Package: cloud-volume
CloudVolume: IO for Neuroglancer DatasetsfromcloudvolumeimportCloudVolumevol=CloudVolume('gs://mylab/mouse/image',parallel=True,progress=True)image=vol[:,:,:]# Download a whole image stack into a numpy array from the cloudvol[:,:,:]=image# Upload an entire image stack from a numpy array to the cloudlabel=1mesh=vol.mesh.get(label)skel=vol.skeleton.get(label)CloudVolume is a serverless Python client for random access reading and writing ofNeuroglancervolumes in "Precomputed" format, a set of representations for arbitrarily large volumetric images, meshes, and skeletons. CloudVolume is typically paired withIgneous, a Kubernetes compatible system for generating image hierarchies, meshes, skeletons, and other dependency free jobs that can be applied to petavoxel scale images.Precomputed volumes are typically stored onAWS S3,Google Storage, or locally. CloudVolume can read and write to these object storage providers given a service account token with appropriate permissions. However, these volumes can be stored on any service, including an ordinary webserver or local filesystem, that supports key-value access.The combination ofNeuroglancer,Igneous, and CloudVolume comprises a system for visualizing, processing, and sharing (via browser viewable URLs) petascale datasets within and between laboratories. A typical example usage would be to visualize raw electron microscope scans of mouse, fish, or fly brains up to a cubic millimeter in physical dimension. Neuroglancer and Igneous would enable you to visualize each step of the process of montaging the image, fine tuning alignment vector fields, creating segmentation layers, ROI masks, or performing other types of analysis. CloudVolume enables you to read from and write to each of these layers. Recently, we have introduced the ability to interact with the graph server ("PyChunkGraph") that backs proofreading automated segmentations via thegraphene://format.You can find a collection of CloudVolume accessible and Neuroglancer viewable datasets athttps://neurodata.io/project/ocp/, an open data project by some of our collaborators.HighlightsRandom access to petavoxel Neuroglancer images, meshes, and skeletons.Nearly all output is immediately visualizable using Neuroglancer.*Reads graph server backed proofreading volumes (viagraphene://).Serverless (exceptgraphene://) and multi-cloud.Detailed HighlightsMulti-threaded, supports multi-process and green threads.Memory optimized, supports shared memory.Lossless connectomics relevant codecs (compressed_segmentation,compresso,crackle(BETA),fpzip,zfpc,png, andbrotli)Understands image hierarchies & anisotropic pixel resolutions.Accomodates downloading missing tiles (fill_missing=True).Accomodates uploading compressed black tiles to erasure coded file systems (delete_black_uploads=True).Growing support for the Neuroglancersharded formatwhich dramatically condenses the number of files required to represent petascale datasets, similar toCloud Optimized GeoTIFF, which can result indramatic cost savings.Reads Precomputed meshes and skeletons.Includes viewers for small images, meshes, and skeletons.Only 3 dimensions + RBG channels currently supported for images.No data versioning.SetupCloud-volume is regularly tested on Ubuntu with 3.7, 3.8, 3.9 and 3.10. We officially support Linux and Mac OS. Windows is community supported. After installation, you'll also need to set up your cloud credentials if you're planning on writing files or reading from a private dataset. Once you're finished setting up, you can tryreading from a public dataset.pipBinary Installationpipinstallcloud-volume# standard installationCloudVolume depends on several PyPI packages which are Cython bindings for C++. We have provided compiled binaries for many platforms and python versions, however if you are on an unsupported system, pip will attempt to install from source. In that case, follow the instructions below.Windows Note:If you get errors related to a missing C++ compiler, this blog post might help you:https://www.scivision.dev/python-windows-visual-c-14-required/Optional DependenciesTagDescriptionDependenciesbossboss://format supportinterntestSupports testingpytestmesh_viewermesh.viewer()GUIvtkskeleton_viewerskeleton.viewer()GUImatplotliball_viewersAll viewers now and in the future.vtk, matplotlibdaskSupports converting to/from dask arraysdask[array]Example:pipinstallcloud-volume[boss,test,all_viewers]pipSource InstallationC++ compiler required.sudoapt-getinstallg++python3-dev# python-dev if you're on python2pipinstallnumpy
