User Guide

Colab Bash1 Bash2 Verilog C1 C2 C3 C++1 C++2 C++3 C++4 C# Java1 Java2 PHP1 PHP2 R Python1 Python2 Python3 Pytorch Manim Flask Django1 Django2 HTML1 HTML2 Sass CSS1 CSS2 AngularJS Ajax JavaScript1 JavaScript2 React1 Recat2 jQuery1 jQuery2 bootstrap Swift Node.js JSP SQL1 SQL2 MySQL SQLite PostgreSQL Hadoop Spark VBA numpy sympy scipy statsmodels pytorch scikit-learn plotly dash-html dash-core dash-bootstrap manim simpy

Manuals

External Libraries :
Applications : SI, FI, BO, CH, EE, AI, RL, DL, ML, FA, ST, NM, PL, UT, MA

Ailever Library

from ailever import dashboard

AIL-F] Forecast

Forecast Package

from ailever.forecast import dashboard
from ailever.forecast.STOCK import krx, Ailf_KR

from ailever.utils import source
source('AIL-F-0000')

Source B C

: github : edu

dashboard STOCK

AIL-A] Analysis

Analysis Package

from ailever.analysis import dashboard

from ailever.utils import source
source('AIL-A-0000')

Source B C

: github : edu

dashboard PHYSICS CHEMISTRY FINANCE ACCOUNTING

AIL-M] Machine

Machine Package

from ailever.machine import dashboard

from ailever.utils import source
source('AIL-M-0000')

Source B C

: github : edu

dashboard RL DL ML ST NM

AIL-L] Language

Language Package

from ailever.language import dashboard

from ailever.utils import source
source('AIL-L-0000')

Source B C

: github : edu

dashboard module

AIL-D] Detection

Detection Package

from ailever.detection import dashboard

from ailever.utils import source
source('AIL-D-0000')

Source B C

: github : edu

dashboard module

AIL-C] Captioning

Captioning Package

from ailever.captioning import dashboard

from ailever.utils import source
source('AIL-C-0000')

Source B C

: github : edu

dashboard module

AIL-U] Utile

Utile Package

from ailever.utils import dashboard
from ailever.utils import source
from ailever.utils import VISUAL

dashboard source VISUAL

External Libraries

lib-PX] plotly-express

from ailever.utils import source
source('lib-PX-0000')

Source latex katex

px.timeline : lib-PX-0000 : edu

px.sunburst : lib-PX-0001 : edu

px.treemap : lib-PX-0002 : edu

lib-GO] plotly graph object

from ailever.utils import source
source('lib-GO-0000')

Source latex katex

go.Bar : lib-GO-0000 : edu

go.Bar : lib-GO-0001 : edu

go.Sankey : lib-GO-0002 : edu

go.Treemap : lib-GO-0003 : edu

go.Sunburst : lib-GO-0004 : edu

Applications

SI] Society Issue

from ailever.utils import source
source('SI-0000')

Source Manual B C

[health] COVID-19 : Global Dataset : github : edu

[health] COVID-19 : Domestic Dataset (Korea) : SI-0000 : edu

: github : edu

FI] Financial Engineering

from ailever.utils import source
source('FI-0000')

Source Manual pandas-datareader quant-c-library

[finance] Korean Stock Item Dataset : From KRX API : github : edu

[finance] Korean Stock Real-Time Price : From KRX API : FI-0001 : edu

[finance] Korean Stock Real-Time Price (with saving .csv file) : From KRX API : FI-0002 : edu

[finance] Korean Stock Dataset : From FinanceDataReader : FI-0000 : edu

[finance] Time Series Analysis For Stock Price : github : edu

[finance] Momentum investing : github : edu

[finance] Bollinger Bands : github : edu

: github : edu

BO] Biological Engineering

from ailever.utils import source
source('BO-0000')

Source Manual B C

: github : edu

CH] Chemical Engineering

from ailever.utils import source
source('CH-0000')

Source Manual B C

: github : edu

EE] Electronic Engineering

from ailever.utils import source
source('EE-0000')

Source Manual PYSCF C

: github : edu

AI] Artificial Intelligence

from ailever.utils import source
source('AI-0000')

Source Manual librosa C

[sound] : github : edu

[computer vision] : github : edu

[natural language processing] Word Cloud : github : edu

: github : edu

RL] Reinforcement Learning

from ailever.utils import source
source('RL-0000')

