FASTR

FASTR is a framework that helps creating workflows of different tools. The workflows created in FASTR are automatically enhanced with flexible data input/output, execution options (local, cluster, etc) and solid provenance.

We chose to create tools by creating wrappers around executables and connecting everything with Python.

Fastr is open-source (licensed under the Apache 2.0 license) and hosted on bitbucket at https://bitbucket.org/bigr_erasmusmc/fastr

For support, go to https://groups.google.com/d/forum/fastr-users

To get yourself a copy, see the Installation

The official documentation can be found at fastr.readthedocs.io

The Fastr workflow system is presented in the following article:

Hakim Achterberg, Marcel Koek, and Wiro Niessen. “Fastr: a workflow engine for advanced data flows in medical image analysis.” Frontiers in ICT 3 (2016): 15.

FASTR Documentation

Introduction

Fastr is a system for creating workflows for automated processing of large scale data. A processing workflow might also be called a processing pipeline, however we feel that a pipeline suggests a linear flow of data. Fastr is designed to handle complex flows of data, so we prefer to use the term network. We see the workflow as a network of processing tools, through which the data will flow.

The original authors work in a medical image analysis group at Erasmus MC. They often had to run analysis that used multiple programs written in different languages. Every time a experiment was set up, the programs had to be glued together by scripts (often in bash or python).

At some point the authors got fed up by doing these things again and again, and so decided to create a flexible, powerful scripting base to easily create these scripts. The idea evolved to a framework in which the building blocks could be defined in XML and the networks could be constructed in very simple scripts (similar to creating a GUI).

Philosophy

Researchers spend a lot of time processing data. In image analysis, this often includes using multiple tools in succession and feeding the output of one tool to the next. A significant amount of time is spent either executing these tools by hand or writing scripts to automate this process. This process is time consuming and error-prone. Considering all these tasks are very similar, we wanted to write one elaborate framework that makes it easy to create pipelines, reduces the risk of errors, generates extensive logs, and guarantees reproducibility.

The Fastr framework is applicable to multiple levels of usage: from a single researcher who wants to design a processing pipeline and needs to get reproducible results for publishing; to applying a consolidated image processing pipeline to a large population imaging study. On all levels of application the pipeline provenance and managed execution of the pipeline enables you to get reliable results.

System overview

There are a few key requirements for the design of the system:

• Any tool that your computer can run using the command line (without user interaction) should be usable by the system without modifying the tool.
• The creation of a workflow should be simple, conceptual and require no real programming.
• Networks, once created, should be usable by anyone like a simple program. All processing should be done automatically.
• All processing of the network should be logged extensively, allowing for complete reproducibility of the system (guaranteeing data provenance).

Using these requirements we define a few key elements in our system:

• A fastr.Tool is a definition of any program that can be used as part of a pipeline (e.g. a segmentation tool)
• A fastr.Node is a single operational step in the workflow. This represents the execution of a fastr.Tool.
• A fastr.Link indicates how the data flows between nodes.
• A fastr.Network is an object containing a collection of fastr.Node and fastr.Link that form a workflow.

With these building blocks, the creation of a pipeline will boil down to just specifying the steps in the pipeline and the flow of the data between them. For example a simple neuro-imaging pipeline could look like:

A simple workflow that registers two images and uses the resulting transform to resample the moving image.

In Fastr this translates to:

• Create a fastr.Network for your pipeline
• Create a fastr.SourceNode for the fixed image
• Create a fastr.SourceNode for the moving image
• Create a fastr.SourceNode for the registration parameters
• Create a fastr.Node for the registration (in this case elastix)
• Create a fastr.Node for the resampling of the image (in this case transformix)
• Create a fastr.SinkNode to save the transformations
• Create a fastr.SinkNode to save the transformed images
• fastr.Link the output of the fixed image source node to the fixed image input of the registration node
• fastr.Link the output of the moving image source node to the moving image input of the registration node
• fastr.Link the output of the registration parameters source node to the registration parameters input of the registration node
• fastr.Link the output transform of the registration node to the transform input of the resampling node
• fastr.Link the output transform of the registration node to the input of transformation SinkNode
• fastr.Link the output image of the resampling node to the input of image SinkNode
• Run the fastr.Network for subjects X

This might seem like a lot of work for a registration, but the Fastr framework manages all other things, executes the pipeline and builds a complete paper trail of all executed operations. The execution can be on any of the supported execution environments (local, cluster, etc). The data can be imported from and exported to any of the supported data connections (file, XNAT, etc). It is also important to keep in mind that this is a simple example, but for more complex pipelines, managing the workflow with Fastr will be easier and less error-prone than writing your own scripts.

Quick start guide

This manual will show users how to install Fastr, configure Fastr, construct and run simple networks, and add tool definitions.

Installation

You can install Fastr either using pip, or from the source code.

Installing via pip

You can simply install fastr using pip:

pip install fastr

Note

You might want to consider installing fastr in a virtualenv

Installing from source code

To install from source code, use Mercurial via the command-line:

hg clone https://<yourusername>@bitbucket.org/bigr_erasmusmc/fastr  # for http
hg clone ssh://hg@bitbucket.org/bigr_erasmusmc/fastr # for ssh

If you prefer a GUI you can try TortoiseHG (Windows, Linux and Mac OS X) or SourceTree (Windows and Mac OS X). The address of the repository is (given for both http and ssh):

https://<yourusername>@bitbucket.org/bigr_erasmusmc/fastr
ssh://hg@bitbucket.org/bigr_erasmusmc/fastr

To install to your current Python environment, run:

cd fastr/
pip install .

This installs the scripts and packages in the default system folders. For windows this is the python site-packages directory for the fastr python library and Scripts directory for the executable scripts. For Ubuntu this is in the /usr/local/lib/python2.7/dist-packages/ and /usr/local/bin/ respectively.

Note

If you want to develop fastr, you might want to use pip install -e . to get an editable install

Note

You might want to consider installing fastr in a virtualenv

Note

• On windows python and the Scripts directory are not on the system PATH by default. You can add these by going to System -> Advanced Options -> Environment variables.
• On mac you need the Xcode Command Line Tools. These can be installed using the command xcode-select --install.

Configuration

Fastr has defaults for all settings so it can be run out of the box to test the examples. However, when you want to create your own Networks, use your own data, or use your own Tools, it is required to edit your config file.

Fastr will search for a config file named config.py in the $FASTRHOME and ~/.fastr/ directories. If both config files contain values for a single setting, the version in ~/.fastr/ has priority.

For a sample configuration file and a complete overview of the options in config.py see the Config file section.

Creating a simple network

If Fastr is properly installed and configured, we can start creating networks. Creating a network is very simple:

>>> import fastr

>>> network = fastr.Network()

Now we have an empty network, the next step is to create some nodes and links. Imagine we want to create the following network:

Creating nodes

We will create the nodes and add them to the network. The easiest way to do this is via the network create_ methods. Let’s create two source nodes, one normal node, and one sink:

>>> source1 = network.create_source('Int', id_='source1')
>>> constant1 = network.create_constant('Int', [1, 3, 3, 7], id_='const1')
>>> sink1 = network.create_sink('Int', id_='sink1')
>>> addint = network.create_node('AddInt', id_='addint')

The functions Network.create_source, Network.create_constant, Network.create_source and Network.create_source are shortcut functions for calling the SourceNode, ConstantNode, SinkNode and Node constructors and adding them to the network. A SourceNode and SinkNode only require the datatype to be specified. A ConstantNode requires both the datatype and the data to be set on creation. A Node requires a Tool template to be instantiated from. The id_ option is optional for all three, but makes it easier to identify the nodes and read the logs.

There is an easier way to add a constant to an input, by using a shortcut method. If you assign a list or tuple to an item in the input list, it will automatically create a ConstantNode and a Link between the contant and the input:

>>> addint.inputs['right_hand'] = [1, 3, 3, 7]

The created constant would have the id addint__right_hand__const as it automatically names the new constant $nodeid__$inputid__const.

In an interactive python session we can simply look at the basic layout of the node using the repr function. Just type the name of the variable holding the node and it will print a human readable representation:

>>> source1
SourceNode source1 (tool: source v1.0)
      Inputs         |       Outputs
-------------------------------------------
                     |  output   (Int)
>>> addint
Node addint (tool: AddInt v1.0)
       Inputs          |       Outputs
---------------------------------------------
left_hand  (Int)       |  result   (Int)
right_hand (Int)       |

This tool has inputs of type Int, so the sources and sinks need to have a matching datatype.

The tools and datatypes available are stored in fastr.toollist and fastr.typelist. These variables are created when fastr is imported for the first time. They contain all the datatype and tools specified by the xml files in the search paths. To get an overview of the tools and datatypes loaded by fastr:

>>> fastr.toollist
ToolManager
Add                            v0.1           :  /home/hachterberg/dev/fastr-develop/fastr/fastr/resources/tools/add/v1_0/add.xml
AddImages                      v0.1           :  /home/hachterberg/dev/fastr-develop/fastr/fastr/resources/tools/addimages/v1_0/addimages.xml
AddInt                         v0.1           :  /home/hachterberg/dev/fastr-develop/fastr/fastr/resources/tools/addint/v1_0/addint.xml

>>> fastr.typelist
DataTypeManager
AnyType                    :  <class 'fastr.datatypes.AnyType'>
Boolean                    :  <class 'fastr.datatypes.Boolean'>
Directory                  :  <class 'fastr.datatypes.Directory'>
Float                      :  <class 'fastr.datatypes.Float'>
Int                        :  <class 'fastr.datatypes.Int'>
String                     :  <class 'fastr.datatypes.String'>

The fastr.toollist variable contains all tools that Fastr could find during initalization. Tools can be chosen in two tways:

• toollist[id] which returns the newest version of the tool
• toollist[id, version] which returns the specified version of the tool

Creating links

So now we have a network with 4 nodes defined, however there is no relation between the nodes yet. For this we have to create some links.

>>> link1 = network.create_link(source1.output, addint.inputs['left_hand'])
>>> link2 = network.create_link(constant1.output, addint.inputs['right_hand'])
>>> link3 = network.create_link(addint.outputs['result'], sink1.inputs['input'])

This asks the network to create links and immediatly store them inside the network. A link always points from an Output to an Input (note that SubOutput or SubInputs are also valid). A SourceNode has only 1 output which is fixed, so it is easy to find. However, addImage has two inputs and one output, this requires us to specify which output we need. A normal node has a mapping with Inputs and one with Outputs. They can be indexed with the approriate id’s. The function returns the links, but you only need that if you are planning to change a link. If not, it is possible to use a short-hand which creates a link but gives you no easy access to it for later.

>>> addint.inputs['left_hand'] = source1.output
>>> addint.inputs['right_hand'] = constant1.output
>>> sink1.inputs['input'] = addint.outputs['result']

Create an image of the Network

For checking your Network it is very useful to have a graphical representation of the network. This can be achieved using the Network.draw_network method.

>>> network.draw_network()
'/home/username/network_layout.dot.svg'

This will create a figure in the path returned by the function that looks like:

Note

for this to work you need to have graphviz installed

Running a Network

Running a network locally is almost as simple as calling the Network.execute method:

>>> source_data = {'source1': {'s1': 4, 's2': 5, 's3': 6, 's4': 7}}
>>> sink_data = {'sink1': 'vfs://tmp/fastr_result_{sample_id}.txt'}
>>> network.execute(source_data, sink_data)

As you can see the execute method needs data for the sources and sinks. This has to be supplied in two dict that have keys matching every source/sink id in the network. Not supplying data for every source and sink will result in an error, although it is possible to pass an empty list to a source.

Note

The values of the source data have to be simple values or urls and values of the sink data have to be url templates. To see what url schemes are available and how they work see IOPlugin Reference. For the sink url templates see SinkNode.set_data

For source nodes you can supply a list or a dict with values. If you supply a dict the keys will be interpreted as sample ids and the values as the corresponding values. If you supply a list, keys will be generated in the form of id_{N} where N will be index of the value in the list.

Warning

As a dict does not have a fixed order, when a dict is supplied the samples are ordered by key to get a fixed order! For a list the original order is retained.

For the sink data, an url template has to be supplied that governs how the data is stored. The mini-lanuage (the replacement fields) are described in the SinkNode.set_data method.

To rerun a stopped/crashed pipeline check the user manual on Continuing a Network

User Manual

In this chapter we will discuss the parts of Fastr in more detail. We will give a more complete overview of the system and describe the more advanced features.

Tools

The Tools in Fastr are the building blocks of each workflow. A tool represents a program/script/binary that can be called by Fastr and can be seens as a template. A Node can be created based on a Tool. The Node will be one processing step in a workflow, and the tool defines what the step does.

On the import of Fastr, all available Tools will be loaded in a default ToolManager that can be accessed via fastr.toollist. To get an overview of the tools in the system, just print the repr() of the ToolManager:

>>> fastr.toollist
AddImages                v0.1    :  /home/hachterberg/dev/fastr/fastr/resources/tools/addimages/v1_0/addimages.xml
AddInt                   v0.1    :  /home/hachterberg/dev/fastr/fastr/resources/tools/addint/v1_0/addint.xml

As you can see it gives the tool id, version and the file from which it was loaded for each tool in the system. To view the layout of a tool, just print the repr() of the tool itself.

>>> fastr.toollist['AddInt']
Tool AddInt v0.1 (Add two integers)
       Inputs          |       Outputs
---------------------------------------------
left_hand  (Int)       |  result   (Int)
right_hand (Int)       |

To add a Tool to the system a file should be added to one of the path in fastr.config.tools_path. The structure of a tool file is described in Tool description

Create your own tool

There are 4 steps in creating a tool:

1. CREATE FOLDERS. We will call the tool ThrowDie. Create the folder throw_die in the folder fastr-tools. In this folder create another folder called bin.

2. PLACE EXECUTABLE IN CORRECT PLACE. In this example we will use a snippet of executable python code:

   #!/usr/bin/env python
   import sys
   import random
   import json
   
   if (len(sys.argv) > 1):
       sides = int(sys.argv[1])
   else:
       sides = 6
   result = [int(random.randint(1, sides ))]
   
   print('RESULT={}'.format(json.dumps(result)))
   

   Save this text in a file called throw_die.py

   Place the executable python script in the folder throw_die/bin

3. CREATE AND EDIT XML FILE FOR TOOL.

   Put the following text in file called throw_die.xml.

   <tool id="ThrowDie" description="Simulates a throw of a die. Number of sides of the die is provided by user"
         name="throw_die" version="1.0">
     <authors>
       <author name="John Doe" />
     </authors>
     <command version="1.0" >
       <authors>
         <author name="John Doe" url="http://a.b/c" />
       </authors>
       <targets>
         <target arch="*" bin="throw_die.py" interpreter="python" os="*" paths='bin/'/>
       </targets>
       <description>
          throw_die.py number_of_sides
          output = simulated die throw
       </description>
     </command>
     <interface>
       <inputs>
         <input cardinality="1" datatype="Int" description="Number of die sides" id="die_sides" name="die sides" nospace="False" order="0" required="True"/>
        </inputs>
       <outputs>
         <output id="output" name="output value" datatype="Int" automatic="True" cardinality="1" method="json" location="^RESULT=(.*)$" />
       </outputs>
     </interface>
   </tool>
   

   Put throw_die.xml in the folder example_tool. All Attributes in the example above are required. For a complete overview of the xml Attributes that can be used to define a tool, check the Tool description. The most important Attributes in this xml are:

   id      : The id is used in in FASTR to create an instance of your tool, this name will appear in the toollist when you type fastr.toollist.
   targets : This defines where the executables are located and on which platform they are available.
   inputs  : This defines the inputs that you want to be used in FASTR, how FASTR should use them and what data is allowed to be put in there.
   

   More xml examples can be found in the fastr-tools folder.

4. EDIT CONFIGURATION FILE. Append the line [PATH TO LOCATION OF FASTR-TOOLS]/fastr-tools/throw_die/ to the the config.py (located in ~/.fastr/ directory) to the tools_path. See Config file for more information on configuration.

   You should now have a working tool. To test that everything is ok do the following in python:

   >>> import fastr
   >>> fastr.toollist
   

Now a list of available tools should be produced, including the tool throw_die

To test the tool create the script test_throwdie.py:

import fastr
network = fastr.Network()
source1 = network.create_source(fastr.typelist['Int'], id_='source1')
sink1 = network.create_sink(fastr.typelist['Int'], id_='sink1')
throwdie = network.create_node(fastr.toollist['ThrowDie'], id_='throwdie')
link1 = network.create_link(source1.output, throwdie.inputs['die_sides'])
link2 = network.create_link(throwdie.outputs['output'], sink1.inputs['input'])
source_data = {'source1': {'s1': 4, 's2': 5, 's3': 6, 's4': 7}}
sink_data = {'sink1': 'vfs://tmp/fastr_result_{sample_id}.txt'}
network.draw_network()
network.execute(source_data, sink_data)

Call the script from commandline by

>>> python test_throwdie.py

An image of the network will be created in the current directory and result files will be put in the tmp directory. The result files are called fastr_result_s1.txt, fastr_result_s2.txt, fastr_result_s3.txt, and fastr_result_s4.txt

Note

If you have code which is operating system depend you will have to edit the xml file. The following gives and example of how the elastix tool does this:

<targets>
      <target os="windows" arch="*" bin="elastix.exe">
        <paths>
          <path type="bin" value="vfs://apps/elastix/4.7/install/" />
          <path type="lib" value="vfs://apps/elastix/4.7/install/lib" />
        </paths>
      </target>
      <target os="linux" arch="*" modules="elastix/4.7" bin="elastix">
        <paths>
          <path type="bin" value="vfs://apps/elastix/4.7/install/" />
          <path type="lib" value="vfs://apps/elastix/4.7/install/lib" />
        </paths>
      </target>
      <target os="darwin" arch="*" modules="elastix/4.7" bin="elastix">
        <paths>
          <path type="bin" value="vfs://apps/elastix/4.7/install/" />
          <path type="lib" value="vfs://apps/elastix/4.7/install/lib" />
        </paths>
      </target>
   </targets>

vfs is the virtual file system path, more information can be found at VirtualFileSystem.

Network

A Network represented an entire workflow. It hold all Nodes, Links and other information required to execute the workflow. Networks can be visualized as a number of building blocks (the Nodes) and links between them:

An empty network is easy to create, all you need is to name it:

>>> network = fastr.Network(id_="network_name")

The Network is the main interface to Fastr, from it you can create all elements to create a workflow. In the following sections the different elements of a Network will be described in more detail.

Node

Nodes are the point in the Network where the processing happens. A Node takes the input data and executes jobs as specified by the underlying Tool. A Nodes can be created in a two different ways:

>>> node1 = fastr.Node(tool, id_='node1', parent=network)
>>> node2 = network.create_node(tool, id_='node2', stepid='step1')

In the first way, we specifically create a Node object. We pass it an id and the parent network. If the parent is None the fastr.curent_network will be used. The Node constructor will automaticaly add the new node to the parent network.

Note

For a Node, the tool can be given both as the Tool class or the id of the

tool.

The second way, we tell the network to create a Node. The network will automatically assign itself as the parent. Optionally you can add define a stepid for the node which is a logical grouping of Nodes that is mostly used for visualization.

A Node contains Inputs and Outputs. To see the layout of the Node one can simply look at the repr().

>>> addint = fastr.Node(fastr.toollist['AddInt'], id_='addint')
>>> addint
Node addint (tool: AddInt v1.0)
       Inputs          |       Outputs
---------------------------------------------
left_hand  (Int)       |  result   (Int)
right_hand (Int)       |

The inputs and outputs are located in mappings with the same name:

>>> addint.inputs
InputDict([('left_hand', <Input: fastr:///networks/unnamed_network/nodelist/addint/inputs/left_hand>), ('right_hand', <Input: fastr:///networks/unnamed_network/nodelist/addint/inputs/right_hand>)])

>>> addint.outputs
OutputDict([('result', Output fastr:///networks/unnamed_network/nodelist/addint/outputs/result)])

The InputDict and OutputDict are classes that behave like mappings. The InputDict also facilitaties the linking shorthand. By assigning an Output to an existing key, the InputDict will create a Link between the InputDict and Output.

SourceNode

A SourceNode is a special kind of node that is the start of a workflow. The SourceNodes are given data at run-time that fetched via IOPlugins. On create, only the datatype of the data that the SourceNode supplied needs to be known. Creating a SourceNode is very similar to an ordinary node:

>>> source1 = fastr.SourceNode('Int', id_='source1')
>>> source2 = network.create_source(fastr.typelist['Int'], id_='source2', stepid='step1')

In both cases, the source is automatically automaticall assigned to a network. In the first case to the fastr.current_network and in the second case to the network used to call the method. A SourceNode only has a single output which has a short-cut access via source.output.

Note

For a source or constant node, the datatype can be given both as the BaseDataType class or the id of the datatype.

ConstantNode

A ConstantNode is another special node. It is a subclass of the SourceNode and has a similar function. However, instead of setting the data at run-time, the data of a constant is given at creation and saved in the object. Creating a ConstantNode is similar as creating a source, but with supplying data:

>>> constant1 = fastr.ConstantNode('Int', [42], id_='constant1')
>>> constant2 = network.create_constant('Int', [42], id_='constant2', stepid='step1')

Often, when a ConstantNode is created, it is created specifically for one input and will not be reused. In this case there is a shorthand to create and link a constant to an input:

>>> addint.inputs['value1'] = [42]

will create a constant node with the value 42 and create a link between the output and input addint.value1.

SinkNode

The SinkNode is the counter-part of the source node. Instead of get data into the workflow, it saves the data resulting from the workflow. For this a rule has to be given at run-time that determines where to store the data. The information about how to create such a rule is described at SinkNode.set_data. At creation time, only the datatype has to be specified:

>>> sink1 = fastr.Sink('Int', id_='sink1')
>>> sink2 = network.create_sink(fastr.typelist['Int'], id_='sink2', stepid='step1')

Link

Links indicate how the data flows between Nodes. Links can be created explicitly using on of the following:

>>> link = fastr.Link(node1.outputs['image'], node2.inputs['image'])
>>> link = network.create_link(node1.outputs['image'], node2.inputs['image'])

or can be create implicitly by assigning an Output to an Input in the InputDict.

# This style of assignment will create a Link similar to above
>>> node2.inputs['image'] = node1.outputs['image']

Note that a Link is also create automatically when using the short-hand for the ConstantNode

Data Flow

The data enters the Network via SourceNodes, flows via other Nodes and leaves the Network via SinkNodes. The flow between Nodes goes from an Output via a Link to an Input. In the following image it is simple to track the data from the SourceNodes at the left to the SinkNodes at right side:

Note that the data in Fastr is stored in the Output and the Link and Input just give access to it (possible while transforming the data).

Data flow inside a Node

In a Node all data from the Inputs will be combined and the jobs will be generated. There are strict rules to how this combination is performed. In the default case all inputs will be used pair-wise, and if there is only a single value for an input, it it will be considered as a constant.

To illustrate this we will consider the following Tool (note this is a simplified version of the real tool):

>>> fastr.toollist['Elastix']
Tool Elastix v4.8 (Elastix Registration)
                         Inputs                            |             Outputs
----------------------------------------------------------------------------------------------
fixed_image       (ITKImageFile)                           |  transform (ElastixTransformFile)
moving_image      (ITKImageFile)                           |
parameters        (ElastixParameterFile)                   |

Also it is important to know that for this tool (by definition) the cardinality of the transform Output will match the cardinality of the parameters Inputs

If we supply a Node based on this Tool with a single sample on each Input, there will be one single matching Output sample created:

If the cardinality of the parameters sample would be increased to 2, the resulting transform sample would also become 2:

Now if the number of samples on fixed_image would be increased to 3, the moving_image and parameters will be considered constant and be repeated, resulting in 3 transform samples.

Then if the amount of samples for moving_image is also increased to 3, the moving_image and fixed_image will be used pairwise and the parameters will be constant.

Advanced flows in a Node

Sometimes the default pairwise behaviour is not desirable. For example if you want to test all combinations of certain input samples. To achieve this we can change the input_group of Inputs to set them apart from the rest. By default all Inputs are assigned to the default input group. Now let us change that:

>>> node = network.create_node('Elastix', id_='elastix')
>>> node.inputs['moving_image'].input_group = 'moving'

This will result in moving_image to be put in a different input group. Now if we would supply fixed_image with 3 samples and moving_image with 4 samples, instead of an error we would get the following result:

Warning

TODO: Expand this section with the merging dimensions

Data flows in a Link

As mentioned before the data flows from an Output to an Input throuhg a Link. By default the Link passed the data as is, however there are two special directives that change the shape of the data:

1. Collapsing flow, this collapses certain dimensions from the sample array into the cardinality. As a user you have to specify the dimension or tuple of dimensions you want to collapse.

   

   This is useful in situation where you want to use a tool that aggregates over a number of samples (e.g. take a mean or sum).

   To achieve this you can set the collapse property of the Link as follows:

   >>> link.collapse = 'dim1'
   >>> link.collapse = ('dim1', 'dim2')  # In case you want to collapse multiple dimensions
   

2. Expanding flow, this turns the cardinality into a new dimension. The new dimension will be named after the Output from which the link originates. It will be in the form of {nodeid}__{outputid}

   

   This flow directive is useful if you want to split a large sample in multiple smaller samples. This could be because processing the whole sample is not feasible because of resource constraints. An example would be splitting a 3D image into slices to process separately to avoid high memory use or to achieve parallelism.

   To achieve this you can set the expand property of the Link to True:

   >>> link.expand = True
   

Note

both collapsing and expanding can be used on the same link, it will executes similar to a expand-collapse sequence, but the newly created expand dimension is ignored in the collapse.

>>> link.collapse = 'dim1'
>>> link.expand = True

Data flows in an Input

If an Inputs has multiple Links attached to it, the data will be combined by concatenating the values for each corresponding sample in the cardinality.

Broadcasting (matching data of different dimensions)

Sometimes you might want to combine data that does not have the same number of dimensions. As long as all dimensions of the lower dimensional datasets match a dimension in the higher dimensional dataset, this can be achieved using broadcasting. The term broadcasting is borrowed from NumPy and described as:

“The term broadcasting describes how numpy treats arrays with different shapes during arithmetic operations. Subject to certain constraints, the smaller array is “broadcast” across the larger array so that they have compatible shapes.”

—NumPy manual on broadcasting

In fastr it works similar, but to combined different Inputs in an InputGroup. To illustrate broadcasting it is best to use an example, the following network uses broadcasting in the transformix Node:

As you can see this visualization prints the dimensions for each Input and Output (e.g. the elastix.fixed_image Input has dimensions [N]). To explain what happens in more detail, we present an image illustrating the details for the samples in elastix and transformix:

In the figure the moving_image (and references to it) are identified with different colors, so they are easy to track across the different steps.

At the top the Inputs for the elastix Node are illustrated. Because the input groups a set differently, output samples are generated for all combinations of fixed_image and moving_image (see Advanced flows in a Node for details).

In the transformix Node, we want to combine a list of samples that is related to the moving_image (it has the same dimension name and sizes) with the resulting transform samples from the elastix Node. As you can see the sizes of the sample collections do not match ([N] vs [N x M]). This is where broadcasting comes into play, it allows the system to match these related sample collections. Because all the dimensions in [N] are known in [N x M], it is possible to match them uniquely. This is done automatically and the result is a new [N xM] sample collection. To create a matching sample collections, the samples in the transformix.image Input are reused as indicated by the colors.

Warning

Note that this might fail when there are data-blocks with non-unique dimension names, as it will be not be clear which of the dimensions with identical names should be matched!

DataTypes

In Fastr all data is contained in object of a specific type. The types in Fastr are represented by classes that subclass BaseDataType. There are a few different other classes under BaseDataType that are each a base class for a family of types:

• DataType – The base class for all types that hold data
  • ValueType – The base class for types that contain simple data (e.g. Int, String) that can be represented as a str
  • EnumType – The base class for all types that are a choice from a set of options
  • URLType – The base class for all types that have their data stored in files (which are referenced by URL)
• TypeGroup – The base class for all types that actually represent a group of types

The relation between the different DataType classes

The types are defined in xml files and created by the DataTypeManager. The DataTypeManager acts as a container containing all Fastr types. It is automatically instantiated as fastr.typelist. In fastr the created DataTypes classes are also automatically place in the fastr.datatypes module once created.

Resolving Datatypes

Outputs in fastr can have a TypeGroup or a number of DataTypes associated with them. The final DataType used will depend on the linked Inputs. The DataType resolving works as a two-step procedure.

1. All possible DataTypes are determined and considered as options.
2. The best possible DataType from options is selected for non-automatic Outputs

The options are defined as the intersection of the set of possible values for the Output and each separate Input connected to the Output. Given the resulting options there are three scenarios:

• If there are no valid DataTypes (options is empty) the result will be None.
• If there is a single valid DataType, then this is automatically the result (even if it is not a preferred DataType).
• If there are multiple valid DataTypes, then the preferred DataTypes are used to resolve conflicts.

There are a number of places where the preferred DataTypes can be set, these are used in the order as given:

1. The preferred keyword argument to match_types
2. The preferred types specified in the fastr.config

Execution

Executing a Network is very simple:

>>> source_data = {'source_id1': ['val1', 'val2'],
                   'source_id2': {'id3': 'val3', 'id4': 'val4'}}
>>> sink_data = {'sink_id1': 'vfs://some_output_location/{sample_id}/file.txt'}
>>> network.execute(source_data, sink_data)

The Network.execute method takes a dict of source data and a dict sink data as arguments. The dictionaries should have a key for each SourceNode or SinkNode.

TODO: add .. figure:: images/execution_layers.*

The execution of a Network uses a layered model:

• Network.execute will analyze the Network and call all Nodes.
• Node.execute will create jobs and fill their payload
• execute_job will execute the job on the execute machine and resolve any deferred values (val:// urls).
• Tool.execute will find the correct target and call the interface and if required resolve vfs:// urls
• Interface.execute will actually run the required command(s)

The ExecutionPlugin will call call the executionscript.py for each job, passing the job as a gzipped pickle file. The executionscript.py will resolve deferred values and then call Tool.execute which analyses the required target and executes the underlying Interface. The Interface actually executes the job and collect the results. The result is returned (via the Tool) to the executionscript.py. There we save the result, provenance and profiling in a new gzipped pickle file. The execution system will use a callback to load the data back into the Network.

The selection and settings of the ExecutionPlugin are defined in the fastr config.

Continuing a Network

Normally a random temporary directory is created for each run. To continue a previously stopped/crashed network, you should call the Network.execute method using the same temporary directory(tmp dir). You can set the temporary directory to a fixed value using the following code:

>>> tmpdir = '/tmp/example_network_rerun'
>>> network.execute(source_data, sink_data, tmpdir=tmpdir)

Warning

Be aware that at this moment, Fastr will rerun only the jobs where not all output files are present or if the job/tool parameters have been changed. It will not rerun if the input data of the node has changed or the actual tools have been adjusted. In these cases you should remove the output files of these nodes, to force a rerun.

IOPlugins

Sources and sink are used to get data in and out of a Network during execution. To make the data retrieval and storage easier, a plugin system was created that selects different plugins based on the URL scheme used. So for example, a url starting with vfs:// will be handles by the VirtualFileSystem plugin. A list of all the IOPlugins known by the system and their use can be found at IOPlugin Reference.

Naming Convention

For the naming convention of the tools we tried to stay close to the Python PEP 8 coding style. In short, we defined toolnames as classes so they should be UpperCamelCased. The inputs and outputs of a tool we considered as functions or method arguments, these should we named lower_case_with_underscores.

An overview of the mapping of Fastr to PEP 8:

Fastr construct	Python PEP8 equivalent	Examples
Network.id	module	brain_tissue_segmentation
Tool.id	class	BrainExtractionTool, ThresholdImage
Node.id	variable name	brain_extraction, threshold_mask
Input/Output.id	method	image, number_of_classes, probability_image

Furthermore there are some small guidelines:

• No input or output in the input or output names. This is already specified when setting or getting the data.

• Add the type of the output that is named. i.e. enum, string, flag, image,

  • No File in the input/output name (Passing files around is what Fastr was developed for).
  • No type necessary where type is implied i.e. lower_threshold, number_of_levels, max_threads.

• Where possible/useful use the fullname instead of an abbreviation.

Provenance

For every data derived data object, Fastr records the Provenance. The SinkNode write provenance records next to every data object it writes out. The records contain information on what operations were performed to obtain the resulting data object.

W3C Prov

The provenance is recorded using the W3C Prov Data Model (PROV-DM). Behind the scences we are using the python prov implementation.

The PROV-DM defines 3 Starting Point Classes and and their relating properties. See Fig. 3 for a graphic representation of the classes and the relations.

The three Starting Point classes and the properties that relate them. The diagrams in this document depict Entities as yellow ovals, Activities as blue rectangles, and Agents as orange pentagons. The responsibility properties are shown in pink. [*]

Implementation

In the workflow document the provenance classes map to fastr concepts in the following way:

Agent:Fastr, Networks, Tools, Nodes Activity:Jobs Entities:Data

Usage

The provenance is stored in ProvDocument objects in pickles. The convenience command line tool fastr prov can be used to extract the provenance in the PROV-N notation and can be serialized to PROV-JSON and PROV-XML. The provenance document can also be vizualized using the fastr prov command line tool.

Footnotes

[*]	This picture and caption is taken from http://www.w3.org/TR/prov-o/ . “Copyright © 2011-2013 World Wide Web Consortium, (MIT, ERCIM, Keio, Beihang). http://www.w3.org/Consortium/Legal/2015/doc-license“

Command Line Tools

command	description
cat	Print information from a job file
execute	Execute a fastr job file
extract_argparse	Create a stub for a Tool based on a python script using argparse
prov	Get PROV information from the result pickle.
run	Run a Network from the commandline
testtool	Run the tests of a tool to verify the proper function
trace	Trace samples/sinks from a run
verify	Print information from a job file
webapp	Start the fastr webapp and open in a new browser tab

cat

usage: fastr cat [-h] __fastr_extra_job_info__.json path

Positional Arguments

__fastr_extra_job_info__.json
	result file to cat
path	path of the data to print

execute

Execute a job or network from commandline

usage: fastr execute [-h] [JOBFILE]

Positional Arguments

JOBFILE

File of the job to execute (default ./__fastr_command__.pickle.gz)

extract_argparse

usage: fastr extract_argparse [-h] SCRIPT.py TOOL.xml

Positional Arguments

SCRIPT.py	Python script to inspect
TOOL.xml	created Tool stub

prov

Get PROV information from the result pickle. When no options are given, the provenance syntax is printed to stdout in PROV-JSON format.

usage: fastr prov [-h] [-so SYNTAX_OUT_FILE] [-sf SYNTAX_FORMAT] [-i INDENT]
                  [-vo VISUALIZE_OUT_FILE]
                  [RESULTFILE]

Positional Arguments

RESULTFILE

File of the job to execute (default ./__fastr_result__.pickle.gz)

Named Arguments

-so, –syntax-out-file
	Write the syntax to file.
-sf, –syntax-format
	Choices are: [json], provn or xml Default: “json”
-i, –indent	Indent size of the serialized documents. Default: 2
-vo, –visualize-out-file
	Visualize the provenance. The most preferred format is svg. You can specify any format pydot supports. Specify the format by postfixing the filename with an extension.

run

Execute a job or network from commandline

usage: fastr run [-h] NETWORKFILE

Positional Arguments

NETWORKFILE

File of the network to execute

testtool

Run the tests for a Tool to check the function

usage: fastr testtool [-h] TOOL

Positional Arguments

TOOL	the id of the tool to test

trace

usage: fastr trace [-h] [--verbose] [--sinks [SINKS [SINKS ...]]]
                   [--nodes [NODES [NODES ...]]]
                   [--samples [SAMPLES [SAMPLES ...]]]
                   [__sink_data__.json]

Positional Arguments

__sink_data__.json
	result file to cat Default: “/home/docs/checkouts/readthedocs.org/user_builds/fastr/checkouts/1.2.2/fastr/doc/__sink_data__.json”

Named Arguments

–verbose, -v	path of the data to print Default: False
–sinks	list results for specified sinks
–nodes	list results for specified nodes
–samples	list result for all samples

verify

usage: fastr verify [-h] TYPE path

Positional Arguments

TYPE	Possible choices: tool Type of resource to verify (e.g. tool)
path	path of the resource to verify

webapp

Fastr web client

usage: fastr webapp [-h] [-d] [-o]

Named Arguments

-d, –debug	Debug mode. Default: False
-o, –openpage	Open web page after start. Default: False

Resource File Formats

This chapter describes the various files fastr uses. The function and format of the files is described allowing the user to configure fastr and add DataTypes and Tools.

Config file

Fastr reads the config files from the following locations by default (in order):

• $FASTRHOME/config.py
• ~/.fastr/config.py

Reading a new config file change or override settings, making the last config file read have the highest priority. All settings have a default value, making config files and all settings within optional.

Example config file

Here is a minimal config file:

# Enable debugging output
debug = False

# Define the path to the tool definitions
tools_path = ['/path/to/tools',
              '/path/to/other/tools'] + tools_path
types_path = ['/path/to/datatypes',
              '/path/to/other/datatypes'] + types_path


# Specify what your preferred output types are.
preferred_types += ["NiftiImageFileCompressed",
                    "NiftiImageFile"]

# Set the tmp mount
mounts['tmp'] = '/path/to/tmpdir'

Format

The config file is actually a python source file. The next syntax applies to setting configuration values:

# Simple values
float_value = 1.0
int_value = 1
str_value = "Some value"
other_str_value = 'name'.capitalize()

# List-like values
list_value = ['over', 'ride', 'values']
other_list_value.prepend('first')
other_list_value.append('list')

# Dict-like values
dict_value = {'this': 1, 'is': 2, 'fixed': 3}
other_dict_value['added'] = 'this key'

Note

Dictionaries and list always have a default, so you can always append or assign elements to them and do not have to create them in a config file. Best practice is to only edit them unless you really want to block out the earliers config files.

Most operations will be assigning values, but for list and dict values a special wrapper object is used that allows manipulations from the default. This limits the operations allowed.

List values in the config.py have the following supported operators/methods:

• +, __add__ and __radd__
• += or __iadd__
• append
• prepend
• extend

Mapping (dict-like) values in the config.py have the following supported operators/methods:

• update
• [] or __getitem__, __setitem__ and __delitem__

Configuration fields

This is a table the known config fields on the system:

name	type	description	default
debug	bool	Flag to enable/disable debugging	False
examplesdir	str	Directory containing the fastr examples	$systemdir/examples
execution_plugin	str	The default execution plugin to use	‘ProcessPoolExecution’
executionscript	str	Execution script location	$systemdir/execution/executionscript.py
logdir	str	Directory where the fastr logs will be placed	$userdir/logs
logtype	str	Type of logging to use	‘default’
mounts	dict	A dictionary containing all mount points in the VFS system	{‘tmp’: ‘$TMPDIR’, ‘home’: ‘~/’, ‘example_data’: ‘$systemdir/examples/data’}
networks_path	list	Directories to scan for networks	[‘$userdir/networks’, ‘$resourcedir/networks’]
pim_host	str	Host of the PIM server to report to	‘’
plugins_path	list	Directories to scan for plugins	[‘$userdir/plugins’, ‘$resourcedir/plugins’]
preferred_types	list	A list indicating the order of the preferred types to use. First item is most preferred.	[]
protected_modules	list	A list of modules in the environmnet modules that are protected against unloading	[]
resourcesdir	str	Directory containing the fastr system resources	$systemdir/resources
schemadir	str	Directory containing the fastr data schemas	$systemdir/schemas
source_job_limit	int	The number of source jobs allowed to run concurrently	4
systemdir	str	Fastr installation directory	‘/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr’
tools_path	list	Directories to scan for tools	[‘$userdir/tools’, ‘$resourcedir/tools’]
types_path	list	Directories to scan for datatypes	[‘$userdir/datatypes’, ‘$resourcedir/datatypes’]
userdir	str	Fastr user configuration directory	~/.fastr
warn_develop	bool	Warning users on import if this is not a production version of fastr	True
web_hostname	str	The hostname to expose the web app for	‘localhost’
web_port	str	The port to expose the web app on	‘5000’
web_secret_key	str	The secret key to use for the flask web app	‘VERYSECRETKEY!’

