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from planet4 import plotting, catalog_production
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rm = catalog_production.ReleaseManager('v1.0b4')
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fans = rm.read_fan_file()
blotches = rm.read_blotch_file()
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cols = ['angle', 'distance', 'tile_id', 'marking_id',
'obsid', 'spread',
'l_s', 'map_scale', 'north_azimuth',
'PlanetographicLatitude',
'PositiveEast360Longitude']
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fans.head()
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fans[cols].rename(dict(PlanetographicLatitude='Latitude',
PositiveEast360Longitude='Longitude'),
axis=1).head()
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fans.columns
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fan_counts = fans.groupby('tile_id').size()
blotch_counts = blotches.groupby('tile_id').size()
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ids = fan_counts[fan_counts > 4][fan_counts < 10].index
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pure_fans = list(set(ids) - set(blotches.tile_id))
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len(ids)
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len(pure_fans)
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rm.savefolder
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%matplotlib ipympl
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plt.close('all')
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from ipywidgets import interact
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id_ = pure_fans[51]
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def do_plot(i):
id_ = pure_fans[i]
plotting.plot_image_id_pipeline(id_, datapath=rm.savefolder, via_obsid=False,
save=True, figsize=(8,4),
saveroot='/Users/klay6683/Dropbox/src/p4_paper1/figures')
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interact(do_plot, i=48)
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from planet4 import markings
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def do_plot(i=0):
plt.close('all')
fig, ax = plt.subplots()
markings.ImageID(pure_fans[i]).show_subframe(ax=ax)
ax.set_title(pure_fans[i])
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interact(do_plot, i=(0,len(pure_fans),1))
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markings.ImageID('6n3').image_name
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from planet4 import markings
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markings.ImageID(pure_fans[15]).image_name
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plotting.plot_raw_fans(id_)
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plotting.plot_finals(id_, datapath=rm.savefolder)
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g_id = fans.groupby('tile_id')
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g_id.size().sort_values(ascending=False).head()
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blotches.groupby('tile_id').size().sort_values(ascending=False).head()
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plotting.plot_finals('6mr', datapath=rm.savefolder)
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plotting.plot_finals('7t9', datapath=rm.savefolder)
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from planet4 import dbscan
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db = dbscan.DBScanner()
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import seaborn as sns
sns.set_context('paper')
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db.parameter_scan(id_, 'fan', [0.13, 0.2], [10, 20, 30], size_to_scan='small')
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plotting.plot_image_id_pipeline('bk7', datapath=rm.savefolder, via_obsid=False,
save=True, figsize=(8,4), do_title=False,
saveroot='/Users/klay6683/Dropbox/src/p4_paper1/figures')
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plotting.plot_image_id_pipeline('ops', datapath=rm.savefolder, via_obsid=False,
save=True, figsize=(8,4), do_title=False,
saveroot='/Users/klay6683/Dropbox/src/p4_paper1/figures')
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plotting.plot_image_id_pipeline('b0t', datapath=rm.savefolder, via_obsid=False,
save=True, figsize=(8,4), do_title=False,
saveroot='/Users/klay6683/Dropbox/src/p4_paper1/figures')
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from astropy.table import Table
tab = Table.read('/Users/klay6683/Dropbox/src/p4_paper1/rois_table.tex')
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rois = tab.to_pandas()
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rois.drop(0, inplace=True)
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rois.head()
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rois.columns = ['Latitude', 'Longitude', 'Informal Name', '# Images (MY29)', '# Images (MY30)']
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rois.head()
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rois.to_csv('/Users/klay6683/Dropbox/data/planet4/p4_analysis/rois.csv')
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