mia_processes.bricks.preprocess.afni package

The atomic calculations from afni.

Submodules

mia_processes.bricks.preprocess.afni.processes module

The afni preprocess library of the mia_processes package.

The purpose of this module is to customise the main afni preprocessing bricks provided by nipype and to correct some things that do not work directly in populse_mia.

Contains:
Class:

  • Automask

  • Calc

  • CalcDropTRs

  • Despike

  • FWHMx

  • GCOR

  • OutlierCount

  • QualityIndex

  • RefitDeoblique

  • SkullStrip

  • TShift

  • TStatMean

  • Volreg

class mia_processes.bricks.preprocess.afni.processes.Automask[source]

Bases: ProcessMIA

Create a brain-only mask of the image using AFNI 3dAutomask command

Please, see the complete documentation for the Automask brick in the mia_processes website

Note

  • Type ‘Automask.help()’ for a full description of this process parameters.

  • Type ‘<Automask>.get_input_spec()’ for a full description of this process input trait types.

  • Type ‘<Automask>.get_output_spec()’ for a full description of this process output trait types.

__init__()[source]

Dedicated to the attributes initialisation/instantiation.

The input and output plugs are defined here. The special ‘self.requirement’ attribute (optional) is used to define the third-party products necessary for the running of the brick.

list_outputs(is_plugged=None, iteration=False)[source]

Dedicated to the initialisation step of the brick.

The main objective of this method is to produce the outputs of the bricks (self.outputs) and the associated tags (self.inheritance_dic), if defined here. In order not to include an output in the database, this output must be a value of the optional key ‘notInDb’ of the self.outputs dictionary. To work properly this method must return self.make_initResult() object.

Parameters:

  • is_plugged – the state, linked or not, of the plugs.

  • iteration – the state, iterative or not, of the process.

Returns:

a dictionary with requirement, outputs and inheritance_dict.

run_process_mia()[source]

Dedicated to the process launch step of the brick.

class mia_processes.bricks.preprocess.afni.processes.Calc[source]

Bases: ProcessMIA

Voxel-by-voxel arithmetic on 3D datasets

Please, see the complete documentation for the Calc brick in the mia_processes website

Note

  • Type ‘Calc.help()’ for a full description of this process parameters.

  • Type ‘<Calc>.get_input_spec()’ for a full description of this process input trait types.

  • Type ‘<Calc>.get_output_spec()’ for a full description of this process output trait types.

__init__()[source]

Dedicated to the attributes initialisation/instantiation.

The input and output plugs are defined here. The special ‘self.requirement’ attribute (optional) is used to define the third-party products necessary for the running of the brick.

list_outputs(is_plugged=None, iteration=False)[source]

Dedicated to the initialisation step of the brick.

The main objective of this method is to produce the outputs of the bricks (self.outputs) and the associated tags (self.inheritance_dic), if defined here. In order not to include an output in the database, this output must be a value of the optional key ‘notInDb’ of the self.outputs dictionary. To work properly this method must return self.make_initResult() object.

Parameters:

  • is_plugged – the state, linked or not, of the plugs.

  • iteration – the state, iterative or not, of the process.

Returns:

a dictionary with requirement, outputs and inheritance_dict.

run_process_mia()[source]

Dedicated to the process launch step of the brick.

class mia_processes.bricks.preprocess.afni.processes.CalcDropTRs[source]

Bases: ProcessMIA

DropTRs of bold datasets (using AFNI 3dCalc command)

Please, see the complete documentation for the CalcDropTRs brick in the mia_processes website

Note

  • Type ‘CalcDropTRs.help()’ for a full description of this process parameters.

  • Type ‘<CalcDropTRs>.get_input_spec()’ for a full description of this process input trait types.

  • Type ‘<CalcDropTRs>.get_output_spec()’ for a full description of this process output trait types.

__init__()[source]

Dedicated to the attributes initialisation/instantiation.

The input and output plugs are defined here. The special ‘self.requirement’ attribute (optional) is used to define the third-party products necessary for the running of the brick.

list_outputs(is_plugged=None, iteration=False)[source]

Dedicated to the initialisation step of the brick.

The main objective of this method is to produce the outputs of the bricks (self.outputs) and the associated tags (self.inheritance_dic), if defined here. In order not to include an output in the database, this output must be a value of the optional key ‘notInDb’ of the self.outputs dictionary. To work properly this method must return self.make_initResult() object.

Parameters:

  • is_plugged – the state, linked or not, of the plugs.

  • iteration – the state, iterative or not, of the process.

