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from planet4 import io
dbname = io.get_current_database_fname()
dbname
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db = io.DBManager()
p4ids = db.image_ids
p4ids[0]
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data = db.get_image_id_markings(p4ids[1])
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%matplotlib notebook
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from planet4 import markings
p4id_data = markings.ImageID(p4ids[1])
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p4id_data.plot_all()
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from planet4.clustering import DBScanner
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from planet4.dbscan import DBScanner
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reduced_fans = DBScanner(p4id_data.get_fans(), kind='fan',
scope='hirise').reduced_data
fans_data = [cluster.data for cluster in reduced_fans]
p4id_data.plot_fans(fans=pd.DataFrame(fans_data))
for fan in reduced_fans:
fan.add_midpoint_pointer(plt.gca())
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reduced_blotches = DBScanner(p4id_data.get_blotches(), kind='blotch',
scope='hirise').reduced_data
blotches_data = [cluster.data for cluster in reduced_blotches]
fig, ax = plt.subplots()
p4id_data.plot_blotches(blotches=pd.DataFrame(blotches_data), ax=ax)
p4id_data.plot_fans(fans=pd.DataFrame(fans_data), ax=ax)
for fan in reduced_fans:
fan.add_midpoint_pointer(plt.gca())
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from numpy.linalg import norm
for blotch in reduced_blotches:
print(blotch.center)
for fan in reduced_fans:
fancoord = fan.base + fan.midpoint
delta = blotch.center - (fan.base+fan.midpoint)
print(norm(delta))
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reduced_blotches[0].data
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p4id_data.plot_fans(fans=pd.DataFrame(fans_data))
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