def create_IQU_cube_data(dataPath,
inCatFile,
startFreq_Hz,
endFreq_Hz,
nChans,
rmsNoise_mJy,
beamMinFWHM_deg,
beamMajFWHM_deg,
beamPA_deg,
pixScale_deg,
xCent_deg,
yCent_deg,
nPixX,
nPixY,
coordSys="EQU",
noiseTmpArr=None,
flagRanges_Hz=[]):
"""
Create a set of Stokes I Q & U data-cubes containing polarised sources.
"""
# Sample frequency space
freqArr_Hz = np.linspace(startFreq_Hz, endFreq_Hz, nChans)
freqNoFlgArr_Hz = freqArr_Hz.copy()
dFreq_Hz = (endFreq_Hz - startFreq_Hz) / (nChans - 1)
print("\nSampling frequencies %.2f - %.2f MHz by %.2f MHz." % \
(freqArr_Hz[0]/1e6, freqArr_Hz[-1]/1e6, dFreq_Hz/1e6))
if len(flagRanges_Hz) > 0:
print("Flagging frequency ranges:")
print("> ", flagRanges_Hz)
for i in range(len(freqArr_Hz)):
for fRng in flagRanges_Hz:
if freqArr_Hz[i] >= fRng[0] and freqArr_Hz[i] <= fRng[1]:
freqArr_Hz[i] = np.nan
# Create normalised noise array from a template or assume all ones.
if noiseTmpArr is None:
print("Assuming flat noise versus frequency curve.")
noiseArr = np.ones(freqArr_Hz.shape, dtype="f4")
else:
print("Scaling noise curve by external template.")
xp = noiseTmpArr[0]
yp = noiseTmpArr[1]
mDict = calc_stats(yp)
yp /= mDict["median"]
noiseArr = extrap(freqArr_Hz, xp, yp)
# Check the catalogue file exists
if not os.path.exists(inCatFile):
print("Err: File does not exist '%s'." % inCatFile)
sys.exit()
catInLst = csv_read_to_list(inCatFile, doFloat=True)
print("Found %d entries in the catalogue." % len(catInLst))
# Create the output directory path
dirs = dataPath.rstrip("/").split("/")
for i in range(1, len(dirs)):
dirStr = "/".join(dirs[:i])
if not os.path.exists(dirStr):
os.mkdir(dirStr)
if os.path.exists(dataPath):
print("\n ", end=' ')
print("*** WARNING ***" * 5)
print(" About to delete existing data directory!", end=' ')
print("Previous results will be deleted.\n")
print("Press <RETURN> to continue ...", end=' ')
input()
shutil.rmtree(dataPath, True)
os.mkdir(dataPath)
# Create simple HDUs in memmory
print("Creating test data in memory.")
