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# algorithms.modelgen

## SpecifyModel

Link to code

Makes a model specification compatible with spm/fsl designers.

The subject_info field should contain paradigm information in the form of a Bunch or a list of Bunch. The Bunch should contain the following information:

    [Mandatory]

    - conditions : list of names
    - onsets : lists of onsets corresponding to each condition
    - durations : lists of durations corresponding to each condition. Should be left to a single 0 if all events are being modelled as impulses.

    [Optional]
    - regressor_names : list of str
        list of names corresponding to each column. Should be None if
        automatically assigned.
    - regressors : list of lists
       values for each regressor - must correspond to the number of
       volumes in the functional run
    - amplitudes : lists of amplitudes for each event. This will be ignored by
      SPM's Level1Design.

    The following two (tmod, pmod) will be ignored by any Level1Design class
    other than SPM:

    - tmod : lists of conditions that should be temporally modulated. Should
      default to None if not being used.
    - pmod : list of Bunch corresponding to conditions
      - name : name of parametric modulator
      - param : values of the modulator
      - poly : degree of modulation

Alternatively, you can provide information through event files.

The event files have to be in 1, 2 or 3 column format with the columns corresponding to Onsets, Durations and Amplitudes and they have to have the name event_name.runXXX... e.g.: Words.run001.txt. The event_name part will be used to create the condition names.

### Examples

    >>> from nipype.interfaces.base import Bunch
    >>> s = SpecifyModel()
    >>> s.inputs.input_units = 'secs'
    >>> s.inputs.functional_runs = ['functional2.nii', 'functional3.nii']
    >>> s.inputs.time_repetition = 6
    >>> s.inputs.high_pass_filter_cutoff = 128.
    >>> info = [Bunch(conditions=['cond1'], onsets=[[2, 50, 100, 180]], durations=[[1]]),             Bunch(conditions=['cond1'], onsets=[[30, 40, 100, 150]], durations=[[1]])]
    >>> s.inputs.subject_info = info

Using pmod:

    >>> info = [Bunch(conditions=['cond1', 'cond2'], onsets=[[2, 50],[100, 180]], durations=[[0],[0]], pmod=[Bunch(name=['amp'], poly=[2], param=[[1, 2]]), None]),         Bunch(conditions=['cond1', 'cond2'], onsets=[[20, 120],[80, 160]], durations=[[0],[0]], pmod=[Bunch(name=['amp'], poly=[2], param=[[1, 2]]), None])]
    >>> s.inputs.subject_info = info

Inputs:

    [Mandatory]
    event_files: (a list of items which are an existing file name)
            list of event description files 1, 2 or 3 column format corresponding to onsets,
            durations and amplitudes
            mutually_exclusive: subject_info
    functional_runs: (a list of items which are an existing file name or an existing file
             name)
            Data files for model. List of 4D files or list oflist of 3D files per session
    high_pass_filter_cutoff: (a float)
            High-pass filter cutoff in secs
    input_units: ('secs' or 'scans')
            Units of event onsets and durations (secs or scans)Output units are always in secs
    subject_info
            Bunch or List(Bunch) subject specific condition information. see :ref:`SpecifyModel` or
            SpecifyModel.__doc__ for details
            mutually_exclusive: event_files
    time_repetition: (a float)
            Time between the start of one volume to the start of the next image volume.

    [Optional]
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    outlier_files: (an existing file name)
            Files containing scan outlier indices that should be tossed
    realignment_parameters: (an existing file name)
            Realignment parameters returned by motion correction algorithm

Outputs:

    session_info
            session info for level1designs

## SpecifySPMModel

Link to code

Adds SPM specific options to SpecifyModel

> adds:
>   - concatenate_runs
>   - output_units

### Examples

    >>> from nipype.interfaces.base import Bunch
    >>> s = SpecifySPMModel()
    >>> s.inputs.input_units = 'secs'
    >>> s.inputs.output_units = 'scans'
    >>> s.inputs.high_pass_filter_cutoff = 128.
    >>> s.inputs.functional_runs = ['functional2.nii', 'functional3.nii']
    >>> s.inputs.time_repetition = 6
    >>> s.inputs.concatenate_runs = True
    >>> info = [Bunch(conditions=['cond1'], onsets=[[2, 50, 100, 180]], durations=[[1]]),             Bunch(conditions=['cond1'], onsets=[[30, 40, 100, 150]], durations=[[1]])]
    >>> s.inputs.subject_info = info

Inputs:

    [Mandatory]
    event_files: (a list of items which are an existing file name)
            list of event description files 1, 2 or 3 column format corresponding to onsets,
            durations and amplitudes
            mutually_exclusive: subject_info
    functional_runs: (a list of items which are an existing file name or an existing file
             name)
            Data files for model. List of 4D files or list oflist of 3D files per session
    high_pass_filter_cutoff: (a float)
            High-pass filter cutoff in secs
    input_units: ('secs' or 'scans')
            Units of event onsets and durations (secs or scans)Output units are always in secs
    subject_info
            Bunch or List(Bunch) subject specific condition information. see :ref:`SpecifyModel` or
            SpecifyModel.__doc__ for details
            mutually_exclusive: event_files
    time_repetition: (a float)
            Time between the start of one volume to the start of the next image volume.