Source Manual gym rlcard Softlearning stable-baselines rl-baselines-zoo policy gradient openai spinningup

[theory] Reinforcement Learning Theory : RL-theory : edu

[value-based] Markov Decision Process : github : edu

[value-based] Synchronous Dynamic Programming : github : edu

[value-based] Asynchronous Dynamic Programming : github : edu

::: In-Place DP (Full Sweeping) : github : edu

::: Prioritized Sweeping : github : edu

::: Real Time DP : github : edu

[value-based] Policy Evaluation : github : edu

::: Monte-Carlo Policy Evaluation : Non-Bootstrap, Unbiased estimator of Value function : github : edu

::: Temporal-Difference Policy Evaluation : Bootstrap, Biased estimator of Value function : github : edu

: github : edu

DL] Deep Learning

from ailever.utils import source
source('DL-0000')

Source Manual pytorch torch-summary geotorch transformers open-mmlab gan

[theory] Deep Learning Theory : DL-theory : edu

[computational resource] Directed Acyclic Graph : DL-0000 : edu

[computational resource] Information for Network Structure : DL-0001 : edu

[computational resource] Checkpoints : DL-0002 : edu

[methodology] Normalization : github : edu

::: Batch Normalization : github : edu

::: Layer Normalization : github : edu

::: Instance Normalization : github : edu

[methodology] Skip connection : github : edu

[methodology] Activation : github : edu

[methodology] Dropout : github : edu

[methodology] Initialization : github : edu

[methodology] Optimizer : github : edu

[lightweight] Pruning : github : edu

[lightweight] Quantization : github : edu

[lightweight] Knowledge Distillation : github : edu

[multi-layer perceptron] Linear Regression : github : edu

[multi-layer perceptron] Logistics Regression : github : edu

[multi-layer perceptron] Support Vector Machine : github : edu

[convolutional] LeNet-5 : github : edu

[convolutional] VGG : github : edu

[convolutional] Inception-v3 : github : edu

[convolutional] ResNet : github : edu

[convolutional] DenseNet : github : edu

[convolutional] SqueezeNet : github : edu

[convolutional] MobileNet : github : edu

[convolutional] ShuffleNet : github : edu

[recurrent] Hopfield Network : github : edu

[recurrent] Vanilla RNN : github : edu

[recurrent] LSTM : DL-0003 : edu

[recurrent] bi-LSTM : github : edu

[recurrent] GRU : github : edu

[recurrent] Attention : DL-0004 : edu

[recurrent] Transformer : github : edu

[recurrent] BERT : github : edu

[recurrent] GPT : github : edu

[generative] Restricted Boltzmann Machine : github : edu

[generative] Denoising AE : github : edu

[generative] Stochastic Contractive AE : github : edu

[generative] Variational AE : github : edu

[generative] Adversarial AE : github : edu

[generative] Conditional AAE : github : edu

[generative] Vanilla GAN : github : edu

[generative] Deep Convolutional GAN : github : edu

[generative] Conditional GAN : github : edu

[bayesian] Hyperparameters Optimization : github : edu

[adversarial] Adversarial Attack : github : edu

: github : edu

ML] Machine Learning

from ailever.utils import source
source('ML-0000')

Source Manual scikit-learn mlxtend

[theory] Machine Learning Theory : ML-theory : edu

[preprocessing] Scaling : github : edu

[classifier] Decision Tree : github : edu

[classifier] Naive Bayes : github : edu

::: Gaussian Naive Bayes : github : edu

::: Bernoulli Naive Bayes : github : edu

::: Multinomial Naive Bayes : github : edu

[classifier] Logistic Regression : github : edu

[classifier] Linear Discriminant Analysis : github : edu

[classifier] Quadratic Discriminant Analysis : github : edu

[classifier] Support Vector Machines | kernel : ‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’ : github : edu

[classifier] K-Nearest Neighbors : github : edu

[clustering] K-Means : github : edu

[clustering] Affinity Propagation : github : edu

[clustering] DBSCAN : github : edu

[manifold] Isomap Embedding : github : edu

[manifold] Locally Linear Embedding : github : edu

[manifold] Multidimensional scaling : github : edu

[manifold] t-distributed Stochastic Neighbor Embedding : github : edu

[manifold] Locally Linear Embedding : github : edu

[feature selection] Recursive Feature Elimination : github : edu

[feature selection] Principal Component Analysis : github : edu

[ensemble] Bagging : github : edu

::: Bagged Decision Trees : github : edu

::: Random Forest : github : edu

::: Extra Trees : github : edu

[ensemble] Boosting : github : edu

::: Adaptive Boosting(AdaBoost) : github : edu

::: Gradient Boosting Machine(GBM) : github : edu

::: eXtreme Gradient Boosting(XGBoost) : github : edu

::: LightGBM : github : edu

[ensemble] Voting : github : edu

[ensemble] Stacking : github : edu

: github : edu

FA] Factor Analysis

from ailever.utils import source
source('FA-0000')

