Fix pycodestyle errors
Pierros Papadeas
parent
658cc9e7c3
commit
eedac86f08
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@ -232,10 +232,10 @@ def extract_tracks(fname, trkrmin, drdtmin, trksig, ntrkmin, path):
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"classfd/classfd.dat")
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elif id.catalog.find("inttles.tle") > 0:
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outfname = os.path.join(path,
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"/classfd/inttles.dat")
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"classfd/inttles.dat")
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else:
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outfname = os.path.join(path,
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"/catalog/catalog.dat")
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"catalog/catalog.dat")
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f = open(outfname, "a")
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f.write("%s\n" % iod_line)
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@ -305,13 +305,13 @@ def extract_tracks(fname, trkrmin, drdtmin, trksig, ntrkmin, path):
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# Copy files
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if id.catalog.find("classfd.tle") > 0:
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copy_files(fname.replace(".fits", "*"),
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os.path.join(path, "/classfd"))
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os.path.join(path, "classfd"))
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elif id.catalog.find("inttles.tle") > 0:
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copy_files(fname.replace(".fits", "*"),
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os.path.join(path, "/classfd"))
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os.path.join(path, "classfd"))
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else:
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copy_files(fname.replace(".fits", "*"),
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os.path.join(path, "/catalog"))
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os.path.join(path, "catalog"))
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elif id.catalog.find("classfd.tle") > 0:
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# Track and stack
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@ -373,11 +373,11 @@ def extract_tracks(fname, trkrmin, drdtmin, trksig, ntrkmin, path):
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print(iod_line)
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if id.catalog.find("classfd.tle") > 0:
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outfname = os.path.join(path, "/classfd/classfd.dat")
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outfname = os.path.join(path, "classfd/classfd.dat")
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elif id.catalog.find("inttles.tle") > 0:
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outfname = os.path.join(path, "/classfd/inttles.dat")
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outfname = os.path.join(path, "classfd/inttles.dat")
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else:
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outfname = os.path.join(path, "/catalog/catalog.dat")
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outfname = os.path.join(path, "catalog/catalog.dat")
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f = open(outfname, "a")
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f.write("%s\n" % iod_line)
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@ -444,13 +444,13 @@ def extract_tracks(fname, trkrmin, drdtmin, trksig, ntrkmin, path):
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# Copy files
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if id.catalog.find("classfd.tle") > 0:
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copy_files(fname.replace(".fits", "*"),
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os.path.join(path, "/classfd"))
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os.path.join(path, "classfd"))
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elif id.catalog.find("inttles.tle") > 0:
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copy_files(fname.replace(".fits", "*"),
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os.path.join(path, "/classfd"))
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os.path.join(path, "classfd"))
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else:
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copy_files(fname.replace(".fits", "*"),
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os.path.joine(path, "/catalog"))
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os.path.joine(path, "catalog"))
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if __name__ == '__main__':
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@ -489,9 +489,12 @@ if __name__ == '__main__':
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# Create output dirs
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path = args.file_dir
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os.makedirs(os.path.join(path, "classfd"))
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os.makedirs(os.path.join(path, "catalog"))
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os.makedirs(os.path.join(path, "unid"))
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if not os.path.exists(os.path.join(path, "classfd")):
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os.makedirs(os.path.join(path, "classfd"))
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if not os.path.exists(os.path.join(path, "catalog")):
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os.makedirs(os.path.join(path, "catalog"))
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if not os.path.exists(os.path.join(path, "unid")):
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os.makedirs(os.path.join(path, "unid"))
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# Get files
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files = sorted(glob.glob(os.path.join(path, "2*.fits")))
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@ -39,7 +39,7 @@ else:
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# Move to processing directory
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os.chdir(args.file_dir)
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table = ascii.read(args.input, format="csv")
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t = Time(table['mjd'], format="mjd", scale="utc")
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@ -49,7 +49,7 @@ if __name__ == "__main__":
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# Move to processing directory
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os.chdir(args.file_dir)
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# Get files
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files = sorted(glob.glob("2*.fits"))
