def create_plot():
(candidatos, porcentajes) = get_plot_data()
# (candidatos, porcentajes) = json_load_unicode("databokeh.json")
primero, segundo = porcentajes[:2]
falta = min(45, max(40 , (segundo + 10))) - primero
if falta <= 0:
sobra = -falta
fserie = [sobra] + [0] * 5
porcentajes[0] -= sobra
resultado = "Gana %s en Primera Vuelta" % candidatos[0]
PALETTE = ["#54BBE2", "#5EDF4D"]
data = OrderedDict([("porcentajes", porcentajes), ("sobra", fserie)])
else:
PALETTE = ["#54BBE2", "#E5162A"]
fserie = [falta] + [0] * 5
resultado = "Hay Ballotage"
data = OrderedDict([("porcentajes", porcentajes), ("falta", fserie)])
# build a dict containing the grouped data
output_file("primervueltometro.html")
# Flyer derecho fijo : 300 x 500
# Zonas de columna auxiliar : 300 x 300 (1er y 2do rolado)
# Puente largo : 975 x 80
# Puente corto : 650 x 80
width, height = 300, 300
z = 2
width *= z
height *= z
params = {
"title": resultado,
"stacked": True,
"width": width,
"height": height,
"tools": "",
"palette": PALETTE,
"legend": 'top_right'
}
bar = Bar(data, candidatos, **params)
show(bar)
def create_plot():
candidatos, (porcentajes, falta) = get_plot_data()
if falta <= 0:
sobra = -falta
fserie = [sobra] + [0] * 5
porcentajes[0] -= sobra
resultado = "Gana %s en Primera Vuelta" % candidatos[0]
data = OrderedDict([("porcentajes", porcentajes), ("sobra", fserie)])
else:
fserie = [falta] + [0] * 5
resultado = "Hay Ballotage"
data = OrderedDict([("porcentajes", porcentajes), ("falta", fserie)])
# build a dict containing the grouped data
output_file("primervueltometro.html")
# Flyer derecho fijo : 300 x 500
# Zonas de columna auxiliar : 300 x 300 (1er y 2do rolado)
# Puente largo : 975 x 80
# Puente corto : 650 x 80
width, height = 300, 300
z = 2
width *= z
height *= z
params = {
"title": "PrimerVueltómetro: %s" % resultado,
"stacked": True,
"width": width,
"height": height,
"tools": "",
"palette": PALETTE,
"legend": 'top_right'
}
bar = Bar(data, candidatos, **params)
show(bar)
def make_bar(color, data):
plot = Bar(data, width=200, height=200, palette=[color, COLOR_PRIMARY_DARK], tools='', stacked=True)
plot.toolbar_location = None
plot.outline_line_color = None
plot.min_border = 5
plot.y_range = Range1d(0, 10)
# Get chart items
legend = plot.select({'type': Legend})
hover = plot.select({'type': HoverTool})
glyphs = plot.select({'type': GlyphRenderer})
xaxis = plot.select({'type': CategoricalAxis})
yaxis = plot.select({'type': LinearAxis})
ygrid = plot.select({'type': Grid})
# Format chart properties
plot.toolbar_location = None
plot.background_fill = COLOR_PRIMARY
plot.border_fill = COLOR_PRIMARY
plot.outline_line_color = None
plot.min_border_top = 0
# Format legent
legend.label_text_color = COLOR_PRIMARY_CONTRAST
legend.border_line_color = COLOR_PRIMARY_CONTRAST
# Tweak hover
hover.tooltips = [('hours', '$y')]
hover.point_policy = 'follow_mouse'
# Format plots
for g in glyphs:
g.glyph.fill_alpha = 1
g.glyph.line_color = None
# Set xaxis properties
xaxis.major_label_text_color = COLOR_PRIMARY_CONTRAST
xaxis.major_label_orientation = 0
xaxis.major_label_standoff = 15
xaxis.major_tick_out = None
xaxis.major_tick_in = None
xaxis.axis_line_color = None
