.gitignore

 *.h5
 __pycache__/
 tmp*
+.idea/*
+*.DS_Store
+data/dev-*
+*.tif
+*.swp
+software
+*.zip
+*.n5
+data/bkp
+data/rawdata/evaluation

README.md

-# platy-browser-tables
+# platy-browser-data
 
 Data and data-generation for the [platybrowser](https://github.com/embl-cba/fiji-plugin-platyBrowser).
 
 
 ## Data storage
 
-Image data (only links for the image volumes) and derived data for all versions are stored in the folder `data`.
+Image data and derived data for all versions are stored in the folder `data`.
 We follow a versioning scheme inspired by [semantic versioning](https://semver.org/), hence all version
 numbers are given as `MAJOR.MINOR.PATCH`.
 
-- `PATCH` is increased if the derived data is update, e.g. due to corrections in some segmentation or new attributes in some table. This is usually triggered automatically (see section below).
-- `MINOR` is increased if new derived data is added, e.g. a new segmentation for some structure or a new attribute table. This needs to be done manually.
-- `MAJOR` is increased if new image / raw data is added, e.g. a new animal registered to the atlas or new genes. This needs to be done manually.
+- `PATCH` is increased if the derived data is update, e.g. due to corrections in some segmentation or new attributes in some table. It is increased by `update_patch.py`.
+- `MINOR` is increased if new derived data is added, e.g. a new segmentation for some structure or a new attribute table. It is increased by `update_minor.py`.
+- `MAJOR` is increased if new image / raw data is added, e.g. a new animal registered to the atlas or new genes. It is increased by `update_major.py`.
 
 For a given version `X.Y.Z`, the data is stored in the directory `/data/X.Y.Z/` with subfolders:
 
-- `images`: Raw image or gene expression data. Contains bigdata-viewer xml files with absolute links to h5 files on the embl server.
+- `images`: Raw image or gene expression data. Contains bigdata-viewer xml and hdf5 files. The hdf5 files are not under version control.
 - `misc`: Miscellanous data.
-- `segmentations`: Segmentation volumes derived from the image data. Only xml files.
+- `segmentations`: Segmentation volumes derived from the image data.
 - `tables`: CSV tables with attributes derived from image data and segmentations.
 
+### File naming
+
+Xml / hdf5 filenames must adhere to the following naming scheme in order to clearly identify the origin of the data:
+the names must be prefixed by the header `MODALITY-STAGE-ID-REGION`, where
+- `MODALITY` is a shorthand for the imaging modality used to obtain the data, e.g. `sbem` for serial blockface electron microscopy.
+- `STAGE` is a shorthand for the develpmental stage, e.g. `6dpf` for six day post ferilisation.
+- `ID` is a number that distinguishes individual animals of a given modality and stage or distinguishes different set-ups for averaging based modalities like prospr.
+- `REGION` is a shorthand for the region of the animal covered by the data, e.g. `parapod` for the parapodium or `whole` for the whole animal.
+
+Currently, the data contains the three modalities
+- `sbem-6dpf-1-whole`
+- `prospr-6dpf-1-whole`
 
 ### Table storage
 
@@ -27,18 +39,109 @@ Derived attributes are stored in csv tables. Tables must be associated with a se
 All tables associated with a given segmentation must be stored in the sub-directory `tables/segmentation-name`.
 If this directory exists, it must at least contain the file `default.csv` with spatial attributes of the segmentation objects , which are necessary for the platybrowser table functionality.
 
-TODO think about linking to previous table version. Simplest option would be to replace the directory `tables/segmentation-name` with a
-text file that contains the absolute path to the previous table version.
+If tables do not change between versions, they can be stored as soft-links to the old version.
 
 
-## Data generation
+## Usage
 
-In addition to the data, the scripts for generating the derived data are also collected here.
-`scripts/segmentation` contains the scripts to generate the derived segmentations with automated segmentation approaches.
-The other derived data can be generated for new segmentation versions with the script `update_platy_browser.py`;
-`make_initial_version.py` was used to generate the initial data in `/data/0.0.0`.
+We provide three scripts to update the respective release digit:
+- `update_patch.py`: Create new version folder and update derived data.
+- `update_minor.py`: Create new version folder and add derived data.
+- `update_major.py`: Create new version folder and add primary data. 
+
+All three scripts take the path to a json file as argument. The json needs to encode which data to update/add
+according to the following specifications:
+
+For `update_patch`, the json needs to contain a dictonary with the two keys `segmentations` and `tables`.
+Each key needs to map to a list that contains (valid) names of segmentations. For names listed in `segmentations`,
+the segmentation AND corresponding tables will be updated. For `tables`, only the tables will be updated.
+The following example would trigger segmentation and table update for the cell segmentation and a table update for the nucleus segmentation:
+```
+{"segmentations": ["sbem-6dpf-1-whole-segmented-cells-labels"],
+ "tables": ["sbem-6dpf-1-whole-segmented-nuclei-labels"]}
+```
+
+For `update_minor`, the json needs to contain a list of dictionaries. Each dictionary corresponds to new
+data to add to the platy browser. There are three valid types of data, each with different required and optional fields:
+- `images`: New image data. Required fields are `source`, `name` and `input_path`. `source` refers to the primary data the image data is associated with, see [naming scheme](https://git.embl.de/tischer/platy-browser-tables#file-naming). `name` specifies the name this image data will have, excluding the naming scheme prefix. `input_path` is the path to the data to add, needs to be in bdv hdf5 format. The field `is_private` is optional. If it is `true`, the data will not be exposed in
+  the public big data server.
+- `static segmentations`: New (static) segmentation data. The required fields are `source`, `name` and `segmentation_path` (corresponding to `input_path` in `images`). The fields `table_path_dict` and `is_private` are optional. `table_dict_path` specifies tables associated with the segmentation as a dictionary `{"table_name1": "/path/to/table1.csv", ...}`. If given, one of the table names must be `default`.
+- `dynamic segmentations`: New (dynamic) segmentation data. The required fields are `source`, `name`, `paintera_project` and `resolution`. `paintera_project` specifies path and key of a n5 container storing paintera corrections for this segmentation. `resolution` is the segmentation's resolution in micrometer. The fields `table_update_function` and `is_private` are optional. `table_update_function` can be specified to register a function to generate tables for this segmentation. The function must be
+  importable from `scripts.attributes`.
+The following example would add a new prospr gene to the images and a new static and dynamic segmentation derived from the em data:
+```
+[{"source": "prospr-6dpf-1-whole", "name": "new-gene-MED", "input_path": "/path/to/new-gene-data.xml"}
+ {"source": "sbem-6dpf-1-whole", "name": "new-static-segmentation", "segmentation_path": "/path/to/new-segmentation-data.xml",
+  "table_path_dict": {"default": "/path/to/default-table.csv", "custom": "/path/to/custom-table.csv"}},
+ {"source": "sbem-6dpf-1-whole", "name": "new-dynamic-segmentation", "paintera_project": ["/path/to/dynamic-segmentation.n5", "/paintera/project"],
+  "table_update_function": "new_update_function"}]
+```
+
+For `update_major`, the json needs to contain a dictionary. The dictionary keys correpond to new primary sources (cf. [naming scheme](https://git.embl.de/tischer/platy-browser-tables#file-naming))'
+to add to the platy browser. Each key needs to map to a list of data entries. The specification of these entries corresponds to `update_minor`, except that the field `source` is not necessary.
+The following example would add a new primary data source (FIBSEM) and add the corresponding raw data as private data:
+```
+{"fib-6dpf-1-whole": [{"name": "raw", "input_path": "/path/to/fib-raw.xml", "is_private": "true"}]}
+```
+
+See `example_updates/` for additional json update files.
+
+For now, we do not add any files to version control automatically. So after calling one of the update
+scripts, you must add all new files yourself and then make a release via `git tag -a X.Y.Z -m "DESCRIPTION"`.
+
+In addition, the script `make_dev_folder.py` can be used to create a development folder. It copies the most
+recent release folder into a folder prefixed with  dev-`, that will not be put under version control.
+
+The script `update_registration.py` can be used to update registered data with a new transformation.
+It creates a new patch version folder and updates all relevant data.
 
