DashboardComm

class imagepypelines.DashboardComm

Bases: object

Object to send messages from the pipelines to dashboard(s)

Attributes Summary

clients	class level variable containing a list of all TCPclients that are connected
graphs_msg_cache	cache of pipeline update messages, these are messages that the dashboard needs to interpret the pipeline status messages
total	returns total number of connected dashboards

Methods Summary

connect(name, host, port)	establishes a connection with the Dashboard Chatroom at the given host and port
disconnect(name)	disconnects from an individual dashboard server
disconnect_all()	disconnects from all dashboard servers
read([names])	send delete messages to all dashboard servers
write(msg[, names])	sends the given message to all connected dashboard servers
write_delete(delete_msg)	send delete messages to all dashboard servers
write_error(error_msg)	send error messages to all dashboard servers
write_graph(pipeline_id, graph_msg)	send pipeline graph or task changes to the Dashboard
write_log(log_msg)
write_reset(reset_msg)	send reset messages to all dashboard servers
write_status(status_msg)	send status changes to all Dashboards

Attributes Documentation

clients = {}

class level variable containing a list of all TCPclients that are connected

Type

list of TCPClient

graphs_msg_cache = {'Pipeline#1a9f11': '{"type": "graph", "name": "Pipeline", "id": "Pipeline#1a9f11", "uuid": "04c1c456f5ba46b884cf9d8cdc1a9f11", "source_type": "pipeline", "payload": {"args": ["images"], "block_docs": {"DisplaySafe#0eccb1": {"name": "DisplaySafe", "id": "DisplaySafe#0eccb1", "uuid": "714f8ae1059e4f54853e76baf50eccb1", "args": ["arr"], "types": {"arr": "<class \'numpy.ndarray\'>"}, "shapes": {"arr": null}, "skip_enforcement": false, "batch_type": "each", "tags": [], "class_name": "DisplaySafe", "DOCS": {"class": "normalizes to [0,255] and bins to a displayable bitdepth\\n\\nDefault Enforcement:\\n 1) image\\n type: np.ndarray\\n shapes: [(None,None), (None,None,None)]\\n\\nBatch Size:\\n \\"each\\"", "__init__": "instantiates the block\\n\\nArgs:\\n name(str,None): the name of this block - how it will show up in the\\n graph.\\n batch_type(str, int): the type of the batch processing for your\\n process function. Either \\"all\\" or \\"each\\". \\"all\\" means that all\\n argument data will be passed into to your function at once,\\n \\"each\\" means that each argument datum will be passed in\\n individually\\n types(:obj:`dict`,None): Dictionary of input types. If arg doesn\'t\\n exist as a key, or if the value is None, then no checking is\\n done. If not provided, then will default to args as keys, None\\n as values.\\n shapes(:obj:`dict`,None): Dictionary of input shapes. If arg doesn\'t\\n exist as a key, or if the value is None, then no checking is\\n done. If not provided, then will default to args as keys, None\\n as values.\\n containers(:obj:`dict`,None): Dictionary of input containers. If arg\\n doesn\'t exist as a key, or if the value is None, then no\\n checking is done. If not provided, then will default to args as\\n keys, None as values.\\n *if batch_type is \\"each\\", then the container is irrelevant and can\\n be safely ignored!*\\n void(bool): Boolean value. By default all blocks return a value or\\n values as output. However, if printing to screen, plotting, or\\n saving data to a file, a block may not have a meaningful output\\n that should be stored in a pipeline\'s output dictionary. In this\\n case, void should be set to True, so that the output of the block\\n is ignored. The associated var key in the pipeline output will\\n contain a value of :obj:`None`. Default is False", "process": "normalizes to given range and cast\\n\\nArgs:\\n arr(:obj:`numpy.ndarray`): array of any shape and type\\n\\nReturns:\\n :obj:`numpy.ndarray`: normalized array of same shape and casted\\n to the given type"}}, "compute_stats#e7ff18": {"name": "compute_stats", "id": "compute_stats#e7ff18", "uuid": "357fab3025254d239b1bbfbbc6e7ff18", "args": ["image"], "types": {"image": "None"}, "shapes": {"image": null}, "skip_enforcement": false, "batch_type": "each", "tags": [], "class_name": "FuncBlock", "DOCS": {"class": "Block that will run any function you give it, either unfettered through\\nthe __call__ function, or with optional hardcoded parameters for use in a\\npipeline. Typically the FuncBlock is only used in the :obj:`blockify`\\ndecorator method.