477 lines
22 KiB
Python
Executable File
477 lines
22 KiB
Python
Executable File
"""
|
|
Library function for background estimation steps of the reduction pipeline.
|
|
|
|
prototypes :
|
|
- display_bkg(data, background, std_bkg, headers, histograms, binning, rectangle, savename, plots_folder)
|
|
Display and save how the background noise is computed.
|
|
- bkg_hist(data, error, mask, headers, sub_shape, display, savename, plots_folder) -> n_data_array, n_error_array, headers, background)
|
|
Compute the error (noise) of the input array by computing the base mode of each image.
|
|
- bkg_mini(data, error, mask, headers, sub_shape, display, savename, plots_folder) -> n_data_array, n_error_array, headers, background)
|
|
Compute the error (noise) of the input array by looking at the sub-region of minimal flux in every image and of shape sub_shape.
|
|
"""
|
|
|
|
from copy import deepcopy
|
|
from datetime import datetime, timedelta
|
|
from os.path import join as path_join
|
|
|
|
import matplotlib.dates as mdates
|
|
import matplotlib.pyplot as plt
|
|
import numpy as np
|
|
from astropy.time import Time
|
|
from lib.plots import plot_obs
|
|
from matplotlib.colors import LogNorm
|
|
from matplotlib.patches import Rectangle
|
|
from scipy.optimize import curve_fit
|
|
|
|
|
|
def gauss(x, *p):
|
|
N, mu, sigma = p
|
|
return N * np.exp(-((x - mu) ** 2) / (2.0 * sigma**2))
|
|
|
|
|
|
def gausspol(x, *p):
|
|
N, mu, sigma, a, b, c, d = p
|
|
return N * np.exp(-((x - mu) ** 2) / (2.0 * sigma**2)) + a * np.log(x) + b / x + c * x + d
|
|
|
|
|
|
def bin_centers(edges):
|
|
return (edges[1:] + edges[:-1]) / 2.0
|
|
|
|
|
|
def display_bkg(data, background, std_bkg, headers, histograms=None, binning=None, coeff=None, rectangle=None, savename=None, plots_folder="./"):
|
|
plt.rcParams.update({"font.size": 15})
|
|
convert_flux = np.array([head["photflam"] for head in headers])
|
|
date_time = np.array([Time((headers[i]["expstart"] + headers[i]["expend"]) / 2.0, format="mjd", precision=0).iso for i in range(len(headers))])
|
|
date_time = np.array([datetime.strptime(d, "%Y-%m-%d %H:%M:%S") for d in date_time])
|
|
date_err = np.array([timedelta(seconds=headers[i]["exptime"] / 2.0) for i in range(len(headers))])
|
|
filt = np.array([headers[i]["filtnam1"] for i in range(len(headers))])
|
|
dict_filt = {"POL0": "r", "POL60": "g", "POL120": "b"}
|
|
c_filt = np.array([dict_filt[f] for f in filt])
|
|
|
|
fig, ax = plt.subplots(figsize=(10, 6), constrained_layout=True)
|
|
for f in np.unique(filt):
|
|
mask = [fil == f for fil in filt]
|
|
ax.scatter(date_time[mask], background[mask] * convert_flux[mask], color=dict_filt[f], label="{0:s}".format(f))
|
|
ax.errorbar(date_time, background * convert_flux, xerr=date_err, yerr=std_bkg * convert_flux, fmt="+k", markersize=0, ecolor=c_filt)
|
|
# Date handling
|
|
locator = mdates.AutoDateLocator()
|
|
formatter = mdates.ConciseDateFormatter(locator)
|
|
ax.xaxis.set_major_locator(locator)
|
|
ax.xaxis.set_major_formatter(formatter)