 
 ## Installation
 
-TODO
+The data is currently hosted on the arendt EMBL share, where a conda environment with all necessary dependencies is
+available. This environment is used by default.
+
+It can be installed elsewhere using the `environment.yaml` file we provide:
+```
+conda env create -f environment.yaml
+```
+
+
+## BigDataServer
+
+The platy browser can be served with [BigDataViewerServer](https://github.com/bigdataviewer/bigdataviewer-server).
+On the EMBL server, you can start it from one of the version foldes misc directories:
+
+```
+cd data/X.Y.Z/misc
+java -jar /g/cba/exchange/bigdataserver/bigdataviewer-server-2.1.2-jar-with-dependencies.jar -d bdv_server.txt
+```
+
+
+## Data generation
+
+In addition to the data, the scripts for generating registration and producing derived data are also collected here:
+
+### Registration
+
+The folder `registration` contains the transformations for the different registration versions as well as the scripts
+to generate / curate the registration targets. You can use the script `apply_registration.py` to apply a registration transformation
+to a new input file.
+
+<!---
+- `transfer_ProSPr_data`. This folder contains the scripts needed to copy and process the ProSPr output to 
+'/g/arendt/EM_6dpf_segmentation/platy-browser-data/data/rawdata/prospr'.
+It reads the .tif files that will be registered to the EM (e.g. MEDs, tissue manual segmentations, reference),
+mirrors them in x axis, adds size information (0.55um/px), and deals with gene names.
+run it in the cluster:
+```
+sbatch ./ProSPr_copy_and_mirror.sh
+```
+
+- `ProSPr_files_for_registration`. The three files to guide the transformation of prospr space into the EM.
+--->
+
+### Segmentation
+
+`scripts/segmentation` contains the scripts to generate the derived segmentations with automated segmentation approaches.
+`deprecated/make_initial_version.py` was used to generate the initial data in `/data/0.0.0`.

analysis/cell_volumes.py

+#! /g/arendt/EM_6dpf_segmentation/platy-browser-data/software/conda/miniconda3/envs/platybrowser/bin/python
+
+import argparse
+import os
+import numpy as np
+from mmpb import get_latest_version
+from mmpb.analysis import get_region_ids, get_morphology_attribute
+
+
+def cell_volumes(region_name, version):
+    # path are hard-coded, so we need to change the pwd to '..'
+    os.chdir('..')
+    try:
+        if version == '':
+            version = get_latest_version()
+        region_table_path = "data/%s/tables/sbem-6dpf-1-whole-segmented-cells-labels/regions.csv" % version
+        nucleus_table_path = "data/%s/tables/sbem-6dpf-1-whole-segmented-cells-labels/cells_to_nuclei.csv" % version
+        label_ids = get_region_ids(region_table_path, nucleus_table_path, region_name)
+        morpho_table_path = "data/%s/tables/sbem-6dpf-1-whole-segmented-cells-labels/morphology.csv" % version
+        volumes = get_morphology_attribute(morpho_table_path, "shape_volume_in_microns", query_ids=label_ids)
+        print("Found average volume for region", region_name, ":")
+        print(np.mean(volumes), "+-", np.std(volumes), "cubic micron")
+    except Exception as e:
+        os.chdir('analysis')
+        raise e
+    os.chdir('analysis')
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Compute average volume of cells for a given region.')
+    parser.add_argument('region_name', type=str,
+                        help='Name of the region.')
+    parser.add_argument('--version', type=str, default='',
+                        help='Version of the platy browser data. Default is latest.')
+
+    args = parser.parse_args()
+    cell_volumes(args.region_name, args.version)

analysis/extend_regions/extend_midgut.py

+import h5py
+from pybdv import make_bdv
+import pandas as pd
+import numpy as np
+
+
+def make_midgut_volume():
+    table_path = './corrected_regions_1.csv'
+    table = pd.read_csv(table_path, sep='\t')
+    label_ids = table['label_id'].values
+    midgut = table['midgut'].values
+    assert len(label_ids) == len(midgut)
+    assert np.array_equal(np.unique(midgut), np.array([0, 1]))
+    midgut_ids = label_ids[midgut == 1]
+
+    scale = 4
+    seg_path = '../../data/0.6.5/segmentations/sbem-6dpf-1-whole-segmented-cells-labels.h5'
+    key = 't00000/s00/%i/cells' % scale
+    res = [0.4, 0.32, 0.32]
+
+    with h5py.File(seg_path, 'r') as f:
+        seg = f[key][:]
+
+    midgut_seg = 255 * np.isin(seg, midgut_ids).astype('int8')
+    out_path = './sbem-6dpf-1-whole-segmented-midgut.h5'
+    factors = 3 * [[2, 2, 2]]
+    make_bdv(midgut_seg, out_path, factors, resolution=res, unit='micrometer',
+             convert_dtype=False)
+
+
+if __name__ == '__main__':
+    make_midgut_volume()

