def main():
if len(sys.argv) != 2:
print("Please choose a single .mod1 file")
return
data = get_file(sys.argv[1])
if data is None:
return None
viewer.viewer(data)
print("Finished")
def main():
global led_status
listener = SampleListener()
controller = Leap.Controller()
controller.add_listener(listener)
controller.set_policy(Leap.Controller.POLICY_BACKGROUND_FRAMES)
controller.set_policy(controller.POLICY_IMAGES)
controller.enable_gesture(Leap.Gesture.TYPE_SWIPE)
#Controller.set_policy
v = viewer.viewer()
v.setDaemon(True)
v.start()
while True:
pass
#frame=controller.frame()
pointable = frame.pointables.frontmost
direction = pointable.direction
length = pointable.length
width = pointable.width
stabilizedPosition = pointable.stabilized_tip_position
position = pointable.tip_position
speed = pointable.tip_velocity
touchDistance = pointable.touch_distance
zone = pointable.touch_zone
print "Press Enter to Quit"
try:
sys.stdin.readline()
except KeyboardInterpt:
pass
finally:
controller.remove_listener(listener)
def _init_viewer(self, user, viewlvl):
"""Initializes the viewer and puts the viewer object into viewerlist.
If the viewer doesn't have a relationship with the stream one is
created. If the viewer relationship is different than what is in the database
it is updated.
Since this is called for each iteration of the viewerlist population
the rewards counter is increased in this function as well.
Parameters:
user = Username of the viewer
viewlvl = The permission level of the viewer
"""
if user == self.stream_name: #if the viewer is the streamer set to a streamer relationship
streamReltn = 'streamer'
else:
streamReltn = viewlvl
if user in self.viewerlist: #if the viewer is already in the list make sure the relationships line up
#No rewards for stream
if self._max_tick != 0:
#Updates the viewer's reward tick count as well as reward points if
#they meet the threshhold
if ((self.viewerlist[user].reward_ticks + 1) == self._max_tick):
self.viewerlist[user].reward_ticks = 0
self.viewerlist[user].reward_points += self._reward_rate
else:
self.viewerlist[user].reward_ticks += 1
#update viewer level if it has changed
if streamReltn != self.viewerlist[user].view_lvl:
self.viewerlist[user].view_lvl = streamReltn
self.stream_db.update_person_stream_reltn(self.viewerlist[user].person_id, self.stream_id, streamReltn)
else:
personID = self.stream_db.get_person_id(user)
print('Caputred person_id = ' + str(personID))
if personID < 0: #error in query
print('ERROR GETTING PERSON_ID')
personID = 0
elif personID == 0:
print('Add personID')
personID = self.stream_db.add_person(user, self.stream_id, streamReltn)
if personID < 0:
print('Error adding person')
personID = 0
else:# personID > 0:
self.stream_db.update_person_stream_reltn(personID, self.stream_id, streamReltn)
self.viewerlist[user] = viewer.viewer(personID, user, streamReltn)
#get number of reward points for user in stream
self.viewerlist[user].reward_points = self.stream_db.get_person_stream_rewards(self.stream_id, personID)
def parse(value, full=True):
# URL
value = urlize_html.urlize_html(value)
if full:
# Youtube, NicoNico
value = viewer.viewer(value)
# Commons
value = commons.parse_commons_html(value)
# Calls
value = calls.parse_id_calls_html(value)
return mark_safe(value)
def make_plots(dirname, fnames):
# get the highest-level directory, assume it is the target source ID
obj = os.path.split(dirname)[-1]
for fn in fnames:
# each band gets listed...
if 'band0' not in fn or '.sav' not in fn:
continue
# templates:
# coadd_cleanband0in_clean_music_20130815_jk000.sav
# 130820_ob1_band0i_clean_music_20130815_map.sav
obs = "_".join(fn.split("_")[:2])
#obs = fn[:11]
print "Working on file ",os.path.join(dirname,obs)
data = load_data(os.path.join(dirname,obs))
headers = {k: load_header(data[k].mapstruct) for k in data}
sm,us = convolve_and_match(data,obj,headers=headers,writefits=True,savepath=dirname)
vmin = -1000 #max([sm[2].min(),-1000])
vmax = max([sm[2].max(),5000])
print obj, obs, vmin, vmax
prefix = os.path.join(dirname,obj+obs)
pl.clf()
viewer(data, vmin=vmin, vmax=vmax, cb=True)
pl.suptitle(obj)
pl.savefig(prefix+"_quicklook.png",bbox_inches='tight')
pl.clf()
dictviewer(sm, vmin=vmin, vmax=vmax, cb=True)
pl.suptitle(obj)
pl.savefig(prefix+"_quicklook_smooth.png",bbox_inches='tight')
pl.clf()
dictviewer(us, vmin=vmin, vmax=vmax, cb=True)
pl.suptitle(obj)
pl.savefig(prefix+"_quicklook_unsharp.png",bbox_inches='tight')
def main():
loader = loadFile()
loader.mainloop()
foto = Image.open(loader.get_path())
foto = foto.convert('L')
rows = foto.size[1]
cols = foto.size[0]
data = foto.getdata()
newPic = Image.new('L',foto.size)
newPic.putdata(data)
newPic.save('normal.png')
newPic.close()
foto.close()
create_file(data, 'pic.bin')
create_file([], 'res.bin')
# [[0, -1, 0], [-1, 5, -1], [0, -1, 0]]
sharpen = [[2, 1, 2], [1, 7, 1], [2, 1, 2]]
# [[0,-2,0],[-2, 9, -2], [0, -2, 0]]
oversharpen = [[2, 0, 2], [0, 11, 0], [2, 0, 2]]
create_file(to_list(sharpen), 'kernel.bin')
create_file([], 'res.bin')
# sharpening
os.system("nasm -f elf64 conv.asm -o conv.o")
os.system("ld conv.o -o conv")
os.system("./conv "+str(rows)+" "+str(cols))
os.rename(r'res.bin', r'sharpening.bin')
toPic('sharpening.bin', rows, cols)
# oversharpening
create_file(to_list(oversharpen), 'kernel.bin')
create_file([], 'res.bin')
os.system("./conv "+str(rows)+" "+str(cols))
os.rename(r'res.bin', r'oversharpening.bin')
toPic('oversharpening.bin', rows, cols)
myview = viewer(rows,cols)
myview.mainloop()
from viewer import viewer
from utils import bytes_to_rgb, bgr_weights
from stream import stream_client, unpack
import numpy as np
host = 'localhost'
port = 5556
if len(sys.argv) > 1:
host = sys.argv[1]
if len(sys.argv) > 2:
port = sys.argv[2]
client = stream_client(host=host, port=port)
view = viewer()
i, t1, t = 0, 0, 0
t0 = time.time()
print('Stream from: ', client.addr)
while True:
msg = client.receive()
if msg:
data = unpack(msg)
if data['type'] == 'bgr':
frame = bytes_to_rgb(data['data'], data['size'])
#frame = np.uint8(np.dot(frame, bgr_weights))
elif data['type'] == 'grayscale':
frame = np.frombuffer(data['data'],
def selfcal(vis, spwn=6, doplots=True, INTERACTIVE=False, reclean=True, field='W51 Ku',
outdir_template="spw%i_selfcal_iter/", statsbox='170,50,229,97', ant1list=['ea14','ea05'],
ant2list=['ea16','ea07'], avgchannel_wide='128', avgchannel_narrow='8',
cleanboxes=cleanboxes, refant='ea27', solint='30s', niter=2,
multiscale=[0,5,10,15,25,50], imsize=512, ):
"""
Docstring incomplete
"""
spw = int(spwn)
outdir = outdir_template % spwn
try:
os.mkdir(outdir)
except OSError:
pass
# you're supposed to pass in avg_data as input
avg_data = vis
mytb.open(vis+"/ANTENNA")
antnames = mytb.getcol("NAME")
