def field_stamps(lon):
"""
Return the stamp parameters to classify for each longitude
Returns
-------
A list of lists
"""
f = get_field(lon)
stamps = list(f.all_stamps())
stamps = [s for s in stamps if np.abs(s[1] - lon) <= 0.5]
return sorted(stamps)
def field_stamps(lon):
"""
Return the stamp parameters to classify for each longitude
Returns
-------
A list of lists
"""
f = get_field(lon)
stamps = list(f.all_stamps())
stamps = [s for s in stamps if np.abs(s[1] - lon) <= 0.5]
return sorted(stamps)
def main():
model = ModelGroup.load('../models/full_classifier.dat')
f = get_field(305)
stamps = sorted(list(f.all_stamps()))
df = model.cloud_decision_function(stamps, workers=100)
result = {'stamps': stamps, 'scores': df.tolist()}
with open('../models/l305_scores.json', 'w') as outfile:
json.dump(result, outfile)
def main():
model = ModelGroup.load('../models/full_classifier.dat')
f = get_field(35)
stamps = list(f.small_stamps())
stamps = [s for s in stamps if s[1] > 34.5 and s[1] < 35.5]
df = model.cloud_decision_function(stamps, workers=100)
result = {'stamps': stamps, 'scores': df.tolist()}
with open('../models/l035_small_scores.json', 'w') as outfile:
json.dump(result, outfile)
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')
def extract(self, lon, l, b, r):
shp = self.shp
return get_field(lon).extract_stamp(l, b, r, limits=[30, 98], shp=shp)
