def get_video_info_by_html(self):
"""
The method is using yfconfig to get information of video including title, video_id, data and thumbnail
:rtype: dict
"""
video_page = get(
f'https://www.youtube.com/channel/{self.target_id}/live')
try:
ytplayer_config = json.loads(
re.search(r'ytplayer.config\s*=\s*([^\n]+?});',
video_page).group(1))
player_response = json.loads(
ytplayer_config['args']['player_response'])
video_details = player_response['videoDetails']
# assert to verity live status
assert video_details['isLive']
title = video_details['title']
vid = video_details['videoId']
target = f"https://www.youtube.com/watch?v={vid}"
thumbnails = video_details['thumbnail']['thumbnails'][-1]['url']
return {
'Title': title,
'Ref': vid,
'Date': strftime("%Y-%m-%d", localtime(time())),
'Target': target,
'Thumbnails': thumbnails
}
except KeyError:
self.logger.exception('Get keys error')
return False
def check(self):
try:
html = get(
f'https://www.youtube.com/channel/{self.target_id}/featured')
if '"label":"LIVE NOW"' in html:
# vid = self.get_videoid_by_channel_id()
# get_live_info = self.getlive_vid(vid)
retry_num = 0
while retry_num < 3:
video_dict = self.get_video_info_by_html()
if video_dict:
break
else:
sleep(0.5)
retry_num += 1
else:
video_dict = self.get_videoid_by_channel_id(self.target_id)
video_dict['Provide'] = self.module
# 确定视频不重复
if self.vid != video_dict['Ref']:
self.vid = video_dict['Ref']
process_video(video_dict)
else:
self.logger.warning('Found A Repeated Video. Drop it')
sleep(1)
else:
if 'Upcoming live streams' in html:
self.logger.info(
f'{self.target_id}: Found A Live Upcoming')
else:
self.logger.info(f'{self.target_id}: Not found Live')
except Exception:
self.logger.exception('Check Failed')
def is_live(self):
html = get(f'https://www.openrec.tv/user/{self.target_id}')
dom = etree.HTML(html)
try:
is_live = dom.xpath(
'/html/body/div[1]/div[2]/div[18]/div[2]/div/div[3]/ul/li[1]/div/text()'
)[0]
except IndexError:
return None
if 'Live' in is_live:
info = dom.xpath(
'/html/body/div[1]/div[2]/div[18]/div[2]/div/div[3]/ul/li[1]/ul/li/a[2]'
)[0]
ref = info.xpath('@href')[0]
title = dom.xpath(
'/html/body/div[1]/div[2]/div[18]/div[2]/div/div[3]/ul/li[1]/ul/li/a[2]/text()'
)[0]
target = ref
date = time.strftime("%Y-%m-%d", time.localtime())
live_dict = {
'Title': title,
'Ref': ref,
'Target': target,
'Date': date
}
return live_dict
return None
def get_video_info_by_html(self):
"""
The method is using yfconfig to get information of video including title, video_id, data and thumbnail
:rtype: dict
"""
video_page = get(
f'https://www.youtube.com/channel/{self.target_id}/live')
try:
ytplayer_config = json.loads(
re.search(r'ytplayer.config\s*=\s*([^\n]+?});',
video_page).group(1))
player_response = json.loads(
ytplayer_config['args']['player_response'])
video_details = player_response['videoDetails']
title = video_details['title']
vid = video_details['videoId']
target = f"https://www.youtube.com/watch?v={vid}"
thumbnails = video_details['thumbnail']['thumbnails'][-1]['url']
# date = player_response['playabilityStatus']['liveStreamability']['liveStreamabilityRenderer']['offlineSlate'] \
# ['liveStreamOfflineSlateRenderer']['scheduledStartTime']
return {