Source Manual factor-analyzer semopy

: github : edu

ST] Statistics

from ailever.utils import source
source('ST-0000')

Source Manual numpy scipy symfit statsmodels arch quantecon hmmlearn pymc3

[correlation] Pearson’s Correlation : github : edu

[correlation] Spearman’s Rank Correlation : github : edu

[correlation] Kendall’s Rank Correlation : github : edu

[correlation] Auto-Correlation Function : ST-0003 : edu

[time series] White Noise : github : edu

::: RP-WN Gaussian White Noise : github : edu

::: RP-WN Bernoulli White Noise : github : edu

[time series] SARIMAX : github : edu

::: RP-GN : MA(Moving Average) - (5) : github : edu

::: RP-GN : AR(AutoRegressive) - (5) : github : edu

::: RP-GN : ARMA(AutoRegressive Moving Average) - (2,2) : github : edu

::: RP-GN : SARIMA(Seasonal ARIMA) - (0,0,0)(0,0,1,12) : github : edu

::: RP-GN : SARIMA(Seasonal ARIMA) - (0,0,1)(0,0,1,12) : github : edu

::: RP-GN : SARIMA(Seasonal ARIMA) - (1,0,0)(0,0,1,12) : github : edu

::: RP-GN : SARIMA(Seasonal ARIMA) - (1,0,1)(0,0,1,12) : github : edu

::: RP-GN : SARIMA(Seasonal ARIMA) - (0,0,0)(1,0,0,12) : github : edu

::: RP-GN : SARIMA(Seasonal ARIMA) - (0,0,1)(1,0,0,12) : github : edu

::: RP-GN : SARIMA(Seasonal ARIMA) - (1,0,0)(1,0,0,12) : github : edu

::: RP-GN : SARIMA(Seasonal ARIMA) - (1,0,1)(1,0,0,12) : github : edu

::: RP-GN : SARIMA(Seasonal ARIMA) - (0,0,0)(1,0,1,12) : github : edu

::: RP-GN : SARIMA(Seasonal ARIMA) - (0,0,1)(1,0,1,12) : github : edu

::: RP-GN : SARIMA(Seasonal ARIMA) - (1,0,0)(1,0,1,12) : github : edu

::: RP-GN : SARIMA(Seasonal ARIMA) - (1,0,1)(1,0,1,12) : github : edu

::: RP-BN(Stochastic Trend) : ARIMA(AutoRegressive Integrated Moving Average) - (0,1,0) : github : edu

::: RP-BN(Stochastic Trend) : ARIMA(AutoRegressive Integrated Moving Average) - (0,1,1) : github : edu

::: RP-BN(Stochastic Trend) : ARIMA(AutoRegressive Integrated Moving Average) - (1,1,0) : github : edu

::: RP-BN(Stochastic Trend) : ARIMA(AutoRegressive Integrated Moving Average) - (1,1,1) : github : edu

::: RP-BN(Stochastic Trend) : ARIMA(AutoRegressive Integrated Moving Average) - (2,1,2) : github : edu

::: RP-BN(Stochastic Trend) : SARIMA(Seasonal ARIMA) - (0,1,0)(0,0,1,12) : github : edu

::: RP-BN(Stochastic Trend) : SARIMA(Seasonal ARIMA) - (0,1,0)(1,0,0,12) : github : edu

::: RP-BN(Stochastic Trend) : SARIMA(Seasonal ARIMA) - (0,1,0)(1,0,1,12) : github : edu

::: RP-BN(Stochastic Trend) : SARIMA(Seasonal ARIMA) - (0,1,1)(0,0,1,12) : github : edu

::: RP-BN(Stochastic Trend) : SARIMA(Seasonal ARIMA) - (0,1,1)(1,0,0,12) : github : edu

::: RP-BN(Stochastic Trend) : SARIMA(Seasonal ARIMA) - (0,1,1)(1,0,1,12) : github : edu