Returns:

a dictionary with requirement, outputs and inheritance_dict.

run_process_mia()[source]

Dedicated to the process launch step of the brick.

class mia_processes.bricks.preprocess.afni.processes.Despike[source]

Bases: ProcessMIA

Removes ‘spikes’ from the 3D+time input dataset

Please, see the complete documentation for the Despike brick in the mia_processes website

Note

  • Type ‘Despike.help()’ for a full description of this process parameters.

  • Type ‘<Despike>.get_input_spec()’ for a full description of this process input trait types.

  • Type ‘<Despike>.get_output_spec()’ for a full description of this process output trait types.

__init__()[source]

Dedicated to the attributes initialisation/instantiation.

The input and output plugs are defined here. The special ‘self.requirement’ attribute (optional) is used to define the third-party products necessary for the running of the brick.

list_outputs(is_plugged=None, iteration=False)[source]

Dedicated to the initialisation step of the brick.

The main objective of this method is to produce the outputs of the bricks (self.outputs) and the associated tags (self.inheritance_dic), if defined here. In order not to include an output in the database, this output must be a value of the optional key ‘notInDb’ of the self.outputs dictionary. To work properly this method must return self.make_initResult() object.

Parameters:

  • is_plugged – the state, linked or not, of the plugs.

  • iteration – the state, iterative or not, of the process.

Returns:

a dictionary with requirement, outputs and inheritance_dict.

run_process_mia()[source]

Dedicated to the process launch step of the brick.

class mia_processes.bricks.preprocess.afni.processes.FWHMx[source]

Bases: ProcessMIA

Computes FWHMs for all sub-bricks in the input dataset, each one separately

Please, see the complete documentation for the FWHMx brick in the mia_processes website

Note

  • Type ‘FWHMx.help()’ for a full description of this process parameters.

  • Type ‘<FWHMx>.get_input_spec()’ for a full description of this process input trait types.

  • Type ‘<FWHMx>.get_output_spec()’ for a full description of this process output trait types.

__init__()[source]

Dedicated to the attributes initialisation/instantiation.

The input and output plugs are defined here. The special ‘self.requirement’ attribute (optional) is used to define the third-party products necessary for the running of the brick.

list_outputs(is_plugged=None, iteration=False)[source]

Dedicated to the initialisation step of the brick.

The main objective of this method is to produce the outputs of the bricks (self.outputs) and the associated tags (self.inheritance_dic), if defined here. In order not to include an output in the database, this output must be a value of the optional key ‘notInDb’ of the self.outputs dictionary. To work properly this method must return self.make_initResult() object.

Parameters:

  • is_plugged – the state, linked or not, of the plugs.

  • iteration – the state, iterative or not, of the process.

Returns:

a dictionary with requirement, outputs and inheritance_dict.

run_process_mia()[source]

Dedicated to the process launch step of the brick.

class mia_processes.bricks.preprocess.afni.processes.GCOR[source]

Bases: ProcessMIA

Computes the average correlation between every voxel and every other voxel, over any given mask

Please, see the complete documentation for the GCOR brick in the mia_processes website

Note

  • Type ‘GCOR.help()’ for a full description of this process parameters.

  • Type ‘<GCOR>.get_input_spec()’ for a full description of this process input trait types.

  • Type ‘<GCOR>.get_output_spec()’ for a full description of this process output trait types.

__init__()[source]

Dedicated to the attributes initialisation/instantiation.

The input and output plugs are defined here. The special ‘self.requirement’ attribute (optional) is used to define the third-party products necessary for the running of the brick.

list_outputs(is_plugged=None, iteration=False)[source]

Dedicated to the initialisation step of the brick.

The main objective of this method is to produce the outputs of the bricks (self.outputs) and the associated tags (self.inheritance_dic), if defined here. In order not to include an output in the database, this output must be a value of the optional key ‘notInDb’ of the self.outputs dictionary. To work properly this method must return self.make_initResult() object.

Parameters:

  • is_plugged – the state, linked or not, of the plugs.

  • iteration – the state, iterative or not, of the process.

Returns:

a dictionary with requirement, outputs and inheritance_dict.

run_process_mia()[source]

Dedicated to the process launch step of the brick.

class mia_processes.bricks.preprocess.afni.processes.OutlierCount[source]

Bases: ProcessMIA

Computes outliers for all sub-bricks in the input dataset, each one separately

Please, see the complete documentation for the OutlierCount brick in the mia_processes website

Note

  • Type ‘OutlierCount.help()’ for a full description of this process parameters.

  • Type ‘<OutlierCount>.get_input_spec()’ for a full description of this process input trait types.