'Title': title,
'Ref': vid,
'Date': strftime("%Y-%m-%d", localtime(time())),
'Target': target,
'Thumbnails': thumbnails
}
except KeyError:
self.logger.exception()
def __init__(self, api_url, data_dict):
fields = [
'id',
'code',
'location_code',
'start_date',
'sample_rate',
'sample_rate_unit',
'azimuth',
'azimuth_unit',
'depth',
'depth_unit',
'chains',
'equipments',
'network',
]
super(Channel, self).__init__(api_url, fields, data_dict)
self.station = None
self.parameters = []
# Equipment preparation
self.equipment_links = self.equipments
self.equipments = []
# Fetch equipment regarding chains
for chain_link in self.chains:
c_data = get(chain_link)
c = Chain(api_url, c_data)
if c.equipment:
e_data = get(c.equipment)
e = Equipment(api_url, e_data)
e.chain_order = c.type
e.channel = self
self.equipments.append(e)
# Fetch network
self.network_link = self.network
if self.network_link:
n_data = get(self.network_link)
if n_data:
self.network = Network(api_url, n_data)
# Fix missing degree unit
if not self.azimuth_unit:
self.azimuth_unit = default_degree_unit
# Fix missing rate unit
if not self.sample_rate_unit:
self.sample_rate_unit = default_sample_unit
# Fix missing meter unit
if not self.depth_unit:
self.depth_unit = default_elevation_unit
def get_img():
"""
获得图片
"""
ua = request.headers.get('User-Agent')
image = image_remote(ua)
api_param = api_param_parse(request.args)
count = get(api_param, 'count', '1')
is_phone_ = is_phone(ua)
key = 'image.phone' if is_phone_ == True else 'image.pc'
local_image = get(dataset.data, key, [])
result = []
if len(local_image) <= int(count):
result = img_list(is_phone_, image, count)
dataset.insert_list(key, result)
else:
result = random.sample(dataset.get(key), int(count))
return json.dumps(result)
def check(self):
self.vinfo = self.get_temp_vid(self.vinfo)
self.vid = self.vinfo['Vid']
html = get("https://www.youtube.com/watch?v=" f"{self.vid}")
if r'"isLive\":true' in html:
video_dict = self.getlive_title(self.vid)
process_video(video_dict)
self.db.delete(self.vinfo)
else:
self.logger.info(f'Not found Live')
def _fetch_addresses(self, object_id):
"""
Get addresses from the given equipment.
"""
res = []
ip_url = '%s%s/?format=json' % (self.api_url, api_ip_name)
data = get('%s&equipment=%s' % (ip_url, object_id))
if data:
for element in data:
address = Address(self.api_url, element)
res.append(address)
return res
def _fetch_services(self, object_id):
"""
Get service from the given equipment.
"""
res = []
service_url = '%s%s/?format=json' % (self.api_url, api_service_name)
data = get('%s&equipment=%s' % (service_url, object_id))
if data:
for element in data:
service = Service(self.api_url, element)
res.append(service)
return res
def img_list(is_phone_, image, count=1):
"""
根据数量获得随机图组
"""
result = []
while len(result) < int(count):
ir = requests.get(image, allow_redirects=False)
text = json.loads(ir.text)
img_url = get(text, 'imgurl', '')
min_size = 100 if is_phone_ else 200
if img_remote_size(img_url) > min_size:
result.append(img_url)
return result
def check(self):
html = get(
f'https://www.youtube.com/channel/{self.target_id}/featured')
if '"label":"LIVE NOW"' in html:
