In [ ]:

    

from planet4.plotting import plot_image_id_pipeline
from planet4 import catalog_production as cp
from planet4 import plotting as p4plot


    

In [ ]:

    

%store -r


    

In [ ]:

    

ids = combined  # coming in from the %store


    

In [ ]:

    

ids


    

In [ ]:

    

from planet4.markings import Fan


    

In [ ]:

    

%matplotlib inline


    

In [ ]:

    

rm = cp.ReleaseManager('v1.0b4')


    

In [ ]:

    

rm.savefolder


    

In [ ]:

    

p4plot.plot_finals('br5', datapath=rm.savefolder)


    

In [ ]:

    

fans = rm.read_fan_file()


    

In [ ]:

    

obsdata = fans[fans.image_name=='ESP_012079_0945']


    

In [ ]:

    

for ind in obsdata.groupby('image_id').angle.std().sort_values(ascending=False).head(10).index:
    p4plot.plot_finals(ind, datapath=rm.savefolder);
    plt.gca().set_title(ind);


    

In [ ]:

    

p4plot.plot_finals('cpv', datapath=rm.savefolder)
plt.gcf().savefig('cpv.png', dpi=150)


    

In [ ]:

    

p4plot.plot_finals('cnt', datapath=rm.savefolder)
plt.gcf().savefig('cnt.png', dpi=150)


    

In [ ]:

    

fan = Fan(s)

fan.plot(color='green')


    

In [ ]:

    

meta = pd.read_csv(rm.metadata_path)


    

In [ ]:

    

meta


    

In [ ]:

    

%matplotlib nbagg
import seaborn as sns
sns.set_context('notebook')


    

In [ ]:

    

from nbtools.logging import setup_live_logging
import logging
logger = setup_live_logging('planet4.plotting', logging.DEBUG)


    

In [ ]:

    

dir_ = 'without_large_fan_fnotch_on_p4_coords'


    

In [ ]:

    

kwargs = dict(datapath=dir_,
              saveroot='plots/'+dir_,
              savetitle='withLarge_hirise_coords',
              save=True)


    

In [ ]:

    

for id_ in ids:
    print(id_)
    plot_image_id_pipeline(id_, **kwargs)
    plt.close('all')


    

In [ ]:

    

from planet4.dbscan import DBScanner
from planet4 import markings, plotting, catalog_production


    

In [ ]:

    

dbscan = DBScanner()


    

In [ ]:

    

dbscan.cluster_and_plot('1cl', kind='fan')


    

In [ ]:

    

data = dbscan.p4id.filter_data('fan')


    

In [ ]:

    

fan_noise = data.loc[dbscan.noise[0]]


    

In [ ]:

    

xy = fan_noise[['x','y']].values


    

In [ ]:

    

rm = catalog_production.ReleaseManager('v1.0')


    

In [ ]:

    

%matplotlib ipympl


    

In [ ]:

    

plt.close('all')


    

In [ ]:

    

fig, ax = plt.subplots(ncols=2, figsize=(8,3))
plotting.plot_raw_fans('1cl', ax=ax[0])
plotting.plot_clustered_markings('1cl', 'fan', datapath=rm.savefolder, ax=ax[1])
ax[1].plot(xy[:,0], xy[:,1], 'o', 
           markerfacecolor='white',
           markeredgecolor='black', 
           markeredgewidth=0.2,
           markersize=3)
for axis in ax:
    axis.set_aspect('equal')
ax[0].set_title("Raw markings before clustering")
ax[1].set_title("Clustered and noise markings")
fig.savefig("/Users/klay6683/Dropbox/src/p4_paper1/figures/fans_clustered.png",
            dpi=150)


    

In [ ]:

    

import seaborn as sns
sns.set_context('paper')


    

In [ ]:

    

fig, axes = plt.subplots(ncols=2)
markings.ImageID('1cl').show_subframe(ax=axes[0])
plotting.plot_raw_fans('1cl', ax=axes[1])
id_ = 'APF00001cl'
axes[0].set_title(f"{id_}, original input")
axes[1].set_title(f"{id_}, Planet Four fan markings")
for ax in axes:
    ax.set_aspect('equal')
fig.tight_layout()
fig.savefig("/Users/klay6683/Dropbox/src/p4_paper1/figures/fan_markings.png",
            dpi=150)


    

In [ ]:

    

p4id = markings.ImageID('1cl')


    

In [ ]:

    

p4id.n_marked_classifications


    

In [ ]:

    

plotting.plot_image_id_pipeline('1cl', datapath=rm.savefolder,
                                figsize=(8,4))
plt.savefig("/Users/klay6683/Dropbox/src/p4_paper1/figures/P4_pipeline.png",
            dpi=150)


    

In [ ]:

    

import matplotlib as mpl


    

In [ ]:

    

time = np.arange('2005-02-01', '2005-02-02', dtype='datetime64[h]')

plt.plot(time)


    

In [ ]:

    

time.shape


    

In [ ]: