Metadata-Version: 2.1
Name: cloud-volume
Version: 1.11.0
Summary: A serverless client for reading and writing Neuroglancer Precomputed volumes both locally and on cloud services.
Home-page: https://github.com/seung-lab/cloud-volume/
Author: William Silversmith, Nico Kemnitz, Ignacio Tartavull, and others
Author-email: ws9@princeton.edu
License: BSD 3-Clause
Description: [![Build Status](https://travis-ci.org/seung-lab/cloud-volume.svg?branch=master)](https://travis-ci.org/seung-lab/cloud-volume) [![PyPI version](https://badge.fury.io/py/cloud-volume.svg)](https://badge.fury.io/py/cloud-volume) [![SfN 2018 Poster](https://img.shields.io/badge/poster-SfN%202018-blue.svg)](https://drive.google.com/open?id=1RKtaAGV2f7F13opnkQfbp6YBqmoD3fZi) [![codecov](https://img.shields.io/badge/codecov-link-%23d819a6)](https://codecov.io/gh/seung-lab/cloud-volume)
        
        # CloudVolume
        
        ```python3
        from cloudvolume import CloudVolume
        
        vol = CloudVolume('gs://mylab/mouse/image', parallel=True, progress=True)
        image = vol[:,:,:] # Download a whole image stack into a numpy array from the cloud
        vol[:,:,:] = image # Upload an entire image stack from a numpy array to the cloud
        
        label = 1
        mesh = vol.mesh.get(label)
        skel = vol.skeleton.get(label)
        ```
        
        CloudVolume is a serverless Python client for random access reading and writing [Neuroglancer](https://github.com/google/neuroglancer/) volumes in "[Precomputed](https://github.com/google/neuroglancer/tree/master/src/neuroglancer/datasource/precomputed)" format, a set of representations for arbitrarily large volumetric images, meshes, and skeletons. CloudVolume is typically paired with [Igneous](https://github.com/seung-lab/igneous), a Kubernetes based 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 on [AWS S3](https://aws.amazon.com/s3/) or on [Google Storage](https://cloud.google.com/storage/). 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 of [Neuroglancer](https://github.com/google/neuroglancer/), [Igneous](https://github.com/seung-lab/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 the `graphene://` format.
        
        You can find a collection of CloudVolume accessible and Neuroglancer viewable datasets at https://neurodata.io/ocp/, an open data project by some of our collaborators.
        
        ## Highlights
        
        - Random access to petavoxel Neuroglancer images, meshes\*, and skeletons.
        - Nearly all output is immediately visualizable using Neuroglancer.\*\*
        - Reads graph server backed proofreading volumes (via `graphene://`).
        - Serverless (except `graphene://`) and multi-cloud.
        
        ### Detailed Highlights
        
        - Multi-threaded, supports multi-process and green threads.
        - Memory optimized, supports shared memory.
        - Lossless connectomics relevant codecs ([`compressed_segmentation`](https://github.com/seung-lab/compressedseg), [`fpzip`](https://github.com/seung-lab/fpzip/), [`brotli`](https://en.wikipedia.org/wiki/Brotli))
        - 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 Neuroglancer [sharded format](https://github.com/google/neuroglancer/tree/master/src/neuroglancer/datasource/precomputed) which dramatically condenses the number of files required to represent petascale datasets, similar to [Cloud Optimized GeoTIFF](https://www.cogeo.org/), which can result in [dramatic cost savings](https://github.com/seung-lab/kimimaro/wiki/The-Economics:-Skeletons-for-the-People).
        - Includes viewers for small images, meshes, and skeletons.
        - Only 3 dimensions + RBG channels currently supported for images.
        - No data versioning.
        
        \* Except the new mutli-res format for now.
        \*\* fpzip compressed data, used for 32-bit per pixel vectors, is not currently visualizable.
        
        ## Setup
        
        Cloud-volume is regularly tested on Ubuntu with Python 2.7, 3.5, 3.6, and 3.7 (we've noticed it's faster on Python 3). 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 try [reading from a public dataset](https://github.com/seung-lab/cloud-volume/wiki/Reading-Public-Data-Examples).
        
        Note that Python 2.7 does not currently support the DracoPy library, and therefore does not support the `graphene://` format.
        
