#!/usr/bin/env python3

import getopt
import re
import sys
from dfatool import plotter
from dfatool.dfatool import RawData, pta_trace_to_aggregate
from dfatool.dfatool import gplearn_to_function
from dfatool.model import PTAModel

opt = dict()


def print_model_quality(results):
    for state_or_tran in results.keys():
        print()
        for key, result in results[state_or_tran].items():
            if "smape" in result:
                print(
                    "{:20s} {:15s} {:.2f}% / {:.0f}".format(
                        state_or_tran, key, result["smape"], result["mae"]
                    )
                )
            else:
                print("{:20s} {:15s} {:.0f}".format(state_or_tran, key, result["mae"]))


def format_quality_measures(result):
    if "smape" in result:
        return "{:6.2f}% / {:9.0f}".format(result["smape"], result["mae"])
    else:
        return "{:6}    {:9.0f}".format("", result["mae"])


def model_quality_table(result_lists, info_list):
    for state_or_tran in result_lists[0]["by_name"].keys():
        for key in result_lists[0]["by_name"][state_or_tran].keys():
            buf = "{:20s} {:15s}".format(state_or_tran, key)
            for i, results in enumerate(result_lists):
                info = info_list[i]
                buf += "  |||  "
                if info == None or info(state_or_tran, key):
                    result = results["by_name"][state_or_tran][key]
                    buf += format_quality_measures(result)
                else:
                    buf += "{:6}----{:9}".format("", "")
            print(buf)


def model_summary_table(result_list):
    buf = "transition duration"
    for results in result_list:
        if len(buf):
            buf += "  |||  "
        buf += format_quality_measures(results["duration_by_trace"])
    print(buf)
    buf = "total energy       "
    for results in result_list:
        if len(buf):
            buf += "  |||  "
        buf += format_quality_measures(results["energy_by_trace"])
    print(buf)
    buf = "transition timeout "
    for results in result_list:
        if len(buf):
            buf += "  |||  "
        buf += format_quality_measures(results["timeout_by_trace"])
    print(buf)


def print_text_model_data(model, pm, pq, lm, lq, am, ai, aq):
    print("")
    print(r"key attribute $1 - \frac{\sigma_X}{...}$")
    for state_or_tran in model.by_name.keys():
        for attribute in model.by_name[state_or_tran]["attributes"]:
            print(
                "{} {} {:.8f}".format(
                    state_or_tran,
                    attribute,
                    model.generic_param_dependence_ratio(state_or_tran, attribute),
                )
            )

    print("")
    print(r"key attribute parameter $1 - \frac{...}{...}$")
    for state_or_tran in model.by_name.keys():
        for attribute in model.by_name[state_or_tran]["attributes"]:
            for param in model.parameters():
                print(
                    "{} {} {} {:.8f}".format(
                        state_or_tran,
                        attribute,
                        param,
                        model.param_dependence_ratio(state_or_tran, attribute, param),
                    )
                )
            if state_or_tran in model._num_args:
                for arg_index in range(model._num_args[state_or_tran]):
                    print(
                        "{} {} {:d} {:.8f}".format(
                            state_or_tran,
                            attribute,
                            arg_index,
                            model.arg_dependence_ratio(
                                state_or_tran, attribute, arg_index
                            ),
                        )
                    )


if __name__ == "__main__":

    ignored_trace_indexes = None
    discard_outliers = None
    safe_functions_enabled = False
    function_override = {}
    show_models = []
    show_quality = []

    try:
        optspec = (
            "plot-unparam= plot-param= show-models= show-quality= "
            "ignored-trace-indexes= discard-outliers= function-override= "
            "with-safe-functions"
        )
        raw_opts, args = getopt.getopt(sys.argv[1:], "", optspec.split(" "))

        for option, parameter in raw_opts:
            optname = re.sub(r"^--", "", option)
            opt[optname] = parameter

            if "ignored-trace-indexes" in opt:
                ignored_trace_indexes = list(
                    map(int, opt["ignored-trace-indexes"].split(","))
                )
                if 0 in ignored_trace_indexes:
                    print("[E] arguments to --ignored-trace-indexes start from 1")

            if "discard-outliers" in opt:
                discard_outliers = float(opt["discard-outliers"])

            if "function-override" in opt:
                for function_desc in opt["function-override"].split(";"):
                    state_or_tran, attribute, *function_str = function_desc.split(" ")
                    function_override[(state_or_tran, attribute)] = " ".join(
                        function_str
                    )

            if "show-models" in opt:
                show_models = opt["show-models"].split(",")

            if "show-quality" in opt:
                show_quality = opt["show-quality"].split(",")

            if "with-safe-functions" in opt:
                safe_functions_enabled = True

    except getopt.GetoptError as err:
        print(err)
        sys.exit(2)

    raw_data = RawData(args)

    preprocessed_data = raw_data.get_preprocessed_data()
    by_name, parameters, arg_count = pta_trace_to_aggregate(preprocessed_data)

