"""Licensed under a 3-clause BSD style license - see LICENSE.rst.
This class implements IRAF/imexamine type capabilities
for providing powerful diagnostic quick-look tools.
However, the power of this python tool is that it is essentially a library
of plotting and analysis routines which can be directed towards
any viewer. It can also be used without connecting to any viewer
since the calls take only data,x,y information. This means that
given a data array and a list of x,y positions you can creates
plots without havin to interact with the viewers.
Users can also register a custom function with the class
and have it available for use in either case.
The plots which are made are fully customizable
"""
import matplotlib.pyplot as plt
# turn on interactive mode for plotting
# so that plotting becomes non-blocking
# if not plt.isinteractive():
# plt.ion()
import warnings
import numpy as np
import sys
import logging
import tempfile
from copy import deepcopy
from matplotlib import get_backend
from astropy.io import fits
from astropy.modeling import models
from astropy.visualization import ZScaleInterval
try:
from scipy import stats
scipy_installed = True
except ImportError:
scipy_installed = False
print("Scipy not installed, describe stat unavailable")
from . import math_helper
from . import imexam_defpars
from .util import set_logging
if sys.version_info.major < 3:
PY3 = False
else:
PY3 = True
# enable display plot in iPython notebook
try:
from io import StringIO
except ImportError:
from cString import StringIO
try:
import photutils
photutils_installed = True
from photutils.centroids import centroid_com
# account for API change
from packaging import version
photutils_version = version.parse(photutils.__version__)
except ImportError:
print("photutils not installed, photometry functionality "
"in imexam() not available")
photutils_installed = False
__all__ = ["Imexamine"]
[docs]class Imexamine:
"""The imexamine class controls plotting and analysis functions."""
def __init__(self):
"""do imexamine like routines on the current frame.
read the returned cursor key value to decide what to do
region_size is the default radius or side of the square for stat info
"""
self.set_option_funcs() # define the dictionary of keys and functions
self._data = np.zeros(0) # the data array
self._datafile = "" # the file from which the data came
# read from imexam_defpars which contains dicts
self._define_default_pars()
# default plot name saved with "s" key
self.plot_name = "imexam_plot.pdf"
# let users have multiple plot windows, the list stores their names
self._plot_windows = list()
# this contains the name of the current plotting window
self._figure_name = "imexam"
self._plot_windows.append(self._figure_name)
self._reserved_keys = ['q', '2'] # not to be changed with user funcs
self._fit_models = ["Gaussian1D",
"Moffat1D",
"MexicanHat1D",
"AiryDisk2D",
"Polynomial1D"]
# see if the package logger was already started
self.log = logging.getLogger(__name__)
self.log = set_logging()
# save the backend that is in use for plotting reference
self._mpl_backend = get_backend().lower()
[docs] def setlog(self, filename=None, on=True, level=logging.INFO):
"""Turn on and off logging to a logfile or the screen.
Parameters
----------
filename: str, optional
Name of the output file to record log information
on: bool, optional
True by default, turn the logging on or off
level: logging class, optional
set the level for logging messages, turn off screen messages
by setting to logging.CRITICAL
"""
self.log = set_logging(filename, on, level)
def _close_plots(self):
"""Make sure to release plot memory at end of exam loop."""
for plot in self._plot_windows:
plt.close()
[docs] def close(self):
"""For use with the Imexamine object standalone."""
self._close_plots()
self.log = set_logging(on=False)
[docs] def show_fit_models(self):
"""Print the available astropy models for plot fits."""
self.log.info(f"The available astropy models for fitting"
f"are: {self._fit_models}")
[docs] def set_option_funcs(self):
"""Define the dictionary which maps imexam keys to their functions.
Notes
-----
The user can modify this dictionary to add or change options,
the first item in the tuple is the associated function
the second item in the tuple is the description of what the function
does when that key is pressed
"""
self.imexam_option_funcs = {'a': (self.aper_phot, 'Aperture sum, with radius region_size '),
'j': (self.line_fit, '1D [Gaussian1D default] line fit '),
'k': (self.column_fit, '1D [Gaussian1D default] column fit'),
'm': (self.report_stat, 'Square region stats, in [region_size],default is median'),
'x': (self.show_xy_coords, 'Return x,y,value of pixel'),
'y': (self.show_xy_coords, 'Return x,y,value of pixel'),
'l': (self.plot_line, 'Return line plot'),
'c': (self.plot_column, 'Return column plot'),
'g': (self.curve_of_growth, 'Return curve of growth plot'),
'r': (self.radial_profile, 'Return the radial profile plot'),
'h': (self.histogram, 'Return a histogram in the region around the cursor'),
'e': (self.contour, 'Return a contour plot in a region around the cursor'),
's': (self.save_figure, 'Save current figure to disk as [plot_name]'),
'b': (self.gauss_center, 'Return the 2D gauss fit center of the object'),
'd': (self.com_center, 'Return the Center of Mass fit center of the object'),
'w': (self.surface, 'Display a surface plot around the cursor location'),
'2': (self.new_plot_window, 'Make the next plot in a new window'),
't': (self.cutout, 'Make a fits image cutout using pointer location')
}
[docs] def print_options(self):
"""Print the imexam options to screen."""
keys = self.get_options()
for key in keys:
print(f"{key} {self.option_descrip(key)}")
[docs] def do_option(self, x, y, key):
"""Run the imexam option.
Parameters
----------
x: int
The x location of the cursor or data point
y: int
The y location of the cursor or data point
key: string
The key which was pressed
"""
self.log.debug(f"pressed: {key}, {self.imexam_option_funcs[key][0].__name__}")
# dont require input for saving the active figure
if key == 's':
self.imexam_option_funcs[key][0]()
else:
self.imexam_option_funcs[key][0](x, y, self._data)
[docs] def get_options(self):
"""Return the imexam options as a key list."""
keys = sorted(self.imexam_option_funcs.keys())
return keys
[docs] def option_descrip(self, key, field=1):
"""Return the looked up dictionary of options.
Parameters
----------
key: string
The key which was pressed, it relates to the function to call
field: int
This tells where in the option dictionary the function name
can be found
"""
return self.imexam_option_funcs[key][field]
[docs] def set_data(self, data=np.zeros(0)):
"""initialize the data that imexamine uses."""
self._data = data
[docs] def set_plot_name(self, filename=None):
"""set the default plot name for the "s" key.
