"""Construct model of telluric Na emission. See `NaBack.best_back`"""

import gc

import os

from cycler import cycler

import numpy as np

import matplotlib.pyplot as plt

from matplotlib.dates import julian2num

import pandas as pd

from astropy import log

from astropy import units as u

from astropy.table import QTable

from astropy.time import Time

from astropy.nddata import CCDData

from astropy.stats import mad_std, biweight_location

from astropy.coordinates import Angle, SkyCoord

from astropy.coordinates import EarthLocation

from astropy.coordinates import solar_system_ephemeris, get_body

from astropy.modeling import models, fitting

from ccdproc import ImageFileCollection

from bigmultipipe import no_outfile, cached_pout, prune_pout

from precisionguide import pgproperty

from IoIO.utils import (Lockfile, reduced_dir, get_dirs_dates,

closest_in_time, valid_long_exposure, im_med_min_max,

add_history, cached_csv, iter_polyfit,

savefig_overwrite)

from IoIO.simple_show import simple_show

from IoIO.cordata_base import CorDataBase

from IoIO.cormultipipe import (IoIO_ROOT, RAW_DATA_ROOT,

CorMultiPipeBase, angle_to_major_body,

nd_filter_mask, combine_masks)

from IoIO.calibration import Calibration, CalArgparseHandler

from IoIO.standard_star import (extinction_correct,

StandardStar, SSArgparseHandler)

# --> Consider making this CorPhotometry and allowing astrometric and

# --> photometric information to be siphoned off

from IoIO.photometry import Photometry

NA_BACK_ROOT = os.path.join(IoIO_ROOT, 'Na_back')

LOCKFILE = '/tmp/na_back_reduce.lock'

# For Photometry -- number of boxes for background calculation

N_BACK_BOXES = 20

def to_numpy(series_or_numpy):

return series_or_numpy

def sun_angle(ccd,

return ccd

def na_back_process(data,

return data

def na_back_pipeline(directory=None, # raw directory

return pout

def na_back_directory(directory,

return na_back_list

# Keep this stuff around just in case I want to do individual day

# stuff, though I have the dataset as a whole in NaBack to play with

# #tdf = df.loc[df['airmass'] < 2.0]

# tdf = df.loc[df['airmass'] < 2.5]

# mean_back = np.mean(tdf['best_back'])

# std_back = np.std(tdf['best_back'])

# biweight_back = biweight_location(tdf['best_back'])

# mad_std_back = mad_std(tdf['best_back'])

#

#

# # https://stackoverflow.com/questions/20664980/pandas-iterate-over-unique-values-of-a-column-that-is-already-in-sorted-order

# # and

# # https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html

#

# #https://matplotlib.org/stable/tutorials/intermediate/color_cycle.html

# offset_cycler = cycler(color=['r', 'g', 'b', 'y'])

# plt.rc('axes', prop_cycle=offset_cycler)

#

# f = plt.figure(figsize=[8.5, 11])

# plt.suptitle(f"Na background {just_date}")

# offset_groups = df.groupby(['raoff', 'decoff']).groups

# ax = plt.subplot(3, 1, 1)

# for offset_idx in offset_groups:

# gidx = offset_groups[offset_idx]

# gdf = df.iloc[gidx]

# plot_dates = julian2num(gdf['jd'])

# plt.plot_date(plot_dates, gdf['best_back'],

# label=f"dRA {gdf.iloc[0]['raoff']} "

# f"dDEC {gdf.iloc[0]['decoff']} armin")

# plt.axhline(y=biweight_back, color='red')

# plt.axhline(y=biweight_back+mad_std_back,

# linestyle='--', color='k', linewidth=1)

# plt.axhline(y=biweight_back-mad_std_back,

# linestyle='--', color='k', linewidth=1)

# plt.text(0.5, biweight_back + 0.1*mad_std_back,

# f'{biweight_back:.4f} +/- {mad_std_back:.4f}',

# ha='center', transform=ax.get_yaxis_transform())

# plt.xlabel('date')

# plt.ylabel('electron/s')

# ax.legend()

#

# ax = plt.subplot(3, 1, 2)

# for offset_idx in offset_groups:

# gidx = offset_groups[offset_idx]

# gdf = df.iloc[gidx]

# plt.plot(gdf['airmass'], gdf['instr_mag'], '.')

# #plt.axhline(y=biweight_back, color='red')

# #plt.axhline(y=biweight_back+mad_std_back,

# # linestyle='--', color='k', linewidth=1)

# #plt.axhline(y=biweight_back-mad_std_back,

# # linestyle='--', color='k', linewidth=1)

# plt.xlabel('Airmass')

# plt.ylabel('mag (electron/s/pix^2')

#

# ax = plt.subplot(3, 1, 3)

# for offset_idx in offset_groups:

# gidx = offset_groups[offset_idx]

# gdf = df.iloc[gidx]

# plt.plot(gdf['alt'], gdf['best_back'], '.')

# plt.axhline(y=biweight_back, color='red')

# plt.axhline(y=biweight_back+mad_std_back,

# linestyle='--', color='k', linewidth=1)

# plt.axhline(y=biweight_back-mad_std_back,

# linestyle='--', color='k', linewidth=1)

# plt.xlabel('Alt')

# plt.ylabel('electron/s')

#

# f.subplots_adjust(hspace=0.3)

# if write_plot is True:

# write_plot = os.path.join(rd, 'Na_back.png')

# if isinstance(write_plot, str):

# savefig_overwrite(write_plot, transparent=True)

# if show:

# plt.show()

# plt.close()

#

# # Problem discussed in https://mail.python.org/pipermail/tkinter-discuss/2019-December/004153.html

# gc.collect()

#

# return {'date': just_date,

# 'jd': np.floor(df['jd'].iloc[0]),

# 'biweight_back': biweight_back,

# 'mad_std_back': mad_std_back,

# 'na_back_list': na_back_list}

def na_back_tree(data_root=RAW_DATA_ROOT,

return to_plot_list

class NaBack():