pipinstallcloud-volumeDue to packaging problems endemic to Python, Cython packages that depend on numpy require numpy header files be installed before attempting to install the package you want. The numpy headers are not recognized unless numpy is installed in a seperate process that runs first. There are hacks for this issue, but I haven't gotten them to work. If you think binaries should be available for your platform, please let us know by opening an issue.Manual InstallationThis can be desirable if you want to hack on CloudVolume itself.gitclonegit@github.com:seung-lab/cloud-volume.gitcdcloud-volume# With virtualenvwrappermkvirtualenvcv
workoncv# With only virtualenvvirtualenvvenvsourcevenv/bin/activate

sudoapt-getinstallg++python3-dev# python-dev if you're on python2pipinstallnumpy# additional step needed for accelerated compressed_segmentation and fpzippipinstall-e.# without optional dependenciespipinstall-e.[all_viewers]# with e.g. the all_viewers optional dependencyCredentialsBy default, CloudVolume's configuration and cache files are stored in$HOME/.cloudvolume(or in$HOME/.cloudfilessince we use CloudFiles for the backend). You can configure where CloudVolume looks for these files with the environment variable$CLOUD_VOLUME_DIR.Credentials are stored in$CLOUD_VOLUME_DIR/secrets. You'll need credentials only for the services you'll use. If you plan to use the local filesystem, you won't need any. For Google Storage (setup instructions here), default account credentials will be used if available and no service account is provided.If neither of those two conditions apply, you need a service account credential. If you have your credentials handy, you can provide them like so as a dict, JSON string, or a bare token if the service will accept that.cv=CloudVolume(...,secrets=...)However, it may be simpler to save your credential to disk so you don't have to always provide it.google-secret.jsonis a service account credential for Google Storage,aws-secret.jsonis a service account for S3, etc. You can support multiple projects at once by prefixing the bucket you are planning to access to the credential filename.google-secret.jsonwill be your defaut service account, but if you also want to also access bucket ABC, you can provideABC-google-secret.jsonand you'll have simultaneous access to your ordinary buckets and ABC. The secondary credentials are accessed on the basis of the bucket name, not the project name.mkdir-p~/.cloudvolume/secrets/
mvaws-secret.json~/.cloudvolume/secrets/# needed for Amazonmvgoogle-secret.json~/.cloudvolume/secrets/# needed for Googlemvboss-secret.json~/.cloudvolume/secrets/# needed for the BOSSmvmatrix-secret.json~/.cloudvolume/secrets/# needed for Matrixmvtigerdata-secret.json~/.cloudvolume/secrets/# needed for Tigerdataaws-secret.jsonandmatrix-secret.jsonCreate anIAM user service accountthat can read, write, and delete objects from at least one bucket.{"AWS_ACCESS_KEY_ID":"$MY_AWS_ACCESS_KEY_ID","AWS_SECRET_ACCESS_KEY":"$MY_SECRET_ACCESS_TOKEN"}google-secret.jsonYou can create thegoogle-secret.jsonfilehere. You don't need to manually fill in JSON by hand, the below example is provided to show you what the end result should look like. You should be able to read, write, and delete objects from at least one bucket.{"type":"service_account","project_id":"$YOUR_GOOGLE_PROJECT_ID","private_key_id":"...","private_key":"...","client_email":"...","client_id":"...","auth_uri":"https://accounts.google.com/o/oauth2/auth","token_uri":"https://accounts.google.com/o/oauth2/token","auth_provider_x509_cert_url":"https://www.googleapis.com/oauth2/v1/certs","client_x509_cert_url":""}cave-secret.jsonNote: used to be called chunkedgraph-secret.json. This is still supported but deprecated.If you have a token from Graphene/Chunkedgraph server, create thecave-secret.jsonfile as shown in the example below. You may also pass the token toCloudVolume(..., secrets=token).