Parameters
----------
filename: string
The name which is used to save the current plotting window to a file
The extension on the name decides which file type is used
"""
if filename is None:
warnings.warn("No filename provided")
else:
self.plot_name = filename
[docs] def get_plot_name(self):
"""return the default plot name."""
return self.plot_name
def _define_default_pars(self):
"""Set all pars to their defaults, stored in a file with dicts."""
self.aper_phot_def_pars = imexam_defpars.aper_phot_pars
self.radial_profile_def_pars = imexam_defpars.radial_profile_pars
self.curve_of_growth_def_pars = imexam_defpars.curve_of_growth_pars
self.surface_def_pars = imexam_defpars.surface_pars
self.line_fit_def_pars = imexam_defpars.line_fit_pars
self.column_fit_def_pars = imexam_defpars.column_fit_pars
self.contour_def_pars = imexam_defpars.contour_pars
self.histogram_def_pars = imexam_defpars.histogram_pars
self.lineplot_def_pars = imexam_defpars.lineplot_pars
self.colplot_def_pars = imexam_defpars.colplot_pars
self.histogram_def_pars = imexam_defpars.histogram_pars
self.contour_def_pars = imexam_defpars.contour_pars
self.report_stat_def_pars = imexam_defpars.report_stat_pars
self.cutout_def_pars = imexam_defpars.cutout_pars
self.com_center_def_pars = imexam_defpars.com_center_pars
self._define_local_pars()
def _define_local_pars(self):
"""Set a copy of the default pars that users can alter."""
self.aper_phot_pars = deepcopy(self.aper_phot_def_pars)
self.radial_profile_pars = deepcopy(self.radial_profile_def_pars)
self.curve_of_growth_pars = deepcopy(self.curve_of_growth_def_pars)
self.surface_pars = deepcopy(self.surface_def_pars)
self.line_fit_pars = deepcopy(self.line_fit_def_pars)
self.column_fit_pars = deepcopy(self.column_fit_def_pars)
self.contour_pars = deepcopy(self.contour_def_pars)
self.histogram_pars = deepcopy(self.histogram_def_pars)
self.lineplot_pars = deepcopy(self.lineplot_def_pars)
self.colplot_pars = deepcopy(self.colplot_def_pars)
self.histogram_pars = deepcopy(self.histogram_def_pars)
self.contour_pars = deepcopy(self.contour_def_pars)
self.report_stat_pars = deepcopy(self.report_stat_def_pars)
self.cutout_pars = deepcopy(self.cutout_def_pars)
self.com_center_pars = deepcopy(self.com_center_def_pars)
[docs] def unlearn_all(self):
"""reset the default parameters for all functions."""
self._define_local_pars()
[docs] def new_plot_window(self, x, y, data=None):
"""make the next plot in a new plot window.
Notes
-----
x,y, data, are not used here, but the calls are setup to take them
for all imexam options. Is there a better way to do the calls in
general? Once the new plotting window is open all plots will be
directed towards it. The old window cannot be used again.
"""
if data is None:
data = self._data
self._figure_name = "imexam" + str(len(self._plot_windows) + 1)
self._plot_windows.append(self._figure_name)
self.log.info(f"Plots now directed towards {self._figure_name}")
[docs] def plot_line(self, x, y, data=None, fig=None):
"""line plot of data at point x.
Parameters
----------
x: int
The x location of the object
y: int
The y location of the object
data: numpy array
The data array to work on
fig: figure object for redirect
Used for interaction with the ginga GUI
"""
if data is None:
data = self._data
x = int(x)
y = int(y)
self.log.info(f"Line at {x} {y}")
pfig = fig
if fig is None:
fig = plt.figure(self._figure_name)
fig.clf()
fig.add_subplot(111)
ax = fig.gca()
if self.lineplot_pars["title"][0] is None:
ax.set_title(f"{self._datafile} line at {y}")
ax.set_xlabel(self.lineplot_pars["xlabel"][0])
ax.set_ylabel(self.lineplot_pars["ylabel"][0])
if not self.lineplot_pars["xmax"][0]:
xmax = len(data[y, :])
else:
xmax = self.lineplot_pars["xmax"][0]
ax.set_xlim(self.lineplot_pars["xmin"][0], xmax)
if self.lineplot_pars["logx"][0]:
ax.set_xscale("log")
if self.lineplot_pars["logy"][0]:
ax.set_yscale("log")
if bool(self.lineplot_pars["pointmode"][0]):
ax.plot(data[y, :], self.lineplot_pars["marker"][0])
else:
ax.plot(data[y, :])
if pfig is None and 'nbagg' not in self._mpl_backend:
plt.draw()
plt.pause(0.001)
else:
fig.canvas.draw_idle()
[docs] def plot_column(self, x, y, data=None, fig=None):
"""column plot of data at point y.
Parameters
----------
x: int
The x location of the object
y: int
The y location of the object
data: numpy array
The data array to work on
fig: figure name for redirect
Used for interaction with the ginga GUI
"""
if data is None:
data = self._data
x = int(x)
y = int(y)
self.log.info(f"Column at {x} {y}")
pfig = fig
if fig is None:
fig = plt.figure(self._figure_name)
fig.clf()
fig.add_subplot(111)
ax = fig.gca()
if self.colplot_pars["title"][0] is None:
ax.set_title(f"{self._datafile} column at {x}")
else:
ax.set_title(self.colplot_pars["title"][0])
ax.set_xlabel(self.colplot_pars["xlabel"][0])
ax.set_ylabel(self.colplot_pars["ylabel"][0])
if not self.colplot_pars["xmax"][0]:
xmax = len(data[:, x])
else:
xmax = self.colplot_pars["xmax"][0]
ax.set_xlim(self.colplot_pars["xmin"][0], xmax)
if self.colplot_pars["logx"][0]:
ax.set_xscale("log")
if self.colplot_pars["logy"][0]:
ax.set_yscale("log")
if bool(self.colplot_pars["pointmode"][0]):
ax.plot(data[:, x], self.colplot_pars["marker"][0])
else:
ax.plot(data[:, x])
if pfig is None and 'nbagg' not in self._mpl_backend:
plt.draw()
plt.pause(0.001)
else:
fig.canvas.draw_idle()
[docs] def show_xy_coords(self, x, y, data=None):
"""print the x,y,value to the screen.
Parameters
----------
x: int
The x location of the object
y: int
The y location of the object
data: numpy array
The data array to work on
"""
if data is None:
data = self._data
info = f"{x} {y} {data[int(y), int(x)]}"
self.log.info(info)
[docs] def report_stat(self, x, y, data=None):
"""report the statisic of values in a box with side region_size.
