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TDSS_VarStar_ViP_GUI.py
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TDSS_VarStar_ViP_GUI.py
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import matplotlib
matplotlib.use('TkAgg')
import matplotlib.style as mplstyle
mplstyle.use('fast')
from matplotlib.ticker import NullFormatter # useful for `logit` scale
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
import PySimpleGUI as sg
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
from matplotlib.gridspec import GridSpec
import matplotlib.image as mpimg
from mpl_toolkits.axes_grid1 import make_axes_locatable
from urllib.parse import urlencode
from urllib.request import urlretrieve
import numpy as np
import numpy.core.defchararray as np_f
import pandas as pd
import scipy as sci
from scipy.stats import f
from scipy.stats import kde
from subprocess import *
import os
import glob
from pathlib import Path
import re
from astropy.table import Table
from astropy import constants as const
from astropy import units as u
from astropy.io import fits
from astropy import coordinates as coords
from astropy.coordinates import SkyCoord
import importlib
import tqdm
import time
import warnings
import ResearchTools.LCtools as LCtools
import VarStar_Vi_plot_functions as vi
from astropy.timeseries import LombScargle
from sklearn.cluster import MeanShift, estimate_bandwidth
# ------------------------------- ViP Initialization CODE -------------------------------
def freq2per(frequency, period_unit=u.d):
return (frequency**-1).to(period_unit)
def per2freq(period, frequency_unit=u.microHertz):
return (period**-1).to(frequency_unit)
spec_dir = "/Users/benjaminroulston/Dropbox/Research/TDSS/Variable_Stars/HARD_COPY_ORGINAL_DATA/SDSS_spec/02-26-2020/SDSSspec/"
CSS_LC_dir = "/Users/benjaminroulston/Dropbox/Research/TDSS/Variable_Stars/HARD_COPY_ORGINAL_DATA/CSS_LCs/csvs/"
ZTF_LC_dir = "/Users/benjaminroulston/Dropbox/Research/TDSS/Variable_Stars/HARD_COPY_ORGINAL_DATA/ZTF/DATA/06-24-2020/"
ZTF_filters = ['g', 'r']
ZTF_LC_file_names = [f'TDSS_SES+PREV_DR16DR12griLT20_GAIADR2_Drake2014PerVar_ZTF_{ZTF_filter}_epochGT10_GroupID.fits' for ZTF_filter in ZTF_filters]
ZTF_g_LCs = Table.read(ZTF_LC_dir + ZTF_LC_file_names[0])
ZTF_r_LCs = Table.read(ZTF_LC_dir + ZTF_LC_file_names[1])
prop_out_dir, CSS_LC_plot_dir, ZTF_LC_plot_dir, Vi_plots_dir, datestr = vi.makeViDirs()
nbins = 50
TDSSprop = vi.TDSSprop(nbins)
latestFullVartoolsRun_filename = "completed_Vi_prop_2020-07-16.csv"
latestFullVartoolsRun = vi.latestFullVartoolsRun(prop_out_dir + latestFullVartoolsRun_filename)
hasViRun, prop_id_last, properties = vi.checkViRun() # if Vi has run, this will find where it let off and continue propid from there
prop_col_names_prefix = ['CSS_', 'ZTF_g_', 'ZTF_r_']
runLS = True
plotLCerr = True
plt_resid = False
plt_subLC = True
plot_rejected = False