analysis/extend_regions/recompute_table.py

+import os
+import h5py
+import numpy as np
+
+from mmpb.default_config import write_default_global_config
+from mmpb.attributes.region_attributes import region_attributes
+from mmpb.files.xml_utils import get_h5_path_from_xml
+
+
+def recompute_table(version):
+    version_folder = '../../data/%s' % version
+    image_folder = os.path.join(version_folder, 'images')
+    seg_folder = os.path.join(version_folder, 'segmentations')
+    table_folder = os.path.join(version_folder, 'tables')
+
+    seg_path = os.path.join(seg_folder, 'sbem-6dpf-1-whole-segmented-cells-labels.xml')
+    seg_path = get_h5_path_from_xml(seg_path, return_absolute_path=True)
+
+    with h5py.File(seg_path, 'r') as f:
+        n_labels = int(f['t00000/s00/0/cells'].attrs['maxId'] + 1)
+    label_ids = np.arange(n_labels)
+
+    tmp_folder = './tmp_regions'
+    write_default_global_config(os.path.join(tmp_folder, 'configs'))
+    target = 'local'
+    max_jobs = 48
+
+    region_out = os.path.join(table_folder, 'sbem-6dpf-1-whole-segmented-cells-labels', 'regions.csv')
+
+    region_attributes(seg_path, region_out,
+                      image_folder, seg_folder,
+                      label_ids, tmp_folder, target, max_jobs)
+
+
+if __name__ == '__main__':
+    recompute_table('0.6.5')

analysis/extent.py

+import h5py
+import numpy as np
+
+
+def number_of_voxels():
+    p = '../data/rawdata/sbem-6dpf-1-whole-raw.h5'
+    with h5py.File(p, 'r') as f:
+        ds = f['t00000/s00/0/cells']
+        shape = ds.shape
+    n_vox = np.prod(list(shape))
+    print("Number of voxel:")
+    print(n_vox)
+    print("corresponds to")
+    print(float(n_vox) / 1e12, "TVoxel")
+
+
+def animal():
+    p = '../data/rawdata/sbem-6dpf-1-whole-mask-inside.h5'
+    with h5py.File(p, 'r') as f:
+        mask = f['t00000/s00/0/cells'][:]
+
+    bb = np.where(mask > 0)
+    mins = [b.min() for b in bb]
+    maxs = [b.max() for b in bb]
+    size = [ma - mi for mi, ma in zip(mins, maxs)]
+
+    print("Animal size in pixel:")
+    print(size)
+    res = [.4, .32, .32]
+
+    size = [si * re for si, re in zip(size, res)]
+    print("Animal size in micron:")
+    print(size)
+
+
+if __name__ == '__main__':
+    number_of_voxels()
+    animal()

analysis/ganglia/write_ganglion_segmentation.py

+import os
+import z5py
+import h5py
+import numpy as np
+import pandas as pd
+import nifty.tools as nt
+from pybdv import make_bdv
+from mmpb.attributes.base_attributes import base_attributes
+
+
+# Segmentation version: 0.2.1
+def write_assignments():
+    tab = pd.read_csv('./head_ganglions_table_v1.csv', sep='\t')
+    g = tab['Head_ganglion_1'].values.astype('uint32')
+    print(g.shape)
+
+    with z5py.File('ganglion.n5') as f:
+        f.create_dataset('assignments', data=g, compression='gzip', chunks=(10000,))
+
+
+def write_ganglia():
+    tab = pd.read_csv('./head_ganglions_table_v1.csv', sep='\t')
+    g = tab['Head_ganglion_1'].values.astype('uint32')
+    g[0] = 0
+    max_id = int(g.max())
+    print("Max-id:", max_id)
+
+    seg_path = os.path.join('/g/arendt/EM_6dpf_segmentation/platy-browser-data/data/0.2.1',
+                            'segmentations/sbem-6dpf-1-whole-segmented-cells-labels.h5')
+    seg_key = 't00000/s00/3/cells'
+
+    out_path = './sbem-6dpf-1-whole-segmented-ganglia-labels.h5'
+
+    print("Reading segmentation ...")
+    with h5py.File(seg_path, 'r') as f:
+        ds = f[seg_key]
+        seg = ds[:].astype('uint32')
+
+    print("To ganglion segmentation ...")
+    seg = nt.take(g, seg)
+    seg = seg.astype('int16')
+
+    print("Writing segmentation ...")
+    n_scales = 4
+    res = [.2, .16, .16]
+    downscale_factors = n_scales * [[2, 2, 2]]
+    make_bdv(seg, out_path, downscale_factors,
+             resolution=res, unit='micrometer')
+    with h5py.File(out_path) as f:
+        ds = f['t00000/s00/0/cells']
+        ds.attrs['maxId'] = max_id
+
+
+def make_table():
+    input_path = './sbem-6dpf-1-whole-segmented-ganglia-labels.h5'
+    input_key = 't00000/s00/0/cells'
+    output_path = './default.csv'
+    resolution = [.2, .16, .16]
+    tmp_folder = './tmp_ganglia'
+    target = 'local'
+    max_jobs = 48
+    base_attributes(input_path, input_key, output_path, resolution,
+                    tmp_folder, target, max_jobs, correct_anchors=False)
+
+
+def make_overlap_table():
+    from mmpb.attributes.util import write_csv, node_labels
+    tmp_folder = './tmp_ganglia'
+    target = 'slurm'
+    max_jobs = 200
+
+    seg_path = os.path.join('/g/arendt/EM_6dpf_segmentation/platy-browser-data/data/0.6.5',
+                            'segmentations/sbem-6dpf-1-whole-segmented-cells-labels.h5')
+    seg_key = 't00000/s00/0/cells'
+
+    input_path = './sbem-6dpf-1-whole-segmented-ganglia-labels.h5'
+    input_key = 't00000/s00/0/cells'
+
+    prefix = 'ganglia'
+
+    ganglia_labels = node_labels(seg_path, seg_key,
+                                 input_path, input_key, prefix,
+                                 tmp_folder, target, max_jobs)
+
+    n_labels = len(ganglia_labels)
+    n_ganglia = ganglia_labels.max() + 1
+    assert n_ganglia == 19
+    col_names = ['label_id', 'ganglion_id']
+    n_cols = len(col_names)
+
+    reg_table = os.path.join('/g/arendt/EM_6dpf_segmentation/platy-browser-data/data/0.6.5',
+                             'tables/sbem-6dpf-1-whole-segmented-cells-labels/regions.csv')
+    reg_table = pd.read_csv(reg_table, sep='\t')
+    print(reg_table.columns)
+    assert len(reg_table) == len(ganglia_labels)
+
+    have_ganglia = ganglia_labels > 0
+
+    head_ids = reg_table['head'].values > 0
+    head_ids = np.logical_and(head_ids, ~have_ganglia)
+    print("Rest of head:", head_ids.sum())
+    ganglia_labels[head_ids] = n_ganglia
+
+    have_labels = ganglia_labels > 0
+    muscle_ids = reg_table['muscle'].values > 0
+    muscle_ids = np.logical_and(muscle_ids, have_labels)
+    print("Muscles in labels:", muscle_ids.sum())
+    ganglia_labels[muscle_ids] = 0
+
+    table = np.zeros((n_labels, n_cols))
+    table[:, 0] = np.arange(n_labels)
+    table[:, 1] = ganglia_labels
+
+    out_path = './ganglia_ids.csv'
+    write_csv(out_path, table, col_names)
+
+
+if __name__ == '__main__':
+    # write_ganglia()
+    # make_table()
+    make_overlap_table()

