In [1]:

    

%matplotlib inline

import json
import os
os.environ['XPA_METHOD'] = 'local'
import numpy as np

from bubbly.viewer import BubblyViewer
from bubbly.extractors import RGBExtractor
from bubbly.cluster import merge, xmatch
import matplotlib.pyplot as plt
import matplotlib
matplotlib.rcParams['axes.grid'] = False


    

    




DEBUG:Cloud:Log file (/Users/beaumont/.picloud/cloud.log) opened

In [9]:

    

bv = BubblyViewer()
bv.load_longitude(305)


    

In [2]:

    

import h5py
with h5py.File('../data/full_search/305.h5', 'r') as infile:
    stamps = infile['stamps'][...]
    scores = infile['scores'][...]
    
mask = np.isfinite(scores)
stamps = stamps[mask]
scores = scores[mask]

rank = np.argsort(scores)[::-1]
lohi = np.argsort(scores)


    

In [37]:

    

#data = json.load(open('../models/l305_scores.json'))

#jstamps = np.array(data['stamps'])
#jscores = np.array(data['scores'])

#jmask = (np.abs(jstamps[:, 1] - 305) < 0.5) & np.isfinite(jscores)
#jstamps = jstamps[jmask]
#jscores = jscores[jmask]

#jrank = np.argsort(scores)[::-1]
#jlohi = np.argsort(scores)


    

In [3]:

    

plt.hist(scores, histtype='step', bins=np.linspace(-1, 1, 15))
plt.yscale('log')

plt.xlabel("Scores")
plt.ylabel("N")


    

    
Out[3]:





<matplotlib.text.Text at 0x108478910>






    

In [4]:

    

ex = RGBExtractor()
ex.shp = (100, 100)

ss = np.linspace(1, -1, 20)
ss += np.random.random(ss.size) * .01
inds = lohi[np.minimum(np.searchsorted(scores[lohi], ss), lohi.size-1)]

ims = [ex.extract(*stamps[i]) for i in inds]

nx = 5
ny = int(np.ceil(inds.size / 5))

im = np.vstack(np.hstack(ims[i:i+nx]) for i in range(0, inds.size, nx))

plt.figure(figsize=(15, 15))
plt.imshow(im, origin='upper')

opts = dict(color = '#0000ff', ha='center', va='center', fontsize=18)
for i in range(inds.size):
    x = (i % nx + 0.5) * ex.shp[0]
    y = (i / nx + 0.5) * ex.shp[0]    
    plt.annotate("%0.2f" % scores[inds[i]], xy = (x, y), **opts)


    

    




Loading a new field at l=305
DEBUG:bubbly.field:Loading a new field at l=305






    

In [17]:

    

from bubbly.field import get_field
from bubbly.util import scale
from bubbly.dr1 import bubble_params

f = get_field(305)
g = scale(f.i4[::7, ::7])
r = scale(f.mips[::7, ::7])

b = r * 0
im = np.dstack((r, g, b))

def plot_stamps(stamps, **kwargs):
    kwargs.setdefault('facecolor', 'none')
    kwargs.setdefault('edgecolor', 'b')
    kwargs.setdefault('alpha', .1)
    
    ax = plt.gca()
    for s in stamps:
        r = plt.Rectangle((s[1] - s[-1], s[2] - s[-1]), width = 2 * s[-1], height = 2 * s[-1], **kwargs)
        ax.add_patch(r)

mwp = [b for b in bubble_params() if np.abs(b[1] - 305) <= .5]

unmerged = stamps[scores > .2]
merged, mscores = merge(unmerged, scores[scores > 0.2])


    

In [18]:

    

plt.figure(figsize=(10, 10), dpi=100)
plt.imshow(im, extent=[306, 304, -1, 1])
plt.axvline(304.5)
plt.axvline(305.5)
plot_stamps(unmerged)
plot_stamps(merged, edgecolor='r', alpha=1)
plt.xlim(306, 304)
plt.ylim(-1, 1)
plt.gca().set_aspect('equal')


    

In [19]:

    

match = xmatch(merged, mwp)
print 'Bubbly -> mwp: (%i)' % ((match >= 0).sum())
for i, j in enumerate(match):    
    if j < 0:
        continue
    print '%i -> %i' % (i, j)
    
print 'Unmatched Bubbly (%i / %i)' % ((match == -1).sum(), merged.shape[0])
print np.where(match == -1)[0].tolist()

print 'Unmatched mwp (%i / %i)' % (len(mwp) - (match >= 0).sum(), len(mwp))
z = np.zeros(len(mwp))
z[match] = 1
print np.where(z == 0)[0].tolist()


    

    




Bubbly -> mwp: (8)
0 -> 36
3 -> 24
5 -> 4
6 -> 1
10 -> 3
13 -> 0
14 -> 15
15 -> 21
Unmatched Bubbly (9 / 17)
[1, 2, 4, 7, 8, 9, 11, 12, 16]
Unmatched mwp (40 / 48)
[2, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 22, 23, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46]

In [10]:

    

#bubbly in blue, MWP in red
bv.clear()
bv.outline(merged, color='red')
bv.outline(mwp)


    

In [20]:

    

order = np.argsort(mscores)[::-1]
for m, s in zip(merged[order], mscores[order]):
    plt.imshow(ex.extract(*m))
    plt.title(s)
    plt.show()


    

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