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astro_exp_funcs.py
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import numpy as np
from astropy import units as u
import plotly.graph_objects as go
import lightkurve as lk
import itertools
from wotan import slide_clip
from wotan import flatten
from wotan import t14
from transitleastsquares import cleaned_array
from transitleastsquares import transitleastsquares
from transitleastsquares import catalog_info
from detecta import detect_peaks
colors=["#bcb056", "#d98556", "#d2617a", "#9c5aa2", "#1b60ad",
"#20ff0b", "#00dfb4", "#00b2ff", "#0078f6","#6a309a",
"#65c9ff","#8baaff", "#df75ea", "#ff158d", "#ff0000",
"#d25dfa", "#ff138a","#fa5916", "#a88d00", "#12a404",
"#a4eeff", "#96d5ff", "#c7afff", "#fb80c4", "#ff6363"]
def window_len(data, return_lc=False):
stellar_properties=catalog_info(KIC_ID=int(data[0].meta['KEPLERID']))
mass=float(stellar_properties[1])
radius=float(stellar_properties[4])
stitched=data.stitch()
quality_index = np.where(stitched['quality'] == 0)[0]
stitched=stitched[quality_index]
lc=stitched.flatten(window_length=901).remove_outliers(sigma_lower=np.Inf, sigma_upper=2)
period_guess = np.linspace(1, 150, 20000)
pg = lc.to_periodogram(method='bls', period=period_guess, frequency_factor=700)
period = float(pg.period_at_max_power/u.d)
tdur = t14(R_s=radius, M_s=mass, P=period, small_planet=True)
window_leng=3*tdur
if return_lc==False:
return(window_leng, period)
if return_lc==True:
return(window_leng, period, lc)
def tls_peak_periods(tls_results, mult=1.5):
remove_noise=list(tls_results.power[(tls_results.power > tls_results.stdev)])
avg=np.average(remove_noise)
dist=round(len(tls_results.periods)*0.0005)
peak_index=detect_peaks(tls_results.power, mph=mult*avg, mpd=dist,show=False)
max_peak_index= np.where(tls_results.periods == tls_results.period)[0][0]
peak_index=peak_index[peak_index!=max_peak_index]
peak_periods=tls_results.periods[peak_index]
return(peak_periods)
def harmonic_frac_trace(tls_results, mult=1.5):
harm_periods=tls_peak_periods(tls_results, mult=mult)
nones=[None] * len(harm_periods)
x_comp=list(itertools.chain(*zip(harm_periods, harm_periods, nones)))
x_comp=x_comp[:-1]
y_comp_pattern=[max(tls_results.power)+0.05*max(tls_results.power), -0.05*max(tls_results.power), None]
y_comp=y_comp_pattern*len(harm_periods)
y_comp=y_comp[:-1]
harm_frac_lines=go.Scattergl(x=x_comp,y=y_comp, mode='lines',
line=dict(color="#1fb927",
width=3,
dash="dot"), name='Other Significant <br> Peaks', opacity=0.5, )
return(harm_frac_lines)
def lightcurve_data(data, window_length=None, method='biweight', break_tolerance=0.5):
SAP=[]
PDCSAP=[]
norm=[]
time=[]
for x in range(0, len(data)-1):
sap_ind=data[x]['sap_flux'].to_value().tolist()
pdcsap_ind=data[x]['flux'].to_value().tolist() # source of the flux in the table is PDCSAP FLUX
norm_ind=data[x].normalize()['flux'].to_value().tolist()
time_ind=data[x]['time'].to_value('bkjd').tolist()
SAP.append(sap_ind)
PDCSAP.append(pdcsap_ind)
norm.append(norm_ind)
time.append(time_ind)
stitched=data.stitch()
quality_index = np.where(stitched['quality'] == 0)[0]
stitched=stitched[quality_index]
stitched_flux=stitched['pdcsap_flux'].to_value().tolist()
stitched_time=stitched['time'].to_value('bkjd').tolist()
if window_length == 'calc':
window_length=window_len(data)[0]
period=window_len(data)[1]
clipped_flux = slide_clip(stitched_time, stitched_flux,
window_length=window_length,
low=3,
