Istres contra Cesson

Analyse of Odds and Adjustments (Available Capital)

• Betting on Odds

  versus Expert Win: 1.65 – Freiburg
• Odds Comparison: 2/1 H2H / Freestyle Showdown:Odds Analysis: When examining the odds of specific events such as under/over 2|  }}
. To meet both semantic requirements. However thorough analysis could. The second paragraph must go after the closing of the first opening and start of the paragraph must be included in this description %

Istres

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-

Cesson

WLWWL

Date: 2025-06-07

Time: 18:30

Venue: Not Available Yet

Predictions:

Market	Prediction	Odd	Result

 #!/usr/bin/env python # -*- coding: utf-8 -*- # coding=utf-8 »’ Created on 21/1/2015 @author: Apurva Hegde »’ import os import pandas as pd def get_context_type(row): if row[‘CONTEXT’][:4] in [«head»,»loss»,»gain»,»gain»]: return 1 else: return 0 def god_context(row): retval= «» for x in row[«CONSTRUCTS»].split( ) : if x in [ «noise»,»life events»,»events»,»behavior»] :return 1 return False from splunklib.binding import * # Import splunklib component from splunklib import authentication # Import python libs import logging import re import json from splunklib.modularinput import * from splunklib.searchcommands import dispatch, Response, option, Command, Configuration, threading logger = logging.getLogger(__name__) class ExampleCommand(Command): «»»Example Command.»»» context = Option(require=False) def stream_events(self, events): for event in events: yield event def process(self): response = Response() page_size = 100 #Splunk will page through the results for you start = 0 #start at the beginning end = None #get all remaining kwargs = {«search_mode»: «normal»} if self.options.ce: kwargs[«count_events»] = True for events in self.service.jobs.fetchj(self.options.sid, output_mode = «raw», executor=self.stream_events, **kwargs): if self.options.ce: response.succeed(json.dumps({«response»: {«next»: -1, «count»: events.count}})) break next_start = events.count + start if next_start > end or end is None: for e in events: response.succeed(json.dumps(e)) break start = next_start @dispatch() class ExampleInput(Module): «»»Example input handler.»»» service = Option(require=False) def stream_events(self, data): for line in io.BufferedReader(io.BytesIO(data), buffer_size=4096): try: yield json.loads(line) except Exception: pass def handle(self, input_type=None): logger.debug(«Handling input of type: {}».format(input_type)) kwargs = {} #don’t touch me if self.options.sid: kwargs[«job»] = self.options.sid if input_type == «events»: kwargs[«output_mode»] = «json» if self.options.ce: kwargs[«count_events»] = True for events in self.service.jobs.fetchj(**kwargs): if self.options.ce: response = {«response»: {«next»: -1, «count»: events.count}} break for e in events: yield json.dumps(e) next_start = events.count + kwargs.get(«start»,0) if next_start > kwargs.get(«end»,None) or kwargs.get(«end»,None) is None: break kwargs[«start»] = next_start #def get_args(): # import argparse # parser = argparse.ArgumentParser( # description=__doc__, # formatter_class=argparse.ArgumentDefaultsHelpFormatter # ) # # parser.add_argument(‘–url’, dest=’url’, required=True) # parser.add_argument(‘–username’, dest=’username’, required=True) # parser.add_argument(‘–password’, dest=’password’, required=True) # # return parser.parse_args() # # #def main(): # # args = get_args() # # service = authentication.connect( # username=args.username, # password=args.password, # app=»search», # host=args.url, protocol=’https’, # port=’8089′, scheme=’https’ # ) # # dispatch(ExampleInput, service) # #if __name__ == «__main__»: # main() aphegde/MDA-Project/requirements.txt numpy==1.11.0 matplotlib==1.5.0 splunk-sdk==1.4.0 splunklib==0.12 scikit_learn==0.17.dev0 pandas==0.17.1 tqdm==4.4.1 HDF5==2.7.8aphegde/MDA-Project/Step_2_parallel/read_data_chunks.py ../Step_1/read_data_chunks.py#!