# (1) Clean a single SPW *interactively*, boxing the brightest regions and not
# cleaning very deeply (maybe 100 iterations). Keep this model in the header
# -- it's what you'll use for the first round of self-calibration.
if reclean:
imagename="average_spw%i_shallowclean_masked" % spwn
for suffix in clean_output_suffixes:
os.system("rm -rf "+imagename+suffix)
clean(vis=avg_data, field=field, imagename=imagename, mode='mfs',
psfmode='hogbom',multiscale=multiscale,
weighting='briggs', robust=0.0, niter=100, imsize=imsize,
mask=cleanboxes,
nterms=2,
usescratch=True)
viewer(imagename+".image.tt0",
outfile=outdir+imagename+".image.tt0.png",
outformat='png',
gui=False)
exportfits(imagename=imagename+".image.tt0", fitsimage=imagename+".fits", overwrite=True)
imrms = [imstat(imagename+".image.tt0",box=statsbox)['rms']]
for calnum in xrange(niter):
# for Ku D W51 Ku spw 2
if reclean:
first_image = 'spw%i_ku_d_firstim_selfcal%i' % (spwn,calnum)
for suffix in clean_output_suffixes:
os.system("rm -rf "+first_image+suffix)
clean(vis=avg_data,imagename=first_image,field=field, mode='mfs',
psfmode='hogbom',multiscale=multiscale,
weighting='briggs', robust=0.0, niter=100, imsize=imsize,
mask=cleanboxes,
nterms=2,
usescratch=True)
exportfits(imagename=first_image+".image.tt0", fitsimage=first_image+".fits", overwrite=True)
viewer(first_image+".image.tt0",
outfile=outdir+first_image+".image.tt0.png",
outformat='png',
gui=False)
caltable = 'selfcal%i_%s_spw%i.pcal' % (calnum,field.replace(" ",""),spwn)
if reclean:
os.system('rm -rf '+caltable)
gaincal(vis=avg_data,
field='',
caltable=caltable,
spw='',
# gaintype = 'T' could reduce failed fit errors by averaging pols...
gaintype='G', # 'G' from http://casaguides.nrao.edu/index.php?title=EVLA_Advanced_Topics_3C391
solint=solint,
refant=refant,
calmode='p',
combine='scan',
minblperant=4)
if reclean:
applycal(vis=avg_data,
gaintable=caltable,
interp='linear',
flagbackup=True) # was False when flagmanager was used
if reclean:
selfcal_image = 'spw%i_ku_d_selfcal%i' % (spwn,calnum)
for suffix in clean_output_suffixes:
os.system("rm -rf "+selfcal_image+suffix)
clean(vis=avg_data,imagename=selfcal_image,field=field, mode='mfs',
psfmode='hogbom',multiscale=multiscale,
weighting='briggs', robust=0.5, niter=1000, imsize=imsize,
nterms=2,
mask=cleanboxes,
usescratch=True)
exportfits(imagename=selfcal_image+".image.tt0", fitsimage=selfcal_image+".fits", overwrite=True)
imrms.append(imstat(selfcal_image+".image.tt0",box=statsbox)['rms'])
viewer(selfcal_image+".image.tt0",
outfile=outdir+selfcal_image+".image.tt0.png",
outformat='png',
gui=False)
print "FINISHED ITERATION %i" % calnum
print "FINISHED ITERATING!!! YAY!"
# final phase + gain cal:
# http://casaguides.nrao.edu/index.php?title=Calibrating_a_VLA_5_GHz_continuum_survey#One_Last_Iteration:_Amplitude_.26_Phase_Self_Calibration
aptable = 'selfcal_ap_%s_spw%i.gcal' % (field.replace(" ",""),spwn)
gaincal(vis=avg_data, field='', caltable=aptable, gaintable=caltable, spw='',
solint='inf', refant=refant, calmode='ap', combine='', minblperant=4)
plotcal(caltable=aptable,
xaxis='phase', yaxis='amp',
plotrange=[-50,50,0.5,1.5],
showgui=INTERACTIVE,
figfile='' if INTERACTIVE else outdir+'selfcal%i_spw%i_ampvsphase_final.png' % (calnum,spwn),
iteration='spw' if INTERACTIVE else '')#, subplot = 221)
applycal(vis=avg_data,
gaintable=[aptable,caltable],
interp='linear',
flagbackup=True) # was False when flagmanager was used
selfcal_image = 'spw%i_ku_d_selfcal%i_final' % (spwn,calnum)
for suffix in clean_output_suffixes:
os.system("rm -rf "+selfcal_image+suffix)
clean(vis=avg_data,imagename=selfcal_image,field=field, mode='mfs', mask=cleanboxes,
weighting='briggs', robust=0.5, niter=10000, imsize=imsize,
nterms=2,
usescratch=True)
exportfits(imagename=selfcal_image+".image.tt0", fitsimage=selfcal_image+".fits", overwrite=True)
plotms(vis=avg_data, spw='0', xaxis='baseline', yaxis='amp', avgtime='1e8',
ydatacolumn='corrected-model',
avgscan=T, coloraxis='baseline', iteraxis='', xselfscale=T,
yselfscale=T,
title='Residual vs. Baseline after CSCLEAN iter %i' % calnum,
plotfile=outdir+'post_selfcal%i_spw%i_residVSbaseline.png' % (calnum,spwn),
field=field,
overwrite=True,
)
plotms(vis=avg_data, spw='0', xaxis='time', yaxis='amp', avgtime='5s',
ydatacolumn='corrected-model',
coloraxis='baseline', iteraxis='', xselfscale=T,
yselfscale=T,
title='Residual vs. Time after CSCLEAN iter %i' % (calnum),
plotfile=outdir+'post_selfcal%i_spw%i_residVStime.png' % (calnum,spwn),
field=field,
overwrite=True,
)
plotms(vis=avg_data, spw='0', xaxis='uvdist', yaxis='amp', avgtime='1e8',
ydatacolumn='corrected-model',
avgscan=T, coloraxis='baseline', iteraxis='', xselfscale=T,
yselfscale=T,
title='Residual vs. UVDIST after CSCLEAN iter %i' % (calnum) ,
plotfile=outdir+'post_selfcal%i_spw%i_residVSuvdist.png' % (calnum,spwn),
field=field,
overwrite=True,
)
selfcal_image = 'spw%i_ku_d_selfcal%i_final_multiscale' % (spwn,calnum)
for suffix in clean_output_suffixes:
os.system("rm -rf "+selfcal_image+suffix)
clean(vis=avg_data,imagename=selfcal_image,field=field, mode='mfs', imagermode='csclean',# mask=cleanboxes,
multiscale=multiscale, psfmode='hogbom',
nterms=2,
weighting='briggs', robust=0.5, niter=10000, imsize=imsize,
usescratch=True)
exportfits(imagename=selfcal_image+".image.tt0", fitsimage=selfcal_image+".fits", overwrite=True)
plotms(vis=avg_data, spw='0', xaxis='baseline', yaxis='amp', avgtime='1e8',
ydatacolumn='corrected-model',
avgscan=T, coloraxis='baseline', iteraxis='', xselfscale=T,
yselfscale=T,
title='Residual vs. Baseline after multiscale CLEAN iter %i' % (calnum),