|
|
# ax.set_ylim(bottom=0.)
|
|
ax.set_yscale("log")
|
|
ax.set_xlabel("Observation date and time")
|
|
ax.set_ylabel(r"Flux [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]")
|
|
plt.legend()
|
|
if savename is not None:
|
|
this_savename = deepcopy(savename)
|
|
if savename[-4:] not in [".png", ".jpg", ".pdf"]:
|
|
this_savename += "_background_flux.pdf"
|
|
else:
|
|
this_savename = savename[:-4] + "_background_flux" + savename[-4:]
|
|
fig.savefig(path_join(plots_folder, this_savename), bbox_inches="tight")
|
|
|
|
if histograms is not None:
|
|
filt_obs = {"POL0": 0, "POL60": 0, "POL120": 0}
|
|
fig_h, ax_h = plt.subplots(figsize=(10, 8), constrained_layout=True)
|
|
for i, (hist, bins) in enumerate(zip(histograms, binning)):
|
|
filt_obs[headers[i]["filtnam1"]] += 1
|
|
ax_h.plot(
|
|
bins * convert_flux[i],
|
|
hist,
|
|
"+",
|
|
color="C{0:d}".format(i),
|
|
alpha=0.8,
|
|
label=headers[i]["filtnam1"] + " (Obs " + str(filt_obs[headers[i]["filtnam1"]]) + ")",
|
|
)
|
|
ax_h.plot([background[i] * convert_flux[i], background[i] * convert_flux[i]], [hist.min(), hist.max()], "x--", color="C{0:d}".format(i), alpha=0.8)
|
|
if coeff is not None:
|
|
# ax_h.plot(bins*convert_flux[i], gausspol(bins, *coeff[i]), '--', color="C{0:d}".format(i), alpha=0.8)
|
|
ax_h.plot(bins * convert_flux[i], gauss(bins, *coeff[i]), "--", color="C{0:d}".format(i), alpha=0.8)
|
|
ax_h.set_xscale("log")
|
|
ax_h.set_ylim([0.0, np.max([hist.max() for hist in histograms])])
|
|
ax_h.set_xlim([np.min(background * convert_flux) * 1e-2, np.max(background * convert_flux) * 1e2])
|
|
ax_h.set_xlabel(r"Flux [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]")
|
|
ax_h.set_ylabel(r"Number of pixels in bin")
|
|
ax_h.set_title("Histogram for each observation")
|
|
plt.legend()
|
|
if savename is not None:
|
|
this_savename = deepcopy(savename)
|
|
if savename[-4:] not in [".png", ".jpg", ".pdf"]:
|
|
this_savename += "_histograms.pdf"
|
|
else:
|
|
this_savename = savename[:-4] + "_histograms" + savename[-4:]
|
|
fig_h.savefig(path_join(plots_folder, this_savename), bbox_inches="tight")
|
|
|
|
fig2, ax2 = plt.subplots(figsize=(10, 10))
|
|
data0 = data[0] * convert_flux[0]
|
|
bkg_data0 = data0 <= background[0] * convert_flux[0]
|
|
instr = headers[0]["instrume"]
|
|
rootname = headers[0]["rootname"]
|
|
exptime = headers[0]["exptime"]
|
|
filt = headers[0]["filtnam1"]
|
|
# plots
|
|
im2 = ax2.imshow(data0, norm=LogNorm(data0[data0 > 0.0].mean() / 10.0, data0.max()), origin="lower", cmap="gray")
|
|
ax2.imshow(bkg_data0, origin="lower", cmap="Reds", alpha=0.5)
|
|
if rectangle is not None:
|
|
x, y, width, height, angle, color = rectangle[0]
|
|
ax2.add_patch(Rectangle((x, y), width, height, edgecolor=color, fill=False, lw=2))
|
|
ax2.annotate(
|
|
instr + ":" + rootname, color="white", fontsize=10, xy=(0.01, 1.00), xycoords="axes fraction", verticalalignment="top", horizontalalignment="left"
|
|
)
|
|
ax2.annotate(filt, color="white", fontsize=14, xy=(0.01, 0.01), xycoords="axes fraction", verticalalignment="bottom", horizontalalignment="left")
|
|
ax2.annotate(
|
|
str(exptime) + " s", color="white", fontsize=10, xy=(1.00, 0.01), xycoords="axes fraction", verticalalignment="bottom", horizontalalignment="right"
|
|
)
|
|
ax2.set(xlabel="pixel offset", ylabel="pixel offset", aspect="equal")
|
|
|
|
fig2.subplots_adjust(hspace=0, wspace=0, right=1.0)
|
|
fig2.colorbar(im2, ax=ax2, location="right", aspect=50, pad=0.025, label=r"Flux [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]")
|
|
|
|
if savename is not None:
|
|
this_savename = deepcopy(savename)
|
|
if savename[-4:] not in [".png", ".jpg", ".pdf"]:
