def main():
data = json.load(open('../models/l035_scores.json'))
stamps = np.array(data['stamps'])
scores = np.array(data['scores'])
unmerged = stamps[scores > .2]
merged = merge(unmerged, scores[scores > 0.2])
unambig = [1, 3, 4, 6, 7, 11, 12, 13, 15, 16, 17, 18, 19]
ambig = [2, 9]
new = [14]
fp = [0, 5]
neb = [8, 10]
groups = [unambig, ambig, new, fp, neb]
ims = [ex(merged[i]) for g in groups for i in g]
plt.figure(dpi=400, tight_layout=True)
collage(ims, 5, 4)
trace_groups(groups, 'rgbcm', 5, 4)
hide_axes()
plt.savefig('l35.eps')
def chunked_merge(stamps, scores):
stamps[stamps[:, 1] > 180, 1] -= 360
lon = stamps[:, 1]
ostamps, oscores = [], []
for lcen in np.arange(lon.min(), lon.max() + 1, 1):
good = np.abs(lon - lcen) < 1
if good.sum() == 0:
continue
st, sc = merge(stamps[good], scores[good])
good = np.abs(st[:, 1] - lcen) < .5
if good.sum() == 0:
continue
ostamps.append(st[good])
oscores.append(sc[good])
print lcen, good.sum()
result = merge(np.vstack(ostamps), np.hstack(oscores))
result[0][result[0][:, 1] < 0, 1] += 360
return result
def chunked_merge(stamps, scores):
stamps[stamps[:, 1] > 180, 1] -= 360
lon = stamps[:, 1]
ostamps, oscores = [], []
for lcen in np.arange(lon.min(), lon.max() + 1, 1):
good = np.abs(lon - lcen) < 1
if good.sum() == 0:
continue
st, sc = merge(stamps[good], scores[good])
good = np.abs(st[:, 1] - lcen) < .5
if good.sum() == 0:
continue
ostamps.append(st[good])
oscores.append(sc[good])
print lcen, good.sum()
result = merge(np.vstack(ostamps), np.hstack(oscores))
result[0][result[0][:, 1] < 0, 1] += 360
return result
def main():
np.random.seed(42)
data = json.load(open('../models/l035_scores.json'))
stamps = np.array(data['stamps'])
scores = np.array(data['scores'])
l = stamps[:, 1]
b = stamps[:, 2]
good = (scores > .1) & (l < 34.8) & (l > 34.6) & (b > -.4) & (b < -0.2)
assert good.sum() > 0
stamps = stamps[good]
scores = scores[good]
merged, ms = merge(stamps, scores)
f = get_field(35)
g = scale(f.i4, limits=[70, 99])
r = scale(f.mips, limits=[70, 99])
b = r * 0
im = np.dstack((r, g, b))
plt.figure(dpi=200, tight_layout=True)
plt.imshow(im, extent=[36, 34, -1, 1], interpolation="bicubic")
plot_stamps(stamps, linewidth=1, edgecolor='white', label='Raw',
alpha=1)
plot_stamps(merged, edgecolor='red', alpha=1, linewidth=2,
label='Merged')
plt.xlim(34.795, 34.695)
plt.ylim(-.365, -.265)
plt.xlabel("$\ell$ ($^\circ$)")
plt.ylabel("b ($^\circ$)")
leg = plt.legend(loc='upper left', frameon=False)
for text in leg.get_texts():
text.set_color('white')
plt.savefig('cluster.eps')
def main():
data = json.load(open('../models/l035_scores.json'))
stamps = np.array(data['stamps'])
scores = np.array(data['scores'])
l = stamps[:, 1]
b = stamps[:, 2]
good = (scores > .1) & (l < 35.17) & (l > 34.9) & (b > -.9) & (b < -0.6)
stamps = stamps[good]
scores = scores[good]
merged = merge(stamps, scores)
mwp = np.array(bubble_params())
mwp = mwp[(mwp[:, 1] < 35.3) & (mwp[:, 1] > 35)]
f = get_field(35)
bad = f.mips == 0
g = scale(f.i4, limits=[30, 99.8])
r = scale(f.mips, limits=[30, 99.7])
r[bad] = 255
b = r * 0
im = np.dstack((r, g, b))
plt.figure(dpi=200, tight_layout=True)
plt.imshow(im, extent=[36, 34, -1, 1], interpolation="bicubic")
plot_stamps(merged, edgecolor='#7570b3', linewidth=2, label='Brut')
plot_stamps(mwp, edgecolor='#e7298a', linewidth=2, label='MWP')
plt.xlim(35.2, 35)
plt.ylim(-.825, -.625)
plt.legend(loc='upper right')
plt.xlabel("$\ell$ ($^\circ$)")
plt.ylabel("b ($^\circ$)")
plt.savefig('cluster_confusion.eps')
def main():
np.random.seed(42)
data = json.load(open('../models/l035_scores.json'))
stamps = np.array(data['stamps'])
scores = np.array(data['scores'])
l = stamps[:, 1]
b = stamps[:, 2]
good = (scores > .1) & (l < 34.8) & (l > 34.6) & (b > -.4) & (b < -0.2)
assert good.sum() > 0
stamps = stamps[good]
scores = scores[good]
merged, ms = merge(stamps, scores)
f = get_field(35)
g = scale(f.i4, limits=[70, 99])
r = scale(f.mips, limits=[70, 99])
b = r * 0
im = np.dstack((r, g, b))
plt.figure(dpi=200, tight_layout=True)
plt.imshow(im, extent=[36, 34, -1, 1], interpolation="bicubic")
plot_stamps(stamps, linewidth=1, edgecolor='white', label='Raw', alpha=1)
plot_stamps(merged, edgecolor='red', alpha=1, linewidth=2, label='Merged')
plt.xlim(34.795, 34.695)
plt.ylim(-.365, -.265)
plt.xlabel("$\ell$ ($^\circ$)")
plt.ylabel("b ($^\circ$)")
leg = plt.legend(loc='upper left', frameon=False)
for text in leg.get_texts():
text.set_color('white')
plt.savefig('cluster.eps')
def main():
data = json.load(open('../models/l035_scores.json'))
stamps = np.array(data['stamps'])
scores = np.array(data['scores'])
l = stamps[:, 1]
b = stamps[:, 2]
good = (scores > .1) & (l < 35.17) & (l > 34.9) & (b > -.9) & (b < -0.6)
stamps = stamps[good]
scores = scores[good]
merged = merge(stamps, scores)
mwp = np.array(bubble_params())
mwp = mwp[(mwp[:, 1] < 35.3) & (mwp[:, 1] > 35)]
f = get_field(35)
bad = f.mips == 0
g = scale(f.i4, limits=[30, 99.8])
r = scale(f.mips, limits=[30, 99.7])
r[bad] = 255
b = r * 0
im = np.dstack((r, g, b))
plt.figure(dpi=200, tight_layout=True)
plt.imshow(im, extent=[36, 34, -1, 1], interpolation="bicubic")
plot_stamps(merged, edgecolor='#7570b3', linewidth=2, label='Brut')
plot_stamps(mwp, edgecolor='#e7298a', linewidth=2, label='MWP')
plt.xlim(35.2, 35)
plt.ylim(-.825, -.625)
plt.legend(loc='upper right')
plt.xlabel("$\ell$ ($^\circ$)")
plt.ylabel("b ($^\circ$)")
plt.savefig('cluster_confusion.eps')