def plot_regions(regions, bbox, tag):
"""Output one shapefile for each region (represented by its bottom left and
upper right index in the grid) with color depending of its discrepancy."""
# TODO not unicode safe
discrepancies = [v[0] for v in regions]
colormap = cm.ScalarMappable(
mcolor.Normalize(min(discrepancies), max(discrepancies)), 'YlOrBr')
schema = {'geometry': 'Polygon', 'properties': {}}
style = []
KARTO_CONFIG['bounds']['data'] = [BBOX[1], BBOX[0], BBOX[3], BBOX[2]]
polys = [{'geometry': mapping(r[1]), 'properties': {}} for r in regions]
for i, r in enumerate(regions):
color = to_css_hex(colormap.to_rgba(r[0]))
name = u'disc_{}_{:03}'.format(tag, i + 1)
KARTO_CONFIG['layers'][name] = {'src': name + '.shp'}
color = 'red'
style.append(CSS.format(name, color, 'black'))
# style.append(CSS.format(name, color, color))
with fiona.collection(mkpath('disc', name + '.shp'), "w",
"ESRI Shapefile", schema) as f:
f.writerecords(polys)
break
with open(mkpath('disc', 'photos.json'), 'w') as f:
json.dump(KARTO_CONFIG, f)
with open(mkpath('disc', 'photos.css'), 'w') as f:
f.write('\n'.join(style))
def tag_cloud(words, coords, label):
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)
def plot_regions(regions, bbox, tag):
"""Output one shapefile for each region (represented by its bottom left and
upper right index in the grid) with color depending of its discrepancy."""
# TODO not unicode safe
discrepancies = [v[0] for v in regions]
colormap = cm.ScalarMappable(mcolor.Normalize(min(discrepancies),
max(discrepancies)),
'YlOrBr')
schema = {'geometry': 'Polygon', 'properties': {}}
style = []
KARTO_CONFIG['bounds']['data'] = [BBOX[1], BBOX[0],
BBOX[3], BBOX[2]]
polys = [{'geometry': mapping(r[1]), 'properties': {}} for r in regions]
for i, r in enumerate(regions):
color = to_css_hex(colormap.to_rgba(r[0]))
name = u'disc_{}_{:03}'.format(tag, i+1)
KARTO_CONFIG['layers'][name] = {'src': name+'.shp'}
color = 'red'
style.append(CSS.format(name, color, 'black'))
# style.append(CSS.format(name, color, color))
with fiona.collection(mkpath('disc', name+'.shp'),
"w", "ESRI Shapefile", schema) as f:
f.writerecords(polys)
break
with open(mkpath('disc', 'photos.json'), 'w') as f:
json.dump(KARTO_CONFIG, f)
with open(mkpath('disc', 'photos.css'), 'w') as f:
f.write('\n'.join(style))
def plot_polygons(bucket, tag, nb_inter, minv, maxv):
schema = {'geometry': 'Polygon', 'properties': {}}
colormap_ = matplotlib.cm.ScalarMappable(cmap='YlOrBr')
vrange = np.linspace(minv, maxv, nb_inter)
colormap = [to_css_hex(c) for c in colormap_.to_rgba(vrange)]
style = []
for i in range(nb_inter):
if len(bucket[i]) > 0:
name = u'{}_freq_{:03}'.format(tag, i + 1)
KARTO_CONFIG['layers'][name] = {'src': name + '.shp'}
style.append(CSS.format(name, colormap[i], colormap[i]))
with fiona.collection(name + '.shp', "w", "ESRI Shapefile",
schema) as f:
f.writerecords(bucket[i])
with open('photos.json', 'w') as f:
json.dump(KARTO_CONFIG, f)
with open('photos.css', 'w') as f:
f.write('\n'.join(style))
def plot_polygons(bucket, tag, nb_inter, minv, maxv):
schema = {'geometry': 'Polygon', 'properties': {}}
colormap_ = matplotlib.cm.ScalarMappable(cmap='YlOrBr')
vrange = np.linspace(minv, maxv, nb_inter)
colormap = [to_css_hex(c) for c in colormap_.to_rgba(vrange)]
style = []
for i in range(nb_inter):
if len(bucket[i]) > 0:
name = u'{}_freq_{:03}'.format(tag, i+1)
KARTO_CONFIG['layers'][name] = {'src': name+'.shp'}
style.append(CSS.format(name, colormap[i], colormap[i]))
with fiona.collection(name+'.shp', "w",
"ESRI Shapefile", schema) as f:
f.writerecords(bucket[i])
with open('photos.json', 'w') as f:
json.dump(KARTO_CONFIG, f)
with open('photos.css', 'w') as f:
f.write('\n'.join(style))
def tag_cloud(words, coords, label):
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)