    ref_model = PTAModel(
        by_name,
        parameters,
        arg_count,
        traces=preprocessed_data,
        ignore_trace_indexes=ignored_trace_indexes,
        discard_outliers=discard_outliers,
        function_override=function_override,
        use_corrcoef=False,
    )
    model = PTAModel(
        by_name,
        parameters,
        arg_count,
        traces=preprocessed_data,
        ignore_trace_indexes=ignored_trace_indexes,
        discard_outliers=discard_outliers,
        function_override=function_override,
        use_corrcoef=True,
    )

    if "plot-unparam" in opt:
        for kv in opt["plot-unparam"].split(";"):
            state_or_trans, attribute = kv.split(" ")
            plotter.plot_y(model.by_name[state_or_trans][attribute])

    if len(show_models):
        print("--- simple static model ---")
    static_model = model.get_static()
    ref_static_model = ref_model.get_static()
    if "static" in show_models or "all" in show_models:
        for state in model.states():
            print(
                "{:10s}: {:.0f} µW  ({:.2f})".format(
                    state,
                    static_model(state, "power"),
                    model.generic_param_dependence_ratio(state, "power"),
                )
            )
            for param in model.parameters():
                print(
                    "{:10s}  dependence on {:15s}: {:.2f}".format(
                        "", param, model.param_dependence_ratio(state, "power", param)
                    )
                )
        for trans in model.transitions():
            print(
                "{:10s}: {:.0f} / {:.0f} / {:.0f} pJ  ({:.2f} / {:.2f} / {:.2f})".format(
                    trans,
                    static_model(trans, "energy"),
                    static_model(trans, "rel_energy_prev"),
                    static_model(trans, "rel_energy_next"),
                    model.generic_param_dependence_ratio(trans, "energy"),
                    model.generic_param_dependence_ratio(trans, "rel_energy_prev"),
                    model.generic_param_dependence_ratio(trans, "rel_energy_next"),
                )
            )
            print("{:10s}: {:.0f} µs".format(trans, static_model(trans, "duration")))
    static_quality = model.assess(static_model)
    ref_static_quality = ref_model.assess(ref_static_model)

    if len(show_models):
        print("--- LUT ---")
    lut_model = model.get_param_lut()
    lut_quality = model.assess(lut_model)
    ref_lut_model = ref_model.get_param_lut()
    ref_lut_quality = ref_model.assess(ref_lut_model)

    if len(show_models):
        print("--- param model ---")
    param_model, param_info = model.get_fitted(
        safe_functions_enabled=safe_functions_enabled
    )
    ref_param_model, ref_param_info = ref_model.get_fitted(
        safe_functions_enabled=safe_functions_enabled
    )
    print("")
    print("")
    print("state_or_trans attribute param stddev_ratio corrcoef")
    for state in model.states():
        for attribute in model.attributes(state):
            for param in model.parameters():
                print(
                    "{:10s} {:10s} {:10s} {:f} {:f}".format(
                        state,
                        attribute,
                        param,
                        ref_model.param_dependence_ratio(state, attribute, param),
                        model.param_dependence_ratio(state, attribute, param),
                    )
                )
    for trans in model.transitions():
        for attribute in model.attributes(trans):
            for param in model.parameters():
                print(
                    "{:10s} {:10s} {:10s} {:f} {:f}".format(
                        trans,
                        attribute,
                        param,
                        ref_model.param_dependence_ratio(trans, attribute, param),
                        model.param_dependence_ratio(trans, attribute, param),
                    )
                )
    print("")
    print("")
    analytic_quality = model.assess(param_model)
    ref_analytic_quality = ref_model.assess(ref_param_model)

    if "tex" in show_models or "tex" in show_quality:
        print_text_model_data(
            model,
            static_model,
            static_quality,
            lut_model,
            lut_quality,
            param_model,
            param_info,
            analytic_quality,
        )

    if "table" in show_quality or "all" in show_quality:
        print("corrcoef:")
        model_quality_table(
            [static_quality, analytic_quality, lut_quality], [None, param_info, None]
        )
        print("heuristic:")
        model_quality_table(
            [ref_static_quality, ref_analytic_quality, ref_lut_quality],
            [None, ref_param_info, None],
        )
    if "summary" in show_quality or "all" in show_quality:
        print("corrcoef:")
        model_summary_table([static_quality, analytic_quality, lut_quality])
        print("heuristic:")
        model_summary_table([ref_static_quality, ref_analytic_quality, ref_lut_quality])

    if "plot-param" in opt:
        for kv in opt["plot-param"].split(";"):
            state_or_trans, attribute, param_name, *function = kv.split(" ")
            if len(function):
                function = gplearn_to_function(" ".join(function))
            else:
                function = None
            plotter.plot_param(
                model,
                state_or_trans,
                attribute,
                model.param_index(param_name),
                extra_function=function,
            )

    sys.exit(0)