A Python program for the implementation of the Γ-method for Monte Carlo simulations
We present a modular analysis program written in Python devoted to the estimation of autocorrelation times for Monte Carlo simulations by means of the Γ-method algorithm. We give a brief review of this method and describe the main features of the program. The latter is characterized by a user-friend...
Saved in:
| Published in: | Computer physics communications Vol. 234; pp. 294 - 301 |
|---|---|
| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier B.V
01.01.2019
|
| Subjects: | |
| ISSN: | 0010-4655, 1879-2944 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | We present a modular analysis program written in Python devoted to the estimation of autocorrelation times for Monte Carlo simulations by means of the Γ-method algorithm. We give a brief review of this method and describe the main features of the program. The latter is characterized by a user-friendly interface and an open source environment which, along with its modularity, make it a versatile tool. Finally we present a simple application as an operational test for the program.
Program Title: UNEW
Program Files doi:http://dx.doi.org/10.17632/hvtvnjsg3h.1
Licensing provisions: MIT
Programming language: Python
Nature of problem: Computation of autocorrelation time for Monte Carlo generated data in an open source environment.
Solution method: Modular package implementing the Γ-method with advanced data handling. |
|---|---|
| AbstractList | We present a modular analysis program written in Python devoted to the estimation of autocorrelation times for Monte Carlo simulations by means of the Γ-method algorithm. We give a brief review of this method and describe the main features of the program. The latter is characterized by a user-friendly interface and an open source environment which, along with its modularity, make it a versatile tool. Finally we present a simple application as an operational test for the program.
Program Title: UNEW
Program Files doi:http://dx.doi.org/10.17632/hvtvnjsg3h.1
Licensing provisions: MIT
Programming language: Python
Nature of problem: Computation of autocorrelation time for Monte Carlo generated data in an open source environment.
Solution method: Modular package implementing the Γ-method with advanced data handling. |
| Author | De Palma, Barbara Mantovani, Luca Mosco, Nicola Erba, Marco |
| Author_xml | – sequence: 1 givenname: Barbara surname: De Palma fullname: De Palma, Barbara – sequence: 2 givenname: Marco orcidid: 0000-0002-2172-592X surname: Erba fullname: Erba, Marco email: marco.erba@unipv.it – sequence: 3 givenname: Luca surname: Mantovani fullname: Mantovani, Luca – sequence: 4 givenname: Nicola surname: Mosco fullname: Mosco, Nicola |
| BookMark | eNp9kE1OwzAQRi1UJErhAOx8gYRxYsexWFUVf1IRLMrach2bukriyDZIPQf34kykLSsWXY00872RvneJJr3vDUI3BHICpLrd5nrQeQGkzoHnAPQMTUnNRVYISidoCkAgoxVjF-gyxi0AcC7KKVrN8dsubXyPh-A_guqw9QGnjcGuG1rTmT6p5Mazt4ftz3fWmTHfHHIvvk8GL1RoPY6u-2wP2XiFzq1qo7n-mzP0_nC_Wjxly9fH58V8memi4CmraUGIataW1koQxSlToIilQlsmGAjBeFNWJWW1sGureFEAt0rVzDIrKKnKGSLHvzr4GIOxcgiuU2EnCci9FrmVoxa51yKBy1HLyPB_jHbHiiko154k746kGSt9ORNk1M702jQuGJ1k490J-hf0J3-c |
| CitedBy_id | crossref_primary_10_1007_JHEP01_2023_155 crossref_primary_10_1007_JHEP08_2022_220 crossref_primary_10_1016_j_cpc_2023_108750 crossref_primary_10_1155_2022_1383520 |
| Cites_doi | 10.1016/0370-2693(89)91563-3 10.1016/S0010-4655(03)00467-3 10.1063/1.1699114 10.1016/j.cpc.2004.10.004 10.1103/PhysRevLett.62.361 10.1016/j.cpc.2006.12.001 10.1007/BF01022990 |
