --git a/resources/Projection-Mix/2023-02-21/1334/Projection-Mix_E07_s1_w1_thumb4BFD4018-E675-475E-B5AB-2E959E6B6DA1.tif b/resources/Projection-Mix/2023-02-21/1334/Projection-Mix_E07_s1_w1_thumb4BFD4018-E675-475E-B5AB-2E959E6B6DA1.tif
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diff --git a/resources/Projection-Mix/2023-02-21/1334/Projection-Mix_E07_s1_w2B14915F6-0679-4494-82D1-F80894B32A66.tif b/resources/Projection-Mix/2023-02-21/1334/Projection-Mix_E07_s1_w2B14915F6-0679-4494-82D1-F80894B32A66.tif
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diff --git a/resources/Projection-Mix/2023-02-21/1334/Projection-Mix_E07_s1_w2_thumbE5A6D08C-AF44-4CA9-A6E8-7DC9BC9565F2.tif b/resources/Projection-Mix/2023-02-21/1334/Projection-Mix_E07_s1_w2_thumbE5A6D08C-AF44-4CA9-A6E8-7DC9BC9565F2.tif
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diff --git a/resources/Projection-Mix/2023-02-21/1334/Projection-Mix_E07_s1_w3BB87F860-FC67-4B3A-A740-A9EACF8A8F5F.tif b/resources/Projection-Mix/2023-02-21/1334/Projection-Mix_E07_s1_w3BB87F860-FC67-4B3A-A740-A9EACF8A8F5F.tif
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diff --git a/resources/Projection-Mix/2023-02-21/1334/Projection-Mix_E07_s1_w3_thumbD739A948-8CA4-4559-910C-79D42CEC98A0.tif b/resources/Projection-Mix/2023-02-21/1334/Projection-Mix_E07_s1_w3_thumbD739A948-8CA4-4559-910C-79D42CEC98A0.tif
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diff --git a/setup.cfg b/setup.cfg
index 068a1a5c..958ff78b 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -1,6 +1,6 @@
 [metadata]
 name = faim_hcs
-description = Package to organize high content screening experiments.
+description = Tools used at FMI-FAIM to handle HCS data.
 long_description = file: README.md
 long_description_content_type = text/markdown
 url = https://github.com/fmi-faim/faim-hcs.git
@@ -16,10 +16,6 @@ classifiers =
     Programming Language :: Python
     Programming Language :: Python :: 3
     Programming Language :: Python :: 3 :: Only
-    Programming Language :: Python :: 3.7
-    Programming Language :: Python :: 3.8
-    Programming Language :: Python :: 3.9
-    Programming Language :: Python :: 3.10
     Topic :: Software Development :: Testing
 project_urls =
     Bug Tracker = https://github.com/fmi-faim/faim-hcs/issues
@@ -30,11 +26,15 @@ project_urls =
 [options]
 packages = find:
 install_requires =
+    imagecodecs
+    matplotlib
     numpy
+    ome-zarr
     pandas
-    scikit-image
+    tifffile
     tqdm
-python_requires = >=3.7
+    zarr
+python_requires = >=3.9
 package_dir =
     = src
 setup_requires =
diff --git a/src/faim_hcs/MetaSeriesUtils.py b/src/faim_hcs/MetaSeriesUtils.py
new file mode 100644
index 00000000..994394ba
--- /dev/null
+++ b/src/faim_hcs/MetaSeriesUtils.py
@@ -0,0 +1,391 @@
+from decimal import Decimal
+from typing import Any, Callable, Optional, Union
+
+import numpy as np
+import pandas as pd
+from numpy._typing import ArrayLike
+
+from faim_hcs.io.MetaSeriesTiff import load_metaseries_tiff
+from faim_hcs.UIntHistogram import UIntHistogram
+from faim_hcs.utils import rgb_to_hex, wavelength_to_rgb
+
+
+def _build_ch_metadata(metaseries_ch_metadata: dict):
+    """Build channel metadata from metaseries metadata."""
+
+    def get_wavelength_power(channel):
+        if channel["Lumencor Cyan Intensity"] > 0.0:
+            wl = "cyan"
+            power = channel["Lumencor Cyan Intensity"]
+            assert channel["Lumencor Green Intensity"] == 0.0
+            assert channel["Lumencor Red Intensity"] == 0.0
+            assert channel["Lumencor Violet Intensity"] == 0.0
+            assert channel["Lumencor Yellow Intensity"] == 0.0
+            return wl, power
+        if channel["Lumencor Green Intensity"] > 0.0:
+            wl = "green"
+            power = channel["Lumencor Green Intensity"]
+            assert channel["Lumencor Cyan Intensity"] == 0.0
+            assert channel["Lumencor Red Intensity"] == 0.0
+            assert channel["Lumencor Violet Intensity"] == 0.0
+            assert channel["Lumencor Yellow Intensity"] == 0.0
+            return wl, power
+        if channel["Lumencor Red Intensity"] > 0.0:
+            wl = "red"
+            power = channel["Lumencor Red Intensity"]
+            assert channel["Lumencor Cyan Intensity"] == 0.0
+            assert channel["Lumencor Green Intensity"] == 0.0
+            assert channel["Lumencor Violet Intensity"] == 0.0
+            assert channel["Lumencor Yellow Intensity"] == 0.0
+            return wl, power
+        if channel["Lumencor Violet Intensity"] > 0.0:
+            wl = "violet"
+            power = channel["Lumencor Violet Intensity"]
+            assert channel["Lumencor Cyan Intensity"] == 0.0
+            assert channel["Lumencor Green Intensity"] == 0.0
+            assert channel["Lumencor Red Intensity"] == 0.0
+            assert channel["Lumencor Yellow Intensity"] == 0.0
+            return wl, power
+        if channel["Lumencor Yellow Intensity"] > 0.0:
+            wl = "yellow"
+            power = channel["Lumencor Yellow Intensity"]
+            assert channel["Lumencor Cyan Intensity"] == 0.0
+            assert channel["Lumencor Green Intensity"] == 0.0
+            assert channel["Lumencor Red Intensity"] == 0.0
+            assert channel["Lumencor Violet Intensity"] == 0.0
+            return wl, power
+        return None, None
+
+    def get_exposure_time_unit(ch):
+        time, unit = ch["Exposure Time"].split(" ")
+        time = float(time)
+        return time, unit
+
+    wavelength, power = get_wavelength_power(metaseries_ch_metadata)
+    time, unit = get_exposure_time_unit(metaseries_ch_metadata)
+    display_color = rgb_to_hex(*wavelength_to_rgb(metaseries_ch_metadata["wavelength"]))
+    metadata = {
+        "wavelength": wavelength,
+        "power": power,
+        "exposure-time": time,
+        "exposure-time-unit": unit,
+        "shading-correction": metaseries_ch_metadata["ShadingCorrection"] == "On",
+        "channel-name": metaseries_ch_metadata["_IllumSetting_"],
+        "objective-numerical-aperture": metaseries_ch_metadata["_MagNA_"],
+        "objective": metaseries_ch_metadata["_MagSetting_"],
+        "display-color": display_color,
+    }
+    if "Z Projection Method" in metaseries_ch_metadata:
+        metadata["z-projection-method"] = metaseries_ch_metadata["Z Projection Method"]
+    return metadata
+
+
+def _z_metadata(metaseries_ch_metadata: dict):
+    return {"z-position": metaseries_ch_metadata["z-position"]}
+
+
+def _get_molecular_devices_well_bbox_2D(
+    data: list[tuple[ArrayLike, dict]]
+) -> tuple[Optional[Any], Optional[Any], Optional[Any], Optional[Any]]:
+    """Compute well-shape based on stage position metadata."""
+    assert "stage-position-x" in data[0][1].keys(), "Missing metaseries metadata."
+    assert "stage-position-y" in data[0][1].keys(), "Missing metaseries metadata."
+    assert "spatial-calibration-x" in data[0][1].keys(), "Missing metaseries metadata."
+    assert "spatial-calibration-y" in data[0][1].keys(), "Missing metaseries metadata."
+
+    min_x, max_x, min_y, max_y = None, None, None, None
+    for d in data:
+        pos_x = d[1]["stage-position-x"]
+        pos_y = d[1]["stage-position-y"]
+        res_x = d[1]["spatial-calibration-x"]
+        res_y = d[1]["spatial-calibration-y"]
+
+        if min_x is None:
+            min_x = pos_x / res_x
+            max_x = min_x + d[0].shape[1]
+        elif min_x > (pos_x / res_x):
+            min_x = pos_x / res_x
+
+        if max_x < (pos_x / res_x) + d[0].shape[1]:
+            max_x = (pos_x / res_x) + d[0].shape[1]
+
+        if min_y is None:
+            min_y = pos_y / res_y
+            max_y = min_y + d[0].shape[0]
+        elif min_y > pos_y / res_y:
+            min_y = pos_y / res_y
+
+        if max_y < (pos_y / res_y) + d[0].shape[0]:
+            max_y = (pos_y / res_y) + d[0].shape[0]
+
+    return min_y, min_x, max_y, max_x
+
+
+def montage_stage_pos_image_YX(data):
+    """Montage 2D fields based on stage position metadata."""
+
+    def sort_key(d):
+        label = d[1]["stage-label"]
+
+        label = label.split(":")
+
+        if len(label) == 1:
+            return label
+        else:
+            return int(label[1].replace("Site", ""))
+
+    data.sort(key=sort_key, reverse=True)
+
+    min_y, min_x, max_y, max_x = _get_molecular_devices_well_bbox_2D(data)
+
+    shape = (int(np.round(max_y - min_y)), int(np.round(max_x - min_x)))
+
+    img = np.zeros(shape, dtype=data[0][0].dtype)
+
+    for d in data:
+        pos_y = int(
+            np.round(d[1]["stage-position-y"] / d[1]["spatial-calibration-y"] - min_y)
+        )
+        pos_x = int(
+            np.round(d[1]["stage-position-x"] / d[1]["spatial-calibration-x"] - min_x)
+        )
+
+        img[pos_y : pos_y + d[0].shape[0], pos_x : pos_x + d[0].shape[1]] = d[0]
+
+    return img
+
+
+def _pixel_pos(dim: str, data: dict):
+    return np.round(data[f"stage-position-{dim}"] / data[f"spatial-calibration-{dim}"])
+
+
+def montage_grid_image_YX(data):
+    """Montage 2D fields into fixed grid, based on stage position metadata."""
+    min_y = min(_pixel_pos("y", d[1]) for d in data)
+    min_x = min(_pixel_pos("x", d[1]) for d in data)
+    max_y = max(_pixel_pos("y", d[1]) for d in data)
+    max_x = max(_pixel_pos("x", d[1]) for d in data)
+
+    assert all([d[0].shape for d in data])
+    step_y = data[0][0].shape[0]
+    step_x = data[0][0].shape[1]
+
+    shape = (
+        int(np.round((max_y - min_y) / step_y + 1) * step_y),
+        int(np.round((max_x - min_x) / step_x + 1) * step_x),
+    )
+    img = np.zeros(shape, dtype=data[0][0].dtype)
+
+    for d in data:
+        pos_x = int(np.round((_pixel_pos("x", d[1]) - min_x) / step_x))
+        pos_y = int(np.round((_pixel_pos("y", d[1]) - min_y) / step_y))
+        img[
+            pos_y * step_y : (pos_y + 1) * step_y, pos_x * step_x : (pos_x + 1) * step_x
+        ] = d[0]
+
+    return img
+
+
+def verify_integrity(field_metadata: list[dict]):
+    metadata = field_metadata[0]
+    for fm in field_metadata:
+        assert fm == metadata, "Metadata is not consistent across fields."
+
+    return metadata
+
+
+def build_stack(imgs: list):
+    # Build zero-stack
+    stack = None
+    for img in imgs:
+        if img is not None:
+            shape = img.shape
+            stack = np.zeros((len(imgs), *shape), dtype=img.dtype)
+            break
+
+    # Fill in existing data
+    for i, img in enumerate(imgs):
+        if img is not None:
+            stack[i] = img
+
+    return stack
+
+
+def compute_z_sampling(ch_z_positions: list[list[Union[None, float]]]):
+    z_samplings = []
+    for z_positions in ch_z_positions:
+        if z_positions is not None and None not in z_positions:
+            precision = -Decimal(str(z_positions[0])).as_tuple().exponent
+            z_step = np.round(np.mean(np.diff(z_positions)), decimals=precision)
+            z_samplings.append(z_step)
+
+    return np.mean(z_samplings)
+
+
+def roll_single_plane(stacks, ch_z_positions):
+    min_z, max_z = [], []
+    for z_positions in ch_z_positions:
+        if z_positions is not None and None not in z_positions:
+            min_z.append(np.min(z_positions))
+            max_z.append(np.max(z_positions))
+
+    min_z, max_z = np.mean(min_z), np.mean(max_z)
+
+    for i, z_positions in enumerate(ch_z_positions):
+        if z_positions is not None and None in z_positions:
+            # Single planes are always acquired in first z-step
+            step_size = (max_z - min_z) / len(z_positions)
+            shift_z = int((z_positions[0] - min_z) // step_size)
+            stacks[i] = np.roll(stacks[i], shift_z, axis=0)
+
+
+def get_well_image_CZYX(
+    well_files: pd.DataFrame,
+    channels: list[str],
+    assemble_fn: Callable = montage_grid_image_YX,
+) -> tuple[ArrayLike, list[UIntHistogram], list[dict], dict]:
+    """Assemble image data for the given well-files."""
+    planes = well_files["z"].unique()
+
+    stacks = []
+    channel_histograms = []
+    channel_metadata = []
+    px_metadata = None
+    z_positions = []
+
+    for ch in channels:
+        channel_files = well_files[well_files["channel"] == ch]
+        if len(channel_files) > 0:
+            plane_imgs = []
+            z_plane_positions = []
+            ch_metadata = None
+            for z in sorted(planes, key=int):
+                plane_files = channel_files[channel_files["z"] == z]
+
+                if len(plane_files) > 0:
+                    px_metadata, img, ch_meta, z_position = get_img_YX(
+                        assemble_fn=assemble_fn, files=plane_files
+                    )
+
+                    if ch_metadata is None:
+                        ch_metadata = ch_meta
+
+                    plane_imgs.append(img)
+                    z_plane_positions.append(z_position)
+                else:
+                    plane_imgs.append(None)
+                    z_plane_positions.append(None)
+
+            zyx = build_stack(plane_imgs)
+            stacks.append(zyx)
+            channel_histograms.append(UIntHistogram(stacks[-1]))
+            channel_metadata.append(ch_metadata)
+            z_positions.append(z_plane_positions)
+        else:
+            stacks.append(None)
+            channel_histograms.append(UIntHistogram())
+            channel_metadata.append(
+                {
+                    "channel-name": "empty",
+                    "display-color": "000000",
+                }
+            )
+            z_positions.append(None)
+
+    z_sampling = compute_z_sampling(z_positions)
+    px_metadata["z-scaling"] = z_sampling
+
+    roll_single_plane(stacks, z_positions)
+
+    czyx = build_stack(stacks)
+
+    for i in range(len(channel_histograms)):
+        if channel_histograms[i] is None:
+            channel_histograms[i] = UIntHistogram()
+            assert channel_metadata[i] is None
+            channel_metadata[i] = {
+                "channel-name": "empty",
+                "display-color": "000000",
+            }
+
+    return czyx, channel_histograms, channel_metadata, px_metadata
+
+
+def get_well_image_CYX(
+    well_files: pd.DataFrame,
+    channels: list[str],
+    assemble_fn: Callable = montage_grid_image_YX,
+    include_z_position: bool = False,
+) -> tuple[ArrayLike, list[UIntHistogram], list[dict], dict]:
+    """Assemble image data for the given well-files.
+
+    For each channel a single 2D image is computed. If the well has multiple
+    fields per channel the `assemble_fn` has to montage or stitch the fields
+    accordingly.
+
+    :param well_files: all files corresponding to the well
+    :param channels: list of required channels
+    :param assemble_fn: creates a single image for each channel
+    :param include_z_position: whether to include z-position metadata
+    :return: CYX image, channel-histograms, channel-metadata, general-metadata
+    """
+    channel_imgs = {}
+    channel_histograms = {}
+    channel_metadata = {}
+    px_metadata = None
+    for ch in channels:
+        channel_files = well_files[well_files["channel"] == ch]
+
+        if len(channel_files) > 0:
+            px_metadata, img, ch_metadata, z_position = get_img_YX(
+                assemble_fn, channel_files
+            )
+
+            channel_imgs[ch] = img
+            channel_histograms[ch] = UIntHistogram(img)
+
+            channel_metadata[ch] = ch_metadata
+
+    cyx = np.zeros((len(channels), *img.shape), dtype=img.dtype)
+
+    channel_hists = []
+    channel_meta = []
+    for i, ch in enumerate(channels):
+        if ch in channel_imgs.keys():
+            cyx[i] = channel_imgs[ch]
+            channel_hists.append(channel_histograms[ch])
+            channel_meta.append(channel_metadata[ch])
+        else:
+            channel_hists.append(UIntHistogram())
+            channel_meta.append(
+                {
+                    "channel-name": "empty",
+                    "display-color": "000000",
+                }
+            )
+
+    return cyx, channel_hists, channel_meta, px_metadata
+
+
+def get_img_YX(assemble_fn, files):
+    imgs = []
+    field_metadata = []
+    z_positions = []
+    general_metadata = None
+    for f in sorted(files["path"]):
+        img, ms_metadata = load_metaseries_tiff(f)
+        ch_metadata = _build_ch_metadata(ms_metadata)
+        z_positions.append(_z_metadata(ms_metadata))
+        imgs.append((img, ms_metadata))
+        field_metadata.append(ch_metadata)
+        if general_metadata is None:
+            general_metadata = {
+                "spatial-calibration-x": ms_metadata["spatial-calibration-x"],
+                "spatial-calibration-y": ms_metadata["spatial-calibration-y"],
+                "spatial-calibration-units": ms_metadata["spatial-calibration-units"],
+                "pixel-type": ms_metadata["PixelType"],
+            }
+    img = assemble_fn(imgs)
+    metadata = verify_integrity(field_metadata)
+    zs = [z["z-position"] for z in z_positions]
+    return general_metadata, img, metadata, np.mean(zs)
diff --git a/src/faim_hcs/UIntHistogram.py b/src/faim_hcs/UIntHistogram.py
new file mode 100644
index 00000000..7fe7fd31
--- /dev/null
+++ b/src/faim_hcs/UIntHistogram.py
@@ -0,0 +1,263 @@
+import numpy as np
+from matplotlib import pyplot as plt
+
+
+class UIntHistogram:
+    def __init__(self, data=None):
+        """
+        An unsigned integer histogram class that can be updated with new data.
+
+        :param data: array[uint]
+            Optional data of which the initial histogram is built.
+        """
+        if data is not None:
+            assert data.min() >= 0, "Negative data is not supported."
+            self.offset, self.bins, self.frequencies = self._get_hist(data)
+        else:
+            self.offset, self.bins, self.frequencies = None, None, None
+
+    @staticmethod
+    def _add(list_a, list_b):
+        """
+        Add two lists element-wise.
+        :param list_a: list(numeric)
+        :param list_b: list(numeric)
+        :return: list(numeric)
+        """
+        return list(map(lambda e: e[0] + e[1], zip(list_a, list_b)))
+
+    @staticmethod
+    def _get_hist(data):
+        """
+        Compute histogram with integer bins.
+        :param data: array[numeric]
+        :return: offset, bins, frequencies
+        """
+        offset = int(data.min())
+        bins = int(data.max()) + 2 - offset
+        freq = np.histogram(data, np.arange(offset, offset + bins))[0].tolist()
+        return offset, bins, freq
+
+    def _aggregate_histograms(self, offset_data, bins, freq):
+        """
+        Integrate new frequencies into existing histogram.
+
+        :param offset_data:
+        :param bins:
+        :param freq:
+        :return:
+        """
+        assert isinstance(freq, list), "freq must be of type List."
+        assert isinstance(self.frequencies, list), (
+            "frequencies must be of " "type List."
+        )
+        lower_shift = offset_data - self.offset
+        upper_shift = self.offset + self.bins - (offset_data + bins)
+
+        if lower_shift == 0 and upper_shift == 0:
+            # Old and new frequencies cover the same range:
+            # [old frequencies]
+            # [new frequencies]
+            self.frequencies = self._add(self.frequencies, freq)
+        elif (
+            lower_shift < 0
+            and (offset_data + bins - 1 >= self.offset)
+            and (offset_data + bins - 1 <= self.offset + self.bins - 1)
+        ):
+            # New frequencies have additional lower ones.
+            #     [old frequencies]
+            # [new frequencies]
+            frequencies_to = offset_data + bins - self.offset - 1
+            freq_from = self.offset - offset_data
+            self.frequencies[:frequencies_to] = self._add(
+                self.frequencies[:frequencies_to],
+                freq[freq_from:],
+            )
+            self.frequencies = freq[:freq_from] + self.frequencies
+            self.offset = offset_data
+            self.bins = len(self.frequencies) + 1
+        elif lower_shift < 0 and (offset_data + bins - 1 < self.offset):
+            # New frequencies only have additional lower ones.
+            #                       [old frequencies]
+            # [new frequencies]
+            gap_freq = [
+                0,
+            ] * (self.offset - offset_data - bins + 1)
+            self.frequencies = freq + gap_freq + self.frequencies
+            self.offset = offset_data
+            self.bins = len(self.frequencies) + 1
+        elif (
+            self.offset
+            <= offset_data
+            <= (self.offset + self.bins - 2)
+            < offset_data + bins - 2
+        ):
+            # New frequencies have additional upper ones.
+            #     [old frequencies]
+            #            [new frequencies]
+            from_frequencies = self.offset + self.bins - offset_data - 1
+            to_freq = self.offset + self.bins - offset_data
+            self.frequencies[-from_frequencies:] = self._add(
+                self.frequencies[-from_frequencies:],
+                freq[:to_freq],
+            )
+            self.frequencies = self.frequencies + freq[from_frequencies:]
+            self.bins = len(self.frequencies) + 1
+        elif (offset_data) > (self.offset + self.bins - 2):
+            # New frequencies have only additional upper ones.
+            #     [old frequencies]
+            #                           [new frequencies]
+            gap_freq = [
+                0,
+            ] * (offset_data - self.offset - self.bins + 1)
+            self.frequencies = self.frequencies + gap_freq + freq
+            self.bins = len(self.frequencies) + 1
+        elif lower_shift >= 0 and upper_shift >= 0:
+            # New frequencies are completely covered.
+            # [          old frequencies          ]
+            #           [new frequencies]
+            if upper_shift == 0:
+                self.frequencies[lower_shift:] = self._add(
+                    self.frequencies[lower_shift:], freq
+                )
+            else:
+                self.frequencies[lower_shift:-upper_shift] = self._add(
+                    self.frequencies[lower_shift:-upper_shift], freq
+                )
+        else:
+            # Old frequencies are completely covered.
+            #     [old frequencies]
+            # [    new frequencies    ]
+            from_ = self.offset - offset_data
+            to = from_ + self.bins - 1
+            self.frequencies = self._add(
+                self.frequencies,
+                freq[from_:to],
+            )
+            self.frequencies = freq[:from_] + self.frequencies + freq[to:]
+            self.offset = offset_data
+            self.bins = len(self.frequencies) + 1
+
+    def combine(self, histogram):
+        """
+        Combine this histogram with another UIntHistogram.
+
+        :param histogram: to merge with this
+        """
+        if self.frequencies is None:
+            self.frequencies = histogram.frequencies
+            self.bins = histogram.bins
+            self.offset = histogram.offset
+        else:
+            if histogram.frequencies is not None:
+                self._aggregate_histograms(
+                    offset_data=histogram.offset,
+                    bins=histogram.bins,
+                    freq=histogram.frequencies,
+                )
+
+        return self
+
+    def update(self, data):
+        """
+        Update histogram by adding more data.
+
+        :param data: array(uint)
+            Data to be added to the histogram.
+        """
+        assert data.min() >= 0, "Negative data is not supported."
+
+        if self.frequencies is None:
+            self.offset, self.bins, self.frequencies = self._get_hist(data)
+        else:
+            offset_data, bins, freq = self._get_hist(data)
+            self._aggregate_histograms(offset_data=offset_data, bins=bins, freq=freq)
+
+        return self
+
+    def plot(self, width=1):
+        """
+        Plot histogram.
+        """
+        if width > 1:
+            heights = []
+            for i in range(self.offset, self.offset + self.bins - 1, width):
+                heights.append(np.sum(self.frequencies[i : i + width]))
+        else:
+            heights = self.frequencies
+
+        plt.bar(
+            np.arange(self.offset, self.offset + self.bins - 1, width),
+            heights,
+            width=width,
+        )
+        plt.show()
+
+    def mean(self):
+        """
+        Get histogram mean.
+        :return: float
+        """
+        if self.frequencies is None:
+            return 0
+        return np.sum(
+            np.arange(self.offset, self.offset + self.bins - 1) * self.frequencies
+        ) / np.sum(self.frequencies)
+
+    def std(self):
+        """
+        Get histogram standard deviation.
+        :return: float
+        """
+        if self.frequencies is None:
+            return 0
+        return np.sqrt(
+            np.sum(
+                (np.arange(self.offset, self.offset + self.bins - 1) - self.mean()) ** 2
+                * self.frequencies
+            )
+            / np.sum(self.frequencies)
+        )
+
+    def quantile(self, q):
+        """
+        Get quantile `q`.
+        :param q: uint
+        :return: quantile
+        """
+        assert q >= 0 and q <= 1
+        if self.frequencies is None:
+            return 0
+        return self.offset + np.argmax(
+            np.cumsum(self.frequencies) / np.sum(self.frequencies) >= q
+        )
+
+    def min(self):
+        """
+        Get minimum.
+        :return: uint
+        """
+        if self.frequencies is None:
+            return 0
+        return self.offset
+
+    def max(self):
+        """
+        Get maximum.
+        :return: uint
+        """
+        if self.frequencies is None:
+            return 0
+        return self.offset + self.bins - 2
+
+    def save(self, path):
+        np.savez(path, frequencies=self.frequencies, offset=self.offset, bins=self.bins)
+
+    @staticmethod
+    def load(path):
+        storage = np.load(path, allow_pickle=True)
+        hist = UIntHistogram()
+        hist.frequencies = storage["frequencies"].tolist()
+        hist.offset = storage["offset"]
+        hist.bins = storage["bins"]
+        return hist
diff --git a/src/faim_hcs/Zarr.py b/src/faim_hcs/Zarr.py
new file mode 100644
index 00000000..152ecd69
--- /dev/null
+++ b/src/faim_hcs/Zarr.py
@@ -0,0 +1,368 @@
+import os
+from enum import IntEnum
+from os.path import join
+from pathlib import Path
+from typing import Union
+
+import numpy as np
+import pandas as pd
+import zarr
+from numpy._typing import ArrayLike
+from ome_zarr.io import parse_url
+from ome_zarr.scale import Scaler
+from ome_zarr.writer import (
+    write_image,
+    write_multiscales_metadata,
+    write_plate_metadata,
+    write_well_metadata,
+)
+from zarr import Group
+
+from faim_hcs.UIntHistogram import UIntHistogram
+
+
+class PlateLayout(IntEnum):
+    """Plate layout, 96-well or 384-well."""
+
+    I96 = 96
+    I384 = 384
+
+
+def _get_row_cols(layout: Union[PlateLayout, int]) -> tuple[list[str], list[str]]:
+    """Return rows and columns for requested layout."""
+    if layout == PlateLayout.I96:
+        rows = ["A", "B", "C", "D", "E", "F", "G", "H"]
+        cols = [str(i) for i in range(1, 13)]
+        assert len(rows) * len(cols) == 96
+    elif layout == PlateLayout.I384:
+        rows = [
+            "A",
+            "B",
+            "C",
+            "D",
+            "E",
+            "F",
+            "G",
+            "H",
+            "I",
+            "J",
+            "K",
+            "L",
+            "M",
+            "N",
+            "O",
+            "P",
+        ]
+        cols = [str(i) for i in range(1, 25)]
+        assert len(rows) * len(cols) == 384
+    else:
+        raise NotImplementedError(f"{layout} layout not supported.")
+
+    return rows, cols
+
+
+def _create_zarr_plate(
+    root_dir: Path,
+    name: str,
+    layout: PlateLayout,
+    files: pd.DataFrame,
+    order_name: str,
+    barcode: str,
+) -> Group:
+    """Create plate layout according to ome-zarr NGFF.
+
+    Additionally the `order_name` and `barcode` is added to the plate.attrs.
+
+    :param root_dir: where the zarr is stored
+    :param layout: plate layout
+    :param files: table of all image files
+    :param order_name: plate order name
+    :param barcode: plate barcode
+    :return: zarr group
+    """
+    rows, cols = _get_row_cols(layout=layout)
+
+    plate_path = join(root_dir, name + ".zarr")
+    os.makedirs(plate_path, exist_ok=False)
+
+    store = parse_url(plate_path, mode="w").store
+    plate = zarr.group(store=store)
+
+    write_plate_metadata(
+        plate,
+        columns=cols,
+        rows=rows,
+        wells=[f"{w[0]}/{str(int(w[1:]))}" for w in files["well"].unique()],
+        name=name,
+        field_count=1,
+    )
+
+    attrs = plate.attrs.asdict()
+    attrs["order_name"] = order_name
+    attrs["barcode"] = barcode
+    plate.attrs.put(attrs)
+
+    return plate
+
+
+def _add_wells_to_plate(plate: Group, files: pd.DataFrame) -> None:
+    """Add wells to zarr-plate according to ome-zarr NGFF."""
+    for well in files["well"].unique():
+        row, col = well[0], str(int(well[1:]))
+
+        if row not in plate:
+            plate.create_group(row)
+
+        if col not in plate[row]:
+            plate[row].create_group(col).create_group("0")
+            write_well_metadata(plate[row][col], [{"path": "0"}])
+
+
+def build_zarr_scaffold(
+    root_dir: Union[str, Path],
+    files: pd.DataFrame,
+    name: str = None,
+    layout: Union[PlateLayout, int] = PlateLayout.I96,
+    order_name: str = "order-name",
+    barcode: str = "barcode",
+) -> Group:
+    """Build empty zarr scaffold of a ome-zarr NGFF conform HCS experiment.
+
+    Additionally `order_name` and `barcode` are added to the plate.attrs.
+
+    :param root_dir: where the zarr is stored
+    :param files: table of image files
+    :param name: Name of the plate-zarr. By default taken from metadata.
+    :param layout: plate layout
+    :param order_name: plate order name
+    :param barcode: plate barcode
+    :return: zarr plate group
+    """
+    names = files["name"].unique()
+    assert len(names) == 1, "Files do belong to more than one plate."
+    if name is None:
+        name = files["name"].unique()[0]
+
+    plate = _create_zarr_plate(
+        root_dir=root_dir,
+        name=name,
+        layout=layout,
+        files=files,
+        order_name=order_name,
+        barcode=barcode,
+    )
+
+    _add_wells_to_plate(plate=plate, files=files)
+
+    return plate
+
+
+def _add_image_metadata(
+    img_group: Group,
+    ch_metadata: dict,
+    dtype: type,
+    histograms: list[UIntHistogram],
+):
+    attrs = img_group.attrs.asdict()
+
+    # Add omero metadata
+    attrs["omero"] = build_omero_channel_metadata(ch_metadata, dtype, histograms)
+
+    # Add metaseries metadta
+    attrs["acquisition_metadata"] = {"channels": ch_metadata}
+
+    # Save histograms and add paths to attributes
+    histogram_paths = []
+    for i, (ch, hist) in enumerate(zip(ch_metadata, histograms)):
+        ch_name = ch["channel-name"].replace(" ", "_")
+        hist_name = f"C{str(i).zfill(2)}_{ch_name}_histogram.npz"
+        hist.save(join(img_group.store.path, img_group.path, hist_name))
+        histogram_paths.append(hist_name)
+
+    attrs["histograms"] = histogram_paths
+
+    img_group.attrs.put(attrs)
+
+
+def _compute_chunk_size_cyx(
+    img: ArrayLike,
+    max_levels: int = 4,
+    max_size: int = 2048,
+    write_empty_chunks: bool = True,
+) -> tuple[list[dict[str, list[int]]], int]:
+    """Compute chunk-size for zarr storage.