analysis/gene_expression.py

+#! /g/arendt/EM_6dpf_segmentation/platy-browser-data/software/conda/miniconda3/envs/platybrowser/bin/python
+import argparse
+import os
+import json
+import numpy as np
+from mmpb import get_latest_version
+from mmpb.analysis import get_cells_expressing_genes
+
+
+def count_gene_expression(gene_names, threshold, version, query):
+
+    if args.query != '':
+        assert os.path.exists(query)
+        with open(query) as f:
+            query_ids = np.array(json.load(f))
+    else:
+        query_ids = None
+
+    # path are hard-coded, so we need to change the pwd to '..'
+    os.chdir('..')
+    try:
+        if version == '':
+            version = get_latest_version()
+
+        # TODO enable using vc assignments once we have them on master
+        table_path = 'data/%s/tables/sbem-6dpf-1-whole-segmented-cells-labels/genes.csv' % version
+        ids = get_cells_expressing_genes(table_path, threshold, gene_names)
+
+        if query_ids is not None:
+            ids = ids[np.isin(ids, query_ids)]
+
+        n = len(ids)
+        print("Found", n, "cells expressing:", ",".join(gene_names))
+
+    except Exception as e:
+        os.chdir('analysis')
+        raise e
+
+    os.chdir('analysis')
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Compute number of cells co-expressing genes.')
+    parser.add_argument('gene_names', type=str, nargs='+',
+                        help='Names of the genes for which to express co-expression.')
+    parser.add_argument('--threshold', type=float, default=.5,
+                        help='Threshold to count gene expression. Default is 0.5.')
+    parser.add_argument('--version', type=str, default='',
+                        help='Version of the platy browser data. Default is latest.')
+    parser.add_argument('--query', type=str, default='',
+                        help='Path to json with list of cell ids to restrict the query to.')
+
+    args = parser.parse_args()
+    count_gene_expression(args.gene_names, args.threshold, args.version, args.query)

analysis/nephridia/20191030_table_ciliaID_cellID

+cilia_id	cell_id
+0	0
+1	0
+2	0
+3	0
+4	0
+5	0
+6	0
+7	0
+8	0
+9	0
+10	0
+11	21590
+12	21590
+13	0
+14	0
+15	21590
+16	0
+17	0
+18	0
+19	0
+20	21911
+21	21911
+22	21910
+23	21910
+24	21910
+25	21910
+26	21910
+27	0
+28	21910
+29	21910
+30	21910
+31	21910
+32	21594
+33	
+34	21594
+35	21911
+36	21911
+37	0
+38	0
+39	21594
+40	21590
+41	21590
+42	21590
+43	21594
+44	21594
+45	21594
+46	0
+47	
+48	0
+49	0
+50	0
+51	0
+52	0
+53	21594
+54	21910
+55	21910
+56	21910
+57	21910
+58	21910
+59	21911
+60	21911
+61	
+62	0
+63	0
+64	0
+65	0
+66	0
+67	21910
+68	21910
+69	21910
+70	21910
+71	0
+72	21911
+73	21911
+74	21911
+75	0
+76	21911
+77	0
+78	
+79	0
+80	0
+81	0
+82	
+83	
+84	0
+85	0
+86	0
+87	21915
+88	21915
+89	21915
+90	0
+91	21915
+92	21915
+93	21911
+94	0
+95	0
+96	
+97	0
+98	0
+99	21915
+100	21915
+101	0
+102	21915
+103	21911
+104	21915
+105	21911
+106	0
+107	21910
+108	0
+109	0
+110	21915
+111	0
+112	0
+113	0
+114	0
+115	0
+116	0
+117	21910
+118	21911
+119	0
+120	0
+121	0
+122	21915
+123	0
+124	0
+125	0
+126	
+127	0
+128	
+129	
+130	0
+131	0
+132	0
+133	0
+134	21904
+135	21904
+136	21915
+137	21915
+138	
+139	0
+140	0
+141	
+142	0
+143	0
+144	0
+145	0
+146	0
+147	0
+148	0
+149	22699
+150	22515
+151	22515
+152	22182
+153	22515
+154	22515
+155	22515
+156	22515
+157	22515
+158	22515
+159	22515
+160	22515
+161	22515
+162	22182
+163	22515
+164	22515
+165	22515
+166	22515
+167	22515
+168	22515
+169	21904
+170	
+171	
+172	0
+173	0
+174	0
+175	0
+176	0
+177	0
+178	0
+179	0
+180	22182
+181	22182
+182	22182
+183	22182
+184	22515
+185	22515
+186	22515
+187	22699
+188	22699
+189	0
+190	22699
+191	22699
+192	0
+193	0
+194	22699
+195	22699
+196	22699
+197	22699
+198	22699
+199	22699
+200	22699
+201	0
+202	22699
+203	22699
+204	22700
+205	22700
+206	22700
+207	22700
+208	22700
+209	22700
+210	22700
+211	
+212	
+213	
+214	
+215	
+216	22181
+217	22181
+218	22181
+219	22181
+220	22182
+221	0
+222	0
+223	0
+224	22515
+225	22699
+226	0
+227	22515
+228	0
+229	0
+230	0
+231	22699
+232	21904
+233	0
+234	0
+235	22181
+236	0
+237	0
+238	0
+239	0
+240	0
+241	0
+242	0
+243	0
+244	0
+245	0
+246	0
+247	0
+248	0
+249	0
+250	0
+251	0
+252	0
+253	22699
+254	0
+255	22181
+256	0
+257	
+258	
+259	22182
+260	0
+261	0
+262	0
+263	0
+264	0
+265	0
+266	0
+267	0
+268	0
+269	0
+270	0
+271	0
+272	22700
+273	22700
+274	0
+275	0
+276	0
+277	0
+278	0
+279	0
+280	0
+281	0
+282	0
+283	0
+284	0
+285	0
+286	0
+287	0
+288	0
+289	0
+290	0
+291	0
+292	22700
+293	0
+294	22700
+295	22700
+296	22700
+297	22700
+298	22700
+299	22700
+300	22700
+301	22700
+302	0
+303	0
+304	0
+305	0
+306	0
+307	0
+308	0
+309	0
+310	0
+311	0
+312	0
+313	0
+314	22584
+315	22584
+316	0
+317	0
+318	0
+319	22584
+320	0
+321	22584
+322	22925
+323	22925
+324	22584
+325	22925
+326	22925
+327	0
+328	0
+329	0
+330	22584
+331	0
+332	22925
+333	0
+334	0
+335	0
+336	0
+337	22584
+338	0
+339	0
+340	24024
+341	0
+342	0
+343	0
+344	0
+345	24024
+346	24024
+347	0
+348	0
+349	0
+350	0
+351	0
+352	24024
+353	24024
+354	0
+355	0
+356	0
+357	0
+358	0
+359	0
+360	0
+361	0
+362	0
+363	0
+364	0
+365	0
+366	0
+367	0
+368	24024
+369	0
+370	0
+371	0
+372	0
+373	0
+374	0
+375	0
+376	0
+377	0
+378	0
+379	0
+380	0
+381	0
+382	0
+383	0
+384	0
+385	21904
+386	21910
+387	21910
+388	21910
+389	22181
+390	22925
+391	0
+392	0
+393	0
+394	0
+395	0
+396	0
+397	0
+398	0
+399	0
+400	0
+401	0
+402	0
+403	0
+404	0
+405	0
+406	0
+407	0
+408	0
+409	0
+410	0
+411	0
+412	0
+413	0
+414	0
+415	0
+416	0
+417	0
+418	0
+419	0
+420	0
+421	0
+422	0
+423	0
+424	0
+425	0
+426	0
+427	0
+428	0
+429	0
+430	0
+431	0
+432	0
+433	0
+434	0
+435	0
+436	0
+437	0
+438	0
+439	0
+440	0
+441	0
+442	0
+443	0
+444	0
+445	0
+446	0
+447	0
+448	0
+449	0
+450	0
+451	0
+452	0
+453	0
+454	0
+455	0
+456	0
+457	0
+458	0
+459	0
+460	0
+461	0
+462	0
+463	0

