def get_updates(self):
es = self.es
index = self.index
xname, xmin, xmax, xbins = self.xname, self.xmin, self.xmax, self.xbins
yname, ymin, ymax, ybins = self.yname, self.ymin, self.xmax, self.xbins
x, y, c = get_2d_hist(xname, xmin, xmax, xbins, yname, ymin, ymax, ybins, es, index=index)
x, y, c = map(np.array, [x, y, c])
# compute color codes using the given cmap
cmap = self.cmap
norm = mpl.colors.Normalize(vmin=0, vmax=np.percentile(c, 90), clip=True)
mapper = cm.ScalarMappable(norm=norm, cmap=cmap)
colors_int = (mapper.to_rgba(c) * 256).astype(int)
ascode = lambda (r, g, b, a): "#{:02x}{:02x}{:02x}".format(r, g, b)
color_codes = np.apply_along_axis(ascode, -1, colors_int)
quads_renderer = self.fig.select(dict(name="quads"))[0]
quads_ds = quads_renderer.data_source
quads_ds.data["fill_color"] = color_codes
quads_ds.data["line_color"] = color_codes
return [quads_ds]
def draw_2d_hist_from_es(xname,xmin,xmax,xbins,
yname,ymin,ymax,ybins,
es,index="*",ax = None):
"""i plot the 2d histogram (heatmap) of a variables xname and yname
between [xmin,xmax],[ymin,ymax] with [xbins,ybins] uniform bins respectively.
i require es to be an elasticsearch.Elasticsearch client"""
if ax is None:
ax = figure()
x,y,c = get_2d_hist(xname,xmin,xmax,xbins,
yname,ymin,ymax,ybins,
es,index=index)
bin_w = (xmax-xmin)/float(xbins)
bin_h = (ymax-ymin)/float(ybins)
x = map(lambda x_i: int(x_i/bin_w), x)
y = map(lambda y_i: int(y_i/bin_h), y)
ax.x_range= brange(start=xmin,end=xmax)
ax.y_range= brange(start=xmax,end=xmin)
img = np.zeros([xbins,ybins])
img[x,y] = c
return ax.image(image=[img], x=[xmin], y=[ymax],
dw=[xmax-xmin], dh=[ymax-ymin], palette='RdYlBu11')
def draw_2d_hist_from_es(xname,xmin,xmax,xbins,
yname,ymin,ymax,ybins,
es,index="*",ax = None,
cmap = cm.gist_earth,quad_name="quads"):
"""i plot the 2d histogram (heatmap) of a variables xname and yname
between [xmin,xmax],[ymin,ymax] with [xbins,ybins] uniform bins respectively.
i require es to be an elasticsearch.Elasticsearch client"""
x,y,c = get_2d_hist(xname,xmin,xmax,xbins,
yname,ymin,ymax,ybins,
es,index=index)
x,y,c = map(np.array,[x,y,c])
ax = classic_heatmap(x,y,c,xmin,xmax,xbins,ymin,ymax,ybins,
fig=ax,cmap=cmap,quad_name=quad_name)
return ax
def __init__(self, xname, xmin, xmax, xbins, yname, ymin, ymax, ybins, es, index="run*", fig=None, cmap=cm.RdYlBu):
if fig is None:
fig = figure()
self.es = es
self.index = index
self.xname, self.xmin, self.xmax, self.xbins = xname, xmin, xmax, xbins
self.yname, self.ymin, self.ymax, self.ybins = yname, ymin, ymax, ybins
# get first histogram
x, y, c = get_2d_hist(xname, xmin, xmax, xbins, yname, ymin, ymax, ybins, es, index=index)
x, y, c = map(np.array, [x, y, c])
self.cmap = cmap
self.fig = vis_bokeh.classic_heatmap(x, y, c, xmin, xmax, xbins, ymin, ymax, ybins, fig=fig, cmap=cmap)