{"token":"<your_token>"}Note that to take advantage of multiple credential files, prepend the fully qualified domain name (FQDN) of the server instead of the bucket for GCS and S3. For example,sudomain.domain.com-cave-secret.json.UsageCloudVolume supports reading and writing to Neuroglancer data layers on Amazon S3, Google Storage, The BOSS, and the local file system.Supported URLs are of the forms:$FORMAT://$PROTOCOL://$BUCKET/$DATASET/$LAYERThe format or protocol fields may be omitted where required. In the case of the precomputed format, the format specifier is optional.FormatProtocolsDefaultExampleprecomputedgs, s3, http, https, file, matrix, tigerdataYesgs://mybucket/dataset/layergraphenegs, s3, http, https, file, matrix, tigerdatagraphene://gs://mybucket/dataset/layerbossN/Aboss://collection/experiment/channeln5gs, s3, http, https, file, matrix, tigerdatan5://gs://mybucket/dataset/layerSupported Formatsprecomputed: Neuroglancer's native format. (specification)graphene: Precomputed based format used by the PyChunkGraph server.boss: The BOSS (https://docs.theboss.io/docs)n5: Not HDF5 (https://github.com/saalfeldlab/n5) Read-only support. Supports raw, gzip, bz2, and xz but not lz4 compression. mode 0 datasets only.Supported Protocolsgs:   Google Storages3:   Amazon S3http(s): (read-only) Ordinary Web Serversfile: Local File System (absolute path)matrix: Princeton Internal System (run in large part by Seung Lab)tigerdata: Princeton Internal System (run by Princeton OIT)CloudVolume also supportsalternative s3 aliasesvia CloudFiles.infoFiles - New DatasetNeuroglancer relies on aninfofile located at the root of a dataset layer to tell it how to compute file locations and interpret the data in each file. CloudVolume piggy-backs on this functionality.In the below example, assume you are creating a new segmentation volume from a 3d numpy array "rawdata". Note Precomputed stores data in Fortran (column major, aka CZYX) order. You should do a small test to see if the image is written transposed. You can fix this by uploadingrawdata.T. A more detailed example for uploading a local volumeis located here.fromcloudvolumeimportCloudVolumeinfo=CloudVolume.create_new_info(num_channels=1,layer_type='segmentation',data_type='uint64',# Channel images might be 'uint8'# raw, png, jpeg, compressed_segmentation, fpzip, kempressed, zfpc, compresso, crackleencoding='raw',resolution=[4,4,40],# Voxel scaling, units are in nanometersvoxel_offset=[0,0,0],# x,y,z offset in voxels from the originmesh='mesh',# Pick a convenient size for your underlying chunk representation# Powers of two are recommended, doesn't need to cover image exactlychunk_size=[512,512,16],# units are voxelsvolume_size=[250000,250000,25000],# e.g. a cubic millimeter dataset)vol=CloudVolume(cfg.path,info=info)vol.commit_info()vol[cfg.x:cfg.x+cfg.length,cfg.y:cfg.y+cfg.length,cfg.z:cfg.z+cfg.length]=rawdata[:,:,:]EncodingImage TypeLosslessNeuroglancer ViewableDescriptionrawAnyYYSerialized numpy arrays.pngImageYYMultiple slices stiched into a single PNG.jpegImageNYMultiple slices stiched into a single JPEG.compressed_segmentationSegmentationYYRenumbered numpy arrays to reduce data width. Also used by Neuroglancer internally.compressoSegmentationYYLossless high compression algorithm for connectomics segmentation.crackleSegmentationYY*Lossless high compression algorithm for connectomics segmentation.fpzipFloating PointYY*Takes advantage of IEEE 754 structure + L1 Lorenzo predictor to get higher compression.kempressedAnisotropic Z Floating PointN**Y*Adds manipulations on top of fpzip to achieve higher compression.zfpcAlignment Vector FieldsN***Y*zfp stream container.* Not integrated into official Neuroglancer yet, but available on aforkwhich can be seen on Githubhere.
** Lossless if your data can handle adding and then subtracting 2.
*** Lossless by default, but you probably want to use the lossy mode.Note oncompressed_segmentation: To use, make surecompressed_segmentation_block_sizeis specified (usually[8,8,8]. This field will appear in theinfofile in the relevant scale.Note onzfpc: To configure, use the fieldszfpc_rate,zfpc_precision,zfpc_tolerance,zfpc_correlated_dimsin the relevant scale of theinfofile.Examples# Basic Examplesvol=CloudVolume('gs://mybucket/retina/image')vol=CloudVolume('gs://mybucket/retina/image',secrets=token,dictorjson)vol=CloudVolume('gs://bucket/dataset/channel',mip=0,bounded=True,fill_missing=False)vol=CloudVolume('gs://bucket/dataset/channel',mip=[8,8,40],bounded=True,fill_missing=False)# set mip at this resolutionvol=CloudVolume('gs://bucket/datasset/channel',info=info)# New info file from scratchimage=vol[:,:,:]# Download the entire image stack into a numpy arrayimage=vol.download(bbox,mip=2,renumber=True)# download w/ smaller dtypeuniq=vol.unique(bbox,mip=0)# efficient extraction of unique labelslisting=vol.exists(np.s_[0:64,0:128,0:64])# get a report on which chunks actually existexists=vol.image.has_data(mip=0)# boolean check to see if any data is therelisting=vol.delete(np.s_[0:64,0:128,0:64])# delete this region (bbox must