plotfile=outdir+'post_selfcal%i_spw%i_residVSbaseline_multiscale.png' % (calnum,spwn),
field=field,
overwrite=True,
)
plotms(vis=avg_data, spw='0', xaxis='time', yaxis='amp', avgtime='5s',
ydatacolumn='corrected-model',
coloraxis='baseline', iteraxis='', xselfscale=T,
yselfscale=T,
title='Residual vs. Time after multiscale CLEAN iter %i' % (calnum),
plotfile=outdir+'post_selfcal%i_spw%i_residVStime_multiscale.png' % (calnum,spwn),
field=field,
overwrite=True,
)
plotms(vis=avg_data, spw='0', xaxis='uvdist', yaxis='amp', avgtime='1e8',
ydatacolumn='corrected-model',
avgscan=T, coloraxis='baseline', iteraxis='', xselfscale=T,
yselfscale=T,
title='Residual vs. UVDIST after multiscale CLEAN iter %i' % (calnum),
plotfile=outdir+'post_selfcal%i_spw%i_residVSuvdist_multiscale.png' % (calnum,spwn),
field=field,
overwrite=True,
)
return imrms
def __init__(self):
# Current coords
self.z = 0;
self.imagesLoaded = 300
# In pyqt widgets must be stacked vertically or horizontally. To achive
# to achive the desired layout they are stored in larger containers
### Create viewport
self.viewport = viewer("/home/alex/Documents/Uni/Project/data/HV01/HV01-B1_800nm_sweep006.tiff", self.imagesLoaded);
### Basic saturaton controls
# Create widgits
self.saturationControl = QVBoxLayout();
self.xControl = sliderControl("x", self.viewport.layers - 1);
self.alphaControl = sliderControl("Saturaton", 100);
self.betaControl = sliderControl("Brightness", 100);
# Connect widgits
self.xControl.slider.valueChanged.connect(lambda: self.updateCoords(self.xControl.value));
self.alphaControl.slider.valueChanged.connect(lambda: self.updateSaturation(self.alphaControl.value, self.betaControl.value));
self.betaControl.slider.valueChanged.connect(lambda: self.updateSaturation(self.alphaControl.value, self.betaControl.value));
# Create layout
self.saturationControl.addLayout(self.xControl.layout);
self.saturationControl.addLayout(self.alphaControl.layout);
self.saturationControl.addLayout(self.betaControl.layout);
### Circular saturaton controls
# Create widgets
self.circularSaturationControl = QVBoxLayout();
self.xScaleControl = sliderControl("xScale", 100);
self.yScaleControl = sliderControl("yScale", 100);
self.circularAlphaControl = sliderControl("Circular Saturaton", 100);
self.circularBetaControl = sliderControl("Circular Brightness", 100);
# Connect widgits
self.xScaleControl.slider.valueChanged.connect(lambda: self.updateCircularSaturation(self.circularAlphaControl.value, self.circularBetaControl.value, self.xScaleControl.value, self.yScaleControl.value));
self.yScaleControl.slider.valueChanged.connect(lambda: self.updateCircularSaturation(self.circularAlphaControl.value, self.circularBetaControl.value, self.xScaleControl.value, self.yScaleControl.value));
self.circularAlphaControl.slider.valueChanged.connect(lambda: self.updateCircularSaturation(self.circularAlphaControl.value, self.circularBetaControl.value, self.xScaleControl.value, self.yScaleControl.value));
self.circularBetaControl.slider.valueChanged.connect(lambda: self.updateCircularSaturation(self.circularAlphaControl.value, self.circularBetaControl.value, self.xScaleControl.value, self.yScaleControl.value));
# Create layout
self.circularSaturationControl.addLayout(self.xScaleControl.layout);
self.circularSaturationControl.addLayout(self.yScaleControl.layout);
self.circularSaturationControl.addLayout(self.circularAlphaControl.layout);
self.circularSaturationControl.addLayout(self.circularBetaControl.layout);
### Connect saturation and circular saturation layouts
self.allSaturations = QHBoxLayout();
self.allSaturations.addLayout(self.saturationControl);
self.allSaturations.addLayout(self.circularSaturationControl);
### Noise filtering controls (for opencv's fastNlMeansDenoising)
# Create widgets
self.noiseHVal = labeledEdit("h value");
self.noiseTemplateWindowSize = labeledEdit("Template window size")
self.noiseSearchWindowSize = labeledEdit("Search window size");
self.denoiseButton = QPushButton("denoise");
# Connect widgets
self.denoiseButton.clicked.connect(lambda: self.updateFilter(self.noiseHVal.getVal(), self.noiseTemplateWindowSize.getVal(), self.noiseSearchWindowSize.getVal()));
# Create layout
self.noiseControls = QVBoxLayout();
self.noiseControls.addLayout(self.noiseHVal.layout);
self.noiseControls.addLayout(self.noiseTemplateWindowSize.layout);
self.noiseControls.addLayout(self.noiseSearchWindowSize.layout);
self.noiseControls.addWidget(self.denoiseButton);
### Connect noise filter to saturatoin layout
self.filterPanel = QVBoxLayout();
self.filterPanel.addLayout(self.allSaturations);
self.filterPanel.addLayout(self.noiseControls);
### Allow files to be loaded
# Create widgets
self.loadPath = QLineEdit();
self.loadButton = QPushButton("load");
self.loadLabel = QLabel("Images filepath");
# Connect widgets
self.loadButton.clicked.connect(lambda: self.load(self.loadPath.text(), self.imagesLoaded))
# Create layout
self.loadLayout = QHBoxLayout();
self.loadLayout.addWidget(self.loadLabel);
self.loadLayout.addWidget(self.loadPath);
self.loadLayout.addWidget(self.loadButton);
### Allow cleaned files to be saved
# Create widgets
self.savePathSelect = labeledEdit("Save path");
self.saveButton = QPushButton("Save");
self.startPoint = labeledEdit("Start");
self.endPoint = labeledEdit("End");
# Connect widgets
self.saveButton.clicked.connect(lambda: self.saveImages(self.savePathSelect.getText(), self.startPoint.getVal(), self.endPoint.getVal()))
# Create layout
self.saveControlLayout = QHBoxLayout();
self.saveControlLayout.addLayout(self.startPoint.layout);
self.saveControlLayout.addLayout(self.endPoint.layout);
self.saveControlLayout.addWidget(self.saveButton);