  • Type ‘<OutlierCount>.get_output_spec()’ for a full description of this process output trait types.

__init__()[source]

Dedicated to the attributes initialisation/instantiation.

The input and output plugs are defined here. The special ‘self.requirement’ attribute (optional) is used to define the third-party products necessary for the running of the brick.

list_outputs(is_plugged=None, iteration=False)[source]

Dedicated to the initialisation step of the brick.

The main objective of this method is to produce the outputs of the bricks (self.outputs) and the associated tags (self.inheritance_dic), if defined here. In order not to include an output in the database, this output must be a value of the optional key ‘notInDb’ of the self.outputs dictionary. To work properly this method must return self.make_initResult() object.

Parameters:

  • is_plugged – the state, linked or not, of the plugs.

  • iteration – the state, iterative or not, of the process.

Returns:

a dictionary with requirement, outputs and inheritance_dict.

run_process_mia()[source]

Dedicated to the process launch step of the brick.

class mia_processes.bricks.preprocess.afni.processes.QualityIndex[source]

Bases: ProcessMIA

Computes a quality index for each sub-brick in a 3D+time dataset

Please, see the complete documentation for the QualityIndex brick in the mia_processes website

Note

  • Type ‘QualityIndex.help()’ for a full description of this process parameters.

  • Type ‘<QualityIndex>.get_input_spec()’ for a full description of this process input trait types.

  • Type ‘<QualityIndex>.get_output_spec()’ for a full description of this process output trait types.

__init__()[source]

Dedicated to the attributes initialisation/instantiation.

The input and output plugs are defined here. The special ‘self.requirement’ attribute (optional) is used to define the third-party products necessary for the running of the brick.

list_outputs(is_plugged=None, iteration=False)[source]

Dedicated to the initialisation step of the brick.

The main objective of this method is to produce the outputs of the bricks (self.outputs) and the associated tags (self.inheritance_dic), if defined here. In order not to include an output in the database, this output must be a value of the optional key ‘notInDb’ of the self.outputs dictionary. To work properly this method must return self.make_initResult() object.

Parameters:

  • is_plugged – the state, linked or not, of the plugs.

  • iteration – the state, iterative or not, of the process.

Returns:

a dictionary with requirement, outputs and inheritance_dict.

run_process_mia()[source]

Dedicated to the process launch step of the brick.

class mia_processes.bricks.preprocess.afni.processes.RefitDeoblique[source]

Bases: ProcessMIA

Deoblique dataset

Please, see the complete documentation for the RefitDeoblique brick in the mia_processes website

Note

  • Type ‘RefitDeoblique.help()’ for a full description of this process parameters.

  • Type ‘<RefitDeoblique>.get_input_spec()’ for a full description of this process input trait types.

  • Type ‘<RefitDeoblique>.get_output_spec()’ for a full description of this process output trait types.

__init__()[source]

Dedicated to the attributes initialisation/instantiation.

The input and output plugs are defined here. The special ‘self.requirement’ attribute (optional) is used to define the third-party products necessary for the running of the brick.

list_outputs(is_plugged=None, iteration=False)[source]

Dedicated to the initialisation step of the brick.

The main objective of this method is to produce the outputs of the bricks (self.outputs) and the associated tags (self.inheritance_dic), if defined here. In order not to include an output in the database, this output must be a value of the optional key ‘notInDb’ of the self.outputs dictionary. To work properly this method must return self.make_initResult() object.

Parameters:

  • is_plugged – the state, linked or not, of the plugs.

  • iteration – the state, iterative or not, of the process.

Returns:

a dictionary with requirement, outputs and inheritance_dict.

run_process_mia()[source]

Dedicated to the process launch step of the brick.

class mia_processes.bricks.preprocess.afni.processes.SkullStrip[source]

Bases: ProcessMIA

From MRI T1-weighted images, extract the brain from surrounding tissue

Please, see the complete documentation for the SkullStrip brick in the mia_processes website

Note

  • Type ‘SkullStrip.help()’ for a full description of this process parameters.

  • Type ‘<SkullStrip>.get_input_spec()’ for a full description of this process input trait types.

  • Type ‘<SkullStrip>.get_output_spec()’ for a full description of this process output trait types.

__init__()[source]

Dedicated to the attributes initialisation/instantiation.

The input and output plugs are defined here. The special ‘self.requirement’ attribute (optional) is used to define the third-party products necessary for the running of the brick.

list_outputs(is_plugged=None, iteration=False)[source]

Dedicated to the initialisation step of the brick.

