TRICERATOPS

https://github.com/stevengiacalone/triceratops
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TRICERATOPS is a statistical validation tool for vetting and validating transiting exoplanet...
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### Install via PyPI

Source: https://github.com/stevengiacalone/triceratops/blob/master/docs/user/install.md

Installs the most recently released version of the tool via PyPI.

```bash
pip install triceratops
```

--------------------------------

### Install in a conda environment

Source: https://github.com/stevengiacalone/triceratops/blob/master/docs/user/install.md

Creates and activates a new conda environment, then installs the tool and jupyterlab.

```bash
conda create -n myenv python=3.8
conda activate myenv
pip install triceratops jupyterlab
```

--------------------------------

### Example initialization

Source: https://github.com/stevengiacalone/triceratops/blob/master/examples/kepler_example.ipynb

This code snippet demonstrates how to initialize and use the .calc_prob method.

```python
import numpy as np
from triceratops.triceratops import Triceratops

# Example data (replace with your actual data)
time = np.linspace(0, 1, 100) # Example time in days
flux_0 = np.ones(100) # Example normalized flux
flux_err_0 = np.full(100, 0.01) # Example flux error
P_orb = 1.5 # Example orbital period in days

triceratops_instance = Triceratops()
probabilities = triceratops_instance.calc_prob(time, flux_0, flux_err_0, P_orb)
print(probabilities)
```

--------------------------------

### Installation

Source: https://github.com/stevengiacalone/triceratops/blob/master/README.rst

You can install the most recently released version of this tool via PyPI.

```bash
$ pip install triceratops

```

--------------------------------

### Example Data and Method Call

Source: https://github.com/stevengiacalone/triceratops/blob/master/examples/example.ipynb

Shows example data that might be used with the calc_prob method and the method signature.

```python
20       NaN     NaN       NaN       169.752      222.255  1.765258e-21   
21       NaN     NaN  0.089077       172.176      124.825  4.631424e-20   
22       NaN     NaN       NaN       185.052      303.511  4.447183e-26   
23  0.496960  5080.0  0.683115       190.431      134.474  3.037689e-24   
24       NaN  3583.0  0.907077       193.543      311.921  2.707183e-27   
25       NaN     NaN       NaN       202.347      307.577  1.995252e-30   

      tdepth  
0   0.005007  
1   0.000000  
2   0.000000  
3   0.000000  
4   0.000000  
5   0.000000  
6   0.000000  
7   0.000000  
8   0.000000  
9   0.000000  
10  0.000000  
11  0.000000  
12  0.000000  
13  0.000000  
14  0.000000  
15  0.000000  
16  0.000000  
17  0.000000  
18  0.000000  
19  0.000000  
20  0.000000  
21  0.000000  
22  0.000000  
23  0.000000  
24  0.000000  
25  0.000000  

```

--------------------------------

### Example Data Output

Source: https://github.com/stevengiacalone/triceratops/blob/master/docs/tutorials/example.ipynb

This snippet shows a sample output from a data processing step.

```text
28   0.762538       184.552       73.669  1.345936e-22  0.000000  
29   0.802713       196.897      151.108  5.313263e-24  0.000000  
30   0.194394       198.877      192.424  1.544053e-25  0.000000  
31   1.127660       200.705      343.072  1.367531e-23  0.000000  
```

--------------------------------

### Plot Transit Fits

Source: https://github.com/stevengiacalone/triceratops/blob/master/docs/tutorials/kepler_example.ipynb

Plots the transit fits for each scenario.

```python
target.plot_fits(time=lc.time.value, flux_0=lc.flux.value, flux_err_0=np.mean(lc.flux_err.value))
```

--------------------------------

### Read in the light curve

Source: https://github.com/stevengiacalone/triceratops/blob/master/docs/tutorials/example.ipynb

Reads a CSV light curve file and initializes a TessLightCurve object.

```python
lc = pd.read_csv("TOI465_01_lightcurve.csv", header=None)
time, flux, flux_err = lc[0].values, lc[1].values, lc[2].values
P_orb = 3.836169

lc_binsize = (time.max()-time.min())/100
lc = TessLightCurve(time=time, flux=flux, flux_err=flux_err).bin(time_bin_size=lc_binsize)

target.calc_probs(time=lc.time.value, flux_0=lc.flux.value, flux_err_0=np.mean(lc.flux_err.value), P_orb=P_orb)
```

