Python compiler

Minimal Python IDE for GNU Emacs

An open source python version of the Lindenmayer Systems.

Material of the course: exercises

A Blender add-on for Lindenmayer systems (aka L-systems), based on the Boudon et al L-Py library.

LPython extension to VSCode

Lpyexplore的Vue.js的Ui组件库

LLM Python

A conda-smithy repository for lpython.

Training material for L-Py

关于json视图工具、coreText、定时器、转场动画、图片压缩、事件传递。。。工具类

OpenAlea Widgets for Jupyter Kernel

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Keygen for the D-link DIR-640L with SSID dlink-XXXX

Learn Python

simplest Python interpreter with many many bugs and slow speed

ANALYZING ROAD SAFETY & TRAFFIC DEMOGRAPHICS IN THE UK (Multi-class Classification) SUMMARY Here, I am aim to analyze the Road Safety and Traffic Demographics dataset (UK), containing accidents reported by the police between the years of 2004 - 2017. PROJECT GOALS: Identify factors responsible for most of the reported accidents. Build a machine learning model that is capable of accurately predicting the severity of an accident. Provide recommendations to the Department of Transport (UK Government), to improve road safety policies and prevent recurrences of severe accidents where possible. PACKAGES USED: Scikit-learn, numpy, pandas, imblearn (imbalanced-learn), seaborn, Matplotlib MOTIVATION World Health Organization (WHO) reported that more than 1.25 million people die each year while 50 million are injured as a result of road accidents worldwide. Road accidents are the 10th leading cause of death globally. On current trends, road traffic accidents are to become the 7th leading cause of death by 2030 making it a major public health concern. Between the years 2005 and 2016, there were roughly 2 million road accidents reported in the United Kingdom (UK) alone of which 16,000 were fatal. As a big data project, I wanted to explore the traffic demographics data in greater detail using machine learning! CONTEXT The UK government amassed traffic data from 2004 to 2017, recording over 2 million accidents in the process and making this one of the most comprehensive traffic data sets out there. It's a huge picture of a country undergoing change. Note that all the contained accident data comes from police reports, so this data does not include minor incidents. For steps undertaken to pre-process and clean the data, please view the "Data Cleansing & Descriptive Analysis_UK Traffic Demographics.ipynb" file DESCRIPTIVE ANALYTICS (EDA) Tools used include Python, Tableau, MS PowerBI Percent (%) distribution of target classes Percent dist of Accident Severity As seen above, the data is highly imbalanced. For detailed steps undertaken to deal with the imbalanced data, please view the "Modelling_Predictive Analytics_UK Traffic Demographics.ipynb" file. This article provides some great tips on utilizing the correct performance metrics when analyzing a models performance trained on an imbalanced dataset. This article describes several strategies that can help combat the case of a severly imbalanced dataset. Methods include: Resampling strategies (under - Tomek Links, Cluster Centroids, over sampling - SMOTE) Using Decision Tree based models Using Cost-Sensitive training (Penalize algorithms) Number of accidents by Year and Accident Severity Total accidents by year and severity It can be seen above that the trend seems to be increasing as the years go. In addition, the spike between 2008 - 2009 was because of a enhancement in the reporting system introduced in the UK in 2009, where all accident including minor accidents needed to be reported by the police so as to match the counts represented by hospitals, insurance claims etc. Accidents density by Location geomap Most accidents took place in major cities - Birmingham, London, leeds, Newcastle Accidents by Gender and Age Accidents by gender and age Accidents by Day of the week and Year Accidents by year and weekday Most accidents take place on a Friday Vehicle Manoever at time of accident Vehicle Manoever at time of accident Most accidents take place as a result of overtaking For more findings, please go to the "Images" folder. For steps undertaken to carry out some predictive modeling and hyper-parameter tuning, please view the "Modelling_Predictive Analytics_UK Traffic Demographics.ipynb" file. RECOMMENDATIONS TO THE DEPARTMENT OF TRANSPORT (UK) Decrease emergency response times during afternoon rush-hours (15-19) especially on Fridays. Allocate resources to investigate high density traffic points and identify new infrastructure needs to divert traffic from dual-carriage ways. Explore conditions of vehicles and casualties such as vehicle type, age of vehicles registered, pedestrian movements, etc. for policy makers. Adopt comprehensive distracted driving laws that increase penalties for drivers who commit traffic violations like aggressive overtaking. ACKNOWLEDGEMENTS The license for this dataset is the Open Givernment Licence used by all data on data.gov.uk. The raw datasets are available from the UK Department of Transport website. I had a lot of fun working on this dataset and learned a lot in the process. I plan to further my research in the area of predictive modeling using imabalanced data and how to effectively build a highly robust model for future projects. About Here, I analyze the Road Safety and Traffic Demographics dataset (UK), containing accidents reported by the police between the years of 2004 - 2017. Topics accident-rate accident-severity imbalanced-data imbalanced-learning road-accident reported-accidents road-safety uk-government transport traffic-demographics severe-accidents pca classification Resources Readme Releases No releases published Packages No packages published Languages Jupyter Notebook 100.0% © 2020 GitHub, Inc.

Generates 3D models of trees along with corresponding metadata (Segmentation info, 3D info) based on predefined pruning and tying rules as seen in modern fruit orchards

爬虫学习

SparkFun Gyro Breakout - LPY503AL (Dual 30°/s)

This is an addon to blender that loads in 3D models of trees (Generated using LPy treesim (https://github.com/OSUrobotics/lpy_treesim) to generate virtual orchards of vartying features. These orchards can further be used to generate data for tasks like segmentation and planning or used to test agricultural robots.

Sources of deployed webpage behind https://lpython.org/

L-system based generation of a strawberry plant using lpy

第一个仓库

A powerful python operations tool

Learning python

note8888

Java interface for Yahoo Finance API

顶部信息View+工具条+底部的ScrollView（ScrollView中又有n个View（可以是tableview 或者CollctionView））（不会有手势冲突）实现功能：根据底部的scrollView的拖动，设置顶部的View向上偏移

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