analysis/nephridia/analyse_cilia.py

+import numpy as np
+import pandas as pd
+from matplotlib import pyplot as plt
+
+
+def get_nephr_ids(version):
+    table_path = '../../data/%s/tables/sbem-6dpf-1-whole-segmented-cells-labels/regions.csv' % version
+    table = pd.read_csv(table_path, sep='\t')
+    nephr_ids = table['nephridia'].values
+    right_nephr_ids = np.where(nephr_ids == 1)[0]
+    left_nephr_ids = np.where(nephr_ids == 2)[0]
+    return right_nephr_ids, left_nephr_ids
+
+
+def check_cell_ids(version):
+    table_path = '../../data/%s/tables/sbem-6dpf-1-whole-segmented-cilia-labels/cell_mapping.csv' % version
+
+    right_nephr_ids, left_nephr_ids = get_nephr_ids(version)
+    table = pd.read_csv(table_path, sep='\t')
+    cell_ids = table['cell_id'].values
+
+    matched_right = []
+    matched_left = []
+    for cell_id in cell_ids:
+        if cell_id in right_nephr_ids:
+            matched_right.append(cell_id)
+        elif cell_id in left_nephr_ids:
+            matched_left.append(cell_id)
+        else:
+            assert cell_id == 0 or np.isnan(cell_id), str(cell_id)
+
+    matched_right = set(matched_right)
+    matched_left = set(matched_left)
+
+    # unmatched_right = set(right_nephr_ids) - matched_right
+    # unmatched_left = set(left_nephr_ids) - matched_left
+
+    print("Number of cells right:")
+    print("Total:", len(right_nephr_ids))
+    print("With cilia:", len(matched_right))
+
+    print("Number of cells left:")
+    print("Total:", len(left_nephr_ids))
+    print("With cilia:", len(matched_left))
+
+
+def plot_cilia_per_cell(version):
+    counts_left = []
+    counts_right = []
+
+    table_path = '../../data/%s/tables/sbem-6dpf-1-whole-segmented-cilia-labels/cell_mapping.csv' % version
+    table = pd.read_csv(table_path, sep='\t')
+    cell_ids = table['cell_id']
+    cell_ids = cell_ids[cell_ids != 0]
+    cell_ids = cell_ids[~np.isnan(cell_ids)]
+    cell_ids = cell_ids.astype('uint32')
+
+    right_nephr_ids, left_nephr_ids = get_nephr_ids(version)
+    unique_cell_ids = np.unique(cell_ids)
+
+    total_count = 0
+    for cell_id in unique_cell_ids:
+        n_cilia = np.sum(cell_ids == cell_id)
+        total_count += n_cilia
+        if cell_id in right_nephr_ids:
+            counts_right.append(n_cilia)
+        elif cell_id in left_nephr_ids:
+            counts_left.append(n_cilia)
+        else:
+            print(cell_id)
+
+    counts_right.sort(reverse=True)
+    counts_left.sort(reverse=True)
+    assert len(counts_right) == len(counts_left)
+    print("Total number of cilia:", total_count)
+    print("Right counts:")
+    print(sum(counts_right))
+    print("Left counts:")
+    print(sum(counts_left))
+
+    fig, axes = plt.subplots(2)
+    x = np.arange(7)
+
+    ax = axes[0]
+    ax.set_title('Right nephridium')
+    ax.bar(x, height=counts_right)
+    ax.set_ylabel('Cilia per cell')
+
+    ax = axes[1]
+    ax.set_title('Left nephridium')
+    ax.bar(x, height=counts_left)
+    ax.set_ylabel('Cilia per cell')
+
+    plt.show()
+
+
+if __name__ == '__main__':
+    version = '0.6.5'
+    check_cell_ids(version)
+    plot_cilia_per_cell(version)