Tool description

Tools are the building blocks in the fastr network. To add new Tools to fastr, XML/json files containing a Tool definition can be added. These files have the following layout:

Attribute			Description
id			The id of this Tool (used internally in fastr)
name			The name of the Tool, for human readability
version			The version of the Tool wrapper (not the binary)
url			The url of the Tool wrapper
authors[]			List of authors of the Tools wrapper
	name		Name of the author
	email		Email address of the author
	url		URL of the website of the author
tags	tag[]		List of tags describing the Tool
command			Description of the underlying command
	version		Version of the tool that is wrapped
	url		Website where the tools that is wrapped can be obtained
	targets[]		Description of the target binaries/script of this Tool
		os	OS targetted (windows, linux, macos or * (for any)
		arch	Architecture targetted 32, 64 or * (for any)
		module	Environment module giving access to the Tool
		location	If the module is not found, try using this location to find the Tool
		interpreter	Interpreter to use to call the bin with (e.g. bash, python, Rscript)
		bin	Name of the Tool binary (e.g. toolname, toolname.exe, toolname.py
	description		Description of the Tool
	license		License of the Tool, either full license or a clear name (e.g. LGPL, GPL v2)
	authors[]		List of authors of the Tool (not the wrapper!)
		name	Name of the authors
		email	Email address of the author
		url	URL of the website of the author
inputs[]			List of Inputs that can are accepted by the Tool
	id		ID of the Input
	name		Longer name of the Input (more human readable)
	datatype		The ID of the DataType of the Input [1]
	enum[]		List of possible values for an EnumType (created on the fly by fastr) [1]
	prefix		Commandline prefix of the Input (e.g. –in, -i)
	cardinality		Cardinality of the Input
	repeat_prefix		Flag indicating if for every value of the Input the prefix is repeated
	required		Flag indicating if the input is required
	nospace		Flag indicating if there is no space between prefix and value (e.g. –in=val)
	format		For DataTypes that have multiple representations, indicate which one to use
	default		Default value for the Input
	description		Long description for an input
outputs[]			List of Outputs that are generated by the Tool (and accessible to fastr)
	id		ID of the Output
	name		Longer name of the Output (more human readable)
	datatype		The ID of the DataType of the Output [1]
	enum[]		List of possible values for an EnumType (created on the fly by fastr) [1]
	prefix		Commandline prefix of the Output (e.g. –out, -o)
	cardinality		Cardinality of the Output
	repeat_prefix		Flag indicating if for every value of the Output the prefix is repeated
	required		Flag indicating if the input is required
	nospace		Flag indicating if there is no space between prefix and value (e.g. –out=val)
	format		For DataTypes that have multiple representations, indicate which one to use
	description		Long description for an input
	action		Special action (defined per DataType) that needs to be performed before creating output value (e.g. ‘ensure’ will make sure an output directory exists)
	automatic		Indicate that output doesn’t require commandline argument, but is created automatically by a Tool [2]
	method		Method to acquire output value from the Tool can be ‘path’ or ‘stdout’ [2]
	location		Definition where to an automatically, usage depends on the method [2]
help			Help text explaining the use of the Tool
cite			Bibtext of the Citation(s) to reference when using this Tool for a publication

Footnotes

[1]	(1, 2, 3, 4) datatype and enum are conflicting entries, if both specified datatype has presedence

[2]	(1, 2, 3) More details on defining automatica output are given in [TODO]

Resource Reference

In this chapter we describe the different plugins bundled with Fastr (e.g. IOPlugins, ExecutionPlugins). The reference is build automatically from code, so after installing a new plugin the documentation has to be rebuild for it to be included in the docs.

CollectorPlugin Reference

CollectorPlugins are used for finding and collecting the output data of outputs part of a FastrInterface

scheme	CollectorPlugin
JsonCollector	JsonCollector
PathCollector	PathCollector
StdoutCollector	StdoutCollector

JsonCollector

The JsonCollector plugin allows a program to print out the result in a pre-defined JSON format. It is then used as values for fastr.

The working is as follows:

1. The location of the output is taken
2. If the location is None, go to step 5
3. The substitutions are performed on the location field (see below)
4. The location is used as a regular expression and matched to the stdout line by line
5. The matched string (or entire stdout if location is None) is loaded as a json
6. The data is parsed by set_result

The structure of the JSON has to follow the a predefined format. For normal Nodes the format is in the form:

[value1, value2, value3]

where the multiple values represent the cardinality.

For a FlowNodes the format is the form:

{
  'sample_id1': [value1, value2, value3],
  'sample_id2': [value4, value5, value6]
}

This allows the tool to create multiple output samples in a single run.

PathCollector

The PathCollector plugin for the FastrInterface. This plugin uses the location fields to find data on the filesystem. To use this plugin the method of the output has to be set to path

The general working is as follows:

1. The location field is taken from the output
2. The substitutions are performed on the location field (see below)
3. The updated location field will be used as a regular expression filter
4. The filesystem is scanned for all matching files/directory

The special substitutions performed on the location use the Format Specification Mini-Language Format Specification Mini-Language. The predefined fields that can be used are:

• inputs, an objet with the input values (use like {inputs.image[0]}) The input contains the following attributes that you can access:
  • .directory for the directory name (use like input.image[0].directory) The directory is the same as the result of os.path.dirname
  • .filename is the result of os.path.basename on the path
  • .basename for the basename name (use like input.image[0].basename) The basename is the same as the result of os.path.basename and the extension stripped. The extension is considered to be everything after the first dot in the filename.
  • .extension for the extension name (use like input.image[0].extension)
• output, an object with the output values (use like {outputs.result[0]}) It contains the same attributes as the input
  • special.cardinality, the index of the current cardinality
  • special.extension, is the extension for the output DataType

Example use:

<output ... method="path" location="{output.directory[0]}/TransformParameters.{special.cardinality}.{special.extension}"/>

Given the output directory ./nodeid/sampleid/result, the second sample in the output and filetype with a txt extension, this would be translated into:

<output ... method="path" location="./nodeid/sampleid/result/TransformParameters.1.txt>

StdoutCollector

The StdoutCollector can collect data from the stdout stream of a program. It filters the stdout line by line matching a predefined regular expression.

The general working is as follows:

1. The location field is taken from the output
2. The substitutions are performed on the location field (see below)
3. The updated location field will be used as a regular expression filter
4. The stdout is scanned line by line and the regular expression filter is applied

The special substitutions performed on the location use the Format Specification Mini-Language Format Specification Mini-Language. The predefined fields that can be used are:

• inputs, an objet with the input values (use like {inputs.image[0]})
• outputs, an object with the output values (use like {outputs.result[0]})
• special which has two subfields:
  • special.cardinality, the index of the current cardinality
  • special.extension, is the extension for the output DataType

Note

because the plugin scans line by line, it is impossible to catch multi-line output into a single value

ExecutionPlugin Reference

This class is the base for all Plugins to execute jobs somewhere. There are many methods already in place for taking care of stuff.

There are fall-backs for certain features, but if a system already implements those it is usually preferred to skip the fall-back and let the external system handle it. There are a few flags to enable disable these features:

• cls.SUPPORTS_CANCEL indicates that the plugin can cancel queued jobs

• cls.SUPPORTS_HOLD_RELEASE indicates that the plugin can queue jobs in a hold state and can release them again (if not, the base plugin will create a hidden queue for held jobs). The plugin should respect the Job.status == JobState.hold when queueing jobs.

• cls.SUPPORTS_DEPENDENCY indicate that the plugin can manage job dependencies, if not the base plugin job dependency system will be used and jobs with only be submitted when all dependencies are met.

• cls.CANCELS_DEPENDENCIES indicates that if a job is cancelled it will automatically cancel all jobs depending on that job. If not the plugin traverse the dependency graph and kill each job manual.

  Note

  If a plugin supports dependencies it is assumed that when a job gets cancelled, the depending job also get cancelled automatically!

Most plugins should only need to redefine a few abstract methods:

• __init__ the constructor
• cleanup a clean up function that frees resources, closes connections, etc
• _queue_job the method that queues the job for execution

Optionally an extra job finished callback could be added:

• _job_finished extra callback for when a job finishes

If SUPPORTS_CANCEL is set to True, the plugin should also implement:

• _cancel_job cancels a previously queued job

If SUPPORTS_HOLD_RELEASE is set to True, the plugin should also implement:

• _hold_job hold_job a job that is currently held
• _release_job releases a job that is currently held

If SUPPORTED_DEPENDENCY is set to True, the plugin should:

• Make sure to use the Job.hold_jobs as a list of its dependencies

Not all of the functions need to actually do anything for a plugin. There are examples of plugins that do not really need a cleanup, but for safety you need to implement it. Just using a pass for the method could be fine in such a case.

Warning

When overwriting other functions, extreme care must be taken not to break the plugins working, as there is a lot of bookkeeping that can go wrong.

scheme	ExecutionPlugin
BlockingExecution	BlockingExecution
DRMAAExecution	DRMAAExecution
LinearExecution	LinearExecution
ProcessPoolExecution	ProcessPoolExecution
RQExecution	RQExecution

BlockingExecution

The blocking execution plugin is a special plugin which is meant for debug purposes. It will not queue jobs but immediately execute them inline, effectively blocking fastr until the Job is finished. It is the simplest execution plugin and can be used as a template for new plugins or for testing purposes.

DRMAAExecution

A DRMAA execution plugin to execute Jobs on a Grid Engine cluster. It uses a configuration option for selecting the queue to submit to. It uses the python drmaa package.

Note

To use this plugin, make sure the drmaa package is installed and that the execution is started on an SGE submit host with DRMAA libraries installed.

Note

This plugin is at the moment tailored to SGE, but it should be fairly easy to make different subclasses for different DRMAA supporting systems.

Configuration fields

name	type	description	default
drmaa_queue	str	The default queue to use for jobs send to the scheduler	‘week’

LinearExecution

An execution engine that has a background thread that executes the jobs in order. The queue is a simple FIFO queue and there is one worker thread that operates in the background. This plugin is meant as a fallback when other plugins do not function properly. It does not multi-processing so it is safe to use in environments that do no support that.

ProcessPoolExecution

A local execution plugin that uses multiprocessing to create a pool of worker processes. This allows fastr to execute jobs in parallel with true concurrency. The number of workers can be specified in the fastr configuration, but the default amount is the number of cores - 1 with a minimum of 1.

Warning

The ProcessPoolExecution does not check memory requirements of jobs and running many workers might lead to memory starvation and thus an unresponsive system.

Configuration fields

name	type	description	default
process_pool_worker_number	int	Number of workers to use in a process pool	3

RQExecution

A execution plugin based on Redis Queue. Fastr will submit jobs to the redis queue and workers will peel the jobs from the queue and process them.

This system requires a running redis database and the database url has to be set in the fastr configuration.

Note

This execution plugin required the redis and rq packages to be installed before it can be loaded properly.

Configuration fields

name	type	description	default
rq_queue	str	The redis queue to use	‘default’
rq_host	str	The url of the redis serving the redis queue	‘redis://localhost:6379/0’

FlowPlugin Reference

Plugin that can manage an advanced data flow. The plugins override the execution of node. The execution receives all data of a node in one go, so not split per sample combination, but all data on all inputs in one large payload. The flow plugin can then re-order the data and create resulting samples as it sees fits. This can be used for all kinds of specialized data flows, e.g. cross validation.

To create a new FlowPlugin there is only one method that needs to be implemented: execute.

scheme	FlowPlugin
CrossValidation	CrossValidation

CrossValidation

Advanced flow plugin that generated a cross-validation data flow. The node need an input with data and an input number of folds. Based on that the outputs test and train will be supplied with a number of data sets.

IOPlugin Reference

IOPlugins are used for data import and export for the sources and sinks. The main use of the IOPlugins is during execution (see Execution). The IOPlugins can be accessed via fastr.ioplugins, but generally there should be no need for direct interaction with these objects. The use of is mainly via the URL used to specify source and sink data.

scheme	IOPlugin
CommaSeperatedValueFile	CommaSeperatedValueFile
FileSystem	FileSystem
Null	Null
Reference	Reference
VirtualFileSystem	VirtualFileSystem
VirtualFileSystemRegularExpression	VirtualFileSystemRegularExpression
VirtualFileSystemValueList	VirtualFileSystemValueList
XNATStorage	XNATStorage

CommaSeperatedValueFile

The CommaSeperatedValueFile an expand-only type of IOPlugin. No URLs can actually be fetched, but it can expand a single URL into a larger amount of URLs.

The csv:// URL is a vfs:// URL with a number of query variables available. The URL mount and path should point to a valid CSV file. The query variable then specify what column(s) of the file should be used.

The following variable can be set in the query:

variable	usage
value	the column containing the value of interest, can be int for index or string for key
id	the column containing the sample id (optional)
header	indicates if the first row is considered the header, can be true or false (optional)
delimiter	the delimiter used in the csv file (optional)
quote	the quote character used in the csv file (optional)
reformat	a reformatting string so that value = reformat.format(value) (used before relative_path)
relative_path	indicates the entries are relative paths (for files), can be true or false (optional)

The header is by default false if the neither the value and id are set as a string. If either of these are a string, the header is required to define the column names and it automatically is assumed true

The delimiter and quota characters of the file should be detected automatically using the Sniffer, but can be forced by setting them in the URL.

Example of valid csv URLs:

# Use the first column in the file (no header row assumed)
csv://mount/some/dir/file.csv?value=0

# Use the images column in the file (first row is assumed header row)
csv://mount/some/dir/file.csv?value=images

# Use the segmentations column in the file (first row is assumed header row)
# and use the id column as the sample id
csv://mount/some/dir/file.csv?value=segmentations&id=id

# Use the first column as the id and the second column as the value
# and skip the first row (considered the header)
csv://mount/some/dir/file.csv?value=1&id=0&header=true

# Use the first column and force the delimiter to be a comma
csv://mount/some/dir/file.csv?value=0&delimiter=,

FileSystem

The FileSystem plugin is create to handle file:// type or URLs. This is generally not a good practice, as this is not portable over between machines. However, for test purposes it might be useful.

The URL scheme is rather simple: file://host/path (see wikipedia for details)

We do not make use of the host part and at the moment only support localhost (just leave the host empty) leading to file:/// URLs.

Warning

This plugin ignores the hostname in the URL and does only accept driver letters on Windows in the form c:/

Null

The Null plugin is create to handle null:// type or URLs. These URLs are indicating the sink should not do anything. The data is not written to anywhere. Besides the scheme, the rest of the URL is ignored.

Reference

The Reference plugin is create to handle ref:// type or URLs. These URLs are to make the sink just write a simple reference file to the data. The reference file contains the DataType and the value so the result can be reconstructed. It for files just leaves the data on disk by reference. This plugin is not useful for production, but is used for testing purposes.

VirtualFileSystem

The virtual file system class. This is an IOPlugin, but also heavily used internally in fastr for working with directories. The VirtualFileSystem uses the vfs:// url scheme.

A typical virtual filesystem url is formatted as vfs://mountpoint/relative/dir/from/mount.ext

Where the mountpoint is defined in the Config file. A list of the currently known mountpoints can be found in the fastr.config object

>>> fastr.config.mounts
{'example_data': '/home/username/fastr-feature-documentation/fastr/fastr/examples/data',
 'home': '/home/username/',
 'tmp': '/home/username/FastrTemp'}

This shows that a url with the mount home such as vfs://home/tempdir/testfile.txt would be translated into /home/username/tempdir/testfile.txt.

There are a few default mount points defined by Fastr (that can be changed via the config file).

mountpoint	default location
home	the users home directory (expanduser('~/'))
tmp	the fastr temprorary dir, defaults to tempfile.gettempdir()
example_data	the fastr example data directory, defaults $FASTRDIR/example/data

VirtualFileSystemRegularExpression

The VirtualFileSystemValueList an expand-only type of IOPlugin. No URLs can actually be fetched, but it can expand a single URL into a larger amount of URLs.

A vfsregex:// URL is a vfs URL that can contain regular expressions on every level of the path. The regular expressions follow the re module definitions.

An example of a valid URLs would be:

vfsregex://tmp/network_dir/.*/.*/__fastr_result__.pickle.gz
vfsregex://tmp/network_dir/nodeX/(?P<id>.*)/__fastr_result__.pickle.gz

The first URL would result in all the __fastr_result__.pickle.gz in the working directory of a Network. The second URL would only result in the file for a specific node (nodeX), but by adding the named group id using (?P<id>.*) the sample id of the data is automatically set to that group (see Regular Expression Syntax under the special characters for more info on named groups in regular expression).

Concretely if we would have a directory vfs://mount/somedir containing:

image_1/Image.nii
image_2/image.nii
image_3/anotherimage.nii
image_5/inconsistentnamingftw.nii

we could match these files using vfsregex://mount/somedir/(?P<id>image_\d+)/.*\.nii which would result in the following source data after expanding the URL:

{'image_1': 'vfs://mount/somedir/image_1/Image.nii',
 'image_2': 'vfs://mount/somedir/image_2/image.nii',
 'image_3': 'vfs://mount/somedir/image_3/anotherimage.nii',
 'image_5': 'vfs://mount/somedir/image_5/inconsistentnamingftw.nii'}

Showing the power of this regular expression filtering. Also it shows how the ID group from the URL can be used to have sensible sample ids.

Warning

due to the nature of regexp on multiple levels, this method can be slow when having many matches on the lower level of the path (because the tree of potential matches grows) or when directories that are parts of the path are very large.

VirtualFileSystemValueList

The VirtualFileSystemValueList an expand-only type of IOPlugin. No URLs can actually be fetched, but it can expand a single URL into a larger amount of URLs. A vfslist:// URL basically is a url that points to a file using vfs. This file then contains a number lines each containing another URL.

If the contents of a file vfs://mount/some/path/contents would be:

vfs://mount/some/path/file1.txt
vfs://mount/some/path/file2.txt
vfs://mount/some/path/file3.txt
vfs://mount/some/path/file4.txt

Then using the URL vfslist://mount/some/path/contents as source data would result in the four files being pulled.

Note

The URLs in a vfslist file do not have to use the vfs scheme, but can use any scheme known to the Fastr system.

XNATStorage

Warning

As this IOPlugin is under development, it has not been thoroughly tested.

The XNATStorage plugin is an IOPlugin that can download data from and upload data to an XNAT server. It uses its own xnat:// URL scheme. This is a scheme specific for this plugin and though it looks somewhat like the XNAT rest interface, a different type or URL.

Data resources can be access directly by a data url:

xnat://xnat.example.com/data/archive/projects/sandbox/subjects/subject001/experiments/experiment001/scans/T1/resources/DICOM
xnat://xnat.example.com/data/archive/projects/sandbox/subjects/subject001/experiments/*_BRAIN/scans/T1/resources/DICOM

In the second URL you can see a wildcard being used. This is possible at long as it resolves to exactly one item.

The id query element will change the field from the default experiment to subject and the label query element sets the use of the label as the fastr id (instead of the XNAT id) to True (the default is False)

To disable https transport and use http instead the query string can be modified to add insecure=true. This will make the plugin send requests over http:

xnat://xnat.example.com/data/archive/projects/sandbox/subjects/subject001/experiments/*_BRAIN/scans/T1/resources/DICOM?insecure=true

For sinks it is import to know where to save the data. Sometimes you want to save data in a new assessor/resource and it needs to be created. To allow the Fastr sink to create an object in XNAT, you have to supply the type as a query parameter:

xnat://xnat.bmia.nl/data/archive/projects/sandbox/subjects/S01/experiments/_BRAIN/assessors/test_assessor/resources/IMAGE/files/image.nii.gz?resource_type=xnat:resourceCatalog&assessor_type=xnat:qcAssessmentData

Valid options are: subject_type, experiment_type, assessor_type, scan_type, and resource_type.

If you want to do a search where multiple resources are returned, it is possible to use a search url:

xnat://xnat.example.com/search?projects=sandbox&subjects=subject[0-9][0-9][0-9]&experiments=*_BRAIN&scans=T1&resources=DICOM

This will return all DICOMs for the T1 scans for experiments that end with _BRAIN that belong to a subjectXXX where XXX is a 3 digit number. By default the ID for the samples will be the experiment XNAT ID (e.g. XNAT_E00123). The wildcards that can be the used are the same UNIX shell-style wildcards as provided by the module fnmatch.

It is possible to change the id to a different fields id or label. Valid fields are project, subject, experiment, scan, and resource:

xnat://xnat.example.com/search?projects=sandbox&subjects=subject[0-9][0-9][0-9]&experiments=*_BRAIN&scans=T1&resources=DICOM&id=subject&label=true

The following variables can be set in the search query:

variable	default	usage
projects	*	The project(s) to select, can contain wildcards (see fnmatch)
subjects	*	The subject(s) to select, can contain wildcards (see fnmatch)
experiments	*	The experiment(s) to select, can contain wildcards (see fnmatch)
scans	*	The scan(s) to select, can contain wildcards (see fnmatch)
resources	*	The resource(s) to select, can contain wildcards (see fnmatch)
id	experiment	What field to use a the id, can be: project, subject, experiment, scan, or resource
label	false	Indicate the XNAT label should be used as fastr id, options true or false
insecure	false	Change the url scheme to be used to http instead of https
regex	false	Change search to use regex re.match() instead of fnmatch for matching

For storing credentials the .netrc file can be used. This is a common way to store credentials on UNIX systems. It is required that the file is only accessible by the owner only or a NetrcParseError will be raised. A netrc file is really easy to create, as its entries look like:

machine xnat.example.com
        login username
        password secret123

See the netrc module or the GNU inet utils website for more information about the .netrc file.

Note

On windows the location of the netrc file is assumed to be os.path.expanduser('~/_netrc'). The leading underscore is because windows does not like filename starting with a dot.

Note

For scan the label will be the scan type (this is initially the same as the series description, but can be updated manually or the XNAT scan type cleanup).

Warning

labels in XNAT are not guaranteed to be unique, so be careful when using them as the sample ID.

For background on XNAT, see the XNAT API DIRECTORY for the REST API of XNAT.

Interface Reference

Abstract base class of all Interfaces. Defines the minimal requirements for all Interface implementations.

scheme	Interface
FastrInterface	FastrInterface
FlowInterface	FlowInterface
NipypeInterface	NipypeInterface

FastrInterface

The default Interface for fastr. For the command-line Tools as used by fastr.

FlowInterface

The Interface use for AdvancedFlowNodes to create the advanced data flows that are not implemented in the fastr. This allows nodes to implement new data flows using the plugin system.

The definition of FlowInterfaces are very similar to the default FastrInterfaces.

Note

A flow interface should be using a specific FlowPlugin

NipypeInterface

Experimental interfaces to using nipype interfaces directly in fastr tools, only using a simple reference.

To create a tool using a nipype interface just create an interface with the correct type and set the nipype argument to the correct class. For example in an xml tool this would become:

<interface class="NipypeInterface">
  <nipype_class>nipype.interfaces.elastix.Registration</nipype_class>
</interface>

Note

To use these interfaces nipype should be installed on the system.

Warning

This interface plugin is basically functional, but highly experimental!

Development and Design Documentation

In this chapter we will discuss the design of Fastr in more detail. We give pointers for development and add the design documents as we currently envision Fastr. This is both for people who are interested in the Fastr develop and for current developers to have an archive of the design decision agreed upon.

Sample flow in Fastr

The current Sample flow is the following:

The idea is that we make a common interface for all classes that are related to the flow of Samples. For this we propose the following mixin classes that provide the interface and allow for better code sharing. The basic structure of the classes is given in the following diagram:

The abstract and mixin methods are as follows:

ABC	Inherits from	Abstract Methods	Mixin methods
HasDimensions		dimensions	size dimnames
HasSamples	HasDimensions	__getitem__	__contains__ __iter__ iteritems items indexes ids
ContainsSamples	HasSamples	samples	__getitem__ __setitem__ dimensions
ForwardsSamples	HasSamples	source index_to_target index_to_source combine_samples combine_dimensions	__getitem__ dimensions

Note

Though the flow is currently working like this, the mixins are not yet created.

Network Execution

The network execution should contain a number of steps:

• Network
  • Creates a NetworkRun based on the current layout
• NetworkRun
  • Transform the Network (possibly joining Nodes of certain interface into a combined Node etc)
  • Start generation of the Job Direct Acyclic Graph (DAG)
• SchedulingPlugin
  • Prioritize Jobs based on some predefined rules
  • Combine certain Jobs to improve efficiency (e.g. minimize i/o on a grid)
• ExecutionPlugin
  • Run a (list of) Jobs. If there is more than one jobs, run them sequentially on same execution host using a local temp for intermediate files.
  • On finished callback: Updated DAG with newly ready jobs, or remove cancelled jobs

This could be visualized as the following loop:

The callback of the ExecutionPlugin to the NetworkRun would trigger the execution of the relevant NodeRuns and the addition of more Jobs to the JobDAG.

Note

The Job DAG should be thread-safe as it could be both read and extended at the same time.

Note

If a list of jobs is send to the ExecutionPlugin to be run as on Job on an external execution platform, the resources should be combined as follows: memory=max, cores=max, runtime=sum

Note

If there are execution hosts that have mutliple cores the ExecutionPlugin should manage this (for example by using pilot jobs). The SchedulingPlugin creates units that should be run sequentially on the resources noted and will not attempt parallelization

A NetworkRun would be contain similar information as the Network but not have functionality for editting/changing it. It would contain the functionality to execute the Network and track the status and samples. This would allow Network.execute to create multiple concurent runs that operate indepent of each other. Also editting a Network after the run started would have no effect on that run.

Note

This is a plan, not yet implemented

Note

For this to work, it would be important for a Jobs to have forward and backward dependency links.

SchedulingPlugins

The idea of the plugin is that it would give a priority on Jobs created by a Network. This could be done based on different strategies:

• Based on (sorted) sample id’s, so that one sample is always prioritized over others. The idea is that samples are process as much as possible in order, finishing the first sample first. Only processing other samples if there is left-over capacity.
• Based on distance to a (particular) Sink. This is to generate specific results as quick as possible. It would not focus on specific samples, but give priority to whatever sample is closest to being finished.
• Based on the distance to from a Souce. Based on the sign of the weight it would either keep all samples on the same stage as much as possible, only progressing to a new Node when all samples are done with the previous Node, or it would push samples with accelerated rates.

Additionally it will group Jobs to be executed on a single host. This could reduce i/o and limited the number of jobs an external scheduler has to track.

Note

The interface for such a plugin has not yet been established.

Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog and this project adheres to Semantic Versioning

1.2.2 - 2017-08-24

Fixed

• Fixed a bug breaking the XNAT IOPlugin due to an xnatpy version update.

1.2.1 - 2017-04-04

Added

• A FastrInterface can now specify a negate flag on an automatic output that also has a prefix, which will negate the flag. This is useful for flag the suppress the creation of an output (e.g. no_mask). An example is given in the Tool fastr.util.AutoPrefixNegateTest.

Changed

• The provenance and extra information of a Job now is not serialized in the Job, but exported to separate files next to the job file __fastr_prov__.json and __fastr_extra_job_info__.json which makes the information more accessible and reduces the memory footprint of the main process hugely as it will not read this information back anymore.
• Most execution plugin will not overwrite the executionscript stdout and stderr but rather append it. This is only relevant when continuing a run in the an existing temporary directory, but avoids loss of information.

Fixed

• Bug that stopped the Link.append function from returning the newly created link
• Bugs that caused some cardinality computations of the output to fail during execution
• Bug in the job.tmpurl that caused double slashes somewhere. Some tools chocked on this when it was used for parameters.

1.2.0 - 2017-03-15

Added

• Failed sample annotation: when a job fails, the result is annotated and forwarded until a SinkNode, where we can determine the status and possibly point of failure of the Sample.
• Commandline tool fastr trace that can inspect a workflow run and help trace errors and print debug information
• Supported for Lmod modules environment next to the old environmentmodules
• BaseDataType descendants are now (un)picklable (including EnumTypes)
• Option to use {extension} field in sink_data, which differs from {ext} in that it doesn’t include a leading dot.
• Support for Docker targets. A Docker target will execute a command inside of a specified docker container, allowing Tools to use Docker for distribution
• Using the right and left shift operator (<< and >>) for creating links to Inputs using input << output or output >> input.
• In the FastrInterfaces, automatic outputs can have a prefix for a flag that should be set for the output to be actually generated.
• Fastr is now able to limit the amount of SourceJobs that are allowed to run concurrently.
• Ability to report progress to PIM (use the pim_host field in the config)

Changed

• Version can now also accept a format based on a date (e.g. 2017-02-17_bananas) which will be parsed the same way as 2017.02.17_bananas
• Work on the ExecutionPlugin and the corresponding API. Has better fall-backs and a mechanism to advertise plugin capabilities.
• The collector plugins have the input and input_parts fields merged, and the output and output_parts fields merged.

Fixed

• In some cases the log directory was not created properly, causing an handled exception
• A bug making the handling of Booleans incorrect for the FastrInterface, when a Boolean was given a flag would also appear when it was False
• Serialization of the namespace of a Network was not correct
• Check version of Fastr that creates and executes a Job against each other
• load_gpickle helper can handle data with Enums that use to cause an AttributeError
• Output validation of Jobs did not work correctly for automatic outputs

1.1.2 - 2016-12-22

Fixed

• The example network in resources/networks/add_ints.json was using an old serialization format making it non-functions. Replaced by a new network file.

1.1.1 - 2016-12-22

Fixed

• Network runs called from an interpreter (and not file) caused a crash because the network tried to report the file used. Better handling of these situations.

1.1.0 - 2016-12-08

Added

• Namespaces for resources (tools and networks)
• Network manager located at fastr.networklist
• RQExecution plugin. This plugin uses python-rq to manage a job queue.
• LinearExecution plugin. This plugin uses a background thread for execution.
• BlockingExecution plugin. This plugin executes jobs in a blocking fashion.
• Automatic generation of documentation for all plugins, the configuration fields and all commandline tools.

Changed

• Provenance is updated with a network dump and used tool definitions.
• New configuration system that uses python files
• New plugin system that integrates with the new configuration system and enables automatic importing of plugins
• The fastr command line tools now use an entrypoint which is located in fastr.utils.cmd. This code also dispatches the sub commands.

Removed

• fastr.config file. This is replaced by the config.py file. Go to the docs!

Fixed

• Adds explicit tool namespace and version to the provenance document.

FASTR User reference

Fastr User Reference

fastr.toollist

A ToolManager containing all versions of all Tools loaded into the FASTR environment. The ToolManager can be indexed using the Tool id string or a tool id string and a version. For example if you have two versions (4.5 and 4.8) of a tool called Elastix:

>>> fastr.toollist['elastix.Elastix']
Tool Elastix v4.8 (Elastix Registration)
                           Inputs                              |             Outputs
--------------------------------------------------------------------------------------------------
fixed_image       (ITKImageFile)                               |  directory (Directory)
moving_image      (ITKImageFile)                               |  transform (ElastixTransformFile)
parameters        (ElastixParameterFile)                       |  log_file  (ElastixLogFile)
fixed_mask        (ITKImageFile)                               |
moving_mask       (ITKImageFile)                               |
initial_transform (ElastixTransformFile)                       |
priority          (__Elastix_4.8_interface__priority__Enum__)  |
threads           (Int)                                        |

>>> fastr.toollist['elastix.Elastix', '4.5']
Tool Elastix v4.5 (Elastix Registration)
                           Inputs                              |             Outputs
--------------------------------------------------------------------------------------------------
fixed_image       (ITKImageFile)                               |  directory (Directory)
moving_image      (ITKImageFile)                               |  transform (ElastixTransformFile)
parameters        (ElastixParameterFile)                       |  log_file  (ElastixLogFile)
fixed_mask        (ITKImageFile)                               |
moving_mask       (ITKImageFile)                               |
initial_transform (ElastixTransformFile)                       |
priority          (__Elastix_4.5_interface__priority__Enum__)  |
threads           (Int)                                        |

fastr.typelist

A dictionary containing all types loaded into the FASTR environment. The keys are the typenames and the values are the classes.

class fastr.Network(id_='unnamed_network', version=None)

The Network class represents a workflow. This includes all Nodes (including ConstantNodes, SourceNodes and Sinks) and Links.

add_link(link)

Add a Link to the Network. Make sure the link is in the link list and the link parent is set to this Network

Parameters:

link (Link) – link to add

Raises:

• FastrTypeError – if link is incorrectly typed
• FastrNetworkMismatchError – if the link already belongs to another Network

add_node(node)

Add a Node to the Network. Make sure the node is in the node list and the node parent is set to this Network

Parameters:node (Node) – node to add Raises:FastrTypeError – if node is incorrectly typed

create_link(source, target, id_=None, collapse=None, expand=None)

Create a link between two Nodes and add it to the current Network.

Parameters:

• source (BaseOutput) – the output that is the source of the link
• target (BaseInput) – the input that is the target of the link
• id (str) – the id of the link

Returns:

the created link

Type:

Link

create_node(tool, id_=None, stepid=None, cores=None, memory=None, walltime=None, nodegroup=None)

Create a Node in this Network. The Node will be automatically added to the Network.

Parameters:

• tool (Tool) – The Tool to base the Node on
• id (str) – The id of the node to be created
• stepid (str) – The stepid to add the created node to
• nodegroup (str) – The group the node belongs to, this can be important for FlowNodes and such, as they will have matching dimension names.

Returns:

the newly created node

Return type:

Node

create_sink(datatype, id_=None, stepid=None)

Create a SinkNode in this Network. The Node will be automatically added to the Network.

Parameters:

• datatype (BaseDataType) – The DataType of the sink node
• id (str) – The id of the sink node to be created
• stepid (str) – The stepid to add the created sink node to

Returns:

the newly created sink node

Return type:

SinkNode

create_source(datatype, id_=None, stepid=None, nodegroup=None, sourcegroup=None)

Create a SourceNode in this Network. The Node will be automatically added to the Network.

Parameters:

• datatype (BaseDataType) – The DataType of the source source_node
• id (str) – The id of the source source_node to be created
• stepid (str) – The stepid to add the created source source_node to
• nodegroup (str) – The group the node belongs to, this can be important for FlowNodes and such, as they will have matching dimension names.
• sourcegroup (str) – DEPRECATED! The nodegroup this SourceNode will be added to

Returns:

the newly created source source_node

Return type:

SourceNode

draw_network(name='network_layout', img_format='svg', draw_dimension=False)

Output a dot file and try to convert it to an image file.

Parameters:img_format (str) – extension of the image format to convert to Returns:path of the image created or None if failed Return type:str or None

class fastr.Link(source, target, parent=None, id_=None, collapse=None, expand=None)

Class for linking outputs (BaseOutput) to inputs (BaseInput)

Examples:

>>> import fastr
>>> network = fastr.Network()
>>> link1 = network.create_link( n1.ouputs['out1'], n2.inputs['in2'] )

link2 = Link()
link2.source = n1.ouputs['out1']
link2.target = n2.inputs['in2']

source

The source BaseOutput of the Link. Setting the source will automatically register the Link with the source BaseOutput. Updating source will also make sure the Link is unregistered with the previous source.

Raises:FastrTypeError – if assigning a non BaseOutput

target

The target BaseInput of the Link. Setting the target will automatically register the Link with the target BaseInput. Updating target will also make sure the Link is unregistered with the previous target.

Raises:FastrTypeError – if assigning a non BaseInput

class fastr.Node(tool, id_=None, parent=None, cores=None, memory=None, walltime=None)

The class encapsulating a node in the network. The node is responsible for setting and checking inputs and outputs based on the description provided by a tool instance.

id_ = None

The Node id s a unique string identifying the Node

inputs = None

A list of inputs of this Node

name

Name of the Tool the Node was based on. In case a Toolless Node was used the class name is given.

outputs = None

A list of outputs of this Node

class fastr.ConstantNode(datatype, data, id_=None)

Class encapsulating one output for which a value can be set. For example used to set a scalar value to the input of a node.

name

Name of the Tool the Node was based on. In case a Toolless Node was used the class name is given.

class fastr.SourceNode(datatype, id_=None)

Class providing a connection to data resources. This can be any kind of file, stream, database, etc from which data can be received.

name

Name of the Tool the Node was based on. In case a Toolless Node was used the class name is given.

class fastr.SinkNode(datatype, id_=None)

Class which handles where the output goes. This can be any kind of file, e.g. image files, textfiles, config files, etc.

name

Name of the Tool the Node was based on. In case a Toolless Node was used the class name is given.

FASTR REST API reference

REST API

Fastr includes a webapp and a REST API. This section contains a listing of all REST paths. The full documentation is available via swagger with the paths /swagger.json for the schema or /api/doc/ for the swagger UI.

Quick reference

Resource	Operation	Description
	GET /
	GET /api/doc/
	GET /api/networks
	GET /api/networks/(id)
	POST /api/runs
	GET /api/runs
	DELETE /api/runs/(id)
	GET /api/runs/(id)
	GET /api/runs/(id)/status
	GET /api/tools
	GET /api/tools/(id)
	GET /api/tools/(id)/(version)
	GET /doc
	GET /index
	GET /
	GET /network/(name)
	GET /networks
	GET /prov
	GET /shutdown
	GET /static/(path:filename)
	GET /swagger.json
	GET /swaggerui/(path:filename)
	GET /tool/(toolname)/(version)
	GET /tool/(toolname)
	GET /tool
	GET /websocketclient

GET /api/networks

Get a list of the networks

GET /api/tools

Get a list of all Tools known to the server

POST /api/runs

Create a new Network run and start execution

GET /api/runs

Get a list of all Network runs on the server

GET /api/doc/

Override this method to customize the documentation page

GET /swagger.json

Render the Swagger specifications as JSON

GET /api/tools/(id)/(version)

Get a Tool json description from the server

GET /api/runs/(id)/status

Get the status of a Network Run on the server

GET /api/networks/(id)

Get a Network json description from the server

GET /api/tools/(id)

Get a Tool json description from the server

DELETE /api/runs/(id)

Abort a Network run and stop all associated execution

GET /api/runs/(id)

Get information about a Network run

GET /swaggerui/(path: filename)

Function used internally to send static files from the static folder to the browser.

New in version 0.5.

GET /static/(path: filename)

Function used internally to send static files from the static folder to the browser.

New in version 0.5.

FASTR Developer Module reference

fastr Package

fastr Package

FASTR is a top level package which includes all parts required to create networks and edit networks.

class fastr.__init__.Network

The class representing a Network, this is in fact a reference to fastr.core.network.Network.

class fastr.__init__.Node

The class representing a Node, this is in fact a reference to fastr.core.node.Node.

class fastr.__init__.Link

The class representing a Link, this is in fact a reference to fastr.core.link.Link.

class fastr.__init__.SourceNode

The class representing a data source, this is in fact a reference to fastr.core.node.SourceNode.

class fastr.__init__.SinkNode

The class representing a data sink, this is in fact a reference to fastr.core.node.SinkNode.

class fastr.__init__.ConstantNode

The class representing a constant data source, this is in fact a reference to fastr.core.node.ConstantNode.

fastr.__init__.toollist

A fastr.core.toolmanager.ToolManager containing all Tools known to the FASTR environment. The toollist can be accessed in a similar way to a dict. Indexing with a tool id will return the newest version of the Tool. If a specific version of the tool is required a tuple can be used as the index:

>>> import fastr
>>> fastr.toollist['testtool']
<Tool: testtool version: 4.2>
>>> fastr.toollist['testtool', '2.0']
<Tool: testtool version: 2.0>

fastr.__init__.typelist

A fastr.core.datatypemanager.DataTypeManager containing all Types known to the FASTR environment. This is usuable as a dict where the key is the datatype id and the value is the datatype itself.

class fastr.__init__.Network(id_='unnamed_network', version=None)

Bases: fastr.core.serializable.Serializable

The Network class represents a workflow. This includes all Nodes (including ConstantNodes, SourceNodes and Sinks) and Links.

NETWORK_DUMP_FILE_NAME = '__fastr_network__.json'

SINK_DUMP_FILE_NAME = '__sink_data__.json'

SOURCE_DUMP_FILE_NAME = '__source_data__.pickle.gz'

__dataschemafile__ = 'Network.schema.json'

__eq__(other)

Compare two Networks and see if they are equal.

Parameters:other (Network) – Returns:flag indicating that the Networks are the same Return type:bool

__getitem__(item)

Get an item by its fullid. The fullid can point to a link, node, input, output or even subinput/suboutput.