be chunk aligned)vol[64:128,64:128,64:128]=image# Write a 64^3 image to the volumeimg=vol.download_point((x,y,z),size=256,mip=3)# download region around (mip 0) x,y,z at mip 3pts=vol.scattered_points([(x1,y1,z1),(x2,y2,z2)])# download voxel labels located at indicated points# download image files without decompressing or rendering them. Good for caching!files=vol.download_files(bbox,mip,decompress=False)# Servervol.viewer()# launches neuroglancer compatible web server on http://localhost:1337# Microviewerimg=vol[64:1028,64:1028,64:128]img.viewer()# launches web viewer on http://localhost:8080# Meshesvol.mesh.save(12345)# save 12345 as ./12345.ply on diskvol.mesh.save([12345,12346,12347])# merge three segments into one filevol.mesh.save(12345,file_format='obj')# 'ply' and 'obj' are both supportedvol.mesh.get(12345)# return the mesh as vertices and faces instead of writing to diskvol.mesh.get([12345,12346])# return these two segids fused into a single meshvol.mesh.get([12345,12346],fuse=False)# return { 12345: mesh, 12346: mesh }vol.mesh.put(meshes)# works for unsharded legacy onlyvol.mesh.delete(segids)# works for unsharded meshes onlymesh.viewer()# Opens GUI. Requires vtk.# Skeletonsskel=vol.skeleton.get(12345)vol.skeleton.upload_raw(segid,skel.vertices,skel.edges,skel.radii,skel.vertex_types)vol.skeleton.upload(skel)# specified in nm, only available for datasets with a generated indexskels=vol.skeleton.get_by_bbox(Bbox((0,0,0),(500,500,500)))vol.skeleton.spatial_index# None if not availableskel.empty()# booleanbytes=skel.encode()# encode to Precomputed format (bytes)skel=Skeleton.decode(bytes)# decode from PrecomputedFormatskel=skel.crop(slicesorbbox)# eliminate vertices and edges outside bboxskel=skel.consolidate()# eliminate duplicate vertices and edgesskel3=skel.merge(skel2)# merge two skeletons into oneskel=skel.clone()# create copyskel=Skeleton.from_swc(swcstr)# decode an SWC fileskel_str=skel.to_swc()# convert to SWC file in string representationskel.viewer()# Opens GUI. Requires matplotlibskel.cable_length()# sum of all edge lengthsskel=skel.downsample(2)# reduce size of skeleton by factor of 2skel1==skel2# check if contents of internal arrays matchSkeleton.equivalent(skel1,skel2)# ...even if there are differences like differently numbered edges# Parallel Operationvol=CloudVolume('gs://mybucket/retina/image',parallel=True)# Use all coresvol.parallel=4# e.g. any number > 1, use this many coresdata=vol[:]# uses shared memory to coordinate processes under the hood# Shared Memory Output (can be used by other processes)vol=CloudVolume(...)# data backed by a shared memory buffer# location is optional (defaults to vol.shared_memory_id)data=vol.download_to_shared_memory(np.s_[:],location='some-example')vol.unlink_shared_memory()# delete the shared memory associated with this cloudvolumevol.shared_memory_id# get/set the default shared memory location for this instance# Shared Memory Uploadvol=CloudVolume(...)vol.upload_from_shared_memory('my-shared-memory-id',# do not prefix with /dev/shmbbox=Bbox((0,0,0),(10000,7500,64)))# Download or Upload directly with Files# The files must be in Precomputed raw format.vol.download_to_file('/path/to/file',bbox=Bbox(...),mip=0)# bbox is the download regionvol.upload_from_file('/path/to/file',bbox=Bbox(...),mip=0)# bbox is the region it represents# Transfer w/o Excess Memory Allocationvol=CloudVolume(...)# single core, send all of vol to destination, no painting memoryvol.transfer_to('gs://bucket/dataset/layer',vol.bounds)# Caching, default located at $HOME/.cloudvolume/cache/$PROTOCOL/$BUCKET/$DATASET/$LAYER/$RESOLUTION# You can also set the cache location using# cache=str or with environment variable CLOUD_VOLUME_CACHE_DIRvol=CloudVolume('gs://mybucket/retina/image',cache=True)# Basic Exampleimage=vol[0:10,0:10,0:10]# Download partial image and cachevol[0:10,0:10,0:10]=image# Upload partial image and cache# Resizing and clearing the LRU in-memory cachevol=CloudVolume(...,lru_bytes=num_bytes)# >= 0, 0 means disabledvol.image.lru.resize(num_bytes)# samevol.image.lru.clear()len(vol.image.lru)# number of items in lruvol.image.lru.nbytes# size in bytes (not counting LRU structures, nor recursive)vol.image.lru.items()# etc, also functions as a dict# Evaluating the on-disk Cachevol.cache.list()# list files in cache at this mip levelvol.cache.list(mip=1)# list files in cache at mip 1vol.cache.list_meshes()vol.cache.list_skeletons()vol.cache.num_files()# number of files at this mip levelvol.cache.num_bytes(all_mips=True)# Return num files for each mip level in a listvol.cache.num_bytes()# number of bytes taken up by files, size on disk can be biggervol.cache.num_bytes(all_mips=True)# Return num bytes for each mip level in a listvol.cache.enabled=True/False# Turn the cache on/offvol.cache.path=Str# set the cache locationvol.cache.compress=None/True/False# None: Link to cloud setting, Boolean: Force cache to compressed (True) or uncompressed (False)# Deleting Cachevol.cache.flush()# Delete local cache for this layer at this mip levelvol.cache.flush(preserve=Bbox(...))