+
+    :param img: to be saved
+    :param max_levels: max resolution pyramid levels
+    :param max_size: chunk size maximum
+    :return: storage options, number of pyramid levels
+    """
+    storage_options = []
+    chunks = [
+        1,
+    ] * img.ndim
+    for i in range(max_levels + 1):
+        h = min(max_size, img.shape[-2] // 2**i)
+        w = min(max_size, img.shape[-1] // 2**i)
+        chunks[-2] = h
+        chunks[-1] = w
+        storage_options.append(
+            {
+                "chunks": chunks,
+                "write_empty_chunks": write_empty_chunks,
+            }
+        )
+        if h <= max_size / 2 and w <= max_size / 2:
+            return storage_options, i
+    return storage_options, max_levels
+
+
+def _get_axis_scale(axis, meta):
+    if axis["name"] == "x":
+        return meta["spatial-calibration-x"]
+    if axis["name"] == "y":
+        return meta["spatial-calibration-y"]
+    if axis["name"] == "z":
+        return meta["z-scaling"]
+    return 1
+
+
+def _set_multiscale_metadata(group: Group, general_metadata: dict, axes: list[dict]):
+    datasets = group.attrs.asdict()["multiscales"][0]["datasets"]
+    scaling = np.array([_get_axis_scale(axis, general_metadata) for axis in axes])
+
+    for ct in datasets:
+        rescaled = ct["coordinateTransformations"][0]["scale"] * scaling
+        ct["coordinateTransformations"][0]["scale"] = list(rescaled)
+
+    write_multiscales_metadata(group, datasets=datasets, axes=axes)
+
+
+def write_image_to_well(
+    img: ArrayLike,
+    axes: list[dict],
+    histograms: list[UIntHistogram],
+    ch_metadata: list[dict],
+    general_metadata: dict,
+    group: Group,
+    write_empty_chunks: bool = True,
+):
+    storage_options, max_layer = _compute_chunk_size_cyx(
+        img, write_empty_chunks=write_empty_chunks
+    )
+
+    scaler = Scaler(max_layer=max_layer)
+
+    write_image(
+        img, group=group, axes=axes, storage_options=storage_options, scaler=scaler
+    )
+
+    _set_multiscale_metadata(group=group, general_metadata=general_metadata, axes=axes)
+
+    _add_image_metadata(
+        img_group=group,
+        ch_metadata=ch_metadata,
+        dtype=img.dtype,
+        histograms=histograms,
+    )
+
+
+def write_cyx_image_to_well(
+    img: ArrayLike,
+    histograms: list[UIntHistogram],
+    ch_metadata: list[dict],
+    general_metadata: dict,
+    group: Group,
+    write_empty_chunks: bool = True,
+):
+    if general_metadata["spatial-calibration-units"] == "um":
+        axes = [
+            {"name": "c", "type": "channel"},
+            {"name": "y", "type": "space", "unit": "micrometer"},
+            {"name": "x", "type": "space", "unit": "micrometer"},
+        ]
+    else:
+        raise NotImplementedError("Spatial unit unknown.")
+
+    write_image_to_well(
+        img=img,
+        axes=axes,
+        histograms=histograms,
+        ch_metadata=ch_metadata,
+        general_metadata=general_metadata,
+        group=group,
+        write_empty_chunks=write_empty_chunks,
+    )
+
+
+def write_czyx_image_to_well(
+    img: ArrayLike,
+    histograms: list[UIntHistogram],
+    ch_metadata: list[dict],
+    general_metadata: dict,
+    group: Group,
+    write_empty_chunks: bool = True,
+):
+    if general_metadata["spatial-calibration-units"] == "um":
+        axes = [
+            {"name": "c", "type": "channel"},
+            {"name": "z", "type": "space", "unit": "micrometer"},
+            {"name": "y", "type": "space", "unit": "micrometer"},
+            {"name": "x", "type": "space", "unit": "micrometer"},
+        ]
+    else:
+        raise NotImplementedError("Spatial unit unknown.")
+
+    write_image_to_well(
+        img=img,
+        axes=axes,
+        histograms=histograms,
+        ch_metadata=ch_metadata,
+        general_metadata=general_metadata,
+        group=group,
+        write_empty_chunks=write_empty_chunks,
+    )
+
+
+def build_omero_channel_metadata(
+    ch_metadata: dict, dtype: type, histograms: list[UIntHistogram]
+):
+    """Build omero conform channel metadata to be stored in zarr attributes.
+
+    * Color is computed from the metaseries wavelength metadata.
+    * Label is the set to the metaseries _IllumSetting_ metadata.
+    * Intensity scaling is obtained from the data histogram [0.01,
+    0.99] quantiles.
+
+    :param ch_metadata: channel metadata from tiff-tags
+    :param dtype: data type
+    :param histograms: histograms of channels
+    :return: omero metadata dictionary
+    """
+    channels = []
+    for i, (ch, hist) in enumerate(zip(ch_metadata, histograms)):
+        label = ch["channel-name"]
+        if "z-projection-method" in ch.keys():
+            proj_method = ch["z-projection-method"]
+            proj_method = proj_method.replace(" ", "-")
+            label = f"{proj_method}-Projection_{label}"
+
+        channels.append(
+            {
+                "active": True,
+                "coefficient": 1,
+                "color": ch["display-color"],
+                "family": "linear",
+                "inverted": False,
+                "label": label,
+                "wavelength_id": f"C{str(i+1).zfill(2)}",
+                "window": {
+                    "min": np.iinfo(dtype).min,
+                    "max": np.iinfo(dtype).max,
+                    "start": hist.quantile(0.01),
+                    "end": hist.quantile(0.99),
+                },
+            }
+        )
+
+    return {"channels": channels}
diff --git a/src/faim_hcs/hcs/Experiment.py b/src/faim_hcs/hcs/Experiment.py
deleted file mode 100644
index b2dbef90..00000000
--- a/src/faim_hcs/hcs/Experiment.py
+++ /dev/null
@@ -1,149 +0,0 @@
-from logging import Logger
-from os import mkdir
-from os.path import basename, dirname, exists, join
-
-import pandas as pd
-
-from ..records.PlateRecord import PlateRecord
-
-
-class Experiment:
-    def __init__(self, name: str = None, root_dir: str = None, save_dir: str = None):
-        self.logger = Logger(f"HCS Experiment {name}")
-        self.name = name
-
-        if root_dir is not None:
-            assert exists(root_dir), f"{root_dir} does not exist."
-        self.root_dir = root_dir
-        if self.root_dir is not None and self.root_dir[-1] != "/":
-            self.root_dir = self.root_dir + "/"
-
-        self.plates = {}
-        self.current_plate = None
-        self.plate_iter = iter(self.plates.values())
-
-        self.iter_plates_only = False
-        self.iter_wells_only = False
-        self.save_dir = save_dir
-        if self.save_dir is not None:
-            assert exists(save_dir), "Save directory does not exist."
-            if not exists(self.get_experiment_dir()):
-                mkdir(self.get_experiment_dir())
-
-    def register_plate(self, plate: PlateRecord):
-        self.plates[plate.plate_id] = plate
-        self.reset_iterator()
-
-    def add_plate(self, path: str):
-        plate_id = basename(path)
-        PlateRecord(self, plate_id=plate_id)
-
-    def get_dataframe(self):
-        plates = [p.plate_id for p in self.plates.values()]
-        if len(plates) == 0:
-            plates = [None]
-        return pd.DataFrame(
-            {
-                "hcs_experiment": self.name,
-                "root_dir": self.root_dir,
-                "save_dir": self.save_dir,
-                "plate": plates,
-            }
-        )
-
-    def build_overview(self):
-        df = self.get_dataframe()
-        plate_overviews = []
-        for plate in self.plates.values():
-            plate_overviews.append(plate.build_overview())
-
-        return df.merge(pd.concat(plate_overviews), on="plate", how="outer")
-
-    def get_organoid_raw_files(self, name: str):
-        df = self.get_dataframe()
-        plate_raw_files = []
-        for plate in self.plates.values():
-            plate_raw_files.append(plate.get_organoid_raw_files(name))
-
-        return df.merge(pd.concat(plate_raw_files), on="plate", how="outer")
-
-    def get_organoid_segmentation_files(self, name: str):
-        df = self.get_dataframe()
-        plate_seg_files = []
-        for plate in self.plates.values():
-            plate_seg_files.append(plate.get_organoid_segmentation_files(name))
-
-        return df.merge(pd.concat(plate_seg_files), on="plate", how="outer")
-
-    def get_organoid_raw_segmentation_files(
-        self, raw_name: str, segmentation_name: str
-    ):
-        df = self.get_dataframe()
-        plate_raw_seg_files = []
-        for plate in self.plates.values():
-            plate_raw_seg_files.append(
-                plate.get_organoid_raw_segmentation_files(raw_name, segmentation_name)
-            )
-
-        return df.merge(pd.concat(plate_raw_seg_files), on="plate", how="outer")
-
-    def get_experiment_dir(self):
-        return join(self.save_dir, self.name)
-
-    def save(self):
-        if self.save_dir is not None:
-            path_ = self.get_experiment_dir()
-            if not exists(path_):
-                mkdir(path_)
-
-            df = self.get_dataframe()
-
-            saved_plates = []
-            for plate in self.plates.values():
-                saved_plates.append(plate.save())
-
-            if len(saved_plates) > 0:
-                df = df.merge(pd.concat(saved_plates), on="plate", how="outer")
-
-            df.to_csv(join(path_, "summary.csv"))
-
-    def load(self, path):
-        self.save_dir = dirname(path)
-        df = pd.read_csv(path)
-        self.name = df.iloc[0]["hcs_experiment"]
-        self.root_dir = df.iloc[0]["root_dir"]
-        self.save_dir = df.iloc[0]["save_dir"]
-        self.plates = {}
-        for plate_id in df.plate.unique():
-            pr = PlateRecord(self, plate_id, self.get_experiment_dir())
-            pr.load(df.query(f"plate == '{plate_id}'"), None)
-            self.plates[plate_id] = pr
-
-    def __iter__(self):
-        return self
-
-    def __next__(self):
-        if self.current_plate is None:
-            if self.iter_plates_only:
-                return next(self.plate_iter)
-            else:
-                self.current_plate = next(self.plate_iter)
-                self.current_plate.iter_wells_only = self.iter_wells_only
-
-        try:
-            return next(self.current_plate)
-        except StopIteration:
-            self.current_plate = None
-            return next(self)
-
-    def reset_iterator(self):
-        self.current_plate = None
-        self.plate_iter = iter(self.plates.values())
-        for plate in self.plates.values():
-            plate.reset_iterator()
-
-    def only_iterate_over_plates(self, b: bool = False):
-        self.iter_plates_only = b
-
-    def only_iterate_over_wells(self, b: bool = False):
-        self.iter_wells_only = b
diff --git a/src/faim_hcs/io/MetaSeriesTiff.py b/src/faim_hcs/io/MetaSeriesTiff.py
new file mode 100644
index 00000000..a36cabac
--- /dev/null
+++ b/src/faim_hcs/io/MetaSeriesTiff.py
@@ -0,0 +1,71 @@
+from pathlib import Path
+
+from numpy._typing import ArrayLike
+from tifffile import tifffile
+
+
+def load_metaseries_tiff(path: Path) -> tuple[ArrayLike, dict]:
+    """Load metaseries tiff file and parts of its metadata.
+
+    The following metadata is collected:
+    * _IllumSetting_
+    * spatial-calibration-x
+    * spatial-calibration-y
+    * spatial-calibration-units
+    * stage-position-x
+    * stage-position-y
+    * z-position
+    * PixelType
+    * _MagNA_
+    * _MagSetting_
+    * Exposure Time
+    * Lumencor Cyan Intensity
+    * Lumencor Green Intensity
+    * Lumencor Red Intensity
+    * Lumencor Violet Intensity
+    * Lumencor Yellow Intensity
+    * ShadingCorrection
+    * stage-label
+    * SiteX
+    * SiteY
+    * wavelength
+    * Z Step (if existent)
+    * Z Projection Method (if existent)
+
+    :param path:
+    :return:
+    image_data, metadata-dict
+    """
+    with tifffile.TiffFile(path) as tiff:
+        assert tiff.is_metaseries, f"{path} is not a metamorph file."
+        data = tiff.asarray()
+        selected_keys = [
+            "_IllumSetting_",
+            "spatial-calibration-x",
+            "spatial-calibration-y",
+            "spatial-calibration-units",
+            "stage-position-x",
+            "stage-position-y",
+            "z-position",
+            "_MagNA_",
+            "_MagSetting_",
+            "Exposure Time",
+            "Lumencor Cyan Intensity",
+            "Lumencor Green Intensity",
+            "Lumencor Red Intensity",
+            "Lumencor Violet Intensity",
+            "Lumencor Yellow Intensity",
+            "ShadingCorrection",
+            "stage-label",
+            "SiteX",
+            "SiteY",
+            "wavelength",
+            "Z Step",  # optional
+            "Z Projection Method",  # optional
+            "Z Projection Step Size",  # optional
+        ]
+        plane_info = tiff.metaseries_metadata["PlaneInfo"]
+        metadata = {k: plane_info[k] for k in selected_keys if k in plane_info}
+        metadata["PixelType"] = str(data.dtype)
+
+    return data, metadata
diff --git a/src/faim_hcs/io/MolecularDevicesImageXpress.py b/src/faim_hcs/io/MolecularDevicesImageXpress.py
new file mode 100644
index 00000000..f816d975
--- /dev/null
+++ b/src/faim_hcs/io/MolecularDevicesImageXpress.py
@@ -0,0 +1,135 @@
+"""Methods to parse image files acquired with a MolecularDevices ImageXpress system."""
+
+import os
+import re
+from pathlib import Path
+from typing import Union
+
+import pandas as pd
+
+_METASERIES_FILENAME_PATTERN = r"(?P<name>.*)_(?P<well>[A-Z]+\d{2})_(?P<field>s\d+)_(?P<channel>w[1-9]{1})(?!_thumb)(?P<md_id>.*)(?P<ext>.tif)"
+_METASERIES_FOLDER_PATTERN = r".*[\/\\](?P<date>\d{4}-\d{2}-\d{2})[\/\\](?P<acq_id>\d+)(?:[\/\\]ZStep_(?P<z>\d+))?.*"
+_METASERIES_MAIN_FOLDER_PATTERN = (
+    r".*[\/\\](?P<date>\d{4}-\d{2}-\d{2})[\/\\](?P<acq_id>\d+)(?![\/\\]ZStep_.*)"
+)
+_METASERIES_ZSTEP_FOLDER_PATTERN = (
+    r".*[\/\\](?P<date>\d{4}-\d{2}-\d{2})[\/\\](?P<acq_id>\d+)[\/\\]ZStep_(?P<z>\d+).*"
+)
+
+
+def parse_single_plane_multi_fields(acquisition_dir: Union[Path, str]) -> pd.DataFrame:
+    """Parse top folder (single planes) of an acquisition of a MolecularDevices ImageXpress Micro Confocal system.
+
+    Storage layout on disk for 2 wells with 2 fields and 2 channels::
+
+        MIP-2P-2sub --> {name} [Optional]
+        └── 2022-07-05 --> {date}
+            └── 1075 --> {acquisition id}
+                ├── MIP-2P-2sub_C05_s1_w146C9B2CD-0BB3-4B8A-9187-2805F4C90506.tif
+                ├── MIP-2P-2sub_C05_s1_w1_thumb6EFE77C6-B96D-412A-9FD1-710DBDA32821.tif
+                ├── MIP-2P-2sub_C05_s1_w2B90625C8-6EA7-4E54-8289-C539EB75263E.tif
+                ├── MIP-2P-2sub_C05_s1_w2_thumbEDDF803A-AE5E-4190-8C06-F54341AEC4A6.tif
+                ├── MIP-2P-2sub_C05_s2_w1E2913F7F-E229-4B6A-BFED-02BCF54561FA.tif
+                ├── MIP-2P-2sub_C05_s2_w1_thumb72E3641A-C91B-4501-900A-245BAC58FF46.tif
+                ├── MIP-2P-2sub_C05_s2_w241C38630-BCFD-4393-8706-58755CECE059.tif
+                ├── MIP-2P-2sub_C05_s2_w2_thumb5377A5AC-9BBF-4BAF-99A2-24896E3373A2.tif
+                ├── MIP-2P-2sub_C06_s1_w152C23B9A-EB4C-4DF6-8A7F-F4147A9E7DDE.tif
+                ├── MIP-2P-2sub_C06_s1_w1_thumb541AA634-387C-4B84-B0D8-EE4CB1C88E81.tif
+                ├── MIP-2P-2sub_C06_s1_w2FB0D7D9B-3EA0-445E-9A05-7D01154A9A5C.tif
+                ├── MIP-2P-2sub_C06_s1_w2_thumb8FA1E466-57CD-4237-B09B-CAB48154647D.tif
+                ├── MIP-2P-2sub_C06_s2_w1F365E60C-BCC2-4B74-9856-BCE07C8B0FD3.tif
+                ├── MIP-2P-2sub_C06_s2_w1_thumb9652366E-36A0-4B7F-8B18-DA89D7DB41BD.tif
+                ├── MIP-2P-2sub_C06_s2_w20EEC6AEA-1727-41E6-806C-40FF6AF68B6C.tif
+                └── MIP-2P-2sub_C06_s2_w2_thumb710CD846-0185-4362-BBAF-C700AE0013B3.tif
+
+    Image data is stored in {name}_{well}_{field}_w{channel}{md_id}.tif.
