Proceedings of the Python in Science Conference最新文献

An Accessible Python based Author Identification Process 一个可访问的基于Python的作者识别过程

Proceedings of the Python in Science Conference Pub Date : 1900-01-01 DOI: 10.25080/gerudo-f2bc6f59-003

Anthony F. Breitzman

{"title":"An Accessible Python based Author Identification Process","authors":"Anthony F. Breitzman","doi":"10.25080/gerudo-f2bc6f59-003","DOIUrl":"https://doi.org/10.25080/gerudo-f2bc6f59-003","url":null,"abstract":"—Author identification also known as ‘author attribution’ and more recently ‘forensic linguistics’ involves identifying true authors of anonymous texts. The Federalist Papers are 85 documents written anonymously by a combination of Alexander Hamilton, John Jay, and James Madison in the late 1780’s supporting adoption of the American Constitution. All but 12 documents have confirmed authors based on lists provided before the author’s deaths. Mosteller and Wallace in 1963 provided evidence of authorship for the 12 disputed documents, however the analysis is not readily accessible to non-statisticians. In this paper we replicate the analysis but in a much more accessible way using modern text mining methods and Python. One surprising result is the usefulness of filler-words in identifying writing styles. The method described here can be applied to other authorship questions such as linking the Unabomber manifesto with Ted Kaczynski, identifying Shakespeare’s collaborators, etc. Although the question of authorship of the Federalist Papers has been studied before, what is new in this paper is we highlight a process and tools that can be easily used by Python programmers, and the methods do not rely on any knowledge of statistics or machine learning.","PeriodicalId":364654,"journal":{"name":"Proceedings of the Python in Science Conference","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116938521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

causal-curve: tools to perform causal inference given a continuous treatment 因果曲线:在给定连续处理的情况下进行因果推理的工具

Proceedings of the Python in Science Conference Pub Date : 1900-01-01 DOI: 10.25080/majora-1b6fd038-01d

R. Kobrosly

引用次数: 0

Bayesian Statistics with Python, No Resampling Necessary 贝叶斯统计与Python，没有重新采样的必要

Proceedings of the Python in Science Conference Pub Date : 1900-01-01 DOI: 10.25080/gerudo-f2bc6f59-005

C. Lindsey

引用次数: 0

EEG-to-fMRI Neuroimaging Cross Modal Synthesis in Python Python中的eeg - fmri神经成像交叉模态合成

Proceedings of the Python in Science Conference Pub Date : 1900-01-01 DOI: 10.25080/gerudo-f2bc6f59-007

David Calhas

引用次数: 0

NumPy – Annual Update NumPy -年度更新

Proceedings of the Python in Science Conference Pub Date : 1900-01-01 DOI: 10.25080/majora-1b6fd038-026

Inessa Pawson

引用次数: 2

Inside NumPy: Preparing for the next decade NumPy内部:为下一个十年做准备

Proceedings of the Python in Science Conference Pub Date : 1900-01-01 DOI: 10.25080/majora-7ddc1dd1-01d

R. Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, S. Walt, Charles R. Harris

引用次数: 1

Using Python to Model Biomass Pyrolysis Reactors 使用Python模拟生物质热解反应器

Proceedings of the Python in Science Conference Pub Date : 1900-01-01 DOI: 10.25080/MAJORA-7DDC1DD1-01A

G. Wiggins

引用次数: 0

Spatial Microsimulation and Activity Allocation in Python: An Update on the Likeness Toolkit 空间微模拟和活动分配在Python:对相似性工具包的更新

Proceedings of the Python in Science Conference Pub Date : 1900-01-01 DOI: 10.25080/gerudo-f2bc6f59-00c

Joseph V. Tuccillo, James D. Gaboardi

{"title":"Spatial Microsimulation and Activity Allocation in Python: An Update on the Likeness Toolkit","authors":"Joseph V. Tuccillo, James D. Gaboardi","doi":"10.25080/gerudo-f2bc6f59-00c","DOIUrl":"https://doi.org/10.25080/gerudo-f2bc6f59-00c","url":null,"abstract":"—Understanding human security and social equity issues within human systems requires large-scale models of population dynamics that simulate high-fidelity representations of individuals and access to essential activities (work/school, social, errands, health). Likeness is a Python toolkit that provides these capabilities for Oak Ridge National Laboratory’s (ORNL) UrbanPop spatial microsimulation project. In step with the initial development phase for Likeness (2021 - 2022), we built out several foundational examples of work/school and health service access. In this paper, we describe expansion and scaling of Likeness capabilities to metropolitan areas in the United States. We then provide an integrated demonstration of our methods based on a case study of Leon County, FL and perform validation exercises on 1) neighborhood demographic composition and 2) visits by demographic cohorts (gender/age) obtained from point of interest (POI) footfall data for essential services (grocery stores). Taking into account lessons learned from our case study, we scope improvements to our model as well as provide a roadmap of the anticipated Likeness development cycle into 2023 - 2024.","PeriodicalId":364654,"journal":{"name":"Proceedings of the Python in Science Conference","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121960650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data Reduction Network 数据简化网络

Proceedings of the Python in Science Conference Pub Date : 1900-01-01 DOI: 10.25080/gerudo-f2bc6f59-012

Haoyin Xu, Haw-minn Lu, J. Unpingco

{"title":"Data Reduction Network","authors":"Haoyin Xu, Haw-minn Lu, J. Unpingco","doi":"10.25080/gerudo-f2bc6f59-012","DOIUrl":"https://doi.org/10.25080/gerudo-f2bc6f59-012","url":null,"abstract":"—Multidimensional categorical data is widespread but not easily visualized using standard methods. For example, questionnaire (e.g. survey) data generally consists of questions with categorical responses (e.g., yes/no, hate/dislike/neutral/like/love). Thus, a questionnaire with 10 questions, each with five mutually exclusive responses, gives a dataset of 5 10 possible observations, an amount of data that would be hard to reasonably collect. Hence, this type of dataset is necessarily sparse. Popular methods of handling categorical data include one-hot encoding (which exacerbates the dimensionality problem) and enumeration, which applies an unwarranted and potentially misleading notional order to the data. To address this, we introduce a novel visualization method named Data Reduction Network (DRN). Using a network-graph structure, the DRN denotes each categorical feature as a node with interrelationships between nodes denoted by weighted edges. The graph is statistically reduced to reveal the strongest or weakest path-wise relationships between features and to reduce visual clutter. A key advantage is that it does not “lose” features, but rather represents interrelationships across the entire categorical feature set without eliminating weaker relationships or features. Indeed, the graph representation can be inverted so that instead of visualizing the strongest interrelationships, the weakest can be surfaced. The DRN is a powerful visualization tool for multi-dimensional categorical data and in particular data derived from surveys and questionaires.","PeriodicalId":364654,"journal":{"name":"Proceedings of the Python in Science Conference","volume":"136-137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117146549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Visualize 3D scientific data in a Pythonic way like matplotlib 以python方式可视化3D科学数据，如matplotlib

Proceedings of the Python in Science Conference Pub Date : 1900-01-01 DOI: 10.25080/majora-1b6fd038-01c

T. Koyama

引用次数: 0