--------------------------------

### Help - Create Conda Environment

Source: https://github.com/stevengiacalone/triceratops/blob/master/README.rst

If you are having trouble getting triceratops working on your machine, I recommend installing it in a fresh conda environment. You can download the latest distribution of anaconda here. After doing so, run the following in terminal:

```bash
$ conda create -n myenv python=3.8
$ conda activate myenv
(myenv) $ pip install triceratops jupyterlab

```

--------------------------------

### Import necessary libraries

Source: https://github.com/stevengiacalone/triceratops/blob/master/docs/tutorials/kepler_example.ipynb

Imports the required libraries for the TRICERATOPS analysis, including numpy, pandas, lightkurve, sys, matplotlib, and triceratops.

```python
import numpy as np
import pandas as pd
import time
from lightkurve import KeplerLightCurve
import sys
import matplotlib.pyplot as plt
%matplotlib inline

import triceratops.triceratops as tr

import warnings
warnings.filterwarnings('ignore')
```

--------------------------------

### Calculate Probabilities

Source: https://github.com/stevengiacalone/triceratops/blob/master/docs/tutorials/kepler_example.ipynb

Calculates the probabilities for different scenarios (TP, EB, EBx2P, etc.) using the calc_probs method.

```python
%%time

target.calc_probs(time=lc.time.value, flux_0=lc.flux.value, flux_err_0=np.mean(lc.flux_err.value), 
                  P_orb=P_orb, parallel=True)
```

--------------------------------

### Initialize target object

Source: https://github.com/stevengiacalone/triceratops/blob/master/docs/tutorials/example.ipynb

Initializes the target object with the star's ID and the sectors it was observed in.

```python
ID = 100990000
sectors = np.array([3,4])
target = tr.target(ID=ID, sectors=sectors)
```

--------------------------------

### Calculate Probabilities

Source: https://github.com/stevengiacalone/triceratops/blob/master/examples/kepler_example.ipynb

Calculates transit, eclipse, and other scenario probabilities using the light curve data.

```python
target.calc_probs(time=lc.time.value, flux_0=lc.flux.value, flux_err_0=np.mean(lc.flux_err.value), 
                  P_orb=P_orb, parallel=True)
```

--------------------------------

### Calculate Probability

Source: https://github.com/stevengiacalone/triceratops/blob/master/docs/tutorials/kepler_example.ipynb

This code snippet shows how to calculate the probability using the .calc_prob method.

```python
tdepth
0    0.00019
1    0.00000
2    0.00000
3    0.00000
4    0.00000
5    0.00000

```

```python
After doing this, we can calculate the probability of each scenario using the .calc_prob(time, flux_0, flux_err_0, P_orb) method, which requires as arguments time (times from phase-folded light curve in units of days from transit center), flux_0 (normalized flux from phase-folded light curve), flux_err_0 (flux error values of the target's phase-folded light curve), and P_orb (orbital period of the TOI in days).
```

--------------------------------

### Example Data

Source: https://github.com/stevengiacalone/triceratops/blob/master/docs/tutorials/example.ipynb

A sample of tabular data with NaN values and scientific notation.

```text
20       NaN     NaN       NaN       169.752      222.255  1.765258e-21   
21       NaN     NaN  0.089077       172.176      124.825  4.631424e-20   
22       NaN     NaN       NaN       185.052      303.511  4.447183e-26   
23  0.496960  5080.0  0.683115       190.431      134.474  3.037689e-24   
24       NaN  3583.0  0.907077       193.543      311.921  2.707183e-27   
25       NaN     NaN       NaN       202.347      307.577  1.995252e-30   

      tdepth  
0   0.005007  
1   0.000000  
2   0.000000  
3   0.000000  
4   0.000000  
5   0.000000  
6   0.000000  
7   0.000000  
8   0.000000  
9   0.000000  
10  0.000000  
11  0.000000  
12  0.000000  
13  0.000000  
14  0.000000  
15  0.000000  
16  0.000000  
17  0.000000  
18  0.000000  
19  0.000000  
20  0.000000  
21  0.000000  
22  0.000000  
23  0.000000  
24  0.000000  
25  0.000000  
```

--------------------------------

### Time Measurement

Source: https://github.com/stevengiacalone/triceratops/blob/master/docs/tutorials/example.ipynb

A Python code snippet to measure execution time.

```python
%%time


```

--------------------------------

### Read Kepler-10b Light Curve

Source: https://github.com/stevengiacalone/triceratops/blob/master/docs/tutorials/kepler_example.ipynb