hduI = create_simple_fits_hdu(shape=(1, nChans, nPixY, nPixX),
freq_Hz=startFreq_Hz,
dFreq_Hz=dFreq_Hz,
xCent_deg=xCent_deg,
yCent_deg=yCent_deg,
beamMinFWHM_deg=beamMinFWHM_deg,
beamMajFWHM_deg=beamMajFWHM_deg,
beamPA_deg=beamPA_deg,
pixScale_deg=pixScale_deg,
stokes="I",
system=coordSys)
head2D = strip_fits_dims(header=hduI.header, minDim=2, forceCheckDims=4)
wcs2D = pw.WCS(head2D)
shape2D = (nPixY, nPixX)
hduQ = hduI.copy()
hduQ.header["CRVAL4"] = 2.0
hduU = hduI.copy()
hduU.header["CRVAL4"] = 3.0
# Calculate some beam parameters
gfactor = 2.0 * m.sqrt(2.0 * m.log(2.0))
beamMinSigma_deg = beamMinFWHM_deg / gfactor
beamMajSigma_deg = beamMajFWHM_deg / gfactor
beamMinSigma_pix = beamMinSigma_deg / pixScale_deg
beamMajSigma_pix = beamMajSigma_deg / pixScale_deg
beamPA_rad = m.radians(beamPA_deg)
# Loop through the sources, calculate the spectra and pixel position
spectraILst = []
spectraQLst = []
spectraULst = []
coordLst_deg = []
coordLst_pix = []
successCount = 0
for i in range(len(catInLst)):
e = catInLst[i]
modelType = int(e[0])
# Type 1 = multiple Burn depolarisation affected components
if modelType == 1:
# Parse the parameters of multiple components
preLst, parmArr = split_repeat_lst(e[1:], 7, 4)
# Create the model spectra from multiple thin components
# modified by external depolarisation
IArr_Jy, QArr_Jy, UArr_Jy = \
create_IQU_spectra_burn(freqArr_Hz = freqArr_Hz,
fluxI = preLst[5]/1e3, # mJy->Jy
SI = preLst[6],
fracPolArr = parmArr[0],
psi0Arr_deg = parmArr[1],
RMArr_radm2 = parmArr[2],
sigmaRMArr_radm2 = parmArr[3],
freq0_Hz = freq0_Hz)
# Type 2 = multiple internal depolarisation affected components
elif modelType == 2:
# Parse the parameters of multiple components
preLst, parmArr = split_repeat_lst(e[1:], 7, 3)
# Create the model spectra from multiple components
# modified by internal Faraday depolarisation
IArr_Jy, QArr_Jy, UArr_Jy = \
create_IQU_spectra_diff(freqArr_Hz = freqArr_Hz,
fluxI = preLst[5]/1e3, # mJy->Jy
SI = preLst[6],
fracPolArr = parmArr[0],
psi0Arr_deg = parmArr[1],
RMArr_radm2 = parmArr[2],
freq0_Hz = freq0_Hz)
else:
continue
spectraILst.append(IArr_Jy)
spectraQLst.append(QArr_Jy)
spectraULst.append(UArr_Jy)
coordLst_deg.append([preLst[0], preLst[1]])
[(x_pix, y_pix)] = wcs2D.wcs_world2pix([(preLst[0], preLst[1])], 0)
coordLst_pix.append([x_pix, y_pix])
successCount += 1
# Loop through the frequency channels & insert the IQU planes
print("Looping through %d frequency channels:" % nChans)
progress(40, 0.0)
for iChan in range(len(freqArr_Hz)):
progress(40, (100.0 * (iChan + 1) / nChans))
for iSrc in range(len(spectraILst)):
params = [
spectraILst[iSrc][iChan], # amplitude
coordLst_pix[iSrc][0], # X centre (pix)
coordLst_pix[iSrc][1], # Y centre
beamMinSigma_pix, # width (sigma)
beamMajSigma_pix, # height (sigma)
beamPA_rad
] # PA (rad) W of N (clockwise)
planeI = twodgaussian(params, shape2D).reshape((nPixY, nPixX))