::: RP-BN(Stochastic Trend) : SARIMA(Seasonal ARIMA) - (1,1,0)(0,0,1,12) : github : edu

::: RP-BN(Stochastic Trend) : SARIMA(Seasonal ARIMA) - (1,1,0)(1,0,0,12) : github : edu

::: RP-BN(Stochastic Trend) : SARIMA(Seasonal ARIMA) - (1,1,0)(1,0,1,12) : github : edu

::: RP-BN(Stochastic Seasonality) : SARIMA(Seasonal ARIMA) - (0,0,0)(0,1,0,12) : github : edu

::: RP-BN(Stochastic Seasonality) : SARIMA(Seasonal ARIMA) - (0,0,1)(0,1,0,12) : github : edu

::: RP-BN(Stochastic Seasonality) : SARIMA(Seasonal ARIMA) - (1,0,0)(0,1,0,12) : github : edu

::: RP-BN(Stochastic Seasonality) : SARIMA(Seasonal ARIMA) - (1,0,1)(0,1,0,12) : github : edu

::: RP-BN(Stochastic Seasonality) : SARIMA(Seasonal ARIMA) - (0,0,0)(0,1,1,12) : github : edu

::: RP-BN(Stochastic Seasonality) : SARIMA(Seasonal ARIMA) - (0,0,1)(0,1,1,12) : github : edu

::: RP-BN(Stochastic Seasonality) : SARIMA(Seasonal ARIMA) - (1,0,0)(0,1,1,12) : github : edu

::: RP-BN(Stochastic Seasonality) : SARIMA(Seasonal ARIMA) - (1,0,1)(0,1,1,12) : github : edu

::: RP-BN(Stochastic Seasonality) : SARIMA(Seasonal ARIMA) - (0,0,0)(1,1,0,12) : github : edu

::: RP-BN(Stochastic Seasonality) : SARIMA(Seasonal ARIMA) - (0,0,1)(1,1,0,12) : github : edu

::: RP-BN(Stochastic Seasonality) : SARIMA(Seasonal ARIMA) - (1,0,0)(1,1,0,12) : github : edu

::: RP-BN(Stochastic Seasonality) : SARIMA(Seasonal ARIMA) - (1,0,1)(1,1,0,12) : github : edu

::: RP-BN(Stochastic Seasonality) : SARIMA(Seasonal ARIMA) - (0,0,0)(1,1,1,12) : github : edu

::: RP-BN(Stochastic Seasonality) : SARIMA(Seasonal ARIMA) - (0,0,1)(1,1,1,12) : github : edu

::: RP-BN(Stochastic Seasonality) : SARIMA(Seasonal ARIMA) - (1,0,0)(1,1,1,12) : github : edu

::: RP-BN(Stochastic Seasonality) : SARIMA(Seasonal ARIMA) - (1,0,1)(1,1,1,12) : github : edu

::: RP-BN(Stochastic Trend&Seasonality) : SARIMA(Seasonal ARIMA) - (0,1,0)(0,1,0,12) : github : edu

::: RP : SARIMAX(SARIMA with exogenous variables) : github : edu

::: Forecasting Gray Noise : MA(Moving Average) : github : edu

::: Forecasting Gray Noise : AR(AutoRegressive) : github : edu

::: Forecasting Gray Noise : ARMA(AutoRegressive Moving Average) : github : edu

::: FSC(Black Noise) ARIMA(AutoRegressive Integrated Moving Average) : ST-0000 : edu

::: FSC(Black Noise) SARIMA(Seasonal ARIMA) : ST-0001 : edu

::: SARIMA(Seasonal ARIMA) Equation : ST-0002 : edu

::: Forecasting : SARIMAX(SARIMA with exogenous variables) : github : edu

[time series] Multi-variate Random Process : github : edu

::: RP : VAR(Vector AutoRegression) : ST-0014 : edu

::: Forecasting Gray Noise : VAR(Vector AutoRegression) : github : edu

::: IRP(Impulse Response Precursor) through VAR : ST-0015 : edu

::: Granger Causality : ST-0016 : edu

::: Cointegration : ST-0017 : edu

[time series] Exponential Smoothing : github : edu

::: FDC : TS( N,N) - Simple Exponential Smoothing : ST-0004 : Statistics : manual

::: FDC : TS( A,N) - Holt Linear Method : ST-0005 : Statistics : manual

::: FDC : TS(Ad,N) - Additive Damped Trend Method : ST-0006 : Statistics : manual

