def test_equal_branches(self):
'''
Ensure the filament arrays are equal with and without computing the
RHT branches.
'''
test1 = fil_finder_2D(img, header=hdr, beamwidth=10.0 * u.arcsec,
flatten_thresh=95,
distance=260 * u.pc, size_thresh=430,
glob_thresh=20, save_name="test1")
test1.create_mask(border_masking=False)
test1.medskel()
test1.analyze_skeletons()
test1.exec_rht(branches=True)
test_copy = deepcopy(test1)
test_copy.exec_rht(branches=False)
for arr1, arr2 in zip(test1.filament_arrays['final'],
test_copy.filament_arrays['final']):
assert np.allclose(arr1, arr2)
for arr1, arr2 in zip(test1.filament_arrays['long path'],
test_copy.filament_arrays['long path']):
assert np.allclose(arr1, arr2)
def test_equal_branches(self):
'''
Ensure the filament arrays are equal with and without computing the
RHT branches.
'''
test1 = fil_finder_2D(img,
header=hdr,
beamwidth=10.0 * u.arcsec,
flatten_thresh=95,
distance=260 * u.pc,
size_thresh=430,
glob_thresh=20,
save_name="test1")
test1.create_mask(border_masking=False)
test1.medskel()
test1.analyze_skeletons()
test1.exec_rht(branches=True)
test_copy = deepcopy(test1)
test_copy.exec_rht(branches=False)
for arr1, arr2 in zip(test1.filament_arrays['final'],
test_copy.filament_arrays['final']):
assert np.allclose(arr1, arr2)
for arr1, arr2 in zip(test1.filament_arrays['long path'],
test_copy.filament_arrays['long path']):
assert np.allclose(arr1, arr2)
def test_with_rht_branches(self):
test1 = fil_finder_2D(img, hdr, 10.0, flatten_thresh=95,
distance=260, size_thresh=430,
glob_thresh=20, save_name="test1")
test1.create_mask(border_masking=False)
test1.medskel()
test1.analyze_skeletons()
test1.exec_rht(branches=True)
test1.find_widths()
test1.compute_filament_brightness()
for i, param in enumerate(test1.width_fits["Names"]):
assert np.allclose(test1.width_fits["Parameters"][:, i],
np.asarray(table1[param]))
assert np.allclose(test1.width_fits["Errors"][:, i],
np.asarray(table1[param+" Error"]))
assert np.allclose(test1.lengths,
np.asarray(table1['Lengths']))
assert (test1.width_fits['Type'] == table1['Fit Type']).all()
assert np.allclose(test1.total_intensity,
np.asarray(table1['Total Intensity']))
assert np.allclose(test1.filament_brightness,
np.asarray(table1['Median Brightness']))
assert np.allclose(test1.branch_properties["number"],
np.asarray(table1['Branches']))
def test_without_header_distance(self):
beamwidth = 10.0*u.pix
test1 = fil_finder_2D(img, header=None, beamwidth=beamwidth,
flatten_thresh=95,
distance=None, size_thresh=430,
glob_thresh=20, save_name="test1")
beamwidth = beamwidth / FWHM_FACTOR
imgscale = 1.0
npt.assert_equal(test1.imgscale, imgscale)
npt.assert_equal(test1.beamwidth, beamwidth.value)
def test_without_distance(self):
beamwidth = 10.0*u.arcsec
test1 = fil_finder_2D(img, header=hdr, beamwidth=beamwidth,
flatten_thresh=95,
distance=None, size_thresh=430,
glob_thresh=20, save_name="test1")
beamwidth = (beamwidth.to(u.deg) /
(hdr["CDELT2"] * u.deg)) / FWHM_FACTOR
imgscale = 1.0
npt.assert_equal(test1.imgscale, imgscale)
npt.assert_equal(test1.beamwidth, beamwidth.value)
def test_header_beam(self):
beam_hdr = hdr.copy()
# It only looks for BMAJ at the moment.
beam_hdr["BMAJ"] = 10.0
test1 = fil_finder_2D(img, header=beam_hdr, beamwidth=None,
flatten_thresh=95,
distance=None, size_thresh=430,
glob_thresh=20, save_name="test1")
beamwidth = (10.0 * u.deg / (hdr["CDELT2"] * u.deg)) / FWHM_FACTOR
imgscale = 1.0
npt.assert_equal(test1.imgscale, imgscale)
npt.assert_equal(test1.beamwidth, beamwidth.value)
def test_without_rht_branches(self):
# Non-branches
test2 = fil_finder_2D(img,
header=hdr,
beamwidth=10.0 * u.arcsec,
flatten_thresh=95,
distance=260 * u.pc,
size_thresh=430,
glob_thresh=20,
save_name="test2")
test2.create_mask(border_masking=False)
test2.medskel()
test2.analyze_skeletons()
test2.exec_rht(branches=False)
test2.find_widths()
test2.compute_filament_brightness()
assert test2.number_of_filaments == len(table2["Lengths"])
for i, param in enumerate(test2.width_fits["Names"]):
npt.assert_allclose(test2.width_fits["Parameters"][:, i],
np.asarray(table2[param]),
rtol=1e-4)
npt.assert_allclose(test2.width_fits["Errors"][:, i],
np.asarray(table2[param + " Error"]),
rtol=1e-4)
assert np.allclose(test2.lengths, np.asarray(table2['Lengths']))
assert (test2.width_fits['Type'] == table2['Fit Type']).all()
assert np.allclose(test2.total_intensity,
np.asarray(table2['Total Intensity']))
assert np.allclose(test2.filament_brightness,
np.asarray(table2['Median Brightness']))
assert np.allclose(test2.branch_properties["number"],
np.asarray(table2['Branches']))
assert np.allclose(test2.rht_curvature['Orientation'],
np.asarray(table2['Orientation']))
assert np.allclose(test2.rht_curvature['Curvature'],
np.asarray(table2['Curvature']))
def test_with_distance(self):
distance = 260*u.pc
beamwidth = 10.0*u.arcsec
# Divide by FWHM factor
width = beamwidth / (2*np.sqrt(2*np.log(2)))
test1 = fil_finder_2D(img, header=hdr, beamwidth=beamwidth,
flatten_thresh=95,
distance=distance, size_thresh=430,
glob_thresh=20, save_name="test1")
imgscale = hdr['CDELT2'] * \
(np.pi / 180.0) * distance.to(u.pc).value
beamwidth = (width.to(u.arcsec).value / 206265.) * distance.value
npt.assert_equal(test1.imgscale, imgscale)
npt.assert_equal(test1.beamwidth, beamwidth)
def test_without_rht_branches(self):
# Non-branches
test2 = fil_finder_2D(img, header=hdr, beamwidth=10.0 * u.arcsec,
flatten_thresh=95,
distance=260 * u.pc, size_thresh=430,
glob_thresh=20, save_name="test2")
test2.create_mask(border_masking=False)
test2.medskel()
test2.analyze_skeletons()
test2.exec_rht(branches=False)
test2.find_widths()
test2.compute_filament_brightness()
assert test2.number_of_filaments == len(table2["Lengths"])
for i, param in enumerate(test2.width_fits["Names"]):
npt.assert_allclose(test2.width_fits["Parameters"][:, i],
np.asarray(table2[param]), rtol=1e-4)
npt.assert_allclose(test2.width_fits["Errors"][:, i],
np.asarray(table2[param + " Error"]), rtol=1e-4)
assert np.allclose(test2.lengths,
np.asarray(table2['Lengths']))
assert (test2.width_fits['Type'] == table2['Fit Type']).all()
assert np.allclose(test2.total_intensity,
np.asarray(table2['Total Intensity']))
assert np.allclose(test2.filament_brightness,
np.asarray(table2['Median Brightness']))
assert np.allclose(test2.branch_properties["number"],
np.asarray(table2['Branches']))
assert np.allclose(test2.rht_curvature['Orientation'],
np.asarray(table2['Orientation']))
assert np.allclose(test2.rht_curvature['Curvature'],
np.asarray(table2['Curvature']))
def make_fil_spine(self,beamwidth=None,verbose=False):
"""
Create filament spine using the FilFinder package 'shortest path' option
Parameters:
----------
beamwidth: float
A float in units of arcseconds indicating the beamwidth of the image
array. When corresponding keys are in the header file, the header
will be used to determine the beamwidth. Only when the header does
not containt information regarding the beamwidth, is the input
`beamwidth` used in the calculation.
verbose: boolean
A boolean indicating whether you want to enable FilFinder plotting of filament spine
Attributes
----------
filspine : numpy.ndarray
A 2D array of 1s and 0s defining the longest path through the filament mask
"""
# Read beamwidth
if isinstance(beamwidth, numbers.Number):
if (self.header is not None):
self.beamwidth = beamwidth * u.arcsec
else:
self.beamwidth = beamwidth * u.pix
else:
self.beamwidth = None
raise TypeError("A beamwidth is needed if the header does not contain the beam information.")