+    The *_thumb*.tif files, used by Molecular Devices as preview, are ignored.
+
+    :param acquisition_dir: Path to acquisition directory.
+    :return: table of all acquired image data.
+    """
+    return parse_files(acquisition_dir=acquisition_dir, mode="top-level")
+
+
+def parse_multi_field_stacks(acquisition_dir: Union[Path, str]) -> pd.DataFrame:
+    """Parse ZStep folders of an acquisition of a Molecular Devices ImageXpress Micro Confocal system.
+
+    Storage hierarchy on disk for 2 wells with 2 fields and 2 channels::
+
+        MIP-2P-2sub --> {name} [Optional]
+        └── 2022-07-05 --> {date}
+            └── 1075 --> {acquisition id}
+                ├── MIP-2P-2sub_C05_s1_w146C9B2CD-0BB3-4B8A-9187-2805F4C90506.tif
+                ├── MIP-2P-2sub_C05_s1_w1_thumb6EFE77C6-B96D-412A-9FD1-710DBDA32821.tif
+                ├── MIP-2P-2sub_C05_s1_w2B90625C8-6EA7-4E54-8289-C539EB75263E.tif
+                ├── MIP-2P-2sub_C05_s1_w2_thumbEDDF803A-AE5E-4190-8C06-F54341AEC4A6.tif
+                ├── MIP-2P-2sub_C05_s2_w1E2913F7F-E229-4B6A-BFED-02BCF54561FA.tif
+                ├── MIP-2P-2sub_C05_s2_w1_thumb72E3641A-C91B-4501-900A-245BAC58FF46.tif
+                ├── MIP-2P-2sub_C05_s2_w241C38630-BCFD-4393-8706-58755CECE059.tif
+                ├── MIP-2P-2sub_C05_s2_w2_thumb5377A5AC-9BBF-4BAF-99A2-24896E3373A2.tif
+                ├── MIP-2P-2sub_C06_s1_w152C23B9A-EB4C-4DF6-8A7F-F4147A9E7DDE.tif
+                ├── MIP-2P-2sub_C06_s1_w1_thumb541AA634-387C-4B84-B0D8-EE4CB1C88E81.tif
+                ├── MIP-2P-2sub_C06_s1_w2FB0D7D9B-3EA0-445E-9A05-7D01154A9A5C.tif
+                ├── MIP-2P-2sub_C06_s1_w2_thumb8FA1E466-57CD-4237-B09B-CAB48154647D.tif
+                ├── MIP-2P-2sub_C06_s2_w1F365E60C-BCC2-4B74-9856-BCE07C8B0FD3.tif
+                ├── MIP-2P-2sub_C06_s2_w1_thumb9652366E-36A0-4B7F-8B18-DA89D7DB41BD.tif
+                ├── MIP-2P-2sub_C06_s2_w20EEC6AEA-1727-41E6-806C-40FF6AF68B6C.tif
+                └── MIP-2P-2sub_C06_s2_w2_thumb710CD846-0185-4362-BBAF-C700AE0013B3.tif
+
+    Image data is stored in {name}_{well}_{field}_w{channel}{md_id}.tif.
+    The *_thumb*.tif files, used by Molecular Devices as preview, are ignored.
+
+    :param acquisition_dir: Path to acquisition directory.
+    :return: table of all acquired image data.
+    """
+    return parse_files(acquisition_dir=acquisition_dir, mode="z-steps")
+
+
+def parse_files(acquisition_dir: Union[Path, str], mode: str = "all") -> pd.DataFrame:
+    """Parse any multi-field acquisition of a Molecular Devices ImageXpress Micro Confocal system.
+
+    Storage layout on disk::
+
+        Experiment --> {name} [Optional]
+        └── 2023-02-22 --> {date}
+            └── 1099 --> {acquisition id}
+                ├── ZStep_1
+                TODO fix file tree
+
+    Image data is stored in {name}_{well}_{field}_w{channel}{md_id}.tif.
+    The *_thumb*.tif files, used by Molecular Devices as preview, are ignored.
+
+    :param acquisition_dir: Path to acquisition directory.
+    :param mode: whether to parse 'top-level' file only, 'z-steps' files only, or 'all' (default).
+    :return: table of all acquired image data.
+    """
+    if mode == "top-level":
+        root_pattern = _METASERIES_MAIN_FOLDER_PATTERN
+    elif mode == "z-steps":
+        root_pattern = _METASERIES_ZSTEP_FOLDER_PATTERN
+    else:
+        root_pattern = _METASERIES_FOLDER_PATTERN
+    return pd.DataFrame(
+        _list_dataset_files(
+            root_dir=acquisition_dir,
+            root_re=re.compile(root_pattern),
+            filename_re=re.compile(_METASERIES_FILENAME_PATTERN),
+        )
+    )
+
+
+def _list_dataset_files(
+    root_dir: Union[Path, str], root_re: re.Pattern, filename_re: re.Pattern
+) -> list[str]:
+    files = []
+    for root, _, filenames in os.walk(root_dir):
+        m_root = root_re.fullmatch(root)
+        if m_root:
+            for f in filenames:
+                m_filename = filename_re.fullmatch(f)
+                if m_filename:
+                    row = m_root.groupdict()
+                    row |= m_filename.groupdict()
+                    row["path"] = str(Path(root).joinpath(f))
+                    files.append(row)
+    return files
diff --git a/src/faim_hcs/records/__init__.py b/src/faim_hcs/io/__init__.py
similarity index 100%
rename from src/faim_hcs/records/__init__.py
rename to src/faim_hcs/io/__init__.py
diff --git a/src/faim_hcs/mobie.py b/src/faim_hcs/mobie.py
new file mode 100644
index 00000000..294218cc
--- /dev/null
+++ b/src/faim_hcs/mobie.py
@@ -0,0 +1,302 @@
+from copy import copy
+from os.path import join
+
+import pandas as pd
+import zarr
+from mobie import create_view
+from mobie.metadata import (
+    add_regions_to_dataset,
+    add_source_to_dataset,
+    get_default_view,
+)
+
+from faim_hcs.UIntHistogram import UIntHistogram
+
+
+def hex_to_rgba(h) -> str:
+    """Convert a hex-color-code to RGBA values.
+
+    :param h: A hex-color-code
+    :return: RGBA color-code
+    """
+    if not isinstance(h, str):
+        return "r=255,g=255,b=255,a=255"
+    return f"r={int(h[0:2], 16)},g={int(h[2:4], 16)},b={int(h[4:6], 16)},a=255"
+
+
+def get_largest_yx_field_shape(plate: zarr.Group) -> tuple[int, int]:
+    """Compute the YX extend of the largest field in the plate.
+
+    :param plate: OME-Zarr plate
+    :return: (y, x) shape
+    """
+    max_y, max_x = 0, 0
+    for row in plate.group_keys():
+        for col in plate[row].group_keys():
+            shape = plate[row][col][0][0].shape
+            if max_y < shape[-2]:
+                max_y = shape[-2]
+
+            if max_x < shape[-1]:
+                max_x = shape[-1]
+
+    return max_y, max_x
+
+
+def add_group_to_project(
+    dataset_folder: str,
+    group: zarr.Group,
+    group_name: str,
+    yx_well_coordinates: tuple[int, int],
+    yx_shape: tuple[int, int],
+    hists: list[UIntHistogram],
+    plate_hists: dict,
+    sources: dict,
+    source_transforms: dict,
+    display_settings: dict,
+    gap: int = 50,
+):
+    """Add a ome-zarr HCS well group to a MoBIE project.
+
+    :param dataset_folder: mobie-dataset folder
+    :param group: zarr group
+    :param group_name: name of the zarr group
+    :param yx_well_coordinates: well position
+    :param yx_shape: max shape of the largest field over all wells
+    :param hists: list of channel histograms
+    :param plate_hists: dict mapping channels to accumulated histograms for
+    plate
+    :param sources: dict mapping channels to all well-sources
+    :param source_transforms: dict mapping channels to source transformations
+    :param display_settings: dict mapping channels to display settings
+    :param gap: between wells in the viewer
+    :return:
+    """
+    y_wc, x_wc = yx_well_coordinates
+    y_max, x_max = yx_shape
+
+    path = join(group.store.path, group.path)
+
+    attrs = group.attrs.asdict()
+    scale = attrs["multiscales"][0]["datasets"][0]["coordinateTransformations"][0][
+        "scale"
+    ]
+    y_spacing, x_spacing = scale[-2], scale[-1]
+
+    for k, ch in enumerate(attrs["omero"]["channels"]):
+        key = f"{ch['wavelength_id']}_{ch['label']}"
+
+        name = f"{group_name}_{key}"
+        source_transform = {
+            "sources": [name],
+            "parameters": [
+                1.0,
+                0.0,
+                0.0,
+                (x_max + gap) * x_spacing * x_wc,
+                0.0,
+                1.0,
+                0.0,
+                (y_max + gap) * y_spacing * y_wc,
+                0.0,
+                0.0,
+                1.0,
+                0.0,
+            ],
+        }
+        view = get_default_view(
+            source_type="image",
+            source_name=name,
+            menu_name="Wells",
+            source_transform=source_transform,
+            color=hex_to_rgba(ch["color"]),
+            contrastLimits=[hists[k].quantile(0.01), hists[k].quantile(0.99)],
+            opacity=1.0,
+            sources=[
+                name,
+            ],
+        )
+
+        add_source_to_dataset(
+            dataset_folder=dataset_folder,
+            source_type="image",
+            source_name=name,
+            image_metadata_path=path,
+            file_format="ome.zarr",
+            channel=k,
+            view=view,
+        )
+
+        if key not in plate_hists.keys():
+            plate_hists[key] = copy(hists[k])
+        else:
+            plate_hists[key].combine(hists[k])
+
+        if key not in sources.keys():
+            sources[key] = [name]
+        else:
+            sources[key].append(name)
+
+        if key not in source_transforms.keys():
+            source_transforms[key] = [{"affine": source_transform}]
+        else:
+            source_transforms[key].append({"affine": source_transform})
+
+        if key not in display_settings.keys():
+            display_settings[key] = {
+                "color": hex_to_rgba(ch["color"]),
+                "opacity": 1.0,
+                "sources": [
+                    name,
+                ],
+            }
+        else:
+            display_settings[key]["sources"].append(name)
+
+
+def add_wells_to_project(
+    plate: zarr.Group,
+    dataset_folder: str,
+    well_group: str = "0",
+    view_name: str = "default",
+    gap: int = 50,
+):
+    """Add well groups of a ome-zarr plate to project
+
+    :param plate: ome-zarr plate
+    :param dataset_folder: mobie dataset folder
+    :param well_group: zarr-index to zarr array. Defaults into zeroth field
+    :param view_name: mobie view name
+    :param gap: between wells in viewer
+    :return:
+    """
+    y_max, x_max = get_largest_yx_field_shape(plate)
+
+    sources = {}
+    source_transforms = {}
+    display_settings = {}
+    plate_hists = {}
+
+    # Add wells as individual sources
+    wells = []
+    for i, row in enumerate(plate.group_keys()):
+        for j, col in enumerate(plate[row].group_keys()):
+            attrs = plate[row][col][well_group].attrs.asdict()
+            wells.append(row + col.zfill(2))
+            path = join(plate.store.path, row, col, well_group)
+
+            hists = [
+                UIntHistogram.load(join(path, h_path)) for h_path in attrs["histograms"]
+            ]
+
+            add_group_to_project(
+                dataset_folder=dataset_folder,
+                group=plate[row][col][well_group],
+                group_name=f"{row}{col.zfill(2)}",
+                yx_well_coordinates=(i, j),
+                yx_shape=(y_max, x_max),
+                hists=hists,
+                plate_hists=plate_hists,
+                sources=sources,
+                source_transforms=source_transforms,
+                display_settings=display_settings,
+                gap=gap,
+            )
+
+    _add_well_regions(dataset_folder, wells)
+
+    # Creates an overview of all wells for each channel
+    disp_settings, group_names, sources_ = _add_channel_plate_overviews(
+        dataset_folder, display_settings, plate_hists, source_transforms, sources
+    )
+
+    # Create default overview with all channels and all wells
+    wells_per_channel = _get_well_sources_per_channel(sources)
+    well_source_transformations = _get_well_source_transformations(source_transforms)
+
+    create_view(
+        dataset_folder=dataset_folder,
+        view_name=view_name,
+        display_settings=disp_settings,
+        display_group_names=group_names,
+        sources=sources_,
+        source_transforms=well_source_transformations,
+        region_displays={
+            "Wells": {
+                "opacity": 0.5,
+                "lut": "glasbey",
+                "sources": wells_per_channel,
+                "tableSource": "wells",
+                "visible": True,
+                "showAsBoundaries": True,
+            }
+        },
+        menu_name="Overview",
+    )
+
+
+def _get_well_source_transformations(source_transforms):
+    first_key = list(source_transforms.keys())[0]
+    well_source_transformations = source_transforms[first_key]
+    for c in filter(lambda k: k != first_key, source_transforms.keys()):
+        for i, trafo in enumerate(source_transforms[c]):
+            well_source_transformations[i]["affine"]["sources"].extend(
+                trafo["affine"]["sources"]
+            )
+    return well_source_transformations
+
+
+def _add_channel_plate_overviews(
+    dataset_folder, display_settings, plate_hists, source_transforms, sources
+):
+    disp_settings = []
+    group_names = []
+    sources_ = []
+    for ch in display_settings.keys():
+        ds = display_settings[ch]
+        ds["contrastLimits"] = [
+            plate_hists[ch].quantile(0.01),
+            plate_hists[ch].quantile(0.99),
+        ]
+        disp_settings.append(ds)
+
+        group_names.append(ch)
+
+        sources_.append(sources[ch])
+
+        create_view(
+            dataset_folder=dataset_folder,
+            view_name=ch,
+            display_settings=[ds],
+            display_group_names=[ch],
+            sources=[sources[ch]],
+            source_transforms=source_transforms[ch],
+            menu_name="Channels",
+            is_exclusive=False,
+        )
+    return disp_settings, group_names, sources_
+
+
+def _get_well_sources_per_channel(sources):
+    wells_per_channel = {}
+    for ch in sources.keys():
+        for well in sources[ch]:
+            if well[:3] not in wells_per_channel.keys():
+                wells_per_channel[well[:3]] = [well]
+            else:
+                wells_per_channel[well[:3]].append(well)
+
+    return wells_per_channel
+
+
+def _add_well_regions(dataset_folder, wells):
+    well_table = pd.DataFrame(
+        {
+            "region_id": wells,
+            "treatment": [
+                "Unknown",
+            ]
+            * len(wells),
+        }
+    )
+    add_regions_to_dataset(dataset_folder, "wells", well_table)
diff --git a/src/faim_hcs/records/DefaultRecord.py b/src/faim_hcs/records/DefaultRecord.py
deleted file mode 100644
index 69d8bb43..00000000
--- a/src/faim_hcs/records/DefaultRecord.py
+++ /dev/null
@@ -1,104 +0,0 @@
-import json
-from logging import Logger
-from os.path import join
-
-import pandas as pd
-from skimage.io import imread
-
-
-class DefaultRecord:
-    def __init__(self, record_id: str):
-        self.logger = Logger(f"Record {record_id}")
-        self.record_id = record_id
-
-        self.raw_files = {}
-        self.spacings = {}
-        self.segmentations = {}
-        self.measurements = {}
-
-    def add_raw_data(self, name: str, path: str, spacing: tuple):
-        location = self._get_relative_location(path)
-        self.raw_files[name] = location
-        self.spacings[name] = spacing
-
-    def get_raw_data(self, name: str):
-        if name in self.raw_files.keys():
-            location = self._get_source_file_location(self.raw_files[name])
-            return imread(location)
-        else:
-            self.logger.warning(
-                f"Raw file {name} not in {self.record_id}:"
-                f"{list(self.raw_files.keys())}"
-            )
-
-    def add_segmentation(self, name: str, path: str):
-        location = self._get_relative_location(path)
-        self.segmentations[name] = location
-
-    def get_segmentation(self, name: str):
-        if name in self.segmentations.keys():
-            location = self._get_source_file_location(self.segmentations[name])
-            return imread(location)
-        else:
-            self.logger.warning(
-                f"Segmentation {name} does not exist in "
-                f"{self.record_id}:"
-                f"{list(self.segmentations.keys())}."