Reads the Kepler-10b light curve data from a CSV file and initializes a KeplerLightCurve object.

```python
import pandas as pd
import numpy as np
from triceratops.analysis import KeplerLightCurve

# read in the light curve
lc = pd.read_csv("Kepler10b_lightcurve.csv", header=None)
time, flux, flux_err = lc[0].values, lc[1].values, lc[2].values
P_orb = 0.837

mask = ~np.isnan(flux)
lc = KeplerLightCurve(time=time[mask], flux=flux[mask], flux_err=flux_err[mask])

lc.plot();
```

--------------------------------

### Plot transit fits

Source: https://github.com/stevengiacalone/triceratops/blob/master/docs/tutorials/example.ipynb

Plots the transit fits for the calculated scenarios.

```python
target.plot_fits(time=time, flux_0=flux, flux_err_0=np.mean(flux_err))
```

--------------------------------

### Deactivate conda environment

Source: https://github.com/stevengiacalone/triceratops/blob/master/docs/user/install.md

Exits the current conda environment.

```bash
conda deactivate
```

--------------------------------

### Get SPOC apertures

Source: https://github.com/stevengiacalone/triceratops/blob/master/examples/TSCIII_tutorial.ipynb

Retrieves the SPOC aperture used for the target in any given sector.

```python
aps = target.get_spoc_apertures()
print(aps)
```

--------------------------------

### Read Light Curve and Calculate Probabilities

Source: https://github.com/stevengiacalone/triceratops/blob/master/docs/tutorials/example.ipynb

Reads a CSV light curve, processes it, and calculates scenario probabilities.

```python
lc = pd.read_csv("TOI411_02_lightcurve.csv", header=None)
time, flux, flux_err = lc[0].values, lc[1].values, lc[2].values
P_orb = 4.040051

lc_binsize = (time.max()-time.min())/100
lc = TessLightCurve(time=time, flux=flux, flux_err=flux_err).bin(time_bin_size=lc_binsize)

target.calc_probs(time=lc.time.value, flux_0=lc.flux.value, flux_err_0=np.mean(lc.flux_err.value), P_orb=P_orb,
                  parallel=True)
```

--------------------------------

### Display Results Table and Calculate FPP/NFPP

Source: https://github.com/stevengiacalone/triceratops/blob/master/docs/tutorials/kepler_example.ipynb

Displays the results table, calculates the False Positive Probability (FPP) and Nearby False Positive Probability (NFPP), and prints them.

```python
df_results = target.probs
print("FPP =", target.FPP)
print("NFPP =", target.NFPP)
df_results
```

--------------------------------

### TRILEGAL Calculation Output

Source: https://github.com/stevengiacalone/triceratops/blob/master/examples/example.ipynb

The output from the TRILEGAL calculation, showing the progress and CPU times.

```shell
TRILEGAL form submitted.
Calculating TP, EB, and EBx2P scenario probabilities for 100990000.
Calculating PTP, PEB, and PEBx2P scenario probabilities for 100990000.
Calculating STP, SEB, and SEBx2P scenario probabilities for 100990000.
Calculating DTP, DEB, and DEBx2P scenario probabilities for 100990000.
...
Calculating BTP, BEB, and BEBx2P scenario probabilities for 100990000.
CPU times: user 50.2 s, sys: 4.42 s, total: 54.6 s
Wall time: 1min 3s
```

--------------------------------

### Import necessary libraries

Source: https://github.com/stevengiacalone/triceratops/blob/master/examples/kepler_example.ipynb

Imports common Python libraries for data analysis, plotting, and the triceratops library, along with system information and warning filters.

```python
import numpy as np
import pandas as pd
import time
from lightkurve import KeplerLightCurve
import matplotlib.pyplot as plt
import triceratops.triceratops as tr
import sys

%matplotlib inline
print(sys.version)

import warnings
warnings.filterwarnings('ignore')
```

--------------------------------

### Python code for calculating depths

Source: https://github.com/stevengiacalone/triceratops/blob/master/docs/tutorials/example.ipynb

This code snippet demonstrates how to calculate depths using the calc_depths method and access star properties.

```python
target.calc_depths(tdepth=0.000166, all_ap_pixels=apertures)
target.stars
```

--------------------------------

### Delete conda environment

Source: https://github.com/stevengiacalone/triceratops/blob/master/docs/user/install.md

Removes a specified conda environment.

```bash
conda remove --name myenv --all
```