high=2,
method='mad', # mad or std
center='median' # median or mean
)
if method == 'biweight':
flatten_lc=flatten(
stitched_time*u.day, # Array of time values
clipped_flux, # Array of flux values
window_length=window_length, method='biweight', return_trend=False)
if method== 'cofiam':
flatten_lc=flatten(
stitched_time*u.day, # Array of time values
clipped_flux, method='cofiam',
window_length=window_length, # Protected window span in units of ``time``
break_tolerance=break_tolerance,
return_trend=False, # Return trend and flattened light curve
)
detrended_time, detrended_flux= cleaned_array(stitched_time, flatten_lc)
return(PDCSAP, SAP, norm, time, detrended_flux, detrended_time, stitched, window_length, period)
if window_length == None:
window_length=0.5
clipped_flux = slide_clip(stitched_time, stitched_flux,
window_length=window_length,
low=3,
high=2,
method='mad', # mad or std
center='median' # median or mean
)
if method == 'biweight':
flatten_lc=flatten(
stitched_time*u.day, # Array of time values
clipped_flux, # Array of flux values
window_length=window_length, method='biweight', return_trend=False)
if method== 'cofiam':
flatten_lc=flatten(
stitched_time*u.day, # Array of time values
clipped_flux, method='cofiam',
window_length=window_length, # Protected window span in units of ``time``
break_tolerance=break_tolerance,
return_trend=False, # Return trend and flattened light curve
)
detrended_time, detrended_flux= cleaned_array(stitched_time, flatten_lc)
return (PDCSAP, SAP, norm, time, detrended_flux, detrended_time, stitched)
def lightcurve_traces(data, window_length=None, method='biweight', break_tolerance=0.5):
upper_range=len(data)-1
data2plot=lightcurve_data(data, window_length=window_length, method=method, break_tolerance=break_tolerance)
SAP_traces={}
PDCSAP_traces={}
norm_traces={}
fully_norm_flux_trace=go.Scattergl(x=data2plot[5], y=data2plot[4], mode='markers', marker_size=2, marker_color="#006e99")
for x in range(0, upper_range):
SAP_traces['SAP_trace_' + str(x)]=go.Scattergl(x=data2plot[3][x], name=f"Quarter {data[x].quarter}", y=data2plot[1][x], mode='markers', marker_size=2, marker_color=colors[x])
PDCSAP_traces['SAP_trace_' + str(x)]=go.Scattergl(x=data2plot[3][x], name=f"Quarter {data[x].quarter}", y=data2plot[0][x], mode='markers', marker_size=2, marker_color=colors[x])
norm_traces['SAP_trace_' + str(x)]=go.Scattergl(x=data2plot[3][x], name=f"Quarter {data[x].quarter}", y=data2plot[2][x], mode='markers', marker_size=2, marker_color=colors[x])
return (PDCSAP_traces, SAP_traces, norm_traces, fully_norm_flux_trace)
def lightcurve_plot(data, window_length=None, method='biweight', break_tolerance=0.5):
traces2plot=lightcurve_traces(data, window_length=window_length, method=method,break_tolerance=break_tolerance)
flux_unit=data[0]['flux'].unit.to_string()
time_unit=data[0]['time'].format
lc_name=data[0].meta['OBJECT']
t_ini_limit=float(data[0].meta['TSTART'])
t_fin_limit=float(data[len(data)-1].meta['TSTOP'])
PDCSAP_fig_data, SAP_fig_data=list(traces2plot[0].values()), list(traces2plot[1].values())
norm_fig_data, fully_norm_flux_fig_data=list(traces2plot[2].values()),traces2plot[3]
layout=go.Layout(xaxis=dict(range=[t_ini_limit-20, t_fin_limit+20], autorange=False, zeroline=False))
PDCSAP_fig, SAP_fig=go.Figure(PDCSAP_fig_data, layout), go.Figure(SAP_fig_data, layout)
norm_fig, fully_norm_flux_fig=go.Figure(norm_fig_data, layout), go.Figure(fully_norm_flux_fig_data, layout)
