.. glmtools documentation master file, created by
   sphinx-quickstart on Mon Oct 19 13:42:29 2020.
   You can adapt this file completely to your liking, but it should at least
   contain the root `toctree` directive.

Welcome to glmtools's documentation!
====================================

.. toctree::
   :maxdepth: 2
   :caption: Contents:

::

    import yaml
    import numpy as np
    import glmtools as glm


    # Design config object contains methods for adding regressors, contrasts
    # and ftests.
    DC = glm.design.DesignConfig()
    DC.add_regressor(name='Cond1',rtype='Categorical',codes=1)
    DC.add_regressor(name='Cond2',rtype='Categorical',codes=2)
    DC.add_regressor(name='Covariate', rtype='Parametric', datainfo='covariate', preproc='z')
    DC.add_simple_contrasts()

    for subj in range(1):
        # Make a fake dataset
        condition = np.tile( [1,2],4 )
        session = np.repeat([1,2,3,4],2)
        covariate = np.array([-3,-2,-1,0,0,1,2,3])

        # Some random data
        X = np.random.randn(8,)

        # The data object must contain a 'data' kwarg pointing to the array to
        # be analysed. Any other kwargs are considered to be meta-data and
        # pushed into a dict in data.info
        data = glm.data.TrialGLMData(data=X,
                                     category_list=condition,
                                     covariate=covariate,
                                     session=session)

        des = DC.design_from_datainfo(data.info)
        des.plot_summary()

        model = glm.fit.OLSModel(des,data)

Can also define DesignConfigs from yaml

.. image:: _static/simple_design1.png

::

    yml = """
    NewDesign:
      regressors:
      - {name: Cond1, rtype: Categorical, codes: 1}
      - {name: Cond2, rtype: Categorical, codes: 2}
      contrasts:
      - {name: Cond1, values: 1 0}
      - {name: Cond2, values: 0 1}
    """

    DC = glm.design.DesignConfig(yaml_text=yml)

For contrasts in complicated designs we can specify regressors via a dict

::

    y2 = """
    NewDesign:
      regressors:
      - {name: Cond1, rtype: Categorical, codes: 1}
      - {name: Cond2, rtype: Categorical, codes: 2}
      - {name: Session, rtype: MeanEffects, datainfo: session}
      contrasts:
      - {name: Cond1, values: {Cond1: 1}}
      - {name: Cond1, values: {Cond2: 1}}
      - {name: CondDiff, values: {Cond1: 1, Cond2: -1}}
    """

    DC = glm.design.DesignConfig(yaml_text=y2)

This can extend to some more complicated designs

::

    Y = np.random.randn(40,8)

    sessions = np.tile(np.arange(10),4)
    subjs = np.repeat(np.arange(4),10)
    covariate = np.random.randn(40,)

    group_data = glm.data.TrialGLMData(data=Y,
                                       sessions=sessions,
                                       subjs=subjs,
                                       covariate=covariate)


    DC = glm.design.DesignConfig()
    DC.add_regressor(name='Constant',rtype='Constant')
    DC.add_regressor(name='Sessions',rtype='Parametric',datainfo='sessions',preproc='z')
    DC.add_regressor(name='Covariate',rtype='Parametric',datainfo='covariate',preproc='z')
    DC.add_regressor(name='Subj_{0}',rtype='MeanEffects',datainfo='subjs')

    DC.add_contrast(name='GroupMean', values={'Constant':1})
    DC.add_contrast(name='SessionEffect', values={'Sessions':1})
    DC.add_contrast(name='CovariateEffect', values={'Covariate':1})

    des = DC.design_from_datainfo(group_data.info)
    des.plot_summary()

.. image:: _static/simple_design2.png



::

    yml = """
    NewDesign:
      contrasts:
      - name: GroupMean
        values: {Constant: 1}
      - name: SessionEffect
        values: {Sessions: 1}
      - name: CovariateEffect
        values: {Covariate: 1}
      regressors:
      - {name: Constant, regressor: ConstantRegressor}
      - {datainfo: sessions, name: Sessions, preproc: z, regressor: ParametricRegressor}
      - {datainfo: covariate, name: Covariate, preproc: z, regressor: ParametricRegressor}
      - {datainfo: subjs, name: 'Subj_{0}', regressor: MeanEffectsRegressor}
    """
    DC = glm.design.DesignConfig(yaml_text=yml)


Some permutation features

::

    # Permutations
    P = glm.permutations.MaxStatPermutation(design, data, 1,
                                            nperms,
                                            metric='tstats',
                                            perm_args=perm_args)

    # 2d cluster permutations
    perm_args = {'cluster_forming_threshold': 2.3, 'pooled_dims': (1, 2)}
    CP = permutations.ClusterPermutation(design, data, cont,
                                         500,
                                         perm_args=perm_args,
                                         metric='tstats')
    cluster_masks, cluster_stats = CP.get_sig_clusters(data, 99)


Using sklearn model fits

::

    # glmtools OLS model
    model = glm.fit.OLSModel(design,data)
    # sklearn OLS model
    skmodel1 = glm.fit.SKLModel(design,data)
    # sklearn robust regression
    skmodel2 = glm.fit.SKLModel(design,data,fit_args={'lm':'RANSACRegressor'})



Indices and tables
==================

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`