import numpy as np
from astropy import units as u
from matplotlib import pyplot as plt
__all__ = ["plot_many_factors", "bin_data", "scale_and_shift"]
[docs]
def plot_many_factors(photometry, shift, scale, ax=None):
"""Plots many factors of photometry against each other.
Parameters
----------
photometry : `stellarphot.PhotometryData`
The photometry table to plot.
shift : float
The amount to shift the data by.
scale : float
The amount to scale the data by.
ax : `matplotlib.axes.Axes`, optional
The axes to plot on.
Returns
-------
None
Added features to the plot directly.
"""
airmass = photometry["airmass"] / np.mean(photometry["airmass"])
x = photometry["xcenter"] / np.mean(photometry["xcenter"])
y = photometry["ycenter"] / np.mean(photometry["ycenter"])
comp_counts = photometry["comparison counts"] / np.mean(
photometry["comparison counts"]
)
sky_per_pix = photometry["sky_per_pix_avg"] / np.mean(photometry["sky_per_pix_avg"])
width = photometry["width"] / np.mean(photometry["width"])
scale_airmass = scale_and_shift(airmass, scale, 0.75 * shift, pos=False)
scale_x = scale_and_shift(x, scale, shift, pos=True)
scale_y = scale_and_shift(y, scale, shift, pos=True)
scale_sky_pix = scale_and_shift(sky_per_pix, scale, shift, pos=True)
scale_counts = scale_and_shift(comp_counts, scale, shift, pos=True)
scale_width = scale_and_shift(width, scale, shift, pos=True)
x_times = (photometry["bjd"] - 2400000 * u.day).jd
if ax is None:
ax = plt.gca()
print(f"{scale_airmass.min()} {scale_airmass.max()}")
ax.plot(
x_times,
scale_counts,
".",
c="brown",
label="tot_C_cnts (arbitrarily scaled and shifted)",
alpha=0.5,
ms=4,
)
ax.plot(
x_times,
scale_airmass,
"c-",
label="AIRMASS (arbitrarily scaled and shifted)",
ms=4,
)
ax.plot(
x_times,
scale_sky_pix,
c="gold",
label="Sky/Pixel_T1 (arbitrarily scaled and shifted)",
ms=4,
)
ax.plot(
x_times,
scale_width,
"-",
c="gray",
label="Width_T1 (arbitrarily scaled and shifted)",
ms=4,
)
ax.plot(
x_times,
scale_x,
"-",
c="pink",
label="X(FITS)_T1 (arbitrarily scaled and shifted)",
ms=4,
)
ax.plot(
x_times,
scale_y,
"-",
c="lightblue",
label="Y(FITS)_T1 (arbitrarily scaled and shifted)",
ms=4,
)
[docs]
def bin_data(data_set, num=3, error_set=None):
"""Bins data into groups of num.
Parameters
----------
data_set : array-like
The data to bin.
num : int, optional
The number of data points to bin together. Default is 3.
error_set : array-like
The error on the data set. Default is None.
Returns
-------
binned_set : array-like
The binned data set.
error : array-like, optional
The error on the binned data set.
"""
binned_set = []
error = []
for i in range(0, len(data_set), num):
binned_set.append(data_set[i : i + num].mean())
if error_set is not None:
error_bin = error_set[i : i + num] ** 2
error.append(error_bin.sum() / num)
return np.array(binned_set), np.array(error)
[docs]
def scale_and_shift(data_set, scale, shift, pos=True):
"""Scales and shifts data set passed in.
Parameters
----------
data_set : array-like
The data to scale and shift.
scale : float
The amount to scale the data by.
shift : float
The amount to shift the data by (in scaled units).
pos : bool, optional
Is data displayed in positive or negative direction? Default is True.
Returns
-------
data_set : array-like
The scaled and shifted data.
"""
if not pos:
data_set = 1 - scale * (data_set - data_set.min()) / (
data_set.max() - data_set.min()
)
else:
data_set = 1 + scale * (data_set - data_set.min()) / (
data_set.max() - data_set.min()
)
data_set += shift
return data_set