# Set yaxis properties
yaxis.major_label_text_color = COLOR_PRIMARY_CONTRAST
yaxis.major_tick_out = None
yaxis.major_tick_in = None
yaxis.axis_line_color = None
yaxis.ticker = SingleIntervalTicker(interval=3)
ygrid.grid_line_color = None
return plot
def bokehplot():
try:
rundate = str(
subprocess.check_output(
'ls ' + logfilepath +
'| grep * | grep -o "[0-9]\+" | cut -c 1-8',
shell=True))
except:
rundate = str(time.strftime("%Y%m%d"))
path = logfilepath + '/*.log'
files = glob.glob(path)
allduration = []
allstatus = []
allstart = []
allend = []
onlyfilenamelist = []
for file in files:
head, onlyfilename = os.path.split(file)
onlyfilename = onlyfilename[:-13]
onlyfilenamelist.append(onlyfilename)
try:
# dont think we need complete
complete = subprocess.check_output(
'tail -8 ' + file +
' | grep -o "Query complete:.*" | cut -f 2 -d ":" ',
shell=True)
duration = subprocess.check_output(
'tail -8 ' + file +
' | grep -o "Duration.*" | cut -f 2 -d ":" ',
shell=True)
status = subprocess.check_output(
'tail -8 ' + file + ' | grep -o "Status.*" | cut -f 2 -d ":" ',
shell=True)
starttime = subprocess.check_output(
'tail -8 ' + file + '| grep -e Start' +
'| awk -F "Start Time:"' + " '{print $2}' ",
shell=True)
endtime = subprocess.check_output(
'tail -8 ' + file + '| grep -e End' + '| awk -F "End Time:"' +
" '{print $2}' ",
shell=True)
allduration.append(duration)
allstatus.append(status)
allstart.append(starttime)
allend.append(endtime)
except:
pass
cleanupalllines = map(lambda s: s.strip(), onlyfilenamelist)
cleanupduration = map(lambda s: s.strip(), allduration)
cleanupstatus = map(lambda s: s.strip(), allstatus)
cleanupstart = map(lambda s: s.strip(), allstart)
cleanupend = map(lambda s: s.strip(), allend)
sumd = 0
floatallduration = []
for item in cleanupduration:
try:
floatallduration.append(float(item))
sumd += float(item)
except:
pass
zipper = zip(cleanupalllines, floatallduration, cleanupstatus,
cleanupstart, cleanupend)
df = pd.DataFrame(zipper)
df = df.rename(
columns={
0: 'QUERY',
1: 'DURATION(MINS)',
2: 'STATUS',
3: 'START TIME',
4: 'END TIME'
})
df['Col5'] = df['QUERY'].map(lambda x: str(x)[-8:])
df['QUERY'] = df['QUERY'].map(lambda x: str(x)[:-13])
df = df.rename(columns={'Col5': 'RunDate'})
df = df.sort_values(by='QUERY', ascending=True)
df = df[df['STATUS'].isin(["SUCCESS"])]
pivot = pd.pivot_table(df.reset_index(),
index='QUERY',
columns='RunDate',
values='DURATION(MINS)')
pivot = pivot.fillna(0)
TOOLS = 'crosshair,pan,wheel_zoom,box_zoom,reset,hover,previewsave'
p = Bar(pivot,
xlabel='QUERY',
stacked=True,
title="Log Query Comparison",
legend='top_right',
width=1100,
height=800,
tools=TOOLS)
hover = p.select(dict(type=HoverTool))
script, div = components(p, INLINE)
# output_file("bar.html")
# show(p)
html = render_template("bokeh.html", script=script, div=div)
return encode_utf8(html)
def bokehplot():
try:
rundate = str(subprocess.check_output(
'ls ' + logfilepath + '| grep * | grep -o "[0-9]\+" | cut -c 1-8',
shell=True))
except:
rundate = str(time.strftime("%Y%m%d"))
path = logfilepath + '/*.log'
files = glob.glob(path)
allduration = []
allstatus = []
allstart = []
allend = []
onlyfilenamelist = []
