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quickwlmodel.py
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quickwlmodel.py
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#!/bin/env python
import os
import sys
import numpy
from astropy.io import fits
import logging
import scipy
import scipy.interpolate
import time
import find_sources
import tracespec
import pysalt.mp_logging
def find_additional_basepoints(data, d_step=4, presmooth=2,
debug=False):
logger = logging.getLogger("FindAddtlBasepoints")
wl = data[:,0]
_flux = data[:,2].copy()
logger.debug("Input: %d sky-samples" % (wl.shape[0]))
logger.debug("gaussian pre-smooth: %.1f pixels" % (presmooth))
logger.debug("slope delta-step: %d intervals" % (d_step))
smoothed = numpy.pad(
scipy.ndimage.filters.gaussian_filter(
input=_flux, sigma=presmooth,
order=0, output=None,
mode='constant', cval=0.0,
), pad_width=2, mode='constant',
)
#_delta_flux = _flux[d_step:] - _flux[:-d_step]
delta_flux = smoothed[d_step:] - smoothed[:-d_step]
_delta_flux = delta_flux.copy()
for iter in range(3):
_stats = numpy.nanpercentile(_delta_flux, [16, 50, 84])
one_sigma = 0.5 * (_stats[2] - _stats[0])
median = _stats[1]
bad = (_delta_flux > median + 3 * one_sigma) | \
(_delta_flux < median - 3 * one_sigma)
_delta_flux[bad] = numpy.NaN
# print "delta-flux:", iter, median, one_sigma
logger.debug("Iteration %d: typical gradients: %f +/- %f" % (
iter, median, one_sigma
))
significant = numpy.fabs(delta_flux) > 3*one_sigma
adtl_basepoints = wl[significant]
if (debug):
numpy.savetxt("quickspec.deltaflux.trim", _delta_flux)
numpy.savetxt("quickspec.deltaflux", delta_flux)
numpy.savetxt("quickspec.basepoints", adtl_basepoints)
logger.debug("Found %d points of significant slope" % (
adtl_basepoints.shape[0])
)
return adtl_basepoints
def find_additional_basepoints_old(data, bs=5):
# now reshape the input array and try to find regions where the
# flux-level changes rapidly
print data.shape
# binsize = 10
# n_points = data.shape[0] % binsize
# good_data = data[:-n_points]
# print good_data.shape
local_var = numpy.empty((data.shape[0]))
local_var[:] = numpy.NaN
for i in range(data.shape[0]):
local_var[i] = numpy.var(data[i-bs:i+bs+1, 1])
numpy.savetxt("quickspec.localvar",
numpy.array([data[:,0], local_var]).T)
_var = local_var.copy()
for iter in range(3):
_stats = numpy.nanpercentile(_var, [16, 50, 84])
one_sigma = 0.5 * (_stats[2] - _stats[0])
median = _stats[1]
bad = (_var > median+3*one_sigma) | (_var < median-3*one_sigma)
_var[bad] = numpy.NaN
print "localvar", iter, median, one_sigma
strong_gradient = numpy.isfinite(local_var) & \
(local_var > median+3*one_sigma)
basepoints = data[:,0][strong_gradient]
return basepoints
if __name__ == "__main__":
logsetup = pysalt.mp_logging.setup_logging()
fn = sys.argv[1]
data = numpy.loadtxt(fn)
good = numpy.isfinite(data[:,0]) & numpy.isfinite(data[:,1]) & \
numpy.isfinite(data[:,2])
data = data[good]
wl = data[:,0]
flux = data[:,2]
noise = data[:,1]
wl_min, wl_max = numpy.min(wl), numpy.max(wl)
spline_iter = scipy.interpolate.LSQUnivariateSpline(
x=wl,
y=flux,
t=wl[1:-1][::3],
# t=k_iter_good, #k_wl,
w=noise, # no weights (for now)
bbox=[wl_min, wl_max],
k=3, # use a cubic spline fit
)
highres_x = numpy.linspace(wl_min, wl_max, 25000)
highres_flux = spline_iter(highres_x)
numpy.savetxt("quickspec.txt",
numpy.array([highres_x, highres_flux]).T)
print highres_flux
adtl_basepoints = find_additional_basepoints(
data=data,
debug=True,
)
print "Found %d new basepoints" % (adtl_basepoints.shape[0])
# now reshape the input array and try to find regions where the
# flux-level changes rapidly
print data.shape
binsize = 10
n_points = data.shape[0] % binsize
good_data = data[:-n_points]
print good_data.shape
local_var = numpy.empty((data.shape[0]))
local_var[:] = numpy.NaN
bs = 5
for i in range(data.shape[0]):
local_var[i] = numpy.var(data[i-bs:i+bs+1, 1])
numpy.savetxt("quickspec.localvar",
numpy.array([data[:,0], local_var]).T)
_var = local_var.copy()
for iter in range(3):
_stats = numpy.nanpercentile(_var, [16, 50, 84])
one_sigma = 0.5 * (_stats[2] - _stats[0])
median = _stats[1]
bad = (_var > median+3*one_sigma) | (_var < median-3*one_sigma)
_var[bad] = numpy.NaN
print "localvar", iter, median, one_sigma
good_reshaped = good_data.reshape((-1, binsize, data.shape[1]))
print good_reshaped.shape
edge_var = numpy.var(good_reshaped, axis=1)
edge_median = numpy.mean(good_reshaped, axis=1)
print edge_var.shape, edge_median.shape
numpy.savetxt("quickspec.X",
numpy.array([edge_median[:,0], edge_var[:,1]]).T)
valid_var = edge_var[:,1].copy()
for i in range(3):
_stats = numpy.nanpercentile(valid_var, [16,50,84])
one_sigma = 0.5*(_stats[2]-_stats[0])
median = _stats[1]
print i, median, one_sigma
bad = (valid_var > (median+3*one_sigma)) | (valid_var < (
median-3*one_sigma))
valid_var[bad] = numpy.NaN
numpy.savetxt("quickspec.X.%d" % (i+1),
numpy.array([edge_median[:,0], valid_var]).T)
pysalt.mp_logging.shutdown_logging(logsetup)
os._exit(0)
# fits_fn = sys.argv[2]
#
# hdulist = fits.open(fits_fn)
# obj_wl = hdulist['WAVELENGTH'].data
# padded = numpy.empty((obj_wl.shape[0], obj_wl.shape[1] + 2))
# padded[:, 1:-1] = obj_wl[:, :]
# padded[:, 0] = obj_wl[:, 0]
# padded[:, -1] = obj_wl[:, -1]
# from_wl = 0.5 * (padded[:, 0:-2] + padded[:, 1:-1])
# to_wl = 0.5 * (padded[:, 1:-1] + padded[:, 2:])
#
# print("computing full-res sky frame")
# t0 = time.time()
# sky2d = numpy.array([spline_iter.integral(a, b) for a, b in
# zip(from_wl.ravel(), to_wl.ravel())]).reshape(
# obj_wl.shape)
# print("done after %f seconds" % (time.time()-t0))
#
#
# fits.PrimaryHDU(data=sky2d).writeto("quicksky.fits", clobber=True)
# print("all done!")