        #### `pip` Binary Installation
        
        ```bash
        pip install cloud-volume # standard installation
        ```
        
        CloudVolume depends on the PyPI packages [`fpzip`](https://github.com/seung-lab/fpzip) and [`compressed_segmentation`](https://github.com/seung-lab/compressedseg), 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.
        
        #### Optional Dependencies
        
        | Tag             | Description                             | Dependencies          |
        |-----------------|-----------------------------------------|-----------------------|
        | boss            | `boss://` format support                | intern                |
        | test            | Supports testing                        | pytest                |
        | mesh_viewer     | `mesh.viewer()` GUI                     | vtk                   |
        | skeleton_viewer | `skeleton.viewer()` GUI                 | matplotlib            |
        | all_viewers     | All viewers now and in the future.      | vtk, matplotlib       |
        | dask            | Supports converting to/from dask arrays | dask\[array\]         |
        
        Example:
        
        ```bash
        pip install cloud-volume[boss,test,all_viewers]
        ```
        
        #### `pip` Source Installation
        
        *C++ compiler required.*
        
        ```bash
        sudo apt-get install g++ python3-dev # python-dev if you're on python2
        pip install numpy
        pip install cloud-volume
        ```
        
        Due 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.
        
        The libraries depending on numpy are:
        - compressed_segmentation: Smaller and faster segmentation files. A pure python fallback is present. When the accelerated version is present, IO is faster than with gzip alone.
        - fpzip: A lossless compression library for 3D & 4D floating point data.
        
        #### Manual Installation
        
        This can be desirable if you want to hack on CloudVolume itself.
        
        ```bash
        git clone git@github.com:seung-lab/cloud-volume.git
        cd cloud-volume
        
        # With virtualenvwrapper
        mkvirtualenv cv
        workon cv
        # With only virtualenv
        virtualenv venv
        source venv/bin/activate
        
        sudo apt-get install g++ python3-dev # python-dev if you're on python2
        pip install numpy # additional step needed for accelerated compressed_segmentation and fpzip
        pip install -e . # without optional dependencies
        pip install -e .[all_viewers] # with e.g. the all_viewers optional dependency
        ```
        
        ### Credentials
        
        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](https://github.com/seung-lab/cloud-volume/wiki/Setting-up-Google-Cloud-Storage)), 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. `google-secret.json` is a service account credential for Google Storage, `aws-secret.json` is 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.json` will be your defaut service account, but if you also want to also access bucket ABC, you can provide `ABC-google-secret.json` and 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.
        
        ```bash
        mkdir -p ~/.cloudvolume/secrets/
        mv aws-secret.json ~/.cloudvolume/secrets/ # needed for Amazon
        mv google-secret.json ~/.cloudvolume/secrets/ # needed for Google
        mv boss-secret.json ~/.cloudvolume/secrets/ # needed for the BOSS
        mv matrix-secret.json ~/.cloudvolume/secrets/ # needed for Matrix
        ```
        
        #### `aws-secret.json` and `matrix-secret.json`
        
        Create an [IAM user service account](https://docs.aws.amazon.com/IAM/latest/UserGuide/id_users.html) that can read, write, and delete objects from at least one bucket.
        
        ```json
        {
        	"AWS_ACCESS_KEY_ID": "$MY_AWS_ACCESS_KEY_ID",
        	"AWS_SECRET_ACCESS_KEY": "$MY_SECRET_ACCESS_TOKEN"
        }
        ```
        
        #### `google-secret.json`
        
        You can create the `google-secret.json` file [here](https://console.cloud.google.com/iam-admin/serviceaccounts). 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.
        
        ```json
        {
          "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": ""
        }
        ```
        
        #### `chunkedgraph-secret.json`
        
        If you have a token from Graphene/Chunkedgraph server, create the `chunkedgraph-secret.json` file as shown in the example below.
        You may also pass the token to `GrapheneMetadata` class `auth_token="<your_token>"`, this will override the token from `json` file.
        