Parameters:item (str,unicode) – fullid of the item to retrieve Returns:the requested item

__getstate__()

Retrieve the state of the Network

Returns:the state of the object Rtype dict:

__init__(id_='unnamed_network', version=None)

Create a new, empty Network

Parameters:name (str) – name of the Network Returns:newly created Network Raises:OSError – if the tmp mount in the config is not a writable directory

__module__ = 'fastr.core.network'

__ne__(other)

Tests for non-equality, this is the negated version __eq__

__repr__()

__setstate__(state)

Set the state of the Network by the given state. This completely overwrites the old state!

Parameters:state (dict) – The state to populate the object with Returns:None

abort()

add_link(link)

Add a Link to the Network. Make sure the link is in the link list and the link parent is set to this Network

Parameters:

link (Link) – link to add

Raises:

• FastrTypeError – if link is incorrectly typed
• FastrNetworkMismatchError – if the link already belongs to another Network

add_node(node)

Add a Node to the Network. Make sure the node is in the node list and the node parent is set to this Network

Parameters:node (Node) – node to add Raises:FastrTypeError – if node is incorrectly typed

add_stepid(stepid, node)

Add a Node to a specific step id

Parameters:

• stepid (str) – the stepid that the node will be added to
• node (Node) – the node to add to the stepid

check_id(id_)

Check if an id for an object is valid and unused in the Network. The method will always returns True if it does not raise an exception.

Parameters:

id (str) – the id to check

Returns:

True

Raises:

• FastrValueError – if the id is not correctly formatted
• FastrValueError – if the id is already in use

create_constant(datatype, data, id_=None, stepid=None, nodegroup=None, sourcegroup=None)

Create a ConstantNode in this Network. The Node will be automatically added to the Network.

Parameters:

• datatype (BaseDataType) – The DataType of the constant node
• data (datatype or list of datatype) – The data to hold in the constant node
• id (str) – The id of the constant node to be created
• stepid (str) – The stepid to add the created constant node to
• nodegroup (str) – The group the node belongs to, this can be important for FlowNodes and such, as they will have matching dimension names.

Returns:

the newly created constant node

Return type:

ConstantNode

create_link(source, target, id_=None, collapse=None, expand=None)

Create a link between two Nodes and add it to the current Network.

Parameters:

• source (BaseOutput) – the output that is the source of the link
• target (BaseInput) – the input that is the target of the link
• id (str) – the id of the link

Returns:

the created link

Type:

Link

create_macro(network, id_=None)

create_node(tool, id_=None, stepid=None, cores=None, memory=None, walltime=None, nodegroup=None)

Create a Node in this Network. The Node will be automatically added to the Network.

Parameters:

• tool (Tool) – The Tool to base the Node on
• id (str) – The id of the node to be created
• stepid (str) – The stepid to add the created node to
• nodegroup (str) – The group the node belongs to, this can be important for FlowNodes and such, as they will have matching dimension names.

Returns:

the newly created node

Return type:

Node

create_reference(source_data, output_directory)

create_sink(datatype, id_=None, stepid=None)

Create a SinkNode in this Network. The Node will be automatically added to the Network.

Parameters:

• datatype (BaseDataType) – The DataType of the sink node
• id (str) – The id of the sink node to be created
• stepid (str) – The stepid to add the created sink node to

Returns:

the newly created sink node

Return type:

SinkNode

create_source(datatype, id_=None, stepid=None, nodegroup=None, sourcegroup=None)

Create a SourceNode in this Network. The Node will be automatically added to the Network.

Parameters:

• datatype (BaseDataType) – The DataType of the source source_node
• id (str) – The id of the source source_node to be created
• stepid (str) – The stepid to add the created source source_node to
• nodegroup (str) – The group the node belongs to, this can be important for FlowNodes and such, as they will have matching dimension names.
• sourcegroup (str) – DEPRECATED! The nodegroup this SourceNode will be added to

Returns:

the newly created source source_node

Return type:

SourceNode

draw_network(name='network_layout', img_format='svg', draw_dimension=False)

Output a dot file and try to convert it to an image file.

Parameters:img_format (str) – extension of the image format to convert to Returns:path of the image created or None if failed Return type:str or None

execute(sourcedata, sinkdata, execution_plugin=None, tmpdir=None, cluster_queue=None)

Execute the Network with the given data. This will analyze the Network, create jobs and send them to the execution backend of the system.

Parameters:

• sourcedata (dict) – dictionary containing all data for the sources
• sinkdata (dict) – dictionary containing directives for the sinks
• execution_plugin (str) – the execution plugin to use (None will use the config value)

Raises:

• FastrKeyError – if a source has not corresponding key in sourcedata
• FastrKeyError – if a sink has not corresponding key in sinkdata

fullid

The fullid of the Network

id

The id of the Network. This is a read only property.

is_valid()

job_finished(job, execution_interface)

Call-back handler for when a job is finished. Will collect the results and handle blocking jobs. This function is automatically called when the execution plugin finished a job.

Parameters:job (Job) – the job that finished

remove(value)

Remove an item from the Network.

Parameters:value (Node or Link) – the item to remove

run_id

The run id of the network

test(reference_data_dir, source_data=None)

Execute the network with the source data specified and test the results against the refence data. This effectively tests the network execution.

Parameters:

• reference_data_dir (str) – The path or vfs url of reference data to compare with
• source_data (dict) – The source data to use

class fastr.__init__.Link(source, target, parent=None, id_=None, collapse=None, expand=None)

Bases: fastr.core.updateable.Updateable, fastr.core.serializable.Serializable

Class for linking outputs (BaseOutput) to inputs (BaseInput)

Examples:

>>> import fastr
>>> network = fastr.Network()
>>> link1 = network.create_link( n1.ouputs['out1'], n2.inputs['in2'] )

link2 = Link()
link2.source = n1.ouputs['out1']
link2.target = n2.inputs['in2']

__abstractmethods__ = frozenset([])

__dataschemafile__ = 'Link.schema.json'

__eq__(other)

Test for equality between two Links

Parameters:other (Link) – object to test against Returns:True for equality, False otherwise Return type:bool

__getitem__(index)

Get a an item for this Link. The item will be retrieved from the connected output, but a diverging or converging flow can change the number of samples/cardinality.

Parameters:index (SampleIndex) – index of the item to retrieve Returns:the requested item Return type:SampleItem Raises:FastrIndexError – if the index length does not match the number dimensions in the source data (after collapsing/expanding)

__getstate__()

Retrieve the state of the Link

Returns:the state of the object Rtype dict:

__init__(source, target, parent=None, id_=None, collapse=None, expand=None)

Create a new Link in a Network.

Parameters:

• source (BaseOutput) – the source output
• target (BaseInput) – the target input
• parent (Network or None) – the parent network, if None is given the fastr.current_network is assumed to be the parent
• id (str or None) – the id of the link, if no id_ is given, the id will be in the form of “link_{:d}”
• collapse (int, str, or tuple of int/str) – the dimensions that the link has to collapse on
• expand (bool) – Does this link need to expand the cardinality into a new sample dimension

Returns:

newly created Link

Raises:

• FastrValueError – if parent is not given and fastr.current_network is not set
• FastrValueError – if the source output is not in the same network as the Link
• FastrValueError – if the target input is not in the same network as the Link

__module__ = 'fastr.core.link'

__repr__()

Get a string representation for the Link

Returns:the string representation Return type:str

__setstate__(state)

Set the state of the Link by the given state.

Parameters:state (dict) – The state to populate the object with Returns:None Raises:FastrValueError – if the parent network and fastr.current_network are not set

cardinality(index=None)

Cardinality for a Link is given by source Output and the collapse/expand settings

Parameters:key (SampleIndex) – key for a specific sample (can be only a sample index!) Returns:the cardinality Return type:int, sympy.Symbol Raises:FastrIndexError – if the index length does not match the number of dimension in the data

collapse

The converging dimensions of this link. Collapsing changes some dimensions of sample lists into cardinality, reshaping the data.

Collapse can be set to a tuple or an int/str, in which case it will be automatically wrapped in a tuple. The int will be seen as indices of the dimensions to collapse. The str will be seen as the name of the dimensions over which to collapse.

Raises:FastrTypeError – if assigning a collapse value of a wrong type

collapse_indexes

The converging dimensions of this link as integers. Dimension names are replaces with the corresponding int.

Collapsing changes some dimensions of sample lists into cardinality, reshaping the data

classmethod createobj(state, network=None)

Create object function for Link

Parameters:

• cls – The class to create
• state – The state to use to create the Link
• network – the parent Network

Returns:

newly created Link

destroy()

The destroy function of a link removes all default references to a link. This means the references in the network, input and output connected to this link. If there is no references in other places in the code, it will destroy the link (reference count dropping to zero).

This function is called when a source for an input is set to another value and the links becomes disconnected. This makes sure there is no dangling links.

dimnames

The dimension names for this Link. The dimension names depend on the connected source output and the collapse/expand.

expand

Flag indicating that the link will expand the cardininality into a new sample dimension to be created.

fullid

The full defining ID for the Input

parent

The Network to which this Link belongs.

size

The size of the data delivered by the link. This can be different from the source size because the link can make data collapse or expand.

source

The source BaseOutput of the Link. Setting the source will automatically register the Link with the source BaseOutput. Updating source will also make sure the Link is unregistered with the previous source.

Raises:FastrTypeError – if assigning a non BaseOutput

status

target

The target BaseInput of the Link. Setting the target will automatically register the Link with the target BaseInput. Updating target will also make sure the Link is unregistered with the previous target.

Raises:FastrTypeError – if assigning a non BaseInput

class fastr.__init__.Node(tool, id_=None, parent=None, cores=None, memory=None, walltime=None)

Bases: fastr.core.updateable.Updateable, fastr.core.serializable.Serializable

The class encapsulating a node in the network. The node is responsible for setting and checking inputs and outputs based on the description provided by a tool instance.

__abstractmethods__ = frozenset([])

__dataschemafile__ = 'Node.schema.json'

__eq__(other)

Compare two Node instances with each other. This function ignores the parent and update status, but tests rest of the dict for equality. equality

Parameters:other (Node) – the other instances to compare to Returns:True if equal, False otherwise

__getstate__()

Retrieve the state of the Node

Returns:the state of the object Rtype dict:

__init__(tool, id_=None, parent=None, cores=None, memory=None, walltime=None)

Instantiate a node.

Parameters:

• tool (Tool) – The tool to base the node on
• id (str) – the id of the node
• parent (Network) – the parent network of the node
• cores (int) – number of cores required for executing this Node
• memory (str) – amount of memory required in the form d+[mMgG] where M is for megabyte and G for gigabyte
• walltime (str) – amount of time required in second or in the form HOURS:MINUTES:SECOND

Returns:

the newly created Node

__metaclass__

alias of ABCMeta

__module__ = 'fastr.core.node'

__repr__()

Get a string representation for the Node

Returns:the string representation Return type:str

__setstate__(state)

Set the state of the Node by the given state.

Parameters:state (dict) – The state to populate the object with Returns:None

__str__()

Get a string version for the Node

Returns:the string version Return type:str

blocking

Indicate that the results of this Node cannot be determined without first executing the Node, causing a blockage in the creation of jobs. A blocking Nodes causes the Chunk borders.

create_job(sample_id, sample_index, job_data, job_dependencies, **kwargs)

Create a job based on the sample id, job data and job dependencies.

Parameters:

• sample_id (SampleId) – the id of the corresponding sample
• sample_index (SampleIndex) – the index of the corresponding sample
• job_data (dict) – dictionary containing all input data for the job
• job_dependencies – other jobs that need to finish before this job can run

Returns:

the created job

Return type:

Job

classmethod createobj(state, network=None)

dimnames

Names of the dimensions in the Node output. These will be reflected in the SampleIdList of this Node.

execute()

Execute the node and create the jobs that need to run

Returns:list of jobs to run Return type:list of Jobs

find_source_index(target_index, target, source)

fullid

The full defining ID for the Node

get_sourced_nodes()

A list of all Nodes connected as sources to this Node

Returns:list of all nodes that are connected to an input of this node

id

The id of the Node

input_groups

A list of input groups for this Node. An input group is InputGroup

object filled according to the Node

listeners

All the listeners requesting output of this node, this means the listeners of all Outputs and SubOutputs

merge_dimensions

name

Name of the Tool the Node was based on. In case a Toolless Node was used the class name is given.

nodegroup

outputsize

Size of the outputs in this Node

parent

The parent network of this node.

prepare()

Prepare the node for execution. It will create a SampleIdList of the correct size and prepare the outputs.

required_cores

Number of cores required for the execution of this Node

required_memory

Amount of memory required for the execution of this Node. Follows the format d+[mMgG] so 500M or 4g would be valid ways to specify 500 megabytes or 4 gigabyte of memory.

required_time

Amount of time required for the execution of this Node. Follows the format of a number of second or H:M:S, with H the number of hours, M the number of minutes and S the number of seconds.

set_result(job, failed_annotation)

Incorporate result of a job into the Node.

Parameters:

• job (Type) – job of which the result to store
• failed_annotation – A set of annotations, None if no errors else containing a tuple describing the errors

status

tool

update_input_groups()

Update all input groups in this node

class fastr.__init__.ConstantNode(datatype, data, id_=None)

Bases: fastr.core.node.SourceNode

Class encapsulating one output for which a value can be set. For example used to set a scalar value to the input of a node.

__abstractmethods__ = frozenset([])

__dataschemafile__ = 'ConstantNode.schema.json'

__getstate__()

Retrieve the state of the ConstantNode

Returns:the state of the object Rtype dict:

__init__(datatype, data, id_=None)

Instantiation of the ConstantNode.

Parameters:

• datatype – The datatype of the output.
• data – the prefilled data to use.
• id – The url pattern.

This class should never be instantiated directly (unless you know what you are doing). Instead create a constant using the network class like shown in the usage example below.

usage example:

>>> import fastr
>>> network = fastr.Network()
>>> source = network.create_source(datatype=fastr.typelist['ITKImageFile'], id_='sourceN')

or alternatively create a constant node by assigning data to an item in an InputDict:

>>> node_a.inputs['in'] = ['some', 'data']

which automatically creates and links a ConstantNode to the specified Input

__module__ = 'fastr.core.node'

__setstate__(state)

Set the state of the ConstantNode by the given state.

Parameters:state (dict) – The state to populate the object with Returns:None

data

The data stored in this constant node

execute()

Execute the constant node and create the jobs that need to run

Returns:list of jobs to run Return type:list of Jobs

set_data(data=None, ids=None)

Set the data of this constant node in the correct way. This is mainly for compatibility with the parent class SourceNode

Parameters:

• data (dict or list of urls) – the data to use
• ids – if data is a list, a list of accompanying ids

class fastr.__init__.SourceNode(datatype, id_=None)

Bases: fastr.core.node.FlowNode

Class providing a connection to data resources. This can be any kind of file, stream, database, etc from which data can be received.

__abstractmethods__ = frozenset([])

__dataschemafile__ = 'SourceNode.schema.json'

__eq__(other)

Compare two Node instances with each other. This function ignores the parent and update status, but tests rest of the dict for equality. equality

Parameters:other (Node) – the other instances to compare to Returns:True if equal, False otherwise

__getstate__()

Retrieve the state of the SourceNode

Returns:the state of the object Rtype dict:

__init__(datatype, id_=None)

Instantiation of the SourceNode.

Parameters:

• datatype – The (id of) the datatype of the output.
• id – The url pattern.

This class should never be instantiated directly (unless you know what you are doing). Instead create a source using the network class like shown in the usage example below.

usage example:

>>> import fastr
>>> network = fastr.Network()
>>> source = network.create_source(datatype=fastr.typelist['ITKImageFile'], id_='sourceN')

__module__ = 'fastr.core.node'

__setstate__(state)

Set the state of the SourceNode by the given state.

Parameters:state (dict) – The state to populate the object with Returns:None

create_job(sample_id, sample_index, job_data, job_dependencies)

datatype

The datatype of the data this source supplies.

dimnames

Names of the dimensions in the SourceNode output. These will be reflected in the SampleIdLists.

execute()

Execute the source node and create the jobs that need to run

Returns:list of jobs to run Return type:list of Jobs

output

Shorthand for self.outputs['output']

outputsize

The size of output of this SourceNode

set_data(data, ids=None)

Set the data of this source node.

Parameters:

• data (dict, OrderedDict or list of urls) – the data to use
• ids – if data is a list, a list of accompanying ids

sourcegroup

valid

This does nothing. It only overloads the valid method of Node(). The original is intended to check if the inputs are connected to some output. Since this class does not implement inputs, it is skipped.

configmanager Module

This module defines the Fastr Config class for managing the configuration of Fastr. The config object is stored directly in the fastr top-level module.

class fastr.configmanager.Config(*configfiles)

Bases: object

Class contain the fastr configuration

DEFAULT_FIELDS = {'resourcesdir': (<type 'str'>, '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources', 'Directory containing the fastr system resources', '$systemdir/resources'), 'examplesdir': (<type 'str'>, '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/examples', 'Directory containing the fastr examples', '$systemdir/examples'), 'plugins_path': (<type 'list'>, ['/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/plugins'], 'Directories to scan for plugins', ['$userdir/plugins', '$resourcedir/plugins']), 'executionscript': (<type 'str'>, '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/execution/executionscript.py', 'Execution script location', '$systemdir/execution/executionscript.py'), 'protected_modules': (<type 'list'>, [], 'A list of modules in the environmnet modules that are protected against unloading'), 'warn_develop': (<type 'bool'>, True, 'Warning users on import if this is not a production version of fastr'), 'preferred_types': (<type 'list'>, [], 'A list indicating the order of the preferred types to use. First item is most preferred.'), 'pim_host': (<type 'str'>, '', 'Host of the PIM server to report to'), 'logtype': (<type 'str'>, 'default', 'Type of logging to use'), 'systemdir': (<type 'str'>, '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr', 'Fastr installation directory'), 'execution_plugin': (<type 'str'>, 'ProcessPoolExecution', 'The default execution plugin to use'), 'logdir': (<type 'str'>, '/home/docs/.fastr/logs', 'Directory where the fastr logs will be placed', '$userdir/logs'), 'web_secret_key': (<type 'str'>, 'VERYSECRETKEY!', 'The secret key to use for the flask web app'), 'types_path': (<type 'list'>, ['/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/datatypes'], 'Directories to scan for datatypes', ['$userdir/datatypes', '$resourcedir/datatypes']), 'mounts': (<type 'dict'>, {'tmp': '/tmp', 'home': '/home/docs/', 'example_data': '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/examples/data'}, 'A dictionary containing all mount points in the VFS system', {'tmp': '$TMPDIR', 'home': '~/', 'example_data': '$systemdir/examples/data'}), 'tools_path': (<type 'list'>, ['/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/tools'], 'Directories to scan for tools', ['$userdir/tools', '$resourcedir/tools']), 'source_job_limit': (<type 'int'>, 4, 'The number of source jobs allowed to run concurrently'), 'web_port': (<type 'str'>, '5000', 'The port to expose the web app on'), 'web_hostname': (<type 'str'>, 'localhost', 'The hostname to expose the web app for'), 'networks_path': (<type 'list'>, ['/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/networks'], 'Directories to scan for networks', ['$userdir/networks', '$resourcedir/networks']), 'userdir': (<type 'str'>, '/home/docs/.fastr', 'Fastr user configuration directory', '~/.fastr'), 'schemadir': (<type 'str'>, '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/schemas', 'Directory containing the fastr data schemas', '$systemdir/schemas'), 'debug': (<type 'bool'>, False, 'Flag to enable/disable debugging')}

__dict__ = dict_proxy({'resourcesdir': <property object>, '__module__': 'fastr.configmanager', 'plugins_path': <property object>, 'executionscript': <property object>, '_field_property': <staticmethod object>, 'warn_develop': <property object>, 'protected_modules': <property object>, 'web_url': <function web_url>, '__dict__': <attribute '__dict__' of 'Config' objects>, '__weakref__': <attribute '__weakref__' of 'Config' objects>, '__init__': <function __init__>, 'DEFAULT_FIELDS': {'resourcesdir': (<type 'str'>, '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources', 'Directory containing the fastr system resources', '$systemdir/resources'), 'examplesdir': (<type 'str'>, '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/examples', 'Directory containing the fastr examples', '$systemdir/examples'), 'plugins_path': (<type 'list'>, ['/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/plugins'], 'Directories to scan for plugins', ['$userdir/plugins', '$resourcedir/plugins']), 'executionscript': (<type 'str'>, '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/execution/executionscript.py', 'Execution script location', '$systemdir/execution/executionscript.py'), 'protected_modules': (<type 'list'>, [], 'A list of modules in the environmnet modules that are protected against unloading'), 'warn_develop': (<type 'bool'>, True, 'Warning users on import if this is not a production version of fastr'), 'preferred_types': (<type 'list'>, [], 'A list indicating the order of the preferred types to use. First item is most preferred.'), 'pim_host': (<type 'str'>, '', 'Host of the PIM server to report to'), 'logtype': (<type 'str'>, 'default', 'Type of logging to use'), 'systemdir': (<type 'str'>, '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr', 'Fastr installation directory'), 'execution_plugin': (<type 'str'>, 'ProcessPoolExecution', 'The default execution plugin to use'), 'logdir': (<type 'str'>, '/home/docs/.fastr/logs', 'Directory where the fastr logs will be placed', '$userdir/logs'), 'web_secret_key': (<type 'str'>, 'VERYSECRETKEY!', 'The secret key to use for the flask web app'), 'types_path': (<type 'list'>, ['/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/datatypes'], 'Directories to scan for datatypes', ['$userdir/datatypes', '$resourcedir/datatypes']), 'mounts': (<type 'dict'>, {'tmp': '/tmp', 'home': '/home/docs/', 'example_data': '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/examples/data'}, 'A dictionary containing all mount points in the VFS system', {'tmp': '$TMPDIR', 'home': '~/', 'example_data': '$systemdir/examples/data'}), 'tools_path': (<type 'list'>, ['/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/tools'], 'Directories to scan for tools', ['$userdir/tools', '$resourcedir/tools']), 'source_job_limit': (<type 'int'>, 4, 'The number of source jobs allowed to run concurrently'), 'web_port': (<type 'str'>, '5000', 'The port to expose the web app on'), 'web_hostname': (<type 'str'>, 'localhost', 'The hostname to expose the web app for'), 'networks_path': (<type 'list'>, ['/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/networks'], 'Directories to scan for networks', ['$userdir/networks', '$resourcedir/networks']), 'userdir': (<type 'str'>, '/home/docs/.fastr', 'Fastr user configuration directory', '~/.fastr'), 'schemadir': (<type 'str'>, '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/schemas', 'Directory containing the fastr data schemas', '$systemdir/schemas'), 'debug': (<type 'bool'>, False, 'Flag to enable/disable debugging')}, 'preferred_types': <property object>, 'pim_host': <property object>, 'userdir': <property object>, 'examplesdir': <property object>, 'register_fields': <function register_fields>, 'set_field': <function set_field>, 'execution_plugin': <property object>, 'logdir': <property object>, 'get_field': <function get_field>, 'read_config': <function read_config>, '__doc__': '\n Class contain the fastr configuration\n ', 'web_secret_key': <property object>, 'types_path': <property object>, 'mounts': <property object>, 'tools_path': <property object>, '_create_field_properties': <classmethod object>, '_update_logging': <function _update_logging>, 'source_job_limit': <property object>, 'web_port': <property object>, 'systemdir': <property object>, 'web_hostname': <property object>, 'process_pool_worker_number': <property object>, 'networks_path': <property object>, 'logtype': <property object>, '__repr__': <function __repr__>, 'schemadir': <property object>, 'debug': <property object>, 'x': '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/plugins'})

__init__(*configfiles)

__module__ = 'fastr.configmanager'

__repr__()

__weakref__

list of weak references to the object (if defined)

debug

examplesdir

execution_plugin

executionscript

get_field(item)

logdir

logtype

mounts

networks_path

pim_host

plugins_path

preferred_types

process_pool_worker_number

protected_modules

read_config(filename)

Read a configuration and update the configuration object accordingly

Parameters:filename – the configuration file to read

read_config_files = None

Trace of the config files read by this object

register_fields(fields_spec)

Register extra fields to the configuration manager.

resourcesdir

schemadir

set_field(item, value)

source_job_limit

systemdir

tools_path

types_path

userdir

warn_develop

web_hostname

web_port

web_secret_key

web_url()

Construct a fqdn from the web[‘hostname’] and web[‘port’] settings. :return: FQDN :rtype: str

x = '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/plugins'

class fastr.configmanager.EmptyDefault(data=None)

Bases: object

__add__(right)

__delitem__(key)

__dict__ = dict_proxy({'extend': <function extend>, '__delitem__': <function __delitem__>, '__module__': 'fastr.configmanager', '__getitem__': <function __getitem__>, 'asdict': <function asdict>, 'update': <function update>, '__iadd__': <function __iadd__>, '__radd__': <function __radd__>, '__setitem__': <function __setitem__>, 'append': <function append>, '__add__': <function __add__>, '__dict__': <attribute '__dict__' of 'EmptyDefault' objects>, 'prepend': <function prepend>, '__weakref__': <attribute '__weakref__' of 'EmptyDefault' objects>, '__doc__': None, '__init__': <function __init__>, 'aslist': <function aslist>})

__getitem__(item)

__iadd__(right)

__init__(data=None)

__module__ = 'fastr.configmanager'

__radd__(other)

__setitem__(key, value)

__weakref__

list of weak references to the object (if defined)

append(value)

asdict()

aslist()

extend(other)

prepend(value)

update(other)

datatypes Module

The datatypes module holds all DataTypes generated by fastr and all the base classes for these datatypes.

class fastr.datatypes.AnalyzeImageFile(value=None, format_=None)

Bases: fastr.datatypes.URLType

__abstractmethods__ = frozenset([])

__module__ = 'fastr.datatypes'

classmethod content(invalue, outvalue=None)

description = 'Analyze Image file formate'

extension = 'hdr'

filename = '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/datatypes/AnalyzeImageFile.py'

module = <module 'AnalyzeImageFile' from '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/datatypes/AnalyzeImageFile.pyc'>

class fastr.datatypes.AnyFile(value=None)

Bases: fastr.datatypes.TypeGroup

Special Datatype in fastr that is a TypeGroup with all known DataTypes as its members.

__abstractmethods__ = frozenset([])

__module__ = 'fastr.datatypes'

description = 'TypeGroup AnyFile\nAnyFile (AnyFile) is a group of consisting of all URLTypes known by fastr, currently:\n - <URLType: Directory class [\x1b[37m\x1b[42m\x1b[1mLoaded\x1b[0m]>\n - <URLType: TifImageFile class [\x1b[37m\x1b[42m\x1b[1mLoaded\x1b[0m]>\n - <URLType: NiftiImageFileCompressed class [\x1b[37m\x1b[42m\x1b[1mLoaded\x1b[0m]>\n - <URLType: FilePrefix class [\x1b[37m\x1b[42m\x1b[1mLoaded\x1b[0m]>\n - <URLType: MetaImageFile class [\x1b[37m\x1b[42m\x1b[1mLoaded\x1b[0m]>\n - <URLType: NiftiImageFileUncompressed class [\x1b[37m\x1b[42m\x1b[1mLoaded\x1b[0m]>\n - <URLType: TxtFile class [\x1b[37m\x1b[42m\x1b[1mLoaded\x1b[0m]>\n - <URLType: ProvNFile class [\x1b[37m\x1b[42m\x1b[1mLoaded\x1b[0m]>\n - <URLType: AnalyzeImageFile class [\x1b[37m\x1b[42m\x1b[1mLoaded\x1b[0m]>\n - <URLType: JsonFile class [\x1b[37m\x1b[42m\x1b[1mLoaded\x1b[0m]>'

class fastr.datatypes.AnyType(value=None)

Bases: fastr.datatypes.TypeGroup

Special Datatype in fastr that is a TypeGroup with all known DataTypes as its members.

__abstractmethods__ = frozenset([])

__module__ = 'fastr.datatypes'

description = 'TypeGroup AnyType\nAnyType (AnyType) is a group of consisting of all DataTypes known by fastr, currently:\n - <URLType: Directory class [\x1b[37m\x1b[42m\x1b[1mLoaded\x1b[0m]>\n - <ValueType: Boolean class [\x1b[37m\x1b[42m\x1b[1mLoaded\x1b[0m]>\n - <URLType: TifImageFile class [\x1b[37m\x1b[42m\x1b[1mLoaded\x1b[0m]>\n - <ValueType: Float class [\x1b[37m\x1b[42m\x1b[1mLoaded\x1b[0m]>\n - <URLType: NiftiImageFileCompressed class [\x1b[37m\x1b[42m\x1b[1mLoaded\x1b[0m]>\n - <URLType: FilePrefix class [\x1b[37m\x1b[42m\x1b[1mLoaded\x1b[0m]>\n - <DataType: Deferred class [\x1b[37m\x1b[42m\x1b[1mLoaded\x1b[0m]>\n - <URLType: NiftiImageFileUncompressed class [\x1b[37m\x1b[42m\x1b[1mLoaded\x1b[0m]>\n - <ValueType: String class [\x1b[37m\x1b[42m\x1b[1mLoaded\x1b[0m]>\n - <EnumType: __source-interface__behaviour__Enum__ class [\x1b[37m\x1b[42m\x1b[1mLoaded\x1b[0m]>\n - <URLType: TxtFile class [\x1b[37m\x1b[42m\x1b[1mLoaded\x1b[0m]>\n - <URLType: ProvNFile class [\x1b[37m\x1b[42m\x1b[1mLoaded\x1b[0m]>\n - <EnumType: __ioplugin__behaviour__Enum__ class [\x1b[37m\x1b[42m\x1b[1mLoaded\x1b[0m]>\n - <EnumType: __CrossValidation_0.1_interface__method__Enum__ class [\x1b[37m\x1b[42m\x1b[1mLoaded\x1b[0m]>\n - <ValueType: UnsignedInt class [\x1b[37m\x1b[42m\x1b[1mLoaded\x1b[0m]>\n - <ValueType: Int class [\x1b[37m\x1b[42m\x1b[1mLoaded\x1b[0m]>\n - <URLType: MetaImageFile class [\x1b[37m\x1b[42m\x1b[1mLoaded\x1b[0m]>\n - <URLType: AnalyzeImageFile class [\x1b[37m\x1b[42m\x1b[1mLoaded\x1b[0m]>\n - <URLType: JsonFile class [\x1b[37m\x1b[42m\x1b[1mLoaded\x1b[0m]>'

class fastr.datatypes.BaseDataType(value=None, format_=None)

Bases: fastr.core.baseplugin.BasePlugin

The base class for all datatypes in the fastr type system.

__abstractmethods__ = frozenset(['__init__'])

__eq__(other)

Test the equality of two DataType objects

Parameters:other (DataType) – the object to compare against Returns:flag indicating equality Return type:bool

__getstate__()

__init__(value=None, format_=None)

The BaseDataType constructor.

Parameters:

• value – value to assign to the new BaseDataType object
• format – the format used for the ValueType

Returns:

new BaseDataType object

Raises:

FastrNotImplementedError – if id, name, version or description is None

__module__ = 'fastr.datatypes'

__ne__(other)

Test if two objects are not equal. This is by default done by negating the __eq__ operator

Parameters:other (DataType) – the object to compare against Returns:flag indicating equality Return type:bool

__reduce_ex__(*args, **kwargs)

__repr__()

Returns string representation of the BaseDataType

Returns:string represenation Return type:str

__setstate__(state)

__str__()

Returns the string version of the BaseDataType

Returns:string version Return type:str

checksum()

Generate a checksum for the value of this DataType

Returns:the checksum of the value Return type:str

description = ''

extension = None

filename = '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/datatypes.pyc'

fullid = 'fastr://typelist/BaseDataType'

id = 'BaseDataType'

classmethod isinstance(value)

Indicate whether value is an instance for this DataType.

Returns:the flag indicating the value is of this DataType Return type:bool

name = 'BaseDataType'

parent = DataTypeManager AnalyzeImageFile : <URLType: AnalyzeImageFile> AnyFile : <TypeGroup: AnyFile> AnyType : <TypeGroup: AnyType> Boolean : <ValueType: Boolean> Deferred : <DataType: Deferred> Directory : <URLType: Directory> FilePrefix : <URLType: FilePrefix> Float : <ValueType: Float> ITKImageFile : <TypeGroup: ITKImageFile> Int : <ValueType: Int> JsonFile : <URLType: JsonFile> MetaImageFile : <URLType: MetaImageFile> NiftiImageFile : <TypeGroup: NiftiImageFile> NiftiImageFileCompressed : <URLType: NiftiImageFileCompressed> NiftiImageFileUncompressed : <URLType: NiftiImageFileUncompressed> Number : <TypeGroup: Number> ProvNFile : <URLType: ProvNFile> String : <ValueType: String> TifImageFile : <URLType: TifImageFile> TxtFile : <URLType: TxtFile> UnsignedInt : <ValueType: UnsignedInt> VirtualFileSystem : <IOPlugin: VirtualFileSystem>

parsed_value

The parsed value of object instantiation of this DataType.

raw_value

The raw value of object instantiation of this DataType. For datatypes that override value (like Deferred) this is the way to access the _value field.

classmethod test()

Define the test for the BasePluginManager. Make sure we are not one of the base classes

valid

A boolean flag that indicates weather or not the value assigned to this DataType is valid. This property is generally overwritten by implementation of specific DataTypes.

value

The value of object instantiation of this DataType.

version = <Version: 1.0>

class fastr.datatypes.Boolean(value=None, format_=None)

Bases: fastr.datatypes.ValueType

Datatype representing a boolean

__abstractmethods__ = frozenset([])

__module__ = 'fastr.datatypes'

__str__()

description = 'A boolean value (True of False)'

filename = '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/datatypes/Boolean.py'

module = <module 'Boolean' from '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/datatypes/Boolean.pyc'>

value

The value of object instantiation of this DataType.

class fastr.datatypes.DataType(value=None, format_=None)

Bases: fastr.datatypes.BaseDataType

This class is the base class for all DataTypes that can hold a value.

__abstractmethods__ = frozenset(['__init__'])

__init__(value=None, format_=None)

The DataType constructor.

Parameters:

• value – value to assign to the new DataType object
• format – the format used for the ValueType

Returns:

new DataType object

__module__ = 'fastr.datatypes'

action(name)

This function can be overwritten by subclasses to implement certain action that should be performed. For example, the Directory DataType has an action ensure. This method makes sure the Directory exists. A Tool can indicate an action that should be called for an Output which will be called before execution.

Parameters:name (str) – name of the action to execute Returns:None

class fastr.datatypes.Deferred(value=None, format_=None)

Bases: fastr.datatypes.DataType

__abstractmethods__ = frozenset([])

__getstate__()

__init__(value=None, format_=None)

The Deferred constructor.

Parameters:

• value – value to assign to the new DataType object
• format – This is ignore but here for compatibility

Returns:

new Deferred object

__module__ = 'fastr.datatypes'

__repr__()

Returns string representation of the BaseDataType

Returns:string represenation Return type:str

__setstate__(state)

checksum()

Generate a checksum for the value of this DataType

Returns:the checksum of the value Return type:str

job

classmethod lookup(value)

Look up the deferred target and return that object

Param:

value

Returns:

The value the deferred points to

Return type:

DataType

Raises:

• FastrKeyError – if the deferred is not available (yet)
• FastrValueError – if the value is not a valid deferrred url

parsed_value

The value of object instantiation of this DataType.

provenance

target

Target object for this deferred.

Raises:

• FastrKeyError – if the deferred is not available (yet)
• FastrValueError – if the value is not a valid deferrred url

value

The value of object instantiation of this DataType.

class fastr.datatypes.Directory(value=None, format_=None)

Bases: fastr.datatypes.URLType

DataType representing a directory.

__abstractmethods__ = frozenset([])

__eq__(other)

Directories are equal by default as long as the validatity matches.

Parameters:other (Directory) – other to compare against Returns:equality flag

__module__ = 'fastr.datatypes'

action(name)

This method makes sure the Directory exists. A Tool can indicate an action that should be called for an Output which will be called before execution.

Parameters:name (str) – name of the action to execute Returns:None

description = 'A directory on the disk'

extension = None

filename = '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/datatypes/Directory.py'

module = <module 'Directory' from '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/datatypes/Directory.pyc'>

class fastr.datatypes.EnumType(value=None, format_=None)

Bases: fastr.datatypes.DataType

The EnumType is the base for DataTypes that can have a value which is an option from a predefined set of possibilities (similar to an enum type in many programming languages).

__abstractmethods__ = frozenset([])

__init__(value=None, format_=None)

The EnumType constructor.

Parameters:

• value – value to assign to the new EnumType object
• format – the format used for the ValueType

Returns:

new EnumType object

Raises:

FastrDataTypeNotInstantiableError – if not subclassed

__module__ = 'fastr.datatypes'

__reduce_ex__(*args, **kwargs)

description = 'EnumType (EnumType) is a enumerate type with options:\n\n\nEnumType can take the value of any of the option, but any other value is considered invalid.'

options = frozenset([])

version = <Version: 1.0>

class fastr.datatypes.FilePrefix(value=None, format_=None)

Bases: fastr.datatypes.URLType

__abstractmethods__ = frozenset([])

__module__ = 'fastr.datatypes'

checksum()

description = 'Prefix for another file'

extension = 'prefix'

filename = '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/datatypes/FilePrefix.py'

module = <module 'FilePrefix' from '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/datatypes/FilePrefix.pyc'>

class fastr.datatypes.Float(value=None, format_=None)

Bases: fastr.datatypes.ValueType

__abstractmethods__ = frozenset([])

__module__ = 'fastr.datatypes'

description = 'A floating point value'

filename = '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/datatypes/Float.py'

module = <module 'Float' from '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/datatypes/Float.pyc'>

value

The value of object instantiation of this DataType.

class fastr.datatypes.ITKImageFile(value=None)

Bases: fastr.datatypes.TypeGroup

__abstractmethods__ = frozenset([])

__module__ = 'fastr.datatypes'

description = 'Text file to store point coordinates'

filename = '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/datatypes/ITKImageFile.py'

module = <module 'ITKImageFile' from '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/datatypes/ITKImageFile.pyc'>

class fastr.datatypes.Int(value=None, format_=None)

Bases: fastr.datatypes.ValueType

__abstractmethods__ = frozenset([])

__module__ = 'fastr.datatypes'

description = 'an integer value'

filename = '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/datatypes/Int.py'

module = <module 'Int' from '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/datatypes/Int.pyc'>

value

The value of object instantiation of this DataType.

class fastr.datatypes.JsonFile(value=None, format_=None)

Bases: fastr.datatypes.URLType

__abstractmethods__ = frozenset([])

__module__ = 'fastr.datatypes'

checksum()

description = 'json file'

extension = 'json'

filename = '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/datatypes/JsonFile.py'

module = <module 'JsonFile' from '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/datatypes/JsonFile.pyc'>

class fastr.datatypes.MetaImageFile(value=None, format_=None)

Bases: fastr.datatypes.URLType

__abstractmethods__ = frozenset([])

__eq__(other)

__module__ = 'fastr.datatypes'

checksum()

Return the checksum of this MetaImageFile

Returns:checksum string Return type:str

classmethod content(invalue, outvalue=None)

description = 'Meta Image file format'

extension = 'mhd'

filename = '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/datatypes/MetaImageFile.py'

module = <module 'MetaImageFile' from '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/datatypes/MetaImageFile.pyc'>

class fastr.datatypes.NiftiImageFile(value=None)

Bases: fastr.datatypes.TypeGroup

__abstractmethods__ = frozenset([])

__module__ = 'fastr.datatypes'

description = 'NiftiTypeGroup'

filename = '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/datatypes/NiftiImageFile.py'

module = <module 'NiftiImageFile' from '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/datatypes/NiftiImageFile.pyc'>

class fastr.datatypes.NiftiImageFileCompressed(value=None, format_=None)

Bases: fastr.datatypes.URLType

__abstractmethods__ = frozenset([])

__module__ = 'fastr.datatypes'

description = 'Compressed Nifti Image File format'

extension = 'nii.gz'

filename = '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/datatypes/NiftiImageFileCompressed.py'

module = <module 'NiftiImageFileCompressed' from '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/datatypes/NiftiImageFileCompressed.pyc'>

class fastr.datatypes.NiftiImageFileUncompressed(value=None, format_=None)

Bases: fastr.datatypes.URLType

__abstractmethods__ = frozenset([])

__module__ = 'fastr.datatypes'

description = 'Nifti Image File format'

extension = 'nii'

filename = '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/datatypes/NiftiImageFileUncompressed.py'

module = <module 'NiftiImageFileUncompressed' from '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/datatypes/NiftiImageFileUncompressed.pyc'>

class fastr.datatypes.Number(value=None)

Bases: fastr.datatypes.TypeGroup

__abstractmethods__ = frozenset([])

__module__ = 'fastr.datatypes'

description = 'an numeric value'

filename = '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/datatypes/Number.py'

module = <module 'Number' from '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/datatypes/Number.pyc'>

class fastr.datatypes.ProvNFile(value=None, format_=None)

Bases: fastr.datatypes.URLType

__abstractmethods__ = frozenset([])

__module__ = 'fastr.datatypes'

description = 'Provenance file in the prov N format'

extension = 'provn'

filename = '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/datatypes/ProvNFile.py'

module = <module 'ProvNFile' from '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/datatypes/ProvNFile.pyc'>

class fastr.datatypes.String(value=None, format_=None)

Bases: fastr.datatypes.ValueType

__abstractmethods__ = frozenset([])

__module__ = 'fastr.datatypes'

description = 'A simple string value'

filename = '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/datatypes/String.py'

module = <module 'String' from '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/datatypes/String.pyc'>

class fastr.datatypes.TifImageFile(value=None, format_=None)

Bases: fastr.datatypes.URLType

__abstractmethods__ = frozenset([])

__module__ = 'fastr.datatypes'

description = 'Tif Image File format'

extension = 'tif'

filename = '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/datatypes/TifImageFile.py'

module = <module 'TifImageFile' from '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/datatypes/TifImageFile.pyc'>

class fastr.datatypes.TxtFile(value=None, format_=None)

Bases: fastr.datatypes.URLType

__abstractmethods__ = frozenset([])

__module__ = 'fastr.datatypes'

description = 'General text file'

extension = 'txt'

filename = '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/datatypes/TxtFile.py'

module = <module 'TxtFile' from '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/datatypes/TxtFile.pyc'>

class fastr.datatypes.TypeGroup(value=None)

Bases: fastr.datatypes.BaseDataType

The TypeGroup is a special DataType that does not hold a value of its own but is used to group a number of DataTypes. For example ITK has a list of supported file formats that all tools build on ITK support. A group can be used to conveniently specify this in multiple Tools that use the same set DataTypes.

