K2-CPM

Causal Pixel Model for K2 data

How to use

$ python run_cpm.py

positional arguments:
  epic          int k2 epic number
  campaign      int campaign number, 91 for phase a, 92 for phase b
  n_predictors  int number of the predictors pixels
  l2            float strength of l2 regularization
  n_pca         int number of the PCA components to use, if 0, no PCA 
  distance      int distance between target pixel and predictor pixels
  exclusion     int how many rows and columns that are excluded around the target pixel
  input_dir     str directory to the target pixel files
  output_dir    str path to the output file

optional arguments:
  -p [pixel_list], --pixel [pixel_list]
                str path to the pixel list file that specify which pixels to be modelled. If not provided, the whole target pixel file will be modeled

Example

$ python run_cpm.py 200069974 92 800 1e3 0 16 5 ./tpf ./output/200069974 -p ./test_pixel.dat

The C++ alternative

Suggestions about the C++ version are very welcome! Please contact Clément Ranc.

Introduction

The C++ version replaces the calculations performed when running cpm_part2.py, but not the ones done by cpm_part1.py. The C++ files must be compiled by hand prior using it for the first time, as explained below.

Files

The C++ version consists in the following files.

• table.cpp and the corresponding header file table.h: these files define a new class of one, two or three dimensional tables.

• matrix.cpp and the corresponding header file matrix.h: these files define a new class of square matrix that includes some very commun operations such as Cholesky's decomposition and a linear system solver.

• libcpm.cpp and the corresponding header file libcpm.h: these files corresponds to the main code that should be executed.

• Makefile: this file allow compilation of the C++ version on most OS.

Compilation

In linux and Mac OS, the C++ version can be compiled by the following commands:

$ cd path-to-main-directory/source/K2CPM/code/
$ make

where path-to-main-directory is the full path to the directory K2-CPM on your machine.

This Makefile will test the OS and adapt the compiler accordingly. If the command make returns an error though, it might be because your C++ compiler is not found or because you have not a C++ compiler installed.

If the first case, please edit the lines 24-25 in Makefile:

# CC = your-own-compiler
# CFLAGS = -Wall -YourFlags

and uncomment them by removing # .

In the second case, it is necessary to install a C++ compiler. A free option is to install GCC 5 or later (see https://gcc.gnu.org).

Usage

Part 1

The first step is to follow the instructions provided in tutorial.md up to the title CPM_PART2. Then, it is necessary to prepare the files read by the C++ code. From a shell,

$ path-to-main-directory/source/K2CPM/code/
$ python conversion2cpp.py -p path-inputoutput

where path-inputoutput/ is the path you are running the tutorial.md.

Part 2

Without microlensing model

Then, the second part of the CPM will be run in C++. Once compiled, the C++ version can be run directly, e.g. as follow,

$ path-to-main-directory/source/K2CPM/code/
$ ./libcpm path-inputoutput/ reference l2

where path-to-main-directory is the full path to the directory K2-CPM, path-inputoutput/ is the path you are using to follow CPM_PART1 (see tutorial.md), reference is the content of the variable stem from CPM_PART1 (see tutorial.md), including _ at the end (if stem is 91_49_1022_119, the reference is 91_49_1022_119_) and l2 is the regularization strengh (e.g. 1000).

The above command run calculations and write the results is two files, in the directory path-inputoutput/:

• reference_results.dat: solution of the linear system;

• reference_cpmflux.dat: the first column is the date, the second is the target flux prediction, the third is the difference flux.

Include a microlensing model (still beta version)

The current version may be used from a microlensing modeling code. Only the Part 2 may be affected by a microlensing model.

For now, several files are used to interact with a modeling code. For every position in the parameter space, a new file must be created in the same directory we have run the Part 1 (path-inputoutput/). If we come back to the above example, the magnification at each epoch of the file 91_49_1022_119_epochs_ml.dat should be computed and saved in a new file called 91_49_1022_119_magnification_ml.dat. This file should have the same number of lines than 91_49_1022_119_epochs_ml.dat and only one column, corresponding to the value of the magnification at the given epoch.

Also, it is necessary to say that we want to use a microlensing model to the CPM Part 2. For that, the C++ code should be run as follow:

$ ./libcpm path-inputoutput/ reference l2 1

where the last integer 1 is a flag that makes the code to load the magnification at each epoch.