-            )
-
-    def add_measurement(self, name: str, path: str):
-        location = self._get_relative_location(path)
-        # TODO: validate file structure --> Columns
-        self.measurements[name] = location
-
-    def get_measurement(self, name: str):
-        if name in self.measurements.keys():
-            location = self._get_source_file_location(self.measurements[name])
-            return pd.read_csv(location)
-        else:
-            self.logger.warning(
-                f"Measurment {name} does not exist in "
-                f"{self.record_id}:"
-                f"{list(self.measurements.keys())}."
-            )
-
-    def _get_relative_location(self, path: str):
-        raise NotImplementedError
-
-    def _get_source_file_location(self, path: str):
-        raise NotImplementedError
-
-    def build_overview(self):
-        raise NotImplementedError
-
-    def save(self, path, name):
-
-        location = join(path, name + ".json")
-
-        obj_dict = {
-            "record_id": self.record_id,
-            "raw_files": self.raw_files,
-            "segmentations": self.segmentations,
-            "measurements": self.measurements,
-            "spacings": self.spacings,
-        }
-        with open(location, "w") as f:
-            json.dump(obj_dict, f, indent=4)
-
-        return location
-
-    def load(self, df, column):
-        if len(df.index) > 0:
-            assert len(
-                df[column].unique()
-            ), "There should only be 1 record. " "Found {} records.".format(
-                len(df[column].unique())
-            )
-            with open(df[column].iloc[0]) as f:
-                obj_dict = json.load(f)
-
-            assert obj_dict["record_id"] == self.record_id
-            self.raw_files = obj_dict["raw_files"]
-            self.segmentations = obj_dict["segmentations"]
-            self.measurements = obj_dict["measurements"]
-            self.spacings = obj_dict["spacings"]
diff --git a/src/faim_hcs/records/OrganoidRecord.py b/src/faim_hcs/records/OrganoidRecord.py
deleted file mode 100644
index 0853bbbc..00000000
--- a/src/faim_hcs/records/OrganoidRecord.py
+++ /dev/null
@@ -1,59 +0,0 @@
-from __future__ import annotations
-
-from logging import Logger
-from os import mkdir
-from os.path import exists, join
-from typing import TYPE_CHECKING
-
-import pandas as pd
-
-from ..records.DefaultRecord import DefaultRecord
-
-if TYPE_CHECKING:
-    from ..records.WellRecord import WellRecord
-
-
-class OrganoidRecord(DefaultRecord):
-    def __init__(self, well: WellRecord, organoid_id: str, save_dir: str = "."):
-        super().__init__(organoid_id)
-        self.logger = Logger(f"Organoid {organoid_id}")
-        self.organoid_id = self.record_id
-        self.well = well
-
-        self.organoid_dir = join(save_dir, self.organoid_id)
-        if not exists(self.organoid_dir):
-            mkdir(self.organoid_dir)
-
-        if self.well is not None:
-            self.well.register_organoid(self)
-
-    def _get_relative_location(self, path):
-        if path.startswith(self.well.plate.experiment.root_dir):
-            assert exists(path), f"File {path} does not exist."
-            return path.replace(self.well.plate.experiment.root_dir, "")
-        else:
-            path_ = join(self.well.plate.experiment.root_dir, path)
-            assert exists(path_), f"File {path_} does not exist."
-            return path
-
-    def _get_source_file_location(self, path):
-        return join(self.well.plate.experiment.root_dir, path)
-
-    def build_overview(self):
-        summary = {"organoid_id": [self.record_id]}
-        for k in self.raw_files.keys():
-            summary[k] = self.raw_files[k]
-
-        for k in self.segmentations.keys():
-            summary[k] = self.segmentations[k]
-
-        for k in self.measurements.keys():
-            summary[k] = self.measurements[k]
-
-        return pd.DataFrame(summary)
-
-    def save(self, path: str = None, name="organoid_summary"):
-        if path is None:
-            return super().save(self.organoid_dir, name=name)
-        else:
-            return super().save(path, name=name)
diff --git a/src/faim_hcs/records/PlateRecord.py b/src/faim_hcs/records/PlateRecord.py
deleted file mode 100644
index e1a8217e..00000000
--- a/src/faim_hcs/records/PlateRecord.py
+++ /dev/null
@@ -1,122 +0,0 @@
-from __future__ import annotations
-
-from logging import Logger
-from os import mkdir
-from os.path import exists, join
-from typing import TYPE_CHECKING
-
-import pandas as pd
-
-from ..records.WellRecord import WellRecord
-
-if TYPE_CHECKING:
-    from src.faim_hcs.hcs.Experiment import Experiment
-
-
-class PlateRecord:
-    def __init__(self, experiment: Experiment, plate_id: str, save_dir: str = "."):
-        self.logger = Logger(f"Plate {plate_id}")
-        self.experiment = experiment
-        self.plate_id = plate_id
-
-        self.plate_dir = join(save_dir, self.plate_id)
-        if not exists(self.plate_dir):
-            mkdir(self.plate_dir)
-
-        self.iter_wells_only = False
-
-        self.wells = {}
-        self.current_well = None
-        self.well_iter = iter(self.wells.values())
-
-        if self.experiment is not None:
-            self.experiment.register_plate(self)
-
-    def register_well(self, well: WellRecord):
-        self.wells[well.record_id] = well
-        self.reset_iterator()
-
-    def get_dataframe(self):
-        return pd.DataFrame(
-            {"plate": self.plate_id, "well": [w.record_id for w in self.wells.values()]}
-        )
-
-    def build_overview(self):
-        df = self.get_dataframe()
-        well_overviews = []
-        for well in self.wells.values():
-            well_overviews.append(well.build_overview())
-
-        return df.merge(pd.concat(well_overviews), on="well", how="outer")
-
-    def get_organoid_raw_files(self, name: str):
-        df = self.get_dataframe()
-        well_raw_files = []
-        for well in self.wells.values():
-            well_raw_files.append(well.get_organoid_raw_files(name))
-
-        return df.merge(pd.concat(well_raw_files), on="well", how="outer")
-
-    def get_organoid_segmentation_files(self, name: str):
-        df = self.get_dataframe()
-        well_seg_files = []
-        for well in self.wells.values():
-            well_seg_files.append(well.get_organoid_segmentation_files(name))
-
-        return df.merge(pd.concat(well_seg_files), on="well", how="outer")
-
-    def get_organoid_raw_segmentation_files(
-        self, raw_name: str, segmentation_name: str
-    ):
-        df = self.get_dataframe()
-        well_raw_seg_files = []
-        for well in self.wells.values():
-            well_raw_seg_files.append(
-                well.get_organoid_raw_segmentation_files(raw_name, segmentation_name)
-            )
-
-        return df.merge(pd.concat(well_raw_seg_files), on="well", how="outer")
-
-    def save(self):
-        df = self.get_dataframe()
-
-        wells = []
-        for well in self.wells.values():
-            wells.append(well.save(name="well_summary"))
-
-        if len(wells) > 0:
-            df = df.merge(pd.concat(wells), on="well", how="outer")
-        return df
-
-    def load(self, df, column):
-
-        for well_id in df.well.unique():
-            if str(well_id) != "nan":
-                well_id = str(well_id)
-                wr = WellRecord(self, well_id, self.plate_dir)
-                wr.load(df.query(f"well == '{well_id}'"), "well_summary")
-                self.wells[well_id] = wr
-
-    def __iter__(self):
-        return self
-
-    def __next__(self):
-        if self.current_well is None:
-            if self.iter_wells_only:
-                return next(self.well_iter)
-            else:
-                self.current_well = next(self.well_iter)
-        try:
-            return next(self.current_well)
-        except StopIteration:
-            self.current_well = None
-            return next(self)
-
-    def reset_iterator(self):
-        self.current_well = None
-        self.well_iter = iter(self.wells.values())
-        for well in self.wells.values():
-            well.reset_iterator()
-
-    def only_iterate_over_wells(self, b: bool = False):
-        self.iter_wells_only = b
diff --git a/src/faim_hcs/records/WellRecord.py b/src/faim_hcs/records/WellRecord.py
deleted file mode 100644
index 2fe83914..00000000
--- a/src/faim_hcs/records/WellRecord.py
+++ /dev/null
@@ -1,166 +0,0 @@
-from __future__ import annotations
-
-from logging import Logger
-from os import mkdir
-from os.path import exists, join
-from typing import TYPE_CHECKING
-
-import pandas as pd
-
-from ..records.DefaultRecord import DefaultRecord
-from ..records.OrganoidRecord import OrganoidRecord
-
-if TYPE_CHECKING:
-    from ..records.PlateRecord import PlateRecord
-
-
-class WellRecord(DefaultRecord):
-    def __init__(self, plate: PlateRecord, well_id: str, save_dir: str = "."):
-        super().__init__(well_id)
-        self.logger = Logger(f"Well {well_id}")
-        self.well_id = self.record_id
-        self.plate = plate
-
-        self.well_dir = join(save_dir, self.well_id)
-        if not exists(self.well_dir):
-            mkdir(self.well_dir)
-
-        self.organoids = {}
-        self.organoid_iter = iter(self.organoids.values())
-
-        if self.plate is not None:
-            self.plate.register_well(self)
-
-    def register_organoid(self, organoid: OrganoidRecord):
-        self.organoids[organoid.record_id] = organoid
-        self.reset_iterator()
-
-    def get_dataframe(self):
-        organoid_id = [organoid.record_id for organoid in self.organoids.values()]
-        if len(organoid_id) == 0:
-            organoid_id = [None]
-        return pd.DataFrame(
-            {
-                "well": self.record_id,
-                "organoid_id": organoid_id,
-            }
-        )
-
-    def build_overview(self):
-        df = self.get_dataframe()
-        organoid_summaries = []
-        for organoid in self.organoids.values():
-            organoid_summaries.append(organoid.build_overview())
-
-        if len(organoid_summaries) > 0:
-            return df.merge(
-                pd.concat(organoid_summaries), on="organoid_id", how="outer"
-            )
-        else:
-            return df
-
-    def get_organoid_raw_files(self, name: str):
-        df = self.get_dataframe()
-        organoid_raw_files = []
-        for organoid in self.organoids.values():
-            if name in organoid.raw_files.keys():
-                organoid_raw_files.append(
-                    pd.DataFrame(
-                        {
-                            "organoid_id": [organoid.record_id],
-                            f"{name}": organoid.raw_files[name],
-                        }
-                    )
-                )
-
-        if len(organoid_raw_files) > 0:
-            return df.merge(
-                pd.concat(organoid_raw_files), on="organoid_id", how="outer"
-            )
-        else:
-            return df
-
-    def get_organoid_segmentation_files(self, name: str):
-        df = self.get_dataframe()
-        organoid_seg_files = []
-        for organoid in self.organoids.values():
-            if name in organoid.segmentations.keys():
-                organoid_seg_files.append(
-                    pd.DataFrame(
-                        {
-                            "organoid_id": [organoid.record_id],
-                            f"{name}": organoid.segmentations[name],
-                        }
-                    )
-                )
-
-        if len(organoid_seg_files) > 0:
-            return df.merge(
-                pd.concat(organoid_seg_files), on="organoid_id", how="outer"
-            )
-        else:
-            return df
-
-    def get_organoid_raw_segmentation_files(
-        self, raw_name: str, segmentation_name: str
-    ):
-        raw_files = self.get_organoid_raw_files(raw_name)
-        seg_files = self.get_organoid_segmentation_files(segmentation_name)
-
-        return raw_files.merge(seg_files)
-
-    def _get_relative_location(self, path: str):
-        if path.startswith(self.plate.experiment.root_dir):
-            assert exists(path), f"File {path} does not exist."
-            return path.replace(self.plate.experiment.root_dir, "")
-        else:
-            path_ = join(self.plate.experiment.root_dir, path)
-            assert exists(path_), f"File {path_} does not exist."
-            return path
-
-    def _get_source_file_location(self, path):
-        return join(self.plate.experiment.root_dir, path)
-
-    def save(self, path: str = None, name: str = "well_summary"):
-        if path is None:
-            well_summary_location = super().save(self.well_dir, name)
-        else:
-            well_summary_location = super().save(path, name)
-        organoid_id = []
-        organoid_summary = []
-        for obj in self.organoids.values():
-            organoid_id.append(obj.record_id)
-            organoid_summary.append(obj.save(name="organoid_summary"))
-
-        df = self.get_dataframe()
-        df["well_summary"] = [
-            well_summary_location,
-        ] * max(1, len(df))
-        return df.merge(
-            pd.DataFrame(
-                {"organoid_id": organoid_id, "organoid_summary": organoid_summary}
-            ),
-            on="organoid_id",
-            how="left",
-        )
-
-    def load(self, df, column):
-        super().load(df, column)
-        self.well_id = self.record_id
-        for organoid_id in df.organoid_id.unique():
-            if str(organoid_id) != "nan":
-                organoid_id = str(organoid_id)
-                organoid = OrganoidRecord(self, organoid_id, self.well_dir)
-                organoid.load(
-                    df.query(f"organoid_id == '{organoid_id}'"), "organoid_summary"
-                )
-                self.organoids[organoid_id] = organoid
-
-    def __iter__(self):
-        return self
-
-    def __next__(self):
-        return next(self.organoid_iter)
-
-    def reset_iterator(self):
-        self.organoid_iter = iter(self.organoids.values())
diff --git a/src/faim_hcs/utils.py b/src/faim_hcs/utils.py
new file mode 100644
index 00000000..87fc7d75
--- /dev/null
+++ b/src/faim_hcs/utils.py
@@ -0,0 +1,51 @@
+def wavelength_to_rgb(wavelength, gamma=0.8):
+    """This converts a given wavelength of light to an
+    approximate RGB color value. The wavelength must be given
+    in nanometers in the range from 380 nm through 750 nm
+    (789 THz through 400 THz).
+    Based on code by Dan Bruton
+    http://www.physics.sfasu.edu/astro/color/spectra.html
+
+    Obtained from https://gist.github.com/error454/65d7f392e1acd4a782fc
+    """
+
+    wavelength = float(wavelength)
+    if 380 <= wavelength <= 440:
+        attenuation = 0.3 + 0.7 * (wavelength - 380) / (440 - 380)
+        R = ((-(wavelength - 440) / (440 - 380)) * attenuation) ** gamma
+        G = 0.0
+        B = (1.0 * attenuation) ** gamma
+    elif 440 <= wavelength <= 490:
+        R = 0.0
+        G = ((wavelength - 440) / (490 - 440)) ** gamma
+        B = 1.0
+    elif 490 <= wavelength <= 510:
+        R = 0.0
+        G = 1.0
+        B = (-(wavelength - 510) / (510 - 490)) ** gamma
+    elif 510 <= wavelength <= 580:
+        R = ((wavelength - 510) / (580 - 510)) ** gamma
+        G = 1.0
+        B = 0.0
+    elif 580 <= wavelength <= 645:
+        R = 1.0
+        G = (-(wavelength - 645) / (645 - 580)) ** gamma
+        B = 0.0
+    elif 645 <= wavelength <= 750:
+        attenuation = 0.3 + 0.7 * (750 - wavelength) / (750 - 645)
+        R = (1.0 * attenuation) ** gamma
+        G = 0.0
+        B = 0.0
+    else:
+        R = 0.0
+        G = 0.0
+        B = 0.0
+    R *= 255
+    G *= 255
+    B *= 255
+    return int(R), int(G), int(B)
+
+
+def rgb_to_hex(r, g, b):
+    """Convert RGB values into hex number."""
