def test_equalize(filename, result_dir):
input_img = Image.open(filename)
output_img = equalize_hist(input_img)
result_path = os.path.join(result_dir, 'equalize.png')
output_img.save(result_path)
print '[Saved] ' + result_path
fig = plot_hist(output_img)
plot_path = os.path.join(result_dir, 'hist-equalize.png')
fig.savefig(plot_path)
print '[Saved] ' + plot_path
def test_equalize(filename, result_dir):
input_img = Image.open(filename)
output_img = equalize_hist(input_img)
result_path = os.path.join(result_dir, 'equalize.png')
output_img.save(result_path)
print '[Saved] ' + result_path
fig = plot_hist(output_img)
plot_path = os.path.join(result_dir, 'hist-equalize.png')
fig.savefig(plot_path)
print '[Saved] ' + plot_path
units='msun')
gas_halos = numpy.loadtxt(
'/cosma5/data/durham/dc-murr1/simba_gas_neighbour_halos.txt')
gas_halos = gas_halos.astype(int)
frac_distance = distance / radius
mask = numpy.where(mass > 0)[0]
spread = spread[mask]
frac_distance = frac_distance[mask]
mass = mass[mask]
gas_halos = gas_halos[mask]
radius = radius[mask]
hist.plot_hist(mass, spread, r'Nearest halo mass [$M_{\odot}$]',
'Spread metric / halo radius',
'/cosma5/data/durham/dc-murr1/simba_gas_spread_mass.png')
where = numpy.where(gas_halos >= 0)[0]
spread = spread[where]
frac_distance = frac_distance[where]
mass = mass[where]
radius = radius[where]
'''
hist.plot_hist(
mass,
frac_spread,
r'Nearest halo mass [$M_{\odot}$]',
'Spread metric / halo radius',
'/cosma5/data/durham/dc-murr1/simba_gas_neighbour_spread_mass.png'
)
import numpy
import unyt
import hist
spread = unyt.unyt_array(
numpy.loadtxt('/cosma5/data/durham/dc-murr1/adiabatic_gas_spread.txt'),
units='Mpc')
mass = unyt.unyt_array(numpy.loadtxt(
'/cosma5/data/durham/dc-murr1/adi_gas_nearest_halo_mass.txt'),
units='msun')
neighbour_halos = numpy.loadtxt(
'/cosma5/data/durham/dc-murr1/adi_gas_neighbour_halos.txt')
where = numpy.where(neighbour_halos >= 0)[0]
spread = spread[where]
mass = mass[where]
hist.plot_hist(
mass, spread, r'Mass of nearest halo [$M_{\odot}$]', 'Spread Metric [Mpc]',
'/cosma5/data/durham/dc-murr1/adi_gas_neighbour_spread_mass.png')
numpy.loadtxt('/cosma5/data/durham/dc-murr1/dm_nearest_halo_mass.txt'),
units='msun')
dm_halos = numpy.loadtxt('/cosma5/data/durham/dc-murr1/dm_neighbour_halos.txt')
dm_halos = dm_halos.astype(int)
frac_distance = distance / radius
mask = numpy.where(frac_distance > 10**(-4))[0]
spread = spread[mask]
frac_distance = frac_distance[mask]
mass = mass[mask]
dm_halos = dm_halos[mask]
radius = radius[mask]
hist.plot_hist(frac_distance, spread, 'Distance to nearest halo / halo radius',
'Spread metric / halo radius',
'/cosma5/data/durham/dc-murr1/dm_spread_distance.png')
where = numpy.where(dm_halos >= 0)[0]
spread = spread[where]
frac_distance = frac_distance[where]
mass = mass[where]
radius = radius[where]
'''
hist.plot_hist(
frac_distance,
frac_spread,
'Distance to nearest halo / halo radius',
'Spread metric / halo radius',
'/cosma5/data/durham/dc-murr1/dm_neighbour_spread_distance.png'
)
units='msun')
dm_halos = numpy.loadtxt(
'/cosma5/data/durham/dc-murr1/gas_neighbour_halos.txt')
dm_halos = dm_halos.astype(int)
frac_spread = spread / radius
frac_distance = distance / radius
mask = numpy.where(frac_distance > 10**(-4))[0]
frac_spread = frac_spread[mask]
frac_distance = frac_distance[mask]
mass = mass[mask]
dm_halos = dm_halos[mask]
hist.plot_hist(frac_distance, frac_spread,
'Distance to nearest halo / halo radius',
'Spread metric / halo radius',
'/cosma5/data/durham/dc-murr1/gas_spread_distance.png')
