Challenger Montevideo Predictions

The Thrilling Tennis Challenger Montevideo Uruguay: Tomorrow's Matches

The Tennis Challenger Montevideo in Uruguay is one of the most anticipated events on the ATP Challenger Tour. With a rich history and a vibrant atmosphere, this tournament attracts top talent from around the globe. As we look ahead to tomorrow's matches, there are several key players and matchups that have caught the attention of both fans and betting experts alike.

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Key Matchups to Watch

Player A vs. Player B: This match is expected to be a thrilling encounter as both players have been in excellent form recently. Player A, known for his aggressive baseline play, will face off against Player B, who excels in net play and tactical intelligence.
Player C vs. Player D: A classic clash of styles, with Player C's powerful serve and groundstrokes going up against Player D's exceptional defensive skills and endurance.
Player E vs. Player F: An exciting matchup featuring two young talents who have been making waves on the tour. Both players are known for their versatility and ability to adapt their game plans mid-match.

Betting Predictions

Betting experts have provided some insightful predictions for tomorrow's matches. Here are some key insights:

Player A vs. Player B

Betting odds suggest that Player A is slightly favored to win this match due to his recent performances on similar surfaces. However, Player B's experience at crucial moments could make this a closely contested battle.

Player C vs. Player D

This match is considered one of the most unpredictable of the day. Betting lines are nearly even, reflecting the uncertainty surrounding how each player will handle the pressure.

Player E vs. Player F

With both players being relatively new to high-stakes matches, betting experts are divided. Some favor Player E due to his strong serve, while others believe Player F's all-court game gives him an edge.

In-Depth Analysis: Key Players

Player A: The Aggressive Baseline Master

Player A has been making headlines with his powerful baseline game and impressive win-loss record this season. Known for his ability to dictate play from the back of the court, he has consistently outperformed opponents with his speed and precision.

Player B: The Tactical Net Specialist

A seasoned veteran, Player B brings a wealth of experience to the court. His strategic approach and exceptional net play make him a formidable opponent, especially in close matches where every point counts.

Tournament Overview

The Venue: Estadio Luis Troccoli

The Estadio Luis Troccoli in Montevideo provides a unique playing environment with its clay courts that challenge players' adaptability and strategic thinking. The stadium's passionate crowd adds an extra layer of excitement to each match.

The Prize Pool: Attracting Top Talent

This year's tournament boasts an impressive prize pool that has attracted some of the best players on the ATP Challenger Tour. Competitors vie not only for ranking points but also for significant financial rewards.

Tactics and Strategies

The Importance of Serve-and-Volley Play

In today's tennis landscape, serve-and-volley tactics can be particularly effective on clay courts like those at Estadio Luis Troccoli. Players who excel at this strategy often disrupt their opponents' rhythm and gain an advantage at crucial points in the match.

Court Positioning:

Maintaining optimal positioning allows players to cover shots effectively while minimizing movement fatigue.
A well-placed serve followed by quick net approaches can put pressure on opponents during rallies.

Variety in Shot Selection:

Mixing up shot selection keeps opponents guessing and prevents them from settling into a rhythm during rallies.
Incorporating drop shots or lobs can catch opponents off guard when they least expect it.

Focusing on Mental Toughness:

Mental resilience is crucial during high-pressure situations such as tiebreaks or deciding sets.
Focusing on staying calm under pressure helps maintain composure throughout long matches or grueling tournaments like this one.

Betting Tips from Experts: Maximizing Your Wagering Success

Understanding Odds: How They Work & What They Mean For You As A Bettor

Odds represent your potential payout based on your wager amount; higher odds indicate lower probability but greater returns if successful.

Analyzing past performance data can provide valuable insights into how certain players perform under specific conditions.

Surface preferences matter; some players excel on hard courts while others thrive on clay or grass surfaces.

Favorable weather conditions can influence gameplay; consider how temperature or wind might affect outcomes.

Tiebreaks often decide matches; evaluate which player handles these high-pressure situations better.

Daily form fluctuations mean yesterday’s hero may not perform equally well today; stay updated with latest news.

Injuries or recent changes in coaching staff might impact performance unexpectedly; keep an eye out for such developments.

Sometimes going against popular opinion pays off – don’t be afraid to take calculated risks based on your own analysis rather than following trends blindly.

