def find_optimal_box(box, display=False):
actual_area = ACQUISITION_BOX_SIZE * ACQUISITION_BOX_SIZE # arcseconds^2
scidata = box.get_data()
best_box = find_optimal(scidata,
measure_box,
get_box_error_func(actual_area, box.unbinned_pixel_scale()))
if display:
plotting.put_plot(plot_box_measurement, (box, best_box))
return MosaicTile(box, best_box)
def find_optimal_slit(acqimage, slitxpos, slitypos, display):
actual_width = acqimage.get_mask_width()
scidata = acqimage.get_science_data()
best_slit = find_optimal(scidata,
measure_slit,
get_error_func(actual_width, acqimage.unbinned_pixel_scale()),
slitxpos,
slitypos)
if display:
plotting.put_plot(plot_slit_measurement, (acqimage, best_slit))
return best_slit
def find_fiber(fiberstamp, position, display_plot=False):
fiber_data = fiberstamp.get_data()
xmin, xmax, ymin, ymax = get_window(fiber_data, position, 10)
debug("...searching for the fiber in the window = [%i:%i,%i:%i]" % (ymin, ymax, xmin, xmax))
window = fiber_data[ymin:ymax,xmin:xmax]
origin = np.array((xmin, ymin))
cursor_point = position - origin
debug("...contour must contain the point =", cursor_point)
median = np.median(window)
maximum = window.max()
fibers = []
detector_offset = fiberstamp.get_detector_offset()
for contour_level in np.linspace(maximum, median):
contours = measure.find_contours(window, contour_level)
for contour in contours:
if not is_closed_contour(contour):
continue
fiber = Fiber(detector_offset, origin, contour, contour_level)
if not fiber.bounding_box_contains(cursor_point):
continue
fibers.append(fiber)
def compare_fibers(fiber1, fiber2):
diff1 = abs(fiber1.get_area() - FIBER_AREA)
diff2 = abs(fiber2.get_area() - FIBER_AREA)
return cmp(diff1, diff2)
fibers.sort(cmp=compare_fibers)
if display_plot:
for fiber in fibers:
plotting.put_plot(plot_fiber_measurement, (window, fiber, FIBER_AREA))
break
if fibers:
return fibers[0]
return None
def get_selection_peak(input_data, curcrds, pixscale,
verbose=False, pixel_buffer=None, cursor_listener=None, radial=False):
seeing_estimate = 0.8
default_fwhm = seeing_estimate / pixscale
if pixel_buffer is None:
pixel_buffer = max(int(round(default_fwhm)), 1)
best_selection = None
prev_shape = (None, None)
for i in range(10):
debug("...using a pixel_buffer =", pixel_buffer)
xmin, xmax, ymin, ymax = get_window(input_data, curcrds, pixel_buffer)
stamp_data = input_data[ymin:ymax, xmin:xmax]
window = (ymin, ymax, xmin, xmax)
map(assert_int, window)
debug("...moffat optimization for the data in the window [%i:%i,%i:%i]" % window)
ydim, xdim = stamp_data.shape
assert ydim != 0
assert xdim != 0
if prev_shape == stamp_data.shape:
debug("...already attempted to fit a function to a window this size, terminating search.")
break
prev_shape = stamp_data.shape
# fit the subset of data to a moffat function
fitted_function = get_fitted_function(stamp_data, default_fwhm)
center_x, center_y = fitted_function.get_center()
# convert to 1-based indicing and original image
measured_center = np.array([xmin + center_x + 1, ymin + center_y + 1])
selection = Selection(measured_center, window, fitted_function)
if is_debug_mode():
plotting.put_plot(plot_selection, (selection, pixscale, cursor_listener))
# note, the first time through the loop best_selection is None, this is ok!
best_selection = max(best_selection, selection)
if fitted_function.get_success() <= 3:
debug("...moffat optimization converged.")
break
rsquared = fitted_function.get_rsquared()
if rsquared >= 0.90:
debug("...moffat optimization did not converge, but R^2 is greater than 0.9 (%.2f)" % rsquared)
break
debug("...moffat optimization did not converge.")
mindim = min(input_data.shape)
if mindim > 300:
if pixel_buffer > int(float(mindim) * 0.1):
debug("...window is already greater than 20% of the field of view, not going to try again.")
break
debug("...doubling the window size and trying again...")
pixel_buffer *= 2
if verbose and (not is_debug_mode() or radial):
plotting.put_plot(plot_selection, (best_selection, pixscale, cursor_listener))
if radial:
plotting.put_plot(plot_radial, (best_selection, pixscale))
return best_selection