#!/usr/bin/env python3

"""analyze-log - Generate a model from performance benchmarks log files

foo
"""

import argparse
import dfatool.cli
import dfatool.plotter
import dfatool.utils
from dfatool.model import AnalyticModel
from dfatool.validation import CrossValidator
import re


def kv_to_param(kv_str, cast):
    key, value = kv_str.split("=")
    value = cast(value)
    return key, value


def kv_to_param_f(kv_str):
    return kv_to_param(kv_str, dfatool.utils.soft_cast_float)


def kv_to_param_i(kv_str):
    return kv_to_param(kv_str, dfatool.utils.soft_cast_int)


def parse_logfile(filename):
    observations = list()

    with open(filename, "r") as f:
        for line in f:
            m = re.search(r"\[::\] *([^|]*?) *[|] *([^|]*?) *[|] *(.*)", line)
            if m:
                name_str = m.group(1)
                param_str = m.group(2)
                attr_str = m.group(3)
                param = dict(map(kv_to_param_i, param_str.split(" ")))
                attr = dict(map(kv_to_param_f, attr_str.split(" ")))
                observations.append(
                    {
                        "name": name_str,
                        "param": param,
                        "attribute": attr,
                    }
                )

    return observations


def main():
    parser = argparse.ArgumentParser(
        formatter_class=argparse.RawDescriptionHelpFormatter, description=__doc__
    )
    dfatool.cli.add_standard_arguments(parser)
    parser.add_argument(
        "--plot-unparam",
        metavar="<name>:<attribute>:<Y axis label>[;<name>:<attribute>:<label>;...]",
        type=str,
        help="Plot all mesurements for <name> <attribute> without regard for parameter values. "
        "X axis is measurement number/id.",
    )
    parser.add_argument(
        "--plot-param",
        metavar="<name> <attribute> <parameter> [gplearn function][;<name> <attribute> <parameter> [function];...])",
        type=str,
        help="Plot measurements for <name> <attribute> by <parameter>. "
        "X axis is parameter value. "
        "Plots the model function as one solid line for each combination of non-<parameter> parameters. "
        "Also plots the corresponding measurements. "
        "If gplearn function is set, it is plotted using dashed lines.",
    )
    parser.add_argument(
        "--show-model",
        choices=["static", "paramdetection", "param", "all"],
        action="append",
        default=list(),
        help="static: show static model values as well as parameter detection heuristic.\n"
        "paramdetection: show stddev of static/lut/fitted model\n"
        "param: show parameterized model functions and regression variable values\n"
        "all: all of the above\n",
    )
    parser.add_argument(
        "--show-quality",
        choices=["table", "summary", "all"],
        action="append",
        default=list(),
        help="table: show static/fitted/lut SMAPE and MAE for each name and attribute.\n"
        "summary: show static/fitted/lut SMAPE and MAE for each attribute, averaged over all states/transitions.\n"
        "all: all of the above.\n",
    )
    parser.add_argument(
        "--force-tree",
        action="store_true",
        help="Build decision tree without checking for analytic functions first",
    )
    parser.add_argument("logfile", type=str, help="Path to benchmark output")
    args = parser.parse_args()

    if args.filter_param:
        args.filter_param = list(
            map(lambda x: x.split("="), args.filter_param.split(","))
        )
    else:
        args.filter_param = list()

    observations = parse_logfile(args.logfile)
    by_name, parameter_names = dfatool.utils.observations_to_by_name(observations)
    del observations

    dfatool.utils.filter_aggregate_by_param(by_name, parameter_names, args.filter_param)

    model = AnalyticModel(
        by_name,
        parameter_names,
        force_tree=args.force_tree,
    )

    if args.info:
        dfatool.cli.print_info_by_name(model, by_name)

    if args.export_pgf_unparam:
        dfatool.cli.export_pgf_unparam(model, args.export_pgf_unparam)

    if args.plot_unparam:
        for kv in args.plot_unparam.split(";"):
            state_or_trans, attribute, ylabel = kv.split(":")
            fname = "param_y_{}_{}.pdf".format(state_or_trans, attribute)
            dfatool.plotter.plot_y(
                model.by_name[state_or_trans][attribute],
                xlabel="measurement #",
                ylabel=ylabel,
                # output=fname,
            )

    if args.cross_validate:
        xv_method, xv_count = args.cross_validate.split(":")
        xv_count = int(xv_count)
        xv = CrossValidator(
            AnalyticModel,
            by_name,
            parameter_names,
            force_tree=args.force_tree,
            max_std=max_std,
            compute_stats=not args.skip_param_stats,
        )
        xv.parameter_aware = args.parameter_aware_cross_validation
    else:
        xv_method = None

    static_model = model.get_static()
    try:
        lut_model = model.get_param_lut()
    except RuntimeError as e:
        if args.force_tree:
            # this is to be expected
            logging.debug(f"Skipping LUT model: {e}")
        else:
            logging.warning(f"Skipping LUT model: {e}")
        lut_model = None

    param_model, param_info = model.get_fitted()
    static_quality = model.assess(static_model)
    analytic_quality = model.assess(param_model)
    if lut_model:
        lut_quality = model.assess(lut_model)
    else:
        lut_quality = None

    if "static" in args.show_model or "all" in args.show_model:
        print("--- static model ---")
        for name in model.names:
            for attribute in model.attributes(name):
                dfatool.cli.print_static(model, static_model, name, attribute)

    if "table" in args.show_quality or "all" in args.show_quality:
        if xv_method is not None:
            print(f"Model error after cross validation ({xv_method}, {xv_count}):")
        else:
            print("Model error on training data:")
        dfatool.cli.model_quality_table(
            ["static", "parameterized", "LUT"],
            [static_quality, analytic_quality, lut_quality],
            [None, param_info, None],
        )

    if args.plot_param:
        for kv in args.plot_param.split(";"):
            try:
                state_or_trans, attribute, param_name = kv.split(":")
            except ValueError:
                print(
                    "Usage: --plot-param='state_or_trans:attribute:param_name'",
                    file=sys.stderr,
                )
                sys.exit(1)
            dfatool.plotter.plot_param(
                model,
                state_or_trans,
                attribute,
                model.param_index(param_name),
            )


if __name__ == "__main__":
    main()