<|repo_name|>SeoOptimizationGPT/seo_optimization_gpt<|file_sep|>/Dockerfile FROM python:alpine RUN pip install selenium==3.x requests beautifulsoup4 lxml==5.x COPY . /app WORKDIR /app CMD ["python", "generate_seo_text.py"]*** Excerpt *** The mechanism by which caffeine enhances arousal is still unclear (Landolt et al., 2000). Several theories exist about caffeine’s effects (for review see Rogers et al., 1999). One theory suggests that caffeine blocks adenosine receptors (Fredholm et al., 1999), another suggests that caffeine increases catecholamine release (Rogers et al., 1999), yet another proposes that caffeine enhances noradrenergic function (Lagopoulos et al., 2005). In our study we found no effect of caffeine administration on dopamine release measured by PET scan using [11C]raclopride as radiotracer (Figures S2–S7). It should be noted however that [11C]raclopride binds specifically to D2/D3 receptors only whereas other subtypes such as D1 receptors were not studied here. *** Revision 0 *** ## Plan To create an exercise that challenges advanced understanding and requires additional factual knowledge beyond what is presented in the excerpt, we must first enhance its complexity by incorporating more detailed scientific terminology related to neurochemistry and pharmacology while embedding logical deductions within conditional statements. The rewritten excerpt should include references to specific biochemical pathways affected by caffeine consumption, mention additional neurotransmitter systems potentially influenced by caffeine beyond what was originally mentioned (e.g., serotonin), incorporate hypothetical scenarios based on experimental conditions not explicitly stated but logically inferred from given data (e.g., effects under different dosages or genetic predispositions affecting receptor sensitivity), and use complex sentence structures including nested conditionals. This approach ensures that solving the exercise demands not only a profound understanding of neuropharmacology but also critical thinking skills to navigate through intricate logical constructs. ## Rewritten Excerpt "The precise biochemical cascade facilitating caffeine-induced arousal remains enigmatic despite extensive research (Landolt et al., 2000). Numerous hypotheses have been posited concerning caffeine’s neuromodulatory actions (comprehensively reviewed by Rogers et al., 1999). One prevailing hypothesis posits that caffeine acts predominantly through antagonism at adenosine receptors (Fredholm et al., 1999), thereby preventing adenosine-mediated inhibitory effects within neural circuits implicated in wakefulness regulation. Conversely, alternative propositions suggest either augmentation in catecholamine discharge—namely dopamine and norepinephrine—or direct enhancement of noradrenergic transmission mechanisms (Lagopoulos et al., 2005) as primary pathways through which caffeine exerts its stimulatory effects. In our investigation employing positron emission tomography ([PET]) with [11C]raclopride as a radiotracer—a compound exhibiting high affinity exclusively for D2/D3 dopamine receptors—we observed no significant modulation in dopamine release subsequent to acute caffeine administration across various dosages tested (referenced data omitted here). It warrants mention that our methodology did not extend scrutiny towards other dopaminergic receptor subtypes such as D1 receptors nor did it explore potential modulatory effects upon other neurotransmitter systems potentially implicated alongside dopaminergic pathways—such as serotonergic or GABAergic systems—in mediating cognitive arousal states induced by caffeine." ## Suggested Exercise Given the enhanced understanding of caffeine’s neuromodulatory mechanisms discussed above: Which statement best encapsulates a comprehensive interpretation considering all information provided? A) Caffeine exclusively functions through inhibition at adenosine receptors without affecting other neurotransmitter systems. B) Despite evidence suggesting no significant alteration in dopamine release via D2/D3 receptor interaction post-caffeine intake, it remains plausible that unexamined dopaminergic receptor subtypes or alternate neurotransmitter systems might mediate its arousing effects. C) The lack of observable effect on dopamine release via [11C]raclopride binding conclusively disproves any role of dopamine in mediating caffeine’s stimulatory action. D) Caffeine directly increases catecholamine release uniformly across all brain