+    return f"{r:02x}{g:02x}{b:02x}"
diff --git a/tox.ini b/tox.ini
index 8c1d6442..a8f9a8a3 100644
--- a/tox.ini
+++ b/tox.ini
@@ -1,33 +1,28 @@
 # For more information about tox, see https://tox.readthedocs.io/en/latest/
 [tox]
-;envlist = py{37,38,39,310}-{linux,macos,windows}
-envlist = py{37,38,39,310}-{linux}
+envlist = py{39,310}-{linux,macos,windows}
 isolated_build=true
 
 [gh-actions]
 python =
-    3.7: py37
-    3.8: py38
     3.9: py39
     3.10: py310
 
 [gh-actions:env]
 PLATFORM =
     ubuntu-latest: linux
-;    macos-latest: macos
-;    windows-latest: windows
+    macos-latest: macos
+    windows-latest: windows
 
 [testenv]
 platform =
-;    macos: darwin
+    macos: darwin
     linux: linux
-;    windows: win32
+    windows: win32
 passenv =
     CI
     GITHUB_ACTIONS
-    DISPLAY XAUTHORITY
     NUMPY_EXPERIMENTAL_ARRAY_FUNCTION
-    PYVISTA_OFF_SCREEN
 deps =
     pytest  # https://docs.pytest.org/en/latest/contents.html
     pytest-cov  # https://pytest-cov.readthedocs.io/en/latest/


Test patch:
diff --git a/.github/workflows/test_and_deploy.yml b/.github/workflows/test_and_deploy.yml
index 68aa810d..fd31a170 100644
--- a/.github/workflows/test_and_deploy.yml
+++ b/.github/workflows/test_and_deploy.yml
@@ -24,7 +24,7 @@ jobs:
       matrix:
 #        platform: [ubuntu-latest, windows-latest, macos-latest]
         platform: [ubuntu-latest]
-        python-version: [3.7, 3.8, 3.9]
+        python-version: [3.9, '3.10']
 
     steps:
       - uses: actions/checkout@v2
diff --git a/tests/__init__.py b/tests/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/tests/hcs/__init__.py b/tests/hcs/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/tests/hcs/test_experiment.py b/tests/hcs/test_experiment.py
deleted file mode 100644
index a7b235dd..00000000
--- a/tests/hcs/test_experiment.py
+++ /dev/null
@@ -1,140 +0,0 @@
-import os
-import shutil
-import tempfile
-from os.path import join
-from unittest import TestCase
-
-from faim_hcs.hcs.Experiment import Experiment
-from faim_hcs.records.OrganoidRecord import OrganoidRecord
-from faim_hcs.records.PlateRecord import PlateRecord
-from faim_hcs.records.WellRecord import WellRecord
-
-
-class ExperimentTest(TestCase):
-    def setUp(self) -> None:
-        self.tmp_dir = tempfile.mkdtemp()
-        self.root_dir = join(self.tmp_dir, "root_dir")
-        self.exp_dir = join(self.tmp_dir, "exp_dir")
-        os.mkdir(self.root_dir)
-        os.mkdir(self.exp_dir)
-
-    def tearDown(self) -> None:
-        shutil.rmtree(self.tmp_dir)
-
-    def test_create(self):
-        exp = Experiment("Experiment", root_dir=self.root_dir, save_dir=self.exp_dir)
-        plate = PlateRecord(
-            experiment=exp,
-            plate_id="plate",
-            save_dir=join(exp.get_experiment_dir()),
-        )
-
-        well = WellRecord(plate=plate, well_id="well", save_dir=plate.plate_dir)
-
-        well1 = WellRecord(plate=plate, well_id="well1", save_dir=plate.plate_dir)
-
-        org = OrganoidRecord(well=well, organoid_id="org_0", save_dir=well.well_dir)
-
-        org.raw_files["raw"] = "/test/raw.tif"
-        org.raw_files["raw1"] = "/test/raw1.tif"
-        org.segmentations["seg"] = "/test/seg.tif"
-        org.measurements["meas"] = "/test/meas.csv"
-        org.spacings["raw"] = [2, 0.161, 0.161]
-        org.spacings["raw1"] = [1, 0.1, 0.1]
-
-        org1 = OrganoidRecord(well=well, organoid_id="org_1", save_dir=well.well_dir)
-
-        org1.raw_files["raw"] = "/test1/raw.tif"
-        org1.raw_files["raw1"] = "/test1/raw1.tif"
-        org1.segmentations["seg"] = "/test1/seg.tif"
-        org1.measurements["meas"] = "/test1/meas.csv"
-        org1.spacings["raw"] = [2, 0.161, 0.161]
-        org1.spacings["raw1"] = [1, 0.1, 0.1]
-
-        org2 = OrganoidRecord(well=well1, organoid_id="org_2", save_dir=well1.well_dir)
-
-        org2.raw_files["raw"] = "/test2/raw.tif"
-        org2.raw_files["raw1"] = "/test2/raw1.tif"
-        org2.segmentations["seg"] = "/test2/seg.tif"
-        org2.measurements["meas"] = "/test2/meas.csv"
-        org2.spacings["raw"] = [2, 0.161, 0.161]
-        org2.spacings["raw1"] = [1, 0.1, 0.1]
-
-        overview = exp.build_overview()
-
-        assert len(overview) == 3
-        assert overview["organoid_id"][0] == "org_0"
-        assert overview["organoid_id"][1] == "org_1"
-        assert overview["organoid_id"][2] == "org_2"
-        assert overview["well"][0] == "well"
-        assert overview["well"][1] == "well"
-        assert overview["well"][2] == "well1"
-
-        exp.reset_iterator()
-        exp.only_iterate_over_plates(False)
-        exp.only_iterate_over_wells(False)
-        assert next(exp).organoid_id == "org_0"
-        assert next(exp).organoid_id == "org_1"
-        assert next(exp).organoid_id == "org_2"
-
-        exp.reset_iterator()
-        exp.only_iterate_over_plates(False)
-        exp.only_iterate_over_wells(True)
-        assert next(exp).well_id == "well"
-        assert next(exp).well_id == "well1"
-
-        exp.reset_iterator()
-        exp.only_iterate_over_plates(True)
-        exp.only_iterate_over_wells(True)
-        assert next(exp).plate_id == "plate"
-
-        exp.save()
-
-        exp_loaded = Experiment()
-        exp_loaded.load(join(self.exp_dir, "Experiment", "summary.csv"))
-
-        organoids = exp_loaded.get_organoid_raw_files("raw")
-        assert len(organoids) == 3
-        assert all(
-            organoids.columns
-            == [
-                "hcs_experiment",
-                "root_dir",
-                "save_dir",
-                "plate",
-                "well",
-                "organoid_id",
-                "raw",
-            ]
-        )
-
-        organoids = exp_loaded.get_organoid_segmentation_files("seg")
-        assert len(organoids) == 3
-        assert all(
-            organoids.columns
-            == [
-                "hcs_experiment",
-                "root_dir",
-                "save_dir",
-                "plate",
-                "well",
-                "organoid_id",
-                "seg",
-            ]
-        )
-
-        organoids = exp_loaded.get_organoid_raw_segmentation_files("raw", "seg")
-        assert len(organoids) == 3
-        assert all(
-            organoids.columns
-            == [
-                "hcs_experiment",
-                "root_dir",
-                "save_dir",
-                "plate",
-                "well",
-                "organoid_id",
-                "raw",
-                "seg",
-            ]
-        )
diff --git a/tests/io/test_MetaSeriesTiff.py b/tests/io/test_MetaSeriesTiff.py
new file mode 100644
index 00000000..fee3f9bc
--- /dev/null
+++ b/tests/io/test_MetaSeriesTiff.py
@@ -0,0 +1,54 @@
+# SPDX-FileCopyrightText: 2023 Friedrich Miescher Institute for Biomedical Research (FMI), Basel (Switzerland)
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+import unittest
+from os.path import join
+from pathlib import Path
+
+import numpy as np
+
+from faim_hcs.io.MetaSeriesTiff import load_metaseries_tiff
+
+ROOT_DIR = Path(__file__).parent.parent
+
+
+class TestMetaSeriesTiff(unittest.TestCase):
+    def test_load_metaseries_tiff(self):
+        path = join(
+            ROOT_DIR.parent,
+            "resources",
+            "Projection-Mix",
+            "2023-02-21",
+            "1334",
+            "Projection-Mix_E07_s1_w1E94C24BD-45E4-450A-9919-257C714278F7.tif",
+        )
+
+        img, metadata = load_metaseries_tiff(path=path)
+
+        assert img.shape == (512, 512)
+        assert img.dtype == np.uint16
+
+        assert metadata["_IllumSetting_"] == "FITC_05"
+        assert metadata["spatial-calibration-x"] == 1.3668
+        assert metadata["spatial-calibration-y"] == 1.3668
+        assert metadata["spatial-calibration-units"] == "um"
+        assert metadata["stage-position-x"] == 79813.4
+        assert metadata["stage-position-y"] == 41385.4
+        assert metadata["z-position"] == 9343.19
+        assert metadata["PixelType"] == "uint16"
+        assert metadata["_MagNA_"] == 0.75
+        assert metadata["_MagSetting_"] == "20X Plan Apo Lambda"
+        assert metadata["Exposure Time"] == "15 ms"
+        assert metadata["Lumencor Cyan Intensity"] == 5.09804
+        assert metadata["Lumencor Green Intensity"] == 0
+        assert metadata["Lumencor Red Intensity"] == 0
+        assert metadata["Lumencor Violet Intensity"] == 0
+        assert metadata["Lumencor Yellow Intensity"] == 0
+        assert metadata["ShadingCorrection"] == "Off"
+        assert metadata["stage-label"] == "E07 : Site 1"
+        assert metadata["SiteX"] == 1
+        assert metadata["SiteY"] == 1
+        assert metadata["wavelength"] == 536
+        assert metadata["Z Projection Method"] == "Maximum"
+        assert metadata["Z Projection Step Size"] == 5
diff --git a/tests/io/test_MolecularDevicesImageXpress.py b/tests/io/test_MolecularDevicesImageXpress.py
new file mode 100644
index 00000000..187edd6c
--- /dev/null
+++ b/tests/io/test_MolecularDevicesImageXpress.py
@@ -0,0 +1,83 @@
+# SPDX-FileCopyrightText: 2023 Friedrich Miescher Institute for Biomedical Research (FMI), Basel (Switzerland)
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+import unittest
+from os.path import exists, join
+from pathlib import Path
+
+from faim_hcs.io.MolecularDevicesImageXpress import (
+    parse_files,
+    parse_multi_field_stacks,
+    parse_single_plane_multi_fields,
+)
+
+ROOT_DIR = Path(__file__).parent.parent
+
+
+class TestMolecularDevicesImageXpress(unittest.TestCase):
+    def test_parse_single_plane_multi_fields(self):
+        acquisition_dir = join(ROOT_DIR.parent, "resources", "Projection-Mix")
+
+        files = parse_single_plane_multi_fields(acquisition_dir=acquisition_dir)
+
+        assert len(files) == 12
+        assert files["name"].unique() == ["Projection-Mix"]
+        self.assertCountEqual(files["well"].unique(), ["E07", "E08"])
+        self.assertCountEqual(files["field"].unique(), ["s1", "s2"])
+        self.assertCountEqual(files["channel"].unique(), ["w1", "w2", "w3"])
+        for item in files["path"]:
+            assert exists(item)
+            assert "thumb" not in item
+
+    def test_parse_multi_field_stacks(self):
+        acquisition_dir = ROOT_DIR.parent / "resources" / "Projection-Mix"
+
+        files = parse_multi_field_stacks(acquisition_dir=acquisition_dir)
+
+        assert len(files) == 84
+        assert files["name"].unique() == ["Projection-Mix"]
+        self.assertCountEqual(files["well"].unique(), ["E07", "E08"])
+        self.assertCountEqual(files["field"].unique(), ["s1", "s2"])
+        self.assertCountEqual(files["channel"].unique(), ["w1", "w2", "w4"])
+
+        for item in files["path"]:
+            assert exists(item)
+            assert "thumb" not in item
+
+    def test_parse_files(self):
+        acquisition_dir = ROOT_DIR.parent / "resources" / "Projection-Mix"
+
+        files = parse_files(acquisition_dir=acquisition_dir)
+
+        assert len(files) == (2 * 2 * (10 + 1)) + (2 * 2 * (10 + 1)) + (2 * 2 * 1) + (
+            2 * 2 * 1
+        )
+        assert files["name"].unique() == ["Projection-Mix"]
+        assert len(files[files["channel"] == "w1"]) == 2 * 2 * (10 + 1)
+        assert len(files[files["channel"] == "w2"]) == 2 * 2 * (10 + 1)
+        assert len(files[files["channel"] == "w3"]) == 2 * 2
+        assert len(files[files["channel"] == "w4"]) == 2 * 2
+
+        assert (
+            len(files[files["z"].isnull()]) == 2 * 2 * 3
+        )  # 2 well, 2 fields, 3 channels (1,2,3)
+        assert (
+            len(files[files["z"] == "1"]) == 2 * 2 * 3
+        )  # 2 well, 2 fields, 3 channels (1,2,4)
+        assert (
+            len(files[files["z"] == "10"]) == 2 * 2 * 2
+        )  # 2 well, 2 fields, 2 channels (1,2)
+
+        assert sorted(files[~files["z"].isnull()]["z"].unique(), key=int) == [
+            "1",
+            "2",
+            "3",
+            "4",
+            "5",
+            "6",
+            "7",
+            "8",
+            "9",
+            "10",
+        ]
diff --git a/tests/records/__init__.py b/tests/records/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/tests/records/test_DefaultRecord.py b/tests/records/test_DefaultRecord.py
deleted file mode 100644
index 4f989d6a..00000000
--- a/tests/records/test_DefaultRecord.py
+++ /dev/null
@@ -1,87 +0,0 @@
-import shutil
-import tempfile
-from os.path import exists, join
-from unittest import TestCase
-
-from pandas import DataFrame
-
-from faim_hcs.records.DefaultRecord import DefaultRecord
-
-
-class DefaultRecordTest(TestCase):
-    def setUp(self) -> None:
-        self.tmp_dir = tempfile.mkdtemp()
-
-    def tearDown(self) -> None:
-        shutil.rmtree(self.tmp_dir)
-
-    def test_create(self):
-        dr = DefaultRecord("record_id")
-
-        assert dr.record_id == "record_id"
-        assert len(dr.raw_files.items()) == 0
-        assert len(dr.spacings.items()) == 0
-        assert len(dr.segmentations.items()) == 0
-        assert len(dr.measurements.items()) == 0
-
-    def test_add_raw_data(self):
-        dr = DefaultRecord("record_id")
-
-        self.assertRaises(NotImplementedError, dr.add_raw_data, "name", "path", (2, 2))
-
-    def test_get_raw_data(self):
-        dr = DefaultRecord("record_id")
-
-        # Put key into dict to get NotImplementedError
-        dr.raw_files["name"] = None
-        self.assertRaises(NotImplementedError, dr.get_raw_data, "name")
-
-    def test_add_segmentation(self):
-        dr = DefaultRecord("record_id")
-
-        self.assertRaises(NotImplementedError, dr.add_segmentation, "name", "path")
-
-    def test_get_segmentation(self):
-        dr = DefaultRecord("record_id")
-
-        # Put key into dict to get NotImplementedError
-        dr.segmentations["name"] = None
-        self.assertRaises(NotImplementedError, dr.get_segmentation, "name")
-
-    def test_add_measurement(self):
-        dr = DefaultRecord("record_id")
-
-        self.assertRaises(NotImplementedError, dr.add_measurement, "name", "path")
-
-    def test_get_measurement(self):
-        dr = DefaultRecord("record_id")
-
-        # Put key into dict to get NotImplementedError
-        dr.measurements["name"] = None
-        self.assertRaises(NotImplementedError, dr.get_measurement, "name")
-
-    def test_save(self):
-        dr = DefaultRecord(record_id="record_id")
-
-        dr.save(self.tmp_dir, "DefaultRecord")
-
-        assert exists(join(self.tmp_dir, "DefaultRecord.json"))
-
-    def test_load(self):
-        dr = DefaultRecord(record_id="record_id")
-        dr.raw_files["raw"] = "/test/raw.tif"