params[0] = spectraQLst[iSrc][iChan]
planeQ = twodgaussian(params, shape2D).reshape((nPixY, nPixX))
params[0] = spectraULst[iSrc][iChan]
planeU = twodgaussian(params, shape2D).reshape((nPixY, nPixX))
hduI.data[0, iChan, :, :] += planeI
hduQ.data[0, iChan, :, :] += planeQ
hduU.data[0, iChan, :, :] += planeU
# Add the noise
rmsNoise_Jy = rmsNoise_mJy / 1e3
hduI.data[0, iChan, :, :] += (
np.random.normal(scale=rmsNoise_Jy, size=(nPixY, nPixX)) *
noiseArr[iChan])
hduQ.data[0, iChan, :, :] += (
np.random.normal(scale=rmsNoise_Jy, size=(nPixY, nPixX)) *
noiseArr[iChan])
hduU.data[0, iChan, :, :] += (
np.random.normal(scale=rmsNoise_Jy, size=(nPixY, nPixX)) *
noiseArr[iChan])
# DEBUG
if False:
# Mask a sub=cube
hduI.data[0, 20:50, 20:40, 20:40] = np.nan
hduQ.data[0, 20:50, 20:40, 20:40] = np.nan
hduU.data[0, 20:50, 20:40, 20:40] = np.nan
# Mask a sub=cube
hduI.data[0, 60:90, 50:70, 50:70] = np.nan
hduQ.data[0, 60:90, 50:70, 50:70] = np.nan
hduU.data[0, 60:90, 50:70, 50:70] = np.nan
if False:
# Mask full planes
hduI.data[0, 50:60, :, :] = np.nan
hduQ.data[0, 50:60, :, :] = np.nan
hduU.data[0, 50:60, :, :] = np.nan
if False:
# Mask full spectra
hduI.data[0, :, 20:60, 20:70] = np.nan
hduQ.data[0, :, 20:60, 20:70] = np.nan
hduU.data[0, :, 20:60, 20:70] = np.nan
# Write to the FITS files
print("Saving the FITS files to disk in directory '%s'" % dataPath)
sys.stdout.flush()
fitsFileOut = dataPath + "/StokesI.fits"
print("> %s" % fitsFileOut)
hduI.writeto(fitsFileOut, clobber=True)
fitsFileOut = dataPath + "/StokesQ.fits"
print("> %s" % fitsFileOut)
hduQ.writeto(fitsFileOut, clobber=True)
fitsFileOut = dataPath + "/StokesU.fits"
print("> %s" % fitsFileOut)
hduU.writeto(fitsFileOut, clobber=True)
# Save a vector of frequency values
freqFileOut = dataPath + "/freqs_Hz.dat"
print("> %s" % freqFileOut)
np.savetxt(freqFileOut, freqNoFlgArr_Hz)
return successCount
def create_IQU_ascii_data(dataPath, inCatFile, startFreq_Hz, endFreq_Hz,
nChans, rmsNoise_mJy, noiseTmpArr=None,
flagRanges_Hz=[], freq0_Hz=None):
"""
Create a set of ASCII files containing Stokes I Q & U spectra.
"""
# Sample frequency space
freqArr_Hz = np.linspace(startFreq_Hz, endFreq_Hz, nChans)
freqNoFlgArr_Hz = freqArr_Hz.copy()
dFreq_Hz = (endFreq_Hz - startFreq_Hz)/ (nChans-1)
print("\nSampling frequencies %.2f - %.2f MHz by %.2f MHz." % \
(freqArr_Hz[0]/1e6, freqArr_Hz[-1]/1e6, dFreq_Hz/1e6))
if len(flagRanges_Hz)>0:
print("Flagging frequency ranges:")
print("> ", flagRanges_Hz)
for i in range(len(freqArr_Hz)):
for fRng in flagRanges_Hz:
if freqArr_Hz[i]>=fRng[0] and freqArr_Hz[i]<=fRng[1]:
freqArr_Hz[i]=np.nan
# Create normalised noise array from a template or assume all ones.
print("Input RMS noise is %.3g mJy" % rmsNoise_mJy)
if noiseTmpArr is None:
print("Assuming flat noise versus frequency curve.")
noiseArr = np.ones(freqArr_Hz.shape, dtype="f8")
else:
print("Scaling noise by external template curve.")
