def plot3():
"""
12 plots (eight like plot2), plus also zoomins on filaments for each distance.
"""
f, ((ax1, ax2, ax3, ax4),(ax11, ax22, ax33, ax44),(ax111, ax222, ax333, ax444)) = plt.subplots(3, 4, figsize = (10.5, 10.))
for distance,axistop,axismid,axisbot in zip(['50Mpc','100Mpc','200Mpc','500Mpc'],[ax1,ax2,ax3,ax4],[ax11,ax22,ax33,ax44],[ax111,ax222,ax333,ax444]):
data_tuple = data_dict[distance]
factor = data_tuple[2]; newsize = data_tuple[1]; data = data_tuple[0]; resolution = 100.
xystarts = [x_center-x_FOV[distance]/2.,y_center-y_FOV[distance]/2.]
size = [x_FOV[distance], y_FOV[distance]]
x1 = ((x_center-x_FOV[distance]/2.)/100.*(newsize/factor))
x2 = ((x_center+x_FOV[distance]/2.)/100.*(newsize/factor))
y1 = ((y_center-y_FOV[distance]/2.)/100.*(newsize/factor))
y2 = ((y_center+y_FOV[distance]/2.)/100.*(newsize/factor))
data_FOV = data[int(x1):int(x2),int(y1):int(y2)]
get_halpha_SB.makemap(data_FOV,size,axistop,xystarts = xystarts)
label = 'Mock observation at '+distance; exptime=10**7*3600.; map='bone'
plotit_general(data_FOV,size,xystarts,axismid,float(exptime), map, float(resolution), label = label)
# do the zoom-ins:
xzoomin = np.array([47.4,48.2])/100.*(newsize/factor)
yzoomin = np.array([11.2,12.4])/100.*(newsize/factor)
data_zoomin = data[int(xzoomin[0]):int(xzoomin[1]),int(yzoomin[0]):int(yzoomin[1])]
xystarts = [47.4,11.2]
size = [1.0,1.2]
label = 'Mock observation at '+distance; exptime=10**7*3600.; map='bone'
plotit_general(data_zoomin,size,xystarts,axisbot,float(exptime), map, float(resolution), label = label)
def plot2():
"""
Only eight plots, half raw EAGLE at different distances and half mock obs at different distances
"""
f, ((ax1, ax2, ax3, ax4),(ax11, ax22, ax33, ax44)) = plt.subplots(2, 4, figsize = (15.5, 15.))
for distance,axistop,axisbot in zip(['50Mpc','100Mpc','200Mpc','500Mpc'],[ax1,ax2,ax3,ax4],[ax11,ax22,ax33,ax44]):
data_tuple = data_dict[distance]
factor = data_tuple[2]; newsize = data_tuple[1]; data = data_tuple[0]; resolution = 100.
xystarts = [x_center-x_FOV[distance]/2.,y_center-y_FOV[distance]/2.]
size = [x_FOV[distance], y_FOV[distance]]
x1 = ((x_center-x_FOV[distance]/2.)/100.*(newsize/factor))
x2 = ((x_center+x_FOV[distance]/2.)/100.*(newsize/factor))
y1 = ((y_center-y_FOV[distance]/2.)/100.*(newsize/factor))
y2 = ((y_center+y_FOV[distance]/2.)/100.*(newsize/factor))
data_FOV = data[int(x1):int(x2),int(y1):int(y2)]
get_halpha_SB.makemap(data_FOV,size,axistop,xystarts = xystarts)
label = 'Mock observation at '+distance; exptime=10**5*3600.; map='bone'
plotit_general(data_FOV,size,xystarts,axisbot,float(exptime), map, float(resolution), label = label)
# put little boxes where zoomins are pulling out
xystarts = [47.4,11.2]
size = [1.0,1.2]
axisbot.plot([47.4,47.4,47.4+1.0,47.4+1.0,47.4],[11.2,11.2+1.2,11.2+1.2,11.2,11.2],'r-')
axisbot.set_xlim((x_center-x_FOV[distance]/2.),(x_center-x_FOV[distance]/2.)+x_FOV[distance])
axisbot.set_ylim((y_center-y_FOV[distance]/2.),(y_center-y_FOV[distance]/2.)+y_FOV[distance])
def testplot():
# plot them all together
f, ((ax1, ax2, ax3, ax4)) = plt.subplots(1, 4, figsize=(15.5, 15.))
for distance, data, axis in zip([
'50Mpc', '100Mpc', '200Mpc', '500Mpc'
], [data_50_100_FOV, data_100_100_FOV, data_200_100_FOV, data_500_100_FOV],
[ax1, ax2, ax3, ax4]):
xystarts = [
x_center - x_FOV[distance] / 2., y_center - y_FOV[distance] / 2.
]
size = [x_FOV[distance], y_FOV[distance]]
get_halpha_SB.makemap(data, size, axis, xystarts=xystarts)
def plotit_general(data, size, xystarts, ax1, exptime, mymap='gist_gray', res=100, label=''):
data_obs = HalphaSBplot_addnoise.addnoise(data,resolution,exptime=exptime,CMOS=True)
# Plot the subtracted noiseadded data
#fig = plt.figure(figsize = (9.5, 5.))