__abstractmethods__ = frozenset(['_members'])

__init__(value=None)

Dummy constructor. TypeGroups are not instantiable and cannot hold a value of its own.

Raises:FastrDataTypeNotInstantiableError – if called

__module__ = 'fastr.datatypes'

static __new__(value=None, format_=None)

Instantiate a TypeGroup. This will for match the value to the best matching type and instantiate that. Not that the returned object will not be of type TypeGroup but one of the TypeGroup members.

classmethod isinstance(value)

members

A descriptor that can act like a property for a class.

class fastr.datatypes.URLType(value=None, format_=None)

Bases: fastr.datatypes.DataType

The URLType is the base for DataTypes that point to a resource somewhere else (typically a filesystem). The true value is actually the resource referenced by the value in this object.

__abstractmethods__ = frozenset([])

__eq__(other)

Test the equality of two DataType objects

Parameters:other (URLType) – the object to compare against Returns:flag indicating equality Return type:bool

__init__(value=None, format_=None)

The URLType constructor

Parameters:

• value – value to assign to the new URLType
• format – the format used for the ValueType

Returns:

new URLType object

__module__ = 'fastr.datatypes'

checksum()

Return the checksum of this URL type

Returns:checksum string Return type:str

classmethod content(inval, outval=None)

Give the contents of a URLType, this is generally useful for filetypes that consists of multiple files (e.g. AnalyzeImageFile, DICOM). The value will indicate the main file, and the contents function can determine all files that form a single data value.

Parameters:

• inval – a value to figure out contents for this type
• outval – the place where the copy should point to

Returns:

a list of all files part of the value (e.g. header and data file)

Return type:

list

parsed_value

The parsed value of object instantiation of this DataType.

valid

A boolean flag that indicates weather or not the value assigned to this DataType is valid. This property is generally overwritten by implementation of specific DataTypes.

class fastr.datatypes.UnsignedInt(value=None, format_=None)

Bases: fastr.datatypes.ValueType

__abstractmethods__ = frozenset([])

__module__ = 'fastr.datatypes'

description = 'an unsigned integer value'

filename = '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/datatypes/UnsignedInt.py'

module = <module 'UnsignedInt' from '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/datatypes/UnsignedInt.pyc'>

value

The value of object instantiation of this DataType.

class fastr.datatypes.ValueType(value=None, format_=None)

Bases: fastr.datatypes.DataType

The ValueType is the base for DataTypes that hold simple values (not an EnumType and not a file/URL). The values is generally represented by a string.

__abstractmethods__ = frozenset([])

__init__(value=None, format_=None)

The ValueType constructor

Parameters:

• value – value to assign to the new ValueType
• format – the format used for the ValueType

Returns:

new ValueType object

__module__ = 'fastr.datatypes'

fastr.datatypes.fastr_isinstance(obj, datatype)

Check if an object is of a specific datatype.

Parameters:

• obj – Object to inspect
• datatype (tuple, BaseDataType) – The datatype(s) to check

Returns:

flag indicating object is of datatype

Return type:

bool

exceptions Module

This module contains all Fastr-related Exceptions

exception fastr.exceptions.FastrAttributeError(*args, **kwargs)

Bases: fastr.exceptions.FastrError, exceptions.AttributeError

AttributeError in the fastr system

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrCannotChangeAttributeError(*args, **kwargs)

Bases: fastr.exceptions.FastrError

Attempting to change an attribute of an object that can be set only once.

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrCardinalityError(*args, **kwargs)

Bases: fastr.exceptions.FastrError

The description of the cardinality is not valid.

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrCollectorError(*args, **kwargs)

Bases: fastr.exceptions.FastrError

Cannot collect the results from a Job because of an error

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrDataTypeFileNotReadable(*args, **kwargs)

Bases: fastr.exceptions.FastrError

Could not read the datatype file.

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrDataTypeMismatchError(*args, **kwargs)

Bases: fastr.exceptions.FastrError

When using a DataType as the key for the DataTypeManager, the DataTypeManager found another DataType with the same name already in the DataTypeManager. The means fastr has two version of the same DataType in the system, which should never happen!

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrDataTypeNotAvailableError(*args, **kwargs)

Bases: fastr.exceptions.FastrError

The DataType requested is not found by the fastr system. Typically this means that no matching DataType is found in the DataTypeManager.

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrDataTypeNotInstantiableError(*args, **kwargs)

Bases: fastr.exceptions.FastrError

The base classes for DataTypes cannot be instantiated and should always be sub-classed.

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrDataTypeValueError(*args, **kwargs)

Bases: fastr.exceptions.FastrError

This value in fastr did not pass the validation specificied for its DataType, typically means that the data is missing or corrupt.

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrError(*args, **kwargs)

Bases: exceptions.Exception

This is the base class for all fastr related exceptions. Catching this class of exceptions should ensure a proper execution of fastr.

__init__(*args, **kwargs)

Constructor for all exceptions. Saves the caller object fullid (if found) and the file, function and line number where the object was created.

__module__ = 'fastr.exceptions'

__str__()

String representation of the error

Returns:error string Return type:str

__weakref__

list of weak references to the object (if defined)

excerpt()

Return a excerpt of the Error as a tuple.

exception fastr.exceptions.FastrErrorInSubprocess(*args, **kwargs)

Bases: fastr.exceptions.FastrExecutionError

Encountered an error in the subprocess started by the execution script

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrExecutableNotFoundError(executable=None, *args, **kwargs)

Bases: fastr.exceptions.FastrExecutionError

The executable could not be found!

__init__(executable=None, *args, **kwargs)

__module__ = 'fastr.exceptions'

__str__()

String representation of the error

exception fastr.exceptions.FastrExecutionError(*args, **kwargs)

Bases: fastr.exceptions.FastrError

Base class for all fastr execution related errors

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrIOError(*args, **kwargs)

Bases: fastr.exceptions.FastrError, exceptions.IOError

IOError in the fastr system

__module__ = 'fastr.exceptions'

__weakref__

list of weak references to the object (if defined)

exception fastr.exceptions.FastrImportError(*args, **kwargs)

Bases: fastr.exceptions.FastrError, exceptions.ImportError

ImportError in the fastr system

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrIndexError(*args, **kwargs)

Bases: fastr.exceptions.FastrError, exceptions.IndexError

IndexError in the fastr system

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrIndexNonexistent(*args, **kwargs)

Bases: fastr.exceptions.FastrIndexError

This is an IndexError for samples requested from a sparse data array. The sample is not there but is probably not there because of sparseness rather than being a missing sample (e.g. out of bounds).

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrKeyError(*args, **kwargs)

Bases: fastr.exceptions.FastrError, exceptions.KeyError

KeyError in the fastr system

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrLookupError(*args, **kwargs)

Bases: fastr.exceptions.FastrError

Could not find specified object in the fastr environment.

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrMountUnknownError(*args, **kwargs)

Bases: fastr.exceptions.FastrKeyError

Trying to access an undefined mount

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrNetworkMismatchError(*args, **kwargs)

Bases: fastr.exceptions.FastrError

Two interacting objects belong to different fastr network.

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrNetworkUnknownError(*args, **kwargs)

Bases: fastr.exceptions.FastrKeyError

Reference to a Tool that is not recognised by the fastr system. This typically means the specific id/version combination of the requested tool has not been loaded by the ToolManager.

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrNoValidTargetError(*args, **kwargs)

Bases: fastr.exceptions.FastrKeyError

Cannot find a valid target for the tool

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrNodeAreadyPreparedError(*args, **kwargs)

Bases: fastr.exceptions.FastrStateError

A attempt is made at preparing a Node for the second time. This is not allowed as it would wipe the current execution data and cause data-loss.

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrNodeNotPreparedError(*args, **kwargs)

Bases: fastr.exceptions.FastrStateError

When trying to access executation data of a Node, the Node must be prepare. The Node has not been prepared by the execution, so the data is not available!

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrNodeNotValidError(*args, **kwargs)

Bases: fastr.exceptions.FastrStateError

A Node is not in a valid state where it should be, typically an invalid Node is passed to the executor causing trouble.

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrNotExecutableError(*args, **kwargs)

Bases: fastr.exceptions.FastrExecutionError

The command invoked by subprocess is not executable on the system

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrNotImplementedError(*args, **kwargs)

Bases: fastr.exceptions.FastrError, exceptions.NotImplementedError

This function/method has not been implemented on purpose (e.g. should be overwritten in a sub-class)

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrOSError(*args, **kwargs)

Bases: fastr.exceptions.FastrError, exceptions.OSError

OSError in the fastr system

__module__ = 'fastr.exceptions'

__weakref__

list of weak references to the object (if defined)

exception fastr.exceptions.FastrObjectUnknownError(*args, **kwargs)

Bases: fastr.exceptions.FastrKeyError

Reference to a Tool that is not recognised by the fastr system. This typically means the specific id/version combination of the requested tool has not been loaded by the ToolManager.

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrOptionalModuleNotAvailableError(*args, **kwargs)

Bases: fastr.exceptions.FastrNotImplementedError

A optional modules for Fastr is needed for this function, but is not available on the current python installation.

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrOutputValidationError(*args, **kwargs)

Bases: fastr.exceptions.FastrExecutionError

An output of a Job does not pass validation

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrParentMismatchError(*args, **kwargs)

Bases: fastr.exceptions.FastrError

Two interactive objects have different parent where they should be the same

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrPluginCapabilityNotImplemented(*args, **kwargs)

Bases: fastr.exceptions.FastrNotImplementedError

A plugin did not implement a capability that it advertised.

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrPluginNotAvailable(*args, **kwargs)

Bases: fastr.exceptions.FastrKeyError

Indicates that a requested Plugin was not found on the system.

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrPluginNotLoaded(*args, **kwargs)

Bases: fastr.exceptions.FastrStateError

The plugin was not successfully loaded. This means the plugin class cannot be instantiated.

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrResultFileNotFound(*args, **kwargs)

Bases: fastr.exceptions.FastrExecutionError

Could not found the result file of job that finished. This means the executionscript process was killed during interruption. Generally this means a scheduler killed it because of resource shortage.

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrSerializationError(message, serializer, original_exception=None)

Bases: fastr.exceptions.FastrError

The serialization encountered a serious problem

__init__(message, serializer, original_exception=None)

__module__ = 'fastr.exceptions'

__repr__()

Simple string representation of the exception

__str__()

Advanced string representation of the exception including the data about where in the schema things went wrong.

exception fastr.exceptions.FastrSerializationIgnoreDefaultError(message, serializer, original_exception=None)

Bases: fastr.exceptions.FastrSerializationError

The value and default are both None, so the value should not be serialized.

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrSerializationInvalidDataError(message, serializer, original_exception=None)

Bases: fastr.exceptions.FastrSerializationError

Encountered data to serialize that is invalid given the serialization schema.

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrSerializationMethodError(*args, **kwargs)

Bases: fastr.exceptions.FastrKeyError

The desired serialization method does not exist.

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrSinkDataUnavailableError(*args, **kwargs)

Bases: fastr.exceptions.FastrKeyError

Could not find the Sink data for the desire sink.

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrSizeInvalidError(*args, **kwargs)

Bases: fastr.exceptions.FastrError

The given size cannot be valid.

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrSizeMismatchError(*args, **kwargs)

Bases: fastr.exceptions.FastrError

The size of two object in fastr is not matching where it should.

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrSizeUnknownError(*args, **kwargs)

Bases: fastr.exceptions.FastrError

The size of object is not (yet) known and only a theoretical estimate is available at the moment.

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrSourceDataUnavailableError(*args, **kwargs)

Bases: fastr.exceptions.FastrKeyError

Could not find the Source data for the desire source.

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrStateError(*args, **kwargs)

Bases: fastr.exceptions.FastrError

An object is in an invalid/unexpected state.

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrSubprocessNotFinished(*args, **kwargs)

Bases: fastr.exceptions.FastrExecutionError

Encountered an error before the subprocess call by the execution script was properly finished.

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrToolNotAvailableError(*args, **kwargs)

Bases: fastr.exceptions.FastrError

The tool used is not available on the current platform (OS and architecture

combination) and cannot be used.

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrToolTargetNotFound(*args, **kwargs)

Bases: fastr.exceptions.FastrError

Could not determine the location of the tools target binary/script. The tool cannot be used.

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrToolUnknownError(*args, **kwargs)

Bases: fastr.exceptions.FastrKeyError

Reference to a Tool that is not recognised by the fastr system. This typically means the specific id/version combination of the requested tool has not been loaded by the ToolManager.

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrTypeError(*args, **kwargs)

Bases: fastr.exceptions.FastrError, exceptions.TypeError

TypeError in the fastr system

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrUnknownURLSchemeError(*args, **kwargs)

Bases: fastr.exceptions.FastrKeyError

Fastr encountered a data URL with a scheme that was not recognised by the IOPlugin manager.

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrValueError(*args, **kwargs)

Bases: fastr.exceptions.FastrError, exceptions.ValueError

ValueError in the fastr system

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrVersionInvalidError(*args, **kwargs)

Bases: fastr.exceptions.FastrValueError

The string representation of the version is malformatted.

__module__ = 'fastr.exceptions'

exception fastr.exceptions.FastrVersionMismatchError(*args, **kwargs)

Bases: fastr.exceptions.FastrValueError

There is a mismatch between different parts of the Fastr environment and integrity is compromised.

__module__ = 'fastr.exceptions'

plugins Module

The plugins module holds all plugins loaded by Fastr. It is empty on start and gets filled by the BasePluginManager

class fastr.plugins.BlockingExecution(finished_callback=None, cancelled_callback=None, status_callback=None)

Bases: fastr.execution.executionpluginmanager.ExecutionPlugin

The blocking execution plugin is a special plugin which is meant for debug purposes. It will not queue jobs but immediately execute them inline, effectively blocking fastr until the Job is finished. It is the simplest execution plugin and can be used as a template for new plugins or for testing purposes.

__abstractmethods__ = frozenset([])

__init__(finished_callback=None, cancelled_callback=None, status_callback=None)

__module__ = 'fastr.plugins'

cleanup()

filename = '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/plugins/executionplugins/blockingexecution.py'

module = <module 'blockingexecution' from '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/plugins/executionplugins/blockingexecution.pyc'>

class fastr.plugins.CommaSeperatedValueFile

Bases: fastr.core.ioplugin.IOPlugin

The CommaSeperatedValueFile an expand-only type of IOPlugin. No URLs can actually be fetched, but it can expand a single URL into a larger amount of URLs.

The csv:// URL is a vfs:// URL with a number of query variables available. The URL mount and path should point to a valid CSV file. The query variable then specify what column(s) of the file should be used.

The following variable can be set in the query:

variable	usage
value	the column containing the value of interest, can be int for index or string for key
id	the column containing the sample id (optional)
header	indicates if the first row is considered the header, can be true or false (optional)
delimiter	the delimiter used in the csv file (optional)
quote	the quote character used in the csv file (optional)
reformat	a reformatting string so that value = reformat.format(value) (used before relative_path)
relative_path	indicates the entries are relative paths (for files), can be true or false (optional)

The header is by default false if the neither the value and id are set as a string. If either of these are a string, the header is required to define the column names and it automatically is assumed true

The delimiter and quota characters of the file should be detected automatically using the Sniffer, but can be forced by setting them in the URL.

Example of valid csv URLs:

# Use the first column in the file (no header row assumed)
csv://mount/some/dir/file.csv?value=0

# Use the images column in the file (first row is assumed header row)
csv://mount/some/dir/file.csv?value=images

# Use the segmentations column in the file (first row is assumed header row)
# and use the id column as the sample id
csv://mount/some/dir/file.csv?value=segmentations&id=id

# Use the first column as the id and the second column as the value
# and skip the first row (considered the header)
csv://mount/some/dir/file.csv?value=1&id=0&header=true

# Use the first column and force the delimiter to be a comma
csv://mount/some/dir/file.csv?value=0&delimiter=,

__abstractmethods__ = frozenset([])

__init__()

__module__ = 'fastr.plugins'

expand_url(url)

filename = '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/plugins/ioplugins/commaseperatedvaluefile.py'

module = <module 'commaseperatedvaluefile' from '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/plugins/ioplugins/commaseperatedvaluefile.pyc'>

scheme = 'csv'

class fastr.plugins.CrossValidation

Bases: flowinterface.FlowPlugin

Advanced flow plugin that generated a cross-validation data flow. The node need an input with data and an input number of folds. Based on that the outputs test and train will be supplied with a number of data sets.

__abstractmethods__ = frozenset([])

__module__ = 'fastr.plugins'

static execute(payload)

filename = '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/plugins/flowplugins/crossvalidation.py'

module = <module 'crossvalidation' from '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/plugins/flowplugins/crossvalidation.pyc'>

class fastr.plugins.DRMAAExecution(finished_callback=None, cancelled_callback=None, status_callback=None)

Bases: fastr.execution.executionpluginmanager.ExecutionPlugin

A DRMAA execution plugin to execute Jobs on a Grid Engine cluster. It uses a configuration option for selecting the queue to submit to. It uses the python drmaa package.

Note

To use this plugin, make sure the drmaa package is installed and that the execution is started on an SGE submit host with DRMAA libraries installed.

Note

This plugin is at the moment tailored to SGE, but it should be fairly easy to make different subclasses for different DRMAA supporting systems.

CANCELS_DEPENDENCIES = False

SUPPORTS_CANCEL = True

SUPPORTS_DEPENDENCY = True

SUPPORTS_HOLD_RELEASE = True

__abstractmethods__ = frozenset([])

__init__(finished_callback=None, cancelled_callback=None, status_callback=None)

__module__ = 'fastr.plugins'

cleanup()

collect_jobs()

configuration_fields = {'drmaa_queue': (<type 'str'>, 'week', 'The default queue to use for jobs send to the scheduler')}

filename = '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/plugins/executionplugins/drmaaexecution.py'

module = <module 'drmaaexecution' from '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/plugins/executionplugins/drmaaexecution.pyc'>

send_job(command, arguments, queue=None, walltime=None, job_name=None, memory=None, ncores=None, joinLogFiles=False, outputLog=None, errorLog=None, hold_job=None, hold=False)

submit_jobs()

classmethod test()

class fastr.plugins.FastrInterface(id_, document)

Bases: fastr.core.interface.Interface

The default Interface for fastr. For the command-line Tools as used by fastr.

__abstractmethods__ = frozenset([])

__dataschemafile__ = 'FastrInterface.schema.json'

__eq__(other)

__getstate__()

Get the state of the FastrInterface object.

Returns:state of interface Return type:dict

__init__(id_, document)

__module__ = 'fastr.plugins'

__setstate__(state)

Set the state of the Interface

check_input_id(id_)

Check if an id for an object is valid and unused in the Tool. The method will always returns True if it does not raise an exception.

Parameters:

id (str) – the id to check

Returns:

True

Raises:

• FastrValueError – if the id is not correctly formatted
• FastrValueError – if the id is already in use

check_output_id(id_)

Check if an id for an object is valid and unused in the Tool. The method will always returns True if it does not raise an exception.

Parameters:

id (str) – the id to check

Returns:

True

Raises:

• FastrValueError – if the id is not correctly formatted
• FastrValueError – if the id is already in use

collect_results(result)

Collect all results of the interface

collector_plugin_type

alias of CollectorPlugin

collectors = CollectorPluginManager Loaded json : <CollectorPlugin: JsonCollector> Loaded path : <CollectorPlugin: PathCollector> Loaded stdout : <CollectorPlugin: StdoutCollector>

execute(target, payload)

Execute the interface using a specific target and payload (containing a set of values for the arguments)

Parameters:

• target (SampleId) – the target to use
• payload (dict) – the values for the arguments

Returns:

result of the execution

Return type:

InterfaceResult

expanding

filename = '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/plugins/interfaceplugins/fastrinterface.py'

get_arguments(values)

Get the argument list for this interface

Returns:return list of arguments

get_specials(payload, output, cardinality_nr)

Get special attributes. Returns tuples for specials, inputs and outputs that are used for formatting substitutions.

Parameters:

• output – Output for which to get the specials
• cardinality_nr (int) – the cardinality number

inputs

module = <module 'fastrinterface' from '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/plugins/interfaceplugins/fastrinterface.pyc'>

outputs

class fastr.plugins.FileSystem

Bases: fastr.core.ioplugin.IOPlugin

The FileSystem plugin is create to handle file:// type or URLs. This is generally not a good practice, as this is not portable over between machines. However, for test purposes it might be useful.

The URL scheme is rather simple: file://host/path (see wikipedia for details)

We do not make use of the host part and at the moment only support localhost (just leave the host empty) leading to file:/// URLs.

Warning

This plugin ignores the hostname in the URL and does only accept driver letters on Windows in the form c:/

__abstractmethods__ = frozenset([])

__init__()

__module__ = 'fastr.plugins'

fetch_url(inurl, outpath)

Fetch the files from the file.

Parameters:

• inurl – url to the item in the data store, starts with file://
• outpath – path where to store the fetch data locally

fetch_value(inurl)

Fetch a value from an external file file.

Parameters:inurl – url of the value to read Returns:the fetched value

filename = '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/plugins/ioplugins/filesystem.py'

module = <module 'filesystem' from '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/plugins/ioplugins/filesystem.pyc'>

path_to_url(path, mountpoint=None)

Construct an url from a given mount point and a relative path to the mount point.

put_url(inpath, outurl)

Put the files to the external data store.

Parameters:

• inpath – path of the local data
• outurl – url to where to store the data, starts with file://

put_value(value, outurl)

Put the value in the external data store.

Parameters:

• value – value to store
• outurl – url to where to store the data, starts with file://

scheme = 'file'

url_to_path(url)

Get the path to a file from a url. Currently supports the file:// scheme

Examples:

>>> 'file:///d:/data/project/file.ext'
'd:\data\project\file.ext'

Warning

file:// will not function cross platform and is mainly for testing

class fastr.plugins.FlowInterface(id_, document)

Bases: fastr.core.interface.Interface

The Interface use for AdvancedFlowNodes to create the advanced data flows that are not implemented in the fastr. This allows nodes to implement new data flows using the plugin system.

The definition of FlowInterfaces are very similar to the default FastrInterfaces.

Note

A flow interface should be using a specific FlowPlugin

__abstractmethods__ = frozenset([])

__dataschemafile__ = 'FastrInterface.schema.json'

__eq__(other)

__getstate__()

Get the state of the FastrInterface object.

Returns:state of interface Return type:dict

__init__(id_, document)

__module__ = 'fastr.plugins'

__setstate__(state)

Set the state of the Interface

execute(target, payload)

expanding

filename = '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/plugins/interfaceplugins/flowinterface.py'

flow_plugin_type

alias of FlowPlugin

flow_plugins = FlowPluginManager Loaded CrossValidation : <FlowPlugin: CrossValidation>

inputs

module = <module 'flowinterface' from '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/plugins/interfaceplugins/flowinterface.pyc'>

outputs

class fastr.plugins.LinearExecution(finished_callback=None, cancelled_callback=None, status_callback=None)

Bases: fastr.execution.executionpluginmanager.ExecutionPlugin

An execution engine that has a background thread that executes the jobs in order. The queue is a simple FIFO queue and there is one worker thread that operates in the background. This plugin is meant as a fallback when other plugins do not function properly. It does not multi-processing so it is safe to use in environments that do no support that.

__abstractmethods__ = frozenset([])

__init__(finished_callback=None, cancelled_callback=None, status_callback=None)

__module__ = 'fastr.plugins'

cleanup()

exec_worker()

filename = '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/plugins/executionplugins/linearexecution.py'

module = <module 'linearexecution' from '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/plugins/executionplugins/linearexecution.pyc'>

class fastr.plugins.NipypeInterface(id_, nipype_cls=None, document=None)

Bases: fastr.core.interface.Interface

Experimental interfaces to using nipype interfaces directly in fastr tools, only using a simple reference.

To create a tool using a nipype interface just create an interface with the correct type and set the nipype argument to the correct class. For example in an xml tool this would become:

<interface class="NipypeInterface">
  <nipype_class>nipype.interfaces.elastix.Registration</nipype_class>
</interface>

Note

To use these interfaces nipype should be installed on the system.

Warning

This interface plugin is basically functional, but highly experimental!

__abstractmethods__ = frozenset([])

__eq__(other)

__getstate__()

__init__(id_, nipype_cls=None, document=None)

__module__ = 'fastr.plugins'

__setstate__(state)

execute(target, payload)

Execute the interface using a specific target and payload (containing a set of values for the arguments)

Parameters:

• target (SampleId) – the target to use
• payload (dict) – the values for the arguments

Returns:

result of the execution

Return type:

InterfaceResult

expanding

filename = '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/plugins/interfaceplugins/nipypeinterface.py'

get_type(trait)

inputs

module = <module 'nipypeinterface' from '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/plugins/interfaceplugins/nipypeinterface.pyc'>

outputs

classmethod test()

class fastr.plugins.Null

Bases: fastr.core.ioplugin.IOPlugin

The Null plugin is create to handle null:// type or URLs. These URLs are indicating the sink should not do anything. The data is not written to anywhere. Besides the scheme, the rest of the URL is ignored.

__abstractmethods__ = frozenset([])

__init__()

__module__ = 'fastr.plugins'

filename = '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/plugins/ioplugins/null.py'

module = <module 'null' from '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/plugins/ioplugins/null.pyc'>

put_url(inpath, outurl)

Put the files to the external data store.

Parameters:

• inpath – path of the local data
• outurl – url to where to store the data, starts with file://

put_value(value, outurl)

Put the value in the external data store.

Parameters:

• value – value to store
• outurl – url to where to store the data, starts with file://

scheme = 'null'

class fastr.plugins.ProcessPoolExecution(finished_callback=None, cancelled_callback=None, status_callback=None, nr_of_workers=None)

Bases: fastr.execution.executionpluginmanager.ExecutionPlugin

A local execution plugin that uses multiprocessing to create a pool of worker processes. This allows fastr to execute jobs in parallel with true concurrency. The number of workers can be specified in the fastr configuration, but the default amount is the number of cores - 1 with a minimum of 1.

Warning

The ProcessPoolExecution does not check memory requirements of jobs and running many workers might lead to memory starvation and thus an unresponsive system.

__abstractmethods__ = frozenset([])

__init__(finished_callback=None, cancelled_callback=None, status_callback=None, nr_of_workers=None)

__module__ = 'fastr.plugins'

cleanup()

configuration_fields = {'process_pool_worker_number': (<type 'int'>, 3, 'Number of workers to use in a process pool')}

filename = '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/plugins/executionplugins/processpoolexecution.py'

module = <module 'processpoolexecution' from '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/plugins/executionplugins/processpoolexecution.pyc'>

classmethod test()

class fastr.plugins.RQExecution(finished_callback=None, cancelled_callback=None, status_callback=None)

Bases: fastr.execution.executionpluginmanager.ExecutionPlugin

A execution plugin based on Redis Queue. Fastr will submit jobs to the redis queue and workers will peel the jobs from the queue and process them.

This system requires a running redis database and the database url has to be set in the fastr configuration.

Note

This execution plugin required the redis and rq packages to be installed before it can be loaded properly.

__abstractmethods__ = frozenset([])

__init__(finished_callback=None, cancelled_callback=None, status_callback=None)

__module__ = 'fastr.plugins'

check_finished()

cleanup()

configuration_fields = {'rq_queue': (<type 'str'>, 'default', 'The redis queue to use'), 'rq_host': (<type 'str'>, 'redis://localhost:6379/0', 'The url of the redis serving the redis queue')}

filename = '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/plugins/executionplugins/rqexecution.py'

module = <module 'rqexecution' from '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/plugins/executionplugins/rqexecution.pyc'>

classmethod run_job(job_id, job_command, job_stdout, job_stderr)

class fastr.plugins.Reference

Bases: fastr.core.ioplugin.IOPlugin

The Reference plugin is create to handle ref:// type or URLs. These URLs are to make the sink just write a simple reference file to the data. The reference file contains the DataType and the value so the result can be reconstructed. It for files just leaves the data on disk by reference. This plugin is not useful for production, but is used for testing purposes.

__abstractmethods__ = frozenset([])

__init__()

__module__ = 'fastr.plugins'

filename = '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/plugins/ioplugins/reference.py'

module = <module 'reference' from '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/plugins/ioplugins/reference.pyc'>

push_sink_data(value, outurl, datatype=None)

Write out the sink data from the inpath to the outurl.

Parameters:

• value (str) – the path of the data to be pushed
• outurl (str) – the url to write the data to
• datatype (DataType) – the datatype of the data, used for determining the total contents of the transfer

Returns:

None

scheme = 'ref'

class fastr.plugins.VirtualFileSystem

Bases: fastr.core.ioplugin.IOPlugin

The virtual file system class. This is an IOPlugin, but also heavily used internally in fastr for working with directories. The VirtualFileSystem uses the vfs:// url scheme.

A typical virtual filesystem url is formatted as vfs://mountpoint/relative/dir/from/mount.ext

Where the mountpoint is defined in the Config file. A list of the currently known mountpoints can be found in the fastr.config object

>>> fastr.config.mounts
{'example_data': '/home/username/fastr-feature-documentation/fastr/fastr/examples/data',
 'home': '/home/username/',
 'tmp': '/home/username/FastrTemp'}

This shows that a url with the mount home such as vfs://home/tempdir/testfile.txt would be translated into /home/username/tempdir/testfile.txt.

There are a few default mount points defined by Fastr (that can be changed via the config file).

mountpoint	default location
home	the users home directory (expanduser('~/'))
tmp	the fastr temprorary dir, defaults to tempfile.gettempdir()
example_data	the fastr example data directory, defaults $FASTRDIR/example/data

__abstractmethods__ = frozenset([])

__init__()

Instantiate the VFS plugin

Returns:the VirtualFileSysten plugin

__module__ = 'fastr.plugins'

static copy_file_dir(inpath, outpath)

Helper function, copies a file or directory not caring what the inpath actually is

Parameters:

• inpath – path of the things to be copied
• outpath – path of the destination

Returns:

the result of shutil.copy2 or shutil.copytree (depending on inpath pointing to a file or directory)

expand_url(url)

Try to expand the url. For vfs with will return the original url.

Parameters:url – url to expand Returns:the expanded url (same as url)

fetch_url(inurl, outpath)

Fetch the files from the vfs.

Parameters:

• inurl – url to the item in the data store, starts with vfs://
• outpath – path where to store the fetch data locally

fetch_value(inurl)

Fetch a value from an external vfs file.

Parameters:inurl – url of the value to read Returns:the fetched value

path_to_url(path, mountpoint=None, scheme=None)

Construct an url from a given mount point and a relative path to the mount point.

Parameters:path (str) – the path to find the url for Mountpoint str:mountpoint the url should be under Returns:url of the

put_url(inpath, outurl)

Put the files to the external data store.

Parameters:

• inpath – path of the local data
• outurl – url to where to store the data, starts with vfs://

put_value(value, outurl)

Put the value in the external data store.

Parameters:

• value – value to store
• outurl – url to where to store the data, starts with vfs://

scheme

setup()

The plugin setup, does nothing but needs to be implemented

url_to_path(url, scheme=None)

Get the path to a file from a vfs url

Parameters:

url (str) – url to get the path for

Returns:

the matching path

Return type:

str

Raises:

• FastrMountUnknownError – if the mount in url is unknown
• FastrUnknownURLSchemeError – if the url scheme is not correct

Example (the mountpoint tmp points to /tmp):

>>> fastr.vfs.url_to_path('vfs://tmp/file.ext')
'/tmp/file.ext'

class fastr.plugins.VirtualFileSystemRegularExpression

Bases: fastr.core.ioplugin.IOPlugin

The VirtualFileSystemValueList an expand-only type of IOPlugin. No URLs can actually be fetched, but it can expand a single URL into a larger amount of URLs.

A vfsregex:// URL is a vfs URL that can contain regular expressions on every level of the path. The regular expressions follow the re module definitions.

An example of a valid URLs would be:

vfsregex://tmp/network_dir/.*/.*/__fastr_result__.pickle.gz
vfsregex://tmp/network_dir/nodeX/(?P<id>.*)/__fastr_result__.pickle.gz

The first URL would result in all the __fastr_result__.pickle.gz in the working directory of a Network. The second URL would only result in the file for a specific node (nodeX), but by adding the named group id using (?P<id>.*) the sample id of the data is automatically set to that group (see Regular Expression Syntax under the special characters for more info on named groups in regular expression).

Concretely if we would have a directory vfs://mount/somedir containing:

image_1/Image.nii
image_2/image.nii
image_3/anotherimage.nii
image_5/inconsistentnamingftw.nii

we could match these files using vfsregex://mount/somedir/(?P<id>image_\d+)/.*\.nii which would result in the following source data after expanding the URL:

{'image_1': 'vfs://mount/somedir/image_1/Image.nii',
 'image_2': 'vfs://mount/somedir/image_2/image.nii',
 'image_3': 'vfs://mount/somedir/image_3/anotherimage.nii',
 'image_5': 'vfs://mount/somedir/image_5/inconsistentnamingftw.nii'}

Showing the power of this regular expression filtering. Also it shows how the ID group from the URL can be used to have sensible sample ids.

Warning

due to the nature of regexp on multiple levels, this method can be slow when having many matches on the lower level of the path (because the tree of potential matches grows) or when directories that are parts of the path are very large.

__abstractmethods__ = frozenset([])

__init__()

__module__ = 'fastr.plugins'

expand_url(url)

filename = '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/plugins/ioplugins/virtualfilesystemregularexpression.py'

module = <module 'virtualfilesystemregularexpression' from '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/plugins/ioplugins/virtualfilesystemregularexpression.pyc'>

scheme = 'vfsregex'

class fastr.plugins.VirtualFileSystemValueList

Bases: fastr.core.ioplugin.IOPlugin

The VirtualFileSystemValueList an expand-only type of IOPlugin. No URLs can actually be fetched, but it can expand a single URL into a larger amount of URLs. A vfslist:// URL basically is a url that points to a file using vfs. This file then contains a number lines each containing another URL.

If the contents of a file vfs://mount/some/path/contents would be:

vfs://mount/some/path/file1.txt
vfs://mount/some/path/file2.txt
vfs://mount/some/path/file3.txt
vfs://mount/some/path/file4.txt

Then using the URL vfslist://mount/some/path/contents as source data would result in the four files being pulled.

Note

The URLs in a vfslist file do not have to use the vfs scheme, but can use any scheme known to the Fastr system.

__abstractmethods__ = frozenset([])

__init__()

__module__ = 'fastr.plugins'

expand_url(url)

filename = '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/plugins/ioplugins/virtualfilesystemvaluelist.py'

module = <module 'virtualfilesystemvaluelist' from '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/plugins/ioplugins/virtualfilesystemvaluelist.pyc'>

scheme = 'vfslist'

class fastr.plugins.XNATStorage

Bases: fastr.core.ioplugin.IOPlugin

Warning

As this IOPlugin is under development, it has not been thoroughly tested.

The XNATStorage plugin is an IOPlugin that can download data from and upload data to an XNAT server. It uses its own xnat:// URL scheme. This is a scheme specific for this plugin and though it looks somewhat like the XNAT rest interface, a different type or URL.

Data resources can be access directly by a data url:

xnat://xnat.example.com/data/archive/projects/sandbox/subjects/subject001/experiments/experiment001/scans/T1/resources/DICOM
xnat://xnat.example.com/data/archive/projects/sandbox/subjects/subject001/experiments/*_BRAIN/scans/T1/resources/DICOM

In the second URL you can see a wildcard being used. This is possible at long as it resolves to exactly one item.

The id query element will change the field from the default experiment to subject and the label query element sets the use of the label as the fastr id (instead of the XNAT id) to True (the default is False)

To disable https transport and use http instead the query string can be modified to add insecure=true. This will make the plugin send requests over http:

xnat://xnat.example.com/data/archive/projects/sandbox/subjects/subject001/experiments/*_BRAIN/scans/T1/resources/DICOM?insecure=true

For sinks it is import to know where to save the data. Sometimes you want to save data in a new assessor/resource and it needs to be created. To allow the Fastr sink to create an object in XNAT, you have to supply the type as a query parameter:

xnat://xnat.bmia.nl/data/archive/projects/sandbox/subjects/S01/experiments/_BRAIN/assessors/test_assessor/resources/IMAGE/files/image.nii.gz?resource_type=xnat:resourceCatalog&assessor_type=xnat:qcAssessmentData

Valid options are: subject_type, experiment_type, assessor_type, scan_type, and resource_type.

If you want to do a search where multiple resources are returned, it is possible to use a search url:

xnat://xnat.example.com/search?projects=sandbox&subjects=subject[0-9][0-9][0-9]&experiments=*_BRAIN&scans=T1&resources=DICOM

This will return all DICOMs for the T1 scans for experiments that end with _BRAIN that belong to a subjectXXX where XXX is a 3 digit number. By default the ID for the samples will be the experiment XNAT ID (e.g. XNAT_E00123). The wildcards that can be the used are the same UNIX shell-style wildcards as provided by the module fnmatch.

It is possible to change the id to a different fields id or label. Valid fields are project, subject, experiment, scan, and resource:

xnat://xnat.example.com/search?projects=sandbox&subjects=subject[0-9][0-9][0-9]&experiments=*_BRAIN&scans=T1&resources=DICOM&id=subject&label=true

The following variables can be set in the search query:

variable	default	usage
projects	*	The project(s) to select, can contain wildcards (see fnmatch)
subjects	*	The subject(s) to select, can contain wildcards (see fnmatch)
experiments	*	The experiment(s) to select, can contain wildcards (see fnmatch)
scans	*	The scan(s) to select, can contain wildcards (see fnmatch)
resources	*	The resource(s) to select, can contain wildcards (see fnmatch)
id	experiment	What field to use a the id, can be: project, subject, experiment, scan, or resource
label	false	Indicate the XNAT label should be used as fastr id, options true or false
insecure	false	Change the url scheme to be used to http instead of https
regex	false	Change search to use regex re.match() instead of fnmatch for matching

For storing credentials the .netrc file can be used. This is a common way to store credentials on UNIX systems. It is required that the file is only accessible by the owner only or a NetrcParseError will be raised. A netrc file is really easy to create, as its entries look like:

machine xnat.example.com
        login username
        password secret123

See the netrc module or the GNU inet utils website for more information about the .netrc file.