--------------------------------

### Plot Result

Source: https://github.com/stevengiacalone/triceratops/blob/master/examples/example.ipynb

The result of the plot_fits function, indicating the figure size.

```text
Result:
<Figure size 864x1440 with 15 Axes>
```

--------------------------------

### Define target star object

Source: https://github.com/stevengiacalone/triceratops/blob/master/docs/tutorials/kepler_example.ipynb

Defines the target star object using the `target(ID, sectors)` class from TRICERATOPS. The arguments are the KIC ID and the observed sectors.

```python
ID = 11904151
sectors = np.array([3])
target = tr.target(ID=ID, sectors=sectors, mission="Kepler")
```

--------------------------------

### Load and Plot Contrast Curve

Source: https://github.com/stevengiacalone/triceratops/blob/master/docs/tutorials/example.ipynb

Loads a contrast curve from a CSV file and plots it. The CSV should have no headers, with the first column as angular separation and the second as Delta mag.

```python
contrastcurve_file = "TOI465_01_contrastcurve.csv"
cc = pd.read_csv(contrastcurve_file, header=None)
sep, dmag = cc[0].values, cc[1].values

plt.plot(sep, dmag, 'k-');
plt.ylim(9,0);
plt.ylabel("$\Delta K_s$", fontsize=20);
plt.xlabel("separation ('')", fontsize=20);
```

--------------------------------

### Calculate FPP multiple times with parallelization

Source: https://github.com/stevengiacalone/triceratops/blob/master/docs/tutorials/example.ipynb

Calculates the FPP 20 times using parallel processing to demonstrate stability and then prints the mean and standard deviation.

```python
FPPs = np.zeros(20)
for i in range(20):
    target.calc_probs(time=lc.time.value, 
                      flux_0=lc.flux.value, 
                      flux_err_0=np.mean(lc.flux_err.value), 
                      P_orb=P_orb,
                      parallel=True,
                      verbose=0)
    FPPs[i] = target.FPP

meanFPP = np.round(np.mean(FPPs), 4)
stdvFPP = np.round(np.std(FPPs), 4)
print("FPP =", meanFPP, "+", stdvFPP)
```

--------------------------------

### Define Target Star

Source: https://github.com/stevengiacalone/triceratops/blob/master/docs/tutorials/example.ipynb

Defines the target star object using its TIC ID and observed sectors.

```python
ID = 270380593
sectors = np.array([4])
target = tr.target(ID=ID, sectors=sectors)
```

--------------------------------

### Calculate Transit Depths

Source: https://github.com/stevengiacalone/triceratops/blob/master/docs/tutorials/example.ipynb

This code snippet calculates the transit depths for stars in an aperture using the `.calc_depths` method.

```python
apertures = np.array([ap])
target.calc_depths(tdepth=0.005, all_ap_pixels=apertures)

target.stars
```

--------------------------------

### Calculate Probabilities with Follow-up Data

Source: https://github.com/stevengiacalone/triceratops/blob/master/docs/tutorials/example.ipynb

Calculates probabilities by iterating 20 times, incorporating the contrast curve data, and then prints the mean and standard deviation of the FPP.

```python
%%time

FPPs = np.zeros(20)
for i in range(20):
    target.calc_probs(time=lc.time.value, 
                      flux_0=lc.flux.value, 
                      flux_err_0=np.mean(lc.flux_err.value), 
                      P_orb=P_orb,
                      contrast_curve_file=contrastcurve_file,
                      parallel=True,
                      verbose=0)
    FPPs[i] = target.FPP

meanFPP = np.round(np.mean(FPPs), 4)
stdvFPP = np.round(np.std(FPPs), 4)
print("FPP =", meanFPP, "+/-", stdvFPP)
```

--------------------------------

### Calculate flux ratios and transit depths

Source: https://github.com/stevengiacalone/triceratops/blob/master/docs/tutorials/kepler_example.ipynb

Calculates the flux ratio contributed by each star within the aperture and the potential transit depth for each star using the `.calc_depths()` method. This helps identify which stars might be responsible for the observed transit signal.

```python
apertures = np.array([ap])
target.calc_depths(tdepth=0.00019, all_ap_pixels=apertures)
target.stars
```

--------------------------------

### Define apertures and plot fields

Source: https://github.com/stevengiacalone/triceratops/blob/master/docs/tutorials/example.ipynb