The statistic can be any numpy function
Parameters
----------
x: int
The x location of the object
y: int
The y location of the object
data: numpy array
The data array to work on
"""
if data is None:
data = self._data
region_size = self.report_stat_pars["region_size"][0]
name = self.report_stat_pars["stat"][0]
dist = region_size / 2
xmin = int(x - dist)
xmax = int(x + dist)
ymin = int(y - dist)
ymax = int(y + dist)
if (("describe" in name) and (scipy_installed)):
try:
stat = getattr(stats, "describe")
nobs, minmax, mean, var, skew, kurt = stat(data[ymin:ymax,
xmin:xmax].flatten())
pstr = (f"[{ymin}:{ymax},{xmin}:{xmax}] {name}: \nnobs: "
f"{nobs}\nminmax: {minmax}\nmean {mean}\nvariance: {var}\nskew: "
f"{skew}\nkurtosis: {kurt}")
except AttributeError:
warnings.warn("Invalid stat specified")
else:
try:
stat = getattr(np, name)
pstr = f"[{ymin}:{ymax},{xmin}:{xmax}] {name}: {stat(data[ymin:ymax, xmin:xmax])}"
except AttributeError:
warnings.warn("Invalid stat specified")
self.log.info(pstr)
[docs] def save(self, filename=None, fig=None):
"""Save to file the figure that's currently displayed.
this is used for the standalone plotting
Parameters
----------
filename: string
Name of the file the plot will be saved to. The extension
on the filename determines the filetype
fig: figure name for redirect
Used for interaction with the ginga GUI
"""
if filename:
self.set_plot_name(filename)
else:
self.set_plot_name(self._figure_name + ".pdf")
if fig is None:
fig = plt.figure(self._figure_name)
ax = fig.gca()
fig.savefig(self.plot_name)
pstr = f"plot saved to {self.plot_name}"
self.log.info(pstr)
[docs] def aper_phot(self, x, y, data=None,
genplot=True, fig=None,
error=None):
"""Perform aperture photometry.
Uses photutils functions, photutils must be available
Parameters
----------
x: int
The x location of the object
y: int
The y location of the object
data: numpy array
The data array to work on
genplot: bool
plot the apertures to a figure; if false then the
tuple of (apertures, annulus_apertures,rawflux_table, sky_per_pix)
is returned.
fig: figure object for redirect
Used for interaction with the ginga GUI
error: float array
If error is not None, then it should be given the
error array for the corresponding data image.
error is assumed to include all sources of error,
including the Poisson error of the sources
See the docs for photutils for more details.
The returned table will include a 'aperture_sum_err' column
in addition to 'aperture_sum'. 'aperture_sum_err'
provides the propagated uncertainty associated with
'aperture_sum'.
Returns
-------
plot or the tuple of apertures, annulus_apertures, rawflux_table, sky_per_pix.
Where apertures and annulus_apertures are photuils objects, or None
"""
if data is None:
data = self._data
if error is not None:
if (data.shape != error.shape):
raise AttributeError("Data and error arrays don't match")
center = False
if not photutils_installed:
self.log.warning("Install photutils to enable")
else:
if self.aper_phot_pars["center"][0]:
center = True
delta = int(self.aper_phot_pars["delta"][0])
if self.aper_phot_pars["center_com"][0]:
# get center from center of mass
xx, yy = self.com_center(x, y, data=data, delta=delta)
sigma = 0.
sigmay = 0.
else:
amp, xx, yy, sigma, sigmay = self.gauss_center(x, y, data,
delta=delta)
radius = self.aper_phot_pars["radius"][0]
width = int(self.aper_phot_pars["width"][0])
inner = int(self.aper_phot_pars["skyrad"][0])
subsky = bool(self.aper_phot_pars["subsky"][0])
outer = inner + width
apertures = photutils.CircularAperture((xx, yy), radius)
rawflux_table = photutils.aperture_photometry(
data,
apertures,
subpixels=1,
error=error,
method="center")
sky_per_pix = 0.
annulus_apertures = None
if subsky:
annulus_apertures = photutils.CircularAnnulus(
(xx, yy), r_in=inner, r_out=outer)
bkgflux_table = photutils.aperture_photometry(
data,
annulus_apertures)
# to calculate the mean local background, divide the circular
# annulus aperture sums by the area fo the circular annulus.
# The bkg sum with the circular aperture is then
# then mean local background tims the circular apreture area.
if photutils_version >= version.parse('0.7'):
aperture_area = apertures.area
annulus_area = annulus_apertures.area
else:
aperture_area = apertures.area()
annulus_area = annulus_apertures.area()
bkg_sum = float(
(bkgflux_table['aperture_sum'] *
aperture_area /
annulus_area)[0])
total_flux = rawflux_table['aperture_sum'][0] - bkg_sum
sky_per_pix = float(bkgflux_table['aperture_sum'] /
annulus_area)
else:
total_flux = float(rawflux_table['aperture_sum'][0])
# compute the magnitude of the sky corrected flux
magzero = float(self.aper_phot_pars["zmag"][0])
mag = magzero - 2.5 * (np.log10(total_flux))
# Construct the output strings (header and parameter values)
pheader = f"x\ty\tradius\tflux\tmag(zpt={magzero:0.2})\t"
pstr = f"\n{x:.2f}\t{y:0.2f}\t{radius}\t{total_flux:0.2}\t{mag:0.2}\t"
if subsky:
pheader += "sky/pix\t"
pstr += f"{sky_per_pix:0.3f}\t"
if center:
# center of mass estimator
if self.aper_phot_pars["center_com"][0]:
pheader += "center of mass(x,y)"
pstr += f"{xx:0.2f},{yy:0.2f}"
else:
pheader += "fwhm(x,y)"
fwhmx, fwhmy = math_helper.gfwhm(sigma, sigmay)
pstr += f"{sigma:0.2f},{sigmay:0.2f}"
pheader = pheader.expandtabs(15)
pstr = pstr.expandtabs(15)
self.log.info(pheader + pstr)
if genplot:
pfig = fig
if fig is None:
# Make sure figure is square so round stars look round
fig = plt.figure(self._figure_name, figsize=[5, 5])
fig.clf()
fig.add_subplot(111)
ax = fig.gca()
if self.aper_phot_pars["title"][0] is None:
title = f"x= {xx:0.2f}, y={yy:0.2f}, flux={total_flux:0.1f}, \nmag={mag:0.1f}, sky={sky_per_pix:0.1f}"
if center:
if self.aper_phot_pars["center_com"][0]:
title+= f", CoM({xx:0.2f},{yy:0.2f})"
else:
title += f", FWHM={math_helper.gfwhm(sigma)[0]:0.2f}"
ax.set_title(title)
else:
ax.set_title(self.aper_phot_pars["title"][0])
if self.aper_phot_pars['scale'][0] == 'zscale':
zs = ZScaleInterval()
color_range = zs.get_limits(data)
else:
color_range = [self.aper_phot_pars['color_min'][0],
self.aper_phot_pars['color_max'][0]]
pad = outer * 1.2 # XXX TODO: Bad magic number
print(xx,yy,pad)
ax.imshow(data[int(yy - pad):int(yy + pad),
int(xx - pad):int(xx + pad)],
vmin=color_range[0], vmax=color_range[1],
extent=[int(xx - pad), int(xx + pad),
int(yy - pad), int(yy + pad)], origin='lower',
cmap=self.aper_phot_pars['cmap'][0])
apertures.plot(ax=ax, color='green', alpha=0.75, lw=3)
if subsky:
annulus_apertures.plot(ax=ax, color='red', alpha=0.75, lw=3)
if pfig is None:
plt.draw()
plt.pause(0.001)
else:
fig.canvas.draw_idle()
else:
return (apertures, annulus_apertures, rawflux_table, sky_per_pix)
[docs] def line_fit(self, x, y, data=None, form=None, genplot=True, fig=None, col=False):
"""compute the 1D fit to the line of data using the specified form.