Note

On windows the location of the netrc file is assumed to be os.path.expanduser('~/_netrc'). The leading underscore is because windows does not like filename starting with a dot.

Note

For scan the label will be the scan type (this is initially the same as the series description, but can be updated manually or the XNAT scan type cleanup).

Warning

labels in XNAT are not guaranteed to be unique, so be careful when using them as the sample ID.

For background on XNAT, see the XNAT API DIRECTORY for the REST API of XNAT.

__abstractmethods__ = frozenset([])

__init__()

__module__ = 'fastr.plugins'

cleanup()

connect(server, path='', insecure=False)

expand_url(url)

fetch_url(inurl, outpath)

Get the file(s) or values from XNAT.

Parameters:

• inurl – url to the item in the data store
• outpath – path where to store the fetch data locally

filename = '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/plugins/ioplugins/xnatstorage.py'

module = <module 'xnatstorage' from '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/plugins/ioplugins/xnatstorage.pyc'>

put_url(inpath, outurl)

Upload the files to the XNAT storage

Parameters:

• inpath – path to the local data
• outurl – url to where to store the data in the external data store.

scheme = 'xnat'

server

xnat

fastr.plugins.json

alias of JsonCollector

fastr.plugins.path

alias of PathCollector

fastr.plugins.stdout

alias of StdoutCollector

version Module

This module keeps track of the version of the currently used Fastr framework. It can check its version from mercurial or a saved file

fastr.version.clear_version()

Remove the cached version info

fastr.version.get_base_version()

Get the version from the top-level version file

Returns:the version Rtype str:

fastr.version.get_hg_info()

Read information about the current mercurial branch and revision

Returns:tuple containing head revision and branch

fastr.version.get_saved_version()

Get cached version from file

Returns:tuple with version, head revision and branch

fastr.version.save_version(current_version, current_hg_head, current_hg_branch)

Cache the version information (useful for when installing)

Parameters:

• current_version (str) – version
• current_hg_head (str) – mercurial head revision
• current_hg_branch (str) – mercurial branch

Returns:

Subpackages

core Package

core Package

This module contains all of the core components of fastr. It has the classes to create networks and work with them.

basemanager Module

This module contains the core class for all managers

class fastr.core.basemanager.BaseManager(path=None, recursive=False)

Bases: _abcoll.MutableMapping

Baseclass for a Manager, subclasses needs to override the following methods:

BaseManager._item_extension, BaseManager._load_item()

_item_extension()

Abstract property that sets the extension of the files to be loaded by the BaseManager. When scanning for items, only files with this extension will be loaded.

Returns:desired extension Return type:str Raises:FastrNotImplementedError – if property not reimplemented in subclass

_load_item(filepath, namespace)

Abstract method to load an item of the BaseManager. This function is not implemented and needs to be reimplemented by a subclass.

Parameters:

• filepath (str) – path of the item to load
• namespace (str) – the namespace of the item to be loaded

Returns:

the loaded item

Raises:

FastrNotImplementedError – if called without being reimplemented by a subclass

__abstractmethods__ = frozenset(['_load_item', '_item_extension'])

__delitem__(key)

Remove item from the BaseManager

Parameters:key – key of the item to remove Returns:None Raises:FastrKeyError – if the key is not found in the BaseManager

__getitem__(key)

Retrieve item from BaseManager

Parameters:key – the key of the item to retrieve Returns:the value indicated by the key Raises:FastrKeyError – if the key is not found in the BaseManager

__init__(path=None, recursive=False)

The BaseManager constructor

Parameters:

• path (str or None) – path to scan for items, or None for no path
• recursive (bool) – Flag to indicate a recursive search is desired

Returns:

the newly created BaseManager

Return type:

BaseManager

__iter__()

Get an iterator from the BaseManager. The iterator will iterate over the keys of the BaseManager.

Returns:the iterator Return type:dictionary-keyiterator

__keytransform__(key)

Identity transform for the keys. This function can be reimplemented by a subclass to implement a different key transform.

Parameters:key – key to transform Returns:the transformed key (in this case the same key as inputted)

__len__()

Return the number of items in the BaseManager

Returns:number of items in the BaseManager Return type:int

__metaclass__

alias of ABCMeta

__module__ = 'fastr.core.basemanager'

__repr__()

Convert the BaseManager to a representation string.

Returns:Representation string Return type:str

__setitem__(key, value)

Set item in the BaseManager

Parameters:

• key – the key of the item to store
• value – the value of the item to store

Returns:

None

data

The actual data dict underlying this Manager

match_filename(filename)

Check if the filename matches the pattern the manager expects.

Parameters:filename – filename to match Returns:flag indicating that the filename matches

populate()

Populate the manager with the data. This is a method that will be called when the Managers data is first accessed. This way we avoid doing expensive directory scans when the data is never requested.

reload()

Reload entire contents of this manager.

baseplugin Module

The base class for all Plugins in the fastr system

class fastr.core.baseplugin.BasePlugin

Bases: object

Base class for Plugins in the fastr system.

__abstractmethods__ = frozenset([])

__dict__ = dict_proxy({'status': <fastr.utils.classproperty.ClassPropertyDescriptor object>, '__module__': 'fastr.core.baseplugin', '__abstractmethods__': frozenset([]), '__metaclass__': <class 'fastr.core.baseplugin.PluginMeta'>, '_abc_negative_cache': <_weakrefset.WeakSet object>, '__str__': <function __str__>, '_instantiate': False, 'module': None, '__dict__': <attribute '__dict__' of 'BasePlugin' objects>, 'fullid': <fastr.utils.classproperty.ClassPropertyDescriptor object>, '_source_code': None, 'source_code': <fastr.utils.classproperty.ClassPropertyDescriptor object>, '__weakref__': <attribute '__weakref__' of 'BasePlugin' objects>, 'id': <fastr.utils.classproperty.ClassPropertyDescriptor object>, '__init__': <function __init__>, '_abc_cache': <_weakrefset.WeakSet object>, 'register_configuration': <classmethod object>, 'status_message': <fastr.utils.classproperty.ClassPropertyDescriptor object>, 'instantiate': <fastr.utils.classproperty.ClassPropertyDescriptor object>, 'configuration_fields': <fastr.utils.classproperty.ClassPropertyDescriptor object>, '_abc_registry': <_weakrefset.WeakSet object>, 'cleanup': <function cleanup>, '_abc_negative_cache_version': 34, '__repr__': <function __repr__>, 'set_status': <classmethod object>, 'test': <classmethod object>, '_status': (<PluginState.uninitialized: '\x1b[46mUnInitialized\x1b[0m'>, 'Plugin object created', None), 'set_code': <classmethod object>, '__doc__': '\n Base class for Plugins in the fastr system.\n '})

__init__()

The BasePlugin constructor.

Returns:the created plugin Return type:BasePlugin Raises:FastrPluginNotLoaded – if the plugin did not load correctly

__metaclass__

alias of PluginMeta

__module__ = 'fastr.core.baseplugin'

__repr__()

__str__()

Creare string representation of the plugin.

Returns:string represenation Return type:str

__weakref__

list of weak references to the object (if defined)

cleanup()

Perform any cleanup action needed when the plugin use ended. This can be closing files/streams etc.

configuration_fields = {}

fullid = 'fastr://plugins/BasePlugin'

id = 'BasePlugin'

instantiate = False

module = None

classmethod register_configuration()

Register and test the configuation fields of the plugin

classmethod set_code(source_code)

Set the filename and source code of the plugin

Parameters:source_code (str) – the source code of the plugin

classmethod set_status(status, message, exception=None)

Update the status of the plugin

Parameters:

• status (str) – the new status
• message (str) – message explaining the status change
• exception (str) – stacktrace of the exception causing the failed load

source_code

A descriptor that can act like a property for a class.

status = '\x1b[46mUnInitialized\x1b[0m'

status_message = 'Plugin object created'

classmethod test()

Test the plugin, default behaviour is just to instantiate the plugin

class fastr.core.baseplugin.Plugin

Bases: fastr.core.baseplugin.BasePlugin

__abstractmethods__ = frozenset([])

__module__ = 'fastr.core.baseplugin'

class fastr.core.baseplugin.PluginMeta

Bases: abc.ABCMeta

__module__ = 'fastr.core.baseplugin'

__repr__()

class fastr.core.baseplugin.PluginState

Bases: enum.Enum

__format__(format_spec)

__module__ = 'fastr.core.baseplugin'

static __new__(value)

__reduce_ex__(proto)

__repr__()

__str__()

failed = '\x1b[37m\x1b[41m\x1b[1mFailed\x1b[0m'

loaded = '\x1b[37m\x1b[42m\x1b[1mLoaded\x1b[0m'

preload = '\x1b[102mPreLoad\x1b[0m'

uninitialized = '\x1b[46mUnInitialized\x1b[0m'

unloaded = '\x1b[46mUnLoaded\x1b[0m'

datatypemanager Module

This module manages datatypes. These datatypes are python classes generated from the XML/JSON datatype files.

class fastr.core.datatypemanager.DataTypeManager

Bases: fastr.core.pluginmanager.BasePluginManager

The DataTypeManager hold a mapping of all DataTypes in the fast system and can create new DataTypes from files/data structures.

__abstractmethods__ = frozenset([])

__init__()

The DataTypeManager constructor will create a new DataTypeManager and populate it with all DataTypes it can find in the paths set in fastr.config.types_path.

Returns:the created DataTypeManager

__keytransform__(key)

Key transformation for this mapping. The key transformation allows indexing by both the DataType name as well as the DataType it self.

Parameters:key (fastr.datatypes.BaseDataType or str) – The name of the requested datatype or the datatype itself Returns:The requested datatype

__module__ = 'fastr.core.datatypemanager'

create_enumtype(type_id, options, name=None)

Create a python class based on an XML file. This function return a completely functional python class based on the contents of a DataType XML file.

Such a class will be of type EnumType.

Parameters:

• type_id (str) – the id of the new class
• options (iterable) – an iterable of options, each option should be str

Returns:

the newly created subclass of EnumType

Raises:

FastrTypeError – if the options is not an iterable of str

fullid

The fullid of the datatype manager

get_type(name)

Read a type given a typename. This will scan all directories in types_path and attempt to load the newest version of the DataType.

Parameters:name (str) – Name of the datatype that should be imported in the system Returns:the datatype with the requested name, or None if datatype is not found

Note

If type is already in TypeManager it will not load anything and return the already loaded version.

guess_type(value, exists=True, options=None, preferred=None)

Guess the DataType based on a value str.

Parameters:

• value (str) – the value to guess the type for
• options (TypeGroup, DataType or tuple of DataTypes) – The options that are allowed to be guessed from
• extists (bool) – Indicate the value exists (if file) and can be checked for validity, if false skip validity check
• preferred (iterable) – An iterable of preferred types in case multiple types match.

Returns:

The resulting DataType or None if no match was found

Raises:

FastrTypeError – if the options argument is of the wrong type

The function will first create a list of all candidate DataTypes. Subsequently, it will check for each candidate if the value would valid. If there are multiple matches, the config value for preferred types is consulted to break the ties. If non of the DataTypes are in the preferred types list, a somewhat random DataType will be picked as the most optimal result.

has_type(name)

Check if the datatype with requested name exists

Parameters:name (str) – the name of the requested datatype Returns:flag indicating if the datatype exists Return type:bool

static isdatatype(item)

Check if item is a valid datatype for the fastr system.

Parameters:item – item to check Returns:flag indicating if the item is a fastr datatype Return type:bool

match_types(*args, **kwargs)

Find the match between a list of DataTypes/TypeGroups, see resolve-datatype for details

Parameters:

• args – A list of DataType/TypeGroup objects to match
• kwargs – A ‘preferred’ keyword argument can be used to indicate a list of DataTypes to prefer in case of ties (first has precedence over later in list)

Returns:

The best DataType match, or None if no match is possible.

Raises:

FastrTypeError – if not all args are subclasses of BaseDataType

match_types_any(*args)

Find the match between a list of DataTypes/TypeGroups, see resolve-datatype for details

Parameters:args – A list of DataType/TypeGroup objects to match Returns:A set with all DataTypes that match. Return type:set Raises:FastrTypeError – if not all args are subclasses of BaseDataType

plugin_class

The PluginClass of the items of the BasePluginManager

poll_datatype(filename)

Poll an xml file to see if there is a definition of a datatype in it.

Parameters:filename (str) – path of the file to poll Returns:tuple with (id, version, basetype) if a datatype is found or (None, None, None) if no datatype is found

populate()

Populate Manager. After scanning for DataTypes, create the AnyType and set the preferred types

dimension Module

class fastr.core.dimension.Dimension(name, size)

Bases: object

A class representing a dimension. It contains the name and size of the dimension.

__dict__ = dict_proxy({'__dict__': <attribute '__dict__' of 'Dimension' objects>, '__module__': 'fastr.core.dimension', '__weakref__': <attribute '__weakref__' of 'Dimension' objects>, '__doc__': '\n A class representing a dimension. It contains the name and size of the\n dimension.\n ', '__init__': <function __init__>})

__init__(name, size)

The constructor for the dimension.

Parameters:

• name (str) – Name of the dimension
• size (int or sympy.Symbol) – Size fo the dimension

__module__ = 'fastr.core.dimension'

__weakref__

list of weak references to the object (if defined)

class fastr.core.dimension.HasDimensions

Bases: object

A Mixin class for any object that has a notion of dimensions and size. It uses the dimension property to expose the dimension name and size.

__abstractmethods__ = frozenset(['dimensions'])

__dict__ = dict_proxy({'__module__': 'fastr.core.dimension', '__metaclass__': <class 'abc.ABCMeta'>, '_abc_negative_cache': <_weakrefset.WeakSet object>, '__dict__': <attribute '__dict__' of 'HasDimensions' objects>, '__weakref__': <attribute '__weakref__' of 'HasDimensions' objects>, 'dimnames': <property object>, 'size': <property object>, '_abc_cache': <_weakrefset.WeakSet object>, 'dimensions': <abc.abstractproperty object>, '__abstractmethods__': frozenset(['dimensions']), '_abc_negative_cache_version': 34, '_abc_registry': <_weakrefset.WeakSet object>, '__doc__': '\n A Mixin class for any object that has a notion of dimensions and size. It\n uses the dimension property to expose the dimension name and size.\n '})

__metaclass__

alias of ABCMeta

__module__ = 'fastr.core.dimension'

__weakref__

list of weak references to the object (if defined)

dimensions

The dimensions has to be implemented by any subclass. It has to provide a tuple of Dimensions.

Returns:dimensions Return type:tuple

dimnames

A tuple containing the dimension names of this object. All items of the tuple are of type str.

size

A tuple containing the size of this object. All items of the tuple are of type int or sympy.Symbol.

inputoutput Module

Classes for arranging the input and output for nodes.

Exported classes:

Input – An input for a node (holding datatype). Output – The output of a node (holding datatype and value). ConstantOutput – The output of a node (holding datatype and value).

Warning

Don’t mess with the Link, Input and Output internals from other places. There will be a huge chances of breaking the network functionality!

class fastr.core.inputoutput.AdvancedFlowOutput(node, description)

Bases: fastr.core.inputoutput.Output

__abstractmethods__ = frozenset([])

__module__ = 'fastr.core.inputoutput'

dimnames

The dimnames of AdvancedFlowNodes have the output id appended, as the sizes per output can be different.

class fastr.core.inputoutput.BaseInput(node, description)

Bases: fastr.core.inputoutput.BaseInputOutput

Base class for all inputs.

__abstractmethods__ = frozenset(['_update', 'fullid', '__getitem__', 'num_subinput', 'itersubinputs', 'size'])

__init__(node, description)

Instantiate a BaseInput

Parameters:

• node – the parent node the input/output belongs to.
• description – the ParameterDescription describing the input/output.

Returns:

the created BaseInput

Raises:

• FastrTypeError – if description is not of class ParameterDescription
• FastrDataTypeNotAvailableError – if the DataType requested cannot be found in the fastr.typelist

__lshift__(other)

__module__ = 'fastr.core.inputoutput'

__rrshift__(other)

create_link_from(value)

itersubinputs()

Iterator over the SubInputs

Returns:iterator

example:

>>> for subinput in input_a.itersubinputs():
        print subinput

num_subinput

The number of SubInputs in this Input

class fastr.core.inputoutput.BaseInputOutput(node, description)

Bases: fastr.core.samples.HasSamples, fastr.core.updateable.Updateable, fastr.core.serializable.Serializable

Base class for Input and Output classes. It mainly implements the properties to access the data from the underlying ParameterDescription.

__abstractmethods__ = frozenset(['_update', 'fullid', '__getitem__', 'size'])

__getstate__()

Retrieve the state of the BaseInputOutput

Returns:the state of the object Rtype dict:

__init__(node, description)

Instantiate a BaseInputOutput

Parameters:

• node – the parent node the input/output belongs to.
• description – the ParameterDescription describing the input/output.

Returns:

created BaseInputOutput

Raises:

• FastrTypeError – if description is not of class ParameterDescription
• FastrDataTypeNotAvailableError – if the DataType requested cannot be found in the fastr.typelist

__iter__()

This function is blocked to avoid support for iteration using a lecacy __getitem__ method.

Returns:None Raises:FastrNotImplementedError – always

__module__ = 'fastr.core.inputoutput'

__repr__()

Get a string representation for the Input/Output

Returns:the string representation Return type:str

__setstate__(state)

Set the state of the BaseInputOutput by the given state.

Parameters:state (dict) – The state to populate the object with Returns:None

cardinality(key=None, job_data=None)

Determine the cardinality of this Input/Output. Optionally a key can be given to determine for a sample.

Parameters:key – key for a specific sample Returns:the cardinality Return type:int, sympy.Symbol, or None

check_cardinality(key=None)

Check if the actual cardinality matches the cardinality specified in the ParameterDescription. Optionally you can use a key to test for a specific sample.

Parameters:key – sample_index (tuple of int) or SampleId for desired sample Returns:flag indicating that the cardinality is correct Return type:bool Raises:FastrCardinalityError – if the Input/Output has an incorrect cardinality description.

datatype

The datatype of this Input/Output

description

The description object of this input/output

fullid

The fullid of the Input/Output, the fullid should be unnique and makes the object retrievable by the network.

id

Id of the Input/Output

node

The Node to which this Input/Output belongs

numel

The number of elements in this Input/Output

required

Flag indicating that the Input/Output is required

size

The size of the Input/Output

class fastr.core.inputoutput.BaseOutput(node, description)

Bases: fastr.core.inputoutput.BaseInputOutput

Base class for all outputs.

__abstractmethods__ = frozenset(['_update', 'fullid', '__getitem__', 'size'])

__init__(node, description)

Instantiate a BaseOutput

Parameters:

• node – the parent node the output belongs to.
• description – the ParameterDescription describing the output.

Returns:

created BaseOutput

Raises:

• FastrTypeError – if description is not of class ParameterDescription
• FastrDataTypeNotAvailableError – if the DataType requested cannot be found in the fastr.typelist

__module__ = 'fastr.core.inputoutput'

automatic

Flag indicating that the Output is generated automatically without being specified on the command line

class fastr.core.inputoutput.Input(node, description)

Bases: fastr.core.inputoutput.BaseInput

Class representing an input of a node. Such an input will be connected to the output of another node or the output of an constant node to provide the input value.

__abstractmethods__ = frozenset([])

__eq__(other)

Compare two Input instances with each other. This function ignores the parent node and update status, but tests rest of the dict for equality.

Parameters:other (Input) – the other instances to compare to Returns:True if equal, False otherwise Return type:bool

__getitem__(key)

Retrieve an item from this Input.

Parameters:

key (str, SampleId or tuple) – the key of the requested item, can be a key str, sample index tuple or a SampleId

Returns:

the return value depends on the requested key. If the key was an int the corresponding SubInput will be returned. If the key was a SampleId or sample index tuple, the corresponding SampleItem will be returned.

Return type:

SampleItem or SubInput

Raises:

• FastrTypeError – if key is not of a valid type
• FastrKeyError – if the key is not found

__getstate__()

Retrieve the state of the Input

Returns:the state of the object Rtype dict:

__init__(node, description)

Instantiate an input.

Parameters:

• node (Node) – the parent node of this input.
• description (ParameterDescription) – the ParameterDescription of the input.

Returns:

the created Input

__module__ = 'fastr.core.inputoutput'

__setitem__(key, value)

Create a link between a SubInput of this Inputs and an Output/Constant

Parameters:

• key (int, str) – the key of the SubInput
• value (BaseOutput, list, tuple, dict, OrderedDict) – the target to link, can be an output or a value to create a constant for

Raises:

FastrTypeError – if key is not of a valid type

__setstate__(state)

Set the state of the Input by the given state.

Parameters:state (dict) – The state to populate the object with Returns:None

__str__()

Get a string version for the Input

Returns:the string version Return type:str

append(value)

When you want to append a link to an Input, you can use the append property. This will automatically create a new SubInput to link to.

example:

>>> link = node2['input'].append(node1['output'])

will create a new SubInput in node2[‘input’] and link to that.

cardinality(key=None, job_data=None)

Cardinality for an Input is the sum the cardinalities of the SubInputs, unless defined otherwise.

Parameters:key (tuple of int or SampleId) – key for a specific sample, can be sample index or id Returns:the cardinality Return type:int, sympy.Symbol, or None

clear()

datatype

The datatype of this Input

dimnames

The list names of the dimensions in this Input. This will be a list of str.

fullid

The full defining ID for the Input

get_sourced_nodes()

Get a list of all Nodes connected as sources to this Input

Returns:list of all connected Nodes Return type:list

get_sourced_outputs()

Get a list of all Outputs connected as sources to this Input

Returns:tuple of all connected Outputs Return type:tuple

get_subinput(key)

Get a requested SubInput

Parameters:key (int) – the index of the SubInput to retrieve Returns:requested SubInput

index(value)

Find index of a SubInput

Parameters:value (SubInput) – the SubInput to find the index of Returns:key Return type:int, str

input_group

The id of the InputGroup this Input belongs to.

insert(index)

Insert a new SubInput at index in the sources list

Parameters:key (int) – positive integer for position in _source list to insert to Returns:newly inserted SubInput Return type:SubInput

itersubinputs()

Iterate over the SubInputs in this Input.

Returns:iterator yielding SubInput

example:

>>> for subinput in input_a.itersubinputs():
        print subinput

num_subinput

The number of SubInputs in this Input

prepare(sample_size=None)

This function makes sure the SampleIdList has the correct size.

Parameters:sample_size (tuple of int) – the required size of the SampleIdList. If no size is given, self.size will be used by default.

remove(value)

Remove a SubInput from the SubInputs list based on the connected Link.

Parameters:value (SubInput, <fastr.core.inputoutput.SubInput>`) – the SubInput or SubLink to removed from this Input

set_subinput(key, value)

Set a specified SubInput.

Parameters:

• key (int) – positive integer for position in _source list
• value – new SubInput to assign to the selected location

size

The size of the sample collections that can accessed via this Input.

source

The mapping of SubInputs that are connected and have more than 0 elements.

class fastr.core.inputoutput.Output(node, description)

Bases: fastr.core.inputoutput.BaseOutput

Class representing an output of a node. It holds the output values of the tool ran. Output fields can be connected to inputs of other nodes.

__abstractmethods__ = frozenset([])

__eq__(other)

Compare two Output instances with each other. This function ignores the parent node, listeners and update status, but tests rest of the dict for equality.

Parameters:other (Output) – the other instances to compare to Returns:True if equal, False otherwise Return type:bool

__getitem__(key)

Retrieve an item from this Output. The returned value depends on what type of key used:

• Retrieving data using index tuple: [index_tuple]
• Retrieving data sample_id str: [SampleId]
• Retrieving a list of data using SampleId list: [sample_id1, ..., sample_idN]
• Retrieving a SubOutput using an int or slice: [n] or [n:m]

Parameters:

key (int, slice, SampleId or tuple) – the key of the requested item, can be a number, slice, sample index tuple or a SampleId

Returns:

the return value depends on the requested key. If the key was an int or slice the corresponding SubOutput will be returned (and created if needed). If the key was a SampleId or sample index tuple, the corresponding SampleItem will be returned. If the key was a list of SampleId a tuple of SampleItem will be returned.

Return type:

SubInput or SampleItem or list of SampleItem

Raises:

• FastrTypeError – if key is not of a valid type
• FastrKeyError – if the parent Node has not been executed

__getstate__()

Retrieve the state of the Output

Returns:the state of the object Rtype dict:

__init__(node, description)

Instantiate an Output

Parameters:

• node – the parent node the output belongs to.
• description – the ParameterDescription describing the output.

Returns:

created Output

Raises:

• FastrTypeError – if description is not of class ParameterDescription
• FastrDataTypeNotAvailableError – if the DataType requested cannot be found in the fastr.typelist

__module__ = 'fastr.core.inputoutput'

__setitem__(key, value)

Store an item in the Output

Parameters:

• key (tuple of int or SampleId) – key of the value to store
• value – the value to store

Returns:

None

Raises:

FastrTypeError – if key is not of correct type

__setstate__(state)

Set the state of the Output by the given state.

Parameters:state (dict) – The state to populate the object with Returns:None

__str__()

Get a string version for the Output

Returns:the string version Return type:str

blocking

Flag indicating that this Output will cause blocking in the execution

cardinality(key=None, job_data=None)

Cardinality of this Output, may depend on the inputs of the parent Node.

Parameters:

key (tuple of int or SampleId) – key for a specific sample, can be sample index or id

Returns:

the cardinality

Return type:

int, sympy.Symbol, or None

Raises:

• FastrCardinalityError – if cardinality references an invalid Input
• FastrTypeError – if the referenced cardinality values type cannot be case to int
• FastrValueError – if the referenced cardinality value cannot be case to int

static create_output_cardinality(desc)

Create a lambda function that returns an integer value of the cardinality.

param str desc:The cardinality description string return:output cardinality description rtype tuple:

The description string can be one of the following forms:

• N: N number of values needed.
• as:input_name: the same number of values as attached to input_name are needed.
• val:input_name: the number of values needed is the value of input_name.
• unknown: the output cardinality cannot be estimated a priori

datatype

The datatype of this Output

dimnames

The list names of the dimensions in this Output. This will be a list of str.

fullid

The full defining ID for the Output

iterconvergingindices(collapse_dims)

Iterate over all data, but collapse certain dimension to create lists of data.

Parameters:collapse_dims (iterable of int) – dimension to collapse Returns:iterator SampleIndex (possibly containing slices)

listeners

The list of Links connected to this Output.

ndims

The number of dimensions in this Output

preferred_types

The list of preferred DataTypes for this Output.

prepare()

This function makes sure that a value storage will be created

resulting_datatype

The DataType that will the results of this Output will have.

samples

The SampleCollection of the samples in this Output. None if the Node has not yet been executed. Otherwise a SampleCollection.

size

The sample size of the Output

valid

Check if the output is valid, i.e. has a valid cardinality

class fastr.core.inputoutput.SourceOutput(node, description)

Bases: fastr.core.inputoutput.Output

Output for a SourceNode, this type of Output determines the cardinality in a different way than a normal Node.

__abstractmethods__ = frozenset([])

__getitem__(item)

Retrieve an item from this Output. The returned value depends on what type of key used:

• Retrieving data using index tuple: [index_tuple]
• Retrieving data sample_id str: [SampleId]
• Retrieving a list of data using SampleId list: [sample_id1, ..., sample_idN]
• Retrieving a SubOutput using an int or slice: [n] or [n:m]

Parameters:

key (int, slice, SampleId or tuple) – the key of the requested item, can be a number, slice, sample index tuple or a SampleId

Returns:

the return value depends on the requested key. If the key was an int or slice the corresponding SubOutput will be returned (and created if needed). If the key was a SampleId or sample index tuple, the corresponding SampleItem will be returned. If the key was a list of SampleId a tuple of SampleItem will be returned.

Return type:

SubInput or SampleItem or list of SampleItem

Raises:

• FastrTypeError – if key is not of a valid type
• FastrKeyError – if the parent Node has not been executed

__init__(node, description)

Instantiate a FlowOutput

Parameters:

• node – the parent node the output belongs to.
• description – the ParameterDescription describing the output.

Returns:

created FlowOutput

Raises:

• FastrTypeError – if description is not of class ParameterDescription
• FastrDataTypeNotAvailableError – if the DataType requested cannot be found in the fastr.typelist

__module__ = 'fastr.core.inputoutput'

__setitem__(key, value)

Store an item in the Output

Parameters:

• key (tuple of int or SampleId) – key of the value to store
• value – the value to store

Returns:

None

Raises:

FastrTypeError – if key is not of correct type

cardinality(key=None, job_data=None)

Cardinality of this SourceOutput, may depend on the inputs of the parent Node.

Parameters:key (tuple of int or SampleId) – key for a specific sample, can be sample index or id Returns:the cardinality Return type:int, sympy.Symbol, or None

linearized

A linearized version of the sample data, this is lazily cached linearized version of the underlying SampleCollection.

ndims

The number of dimensions in this SourceOutput

size

The sample size of the SourceOutput

class fastr.core.inputoutput.SubInput(input_)

Bases: fastr.core.inputoutput.BaseInput

This class is used by Input to allow for multiple links to an Input. The SubInput class can hold only a single Link to a (Sub)Output, but behaves very similar to an Input otherwise.

__abstractmethods__ = frozenset([])

__eq__(other)

Compare two SubInput instances with each other. This function ignores the parent, node, source and update status, but tests rest of the dict for equality.

Parameters:other (SubInput) – the other instances to compare to Returns:True if equal, False otherwise

__getitem__(key)

Retrieve an item from this SubInput.

Parameters:key (int, SampleId or SampleIndex) – the key of the requested item, can be a number, sample index tuple or a SampleId Returns:the return value depends on the requested key. If the key was an int the corresponding SubInput will be returned. If the key was a SampleId or sample index tuple, the corresponding SampleItem will be returned. Return type:SampleItem or SubInput Raises:FastrTypeError – if key is not of a valid type

Note

As a SubInput has only one SubInput, only requesting int key 0 or -1 is allowed, and it will return self

__getstate__()

Retrieve the state of the SubInput

Returns:the state of the object Rtype dict:

__init__(input_)

Instantiate an SubInput.

Parameters:input (Input) – the parent of this SubInput. Returns:the created SubInput

__module__ = 'fastr.core.inputoutput'

__setstate__(state)

Set the state of the SubInput by the given state.

Parameters:state (dict) – The state to populate the object with Returns:None

__str__()

Get a string version for the SubInput

Returns:the string version Return type:str

cardinality(key=None, job_data=None)

Get the cardinality for this SubInput. The cardinality for a SubInputs is defined by the incoming link.

Parameters:key (SampleIndex or SampleId) – key for a specific sample, can be sample index or id Returns:the cardinality Return type:int, sympy.Symbol, or None

description

dimnames

List of dimension names for this SubInput

fullid

The full defining ID for the SubInput

get_sourced_nodes()

Get a list of all Nodes connected as sources to this SubInput

Returns:list of all connected Nodes Return type:list

get_sourced_outputs()

Get a list of all Outputs connected as sources to this SubInput

Returns:list of all connected Outputs Return type:list

input_group

The id of the InputGroup this SubInputs parent belongs to.

iteritems()

Iterate over the SampleItems that are in the SubInput.

Returns:iterator yielding SampleItem objects

itersubinputs()

Iterate over SubInputs (for a SubInput it will yield self and stop iterating after that)

Returns:iterator yielding SubInput

example:

>>> for subinput in input_a.itersubinputs():
        print subinput

node

The Node to which this SubInputs parent belongs

num_subinput

The number of SubInputs in this SubInput, this is always 1.

remove(value)

Remove a SubInput from parent Input.

Parameters:value (SubInput) – the SubInput to removed from this Input

size

The sample size of the SubInput

source

A list with the source Link. The list is to be compatible with Input

source_output

The Output linked to this SubInput

class fastr.core.inputoutput.SubOutput(output, index)

Bases: fastr.core.inputoutput.Output

The SubOutput is an Output that represents a slice of another Output.

__abstractmethods__ = frozenset([])

__eq__(other)

Compare two SubOutput instances with each other. This function ignores the parent, node and update status, but tests rest of the dict for equality. equality

Parameters:other (SubOutput) – the other instances to compare to Returns:True if equal, False otherwise Return type:bool

__getitem__(key)

Retrieve an item from this SubOutput. The returned value depends on what type of key used:

• Retrieving data using index tuple: [index_tuple]
• Retrieving data sample_id str: [SampleId]
• Retrieving a list of data using SampleId list: [sample_id1, ..., sample_idN]
• Retrieving a SubOutput using an int or slice: [n] or [n:m]

Parameters:key (int, slice, SampleId or tuple) – the key of the requested item, can be a number, slice, sample index tuple or a SampleId Returns:the return value depends on the requested key. If the key was an int or slice the corresponding SubOutput will be returned (and created if needed). If the key was a SampleId or sample index tuple, the corresponding SampleItem will be returned. If the key was a list of SampleId a tuple of SampleItem will be returned. Return type:SubInput or SampleItem or list of SampleItem Raises:FastrTypeError – if key is not of a valid type

__getstate__()

Retrieve the state of the SubOutput

Returns:the state of the object Rtype dict:

__init__(output, index)

Instantiate a SubOutput

Parameters:

• output – the parent output the suboutput slices.
• index (int or slice) – the way to slice the parent output

Returns:

created SubOutput

Raises:

• FastrTypeError – if the output argument is not an instance of Output
• FastrTypeError – if the index argument is not an int or slice

__len__()

Return the length of the Output.

Note

In a SubOutput this is always 1.

__module__ = 'fastr.core.inputoutput'

__setitem__(key, value)

A function blocking the assignment operator. Values cannot be assigned to a SubOutput.

Raises:FastrNotImplementedError – if called

__setstate__(state)

Set the state of the SubOutput by the given state.

Parameters:state (dict) – The state to populate the object with Returns:None

__str__()

Get a string version for the SubOutput

Returns:the string version Return type:str

cardinality(key=None, job_data=None)

Cardinality of this SubOutput depends on the parent Output and self.index

Parameters:

key (tuple of int or SampleId) – key for a specific sample, can be sample index or id

Returns:

the cardinality

Return type:

int, sympy.Symbol, or None

Raises:

• FastrCardinalityError – if cardinality references an invalid Input
• FastrTypeError – if the referenced cardinality values type cannot be case to int
• FastrValueError – if the referenced cardinality value cannot be case to int

datatype

The datatype of this SubOutput

fullid

The full defining ID for the SubOutput

indexrep

Simple representation of the index.

listeners

The list of Links connected to this Output.

node

The Node to which this SubOutput belongs

preferred_types

The list of preferred DataTypes for this SubOutput.

resulting_datatype

The DataType that will the results of this SubOutput will have.

samples

The SampleCollection for this SubOutput

interface Module

A module that describes the interface of a Tool. It specifies how a set of

input values will be translated to commands to be executed. This creates a generic interface to different ways of executing underlying software.

class fastr.core.interface.InputSpec

Bases: fastr.core.interface.InputSpec

__dict__ = dict_proxy({'__dict__': <attribute '__dict__' of 'InputSpec' objects>, '__module__': 'fastr.core.interface', '__new__': <staticmethod object>, '__doc__': None})

__module__ = 'fastr.core.interface'

static __new__(id_, cardinality, datatype, required=False, description='', default=None, hidden=False)

fastr.core.interface.InputSpecBase

alias of InputSpec

class fastr.core.interface.Interface

Bases: fastr.core.baseplugin.Plugin, fastr.core.serializable.Serializable

Abstract base class of all Interfaces. Defines the minimal requirements for all Interface implementations.

__abstractmethods__ = frozenset(['inputs', 'execute', '__setstate__', 'expanding', '__getstate__', 'outputs'])

__getstate__()

Retrieve the state of the Interface

Returns:the state of the object Rtype dict:

__metaclass__

alias of ABCMeta

__module__ = 'fastr.core.interface'

__setstate__(state)

Set the state of the Interface

execute(target, payload)

Execute the interface given the a target and payload. The payload should have the form {‘input’: {‘input_id_a’: (value, value), ‘input_id_b’: (value, value)}, ‘output’: {‘output_id_a’: (value, value), ‘output_id_b’: (value, value)}}

Parameters:

• target – the target to call
• payload – the payload to use

Returns:

the result of the execution

Return type:

(tuple of) InterfaceResult

expanding

Indicates whether or not this Interface will result in multiple samples per run. If the flow is unaffected, this will be zero, if it is nonzero it means that number of dimension will be added to the sample array.

inputs

OrderedDict of Inputs connected to the Interface. The format should be {input_id: InputSpec}.

outputs

OrderedDict of Output connected to the Interface. The format should be {output_id: OutputSpec}.

classmethod test()

Test the plugin, interfaces do not need to be tested on import

class fastr.core.interface.InterfacePluginManager

Bases: fastr.core.pluginmanager.PluginSubManager

Container holding all the CollectorPlugins

__abstractmethods__ = frozenset([])

__init__()

Create the Coll :param path: :param recursive: :return:

__module__ = 'fastr.core.interface'

class fastr.core.interface.InterfaceResult(result_data, log_data, payload, sample_index=None, sample_id=None, errors=None)

Bases: object

The class in which Interfaces should wrap their results to be picked up by fastr

__dict__ = dict_proxy({'__dict__': <attribute '__dict__' of 'InterfaceResult' objects>, '__module__': 'fastr.core.interface', '__weakref__': <attribute '__weakref__' of 'InterfaceResult' objects>, '__doc__': '\n The class in which Interfaces should wrap their results to be picked up by fastr\n ', '__init__': <function __init__>})

__init__(result_data, log_data, payload, sample_index=None, sample_id=None, errors=None)

__module__ = 'fastr.core.interface'

__weakref__

list of weak references to the object (if defined)

class fastr.core.interface.OutputSpec

Bases: fastr.core.interface.OutputSpec

__dict__ = dict_proxy({'__dict__': <attribute '__dict__' of 'OutputSpec' objects>, '__module__': 'fastr.core.interface', '__new__': <staticmethod object>, '__doc__': None})

__module__ = 'fastr.core.interface'

static __new__(id_, cardinality, datatype, automatic=True, required=False, description='', hidden=False)

fastr.core.interface.OutputSpecBase

alias of OutputSpec

ioplugin Module

This module contains the manager class for IOPlugins and the base class for all IOPlugins

class fastr.core.ioplugin.IOPlugin

Bases: fastr.core.baseplugin.Plugin

IOPlugins are used for data import and export for the sources and sinks. The main use of the IOPlugins is during execution (see Execution). The IOPlugins can be accessed via fastr.ioplugins, but generally there should be no need for direct interaction with these objects. The use of is mainly via the URL used to specify source and sink data.

__abstractmethods__ = frozenset(['scheme'])

__init__()

Initialization for the IOPlugin

Returns:newly created IOPlugin

__metaclass__

alias of ABCMeta

__module__ = 'fastr.core.ioplugin'

cleanup()

(abstract) Clean up the IOPlugin. This is to do things like closing files or connections. Will be called when the plugin is no longer required.

expand_url(url)

(abstract) Expand an URL. This allows a source to collect multiple samples from a single url. The URL will have a wildcard or point to something with info and multiple urls will be returned.

Parameters:url (str) – url to expand Returns:the resulting url(s), a tuple if multiple, otherwise a str Return type:str or tuple of str

fetch_url(inurl, outfile)

(abstract) Fetch a file from an external data source.

Parameters:

• inurl – url to the item in the data store
• outpath – path where to store the fetch data locally

fetch_value(inurl)

(abstract) Fetch a value from an external data source.

Parameters:inurl – the url of the value to retrieve Returns:the fetched value

static isurl(string)

Test if given string is an url.

Parameters:string (str) – string to test Returns:True if the string is an url, False otherwise Return type:bool

path_to_url(path, mountpoint=None)

(abstract) Construct an url from a given mount point and a relative path to the mount point.

Parameters:

• path (str) – the path to determine the url for
• mountpoint (str or None) – the mount point to use, will be automatically detected if None is given

Returns:

url matching the path

Return type:

str

static print_result(result)

Print the result of the IOPlugin to stdout to be picked up by the tool

Parameters:result – value to print as a result Returns:None

pull_source_data(inurl, outdir, sample_id, datatype=None)

Transfer the source data from inurl to be available in outdir.