# vid = self.get_videoid_by_channel_id()
# get_live_info = self.getlive_vid(vid)
video_dict = self.get_videoid_by_channel_id(self.target_id)
video_dict['Provide'] = self.module
process_video(video_dict)
else:
if 'Upcoming live streams' in html:
self.logger.info(f'{self.target_id}: Found A Live Upcoming')
else:
self.logger.info(f'{self.target_id}: Not found Live')
def policy_gradient(d, g):
d['day'] = ut.get(d, 'date')
t = g
rate = 0.00002
delta = 0.05
res = pd.DataFrame(index=d.day.unique(),
columns=['pnl_naive', 'pnl_dynamic', 'q'])
for day in d.day.unique():
batch = d[d.day == day]
pnl = backtest(batch, t, g)
grad = 0.5 * (backtest(batch, t + delta, g) -
backtest(batch, t - delta, g)) / delta
t = max(t + rate * grad, 0)
res.loc[day] = [backtest(batch, g, g), pnl, t]
#print('Day: ' + day.strftime(format='%Y-%m-%d') + ' PNL: ' + str(pnl) + ' t:'+str(t))
return res
def check(self):
try:
html = get(
f'https://www.youtube.com/channel/{self.target_id}/featured')
if '"label":"LIVE NOW"' in html:
video_dict = self.get_video_info_by_html()
if video_dict:
video_dict['Provide'] = self.module
process_video(video_dict)
else:
if 'Upcoming live streams' in html:
self.logger.info(
f'{self.target_id}: Found A Live Upcoming')
else:
self.logger.info(f'{self.target_id}: Not found Live')
except Exception:
self.logger.exception('Check Failed')
def seek(self):
make = {}
table = tools.get(self.loop) # 경우의 수를 모두 구하여 키를 만듬
for val in table: # 여기서 모든 경우의 수를 다 구한다
make[val] = {"time": 0, "money": 0}
for n in range(len(val)):
make[val]["time"] += self.data[n][self.iterTable[int(
val[n])]][0]
make[val]["money"] += self.data[n][self.iterTable[int(
val[n])]][1]
temp = []
for key in make.keys():
#print(n, "OVER" if make[n]['time'] > self.maxTime else make[n]['time'])
if make[key]['time'] <= self.maxTime:
temp.append([key, make[key]["money"]])
return max(temp, key=lambda x: x[1])[1]
def __init__(self, api_url, data_dict):
fields = [
'id',
'code',
'latitude',
'latitude_unit',
'longitude',
'longitude_unit',
'elevation',
'elevation_unit',
'name',
'region',
'county',
'country',
'town',
'geology',
'operator',
]
super(Station, self).__init__(api_url, fields, data_dict)
self.channels = []
self.networks = []
self.equipments = []
# Fetch operator if needed
self.operator_link = self.operator
self.operator = ''
if self.operator_link:
data = get(self.operator_link)
if data:
self.operator = Organism(api_url, data)
# Fix unit for longitude, latitude, azimuth
for field in ['latitude', 'longitude']:
field_unit = '_'.join([field, 'unit'])
if not getattr(self, field_unit, None):
setattr(self, field_unit, default_degree_unit)
# Then elevation
if not self.elevation_unit:
self.elevation_unit = default_elevation_unit
# Geology
if not self.geology:
self.geology = 'Unknown'
def open_link(self, device = "inst0", autoid=True):
res = self._make_call(
create_link,
Create_LinkParms(
clientId = self.get_unique_id(),
lockDevice = 0,
lock_timeout = VXI11_DEFAULT_TIMEOUT,
device=device,
),
self.p.pack_Create_LinkParms,
self.un_p.unpack_Create_LinkResp,
timeout=VXI11_DEFAULT_TIMEOUT,
)
# check for errors, this raises exceptions on errors
VXI11Error.check(res.error)
self.links[res.lid] = (device,res)