# fil_finder
## Let fil_fineder deal with the beamwidth
if (self.header is not None):
fils = fil_finder_2D(self.image,
header = self.header,
beamwidth = self.beamwidth,
distance = self.distance,
mask = self.mask)
## scale-free
else:
fils = fil_finder_2D(self.image,
beamwidth = self.beamwidth,
skel_thresh = 15,
mask = self.mask)
## 15 is chosen to be roughly 0.3 pc at the distance to Per B5 (260 pc).
## Consider allowing users to input in the future.
# do the skeletonization
fils.medskel(verbose=verbose)
# Find shortest path through skeleton
analysis = fils.analyze_skeletons(verbose=verbose)
# Return the reults.
self.filspine = fils.skeleton_longpath.astype(bool)
if (self.header is not None):
self.length = np.sum(analysis.lengths) * u.pc
self.imgscale = fils.imgscale * u.pc
else:
self.length = np.sum(analysis.lengths) * u.pix
self.imgscale = fils.imgscale * u.pix
# Return a dictionary to store the key setup Parameters
self._params['__init__']['imgscale'] = self.imgscale
params = {'beamwidth': self.beamwidth}
self._params['make_fil_spine'] = params
# Return a dictionary to store the results
self._results['make_fil_spine']['filspine'] = self.filspine
self._results['make_fil_spine']['length'] = self.length
return self
# Licensed under an MIT open source license - see LICENSE
from fil_finder import fil_finder_2D
from astropy.io.fits import getdata
img, hdr = getdata("filaments_updatedhdr.fits", header=True)
# This one uses RHT on branches
test1 = fil_finder_2D(img, hdr, 10.0, flatten_thresh=95, distance=260,
size_thresh=430, glob_thresh=20, save_name="test1")
test1.create_mask(border_masking=False)
test1.medskel()
test1.analyze_skeletons()
test1.exec_rht(branches=True)
test1.find_widths()
test1.compute_filament_brightness()
test1.save_table(save_name="test1",
branch_table_type='hdf5', table_type='hdf5')
test1.save_fits(save_name="test1")
# This one does not
test2 = fil_finder_2D(img, hdr, 10.0, flatten_thresh=95, distance=260,
size_thresh=430, glob_thresh=20, save_name="test2")
test2.create_mask(border_masking=False)
test2.medskel()
test2.analyze_skeletons()
test2.exec_rht(branches=False)
def make_fil_spine(self,beamwidth = None,verbose = False):
"""
Create filament spine using the FilFinder package 'longest path' option
Parameters:
----------
verbose: boolean
A boolean indicating whether you want to enable FilFinder plotting of filament spine
Attributes
----------
filspine : numpy.ndarray
A 2D boolean array defining the longest path through the filament mask
length: float
The length of the filament; only accessible if make_fil_spine is called
"""
try:
from fil_finder import fil_finder_2D
except ImportError:
raise ImportError("To use this method, you must install the fil_finder package to continue.")
# Read beamwidth
if isinstance(beamwidth, numbers.Number):
if (self.header is not None):
self.beamwidth = beamwidth * u.arcsec
else:
self.beamwidth = beamwidth * u.pix
else:
self.beamwidth = None
raise TypeError("A beamwidth is required")
# fil_finder
## Let fil_fineder deal with the beamwidth
if (self.header is not None):
fils = fil_finder_2D(self.image,
header = self.header,
beamwidth = self.beamwidth,
distance = self.distance,
mask = self.mask)
## scale-free
else:
fils = fil_finder_2D(self.image,
beamwidth = self.beamwidth,
skel_thresh = 15,
mask = self.mask)