        ```json
        {
          "token": "<your_token>"
        }
        ```
        
        ## Usage
        
        CloudVolume 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/$LAYER`
        
        The format or protocol fields may be omitted where required. In the case of the precomputed format, the format specifier is optional.
        
        | Format      | Protocols                         | Default | Example                                |
        |-------------|-----------------------------------|---------|----------------------------------------|
        | precomputed | gs, s3, http, https, file, matrix | Yes     | gs://mybucket/dataset/layer            |
        | graphene    | gs, s3, http, https, file, matrix |         | graphene://gs://mybucket/dataset/layer |
        | boss        | N/A                               |         | boss://collection/experiment/channel   |
        
        ### Supported Formats
        
        * precomputed: Neuroglancer's native format. ([specification](https://github.com/google/neuroglancer/tree/master/src/neuroglancer/datasource/precomputed))
        * graphene: Precomputed based format used by the PyChunkGraph server.
        * boss: The BOSS (https://docs.theboss.io/docs)
        
        We currently support reading the sharded skeleton format within Precomputed that is used in some newer datasets. Other data types are forthcoming.
        
        ### Supported Protocols
        * gs:   Google Storage
        * s3:   Amazon S3
        * http(s): (read-only) Ordinary Web Servers
        * file: Local File System (absolute path)
        * matrix: Princeton Internal System
        
        
        ### `info` Files - New Dataset
        
        Neuroglancer relies on an [`info`](https://github.com/google/neuroglancer/tree/master/src/neuroglancer/datasource/precomputed#info-json-file-specification) file 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 uploading `rawdata.T`. A more detailed example for uploading a local volume [is located here](https://github.com/seung-lab/cloud-volume/wiki/Example-Single-Machine-Dataset-Upload).
        
        ```python3
        from cloudvolume import CloudVolume
        
        info = CloudVolume.create_new_info(
            num_channels    = 1,
            layer_type      = 'segmentation',
            data_type       = 'uint64', # Channel images might be 'uint8'
            encoding        = 'raw', # raw, jpeg, compressed_segmentation, fpzip, kempressed
            resolution      = [4, 4, 40], # Voxel scaling, units are in nanometers
            voxel_offset    = [0, 0, 0], # x,y,z offset in voxels from the origin
            mesh            = 'mesh',
            # Pick a convenient size for your underlying chunk representation
            # Powers of two are recommended, doesn't need to cover image exactly
            chunk_size      = [ 512, 512, 16 ], # units are voxels
            volume_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[:,:,:]
        ```
        | Encoding                | Image Type                 | Lossless | Neuroglancer Viewable | Description                                                                              |
        |-------------------------|----------------------------|----------|-------------|------------------------------------------------------------------------------------------|
        | raw                     | Any                        | Y        | Y           | Serialized numpy arrays.                                                                 |
        | jpeg                    | Image                      | N        | Y           | Multiple slices stiched into a single JPEG.                                              |
        | compressed_segmentation | Segmentation               | Y        | Y           | Renumbered numpy arrays to reduce data width. Also used by Neuroglancer internally.      |
        | fpzip                   | Floating Point             | Y        | N*           | Takes advantage of IEEE 754 structure + L1 Lorenzo predictor to get higher compression.  |
        | kempressed              | Anisotropic Z Floating Point | N        | N*           | Adds manipulations on top of fpzip to achieve higher compression.                        |
        
        \* Coming soon.
        
        
        ### Examples
        
        ```python3
        # Basic Examples
        vol = CloudVolume('gs://mybucket/retina/image')
        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 resolution
        vol = CloudVolume('gs://bucket/datasset/channel', info=info) # New info file from scratch
        image = vol[:,:,:] # Download the entire image stack into a numpy array
        listing = vol.exists( np.s_[0:64, 0:128, 0:64] ) # get a report on which chunks actually exist
        listing = 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 volume
        img = vol.download_point( (x,y,z), size=256, mip=3 ) # download region around (mip 0) x,y,z at mip 3
        
        # Server
        vol.viewer() # launches neuroglancer compatible web server on http://localhost:1337
        
        # Microviewer
        img = vol[64:1028, 64:1028, 64:128]
        img.viewer() # launches web viewer on http://localhost:8080
        
        # Meshes
        vol.mesh.save(12345) # save 12345 as ./12345.ply on disk
        vol.mesh.save([12345, 12346, 12347]) # merge three segments into one file
        vol.mesh.save(12345, file_format='obj') # 'ply' and 'obj' are both supported
        vol.mesh.get(12345) # return the mesh as vertices and faces instead of writing to disk
        vol.mesh.get([ 12345, 12346 ]) # return these two segids fused into a single mesh
        vol.mesh.get([ 12345, 12346 ], fuse=False) # return { 12345: mesh, 12346: mesh }
        
        mesh.viewer() # Opens GUI. Requires vtk.
        