regions without engaging any specific receptor subtype interactions. *** Revision 1 *** check requirements: - req_no: 1 discussion: The draft does not require external knowledge beyond what is presented. It only asks about information contained within the excerpt. score: 0 - req_no: 2 discussion: Understanding subtleties is necessary but does not require external advanced knowledge. score: 2 - req_no: 3 discussion: The excerpt meets length requirements but could integrate more complex, nuanced details related specifically to neuropharmacology. score: 2 - req_no: 4 discussion: The choices are misleading enough but could benefit from integration with external knowledge for increased difficulty. score: 2 - req_no: '5' discussion: Without requiring external knowledge, it falls short of challenging someone with advanced undergraduate knowledge. score: 1 - req_no_no': '6' discussion': All choices seem plausible without referring back to specifics within the question context; however integrating external knowledge would enhance this. ' score': '1' external fact': Knowledge about specific pharmacological actions beyond those mentioned, such as how genetic variations might influence individual responses to substances, could add depth. revision suggestion": "To satisfy requirement #1 more fully while enhancing overall difficulty per requirements #5 & #6, consider revising the exercise question so it compares findings from different studies involving similar methodologies but varying substances or genetic predispositions among subjects - e.g., comparing responses between individuals with different genotypes affecting adenosine receptor density. This comparison would necessitate outside knowledge about genetics' role in pharmacodynamics." revised exercise": |- Given your understanding from both this study described above and general pharmacogenomic principles: Which statement best encapsulates how genetic variability might influence individual responses observed when using [11C]raclopride PET scans after administering substances like caffeine? correct choice: Variations in adenosine receptor density due to genetic differences could explain why some individuals exhibit minimal changes in dopaminergic activity, despite similar doses administered compared with others showing significant changes. incorrect choices: - All individuals show uniform responses irrespective of genetic makeup when subjected to similar doses of psychoactive substances like caffeine. - Genetic factors do not significantly influence neurotransmitter system responses, making environmental factors more pivotal. - Genetic variations primarily affect metabolic rates rather than direct neuromodulation, thus having negligible impact on PET scan results after substance administration. *** Revision 2 *** check requirements: - req_no: '1' discussion: Draft relies solely on information within excerpt without requiring external, advanced knowledge for solution. score: '0' - req_no: '2' discussion: Subtleties are necessary for understanding but do not reach beyond provided information. score: '2' - req_no: '3' discussion: Excerpt lacks integration with complex neuropharmacological concepts; could be improved by linking directly relevant theories or models. score: '1' - req_no: '4' discussion': Choices need stronger linkage with external academic facts or theories; currently too reliant on excerpt content.' score': '1' external fact': Incorporate knowledge regarding pharmacogenomics specifically focusing comparison between known polymorphisms affecting adenosine receptor genes (e.g., ADORA2A) and their impact on drug response variability among individuals consuming stimulants-like-caffeine.' revision suggestion": "To address requirement #1 effectively while also enhancing overall challenge per requirements #5 & #6, modify exercise question so it draws comparison/conclusion/linkage/correlation/ ? between findings from current study described above about generic response patterns using [11C]raclopride PET scans after administering substances like caffeinea nd existing research/data regarding genetic polymorphisms affecting adenosine receptors impacting individual reactions differently? This adjustment will necessitate deeper comprehension involving pharmacogenomics principles.nnAlso revise choices so they reflect consequences/effects derived from such comparisons.", "revised exercise": |- Given your understanding from both this study described above involving [11C]raclopride PET scans after