self.saveLayout = QVBoxLayout();
self.saveLayout.addLayout(self.savePathSelect.layout);
self.saveLayout.addLayout(self.saveControlLayout);
### Display image
self.image = QLabel("loop");
self.pixmap = QPixmap('Flamboyant_Potato.jpg');
self.image.setPixmap(self.pixmap);
### Connect image to control layouts
self.imgControl = QHBoxLayout();
self.imgControl.addWidget(self.image);
self.imgControl.addLayout(self.filterPanel);
# Full interface layout
self.layout = QVBoxLayout();
self.layout.addLayout(self.loadLayout);
self.layout.addLayout(self.imgControl);
self.layout.addLayout(self.saveLayout);
args = parser.parse_args()
# Load input file
pcd_list = np.array(ut.loadPoints(args.input, [0,1,2]))
# Compute the number of iterations to do given the slice size
to_process = len(pcd_list)
slice_size = args.slice
iterations = to_process/slice_size
if (iterations == 0 or to_process%slice_size == 0):
iterations += 1
# Initialize the iteration variable
i = 0
# Get the matplotlib object that shows the point cloud
plt = viewer(pcd_list, get=True)
# Compute the fitting polynoms and plot them
for i in range(iterations):
begin = i*slice_size
if (i+1)*slice_size < to_process:
end = (i+1)*slice_size
else:
end = to_process
points_slice = pcd_list[begin:end]
xp = np.linspace(points_slice[0,0], points_slice[end-begin-1,0], 100)
pl = np.poly1d(np.polyfit(points_slice[:,0], points_slice[:,1], args.degree))
plt.plot(xp, pl(xp), 'r-', linewidth=3.0)
plt.show()
soc.connect(("localhost",5555))
soc.send("addlogger:root.moje,")
time.sleep(0.1)
soc.send("port:6868,")
time.sleep(0.1)
soc.send("level:0,endaddlogger")
try:
while True:
time.sleep(0.1)
d = soc.recv(32)
if (d == ""):
print "Connection Closed"
break
except KeyboardInterrupt:
print "Ctrl + C pressed"
soc.close()
if __name__ == '__main__':
app = QtGui.QApplication(sys.argv)
#w = logger.superLogger()
w = viewer.viewer()
w.show()
app.exec_()
pass
def buildThread():
v = viewer.viewer()
v.setDaemon(True)
v.start()
parser = argparse.ArgumentParser()
parser.add_argument("input", help='input file that contains the point cloud', type=str)
parser.add_argument("output", help='path to the output file', type=str)
parser.add_argument("--slice", "-s", help='slice size (default: 15000)', type=int, default=15000)
args = parser.parse_args()
# Load input file
pcd_list = ut.loadPoints(args.input, [2,3,4])
# Compute the number of iterations to do given the slice size
to_process = len(pcd_list)
slice_size = args.slice
iterations = to_process/slice_size
if (iterations == 0 or to_process%slice_size == 0):
iterations += 1
largest_clusters = []
i = 0
for i in range(iterations):
begin = i*slice_size
if (i+1)*slice_size < to_process:
end = (i+1)*slice_size
else:
end = to_process
# Get the largest clusters
largest_clusters += noise_removal(pcd_list[begin:end], ratio=0.1)
largest_clusters = np.array(largest_clusters)
ut.savePoints(largest_clusters, args.output, [0,1,2])
viewer(largest_clusters, c=largest_clusters[:,2], s=2)
def compute_background(img):
colors = np.ndarray((img.shape[0], img.shape[1]), dtype=np.uint32)
colors[:, :] = np.uint32(img[:, :, 0]) << 16
colors[:, :] += np.uint32(img[:, :, 1]) << 8
colors[:, :] += np.uint32(img[:, :, 2]) << 0
(values, counts) = np.unique(colors, return_counts=True)
pix = values[np.argmax(counts)]
bg = np.array([(pix >> 16) & 0xFF, (pix >> 8) & 0xFF, (pix >> 0) & 0xFF],
dtype=np.uint8)
return bg
slides = []
viewer = viewer(interactive=1)
SDL_Init(SDL_INIT_EVERYTHING)
window = SDL_CreateWindow("Futhark Presents!", SDL_WINDOWPOS_UNDEFINED,
SDL_WINDOWPOS_UNDEFINED, width, height,
SDL_WINDOW_SHOWN)
def reWindow(window):
window_surface = SDL_GetWindowSurface(window)
frame_py = np.ndarray(shape=(height, width), dtype=np.int32, order='C')
surface = SDL_CreateRGBSurfaceFrom(frame_py.ctypes.data, width, height, 32,
width * 4, 0xFF0000, 0xFF00, 0xFF,
0x00000000)
return (window_surface, frame_py, surface)
def selfcal(vis, spwn=6, doplots=True, INTERACTIVE=False, reclean=True, field='W51 Ku',
outdir_template="spw%i_selfcal_iter/", statsbox='170,50,229,97', ant1list=['ea14','ea05'],
ant2list=['ea16','ea07'], avgchannel_wide='128', avgchannel_narrow='8',
cleanboxes=cleanboxes, refant='ea27', solint='30s', niter=2,
multiscale=[0,5,10,15,25,50], imsize=512, robust=0.0 ):
"""
Docstring incomplete
"""
spw = int(spwn)
outdir = outdir_template % spwn
try:
os.mkdir(outdir)
except OSError:
pass
# you're supposed to pass in avg_data as input
avg_data = vis
mytb.open(vis+"/ANTENNA")
antnames = mytb.getcol("NAME")
# plot each antenna's ampl vs time for flagging purposes
for ant2 in ant2list:
for ant in ant1list:
plotms(vis=vis, spw=str(spwn), xaxis='time', yaxis='amp', avgchannel=avgchannel_wide,
avgscan=F, coloraxis='baseline', iteraxis='', xselfscale=T,
yselfscale=T,
antenna=ant+"&"+ant2,
title='Amp vs Time before averaging for spw %i ant %s-%s' % (spwn,ant,ant2),
plotfile=outdir+'ampvstime_spw%i_ant%s-%s.png' % (spwn,ant,ant2),
field=field,
overwrite=True,
)
plotms(vis=vis, spw=str(spwn), xaxis='freq', yaxis='phase', avgtime='1e8',
avgscan=T, coloraxis='corr', iteraxis='baseline', xselfscale=T,
yselfscale=T,
antenna=ant+"&"+ant2,
title='Phase vs Freq with time averaging for spw %i ant %s-%s' % (spwn,ant,ant2),
plotfile=outdir+'phasevsfreq_spw%i_ant%s-%s.png' % (spwn,ant,ant2),
field=field,
overwrite=True,
)
plotms(vis=vis, spw=str(spwn), xaxis='amp', yaxis='phase', avgtime='1e8',
avgscan=T, coloraxis='corr', iteraxis='baseline', xselfscale=T,
yselfscale=T,
antenna=ant+"&"+ant2,
title='Phase vs Amp with time averaging for spw %i ant %s-%s' % (spwn,ant,ant2),
plotfile=outdir+'phasevsamp_spw%i_ant%s-%s.png' % (spwn,ant,ant2),
field=field,
overwrite=True,
)