# Same, but preserve cache in a region of spacevol.cache.flush_region(region=Bbox(...),mips=[...])# Delete the cached files in this region at these mip levels (default all mips)vol.cache.flush_info()vol.cache.flush_provenance()# Using Green Threadsimportgevent.monkeygevent.monkey.patch_all(thread=False)cv=CloudVolume(...,green_threads=True)img=cv[...]# now green threads will be used# Dask Interface (requires dask installation)arr=cv.to_dask()arr=cloudvolume.dask.from_cloudvolume(cloudpath)# same as to_daskres=cloudvolume.dask.to_cloudvolume(arr,cloudpath,compute=bool,return_store=bool)CloudVolume ConstructorCloudVolume(cloudpath:str,mip:int=0,bounded:bool=True,autocrop:bool=False,fill_missing:bool=False,cache:CacheType=False,compress_cache:CompressType=None,cdn_cache:bool=True,progress:bool=INTERACTIVE,info:dict=None,provenance:dict=None,compress:CompressType=None,compress_level:Optional[int]=None,non_aligned_writes:bool=False,parallel:ParallelType=1,delete_black_uploads:bool=False,background_color:int=0,green_threads:bool=False,use_https:bool=False,max_redirects:int=10,mesh_dir:Optional[str]=None,skel_dir:Optional[str]=None,agglomerate:bool=False,secrets:SecretsType=None,spatial_index_db:Optional[str]=None,lru_bytes:int=0,cache_locking:bool=True)agglomerate: (bool, graphene only) sets the default mode for downloading
  images to agglomerated (True) vs watershed (False).autocrop: (bool) If the specified retrieval bounding box exceeds the
    volume bounds, process only the area contained inside the volume. 
    This can be useful way to ensure that you are staying inside the 
    bounds when `bounded=False`.background_color: (number) Specifies what the "background value" of the
  volume is (traditionally 0). This is mainly for changing the behavior
  of delete_black_uploads.bounded: (bool) If a region outside of volume bounds is accessed:
    True: Throw an error
    False: Allow accessing the region. If no files are present, an error 
        will still be thrown. Consider combining this option with 
        `fill_missing=True`. However, this can be dangrous as it allows
        missing files and potentially network errors to be intepreted as 
        zeros.cache: (bool or str) Store downs and uploads in a cache on disk
      and preferentially read from it before redownloading.
    - falsey value: no caching will occur.
    - True: cache will be located in a standard location.
    - non-empty string: cache is located at this file path

    After initialization, you can adjust this setting via:
    `cv.cache.enabled = ...` which accepts the same values.

    Note: This cache is totally separate from the LRU controlled by 
    lru_bytes.cache_locking: (bool) The local cache will use file locks via fasteners to prevent issues with multi-process cache access. If this is not a concern, performance can be slightly improved by setting this to False. This uses CloudFiles' locking mechanism.cdn_cache: (int, bool, or str) Sets Cache-Control HTTP header on uploaded 
  image files. Most cloud providers perform some kind of caching. As of 
  this writing, Google defaults to 3600 seconds. Most of the time you'll 
  want to go with the default. 
  - int: number of seconds for cache to be considered fresh (max-age)
  - bool: True: max-age=3600, False: no-cache
  - str: set the header manuallycompress: (bool, str, None) pick which compression method to use.None: (default) gzip for raw arrays and no additional compression
      for compressed_segmentation and fpzip.
    bool: 
      True=gzip, 
      False=no compression, Overrides defaults
    str: 
      'gzip': Extension so that we can add additional methods in the future 
              like lz4 or zstd. 
      'br': Brotli compression, better compression rate than gzip
      '': no compression (same as False).compress_level: (int, None) level for compression. Higher number results
    in better compression but takes longer.
  Defaults to 9 for gzip (ranges from 0 to 9).