analysis/nephridia/make_nephridia_segmentation.py

+import numpy as np
+import pandas as pd
+import elf.parallel
+import vigra
+from elf.io import open_file
+from pybdv import make_bdv
+
+
+# leaving this here to reproduce issues with elf.parallel.label
+def make_nephridia_segmentation_depr():
+    table_path = '../../data/0.6.5/tables/sbem-6dpf-1-whole-segmented-cilia-labels/cell_mapping.csv'
+    seg_path = '../../data/0.6.5/segmentations/sbem-6dpf-1-whole-segmented-cells-labels.h5'
+    # TODO
+    out_path = '/g/kreshuk/pape/Work/nephr_tmp.n5'
+    out_key = 'test'
+
+    table = pd.read_csv(table_path, sep='\t')
+    cell_ids = table['cell_id'].values
+    cell_ids = np.unique(cell_ids)
+    if cell_ids[0] == 0:
+        cell_ids = cell_ids[1:]
+    print(cell_ids)
+
+    scale = 4
+    key = 't00000/s00/%i/cells' % scale
+    with open_file(seg_path, 'r') as f, open_file(out_path) as f_out:
+        ds = f[key]
+        print(ds.shape)
+
+        out = f_out.require_dataset(out_key, shape=ds.shape, chunks=ds.chunks, compression='gzip',
+                                    dtype='uint8')
+
+        print("Isin ...")
+        out = elf.parallel.isin(ds, cell_ids, out=out, n_threads=16, verbose=True)
+        print("Label ...")
+        # FIXME label is still not doing a completely accurate job of merging across block boundaries
+        out = elf.parallel.label(out, out, n_threads=16, verbose=True)
+        print("Compute max ...")
+        max_id = elf.parallel.max(out, n_threads=16, verbose=True)
+        print("Max component id:", max_id)
+
+
+# leaving this here to reproduce issues with elf.parallel.label
+def make_nephridia_segmentation():
+    table_path = '../../data/0.6.5/tables/sbem-6dpf-1-whole-segmented-cilia-labels/cell_mapping.csv'
+    seg_path = '../../data/0.6.5/segmentations/sbem-6dpf-1-whole-segmented-cells-labels.h5'
+
+    out_path = '../../data/0.6.5/segmentations/sbem-6dpf-1-whole-segmented-nephridia.xml'
+
+    table = pd.read_csv(table_path, sep='\t')
+    cell_ids = table['cell_id'].values
+    cell_ids = np.unique(cell_ids)
+    if cell_ids[0] == 0:
+        cell_ids = cell_ids[1:]
+    print(cell_ids)
+
+    scale = 4
+    key = 't00000/s00/%i/cells' % scale
+    with open_file(seg_path, 'r') as f:
+        ds = f[key]
+        seg = ds[:].astype('uint32')
+        bshape = (32, 256, 256)
+
+        tmp = np.zeros_like(seg)
+        print("Isin ...")
+        tmp = elf.parallel.isin(seg, cell_ids, out=tmp, n_threads=16, verbose=True, block_shape=bshape)
+        print("Label ...")
+        tmp = vigra.analysis.labelVolumeWithBackground(tmp)
+
+        print("Size filter ...")
+        ids, counts = elf.parallel.unique(tmp, return_counts=True, n_threads=16, verbose=True, block_shape=bshape)
+        keep_ids = np.argsort(counts)[::-1]
+        keep_ids = ids[keep_ids[:3]]
+        assert keep_ids[0] == 0
+
+        out = np.zeros(tmp.shape, dtype='uint8')
+        for new_id, keep_id in enumerate(keep_ids[1:], 1):
+            out[tmp == keep_id] = new_id
+
+    factors = 3 * [[2, 2, 2]]
+    res = [.4, .32, .32]
+    make_bdv(out, out_path, factors, resolution=res, unit='micrometer')
+
+
+def append_nephridia_table():
+    table_path = '../../data/0.6.5/tables/sbem-6dpf-1-whole-segmented-cilia-labels/cell_mapping.csv'
+    table = pd.read_csv(table_path, sep='\t')
+    cell_ids = table['cell_id'].values
+    cell_ids = np.unique(cell_ids)
+    if cell_ids[0] == 0:
+        cell_ids = cell_ids[1:]
+
+    out_table_path = '../../data/0.6.5/tables/sbem-6dpf-1-whole-segmented-cells-labels/regions.csv'
+    seg_path = '../../data/0.6.5/segmentations/sbem-6dpf-1-whole-segmented-cells-labels.h5'
+    nep_path = '../../data/0.6.5/segmentations/sbem-6dpf-1-whole-segmented-nephridia.h5'
+
+    table = pd.read_csv(out_table_path, sep='\t')
+    new_col = np.zeros(len(table), dtype='float32')
+
+    print("Loading volumes ...")
+    scale = 4
+    key = 't00000/s00/%i/cells' % scale
+    with open_file(seg_path, 'r') as f:
+        seg = f[key][:]
+    scale = 0
+    key = 't00000/s00/%i/cells' % scale
+    with open_file(nep_path, 'r') as f:
+        nep = f[key][:]
+    assert nep.shape == seg.shape
+
+    print("Iterating over cells ...")
+    for cid in cell_ids:
+        nid = np.unique(nep[seg == cid])
+        if 0 in nid:
+            nid = nid[1:]
+        assert len(nid) == 1
+        new_col[cid] = nid
+
+    table['nephridia'] = new_col
+    table.to_csv(out_table_path, sep='\t', index=False)
+
+
+if __name__ == '__main__':
+    # make_nephridia_segmentation()
+    append_nephridia_table()