        # Skeletons
        skel = 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 index
        skels = vol.skeleton.get_by_bbox( Bbox( (0,0,0), (500, 500, 500) ) )
        vol.skeleton.spatial_index # None if not available
        
        skel.empty() # boolean
        
        bytes = skel.encode() # encode to Precomputed format (bytes)
        skel = Skeleton.decode(bytes) # decode from PrecomputedFormat
        
        skel = skel.crop(slices or bbox) # eliminate vertices and edges outside bbox
        skel = skel.consolidate() # eliminate duplicate vertices and edges
        skel3 = skel.merge(skel2) # merge two skeletons into one
        skel = skel.clone() # create copy
        skel = Skeleton.from_swc(swcstr) # decode an SWC file
        skel_str = skel.to_swc() # convert to SWC file in string representation
        skel.viewer() # Opens GUI. Requires matplotlib
        
        skel.cable_length() # sum of all edge lengths
        skel = skel.downsample(2) # reduce size of skeleton by factor of 2
        
        skel1 == skel2 # check if contents of internal arrays match
        Skeleton.equivalent(skel1, skel2) # ...even if there are differences like differently numbered edges
        
        # Parallel Operation
        vol = CloudVolume('gs://mybucket/retina/image', parallel=True) # Use all cores
        vol.parallel = 4 # e.g. any number > 1, use this many cores
        data = 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 cloudvolume
        vol.shared_memory_id # get/set the default shared memory location for this instance
        
        # Shared Memory Upload
        vol = CloudVolume(...)
        vol.upload_from_shared_memory('my-shared-memory-id', # do not prefix with /dev/shm
            bbox=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 region
        vol.upload_from_file('/path/to/file', bbox=Bbox(...), mip=0) # bbox is the region it represents
        
        # Transfer w/o Excess Memory Allocation
        vol = CloudVolume(...)
        # single core, send all of vol to destination, no painting memory
        vol.transfer_to('gs://bucket/dataset/layer', vol.bounds)
        
        # Caching, located at $HOME/.cloudvolume/cache/$PROTOCOL/$BUCKET/$DATASET/$LAYER/$RESOLUTION
        vol = CloudVolume('gs://mybucket/retina/image', cache=True) # Basic Example
        image = vol[0:10,0:10,0:10] # Download partial image and cache
        vol[0:10,0:10,0:10] = image # Upload partial image and cache
        
        # Evaluating the Cache
        vol.cache.list() # list files in cache at this mip level
        vol.cache.list(mip=1) # list files in cache at mip 1
        vol.cache.list_meshes()
        vol.cache.list_skeletons()
        vol.cache.num_files() # number of files at this mip level
        vol.cache.num_bytes(all_mips=True) # Return num files for each mip level in a list
        vol.cache.num_bytes() # number of bytes taken up by files, size on disk can be bigger
        vol.cache.num_bytes(all_mips=True) # Return num bytes for each mip level in a list
        
        vol.cache.enabled = True/False # Turn the cache on/off
        vol.cache.path = Str # set the cache location
        vol.cache.compress = None/True/False # None: Link to cloud setting, Boolean: Force cache to compressed (True) or uncompressed (False)
        
        # Deleting Cache
        vol.cache.flush() # Delete local cache for this layer at this mip level
        vol.cache.flush(preserve=Bbox(...)) # Same, but preserve cache in a region of space
        vol.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 Threads
        import gevent.monkey
        gevent.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_dask
        res = cloudvolume.dask.to_cloudvolume(arr, cloudpath, compute=bool, return_store=bool)
        ```
        
        ### CloudVolume Constructor
        
        ```python3
        CloudVolume(cloudpath,
             mip=0, bounded=True, fill_missing=False, autocrop=False,
             cache=False, compress_cache=None, cdn_cache=False, progress=INTERACTIVE, info=None, provenance=None,
             compress=None, non_aligned_writes=False, parallel=1,
             delete_black_uploads=False, background_color=0,
             green_threads=False, use_https=False,
             max_redirects=10)
        ```
        