    [Optional]
    concatenate_runs: (a boolean, nipype default value: False)
            Concatenate all runs to look like a single session.
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    outlier_files: (an existing file name)
            Files containing scan outlier indices that should be tossed
    output_units: ('secs' or 'scans', nipype default value: secs)
            Units of design event onsets and durations (secs or scans)
    realignment_parameters: (an existing file name)
            Realignment parameters returned by motion correction algorithm

Outputs:

    session_info
            session info for level1designs

## SpecifySparseModel

Link to code

Specify a sparse model that is compatible with spm/fsl designers

### References

[1] | Ghosh et al. (2009) OHBM http://dl.dropbox.com/u/363467/OHBM2009_HRF.pdf

### Examples

    >>> from nipype.interfaces.base import Bunch
    >>> s = SpecifySparseModel()
    >>> s.inputs.input_units = 'secs'
    >>> s.inputs.functional_runs = ['functional2.nii', 'functional3.nii']
    >>> s.inputs.time_repetition = 6
    >>> s.inputs.time_acquisition = 2
    >>> s.inputs.high_pass_filter_cutoff = 128.
    >>> s.inputs.model_hrf = True
    >>> info = [Bunch(conditions=['cond1'], onsets=[[2, 50, 100, 180]], durations=[[1]]),             Bunch(conditions=['cond1'], onsets=[[30, 40, 100, 150]], durations=[[1]])]
    >>> s.inputs.subject_info = info

Inputs:

    [Mandatory]
    event_files: (a list of items which are an existing file name)
            list of event description files 1, 2 or 3 column format corresponding to onsets,
            durations and amplitudes
            mutually_exclusive: subject_info
    functional_runs: (a list of items which are an existing file name or an existing file
             name)
            Data files for model. List of 4D files or list oflist of 3D files per session
    high_pass_filter_cutoff: (a float)
            High-pass filter cutoff in secs
    input_units: ('secs' or 'scans')
            Units of event onsets and durations (secs or scans)Output units are always in secs
    subject_info
            Bunch or List(Bunch) subject specific condition information. see :ref:`SpecifyModel` or
            SpecifyModel.__doc__ for details
            mutually_exclusive: event_files
    time_acquisition: (a float)
            Time in seconds to acquire a single image volume
    time_repetition: (a float)
            Time between the start of one volume to the start of the next image volume.

    [Optional]
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    model_hrf: (a boolean)
            model sparse events with hrf
    outlier_files: (an existing file name)
            Files containing scan outlier indices that should be tossed
    realignment_parameters: (an existing file name)
            Realignment parameters returned by motion correction algorithm
    save_plot: (a boolean)
            save plot of sparse design calculation (Requires matplotlib)
    scale_regressors: (a boolean, nipype default value: True)
            Scale regressors by the peak
    scan_onset: (a float, nipype default value: 0.0)
            Start of scanning relative to onset of run in secs
    stimuli_as_impulses: (a boolean, nipype default value: True)
            Treat each stimulus to be impulse like.
    use_temporal_deriv: (a boolean)
            Create a temporal derivative in addition to regular regressor
            requires: model_hrf
    volumes_in_cluster: (an integer >= 1, nipype default value: 1)
            Number of scan volumes in a cluster

Outputs:

    session_info
            session info for level1designs
    sparse_png_file: (a file name)
            PNG file showing sparse design
    sparse_svg_file: (a file name)
            SVG file showing sparse design

## `gcd()`

Link to code

Returns the greatest common divisor of two integers

uses Euclid’s algorithm

    >>> gcd(4, 5)
    ~
    >>> gcd(4, 8)
    ~
    >>> gcd(22, 55)
    ~~

## `gen_info()`

Link to code

Generate subject_info structure from a list of event files

## `orth()`

Link to code

Orthoganlize y_in with respect to x_in

    >>> err = np.abs(np.array(orth([1, 2, 3],[4, 5, 6]) - np.array([1.7142857142857144, 0.42857142857142883, -0.85714285714285676])))
    >>> all(err<np.finfo(float).eps)
    True

## `scale_timings()`

Link to code

Scales timings given input and output units (scans/secs)

### Parameters

timelist: list of times to scale input_units: ‘secs’ or ‘scans’ output_units: Ibid. time_repetition: float in seconds

## `spm_hrf()`

Link to code

python implementation of spm_hrf

see spm_hrf for implementation details

% RT - scan repeat time % p - parameters of the response function (two gamma % functions) % defaults (seconds) % p(0) - delay of response (relative to onset) 6 % p(1) - delay of undershoot (relative to onset) 16 % p(2) - dispersion of response 1 % p(3) - dispersion of undershoot 1 % p(4) - ratio of response to undershoot 6 % p(5) - onset (seconds) 0 % p(6) - length of kernel (seconds) 32 ~ % hrf - hemodynamic response function % p - parameters of the response function

the following code using scipy.stats.distributions.gamma doesn’t return the same result as the spm_Gpdf function hrf = gamma.pdf(u, p[0]/p[2], scale=dt/p[2]) - gamma.pdf(u, p[1]/p[3], scale=dt/p[3])/p[4]

    >>> print spm_hrf(2)
    [  0.00000000e+00   8.65660810e-02   3.74888236e-01   3.84923382e-01
       2.16117316e-01   7.68695653e-02   1.62017720e-03  -3.06078117e-02
      -3.73060781e-02  -3.08373716e-02  -2.05161334e-02  -1.16441637e-02
      -5.82063147e-03  -2.61854250e-03  -1.07732374e-03  -4.10443522e-04
      -1.46257507e-04]