Cryptocurrency

Python Dash app that tracks whale activity in cryptocurrency markets

crypto-whale-watching-app Welcome! This is a Python-based Dash app meant to track whale activity in buy / sell walls on crypto-currency exchanges (presently just operational for GDAX, but more exchanges to come). This document aims to explain the purpose, functionality, and future of this project. Please do share this with your fellow coders / traders / crypto-aficionados, and contribute to the future of this project by calling out issues, requesting new features, and submitting pull requests to improve the app. The […]

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Network

A simple URL shortener app using AWS Chalice

url-shortener-chalice A simple URL shortener app using AWS Chalice. Please make sure your to configure your AWS credentials before starting with deploying things onto AWS. aws configure Dependencies are included in the file: requirements.txt Please note the below chalice scheduler is configured to clean up the dynamo-db table entries every 24 hours. Deployment steps: aws cloudformation deploy –template-file .chalicedynamodb_cf_template.yaml –stack-name “url-shortner-stack” chalice deploy Testing steps screenshots: Teardown steps: chalice delete aws cloudformation delete-stack –stack-name “url-shortner-stack” GitHub https://github.com/rg666/url-shortener-chalice    

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Tool

Fitting thermodynamic models with pycalphad

ESPEI ESPEI, or Extensible Self-optimizing Phase Equilibria Infrastructure, is a tool for thermodynamic database development within the CALPHAD method. It uses pycalphad for calculating Gibbs free energies of thermodynamic models. Installation Anaconda (recommended) ESPEI does not require any special compiler, but several dependencies do. Therefore it is suggested to install ESPEI from conda-forge. conda install -c conda-forge espei What is ESPEI? ESPEI parameterizes CALPHAD models with enthalpy, entropy, and heat capacity data using the corrected Akiake Information Criterion (AICc). This […]

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Neural Machine Translation (NMT), NMT Leave a comment

Machine Translation Weekly 83: On Language Indentity and Zero-Shot Transfer

This week I will comment on two papers on zero-shot cross-lingual model transfer which do not focus on the representation quality but on the transfer itself. The title of the first one is Language Embeddings for Typology and Cross-lingual Transfer Learning and has authors from UC Davis. The second is Syntax-augmented Multilingual BERT for Cross-lingual Transfer and has authors from UC LA and Facebook AI. Both papers will appear at this year’s ACL. Just a reminder, zero-shot model transfer means […]

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Python

Python Practice Problems: Parsing CSV Files

Day,MxT,MnT,AvT,AvDP,1HrP TPcpn,PDir,AvSp,Dir,MxS,SkyC,MxR,Mn,R AvSLP 1,88,59,74,53.8,0,280,9.6,270,17,1.6,93,23,1004.5 2,79,63,71,46.5,0,330,8.7,340,23,3.3,70,28,1004.5 3,77,55,66,39.6,0,350,5,350,9,2.8,59,24,1016.8 4,77,59,68,51.1,0,110,9.1,130,12,8.6,62,40,1021.1 5,90,66,78,68.3,0,220,8.3,260,12,6.9,84,55,1014.4 6,81,61,71,63.7,0,30,6.2,30,13,9.7,93,60,1012.7 7,73,57,65,53,0,50,9.5,50,17,5.3,90,48,1021.8 8,75,54,65,50,0,160,4.2,150,10,2.6,93,41,1026.3 9,86,32,59,61.5,0,240,7.6,220,12,6,78,46,1018.6 10,84,64,74,57.5,0,210,6.6,50,9,3.4,84,40,1019 11,91,59,75,66.3,0,250,7.1,230,12,2.5,93,45,1012.6 12,88,73,81,68.7,0,250,8.1,270,21,7.9,94,51,1007 13,70,59,65,55,0,150,3,150,8,10,83,59,1012.6 14,61,59,60,55.9,0,60,6.7,80,9,10,93,87,1008.6 15,64,55,60,54.9,0,40,4.3,200,7,9.6,96,70,1006.1 16,79,59,69,56.7,0,250,7.6,240,21,7.8,87,44,1007 17,81,57,69,51.7,0,260,9.1,270,29,5.2,90,34,1012.5 18,82,52,67,52.6,0,230,4,190,12,5,93,34,1021.3 19,81,61,71,58.9,0,250,5.2,230,12,5.3,87,44,1028.5 20,84,57,71,58.9,0,150,6.3,160,13,3.6,90,43,1032.5 21,86,59,73,57.7,0,240,6.1,250,12,1,87,35,1030.7 22,90,64,77,61.1,0,250,6.4,230,9,0.2,78,38,1026.4 23,90,68,79,63.1,0,240,8.3,230,12,0.2,68,42,1021.3 24,90,77,84,67.5,0,350,8.5,10,14,6.9,74,48,1018.2 25,90,72,81,61.3,0,190,4.9,230,9,5.6,81,29,1019.6 26,97,64,81,70.4,0,50,5.1,200,12,4,107,45,1014.9 27,91,72,82,69.7,0,250,12.1,230,17,7.1,90,47,1009 28,84,68,76,65.6,0,280,7.6,340,16,7,100,51,1011 29,88,66,77,59.7,0,40,5.4,20,9,5.3,84,33,1020.6 30,90,45,68,63.6,0,240,6,220,17,4.8,200,41,1022.7    