-        dr.segmentations["seg"] = "/test/seg.tif"
-        dr.measurements["meas"] = "/test/meas.csv"
-        dr.spacings["raw"] = [2, 0.161, 0.161]
-
-        dr.save(self.tmp_dir, "DefaultRecord")
-
-        df = DataFrame({"path": [join(self.tmp_dir, "DefaultRecord.json")]})
-
-        dr.load(df, "path")
-
-        assert dr.record_id == "record_id"
-        assert dr.raw_files == {"raw": "/test/raw.tif"}
-        assert dr.segmentations == {"seg": "/test/seg.tif"}
-        assert dr.measurements == {"meas": "/test/meas.csv"}
-        assert dr.spacings == {"raw": [2, 0.161, 0.161]}
diff --git a/tests/records/test_OrganoidRecord.py b/tests/records/test_OrganoidRecord.py
deleted file mode 100644
index 464ac4fc..00000000
--- a/tests/records/test_OrganoidRecord.py
+++ /dev/null
@@ -1,96 +0,0 @@
-import os
-import shutil
-import tempfile
-from os.path import exists, join
-from unittest import TestCase
-
-from pandas import DataFrame
-
-from faim_hcs.hcs.Experiment import Experiment
-from faim_hcs.records.OrganoidRecord import OrganoidRecord
-from faim_hcs.records.PlateRecord import PlateRecord
-from faim_hcs.records.WellRecord import WellRecord
-
-
-class OrganoidRecordTest(TestCase):
-    def setUp(self) -> None:
-        self.tmp_dir = tempfile.mkdtemp()
-        root_dir = join(self.tmp_dir, "root_dir")
-        exp_dir = join(self.tmp_dir, "exp_dir")
-        os.mkdir(root_dir)
-        os.mkdir(exp_dir)
-        self.exp = Experiment("Experiment", root_dir=root_dir, save_dir=exp_dir)
-        self.plate = PlateRecord(
-            experiment=self.exp,
-            plate_id="plate",
-            save_dir=join(self.exp.get_experiment_dir()),
-        )
-        self.well = WellRecord(
-            plate=self.plate, well_id="well", save_dir=self.plate.plate_dir
-        )
-
-    def tearDown(self) -> None:
-        shutil.rmtree(self.tmp_dir)
-
-    def test_create(self):
-        org = OrganoidRecord(
-            well=self.well, organoid_id="org_0", save_dir=self.well.well_dir
-        )
-
-        assert org.organoid_id == "org_0"
-        assert org.well == self.well
-        assert org.organoid_dir == join(self.well.well_dir, "org_0")
-        assert self.well.organoids["org_0"] == org
-
-        assert exists(join(self.well.well_dir, "org_0"))
-
-    def test_build_overview(self):
-        org = OrganoidRecord(
-            well=self.well, organoid_id="org_1", save_dir=self.well.well_dir
-        )
-
-        org.raw_files["raw"] = "/test/raw.tif"
-        org.raw_files["raw1"] = "/test/raw1.tif"
-        org.segmentations["seg"] = "/test/seg.tif"
-        org.measurements["meas"] = "/test/meas.csv"
-        org.spacings["raw"] = [2, 0.161, 0.161]
-        org.spacings["raw1"] = [1, 0.1, 0.1]
-
-        overview = org.build_overview()
-
-        assert len(overview) == 1
-        assert overview["organoid_id"].iloc[0] == "org_1"
-        assert overview["raw"].iloc[0] == "/test/raw.tif"
-        assert overview["raw1"].iloc[0] == "/test/raw1.tif"
-        assert overview["seg"].iloc[0] == "/test/seg.tif"
-        assert overview["meas"].iloc[0] == "/test/meas.csv"
-        assert len(overview.columns) == 5
-
-    def test_save_and_load(self):
-        org = OrganoidRecord(
-            well=self.well, organoid_id="org_1", save_dir=self.well.well_dir
-        )
-
-        org.raw_files["raw"] = "/test/raw.tif"
-        org.segmentations["seg"] = "/test/seg.tif"
-        org.measurements["meas"] = "/test/meas.csv"
-        org.spacings["raw"] = [2, 0.161, 0.161]
-
-        org.save()
-        assert exists(join(self.well.well_dir, "org_1", "organoid_summary.json"))
-
-        org.save(path=self.tmp_dir, name="organoid")
-        assert exists(join(self.tmp_dir, "organoid.json"))
-
-        df = DataFrame({"path": [join(self.tmp_dir, "organoid.json")]})
-        org_loaded = OrganoidRecord(
-            well=self.well, organoid_id="org_1", save_dir=self.well.well_dir
-        )
-        org_loaded.load(df, "path")
-        assert org_loaded.organoid_id == "org_1"
-        assert org_loaded.raw_files["raw"] == "/test/raw.tif"
-        assert org_loaded.segmentations["seg"] == "/test/seg.tif"
-        assert org_loaded.measurements["meas"] == "/test/meas.csv"
-        assert org_loaded.spacings["raw"] == [2, 0.161, 0.161]
-
-        org_loaded.save(path=self.tmp_dir, name="organoid")
diff --git a/tests/records/test_WellRecord.py b/tests/records/test_WellRecord.py
deleted file mode 100644
index aaa06222..00000000
--- a/tests/records/test_WellRecord.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import os
-import shutil
-import tempfile
-from os.path import exists, join
-from unittest import TestCase
-
-from faim_hcs.hcs.Experiment import Experiment
-from faim_hcs.records.OrganoidRecord import OrganoidRecord
-from faim_hcs.records.PlateRecord import PlateRecord
-from faim_hcs.records.WellRecord import WellRecord
-
-
-class WellRecordTest(TestCase):
-    def setUp(self) -> None:
-        self.tmp_dir = tempfile.mkdtemp()
-        root_dir = join(self.tmp_dir, "root_dir")
-        exp_dir = join(self.tmp_dir, "exp_dir")
-        os.mkdir(root_dir)
-        os.mkdir(exp_dir)
-        self.exp = Experiment("Experiment", root_dir=root_dir, save_dir=exp_dir)
-        self.plate = PlateRecord(
-            experiment=self.exp,
-            plate_id="plate",
-            save_dir=join(self.exp.get_experiment_dir()),
-        )
-
-    def tearDown(self) -> None:
-        shutil.rmtree(self.tmp_dir)
-
-    def test_create(self):
-        well = WellRecord(
-            plate=self.plate, well_id="well", save_dir=self.plate.plate_dir
-        )
-
-        assert self.plate.wells["well"] == well
-        assert well.well_id == "well"
-        assert well.plate == self.plate
-
-    def test_save_and_load(self):
-        well = WellRecord(
-            plate=self.plate, well_id="E03", save_dir=self.plate.plate_dir
-        )
-
-        org = OrganoidRecord(well=well, organoid_id="org_1", save_dir=well.well_dir)
-
-        org.raw_files["raw"] = "/test/raw.tif"
-        org.segmentations["seg"] = "/test/seg.tif"
-        org.measurements["meas"] = "/test/meas.csv"
-        org.spacings["raw"] = [2, 0.161, 0.161]
-
-        df = well.save()
-        assert exists(
-            join(self.plate.plate_dir, "E03", "org_1", "organoid_summary.json")
-        )
-        assert exists(join(self.plate.plate_dir, "E03", "well_summary.json"))
-
-        well_loaded = WellRecord(
-            plate=self.plate, well_id="E03", save_dir=self.plate.plate_dir
-        )
-        well_loaded.load(df, "well_summary")
-        assert well_loaded.well_id == "E03"
-        assert well_loaded.organoids["org_1"].organoid_id == "org_1"
diff --git a/tests/test_MetaSeriesUtils.py b/tests/test_MetaSeriesUtils.py
new file mode 100644
index 00000000..82c22dc7
--- /dev/null
+++ b/tests/test_MetaSeriesUtils.py
@@ -0,0 +1,94 @@
+# SPDX-FileCopyrightText: 2023 Friedrich Miescher Institute for Biomedical Research (FMI), Basel (Switzerland)
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+from faim_hcs.io.MolecularDevicesImageXpress import parse_files
+from faim_hcs.MetaSeriesUtils import (
+    get_well_image_CYX,
+    get_well_image_CZYX,
+    montage_grid_image_YX,
+    montage_stage_pos_image_YX,
+)
+
+ROOT_DIR = Path(__file__).parent.parent
+
+
+@pytest.fixture
+def files():
+    return parse_files(ROOT_DIR / "resources" / "Projection-Mix")
+
+
+def test_get_well_image_CYX(files):
+    files2d = files[(files["z"].isnull()) & (files["channel"].isin(["w1", "w2"]))]
+    for well in files2d["well"].unique():
+        img, hists, ch_metadata, metadata = get_well_image_CYX(
+            files2d[files2d["well"] == well],
+            channels=["w1", "w2"],
+            assemble_fn=montage_stage_pos_image_YX,
+        )
+        assert img.shape == (2, 512, 1024)
+        assert len(hists) == 2
+        assert "z-scaling" not in metadata
+        for ch_meta in ch_metadata:
+            assert "z-projection-method" in ch_meta
+
+
+def test_get_well_image_CYX_well_E07(files):
+    files2d = files[(files["z"].isnull()) & (files["channel"].isin(["w1", "w2"]))]
+    cyx, hists, ch_meta, metadata = get_well_image_CYX(
+        well_files=files2d[files2d["well"] == "E07"], channels=["w1", "w2"]
+    )
+
+    assert cyx.shape == (2, 512, 1024)
+    assert cyx.dtype == np.uint16
+    assert len(hists) == 2
+    assert hists[0].min() == 114
+    assert hists[1].min() == 69
+    assert ch_meta[0] == {
+        "channel-name": "FITC_05",
+        "display-color": "73ff00",
+        "exposure-time": 15.0,
+        "exposure-time-unit": "ms",
+        "objective": "20X Plan Apo Lambda",
+        "objective-numerical-aperture": 0.75,
+        "power": 5.09804,
+        "shading-correction": False,
+        "wavelength": "cyan",
+        "z-projection-method": "Maximum",
+    }
+    assert ch_meta[1] == {
+        "channel-name": "FITC_05",
+        "display-color": "73ff00",
+        "exposure-time": 15.0,
+        "exposure-time-unit": "ms",
+        "objective": "20X Plan Apo Lambda",
+        "objective-numerical-aperture": 0.75,
+        "power": 5.09804,
+        "shading-correction": False,
+        "wavelength": "cyan",
+        "z-projection-method": "Best Focus",
+    }
+    assert metadata == {
+        "pixel-type": "uint16",
+        "spatial-calibration-units": "um",
+        "spatial-calibration-x": 1.3668,
+        "spatial-calibration-y": 1.3668,
+    }
+
+
+def test_get_well_image_ZCYX(files):
+    files3d = files[(~files["z"].isnull()) & (files["channel"].isin(["w1", "w2"]))]
+    for well in files3d["well"].unique():
+        img, hists, ch_metadata, metadata = get_well_image_CZYX(
+            files3d[files3d["well"] == well],
+            channels=["w1", "w2"],
+            assemble_fn=montage_grid_image_YX,
+        )
+        assert img.shape == (2, 10, 512, 1024)
+        assert len(hists) == 2
+        assert "z-scaling" in metadata
diff --git a/tests/test_UIntHistogram.py b/tests/test_UIntHistogram.py
new file mode 100644
index 00000000..01db2264
--- /dev/null
+++ b/tests/test_UIntHistogram.py
@@ -0,0 +1,341 @@
+import tempfile
+import unittest
+from os.path import join
+
+import numpy as np
+from numpy.testing import assert_almost_equal, assert_array_equal, assert_equal
+
+from faim_hcs.UIntHistogram import UIntHistogram
+
+
+class TestUIntHistogram(unittest.TestCase):
+    def test_bins(self):
+        data = np.array([4, 5, 6])
+        hist = UIntHistogram(data)
+        assert hist.bins == len(hist.frequencies) + 1
+
+    def test_update_same_length(self):
+        # Old and new frequencies cover the same range:
+        # [old frequencies]
+        # [new frequencies]
+        data = np.array([4, 5, 6])
+        hist = UIntHistogram(data)
+        hist.update(data)
+
+        assert_array_equal(hist.frequencies, np.array([2, 2, 2]))
+        assert_equal(hist.bins, len(hist.frequencies) + 1)
+        assert_equal(hist.offset, 4)
+
+    def test_update_lower_overlap(self):
+        # New frequencies have additional lower ones.
+        #     [old frequencies]
+        # [new frequencies]
+        data = np.array([4, 5, 6])
+        update_data = np.array([2, 3, 4])
+        hist = UIntHistogram(data)
+        hist.update(update_data)
+
+        assert_array_equal(hist.frequencies, np.array([1, 1, 2, 1, 1]))
+        assert_equal(hist.bins, len(hist.frequencies) + 1)
+        assert_equal(hist.offset, 2)
+
+        update_data = np.array([2, 3, 4, 5, 6])
+        hist = UIntHistogram(data)
+        hist.update(update_data)
+
+        assert_array_equal(hist.frequencies, np.array([1, 1, 2, 2, 2]))
+        assert_equal(hist.bins, len(hist.frequencies) + 1)
+        assert_equal(hist.offset, 2)
+
+    def test_update_lower_concat(self):
+        # New frequencies only have additional lower ones.
+        #                  [old frequencies]
+        # [new frequencies]
+        data = np.array([4, 5, 6])
+        update_data = np.array([1, 2, 3])
+        hist = UIntHistogram(data)
+        hist.update(update_data)
+
+        assert_array_equal(hist.frequencies, np.array([1, 1, 1, 1, 1, 1]))
+        assert_equal(hist.bins, len(hist.frequencies) + 1)
+        assert_equal(hist.offset, 1)
+
+    def test_update_lower_gap(self):
+        # New frequencies only have additional lower ones.
+        #                       [old frequencies]
+        # [new frequencies]
+        data = np.array([4, 5, 6])
+        update_data = np.array([1, 2])
+        hist = UIntHistogram(data)
+        hist.update(update_data)
+
+        assert_array_equal(hist.frequencies, np.array([1, 1, 0, 1, 1, 1]))
+        assert_equal(hist.bins, len(hist.frequencies) + 1)
+        assert_equal(hist.offset, 1)
+
+    def test_update_upper_overlap(self):
+        # New frequencies have additional upper ones.
+        #     [old frequencies]
+        #            [new frequencies]
+        data = np.array([4, 5, 6])
+        update_data = np.array([5, 6, 7])
+        hist = UIntHistogram(data)
+        hist.update(update_data)
+
+        assert_array_equal(hist.frequencies, np.array([1, 2, 2, 1]))
+        assert_equal(hist.bins, len(hist.frequencies) + 1)
+        assert_equal(hist.offset, 4)
+
+        update_data = np.array([4, 5, 6, 7])
+        hist = UIntHistogram(data)
+        hist.update(update_data)
+
+        assert_array_equal(hist.frequencies, np.array([2, 2, 2, 1]))
+        assert_equal(hist.bins, len(hist.frequencies) + 1)
+        assert_equal(hist.offset, 4)
+
+    def test_update_upper_concat(self):
+        # New frequencies have only additional upper ones.
+        #     [old frequencies]
+        #                      [new frequencies]
+        data = np.array([4, 5, 6])
+        update_data = np.array([7, 8])
+        hist = UIntHistogram(data)
+        hist.update(update_data)
+
+        assert_array_equal(hist.frequencies, np.array([1, 1, 1, 1, 1]))
+        assert_equal(hist.bins, len(hist.frequencies) + 1)
+        assert_equal(hist.offset, 4)
+
+    def test_update_upper_gap(self):
+        # New frequencies have only additional upper ones.
+        #     [old frequencies]
+        #                           [new frequencies]
+        data = np.array([4, 5, 6])
+        update_data = np.array([8, 9])
+        hist = UIntHistogram(data)
+        hist.update(update_data)
+
+        assert_array_equal(hist.frequencies, np.array([1, 1, 1, 0, 1, 1]))
+        assert_equal(hist.bins, len(hist.frequencies) + 1)
+        assert_equal(hist.offset, 4)
+
+    def test_update_covered(self):
+        # New frequencies are completely covered.