checkHarmonic = True
logProblimit = -10
Nepochs_required = 10
minP = 0.1
maxP = 100.0
nterms_LS = 1
prop = Table.read("/Users/benjaminroulston/Dropbox/Research/TDSS/Variable_Stars/HARD_COPY_ORGINAL_DATA/PROGRAM_SAMPLE/2020-06-24/FINAL_FILES/TDSS_SES+PREV_DR16DR12griLT20_GAIADR2_Drake2014PerVar_VSX_CSSID_ZTFIDs_LCpointer_PyHammer_VI_ALLPROP_07-27-2020.fits")
def get_objectLC(prop_id, TDSSprop, LCsurvey):
# prop_id = 5 # 12
ROW = TDSSprop[prop_id]
object_ra = ROW['ra']
object_dec = ROW['dec']
ra_string = '{:0>9.5f}'.format(object_ra)
dec_string = '{:0=+9.5f}'.format(object_dec)
is_CSS = ROW['CSSLC']
is_ZTF_g = np.isfinite(ROW['ZTF_g_GroupID'])
is_ZTF_r = np.isfinite(ROW['ZTF_r_GroupID'])
if (LCsurvey == 'CSS') and is_CSS:
lc_file = CSS_LC_dir+str(ROW['CSSID'])+'.dat'
CSS_lc_data = Table.read(lc_file, format='ascii', names=['mjd', 'mag', 'magerr'])
CSS_lc_data.sort('mjd')
return ra_string, dec_string, CSS_lc_data
if (LCsurvey == 'ZTF_g') and is_ZTF_g:
ZTF_g_lc_data = ZTF_g_LCs[(ZTF_g_LCs['GroupID'] == ROW['ZTF_g_GroupID'])]['mjd', 'mag', 'magerr']
ZTF_g_lc_data.sort('mjd')
return ra_string, dec_string, ZTF_g_lc_data
if (LCsurvey == 'ZTF_r') and is_ZTF_r:
ZTF_r_lc_data = ZTF_r_LCs[(ZTF_r_LCs['GroupID'] == ROW['ZTF_r_GroupID'])]['mjd', 'mag', 'magerr']
ZTF_r_lc_data.sort('mjd')
return ra_string, dec_string, ZTF_r_lc_data
return None, None, None
def process_LC(ra_string, dec_string, lc_data, LCsurvey):
flc_data, LC_stat_properties = LCtools.process_LC(lc_data.copy(), fltRange=5.0)
select_properties, all_properties = LCtools.perdiodSearch(flc_data, minP=0.1, maxP=100.0)
# {'P': best_period, 'omega_best': omega_best, 'is_Periodic': is_Periodic,
# 'logProb': best_period_FAP, 'Amp': Amp, 'isAlias': isAlias,
# 'time_whittened': time_whittened, 'ls': ls, 'frequency': frequency, 'power': power,
# 'minP': minP, 'maxP': maxP, 'AFD': AFD_data, 't0': t0}
P = all_properties["P"]
freq_grid = all_properties['frequency']
power = all_properties['power']
logFAP_limit = -10
FAP_power_peak = all_properties['ls'].false_alarm_level(10**logFAP_limit)
df = (1 * u.d)**-1
title = "RA: {!s} DEC: {!s} {!s}".format(ra_string, dec_string, LCsurvey)
return flc_data, P, freq_grid, power, FAP_power_peak, logFAP_limit, df, title, all_properties
# LCsurvey = 'CSS'
# ra_string, dec_string, lc_data = get_objectLC(5, TDSSprop, LCsurvey)
# flc_data, P, freq_grid, power, FAP_power_peak, logFAP_limit, df, title, all_properties = process_LC(ra_string, dec_string, lc_data, LCsurvey)
# fig = plot_LC_analysis_ALLaliases(flc_data, P, freq_grid, power, FAP_power_peak=FAP_power_peak, logFAP_limit=logFAP_limit, df=df, title=title)
# def plot_LC_analysis_ALLaliases(lc_data, P, frequency, power, FAP_power_peak=None, logFAP_limit=None, df=None, title=""):
# fig = plt.figure(figsize=(18, 9), constrained_layout=False)
# gs = GridSpec(6, 4, figure=fig)
# ax1 = fig.add_subplot(gs[0, :2])
# ax2 = fig.add_subplot(gs[1, :2])
# ax3 = fig.add_subplot(gs[:2, 2])
# ax4 = fig.add_subplot(gs[:2, 3])
# ax5 = fig.add_subplot(gs[2:4, 0])
# ax6 = fig.add_subplot(gs[2:4, 1])
# ax7 = fig.add_subplot(gs[2:4, 2])
# ax8 = fig.add_subplot(gs[2:4, 3])
# ax9 = fig.add_subplot(gs[4:, 0])
# ax10 = fig.add_subplot(gs[4:, 1])
# ax11 = fig.add_subplot(gs[4:, 2])
# ax12 = fig.add_subplot(gs[4:, 3])
# LCtools.plot_powerspec(frequency, power, ax1=ax1, ax2=ax2, FAP_power_peak=FAP_power_peak, logFAP_limit=logFAP_limit, alias_df=df, title=title)
# LCtools.plt_any_lc_ax(lc_data, P, is_Periodic=False, ax=ax3, title="", phasebin=True, bins=25)
# LCtools.plt_any_lc_ax(lc_data, 1.0*P, is_Periodic=True, ax=ax4, title="", phasebin=True, bins=25)
# LCtools.plt_any_lc_ax(lc_data, 0.5*P, is_Periodic=True, ax=ax5, title="(1/2)", phasebin=True, bins=25)
# LCtools.plt_any_lc_ax(lc_data, 2.0* P, is_Periodic=True, ax=ax9, title="2", phasebin=True, bins=25)
# LCtools.plt_any_lc_ax(lc_data, (1/3)*P, is_Periodic=True, ax=ax6, title="(1/3)", phasebin=True, bins=25)
# LCtools.plt_any_lc_ax(lc_data, 3.0* P, is_Periodic=True, ax=ax10, title="3", phasebin=True, bins=25)
# LCtools.plt_any_lc_ax(lc_data, 0.25*P, is_Periodic=True, ax=ax7, title="(1/4)", phasebin=True, bins=25)
# LCtools.plt_any_lc_ax(lc_data, 4.0* P, is_Periodic=True, ax=ax11, title="4", phasebin=True, bins=25)
# LCtools.plt_any_lc_ax(lc_data, 0.2*P, is_Periodic=True, ax=ax8, title="(1/5)", phasebin=True, bins=25)
# LCtools.plt_any_lc_ax(lc_data, 5.0* P, is_Periodic=True, ax=ax12, title="5", phasebin=True, bins=25)
# ax3.set_title("")
# fig.tight_layout()
# return fig
def plot_LC_analysis_ALLaliases(lc_data, P, frequency, power, FAP_power_peak=None, logFAP_limit=None, df=None, title=""):
fig = plt.figure(figsize=(18, 9), constrained_layout=False)
gs = GridSpec(2, 6, figure=fig)
ax1 = fig.add_subplot(gs[0, 0:2])
ax2 = fig.add_subplot(gs[0, 2:4])
ax3 = fig.add_subplot(gs[0, 4:6])
ax4 = fig.add_subplot(gs[1, 1:3])
ax5 = fig.add_subplot(gs[1, 3:5])
LCtools.plt_any_lc_ax(lc_data, 1.0*P, is_Periodic=True, ax=ax2, title="", phasebin=True, bins=25)
LCtools.plt_any_lc_ax(lc_data, 0.5*P, is_Periodic=True, ax=ax1, title="(1/2)", phasebin=True, bins=25)
LCtools.plt_any_lc_ax(lc_data, 2.0* P, is_Periodic=True, ax=ax3, title="2", phasebin=True, bins=25)
LCtools.plt_any_lc_ax(lc_data, (1/3)*P, is_Periodic=True, ax=ax4, title="(1/3)", phasebin=True, bins=25)
LCtools.plt_any_lc_ax(lc_data, 3.0* P, is_Periodic=True, ax=ax5, title="3", phasebin=True, bins=25)
fig.tight_layout()
return fig
# ------------------------------- START OF YOUR MATPLOTLIB CODE -------------------------------
def draw_plot(n):
fig = matplotlib.figure.Figure(figsize=(18, 9), dpi=100)
t = np.arange(0, 3, .01)
fig.add_subplot(111).plot(t, 2 * np.sin(2 * np.pi * t / n))
#fig.tight_layout()
return fig