LinkClass = Link
link = Link(LinkResp=res,client=self)
if autoid:
import tools # delayed to allow complete loading
return tools.get( link.query('*IDN?') )(link)
return link
def get(self, key):
result = get(self.data, key, None)
return result
in_list = []
in_list_JES_up = []
in_list_JES_down = []
in_list_PU_up = []
in_list_PU_down = []
for c in categories:
for s in signals:
in_list.append((filename, 'signal_histos/%s_%s' % (c, s))),
in_list_JES_up.append((filename, 'signal_histos/%s_%s_JES_up' % (c, s))),
in_list_JES_down.append((filename, 'signal_histos/%s_%s_JES_down' % (c, s))),
in_list_PU_up.append((filename, 'signal_histos/%s_%s_PU_up' % (c, s))),
in_list_PU_down.append((filename, 'signal_histos/%s_%s_PU_down' % (c, s))),
std_plots = tools.get(in_list)
JES_up_plots = tools.get(in_list_JES_up)
JES_down_plots = tools.get(in_list_JES_down)
PU_up_plots = tools.get(in_list_PU_up)
PU_down_plots = tools.get(in_list_PU_down)
print ""
print "=========================================="
print "JES Down/Up"
print "=========================================="
for i in range(0,len(std_plots)):
if i%5 == 0: print ""
JES_up_ratio = JES_up_plots[i].Integral()/std_plots[i].Integral()
JES_down_ratio = JES_down_plots[i].Integral()/std_plots[i].Integral()
print "JES %s: %5.3f/%5.3f" % (std_plots[i].GetName(), JES_down_ratio, JES_up_ratio)
category = 'cAll'
vbf_samples = ['H2Mu_VBF', 'H2Mu_VBF_120', 'H2Mu_VBF_130']
gg_samples = ['H2Mu_gg', 'H2Mu_gg_120', 'H2Mu_gg_130']
filename = dirname+filename
gg_getlist = []
vbf_getlist = []
for sample in gg_samples:
gg_getlist.append((filename, 'signal_histos/%s_%s' % (category, sample)))
for sample in vbf_samples:
vbf_getlist.append((filename, 'signal_histos/%s_%s' % (category, sample)))
print "\n =========== GET ================== \n"
gg_hlist = tools.get(gg_getlist)
vbf_hlist = tools.get(vbf_getlist)
# normalize histograms
for h in gg_hlist:
h.Scale(1/h.Integral())
for h in vbf_hlist:
h.Scale(1/h.Integral())
# -- ggf -----------------------
gg_h125 = gg_hlist[0]
gg_h120 = gg_hlist[1]
gg_h130 = gg_hlist[2]
gg_h125.SetTitle("GGF_M125");
##############################################
# make_bdt_score_overlay.py #
##############################################
import tools as tools
from categories_and_samples import *
from ROOT import *
#============================================
# code
#============================================
filename = '../../UFDimuAnalysis_v2/bin/rootfiles/validate_bdt_score_-1_1_categories2_36814_dyAMC-J_minpt20_b1_sig-xlumi1.root'
in_list = []
c = 'cAll'
for s in net_samples:
if 'Data' in s or 'Diboson' in s or 'VH' in s:
continue
in_list.append( (filename, 'net_histos/%s_%s' % (c, s)) )
plot_list = tools.get(in_list)
i=2
for h in plot_list:
h.Scale(1/h.Integral())
i+=1
tools.overlay(plot_list, title="BDT Score", savename="bdt_score_overlay_%s" % c,
draw="fill-transparent", xtitle="BDT score", ytitle="", ldim=[0.5, 0.7, 0.88, 0.88], yrange =(0,0.15))
from skimage.color import rgb2gray
from skimage.transform import resize
import sys
from tools import get
#root directory
root_directory=sys.argv[1]
copy_root_directory="Pkl/"
#make Pkl direcory