  Defaults to 5 for brotli (ranges from 0 to 11).compress_cache: (None or bool) If not None, override default compression 
    behavior for the cache.delete_black_uploads: (bool) If True, on uploading an entirely black chunk,
    issue a DELETE request instead of a PUT. This can be useful for avoiding storing
    tiny files in the region around an ROI. Some storage systems using erasure coding 
    don't do well with tiny file sizes.fill_missing: (bool) If a chunk file is unable to be fetched:
    True: Use a block of zeros
    False: Throw an errorgreen_threads: (bool) Use green threads instead of preemptive threads. This
  can result in higher download performance for some compression types. Preemptive
  threads seem to reduce performance on multi-core machines that aren't densely
  loaded as the CPython threads are assigned to multiple cores and the thrashing
  + GIL reduces performance. You'll need to add the following code to the top
  of your program to use green threads:

      import gevent.monkey
      gevent.monkey.patch_all(threads=False)lru_bytes: (int) number of bytes used to cache recently used image 
  tiles in memory. This is an in-memory cache and is completely separate from
  the `cache` parameter that handles disk IO. Tiles are stripped over only their
  second stage compression.info: (dict) In lieu of fetching a neuroglancer info file, use this one.
    This is useful when creating new datasets and for repeatedly initializing
    a new cloudvolume instance.max_redirects: (int) if > 0, allow up to this many redirects via info file 'redirect'
    data fields. If <= 0, allow no redirections and access the current info file directly
    without raising an error.mesh_dir: (str) if not None, override the info['mesh'] key before pulling the
  mesh info file.mip: (int or iterable) Which level of downsampling to read and write from.
    0 is the highest resolution. You can also specify the voxel resolution
    like mip=[6,6,30] which will search for the appropriate mip level.non_aligned_writes: (bool) Enable non-aligned writes. Not multiprocessing 
    safe without careful design. When not enabled, a 
    cloudvolume.exceptions.AlignmentError is thrown for non-aligned writes. 
    
    https://github.com/seung-lab/cloud-volume/wiki/Advanced-Topic:-Non-Aligned-Writes

parallel (int: 1, bool): Number of extra processes to launch, 1 means only 
    use the main process. If parallel is True use the number of CPUs 
    returned by multiprocessing.cpu_count(). When parallel > 1, shared
    memory (Linux) or emulated shared memory via files (other platforms) 
    is used by the underlying download.progress: (bool) Show progress bars. 
    Defaults to True in interactive python, False in script execution mode.provenance: (string, dict) In lieu of fetching a provenance 
    file, use this one.secrets: (dict) provide per-instance authorization tokens. If not provided,
  defaults to looking in .cloudvolume/secrets for necessary tokens.skel_dir: (str) if not None, override the info['skeletons'] key before 
  pulling the skeleton info file.spatial_index_db: (str) A path to an sqlite3 or mysql database that follows 
  the following uri schema. sqlite is assumed if no scheme is present in 
  the uri.
    [sqlite://]filename.db
    mysql://<username>:<password>@<host>:<port>/<db_name>

  Igneous generated datasets include a JSON based spatial
  database that tiles the dataset. This can be fast enough up to about 100 TVx
  datasets. Above that, a proper database is required for efficient queries.
  We provide multiple SQL database types that the index can be hosted on.use_https: (bool) maps gs:// and s3:// to their respective https paths. The 
  https paths hit a cached, read-only version of the data and may be faster.CloudVolume MethodsBetter documentation coming later, but for now, here's a summary of the most useful method calls. Use help(cloudvolume.CloudVolume.$method) for more info.create_new_info (class method) - Helper function for creating info files for creating new data layers.refresh_info - Repull the info file.refresh_provenance - Repull the provenance file.bbox_to_mip - Covert a bounding box or slice from one mip level to another.slices_from_global_coords -deprecated, why not use bbox_to_mip?Find the CloudVolume slice from MIP 0 coordinates if you're on a different MIP. Often used in combination with neuroglancer.reset_scales - Delete mips other than 0 in the info file. Does not autocommit.add_scale - Generate a new mip level in the info property. Does not autocommit.commit_info - Push the current info property into the cloud as a JSON file.commit_provenance - Push the current provenance property into the cloud as a JSON file.image - Access image operations directly.download - Download bounding boxes from a given mip level.upload - Upload images to bounding boxes at a given mip level.transfer_to - Transfer data without painting a container array to avoid out of memory errors.exists - Check which chunk files exist in a given bounding box.delete - Delete chunks in a given bounding box at a given mip level.mesh - Access mesh operationsget - Download an object. Can merge multiple segmentidssave - Download an object and save it in.objformat. You can combine equivialences into a single object too.skeleton - Access Skeletonsget - Download an object.upload - Save a skeleton object to the cloud.cache - Access cache operationsenabled - Boolean switch to enable/disable cache. If true, on reading, check local disk cache before downloading, and save downloaded chunks to cache. When writing, write to the cloud then save the chunks you wrote to cache. If false, bypass cache completely. The cache is located at$HOME/.cloudvolume/cache.path - Property that shows the current filesystem path to the cachelist - List files in cachenum_files - Number of files in cache at this mip level , use all_mips=True to get them allnum_bytes - Return the number of bytes in cache at this mip level, all_mips=True to get them allflush - Delete the cache at this mip level, preserve=Bbox/slice to save a spatial regionflush_region - Delete a spatial region at this mip levelexists - Generate a report on which chunks within a bounding box exist.delete - Delete the chunks within this bounding box.transfer_to - Transfer data from a bounding box to another data storage location. Does not allocate memory and transfers in blocks, so can transfer large volumes of data. May be less efficient than a dedicated tool likegsutiloraws s3.unlink_shared_memory - Delete shared memory associated with this instance (vol.shared_memory_id)generate_shared_memory_location - Create a new unique shared memory identifier string. No side effects.download_to_shared_memory - Instead of using ordinary numpy memory allocations, download to shared memory.