# imagename = "noaverage_spw%i" % spwn
# os.system("rm -rf "+imagename+".image")
# os.system("rm -rf "+imagename+".model")
# os.system("rm -rf "+imagename+".flux")
# os.system("rm -rf "+imagename+".psf")
# os.system("rm -rf "+imagename+".residual")
# clean(vis=vis, field=field, imagename=imagename, mode='mfs',
# weighting='briggs', robust=robust, niter=500, imsize=512)
# viewer(imagename+".image",
# outfile=outdir+imagename+".image.png",
# outformat='png',
# gui=False)
# exportfits(imagename=imagename+".image", fitsimage=imagename+".fits", overwrite=True)
#width = 10 # for TW Hydra
# width = 4 # for NGC 3256
# (0) Using your split-off, calibrated data, plot the "model" in this MS using
# plotms. It should be unit-valued for all data. If not, run delmod to get
# rid of any model that might still be lurking in the header, and/or clearcal
# to set to 1 any MODEL data.
plotms(vis=avg_data, spw='0', xaxis='time', yaxis='amp',
avgchannel=avgchannel_wide, xdatacolumn='model', ydatacolumn='model', avgscan=F,
coloraxis='baseline', iteraxis='', xselfscale=T, yselfscale=T,
title='Model Amp vs Time after split for spw %i. Should be all 1s' % spwn,
plotfile=outdir+'ampvstime_model_shouldbe1.png', field=field,
overwrite=True,)
delmod(vis=avg_data)
plotms(vis=avg_data, spw='0', xaxis='phase', yaxis='amp',
avgchannel=avgchannel_wide, xdatacolumn='data', ydatacolumn='data', avgscan=F,
coloraxis='baseline', iteraxis='', xselfscale=T, yselfscale=T,
title='Corrected Phase vs Amp after split',
plotfile=outdir+'ampvsphase_corrected_avg_spw%i.png' % spwn, field=field,
overwrite=True,)
# (0.5) Run clean non-interactively with some set number of iterations, and be
# sure to keep the image around for comparison later. Run delmod to get rid of
# the model it saved to the MS header.
#if reclean:
# imagename="average_spw%i_shallowclean" % spwn
# for suffix in clean_output_suffixes:
# os.system("rm -rf "+imagename+suffix)
# clean(vis=avg_data, field=field, imagename=imagename, mode='mfs',
# weighting='briggs', robust=robust, niter=100, imsize=512)
# viewer(imagename+".image",
# outfile=outdir+imagename+".image.png",
# outformat='png',
# gui=False)
# exportfits(imagename=imagename+".image", fitsimage=imagename+".fits", overwrite=True)
# delmod(avg_data,scr=True)
# (1) Clean a single SPW *interactively*, boxing the brightest regions and not
# cleaning very deeply (maybe 100 iterations). Keep this model in the header
# -- it's what you'll use for the first round of self-calibration.
if reclean:
imagename="average_spw%i_shallowclean_masked" % spwn
for suffix in clean_output_suffixes:
os.system("rm -rf "+imagename+suffix)
clean(vis=avg_data, field=field, imagename=imagename, mode='mfs',
psfmode='hogbom',multiscale=multiscale,
weighting='briggs', robust=robust, niter=100, imsize=imsize,
mask=cleanboxes,
nterms=2,
usescratch=True)
viewer(imagename+".image.tt0",
outfile=outdir+imagename+".image.tt0.png",
outformat='png',
gui=False)
exportfits(imagename=imagename+".image.tt0", fitsimage=imagename+".fits", overwrite=True)
imrms = [imstat(imagename+".image.tt0",box=statsbox)['rms']]
# FAILS!!!!
#plotms(vis=avg_data, spw='0', xaxis='time', yaxis='amp',
# avgchannel='128', xdatacolumn='model', ydatacolumn='model', avgscan=F,
# coloraxis='baseline', iteraxis='', xselfscale=T, yselfscale=T,
# title='Model Amp vs Time after shallow clean for spw %i.' % spwn,
# plotfile=outdir+'ampvstime_model_shallowclean_spw%i.png' % spwn, field=field,
# overwrite=True,)
for calnum in xrange(niter):
# for Ku D W51 Ku spw 2
if reclean:
first_image = 'spw%i_C_C_firstim_selfcal%i' % (spwn,calnum)
for suffix in clean_output_suffixes:
os.system("rm -rf "+first_image+suffix)
clean(vis=avg_data,imagename=first_image,field=field, mode='mfs',
psfmode='hogbom',multiscale=multiscale,
weighting='briggs', robust=robust, niter=100, imsize=imsize,
mask=cleanboxes,
nterms=2,
usescratch=True)
exportfits(imagename=first_image+".image.tt0", fitsimage=first_image+".fits", overwrite=True)
viewer(first_image+".image.tt0",
outfile=outdir+first_image+".image.tt0.png",
outformat='png',
gui=False)
# this fails?
#plotms(vis=avg_data, spw='0', xaxis='time', yaxis='amp',
# avgchannel='128', xdatacolumn='model', ydatacolumn='model', avgscan=F,
# coloraxis='baseline', iteraxis='', xselfscale=T, yselfscale=T,
# title='Model Amp vs Time after shallow clean for spw %i iter %i.' % (spwn,calnum),
# plotfile=outdir+'ampvstime_model_shallowclean_spw%i_iter%i.png' % (spwn,calnum), field=field,
# overwrite=True,)
# DONE avg/split ing
caltable = 'selfcal%i_%s_spw%i.gcal' % (calnum,field.replace(" ",""),spwn)
if reclean:
os.system('rm -rf '+caltable)
gaincal(vis=avg_data,
field='',
caltable=caltable,
spw='',
# gaintype = 'T' could reduce failed fit errors by averaging pols...
gaintype='G', # 'G' from http://casaguides.nrao.edu/index.php?title=EVLA_Advanced_Topics_3C391
solint=solint,
refant=refant,
calmode='p',
combine='scan',
minblperant=4)