## 15 is chosen to be roughly 0.3 pc at the distance to Per B5 (260 pc).
## Consider allowing users to input in the future.
# do the skeletonization
fils.medskel(verbose=verbose)
# Find shortest path through skeleton
analysis = fils.analyze_skeletons(verbose=verbose)
# Return the reults.
self.filspine = fils.skeleton_longpath.astype(bool)
if (self.header is not None):
self.length = np.sum(analysis.lengths) * u.pc
self.imgscale = fils.imgscale * u.pc
else:
self.length = np.sum(analysis.lengths) * u.pix
self.imgscale = fils.imgscale * u.pix
# Return a dictionary to store the key setup Parameters
self._params['__init__']['imgscale'] = self.imgscale
params = {'beamwidth': self.beamwidth}
self._params['make_fil_spine'] = params
# Return a dictionary to store the results
self._results['make_fil_spine']['filspine'] = self.filspine
self._results['make_fil_spine']['length'] = self.length
return self
# Licensed under an MIT open source license - see LICENSE
from fil_finder import fil_finder_2D
from astropy.io.fits import getdata
img, hdr = getdata("chamaeleonI-250_normed.fits",
header=True)
# Utilize fil_finder_2D class
# See filfind_class.py for inputs
# , region_slice=[580,1620,470,1120])
test = fil_finder_2D(img, hdr, 15.1, 30, 5, 50, distance=170., glob_thresh=20)
test.create_mask(verbose=False, border_masking=True)
test.medskel(verbose=False)
test.analyze_skeletons()
test.compute_filament_brightness()
test.exec_rht(verbose=True, branches=False)
# test.find_widths(verbose=False)
# Or run:
# test.run(verbose=True, save_name="chamaeleonI-250", save_plots=False) ##
# Run entire algorithm
cube = SpectralCube.read(
paths.Fpath('12m/SgrB2_b3_12M.HC3N.image.pbcor.contsub.fits'))
subcube = cube[100:155].minimal_subcube()
newcube = np.empty_like(subcube, dtype='int16')
for ii, imslice in enumerate(ProgressBar(subcube)):
img = imslice.value
hdr = header = imslice.header
try:
ff = fil_finder.fil_finder_2D(
img,
hdr,
beamwidth=3.5,
distance=8500,
skel_thresh=20,
branch_thresh=10,
glob_thresh=65,
adapt_thresh=10,
pad_size=1,
)
except ValueError:
print("FilFinder creation failed on iter {0}".format(ii))
continue
try:
mask = ff.create_mask(verbose=True)
except ValueError:
print("Mask creation failed on iter {0}".format(ii))
continue
try:
skels = ff.medskel(verbose=True)
p.imshow(np.arctan(pipe_img /
np.percentile(pipe_img[np.isfinite(pipe_img)], 95)),
origin="lower",
interpolation="nearest")
p.subplot(122)
p.imshow(np.arctan(
pipe_degraded /
np.percentile(pipe_degraded[np.isfinite(pipe_degraded)], 95)),
origin="lower",
interpolation="nearest")
p.show()
filfind = fil_finder_2D(pipe_degraded,
pipe_hdr,
18.2,
30,
15,
30,
distance=400,
glob_thresh=20)
filfind.run(verbose=False, save_name="degraded_pipe", save_plots=False)
## Analysis
if output:
from astropy.table import Table
deg_pipe_analysis = Table.read("degraded_pipe_table.fits")
pipe_analysis = Table.read(
"pipeCenterB59-250/pipeCenterB59-250_table.fits")
# Plot lengths, widths, orientation, curvature. Adjust for distance difference
# Add some noise
import numpy as np
np.random.seed(500)
threshs = [75, 95, 99]
patches = [7, 13, 26]
figure, grid = p.subplots(3, 3, sharex=True, sharey=True, figsize=(12, 12))
for i, patch in enumerate(patches[::-1]):
for j, thresh in enumerate(threshs):
noiseimg = img + np.random.normal(0, 0.05, size=img.shape)
test = fil_finder_2D(noiseimg, hdr, 0.0, 30, 5, 10,
flatten_thresh=thresh,
distance=260)
test.create_mask(verbose=False, smooth_size=3.0, adapt_thresh=patch,
size_thresh=180, regrid=False, border_masking=False,
zero_border=True)
test.medskel(verbose=False)
test.analyze_skeletons(verbose=False)
grid[i, j].imshow(test.mask[10:266, 10:266],
cmap='binary', interpolation='nearest')