Defines the aperture pixels for sectors 3 and 4, then iterates through the sectors to plot the field with the specified apertures.

```python
ap_s3 = np.array([                            [185, 1320], [186, 1320],
                                 [184, 1319], [185, 1319], [186, 1319], [187, 1319],
                    [183, 1318], [184, 1318], [185, 1318], [186, 1318], [187, 1318],
                    [183, 1317], [184, 1317], [185, 1317], [186, 1317], [187, 1317],
                                 [184, 1316], [185, 1316], [186, 1316], [187, 1316]])

ap_s4 = np.array([  [414, 998], [415, 998], [416, 998],
                    [414, 997], [415, 997], [416, 997], [417, 997],
                    [414, 996], [415, 996], [416, 996], [417, 996],
                    [414, 995], [415, 995], [416, 995], [417, 995],
                    [414, 994], [415, 994], [416, 994], [417, 994],
                                            [416, 993]])

apertures = np.array([ap_s3, ap_s4])

for i,(sector,aperture) in enumerate(zip(sectors,apertures)):
    print("Sector", sector)
    target.plot_field(sector=sector, ap_pixels=aperture)

target.stars
```

--------------------------------

### Calculate and display FPP and NFPP

Source: https://github.com/stevengiacalone/triceratops/blob/master/docs/tutorials/example.ipynb

Calculates the False Positive Probability (FPP) and Nearby False Positive Probability (NFPP) and prints them. It also displays a table of results.

```python
df_results = target.probs
print("FPP =", np.round(target.FPP, 4))
print("NFPP =", np.round(target.NFPP, 4))
df_results
```

--------------------------------

### Display Results and Plot Fits

Source: https://github.com/stevengiacalone/triceratops/blob/master/examples/kepler_example.ipynb

Prints the False Positive Probability (FPP) and Nearby False Positive Probability (NFPP), displays the results table, and plots the transit fits for each scenario.

```python
df_results = target.probs
print("FPP =", target.FPP)
print("NFPP =", target.NFPP)
df_results
```

```python
target.plot_fits(time=lc.time.value, flux_0=lc.flux.value, flux_err_0=np.mean(lc.flux_err.value))
```

--------------------------------

### Define Aperture and Plot Field

Source: https://github.com/stevengiacalone/triceratops/blob/master/docs/tutorials/example.ipynb

Defines the pixel aperture for light curve extraction, plots the field image, and displays nearby stars.

```python
ap = np.array([ [1180, 763], [1181, 763], [1182, 763], 
                [1180, 762], [1181, 762], [1182, 762], [1183, 762], 
                [1180, 761], [1181, 761], [1182, 761], [1183, 761], 
                                          [1182, 760]])

target.plot_field(sector=4, ap_pixels=ap)
 
target.stars
```

--------------------------------

### Probability Results Table

Source: https://github.com/stevengiacalone/triceratops/blob/master/examples/example.ipynb

The detailed table of probabilities for different scenarios (TP, EB, EBx2P, PTP, PEB, PEBx2P, STP, SEB, SEBx2P, DTP, DEB, DEBx2P, BTP, BEB, BEBx2P).

```text
Result:
           ID scenario       M_s       R_s     P_orb        inc       ecc  \
0   100990000       TP  1.170000  1.116720  4.040051  87.430810  0.619036   
1   100990000       EB  1.170000  1.116720  4.040051  82.582402  0.618804   
2   100990000    EBx2P  1.170000  1.116720  8.080102  85.792644  0.828554   
3   100990000      PTP  1.170000  1.116720  4.040051  87.424252  0.397792   
4   100990000      PEB  1.170000  1.116720  4.040051  78.964877  0.282007   
5   100990000   PEBx2P  1.170000  1.116720  8.080102  85.522643  0.598110   
6   100990000      STP  1.060924  1.116720  4.040051  87.249296  0.341908   
7   100990000      SEB  0.213924  0.242618  4.040051  88.345399  0.931711   
8   100990000   SEBx2P  0.351836  0.361533  8.080102  88.270721  0.900819   
9   100990000      DTP  1.170000  1.116720  4.040051  87.282509  0.368578   
10  100990000      DEB  1.170000  1.116720  4.040051  79.895366  0.277093   
11  100990000   DEBx2P  1.170000  1.116720  8.080102  85.574137  0.822941   
12  100990000      BTP  1.034000  0.983545  4.040051  89.340025  0.553490   
13  100990000      BEB  0.866000  1.283195  4.040051  88.984593  0.274950   
14  100990000   BEBx2P  1.257000  1.427912  8.080102  85.359789  0.089682   

             w        R_p      M_EB      R_EB          prob  
0   329.591045   1.606318  0.000000  0.000000  7.506777e-01  
1   306.214282   0.000000  1.075974  1.116720  1.139451e-58  
2   248.078683   0.000000  1.136131  1.116720  1.811696e-48  
3   323.801387   1.585492  0.000000  0.000000  1.188953e-01  
4    23.772130   0.000000  1.087470  1.116720  1.765332e-60  
5   304.412358   0.000000  1.134997  1.116720  2.087916e-53  
6   325.056354   2.677714  0.000000  0.000000  3.378508e-02  
7   249.880757   0.000000  0.042947  0.100000  5.009660e-18  
8   254.730922   0.000000  0.345782  0.356475  4.586207e-15  
9   313.623482   1.567885  0.000000  0.000000  9.049978e-02  
10   12.269202   0.000000  0.940363  0.992628  3.327247e-61  
11  245.756969   0.000000  1.124392  1.116720  3.731821e-52  
12  327.379648  18.977031  0.000000  0.000000  1.001123e-04  
13  354.593941   0.000000  0.303919  0.321197  5.996511e-03  
14  302.623192   0.000000  1.255807  1.402719  4.546522e-05  
```