#
# Watch out for failed solutions noted in the terminal during this
# solution. If you see a large fraction (really more than 1 or 2) of
# your antennas failing to converge in many time intervals then you
# may need to lengthen the solution interval.
#
# =%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%
# INSPECT THE CALIBRATION
# =%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%
#
# After you have run the gaincal, you want to inspect the
# solution. Use PLOTCAL to look at the solution (here broken into
# panels by SPW with individual antennas mapped to colors). Look at
# the overall magnitude of the correction to get an idea of how
# important the selfcal is and at how quickly it changes with time to
# get an idea of how stable the instrument and atmosphere were.
#
if doplots:
for ant2 in ant2list:
for ant in ant1list:
# (4) Have a look at the gain solutions by antenna. Which antennas
# have the largest phase corrections? Before applying the
# calibration, use plotms to display the corrected phase vs. amp
# for these antennas, to compare with *after* the correction is
# applied.
plotcal(caltable=caltable,
xaxis='time', yaxis='phase',
showgui=False,
antenna=ant+'&'+ant2,
figfile=outdir+'selfcal%i_spw%i_phasevstime_ant%s-%s.png' % (calnum,spwn,ant,ant2),
iteration='')#, subplot = 221)
#plotcal(caltable=caltable, xaxis='amp', yaxis='phase',
# showgui=False,
# antenna=ant,
# figfile=outdir+'selfcal%i_spw%i_phasevsamp_ant%s.png' % (calnum,spwn,ant),
# iteration='')#, subplot = 221)
if calnum == 0:
datacol='data'
else:
datacol='corrected'
plotms(vis=avg_data, xaxis='time', yaxis='phase',
xdatacolumn=datacol, ydatacolumn=datacol,
avgtime='15s', avgchannel=avgchannel_narrow, coloraxis='corr',
antenna=ant+'&'+ant2,
overwrite=True, title='Iteration %i for spw %i and ant %s-%s. datacol=%s' % (calnum,spwn,ant,ant2,datacol),
plotfile=outdir+'selfcal%i_spw%i_ant%s-%s_phasetime.png' % (calnum,spwn,ant,ant2),)
plotms(vis=avg_data, xaxis='time', yaxis='amp',
xdatacolumn=datacol, ydatacolumn=datacol,
avgtime='15s', avgchannel=avgchannel_narrow, coloraxis='corr',
antenna=ant+'&'+ant2,
overwrite=True, title='Iteration %i for spw %i and ant %s-%s. datacol=%s' % (calnum,spwn,ant,ant2,datacol),
plotfile=outdir+'selfcal%i_spw%i_ant%s-%s_amptime.png' % (calnum,spwn,ant,ant2),)
plotms(vis=avg_data, xaxis='phase', yaxis='amp',
xdatacolumn=datacol, ydatacolumn=datacol,
avgtime='60s', avgchannel=avgchannel_narrow, coloraxis='corr',
antenna=ant+'&'+ant2,
overwrite=True, title='Iteration %i for spw %i and ant %s-%s. datacol=%s' % (calnum,spwn,ant,ant2,datacol),
plotfile=outdir+'selfcal%i_spw%i_ant%s-%s_phaseamp.png' % (calnum,spwn,ant,ant2),)
plotcal(caltable=caltable,
xaxis='time', yaxis='phase',
plotrange=[0,0,-180,180],
showgui=INTERACTIVE,
figfile='' if INTERACTIVE else outdir+'selfcal%i_spw%i_phasevstime.png' % (calnum,spwn),
iteration='spw' if INTERACTIVE else '')#, subplot = 221)
plotcal(caltable=caltable,
xaxis='antenna', yaxis='phase',
showgui=INTERACTIVE,
figfile=outdir+'selfcal%i_spw%i_phasevsantenna.png' % (calnum,spwn),
iteration='')
plotcal(caltable=caltable,
xaxis='time', yaxis='amp',
plotrange=[0,0,0.5,1.5],
showgui=INTERACTIVE,
figfile='' if INTERACTIVE else outdir+'selfcal%i_spw%i_ampvstime.png' % (calnum,spwn),
iteration='spw' if INTERACTIVE else '')#, subplot = 221)
#plotcal(caltable=caltable,
# xaxis='phase', yaxis='amp',
# plotrange=[-50,50,0.5,1.5],
# showgui=INTERACTIVE,
# figfile='' if INTERACTIVE else outdir+'selfcal%i_spw%i_ampvsphase.png' % (calnum,spwn),
# iteration='spw' if INTERACTIVE else '')#, subplot = 221)
# THERE WILL BE WEIRD "LUSTRE" ERRORS GENERATED BY THE FILE SYSTEM. DO
# NOT FREAK OUT. These are just a feature of our fast file
# system. Plotcal will still work.
# It can be useful useful to plot the X-Y solutions (i.e., differences
# between polarizations) as an indicator of the noise in the
# solutions.
plotcal(caltable=caltable,
xaxis='time',
yaxis='phase',
plotrange=[0,0,-25, 25],
poln = '/',
showgui=INTERACTIVE,
iteration='spw,antenna' if INTERACTIVE else '',
figfile='' if INTERACTIVE else outdir+'selfcal%i_spw%i_poldiff.png' % (calnum,spwn),
subplot = 221 if INTERACTIVE else 111)
plotms(vis=avg_data,
xaxis='uvdist',
yaxis='amp',
xdatacolumn='corrected',
ydatacolumn='corrected',
avgtime='1e8s',
avgchannel=avgchannel_narrow,
coloraxis='baseline',
overwrite=True,
title='Iteration %i for spw %i' % (calnum,spw),
plotfile='' if INTERACTIVE else outdir+'selfcal%i_spw%i_uvdistamp.png' % (calnum,spwn),
)
#plotms(vis=avg_data,
# xaxis='phase',
# yaxis='amp',
# xdatacolumn='corrected',
# ydatacolumn='corrected',
# avgtime='60s',
# avgchannel=avgchannel_narrow,
# coloraxis='corr',
# overwrite=True,
# title='Iteration %i for spw %i' % (calnum,spw),
# plotfile='' if INTERACTIVE else outdir+'selfcal%i_spw%i_phaseamp.png' % (calnum,spwn),
# )
plotms(vis=avg_data,
xaxis='time',
yaxis='amp',
xdatacolumn='corrected',
ydatacolumn='corrected',
avgtime='10s',
avgchannel=avgchannel_narrow,
coloraxis='baseline',
overwrite=True,
title='Iteration %i for spw %i' % (calnum,spw),
plotfile='' if INTERACTIVE else outdir+'selfcal%i_spw%i_amptime.png' % (calnum,spwn),
)
