# Quick script to overplot power colour values
# Written by David Gardenier, 2015-2016

import os
import glob
import pandas as pd
from math import atan2, degrees, pi, log10, sqrt, radians
import matplotlib.pyplot as plt
from collections import defaultdict
from pyx import *

import pandas as pd
import os

# Import file with bursty obsids
path = os.path.dirname(os.path.realpath(__file__))
file_name = path + '/bursts_list.txt'
db_bursts = pd.read_csv(file_name, sep=' ')
obsids_bursts = db_bursts.obsid.values.tolist()


def filter_bursts(df):
    """Filter out bursts found by Phil"""
    df = df[~df['obsids'].isin(obsids_bursts)]
    df = df[~df['obsids'].astype(str).str.startswith('20161')]
    return df


def path(o):
    return '/scratch/david/master_project/' + o + '/info/database_' + o + '.csv'


def findbestres(res):
    '''Find the smallest resolution from a list of resolutions'''

    # Split resolutions into values and units
    heads = []
    tails = []
    for s in res:
        unit = s.strip('0123456789')
        num = s[:-len(unit)]
        heads.append(num)
        tails.append(unit)

    # Sort by unit, then by value
    unitorder = ['us','ms','s']
    for u in unitorder:
        if u in tails:
            indices = [i for i, x in enumerate(tails) if x==u]
            sameunits = [heads[i] for i in indices]
            sortvalues = sorted(sameunits)
            return sortvalues[0]+u


def findbestdataperobsid(df):
    '''Select the data with best mode and resolution'''
    ordering = ['gx1','gx2','event','binned','std2']
    for mode in ordering:
        if mode in df.modes.values:
            df = df[df.modes==mode]
            if df.shape[0] > 1:
                bestres = findbestres(df.resolutions.values)
                return df[df.resolutions==bestres].iloc[0]
            else:
                return df.iloc[0]


def findbestdata(db):
    # Apply constraint to the data
    db = db[(db.pc1.notnull() & db.lt3sigma==True)]
    db = db.groupby('obsids').apply(findbestdataperobsid)
    return db


def findhardness(db, obsid):
    db = db[(db.obsids==obsid) & (db.modes=='std2') & (db.hardness_err_i3t16_s6p4t9p7_h9p7t16.notnull())]
    hardness = db.hardness_i3t16_s6p4t9p7_h9p7t16.values[0]
    hardness_error = db.hardness_err_i3t16_s6p4t9p7_h9p7t16.values[0]
    return hardness, hardness_error

def cal_hue(x,y,xerr,yerr):
    '''
    Function to calculate the hue on basis of power colour-ratio values.

    Assuming:
     - errors symmetric along either axis
     - errors uncorrelated with each other
     - errors given relative to a value

    Returns:
     - [tuple] hue, hue_error
    '''
    # Central point
    x0 = 4.51920
    y0 = 0.453724

    x = float(x)
    y = float(y)

    # Angles are defined in log-space
    dx = log10(x) - log10(x0)
    dy = log10(y) - log10(y0)

    # Calculate angle
    rads = atan2(dy,dx)
    rads %= 2*pi
    # Add 135 degrees as the hue angle is defined
    # from the line extending in north-west direction
    degs = -(rads*(180/pi)) + 135
    # Fixing things with minus degrees
    if degs < 0:
        degs = (180 - abs(degs)) + 180

    # Calculate errors with error propagation
    above = (yerr*x*log10(x/x0))**2+(xerr*y*log10(y/y0))**2
    below = (x*y*(log10(x/x0)**2 + log10(y/y0)**2))**2
    radserr = sqrt(above/float(below))
    radserr %= 2*pi
    degserr = radserr*180/pi

    return degs, degserr


def plot_allpcs():
    import numpy as np
    import itertools

    objects=[
            ('4U_0614p09', '4U 0614+09'),
            ('4U_1636_m53', '4U 1636-53'),
            ('4U_1702m43', '4U 1702-43'),
            ('4u_1705_m44', '4U 1705-44'),
            ('4U_1728_34', '4U 1728-34'),
            ('aquila_X1', 'Aql X-1'),
            ('cyg_x2', 'Cyg X-2'),
            ('gx_17p2', 'GX 17+2'),
            ('gx_340p0', 'GX 340+0'),
            ('gx_349p2', 'GX 349+2'), #Only 3 points
            ('gx_5m1', 'GX 5-1'), # Only 4 points
            ('HJ1900d1_2455', 'HETE J1900.1-2455'),
            ('IGR_J00291p5934', 'IGR J00291+5934'),
            ('IGR_J17480m2446', 'IGR J17480-2446'),
            ('IGR_J17498m2921', 'IGR J17498-2921'), #Only 1 point
            ('KS_1731m260', 'KS 1731-260'),
            ('xte_J1808_369', 'SAX J1808.4-3648'),
            ('S_J1756d9m2508', 'SWIFT J1756.9-2508'),
            ('sco_x1', 'Sco X-1'),
            ('sgr_x1', 'Sgr X-1'),
            ('sgr_x2', 'Sgr X-2'),
            ('v4634_sgr', 'V4634 Sgr'),
            ('XB_1254_m690', 'XB 1254-690'), #Only 1 point
            ('xte_J0929m314', 'XTE J0929-314'),
            ('J1701_462', 'XTE J1701-462'),
            ('xte_J1751m305', 'XTE J1751-305'),
            ('xte_J1807m294', 'XTE J1807-294'), # Only 2 points
            ('xte_J1814m338', 'XTE J1814-338'),  # Only 3 points
            ('gx_339_d4', 'GX 339-4'), # BH system
            ('H1743m322', 'H1743-322'),  # BH system
            ('xte_J1550m564', 'XTE J1550-564'), #BH system
            ]

    allhues = []
    for i, o in enumerate(objects):
        name = o[-1]
        o = o[0]
        p = path(o)
        db = pd.read_csv(p)
        # Determine pc values
        bestdata = findbestdata(db)
        bestdata = filter_bursts(bestdata)
        print(name, len(bestdata))

        # Calculate hues
        for i in range(len(bestdata.pc1.values)):
            # Determine input parameters
            obsid = bestdata.obsids.values[i]
            mode = bestdata.modes.values[i]
            pc1 = bestdata.pc1.values[i]
            pc2 = bestdata.pc2.values[i]
            pc1err = bestdata.pc1_err.values[i]
            pc2err = bestdata.pc2_err.values[i]
            hue, hue_err = cal_hue(pc1,pc2,pc1err,pc2err)

            hardness, hardness_err = findhardness(db, obsid)
            allhues.append((o, obsid, mode, pc1, pc1err, pc2, pc2err, hue, hue_err, hardness, hardness_err))

    # Print
    with open('chromos_results.txt', 'w') as f:
        f.write('object obsid mode pc1 pc1_err pc2 pc2_err hue hue_err hardness hardness_err\n')

        for t in allhues:
            strs = ' '.join(str(s) for s in t)
            f.write(strs + '\n')

if __name__=='__main__':
    plot_allpcs()