# def draw_plot(lc_data):
# fig = plt.figure(figsize=(5, 4))
# fig.add_subplot(111).scatter(lc_data['mjd'], lc_data['mag'])
# return fig
# fig = draw_plot(flc_data)
# ------------------------------- END OF YOUR MATPLOTLIB CODE -------------------------------
# ------------------------------- Beginning of Matplotlib helper code -----------------------
def draw_figure(canvas, figure):
figure_canvas_agg = FigureCanvasTkAgg(figure, canvas)
figure_canvas_agg.draw()
figure_canvas_agg.get_tk_widget().pack(side='top', fill='both', expand=1)
return figure_canvas_agg
def delete_figure_agg(figure_agg):
figure_agg.get_tk_widget().forget()
plt.close('all')
# ------------------------------- Beginning of GUI CODE -------------------------------
# fig = draw_plot(n)
# fig_canvas_agg = draw_figure(window['-CANVAS-'].TKCanvas, fig)
vartypes = np.array(['RRab', 'RRc', 'EA', 'EB/EW', 'Single Min', 'Delta Scuti', 'Unknown', 'Non-periodic'])
harmonics = np.array([1 / 3, 1 / 2, 1, 2, 3])
LCsurvey = 'CSS'
prop = Table.read("/Users/benjaminroulston/Dropbox/Research/TDSS/Variable_Stars/HARD_COPY_ORGINAL_DATA/PROGRAM_SAMPLE/2020-06-24/FINAL_FILES/TDSS_SES+PREV_DR16DR12griLT20_GAIADR2_Drake2014PerVar_VSX_CSSID_ZTFIDs_LCpointer_PyHammer_VI_ALLPROP_07-27-2020.fits")
periodic_prop = Table.read("/Users/benjaminroulston/Desktop/completed_Vi_prop_2020-08-12_speedtest_PeriodOnly.fits")
all_coords = SkyCoord(ra=prop['ra'] * u.degree, dec=prop['dec'] * u.degree, frame='icrs')
periodic_coords = SkyCoord(ra=periodic_prop['ra'] * u.degree, dec=periodic_prop['dec'] * u.degree, frame='icrs')
all_prop_index = []
for ii in range(len(periodic_prop)):
this_prop_index = np.argmin(all_coords.separation(periodic_coords[ii]))
all_prop_index.append(this_prop_index)
for key in periodic_prop.columns.keys()[4:]:
prop[this_prop_index][key] = periodic_prop[ii][key]
all_prop_index = np.array(all_prop_index)
prop.add_column(col=prop['gmag_SDSSDR12'] - prop['imag_SDSSDR12'], name='gmi')
lc_prefixs = ['CSS_', 'ZTF_g_', 'ZTF_r_']
prop_col_names = ['lc_id', 'P', 'logProb', 'Amp', 'isAlias', 'time_whittened']
this_lc_index = np.where(prop[all_prop_index][f"{LCsurvey}_P"] != 0)[0]
def run_plot(n, prop, LCsurvey):
ra_string, dec_string, lc_data = get_objectLC(n, prop, LCsurvey)
if ra_string is None:
return None, None
flc_data, P, freq_grid, power, FAP_power_peak, logFAP_limit, df, title, all_properties = process_LC(ra_string, dec_string, lc_data, LCsurvey)
fig = plot_LC_analysis_ALLaliases(flc_data, P, freq_grid, power, FAP_power_peak=FAP_power_peak, logFAP_limit=logFAP_limit, df=df, title=title)
return fig, all_properties
ii = 0 # ii runs over the length of the peropdic objects
n = all_prop_index[this_lc_index[ii]] # n convets ii from the range of only periodic objects into the entire 23,595 prop Table
total_N = this_lc_index.size
period_alias_frac = np.ones(total_N)
variable_type = np.empty(total_N, dtype="<U15")