where = numpy.where(dm_halos >= 0)[0]
frac_spread = frac_spread[where]
frac_distance = frac_distance[where]
mass = mass[where]
hist.plot_hist(
frac_distance, frac_spread, 'Distance to nearest halo / halo radius',
'Spread metric / halo radius',
'/cosma5/data/durham/dc-murr1/gas_neighbour_spread_distance.png')
one = numpy.where(
numpy.logical_and(
numpy.amin(mass) < mass, mass < numpy.percentile(mass, 25)))[0]
spread = unyt.unyt_array(
numpy.loadtxt('/cosma5/data/durham/dc-murr1/gas_spread.txt'), units='Mpc')
bh_mass = unyt.unyt_array(
numpy.loadtxt('/cosma5/data/durham/dc-murr1/gas_nearest_bh_mass.txt'),
units='msun')
mass = unyt.unyt_array(
numpy.loadtxt('/cosma5/data/durham/dc-murr1/gas_nearest_halo_mass.txt'),
units='msun')
where_bh = numpy.where(bh_mass > 0)[0]
spread = spread[where_bh]
bh_mass = bh_mass[where_bh]
mass = mass[where_bh]
hist.plot_hist(bh_mass, spread,
r"Mass of nearest halo's central BH [$M_{\odot}$]",
'Spread metric [Mpc]',
'/cosma5/data/durham/dc-murr1/gas_spread_bh.png')
dm_halos = numpy.loadtxt(
'/cosma5/data/durham/dc-murr1/gas_neighbour_halos.txt')
dm_halos = dm_halos.astype(int)
dm_halos = dm_halos[where_bh]
where = numpy.where(dm_halos >= 0)[0]
spread = spread[where]
bh_mass = bh_mass[where]
mass = mass[where]
hist.plot_hist(bh_mass, spread,
r"Mass of nearest halo's central BH [$M_{\odot}$]",
def test_plot(filename, result_dir):
input_img = Image.open(filename)
fig = plot_hist(input_img)
result_path = os.path.join(result_dir, 'hist.png')
fig.savefig(result_path)
print '[Saved] ' + result_path
units='msun')
dm_halos = numpy.loadtxt('/cosma5/data/durham/dc-murr1/dm_neighbour_halos.txt')
dm_halos = dm_halos.astype(int)
spread = spread
frac_distance = distance / radius
mask = numpy.where(mass > 0)[0]
spread = spread[mask]
frac_distance = frac_distance[mask]
mass = mass[mask]
dm_halos = dm_halos[mask]
radius = radius[mask]
hist.plot_hist(mass, spread, r'Nearest halo mass [$M_{\odot}$]',
'Spread metric / halo radius',
'/cosma5/data/durham/dc-murr1/dm_spread_mass.png')
where = numpy.where(dm_halos >= 0)[0]
spread = spread[where]
frac_distance = frac_distance[where]
mass = mass[where]
radius = radius[where]
hist.plot_hist(mass, spread, r'Nearest halo mass [$M_{\odot}$]',
'Spread metric / halo radius',
'/cosma5/data/durham/dc-murr1/dm_neighbour_spread_mass.png')
one = numpy.where(
numpy.logical_and(
numpy.amin(frac_distance) < frac_distance,
def load_prob_stats_and_calc_hist_thresh(stats_fn,
num_ids,
label1=None,
num_images=-1,
stats_fn2=None,
num_ids2=None,
label2=None,
num_images2=-1,
only_after_bin_val=None,
show_hist=True,
save_dir=None,
label_list=None):
print 'file1: ', stats_fn
print 'file2: ', stats_fn2
if save_dir is None:
save_dir = './rlt_hist_output/'
if not osp.exists(save_dir):
os.makedirs(save_dir)
if not label1:
label1 = get_auto_label(stats_fn)
prob_avg_vec, max_label_vec = load_prob_stats(stats_fn, num_ids)
print 'prob_avg_vec.shape: ', prob_avg_vec.shape
hist, bins = get_hist(prob_avg_vec)
thresh = calc_otsu_threshold(hist, bins, only_after_bin_val)
print 'Otsu_threshold for file1: ', thresh
# out_prefix = osp.splitext(stats_fn)[0]
# out_prefix = osp.join(save_dir, osp.basename(out_prefix))
out_prefix = osp.join(save_dir, label1)
hist_out_fn = out_prefix + '_thr%g.png' % thresh
plot_hist(prob_avg_vec, bins, show_hist, hist_out_fn)
thresh_out_fn = out_prefix + '_thr%g.txt' % thresh
cnt_gteq_thresh = np.sum(prob_avg_vec >= thresh)
cnt_lt_thresh = prob_avg_vec.size - cnt_gteq_thresh
fp = open(thresh_out_fn, 'w')
