def icons(users, distance):
"""Visualization using user profile images as the points."""
# It would be pretty cool to put user thumbails where points are.
# but i'm still not sure how to do this yet.
images = []
try:
print 'getting images..'
for p in users:
print p
f = p.image
img = imread('image.tmp')
images.append(img)
except Exception as e:
print 'got an error...'
import traceback
etype, evalue, tb = sys.exc_info()
print yellow % '\n'.join(traceback.format_exception(etype, evalue, tb))
ip()
(W, H, _) = shape(img) # thumbnails should all be the same size
count = len(images)
pl.figure()
P2, _ = mds(distance, 2)
X, Y = P2[:, 0], P2[:, 1]
## XXX: not a great transformation b/c we might stretch more in one dimension
def N(x):
"force x to fit in interval [0,1]"
x = (x - x.min())
x = x / x.max()
assert all(x >= 0) and all(x <= 1)
return x
X = N(X) * 475
Y = N(Y) * 425
figimages = [
pl.figimage(img, xo=x, yo=y) for img, x, y in zip(images, X, Y)
]
def icons(users, distance):
"""Visualization using user profile images as the points."""
# It would be pretty cool to put user thumbails where points are.
# but i'm still not sure how to do this yet.
images = []
try:
print 'getting images..'
for p in users:
print p
f = p.image
img = imread('image.tmp')
images.append(img)
except Exception as e:
print 'got an error...'
import traceback
etype, evalue, tb = sys.exc_info()
print yellow % '\n'.join(traceback.format_exception(etype, evalue, tb))
ip()
(W, H, _) = shape(img) # thumbnails should all be the same size
count = len(images)
pl.figure()
P2, _ = mds(distance, 2)
X,Y = P2[:,0], P2[:,1]
## XXX: not a great transformation b/c we might stretch more in one dimension
def N(x):
"force x to fit in interval [0,1]"
x = (x - x.min())
x = x / x.max()
assert all(x >= 0) and all(x <= 1)
return x
X = N(X)*475
Y = N(Y)*425
figimages = [pl.figimage(img, xo=x, yo=y) for img, x, y in zip(images, X, Y)]
def main(usecached=True):
if usecached and path.exists(tweet_cache):
(users, distance) = pickle.load(file(tweet_cache, 'r'))
else:
sets = example_data()
users = list(reduce(set.union, sets))
N = len(users)
distance = zeros((N, N))
for (i, xi) in enumerate(users):
for (j, xj) in enumerate(users):
distance[i,j] = xi.similarity(xj)
pickle.dump((users, distance), file(tweet_cache, 'wb'))
pl.figure()
pl.grid(True)
labels = [u.username for u in users]
[Y, _] = mds(distance)
print users
X = pandas.DataFrame({
'x': Y[:,0],
'y': Y[:,1],
'user': labels,
'obj': users,
})
sct = pl.scatter(Y[:,0], Y[:,1], s=30*np.ones_like(users), c='b', lw=0)
for user, x, y in zip(labels, Y[:,0], Y[:,1]):
pl.text(x, y, user, fontsize=7)
pl.grid(False)
threshold = 0.05
for i in xrange(len(users)):
for j in xrange(i + 1, len(users)):
x = Y[[i,j], 0]
y = Y[[i,j], 1]
if distance[i,j] >= threshold:
pl.plot(x, y, lw=10.0*distance[i,j], alpha=distance[i,j], c='k')
if 0:
# TODO: I want points to resize according proportional to similarity to
# selected point. I'm having trouble getting matplotlib to do this for
# me -- such an inconsistent API!
def callback(br, m):
_ = users, sct # FIXME: ip only seems to take local and global; sct is neither..
newsizes = np.array(map(m.ix[0].similarity, users)) * 30
#s = sct.get_sizes()
#s[:] = newsizes
sct._sizes = newsizes
ip()
b = PointBrowser(X, ax=sct.get_axes(), callback=callback)
else:
def callback(m):
if m.empty:
return
print '***********************************'
common_words = reduce(set.intersection, m['obj'].map(lambda x: x.features))
print common_words
print '***********************************'
b = LassoBrowser(X, ax=sct.get_axes(), callback=callback)
#mds_plot2D(labels, distance)
#mds_plot3D(labels, distance)
if 'icons' in sys.argv:
icons(users, distance)
b.ax.figure.set_facecolor('white')
b.ax.set_axis_off()
# pl.ion()
pl.show()
def main(usecached=True):
if usecached and path.exists(tweet_cache):
(users, distance) = pickle.load(file(tweet_cache, 'r'))
else:
sets = example_data()
users = list(reduce(set.union, sets))
N = len(users)
distance = zeros((N, N))
for (i, xi) in enumerate(users):
for (j, xj) in enumerate(users):
distance[i, j] = xi.similarity(xj)
pickle.dump((users, distance), file(tweet_cache, 'wb'))
pl.figure()
pl.grid(True)
labels = [u.username for u in users]
[Y, _] = mds(distance)
print users
X = pandas.DataFrame({
'x': Y[:, 0],
'y': Y[:, 1],
'user': labels,
'obj': users,
})
sct = pl.scatter(Y[:, 0], Y[:, 1], s=30 * np.ones_like(users), c='b', lw=0)
for user, x, y in zip(labels, Y[:, 0], Y[:, 1]):
pl.text(x, y, user, fontsize=7)
pl.grid(False)
threshold = 0.05
for i in xrange(len(users)):
for j in xrange(i + 1, len(users)):
x = Y[[i, j], 0]
y = Y[[i, j], 1]
if distance[i, j] >= threshold:
pl.plot(x,
y,
lw=10.0 * distance[i, j],
alpha=distance[i, j],
c='k')
if 0:
# TODO: I want points to resize according proportional to similarity to
# selected point. I'm having trouble getting matplotlib to do this for
# me -- such an inconsistent API!
def callback(br, m):
_ = users, sct # FIXME: ip only seems to take local and global; sct is neither..
newsizes = np.array(map(m.ix[0].similarity, users)) * 30
#s = sct.get_sizes()
#s[:] = newsizes
sct._sizes = newsizes
ip()
b = PointBrowser(X, ax=sct.get_axes(), callback=callback)
else:
def callback(m):
if m.empty:
return
print '***********************************'
common_words = reduce(set.intersection,
m['obj'].map(lambda x: x.features))
print common_words
print '***********************************'
b = LassoBrowser(X, ax=sct.get_axes(), callback=callback)
#mds_plot2D(labels, distance)
#mds_plot3D(labels, distance)
if 'icons' in sys.argv:
icons(users, distance)
b.ax.figure.set_facecolor('white')
b.ax.set_axis_off()
# pl.ion()
pl.show()
