@@ -53,9 +53,13 @@ def sample(problem, N, num_levels, grid_jump, optimal_trajectories=None,
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"""Generate model inputs using the Method of Morris
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Returns a NumPy matrix containing the model inputs required for Method of
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- Morris. The resulting matrix has :math:`(D+1)*N` rows and :math:`D`
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- columns, where :math:`D` is the number of parameters. These model inputs
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- are intended to be used with :func:`SALib.analyze.morris.analyze`.
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+ Morris. The resulting matrix has :math:`(G+1)*T` rows and :math:`D`
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+ columns, where :math:`D` is the number of parameters, :math:`G` is the
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+ number of groups (if no groups are selected, the number of parameters).
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+ :math:`T` is the number of trajectories :math:`N`,
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+ or `optimal_trajectories` if selected.
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+ These model inputs are intended to be used with
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+ :func:`SALib.analyze.morris.analyze`.
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Parameters
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----------
@@ -79,8 +83,8 @@ def sample(problem, N, num_levels, grid_jump, optimal_trajectories=None,
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-------
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sample : numpy.ndarray
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Returns a numpy.ndarray containing the model inputs required for Method
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- of Morris. The resulting matrix has :math:`(D+1)*N` rows and :math:`D`
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- columns, where :math:`D` is the number of parameters.
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+ of Morris. The resulting matrix has :math:`(G/ D+1)*N/T ` rows and
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+ :math:`D` columns, where :math:`D` is the number of parameters.
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"""
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if grid_jump >= num_levels :
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raise ValueError ("grid_jump must be less than num_levels" )
@@ -161,8 +165,9 @@ def _sample_oat(problem, N, num_levels, grid_jump):
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def _sample_groups (problem , N , num_levels , grid_jump ):
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"""Generate trajectories for groups
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- Returns an N(g+1)-by-k array of N trajectories;
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- where g is the number of groups and k is the number of factors
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+ Returns an :math:`N(g+1)`-by-:math:`k` array of `N` trajectories,
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+ where :math:`g` is the number of groups and :math:`k` is the number
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+ of factors
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Arguments
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---------
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