analysis/nephridia/morphology.py

+import os
+import numpy as np
+import pandas as pd
+from mmpb.analysis.nephridia import filter_by_size, match_cilia_to_cells
+
+cell_ids = [24449, 22584, 21904, 21590, 21594, 21595, 21910, 21911, 21915]
+print("Number of cells:", len(cell_ids))
+
+tmp_path = './cilia_ids.npy'
+if os.path.exists(tmp_path):
+    cilia_ids = np.load(tmp_path)
+else:
+    cell_table = '../data/0.5.2/tables/sbem-6dpf-1-whole-segmented-cells-labels/default.csv'
+    cilia_path = '../data/0.5.2/segmentations/sbem-6dpf-1-whole-segmented-cilia-labels.h5'
+    cilia_key = 't00000/s00/2/cells'
+    cilia_res = [0.1, 0.04, 0.04]
+    cilia_ids = match_cilia_to_cells(cell_ids, cell_table,
+                                     cilia_path, cilia_key, cilia_res)
+    np.save(tmp_path, cilia_ids)
+
+table_path = '../data/0.5.2/tables/sbem-6dpf-1-whole-segmented-cilia-labels/default.csv'
+table_path2 = '../data/0.5.2/tables/sbem-6dpf-1-whole-segmented-cilia-labels/cilia.csv'
+
+table1 = pd.read_csv(table_path, sep='\t')
+table2 = pd.read_csv(table_path2, sep='\t')
+table2 = table2[['length', 'diameter_mean']]
+table = pd.concat([table1, table2], axis=1)
+assert len(table1) == len(table2) == len(table)
+
+table.set_index('label_id')
+table = table[3:]
+table = table.loc[table['length'] > 0.1]
+ids = table['label_id'].values
+ids = ids[np.isin(ids, cilia_ids)]
+print(ids)
+table = table.loc[ids]
+
+# plot_sizes(table)
+size_threshold = 5000
+table = filter_by_size(table, size_threshold)
+print("Number of cilia:", len(table))
+
+lens = table["length"].values
+for idd, leng in zip(ids, lens):
+    print(idd, leng)
+print("Average len:", np.mean(lens), "+-", np.std(lens))
+
+diameters = table["diameter_mean"].values
+print("Average diameter:", np.mean(diameters), "+-", np.std(diameters))

analysis/nephridia/propagate_table_ids.py

+import json
+import numpy as np
+import pandas as pd
+import nifty.tools as nt
+
+# the current table is w.r.t.
+# cilia ids from 0.5.3 and we need 0.6.2
+# cell ids from 0.3.1 and we need 0.6.1
+
+
+def propagate_table():
+    table_path = './20191030_table_ciliaID_cellID'
+    table = pd.read_csv(table_path, sep='\t')
+    cilia_ids = table['cilia_id'].values.astype('uint32')
+    cell_ids = table['cell_id'].values
+    cell_ids[np.isinf(cell_ids)] = 0
+    cell_ids[np.isnan(cell_ids)] = 0
+    cell_ids = cell_ids.astype('uint32')
+
+    cell_id_mapping = {24024: 24723, 22925: 23531, 22700: 23296, 22699: 23295,
+                       22584: 23199, 22515: 23132, 22182: 22827, 22181: 22826,
+                       21915: 22549, 21911: 22546, 21910: 22545, 21904: 22541,
+                       21594: 22214, 21590: 22211, 0: 0}
+    unique_vals, unique_counts = np.unique(list(cell_id_mapping.values()), return_counts=True)
+    print(unique_vals)
+    assert (unique_counts == 1).all()
+    cell_ids = nt.takeDict(cell_id_mapping, cell_ids)
+
+    cilia_id_mapping = '../../data/0.6.2/misc/new_id_lut_sbem-6dpf-1-whole-segmented-cilia-labels.json'
+    with open(cilia_id_mapping) as f:
+        cilia_id_mapping = json.load(f)
+    cilia_id_mapping = {int(k): v for k, v in cilia_id_mapping.items()}
+
+    cilia_ids = [cilia_id_mapping.get(cil_id, 0) for cil_id in cilia_ids]
+    cilia_ids = np.array(cilia_ids)
+
+    valid_mask = ~(cilia_ids == 0)
+    cilia_ids = cilia_ids[valid_mask]
+    cell_ids = cell_ids[valid_mask]
+    sorter = np.argsort(cilia_ids)
+    cilia_ids = cilia_ids[sorter]
+    cell_ids = cell_ids[sorter]
+
+    table_out = './20191030_table_ciliaID_cellID_out'
+    new_table = np.concatenate([cilia_ids[:, None], cell_ids[:, None]], axis=1)
+    new_table = pd.DataFrame(new_table, columns=['label_id', 'cell_id'])
+    new_table.to_csv(table_out, sep='\t', index=False)
+
+
+if __name__ == '__main__':
+    propagate_table()

data/0.0.0/images/local/prospr-6dpf-1-whole-ache.xml

+<SpimData version="0.2">
+  <BasePath type="relative">.</BasePath>
+  <SequenceDescription>
+    <ImageLoader format="bdv.hdf5">
+      <hdf5 type="relative">prospr-6dpf-1-whole-ache.h5</hdf5>
+    </ImageLoader>
+    <ViewSetups>
+      <ViewSetup>
+        <id>0</id>
+        <name>channel 1</name>
+        <size>550 518 570</size>
+        <voxelSize>
+          <unit>micrometer</unit>
+          <size>0.5 0.5 0.5</size>
+        </voxelSize>
+        <attributes>
+          <channel>1</channel>
+        </attributes>
+      </ViewSetup>
+      <Attributes name="channel">
+        <Channel>
+          <id>1</id>
+          <name>1</name>
+        </Channel>
+      </Attributes>
+    </ViewSetups>
+    <Timepoints type="range">
+      <first>0</first>
+      <last>0</last>
+    </Timepoints>
+  </SequenceDescription>
+  <ViewRegistrations>
+    <ViewRegistration setup="0" timepoint="0">
+      <ViewTransform type="affine">
+        <affine>0.5 0.0 0.0 0.0 0.0 0.5 0.0 0.0 0.0 0.0 0.5 0.0</affine>
+      </ViewTransform>
+    </ViewRegistration>
+  </ViewRegistrations>
+</SpimData>

data/0.0.0/images/local/prospr-6dpf-1-whole-allcr1.xml

+<SpimData version="0.2">
+  <BasePath type="relative">.</BasePath>
+  <SequenceDescription>
+    <ImageLoader format="bdv.hdf5">
+      <hdf5 type="relative">prospr-6dpf-1-whole-allcr1.h5</hdf5>
+    </ImageLoader>
+    <ViewSetups>
+      <ViewSetup>
+        <id>0</id>
+        <name>channel 1</name>
+        <size>550 518 570</size>
+        <voxelSize>
+          <unit>micrometer</unit>
+          <size>0.5 0.5 0.5</size>
+        </voxelSize>
+        <attributes>
+          <channel>1</channel>
+        </attributes>
+      </ViewSetup>
+      <Attributes name="channel">
+        <Channel>
+          <id>1</id>
+          <name>1</name>
+        </Channel>
+      </Attributes>
+    </ViewSetups>
+    <Timepoints type="range">
+      <first>0</first>
+      <last>0</last>
+    </Timepoints>
+  </SequenceDescription>
+  <ViewRegistrations>
+    <ViewRegistration setup="0" timepoint="0">
+      <ViewTransform type="affine">
+        <affine>0.5 0.0 0.0 0.0 0.0 0.5 0.0 0.0 0.0 0.0 0.5 0.0</affine>
+      </ViewTransform>
+    </ViewRegistration>
+  </ViewRegistrations>
+</SpimData>