Be careful, shared memory is like a file and doesn't disappear unless explicitly unlinked. (vol.unlink_shared_memory())upload_from_shared_memory - Upload from a given shared memory block without making a copy.download_point - Download the region around this mip 0 coordinate at a given mip level.CloudVolume PropertiesAccessed asvol.$PROPERTYlikevol.mip. Parens next to each property mean (data type:default, writability). (r) means read only, (w) means write only, (rw) means read/write.mip (uint:0, rw) - Read from and write to this mip level (0 is highest res). Each additional increment in the number is typically a 2x reduction in resolution.bounded (bool:True, rw) - If a region outside of volume bounds is accessed throw an error if True or Fill the region with black (useful for e.g. marching cubes's 1px boundary) if False.autocrop (bool:False, rw) - If bounded is False and this option is True, automatically crop requested uploads and downloads to the volume boundary.fill_missing (bool:False, rw) - If a file inside volume bounds is unable to be fetched use a block of zeros if True, else throw an error.delete_black_uploads (bool:False, rw) - If True, issue a DELETE http request instead of a PUT when an individual uploaded chunk is all zeros.info (dict, rw) - Python dict representation of Neuroglancer info JSON file. You must callvol.commit_info()to save your changes to storage.provenance (dict-like, rw) - Data layer provenance file representation. You must callvol.commit_provenance()to save your changes to storage.available_mips (list of ints, r) - Query which mip levels are defined for reading and writing.dataset_name (str, rw) - Which dataset (e.g. test_v0, snemi3d_v0) on S3, GS, or FS you're reading and writing to. Known as an "experiment" in BOSS terminology. Writing to this property triggers an info refresh.layer (str, rw) - Which data layer (e.g. image, segmentation) on S3, GS, or FS you're reading and writing to. Known as a "channel" in BOSS terminology. Writing to this property triggers an info refresh.base_cloudpath (str, r) - The cloud path to the dataset e.g. s3://bucket/dataset/layer_cloudpath (str, r) - The cloud path to the data layer e.g. gs://bucket/dataset/imageinfo_cloudpath (str, r) - Generate the cloud path to this data layer's info file.scales (dict, r) - Shortcut to the 'scales' property of the info objectscale (dict, rw)* - Shortcut to the working scale of the current mip levelshape (Vec4, r)* - Like numpy.ndarray.shape for the entire data layer.volume_size (Vec3, r)* - Like shape, but omits channel (x,y,z only).num_channels (int, r) - The number of channels, the last element of shape.layer_type (str, r) - The neuroglancer info type, 'image' or 'segmentation'.dtype (str, r) - The info data_type of the volume, e.g. uint8, uint32, etc. Similar to numpy.ndarray.dtype.encoding (str, r) - The neuroglancer info encoding. e.g. 'raw', 'jpeg', 'npz'resolution (Vec3, r)* - The 3D physical resolution of a voxel in nanometers at the working mip level.downsample_ratio (Vec3, r) - Ratio of the current resolution to the highest resolution mip available.chunk_size (Vec3, r)* - Size of the underlying chunks that constitute the volume in storage. e.g. Vec(64, 64, 64)key (str, r)* - The 'directory' we're accessing the current working mip level from within the data layer. e.g. '6_6_30'bounds (Bbox, r)* - A Bbox object that represents the bounds of the entire volume.shared_memory_id (str, rw) - Shared memory location used for parallel operation or for output.* These properties can also be accessed with a function named likevol.mip_$PROPERTY($MIP). By default they return the current mip level assigned to the CloudVolume, but any mip level can be accessed via the correspondingmip_function. Example:vol.mip_resolution(2)would return the resolution of mip 2.VolumeCutout FunctionsWhen you download an image using CloudVolume it gives you aVolumeCutout. These arenumpy.ndarraysubclasses that support a few extra properties to help make book keeping easier. The major advantage issave_images()which can help you view your dataset as PNG slices.dataset_name- The dataset this image came from.layer- Which