administering substances like caffeine AND existing literature concerning genetic polymorphisms impacting adenosine receptors: Which statement best encapsulates potential explanations for differential responses observed among individuals? correct choice": Variations in adenosine receptor density due to specific gene polymorphisms could explain differing levels of dopaminergic activity seen across individuals following similar doses." incorrect choices: - All individuals show uniform neurological responses irrespective of genetic makeup, ? when subjected similarly dosed psychoactive substances like caffeinea nbsp; ? Genetic factors primarily alter metabolic processing speeds rather than influencing neurotransmission dynamics directly, ? hence having minimal effect variations observed during PET scan analyses post-substance administration? *** Revision ### Revision ### check requirements: - req_no: '1' discussion: No incorporation of required advanced external knowledge related directly; relies entirely on content within excerpt itself. ? score ': : N/A ? correct choice : N/A ? incorrect choices : N/A ? : N/A ? : N/A ? : N/A ? : N/A ? revised exercise : N/A ? : N/A ? : N/A ? revision suggestion : To fulfill requirement #1 effectively while also enhancing overall challenge per requirements #5 & #6, ? modify exercise question so it draws comparison/conclusion/linkage/correlation between findings from current study described above about generic response patterns using [11C]raclopride PET scans after administering substances like caffeinea nd existing research/data regarding genetic polymorphisms affecting adenosine receptors impacting individual reactions differently? This adjustment will necessitate deeper comprehension involving pharmacogenomics principles.nnAlso revise choices so they reflect consequences/effects derived from such comparisons." [0]: import json [1]: import os [2]: import re [3]: import sys [4]: def _print(*args): [5]: print(" ".join([str(arg) if arg else '' for arg in args])) [6]: def _get_object_id(obj): [7]: return obj["id"] if isinstance(obj.get("id"), str) else obj["name"] [8]: class Object: [9]: """ [10]: Represents object. [11]: """ [12]: def __init__(self, [13]: obj=None, [14]: parent=None, [15]: children=None): [16]: self.parent = parent [17]: self.children = children if children else [] [18]: self._obj = obj <|repo_name|>omaralshamrani/Humanoid-Robotics-Laboratory---UMass-Lowell-Coursework---2018-Fall---Robotics-I---Lab-Simulator-for-Autonomous-Robots-in-Dynamic-Uncertain-and-Unstructured-Urban-Emergency-Situations-using-Python-and-Cyberbotics-TurtleBot-XinuCode-GUI-based-Framework-with-Simulated-Navigation-and-Vision-Sensors-to-be-integrated-with-the-Autonomous-Robotics-Benchmarks-for-Emergency-Situations-(AR-BES)-Platform-developed-by-USF)<|file_sep[master] host = github.com [user] name = omaralshamrani [url "[email protected]:"] insteadOf = https://github.com/ [url "ssh://[email protected]"] insteadOf = https://github.com/ [url "ssh://[email protected]"] insteadOf = https://gitlab.com/ [url "ssh://[email protected]"] insteadOf = https://bitbucket.org/ # ============================================================================== # GITHUB SSH CONFIGURATION FOR WINDOWS AND LINUX USERS USING PUTTY TO GENERATE SSH KEYS IN PEM FORMAT AND CONVERT THEM TO OPENSSH FORMAT USING PUTTYGEN TOOL WITH THE FOLLOWING STEPS : # STEP ONE - Generate SSH Keys Using Puttygen Tool : # Open PuttyGen tool then click Load button then choose None button then click Generate button then move mouse around until you see progress bar fills up completely then copy public key generated under Label field then open Git Bash terminal then paste following command replacing {your_username} with your actual GitHub username : # # ssh-keygen -t rsa -b4096 -f ~/.ssh/id_rsa_{your_username} -N "" # # Then open PuttyGen tool again then click Load button then choose Private Key (*.ppk) file format option then load private key file generated previously under {your_username} directory inside .ssh directory located inside user home directory i.e ~/.ssh/id_rsa_{your_username}.ppk file path then click Conversions menu item then click Export OpenSSH key button then save exported private key file inside {your_username} directory located inside .ssh directory located inside user home directory i.e ~/.ssh/id_rsa_{your_username}.pub file path . # # STEP TWO - Add SSH Keys To GitHub Account : # Copy public key generated previously under Label field inside PuttyGen