Parameters:

• inurl (str) – the input url to fetch data from
• outdir (str) – the directory to write the data to
• datatype (DataType) – the datatype of the data, used for determining the total contents of the transfer

Returns:

None

push_sink_data(inpath, outurl, datatype=None)

Write out the sink data from the inpath to the outurl.

Parameters:

• inpath (str) – the path of the data to be pushed
• outurl (str) – the url to write the data to
• datatype (DataType) – the datatype of the data, used for determining the total contents of the transfer

Returns:

None

put_url(inpath, outurl)

(abstract) Put the files to the external data store.

Parameters:

• inpath – path to the local data
• outurl – url to where to store the data in the external data store.

put_value(value, outurl)

(abstract) Put the files to the external data store.

Parameters:

• value – the value to store
• outurl – url to where to store the data in the external data store.

scheme

(abstract) This abstract property is to be overwritten by a subclass to indicate the url scheme associated with the IOPlugin.

setup(*args, **kwargs)

(abstract) Setup before data transfer. This can be any function that needs to be used to prepare the plugin for data transfer.

url_to_path(url)

(abstract) Get the path to a file from a url.

Parameters:url (str) – the url to retrieve the path for Returns:the corresponding path Return type:str

class fastr.core.ioplugin.IOPluginManager

Bases: fastr.core.pluginmanager.PluginSubManager

A mapping containing the IOPlugins known to this system

__abstractmethods__ = frozenset([])

__init__()

Create the IOPluginManager and populate it.

Returns:newly created IOPluginManager

__iter__()

__keytransform__(key)

__module__ = 'fastr.core.ioplugin'

cleanup()

Cleanup all plugins, this closes files, connections and other things that could be left dangling otherwise.

static create_ioplugin_tool()

Create the tools which handles sinks and sources. The command of this tool is the main of core.ioplugin.

expand_url(url)

Expand the url by filling the wildcards. This function checks the url scheme and uses the expand function of the correct IOPlugin.

Parameters:url (str) – url to expand Returns:list of urls Return type:list of str

populate()

Populate the IOPlugins manager. After the default directory scan, add the vfs IOPlugin and create the Tools for the IOPlugins

pull_source_data(url, outdir, sample_id, datatype=None)

Retrieve data from an external source. This function checks the url scheme and selects the correct IOPlugin to retrieve the data.

Parameters:

• url – url to pull
• outdir (str) – the directory to write the data to
• datatype (DataType) – the datatype of the data, used for determining the total contents of the transfer

Returns:

None

push_sink_data(inpath, outurl, datatype=None)

Send data to an external source. This function checks the url scheme and selects the correct IOPlugin to retrieve the data.

Parameters:

• inpath (str) – the path of the data to be pushed
• outurl (str) – the url to write the data to
• datatype (DataType) – the datatype of the data, used for determining the total contents of the transfer

put_url(inpath, outurl)

Put the files to the external data store.

Parameters:

• inpath – path to the local data
• outurl – url to where to store the data in the external data store.

static register_url_scheme(scheme)

Register a custom scheme to behave http like. This is needed to parse all things properly with urlparse.

Parameters:scheme – the scheme to register

url_to_path(url)

Retrieve the path for a given url

Parameters:url (str) – the url to parse Returns:the path corresponding to the input url Return type:str

fastr.core.ioplugin.main()

The main entry point for command line access to the IOPlugin

link Module

The link module contain the Link class. This class represents the links in a network. These links lead from an output (BaseOutput) to an input (BaseInput) and indicate the desired data flow. Links are smart objects, in the sense that when you set their start or end point, they register themselves with the Input and Output. They do all the book keeping, so as long as you only set the source and target of the Link, the link should be valid.

Warning

Don’t mess with the Link, Input and Output internals from other places. There will be a huge chances of breaking the network functionality!

class fastr.core.link.Link(source, target, parent=None, id_=None, collapse=None, expand=None)

Bases: fastr.core.updateable.Updateable, fastr.core.serializable.Serializable

Class for linking outputs (BaseOutput) to inputs (BaseInput)

Examples:

>>> import fastr
>>> network = fastr.Network()
>>> link1 = network.create_link( n1.ouputs['out1'], n2.inputs['in2'] )

link2 = Link()
link2.source = n1.ouputs['out1']
link2.target = n2.inputs['in2']

__abstractmethods__ = frozenset([])

__dataschemafile__ = 'Link.schema.json'

__eq__(other)

Test for equality between two Links

Parameters:other (Link) – object to test against Returns:True for equality, False otherwise Return type:bool

__getitem__(index)

Get a an item for this Link. The item will be retrieved from the connected output, but a diverging or converging flow can change the number of samples/cardinality.

Parameters:index (SampleIndex) – index of the item to retrieve Returns:the requested item Return type:SampleItem Raises:FastrIndexError – if the index length does not match the number dimensions in the source data (after collapsing/expanding)

__getstate__()

Retrieve the state of the Link

Returns:the state of the object Rtype dict:

__init__(source, target, parent=None, id_=None, collapse=None, expand=None)

Create a new Link in a Network.

Parameters:

• source (BaseOutput) – the source output
• target (BaseInput) – the target input
• parent (Network or None) – the parent network, if None is given the fastr.current_network is assumed to be the parent
• id (str or None) – the id of the link, if no id_ is given, the id will be in the form of “link_{:d}”
• collapse (int, str, or tuple of int/str) – the dimensions that the link has to collapse on
• expand (bool) – Does this link need to expand the cardinality into a new sample dimension

Returns:

newly created Link

Raises:

• FastrValueError – if parent is not given and fastr.current_network is not set
• FastrValueError – if the source output is not in the same network as the Link
• FastrValueError – if the target input is not in the same network as the Link

__module__ = 'fastr.core.link'

__repr__()

Get a string representation for the Link

Returns:the string representation Return type:str

__setstate__(state)

Set the state of the Link by the given state.

Parameters:state (dict) – The state to populate the object with Returns:None Raises:FastrValueError – if the parent network and fastr.current_network are not set

cardinality(index=None)

Cardinality for a Link is given by source Output and the collapse/expand settings

Parameters:key (SampleIndex) – key for a specific sample (can be only a sample index!) Returns:the cardinality Return type:int, sympy.Symbol Raises:FastrIndexError – if the index length does not match the number of dimension in the data

collapse

The converging dimensions of this link. Collapsing changes some dimensions of sample lists into cardinality, reshaping the data.

Collapse can be set to a tuple or an int/str, in which case it will be automatically wrapped in a tuple. The int will be seen as indices of the dimensions to collapse. The str will be seen as the name of the dimensions over which to collapse.

Raises:FastrTypeError – if assigning a collapse value of a wrong type

collapse_indexes

The converging dimensions of this link as integers. Dimension names are replaces with the corresponding int.

Collapsing changes some dimensions of sample lists into cardinality, reshaping the data

classmethod createobj(state, network=None)

Create object function for Link

Parameters:

• cls – The class to create
• state – The state to use to create the Link
• network – the parent Network

Returns:

newly created Link

destroy()

The destroy function of a link removes all default references to a link. This means the references in the network, input and output connected to this link. If there is no references in other places in the code, it will destroy the link (reference count dropping to zero).

This function is called when a source for an input is set to another value and the links becomes disconnected. This makes sure there is no dangling links.

dimnames

The dimension names for this Link. The dimension names depend on the connected source output and the collapse/expand.

expand

Flag indicating that the link will expand the cardininality into a new sample dimension to be created.

fullid

The full defining ID for the Input

parent

The Network to which this Link belongs.

size

The size of the data delivered by the link. This can be different from the source size because the link can make data collapse or expand.

source

The source BaseOutput of the Link. Setting the source will automatically register the Link with the source BaseOutput. Updating source will also make sure the Link is unregistered with the previous source.

Raises:FastrTypeError – if assigning a non BaseOutput

status

target

The target BaseInput of the Link. Setting the target will automatically register the Link with the target BaseInput. Updating target will also make sure the Link is unregistered with the previous target.

Raises:FastrTypeError – if assigning a non BaseInput

network Module

Network module containing Network facilitators and analysers.

class fastr.core.network.Network(id_='unnamed_network', version=None)

Bases: fastr.core.serializable.Serializable

The Network class represents a workflow. This includes all Nodes (including ConstantNodes, SourceNodes and Sinks) and Links.

NETWORK_DUMP_FILE_NAME = '__fastr_network__.json'

SINK_DUMP_FILE_NAME = '__sink_data__.json'

SOURCE_DUMP_FILE_NAME = '__source_data__.pickle.gz'

__dataschemafile__ = 'Network.schema.json'

__eq__(other)

Compare two Networks and see if they are equal.

Parameters:other (Network) – Returns:flag indicating that the Networks are the same Return type:bool

__getitem__(item)

Get an item by its fullid. The fullid can point to a link, node, input, output or even subinput/suboutput.

Parameters:item (str,unicode) – fullid of the item to retrieve Returns:the requested item

__getstate__()

Retrieve the state of the Network

Returns:the state of the object Rtype dict:

__init__(id_='unnamed_network', version=None)

Create a new, empty Network

Parameters:name (str) – name of the Network Returns:newly created Network Raises:OSError – if the tmp mount in the config is not a writable directory

__module__ = 'fastr.core.network'

__ne__(other)

Tests for non-equality, this is the negated version __eq__

__repr__()

__setstate__(state)

Set the state of the Network by the given state. This completely overwrites the old state!

Parameters:state (dict) – The state to populate the object with Returns:None

abort()

add_link(link)

Add a Link to the Network. Make sure the link is in the link list and the link parent is set to this Network

Parameters:

link (Link) – link to add

Raises:

• FastrTypeError – if link is incorrectly typed
• FastrNetworkMismatchError – if the link already belongs to another Network

add_node(node)

Add a Node to the Network. Make sure the node is in the node list and the node parent is set to this Network

Parameters:node (Node) – node to add Raises:FastrTypeError – if node is incorrectly typed

add_stepid(stepid, node)

Add a Node to a specific step id

Parameters:

• stepid (str) – the stepid that the node will be added to
• node (Node) – the node to add to the stepid

check_id(id_)

Check if an id for an object is valid and unused in the Network. The method will always returns True if it does not raise an exception.

Parameters:

id (str) – the id to check

Returns:

True

Raises:

• FastrValueError – if the id is not correctly formatted
• FastrValueError – if the id is already in use

create_constant(datatype, data, id_=None, stepid=None, nodegroup=None, sourcegroup=None)

Create a ConstantNode in this Network. The Node will be automatically added to the Network.

Parameters:

• datatype (BaseDataType) – The DataType of the constant node
• data (datatype or list of datatype) – The data to hold in the constant node
• id (str) – The id of the constant node to be created
• stepid (str) – The stepid to add the created constant node to
• nodegroup (str) – The group the node belongs to, this can be important for FlowNodes and such, as they will have matching dimension names.

Returns:

the newly created constant node

Return type:

ConstantNode

create_link(source, target, id_=None, collapse=None, expand=None)

Create a link between two Nodes and add it to the current Network.

Parameters:

• source (BaseOutput) – the output that is the source of the link
• target (BaseInput) – the input that is the target of the link
• id (str) – the id of the link

Returns:

the created link

Type:

Link

create_macro(network, id_=None)

create_node(tool, id_=None, stepid=None, cores=None, memory=None, walltime=None, nodegroup=None)

Create a Node in this Network. The Node will be automatically added to the Network.

Parameters:

• tool (Tool) – The Tool to base the Node on
• id (str) – The id of the node to be created
• stepid (str) – The stepid to add the created node to
• nodegroup (str) – The group the node belongs to, this can be important for FlowNodes and such, as they will have matching dimension names.

Returns:

the newly created node

Return type:

Node

create_reference(source_data, output_directory)

create_sink(datatype, id_=None, stepid=None)

Create a SinkNode in this Network. The Node will be automatically added to the Network.

Parameters:

• datatype (BaseDataType) – The DataType of the sink node
• id (str) – The id of the sink node to be created
• stepid (str) – The stepid to add the created sink node to

Returns:

the newly created sink node

Return type:

SinkNode

create_source(datatype, id_=None, stepid=None, nodegroup=None, sourcegroup=None)

Create a SourceNode in this Network. The Node will be automatically added to the Network.

Parameters:

• datatype (BaseDataType) – The DataType of the source source_node
• id (str) – The id of the source source_node to be created
• stepid (str) – The stepid to add the created source source_node to
• nodegroup (str) – The group the node belongs to, this can be important for FlowNodes and such, as they will have matching dimension names.
• sourcegroup (str) – DEPRECATED! The nodegroup this SourceNode will be added to

Returns:

the newly created source source_node

Return type:

SourceNode

draw_network(name='network_layout', img_format='svg', draw_dimension=False)

Output a dot file and try to convert it to an image file.

Parameters:img_format (str) – extension of the image format to convert to Returns:path of the image created or None if failed Return type:str or None

execute(sourcedata, sinkdata, execution_plugin=None, tmpdir=None, cluster_queue=None)

Execute the Network with the given data. This will analyze the Network, create jobs and send them to the execution backend of the system.

Parameters:

• sourcedata (dict) – dictionary containing all data for the sources
• sinkdata (dict) – dictionary containing directives for the sinks
• execution_plugin (str) – the execution plugin to use (None will use the config value)

Raises:

• FastrKeyError – if a source has not corresponding key in sourcedata
• FastrKeyError – if a sink has not corresponding key in sinkdata

fullid

The fullid of the Network

id

The id of the Network. This is a read only property.

is_valid()

job_finished(job, execution_interface)

Call-back handler for when a job is finished. Will collect the results and handle blocking jobs. This function is automatically called when the execution plugin finished a job.

Parameters:job (Job) – the job that finished

namespace = None

The namespace this network lives in, this will be set by the NetworkManager on load

remove(value)

Remove an item from the Network.

Parameters:value (Node or Link) – the item to remove

run_id

The run id of the network

test(reference_data_dir, source_data=None)

Execute the network with the source data specified and test the results against the refence data. This effectively tests the network execution.

Parameters:

• reference_data_dir (str) – The path or vfs url of reference data to compare with
• source_data (dict) – The source data to use

networkmanager Module

This module contains the tool manager class

class fastr.core.networkmanager.NetworkManager(path)

Bases: fastr.core.objectmanager.ObjectManager

__abstractmethods__ = frozenset([])

__module__ = 'fastr.core.networkmanager'

get_object_version(obj)

object_class

fastr.core.networkmanager.networklist = NetworkManager add_ints v0.0 : /home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/networks/add_ints.json

The fastr networklist

node Module

A module to maintain a network node.

Exported classes:

Node – A class encapsulating a tool. ConstantNode – A node encapsulating an Output to set scalar values. SourceNode – A class providing a handle to a file.

class fastr.core.node.AdvancedFlowNode(tool, id_=None, parent=None, cores=None, memory=None, walltime=None)

Bases: fastr.core.node.FlowNode

__abstractmethods__ = frozenset([])

__module__ = 'fastr.core.node'

execute()

Execute the node and create the jobs that need to run

Returns:list of jobs to run Return type:list of Jobs

set_result(job, failed_annotation)

class fastr.core.node.ConstantNode(datatype, data, id_=None)

Bases: fastr.core.node.SourceNode

Class encapsulating one output for which a value can be set. For example used to set a scalar value to the input of a node.

__abstractmethods__ = frozenset([])

__dataschemafile__ = 'ConstantNode.schema.json'

__getstate__()

Retrieve the state of the ConstantNode

Returns:the state of the object Rtype dict:

__init__(datatype, data, id_=None)

Instantiation of the ConstantNode.

Parameters:

• datatype – The datatype of the output.
• data – the prefilled data to use.
• id – The url pattern.

This class should never be instantiated directly (unless you know what you are doing). Instead create a constant using the network class like shown in the usage example below.

usage example:

>>> import fastr
>>> network = fastr.Network()
>>> source = network.create_source(datatype=fastr.typelist['ITKImageFile'], id_='sourceN')

or alternatively create a constant node by assigning data to an item in an InputDict:

>>> node_a.inputs['in'] = ['some', 'data']

which automatically creates and links a ConstantNode to the specified Input

__module__ = 'fastr.core.node'

__setstate__(state)

Set the state of the ConstantNode by the given state.

Parameters:state (dict) – The state to populate the object with Returns:None

data

The data stored in this constant node

execute()

Execute the constant node and create the jobs that need to run

Returns:list of jobs to run Return type:list of Jobs

set_data(data=None, ids=None)

Set the data of this constant node in the correct way. This is mainly for compatibility with the parent class SourceNode

Parameters:

• data (dict or list of urls) – the data to use
• ids – if data is a list, a list of accompanying ids

class fastr.core.node.DefaultInputGroupCombiner(parent_node)

Bases: object

__dict__ = dict_proxy({'__module__': 'fastr.core.node', 'merge_sample_jobs': <function merge_sample_jobs>, 'update': <function update>, '__iter__': <function __iter__>, 'merge_sample_data': <function merge_sample_data>, 'fullid': <property object>, '__dict__': <attribute '__dict__' of 'DefaultInputGroupCombiner' objects>, 'merge_sample_index': <function merge_sample_index>, 'unmerge': <function unmerge>, 'dimnames': <property object>, 'merge_failed_annotations': <function merge_failed_annotations>, '__weakref__': <attribute '__weakref__' of 'DefaultInputGroupCombiner' objects>, 'merge_sample_id': <function merge_sample_id>, 'iter_input_groups': <function iter_input_groups>, 'merge': <function merge>, 'merge_payloads': <function merge_payloads>, '__init__': <function __init__>, 'outputsize': <property object>, 'input_groups': <property object>, '__doc__': None})

__init__(parent_node)

__iter__()

__module__ = 'fastr.core.node'

__weakref__

list of weak references to the object (if defined)

dimnames

fullid

The full id of the InputGroupCombiner

input_groups

The input groups under management

iter_input_groups()

merge(list_of_items)

Given a list of items for each input group, it returns the combined list of items.

Parameters:list_of_items (list) – items to combine Returns:combined list

merge_failed_annotations(list_of_failed_annotations)

merge_payloads(sample_payloads)

merge_sample_data(list_of_sample_data)

merge_sample_id(list_of_sample_ids)

merge_sample_index(list_of_sample_indexes)

merge_sample_jobs(list_of_sample_jobs)

outputsize

unmerge(item)

Given a item it will recreate the seperate items, basically this is the inverse operation of merge. However, this create an OrderedDict so that specific input groups can be easily retrieved. To get a round trip, the values of the OrderedDict should be taken:

>>> list_of_items = combiner.unmerge(item)
>>> item = combiner.merge(list_of_items.values())

Parameters:item (list) – the item to unmerge Returns:items Return type:OrderedDict

update()

class fastr.core.node.FlowNode(tool, id_=None, parent=None, cores=None, memory=None, walltime=None)

Bases: fastr.core.node.Node

A Flow Node is a special subclass of Nodes in which the amount of samples can vary per Output. This allows non-default data flows.

__abstractmethods__ = frozenset([])

__init__(tool, id_=None, parent=None, cores=None, memory=None, walltime=None)

Instantiate a flow node.

Parameters:

• tool (Tool) – The tool to base the node on
• id (str) – the id of the node
• parent (Network) – the parent network of the node

Returns:

the newly created FlowNode

__module__ = 'fastr.core.node'

blocking

A FlowNode is (for the moment) always considered blocking.

Returns:True

dimnames

Names of the dimensions in the Node output. These will be reflected in the SampleIdList of this Node.

outputsize

Size of the outputs in this Node

set_result(job, failed_annotation)

Incorporate result of a job into the FlowNode.

Parameters:job (Type) – job of which the result to store

class fastr.core.node.InputDict(*args, **kwds)

Bases: collections.OrderedDict

The container containing the Inputs of Node. Implements helper functions for the easy linking syntax.

__module__ = 'fastr.core.node'

__setitem__(key, value, dict_setitem=<slot wrapper '__setitem__' of 'dict' objects>)

Set an item in the input dictionary. The behaviour depends on the type of the value. For a BaseInput, the input will simply be added to the list of inputs. For a BaseOutput, a link between the output and input will be created.

Parameters:

• key (str) – id of the input to assign/link
• value (BaseInput or BaseOutput) – either the input to add or the output to link
• dict_setitem – the setitem function to use for the underlying OrderedDict insert

class fastr.core.node.InputGroup(*args, **kwargs)

Bases: collections.OrderedDict

A class representing a group of inputs. Input groups allow the

__abstractmethods__ = frozenset([])

__delitem__(*args, **kwargs)

od.__delitem__(y) <==> del od[y]

Note

This is a wrapped version of collections.__delitem__ which triggers an update of the object after being called

__getitem__(key)

__init__(*args, **kwargs)

Create a new InputGroup representation

Parameters:

• parent (Node) – the parent node
• id (str) – the id of the input group

Raises:

FastrTypeError – if parent is not a Node

Note

This is a wrapped version of fastr.core.node.__init__ which triggers an update of the object after being called

__metaclass__

alias of UpdateableMeta

__module__ = 'fastr.core.node'

__setitem__(*args, **kwargs)

Assign an input to this input group.

Parameters:

• key (str) – id of the input
• value (Input) – the input to assign

Raises:

FastrTypeError – if value of valid type

Note

This is a wrapped version of fastr.core.node.__setitem__ which triggers an update of the object after being called

__updatefunc__()

Update the InputGroup. Triggers when a change is made to the content of the InputGroup. Automatically recalculates the size, primary Input etc.

__updatetriggers__ = ['__init__', '__setitem__', '__delitem__', 'clear', 'pop', 'popitem', 'setdefault', 'update']

clear() → None. Remove all items from od.

Note

This is a wrapped version of collections.clear which triggers an update of the object after being called

dimnames

The names of the dimensions in this InputGroup

empty

Bool indicating that this InputGroup is empty (has no data connected)

classmethod find_source_index(target_size, target_dimnames, source_size, source_dimnames, target_index)

fullid

The full id of the InputGroup

iterinputvalues

Iterate over the item in this InputGroup

Returns:iterator yielding SampleItems

parent

The parent node of this InputGroup

pop(k[, d]) → v, remove specified key and return the corresponding

value. If key is not found, d is returned if given, otherwise KeyError

is raised.

Note

This is a wrapped version of collections.pop which triggers an update of the object after being called

popitem() → (k, v), return and remove a (key, value) pair.

Pairs are returned in LIFO order if last is true or FIFO order if false.

Note

This is a wrapped version of collections.popitem which triggers an update of the object after being called

primary

The primary Input in this InputGroup. The primary Input is the Input that defines the size of this InputGroup. In case of ties it will be the first in the tool definition.

setdefault(k[, d]) → od.get(k,d), also set od[k]=d if k not in od

Note

This is a wrapped version of collections.setdefault which triggers an update of the object after being called

size

The sample size of this InputGroup

classmethod solve_broadcast(target_size, target_dimnames, source_size, source_dimnames, target_index, nodegroups=None)

update([E, ]**F) → None. Update D from mapping/iterable E and F.

If E present and has a .keys() method, does: for k in E: D[k] = E[k]

If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v

Note

This is a wrapped version of _abcoll.update which triggers an update of the object after being called

class fastr.core.node.MacroNode(network, id_=None, parent=None, cores=None, memory=None, walltime=None)

Bases: fastr.core.node.Node

MacroNode encapsulates an entire network in a single node.

__abstractmethods__ = frozenset([])

__getstate__()

Retrieve the state of the MacroNode

Returns:the state of the object Rtype dict:

__init__(network, id_=None, parent=None, cores=None, memory=None, walltime=None)

Parameters:network (Network) – network to create macronode for

__module__ = 'fastr.core.node'

__setstate__(state)

execute()

class fastr.core.node.MergingInputGroupCombiner(parent_node, merge_dimension)

Bases: fastr.core.node.DefaultInputGroupCombiner

__init__(parent_node, merge_dimension)

__module__ = 'fastr.core.node'

iter_input_groups()

merge(list_of_items)

unmerge(item)

update()

class fastr.core.node.Node(tool, id_=None, parent=None, cores=None, memory=None, walltime=None)

Bases: fastr.core.updateable.Updateable, fastr.core.serializable.Serializable

The class encapsulating a node in the network. The node is responsible for setting and checking inputs and outputs based on the description provided by a tool instance.

__abstractmethods__ = frozenset([])

__dataschemafile__ = 'Node.schema.json'

__eq__(other)

Compare two Node instances with each other. This function ignores the parent and update status, but tests rest of the dict for equality. equality

Parameters:other (Node) – the other instances to compare to Returns:True if equal, False otherwise

__getstate__()

Retrieve the state of the Node

Returns:the state of the object Rtype dict:

__init__(tool, id_=None, parent=None, cores=None, memory=None, walltime=None)

Instantiate a node.

Parameters:

• tool (Tool) – The tool to base the node on
• id (str) – the id of the node
• parent (Network) – the parent network of the node
• cores (int) – number of cores required for executing this Node
• memory (str) – amount of memory required in the form d+[mMgG] where M is for megabyte and G for gigabyte
• walltime (str) – amount of time required in second or in the form HOURS:MINUTES:SECOND

Returns:

the newly created Node

__metaclass__

alias of ABCMeta

__module__ = 'fastr.core.node'

__repr__()

Get a string representation for the Node

Returns:the string representation Return type:str

__setstate__(state)

Set the state of the Node by the given state.

Parameters:state (dict) – The state to populate the object with Returns:None

__str__()

Get a string version for the Node

Returns:the string version Return type:str

blocking

Indicate that the results of this Node cannot be determined without first executing the Node, causing a blockage in the creation of jobs. A blocking Nodes causes the Chunk borders.

create_job(sample_id, sample_index, job_data, job_dependencies, **kwargs)

Create a job based on the sample id, job data and job dependencies.

Parameters:

• sample_id (SampleId) – the id of the corresponding sample
• sample_index (SampleIndex) – the index of the corresponding sample
• job_data (dict) – dictionary containing all input data for the job
• job_dependencies – other jobs that need to finish before this job can run

Returns:

the created job

Return type:

Job

classmethod createobj(state, network=None)

dimnames

Names of the dimensions in the Node output. These will be reflected in the SampleIdList of this Node.

execute()

Execute the node and create the jobs that need to run

Returns:list of jobs to run Return type:list of Jobs

find_source_index(target_index, target, source)

fullid

The full defining ID for the Node

get_sourced_nodes()

A list of all Nodes connected as sources to this Node

Returns:list of all nodes that are connected to an input of this node

id

The id of the Node

id_ = None

The Node id s a unique string identifying the Node

input_groups

A list of input groups for this Node. An input group is InputGroup

object filled according to the Node

inputs = None

A list of inputs of this Node

listeners

All the listeners requesting output of this node, this means the listeners of all Outputs and SubOutputs

merge_dimensions

name

Name of the Tool the Node was based on. In case a Toolless Node was used the class name is given.

nodegroup

outputs = None

A list of outputs of this Node

outputsize

Size of the outputs in this Node

parent

The parent is the Network this Node is part of

prepare()

Prepare the node for execution. It will create a SampleIdList of the correct size and prepare the outputs.

required_cores

Number of cores required for the execution of this Node

required_memory

Amount of memory required for the execution of this Node. Follows the format d+[mMgG] so 500M or 4g would be valid ways to specify 500 megabytes or 4 gigabyte of memory.

required_time

Amount of time required for the execution of this Node. Follows the format of a number of second or H:M:S, with H the number of hours, M the number of minutes and S the number of seconds.

set_result(job, failed_annotation)

Incorporate result of a job into the Node.

Parameters:

• job (Type) – job of which the result to store
• failed_annotation – A set of annotations, None if no errors else containing a tuple describing the errors

status

tool

update_input_groups()

Update all input groups in this node

class fastr.core.node.OutputDict(*args, **kwds)

Bases: collections.OrderedDict

The container containing the Inputs of Node. Only checks if the inserted values are actually outputs.

__module__ = 'fastr.core.node'

__setitem__(key, value, dict_setitem=<slot wrapper '__setitem__' of 'dict' objects>)

Set an output.

Parameters:

• key (str) – the of the item to set
• value (BaseOutput) – the output to set
• dict_setitem – the setitem function to use for the underlying OrderedDict insert

class fastr.core.node.SinkNode(datatype, id_=None)

Bases: fastr.core.node.Node

Class which handles where the output goes. This can be any kind of file, e.g. image files, textfiles, config files, etc.

__abstractmethods__ = frozenset([])

__dataschemafile__ = 'SinkNode.schema.json'

__getstate__()

__init__(datatype, id_=None)

Instantiation of the SourceNode.

Parameters:

• datatype – The datatype of the output.
• id – the id of the node to create

Returns:

newly created sink node

usage example:

>>> import fastr
>>> network = fastr.Network()
>>> sink = network.create_sink(datatype=fastr.typelist['ITKImageFile'], id_='SinkN')

__module__ = 'fastr.core.node'

__setstate__(state)

create_job(sample_id, sample_index, job_data, job_dependencies)

Create a job for a sink based on the sample id, job data and job dependencies.

Parameters:

• sample_id (SampleId) – the id of the corresponding sample
• job_data (dict) – dictionary containing all input data for the job
• job_dependencies – other jobs that need to finish before this job can run

Returns:

the created job

Return type:

Job

datatype

The datatype of the data this sink can store.

execute()

Execute the sink node and create the jobs that need to run

Returns:list of jobs to run Return type:list of Jobs

input

The default input of the sink Node

set_data(data)

Set the targets of this sink node.

Parameters:data (dict or list of urls) – the targets rules for where to write the data

The target rules can include a few fields that can be filled out:

field	description
sample_id	the sample id of the sample written in string form
cardinality	the cardinality of the sample written
ext	the extension of the datatype of the written data, including the .
extension	the extension of the datatype of the written data, excluding the .
network	the id of the network the sink is part of
node	the id of the node of the sink
timestamp	the iso formatted datetime the network execution started
uuid	the uuid of the network run (generated using uuid.uuid1)

An example of a valid target could be:

>>> target = 'vfs://output_mnt/some/path/image_{sample_id}_{cardinality}{ext}'

Note

The {ext} and {extension} are very similar but are both offered. In many cases having a name.{extension} will feel like the correct way to do it. However, if you have DataTypes with and without extension that can both exported by the same sink, this would cause either name.ext or name. to be generated. In this particular case name{ext} can help as it will create either name.ext or name.

set_result(job, failed_annotation)

Incorporate result of a sink job into the Network.

Parameters:job (Type) – job of which the result to store Failed_annotation:  A set of annotations, None if no errors else containing a tuple describing the errors

class fastr.core.node.SourceNode(datatype, id_=None)

Bases: fastr.core.node.FlowNode

Class providing a connection to data resources. This can be any kind of file, stream, database, etc from which data can be received.

__abstractmethods__ = frozenset([])

__dataschemafile__ = 'SourceNode.schema.json'

__eq__(other)

Compare two Node instances with each other. This function ignores the parent and update status, but tests rest of the dict for equality. equality

Parameters:other (Node) – the other instances to compare to Returns:True if equal, False otherwise

__getstate__()

Retrieve the state of the SourceNode

Returns:the state of the object Rtype dict:

__init__(datatype, id_=None)

Instantiation of the SourceNode.

Parameters:

• datatype – The (id of) the datatype of the output.
• id – The url pattern.

This class should never be instantiated directly (unless you know what you are doing). Instead create a source using the network class like shown in the usage example below.

usage example:

>>> import fastr
>>> network = fastr.Network()
>>> source = network.create_source(datatype=fastr.typelist['ITKImageFile'], id_='sourceN')

__module__ = 'fastr.core.node'

__setstate__(state)

Set the state of the SourceNode by the given state.

Parameters:state (dict) – The state to populate the object with Returns:None

create_job(sample_id, sample_index, job_data, job_dependencies)

datatype

The datatype of the data this source supplies.

dimnames

Names of the dimensions in the SourceNode output. These will be reflected in the SampleIdLists.

execute()

Execute the source node and create the jobs that need to run

Returns:list of jobs to run Return type:list of Jobs

output

Shorthand for self.outputs['output']

outputsize

The size of output of this SourceNode

set_data(data, ids=None)

Set the data of this source node.

Parameters:

• data (dict, OrderedDict or list of urls) – the data to use
• ids – if data is a list, a list of accompanying ids

sourcegroup

valid

This does nothing. It only overloads the valid method of Node(). The original is intended to check if the inputs are connected to some output. Since this class does not implement inputs, it is skipped.

objectmanager Module

This module contains the object manager class

class fastr.core.objectmanager.ObjectManager(path)

Bases: fastr.core.basemanager.BaseManager

Class for managing all the objects loaded in the fastr system

__abstractmethods__ = frozenset(['object_class', 'get_object_version'])

__contains__(key)

Check if an item is in the ObjectManager

Parameters:key (str or tuple) – object id or tuple (Objectid, version) Returns:flag indicating the item is in the manager

__getitem__(key)

Retrieve a Object from the ObjectManager. You can request by only an id, which results in the newest version of the Object being returned, or request using both an id and a version.

Parameters:key (str or tuple) – object id or tuple (Objectid, version) Returns:the requested Object Raises:FastrObjectUnknownError – if a non-existing Object was requested

__init__(path)

Create a ObjectManager and scan path to search for Objects

Parameters:path (str or iterable of str) – the path(s) to scan for Objects Returns:newly created ObjectManager

__keytransform__(key)

Key transform, used for allowing indexing both by id-only and by (id, version)

Parameters:key – key to transform Returns:key in form (id, version)

__module__ = 'fastr.core.objectmanager'

get_object_version(obj)

Get the version of a given object

Parameters:object – the object to use Returns:the version of the object

object_class

The class of the objects to populate the manager with

objectversions(obj)

Return a list of available versions for the object

Parameters:object – The object to check the versions for. Can be either a Object or a str. Returns:List of version objects. Returns None when the given object is not known.

todict()

Return a dictionary version of the Manager

Returns:manager as a dict

pluginmanager Module

This module contains the Manager class for Plugins in the fastr system

class fastr.core.pluginmanager.BasePluginManager(path=None, recursive=False)

Bases: fastr.core.basemanager.BaseManager

Baseclass for PluginManagers, need to override the self._plugin_class

__abstractmethods__ = frozenset(['plugin_class'])

__getitem__(key)

Retrieve item from BaseManager

Parameters:key – the key of the item to retrieve Returns:the value indicated by the key Raises:FastrKeyError – if the key is not found in the BaseManager

__init__(path=None, recursive=False)

Create a BasePluginManager and scan the give path for matching plugins

Parameters:

• path (str) – path to scan
• recursive (bool) – flag to indicate a recursive search

Returns:

newly created plugin manager

Raises:

FastrTypeError – if self._plugin_class is set to a class not subclassing BasePlugin

__module__ = 'fastr.core.pluginmanager'

load_plugin(plugin_key)

plugin_class

The class from which the plugins must be subclassed

populate()

Populate the manager with the data. This is a method that will be called when the Managers data is first accessed. This way we avoid doing expensive directory scans when the data is never requested.

class fastr.core.pluginmanager.LazyModule(name, parent, plugin_manager)

Bases: module

A module that allows content to be loaded lazily from plugins. It generally is (almost) empty and gets (partially) populated when an attribute cannot be found. This allows lazy loading and plugins depending on other plugins.

__getattr__(item)

The getattr is called when getattribute does not return a value and is used as a fallback. In this case we try to find the value normally and will trigger the plugin manager if it cannot be found.

Parameters:item (str) – attribute to retrieve Returns:the requested attribute

__init__(name, parent, plugin_manager)

__module__ = 'fastr.core.pluginmanager'

__repr__()

__weakref__

list of weak references to the object (if defined)

class fastr.core.pluginmanager.PluginManager(path=None)

Bases: fastr.core.pluginmanager.BasePluginManager

__abstractmethods__ = frozenset([])

__init__(path=None)

__module__ = 'fastr.core.pluginmanager'

__setitem__(key, value)

Store an item in the BaseManager, will ignore the item if the key is already present in the BaseManager.

Parameters:

• name – the key of the item to save
• value – the value of the item to save

Returns:

None

plugin_class

The plugin manager contains any Plugin subclass

class fastr.core.pluginmanager.PluginSubManager(parent, plugin_class)

Bases: fastr.core.pluginmanager.BasePluginManager

A PluginManager that is a selection of a parent plugin manger. It uses the PluginsView to only exponse part of the parent PluginManager. This is used to create plugin managers for only certain types of plugins (e.g. IOPlugins) without loading them multiple times.

__abstractmethods__ = frozenset([])

__init__(parent, plugin_class)

__module__ = 'fastr.core.pluginmanager'

data

plugin_class

PluginSubManagers only expose the plugins of a certain class

class fastr.core.pluginmanager.PluginsView(parent, plugin_class)

Bases: _abcoll.MutableMapping

A collection that acts like view of the plugins of another plugin manager. This is a proxy object that only gives access the plugins of a certain plugin class. It behaves like a mapping and is used as the data object for a PluginSubManager.

__abstractmethods__ = frozenset([])

__delitem__(key)

__getitem__(item)

__init__(parent, plugin_class)

Constructor for the plugins view

Parameters:

• parent (BasePluginManager) – the parent plugin manager
• plugin_class (class) – the class of the plugins to expose

__iter__()

__len__()

__module__ = 'fastr.core.pluginmanager'

__setitem__(key, value)

filter_plugin(plugin)

class fastr.core.pluginmanager.plugin_option_type(filename, name, namespace, id)

Bases: tuple

__dict__ = dict_proxy({'__module__': 'fastr.core.pluginmanager', '_make': <classmethod object>, '_replace': <function _replace>, '_asdict': <function _asdict>, '__dict__': <property object>, '__getnewargs__': <function __getnewargs__>, 'id': <property object>, 'name': <property object>, '_fields': ('filename', 'name', 'namespace', 'id'), '__new__': <staticmethod object>, 'namespace': <property object>, 'filename': <property object>, '__slots__': (), '__repr__': <function __repr__>, '__getstate__': <function __getstate__>, '__doc__': 'plugin_option_type(filename, name, namespace, id)'})

__getnewargs__()

Return self as a plain tuple. Used by copy and pickle.

__getstate__()

Exclude the OrderedDict from pickling

__module__ = 'fastr.core.pluginmanager'

static __new__(_cls, filename, name, namespace, id)

Create new instance of plugin_option_type(filename, name, namespace, id)

__repr__()

Return a nicely formatted representation string

__slots__ = ()

filename

Alias for field number 0

id

Alias for field number 3

name

Alias for field number 1

namespace

Alias for field number 2

provenance Module

class fastr.core.provenance.Provenance(host=None)

Bases: object

The Provenance object keeps track of everything that happens to a data object.

__dict__ = dict_proxy({'__module__': 'fastr.core.provenance', 'init_provenance': <function init_provenance>, 'collect_input_argument_provenance': <function collect_input_argument_provenance>, 'data_uri': <staticmethod object>, 'serialize': <function serialize>, 'get_parent_provenance': <staticmethod object>, 'agent': <function agent>, 'entity': <function entity>, '__init__': <function __init__>, 'activity': <function activity>, '__dict__': <attribute '__dict__' of 'Provenance' objects>, '_add_namespace': <function _add_namespace>, '__weakref__': <attribute '__weakref__' of 'Provenance' objects>, '__doc__': '\n The Provenance object keeps track of everything that happens to a data object.\n ', 'collect_provenance': <function collect_provenance>})

__init__(host=None)

__module__ = 'fastr.core.provenance'

__weakref__

list of weak references to the object (if defined)

activity(identifier, start_time=None, end_time=None, other_attributes=None)

agent(identifier, other_attributes=None)

collect_input_argument_provenance(input_argument)

collect_provenance(job, advanced_flow=False)

Collect the provenance for this job

static data_uri(value, job)

entity(identifier, other_attributes=None)

static get_parent_provenance(value)

Find the provenance of the parent job

Parameters:

value (str) – url for the value for which to find the job

Returns:

the provenance of the job that created the value

Raises:

• FastrKeyError – if the deferred is not available (yet)
• FastrValueError – if the value is not a valid deferred url

init_provenance(job)

Create initial provenance document

serialize(filename, format)

samples Module

This package holds the classes for working with samples.

class fastr.core.samples.HasSamples

Bases: object

Base class for all classes that supply samples. This base class allows to only define __getitem__ and size and get all other basic functions mixed in so that the object behaves similar to a Mapping.