Parameters
----------
x: int
The x location of the object
y: int
The y location of the object
data: numpy array
The data array to work on
form: string
This is the functional form specified in the line fit parameters
see show_fit_models()
genplot: bool
produce the plot or return the fit
fig: figure for redirect
Used for interaction with the ginga GUI
col: bool (False)
Plot column instead of line
Notes
-----
The background is currently ignored
If centering is True in the parameter set, then the center
is fit with a 2d gaussian, not performed for Polynomial1D
"""
# Set which parameters to use
if col:
pars = self.column_fit_pars
else:
pars = self.line_fit_pars
if data is None:
data = self._data
if form is None:
fitform = getattr(models, pars["func"][0])
else:
if form in self._fit_models:
fitform = getattr(models, form)
else:
raise ValueError(f"Functional form not in available: {self._fit_models}")
self.log.info(f"using model: {fitform}")
# Used for Polynomial1D fitting
degree = int(pars["order"][0])
delta = int(pars["rplot"][0])
if delta >= len(data) / 4: # help with small data arrays and defaults
delta = delta / 2
delta = int(delta)
xx = int(x)
yy = int(y)
# fit the center with a 2d gaussian
if pars["center"][0]:
if fitform.name != "Polynomial1D":
amp, xout, yout, sigma, sigmay = self.gauss_center(xx,
yy,
data,
delta=delta)
if (xout < 0 or yout < 0 or xout > data.shape[1] or
yout > data.shape[0]):
self.log.warning("Problem with centering, "
"pixel coords")
else:
xx = int(xout)
yy = int(yout)
if col:
line = data[:, xx]
chunk = line[yy - delta:yy + delta]
delta_add = yy - delta
else:
line = data[yy, :]
chunk = line[xx - delta: xx + delta]
delta_add = xx - delta
# This factor is passed to the fitter
if pars["clip"][0]:
sig_factor = pars["sigma"][0]
else:
sig_factor = 0
# fit model to data
if fitform.name == "Gaussian1D":
xr = np.arange(len(chunk))
fitted = math_helper.fit_gauss_1d(xr, chunk, sigma_factor=sig_factor)
fitted.mean_0.value += delta_add
elif fitform.name == "Moffat1D":
fitted = math_helper.fit_moffat_1d(chunk, sigma_factor=sig_factor)
fitted.x_0_0.value += delta_add
elif fitform.name == "MexicanHat1D":
fitted = math_helper.fit_mex_hat_1d(chunk, sigma_factor=sig_factor)
fitted.x_0_0.value += delta_add
elif fitform.name == "Polynomial1D":
fitted = math_helper.fit_poly_n(chunk, deg=degree, sigma_factor=sig_factor)
if fitted is None:
raise ValueError("Problem with the Poly1D fit")
elif fitform.name == "AiryDisk2D":
fitted = math_helper.fit_airy_2d(chunk, sigma_factor=sig_factor)
if fitted is None:
raise ValueError("Problem with the AiryDisk2D fit")
fitted.x_0_0.value += (xx - delta)
fitted.y_0_0.value += (yy - delta)
xline = np.arange(len(chunk)) + delta_add
fline = np.linspace(xline[0], xline[-1], 100) # finer sample
if fitform.name == "AiryDisk2D":
yfit = fitted(fline, fline * 0 + fitted.y_0_0.value)
else:
yfit = fitted(fline)
# make a plot
if pars["title"][0] is None:
title = f"{self._datafile}: {int(x)} {int(y)}\n"
else:
title = pars["title"][0]
if genplot:
pfig = fig
if fig is None:
fig = plt.figure(self._figure_name)
fig.clf()
fig.add_subplot(111)
ax = fig.gca()
ax.set_xlabel(pars["xlabel"][0])
ax.set_ylabel(pars["ylabel"][0])
if pars["logx"][0]:
ax.set_xscale("log")
if pars["logy"][0]:
ax.set_yscale("log")
if bool(pars["pointmode"][0]):
ax.plot(xline, chunk, 'o', label="data")
else:
ax.plot(xline, chunk, label="data", linestyle='-')
if fitform.name == "Gaussian1D":
fwhmx, fwhmy = math_helper.gfwhm(fitted.stddev_0.value)
ax.set_title(f"{title} amp={fitted.amplitude_0.value:8.2f}"
f" mean={fitted.mean_0.value:9.2f},"
f"fwhm={fwhmx:9.2f}")
pstr = f"({int(x):d},{int(y):d}) mean={fitted.mean_0.value:9.2f}, fwhm={fwhmx:9.2f}"
self.log.info(pstr)
elif fitform.name == "Moffat1D":
mfwhm = math_helper.mfwhm(fitted.alpha_0.value,
fitted.gamma_0.value)
ax.set_title(f"{title} amp={fitted.amplitude_0.value:8.2f}"
f" fwhm={mfwhm:9.2f}")
pstr = f"({int(x):d},{int(y):d}) amp={fitted.amplitude_0.value:8.2f} fwhm={mfwhm:9.2f}"
self.log.info(pstr)
elif fitform.name == "MexicanHat1D":
ax.set_title(f"{title} amp={fitted.amplitude_0.value:8.2f} sigma={fitted.sigma_0.value:8.2f}")
pstr = f"({int(x):d},{int(y):d}) amp={fitted.amplitude_0.value:8.2f} sigma={fitted.sigma_0.value:9.2f}"
self.log.info(pstr)
elif fitform.name == "Polynomial1D":
ax.set_title(f"{title} degree={degree}")
pstr = f"({int(x):d},{int(y):d}) degree={degree}"
self.log.info(fitted.parameters)
self.log.info(pstr)
elif fitform.name == "AiryDisk2D":
ax.set_title(f"{title} amp={fitted.amplitude_0.value:8.2f} radius={fitted.radius_0.value:8.2f}")
pstr = f"({int(x):d},{int(y):d}) amp={fitted.amplitude_0.value:8.2f} radius={fitted.radius_0.value:9.2f}"
self.log.info(pstr)
else:
warnings.warn("Unsupported functional form specified for fit")
raise ValueError
ax.plot(fline, yfit, c='r', label=str(fitform.__name__) + " fit")
ax.legend()
if pfig is None and 'nbagg' not in self._mpl_backend:
plt.draw()
plt.pause(0.001)
else:
fig.canvas.draw_idle()
else:
return fitted
[docs] def column_fit(self, x, y, data=None, form=None, genplot=True, fig=None):
"""Compute the 1d fit to the column of data.