tool window then go inside GitHub account settings page https://github.com/settings/profile , scroll down till SSH keys section , click Add SSH Key button , give title name i.e {your_username}_Windows_Putty_Generated_SSH_Key , paste public key copied previously into Key field , finally click Add SSH Key button . # # STEP THREE - Set Up Environment Variables For Windows Users Using Git Bash Terminal : # First set environment variables using following command replacing {your_username} with your actual GitHub username : # # export GIT_SSH_COMMAND='ssh.exe -i ~/.ssh/id_rsa_{your_username}' # # Second add following lines replacing {your_username} again with your actual GitHub username : # # export GIT_SSH_COMMAND='ssh.exe -i ~/.ssh/id_rsa_{your_username}' # # Inside $HOME/.bashrc file located inside user home directory i.e ~/ directory . # # Third run following command replacing {username} again with your actual GitHub username : # # git config --global core.sshCommand ssh.exe -i ~/.ssh/id_rsa_{username} # # [color] ui = true [pull] rebase = false [pager] diff = log = showbranch = status = [rerere] enabled = true [user] name = email = #[core] autocrlf = input #[diff] tool = [difftool] prompt = false #[merge] tool = #[mergetool] [difftool "meld"] cmd =""$HOME/bin/meld" "$LOCAL" "$REMOTE"" [color "diff-highlight"] oldNormal = red bold strike-through underline background:black70 oldWhite black70 reverse bold strike-through underline background:black70 oldWhite black70 reverse bold strike-through underline background:black70 oldWhite black70 reverse bold strike-through underline background:black70 oldWhite black70 reverse bold strike-through underline background:black70 oldWhite black70 reverse bold strike-through underline background:black70 oldWhite black70 reverse bold strike-through underline background:black70 oldWhite black70 reverse bold strike-through underline background:black70 oldWhite black70 reverse bold strike-through underline background:black70 oldWhite black70 reverse bold strike-through underline background:black70 oldWhite black71 reverse bold strike-through underline background:black71" newNormal = green normal inverse white green inverse white green inverse white green inverse white green inverse white green inverse white green inverse white green inverse white green inverse white green inverse white green inverse white green inverse white green normal inverse white [color "diff"] meta = yellow italicized foreground:white20 bg:red30 dimmed italicized foreground:white20 bg:red30 dimmed italicized foreground:white20 bg:red30 dimmed italicized foreground:white20 bg:red30 dimmed italicized foreground:white20 bg:red30 dimmed italicized foreground:white20 bg:red30 dimmed italicized foreground:white20 bg:red30 dimmed italicized foreground:white20 bg:red30 dimmed italicized foreground:white20 bg:red30 dimmed italicized foreground:white20 bg:red30 dimmed italicized foreground:white20 bg:red30 dimmed italicized foreground:white20 bg:red30 dimmed italicized foreground:white20 bg:red30 dimmed italicized foreground:white21 bg:red31 normal reversed yellow normal reversed yellow normal reversed yellow normal reversed yellow normal reversed yellow normal reversed yellow normal reversed yellow normal reversed yellow normal reversed yellow normal reversed yellow normal reversed yellow normal reversed yellow normal reversed yellow old = red strikethrough red strikethrough red strikethrough red strikethrough red strikethrough red strikethrough red strikethrough red strikethrough red strikethrough red strikethrough red strikethrough red stroked fore:green80 stroked fore:green80 stroked fore:green80 stroked fore:green80 stroked fore:green80 stroked fore:green80 stroked fore:green80 stroked fore:green80 stroked fore:green80 stroked fore:green80 stroked fore:green81 stroke fore:#ff0000 stroke fore:#ff0000 stroke fore:#ff0000 stroke fore:#ff0000 stroke fore:#ff0000 stroke fore:#ff0000 stroke fore:#ff0000 stroke fore:#ff0000 stroke fore:#ff0000 stroke fore:#ff0000 stroke new = none none none none none none none none none none none none none blue inverted blue inverted blue inverted blue inverted blue inverted blue inverted blue inverted blue inverted blue inverted blue inverted blue inverted blue inverted whitespace = gray25 gray25 gray25 gray25 gray25 gray25 gray25 gray25 gray25 gray25 gray25 gray25 gray25 grey24 grey24 grey24 grey24 grey24 grey24 grey24 grey24 grey24 [color "status"] added = changed = untracked = [color] diff = diff-highlight = meta = frag = old = new = branch = intersect = untracked = whitespace = [color "branch"] current = local = remote = merged = [color] [giggle] mainwindowmaximized=true mainwindowgeometry=800x600+192+100 [giggle] showstatustext=false [giggle] sortbycolumn=false [giggle] showstatusbar=true [giggle] showlinenumbers=true [giggle] splitviewhorizontal=false [giggle] fuzzysearchlimit=100 [giggle] fuzzymatchthreshold=-50 [giggle] limitresultspagecount=-99999999 [giggle] resultspagecount=-99999999 [giggle] maxresultspagecount=-99999999 <|file_sep|>#include #include #include #include using namespace std; int main(){ int num; cin>>num; vectorv(num); for(int i=0;i>v[i]; } int target; cin>>target; int min_sum=v[target]; for(int i=target;iyunjunzhang/TTreeManager<|file_sep-modified version--20200408--add treeinfo.cxx treeinfo.h header files. TTreeManager library was designed mainly used internally at IHEP. It includes many useful classes/functions which help you manage TTree. You can find detailed documentation here: https://twiki.cern.ch/twiki/bin/viewauth/Cms/TtreeManagerUserGuide https://twiki.cern.ch/twiki/bin/viewauth/Cms/TtreeManagerSourceCodeDocumentation https://twiki.cern.ch/twiki/bin/viewauth/Cms/TtreeManagerSourceCodeDocumentationV123X https://twiki.cern.ch/twiki/bin/viewauth/Cms/TtreeManagerSourceCodeDocumentationV124X https://twiki.cern.ch/twiki/bin/viewauth/Cms/TtreeManagerSourceCodeDocumentationV125X https://twiki.cern.ch/twiki/bin/viewauth/Cms/TtreeManagerSourceCodeDocumentationV126X https://twiki.cern.ch/twiki/bin/viewauth/Cms/TtreeManagerSourceCodeDocumentationV127X Here you can find code examples: https://twiki.cern.ch/twiki/pub/auth/Cms/TTreeManagerExamples.tar.gz If you want compile TTreeManager library locally, you need ROOT installed first. then just run make to build TTreeManager library. Now you can use TTree Manager! Have fun! Yunjun Zhang @ IHEP CAS China. E-mail:[email protected] <|repo_name|>yunjunzhang/TTreeManager<|file_sep�� ֻ��һ��Ĳ��һ��ʱ��ڸ�Ҫ��ȥ��һ��ͼ��ʱ�ϵģ�Ӧ��Ҫȥ��Խ��һ��ͼ�Ĳ��ȥ��˵��һ��ͼ�Ĳ��Ϊָ��Ϊ��Ϊָʱ�䣬��һ��ͼ�Ĳ��Ϊָʱ�䣬ֵΪֵָ��Ƿ��ڵ㡣 #include #include #include using namespace std; struct event{ int v; }; bool comp(event& e,int x){ return e.v>num; vectorv(num); for(int i=0;i>v[i]; } vector::iterator low_bnd=v.begin(); low_bnd=find_if(low_bnd,v.end(),comp(v.front())); low_bnd++; vector::iterator up_bnd=v.end()-find_if(v.rbegin(),v.rend(),comp(v.back())); up_bnd--; if(low_bnd==up_bnd){ cout<<"No"<=10); cout<v=e.v;//access member via pointer member ev_ptr->v=ev_ptr->v+10;//access member via array member e.ev_ptr=&(pe->v);//access pointer member via pointer member ev_ptr=&(pe->ev_ptr->v);//access array member via array member return EXIT_SUCCESS; }<|repo_name|>yunjunzhang/TTreeManager<|file_sepcpp #ifndef TTREEMANAGER_TREEINFO_H #define TTREEMANAGER_TREEINFO_H #include//strlen #include//string #include//vector #include//cout,cin,cerr,clog,cerr,clog,sstream using namespace std; class TreeInfo{ public:\static const int kMaxBranchNameLength=kMaxBranchNameLength;kMaxBranchNameLength=kMaxBranchNameLength;kMaxBranchNameLength=kMaxBranchNameLength;kMaxBranchNameLength;kMaxBranchNameLength=kMaxBranchNameLength;kMaxBranchNameLength=kMaxBranchNameLength;kMaxBranchNameLength=kMaxBranchNameLength;kMaxBranchNameLength=kMaxBranchNameLength;kMaxBranchNameLength=kMaxBranchNameLength; //define maximum branch name length kMinEntryNumForAvgFillRatePerEvent //define minimum number entry used calculate average fill rate per event kMinEntryNumForAvgSizePerEvent //define minimum number entry used calculate average size per event kMinEntryNumForAvgTimePerEvent //define minimum number entry used calculate average time per event kDefaultPrintProgressEveryEntriesNumber //define default number entries print progress every time kDefaultPrintProgressEverySeconds //define default seconds print progress every time kDefaultPrintProgressEveryEntriesNumberWhenSaveProgressToFile //define default number entries print progress every time when save progress info into file kDefaultPrintProgressEverySecondsWhenSaveProgressToFile //define default seconds print progress every time when save progress info into file kDefaultSaveProgressInfoIntoFileEveryEntriesNumber //define default number entries save progress info into file every time kDefaultSaveProgressInfoIntoFileEverySeconds //define default seconds save progress info into file every time kDefaultAutoSaveEveryEntriesNumber //define default number entries auto save every time kDefaultAutoSaveEverySeconds //define default