prop_col_names = ['lc_id', 'P', 'logProb', 'Amp', 'isAlias', 'time_whittened']
recalc_stats_keys = ["ra", "dec"]
recalc_stats_keys.extend([f"{LCsurvey}_{col}" for col in prop_col_names])
new_Lc_prop = prop[all_prop_index[this_lc_index]][recalc_stats_keys].copy()
new_Lc_prop.add_column(np.ones(len(new_Lc_prop)), name=f"{LCsurvey}_Pharm")
new_Lc_prop.add_column(np.empty(len(new_Lc_prop), dtype="<U15"), name=f"{LCsurvey}_VarType")
col_HarmonicSelector = sg.Col([[sg.Radio('1/3 P', "Harmonic", default=False, key="-1/3P-")],
[sg.Radio('1/2 P', "Harmonic", default=False, key="-1/2P-")],
[sg.Radio(' P', "Harmonic", default=True, key="-1P-")],
[sg.Radio(' 2P', "Harmonic", default=False, key="-2P-")],
[sg.Radio(' 3P', "Harmonic", default=False, key="-3P-")]])
col_VarTypeSelector = sg.Col([[sg.Radio('RRab', "Type", default=False, key="-RRab-")],
[sg.Radio('RRc', "Type", default=False, key="-RRc-")],
[sg.Radio('EA', "Type", default=False, key="-EA-")],
[sg.Radio('EB/EW', "Type", default=False, key="-EB/EW-")],
[sg.Radio('Single Min', "Type", default=False, key="-SingleMin-")],
[sg.Radio('Delta Scuti', "Type", default=False, key="-DeltaScuti-")],
[sg.Radio('Unknown', "Type", default=True, key="-Unknown-")],
[sg.Radio('Non-periodic', "Type", default=True, key="-Non-periodic-")]])
col_previous = sg.Col([[sg.Button("Previous Object")], [sg.Button("Previous LC Survey")]])
col_next = sg.Col([[sg.Button("Next Object")], [sg.Button("Next LC Survey")]])
sg.theme('Default')
title = "TDSS VarStar Period ViP"
figure_w, figure_h = 2048, 1280
plt_w, plt_h = 2000, 800
layout = [[sg.Text(title)],
[sg.Canvas(key='-CANVAS-', size=(plt_w, plt_h))],
[sg.Button('Exit')],
[col_HarmonicSelector, col_VarTypeSelector, sg.Button("Previous"), sg.Button("Next")]]
# create the form and show it without the plot
window = sg.Window('TDSS Variable Star Visual Inspection - Period Aliasing', layout,
size=(figure_w, figure_h), resizable=True, finalize=True,
element_justification='center', font='Helvetica 18')
# add the plot to the window
ra_string, dec_string, lc_data = get_objectLC(n, prop, LCsurvey)
flc_data, P, freq_grid, power, FAP_power_peak, logFAP_limit, df, title, all_properties = process_LC(ra_string, dec_string, lc_data, LCsurvey)
fig = plot_LC_analysis_ALLaliases(flc_data, P, freq_grid, power, FAP_power_peak=FAP_power_peak, logFAP_limit=logFAP_limit, df=df, title=title)
fig_canvas_agg = draw_figure(window['-CANVAS-'].TKCanvas, fig)
while True:
print(ii, n)
event, values = window.read()
print(event, values)
if (event == 'Exit') or (event == sg.WIN_CLOSED):
period_alias_frac[ii] = harmonics[np.where(np.array([values["-1/3P-"], values["-1/2P-"], values["-1P-"], values["-2P-"], values["-3P-"]]))[0][0]]
variable_type[ii] = vartypes[np.where(np.array([values["-RRab-"], values["-RRc-"], values["-EA-"], values["-EB/EW-"], values["-SingleMin-"], values["-DeltaScuti-"], values["-Unknown-"], values["-Non-periodic-"]]))[0][0]]