grid[i, j].contour(test.skeleton[10:266, 10:266], colors='gray',
linewidths=0.3)
grid[i, j].set_xlim([0, 256])
grid[i, j].set_ylim([0, 256])
if i == 2:
# Licensed under an MIT open source license - see LICENSE
from fil_finder import fil_finder_2D
from astropy.io.fits import getdata
img, hdr = getdata("filaments_updatedhdr.fits", header=True)
# This one uses RHT on branches
test1 = fil_finder_2D(img,
hdr,
10.0,
flatten_thresh=95,
distance=260,
size_thresh=430,
glob_thresh=20,
save_name="test1")
test1.create_mask(border_masking=False)
test1.medskel()
test1.analyze_skeletons()
test1.exec_rht(branches=True)
test1.find_widths()
test1.compute_filament_brightness()
test1.save_table(save_name="test1",
branch_table_type='hdf5',
table_type='hdf5')
test1.save_fits(save_name="test1")
# This one does not
from astropy.utils.console import ProgressBar
import fil_finder
from spectral_cube import SpectralCube
cube = SpectralCube.read(paths.Fpath('12m/SgrB2_b3_12M.HC3N.image.pbcor.contsub.fits'))
subcube = cube[100:155].minimal_subcube()
newcube = np.empty_like(subcube, dtype='int16')
for ii,imslice in enumerate(ProgressBar(subcube)):
img = imslice.value
hdr = header = imslice.header
try:
ff = fil_finder.fil_finder_2D(img, hdr, beamwidth=3.5, distance=8500,
skel_thresh=20, branch_thresh=10,
glob_thresh=65, adapt_thresh=10,
pad_size=1,
)
except ValueError:
print("FilFinder creation failed on iter {0}".format(ii))
continue
try:
mask = ff.create_mask(verbose=True)
except ValueError:
print("Mask creation failed on iter {0}".format(ii))
continue
try:
skels = ff.medskel(verbose=True)
except ValueError:
print("Skeleton creation failed on iter {0}".format(ii))
continue
Example of the new radial profiling code for FilFinder.
This functionality is still in testing!
'''
hdu = fits.open("filaments_updatedhdr.fits")[0]
img, hdr = hdu.data, hdu.header
# Add some noise
np.random.seed(500)
noiseimg = img + np.random.normal(0, 0.05, size=img.shape)
# We need the finalized skeletons, so run the first few portions of the
# normal algorithm.
test = fil_finder_2D(noiseimg, header=hdr, beamwidth=10.0*u.arcsec,
flatten_thresh=95, distance=260*u.pc,
glob_thresh=20)
test.create_mask(verbose=False, border_masking=False, size_thresh=430)
test.medskel(verbose=False)
test.analyze_skeletons(verbose=False)
# Now choose one the longest path skeletons from the labeled array
labels, num = nd.label(test.skeleton_longpath, eight_con())
# Number 3 isn't too long and is in a relatively uncrowded region.
# We enable verbose, which will plot the profile normal to each pixel in the
# skeleton.
# The noise parameter allows an array, of the same shape as the image, to be
# passed and used as weights in the fitting.
# NOTE: If you don't want a million plots showing up, uncomment the next line:
# Licensed under an MIT open source license - see LICENSE
from fil_finder import fil_finder_2D
from astropy.io.fits import getdata
img, hdr = getdata("filaments_updatedhdr.fits", header=True)
# Add some noise
import numpy as np
np.random.seed(500)
noiseimg = img + np.random.normal(0,0.05,size=img.shape)
## Utilize fil_finder_2D class
## See filfind_class.py for inputs
test = fil_finder_2D(noiseimg, hdr, 10.0, flatten_thresh=95, distance=260)
test.create_mask(verbose=True, smooth_size=3.0, adapt_thresh=13.0,
size_thresh=180, regrid=False, border_masking=False,
zero_border=True)
test.medskel(verbose=False)
test.analyze_skeletons(verbose=False)
test.exec_rht(verbose=False)
test.find_widths(verbose=False)
# test.save_table(save_name="sim_filaments")
# test.save_fits(save_name="sim_filaments")
#def run_filfinder(label='mycloud', cubefile=None, mom8file=None, mom0file=None, redo=False,
#distpc=4.8e4):
plotfiledir = label + ".plots" # NO TRAILING / !