        * mip - Which mip level to access
        * bounded - Whether access is allowed outside the bounds defined in the info file
        * fill_missing - If a chunk is missing, should it be zero filled or throw an EmptyVolumeException? Note that under conditions of high load, it's possible for fill_missing to be activated for existing files. Set to false for extra safety.
        * cache - Save uploads/downloads to disk. You can also provide a string path instead of a boolean to specify a custom cache location.
        * compress_cache - Override default cache compression behavior if set to a boolean.
        * autocrop - If bounded is False, automatically crop requested uploads and downloads to the volume boundary.
        * cdn_cache - Set the HTTP Cache-Control header on uploaded image chunks.
        * progress - Show progress bars. Defaults to True if in python interactive mode else default False.
        * info - Use this info object rather than pulling from the cloud (useful for creating new layers).
        * provenance - Use this object as the provenance file.
        * compress - None, 'gzip' or 'br', force this compression algorithm to be used for upload
        * non_aligned_writes - True/False. If False, non-chunk-aligned writes will trigger an error with a helpful message. If True,
            Non-aligned writes will proceed. Be careful, non-aligned writes are wasteful in memory and bandwidth, and in a mulitprocessing environment, are subject to an ugly race condition. (c.f. https://github.com/seung-lab/cloud-volume/wiki/Advanced-Topic:-Non-Aligned-Writes)
        * parallel - True/False/(int > 0), If False or 1, use a single process. If > 1, use that number of processes for downloading
           that coordinate over shared memory. If True, use a number of processes equal to the number of available cores.
        * delete_black_uploads - True/False. If True, issue a DELETE http request instead of a PUT when an individual uploaded chunk is all background (usually all zeros). This is useful for avoiding creating many tiny files, which some storage system designs do not handle well.
        * background_color - Number. Determines the background color that `delete_black_uploads` will scan for (typically zero).
        * green_threads - True/False. If True, use the gevent cooperative threading library instead of preemptive threads. This requires monkey patching your program which may be undesirable. However, for certain workloads this can be a significant performance improvement on multi-core devices.
        * use_https - True/False. If True, use the same read-only access urls that neuroglancer does that may be cached vs the secured read/write strongly consistent API. Use this when you do not have credentials.
        * max_redirects - Integer. If > 0, allow info files containing a 'redirect' field to forward the CloudVolume instance across this many hops before raising an error. If set to <= 0, then do not allow redirection, but also do not raise an error (which allows for easy editing of info files with a redirect in them).
        
        ### CloudVolume Methods
        
        Better 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 operations
        	* get - Download an object. Can merge multiple segmentids
        	* save - Download an object and save it in `.obj` format. You can combine equivialences into a single object too.
        * skeleton - Access Skeletons
          * get - Download an object.
          * upload - Save a skeleton object to the cloud.
        * cache - Access cache operations
        	* enabled - 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 cache
        	* list - List files in cache
        	* num_files - Number of files in cache at this mip level , use all_mips=True to get them all
        	* num_bytes - Return the number of bytes in cache at this mip level, all_mips=True to get them all
        	* flush - Delete the cache at this mip level, preserve=Bbox/slice to save a spatial region
        	* flush_region - Delete a spatial region at this mip level
        * exists - 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 like `gsutil` or `aws 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 Properties
        
        Accessed as `vol.$PROPERTY` like `vol.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 call `vol.commit_info()` to save your changes to storage.
        * provenance (dict-like, rw) - Data layer provenance file representation. You must call `vol.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/image
        * info_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 object
        * scale (dict, rw)* - Shortcut to the working scale of the current mip level
        * shape (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 like `vol.mip_$PROPERTY($MIP)`. By default they return the current mip level assigned to the CloudVolume, but any mip level can be accessed via the corresponding `mip_` function. Example: `vol.mip_resolution(2)` would return the resolution of mip 2.
        
        ### VolumeCutout Functions
        
        When you download an image using CloudVolume it gives you a `VolumeCutout`. These are `numpy.ndarray` subclasses that support a few extra properties to help make book keeping easier. The major advantage is `save_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 from
        * `layer_type` - "image" or "segmentation"
        * `bounds` - The bounding box of the cutout
        * `num_channels` - Alias for `vol.shape[3]`
        * `save_images()` - Save Z slice PNGs of the current image to `./saved_images` for manual inspection
        * `viewer()` - Start a local web server (http://localhost:8080) that can view small volumes interactively. This was recently changed from `view` as `view` is a useful numpy method.
        