|
|
this_savename += "_" + filt + "_background_location.pdf"
|
|
else:
|
|
this_savename = savename[:-4] + "_" + filt + "_background_location" + savename[-4:]
|
|
fig2.savefig(path_join(plots_folder, this_savename), bbox_inches="tight")
|
|
if rectangle is not None:
|
|
plot_obs(
|
|
data,
|
|
headers,
|
|
vmin=data[data > 0.0].min() * convert_flux.mean(),
|
|
vmax=data[data > 0.0].max() * convert_flux.mean(),
|
|
rectangle=rectangle,
|
|
savename=savename + "_background_location",
|
|
plots_folder=plots_folder,
|
|
)
|
|
elif rectangle is not None:
|
|
plot_obs(data, headers, vmin=data[data > 0.0].min(), vmax=data[data > 0.0].max(), rectangle=rectangle)
|
|
|
|
plt.show()
|
|
|
|
|
|
def sky_part(img):
|
|
rand_ind = np.unique((np.random.rand(np.floor(img.size / 4).astype(int)) * 2 * img.size).astype(int) % img.size)
|
|
rand_pix = img.flatten()[rand_ind]
|
|
# Intensity range
|
|
sky_med = np.median(rand_pix)
|
|
sig = np.min([img[img < sky_med].std(), img[img > sky_med].std()])
|
|
sky_range = [sky_med - 2.0 * sig, np.max([sky_med + sig, 7e-4])] # Detector background average FOC Data Handbook Sec. 7.6
|
|
|
|
sky = img[np.logical_and(img >= sky_range[0], img <= sky_range[1])]
|
|
return sky, sky_range
|
|
|
|
|
|
def bkg_estimate(img, bins=None, chi2=None, coeff=None):
|
|
if bins is None or chi2 is None or coeff is None:
|
|
bins, chi2, coeff = [8], [], []
|
|
else:
|
|
try:
|
|
bins.append(int(3.0 / 2.0 * bins[-1]))
|
|
except IndexError:
|
|
bins, chi2, coeff = [8], [], []
|
|
hist, bin_edges = np.histogram(img[img > 0], bins=bins[-1])
|
|
binning = bin_centers(bin_edges)
|
|
peak = binning[np.argmax(hist)]
|
|
bins_stdev = binning[hist > hist.max() / 2.0]
|
|
stdev = bins_stdev[-1] - bins_stdev[0]
|
|
# p0 = [hist.max(), peak, stdev, 1e-3, 1e-3, 1e-3, 1e-3]
|
|
p0 = [hist.max(), peak, stdev]
|
|
try:
|
|
# popt, pcov = curve_fit(gausspol, binning, hist, p0=p0)
|
|
popt, pcov = curve_fit(gauss, binning, hist, p0=p0)
|
|
except RuntimeError:
|
|
popt = p0
|
|
# chi2.append(np.sum((hist - gausspol(binning, *popt))**2)/hist.size)
|
|
chi2.append(np.sum((hist - gauss(binning, *popt)) ** 2) / hist.size)
|
|
coeff.append(popt)
|
|
return bins, chi2, coeff
|
|
|
|
|
|
def bkg_fit(data, error, mask, headers, subtract_error=True, display=False, savename=None, plots_folder=""):
|
|
"""
|
|
----------
|
|
Inputs:
|
|
data : numpy.ndarray
|
|
Array containing the data to study (2D float arrays).
|
|
error : numpy.ndarray
|
|
Array of images (2D floats, aligned and of the same shape) containing
|
|
the error in each pixel of the observation images in data_array.
|
|
mask : numpy.ndarray
|
|
2D boolean array delimiting the data to work on.
|
|
headers : header list
|
|
Headers associated with the images in data_array.
|
|
subtract_error : float or bool, optional
|
|
If float, factor to which the estimated background should be multiplied
|
|
If False the background is not subtracted.
|
|
Defaults to True (factor = 1.).
|
|
display : boolean, optional
|
|
If True, data_array will be displayed with a rectangle around the
|
|
sub-image selected for background computation.
|
|
Defaults to False.
|
|
savename : str, optional
|
|
Name of the figure the map should be saved to. If None, the map won't
|
|
be saved (only displayed). Only used if display is True.
|
|
Defaults to None.CNRS-Unistra Labo ObsAstroS
|
|
plots_folder : str, optional
|
|
Relative (or absolute) filepath to the folder in wich the map will
|
|
be saved. Not used if savename is None.
|
|
Defaults to current folder.
|
|
----------
|
|
Returns:
|
|
data_array : numpy.ndarray
|
|
Array containing the data to study minus the background.