Parameters
----------
x: int
The x location of the object
y: int
The y location of the object
data: numpy array
The data array to work on
form: string
This is the functional form specified in the column fit parameters
genplot: int
produce the plot or return the fit model
fig: figure name for redirect
Used for interaction with the ginga GUI
Notes
-----
delta is the range of data values to use around the x,y location
The background is currently ignored
if centering is True, then the center is fit with a 2d gaussian,
but this is currently not done for Polynomial1D
"""
result = self.line_fit(x, y, data=data, form=form,
genplot=genplot, fig=fig, col=True)
if not genplot:
return result
[docs] def com_center(self, x, y, data=None, delta=None, oversampling=1.):
""" Return the center of mass of the object at x,y
Parameters
----------
x: int
The x location of the object
y: int
The y location of the object
data: numpy array
The data array to work on
delta: int
The range of data values (bounding box) to use around the x,y
location for calculating the center
oversampling: int
Oversampling factors of pixel indices. If oversampling
is a scalar this is treated as both x and y directions
having the same oversampling factor; otherwise it is
treated as (x_oversamp, y_oversamp)
"""
if data is None:
data = self._data
if delta is None:
delta = int(self.com_center_pars['delta'][0])
# reset delta for small arrays
if delta >= len(data) / 4:
delta = delta // 2
if delta is None:
delta = int(self.com_center_pars['delta'][0])
xx = int(x)
yy = int(y)
# flipped from xpa
chunk = data[yy - delta:yy + delta, xx - delta:xx + delta]
try:
xcenter, ycenter = centroid_com(chunk, oversampling=oversampling)
pstr = f"xc={(xcenter + xx - delta):.4f}\tyc={(ycenter + yy - delta):.4f}"
except AttributeError:
raise AttributeError("Problem with center of mass")
self.log.info(pstr)
return (xcenter + xx - delta,
ycenter + yy - delta)
[docs] def gauss_center(self, x, y, data=None, delta=10,
sigma_factor=0):
"""Return the Gaussian 2D fit center of the object at (x,y).
Parameters
----------
x: int
The x location of the object
y: int
The y location of the object
data: numpy array
The data array to work on
delta: int
The range of data values (bounding box) to use around the x,y
location for calculating the center
sigma_factor: float, optional
The sigma clipping factor to use on the data fit
"""
if data is None:
data = self._data
# reset delta for small arrays
if delta >= len(data) / 4:
delta = delta / 2
delta = int(delta)
xx = int(x)
yy = int(y)
# flipped from xpa
chunk = data[yy - delta:yy + delta, xx - delta:xx + delta]
try:
fit = math_helper.fit_gaussian_2d(chunk, sigma_factor=sigma_factor)
amp = fit.amplitude_0.value
xcenter = fit.x_mean_0.value
ycenter = fit.y_mean_0.value
xsigma = fit.x_stddev_0.value
ysigma = fit.y_stddev_0.value
pstr = f"xc={(xcenter + xx - delta):.4f}\tyc={(ycenter + yy - delta):.4f}"
self.log.info(pstr)
return (amp,
xcenter + xx - delta,
ycenter + yy - delta,
xsigma,
ysigma)
except (RuntimeError, UserWarning) as e:
self.log.info(f"Warning: {str(e)}, returning zeros for fit")
return (0, 0, 0, 0, 0)
[docs] def radial_profile(self, x, y, data=None, form=None,
genplot=True, fig=None):
"""Display the radial profile plot (intensity vs radius) for the object.
From the parameters Dictionary:
If pixel is True, then every pixel at each radius is plotted.
If pixel is False, then the sum of all pixels in integer bins is plotted
Background may be subtracted and centering can be done with a
2D Gaussian fit. These options are read from the plot parameters dict.
Parameters
----------
x: int
The x location of the object
y: int
The y location of the object
data: numpy array
The data array to work on
form: string
The string name of the form of the fit to use
genplot: bool
Generate the plot if True, else retfurn the fit data
"""
pars = self.radial_profile_pars
subtract_background = bool(pars["background"][0])
if not photutils_installed and subtract_background:
self.log.warning("Install photutils to enable "
"background subtraction")
subtract_background = False
if data is None:
data = self._data
getdata = bool(pars["getdata"][0])
center = pars["center"][0]
# be careful with the clipping since most
# of the data will be near the low value
clip_on = pars["clip"][0]
if clip_on:
sig_factor = pars["sigma"][0]
else:
sig_factor = 0
fitplot = bool(pars["fitplot"][0])
if fitplot:
if form is None:
fitform = getattr(models, pars["func"][0])
else:
if form not in self._fit_models:
msg = f"{form} not supported for fitting"
self.log.info(msg)
raise ValueError(msg)
else:
fitform = getattr(models, form)
# cut the data down to size and center cutout
datasize = int(pars["rplot"][0])
if datasize < 3:
self.log.info("Insufficient pixels, resetting chunk size to 3.")
datasize = 3
if center:
# reset delta for small arrays
# make it odd if it's even
if ((datasize % 2) == 0):
datasize = datasize + 1
xx = int(x)
yy = int(y)
# flipped from xpa
data_chunk = data[yy - datasize:yy + datasize,
xx - datasize:xx + datasize]
amp, centerx, centery, sigmax, sigmay = self.gauss_center(xx, yy, data, delta=datasize)
else:
centery = y
centerx = x
icenterx = int(centerx)
icentery = int(centery)
# fractional center, help with precision errors to 1000th pixel
xfrac = round(centerx - icenterx, 2)
yfrac = round(centery - icentery, 2)
# just grab the data box centered on the object
data_chunk = data[icentery - datasize:icentery + datasize,
icenterx - datasize:icenterx + datasize]
y, x = np.indices(data_chunk.shape) # index of all pixels
y = y - datasize
x = x - datasize
r = np.sqrt((x-xfrac)**2 + (y-yfrac)**2)
indices = np.argsort(r.flat) # sorted indices
if pars["pixels"][0]:
flux = data_chunk.ravel()[indices]
radius = r.ravel()[indices]
else: # sum the flux in integer bins
radius = r.ravel()[indices].astype(np.int)
flux = np.bincount(radius, data_chunk.ravel()[indices])
radbc = np.bincount(radius)
flux = flux / radbc
radius = np.arange(len(flux))
# Get a background measurement
if subtract_background:
inner = pars["skyrad"][0]
width = pars["width"][0]
annulus_apertures = photutils.CircularAnnulus((centerx, centery),
r_in=inner,
r_out=inner+width)
bkgflux_table = photutils.aperture_photometry(data,
annulus_apertures)
# to calculate the mean local background, divide the circular
# annulus aperture sums by the area of the circular annulus.
# The bkg sum with the circular aperture is then
# the mean local background times the circular apreture area.
if photutils_version >= version.parse('0.7'):
annulus_area = annulus_apertures.area
else:
annulus_area = annulus_apertures.area()
sky_per_pix = float(bkgflux_table['aperture_sum'] /
annulus_area)
# don't add flux
if sky_per_pix < 0.:
sky_per_pix = 0.