filpropfile = label + ".filprops.pkl" # phys props
# initial fil finding in mom8 image:
fits_hdu = fits.open(mom8file)[0]
bmwidth = np.sqrt(
fits_hdu.header['BMIN'] * fits_hdu.header['BMAJ']) * 3600 # arcsec
# setting beamwidth will have no effect if a fits header is given
# beamwidth=bmwidth*u.arcsec,
# it turns out it uses sigma not FWHM so later, nbeam=#sigma=1.6pix here
fils = fil_finder_2D(fits_hdu,
distance=distpc * u.pc,
glob_thresh=50,
flatten_thresh=90)
# 30Dor: glob=72, flatten=100
# GMC1 1st time: 50, 85
fils.save_name = plotfiledir
if not os.path.exists(plotfiledir):
os.mkdir(plotfiledir)
redo = True
# give radial profiles more breathing room.
fils.skeleton_pad_size = 5
# 30dor: adapt=75, size=800
# GMC1 first time: 50,500
# can lower glob_thresh, get more small things, then lower adapt_thresh to
# Remove the old outputs
dir_path = os.path.dirname(__file__)
test1_path = os.path.join(dir_path, "test1")
for file in os.listdir(test1_path):
if file.startswith("test1_"):
os.remove(os.path.join(test1_path, file))
img, hdr = getdata("filaments_updatedhdr.fits", header=True)
# This one uses RHT on branches
test1 = fil_finder_2D(img,
header=hdr,
beamwidth=10.0 * u.arcsec,
flatten_thresh=95,
distance=260 * u.pc,
size_thresh=430,
glob_thresh=20,
save_name="test1")
test1.create_mask(border_masking=False)
test1.medskel()
test1.analyze_skeletons()
test1.exec_rht(branches=True)
test1.find_widths()
test1.compute_filament_brightness()
test1.save_table(save_name="test1",
branch_table_type='hdf5',
table_type='hdf5')
test1.save_fits(save_name="test1")
def insert_rasterized_contour_plot(c, ax):
collections = c.collections
for _c in collections:
_c.remove()
cc = ListCollection(collections, rasterized=True)
ax.add_artist(cc)
return cc
img, hdr = fits.getdata('pipeCenterB59-350.fits', header=True)
beam = 24.9
img = img + 31.697
filfind = fil_finder_2D(img, hdr, beam, glob_thresh=20,
distance=145.)
filfind.create_mask()#size_thresh=400)
filfind.medskel()
filfind.analyze_skeletons()
filfind.exec_rht()
filfind.find_widths(verbose=False)
r = 460. / 145.
conv = np.sqrt(r ** 2. - 1) * \
(beam / np.sqrt(8*np.log(2)) / (np.abs(hdr["CDELT2"]) * 3600.))
kernel = convolution.Gaussian2DKernel(conv)
good_pixels = np.isfinite(img)
nan_pix = np.ones(img.shape)
nan_pix[good_pixels == 0] = np.NaN
conv_img = convolution.convolve(img, kernel, boundary='fill',
from astropy import units as u
import os
# Remove the old outputs
dir_path = os.path.dirname(__file__)
test1_path = os.path.join(dir_path, "test1")
for file in os.listdir(test1_path):
if file.startswith("test1_"):
os.remove(os.path.join(test1_path, file))
img, hdr = getdata("filaments_updatedhdr.fits", header=True)
# This one uses RHT on branches
test1 = fil_finder_2D(img, header=hdr, beamwidth=10.0 * u.arcsec,
flatten_thresh=95,
distance=260 * u.pc, size_thresh=430,
glob_thresh=20, save_name="test1")
test1.create_mask(border_masking=False)
test1.medskel()
test1.analyze_skeletons()
test1.exec_rht(branches=True)
test1.find_widths()
test1.compute_filament_brightness()
test1.save_table(save_name="test1",
branch_table_type='hdf5', table_type='hdf5')
test1.save_fits(save_name="test1")
# This one does not
test2_path = os.path.join(dir_path, "test2")
from astropy.io import fits
import astropy.units as u
import numpy as np
hdu = fits.open("filaments_updatedhdr.fits")[0]
