def selection_change(self, obj, attrname, old, new):
#self.make_brand_plot
#self.set_children()
#curdoc().add(self)
bdf = self.brand_df
min_idx, max_idx = shoes_func.min_max_range(ranges, bdf['price'])
self.configure_brand_source(min_idx, max_idx)
self.make_brand_source()
self.make_brand_plot(min_idx, max_idx)
self.set_children()
curdoc().add(self)
def selection_change(self, obj, attrname, old, new):
#self.make_brand_plot
#self.set_children()
#curdoc().add(self)
bdf = self.brand_df
min_idx, max_idx = shoes_func.min_max_range(ranges, bdf['price'])
self.configure_brand_source(min_idx, max_idx)
self.make_brand_source()
self.make_brand_plot(min_idx, max_idx)
self.set_children()
curdoc().add(self)
def configure_brand_source(self, min_idx=0, max_idx=MAX_IDX):
bdf = self.brand_df
#min_idx, max_idx = shoes_func.min_max_range(ranges, bdf['price'])
min_idx, max_idx = shoes_func.min_max_range(orig_order, bdf['price'])
counts = [0] * (max_idx - min_idx + 1)
for i in bdf.index:
ans = bdf.ix[i]
#idx = int(ans['price'] + 100)//100 + 1
idx = int(ans['price'] // 100)
print "idx is ", idx
print "price is ", ans['price']
#Categorical stuff
#xcat.append(ranges[idx-2])
#_shoedf.loc[i, 'brand_y'] = (counts[idx - min_idx] - 0.5*counts[idx - min_idx])
_shoedf.loc[i, 'brand_y'] = (counts[idx - min_idx]) + 0.5
counts[idx - min_idx] = counts[idx - min_idx] + 1
#quad stuff
#xvals.append(idx - 0.25)
#rightvals.append(idx + 0.25)
#bottoms.append(counts[idx]/2)
#tops.append(counts[idx] + 0.5)
#fills.append(brand_to_color[ans['brand']])
return None
def configure_brand_source(self, min_idx=0, max_idx=MAX_IDX):
bdf = self.brand_df
#min_idx, max_idx = shoes_func.min_max_range(ranges, bdf['price'])
min_idx, max_idx = shoes_func.min_max_range(orig_order, bdf['price'])
counts = [0] * (max_idx - min_idx + 1)
for i in bdf.index:
ans = bdf.ix[i]
#idx = int(ans['price'] + 100)//100 + 1
idx = int(ans['price']//100)
print "idx is ", idx
print "price is ", ans['price']
#Categorical stuff
#xcat.append(ranges[idx-2])
#_shoedf.loc[i, 'brand_y'] = (counts[idx - min_idx] - 0.5*counts[idx - min_idx])
_shoedf.loc[i, 'brand_y'] = (counts[idx - min_idx]) + 0.5
counts[idx - min_idx] = counts[idx - min_idx] + 1
#quad stuff
#xvals.append(idx - 0.25)
#rightvals.append(idx + 0.25)
#bottoms.append(counts[idx]/2)
#tops.append(counts[idx] + 0.5)
#fills.append(brand_to_color[ans['brand']])
return None