self.log.info("Sky background negative, setting to zero")
self.log.info(f"Background per pixel: {sky_per_pix}")
flux -= sky_per_pix
if getdata:
self.log.info(f"Sky per pixel: {sky_per_pix} using "
f"(rad={inner}->{inner + width})")
if getdata:
info = f"\nat (x,y)={centerx},{centery}\n"
self.log.info(info)
self.log.info(radius, flux)
# Fit the functional form to the radial profile flux
# TODO: Ignore sky subtracted pixels that push flux
# below zero?
if fitplot:
fline = np.linspace(0, datasize, 100) # finer sample
# fit model to data
if fitform.name == "Gaussian1D":
fitted = math_helper.fit_gauss_1d(radius, flux,
sigma_factor=sig_factor,
center_at=0,
weighted=True)
fwhmx, fwhmy = math_helper.gfwhm(fitted.stddev_0.value)
legend = (f"Max. pix. flux = {np.max(flux):9.3f}\n"
f"amp = {fitted.amplitude_0.value:9.3f}\n"
f"fwhm = {fwhmx:9.3f}")
self.log.info(legend)
legendx = datasize / 2
legendy = np.max(flux) / 2
elif fitform.name == "Moffat1D":
fitted = math_helper.fit_moffat_1d(flux,
sigma_factor=sig_factor,
center_at=0,
weighted=True)
mfwhm = math_helper.mfwhm(fitted.alpha_0.value,
fitted.gamma_0.value)
legend = (f"Max. pix. flux = {np.max(flux):9.3f}\n"
f"amp = {fitted.amplitude_0.value:9.3f}\n"
f"fwhm = {mfwhm:9.3f}")
legendx = datasize / 2
legendy = np.max(flux) / 2
elif fitform.name == "MexicanHat1D":
fitted = math_helper.fit_mex_hat_1d(flux,
sigma_factor=sig_factor,
center_at=0,
weighted=True)
legend = (f"Max. pix. flux = {np.max(flux):9.3f}\n")
legendx = datasize / 2
legendy = np.max(flux) / 2
if fitted is None:
msg = f"Problem with the {fitform.name} fit"
self.log.info(msg)
raise ValueError(msg)
yfit = fitted(fline)
# finish the plot
# TODO: users may get error if they use this without a display
# and request data back but forget to set genplot=False
if genplot:
pfig = fig
if fig is None:
fig = plt.figure(self._figure_name)
fig.clf()
fig.add_subplot(111)
ax = fig.gca()
if subtract_background:
ytitle = (f"Flux ( sky/pix = {sky_per_pix:8.2f} )")
else:
ytitle = pars["ylabel"][0]
ax.set_xlabel(pars["xlabel"][0])
ax.set_ylabel(ytitle)
if bool(pars["pointmode"][0]):
ax.plot(radius, flux, pars["marker"][0])
else:
ax.plot(radius, flux)
ax.set_ylim(0,)
if pars["title"][0] is None:
if fitplot:
title = f"Radial Profile at ({icenterx},{icentery}) with {fitform.name}"
else:
title = f"Radial Profile for {icenterx} {icentery}"
else:
title = pars["title"][0]
if fitplot:
ax.plot(fline, yfit, linestyle='-', c='r', label=fitform.name)
ax.set_xlim(0, datasize, 0.5)
ax.text(legendx, legendy, legend)
ax.set_title(title)
if pfig is None and 'nbagg' not in self._mpl_backend:
plt.draw()
plt.pause(0.001)
else:
fig.canvas.draw_idle()
else:
return radius, flux
[docs] def curve_of_growth(self, x, y, data=None, genplot=True, fig=None):
"""Display a curve of growth plot.
Parameters
----------
x: int
The x location of the object
y: int
The y location of the object
data: numpy array
The data array to work on
fig: figure name for redirect
Used for interaction with the ginga GUI
Notes
-----
the object photometry is taken from photutils
"""
if not photutils_installed:
self.log.warning("Install photutils to enable")
else:
if data is None:
data = self._data
delta = 10 # chunk size to find center
subpixels = 10 # for line fit later
# center using a center of mass
if self.curve_of_growth_pars["center"][0]:
if self.aper_phot_pars["center_com"][0]:
# use the center of mass
centerx, centery = self.com_center(x, y, delta=delta)
else:
# user the gaussian2d
amp, centerx, centery, sigma, sigmay = \
self.gauss_center(x, y, data, delta=delta)
else:
centery = y
centerx = x
centerx = int(centerx)
centery = int(centery)
# now grab aperture sums going out from that central pixel
inner = self.curve_of_growth_pars["buffer"][0]
width = self.curve_of_growth_pars["width"][0]
router = self.curve_of_growth_pars["rplot"][0]
getdata = bool(self.curve_of_growth_pars["getdata"][0])
radius = list()
flux = list()
rapert = int(router) + 1
for rad in range(1, rapert, 1):
aper_flux, annulus_sky, skysub_flux = self._aperture_phot(
centerx, centery, data, radsize=rad, sky_inner=inner,
skywidth=width, method="exact", subpixels=subpixels)
radius.append(rad)
if self.curve_of_growth_pars["background"][0]:
if inner < router:
warnings.warn(
"Your sky annulus is inside your \
photometry radius rplot")
flux.append(skysub_flux)
else:
flux.append(aper_flux)
if getdata:
rapert = np.arange(1, rapert, 1)
info = f"\nat (x,y)={int(centerx)},{int(centery)}\nradii:{rapert}\nflux:{flux}"
self.log.info(info)
if genplot:
pfig = fig
if fig is None:
fig = plt.figure(self._figure_name)
fig.clf()
fig.add_subplot(111)
ax = fig.gca()
if self.curve_of_growth_pars["title"][0] is None:
title = f"{self._datafile}: {int(x)} {int(y)}\n"
else:
title = self.curve_of_growth_pars["title"][0]
ax.set_xlabel(self.curve_of_growth_pars["xlabel"][0])
ax.set_ylabel(self.curve_of_growth_pars["ylabel"][0])
ax.plot(radius, flux, 'o')
ax.set_title(title)
if pfig is None and 'nbagg' not in self._mpl_backend:
plt.draw()
plt.pause(0.001)
else:
fig.canvas.draw_idle()
else:
return rapert, flux
def _aperture_phot(self, x, y, data=None, radsize=1,
sky_inner=5, skywidth=5,
method="subpixel", subpixels=4):
"""Perform sky subtracted aperture photometry.
