#!/usr/bin/env python

# Copyright (C) 2016 Christopher M. Biwer
#
# This program is free software; you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by the
# Free Software Foundation; either version 3 of the License, or (at your
# option) any later version.
#
# This program is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General
# Public License for more details.
#
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
""" Plots samples from inference sampler.
"""

import argparse
import logging
import sys

from matplotlib import use
use('agg')
from matplotlib import pyplot as plt

import pycbc
from pycbc import (results, transforms)

from gwin import option_utils

# command line usage
parser = argparse.ArgumentParser(usage=__file__ + " [--options]",
                                 description=__doc__)

# verbose option
parser.add_argument("--verbose", action="store_true", default=False,
                    help="Print logging info.")

# output plot options
parser.add_argument("--output-file", type=str, required=True,
                    help="Path to output plot.")

# add results group options
option_utils.add_inference_results_option_group(parser)

# parse the command line
opts = parser.parse_args()

# setup log
pycbc.init_logging(opts.verbose)

# load the results
fp, parameters, labels, _ = option_utils.results_from_cli(opts,
                                                          load_samples=False)

# get number of dimensions
ndim = len(parameters)

# plot samples
# plot each parameter as a different subplot
logging.info("Plotting samples")
fig, axs = plt.subplots(ndim, sharex=True)
plt.xlabel("Iteration")

# loop over parameters
axs = [axs] if not hasattr(axs, "__iter__") else axs
for i, arg in enumerate(parameters):

    # loop over walkers
    for j in range(fp.nwalkers):

        # plot each walker as a different line on the subplot
        file_parameters, cs = transforms.get_common_cbc_transforms(
                                                 parameters, fp.variable_args)
        y = fp.read_samples(file_parameters, walkers=j,
                            thin_start=opts.thin_start,
                            thin_interval=opts.thin_interval,
                            thin_end=opts.thin_end)
        y = transforms.apply_transforms(y, cs)
        axs[i].plot(y[arg], alpha=0.25)

        # y-axis label
        axs[i].set_ylabel(labels[i])

# save figure with meta-data
caption_kwargs = {
    "parameters" : ", ".join(labels),
}
caption = r"""All samples from all the walker chains for the parameters. Each
line is a different chain of walker samples."""
title = "Samples for {parameters}".format(**caption_kwargs)
results.save_fig_with_metadata(fig, opts.output_file,
                               cmd=" ".join(sys.argv),
                               title=title,
                               caption=caption)
plt.close()

# exit
fp.close()
logging.info("Done")