|
|
headers : header list
|
|
Updated headers associated with the images in data_array.
|
|
error_array : numpy.ndarray
|
|
Array containing the background values associated to the images in
|
|
data_array.
|
|
background : numpy.ndarray
|
|
Array containing the pixel background value for each image in
|
|
data_array.
|
|
"""
|
|
n_data_array, n_error_array = deepcopy(data), deepcopy(error)
|
|
error_bkg = np.ones(n_data_array.shape)
|
|
std_bkg = np.zeros((data.shape[0]))
|
|
background = np.zeros((data.shape[0]))
|
|
histograms, binning = [], []
|
|
|
|
for i, image in enumerate(data):
|
|
# Compute the Count-rate histogram for the image
|
|
sky, sky_range = sky_part(image[image > 0.0])
|
|
|
|
bins, chi2, coeff = bkg_estimate(sky)
|
|
while bins[-1] < 256:
|
|
bins, chi2, coeff = bkg_estimate(sky, bins, chi2, coeff)
|
|
hist, bin_edges = np.histogram(sky, bins=bins[-1])
|
|
histograms.append(hist)
|
|
binning.append(bin_centers(bin_edges))
|
|
chi2, coeff = np.array(chi2), np.array(coeff)
|
|
weights = 1 / chi2**2
|
|
weights /= weights.sum()
|
|
|
|
bkg = np.sum(weights * (coeff[:, 1] + np.abs(coeff[:, 2]) * subtract_error))
|
|
|
|
error_bkg[i] *= bkg
|
|
|
|
n_error_array[i] = np.sqrt(n_error_array[i] ** 2 + error_bkg[i] ** 2)
|
|
|
|
# Substract background
|
|
if subtract_error > 0:
|
|
n_data_array[i][mask] = n_data_array[i][mask] - bkg
|
|
n_data_array[i][np.logical_and(mask, n_data_array[i] <= 1e-3 * bkg)] = 1e-3 * bkg
|
|
|
|
std_bkg[i] = image[np.abs(image - bkg) / bkg < 1.0].std()
|
|
background[i] = bkg
|
|
|
|
if display:
|
|
display_bkg(data, background, std_bkg, headers, histograms=histograms, binning=binning, coeff=coeff, savename=savename, plots_folder=plots_folder)
|
|
return n_data_array, n_error_array, headers, background
|
|
|
|
|
|
def bkg_hist(data, error, mask, headers, sub_type=None, subtract_error=True, display=False, savename=None, plots_folder=""):
|
|
"""
|
|
----------
|
|
Inputs:
|
|
data : numpy.ndarray
|
|
Array containing the data to study (2D float arrays).
|
|
error : numpy.ndarray
|
|
Array of images (2D floats, aligned and of the same shape) containing
|
|
the error in each pixel of the observation images in data_array.
|
|
mask : numpy.ndarray
|
|
2D boolean array delimiting the data to work on.
|
|
headers : header list
|
|
Headers associated with the images in data_array.
|
|
sub_type : str or int, optional
|
|
If str, statistic rule to be used for the number of bins in counts/s.
|
|
If int, number of bins for the counts/s histogram.
|
|
Defaults to "Freedman-Diaconis".
|
|
subtract_error : float or bool, optional
|
|
If float, factor to which the estimated background should be multiplied
|
|
If False the background is not subtracted.
|
|
Defaults to True (factor = 1.).
|
|
display : boolean, optional
|
|
If True, data_array will be displayed with a rectangle around the
|
|
sub-image selected for background computation.
|
|
Defaults to False.
|
|
savename : str, optional
|
|
Name of the figure the map should be saved to. If None, the map won't
|
|
be saved (only displayed). Only used if display is True.
|
|
Defaults to None.
|
|
plots_folder : str, optional
|
|
Relative (or absolute) filepath to the folder in wich the map will
|
|
be saved. Not used if savename is None.
|
|
Defaults to current folder.
|
|
----------
|
|
Returns:
|
|
data_array : numpy.ndarray
|
|
Array containing the data to study minus the background.
|
|
headers : header list
|
|
Updated headers associated with the images in data_array.
|
|
error_array : numpy.ndarray
|
|
Array containing the background values associated to the images in
|
|
data_array.
|
|
background : numpy.ndarray
|
|
Array containing the pixel background value for each image in
|
|
data_array.