# The rms noise is about 4 to 8 deg, depending on antenna, but the
# phase changes are considerably larger. This indicates that the
# application of this solution will improve the image.
# =%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%
# APPLY THE CALIBRATION
# =%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%=%
#
# If you are satisfied with your solution, you can now apply it to the
# data to generate a new corrected data column, which you can then
# image. Be sure to save the previous flags before you do so because
# applycal will flag data without good solutions. The commented
# command after the applycal will roll back to the saved solution in
# case you get in trouble.
#
# flagmanager(vis=avg_data,
# mode='save',
# versionname='before_selfcal_apply')
# 2013-03-04 19:53:37 SEVERE agentflagger:: (file /opt/casa/stable-2013-02/gcwrap/tools/flagging/agentflagger_cmpt.cc, line 37) Exception Reported: Invalid Table operation: ArrayColumn::setShape; shape cannot be changed for row 0 column FLAG
# *** Error *** Invalid Table operation: ArrayColumn::setShape; shape cannot be changed for row 0 column FLAG
if reclean:
applycal(vis=avg_data,
gaintable=caltable,
interp='linear',
flagbackup=True) # was False when flagmanager was used
# (6) Plot corrected phase vs. amp for the antennas you picked out in (4),
# to check that in fact the corrections have been applied as expected.
for ant2 in ant2list:
for ant in ant1list:
plotms(vis=avg_data, xaxis='time', yaxis='phase',
xdatacolumn='corrected', ydatacolumn='corrected',
avgtime='15s', avgchannel=avgchannel_narrow, coloraxis='corr',
antenna=ant+'&'+ant2,
overwrite=True, title='Iteration %i for spw %i and ant %s-%s' % (calnum,spwn,ant,ant2),
plotfile=outdir+'selfcal%i_spw%i_ant%s-%s_phasetime_applied.png' % (calnum,spwn,ant,ant2),)
plotms(vis=avg_data, xaxis='time', yaxis='amp',
xdatacolumn='corrected', ydatacolumn='corrected',
avgtime='60s', avgchannel=avgchannel_narrow, coloraxis='corr',
antenna=ant+'&'+ant2,
overwrite=True, title='Iteration %i for spw %i and ant %s-%s' % (calnum,spwn,ant,ant2),
plotfile=outdir+'selfcal%i_spw%i_ant%s-%s_amptime_applied.png' % (calnum,spwn,ant,ant2),)
plotms(vis=avg_data, xaxis='phase', yaxis='amp',
xdatacolumn='corrected', ydatacolumn='corrected',
avgtime='60s', avgchannel=avgchannel_narrow, coloraxis='corr',
antenna=ant+'&'+ant2,
overwrite=True, title='Iteration %i for spw %i and ant %s-%s' % (calnum,spwn,ant,ant2),
plotfile=outdir+'selfcal%i_spw%i_ant%s-%s_phaseamp_applied.png' % (calnum,spwn,ant,ant2),)
plotms(vis=vis, spw='0', xaxis='freq', yaxis='phase', avgtime='1e8',
avgscan=T, coloraxis='corr', iteraxis='baseline', xselfscale=T,
yselfscale=T,
antenna=ant+'&'+ant2,
title='Phase vs Freq with time averaging for spw %i ant %s-%s iter %i' % (spwn,ant,ant2,calnum),
plotfile=outdir+'phasevsfreq_spw%i_ant%s-%s_selfcal%i.png' % (spwn,ant,ant2,calnum),
field=field,
overwrite=True,
)
# Use this command to roll back to the previous flags in the event of
# an unfortunate applycal.
#flagmanager(vis=avg_data,
# mode='restore',
# versionname='before_selfcal_apply')
if reclean:
selfcal_image = 'spw%i_C_C_selfcal%i' % (spwn,calnum)
for suffix in clean_output_suffixes:
os.system("rm -rf "+selfcal_image+suffix)
clean(vis=avg_data,imagename=selfcal_image,field=field, mode='mfs',
psfmode='hogbom',multiscale=multiscale,
weighting='briggs', robust=robust, niter=1000, imsize=imsize,
nterms=2,
mask=cleanboxes,
usescratch=True)
exportfits(imagename=selfcal_image+".image.tt0", fitsimage=selfcal_image+".fits", overwrite=True)
plotms(vis=avg_data, spw='0', xaxis='baseline', yaxis='amp', avgtime='1e8',
ydatacolumn='corrected-model',
avgscan=T, coloraxis='baseline', iteraxis='', xselfscale=T,
yselfscale=T,
title='Residual vs. Baseline after CSCLEAN iter %i' % calnum,
plotfile=outdir+'post_selfcal%i_spw%i_residVSbaseline.png' % (calnum,spwn),
field=field,
overwrite=True,
)
plotms(vis=avg_data, spw='0', xaxis='time', yaxis='amp', avgtime='5s',
ydatacolumn='corrected-model',
coloraxis='baseline', iteraxis='', xselfscale=T,
yselfscale=T,
title='Residual vs. Time after CSCLEAN iter %i' % (calnum),
plotfile=outdir+'post_selfcal%i_spw%i_residVStime.png' % (calnum,spwn),
field=field,
overwrite=True,
)
plotms(vis=avg_data, spw='0', xaxis='uvdist', yaxis='amp', avgtime='1e8',
ydatacolumn='corrected-model',
avgscan=T, coloraxis='baseline', iteraxis='', xselfscale=T,
yselfscale=T,
title='Residual vs. UVDIST after CSCLEAN iter %i' % (calnum) ,
plotfile=outdir+'post_selfcal%i_spw%i_residVSuvdist.png' % (calnum,spwn),
field=field,
overwrite=True,
)
imrms.append(imstat(selfcal_image+".image.tt0",box=statsbox)['rms'])
viewer(selfcal_image+".image.tt0",
outfile=outdir+selfcal_image+".image.tt0.png",
outformat='png',
gui=False)
print "FINISHED ITERATION %i" % calnum
print "FINISHED ITERATING!!! YAY!"
