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-rw-r--r--python/msvst_starlet.py (renamed from python/starlet.py)101
1 files changed, 95 insertions, 6 deletions
diff --git a/python/starlet.py b/python/msvst_starlet.py
index 67e69ba..b90adf9 100644
--- a/python/starlet.py
+++ b/python/msvst_starlet.py
@@ -1,3 +1,4 @@
+#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# References:
@@ -10,23 +11,36 @@
#
# Aaron LI
# Created: 2016-03-17
-# Updated: 2016-03-22
+# Updated: 2016-04-20
+#
+# ChangeLog:
+# 2016-04-20:
+# * Add argparse and main() for scripting
#
"""
Starlet wavelet transform, i.e., isotropic undecimated wavelet transform
(IUWT), or à trous wavelet transform.
-And multi-scale variance stabling transform (MS-VST).
+And multi-scale variance stabling transform (MS-VST), which can be used
+to effectively remove the Poisson noises.
"""
+__version__ = "0.2.0"
+__date__ = "2016-04-20"
+
+
import sys
+import os
+import argparse
+from datetime import datetime
import numpy as np
import scipy as sp
from scipy import signal
+from astropy.io import fits
-class B3Spline:
+class B3Spline: # {{{
"""
B3-spline wavelet.
"""
@@ -35,9 +49,10 @@ class B3Spline:
dec_hi = np.array([-1.0, -4.0, 10.0, -4.0, -1.0]) / 16
rec_lo = np.array([0.0, 0.0, 1.0, 0.0, 0.0])
rec_hi = np.array([0.0, 0.0, 1.0, 0.0, 0.0])
+# B3Spline }}}
-class IUWT:
+class IUWT: # {{{
"""
Isotropic undecimated wavelet transform.
"""
@@ -239,9 +254,10 @@ class IUWT:
Get the approximation coefficients of the largest scale.
"""
return self.decomposition[-1]
+# IUWT }}}
-class IUWT_VST(IUWT):
+class IUWT_VST(IUWT): # {{{
"""
IUWT with Multi-scale variance stabling transform.
@@ -367,6 +383,8 @@ class IUWT_VST(IUWT):
"""
Multiple hypothesis testing with false discovery rate (FDR) control.
+ `independent': whether the test statistics of all the null
+ hypotheses are independent.
If `independent=True': FDR <= (m0/m) * q
otherwise: FDR <= (m0/m) * q * (1 + 1/2 + 1/3 + ... + 1/m)
@@ -482,9 +500,12 @@ class IUWT_VST(IUWT):
iuwt = IUWT(level=self.level)
iuwt.calc_filters()
# iterative reconstruction
+ if verbose:
+ print("Iteratively reconstructing (%d times): " % niter,
+ end="", flush=True, file=sys.stderr)
for i in range(niter):
if verbose:
- print("iter: %d" % i+1, file=sys.stderr)
+ print("%d..." % i, end="", flush=True, file=sys.stderr)
tempd = self.data.copy()
solution_decomp = []
for scale in range(1, self.level+1):
@@ -507,7 +528,75 @@ class IUWT_VST(IUWT):
solution[solution < 0] = 0.0
#
lbd -= delta
+ if verbose:
+ print("DONE!", flush=True, file=sys.stderr)
#
self.reconstruction = solution
return self.reconstruction
+# IUWT_VST }}}
+
+
+def main():
+ # commandline arguments parser
+ parser = argparse.ArgumentParser(
+ description="Poisson Noise Removal with Multi-scale Variance " + \
+ "Stabling Transform and Wavelet Transform",
+ epilog="Version: %s (%s)" % (__version__, __date__))
+ parser.add_argument("-l", "--level", dest="level",
+ type=int, default=5,
+ help="level of the IUWT decomposition")
+ parser.add_argument("-r", "--fdr", dest="fdr",
+ type=float, default=0.1,
+ help="false discovery rate")
+ parser.add_argument("-I", "--fdr-independent", dest="fdr_independent",
+ action="store_true", default=False,
+ help="whether the FDR null hypotheses are independent")
+ parser.add_argument("-n", "--niter", dest="niter",
+ type=int, default=20,
+ help="number of iterations for reconstruction")
+ parser.add_argument("-v", "--verbose", dest="verbose",
+ action="store_true", default=False,
+ help="show verbose progress")
+ parser.add_argument("-C", "--clobber", dest="clobber",
+ action="store_true", default=False,
+ help="overwrite output file if exists")
+ parser.add_argument("infile", help="input image with Poisson noises")
+ parser.add_argument("outfile", help="output denoised image")
+ args = parser.parse_args()
+
+ if args.verbose:
+ print("infile: '%s'" % args.infile, file=sys.stderr)
+ print("outfile: '%s'" % args.outfile, file=sys.stderr)
+ print("level: %s" % args.level, file=sys.stderr)
+ print("fdr: %s" % args.fdr, file=sys.stderr)
+ print("fdr_independent: %s" % args.fdr_independent, file=sys.stderr)
+ print("niter: %s\n" % args.niter, file=sys.stderr)
+
+ imgfits = fits.open(args.infile)
+ img = imgfits[0].data
+ # Remove Poisson noises
+ msvst = IUWT_VST(data=img)
+ if args.verbose:
+ print("INFO: MSVST decomposing ...", file=sys.stderr)
+ msvst.decompose(level=args.level)
+ if args.verbose:
+ print("INFO: MSVST denosing ...", file=sys.stderr)
+ msvst.denoise(fdr=args.fdr, fdr_independent=args.fdr_independent)
+ if args.verbose:
+ print("INFO: MSVST reconstructing (this may take a while) ...",
+ file=sys.stderr)
+ msvst.reconstruct(denoised=True, niter=args.niter, verbose=args.verbose)
+ img_denoised = msvst.reconstruction
+ # Output
+ imgfits[0].data = img_denoised
+ imgfits[0].header.add_history("%s: Removed Poisson Noises @ %s" % (
+ os.path.basename(sys.argv[0]), datetime.utcnow().isoformat()))
+ imgfits[0].header.add_history(" TOOL: %s (v%s)" % (
+ os.path.basename(sys.argv[0]), __version__))
+ imgfits[0].header.add_history(" PARAM: %s" % " ".join(sys.argv[1:]))
+ imgfits.writeto(args.outfile, checksum=True, clobber=args.clobber)
+
+
+if __name__ == "__main__":
+ main()