PDCSAP_fig.update_layout(hovermode=False, font_family="sans-serif", title={
'text': f"{lc_name}'s PDCSAP Lightcurve",
'y':0.85,
'x':0.5,
'xanchor': 'center',
'yanchor': 'top'}, plot_bgcolor='rgba(0,0,0,0)', yaxis_title=f"PDCSAP Flux [{flux_unit}]",
xaxis_title=f"Days [{time_unit}]", legend_title="Observation Quarters",
xaxis=dict(showline=True, showgrid=False, showticklabels=True, mirror=True,linecolor='black', linewidth=1.5, ticks='outside'),
yaxis=dict(showline=True, showgrid=False, showticklabels=True, mirror=True,linecolor='black', linewidth=1.5, ticks='outside'),
legend=dict(itemsizing='constant', font=dict(size=10, )))
SAP_fig.update_layout(hovermode=False, font_family="sans-serif", title={
'text': f"{lc_name}'s SAP Lightcurve",
'y':0.85,
'x':0.5,
'xanchor': 'center',
'yanchor': 'top'}, plot_bgcolor='rgba(0,0,0,0)', yaxis_title=f"SAP Flux [{flux_unit}]",
xaxis_title=f"Days [{time_unit}]",
xaxis=dict(showline=True, showgrid=False, showticklabels=True, mirror=True,linecolor='black', linewidth=1.5, ticks='outside'),
yaxis=dict(showline=True, showgrid=False, showticklabels=True, mirror=True,linecolor='black', linewidth=1.5, ticks='outside'),
legend_title="Observation Quarters", legend=dict(itemsizing='constant', font=dict(size=10, )))
norm_fig.update_layout(hovermode=False, font_family="sans-serif", title={
'text': f"{lc_name}'s Normalized Lightcurve",
'y':0.85,
'x':0.5,
'xanchor': 'center',
'yanchor': 'top'}, plot_bgcolor='rgba(0,0,0,0)', yaxis_title=f"Normalized Flux",
xaxis_title=f"Days [{time_unit}]",xaxis=dict(showline=True, showgrid=False, showticklabels=True, mirror=True,linecolor='black', linewidth=1.5, ticks='outside'),
yaxis=dict(showline=True, showgrid=False, showticklabels=True, mirror=True,linecolor='black', linewidth=1.5, ticks='outside'),
legend_title="Observation Quarters", legend=dict(itemsizing='constant', font=dict(size=10, )))
fully_norm_flux_fig.update_layout(hovermode=False, font_family="sans-serif", title={
'text': f"{lc_name}'s Normalized and Detrended Lightcurve",
'y':0.85,
'x':0.5,
'xanchor': 'center',
'yanchor': 'top'}, plot_bgcolor='rgba(0,0,0,0)', yaxis_title=f"Normalized Flux",
xaxis_title=f"Days [{time_unit}]",
xaxis=dict(showline=True, showgrid=False, showticklabels=True, mirror=True,linecolor='black', linewidth=1.5, ticks='outside'),
yaxis=dict(showline=True, showgrid=False, showticklabels=True, mirror=True,linecolor='black', linewidth=1.5, ticks='outside'),
legend=dict(itemsizing='constant', font=dict(size=10)))
return(PDCSAP_fig, SAP_fig, norm_fig, fully_norm_flux_fig)
def tls_results(data, max_period=None, period_window=0.6, method='biweight', break_tolerance=0.5):
flux_unit=data[0]['flux'].unit.to_string()
time_unit=data[0]['time'].format
if max_period==None or max_period=='calc':
model_data=lightcurve_data(data, window_length='calc', method=method, break_tolerance=break_tolerance)
tls_model=transitleastsquares(model_data[5], model_data[4])
results=tls_model.power(period_min=model_data[8]*period_window, period_max=(1+period_window)*model_data[8])
results.name=data[0].meta['OBJECT']
results.flux_unit=flux_unit
results.time_unit=time_unit
results.stdev=results.depth_mean[0]*np.sqrt(results.in_transit_count)/results.snr
return(results)
else:
model_data=lightcurve_data(data, window_length=None, method=method, break_tolerance=break_tolerance)
tls_model=transitleastsquares(model_data[5], model_data[4])