os.makedirs(copy_root_directory,exist_ok=True)
#get all the user_directory in the directory
users_directory=os.listdir(root_directory)
#image characterist
image_size=int(get("image_size"))
num_characters=int(get("num_characters"))
def hotfixLabel(n):
label=[0]*num_characters
label[n]=1
return label
total_captured=0;
flag=True
for user in users_directory:
#print("Processing Directory:"+str(user))
# define images and labels
images=[]
labels=[]
]
directory = '/home/puno/h2mumu/UFDimuAnalysis_v2/bin/rootfiles/'
roch_filename = directory+'validate_UNBLINDED_dimu_mass_Roch_110_160_categories3_tree_categorization_final_36814_dyAMC_minpt10.root'
kamu_filename = directory+'validate_UNBLINDED_dimu_mass_KaMu_110_160_categories3_tree_categorization_final_36814_dyAMC_minpt10.root'
roch_list = []
kamu_list = []
for c in categories:
for s in signals:
roch_list.append((roch_filename, 'signal_histos/%s_%s' % (c, s))),
kamu_list.append((kamu_filename, 'signal_histos/%s_%s' % (c, s))),
print "\n =========== GET ================== \n"
roch_plots = tools.get(roch_list)
kamu_plots = tools.get(kamu_list)
#print "\n =========== FIT ================ \n"
#
#ggf_ratios = []
#ggf_mean_roch = []
#ggf_mean_kamu = []
#ggf_sigma_roch = []
#ggf_sigma_kamu = []
#
#vbf_ratios = []
#vbf_mean_roch = []
#vbf_mean_kamu = []
#vbf_sigma_roch = []
#vbf_sigma_kamu = []
'c_01_Jet_Loose_OO',
'c_01_Jet_Loose_OE',
'c_01_Jet_Loose_EE'
]
category = 'c_01_Jet_Tight_BB'
samples = ['VH', 'H2Mu_VBF', 'H2Mu_gg', 'Diboson_plus', 'TTbar_Plus_SingleTop', 'Drell_Yan', 'Net_Data']
filename = dirname+filenames[2]
getlist = []
#for sample in samples:
# getlist.append((filename, 'net_histos/%s_%s' % (category, sample)))
getlist.append( (filename, 'Net_DY_HT') )
getlist.append( (filename, 'ZJets_AMC') )
print "\n =========== GET ================== \n"
hlist = tools.get(getlist)
print "\n =========== REBIN ================ \n"
hmc = tools.add(hlist[0:-1])
hdata = hlist[-1]
newBins = tools.getRebinEdges(hdata, hmc, max_err=0.1)
print newBins
rebin_hlist = tools.rebin(hlist, newBins) #rebinned to var bin width
srebin_hlist = tools.scaleByBinWidth(rebin_hlist, newBins) # rebinned to var bin width and wide bins are scaled down
srebin_hlist[0].SetTitle('ZJets_MG');
print "\n =========== STACK AND RATIO ====== \n"
tools.stackAndRatio(srebin_hlist, title='Drell_Yan_MC_AMC_vs_Madgraph', ytitleratio='AMC/MG', yrange=(1e3, 1e7))
samples = [
'VH', 'H2Mu_VBF', 'H2Mu_gg', 'Diboson_plus', 'TTbar_Plus_SingleTop',
'Drell_Yan', 'Net_Data'
]
filename = dirname + filenames[2]
getlist = []
#for sample in samples:
# getlist.append((filename, 'net_histos/%s_%s' % (category, sample)))
getlist.append((filename, 'Net_DY_HT'))
getlist.append((filename, 'ZJets_AMC'))
print "\n =========== GET ================== \n"
hlist = tools.get(getlist)
print "\n =========== REBIN ================ \n"
hmc = tools.add(hlist[0:-1])
hdata = hlist[-1]
newBins = tools.getRebinEdges(hdata, hmc, max_err=0.1)
print newBins
rebin_hlist = tools.rebin(hlist, newBins) #rebinned to var bin width
srebin_hlist = tools.scaleByBinWidth(
rebin_hlist,
newBins) # rebinned to var bin width and wide bins are scaled down
srebin_hlist[0].SetTitle('ZJets_MG')