layer it came from.mip- Which mip it came fromlayer_type- "image" or "segmentation"bounds- The bounding box of the cutoutnum_channels- Alias forvol.shape[3]save_images()- Save Z slice PNGs of the current image to./saved_imagesfor manual inspectionviewer()- Start a local web server (http://localhost:8080) that can view small volumes interactively. This was recently changed fromviewasviewis a useful numpy method.Viewing a Precomputed Volume on DiskIf you have Precomputed volume onto local disk and would like to point neuroglancer to it:vol=CloudVolume(...)vol.viewer()You can then point any version of neuroglancer at it usingprecomputed://http://localhost:1337/NAME_OF_LAYER.MicroviewerCloudVolume includes a built-in dependency free viewer for 3D volumetric datasets smaller than about 2GB uncompressed. It supports bool, uint8, uint16, uint32, float32, and float64 numpy data types for both images and segmentation and can render a composite overlay of image and segmentation.You can launch a viewer using the.viewer()method of a VolumeCutout object or by using theview(...)orhyperview(...)functions that come with the cloudvolume module. This launches a web server onhttp://localhost:8080. You can read moreon the wiki.fromcloudvolumeimportCloudVolume,view,hyperviewchannel_vol=CloudVolume(...)seg_vol=CloudVolume(...)img=vol[...]seg=vol[...]img.viewer()# works on VolumeCutoutsseg.viewer()# segmentation type derived from infoview(img)# alternative for arbitrary numpy arraysview(seg,segmentation=True)hyperview(img,seg)# img and seg shape must match>>>Viewerserverlisteningtohttp://localhost:8080There are also seperate viewers for skeleton and mesh objects that can be invoked by calling.viewer()on either object. However, skeletons depend onmatplotliband meshes depend onvtkand OpenGL to function.pipinstallvtkmatplotlibPython 2.7 End of LifePython 2.7 is no longer supported by CloudVolume. Updated versions ofpipwill download the last supported release 1.21.1. You can read more on the policy page:https://github.com/seung-lab/cloud-volume/wiki/Policy#python-27-end-of-lifeRelated ProjectsIgneous: Computational pipeline for visualizing neuroglancer volumes.CloudVolume.jl: CloudVolume in Juliafpzip: A Python Package for the C++ code by Lindstrom et al.compressed_segmentation: A Python Package wrapping the code for the compressed_segmentation format developed by Jeremy Maitin-Shepard and Stephen Plaza.Kimimaro: High performance skeletonization of densely labeled 3D volumes.compresso: High lossless compression of connectomics segmentation. Algorithm by and code derived from Matejek et al.zfpc: Optimized zfp multi-stream container for alignment vector fields (and similar floating point data).crackle: Lossless high compression of connectomics segmentation. (BETA)AcknowledgmentsThank you to everyone that has contributed past or current to CloudVolume or the ecosystem it serves. We love you!Jeremy Maitin-Shepard createdNeuroglancerand defined the Precomputed format. Yann Leprince provided apure Python codecfor the compressed_segmentation format. Jeremy Maitin-Shepard and Stephen Plaza created C++ code defining the compression and decompression (respectively) protocol forcompressed_segmentation. Peter Lindstrom et al. createdthe fpzip algorithm, and contributed a C++ implementation and advice. Nico Kemnitz adapted our data to fpzip using the "Kempression" protocol (we named it, not him). Dan Bumbarger contributed code and information helpful for getting CloudVolume working on Windows. Fredrik Kihlander'spure python implementationof murmurhash3 andAustin Applebydeveloped murmurhash3 which is necessary for the sharded format. Ben Falk advocated for and did the bulk of the work on brotli compression. Some of the ideas in CloudVolume are based on work by Jingpeng Wu inBigArrays.jl.  Sven Dorkenwald, Manuel Castro, and Akhilesh Halageri contributed advice and code towards implementing the graphene interface. Oluwaseun Ogedengbe contributed documentation for the sharded format. Eric Perlman wrote the reader for Neuroglancer Multi-LOD meshes. Ignacio Tartavull and William Silversmith wrote the initial version of CloudVolume.