data/0.0.0/images/local/prospr-6dpf-1-whole-allcr2.xml

+<SpimData version="0.2">
+  <BasePath type="relative">.</BasePath>
+  <SequenceDescription>
+    <ImageLoader format="bdv.hdf5">
+      <hdf5 type="relative">prospr-6dpf-1-whole-allcr2.h5</hdf5>
+    </ImageLoader>
+    <ViewSetups>
+      <ViewSetup>
+        <id>0</id>
+        <name>channel 1</name>
+        <size>550 518 570</size>
+        <voxelSize>
+          <unit>micrometer</unit>
+          <size>0.5 0.5 0.5</size>
+        </voxelSize>
+        <attributes>
+          <channel>1</channel>
+        </attributes>
+      </ViewSetup>
+      <Attributes name="channel">
+        <Channel>
+          <id>1</id>
+          <name>1</name>
+        </Channel>
+      </Attributes>
+    </ViewSetups>
+    <Timepoints type="range">
+      <first>0</first>
+      <last>0</last>
+    </Timepoints>
+  </SequenceDescription>
+  <ViewRegistrations>
+    <ViewRegistration setup="0" timepoint="0">
+      <ViewTransform type="affine">
+        <affine>0.5 0.0 0.0 0.0 0.0 0.5 0.0 0.0 0.0 0.0 0.5 0.0</affine>
+      </ViewTransform>
+    </ViewRegistration>
+  </ViewRegistrations>
+</SpimData>

data/0.0.0/images/local/prospr-6dpf-1-whole-anpra.xml

+<SpimData version="0.2">
+  <BasePath type="relative">.</BasePath>
+  <SequenceDescription>
+    <ImageLoader format="bdv.hdf5">
+      <hdf5 type="relative">prospr-6dpf-1-whole-anpra.h5</hdf5>
+    </ImageLoader>
+    <ViewSetups>
+      <ViewSetup>
+        <id>0</id>
+        <name>channel 1</name>
+        <size>550 518 570</size>
+        <voxelSize>
+          <unit>micrometer</unit>
+          <size>0.5 0.5 0.5</size>
+        </voxelSize>
+        <attributes>
+          <channel>1</channel>
+        </attributes>
+      </ViewSetup>
+      <Attributes name="channel">
+        <Channel>
+          <id>1</id>
+          <name>1</name>
+        </Channel>
+      </Attributes>
+    </ViewSetups>
+    <Timepoints type="range">
+      <first>0</first>
+      <last>0</last>
+    </Timepoints>
+  </SequenceDescription>
+  <ViewRegistrations>
+    <ViewRegistration setup="0" timepoint="0">
+      <ViewTransform type="affine">
+        <affine>0.5 0.0 0.0 0.0 0.0 0.5 0.0 0.0 0.0 0.0 0.5 0.0</affine>
+      </ViewTransform>
+    </ViewRegistration>
+  </ViewRegistrations>
+</SpimData>

data/0.0.0/images/local/prospr-6dpf-1-whole-anprb.xml

+<SpimData version="0.2">
+  <BasePath type="relative">.</BasePath>
+  <SequenceDescription>
+    <ImageLoader format="bdv.hdf5">
+      <hdf5 type="relative">prospr-6dpf-1-whole-anprb.h5</hdf5>
+    </ImageLoader>
+    <ViewSetups>
+      <ViewSetup>
+        <id>0</id>
+        <name>channel 1</name>
+        <size>550 518 570</size>
+        <voxelSize>
+          <unit>micrometer</unit>
+          <size>0.5 0.5 0.5</size>
+        </voxelSize>
+        <attributes>
+          <channel>1</channel>
+        </attributes>
+      </ViewSetup>
+      <Attributes name="channel">
+        <Channel>
+          <id>1</id>
+          <name>1</name>
+        </Channel>
+      </Attributes>
+    </ViewSetups>
+    <Timepoints type="range">
+      <first>0</first>
+      <last>0</last>
+    </Timepoints>
+  </SequenceDescription>
+  <ViewRegistrations>
+    <ViewRegistration setup="0" timepoint="0">
+      <ViewTransform type="affine">
+        <affine>0.5 0.0 0.0 0.0 0.0 0.5 0.0 0.0 0.0 0.0 0.5 0.0</affine>
+      </ViewTransform>
+    </ViewRegistration>
+  </ViewRegistrations>
+</SpimData>

data/0.0.0/images/local/prospr-6dpf-1-whole-ap2.xml

+<SpimData version="0.2">
+  <BasePath type="relative">.</BasePath>
+  <SequenceDescription>
+    <ImageLoader format="bdv.hdf5">
+      <hdf5 type="relative">prospr-6dpf-1-whole-ap2.h5</hdf5>
+    </ImageLoader>
+    <ViewSetups>
+      <ViewSetup>
+        <id>0</id>
+        <name>channel 1</name>
+        <size>550 518 570</size>
+        <voxelSize>
+          <unit>micrometer</unit>
+          <size>0.5 0.5 0.5</size>
+        </voxelSize>
+        <attributes>
+          <channel>1</channel>
+        </attributes>
+      </ViewSetup>
+      <Attributes name="channel">
+        <Channel>
+          <id>1</id>
+          <name>1</name>
+        </Channel>
+      </Attributes>
+    </ViewSetups>
+    <Timepoints type="range">
+      <first>0</first>
+      <last>0</last>
+    </Timepoints>
+  </SequenceDescription>
+  <ViewRegistrations>
+    <ViewRegistration setup="0" timepoint="0">
+      <ViewTransform type="affine">
+        <affine>0.5 0.0 0.0 0.0 0.0 0.5 0.0 0.0 0.0 0.0 0.5 0.0</affine>
+      </ViewTransform>
+    </ViewRegistration>
+  </ViewRegistrations>
+</SpimData>