|
|
"""
|
|
n_data_array, n_error_array = deepcopy(data), deepcopy(error)
|
|
error_bkg = np.ones(n_data_array.shape)
|
|
std_bkg = np.zeros((data.shape[0]))
|
|
background = np.zeros((data.shape[0]))
|
|
histograms, binning, coeff = [], [], []
|
|
|
|
for i, image in enumerate(data):
|
|
# Compute the Count-rate histogram for the image
|
|
n_mask = np.logical_and(mask, image > 0.0)
|
|
if sub_type is not None:
|
|
if isinstance(sub_type, int):
|
|
n_bins = sub_type
|
|
elif sub_type.lower() in ["sqrt"]:
|
|
n_bins = np.fix(np.sqrt(image[n_mask].size)).astype(int) # Square-root
|
|
elif sub_type.lower() in ["sturges"]:
|
|
n_bins = np.ceil(np.log2(image[n_mask].size)).astype(int) + 1 # Sturges
|
|
elif sub_type.lower() in ["rice"]:
|
|
n_bins = 2 * np.fix(np.power(image[n_mask].size, 1 / 3)).astype(int) # Rice
|
|
elif sub_type.lower() in ["scott"]:
|
|
n_bins = np.fix((image[n_mask].max() - image[n_mask].min()) / (3.5 * image[n_mask].std() / np.power(image[n_mask].size, 1 / 3))).astype(
|
|
int
|
|
) # Scott
|
|
else:
|
|
n_bins = np.fix(
|
|
(image[n_mask].max() - image[n_mask].min())
|
|
/ (2 * np.subtract(*np.percentile(image[n_mask], [75, 25])) / np.power(image[n_mask].size, 1 / 3))
|
|
).astype(int) # Freedman-Diaconis
|
|
else:
|
|
n_bins = np.fix(
|
|
(image[n_mask].max() - image[n_mask].min()) / (2 * np.subtract(*np.percentile(image[n_mask], [75, 25])) / np.power(image[n_mask].size, 1 / 3))
|
|
).astype(int) # Freedman-Diaconis
|
|
|
|
hist, bin_edges = np.histogram(np.log(image[n_mask]), bins=n_bins)
|
|
histograms.append(hist)
|
|
binning.append(np.exp(bin_centers(bin_edges)))
|
|
|
|
# Fit a gaussian to the log-intensity histogram
|
|
bins_stdev = binning[-1][hist > hist.max() / 2.0]
|
|
stdev = bins_stdev[-1] - bins_stdev[0]
|
|
# p0 = [hist.max(), binning[-1][np.argmax(hist)], stdev, 1e-3, 1e-3, 1e-3, 1e-3]
|
|
p0 = [hist.max(), binning[-1][np.argmax(hist)], stdev]
|
|
# popt, pcov = curve_fit(gausspol, binning[-1], hist, p0=p0)
|
|
popt, pcov = curve_fit(gauss, binning[-1], hist, p0=p0)
|
|
coeff.append(popt)
|
|
bkg = popt[1] + np.abs(popt[2]) * subtract_error
|
|
|
|
error_bkg[i] *= bkg
|
|
|
|
n_error_array[i] = np.sqrt(n_error_array[i] ** 2 + error_bkg[i] ** 2)
|
|
|
|
# Substract background
|
|
if subtract_error > 0:
|
|
n_data_array[i][mask] = n_data_array[i][mask] - bkg
|
|
n_data_array[i][np.logical_and(mask, n_data_array[i] <= 1e-3 * bkg)] = 1e-3 * bkg
|
|
|
|
std_bkg[i] = image[np.abs(image - bkg) / bkg < 1.0].std()
|
|
background[i] = bkg
|
|
|
|
if display:
|
|
display_bkg(data, background, std_bkg, headers, histograms=histograms, binning=binning, coeff=coeff, savename=savename, plots_folder=plots_folder)
|
|
return n_data_array, n_error_array, headers, background
|
|
|
|
|
|
def bkg_mini(data, error, mask, headers, sub_shape=(15, 15), subtract_error=True, display=False, savename=None, plots_folder=""):
|
|
"""
|
|
Look for sub-image of shape sub_shape that have the smallest integrated
|
|
flux (no source assumption) and define the background on the image by the
|
|
standard deviation on this sub-image.
|
|
----------
|
|
Inputs:
|
|
data : numpy.ndarray
|
|
Array containing the data to study (2D float arrays).
|
|
error : numpy.ndarray
|
|
Array of images (2D floats, aligned and of the same shape) containing
|
|
the error in each pixel of the observation images in data_array.