--------------------------------

### Calculate Depths

Source: https://github.com/stevengiacalone/triceratops/blob/master/examples/example.ipynb

This code snippet shows how to use the `.calc_depths()` method to calculate the flux ratio and transit depth for each star in the aperture. It takes the transit depth of the candidate and a numpy array of all apertures as arguments.

```python
apertures = np.array([ap])
target.calc_depths(tdepth=0.005, all_ap_pixels=apertures)
```

--------------------------------

### Define aperture and plot field

Source: https://github.com/stevengiacalone/triceratops/blob/master/docs/tutorials/kepler_example.ipynb

Defines the aperture for light curve extraction, plots an image of the field using the defined aperture, and displays a table of nearby stars. The aperture is a 2D numpy array of pixel coordinates. The `.plot_field()` method visualizes the field, and the `.stars` attribute provides information about nearby stars.

```python
ap = np.array([
               [655, 247],
               [654, 246], [655, 246], [656, 246],
                           [655, 245]             ])

target.plot_field(sector=3, ap_pixels=ap)
 
target.stars
```

--------------------------------

### Load and plot initial light curve data

Source: https://github.com/stevengiacalone/triceratops/blob/master/examples/TSCIII_tutorial.ipynb

Loads the folded TESS light curve data from a CSV file and plots it.

```python
lc = pd.read_csv("TOI1228_folded_lightcurve.csv")
t = lc.x_fold.values
y = lc.y.values + 1

plt.scatter(t, y, marker='.')
plt.xlabel("Time from Transit Midpoint (days)", fontsize=14)
plt.ylabel("Normalized Flux", fontsize=14)
plt.show()
```

--------------------------------

### Import necessary libraries

Source: https://github.com/stevengiacalone/triceratops/blob/master/examples/TSCIII_tutorial.ipynb

Imports the required Python libraries for data manipulation, light curve analysis, and TRICERATOPS.

```python
import numpy as np
import pandas as pd
import time
from lightkurve import TessLightCurve
import matplotlib.pyplot as plt
%matplotlib inline

import triceratops.triceratops as tr
```

--------------------------------

### Load contrast curve data

Source: https://github.com/stevengiacalone/triceratops/blob/master/examples/TSCIII_tutorial.ipynb

Loads the speckle contrast curve data from a table file.

```python
cc = pd.read_csv("TOI1228_cc.tbl", header=None)
cc
```

--------------------------------

### Display Scenario Probabilities

Source: https://github.com/stevengiacalone/triceratops/blob/master/examples/TSCIII_tutorial.ipynb

Prints a table containing the relative probability of each scenario, including parameters like stellar mass (M_s), stellar radius (R_s), orbital period (P_orb), inclination (inc), impact parameter (b), eccentricity (ecc), argument of periastron (w), planetary radius (R_p), companion stellar mass (M_EB), companion stellar radius (R_EB), and probability (prob).

```python
target.probs
```

--------------------------------

### Calculate Probabilities

Source: https://github.com/stevengiacalone/triceratops/blob/master/examples/TSCIII_tutorial.ipynb