prop_col_names = ['P', 'logProb', 'Amp', 'isAlias', 'time_whittened']
recalc_stats_keys = [f"{LCsurvey}_{col}" for col in prop_col_names]
new_Lc_prop[ii][recalc_stats_keys] = all_properties['P'], all_properties['logProb'], all_properties['Amp'], all_properties['isAlias'], all_properties['time_whittened']
new_Lc_prop[ii][f"{LCsurvey}_Pharm"] = period_alias_frac[ii]
new_Lc_prop[ii][f"{LCsurvey}_VarType"] = variable_type[ii]
new_Lc_prop.write(f"temp_prop_final_{LCsurvey}.fits", format='fits', overwrite=True)
window.close()
break
if fig_canvas_agg:
# ** IMPORTANT ** Clean up previous drawing before drawing again
delete_figure_agg(fig_canvas_agg)
if event == 'Previous':
period_alias_frac[ii] = harmonics[np.where(np.array([values["-1/3P-"], values["-1/2P-"], values["-1P-"], values["-2P-"], values["-3P-"]]))[0][0]]
variable_type[ii] = vartypes[np.where(np.array([values["-RRab-"], values["-RRc-"], values["-EA-"], values["-EB/EW-"], values["-SingleMin-"], values["-DeltaScuti-"], values["-Unknown-"], values["-Non-periodic-"]]))[0][0]]
prop_col_names = ['P', 'logProb', 'Amp', 'isAlias', 'time_whittened']
recalc_stats_keys = [f"{LCsurvey}_{col}" for col in prop_col_names]
new_Lc_prop[ii][recalc_stats_keys] = all_properties['P'], all_properties['logProb'], all_properties['Amp'], all_properties['isAlias'], all_properties['time_whittened']
new_Lc_prop[ii][f"{LCsurvey}_Pharm"] = period_alias_frac[ii]
new_Lc_prop[ii][f"{LCsurvey}_VarType"] = variable_type[ii]
if ii != 0:
ii -= 1
n = all_prop_index[this_lc_index[ii]]
fig, all_properties = run_plot(n, prop, LCsurvey)
fig_canvas_agg = draw_figure(window['-CANVAS-'].TKCanvas, fig)
elif event == 'Next':
period_alias_frac[ii] = harmonics[np.where(np.array([values["-1/3P-"], values["-1/2P-"], values["-1P-"], values["-2P-"], values["-3P-"]]))[0][0]]
variable_type[ii] = vartypes[np.where(np.array([values["-RRab-"], values["-RRc-"], values["-EA-"], values["-EB/EW-"], values["-SingleMin-"], values["-DeltaScuti-"], values["-Unknown-"], values["-Non-periodic-"]]))[0][0]]
prop_col_names = ['P', 'logProb', 'Amp', 'isAlias', 'time_whittened']
recalc_stats_keys = [f"{LCsurvey}_{col}" for col in prop_col_names]
new_Lc_prop[ii][recalc_stats_keys] = all_properties['P'], all_properties['logProb'], all_properties['Amp'], all_properties['isAlias'], all_properties['time_whittened']
new_Lc_prop[ii][f"{LCsurvey}_Pharm"] = period_alias_frac[ii]
new_Lc_prop[ii][f"{LCsurvey}_VarType"] = variable_type[ii]
if ii != total_N:
ii += 1
n = all_prop_index[this_lc_index[ii]]
elif ii == total_N: # end of inspection
window.close()
break
fig, all_properties = run_plot(n, prop, LCsurvey)
fig_canvas_agg = draw_figure(window['-CANVAS-'].TKCanvas, fig)
print(ii, n)
if (ii % 100) == 0:
new_Lc_prop.write(f"temp_prop_{LCsurvey}.fits", format='fits', overwrite=True)