|
|
mask : numpy.ndarray
|
|
2D boolean array delimiting the data to work on.
|
|
headers : header list
|
|
Headers associated with the images in data_array.
|
|
sub_shape : tuple, optional
|
|
Shape of the sub-image to look for. Must be odd.
|
|
Defaults to 10% of input array.
|
|
subtract_error : float or bool, optional
|
|
If float, factor to which the estimated background should be multiplied
|
|
If False the background is not subtracted.
|
|
Defaults to True (factor = 1.).
|
|
display : boolean, optional
|
|
If True, data_array will be displayed with a rectangle around the
|
|
sub-image selected for background computation.
|
|
Defaults to False.
|
|
savename : str, optional
|
|
Name of the figure the map should be saved to. If None, the map won't
|
|
be saved (only displayed). Only used if display is True.
|
|
Defaults to None.
|
|
plots_folder : str, optional
|
|
Relative (or absolute) filepath to the folder in wich the map will
|
|
be saved. Not used if savename is None.
|
|
Defaults to current folder.
|
|
----------
|
|
Returns:
|
|
data_array : numpy.ndarray
|
|
Array containing the data to study minus the background.
|
|
headers : header list
|
|
Updated headers associated with the images in data_array.
|
|
error_array : numpy.ndarray
|
|
Array containing the background values associated to the images in
|
|
data_array.
|
|
background : numpy.ndarray
|
|
Array containing the pixel background value for each image in
|
|
data_array.
|
|
"""
|
|
sub_shape = np.array(sub_shape)
|
|
# Make sub_shape of odd values
|
|
if not (np.all(sub_shape % 2)):
|
|
sub_shape += 1 - sub_shape % 2
|
|
shape = np.array(data.shape)
|
|
diff = (sub_shape - 1).astype(int)
|
|
temp = np.zeros((shape[0], shape[1] - diff[0], shape[2] - diff[1]))
|
|
|
|
n_data_array, n_error_array = deepcopy(data), deepcopy(error)
|
|
error_bkg = np.ones(n_data_array.shape)
|
|
std_bkg = np.zeros((data.shape[0]))
|
|
background = np.zeros((data.shape[0]))
|
|
rectangle = []
|
|
|
|
for i, image in enumerate(data):
|
|
# Find the sub-image of smallest integrated flux (suppose no source)
|
|
# sub-image dominated by background
|
|
fmax = np.finfo(np.double).max
|
|
img = deepcopy(image)
|
|
img[1 - mask] = fmax / (diff[0] * diff[1])
|
|
for r in range(temp.shape[1]):
|
|
for c in range(temp.shape[2]):
|
|
temp[i][r, c] = np.where(mask[r, c], img[r : r + diff[0], c : c + diff[1]].sum(), fmax / (diff[0] * diff[1]))
|
|
|
|
minima = np.unravel_index(np.argmin(temp.sum(axis=0)), temp.shape[1:])
|
|
|
|
for i, image in enumerate(data):
|
|
rectangle.append([minima[1], minima[0], sub_shape[1], sub_shape[0], 0.0, "r"])
|
|
# Compute error : root mean square of the background
|
|
sub_image = image[minima[0] : minima[0] + sub_shape[0], minima[1] : minima[1] + sub_shape[1]]
|
|
# bkg = np.std(sub_image) # Previously computed using standard deviation over the background
|
|
bkg = np.sqrt(np.sum(sub_image**2) / sub_image.size) * subtract_error if subtract_error > 0 else np.sqrt(np.sum(sub_image**2) / sub_image.size)
|
|
error_bkg[i] *= bkg
|
|
|
|
n_error_array[i] = np.sqrt(n_error_array[i] ** 2 + error_bkg[i] ** 2)
|
|
|
|
# Substract background
|
|
if subtract_error > 0.0:
|
|
n_data_array[i][mask] = n_data_array[i][mask] - bkg
|
|
n_data_array[i][np.logical_and(mask, n_data_array[i] <= 1e-3 * bkg)] = 1e-3 * bkg
|
|
|
|
std_bkg[i] = image[np.abs(image - bkg) / bkg < 1.0].std()
|
|
background[i] = bkg
|
|
|
|
if display:
|
|
display_bkg(data, background, std_bkg, headers, rectangle=rectangle, savename=savename, plots_folder=plots_folder)
|
|
return n_data_array, n_error_array, headers, background
|