This code snippet demonstrates how to use the target.calc_probs function to calculate false positive probabilities, with detailed comments explaining each argument.

```python
target.calc_probs(time=t_binned,                               # binned time data
                  flux_0=y_binned,                             # binned flux data
                  flux_err_0=np.std(y_binned[:50]),            # standard deviation of (out-of-transit) flux data
                  P_orb=29.04992,                              # best-fit orbital period of planet candidate
                  contrast_curve_file="TOI1228_cc.tbl",        # contrast curve file to use (str)
                  filt="TESS",                                 # passband of contrast curve ("TESS", "J", "H", or "K")
                  N=int(1e6),                                  # number of instances to simulate for each scenario (int, usually fine to leave alone)
                  parallel=True,                               # whether or not to use parallel processing (bool)
                  drop_scenario=[],                            # scenarios to ignore (e.g., "EB")
                  verbose=1,                                   # whether or not to print updates (0 or 1)
                  flatpriors=False,                            # whether or not to assume planet priors (bool)
                  exptime=0.00139,                             # exposure time of TESS data (float, in days)
                  nsamples=20,                                 # number of samples to use for supersampling (int, leave alone)
                  molusc_file="TOI1228_molusc_kept.csv")       # path to molusc file (str, optional)
```

--------------------------------

### Calculate Depths

Source: https://github.com/stevengiacalone/triceratops/blob/master/examples/TSCIII_tutorial.ipynb

Calculates the transit depths considering the planet candidate's depth and the apertures used for calculation.

```python
target.calc_depths(tdepth=0.000407,           # transit depth of the planet candidate in the TESS data (float)
                   all_ap_pixels=aps)         # apertures to use for calculation (list of numpy arrays)
```

--------------------------------

### Trim and bin light curve data

Source: https://github.com/stevengiacalone/triceratops/blob/master/examples/TSCIII_tutorial.ipynb

Trims the light curve data to focus around the transit and bins it down to approximately 200 points for efficiency.

```python
transit_mask = (np.abs(t) < 0.4)
lc_binned = TessLightCurve(time=t[transit_mask], flux=y[transit_mask]).bin(time_bin_size=2*np.max(t[transit_mask])/200)
t_binned = lc_binned.time.value
y_binned = lc_binned.flux.value

plt.scatter(t_binned, y_binned, marker='.')
plt.xlabel("Time from Transit Midpoint (days)", fontsize=14)
plt.ylabel("Normalized Flux", fontsize=14)
plt.show()
```

--------------------------------

### Initialize TRICERATOPS target object

Source: https://github.com/stevengiacalone/triceratops/blob/master/examples/TSCIII_tutorial.ipynb

Initializes the TRICERATOPS target object with the TIC ID, observed sectors, and other optional parameters.

```python
ID = 300038935
sectors = np.array([1])
target = tr.target(ID=ID,                       # TIC ID (int)
                   sectors=sectors,             # TESS sectors (numpy array)
                   search_radius=10,            # number of pixels to search from target star (int)
                   mission="TESS",              # which mission you are using data from ("TESS" or "Kepler" currently supported)
                   lightkurve_cache_dir=None,   # directory containing lightkurve data products (str)
                   trilegal_fname=None,         # name of TRILEGAL query results (str, generated after first run)
                   ra=None,                     # ra if target ID is unknown (float, degrees)
                   dec=None)                    # dec if target ID is unknown (float, degrees)
                  
```

--------------------------------

### Access nearby stars dataframe

Source: https://github.com/stevengiacalone/triceratops/blob/master/examples/TSCIII_tutorial.ipynb

Prints a dataframe containing the properties and positions of all nearby stars, ordered by distance from the target.

```python
target.stars
```

--------------------------------

### Plot Maximum-Likelihood Transit Model

Source: https://github.com/stevengiacalone/triceratops/blob/master/examples/TSCIII_tutorial.ipynb

Plots the maximum-likelihood transit model for each scenario using binned time and flux data, along with the standard deviation of the out-of-transit flux.

```python
target.plot_fits(time=t_binned,                         # binned time data
                 flux_0=y_binned,                       # binned flux data
                 flux_err_0=np.std(y_binned[:50]),      # standard deviation of (out-of-transit) flux data
                 save=False,                            # whether or not to save the plot
                 fname=None)                            # filename, if saved
```

--------------------------------

### Plot field with aperture

Source: https://github.com/stevengiacalone/triceratops/blob/master/examples/TSCIII_tutorial.ipynb