The main objective of this method is to produce the outputs of the bricks (self.outputs) and the associated tags (self.inheritance_dic), if defined here. In order not to include an output in the database, this output must be a value of the optional key ‘notInDb’ of the self.outputs dictionary. To work properly this method must return self.make_initResult() object.

Parameters:

  • is_plugged – the state, linked or not, of the plugs.

  • iteration – the state, iterative or not, of the process.

Returns:

a dictionary with requirement, outputs and inheritance_dict.

run_process_mia()[source]

Dedicated to the process launch step of the brick.

class mia_processes.bricks.preprocess.afni.processes.TShift[source]

Bases: ProcessMIA

Slice-time correction of bold images

Please, see the complete documentation for the TShift brick in the mia_processes website

Note

  • Type ‘TShift.help()’ for a full description of this process parameters.

  • Type ‘<TShift>.get_input_spec()’ for a full description of this process input trait types.

  • Type ‘<TShift>.get_output_spec()’ for a full description of this process output trait types.

__init__()[source]

Dedicated to the attributes initialisation/instantiation.

The input and output plugs are defined here. The special ‘self.requirement’ attribute (optional) is used to define the third-party products necessary for the running of the brick.

list_outputs(is_plugged=None, iteration=False)[source]

Dedicated to the initialisation step of the brick.

The main objective of this method is to produce the outputs of the bricks (self.outputs) and the associated tags (self.inheritance_dic), if defined here. In order not to include an output in the database, this output must be a value of the optional key ‘notInDb’ of the self.outputs dictionary. To work properly this method must return self.make_initResult() object.

Parameters:

  • is_plugged – the state, linked or not, of the plugs.

  • iteration – the state, iterative or not, of the process.

Returns:

a dictionary with requirement, outputs and inheritance_dict.

run_process_mia()[source]

Dedicated to the process launch step of the brick.

class mia_processes.bricks.preprocess.afni.processes.TStatMean[source]

Bases: ProcessMIA

Mean of bold images (using AFNI 3dTstat)

Please, see the complete documentation for the TStatMean brick in the mia_processes website

Note

  • Type ‘TStatMean.help()’ for a full description of this process parameters.

  • Type ‘<TStatMean>.get_input_spec()’ for a full description of this process input trait types.

  • Type ‘<TStatMean>.get_output_spec()’ for a full description of this process output trait types.

__init__()[source]

Dedicated to the attributes initialisation/instantiation.

The input and output plugs are defined here. The special ‘self.requirement’ attribute (optional) is used to define the third-party products necessary for the running of the brick.

list_outputs(is_plugged=None, iteration=False)[source]

Dedicated to the initialisation step of the brick.

The main objective of this method is to produce the outputs of the bricks (self.outputs) and the associated tags (self.inheritance_dic), if defined here. In order not to include an output in the database, this output must be a value of the optional key ‘notInDb’ of the self.outputs dictionary. To work properly this method must return self.make_initResult() object.

Parameters:

  • is_plugged – the state, linked or not, of the plugs.

  • iteration – the state, iterative or not, of the process.

Returns:

a dictionary with requirement, outputs and inheritance_dict.

run_process_mia()[source]

Dedicated to the process launch step of the brick.

class mia_processes.bricks.preprocess.afni.processes.Volreg[source]

Bases: ProcessMIA

Register an input volume to a base volume using AFNI 3dvolreg

Please, see the complete documentation for the Volreg brick in the mia_processes website

Note

  • Type ‘Volreg.help()’ for a full description of this process parameters.

  • Type ‘<Volreg>.get_input_spec()’ for a full description of this process input trait types.

  • Type ‘<Volreg>.get_output_spec()’ for a full description of this process output trait types.

__init__()[source]

Dedicated to the attributes initialisation/instantiation.

The input and output plugs are defined here. The special ‘self.requirement’ attribute (optional) is used to define the third-party products necessary for the running of the brick.

list_outputs(is_plugged=None, iteration=False)[source]

Dedicated to the initialisation step of the brick.

The main objective of this method is to produce the outputs of the bricks (self.outputs) and the associated tags (self.inheritance_dic), if defined here. In order not to include an output in the database, this output must be a value of the optional key ‘notInDb’ of the self.outputs dictionary. To work properly this method must return self.make_initResult() object.

Parameters:

  • is_plugged – the state, linked or not, of the plugs.

  • iteration – the state, iterative or not, of the process.

Returns:

a dictionary with requirement, outputs and inheritance_dict.

run_process_mia()[source]

Dedicated to the process launch step of the brick.