/usr/bin/env python # coding=utf-8 import sys,time from multi_mda_stream import MultiMDAstream as mda_stream from multi_mda_functions import concatenate_nios,pattern_out_data,build_test_output if len( sys.argv ) != 2 : print «n Usage: %s [stream_id]» % ( sys.argv[0] ) ; sys.exit( -1 ) s_id = int( sys.argv[1] ) print «Running with stream_id = «, s_id S=mda_stream(sample_size=512000,outfile=»../example_mda_output_%d.csv» % s_id) while 1 : start=time.time() S.read_chunk() concatenate_nios(S.N[s_id],»../concat_out_%d.csv» % s_id) print «Concatenation finished in «, (time.time() – start) output=pattern_out_data(S.N[s_id]) print «Pattern Matched in «, (time.time() – start) print «Data Saved in file «, ( build_test_output(output,S.s_id) )aphegde/MDA-Project/read_data_chunks.py »’ Created on 13/1/2015 @author: Apurva Hegde »’ import json import pandas as pd if __name__ == ‘__main__’: processing_list=[None] context_type=[0] score_caps=[None] #print large_df_tr.head() def get_chunk(sid,s_i,parsity_ids=None): for events in sid.jobs.fetchj(sid_job.id, output_mode = «json», count_events=True, start=s_i*(parsity_ids.shape[0] if parsity_ids else chunk_size), end=(s_i+1)*(parsity_ids.shape[0] if parsity_ids else chunk_size), search_mode=»normal», columns=None ): results=json.load(events) print results[«response»][«count»] if results[«response»][«count»] == 0 : break #Store the results in a string result_string=»n».join([str(e) for e in results[«events»]]) if isinstance(results[«response»][«next»],int): done=results[«response»][«next»] == -1 file_name=»large_df_tr_%d.csv» % sid_job.id large_df_tr=pd.read_csv(file_name) large_df=pd.DataFrame.from_csv(result_string) large_df_tr=large_df_tr.append(large_df) l=len(large_df_tr) large_df_tr.to_csv(file_name) return l,done #Store the results in a string result_string=»n».join([str(e) for e in results[«events»]]) large_df=pd.DataFrame.from_csv(result_string) large_df_tr=pd.read_csv(file_name) large_df_tr=large_df_tr.append(large_df) l=len(large_df_tr) large_df_tr.to_csv(file_name) return l,False def pattern_record(contexts,caps,score): global processing_list #Keep track of processing contexts and score_caps processing_list.append((contexts,caps)) #Provide context_type as dataframe index for each context with score_caps bounds context_type.append( get_context_type(contexts,caps,score) ) def pattern_record(context,score): global context_type context_type.append( get_context_type(context,score) ) def chunk_info(sid_job,s_id,parsity_ids=None): chunk_size=10000 #Only fetch counts default sid=sid_job.service l=0 done=False while not done : l,done=get_chunk(sid_job,s_id,parsity_ids) if l % 100000 == 0 : print «Collected %d events» % l def s_id(sid_job,s_id): chunk_info(sid_job,s_id) def s_ids(sid_job): chunk_info(sid_job) def get_data(sid): from multiprocessing import Pool N=100 pool=Pool() jobs=[pool.apply_async(s_id,(sid,i)) for i in range(N+1)] pool.close() def truncate_results(df,caps,score): print df.columns df=df[ [s for s in df.columns.values.tolist() if float(s)>score] ] print df.columns return df.sort(columns=[«%d» % score], axis=0) aphegde/MDA-Project/Step_1/get_wstats.py #!/usr/bin/env python # -*- coding: utf-8 -*- »’ Created on 13/9/2014 @author: Apurva Hegde Returnes word statistics by parsing content fields of a search job. Stores documents in two classes : 1) Non contiguous : No consecutive appearances of a word