+        # [          old frequencies          ]
+        #           [new frequencies]
+        data = np.array([4, 5, 6])
+        update_data = np.array([5])
+        hist = UIntHistogram(data)
+        hist.update(update_data)
+
+        assert_array_equal(hist.frequencies, np.array([1, 2, 1]))
+        assert_equal(hist.bins, len(hist.frequencies) + 1)
+        assert_equal(hist.offset, 4)
+
+        data = np.array([4, 5, 6, 7])
+        update_data = np.array([5, 6])
+        hist = UIntHistogram(data)
+        hist.update(update_data)
+
+        assert_array_equal(hist.frequencies, np.array([1, 2, 2, 1]))
+        assert_equal(hist.bins, len(hist.frequencies) + 1)
+        assert_equal(hist.offset, 4)
+
+        data = np.array([4, 5, 6, 7])
+        update_data = np.array([4, 5, 6])
+        hist = UIntHistogram(data)
+        hist.update(update_data)
+
+        assert_array_equal(hist.frequencies, np.array([2, 2, 2, 1]))
+        assert_equal(hist.bins, len(hist.frequencies) + 1)
+        assert_equal(hist.offset, 4)
+
+        data = np.array([4, 5, 6, 7])
+        update_data = np.array([5, 6, 7])
+        hist = UIntHistogram(data)
+        hist.update(update_data)
+
+        assert_array_equal(hist.frequencies, np.array([1, 2, 2, 2]))
+        assert_equal(hist.bins, len(hist.frequencies) + 1)
+        assert_equal(hist.offset, 4)
+
+    def test_old_frequencies_covered(self):
+        # Old frequencies are completely covered.
+        #     [old frequencies]
+        # [    new frequencies    ]
+        data = np.array([5])
+        update_data = np.array([4, 5, 6])
+        hist = UIntHistogram(data)
+        hist.update(update_data)
+
+        assert_array_equal(hist.frequencies, np.array([1, 2, 1]))
+        assert_equal(hist.bins, len(hist.frequencies) + 1)
+        assert_equal(hist.offset, 4)
+
+        data = np.array([5])
+        update_data = np.array([3, 5, 6])
+        hist = UIntHistogram(data)
+        hist.update(update_data)
+
+        assert_array_equal(hist.frequencies, np.array([1, 0, 2, 1]))
+        assert_equal(hist.bins, len(hist.frequencies) + 1)
+        assert_equal(hist.offset, 3)
+
+        data = np.array([5])
+        update_data = np.array([4, 5, 7])
+        hist = UIntHistogram(data)
+        hist.update(update_data)
+
+        assert_array_equal(hist.frequencies, np.array([1, 2, 0, 1]))
+        assert_equal(hist.bins, len(hist.frequencies) + 1)
+        assert_equal(hist.offset, 4)
+
+        data = np.array([5])
+        update_data = np.array([3, 5, 7])
+        hist = UIntHistogram(data)
+        hist.update(update_data)
+
+        assert_array_equal(hist.frequencies, np.array([1, 0, 2, 0, 1]))
+        assert_equal(hist.bins, len(hist.frequencies) + 1)
+        assert_equal(hist.offset, 3)
+
+    def test_mean(self):
+        data = np.random.randint(0, 255, (100, 100))
+        hist = UIntHistogram(data)
+        assert_almost_equal(hist.mean(), np.mean(data), decimal=10)
+
+        update_data = np.random.randint(9, 127, (100, 100))
+        hist.update(update_data)
+        assert_almost_equal(
+            hist.mean(),
+            np.mean(np.concatenate([data.ravel(), update_data.ravel()])),
+            decimal=10,
+        )
+
+    def test_std(self):
+        data = np.random.randint(0, 255, (100, 100))
+        hist = UIntHistogram(data)
+        assert_almost_equal(hist.std(), np.std(data), decimal=10)
+
+        update_data = np.random.randint(9, 127, (100, 100))
+        hist.update(update_data)
+        assert_almost_equal(
+            hist.std(),
+            np.std(np.concatenate([data.ravel(), update_data.ravel()])),
+            decimal=10,
+        )
+
+    def test_quantile(self):
+        data = np.random.randint(50, 150, (100, 100))
+
+        hist = UIntHistogram()
+        hist.update(data)
+        assert_equal(
+            hist.quantile(0.0), np.quantile(data, 0.0, method="closest_observation")
+        )
+        assert_equal(
+            hist.quantile(0.25), np.quantile(data, 0.25, method="closest_observation")
+        )
+        assert_equal(
+            hist.quantile(0.5), np.quantile(data, 0.5, method="closest_observation")
+        )
+        assert_equal(
+            hist.quantile(0.75), np.quantile(data, 0.75, method="closest_observation")
+        )
+        assert_equal(
+            hist.quantile(1.0), np.quantile(data, 1.0, method="closest_observation")
+        )
+
+        update_data = np.random.randint(9, 227, (100, 100))
+
+        hist.update(update_data)
+        assert_equal(
+            hist.quantile(0.0),
+            np.quantile(
+                np.concatenate([data.ravel(), update_data.ravel()]),
+                0.0,
+                method="closest_observation",
+            ),
+        )
+        assert_equal(
+            hist.quantile(0.25),
+            np.quantile(
+                np.concatenate([data.ravel(), update_data.ravel()]),
+                0.25,
+                method="closest_observation",
+            ),
+        )
+        assert_equal(
+            hist.quantile(0.5),
+            np.quantile(
+                np.concatenate([data.ravel(), update_data.ravel()]),
+                0.5,
+                method="closest_observation",
+            ),
+        )
+        assert_equal(
+            hist.quantile(0.75),
+            np.quantile(
+                np.concatenate([data.ravel(), update_data.ravel()]),
+                0.75,
+                method="closest_observation",
+            ),
+        )
+        assert_equal(
+            hist.quantile(1.0),
+            np.quantile(
+                np.concatenate([data.ravel(), update_data.ravel()]),
+                1.0,
+                method="closest_observation",
+            ),
+        )
+
+    def test_min(self):
+        data = np.random.randint(0, 255, (100, 100))
+        hist = UIntHistogram(data)
+        assert_almost_equal(hist.min(), np.min(data), decimal=10)
+
+        update_data = np.random.randint(9, 127, (100, 100))
+        hist.update(update_data)
+        assert_almost_equal(
+            hist.min(),
+            np.min(np.concatenate([data.ravel(), update_data.ravel()])),
+            decimal=10,
+        )
+
+    def test_max(self):
+        data = np.random.randint(0, 255, (100, 100))
+        hist = UIntHistogram(data)
+        assert_almost_equal(hist.max(), np.max(data), decimal=10)
+
+        update_data = np.random.randint(0, 127, (100, 100))
+        hist1 = UIntHistogram(update_data)
+        hist.combine(hist1)
+        assert_almost_equal(
+            hist.max(),
+            np.max(np.concatenate([data.ravel(), update_data.ravel()])),
+            decimal=10,
+        )
+
+    def test_save_load(self):
+        with tempfile.TemporaryDirectory() as temp_dir:
+            data = np.array([4, 5, 6])
+            hist = UIntHistogram(data)
+
+            hist.save(join(temp_dir, "hist.npz"))
+
+            hist_ = UIntHistogram.load(join(temp_dir, "hist.npz"))
+
+            assert_equal(hist_.bins, hist.bins)
+            assert_equal(hist_.offset, hist.offset)
+            assert_array_equal(hist_.frequencies, hist.frequencies)
+
+            assert hist != hist_
+            assert isinstance(hist.frequencies, list)
+            assert isinstance(hist.bins, int)
+            assert isinstance(hist.offset, int)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/test_Zarr.py b/tests/test_Zarr.py
new file mode 100644
index 00000000..701ad423
--- /dev/null
+++ b/tests/test_Zarr.py
@@ -0,0 +1,283 @@
+# SPDX-FileCopyrightText: 2023 Friedrich Miescher Institute for Biomedical Research (FMI), Basel (Switzerland)
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+import shutil
+import tempfile
+import unittest
+from os.path import exists, join
+from pathlib import Path
+
+from faim_hcs.io.MolecularDevicesImageXpress import (
+    parse_multi_field_stacks,
+    parse_single_plane_multi_fields,
+)
+from faim_hcs.MetaSeriesUtils import get_well_image_CYX, get_well_image_CZYX
+from faim_hcs.Zarr import (
+    PlateLayout,
+    build_zarr_scaffold,
+    write_cyx_image_to_well,
+    write_czyx_image_to_well,
+)
+
+ROOT_DIR = Path(__file__).parent
+
+
+class TestZarr(unittest.TestCase):
+    def setUp(self) -> None:
+        self.tmp_dir = tempfile.mkdtemp()
+
+        self.files = parse_single_plane_multi_fields(
+            join(ROOT_DIR.parent, "resources", "Projection-Mix")
+        )
+
+        self.files3d = parse_multi_field_stacks(
+            join(ROOT_DIR.parent, "resources", "Projection-Mix")
+        )
+
+        self.zarr_root = Path(self.tmp_dir, "zarr-files")
+        self.zarr_root.mkdir()
+
+    def tearDown(self) -> None:
+        shutil.rmtree(self.tmp_dir)
+
+    def test_plate_scaffold_96(self):
+        plate = build_zarr_scaffold(
+            root_dir=self.zarr_root,
+            files=self.files,
+            layout=PlateLayout.I96,
+            order_name="test-order",
+            barcode="test-barcode",
+        )
+
+        assert exists(join(self.zarr_root, "Projection-Mix.zarr", "E", "7", "0"))
+        assert exists(join(self.zarr_root, "Projection-Mix.zarr", "E", "8", "0"))
+
+        attrs = plate.attrs.asdict()
+        assert attrs["order_name"] == "test-order"
+        assert attrs["barcode"] == "test-barcode"
+        assert len(attrs["plate"]["columns"]) * len(attrs["plate"]["rows"]) == 96
+
+    def test_plate_scaffold_384(self):
+        plate = build_zarr_scaffold(
+            root_dir=self.zarr_root,
+            files=self.files,
+            layout=PlateLayout.I384,
+            order_name="test-order",
+            barcode="test-barcode",
+        )
+
+        assert exists(join(self.zarr_root, "Projection-Mix.zarr", "E", "7", "0"))
+        assert exists(join(self.zarr_root, "Projection-Mix.zarr", "E", "8", "0"))
+
+        attrs = plate.attrs.asdict()
+        assert attrs["order_name"] == "test-order"
+        assert attrs["barcode"] == "test-barcode"
+        assert len(attrs["plate"]["columns"]) * len(attrs["plate"]["rows"]) == 384
+
+    def test_plate_scaffold_24(self):
+        self.assertRaises(
+            NotImplementedError,
+            build_zarr_scaffold,
+            root_dir=self.zarr_root,
+            files=self.files,
+            layout=24,
+            order_name="test-order",
+            barcode="test-barcode",
+        )
+
+    def test_write_cyx_image_to_well(self):
+        plate = build_zarr_scaffold(
+            root_dir=self.zarr_root,
+            files=self.files,
+            layout=96,
+            order_name="test-order",
+            barcode="test-barcode",
+        )
+
+        for well in self.files["well"].unique():
+            well_files = self.files[self.files["well"] == well]
+            img, hists, ch_metadata, metadata = get_well_image_CYX(
+                well_files=well_files, channels=["w1", "w2", "w3", "w4"]
+            )
+
+            field = plate[well[0]][str(int(well[1:]))][0]
+            write_cyx_image_to_well(img, hists, ch_metadata, metadata, field)
+
+        e07 = plate["E"]["7"]["0"].attrs.asdict()
+        assert (
+            self.zarr_root
+            / "Projection-Mix.zarr"
+            / "E"
+            / "7"
+            / "0"
+            / "C00_FITC_05_histogram.npz"
+        ).exists()
+        assert (
+            self.zarr_root
+            / "Projection-Mix.zarr"
+            / "E"
+            / "7"
+            / "0"
+            / "C01_FITC_05_histogram.npz"
+        ).exists()
+        assert (
+            self.zarr_root
+            / "Projection-Mix.zarr"
+            / "E"
+            / "7"
+            / "0"
+            / "C02_FITC_05_histogram.npz"
+        ).exists()
+        assert (
+            self.zarr_root
+            / "Projection-Mix.zarr"
+            / "E"
+            / "7"
+            / "0"
+            / "C03_empty_histogram.npz"
+        ).exists()
+        assert "histograms" in e07.keys()
+        assert "acquisition_metadata" in e07.keys()
+        assert e07["multiscales"][0]["datasets"][0]["coordinateTransformations"][0][
+            "scale"
+        ] == [1.0, 1.3668, 1.3668]
+
+        e08 = plate["E"]["8"]["0"].attrs.asdict()
+        assert (
+            self.zarr_root
+            / "Projection-Mix.zarr"
+            / "E"
+            / "8"
+            / "0"
+            / "C00_FITC_05_histogram.npz"
+        ).exists()
+        assert (
+            self.zarr_root
+            / "Projection-Mix.zarr"
+            / "E"
+            / "8"
+            / "0"
+            / "C01_FITC_05_histogram.npz"
+        ).exists()
+        assert (
+            self.zarr_root
+            / "Projection-Mix.zarr"
+            / "E"
+            / "8"
+            / "0"
+            / "C02_FITC_05_histogram.npz"
+        ).exists()
+        assert (
+            self.zarr_root
+            / "Projection-Mix.zarr"
+            / "E"
+            / "8"
+            / "0"
+            / "C03_empty_histogram.npz"
+        ).exists()
+        assert "histograms" in e08.keys()
+        assert "acquisition_metadata" in e08.keys()
+        assert e08["multiscales"][0]["datasets"][0]["coordinateTransformations"][0][
+            "scale"
+        ] == [1.0, 1.3668, 1.3668]
+
+    def test_write_czyx_image_to_well(self):
+        plate = build_zarr_scaffold(
+            root_dir=self.zarr_root,
+            files=self.files3d,
+            layout=96,
+            order_name="test-order",
+            barcode="test-barcode",
+        )
+
+        for well in self.files3d["well"].unique():
+            well_files = self.files3d[self.files3d["well"] == well]
+            img, hists, ch_metadata, metadata = get_well_image_CZYX(
+                well_files=well_files, channels=["w1", "w2", "w3", "w4"]
+            )
+
+            field = plate[well[0]][str(int(well[1:]))][0]
+            write_czyx_image_to_well(img, hists, ch_metadata, metadata, field)
+
+        e07 = plate["E"]["7"]["0"].attrs.asdict()
+        assert (
+            self.zarr_root
+            / "Projection-Mix.zarr"
+            / "E"
+            / "7"
+            / "0"
+            / "C00_FITC_05_histogram.npz"
+        ).exists()
+        assert (
+            self.zarr_root
+            / "Projection-Mix.zarr"
+            / "E"
+            / "7"
+            / "0"
+            / "C01_FITC_05_histogram.npz"
+        ).exists()
+        assert (
+            self.zarr_root
+            / "Projection-Mix.zarr"
+            / "E"
+            / "7"
+            / "0"
+            / "C02_empty_histogram.npz"
+        ).exists()
+        assert (
+            self.zarr_root
+            / "Projection-Mix.zarr"
+            / "E"
+            / "7"
+            / "0"
+            / "C03_FITC_05_histogram.npz"
+        ).exists()
+        assert "histograms" in e07.keys()
+        assert "acquisition_metadata" in e07.keys()
+        assert e07["multiscales"][0]["datasets"][0]["coordinateTransformations"][0][
+            "scale"
+        ] == [1.0, 5.02, 1.3668, 1.3668]
+
+        e08 = plate["E"]["8"]["0"].attrs.asdict()
+        assert (
+            self.zarr_root
+            / "Projection-Mix.zarr"
+            / "E"
+            / "8"
+            / "0"
+            / "C00_FITC_05_histogram.npz"
+        ).exists()
+        assert (
+            self.zarr_root
+            / "Projection-Mix.zarr"
+            / "E"
+            / "8"
+            / "0"
+            / "C01_FITC_05_histogram.npz"
+        ).exists()
+        assert (
+            self.zarr_root
+            / "Projection-Mix.zarr"
+            / "E"
+            / "8"
+            / "0"
+            / "C02_empty_histogram.npz"
+        ).exists()
+        assert (
+            self.zarr_root
+            / "Projection-Mix.zarr"
+            / "E"
+            / "8"
+            / "0"
+            / "C03_FITC_05_histogram.npz"
+        ).exists()
+        assert "histograms" in e08.keys()
+        assert "acquisition_metadata" in e08.keys()
+        assert e08["multiscales"][0]["datasets"][0]["coordinateTransformations"][0][
+            "scale"
+        ] == [1.0, 5.0, 1.3668, 1.3668]
+
+
+if __name__ == "__main__":
+    unittest.main()