"""
Option parser for bumps command line
"""
from __future__ import print_function

import sys
from .fitters import FITTERS, FIT_AVAILABLE_IDS, FIT_ACTIVE_IDS, FIT_DEFAULT_ID

class ParseOpts(object):
    """
    Options parser.

    Subclass should define *MINARGS*, *FLAGS*, *VALUES* and *USAGE*.

    *MINARGS* is the minimum number of positional arguments.

    *FLAGS* is a set of arguments that may be present or absent.

    *VALUES* is a set of arguments that take values.  Value checking
    can be done in the setter for each argument in the set.  Default
    values should be set in the corresponding object attribute.

    *USAGE* is the help string to display for option "help".

    The constructor will invoke the command line parser, leaving the
    values set by the command line as attribute values.   Flag options
    will be True or False.
    """
    MINARGS = 0
    FLAGS = set()
    VALUES = set()
    USAGE = ""

    def __init__(self, args):
        self._parse(args)

    def _parse(self, args):
        if self.VALUES & self.FLAGS:
            raise TypeError("option used as both a flag and a value: %s"%
                            ",".join(self.VALUES&self.FLAGS))
        flagargs = [v
                    for v in sys.argv[1:]
                    if v.startswith('--') and not '=' in v]
        flags = set(v[2:] for v in flagargs)
        if 'help' in flags or '-h' in sys.argv[1:] or '-?' in sys.argv[1:]:
            print(self.USAGE)
            sys.exit()
        unknown = flags - self.FLAGS
        if any(unknown):
            raise ValueError("Unknown options --%s.  Use -? for help."
                             % ", --".join(unknown))
        for f in self.FLAGS:
            setattr(self, f, (f in flags))

        valueargs = [v
                     for v in sys.argv[1:]
                     if v.startswith('--') and '=' in v]
        for f in valueargs:
            idx = f.find('=')
            name = f[2:idx]
            value = f[idx + 1:]
            if name not in self.VALUES:
                raise ValueError(
                    "Unknown option --%s. Use -? for help." % name)
            setattr(self, name, value)

        positionargs = [v for v in sys.argv[1:] if not v.startswith('--')]
        self.args = positionargs


# === Fitter option parsing ===

class ChoiceList(object):

    def __init__(self, *choices):
        self.choices = choices

    def __call__(self, value):
        if not value in self.choices:
            raise ValueError('invalid option "%s": use %s'
                             % (value, '|'.join(self.choices)))
        else:
            return value


def yesno(value):
    if value.lower() in ('true', 'yes', 'on', '1'):
        return True
    elif value.lower() in ('false', 'no', 'off', '0'):
        return False
    raise ValueError('invalid option "%s": use yes|no')


def parse_int(value):
    float_value = float(value)
    if int(float_value) != float_value:
        raise ValueError("integer expected")
    return int(float_value)


FIT_FIELDS = dict(
    starts=("Starts", parse_int),
    steps=("Steps", parse_int),
    samples=("Samples", parse_int),
    xtol=("x tolerance", float),
    ftol=("f(x) tolerance", float),
    stop=("Stopping criteria", str),
    thin=("Thinning", parse_int),
    burn=("Burn-in Steps", parse_int),
    pop=("Population", float),
    init=("Initializer", ChoiceList("eps", "lhs", "cov", "random")),
    CR=("Crossover ratio", float),
    F=("Scale", float),
    nT=("# Temperatures", parse_int),
    Tmin=("Min temperature", float),
    Tmax=("Max temperature", float),
    radius=("Simplex radius", float),
    )

# Make sure all settings are parseable
for fit in FITTERS:
    assert all(opt in FIT_FIELDS for opt, _ in fit.settings), \
        "Fitter %s contains unknown settings"%fit.id
del fit

class FitConfig(object):
    """
    Fit settings configuration object.

    The command line parser will define a FitConfig object which contains
    the fitter that was given on the command line and all its options.  For
    embedded bumps, which does not use the bumps command line parser, a
    new FitConfig object can be created with its own selected options.

    Attributes
    ----------

    *ids = [id, id, ...]* is a list available fitters in "preferred" order.
    Depending on usage, you may want to sort them, or alternatively, sort
    by long name with *[id for _,id in sorted((v,k) for k,v in self.names]*

    *fitters = {id: fitclasss}* maps ids to fitters.

    *names* = {id: name}* maps ids to long names

    *settings = {id: [(option, default), ...]}* maps ids to default settings.
    The order of the settings is the preferred order to present the settings
    to the user in a GUI dialog for example.