        ### Viewing a Precomputed Volume on Disk
        
        If you have Precomputed volume onto local disk and would like to point neuroglancer to it:
        
        ```python
        vol = CloudVolume(...)
        vol.viewer()
        ```
        
        You can then point any version of neuroglancer at it using `precomputed://http://localhost:1337/NAME_OF_LAYER`.
        
        ### Microviewer
        
        CloudVolume 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 the `view(...)` or `hyperview(...)` functions that come with the cloudvolume module. This launches a web server on `http://localhost:8080`. You can read more [on the wiki](https://github.com/seung-lab/cloud-volume/wiki/%CE%BCViewer).
        
        ```python3
        from cloudvolume import CloudVolume, view, hyperview
        
        channel_vol = CloudVolume(...)
        seg_vol = CloudVolume(...)
        img = vol[...]
        seg = vol[...]
        
        img.viewer() # works on VolumeCutouts
        seg.viewer() # segmentation type derived from info
        view(img) # alternative for arbitrary numpy arrays
        view(seg, segmentation=True)
        hyperview(img, seg) # img and seg shape must match
        
        >>> Viewer server listening to http://localhost:8080
        ```
        
        There are also seperate viewers for skeleton and mesh objects that can be invoked by calling `.viewer()` on either object. However, skeletons depend on `matplotlib` and meshes depend on `vtk` and OpenGL to function.
        
        ```bash
        pip install vtk matplotlib
        ```
        
        ## Python 2.7 End of Life
        
        Python 2.7's [End of Life date](https://pythonclock.org/) is January, 1 2020. CloudVolume will support Python 2.7 up to that point and possibly a few months after. Numpy will be dropping support on January 1. We may accelerate the deprecation schedule if substantial technical problems arise with supporting Python 2.7, but so far the impact has been mostly limited.
        
        ## Related Projects
        
        1. [Igneous](https://github.com/seung-lab/igneous): Computational pipeline for visualizing neuroglancer volumes.
        2. [CloudVolume.jl](https://github.com/seung-lab/CloudVolume.jl): CloudVolume in Julia
        3. [fpzip](https://github.com/seung-lab/fpzip): A Python Package for the C++ code by Lindstrom et al.
        4. [compressed_segmentation](https://github.com/seung-lab/compressedseg): A Python Package wrapping the code for the compressed_segmentation format developed by Jeremy Maitin-Shepard and Stephen Plaza.
        5. [Kimimaro](https://github.com/seung-lab/kimimaro): High performance skeletonization of densely labeled 3D volumes.
        
        ## Acknowledgments
        
        Thank you to everyone that has contributed past or current to CloudVolume or the ecosystem it serves. We love you!  
        
        Jeremy Maitin-Shepard created [Neuroglancer](https://github.com/google/neuroglancer) and defined the Precomputed format. Yann Leprince provided a [pure Python codec](https://github.com/HumanBrainProject/neuroglancer-scripts) for the compressed_segmentation format. Jeremy Maitin-Shepard and Stephen Plaza created C++ code defining the compression and decompression (respectively) protocol for [compressed_segmentation](https://github.com/janelia-flyem/compressedseg). Peter Lindstrom et al. created [the fpzip algorithm](https://computation.llnl.gov/projects/floating-point-compression), 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's [pure python implementation](https://github.com/wc-duck/pymmh3) of murmurhash3 and [Austin Appleby](https://github.com/aappleby/smhasher) developed 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 in [BigArrays.jl](https://github.com/seung-lab/BigArrays.jl).  Sven Dorkenwald, Manuel Castro, and Akhilesh Hagaldi contributed advice and code towards implementing the graphene interface. Oluwaseun Ogedengbe contributed documentation for the sharded format. Ignacio Tartavull and William Silversmith wrote the initial version of CloudVolume.
        
Keywords: neuroglancer volumetric-data numpy connectomics microscopy image-processing biomedical-image-processing s3 gcs mesh meshes skeleton skeletons
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Development Status :: 5 - Production/Stable
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: POSIX
Classifier: Operating System :: MacOS
Classifier: Operating System :: Microsoft :: Windows :: Windows 10
Classifier: Topic :: Utilities
Description-Content-Type: text/markdown
Provides-Extra: boss
Provides-Extra: mesh_viewer
Provides-Extra: skeleton_viewer
Provides-Extra: all_viewers
Provides-Extra: dask
Provides-Extra: test