print "\n =========== STACK AND RATIO ====== \n"
tools.stackAndRatio(srebin_hlist,
title='Drell_Yan_MC_AMC_vs_Madgraph',
ytitleratio='AMC/MG',
smean_mc = []
sres_data = []
sres_mc=[]
calibs = ['PF', 'Roch', 'KaMu']
# Make
for c in calibs:
#print " /// (%d) Looking at sample %s ..." % (dy_bug, s)
smean_data.append( (filename, 'plots/mean_Net_Data_mass_%s_%s' % (c, varname)) )
smean_mc.append( (filename, 'plots/mean_ZJets_AMC_mass_%s_%s' % (c, varname)) )
sres_data.append( (filename, 'plots/resolution_Net_Data_mass_%s_%s' % (c, varname)) )
sres_mc.append( (filename, 'plots/resolution_ZJets_AMC_mass_%s_%s' % (c, varname)) )
mean_data = tools.get(smean_data)
mean_mc = tools.get(smean_mc)
res_data = tools.get(sres_data)
res_mc = tools.get(sres_mc)
mean_all = []
mean_all.extend(mean_data)
mean_all.extend(mean_mc)
res_all = []
res_all.extend(res_data)
res_all.extend(res_mc)
mean_minmax = (999999,-999999)
res_minmax = (999999,-999999)
def insert_list(self, key, values):
target = get(self.data, key, [])
target.extend(values)
self.insert(key, target)
import numpy as np
import matplotlib.pyplot as plt
import pickle
import os
import sys
from collections import deque
import model
from tools import get
import Loader
#Specify which user is writing
user=int(sys.argv[1])
#image parameters
image_size=int(get("image_size"))
num_characters=int(get("num_characters"))
# prepare code to fetch datasets
users=deque(os.listdir("Pickles/Pkl"))
def load_next_batch():
#return image
file_c=users.popleft()
print("Batch Name:",file_c)
users.append(file_c)
pickle_file="Pickles/Pkl/"+file_c
pickle_file=open(pickle_file,"rb")
save=pickle.load(pickle_file)
image_r=save["images"].reshape([-1,image_size*image_size])
label_r=save["labels"]
if(len(label_r)==0):
print("Error in loading .. Continuing for the nest iteration")
for s in net_samples:
if s == 'Drell_Yan' and dy_bug:
s += '_'
#print " /// (%d) Looking at sample %s ..." % (dy_bug, s)
# Histos to get for this category
in_list.append((filename, 'net_histos/%s_%s' % (c, s)))
# Up and Down uncertainty histograms to get for this category, e.g. JES_up, JES_down, ...
if s == 'Net_Data':
continue # Data does not have up/down uncertainty histograms, only MC
for key, value in in_unc_map.iteritems():
value.append((filename, 'net_histos/%s_%s_%s' % (c, s, key)))
# list of histograms to make the stack and ratio plots for this category
hlist = tools.get(in_list)
# map to lists of up/down uncertainty histograms for this category
# 'JES_up' -> list of JES_up histos for this category
for in_key, in_value in in_unc_map.iteritems():
unc_hist_map[in_key] = tools.get(in_value)
#hmc = tools.add(hlist[0:-1])
#hdata = hlist[-1]
#newBins = tools.getRebinEdges(hdata, hmc, max_err=0.05)
#print newBins
#rebin_hlist = tools.rebin(hlist, newBins) #rebinned to var bin width
#srebin_hlist = tools.scaleByBinWidth(rebin_hlist, newBins) # rebinned to var bin width and wide bins are scaled down
print "\n =========== STACK AND RATIO ====== \n"
for s in net_samples:
if s == 'Drell_Yan' and dy_bug:
s+='_'
#print " /// (%d) Looking at sample %s ..." % (dy_bug, s)
# Histos to get for this category
in_list.append( (filename, 'net_histos/%s_%s' % (c, s)) )