img, hdr = hdu.data, hdu.header
# Add some noise
np.random.seed(500)
noiseimg = img + np.random.normal(0, 0.05, size=img.shape)
# Utilize fil_finder_2D class
# See filfind_class.py for inputs
test = fil_finder_2D(noiseimg,
header=hdr,
beamwidth=10.0 * u.arcsec,
flatten_thresh=95,
distance=260 * u.pc,
glob_thresh=20)
test.create_mask(verbose=True, border_masking=False, size_thresh=430)
# test = fil_finder_2D(noiseimg, hdr, 10.0, flatten_thresh=95, distance=260)
# test.create_mask(verbose=True, smooth_size=3.0, adapt_thresh=13.0,
# size_thresh=180, regrid=False, border_masking=False,
# zero_border=True)
test.medskel(verbose=False)
test.analyze_skeletons(verbose=False)
test.exec_rht(verbose=False)
test.find_widths(verbose=False)
# test.save_table(save_name="sim_filaments")
# test.save_fits(save_name="sim_filaments")
import numpy as np
np.random.seed(500)
threshs = [75, 95, 99]
patches = [7, 13, 26]
figure, grid = p.subplots(3, 3, sharex=True, sharey=True, figsize=(12, 12))
for i, patch in enumerate(patches[::-1]):
for j, thresh in enumerate(threshs):
noiseimg = img + np.random.normal(0, 0.05, size=img.shape)
test = fil_finder_2D(noiseimg,
hdr,
0.0,
30,
5,
10,
flatten_thresh=thresh,
distance=260)
test.create_mask(verbose=False,
smooth_size=3.0,
adapt_thresh=patch,
size_thresh=180,
regrid=False,
border_masking=False,
zero_border=True)
test.medskel(verbose=False)
test.analyze_skeletons(verbose=False)
grid[i, j].imshow(test.mask[10:266, 10:266],
cmap='binary',
# Licensed under an MIT open source license - see LICENSE
from fil_finder import fil_finder_2D
from astropy.io.fits import getdata
img, hdr = getdata("filaments_updatedhdr.fits", header=True)
# Add some noise
import numpy as np
np.random.seed(500)
noiseimg = img + np.random.normal(0, 0.05, size=img.shape)
## Utilize fil_finder_2D class
## See filfind_class.py for inputs
test = fil_finder_2D(noiseimg, hdr, 10.0, flatten_thresh=95, distance=260)
test.create_mask(verbose=True,
smooth_size=3.0,
adapt_thresh=13.0,
size_thresh=180,
regrid=False,
border_masking=False,
zero_border=True)
test.medskel(verbose=False)
test.analyze_skeletons(verbose=False)
test.exec_rht(verbose=False)
test.find_widths(verbose=False)
# test.save_table(save_name="sim_filaments")
# test.save_fits(save_name="sim_filaments")
if compute:
kernel = convolution.Gaussian2DKernel(conv)
pipe_degraded = convolution.convolve(pipe_img, kernel, boundary='fill',
fill_value=np.NaN)
p.subplot(121)
p.imshow(np.arctan(pipe_img/np.percentile(pipe_img[np.isfinite(pipe_img)], 95)),
origin="lower", interpolation="nearest")
p.subplot(122)
p.imshow(np.arctan(pipe_degraded/np.percentile(pipe_degraded[np.isfinite(pipe_degraded)], 95)),
origin="lower", interpolation="nearest")
p.show()
filfind = fil_finder_2D(pipe_degraded, pipe_hdr, 18.2, 30, 15, 30, distance=400, glob_thresh=20)
filfind.run(verbose=False, save_name="degraded_pipe", save_plots=False)
## Analysis
if output:
from astropy.table import Table
deg_pipe_analysis = Table.read("degraded_pipe_table.fits")
pipe_analysis = Table.read("pipeCenterB59-250/pipeCenterB59-250_table.fits")
# Plot lengths, widths, orientation, curvature. Adjust for distance difference
# p.subplot2grid((4,2), (0,0))
p.subplot(411)
num1 = int(np.sqrt(deg_pipe_analysis["FWHM"][np.isfinite(deg_pipe_analysis["FWHM"])].size))