uses photutils functions, photutil must be installed
Parameters
----------
x: int
The x location of the object
y: int
The y location of the object
data: numpy array
The data array to work on
radsize: int
Size of the radius
sky_inner: int
Inner radius of the sky annulus
skywidth: int
Width of the sky annulus
method: string
Pixel sampling method to use
subpixels: int
How many subpixels to use
Notes
-----
background is taken from sky annulus pixels, check into
masking bad pixels
"""
if not photutils_installed:
self.log.warning("Install photutils to enable")
else:
if data is None:
data = self._data
apertures = photutils.CircularAperture((x, y), radsize)
rawflux_table = photutils.aperture_photometry(
data,
apertures,
subpixels=1,
method="center")
outer = sky_inner + skywidth
annulus_apertures = photutils.CircularAnnulus(
(x, y), r_in=sky_inner, r_out=outer)
bkgflux_table = photutils.aperture_photometry(
data,
annulus_apertures)
# to calculate the mean local background, divide the circular
# annulus aperture sums
# by the area of the circular annulus. The bkg sum within the
# circular aperture is then
# then mean local background times the circular apreture area.
if photutils_version >= version.parse('0.7'):
aperture_area = apertures.area
annulus_area = annulus_apertures.area
else:
aperture_area = apertures.area()
annulus_area = annulus_apertures.area()
bkg_sum = (
bkgflux_table['aperture_sum'] *
aperture_area /
annulus_area)[0]
skysub_flux = rawflux_table['aperture_sum'][0] - bkg_sum
return (
float(rawflux_table['aperture_sum'][0]), bkg_sum, skysub_flux)
[docs] def histogram(self, x, y, data=None, genplot=True, fig=None):
"""Calulate a histogram of the data values.
Parameters
----------
x: int, required
The x location of the object
y: int, required
The y location of the object
data: numpy array, optional
The data array to work on
genplot: boolean, optional
If false, returns the hist, bin_edges tuple
fig: figure name for redirect
Used for interaction with the ginga GUI
Notes
-----
This functional originally used the pylab histogram routine for
plotting. In order to accomodate returning just the histogram data,
this was changed to the numpy histogram, with a subsequent plot if
genplot is True.
Does not yet support numpy v1.11 strings for bin estimation.
"""
if data is None:
data = self._data
deltax = int(self.histogram_pars["ncolumns"][0] / 2.)
deltay = int(self.histogram_pars["nlines"][0] / 2.)
yf = int(y)
xf = int(x)
data_cut = data[yf - deltay:yf + deltay, xf - deltax:xf + deltax]
# mask data for min and max intensity specified
if self.histogram_pars["z1"][0]:
mini = float(self.histogram_pars["z1"][0])
else:
mini = np.min(data_cut)
if self.histogram_pars["z2"][0]:
maxi = float(self.histogram_pars["z2"][0])
else:
maxi = np.max(data_cut)
lt = (data_cut < maxi)
gt = (data_cut > mini)
total_mask = lt * gt
flat_data = data_cut[total_mask].flatten()
if not maxi:
maxi = np.max(data_cut)
if not mini:
mini = np.min(data_cut)
num_bins = int(self.histogram_pars["nbins"][0])
if genplot:
pfig = fig
if fig is None:
fig = plt.figure(self._figure_name)
fig.clf()
fig.add_subplot(111)
ax = fig.gca()
if self.histogram_pars["title"][0] is None:
title = f"{self._datafile}: {int(x)} {int(y)}\n"
else:
title = self.histogram_pars["title"][0]
ax.set_title(title)
ax.set_xlabel(self.histogram_pars["xlabel"][0])
ax.set_ylabel(self.histogram_pars["ylabel"][0])
if self.histogram_pars["logx"][0]:
ax.set_xscale("log")
if self.histogram_pars["logy"][0]:
ax.set_yscale("log")
n, bins, patches = ax.hist(flat_data, num_bins,
range=[mini, maxi],
density=False,
facecolor='green',
alpha=0.5,
histtype='bar')
self.log.info(f"{num_bins} bins "
f"range:[{mini},{maxi}]")
if pfig is None and 'nbagg' not in self._mpl_backend:
plt.draw()
plt.pause(0.001)
else:
fig.canvas.draw_idle()
else:
hist, bin_edges = np.histogram(flat_data,
num_bins,
range=[mini, maxi],
density=False)
return hist, bin_edges
[docs] def contour(self, x, y, data=None, fig=None):
"""plot contours in a region around the specified location.
Parameters
----------
x: int
The x location of the object
y: int
The y location of the object
data: numpy array
The data array to work on
fig: figure for redirect
Used for interaction with the ginga GUI
"""
if data is None:
data = self._data
pfig = fig
if fig is None:
fig = plt.figure(self._figure_name)
fig.clf()
fig.add_subplot(111)
ax = fig.gca()
if self.contour_pars["title"][0] is None:
title = f"{self._datafile} {int(x)} {int(y)}"
else:
title = self.contour_pars["title"][0]
ax.set_title(title)
ax.set_xlabel(self.contour_pars["xlabel"][0])
ax.set_ylabel(self.contour_pars["ylabel"][0])
ncont = self.contour_pars["ncontours"][0]
colormap = self.contour_pars["cmap"][0]
lsty = self.contour_pars["linestyles"][0]
self.log.info(f"contour centered at: {x} {y}")
deltax = int(self.contour_pars["ncolumns"][0] / 2.)
deltay = int(self.contour_pars["nlines"][0] / 2.)
xx = int(x)
yy = int(y)
data_cut = data[yy - deltay:yy + deltay, xx - deltax:xx + deltax]
plt.rcParams['xtick.direction'] = 'out'
plt.rcParams['ytick.direction'] = 'out'
X, Y = np.meshgrid(np.arange(0, deltax, 0.5) + x - deltax / 2.,
np.arange(0, deltay, 0.5) + y - deltay / 2.)
C = ax.contour(
X,
Y,
data_cut,
ncont,
linewidths=.5,
colors='black',
linestyles=lsty)
# make the filled contour
ax.contourf(X, Y, data_cut, ncont, alpha=.75, cmap=colormap)
if self.contour_pars["label"][0]:
ax.clabel(C, inline=1, fontsize=10, fmt="%5.3f")
if pfig is None and 'nbagg' not in self._mpl_backend:
plt.draw()
plt.pause(0.001)
else:
fig.canvas.draw_idle()
[docs] def surface(self, x, y, data=None, fig=None):
"""plot a surface around the specified location.