#ax1 = plt.subplot(111)
# Plot the data nicely
median = np.median(data_obs);
sig = np.sqrt(median)
mymax = median + 40*sig
mymin = median - 5*sig
SBdata_clipped = data_obs
SBdata_clipped[SBdata_clipped < mymin] = mymin
SBdata_clipped[SBdata_clipped > mymax] = mymax
SBdata_clipped = SBdata_clipped - mymin
get_halpha_SB.makemap(np.log10(SBdata_clipped**0.5),size,ax1,xystarts=xystarts,colmap=mymap,label=label,colorbar=False)
#data_trim = data[0:31800,0:31800]
#data_200 = get_halpha_SB.imreduce(data_trim, int(round(factor_200Mpc)), log=True, method = 'average')
#data_trim = data[0:31500,0:31500]
#data_1000 = get_halpha_SB.imreduce(data_trim, int(round(factor_1000Mpc)), log=True, method = 'average')
# Full Plot #
# Use data_50 because if don't bin at all, end up with some weird bright pixels that are not physical
xystarts = [38., 0.]
size = [30., 18.]
factor = int(round(factor_50Mpc))
fig = plt.figure(figsize=(16.5, 15.))
ax1 = plt.subplot(111)
get_halpha_SB.makemap(
data_50[(38. / 100. * (32000. / factor)):(68. / 100. * (32000. / factor)),
(0. / 100. * (32000. / factor)):(18. / 100. * (32000. / factor))],
size,
ax1,
xystarts=xystarts)
# Plot the FOV of dragonfly on top
# snapnum 28:
scale_50 = 0.206 #kpc/" ## at z = 0.01 (about 50 Mpc)
scale_100 = 0.467 #kpc/" ## at z = 0.023 (about 100 Mpc)
scale_200 = 0.928 #kpc/" ## at z = 0.047. (about 200 Mpc)
scale_1000 = 2.178 #kpc/" ## at z = 0.12 (about 1000 Mpc)
x_angFOV = 3. * 60. * 60. # "
y_angFOV = 2. * 60. * 60. # "
x_FOV_50 = x_angFOV * scale_50 / 1000. # Mpc
y_FOV_50 = y_angFOV * scale_50 / 1000. # Mpc
x_FOV_100 = x_angFOV * scale_100 / 1000. # Mpc
data_5, data_10, data_15, data_20 = loaddata1('chinook')
if verbose:
print('Plot an example region (20 Mpc box)...')
# Plotting parameters
save = False
plotDragonflyFOV = True
xystarts = [40., 0.]
size = 20.
fig = plt.figure(figsize=(16.5, 15.))
ax1 = plt.subplot(111)
get_halpha_SB.makemap(data_5[(40. / 100. * 3200.):(60. / 100. * 3200.),
0:(20. / 100. * 3200.)],
size,
ax1,
xystarts=xystarts)
if verbose:
print(
'Plot the regions... (slightly shifted for the different snapnums)'
)
if snapnum == 27:
# Pick out regions along the filaments in the map (snapnum 27)
ax1.plot([53, 53, 56, 56, 53], [9.2, 10, 8.5, 7.7, 9.2],
color='r',
label='Region 3')
ax1.plot(np.array([46.2, 47.2, 48.2, 47.2, 46.2]),
[14, 14, 10, 10, 14],
# exposure time
exptime = 60. * 60. * 10**5 # seconds (10^5 hours)
# load data and calculate signal in exposure time
data_20 = loaddata(machine)
totsignal = np.log10(10**data_20 * exptime) # log( photons / cm^2 /sr )
print('Plotting without the noise added, small region (20 Mpc box)...')
# Plotting parameters
xystarts = [40., 0.]
size = 20.
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(10, 10))
get_halpha_SB.makemap(
totsignal[(xystarts[0] / 100. * 3200.):((xystarts[0] + size) / 100. *
3200.),
(xystarts[1] / 100. * 3200.):((xystarts[1] + size) / 100. *
3200.)],
size,
ax1,
xystarts=xystarts)
ax1.set_title('sky signal, no noise')
# calculate the number of detected electrons
# how much are we going to bin? 100" square, so that is
binpix_size = 100. # arcsec
numpixel = round((binpix_size / pix_size)**2)
detsignal = np.log10(10**totsignal * QE_old * tau_l * tau_f * area_lens *
ang_size_pixel * numpixel)
print(
'The total detected signal per pixel (taking into account transmittance of the lens, quantum efficiency, and the pixel size) is %s.'
% detsignal)
def maskgalaxies_infilament(resolution, distance):
# load the full 100Mpc x 100Mpc box size data first
data_tuple = (np.load('data_%s_%sarcsec.npz' %
(distance, resolution))['arr_0'])
factor = data_tuple[2]
newsize = data_tuple[1]
data = data_tuple[0]
# select out a region around the filament 1
xmin = np.min(xbox_1) - 1.
xmax = np.max(xbox_1) + 1.
ymin = np.min(ybox_1) - 1.
ymax = np.max(ybox_1) + 1.
xystarts = [
xmin - pixscale[distance] * resolution / 2.,
ymin - pixscale[distance] * resolution / 2.
