def closest_bubble(p):
bubbles = np.array(bubble_params())
l0, b0 = bubbles[:, 1], bubbles[:, 2]
d = np.hypot(l0 - p[1], b0 - p[2])
ind = np.argmin(d)
return bubbles[ind], d[ind]
def closest_bubble(p):
bubbles = np.array(bubble_params())
l0, b0 = bubbles[:, 1], bubbles[:, 2]
d = np.hypot(l0 - p[1], b0 - p[2])
ind = np.argmin(d)
return bubbles[ind], d[ind]
def main():
model = ModelGroup.load('../models/full_classifier.dat')
bubbles = sorted(bubble_params())
scores = model.decision_function(bubbles)
result = {'params': bubbles, 'scores': scores.tolist()}
with open('../models/bubble_scores.json', 'w') as outfile:
json.dump(result, outfile)
def collage(bubbles):
ex = RGBExtractor()
ex.shp = (200, 200)
images = [ex.extract(*p) for p in bubble_params(bubbles)]
if len(images) == 3:
return np.vstack(images)
r, g, b = tuple(
montage2d(np.array([a[:, :, i] for a in images])) for i in range(3))
return np.dstack((r, g, b)).astype(np.uint8)
def collage(bubbles):
ex = RGBExtractor()
ex.shp = (200, 200)
images = [ex.extract(*p) for p in bubble_params(bubbles)]
if len(images) == 3:
return np.vstack(images)
r, g, b = tuple(montage2d(np.array([a[:, :, i] for a in images]))
for i in range(3))
return np.dstack((r, g, b)).astype(np.uint8)
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():
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 bubble_names(bubbles):
params = bubble_params(bubbles)
return ['%6.6i%+5.5i' % (p[1] * 1000, p[2] * 10000) for p in params]
def bubble_names(bubbles):
params = bubble_params(bubbles)
return ['%6.6i%+5.5i' % (p[1] * 1000, p[2] * 10000)
for p in params]