__abstractmethods__ = frozenset(['__getitem__', 'size'])

__contains__(item)

__dict__ = dict_proxy({'_abc_cache': <_weakrefset.WeakSet object>, '__module__': 'fastr.core.samples', '__metaclass__': <class 'abc.ABCMeta'>, '_abc_negative_cache': <_weakrefset.WeakSet object>, '__getitem__': <function __getitem__>, '__contains__': <function __contains__>, 'items': <function items>, '__abstractmethods__': frozenset(['__getitem__', 'size']), 'ids': <function ids>, 'indexes': <function indexes>, '_abc_registry': <_weakrefset.WeakSet object>, '__iter__': <function __iter__>, '_abc_negative_cache_version': 34, 'iteritems': <function iteritems>, '__dict__': <attribute '__dict__' of 'HasSamples' objects>, '__weakref__': <attribute '__weakref__' of 'HasSamples' objects>, '__doc__': '\n Base class for all classes that supply samples. This base class allows\n to only define __getitem__ and size and get all other basic functions mixed\n in so that the object behaves similar to a Mapping.\n ', 'size': <abc.abstractproperty object>})

__getitem__(item)

__iter__()

__metaclass__

alias of ABCMeta

__module__ = 'fastr.core.samples'

__weakref__

list of weak references to the object (if defined)

ids()

indexes()

items()

iteritems()

size

class fastr.core.samples.SampleBaseId

Bases: tuple

This class represents a sample id. A sample id is a multi-dimensional id that has a simple, consistent string representation.

__add__(other)

Add another SampleId, this allows to add parts to the SampleId in a convenient way.

__dict__ = dict_proxy({'__module__': 'fastr.core.samples', '__new__': <staticmethod object>, '__str__': <function __str__>, '__radd__': <function __radd__>, '__repr__': <function __repr__>, '__add__': <function __add__>, '__dict__': <attribute '__dict__' of 'SampleBaseId' objects>, '__doc__': '\n This class represents a sample id. A sample id is a multi-dimensional\n id that has a simple, consistent string representation.\n ', '_element_type': <type 'NoneType'>})

__module__ = 'fastr.core.samples'

static __new__(*args)

Create a new SampleId

Parameters:args (iterator/iterable of element type or element type) – the strings to make sample id for

__radd__(other)

Add another SampleId, this allows to add parts to the SampleId in a convenient way. This is the right-hand version of the operator.

__repr__()

Get a string representation for the SampleBaseId

Returns:the string representation Return type:str

__str__()

Get a string version for the SampleId, joins the SampleId with __ to create a single string version.

Returns:the string version Return type:str

class fastr.core.samples.SampleCollection(dimnames, parent)

Bases: _abcoll.MutableMapping

The SampleCollections is a class that contains the data including a form of ordering. Each sample is reachable both by its SampleId and a SampleIndex. The object is sparse, so not all SampleId have to be defined allowing for non-rectangular data shapes.

Note

This object is meant to replace both the SampleIdList and the ValueStorage.

__abstractmethods__ = frozenset([])

__contains__(item)

Check if an item is in the SampleCollection. The item can be a SampleId or SampleIndex. If the item is a slicing SampleIndex, then check if it would return any data (True) or no data (False)

Parameters:item (SampleId, SampleIndex) – the item to check for Returns:flag indicating item is in the collections Return type:bool

__delitem__(key)

Remove an item from the SampleCollection

Parameters:key (SampleId, SampleIndex, tuple of both, or SampleItem) – the key of the item to remove

__getitem__(item)

Retrieve (a) SampleItem(s) from the SampleCollection using the SampleId or SampleIndex. If the item is a tuple, it should be valid tuple for constructing either a SampleId or SampleIndex.

Parameters:

item (SampleId, SampleIndex, or tuple) – the identifier of the item to retrieve

Returns:

the requested item

Return type:

SampleItem

Raises:

• FastrTypeError – if the item parameter is of incorrect type
• KeyError – if the item is not found

__init__(dimnames, parent)

Createa a new SampleCollection

__iter__()

Iterate over the indices

__len__()

Get the number of samples in the SampleCollections.

__module__ = 'fastr.core.samples'

__repr__()

__setitem__(key, value)

Set an item to the SampleCollection. The key can be a SampleId, SampleIndex or a tuple containing a SampleId and SampleIndex. The value can be a SampleItem (with the SampleId and SampleIndex matching), a tuple with values (assuming no depending jobs), or a with a list of values and a set of depending jobs.

Parameters:

• key (SampleId, SampleIndex, tuple of both, or SampleItem) – the key of the item to store
• value (SampleItem, tuple of values, or tuple of tuple of values and set of depending jobs) – the value of the SampleItem to store

Raises:

• FastrTypeError – if the key or value types are incorrect
• FastrValueError – if the id or values are incorrectly formed

dimnames

The dimnames of the SampleCollection

fullid

The full defining ID for the SampleIdList

ndims

The number of dimensions in this SampleCollection

parent

The parent object holding the SampleCollection

size

The size of the SampleCollection. The size is the largest index in every dimension. For a 2D SampleCollection with 2 entries (10, 2) and (6, 6) the size would be (10, 6). As that is the rectangular grid that contains all data points.

class fastr.core.samples.SampleId

Bases: fastr.core.samples.SampleBaseId

SampleId is an identifier for data using human readable strings

__module__ = 'fastr.core.samples'

class fastr.core.samples.SampleIndex

Bases: fastr.core.samples.SampleBaseId

SampleId is an identifier for data using the location in the N-d data structure.

__module__ = 'fastr.core.samples'

__repr__()

Get a string representation for the SampleIndex

Returns:the string representation Return type:str

__str__()

Get a string version for the SampleId, joins the SampleId with __ to create a single string version.

Returns:the string version Return type:str

expand(size)

Function expanding a slice SampleIndex into a list of non-slice SampleIndex objects

Parameters:size – the size of the collection to slice

isslice

Flag indicating that the SampleIndex is a slice (as opposed to a simple single index).

class fastr.core.samples.SampleItem

Bases: fastr.core.samples.SampleItemBase

__module__ = 'fastr.core.samples'

static __new__(index, id_, data, jobs=None, failed_annotations=None)

Create a SampleItem. Data should be an OrderedDict of tuples.

Parameters:

• index (tuple, slice) – the sample index
• id (SampleId) – the sample id
• data (SampleValue, Mapping) – the data values
• jobs (set) – set of jobs on which this SampleItems data depends.
• failed_annotations (set) – set of tuples. The tuple is contructed like follows: (job_id, reason).

class fastr.core.samples.SampleItemBase

Bases: tuple

This class represents a sample item, a combination of a SampleIndex, SampleID, value and required jobs. The SampleItem based on a named tuple and has some extra methods to combine SampleItems easily.

__add__(other)

The addition operator combines two SampleItems into a single SampleItems. It merges the data and jobs and takes the index and id of the left-hand item.

Parameters:other (SampleItem) – The other item to add to this one Returns:the combined SampleItem Return type:SampleItem

__dict__ = dict_proxy({'index': <property object>, '__module__': 'fastr.core.samples', 'dimensionality': <property object>, 'jobs': <property object>, '__new__': <staticmethod object>, 'data': <property object>, '__doc__': '\n This class represents a sample item, a combination of a SampleIndex,\n SampleID, value and required jobs. The SampleItem based on a named\n tuple and has some extra methods to combine SampleItems easily.\n ', 'combine': <staticmethod object>, '__repr__': <function __repr__>, '__add__': <function __add__>, '__dict__': <attribute '__dict__' of 'SampleItemBase' objects>, 'cardinality': <property object>, '__getnewargs__': <function __getnewargs__>, 'id': <property object>, 'failed_annotations': <property object>})

__getnewargs__()

Get new args gives the arguments to use to re-create this object, This is used for serialization.

__module__ = 'fastr.core.samples'

static __new__(index, id_, data, jobs=None, failed_annotations=None)

Create a SampleItem. Data should be an OrderedDict of tuples.

Parameters:

• index (tuple, slice) – the sample index
• id (SampleId) – the sample id
• data (SampleValue, Mapping) – the data values
• jobs (set) – set, tuple or list of jobs on which this SampleItems data depends.
• failed_annotations (set) – set of tuples. The tuple is contructed like follows: (job_id, reason).

__repr__()

Get a string representation for the SampleItem

Returns:the string representation Return type:str

cardinality

The cardinality of this Sample

static combine(*args)

Combine a number of SampleItems into a new one.

Parameters:*args –

the SampleItems to combine

Returns:the combined SampleItem Return type:SampleItem

It is possible to both give multiple arguments, where each argument is a SampleItem, or a single argument which is an iterable yielding SampleItems.

# variables a, b, c, d are SampleItems to combine
# These are all valid ways of combining the SampleItems
comb1 = SampleItem.combine(a, b, c, d)  # Using multiple arguments
l = [a, b, c, d]
comb2 = SampleItem.combine(l)  # Using a list of arguments
comb3 = SampleItem.combine(l.__iter__())  # Using an iterator

data

The data SampleValue of the SampleItem

Returns:The value of this SampleItem Return type:SampleValue

dimensionality

The dimensionality of this Sample

failed_annotations

id

The sample id of the SampleItem

Returns:The id of this SampleItem Return type:SampleId

index

The index of the SampleItem

Returns:The index of this SampleItem Return type:SampleIndex

jobs

The set of the jobs on which this SampleItem depends

Returns:The jobs that generated the data for this SampleItem Return type:set

class fastr.core.samples.SamplePayload

Bases: fastr.core.samples.SampleItemBase

__add__(other)

The addition operator combines two SampleItems into a single SampleItems. It merges the data and jobs and takes the index and id of the left-hand item.

Parameters:other (SampleItem) – The other item to add to this one Returns:the combined SamplePayload Return type:SamplePayload

__module__ = 'fastr.core.samples'

static __new__(index, id_, data, jobs=None, failed_annotations=None)

Create a SampleItem. Data should be an OrderedDict of tuples.

Parameters:

• index (tuple, slice) – the sample index
• id (SampleId) – the sample id
• data (SampleValue, Mapping) – the data values
• jobs (set) – set of jobs on which this SampleItems data depends.
• failed_annotations (set) – set of tuples. The tuple is contructed like follows: (job_id, reason).

class fastr.core.samples.SampleValue(*args, **kwargs)

Bases: _abcoll.MutableMapping

A collection containing the content of a sample

__abstractmethods__ = frozenset([])

__add__(other)

__delitem__(key)

__getitem__(item)

__getstate__()

__init__(*args, **kwargs)

__iter__()

__len__()

__module__ = 'fastr.core.samples'

__radd__(other)

__repr__()

__setitem__(key, value)

__setstate__(state)

cast(datatype)

is_mapping

is_sequence

iterelements()

mapping_part()

sequence_part()

serializable Module

This package contains the base class and meta class for all serializable objects in the Fastr system.

class fastr.core.serializable.PassThroughSerializer

Bases: object

__dict__ = dict_proxy({'__module__': 'fastr.core.serializable', 'dumps': <staticmethod object>, '__dict__': <attribute '__dict__' of 'PassThroughSerializer' objects>, 'loads': <staticmethod object>, '__weakref__': <attribute '__weakref__' of 'PassThroughSerializer' objects>, '__doc__': None})

__module__ = 'fastr.core.serializable'

__weakref__

list of weak references to the object (if defined)

static dumps(data)

static loads(data)

class fastr.core.serializable.Serializable

Bases: object

Superclass for all classes that can be serialized.

SERIALIZERS = {'/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/schemas/Tool.schema.json': <fastr.utils.jsonschemaparser.Blueprinter object>, '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/schemas/Network.schema.json': <fastr.utils.jsonschemaparser.Blueprinter object>, '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/schemas/FastrInterface.schema.json': <fastr.utils.jsonschemaparser.Blueprinter object>}

__dict__ = dict_proxy({'load': <classmethod object>, '__module__': 'fastr.core.serializable', 'SERIALIZERS': {'/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/schemas/Tool.schema.json': <fastr.utils.jsonschemaparser.Blueprinter object>, '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/schemas/Network.schema.json': <fastr.utils.jsonschemaparser.Blueprinter object>, '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/schemas/FastrInterface.schema.json': <fastr.utils.jsonschemaparser.Blueprinter object>}, 'dump': <function dump>, '_unicode_convert': <classmethod object>, 'get_serializer': <classmethod object>, 'dumpfuncs': {'xml': <module 'fastr.utils.xmltodict' from '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/utils/xmltodict.pyc'>, 'pickle': <module 'pickle' from '/home/docs/.pyenv/versions/2.7.13/lib/python2.7/pickle.pyc'>, 'json': <module 'json' from '/home/docs/.pyenv/versions/2.7.13/lib/python2.7/json/__init__.pyc'>, 'dict': <class 'fastr.core.serializable.PassThroughSerializer'>, 'marshall': <module 'marshal' (built-in)>}, '_dumps': <classmethod object>, '_dump': <classmethod object>, '__dict__': <attribute '__dict__' of 'Serializable' objects>, 'loadf': <classmethod object>, '_unserialize': <classmethod object>, '_load': <classmethod object>, '_dumpf': <classmethod object>, '_loads': <classmethod object>, 'dumpf': <function dumpf>, '__weakref__': <attribute '__weakref__' of 'Serializable' objects>, '__doc__': '\n Superclass for all classes that can be serialized.\n ', 'dumps': <function dumps>, '_loadf': <classmethod object>, '_serialize': <function _serialize>, 'loads': <classmethod object>, '__getstate__': <function __getstate__>, 'createobj': <classmethod object>})

__getstate__()

__module__ = 'fastr.core.serializable'

__weakref__

list of weak references to the object (if defined)

classmethod createobj(state, _=None)

Create object function for generic objects

Parameters:

• cls – The class to create
• state – The state to use to create the Link
• network – the parent Network

Returns:

newly created Link

dump(file_handle, method='json', **kwargs)

Dump the object to a file like object.

Parameters:

• file_handle – file descriptor to write the data to
• method (str) – method of final serialization to use (e.g. json, xml, pickle)
• kwargs – extra arguments passed to the final serializer

dumpf(path, method=None, **kwargs)

Dump the object to a file

Parameters:

• path – path where to write the file
• method (str) – method of final serialization to use (e.g. json, xml, pickle)
• kwargs – extra arguments passed to the final serializer

Note

The dumpf function can determine the method based on the desired output filename. Also, if the filename ends with .gz it will continue search for another extension (so .json.gz could be found) and will then compress the result with gzip.

dumpfuncs = {'xml': <module 'fastr.utils.xmltodict' from '/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/utils/xmltodict.pyc'>, 'pickle': <module 'pickle' from '/home/docs/.pyenv/versions/2.7.13/lib/python2.7/pickle.pyc'>, 'json': <module 'json' from '/home/docs/.pyenv/versions/2.7.13/lib/python2.7/json/__init__.pyc'>, 'dict': <class 'fastr.core.serializable.PassThroughSerializer'>, 'marshall': <module 'marshal' (built-in)>}

dumps(method='json', **kwargs)

Dump the object to a string

Parameters:

• method (str) – method of final serialization to use (e.g. json, xml, pickle)
• kwargs – extra arguments passed to the final serializer

Returns:

serialization string

Return type:

str

classmethod get_serializer(filename=None)

classmethod load(file_handle, method=None, network=None, **kwargs)

Load the object from a file-like object

Parameters:

• cls – class of the object
• file_handle – file descriptor to write the data to
• method (str) – method of final serialization to use (e.g. json, xml, pickle)
• network – network in which to place the loaded object
• kwargs – extra arguments passed to the final serializer

Returns:

newly created object

Warning

Unlike the loadf functions, this function does not automatically detect gzip compression. You read a gzip using the gzip.open method, but not but simply opening a stream and hopeing this function will function.

classmethod loadf(path, method=None, network=None, **kwargs)

Load the object from a file

Parameters:

• cls – class of the object
• path – path where to write the file
• method (str) – method of final serialization to use (e.g. json, xml, pickle)
• network – network in which to place the loaded object
• kwargs – extra arguments passed to the final serializer

Returns:

newly created object

Note

The loadf function can determine the method of loading based on the filename. Also it can automatically determine whether a file is gzipped.

classmethod loads(string, method=None, network=None, **kwargs)

Load the object from a string

Parameters:

• cls – class of the object
• string (str) – the string containing the serialized data
• method (str) – method of final serialization to use (e.g. json, xml, pickle)
• network – network in which to place the loaded object
• kwargs – extra arguments passed to the final serializer

Returns:

newly created object

target Module

The module containing the classes describing the targets.

class fastr.core.target.DockerTarget(binary, docker_image)

Bases: fastr.core.target.Target

A tool target that is located in a Docker images. Can be run using docker-py.

__abstractmethods__ = frozenset([])

__enter__()

__exit__(exc_type, exc_value, traceback)

__init__(binary, docker_image)

Define a new docker target.

Parameters:docker_image (str) – Docker image to use

__module__ = 'fastr.core.target'

container

docker_api = None

Docker api to use for docker target

monitor_docker(container, resources)

Monitor a process and profile the cpu, memory and io use. Register the resource use every _MONITOR_INTERVAL seconds.

Parameters:

• process (subproces.Popen) – process to monitor
• resources – list to append measurements to

run_command(command)

class fastr.core.target.LocalBinaryTarget(binary, paths=None, environment_variables=None, initscripts=None, modules=None, interpreter=None, **kwargs)

Bases: fastr.core.target.Target

A tool target that is a local binary on the system. Can be found using environmentmodules or vfs-path on the executing machine

DYNAMIC_LIBRARY_PATH_DICT = {'windows': 'PATH', 'darwin': 'DYLD_LIBRARY_PATH', 'linux': 'LD_LIBRARY_PATH'}

__abstractmethods__ = frozenset([])

__enter__()

Set the environment in such a way that the target will be on the path.

__exit__(exc_type, exc_value, traceback)

Cleanup the environment

__init__(binary, paths=None, environment_variables=None, initscripts=None, modules=None, interpreter=None, **kwargs)

Define a new local binary target. Must be defined either using paths and optionally environment_variables and initscripts, or enviroment modules.

__module__ = 'fastr.core.target'

call_subprocess(command)

Call a subprocess with logging/timing/profiling

Parameters:command (list) – the command to execute Returns:execution info Return type:dict

monitor_process(process, resources)

Monitor a process and profile the cpu, memory and io use. Register the resource use every _MONITOR_INTERVAL seconds.

Parameters:

• process (subproces.Popen) – process to monitor
• resources – list to append measurements to

run_command(command)

class fastr.core.target.ProcessUsageCollection

Bases: _abcoll.Sequence

__abstractmethods__ = frozenset([])

__getitem__(item)

__init__()

__len__()

__module__ = 'fastr.core.target'

aggregate(number_of_points)

append(value)

usage_type

alias of SystemUsageInfo

class fastr.core.target.SystemUsageInfo(timestamp, cpu_percent, vmem, rmem, read_bytes, write_bytes)

Bases: tuple

__dict__ = dict_proxy({'__module__': 'fastr.core.target', '_make': <classmethod object>, 'timestamp': <property object>, '_replace': <function _replace>, 'read_bytes': <property object>, '_asdict': <function _asdict>, 'rmem': <property object>, '__dict__': <property object>, '__getnewargs__': <function __getnewargs__>, 'write_bytes': <property object>, '_fields': ('timestamp', 'cpu_percent', 'vmem', 'rmem', 'read_bytes', 'write_bytes'), '__new__': <staticmethod object>, 'cpu_percent': <property object>, 'vmem': <property object>, '__slots__': (), '__repr__': <function __repr__>, '__getstate__': <function __getstate__>, '__doc__': 'SystemUsageInfo(timestamp, cpu_percent, vmem, rmem, read_bytes, write_bytes)'})

__getnewargs__()

Return self as a plain tuple. Used by copy and pickle.

__getstate__()

Exclude the OrderedDict from pickling

__module__ = 'fastr.core.target'

static __new__(_cls, timestamp, cpu_percent, vmem, rmem, read_bytes, write_bytes)

Create new instance of SystemUsageInfo(timestamp, cpu_percent, vmem, rmem, read_bytes, write_bytes)

__repr__()

Return a nicely formatted representation string

__slots__ = ()

cpu_percent

Alias for field number 1

read_bytes

Alias for field number 4

rmem

Alias for field number 3

timestamp

Alias for field number 0

vmem

Alias for field number 2

write_bytes

Alias for field number 5

class fastr.core.target.Target

Bases: object

The abstract base class for all targets. Execution with a target should follow the following pattern:

>>> with Target() as target:
...     target.run_commmand(['sleep', '10'])
...     target.run_commmand(['sleep', '10'])
...     target.run_commmand(['sleep', '10'])

The Target context operator will set the correct paths/initialization. Within the context command can be ran and when leaving the context the target reverts the state before.

__abstractmethods__ = frozenset(['run_command'])

__dict__ = dict_proxy({'__module__': 'fastr.core.target', '__abstractmethods__': frozenset(['run_command']), '__exit__': <function __exit__>, '_abc_negative_cache': <_weakrefset.WeakSet object>, '__dict__': <attribute '__dict__' of 'Target' objects>, '__metaclass__': <class 'abc.ABCMeta'>, '_NIPYPE_RUN_COMMAND': None, '__weakref__': <attribute '__weakref__' of 'Target' objects>, '_abc_cache': <_weakrefset.WeakSet object>, '__enter__': <function __enter__>, '_abc_negative_cache_version': 34, '_abc_registry': <_weakrefset.WeakSet object>, '__doc__': "\n The abstract base class for all targets. Execution with a target should\n follow the following pattern:\n\n >>> with Target() as target:\n ... target.run_commmand(['sleep', '10'])\n ... target.run_commmand(['sleep', '10'])\n ... target.run_commmand(['sleep', '10'])\n\n The Target context operator will set the correct paths/initialization.\n Within the context command can be ran and when leaving the context the\n target reverts the state before.\n ", 'run_command': <function run_command>})

__enter__()

Set the environment in such a way that the target will be on the path.

__exit__(exc_type, exc_value, traceback)

Cleanup the environment where needed

__metaclass__

alias of ABCMeta

__module__ = 'fastr.core.target'

__weakref__

list of weak references to the object (if defined)

run_command(command)

tool Module

A module to maintain a tool.

Exported classes:

• Tool – A class encapsulating a tool.
• ParameterDescription – The base class containing the shared description of a parameter (both input and ouput).
• InputParameterDescription – A class containing the description of an input parameter.
• Output ParameterDescription – A class containing the description of an output parameter.

class fastr.core.tool.Tool(doc=None)

Bases: fastr.core.serializable.Serializable

The class encapsulating a tool.

__dataschemafile__ = 'Tool.schema.json'

__eq__(other)

Compare two Tool instances with each other.

Parameters:other (Tool) – the other instances to compare to Returns:True if equal, False otherwise

__getstate__()

Retrieve the state of the Tool

Returns:the state of the object Rtype dict:

__init__(doc=None)

Create a new Tool :param doc: path of toolfile or a dict containing the tool data :type doc: str or dict

__module__ = 'fastr.core.tool'

__repr__()

Get a string representation for the Tool. This will show the inputs and output defined in a table-like structure.

Returns:the string representation Return type:str

__setstate__(state)

Set the state of the Tool by the given state.

Parameters:state (dict) – The state to populate the object with

__str__()

Get a string version for the Tool

Returns:the string version Return type:str

authors = None

List of authors of the tool. These people wrapped the executable but are not responsible for executable itself.

cite = None

This holds the citation you should use when publishing something based on this Tool

command = None

Command is a dictionary contain information about the command which is called by this Tool: command[‘interpreter’] holds the (possible) interpreter to use command[‘targets’] holds a per os/arch dictionary of files that should be executed command[‘url’] is the webpage of the command to be called command[‘version’] is the version of the command used command[‘description’] can help a description of the command command[‘authors’] lists the original authors of the command

command_version

description = None

Description of the tool and it’s functionality

execute(payload=None, **kwargs)

Execute a Tool given the payload for a single run

Parameters:payload – the data to execute the Tool with Returns:The result of the execution Return type:InterFaceResult

fullid

The full id of this tool

hash

help = None

Man page for the Tool. Here usage and examples can be described in detail

inputs

interface_class = None

Create the Interface based on the class specified in the tool file

name = None

Name of the tool, this should be a descriptive, human readable name.

namespace = None

The namespace this tools lives in, this will be set by the ToolManager on load

node_class = None

Class for of the Node to use

ns_id

The namespace and id of the Tool

outputs

path

The path of the directory in which the tool definition file was located.

references = None

A list of documents and in depth reading about the methods used in this tool

regex = None

Identifier for the tool

requirements = None

Requirements for this Tool

Warning

Not yet implemented

tags = None

List of tags for this tool

target

The OS and arch matched target definition.

test()

Run the tests for this tool

test_spec

alias of TestSpecification

tests = None

Test for this tool. A test should be a collection of inputs, parameters and outputs to verify the proper functioning of the Tool.

The format of the tests is a list of namedtuples, that have 3 fields: - input: a dict of the input data - command: a list given the expected command-line arguments - output: a dict of the output data to validate

Warning

Not yet implemented

url = None

URL to website where this tool can be downloaded from

version = None

Version of the tool, not of the underlying software

toolmanager Module

This module contains the tool manager class

class fastr.core.toolmanager.ToolManager(path)

Bases: fastr.core.objectmanager.ObjectManager

__abstractmethods__ = frozenset([])

__module__ = 'fastr.core.toolmanager'

get_object_version(obj)

object_class

populate()

toolversions(tool)

Return a list of available versions for the tool

Parameters:tool – The tool to check the versions for. Can be either a Tool or a str. Returns:List of version objects. Returns None when the given tool is not known.

fastr.core.toolmanager.toollist = ToolManager fastr.Sink v1.0 : /home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/core/ioplugin.pyc fastr.Source v1.0 : /home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/core/ioplugin.pyc fastr.flow.CrossValidation v0.1 : /home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/tools/fastr/flow/0.1/crossvalidation.xml fastr.math.Add v0.1 : /home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/tools/fastr/math/0.1/add.xml fastr.math.AddInt v0.1 : /home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/tools/fastr/math/0.1/addint.xml fastr.math.Divide v0.1 : /home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/tools/fastr/math/0.1/divide.xml fastr.math.IntegerDivide v0.1 : /home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/tools/fastr/math/0.1/intdivide.xml fastr.math.Max v0.1 : /home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/tools/fastr/math/0.1/max.xml fastr.math.Min v0.1 : /home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/tools/fastr/math/0.1/min.xml fastr.math.Multiply v0.1 : /home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/tools/fastr/math/0.1/multiply.xml fastr.math.Subtract v0.1 : /home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/tools/fastr/math/0.1/subtract.xml fastr.math.Sum v0.1 : /home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/tools/fastr/math/0.1/sum.xml fastr.test.0.1.BetDocker v4.1.9 : /home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/tools/fastr/test/0/1/bet_docker.xml fastr.util.AutoPrefixNegateTest v0.1 : /home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/tools/fastr/util/0.1/auto_prefix_negate.xml fastr.util.AutoPrefixTest v0.1 : /home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/tools/fastr/util/0.1/auto_prefix.xml fastr.util.Delay v0.1 : /home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/tools/fastr/util/0.1/delay.xml fastr.util.Fail v0.1 : /home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/tools/fastr/util/0.1/fail.xml fastr.util.PassThroughAuto v0.1 : /home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/tools/fastr/util/0.1/passthroughauto.xml fastr.util.Range v0.1 : /home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/lib/python2.7/site-packages/fastr-1.2.2-py2.7.egg/fastr/resources/tools/fastr/util/0.1/range.xml

The fastr toollist

updateable Module

This module contains both the Updateable class and UpdateableMeta meta-class for objects which support updates within the fastr system

class fastr.core.updateable.Updateable

Bases: object

Super class for all classes that can be updated and have a status. These objects can be valid/invalid and ready/not-ready depending on their state. These states are set by the function update. This allows for interactively checking the network.

__abstractmethods__ = frozenset(['_update'])

__dict__ = dict_proxy({'__module__': 'fastr.core.updateable', '_update': <function _update>, '__metaclass__': <class 'fastr.core.updateable.UpdateableMeta'>, '__updatefunc__': <function update>, '_abc_negative_cache': <_weakrefset.WeakSet object>, 'ready': <property object>, '__dict__': <attribute '__dict__' of 'Updateable' objects>, '__weakref__': <attribute '__weakref__' of 'Updateable' objects>, '__init__': <function __init__>, '_abc_cache': <_weakrefset.WeakSet object>, '__abstractmethods__': frozenset(['_update']), 'valid': <property object>, '__getstate__': <function __getstate__>, '__doc__': '\n Super class for all classes that can be updated and have a status.\n These objects can be valid/invalid and ready/not-ready depending on their\n state. These states are set by the function update. This allows for\n interactively checking the network.\n ', '__setstate__': <function __setstate__>, 'update': <function update>, '__updating__': True, 'messages': <property object>, '_abc_negative_cache_version': 34, '__updatetriggers__': [], '__updateinprogress__': <thread.lock object>, '_abc_registry': <_weakrefset.WeakSet object>})

__getstate__()

Retrieve the state of the object, make sure the status is not part of the description as it will not be valid after re-creating the object.

Returns:the state of the object Rtype dict:

__init__()

Constructor, creates the status field

Returns:newly created object

__metaclass__

alias of UpdateableMeta

__module__ = 'fastr.core.updateable'

__setstate__(state)

Set the state of the object by the given state. This adds a clean status field, making sure it is not unintended, outdated information from before serialization.

Parameters:state (dict) – The state to populate the object with

__updatefunc__(key=None, forward=True, backward=False)

Default function for updating, it can be called without key to have a new update started with a new key.

Parameters:

• key (int) – a key for this update, should be different than the last update key
• forward (bool) – flag indicating to update forward in the network
• backward (bool) – flag indicating to update backward in the network

__updateinprogress__ = <thread.lock object>

Lock to avoid multiple updates happening at the same time

__updatetriggers__ = []

Which methods need to be wrapped to trigger an update. Override this value to have the functions automatically wrapped. E.g. __update_triggers__ = ['append', 'insert', '__setitem__'] to have these functions wrapped.

__updating__ = True

Flag to indicate that this object is allowed to update

__weakref__

list of weak references to the object (if defined)

messages

The messages of the last update

ready

Flag indicating that the object is ready

update(key=None, forward=True, backward=False)

Default function for updating, it can be called without key to have a new update started with a new key.

Parameters:

• key (int) – a key for this update, should be different than the last update key
• forward (bool) – flag indicating to update forward in the network
• backward (bool) – flag indicating to update backward in the network

valid

Flag indicating that the object is valid

class fastr.core.updateable.UpdateableMeta

Bases: abc.ABCMeta

A metaclass for objects which are updateable and need some methods/properties to trigger an update.

__module__ = 'fastr.core.updateable'

static __new__(mcs, name, parents, dct)

classmethod calcmro(mcs, bases)

Calculate the Method Resolution Order of bases using the C3 algorithm.

Suppose you intended creating a class K with the given base classes. This function returns the MRO which K would have, excluding K itself (since it doesn’t yet exist), as if you had actually created the class.

Another way of looking at this, if you pass a single class K, this will return the linearization of K (the MRO of K, including itself).

Parameters:bases – the list of bases for which create the MRO Returns:the list representing the entire MRO, except the (non-existing) class itself

Note: Taken from http://code.activestate.com/recipes/577748-calculate-the-mro-of-a-class/

Created by Steven D’Aprano and licensed under the MIT license

classmethod find_member(mcs, name, parents, dct)

Find a member of the class in the same way as Python would if it had a given dict and set of bases

Parameters:

• mcs – metaclass at work
• name – name of the class to be created
• parents – list of the bases for the new class
• dct – the dict of the class being created

Returns:

the firstly resolved member or None if nothing found

static updatetrigger(fnc)

Function decorator to make a function trigger an update after being called. This is a way to easily have function trigger an update after setting a value without writing tons of wrapper functions. The function keeps the original docstring and appends a note to it.

version Module

Module containing the class that represent versions

class fastr.core.version.Version

Bases: tuple

Class representing a software version definition. Allows for sorting and extraction of parts.

__dict__ = dict_proxy({'status': <property object>, '__module__': 'fastr.core.version', '__new__': <staticmethod object>, 'extra': <property object>, '__str__': <function __str__>, 'major': <property object>, '__dict__': <attribute '__dict__' of 'Version' objects>, 'build': <property object>, '__repr__': <function __repr__>, 'extra_string': <property object>, 'version_matcher': <_sre.SRE_Pattern object at 0x2ffa010>, 'date_version_matcher': <_sre.SRE_Pattern object>, '__doc__': '\n Class representing a software version definition. Allows for sorting and\n extraction of parts.\n ', 'minor': <property object>, 'suffix': <property object>})

__module__ = 'fastr.core.version'

static __new__(*version)

Class containing a version

Can be constructed by:

Version( 'major.$minor.$extra[0].$extra[1]$seperator$status$build$suffix' )
Version( major, minor, extra, status, build, suffix, seperator )
Version( (major, minor, extra, status, build, suffix, seperator) )
Version( [major, minor, extra, status, build, suffix, seperator] )

Parameters:

• major (int) – interger giving major version
• minor (int) – is an integer (required)
• extra (list of int) – is a list of integers
• status (str) – can be “a”, “alpha”, “b”, “beta”, “rc”, or “r”
• build (int) – is an integer
• suffix (str) – can contain any combination of alpha-numeric character and ”._-“
• seperator (str) – is any of ”.”, “-”, or “_”, which is located between $extra and $build

Note

The method based on strings is the recommended method. For strings the major and minor version are required, where for tuple and list constructors all seven elements are optional.

Examples:

>>> a = Version('0.1')
>>> print(tuple(a))
(0, 1, None, None, None, '', None)
>>> b = Version('2.5.3-rc2')
>>> print(tuple(b))
(2, 5, [3], 'rc', 2, '', '-')
>>> c = Version('1.2.3.4.5.6.7-beta8_with_suffix')
>>> print(tuple(c))
(1, 2, [3, 4, 5, 6, 7], 'beta', 8, '_with_suffix', '-')

__repr__()

Return a in-editor representation of the version

__str__()

Return a string representation of the version

build

the build number, this is following the status (e.g. for 3.2-beta4, this would be 4)

date_version_matcher = <_sre.SRE_Pattern object>

extra

extra version extension as a list

extra_string

extra version extension as a string

major

major version

minor

minor version

status

the status of the version (a, alpha, b, beta, rc or r)

suffix

the remainder of the version which was not formatted in a known way

version_matcher = <_sre.SRE_Pattern object at 0x2ffa010>

vfs Module

This module contains the virtual file system code. This is both an internally used object as well as an IOPlugin.

Subpackages

test Package

test Package

testdatatypemanager Module

testdimension Module

testnetwork Module

testnode Module

testsamples Module

testtool Module

testversion Module

testvfs Module

data Package

data Package

Package containig data related modules

url Module

Module providing tools to parse and create valid urls and paths.

usage example:

When in fastr.config under the mounts section the data mount is set to /media/data, you will get the following. .. code-block:: python

>>> import fastrdata.url import get_path_from_url
>>> get_path_from_url('vfs://data/temp/blaat1.png')
'/media/data/temp/blaat1.png'

fastr.data.url.basename(url)

Get basename of url

Parameters:url (str) – the url Returns:the basename of the path in the url

fastr.data.url.create_vfs_url(mountpoint, path)

Construct an url from a given mount point and a relative path to the mount point.

Parameters:

• mountpoint (str) – the name of the mountpoint
• path (str) – relative path from the mountpoint

Returns:

the created vfs url

fastr.data.url.dirname(url)

Get the dirname of the url

Parameters:url (str) – the url Returns:the dirname of the path in the url

fastr.data.url.dirurl(url)

Get the a new url only having the dirname as the path

Parameters:url (str) – the url Returns:the modified url with only dirname as path

fastr.data.url.full_split(urlpath)

Split the path in the url in a list of parts

Parameters:urlpath – the url path Returns:a list of parts

fastr.data.url.get_path_from_url(url)

Get the path to a file from a url. Currently supports the file:// and vfs:// scheme’s

Examples:

>>> url.get_path_from_url('vfs://neurodata/user/project/file.ext')
'Y:\neuro3\user\project\file.ext'


>>> 'file:///d:/data/project/file.ext'
'd:\data\project\file.ext'

Warning

file:// will not function cross platform and is mainly for testing

fastr.data.url.get_url_scheme(url)

Get the schem of the url

Parameters:url (str) – url to extract scheme from Returns:the url scheme Return type:str

fastr.data.url.isurl(string)

Check if string is a valid url

Parameters:string (str) – potential url Returns:flag indicating if string is a valid url

fastr.data.url.join(url, *p)

Join the path in the url with p

Parameters:

• url (str) – the base url to join with
• p – additional parts of the path

Returns:

the url with the parts added to the path

fastr.data.url.normurl(url)

Normalized the path of the url

Parameters:url (str) – the url Returns:the normalized url

fastr.data.url.register_url_scheme(scheme)

Register a custom scheme to behave http like. This is needed to parse all things properly.

fastr.data.url.split(url)

Split a url in a url with the dirname and the basename part of the path of the url

Parameters:url (str) – the url Returns:a tuple with (dirname_url, basename)

examples Package

examples Package

__generate_reference__ Module

fastr.examples.__generate_reference__.create_reference(example_path)

fastr.examples.__generate_reference__.generate_all_references()

add_ints Module

fastr.examples.add_ints.create_network()

fastr.examples.add_ints.main()

fastr.examples.add_ints.sink_data(network)

fastr.examples.add_ints.source_data(network)

auto_prefix Module

fastr.examples.auto_prefix.create_network()

fastr.examples.auto_prefix.main()

collapse Module

fastr.examples.collapse.create_network()

fastr.examples.collapse.main()

fastr.examples.collapse.sink_data(network)

fastr.examples.collapse.source_data(network)

collapse_expand Module

fastr.examples.collapse_expand.create_network()

fastr.examples.collapse_expand.main()

fastr.examples.collapse_expand.sink_data(network)

fastr.examples.collapse_expand.source_data(network)

cross_validation Module

fastr.examples.cross_validation.create_network()

fastr.examples.cross_validation.main()

fastr.examples.cross_validation.sink_data(network)

fastr.examples.cross_validation.source_data(network)

elastix_nipype Module

fastr.examples.elastix_nipype.create_network()

fastr.examples.elastix_nipype.main()

fastr.examples.elastix_nipype.sink_data(network)

fastr.examples.elastix_nipype.source_data(network)

expand Module

fastr.examples.expand.create_network()

fastr.examples.expand.main()

fastr.examples.expand.sink_data(network)

fastr.examples.expand.source_data(network)

failing_network Module

fastr.examples.failing_network.create_network()

fastr.examples.failing_network.main()

fastr.examples.failing_network.sink_data(network)

fastr.examples.failing_network.source_data(network)

filecopy Module

fastr.examples.filecopy.create_network()

fastr.examples.filecopy.main()

fastr.examples.filecopy.sink_data(network)

fastr.examples.filecopy.source_data(network)

macro_node Module

fastr.examples.macro_node.create_macro_network()

fastr.examples.macro_node.create_network()

fastr.examples.macro_node.create_super_macro_node()

fastr.examples.macro_node.main()

fastr.examples.macro_node.sink_data(network)

fastr.examples.macro_node.source_data(network)

shift_links Module

fastr.examples.shift_links.create_network()

fastr.examples.shift_links.main()

fastr.examples.shift_links.sink_data(network)

fastr.examples.shift_links.source_data(network)

source_sink Module

fastr.examples.source_sink.create_network()

fastr.examples.source_sink.main()

fastr.examples.source_sink.sink_data(network)

fastr.examples.source_sink.source_data(network)

Subpackages

test Package

test_examples Module

execution Package

execution Package

This package contains all modules related directly to the execution

environmentmodules Module

This module contains a class to interact with EnvironmentModules

class fastr.execution.environmentmodules.EnvironmentModules(protected=None)

Bases: object

This class can control the module environments in python. It can list, load and unload environmentmodules. These modules are then used if subprocess is

called from python.

