def increase_coverage(upto=5000):
"""Save `upto` unprocessed San Francisco tags"""
from more_query import get_top_tags
sup = persistent.load_var('supported')
more = get_top_tags(upto, 'nsf_tag.dat')
already = [v[0] for v in sup]
addition = set(more).difference(set(already))
persistent.save_var('addition', addition)
#! /usr/bin/python2
# vim: set fileencoding=utf-8
import more_query as mq
if __name__ == '__main__':
sf = mq.get_top_tags(300)
us = mq.get_top_tags(300, 'us_tags.dat')
ca = mq.get_top_tags(300, 'ca_tags.dat')
sfca = [s for s in sf if s in ca]
sfus = [s for s in sf if s in us]
sfusca = [s for s in sfca if s in us]
import matplotlib.colors as colors
matplotlib.use('PDF')
import matplotlib.pyplot as plt
plt.figure(dpi=200)
H = read_entropy()
colormap_ = cm.ScalarMappable(
colors.Normalize(min(H.values()), max(H.values())), 'Blues')
font0 = FontProperties()
pos = {'horizontalalignment': 'center', 'verticalalignment': 'center'}
font0.set_size(1.4)
for w, c in zip(words, coords):
plt.plot(c[0], c[1], '.', ms=5, c=to_css_hex(colormap_.to_rgba(H[w])))
if label:
plt.text(c[0],
c[1],
w,
fontproperties=font0,
rotation=randint(-28, 28),
**pos)
plt.axis('off')
plt.savefig('red.pdf', format='pdf', transparent=True)
if __name__ == '__main__':
import more_query as mq
data = mq.sio.loadmat('MDS1.mat')['A']
words = mq.get_top_tags(mq.np.size(data, 0), 'nsf_tag.dat')
coords = [(p[0], p[1]) for p in data]
tag_cloud(words, coords, True)
bx, by, tx, ty = [float(v) for v in request.POST.getlist('coords[]')]
long_step = (bbox[3] - bbox[1]) / k
lat_step = (bbox[2] - bbox[0]) / k
x0 = bbox[1]
y0 = bbox[0]
bx, tx = (bx, tx) if tx > bx else (tx, bx)
by, ty = (by, ty) if ty > by else (ty, by)
return '\n'.join([
u'{}: {:.4f}'.format(t, float(v)) for t, v in get_best_tags(
box(x0 + bx * long_step, y0 + by * lat_step, x0 +
tx * long_step, y0 + ty * lat_step))
])
# for t, v in get_best_tags(Point(x0+x*long_step,
# y0+y*lat_step))])
@post('/cover')
def cover():
n = int(request.POST.get('n'))
w = int(request.POST.get('w'))
h = int(request.POST.get('h'))
overlap = request.POST.get('overlap') == 'true'
return js_some(d, n, w, h, overlap=overlap)
t = persistent.load_var('disc/all')
top = get_top_tags(500, 'nsf_tag.dat')
supported = [v[0] for v in persistent.load_var('supported')][:600]
d = top_discrepancy(t, supported)
run(host='localhost', port=8080, debug=True, reloader=True)
from matplotlib.font_manager import FontProperties
import matplotlib
import matplotlib.cm as cm
import matplotlib.colors as colors
matplotlib.use("PDF")
import matplotlib.pyplot as plt
plt.figure(dpi=200)
H = read_entropy()
colormap_ = cm.ScalarMappable(colors.Normalize(min(H.values()), max(H.values())), "Blues")
font0 = FontProperties()
pos = {"horizontalalignment": "center", "verticalalignment": "center"}
font0.set_size(1.4)
for w, c in zip(words, coords):
plt.plot(c[0], c[1], ".", ms=5, c=to_css_hex(colormap_.to_rgba(H[w])))
if label:
plt.text(c[0], c[1], w, fontproperties=font0, rotation=randint(-28, 28), **pos)
plt.axis("off")
plt.savefig("red.pdf", format="pdf", transparent=True)
if __name__ == "__main__":
import more_query as mq
data = mq.sio.loadmat("MDS1.mat")["A"]
words = mq.get_top_tags(mq.np.size(data, 0), "nsf_tag.dat")
coords = [(p[0], p[1]) for p in data]
tag_cloud(words, coords, True)
# y = float(request.POST.get('y'))
bx, by, tx, ty = [float(v) for v in request.POST.getlist('coords[]')]
long_step = (bbox[3] - bbox[1])/k
lat_step = (bbox[2] - bbox[0])/k
x0 = bbox[1]
y0 = bbox[0]
bx, tx = (bx, tx) if tx > bx else (tx, bx)
by, ty = (by, ty) if ty > by else (ty, by)
return '\n'.join([u'{}: {:.4f}'.format(t, float(v))
for t, v in get_best_tags(box(x0+bx*long_step,
y0+by*lat_step,
x0+tx*long_step,
y0+ty*lat_step))])
# for t, v in get_best_tags(Point(x0+x*long_step,
# y0+y*lat_step))])
@post('/cover')
def cover():
n = int(request.POST.get('n'))
w = int(request.POST.get('w'))
h = int(request.POST.get('h'))
overlap = request.POST.get('overlap') == 'true'
return js_some(d, n, w, h, overlap=overlap)
t = persistent.load_var('disc/all')
top = get_top_tags(500, 'nsf_tag.dat')
supported = [v[0] for v in persistent.load_var('supported')][:600]
d = top_discrepancy(t, supported)
run(host='localhost', port=8080, debug=True, reloader=True)