::: FDC : TS( A,A) - Additive Holt-Winters' Method : ST-0007 : Statistics : manual

::: FDC : TS( A,M) - Multiplicative Holt-Winters' Method : ST-0008 : Statistics : manual

::: FDC : TS(Ad,M) - Holt-Winters Damped Method : ST-0009 : Statistics : manual

::: FDC : ETS(A,N,N) - Simple Exponential Smoothing with Additive Errors : ST-0010 : Statistics : manual

::: FDC : ETS(M,N,N) - Simple Exponential Smoothing with Multiplicative Errors : ST-0011 : Statistics : manual

::: FDC : ETS(A,A,N) - Holt’s Linear Method with Additive Errors : ST-0012 : Statistics : manual

::: FDC : ETS(M,A,N) - Holt’s Linear Method with Multiplicative Errors : ST-0013 : Statistics : manual

[time series] Non-Linear Random Process : github : edu

::: Dynamics Linear Model : github : edu

::: Local Level Model : github : edu

::: Structural Model : github : edu

::: Stochastic Volatility Model : github : edu

::: ARCH(Autoregressive conditional heteroskedasticity) : github : edu

::: GARCH(Generalized Autoregressive conditional heteroskedasticity) : github : edu

::: HMM: Hidden Markov Model : github : edu

::: Independent Mixture Model : github : edu

::: Kalman Filter : github : edu

[time series] Autocorrelation : github : edu

::: Ljung–Box test : github : edu

::: Portmanteau test : github : edu

::: Breusch–Godfrey test : github : edu

::: Durbin–Watson test : github : edu

[time series] Homoscedasticity/Heteroscedasticity : github : edu

::: Goldfeld–Quandt test : github : edu

::: Breusch–Pagan test : github : edu

::: Bartlett's test : github : edu

[time series] Stationary : github : edu

::: ADF(Augmented Dickey-Fuller) test : github : edu

::: ADF-GLS test : github : edu

::: PP(Phillips–Perron) test : github : edu

::: KPSS(Kwiatkowski Phillips Schmidt Shin) test : github : edu

::: Detrending : with Differencing : ST-0018 : edu

::: Detrending : with Modeling : ST-0019 : edu

::: Deseasonalizing : with Differencing : ST-0020 : edu

::: Deseasonalizing : with Modeling : ST-0021 : edu

[regression] Linear Regression : github : edu

::: Analytic solution : statsmodels : github : edu

::: Analytic solution : numpy : github : edu

::: Solution via QR Decomposition : github : edu

::: Solution via truncated SVD : github : edu

[regression] Residual Analysis : github : edu

[regression] Multicollinearity : github : edu

[hypothesis test] ANOVA : github : edu

::: one-way(independent) : github : edu

::: one-way(paired) : repeated measure : github : edu

::: two-way(independent) : github : edu

::: two-way(paired) : repeated measure : github : edu

::: three-way(independent) : github : edu

::: three-way(paired) : repeated measure : github : edu

::: higher multivariate(independent) : github : edu

::: higher multivariate(paired) : repeated measure : github : edu

[hypothesis test] variance-test : github : edu

[hypothesis test] t-test : github : edu

::: independent t-test : github : edu

::: paired t-test : github : edu

[hypothesis test] z-test : github : edu

[hypothesis test] binomial-test : github : edu

[hypothesis test] Cohen's measure : github : edu

[hypothesis test] Nonparametric : github : edu

::: Mann-Whitney U Test : nonparametric statistical significance test for determining whether two independent samples were drawn from a population with the same distribution : github : edu

::: Wilcoxon Signed-Rank Test : nonparametric version of the paired Student’s t-test : github : edu

::: Kruskal-Wallis H Test : nonparametric version of the one-way analysis of variance test or ANOVA : github : edu

::: Friedman Test : nonparametric version of the repeated measures analysis of variance test, or repeated measures ANOVA : github : edu

[theory] Statistics Theory : ST-theory : edu

[theory] Normality : github : edu

::: Shapiro–Wilk test : github : edu

::: Kolmogorov–Smirnov test : github : edu

::: Lilliefors test : github : edu

::: Anderson–Darling test : github : edu

::: Jarque–Bera test : github : edu

::: Pearson's chi-squared test : github : edu

::: D'Agostino's K-squared test : github : edu

[estimation] Interval Estimation : github : edu

::: Tolerance intervals : github : edu

::: Confidence intervals : github : edu

::: Prediction intervals : github : edu

[estimation] Density Estimation : github : edu

[stochastic process] Hidden Markov model : github : edu

[stochastic process] Markov process : github : edu

[stochastic process] ◆Gaussian process : ST-0021 : edu

: github : edu

NM] Numerical Method

from ailever.utils import source
source('NM-0000')