__dict__ = dict_proxy({'load': <function load>, '__module__': 'fastr.execution.environmentmodules', '__repr__': <function __repr__>, '__dict__': <attribute '__dict__' of 'EnvironmentModules' objects>, '_sync_avail': <function _sync_avail>, 'sync': <function sync>, '_run_commands_string': <function _run_commands_string>, 'reload': <function reload>, '_sync_loaded': <function _sync_loaded>, '_module_settings_warning': 'Cannot find Environment Modules home directory (environment variables not setup properly?)', '__weakref__': <attribute '__weakref__' of 'EnvironmentModules' objects>, 'tostring_modvalue': <staticmethod object>, '__init__': <function __init__>, '_module': <function _module>, 'exception': FastrImportError('Cannot find Environment Modules home directory (environment variables not setup properly?)',), 'avail_modules': <property object>, 'unload': <function unload>, 'clear': <function clear>, '_module_settings_loaded': False, 'isloaded': <function isloaded>, 'avail': <function avail>, 'totuple_modvalue': <staticmethod object>, 'swap': <function swap>, '__doc__': '\n This class can control the module environments in python. It can list, load\n and unload environmentmodules. These modules are then used if subprocess is\n called from python.\n ', 'loaded_modules': <property object>})

__init__(protected=None)

Create the environmentmodules control object

Parameters:protected (list) – list of modules that should never be unloaded Returns:newly created EnvironmentModules

__module__ = 'fastr.execution.environmentmodules'

__repr__()

__weakref__

list of weak references to the object (if defined)

avail(namestart=None)

Print available modules in same way as commandline version

Parameters:namestart – filter on modules that start with namestart

avail_modules

List of avaible modules

clear()

Unload all modules (except the protected modules as they cannot be unloaded). This should result in a clean environment.

exception = FastrImportError('Cannot find Environment Modules home directory (environment variables not setup properly?)',)

isloaded(module)

Check if a specific module is loaded

Parameters:module – module to check Returns:flag indicating the module is loaded

load(module)

Load specified module

Parameters:module – module to load

loaded_modules

List of currently loaded modules

reload(module)

Reload specified module

Parameters:module – module to reload

swap(module1, module2)

Swap one module for another one

Parameters:

• module1 – module to unload
• module2 – module to load

sync()

Sync the object with the underlying enviroment. Re-checks the available and loaded modules

static tostring_modvalue(value)

Turn a representation of a module into a string representation

Parameters:value – module representation (either str or tuple) Returns:string representation

static totuple_modvalue(value)

Turn a representation of a module into a tuple representation

Parameters:value – module representation (either str or tuple) Returns:tuple representation (name, version, default)

unload(module)

Unload specified module

Parameters:module – module to unload

class fastr.execution.environmentmodules.ModuleSystem

Bases: enum.Enum

__format__(format_spec)

__module__ = 'fastr.execution.environmentmodules'

static __new__(value)

__reduce_ex__(proto)

__repr__()

__str__()

envmod = 'enviromentmodules'

lmod = 'Lmod'

executionpluginmanager Module

This module holds the ExecutionPluginManager as well as the base-class for all ExecutionPlugins.

class fastr.execution.executionpluginmanager.ExecutionPlugin(finished_callback=None, cancelled_callback=None, status_callback=None)

Bases: fastr.core.baseplugin.Plugin

This class is the base for all Plugins to execute jobs somewhere. There are many methods already in place for taking care of stuff.

There are fall-backs for certain features, but if a system already implements those it is usually preferred to skip the fall-back and let the external system handle it. There are a few flags to enable disable these features:

• cls.SUPPORTS_CANCEL indicates that the plugin can cancel queued jobs

• cls.SUPPORTS_HOLD_RELEASE indicates that the plugin can queue jobs in a hold state and can release them again (if not, the base plugin will create a hidden queue for held jobs). The plugin should respect the Job.status == JobState.hold when queueing jobs.

• cls.SUPPORTS_DEPENDENCY indicate that the plugin can manage job dependencies, if not the base plugin job dependency system will be used and jobs with only be submitted when all dependencies are met.

• cls.CANCELS_DEPENDENCIES indicates that if a job is cancelled it will automatically cancel all jobs depending on that job. If not the plugin traverse the dependency graph and kill each job manual.

  Note

  If a plugin supports dependencies it is assumed that when a job gets cancelled, the depending job also get cancelled automatically!

Most plugins should only need to redefine a few abstract methods:

• __init__ the constructor
• cleanup a clean up function that frees resources, closes connections, etc
• _queue_job the method that queues the job for execution

Optionally an extra job finished callback could be added:

• _job_finished extra callback for when a job finishes

If SUPPORTS_CANCEL is set to True, the plugin should also implement:

• _cancel_job cancels a previously queued job

If SUPPORTS_HOLD_RELEASE is set to True, the plugin should also implement:

• _hold_job hold_job a job that is currently held
• _release_job releases a job that is currently held

If SUPPORTED_DEPENDENCY is set to True, the plugin should:

• Make sure to use the Job.hold_jobs as a list of its dependencies

Not all of the functions need to actually do anything for a plugin. There are examples of plugins that do not really need a cleanup, but for safety you need to implement it. Just using a pass for the method could be fine in such a case.

Warning

When overwriting other functions, extreme care must be taken not to break the plugins working, as there is a lot of bookkeeping that can go wrong.

CANCELS_DEPENDENCIES = False

Indicates that when a job is cancelled the dependenceis

SUPPORTS_CANCEL = False

Indicates if the plugin can cancel queued jobs

SUPPORTS_DEPENDENCY = False

Indicate if the plugin can manage job dependencies, if not the base plugin job dependency system will be used and jobs with only be submitted when all dependencies are met.

SUPPORTS_HOLD_RELEASE = False

Indicates if the plugin can queue jobs in a hold state and can release them again (if not, the base plugin will create a hidden queue for held

jobs)

__abstractmethods__ = frozenset(['cleanup', '_queue_job', '__init__'])

__del__()

Cleanup if the variable was deleted on purpose

__enter__()

__exit__(type_, value, tb)

__init__(finished_callback=None, cancelled_callback=None, status_callback=None)

Setup the ExecutionPlugin

Parameters:

• finished_callback – the callback to call after a job finished
• cancelled_callback – the callback to call after a job cancelled

Returns:

newly created ExecutionPlugin

__module__ = 'fastr.execution.executionpluginmanager'

cancel_job(job)

Cancel a job previously queued

Parameters:job – job to cancel

check_job_requirements(job_id)

Check if the requirements for a job are fulfilled.

Parameters:job_id – job to check Returns:directive what should happen with the job Return type:JobAction

check_job_status(job_id)

Get the status of a specified job

Parameters:job_id – the target job Returns:the status of the job (or None if job not found)

cleanup()

Method to call to clean up the ExecutionPlugin. This can be to clear temporary data, close connections, etc.

Parameters:force – force cleanup (e.g. kill instead of join a process)

get_job(job_id)

get_status(job)

hold_job(job)

job_finished(job, errors=None, blocking=False)

The default callback that is called when a Job finishes. This will create a new thread that handles the actual callback.

Parameters:

• job (Job) – the job that finished
• errors – optional list of errors encountered
• blocking (bool) – if blocking, do not create threads

Returns:

queue_job(job)

Add a job to the execution queue

Parameters:job (Job) – job to add

register_job(job)

release_job(job)

Release a job that has been put on hold

Parameters:job – job to release

show_jobs(req_status=None)

List the queued jobs, possible filtered by status

Parameters:req_status – requested status to filter on Returns:list of jobs

class fastr.execution.executionpluginmanager.ExecutionPluginManager

Bases: fastr.core.pluginmanager.PluginSubManager

Container holding all the ExecutionPlugins known to the Fastr system

__abstractmethods__ = frozenset([])

__init__()

Initialize a ExecutionPluginManager and load plugins.

Parameters:

• path – path to search for plugins
• recursive – flag for searching recursively

Returns:

newly created ExecutionPluginManager

__module__ = 'fastr.execution.executionpluginmanager'

class fastr.execution.executionpluginmanager.JobAction

Bases: enum.Enum

Job actions that can be performed. This is used for checking if held jobs should be queued, held longer or be cancelled.

__format__(format_spec)

__module__ = 'fastr.execution.executionpluginmanager'

static __new__(value)

__reduce_ex__(proto)

__repr__()

__str__()

cancel = 'cancel'

hold = 'hold'

queue = 'queue'

executionscript Module

The executionscript is the script that wraps around a tool executable. It takes a job, builds the command, executes the command (while profiling it) and collects the results.

fastr.execution.executionscript.execute_job(job)

Execute a Job and save the result to disk

Parameters:job – the job to execute

fastr.execution.executionscript.main(joblist=None)

This is the main code. Wrapped inside a function to avoid the variables being seen as globals and to shut up pylint. Also if the joblist argument is given it can run any given job, otherwise it takes the first command line argument.

job Module

This module contains the Job class and some related classes.

class fastr.execution.job.InlineJob(*args, **kwargs)

Bases: fastr.execution.job.Job

__init__(*args, **kwargs)

__module__ = 'fastr.execution.job'

get_result()

class fastr.execution.job.Job(node, sample_id, sample_index, input_arguments, output_arguments, hold_jobs=None, status_callback=None, preferred_types=None)

Bases: fastr.core.serializable.Serializable

Class describing a job.

Arguments: tool_name - the name of the tool (str) tool_version - the version of the tool (Version) argument - the arguments used when calling the tool (list) tmpdir - temporary directory to use to store output data hold_jobs - list of jobs that need to finished before this job can run (list)

COMMAND_DUMP = '__fastr_command__.pickle.gz'

INFO_DUMP = '__fastr_extra_job_info__.json'

PROV_DUMP = '__fastr_prov__.json'

RESULT_DUMP = '__fastr_result__.pickle.gz'

STDERR_DUMP = '__fastr_stderr__.txt'

STDOUT_DUMP = '__fastr_stdout__.txt'

__getstate__()

Get the state of the job

Returns:job state Return type:dict

__init__(node, sample_id, sample_index, input_arguments, output_arguments, hold_jobs=None, status_callback=None, preferred_types=None)

Create a job

Parameters:

• node (Node) – the node the job is based on
• sample_id (fastr.core.samples.SampleId) – the id of the sample
• sample_index (fastr.core.samples.SampleIndex) – the index of the sample
• input_arguments (list[dict]) – the argument list
• output_arguments (list[dict]) – the argument list
• hold_jobs (list[str]) – the jobs on which this jobs depend
• status_callback (callable) – The callback to call when the status changed
• preferred_types – The list of preferred types to use

Returns:

__module__ = 'fastr.execution.job'

__repr__()

String representation of the Job

__setstate__(state)

Set the state of the job

Parameters:state (dict) –

static calc_cardinality(description, payload)

commandfile

The path of the command pickle

commandurl

The url of the command pickle

create_payload()

Create the payload for this object based on all the input/output arguments

Returns:the payload Return type:dict

ensure_tmp_dir()

execute()

Execute this job

Returns:The result of the execution Return type:InterFaceResult

extrainfofile

The path where the extra job info document is saved

extrainfourl

The url where the extra job info document is saved

fill_output_argument(output_spec, cardinality, desired_type, requested)

This is an abstract class method. The method should take the argument_dict generated from calling self.get_argument_dict() and turn it into a list of commandline arguments that represent this Input/Output.

Parameters:

• cardinality (int) – the cardinality for this output (can be non for automatic outputs)
• desired_type (DataType) – the desired datatype for this output
• requested (bool) – flag to indicate that the output is requested by Fastr

Returns:

the values for this output

Return type:

list

fullid

The full id of the job

get_deferred(output_id, cardinality_nr, sample_id=None)

Get a deferred pointing to a specific output value in the Job

Parameters:

• output_id (str) – the output to select from
• cardinality_nr (int) – the index of the cardinality
• sample_id (str) – the sample id to select (optional)

Returns:

The deferred

get_output_datatype(output_id)

Get the datatype for a specific output

Parameters:output_id (str) – the id of the output to get the datatype for Returns:the requested datatype Return type:BaseDataType

get_result()

Get the result of the job if it is available. Load the output file if found and check if the job matches the current object. If so, load and return the result.

Returns:Job after execution or None if not available Return type:Job | None

classmethod get_value(value)

Get a value

Parameters:

• value – the url of the value
• datatype – datatype of the value

Returns:

the retrieved value

hash_inputs()

Create hashes for all input values and store them in the info store

hash_results()

Create hashes of all output values and store them in the info store

id

The id of this job

logfile

The path of the result pickle

logurl

The url of the result pickle

provfile

The path where the prov document is saved

provurl

The url where the prov document is saved

required_cores

Number of required cores

required_memory

Number of required memory

required_time

Number of required runtime

status

The status of the job

stderrfile

The path where the stderr text is saved

stderrurl

The url where the stderr text is saved

stdoutfile

The path where the stdout text is saved

stdouturl

The url where the stdout text is saved

tmpurl

The URL of the tmpdir to use

tool

translate_argument(value)

Translate an argument from a URL to an actual path.

Parameters:

• value – value to translate
• datatype – the datatype of the value

Returns:

the translated value

translate_results(result)

Translate the results of an interface (using paths etc) to the proper form using URI’s instead.

Parameters:result (dict) – the result data of an interface Returns:the translated result Return type:dict

validate_results(payload)

Validate the results of the Job

Returns:flag indicating the results are complete and valid

write()

class fastr.execution.job.JobState(_, stage, error)

Bases: enum.Enum

The possible states a Job can be in. An overview of the states and the adviced transitions are depicted in the following figure:

__format__(format_spec)

__init__(_, stage, error)

__module__ = 'fastr.execution.job'

static __new__(value)

__reduce_ex__(proto)

__repr__()

__str__()

cancelled = ('cancelled', 'done', True)

created = ('created', 'idle', False)

done

execution_done = ('execution_done', 'in_progress', False)

execution_failed = ('execution_failed', 'in_progress', True)

failed = ('failed', 'done', True)

finished = ('finished', 'done', False)

hold = ('hold', 'idle', False)

in_progress

nonexistent = ('nonexistent', 'idle', False)

processing_callback = ('processing_callback', 'in_progress', False)

queued = ('queued', 'idle', False)

running = ('running', 'in_progress', False)

class fastr.execution.job.SinkJob(node, sample_id, sample_index, input_arguments, output_arguments, hold_jobs=None, substitutions=None, status_callback=None, preferred_types=None)

Bases: fastr.execution.job.Job

Special SinkJob for the Sink

__init__(node, sample_id, sample_index, input_arguments, output_arguments, hold_jobs=None, substitutions=None, status_callback=None, preferred_types=None)

__module__ = 'fastr.execution.job'

__repr__()

String representation for the SinkJob

create_payload()

Create the payload for this object based on all the input/output arguments

Returns:the payload Return type:dict

get_result()

Get the result of the job if it is available. Load the output file if found and check if the job matches the current object. If so, load and return the result.

Returns:Job after execution

hash_inputs()

Create hashes for all input values and store them in the info store

id

The id of this job

substitute(value, datatype=None)

Substitute the special fields that can be used in a SinkJob.

Parameters:

• value (str) – the value to substitute fields in
• datatype (BaseDataType) – the datatype for the value

Returns:

string with substitutions performed

Return type:

str

tmpurl

The URL of the tmpdir to use

validate_results(payload)

Validate the results of the SinkJob

Returns:flag indicating the results are complete and valid

class fastr.execution.job.SourceJob(node, sample_id, sample_index, input_arguments, output_arguments, hold_jobs=None, status_callback=None, preferred_types=None)

Bases: fastr.execution.job.Job

Special SourceJob for the Source

__module__ = 'fastr.execution.job'

__repr__()

String representation for the SourceJob

get_output_datatype(output_id)

Get the datatype for a specific output

Parameters:output_id (str) – the id of the output to get the datatype for Returns:the requested datatype Return type:BaseDataType

hash_inputs()

Create hashes for all input values and store them in the info store

validate_results(payload)

Validate the results of the Job

Returns:flag indicating the results are complete and valid

networkanalyzer Module

Module that defines the NetworkAnalyzer and holds the reference implementation.

class fastr.execution.networkanalyzer.DefaultNetworkAnalyzer

Bases: fastr.execution.networkanalyzer.NetworkAnalyzer

Default implementation of the NetworkAnalyzer.

__module__ = 'fastr.execution.networkanalyzer'

analyze_network(network, chunk)

Analyze a chunk of a Network. Simply process the Nodes in the chunk sequentially.

Parameters:

• network – Network corresponding with the chunk
• chunk – The chunk of the network to analyze

class fastr.execution.networkanalyzer.NetworkAnalyzer

Bases: object

Base class for NetworkAnalyzers

__dict__ = dict_proxy({'__dict__': <attribute '__dict__' of 'NetworkAnalyzer' objects>, '__weakref__': <attribute '__weakref__' of 'NetworkAnalyzer' objects>, '__module__': 'fastr.execution.networkanalyzer', 'analyze_network': <function analyze_network>, '__doc__': '\n Base class for NetworkAnalyzers\n '})

__module__ = 'fastr.execution.networkanalyzer'

__weakref__

list of weak references to the object (if defined)

analyze_network(network, chunk)

Analyze a chunk of a Network.

Parameters:

• network – Network corresponding with the chunk
• chunk – The chunk of the network to analyze

networkchunker Module

This module contains the NetworkChunker class and its default implementation the DefaultNetworkChunker

class fastr.execution.networkchunker.DefaultNetworkChunker

Bases: fastr.execution.networkchunker.NetworkChunker

The default implementation of the NetworkChunker. It tries to create as large as possible chunks so the execution blocks as little as possible.

__init__()

__module__ = 'fastr.execution.networkchunker'

chunck_network(network)

Create a list of Network chunks that can be pre-analyzed completely. Each chunk needs to be executed before the next can be analyzed and executed.

The returned chunks are (at the moment) in the format of a tuple (start, nodes) which are both tuples. The tuple contain the nodes where to start execution (should ready if previous chunks are done) and all nodes of the chunk respectively.

Parameters:network – Network to split into chunks Returns:tuple containing chunks

class fastr.execution.networkchunker.NetworkChunker

Bases: object

The base class for NetworkChunkers. A Network chunker is a class that takes a Network and produces a list of chunks that can each be analyzed and executed in one go.

__dict__ = dict_proxy({'__dict__': <attribute '__dict__' of 'NetworkChunker' objects>, '__module__': 'fastr.execution.networkchunker', 'chunck_network': <function chunck_network>, '__weakref__': <attribute '__weakref__' of 'NetworkChunker' objects>, '__doc__': '\n The base class for NetworkChunkers. A Network chunker is a class that takes\n a Network and produces a list of chunks that can each be analyzed and\n executed in one go.\n '})

__module__ = 'fastr.execution.networkchunker'

__weakref__

list of weak references to the object (if defined)

chunck_network(network)

Create a list of Network chunks that can be pre-analyzed completely. Each chunk needs to be executed before the next can be analyzed and executed.

Parameters:network – Network to split into chunks Returns:list containing chunks

resources Package

resources Package

Subpackages

datatypes Package

datatypes Package

AnalyzeImageFile Module

Boolean Module

Directory Module

FilePrefix Module

Float Module

ITKImageFile Module

Int Module

JsonFile Module

MetaImageFile Module

NiftiImageFile Module

NiftiImageFileCompressed Module

NiftiImageFileUncompressed Module

Number Module

ProvNFile Module

String Module

TifImageFile Module

TxtFile Module

UnsignedInt Module

tools Package

tools Package

test Package

test Package

testdatatypes Module

utils Package

utils Package

A collections of utils for fastr (command line tools or non-core functionality)

checksum Module

This module contains a number of functions for checksumming files and objects

fastr.utils.checksum.checksum(filepath, algorithm='md5', hasher=None)

Generate the checksum of a file

Parameters:

• filepath (str, list) – path of the file(s) to checksum
• algorithm (str) – the algorithm to use
• hasher (_hashlib.HASH) – a hasher to continue updating (rather then creating a new one)

Returns:

the checksum

Return type:

str

fastr.utils.checksum.checksum_directory(directory, algorithm='md5', hasher=None)

Generate the checksum of an entire directory

Parameters:

• directory (str) – path of the file(s) to checksum
• algorithm (str) – the algorithm to use
• hasher (_hashlib.HASH) – a hasher to continue updating (rather then creating a new one)

Returns:

the checksum

Return type:

str

fastr.utils.checksum.hashsum(objects, hasher=None)

Generate the md5 checksum of (a) python object(s)

Parameters:

• objects – the objects to hash
• hasher – the hasher to use as a base

Returns:

the hash generated

Return type:

str

fastr.utils.checksum.md5_checksum(filepath)

Generate the md5 checksum of a file

Parameters:filepath (str, list) – path of the file(s) to checksum Returns:the checksum Return type:str

fastr.utils.checksum.sha1_checksum(filepath)

Generate the sha1 checksum of a file

Parameters:filepath (str, list) – path of the file(s) to checksum Returns:the checksum Return type:str

classproperty Module

Module containing the code to create class properties.

class fastr.utils.classproperty.ClassPropertyDescriptor(fget)

Bases: object

A descriptor that can act like a property for a class.

__dict__ = dict_proxy({'__module__': 'fastr.utils.classproperty', '__dict__': <attribute '__dict__' of 'ClassPropertyDescriptor' objects>, '__weakref__': <attribute '__weakref__' of 'ClassPropertyDescriptor' objects>, '__doc__': '\n A descriptor that can act like a property for a class.\n ', '__init__': <function __init__>, '__get__': <function __get__>})

__get__(obj, cls=None)

__init__(fget)

__module__ = 'fastr.utils.classproperty'

__weakref__

list of weak references to the object (if defined)

fastr.utils.classproperty.classproperty(func)

Decorator to create a “class property”

Parameters:func – the function to wrap Returns:a class property Return type:ClassPropertyDescriptor

clear_pycs Module

A small tool to wipe all .pyc files from fastr

fastr.utils.clear_pycs.dir_list(directory)

Find all .pyc files

Parameters:directory (str) – directory to search Returns:all .pyc files Return type:list

fastr.utils.clear_pycs.main()

Main entry poitn

compare Module

Module to compare various fastr specific things such as a execution directory or a reference directory.

fastr.utils.compare.compare_execution_dir(path1, path2)

dicteq Module

Some helper function to compare dictionaries and find the parts of the dict that are different. This is mostly to help in debugging.

fastr.utils.dicteq.dicteq(self, other)

Compare two dicts for equality

Parameters:

• self – the first object to compare
• other – the oth

Returns:

fastr.utils.dicteq.diffdict(self, other, path=None, visited=None)

Find the differences in two dictionaries.

Parameters:

• self – the first object to compare
• other (dict) – other dictionary
• path (list) – the path for nested dicts (too keep track of recursion)

Returns:

list of messages indicating the differences

Return type:

list

fastr.utils.dicteq.diffobj(self, other, path=None, visited=None)

Compare two objects by comparing their __dict__ entries

Parameters:

• self – the first object to compare
• other – other objects to compare
• path (list) – the path for nested dicts (too keep track of recursion)

Returns:

list of messages

Return type:

list

fastr.utils.dicteq.diffobj_str(self, other)

Compare two objects by comparing their __dict__ entries, but returns the differences in a single string ready for logging.

Parameters:

• self – the first object to compare
• other – other object to compare to

Returns:

the description of the differences

Return type:

str

gettools Module

fastr.utils.gettools.main()

iohelpers Module

fastr.utils.iohelpers.load_gpickle(path, retry_scheme=None)

fastr.utils.iohelpers.load_json(path)

fastr.utils.iohelpers.save_gpickle(path, data)

fastr.utils.iohelpers.save_json(path, data, indent=2)

jsonschemaparser Module

The JSON schema parser validates a json data structure and if possible casts data to the correct type and fills out default values. The result in a valid document that can be used to construct objects.

class fastr.utils.jsonschemaparser.FastrRefResolver(base_uri, referrer, store=(), cache_remote=True, handlers=())

Bases: jsonschema.validators.RefResolver

Adapted version of the RefResolver for handling inter-file references more to our liking

__init__(base_uri, referrer, store=(), cache_remote=True, handlers=())

Create a new FastrRefResolver

Parameters:

• base_uri (str) – URI of the referring document
• referrer – the actual referring document
• store (dict) – a mapping from URIs to documents to cache
• cache_remote (bool) – whether remote refs should be cached after first resolution
• handlers (dict) – a mapping from URI schemes to functions that should be used to retrieve them

__module__ = 'fastr.utils.jsonschemaparser'

classmethod from_schema(schema, *args, **kwargs)

Instantiate a RefResolver based on a schema

static readfastrschema(name)

Open a json file based on a fastr:// url that points to a file in the fastr.schemadir

Parameters:name (str) – the url of the file to open Returns:the resulting json schema data

static readfile(filename)

Open a json file based on a simple filename

Parameters:filename (str) – the path of the file to read Returns:the resulting json schema data

fastr.utils.jsonschemaparser.any_of_draft4(validator, any_of, instance, schema)

The oneOf directory needs to be done stepwise, because a validation even if it fails will try to change types / set defaults etc. Therefore we first create a copy of the data per subschema and test if they match. Then for all the schemas that are valid, we perform the validation on the actual data so that only the valid subschemas will effect the data.

Parameters:

• validator – the json schema validator
• any_of (dict) – the current oneOf
• instance – the current object instance
• schema (dict) – the current json schema

fastr.utils.jsonschemaparser.extend(validator_cls)

Extend the given jsonschema.IValidator with the Seep layer.

fastr.utils.jsonschemaparser.getblueprinter(uri, blueprint=None)

Instantiate the given data using the blueprinter.

Parameters:blueprint – a blueprint (JSON Schema with Seep properties)

fastr.utils.jsonschemaparser.items_prevalidate(validator, items, instance, schema)

The pre-validation function for items

Parameters:

• validator – the json schema validator
• items (dict) – the current items
• instance – the current object instance
• schema (dict) – the current json schema

fastr.utils.jsonschemaparser.not_draft4(validator, not_schema, instance, schema)

The not needs to use a temporary copy of the instance, not to change the instance with the invalid schema

Parameters:

• validator – the json schema validator
• not_schema (dict) – the current oneOf
• instance – the current object instance
• schema (dict) – the current json schema

fastr.utils.jsonschemaparser.one_of_draft4(validator, one_of, instance, schema)

The one_of directory needs to be done stepwise, because a validation even if it fails will try to change types / set defaults etc. Therefore we first create a copy of the data per subschema and test if they match. Once we found a proper match, we only validate that branch on the real data so that only the valid piece of schema will effect the data.

Parameters:

• validator – the json schema validator
• one_of (dict) – the current one_of
• instance – the current object instance
• schema (dict) – the current json schema

fastr.utils.jsonschemaparser.pattern_properties_prevalid(validator, pattern_properties, instance, schema)

The pre-validation function for patternProperties

Parameters:

• validator – the json schema validator
• pattern_properties (dict) – the current patternProperties
• instance (dict) – the current object instance
• schema (dict) – the current json schema

fastr.utils.jsonschemaparser.properties_postvalidate(validator, properties, instance, schema)

# All arguments must be used because this function is called like this # pylint: disable=unused-argument The post-validation function for properties

Parameters:

• validator – the json schema validator
• properties (dict) – the current properties
• instance – the current object instance
• schema (dict) – the current json schema

fastr.utils.jsonschemaparser.properties_prevalidate(validator, properties, instance, schema)

The pre-validation function for properties

Parameters:

• validator – the json schema validator
• properties (dict) – the current properties
• instance – the current object instance
• schema (dict) – the current json schema

multiprocesswrapper Module

fastr.utils.multiprocesswrapper.function_wrapper(filepath, fnc_name, *args, **kwargs)

pim_publisher Module

class fastr.utils.pim_publisher.PimPublisher(uri=None)

Bases: object

Class to publish to PIM

PIM_STATUS_MAPPING = {<JobState.execution_done: ('execution_done', 'in_progress', False)>: 'running', <JobState.nonexistent: ('nonexistent', 'idle', False)>: 'unknown', <JobState.running: ('running', 'in_progress', False)>: 'running', <JobState.processing_callback: ('processing_callback', 'in_progress', False)>: 'running', <JobState.hold: ('hold', 'idle', False)>: 'idle', <JobState.created: ('created', 'idle', False)>: 'idle', <JobState.finished: ('finished', 'done', False)>: 'success', <JobState.failed: ('failed', 'done', True)>: 'failed', <JobState.execution_failed: ('execution_failed', 'in_progress', True)>: 'running', <JobState.cancelled: ('cancelled', 'done', True)>: 'failed', <JobState.queued: ('queued', 'idle', False)>: 'idle'}

__dict__ = dict_proxy({'__dict__': <attribute '__dict__' of 'PimPublisher' objects>, '__module__': 'fastr.utils.pim_publisher', '__init__': <function __init__>, 'PIM_STATUS_MAPPING': {<JobState.execution_done: ('execution_done', 'in_progress', False)>: 'running', <JobState.nonexistent: ('nonexistent', 'idle', False)>: 'unknown', <JobState.running: ('running', 'in_progress', False)>: 'running', <JobState.processing_callback: ('processing_callback', 'in_progress', False)>: 'running', <JobState.hold: ('hold', 'idle', False)>: 'idle', <JobState.created: ('created', 'idle', False)>: 'idle', <JobState.finished: ('finished', 'done', False)>: 'success', <JobState.failed: ('failed', 'done', True)>: 'failed', <JobState.execution_failed: ('execution_failed', 'in_progress', True)>: 'running', <JobState.cancelled: ('cancelled', 'done', True)>: 'failed', <JobState.queued: ('queued', 'idle', False)>: 'idle'}, 'pim_register_run': <function pim_register_run>, 'pim_serialize_network': <staticmethod object>, '__weakref__': <attribute '__weakref__' of 'PimPublisher' objects>, '__doc__': '\n Class to publish to PIM\n ', 'pim_update_status': <function pim_update_status>})

__init__(uri=None)

__module__ = 'fastr.utils.pim_publisher'

__weakref__

list of weak references to the object (if defined)

pim_register_run(network)

static pim_serialize_network(network)

Serialize Network in the correct for to use with PIM.

Returns:json data for PIM

pim_update_status(job)

procutils Module

fastr.utils.procutils.which(name)

Find executable by name on the PATH, returns the executable that will be

found in case it is used for a Popen call

rest_generation Module

fastr.utils.rest_generation.create_rest_table(data, headers)

Create a ReST table from data. The data should be a list of columns and the headers should be a list of column names.

Parameters:

• data (list) – List of lists/tuples representing the columns
• headers (list) – List of strings for the column names

Returns:

a string representing the table in ReST

Return type:

str

schematotable Module

A module to generate reStructuredText tables from json schema files

class fastr.utils.schematotable.SchemaPrinter(schema, skipfirst=False)

Bases: object

Object that create a table in reStructuedText from a json schema

__dict__ = dict_proxy({'parse': <function parse>, '__module__': 'fastr.utils.schematotable', 'descend': <function descend>, '__dict__': <attribute '__dict__' of 'SchemaPrinter' objects>, '__weakref__': <attribute '__weakref__' of 'SchemaPrinter' objects>, '__str__': <function __str__>, 'printlines': <function printlines>, '__doc__': '\n Object that create a table in reStructuedText from a json schema\n ', '__init__': <function __init__>})

__init__(schema, skipfirst=False)

Create the printer object

Parameters:

• schema (dict) – the json schema to print
• skipfirst (bool) – flag to indicate that the first line should not be printed

__module__ = 'fastr.utils.schematotable'

__str__()

String representation of json schema (that is the printed table)

__weakref__

list of weak references to the object (if defined)

descend(properties)

Descend into a subschema

Parameters:properties (dict) – the properties in the subschema

parse(schema=None)

Parse a schema

Parameters:schema (dict) – the schema to parse

printlines()

Given a parsed schema (parsing happens when the object is constructed), print all the lines

Returns:the printed table Return type:str

sysinfo Module

This module contains function to help gather system information use for the provenance of the Job execution.

fastr.utils.sysinfo.get_cpu_usage()

Get the current CPU usage

Returns:CPU usage info Return type:dict

fastr.utils.sysinfo.get_drmaa_info()

Get information about the SGE cluster (if applicable)

Returns:cluster info Return type:dict

fastr.utils.sysinfo.get_hostinfo()

Get all information about the current host machine

Returns:host info Return type:dict

fastr.utils.sysinfo.get_memory_usage()

Get the current memory usage

Returns:memory usage info Return type:dict

fastr.utils.sysinfo.get_mounts()

Get the current mounts known on the system

Returns:mount info Return type:dict

fastr.utils.sysinfo.get_os()

Get information about the OS

Returns:OS information Return type:dict

fastr.utils.sysinfo.get_processes()

Get a list of all currently running processes

Returns:process information Return type:list

fastr.utils.sysinfo.get_python()

Get information about the currently used Python implementation

Returns:python info Return type:dict

fastr.utils.sysinfo.get_sysinfo()

Get system information (cpu, memory, mounts and users)

Returns:system information Return type:dict

fastr.utils.sysinfo.get_users()

Get current users on the system

Returns:user info Return type:dict

fastr.utils.sysinfo.namedtuple_to_dict(ntuple)

Helper function to convert a named tuple into a dict

Parameters:ntuple (namedtuple) – the namedtuple to convert Returns:named tuple as a dict Return type:dict

verify Module

fastr.utils.verify.verify_resource_loading(filename)

fastr.utils.verify.verify_tool(filename)

Verify that a file

xmltodict Module

This module contains tool for converting python dictionaries into XML object and vice-versa.

fastr.utils.xmltodict.dump(data, filehandle)

Write a dict to an XML file

Parameters:

• data – data to write
• filehandle – file handle to write to

fastr.utils.xmltodict.dumps(data)

Write a dict to an XML string

Parameters:data – data to write Returns:the XML data Return type:str

fastr.utils.xmltodict.load(filehandle)

Load an xml file and parse it to a dict

Parameters:filehandle – file handle to load Returns:the parsed data

fastr.utils.xmltodict.loads(data)

Load an xml string and parse it to a dict

Parameters:data (str) – the xml data to load Returns:the parsed data

Subpackages

cmd Package

cmd Package

fastr.utils.cmd.find_commands()

fastr.utils.cmd.get_command_module(command)

fastr.utils.cmd.main()

fastr.utils.cmd.print_help(commands=None)

cat Module

fastr.utils.cmd.cat.fastr_cat(infile, path)

fastr.utils.cmd.cat.get_parser()

fastr.utils.cmd.cat.main()

Print information from a job file

execute Module

fastr.utils.cmd.execute.get_parser()

fastr.utils.cmd.execute.main()

Execute a fastr job file

extract_argparse Module

fastr.utils.cmd.extract_argparse.cardinality_from_nargs(value)

fastr.utils.cmd.extract_argparse.datatype_from_type(type_, metavar)

fastr.utils.cmd.extract_argparse.extract_argparser(filepath)

fastr.utils.cmd.extract_argparse.find_argparser(entry, basename='/home/docs/checkouts/readthedocs.org/user_builds/fastr/envs/1.2.2/bin/sphinx-build')

fastr.utils.cmd.extract_argparse.get_parser()

fastr.utils.cmd.extract_argparse.main()

Create a stub for a Tool based on a python script using argparse

prov Module

fastr.utils.cmd.prov.get_parser()

fastr.utils.cmd.prov.get_prov_document(result)

fastr.utils.cmd.prov.main()

Get PROV information from the result pickle.

run Module

fastr.utils.cmd.run.create_network_parser(network)

fastr.utils.cmd.run.get_parser()

fastr.utils.cmd.run.main()

Run a Network from the commandline

testtool Module

fastr.utils.cmd.testtool.get_parser()

fastr.utils.cmd.testtool.main()

Run the tests of a tool to verify the proper function

trace Module

fastr.utils.cmd.trace.get_parser()

fastr.utils.cmd.trace.main()

Trace samples/sinks from a run

fastr.utils.cmd.trace.print_job_result(job_file)

fastr.utils.cmd.trace.print_sample_node(sink_data, dirname, sample_node_tuples, verbose)

fastr.utils.cmd.trace.print_sample_sink(sink_data, dirname, sample_sink_tuples, verbose)

fastr.utils.cmd.trace.print_samples(sink_data, sample_ids, verbose)

fastr.utils.cmd.trace.print_sinks(sink_data, sink_ids, verbose)

fastr.utils.cmd.trace.read_sink_data(infile)

fastr.utils.cmd.trace.switch_sample_sink(sink_data)

verify Module

fastr.utils.cmd.verify.get_parser()

fastr.utils.cmd.verify.main()

Print information from a job file

webapp Module

fastr.utils.cmd.webapp.get_parser()

fastr.utils.cmd.webapp.main()

Start the fastr webapp and open in a new browser tab

fastr.utils.cmd.webapp.open_url(url)

web Package

web Package

api Module

class fastr.web.api.NetworkApi(api=None, *args, **kwargs)

Bases: flask_restplus.resource.Resource

__module__ = 'fastr.web.api'

endpoint = 'api_network'

get(id_)

Get a Network json description from the server

mediatypes(resource_cls)

methods = ['GET']

class fastr.web.api.NetworkListApi(api=None, *args, **kwargs)

Bases: flask_restplus.resource.Resource

__module__ = 'fastr.web.api'

endpoint = 'api_networks'

get(*args, **kwargs)

Get a list of the networks

mediatypes(resource_cls)

methods = ['GET']

class fastr.web.api.ObjectUrl(object_classs, **kwargs)

Bases: flask_restplus.fields.Raw

__init__(object_classs, **kwargs)

__module__ = 'fastr.web.api'

__schema_type__ = 'string'

__slotnames__ = []

format(value)

class fastr.web.api.Run(id_, network, source_data, sink_data)

Bases: object

__dict__ = dict_proxy({'status': <function status>, '__module__': 'fastr.web.api', 'abort': <function abort>, '__dict__': <attribute '__dict__' of 'Run' objects>, '__weakref__': <attribute '__weakref__' of 'Run' objects>, '__doc__': None, '__init__': <function __init__>, 'run_network': <function run_network>})

__init__(id_, network, source_data, sink_data)

__module__ = 'fastr.web.api'

__weakref__

list of weak references to the object (if defined)

abort()

run_network(network, source_data, sink_data, abort_lock)

status()

class fastr.web.api.RunApi(api=None, *args, **kwargs)

Bases: flask_restplus.resource.Resource

Run API documentation

__module__ = 'fastr.web.api'

delete(id_)

Abort a Network run and stop all associated execution

endpoint = 'api_run'

get(*args, **kwargs)

Get information about a Network run

mediatypes(resource_cls)

methods = ['DELETE', 'GET']

class fastr.web.api.RunListApi(api=None, *args, **kwargs)

Bases: flask_restplus.resource.Resource

__module__ = 'fastr.web.api'

endpoint = 'api_runs'

get(*args, **kwargs)

Get a list of all Network runs on the server

mediatypes(resource_cls)

methods = ['GET', 'POST']

post()

Create a new Network run and start execution

request_parser = <flask_restplus.reqparse.RequestParser object>

class fastr.web.api.StatusApi(api=None, *args, **kwargs)

Bases: flask_restplus.resource.Resource

__module__ = 'fastr.web.api'

endpoint = 'api_status'

get(id_)

Get the status of a Network Run on the server

mediatypes(resource_cls)

methods = ['GET']

class fastr.web.api.SubUrl(object_classs, subfield, **kwargs)

Bases: flask_restplus.fields.Raw

__init__(object_classs, subfield, **kwargs)

__module__ = 'fastr.web.api'

__schema_type__ = 'string'

format(value)

class fastr.web.api.ToolApi(api=None, *args, **kwargs)

Bases: flask_restplus.resource.Resource

__module__ = 'fastr.web.api'

endpoint = 'api_version_tool'

get(id_, version=None)

Get a Tool json description from the server

mediatypes(resource_cls)

methods = ['GET']

class fastr.web.api.ToolListApi(api=None, *args, **kwargs)

Bases: flask_restplus.resource.Resource

__module__ = 'fastr.web.api'

endpoint = 'api_tools'

get(*args, **kwargs)

Get a list of all Tools known to the server

mediatypes(resource_cls)

methods = ['GET']

fastr.web.api.network_lock_thread(lock, network)

fastr.web.api.network_runner(network, source_data, sink_data, chuck_status, job_status, job_results, abort_lock)

fastr.web.api.update_job_result(job, job_status, job_results)

fastr.web.api.update_status(job, job_status)

run Module

fastr.web.run.main()

fastr.web.run.runapp(debug=False)

views Module

fastr.web.views.doc()

fastr.web.views.index()

fastr.web.views.network(name=None)

fastr.web.views.networks()

fastr.web.views.prov()

fastr.web.views.shutdown()

fastr.web.views.shutdown_server()

fastr.web.views.tool(toolname=None, version=None)

fastr.web.views.websocket_client()

Indices and tables

• Index
• Module Index
• Search Page