    *values = {id: {option: value, ...}}* maps ids to the settings for
    each fitter.  Note that in the GUI, different fitters may have there
    settings recorded and preserved even when not selected.

    *active_ids = [id, id, ...]* is the list of fitters to show the user in
    a GUI dialog for example.  The other fitters should still be available from
    the command line.

    *default_id = id* is the fitter to use by default.

    *selected_id = id* is the fitter that was selected, either by command line
    or by GUI.

    *selected_values = {option: value}* returns the settings for the current
    fitter.

    *selected_name = name* returns the name of the selected fitter.

    *selected_fitter = FitClass* returns the class of the selected fitter.

    """
    def __init__(self, default=FIT_DEFAULT_ID, active=FIT_ACTIVE_IDS):
        # Keep a private copy of the configure settings rather than modifying
        # the global defaults
        self.ids = [fit.id for fit in FITTERS]
        # FITTERS is a list of FitBase classes
        # Each class has:
        #     fit.id: the short name used on the command line
        #     fit.name: the long name used in the GUI
        #     fit.settings: available options: [(key,default value), ...]
        self.fitters = dict((fit.id, fit) for fit in FITTERS)
        self.names = dict((fit.id, fit.name) for fit in FITTERS)
        self.settings = dict((fit.id, fit.settings) for fit in FITTERS)
        self.values = dict((fit.id, dict(fit.settings)) for fit in FITTERS)
        if not all(k in self.ids for k in active):
            raise ValueError("Some active fitters are not available")
        if default not in active:
            raise ValueError("default fitter is not active")
        self.active_ids = active
        self.default_id = default
        self.selected_id = default

    def set_from_cli(self, opts):
        """
        Use the BumpsOpts command line parser values to set the selected
        fitter and its configuration options.
        """
        fitter = opts.fit
        self.selected_id = fitter
        # Convert supplied options to the correct types and save them in value
        for field, reset_value in self.settings[fitter]:
            value = getattr(opts, field, None)
            parse = FIT_FIELDS[field][1]
            if value is not None:
                try:
                    self.values[fitter][field] = parse(value)
                except Exception as exc:
                    raise ValueError("error in --%s: %s" % (field, str(exc)))
                    # print("options=%s"%(str(self.options)))

    @property
    def selected_values(self):
        return self.values[self.selected_id]

    @property
    def selected_name(self):
        return self.names[self.selected_id]

    @property
    def selected_fitter(self):
        return self.fitters[self.selected_id]

# FitConfig singleton for the common case in which only one config is needed.
# There may be other use cases, such as saving the fit config along with the
# rest of the state so that on resume the fit options are restored, but in that
# case the application will not be using the singleton.
FIT_CONFIG = FitConfig()

# === Bumps options parsing ===
class BumpsOpts(ParseOpts):
    """
    Option parser for bumps.
    """
    MINARGS = 1
    FLAGS = set(("preview", "chisq", "profile", "time_model",
                 "simulate", "simrandom", "shake", "worker",
                 "batch", "noshow", "overwrite", "stepmon",
                 "err", "cov", "entropy",
                 "remote", "staj", "edit", "mpi", "keep_best",
                 # passed in when app is a frozen image
                 "multiprocessing-fork",
                 # passed when not running bumps, but instead using a
                 # bundled application as a python distribution with domain
                 # specific models pre-defined.
                 "i",
                ))
    VALUES = set(("plot", "store", "resume", "fit", "noise", "seed", "pars",
                  "resynth", "transport", "notify", "queue", "time",
                  "m", "c", "p", "parallel", "view",
                 ))
    # Add in parameters from the fitters
    VALUES |= set(FIT_FIELDS.keys())
    pars = None
    notify = ""
    queue = "http://reflectometry.org/queue"
    resynth = "0"
    noise = "5"
    starts = "1"
    seed = ""
    time = "inf"
    parallel = ""
    view = None
    PLOTTERS = "linear", "log", "residuals"
    USAGE = """\
Usage: bumps [options] modelfile [modelargs]

The modelfile is a Python script (i.e., a series of Python commands)
which sets up the data, the models, and the fittable parameters.
The model arguments are available in the modelfile as sys.argv[1:].
Model arguments may not start with '-'.