\\n\\nAttributes:\\n func(function): the function to call internally\\n preset_kwargs(dict): preset keyword arguments, typically used for\\n arguments that are not data to process", "__init__": "instantiates the function block\\n\\nArgs:\\n func (function): the function you desire to turn into a block\\n preset_kwargs (dict): preset keyword arguments, typically used for\\n arguments that are not data to process\\n **block_kwargs: keyword arguments for :obj:`Block` instantiation", "process": null}}, "equalization_LUT#fa4a8c": {"name": "equalization_LUT", "id": "equalization_LUT#fa4a8c", "uuid": "011e5ea7d6dc4c198d68f16391fa4a8c", "args": ["image_cdf"], "types": {"image_cdf": "None"}, "shapes": {"image_cdf": null}, "skip_enforcement": false, "batch_type": "each", "tags": [], "class_name": "FuncBlock", "DOCS": {"class": "Block that will run any function you give it, either unfettered through\\nthe __call__ function, or with optional hardcoded parameters for use in a\\npipeline. Typically the FuncBlock is only used in the :obj:`blockify`\\ndecorator method.\\n\\nAttributes:\\n func(function): the function to call internally\\n preset_kwargs(dict): preset keyword arguments, typically used for\\n arguments that are not data to process", "__init__": "instantiates the function block\\n\\nArgs:\\n func (function): the function you desire to turn into a block\\n preset_kwargs (dict): preset keyword arguments, typically used for\\n arguments that are not data to process\\n **block_kwargs: keyword arguments for :obj:`Block` instantiation", "process": null}}, "CompareView#8524a3": {"name": "CompareView", "id": "CompareView#8524a3", "uuid": "a088946f54a2477ca5ca46b39d8524a3", "args": ["image", "image2"], "types": {"image": "<class \'numpy.ndarray\'>", "image2": "<class \'numpy.ndarray\'>"}, "shapes": {"image": [[null, null], [null, null, null]], "image2": [[null, null], [null, null, null]]}, "skip_enforcement": false, "batch_type": "each", "tags": ["imagery"], "class_name": "CompareView", "DOCS": {"class": "Image Viewer that uses matplotlib internally to compare 2 images.\\nNearly always guarenteed to work, but timing will be less accurate\\nespecially for short timeframes\\n\\nThis viewer will work with online sphinx-generated examples\\n\\nAttributes:\\n pause_for(int): the amount of time in milliseconds to pause\\n between images\\n\\nDefault Enforcement:\\n 1) image\\n type: np.ndarray\\n shapes: [(None,None), (None,None,None)]\\n 2) image2\\n type: np.ndarray\\n shapes: [(None,None), (None,None,None)]\\nBatch Size:\\n \\"each\\"", "__init__": "Instantiates the Matplotlib Viewer\\n\\nArg:\\n pause_for(int): the amount of time in milliseconds to pause\\n between images. defaults to 500ms\\n close_fig(bool): whether or not to close the matplotlib figure after\\n processing is done. defaults to False", "process": "Displays the image in a matplotlib figure\\n\\nArgs:\\n image (np.ndarray): image\\n image2 (np.ndarray): second image\\n\\nReturns:\\n None"}}, "InputNone#578055": {"name": "InputNone", "id": "InputNone#578055", "uuid": "347b4c64dca14874a0becbf74c578055", "args": [], "types": {}, "shapes": {}, "skip_enforcement": false, "batch_type": "all", "tags": [], "class_name": "Input", "DOCS": {"class": "An object to inject data into the graph\\n\\nAttributes:\\n data(any type):\\n loaded(bool): where", "__init__": "instantiates the Input\\n\\nArgs:\\n index(int,None): index of the input into the Pipeline", "process": "returns the loaded data"}}, "compare_stats#7c82ae": {"name": "compare_stats", "id": "compare_stats#7c82ae", "uuid": "06d41c1187a9471b95427e175f7c82ae", "args": ["original", "equalized"], "types": {"original": "None", "equalized": "None"}, "shapes": {"original": null, "equalized": null}, "skip_enforcement": false, "batch_type": "each", "tags": [], "class_name": "FuncBlock", "DOCS": {"class": "Block that will run any function you give it, either unfettered through\\nthe __call__ function, or with optional hardcoded parameters for use in a\\npipeline. Typically the FuncBlock is only used in the :obj:`blockify`\\ndecorator method.\\n\\nAttributes:\\n func(function): the function to call internally\\n preset_kwargs(dict): preset keyword arguments, typically used for\\n arguments that are not data to process", "__init__": "instantiates the function block\\n\\nArgs:\\n func (function): the function you desire to turn into a block\\n preset_kwargs (dict): preset keyword arguments, typically used for\\n arguments that are not data to process\\n **block_kwargs: keyword arguments for :obj:`Block` instantiation", "process": null}}, "apply_LUT#50102a": {"name": "apply_LUT", "id": "apply_LUT#50102a", "uuid": "b30c5e39d0f94e7885ddf20d9d50102a", "args": ["image", "lut"], "types": {"image": "None", "lut": "None"}, "shapes": {"image": null, "lut": null}, "skip_enforcement": false, "batch_type": "each", "tags": [], "class_name": "FuncBlock", "DOCS": {"class": "Block that will run any function you give it, either unfettered through\\nthe __call__ function, or with optional hardcoded parameters for use in a\\npipeline. Typically the FuncBlock is only used in the :obj:`blockify`\\ndecorator method.