write_string = ('thresh: %g\ncnt(>=thresh):%d\ncnt(<thresh):%d' %
(thresh, cnt_gteq_thresh, cnt_lt_thresh))
fp.write(write_string)
fp.close()
if stats_fn2:
if not label2:
label2 = get_auto_label(stats_fn2)
prob_avg_vec2, max_label_vec2 = load_prob_stats(stats_fn2, num_ids2)
print 'prob_avg_vec2.shape: ', prob_avg_vec2.shape
hist, bins = get_hist(prob_avg_vec2)
thresh2 = calc_otsu_threshold(hist, bins, only_after_bin_val)
print 'Otsu_threshold for file2: ', thresh2
# out_prefix2 = osp.splitext(stats_fn2)[0]
# out_prefix2 = osp.join(save_dir, osp.basename(out_prefix2))
out_prefix2 = osp.join(save_dir, label2)
hist_out_fn2 = out_prefix2 + '_thr%g.png' % thresh2
plot_hist(prob_avg_vec2, bins, show_hist, hist_out_fn2)
thresh_out_fn = out_prefix2 + '_thr%g.txt' % thresh2
cnt_gteq_thresh = np.sum(prob_avg_vec2 >= thresh2)
cnt_lt_thresh = prob_avg_vec2.size - cnt_gteq_thresh
fp = open(thresh_out_fn, 'w')
write_string = ('thresh: %g\ncnt(>=thresh):%d\ncnt(<thresh):%d' %
(thresh2, cnt_gteq_thresh, cnt_lt_thresh))
fp.write(write_string)
fp.close()
prob_avg_vec3 = np.hstack((prob_avg_vec, prob_avg_vec2))
print 'prob_avg_vec.shape: ', prob_avg_vec3.shape
hist, bins = get_hist(prob_avg_vec3)
thresh3 = calc_otsu_threshold(hist, bins, only_after_bin_val)
out_prefix3 = out_prefix + '_and_' + osp.basename(out_prefix2)
hist_out_fn3 = out_prefix3 + '_thr%g.png' % thresh3
plot_hist(prob_avg_vec3, None, show_hist, hist_out_fn3)
print 'Otsu_threshold for file1 and file2: ', thresh3
thresh_out_fn = out_prefix3 + '_thr%g.txt' % thresh3
cnt_gteq_thresh = np.sum(prob_avg_vec3 >= thresh3)
cnt_lt_thresh = prob_avg_vec3.size - cnt_gteq_thresh
fp = open(thresh_out_fn, 'w')
write_string = ('thresh: %g\ncnt(>=thresh):%d\ncnt(<thresh):%d' %
(thresh3, cnt_gteq_thresh, cnt_lt_thresh))
fp.write(write_string)
fp.close()
mask = numpy.where(mass > 0)[0]
spread = spread[mask]
mass = mass[mask]
dm_halos = dm_halos[mask]
radius = radius[mask]
density = density[mask]
where = numpy.where(dm_halos >= 0)[0]
spread = spread[where]
mass = mass[where]
radius = radius[where]
density = density[where]
hist.plot_hist(
density, spread, 'Number of gas particles within 500kpc radius',
'Spread metric [Mpc]',
'/cosma5/data/durham/dc-murr1/adi_gas_neighbour_spread_density.png')
mass_cutoff = numpy.where(numpy.logical_and(mass > 10**8, mass < 10**10))[0]
spread_after = spread[mass_cutoff]
density_after = density[mass_cutoff]
spread_cutoff = numpy.where(
numpy.logical_and(spread_after > 10**(-1), spread_after < 1))[0]
density_after = density_after[spread_cutoff]
spread_after = spread_after[spread_cutoff]
xbins = numpy.logspace(numpy.log10(numpy.amin(density)),
numpy.log10(numpy.amax(density)),
num=50)
ybins = numpy.logspace(numpy.log10(numpy.amin(spread)),
import numpy
import unyt
import hist
spread = unyt.unyt_array(
numpy.loadtxt('/cosma5/data/durham/dc-murr1/gas_spread.txt'), units='Mpc')
mass = unyt.unyt_array(
numpy.loadtxt('/cosma5/data/durham/dc-murr1/gas_nearest_halo_mass.txt'),
units='msun')
neighbour_halos = numpy.loadtxt(
'/cosma5/data/durham/dc-murr1/gas_neighbour_halos.txt')
where = numpy.where(neighbour_halos >= 0)[0]
spread = spread[where]
mass = mass[where]
hist.plot_hist(mass, spread, r'Nearest halo mass [$M_{\odot}$]',
'Spread metric [Mpc]',
'/cosma5/data/durham/dc-murr1/gas_neighbour_spread_mass.png')
def test_plot(filename, result_dir):
input_img = Image.open(filename)
fig = plot_hist(input_img)
result_path = os.path.join(result_dir, 'hist.png')
fig.savefig(result_path)
print '[Saved] ' + result_path