xp = noiseTmpArr[0]
yp = noiseTmpArr[1]
mDict = calc_stats(yp)
yp /= mDict["median"]
noiseArr = extrap(freqArr_Hz, xp, yp)
# Check the catalogue file exists
if not os.path.exists(inCatFile):
print("Err: File does not exist '%s'." % inCatFile)
sys.exit()
catInLst = csv_read_to_list(inCatFile, doFloat=True)
print("Found %d entries in the catalogue." % len(catInLst))
# Set the frequency at which the flux has been defined
if freq0_Hz is None:
freq0_Hz = startFreq_Hz
print("Catalogue flux is defined at %.3f MHz" % (freq0_Hz/1e6))
# Create the output directory path
dataPath = dataPath.rstrip("/")
print("Creating test dataset in '%s/'" % dataPath)
dirs = dataPath.split("/")
for i in range(1, len(dirs)):
dirStr = "/".join(dirs[:i])
if not os.path.exists(dirStr):
os.mkdir(dirStr)
if os.path.exists(dataPath):
print("\n ", end=' ')
print("*** WARNING ***" *5)
print(" About to delete existing data directory!", end=' ')
print("Previous results will be deleted.\n")
print("Press <RETURN> to continue ...", end=' ')
input()
shutil.rmtree(dataPath, True)
os.mkdir(dataPath)
# Loop through the sources, calculate the spectra and save to disk
successCount = 0
for i in range(len(catInLst)):
e = catInLst[i]
modelType = int(e[0])
# Type 1 = multiple Burn depolarisation affected components
if modelType==1:
# Parse the parameters of multiple components
preLst, parmArr = split_repeat_lst(e[1:],7,4)
# Create the model spectra from multiple thin components
# modified by external depolarisation
IArr_Jy, QArr_Jy, UArr_Jy = \
create_IQU_spectra_burn(freqArr_Hz = freqArr_Hz,
fluxI = preLst[5]/1e3, # mJy->Jy
SI = preLst[6],
fracPolArr = parmArr[0],
psi0Arr_deg = parmArr[1],
RMArr_radm2 = parmArr[2],
sigmaRMArr_radm2 = parmArr[3],
freq0_Hz = freq0_Hz)
# Type 2 = multiple internal depolarisation affected components
elif modelType==2:
# Parse the parameters of multiple components
preLst, parmArr = split_repeat_lst(e[1:],7,3)
# Create the model spectra from multiple components
# modified by internal Faraday depolarisation
IArr_Jy, QArr_Jy, UArr_Jy = \
create_IQU_spectra_diff(freqArr_Hz = freqArr_Hz,
fluxI = preLst[5]/1e3, # mJy->Jy
SI = preLst[6],
fracPolArr = parmArr[0],
psi0Arr_deg = parmArr[1],
RMArr_radm2 = parmArr[2],
freq0_Hz = freq0_Hz)
else:
continue
# Add scatter to the data to simulate noise
rmsNoise_Jy = rmsNoise_mJy/1e3
IArr_Jy += (np.random.normal(scale=rmsNoise_Jy, size=IArr_Jy.shape)
* noiseArr)
QArr_Jy += (np.random.normal(scale=rmsNoise_Jy, size=QArr_Jy.shape)
* noiseArr)
UArr_Jy += (np.random.normal(scale=rmsNoise_Jy, size=UArr_Jy.shape)
* noiseArr)
dIArr_Jy = noiseArr * rmsNoise_Jy
dIArr_Jy *= np.random.normal(loc=1.0, scale=0.05, size=noiseArr.shape)
dQArr_Jy = noiseArr * rmsNoise_Jy
dQArr_Jy *= np.random.normal(loc=1.0, scale=0.05, size=noiseArr.shape)
dUArr_Jy = noiseArr * rmsNoise_Jy
dUArr_Jy *= np.random.normal(loc=1.0, scale=0.05, size=noiseArr.shape)
# Save spectra to disk
outFileName = "Source%d.dat" % (i+1)
outFilePath = dataPath + "/" + outFileName
print("> Writing ASCII file '%s' ..." % outFileName, end=' ')
np.savetxt(outFilePath,
np.column_stack((freqArr_Hz,
IArr_Jy,
QArr_Jy,
UArr_Jy,
dIArr_Jy,
dQArr_Jy,
dUArr_Jy)))
print("done.")
successCount += 1
return successCount