seconds auto save every time static const int max_branch_name_length=max_branch_name_length,max_branch_name_length,max_branch_name_length,max_branch_name_length,max_branch_name_length,max_branch_name_length,max_branch_name_length,max_branch_name_length,max_branch_name_length,k_max_tree_info_entry_num_for_avg_fill_rate_per_event,k_max_tree_info_entry_num_for_avg_size_per_event,k_max_tree_info_entry_num_for_avg_time_per_event,k_default_print_progress_every_entries_number,k_default_print_progress_every_seconds,k_default_print_progress_every_entries_number_when_save_progress_to_file,k_default_print_progress_every_seconds_when_save_progress_to_file,k_default_save_progress_info_into_file_every_entries_number,k_default_save_progress_info_into_file_every_seconds,k_default_auto_save_every_entries_number,k_default_auto_save_every_seconds,const char* tree_info_filename_format,const char* tree_info_filename_format_when_run_in_batch_mode,const char* tree_info_filename_format_when_run_in_batch_mode_with_ntuple_num,const char* branch_key_suffix,const char* branch_value_suffix,const char* branch_key_value_separator,char separator_between_each_treeinfo_line,char separator_between_each_treeinfo_line_when_run_in_batch_mode,char separator_between_each_treeinfo_line_when_run_in_batch_mode_with_ntuple_num,char separator_between_each_treeinfo_line_when_run_in_batch_mode_with_ntuple_num_and_subrun_index,char separator_between_each_subruninfo_line,char separator_between_each_subruninfo_line_when_run_in_batch_mode,char separator_between_each_subruninfo_line_when_run_in_batch_mode_with_ntuple_num,char separator_between_each_subruninfo_line_when_run_in_batch_mode_with_ntuple_num_and_subrun_index,int max_entry_num_to_check_fill_rate_per_event,int max_entry_num_to_check_size_per_event,int max_entry_num_to_check_time_per_event,int min_entry_num_for_avg_fill_rate_per_event,int min_entry_num_for_avg_size_per_event,int min_entry_num_for_avg_time_per_event,int default_print_progress_every_entries_number,int default_print_progress_every_seconds,int default_print_progress_every_entries_number_when_save_progress_to_file,int default_print_progress_every_seconds_when_save_progress_to_file,int default_save_progress_info_into_file_every_entries_number,int default_save_progress_info_into_file_every_seconds,int default_auto_save_every_entries_number,int default_auto_save_every_seconds,std::string tree_info_filename_format,std::string tree_info_filename_format_when_run_in_batch_mode,std::string tree_info_filename_format_when_run_in_batch_mode_with_ntuple_num,std::string branch_key_suffix,std::string branch_value_suffix,std::string branch_key_value_separator,std::string separator_between_each_treeinfo_line,std::string separator_between_each_treeinfo_line_when_run_in_batch_mode,std::string separator_between_each_treeinfo_line_when_run_in_batch_mode_with_ntuple_num,std::string separator_between_each_treeinfo_line_when_run_in_batch_mode_with_ntuple_num_and_subrun_index,std::string separator_between_each_subruninfo_line,std::string separator_between_each_subruninfo_line_when_run_in_batch_mode,std::string separator_between_each_subruninfo_line_when_run_in_batch_mode_with_ntuple_num,std::string separator_between_each_subruninfo_line_when_run_in_batch_mode_with_ntuple_num_and_subrun_index,bool check_fill_rate,is_debug_on,is_debug_on,is_debug_on,is_debug_on,is_debug_on,is_debug_on,is_debug_on,is_debug_on,is_debug_on,is_debug_on,bool check_size,bool check_time,bool check_size,bool check_time,bool check_size,bool check_time,bool check_size,bool check_time,bool check_size,bool check_time bool auto_clear_mem_after_read bool auto_clear_mem_after_read bool auto_clear_mem_after_read bool auto_clear_mem_after_read bool auto_clear_mem_after_read bool auto_clear_mem_after_read bool auto_clear_mem_after_read bool auto_clear_mem_after_read bool clear_all_mems_before_write clear_all_mems_before_write clear_all_mems_before_write clear_all_mems_before_write clear_all_mems_before_write clear_all_mems_before_write clear_all_mems_before_write clear_all_mems_before_write void set_max_entry_nume_ry_to_check_fill_rate_pe_reevent(void)set_max_entry_nume_ry_to_check_fill_rate_pe_reevent(void)set_max_entry_nume_ry_to_check_fill_rate_pe_reevent(void)set_max_entry_nume_ry_to_check_fill_rate_pe_reevent(void)set_max_entry_nume_ry_to_check_fill_rate_pe_reevent(void)set_max_entry_nume_ry_to_check_fill_rate_pe_reevent(void)set_max_entry_nume_ry_to_check_fille_rat_e_pereve_ent,void set_max_entry_nume_r_y_t_o_c_h_e_c_k_s_i_z_e