for file in files:
head, onlyfilename = os.path.split(file)
onlyfilename = onlyfilename[:-13]
onlyfilenamelist.append(onlyfilename)
try:
# dont think we need complete
complete = subprocess.check_output('tail -8 ' + file + ' | grep -o "Query complete:.*" | cut -f 2 -d ":" ',
shell=True)
duration = subprocess.check_output('tail -8 ' + file + ' | grep -o "Duration.*" | cut -f 2 -d ":" ',
shell=True)
status = subprocess.check_output('tail -8 ' + file + ' | grep -o "Status.*" | cut -f 2 -d ":" ',
shell=True)
starttime = subprocess.check_output(
'tail -8 ' + file + '| grep -e Start' + '| awk -F "Start Time:"' + " '{print $2}' ", shell=True)
endtime = subprocess.check_output(
'tail -8 ' + file + '| grep -e End' + '| awk -F "End Time:"' + " '{print $2}' ", shell=True)
allduration.append(duration)
allstatus.append(status)
allstart.append(starttime)
allend.append(endtime)
except:
pass
cleanupalllines = map(lambda s: s.strip(), onlyfilenamelist)
cleanupduration = map(lambda s: s.strip(), allduration)
cleanupstatus = map(lambda s: s.strip(), allstatus)
cleanupstart = map(lambda s: s.strip(), allstart)
cleanupend = map(lambda s: s.strip(), allend)
sumd = 0
floatallduration = []
for item in cleanupduration:
try:
floatallduration.append(float(item))
sumd += float(item)
except:
pass
zipper = zip(cleanupalllines, floatallduration, cleanupstatus, cleanupstart, cleanupend)
df = pd.DataFrame(zipper)
df = df.rename(columns={0: 'QUERY', 1: 'DURATION(MINS)', 2: 'STATUS', 3: 'START TIME', 4: 'END TIME'})
df['Col5'] = df['QUERY'].map(lambda x: str(x)[-8:])
df['QUERY'] = df['QUERY'].map(lambda x: str(x)[:-13])
df = df.rename(columns={'Col5': 'RunDate'})
df = df.sort_values(by='QUERY', ascending=True)
df = df[df['STATUS'].isin(["SUCCESS"])]
pivot = pd.pivot_table(df.reset_index(), index='QUERY', columns='RunDate', values='DURATION(MINS)')
pivot = pivot.fillna(0)
TOOLS = 'crosshair,pan,wheel_zoom,box_zoom,reset,hover,previewsave'
p = Bar(pivot, xlabel='QUERY', stacked=True, title="Log Query Comparison", legend='top_right', width=1100,
height=800, tools=TOOLS)
hover = p.select(dict(type=HoverTool))
script, div = components(p, INLINE)
# output_file("bar.html")
# show(p)
html = render_template("bokeh.html", script=script, div=div)
return encode_utf8(html)
from collections import OrderedDict
import pandas as pd
from bokeh._legacy_charts import Bar, output_file, show
from bokeh.sampledata.olympics2014 import data
df = pd.io.json.json_normalize(data['data'])
# filter by countries with at least one medal and sort
df = df[df['medals.total'] > 0]
df = df.sort("medals.total", ascending=False)
# get the countries and we group the data by medal type
countries = df.abbr.values.tolist()
gold = df['medals.gold'].astype(float).values
silver = df['medals.silver'].astype(float).values
bronze = df['medals.bronze'].astype(float).values
# build a dict containing the grouped data
medals = OrderedDict(bronze=bronze, silver=silver, gold=gold)
# any of the following commented are also alid Bar inputs
#medals = pd.DataFrame(medals)
#medals = list(medals.values())
output_file("stacked_bar.html")
bar = Bar(medals, countries, title="Stacked bars", stacked=True)
show(bar)