# Up and Down uncertainty histograms to get for this category, e.g. JES_up, JES_down, ...
if s == 'Net_Data':
continue # Data does not have up/down uncertainty histograms, only MC
for key,value in in_unc_map.iteritems():
value.append( (filename, 'net_histos/%s_%s_%s' % (c, s, key)) )
# list of histograms to make the stack and ratio plots for this category
hlist = tools.get(in_list)
# map to lists of up/down uncertainty histograms for this category
# 'JES_up' -> list of JES_up histos for this category
for in_key, in_value in in_unc_map.iteritems():
unc_hist_map[in_key] = tools.get(in_value)
#hmc = tools.add(hlist[0:-1])
#hdata = hlist[-1]
#newBins = tools.getRebinEdges(hdata, hmc, max_err=0.05)
#print newBins
#rebin_hlist = tools.rebin(hlist, newBins) #rebinned to var bin width
#srebin_hlist = tools.scaleByBinWidth(rebin_hlist, newBins) # rebinned to var bin width and wide bins are scaled down
print "\n =========== STACK AND RATIO ====== \n"
category = 'root'
vbf_samples = ['H2Mu_VBF', 'H2Mu_VBF_120', 'H2Mu_VBF_130']
gg_samples = ['H2Mu_gg', 'H2Mu_gg_120', 'H2Mu_gg_130']
filename = dirname+filename
gg_getlist = []
vbf_getlist = []
for sample in gg_samples:
gg_getlist.append((filename, 'signal_histos/%s_%s' % (category, sample)))
for sample in vbf_samples:
vbf_getlist.append((filename, 'signal_histos/%s_%s' % (category, sample)))
print "\n =========== GET ================== \n"
gg_hlist = tools.get(gg_getlist)
vbf_hlist = tools.get(vbf_getlist)
# normalize histograms
for h in gg_hlist:
h.Scale(1/h.Integral())
for h in vbf_hlist:
h.Scale(1/h.Integral())
# -- ggf -----------------------
gg_h125 = gg_hlist[0]
gg_h120 = gg_hlist[1]
gg_h130 = gg_hlist[2]
gg_h125.SetTitle("GGF_M125");
calibs = ['PF', 'Roch', 'KaMu']
# Make
for c in calibs:
#print " /// (%d) Looking at sample %s ..." % (dy_bug, s)
smean_data.append(
(filename, 'plots/mean_Net_Data_mass_%s_%s' % (c, varname)))
smean_mc.append(
(filename, 'plots/mean_ZJets_AMC_mass_%s_%s' % (c, varname)))
sres_data.append(
(filename, 'plots/resolution_Net_Data_mass_%s_%s' % (c, varname)))
sres_mc.append(
(filename, 'plots/resolution_ZJets_AMC_mass_%s_%s' % (c, varname)))
mean_data = tools.get(smean_data)
mean_mc = tools.get(smean_mc)
res_data = tools.get(sres_data)
res_mc = tools.get(sres_mc)
mean_all = []
mean_all.extend(mean_data)
mean_all.extend(mean_mc)
res_all = []
res_all.extend(res_data)
res_all.extend(res_mc)
mean_minmax = (999999, -999999)
res_minmax = (999999, -999999)
#============================================
# code
#============================================
print "\n /// Overlaying backgrounds for %s... \n" % filename
# List of histograms to make the overlay from -----------------
in_list = []
for c in bdt_categories:
for s in net_samples_all:
if s == 'Net_Data':
in_list.append((filename, 'net_histos/%s_%s' % (c, s))),
print "\n =========== GET ================== \n"
hlist = tools.get(in_list)
edges = [x for x in range(110, 161) if x % 2 == 0]
for e in edges:
print e
hlist = tools.rebin(hlist, edges)
inclusive = 0
# Normalize all of the backgrounds to the same scale
for i, h in enumerate(hlist):
h.Scale(1 / h.Integral())
#h.SetName(bdt_categories[i])
h.SetTitle(bdt_categories[i])
if 'cAll' in h.GetName():
h.SetTitle("Inclusive")
inclusive = h
]
h = 'H2Mu_gg'
i = 0
for c in categories:
in_list = [