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Advanced, Listicle, NLP, Python, Text, Unstructured Data

Must Known Techniques for text preprocessing in NLP

This article was published as a part of the Data Science Blogathon In any Machine learning task, cleaning or preprocessing the data is as important as model building. Text data is one of the most unstructured forms of available data and when comes to deal with Human language then it’s too complex. Have you ever wondered how Alexa, Siri, Google assistant can understand, process, and respond in Human language. NLP is a technology that works behind it where before any response […]

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NLP

3 Painful Mistakes Leaders Can Avoid When Buying AI Solutions

85% of global executives believe that AI can become their competitive advantage. So, the rush to AI adoption is understandable. Unfortunately, implementing AI from scratch takes time, and success comes with experience in building and deploying solutions. To speed things up, “buying” instead of building from scratch seems like a sensible way to get started; You don’t have to hire a team of data scientists, spend on additional infrastructure, or have support staff on call to troubleshoot model problems. Plus, […]

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Text

Python 3 module to print out long strings of text with intervals of time inbetween

Python-Fastprint Python 3 module to print out long strings of text with intervals of time inbetween Install:pip install fastprint Sync Usage: from fastprint import pr pr(“longntext”) # each line takes 1 second pr(“othernlongtext”, 0.2) # each line takes 0.2 seconds Async usage: from async_fastprint import async_pr async def foo: return async_pr(“Thisnisnasynchrounous!”) Check out example.py for more GitHub https://github.com/ThatOneCalculator/Python-Fastprint    

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Simulation

A Scheil-Gulliver simulation tool using pycalphad

scheil A Scheil-Gulliver simulation tool using pycalphad. import matplotlib.pyplot as plt from pycalphad import Database, variables as v from scheil import simulate_scheil_solidification # setup the simulation parameters dbf = Database(‘alzn_mey.tdb’) comps = [‘AL’, ‘ZN’, ‘VA’] phases = sorted(dbf.phases.keys()) liquid_phase_name = ‘LIQUID’ initial_composition = {v.X(‘ZN’): 0.3} start_temperature = 850 # perform the simulation sol_res = simulate_scheil_solidification(dbf, comps, phases, initial_composition, start_temperature, step_temperature=1.0) # plot the result for phase_name, amounts in sol_res.cum_phase_amounts.items(): plt.plot(sol_res.temperatures, amounts, label=phase_name) plt.plot(sol_res.temperatures, sol_res.fraction_liquid, label=’LIQUID’) plt.ylabel(‘Phase Fraction’) plt.xlabel(‘Temperature (K)’) plt.title(‘Al-30Zn […]

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RESTful API

A friendly Python wrapper around the Deezer API

Deezer Python Client A friendly Python wrapper around the Deezer API. Installation The package is published onPyPI and can be installed by running: pip install deezer-python Basic Use Easily query the Deezer API from you Python code. The data returned by the DeezerAPI is mapped to python resources: >>> client = deezer.Client() >>> client.get_album(680407).title ‘Monkey Business’    

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