| ContentType | Journal Article |
| Copyright | 2018 Elsevier B.V. |
| Copyright_xml | – notice: 2018 Elsevier B.V. |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.cpc.2018.07.004 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Physics |
| EISSN | 1879-2944 |
| EndPage | 301 |
| ExternalDocumentID | 10_1016_j_cpc_2018_07_004 S0010465518302534 |
| GroupedDBID | --K --M -~X .DC .~1 0R~ 1B1 1RT 1~. 1~5 29F 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AARLI AAXUO AAYFN ABBOA ABFNM ABMAC ABNEU ABQEM ABQYD ABXDB ABYKQ ACDAQ ACFVG ACGFS ACLVX ACNNM ACRLP ACSBN ACZNC ADBBV ADECG ADEZE ADJOM ADMUD AEBSH AEKER AENEX AFKWA AFTJW AFZHZ AGHFR AGUBO AGYEJ AHHHB AHZHX AI. AIALX AIEXJ AIKHN AITUG AIVDX AJBFU AJOXV AJSZI ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD ASPBG ATOGT AVWKF AXJTR AZFZN BBWZM BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FLBIZ FNPLU FYGXN G-2 G-Q GBLVA GBOLZ HLZ HME HMV HVGLF HZ~ IHE IMUCA J1W KOM LG9 LZ4 M38 M41 MO0 N9A NDZJH O-L O9- OAUVE OGIMB OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SBC SCB SDF SDG SES SEW SHN SPC SPCBC SPD SPG SSE SSK SSQ SSV SSZ T5K TN5 UPT VH1 WUQ ZMT ~02 ~G- 9DU AATTM AAXKI AAYWO AAYXX ABJNI ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD |
| ID | FETCH-LOGICAL-c227t-84211adbf48a91a745a0a1f49cf59509957d3634589fbfa72207faa85f5f94163 |
| ISICitedReferencesCount | 6 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000449126400026&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0010-4655 |
| IngestDate | Tue Nov 18 21:56:34 EST 2025 Sat Nov 29 03:58:14 EST 2025 Fri Feb 23 02:28:55 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Monte Carlo simulations Statistical mechanics Python Autocorrelation time |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c227t-84211adbf48a91a745a0a1f49cf59509957d3634589fbfa72207faa85f5f94163 |
| ORCID | 0000-0002-2172-592X |
| PageCount | 8 |
| ParticipantIDs | crossref_primary_10_1016_j_cpc_2018_07_004 crossref_citationtrail_10_1016_j_cpc_2018_07_004 elsevier_sciencedirect_doi_10_1016_j_cpc_2018_07_004 |
| PublicationCentury | 2000 |
| PublicationDate | January 2019 2019-01-00 |
| PublicationDateYYYYMMDD | 2019-01-01 |
| PublicationDate_xml | – month: 01 year: 2019 text: January 2019 |
| PublicationDecade | 2010 |
| PublicationTitle | Computer physics communications |
| PublicationYear | 2019 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Metropolis, Rosenbluth, Rosenbluth, Teller, Teller (b2) 1953; 21 Priestley (b6) 1981 Wolff (b4) 1989; 228 Wolff (b3) 1989; 62 Wolff (b8) 2007; 176 Madras, Sokal (b5) 1988; 50 Wolff (b1) 2004; 156 Luscher (b7) 2005; 165 Wolff (10.1016/j.cpc.2018.07.004_b1) 2004; 156 Wolff (10.1016/j.cpc.2018.07.004_b4) 1989; 228 Metropolis (10.1016/j.cpc.2018.07.004_b2) 1953; 21 Wolff (10.1016/j.cpc.2018.07.004_b3) 1989; 62 Madras (10.1016/j.cpc.2018.07.004_b5) 1988; 50 Wolff (10.1016/j.cpc.2018.07.004_b8) 2007; 176 Luscher (10.1016/j.cpc.2018.07.004_b7) 2005; 165 Priestley (10.1016/j.cpc.2018.07.004_b6) 1981 |
| References_xml | – volume: 21 start-page: 1087 year: 1953 end-page: 1092 ident: b2 publication-title: J. Chem. Phys. – volume: 62 start-page: 361 year: 1989 end-page: 364 ident: b3 publication-title: Phys. Rev. Lett. – volume: 50 start-page: 109 year: 1988 end-page: 186 ident: b5 publication-title: J. Stat. Phys. – volume: 156 start-page: 143 year: 2004 end-page: 153 ident: b1 publication-title: Comput. Phys. Comm. – volume: 165 start-page: 199 year: 2005 end-page: 220 ident: b7 publication-title: Comput. Phys. Comm. – volume: 228 start-page: 379 year: 