# academic-drawing

**Repository Path**: deep_seismology/academic-drawing

## Basic Information

- **Project Name**: academic-drawing
- **Description**: Providing codes (including Matlab and Python) for presenting experiment results.
- **Primary Language**: Matlab
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2019-04-21
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

Academic drawing
-----------------

This is a project providing source codes (including Matlab and Python) for presenting experiment results.

Contents
--------

-   [Usage](#usage)
-   [Our examples](#our-examples)
-   [Our publications](#our-publications)

Usage
--------------

> It is not necessary to open each file in this repository because you can follow this readme document to find your needs.

Our examples
--------------

- **Download**
  - [mu10.mat](https://github.com/xinychen/academic-drawing/blob/master/curves/mu10.mat)
  - [mu_curve10.m](https://github.com/xinychen/academic-drawing/blob/master/curves/mu_curve10.m)

and evaluate these in Matlab, then, you will see the following picture:

![mu_curve10](https://github.com/xinychen/academic-drawing/blob/master/curves/mu_curve10.png)


- **Download**
  - [BCPF_fiber_rmselb_m30_r5.csv](https://github.com/xinychen/academic-drawing/blob/master/curves/BCPF_fiber_rmselb_m30_r5.csv)
  - [BCPF_fiber_rmselb_m30_r10.csv](https://github.com/xinychen/academic-drawing/blob/master/curves/BCPF_fiber_rmselb_m30_r10.csv)
  - [overfitting.m](https://github.com/xinychen/academic-drawing/blob/master/curves/overfitting.m)

and evaluate these in Matlab, then, you will see the following pictures:

![overfitting_ms30_r5](https://github.com/xinychen/academic-drawing/blob/master/curves/overfitting_ms30_r5.png)
![overfitting_ms30_r10](https://github.com/xinychen/academic-drawing/blob/master/curves/overfitting_ms30_r10.png)


- **Download**
  - [bias10.mat](https://github.com/xinychen/academic-drawing/blob/master/heat-maps/bias10.mat)
  - [heat_map10.m](https://github.com/xinychen/academic-drawing/blob/master/heat-maps/heat_map10.m)

and evaluate these in Matlab, then, you will see the following picture:

![heat_map10](https://github.com/xinychen/academic-drawing/blob/master/heat-maps/heat_map10.png)


- **Download**
  - [latent_factors.mat](https://github.com/xinychen/academic-drawing/blob/master/heat-maps/latent_factors.mat)
  - [latent_factors.m](https://github.com/xinychen/academic-drawing/blob/master/heat-maps/latent_factors.m)

and evaluate these in Matlab, then, you will see the following pictures:

  ![factor2](https://github.com/xinychen/academic-drawing/blob/master/heat-maps/factor2.png)
  ![factor3](https://github.com/xinychen/academic-drawing/blob/master/heat-maps/factor3.png)


- **Download**
  - [rmse.mat](https://github.com/xinychen/academic-drawing/blob/master/rmse-curves/rmse.mat)
  - [rmse_curves.m](https://github.com/xinychen/academic-drawing/blob/master/rmse-curves/rmse_curves.m)

and evaluate these in Matlab, then, you will see the following picture:

![rmse_curve](https://github.com/xinychen/academic-drawing/blob/master/rmse-curves/rmse_curve.png)


- **Download**
  - [rmse10.mat](https://github.com/xinychen/academic-drawing/blob/master/rmse-curves/rmse10.mat)
  - [rmse_curve10.m](https://github.com/xinychen/academic-drawing/blob/master/rmse-curves/rmse_curve10.m)

and evaluate these in Matlab, then, you will see the following picture:

![rmse_curve10](https://github.com/xinychen/academic-drawing/blob/master/rmse-curves/rmse_curve10.png)


- **Download**
  - [road1_fiber_ms50_r10.csv](https://github.com/xinychen/academic-drawing/blob/master/time-series/road1_fiber_ms50_r10.csv)
  - [time_series_speed1.py](https://github.com/xinychen/academic-drawing/blob/master/time-series/time_series_speed1.py)
  - [time_series_speed2.py](https://github.com/xinychen/academic-drawing/blob/master/time-series/time_series_speed2.py)

and evaluate these in Python, then, you will see the following pictures:

![time_series_speed1](https://github.com/xinychen/academic-drawing/blob/master/time-series/time_series_speed1.png)

![time_series_speed2](https://github.com/xinychen/academic-drawing/blob/master/time-series/time_series_speed2.png)


- **Download**
  - [road1_fiber_ms50_r10.csv](https://github.com/xinychen/academic-drawing/blob/master/time-series/road1_fiber_ms50_r10.csv)
  - [speed_curve.m](https://github.com/xinychen/academic-drawing/blob/master/time-series/speed_curve.m)

and evaluate these in Matlab, then, you will see the following picture:

![speed_curve](https://github.com/xinychen/academic-drawing/blob/master/time-series/speed_curve.png)


- **Download**
  - [nyc_data_completeness.mat](https://github.com/xinychen/academic-drawing/blob/master/time-series/nyc_data_completeness.mat)
  - [nyc_data_completeness.m](https://github.com/xinychen/academic-drawing/blob/master/time-series/nyc_data_completeness.m)

and evaluate these in Matlab, then, you will see the following picture:

![nyc_data_completeness](https://github.com/xinychen/academic-drawing/blob/master/time-series/nyc_data_completeness.png)

Our publications
--------------

  - Xinyu Chen, Zhaocheng He, Jiawei Wang, 2018. [*Spatial-temporal traffic speed patterns discovery and incomplete data recovery via SVD-combined tensor decomposition*](https://doi.org/10.1016/j.trc.2017.10.023). Transportation Research Part C: Emerging Technologies, 86: 59-77.

  - Xinyu Chen, Zhaocheng He, Lijun Sun, 2019. [*A Bayesian tensor decomposition approach for spatiotemporal traffic data imputation*](https://doi.org/10.1016/j.trc.2018.11.003). Transportation Research Part C: Emerging Technologies, 98: 73-84. [[Matlab code](https://github.com/lijunsun/bgcp_imputation)]

  >Please consider citing our papers if they help your research.