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@ -70,8 +70,8 @@ if __name__ == "__main__":
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generate_satellite_predictions(fname)
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# Extract lines with 3D Hough transform
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ids=find_hough3d_lines(fname)
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ids = find_hough3d_lines(fname)
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# Stars available and used
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nused = np.sum(pix_catalog.flag == 1)
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nstars = pix_catalog.nstars
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@ -7,36 +7,40 @@ from stvid.stio import satid
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from stvid.stio import observation
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from tempfile import mkstemp
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def generate_satellite_predictions(fname):
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# Format command
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command = "satid %s %s.png/png"%(fname, fname)
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command = "satid %s %s.png/png" % (fname, fname)
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# Run command
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output = subprocess.check_output(command, shell=True,
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stderr=subprocess.STDOUT)
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return
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return output
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def find_hough3d_lines(fname,ntrkmin=20,dr=8):
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def find_hough3d_lines(fname, ntrkmin=20, dr=8):
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# Read four frame
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ff=fourframe(fname)
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ff = fourframe(fname)
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# Mask frame
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ff.mask(10,10,5,5)
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ff.mask(10, 10, 5, 5)
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# Compute selection mask
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x,y,z,t,sig=ff.selection_mask(5.0,40.0)
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x, y, z, t, sig = ff.selection_mask(5.0, 40.0)
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# Save points to temporary file
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f,fpath=mkstemp()
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with open(fpath,"w") as f:
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f, fpath = mkstemp()
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with open(fpath, "w") as f:
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for i in range(len(t)):
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f.write("%f,%f,%f\n"%(x[i],y[i],z[i]))
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f.write("%f,%f,%f\n" % (x[i], y[i], z[i]))
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f.close()
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# Run 3D Hough line-finding algorithm
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command="hough3dlines -dx %d -minvotes %d %s"%(dr,ntrkmin,fpath)
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output=subprocess.check_output(command,shell=True,stderr=subprocess.STDOUT)
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command = "hough3dlines -dx %d -minvotes %d %s" % (dr, ntrkmin, fpath)
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output = subprocess.check_output(command,
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shell=True,
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stderr=subprocess.STDOUT)
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# Remove file
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os.remove(fpath)
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@ -44,22 +48,22 @@ def find_hough3d_lines(fname,ntrkmin=20,dr=8):
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# Clean output (a bit cluncky)
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cleaned_output = output.replace("npoints=", "")
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cleaned_output = cleaned_output.replace(", a=(", " ")
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cleaned_output = cleaned_output.replace("), b=("," ")
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cleaned_output = cleaned_output.replace("), b=(", " ")
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cleaned_output = cleaned_output.replace(")", "")
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cleaned_output = cleaned_output.replace(",", " ")
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print(cleaned_output)
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# Generate identifications
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lines=[]
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s=cleaned_output.split()
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lines = []
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s = cleaned_output.split()
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for i in range(len(s)//7):
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x0,y0,z0=float(s[1+7*i]),float(s[2+7*i]),float(s[3+7*i])
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dx,dy,dz=float(s[4+7*i]),float(s[5+7*i]),float(s[6+7*i])
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xmin=x0-z0*dx/dz
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xmax=x0+(ff.nz-z0)*dx/dz
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ymin=y0-z0*dy/dz
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ymax=y0+(ff.nz-z0)*dy/dz
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line="%.23s %8.3f %8.3f %8.3f %8.3f %8.5f %s unidentified sunlit"%(ff.nfd,xmin,ymin,xmax,ymax,ff.texp,99999)
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x0, y0, z0 = float(s[1+7*i]), float(s[2+7*i]), float(s[3+7*i])
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dx, dy, dz = float(s[4+7*i]), float(s[5+7*i]), float(s[6+7*i])
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xmin = x0-z0*dx/dz
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xmax = x0+(ff.nz-z0)*dx/dz
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ymin = y0-z0*dy/dz
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ymax = y0+(ff.nz-z0)*dy/dz
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line = "%.23s %8.3f %8.3f %8.3f %8.3f %8.5f %s unidentified sunlit" %\
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(ff.nfd, xmin, ymin, xmax, ymax, ff.texp, 99999)
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lines.append(line)
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return [satid(line) for line in lines]
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@ -4,6 +4,7 @@ import os
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import subprocess
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import numpy as np
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class pixel_catalog:
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"""Pixel catalog"""
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@ -6,6 +6,7 @@ from astropy.time import Time
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from astropy import wcs
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from scipy import ndimage
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class observation:
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"""Satellite observation"""
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@ -94,8 +95,8 @@ class fourframe:
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self.zavg, self.zstd, self.zmax, self.znum = hdu[0].data
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# Generate sigma frame
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self.zsig=(self.zmax-self.zavg)/(self.zstd+1e-9)
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self.zsig = (self.zmax-self.zavg)/(self.zstd+1e-9)
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# Frame properties
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self.ny, self.nx = self.zavg.shape
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self.nz = hdu[0].header['NFRAMES']
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@ -144,46 +145,46 @@ class fourframe:
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self.w.wcs.ctype = self.ctype
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self.w.wcs.set_pv([(2, 1, 45.0)])
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def mask(self, xmin, xmax, ymin, ymax):
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x, y = np.meshgrid(np.arange(self.nx), np.arange(self.ny))
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c = (x >= xmin) & (x <= self.nx-xmax)\
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& (y >= ymin)\
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& (y <= self.ny-ymax)
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self.mask = np.ones_like(self.zavg)
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self.mask[~c] = 0.0
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self.zavg *= self.mask
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self.zstd *= self.mask
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self.zmax *= self.mask
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self.znum *= self.mask
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self.zsig *= self.mask
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def mask(self,xmin,xmax,ymin,ymax):
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x,y=np.meshgrid(np.arange(self.nx),np.arange(self.ny))
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c=(x>=xmin) & (x<=self.nx-xmax) & (y>=ymin) & (y<=self.ny-ymax)
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self.mask=np.ones_like(self.zavg)
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self.mask[~c]=0.0
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self.zavg*=self.mask
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self.zstd*=self.mask
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self.zmax*=self.mask
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self.znum*=self.mask
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self.zsig*=self.mask
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def selection_mask(self,sigma,zstd):
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def selection_mask(self, sigma, zstd):
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"""Create a selection mask"""
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c1=ndimage.uniform_filter(self.znum,3,mode='constant')
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c2=ndimage.uniform_filter(self.znum*self.znum,3,mode='constant')
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z=np.sqrt(c2-c1*c1)
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c1 = ndimage.uniform_filter(self.znum, 3, mode='constant')
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c2 = ndimage.uniform_filter(self.znum*self.znum, 3, mode='constant')
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z = np.sqrt(c2-c1*c1)
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# Standard deviation mask
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c=z<zstd
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m1=np.zeros_like(self.zavg)
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m1[c]=1.0
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c = z < zstd
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m1 = np.zeros_like(self.zavg)
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m1[c] = 1.0
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# Sigma mask
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c=self.zsig<sigma
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m2=np.zeros_like(self.zavg)
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m2[~c]=1.0
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self.zsel=m1*m2
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c = self.zsig < sigma
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m2 = np.zeros_like(self.zavg)
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m2[~c] = 1.0
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self.zsel = m1*m2
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# Generate points
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c=self.zsel==1.0
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xm,ym=np.meshgrid(np.arange(self.nx),np.arange(self.ny))
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x,y=np.ravel(xm[c]),np.ravel(ym[c])
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inum=np.ravel(self.znum[c]).astype('int')
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sig=np.ravel(self.zsig[c])
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t=np.array([self.dt[i] for i in inum])
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c = self.zsel == 1.0
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xm, ym = np.meshgrid(np.arange(self.nx), np.arange(self.ny))
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x, y = np.ravel(xm[c]), np.ravel(ym[c])
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inum = np.ravel(self.znum[c]).astype('int')
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sig = np.ravel(self.zsig[c])
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t = np.array([self.dt[i] for i in inum])
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return x,y,inum,t,sig
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return x, y, inum, t, sig
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def significant_pixels_along_track(self, sigma, x0, y0,
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dxdt, dydt, rmin=10.0):
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"""Extract significant pixels along a track"""
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