] ###
size = [xmax - xmin, ymax - ymin]
data_aroundfilament1 = data[(xmin / 100. *
(newsize / factor)):(xmax / 100. *
(newsize / factor)),
(ymin / 100. *
(newsize / factor)):(ymax / 100. *
(newsize / factor))]
if plotchecks:
fig = plt.figure(figsize=[5, 5])
axis = plt.subplot(111)
get_halpha_SB.makemap(data_aroundfilament1,
size,
axis,
xystarts=xystarts)
plt.show()
# load the galaxy catalogue
zmin = 10.
zmax = 15.
xgal, ygal, mgal, rhgas, rhstar = searchgals(xmin, xmax, ymin, ymax,
zmin, zmax)
if plotchecks:
fig = plt.figure(figsize=[5, 5])
axis = plt.subplot(111)
get_halpha_SB.makemap(data_aroundfilament1,
size,
axis,
xystarts=xystarts)
plotgals(xgal, ygal, rhgas, rhstar, axis)
plt.show()
# mask the galaxies in the EAGLE data
data_masked = maskgals2(
xgal, ygal, rhgas, data, [0, 0], [100., 100.],
distance) # using the full 100Mpcx100Mpc data for the next steps
if plotchecks:
fig = plt.figure(figsize=[5, 10])
axis = plt.subplot(121)
get_halpha_SB.makemap(
data_masked[(xmin / 100. *
(newsize / factor)):(xmax / 100. *
(newsize / factor)),
(ymin / 100. *
(newsize / factor)):(ymax / 100. *
(newsize / factor))],
size,
axis,
xystarts=xystarts)
plotgals(xgal, ygal, rhgas, rhstar, axis)
axis2 = plt.subplot(122)
get_halpha_SB.makemap(
data[(xmin / 100. * (newsize / factor)):(xmax / 100. *
(newsize / factor)),
(ymin / 100. * (newsize / factor)):(ymax / 100. *
(newsize / factor))],
size,
axis2,
xystarts=xystarts)
plotgals(xgal, ygal, rhgas, rhstar, axis2)
plt.show()
# select out the filament region
xboxes, yboxes = define_filament_boxes(data_masked)
xfull, yfull = get_halpha_SB.indices_region(xboxes[boxnum].astype(int),
yboxes[boxnum].astype(int))
SBdata = extractdata(xfull, yfull, data_masked)
if plotchecks:
SBdata_notmasked = np.load('SBdata_%sarcsec.npz' %
resolution)['arr_0']
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(8.5, 7.))
map = 'viridis'
get_halpha_SB.makemapfilament(SBdata_notmasked,
ax1,
colmap=map,
labelaxes=True)
get_halpha_SB.makemapfilament(SBdata,
ax2,
colmap=map,
labelaxes=True)
plt.show()
np.savez('SBdata_%sarcsec_%s_masked.npz' % (resolution, distance))
return SBdata
fname = 'emission_halpha_L0100N1504_28_test2_SmAb_C2Sm_32000pix_5.000000slice_zcen12.5_'+resolution+'.npz'
if os.path.isfile(fname):
print("data exists, loading %s now..."%fname)
sl = [slice(None,None,None), slice(None,None,None)]
data = (np.load(fname)['arr_0'])[sl]
else:
print("data not saved, loading from original files now...")
data = loaddata()
np.savez(fname,data)
xystarts = [38.,0.]
size = [30.,18.]
#factor = int(round(factor_50Mpc))
fig = plt.figure(figsize = (6.5, 5.))
ax1 = plt.subplot(111)
get_halpha_SB.makemap(data[(38./100.*(32000./factor)):(68./100.*(32000./factor)),(0./100.*(32000./factor)):(18./100.*(32000./factor))],size,ax1,xystarts = xystarts)
ax1.set_xlim(38.,68.)
ax1.set_ylim(0.,18.)
for distance in ['50Mpc','100Mpc','200Mpc','500Mpc']:
ax1.plot([x_center-x_FOV[distance]/2.,x_center+x_FOV[distance]/2.,x_center+x_FOV[distance]/2.,x_center-x_FOV[distance]/2.,x_center-x_FOV[distance]/2.],
[y_center+y_FOV[distance]/2.,y_center+y_FOV[distance]/2.,y_center-y_FOV[distance]/2.,y_center-y_FOV[distance]/2.,y_center+y_FOV[distance]/2.],color='gray')
#ax1.text(x_center-x_FOV[distance]/2.,y_center+y_FOV[distance]/2.,distance+' away')
ax1.text(x_center-x_FOV[distance]/2.,y_center,distance+' away',rotation='vertical',va='center')
plt.show()
elif allFOVs:
print("congrats, you picked allFOVs! These are the all the same resolution, different distances plots.")
# load the data: