"""Forward facing lyman tools with information about ecosystem."""
import os
import re
import sys
import imp
import os.path as op

import numpy as np


def gather_project_info():
    """Import project information based on environment settings."""
    lyman_dir = os.environ["LYMAN_DIR"]
    proj_file = op.join(lyman_dir, "project.py")
    try:
        project = sys.modules["project"]
    except KeyError:
        project = imp.load_source("project", proj_file)

    project_dict = dict()
    for dir in ["data", "analysis", "working", "crash"]:
        path = op.abspath(op.join(lyman_dir, getattr(project, dir + "_dir")))
        project_dict[dir + "_dir"] = path
    project_dict["default_exp"] = project.default_exp
    project_dict["rm_working_dir"] = project.rm_working_dir

    if hasattr(project, "ants_normalization"):
        use_ants = project.ants_normalization
        project_dict["normalization"] = "ants" if use_ants else "fsl"
    else:
        project_dict["normalization"] = "fsl"

    return project_dict


def gather_experiment_info(exp_name=None, altmodel=None):
    """Import an experiment module and add some formatted information."""
    lyman_dir = os.environ["LYMAN_DIR"]

    # Allow easy use of default experiment
    if exp_name is None:
        project = gather_project_info()
        exp_name = project["default_exp"]

    # Import the base experiment
    try:
        exp = sys.modules[exp_name]
    except KeyError:
        exp_file = op.join(lyman_dir, exp_name + ".py")
        exp = imp.load_source(exp_name, exp_file)

    exp_dict = default_experiment_parameters()

    def keep(k):
        return not re.match("__.*__", k)

    exp_dict.update({k: v for k, v in exp.__dict__.items() if keep(k)})

    # Possibly import the alternate model details
    if altmodel is not None:
        try:
            alt = sys.modules[altmodel]
        except KeyError:
            alt_file = op.join(lyman_dir, "%s-%s.py" % (exp_name, altmodel))
            alt = imp.load_source(altmodel, alt_file)

        alt_dict = {k: v for k, v in alt.__dict__.items() if keep(k)}

        # Update the base information with the altmodel info
        exp_dict.update(alt_dict)

    # Save the __doc__ attribute to the dict
    exp_dict["comments"] = "" if exp.__doc__ is None else exp.__doc__
    if altmodel is not None:
        exp_dict["comments"] += "\n"
        exp_dict["comments"] += "" if alt.__doc__ is None else alt.__doc__

    # Check if it looks like this is a partial FOV acquisition
    exp_dict["partial_brain"] = bool(exp_dict.get("whole_brain_template"))

    # Temporal resolution. Mandatory.
    exp_dict["TR"] = float(exp_dict["TR"])

    # Set up the default contrasts
    if exp_dict["condition_names"] is not None:
        cs = [(name, [name], [1]) for name in exp_dict["condition_names"]]
        exp_dict["contrasts"] = cs + exp_dict["contrasts"]

    # Build contrasts list if neccesary
    exp_dict["contrast_names"] = [c[0] for c in exp_dict["contrasts"]]

    return exp_dict


def default_experiment_parameters():
    """Return default values for experiments."""
    exp = dict(

        source_template="",
        whole_brain_template="",
        fieldmap_template="",
        n_runs=0,

        TR=2,
        frames_to_toss=0,
        fieldmap_pe=("y", "y-"),
        temporal_interp=False,
        interleaved=True,
        coreg_init="fsl",
        slice_order="up",
        intensity_threshold=4.5,
        motion_threshold=1,
        spike_threshold=None,
        smooth_fwhm=6,
        hpf_cutoff=128,

        design_name=None,
        condition_names=None,
        regressor_file=None,
        regressor_names=None,
        hrf_model="GammaDifferenceHRF",
        temporal_deriv=False,
        confound_pca=False,
        hrf_params={},
        contrasts=[],
        memory_request=5,

        flame_mode="flame1",
        cluster_zthresh=2.3,
        grf_pthresh=0.05,
        peak_distance=30,
        surf_name="inflated",
        surf_smooth=5,
        sampling_units="frac",
        sampling_method="average",
        sampling_range=(0, 1, .1),
        surf_corr_sign="pos",

        )

    return exp


def determine_subjects(subject_arg=None):
    """Intelligently find a list of subjects in a variety of ways."""
    if subject_arg is None:
        subject_file = op.join(os.environ["LYMAN_DIR"], "subjects.txt")
        subjects = np.loadtxt(subject_file, str).tolist()
    elif op.isfile(subject_arg[0]):
        subjects = np.loadtxt(subject_arg[0], str).tolist()
    else:
        try:
            subject_file = op.join(os.environ["LYMAN_DIR"],
                                   subject_arg[0] + ".txt")
            subjects = np.loadtxt(subject_file, str).tolist()
        except IOError:
            subjects = subject_arg
    return subjects


def determine_engine(args):
    """Read command line args and return Workflow.run() args."""
    plugin_dict = dict(linear="Linear", multiproc="MultiProc",
                       ipython="IPython", torque="PBS", sge="SGE",
                       slurm="SLURM")

    plugin = plugin_dict[args.plugin]

    plugin_args = dict()
    qsub_args = ""

    if plugin == "MultiProc":
        plugin_args['n_procs'] = args.nprocs
    elif plugin in ["SGE", "PBS"]:
        qsub_args += "-V -e /dev/null -o /dev/null "

    if args.queue is not None:
        qsub_args += "-q %s " % args.queue

    plugin_args["qsub_args"] = qsub_args

    return plugin, plugin_args


def run_workflow(wf, name=None, args=None):
    """Run a workflow, if we asked to do so on the command line."""
    plugin, plugin_args = determine_engine(args)
    if (name is None or name in args.workflows) and not args.dontrun:
        wf.run(plugin, plugin_args)

@@ -3499,24 +3499,49 @@
 shold=None,%0A
+        wm_components=6,%0A
         smoo
@@ -3683,32 +3683,69 @@
 sor_names=None,%0A
+        confound_sources=%5B%22motion%22%5D,%0A
         hrf_mode

Add new parameters to experiment defaults