1989 end-page: 382 ident: b4 publication-title: Phys. Lett. B – year: 1981 ident: b6 article-title: Spectral Analysis and Time Series/ M.B. Priestley – volume: 176 start-page: 383 year: 2007 ident: b8 publication-title: Comput. Phys. Comm. – volume: 228 start-page: 379 issue: 3 year: 1989 ident: 10.1016/j.cpc.2018.07.004_b4 publication-title: Phys. Lett. B doi: 10.1016/0370-2693(89)91563-3 – year: 1981 ident: 10.1016/j.cpc.2018.07.004_b6 – volume: 156 start-page: 143 year: 2004 ident: 10.1016/j.cpc.2018.07.004_b1 publication-title: Comput. Phys. Comm. doi: 10.1016/S0010-4655(03)00467-3 – volume: 21 start-page: 1087 issue: 6 year: 1953 ident: 10.1016/j.cpc.2018.07.004_b2 publication-title: J. Chem. Phys. doi: 10.1063/1.1699114 – volume: 165 start-page: 199 year: 2005 ident: 10.1016/j.cpc.2018.07.004_b7 publication-title: Comput. Phys. Comm. doi: 10.1016/j.cpc.2004.10.004 – volume: 62 start-page: 361 year: 1989 ident: 10.1016/j.cpc.2018.07.004_b3 publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.62.361 – volume: 176 start-page: 383 issue: 5 year: 2007 ident: 10.1016/j.cpc.2018.07.004_b8 publication-title: Comput. Phys. Comm. doi: 10.1016/j.cpc.2006.12.001 – volume: 50 start-page: 109 issue: 1 year: 1988 ident: 10.1016/j.cpc.2018.07.004_b5 publication-title: J. Stat. Phys. doi: 10.1007/BF01022990 |
| SSID | ssj0007793 |
| Score | 2.2973263 |
| Snippet | We present a modular analysis program written in Python devoted to the estimation of autocorrelation times for Monte Carlo simulations by means of the Γ-method... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 294 |
| SubjectTerms | Autocorrelation time Monte Carlo simulations Python Statistical mechanics |
| Title | A Python program for the implementation of the Γ-method for Monte Carlo simulations |
| URI | https://dx.doi.org/10.1016/j.cpc.2018.07.004 |
| Volume | 234 |
| WOSCitedRecordID | wos000449126400026&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1879-2944 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0007793 issn: 0010-4655 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1db9MwFLXKBhIviE8xGMgP7IUqKHHs2H6spk6A2FSJIvUtcj4sdeqSaumq8Tfgf-037Tq2k4wBAiReotZymsr3xPfk5t5zEXoDpEKHOSuCkGsS0FInQaZKHVAlIyGLuGREt80m-MmJWCzkbDT65mthtiteVeLyUq7_q6lhDIxtSmf_wtzdj8IAfAajwxHMDsc_MvxkPPtqBAF86lWXSLg887niniWa0YPD6YGMA9tJup17bASrTCbIqh43yzPX36sZ0ljfC8IFRhqTmd7XmfS96sux6c-iBi82OgYP31ytUF73YfFqU29tl6nxp4s-i-i4bvK6A64ahipMddSNUMXtGhq7J4MnMCpu1iPZbVhwGRBplSH9Pk1c1NPttLY3snPasb3MLX9gQxOn7_K1kauMRCvUGtLe-XUpiZ9bpaLECNTFwANjegftEs4k7JS7kw_TxcfOv3PupJzd__bvytuswR8u9HO2M2Aw84fogXv0wBMLmUdoVFaP0b2ZteATNJ9gCxzsgIMBDBgggm8CB9e6Hb367kDTzmtBg1vQ4AFonqIvR9P54fvA9dwIckL4JhCURJEqMk0F3K2KU6ZCFWkqc80kkEvJeBEnMWVC6kwrTgjc5EoJppmWhtw_QztVXZXPEWbApkkmGC2SjHKmRZHTWIWZ4lmUSBbvodCvTZo7QXrTF2WV-szD0xSWMzXLmYYmTYLuobfdKWurxvK7ydQveOropKWJKaDj16e9-LfTXqL7PeT30c7m_KJ8he7m282yOX_tMHQNBf2YJQ |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+Python+program+for+the+implementation+of+the+%CE%93-method+for+Monte+Carlo+simulations&rft.jtitle=Computer+physics+communications&rft.au=De+Palma%2C+Barbara&rft.au=Erba%2C+Marco&rft.au=Mantovani%2C+Luca&rft.au=Mosco%2C+Nicola&rft.date=2019-01-01&rft.pub=Elsevier+B.V&rft.issn=0010-4655&rft.eissn=1879-2944&rft.volume=234&rft.spage=294&rft.epage=301&rft_id=info:doi/10.1016%2Fj.cpc.2018.07.004&rft.externalDocID=S0010465518302534 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0010-4655&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0010-4655&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0010-4655&client=summon |