('../../bin/rootfiles/validate_M120_blinded_dimu_mass_PF_110_160_nolow_categories3_tree_categorization_final_36814_dyMG.root',
'net_histos/%s_%s_120' % (c, h)),
('../../bin/rootfiles/validate_M130_blinded_dimu_mass_PF_110_160_nolow_categories3_tree_categorization_final_36814_dyMG.root',
'net_histos/%s_%s_130' % (c, h)),
('../../bin/rootfiles/validate_blinded_dimu_mass_PF_110_160_nolow_categories3_tree_categorization_final_36814_dyMG.root',
'net_histos/%s_%s' % (c, h))
]
plot_list = tools.get(in_list)
plot_list[0].SetName('M120')
plot_list[0].SetTitle('M120')
plot_list[1].SetName('M130')
plot_list[1].SetTitle('M130')
plot_list[2].SetName('M125')
plot_list[2].SetTitle('M125')
tools.overlay(plot_list,
title="c%d_120_125_130_%s" % (i, h),
savename="c%d_120_125_130_%s" % (i, h),
xtitle="dimu_mass",
ytitle="Events / 1 GeV",
ldim=[0.5, 0.7, 0.88, 0.88])
i += 1
searched_site_code = 'CHMF'
# Configuration
server = 'localhost'
port = '8002'
if port:
server = ':'.join([server, port])
api_url = 'http://%s/api/v1/' % server
site_url = api_url + 'sites/?format=json'
channel_url = api_url + 'channels/?format=json'
equipment_url = api_url + 'equipments/?format=json'
params_url = api_url + 'channel_parameters/?format=json'
# Search Charmoille site
url = site_url + '&code=%s' % searched_site_code
stations = get(url)
# Then BROWSE data to fetch ALL NEEDED information
result = []
for station in stations:
# Fetch its ID and some info
s = Station(api_url, station)
# Search linked channels
url = channel_url + '&station=%s' % s.code
channels = get(url)
for channel in channels:
c = Channel(api_url, channel)
c.station = s # Add a link from channel to station
s.channels.append(c)
if c.network and c.network.code not in s.networks:
s.networks.append(c.network.code)
name = key.GetName()
#print "(%d) Looping over %s ..." % (dy_bug, key)
if 'Drell_Yan_' in name and name[-1] == '_':
dy_bug = True
break
# List of histograms to make the table from -----------------
in_list = []
for c in bdt_categories:
for s in net_samples_all:
if s == 'Drell_Yan' and dy_bug:
s+='_'
in_list.append((filename, 'net_histos/%s_%s' % (c, s))),
print "\n =========== GET ================== \n"
hlist = tools.get(in_list)
fwhm_widths = []
ffwhm_widths = []
fwhm_bounds = []
ffwhm_bounds = []
signal_net = []
signal_fwhm = []
bkg_fwhm = []
vbf_net = []
vbf_fwhm = []
ggf_net = []
ggf_fwhm = []
vh_net = []
vh_fwhm = []
]
directory = '/home/puno/h2mumu/UFDimuAnalysis_v2/bin/rootfiles/'
roch_filename = directory + 'validate_UNBLINDED_dimu_mass_Roch_110_160_categories3_tree_categorization_final_36814_dyAMC_minpt10.root'
kamu_filename = directory + 'validate_UNBLINDED_dimu_mass_KaMu_110_160_categories3_tree_categorization_final_36814_dyAMC_minpt10.root'
roch_list = []
kamu_list = []
for c in categories:
for s in signals:
roch_list.append((roch_filename, 'signal_histos/%s_%s' % (c, s))),
kamu_list.append((kamu_filename, 'signal_histos/%s_%s' % (c, s))),
print "\n =========== GET ================== \n"
roch_plots = tools.get(roch_list)
kamu_plots = tools.get(kamu_list)
#print "\n =========== FIT ================ \n"
#
#ggf_ratios = []
#ggf_mean_roch = []
#ggf_mean_kamu = []
#ggf_sigma_roch = []
#ggf_sigma_kamu = []
#
#vbf_ratios = []
#vbf_mean_roch = []
#vbf_mean_kamu = []
#vbf_sigma_roch = []
#vbf_sigma_kamu = []