Parameters
----------
x: int
The x location of the object
y: int
The y location of the object
data: numpy array
The data array to work on
fig: figure for redirect
Used for interaction with the ginga GUI
"""
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.ticker import LinearLocator, FormatStrFormatter
if data is None:
data = self._data
pfig = fig
if fig is None:
fig = plt.figure(self._figure_name)
fig.clf()
fig.add_subplot(111)
ax = fig.gca(projection='3d')
title = self.surface_pars["title"][0]
if title is None:
title = f"{self._datafile}: {int(x)} {int(y)}"
ax.set_title(title)
ax.set_xlabel(self.surface_pars["xlabel"][0])
ax.set_ylabel(self.surface_pars["ylabel"][0])
if self.surface_pars["zlabel"][0]:
ax.set_zlabel("Flux")
fancy = self.surface_pars["fancy"][0]
deltax = self.surface_pars["ncolumns"][0]
deltay = self.surface_pars["nlines"][0]
minx = int(x - deltax) if x - deltax > 0 else 0
miny = int(y - deltay) if y - deltay > 0 else 0
maxx = int(x + deltax) if x + deltax < data.shape[-1] else data.shape[0]
maxy = int(y + deltay) if y + deltay < data.shape[0] else data.shape[0]
X = np.arange(minx, maxx, 1)
Y = np.arange(miny, maxy, 1)
X, Y = np.meshgrid(X, Y)
Z = data[miny:maxy, minx:maxx]
if self.surface_pars["floor"][0]:
zmin = float(self.surface_pars["floor"][0])
else:
zmin = np.min(Z)
if self.surface_pars["ceiling"][0]:
zmax = float(self.surface_pars["ceiling"][0])
else:
zmax = np.max(Z)
stride = int(self.surface_pars["stride"][0])
if fancy:
surf = ax.plot_surface(
X, Y, Z, rstride=stride, cstride=stride,
cmap=self.surface_pars["cmap"][0], alpha=0.6)
else:
surf = ax.plot_surface(X, Y, Z, rstride=stride, cstride=stride,
cmap=self.surface_pars["cmap"][0],
linewidth=0, antialiased=False)
ax.zaxis.set_major_locator(LinearLocator(10))
ax.zaxis.set_major_formatter(FormatStrFormatter('%.0f'))
ax.set_zlim(zmin, zmax)
if fancy:
xmin = minx
ymax = maxy
cset = ax.contour(
X,
Y,
Z,
zdir='z',
offset=zmax,
cmap=self.surface_pars["cmap"][0])
cset = ax.contour(
X,
Y,
Z,
zdir='x',
offset=xmin,
cmap=self.surface_pars["cmap"][0])
cset = ax.contour(
X,
Y,
Z,
zdir='y',
offset=ymax,
cmap=self.surface_pars["cmap"][0])
fig.colorbar(surf, shrink=0.5, aspect=5)
if self.surface_pars["azim"][0]:
ax.view_init(elev=10., azim=float(self.surface_pars["azim"][0]))
if pfig is None and 'nbagg' not in self._mpl_backend:
plt.draw()
plt.pause(0.001)
else:
fig.canvas.draw_idle()
[docs] def cutout(self, x, y, data=None, size=None, fig=None):
"""Make a fits cutout around the pointer location without wcs.
Parameters
----------
x: int
The x location of the object
y: int
The y location of the object
data: numpy array
The data array to work on
size: int
The radius of the cutout region
fig: figure for redirect
Used for interaction with the ginga GUI
"""
if data is None:
data = self._data
if size is None:
size = self.cutout_pars["size"][0]
xx = int(x)
yy = int(y)
prefix = f"cutout_{xx}_{yy}_"
fname = tempfile.mkstemp(prefix=prefix, suffix=".fits", dir="./")[-1]
cutout = data[yy - size:yy + size, xx - size:xx + size]
hdu = fits.PrimaryHDU(cutout)
hdulist = fits.HDUList([hdu])
hdulist[0].header['EXTEND'] = False
hdulist.writeto(fname)
self.log.info(f"Cutout at ({xx},{yy}) saved to {fname}")
[docs] def register(self, user_funcs):
"""register a new imexamine function made by the user as an option.
Parameters
----------
user_funcs: dict
Contains a dictionary where each key is the binding for the
(function,description) tuple
Notes
-----
The new binding will be added to the dictionary of imexamine functions
as long as the key is unique. The new functions do not have to have
default dictionaries associated with them.
"""
if not isinstance(user_funcs, type(dict())):
warnings.warn("Your input needs to be a dictionary")
for key in user_funcs.keys():
if key in self.imexam_option_funcs.keys():
warnings.warn(f"{key} is not a unique key")
warnings.warn(f"{self.imexam_option_funcs[key]}")
raise ValueError(f"{key} is not a unique key")
elif key == 'q':
warnings.warn("q is reserved as the quit key")
raise ValueError("q is reserved for the quit key")
else:
func_name = user_funcs[key][0].__name__
self._add_user_function(user_funcs[key][0])
self.imexam_option_funcs[key] = (
self.__getattribute__(func_name), user_funcs[key][1])
self.log.info(f"User function: {func_name} added to imexam options with "
f"key {key}")
@classmethod
def _add_user_function(cls, func):
import types
if PY3:
return setattr(cls, func.__name__, types.MethodType(func, cls))
else:
return setattr(cls, func.__name__,
types.MethodType(func, None, cls))
[docs] def set_aper_phot_pars(self, user_dict=None):
"""the user may supply a dictionary of par settings."""
if not user_dict:
self.aper_phot_pars = imexam_defpars.aper_phot_pars
else:
self.aper_phot_pars = user_dict
[docs] def set_com_center_pars(self):
""" set paramters for the center of mass function"""
self.com_center_pars = imexam_defpars.radial_profile_pars
[docs] def set_radial_pars(self):
"""set parameters for radial profile plots."""
self.radial_profile_pars = imexam_defpars.radial_profile_pars
[docs] def set_curve_pars(self):
"""set parameters for curve of growth plots."""
self.curve_of_growth_pars = imexam_defpars.curve_of_growth_pars
[docs] def set_surface_pars(self):
"""set parameters for surface plots."""
self.surface_pars = imexam_defpars.surface_pars
[docs] def set_line_fit_pars(self):
"""set parameters for 1D line fit plots."""
self.line_fit_pars = imexam_defpars.line_fit_pars
[docs] def set_column_fit_pars(self):
"""set parameters for 1D line fit plots."""
self.column_fit_pars = imexam_defpars.column_fit_pars
[docs] def set_contour_pars(self):
"""set parameters for contour plots."""
self.contour_pars = imexam_defpars.contour_pars
[docs] def set_histogram_pars(self):
"""set parameters for histogram plots."""
self.histogram_pars = imexam_defpars.histogram_pars
[docs] def set_lineplot_pars(self):
"""set parameters for line plots."""
self.lineplot_pars = imexam_defpars.lineplot_pars
[docs] def set_colplot_pars(self):
"""set parameters for column plots."""
self.colplot_pars = imexam_defpars.colplot_pars
[docs] def set_cutout_pars(self):
"""set parameters for cutout images."""
self.cutout_pars = imexam_defpars.cutout_pars
[docs] def reset_defpars(self):
"""set all pars to their defaults."""
self._define_pars()