# final phase + gain cal:
# http://casaguides.nrao.edu/index.php?title=Calibrating_a_VLA_5_GHz_continuum_survey#One_Last_Iteration:_Amplitude_.26_Phase_Self_Calibration
aptable = 'selfcal_ap_%s_spw%i.gcal' % (field.replace(" ",""),spwn)
gaincal(vis=avg_data, field='', caltable=aptable, gaintable=caltable, spw='',
solint='inf', refant=refant, calmode='ap', combine='', minblperant=4)
plotcal(caltable=aptable,
xaxis='phase', yaxis='amp',
plotrange=[-50,50,0.5,1.5],
showgui=INTERACTIVE,
figfile='' if INTERACTIVE else outdir+'selfcal%i_spw%i_ampvsphase_final.png' % (calnum,spwn),
iteration='spw' if INTERACTIVE else '')#, subplot = 221)
applycal(vis=avg_data,
gaintable=[aptable,caltable],
interp='linear',
flagbackup=True) # was False when flagmanager was used
selfcal_image = 'spw%i_C_C_selfcal%i_final' % (spwn,calnum)
for suffix in clean_output_suffixes:
os.system("rm -rf "+selfcal_image+suffix)
clean(vis=avg_data,imagename=selfcal_image,field=field, mode='mfs', mask=cleanboxes,
weighting='briggs', robust=robust, niter=10000, imsize=imsize,
nterms=2,
usescratch=True)
exportfits(imagename=selfcal_image+".image.tt0", fitsimage=selfcal_image+".fits", overwrite=True)
plotms(vis=avg_data, spw='0', xaxis='baseline', yaxis='amp', avgtime='1e8',
ydatacolumn='corrected-model',
avgscan=T, coloraxis='baseline', iteraxis='', xselfscale=T,
yselfscale=T,
title='Residual vs. Baseline after CSCLEAN iter %i' % calnum,
plotfile=outdir+'post_selfcal%i_spw%i_residVSbaseline.png' % (calnum,spwn),
field=field,
overwrite=True,
)
plotms(vis=avg_data, spw='0', xaxis='time', yaxis='amp', avgtime='5s',
ydatacolumn='corrected-model',
coloraxis='baseline', iteraxis='', xselfscale=T,
yselfscale=T,
title='Residual vs. Time after CSCLEAN iter %i' % (calnum),
plotfile=outdir+'post_selfcal%i_spw%i_residVStime.png' % (calnum,spwn),
field=field,
overwrite=True,
)
plotms(vis=avg_data, spw='0', xaxis='uvdist', yaxis='amp', avgtime='1e8',
ydatacolumn='corrected-model',
avgscan=T, coloraxis='baseline', iteraxis='', xselfscale=T,
yselfscale=T,
title='Residual vs. UVDIST after CSCLEAN iter %i' % (calnum) ,
plotfile=outdir+'post_selfcal%i_spw%i_residVSuvdist.png' % (calnum,spwn),
field=field,
overwrite=True,
)
selfcal_image = 'spw%i_C_C_selfcal%i_final_multiscale' % (spwn,calnum)
for suffix in clean_output_suffixes:
os.system("rm -rf "+selfcal_image+suffix)
clean(vis=avg_data,imagename=selfcal_image,field=field, mode='mfs', imagermode='csclean',# mask=cleanboxes,
multiscale=multiscale, psfmode='hogbom',
nterms=2,
weighting='briggs', robust=robust, niter=10000, imsize=imsize,
usescratch=True)
exportfits(imagename=selfcal_image+".image.tt0", fitsimage=selfcal_image+".fits", overwrite=True)
plotms(vis=avg_data, spw='0', xaxis='baseline', yaxis='amp', avgtime='1e8',
ydatacolumn='corrected-model',
avgscan=T, coloraxis='baseline', iteraxis='', xselfscale=T,
yselfscale=T,
title='Residual vs. Baseline after multiscale CLEAN iter %i' % (calnum),
plotfile=outdir+'post_selfcal%i_spw%i_residVSbaseline_multiscale.png' % (calnum,spwn),
field=field,
overwrite=True,
)
plotms(vis=avg_data, spw='0', xaxis='time', yaxis='amp', avgtime='5s',
ydatacolumn='corrected-model',
coloraxis='baseline', iteraxis='', xselfscale=T,
yselfscale=T,
title='Residual vs. Time after multiscale CLEAN iter %i' % (calnum),
plotfile=outdir+'post_selfcal%i_spw%i_residVStime_multiscale.png' % (calnum,spwn),
field=field,
overwrite=True,
)
plotms(vis=avg_data, spw='0', xaxis='uvdist', yaxis='amp', avgtime='1e8',
ydatacolumn='corrected-model',
avgscan=T, coloraxis='baseline', iteraxis='', xselfscale=T,
yselfscale=T,
title='Residual vs. UVDIST after multiscale CLEAN iter %i' % (calnum),
plotfile=outdir+'post_selfcal%i_spw%i_residVSuvdist_multiscale.png' % (calnum,spwn),
field=field,
overwrite=True,
)
return imrms
parser.add_argument("--eps", help='epsilon value for DBSCAN (default: 0.00005)', type=float, default=0.00005)
args = parser.parse_args()
method = args.meth
if method == "kmeans":
sys.stderr.write("Clustering with K-Means method...\n")
options = {'init':'random', 'n_clusters':args.n, 'n_jobs':-1, 'n_init':10}
elif method == "affinity":
sys.stderr.write("Clustering with Affinity Propagation method...\n")
options = {'preference':-50}
elif method == "dbscan":
sys.stderr.write("Clustering with DBScan method...\n")
options = {'eps':args.eps}
elif method == "ward":
sys.stderr.write("Clustering with Ward method...\n")
options = {'n_clusters':args.n}
elif method == "meanshift":
sys.stderr.write("Clustering with Mean Shift method...\n")
options = {'bandwidth':bandwidth, 'bin_seeding':True}
else:
sys.stderr.write("Error: Unsupported clustering method.\n")
exit(0)
# Load file
pcd_list = ut.loadPoints(args.file, [2,3,4])
# Cluster the points
clusters = cluster(pcd_list, method, options)
# Show the clusters
viewer(clusters, draw_elevation=False, c=clusters[:,2])
# Load input file
pcd_list = np.array(ut.loadPoints(args.input, [0, 1, 2]))
# Compute the number of iterations to do given the slice size
to_process = len(pcd_list)
slice_size = args.slice
iterations = to_process / slice_size
if (iterations == 0 or to_process % slice_size == 0):
iterations += 1
# Initialize the iteration variable
i = 0
# Get the matplotlib object that shows the point cloud
plt = viewer(pcd_list, get=True)
# Compute the fitting polynoms and plot them
for i in range(iterations):
begin = i * slice_size
if (i + 1) * slice_size < to_process:
end = (i + 1) * slice_size
else:
end = to_process
points_slice = pcd_list[begin:end]
xp = np.linspace(points_slice[0, 0], points_slice[end - begin - 1, 0],
100)
pl = np.poly1d(
np.polyfit(points_slice[:, 0], points_slice[:, 1], args.degree))
plt.plot(xp, pl(xp), 'r-', linewidth=3.0)
plt.show()
sys.stderr.write("Clustering with K-Means method...\n")
options = {
'init': 'random',
'n_clusters': args.n,
'n_jobs': -1,
'n_init': 10
}
elif method == "affinity":
sys.stderr.write("Clustering with Affinity Propagation method...\n")
options = {'preference': -50}
elif method == "dbscan":
sys.stderr.write("Clustering with DBScan method...\n")
options = {'eps': args.eps}
elif method == "ward":
sys.stderr.write("Clustering with Ward method...\n")
options = {'n_clusters': args.n}
elif method == "meanshift":
sys.stderr.write("Clustering with Mean Shift method...\n")
options = {'bandwidth': bandwidth, 'bin_seeding': True}
else:
sys.stderr.write("Error: Unsupported clustering method.\n")
exit(0)
# Load file
pcd_list = ut.loadPoints(args.file, [2, 3, 4])
# Cluster the points
clusters = cluster(pcd_list, method, options)
# Show the clusters
viewer(clusters, draw_elevation=False, c=clusters[:, 2])