Plots the field of stars and highlights the photometric aperture. Allows customization of aperture color and saving the figure.

```python
target.plot_field(sector=sectors[0],     # sector to plot (int)
                  ap_pixels=aps[0],      # which aperture to plot (numpy array, optional)
                  ap_color="red",        # color to make aperture (str, red is default)
                  save=False,            # whether or not to save the figure (bool)
                  fname=None)            # name of file, if saved (str)
```

--------------------------------

### Print False Positive Probabilities

Source: https://github.com/stevengiacalone/triceratops/blob/master/examples/TSCIII_tutorial.ipynb

Calculates and prints the false positive probability (FPP) and the nearby false positive probability (NFPP). A planet candidate is generally validated if FPP < 1% and NFPP < 0.1%.

```python
print("FPP =", target.FPP)
print("NFPP =", target.NFPP)
```

--------------------------------

### Display Stars Dataframe with Flux Contribution

Source: https://github.com/stevengiacalone/triceratops/blob/master/examples/TSCIII_tutorial.ipynb

Displays the stars dataframe, filtered to show only stars with a non-zero tdepth, indicating their potential to cause the observed signal. This includes flux ratio and true transit depth.

```python
target.stars[target.stars.tdepth > 0]
```

--------------------------------

### Help - Delete Conda Environment

Source: https://github.com/stevengiacalone/triceratops/blob/master/README.rst

To delete this environment, run:

```bash
$ conda remove --name myenv --all

```

--------------------------------

### Help - Deactivate Conda Environment

Source: https://github.com/stevengiacalone/triceratops/blob/master/README.rst

To exit this environment, run:

```bash
(myenv) $ conda deactivate

```

--------------------------------

### Code Citation

Source: https://github.com/stevengiacalone/triceratops/blob/master/docs/user/cite.md

BibTeX entry for the TRICERATOPS software.

```bibtex
@MISC{2020ascl.soft02004G,
       author = {{Giacalone}, Steven and {Dressing}, Courtney D.},
        title = "{triceratops: Candidate exoplanet rating tool}",
     keywords = {Software, NASA, TESS},
         year = 2020,
        month = feb,
          eid = {ascl:2002.004},
        pages = {ascl:2002.004},
archivePrefix = {ascl},
       eprint = {2002.004},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2020ascl.soft02004G},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

```

--------------------------------

### Paper Citation

Source: https://github.com/stevengiacalone/triceratops/blob/master/docs/user/cite.md

BibTeX entry for the research paper associated with TRICERATOPS.

```bibtex
@ARTICLE{2021AJ....161...24G,
       author = {{Giacalone}, Steven and {Dressing}, Courtney D. and {Jensen}, Eric L.~N. and {Collins}, Karen A. and {Ricker}, George R. and {Vanderspek}, Roland and {Seager}, S. and {Winn}, Joshua N. and {Jenkins}, Jon M. and {Barclay}, Thomas and {Barkaoui}, Khalid and {Cadieux}, Charles and {Charbonneau}, David and {Collins}, Kevin I. and {Conti}, Dennis M. and {Doyon}, Ren{\'e} and {Evans}, Phil and {Ghachoui}, Mourad and {Gillon}, Micha{"e}l and {Guerrero}, Natalia M. and {Hart}, Rhodes and {Jehin}, Emmanu{"e}l and {Kielkopf}, John F. and {McLean}, Brian and {Murgas}, Felipe and {Palle}, Enric and {Parviainen}, Hannu and {Pozuelos}, Francisco J. and {Relles}, Howard M. and {Shporer}, Avi and {Socia}, Quentin and {Stockdale}, Chris and {Tan}, Thiam-Guan and {Torres}, Guillermo and {Twicken}, Joseph D. and {Waalkes}, William C. and {Waite}, Ian A.},
        title = "{Vetting of 384 TESS Objects of Interest with TRICERATOPS and Statistical Validation of 12 Planet Candidates}",
      journal = {\aj},
     keywords = {Exoplanet astronomy, Astrostatistics, Planet hosting stars, Exoplanets, 486, 1882, 1242, 498, Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Solar and Stellar Astrophysics},
         year = 2021,
        month = jan,
       volume = {161},
       number = {1},
          eid = {24},
        pages = {24},
          doi = {10.3847/1538-3881/abc6af},
archivePrefix = {arXiv},
       eprint = {2002.00691},
 primaryClass = {astro-ph.EP},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2021AJ....161...24G},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

```
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