results=tls_model.power(period_min=max_period*period_window, period_max=max_period)
results.name=data[0].meta['OBJECT']
results.stdev=results.depth_mean[0]*np.sqrt(results.in_transit_count)/results.snr
return(results)
def tls_plotting(tls_results, mult=1.5, window=0.5):
y_axis_min=-0.05*max(tls_results.power)
y_axis_max=max(tls_results.power)+0.05*max(tls_results.power)
folded_mid_point=np.round(tls_results.model_folded_phase[np.where(tls_results.model_folded_model== min(tls_results.model_folded_model))][0],4)
model_phase_max_height=np.average(tls_results.folded_y)+2*np.std(tls_results.folded_y)
model_phase_min_height=min(tls_results.model_folded_model)-1.5*np.std(tls_results.folded_y)
harm_frac_lines=harmonic_frac_trace(tls_results, mult)
data_tls=go.Scattergl(x=tls_results.periods, y=tls_results.power, mode='lines',
line=dict(color="#468eb5", width=1.5),
name="SDE", showlegend=False),
layout_tls=go.Layout(xaxis=dict(range=[min(tls_results.periods), max(tls_results.periods)],
autorange=False, zeroline=False),
yaxis=dict(range=[y_axis_min,
y_axis_max],
autorange=False, zeroline=False)
)
tls_fig=go.Figure(data=data_tls, layout=layout_tls)
tls_fig.add_trace(harm_frac_lines)
tls_fig.add_trace(go.Scattergl(x=[tls_results.period, tls_results.period],y=[y_axis_min, y_axis_max], mode='lines',
line=dict(color="#a35680",
width=5,
),
name='Maximum Peak', opacity=0.4
)
)
tls_fig.update_layout(hovermode=False, font_family="sans-serif", yaxis_title="Spectral Density Estimation (SDE)", xaxis_title="Period [days]",
legend_font_size=10,
title={
'text': f"{tls_results.name}'s TLS Periodogram",
'y':0.85,
'x':0.5,
'xanchor': 'center',
'yanchor': 'top'},
legend={ "x": 1.01,
"y": 0.9,
"xanchor":"left",
"xref": "container",
"yref": "container",
"itemsizing":'constant',
"font":{"size":10}
},
plot_bgcolor='rgba(0,0,0,0)',
xaxis=dict(showline=True, showgrid=False, showticklabels=True, mirror=True,linecolor='black', linewidth=1.5, ticks='outside'),
yaxis=dict(showline=True, showgrid=False, showticklabels=True, mirror=True,linecolor='black', linewidth=1.5, ticks='outside')
)
tls_folded=go.Scattergl(x=tls_results.folded_phase, y=tls_results.folded_y, mode='markers', marker_size=2, marker=dict(color="#9e4d79"), name= "Binned Flux")
tls_model=go.Scattergl(x=tls_results.model_folded_phase, y=tls_results.model_folded_model, name= "TLS Model Flux", mode='lines',
line=dict(color= "#27685d", width=2))
tls_folded_data=[tls_folded, tls_model]
layout_tls_folded=go.Layout(xaxis=dict(range=[folded_mid_point*(1-window), folded_mid_point*(1+window)],
autorange=False, zeroline=False),
yaxis=dict(range=[model_phase_min_height,
model_phase_max_height],
autorange=False, zeroline=False)
)
tls_folded_fig=go.Figure(data=tls_folded_data, layout=layout_tls_folded)
tls_folded_fig.update_layout(hovermode=False, font_family="sans-serif", yaxis_title="Normalized Flux", xaxis_title="Phase",
legend_font_size=10,
title={
'text': f"{tls_results.name}'s Folded Lightcurve",
'y':0.85,
'x':0.5,
'xanchor': 'center',
'yanchor': 'top'},
legend={ "x": 1.01,
"y": 0.9,
"xanchor":"left",
"xref": "container",
"yref": "container",
"itemsizing":'constant',
"font":{"size":10}
},
plot_bgcolor='rgba(0,0,0,0)',
xaxis=dict(showline=True, showgrid=False, showticklabels=True, mirror=True,linecolor='black', linewidth=1.5, ticks='outside'),
yaxis=dict(showline=True, showgrid=False, showticklabels=True, mirror=True,linecolor='black', linewidth=1.5, ticks='outside'),
)
return(tls_fig, tls_folded_fig)