Options:

    --preview
        display model but do not perform a fitting operation
    --pars=filename or store path
        initial parameter values; fit results are saved as path/<modelname>.par
    --plot=log      [%(plotter)s]
        type of plot to display
    --simulate
        simulate a dataset using the initial problem parameters
    --simrandom
        simulate a dataset using random problem parameters
    --shake
        set random parameters before fitting
    --noise=5%%
        percent noise to add to the simulated data
    --seed=integer
        random number seed
    --err
        show uncertainty estimate from curvature at the minimum
    --cov
        show the covariance matrix for the model when done
    --entropy
        compute entropy for the model when done [dream only]
    --staj
        output staj file when done
    --edit
        start the gui
    --view=linear|log
        one of the predefined problem views; reflectometry also has fresnel,
        logfresnel, q4 and residuals

    --store=path
        output directory for plots and models
    --overwrite
        if store already exists, replace it
    --resume=path    [dream]
        resume a fit from previous stored state
    --parallel=n
        run fit using multiprocessing for parallelism; use --parallel=0 for all cpus
    --mpi
        run fit using MPI for parallelism (use command "mpirun -n cpus ...")
    --batch
        batch mode; save output in .mon file and don't show plots after fit
    --noshow
        semi-batch; send output to console but don't show plots after fit
    --remote
        queue fit to run on remote server
    --notify=user@email
        remote fit notification
    --queue=http://reflectometry.org
        remote job queue
    --time=inf
        run for a maximum number of hours

    --fit=amoeba    [%(fitter)s]
        fitting engine to use; see manual for details
    --steps=400    [%(fitter)s]
        number of fit iterations after any burn-in time
    --samples=1e4   [dream]
        set steps so the target number of samples is drawn
    --xtol=1e-4     [de, amoeba]
        minimum population diameter
    --ftol=1e-4     [de, amoeba]
        minimum population flatness
    --pop=10        [dream, de, rl, ps]
        population size
    --burn=100      [dream, pt]
        number of burn-in iterations before accumulating stats
    --thin=1        [dream]
        number of fit iterations between steps
    --nT=25
    --Tmin=0.1
    --Tmax=10       [pt]
        temperatures vector; use a higher maximum temperature and a larger
        nT if your fit is getting stuck in local minima
    --CR=0.9        [de, rl, pt]
        crossover ratio for population mixing
    --starts=1      [newton, rl, amoeba]
        number of times to run the fit from random starting points.
    --keep_best
        when running with multiple starts, restart from a point near the
        last minimum rather than using a completely random starting point.
    --init=eps      [dream]
        population initialization method:
          eps:    ball around initial parameter set
          lhs:    latin hypercube sampling
          cov:    normally distributed according to covariance matrix
          random: uniformly distributed within parameter ranges
    --stepmon
        show details for each step
    --resynth=0
        run resynthesis error analysis for n generations

    --time_model
        run the model --steps times in order to estimate total run time.
    --profile
        run the python profiler on the model; use --steps to run multiple
        models for better statistics
    --chisq
        print the model description and chisq value and exit
    -m/-c/-p command
        run the python interpreter with bumps on the path:
            m: command is a module such as bumps.cli, run as __main__
            c: command is a python one-line command
            p: command is the name of a python script
    -i
        start the interactive interpreter
    -?/-h/--help
        display this help
""" % {'fitter': '|'.join(sorted(FIT_AVAILABLE_IDS)),
       'plotter': '|'.join(PLOTTERS),
      }

#    --transport=mp  {amqp|mp|mpi}
#        use amqp/multiprocessing/mpi for parallel evaluation

    _plot = 'log'

    def _set_plot(self, value):
        if value not in set(self.PLOTTERS):
            raise ValueError("unknown plot type %s; use %s"
                             % (value, "|".join(self.PLOTTERS)))
        self._plot = value
    plot = property(fget=lambda self: self._plot, fset=_set_plot)
    store = None
    resume = None
    _fit = FIT_DEFAULT_ID
    @property
    def fit(self):
        return self._fit
    @fit.setter
    def fit(self, value):
        if value not in FIT_AVAILABLE_IDS:
            raise ValueError("unknown fitter %s; use %s"
                             % (value, "|".join(sorted(FIT_AVAILABLE_IDS))))
        self._fit = value
    fit_config = FIT_CONFIG
    TRANSPORTS = 'amqp', 'mp', 'mpi', 'celery'
    _transport = 'mp'

    def _set_transport(self, value):
        if value not in self.TRANSPORTS:
            raise ValueError("unknown transport %s; use %s"
                             % (value, "|".join(self.TRANSPORTS)))
        self._transport = value
    transport = property(fget=lambda self: self._transport, fset=_set_transport)
    meshsteps = 40


def getopts():
    """
    Process command line options.

    Option values will be stored as attributes in the returned object.
    """
    opts = BumpsOpts(sys.argv)
    opts.resynth = int(opts.resynth)
    opts.seed = int(opts.seed) if opts.seed != "" else None
    opts.fit_config.set_from_cli(opts)
    return opts