Source Manual FEniCS scipy sympy symfit

[theory] Numerical Methods Theory : NM-theory : edu

[fitting] Fourier Series Fitting : github : edu

[fitting] Taylor Series Fitting : github : edu

[Optimize] : github : edu

[linear algebra] Inverse Matrix : github : edu

[linear algebra] LU Decomposition : github : edu

[linear algebra] QR Decomposition : github : edu

[linear algebra] Cholesky Decomposition : github : edu

[linear algebra] Eigen Decomposition : github : edu

[linear algebra] Singular Value Decomposition : github : edu

[linear algebra] Eigen-Value Problems : github : edu

[ordinary differential equation] Euler method : github : edu

[ordinary differential equation] Runge–Kutta : github : edu

[partial differential equation] Finite difference method : github : edu

[partial differential equation] Finite element method : github : edu

[partial differential equation] Finite volume method : github : edu

[symbolic computing] Mathematical Formula Expression : github : edu

[symbolic computing] Matrix : github : edu

[symbolic computing] Polynomial : github : edu

: github : edu

PL] Programming Language

from ailever.utils import source
source('PL-0000')

Source Manual ipaddress tcpschool repl w3schools devkuma rosettacode isolution SQL C++ Java JavaScript

[security] : github : edu

: github : edu

UT] Utils

from ailever.utils import source
source('UT-0000')

Source Manual pyautogui urllib requests beautifulsoup4 selenium scrapy pandas visdom plotly plotly-figure plotly-api dash-callback dash-core dash-html dash-bootstrap

[automation] Encode : percentage encoder : github : edu

[automation] Time Object : python : github : edu

[automation] Time Object : numpy : github : edu

[automation] Time Object : pandas : github : edu

[automation] Scheduler : Bash : github : edu

[automation] Scheduler : Python : github : edu

[automation] Argument Parser : Bash : github : edu

[automation] Argument Parser : Python : github : edu

[automation] Control : Mouse & Keyboard & Messagebox : github : edu

[automation] Email : github : edu

[automation] Clipboard Control : github : edu

[dataset] Dataset Links : github : edu

[dataset] Downloader : UT-0003 : edu

[dataset] Crawler : urllib : UT-0002 : edu

[dataset] Crawler : requests : UT-0001 : edu

[dataset] Crawler : selenium : UT-0000 : edu

[dataset] Crawler : scrapy : github : edu

::: Crawler : scrapy | search.naver.com : github : edu : manual

[programming] MetaClass : github : edu

[container] Query : python : github : edu

[container] Query : numpy : github : edu

[container] Query : pandas : github : edu

[container] Query : pytorch : github : edu

[documentation] Dash Template - structure : raw : github : edu

[documentation] Dash Template - Simple Project : UT-0004 : edu

[documentation] Dash Template - Real Time Analysis : UT-0005 : edu

[visualization] matplotlib : multiple axes : github : edu

[visualization] matplotlib : direction field : github : edu : manual

[visualization] matplotlib : 3d scatter plot : github : edu : manual

[visualization] matplotlib : 3d surface plot : github : edu : manual

[visualization] matplotlib : animate one figure with IPython.display : github : edu

[visualization] matplotlib : animate multiple figure with IPython.display : github : edu

[visualization] matplotlib : trajectory-2d with IPython.display : github : edu

[visualization] matplotlib : trajectory-3d with IPython.display : github : edu

[visualization] matplotlib : UI Interface for one figure with ipywidgets : github : edu

[visualization] matplotlib : UI Interface for multiple figure with ipywidgets : github : edu

[visualization] matplotlib : UI Interface for trajectory with ipywidgets : github : edu

[visualization] plotly : subplots : github : edu

[visualization] plotly : Parallel Coordinates Chart : github : edu

[visualization] pyflowchart : Flow Chart : github : edu : manual

: github : edu

MA] Materials

from ailever.utils import source
source('MA-0000')

Source Manual latex katex

[analysis] SARIMA : MA-0000 : edu

[analysis] COVID-19 : MA-0001 : edu

: github : edu

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