Creates a plot of a K2 target's centroids, with and without mid-campaign offset.

plot-k2-centroids.py

"""Creates a plot of a K2 target's centroids, with and without mid-campaign offset.
"""
import numpy as np
import matplotlib.pyplot as pl
from matplotlib.ticker import MultipleLocator, FormatStrFormatter
import seaborn

from astropy.io import fits


def plot_centroids(ax, col, row, xlim, ylim, title=""):
    sc = ax.scatter(col, row, c=range(len(col)), s=4, lw=0, cmap="viridis")
    cbar = fig.colorbar(sc, ax=ax, orientation="horizontal")
    cbar.set_label('Cadence')
    ax.grid(True)
    ax.xaxis.set_major_locator(MultipleLocator(1))
    ax.yaxis.set_major_locator(MultipleLocator(1))
    ax.xaxis.set_minor_locator(MultipleLocator(0.1))
    ax.yaxis.set_minor_locator(MultipleLocator(0.1))
    ax.xaxis.set_major_formatter(FormatStrFormatter('%.0f'))
    ax.yaxis.set_major_formatter(FormatStrFormatter('%.0f'))
    ax.set_xlim(xlim)
    ax.set_ylim(ylim)
    ax.set_xlabel("Column (pixels)")
    ax.set_ylabel("Row (pixels)")
    ax.text(0.05, 0.9, title, ha="left", transform=ax.transAxes, fontsize=24)


if __name__ == "__main__":
    EPIC = "212126314-c05"

    # Load centroid positions
    fts = fits.open("ktwo{}_llc.fits".format(EPIC))
    col = fts[1].data["MOM_CENTR1"]
    row = fts[1].data["MOM_CENTR2"]
    mean_col, mean_row = np.nanmean(col), np.nanmean(row)
    xlim = [mean_col-1.5, mean_col+1.5]
    ylim = [mean_row-1.5, mean_row+1.5]

    # Simulate C9
    col_c9, row_c9 = col.copy(), row.copy()
    row_c9[int(len(row)/2):] -= 0.5

    # Make the plot pretty
    seaborn.set(context="notebook", style="ticks", font_scale=2)
    seaborn.set_palette("RdBu")

    fig = pl.figure(figsize=(16, 9))
    ax1 = fig.add_subplot(1, 2, 1)
    plot_centroids(ax1, col, row, xlim, ylim, title="Nominal")
    ax2 = fig.add_subplot(1, 2, 2)
    plot_centroids(ax2, col_c9, row_c9, xlim, ylim, title="With mid-campaign offset")

    fig.tight_layout()
    output_fn = "epic-{}.png".format(EPIC)
    fig.savefig(output_fn)