\\n\\nAttributes:\\n func(function): the function to call internally\\n preset_kwargs(dict): preset keyword arguments, typically used for\\n arguments that are not data to process", "__init__": "instantiates the function block\\n\\nArgs:\\n func (function): the function you desire to turn into a block\\n preset_kwargs (dict): preset keyword arguments, typically used for\\n arguments that are not data to process\\n **block_kwargs: keyword arguments for :obj:`Block` instantiation", "process": null}}}, "nodes": {"InputNone0121981b3d194d82bdd7793436fab75f-node": {"args": [], "outputs": ["images"], "validation_time": null, "processing_time": null, "avg_time_per_datum": null, "num_in": null, "n_batches": null, "pid": null, "status": "not started", "name": "InputNone", "class_name": "Input", "batch_type": "all", "display_as": "input"}, "compute_stats1df35f308b284a31a48bfdbca7bac00e-node": {"args": ["images"], "outputs": ["hists", "pdfs", "cdfs"], "validation_time": null, "processing_time": null, "avg_time_per_datum": null, "num_in": null, "n_batches": null, "pid": null, "status": "not started", "name": "compute_stats", "class_name": "FuncBlock", "batch_type": "each", "display_as": "block"}, "equalization_LUT5900bdb7fac643199907bd65832a560b-node": {"args": ["cdfs"], "outputs": ["LUTs"], "validation_time": null, "processing_time": null, "avg_time_per_datum": null, "num_in": null, "n_batches": null, "pid": null, "status": "not started", "name": "equalization_LUT", "class_name": "FuncBlock", "batch_type": "each", "display_as": "block"}, "apply_LUT990d55f934e842c68a2453dfcbbfa38c-node": {"args": ["images", "LUTs"], "outputs": ["applied"], "validation_time": null, "processing_time": null, "avg_time_per_datum": null, "num_in": null, "n_batches": null, "pid": null, "status": "not started", "name": "apply_LUT", "class_name": "FuncBlock", "batch_type": "each", "display_as": "block"}, "DisplaySafec48d3d2df4c8458db1f8902213e55e63-node": {"args": ["applied"], "outputs": ["safe"], "validation_time": null, "processing_time": null, "avg_time_per_datum": null, "num_in": null, "n_batches": null, "pid": null, "status": "not started", "name": "DisplaySafe", "class_name": "DisplaySafe", "batch_type": "each", "display_as": "block"}, "CompareView00b53cb625d54d45a6e88cab4630bbfe-node": {"args": ["images", "safe"], "outputs": ["viewing"], "validation_time": null, "processing_time": null, "avg_time_per_datum": null, "num_in": null, "n_batches": null, "pid": null, "status": "not started", "name": "CompareView", "class_name": "CompareView", "batch_type": "each", "display_as": "block"}, "compute_statsbfd595f66ef243b0b9bfbce5e734deda-node": {"args": ["applied"], "outputs": ["eq_hists", "eq_pdfs", "eq_cdfs"], "validation_time": null, "processing_time": null, "avg_time_per_datum": null, "num_in": null, "n_batches": null, "pid": null, "status": "not started", "name": "compute_stats", 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cache of pipeline update messages, these are messages that the dashboard needs to interpret the pipeline status messages

total

returns total number of connected dashboards

Methods Documentation

classmethod connect(name, host, port)

establishes a connection with the Dashboard Chatroom at the given host and port

Parameters

• name (str) – human readable name of

• host (str) – ip address for the dashboard

• port (int) – port on host for the dashboard

classmethod disconnect(name)

disconnects from an individual dashboard server

classmethod disconnect_all()

disconnects from all dashboard servers

read(names=None)

send delete messages to all dashboard servers

write(msg, names=None)

sends the given message to all connected dashboard servers

Parameters

• msg (str) – a string to send

• names (tuple(str)) – iterable of names specifying a whitelist

write_delete(delete_msg)

send delete messages to all dashboard servers

write_error(error_msg)

send error messages to all dashboard servers

write_graph(pipeline_id, graph_msg)

send pipeline graph or task changes to the Dashboard

write_log(log_msg)

write_reset(reset_msg)

send reset messages to all dashboard servers

write_status(status_msg)

send status changes to all Dashboards